references.bib

@article{adhuryaNovelMethodPredicting2024,
  title = {A Novel Method for Predicting Ecological Interactions with an Unsupervised Machine Learning Algorithm},
  author = {Adhurya, Sagar and Park, Young-Seuk},
  year = {2024},
  journal = {Methods in Ecology and Evolution},
  volume = {n/a},
  number = {n/a},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.14358},
  urldate = {2024-06-03},
  abstract = {This gap in knowledge regarding ecological interactions has prompted the development of various predictive approaches. Traditionally, ecological interactions have been inferred using traits. However, the lack of trait information for numerous organisms necessitates using phylogenetic data and statistical insights from interaction matrices for prediction. Previous studies have overlooked the validation of model-predicted interactions. This study used a novel method in predicting ecological interactions using a self-organizing map (SOM), an unsupervised machine learning algorithm. The SOM learns from the input interaction matrix by grouping the nodes into output layers based on their interactions. Subsequently, the trained model predicts the interactions as scores. To distinguish between interactions and non-interactions, we employed F1 score maximization, setting scores above a specific threshold as interactions and the remainder as non-interactions. We applied this method to three unipartite metawebs and one bipartite metaweb and subsequently validated the predicted interactions using two innovative approaches: taxonomic and interaction recovery validation. Our method exhibited outstanding predictive performance, particularly for large networks. Various binary classification performance indicators, including F1 score (0.84--0.97) and accuracy (0.97--0.99), indicated high performance. Moreover, the method generated minimal predicted interactions, signifying low noise in the predictions, particularly for large networks. Taxonomic validation excels in metawebs with a connectance {$>$}0.1 but performs poorly in metawebs with very low connectance. In contrast, interaction recovery was most effective in larger metawebs. Our proposed method excels at making highly accurate predictions of ecological interactions with minimal noise, solely utilizing input interaction data without relying on traits or phylogenetic information regarding interacting nodes. These predictions are particularly precise for large networks, underscoring their potential to address knowledge gaps in emerging extensive metawebs. Notably, taxonomic validation and interaction recovery methods are sensitive to connectance and network size, respectively, suggesting prospects for developing robust interaction validation methods.},
  copyright = {{\copyright} 2024 The Author(s). Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {ecological interaction,ecological network,Eltonian shortfall,interaction prediction,interaction validation,metaweb,network prediction,self-organizing map (SOM)},
  file = {/Users/tanyastrydom/Zotero/storage/6MDYZGGG/Adhurya and Park - A novel method for predicting ecological interacti.pdf;/Users/tanyastrydom/Zotero/storage/EGHM9LFC/2041-210X.html}
}

@article{albouyMarineFishFood2019,
  title = {The Marine Fish Food Web Is Globally Connected},
  author = {Albouy, Camille and Archambault, Philippe and Appeltans, Ward and Ara{\'u}jo, Miguel B. and Beauchesne, David and Cazelles, Kevin and Cirtwill, Alyssa R. and Fortin, Marie-Jos{\'e}e and Galiana, Nuria and Leroux, Shawn J. and Pellissier, Lo{\"i}c and Poisot, Timoth{\'e}e and Stouffer, Daniel B. and Wood, Spencer A. and Gravel, Dominique},
  year = {2019},
  month = aug,
  journal = {Nature Ecology \& Evolution},
  volume = {3},
  number = {8},
  pages = {1153--1161},
  publisher = {Nature Publishing Group},
  issn = {2397-334X},
  doi = {10.1038/s41559-019-0950-y},
  urldate = {2021-05-14},
  abstract = {The productivity of marine ecosystems and the services they provide to humans are largely dependent on complex interactions between prey and predators. These are embedded in a diverse network of trophic interactions, resulting in a cascade of events following perturbations such as species extinction. The sheer scale of oceans, however, precludes the characterization of marine feeding networks through de novo sampling. This effort ought instead to rely on a combination of extensive data and inference. Here we investigate how the distribution of trophic interactions at the global scale shapes the marine fish food web structure. We hypothesize that the heterogeneous distribution of species ranges in biogeographic regions should concentrate interactions in the warmest areas and within species groups. We find that the inferred global metaweb of marine fish---that is, all possible potential feeding links between co-occurring species---is highly connected geographically with a low degree of spatial modularity. Metrics of network structure correlate with sea surface temperature and tend to peak towards the tropics. In contrast to open-water communities, coastal food webs have greater interaction redundancy, which may confer robustness to species extinction. Our results suggest that marine ecosystems are connected yet display some resistance to perturbations because of high robustness at most locations.},
  copyright = {2019 The Author(s), under exclusive licence to Springer Nature Limited},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/2QX9YAVK/s41559-019-0950-y.html}
}

@article{allesinaFoodWebModels2009,
  title = {Food Web Models: A Plea for Groups},
  shorttitle = {Food Web Models},
  author = {Allesina, Stefano and Pascual, Mercedes},
  year = {2009},
  journal = {Ecology Letters},
  volume = {12},
  number = {7},
  pages = {652--662},
  issn = {1461-0248},
  doi = {10.1111/j.1461-0248.2009.01321.x},
  urldate = {2024-03-27},
  abstract = {The concept of a group is ubiquitous in biology. It underlies classifications in evolution and ecology, including those used to describe phylogenetic levels, the habitat and functional roles of organisms in ecosystems. Surprisingly, this concept is not explicitly included in simple models for the structure of food webs, the ecological networks formed by consumer--resource interactions. We present here the simplest possible model based on groups, and show that it performs substantially better than current models at predicting the structure of large food webs. Our group-based model can be applied to different types of biological and non-biological networks, and for the first time merges in the same framework two important notions in network theory: that of compartments (sets of highly interacting nodes) and that of roles (sets of nodes that have similar interaction patterns). This model provides a basis to examine the significance of groups in biological networks and to develop more accurate models for ecological network structure. It is especially relevant at a time when a new generation of empirical data is providing increasingly large food webs.},
  copyright = {{\copyright} 2009 Blackwell Publishing Ltd/CNRS},
  langid = {english},
  keywords = {Akaike information criterion,clustering algorithm,compartment,connectance,food web model,group,likelihood,model selection,species richness,trophic role,trophospecies},
  file = {/Users/tanyastrydom/Zotero/storage/U4TH9D72/Allesina and Pascual - 2009 - Food web models a plea for groups.pdf;/Users/tanyastrydom/Zotero/storage/7DJ8IDCQ/j.1461-0248.2009.01321.html}
}

@article{allesinaGeneralModelFood2008,
  title = {A {{General Model}} for {{Food Web Structure}}},
  author = {Allesina, Stefano and Alonso, David and Pascual, Mercedes},
  year = {2008},
  month = may,
  journal = {Science},
  volume = {320},
  number = {5876},
  pages = {658--661},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/science.1156269},
  urldate = {2024-03-27},
  abstract = {A central problem in ecology is determining the processes that shape the complex networks known as food webs formed by species and their feeding relationships. The topology of these networks is a major determinant of ecosystems' dynamics and is ultimately responsible for their responses to human impacts. Several simple models have been proposed for the intricate food webs observed in nature. We show that the three main models proposed so far fail to fully replicate the empirical data, and we develop a likelihood-based approach for the direct comparison of alternative models based on the full structure of the network. Results drive a new model that is able to generate all the empirical data sets and to do so with the highest likelihood.},
  file = {/Users/tanyastrydom/Zotero/storage/E6FSXQTZ/Allesina et al. - 2008 - A General Model for Food Web Structure.pdf}
}

@article{bambachAutecologyFillingEcospace2007,
  title = {Autecology and the {{Filling}} of {{Ecospace}}: {{Key Metazoan Radiations}}},
  shorttitle = {Autecology and the {{Filling}} of {{Ecospace}}},
  author = {Bambach, Richard K. and Bush, Andrew M. and Erwin, Douglas H.},
  year = {2007},
  journal = {Palaeontology},
  volume = {50},
  number = {1},
  pages = {1--22},
  issn = {1475-4983},
  doi = {10.1111/j.1475-4983.2006.00611.x},
  urldate = {2024-02-08},
  abstract = {Abstract: All possible combinations of six tiering positions in relation to the substratum/water interface, six motility levels and six feeding strategies define a complete theoretical ecospace of 216 potential modes of life for marine animals. The number of modes of life actually utilized specifies realized ecospace. Owing to constraints of effectiveness and efficiency the modern marine fauna utilizes only about half the potential number of modes of life, two-thirds of which (62 of 92) are utilized by animals with readily preserved, mineralized hard parts. Realized ecospace has increased markedly since the early evolution of animal ecosystems. The Ediacaran fauna utilized at most 12 modes of life, with just two practised by skeletal organisms. A total of 30 modes of life are recorded in the Early and Middle Cambrian, 19 of which were utilized by skeletal organisms. The other 11 are documented from soft-bodied animals preserved in the Chengjiang and Burgess Shale Konservat-Lagerst{\"a}tten. The number of modes of life utilized by skeletal organisms increased by more than 50 per cent during the Ordovician radiation to a Late Ordovician total of 30. Between the Late Ordovician and the Recent the number of utilized modes of life has doubled again. The autecological and taxonomic diversity histories of the marine metazoa appear to be broadly parallel, and future studies of theoretical ecospace utilization should provide more detailed tests of pattern and process in the ecological history of the metazoa.},
  langid = {english},
  keywords = {ecological complexity,evolutionary constraint,feeding,mode of life,motility,theoretical ecospace,tiering},
  file = {/Users/tanyastrydom/Zotero/storage/QKII8I3E/Bambach et al. - 2007 - Autecology and the Filling of Ecospace Key Metazo.pdf;/Users/tanyastrydom/Zotero/storage/AL3PXAC5/j.1475-4983.2006.00611.html}
}

@misc{banvilleDecipheringProbabilisticSpecies2024,
  title = {Deciphering Probabilistic Species Interaction Networks},
  author = {Banville, Francis and Strydom, Tanya and Blyth, Penelope and Brimacombe, Chris and Catchen, Michael D. and Dansereau, Gabriel and Higino, Gracielle and Malpas, Thomas and Mayall, Hana and Norman, Kari and Gravel, Dominique and Poisot, Timoth{\'e}e},
  year = {2024},
  month = jul,
  publisher = {EcoEvoRxiv},
  doi = {10.32942/X28G8Z},
  urldate = {2024-07-25},
  abstract = {Representing species interactions probabilistically (how likely are they to occur?) as opposed to deterministically (are they occurring?) conveys uncertainties in our knowledge of interactions and information on their variability. The sources of uncertainty captured by interaction probabilities depend on the method used to evaluate them: uncertainty of predictive models, subjective assessment of experts, or empirical measurement of interaction spatiotemporal variability. However, guidelines for the estimation and documentation of probabilistic interaction data are lacking. This is concerning because our understanding and analysis of interaction probabilities depend on their sometimes elusive definition and uncertainty sources. We review how probabilistic interactions are defined at different spatial scales, from local interactions to regional networks (metawebs), with a strong emphasis on host-parasite and trophic (predatory and herbivory) interactions. These definitions are based on the distinction between the realization of an interaction at a specific time and space (local) and its biological or ecological feasibility (regional). Using host-parasite interactions in Europe, we illustrate how these two network representations differ in their statistical properties, specifically: how local networks and metawebs differ in their spatial and temporal scaling of probabilistic interactions, but not in their taxonomic scaling. We present two approaches to inferring binary interactions from probabilistic ones that account for these differences and show that systematic biases arise when directly inferring local networks from metawebs. Our results underscore the importance of more rigorous descriptions of probabilistic species interaction networks that specify their type of interaction (local or regional), conditional variables and uncertainty sources.},
  archiveprefix = {EcoEvoRxiv},
  copyright = {CC-By Attribution-ShareAlike 4.0 International},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/BYNFU5RC/Banville et al. - 2024 - Deciphering probabilistic species interaction netw.pdf}
}

@article{banvilleMangaljlEcologicalNetworksjlTwo2021,
  title = {Mangal.Jl and {{EcologicalNetworks}}.Jl: {{Two}} Complementary Packages for Analyzing Ecological Networks in {{Julia}}},
  shorttitle = {Mangal.Jl and {{EcologicalNetworks}}.Jl},
  author = {Banville, Francis and Vissault, Steve and Poisot, Timoth{\'e}e},
  year = {2021},
  month = may,
  journal = {Journal of Open Source Software},
  volume = {6},
  number = {61},
  pages = {2721},
  issn = {2475-9066},
  doi = {10.21105/joss.02721},
  urldate = {2021-05-12},
  abstract = {Network ecology is an emerging field of study describing species interactions (e.g. predation, pollination) in a biological community. Ecological networks, in which two species are connected if they can interact, are a mathematical representation of all interactions encountered in a given ecosystem. Anchored in graph theory, the methods developed in network ecology are remarkably rigorous and biologically insightful. Indeed, many ecological and evolutionary processes are driven by species interactions and network structure (i.e. the arrangement of links in ecological networks). The study of ecological networks, from data importation and simulation to data analysis and visualization, requires a coherent and efficient set of numerical tools. With its powerful and dynamic type system, the Julia programming language provides a very good and fast computing environment suitable for ecological research. Julia's typing system notably ensures that appropriate methods are used when a function is applied to different types of ecological data, which therefore minimizes the risk of committing errors of analysis and interpretation in network ecology. Mangal.jl and EcologicalNetworks.jl are two novel and complementary Julia packages designed to conduct research in network ecology efficiently. Researchers studying a broad range of ecological networks can read the data and metadata of numerous well-documented networks using Mangal.jl, and analyze their properties using EcologicalNetworks.jl.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/T35JN8KF/Banville et al. - 2021 - Mangal.jl and EcologicalNetworks.jl Two complemen.pdf}
}

@article{banvilleWhatConstrainsFood2023,
  title = {What Constrains Food Webs? {{A}} Maximum Entropy Framework for Predicting Their Structure with Minimal Biases},
  shorttitle = {What Constrains Food Webs?},
  author = {Banville, Francis and Gravel, Dominique and Poisot, Timoth{\'e}e},
  year = {2023},
  month = sep,
  journal = {PLOS Computational Biology},
  volume = {19},
  number = {9},
  pages = {e1011458},
  publisher = {Public Library of Science},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1011458},
  urldate = {2023-09-14},
  abstract = {Food webs are complex ecological networks whose structure is both ecologically and statistically constrained, with many network properties being correlated with each other. Despite the recognition of these invariable relationships in food webs, the use of the principle of maximum entropy (MaxEnt) in network ecology is still rare. This is surprising considering that MaxEnt is a statistical tool precisely designed for understanding and predicting many types of constrained systems. This principle asserts that the least-biased probability distribution of a system's property, constrained by prior knowledge about that system, is the one with maximum information entropy. MaxEnt has been proven useful in many ecological modeling problems, but its application in food webs and other ecological networks is limited. Here we show how MaxEnt can be used to derive many food-web properties both analytically and heuristically. First, we show how the joint degree distribution (the joint probability distribution of the numbers of prey and predators for each species in the network) can be derived analytically using the number of species and the number of interactions in food webs. Second, we present a heuristic and flexible approach of finding a network's adjacency matrix (the network's representation in matrix format) based on simulated annealing and SVD entropy. We built two heuristic models using the connectance and the joint degree sequence as statistical constraints, respectively. We compared both models' predictions against corresponding null and neutral models commonly used in network ecology using open access data of terrestrial and aquatic food webs sampled globally (N = 257). We found that the heuristic model constrained by the joint degree sequence was a good predictor of many measures of food-web structure, especially the nestedness and motifs distribution. Specifically, our results suggest that the structure of terrestrial and aquatic food webs is mainly driven by their joint degree distribution.},
  langid = {english}
}

@article{barberanUsingNetworkAnalysis2012,
  title = {Using Network Analysis to Explore Co-Occurrence Patterns in Soil Microbial Communities},
  author = {Barber{\'a}n, Albert and Bates, Scott T. and Casamayor, Emilio O. and Fierer, Noah},
  year = {2012},
  month = feb,
  journal = {The ISME Journal},
  volume = {6},
  number = {2},
  pages = {343--351},
  publisher = {Nature Publishing Group},
  issn = {1751-7370},
  doi = {10.1038/ismej.2011.119},
  urldate = {2024-05-14},
  abstract = {Exploring large environmental datasets generated by high-throughput DNA sequencing technologies requires new analytical approaches to move beyond the basic inventory descriptions of the composition and diversity of natural microbial communities. In order to investigate potential interactions between microbial taxa, network analysis of significant taxon co-occurrence patterns may help to decipher the structure of complex microbial communities across spatial or temporal gradients. Here, we calculated associations between microbial taxa and applied network analysis approaches to a 16S rRNA gene barcoded pyrosequencing dataset containing {$>$}160\,000 bacterial and archaeal sequences from 151 soil samples from a broad range of ecosystem types. We described the topology of the resulting network and defined operational taxonomic unit categories based on abundance and occupancy (that is, habitat generalists and habitat specialists). Co-occurrence patterns were readily revealed, including general non-random association, common life history strategies at broad taxonomic levels and unexpected relationships between community members. Overall, we demonstrated the potential of exploring inter-taxa correlations to gain a more integrated understanding of microbial community structure and the ecological rules guiding community assembly.},
  copyright = {2012 International Society for Microbial Ecology},
  langid = {english},
  keywords = {Ecology,Evolutionary Biology,general,Life Sciences,Microbial Ecology,Microbial Genetics and Genomics,Microbiology},
  file = {/Users/tanyastrydom/Zotero/storage/RDTLUN5Q/Barberán et al. - 2012 - Using network analysis to explore co-occurrence pa.pdf}
}

@article{bartomeusCommonFrameworkIdentifying2016,
  title = {A Common Framework for Identifying Linkage Rules across Different Types of Interactions},
  author = {Bartomeus, Ignasi and Gravel, Dominique and Tylianakis, Jason M. and Aizen, Marcelo A. and Dickie, Ian A. and Bernard-Verdier, Maud},
  year = {2016},
  journal = {Functional Ecology},
  volume = {30},
  number = {12},
  pages = {1894--1903},
  issn = {1365-2435},
  doi = {10.1111/1365-2435.12666},
  urldate = {2020-11-02},
  abstract = {Species interactions, ranging from antagonisms to mutualisms, form the architecture of biodiversity and determine ecosystem functioning. Understanding the rules responsible for who interacts with whom, as well as the functional consequences of these interspecific interactions, is central to predict community dynamics and stability. Species traits sensu lato may affect different ecological processes by determining species interactions through a two-step process. First, ecological and life-history traits govern species distributions and abundance, and hence determine species co-occurrence and the potential for species to interact. Secondly, morphological or physiological traits between co-occurring potential interaction partners should match for the realization of an interaction. Here, we review recent advances on predicting interactions from species co-occurrence and develop a probabilistic model for inferring trait matching. The models proposed here integrate both neutral and trait-matching constraints, while using only information about known interactions, thereby overcoming problems originating from undersampling of rare interactions (i.e. missing links). They can easily accommodate qualitative or quantitative data and can incorporate trait variation within species, such as values that vary along developmental stages or environmental gradients. We use three case studies to show that the proposed models can detect strong trait matching (e.g. predator--prey system), relaxed trait matching (e.g. herbivore--plant system) and barrier trait matching (e.g. plant--pollinator systems). Only by elucidating which species traits are important in each process (i.e. in determining interaction establishment and frequency), we can advance in explaining how species interact and the consequences of these interactions for ecosystem functioning. A lay summary is available for this article.},
  copyright = {{\copyright} 2016 The Authors. Functional Ecology {\copyright} 2016 British Ecological Society},
  langid = {english},
  keywords = {functional traits,herbivory,interaction networks,mutualisms,parasitism,pollination,predation,spatial,trait matching,trophic interactions},
  file = {/Users/tanyastrydom/Zotero/storage/W5IQICSP/Bartomeus et al. - 2016 - A common framework for identifying linkage rules a.pdf}
}

@article{bascompteNestedAssemblyPlantanimal2003,
  title = {The Nested Assembly of Plant-Animal Mutualistic Networks},
  author = {Bascompte, J. and Jordano, P. and Melian, C. J. and Olesen, J. M.},
  year = {2003},
  month = aug,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {100},
  number = {16},
  pages = {9383--9387},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.1633576100},
  urldate = {2016-10-12},
  abstract = {Most studies of plant--animal mutualisms involve a small number of species. There is almost no information on the structural organization of species-rich mutualistic networks despite its potential importance for the maintenance of diversity. Here we analyze 52 mutualistic networks and show that they are highly nested; that is, the more specialist species interact only with proper subsets of those species interacting with the more generalists. This assembly pattern generates highly asymmetrical interactions and organizes the community cohesively around a central core of interactions. Thus, mutualistic networks are neither randomly assembled nor organized in compartments arising from tight, parallel specialization. Furthermore, nestedness increases with the complexity (number of interactions) of the network: for a given number of species, communities with more interactions are significantly more nested. Our results indicate a nonrandom pattern of community organization that may be relevant for our understanding of the organization and persistence of biodiversity.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/TUGZVJ6V/Bascompte et al. - 2003 - The nested assembly of plant–animal mutualistic ne.pdf;/Users/tanyastrydom/Zotero/storage/H3SRZB63/9383.html}
}

@article{beckermanForagingBiologyPredicts2006,
  title = {Foraging Biology Predicts Food Web Complexity},
  author = {Beckerman, Andrew P. and Petchey, Owen L. and Warren, Philip H.},
  year = {2006},
  month = sep,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {103},
  number = {37},
  pages = {13745--13749},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0603039103},
  urldate = {2024-01-15},
  abstract = {Food webs, the networks of feeding links between species, are central to our understanding of ecosystem structure, stability, and function. One of the key aspects of food web structure is complexity, or connectance, the number of links expressed as a proportion of the total possible number of links. Connectance (complexity) is linked to the stability of webs and is a key parameter in recent models of other aspects of web structure. However, there is still no fundamental biological explanation for connectance in food webs. Here, we propose that constraints on diet breadth, driven by optimal foraging, provide such an explanation. We show that a simple diet breadth model predicts highly constrained values of connectance as an emergent consequence of individual foraging behavior. When combined with features of real food web data, such as taxonomic and trophic aggregation and cumulative sampling of diets, the model predicts well the levels of connectance and scaling of connectance with species richness, seen in real food webs. This result is a previously undescribed synthesis of foraging theory and food web theory, in which network properties emerge from the behavior of individuals and, as such, provides a mechanistic explanation of connectance currently lacking in food web models.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/XHZHJMNI/Beckerman et al. - 2006 - Foraging biology predicts food web complexity.pdf}
}

@article{beckerOptimisingPredictiveModels2022,
  title = {Optimising Predictive Models to Prioritise Viral Discovery in Zoonotic Reservoirs},
  author = {Becker, Daniel J. and Albery, Gregory F. and Sjodin, Anna R. and Poisot, Timoth{\'e}e and Bergner, Laura M. and Chen, Binqi and Cohen, Lily E. and Dallas, Tad A. and Eskew, Evan A. and Fagre, Anna C. and Farrell, Maxwell J. and Guth, Sarah and Han, Barbara A. and Simmons, Nancy B. and Stock, Michiel and Teeling, Emma C. and Carlson, Colin J.},
  year = {2022},
  month = aug,
  journal = {The Lancet Microbe},
  volume = {3},
  number = {8},
  pages = {e625-e637},
  publisher = {Elsevier},
  issn = {2666-5247},
  doi = {10.1016/S2666-5247(21)00245-7},
  urldate = {2024-03-12},
  langid = {english},
  pmid = {35036970},
  file = {/Users/tanyastrydom/Zotero/storage/W4SKQ7F5/Becker et al. - 2022 - Optimising predictive models to prioritise viral d.pdf}
}

@article{benadiQuantitativePredictionInteractions2022,
  title = {Quantitative {{Prediction}} of {{Interactions}} in {{Bipartite Networks Based}} on {{Traits}}, {{Abundances}}, and {{Phylogeny}}},
  author = {Benadi, Gita and Dormann, Carsten F. and Fr{\"u}nd, Jochen and Stephan, Ruth and V{\'a}zquez, Diego P.},
  year = {2022},
  month = jun,
  journal = {The American Naturalist},
  volume = {199},
  number = {6},
  pages = {841--854},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/714420},
  urldate = {2024-10-29},
  abstract = {Ecological interactions link species in networks. Loss of species from or introduction of new species into an existing network may have substantial effects for interaction patterns. Predicting changes in interaction frequency while allowing for rewiring of existing interactions---and hence estimating the consequences of community compositional changes---is thus a central challenge for network ecology. Interactions between species groups, such as pollinators and flowers or parasitoids and hosts, are moderated by matching morphological traits or sensory clues, most of which are unknown to us. If these traits are phylogenetically conserved, however, we can use phylogenetic distances to construct latent, surrogate traits and try to match those across groups, in addition to observed traits. Understanding how important traits and trait matching are, relative to abundances and chance, is crucial to estimating the fundamental predictability of network interactions. Here, we present a statistically sound approach (``tapnet'') to fitting abundances, traits, and phylogeny to observed network data to predict interaction frequencies. We thereby expand existing approaches to quantitative bipartite networks, which so far have failed to correctly represent the nonindependence of network interactions. Furthermore, we use simulations and cross validation on independent data to evaluate the predictive power of the fit. Our results show that tapnet is on a par with abundance-only, matching centrality, and machine learning approaches. This approach also allows us to evaluate how well current concepts of trait matching work. On the basis of our results, we expect that interactions in well-sampled networks can be well predicted if traits and abundances are the main driver of interaction frequency.}
}

@article{berlowGoldilocksFactorFood2008,
  title = {The ``{{Goldilocks}} Factor'' in Food Webs},
  author = {Berlow, Eric L. and Brose, Ulrich and Martinez, Neo D.},
  year = {2008},
  month = mar,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {105},
  number = {11},
  pages = {4079--4080},
  publisher = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.0800967105},
  urldate = {2024-02-09}
}

@article{berlowInteractionStrengthsFood2004,
  title = {Interaction Strengths in Food Webs: Issues and Opportunities},
  shorttitle = {Interaction Strengths in Food Webs},
  author = {Berlow, Eric L. and Neutel, Anje-Margiet and Cohen, Joel E. and {de Ruiter}, Peter C. and Ebenman, Bo and Emmerson, Mark and Fox, Jeremy W. and Jansen, Vincent A. A. and Iwan Jones, J. and Kokkoris, Giorgos D. and Logofet, Dmitrii O. and McKane, Alan J. and Montoya, Jose M. and Petchey, Owen},
  year = {2004},
  month = may,
  journal = {Journal of Animal Ecology},
  volume = {73},
  number = {3},
  pages = {585--598},
  issn = {0021-8790, 1365-2656},
  doi = {10.1111/j.0021-8790.2004.00833.x},
  urldate = {2020-11-11},
  langid = {english}
}

@article{bhatiaNetworkbasedRestorationStrategies2023,
  title = {Network-Based Restoration Strategies Maximize Ecosystem Recovery},
  author = {Bhatia, Udit and Dubey, Sarth and Gouhier, Tarik C. and Ganguly, Auroop R.},
  year = {2023},
  month = dec,
  journal = {Communications Biology},
  volume = {6},
  number = {1},
  pages = {1--10},
  publisher = {Nature Publishing Group},
  issn = {2399-3642},
  doi = {10.1038/s42003-023-05622-3},
  urldate = {2024-05-02},
  abstract = {Redressing global patterns of biodiversity loss requires quantitative frameworks that can predict ecosystem collapse and inform restoration strategies. By applying a network-based dynamical approach to synthetic and real-world mutualistic ecosystems, we show that biodiversity recovery following collapse is maximized when extirpated species are reintroduced based solely on their total number of connections in the original interaction network. More complex network-based strategies that prioritize the reintroduction of species that improve `higher order' topological features such as compartmentalization do not provide meaningful performance improvements. These results suggest that it is possible to design nearly optimal restoration strategies that maximize biodiversity recovery for data-poor ecosystems in order to ensure the delivery of critical natural services that fuel economic development, food security, and human health around the globe.},
  copyright = {2023 The Author(s)},
  langid = {english},
  keywords = {Ecology,Plant sciences},
  file = {/Users/tanyastrydom/Zotero/storage/3IDEDJLW/Bhatia et al. - 2023 - Network-based restoration strategies maximize ecos.pdf}
}

@article{bitonInductiveLinkPrediction2024,
  title = {Inductive Link Prediction Boosts Data Availability and Enables Cross-Community Link Prediction in Ecological Networks},
  author = {Biton, Barry and Puzis, Rami and Pilosof, Shai},
  year = {2024},
  month = aug,
  publisher = {EcoEvoRxiv},
  urldate = {2024-09-16},
  abstract = {Predicting species interactions within ecological networks is vital for understanding ecosystem functioning and the response of communities to changing environments. Traditional link prediction models often fall short due to sparse and incomplete data and are limited to single networks. Here, we present a novel approach using inductive link prediction (ILP), which leverages structural similarities across diverse ecological networks. Our model pools data across communities, and uses transfer learning to enable prediction within and between different ecological communities. We applied our model to 538 networks across four community types: plant-seed disperser, plant-pollinator, host-parasite, and plant-herbivore. ILP outperforms non-ILP models, particularly in host-parasite and plant-seed disperser networks. However, the efficacy of cross-community predictions varies, with plant-pollinator networks consistently under-performing as train and test sets. Moreover, we developed the first method to computationally estimate the limits of link prediction given a certain proportion of missing links, in which ILP performs better than a non-ILP model. This study underscores the potential of ILP to generalize link prediction across different ecological contexts.},
  copyright = {CC-BY Attribution-NonCommercial-ShareAlike 4.0 International},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/DQFCERTK/Biton et al. - 2024 - Inductive link prediction boosts data availability.pdf}
}

@article{blanchetCooccurrenceNotEvidence2020,
  ids = {Blanchet2020CooNot},
  title = {Co-Occurrence Is Not Evidence of Ecological Interactions},
  author = {Blanchet, F. Guillaume and Cazelles, Kevin and Gravel, Dominique},
  year = {2020},
  journal = {Ecology Letters},
  volume = {23},
  number = {7},
  pages = {1050--1063},
  issn = {1461-0248},
  doi = {10.1111/ele.13525},
  urldate = {2020-10-30},
  abstract = {There is a rich amount of information in co-occurrence (presence--absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co-occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co-occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co-occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co-occurrence, along with potential avenues to extract as much information as possible from such data.},
  langid = {english}
}

@article{bluthgenCriticalEvaluationNetwork2024,
  title = {A {{Critical Evaluation}} of {{Network Approaches}} for {{Studying Species Interactions}}},
  author = {Bl{\"u}thgen, Nico and Staab, Michael},
  year = {2024},
  month = nov,
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {55},
  number = {1},
  pages = {65--88},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev-ecolsys-102722-021904},
  urldate = {2024-11-06},
  abstract = {Ecological networks of species interactions are popular and provide powerful analytical tools for understanding variation in community structure and ecosystem functioning. However, network analyses and commonly used metrics such as nestedness and connectance have also attracted criticism. One major concern is that observed patterns are misinterpreted as niche properties such as specialization, whereas they may instead merely reflect variation in sampling, abundance, and/or diversity. As a result, studies potentially draw flawed conclusions about ecological function, stability, or coextinction risks. We highlight potential biases in analyzing and interpreting species-interaction networks and review the solutions available to overcome them, among which we particularly recommend the use of null models that account for species abundances. We show why considering variation across species and networks is important for understanding species interactions and their consequences. Network analyses can advance knowledge on the principles of species interactions but only when judiciously applied.},
  copyright = {http://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/S5JU9XW4/Blüthgen and Staab - 2024 - A Critical Evaluation of Network Approaches for St.pdf}
}

@article{bluthgenEcologyMammalsInteraction2021,
  title = {Ecology: {{Mammals}}, Interaction Networks and the Relevance of Scale},
  shorttitle = {Ecology},
  author = {Bl{\"u}thgen, Nico and Staab, Michael},
  year = {2021},
  month = jul,
  journal = {Current Biology},
  volume = {31},
  number = {13},
  pages = {R850-R853},
  issn = {0960-9822},
  doi = {10.1016/j.cub.2021.05.032},
  urldate = {2024-11-06},
  abstract = {A new study shows that large mammals in an African savanna not only modify the vegetation but also strongly alter interaction networks between plants and pollinators. These insights raise fundamental yet unresolved questions about spatial dimensions of experiments, species interaction networks and ecosystems.},
  file = {/Users/tanyastrydom/Zotero/storage/TG7Y45HV/Blüthgen and Staab - 2021 - Ecology Mammals, interaction networks and the rel.pdf;/Users/tanyastrydom/Zotero/storage/BTGZD5EV/S0960982221007302.html}
}

@article{bluthgenWhyNetworkAnalysis2010,
  title = {Why Network Analysis Is Often Disconnected from Community Ecology: {{A}} Critique and an Ecologist's Guide},
  shorttitle = {Why Network Analysis Is Often Disconnected from Community Ecology},
  author = {Bl{\"u}thgen, Nico},
  year = {2010},
  month = may,
  journal = {Basic and Applied Ecology},
  volume = {11},
  number = {3},
  pages = {185--195},
  issn = {1439-1791},
  doi = {10.1016/j.baae.2010.01.001},
  urldate = {2024-11-06},
  abstract = {Network analyses of mutualistic or antagonistic interactions between species are very popular, but their biological interpretations are often unclear and incautious. Here I propose to distinguish two possible implications of network patterns in conjunction with solutions to avoid misinterpretations. Interpretations can be either(1)niche-based, describing specialisation, trait (mis-)matching between species, niche breadth and niche overlap and their relationship to interspecific competition and species coexistence, or(2)impact-based, focusing on frequencies of interactions between species such as predation or infection rates and mutualistic services, aiming to quantify each species' relative contribution to an ecological effect. For niche-based implications, it is crucial to acknowledge the sampling limitations of a network and thus control for the number of observations of each species. This is particularly important for those kinds of networks that summarise observed interactions in communities (e.g. bipartite host--parasitoid or plant--animal networks), rather than compile information from different sources or experiments (as in many food webs). Variation in total observation frequencies may alone explain network patterns that have often been interpreted as `specialisation asymmetries' (nestedness, dependence asymmetries). I show analytically that `dependence asymmetries' between two species (or two guilds) only reflect variation in their total observation frequencies. To depict true asymmetries in niche breadth, independent data are required for both species. Moreover, simulated co-extinction scenarios assume that each species `depends' on its associated partners in the network (again niche-based), but species that appear most endangered are simply those with one or very few observations and are not necessarily specialised. Distinguishing niche-based and impact-based interpretations may help to bridge terminological and conceptual gaps between network pattern analyses and traditional community ecology. Zusammenfassung Mutualistische oder antagonistische Beziehungen zwischen Arten einer Gemeinschaft werden derzeit h{\"a}ufig mit Hilfe von Netzwerkanalysen beschrieben. Da die biologische Deutung solcher Analysen oft missverst{\"a}ndlich ist, wird in diesem Artikel vorgeschlagen, zwei Interpretationsarten zu unterscheiden:(1){\"O}kologische Nische, z.B. Spezialisierung, Nischenbreite und {\"u}berlappung, sowie Kompatibilit{\"a}t von Merkmalen zwischen Arten.(2)Interaktionseffekte, die von der relativen H{\"a}ufigkeit der Wechselwirkungen abh{\"a}ngig sind, z.B. Pr{\"a}dations- und Infektionsraten oder mutualistische Funktionen. Bei nischenbezogenen Deutungen von Netzwerken, die auf beobachteten Interaktionen basieren, muss jedoch ber{\"u}cksichtigt werden, dass die Gesamtzahl der Beobachtungen pro Art limitiert ist und sich zwischen Arten stark unterscheidet. Allein diese Variation kann viele Netzwerkmuster erkl{\"a}ren, beispielsweise ``Nestedness'', was oft als asymmetrische Spezialisierung missverstanden wurde. Hier wird analytisch bewiesen, dass eine mutma{\ss}liche ``Spezialisierungs-Asymmetrie'' zwischen zwei Arten allein auf deren unterschiedliche Beobachtungsh{\"a}ufigkeit zur{\"u}ckgef{\"u}hrt werden kann. Unhabh{\"a}ngig erhobene Daten f{\"u}r beide Arten sind notwendig, um diesen Trugschluss zu vermeiden. Das Aussterben von Arten durch Verlust des Assoziationspartners (Koextinktion) wurde in mehreren publizierten Studien modelliert. Solche Simulationen basieren auf der Annahme, dass jede Art von seinen beobachteten Assoziationspartnern abh{\"a}ngig ist (nischenbasierte Deutung). Hier kann jedoch gezeigt werden, dass vor allem solche Arten scheinbar gef{\"a}hrdet sind, die nur ein- oder wenige Male beobachtet wurden, also nicht notwendigerweise Spezialisten darstellen. Die explizite Unterscheidung zwischen nischen- und effektbasierter Interpretation k{\"o}nnte demnach eine hilfreiche konzeptionelle Br{\"u}cke darstellen, um Netzwerkanalysen und klassische Gemeinschafts{\"o}kologie zusammenzuf{\"u}hren.},
  file = {/Users/tanyastrydom/Zotero/storage/6TLZRR3U/S1439179110000125.html}
}

@article{bonnaffeNeuralOrdinaryDifferential2021,
  title = {Neural Ordinary Differential Equations for Ecological and Evolutionary Time-Series Analysis},
  author = {Bonnaff{\'e}, Willem and Sheldon, Ben C. and Coulson, Tim},
  year = {2021},
  journal = {Methods in Ecology and Evolution},
  volume = {12},
  number = {7},
  pages = {1301--1315},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13606},
  urldate = {2024-07-02},
  abstract = {Inferring the functional shape of ecological and evolutionary processes from time-series data can be challenging because processes are often not describable with simple equations. The dynamical coupling between variables in time series further complicates the identification of equations through model selection as the inference of a given process is contingent on the accurate depiction of all other processes. We present a novel method, neural ordinary differential equations (NODEs), for learning ecological and evolutionary processes from time-series data by modelling dynamical systems as ordinary differential equations and dynamical functions with artificial neural networks (ANNs). Upon successful training, the ANNs converge to functional shapes that best describe the biological processes underlying the dynamics observed, in a way that is robust to mathematical misspecifications of the dynamical model. We demonstrate NODEs in a population dynamic context and show how they can be used to infer ecological interactions, dynamical causation and equilibrium points. We tested NODEs by analysing well-understood hare and lynx time-series data, which revealed that prey--predator oscillations were mainly driven by the interspecific interaction, as well as intraspecific densitydependence, and characterised by a single equilibrium point at the centre of the oscillation. Our approach is applicable to any system that can be modelled with differential equations, and particularly suitable for linking ecological, evolutionary and environmental dynamics where parametric approaches are too challenging to implement, opening new avenues for theoretical and empirical investigations.},
  copyright = {{\copyright} 2021 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society},
  langid = {english},
  keywords = {artificial neural networks,ecological dynamics,evolutionary dynamics,Geber method,neural ordinary differential equations,ordinary differential equations,prey-predator dynamics,time-series analysis},
  file = {/Users/tanyastrydom/Zotero/storage/ZV23C5IN/Bonnaffé et al. - 2021 - Neural ordinary differential equations for ecologi.pdf;/Users/tanyastrydom/Zotero/storage/ZVSUHA33/2041-210X.html}
}

@article{borrettWalkPartitionsFlow2019,
  title = {Walk Partitions of Flow in {{Ecological Network Analysis}}: {{Review}} and Synthesis of Methods and Indicators},
  shorttitle = {Walk Partitions of Flow in {{Ecological Network Analysis}}},
  author = {Borrett, Stuart R. and Scharler, Ursula M.},
  year = {2019},
  month = nov,
  journal = {Ecological Indicators},
  volume = {106},
  pages = {105451},
  issn = {1470-160X},
  doi = {10.1016/j.ecolind.2019.105451},
  urldate = {2021-06-18},
  abstract = {Ecological Network Analysis (ENA) has provided insights into the structure, function, and transformation of ecosystems for more than forty years. Key insights from ENA focus on how the patterns of directed weighted transactions among system components (e.g., species, functional groups, economic sectors) create emergent and often unexpected relationships in ecosystems that affect system function and sustainability. Flow analysis, also called throughflow analysis, is one of several core techniques in ENA. Generally, it traces the flux of energy or matter through the network from inputs to outputs. During the forty-years of development, flow analysis has accreted multiple extensions and modifications. In this concept and synthesis paper, we review four flow analyses and show how they are conceptually linked by partitioning flows across subsets of pathways within networks. These flow analyses include: (1) the definition of throughflow, a measure of the total processing power of a network; (2) Leontief's decomposition based on walk length, indicating the direction and distance of energy or matter flow; (3) Finn's measure of recycling of matter in networks; and (4) five mode analysis, characterizing flows according to their origin and destination. Presenting these techniques side-by-side with a common conceptual framework reveals overlaps and distinctive elements among the analytic products. This synthesis clarifies the flow analyses tools and their applications to ecological and socio-economic networks and provides example applications. Further, new insights are presented by combining existing flow analyses to calculate novel indices that further characterize the flow structure of networks. For example, both indirect flows in networks and cycling are highly important features in networks. In order to determine the proportion of indirect flows generated through cycling, we can use the ratio of Cycled Flow identified from Finn's analysis and the indirect flows identified in the Leontief analysis. As ENA matures through additional analysis development and applications, it will continue to provide insights into ecosystems and contribute to the broader area of network science.},
  langid = {english}
}

@misc{boussangePartitioningTimeSeries2024,
  title = {Partitioning Time Series to Improve Process-Based Models with Machine Learning},
  author = {Boussange, Victor and Aceituno, Pau Vilimelis and Schaefer, Frank and Pellissier, Loic},
  year = {2024},
  month = apr,
  primaryclass = {New Results},
  pages = {2022.07.25.501365},
  publisher = {bioRxiv},
  doi = {10.1101/2022.07.25.501365},
  urldate = {2024-04-11},
  abstract = {Process-based community models are required to extrapolate beyond current dynamics and anticipate biodiversity response to global change, but their adoption has been limited in practice because of issues with model parameter estimation and model inaccuracies. While observation data could be used to directly estimate parameters and refine model structures, model nonlinearities, sparse and noisy datasets and the complexity of ecological dynamics complicate this process. Here, we propose an inverse modelling framework designed for addressing these issues, that relies on a segmentation method combined with automatic differentiation, deep learning gradient-based optimizers, minibatching, and a parameter normalization technique. By partitioning the data into time series with a short time horizon, we show that the segmentation method regularizes the inverse problem. Implemented in the Julia package PiecewiseInference.jl, the resulting framework enables the efficient calibration of realistic dynamical community models and supports the use of neural networks to represent complex ecological processes within the model. We evaluate the performance of the inverse modelling framework on simulated food-web dynamics of increasing complexity, including scenarios with partial observations. Our results demonstrate its performance in inferring model parameters and providing forecasts. We further showcase that the regularization induced by the framework, together with its efficiency, are instrumental for the online training of neural network-based parametrizations, alongside the other model parameters. Neural network-based parametrizations not only mitigate model inaccuracies but can also contribute to the refinement of ecological theory through their offline examination. The proposed inverse modelling framework is data-efficient, interpretable and capable of extrapolating beyond observed dynamics, showing promise in improving our ability to understand and forecast biodiversity dynamics.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/JGSKMR5U/Boussange et al. - 2024 - Partitioning time series to improve process-based .pdf}
}

@article{bramonmoraIdentifyingCommonBackbone2018,
  ids = {Mora2018IdeCom},
  title = {Identifying a Common Backbone of Interactions Underlying Food Webs from Different Ecosystems},
  author = {Bramon Mora, Bernat and Gravel, Dominique and Gilarranz, Luis J. and Poisot, Timoth{\'e}e and Stouffer, Daniel B.},
  year = {2018},
  month = dec,
  journal = {Nature Communications},
  volume = {9},
  number = {1},
  pages = {2603},
  publisher = {Nature Publishing Group},
  address = {London, United States},
  issn = {2041-1723},
  doi = {10.1038/s41467-018-05056-0},
  urldate = {2021-03-26},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/MECAHV9I/Mora et al. - 2018 - Identifying a common backbone of interactions unde.pdf;/Users/tanyastrydom/Zotero/storage/YNKV9HFU/Bramon Mora et al. - 2018 - Identifying a common backbone of interactions unde.pdf}
}

@misc{brimacombeApplyingMethodIts2024,
  title = {Applying a Method before Its Proof-of-Concept: {{A}} Cautionary Tale Using Inferred Food Webs},
  shorttitle = {Applying a Method before Its Proof-of-Concept},
  author = {Brimacombe, Chris and Bodner, Korryn and Fortin, Marie-Josee},
  year = {2024},
  month = apr,
  doi = {10.13140/RG.2.2.22076.65927},
  file = {/Users/tanyastrydom/Zotero/storage/I2XM9S7Z/Brimacombe et al. - 2024 - Applying a method before its proof-of-concept A c.pdf}
}

@article{brimacombeHowNetworkSize2022,
  title = {How Network Size Strongly Determines Trophic Specialisation: {{A}} Technical Comment on {{Luna}} et al. (2022)},
  shorttitle = {How Network Size Strongly Determines Trophic Specialisation},
  author = {Brimacombe, Chris and Bodner, Korryn and Fortin, Marie-Jos{\'e}e},
  year = {2022},
  journal = {Ecology Letters},
  volume = {25},
  number = {8},
  pages = {1914--1916},
  issn = {1461-0248},
  doi = {10.1111/ele.14029},
  urldate = {2024-11-06},
  abstract = {Luna et al. (2022) concluded that the environment contributes to explaining specialisation in open plant--pollinator networks. When reproducing their study, we instead found that network size alone largely explained the variation in their specialisation metrics. Thus, we question whether empirical network specialisation is driven by the environment.},
  copyright = {{\copyright} 2022 John Wiley \& Sons Ltd.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/VLBNACHK/Brimacombe et al. - 2022 - How network size strongly determines trophic speci.pdf;/Users/tanyastrydom/Zotero/storage/F9I2A6VC/ele.html}
}

@article{brimacombeInferredSeasonalInteraction2021,
  title = {Inferred Seasonal Interaction Rewiring of a Freshwater Stream Fish Network},
  author = {Brimacombe, Chris and Bodner, Korryn and Fortin, Marie-Jos{\'e}e},
  year = {2021},
  journal = {Ecography},
  volume = {44},
  number = {2},
  pages = {219--230},
  issn = {1600-0587},
  doi = {10.1111/ecog.05452},
  urldate = {2024-04-15},
  abstract = {Despite evidence that seasonal variation may lead to the persistence of competing species, studies on the effect of seasonality on community network structures are sparse. Identifying whether seasonal network changes are the result of turnover or rewiring (i.e. rearrangement of interactions among species), also remains understudied in multi-trophic communities. Using species abundance data for 38 species over three years (from nine sites across central/eastern United States) and a novel tree-based inference method, we constructed seasonal networks for a multi-trophic freshwater stream fish community. We found that seasonality influences species interactions, particularly through rewiring (81\%) as compared to species turnover (19\%). Moreover, the number of rewiring interactions was best explained by fish status as a piscivore/non-piscivore and species maximum length (R2 = 0.41). Our findings suggest that rewiring may be a dominant process in stream fish communities experiencing seasonal environments and that traits linked to trophic-level could be a good indicator of a species contribution to rewiring. As networks dominated by rewiring may be more robust, this network approach could be a valuable conservation tool for identifying which biological relationships must be retained for communities to avoid extinction.},
  copyright = {{\copyright} 2020 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/JXBQV7IY/Brimacombe et al. - 2021 - Inferred seasonal interaction rewiring of a freshw.pdf}
}

@article{brimacombePublicationdrivenConsistencyFood2024,
  title = {Publication-Driven Consistency in Food Web Structures: {{Implications}} for Comparative Ecology},
  shorttitle = {Publication-Driven Consistency in Food Web Structures},
  author = {Brimacombe, Chris and Bodner, Korryn and Gravel, Dominique and Leroux, Shawn J. and Poisot, Timoth{\'e}e and Fortin, Marie-Jos{\'e}e},
  year = {2024},
  journal = {Ecology},
  volume = {n/a},
  number = {n/a},
  pages = {e4467},
  issn = {1939-9170},
  doi = {10.1002/ecy.4467},
  urldate = {2024-11-25},
  abstract = {Large collections of freely available food webs are commonly reused by researchers to infer how biological or environmental factors influence the structure of ecological communities. Although reusing food webs expands sample sizes for community analysis, this practice also has significant drawbacks. As food webs are meticulously crafted by researchers for their own specific research endeavors and resulting publications (i.e., books and scientific articles), the structure of these webs inherently reflects the unique methodologies and protocols of their source publications. Consequently, combining food webs sourced from different publications without accounting for discrepancies that influence network structure may be problematic. Here, we investigate the determinants of structure in freely available food webs sourced from different publications, examining potential disparities that could hinder their effective comparison. Specifically, we quantify structural similarity across 274 commonly reused webs sourced from 105 publications using a subgraph technique. Surprisingly, we found no increased structural similarity between webs from the same ecosystem nor webs built using similar network construction methodologies. Yet, webs sourced from the same publication were very structurally similar with this degree of similarity increasing over time. As webs sourced from the same publication are typically sampled, constructed, and/or exposed to similar biological and environmental factors, publications likely holistically drive their own webs' structure to be similar. Our findings demonstrate the large effect that publications have on the structure of their own webs, which stymies inference when comparing the structure of webs sourced from different publications. We conclude by proposing different approaches that may be useful for reducing these publication-related structural issues.},
  copyright = {{\copyright} 2024 The Author(s). Ecology published by Wiley Periodicals LLC on behalf of The Ecological Society of America.},
  langid = {english},
  keywords = {communities,methodology,networks,sampling,species interactions,subgraphs,trophic relationships},
  file = {/Users/tanyastrydom/Zotero/storage/U2RBHXSU/Brimacombe et al. - Publication-driven consistency in food web structu.pdf;/Users/tanyastrydom/Zotero/storage/AMWU6P7I/ecy.html}
}

@article{brimacombeShortcomingsReusingSpecies2023,
  title = {Shortcomings of Reusing Species Interaction Networks Created by Different Sets of Researchers},
  author = {Brimacombe, Chris and Bodner, Korryn and {Michalska-Smith}, Matthew and Poisot, Timoth{\'e}e and Fortin, Marie-Jos{\'e}e},
  year = {2023},
  month = apr,
  journal = {PLOS Biology},
  volume = {21},
  number = {4},
  pages = {e3002068},
  publisher = {Public Library of Science},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.3002068},
  urldate = {2023-09-05},
  abstract = {Given the requisite cost associated with observing species interactions, ecologists often reuse species interaction networks created by different sets of researchers to test their hypotheses regarding how ecological processes drive network topology. Yet, topological properties identified across these networks may not be sufficiently attributable to ecological processes alone as often assumed. Instead, much of the totality of topological differences between networks---topological heterogeneity---could be due to variations in research designs and approaches that different researchers use to create each species interaction network. To evaluate the degree to which this topological heterogeneity is present in available ecological networks, we first compared the amount of topological heterogeneity across 723 species interaction networks created by different sets of researchers with the amount quantified from non-ecological networks known to be constructed following more consistent approaches. Then, to further test whether the topological heterogeneity was due to differences in study designs, and not only to inherent variation within ecological networks, we compared the amount of topological heterogeneity between species interaction networks created by the same sets of researchers (i.e., networks from the same publication) with the amount quantified between networks that were each from a unique publication source. We found that species interaction networks are highly topologically heterogeneous: while species interaction networks from the same publication are much more topologically similar to each other than interaction networks that are from a unique publication, they still show at least twice as much heterogeneity as any category of non-ecological networks that we tested. Altogether, our findings suggest that extra care is necessary to effectively analyze species interaction networks created by different researchers, perhaps by controlling for the publication source of each network.},
  langid = {english}
}

@article{brownMetabolicTheoryEcology2004,
  title = {Toward a {{Metabolic Theory}} of {{Ecology}}},
  author = {Brown, James H. and Gillooly, James F. and Allen, Andrew P. and Savage, Van M. and West, Geoffrey B.},
  year = {2004},
  journal = {Ecology},
  volume = {85},
  number = {7},
  pages = {1771--1789},
  issn = {1939-9170},
  doi = {10.1890/03-9000},
  urldate = {2024-10-03},
  abstract = {Metabolism provides a basis for using first principles of physics, chemistry, and biology to link the biology of individual organisms to the ecology of populations, communities, and ecosystems. Metabolic rate, the rate at which organisms take up, transform, and expend energy and materials, is the most fundamental biological rate. We have developed a quantitative theory for how metabolic rate varies with body size and temperature. Metabolic theory predicts how metabolic rate, by setting the rates of resource uptake from the environment and resource allocation to survival, growth, and reproduction, controls ecological processes at all levels of organization from individuals to the biosphere. Examples include: (1) life history attributes, including development rate, mortality rate, age at maturity, life span, and population growth rate; (2) population interactions, including carrying capacity, rates of competition and predation, and patterns of species diversity; and (3) ecosystem processes, including rates of biomass production and respiration and patterns of trophic dynamics. Data compiled from the ecological literature strongly support the theoretical predictions. Eventually, metabolic theory may provide a conceptual foundation for much of ecology, just as genetic theory provides a foundation for much of evolutionary biology.},
  copyright = {{\copyright} 2004 by the Ecological Society of America},
  langid = {english},
  keywords = {allometry,biogeochemical cycles,body size,development,ecological interactions,ecological theory,metabolism,population growth,production,stoichiometry,temperature,trophic dynamics},
  file = {/Users/tanyastrydom/Zotero/storage/7UHBWMNR/03-9000.html}
}

@article{bucheMultitrophicHigherOrderInteractions2024,
  title = {Multitrophic {{Higher-Order Interactions Modulate Species Persistence}}},
  author = {Buche, Lisa and Bartomeus, Ignasi and Godoy, Oscar},
  year = {2024},
  month = apr,
  journal = {The American Naturalist},
  volume = {203},
  number = {4},
  pages = {458--472},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/729222},
  urldate = {2024-09-30},
  abstract = {Ecologists increasingly recognize that interactions between two species can be affected by the density of a third species. How these higher-order interactions (HOIs) affect species persistence remains poorly understood. To explore the effect of HOIs stemming from multiple trophic layers on a plant community composition, we experimentally built a mesocosm with three plants and three pollinator species arranged in a fully nested and modified network structure. We estimated pairwise interactions among plants and between plants and pollinators, as well as HOIs initiated by a plant or a pollinator affecting plant species pairs. Using a structuralist approach, we evaluated the consequences of the statistically supported HOIs on the persistence probability of each of the three competing plant species and their combinations. HOIs substantially redistribute the strength and sign of pairwise interactions between plant species, promoting the opportunities for multispecies communities to persist compared with a non-HOI scenario. However, the physical elimination of a plant-pollinator link in the modified network structure promotes changes in per capita pairwise interactions and HOIs, resulting in a single-species community. Our study provides empirical evidence of the joint importance of HOIs and network structure in determining species persistence within diverse communities.},
  keywords = {coexistence,multitrophic interactions,per capita effects,plant-pollinator network},
  file = {/Users/tanyastrydom/Zotero/storage/GMRSTM8E/Buche et al. - 2024 - Multitrophic Higher-Order Interactions Modulate Sp.pdf}
}

@article{canardEmergenceStructuralPatterns2012,
  title = {Emergence of {{Structural Patterns}} in {{Neutral Trophic Networks}}},
  author = {Canard, Elsa and Mouquet, Nicolas and Marescot, Lucile and Gaston, Kevin J. and Gravel, Dominique and Mouillot, David},
  year = {2012},
  month = aug,
  journal = {PLOS ONE},
  volume = {7},
  number = {8},
  pages = {e38295},
  publisher = {Public Library of Science},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0038295},
  urldate = {2024-07-05},
  abstract = {Interaction networks are central elements of ecological systems and have very complex structures. Historically, much effort has focused on niche-mediated processes to explain these structures, while an emerging consensus posits that both niche and neutral mechanisms simultaneously shape many features of ecological communities. However, the study of interaction networks still lacks a comprehensive neutral theory. Here we present a neutral model of predator-prey interactions and analyze the structural characteristics of the simulated networks. We find that connectance values (complexity) and complexity-diversity relationships of neutral networks are close to those observed in empirical bipartite networks. High nestedness and low modularity values observed in neutral networks fall in the range of those from empirical antagonist bipartite networks. Our results suggest that, as an alternative to niche-mediated processes that induce incompatibility between species (``niche forbidden links''), neutral processes create ``neutral forbidden links'' due to uneven species abundance distributions and the low probability of interaction between rare species. Neutral trophic networks must be seen as the missing endpoint of a continuum from niche to purely stochastic approaches of community organization.},
  langid = {english},
  keywords = {Community structure,Ecological niches,Network analysis,Predation,Relative abundance distribution,Species diversity,Species interactions,Trophic interactions},
  file = {/Users/tanyastrydom/Zotero/storage/2NWZC39M/Canard et al. - 2012 - Emergence of Structural Patterns in Neutral Trophi.pdf}
}

@article{canardEmpiricalEvaluationNeutral2014,
  title = {Empirical {{Evaluation}} of {{Neutral Interactions}} in {{Host-Parasite Networks}}.},
  author = {Canard, E. F. and Mouquet, N. and Mouillot, D. and Stanko, M. and Miklisova, D. and Gravel, D.},
  year = {2014},
  month = apr,
  journal = {The American Naturalist},
  volume = {183},
  number = {4},
  pages = {468--479},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/675363},
  urldate = {2024-11-22},
  abstract = {While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization.},
  keywords = {host-parasite network,network structure,neutrality,null model,species abundance distribution},
  file = {/Users/tanyastrydom/Zotero/storage/C288TWUC/Canard et al. - 2014 - Empirical Evaluation of Neutral Interactions in Ho.pdf}
}

@article{caronAddressingEltonianShortfall2022,
  title = {Addressing the {{Eltonian}} Shortfall with Trait-Based Interaction Models},
  author = {Caron, Dominique and Maiorano, Luigi and Thuiller, Wilfried and Pollock, Laura J.},
  year = {2022},
  journal = {Ecology Letters},
  volume = {25},
  number = {4},
  pages = {889--899},
  issn = {1461-0248},
  doi = {10.1111/ele.13966},
  urldate = {2023-09-15},
  abstract = {We have very limited knowledge of how species interact in most communities and ecosystems despite trophic relationships being fundamental for linking biodiversity to ecosystem functioning. A promising approach to fill this gap is to predict interactions based on functional traits, but many questions remain about how well we can predict interactions for different taxa, ecosystems and amounts of input data. Here, we built a new traits-based model of trophic interactions for European vertebrates and found that even models calibrated with 0.1\% of the interactions (100 out of 71 k) estimated the full European vertebrate food web reasonably well. However, predators were easier to predict than prey, especially for some clades (e.g. fowl and storks) and local food web connectance was consistently overestimated. Our results demonstrate the ability to rapidly generate food webs when empirical data are lacking---an important step towards a more complete and spatially explicit description of food webs.},
  copyright = {{\copyright} 2022 John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {ecological networks,ecological predictions,food web,model transferability,terrestrial vertebrates,trait matching,trophic interactions},
  file = {/Users/tanyastrydom/Zotero/storage/YIYHD8HS/ele.html}
}

@article{caronTraitmatchingModelsPredict2024,
  title = {Trait-Matching Models Predict Pairwise Interactions across Regions, Not Food Web Properties},
  author = {Caron, Dominique and Brose, Ulrich and Lurgi, Miguel and Blanchet, F. Guillaume and Gravel, Dominique and Pollock, Laura J.},
  year = {2024},
  journal = {Global Ecology and Biogeography},
  volume = {33},
  number = {4},
  pages = {e13807},
  issn = {1466-8238},
  doi = {10.1111/geb.13807},
  urldate = {2024-03-21},
  abstract = {Aim Food webs are essential for understanding how ecosystems function, but empirical data on the interactions that make up these ecological networks are lacking for most taxa in most ecosystems. Trait-based models can fill these data gaps, but their ability to do so has not been widely tested. We test how well these models can extrapolate to new ecological communities both in terms of pairwise predator--prey interactions and higher level food web attributes (i.e. species position, food web-level properties). Location Canada, Europe, Tanzania. Time Period Current. Major Taxa Studied Terrestrial vertebrates. Methods We train trait-based models of pairwise trophic interactions on four independent vertebrate food webs (Canadian tundra, Serengeti, alpine south-eastern Pyrenees and Europe) and evaluate how well these models predict pairwise interactions and network properties of each food web. Results We find that, overall, trait-based models predict most interactions and their absence correctly. Performance was best for training and testing on the same food web (AUC {$>$} 0.90) and declined with environmental and phylogenetic distances with the strongest loss of performance for the tundra-Serengeti ecosystems (AUC {$>$} 0.75). Network metrics were less well-predicted than single interactions by our models with predicted food webs being more connected, less modular, and with higher mean trophic levels than observed. Main Conclusions Theory predicts that the variability observed in food webs can be explained by differences in trait distributions and trait-matching relationships. Our finding that trait-based models can predict many trophic interactions, even in contrasting environments, adds to the growing body of evidence that there are general constraints on interactions and that trait-based methods can serve as a useful first approximation of food webs in unknown areas. However, food webs are more than the sum of their parts, and predicting network attributes will likely require models that simultaneously predict individual interactions and community constraints.},
  langid = {english},
  keywords = {ecological predictions,food web,model transferability,terrestrial vertebrates,trait matching,trophic interactions},
  file = {/Users/tanyastrydom/Zotero/storage/5WSV2KMN/Caron et al. - 2024 - Trait-matching models predict pairwise interaction.pdf;/Users/tanyastrydom/Zotero/storage/IUZB465T/geb.html}
}

@article{catchenMissingLinkDiscerning2023,
  title = {The Missing Link: Discerning True from False Negatives When Sampling Species Interaction Networks},
  shorttitle = {The Missing Link},
  author = {Catchen, Michael D. and Poisot, Timoth{\'e}e and Pollock, Laura J. and Gonzalez, Andrew},
  year = {2023},
  month = jan,
  publisher = {EcoEvoRxiv},
  urldate = {2023-09-15},
  abstract = {Ecosystems are composed of networks of interacting species. These interactions allow communities of species to persist through time through both neutral and adaptive processes. Despite their importance, a robust understanding of (and ability to predict and forecast) interactions among species remains elusive. This knowledge-gap is largely driven by a shortfall of data---although species occurrence data has rapidly increased in the last decade, species interaction data has not kept pace, largely due to the effort required to sample interactions. This means there are many interactions between species that occur in nature, but we do not know these interactions occur because we have never observed them. These so-called ``false-negatives'' bias data and hinder inference about the structure and dynamics of interaction networks. Here, we demonstrate the realized rate of false-negatives in data can be quite high, even in thoroughly sampled systems, due to the intrinsic variation in abundances across species in a community. We illustrate how a null model of occurrence detection can be used to estimate the false-negative rate in a given dataset. We also show how to directly incorporate uncertainty due to observation error into model-based predictions of interaction probabilities between species. One hypothesis is that interactions between ``rare'' species are themselves rare because these species are less likely to encounter one-another than species of higher relative abundance, and that this can (in part) explain the common pattern of nestedness in bipartite interaction networks. However, we demonstrate that across several datasets of spatial or temporally replicated networks, there are positive associations between species co-occurrence and interactions, which suggests these interactions among ``rare'' species actually exist but simply are not observed. Finally, we assess how false negatives influence various models of network prediction, and recommend directly accounting for observation error in predictive models. We conclude by discussing how the understanding of false-negatives can inform how we design monitoring schemes for species interactions.},
  copyright = {CC BY Attribution 4.0 International},
  langid = {english}
}

@article{cattinPhylogeneticConstraintsAdaptation2004,
  title = {Phylogenetic Constraints and Adaptation Explain Food-Web Structure},
  author = {Cattin, Marie-France and Bersier, Louis-F{\'e}lix and {Bana{\v s}ek-Richter}, Carolin and Baltensperger, Richard and Gabriel, Jean-Pierre},
  year = {2004},
  month = feb,
  journal = {Nature},
  volume = {427},
  number = {6977},
  pages = {835--839},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/nature02327},
  urldate = {2024-02-01},
  abstract = {Food webs are descriptions of who eats whom in an ecosystem. Although extremely complex and variable, their structure possesses basic regularities1,2,3,4,5,6. A fascinating question is to find a simple model capturing the underlying processes behind these repeatable patterns. Until now, two models have been devised for the description of trophic interactions within a natural community7,8. Both are essentially based on the concept of ecological niche, with the consumers organized along a single niche dimension; for example, prey size8,9. Unfortunately, they fail to describe adequately recent and high-quality data. Here, we propose a new model built on the hypothesis that any species' diet is the consequence of phylogenetic constraints and adaptation. Simple rules incorporating both concepts yield food webs whose structure is very close to real data. Consumers are organized in groups forming a nested hierarchy, which better reflects the complexity and multidimensionality of most natural systems.},
  copyright = {2004 Macmillan Magazines Ltd.},
  langid = {english},
  keywords = {Humanities and Social Sciences,multidisciplinary,Science},
  file = {/Users/tanyastrydom/Zotero/storage/CVEVP4AX/Cattin et al. - 2004 - Phylogenetic constraints and adaptation explain fo.pdf}
}

@article{chenTransientDynamicsFood2001,
  title = {Transient Dynamics and Food--Web Complexity in the {{Lotka}}--{{Volterra}} Cascade Model},
  author = {Chen, X. and Cohen, J. E.},
  year = {2001},
  month = apr,
  journal = {Proceedings of the Royal Society of London. Series B: Biological Sciences},
  volume = {268},
  number = {1469},
  pages = {869--877},
  publisher = {Royal Society},
  doi = {10.1098/rspb.2001.1596},
  urldate = {2024-02-01},
  abstract = {How does the long--term behaviour near equilibrium of model food webs correlate with their short--term transient dynamics? Here, simulations of the Lotka--Volterra cascade model of food webs provide the first evidence to answer this question. Transient behaviour is measured by resilience, reactivity, the maximum amplification of a perturbation and the time at which the maximum amplification occurs. Model food webs with a higher probability of local asymptotic stability may be less resilient and may have a larger transient growth of perturbations. Given a fixed connectance, the sizes and durations of transient responses to perturbations increase with the number of species. Given a fixed number of species, as connectance increases, the sizes and durations of transient responses to perturbations may increase or decrease depending on the type of link that is varied. Reactivity is more sensitive to changes in the number of donor--controlled links than to changes in the number of recipient--controlled links, while resilience is more sensitive to changes in the number of recipient--controlled links than to changes in the number of donor--controlled links. Transient behaviour is likely to be one of the important factors affecting the persistence of ecological communities.},
  keywords = {food webs,persistence,perturbation,reactivity,resilience,stability},
  file = {/Users/tanyastrydom/Zotero/storage/U3EH86TC/Chen and Cohen - 2001 - Transient dynamics and food–web complexity in the .pdf}
}

@article{cherifEnvironmentRescueCan2024,
  title = {The Environment to the Rescue: Can Physics Help Predict Predator--Prey Interactions?},
  shorttitle = {The Environment to the Rescue},
  author = {Cherif, Mehdi and Brose, Ulrich and Hirt, Myriam R. and Ryser, Remo and Silve, Violette and Albert, Georg and Arnott, Russell and Berti, Emilio and Cirtwill, Alyssa and Dyer, Alexander and Gauzens, Benoit and Gupta, Anhubav and Ho, Hsi-Cheng and Portalier, S{\'e}bastien M. J. and Wain, Danielle and Wootton, Kate},
  year = {2024},
  journal = {Biological Reviews},
  volume = {138},
  number = {1},
  issn = {1469-185X},
  doi = {10.1111/brv.13105},
  urldate = {2024-06-17},
  abstract = {Understanding the factors that determine the occurrence and strength of ecological interactions under specific abiotic and biotic conditions is fundamental since many aspects of ecological community stability and ecosystem functioning depend on patterns of interactions among species. Current approaches to mapping food webs are mostly based on traits, expert knowledge, experiments, and/or statistical inference. However, they do not offer clear mechanisms explaining how trophic interactions are affected by the interplay between organism characteristics and aspects of the physical environment, such as temperature, light intensity or viscosity. Hence, they cannot yet predict accurately how local food webs will respond to anthropogenic pressures, notably to climate change and species invasions. Herein, we propose a framework that synthesises recent developments in food-web theory, integrating body size and metabolism with the physical properties of ecosystems. We advocate for combination of the movement paradigm with a modular definition of the predation sequence, because movement is central to predator--prey interactions, and a generic, modular model is needed to describe all the possible variation in predator--prey interactions. Pending sufficient empirical and theoretical knowledge, our framework will help predict the food-web impacts of well-studied physical factors, such as temperature and oxygen availability, as well as less commonly considered variables such as wind, turbidity or electrical conductivity. An improved predictive capability will facilitate a better understanding of ecosystem responses to a changing world.},
  langid = {english},
  keywords = {food webs,functional response,internal state,motion,movement paradigm,navigation,physical factors,predation sequence,predictability}
}

@article{chiccoAdvantagesMatthewsCorrelation2020,
  title = {The Advantages of the {{Matthews}} Correlation Coefficient ({{MCC}}) over {{F1}} Score and Accuracy in Binary Classification Evaluation},
  author = {Chicco, Davide and Jurman, Giuseppe},
  year = {2020},
  month = jan,
  journal = {BMC Genomics},
  volume = {21},
  number = {1},
  pages = {6},
  issn = {1471-2164},
  doi = {10.1186/s12864-019-6413-7},
  urldate = {2024-03-22},
  abstract = {To evaluate binary classifications and their confusion matrices, scientific researchers can employ several statistical rates, accordingly to the goal of the experiment they are investigating. Despite being a crucial issue in machine learning, no widespread consensus has been reached on a unified elective chosen measure yet. Accuracy and F1 score computed on confusion matrices have been (and still are) among the most popular adopted metrics in binary classification tasks. However, these statistical measures can dangerously show overoptimistic inflated results, especially on imbalanced datasets.},
  keywords = {Accuracy,Binary classification,Biostatistics,Confusion matrices,Dataset imbalance,F1 score,Genomics,Machine learning,Matthews correlation coefficient},
  file = {/Users/tanyastrydom/Zotero/storage/4KTILJ3D/Chicco and Jurman - 2020 - The advantages of the Matthews correlation coeffic.pdf;/Users/tanyastrydom/Zotero/storage/TNPWRPFX/s12864-019-6413-7.html}
}

@article{cirtwillQuantitativeFrameworkInvestigating2019,
  title = {A Quantitative Framework for Investigating the Reliability of Empirical Network Construction},
  author = {Cirtwill, Alyssa R. and Ekl{\"{\cyrchar\cyro}}f, Anna and Roslin, Tomas and Wootton, Kate and Gravel, Dominique},
  year = {2019},
  journal = {Methods in Ecology and Evolution},
  volume = {10},
  number = {6},
  pages = {902--911},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13180},
  urldate = {2019-04-03},
  abstract = {1.Descriptions of ecological networks typically assume that the same interspecific interactions occur each time a community is observed. This contrasts with the known stochasticity of ecological communities: community composition, species abundances, and link structure all vary in space and time. Moreover, finite sampling generates variation in the set of interactions actually observed. For interactions that have not been observed, most data sets will not contain enough information for the ecologist to be con\_dent that unobserved interactions truly did not occur. 2. Here we develop the conceptual and analytical tools needed to capture uncertainty in the estimation of pairwise interactions. To define the problem, we identify the different contributions to the uncertainty of an interaction. We then outline a framework to quantify the uncertainty around each interaction by combining data on observed co-occurrences with prior knowledge. We illustrate this framework using perhaps the most extensively sampled network to date. 3. We found significant uncertainty in estimates for the probability of most pairwise interactions. This uncertainty can, however, be constrained with informative priors. This uncertainty scaled up to summary measures of network structure such as connectance and nestedness. Even with informative priors, we are likely to miss many interactions that may occur rarely or under different local conditions. 4. Overall, we demonstrate the importance of acknowledging the uncertainty inherent in network studies, and the utility of treating interactions as probabilities in pinpointing areas where more study is needed. Most importantly, we stress that networks are best thought of as systems constructed from random variables, the stochastic nature of which must be acknowledged for an accurate representation. Doing so will fundamentally change networks analyses and yield greater realism. This article is protected by copyright. All rights reserved.},
  copyright = {This article is protected by copyright. All rights reserved.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/UUS39J48/Cirtwill et al. - 2019 - A quantitative framework for investigating the rel.pdf;/Users/tanyastrydom/Zotero/storage/RBZS26LE/Cirtwill et al. - A quantitative framework for investigating the rel.html}
}

@article{cleggImpactIntraspecificVariation2018,
  title = {The Impact of Intraspecific Variation on Food Web Structure},
  author = {Clegg, Tom and Ali, Mohammad and Beckerman, Andrew P.},
  year = {2018},
  journal = {Ecology},
  volume = {99},
  number = {12},
  pages = {2712--2720},
  issn = {1939-9170},
  doi = {10.1002/ecy.2523},
  urldate = {2024-05-15},
  abstract = {Accounting for the variation that occurs within species in food webs can theoretically result in significant changes in both network structure and dynamics. However, there has been little work exploring their role with empirical data. In particular, the variation associated with species' life cycles, which is prevalent and represents both trait variation and taxonomic identity, has received little attention. Here, we characterize the structural consequences of life stage variation in five food webs, including a newly compiled web from the Arabian Gulf. We show that making life stage variation explicit in food webs results in larger food webs that possess consistent structural changes that are separate from the changes in structure that come simply from increasing the number of nodes in the webs. Furthermore, we show that the magnitude of these changes is related to ontogenetic specialism, the degree of overlap in the ecological niches of life stages. These results demonstrate the capacity of intraspecific variation to affect ecological networks and indicate the potential usefulness of stage-structured food webs, which capture size and taxonomic information, to represent variation below the species level.},
  copyright = {{\copyright} 2018 by the Ecological Society of America},
  langid = {english},
  keywords = {ecological network,empirical food web,ontogenetic niche shift,ontogeny,predator-prey,stage structure},
  file = {/Users/tanyastrydom/Zotero/storage/C5ZVCPUU/Clegg et al. - 2018 - The impact of intraspecific variation on food web .pdf;/Users/tanyastrydom/Zotero/storage/UE32P5ND/ecy.html}
}

@book{cohenCommunityFoodWebs1990,
  title = {Community {{Food Webs}}: {{Data}} and {{Theory}}},
  shorttitle = {Community {{Food Webs}}},
  author = {Cohen, Joel E and Briand, Frederic and Newman, Charles},
  year = {1990},
  series = {Biomathematics},
  publisher = {Springer-Verlag},
  address = {Berlin Heidelberg},
  urldate = {2019-08-07},
  abstract = {Food webs hold a central place in ecology. They describe which organisms feed on which others in natural habitats. This book describes recently discovered empirical regularities in real food webs: it proposes a novel theory unifying many of these regularities, as well as extensive empirical data. After a general introduction, reviewing the empirical and theoretical discoveries about food webs, the second portion of the book shows that community food webs obey several striking phenomenological regularities. Some of these unify, regardless of habitat. Others differentiate, showing that habitat significantly influences structure. The third portion of the book presents a theoretical analysis of some of the unifying empirical regularities. The fourth portion of the book presents 13 community food webs. Collected from scattered sources and carefully edited, they are the empirical basis for the results in the volume. The largest available set of data on community food webs provides a valuable foundation for future studies of community food webs. The book is intended for graduate students, teachers and researchers primarily in ecology. The theoretical portions of the book provide materials useful to teachers of applied combinatorics, in particular, random graphs. Researchers in random graphs will find here unsolved mathematical problems.},
  isbn = {978-3-642-83786-9},
  langid = {english}
}

@article{cohenFoodWebsDimensionality1977,
  title = {Food Webs and the Dimensionality of Trophic Niche Space},
  author = {Cohen, Joel E.},
  year = {1977},
  month = oct,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {74},
  number = {10},
  pages = {4533--4536},
  publisher = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.74.10.4533},
  urldate = {2024-02-01},
  abstract = {If the trophic niche of a kind of organism is a connected region in niche space, then it is possible for trophic niche overlaps to be described in a one-dimensional niche space if and only if the trophic niche overlap graph is an interval graph. An analysis of 30 food webs, using the combinatorial theory of interval graphs, suggests that a niche space of dimension 1 suffices, with unexpectedly high frequency and perhaps always, to describe the trophic niche overlaps implied by real food webs in single habitats. Consequently, real food webs fall in a small subset of the set of mathematically possible food webs. That real food webs are compatible with one-dimensional trophic niche spaces, more often than can be explained by chance alone, has not been noticed previously.},
  file = {/Users/tanyastrydom/Zotero/storage/9V5LRTIJ/Cohen - 1977 - Food webs and the dimensionality of trophic niche .pdf}
}

@article{cohenStochasticTheoryCommunity1985,
  title = {A Stochastic Theory of Community Food Webs {{I}}. {{Models}} and Aggregated Data},
  author = {Cohen, Joel E. and Newman, C. M. and Steele, John Hyslop},
  year = {1985},
  journal = {Proceedings of the Royal Society of London. Series B. Biological Sciences},
  volume = {224},
  number = {1237},
  pages = {421--448},
  publisher = {Royal Society},
  doi = {10.1098/rspb.1985.0042},
  urldate = {2024-02-01},
  abstract = {Three recently discovered quantitative empirical generalizations describe major features of the structure of community food webs. These generalizations are: (i) a species scaling law: the mean proportions of basal, intermediate and top species remain invariant at approximately 0.19, 0.53, and 0.29, respectively, over the range of variation in the number of species in a web; (ii) a link scaling law : the mean proportions of trophic links in the categories basal-intermediate, basal-top, intermediate--intermediate, and intermediate--top remain invariant at approximately 0.27, 0.08, 0.30 and 0.35, respectively, over the range of variation in the number of species in a web; and (iii) a link-species scaling law: the ratio of mean trophic links to species remains invariant at approximately 1.86, over the range of variation in the number of species in a web. This paper presents a model, the only successful one among several attempts, in which the first two of these empirical generalizations can be derived as a consequence of the third. The model assumes that species are ordered in a cascade or hierarchy such that a given species can prey on only those species below it and can be preyed on by only those species above it in the hierarchy.},
  file = {/Users/tanyastrydom/Zotero/storage/ZGDQ9C63/Cohen et al. - 1997 - A stochastic theory of community food webs I. Mode.pdf}
}

@article{cooperDeepDiveEstimatingGlobal2024,
  title = {{{DeepDive}}: Estimating Global Biodiversity Patterns through Time Using Deep Learning},
  shorttitle = {{{DeepDive}}},
  author = {Cooper, Rebecca B. and {Flannery-Sutherland}, Joseph T. and Silvestro, Daniele},
  year = {2024},
  month = may,
  journal = {Nature Communications},
  volume = {15},
  number = {1},
  pages = {4199},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-024-48434-7},
  urldate = {2024-06-04},
  abstract = {Understanding how biodiversity has changed through time is a central goal of evolutionary biology. However, estimates of past biodiversity are challenged by the inherent incompleteness of the fossil record, even when state-of-the-art statistical methods are applied to adjust estimates while correcting for sampling biases. Here we develop an approach based on stochastic simulations of biodiversity and a deep learning model to infer richness at global or regional scales through time while incorporating spatial, temporal and taxonomic sampling variation. Our method outperforms alternative approaches across simulated datasets, especially at large spatial scales, providing robust palaeodiversity estimates under a wide range of preservation scenarios. We apply our method on two empirical datasets of different taxonomic and temporal scope: the Permian-Triassic record of marine animals and the Cenozoic evolution of proboscideans. Our estimates provide a revised quantitative assessment of two mass extinctions in the marine record and reveal rapid diversification of proboscideans following their expansion out of Africa and a\,{$>$}70\% diversity drop in the Pleistocene.},
  copyright = {2024 The Author(s)},
  langid = {english},
  keywords = {Biodiversity,Machine learning,Palaeontology,Taxonomy},
  file = {/Users/tanyastrydom/Zotero/storage/TPYM2GVT/Cooper et al. - 2024 - DeepDive estimating global biodiversity patterns .pdf}
}

@article{curtsdotterEcosystemFunctionPredator2019,
  title = {Ecosystem Function in Predator--Prey Food Webs---Confronting Dynamic Models with Empirical Data},
  author = {Curtsdotter, Alva and Banks, H. Thomas and Banks, John E. and Jonsson, Mattias and Jonsson, Tomas and Laubmeier, Amanda N. and Traugott, Michael and Bommarco, Riccardo},
  year = {2019},
  journal = {Journal of Animal Ecology},
  volume = {88},
  number = {2},
  pages = {196--210},
  issn = {1365-2656},
  doi = {10.1111/1365-2656.12892},
  urldate = {2024-09-19},
  abstract = {Most ecosystem functions and related services involve species interactions across trophic levels, for example, pollination and biological pest control. Despite this, our understanding of ecosystem function in multitrophic communities is poor, and research has been limited to either manipulation in small communities or statistical descriptions in larger ones. Recent advances in food web ecology may allow us to overcome the trade-off between mechanistic insight and ecological realism. Molecular tools now simplify the detection of feeding interactions, and trait-based approaches allow the application of dynamic food web models to real ecosystems. We performed the first test of an allometric food web model's ability to replicate temporally nonaggregated abundance data from the field and to provide mechanistic insight into the function of predation. We aimed to reproduce and explore the drivers of the population dynamics of the aphid herbivore Rhopalosiphum padi observed in ten Swedish barley fields. We used a dynamic food web model, taking observed interactions and abundances of predators and alternative prey as input data, allowing us to examine the role of predation in aphid population control. The inverse problem methods were used for simultaneous model fit optimization and model parameterization. The model captured {$>$}70\% of the variation in aphid abundance in five of ten fields, supporting the model-embodied hypothesis that body size can be an important determinant of predation in the arthropod community. We further demonstrate how in-depth model analysis can disentangle the likely drivers of function, such as the community's abundance and trait composition. Analysing the variability in model performance revealed knowledge gaps, such as the source of episodic aphid mortality, and general method development needs that, if addressed, would further increase model success and enable stronger inference about ecosystem function. The results demonstrate that confronting dynamic food web models with abundance data from the field is a viable approach to evaluate ecological theory and to aid our understanding of function in real ecosystems. However, to realize the full potential of food web models, in ecosystem function research and beyond, trait-based parameterization must be refined and extended to include more traits than body size.},
  copyright = {{\copyright} 2018 The Authors. Journal of Animal Ecology {\copyright} 2018 British Ecological Society},
  langid = {english}
}

@article{daleGraphsSpatialGraphs2010,
  title = {From {{Graphs}} to {{Spatial Graphs}}},
  author = {Dale, M.R.T. and Fortin, M.-J.},
  year = {2010},
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {41},
  eprint = {27896212},
  eprinttype = {jstor},
  pages = {21--38},
  publisher = {Annual Reviews},
  issn = {1543-592X},
  urldate = {2021-05-06},
  abstract = {Graph theory is a powerful body of mathematical knowledge, based on simple concepts, in which structural units are depicted as nodes with relationships between them depicted as lines. The nodes may have qualitative and quantitative characteristics, and the edges may have properties such as weights and directions. Graph theory provides a flexible conceptual model that can clarify the relationship between structures and processes, including the mechanisms of configuration effects and compositional differences. Graph concepts apply to many ecological and evolutionary phenomena, including interspecific associations, spatial structure, dispersal in landscapes, and relationships within metapopulations and metacommunities. We review applications of graph theory in biology, emphasizing graphs with spatial contexts. We show how spatial graph properties can be used for description and comparison as well as to test specific hypotheses. We suggest that future applications should include explicit spatial elements for landscape studies of ecological, genetic and epidemiological phenomena.}
}

@article{dallarivaExploringEvolutionarySignature2016,
  title = {Exploring the Evolutionary Signature of Food Webs' Backbones Using Functional Traits},
  author = {Dalla Riva, Giulio V and Stouffer, Daniel B.},
  year = {2016},
  journal = {Oikos},
  volume = {125},
  number = {4},
  pages = {446--456},
  issn = {1600-0706},
  doi = {10.1111/oik.02305},
  urldate = {2020-09-17},
  abstract = {Increasing evidence suggests that an appropriate model for food webs, the network of feeding links in a community of species, should take into account the inherent variability of ecological interactions. Harnessing this variability, we will show that it is useful to interpret empirically observed food webs as realisations of a family of stochastic processes, namely random dot-product graph models. These models provide an ideal extension of food-web models beyond the limitations of current deterministic or partially probabilistic models. As an additional benefit, our RDPG framework enables us to identify the pairwise distance structure given by species' functional food-web traits: this allows for the natural emergence of ecologically meaningful species groups. Lastly, our results suggest the notion that the evolutionary signature in food webs is already detectable in their stochastic backbones, while the contribution of their fine wiring is arguable. Synthesis Food webs are influenced by many stochastic processes and are constantly evolving. Here, we treat observed food webs as realisations of random dot-product graph models (RDPG), extending food-web modelling beyond the limitations of current deterministic or partially probabilistic models. Our RDPG framework enables us to identify the pairwise-distance structure given by species' functional food-web traits, which in turn allows for the natural emergence of ecologically meaningful species groups. It also provides a way to measure the phylogenetic signal present in food webs, which we find is strongest in webs' low-dimensional backbones.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/6UJMKH3J/Riva and Stouffer - 2016 - Exploring the evolutionary signature of food webs'.pdf;/Users/tanyastrydom/Zotero/storage/D5LDWZI2/oik.html}
}

@article{dallasPredictingCrypticLinks2017,
  title = {Predicting Cryptic Links in Host-Parasite Networks},
  author = {Dallas, Tad and Park, Andrew W. and Drake, John M.},
  year = {2017},
  month = may,
  journal = {PLOS Computational Biology},
  volume = {13},
  number = {5},
  pages = {e1005557},
  publisher = {Public Library of Science},
  issn = {1553-7358},
  doi = {10.1371/journal.pcbi.1005557},
  urldate = {2021-10-21},
  abstract = {Networks are a way to represent interactions among one (e.g., social networks) or more (e.g., plant-pollinator networks) classes of nodes. The ability to predict likely, but unobserved, interactions has generated a great deal of interest, and is sometimes referred to as the link prediction problem. However, most studies of link prediction have focused on social networks, and have assumed a completely censused network. In biological networks, it is unlikely that all interactions are censused, and ignoring incomplete detection of interactions may lead to biased or incorrect conclusions. Previous attempts to predict network interactions have relied on known properties of network structure, making the approach sensitive to observation errors. This is an obvious shortcoming, as networks are dynamic, and sometimes not well sampled, leading to incomplete detection of links. Here, we develop an algorithm to predict missing links based on conditional probability estimation and associated, node-level features. We validate this algorithm on simulated data, and then apply it to a desert small mammal host-parasite network. Our approach achieves high accuracy on simulated and observed data, providing a simple method to accurately predict missing links in networks without relying on prior knowledge about network structure.},
  langid = {english}
}

@misc{danetResponseDiversityMajor2024,
  title = {Response Diversity Is a Major Driver of Temporal Stability in Complex Food Webs},
  author = {Danet, Alain and K{\'e}fi, Sonia and Johnson, Thomas F. and Beckerman, Andrew P.},
  year = {2024},
  month = aug,
  primaryclass = {New Results},
  pages = {2024.08.29.610288},
  publisher = {bioRxiv},
  doi = {10.1101/2024.08.29.610288},
  urldate = {2024-11-24},
  abstract = {Global change constitutes a suite of major threats to biodiversity and ecosystem functioning. These threats can materialise via changes in the temporal stability of ecological communities and the services they provide. However, the majority of research on stability has focused on single trophic level communities and has not yet integrated classic theory about species richness and food web structure with more recent theory centred on response diversity and stochasticity. Using a stochastic bioenergenetic food-web model, we reveal that response diversity is a major driver of community stability. Moreover, positive stability-richness relationships emerge only in the presence of response diversity. In contrast to previous work, food-web structural properties are only secondary drivers of overall community stability, but interact with response diversity to determine the sign of the stability-richness relationship. Our study highlights the complex pathways by which food-web structure and response diversity drive community stability, and raises concerns about how the loss of response diversity may lead to a breakdown of stability and the capacity for these communities to deliver functions and services to human societies.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/LQYQF52Y/Danet et al. - 2024 - Response diversity is a major driver of temporal s.pdf}
}

@article{dansereauOvercomingDisconnectInteraction2024,
  title = {Overcoming the Disconnect between Interaction Networks and Biodiversity Conservation and Management},
  author = {Dansereau, Gabriel and Braga, Jo{\~a}o and Ficetola, Gentile Francesco and Galiana, Nur{\'i}a and Gravel, Dominique and Maiorano, Luigi and Montoya, Jos{\'e} M. and O'Connor, Louise and Pollock, Laura J. and Thuiller, Wilfried and Poisot, Timoth{\'e}e and Barros, Ceres},
  year = {2024},
  month = nov,
  publisher = {EcoEvoRxiv},
  urldate = {2024-12-04},
  abstract = {Decision-makers need to act now to halt biodiversity loss, and ecologists must provide them with relevant species interaction indicators to inform on community- and ecosystem-level changes. Yet, the integration of ecological networks into conservation is still virtually nonexistent. Here, we discuss challenges and opportunities related to uncertainty, interpretability and relevance of network metrics applied to conservation. We argue that existing data and methodologies are sufficient to generate network information usable for conservation, and to overcome existing challenges. Interaction network indicators must meet criteria important to decision-makers and be tied to specific conservation goals, which requires academics to better engage with practitioners. We suggest network robustness as an indicator for biodiversity management and showcase it in a workflow to inform decision-making.},
  copyright = {CC BY Attribution 4.0 International},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/FNSJNYIY/Dansereau et al. - 2024 - Overcoming the disconnect between interaction netw.pdf}
}

@article{dansereauSpatiallyExplicitPredictions2024,
  title = {Spatially Explicit Predictions of Food Web Structure from Regional-Level Data},
  author = {Dansereau, Gabriel and Barros, Ceres and Poisot, Timoth{\'e}e},
  year = {2024},
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {379},
  number = {1909},
  publisher = {EcoEvoRxiv},
  doi = {10.1098/rstb.2023.0166},
  urldate = {2023-09-18},
  abstract = {Knowledge about how ecological networks vary across global scales is currently limited given the complexity of acquiring repeated data for species interactions. Yet, recent developments of metawebs highlight efficient ways to first document possible interactions within regional species pools. Downscaling metawebs towards local network predictions is a promising approach to use current data to investigate the variation of networks across space. However, issues remain in how to represent the spatial variability and uncertainty of species interactions, especially for large scale food webs. Here, we present a probabilistic framework to downscale a metaweb based on the Canadian mammal metaweb and species occurrences from GBIF. We investigate how our approach can be used to represent the variability of networks and communities between ecoregions in Canada. Our results highlighted mismatches in the distribution of species richness and interactions, especially in their within-ecoregion variability, indicating that interactions vary differently than species distributions over continental-scale gradients. Results summarized by ecoregion showed non-constant variation within and between ecologically meaningful biogeographic boundaries and identified contrasting diversity hotspots. Our method offers the potential to bring global predictions down to a more actionable local scale, and increases the diversity of ecological networks that can be projected in space.},
  copyright = {CC-By Attribution-ShareAlike 4.0 International},
  langid = {english}
}

@book{darwinOriginSpeciesMeans1859,
  title = {On the {{Origin}} of {{Species}} by {{Means}} of {{Natural Selection}}, or the {{Preservation}} of {{Favoured Races}} in the {{Struggle}} for {{Life}}},
  author = {Darwin, Charles},
  year = {1859},
  publisher = {J. Murray},
  address = {London}
}

@article{deangelisModelTropicInteraction1975,
  title = {A {{Model}} for {{Tropic Interaction}}},
  author = {DeAngelis, D. L. and Goldstein, R. A. and O'Neill, R. V.},
  year = {1975},
  month = jul,
  journal = {Ecology},
  volume = {56},
  number = {4},
  pages = {881--892},
  issn = {0012-9658, 1939-9170},
  doi = {10.2307/1936298},
  urldate = {2024-03-25},
  abstract = {A nonlinear function general enough to include the effects of feeding saturation and intraspecific consumer interference is used to represent the transfer of material or energy from one trophic level to another. The function agrees with some recent experimental data on feeding rates. A model using this feeding rate function is subjected to equilibrium and stability analyses to ascertain its mathematical implications. The analyses lead to several observations; for example, increases in maximum feeding rate may, under certain circumstances, result in decreases in consumer population and mutual interference between consumers is a major stabilizing factor in a nonlinear system. The analyses also suggest that realistic classes of consumer-resourcemodels exist which do not obey Kolmogorov's Criteria but are nevertheless globally stable.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/6BD8CY8E/DeAngelis et al. - 1975 - A Model for Tropic Interaction.pdf}
}

@article{deangelisNutrientDynamicsFoodWeb1989,
  title = {Nutrient {{Dynamics}} and {{Food-Web Stability}}},
  author = {DeAngelis, D. L. and Mulholland, P. J. and Palumbo, A. V. and Steinman, A. D. and Huston, M. A. and Elwood, J. W.},
  year = {1989},
  month = nov,
  journal = {Annual Review of Ecology, Evolution and Systematics},
  volume = {20},
  number = {Volume 20, 1989},
  pages = {71--95},
  publisher = {Annual Reviews},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev.es.20.110189.000443},
  urldate = {2024-04-09},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/XGSZDMIW/annurev.es.20.110189.html}
}

@article{delmasAnalysingEcologicalNetworks2019,
  title = {Analysing Ecological Networks of Species Interactions},
  author = {Delmas, Eva and Besson, Mathilde and Brice, Marie-H{\'e}l{\`e}ne and Burkle, Laura A. and Riva, Giulio V. Dalla and Fortin, Marie-Jos{\'e}e and Gravel, Dominique and Guimar{\~a}es, Paulo R. and Hembry, David H. and Newman, Erica A. and Olesen, Jens M. and Pires, Mathias M. and Yeakel, Justin D. and Poisot, Timoth{\'e}e},
  year = {2019},
  journal = {Biological Reviews},
  volume = {94},
  number = {1},
  pages = {16--36},
  issn = {1469-185X},
  doi = {10.1111/brv.12433},
  urldate = {2020-08-24},
  abstract = {Network approaches to ecological questions have been increasingly used, particularly in recent decades. The abstraction of ecological systems -- such as communities -- through networks of interactions between their components indeed provides a way to summarize this information with single objects. The methodological framework derived from graph theory also provides numerous approaches and measures to analyze these objects and can offer new perspectives on established ecological theories as well as tools to address new challenges. However, prior to using these methods to test ecological hypotheses, it is necessary that we understand, adapt, and use them in ways that both allow us to deliver their full potential and account for their limitations. Here, we attempt to increase the accessibility of network approaches by providing a review of the tools that have been developed so far, with -- what we believe to be -- their appropriate uses and potential limitations. This is not an exhaustive review of all methods and metrics, but rather, an overview of tools that are robust, informative, and ecologically sound. After providing a brief presentation of species interaction networks and how to build them in order to summarize ecological information of different types, we then classify methods and metrics by the types of ecological questions that they can be used to answer from global to local scales, including methods for hypothesis testing and future perspectives. Specifically, we show how the organization of species interactions in a community yields different network structures (e.g., more or less dense, modular or nested), how different measures can be used to describe and quantify these emerging structures, and how to compare communities based on these differences in structures. Within networks, we illustrate metrics that can be used to describe and compare the functional and dynamic roles of species based on their position in the network and the organization of their interactions as well as associated new methods to test the significance of these results. Lastly, we describe potential fruitful avenues for new methodological developments to address novel ecological questions.},
  langid = {english},
  keywords = {biogeography,community ecology,DelmasE,ecological networks,graph theory,interactions,networks,PoisotT,Recommended_Tim,Workshop_Oct},
  file = {/Users/tanyastrydom/Zotero/storage/7ZDGIXPL/Delmas et al. - 2019 - Analysing ecological networks of species interacti.pdf;/Users/tanyastrydom/Zotero/storage/EU2YG29S/brv.html}
}

@article{delmasSimulationsBiomassDynamics2017,
  title = {Simulations of Biomass Dynamics in Community Food Webs},
  author = {Delmas, Eva and Brose, Ulrich and Gravel, Dominique and Stouffer, Daniel B. and Poisot, Timoth{\'e}e},
  year = {2017},
  journal = {Methods in Ecology and Evolution},
  volume = {8},
  number = {7},
  pages = {881--886},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12713},
  urldate = {2024-01-17},
  abstract = {Food webs are the backbone upon which biomass flows through ecosystems. Dynamical models of biomass can reveal how the structure of food webs is involved in many key ecosystem properties, such as persistence, stability, etc. In this contribution, we present BioEnergeticFoodWebs, an implementation of Yodzis \& Innes (The American Naturalist 139, 1151?1175, 1992) bio-energetic model, in the high-performance computing language Julia. We illustrate how this package can be used to conduct numerical experiments in a reproducible and standard way. A reference implementation of this widely used model will ease reproducibility and comparison of results across studies.},
  copyright = {{\copyright} 2016 The Authors. Methods in Ecology and Evolution {\copyright} 2016 British Ecological Society},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/KXEU2FVE/2041-210X.html}
}

@article{desjardins-proulxEcologicalInteractionsNetflix2017,
  title = {Ecological Interactions and the {{Netflix}} Problem},
  author = {{Desjardins-Proulx}, Philippe and Laigle, Idaline and Poisot, Timoth{\'e}e and Gravel, Dominique},
  year = {2017},
  journal = {PeerJ},
  volume = {5},
  pages = {e3644},
  issn = {2167-8359},
  doi = {10.7717/peerj.3644},
  abstract = {Species interactions are a key component of ecosystems but we generally have an incomplete picture of who-eats-who in a given community. Different techniques have been devised to predict species interactions using theoretical models or abundances. Here, we explore the K nearest neighbour approach, with a special emphasis on recommendation, along with a supervised machine learning technique. Recommenders are algorithms developed for companies like Netflix to predict whether a customer will like a product given the preferences of similar customers. These machine learning techniques are well-suited to study binary ecological interactions since they focus on positive-only data. By removing a prey from a predator, we find that recommenders can guess the missing prey around 50\% of the times on the first try, with up to 881 possibilities. Traits do not improve significantly the results for the K nearest neighbour, although a simple test with a supervised learning approach (random forests) show we can predict interactions with high accuracy using only three traits per species. This result shows that binary interactions can be predicted without regard to the ecological community given only three variables: body mass and two variables for the species' phylogeny. These techniques are complementary, as recommenders can predict interactions in the absence of traits, using only information about other species' interactions, while supervised learning algorithms such as random forests base their predictions on traits only but do not exploit other species' interactions. Further work should focus on developing custom similarity measures specialized for ecology to improve the KNN algorithms and using richer data to capture indirect relationships between species.},
  langid = {english},
  pmcid = {PMC5554597},
  pmid = {28828250},
  file = {/Users/tanyastrydom/Zotero/storage/CE4SMGUT/Desjardins-Proulx et al. - 2017 - Ecological interactions and the Netflix problem.pdf}
}

@incollection{dormannRisePossibleFall2023,
  title = {The Rise, and Possible Fall, of Network Ecology},
  booktitle = {Defining {{Agroecology}} -- {{A Festschrift}} for {{Teja Tscharntke}}},
  author = {Dormann, Carsten F},
  year = {2023},
  pages = {143--159.},
  publisher = {Tredition},
  address = {Hamburg},
  abstract = {Ecology, much like any other discipline, has its fashions and fads, and each brings its own buzzwords and jargon. ``Network ecology'' is an example of such a fashion, which has for a few decades imprinted heavily on ecological publications. However, the topics of network ecology are of course much older, as are most of its methods. This invites the question whether network ecology is on a good path to providing di{\dbend}erent viewpoints and new insights. I here try to outline a somewhat opinionated view of why there is a high risk that this avenue of research may prove to be a cul-de-sac, for two reasons. On the one hand, the word ``network'' has become an empty buzzword void of speci{\dbend}c meaning. On the other, there are six problems that I deem to be ``deal breakers'' in research on interaction networks: unless they all are resolved, this approach cannot make meaningful contributions. They are: ({\dbend}) sampling bias; ({\dbend}) ecological meaning of recorded interactions; ({\dbend}) data aggregation over individuals; ({\dbend}) lack of quantitative expectation; and ({\dbend}) ecologically meaningless indices. Together they lead to the biggest problem ({\dbend}) confusion in what it all means ecologically. Until these issues are being tackled by improved {\dbend}eld and computational research, there seem to be little progress possible in our understanding of assemblages of interacting species under the header of ``network ecology''.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/3DRQ7ZKD/Dormann - The rise, and possible fall, of network ecology.pdf}
}

@misc{duchenneProbabilisticViewForbidden2024a,
  title = {A Probabilistic View of Forbidden Links: Their Prevalence and Their Consequences for the Robustness of Mutualistic Networks},
  shorttitle = {A Probabilistic View of Forbidden Links},
  author = {Duchenne, Fran{\c c}ois and Barreto, Elisa and Guevara, Esteban A. and Beck, Holger and Bello, Carolina and Bobato, Rafaela and B{\^o}lla, Daniela and Brenes, Emanuel and B{\"u}ttner, Nicole and Caron, Ana P. and Elizondo, Nelson and {Restrepo-Gonz{\'a}lez}, Alejandro and Castro, Jos{\'e} A. and Kaehler, Miriam and {Machado-de-Souza}, Tiago and {Machnicki-Reis}, Miguel and {Marcayata-Fajardo}, Andr{\'e}s S. and De Menezes, Cau{\~a} G. and Nieto, Andrea and De Oliveira, Rafael and De Oliveira, Ricardo A. C. and Richter, Friederike and Rojas, Bryan G. and Romanowski, Luciele L. and De Souza, Wellinton L and Veluza, Danila S. and Weinstein, Ben and W{\"u}est, Rafael and Zanata, Thais B. and Zuniga, Krystal and Maglianesi, Mar{\'i}a A. and Santander, Tatiana and Varassin, Isabella G. and Graham, Catherine H.},
  year = {2024},
  month = jul,
  doi = {10.1101/2024.07.04.602032},
  urldate = {2024-07-12},
  abstract = {Understanding how species extinctions affect communities of interacting species is an important challenge of ecology. The presence of unfeasible interactions, termed forbidden links, due to physiological or morphological barriers, is likely to decrease the plasticity of interaction networks, affecting their robustness to species extinctions. However, the existence of these forbidden links has been long debated, stressing the need to learn more about their prevalence and putative consequences for community robustness to extinctions. To tackle this gap, we used a dataset of plant-hummingbird interactions collected in Brazil, Costa Rica and Ecuador along elevation gradients. We used a Bayesian hierarchical model, to assess the probabilistic importance of exploitation barriers in determining species interactions. We found evidence for exploitation barriers between flowers and hummingbirds with a shorter bill than the corolla. We also showed that the proportion of forbidden links can change drastically among communities, because of changes in trait distributions. Finally we showed that networks with a high proportion of forbidden links had low robustness, because of constraints on interaction rewiring. Our results suggest that exploitation barriers are not rare and strongly limit interaction rewiring, and thus the rescue of species experiencing partner extinction may be limited.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/IF3AEZ69/Duchenne et al. - 2024 - A probabilistic view of forbidden links their pre.pdf}
}

@article{dunhillExtinctionCascadesCommunity2024,
  title = {Extinction Cascades, Community Collapse, and Recovery across a {{Mesozoic}} Hyperthermal Event},
  author = {Dunhill, Alexander M. and Zarzyczny, Karolina and Shaw, Jack O. and Atkinson, Jed W. and Little, Crispin T. S. and Beckerman, Andrew P.},
  year = {2024},
  month = oct,
  journal = {Nature Communications},
  volume = {15},
  number = {1},
  pages = {8599},
  publisher = {Nature Publishing Group},
  issn = {2041-1723},
  doi = {10.1038/s41467-024-53000-2},
  urldate = {2024-10-07},
  abstract = {Mass extinctions are considered to be quintessential examples of Court Jester drivers of macroevolution, whereby abiotic pressures drive a suite of extinctions leading to huge ecosystem changes across geological timescales. Most research on mass extinctions ignores species interactions and community structure, limiting inference about which and why species go extinct, and how Red Queen processes that link speciation to extinction rates affect the subsequent recovery of biodiversity, structure and function. Here, we apply network reconstruction, secondary extinction modelling and community structure analysis to the Early Toarcian (Lower Jurassic; 183\,Ma) Extinction Event and recovery. We find that primary extinctions targeted towards infaunal guilds, which caused secondary extinction cascades to higher trophic levels, reproduce the empirical post-extinction community most accurately. We find that the extinction event caused a switch from a diverse community with high levels of functional redundancy to a less diverse, more densely connected community of generalists. Recovery was characterised by a return to pre-extinction levels of some elements of community structure and function prior to the recovery of biodiversity. Full ecosystem recovery took {\textasciitilde}7 million years at which point we see evidence of dramatically increased vertical structure linked to the Mesozoic Marine Revolution and modern marine ecosystem structure.},
  copyright = {2024 The Author(s)},
  langid = {english}
}

@article{dunneCompilationNetworkAnalyses2008,
  title = {Compilation and {{Network Analyses}} of {{Cambrian Food Webs}}},
  author = {Dunne, Jennifer A. and Williams, Richard J. and Martinez, Neo D. and Wood, Rachel A. and Erwin, Douglas H.},
  year = {2008},
  month = apr,
  journal = {PLOS Biology},
  volume = {6},
  number = {4},
  pages = {e102},
  publisher = {Public Library of Science},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.0060102},
  urldate = {2024-01-15},
  abstract = {A rich body of empirically grounded theory has developed about food webs---the networks of feeding relationships among species within habitats. However, detailed food-web data and analyses are lacking for ancient ecosystems, largely because of the low resolution of taxa coupled with uncertain and incomplete information about feeding interactions. These impediments appear insurmountable for most fossil assemblages; however, a few assemblages with excellent soft-body preservation across trophic levels are candidates for food-web data compilation and topological analysis. Here we present plausible, detailed food webs for the Chengjiang and Burgess Shale assemblages from the Cambrian Period. Analyses of degree distributions and other structural network properties, including sensitivity analyses of the effects of uncertainty associated with Cambrian diet designations, suggest that these early Paleozoic communities share remarkably similar topology with modern food webs. Observed regularities reflect a systematic dependence of structure on the numbers of taxa and links in a web. Most aspects of Cambrian food-web structure are well-characterized by a simple ``niche model,'' which was developed for modern food webs and takes into account this scale dependence. However, a few aspects of topology differ between the ancient and recent webs: longer path lengths between species and more species in feeding loops in the earlier Chengjiang web, and higher variability in the number of links per species for both Cambrian webs. Our results are relatively insensitive to the exclusion of low-certainty or random links. The many similarities between Cambrian and recent food webs point toward surprisingly strong and enduring constraints on the organization of complex feeding interactions among metazoan species. The few differences could reflect a transition to more strongly integrated and constrained trophic organization within ecosystems following the rapid diversification of species, body plans, and trophic roles during the Cambrian radiation. More research is needed to explore the generality of food-web structure through deep time and across habitats, especially to investigate potential mechanisms that could give rise to similar structure, as well as any differences.},
  langid = {english},
  keywords = {Cambrian period,Ecological niches,Ecosystems,Food,Food web structure,Paleoecology,Shale,Species interactions}
}

@incollection{dunneNetworkStructureFood2006,
  title = {The {{Network Structure}} of {{Food Webs}}},
  booktitle = {Ecological Networks: {{Linking}} Structure and Dynamics},
  author = {Dunne, Jennifer A},
  editor = {Dunne, Jennifer A and Pascual, Mercedes},
  year = {2006},
  pages = {27--86},
  publisher = {Oxford University Press}
}

@article{dunneNetworkStructureRobustness2004,
  title = {Network Structure and Robustness of Marine Food Webs},
  author = {Dunne, Jennifer A. and Williams, Richard J. and Martinez, Neo D.},
  year = {2004},
  journal = {Marine Ecology Progress Series},
  volume = {273},
  pages = {291--302},
  issn = {0171-8630, 1616-1599},
  doi = {10.3354/meps273291},
  urldate = {2024-02-13},
  abstract = {Previous studies suggest that food-web theory has yet to account for major differences in food-web properties of marine versus other types of ecosystems. We examined this issue by analyzing the network structure of food webs for the Northeast US Shelf, a Caribbean reef, and Benguela, off South Africa. The values of connectance (links per species2), link density (links per species), mean chain length, and fractions of intermediate, omnivorous, and cannibalistic taxa of these marine webs are somewhat high but still within the ranges observed in other webs. We further compared the marine webs by using the empirically corroborated `niche model' that accounts for observed variation in diversity (taxon number) and complexity (connectance). Our results substantiate previously reported results for estuarine, fresh-water, and terrestrial datasets, which suggests that food webs from different types of ecosystems with variable diversity and complexity share fundamental structural and ordering characteristics. Analyses of potential secondary extinctions resulting from species loss show that the structural robustness of marine food webs is also consistent with trends from other food webs. As expected, given their relatively high connectance, marine food webs appear fairly robust to loss of most-connected taxa as well as random taxa. Still, the short average path length between marine taxa (1.6 links) suggests that effects from perturbations, such as overfishing, can be transmitted more widely throughout marine ecosystems than previously appreciated.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/HG59NFGA/Dunne et al. - 2004 - Network structure and robustness of marine food we.pdf}
}

@article{dunnSixthMassCoextinction2009,
  title = {The Sixth Mass Coextinction: Are Most Endangered Species Parasites and Mutualists?},
  shorttitle = {The Sixth Mass Coextinction},
  author = {Dunn, Robert R. and Harris, Nyeema C. and Colwell, Robert K. and Koh, Lian Pin and Sodhi, Navjot S.},
  year = {2009},
  month = sep,
  journal = {Proceedings. Biological Sciences},
  volume = {276},
  number = {1670},
  pages = {3037--3045},
  issn = {0962-8452},
  doi = {10.1098/rspb.2009.0413},
  abstract = {The effects of species declines and extinction on biotic interactions remain poorly understood. The loss of a species is expected to result in the loss of other species that depend on it (coextinction), leading to cascading effects across trophic levels. Such effects are likely to be most severe in mutualistic and parasitic interactions. Indeed, models suggest that coextinction may be the most common form of biodiversity loss. Paradoxically, few historical or contemporary coextinction events have actually been recorded. We review the current knowledge of coextinction by: (i) considering plausible explanations for the discrepancy between predicted and observed coextinction rates; (ii) exploring the potential consequences of coextinctions; (iii) discussing the interactions and synergies between coextinction and other drivers of species loss, particularly climate change; and (iv) suggesting the way forward for understanding the phenomenon of coextinction, which may well be the most insidious threat to global biodiversity.},
  langid = {english},
  pmcid = {PMC2817118},
  pmid = {19474041},
  keywords = {Animals,Biological Evolution,Conservation of Natural Resources,Ecosystem,Extinction Biological,Food Chain,Greenhouse Effect,Host-Parasite Interactions,Models Biological,Population Dynamics,Species Specificity,Symbiosis},
  file = {/Users/tanyastrydom/Zotero/storage/SZTQESP6/Dunn et al. - 2009 - The sixth mass coextinction are most endangered s.pdf}
}

@article{eichenwaldPotentialExtinctionCascades2024,
  title = {Potential Extinction Cascades in a Desert Ecosystem: {{Linking}} Food Web Interactions to Community Viability},
  shorttitle = {Potential Extinction Cascades in a Desert Ecosystem},
  author = {Eichenwald, Adam J. and Fefferman, Nina H. and Reed, J. Michael},
  year = {2024},
  journal = {Ecology and Evolution},
  volume = {14},
  number = {2},
  pages = {e10930},
  issn = {2045-7758},
  doi = {10.1002/ece3.10930},
  urldate = {2024-02-20},
  abstract = {Desert communities are threatened with species loss due to climate change, and their resistance to such losses is unknown. We constructed a food web of the Mojave Desert terrestrial community (300 nodes, 4080 edges) to empirically examine the potential cascading effects of bird extinctions on this desert network, compared to losses of mammals and lizards. We focused on birds because they are already disappearing from the Mojave, and their relative thermal vulnerabilities are known. We quantified bottom-up secondary extinctions and evaluated the relative resistance of the community to losses of each vertebrate group. The impact of random bird species loss was relatively low compared to the consequences of mammal (causing the greatest number of cascading losses) or reptile loss, and birds were relatively less likely to be in trophic positions that could drive top-down effects in apparent competition and tri-tropic cascade motifs. An avian extinction cascade with year-long resident birds caused more secondary extinctions than the cascade involving all bird species for randomized ordered extinctions. Notably, we also found that relatively high interconnectivity among avian species has formed a subweb, enhancing network resistance to bird losses.},
  langid = {english},
  keywords = {biodiversity,climate change,community viability analysis,CVA,ecological networks,Mojave Desert,secondary extinction,trophic cascade},
  file = {/Users/tanyastrydom/Zotero/storage/Q2HVXRWH/Eichenwald et al. - 2024 - Potential extinction cascades in a desert ecosyste.pdf;/Users/tanyastrydom/Zotero/storage/XLM4DNPZ/ece3.html}
}

@article{eklofDimensionalityEcologicalNetworks2013,
  title = {The Dimensionality of Ecological Networks},
  author = {Ekl{\"o}f, Anna and Jacob, Ute and Kopp, Jason and Bosch, Jordi and Castro-Urgal, Roc{\'i}o and Chacoff, Natacha P. and Dalsgaard, Bo and de Sassi, Claudio and Galetti, Mauro and Guimar{\~a}es, Paulo R. and Lom{\'a}scolo, Silvia Beatriz and Gonz{\'a}lez, Ana M. Mart{\'i}n and Pizo, Marco Aurelio and Rader, Romina and Rodrigo, Anselm and Tylianakis, Jason M. and V{\'a}zquez, Diego P. and Allesina, Stefano},
  year = {2013},
  journal = {Ecology Letters},
  volume = {16},
  number = {5},
  pages = {577--583},
  issn = {1461-0248},
  doi = {10.1111/ele.12081},
  urldate = {2020-08-28},
  abstract = {How many dimensions (trait-axes) are required to predict whether two species interact? This unanswered question originated with the idea of ecological niches, and yet bears relevance today for understanding what determines network structure. Here, we analyse a set of 200 ecological networks, including food webs, antagonistic and mutualistic networks, and find that the number of dimensions needed to completely explain all interactions is small ( {$<$} 10), with model selection favouring less than five. Using 18 high-quality webs including several species traits, we identify which traits contribute the most to explaining network structure. We show that accounting for a few traits dramatically improves our understanding of the structure of ecological networks. Matching traits for resources and consumers, for example, fruit size and bill gape, are the most successful combinations. These results link ecologically important species attributes to large-scale community structure.},
  copyright = {{\copyright} 2013 Blackwell Publishing Ltd/CNRS},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/33I46ZGY/ele.html}
}

@article{eklofSecondaryExtinctionsFood2013,
  title = {Secondary Extinctions in Food Webs: A {{Bayesian}} Network Approach},
  shorttitle = {Secondary Extinctions in Food Webs},
  author = {Ekl{\"o}f, Anna and Tang, Si and Allesina, Stefano},
  editor = {Metcalf, Jessica},
  year = {2013},
  month = aug,
  journal = {Methods in Ecology and Evolution},
  volume = {4},
  number = {8},
  pages = {760--770},
  issn = {2041-210X, 2041-210X},
  doi = {10.1111/2041-210X.12062},
  urldate = {2024-01-15},
  abstract = {Summary                                                                Ecological communities are composed of populations connected in tangled networks of ecological interactions. Therefore, the extinction of a species can reverberate through the network and cause other (possibly distantly connected) species to go extinct as well. The study of these secondary extinctions is a fertile area of research in ecological network theory.                                                     However, to facilitate practical applications, several improvements to the current analytical approaches are needed. In particular, we need to consider that (i) species have different `a priori' probabilities of extinction, (ii) disturbances can simultaneously affect several species, and (iii) extinction risk of consumers likely grows with resource loss. All these points can be included in dynamical models, which are, however, difficult to parameterize.                                                     Here we advance the study of secondary extinctions with Bayesian networks. We show how this approach can account for different extinction responses using binary -- where each resource has the same importance -- and quantitative data -- where resources are weighted by their importance. We simulate ecological networks using a popular dynamical model (the Allometric Trophic Network model) and use it to test our method.                                                     We find that the Bayesian network model captures the majority of the secondary extinctions produced by the dynamical model and that consumers' responses to species loss are best modelled using a nonlinear sigmoid function. We also show that an approach based exclusively on food web structure loses power when species at higher trophic levels are preferentially lost. Because the loss of apex predators is unfortunately widespread, the results highlight a serious limitation of studies on network robustness.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/WPTP7LXF/Eklöf et al. - 2013 - Secondary extinctions in food webs a Bayesian net.pdf}
}

@book{eltonAnimalEcology2001,
  title = {Animal {{Ecology}}},
  author = {Elton, Charles S.},
  editor = {Leibold, Matthew A. and Wootton, Timothy J.},
  year = {2001},
  month = jun,
  publisher = {University of Chicago Press},
  address = {Chicago, IL},
  urldate = {2024-10-04},
  abstract = {Charles Elton was one of the founders of ecology, and his Animal Ecology was one of the seminal works that defined the field. In this book Elton introduced and drew together many principles still central to ecology today, including succession, niche, food webs, and the links between communities and ecosystems, each of which he illustrated with well-chosen examples. Many of Elton's ideas have proven remarkably prescient---for instance, his emphasis on the role climatic changes play in population fluctuations anticipated recent research in this area stimulated by concerns about global warming.For Chicago's reprint of this classic work, ecologists Mathew A. Leibold and J. Timothy Wootton have provided new introductions to each chapter, placing Elton's ideas in historical and scientific context. They trace modern developments in each of the key themes Elton introduced, and provide references to the most current literature. The result will be an important work for ecologists interested in the roots of their discipline, for educated readers looking for a good overview of the field, and for historians of science.},
  isbn = {978-0-226-20639-4},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/23HVN2K3/bo25281897.html}
}

@article{erdosRandomGraphs1959,
  title = {On {{Random Graphs I}}},
  author = {Erd{\H o}s, Paul and R{\'e}nyi, Alfr{\'e}d},
  year = {1959},
  journal = {Publicationes Mathematicae},
  doi = {10.5486/PMD.1959.6.3-4.12},
  file = {/Users/tanyastrydom/Zotero/storage/YP5UCPQC/erdos59random.pdf}
}

@article{estayEditorialPatternsProcesses2023,
  title = {Editorial: {{Patterns}} and Processes in Ecological Networks over Space},
  shorttitle = {Editorial},
  author = {Estay, Sergio A. and Fortin, Marie-Jos{\'e}e and L{\'o}pez, Daniela N.},
  year = {2023},
  journal = {Frontiers in Ecology and Evolution},
  volume = {11},
  issn = {2296-701X},
  urldate = {2023-09-26}
}

@misc{fairbanksGraphsJlOptimized2021,
  title = {Graphs.Jl: An Optimized Graphs Package for the {{Julia}} Programming Language},
  author = {Fairbanks, James and Besan{\c c}on, Mathieu and Sch{\"o}lly, Simon and Hoffiman, J{\'u}lio and Eubank, Nick and Karpinski, Stefan},
  year = {2021}
}

@article{fortinNetworkEcologyDynamic2021,
  ids = {Fortin2021NetEco},
  title = {Network Ecology in Dynamic Landscapes},
  author = {Fortin, Marie-Jos{\'e}e and Dale, Mark R. T. and Brimacombe, Chris},
  year = {2021},
  month = apr,
  journal = {Proceedings of the Royal Society B: Biological Sciences},
  volume = {288},
  number = {1949},
  pages = {rspb.2020.1889, 20201889},
  issn = {0962-8452, 1471-2954},
  doi = {10.1098/rspb.2020.1889},
  urldate = {2021-05-04},
  abstract = {Network ecology is an emerging field that allows researchers to conceptualize and analyse ecological networks and their dynamics. Here, we focus on the dynamics of ecological networks in response to environmental changes. Specifically, we formalize how network topologies constrain the dynamics of ecological systems into a unifying framework in network ecology that we refer to as the `ecological network dynamics framework'. This framework stresses that the interplay between species interaction networks and the spatial layout of habitat patches is key to identifying which network properties (number and weights of nodes and links) and trade-offs among them are needed to maintain species interactions in dynamic landscapes. We conclude that to be functional, ecological networks should be scaled according to species dispersal abilities in response to landscape heterogeneity. Determining how such effective ecological networks change through space and time can help reveal their complex dynamics in a changing world.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/U23JDCEI/Fortin et al. - 2021 - Network ecology in dynamic landscapes.pdf;/Users/tanyastrydom/Zotero/storage/VDTUBKIF/Fortin et al. - 2021 - Network ecology in dynamic landscapes.pdf}
}

@article{fortinSpatialStatisticsSpatial2012,
  title = {Spatial Statistics, Spatial Regression, and Graph Theory in Ecology},
  author = {Fortin, Marie-Jos{\'e}e and James, Patrick M. A. and MacKenzie, Alistair and Melles, Stephanie J. and Rayfield, Bronwyn},
  year = {2012},
  month = may,
  journal = {Spatial Statistics},
  volume = {1},
  pages = {100--109},
  issn = {2211-6753},
  doi = {10.1016/j.spasta.2012.02.004},
  urldate = {2024-05-02},
  abstract = {A critical part of ecological studies is to quantify how landscape spatial heterogeneity affects species' distributions. With advancements in remote sensing technology and GIS, we now live in a data-rich era allowing us to investigate species--environment relationships in heterogeneous landscapes at multiple spatial scales. However, the degree and type of spatial heterogeneity changes depending on the spatial scale at which species--environment relationships are analysed. Here we present the current spatial analytic methods used in ecological studies to quantify ecological spatial heterogeneity. To determine the key spatial scales at which underlying ecological processes act upon species, we recommend use of spectral decomposition techniques such as wavelet analysis or Moran's eigenvector maps. Following this, a suite of spatial regression methods can be used to quantify the relative influence of environmental factors on species' distributions. Finally, spatial graph metrics can be employed to quantify the effects of spatial heterogeneity on landscape connectivity across or within species' ranges and can be used as additional predictors in spatial regression models. We emphasize how spatial statistics, spatial regression, and spatial graph theory can be used to provide insights into how landscape spatial complexity influences species distributions and to better understand species response to global change.},
  keywords = {Connectivity,Fragmentation,Spatial heterogeneity,Spatial scales,Species distribution,Wavelet},
  file = {/Users/tanyastrydom/Zotero/storage/UUUSJLTW/S221167531200005X.html}
}

@article{fortunaHabitatLossStructure2006,
  ids = {FortBasc06},
  title = {Habitat Loss and the Structure of Plant-Animal Mutualistic Networks: {{Mutualistic}} Networks and Habitat Loss},
  shorttitle = {Habitat Loss and the Structure of Plant-Animal Mutualistic Networks},
  author = {Fortuna, Miguel A. and Bascompte, Jordi},
  year = {2006},
  month = mar,
  journal = {Ecology Letters},
  volume = {9},
  number = {3},
  pages = {281--286},
  issn = {1461023X},
  doi = {10.1111/j.1461-0248.2005.00868.x},
  urldate = {2018-02-16},
  langid = {english}
}

@article{frickeCollapseTerrestrialMammal2022,
  title = {Collapse of Terrestrial Mammal Food Webs since the {{Late Pleistocene}}},
  author = {Fricke, Evan C. and Hsieh, Chia and Middleton, Owen and Gorczynski, Daniel and Cappello, Caroline D. and Sanisidro, Oscar and Rowan, John and Svenning, Jens-Christian and Beaudrot, Lydia},
  year = {2022},
  month = aug,
  journal = {Science},
  volume = {377},
  number = {6609},
  pages = {1008--1011},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/science.abn4012},
  urldate = {2024-06-11},
  abstract = {Food webs influence ecosystem diversity and functioning. Contemporary defaunation has reduced food web complexity, but simplification caused by past defaunation is difficult to reconstruct given the sparse paleorecord of predator-prey interactions. We identified changes to terrestrial mammal food webs globally over the past {\textasciitilde}130,000 years using extinct and extant mammal traits, geographic ranges, observed predator-prey interactions, and deep learning models. Food webs underwent steep regional declines in complexity through loss of food web links after the arrival and expansion of human populations. We estimate that defaunation has caused a 53\% decline in food web links globally. Although extinctions explain much of this effect, range losses for extant species degraded food webs to a similar extent, highlighting the potential for food web restoration via extant species recovery.},
  file = {/Users/tanyastrydom/Zotero/storage/C6BQGXPV/Fricke et al. - 2022 - Collapse of terrestrial mammal food webs since the.pdf}
}

@article{galianaSpatialScalingSpecies2018,
  title = {The Spatial Scaling of Species Interaction Networks},
  author = {Galiana, N{\'u}ria and Lurgi, Miguel and {Claramunt-L{\'o}pez}, Bernat and Fortin, Marie-Jos{\'e}e and Leroux, Shawn and Cazelles, Kevin and Gravel, Dominique and Montoya, Jos{\'e} M.},
  year = {2018},
  month = may,
  journal = {Nature Ecology \& Evolution},
  volume = {2},
  number = {5},
  pages = {782--790},
  issn = {2397-334X},
  doi = {10.1038/s41559-018-0517-3},
  urldate = {2020-02-09},
  abstract = {How biotic interactions change across spatial scales is not well characterized. Here, the authors outline a theoretical framework to explore the spatial scaling of multitrophic communities, and present testable predictions on network-area relationships (NARs).},
  copyright = {2018 The Author(s)},
  langid = {english}
}

@article{gaoGgVennDiagramIntuitiveEasytoUse2021,
  title = {{{ggVennDiagram}}: {{An Intuitive}}, {{Easy-to-Use}}, and {{Highly Customizable R Package}} to {{Generate Venn Diagram}}},
  shorttitle = {{{ggVennDiagram}}},
  author = {Gao, Chun-Hui and Yu, Guangchuang and Cai, Peng},
  year = {2021},
  journal = {Frontiers in Genetics},
  volume = {12},
  issn = {1664-8021},
  urldate = {2024-02-12},
  abstract = {Venn diagrams are widely used diagrams to show the set relationships in biomedical studies. In this study, we developed ggVennDiagram, an R package that could automatically generate high-quality Venn diagrams with two to seven sets. The ggVennDiagram is built based on ggplot2, and it integrates the advantages of existing packages, such as venn, RVenn, VennDiagram, and sf. Satisfactory results can be obtained with minimal configurations. Furthermore, we designed comprehensive objects to store the entire data of the Venn diagram, which allowed free access to both intersection values and Venn plot sub-elements, such as set label/edge and region label/filling. Therefore, high customization of every Venn plot sub-element can be fulfilled without increasing the cost of learning when the user is familiar with ggplot2 methods. To date, ggVennDiagram has been cited in more than 10 publications, and its source code repository has been starred by more than 140 GitHub users, suggesting a great potential in applications. The package is an open-source software released under the GPL-3 license, and it is freely available through CRAN (https://cran.r-project.org/package=ggVennDiagram).},
  file = {/Users/tanyastrydom/Zotero/storage/WNAJVA7F/Gao et al. - 2021 - ggVennDiagram An Intuitive, Easy-to-Use, and High.pdf}
}

@article{garcia-callejasNonrandomInteractionsGuilds2023,
  title = {Non-Random Interactions within and across Guilds Shape the Potential to Coexist in Multi-Trophic Ecological Communities},
  author = {{Garc{\'i}a-Callejas}, David and Godoy, Oscar and Buche, Lisa and Hurtado, Mar{\'i}a and Lanuza, Jose B. and {Allen-Perkins}, Alfonso and Bartomeus, Ignasi},
  year = {2023},
  journal = {Ecology Letters},
  volume = {26},
  number = {6},
  pages = {831--842},
  issn = {1461-0248},
  doi = {10.1111/ele.14206},
  urldate = {2024-09-30},
  abstract = {Theory posits that the persistence of species in ecological communities is shaped by their interactions within and across trophic guilds. However, we lack empirical evaluations of how the structure, strength and sign of biotic interactions drive the potential to coexist in diverse multi-trophic communities. Here, we model community feasibility domains, a theoretically informed measure of multi-species coexistence probability, from grassland communities comprising more than 45 species on average from three trophic guilds (plants, pollinators and herbivores). Contrary to our hypothesis, increasing community complexity, measured either as the number of guilds or community richness, did not decrease community feasibility. Rather, we observed that high degrees of species self-regulation and niche partitioning allow for maintaining larger levels of community feasibility and higher species persistence in more diverse communities. Our results show that biotic interactions within and across guilds are not random in nature and both structures significantly contribute to maintaining multi-trophic diversity.},
  copyright = {{\copyright} 2023 John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {biotic interactions,coexistence,feasibility domain,multi-trophic communities,niche partitioning,self-regulation},
  file = {/Users/tanyastrydom/Zotero/storage/LPWJBZPX/ele.html}
}

@article{germainSpecialFeatureDemystifying2024,
  title = {Special Feature: {{Demystifying}} Fundamental Theories in Ecology},
  shorttitle = {Special Feature},
  author = {Germain, Rachel M. and Schreiber, Sebastian J.},
  year = {2024},
  month = oct,
  journal = {The American Naturalist},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/733789},
  urldate = {2024-11-05},
  file = {/Users/tanyastrydom/Zotero/storage/JUY2JERW/Germain and Schreiber - 2024 - Special feature Demystifying fundamental theories.pdf}
}

@article{gilljamAdaptiveRewiringAggravates2015,
  title = {Adaptive Rewiring Aggravates the Effects of Species Loss in Ecosystems},
  author = {Gilljam, David and Curtsdotter, Alva and Ebenman, Bo},
  year = {2015},
  month = sep,
  journal = {Nature Communications},
  volume = {6},
  number = {1},
  pages = {8412},
  issn = {2041-1723},
  doi = {10.1038/ncomms9412},
  urldate = {2024-04-10},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/TQ2F6S2F/Gilljam et al. - 2015 - Adaptive rewiring aggravates the effects of specie.pdf}
}

@article{golubskiModifyingModifiersWhat2011,
  title = {Modifying Modifiers: What Happens When Interspecific Interactions Interact?},
  shorttitle = {Modifying Modifiers},
  author = {Golubski, Antonio J. and Abrams, Peter A.},
  year = {2011},
  journal = {Journal of Animal Ecology},
  volume = {80},
  number = {5},
  pages = {1097--1108},
  issn = {1365-2656},
  doi = {10.1111/j.1365-2656.2011.01852.x},
  urldate = {2020-11-03},
  abstract = {1. The strength of the trophic link between any given pair of species in a food web is likely to depend on the presence and/or densities of other species in the community. How these trophic interaction modifications (TIMs) interact with one another to produce a net modifying effect is an important but under-explored issue. 2. We review several specific types of TIMs that are well understood to address whether the magnitude of the net modification changes with the number of modifiers, and whether modifiers usually increase or decrease each other's effects. 3. Modifications of interactions are generally not independent. It is likely that TIMs interact antagonistically in the majority of cases; the magnitudes of TIMs decrease as more modifiers are added, or new TIMs reduce the magnitudes of modifications that are already present. 4. Individual modifications are likely to have a smaller effect in many-species systems than expected from independent combination of modifications measured in systems with relatively few species. Thus, models that lack explicit TIMs may in some cases yield adequate predictions for species-level perturbations, provided that the net effects of TIMs are implicitly included in measured interaction strengths. 5. Many types of TIMs share structural similarities. Nevertheless, a complete understanding of their effects may require theory that distinguishes different `functional groups' of modifiers and addresses how these are structured according to trophic relationships.},
  copyright = {{\copyright} 2011 The Authors. Journal of Animal Ecology {\copyright} 2011 British Ecological Society},
  langid = {english}
}

@article{gomezEcologicalInteractionsAre2010,
  title = {Ecological Interactions Are Evolutionarily Conserved across the Entire Tree of Life},
  author = {G{\'o}mez, Jos{\'e} M. and Verd{\'u}, Miguel and Perfectti, Francisco},
  year = {2010},
  month = jun,
  journal = {Nature},
  volume = {465},
  number = {7300},
  pages = {918--921},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/nature09113},
  urldate = {2020-11-10},
  abstract = {The characteristics that regulate species interactions are largely inherited, so it seems logical to expect that closely related organisms are more likely to have similar ecological interactions than less related ones. Few studies have been done on this relationship, and those that have tend to focus on specialized organisms, such as parasites or insect herbivores. A new analysis of the evolution of host use in a diverse group of interactions comprising both specialist and generalist, acellular, unicellular and multicellular organisms, and including all types of interaction, finds support for the evolutionary conservation of ecological interactions across all species. The same rules seem to drive the evolution of most ecological interactions, and are strongly contributing to the organization of biodiversity on Earth.},
  copyright = {2010 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/I86SF7TE/nature09113.html}
}

@article{gravelBringingEltonGrinnell2019,
  title = {Bringing {{Elton}} and {{Grinnell}} Together: A Quantitative Framework to Represent the Biogeography of Ecological Interaction Networks},
  shorttitle = {Bringing {{Elton}} and {{Grinnell}} Together},
  author = {Gravel, Dominique and Baiser, Benjamin and Dunne, Jennifer A. and Kopelke, Jens-Peter and Martinez, Neo D. and Nyman, Tommi and Poisot, Timoth{\'e}e and Stouffer, Daniel B. and Tylianakis, Jason M. and Wood, Spencer A. and Roslin, Tomas},
  year = {2019},
  journal = {Ecography},
  volume = {42},
  number = {3},
  pages = {401--415},
  issn = {1600-0587},
  doi = {10.1111/ecog.04006},
  urldate = {2020-12-09},
  abstract = {Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but only recently community ecologists realized the need to document their spatial and temporal variation. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time -- i.e. a novel approach to interaction distribution modeling. We apply this framework to host--parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co-occurrence, but not on species interactions. Even where species co-occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host-parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modeling and community ecology.},
  copyright = {{\copyright} 2018 The Authors},
  langid = {english}
}

@article{gravelInferringFoodWeb2013,
  title = {Inferring Food Web Structure from Predator--Prey Body Size Relationships},
  author = {Gravel, Dominique and Poisot, Timoth{\'e}e and Albouy, Camille and Velez, Laure and Mouillot, David},
  year = {2013},
  journal = {Methods in Ecology and Evolution},
  volume = {4},
  number = {11},
  pages = {1083--1090},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12103},
  urldate = {2024-02-13},
  abstract = {Current global changes make it important to be able to predict which interactions will occur in the emerging ecosystems. Most of the current methods to infer the existence of interactions between two species require a good knowledge of their behaviour or a direct observation of interactions. In this paper, we overcome these limitations by developing a method, inspired from the niche model of food web structure, using the statistical relationship between predator and prey body size to infer the matrix of potential interactions among a pool of species. The novelty of our approach is to infer, for any species of a given species pool, the three species-specific parameters of the niche model. The method applies to both local and metaweb scales. It allows one to evaluate the feeding interactions of a new species entering the community. We find that this method gives robust predictions of the structure of food webs and that its efficiency is increased when the strength of the body--size relationship between predators and preys increases. We finally illustrate the potential of the method to infer the metaweb structure of pelagic fishes of the Mediterranean sea under different global change scenarios.},
  copyright = {{\copyright} 2013 The Authors. Methods in Ecology and Evolution {\copyright} 2013 British Ecological Society},
  langid = {english},
  keywords = {Body size,Food web,Metaweb,Niche model},
  file = {/Users/tanyastrydom/Zotero/storage/GDGMRIG5/Gravel et al. - 2013 - Inferring food web structure from predator–prey bo.pdf;/Users/tanyastrydom/Zotero/storage/GNQUTI44/2041-210X.html}
}

@article{grayFORUMEcologicalNetworks2014,
  title = {{{FORUM}}: {{Ecological}} Networks: The Missing Links in Biomonitoring Science},
  shorttitle = {{{FORUM}}},
  author = {Gray, Clare and Baird, Donald J. and Baumgartner, Simone and Jacob, Ute and Jenkins, Gareth B. and O'Gorman, Eoin J. and Lu, Xueke and Ma, Athen and Pocock, Michael J. O. and Schuwirth, Nele and Thompson, Murray and Woodward, Guy},
  year = {2014},
  journal = {Journal of Applied Ecology},
  volume = {51},
  number = {5},
  pages = {1444--1449},
  issn = {1365-2664},
  doi = {10.1111/1365-2664.12300},
  urldate = {2024-06-11},
  abstract = {Monitoring anthropogenic impacts is essential for managing and conserving ecosystems, yet current biomonitoring approaches lack the tools required to deal with the effects of stressors on species and their interactions in complex natural systems. Ecological networks (trophic or mutualistic) can offer new insights into ecosystem degradation, adding value to current taxonomically constrained schemes. We highlight some examples to show how new network approaches can be used to interpret ecological responses. Synthesis and applications. Augmenting routine biomonitoring data with interaction data derived from the literature, complemented with ground-truthed data from direct observations where feasible, allows us to begin to characterise large numbers of ecological networks across environmental gradients. This process can be accelerated by adopting emerging technologies and novel analytical approaches, enabling biomonitoring to move beyond simple pass/fail schemes and to address the many ecological responses that can only be understood from a network-based perspective.},
  copyright = {{\copyright} 2014 The Authors. Journal of Applied Ecology published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {anthropogenic stress,climate change,conservation,food web,global warming,mutualism,pollination}
}

@article{grayJoiningDotsAutomated2015,
  title = {Joining the Dots: {{An}} Automated Method for Constructing Food Webs from Compendia of Published Interactions},
  shorttitle = {Joining the Dots},
  author = {Gray, Clare and Figueroa, David H. and Hudson, Lawrence N. and Ma, Athen and Perkins, Dan and Woodward, Guy},
  year = {2015},
  month = dec,
  journal = {Food Webs},
  volume = {5},
  pages = {11--20},
  issn = {2352-2496},
  doi = {10.1016/j.fooweb.2015.09.001},
  urldate = {2024-04-02},
  abstract = {Food webs are important tools for understanding how complex natural communities are structured and how they respond to environmental change. However their full potential has yet to be realised because of the huge amount of resources required to construct them de novo. Consequently, the current catalogue of networks that are suitable for rigorous and comparative analyses and theoretical development still suffers from a lack of standardisation and replication. Here, we present a novel R function, WebBuilder, which automates the construction of food webs from taxonomic lists, and a dataset of trophic interactions. This function works by matching species against those within a dataset of trophic interactions, and `filling in' missing trophic interactions based on these matches. We also present a dataset of over 20,000 freshwater trophic interactions, and use this and four well-characterised freshwater food webs to test the method. The WebBuilder function facilitates the generation of food webs of comparable quality to the most detailed published food webs, but at a fraction of the research effort or cost. Furthermore, it matched and often outperformed a selection of predictive models, which are currently among the best, in terms of capturing key properties of empirical food webs. The method is simple to use, systematic and, perhaps most importantly, reproducible, which will facilitate (re-) analysis and data sharing. Although developed and tested on a sample of freshwater food webs, this method could easily be extended to cover other types of ecological interactions (such as mutualistic interactions).},
  keywords = {Food web construction,Food web modelling,Gut contents analysis},
  file = {/Users/tanyastrydom/Zotero/storage/Y2JMHB76/S2352249615300045.html}
}

@article{grimmBabelEcologicalStability1997,
  title = {Babel, or the Ecological Stability Discussions: An Inventory and Analysis of Terminology and a Guide for Avoiding Confusion},
  shorttitle = {Babel, or the Ecological Stability Discussions},
  author = {Grimm, V. and Wissel, Christian},
  year = {1997},
  month = feb,
  journal = {Oecologia},
  volume = {109},
  number = {3},
  pages = {323--334},
  issn = {1432-1939},
  doi = {10.1007/s004420050090},
  urldate = {2024-03-25},
  abstract = {We present an inventory and analysis of discussions of ecological stability, considering 163 definitions of 70 different stability concepts. Our aim is to derive a strategy that can help to dispel the existing ''confusion of tongues'' on the subject of ''stability'' and prevent its future recurrence. The strategy consists of three questions that should be kept in mind when communicating about stability properties. These three questions should overcome the three main sources of confusion in terminology. Firstly, which stability properties are being addressed in the stability statement? Our analysis shows that the general term ''stability'' is so ambiguous as to be useless.It can be replaced by the stability properties ''staying essentially unchanged'' (constancy), ''returning to the reference state (or dynamic) after a temporary disturbance'' (resilience), and ''persistence through time of an ecological system'' (persistence). Second, to what ecological situation does the statement refer? An ecological situation is defined by a set of features that, taken as a whole, determine the domain of validity of a stability statement. The six most important features form the ''ecological checklist'', which serves to classify ecological situations and thereby provides a system of coordinates for communication. The six points are: variable of interest, level of description, reference state, disturbance, spatial scale and temporal scale. Thirdly, is the statement anchored in the situation in question, or is there unacceptable generalisation by inferring ''stability'' of the whole system from a certain stability property in a certain ecological ecological situation? This question separates the scientifically valuable content of a statement from the desire for general statements which is often projected through stability statements.},
  langid = {english},
  keywords = {(Ecological) stability,Generality,Key wordsTerminology,Persistence,Resilience},
  file = {/Users/tanyastrydom/Zotero/storage/T76EFL7P/Grimm and Wissel - 1997 - Babel, or the ecological stability discussions an.pdf}
}

@article{guimaraesStructureEcologicalNetworks2020,
  title = {The {{Structure}} of {{Ecological Networks Across Levels}} of {{Organization}}},
  author = {Guimar{\~a}es, Paulo R.},
  year = {2020},
  month = nov,
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {51},
  number = {1},
  pages = {annurev-ecolsys-012220-120819},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev-ecolsys-012220-120819},
  urldate = {2020-09-02},
  abstract = {Interactions connect the units of ecological systems, forming networks. Individual-based networks characterize variation in niches among individuals within populations. These individual-based networks merge with each other, forming species-based networks and food webs that describe the architecture of ecological communities. Networks at broader spatiotemporal scales portray the structure of ecological interactions across landscapes and over macroevolutionary time. Here, I review the patterns observed in ecological networks across multiple levels of biological organization. A fundamental challenge is to understand the amount of interdependence as we move from individual-based networks to species-based networks and beyond. Despite the uneven distribution of studies, regularities in network structure emerge across scales due to the fundamental architectural patterns shared by complex networks and the interplay between traits and numerical effects. I illustrate the integration of these organizational scales by exploring the consequences of the emergence of highly connected species for network structures across scales.             Expected final online publication date for the Annual Review of Ecology, Evolution, and Systematics, Volume 51 is November 2, 2020. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/679TDUM4/Guimarães - 2020 - The Structure of Ecological Networks Across Levels.pdf}
}

@article{herbersteinAnimalTraitsCuratedAnimal2022,
  title = {{{AnimalTraits}} - a Curated Animal Trait Database for Body Mass, Metabolic Rate and Brain Size},
  author = {Herberstein, Marie E. and McLean, Donald James and Lowe, Elizabeth and Wolff, Jonas O. and Khan, Md Kawsar and Smith, Kaitlyn and Allen, Andrew P. and Bulbert, Matthew and Buzatto, Bruno A. and Eldridge, Mark D. B. and Falster, Daniel and Fernandez Winzer, Laura and Griffith, Simon C. and Madin, Joshua S. and Narendra, Ajay and Westoby, Mark and Whiting, Martin J. and Wright, Ian J. and Carthey, Alexandra J. R.},
  year = {2022},
  month = jun,
  journal = {Scientific Data},
  volume = {9},
  number = {1},
  pages = {265},
  publisher = {Nature Publishing Group},
  issn = {2052-4463},
  doi = {10.1038/s41597-022-01364-9},
  urldate = {2024-02-08},
  abstract = {Trait databases have become important resources for large-scale comparative studies in ecology and evolution. Here we introduce the AnimalTraits database, a curated database of body mass, metabolic rate and brain size, in standardised units, for terrestrial animals. The database has broad taxonomic breadth, including tetrapods, arthropods, molluscs and annelids from almost 2000 species and 1000 genera. All data recorded in the database are sourced from their original empirical publication, and the original metrics and measurements are included with each record. This allows for subsequent data transformations as required. We have included rich metadata to allow users to filter the dataset. The additional R scripts we provide will assist researchers with aggregating standardised observations into species-level trait values. Our goals are to provide this resource without restrictions, to keep the AnimalTraits database current, and to grow the number of relevant traits in the future.},
  copyright = {2022 The Author(s)},
  langid = {english},
  keywords = {Behavioural ecology,Evolution}
}

@article{higinoMismatchIUCNRange2023,
  title = {Mismatch between {{IUCN}} Range Maps and Species Interactions Data Illustrated Using the {{Serengeti}} Food Web},
  author = {Higino, Gracielle T. and Banville, Francis and Dansereau, Gabriel and Mu{\~n}oz, Norma Rocio Forero and Windsor, Fredric and Poisot, Timoth{\'e}e},
  year = {2023},
  month = feb,
  journal = {PeerJ},
  volume = {11},
  pages = {e14620},
  publisher = {PeerJ Inc.},
  issn = {2167-8359},
  doi = {10.7717/peerj.14620},
  urldate = {2024-09-09},
  abstract = {Background Range maps are a useful tool to describe the spatial distribution of species. However, they need to be used with caution, as they essentially represent a rough approximation of a species' suitable habitats. When stacked together, the resulting communities in each grid cell may not always be realistic, especially when species interactions are taken into account. Here we show the extent of the mismatch between range maps, provided by the International Union for Conservation of Nature (IUCN), and species interactions data. More precisely, we show that local networks built from those stacked range maps often yield unrealistic communities, where species of higher trophic levels are completely disconnected from primary producers. Methodology We used the well-described Serengeti food web of mammals and plants as our case study, and identify areas of data mismatch within predators' range maps by taking into account food web structure. We then used occurrence data from the Global Biodiversity Information Facility (GBIF) to investigate where data is most lacking. Results We found that most predator ranges comprised large areas without any overlapping distribution of their prey. However, many of these areas contained GBIF occurrences of the predator. Conclusions Our results suggest that the mismatch between both data sources could be due either to the lack of information about ecological interactions or the geographical occurrence of prey. We finally discuss general guidelines to help identify defective data among distributions and interactions data, and we recommend this method as a valuable way to assess whether the occurrence data that are being used, even if incomplete, are ecologically accurate.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/Q4C566LL/Higino et al. - 2023 - Mismatch between IUCN range maps and species inter.pdf}
}

@misc{hongDealingClassImbalance2016,
  title = {Dealing with {{Class Imbalance}} Using {{Thresholding}}},
  author = {Hong, Charmgil and Ghosh, Rumi and Srinivasan, Soundar},
  year = {2016},
  month = jul,
  number = {arXiv:1607.02705},
  eprint = {1607.02705},
  primaryclass = {cs},
  publisher = {arXiv},
  doi = {10.48550/arXiv.1607.02705},
  urldate = {2024-03-22},
  abstract = {We propose thresholding as an approach to deal with class imbalance. We define the concept of thresholding as a process of determining a decision boundary in the presence of a tunable parameter. The threshold is the maximum value of this tunable parameter where the conditions of a certain decision are satisfied. We show that thresholding is applicable not only for linear classifiers but also for non-linear classifiers. We show that this is the implicit assumption for many approaches to deal with class imbalance in linear classifiers. We then extend this paradigm beyond linear classification and show how non-linear classification can be dealt with under this umbrella framework of thresholding. The proposed method can be used for outlier detection in many real-life scenarios like in manufacturing. In advanced manufacturing units, where the manufacturing process has matured over time, the number of instances (or parts) of the product that need to be rejected (based on a strict regime of quality tests) becomes relatively rare and are defined as outliers. How to detect these rare parts or outliers beforehand? How to detect combination of conditions leading to these outliers? These are the questions motivating our research. This paper focuses on prediction of outliers and conditions leading to outliers using classification. We address the problem of outlier detection using classification. The classes are good parts (those passing the quality tests) and bad parts (those failing the quality tests and can be considered as outliers). The rarity of outliers transforms this problem into a class-imbalanced classification problem.},
  archiveprefix = {arXiv},
  keywords = {Computer Science - Machine Learning},
  file = {/Users/tanyastrydom/Zotero/storage/M3NTKJIV/Hong et al. - 2016 - Dealing with Class Imbalance using Thresholding.pdf;/Users/tanyastrydom/Zotero/storage/4PF94AGG/1607.html}
}

@article{hortalSevenShortfallsThat2015,
  title = {Seven {{Shortfalls}} That {{Beset Large-Scale Knowledge}} of {{Biodiversity}}},
  author = {Hortal, Joaqu{\'i}n and {de Bello}, Francesco and {Diniz-Filho}, Jos{\'e} Alexandre F. and Lewinsohn, Thomas M. and Lobo, Jorge M. and Ladle, Richard J.},
  year = {2015},
  month = dec,
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {46},
  number = {1},
  pages = {523--549},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev-ecolsys-112414-054400},
  urldate = {2021-12-14},
  abstract = {Ecologists and evolutionary biologists are increasingly using big-data approaches to tackle questions at large spatial, taxonomic, and temporal scales. However, despite recent efforts to gather two centuries of biodiversity inventories into comprehensive databases, many crucial research questions remain unanswered. Here, we update the concept of knowledge shortfalls and review the tradeoffs between generality and uncertainty. We present seven key shortfalls of current biodiversity data. Four previously proposed shortfalls pinpoint knowledge gaps for species taxonomy (Linnean), distribution (Wallacean), abundance (Prestonian), and evolutionary patterns (Darwinian). We also redefine the Hutchinsonian shortfall to apply to the abiotic tolerances of species and propose new shortfalls relating to limited knowledge of species traits (Raunki{\ae}ran) and biotic interactions (Eltonian). We conclude with a general framework for the combined impacts and consequences of shortfalls of large-scale biodiversity knowledge for evolutionary and ecological research and consider ways of overcoming the seven shortfalls and dealing with the uncertainty they generate.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/MWLVGGWF/Hortal et al. - 2015 - Seven Shortfalls that Beset Large-Scale Knowledge .pdf}
}

@book{hubbellUnifiedNeutralTheory2001,
  title = {The {{Unified Neutral Theory}} of {{Biodiversity}} and {{Biogeography}} ({{MPB-32}})},
  author = {Hubbell, Stephen P.},
  year = {2001},
  eprint = {j.ctt7rj8w},
  eprinttype = {jstor},
  publisher = {Princeton University Press},
  urldate = {2024-05-13},
  abstract = {Despite its supreme importance and the threat of its global crash, biodiversity remains poorly understood both empirically and theoretically. This ambitious book presents a new, general neutral theory to explain the origin, maintenance, and loss of biodiversity in a biogeographic context.  Until now biogeography (the study of the geographic distribution of species) and biodiversity (the study of species richness and relative species abundance) have had largely disjunct intellectual histories. In this book, Stephen Hubbell develops a formal mathematical theory that unifies these two fields. When a speciation process is incorporated into Robert H. MacArthur and Edward O. Wilson's now classical theory of island biogeography, the generalized theory predicts the existence of a universal, dimensionless biodiversity number. In the theory, this fundamental biodiversity number, together with the migration or dispersal rate, completely determines the steady-state distribution of species richness and relative species abundance on local to large geographic spatial scales and short-term to evolutionary time scales.  Although neutral, Hubbell's theory is nevertheless able to generate many nonobvious, testable, and remarkably accurate quantitative predictions about biodiversity and biogeography. In many ways Hubbell's theory is the ecological analog to the neutral theory of genetic drift in genetics. The unified neutral theory of biogeography and biodiversity should stimulate research in new theoretical and empirical directions by ecologists, evolutionary biologists, and biogeographers.},
  isbn = {978-0-691-02128-7}
}

@article{huiHowInvadeEcological2019,
  title = {How to {{Invade}} an {{Ecological Network}}},
  author = {Hui, Cang and Richardson, David M.},
  year = {2019},
  month = feb,
  journal = {Trends in Ecology \& Evolution},
  volume = {34},
  number = {2},
  pages = {121--131},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2018.11.003},
  urldate = {2024-09-27},
  abstract = {Invasion science is in a state of paradox, having low predictability despite strong, identifiable covariates of invasion performance. We propose shifting the foundation metaphor of biological invasions from a linear filtering scheme to one that invokes complex adaptive networks. We link invasion performance and invasibility directly to the loss of network stability and indirectly to network topology through constraints from the emergence of the stability criterion in complex systems. We propose the wind vane of an invaded network -- the major axis of its adjacency matrix -- which reveals how species respond dynamically to invasions. We suggest that invasion ecology should steer away from comparative macroecological studies, to rather explore the ecological network centred on the focal species.},
  file = {/Users/tanyastrydom/Zotero/storage/F2YPA5YH/S016953471830274X.html}
}

@article{hutchinsonSeeingForestTrees2019,
  title = {Seeing the Forest for the Trees: {{Putting}} Multilayer Networks to Work for Community Ecology},
  shorttitle = {Seeing the Forest for the Trees},
  author = {Hutchinson, Matthew C. and Bramon Mora, Bernat and Pilosof, Shai and Barner, Allison K. and K{\'e}fi, Sonia and Th{\'e}bault, Elisa and Jordano, Pedro and Stouffer, Daniel B.},
  editor = {Godoy, Oscar},
  year = {2019},
  month = feb,
  journal = {Functional Ecology},
  volume = {33},
  number = {2},
  pages = {206--217},
  issn = {0269-8463, 1365-2435},
  doi = {10.1111/1365-2435.13237},
  urldate = {2020-11-02},
  langid = {english}
}

@article{ingsEcologicalNetworksbeyondFood2009,
  title = {Ecological Networks--beyond Food Webs},
  author = {Ings, Thomas C. and Montoya, Jos{\'e} M. and Bascompte, Jordi and Bl{\"u}thgen, Nico and Brown, Lee and Dormann, Carsten F. and Edwards, Fran{\c c}ois and Figueroa, David and Jacob, Ute and Jones, J. Iwan and Lauridsen, Rasmus B. and Ledger, Mark E. and Lewis, Hannah M. and Olesen, Jens M. and {van Veen}, F. J. Frank and Warren, Phil H. and Woodward, Guy},
  year = {2009},
  month = jan,
  journal = {The Journal of Animal Ecology},
  volume = {78},
  number = {1},
  pages = {253--269},
  issn = {1365-2656},
  doi = {10.1111/j.1365-2656.2008.01460.x},
  abstract = {1. A fundamental goal of ecological network research is to understand how the complexity observed in nature can persist and how this affects ecosystem functioning. This is essential for us to be able to predict, and eventually mitigate, the consequences of increasing environmental perturbations such as habitat loss, climate change, and invasions of exotic species. 2. Ecological networks can be subdivided into three broad types: 'traditional' food webs, mutualistic networks and host-parasitoid networks. There is a recent trend towards cross-comparisons among network types and also to take a more mechanistic, as opposed to phenomenological, perspective. For example, analysis of network configurations, such as compartments, allows us to explore the role of co-evolution in structuring mutualistic networks and host-parasitoid networks, and of body size in food webs. 3. Research into ecological networks has recently undergone a renaissance, leading to the production of a new catalogue of evermore complete, taxonomically resolved, and quantitative data. Novel topological patterns have been unearthed and it is increasingly evident that it is the distribution of interaction strengths and the configuration of complexity, rather than just its magnitude, that governs network stability and structure. 4. Another significant advance is the growing recognition of the importance of individual traits and behaviour: interactions, after all, occur between individuals. The new generation of high-quality networks is now enabling us to move away from describing networks based on species-averaged data and to start exploring patterns based on individuals. Such refinements will enable us to address more general ecological questions relating to foraging theory and the recent metabolic theory of ecology. 5. We conclude by suggesting a number of 'dead ends' and 'fruitful avenues' for future research into ecological networks.},
  langid = {english},
  pmid = {19120606}
}

@article{jonesPanTHERIASpecieslevelDatabase2009,
  title = {{{PanTHERIA}}: A Species-Level Database of Life History, Ecology, and Geography of Extant and Recently Extinct Mammals},
  shorttitle = {{{PanTHERIA}}},
  author = {Jones, Kate E. and Bielby, Jon and Cardillo, Marcel and Fritz, Susanne A. and O'Dell, Justin and Orme, C. David L. and Safi, Kamran and Sechrest, Wes and Boakes, Elizabeth H. and Carbone, Chris and Connolly, Christina and Cutts, Michael J. and Foster, Janine K. and Grenyer, Richard and Habib, Michael and Plaster, Christopher A. and Price, Samantha A. and Rigby, Elizabeth A. and Rist, Janna and Teacher, Amber and {Bininda-Emonds}, Olaf R. P. and Gittleman, John L. and Mace, Georgina M. and Purvis, Andy},
  year = {2009},
  journal = {Ecology},
  volume = {90},
  number = {9},
  pages = {2648--2648},
  issn = {1939-9170},
  doi = {10.1890/08-1494.1},
  urldate = {2024-02-08},
  abstract = {Analyses of life-history, ecological, and geographic trait differences among species, their causes, correlates, and likely consequences are increasingly important for understanding and conserving biodiversity in the face of rapid global change. Assembling multispecies trait data from diverse literature sources into a single comprehensive data set requires detailed consideration of methods to reliably compile data for particular species, and to derive single estimates from multiple sources based on different techniques and definitions. Here we describe PanTHERIA, a species-level data set compiled for analysis of life history, ecology, and geography of all known extant and recently extinct mammals. PanTHERIA is derived from a database capable of holding multiple geo-referenced values for variables within a species containing 100 740 lines of biological data for extant and recently extinct mammalian species, collected over a period of three years by 20 individuals. PanTHERIA also includes spatial databases of mammalian geographic ranges and global climatic and anthropogenic variables. Here we detail how the data fields are extracted and defined for PanTHERIA using a customized data input format (MammalForm); how data were collected from the literature, species names and sources tracked, error-checking and validation procedures applied, and how data were consolidated into species-level values for each variable. Tables of the consolidated species-level values are made available for each of two recent species-level taxonomic classifications of mammals, as well as associated taxonomic synonymy conversion and data-input files. This study provides a useful guide to prospective researchers on how to structure and codify life-history, ecological, geographic, and taxonomic data and methods to extract meaningful species-level traits. It also provides comprehensive information on traits like size, diet, environmental conditions, and ecology to permit macroecological and macroevolutionary analyses of this important clade. The complete data sets corresponding to abstracts published in the Data Papers section of the journal are published electronically in Ecological Archives at {\textlangle}http://esapubs.org/archive{\textrangle}. (The accession number for each Data Paper is given directly beneath the title.)},
  copyright = {{\copyright} 2009 by the Ecological Society of America},
  langid = {english},
  keywords = {behavior,biogeography,body size,climatic variables,comparative analyses,conservation,ecology,geographic range,life history,mammals,PanTHERIA,population density},
  file = {/Users/tanyastrydom/Zotero/storage/E5KGBX5K/Jones et al. - 2009 - PanTHERIA a species-level database of life histor.pdf;/Users/tanyastrydom/Zotero/storage/FXPJ3H4N/08-1494.html}
}

@article{jordanoChasingEcologicalInteractions2016,
  title = {Chasing {{Ecological Interactions}}},
  author = {Jordano, Pedro},
  year = {2016},
  month = sep,
  journal = {PLOS Biology},
  volume = {14},
  number = {9},
  pages = {e1002559},
  publisher = {Public Library of Science},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.1002559},
  urldate = {2020-11-07},
  abstract = {Basic research on biodiversity has concentrated on individual species---naming new species, studying distribution patterns, and analyzing their evolutionary relationships. Yet biodiversity is more than a collection of individual species; it is the combination of biological entities and processes that support life on Earth. To understand biodiversity we must catalog it, but we must also assess the ways species interact with other species to provide functional support for the Tree of Life. Ecological interactions may be lost well before the species involved in those interactions go extinct; their ecological functions disappear even though they remain. Here, I address the challenges in studying the functional aspects of species interactions and how basic research is helping us address the fast-paced extinction of species due to human activities.},
  langid = {english},
  keywords = {Biodiversity,Ecosystem functioning,Ecosystems,Plant-animal interactions,Seeds,Species extinction,Species interactions,Trophic interactions},
  file = {/Users/tanyastrydom/Zotero/storage/WY59SMBD/Jordano - 2016 - Chasing Ecological Interactions.pdf;/Users/tanyastrydom/Zotero/storage/U7U4ELDB/article.html}
}

@article{jordanoSamplingNetworksEcological2016,
  title = {Sampling Networks of Ecological Interactions},
  author = {Jordano, Pedro},
  year = {2016},
  month = sep,
  journal = {Functional Ecology},
  issn = {02698463},
  doi = {10.1111/1365-2435.12763},
  urldate = {2016-10-12},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/HA5A2J2J/Jordano - 2016 - Sampling networks of ecological interactions.pdf}
}

@article{kamaruDisruptionAntplantMutualism2024,
  title = {Disruption of an Ant-Plant Mutualism Shapes Interactions between Lions and Their Primary Prey},
  author = {Kamaru, Douglas N. and Palmer, Todd M. and Riginos, Corinna and Ford, Adam T. and Belnap, Jayne and Chira, Robert M. and Githaiga, John M. and Gituku, Benard C. and Hays, Brandon R. and Kavwele, Cyrus M. and Kibungei, Alfred K. and Lamb, Clayton T. and Maiyo, Nelly J. and Milligan, Patrick D. and Mutisya, Samuel and Ng'weno, Caroline C. and Ogutu, Michael and Pietrek, Alejandro G. and Wildt, Brendon T. and Goheen, Jacob R.},
  year = {2024},
  month = jan,
  journal = {Science},
  volume = {383},
  number = {6681},
  pages = {433--438},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/science.adg1464},
  urldate = {2024-11-05},
  abstract = {Mutualisms often define ecosystems, but they are susceptible to human activities. Combining experiments, animal tracking, and mortality investigations, we show that the invasive big-headed ant (Pheidole megacephala) makes lions (Panthera leo) less effective at killing their primary prey, plains zebra (Equus quagga). Big-headed ants disrupted the mutualism between native ants (Crematogaster spp.) and the dominant whistling-thorn tree (Vachellia drepanolobium), rendering trees vulnerable to elephant (Loxodonta africana) browsing and resulting in landscapes with higher visibility. Although zebra kills were significantly less likely to occur in higher-visibility, invaded areas, lion numbers did not decline since the onset of the invasion, likely because of prey-switching to African buffalo (Syncerus caffer). We show that by controlling biophysical structure across landscapes, a tiny invader reconfigured predator-prey dynamics among iconic species.},
  file = {/Users/tanyastrydom/Zotero/storage/7DEJT2YT/Kamaru et al. - 2024 - Disruption of an ant-plant mutualism shapes intera.pdf}
}

@article{kefiMoreMealIntegrating2012,
  ids = {KefiBerl12},
  title = {More than a Meal{\dots} Integrating Non-Feeding Interactions into Food Webs: {{More}} than a Meal {\dots}},
  shorttitle = {More than a Meal{\dots} Integrating Non-Feeding Interactions into Food Webs},
  author = {K{\'e}fi, Sonia and Berlow, Eric L. and Wieters, Evie A. and Navarrete, Sergio A. and Petchey, Owen L. and Wood, Spencer A. and Boit, Alice and Joppa, Lucas N. and Lafferty, Kevin D. and Williams, Richard J. and Martinez, Neo D. and Menge, Bruce A. and Blanchette, Carol A. and Iles, Alison C. and Brose, Ulrich},
  year = {2012},
  month = apr,
  journal = {Ecology Letters},
  volume = {15},
  number = {4},
  pages = {291--300},
  issn = {1461023X},
  doi = {10.1111/j.1461-0248.2011.01732.x},
  urldate = {2018-02-19},
  langid = {english}
}

@article{kefiNetworkStructureFood2015,
  title = {Network Structure beyond Food Webs: Mapping Non-Trophic and Trophic Interactions on {{Chilean}} Rocky Shores},
  shorttitle = {Network Structure beyond Food Webs},
  author = {K{\'e}fi, Sonia and Berlow, Eric L. and Wieters, Evie A. and Joppa, Lucas N. and Wood, Spencer A. and Brose, Ulrich and Navarrete, Sergio A.},
  year = {2015},
  journal = {Ecology},
  volume = {96},
  number = {1},
  pages = {291--303},
  issn = {1939-9170},
  doi = {10.1890/13-1424.1},
  urldate = {2024-09-10},
  abstract = {How multiple types of non-trophic interactions map onto trophic networks in real communities remains largely unknown. We present the first effort, to our knowledge, describing a comprehensive ecological network that includes all known trophic and diverse non-trophic links among {$>$}100 coexisting species for the marine rocky intertidal community of the central Chilean coast. Our results suggest that non-trophic interactions exhibit highly nonrandom structures both alone and with respect to food web structure. The occurrence of different types of interactions, relative to all possible links, was well predicted by trophic structure and simple traits of the source and target species. In this community, competition for space and positive interactions related to habitat/refuge provisioning by sessile and/or basal species were by far the most abundant non-trophic interactions. If these patterns are corroborated in other ecosystems, they may suggest potentially important dynamic constraints on the combined architecture of trophic and non-trophic interactions. The nonrandom patterning of non-trophic interactions suggests a path forward for developing a more comprehensive ecological network theory to predict the functioning and resilience of ecological communities.},
  copyright = {{\copyright} 2015 by the Ecological Society of America},
  langid = {english}
}

@article{keyesSynthesisingRelationshipsFood2024,
  title = {Synthesising the {{Relationships Between Food Web Structure}} and {{Robustness}}},
  author = {Keyes, Aislyn A. and Barner, Allison K. and Dee, Laura E.},
  year = {2024},
  journal = {Ecology Letters},
  volume = {27},
  number = {10},
  pages = {e14533},
  issn = {1461-0248},
  doi = {10.1111/ele.14533},
  urldate = {2024-10-29},
  abstract = {For many decades, ecologists have sought to understand the extent to which species losses lead to secondary extinctions---that is, the additional loss of species that occurs when resources or key interactions are lost (i.e. robustness). In particular, ecologists aim to identify generalisable rules that explain which types of food webs are more or less robust to secondary extinctions. Food web structure, or the patterns formed by species and their interactions, has been extensively studied as a potential factor that influences robustness to species loss. We systematically reviewed 28 studies to identify the relationships between food web structure and robustness to species loss and how the conclusions depend on methodological differences. Contrary to popular belief and theory, we found relatively consistent, positive relationships between connectance and robustness, among other generalities. Yet, we also found that conflicting conclusions about structure-robustness relationships can be, in part, attributed to differences in the type of data that studies use, particularly studies that use empirical data versus those generated from theoretical models. This review points towards a need to standardise methodology to answer the open question of whether robustness and its relationship with food web structure and to provide applicable insights for managing complex systems.},
  copyright = {{\copyright} 2024 John Wiley \& Sons Ltd.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/GRSIWHS2/ele.html}
}

@article{krauseCompartmentsRevealedFoodweb2003,
  title = {Compartments Revealed in Food-Web Structure},
  author = {Krause, Ann E. and Frank, Kenneth A. and Mason, Doran M. and Ulanowicz, Robert E. and Taylor, William W.},
  year = {2003},
  month = nov,
  journal = {Nature},
  volume = {426},
  number = {6964},
  pages = {282--285},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/nature02115},
  urldate = {2024-09-13},
  abstract = {Compartments1 in food webs are subgroups of taxa in which many strong interactions occur within the subgroups and few weak interactions occur between the subgroups2. Theoretically, compartments increase the stability in networks1,2,3,4,5, such as food webs. Compartments have been difficult to detect in empirical food webs because of incompatible approaches6,7,8,9 or insufficient methodological rigour8,10,11. Here we show that a method for detecting compartments from the social networking science12,13,14 identified significant compartments in three of five complex, empirical food webs. Detection of compartments was influenced by food web resolution, such as interactions with weights. Because the method identifies compartmental boundaries in which interactions are concentrated, it is compatible with the definition of compartments. The method is rigorous because it maximizes an explicit function, identifies the number of non-overlapping compartments, assigns membership to compartments, and tests the statistical significance of the results12,13,14. A graphical presentation14 reveals systemic relationships and taxa-specific positions as structured by compartments. From this graphic, we explore two scenarios of disturbance to develop a hypothesis for testing how compartmentalized interactions increase stability in food webs15,16,17.},
  copyright = {2003 Macmillan Magazines Ltd.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/EWIFYL5F/Krause et al. - 2003 - Compartments revealed in food-web structure.pdf}
}

@article{krishnaNeutralnicheTheoryNestedness2008,
  title = {A Neutral-Niche Theory of Nestedness in Mutualistic Networks},
  author = {Krishna, Abhay and Guimar{\~a}es Jr, Paulo R. and Jordano, Pedro and Bascompte, Jordi},
  year = {2008},
  journal = {Oikos},
  volume = {117},
  number = {11},
  pages = {1609--1618},
  issn = {1600-0706},
  doi = {10.1111/j.1600-0706.2008.16540.x},
  urldate = {2024-03-12},
  abstract = {Recently, there has been a vigorous interest in community ecology about the structure of mutualistic networks and its importance for species persistence and coevolution. However, the mechanisms shaping mutualistic networks have been rarely explored. Here we extend for the first time the neutral theory of biodiversity to a multi trophic system. We focus on nestedness, a distinctive pattern of mutualistic community assembly showing two characteristics, namely, asymmetrical specialization (specialists interacting with generalists) and a generalist core (generalists interacting with generalists). We investigate the importance of relative species abundance (RSA) for the nested assembly of plant--animal mutualistic networks. Our results show that neutral mutualistic communities give rise to networks considerably more nested than real communities. RSA explains 60--70\% of nested patterns in two real communities studied here, while 30--40\% of nestedness is still unexplained. The nested pattern in real communities is better explained when we introduce interaction-specific species traits such as forbidden links and intensity of dependence (relative importance of fruits for the diet of a frugivore) in our analysis. The fact that neutral mutualistic communities exhibit a perfectly nested structure and do not show a random or compartmentalized structure, underlines the importance of RSA in the assembly of mutualistic networks.},
  langid = {english}
}

@misc{kuschEcologicalNetworkInference2023,
  title = {Ecological Network Inference Is Not Consistent across Scales or Approaches},
  author = {Kusch, Erik and Bimler, Malyon and Lutz, James A. and Ordonez, Alejandro},
  year = {2023},
  month = jul,
  primaryclass = {New Results},
  pages = {2023.07.13.548816},
  publisher = {bioRxiv},
  doi = {10.1101/2023.07.13.548816},
  urldate = {2024-06-07},
  abstract = {Several methods of ecological network inference have been proposed, but their consistency and applicability for use across ecologically relevant scales require further investigation. Here, we infer ecological networks using two data sets (YFDP, FIA) describing distributional and attribute information at local, regional, and continental scales for woody species across North America. We accomplish this inference using four different methodologies (COOCCUR, NETASSOC, HMSC, NDD-RIM), incorporating biological data along an occurrence-performance spectrum while accounting (or not) for various confounding parameters. We contrast 1-1 associations at each evaluated scale to quantify consistency amongst inference approaches. We also assess consistency across scales within each inference approach. Ultimately, we find that inferred networks are inconsistent across scales and methodologies, particularly at continental scales. We highlight how such inconsistencies between network inference methods may be linked to using occurrence or performance information and incorporating or not confounding factors. Finally, we argue that identifying the ``best'' inference method is non-trivial. Thus, to facilitate the choice of inference methods for a given purpose, we suggest aligning specific research questions and the scale applicability of the method when interpreting the inferred links, network topology, and ecological processes governing network assembly.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/W2KJHDCA/Kusch et al. - 2023 - Ecological network inference is not consistent acr.pdf}
}

@misc{kuschNovelSimulationFramework2023,
  title = {A {{Novel Simulation Framework}} for {{Validation}} of {{Ecological Network Inference}}},
  author = {Kusch, Erik and Vinton, Anna C.},
  year = {2023},
  month = oct,
  primaryclass = {New Results},
  pages = {2023.08.05.552122},
  publisher = {bioRxiv},
  doi = {10.1101/2023.08.05.552122},
  urldate = {2024-06-07},
  abstract = {Understanding how the differential magnitude and sign of ecological interactions vary across space is vital to assessing ecosystem resilience to biodiversity loss and predict community assemblies. This necessity for ecological network knowledge and their labour-intensive sampling requirements has spurred the creation of ecological network inference methodology. Recent research has identified inconsistencies in networks inferred using different approaches thus necessitating quantification of inference performance to facilitate choice of network inference approach.Here we develop a data simulation method to generate data products fit for network inference and subsequently quantify the validity of two well-established ecological interaction network inference methods -- HMSC and COOCCUR. The simulation framework we present here can be parameterised using real-world information (e.g., biological interactions observed in-situ and bioclimatic niche preferences) thus representing network inference capabilities in real-world applications. Using this framework, it is thus possible to evaluate the performance of any ecological network inference approach.We identify a concerningly large range in accuracy of inferred networks as compared to true, realisable association networks. These differences in inference accuracy are governed by a paradigm of input data types and environmental parameter estimation as previously suggested. To establish a workflow for quantification of network inference reliability, we suggest analysis procedures with which to explore inference and detection probabilities of association types of different identity and sign with respect to bioclimatic niche preferences and association strength of association partner-species.With this study, we provide the groundwork with which to validate and compare ecological network inference methods, and ultimately vastly increase our ability to understand and predict species biodiversity across space and time.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2023, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/CFVKEZL3/Kusch and Vinton - 2023 - A Novel Simulation Framework for Validation of Eco.pdf}
}

@article{laenderCarbonTransferHerbivore2010,
  title = {Carbon Transfer in a Herbivore- and Microbial Loop-Dominated Pelagic Food Webs in the Southern {{Barents Sea}} during Spring and Summer},
  author = {Laender, Frederik De and Oevelen, Dick Van and Soetaert, Karline and Middelburg, Jack J.},
  year = {2010},
  month = jan,
  journal = {Marine Ecology Progress Series},
  volume = {398},
  pages = {93--107},
  issn = {0171-8630, 1616-1599},
  doi = {10.3354/meps08335},
  urldate = {2024-09-30},
  abstract = {We compared carbon budgets between a herbivore-dominated and a microbial loop-dominated food web and examined the implications of food web structure for fish production. We used the southern Barents Sea as a case study and inverse modelling as an analysis method. In spring, when the system was dominated by the herbivorous web, the diet of protozoa consisted of similar amounts of bacteria and phytoplankton. Copepods showed no clear preference for protozoa. Cod Gadus morhua, a predatory fish preying on copepods and on copepod-feeding capelin Mallotus villosus in spring, moderately depended on the microbial loop in spring, as only 20 to 60\% of its food passed through the microbial loop. In summer, when the food web was dominated by the microbial loop, protozoa ingested 4 times more bacteria than phytoplankton and protozoa formed 80 to 90\% of the copepod diet. Because of this strong link between the microbial loop and copepods (the young cod's main prey item) young cod ({$<$}3 yr) depended more on the microbial loop than on any other food web compartment, as {$>$}60\% of its food passed through the microbial loop in summer. Adult cod ({$\leq$}3 yr) relied far less on the microbial loop than young cod as it preyed on strictly herbivorous krill in summer. Food web efficiency for fish production was comparable between seasons ({\textasciitilde}5 {\texttimes} 10--4) and 2 times higher in summer (5 {\texttimes} 10--2) than in spring for copepod production.},
  langid = {english},
  keywords = {Copepods,Food web,Gadus morhua,Microbial loop,Protozoa},
  file = {/Users/tanyastrydom/Zotero/storage/SFQY9LX8/Laender et al. - 2010 - Carbon transfer in a herbivore- and microbial loop.pdf}
}

@article{laigleSpeciesTraitsDrivers2018,
  title = {Species Traits as Drivers of Food Web Structure},
  author = {Laigle, Idaline and Aubin, Isabelle and Digel, Christoph and Brose, Ulrich and Boulangeat, Isabelle and Gravel, Dominique},
  year = {2018},
  journal = {Oikos},
  volume = {127},
  number = {2},
  pages = {316--326},
  issn = {1600-0706},
  doi = {10.1111/oik.04712},
  urldate = {2024-03-22},
  abstract = {The use of functional traits to describe community structure is a promising approach to reveal generalities across organisms and ecosystems. Plant ecologists have demonstrated the importance of traits in explaining community structure, competitive interactions as well as ecosystem functioning. The application of trait-based methods to more complex communities such as food webs is however more challenging owing to the diversity of animal characteristics and of interactions. The objective of this study was to determine how functional structure is related to food web structure. We consider that food web structure is the result of 1) the match between consumer and resource traits, which determine the occurence of a trophic interaction between them, and 2) the distribution of functional traits in the community. We implemented a statistical approach to assess whether or not 35 466 pairwise interactions between soil organisms are constrained by trait-matching and then used a Procrustes analysis to investigate correlations between functional indices and network properties across 48 sites. We found that the occurrence of trophic interactions is well predicted by matching the traits of the resource with those of the consumer. Taxonomy and body mass of both species were the most important traits for the determination of an interaction. As a consequence, functional evenness and the variance of certain traits in the community were correlated to trophic complementarity between species, while trait identity, more than diversity, was related to network topology. The analysis was however limited by trait data availability, and a coarse resolution of certain taxonomic groups in our dataset. These limitations explain the importance of taxonomy, as well as the complexity of the statistical model needed. Our results outline the important implications of trait composition on ecological networks, opening promising avenues of research into the relationship between functional diversity and ecosystem functioning in multi-trophic systems.},
  copyright = {{\copyright} 2017 The Authors},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/STGAFHMI/Laigle et al. - 2018 - Species traits as drivers of food web structure.pdf;/Users/tanyastrydom/Zotero/storage/VCSL8BM8/oik.html}
}

@misc{lajaaitiEcologicalNetworksDynamicsJlJulia2024,
  title = {{{EcologicalNetworksDynamics}}.Jl {{A Julia}} Package to Simulate the Temporal Dynamics of Complex Ecological Networks},
  author = {Lajaaiti, Isma{\"e}l and Bonnici, Iago and K{\'e}fi, Sonia and Mayall, Hana and Danet, Alain and Beckerman, Andrew P. and Malpas, Thomas and Delmas, Eva},
  year = {2024},
  month = mar,
  primaryclass = {New Results},
  pages = {2024.03.20.585899},
  publisher = {bioRxiv},
  doi = {10.1101/2024.03.20.585899},
  urldate = {2024-06-21},
  abstract = {Species interactions play a crucial role in shaping biodiversity, species coexistence, population dynamics, community stability and ecosystem functioning. Our understanding of the role of the diversity of species interactions driving these species, community and ecosystem features is limited because current approaches often focus only on trophic interactions. This is why a new modelling framework that includes a greater diversity of interactions between species is crucially needed.We developed a modular, user-friendly, and extensible Julia package that delivers the core functionality of the bio-energetic food web model. Moreover, it embeds several ecological interaction types alongside the capacity to manipulate external drivers of ecological dynamics like temperature. These new features represent important processes known to influence biodiversity, coexistence, functioning and stability in natural communities. Specifically, they include: a) an explicit multiple nutrient intake model for producers, b) competition among producers, c) temperature dependence implemented via the Boltzmann-Arhennius rule, and d) the ability to model several non-trophic interactions including competition for space, plant facilitation, predator interference and refuge provisioning.The inclusion of the various features provides users with the ability to ask questions about multiple simultaneous processes and stressor impacts, and thus develop theory relevant to real world scenarios facing complex ecological communities in the Anthropocene. It will allow researchers to quantify the relative importance of different mechanisms to stability and functioning of complex communities.The package was build for theoreticians seeking to explore the effects of different types of species interactions on the dynamics of complex ecological communities, but also for empiricists seeking to confront their empirical findings with theoretical expectations. The package provides a straightforward framework to model explicitly complex ecological communities or provide tools to generate those communities from few parameters.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/CM3HJZC7/Lajaaiti et al. - 2024 - EcologicalNetworksDynamics.jl A Julia package to s.pdf}
}

@article{landiComplexityStabilityEcological2018,
  title = {Complexity and Stability of Ecological Networks: A Review of the Theory},
  shorttitle = {Complexity and Stability of Ecological Networks},
  author = {Landi, Pietro and Minoarivelo, Henintsoa O. and Br{\"a}nnstr{\"o}m, {\AA}ke and Hui, Cang and Dieckmann, Ulf},
  year = {2018},
  month = oct,
  journal = {Population Ecology},
  volume = {60},
  number = {4},
  pages = {319--345},
  issn = {1438-390X},
  doi = {10.1007/s10144-018-0628-3},
  urldate = {2018-11-22},
  abstract = {Our planet is changing at paces never observed before. Species extinction is happening at faster rates than ever, greatly exceeding the five mass extinctions in the fossil record. Nevertheless, our lives are strongly based on services provided by ecosystems, thus the responses to global change of our natural heritage are of immediate concern. Understanding the relationship between complexity and stability of ecosystems is of key importance for the maintenance of the balance of human growth and the conservation of all the natural services that ecosystems provide. Mathematical network models can be used to simplify the vast complexity of the real world, to formally describe and investigate ecological phenomena, and to understand ecosystems propensity of returning to its functioning regime after a stress or a perturbation. The use of ecological-network models to study the relationship between complexity and stability of natural ecosystems is the focus of this review. The concept of ecological networks and their characteristics are first introduced, followed by central and occasionally contrasting definitions of complexity and stability. The literature on the relationship between complexity and stability in different types of models and in real ecosystems is then reviewed, highlighting the theoretical debate and the lack of consensual agreement. The summary of the importance of this line of research for the successful management and conservation of biodiversity and ecosystem services concludes the review.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/K86PGCXN/Landi et al. - 2018 - Complexity and stability of ecological networks a.pdf;/Users/tanyastrydom/Zotero/storage/Q7KG49YB/Landi et al. - 2018 - Complexity and stability of ecological networks a.pdf}
}

@book{levinPrincetonGuideEcology2009,
  title = {The {{Princeton Guide}} to {{Ecology}}},
  author = {Levin, Simon A. and Carpenter, Stephen R. and Godfray, H. Charles J. and Kinzig, Ann P. and Loreau, Michel and Losos, Jonathan B. and Walker, Brian and Wilcove, David S.},
  year = {2009},
  month = jul,
  publisher = {Princeton University Press},
  abstract = {The Princeton Guide to Ecology is a concise, authoritative one-volume reference to the field's major subjects and key concepts. Edited by eminent ecologist Simon Levin, with contributions from an international team of leading ecologists, the book contains more than ninety clear, accurate, and up-to-date articles on the most important topics within seven major areas: autecology, population ecology, communities and ecosystems, landscapes and the biosphere, conservation biology, ecosystem services, and biosphere management. Complete with more than 200 illustrations (including sixteen pages in color), a glossary of key terms, a chronology of milestones in the field, suggestions for further reading on each topic, and an index, this is an essential volume for undergraduate and graduate students, research ecologists, scientists in related fields, policymakers, and anyone else with a serious interest in ecology. Explains key topics in one concise and authoritative volume  Features more than ninety articles written by an international team of leading ecologists  Contains more than 200 illustrations, including sixteen pages in color  Includes glossary, chronology, suggestions for further reading, and index  Covers autecology, population ecology, communities and ecosystems, landscapes and the biosphere, conservation biology, ecosystem services, and biosphere management},
  isbn = {978-1-4008-3302-3},
  langid = {english},
  keywords = {Nature / Ecology,Reference / General,Science / Reference}
}

@article{lindemanTrophicDynamicAspectEcology1942,
  title = {The {{Trophic-Dynamic Aspect}} of {{Ecology}}},
  author = {Lindeman, Raymond L.},
  year = {1942},
  journal = {Ecology},
  volume = {23},
  number = {4},
  eprint = {1930126},
  eprinttype = {jstor},
  pages = {399--417},
  publisher = {Ecological Society of America},
  issn = {0012-9658},
  doi = {10.2307/1930126},
  urldate = {2024-05-02},
  file = {/Users/tanyastrydom/Zotero/storage/9UFIE33S/Lindeman - 1942 - The Trophic-Dynamic Aspect of Ecology.pdf}
}

@article{llewelynPredictingPredatorPrey2023,
  title = {Predicting Predator--Prey Interactions in Terrestrial Endotherms Using Random Forest},
  author = {Llewelyn, John and Strona, Giovanni and Dickman, Christopher R. and Greenville, Aaron C. and Wardle, Glenda M. and Lee, Michael S. Y. and Doherty, Seamus and Shabani, Farzin and Saltr{\'e}, Fr{\'e}d{\'e}rik and Bradshaw, Corey J. A.},
  year = {2023},
  journal = {Ecography},
  volume = {2023},
  number = {9},
  pages = {e06619},
  issn = {1600-0587},
  doi = {10.1111/ecog.06619},
  urldate = {2024-03-22},
  abstract = {Species interactions play a fundamental role in ecosystems. However, few ecological communities have complete data describing such interactions, which is an obstacle to understanding how ecosystems function and respond to perturbations. Because it is often impractical to collect empirical data for all interactions in a community, various methods have been developed to infer interactions. Machine learning is increasingly being used for making interaction predictions, with random forest being one of the most frequently used of these methods. However, performance of random forest in inferring predator-prey interactions in terrestrial vertebrates and its sensitivity to training data quality remain untested. We examined predator--prey interactions in two diverse, primarily terrestrial vertebrate classes: birds and mammals. Combining data from a global interaction dataset and a specific community (Simpson Desert, Australia), we tested how well random forest predicted predator--prey interactions for mammals and birds using species' ecomorphological and phylogenetic traits. We also tested how variation in training data quality -- manipulated by removing records and switching interaction records to non-interactions -- affected model performance. We found that random forest could predict predator--prey interactions for birds and mammals using ecomorphological or phylogenetic traits, correctly predicting up to 88 and 67\% of interactions and non-interactions in the global and community-specific datasets, respectively. These predictions were accurate even when there were no records in the training data for focal species. In contrast, false non-interactions for focal predators in training data strongly degraded model performance. Our results demonstrate that random forest can identify predator--prey interactions for birds and mammals that have few or no interaction records. Furthermore, our study provides guidance on how to prepare training data to optimise machine learning classifiers for predicting species interactions, which could help ecologists 1) address knowledge gaps and explore network-related questions in data-poor situations, and 2) predict interactions for range-expanding species.},
  copyright = {{\copyright} 2023 The Authors. Ecography published by John Wiley \& Sons Ltd on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {ecological network,food web,machine learning,Simpson Desert,species interactions,training data},
  file = {/Users/tanyastrydom/Zotero/storage/YKHUIRTF/Llewelyn et al. - 2023 - Predicting predator–prey interactions in terrestri.pdf;/Users/tanyastrydom/Zotero/storage/U8ZH3IC6/ecog.html}
}

@article{loreauBiodiversityEcosystemStability2013,
  title = {Biodiversity and Ecosystem Stability: A Synthesis of Underlying Mechanisms},
  shorttitle = {Biodiversity and Ecosystem Stability},
  author = {Loreau, Michel and {de Mazancourt}, Claire},
  year = {2013},
  journal = {Ecology Letters},
  volume = {16},
  number = {s1},
  pages = {106--115},
  issn = {1461-0248},
  doi = {10.1111/ele.12073},
  urldate = {2024-11-15},
  abstract = {There is mounting evidence that biodiversity increases the stability of ecosystem processes in changing environments, but the mechanisms that underlie this effect are still controversial and poorly understood. Here, we extend mechanistic theory of ecosystem stability in competitive communities to clarify the mechanisms underlying diversity--stability relationships. We first explain why, contrary to a widely held belief, interspecific competition should generally play a destabilising role. We then explore the stabilising effect of differences in species' intrinsic rates of natural increase and provide a synthesis of various potentially stabilising mechanisms. Three main mechanisms are likely to operate in the stabilising effects of biodiversity on ecosystem properties: (1) asynchrony of species' intrinsic responses to environmental fluctuations, (2) differences in the speed at which species respond to perturbations, (3) reduction in the strength of competition. The first two mechanisms involve temporal complementarity between species, while the third results from functional complementarity. Additional potential mechanisms include selection effects, behavioural changes resulting from species interactions and mechanisms arising from trophic or non-trophic interactions and spatial heterogeneity. We conclude that mechanistic trait-based approaches are key to predicting the effects of diversity on ecosystem stability and to bringing the old diversity--stability debate to a final resolution.},
  copyright = {{\copyright} 2013 John Wiley \& Sons Ltd/CNRS},
  langid = {english},
  keywords = {Biodiversity,complementarity,ecosystem,fast-slow dynamics,overyielding,stability,synchrony},
  file = {/Users/tanyastrydom/Zotero/storage/V3YR2JI9/Loreau and de Mazancourt - 2013 - Biodiversity and ecosystem stability a synthesis .pdf;/Users/tanyastrydom/Zotero/storage/C4ZQFRS2/ele.html}
}

@article{maioranoTETRAEUSpecieslevelTrophic2020,
  title = {{{TETRA-EU}} 1.0: {{A}} Species-Level Trophic Metaweb of {{European}} Tetrapods},
  shorttitle = {{{TETRA-EU}} 1.0},
  author = {Maiorano, Luigi and Montemaggiori, Alessandro and Ficetola, Gentile Francesco and O'Connor, Louise and Thuiller, Wilfried},
  year = {2020},
  month = sep,
  journal = {Global Ecology and Biogeography},
  volume = {29},
  number = {9},
  pages = {1452--1457},
  issn = {1466-8238},
  doi = {10.1111/geb.13138},
  urldate = {2020-07-26},
  abstract = {Motivation Documenting potential interactions between species represents a major step towards understanding and predicting the spatial and temporal structure of multi-trophic communities and their functioning. The metaweb concept summarizes the potential trophic (and non-trophic) interactions in a given species pool. As such, it generalizes the regional species pool of community ecology by incorporating the potential relationships between species from different trophic levels along with their functional characteristics. However, although this concept is very attractive theoretically, it has rarely been used to understand the structure of an ecological network, mostly because of data availability. Here, we provide a continental-scale, species-level metaweb for all tetrapods (mammals, breeding birds, reptiles and amphibians) occurring in Europe and in the Northern Mediterranean basin. This metaweb is based on data extracted from the scientific literature, including published papers, books and grey literature. Main type of variable contained For each species considered, we built the network of potential two-way trophic interactions. Spatial location and grain We considered all species occurring in the entire European subcontinent, from Macaronesia (including only the islands belonging politically to Spain and Portugal) to the Ural Mountains (west to east) and from Fennoscandia and U.K. islands to the Mediterranean (north to south). We included Turkey, geographically part of Asia, to provide a complete picture of the north-eastern Mediterranean coast. Time period The data represent information published and/or collected during the last 50 years. Major taxa studied and level of measurement We focused our metaweb on terrestrial tetrapods occurring in the study area. Only species introduced in historical times and currently naturalized were considered; new introductions were excluded. In total, we included 288 mammals, 509 regularly breeding birds, 250 reptiles and 104 amphibians. Software format Data are supplied as semicolon-separated text files.},
  copyright = {{\copyright} 2020 John Wiley \& Sons Ltd},
  langid = {english}
}

@article{martinsPropagationDisturbancesEcological2024,
  title = {The Propagation of Disturbances in Ecological Networks},
  author = {Martins, Lucas P. and {Garcia-Callejas}, David and Lai, Hao Ran and Wootton, Kate L. and Tylianakis, Jason M.},
  year = {2024},
  month = feb,
  journal = {Trends in Ecology \& Evolution},
  volume = {0},
  number = {0},
  publisher = {Elsevier},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2024.01.009},
  urldate = {2024-04-22},
  langid = {english},
  pmid = {38402007},
  file = {/Users/tanyastrydom/Zotero/storage/LYKLGG77/Martins et al. - 2024 - The propagation of disturbances in ecological netw.pdf}
}

@article{matthewsComparisonPredictedObserved1975,
  title = {Comparison of the Predicted and Observed Secondary Structure of {{T4}} Phage Lysozyme},
  author = {Matthews, B. W.},
  year = {1975},
  month = oct,
  journal = {Biochimica et Biophysica Acta (BBA) - Protein Structure},
  volume = {405},
  number = {2},
  pages = {442--451},
  issn = {0005-2795},
  doi = {10.1016/0005-2795(75)90109-9},
  urldate = {2024-04-30},
  abstract = {Predictions of the secondary structure of T4 phage lysozyme, made by a number of investigators on the basis of the amino acid sequence, are compared with the structure of the protein determined experimentally by X-ray crystallography. Within the amino terminal half of the molecule the locations of helices predicted by a number of methods agree moderately well with the observed structure, however within the carboxyl half of the molecule the overall agreement is poor. For eleven different helix predictions, the coefficients giving the correlation between prediction and observation range from 0.14 to 0.42. The accuracy of the predictions for both {$\beta$}-sheet regions and for turns are generally lower than for the helices, and in a number of instances the agreement between prediction and observation is no better than would be expected for a random selection of residues. The structural predictions for T4 phage lysozyme are much less successful than was the case for adenylate kinase (Schulz et al. (1974) Nature 250, 140--142). No one method of prediction is clearly superior to all others, and although empirical predictions based on larger numbers of known protein structure tend to be more accurate than those based on a limited sample, the improvement in accuracy is not dramatic, suggesting that the accuracy of current empirical predictive methods will not be substantially increased simply by the inclusion of more data from additional protein structure determinations.},
  file = {/Users/tanyastrydom/Zotero/storage/WSAZVBR8/0005279575901099.html}
}

@book{mccuneAnalysisEcologicalCommunities2002,
  title = {Analysis of {{Ecological Communities}}},
  author = {McCune, Bruce and Grace, B, James},
  year = {2002},
  publisher = {MjM Software Design},
  address = {United States of America},
  isbn = {0-9721290-0-6}
}

@article{mcleodSamplingAsymptoticNetwork2021,
  title = {Sampling and Asymptotic Network Properties of Spatial Multi-Trophic Networks},
  author = {McLeod, Anne and Leroux, Shawn J. and Gravel, Dominique and Chu, Cindy and Cirtwill, Alyssa R. and Fortin, Marie-Jos{\'e}e and Galiana, N{\'u}ria and Poisot, Timoth{\'e}e and Wood, Spencer A.},
  year = {2021},
  journal = {Oikos},
  volume = {n/a},
  number = {n/a},
  issn = {1600-0706},
  doi = {10.1111/oik.08650},
  urldate = {2021-11-28},
  abstract = {Collecting well-resolved empirical trophic networks requires significant time, money and expertise, yet we are still lacking knowledge on how sampling effort and bias impact the estimation of network structure. Filling this gap is a critical first step towards creating accurate representations of ecological networks and for teasing apart the impact of sampling compared to ecological and evolutionary processes that are known to create spatio-temporal variation in network structure. We use a well-sampled spatial dataset of lake food webs to examine how sample effort influences network structure. Specifically, we predict asymptotic network properties (ANPs) for our dataset by comparing lake-specific network metrics with increasing sampling effort. We then contrast three sampling strategies -- random, smallest lake to largest lake or largest lake to smallest lake -- to assess which strategy best captures the regional metaweb (i.e. network of all potential interactions) network properties. We demonstrate metric-specific relationships between sample effort and network metrics, often diverging from the ANPs. For example, low sample effort can contribute to much lower and poorer estimates of closeness centralization, as compared to approximations of modularity with similar sample efforts. In fact, many network metrics (e.g. connectance) have a quadratic relationship with sample effort indicating a sampling `sweet spot', which represents optimal sample effort for a close approximation of the ANP. Further, we find that sampling larger lakes followed by smaller lakes is a more optimal sampling strategy for capturing metaweb properties in this lentic ecosystem. Overall, we provide clear ways to better understand the impacts of sampling bias in food-web studies which may be particularly critical given the rapid increase in studies comparing food webs across space and time.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/UBBQ7TK5/McLeod et al. - Sampling and asymptotic network properties of spat.pdf;/Users/tanyastrydom/Zotero/storage/ZPC8GD7H/oik.html}
}

@article{miloNetworkMotifsSimple2002,
  title = {Network {{Motifs}}: {{Simple Building Blocks}} of {{Complex Networks}}},
  shorttitle = {Network {{Motifs}}},
  author = {Milo, R. and {Shen-Orr}, S. and Itzkovitz, S. and Kashtan, N. and Chklovskii, D. and Alon, U.},
  year = {2002},
  month = oct,
  journal = {Science},
  volume = {298},
  number = {5594},
  pages = {824--827},
  publisher = {American Association for the Advancement of Science},
  doi = {10.1126/science.298.5594.824},
  urldate = {2024-07-05},
  abstract = {Complex networks are studied across many fields of science. To uncover their structural design principles, we defined ``network motifs,'' patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.},
  file = {/Users/tanyastrydom/Zotero/storage/YNC2AV7L/Milo et al. - 2002 - Network Motifs Simple Building Blocks of Complex .pdf}
}

@article{momalTreebasedInferenceSpecies2020,
  title = {Tree-Based Inference of Species Interaction Networks from Abundance Data},
  author = {Momal, Rapha{\"e}lle and Robin, St{\'e}phane and Ambroise, Christophe},
  year = {2020},
  journal = {Methods in Ecology and Evolution},
  volume = {11},
  number = {5},
  pages = {621--632},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13380},
  urldate = {2024-08-16},
  abstract = {The behaviour of ecological systems mainly relies on the interactions between the species it involves. We consider the problem of inferring the species interaction network from abundance data. To be relevant, any network inference methodology needs to handle count data and to account for possible environmental effects. It also needs to distinguish between direct interactions and indirect associations and graphical models provide a convenient framework for this purpose. A simulation study shows that the proposed methodology compares well with state-of-the-art approaches, even when the underlying graph strongly differs from a tree. The analysis of two datasets highlights the influence of covariates on the inferred network. Accounting for covariates is critical to avoid spurious edges. The proposed approach could be extended to perform network comparison or to look for missing species.},
  copyright = {{\copyright} 2020 British Ecological Society},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/W3ND2MX6/2041-210X.html}
}

@article{morales-castillaInferringBioticInteractions2015,
  ids = {mora15ibi},
  title = {Inferring Biotic Interactions from Proxies},
  author = {{Morales-Castilla}, Ignacio and Matias, Miguel G. and Gravel, Dominique and Ara{\'u}jo, Miguel B.},
  year = {2015},
  month = jun,
  journal = {Trends in Ecology \& Evolution},
  volume = {30},
  number = {6},
  pages = {347--356},
  doi = {10.1016/j.tree.2015.03.014},
  abstract = {Inferring biotic interactions from functional, phylogenetic and geographical proxies remains one great challenge in ecology. We propose a conceptual framework to infer the backbone of biotic interaction networks within regional species pools. First, interacting groups are identified to order links and remove forbidden interactions between species. Second, additional links are removed by examination of the geographical context in which species co-occur. Third, hypotheses are proposed to establish interaction probabilities between species. We illustrate the framework using published food-webs in terrestrial and marine systems. We conclude that preliminary descriptions of the web of life can be made by careful integration of data with theory.},
  owner = {tpoisot}
}

@article{moralesEffectSpacePlant2008,
  title = {The Effect of Space in Plant--Animal Mutualistic Networks: Insights from a Simulation Study},
  shorttitle = {The Effect of Space in Plant--Animal Mutualistic Networks},
  author = {Morales, Juan M. and V{\'a}zquez, Diego P.},
  year = {2008},
  journal = {Oikos},
  volume = {117},
  number = {9},
  pages = {1362--1370},
  issn = {1600-0706},
  doi = {10.1111/j.0030-1299.2008.16737.x},
  urldate = {2024-11-06},
  abstract = {The topology of plant--animal mutualistic networks has the potential to determine the ecological and evolutionary dynamics of interacting species. Many mechanisms have been proposed as explanations of observed network patterns; however, the fact that plant--animal interactions are inherently spatial has so far been ignored. Using a simulation model of frugivorous birds foraging in spatially explicit landscapes we evaluated how plant distribution and the scale of bird movement decisions influenced species interaction probabilities and the resulting network properties. Spatial aggregation and limited animal mobility restricted encounter probabilities, so that the distribution of animal visits per plant deviated strongly from the binomial distribution expected for a well-mixed system. Lack of mixing in turn resulted in a strong decrease in network connectance, a weak decrease in nestedness, stronger interactions, greater strength asymmetry and the unexpected presence/absence of some interactions. Our results suggest that spatial processes may contribute substantially to structure plant--animal mutualistic networks.},
  copyright = {{\copyright} 2008 The Authors},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/6K266LY4/Morales and Vázquez - 2008 - The effect of space in plant–animal mutualistic ne.pdf;/Users/tanyastrydom/Zotero/storage/RQSQ83ZD/j.0030-1299.2008.16737.html}
}

@misc{moulatletScalingTrophicSpecialization2024,
  title = {The Scaling of Trophic Specialization in Interaction Networks across Levels of Organization},
  author = {Moulatlet, Gabriel and Luna, Pedro and Dattilo, Wesley and Villalobos, Fabricio},
  year = {2024},
  publisher = {Authorea},
  doi = {10.22541/au.172977303.33335171/v1},
  urldate = {2024-11-07},
  abstract = {Natural ecosystems are characterized by a specialization pattern where few species are common, while many others are rare. In ecological networks involving biotic interactions, specialization operates as a continuum at individual, species, and network},
  archiveprefix = {Authorea},
  file = {/Users/tanyastrydom/Zotero/storage/Q9RVFNAQ/Moulatlet et al. - The scaling of trophic specialization in interacti.pdf}
}

@book{newmanNetworksIntroduction2010,
  title = {Networks. {{An}} Introduction},
  author = {Newman, Mark E. J.},
  year = {2010},
  publisher = {Oxford University Press},
  address = {New York, NY}
}

@article{nielsenNaturalSyntheticArtificial2010,
  title = {Natural - Synthetic - Artificial!},
  author = {Nielsen, Peter E.},
  year = {2010},
  month = jul,
  journal = {Artificial DNA: PNA \& XNA},
  volume = {1},
  number = {1},
  pages = {58--59},
  publisher = {Taylor \& Francis},
  issn = {1949-095X},
  doi = {10.4161/adna.1.1.12934},
  urldate = {2024-02-12},
  abstract = {The terms ``natural, ``synthetic'' and ``artificial'' are discussed in relation to synthetic and artificial chromosomes and genomes, synthetic and artificial cells and artificial life.},
  pmid = {21687528},
  file = {/Users/tanyastrydom/Zotero/storage/XCTBYZQG/Nielsen - 2010 - Natural - synthetic - artificial!.pdf}
}

@article{ohlmannMappingImprintBiotic2018,
  title = {Mapping the Imprint of Biotic Interactions on {$\beta$}-Diversity},
  author = {Ohlmann, Marc and Mazel, Florent and Chalmandrier, Lo{\"i}c and Bec, St{\'e}phane and Coissac, Eric and Gielly, Ludovic and Pansu, Johan and Schilling, Vincent and Taberlet, Pierre and Zinger, Lucie and Chave, J{\'e}rome and Thuiller, Wilfried},
  year = {2018},
  journal = {Ecology Letters},
  volume = {21},
  number = {11},
  pages = {1660--1669},
  issn = {1461-0248},
  doi = {10.1111/ele.13143},
  urldate = {2024-04-22},
  abstract = {Investigating how trophic interactions influence the {$\beta$}-diversity of meta-communities is of paramount importance to understanding the processes shaping biodiversity distribution. Here, we apply a statistical method for inferring the strength of spatial dependencies between pairs of species groups. Using simulated community data generated from a multi-trophic model, we showed that this method can approximate biotic interactions in multi-trophic communities based on {$\beta$}-diversity patterns across groups. When applied to soil multi-trophic communities along an elevational gradient in the French Alps, we found that fungi make a major contribution to the structuring of {$\beta$}-diversity across trophic groups. We also demonstrated that there were strong spatial dependencies between groups known to interact specifically (e.g. plant-symbiotic fungi, bacteria-nematodes) and that the influence of environment was less important than previously reported in the literature. Our method paves the way for a better understanding and mapping of multi-trophic communities through space and time.},
  copyright = {{\copyright} 2018 John Wiley \& Sons Ltd/CNRS},
  langid = {english},
  keywords = {-diversity,graphical lasso,graphical model,interaction network,meta-communities,partial correlation networks},
  file = {/Users/tanyastrydom/Zotero/storage/RUPS3ATP/Ohlmann et al. - 2018 - Mapping the imprint of biotic interactions on β-di.pdf;/Users/tanyastrydom/Zotero/storage/K3R3BHFT/ele.html}
}

@article{olivierExploringTemporalVariability2019,
  title = {Exploring the Temporal Variability of a Food Web Using Long-Term Biomonitoring Data},
  author = {Olivier, Pierre and Frelat, Romain and Bonsdorff, Erik and Kortsch, Susanne and Kr{\"o}ncke, Ingrid and M{\"o}llmann, Christian and Neumann, Hermann and Sell, Anne F. and Nordstr{\"o}m, Marie C.},
  year = {2019},
  journal = {Ecography},
  volume = {42},
  number = {12},
  pages = {2107--2121},
  issn = {1600-0587},
  doi = {10.1111/ecog.04461},
  urldate = {2024-10-10},
  abstract = {Ecological communities are constantly being reshaped in the face of environmental change and anthropogenic pressures. Yet, how food webs change over time remains poorly understood. Food web science is characterized by a trade-off between complexity (in terms of the number of species and feeding links) and dynamics. Topological analysis can use complex, highly resolved empirical food web models to explore the architecture of feeding interactions but is limited to a static view, whereas ecosystem models can be dynamic but use highly aggregated food webs. Here, we explore the temporal dynamics of a highly resolved empirical food web over a time period of 18 years, using the German Bight fish and benthic epifauna community as our case study. We relied on long-term monitoring ecosystem surveys (from 1998 to 2015) to build a metaweb, i.e. the meta food web containing all species recorded over the time span of our study. We then combined time series of species abundances with topological network analysis to construct annual food web snapshots. We developed a new approach, `node-weighted' food web metrics by including species abundances to represent the temporal dynamics of food web structure, focusing on generality and vulnerability. Our results suggest that structural food web properties change through time; however, binary food web structural properties may not be as temporally variable as the underlying changes in species composition. Further, the node-weighted metrics enabled us to detect that food web structure was influenced by changes in species composition during the first half of the time series and more strongly by changes in species dominance during the second half. Our results demonstrate how ecosystem surveys can be used to monitor temporal changes in food web structure, which are important ecosystem indicators for building marine management and conservation plans.},
  copyright = {{\copyright} 2019 The Authors. Ecography published by John Wiley \& Sons on behalf of Nordic Society Oikos},
  langid = {english},
  keywords = {food web structure,temporal variability,topology},
  file = {/Users/tanyastrydom/Zotero/storage/C6YX4CZH/Olivier et al. - 2019 - Exploring the temporal variability of a food web u.pdf;/Users/tanyastrydom/Zotero/storage/J73S5VIF/ecog.html}
}

@article{ovaskainenUsingLatentVariable2016,
  title = {Using Latent Variable Models to Identify Large Networks of Species-to-Species Associations at Different Spatial Scales},
  author = {Ovaskainen, Otso and Abrego, Nerea and Halme, Panu and Dunson, David},
  year = {2016},
  journal = {Methods in Ecology and Evolution},
  volume = {7},
  number = {5},
  pages = {549--555},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12501},
  urldate = {2024-06-07},
  abstract = {We present a hierarchical latent variable model that partitions variation in species occurrences and co-occurrences simultaneously at multiple spatial scales. We illustrate how the parameterized model can be used to predict the occurrences of a species by using as predictors not only the environmental covariates, but also the occurrences of all other species, at all spatial scales. We leverage recent progress in Bayesian latent variable models to implement a computationally effective algorithm that enables one to consider large communities and extensive sampling schemes. We exemplify the framework with a community of 98 fungal species sampled in c. 22 500 dead wood units in 230 plots in 29 beech forests. The networks identified by correlations and partial correlations were consistent, as were networks for natural and managed forests, but networks at different spatial scales were dissimilar. Accounting for the occurrences of the other species roughly doubled the predictive powers of the models compared to accounting for environmental covariates only .},
  copyright = {{\copyright} 2015 The Authors. Methods in Ecology and Evolution {\copyright} 2015 British Ecological Society},
  langid = {english},
  keywords = {biotic interaction,co-occurrence,correlation,hierarchical model,joint species distribution model,partial correlation}
}

@article{parkStatisticalMechanicsNetworks2004,
  title = {Statistical Mechanics of Networks},
  author = {Park, Juyong and Newman, M. E. J.},
  year = {2004},
  month = dec,
  journal = {Physical Review E},
  volume = {70},
  number = {6},
  pages = {066117},
  publisher = {American Physical Society},
  doi = {10.1103/PhysRevE.70.066117},
  urldate = {2020-11-05},
  abstract = {We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of the ensemble. Models of this type play the same role in the study of networks as is played by the Boltzmann distribution in classical statistical mechanics; they offer the best prediction of network properties subject to the constraints imposed by a given set of observations. We give exact solutions of models within this class that incorporate arbitrary degree distributions and arbitrary but independent edge probabilities. We also discuss some more complex examples with correlated edges that can be solved approximately or exactly by adapting various familiar methods, including mean-field theory, perturbation theory, and saddle-point expansions.},
  file = {/Users/tanyastrydom/Zotero/storage/BUMDDA2H/Park and Newman - 2004 - Statistical mechanics of networks.pdf;/Users/tanyastrydom/Zotero/storage/48Y39QUE/PhysRevE.70.html}
}

@article{pawarDimensionalityConsumerSearch2012,
  title = {Dimensionality of Consumer Search Space Drives Trophic Interaction Strengths},
  author = {Pawar, Samraat and Dell, Anthony I. and Savage, Van M.},
  year = {2012},
  month = jun,
  journal = {Nature},
  volume = {486},
  number = {7404},
  pages = {485--489},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/nature11131},
  urldate = {2024-06-17},
  abstract = {Trophic interactions govern biomass fluxes in ecosystems, and stability in food webs. Knowledge of how trophic interaction strengths are affected by differences among habitats is crucial for understanding variation in ecological systems. Here we show how substantial variation in consumption-rate data, and hence trophic interaction strengths, arises because consumers tend to encounter resources more frequently in three dimensions (3D) (for example, arboreal and pelagic zones) than two dimensions (2D) (for example, terrestrial and benthic zones). By combining new theory with extensive data (376 species, with body masses ranging from 5.24 {\texttimes} 10-14 kg to 800 kg), we find that consumption rates scale sublinearly with consumer body mass (exponent of approximately 0.85) for 2D interactions, but superlinearly (exponent of approximately 1.06) for 3D interactions. These results contradict the currently widespread assumption of a single exponent (of approximately 0.75) in consumer--resource and food-web research. Further analysis of 2,929 consumer--resource interactions shows that dimensionality of consumer search space is probably a major driver of species coexistence, and the stability and abundance of populations.},
  copyright = {2012 Springer Nature Limited},
  langid = {english},
  keywords = {Animal behaviour,Food webs},
  file = {/Users/tanyastrydom/Zotero/storage/XUPS6A8Q/Pawar et al. - 2012 - Dimensionality of consumer search space drives tro.pdf}
}

@article{pecuchetNovelFeedingInteractions2020,
  title = {Novel Feeding Interactions Amplify the Impact of Species Redistribution on an {{Arctic}} Food Web},
  author = {Pecuchet, Laurene and Blanchet, Marie-Anne and Frainer, Andr{\'e} and Husson, B{\'e}reng{\`e}re and J{\o}rgensen, Lis L. and Kortsch, Susanne and Primicerio, Raul},
  year = {2020},
  journal = {Global Change Biology},
  volume = {26},
  number = {9},
  pages = {4894--4906},
  issn = {1365-2486},
  doi = {10.1111/gcb.15196},
  urldate = {2024-10-10},
  abstract = {Species are redistributing globally in response to climate warming, impacting ecosystem functions and services. In the Barents Sea, poleward expansion of boreal species and a decreased abundance of Arctic species are causing a rapid borealization of the Arctic communities. This borealization might have profound consequences on the Arctic food web by creating novel feeding interactions between previously non co-occurring species. An early identification of new feeding links is crucial to predict their ecological impact. However, detection by traditional approaches, including stomach content and isotope analyses, although fundamental, cannot cope with the speed of change observed in the region, nor with the urgency of understanding the consequences of species redistribution for the marine ecosystem. In this study, we used an extensive food web (metaweb) with nearly 2,500 documented feeding links between 239 taxa coupled with a trait data set to predict novel feeding interactions and to quantify their potential impact on Arctic food web structure. We found that feeding interactions are largely determined by the body size of interacting species, although species foraging habitat and metabolic type are also important predictors. Further, we found that all boreal species will have at least one potential resource in the Arctic region should they redistribute therein. During 2014--2017, 11 boreal species were observed in the Arctic region of the Barents Sea. These incoming species, which are all generalists, change the structural properties of the Arctic food web by increasing connectance and decreasing modularity. In addition, these boreal species are predicted to initiate novel feeding interactions with the Arctic residents, which might amplify their impact on Arctic food web structure affecting ecosystem functioning and vulnerability. Under the ongoing species redistribution caused by environmental change, we propose merging a trait-based approach with ecological network analysis to efficiently predict the impacts of range-shifting species on food webs.},
  copyright = {{\copyright} 2020 The Authors. Global Change Biology published by John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {Arctic,Barents Sea,body size,borealization,ecological network,ecosystem vulnerability,food web structure,prediction of feeding interactions,trait-based approach},
  file = {/Users/tanyastrydom/Zotero/storage/RNDUZ5IB/Pecuchet et al. - 2020 - Novel feeding interactions amplify the impact of s.pdf;/Users/tanyastrydom/Zotero/storage/VBIDUJSK/gcb.html}
}

@misc{peixotoScalableNetworkReconstruction2024,
  title = {Scalable Network Reconstruction in Subquadratic Time},
  author = {Peixoto, Tiago P.},
  year = {2024},
  month = may,
  number = {arXiv:2401.01404},
  eprint = {2401.01404},
  primaryclass = {physics, stat},
  publisher = {arXiv},
  urldate = {2024-05-07},
  abstract = {Network reconstruction consists in determining the unobserved pairwise couplings between \$N\$ nodes given only observational data on the resulting behavior that is conditioned on those couplings -- typically a time-series or independent samples from a graphical model. A major obstacle to the scalability of algorithms proposed for this problem is a seemingly unavoidable quadratic complexity of \${\textbackslash}Omega(N{\textasciicircum}2)\$, corresponding to the requirement of each possible pairwise coupling being contemplated at least once, despite the fact that most networks of interest are sparse, with a number of non-zero couplings that is only \$O(N)\$. Here we present a general algorithm applicable to a broad range of reconstruction problems that significantly outperforms this quadratic baseline. Our algorithm relies on a stochastic second neighbor search (Dong et al., 2011) that produces the best edge candidates with high probability, thus bypassing an exhaustive quadratic search. If we rely on the conjecture that the second-neighbor search finishes in log-linear time (Baron \& Darling, 2020; 2022), we demonstrate theoretically that our algorithm finishes in subquadratic time, with a data-dependent complexity loosely upper bounded by \$O(N{\textasciicircum}\{3/2\}{\textbackslash}log N)\$, but with a more typical log-linear complexity of \$O(N{\textbackslash}log{\textasciicircum}2N)\$. In practice, we show that our algorithm achieves a performance that is many orders of magnitude faster than the quadratic baseline -- in a manner consistent with our theoretical analysis -- allows for easy parallelization, and thus enables the reconstruction of networks with hundreds of thousands and even millions of nodes and edges.},
  archiveprefix = {arXiv},
  langid = {english},
  keywords = {Computer Science - Data Structures and Algorithms,Computer Science - Machine Learning,Physics - Data Analysis Statistics and Probability,Statistics - Computation,Statistics - Machine Learning},
  file = {/Users/tanyastrydom/Zotero/storage/3I6Q92YT/Peixoto - 2024 - Scalable network reconstruction in subquadratic ti.pdf}
}

@article{petcheyFitEfficiencyBiology2011,
  title = {Fit, Efficiency, and Biology: {{Some}} Thoughts on Judging Food Web Models},
  shorttitle = {Fit, Efficiency, and Biology},
  author = {Petchey, Owen L. and Beckerman, Andrew P. and Riede, Jens O. and Warren, Philip H.},
  year = {2011},
  month = jun,
  journal = {Journal of Theoretical Biology},
  volume = {279},
  number = {1},
  pages = {169--171},
  issn = {0022-5193},
  doi = {10.1016/j.jtbi.2011.03.019},
  urldate = {2024-02-09},
  abstract = {Revealing the processes that determine who eats whom, and thereby the structure of food webs, is a long running challenge in ecological research. Recent advances include development of new methods for measuring fit of models to observed food web data, and thereby testing which are the `best' food web models. The best model could be considered the most efficient with relatively few parameters and high explanatory power. Another recent advance involves adding some additional biology to food web models in the form of foraging theory based on maximisation of energy intake as the predictor of species' diets in food webs. While it is interesting to compare efficiency among food web models, we believe that such comparisons at least should be interpreted with caution, since they do not account for any differences in motivation, formulation, and potential that might also exist among models. Furthermore, we see an important but somewhat neglected role for experimental tests of models of food web structure.},
  keywords = {Connectance,Energetics,Foraging,Network,Static food web model},
  file = {/Users/tanyastrydom/Zotero/storage/4J4QM7HS/Petchey et al. - 2011 - Fit, efficiency, and biology Some thoughts on jud.pdf;/Users/tanyastrydom/Zotero/storage/3EPNEJYI/S0022519311001652.html}
}

@article{petcheySizeForagingFood2008,
  title = {Size, Foraging, and Food Web Structure},
  author = {Petchey, Owen L. and Beckerman, Andrew P. and Riede, Jens O. and Warren, Philip H.},
  year = {2008},
  month = mar,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {105},
  number = {11},
  pages = {4191--4196},
  issn = {0027-8424, 1091-6490},
  doi = {10.1073/pnas.0710672105},
  urldate = {2024-01-15},
  abstract = {Understanding what structures ecological communities is vital to answering questions about extinctions, environmental change, trophic cascades, and ecosystem functioning. Optimal foraging theory was conceived to increase such understanding by providing a framework with which to predict species interactions and resulting community structure. Here, we use an optimal foraging model and allometries of foraging variables to predict the structure of real food webs. The qualitative structure of the resulting model provides a more mechanistic basis for the phenomenological rules of previous models. Quantitative analyses show that the model predicts up to 65\% of the links in real food webs. The deterministic nature of the model allows analysis of the model's successes and failures in predicting particular interactions. Predacious and herbivorous feeding interactions are better predicted than pathogenic, parasitoid, and parasitic interactions. Results also indicate that accurate prediction and modeling of some food webs will require incorporating traits other than body size and diet choice models specific to different types of feeding interaction. The model results support the hypothesis that individual behavior, subject to natural selection, determines individual diets and that food web structure is the sum of these individual decisions.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/43Z2U5TP/Petchey et al. - 2008 - Size, foraging, and food web structure.pdf}
}

@misc{philippe-lesaffreAdvancingEcologicalNetworks2024,
  title = {Advancing Ecological Networks: Moving beyond Binary Classification to Probabilistic Interactions},
  shorttitle = {Advancing Ecological Networks},
  author = {{Philippe-Lesaffre}, Martin},
  year = {2024},
  month = feb,
  primaryclass = {New Results},
  pages = {2024.02.07.579289},
  publisher = {bioRxiv},
  doi = {10.1101/2024.02.07.579289},
  urldate = {2024-07-12},
  abstract = {Leveraging trophic interactions to deduce macro-ecological patterns has become a prevalent method, taking advantage of the extensive databases on binary trophic interactions (i.e., prey-predator relationships). However, this binary approach oversimplifies complex ecological dynamics and fails to capture the nuanced structure of food webs. The challenge lies in the scarcity and limited availability of data on non-binary interactions, which are crucial for a more comprehensive understanding of ecological networks. This study explores the use of binary classifiers, particularly the XGBOOST algorithm to address the limitations of traditional binary approaches to prey-predator relationships. By predicting predation probabilities among nine mammalian predators using species traits, my findings demonstrate the classifiers' robust predictive capabilities to binary predictions but also a good correlation between probabilistic predation derived from binary classifiers and observed prey preferences. It also highlighted the importance of selecting informative species traits for predicting interaction, with performance contrastingly superior to null models. Despite a small sample size, this work provides insightful results and sets a foundation for future research to expand these models to broader ecological networks, emphasizing the need for comprehensive prey preference data.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/355RDEG4/Philippe-Lesaffre - 2024 - Advancing ecological networks moving beyond binar.pdf}
}

@article{pichlerMachineLearningAlgorithms2020,
  title = {Machine Learning Algorithms to Infer Trait-Matching and Predict Species Interactions in Ecological Networks},
  author = {Pichler, Maximilian and Boreux, Virginie and Klein, Alexandra-Maria and Schleuning, Matthias and Hartig, Florian},
  year = {2020},
  journal = {Methods in Ecology and Evolution},
  volume = {11},
  number = {2},
  pages = {281--293},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13329},
  urldate = {2024-04-10},
  abstract = {Ecologists have long suspected that species are more likely to interact if their traits match in a particular way. For example, a pollination interaction may be more likely if the proportions of a bee's tongue fit a plant's flower shape. Empirical estimates of the importance of trait-matching for determining species interactions, however, vary significantly among different types of ecological networks. Here, we show that ambiguity among empirical trait-matching studies may have arisen at least in parts from using overly simple statistical models. Using simulated and real data, we contrast conventional generalized linear models (GLM) with more flexible Machine Learning (ML) models (Random Forest, Boosted Regression Trees, Deep Neural Networks, Convolutional Neural Networks, Support Vector Machines, na{\"i}ve Bayes, and k-Nearest-Neighbor), testing their ability to predict species interactions based on traits, and infer trait combinations causally responsible for species interactions. We found that the best ML models can successfully predict species interactions in plant--pollinator networks, outperforming GLMs by a substantial margin. Our results also demonstrate that ML models can better identify the causally responsible trait-matching combinations than GLMs. In two case studies, the best ML models successfully predicted species interactions in a global plant--pollinator database and inferred ecologically plausible trait-matching rules for a plant--hummingbird network from Costa Rica, without any prior assumptions about the system. We conclude that flexible ML models offer many advantages over traditional regression models for understanding interaction networks. We anticipate that these results extrapolate to other ecological network types. More generally, our results highlight the potential of machine learning and artificial intelligence for inference in ecology, beyond standard tasks such as image or pattern recognition.},
  copyright = {{\copyright} 2019 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {bipartite networks,causal inference,deep learning,hummingbirds,insect pollinators,machine learning,pollination syndromes,predictive modelling},
  file = {/Users/tanyastrydom/Zotero/storage/5TG7ZFBD/Pichler et al. - 2020 - Machine learning algorithms to infer trait-matchin.pdf;/Users/tanyastrydom/Zotero/storage/FDHLRZZ9/2041-210X.html}
}

@article{pilosofMultilayerNatureEcological2017,
  title = {The Multilayer Nature of Ecological Networks},
  author = {Pilosof, Shai and Porter, Mason A. and Pascual, Mercedes and K{\'e}fi, Sonia},
  year = {2017},
  month = mar,
  journal = {Nature Ecology \& Evolution},
  volume = {1},
  number = {4},
  pages = {101},
  issn = {2397-334X},
  doi = {10.1038/s41559-017-0101},
  abstract = {Although networks provide a powerful approach to study a large variety of ecological systems, their formulation does not typically account for multiple interaction types, interactions that vary in space and time, and interconnected systems such as networks of networks. The emergent field of 'multilayer networks' provides a natural framework for extending analyses of ecological systems to include such multiple layers of complexity, as it specifically allows one to differentiate and model 'intralayer' and 'interlayer' connectivity. The framework provides a set of concepts and tools that can be adapted and applied to ecology, facilitating research on high-dimensional, heterogeneous systems in nature. Here, we formally define ecological multilayer networks based on a review of previous, related approaches; illustrate their application and potential with analyses of existing data; and discuss limitations, challenges, and future applications. The integration of multilayer network theory into ecology offers largely untapped potential to investigate ecological complexity and provide new theoretical and empirical insights into the architecture and dynamics of ecological systems.},
  langid = {english},
  pmid = {28812678}
}

@article{piresMegafaunalExtinctionsHuman2020,
  title = {Before, during and after Megafaunal Extinctions: {{Human}} Impact on {{Pleistocene-Holocene}} Trophic Networks in {{South Patagonia}}},
  shorttitle = {Before, during and after Megafaunal Extinctions},
  author = {Pires, Mathias M. and Rindel, Diego and Moscardi, Bruno and Cruz, Livia R. and Guimar{\~a}es, Paulo R. and {dos Reis}, Sergio F. and Perez, S. Ivan},
  year = {2020},
  month = dec,
  journal = {Quaternary Science Reviews},
  volume = {250},
  pages = {106696},
  issn = {0277-3791},
  doi = {10.1016/j.quascirev.2020.106696},
  urldate = {2024-02-01},
  abstract = {Worldwide extinctions of large terrestrial vertebrates in the late Pleistocene provide insight on how humans reshape ecological communities. Understanding the ecological causes and consequences of megafaunal extinctions requires integrating approaches to reconstruct the ecological communities from the past. Here, we combined archeological and paleontological evidence with network analyses to understand the changes in ecological communities from late Pleistocene to the Holocene in South Patagonia, the last continental region where the encounter between humans and extinct megafauna occurred. The zooarcheological record suggests humans would have interacted mainly with large-bodied species, which comprise a small subset of the available prey. Accordingly, using network reconstructions and structural analyses, we found that human arrival would have produced minor changes in the overall structure of trophic networks. However, those few novel interactions established by humans would have created multiple indirect paths among megafaunal species. Indirect paths are the route for indirect effects such as competition and increase the vulnerability of interaction networks to perturbations. After the extinctions of most of the megafauna, the impoverished network became structurally simpler and densely connected. Our reconstructions of past trophic networks show that multiple indirect effects, potentially contributing to extinctions, can emerge even from a limited number of novel interactions and illustrate how network organization can affect and be affected by extinctions events.},
  keywords = {Ecological paleocommunities,Faunal diversity,Food webs,Human arrival,Indirect effects,Late quaternary extinctions,Southern south America},
  file = {/Users/tanyastrydom/Zotero/storage/AIHLFW6Q/S0277379120306582.html}
}

@article{poelenGlobalBioticInteractions2014,
  title = {Global Biotic Interactions: {{An}} Open Infrastructure to Share and Analyze Species-Interaction Datasets},
  shorttitle = {Global Biotic Interactions},
  author = {Poelen, Jorrit H. and Simons, James D. and Mungall, Chris J.},
  year = {2014},
  month = nov,
  journal = {Ecological Informatics},
  volume = {24},
  pages = {148--159},
  issn = {1574-9541},
  doi = {10.1016/j.ecoinf.2014.08.005},
  urldate = {2024-04-02},
  abstract = {An intricate network of interactions between organisms and their environment form the ecosystems that sustain life on earth. With a detailed understanding of these interactions, ecologists and biologists can make better informed predictions about the ways different environmental factors will impact ecosystems. Despite the abundance of research data on biotic and abiotic interactions, no comprehensive and easily accessible data collection is available that spans taxonomic, geospatial, and temporal domains. Biotic-interaction datasets are effectively siloed, inhibiting cross-dataset comparisons. In order to pool resources and bring to light individual datasets, specialized research tools are needed to aggregate, normalize, and integrate existing datasets with standard taxonomies, ontologies, vocabularies, and structured data repositories. Global Biotic Interactions (GloBI) provides such tools by way of an open, community-driven infrastructure designed to lower the barrier for researchers to perform ecological systems analysis and modeling. GloBI provides a tool that (a) ingests, normalizes, and aggregates datasets, (b) integrates interoperable data with accepted ontologies (e.g., OBO Relations Ontology, Uberon, and Environment Ontology), vocabularies (e.g., Coastal and Marine Ecological Classification Standard), and taxonomies (e.g., Integrated Taxonomic Information System and National Center for Biotechnology Information Taxonomy Database), (c) makes data accessible through an application programming interface (API) and various data archives (Darwin Core, Turtle, and Neo4j), and (d) houses a data collection of about 700,000 species interactions across about 50,000 taxa, covering over 1100 references from 19 data sources. GloBI has taken an open-source and open-data approach in order to make integrated species-interaction data maximally accessible and to encourage users to provide feedback, contribute data, and improve data access methods. The GloBI collection of datasets is currently used in the Encyclopedia of Life (EOL) and Gulf of Mexico Species Interactions (GoMexSI).},
  keywords = {Data integration,Ontology,Species interactions,Taxonomy},
  file = {/Users/tanyastrydom/Zotero/storage/UTA8HJWG/S1574954114001125.html}
}

@article{poisotDescribeUnderstandPredict2016,
  title = {Describe, Understand and Predict: Why Do We Need Networks in Ecology?},
  shorttitle = {Describe, Understand and Predict},
  author = {Poisot, Timoth{\'e}e and Stouffer, Daniel B. and K{\'e}fi, Sonia},
  year = {2016},
  journal = {Functional Ecology},
  volume = {30},
  number = {12},
  eprint = {48582345},
  eprinttype = {jstor},
  pages = {1878--1882},
  publisher = {[British Ecological Society, Wiley]},
  issn = {0269-8463},
  urldate = {2024-04-10},
  file = {/Users/tanyastrydom/Zotero/storage/W6IYXMDL/Poisot et al. - 2016 - Describe, understand and predict why do we need n.pdf}
}

@article{poisotGlobalKnowledgeGaps2021,
  title = {Global Knowledge Gaps in Species Interaction Networks Data},
  author = {Poisot, Timoth{\'e}e and Bergeron, Gabriel and Cazelles, Kevin and Dallas, Tad and Gravel, Dominique and MacDonald, Andrew and Mercier, Benjamin and Violet, Cl{\'e}ment and Vissault, Steve},
  year = {2021},
  journal = {Journal of Biogeography},
  volume = {48},
  number = {7},
  pages = {1552--1563},
  issn = {1365-2699},
  doi = {10.1111/jbi.14127},
  urldate = {2021-04-29},
  abstract = {Ecological networks are increasingly studied at large spatial scales, expanding their focus from a conceptual tool for community ecology into one that also addresses questions in biogeography and macroecology. This effort is supported by increased access to standardized information on ecological networks, in the form of openly accessible databases. Yet, there has been no systematic evaluation of the fitness for purpose of these data to explore synthesis questions at very large spatial scales. In particular, because the sampling of ecological networks is a difficult task, they are likely to not have a good representation of the diversity of Earth's bioclimatic conditions, likely to be spatially aggregated, and therefore unlikely to achieve broad representativeness. In this paper, we analyse over 1300 ecological networks in the mangal.io database, and discuss their coverage of climates, and the geographic areas in which there is a deficit of data on ecological networks. Taken together, our results suggest that while some information about the global structure of ecological networks is available, it remains fragmented over space, with further differences by types of ecological interactions. This causes great concerns both for our ability to transfer knowledge from one region to the next, but also to forecast the structural change in networks under climate change.},
  copyright = {{\copyright} 2021 John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {data archival,ecoinformatics,ecological networks,network biogeography},
  file = {/Users/tanyastrydom/Zotero/storage/QAR4NWAJ/Poisot et al. - Global knowledge gaps in species interaction netwo.pdf}
}

@article{poisotGuidelinesPredictionSpecies2023,
  title = {Guidelines for the Prediction of Species Interactions through Binary Classification},
  author = {Poisot, Timoth{\'e}e},
  year = {2023},
  journal = {Methods in Ecology and Evolution},
  volume = {14},
  number = {5},
  pages = {1333--1345},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.14071},
  urldate = {2023-09-15},
  abstract = {The prediction of species interactions is gaining momentum as a way to circumvent limitations in data volume. Yet, ecological networks are challenging to predict because they are typically small and sparse. Dealing with extreme class imbalance is a challenge for most binary classifiers, and there are currently no guidelines as to how predictive models can be trained for this specific problem. Using simple mathematical arguments and numerical experiments in which a variety of classifiers (for supervised learning) are trained on simulated networks, we develop a series of guidelines related to the choice of measures to use for model selection, and the ways to assemble the training dataset. Neither classifier accuracy nor the area under the receiver operating characteristic curve (ROC-AUC) are informative measures for the performance of interaction prediction. The area under the precision-recall curve (PR-AUC) is a fairer assessment of performance. In some cases, even standard measures can lead to selecting a more biased classifier because the effect of connectance is strong. The amount of correction to apply to the training dataset depends on network connectance, on the measure to be optimized, and only weakly on the classifier. These results reveal that training machines to predict networks is a challenging task, and that in virtually all cases, the composition of the training set needs to be fine-tuned before performing the actual training. We discuss these consequences in the context of the low volume of data.},
  copyright = {{\copyright} 2023 The Author. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {binary classification,ecological networks,machine learning},
  file = {/Users/tanyastrydom/Zotero/storage/XHFCT84W/2041-210X.html}
}

@article{poisotHostsParasitesTheir2017,
  title = {Hosts, Parasites and Their Interactions Respond to Different Climatic Variables},
  author = {Poisot, Timoth{\'e}e and {Gu{\'e}veneux-Julien}, Cynthia and Fortin, Marie-Jos{\'e}e and Gravel, Dominique and Legendre, Pierre},
  year = {2017},
  journal = {Global Ecology and Biogeography},
  volume = {26},
  number = {8},
  pages = {942--951},
  issn = {1466-8238},
  doi = {10.1111/geb.12602},
  urldate = {2024-11-06},
  abstract = {Aim Although there is a vast body of literature on the causes of variation in species composition in ecological communities, less effort has been invested in understanding how interactions between these species vary. Given that interactions are crucial to the structure and functioning of ecological communities, we need to develop a better understanding of their spatial distribution. Here, we investigate whether species interactions vary more in response to different climate variables than do individual species. Location Eurasia. Time period 2000s. Major taxa Animalia. Methods We used a measure of local contribution to {$\beta$}-diversity to evaluate the compositional uniqueness of 51 host--parasite communities of rodents and their ectoparasitic fleas across Eurasia, using publicly available data. We measured uniqueness based on the species composition and based on potential and realized biotic interactions (here, host--parasite interactions). Results We show that species interactions vary more, across space, than do species. In particular, we show that species interactions respond to some climatic variables that have no effect on species distributions or dissimilarity. Main conclusions Species interactions capture some degree of variation that is not apparent when looking at species occurrences only. In this system, this appeared as hosts and parasites interacting in different ways as a response to different environmental factors, especially the temperature and dryness. We discuss the implications of this finding for the amount of information that should be considered when measuring community dissimilarity.},
  copyright = {{\copyright} 2017 John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {-diversity,climatic niches,data re-use,host-parasite interactions,species distribution models,species interaction networks},
  file = {/Users/tanyastrydom/Zotero/storage/LN7ME9L4/Poisot et al. - 2017 - Hosts, parasites and their interactions respond to.pdf;/Users/tanyastrydom/Zotero/storage/HDNYRJ2T/geb.html}
}

@article{poisotMangalMakingEcological2016,
  title = {Mangal -- Making Ecological Network Analysis Simple},
  author = {Poisot, Timoth{\'e}e and Baiser, Benjamin and Dunne, Jennifer and K{\'e}fi, Sonia and Massol, Fran{\c c}ois and Mouquet, Nicolas and Romanuk, Tamara N. and Stouffer, Daniel B. and Wood, Spencer A. and Gravel, Dominique},
  year = {2016},
  journal = {Ecography},
  volume = {39},
  number = {4},
  pages = {384--390},
  issn = {1600-0587},
  doi = {10.1111/ecog.00976},
  urldate = {2020-11-27},
  abstract = {The study of ecological networks is severely limited by 1) the difficulty to access data, 2) the lack of a standardized way to link meta-data with interactions, and 3) the disparity of formats in which ecological networks themselves are stored and represented. To overcome these limitations, we have designed a data specification for ecological networks. We implemented a database respecting this standard, and released an R package (rmangal) allowing users to programmatically access, curate, and deposit data on ecological interactions. In this article, we show how these tools, in conjunction with other frameworks for the programmatic manipulation of open ecological data, streamlines the analysis process and improves replicability and reproducibility of ecological network studies.},
  copyright = {{\copyright} 2015 The Authors},
  langid = {english}
}

@article{poisotSpeciesWhyEcological2015,
  title = {Beyond Species: Why Ecological Interaction Networks Vary through Space and Time},
  shorttitle = {Beyond Species},
  author = {Poisot, Timoth{\'e}e and Stouffer, Daniel B. and Gravel, Dominique},
  year = {2015},
  journal = {Oikos},
  volume = {124},
  number = {3},
  pages = {243--251},
  issn = {1600-0706},
  doi = {10.1111/oik.01719},
  urldate = {2020-08-28},
  abstract = {Community ecology is tasked with the considerable challenge of predicting the structure, and properties, of emerging ecosystems. It requires the ability to understand how and why species interact, as this will allow the development of mechanism-based predictive models, and as such to better characterize how ecological mechanisms act locally on the existence of inter-specific interactions. Here we argue that the current conceptualization of species interaction networks is ill-suited for this task. Instead, we propose that future research must start to account for the intrinsic variability of species interactions, then scale up from here onto complex networks. This can be accomplished simply by recognizing that there exists intra-specific variability, in traits or properties related to the establishment of species interactions. By shifting the scale towards population-based processes, we show that this new approach will improve our predictive ability and mechanistic understanding of how species interact over large spatial or temporal scales. Synthesis Although species interactions are the backbone of ecological communities, we have little insights on how (and why) they vary through space and time. In this article, we build on existing empirical literature to show that the same species may happen to interact in different ways when their local abundances vary, their trait distribution changes, or when the environment affects either of these factors. We discuss how these findings can be integrated in existing frameworks for the analysis and simulation of species interactions.},
  langid = {english},
  keywords = {GravelD,PoisotT,Tangent_FT&Networks},
  file = {/Users/tanyastrydom/Zotero/storage/3ZD2JY54/Poisot et al. - 2015 - Beyond species why ecological interaction network.pdf;/Users/tanyastrydom/Zotero/storage/2MKHE38V/oik.html}
}

@article{poisotStructureProbabilisticNetworks2016,
  title = {The Structure of Probabilistic Networks},
  author = {Poisot, Timoth{\'e}e and Cirtwill, Alyssa and Cazelles, K{\'e}vin and Gravel, Dominique and Fortin, Marie-Jos{\'e}e and Stouffer, Daniel},
  year = {2016},
  journal = {Methods in Ecology and Evolution},
  volume = {7},
  number = {3},
  pages = {303--312},
  doi = {10},
  abstract = {Summary There is a growing realization among community ecologists that interactions between species vary across space and time and that this variation needs to be quantified. Our current numerical framework to analyse the structure of species interactions, based on graph-theoretical approaches, usually do not consider the variability of interactions. As this variability has been show to hold valuable ecological information, there is a need to adapt the current measures of network structure so that they can exploit it. We present analytical expressions of key measures of network structured, adapted so that they account for the variability of ecological interactions. We do so by modelling each interaction as a Bernoulli event; using basic calculus allows expressing the expected value, and when mathematically tractable, its variance. When applied to non-probabilistic data, the measures we present give the same results as their non-probabilistic formulations, meaning that they can be generally applied. We present three case studies that highlight how these measures can be used, in re-analysing data that experimentally measured the variability of interactions, to alleviate the computational demands of permutation-based approaches, and to use the frequency at which interactions are observed over several locations to infer the structure of local networks. We provide a free and open-source implementation of these measures. We discuss how both sampling and data representation of ecological networks can be adapted to allow the application of a fully probabilistic numerical network approach.}
}

@article{poisotSyntheticDatasetsCommunity2016,
  title = {Synthetic Datasets and Community Tools for the Rapid Testing of Ecological Hypotheses},
  author = {Poisot, Timoth{\'e}e and Gravel, Dominique and Leroux, Shawn and Wood, Spencer A. and Fortin, Marie-Jos{\'e}e and Baiser, Benjamin and Cirtwill, Alyssa R. and Ara{\'u}jo, Miguel B. and Stouffer, Daniel B.},
  year = {2016},
  journal = {Ecography},
  volume = {39},
  number = {4},
  pages = {402--408},
  issn = {1600-0587},
  doi = {10.1111/ecog.01941},
  urldate = {2021-05-14},
  abstract = {The increased availability of both open ecological data, and software to interact with it, allows the fast collection and integration of information at all spatial and taxonomic scales. This offers the opportunity to address macroecological questions in a cost-effective way. In this contribution, we illustrate this approach by forecasting the structure of a stream food web at the global scale. In so doing, we highlight the most salient issues needing to be addressed before this approach can be used with a high degree of confidence.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/A7C7DVDD/Poisot et al. - 2016 - Synthetic datasets and community tools for the rap.pdf;/Users/tanyastrydom/Zotero/storage/C4DFFEM7/ecog.html}
}

@article{poisotWhenEcologicalNetwork2014,
  ids = {pois14wen},
  title = {When Is an Ecological Network Complex? {{Connectance}} Drives Degree Distribution and Emerging Network Properties},
  shorttitle = {When Is an Ecological Network Complex?},
  author = {Poisot, Timoth{\'e}e and Gravel, Dominique},
  year = {2014},
  month = feb,
  journal = {PeerJ},
  volume = {2},
  pages = {e251},
  issn = {2167-8359},
  doi = {10.7717/peerj.251},
  urldate = {2020-02-05},
  abstract = {Connectance and degree distributions are important components of the structure of ecological networks. In this contribution, we use a statistical argument and simple network generating models to show that properties of the degree distribution are driven by network connectance. We discuss the consequences of this finding for (1) the generation of random networks in null-model analyses, and (2) the interpretation of network structure and ecosystem properties in relationship with degree distribution.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/6PEYICPV/Poisot et Gravel - 2014 - When is an ecological network complex Connectance.pdf;/Users/tanyastrydom/Zotero/storage/89UK8TF2/Poisot and Gravel - 2014 - When is an ecological network complex Connectance.pdf;/Users/tanyastrydom/Zotero/storage/TRN7I4CJ/Poisot and Gravel - 2014 - When is an ecological network complex Connectance.pdf;/Users/tanyastrydom/Zotero/storage/VE8JXIJD/251.html;/Users/tanyastrydom/Zotero/storage/VTLJ4KJJ/251.html}
}

@article{polisComplexTrophicInteractions1991,
  title = {Complex {{Trophic Interactions}} in {{Deserts}}: {{An Empirical Critique}} of {{Food-Web Theory}}},
  shorttitle = {Complex {{Trophic Interactions}} in {{Deserts}}},
  author = {Polis, Gary A.},
  year = {1991},
  month = jul,
  journal = {The American Naturalist},
  volume = {138},
  number = {1},
  pages = {123--155},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/285208},
  urldate = {2024-11-08},
  abstract = {Food webs in the real world are much more complex than food-web literature would have us believe. This is illustrated by the web of the sand community in the Coachella Valley desert. The biota include 174 species of vascular plants, 138 species of vertebrates, more than 55 species of arachnids, and an unknown (but great) number of microorganisms, insects (2,000-3,000 estimated species), acari, and nematodes. Trophic relations are presented in a series of nested subwebs and delineations of the community. Complexity arises from the large number of interactive species, the frequency of omnivory, age structure, looping, the lack of compartmentalization, and the complexity of the arthropod and soil faunas. Web features found in the Coachella also characterize other communities and should produce equivalently complex webs. If anything, diversity and complexity in most nondesert habitats are greater than those in deserts. Patterns from the Coachella web are compared with theoretical predictions and "empirical generalizations" derived from catalogs of published webs. The Coachella web differs greatly: chains are longer, omnivory and loops are not rare, connectivity is greater (species interact with many more predators and prey), top predators are rare or nonexistent, and prey-topredator ratios are greater than 1.0. The evidence argues that actual community food webs are extraordinarily more complex than those webs cataloged by theorists. I argue that most cataloged webs are oversimplified caricatures of actual communities. That cataloged webs depict so few species, absurdly low ratios of predators on prey and prey eaten by predators, so few links, so little omnivory, a veritable absence of looping, and such a high proportion of top predators argues strongly that they poorly represent real biological communities. Consequently, the practice of abstracting empirical regularities from such catalogs yields an inaccurate and artifactual view of trophic interactions within communities. Contrary to strong assertions by many theorists, patterns from food webs of real communities generally do not support predictions arising from dynamic and graphic models of food-web structure.}
}

@article{pollockUnderstandingCooccurrenceModelling2014,
  title = {Understanding Co-Occurrence by Modelling Species Simultaneously with a {{Joint Species Distribution Model}} ({{JSDM}})},
  author = {Pollock, Laura J. and Tingley, Reid and Morris, William K. and Golding, Nick and O'Hara, Robert B. and Parris, Kirsten M. and Vesk, Peter A. and McCarthy, Michael A.},
  year = {2014},
  journal = {Methods in Ecology and Evolution},
  volume = {5},
  number = {5},
  pages = {397--406},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.12180},
  urldate = {2024-05-13},
  abstract = {A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology -- habitat modelling and community ecology -- approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species' and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co-occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co-occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co-occurrence patterns into components describing shared environmental responses and residual patterns of co-occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co-occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co-occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.},
  copyright = {{\copyright} 2014 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {amphibians,biotic interactions,community assembly,correlated residuals,Eucalyptus,frogs,species covariance},
  file = {/Users/tanyastrydom/Zotero/storage/IA42RAZT/Pollock et al. - 2014 - Understanding co-occurrence by modelling species s.pdf;/Users/tanyastrydom/Zotero/storage/XQMNJE2K/2041-210X.html}
}

@article{pomeranzInferringPredatorPrey2019,
  title = {Inferring Predator--Prey Interactions in Food Webs},
  author = {Pomeranz, Justin P. F. and Thompson, Ross M. and Poisot, Timoth{\'e}e and Harding, Jon S.},
  year = {2019},
  journal = {Methods in Ecology and Evolution},
  volume = {10},
  number = {3},
  pages = {356--367},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13125},
  urldate = {2024-03-12},
  abstract = {Food webs are a powerful way to represent the diversity, structure, and function of ecological systems. However, the accurate description of food webs requires significant effort in time and resources, limiting their widespread use in ecological studies. Newly published methods allow for the inference of feeding interactions using proxy variables. Here, we compare the accuracy of two recently described methods, as well as describe a composite model of the two, for the inference of feeding interactions using a large, well-described dataset. Both niche and neutral processes are involved in determining whether or not two species will form a feeding link in communities. Three different models for determining niche constraints of feeding interactions are compared, and all three models are extended by incorporating neutral processes, based on relative abundances. The three models compared here infer niche processes through (a) phylogenetic relationships, (b) local species trait distributions (e.g., body size), and (c) a composite of phylogeny and local traits. We show that all three methods perform well at predicting individual species interactions, and that these individual predictions scale up to the network level, resulting in food web structure of inferred networks being similar to their empirical counterparts. Our results indicate that inferring food web structure using phylogenies can be an efficient way of getting summary webs with minimal data, and offers a conservative test of changes in food web structure, particularly when there is low species turnover between sites. Inferences made using traits require more data, but allows for greater understanding of the mechanisms underlying trophic interactions. A composite model of the two methods provides a framework for investigating the importance of how phylogeny, trait distributions, and relative abundances, affect species interactions, and network structure.},
  copyright = {{\copyright} 2018 The Authors. Methods in Ecology and Evolution {\copyright} 2018 British Ecological Society},
  langid = {english},
  keywords = {body size,food web inference,food web structure,neutral theory,niche model,trait-matching,WebBuilder},
  file = {/Users/tanyastrydom/Zotero/storage/L3TGIBKM/Pomeranz et al. - 2019 - Inferring predator–prey interactions in food webs.pdf;/Users/tanyastrydom/Zotero/storage/2GRS36MV/2041-210X.html}
}

@article{portalierMechanicsPredatorPrey2019,
  title = {The Mechanics of Predator--Prey Interactions: {{First}} Principles of Physics Predict Predator--Prey Size Ratios},
  shorttitle = {The Mechanics of Predator--Prey Interactions},
  author = {Portalier, S{\'e}bastien M. J. and Fussmann, Gregor F. and Loreau, Michel and Cherif, Mehdi},
  year = {2019},
  journal = {Functional Ecology},
  volume = {33},
  number = {2},
  pages = {323--334},
  issn = {1365-2435},
  doi = {10.1111/1365-2435.13254},
  urldate = {2024-11-21},
  abstract = {Robust predictions of predator--prey interactions are fundamental for the understanding of food webs, their structure, dynamics, resistance to species loss, response to invasions and ecosystem function. Most current food web models measure parameters at the food web level to predict patterns at the same level. Thus, they are sensitive to the quality of the data and may be ineffective in predicting non-observed interactions and disturbed food webs. There is a need for mechanistic models that predict the occurrence of a predator--prey interaction based on lower levels of organization (i.e. the traits of organisms) and the properties of their environment. Here, we present such a model that focuses on the predation act itself. We built a Newtonian, mechanical model for the processes of searching, capturing and handling of a prey item by a predator. Associated with general metabolic laws, we predict the net energy gain from predation for pairs of pelagic or flying predator species and their prey depending on their body sizes. Predicted interactions match well with data from the most extensive predator--prey database, and overall model accuracy is greater than the allometric niche model. Our model shows that it is possible to accurately predict the structure of food webs using only a few mechanical traits. It underlines the importance of physical constraints in structuring food webs. A plain language summary is available for this article.},
  copyright = {{\copyright} 2018 The Authors. Functional Ecology {\copyright} 2018 British Ecological Society},
  langid = {english},
  keywords = {body size,energy,mechanics,predation,trophic link},
  file = {/Users/tanyastrydom/Zotero/storage/ID2XNUC3/Portalier et al. - 2019 - The mechanics of predator–prey interactions First.pdf;/Users/tanyastrydom/Zotero/storage/FNF3LPCW/1365-2435.html}
}

@article{pringleResolvingFoodWebStructure2020,
  title = {Resolving {{Food-Web Structure}}},
  author = {Pringle, Robert M. and Hutchinson, Matthew C.},
  year = {2020},
  month = nov,
  journal = {Annual Review of Ecology, Evolution and Systematics},
  volume = {51},
  number = {Volume 51, 2020},
  pages = {55--80},
  publisher = {Annual Reviews},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev-ecolsys-110218-024908},
  urldate = {2024-04-09},
  abstract = {Food webs are a major focus and organizing theme of ecology, but the data used to assemble them are deficient. Early debates over food-web data focused on taxonomic resolution and completeness, lack of which had produced spurious inferences. Recent data are widely believed to be much better and are used extensively in theoretical and meta-analytic research on network ecology. Confidence in these data rests on the assumptions (a) that empiricists correctly identified consumers and their foods and (b) that sampling methods were adequate to detect a near-comprehensive fraction of the trophic interactions between species. Abundant evidence indicates that these assumptions are often invalid, suggesting that most topological food-web data may remain unreliable for inferences about network structure and underlying ecological and evolutionary processes. Morphologically cryptic species are ubiquitous across taxa and regions, and many trophic interactions routinely evade detection by conventional methods. Molecular methods have diagnosed the severity of these problems and are a necessary part of the cure.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/SZRGW3YA/Pringle and Hutchinson - 2020 - Resolving Food-Web Structure.pdf;/Users/tanyastrydom/Zotero/storage/AHLT3MUT/annurev-ecolsys-110218-024908.html}
}

@incollection{pringleUntanglingFoodWebs2020,
  title = {Untangling {{Food Webs}}},
  booktitle = {Unsolved {{Problems}} in {{Ecology}}},
  author = {Pringle, Robert M.},
  year = {2020},
  month = jun,
  pages = {225--238},
  publisher = {Princeton University Press},
  doi = {10.1515/9780691195322-020},
  urldate = {2024-04-09},
  abstract = {Untangling Food Webs was published in Unsolved Problems in Ecology on page 225.},
  copyright = {De Gruyter expressly reserves the right to use all content for commercial text and data mining within the meaning of Section 44b of the German Copyright Act.},
  isbn = {978-0-691-19532-2},
  langid = {english}
}

@article{proulxNetworkThinkingEcology2005,
  title = {Network Thinking in Ecology and Evolution},
  author = {Proulx, Stephen R. and Promislow, Daniel E. L. and Phillips, Patrick C.},
  year = {2005},
  month = jun,
  journal = {Trends in Ecology \& Evolution},
  volume = {20},
  number = {6},
  pages = {345--353},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2005.04.004},
  urldate = {2016-10-18},
  abstract = {Although pairwise interactions have always had a key role in ecology and evolutionary biology, the recent increase in the amount and availability of biological data has placed a new focus on the complex networks embedded in biological systems. The increased availability of computational tools to store and retrieve biological data has facilitated wide access to these data, not just by biologists but also by specialists from the social sciences, computer science, physics and mathematics. This fusion of interests has led to a burst of research on the properties and consequences of network structure in biological systems. Although traditional measures of network structure and function have started us off on the right foot, an important next step is to create biologically realistic models of network formation, evolution, and function. Here, we review recent applications of network thinking to the evolution of networks at the gene and protein level and to the dynamics and stability of communities. These studies have provided new insights into the organization and function of biological systems by applying existing techniques of network analysis. The current challenge is to recognize the commonalities in evolutionary and ecological applications of network thinking to create a predictive science of biological networks.},
  langid = {english},
  pmid = {16701391},
  file = {/Users/tanyastrydom/Zotero/storage/2B58K65Z/Proulx et al. - 2005 - Network thinking in ecology and evolution.pdf;/Users/tanyastrydom/Zotero/storage/YFGGXEVM/S0169-5347(05)00088-1.html}
}

@article{pykeOptimalForagingTheory1984,
  title = {Optimal {{Foraging Theory}}: {{A Critical Review}}},
  shorttitle = {Optimal {{Foraging Theory}}},
  author = {Pyke, Graham},
  year = {1984},
  month = nov,
  journal = {Annual Review of Ecology, Evolution and Systematic},
  volume = {15},
  pages = {523--575},
  doi = {10.1146/annurev.ecolsys.15.1.523},
  abstract = {Assumptions behind optimal foraging theory are: 1) an individual's contribution to the next generation (its fitness) depends on behaviour during foraging; 2) there should be a heritable component of foraging behaviour (the actual innate foraging responses or the rules by which such responses are learned), and the proportion of individuals in a population foraging in ways that enhance fitness will tend to increase over time; 3) the relationship between foraging behaviour and fitness is known; 4) evolution of foraging behaviour is not prevented by genetic contraints; 5) such evolution is subject to 'functional' constraints (eg related to the animal's morphology); and 6) foraging behaviour evolves more rapidly than the rate at which relevant environmental conditions change. The review considers recent theoretical and empirical developments, dealing with behaviour of animals while they are foraging (but ignoring the timing of and the amount of time allocated to such behaviour). Ideas considered include risk aversion and risk proneness; optimal diet; optimal patch choice; optimal patch departure rules; optimal movements; and optimal central place foraging. Means of evaluating optimal foraging theory are discussed. -P.J.Jarvis}
}

@misc{quinteroDownscalingMutualisticNetworks2024,
  title = {Downscaling Mutualistic Networks from Species to Individuals Reveals Consistent Interaction Niches and Roles within Plant Populations},
  author = {Quintero, Elena and {Arroyo-Correa}, Blanca and Isla, Jorge and {Rodr{\'i}guez-S{\'a}nchez}, Francisco and Jordano, Pedro},
  year = {2024},
  month = jul,
  primaryclass = {New Results},
  pages = {2024.02.02.578595},
  publisher = {bioRxiv},
  doi = {10.1101/2024.02.02.578595},
  urldate = {2024-10-10},
  abstract = {Species-level networks emerge as the combination of interactions spanning multiple individuals, and their study has received considerable attention over the past 30 years. However, less is known about the structure of individual interaction configurations within species, being the fundamental scale at which ecological interactions occur in nature. We compiled 46 empirical, individual-based, interaction networks on plant-animal seed dispersal mutualisms, comprising 1037 plant individuals across 29 species from various regions. We compare the structure of individual-based networks to that of species-based networks and, by extending the niche concept to interaction assemblages, we explore individual plant specialization. Using a Bayesian framework to account for uncertainty derived from sampling, we examine how plant individuals ``explore'' the interaction niche of their population. Both individual-based and species-based networks exhibited high variability in network properties, lacking remarkable structural and topological differences between them. Within populations, frugivores' interaction allocation among plant individuals was highly heterogeneous, with one to three frugivore species dominating interactions. Regardless of species or bioregion, plant individuals displayed a variety of interaction profiles across populations, with a consistently small percentage of individuals playing a central role and exhibiting high diversity in their interaction assemblage. Plant populations showed varying mid to low levels of niche specialization; on average individuals' interaction niche ``breadth'' accounted for 70\% of the interaction diversity. Our results emphasize the importance of downscaling from species to individual-based networks to understand the structuring of interactions within ecological communities and provide an empirical basis for the extension of niche theory to complex mutualistic networks. Significance Statement Ecological interactions in nature occur between individual partners rather than species, and their outcomes determine fitness variation. By examining among-individual variation in interaction niches, we can bridge evolutionary and ecological perspectives to understand interaction biodiversity. This study investigates individual plant variation in frugivore assemblages worldwide, exploring how plant individuals ``build'' their interaction profiles with animal frugivores. The structure of networks composed of individuals was surprisingly similar to networks composed of species. Within populations, only a few plants played a key role in attracting a high diversity of frugivores, making them central to the overall network structure. Individuals actually explored a substantial diversity of partners, with individual interaction ``breadth'' accounting for up to 70\% of total interaction diversity on average.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial-NoDerivs 4.0 International), CC BY-NC-ND 4.0, as described at http://creativecommons.org/licenses/by-nc-nd/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/MY7AMRV4/Quintero et al. - 2024 - Downscaling mutualistic networks from species to i.pdf}
}

@article{robertsTestingCascadeModel2003,
  title = {Testing the {{Cascade Model}} for {{Food Webs}}},
  author = {Roberts, John M.},
  year = {2003},
  journal = {Journal of Agricultural, Biological, and Environmental Statistics},
  volume = {8},
  number = {2},
  eprint = {1400428},
  eprinttype = {jstor},
  pages = {196--204},
  publisher = {[International Biometric Society, Springer]},
  issn = {1085-7117},
  urldate = {2024-02-13},
  abstract = {The cascade model is a standard model for food web data. This article describes a method for testing the cascade model that complements existing tests. Like existing tests, the new test is conditional on the number of links observed in the predation matrix. One important feature of the cascade model is that an ordering of species is required, but in practice the true order is unknown. This feature has important implications for tests of the model, both for the new test and for existing tests. The analysis here does not require a single assumed order. Some real food webs are used for illustration.},
  file = {/Users/tanyastrydom/Zotero/storage/YN5RJGHZ/Roberts - 2003 - Testing the Cascade Model for Food Webs.pdf}
}

@article{rohrModelingFoodWebs2010,
  title = {Modeling {{Food Webs}}: {{Exploring Unexplained Structure Using Latent Traits}}.},
  shorttitle = {Modeling {{Food Webs}}},
  author = {Rohr, Rudolf~Philippe and Scherer, Heike and Kehrli, Patrik and Mazza, Christian and Bersier, Louis-F{\'e}lix},
  year = {2010},
  month = aug,
  journal = {The American Naturalist},
  volume = {176},
  number = {2},
  pages = {170--177},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/653667},
  urldate = {2024-02-01},
  abstract = {Several stochastic models have tried to capture the architecture of food webs. This approach is interesting, but it is limited by the fact that different assumptions can yield similar results. To overcome this limitation, we develop a purely statistical approach. Body size in terms of an optimal ratio between prey and predator is used as explanatory variable. In 12 observed food webs, this model predicts, on average, 20\% of interactions. To analyze the unexplained part, we introduce a latent term: each species is described by two latent traits, foraging and vulnerability, that represent nonmeasured characteristics of species once the optimal body size has been accounted for. The model now correctly predicts an average of 73\% of links. The key features of our approach are that latent traits quantify the structure that is left unexplained by the explanatory variable and that this quantification allows a test of whether independent biological information, such as microhabitat use, camouflage, or phylogeny, explains this structure. We illustrate this method with phylogeny and find that it is linked to one or both latent traits in nine of 12 food webs. Our approach opens the door to the formulation of more complex models that can be applied to any kind of biological network.},
  keywords = {biological network,body size,community structure,latent variable,phylogeny,statistical model},
  file = {/Users/tanyastrydom/Zotero/storage/VUV72CMZ/Rohr et al. - 2010 - Modeling Food Webs Exploring Unexplained Structur.pdf}
}

@article{rooneyLandscapeTheoryFood2008,
  title = {A Landscape Theory for Food Web Architecture},
  author = {Rooney, Neil and McCann, Kevin S. and Moore, John C.},
  year = {2008},
  journal = {Ecology Letters},
  volume = {11},
  number = {8},
  pages = {867--881},
  issn = {1461-0248},
  doi = {10.1111/j.1461-0248.2008.01193.x},
  urldate = {2024-09-09},
  abstract = {Ecologists have long searched for structures and processes that impart stability in nature. In particular, food web ecology has held promise in tackling this issue. Empirical patterns in food webs have consistently shown that the distributions of species and interactions in nature are more likely to be stable than randomly constructed systems with the same number of species and interactions. Food web ecology still faces two fundamental challenges, however. First, the quantity and quality of food web data required to document both the species richness and the interaction strengths among all species within food webs is largely prohibitive. Second, where food webs have been well documented, spatial and temporal variation in food web structure has been ignored. Conversely, research that has addressed spatial and temporal variation in ecosystems has generally ignored the full complexity of food web architecture. Here, we incorporate empirical patterns, largely from macroecology and behavioural ecology, into a spatially implicit food web structure to construct a simple landscape theory of food web architecture. Such an approach both captures important architectural features of food webs and allows for an exploration of food web structure across a range of spatial scales. Finally, we demonstrated that food webs are hierarchically organized along the spatial and temporal niche axes of species and their utilization of food resources in ways that stabilize ecosystems.},
  copyright = {{\copyright} 2008 Blackwell Publishing Ltd/CNRS},
  langid = {english}
}

@incollection{roopnarineEcologicalModellingPaleocommunity2017,
  title = {Ecological {{Modelling}} of {{Paleocommunity Food Webs}}},
  booktitle = {Conservation {{Paleobiology}}: {{Using}} the {{Past}} to {{Manage}} for the {{Future}}},
  author = {Roopnarine, Peter D},
  year = {2017},
  pages = {201--226},
  publisher = {University of Chicago Press},
  abstract = {Food webs are powerful representations of some of the most significant interspecific relationships in a community. They have been used as primary tools in studies of the relationship between biodiversity and ecological stability, and the robustness and resilience of communities in the face of environmental disturbance. Those topics are central to theoretical studies in conservation biology and biodiversity ecology, and hence conservation paleobiology. Food webs can be constructed from paleocommunity data for studies relevant to conservation paleobiology and biology, given that proper attention is paid to limitations imposed by fossil preservation and the geological record, and that questions are asked at appropriate scales. This paper presents a mathematical model, CEG (Cascading Extinction on Graphs) for the reconstruction and analysis of paleocommunity food webs. The model utilizes combinatoric and network-based mathematics, combining network topological and population demographic processes. CEG is founded upon ecological first principles and effectively mimics fundamental trophic processes, such as trophic cascades, keystone predator effects, bottom-up community collapse, and parasite-mediated suppression of populations. The model is applied to two example paleocommunities, a terrestrial community from the Late Permian of the Karoo Basin in South Africa, and a Neogene coastal marine community from the Dominican Republic. Insights, assumptions and limitations of the model are explored.},
  isbn = {978-0-226-50669-2},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/TG5G6ZA3/Roopnarine - ECOLOGICAL MODELING OF PALEOCOMMUNITY FOOD WEBS.pdf}
}

@article{roopnarineExtinctionCascadesCatastrophe2006,
  title = {Extinction {{Cascades}} and {{Catastrophe}} in {{Ancient Food Webs}}},
  author = {Roopnarine, Peter D.},
  year = {2006},
  journal = {Paleobiology},
  volume = {32},
  number = {1},
  eprint = {4096814},
  eprinttype = {jstor},
  pages = {1--19},
  publisher = {Paleontological Society},
  issn = {0094-8373},
  urldate = {2024-02-01},
  abstract = {A model is developed to explore the potential responses of paleocommunities to disruptions of primary production during times of mass extinction and ecological crisis. Disruptions of primary production are expected to generate bottom-up cascades of secondary extinction, and these are predictable given species richnesses, functional diversity, and trophic link distributions. If, however, consumers are permitted to compensate for the loss of trophic resources by increasing the intensities of their remaining biotic interactions, top-down driven catastrophic increases of secondary extinction emerge from the model. Both bottom-up and top-down effects are themselves controlled by the geometry of the food webs. The general Phanerozoic trends of increasing taxonomic and ecological diversities, as well as the varying strengths of biotic interactions, have led to food webs of increasing complexity. The frequency of catastrophic secondary extinction increases as food web complexity increases, but increased complexity also serves to dampen the magnitude of the secondary extinctions. When intraguild competitive interactions are included in the model, competitively inferior taxa are observed to possess greater probabilities of survival if the guilds are embedded in simple subnetworks of the overall food web. The result is the emergence of post-extinction guilds dominated by those inferior taxa. These results are congruent with empirical observations of "disaster taxa" dominance after some mass extinction events, and provide a mechanism for the reorganization of ecosystems that is observed after those events. The model makes the testable prediction that dominance by disaster taxa, however, should be observed only when bottom-up disruptions have caused ecosystems to collapse catastrophically.},
  file = {/Users/tanyastrydom/Zotero/storage/BSEBTB7B/Roopnarine - 2006 - Extinction Cascades and Catastrophe in Ancient Foo.pdf}
}

@article{roslinUseDNABarcodes2016,
  title = {The Use of {{DNA}} Barcodes in Food Web Construction---Terrestrial and Aquatic Ecologists Unite!},
  author = {Roslin, Tomas and Majaneva, Sanna},
  year = {2016},
  month = sep,
  journal = {Genome},
  volume = {59},
  number = {9},
  pages = {603--628},
  publisher = {NRC Research Press},
  issn = {0831-2796},
  doi = {10.1139/gen-2015-0229},
  urldate = {2024-04-10},
  abstract = {By depicting who eats whom, food webs offer descriptions of how groupings in nature (typically species or populations) are linked to each other. For asking questions on how food webs are built and work, we need descriptions of food webs at different levels of resolution. DNA techniques provide opportunities for highly resolved webs. In this paper, we offer an expos{\'e} of how DNA-based techniques, and DNA barcodes in particular, have recently been used to construct food web structure in both terrestrial and aquatic systems. We highlight how such techniques can be applied to simultaneously improve the taxonomic resolution of the nodes of the web (i.e., the species), and the links between them (i.e., who eats whom). We end by proposing how DNA barcodes and DNA information may allow new approaches to the construction of larger interaction webs, and overcome some hurdles to achieving adequate sample size. Most importantly, we propose that the joint adoption and development of these techniques may serve to unite approaches to food web studies in aquatic and terrestrial systems---revealing the extent to which food webs in these environments are structured similarly to or differently from each other, and how they are linked by dispersal.},
  file = {/Users/tanyastrydom/Zotero/storage/FFEXJVT2/Roslin and Majaneva - 2016 - The use of DNA barcodes in food web construction—t.pdf}
}

@article{rossbergFoodWebsExperts2006,
  title = {Food Webs: {{Experts}} Consuming Families of Experts},
  shorttitle = {Food Webs},
  author = {Rossberg, A. G. and Matsuda, H. and Amemiya, T. and Itoh, K.},
  year = {2006},
  month = aug,
  journal = {Journal of Theoretical Biology},
  volume = {241},
  number = {3},
  pages = {552--563},
  issn = {0022-5193},
  doi = {10.1016/j.jtbi.2005.12.021},
  urldate = {2024-02-01},
  abstract = {Food webs of habitats as diverse as lakes or desert valleys are known to exhibit common ``food-web patterns'', but the detailed mechanisms generating these structures have remained unclear. By employing a stochastic, dynamical model, we show that many aspects of the structure of predatory food webs can be understood as the traces of an evolutionary history where newly evolving species avoid direct competition with their relatives. The tendency to avoid sharing natural enemies (apparent competition) with related species is considerably weaker. Thus, ``experts consuming families of experts'' can be identified as the main underlying food-web pattern. We report the results of a systematic, quantitative model validation showing that the model is surprisingly accurate.},
  keywords = {Competition,Evolution,Food webs,Network structure,Parasites},
  file = {/Users/tanyastrydom/Zotero/storage/TKM2X2U2/Rossberg et al. - 2006 - Food webs Experts consuming families of experts.pdf;/Users/tanyastrydom/Zotero/storage/7H8PCX8N/S0022519305005606.html}
}

@article{saberskiImpactDataResolution2024,
  title = {The Impact of Data Resolution on Dynamic Causal Inference in Multiscale Ecological Networks},
  author = {Saberski, Erik and Lorimer, Tom and Carpenter, Delia and Deyle, Ethan and Merz, Ewa and Park, Joseph and Pao, Gerald M. and Sugihara, George},
  year = {2024},
  month = nov,
  journal = {Communications Biology},
  volume = {7},
  number = {1},
  pages = {1--10},
  publisher = {Nature Publishing Group},
  issn = {2399-3642},
  doi = {10.1038/s42003-024-07054-z},
  urldate = {2024-11-11},
  abstract = {While it is commonly accepted that ecosystem dynamics are nonlinear, what is often not acknowledged is that nonlinearity implies scale-dependence. With the increasing availability of high-resolution ecological time series, there is a growing need to understand how scale and resolution in the data affect the construction and interpretation of causal networks---specifically, networks mapping how changes in one variable drive changes in others as part of a shared dynamic system (``dynamic causation''). We use Convergent Cross Mapping (CCM), a method specifically designed to measure dynamic causation, to study the effects of varying temporal and taxonomic/functional resolution in data when constructing ecological causal networks. As the system is viewed at different scales relationships will appear and disappear. The relationship between data resolution and interaction presence is not random: the temporal scale at which a relationship is uncovered identifies a biologically relevant scale that drives changes in population abundance. Further, causal relationships between taxonomic aggregates (low-resolution) are shown to be influenced by the number of interactions between their component species (high-resolution). Because no single level of resolution captures all the causal links in a system, a more complete understanding requires multiple levels when constructing causal networks.},
  copyright = {2024 The Author(s)},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/2Z9FLMT6/Saberski et al. - 2024 - The impact of data resolution on dynamic causal in.pdf}
}

@article{saraviaEcologicalNetworkAssembly2022,
  title = {Ecological Network Assembly: {{How}} the Regional Metaweb Influences Local Food Webs},
  shorttitle = {Ecological Network Assembly},
  author = {Saravia, Leonardo A. and Marina, Tom{\'a}s I and Kristensen, Nadiah P. and De Troch, Marleen and Momo, Fernando R.},
  year = {2022},
  journal = {Journal of Animal Ecology},
  volume = {91},
  number = {3},
  pages = {630--642},
  publisher = {Wiley},
  address = {Hoboken},
  issn = {0021-8790},
  doi = {10.1111/1365-2656.13652},
  urldate = {2023-05-06},
  abstract = {Local food webs result from a sequence of colonisations and extinctions by species from the regional pool or metaweb, that is, the assembly process. Assembly is theorised to be a selective process: whether or not certain species or network structures can persist is partly determined by local processes including habitat filtering and dynamical constraints. Consequently, local food web structure should reflect these processes. The goal of this study was to test evidence for these selective processes by comparing the structural properties of real food webs to the expected distribution given the metaweb. We were particularly interested in ecological dynamics; if the network properties commonly associated with dynamical stability are indeed the result of stability constraints, then they should deviate from expectation in the direction predicted by theory. To create a null expectation, we used the novel approach of randomly assembling model webs by drawing species and interactions from the empirical metaweb. The assembly model permitted colonisation and extinction, and required a consumer species to have at least one prey, but had no habitat type nor population dynamical constraints. Three datasets were used: (a) the marine Antarctic metaweb, with two local food webs; (b) the 50 lakes of the Adirondacks; and (c) the arthropod community from Florida Keys' classic defaunation experiment. Contrary to our expectations, we found that there were almost no differences between empirical webs and those resulting from the null assembly model. Few empirical food webs showed significant differences with network properties, motif representations and topological roles. Network properties associated with stability did not deviate from expectation in the direction predicted by theory. Our results suggest that-for the commonly used metrics we considered-local food web structure is not strongly influenced by dynamical nor habitat restrictions. Instead, the structure is inherited from the metaweb. This suggests that the network properties typically attributed as causes or consequences of ecological stability are instead a by-product of the assembly process (i.e. spandrels), and may potentially be too coarse to detect the true signal of dynamical constraint.},
  langid = {english},
  annotation = {WOS:000740543600001},
  file = {/Users/tanyastrydom/Zotero/storage/QS5PINH8/Saravia et al. - 2018 - Ecological Network assembly how the regional meta.pdf}
}

@article{schwarzTemporalScaledependencePlant2020,
  title = {Temporal Scale-Dependence of Plant--Pollinator Networks},
  author = {Schwarz, Benjamin and V{\'a}zquez, Diego P. and CaraDonna, Paul J. and Knight, Tiffany M. and Benadi, Gita and Dormann, Carsten F. and Gauzens, Benoit and Motivans, Elena and Resasco, Julian and Bl{\"u}thgen, Nico and Burkle, Laura A. and Fang, Qiang and {Kaiser-Bunbury}, Christopher N. and Alarc{\'o}n, Ruben and Bain, Justin A. and Chacoff, Natacha P. and Huang, Shuang-Quan and LeBuhn, Gretchen and MacLeod, Molly and Petanidou, Theodora and Rasmussen, Claus and Simanonok, Michael P. and Thompson, Amibeth H. and Fr{\"u}nd, Jochen},
  year = {2020},
  journal = {Oikos},
  volume = {129},
  number = {9},
  pages = {1289--1302},
  issn = {1600-0706},
  doi = {10.1111/oik.07303},
  urldate = {2024-11-07},
  abstract = {The study of mutualistic interaction networks has led to valuable insights into ecological and evolutionary processes. However, our understanding of network structure may depend upon the temporal scale at which we sample and analyze network data. To date, we lack a comprehensive assessment of the temporal scale-dependence of network structure across a wide range of temporal scales and geographic locations. If network structure is temporally scale-dependent, networks constructed over different temporal scales may provide very different perspectives on the structure and composition of species interactions. Furthermore, it remains unclear how various factors -- including species richness, species turnover, link rewiring and sampling effort -- act in concert to shape network structure across different temporal scales. To address these issues, we used a large database of temporally-resolved plant--pollinator networks to investigate how temporal aggregation from the scale of one day to multiple years influences network structure. In addition, we used structural equation modeling to explore the direct and indirect effects of temporal scale, species richness, species turnover, link rewiring and sampling effort on network structural properties. We find that plant--pollinator network structure is strongly temporally-scale dependent. This general pattern arises because the temporal scale determines the degree to which temporal dynamics (i.e. phenological turnover of species and links) are included in the network, in addition to how much sampling effort is put into constructing the network. Ultimately, the temporal scale-dependence of our plant--pollinator networks appears to be mostly driven by species richness, which increases with sampling effort, and species turnover, which increases with temporal extent. In other words, after accounting for variation in species richness, network structure is increasingly shaped by its underlying temporal dynamics. Our results suggest that considering multiple temporal scales may be necessary to fully appreciate the causes and consequences of interaction network structure.},
  copyright = {{\copyright} 2020 Nordic Society Oikos. Published by John Wiley \& Sons Ltd},
  langid = {english},
  keywords = {mutualistic networks,phenological turnover,rewiring,sampling effort,temporal dynamics,temporal extent},
  file = {/Users/tanyastrydom/Zotero/storage/2FI8BWBK/Schwarz et al. - 2020 - Temporal scale-dependence of plant–pollinator netw.pdf;/Users/tanyastrydom/Zotero/storage/ATG2565F/oik.html}
}

@article{segarRoleEvolutionShaping2020,
  title = {The {{Role}} of {{Evolution}} in {{Shaping Ecological Networks}}},
  author = {Segar, Simon T. and Fayle, Tom M. and Srivastava, Diane S. and Lewinsohn, Thomas M. and Lewis, Owen T. and Novotny, Vojtech and Kitching, Roger L. and Maunsell, Sarah C.},
  year = {2020},
  month = may,
  journal = {Trends in Ecology \& Evolution},
  volume = {35},
  number = {5},
  pages = {454--466},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2020.01.004},
  urldate = {2024-09-16},
  abstract = {The structure of ecological networks reflects the evolutionary history of their biotic components, and their dynamics are strongly driven by ecoevolutionary processes. Here, we present an appraisal of recent relevant research, in which the pervasive role of evolution within ecological networks is manifest. Although evolutionary processes are most evident at macroevolutionary scales, they are also important drivers of local network structure and dynamics. We propose components of a blueprint for further research, emphasising process-based models, experimental evolution, and phenotypic variation, across a range of distinct spatial and temporal scales. Evolutionary dimensions are required to advance our understanding of foundational properties of community assembly and to enhance our capability of predicting how networks will respond to impending changes.},
  keywords = {adaptive network,ecoevolution,ecophylogenetics,metacommunity,microevolution,phylogeny},
  file = {/Users/tanyastrydom/Zotero/storage/BYKZ5KDV/Segar et al. - 2020 - The Role of Evolution in Shaping Ecological Networ.pdf;/Users/tanyastrydom/Zotero/storage/HP8ZT8JT/S0169534720300069.html}
}

@misc{shawFrameworkReconstructingAncient2024,
  title = {A Framework for Reconstructing Ancient Food Webs Using Functional Trait Data},
  author = {Shaw, Jack O. and Dunhill, Alexander M. and Beckerman, Andrew P. and Dunne, Jennifer A. and Hull, Pincelli M.},
  year = {2024},
  month = jan,
  primaryclass = {New Results},
  pages = {2024.01.30.578036},
  publisher = {bioRxiv},
  doi = {10.1101/2024.01.30.578036},
  urldate = {2024-02-01},
  abstract = {Food webs provide quantitative insights into the structure and dynamics of ecological communities. Previous work has shown their utility in understanding community responses to modern and ancient perturbations, including anthropogenic change and mass extinctions. However, few ancient food webs have been reconstructed due to difficulties assessing trophic interactions amongst extinct species derived from an incomplete fossil record. We present and assess the Paleo Food web Inference Model (PFIM). PFIM uses functional trait data--predictive of interactions in modern ecosystems and commonly available for fossil organisms--to reconstruct ancient food webs. We test the model by (i) applying it to four modern ecosystems with empirical constrained food webs to directly compare PFIM-constructed networks to their empirical counterparts, (ii) by carefully comparing discrepancies between PFIM-inferred and empirical webs in one of those systems, and (iii) by comparing networks describing feasible trophic interactions ("feasible webs") with networks to which we superimpose characteristic interaction distributions derived from modern theory ("realized webs"). As a proof of concept, we then apply the method to faunal data from two Cambrian fossil deposits to reconstruct ancient trophic systems. PFIM-inferred feasible food webs successfully predict {\textasciitilde}70\% of trophic interactions across four modern systems. Furthermore, inferred food webs with enforced interaction distributions (i.e., realized webs) accurately predict {\textasciitilde}90\% of interactions. Comparisons with a global database of trophic interactions and other food web models, suggest that under sampling of empirical webs accounts for up to 21\% of the remaining differences between PFIM and empirical food webs. Food webs can be reasonably approximated by inferring trophic interactions based upon life habit traits. This study provides the foundation to use trait-based inference models across the fossil record to examine ancient food webs and community evolution.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NonCommercial 4.0 International), CC BY-NC 4.0, as described at http://creativecommons.org/licenses/by-nc/4.0/},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/AKAQ984E/Shaw et al. - 2024 - A framework for reconstructing ancient food webs u.pdf}
}

@article{simmonsRefocusingMultipleStressor2021,
  title = {Refocusing Multiple Stressor Research around the Targets and Scales of Ecological Impacts},
  author = {Simmons, Benno I. and Blyth, Penelope S. A. and Blanchard, Julia L. and Clegg, Tom and Delmas, Eva and Garnier, Aur{\'e}lie and Griffiths, Christopher A. and Jacob, Ute and Pennekamp, Frank and Petchey, Owen L. and Poisot, Timoth{\'e}e and Webb, Thomas J. and Beckerman, Andrew P.},
  year = {2021},
  month = nov,
  journal = {Nature Ecology \& Evolution},
  volume = {5},
  number = {11},
  pages = {1478--1489},
  publisher = {Nature Publishing Group},
  issn = {2397-334X},
  doi = {10.1038/s41559-021-01547-4},
  urldate = {2024-09-12},
  abstract = {Ecological communities face a variety of environmental and anthropogenic stressors acting simultaneously. Stressor impacts can combine additively or can interact, causing synergistic or antagonistic effects. Our knowledge of when and how interactions arise is limited, as most models and experiments only consider the effect of a small number of non-interacting stressors at one or few scales of ecological organization. This is concerning because it could lead to significant underestimations or overestimations of threats to biodiversity. Furthermore, stressors have been largely classified by their source rather than by the mechanisms and ecological scales at which they act (the target). Here, we argue, first, that a more nuanced classification of stressors by target and ecological scale can generate valuable new insights and hypotheses about stressor interactions. Second, that the predictability of multiple stressor effects, and consistent patterns in their impacts, can be evaluated by examining the distribution of stressor effects across targets and ecological scales. Third, that a variety of existing mechanistic and statistical modelling tools can play an important role in our framework and advance multiple stressor research.},
  copyright = {2021 Springer Nature Limited},
  langid = {english}
}

@article{soberonGrinnellianEltonianNiches2007,
  title = {Grinnellian and {{Eltonian}} Niches and Geographic Distributions of Species},
  author = {Sober{\'o}n, Jorge},
  year = {2007},
  journal = {Ecology Letters},
  volume = {10},
  number = {12},
  pages = {1115--1123},
  issn = {1461-0248},
  doi = {10.1111/j.1461-0248.2007.01107.x},
  urldate = {2024-10-04},
  abstract = {In the recent past, availability of large data sets of species presences has increased by orders of magnitude. This, together with developments in geographical information systems and statistical methods, has enabled scientists to calculate, for thousands of species, the environmental conditions of their distributional areas. The profiles thus obtained are obviously related to niche concepts in the Grinnell tradition, and separated from those in Elton's tradition. I argue that it is useful to define Grinnellian and Eltonian niches on the basis of the types of variables used to calculate them, the natural spatial scale at which they can be measured, and the dispersal of the individuals over the environment. I use set theory notation and analogies derived from population ecology theory to obtain formal definitions of areas of distribution and several types of niches. This brings clarity to several practical and fundamental questions in macroecology and biogeography.},
  langid = {english},
  keywords = {Bionomic,distributional areas,Elton,Grinnell,Niche,scenopoetic,spatial scaling},
  file = {/Users/tanyastrydom/Zotero/storage/AD6Z8BV5/Soberón - 2007 - Grinnellian and Eltonian niches and geographic dis.pdf;/Users/tanyastrydom/Zotero/storage/S7XTGPG9/j.1461-0248.2007.01107.html}
}

@article{solowLumpingSpeciesFood1998,
  title = {On {{Lumping Species}} in {{Food Webs}}},
  author = {Solow, Andrew R. and Beet, Andrew R.},
  year = {1998},
  journal = {Ecology},
  volume = {79},
  number = {6},
  pages = {2013--2018},
  issn = {1939-9170},
  doi = {10.1890/0012-9658(1998)079[2013:OLSIFW]2.0.CO;2},
  urldate = {2024-02-01},
  abstract = {Species in community food webs are commonly aggregated or lumped on the basis of overlap in predators and prey. This note reports an unexpected result of lumping species in observed food webs and simulated food webs. The main result is that it is much easier to lump species in observed webs than in simulated webs. The reason is that there is much more overlap in predators and prey for species in observed webs than in simulated webs. This points to a fundamental deficiency in web assembly models that assume a random distribution of links.},
  copyright = {{\copyright} 1998 by the Ecological Society of America},
  langid = {english},
  keywords = {cascade model,constant connectance model},
  file = {/Users/tanyastrydom/Zotero/storage/QJLJND2N/Solow and Beet - 1998 - On Lumping Species in Food Webs.pdf;/Users/tanyastrydom/Zotero/storage/UNIDNMV3/0012-9658(1998)079[2013OLSIFW]2.0.html}
}

@misc{songRigorousValidationEcological2024,
  title = {Rigorous (in)Validation of Ecological Models},
  author = {Song, Chuliang and Levine, Jonathan M.},
  year = {2024},
  month = sep,
  primaryclass = {New Results},
  pages = {2024.09.19.613075},
  publisher = {bioRxiv},
  doi = {10.1101/2024.09.19.613075},
  urldate = {2024-09-25},
  abstract = {The complexity of ecosystems poses a formidable challenge for confidently invalidating ecological models, as current practices struggle to distinguish model inadequacies from the confounding effects of unobserved biotic or abiotic factors. The prevailing inability to falsify models has resulted in an accumulation of models but not an accumulation of confidence. Here, we introduce a new approach rooted in queueing theory, termed the covariance criteria, that establishes a rigorous test for model validity based on covariance relationships between observable quantities. These criteria set a high bar for models to pass by specifying necessary conditions that must hold regardless of unobserved factors. We demonstrate the broad applicability and discriminatory power of the covariance criteria by applying them to three long-standing challenges in ecological theory: resolving competing models of predator-prey functional responses, disentangling ecological and evolutionary dynamics in systems with rapid evolution, and detecting the often-elusive influence of higher-order species interactions. Across these diverse case studies, the covariance criteria consistently rule out inadequate models, while building strong confidence in those that provide strategically useful approximations. The covariance criteria approach is mathematically rigorous, computationally efficient, and often non-parametric, making it immediately applicable to existing data and models.},
  archiveprefix = {bioRxiv},
  chapter = {New Results},
  copyright = {{\copyright} 2024, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
  langid = {english}
}

@article{staniczenkoStructuralDynamicsRobustness2010,
  title = {Structural Dynamics and Robustness of Food Webs},
  author = {Staniczenko, Phillip P. A. and Lewis, Owen T. and Jones, Nick S. and {Reed-Tsochas}, Felix},
  year = {2010},
  journal = {Ecology Letters},
  volume = {13},
  number = {7},
  pages = {891--899},
  issn = {1461-0248},
  doi = {10.1111/j.1461-0248.2010.01485.x},
  urldate = {2024-04-03},
  abstract = {Ecology Letters (2010) 13: 891--899 Abstract Food web structure plays an important role when determining robustness to cascading secondary extinctions. However, existing food web models do not take into account likely changes in trophic interactions (`rewiring') following species loss. We investigated structural dynamics in 12 empirically documented food webs by simulating primary species loss using three realistic removal criteria, and measured robustness in terms of subsequent secondary extinctions. In our model, novel trophic interactions can be established between predators and food items not previously consumed following the loss of competing predator species. By considering the increase in robustness conferred through rewiring, we identify a new category of species -- overlap species -- which promote robustness as shown by comparing simulations incorporating structural dynamics to those with static topologies. The fraction of overlap species in a food web is highly correlated with this increase in robustness; whereas species richness and connectance are uncorrelated with increased robustness. Our findings underline the importance of compensatory mechanisms that may buffer ecosystems against environmental change, and highlight the likely role of particular species that are expected to facilitate this buffering.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/T35FFRIS/j.1461-0248.2010.01485.html}
}

@book{stephensForagingTheory1986,
  title = {Foraging {{Theory}}},
  author = {Stephens, David W. and Krebs, John R.},
  year = {1986},
  volume = {1},
  eprint = {j.ctvs32s6b},
  eprinttype = {jstor},
  publisher = {Princeton University Press},
  doi = {10.2307/j.ctvs32s6b},
  urldate = {2024-11-21},
  abstract = {This account of the current state of foraging theory is also a valuable description of the use of optimality theory in behavioral ecology in general. Organizing and introducing the main research themes in economic analyses of animal feeding behavior, the authors analyze the empirical evidence bearing on foraging models and answer criticisms of optimality modeling. They explain the rationale for applying optimality models to the strategies and mechanics of foraging and present the basic "average-rate maximizing" models and their extensions.     The work discusses new directions in foraging research: incorporating incomplete information and risk-sensitive behavior in foraging models; analyzing trade-offs, such as nutrient requirements and the threat of being eaten while foraging; formulating dynamic models; and building constrained optimization models that assume that foragers can use only simple "rules of thumb." As an analysis of these and earlier research developments and as a contribution to debates about the role of theory in evolutionary biology. \emph{Foraging Theory}  will appeal to a wide range of readers, from students to research professionals, in behavioral ecology, population and community ecology, animal behavior, and animal psychology, and especially to those planning empirical tests of foraging models.},
  isbn = {978-0-691-08441-1}
}

@article{stockPairwiseLearningPredicting2021,
  title = {Pairwise Learning for Predicting Pollination Interactions Based on Traits and Phylogeny},
  author = {Stock, Michiel},
  year = {2021},
  journal = {Ecological Modelling},
  pages = {14},
  abstract = {Mutualistic bee--plant interaction networks are a vital part of terrestrial ecosystems. They frequently arise through co-evolutionary processes, which match the traits of both partners, facilitating their interaction. Insights in these interaction mechanisms are vital to be able to manage changing ecosystems. This entails the need for models to predict species interaction networks in general and pollination networks in particular. We show how kernel-based pairwise learning can predict bee--plant interactions based on the traits and the phylogeny of the plant and bee species. The traits and the phylogeny of the plant and bee species proved to be highly predictive. Although the traits were slightly more informative compared to the phylogeny, the best results were obtained by combining both the traits and the phylogeny in the model Notably, the model performance varied greatly depending on whether the goal was to pinpoint missing interactions in the network or to predict interactions for new bee species, new plant species, or both. This issue highlights the importance of proper stratification when fitting biological network prediction models. Our model, however, showed the capacity to generalize beyond the original dataset provided by FlorAbeilles. The model was validated by predicting potentially interacting plant species for the invasive bee species Megachile sculpturalis. Four out of the five plant species identified by the model could be validated based on literature.},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/YF8UP7XG/Stock - 2021 - Pairwise learning for predicting pollination inter.pdf}
}

@article{stoufferAllEcologicalModels2019,
  title = {All Ecological Models Are Wrong, but Some Are Useful},
  author = {Stouffer, Daniel B.},
  year = {2019},
  journal = {Journal of Animal Ecology},
  volume = {88},
  number = {2},
  pages = {192--195},
  issn = {1365-2656},
  doi = {10.1111/1365-2656.12949},
  urldate = {2021-01-27},
  abstract = {In Focus: Curtsdotter, A., Banks, H. T., Banks, J. E., Jonsson, M., Jonsson, T., Laubmeier, A. N., {\dots} Bommarco, R. (2019). Ecosystem function in predator-prey food webs---Confronting dynamic models with empirical data. Journal of Animal Ecology, 88, https://doi.org/10.1111/1365-2656.12892 Species' population dynamics are influenced by a variety of abiotic and biotic factors. Curtsdotter et al. (2019) used a food web model to investigate the role of predator--prey interactions in the population dynamics of the bird cherry-oat aphid Rhopalosiphum padi. Their analysis hinged on linking the observed population dynamics to a mathematical description of the multi-species system via inverse methods---an approach less utilized in ecology but that allows one to search a wide space of possible parameterizations and identify best-fit model parameters. By scrutinizing the fit of this model to observed aphid population dynamics in 10 separate barley fields, they identified fields in which predation was the key driving force; in others, they found that accurate predictions depended on the existence of an unpredictable and unidentified extrinsic driver of aphid mortality. By scrutinizing areas where the model gave poor or biologically counterintuitive fits, their study provides a path forward to better link ecological theory to ecosystem function.},
  copyright = {{\copyright} 2019 The Author. Journal of Animal Ecology {\copyright} 2019 British Ecological Society},
  langid = {english}
}

@article{stoufferEvidenceExistenceRobust2007,
  title = {Evidence for the Existence of a Robust Pattern of Prey Selection in Food Webs},
  author = {Stouffer, Daniel B and Camacho, Juan and Jiang, Wenxin and Nunes Amaral, Lu{\'i}s A},
  year = {2007},
  month = jun,
  journal = {Proceedings of the Royal Society B: Biological Sciences},
  volume = {274},
  number = {1621},
  pages = {1931--1940},
  publisher = {Royal Society},
  doi = {10.1098/rspb.2007.0571},
  urldate = {2024-04-04},
  abstract = {Food webs aim to provide a thorough representation of the trophic interactions found in an ecosystem. The complexity of empirical food webs, however, is leading many ecologists to focus dynamic ecosystem studies on smaller microcosm or mesocosm studies based upon community modules, which comprise three to five species and the interactions likely to have ecological relevance. We provide here a structural counterpart to community modules. We investigate food-web `motifs' which are n-species connected subgraphs found within the food web. Remarkably, we find that the over- and under-representation of three-species motifs in empirical food webs can be understood through comparison to a static food-web model, the niche model. Our result conclusively demonstrates that predation upon species with some `characteristic' niche value is the prey selection mechanism consistent with the structural properties of empirical food webs.},
  keywords = {complex networks,food webs,food-web structure,network motifs,prey selection}
}

@article{stoufferQuantitativePatternsStructure2005,
  title = {Quantitative {{Patterns}} in the {{Structure}} of {{Model}} and {{Empirical Food Webs}}},
  author = {Stouffer, D. B. and Camacho, J. and Guimer{\`a}, R. and Ng, C. A. and Nunes Amaral, L. A.},
  year = {2005},
  journal = {Ecology},
  volume = {86},
  number = {5},
  pages = {1301--1311},
  issn = {1939-9170},
  doi = {10.1890/04-0957},
  urldate = {2024-02-09},
  abstract = {Understanding the structure of food webs, and the mechanisms that give rise to this structure, is one of the most challenging problems in ecology. We analyze from a statistical physics perspective the network structure of model food webs and of 15 community food webs from a variety of environments, including freshwater, marine, estuarine, and terrestrial environments. We perform a theoretical analysis of two recently proposed models for food webs, the niche model of R. J. Williams and N. D. Martinez and the nested-hierarchy model of M.-F. Cattin et al. We find that the two models generate distributions of numbers of prey, predators, and links that are described by the same analytical expressions. Our analytical treatment reveals that a model's capacity to reproduce empirical data is principally determined by its ability to satisfy two conditions: (1) the species' niche values form a totally ordered set and (2) each species has a specific exponentially decaying probability of preying on a given fraction of the species with lower niche values. To test this hypothesis, we generalize the cascade model of J. E. Cohen and C. M. Newman so that it satisfies condition 2 and find that the new model is able to reproduce the properties of empirical food webs, validating our hypothesis. We use our analytical predictions as a guide to the analysis of 15 of the most complete empirical food webs available. We demonstrate that the quantitative unifying patterns that describe the properties of the food-web models considered earlier also describe the majority of the empirical webs considered. We find good agreement between the empirical data and the models for the average distance between species and the average clustering coefficient. Our results strongly support two hypotheses: first, that any model satisfying the two conditions we identify will accurately reproduce a number of the statistical properties of empirical food webs, and second, that the empirical distributions of number of prey and number of predators follow universal functional forms that, without free parameters, match our analytical predictions.},
  copyright = {{\copyright} 2005 by the Ecological Society of America},
  langid = {english},
  keywords = {complex networks,food webs,food-web patterns,food-web structure,model vs. empirical,network structure,scaling,universality},
  file = {/Users/tanyastrydom/Zotero/storage/FEVQ3G5P/Stouffer et al. - 2005 - Quantitative Patterns in the Structure of Model an.pdf;/Users/tanyastrydom/Zotero/storage/QLRI8CZ8/04-0957.html}
}

@article{stoufferRobustMeasureFood2006,
  title = {A Robust Measure of Food Web Intervality},
  author = {Stouffer, Daniel B. and Camacho, Juan and Amaral, Lu{\'i}s A. Nunes},
  year = {2006},
  month = dec,
  journal = {Proceedings of the National Academy of Sciences},
  volume = {103},
  number = {50},
  pages = {19015--19020},
  publisher = {Proceedings of the National Academy of Sciences},
  doi = {10.1073/pnas.0603844103},
  urldate = {2024-02-09},
  abstract = {Intervality of a food web is related to the number of trophic dimensions characterizing the niches in a community. We introduce here a mathematically robust measure for food web intervality. It has previously been noted that empirical food webs are not strictly interval; however, upon comparison to suitable null hypotheses, we conclude that empirical food webs actually do exhibit a strong bias toward contiguity of prey, that is, toward intervality. Further, our results strongly suggest that empirically observed species and their diets can be mapped onto a single dimension. This finding validates a critical assumption in the recently proposed static niche model and provides guidance for ongoing efforts to develop dynamic models of ecosystems.},
  file = {/Users/tanyastrydom/Zotero/storage/BQTL6YME/Stouffer et al. - 2006 - A robust measure of food web intervality.pdf}
}

@article{strydomFoodWebReconstruction2022,
  title = {Food Web Reconstruction through Phylogenetic Transfer of Low-Rank Network Representation},
  author = {Strydom, Tanya and Bouskila, Salom{\'e} and Banville, Francis and Barros, Ceres and Caron, Dominique and Farrell, Maxwell J. and Fortin, Marie-Jos{\'e}e and Hemming, Victoria and Mercier, Benjamin and Pollock, Laura J. and Runghen, Rogini and Dalla Riva, Giulio V. and Poisot, Timoth{\'e}e},
  year = {2022},
  journal = {Methods in Ecology and Evolution},
  volume = {13},
  number = {12},
  pages = {2838--2849},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.13835},
  urldate = {2024-02-06},
  abstract = {Despite their importance in many ecological processes, collecting data and information on ecological interactions is an exceedingly challenging task. For this reason, large parts of the world have a data deficit when it comes to species interactions and how the resulting networks are structured. As data collection alone is unlikely to be sufficient, community ecologists must adopt predictive methods. We present a methodological framework that uses graph embedding and transfer learning to assemble a predicted list of trophic interactions of a species pool for which their interactions are unknown. Specifically, we `learn' the information (latent traits) of species from a known interaction network and infer the latent traits of another species pool for which we have no a priori interaction data based on their phylogenetic relatedness to species from the known network. The latent traits can then be used to predict interactions and construct an interaction network. Here we assembled a metaweb for Canadian mammals derived from interactions in the European food web, despite only 4\% of common species being shared between the two locations. The results of the predictive model are compared against databases of recorded pairwise interactions, showing that we correctly recover 91\% of known interactions. The framework itself is robust even when the known network is incomplete or contains spurious interactions making it an ideal candidate as a tool for filling gaps when it comes to species interactions. We provide guidance on how this framework can be adapted by substituting some approaches or predictors in order to make it more generally applicable.},
  copyright = {{\copyright} 2022 British Ecological Society.},
  langid = {english},
  keywords = {ancestral character estimation,biogeography,ecological networks,network embedding,transfer learning},
  file = {/Users/tanyastrydom/Zotero/storage/MDVN4MY3/Strydom et al. - 2022 - Food web reconstruction through phylogenetic trans.pdf;/Users/tanyastrydom/Zotero/storage/HGJRDYM4/2041-210X.html}
}

@article{strydomGraphEmbeddingTransfer2023,
  title = {Graph Embedding and Transfer Learning Can Help Predict Potential Species Interaction Networks despite Data Limitations},
  author = {Strydom, Tanya and Bouskila, Salom{\'e} and Banville, Francis and Barros, Ceres and Caron, Dominique and Farrell, Maxwell J. and Fortin, Marie-Jos{\'e}e and Mercier, Benjamin and Pollock, Laura J. and Runghen, Rogini and Dalla Riva, Giulio V. and Poisot, Timoth{\'e}e},
  year = {2023},
  journal = {Methods in Ecology and Evolution},
  volume = {14},
  number = {12},
  pages = {2917--2930},
  issn = {2041-210X},
  doi = {10.1111/2041-210X.14228},
  urldate = {2024-02-07},
  abstract = {Metawebs (networks of potential interactions within a species pool) are a powerful abstraction to understand how large-scale species interaction networks are structured. Because metawebs are typically expressed at large spatial and taxonomic scales, assembling them is a tedious and costly process; predictive methods can help circumvent the limitations in data deficiencies, by providing a first approximation of metawebs. One way to improve our ability to predict metawebs is to maximize available information by using graph embeddings, as opposed to an exhaustive list of species interactions. Graph embedding is an emerging field in machine learning that holds great potential for ecological problems. Here, we outline how the challenges associated with inferring metawebs line-up with the advantages of graph embeddings; followed by a discussion as to how the choice of the species pool has consequences on the reconstructed network, specifically as to the role of human-made (or arbitrarily assigned) boundaries and how these may influence ecological hypotheses.},
  copyright = {{\copyright} 2023 The Authors. Methods in Ecology and Evolution published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {ecological networks,network embedding,network macroecology,transfer learning},
  file = {/Users/tanyastrydom/Zotero/storage/DQCWBZ23/Strydom et al. - 2023 - Graph embedding and transfer learning can help pre.pdf;/Users/tanyastrydom/Zotero/storage/GBBDKZNN/2041-210X.html}
}

@article{strydomRoadmapPredictingSpecies2021,
  title = {A Roadmap towards Predicting Species Interaction Networks (across Space and Time)},
  author = {Strydom, Tanya and Catchen, Michael D. and Banville, Francis and Caron, Dominique and Dansereau, Gabriel and {Desjardins-Proulx}, Philippe and {Forero-Mu{\~n}oz}, Norma R. and Higino, Gracielle and Mercier, Benjamin and Gonzalez, Andrew and Gravel, Dominique and Pollock, Laura and Poisot, Timoth{\'e}e},
  year = {2021},
  month = nov,
  journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
  volume = {376},
  number = {1837},
  pages = {20210063},
  publisher = {Royal Society},
  doi = {10.1098/rstb.2021.0063},
  urldate = {2021-10-21},
  abstract = {Networks of species interactions underpin numerous ecosystem processes, but comprehensively sampling these interactions is difficult. Interactions intrinsically vary across space and time, and given the number of species that compose ecological communities, it can be tough to distinguish between a true negative (where two species never interact) from a false negative (where two species have not been observed interacting even though they actually do). Assessing the likelihood of interactions between species is an imperative for several fields of ecology. This means that to predict interactions between species---and to describe the structure, variation, and change of the ecological networks they form---we need to rely on modelling tools. Here, we provide a proof-of-concept, where we show how a simple neural network model makes accurate predictions about species interactions given limited data. We then assess the challenges and opportunities associated with improving interaction predictions, and provide a conceptual roadmap forward towards predictive models of ecological networks that is explicitly spatial and temporal. We conclude with a brief primer on the relevant methods and tools needed to start building these models, which we hope will guide this research programme forward. This article is part of the theme issue `Infectious disease macroecology: parasite diversity and dynamics across the globe'.},
  copyright = {All rights reserved},
  keywords = {biogeography,deep learning,ecological forecasting,ecological networks,machine learning}
}

@article{strydomSVDEntropyReveals2021,
  title = {{{SVD Entropy Reveals}} the {{High Complexity}} of {{Ecological Networks}}},
  author = {Strydom, Tanya and Dalla Riva, Giulio V. and Poisot, Timoth{\'e}e},
  year = {2021},
  journal = {Frontiers in Ecology and Evolution},
  volume = {9},
  publisher = {Frontiers},
  issn = {2296-701X},
  doi = {10.3389/fevo.2021.623141},
  urldate = {2021-06-18},
  abstract = {Quantifying the complexity of ecological networks has remained elusive. Primarily, complexity has been defined on the basis of the structural (or behavioural) complexity of the system. These definitions ignore the notion of 'physical complexity', which can measure the amount of information contained in an ecological network, and how difficult it would be to compress. We present relative rank deficiency and SVD entropy as measures of 'external' and 'internal' complexity respectively. Using bipartite ecological networks, we find that they all show a very high, almost maximal, physical complexity. Pollination networks, in particular, are more complex when compared to other types of interactions. In addition, we find that SVD entropy relates to other structural measures of complexity (nestedness, connectance, and spectral radius), but does not inform about the resilience of a network when using simulated extinction cascades, which has previously been reported for structural measures of complexity. We argue that SVD entropy provides a fundamentally more 'correct' measure of network complexity and should be added to the toolkit of descriptors of ecological networks moving forward.},
  copyright = {All rights reserved},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/7CNY28YW/Strydom et al. - 2021 - SVD Entropy Reveals the High Complexity of Ecologi.pdf}
}

@article{terryFindingMissingLinks2020,
  title = {Finding Missing Links in Interaction Networks},
  author = {Terry, J. Christopher D. and Lewis, Owen T.},
  year = {2020},
  journal = {Ecology},
  volume = {101},
  number = {7},
  pages = {e03047},
  issn = {1939-9170},
  doi = {10.1002/ecy.3047},
  urldate = {2024-06-04},
  abstract = {Documenting which species interact within ecological communities is challenging and labor intensive. As a result, many interactions remain unrecorded, potentially distorting our understanding of network structure and dynamics. We test the utility of four structural models and a new coverage-deficit model for predicting missing links in both simulated and empirical bipartite networks. We find they can perform well, although the predictive power of structural models varies with the underlying network structure. The accuracy of predictions can be improved by ensembling multiple models. Augmenting observed networks with most-likely missing links improves estimates of qualitative network metrics. Tools to identify likely missing links can be simple to implement, allowing the prioritization of research effort and more robust assessment of network properties.},
  copyright = {{\copyright} 2020 The Authors. Ecology published by Wiley Periodicals LLC on behalf of Ecological Society of America.},
  langid = {english},
  keywords = {bipartite network,ecological network,missing links,network metrics,prediction,sampling completeness},
  file = {/Users/tanyastrydom/Zotero/storage/KZKGW2G3/Terry and Lewis - 2020 - Finding missing links in interaction networks.pdf;/Users/tanyastrydom/Zotero/storage/WKFBN4QR/ecy.html}
}

@incollection{thanosAristotleTheophrastusPlantanimal1994,
  title = {Aristotle and {{Theophrastus}} on Plant-Animal Interactions},
  booktitle = {Plant-Animal Interactions in {{Mediterranean-type}} Ecosystems},
  author = {Thanos, Costas A.},
  editor = {Arianoutsou, M. and Groves, R. H.},
  year = {1994},
  pages = {3--11},
  publisher = {Springer Netherlands},
  address = {Dordrecht},
  doi = {10.1007/978-94-011-0908-6_1},
  urldate = {2024-04-25},
  abstract = {Aristotle and Theophrastus, the last great philosophers and scientists of Greek Classical Antiquity, are the founding fathers of Zoology and Botany, respectively; they should also be honoured as the co-founders of Biology. They were close friends and life-long collaborators who evidently decided to pursue an organized study of the living world, probably in Lesbos at 344 BC (the landmark for the creation of the Science of Biology). The product of their division of labour, the voluminous zoological and botanical works of Aristotle and Theophrastus, respectively, were actually used contemporaneously as university textbooks by the students of the Lyceum.},
  isbn = {978-94-011-0908-6},
  langid = {english}
}

@article{thompsonFoodWebsReconciling2012,
  title = {Food Webs: Reconciling the Structure and Function of Biodiversity},
  author = {Thompson, Ross M. and Brose, Ulrich and Dunne, Jennifer and Hall, Robert O. and Hladyz, Sally and Kitching, Roger L. and Martinez, Neo D. and Rantala, Heidi and Romanuk, Tamara N. and Stouffer, Daniel B. and Tylianakis, Jason M.},
  year = {2012},
  month = dec,
  journal = {Trends in Ecology \& Evolution},
  volume = {27},
  number = {12},
  pages = {689--697},
  doi = {10.1016/j.tree.2012.08.005}
}

@article{thuillerNavigatingIntegrationBiotic2024,
  title = {Navigating the Integration of Biotic Interactions in Biogeography},
  author = {Thuiller, Wilfried and {Calder{\'o}n-Sanou}, Irene and Chalmandrier, Lo{\"i}c and Ga{\"u}z{\`e}re, Pierre and O'Connor, Louise M. J. and Ohlmann, Marc and Poggiato, Giovanni and M{\"u}nkem{\"u}ller, Tamara},
  year = {2024},
  journal = {Journal of Biogeography},
  volume = {51},
  number = {4},
  pages = {550--559},
  issn = {1365-2699},
  doi = {10.1111/jbi.14734},
  urldate = {2024-04-16},
  abstract = {Biotic interactions are widely recognised as the backbone of ecological communities, but how best to study them is a subject of intense debate, especially at macro-ecological scales. While some researchers claim that biotic interactions need to be observed directly, others use proxies and statistical approaches to infer them. Despite this ambiguity, studying and predicting the influence of biotic interactions on biogeographic patterns is a thriving area of research with crucial implications for conservation. Three distinct approaches are currently being explored. The first approach involves empirical observation and measurement of biotic interactions' effects on species demography in laboratory or field settings. While these findings contribute to theory and to understanding species' demographies, they can be challenging to generalise on a larger scale. The second approach centers on inferring biotic associations from observed co-occurrences in space and time. The goal is to distinguish the environmental and biotic effects on species distributions. The third approach constructs extensive potential interaction networks, known as metanetworks, by leveraging existing knowledge about species ecology and interactions. This approach analyses local realisations of these networks using occurrence data and allows understanding large distributions of multi-taxa assemblages. In this piece, we appraise these three approaches, highlighting their respective strengths and limitations. Instead of seeing them as conflicting, we advocate for their integration to enhance our understanding and expand applications in the emerging field of interaction biogeography. This integration shows promise for ecosystem understanding and management in the Anthropocene era.},
  copyright = {{\copyright} 2023 The Authors. Journal of Biogeography published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {biodiversity models,biodiversity patterns,biogeography,biotic interactions,networks,species distributions},
  file = {/Users/tanyastrydom/Zotero/storage/U9I4XX9L/Thuiller et al. - 2024 - Navigating the integration of biotic interactions .pdf;/Users/tanyastrydom/Zotero/storage/U3MRM9GJ/jbi.html}
}

@article{tredennickPracticalGuideSelecting2021,
  title = {A Practical Guide to Selecting Models for Exploration, Inference, and Prediction in Ecology},
  author = {Tredennick, Andrew T. and Hooker, Giles and Ellner, Stephen P. and Adler, Peter B.},
  year = {2021},
  journal = {Ecology},
  volume = {102},
  number = {6},
  pages = {e03336},
  issn = {1939-9170},
  doi = {10.1002/ecy.3336},
  urldate = {2024-03-05},
  abstract = {Selecting among competing statistical models is a core challenge in science. However, the many possible approaches and techniques for model selection, and the conflicting recommendations for their use, can be confusing. We contend that much confusion surrounding statistical model selection results from failing to first clearly specify the purpose of the analysis. We argue that there are three distinct goals for statistical modeling in ecology: data exploration, inference, and prediction. Once the modeling goal is clearly articulated, an appropriate model selection procedure is easier to identify. We review model selection approaches and highlight their strengths and weaknesses relative to each of the three modeling goals. We then present examples of modeling for exploration, inference, and prediction using a time series of butterfly population counts. These show how a model selection approach flows naturally from the modeling goal, leading to different models selected for different purposes, even with exactly the same data set. This review illustrates best practices for ecologists and should serve as a reminder that statistical recipes cannot substitute for critical thinking or for the use of independent data to test hypotheses and validate predictions.},
  copyright = {{\copyright} 2021 The Authors. Ecology published by Wiley Periodicals LLC on behalf of Ecological Society of America.},
  langid = {english},
  keywords = {model selection,prediction,validation,variable selection},
  file = {/Users/tanyastrydom/Zotero/storage/QESZ9CIS/Tredennick et al. - 2021 - A practical guide to selecting models for explorat.pdf;/Users/tanyastrydom/Zotero/storage/ZXWWEQS4/ecy.html}
}

@article{uphamInferringMammalTree2019,
  title = {Inferring the Mammal Tree: {{Species-level}} Sets of Phylogenies for Questions in Ecology, Evolution, and Conservation},
  shorttitle = {Inferring the Mammal Tree},
  author = {Upham, Nathan S. and Esselstyn, Jacob A. and Jetz, Walter},
  year = {2019},
  month = dec,
  journal = {PLOS Biology},
  volume = {17},
  number = {12},
  pages = {e3000494},
  publisher = {Public Library of Science},
  issn = {1545-7885},
  doi = {10.1371/journal.pbio.3000494},
  urldate = {2021-05-05},
  abstract = {Big, time-scaled phylogenies are fundamental to connecting evolutionary processes to modern biodiversity patterns. Yet inferring reliable phylogenetic trees for thousands of species involves numerous trade-offs that have limited their utility to comparative biologists. To establish a robust evolutionary timescale for all approximately 6,000 living species of mammals, we developed credible sets of trees that capture root-to-tip uncertainty in topology and divergence times. Our ``backbone-and-patch'' approach to tree building applies a newly assembled 31-gene supermatrix to two levels of Bayesian inference: (1) backbone relationships and ages among major lineages, using fossil node or tip dating, and (2) species-level ``patch'' phylogenies with nonoverlapping in-groups that each correspond to one representative lineage in the backbone. Species unsampled for DNA are either excluded (``DNA-only'' trees) or imputed within taxonomic constraints using branch lengths drawn from local birth--death models (``completed'' trees). Joining time-scaled patches to backbones results in species-level trees of extant Mammalia with all branches estimated under the same modeling framework, thereby facilitating rate comparisons among lineages as disparate as marsupials and placentals. We compare our phylogenetic trees to previous estimates of mammal-wide phylogeny and divergence times, finding that (1) node ages are broadly concordant among studies, and (2) recent (tip-level) rates of speciation are estimated more accurately in our study than in previous ``supertree'' approaches, in which unresolved nodes led to branch-length artifacts. Credible sets of mammalian phylogenetic history are now available for download at http://vertlife.org/phylosubsets, enabling investigations of long-standing questions in comparative biology.},
  langid = {english},
  keywords = {Animal phylogenetics,Fossil calibration,Fossils,Mammals,Paleogenetics,Paleozoology,Phylogenetic analysis,Taxonomy},
  file = {/Users/tanyastrydom/Zotero/storage/NCS3EFN5/Upham et al. - 2019 - Inferring the mammal tree Species-level sets of p.pdf;/Users/tanyastrydom/Zotero/storage/VW98RJTP/article.html}
}

@article{valdovinosBioenergeticFrameworkAboveground2023,
  title = {A Bioenergetic Framework for Aboveground Terrestrial Food Webs},
  author = {Valdovinos, Fernanda S. and Hale, Kayla R. S. and Dritz, Sabine and Glaum, Paul R. and McCann, Kevin S. and Simon, Sophia M. and Th{\'e}bault, Elisa and Wetzel, William C. and Wootton, Kate L. and Yeakel, Justin D.},
  year = {2023},
  month = mar,
  journal = {Trends in Ecology \& Evolution},
  volume = {38},
  number = {3},
  pages = {301--312},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2022.11.004},
  urldate = {2024-01-19},
  abstract = {Bioenergetic approaches have been greatly influential for understanding community functioning and stability and predicting effects of environmental changes on biodiversity. These approaches use allometric relationships to establish species' trophic interactions and consumption rates and have been successfully applied to aquatic ecosystems. Terrestrial ecosystems, where body mass is less predictive of plant--consumer interactions, present inherent challenges that these models have yet to meet. Here, we discuss the processes governing terrestrial plant--consumer interactions and develop a bioenergetic framework integrating those processes. Our framework integrates bioenergetics specific to terrestrial plants and their consumers within a food web approach while also considering mutualistic interactions. Such a framework is poised to advance our understanding of terrestrial food webs and to predict their responses to environmental changes.},
  keywords = {consumer-resource,fast-slow continuum,plant defenses,plant mutualists,stage structure,tissue-specialist herbivores},
  file = {/Users/tanyastrydom/Zotero/storage/Y4DWQ6XM/S0169534722002841.html}
}

@article{vandermeerNicheTheory1972,
  title = {Niche {{Theory}}},
  author = {Vandermeer, John H.},
  year = {1972},
  month = nov,
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {3},
  number = {Volume 3, 1972},
  pages = {107--132},
  publisher = {Annual Reviews},
  issn = {1543-592X, 1545-2069},
  doi = {10.1146/annurev.es.03.110172.000543},
  urldate = {2024-11-22},
  langid = {english},
  file = {/Users/tanyastrydom/Zotero/storage/6SRH8YBD/annurev.es.03.110172.html}
}

@article{vandewalleArthropodFoodWebs2023,
  title = {Arthropod Food Webs Predicted from Body Size Ratios Are Improved by Incorporating Prey Defensive Properties},
  author = {Van De Walle, Ruben and Logghe, Garben and Haas, Nina and Massol, Fran{\c c}ois and Vandegehuchte, Martijn L. and Bonte, Dries},
  year = {2023},
  journal = {Journal of Animal Ecology},
  volume = {92},
  number = {4},
  pages = {913--924},
  issn = {1365-2656},
  doi = {10.1111/1365-2656.13905},
  urldate = {2024-09-30},
  abstract = {Trophic interactions are often deduced from body size differences, assuming that predators prefer prey smaller than themselves because larger prey are more difficult to subdue. This has mainly been confirmed in aquatic ecosystems, but rarely in terrestrial ecosystems, especially in arthropods. Our goal was to validate whether body size ratios can predict trophic interactions in a terrestrial, plant-associated arthropod community and whether predator hunting strategy and prey taxonomy could explain additional variation. We conducted feeding trials with arthropods from marram grass in coastal dunes to test whether two individuals, of the same or different species, would predate each other. From the trial results, we constructed one of the most complete, empirically derived food webs for terrestrial arthropods associated with a single plant species. We contrasted this empirical food web with a theoretical web based on body size ratios, activity period, microhabitat, and expert knowledge. In our feeding trials, predator--prey interactions were indeed largely size-based. Moreover, the theoretical and empirically based food webs converged well for both predator and prey species. However, predator hunting strategy, and especially prey taxonomy improved predictions of predation. Well-defended taxa, such as hard-bodied beetles, were less frequently consumed than expected based on their body size. For instance, a beetle of average size (measuring 4 mm) is 38\% less vulnerable than another average arthropod with the same length. Body size ratios predict trophic interactions among plant-associated arthropods fairly well. However, traits such as hunting strategy and anti-predator defences can explain why certain trophic interactions do not adhere to size-based rules. Feeding trials can generate insights into multiple traits underlying real-life trophic interactions among arthropods.},
  copyright = {{\copyright} 2023 The Authors. Journal of Animal Ecology published by John Wiley \& Sons Ltd on behalf of British Ecological Society.},
  langid = {english},
  keywords = {feeding trials,hunting strategy,insects,invertebrate community,marram grass dunes,trophic interactions}
}

@article{vazquezUnitingPatternProcess2009,
  title = {Uniting Pattern and Process in Plant--Animal Mutualistic Networks: A Review},
  shorttitle = {Uniting Pattern and Process in Plant--Animal Mutualistic Networks},
  author = {V{\'a}zquez, Diego P. and Bl{\"u}thgen, Nico and Cagnolo, Luciano and Chacoff, Natacha P.},
  year = {2009},
  month = jun,
  journal = {Annals of Botany},
  volume = {103},
  number = {9},
  pages = {1445--1457},
  issn = {0305-7364},
  doi = {10.1093/aob/mcp057},
  urldate = {2024-09-16},
  abstract = {Ecologists and evolutionary biologists are becoming increasingly interested in networks as a framework to study plant--animal mutualisms within their ecological context. Although such focus on networks has brought about important insights into the structure of these interactions, relatively little is still known about the mechanisms behind these patterns.The aim in this paper is to offer an overview of the mechanisms influencing the structure of plant--animal mutualistic networks. A brief summary is presented of the salient network patterns, the potential mechanisms are discussed and the studies that have evaluated them are reviewed. This review shows that researchers of plant--animal mutualisms have made substantial progress in the understanding of the processes behind the patterns observed in mutualistic networks. At the same time, we are still far from a thorough, integrative mechanistic understanding. We close with specific suggestions for directions of future research, which include developing methods to evaluate the relative importance of mechanisms influencing network patterns and focusing research efforts on selected representative study systems throughout the world.},
  file = {/Users/tanyastrydom/Zotero/storage/UITY9IDI/Vázquez et al. - 2009 - Uniting pattern and process in plant–animal mutual.pdf;/Users/tanyastrydom/Zotero/storage/ATQTHK8A/146155.html}
}

@article{vermaatMajorDimensionsFoodweb2009,
  title = {Major Dimensions in Food-Web Structure Properties.},
  author = {Vermaat, Jan E and Dunne, Jennifer A and Gilbert, Alison J},
  year = {2009},
  journal = {Ecology},
  volume = {90},
  number = {1},
  eprint = {19294932},
  eprinttype = {pubmed},
  pages = {278--282}
}

@article{wellsSpeciesInteractionsEstimating2013,
  title = {Species Interactions: Estimating per-Individual Interaction Strength and Covariates before Simplifying Data into per-Species Ecological Networks},
  shorttitle = {Species Interactions},
  author = {Wells, Konstans and O'Hara, Robert B.},
  year = {2013},
  journal = {Methods in Ecology and Evolution},
  volume = {4},
  number = {1},
  pages = {1--8},
  issn = {2041-210X},
  doi = {10.1111/j.2041-210x.2012.00249.x},
  urldate = {2024-09-30},
  abstract = {Ecological network models based on aggregated data from species interactions are widely used to make inferences about species specialization, functionality and extinction risk. While increasing number of network data are available and are used in comparative studies, data quality and uncertainty have received little attention. Moreover, key individual-level information such as the proportion of individuals not involved in interactions and underlying processes driving interactions are ignored by aggregated data analysis. We suggest an individual-level hierarchical interaction model as a more flexible approach to considering uncertainty, sampling effort and conditions under which interactions take place and from which network attributes can be derived. We performed a simulation exercise to compare inference under different sample sizes and from aggregated data matrices to those from our individual-level model. Formalizing the process of network formation in an individual-level model made clear that per-species interaction frequencies are not independent of sample size and population pools and also ignore important information given by the proportion of non-interacting individuals. Hierarchical linear models are a possible solution to infer community-level attributes of network formation and allow various kinds of comprehensive model extensions to capture variation of per-individual interactions in space and time that shape upper level organization. Individual-level hierarchical models provide the link between individual behaviour and interactions under variable environmental conditions and can be summarized into networks in a conceptually neat way. Such models may not only help to account for various sources of variation but also conceptualize aspects overlooked in aggregated data. In particular, the quantification of per-individual interactions under different sampling scenarios emphasizes that per-species interaction frequencies at the species level are not necessarily a surrogate of species abundance in natural systems under investigation.},
  copyright = {{\copyright} 2012 The Authors. Methods in Ecology and Evolution {\copyright} 2012 British Ecological Society},
  langid = {english},
  keywords = {biotic interactions,ecological fallacy,ecological networks,hierarchical models,random graphs,species specialization},
  file = {/Users/tanyastrydom/Zotero/storage/3J5ELN9N/j.2041-210x.2012.00249.html}
}

@article{whiteRelationshipsBodySize2007,
  title = {Relationships between Body Size and Abundance in Ecology},
  author = {White, Ethan P. and Ernest, S. K. Morgan and Kerkhoff, Andrew J. and Enquist, Brian J.},
  year = {2007},
  month = jun,
  journal = {Trends in Ecology \& Evolution},
  volume = {22},
  number = {6},
  pages = {323--330},
  issn = {0169-5347},
  doi = {10.1016/j.tree.2007.03.007},
  urldate = {2024-10-29},
  abstract = {Body size is perhaps the most fundamental property of an organism and is related to many biological traits, including abundance. The relationship between abundance and body size has been extensively studied in an attempt to quantify the form of the relationship and to understand the processes that generate it. However, progress has been impeded by the underappreciated fact that there are four distinct, but interrelated, relationships between size and abundance that are often confused in the literature. Here, we review and distinguish between these four patterns, and discuss the linkages between them. We argue that a synthetic understanding of size--abundance relationships will result from more detailed analyses of individual patterns and from careful consideration of how and why the patterns are related.},
  file = {/Users/tanyastrydom/Zotero/storage/66DXE6PT/S0169534707000985.html}
}

@article{williamsProbabilisticNicheModel2010,
  title = {The {{Probabilistic Niche Model Reveals}} the {{Niche Structure}} and {{Role}} of {{Body Size}} in a {{Complex Food Web}}},
  author = {Williams, Richard J. and Anandanadesan, Ananthi and Purves, Drew},
  year = {2010},
  month = aug,
  journal = {PLOS ONE},
  volume = {5},
  number = {8},
  pages = {e12092},
  publisher = {Public Library of Science},
  issn = {1932-6203},
  doi = {10.1371/journal.pone.0012092},
  urldate = {2024-03-27},
  abstract = {The niche model has been widely used to model the structure of complex food webs, and yet the ecological meaning of the single niche dimension has not been explored. In the niche model, each species has three traits, niche position, diet position and feeding range. Here, a new probabilistic niche model, which allows the maximum likelihood set of trait values to be estimated for each species, is applied to the food web of the Benguela fishery. We also developed the allometric niche model, in which body size is used as the niche dimension. About 80\% of the links in the empirical data are predicted by the probabilistic niche model, a significant improvement over recent models. As in the niche model, species are uniformly distributed on the niche axis. Feeding ranges are exponentially distributed, but diet positions are not uniformly distributed below the predator. Species traits are strongly correlated with body size, but the allometric niche model performs significantly worse than the probabilistic niche model. The best-fit parameter set provides a significantly better model of the structure of the Benguela food web than was previously available. The methodology allows the identification of a number of taxa that stand out as outliers either in the model's poor performance at predicting their predators or prey or in their parameter values. While important, body size alone does not explain the structure of the one-dimensional niche.},
  langid = {english},
  keywords = {Diet,Ecological niches,Food,Food web structure,Physiological parameters,Predation,Sharks,Trophic interactions},
  file = {/Users/tanyastrydom/Zotero/storage/J6UM8TGI/Williams et al. - 2010 - The Probabilistic Niche Model Reveals the Niche St.pdf}
}

@article{williamsSimpleRulesYield2000,
  title = {Simple Rules Yield Complex Food Webs},
  author = {Williams, Richard J. and Martinez, Neo D.},
  year = {2000},
  month = mar,
  journal = {Nature},
  volume = {404},
  number = {6774},
  pages = {180--183},
  publisher = {Nature Publishing Group},
  issn = {1476-4687},
  doi = {10.1038/35004572},
  urldate = {2024-02-01},
  abstract = {Several of the most ambitious theories in ecology1,2,3,4,5,6,7,8,9,10,11,12,13,14 describe food webs that document the structure of strong and weak trophic links9 that is responsible for ecological dynamics among diverse assemblages of species4,11,12,13. Early mechanism-based theory asserted that food webs have little omnivory and several properties that are independent of species richness1,2,3,4,6. This theory was overturned by empirical studies that found food webs to be much more complex5,7,8,9,14,15,16,17,18, but these studies did not provide mechanistic explanations for the complexity9. Here we show that a remarkably simple model fills this scientific void by successfully predicting key structural properties of the most complex and comprehensive food webs in the primary literature. These properties include the fractions of species at top, intermediate and basal trophic levels, the means and variabilities of generality, vulnerability and food-chain length, and the degrees of cannibalism, omnivory, looping and trophic similarity. Using only two empirical parameters, species number and connectance, our `niche model' extends the existing `cascade model'3,19 and improves its fit ten-fold by constraining species to consume a contiguous sequence of prey in a one-dimensional trophic niche20.},
  copyright = {2000 Macmillan Magazines Ltd.},
  langid = {english},
  keywords = {Humanities and Social Sciences,multidisciplinary,Science},
  file = {/Users/tanyastrydom/Zotero/storage/9AGQJAV4/Williams and Martinez - 2000 - Simple rules yield complex food webs.pdf}
}

@article{williamsSuccessItsLimits2008,
  title = {Success and Its Limits among Structural Models of Complex Food Webs},
  author = {Williams, Richard J. and Martinez, Neo D.},
  year = {2008},
  journal = {Journal of Animal Ecology},
  volume = {77},
  number = {3},
  pages = {512--519},
  issn = {1365-2656},
  doi = {10.1111/j.1365-2656.2008.01362.x},
  urldate = {2024-02-13},
  abstract = {1 Following the development of the relatively successful niche model, several other simple structural food web models have been proposed. These models predict the detailed structure of complex food webs given only two input parameters, the numbers of species and the number of feeding links among them. 2 The models claim different degrees of success but have not been compared consistently with each other or with the empirical data. We compared the performance of five structural models rigorously against 10 empirical food webs from a variety of aquatic and terrestrial habitats containing 25--92 species and 68--997 links. 3 All models include near-hierarchical ordering of species' consumption and have identical distributions of the number of prey of each consumer species, but differ in the extent to which species' diets are required to be contiguous and the rules used to assign feeding links. 4 The models perform similarly on a range of food-web properties, including the fraction of top, intermediate and basal species, the standard deviations of generality and connectivity and the fraction of herbivores and omnivores. 5 For other properties, including the standard deviation of vulnerability, the fraction of cannibals and species in loops, mean trophic level, path length, clustering coefficient, maximum similarity and diet discontinuity, there are significant differences in the performance of the different models. 6 While the empirical data do not support the niche model's assumption of diet contiguity, models which relax this assumption all have worse overall performance than the niche model. All the models under-estimate severely the fraction of species that are herbivores and exhibit other important failures that need to be addressed in future research.},
  langid = {english},
  keywords = {ecological networks,intervality,network structure,predator-prey network},
  file = {/Users/tanyastrydom/Zotero/storage/L5Y8QUY3/Williams and Martinez - 2008 - Success and its limits among structural models of .pdf;/Users/tanyastrydom/Zotero/storage/T6BDDGG8/j.1365-2656.2008.01362.html}
}

@article{wilmanEltonTraitsSpecieslevelForaging2014,
  title = {{{EltonTraits}} 1.0: {{Species-level}} Foraging Attributes of the World's Birds and Mammals},
  shorttitle = {{{EltonTraits}} 1.0},
  author = {Wilman, Hamish and Belmaker, Jonathan and Simpson, Jennifer and {de la Rosa}, Carolina and Rivadeneira, Marcelo M. and Jetz, Walter},
  year = {2014},
  journal = {Ecology},
  volume = {95},
  number = {7},
  pages = {2027--2027},
  issn = {1939-9170},
  doi = {10.1890/13-1917.1},
  urldate = {2024-02-08},
  abstract = {Species are characterized by physiological, behavioral, and ecological attributes that are all subject to varying evolutionary and ecological constraints and jointly determine species' role and function in ecosystems. Attributes such as diet, foraging strata, foraging time, and body size, in particular, characterize a large portion of the ``Eltonian'' niches of species. Here we present a global species-level compilation of these key attributes for all 9993 and 5400 extant bird and mammal species derived from key literature sources. Global handbooks and monographs allowed the consistent sourcing of attributes for most species. For diet and foraging stratum we followed a defined protocol to translate the verbal descriptions into standardized, semiquantitative information about relative importance of different categories. Together with body size (continuous) and activity time (categorical) this enables a much finer distinction of species' foraging ecology than typical categorical guild assignments allow. Attributes lacking information for specific species are flagged, and interpolated values based on taxonomy are provided instead. The presented data set is limited by, among others, these select cases missing observed data, by errors and uncertainty in the expert assessment as presented in the literature, and by the lack of intraspecific information. However, the standardized and transparent nature and complete global coverage of the data set should support an array of potential studies in biogeography, community ecology, macroevolution, global change biology, and conservation. Potential uses include comparative work involving these traits as focal or secondary variables, ecological research on the trait or trophic structure of communities, or conservation science concerned with the loss of function among species or in ecosystems in a changing world. We hope that this publication will spur the sharing, collaborative curation, and extension of data to the benefit of a more integrative, rigorous, and global biodiversity science.},
  copyright = {{\copyright} 2014 by the Ecological Society of America},
  langid = {english},
  keywords = {bird,body size,diet,eco-informatics,foraging,function,mammal,mass,niche,stratum,traits,vertebrate},
  file = {/Users/tanyastrydom/Zotero/storage/KSCFSFDQ/Wilman et al. - 2014 - EltonTraits 1.0 Species-level foraging attributes.pdf;/Users/tanyastrydom/Zotero/storage/7GQI5YJ8/13-1917.html}
}

@article{windsorUsingEcologicalNetworks2023,
  title = {Using Ecological Networks to Answer Questions in Global Biogeography and Ecology},
  author = {Windsor, Fredric M. and {van den Hoogen}, Johan and Crowther, Thomas W. and Evans, Darren M.},
  year = {2023},
  journal = {Journal of Biogeography},
  volume = {50},
  number = {1},
  pages = {57--69},
  issn = {1365-2699},
  doi = {10.1111/jbi.14447},
  urldate = {2024-04-22},
  abstract = {Ecological networks have classically been studied at site and landscape scales, yet recent efforts have been made to collate these data into global repositories. This offers an opportunity to integrate and upscale knowledge about ecological interactions from local to global scales to gain enhanced insights from the mechanistic information provided by these data. By drawing on existing research investigating patterns in ecological interactions at continental to global scales, we show how data on ecological networks, collected at appropriate scales, can be used to generate an improved understanding of many aspects of ecology and biogeography---for example, species distribution modelling, restoration ecology and conservation. We argue that by understanding the patterns in the structure and function of ecological networks across scales, it is possible to enhance our understanding of the natural world.},
  copyright = {{\copyright} 2022 The Authors. Journal of Biogeography published by John Wiley \& Sons Ltd.},
  langid = {english},
  keywords = {biodiversity,biomonitoring,conservation,ecological functioning,ecological interactions,macroecology,network ecology,restoration},
  file = {/Users/tanyastrydom/Zotero/storage/2RWFRHTE/jbi.html}
}

@article{woosterAustraliasRecentlyEstablished2024,
  title = {Australia's Recently Established Predators Restore Complexity to Food Webs Simplified by Extinction},
  author = {Wooster, Eamonn I. F. and Middleton, Owen S. and Wallach, Arian D. and Ramp, Daniel and Sanisidro, Oscar and Harris, Valerie K. and Rowan, John and Schowanek, Simon D. and Gordon, Chris E. and Svenning, Jens-Christian and Davis, Matt and Scharlemann, J{\"o}rn P. W. and Nimmo, Dale G. and Lundgren, Erick J. and Sandom, Christopher J.},
  year = {2024},
  month = nov,
  journal = {Current Biology},
  volume = {34},
  number = {22},
  pages = {5164-5172.e2},
  publisher = {Elsevier},
  issn = {0960-9822},
  doi = {10.1016/j.cub.2024.09.049},
  urldate = {2024-11-22},
  langid = {english},
  pmid = {39389058}
}

@article{woottonMeasurementInteractionStrength2005,
  title = {Measurement of {{Interaction Strength}} in {{Nature}}},
  author = {Wootton, J. Timothy and Emmerson, Mark},
  year = {2005},
  journal = {Annual Review of Ecology, Evolution, and Systematics},
  volume = {36},
  number = {1},
  pages = {419--444},
  doi = {10.1146/annurev.ecolsys.36.091704.175535},
  urldate = {2020-11-16},
  abstract = {Understanding and predicting the dynamics of multispecies systems generally require estimates of interaction strength among species. Measuring interaction strength is difficult because of the large number of interactions in any natural system, long-term feedback, multiple pathways of effects between species pairs, and possible nonlinearities in interaction-strength functions. Presently, the few studies that extensively estimate interaction strength suggest that distributions of interaction strength tend to be skewed toward few strong and many weak interactions. Modeling studies indicate that such skewed patterns tend to promote system stability and arise during assembly of persistent communities. Methods for estimating interaction strength efficiently from traits of organisms, such as allometric relationships, show some promise. Methods for estimating community response to environmental perturbations without an estimate of interaction strength may also be of use. Spatial and temporal scale may affect patterns of interaction strength, but these effects require further investigation and new multispecies modeling frameworks. Future progress will be aided by development of long-term multispecies time series of natural communities, by experimental tests of different methods for estimating interaction strength, and by increased understanding of nonlinear functional forms.}
}

@article{woottonModularTheoryTrophic2023,
  title = {Towards a Modular Theory of Trophic Interactions},
  author = {Wootton, Kate L. and Curtsdotter, Alva and Roslin, Tomas and Bommarco, Riccardo and Jonsson, Tomas},
  year = {2023},
  journal = {Functional Ecology},
  volume = {37},
  number = {1},
  pages = {26--43},
  issn = {1365-2435},
  doi = {10.1111/1365-2435.13954},
  urldate = {2024-06-20},
  abstract = {Species traits and environmental conditions determine the occurrence and strength of trophic interactions. If we understand the relationship between these factors and trophic interactions, we can make more accurate predictions and build better trophic-interaction models. We can compare traits and conditions by considering their effect on different parts (steps) of a trophic interaction, such as the steps search and pursuit. By linking traits to relevant steps, we can use these relationships to build trophic-interaction models. Currently, this is done ad hoc, defining steps based on the species and traits of interest. This makes it difficult to compare across traits and species and gain an overarching understanding of how traits and the environment drive trophic interactions. We present a comprehensive approach for the explicit choice of interaction steps and species traits or environmental conditions, which is readily integrated into existing models. The core of this framework is that it is modular; we present eight steps that occur in all trophic interactions and use them to build a modular, general dynamic model. When applying the framework, one explicitly selects only the most relevant steps and uses those to build a specific model. To build our modular framework, we revisit and expand the functional and numerical response functions, dividing the trophic interaction into eight steps: (1) search, (2) prey detection, (3) attack decision, (4) pursuit, (5) subjugation, (6) ingestion, (7) digestion and (8) nutrient allocation. Together these steps form a general dynamical model where trophic interactions can be explicitly parameterized for multiple traits and environmental factors. We then concretize this approach by outlining how a specific community can be modelled by selecting key modules (steps) and parameterizing them for relevant factors. This we exemplify for a community of terrestrial arthropods using empirical data on body size and temperature responses. With species interactions at the core of community dynamics, our modular approach allows for quantification and comparisons of the importance of different steps, traits, and abiotic factors across ecosystems and trophic-interaction types, and provides a powerful tool for trait-based prediction of food-web structure and dynamics. A free Plain Language Summary can be found within the Supporting Information of this article.},
  copyright = {{\copyright} 2021 British Ecological Society},
  langid = {english},
  keywords = {body size,ecological interaction networks,food webs,functional response,numerical response,predator-prey interactions,trait-based approaches}
}

@article{xieCompletenessCommunityStructure2017,
  title = {Completeness of {{Community Structure}} in {{Networks}}},
  author = {Xie, Jia-Rong and Zhang, Pan and Zhang, Hai-Feng and Wang, Bing-Hong},
  year = {2017},
  month = jul,
  journal = {Scientific Reports},
  volume = {7},
  number = {1},
  pages = {5269},
  publisher = {Nature Publishing Group},
  issn = {2045-2322},
  doi = {10.1038/s41598-017-05585-6},
  urldate = {2024-03-22},
  abstract = {By defining a new measure to community structure, exclusive modularity, and based on cavity method of statistical physics, we develop a mathematically principled method to determine the completeness of community structure, which represents whether a partition that is either annotated by experts or given by a community-detection algorithm, carries complete information about community structure in the network. Our results demonstrate that the expert partition is surprisingly incomplete in some networks such as the famous political blogs network, indicating that the relation between meta-data and community structure in real-world networks needs to be re-examined. As a byproduct we find that the exclusive modularity, which introduces a null model based on the degree-corrected stochastic block model, is of independent interest. We discuss its applications as principled ways of detecting hidden structures, finding hierarchical structures without removing edges, and obtaining low-dimensional embedding of networks.},
  copyright = {2017 The Author(s)},
  langid = {english},
  keywords = {Complex networks,Statistical physics},
  file = {/Users/tanyastrydom/Zotero/storage/82X2EILP/Xie et al. - 2017 - Completeness of Community Structure in Networks.pdf}
}

@article{yeakelCollapseEcologicalNetwork2014,
  title = {Collapse of an Ecological Network in {{Ancient Egypt}}},
  author = {Yeakel, Justin D. and Pires, Mathias M. and Rudolf, Lars and Dominy, Nathaniel J. and Koch, Paul L. and Guimar{\~a}es, Paulo R. and Gross, Thilo},
  year = {2014},
  month = oct,
  journal = {PNAS},
  volume = {111},
  number = {40},
  pages = {14472--14477},
  doi = {10.1073/pnas.1408471111},
  urldate = {2015-03-03}
}

@article{yodzisBodySizeConsumerResource1992,
  title = {Body {{Size}} and {{Consumer-Resource Dynamics}}},
  author = {Yodzis, P. and Innes, S.},
  year = {1992},
  month = jun,
  journal = {The American Naturalist},
  volume = {139},
  number = {6},
  pages = {1151--1175},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/285380},
  urldate = {2024-09-25},
  abstract = {"Plausible" consumer-resource models, based on energetic reasoning and allometric empiricism, are formulated and their dynamics investigated. Most of the parameters in these models are determined by the body sizes and metabolic categories (endotherm, vertebrate ectotherm, invertebrate ectotherm, or plant) of the populations in question. The remaining parameters have clear biological meanings. The intent of these models is to provide maximum realism from minimum data when needed because of constraints of time or of resources for empirical research. The models also demonstrate the influence of physiological power and body size on consumer-resource dynamics. For a given metabolic type, we define ecological scope as the ratio of maximal ingestion rate to respiration rate. Invertebrate ectotherms have greater ecological scope than do vertebrate ectotherms, which have greater scope than do endotherms. Greater ecological scope implies ability to subsist on scarcer resources and a tendency to more stable dynamics. Changes in dynamics associated with increased resource carrying capacity K (the "paradox of enrichment") are investigated. Robust limit cycles (sustained oscillations) require a Type III functional response, but even in this case the cycles quickly "implode" (with respect to changes in K) to extremely small densities if the resource-consumer body mass ratio is too high. Estimates are given for resource-consumer body mass ratios that permit robust limit cycles, and some pertinent data are discussed. An analytic expression for the periods of limit cycles is derived. In terms of body mass, the periods scale as (consumer body mass)1/8 (resource body mass)1/8.}
}

@article{yodzisCompartmentationRealAssembled1982,
  title = {The {{Compartmentation}} of {{Real}} and {{Assembled Ecosystems}}},
  author = {Yodzis, P.},
  year = {1982},
  month = nov,
  journal = {The American Naturalist},
  volume = {120},
  number = {5},
  pages = {551--570},
  publisher = {The University of Chicago Press},
  issn = {0003-0147},
  doi = {10.1086/284013},
  urldate = {2024-09-13},
  abstract = {The dominant cliques of graph theory provide an unambiguous compartmentation for ecosystems. It is suggested here that it might make ecological sense to regard dominant cliques as something like "trophic guilds." The dominant clique structure of 40 real food webs collected from the published literature by Briand (1982) is investigated. It is found that food webs from environments classifiedd by Briand as "fluctuating" differ significantly, both in number and in average size of dominant cliques, from food webs from environments classified by Briand as "constant." In addition, food web intervality (Cohen 1978) is found to be associated with poverty in dominant cliques. Adding constraints on dominant clique number to "assembled" food web models (Yodzis 1981) yields mathematical food web universes which predict the observed frequency of interval food webs.}
}