Code and data for 'Management thresholds shift under the influence of multiple stressors: Eelgrass meadows as a case study'
Citation: Dunic & Côté. 2023. Management thresholds shift under the influence of multiple stressors: Eelgrass meadows as a case study. Conservation Letters. https://doi.org/10.1111/conl.12938
This repository provides the code and data to reproduce the analysis and figures found in Dunic & Côté (2023).
As human activities increase in intensity and extent, ecosystems face growing threats from multiple stressors. Successful management requires identifying measurable targets, which is challenging because of data limitations, non-linear ecosystem responses, and potentially shifting targets under multiple stressors. To identify critical management values and determine whether these values shift in the presence of multiple stressors, we use eelgrass (Zostera marina) meadows as a model system. We reviewed 20 studies that measured the effects of light and temperature on eelgrass performance, providing 109 unique study-site-treatment combinations. We modelled the interactive effect of temperature and light on eelgrass population growth rate (i.e., lateral shoot production rates) using a hierarchical generalised additive model and predicted population growth rates across a range of light levels and temperatures. We found that two critical performance metrics of population growth, zero-growth and maximum growth rates, shifted across a gradient of light and temperature, suggesting that fixed management targets linked to population growth rates might be unsuitable for managing meadows under multiple stressors. Our approach bridges the gap between data from laboratory and field studies and could be developed into an interactive management tool.
Please reach out if you would like to discuss this more. I am hoping to continue work with this model and possibly write up a shiny app.