Update main.tex

Correction metrics values

papers/Gagnon_Kebe_Tahiri/main.tex

@@ -457,12 +457,12 @@ \section{Results}\label{results}
     \caption{Analysis of fluctuations in four distance metrics using multiple sequence alignment (MSA): a) Least Squares distance, b) Robinson-Foulds distance, c) normalized Robinson-Foulds distance, and d) Euclidean distance. These distances aim to determine the degree of dissimilarity between the 16S rRNA mitochondrial gene region of 62 Cumacea specimens and the variation in O\textsubscript{2} concentration (mg/L) at the sampling sites. \label{fig:fig7}}
 \end{figure}
 
-The divergence between the genetic sequences and two attributes, one climatic (wind speed (m/s) at the start of sampling) and the other environmental (O\textsubscript{2} concentration (mg/L)) is presented in Figure \ref{fig:fig6} and Figure \ref{fig:fig7}. All the attributes given in the first step of the \autoref{aPhyloGeo-software} section were analyzed and their script and figure will be soon available in the $img$ and $script$ python file on \href{https://github.com/tahiri-lab/Cumacea_aPhyloGeo}{GitHub}. However, only these two attributes showed the most interesting mutation rate. Using the four metrics mentioned in the section \autoref{metrics}, we noticed that the Euclidean distance is particularly sensitive to our data, manifesting considerable sequence variation at the position in MSA 520-529 amino acids (aa) (Euclidean distance: 0.8 < x < 0.9; see Figure \ref{fig:fig6}d) and 1190-199 aa (Euclidean distance: 1.2 < x < 1.3; see Figure \ref{fig:fig7}d). Unlike the other windows for this metric in the two figures (see Figure \ref{fig:fig6}d and Figure \ref{fig:fig7}d), the fluctuations in wind speed (m/s) at the start of sampling and in O\textsubscript{2} concentration (mg/L) do not appear to explain the variations in these two specific sequences. This could indicate the absence of directional selection in these sequences due to these habitat attributes, local selective pressures not considered in our analysis, or other evolutionary factors (e.g., genetic drift or biotic interactions) predominate over these two parameters concerning these two sequences. On the other hand, this may suggest that these two attributes could potentially influence the divergent (i.e., genetic diversification) rather than a convergent adaptation of these Cumacea, reflecting unique evolutionary responses to these specific ecological pressures. These results are consistent with the aim of our study, which is to identify the Cumacea genetic region that diverges most as a function of habitat attribute, to determine whether this is due to divergent local adaptation or other evolutionary processes.
+The divergence between the genetic sequences and two attributes, one climatic (wind speed (m/s) at the start of sampling) and the other environmental (O\textsubscript{2} concentration (mg/L)) is presented in Figure \ref{fig:fig6} and Figure \ref{fig:fig7}. All the attributes given in the first step of the \autoref{aPhyloGeo-software} section were analyzed and their script and figure will be soon available in the $img$ and $script$ python file on \href{https://github.com/tahiri-lab/Cumacea_aPhyloGeo}{GitHub}. However, only these two attributes showed the most interesting mutation rate. Using the four metrics mentioned in the section \autoref{metrics}, we noticed that the Euclidean distance is particularly sensitive to our data, manifesting considerable sequence variation at the position in MSA 560-569 amino acids (aa) (Euclidean distance: 0.86; see Figure \ref{fig:fig6}d) and 1210-1219 aa (Euclidean distance: 1.23; see Figure \ref{fig:fig7}d). Unlike the other windows for this metric in the two figures (see Figure \ref{fig:fig6}d and Figure \ref{fig:fig7}d), the fluctuations in wind speed (m/s) at the start of sampling and in O\textsubscript{2} concentration (mg/L) do not appear to explain the variations in these two specific sequences. This could indicate the absence of directional selection in these sequences due to these habitat attributes, local selective pressures not considered in our analysis, or other evolutionary factors (e.g., genetic drift or biotic interactions) predominate over these two parameters concerning these two sequences. On the other hand, this may suggest that these two attributes could potentially influence the divergent (i.e., genetic diversification) rather than a convergent adaptation of these Cumacea, reflecting unique evolutionary responses to these specific ecological pressures. These results are consistent with the aim of our study, which is to identify the Cumacea genetic region that diverges most as a function of habitat attribute, to determine whether this is due to divergent local adaptation or other evolutionary processes.
 
 These results provide important insight into the genetic adaptation of Cumacea to their environment. These results need to be analyzed in greater depth to certify their involvement, especially in contrast with \citep{uhlir_adding_2021}, which investigated similar topics of environmental and climatic effects on Cumacea distribution and genetics. The \textit{aPhyloGeo} package is still in the process of being updated.
 
 \section{Conclusion}\label{conclusion}
-This study examines the effects of meteorological, regional, and ecosystemic attributes on the genetics of Cumacea in the waters surrounding Iceland. Our main objective is to determine whether there is a divergence between precise genetic information of the 16S rRNA mitochondrial gene region (i.e., a window) of Cumacea species and their habitat attributes. In addition to data distribution representations (see Figure \ref{fig:fig2}, Figure \ref{fig:fig3}, Figure \ref{fig:fig4} and Figure \ref{fig:fig5}), DNA sequence analyses have identified specific genetic windows that diverge from atmospheric and biological attributes such as wind speed (m/s) at the start of sampling (Position in MSA: 520-529 aa; Euclidean distance: 0.8 < x < 0.9; see Figure \ref{fig:fig6}d) and O\textsubscript{2} concentration (mg/L) (Position in MSA: 1190-1199 aa; Euclidean distance: 1.2 < x < 1.3; see Figure \ref{fig:fig7}d). These results may imply our sample may have been shaped by these unique local environments, resulting in genetic sequences adapted to their distinctive conditions.
+This study examines the effects of meteorological, regional, and ecosystemic attributes on the genetics of Cumacea in the waters surrounding Iceland. Our main objective is to determine whether there is a divergence between precise genetic information of the 16S rRNA mitochondrial gene region (i.e., a window) of Cumacea species and their habitat attributes. In addition to data distribution representations (see Figure \ref{fig:fig2}, Figure \ref{fig:fig3}, Figure \ref{fig:fig4} and Figure \ref{fig:fig5}), DNA sequence analyses have identified specific genetic windows that diverge from atmospheric and biological attributes such as wind speed (m/s) at the start of sampling (Position in MSA: 560-569 aa; Euclidean distance: 0.86; see Figure \ref{fig:fig6}d) and O\textsubscript{2} concentration (mg/L) (Position in MSA: 1210-1219 aa; Euclidean distance: 1.23; see Figure \ref{fig:fig7}d). These results may imply our sample may have been shaped by these unique local environments, resulting in genetic sequences adapted to their distinctive conditions.
 
 The novelty in our research lies in the exhaustive divergence between habitat attributes and genetic mutability in Cumacea, particularly in identifying genetic windows associated with habitat fluctuations, which has not been widely investigated in previous studies \citep{manel2003landscape, vrijenhoek2009cryptic}. In this case, our integrated method identifies specific genetic regions sensitive to ecosystemic and atmospheric variations. Thus, by seeking to determine which of these two attributes diverges most with the DNA sequences, the eventual identification of proteins linked to one of these variable DNA sequences will make it possible to represent its functional effects in responses to habitat changes. Our future research will focus on verifying the prediction of this protein and assessing its role in the physiological adaptation of Cumacea to fluctuating conditions, adding a link between genetic data and ecological function.