Update main.tex

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papers/Gagnon_Kebe_Tahiri/main.tex

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 \begin{abstract}
 The peracarid taxon Cumacea is an essential indicator of benthic quality in marine ecosystems. This study investigated the influence of environmental (i.e., biological or ecosystemic), climatic (i.e., meteorological or atmospheric), and geographic (i.e., spatial or regional) attributes on their genetic variability in the Northern North Atlantic, focusing on Icelandic waters. We analyzed partial sequences of the 16S rRNA mitochondrial gene from 62 Cumacea specimens. Using the \textit{aPhyloGeo} software, we compared these sequences with relevant parameters such as latitude (decimal degree) at the end of sampling, wind speed (m/s) at the start of sampling, O\textsubscript{2} concentration (mg/L), and depth (m) at the start of sampling.
 
-Our analyses revealed variability in most spatial and biological attributes, reflecting the diversity of ecological requirements and benthic habitats. The most common Cumacea families, Diastylidae and Leuconidae, suggest adaptations to various marine environments. Phylogeographic analysis showed a divergence between specific genetic sequences and two habitat attributes: wind speed (m/s) at the start of sampling and O\tsubscript{2} concentration (mg/L). This may suggest potential divergent local adaptation to these fluctuating conditions.
+Our analyses revealed variability in most spatial and biological attributes, reflecting the diversity of ecological requirements and benthic habitats. The most common Cumacea families, Diastylidae and Leuconidae, suggest adaptations to various marine environments. Phylogeographic analysis showed a divergence between specific genetic sequences and two habitat attributes: wind speed (m/s) at the start of sampling and O\textsubscript{2} concentration (mg/L). This may suggest potential divergent local adaptation to these fluctuating conditions.
 
 These results reinforce the importance of further research into the relationship between Cumacea genetics and global environmental factors. Understanding these relationships is essential for interpreting the evolutionary dynamics and adaptation of deep-sea Cumacea. This study sheds much-needed light on invertebrate acclimatization to climate change, anthropomorphic pressures, and deep-water habitat management. It can contribute to the evolution of more efficient conservation strategies and inform policies that protect vulnerable marine ecosystems. 
 
@@ -15,7 +15,7 @@ \section{Introduction}\label{introduction}
 
 Given the urgency of the above factors, this study aims to analyze the influence of ecological (climatic and environmental) and spatial parameters on the genetic adaptability of Cumacea in the Northern North Atlantic. More specifically, we will examine whether genetic adaptation exists between the genetic structure of a region of a partial sequence of the 16S rRNA mitochondrial gene of the Cumacea species sampled and their habitat attributes. If so, we will determine the attribute that diverges most from a specific gene sequence of this Cumacea gene (i.e., a window) and further explore the potential associated protein using bioinformatics tools to interpret its biological relevance. Our approach includes confirming different {phylogeographic models}\footnote{Phylogeographic models are computational tools that analyze relationships between the genetic structures of populations and their geographic distributions. In our case, by incorporating regional, biological, and atmospheric characteristics, we can interpret their impact on the genetic distribution of Cumacea species.} and updating a Python package (currently in beta), \textit{aPhyloGeo}, to simplify these analyses.
 
-This paper is organized as follows: Section \autoref{related-works} reviews pertinent studies on the biodiversity and biogeography of deep-sea benthic invertebrates; Section \autoref{contribution} summarizes the aims and contributions of this study, highlighting aspects relating to the conservation and adaptation of marine invertebrates to climate change; Section \autoref{materials-methods} describes the data collection, sampling procedures, and genetic analyses; Section \autoref{metrics} describes the metrics used to evaluate the phylogeographic models; Section \autoref{results} presents the results; finally, Section \autoref{conclusion} discusses their implications for future research and conservation efforts.
+This paper is organized as follows: \autoref{related-works} reviews pertinent studies on the biodiversity and biogeography of deep-sea benthic invertebrates; \autoref{contribution} summarizes the aims and contributions of this study, highlighting aspects relating to the conservation and adaptation of marine invertebrates to climate change; \autoref{materials-methods} describes the data collection, sampling procedures, and genetic analyses; \autoref{metrics} describes the metrics used to evaluate the phylogeographic models; \autoref{results} presents the results; finally, \autoref{conclusion} discusses their implications for future research and conservation efforts.
 
 \section{Related Works}\label{related-works}
 Assessing and quantifying the biodiversity of deep-sea benthic invertebrates has become increasingly crucial since it was discovered that their species richness may be underestimated \citep{grassle1992deep}. Subsequent research has highlighted the need for large-scale distribution models to interpret the diversity of these organisms across their ecological and evolutionary contexts \citep{rex1997large}. That is why recent efforts have focused on mapping, managing, and studying the seabed \citep{brown2011benthic}. Advanced technologies such as acoustic detection are improving our knowledge of benthic ecosystem complexity \citep{brown2011benthic}. Integrating genetic and habitat attributes gives a deeper understanding of how ecosystemic, meteorological, and spatial attributes influence the genetic differences, distribution, biodiversity, and resilience of deep-sea benthic organisms \citep{vrijenhoek2009cryptic}.
@@ -35,7 +35,7 @@ \section{Materials and Methods}\label{materials-methods}
 \begin{figure}[htbp]
     \centering
     \includegraphics[width=0.7\textwidth]{diagram.drawio.png}
-    \caption{Flow chart summarizing the Materials and Methods section workflow. Six different colors highlight the blocks. The first block (blue) represents our database. The second block (red) is data pre-processing, where we remove attributes. The third and fourth blocks (orange) implement the \textit{aPhyloGeo} software and its parameters for our phylogeographic analyses (see in the second step of the section \autoref{aPhyloGeo-software}). The fifth block (grey) calculates phylogenetic tree comparison distances. The sixth block (yellow) compares the distances between the phylogenetic and the habitat trees produced. The seventh block (purple) identifies the most divergent habitat parameter(s) of a specific region of the partial sequence of the 16S rRNA mitochondrial gene based on the results of tree comparisons. *See YAML files on \href{https://github.com/tahiri-lab/aPhyloGeo}{GitHub} for more details on these parameters. \label{fig:fig1}}
+    \caption{Flow chart summarizing the Materials and Methods section workflow. Six different colors highlight the blocks. The first block (blue) represents our database. The second block (red) is data pre-processing, where we remove attributes. The third and fourth blocks (orange) implement the \textit{aPhyloGeo} software and its parameters for our phylogeographic analyses (see in the second step of the \autoref{aPhyloGeo-software}). The fifth block (grey) calculates phylogenetic tree comparison distances. The sixth block (yellow) compares the distances between the phylogenetic and the habitat trees produced. The seventh block (purple) identifies the most divergent habitat parameter(s) of a specific region of the partial sequence of the 16S rRNA mitochondrial gene based on the results of tree comparisons. *See YAML files on \href{https://github.com/tahiri-lab/aPhyloGeo}{GitHub} for more details on these parameters. \label{fig:fig1}}
 \end{figure}
 
 \subsection{Description of the data}
@@ -223,12 +223,12 @@ \subsection{{\textit{aPhyloGeo} software}\label{aPhyloGeo-software}}
 \subsection{Metrics}\label{metrics}
 Our phylogeographic study used four distance measures to quantify differences between phylogenetic trees and habitat trees and assess dissimilarities between genetic sequences and our parameters. This enables a comprehensive analysis of the evolutionary dynamics of Cumacea populations in different environmental contexts. 
 
-The following section presents a more concise version of the functions mentioned in the second and third steps of section \autoref{aPhyloGeo-software}:
+The following section presents a more concise version of the functions mentioned in the second and third steps of \autoref{aPhyloGeo-software}:
 
 \subsubsection{Robinson-Foulds distance}\label{RF}
-The Robinson-Foulds (RF) distance calculates the distance between phylogenetic trees built in each sliding window ($T_1$) and the attributes trees ($T_2$) (see the list in the first step of the section \autoref{aPhyloGeo-software}) \citep{tahiri2018new, koshkarov_phylogeography_2022}. This measurement is used to evaluate the topological differences between the two sets of trees (see Equation \eqref{eq:rf} and \autoref{lst:robinsonFoulds}).
+The Robinson-Foulds (RF) distance calculates the distance between phylogenetic trees built in each sliding window ($T_1$) and the attributes trees ($T_2$) (see the list in the first step of the \autoref{aPhyloGeo-software}) \citep{tahiri2018new, koshkarov_phylogeography_2022}. This measurement is used to evaluate the topological differences between the two sets of trees (see Equation \eqref{eq:rf} and \autoref{lst:robinsonFoulds}).
 
-For example, it evaluates the number of division differences between phylogenetic trees built within certain user-defined sliding windows (see the second step of the section \autoref{aPhyloGeo-software}) and geographic trees built with latitude data (DD) at the end of sampling \citep{robinson_comparison_1981}. A high distance between a specific window and other windows considered in the RF distance analysis may imply that the habitat feature has little to no impact on the evolution of this particular DNA sequence and that the fluctuation of this attribute might not explain the genetic divergences observed.
+For example, it evaluates the number of division differences between phylogenetic trees built within certain user-defined sliding windows (see the second step of the \autoref{aPhyloGeo-software}) and geographic trees built with latitude data (DD) at the end of sampling \citep{robinson_comparison_1981}. A high distance between a specific window and other windows considered in the RF distance analysis may imply that the habitat feature has little to no impact on the evolution of this particular DNA sequence and that the fluctuation of this attribute might not explain the genetic divergences observed.
 
 \begin{equation}\label{eq:rf}
     \text{RF}(T_1, T_2) = | \Sigma(T_1) \Delta \Sigma(T_2) |
@@ -407,7 +407,7 @@ \section{Results}\label{results}
     \caption{Analysis of fluctuations in four distance metrics using multiple sequence alignment (MSA): a) Robinson-Foulds distance, b) normalized Robinson-Foulds distance, and c) Euclidean distance. These distances aim to determine the degree of dissimilarity between the partial sequence of the 16S rRNA mitochondrial gene 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 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.
+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 \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.