Update main.tex

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papers/Gagnon_Kebe_Tahiri/main.tex

@@ -173,14 +173,14 @@ \subsection{Least-Squares Distance}\label{LS}
 
 \section{Results}\label{results}
 
+Figure \ref{fig:fig1} shows the extent and variability of the dataset for three geographic attributes, namely latitude (decimal) and longitude (decimal) at the start of sampling and depth (m) at specimen collection, as well as three environmental attributes, such as wind speed at the start of sampling (m/s) and temperature (°C)) and the oxygen concentration of the water (mg/L) based on the depth at which the samples were collected.
+
 \begin{figure}[]
     \centering
     \includegraphics[width=0.7\textwidth]{figure1.jpg}
     \caption{Violin diagrams of six geographical and environmental attributes in our sample. a) Latitude (decimal) at the beginning of sampling (red); b) Longitude (decimal) at the beginning of sampling (yellow); c) Depth (m) at the start of sampling (green); d) Wind speed (m/s) at the beginning of sampling (light blue); e) Water temperature (°C) according to the depth at which the specimens were collected (dark blue); (f) Oxygen concentration (mg/L) according to the depth at which the specimens were sampled (pink). The mean, median, standard deviation (Std, Dev), 1st quartile (Q1), and 3rd quartile (Q3) of the dataset for each attribute are shown in the top right corner of each chart. \label{fig:fig1}}
 \end{figure}
 
-Figure \ref{fig:fig1} shows the extent and variability of the dataset for three geographic attributes, namely latitude (decimal) and longitude (decimal) at the start of sampling and depth (m) at specimen collection, as well as three environmental attributes, such as wind speed at the start of sampling (m/s) and temperature (°C)) and the oxygen concentration of the water (mg/L) based on the depth at which the samples were collected.
-
 These digraphs are critical to designing the conditions of the habitats where the samples were collected, as high variability in the data may indicate heterogeneous habitats, while low variability dictates more homogeneous habitats. These diagrams can provide visual evidence that can support or refute our hypothesis regarding the relationship between these environmental variables and Cumacea genetics. For example, if a correlation is found between temperature and certain genetic variations, this may support that temperature influences the genetic structure of Cumacea populations. Thus, these violin graphs allow us to highlight rare environmental events or unique habitats that can have a significant impact on Cumacea genetics. 
 
 The mean, median, standard deviation, and 1st and 3rd quartiles are represented in the top right corner of each of the six charts. These make it possible to identify general trends in the data as well as irregularities or extreme values in our data. For example, a large dissimilarity between the mean and the median may indicate the presence of extreme values, as is the case for depth (m) at the beginning of sampling. Large variations in standard deviation may indicate that some environmental variables are more variable and could have a different impact on Cumacea genetics, as is also the case for depth at the beginning of sampling. 
@@ -217,20 +217,24 @@ \section{Results}\label{results}
 
 Figures \ref{fig:fig5} and \ref{fig:fig6} below show the degree of correlation between a portion of the multiple sequence alignment (window) of our samples and two climatic parameters: wind speed at the start of sampling (m/s) and the O2 concentration of the water where the samples were collected (mg/L). This correlation was based on 4 metrics: Least-Square Distance, Robinson-Fouls Distance, Normalised Robinson-Foulds Distance and Euclidean Distance. These results were obtained using the functions leastSquare(tree1, tree2), robinsonFoulds(tree1, tree2), euclideanDist(tree1, tree2) from the aPhyloGeo software and were organised by the main function (see \autoref{lst:main}). 
 
+Sequence correlation raises the question of how variations in sequences (windows) respond to or vary with climatic conditions. Conserved positions (low values) could potentially suggest functionally essential regions that do not readily vary with changing climatic conditions, while fluctuating windows (high values) could present specific adaptations to climatic conditions. Analyzing how sequences do or don't vary under these two different climatic conditions can highlight regions in the sequences (windows) that are sensitive or resistant to fluctuations in these two climatic parameters. In our results, we observe a similar fluctuation of the correlation with these two parameters between Figure \ref{fig:fig5} and \ref{fig:fig6}.
+
 \begin{figure}[]
     \centering
     \includegraphics[width=0.7\textwidth]{figure5.png}
     \caption{Variations in the four metrics (Robinson-Foulds distance, generalized Robinson-Foulds, Euclidean distance, and least-square distance) analyzed to elucidate their correlation with fluctuations in wind speed at the start of the sampling (m/s). \label{fig:fig5}}
 \end{figure}
 
-In Figure 5a, the peaks and troughs suggest that some locations across the sample sequences are more conserved and therefore similar (smaller distance), probably indicating potential functional or structural significance, while other positions show more variability (larger distance). In contrast to Figure 5a, the values in Figure 5b are more concentrated on restricted values, suggesting a more uniform fluctuation. In this context, lower variation may indicate that changes in sequences do not completely affect local phylogeny. The same is true of Figure 5c, where the normalized distances are rather homogeneous, dictating that variations in sequence positions have a fairly constant impact on the phylogenic structure of the trees. The Euclidean distance, shown in Figure 5d, appears to be the most sensitive distance to our data, indicating significant variation between nucleotide positions. A higher Euclidean distance means a greater difference between sequences at this position (520-529), suggesting that this site is more variable or evolutionarily susceptible to change. 
+In Figure \ref{fig:fig5}a, the peaks and troughs suggest that some locations across the sample sequences are more conserved and therefore similar (smaller distance), probably indicating potential functional or structural significance, while other positions show more variability (larger distance). In contrast to Figure \ref{fig:fig5}a, the values in Figure \ref{fig:fig5}b are more concentrated on restricted values, suggesting a more uniform fluctuation. In this context, lower variation may indicate that changes in sequences do not completely affect local phylogeny. The same is true of Figure \ref{fig:fig5}c, where the normalized distances are rather homogeneous, dictating that variations in sequence positions have a fairly constant impact on the phylogenic structure of the trees. The Euclidean distance, shown in Figure \ref{fig:fig5}d, appears to be the most sensitive and disparate distance to our data. A higher Euclidean distance means a greater difference between sequences at position 520_529 (see Figure \ref{fig:fig5}d), suggesting that this site is more variable or evolutionarily susceptible to change. 
 
 \begin{figure}[]
     \centering
     \includegraphics[width=0.7\textwidth]{figure6.png}
     \caption{Variations in the four metrics (Robinson-Foulds distance, generalized Robinson-Foulds, Euclidean distance, and least-square distance) analyzed to elucidate their correlation with fluctuations in O2 saturation ground (mg/L). \label{fig:fig6}}
 \end{figure}
 
+Figure \ref{fig:fig6}a is similar to figure \ref{fig:fig5}a, but shows more variation between different window positions. Windows with smaller mean-squared distances are more likely to be evolutionarily conserved, while windows with larger distances show greater instability. The Robinson-Foulds distances in Figure \ref{fig:fig6}b vary with a restricted range of values (50 to 70). These small variations suggest that fluctuations in sequences do not significantly alter local phylogeny. Like the figure above, Figure \ref{fig:fig6}c has a fairly homogeneous distribution. This means that variations in individual window positions exert a fairly uniform influence on the phylogenetic arrangement of trees following normalization. Like Figure \ref{fig:fig5}d, the Euclidean distance presented in Figure \ref{fig:fig6}d shows the greatest sensitivity and heterogeneity from our data. The position with the highest Euclidean distance (1190_1199, see Figure 5d) shows significant dissimilarity between sequences at this position, which may signify a more fluctuating or evolutionarily unstable site. 
+
 All these results will need to be further investigated and analyzed in order to provide a more complete understanding of these results, and thus enable us to draw solid conclusions.
 
 \section{Conclusion}\label{conclusion}