Canonical correspondence analysis for estimating spatial and environmental effects on microsatellite gene diversity in brook charr (<i>Salvelinus fontinalis</i>)

TLDR

Understanding how environmental factors shape evolutionary and ecological processes is crucial, yet quantifying their relationship with genetic variables remains challenging. The study aims to identify and quantify how drainage patterns and environmental factors contribute to genetic diversity in brook charr populations using canonical correspondence analysis. Canonical correspondence analysis simultaneously evaluates multiple environmental and genetic variables to quantify their relative contributions to population genetic structure. The analysis showed that drainage pattern, altitude, and human‑induced factors, along with historical events, shape genetic diversity, demonstrating that canonical correspondence analysis efficiently links genetic variation to environmental descriptors.

Abstract

Abstract The understanding of the relationships between environmental factors and evolutionary forces is of importance to preserve opportunities for the continuation of dynamic evolutionary and ecological processes. This involves the identification and quantification of the relative importance of environmental factors that may influence these processes. Nevertheless, environmental factors are generally interpreted in terms of hypothetical inferences as relationships between environmental and genetic variables are often difficult to quantify. In this study, we used a statistical framework based on canonical correspondence analysis in order to determine the relative contribution of drainage pattern and environmental factors in structuring inter‐ and intrapopulational genetic diversity among brook charr populations as depicted by microsatellite analysis. These procedures simultaneously analyse several sets of variables and determine their relative contribution. The results revealed the influence of drainage pattern, altitude and human‐induced factors on the pattern of genetic diversity and, particularly, the important role of historical events in explaining patterns of contemporary genetic diversity among populations. The statistical framework used in this study provides an efficient way to empirically relate variations of genetic diversity and descriptive variables in general.