QLS Seminar Series - Pedro Peres-Neto

Spatial pattern detection in genetic and ecological data

Pedro Peres-Neto, Concordia University
Tuesday December 7, 12-1pm
Zoom Link: https://mcgill.zoom.us/j/85428056343

Abstract: Landscape genetics studies using neutral markers have focused on the relationship between gene flow and landscape features. In the same way that landscape ecologists are interested in modelling the relationships between ecological data and landscape features. Spatial patterns in the genetic distances among individuals may reflect spatially uneven patterns of gene flow caused by landscape features that influence movement and dispersal. We present a method for identifying spatial patterns in genetic data that adopts a regression framework where the predictors are generated using Moran’s eigenvectors maps (MEM),a multivariate technique developed by us for spatial ecological analyses and applicable to landscape genetics (MEMGENE). Using simulated and real genetic data, we show that our MEMGENE method can recover patterns reflecting the landscape features that influenced gene flow. We developed the MEMGENE package for R to detect and visualize relatively weak or cryptic spatial genetic patterns and aid researchers in generating hypotheses about the ecological processes that may underlie these patterns. MEMGENE provides a flexible set of R functions that can be used to different types of analyses.