Broadscale effects of landscape on genetic structure of polar bears

The interplay between landscape features and genetic processes (e.g., gene flow, genetic drift) ultimately shapes population structure and species distributions. Understanding the evolutionary processes linking species and their environments can inform species’ responses to stochastic or human-induc...

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Bibliographic Details
Main Author: Vanderluit, Sean
Other Authors: Biology, Lougheed, Stephen
Format: Thesis
Language:English
Published: 2022
Subjects:
Online Access:http://hdl.handle.net/1974/30121
Description
Summary:The interplay between landscape features and genetic processes (e.g., gene flow, genetic drift) ultimately shapes population structure and species distributions. Understanding the evolutionary processes linking species and their environments can inform species’ responses to stochastic or human-induced change. Individual dispersal and connectivity among populations can be studied using the rapidly advancing field of landscape genetics through integrative study of spatial genetic patterns and their relation to landscape variables. Studying these dynamics through time is not often done, yet offers greater potential to evaluate the effects of landscapes on gene flow, and enables study of genetic change. The polar bear (Ursus martimus) is a circumpolar, apex arctic predator, considered to be in peril in the context of rapidly changing arctic environments. It is of great cultural and spiritual importance to Inuit peoples, and is hunted across the Arctic. Using over two decades (1997–2020) of polar bear harvest sample data collected by Inuit throughout Nunavut and the Inuvialuit Settlement Region, I conduct a comparison of polar bear spatial genetic structure and landscape resistance through time. I use resistance models to relate landscape variables to genetic structure for each of two periods of sampling across a consistent distribution (1997–2008 and 2009–2020). I observe local changes in spatial genetic structure across the Arctic Archipelago between periods. Resistance models emphasize the importance of both sea ice extents and landcover across this distribution for each period. Spatial autoregressive lag models indicate genetic change is predicted by sea ice resistance change between periods. I thus detect change in genetic structure resulting from environmental change, and identify sea ice as the leading underlying factor. Temporal landscape-genetic studies offer valuable insight on wide-ranging, continuously distributed species for conservation and management decisions. M.Sc.