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Data from: Linking genotype, ecotype, and phenotype in an intensively managed large carnivore
Shafer, Aaron B. A.; Nielsen, Scott E.; Northrup, Joseph M.; Stenhouse, Gordon B. — 2013-09-26 Numerous factors influence fitness of free-ranging animals, yet often these are uncharacterized. We integrated GPS habitat use data and genetic profiling to determine their influence on fitness proxies (mass, length, and body condition) in a threatened population of grizzly bears (Ursus arctos) in Alberta, Canada. We detected distinct genetic and habitat use (ecotype) clusters, with individual cluster assignments, or genotype/ecotype, being correlated (Pearson r = 0.34, P < 0.01). Related individuals showed evidence of similar habitat use patterns, irrespective of geographic distance and sex. Fitness proxies were influenced by sex, age, and habitat use, and homozygosity had a positive effect on these proxies that could be indicative of outbreeding depression. We further documented over 300 translocations occurring in the province since the 1970s, often to areas with significantly different habitat. We argue this could be unintentionally causing the pattern of outbreeding, although the heterozygosity correlation may instead be explained by the energetic costs associated with larger body size. The observed patterns, together with the unprecedented human-mediated migrations, make understanding the link between genotype, ecotype, and phenotype and mechanisms behind the negative heterozygosity-fitness correlations critical for management and conservation of this species.
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Data from: Habitat selection predicts genetic relatedness in an alpine ungulate
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Shafer, Aaron B. A.; Northrup, Joseph M.; White, Kevin S.; Boyce, Mark S.; Côté, Steeve D.; Coltman, David W. — 2012-06-14 Landscape heterogeneity plays an integral role in shaping ecological and evolutionary processes. Despite links between the two disciplines, ecologists and population geneticists have taken different approaches to evaluating habitat selection, animal movement, and gene flow across the landscape. Ecologists commonly use statistical models such as resource selection functions (RSFs) to identify habitat features disproportionately selected by animals, while population genetic approaches model genetic differentiation according to the distribution of habitat variables. We combined ecological and genetic approaches by using RSFs and step-selection functions (SSFs) to predict genetic relatedness across a heterogeneous landscape. We constructed sex and season-specific resistance surfaces based on RSFs and SSFs estimated using data from 102 GPS radiocollared mountain goats (Oreamnos americanus) in southeast Alaska. Based on mountain goat ecology, we hypothesized that summer and male surfaces would be the best predictors of relatedness. All individuals were genotyped at 22 microsatellite loci, which we used to estimate genetic relatedness. Summer resistance surfaces derived from RSFs were the best predictors of genetic relatedness, and winter models the poorest. Male and female specific surfaces were similar, except for winter where male habitat selection better predicted genetic relatedness. The null models of isolation-by-distance and barrier only outperformed the winter models. This study merges high-resolution individual locations through GPS telemetry and genetic data, that can be used to validate and parameterize landscape genetics models, and further elucidates the relationship between landscape heterogeneity and genetic differentiation.
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Data from: Harvest and density-dependent predation drive long-term population decline in a northern ungulate
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Marrotte, Robby R.; Patterson, Brent R.; Northrup, Joseph M. — 2022-02-15 <p><span lang="EN-US">The relative effect of top-down versus bottom-up forces in regulating and limiting wildlife populations is an important theme in ecology. Untangling these effects is critical for a basic understanding of trophic dynamics and effective management. We examined the drivers of moose (<em>Alces alces</em>) population growth by integrating two independent sources of observations within a hierarchical Bayesian population model. This analysis used one of the largest existing spatiotemporal datasets on ungulate population dynamics globally. We documented a 20% population decline over the period examined. Moose population growth was negatively density-dependent. Although the mechanisms producing density-dependent suppression of population growth could not be determined, the relatively low densities at which moose populations were documented suggests it could be due primarily to density-dependent predation. Predation </span>primarily limited population growth, except at low density, where it was regulating. <span lang="EN-US">Harvest appeared to be largely </span>additive<span lang="EN-US"> and contributed to population declines.</span> Our results, highlight how <span lang="EN-US">population dynamics are context dependent and vary strongly across gradients in climate, forest type, and predator abundance. These results help clarify long-standing questions in population ecology and highlight the complex relationships between natural and human-caused mortality in driving ungulate population dynamics. </span></p>

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(Author: Northrup, Joseph M.)

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