Abstract
Understanding how human infrastructure and other landscape attributes affect genetic differentiation in animals is an important step for identifying and maintaining dispersal corridors for these species. We built upon recent advances in the field of landscape genetics by using an individual-based and multiscale approach to predict landscape-level genetic connectivity for grizzly bears (Ursus arctos) across ~100,000 km2 in Canada's southern Rocky Mountains. We used a genetic dataset with 1156 unique individuals genotyped at nine microsatellite loci to identify landscape characteristics that influence grizzly bear gene flow at multiple spatial scales and map predicted genetic connectivity through a matrix of rugged terrain, large protected areas, highways and a growing human footprint. Our corridor-based modelling approach used a machine learning algorithm that objectively parameterized landscape resistance, incorporated spatial cross validation and variable selection and explicitly accounted for isolation by distance. This approach avoided overfitting, discarded variables that did not improve model performance across withheld test datasets and spatial predictive capacity compared to random cross-validation. We found that across all spatial scales, geographic distance explained more variation in genetic differentiation in grizzly bears than landscape variables. Human footprint inhibited connectivity across all spatial scales, while open canopies inhibited connectivity at the broadest spatial scale. Our results highlight the negative effect of human footprint on genetic connectivity, provide strong evidence for using spatial cross-validation in landscape genetics analyses and show that multiscale analyses provide additional information on how landscape variables affect genetic differentiation.
| Original language | English |
|---|---|
| Pages (from-to) | 5211-5227 |
| Number of pages | 17 |
| Journal | Molecular Ecology |
| Volume | 32 |
| Issue number | 19 |
| DOIs | |
| State | Published - Oct 2023 |
Funding
We acknowledge support from the Government of British Columbia, Habitat Conservation Trust Foundation, Teck Coal, Vanier Canada Graduate Scholarship, Fish and Wildlife Compensation Program, Forest Enhancement Society of British Columbia, Columbia Basin Trust, Counter Assault, Safari Club International, Sparwood and District Fish and Wildlife Association, BP Canada, and BC Conservation Corps. We thank Parks Canada and the many partners of the fRI Grizzly Bear Project who provided funding over many years. We thank the South Rockies Grizzly Bear Project volunteers for supporting our research project and helping us out in the field. We thank Barb Johnston of the south‐west Alberta grizzly bear monitoring project for logistical and financial support. We acknowledge support from the Wilburforce Foundation, Western Transportation Institute, Woodcock Foundation, National Fish and Wildlife Foundation, Alberta Conservation Association, Calgary Foundation and Mountain Equipment Co‐operative. We thank Laura Smit, John Paczkowski and Karen Graham for providing data. We acknowledge support from the Government of British Columbia, Habitat Conservation Trust Foundation, Teck Coal, Vanier Canada Graduate Scholarship, Fish and Wildlife Compensation Program, Forest Enhancement Society of British Columbia, Columbia Basin Trust, Counter Assault, Safari Club International, Sparwood and District Fish and Wildlife Association, BP Canada, and BC Conservation Corps. We thank Parks Canada and the many partners of the fRI Grizzly Bear Project who provided funding over many years. We thank the South Rockies Grizzly Bear Project volunteers for supporting our research project and helping us out in the field. We thank Barb Johnston of the south-west Alberta grizzly bear monitoring project for logistical and financial support. We acknowledge support from the Wilburforce Foundation, Western Transportation Institute, Woodcock Foundation, National Fish and Wildlife Foundation, Alberta Conservation Association, Calgary Foundation and Mountain Equipment Co-operative. We thank Laura Smit, John Paczkowski and Karen Graham for providing data. This research was supported in part by the USDA Forest Service, Rocky Mountain Research Station and Aldo Leopold Wilderness Research Institute. The findings and conclusions in this publication are those of the authors and should not be construed to represent any official USDA or US Government determination or policy. Computational resources from the University of Montana's Computational Ecology Lab and Griz Shared Computing Cluster contributed to this research (NSF award numbers 2018112 & 1925267).
| Funders |
|---|
| Aldo Leopold Wilderness Research Institute |
| Government of British Columbia |
| U.S. Forest Service-Retired |
| Alberta Conservation Association |
Keywords
- connectivity
- grizzly bear
- landscape genetics
- machine learning
- spatial cross-validation
- Genetic Drift
- Animals
- Gene Flow
- Humans
- Ecosystem
- Ursidae/genetics
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