Roads, Soil, Snow, and Topography Influence Genetic Connectivity: A Machine Learning Approach for a Peripheral American Badger Population. Palm, E. C., Landguth, E. L., Lamy, K., Gorrell, J. C., Weir, R. D., Richardson, E. L., Forbes, K. J., Davis, H., & Burgar, J. M. Ecology and Evolution, 16(4):e73467, 2026. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.73467![Roads, Soil, Snow, and Topography Influence Genetic Connectivity: A Machine Learning Approach for a Peripheral American Badger Population [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Effective management and conservation of peripheral populations require an understanding of the landscape conditions inhibiting dispersal and spatially explicit predictions of connectivity. Here, we modeled landscape resistance and genetic connectivity for the western population of an American badger subspecies (Taxidea taxus jeffersonii) across 170,000 km2 in southern British Columbia, Canada, using 116 genetic samples genotyped at 14 microsatellite loci. We used gradient boosting machine models in a corridor-based approach to predict genetic distances between pairs of individual badgers as a function of landscape variable data. Spatial genetic autocorrelation tests and our top model predicted that genetic similarities of T. t. jeffersonii were present up to 110 km. Gene diversity was lowest in the Cariboo region in the northwest portion of the study area and highest in the Okanagan region in the southeast. Our analyses suggest that the genetic connectivity of T. t. jeffersonii was impeded by colluvial soil parent material, geographic distance, steep slopes, and major roads, but was facilitated by organic and fluvial soil parent materials, and areas with relatively little snow cover during winter. Our predictive maps of landscape resistance and connectivity can help guide management actions such as habitat protection and underpass placement on major roads to promote genetic connectivity.
@article{palm_roads_2026,
title = {Roads, {Soil}, {Snow}, and {Topography} {Influence} {Genetic} {Connectivity}: {A} {Machine} {Learning} {Approach} for a {Peripheral} {American} {Badger} {Population}},
volume = {16},
copyright = {© 2026 The Author(s). Ecology and Evolution published by British Ecological Society and John Wiley \& Sons Ltd.},
issn = {2045-7758},
shorttitle = {Roads, {Soil}, {Snow}, and {Topography} {Influence} {Genetic} {Connectivity}},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/ece3.73467},
doi = {10.1002/ece3.73467},
abstract = {Effective management and conservation of peripheral populations require an understanding of the landscape conditions inhibiting dispersal and spatially explicit predictions of connectivity. Here, we modeled landscape resistance and genetic connectivity for the western population of an American badger subspecies (Taxidea taxus jeffersonii) across 170,000 km2 in southern British Columbia, Canada, using 116 genetic samples genotyped at 14 microsatellite loci. We used gradient boosting machine models in a corridor-based approach to predict genetic distances between pairs of individual badgers as a function of landscape variable data. Spatial genetic autocorrelation tests and our top model predicted that genetic similarities of T. t. jeffersonii were present up to 110 km. Gene diversity was lowest in the Cariboo region in the northwest portion of the study area and highest in the Okanagan region in the southeast. Our analyses suggest that the genetic connectivity of T. t. jeffersonii was impeded by colluvial soil parent material, geographic distance, steep slopes, and major roads, but was facilitated by organic and fluvial soil parent materials, and areas with relatively little snow cover during winter. Our predictive maps of landscape resistance and connectivity can help guide management actions such as habitat protection and underpass placement on major roads to promote genetic connectivity.},
language = {en},
number = {4},
urldate = {2026-05-28},
journal = {Ecology and Evolution},
author = {Palm, Eric C. and Landguth, Erin L. and Lamy, Karina and Gorrell, Jamieson C. and Weir, Richard D. and Richardson, Emma L. and Forbes, Krystyn J. and Davis, Helen and Burgar, Joanna M.},
year = {2026},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/ece3.73467},
keywords = {NALCMS},
pages = {e73467},
}
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