Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity. Palm, E. C., Landguth, E. L., Holden, Z. A., Day, C. C., Lamb, C. T., Frame, P. F., Morehouse, A. T., Mowat, G., Proctor, M. F., Sawaya, M. A., Stenhouse, G., Whittington, J., & Zeller, K. A. Molecular Ecology, 32(19):5211–5227, 2023. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.17098![Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex 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.
@article{palm_corridor-based_2023,
title = {Corridor-based approach with spatial cross-validation reveals scale-dependent effects of geographic distance, human footprint and canopy cover on grizzly bear genetic connectivity},
volume = {32},
copyright = {© 2023 His Majesty the King in Right of Canada. Molecular Ecology © 2023 John Wiley \& Sons Ltd. Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.},
issn = {1365-294X},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.17098},
doi = {10.1111/mec.17098},
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.},
language = {en},
number = {19},
urldate = {2024-01-31},
journal = {Molecular Ecology},
author = {Palm, Eric C. and Landguth, Erin L. and Holden, Zachary A. and Day, Casey C. and Lamb, Clayton T. and Frame, Paul F. and Morehouse, Andrea T. and Mowat, Garth and Proctor, Michael F. and Sawaya, Michael A. and Stenhouse, Gordon and Whittington, Jesse and Zeller, Katherine A.},
year = {2023},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/mec.17098},
keywords = {NALCMS},
pages = {5211--5227},
}
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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. 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Molecular Ecology © 2023 John Wiley \\& Sons Ltd. Reproduced with the permission of the Minister of Environment and Climate Change Canada. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.},\n\tissn = {1365-294X},\n\turl = {https://onlinelibrary.wiley.com/doi/abs/10.1111/mec.17098},\n\tdoi = {10.1111/mec.17098},\n\tabstract = {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. 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