Improved prediction of Canada lynx distribution through regional model transferability and data efficiency

Ecology and Evolution

Jacob S. Ivan¹  Joseph D. Holbrook²  Arthur Scully³  Dennis Murray³  Michael Lucid⁴  John R. Squires⁵  Lucretia E. Olson⁵  Daniel Thornton⁶  Travis King⁶  Gary Hanvey⁷  Scott Jackson⁷  Robert Naney⁸  John Rohrer⁸  Brian Kertson⁹  Nichole Bjornlie^1,10  Zachary Walker^1,10 

[1] Colorado Parks and Wildlife Fort Collins CO USA;Department of Zoology and Physiology Haub School of Environment and Natural Resources University of Wyoming Laramie WY USA;Environmental and Life Sciences Biology Department Trent University Peterborough ON Canada;Idaho Department of Fish and Game Coeur d'Alene ID USA;Rocky Mountain Research Station United States Forest Service Missoula MT USA;School of the Environment Washington State University Pullman WA USA;United States Department of Agriculture, Northern Region United States Forest Service Missoula MT USA;United States Forest Service Okanogan‐Wenatchee National Forest Winthrop WA USA;Washington Department of Fish and Wildlife Snoqualmie WA USA;Wyoming Game and Fish Department Lander WY USA;

关键词: Canada lynx;    generalizability;    GPS telemetry data;    local adaptation;    Lynx canadensis;    niche similarity;   

DOI  :  10.1002/ece3.7157

来源: DOAJ

【 摘 要 】

Abstract The application of species distribution models (SDMs) to areas outside of where a model was created allows informed decisions across large spatial scales, yet transferability remains a challenge in ecological modeling. We examined how regional variation in animal‐environment relationships influenced model transferability for Canada lynx (Lynx canadensis), with an additional conservation aim of modeling lynx habitat across the northwestern United States. Simultaneously, we explored the effect of sample size from GPS data on SDM model performance and transferability. We used data from three geographically distinct Canada lynx populations in Washington (n = 17 individuals), Montana (n = 66), and Wyoming (n = 10) from 1996 to 2015. We assessed regional variation in lynx‐environment relationships between these three populations using principal components analysis (PCA). We used ensemble modeling to develop SDMs for each population and all populations combined and assessed model prediction and transferability for each model scenario using withheld data and an extensive independent dataset (n = 650). Finally, we examined GPS data efficiency by testing models created with sample sizes of 5%–100% of the original datasets. PCA results indicated some differences in environmental characteristics between populations; models created from individual populations showed differential transferability based on the populations' similarity in PCA space. Despite population differences, a single model created from all populations performed as well, or better, than each individual population. Model performance was mostly insensitive to GPS sample size, with a plateau in predictive ability reached at ~30% of the total GPS dataset when initial sample size was large. Based on these results, we generated well‐validated spatial predictions of Canada lynx distribution across a large portion of the species' southern range, with precipitation and temperature the primary environmental predictors in the model. We also demonstrated substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original.

【 授权许可】

Unknown