Improved prediction of Canada lynx distribution through regional model transferability and data efficiency
Autor: | Joseph D. Holbrook, Daniel H. Thornton, Brian Kertson, Nichole Bjornlie, Dennis L. Murray, John Rohrer, Jacob S. Ivan, Lucretia E. Olson, Michael K. Lucid, Arthur Scully, Scott A. Jackson, Zachary Walker, Travis W. King, Gary Hanvey, Robert H. Naney, John R. Squires |
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Jazyk: | angličtina |
Rok vydání: | 2021 |
Předmět: |
0106 biological sciences
niche similarity Species distribution Population GPS telemetry data 010603 evolutionary biology 01 natural sciences 03 medical and health sciences Ecosystem model lcsh:QH540-549.5 Range (statistics) education generalizability Ecology Evolution Behavior and Systematics 030304 developmental biology Nature and Landscape Conservation Local adaptation Original Research 0303 health sciences education.field_of_study Ecology Ensemble forecasting species distribution model transferability regional variation Canada lynx Lynx canadensis sample size Geography Sample size determination Principal component analysis lcsh:Ecology Cartography local adaptation |
Zdroj: | Ecology and Evolution Ecology and Evolution, Vol 11, Iss 4, Pp 1667-1690 (2021) |
ISSN: | 2045-7758 |
Popis: | 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. Using a collaborative dataset from three populations of Canada lynx in the northwestern United States, we explore the impact of regional variation in animal‐environment relationships on model transferability, as well as how model performance and transferability is affected by the size of the GPS dataset used. Despite the specialist nature of Canada lynx, we found regional differences in lynx‐environment relationships. Model transferability improved as populations became closer in terms of their lynx‐environment relationships. We found substantial redundancy in our large GPS dataset, with predictive performance insensitive to sample sizes above 30% of the original. |
Databáze: | OpenAIRE |
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