A machine‐learning approach for extending classical wildlife resource selection analyses

Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected...

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Detalles Bibliográficos
Autores principales: Shoemaker, Kevin T., Heffelfinger, Levi J., Jackson, Nathan J., Blum, Marcus E., Wasley, Tony, Stewart, Kelley M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2018
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5869366/
https://www.ncbi.nlm.nih.gov/pubmed/29607046
http://dx.doi.org/10.1002/ece3.3936

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5869366/
https://www.ncbi.nlm.nih.gov/pubmed/29607046
http://dx.doi.org/10.1002/ece3.3936

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