Machine learned daily life history classification using low frequency tracking data and automated modelling pipelines: application to North American waterfowl

BACKGROUND: Identifying animal behaviors, life history states, and movement patterns is a prerequisite for many animal behavior analyses and effective management of wildlife and habitats. Most approaches classify short-term movement patterns with high frequency location or accelerometry data. Howeve...

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Detalles Bibliográficos
Autores principales: Overton, Cory, Casazza, Michael, Bretz, Joseph, McDuie, Fiona, Matchett, Elliott, Mackell, Desmond, Lorenz, Austen, Mott, Andrea, Herzog, Mark, Ackerman, Josh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Methodology Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109391/
https://www.ncbi.nlm.nih.gov/pubmed/35578372
http://dx.doi.org/10.1186/s40462-022-00324-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9109391/
https://www.ncbi.nlm.nih.gov/pubmed/35578372
http://dx.doi.org/10.1186/s40462-022-00324-7

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