Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset

To analyze species count data when detection is imperfect, ecologists need models to estimate relative abundance in the presence of unknown sources of heterogeneity. Two candidate models are generalized linear mixed models (GLMMs) and hierarchical N-mixture models. GLMMs are computationally robust b...

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Detalles Bibliográficos
Autores principales: Goldstein, Benjamin R., de Valpine, Perry
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296480/
https://www.ncbi.nlm.nih.gov/pubmed/35853908
http://dx.doi.org/10.1038/s41598-022-16368-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9296480/
https://www.ncbi.nlm.nih.gov/pubmed/35853908
http://dx.doi.org/10.1038/s41598-022-16368-z

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