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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
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 |
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author | Goldstein, Benjamin R. de Valpine, Perry |
author_facet | Goldstein, Benjamin R. de Valpine, Perry |
author_sort | Goldstein, Benjamin R. |
collection | PubMed |
description | 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 but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models—especially N-mixtures with beta-binomial detection submodels—were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models. |
format | Online Article Text |
id | pubmed-9296480 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-92964802022-07-21 Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset Goldstein, Benjamin R. de Valpine, Perry Sci Rep Article 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 but do not explicitly separate detection from abundance patterns. N-mixture models separately estimate detection and abundance via a latent state but are sensitive to violations in assumptions and subject to practical estimation issues. When one can assume that detection is not systematically confounded with ecological patterns of interest, these two models can be viewed as sharing a heuristic framework for relative abundance estimation. Model selection can then determine which predicts observed counts best, for example by AIC. We compared four N-mixture model variants and two GLMM variants for predicting bird counts in local subsets of a citizen science dataset, eBird, based on model selection and goodness-of-fit measures. We found that both GLMMs and N-mixture models—especially N-mixtures with beta-binomial detection submodels—were supported in a moderate number of datasets, suggesting that both tools are useful and that relative fit is context-dependent. We provide faster software implementations of N-mixture likelihood calculations and a reparameterization to interpret unstable estimates for N-mixture models. Nature Publishing Group UK 2022-07-19 /pmc/articles/PMC9296480/ /pubmed/35853908 http://dx.doi.org/10.1038/s41598-022-16368-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Goldstein, Benjamin R. de Valpine, Perry Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset |
title | Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset |
title_full | Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset |
title_fullStr | Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset |
title_full_unstemmed | Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset |
title_short | Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset |
title_sort | comparing n-mixture models and glmms for relative abundance estimation in a citizen science dataset |
topic | Article |
url | 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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