Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset
Abstract 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...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2022-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-022-16368-z |
| _version_ | 1811287147076911104 |
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| author | Benjamin R. Goldstein Perry de Valpine |
| author_facet | Benjamin R. Goldstein Perry de Valpine |
| author_sort | Benjamin R. Goldstein |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-04-13T03:13:34Z |
| format | Article |
| id | doaj.art-e4fcfb9f6af344b19158af8e7031e246 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-13T03:13:34Z |
| publishDate | 2022-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e4fcfb9f6af344b19158af8e7031e2462022-12-22T03:04:59ZengNature PortfolioScientific Reports2045-23222022-07-0112111210.1038/s41598-022-16368-zComparing N-mixture models and GLMMs for relative abundance estimation in a citizen science datasetBenjamin R. Goldstein0Perry de Valpine1Department of Environmental Science, Policy, and Management, University of California, BerkeleyDepartment of Environmental Science, Policy, and Management, University of California, BerkeleyAbstract 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.https://doi.org/10.1038/s41598-022-16368-z |
| spellingShingle | Benjamin R. Goldstein Perry de Valpine Comparing N-mixture models and GLMMs for relative abundance estimation in a citizen science dataset Scientific Reports |
| 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 |
| url | https://doi.org/10.1038/s41598-022-16368-z |
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