Biogeographic multi‐species occupancy models for large‐scale survey data
Abstract Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi‐species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for loc...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-10-01
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| Series: | Ecology and Evolution |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/ece3.9328 |
| _version_ | 1811316287435964416 |
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| author | Jacob B. Socolar Simon C. Mills Torbjørn Haugaasen James J. Gilroy David P. Edwards |
| author_facet | Jacob B. Socolar Simon C. Mills Torbjørn Haugaasen James J. Gilroy David P. Edwards |
| author_sort | Jacob B. Socolar |
| collection | DOAJ |
| description | Abstract Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi‐species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local‐scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating preexisting range information into MSOMs, yielding a “biogeographic multi‐species occupancy model” (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey and entire avian communities in forests and pastures of Colombia's West Andes. Compared with traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species‐specific inference even for never‐observed species. Incorporating preexisting range data enables principled partial pooling of information across species in large‐scale MSOMs. Our biogeographic framework for multi‐species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false absences are modeled in an occupancy framework. |
| first_indexed | 2024-04-13T11:47:25Z |
| format | Article |
| id | doaj.art-a6da94412bfa4ef988aac4bf621e2642 |
| institution | Directory Open Access Journal |
| issn | 2045-7758 |
| language | English |
| last_indexed | 2024-04-13T11:47:25Z |
| publishDate | 2022-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | Ecology and Evolution |
| spelling | doaj.art-a6da94412bfa4ef988aac4bf621e26422022-12-22T02:48:09ZengWileyEcology and Evolution2045-77582022-10-011210n/an/a10.1002/ece3.9328Biogeographic multi‐species occupancy models for large‐scale survey dataJacob B. Socolar0Simon C. Mills1Torbjørn Haugaasen2James J. Gilroy3David P. Edwards4Faculty of the Environment and Natural Resources Management Norwegian University of Life Sciences Ås NorwayEcology and Evolutionary Biology School of Biosciences, University of Sheffield Sheffield UKFaculty of the Environment and Natural Resources Management Norwegian University of Life Sciences Ås NorwaySchool of Environmental Sciences University of East Anglia Norwich UKEcology and Evolutionary Biology School of Biosciences, University of Sheffield Sheffield UKAbstract Ecologists often seek to infer patterns of species occurrence or community structure from survey data. Hierarchical models, including multi‐species occupancy models (MSOMs), can improve inference by pooling information across multiple species via random effects. Originally developed for local‐scale survey data, MSOMs are increasingly applied to larger spatial scales that transcend major abiotic gradients and dispersal barriers. At biogeographic scales, the benefits of partial pooling in MSOMs trade off against the difficulty of incorporating sufficiently complex spatial effects to account for biogeographic variation in occupancy across multiple species simultaneously. We show how this challenge can be overcome by incorporating preexisting range information into MSOMs, yielding a “biogeographic multi‐species occupancy model” (bMSOM). We illustrate the bMSOM using two published datasets: Parulid warblers in the United States Breeding Bird Survey and entire avian communities in forests and pastures of Colombia's West Andes. Compared with traditional MSOMs, the bMSOM provides dramatically better predictive performance at lower computational cost. The bMSOM avoids severe spatial biases in predictions of the traditional MSOM and provides principled species‐specific inference even for never‐observed species. Incorporating preexisting range data enables principled partial pooling of information across species in large‐scale MSOMs. Our biogeographic framework for multi‐species modeling should be broadly applicable in hierarchical models that predict species occurrences, whether or not false absences are modeled in an occupancy framework.https://doi.org/10.1002/ece3.9328community modelhierarchical modeloccupancy modelpoolingspatial scale |
| spellingShingle | Jacob B. Socolar Simon C. Mills Torbjørn Haugaasen James J. Gilroy David P. Edwards Biogeographic multi‐species occupancy models for large‐scale survey data Ecology and Evolution community model hierarchical model occupancy model pooling spatial scale |
| title | Biogeographic multi‐species occupancy models for large‐scale survey data |
| title_full | Biogeographic multi‐species occupancy models for large‐scale survey data |
| title_fullStr | Biogeographic multi‐species occupancy models for large‐scale survey data |
| title_full_unstemmed | Biogeographic multi‐species occupancy models for large‐scale survey data |
| title_short | Biogeographic multi‐species occupancy models for large‐scale survey data |
| title_sort | biogeographic multi species occupancy models for large scale survey data |
| topic | community model hierarchical model occupancy model pooling spatial scale |
| url | https://doi.org/10.1002/ece3.9328 |
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