Effectiveness of joint species distribution models in the presence of imperfect detection
Abstract Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of t...
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Wiley
2021-08-01
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| Series: | Methods in Ecology and Evolution |
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| Online Access: | https://doi.org/10.1111/2041-210X.13614 |
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| author | Stephanie Elizabeth Hogg Yan Wang Lewi Stone |
| author_facet | Stephanie Elizabeth Hogg Yan Wang Lewi Stone |
| author_sort | Stephanie Elizabeth Hogg |
| collection | DOAJ |
| description | Abstract Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise. A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of ‘collapsed data’. A case study of owls and gliders in Victoria, Australia, is also illustrated. Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications. To avoid biased estimates of inter‐species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter‐dependencies and occupancy. |
| format | Article |
| id | doaj-art-cdb849d26f6f444db8b0a0b968583a99 |
| institution | Kabale University |
| issn | 2041-210X |
| language | English |
| publishDate | 2021-08-01 |
| publisher | Wiley |
| record_format | Article |
| series | Methods in Ecology and Evolution |
| spelling | doaj-art-cdb849d26f6f444db8b0a0b968583a992025-02-07T06:21:05ZengWileyMethods in Ecology and Evolution2041-210X2021-08-011281458147410.1111/2041-210X.13614Effectiveness of joint species distribution models in the presence of imperfect detectionStephanie Elizabeth Hogg0Yan Wang1Lewi Stone2Mathematics School of Science RMIT Melbourne AustraliaMathematics School of Science RMIT Melbourne AustraliaMathematics School of Science RMIT Melbourne AustraliaAbstract Joint species distribution models (JSDMs) are a recent development in biogeography and enable the spatial modelling of multiple species and their interactions and dependencies. However, most models do not consider imperfect detection, which can significantly bias estimates. This is one of the first papers to account for imperfect detection when fitting data with JSDMs and to explore the complications that may arise. A multivariate probit JSDM that explicitly accounts for imperfect detection is proposed, and implemented using a Bayesian hierarchical approach. We investigate the performance of the JSDM in the presence of imperfect detection for a range of factors, including varied levels of detection and species occupancy, and varied numbers of survey sites and replications. To understand how effective this JSDM is in practice, we also compare results to those from a JSDM that does not explicitly model detection but instead makes use of ‘collapsed data’. A case study of owls and gliders in Victoria, Australia, is also illustrated. Using simulations, we found that the JSDMs explicitly accounting for detection can accurately estimate intrinsic correlation between species with enough survey sites and replications. Reducing the number of survey sites decreases the precision of estimates, while reducing the number of survey replications can lead to biased estimates. For low probabilities of detection, the model may require a large number of survey replications to remove bias from estimates. However, JSDMs not explicitly accounting for detection may have a limited ability to disentangle detection from occupancy, which substantially reduces their ability to accurately infer the species distribution spatially. Our case study showed positive correlation between Sooty Owls and Greater Gliders, despite a low number of survey replications. To avoid biased estimates of inter‐species correlations and species distributions, imperfect detection needs to be considered. However, for low probability of detection, the JSDMs explicitly accounting for detection is data hungry. Estimates from such models may still be subject to bias. To overcome the bias, researchers need to carefully design surveys and choose appropriate modelling approaches. The survey design should ensure sufficient survey replications for unbiased inferences on species inter‐dependencies and occupancy.https://doi.org/10.1111/2041-210X.13614detection probabilityimperfect detectioninter‐species correlationjoint species distribution modelmultivariate probitoccupancy model |
| spellingShingle | Stephanie Elizabeth Hogg Yan Wang Lewi Stone Effectiveness of joint species distribution models in the presence of imperfect detection Methods in Ecology and Evolution detection probability imperfect detection inter‐species correlation joint species distribution model multivariate probit occupancy model |
| title | Effectiveness of joint species distribution models in the presence of imperfect detection |
| title_full | Effectiveness of joint species distribution models in the presence of imperfect detection |
| title_fullStr | Effectiveness of joint species distribution models in the presence of imperfect detection |
| title_full_unstemmed | Effectiveness of joint species distribution models in the presence of imperfect detection |
| title_short | Effectiveness of joint species distribution models in the presence of imperfect detection |
| title_sort | effectiveness of joint species distribution models in the presence of imperfect detection |
| topic | detection probability imperfect detection inter‐species correlation joint species distribution model multivariate probit occupancy model |
| url | https://doi.org/10.1111/2041-210X.13614 |
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