Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers
Species distribution models (SDMs) link species occurrence to environmental characteristics to predict suitable habitats beyond known occurrences. The conventional procedure to fit SDMs for individual organisms detected at some distance away from observers is to characterize species’ associated habi...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Ecological Indicators |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X25004765 |
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| author | Fang-Yu Shen Fiona Victoria Stanley Jothiraj Rebecca A. Hutchinson Tyler A. Hallman Jenna R. Curtis W. Douglas Robinson |
| author_facet | Fang-Yu Shen Fiona Victoria Stanley Jothiraj Rebecca A. Hutchinson Tyler A. Hallman Jenna R. Curtis W. Douglas Robinson |
| author_sort | Fang-Yu Shen |
| collection | DOAJ |
| description | Species distribution models (SDMs) link species occurrence to environmental characteristics to predict suitable habitats beyond known occurrences. The conventional procedure to fit SDMs for individual organisms detected at some distance away from observers is to characterize species’ associated habitat based on observer’s survey location. However, each surveyed individual may be detected in habitats distinct from those where observers are located. Here, we compared environmental variables centered on the observer and individual bird locations and the consequent effects on SDMs performance. We utilized remote sensing data on observer- and bird-locations to characterize habitat at three radii (pixel radius: 30-m; fixed radius: 100-m; species-specific effective detection radius). We trained Poisson boosted regression tree models for 105 bird species from structured professional surveys. We evaluated models’ predictability with Kendall’s rank correlation coefficient and used linear mixed-effect models to measure the effect of characterization locations and radii. Models based on bird locations exhibited a median increase of 22.9% in predictive performance, demonstrating higher Kendall’s rank correlation coefficients than those based on observer locations, leading to more reliable prediction maps. SDMs of habitat specialists and generalists performed better when habitat characterization was centered on bird instead of surveyor locations. A higher percentage of habitat specialists (72%) than generalists (55%) showed better model performance in bird-location than in observer-location models. Across radii, fixed radius generally performed better than species-specific effective and pixel radii. Our findings emphasize the importance of prioritizing habitat characterizations based on detected individuals’ locations to enhance model performance and improve species distribution predictions. |
| format | Article |
| id | doaj-art-28fd91bd8b3a4b7bb08a5321fc29f70b |
| institution | OA Journals |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-28fd91bd8b3a4b7bb08a5321fc29f70b2025-08-20T02:07:27ZengElsevierEcological Indicators1470-160X2025-07-0117611354610.1016/j.ecolind.2025.113546Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observersFang-Yu Shen0Fiona Victoria Stanley Jothiraj1Rebecca A. Hutchinson2Tyler A. Hallman3Jenna R. Curtis4W. Douglas Robinson5Department of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA; Corresponding author.School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USADepartment of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA; School of Electrical Engineering and Computer Science, Oregon State University, Corvallis, OR 97331, USADepartment of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA; School of Environmental and Natural Sciences, Bangor University, Bangor LL57 2DG, UKDepartment of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USA; Cornell Lab of Ornithology, Cornell University, Ithaca, NY 14850, USADepartment of Fisheries, Wildlife and Conservation Sciences, Oregon State University, Corvallis, OR 97331, USASpecies distribution models (SDMs) link species occurrence to environmental characteristics to predict suitable habitats beyond known occurrences. The conventional procedure to fit SDMs for individual organisms detected at some distance away from observers is to characterize species’ associated habitat based on observer’s survey location. However, each surveyed individual may be detected in habitats distinct from those where observers are located. Here, we compared environmental variables centered on the observer and individual bird locations and the consequent effects on SDMs performance. We utilized remote sensing data on observer- and bird-locations to characterize habitat at three radii (pixel radius: 30-m; fixed radius: 100-m; species-specific effective detection radius). We trained Poisson boosted regression tree models for 105 bird species from structured professional surveys. We evaluated models’ predictability with Kendall’s rank correlation coefficient and used linear mixed-effect models to measure the effect of characterization locations and radii. Models based on bird locations exhibited a median increase of 22.9% in predictive performance, demonstrating higher Kendall’s rank correlation coefficients than those based on observer locations, leading to more reliable prediction maps. SDMs of habitat specialists and generalists performed better when habitat characterization was centered on bird instead of surveyor locations. A higher percentage of habitat specialists (72%) than generalists (55%) showed better model performance in bird-location than in observer-location models. Across radii, fixed radius generally performed better than species-specific effective and pixel radii. Our findings emphasize the importance of prioritizing habitat characterizations based on detected individuals’ locations to enhance model performance and improve species distribution predictions.http://www.sciencedirect.com/science/article/pii/S1470160X25004765SpecialistModel performanceAbundancePositional errorLandsatEffective detection radius |
| spellingShingle | Fang-Yu Shen Fiona Victoria Stanley Jothiraj Rebecca A. Hutchinson Tyler A. Hallman Jenna R. Curtis W. Douglas Robinson Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers Ecological Indicators Specialist Model performance Abundance Positional error Landsat Effective detection radius |
| title | Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers |
| title_full | Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers |
| title_fullStr | Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers |
| title_full_unstemmed | Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers |
| title_short | Species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers |
| title_sort | species distribution model performance improves when habitat characterizations are centered on detected individuals instead of observers |
| topic | Specialist Model performance Abundance Positional error Landsat Effective detection radius |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X25004765 |
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