Models Incorporating Non‐Stationarity Improve Detection of Climate‐Driven Range Shifts in Odontocetes
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Le résumé fourni par la source
ABSTRACT Aim Climate change is causing distributional shifts in many species globally. Identifying and anticipating these shifts is critical to understanding ecosystem impacts and implementing successful management strategies. Species distribution models ( SDMs ) are useful tools often employed to describe current and changing habitat use, particularly for marine predators. However, most SDMs assume the statistical relationships between species and their environment are temporally static, which may not be true. We examined how incorporating temporal variability improved SDM performance and estimated range shifts for six odontocete species. We used a high performing model to quantify changes in odontocete distribution over a 24‐year period. Location Waters of the United States, east coast, from Florida to Nova Scotia. Methods We utilised nearly 1.4 million kilometres of line transect survey data collected from 1997 to 2020 along the East Coast of the United States to evaluate changes in the distribution of six odontocete species. We assessed six model specifications of generalise additive models that varied in the extent of temporal and spatial variability incorporated. Results We found that the best performing model specifications included temporally dynamic species–environment relationships and temporally dynamic spatial terms. These model specifications identified significant poleward range shifts in all species for which we had sufficient data across their range. In contrast, model specifications which only included static terms performed poorly and identified limited or no spatial shifts. Main Conclusions These results advance our predictive capabilities from static species–environment relationships for marine predators and demonstrate the importance of carefully considering assumptions and model specifications when modelling changes to distributions. The odontocete range shifts we identified are likely to have substantial ecosystem impacts, and the framework we present offers a diagnostic approach for modelling and identifying range shifts in other wide‐ranging species.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Models Incorporating Non‐Stationarity Improve Detection of Climate‐Driven Range Shifts in Odontocetes
- Date Crossref
- 01/02/2026
- Éditeur
- Wiley
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Stony Brook University pays non établi dans la noticeUniversité ou école supérieure
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Geospatial Research (United Kingdom) pays non établi dans la noticeEntreprise
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New England Aquarium pays non établi dans la noticeInstitution
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Cabot (United States) pays non établi dans la noticeEntreprise
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NOAA National Marine Fisheries Service Southwest Fisheries Science Center pays non établi dans la noticeOrganisme public
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NOAA National Marine Fisheries Service Northeast Fisheries Science Center pays non établi dans la noticeOrganisme public
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University of North Carolina Wilmington pays non établi dans la noticeUniversité ou école supérieure
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NOAA National Marine Fisheries Service Southeast Fisheries Science Center pays non établi dans la noticeOrganisme public
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Virginia Aquarium & Marine Science Center pays non établi dans la noticeInstitution
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Simmons University pays non établi dans la noticeUniversité ou école supérieure
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New York State Department of Environmental Conservation pays non établi dans la noticeOrganisme public
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Tetra Tech (United States) pays non établi dans la noticeEntreprise
Stony Brook University, Geospatial Research (United Kingdom) et New England Aquarium, avec 9 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.