ABSTRACT Aim Essential habitats are areas that support biological and ecological functions critical for species' survival. For highly mobile and elusive marine species, aggregations in these habitats provide rare opportunities to study their ecology and inform conservation. We aimed to build a dynamic species distribution model (SDM) to predict essential habitats for migratory marine species. The model was tested on whale sharks ( Rhincodon typus ), a species with ~30 documented aggregation sites across their global distribution, addressing a major knowledge gap in the southwest Pacific (SWP). Location Coral Sea, southwest Pacific. Methods High‐resolution, movement‐informed SDMs were built using behaviourally filtered juvenile whale shark satellite tracks (low move‐persistence locations) to quantify key environmental drivers of inferred foraging and predict dynamic suitability of essential habitat in the SWP. An ensemble modelling approach was applied to account for model uncertainty and improve model reliability by combining regression and machine learning algorithms. Results Model predictions indicated high suitability in the northern Great Barrier Reef during the monsoon season (November–April), shifting eastward into the Coral Sea and beyond during the dry season (May–October). Bathymetric variables (depth, distance to deepwater drop‐off) were key drivers of occurrence, while dynamic variables like sea surface temperature and productivity proxies also contributed largely to model predictions. Across algorithms, spatial block cross‐validation and external validation with independent sightings indicated moderate but consistent discriminatory ability. Habitat suitability predictions varied across algorithms, underscoring the advantages of integrating diverse modelling approaches. Main Conclusions This study presents the first movement‐informed predictions of essential habitat suitability for juvenile whale sharks in the SWP, providing a framework for improving population assessments and guiding research and management. The dynamic SDM approach is broadly applicable, facilitating essential habitat identification, research prioritisation in data‐limited regions, and targeted conservation in dynamic marine environments.
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Miller et al. (Wed,) studied this question.
www.synapsesocial.com/papers/69d895d86c1944d70ce06ed4 — DOI: https://doi.org/10.1111/ddi.70186
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:
Ingo B. Miller
Yuri Niella
Vinay Udyawer
Diversity and Distributions
James Cook University
University of the Sunshine Coast
Sydney Institute of Marine Science
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