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Record W4412010056 · doi:10.3354/meps14906

Characterizing seasonal whale shark habitat in the western North Atlantic

2025· article· en· W4412010056 on OpenAlexaboutno aff
Hunter Milles, Eric R. Hoffmayer, Martin C. Arostegui, Rafael de la Parra-Venegas, William B. Driggers, James S. Franks, Rachel T. Graham, Jill M. Hendon, Jennifer A. McKinney, Meaghan Olton, Jennifer V. Schmidt, Julia Robinson Willmott, Rebecca L. Lewison, Camrin D. Braun

Bibliographic record

VenueMarine Ecology Progress Series · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicUnderwater Acoustics Research
Canadian institutionsnot available
Fundersnot available
KeywordsWhaleFisheryHabitatOceanographyGeographyMarine mammalEnvironmental scienceEcologyBiologyGeology

Abstract

fetched live from OpenAlex

There are significant knowledge gaps in the ecology of whale sharks Rhincodon typus beyond well-studied coastal aggregation sites. We synthesize several disparate data types, including scientific aerial and shipboard surveys, environmental impact assessments, and opportunistic citizen science records, in a species distribution model framework to characterize whale shark distribution across the northwest Atlantic Ocean (NWA), Gulf of Mexico, and the Caribbean Sea. Based on 2010 occurrence records spanning 1993-2023 from the Bay of Fundy to the North Brazil Current off the northern coast of South America, we developed a species distribution model to characterize seasonal habitat suitability for whale sharks. The model indicated that bathymetry (46.2%), sea surface temperature (21.0%), and sea surface height (16.1%) explained the most variability in habitat suitability. The model predicted high suitability in known coastal aggregation areas and continental shelf edges along the US east coast from southern Florida to Cape Hatteras year-round, expanding north to the USA-Canada border in summer and autumn. Suitability was also high in the north-central Gulf of Mexico during summer and autumn and in the Yucatan and Caribbean region throughout the year. These findings underscore a broad whale shark distribution across the NWA beyond known aggregation sites, emphasizing seasonal suitability along the US east coast and in the Gulf of Mexico. Given rapid climate-induced changes in the NWA, our findings are a critical step toward understanding climatic impacts on this charismatic species and can support marine spatial planning and conservation efforts.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.014
GPT teacher head0.248
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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