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Record W4281781880 · doi:10.1139/as-2022-0003

Environmental drivers of beluga whale distribution in a changing climate: a case study of summering aggregations in the Mackenzie Estuary and Tarium Niryutait Marine Protected Area

2022· article· en· W4281781880 on OpenAlexafffundvenue
Aurélie Noël, John Iacozza, Emmanuel Devred, Marianne Marcoux, Claire Hornby, Lisa L. Loseto

Bibliographic record

VenueArctic Science · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsBedford Institute of OceanographyUniversity of ManitobaFisheries and Oceans Canada
FundersGoddard Space Flight CenterFisheries and Oceans CanadaNational Aeronautics and Space Administration
KeywordsBeluga WhaleBelugaEstuaryHabitatFisheryLeucasEnvironmental scienceClimate changePopulationGeographyOceanographyCritical habitatEcologyBiologyArcticEndangered speciesGeology

Abstract

fetched live from OpenAlex

During summer, the Eastern Beaufort Sea beluga whale ( Delphinapterus leucas (Pallas, 1776)) population aggregates in the waters of the Mackenzie Estuary and Tarium Niryutait Marine Protected Area (TNMPA). Guided by local communities’ priorities, this study aimed to better understand beluga summer habitat selection and to examine whether shifts in beluga distribution are expected under a changing climate. We used a resource selection function (RSF) based on aerial survey data and satellite remote sensing images to estimate the likelihood of beluga presence as a function of environmental conditions. The RSF revealed that belugas selected warm and turbid waters, with suspended particulate matter concentrations and sea surface temperatures ranging above average estuarine values. These specific conditions support hypotheses on the ecological roles of estuaries for belugas such as providing a thermal advantage for their calves or for beluga epidermal moulting. Using a diachronic analysis, we found a distribution shift towards coastal and inshore waters, areas already experiencing effects of climate change. Thus, the current distribution may reflect beluga responses to a changing climate, selecting warmer and more turbid areas. Our finding provides insight into current and evolving beluga habitat and habitat selection under a changing climate, which may help inform beluga management in the TNMPA.

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)0.001
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 score0.045
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.229
Teacher spread0.213 · 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

Citations8
Published2022
Admission routes3
Has abstractyes

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