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Record W4408943073 · doi:10.3389/fevo.2025.1497949

Temperature and stopover duration carry-over to affect Arctic arrival timing and breeding success in the cackling goose (Branta hutchinsii)

2025· article· en· W4408943073 on OpenAlexafffundabout
Saeedeh Bani Assadi, Frank Baldwin, Leanne R. Neufeld, Kevin C. Fraser

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMarine animal studies overview
Canadian institutionsEnvironment and Climate Change CanadaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGooseBrantaArcticDuration (music)WaterfowlEcologyAffect (linguistics)GeographyFisheryEnvironmental scienceBiologyHabitat

Abstract

fetched live from OpenAlex

Arrival timing in spring may be mediated by conditions experienced during migratory stopovers or staging areas, but our knowledge about their impact on migration timing and reproduction is limited. We explored the role of stopover duration on spring migration timing and successful incubation in cackling geese (Branta hutchinsii), which breed at high arctic latitudes where climate change effects are more pronounced. To track migration phenology and incubation duration, 236 light-level geolocators were deployed on cackling geese during the breeding period at Baffin Island, Nunavut, Canada between 2016 and 2018. Using data available for spring migration in the year following tag deployment (25 tags retrieved), we found that most geese had long, coastal stopovers (8–39 days) before crossing Hudson Bay on the last leg of their spring migration to their Baffin Island breeding area. We show that longer stopover durations at these Hudson Bay Lowland sites were associated with successful completion of incubation (a proxy for breeding success). Although spending more time at the stopover led to a later arrival date at the breeding ground, longer stopovers may increase the energy stores necessary for reproduction in these capital breeders. Stopover duration did not influence the incubation interval (number of days between arrival date at the breeding ground and start of incubation). Lastly, we found that the temperature at stopovers influenced migration timing, with higher temperatures resulting in earlier arrival at the breeding ground. Overall, our results demonstrate that conditions and behaviour at distant stopovers (1700–3000 km away) have important influence on timing and breeding success once birds arrive at their Arctic breeding sites. Therefore, our understanding of climate change impacts on these Arctic-breeding geese must also include the influence of en route conditions

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.060
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2025
Admission routes3
Has abstractyes

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