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Timing of breeding carries over to influence migratory departure in a songbird: an automated radiotracking study

2012· article· en· W2109173834 on OpenAlexafffund
Greg W. Mitchell, Amy E. M. Newman, Martin Wikelski, D. Ryan Norris

Bibliographic record

VenueJournal of Animal Ecology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversity of Guelph
FundersSociety of Canadian OrnithologistsBowdoin CollegeAnimal Behavior SocietyUniversity of Guelph
KeywordsSongbirdFledgeAnnual cyclePopulationEcologyGeographyPhenologyBiologyDemographyPredation

Abstract

fetched live from OpenAlex

1. Determining how events interact across stages of the annual cycle is critical for understanding the factors that affect individual fitness. However, there is currently little information detailing how breeding events influence migratory behaviour. 2. Using an automated digital telemetry array and an isolated island-breeding population of Savannah sparrows Passerculus sandwichensis, we provide the first direct evidence that the timing of breeding events carries over to influence the timing of migration in a songbird and assess for the first time how weather conditions on the breeding grounds also affect departure dates. 3. Date of migratory departure between September and October was strongly influenced by date of breeding completion in adults and fledging date in juveniles from June to July. 4. With respect to weather, adults departed during the first half of high-pressure systems, while juveniles departed throughout the entirety of high-pressure systems (including rainy evenings on the western edge of systems). 5. By combining both ecological and weather data, we could explain almost all variation in departure date for adults (95%), but weather conditions were not a good predictor of departure date for juveniles. 6. Our results provide strong evidence that the timing of breeding events is an important driver of migration timing and that exact departure dates are fine-tuned according to local weather conditions in adults, but not in juveniles.

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 score0.050
Threshold uncertainty score0.559

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.020
GPT teacher head0.312
Teacher spread0.292 · 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

Citations97
Published2012
Admission routes2
Has abstractyes

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