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Partial migration in birds: tests of three hypotheses in a tropical lekking frugivore

2008· article· en· W2155796593 on OpenAlexaff
W. Alice Boyle

Bibliographic record

VenueJournal of Animal Ecology · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsWestern University
Fundersnot available
KeywordsForagingBiologyLek matingEcologyDominance (genetics)Temperate climateContext (archaeology)FrugivoreZoologyMate choiceHabitat

Abstract

fetched live from OpenAlex

1. Partially migratory species provide opportunities to understand which ecological factors cause some animals to migrate when others remain resident year round. Partial migration in birds has been explained by the dominance, arrival-time, and body-size hypotheses. 2. Testing these hypotheses has proven difficult due to the similarities of the predictions they make in temperate-breeding long-distance migrants. In tropical altitudinal migrants, however, these hypotheses make different predictions regarding the sex, age, and condition of migrants and residents. 3. Among white-ruffed manakins in Costa Rica, young birds were not more likely to migrate (as predicted by the dominance hypothesis), nor were females more likely to migrate (as predicted by the arrival-time hypothesis). All condition-related variables interacted with sex, together explaining much of the variation in migratory behaviour. 4. I re-articulate the body-size hypothesis in the context of tropical altitudinal bird migration, focusing explicitly on how limited foraging opportunities and differences in individual condition affect fasting ability during torrential rains. Despite ample food, the smallest birds or those stressed by parasites or moult may risk starvation at breeding elevations due to a reduction in foraging time. These results highlight how intrinsic and extrinsic factors may interact to produce observed patterns of within- and among-species variation in migratory behaviour.

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 score0.022
Threshold uncertainty score0.996

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 science0.0000.000
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 head0.032
GPT teacher head0.260
Teacher spread0.228 · 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

Citations121
Published2008
Admission routes1
Has abstractyes

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