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Record W3167704181 · doi:10.1002/ecy.3442

Flight efficiency explains differences in natal dispersal distances in birds

2021· article· en· W3167704181 on OpenAlexafffund
Santiago Claramunt

Bibliographic record

VenueEcology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsRoyal Ontario MuseumUniversity of Toronto
FundersDirectorate for Biological SciencesRoyal Ontario MuseumBurke Museum, University of WashingtonNatural Sciences and Engineering Research Council of CanadaLouisiana State UniversityUniversity of Washington
KeywordsBiological dispersalEcologyBiologyDemography

Abstract

fetched live from OpenAlex

The factors responsible for variation in dispersal distances across species remain poorly understood. Previous comparative studies found differing results and equivocal support for theoretical predictions. Here I re-examine factors that influence natal dispersal distances in British birds while taking into account the cost of transport as estimated from proxies of long-distance flight efficiency. First, I show that flight efficiency, as estimated by the hand-wing index, the aspect ratio, or the lift-to-drag ratio, is a strong predictor of dispersal distances among resident species. Most migratory species showed a similar pattern, but a group of species with relatively low aerodynamic efficiency showed longer-than-expected dispersal distances, making the overall trend independent of flight efficiency. Ecological, behavioral, and life history factors had a small or nil influence on dispersal distances, with most of their influence likely mediated by adaptations for the use of space reflected in flight efficiency. This suggests that dispersal distances in birds are not determined by adaptive strategies for dispersal per se, but are predominantly influenced by the energetic cost of movement.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.994

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)0.0070.000

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 head0.010
GPT teacher head0.229
Teacher spread0.219 · 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.

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

Citations79
Published2021
Admission routes2
Has abstractyes

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