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Record W4383879058 · doi:10.1093/beheco/arad052

How do resource distribution and taxonomy affect the use of dual foraging in seabirds? A review

2023· review· en· W4383879058 on OpenAlexafffund
Jessica Ann Phillips, Tim Guilford, Annette L. Fayet

Bibliographic record

VenueBehavioral Ecology · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsOcean Networks Canada Society
FundersNatural Sciences and Engineering Research Council of CanadaNorges Forskningsråd
KeywordsForagingSeabirdBiologyHabitatEcologyPredation

Abstract

fetched live from OpenAlex

In many seabird species, parents feeding young switch between short and long foraging excursions in a strategy known as "dual foraging." To investigate whether habitat quality near breeding colonies drives the use of dual foraging, we conducted a review of the seabird literature, compiling the results of 102 studies which identified dual-foraging in 50 species across nine families from all six seabird orders. We estimated the mean distance from the colony of each species' short and long foraging trips and obtained remote-sensed data on chlorophyll-a concentrations within the radius of both short and long trips around each colony. We then assessed, for each seabird family, the relationship between the use of dual foraging strategies and the difference in the quality of foraging locations between short- and long-distance foraging trips. We found that the probability of dual foraging grew with increasing differences in the quality of foraging locations available during short- and long-distance trips. We also found that when controlling for differences in habitat quality, albatrosses and penguins were less likely to use dual foraging than Procellariidae, which in turn were less likely to use dual foraging than Sulids. This study helps clarify how environmental conditions and taxon-specific characteristics influence seabird foraging behavior.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.881
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.170
GPT teacher head0.343
Teacher spread0.173 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations12
Published2023
Admission routes2
Has abstractyes

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