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Record W4200204250 · doi:10.1093/ornithology/ukab078

Endogenous biomarkers reveal diet partitioning among three sympatric species of swallows

2021· article· en· W4200204250 on OpenAlexafffundabout
Kaelyn H Bumelis, Michael D. Cadman, Keith A. Hobson

Bibliographic record

VenueThe Auk · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicIsotope Analysis in Ecology
Canadian institutionsEnvironment and Climate Change CanadaWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHirundoBiologyInsectivoreSympatric speciationEcologyPopulationWarblerPredationInterspecific competitionZoologyHabitat

Abstract

fetched live from OpenAlex

Abstract Since the early 1990s, aerial insectivorous birds have shown serious population declines in North America, but it is not clear if factors common to all species within this guild account for these declines. Among sympatric swallows, population trends differ, and this may be due to differences in ecology operating throughout the annual cycle. Although these species all feed on aerial insects, prey taxa can differ tremendously in their “aeroecology” and use by swallows. We examined the potential for dietary differences among three species of swallows, Barn Swallow (Hirundo rustica), Cliff Swallow (Petrochelidon pyrrhonota), and Tree Swallow (Tachycineta bicolor), breeding sympatrically in southern Ontario, Canada. Potential interspecific differences in nestling diet were examined using two endogenous biomarkers, DNA barcoding of nestling feces and stable isotope analysis (δ 2H, δ 13C, δ 15N) of nestling feathers. We found evidence for differences in dietary sources of provisioned young where Barn Swallows provisioned more terrestrial-based prey, Cliff Swallows provisioned an intermediate diet, and Tree Swallows the most aquatic-emergent insect diet. We suggest this information may help to identify potential factors contributing to differential declines of aerial insectivores operating on the breeding grounds, including diet quality.

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 score0.058
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)0.0050.000

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 head0.021
GPT teacher head0.210
Teacher spread0.189 · 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

Citations16
Published2021
Admission routes3
Has abstractyes

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