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Record W2099073305 · doi:10.1525/auk.2012.11184

Avian use of early-successional boreal forests in the postbreeding period

2012· article· en· W2099073305 on OpenAlexafffundabout
Mélanie Major, André Desrochers

Bibliographic record

VenueThe Auk · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAvian ecology and behavior
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFrugivoreSeed dispersalBorealEcologyTaigaAbundance (ecology)GeographyBiologyBiological dispersalHabitat

Abstract

fetched live from OpenAlex

The postbreeding period is critical for many forest birds and especially for juveniles, which must learn to forage on their own before the fall migration. During this period, many birds of the boreal forest are found in early-successional stands (ESS), where fruit abundance is typically high. Boreal forest birds may use ESS to exploit fruit or for reasons other than access to fruit, namely to forage along forest edges or simply to transit through clearcuts between patches of mature forest. We tested whether frugivory, edge use, and transit through small (<65 ha) clearcuts between mature-forest patches accounted for bird abundance in ESS in a boreal forest of Quebec during the summers 2007 and 2008. Fifteen of the 33 species captured in ESS were postbreeding frugivores. Removal of all fruits from Sambucus racemosa, a dominant fruiting plant, within 10 m of mist-netting sites reduced the number of frugivores captured by 45% but did not affect nonfrugivores. Numbers of birds captured were independent of distance from mature-forest edges, thus refuting the edge hypothesis, at least in a range of 20–60 m. Mist nets placed parallel to mature-forest edges intercepted more mature-forest birds than mist nets placed perpendicular to edges, as would be expected if mature-forest birds traveled straight through ESS. We conclude that frugivory and transit, but not edge proximity, contribute to the postbreeding abundance of mature-forest birds in boreal early-successional stands.

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 score0.004
Threshold uncertainty score0.790

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 head0.034
GPT teacher head0.267
Teacher spread0.233 · 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

Citations17
Published2012
Admission routes3
Has abstractyes

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