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Seed dispersal facilitation and geographic consistency in bird–fruit abundance patterns

2002· article· en· W2019919720 on OpenAlexaboutno aff
Kevin C. Burns

Bibliographic record

VenueGlobal Ecology and Biogeography · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPhenologyBiological dispersalEcologyBiologyAbundance (ecology)Seed dispersalFrugivoreFacilitationHabitatPopulation

Abstract

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Abstract Avian seed dispersal mutualisms are characterized frequently by stochastic interactions between birds and fruits; however, many studies report coarse‐scale correlations in annual abundances of birds and fruits at particular locales (i.e. ‘phenological synchrony’). This study tested the geographical consistency of phenological synchrony in a meta‐analysis of data from 14 biogeographic locations. Data from a single site in British Columbia, Canada, were then used to test the dispersal facilitation hypothesis, which postulates that synchronous bird–fruit abundance patterns result from deterministic seed dispersal processes (i.e. avian fruit consumption). Results showed that phenological synchrony is a geographically consistent pattern. However, fruit production occurred after peak periods of bird abundances in British Columbia. Although phenological patterns were asynchronous at this site, observational and experimental fruit removal patterns supported the dispersal facilitation hypothesis. Avian fruit consumption covaried with bird abundances, suggesting selection may favour earlier fruit production and increased phenological synchrony. Environmental data suggest that earlier fruit production is constrained by cold spring temperatures, which inhibit the activity of pollinators and earlier dates of fruit maturation. Overall, the results show that phenological synchrony is a geographically consistent pattern in seed dispersal mutualisms. However, decoupled bird–fruit abundance patterns may occur despite deterministic processes favouring phenological synchrony.

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 score0.026
Threshold uncertainty score0.991

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations54
Published2002
Admission routes1
Has abstractyes

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