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Record W2052182082 · doi:10.1111/eea.12019

Mate‐finding allee effect in spruce budworm population dynamics

2012· article· en· W2052182082 on OpenAlexaff
Jacques Régnière, Johanne Delisle, Deepa S. Pureswaran, Richard Trudel

Bibliographic record

VenueEntomologia Experimentalis et Applicata · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAnimal Ecology and Behavior Studies
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsAllee effectBiologySpruce budwormPopulationMatingPopulation densityEcologyZoologyLepidoptera genitaliaDemographyTortricidae

Abstract

fetched live from OpenAlex

Abstract Allee effects can cause populations to decline due to decreasing population growth rates with decreasing density and play a major role in population dynamics. Mate‐finding failure, a common mechanism contributing to demographic A llee effects, is usually difficult to demonstrate because of the arduous nature of sampling individuals at very low densities. In a rising outbreak of the eastern spruce budworm, C horistoneura fumiferana ( C lemens) ( L epidoptera: T ortricidae), we used caged and tethered virgin females in traps to measure mating success over population densities ranging from deep endemic to outbreak conditions. We found that mating success increased with increasing population density, and that at endemic population densities, females experienced difficulties attracting males and mating, demonstrating for the first time a mate‐finding A llee effect in the spruce budworm. The relationship between population density and mating success is nonlinear. As population density increased, the proportion of mated females eventually reached a plateau and mating success was not 100% even at the highest moth densities, probably due to female reluctance to mate and perhaps interference competition by males for access to females. Both laboratory‐reared and wild females were equally effective in synthesizing pheromone, attracting males, and mating. Our results strongly suggest that a mate‐finding A llee effect is involved in maintaining low‐density spruce budworm populations below an A llee threshold where they fail to grow. Factors such as changes in predation pressure and immigration could help populations overcome this A llee threshold.

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 score0.068
Threshold uncertainty score1.000

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.322
Teacher spread0.305 · 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

Citations66
Published2012
Admission routes1
Has abstractyes

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