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Record W2969967953 · doi:10.3390/f10080706

Density Dependence of Egg Recruitment and Moth Dispersal in Spruce Budworms

2019· article· en· W2969967953 on OpenAlexaff
Jacques Régnière, Vincent G. Nealis

Bibliographic record

VenueForests · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsBiological dispersalSpruce budwormBiologyEcologyDensity dependenceFecundityPopulation densityPopulationBlack spruceTaigaLepidoptera genitaliaDemography

Abstract

fetched live from OpenAlex

Egg recruitment quantifies the relative importance of realized fecundity and migration rates in the population dynamics of highly mobile insects. We develop here a formal context upon which to base the measurement and interpretation of egg recruitment in population dynamics of eastern and western spruce budworms, two geographically separated species that share a very similar ecology. Under most circumstances, per capita egg recruitment rates in these budworms are higher in low-density populations and lower in high-density populations, relative to the regional mean: Low-density populations are nearly always migration sinks for gravid moths, and dense populations nearly always sources. The slope of this relationship, measured on a log scale, is negatively correlated with migration rate, and ranges between 0 and −1. The steeper the slope, the more marked net migration. Using our western spruce budworm observations, we found strong evidence of density-dependent emigration in budworms, so migration is not simply a random perturbation in the lagged, density-dependent stochastic process leading to budworm outbreaks. It is itself statistically and biologically density-dependent. Therefore, moth migration is a synchronizing factor and a spread mechanism that is essential to understanding the development and expansion of spruce budworm outbreaks at regional scales in the boreal forests of North America.

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 score0.028
Threshold uncertainty score0.986

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)0.0000.000

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 head0.011
GPT teacher head0.233
Teacher spread0.222 · 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

Citations34
Published2019
Admission routes1
Has abstractyes

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