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Record W2479593622 · doi:10.1002/eap.1404

Aggregation and a strong<scp>A</scp>llee effect in a cooperative outbreak insect

2016· article· en· W2479593622 on OpenAlexafffund
Devin W. Goodsman, Dean C. Koch, Caroline Whitehouse, Maya L. Evenden, Barry J. Cooke, Mark A. Lewis

Bibliographic record

VenueEcological Applications · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsCanadian Forest ServiceAlberta Ministry of Agriculture and ForestryAgriculture Food and Rural DevelopmentUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOutbreakInsectEcologyBiologyZoologyVirology

Abstract

fetched live from OpenAlex

Most species that are negatively impacted when their densities are low aggregate to minimize this effect. Aggregation has the potential to change how Allee effects are expressed at the population level. We studied the interplay between aggregation and Allee effects in the mountain pine beetle (Dendroctonus ponderosae Hopkins), an irruptive bark beetle that aggregates to overcome tree defenses. By cooperating to surpass a critical number of attacks per tree, the mountain pine beetle is able to breach host defenses, oviposit, and reproduce. Mountain pine beetles and Hymenopteran parasitoids share some biological features, the most notable of which is obligatory host death as a consequence of parasitoid attack and development. We developed spatiotemporal models of mountain pine beetle dynamics that were based on the Nicholson-Bailey framework but which featured beetle aggregation and a tree-level attack threshold. By fitting our models to data from a local mountain pine beetle outbreak, we demonstrate that due to aggregation, attack thresholds at the tree level can be overcome by a surprisingly low ratio of beetles per susceptible tree at the stand level. This results confirms the importance of considering aggregation in models of organisms that are subject to strong Allee effects.

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 score0.135
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 science0.0000.000
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 head0.009
GPT teacher head0.225
Teacher spread0.216 · 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

Citations36
Published2016
Admission routes2
Has abstractyes

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