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Record W2951491357 · doi:10.1139/cjfr-2018-0304

Factors influencing dispersal by flight in bark beetles (Coleoptera: Curculionidae: Scolytinae): from genes to landscapes

2019· article· en· W2951491357 on OpenAlexaffvenue
Kelsey L. Jones, Victor A. Shegelski, Nathan G. Marculis, Asha Wijerathna, Maya L. Evenden

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiological dispersalBark beetleCurculionidaeBiologyEcologyBark (sound)Abiotic componentPopulation

Abstract

fetched live from OpenAlex

Dispersal by flight is obligatory for bark beetles in the subfamily Scolytinae. Adult bark beetles must leave the natal host and fly to seek new hosts for brood production. Because of the eruptive nature of some bark beetle populations, dispersal capacity has implications for beetle spread and invasion across the landscape. Bark beetle dispersal can occur over short distances within a stand or over long distances above the forest canopy, where wind aids dispersal. Despite the obvious importance of dispersal for predicting population spread, knowledge gaps in understanding factors that influence bark beetle dispersal remain. In this review, we synthesize information on bark beetle flight to gain a better understanding of this important life history trait. We assess the impact of genetic, physiological, and morphological traits on flight in different bark beetle species. We also consider the impact of abiotic and biotic environmental conditions on flight. We discuss how measurements of these factors could contribute to the development of comprehensive models to better predict spread of bark beetle populations. Through the synthesis of flight research on a variety of bark beetle species, this review provides suggestions for future avenues of research on this important aspect of bark beetle ecology.

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)0.001
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 score0.693
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.261
Teacher spread0.244 · 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
Published2019
Admission routes2
Has abstractyes

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