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Record W2736609684 · doi:10.1093/ee/nvx127

Cold Tolerance of Mountain Pine Beetle (Coleoptera: Curculionidae) Eggs From the Historic and Expanded Ranges

2017· article· en· W2736609684 on OpenAlexafffundabout
Katherine P. Bleiker, Gregory D. Smith, Leland M. Humble

Bibliographic record

VenueEnvironmental Entomology · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersfRI Research
KeywordsCurculionidaeBiologyBotanyAgronomyEcology

Abstract

fetched live from OpenAlex

Winter mortality is expected to be a key factor determining the ability of mountain pine beetle, Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae), to expand its range in Canada. We determined the mortality rate and supercooling points of eggs from the beetle's historic range in southern British Columbia as well as the recently expanded range in north-central Alberta and tested if eggs require an extended period of chilling to reach their maximum cold tolerance. We found no effect of population source or acclimation time on egg cold tolerance. Although 50% of eggs can survive brief exposure to -20.5 °C (LT50), storage at 0.3 °C and -7.5 °C for 59 d resulted in 50% and 100% mortality, respectively. Our results indicate that eggs suffer significant prefreeze mortality and are not well-adapted to overwintering: eggs are unlikely to survive winter throughout much of the beetle's range. Our results provide information that can be used to help model the climatic suitability of mountain pine beetle, including how changes in seasonality associated with new or changing climates may affect winter survival. In addition to lower lethal temperatures, it is critical that the duration of exposure to sublethal cold temperatures are considered in a comprehensive index of cold tolerance and incorporated into survival and population models.

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 score0.045
Threshold uncertainty score0.998

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.221
Teacher spread0.213 · 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

Citations30
Published2017
Admission routes3
Has abstractyes

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