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Record W7106011258 · doi:10.7939/83241

The effect of energetic condition on dispersal by flight and response to semiochemical cues by Dendroctonus ponderosae Hopkins (Coleoptera: Curculionidae: Scolytinae) in its expanded range in Alberta

2025· dissertation· en· W7106011258 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2025
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBiological dispersalMountain pine beetleRange (aeronautics)Bark beetlePopulationDendroctonusHost (biology)Colonization

Abstract

fetched live from OpenAlex

Global climate change drives range expansion of various taxa, including the irruptive bark beetle, the mountain pine beetle (Dendroctonus ponderosae; MPB), which recently experienced a rapid range expansion. Dispersal and host colonization dictate the rate of spread and establishment of MPB populations. Mountain pine beetle undergo obligatory dispersal flights for host colonization and brood production, yet dispersal remains a poorly understood aspect of their ecology especially in the endemic population state when beetle densities on the landscape are low. Beetle morphology and energetic condition are heritable traits that explain some variation in MPB flight capacity. Selection could act on traits such as wing area, body size, and lipid reserves via spatial sorting in the expanded range of MPB. Mountain pine beetles show continuous flight polyphenisms despite similar morphology and energetic condition. A potential hypothesis to explain this variable dispersal is that energy use during flight triggers responsiveness to the semiochemicals that orchestrate host colonization of pine trees. Plasticity in host-colonization behavior is characteristic of the endemic population state, and may influence the morphology, energetic condition, and dispersal of MPB in the expanded range in Alberta. Here, I test the hypothesis that flight exercise impacts subsequent behavioral response to semiochemicals in two olfactometer experiments that simulate 1) female beetle pioneer response to host volatiles; and 2) male and female joining beetle response to host volatiles and the female-produced aggregation pheromone, trans-verbenol. I also assess electrophysiological response of antennae of female beetles to α-pinene post-flight in an electroantennogram bioassay. Finally, I use pheromone-baited funnel traps to capture MPB across its expanded range in Alberta, Canada to assess the morphology, energetic condition and dispersal of MPB in the endemic state. These experiments provide seven key findings: (1) female MPB in poor body condition due to energy expenditure respond strongly to host semiochemicals post-flight; (2) antennal response of female MPB to the host volatile, α-pinene, increases with relative lipid content of beetles; (3) male body condition or exercise by flight does not influence behavioral response to semiochemicals; (4) field traps captured 0.4% of beetles at distances indicating long-distance dispersal in the endemic population state; (5) beetles in regions along the leading edge of range expansion in Alberta have larger wings compared to beetles in the core of the expanded range; (6) the morphology and energetic condition of field captured beetles is indicative of beetles in the endemic population state; (7) MPB body lipid dynamics vary between beetles in the endemic and epidemic states. Together, my findings support the need for ongoing monitoring of endemic MPB populations that will form future outbreaks and establish a foundation for understanding of state-dependent modulation in response to semiochemical cues in MPB.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score0.804

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.002
GPT teacher head0.185
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreOther

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

Citations0
Published2025
Admission routes1
Has abstractyes

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