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Record W6903004846 · doi:10.7939/r3-7x7n-rb77

Pine Wars: A New Host Interactions between the mountain pine beetle (Dendroctonus ponderosae Hopkins) and its pine hosts in Canada's boreal forest

2023· dissertation· en· W6903004846 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity of Alberta Library · 2023
Typedissertation
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsPinus contortaMountain pine beetleDendroctonusRange (aeronautics)Bark (sound)Host (biology)TaigaBoreal

Abstract

fetched live from OpenAlex

Mountain pine beetle (MPB) has undergone a climate change facilitated range expansion and has attacked and killed trees at higher latitudes and elevations than has ever been recorded. During outbreaks, MPB attack large healthy pine trees that will fight back against the colonizing beetles using physical and chemical defenses. Attacking beetles communicate with pheromones and will cooperatively “mass attack” and kill these trees. If they win the battle against the tree, the beetles are rewarded with abundant resources under the bark but if they lose, they are poisoned or consumed by pitch. In my PhD research I studied the battle between MPB and its hosts in Alberta. Trees in the expanded range have fewer constitutive defenses and when challenged with simulated MPB attacks, are not able to produce as many toxic chemical defenses as trees in the historic range of MPB. Lodgepole pine is the most common historic host of MPB but the lodgepole pines in Alberta do not have a shared evolutionary history with MPB. I tested the hypothesis that naïve lodgepole pines are more susceptible to mass attack by performing mass attack manipulation experiments in the field. I used aggregation pheromone to attract wild MPB to experimental lodgepole pine trees and stopped the process of attack at different attack densities to determine the minimum density of beetles that can successfully colonize and kill Albertan lodgepole pines. I found variation in the threshold for mass attack across three years of experiments which was best explained by changes in environmental conditions that influenced tree defense. I then performed a similar experiment in jack pine, which is a novel host of MPB. Since there were no wild MPB populations in jack pine stands, I collected MPB from a lodgepole pine forest and transplanted them under caged jack pine trees. The mass attack threshold density in jack pine was half the beetle density typically seen in lodgepole pine which is strong evidence that jack pine is more susceptible to MPB. During the mass attack experiments, I also collected phloem tissue samples to quantify tree chemical response to MPB attack. I found that neither species increased terpene defenses in the 36 weeks after mass attack but all trees had large increases in terpene content the spring following attack, even if the mass attack was unsuccessful. Finally, I performed laboratory experiments to test the preference and performance of MPB that emerged from mass attacked lodgepole and jack pines. I found that beetles that switched hosts from lodgepole pine to jack pine had offspring that were in significantly worse condition compared to beetles that did not switch hosts or switched from jack to lodgepole pine. Although jack pine has a lower mass attack threshold and does not induce defenses quickly, there is a cost to switching from lodgepole pine to jack pine. Jack pine has a thinner phloem layer and so likely has fewer resources available for developing MPB brood. However, adaptation to jack pine could still take place.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.828
Threshold uncertainty score0.343

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 head0.007
GPT teacher head0.189
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
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

Citations0
Published2023
Admission routes1
Has abstractyes

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