Evolution of tree killing in bark beetles (Coleoptera: Curculionidae): trade-offs between the maddening crowds and a sticky situation
Bibliographic record
Abstract
Abstract Bark beetles (Coleoptera: Curculionidae: Scolytinae) play important roles in temperate conifer ecosystems, and also cause substantial economic losses. Although their general life histories are relatively similar, different species vary markedly in the physiological condition of the hosts they select. Most of ∼6000 known species colonise dead or stressed trees, a resource they share with a large diversity of insects and other organisms. A small number of bark beetle species kill healthy, live trees. These few are of particular interest as they compete directly with humans for resources. We propose that tree killing evolved when intense interspecific competition in the ephemeral, scarce resource of defence-impaired trees selected for genotypes that allowed them to escape this limitation by attacking relatively healthy trees. These transitions were uncommon, and we suggest they were facilitated by (a) genetically and phenotypically flexible host selection behaviours, (b) biochemical adaptations for detoxifying a wide range of defence compounds, and (c) associations with symbionts, which together aided bark beetles in overcoming formidable constitutive and induced host defences. The ability to detoxify terpenes influenced the evolutionary course of pheromonal communication. Specifically, a mate attraction system, which was exploited by intraspecific competitors in locating poorly defended hosts, became a system of cooperative attack in which emitters benefit from the contributions responders make in overcoming defence. This functional shift in communication was driven in part by linkage of beetle semiochemistry to host defence chemistry. Behavioural and phenological adaptations also improved the beetles’ abilities to detect when tree defences are impaired, and, where compatible with life history adaptations to other selective forces, for flight to coincide with seasonally predictable host stress agents. We propose a conceptual model, whereby the above mechanisms enable beetles to concentrate on those trees that offer an optimal trade-off between host defence and interspecific competition, along dynamic gradients of tree vigour and stand-level beetle density. We offer suggestions for future research on testing elements of this model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".