Responses of tree-killing bark beetles to a changing climate.
Bibliographic record
Abstract
Abstract Bark beetles cause widespread tree mortality, so understanding how climate change will influence the distribution and magnitude of outbreaks by this group of herbivores is important. We first develop a framework of outbreak dynamics that emphasizes transitions from states dominated by negative feedback to those dominated by positive, density-dependent feedback. We then consider mechanisms by which temperature and precipitation changes can allow populations to breach critical thresholds, and the empirical data relating outbreaks to weather. Finally, we consider how anticipated climatic change, and relationships with new hosts and natural enemy guilds, may influence dynamics in new habitats. There is strong evidence that elevated temperature can increase overwintering survival and decrease generation times of bark beetles, although evolved traits can also constrain response to warming in some habitats. Moreover, combinations of phenotypic plasticity, genotypic variation and physiological thresholds yield a broad range of conditions under which adults emerge synchronously, and thus can mass attack trees. There is likewise strong evidence that severe drought reduces tree defences against attack. Drought stress can occur through lower precipitation and/or higher temperatures that reduce soil moisture and/or raise vapour pressure deficit. We also delineate three categories of range modifications: increased and more persistent establishment in areas where trees experienced only intermittent exposure historically; establishment in areas dominated by host species but where local populations experienced little or no pressure historically; and movement into new areas containing susceptible species that have not been exposed previously. Each of these has been documented for bark beetles. Trees in areas that experienced minor or no exposure generally had lower defences than their more historically exposed counterparts. However, there can be lags in beetle behavioural responses. In some cases, more heterogeneous forest structure, more abundant local predators and competitors and low host nutritional quality could potentially lessen risk in new habitats. Direct comparative studies are needed to evaluate outbreak potentials following range expansions driven by climate warming.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".