Bibliographic record
Abstract
Climate change affects populations of forest insect pests in a number of ways. We reviewed the most recent literature (2013–2017) on this subject including previous reviews on the topic. We provide a comprehensive discussion of the subject, with special attention to insect range expansion, insect abundance, impacts on forest ecosystems, and effects on forest insect communities. We considered forest insects according to their major guilds and biomes. Effects of climate change on forest insects are demonstrated for a number of species and guilds, although generalizations of results available so far are difficult because of species-specific responses to climate change. In addition, disentangling direct and indirect effects of climate change is complex due to the large number of variables affected. Modeling based on climate projections is useful when combined with mechanistic explanations. Expansion of either the true range or the outbreak range is observed in several model species/groups of major insect guilds in boreal and temperate biomes. Mechanistic explanations are provided for a few species and are mainly based on increase in winter temperatures. In relation to insect abundance, climate change can either promote outbreaks or disrupt trophic interactions and decrease the severity of outbreaks. There is good evidence that some recent outbreaks of bark beetles and defoliating insects are influenced by climate change and are having a large impact on ecosystems as well as on communities of forest insects.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".