Bottom-up factors contribute to large-scale synchrony in spruce budworm populations
Bibliographic record
Abstract
Understanding the mechanisms that cause large-scale synchrony in insect population dynamics might yield key insights for predicting potential outbreak occurrence. Here, we evaluated which environmental factors best explain synchronous population fluctuations in the spruce budworm (Choristoneura fumiferana Clem.) (SBW), a major defoliator of coniferous forests in North America. SBW population levels were assessed with pheromone traps during the 1986–2014 period across a 625 000 km 2 territory located in the province of Québec (Canada). The populations were characterized by abundance fluctuations that were often synchronized across the whole study area. Interannual population fluctuations were correlated with host tree cone production (a source of food) and high May temperatures, suggesting that synchrony was influenced by food availability and phenological mismatch during shoot development. Cone production was itself correlated with low precipitation during the previous summer. This study indicates that bottom-up trophic factors can drive spatiotemporal synchrony in insect populations and contribute to explain important and sustained population increases during some years. We also suggest that several biological processes, all influenced by weather, are likely to interact to explain population synchrony during the different phases of the approximately 35 year SBW outbreak cycle, complicating the prediction of climate change effects on this insect.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".