Climatic effects on caterpillar fluctuations in northern hardwood forests
Bibliographic record
Abstract
Fluctuations in the abundance of Lepidoptera are common but inadequately understood. Here we show that caterpillar abundance in the White Mountains of New Hampshire has fluctuated by >20-fold from 1986 to 2005. We report tests of three possible causes: (i) extreme winter cold; (ii) long, warm summers; and (iii) interannual variation in tree growth, which tends to correlate with phytochemistry. Caterpillar fluctuations from summers t to t + 1 were uncorrelated or negatively correlated with minimum air temperature during the intervening winter (does not support the first cause), but were positively correlated with thermal sum during summer t (r = 0.49–0.56) (supports the second cause). There was limited interannual variation in the radial growth of two dominant tree species ( Acer saccharum Marsh. and Betula alleghaniensis Britt.) and no correlation with caterpillar fluctuations (refutes the third cause). Thermal sum might influence caterpillar fluctuations through direct effects on insect development, indirect effects on susceptibility to natural enemies, and (or) indirect effects on plant-insect interactions; the mechanisms are of particular interest because thermal sums have been increasing since local records began in 1957 (r = 0.41–0.45). In hardwoods forests of the northeastern United States, there is some broad-scale driver related to summer temperatures that generates fluctuations in caterpillar abundance, which influences herbivory as well as higher level consumers, such as insectivorous birds.
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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 | 0.001 |
| 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 | 0.000 |
| 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".