Long‐term population dynamics of western tent caterpillars: History, trends and causes of cycles
Bibliographic record
Abstract
This is a story of unique long-term studies of the population ecology of a gregarious, cyclic forest insect, the western tent caterpillar (WTC), Malacosoma californicum pluviale. Early work by W.G. Wellington proposed that variation in the 'quality' and activity of larvae and moths influenced their population ecology. Our subsequent studies monitored six WTC populations over 29-50 years in south-western BC to determine the consistent characteristics of cyclic population dynamics. The six studied populations fluctuated more or less in synchrony with an eight to 11-year periodicity. Fecundity and tent size (an indication of early larval survival) increased with population increase and declined several years before the population peak. Fecundity and tent size were positively related to the population growth rate and declined before the population peak. Mortality from a baculovirus was high at peak densities, and the rate of population growth was negatively related to infection levels. Resistance to the virus varied among families and was higher following the epizootic at peak host density. Factors that might influence changes in fecundity were explored. Viral resistance was not related to moth fecundity, but sublethal effects as a result of surviving virus exposure could reduce fecundity. Declines in fecundity and tent size prior to the peak density could be a result of reduced foliage availability and quality from induced effects of larval feeding. Introduction and cropping experiments were unsuccessful at creating out-of-phase populations, and introduced insects appeared to carry the 'quality' of the source populations and declined synchronously with them. Warming temperatures influence the phenology of egg hatch and leaf development, but field experiments show that WTC larvae are resilient to this variation. No signal of an influence of a warming climate was apparent in long-term data. Longterm field observations indicate that changes in fecundity and viral infection can drive population cycles and inform the theory of cyclic dynamics. The early focus on variation among individuals was a prelude to the eco-evo thinking that has become accepted today but should include both genetic and phenotypic change as being relevant.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".