Plasticity in a key life‐history trait contributes to population cycles in an insect herbivore
Bibliographic record
Abstract
Research Highlight: Myers, J. H., & Cory, J. S. (2025). Long-term population dynamics of western tent caterpillars: History, trends and causes of cycles. Journal of Animal Ecology. https://doi.org/10.1111/1365-2656.70104. For centuries, population cycles have intrigued ecologists and posed challenges for resource managers. These dramatic fluctuations are influenced by strong interactions with natural enemies and/or the climate, yet these external drivers alone are typically insufficient to explain the observed cycles. Cyclic changes in life-history traits (e.g. fecundity) often play a significant role, though the mechanisms underlying these regular phenotypic shifts remain largely undetermined. Here Myers and Cory (2025) convincingly demonstrate the key role of plastic changes in fecundity in driving the 8-11-year population cycles of the western tent caterpillar Malacosoma californicum pluviale. These cycles are partially driven by lethal infections from a specialized baculovirus Malacosoma pluviale nucleopolyhedrovirus. Although tent caterpillars evolve increased resistance to the virus following peak infection periods, this resistance does not incur a fecundity cost, suggesting that eco-evolutionary feedback does not regulate this cycle. Instead, sublethal viral infections induce plastic reductions in fecundity. Declines in food quantity and quality following peak defoliation periods likely further contribute to these plastic changes. While climate variation does influence population growth, future climate change is unlikely to disrupt these cycles. Taken together, this long-term research underscores the importance of phenotypic plasticity in shaping dramatic herbivore population cycles. Future research on eco-evolutionary dynamics should consider, more even-handedly, alternative mechanisms by which the environment can feedback to cause phenotypic change.
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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) | 0.002 | 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".