Warming counteracts defoliation‐induced mismatch by increasing herbivore‐plant phenological synchrony
Bibliographic record
Abstract
Climate change is altering phenology; however, the magnitude of this change varies among taxa. Compared with phenological mismatch between plants and herbivores, synchronization due to climate has been less explored, despite its potential implications for trophic interactions. The earlier budburst induced by defoliation is a phenological strategy for plants against herbivores. Here, we tested whether warming can counteract defoliation-induced mismatch by increasing herbivore-plant phenological synchrony. We compared the larval phenology of spruce budworm and budburst in balsam fir, black spruce, and white spruce saplings subjected to defoliation in a controlled environment at temperatures of 12, 17, and 22°C. Budburst in defoliated saplings occurred 6-24 days earlier than in the controls, thus mismatching needle development from larval feeding. This mismatch decreased to only 3-7 days, however, when temperatures warmed by 5 and 10°C, leading to a resynchronization of the host with spruce budworm larvae. The increasing synchrony under warming counteracts the defoliation-induced mismatch, disrupting trophic interactions and energy flow between forest ecosystem and insect populations. Our results suggest that the predicted warming may improve food quality and provide better growth conditions for larval development, thus promoting longer or more intense insect outbreaks in the future.
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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 | 0.000 |
| 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".