The ecology of predispersal insect herbivory on tree reproductive structures in natural forest ecosystems
Bibliographic record
Abstract
Plant-insect interactions are key model systems to assess how some species affect the distribution, the abundance, and the evolution of others. Tree reproductive structures represent a critical resource for many insect species, which can be likely drivers of demography, spatial distribution, and trait diversification of plants. In this review, we present the ecological implications of predispersal herbivory on tree reproductive structures by insects (PIHR) in forest ecosystems. Both insect's and tree's perspectives are addressed with an emphasis on how spatiotemporal variation and unpredictability in seed availability can shape such particular plant-animal interactions. Reproductive structure insects show strong trophic specialization and guild diversification. Insects evolved host selection and spatiotemporal dispersal strategies in response to variable and unpredictable abundance of reproductive structures in both space and time. If PIHR patterns have been well documented in numerous systems, evidences of the subsequent demographic and evolutionary impacts on tree populations are still constrained by time-scale challenges of experimenting on such long-lived organisms, and modeling approaches of tree dynamics rarely consider PIHR when including biotic interactions in their processes. We suggest that spatially explicit and mechanistic approaches of the interactions between individual tree fecundity and insect dynamics will clarify predictions of the demogenetic implications of PIHR in tree populations. In a global change context, further experimental and theoretical contributions to the likelihood of life-cycle disruptions between plants and their specialized herbivores, and to how these changes may generate novel dynamic patterns in each partner of the interaction are increasingly critical.
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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) | 0.000 | 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".