Does the munch affect the bunch? Using community science to explore insect herbivory and fruit production in an understory plant
Bibliographic record
Abstract
Digital, community-sourced natural history records are valuable for understanding species attributes such as phenology and geographic distribution. When these records include photographs, they can also be analysed for individual phenotypes and species interactions to develop or test ecological hypotheses. Here, we use observational and experimental approaches to assess how insect herbivory affects reproductive success in a widespread forest plant, bunchberry (Cornus canadensis). We queried the Global Biodiversity Information Facility (GBIF) and assembled a dataset of 2,578 photographic records of fruiting plants. Of these, 891 showed evidence of insect herbivory, but herbivory was not significantly associated with fruit production. In a field study we monitored 200 plants over five weeks. Herbivory was widespread (78% of plants showed insect feeding), but damage was generally low—only 5% of plants experienced herbivory ≥40% of total leaf area. No relationship was found between natural herbivory and fruit production. In a second study, we simulated high herbivory by mechanically removing 40% of leaf area in early and mid-season. Early-season herbivory reduced fruit production by 100%, while mid-season herbivory reduced fruit production by 87.3%. These results suggest that although high herbivory early in the season can substantially reduce fruiting, natural levels of herbivory exert limited top-down control on reproduction in bunchberry. By combining large-scale community-sourced records with controlled field experiments, this study demonstrates the value of mixed-methods approaches for testing ecological hypotheses and gaining insight into the processes that shape plant–insect interactions.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".