A pantropical assessment of vertebrate physical damage to forest seedlings and the effects of defaunation
Bibliographic record
Abstract
Many of the forces that shape tropical forest plant communities are facilitated by interactions with animals, which can either promote or inhibit plant reproduction and survival across ontogenetic stages. Hunting-induced defaunation can disrupt these interactions, altering tree recruitment, forest structure, and carbon storage, with strong effects at the seed and seedling stages. Research to date has largely focused on how changes to prominent interactions (especially seed dispersal) affect plant species and communities, while concurrent disruptions to less-studied processes may have opposing effects. With a particularly limited understanding of non-trophic interactions – such as physical damage to seedlings by vertebrate trampling, rooting, and digging – it remains difficult to predict the outcomes of defaunation for tropical forest plant communities. We established 1800 artificial seedlings in 18 intact and disturbed sites across the three main tropical forest regions – the Neotropics (Peru), the Afrotropics (Gabon) and the Indo-Malayan tropics (Malaysian Borneo) – to isolate non-trophic vertebrate physical damage from other causes of seedling mortality (herbivory, pathogens, abiotic desiccation, etc.), and to understand its effects in intact and anthropogenically-disturbed forests. We found that vertebrate physical damage is a consistent force in forests across the tropics, and that hunting significantly alters its strength, with a ∼70% decrease in damage in hunted vs. intact sites that resulted in a ∼3.5-fold (350%) increase in artificial seedling survival. Our results reveal an understudied mechanism that may contribute to changes in seedling survival, stem density, and plant community composition in tropical forests subjected to hunting.
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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".