The combined impacts of experimental defaunation and logging on seedling traits and diversity
Bibliographic record
Abstract
Animals can have both positive (e.g. via seed dispersal) and negative (e.g. via herbivory) impacts on plants. The net effects of these interactions remain difficult to predict and may be affected by overhunting and habitat disturbance, two widespread threats to tropical forests. Recent studies have documented their separate effects on plant recruitment but our understanding of how defaunation and logging interact to influence tropical tree communities is limited. From 2013 to 2016, we followed the fate of marked tree seedlings ( n = 1489) from 81 genera in and outside experimental plots. Our plots differentially excluded small, medium and large-bodied mammal herbivores in logged and unlogged forest in Malaysian Borneo. We assessed the effects of experimental defaunation and logging on taxonomic diversity and plant trait (wood density, specific leaf area, fruit size) composition of seedling communities. Although seedling mortality was highest in the presence of all mammal herbivores (44%), defaunation alone did not alter taxonomic diversity nor plant trait composition. However, herbivores (across all body sizes) significantly reduced mean fruit size across the seedling community over time (95% confidence interval (CI): −0.09 to −0.01), particularly in logged forest (95% CI: −0.12 to −0.003). Our findings suggest that impacts of mammal herbivores on plant communities may be greater in forests with a history of disturbance and could subsequently affect plant functional traits and ecological processes associated with forest regeneration.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".