Defaunation changes leaf trait composition of recruit communities in tropical forests in French Guiana
Bibliographic record
Abstract
Hunting impacts tropical vertebrate populations, causing declines of species that function as seed dispersers and predators, or that browse seedlings and saplings. Whether and how the resulting reductions in seed dispersal, seed predation, and browsing translate to changes in the tree composition is poorly understood. Here, we assess the effect of defaunation on the functional composition of communities of tree recruits in tropical rainforests in French Guiana. We selected eight sites along a gradient of defaunation, caused by differences in hunting pressure, in otherwise intact old-growth forests in French Guiana. We measured shifts in functional composition by comparing leaf and fruit traits and wood density between tree recruits (up to 5 cm diameter at breast height) and adults, and tested whether and how these compositional shifts related to defaunation. We found a positive relationship with defaunation for shifts in specific leaf area, a negative relationship for shifts of leaf toughness and wood density, and a weak relationship for shifts in fruit traits. Our results suggest that the loss of vertebrates affects ecological processes such as seed dispersal and browsing, of which browsing remains understudied. Even though these changes sometimes seem minor, together they result in major shifts in forest composition. These changes have long-term ramifications that may alter forest dynamics for generations.
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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 | 0.001 | 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".