Experimental manipulation of seed shadows of an Afrotropical tree determines drivers of recruitment
Bibliographic record
Abstract
The loss of animals in tropical forests may alter seed dispersal patterns and reduce seedling recruitment of tree species, but direct experimental evidence is scarce. We manipulated dispersal patterns of Manilkara mabokeensis, a monkey-dispersed tree, to assess the extent to which spatial distributions of seeds drive seedling recruitment. Based on the natural seed shadow, we created seed distributions with seeds deposited under the canopy ("no dispersal"), with declining density from the tree ("natural dispersal"), and at uniform densities ("good dispersal"). These distributions mimicked dispersal patterns that could occur with the extirpation of monkeys, low levels of hunting, and high rates of seed dispersal. We monitored seedling emergence and survival for 18 months and recorded the number of leaves and damage to leaves. "Good dispersal" increased seedling survival by 26%, and "no dispersal" decreased survival by 78%, relative to "natural dispersal." Using a mixed-effects survival model, we decoupled the distance and density components of the seed shadow: seedling survival depended on the seed density, but not on the distance from the tree. Although community seedling diversity tended to decrease with longer dispersal distances, we found no conclusive evidence that patterns of seed dispersal influence the diversity of the seedling community. Local seed dispersal does affect seedling recruitment and survival, with better dispersal resulting in higher seedling recruitment; hence the loss of dispersal services that comes with the reduction or extirpation of seed dispersers will decrease regeneration of some tree species.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".