The role of nocturnal omnivorous lemurs as seed dispersers in Malagasy rain forests
Bibliographic record
Abstract
Abstract Fruit‐eating animals play important roles as seed dispersal agents in terrestrial systems. Yet, the extent to which seed dispersal by nocturnal omnivores may facilitate germination and the recruitment of plant communities has rarely been investigated. Characterizing their roles in seed dispersal is necessary to provide a more complete picture of how seed dispersal processes affect ecosystem functioning. We investigated the roles and impacts of two species of nocturnal omnivorous lemur species, Microcebus jollyae and M. rufus , on seed dispersal in Madagascar's rain forests, through analysis of fecal samples and germination experiments. Data show that these lemur species, which are among the world's smallest primates, dispersed 22 plant species from various forest strata and that the defecated seeds germinated faster and at higher rates than control seeds for the eight plant species we tested. Even though mouse lemurs dispersed both native and non‐native plant species, non‐native plant species represented a relatively small proportion (17%). These results demonstrate that overlooked nocturnal omnivores can act as important seed dispersers, which may have critical implications for forest regeneration and the maintenance of plant diversity in fragmented/degraded forests. Finally, we provide critical insights into the previously unobserved behavior and diet of endangered nocturnal lemurs for their effective conservation. Abstract in Malagasy is available with online material.
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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".