Multiple interacting factors affect seed predation in an African savanna small mammal community
Bibliographic record
Abstract
Abstract Multiple factors affect seed predation, including seed traits, habitat type, seed predator community composition, predation risk, and seasonality. How all these factors and their interactions simultaneously influence seed predation has rarely been tested experimentally in situ. Here, we assessed the relative contribution of the factors driving seed predation in an African savanna rodent community, comprising six ecologically similar species. We first conducted seed preference tests under semicaptive conditions to determine which seed trait (size, shell hardness, nutritional content) influenced seed predation. Then we performed in situ experiments to establish whether rodent community composition (diversity and abundance), seed type, habitat type, seasonality, predation risk, and their interactions affected seed predation. Semicaptive experiments showed that rodents preferred smaller, lighter seeds, containing relatively high water content. In situ experiments showed that predation risk was an important factor influencing seed predation, with rodents removing considerably more seeds in areas where predation risk was lower. Habitat type also affected seed predation, but its effects were strongly linked to predation risk. In areas where predation risk was higher, rodents removed more seeds in more heterogeneous habitats, whereas in areas where predation risk was lower, rodents removed more seeds in less heterogeneous habitats. Seasonality was the least influential factor shaping seed predation. Rodents removed more seeds in winter compared to other seasons, but only in areas where predation risk was low. We provide experimental evidence for a multifaceted approach to understanding the relative contribution of the different factors driving variation in seed predation in natural communities and show that these factors are likely hierarchically arranged.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".