Factors affecting the relationship between seed removal and seed mortality
Bibliographic record
Abstract
S.B. Vander Wall et al. (Ecology, 86: 801–806 (2005)) criticized seed dispersal studies that use seed removal as a proxy for seed predation, because secondary dispersal processes following removal are important to seed fates for many plants. We compared seed removal rates with direct estimates of seed mortality and another mortality index, based on a 3-year experiment that included five temperate deciduous tree species and four exclosure treatments designed to identify effects of different seed consumer groups. Patterns of seed removal rates generally did not match patterns of mortality. Removal and mortality rates were both highest in seed-poor years, indicative of response to food limitation, but annual food abundance interacted with seed type differently for removal rates than for mortality rates. The effect of exclosure type (access by different consumers) on removal rates was opposite its effect on mortality rates; seeds were removed fastest from exclosures that allowed access to tree squirrels (genus Sciurus L., 1758), but these seeds had the lowest mortality because Sciurus is an important seed disperser. We discuss types of studies in which seed removal may be a reasonable index of seed mortality, and we stress the importance of justifying assumptions concerning links between removal and predation.
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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 | 0.003 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".