SEASONAL MORTALITY PATTERNS IN NON-HUMAN PRIMATES: IMPLICATIONS FOR VARIATION IN SELECTION PRESSURES ACROSS ENVIRONMENTS
Bibliographic record
Abstract
Examining seasonal mortality patterns can yield insights into the drivers of mortality and thus potential selection pressures acting on individuals in different environments. We compiled adult and juvenile mortality data from nine wild non-human primate taxa to investigate the role of seasonality in patterns of mortality and address the following questions: Is mortality highly seasonal across species? Does greater environmental seasonality lead to more seasonal mortality patterns? If mortality is seasonal, is it higher during wet seasons or during periods of food scarcity? and Do folivores show less seasonal mortality than frugivores? We found seasonal mortality patterns in five of nine taxa, and mortality was more often tied to wet seasons than food-scarce periods, a relationship that may be driven by disease. Controlling for phylogeny, we found a positive relationship between the degree of environmental seasonality and mortality, with folivores exhibiting more seasonal mortality than frugivores. These results suggest that mortality patterns are influenced both by diet and degree of environmental seasonality. Applied to a wider array of taxa, analyses of seasonal mortality patterns may aid understanding of life-history evolution and selection pressures acting across a broad spectrum of environments and spatial and temporal scales.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".