Climate induced stress and mortality in vervet monkeys
Bibliographic record
Abstract
As the effects of global climate change become more apparent, animal species will become increasingly affected by extreme climate and its effect on the environment. There is a pressing need to understand animal physiological and behavioural responses to climatic stressors. We used the reactive scope model as a framework to investigate the influence of drought conditions on vervet monkey ( Chlorocebus pygerythrus ) behaviour, physiological stress and survival across 2.5 years in South Africa. Data were collected on climatic, environmental and behavioural variables and physiological stress via faecal glucocorticoid metabolites (fGCMs). There was a meaningful interaction between water availability and resource abundance: when food availability was high but standing water was unavailable, fGCM concentrations were higher compared to when food was abundant and water was available. Vervet monkeys adapted their behaviour during a drought period by spending a greater proportion of time resting at the expense of feeding, moving and social behaviour. As food availability decreased, vervet mortality increased. Peak mortality occurred when food availability was at its lowest and there was no standing water. A survival analysis revealed that higher fGCM concentrations were associated with an increased probability of mortality. Our results suggest that with continued climate change, the increasing prevalence of drought will negatively affect vervet abundance and distribution in our population. Our study contributes to knowledge of the limits and scope of behavioural and physiological plasticity among vervet monkeys in the face of rapid environmental change.
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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 | 0.000 |
| 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".