Effects of an Extreme Weather Event on Primate Populations
Bibliographic record
Abstract
OBJECTIVES: With contemporary, human-induced climate change at a crisis point, extreme weather events (e.g., cyclones, heatwaves, floods) are becoming more frequent, intense, and difficult to predict. These events can wreak rapid and significant changes on ecosystems; thus, it is imperative to understand how wildlife communities respond to these disruptions. Primates are perceived as being a largely adaptable order, but we often lack the quantitative data to rigorously assess how they are impacted by extreme environmental change. Leveraging detections from a long-term camera trap survey, this opportunistic study reports the effects of an extreme weather event on a little-studied population of free-ranging primates in Gorongosa National Park, Mozambique. MATERIALS AND METHODS: We examined shifts in gray-footed chacma baboon (Papio ursinus griseipes) and vervet monkey (Chlorocebus pygerythrus) spatial distribution and relative abundance following Cyclone Idai-a category four tropical cyclone that struck Mozambique in March 2019. RESULTS: Baboon spatial distributions were impacted in the first month after the cyclone, with more detections in areas where flooding was less severe. Spatial distributions renormalized once floodwaters began to recede. We describe vervet monkey spatial distribution trends, though sample size limitations inhibited statistical analysis. Primate relative abundance did not appear to substantially decrease following the cyclone, suggesting troops were able to adopt behavioral adjustments to evade rising floodwaters. DISCUSSION: These findings highlight the behavioral flexibility of Gorongosa's primates and their ability to adapt to extreme-if temporary-disruptions, with implications for primate conservation in the Anthropocene and research into how rapid climatic events may have shaped primate evolution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".