Spatial and temporal variability in predation on rainforest primates: do forest fragmentation and predation act synergistically?
Bibliographic record
Abstract
Abstract Predation is a constant risk for most primates, impacting demography, population dynamics, activity patterns and social behaviour. Data are limited on both the rates of predation and its spatial and temporal variability. We present long‐term observations of Cryptoprocta ferox predation on rainforest sifakas in Madagascar, Propithecus diadema at Tsinjoarivo (22 group years) and Propithecus edwardsi at Ranomafana (73 group years), derived from intensive observations based on ongoing behavioural studies. Average per capita offtake rates are relatively low (0.06–0.07), but temporal variability is high (kills are clumped in time). This is consistent with Cryptoprocta ecology; individual home ranges are much larger than sifaka ranges, and individuals may hunt in a subsection of their range until prey density is decreased, then move on. These results have broad implications. First, in terms of the evolution of anti‐predator strategies, it now becomes important to ask: (1) whether average or peak predation rates determine the strength of selection and (2) whether antipredator strategies (e.g. vigilance, sleeping site selection) fluctuate interannually, reflecting recent experience. Second, in terms of population ecology, Cryptoprocta may have disproportionately large impacts on the (small) sifaka groups, even driving groups to extinction (as observed at both sites). Third, the disappearance of groups has important implications for conservation. When this happens in continuous forest (as at Ranomafana), home ranges will likely be re‐filled over time, whereas in isolated forest fragments (as at Tsinjoarivo), recolonization is less likely. Thus, conservation planners should consider predation as a potentially important proximate cause of extirpation in fragmented landscapes, even when resource density and quality could otherwise sustain populations. Considering the effects of predation can be useful in (1) decisions regarding the allocation of limited conservation resources, including which landscapes to invest resources in and (2) investigating ways to increase resilience of prey species.
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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 | 0.001 |
| Scholarly communication | 0.001 | 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".