Ecological, Behavioral, and Life‐History Correlates of Mammal Extinctions in West Africa
Bibliographic record
Abstract
Abstract: Identifying the biological traits of species that predispose them to extinction is a focus of research in evolutionary ecology and conservation biology. This research has traditionally been divided between studies of extinction or decline in undisturbed habitat islands and studies of the persistence of species affected adversely by human influence. I combined these approaches to test for correlations between nine ecological, behavioral, and life‐history traits and vulnerability to local extinction for 41 species of carnivores, primates, and ungulates in fragmented and exploited habitats in Ghana, West Africa, while accounting statistically for phylogeny. Species distributed in isolated populations were most prone to local extinction, and monogamous species and those wherein males defended small harems were also prone to extinction. Body size, fecundity, abundance, habitat specialization, trophic group, and the degree to which hunters and consumers preferred a species generally were unrelated to species persistence. Although population isolation and mating system were the only traits that explained a significant amount of the observed variation in persistence of all species, analyses of carnivores, primates, and ungulates as groups yielded varied results. Mammals most prone to local extinction in my study reserves were also those listed by the World Conservation Union as being at greatest risk of global extinction. Thus, my results suggest that the relative isolation of populations and the mating system displayed by mammals may be good general predictors of their persistence.
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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 | 0.002 |
| 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".