Resource selection and landscape change reveal mechanisms suppressing population recovery for the world's most endangered antelope
Bibliographic record
Abstract
Summary Understanding how bottom‐up and top‐down forces affect resource selection can inform restoration efforts. With a global population size of <500 individuals, the hirola Beatragus hunteri is the world's most endangered antelope, with a declining population since the 1970s. While the underlying mechanisms are unclear, some combination of habitat loss and predation are thought to be responsible for low abundances of contemporary populations. Efforts to conserve hirola are hindered by a lack of understanding as to why population density remains low, despite eradication of the viral disease, rinderpest. To elucidate factors underlying chronically low numbers, we examined resource selection and landscape change within the hirola's native range. Because hirola are grazers, we hypothesized that the availability of open areas would be linked both to forage and safety from predators. We quantified: (i) changes in tree cover across the hirola's historical range in eastern Kenya over the past 27 years; (ii) how tree cover has influenced resource selection by hirola; and (iii) interactions between tree cover and predation. Between 1985 and 2012, tree cover increased by 251% across the historical range of hirola. Tree encroachment was associated with a 98% decline of hirola and elephant Loxodonta africana populations, a 74% decline in cattle Bos indicus , an increase in browsing livestock by 327%, and a reduction in rainfall. Although hirola avoided tree cover, we found no evidence that predation on hirola increased with increasing tree cover. Synthesis and applications . Hirola may qualify as a refugee species, in which contemporary populations are restricted to suboptimal habitat and exhibit low survival, reproduction or both. The extinction of hirola would be the first of a mammalian genus on the African continent in modern history. We conclude that contemporary low numbers of hirola are due at least partly to habitat loss via tree encroachment, triggered by some combination of elephant extirpation, overgrazing, drought and perhaps fire suppression. We recommend a combination of rangeland restoration efforts (including conservation of elephants, manual clearing of trees, and grass seeding), increased enforcement of an existing protected area (Arawale National Reserve), and reintroductions to enhance recovery for this endangered species. These efforts will rely on enhanced support from the international conservation community and the cooperation of pastoralist communities with which the hirola coexist.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".