Evaluating support for rangeland‐restoration practices by rural Somalis: an unlikely win‐win for local livelihoods and hirola antelope?
Bibliographic record
Abstract
Abstract In developing countries, governments often lack the authority and resources to implement conservation outside of protected areas. In such situations, the integration of conservation with local livelihoods is crucial to species recovery and reintroduction efforts. The hirola Beatragus hunteri is the world's most endangered antelope, with a population of <500 individuals that is restricted to <5% of its historical geographic range on the Kenya–Somali border. Long‐term hirola declines have been attributed to a combination of disease and rangeland degradation. Tree encroachment—driven by some combination of extirpation of elephants, overgrazing by livestock, and perhaps fire suppression—is at least partly responsible for habitat loss and the decline of contemporary populations. Through interviews in local communities across the hirola's current range, we identified socially acceptable strategies for habitat restoration and hirola recovery. We used classification and regression trees, conditional inference trees, and generalized linear models to identify sociodemographic predictors of support for range‐restoration strategies. Locals supported efforts to conserve elephants (which kill trees and thus facilitate grass growth), seed and fertilize grass, and remove trees, but were opposed to livestock reduction. Locals were ambivalent toward controlled burns and soil ripping (a practice through which soil is broken up to prevent compaction). Livestock ownership and years of residency were key predictors of locals’ perceptions toward rangeland‐restoration practices. Locals owning few livestock were more supportive of elephant conservation, and seeding and fertilization of grass, while longer term residents were more supportive of livestock reduction but were less supportive of elephant conservation. Ultimately, wildlife conservation outside protected areas requires long‐term, community‐based efforts that are compatible with human livelihoods. We recommend elephant conservation, grass seeding and fertilization, manual tree removal and resting range from livestock both to enhance the potential for hirola recovery and to build positive rapport with local communities in the geographic range of this critically endangered species.
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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 | 0.001 |
| 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".