Genetics and community-based restoration can guide conservation of forest fragments for endangered primates
Bibliographic record
Abstract
As deforestation progresses in the tropics, wildlife populations are increasingly restricted to forest fragments. Here we study genetic population structure in the endangered Ashy red colobus (Piliocolobus tephrosceles) population in the forest fragments surrounding Kibale National Park, Uganda. Subsequently, we use landscape features (elevation, road data and distance to the park) to design a feasible strategy to restore forest in a fashion suitable for both the dispersal patterns of the species and land use practices of the local people. A lack of association between geographic distance and pairwise genetic relatedness among localities, the presence of first degree relatives across localities, and a low global Fst value suggest that red colobus individuals have migrated across this landscape in the recent past. Thus, a series of “stepping stone” forests from the fragments to the park will likely maintain viability of red colobus fragment populations. In this area, low-lying valleys are legally protected to prevent flooding and are considered of low-economic value to local people. We identify such valleys for development of community-based forest restoration efforts that will aid in red colobus conservation and provide various ecosystem services. Our study outlines how genetics and community-based restoration can be integrated to provide realistic conservation solutions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".