Capacity for recovery in Bornean orangutan populations if forest fragmentation and offtake is limited
Bibliographic record
Abstract
Forty years of deforestation and logging have degraded and fragmented much of Borneo’s lowland forest. This poses a threat to the island’s unique biodiversity, which can be exacerbated by hunting and killing. Although orangutans sometimes persist in small forest patches, it is unclear if such highly fragmented habitats can sustain viable populations, and whether they facilitate movements across modified landscapes over the long-term. Since longitudinal population data are unavailable, inferences must be made from modelling. We applied a spatially-explicit individual-based model to predict the potential long-term viability of orangutan populations across Borneo. Specifically, we examined how population dynamics and dispersal could be affected by the loss of habitat fragments and removal of individuals through hunting, retaliatory killings and capture and translocation. Small forest fragments facilitated orangutan movement, increasing the number of individuals settling in non-natal patches. However, large rivers remained a substantial barrier, and limited the capacity of orangutan populations to recover from decline. Orangutan populations were also highly vulnerable to even small amounts of offtake, with annual removal of >2% diminishing the positive role that small fragments played in sustaining population connectivity and long-term viability. Our results imply that orangutan populations could grow and recover from recent declines across Borneo if further habitat loss within human-modified landscapes is minimized. However, this will only be achievable if efforts are made to reduce the removal of orangutans by promoting coexistence with people, limiting killings, and only engaging in translocations in rare cases where no suitable alternative exists.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".