Connectivity conservation to mitigate climate and land-cover change impacts on Borneo
Bibliographic record
Abstract
Enhancing connectivity between protected areas is crucial for facilitating species range shifts in response to climate change. Yet spatial planning for this connectivity often overlooks the combined impacts of climate and land-cover change, particularly in tropical regions where habitat loss is a more immediate biodiversity threat. We explore the need for connectivity between protected areas to mitigate the dual impacts of climate- and land-cover change on Borneo. Using habitat suitability models and combined climate and land-cover change forecasts, we develop connectivity models for present and future scenarios, identifying optimal connections between protected areas for 81 species. By considering restoration and opportunity economic costs, we also explore the cost-benefit trade-offs of implementing connectivity plans. Connectivity solutions varied among species, but often converged on the same connections between protected areas, with contemporary connections traversing 6 to 40 km and comprising 67 % forest cover, on average. By the 2080s there were fewer connections, and while many were shorter, they also comprised poorer quality habitat, reflecting reductions in forest cover and species distributions. As a result, the economic cost of creating corridors between protected areas was estimated to be 65 % higher in 2080 than in 2020. Our analysis highlights the urgent need to prioritize connectivity interventions early to maximize long-term benefits for multiple species facing climate-change disruption while minimizing costs. However, conservation planning in tropical regions is complex, given high rates of forest degradation and loss. Implementing our approach at finer spatial scales could help identify cost-effective areas to prioritize landscape connectivity, helping safeguard tropical biodiversity amid changing environmental conditions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".