Optimising different types of biodiversity coverage of protected areas with a case study using Himalayan Galliformes
Bibliographic record
Abstract
International targets have committed governments to expanding the global protected area (PA) network to 17% of the terrestrial surface by 2020. Optimising PA placement in the landscape is challenging due to a poor knowledge of biodiversity distribution and multiple definitions of conservation value. We explore these two issues using a case study of a highly threatened bird Order in a region of conservation concern where PA network effectiveness for biodiversity has not been formally explored previously. To determine if the existing PA network protects the most important areas for 24 species of Himalayan Galliformes, we use a novel method to compare the current network placement to results produced from Zonation prioritisation software and modelled species distributions. Specifically, we identify areas of high species richness and then weight maps by three different species specific conservation values. The current PA network captures ranges poorly. We found statistically significantly poorer fits between the optimal and the existing placement of the Himalayan PA network for Zonation results that were: (i) unweighted; (ii) weighted by Red List score; and (iii) weighted by endemism to the Himalaya. Across these and two other Zonation results, the placement of the optimal PA network covered 58% more of Galliformes distributions than the existing network. We advocate some refinements to the existing PA network to maximise Galliformes coverage and suggest that our method could be used to model the optimal PA network for a wide range of species and/or regions, something which will support the assessment and attainment of CBD targets.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".