A multi-criteria approach to identify priority regions for freshwater biodiversity conservation
Bibliographic record
Abstract
Area-based conservation requires identifying priority areas, but guidance on which ecological criteria to use and how these criteria influence perceived conservation priorities is limited. To assess the sensitivity of spatial prioritization to such decisions, we used three ecological concepts (irreplaceability, taxonomic representativeness, and vulnerability) to create five conservation prioritization strategies (three proactive, one reactive, and one representative). We applied these to prioritize areas for freshwater fish and mussel diversity in Southwestern Ontario, a region with rich freshwater biodiversity and high anthropogenic pressures. Prioritization differed based on the strategies. The proactive 1 strategy, focusing only on vulnerability, identified sub-basins with the lowest diversity, offering limited biodiversity benefits. Conversely, high-priority sub-basins in reactive and proactive (2 and 3) strategies provided similar conservation value, containing similar richness of at-risk species. Sub-basin prioritization varied depending on whether a single-taxon or multi-taxon approach was used, yet overall biodiversity representation was similar. Current protected area coverage, protecting only 0.36% of the region, is inadequate to offer protection to freshwater species. Our findings suggest that implementing conservation measures in sub-basins identified by proactive strategies can maximize gains across multiple taxa, outperforming commonly used single-species strategies.
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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 | 0.020 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".