Prioritization of public and private land to protect species at risk habitat
Bibliographic record
Abstract
Abstract Conservation budgets are limited, requiring strategic prioritization among actions to efficiently protect species. Systematic prioritization approaches typically determine locations for conservation that most effectively balance species protection with cost. Proxies for cost are frequently used in prioritizing land for protection. Here, we combine financial cost estimates for private land acquisition and species habitat models into a spatial prioritization to explore cost‐effective habitat protection, using a case study of species at risk in Ontario, Canada. Our findings suggest a key trade‐off, whereby protecting the areas with the greatest concentration of species at risk may not be the best strategy for protecting these species. Instead, protecting species at risk may be most cost effective in areas where species‐at‐risk richness is still relatively high, but land costs are relatively low, such as in central Ontario. However, the budget required to adequately protect species at risk through land purchase would be much larger than is currently available for conservation efforts, even if public lands are preferentially protected. Therefore, to effectively protect all species at risk in Ontario, we recommend the use of alternative conservation measures, such as easements and incentives for restoration on private land, to supplement already protected areas.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".