Ecosystem services in conservation planning: less costly as costs and side-benefits
Bibliographic record
Abstract
Because of their potential to explicitly link conservation and human well-being, there is growing support to include ecosystem services in conservation planning. In this study, we explored three questions: (1) what is the most effective and efficient method of including ecosystem services in Marxan—the most widely used software tool for conservation reserve network design; (2) what reduction in estimated reserve costs is enabled by the explicit inclusion of ecosystem-service opportunity costs; and (3) what are the relationships between services across space. In conjunction with the Nature Conservancy of Canada, we answered these questions by examining the potential impact of conservation on the supply of these three ecosystem services and biodiversity in the Central Interior of British Columbia, relative to a business-asusual scenario. Our findings suggest that including ecosystem services within a conservation-planning program may be most cost-effective when these services are represented as substitutable costs or benefits (within the cost surface), rather than as targeted features.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".