How to choose a cost‐effective indicator to trigger conservation decisions?
Bibliographic record
Abstract
Abstract Effective biodiversity conservation requires responding to threats in a timely fashion. This requires understanding the impacts of threats on biodiversity and when management needs to be implemented. However, most ecological systems face multiple threats, so monitoring to assess their impacts on biodiversity is a complex task. Indicators help simplify the challenge of monitoring but choosing the best indicator(s) to inform management is not straightforward. We provide a decision framework that can help identify optimal indicators to trigger management in a system faced with multiple threats. The approach evaluates indicators based on criteria spanning monitoring efficiency, management outcomes and the economic constraints for decision‐making. Critical decision factors (or parameters) are identified and detailed in a six‐step process to estimate the cost‐effectiveness of alternate indicators, including threat impacts, sensitivity of indicators to detect change, and the benefits, costs and feasibility of alternative indicators and management actions. Using the Kimberley as a case study, we evaluate 18 indicators for informing management of three key threats in the region: fire and grazing, feral cat predation, and weeds. We show that indicator selection based on our approach can help improve the expected outcome of management decisions under limited resources. By accounting for multiple factors in estimating benefit and costs of monitoring, our approach improves on common approaches that select indicators based only on whether they are sensitive to change and/or cheap to monitor. We also identify how uncertainty in decision factors influences indicator selection. Although cost‐effectiveness analyses are gaining popularity, ours is the first study to integrate multiple selection criteria using a return on investment framework to compare indicators for monitoring multiple threats and triggering management.
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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 | 0.018 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".