Accounting for human–nature linkages in area‐based conservation monitoring through social–ecological indicator bundles
Bibliographic record
Abstract
As the coverage of area-based conservation increases across the globe, it is critical to improve understanding of the social and ecological outcomes of such measures and the pathways to their outcomes. A social-ecological systems approach to monitoring and evaluation is increasingly advocated; yet, applications remain scarce. We sought to facilitate operationalization of this approach through prioritization of indicators when resources are scarce and to improve capture of social-ecological interactions. We convened a working group of practitioners and academics to explore linked social and ecological interactions through a case study of marine protected areas (MPAs). We used causal models (implemented through causal loop diagrams) in participatory and future-oriented approaches to identify interactions among key nodes of the system that can be a focus of monitoring. These nodes and their interactions provided insight into linked indicators of key system components, for example, biomass, compliance, perceived legitimacy, catches, and perceived fairness. We called these indicator bundles. Indicator bundles can be applied to analyze causal modeling diagrams, identify essential elements to monitor, and inform analytical and reporting protocols. The bundles can also help identify key leverage points for adaptive management to improve outcomes of existing interventions. This approach can inform monitoring and evaluation and, ultimately, the design and adaptive management of conservation areas that maximize social and ecological benefits and minimize negative trade-offs.
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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 | 0.000 | 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 | 0.000 |
| 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".