Connecting governance interventions to ecosystem services provision: A social‐ecological network approach
Bibliographic record
Abstract
Abstract The fulfilment of the benefits resulting from services provided by nature requires an integrated framework that combines appropriate ecosystem service governance with spatially explicit models of service provision. Here, we propose using a social‐ecological network approach to develop a ‘landscape governance framework’ that identifies how different types of governance can act on supply, demand and flow of ecosystem services through changes in landscape structure and connections. Starting from undesirable situations where demand exceeds supply, we exemplify the application of this conceptual model considering hierarchical (e.g. creation of protected areas), market (e.g. payments for environmental services) and community‐based (e.g. enhancing links between stakeholders) governance approaches. We show how interventions associated with each of these approaches act in distinct ways to regulate different components of the service provision chain in heterogeneous landscapes. Filling such knowledge gaps can help identify appropriate governance interventions depending on factors that limit provision: restricted supply, demand or flow. The application of the landscape governance framework entails challenges related to availability of data and limited understanding of key underlying mechanisms. However, it opens important new research questions at the interface between governance and ecosystem services, with great potential as a tool for landscape management that aims to achieve ecosystem service sustainability. A free Plain Language Summary can be found within the Supporting Information of this article.
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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".