How spatial feedbacks between institutional and ecological patterns drive landscape dynamics in peri-urban commons
Bibliographic record
Abstract
Rapid urbanization is a critical landscape transition that impacts biodiversity, ecosystem goods and services, and social sustainability. Urbanizing locations may follow different ecological trajectories, with very different consequences for future inhabitants. Ostrom’s design principles assert that effective land governance (that maintains biodiversity and ecosystem services) requires congruence between rules and local social-ecological conditions. However, little is known about how to achieve such congruence in peri-urban systems, partly because local landscape conditions are constantly changing. We used dynamic simulation models to address this gap, testing how spatially explicit feedbacks between ecological patterns and land governance might influence landscape dynamics. We captured the feedbacks by testing the outcomes of decisions made at two different spatial extents, regional (100 km²) and local (4 km²), for two different levels of landscape heterogeneity. Our approach extends and operationalizes Ostrom’s design principle by explicitly defining the term “local” as relative rather than fixed; that is, as a spatial extent of decision-making based on an administrative hierarchy. We found that the rate of urbanization was higher for high heterogeneity landscapes than low heterogeneity landscapes. Further, the urbanization trend differed significantly between the regional and local scales for both high and low heterogeneity landscapes. The analysis shows how spatial mismatches between land governance and spatial processes can arise and explores their consequences for ecosystems in landscapes that are in transition from rural to urban.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".