Historical dynamics in ecosystem service bundles
Bibliographic record
Abstract
Managing multiple ecosystem services (ES), including addressing trade-offs between services and preventing ecological surprises, is among the most pressing areas for sustainability research. These challenges require ES research to go beyond the currently common approach of snapshot studies limited to one or two services at a single point in time. We used a spatiotemporal approach to examine changes in nine ES and their relationships from 1971 to 2006 across 131 municipalities in a mixed-use landscape in Quebec, Canada. We show how an approach that incorporates time and space can improve our understanding of ES dynamics. We found an increase in the provision of most services through time; however, provision of ES was not uniformly enhanced at all locations. Instead, each municipality specialized in providing a bundle (set of positively correlated ES) dominated by just a few services. The trajectory of bundle formation was related to changes in agricultural policy and global trends; local biophysical and socioeconomic characteristics explained the bundles' increasing spatial clustering. Relationships between services varied through time, with some provisioning and cultural services shifting from a trade-off or no relationship in 1971 to an apparent synergistic relationship by 2006. By implementing a spatiotemporal perspective on multiple services, we provide clear evidence of the dynamic nature of ES interactions and contribute to identifying processes and drivers behind these changing relationships. Our study raises questions about using snapshots of ES provision at a single point in time to build our understanding of ES relationships in complex and dynamic social-ecological systems.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".