Managing ecosystem services: tools and theory for understanding the dynamics of multiple ecosystem services on a landscape
Bibliographic record
Abstract
A key challenge for achieving sustainability is understanding how to manage landscapes for multiple ecosystem services, the benefits that humans obtain from ecosystems. Because the enhancement of provisioning ecosystem services, such as food and timber, often leads to declines in regulating and cultural ecosystem services, such as nutrient cycling and tourism, ecological management has often been less successful than it could be. As the demand for all types of ecosystem services increases globally, management that considers and manages ecosystem service interactions is needed to produce better outcomes for societies. This thesis develops new tools and approaches for understanding and managing the multiple ecosystem services provided by landscapes. It does this by combining a global assessment with a regional case study. I assess global trends to explore how human well-being has continued to improve while the condition of many ecosystem services has sharply degraded. This paradox is partially explained by humanity's success in engineering productive food systems and substitutions for ecosystem services, and by time lags in the global system. The analysis concludes that sustainable management of ecosystem services to enhance human well-being requires quantitative methods for analyzing interactions among ecosystem services. I develop and test an approach for analyzing interactions among multiple ecosystem services across space, using a case study of 12 ecosystem services quantified across a landscape in southern Québec. Based on this analysis, I present the first empirical demonstration of ecosystem service bundles, sets of services that appear together repeatedly across a landscape. Bundle analysis demonstrates landscape-scale trade-offs between provisioning and almost all regulating and cultural ecosystem services and that a greater diversity of ecosystem services is positively correlated with regulating ecosystem services. I find that predicting these landscape patterns requires models that integrate social, geographic and ecological drivers. Finally, a novel analysis of the effect of scale on ecosystem service assessment outcomes reveals that both the patterns and interactions of ecosystem services with more spatially clustered distributions change the most as the scale of observation changes. Scale mismatches among production, consumption and management processes of ecosystem services are identified as potential indicators of management problems.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".