Land-use intensity mediates ecosystem service tradeoffs across regional social-ecological systems
Bibliographic record
Abstract
A key sustainability challenge in human-dominated landscapes is how to reconcile competing demands such as food production, water quality, climate regulation, and ecological amenities. Prior research has documented how efforts to prioritize desirable ecosystem services such as food and fiber have often led to tradeoffs with other services. However, the growing literature has revealed different and sometimes contradictory patterns in ecosystem service relationships. It thus remains unclear whether there are generalizable patterns across social-ecological systems, and if not, what factors explain the variations. In this study, we synthesize datasets of five ecosystem services from four social-ecological systems. We ask: (1) Are ecosystem service relationships consistent across distinct regional social-ecological systems? (2) How do ecosystem service relationships vary with land-use intensity at the landscape scale? (3) In case of ecosystem service tradeoffs, how does land-use intensity affect intersection points of tradeoffs along the landscape composition gradient? Our results reveal that land-use intensity increases magnitude of ecosystem service tradeoffs (e.g. food production vs. climate regulation and water quality) across landscapes. Land-use intensity also alters where provisioning and regulating services intersect: in high-intensity systems, food production and regulating services can be both sustained only at smaller proportions of agricultural lands, whereas in low-intensity systems, these services could be both supplied with greater proportions of agricultural lands. Our research demonstrates importance of considering multiple aspects of land uses (landscape composition and land-use intensity), and provides a more nuanced understanding and framework to enhance our ability to predict how land use alters ecosystem service relationships.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".