Ecosystem service trade-offs and synergies misunderstood without landscape history
Bibliographic record
Abstract
Dramatic changes in ecosystem services have motivated recent work characterizing their interactions, including identifying trade-offs and synergies. Although time is arguably implicit in these ideas of trade-offs and synergies (e.g., temporal dynamics or changes in ecosystem services), such interactions are routinely inferred based on the spatial relationships among ecosystem services alone (e.g., spatial concordance of ecosystem services indicates synergies, whereas incongruence signifies trade-offs). The limitations of this approach have not been fully explored. We quantified ecosystem service interactions using correlations among contemporary ecosystem services and compared these results to those derived by incorporating change in ecosystem services from an earlier decade. To document change over ~60 years in an urbanizing floodplain, we used aerial photography to map multiple floodplain-associated ecosystem services. Our results demonstrate how incorporating landscape baselines can influence measured synergies and trade-offs. Spatial correlations among contemporary ecosystem services missed several interactions that were detected when using prior baseline ecosystem services. Ignoring the history of ecosystem services and their change over time may result in missed opportunities to foster their synergies and lead to unnecessary trade-offs. Efforts to incorporate ecosystem services into land management should include long-term monitoring and baseline reconstructions of ecosystem services.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".