Next steps for ecosystem service models: integrating complex interactions and beneficiaries
Bibliographic record
Abstract
Accounting for ecosystem services (ES)—the ways in which society and people directly benefit from ecological processes and functions—is crucial for developing sustainable landscape management approaches that consider the interrelationship between people and nature. Previous research has produced models that estimate the provision of potential ES by landscapes to help inform policy and stakeholder decision-making. However, most modelling efforts do not consider the delivery of ES to specific human populations or communities, making it difficult to evaluate any possible human welfare implications from alternative land use planning scenarios. In this paper, we first explore the recent state of science of ES modelling from the perspective of ES provision and delivery to the people that benefit from them. Second, we propose the addition of some essential aspects of complexity using the classic social–ecological system framework, crucial for developing models to inform pragmatic decision-making. Our propositions are illustrated using simplified examples inspired by sea otter conservation in the seascapes of British Columbia. Integrating these concepts in future ES models should serve as a baseline for future management approaches that more adequately capture the important implications of landscape scenarios on human well-being.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".