Which ecosystems provide which services? A meta-analysis of nine selected ecosystem services assessments
Bibliographic record
Abstract
For ecosystem measurement frameworks to be accepted, operationalised and implemented by diverse international communities, clear and agreeable concepts and classifications are essential. This paper analyses and develops two foundational typology challenges within ecosystem measurement: the classification of ecosystems and the classification of their services. Our aim is to determine if there is sufficient consensus to ascertain “Which ecosystems provide which services?” for standardised ecosystem accounting. This paper first compares classifications used in nine selected ecosystem assessments as input studies that make value statements about multiple ecosystems providing multiple ecosystem services. Given that these nine studies do not use identical concepts, classifications and terminologies, we develop “supersets” that can accommodate the diversity of classifications used in these input studies. Each input study is then corresponded to these new supersets. On the basis of this analysis, substantial consensus was found that some ecosystems are more likely to provide certain services than others are. However, for several ecosystem types, there was little or no consensus on which services they provide. Linkages for which there is consensus can serve as a checklist for future ecosystem services assessments. Both the framework of the supersets and the correspondence and visual methods developed will be useful for integrating information at different scales (for example, linkages from local, ecosystem-specific and ecosystem services-specific studies). This paper also provides guidance to future ecosystem services assessments to use, test and extend the current classifications of ecosystems and 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 | 0.040 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.015 |
| Bibliometrics | 0.024 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".