Monitoring ecosystem services with essential ecosystem service variables
Bibliographic record
Abstract
In the Anthropocene, ecosystems are changing along with their capacity to support human well‐being. Monitoring ecosystem services (ESs) is required to assess the changing state of human–nature interactions. To standardize the monitoring of multiple facets of ESs, the Group on Earth Observations Biodiversity Observation Network (GEO BON) recently proposed the essential ecosystem service variables (EESVs), which are organized into six classes: Ecological Supply , Use , Demand , Anthropogenic Contribution , Instrumental Value , and Relational Value . We apply the EESV framework to three case studies in British Columbia, Canada, each targeting a single ES. Using trend and intervention analysis, we show how EESVs are changing and affected by policy. We discuss key challenges and solutions while providing guidance on how to quantify EESVs. Finally, we demonstrate the potential of EESVs to harmonize metrics across conceptual frameworks, monitor ES change, and provide decision support to assess progress under various international policy conventions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".