Advancing ecosystem service monitoring by mapping the current use of essential ecosystem service variables
Bibliographic record
Abstract
Essential variables are a well-established tool to support the calculation of ecological indicators. The recently conceptualized Essential Ecosystem Service Variables (EESVs) are regrouped into six classes – Ecological Supply , Demand , Use , Relational Value , Instrumental Value , and Anthropogenic Contribution – designed to capture changes in the multiple dimensions of ecosystem services. Prior to the proposal to monitor ecosystem services using EESVs, many variables relevant to these classes were already used in ecosystem services studies. Here, we perform a systematic retrospective analysis across disciplines to determine the potential of EESV classes for monitoring ecosystem services effectively. We conducted a comprehensive keyword search across 439 studies, based on a review paper on ecosystem services. Network analyses revealed that Anthropogenic Contribution had the highest overall presence based on odds ratios, while Relational Value was the least represented, often showing interdependencies with other classes and low network connectivity and centrality. Network centrality metrics identified Ecological Supply as the most interconnected class, reflecting its foundational role across studies. Journal analysis across seven major journal types showed a good overall distribution of EESV classes across fields, while still emphasizing disciplinary priorities. Urban journals focused more on Anthropogenic Contribution and Relational Value while biological journals prioritized Ecological Supply . Agricultural journals often highlighted Use and Demand as well as Instrumental Value and management and policy journals emphasized Instrumental Value . Addressing gaps in EESV class coverage stresses that underrepresented classes like Relational Value are empirically grounded and measurable, yet these classes are essential for monitoring both the ecological and socio-cultural dimensions 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 | 0.013 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".