Monitoring ecosystem services requires a redesign of siloed monitoring programmes
Bibliographic record
Abstract
1. Monitoring ecosystem services is essential for achieving sustainability and biodiversity goals, yet existing monitoring programmes are fragmented, siloed, and not designed to detect or attribute change in ecosystem services. 2. We applied the Essential Ecosystem Service Variables (EESV) framework within a social-ecological network model to integrate three decades of ecological, economic, and social monitoring data from the Pacific salmon fisheries of British Columbia, Canada. Using Bayesian state-space models, we analysed the coupled provisioning (commercial) and cultural (recreational) services provided by five salmon species across six regions. 3. Our models revealed complex, species- and location-specific dynamics, including regional declines in Chum salmon abundance, long-term reductions in commercial fishing effort, and diverging trends between commercial and recreational harvests, with recreational catchability consistently higher than commercial catchability. 4. Trade-offs between provisioning and cultural services were particularly evident for Chinook and Coho salmon, where recreational and commercial harvest rates displayed opposing trends, highlighting competition among user groups. 5. The modelling process exposed the limitations of current monitoring systems: many model structures failed to converge, key external drivers (e.g. sea surface temperature and hatchery releases) could not be reliably incorporated, and predictive accuracy was consistently poor for anthropogenic and governance components, demonstrating that existing monitoring programmes cannot support confident causal attribution of change. 6. Despite these limitations, the integration of siloed datasets recovered known dynamics and provided valuable insights, showing that social-ecological network models can serve both as analytical tools and diagnostics of monitoring capacity, providing an empirically supported mandate for the fundamental redesign of monitoring systems. To effectively manage ecosystem services and meet global sustainability targets, nations must move beyond fragmented data collection and build integrated, holistic monitoring programs that co-measure ecological, social, and governance variables by design, enabling an evidence-based understanding of our planet's vital human-nature systems.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".