Applying essential ecosystem service variables to analyse thirty years of wild salmon provisioning trends in Canada
Bibliographic record
Abstract
Wild salmon commercial fisheries in British Columbia (BC), Canada, have seen decreasing return and catch numbers across multiple salmon populations. Successful management of this ecosystem service (ES) has been elusive, but there is recognition that a wider social-ecological perspective is needed to support recovery. While ES monitoring is essential for evidence-based management, the social-ecological dimensions of ES pose challenges to monitoring: (i) ES-relevant data are siloed and disjointed, and (ii) ES assessments rarely consider the full range of social, economic, and ecological variables shaping ES dynamics. The essential ecosystem service variables (EESV) framework is intended to tackle these challenges but has not been fully implemented in any study to date. We use the EESV framework to analyze 27 years of monitoring data from diverse sources to assess change in the ES provided by wild Pacific salmon fisheries in the first multi-species analysis conducted at the provincial scale of BC. We develop and test a causal model incorporating five essential variables—salmon abundance, fishing effort, salmon catch, landed value and market demand—and seven drivers, including sea surface temperature, hatchery releases, and fishing licenses. Our results show that rising sea surface temperatures negatively impact salmon returns, while population enhancement through hatcheries has mixed effects, with some species benefiting and others declining. We also evaluate the government’s license retirement program, finding limited success in reducing fishing effort and improving the industry’s financial viability. Our Bayesian modeling approach explicitly quantifies uncertainty, revealing that ecological predictions have greater uncertainty than social or economic trends, suggesting that additional unaccounted for mechanisms influence ES supply. We were unable to include any measure of relational value, emphasizing the need to collect and incorporate cultural and relational data into ES monitoring. Overall, our findings underscore the role of market forces in maintaining salmon ES value despite ecological unpredictability and declining catch. By demonstrating how essential variables can be used to integrate diverse data into a unified causal representation, this study advances standardized ES monitoring. This work advances the EESV framework by linking social and ecological data and it offers insight into how to monitor ES in other systems facing similar challenges.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".