Monitoring for fisheries or for fish? Declines in monitoring of salmon spawners continue despite a conservation crisis
Bibliographic record
Abstract
Abstract Monitoring of salmon in Pacific Canada has been declining for decades. Counts of spawning salmon are critically important, enabling researchers to quantify impacts of stressors, identify when and where management interventions are required to avoid extirpations, and evaluate the efficacy of recovery efforts. These data are more important now than ever, as uniquely adapted spawning populations underlie salmon resilience and their ability to adapt to climate change, and fine-scale data can inform sustainable fishing opportunities including the revitalization of terminal fisheries. We revisit the state of Pacific salmon spawner data from the Yukon to southern British Columbia. Almost two-thirds of salmon populations that were historically monitored have no reported estimates in 2014-2023 - the worst decade for data since broadscale spawner surveys began in the 1950s. We found a positive association between the number of populations monitored and landed value for three of the five Pacific salmon species, suggesting that monitoring is, to some extent, motivated by the information needs of commercial fisheries management. We recommend aligning monitoring objectives and strategic investments to improve monitoring outcomes for salmon, ecosystems, and the communities that depend on them. We emphasize data stewardship, as ensuring access to these baseline data is a cornerstone for rebuilding wild Pacific salmon.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".