Salmonid Biomass in Streams Around the World: A Quantitative Synthesis
Bibliographic record
Abstract
ABSTRACT Salmonid fishes are one of the best studied fish taxa, but little is known about their biomass distribution. We created a dataset using published material for over 1000 rivers with estimated salmonid biomass, covering 27 countries, and 11 species. The distribution of salmonid biomass and production across streams was skewed to the right with a mean biomass and production of 5.2 g/m2 (range = 0–70.3 g/m2) and 6.3 g/m2/year (range = 0.03–50.2 g/m2/year), respectively. The top 10% and 1% of salmonid streams in the world had a biomass > 11.9 and 36.5 g/m2, respectively, and a production > 13.9 and 25.6 g/m2/year, respectively. Salmonid production was positively correlated with biomass (r = 0.82, n = 194), with a mean production to biomass (P/B) ratio of 1.08, which differed among species. Mean biomass declined 38% over time, from 8.6 g/m2 before 1980 to 5.4 g/m2 in 2000–2020. Biomass was also higher in small streams (< 10 m wide) and in streams where smaller areas were sampled. Brown trout (Salmo trutta) streams represented a higher proportion of those with biomass > 10 g/m2 than many other species. In addition to the variables mentioned above, salmonid biomass in streams was affected by species, season, method of sampling, elevation, latitude, and migratory strategy. Expanding the list of variables would be useful for developing models to predict salmonid biomass and the conditions for an outstanding salmonid stream, defined as a stream which has a biomass estimate in the top 1% worldwide.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".