Species-specific vulnerability to angling and its size-selectivity in sympatric stream salmonids
Bibliographic record
Abstract
In mixed fisheries where multiple species are caught, to manage resources sustainably, knowledge about the species-specific vulnerability to fishing is equally or even more important than knowledge of size selectivity of the gear. We compared the vulnerability to bait recreational angling in four salmonid species in natural streams in Japan. The ranking of species-specific angling vulnerability was as follows (from highest to lowest): rainbow trout (Oncorhynchus mykiss), masu salmon (Oncorhynchus masou), white-spotted char (Salvelinus leucomaenis), and southern Asian Dolly Varden (Salvelinus curilus). In all species, larger individuals were more vulnerable to angling, but there were differences in the size dependence between species. In rainbow trout and Dolly Varden (which have a nonanadromous life history in the study area), the probability of being caught monotonically increased with body size, while the vulnerability to angling in masu salmon and white-spotted char (which have an anadromous life history in the study area) showed a domed-shaped pattern. We found that across the species the catch per unit effort showed a hyperstable relationship with population density. Therefore, diminishing local populations are prone to collapse, and this collapse would be hard to foresee based on catch rate data alone.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".