Hyperstability in an inland recreational fishery: Are catch-per-unit-effort data masking the magnitude of steelhead declines?
Bibliographic record
Abstract
Abstract Objective Recreational fisheries are complex social–ecological systems, with interactions and feedbacks across local and regional scales and among individual fish, anglers, and managers. Understanding the link between true fish abundance and angler catch per unit effort (CPUE) is crucial to inform management decisions in these systems, especially in the absence of independent monitoring data. For instance, the relationship between fish abundance and CPUE could be linear or nonlinear, such as a hyperstable relationship, which occurs when CPUE remains high even as population abundance declines. We investigated the relationship between fisheries-independent abundance and CPUE in steelhead Oncorhynchus mykiss and evaluated the extent to which hyperstability may obscure underlying population declines. Methods We analyzed the relationship between steelhead abundance and CPUE in 14 streams across the province of British Columbia, Canada. To explore the impact of hyperstability on our capacity to detect changes in abundance, we simulated three scenarios of steelhead decline, comparing the rate of change in CPUE versus abundance. Results Our findings revealed sweeping patterns of hyperstability, indicating that when populations are depressed, CPUE does not decrease as rapidly as abundance. Additionally, we found that the magnitude of CPUE overestimation varied with the extent of population decline. For example, when the population declined by 50%, CPUE decreased by only 40%, representing a 28% overestimation of remaining abundance. This disparity increased in more extreme scenarios of decline. Conclusions Our findings underscore that catch data can mask fish population declines and highlight the need for improved fish population monitoring in recreational fisheries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".