Seasonal Fishery Dynamics of a Previously Unexploited Rainbow Trout Population with Contrasts to Established Fisheries
Bibliographic record
Abstract
Abstract Although predator–prey interactions between anglers and fish populations are well studied and understood, little is known about whether these interactions differ between lightly and fully exploited populations. Furthermore, within-season shifts in catch rates are poorly understood. These differences have been thought to be due in part to changes in fish behavior after catch and release, which reduces the overall catchability of the population. To address these questions, angling was introduced to a previously unexploited population of rainbow trout Oncorhynchus mykiss and the within-season fishery dynamics were contrasted with those of fully exploited populations. We found that catch rates rapidly decreased after the introduction of angling; moreover, once this had occurred, angler effort decreased. Catch per unit effort (CPUE) and catchability of the lightly exploited population were initially quite high compared with most exploited populations but quickly decreased throughout the summer to levels similar to those of fish populations that have been open to angling for decades. Therefore, the differences in catch rates are transitory and the unexploited population quickly becomes indiscernible from fully exploited populations. Seasonality in CPUE was observed in all lakes, with significant decreases in CPUE throughout the summer. These changes in CPUE reflected changes in catchability throughout the season. Although the relative effects of harvest and possible behavioral shifts in fish after catch and release accounted for some variation in catchability, the effects were insufficient to explain total seasonal decreases in catchability in the previously unexploited population. These findings demonstrate that any potential shifts in fish behavior subsequent to catch and release are inadequate to explain seasonal shifts in catch rates. Apparently, seasonal changes in CPUE are driven more by ecological processes than by the fishery.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".