Supply–demand equilibria and the size–number trade‐off in spatially structured recreational fisheries
Bibliographic record
Abstract
Recreational fishing effort varies across complex inland landscapes (e.g., lake-districts) and appears influenced by both angler preferences and qualities of the fishery resource, like fish size and abundance. However, fish size and abundance have an ecological trade-off within a population, thereby structuring equal-quality isopleths expressing this trade-off across the fishing landscape. Since expressed preferences of recreational anglers (i.e., site-selection of high-quality fishing opportunities among many lakes) can be analogous to optimal foraging strategies of natural predators, adopting such concepts can aid in understanding scale-dependence in fish-angler interactions and impacts of fishing across broad landscapes. Here, we assumed a fish supply-angler demand equilibria and adapted a novel bivariate measure of fishing quality based on fish size and catch rates to assess how recreational anglers influence fishing quality among a complex inland landscape. We then applied this metric to evaluate (1) angler preferences for caught and released fish compared to harvested fish, (2) the nonlinear size-numbers trade-off with uncertainty in both traits, and (3) the spatial-scale of the equilibria across 62 lakes and four independent management regions in British Columbia's (BC) rainbow trout Oncorhynchus mykiss fishery. We found anglers had low preference for caught and released fish (~10% of the value compared to harvested fish), which modified anglers' perception of fishing quality. Hence, fishing quality and angler effort was not influenced simply by total fish caught, but largely by harvested fish catch rates. Fishing quality varied from BC's northern regions (larger fish and more abundant) compared to southern regions (smaller fish and less abundant) directly associated with a 2.5 times increase in annual fishing effort in southern regions, suggesting that latent fishing pressure can structure the size-numbers trade-off in rainbow trout populations. The presence of two different equal-quality isopleths suggests at least two effective landscapes support co-occurring ideal free distributions of recreational fishing effort in BC's rainbow fishery. Anglers' expressed preferences among lakes interacted with density dependent growth and survival within lakes to structure a size-numbers trade-off influencing how anglers perceive fishing quality and, ultimately, distribute across complex inland landscapes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".