The importance of understanding angler heterogeneity for managing recreational fisheries
Bibliographic record
Abstract
Human Dimensions (HD) research in recreational fisheries is predicated on the understanding that successful management depends on knowing what anglers want from their fishing experience. While researchers have long recognized that diversity exists among anglers in terms of attitudes and preferences, few comparative studies account for the role played by diverse fishing opportunities in fulfilling anglers’ goals. Instead, most studies focus either on fishing as a general activity or generalize from fishery-specific case studies. Consequently, HD research has faced criticism from fisheries ecologists and managers regarding its management relevance. Leveraging an initiative to develop comprehensive catch and harvest information in the German state of Mecklenburg-Vorpommern (M-V) I collected additional angler information to explore HD constructs. I used recreation specialization as a framework for understanding angler heterogeneity while exploring how resource diversity affects preferred recreational outcomes. First, I examined the link between motivations and behavior, demonstrating that the relative importance of catch and non-catch outcomes depends on target species, and that angler specialization and motivations are related. Second, I used random utility theory to test how well different measures of specialization explain preference heterogeneity observed after accounting for target species, finding centrality-to-lifestyle to be the best predictor. Third, I examined the influence of centrality-to-lifestyle and target species on the importance of several catch and non-catch characteristics related to satisfaction-with-catch. While the model parameters suggested that more and larger fish are universally desired, the relative importance of these characteristics depended on both targeted species and specialization level. For my last study, I presented a case study of particular relevance to conservation of the European eel (Anguilla anguilla) fishery in M-V, by evaluating the effect of proposed regulatory changes on angling effort and harvest. Overall, and regardless of the specialization level, anglers were largely unresponsive to proposed legislation to partially close the fishery, suggesting more drastic measures may be required to meet ecological objectives. Together, these studies reinforce that researchers and practitioners should be wary of applying general insights of HD research to specific situations. Not only does the ‘average’ angler not exist, but neither does the ‘average’ fishing trip.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".