Ecology for Understanding Recreational Fishers and Fisheries
Bibliographic record
Abstract
Abstract We examine recreational fisher behaviour and recreational fishery systems through the lens of an ecologist to understand the dynamical properties of these social-ecological systems. From the perspective of an ecologist, recreational fishers and the fish they capture can be viewed as analogous to predator-prey systems. Our understanding of predator-prey interactions is supported by a richness of empirical and conceptual research, primarily developed within sub-fields of behavioural, population, and community ecology. We develop this analogy between predator-prey ecology and fisher behaviour by examining the key underlying processes within a conceptual framework based on simple models. We then characterize several processes inherent to recreational fisheries that can, at least in part, decouple these simple predator-prey interactions. We examine the impacts on fisher behaviour and fishery outcomes of non-random spatial distributions of fishers and fish, heterogeneity of fisher behaviour, and multi-species fisheries, and develop an enhanced framework to understand these dynamic interactions. Population ecology and density-dependent feedbacks are important concepts underlying fish population dynamics, and also set limits to the sustainability of fisher harvest. Predator-prey theory is helpful in understanding fisher behaviour and its feedback with fish production, fishery quality, and sustainability. As fishers often prefer larger sizes in their catch, the predator-prey dynamic involves ecological concepts related to life-history theory and size-structured interactions. Although some recreational fisheries target a single species, many involve multi-species fisheries, such that food web theory is also important in understanding ecological feedbacks between fisher behaviour and fishery outcomes. In addition, individual recreational fisheries are typically embedded within landscapes of alternative fisheries, so the spatial configuration of fishing opportunities and the spatial behaviour of fishers is central in understanding fishery outcomes across landscapes. We discuss field methods used to measure fisher behaviour and provide empirical examples from well-studied recreational fisheries. Field methods to measure catchability, catch-per-unit effort, and landscape distribution of fisher effort are informed by the ecological theory of functional and numerical responses. Methods and implications of catch-and-release behaviour, multi-species fisheries, and non-catch related fisher site choice are discussed as related to our understanding of fisher behaviour and fisheries outcomes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".