Frontiers in modelling social–ecological dynamics of recreational fisheries: A review and synthesis
Bibliographic record
Abstract
Abstract Recreational fisheries are culturally and economically important around the world. Recent research emphasizes that understanding and managing these systems requires a social–ecological perspective. We systematically reviewed quantitative social–ecological models of marine and freshwater recreational fisheries to summarize their conceptualization of social, ecological, and social–ecological dynamics and identify research frontiers. From a candidate set of 626 studies published between 1975 and 2018, 49 met criteria for inclusion in our review. These studies, though diverse in terms of focal species and processes considered, were geographically limited to a few locations and ignored large regions of the globe where recreational fishing is important. There were also important gaps in the social and ecological processes that were included in published models. Reflecting on these patterns in the context of previous conceptual frameworks, we define five key frontiers for future work: 1) exploring the implications of social and behavioural processes like heuristics, social norms, and information sharing for angler decisions and fishery dynamics; 2) modelling governance with more realistic complexity; 3) incorporating ideas from resilience thinking and complex adaptive systems, including slow variables, destabilizing feedbacks, surprises and diversity; 4) considering key ideas in fisheries systems, including spatial and temporal effort dynamics, catch hyperstability, and stocking; and 5) thinking synthetically about the models that we use to describe social–ecological dynamics in recreational fisheries, via explicit comparisons and formal integration with data. Exploration of these frontiers, while remembering the distinction between model complexity and model usefulness, will improve our ability to understand and sustain recreational fisheries.
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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) | 0.002 | 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) | 0.003 | 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".