Estimating Fish Exploitation and Aquatic Habitat Loss across Diffuse Inland Recreational Fisheries
Bibliographic record
Abstract
The current state of many freshwater fish stocks worldwide is largely unknown but suspected to be vulnerable to exploitation from recreational fisheries and habitat degradation. Both these factors, combined with complex ecological dynamics and the diffuse nature of inland fisheries could lead to an invisible collapse: the drastic decline in fish stocks without great public or management awareness. In this study we provide a method to address the pervasive knowledge gaps in regional rates of exploitation and habitat degradation, and demonstrate its use in one of North America's largest and most diffuse recreational freshwater fisheries (Ontario, Canada). We estimated that (1) fish stocks were highly exploited and in apparent danger of collapse in management zones close to large population centres, and (2) fish habitat was under a low but constant threat of degradation at rates comparable to deforestation in Ontario and throughout Canada. These findings confirm some commonly held, but difficult to quantify, beliefs in inland fisheries management but also provide some further insights including (1) large anthropogenic projects greater than one hectare could contribute much more to fish habitat loss on an area basis than the cumulative effect of smaller projects within one year, (2) hooking mortality from catch-and-release fisheries is likely a greater source of mortality than the harvest itself, and (3) in most northern management zones over 50% of the fisheries resources are not yet accessible to anglers. While this model primarily provides a framework to prioritize management decisions and further targeted stock assessments, we note that our regional estimates of fisheries productivity and exploitation were similar to broadscale monitoring efforts by the Province of Ontario. We discuss the policy implications from our results and extending the model to other jurisdictions and countries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".