Size spectrum model reveals importance of considering species interactions in a freshwater fisheries management context
Bibliographic record
Abstract
Abstract Inland fisheries have a significant cultural and economic value around the globe, providing dietary protein, income, and recreation. Consequently, methods for monitoring and managing these important fisheries are continually being refined. In marine systems, multispecies size spectrum models have been increasingly used to explore management scenarios of important fish stocks within an ecosystem‐based fisheries management framework; however, these models have not been applied as extensively in freshwater systems. In this study, we developed a multispecies size spectrum model for the fish community of Lake Nipissing, a large, productive lake in Ontario, Canada. To the best of our knowledge, this is the first fully calibrated multispecies size spectrum model for an inland fishery. Using this model, we explored the impacts of potential fishing regimes and management scenarios on fish community dynamics while taking species interactions into account. Specifically, we examined how changes in fishing mortality affect (1) species biomass, (2) community size structure, and (3) stock recovery times. We found that community dynamics following changes in fishing mortality were driven by complex interactions among species, including competition and predation. The greatest changes in biomass and community size structure were observed following changes in fishing mortality of top predators, with community size structure most strongly influenced by changes in the mortality of the largest species. Counter to predictions based on generation time, the smallest species in our model exhibited the longest time to recovery due to strong competition and predation. Our results demonstrate the importance of considering species interactions in the management of inland fisheries and highlight the potential of size spectrum model use in freshwater systems.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".