Modelling an exploited marine fish community with 15 parameters – results from a simple size-based model
Bibliographic record
Abstract
Abstract To measure and predict the response of fish communities to exploitation, it is necessary to understand how the direct and indirect effects of fishing interact. Because fishing and predation are size-selective processes, the potential response can be explored with size-based models. We use a simulation approach to describe the relationship between size spectrum slope and overall fishing mortality and to try to understand how a linear spectrum might be maintained. The model uses 15 parameters to describe a 13-“species” fish community, where species are defined by their maximum body size and the general relationship between size and life-history characteristics. The simulations allow us to assess the role of changes in the strength and type of density dependence in controlling the response to fishing, and to investigate the trade-off between catches and stock status of the different species. The outputs showed that the linear slope of the size spectrum was a function of community exploitation rate. Density-dependent controls, specifically predation mortality and the extent of compensation in the stock-recruit relationship, were key mechanisms in maintaining a linear spectrum. A linear spectrum emerged independent of the rate of compensation in the stock-recruit relationship. When this rate was low, the effects of changes in fishing mortality on predator abundance dominated those on spawning-stock biomass, whereas the dominance was reversed when the compensation rate in the stock-recruit relationship was high. The approach allows us to explore the effects of different fishing mortality schedules on properties of the fish community, to assess how fishing affects species with different life histories in mixed fisheries, and to assess the effects of selectively fishing different size classes. The simulations indicate that the size classes to be included when developing and interpreting size-based metrics must be carefully considered in relation to the trophic structure and likely strength of predatory interactions in the community. Runs with different fishing mortality by size suggest that the dynamics of predation cannot compensate fully for changing rates and patterns of exploitation, implying that the effects of selectively fishing different size classes should be assessed on a case-by-case basis.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".