Spatial variation in fishing intensity and its effect on yield
Bibliographic record
Abstract
Fishing mortality is rarely, if ever, evenly distributed over space, yet this is a common assumption of many fisheries models. To evaluate the effect of spatial heterogeneity in fishing mortality on yield, we constructed age-structured models that allowed for differing levels of fishing in three regions within the boundaries of a stock and explored alternative assumptions about the life stage in which density-dependent compensation operates. If the fishing mortality rate (F) is not excessive (i.e., F ≤ FMSY defined for the spatially homogeneous case; MSY, maximum sustainable yield), simulations demonstrated that minor to moderate spatial variation in fishing intensity does not impact sustainable yield. However, if fishing mortality is excessive (F > FMSY), spatial variation in fishing intensity often improves yield and can actually produce yields in excess of MSY when compensation occurs after dispersal, and the density-dependent recruitment rate is a function of the local density of adults. The yield premium generated in these simulations by postdispersal density dependence is due to a low level of compensatory mortality in heavily fished areas coupled with dispersal of propagules into these areas from lightly fished adjacent regions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".