Impacts of population and fishery spatial structures on fishery stock assessment
Bibliographic record
Abstract
Fish populations and fishing efforts in most fisheries exhibit spatial heterogeneity. However, spatial considerations are generally ignored in fishery stock assessment and management because of a lack of spatially explicit data and poor understanding of the spatial dynamics of most fisheries. This study uses a simulation approach to evaluate the consequences of misspecifying spatial structure and migration during the assessment process. We developed an operating model to simulate a fishery using US Atlantic herring (Clupea harengus) as our model species. This population consists of two well-defined spawning substocks distributed and mixed in four management areas. Simulations were done for three alternative “true” populations, each having a different spatial structure both biologically and with regard to the geographic distribution of fishing effort. Stock assessments were then performed for the three simulated “true” populations using standard methodologies and assumptions currently used. Management-area-based assessments lead to overestimation of spawning stock biomass and underestimation of fishing mortality because of the interaction within the management area between the spatial structure of the population and that of the spatially heterogeneous fishery removals. In contrast, when fishing is spatially homogeneous, movement across management boundaries may not be relevant to modeling population dynamics. Such an idealized situation does not typically hold, however.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".