Modeling the spatial distribution of larval fish abundance provides essential information for management
Bibliographic record
Abstract
Productive fisheries are strongly linked to the ecological state of the essential habitats. In this study, we developed a methodology to assess the most important reproduction habitats of fish by using larval survey data and Bayesian species distribution models that predict the spatial distribution and abundance of fish larvae. Our case study with four commercially and ecologically important fish species in the coastal zone of the northern Baltic Sea demonstrated that the production of fish stocks can be concentrated to an extremely limited area compared with the entire suitable production area. The area suitable for larval production varied from 3.7% to 99.8% among species, but the smallest area responsible for 80% of the cumulative larval production was two to five times more limited, varying from 1.4% to 52.9% among species. Hence, instead of the traditional approach of modeling only habitat suitability for fish production, marine spatial planning and management should take into account the areal production potential. Moreover, the developed methodology enables linking of the total production potential across the whole distribution area to fisheries stock assessment and management.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".