An ecosystem-driven model for spatial dynamics and stock assessment of North Atlantic albacore
Bibliographic record
Abstract
An application of the Spatial Ecosystem And POpulation DYnamics Model (SEAPODYM) is developed for the North Atlantic albacore (Thunnus alalunga) population. We investigate the spatiotemporal dynamics of this species, distinguishing the influences of environment and international fishing. Incorporating historical data (1960–2008), a maximum likelihood approach allows the estimation of biological parameters (thermal and oxygen tolerance) and stock spatial distribution varying over time. Juvenile albacore are predicted in warm surface waters, whereas adults inhabit cooler and deeper waters. Positive correlations between juveniles and tropical large-scale climate indices highlight the importance of environmental drivers when estimating stock recruitment biology and spatiotemporal distribution. A methodology is proposed to use SEAPODYM outputs to estimate stock abundance and maximum sustainable yield (MSY). MSY is computed taking into account the spatial dynamics of the species and the environmental variability and is based on a mechanistic modelling of larval recruitment. MSY estimates converge towards an asymptotic value (15 997 t) of the same magnitude than standard stock assessment estimates conducted for the international tuna commission. In agreement with all assessment studies, the stock status is estimated from overfished in the 1990s to recovered in the 2000s. Our results show that the stock recovery results both from fishing actions, including total allowable catches established in the 2000s, and from the beginning of a North Atlantic Oscillation warm phase, leading to more favourable recruitment conditions. Following a parsimonious ecosystemic approach, SEAPODYM offers a faithful and spatially dynamic modelling framework that now includes direct tools for spatialized management advice and for distinction between environmental and fishing effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".