Species distribution models estimate time-varying juvenile salmon distributions in the north- and southeastern Bering Sea
Bibliographic record
Abstract
This study compares alternative implementations of species distribution models (SDMs) for quantifying static and dynamic patterns in marine habitat use, with a case study focusing on juvenile salmon in the eastern Bering Sea. We compare the performance of two prevalent SDM frameworks—generalized additive models (GAMs) and vector autoregressive spatio-temporal (VAST) models—in predicting juvenile salmon distributions and assessing interannual variation in habitat utilization. The two SDM frameworks produced similar spatial predictions but performed differently in tests of within-sample and out-of-sample predictive power. Our findings indicate that VAST models may provide more precise estimates of distribution compared to GAMs. Maps of predicted juvenile salmon distributions showed highest salmon densities in habitats within the 50 m isobath of the continental shelf, underscoring the importance of these coastal areas, although among-species differences were evident. Model performance results suggested evidence for spatial variation in juvenile salmon species’ distributions through time. Our findings suggest that an SDM approach can be effective for estimating static and dynamic juvenile salmon distributions, and for providing insights that are useful in spatial fisheries management contexts.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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".