Predicted shifts of groundfish distribution in the Eastern Bering Sea under climate change, with implications for fish populations and fisheries management
Bibliographic record
Abstract
Abstract Climate-related distribution shifts for marine species are, in general, amplified in northern latitudes. The objective of this study was to predict future distributions of commercially important species in the eastern Bering Sea under six climate scenarios, by incorporating predictions of future oceanographic conditions. We used species distribution modelling to determine potential distribution changes in four time periods (2013–2017, 2030–2039, 2060–2069, and 2090-2099) relative to 1982–2012 for 16 marine fish and invertebrates. Most species were predicted to have significant shifts in the centre of gravity of the predicted abundance, the area occupied, and the proportion of the predicted abundance found in the standard bottom trawl survey area. On average the shifts were modest, averaging 35.2 km (ranging from 1 to 202 km). There were significant differences in the predicted trend for distribution metrics among climate scenarios, with the most extensive changes in distribution resulting from Representative Concentration Pathway 8.5 climate scenarios. The variability in distributional shifts among years and climate scenarios was high, although the magnitudes were low. This study provides a basis for understanding where fish populations might expand or contract in future years. This will provide managers’ information that can help guide appropriate actions under warming conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".