Subregional differences in groundfish distributional responses to anomalous ocean bottom temperatures in the northeast Pacific
Bibliographic record
Abstract
Although climate-induced shifts in fish distribution have been widely reported at the population level, studies that account for ontogenetic shifts and subregional differences when assessing responses are rare.In this study, groundfish distributional changes in depth, latitude, and longitude were assessed at different size classes by species within nine subregions. We examined large, quality-controlled datasets of depth-stratified-random bottom trawl surveys conducted during summer in three large regions-the Gulf of Alaska and the west coasts of Canada and the United States-over the period 1996-2015, a time period punctuated by a marine "heat wave." Temporal biases in bottom temperature were minimized by subdividing each region into three subregions, each with short-duration surveys. Near-bottom temperatures, weighted by stratum area, were unsynchronized across subregions and exhibited varying subregional interannual variability. The weighted mean bottom depths in the subregions also vary largely among subregions. The centroids (centers of gravity) of groundfish distribution were weighted with catch per unit effort and stratum area for 10 commercially important groundfish species by size class and subregion. Our multivariate analyses showed that there were significant differences in aggregate fish movement responses to warm temperatures across subregions but not among species or sizes. Groundfish demonstrated poleward responses to warming temperatures only in a few subregions and moved shallower or deeper to seek colder waters. The temperature responses of groundfish depended on where they were. Under global warming, groundfish may form geographically distinct thermal ecoregions along the northeast Pacific shelf. Shallow-depth species exhibited greatly different distributional responses to temperature changes across subregions while deep-depth species of different subregions tend to have relatively similar temperature responses. Future climate studies would benefit by considering fish distributions on small subregional scales.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".