Climate models drive variation in projections of species distribution on the Grand Banks of Newfoundland
Bibliographic record
Abstract
Species Distribution Models (SDMs) are tools for understanding climate-induced habitat changes, yet their outcomes depend heavily on climate model selection. This study compares biomass projections for three key species on the Grand Banks of Newfoundland that are known to be sensitive to warming—snow crab, yellowtail flounder, and Atlantic cod. We use Earth system models (GFDL-ESM4, IPSL-CM6A-LR) and a regional ocean model system (Atlantic Climate Model (ACM)) under varying climate change emissions scenarios to assess long-term biomass trends and distributional shifts driven by future ocean warming on the Grand Banks. Projections indicate declining biomass for snow crab and yellowtail flounder with rising temperatures, whereas Atlantic cod is anticipated to exhibit biomass gains, particularly in the southern Grand Banks. Variations in biomass projections among climate models were noticeable, with IPSL forecasting the most drastic decline. ACM and GFDL biomass projections were more similar to each other than GFDL and IPSL projections, likely because ACM was downscaled from GFDL. Differences between GFDL and ACM likely arise from the coarse spatial resolution of ESMs, leading to insufficient resolution of the bathymetry and incorrect current patterns, in turn affecting the bottom temperature field. These findings underscore the important role of climate model selection in SDM-derived biomass projections. We partitioned uncertainty by source and found that the relative contribution of variability by component changes by species. As temperatures continue to rise, the urgency of implementing adaptive management strategies to minimize impacts on Newfoundland and Labrador fisheries becomes increasingly evident. SDM outputs can aid in strategic decision making, providing valuable insights for medium and long-term planning in fisheries 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".