Interactive effects of predation and climate on the distributions of marine shellfish in the Northwest Atlantic
Bibliographic record
Abstract
As climate change transforms marine environments globally, species distributions correspondingly shift to locations where conditions have become or remain favourable. The ability to model these distributional shifts has been facilitated by species distribution models (SDMs). However, current SDM approaches have largely ignored climate‐driven changes in species interactions, which ultimately can have an important influence on species distributions. In this study, we utilize a long‐term, large‐scale dataset spanning 48 years and approximately 30 degrees latitude across the Canadian Atlantic shelf. We examine how climate influences the distribution and predation patterns of two invertebrates, northern shrimp Pandalus borealis and snow crab Chionoecetes opilio , aiming to evaluate the impacts of climate change on prey distributions. We found that both invertebrate species have a pronounced predicted response to climate change, with a northern shift in the distribution of northern shrimp and an overall reduction in abundance of both snow crab and shrimp associated with warming temperatures. Including predatory interactions as predictors in the SDMs (either directly via predator densities or via estimated predation rates) improved prediction accuracy for northern shrimp but not for snow crab. This is consistent with the ecology of these two species, as northern shrimp is more vulnerable to predation than snow crab. We found that the projections of future northern shrimp distributions are sensitive to the predicted spatial distribution and abundance of predators, highlighting the inherent complexity of predicting species response to climate change. Collectively, these results contribute to a broader literature that seeks to improve the capabilities of models to predict the effects of species interactions on species distributions under changing ecological 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".