Climate change could disrupt migratory patterns for an Arctic seabird population
Bibliographic record
Abstract
Climate change is altering the marine environment at a global scale, and these changes could affect the distribution and migration patterns of marine species throughout their annual cycle. Arctic regions are already experiencing some of the most dramatic changes in marine climate, and there is a need for predictive models to understand how these changes could alter the spatio-temporal distributions of Arctic marine species. We used a species distribution model to predict potential future changes in the non-breeding distribution of thick-billed murres Uria lomvia from a colony in Hudson Bay, Canada, from 2021 to 2100 using 3 Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios: low (SSP1-2.6), intermediate (SSP2-4.5), and high (SSP5-8.5) emissions. Under the intermediate- and high-emissions scenarios, suitable habitat within Hudson Bay would become available year-round during the next century. This could lead to a portion of this migratory population becoming year-round residents within the next 80 yr. We predicted a significant northward shift in the winter range, such that little or no habitat would be available below 55°N by 2100. This shift would have significant implications for the murre harvest in Canada because the winter distribution would no longer include coastal Newfoundland where most harvesting occurs, particularly if murres from other colonies show a similar shift in distribution. Although there were projected changes in seasonal distributions under all 3 climate scenarios, dramatic re-distribution of non-breeding habitat could be avoided with policies that limit future emissions.
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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 | 0.001 | 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) | 0.002 | 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".