Assessing vulnerability of Arctic fish species to climate change
Bibliographic record
Abstract
Climate change is impacting Arctic marine ecosystems at faster rates than the global average, challenging the management and conservation of biodiversity and living marine resources. This study examined the climate risks and vulnerabilities of 21 Arctic fish species occurring in the western Canadian Arctic using a fuzzy logic approach. Identified climatic hazards to marine species and their habitats are increasing temperature, decreasing sea ice cover, freshening, decreasing oxygen concentration, and acidification. The nature of these hazards included changes in mean conditions by 2050 (2041-2060), compared to the historical period (1979-2015 average) simulated from a regional coupled ice-ocean biogeochemical model and two coupled Earth system models under low and high emissions scenarios. A spatially-explicit algorithm was used to assess the risk and vulnerability in the Beaufort Sea shelf and slope and Amundsen Gulf (BS-AG) based on the species' biological traits, biogeography and their exposure to climatic hazards. The results indicated high to very high exposure and risk of climate impacts across the ecosystem variables. Specifically, shallow areas were projected to be simultaneously exposed to more intense warming, reduced sea ice coverage, freshening, and acidification relative to the regional averages. In addition, for species occurring in the BS-AG, low adaptability and high sensitivity to climate hazards was identified. These applied tools and evaluations can inform marine spatial planning and climate adaptation efforts to help achieve conservation objectives and sustain ecosystem and community health in a changing Arctic climate. Supplementary Information: The online version contains supplementary material available at 10.1007/s44289-025-00056-7.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".