Community structure and range shifts in Arctic marine fish under climate change
Bibliographic record
Abstract
Arctic marine ecosystems are rapidly transforming due to climate change. Warming temperatures and shrinking sea ice are enabling boreal fish to expand northward, possibly disturbing cold‐adapted Arctic species assemblages. Species range shifts have been documented in the Bering and Barents Seas, raising concerns about ecosystem restructuring. Range shifts are especially difficult to detect in the Arctic due to sparse and inconsistent data. Here, we studied fish composition from eDNA water samples taken in East Greenland, Svalbard, the Barents Sea, and the Kara Sea during the TOPtoTOP and Arctic Century expeditions. We examined the environmental drivers of fish community structure using global dissimilarity models. We calculated the decadal rate of temperature change to identify the fastest‐changing areas. We compared fish detections from eDNA with published historical records for the Kara Sea to assess possible range expansions. We found that temperature was the main factor influencing the taxa turnover of fish communities, with Gadidae and Liparis sp. driving the greatest compositional differences. Over the past 30 years, temperatures increased by 0.2 to 0.6°C per decade at our study sites, with the highest increases in western Svalbard and the lowest in the eastern Kara Sea. Despite the apparent dependence on temperature, we identified only one species detected outside its known latitudinal range, and five species in the Kara Sea with recent occurrences or representing an extended distribution. Our study suggests that temperature, the main driver of fish community assembly, is increasing rapidly in the Arctic, and a few species have likely already shifted recently, or at least their detections are new in some areas. While these detections cannot be definitively linked to range shifts, our results highlight the need to improve monitoring of high‐latitude fish communities to detect and predict future ecosystem changes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".