Climate change opens new frontiers for marine species in the Arctic: Current trends and future invasion risks
Bibliographic record
Abstract
Climate change and increased anthropogenic activities are expected to elevate the potential of introducing nonindigenous species (NIS) into the Arctic. Yet, the knowledge base needed to identify gaps and priorities for NIS research and management is limited. Here, we reviewed primary introduction events to each ecoregion of the marine Arctic realm to identify temporal and spatial patterns, likely source regions of NIS, and the putative introduction pathways. We included 54 introduction events representing 34 unique NIS. The rate of NIS discovery ranged from zero to four species per year between 1960 and 2015. The Iceland Shelf had the greatest number of introduction events (n = 14), followed by the Barents Sea (n = 11), and the Norwegian Sea (n = 11). Sixteen of the 54 introduction records had no known origins. The majority of those with known source regions were attributed to the Northeast Atlantic and the Northwest Pacific, 19 and 14 records, respectively. Some introduction events were attributed to multiple possible pathways. For these introductions, vessels transferred the greatest number of aquatic NIS (39%) to the Arctic, followed by natural spread (30%) and aquaculture activities (25%). Similar trends were found for introductions attributed to a single pathway. The phyla Arthropoda and Ochrophyta had the highest number of recorded introduction events, with 19 and 12 records, respectively. Recommendations including vector management, horizon scanning, early detection, rapid response, and a pan-Arctic biodiversity inventory are considered in this paper. Our study provides a comprehensive record of primary introductions of NIS for marine environments in the circumpolar Arctic and identifies knowledge gaps and opportunities for NIS research and management. Ecosystems worldwide will face dramatic changes in the coming decades due to global change. Our findings contribute to the knowledge base needed to address two aspects of global change-invasive species and climate change.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".