Environmental DNA metabarcoding reveals seasonal and spatial variation in the vertebrate fauna of Ilulissat Icefjord, Greenland
Bibliographic record
Abstract
Ilulissat Icefjord in Greenland is experiencing the effects of climate change, with the Sermeq Kujalleq glacier being one of the fastest-moving and most productive ice streams in Greenland. This is likely affecting the distribution of species in the fjord, including those important to local fisheries. Due to heavy ice conditions, few studies on environmental and ecological conditions exist from the fjord. However, new techniques such as environmental DNA (eDNA) metabarcoding now allow deeper insight into the fjord system. Here, we combine local ecological knowledge with data on hydrographic conditions, stable isotopes (δ18O), and eDNA metabarcoding to investigate the spatial and seasonal distribution of marine fish and mammals inside Ilulissat Icefjord. Our eDNA results support local observations that Arctic char migrate to the southern fjord during summer, harp seals forage in large herds in the fjord system, polar cod is the dominant prey fish in the area, and Greenland shark likely does not reside in the fjord system. Lower predation pressure in the Icefjord, due to the absence of Greenland shark and polar bears as well as limited fishing/hunting, is presumably one of the reasons why ringed seals and Greenland halibut are larger in the Icefjord. Furthermore, our results indicate that in summer, the southern branch of the fjord system has a more diverse community of vertebrates and different water masses than the northern branch and main fjord, indicating a time lag between inflows to the different branches of the fjord system. Our approach highlights the value of combining local ecological knowledge with scientific research and represents a potential starting point for monitoring biological responses in Ilulissat Icefjord associated with climate-induced changes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".