Climate change drives coastal oligotrophication in a high-Arctic fjord via terrestrial greening and freshwater input
Bibliographic record
Abstract
Abstract Nutrient inputs from upwelling, ocean currents, advection, and terrestrial sources play a crucial role in driving primary production in Arctic fjords and coastal areas. This study analyzes more than two decades of field measurements across a terrestrial-river-coastal continuum in Arctic Greenland, showing how shifts in coastal inflows, glacial meltwater, and terrestrial inputs control changes in nutrient dynamics in the fjord. Our data indicate oligotrophication, with nitrate concentrations decreasing by ∼49% and phytoplankton biomass by ∼60% over the study period. These changes are associated with a ∼12% increase in catchment vegetation greening, which likely reduced terrestrial nitrate input to the fjord by ∼65%. Nutrient dynamics in the fjord were also influenced by inflows of fresher coastal waters, providing nitrate-poor, silicate-rich waters. Silicate concentrations in the fjord have risen by ∼115% over the past two decades, suggesting increased input from all these sources. Whether these patterns are unique to this fjord or representative of broader Arctic trends remains uncertain and our study highlights the need to further explore the cross-boundary ecological impacts of climate change on Arctic marine and coastal ecosystems.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".