Arctic kelp forest decline -- a consequence of melting glaciers?
Bibliographic record
Abstract
The Arctic archipelago of Svalbard is a hotspot of global warming and many fjords experience a continuous increase in seawater temperature and glacial melt while sea-ice cover declines. In 1996/98 and 2012-14 macroalgal biomass and species diversity were quantified at the study site Hansneset, Kongsfjorden (W-Spitsbergen) in order to identify potential changes over time. In 2021, we repeated the earlier studies by stratified random sampling (1x1 m2, n=3) along a sublittoral depth transect (0, 2.5, 5, 10 and 15m) and investigated the lower depth limits of dominant brown algae between 2-20m. The fresh weight maximum was 11.5 kg m-2 at 2.5m and kelp blades stored 277g carbon m-2 and 18g nitrogen m-2 at this depth. Although biomass did not significantly change since 2012/13, the ‘Digitate Kelps’ community (Laminaria digitata/Hedophyllum nigripes) changed to an Alaria esculenta dominated kelp forest in 2021 and a balanced age structure of kelps (juveniles plus many older kelp individuals) was only apparent at 2.5m. In addition, the abundances and lower depth limits of all dominant brown algae declined noticeably over the last 25 years while the deep red algae flora remained stable. As we revealed that biomass allocation to blades, stipes and holdfasts as well as the ability to store blade carbon and nitrogen was kelp species-specific, a pronounced shift in the functionality of the kelp forest had occurred over time. We propose that the observed changes in the macroalgal community are mainly driven by alterations in underwater light climate as in situ data confirmed increasing turbidity and decreasing irradiance since 2012 and 2017, respectively. As a consequence, the overall retreat of the kelp forest to lower depth levels seems to be a result of strong glacial melt and will possibly continue in future with unforeseen consequences for Arctic coasts and their socio-ecological fjord systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".