Kelp forest community structure and demography in Kongsfjorden (Svalbard) across 25 years of Arctic warming
Bibliographic record
Abstract
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 1996/1998, 2012–2014, and 2021 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 (1 × 1 m2, n = 3) along a sublittoral depth transect (0, 2.5, 5, 10, and 15 m) and investigated the lower depth limits of dominant brown algae between 3 and 19 m. The maximum fresh weight (FW) of all seaweeds was 11.5 kg m−2 at 2.5 m and to 99.9% constituted of kelp. Although biomass distribution along the depth transect in 2021 was not significantly different compared to 2012/2013, the digitate kelp community (Laminaria digitata/Hedophyllum nigripes) had transformed into an Alaria esculenta‐dominated kelp forest. Consequently, a pronounced shift in kelp forest structure occurred over time as we demonstrate that biomass allocation to thallus parts is kelp species‐specific. Over the past decade, kelp demography changed and in 2021 a balanced age structure of kelps (juveniles plus many older kelp individuals) was only apparent at 2.5 m. In addition, the abundances and lower depth limits of all dominant brown algae declined noticeably over the last 25 years while the red algal flora abundance remained unchanged at depth. We propose that the major factor driving the observed changes in the macroalgal community are alterations in underwater light climate, as in situ data showed increasing turbidity and decreasing irradiance since 2012 and 2017, respectively. As a consequence, the interplay between kelp forest retreat to lower depth levels caused by coastal darkening and potential macroalgal biomass gain with increasing temperatures will possibly intensify in the future with unforeseen consequences for melting Arctic coasts and fjord ecosystem services.
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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 | 0.001 | 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".