Increasing depth distribution of Arctic kelp with increasing number of open water days with light
Bibliographic record
Abstract
Kelps are a dominant macrophyte group and primary producer in Arctic nearshore waters that provide significant services to the coastal ecosystem. The quantification of these services in the Arctic is constrained, however, by limited estimates of kelp depth extent, which creates uncertainties in the area covered by kelp. Here, we test the environmental drivers of the depth extent of Arctic kelp. We used Southampton Island (SI), Nunavut, Canada, as an example region after an initial survey found deep Arctic kelp (at depths to at least 50 m) with relatively low grazing pressure within diverse hydrographic conditions. We found abundant rocky substrata, but no influence of substratum type on kelp cover. The kelp cover increased with depth until 20 m and then decreased (the median maximum depth for all stations was 37 m). The best predictor of kelp depth extent was the number of annual open (ice-free) water days with light (r2 = 44–52%); combining depth extent data from SI with published data from Greenland strengthened this relationship (r2 = 58–71%). Using these relationships we estimated the maximum kelp-covered area around SI to be 27,000–28,000 km2, yielding potential primary production between 0.6 and 1.9 Tg Cyr−1. Water transparency was a key determinant of the underwater light environment and was essential for explaining cross-regional differences in kelp depth extent in SI and Greenland. Around SI the minimum underwater light required by kelp was 49 mol photons m−2 yr−1, or 1.4% of annual integrated incident irradiance. Future consideration of seasonal variation in water transparency can improve these underwater light estimations, while future research seeking to understand the kelp depth extent relationship with nutrients and ocean dynamics can further advance estimates of their vertical distribution. Improving our understanding of the drivers of kelp depth extent can reduce uncertainties around the role of kelp in Arctic marine ecosystems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".