Drivers of Biogeochemical Variability in a Central California Kelp Forest: Implications for Local Amelioration of Ocean Acidification
Bibliographic record
Abstract
Abstract Kelp forests are among the world's most productive marine ecosystems, and they have the potential to locally ameliorate ocean acidification (OA). In order to understand the contribution of kelp metabolism to local biogeochemistry, we must first quantify the natural variability and the relative contributions of physical and biological drivers to biogeochemical changes in space and time. We deployed an extensive instrument array in Monterey Bay, CA, inside and outside of a kelp forest to assess the degree to which giant kelp (Macrocystis pyrifera) locally ameliorates present‐day acidic conditions which we expect to be exacerbated by OA. Temperature, pH, and O2 variability occurred at semidiurnal, diurnal (tidal and diel), and longer upwelling event periods. Mean conditions were driven by offshore wind forcing and the delivery of upwelled water via nearshore internal bores. While near‐surface pH and O2 were similar inside and outside the kelp forest, surface pH was elevated inside the kelp compared to outside, suggesting that the kelp canopy locally increased surface pH. We observed the greatest acidification stress deeper in the water column where pCO2 reached levels as high as 1,300 μatm and aragonite undersaturation (ΩAr < 1) occurred on several occasions. At this site, kelp canopy modification of seawater properties, and thus any ameliorating effect against acidification, is greatest in a narrow band of surface water. The spatial disconnect between stress exposure at depth and reduction of acidification stress at the surface warrants further assessment of utilizing kelp forests as provisioners of local OA mitigation.
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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 | 0.000 | 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) | 0.000 | 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".