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 O 2 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 O 2 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 pCO 2 reached levels as high as 1,300 μatm and aragonite undersaturation (Ω Ar < 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 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 | 0.001 |
| 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 | 0.000 | 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 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".