Particulate trace metal dynamics in response to increased CO <sub>2</sub> and iron availability in a coastal mesocosm experiment
Bibliographic record
Abstract
Rising concentrations of atmospheric carbon dioxide are causing ocean acidification and will influence marine processes and trace metal biogeochemistry. In June 2012, in the Raunefjord (Bergen, Norway), we performed a mesocosm experiment, comprised of a fully factorial design of ambient and elevated p CO 2 and/or an addition of the siderophore desferrioxamine B (DFB). In addition, the macronutrient concentrations were manipulated to enhance a bloom of the coccolithophore Emiliania huxleyi . We report the changes in particulate trace metal concentrations during this experiment. Our results show that particulate Ti and Fe were dominated by lithogenic material, while particulate Cu, Co, Mn, Zn, Mo and Cd had a strong biogenic component. Furthermore, significant correlations were found between particulate concentrations of Cu, Co, Zn, Cd, Mn, Mo and P in seawater and phytoplankton biomass (µgC L −1 ), supporting a significant influence of the bloom in the distribution of these particulate elements. The concentrations of these biogenic metals in the E. huxleyi bloom were ranked as follows: Zn < Cu ≈ Mn < Mo < Co < Cd. Changes in CO 2 affected total particulate concentrations and biogenic metal ratios (Me : P) for some metals, while the addition of DFB only significantly affected the concentrations of some particulate metals (mol L −1 ). Variations in CO 2 had the most clear and significant effect on particulate Fe concentrations, decreasing its concentration under high CO 2 . Indeed, high CO 2 and/or DFB promoted the dissolution of particulate Fe, and the presence of this siderophore helped in maintaining high dissolved Fe. This shift between particulate and dissolved Fe concentrations in the presence of DFB, promoted a massive bloom of E. huxleyi in the treatments with ambient CO 2 . Furthermore, high CO 2 decreased the Me : P ratios of Co, Zn and Mn while increasing the Cu : P ratios. These findings support theoretical predictions that the molar ratios of metal to phosphorous (Me : P ratios) of metals whose seawater dissolved speciation is dominated by free ions (e.g., Co, Zn and Mn) will likely decrease or stay constant under ocean acidification. In contrast, high CO 2 is predicted to shift the speciation of dissolved metals associated with carbonates such as Cu, increasing their bioavailability and resulting in higher Me : P ratios.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".