Phytoplankton community structure changes following simulated upwelled iron inputs in the Peru upwelling region
Bibliographic record
Abstract
The effects of iron on phytoplankton community structure in 'High Nutrient Low Chlorophyll' regions of the ocean have been examined using both shipboard batch cultures (growouts) and open ocean mesoscale fertilization experiments. The addition of iron in these areas frequently results in a shift from communities dominated by small non-siliceous species towards ones dominated by larger diatoms. We used a new shipboard continuous culture experimental design in iron-limited Peru upwelling waters to examine shifts in phytoplankton structure and their biogeochemical consequences following simulated upwelled iron inputs. By allowing the added iron to pre-equilibrate with natural seawater ligands, we were able to supply iron in realistic chemical species at rates and concentrations similar to those found in upwelled waters off Peru. The community shifted strongly from cyanobacteria towards diatoms, and the extent of this shift was proportional to the increase in iron supply. Eukaryotic nanophytoplankton were the first to respond to the iron addition, followed by a community dominated by small pennate diatoms by Day 5. These community changes led to increased biogenic silica:particulate organic nitrogen (BSi:PON) and biogenic silica:particulate organic carbon (BSi:POC) production ratios, driven mainly by increases in diatom numbers with increasing iron. Our experiment demonstrated both similarities to and differences with parallel growout experiments and previous mesoscale fertilization experiments, and suggest that the shipboard continuous culture method can be applied to questions that cannot be easily addressed by either of these previous iron addition techniques.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".