Regional impacts of iron-light colimitation in a global biogeochemical model
Bibliographic record
Abstract
Abstract. Laboratory and field studies have revealed that iron has multiple roles in phytoplankton physiology, with particular importance for light-harvesting cellular machinery. However, although iron-limitation is explicitly included in numerous biogeochemical/ecosystem models, its implementation varies, and its effect on the efficiency of light harvesting is often ignored. Given the complexity of the ocean environment, it is difficult to predict the consequences of applying different iron limitation schemes. Here we explore the interaction of iron and nutrient cycles in an ocean general circulation model using a new, streamlined model of ocean biogeochemistry. Building on previously published parameterizations of photoadaptation and export production, the Biogeochemistry with Light Iron Nutrients and Gasses (BLING) model is constructed with only four explicit tracers but including macronutrient and micronutrient limitation, light limitation, and an implicit treatment of community structure. The structural simplicity of this computationally-inexpensive model allows us to clearly isolate the global effect that iron availability has on maximum light-saturated photosynthesis rates vs. the effect iron has on photosynthetic efficiency. We find that the effect on light-saturated photosynthesis rates is dominant, negating the importance of photosynthetic efficiency in most regions, especially the cold waters of the Southern Ocean. The primary exceptions to this occur in iron-rich regions of the Northern Hemisphere, where high light-saturated photosynthesis rates allow photosynthetic efficiency to play a more important role. In other words, the ability to efficiently harvest photons has little effect in regions where light-saturated growth rates are low. Additionally, we speculate that the phytoplankton cells dominating iron-limited regions tend to have relatively high photosynthetic efficiency, due to reduced packaging effects. If this speculation is correct, it would imply that natural communities of iron-stressed phytoplankton may tend to harvest photons more efficiently than would be inferred from iron-limitation experiments with other phytoplankton. We suggest that iron limitation of photosynthetic efficiency has a relatively small impact on global biogeochemistry, though it is expected to impact the seasonal cycle of plankton as well as the vertical structure of primary production.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".