An iron cycle cascade governs the response of equatorial Pacific ecosystems to climate change
Bibliographic record
Abstract
Earth System Models project that global climate change will reduce ocean net primary production (NPP), upper trophic level biota biomass and potential fisheries catches in the future, especially in the eastern equatorial Pacific. However, projections from Earth System Models are undermined by poorly constrained assumptions regarding the biological cycling of iron, which is the main limiting resource for NPP over large parts of the ocean. In this study, we show that the climate change trends in NPP and the biomass of upper trophic levels are strongly affected by modifying assumptions associated with phytoplankton iron uptake. Using a suite of model experiments, we find 21st century climate change impacts on regional NPP range from -12.3% to +2.4% under a high emissions climate change scenario. This wide range arises from variations in the efficiency of iron retention in the upper ocean in the eastern equatorial Pacific across different scenarios of biological iron uptake, which affect the strength of regional iron limitation. Those scenarios where nitrogen limitation replaced iron limitation showed the largest projected NPP declines, while those where iron limitation was more resilient displayed little future change. All model scenarios have similar skill in reproducing past inter-annual variations in regional ocean NPP, largely due to limited change in the historical period. Ultimately, projections of end of century upper trophic level biomass change are altered by 50%-80% across all plausible scenarios. Overall, we find that uncertainties in the biological iron cycle cascade through open ocean pelagic ecosystems, from plankton to fish, affecting their evolution under climate change. This highlights additional challenges to developing effective conservation and fisheries management policies under climate change.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".