The fecal iron pump: Global impact of animals on the iron stoichiometry of marine sinking particles
Bibliographic record
Abstract
The impact of marine animals on the iron (Fe) cycle has mostly been considered in terms of their role in supplying dissolved Fe to phytoplankton at the ocean surface. However, little attention has been paid to how the transformation of ingested food into fecal matter by animals alters the relative Fe-richness of particles, which could have consequences for Fe cycling in the water column and for the food quality of suspended and sinking particles. Here, we compile observations to show that the Fe to carbon (C) ratio (Fe:C) of fecal pellets of various marine animals is consistently enriched compared to their food, often by more than an order of magnitude. We explain this consistent enrichment by the low assimilation rates that have been measured for Fe in animals, together with the respiratory conversion of dietary organic C to excreted dissolved inorganic C. Furthermore, we calculate that this enrichment should cause animal fecal matter to constitute a major fraction of the global sinking flux of biogenic Fe, a component of the marine iron cycle that has been previously unappreciated. We also estimate that this fecal iron pump provides an important source of Fe to marine animals via coprophagy, particularly in the mesopelagic, given that fecal matter Fe:C can be many-fold higher than the Fe:C of local phytoplankton. Our results imply that the fecal iron pump is important both for global Fe cycling and for the iron nutrition of pelagic and mesopelagic communities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| 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".