Spatial abundance distribution of prokaryotes is associated with dissolved organic matter composition and ecosystem function
Bibliographic record
Abstract
Abstract Diverse prokaryotic communities consume and transform a broad suite of molecules in the dissolved organic matter (DOM) pool, which controls major biogeochemical cycles. Despite methodological advancements that provide increasingly more detailed information on the diversity of both prokaryotic communities and DOM components, understanding how these two component parts are structured to influence ecosystem functioning remains a major challenge in microbial ecology. Using empirical data collected along a gradient of productivity in the Labrador Sea, we characterized relationships among DOM compounds, metabolic processing, and prokaryotic diversity by structuring prokaryotic communities using spatial abundance distribution (SpAD) modeling. We identified strong associations of different SpAD taxonomic groups with specific organic substrates as well as with metabolic rates. Amplicon sequence variants (ASVs) with more cosmopolitan distributions (i.e. normal‐like) such as Bacteroidia were related to fresher DOM substrates such as free and combined amino acids whereas rare ASVs (i.e. logistic) like δ‐proteobacteria were associated with complex forms of organic matter. In terms of ecosystem function, rates of respiration and production were most strongly predicted by the abundance of certain SpAD taxonomic groups. Given the importance and complexity of linking environmental conditions, prokaryotic community structure, and ecosystem function, we propose a framework to bridge the gap between prokaryotic diversity, microbial ecology, and biogeochemistry among methods and across scales. Our work suggests that SpAD modeling can be used as an intermediate step to link prokaryotic community structure to both finer DOM details and larger ecosystem scale processes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".