Global Grassland Diazotrophic Communities Are Structured by Combined Abiotic, Biotic, and Spatial Distance Factors but Resilient to Fertilization
Bibliographic record
Abstract
Grassland ecosystems cover around 37% of the ice-free land surface on Earth and have critical socioeconomic importance globally. As in many terrestrial ecosystems, biological dinitrogen (N2) fixation represents an essential natural source of nitrogen (N). The ability to fix atmospheric N2 is limited to diazotrophs, a diverse guild of bacteria and archaea. To elucidate the abiotic (climatic, edaphic), biotic (vegetation), and spatial factors that govern diazotrophic community composition in global grassland soils, amplicon sequencing of the dinitrogenase reductase gene—nifH—was performed on samples from a replicated standardized nutrient [N, phosphorus (P)] addition experiment in 23 grassland sites spanning four continents. Sites harbored distinct and diverse diazotrophic communities, with most of reads assigned to diazotrophic taxa within the Alphaproteobacteria (e.g., Rhizobiales), Cyanobacteria (e.g., Nostocales), and Deltaproteobacteria (e.g., Desulforomonadales) groups. Likely because of the wide range of climatic and edaphic conditions and spatial distance among sampling sites, only a few of the taxa were present at all sites. The best model describing the variation among soil diazotrophic communities at the OTU level combined climate seasonality (temperature in the wettest quarter and precipitation in the warmest quarter) with edaphic (C:N ratio, soil texture) and vegetation factors (various perennial plant covers). Additionally, spatial variables (geographic distance) correlated with diazotrophic community variation, suggesting an interplay of environmental variables and spatial distance. The diazotrophic communities appeared to be resilient to elevated nutrient levels, as 2–4 years of chronic N and P additions had little effect on the community composition. However, it remains to be seen, whether changes in the community composition occur after exposure to long-term, chronic fertilization regimes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".