Effect of Environmental Variation on Estimating the Bacterial Species Richness
Bibliographic record
Abstract
Estimating the species richness of microorganisms is of great importance in predicting, maintaining and managing microbial communities. Although the roles of environmental heterogeneity and geographical distance in structuring soil microbial communities have been studied intensively, the effects of environmental and spatial variation on the species richness estimation have not been examined. To this end, we have explored their effects on estimating the belowground soil bacterial species richness within a 50 ha forest dynamic plot (FDP) using a published massive sequencing dataset with intensive sampling scheme. Our resampling analyses showed that, for a given sequencing depth, increasing the sample size could significantly enhance the detection of rare species by capturing more of the environmental and spatial variation, thus obtaining higher observed and estimated species richness. Additionally, the estimates of bacterial species richness were significantly and positively correlated with environmental variation among samples, indicating that environmental filtering was the main mechanism driving the processes of community assembly for belowground soil bacterial communities in the plot. Moreover, this effect of environmental variation could be markedly alleviated when the sample size was higher than 450, and thus we predicted that there were at least 42,866 soil bacterial species based on 8,296,878 sequences from 550 samples in the whole 50 ha FDP. Furthermore, we built a power law environmental heterogeneity equation (EHE) as a decision-tool to determine an approximate sample size for comprehensively capturing the environmental gradient within a given habitat. Collectively, this work further links the inherent environmental and spatial variation to the estimation of soil bacterial species richness within a given region, and provides a useful tool of sampling design for a better understanding of microbial biogeographic patterns and estimation of microbial biodiversity.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".