Bacterial community structure across a sand dune chronosequence at the Indiana Dunes National Park
Bibliographic record
Abstract
The microbial role in dune succession along the Great Lakes freshwater sand dunes remains poorly understood. A chronosequence study was conducted to understand the relationships among soil bacterial communities, soil chemistry, and prescribed burning at the Indiana Dunes National Park. Soil bacterial communities and chemistry, as well as groundlayer vegetation were sampled during 2015 and 2017 from seven successional stages from the beach (contemporary) to the 14,000-year-old oak forest. Bacterial communities from unburned and burned sites among stages were determined by 16S rRNA gene amplicon sequencing. Soil pH and cations decreased from early (beach, foredune, secondary dune, and woodland transition) to late (oak savanna, woodland, and oak forest) successional stages, while organic matter and organic carbon concentrations increased in the late successional stages. Bacterial alpha diversity showed no significant differences among stages, but a significant interaction was found between stage and prescribed burning (H = 39.7, p < 0.001). Bacterial communities separated mainly along stage by all four beta diversity metrics used (Bray Curtis, Jaccard, and Weighted and Unweighted UniFrac), with the main difference observed along the primary axis (weighted UniFrac, 48 %). Bacterial phyla were differentially abundant in older soil stages compared to beach (ANCOM-BC, q < 0.05); likewise, differential abundances in genera were evident when burned and unburned sites were compared. A Mantel test indicated stronger congruency between the bacterial communities and soil chemistry than between bacterial communities and vegetation. Collectively, soil chemical and microbial parameters along with management practices contribute to dunal successional patterns in the Great Lakes.
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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 | 0.001 | 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".