Niche differentiation of tallgrass prairie plants species along soil hydrological gradients
Bibliographic record
Abstract
This study addressed whether the distribution of species in frequently burned lowland tallgrass prairies is driven by gradients in soil hydrology. On three study sites, the hydrological conditions in 1 m2 vegetation survey plots where quantified as the number of days the soil was anaerobic less than 15 cm below the surface, and the surface of the soil was drier than 0.4 m3·m−3. On each site, the centroid of each species hydrological niche was defined as the hydrological conditions in plots where it occurred, weighted by its abundance. Species found on all sites maintained a consistent ranking between sites along the soil drying gradient, but not the anaerobic gradient. The levels of niche overlap between species pairs along both hydrological gradients were significantly less than the overlap from randomly assigning species to the hydrological gradients. Indicator species analysis suggested that on each site the communities were best described as consisting of two subgroups. The hydrological niches of the species in these subgroups were significantly different from one another, suggesting that these subgroups are associated with wet or dry habitats. Overall, these analyses suggest that hydrology plays a major role in determining the structure of these frequently disturbed communities.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".