Relationships between soil hydrology and forest structure and composition in the southern Brazilian Amazon
Bibliographic record
Abstract
Abstract Question: Is soil hydrology an important niche‐based driver of biodiversity in tropical forests? More specifically, we asked whether seasonal dynamics in soil water regime contributed to vegetation partitioning into distinct forest types. Location: Tropical rain forest in northwestern Mato Grosso, Brazil. Methods: We investigated the distribution of trees and lianas ≥ 1 cm DBH in ten transects that crossed distinct hydrological transitions. Soil water content and depth to water table were measured regularly over a 13‐month period. Results: A detrended correspondence analysis (DCA) of 20 dominant species and structural attributes in 10 × 10 m subplots segregated three major forest types: (1) high‐statured upland forest with intermediate stem density, (2) medium‐statured forest dominated by palms, and (3) low‐statured campinarana forest with high stem density. During the rainy season and transition into the dry season, distinct characteristics of the soil water regime (i.e. hydro‐indicators) were closely associated with each vegetation community. Stand structural attributes and hydro‐indicators were statistically different among forest types. Conclusions: Some upland species appeared intolerant of anaerobic conditions as they were not present in palm and campinarana sites, which experienced prolonged periods of saturation at the soil surface. A shallow impermeable layer restricted rooting depth in the campinarana community, which could heighten drought stress during the dry season. The only vegetation able to persist in campinarana sites were short‐statured trees that appear to be well‐adapted to the dual extremes of inundation and drought.
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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 | 0.000 | 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".