Hydroecological model predictions indicate wetter and more diverse soil water regimes and vegetation types following floodplain restoration
Bibliographic record
Abstract
Transitions between aquatic and terrestrial ecosystems represent zones where soil moisture is a dominant factor influencing vegetation composition. Niche models based on hydrological and vegetation observations can be powerful tools for guiding management of these zones, especially when they are linked with physically based hydrological models. Floodplain restoration represents a unique opportunity to utilize such a predictive vegetation tool when a site's hydrology is altered to create a wetter environment. A variably saturated groundwater flow model was developed and used to simulate the soil moisture regime across a floodplain in Wisconsin where post‐settlement alluvium was removed with the intent of increasing regionally threatened wetland plant species. Hydrological niche models based on simultaneous observations of vegetation composition and surface effective saturation were used to predict probability of presence for two plant species (Carex vulpinoidea (fox sedge) and Elymus canadensis(Canada wildrye)) and wetland indicator score (a composite indicator of relative frequency of species in five habitat categories) based on simulated surface effective saturation. The vegetation predictions following restoration are more wetland‐species dominant overall. However, zones of the study site where a confining layer is present that decouples groundwater from the near‐surface soil zone tend to be drier following restoration due to restricted upward groundwater flow and less soil water storage above the confining layer. As reflected by an increase in the interquartile range in the predicted wetland indicator score, this restoration technique may increase the site‐scale spatial diversity of plant community types while simultaneously accomplishing the goal of increasing wetland plant species occurrence.
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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 | 0.000 | 0.000 |
| 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".