Distribution and abundance of trees in floodplain forests of the Wisconsin River: Environmental influences at different scales
Bibliographic record
Abstract
Abstract Questions: 1. How do physiography, flooding regime, landscape pattern, land‐cover history, and local soil conditions influence the presence, community structure and abundance of overstorey trees? 2. Can broad‐scale factors explain variation in the floodplain forest community, or are locally measured soil conditions necessary? Location: Floodplain of the lower 370 km of the Wisconsin River, Wisconsin, USA. Methods: Floodplain forest was sampled in 10 m × 20 m plots [n= 405) during summers of 1999 and 2000 in six 12‐ to 15‐km reaches. Results: Species observed most frequently were Fraxinus pennsylvanica, Acer saccharinum and Ulmus americana. Physiography (e.g. geographic province) and indicators of flooding regime (e.g. relative elevation and distance from main channel) were consistently important in predicting occurrence, community composition, and abundance of trees. Correspondence analysis revealed that flood‐tolerant and intolerant species segregated along the primary axis, and late‐successional species segregated from flood‐tolerant species along the secondary axis. Current landscape configuration only influenced species presence or abundance in forests that developed during recent decades. Land‐cover history was important for tree species presence and for the abundance of late‐successional species. Comparison of statistical models developed with and without soils data suggested that broad‐scale factors such as geographic province generally performed well. Conclusions: Physiography and indicators of flood regime are particularly useful for explaining floodplain forest structure and composition in floodplains with a relatively high proportion of natural cover types.
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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 | 0.001 | 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".