Effect of flood regime on tree growth in the floodplain and surrounding uplands of the Wisconsin River
Bibliographic record
Abstract
Abstract Flood regime and vegetation flood tolerance interact to influence tree growth in riverine landscapes. We studied tree growth in floodplain and upland forests of the Wisconsin River. About a century ago, levees set back from the river were constructed on this floodplain. The levee restricts some floodplain area from overbank flood events, but leaves a portion of active floodplain still inundated by floods. We addressed two questions: (1) how do growth rates of flood‐tolerant and flood‐intolerant tree species in the floodplain differ with flood regime? (2) At the stand level, how does growth rate differ with flood regime and between floodplain and upland areas? Annual tree growth rates from 1991 to 2000 were determined from tree increment cores for both individual species and stands. Tree growth rates of individual species varied between flood regimes. The most flood‐tolerant species (Betula nigraandFraxinus pennsylvanica) grew faster in areas with active flooding, while the growth of less flood‐tolerant species (Quercus velutinaandQ. ellipsoidalis) was depressed in swales and active floodplain. However, stand‐level tree growth did not differ between the floodplain and upland, or between flood regimes within the floodplain. Therefore, variation in the growth of individual species may not scale up to create differences in stand‐level tree growth because forest community composition varies spatially with flood regime. We suggest that growth rates are similar among sites because each community comprises of species adapted to their current flood regime. Copyright © 2008 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".