Riverine wetland vegetation: importance of small-scale and large-scale environmental variation
Bibliographic record
Abstract
The purpose of this study was to evaluate the relative importance of small-scale variation in abiotic factors and large-scale spatio-temporal variation on the distribution of wetland vegetation of a section of the Upper St. Lawrence River in Quebec. Vegetation data have been classified with agglomerative clustering into 11 community types, from Acer rubrum, Acer saccharinum and Fraxinus swamps, to scrubs dominated by Salix petiolaris, Alnus rugosa var. americana or Myrica gale, to Typha, Typha/Lythrum, Carex lacustris and Calamagrostis canadensis marshes. Canonical Correspondence Analysis (CANOCO) suggested that peat thickness and water level are the most important abiotic variables correlated with plant community composition. As a whole, small-scale variation accounts for 25.6 % of the species variation. Another 21.6% is explained by large-scale variation based on location data summarizing large-scale spatial distribution, and historical landscape dynamics differentiated into (a) no net loss of wetlands, (b) net loss of wetland, and (c) changes within wetlands, which are correlated with the actual variation in herbaceous and scrubby vegetation. The interaction between small-scale and large-scale variations explains another 1.7 %. In total, 48.9% of the species variation is explained by the two data sets, leaving 51.1% unexplained. Whereas the omission of some abiotic variables is possible, it is hypothesized that the abiotic conditions measured in this study play an important role, especially in the distribution of forested swamps. In addition, past history, particularly that of human interventions, becomes another important factor leading to the observed importance of large-scale spatio-temporal variables. This is particularly true for Alnus rugosa var. americana shrublands. Time lag between a relative stabilization of species distribution and the reduction of natural disturbances (water level fluctuations and fires) could be a possible cause of the importance of spatio-temporal variables and the undetermined portion of species variation.
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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 | 0.001 | 0.001 |
| 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".