Riparian vegetation variability in natural boreal forests: Lessons for delineating functional forested strips at the margin of aquatic habitats
Bibliographic record
Abstract
Riparian zones are biodiversity hotspots that play a fundamental role in the functioning of aquatic habitats. In logged boreal forests, the common practice is to maintain a fixed width of vegetation along rivers and lakes, without any other protective measures, and often neglecting small streams and ponds. To preserve the ecosystem services provided by riparian zones, management practices need to better integrate the natural variability in vegetation attributes along the lateral connectivity gradient in different hydrogeomorphic contexts. This study compared patterns of plant diversity and composition at the margin of five aquatic habitats, in four ecoregions of the Canadian boreal forest. Our results showed that species richness, the mean values for wetness requirements and the magnitude of changes in taxa composition decreased consistently along the lateral gradient, but that this effect was mediated by hydrogeomorphic settings. Specifically, we highlighted significant interactions between distance or elevation difference from the waterline and ecoregions or aquatic habitats, showing that the structure and extent of riparian vegetation along the local topographic gradient are shaped differently by the regional (climate) and hydrogeomorphic (flow regime) contexts. In addition, model ranking revealed that elevation difference best predicted changes in riparian vegetation structure and composition than distance from the waterline. For delineating functional forested strips, our results revealed thus the importance of taking into account both elevation and distance criteria but also of modulating the spatial extent of these strips according to the regional climatic context and the types of aquatic habitat.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".