How does the taxonomic and functional structure of plant communities differ between riverine and palustrine swamps?
Bibliographic record
Abstract
Abstract Questions How does the type of swamp, that is, riverine vs palustrine, shape understorey and overstorey plant communities? Beyond swamp type, how do spatial, topographic, soil and landscape characteristics determine the taxonomic and functional structure of swamp communities? Location Southern Québec, Canada. Methods We sampled riverine and palustrine swamp plant communities in two watersheds within two ecoregions with contrasting land use. At the site scale ( n = 56), we analyzed differences between riverine and palustrine swamps in plant richness and cover, species composition, and mean and dispersion values for ecological and morphological traits. At the plot scale ( n = 213), we assessed the relative influence of a set of environmental parameters on species richness and cover, as well as on trait values using mixed models and on species composition using redundancy analysis. Results Species composition and the mean value of traits varied significantly between the two types of swamps. While riverine swamps hosted more non‐native species and were composed of more mesophilic species, shorter in height and with dominant resource acquisition strategies, palustrine swamps sheltered more non‐vascular taxa and tall hygrophilous vascular species with more conservative resource strategies. The surrounding landscape and local microtopography within swamps had a significant effect on plant community structure. Species diversity and trait dispersion increased from agricultural‐dominated to forest‐dominated landscapes, and from homogeneous to heterogeneous substrates. Conclusions Habitats provided by riverine and palustrine swamps are complementary for wetland biodiversity. Our results underline the need to develop conservation plans to protect a wide variety of freshwater swamp types; for example, management actions that maintain or promote heterogeneous topographic forms at the site scale, and continuity of forest cover at the landscape scale.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 teacher head, 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".