Exotic and native plant community distributions within complex riparian landscapes: A positive correlation
Bibliographic record
Abstract
:We studied the riparian vegetation on the agricultural floodplain of the Middle Garonne River (SW France) in order to compare native and exotic plant community patterns. In total, we investigated the vegetation of 1,824 plots during four seasons along 50-m-long transects delineated transversely from the banks of five types of water bodies (subsystems) differing by their exposure to natural and to human-induced disturbance. Exotic species accounted for 21% of the total of 726 species identified. We characterized native and exotic plant communities of each subsystem on the basis of Grime’s ecological strategies, species lifespan, species richness, and species cover. The communities of each subsystem were compared with respect to the landscape structure (35 patch types) and to their distribution according to the distance to the bank of each water body, the distance to the main channel of the river, the annual duration of inundation, the total plant cover, the total species richness, and the proportion of exotics. Although significant contrasts exist between the community structures of each water body, we found a strong correlation between the attributes of native and exotic species pools. Native and exotic species covers were negatively correlated, while native species richness was positively correlated to exotic species richness, both at local and large scale. This positive correlation remained significant when comparing plots within each patch type. Few or no differences were detected between the distribution of native and exotic species according to the six variables of interest, the effect of the origin (exotic or native) of the plants being negligible as a discriminant attribute. The possible role of landscape complexity and the role of combined natural and human-induced disturbances are discussed to explain these patterns.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".