Prediction of macrophyte distribution: The role of natural versus anthropogenic physical disturbances
Bibliographic record
Abstract
Abstract Questions Do natural and anthropogenic physical disturbances equally affect the distribution of aquatic plant communities? Can hydrodynamic and geomorphological features be used to predict the establishment of macrophyte communities at the shoreline scale? Locations Two large, shallow lakes, southwest France. Methods Based on field observations (vegetation occurrence and anthropogenic modifications of the shore) and data generated by a geographic information system (wave exposure, wave‐induced sediment re‐suspension, slope and land cover), we defined sites and community groups using cluster and indicator species analyses. The groups were then analysed by means of a statistical classifier (Random Forest). These different steps in data treatment enabled us to characterize the importance of each physical factor in determining macrophyte occurrence and distribution. As a result, a predictive map to forecast aquatic plant distribution at the shoreline scale was obtained. Results Anthropogenic disturbances were less important parameters than natural physical variables in structuring the distribution of lakeshore macrophytes. Within natural factors, wave‐induced sediment re‐suspension and slope had the most impact; nevertheless, the presence of swimming areas seemed to have a strong impact on aquatic habitats, being correlated with the total absence of aquatic vegetation. The predictive map obtained through the model spatially defined the position and occurrence of suitable sites for the settlement of both invasive and rare and endangered species. Conclusions In this study, natural disturbances play a major role in structuring aquatic plant distribution over physical anthropogenic factors. The model contributes to improving knowledge on plant communities with respect to local hydrodynamic and morphological features of lakeshores. Furthermore, the model provides a predictive map as a useful tool for the management of aquatic vegetation in temperate shallow lakes.
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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 | 0.000 | 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".