Prediction of macrophyte distribution: The role of natural versus anthropogenic physical disturbances
Why this work is in the frame
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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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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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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 it