Aquatic vegetation in deep lakes: Macrophyte co-occurrence patterns and environmental determinants
Bibliographic record
Abstract
Our aims were to test the hypothesis that in deep lakes the co-occurrence patterns of macrophytes are not random, and to compare the relative contribution of the main environmental determinants (light, water and sediment parameters, phytoplankton) in structuring aquatic vegetation. We collected data from five deep Chara-dominated lakes in Central Italy along gradients of depth (33 to 165 m), dimension (1.7 to 114.5 km2) and water trophic conditions (12.4 to 41.3 μg L-1 of total phosphorous). Twenty-five sampling plots per lake were randomly selected at five predetermined depths (1.5, 3.0, 6.0, 12.0 and 20.0 m; n=5) within homogenous littoral sectors. Data were explored by a null model analysis using the checkerboard score (C-score) index, and Canonical Correspondence Analysis. Our data verify the not random co-occurrence patterns of macrophyte’ communities in deep lakes. However, present data suggested that C-scores are strictly dependent on lake’ trophic status: low nutrient loads, in both water and sediments, seemed to be reflected in a not random co-occurrence zonation of macrophytes. Summarizing, it is fundamental evaluate the local effects of lake trophy on the macrophyte community dynamics both in time and space before inquiring about mutual links. If it fails to assess macrophyte co-occurrence patterns, it may be not possible to identify the determinants of the spatial arrangement of macrophytes and, in turn, the conservation status or the ongoing dynamics of 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".