THE INFLUENCE OF SUBMERGED MACROPHYTES ON SEDIMENTARY DIATOM ASSEMBLAGES<sup>1</sup>
Bibliographic record
Abstract
Submerged macrophytes are a central component of lake ecosystems; however, little is known regarding their long-term response to environmental change. We have examined the potential of diatoms as indicators of past macrophyte biomass. We first sampled periphyton to determine whether habitat was a predictor of diatom assemblage. We then sampled 41 lakes in Quebec, Canada, to evaluate whether whole-lake submerged macrophyte biomass (BiomEpiV) influenced surface sediment diatom assemblages. A multivariate regression tree (MRT) was used to construct a semiquantitative model to reconstruct past macrophyte biomass. We determined that periphytic diatom assemblages on macrophytes were significantly different from those on wood and rocks (ANOSIM R = 0.63, P < 0.01). A redundancy analysis (RDA) of the 41-lake data set identified BiomEpiV as a significant (P < 0.05) variable in structuring sedimentary diatom assemblages. The MRT analysis classified the lakes into three groups. These groups were (A) high-macrophyte, nutrient-limited lakes (BiomEpiV ≥525 μg · L(-1) ; total phosphorus [TP] <35 μg · L(-1) ; 23 lakes); (B) low-macrophyte, nutrient-limited lakes (BiomEpiV <525 μg · L(-1) ; TP <35 μg · L(-1) ; 12 lakes); and (C) eutrophic lakes (TP ≥35 μg · L(-1) ; six lakes). A semiquantitative model correctly predicted the MRT group of the lake 71% of the time (P < 0.001). These results suggest that submerged macrophytes have a significant influence on diatom community structure and that sedimentary diatom assemblages can be used to infer past macrophyte abundance.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".