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 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".