Potamoplankton size structure and taxonomic composition: Influence of river size and nutrient concentrations
Bibliographic record
Abstract
We measured the size structure and taxonomic composition of phytoplankton in temperate rivers during base flows of summer to investigate the influence of river size, ambient nutrient concentration, and light availability on potamoplankton community structure. Algal biomass was measured in three size classes (2–20, 20–64, and >64 µm) by microscope enumeration of water samples collected in 31 rivers and by chlorophyll a in water samples collected in 46 rivers in another year across Ontario and western Quebec. Nanoplankton dominated the potamoplankton biomass across the range of river nutrient concentrations (total phosphorus 5–280 µg P L−1), water residence times (1–39 d), and light regimes (euphotic zone to mixing depth ratio 0.1–33). Both nanoplankton (2–20 µm) and total potamoplankton biomass were significantly correlated with water column total phosphorus concentrations and were not related to water residence time or light availability. On average, diatoms contributed the largest percentage of the total biomass, followed by cryptophytes and an equal percentage of chlorophytes and chrysophytes. The contribution of any one division to total biomass was not significantly correlated with nutrients, water residence time, or light regime. In contrast to temperate lake systems, both the proportion of biomass in larger size classes and the contribution of cyanobacteria did not change significantly as a function of nutrient concentrations. However, community size structure varied in relation to river size: netplankton (>64 µm) contributed slightly more to total biomass at sites with both shorter (<2 d) and longer (>10 d) water residence times. These results point to differences between the phytoplankton of lakes and rivers in response to eutrophication.
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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 | 0.000 | 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 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".