Nutrient Control of Phytoplankton Abundance and Biomass, and Microplankton Assemblage Structure in the Lower Columbia River (Vancouver, Washington, USA)
Bibliographic record
Abstract
Nutrient limitation of phytoplankton is common but by no means universal in large temperate rivers. Previous field studies in the Columbia River, USA, are suggestive of nutrient limitations of phytoplankton, especially during summer, but this has never been tested experimentally. We therefore undertook monthly 5-day nutrient amendment incubation experiments from May–September 2018 using Columbia River water collected at Vancouver, Washington, USA. We compared replicate treatment bottles containing natural microplankton assemblages and amended nutrients (NO3, PO4 and SiO4 in combination) with replicate control bottles containing natural microplankton assemblages and ambient nutrients. Phytoplankton abundance and biomass were compared between treatments and controls on each day of each experiment, and microplankton assemblage structure was evaluated using Permutational Multivariate Analysis of Variance and Non-Metric Multi-Dimensional Scaling ordination on Day 0 (ambient) and Day 5 of each experiment. Nutrient amendment significantly affected phytoplankton abundance and biomass, particularly in June–August, although this varied between taxa (e.g., cyanobacteria, dinoflagellates, flagellates and ciliates showed more frequent positive responses than chlorophytes and diatoms did). Abundance-based microplankton assemblage structure was significantly correlated with PO4, SiO4 and NO3 concentrations, and BIOENV procedure in R revealed that the best subset of explanatory variables included SiO4 and NO3 concentrations. Biomass-based assemblage structure was significantly correlated with SiO4 and NO3, although BIOENV explanatory variables included only SiO4. These results are suggestive of summertime nutrient control of phytoplankton abundance and biomass, as well as microplankton composition, in the lower Columbia River, at least during some months. Since eutrophication is increasing in the watershed, this could have important implications for higher level consumers (e.g., zooplankton and out-migrating juvenile salmon).
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".