Differential stimulation and suppression of phytoplankton growth by ammonium enrichment in eutrophic hardwater lakes over 16 years
Bibliographic record
Abstract
Abstract Previous research suggests that fertilization of surface waters with chemically reduced nitrogen (N), including ammonium (NH4+), may either enhance or suppress phytoplankton growth. To identify the factors influencing the net effect of NH4+, we fertilized natural phytoplankton assemblages from two eutrophic hardwater lakes with growth‐saturating concentrations of NH4Cl in 241 incubation experiments conducted biweekly May–August during 1996–2011. Phytoplankton biomass (as chlorophyll a) was significantly (p < 0.05) altered in fertilized trials relative to controls after 72 h in 44.8% of experiments, with a marked rise in both spring suppression and summer stimulation of assemblages over 16 yr, as revealed by generalized additive models (GAMs). Binomial GAMs were used to compare contemporaneous changes in physico‐chemical (temperature, Secchi depth, pH, nutrients; 19.5% deviance explained) and biological parameters (phytoplankton community composition; 40.0% deviance explained) to results from fertilization experiments. Models revealed that that the likelihood of growth suppression by NH4+ increased with abundance of diatoms, cryptophytes, and unicellular cyanobacteria, particularly when water temperatures and soluble reactive phosphorus (SRP) concentrations were low. In contrast, phytoplankton was often stimulated by NH4+ when chlorophytes and non‐N2‐fixing cyanobacteria were abundant, and temperatures and SRP concentrations were high. Progressive intensification of NH4+ effects over 16 yr reflects changes in both spring (cooler water, increased diatoms and cryptophytes) and summer lake conditions (more chlorophytes, earlier cyanobacteria blooms), suggesting that the seasonal effects of NH4+ will vary with future climate change and modes of N enrichment.
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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 | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".