Effects of phosphorus enrichment on <i>Daphnia</i>–algae interactions in laboratory microcosms
Bibliographic record
Abstract
Abstract We examined the effects of phosphorus (P) on algal biomass and stoichiometry and, subsequently, alterations to zooplankton populations. We modified P supply in microcosms and tracked changes in algal and Daphnia populations, and phosphorus concentrations. Even though we found algal biomass increased over the experiment in low P containers, greater food abundance did not increase Daphnia abundance. In low P containers, a high algal biomass was accompanied with elevated C:P ratios, very low soluble reactive P concentrations and low Daphnia fecundity. High algal C:P ratios and low soluble reactive P concentrations in these microcosms indicated a strong P-limitation of algae and food quality constraints on consumer populations. In high P containers, algal biomass initially increased, which led to an increased Daphnia abundance. In most of the high P microcosms, rapid increases in Daphnia populations led to reduced algal biomass and increased concentrations of soluble reactive P. However, in an outlier high P container, we found a different pattern with elevated algal biomass, low soluble reactive P concentrations and a very large Daphnia population. Our results show that P supplies can strongly affect Daphnia–algae interactions, but the nature of these effects are likely complicated by internal feedbacks that affect the gain and loss of both populations.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".