Bacterial infection changes the elemental composition of <i>Daphnia magna</i>
Bibliographic record
Abstract
1. An animal's elemental composition can be an important indicator of its physiological state and role in ecosystem nutrient cycling. We examined the interactive effects of bacterial (Pasteuria ramosa) infection and phosphorus (P)-poor food on the body stoichiometry of Daphnia magna. Daphnia were exposed to or held free of a bacterial parasite and fed algal food of different C:P ratios (100-500) over a 28-day period. 2. To assess the effects of exposure and infection on Daphnia stoichiometry, we measured their whole body content of carbon (C), nitrogen (N), and P on four different days (4, 8, 15, and 28) during the experiment. 3. We found strong effects of infection, food quality, and/or their interactions on the C, N, and P content of Daphnia, especially as the infectious disease progressed. At the end of the experiment, infected animals had significantly more C and less P in their bodies than uninfected conspecifics. Body N content of Daphnia consuming P-rich food was reduced by bacterial infection whereas Daphnia consuming P-poor algae showed increased body N content from infection. 4. Using a mass-balance model, we found that changes in N and P content of host bodies were largely accountable by disease-induced alterations to Daphnia reproduction (i.e. bacterial induced sterility) and the accumulation of Pasteuria spores in the body cavity. Our calculations also show that the observed increase in host C content could not be accounted for by loss of eggs or accumulation of bacterial spores. This instead must result from unidentified changes to underlying daphnid tissue C content. 5. These results demonstrate that intraspecific variation in zooplankton body stoichiometry can be caused by exposure to and infection by bacterial parasites. In addition, these effects were found to depend both upon the stage of the disease and upon the food quality consumed by the host.
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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 | 0.000 | 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) | 0.000 | 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".