Nutrient availability affects the prevalence of a microsporidian parasite
Bibliographic record
Abstract
Defining the relationship between nutrients and parasitism is complicated by shifts in host physiology and population density, which can both mediate the effects of host diet on parasites and vice versa. We examined the relationship between nutrient availability and an abundant parasite capable of both horizontal and vertical transmission (Hamiltosporidium tvaerminnensis) of a planktonic crustacean, Daphnia magna, in rock pools on Baltic Sea Skerry islands. We found that the relative availability of nutrients directly affected infection prevalence; parasite prevalence was higher in pools with higher particulate N:P ratios. Infection prevalence was not related to Daphnia population densities. A complementary experiment that examined host responses to an N:P gradient in mesocosms indicated that high N:P ratios can increase spore load in the hosts. We surmise that high N:P food increases Daphnia feeding rate, which increases their contact with parasite spores and leads to higher prevalence and more intense infections. We found no direct evidence that parasite-induced changes in host nutrient use affected nutrient dynamics in pools. However, the relationship between diet N:P and the parasite's prevalence and load is consistent with previously documented patterns of this parasite's effect on host nutrient use. Taken together, this study suggests that high N:P ratios in food may benefit the parasite in multiple ways and could create environments that favour horizontal transmission over vertical transmission for parasites capable of both transmission routes. If so, nutrient limitation could have long-term consequences for host-parasite evolution.
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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".