Partitioning the mechanisms by which genetic diversity of parasite infections affects total parasite load
Bibliographic record
Abstract
Genetically‐diverse parasite infections are common in nature, however what mechanisms influence parasite load are still under debate. Rauch et al. found consistently lower parasite loads in genetically‐mixed infections compared to uniform infections. Using the additive partition of Loreau and Hector they demonstrated that this lower parasite load was due to negative complementarity effects, but they only found weak selection effects. Complementarity effects arise from differentiation among genotypes that accrue equally to all genotypes, while selection effects arise from unexpectedly high performance of certain genotypes in mixed infections. However, selection effects might arise either because genotypes with certain traits perform unexpectedly well in mixed infections at the expense of other genotypes (‘dominance effects’, DEs), or because genotypes with certain traits perform unexpectedly well, but not at the expense of others genotypes (‘trait dependent complementarity effects’, TDCEs). Here, we reanalyze the data of Rauch et al. using the tripartite partition of Fox to separate DEs, TDCEs and trait‐independent complementarity effects (TICEs, corresponding to the complementarity effect of Loreau and Hector). We found significantly negative TDCEs that contribute strongly to the low parasite loads in mixed infections. We suggest novel, testable hypotheses to explain negative TDCEs. Ours is the first study to demonstrate consistently‐strong TDCEs, which are rare in studies of the productivity of plant mixtures. Our results highlight the importance of testing for TDCEs, rather than assuming them to be small. We discuss the interpretation and value of the tripartite partition as an analytical tool complementary to more mechanistic approaches.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".