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Partitioning the mechanisms by which genetic diversity of parasite infections affects total parasite load

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Rauch,  Gisep
Department Evolutionary Ecology, Max Planck Institute for Evolutionary Biology, Max Planck Society;

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Citation

Fox, J. W., & Rauch, G. (2009). Partitioning the mechanisms by which genetic diversity of parasite infections affects total parasite load. Oikos, 118(10), 1507-1514. doi:10.1111/j.1600-0706.2009.17547.x.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-D564-7
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.