Data from: Linking host traits, interactions with competitors, and disease: mechanistic foundations for disease dilution

1.The size of disease epidemics remains difficult to predict, especially when parasites interact with multiple species. Traits of focal hosts like susceptibility could directly predict epidemic size, while other traits including competitive ability might shape it indirectly in communities with a ‘dilution effect’. 2.In a dilution effect, diluter taxa can reduce disease by regulating (lowering) the density of focal hosts (i.e., through competition), or by reducing encounters between focal hosts and parasites. However, these dilution mechanisms are rarely grounded in focal host traits, and the relative importance of host regulation vs. encounter reduction remains understudied. 3.Here, we map focal host traits to disease—via these dilution mechanisms—in communities with diluters. We measured two traits (competitive ability and susceptibility) for eight genotypes of a focal host (Daphnia), tracked the densities of each genotype in experimental mesocosms (+/- Ceriodaphnia competitor/diluters), and monitored their infections with a virulent fungal parasite (Metschnikowia) over 6-8 host generations. We disentangled the impacts of both traits on the density of infected hosts and partitioned dilution mechanisms using path models. 4.Higher susceptibility directly fueled larger epidemics. Simultaneously, weaker competitive ability indirectly suppressed epidemics by enabling higher densities of diluters. These higher densities of diluters reduced the density of infected hosts indirectly via host regulation. In contrast, encounter reduction was much weaker. 5.Our experiment strengthens the dilution effect paradigm with a predictable, traits-oriented framework. Similar traits—susceptibility, competitive ability, and their covariance—could help predict epidemic severity in a variety of other systems. Partitioning the direct and indirect effects of diluters could also delineate how they impact disease. Such trait-based insights could help broadly predict the size of epidemics in diverse communities