Using gut passage time and mathematical models to predict disease prevalence in Daphnia dentifera

Background/Question/Methods

Successful transmission of parasites between hosts involves many factors including the ability of the parasite to infect the host, within-host growth rate, and the release of infectious propagules. When hosts become infected while foraging, transmission can depend on the host’s resource acquisition traits.  We used the planktonic host Daphnia dentifera infected with the virulent fungal pathogen Metschnikowia bicuspidata to ask how genetically and environmentally determined differences in feeding rate among hosts may influence gut passage time (GPT) and disease prevalence.  We used a series of laboratory assays to investigate the role of host genotype, the presence or absence of predator kairomones and resource levels on GPT in three genotypes of D. dentifera that are known to vary in their feeding rate and susceptibility to Metschnikowia. In addition, we constructed and analyzed a four-population model.  We predicted that increased per-spore infectivity would increase disease prevalence and that longer gut passage times would increase per-spore infectivity. The model was analyzed with a range of values for per-spore infectivity. GPT and disease prevalence were measured in both the presence and absence of a predator (the phantom midge Chaoborus) under two resource conditions (high vs. low quantity). 

Results/Conclusions

The model predicted that an increase in per-spore infectivity would result in increased disease prevalence. The empirical results revealed that GPT was influenced by a significant three way interaction between genotype, kairomone and resource levels. Gut passage times typically were longer under low food conditions, but only one of the three genotypes responded to the presence of predator kairomones. For that genotype, gut passage time was longer in the kairomone treatment, particularly when resource conditions were low. The initial susceptibility assay did not support our initial predictions; no significant difference in disease prevalence was observed the treatments, nor was there a significant correlation between GPT and prevalence. These results suggest that many factors contribute to the differences observed in GPT, but susceptibility may not be directly influenced by GPT.