Empirical Dynamic Modelling Identifies different Responses of Aedes Polynesiensis Subpopulations to Natural Environmental Variables.

To control mosquito populations for managing vector-borne diseases, a critical need is to identify and predict their response to causal environmental variables. However, most existing attempts rely on linear approaches based on correlation, which cannot apply in complex, nonlinear natural systems, because correlation is neither a necessary nor sufficient condition for causation. Applying empirical dynamic modelling that acknowledges nonlinear dynamics on nine subpopulations of tiger mosquitos from three neighbouring reef islets of the Raiatea atoll, we identified temperature, precipitation, dew point, air pressure, and mean tide level as causal environmental variables. Interestingly, responses of subpopulations in close proximity (100-500 m) differed with respect to their causal environmental variables and the time delay of effect, highlighting complexity in mosquito-environment causality network. Moreover, we demonstrated how to explore the effects of changing environmental variables on number and strength of mosquito outbreaks, providing a new framework for pest control and disease vector ecology.

A Comparison of Wolbachia Infection Frequencies in Varroa With Prevalence of Deformed Wing Virus.

Wolbachia are widely distributed bacterial endosymbionts of arthropods and filarial nematodes. These bacteria can affect host fitness in a variety of ways, such as protecting hosts against viruses and other pathogens. Here, we investigate the possible role of Wolbachia in the prevalence of the deformed wing virus (DWV), a highly virulent pathogen of honey bees (Apis mellifera) that is transmitted by parasitic Varroa mites (Varroa destructor). About 180 Varroa mites from 18 beehives were tested for infection with Wolbachia and DWV. We first screened for Wolbachia using two standard primers (wsp and 16S rDNA), and found 26% of the mites to be positive for Wolbachia using the wsp primer and 64% of the mites to be positive using the 16S rDNA primer. Using these intermediate Wolbachia frequencies, we then tested for statistical correlations with virus infection frequencies. The analysis revealed a significant positive correlation between DWV and Wolbachia using the wsp primer, but no significant association between DWV and Wolbachia using the 16S rDNA primer. In conclusion, there is no evidence for an anti-pathogenic effect of Wolbachia in V. destructor, but weak evidence for a pro-pathogenic effect. These results encourage further examination of Wolbachia-virus interactions in Varroa mites since an increased vector competence of the mites may significantly impact disease outbreaks in honey bees.

Experimental evolution in silico: a custom-designed mathematical model for virulence evolution of Bacillus thuringiensis.

Most mathematical models on the evolution of virulence are based on epidemiological models that assume parasite transmission follows the mass action principle. In experimental evolution, however, mass action is often violated due to controlled infection protocols. This "theory-experiment mismatch" raises the question whether there is a need for new mathematical models to accommodate the particular characteristics of experimental evolution. Here, we explore the experimental evolution model system of Bacillus thuringiensis as a parasite and Caenorhabditis elegans as a host. Recent experimental studies with strict control of parasite transmission revealed that one-sided adaptation of B. thuringiensis with non-evolving hosts selects for intermediate or no virulence, sometimes coupled with parasite extinction. In contrast, host-parasite coevolution selects for high virulence and for hosts with strong resistance against B. thuringiensis. In order to explain the empirical results, we propose a new mathematical model that mimics the basic experimental set-up. The key assumptions are: (i) controlled parasite transmission (no mass action), (ii) discrete host generations, and (iii) context-dependent cost of toxin production. Our model analysis revealed the same basic trends as found in the experiments. Especially, we could show that resistant hosts select for highly virulent bacterial strains. Moreover, we found (i) that the evolved level of virulence is independent of the initial level of virulence, and (ii) that the average amount of bacteria ingested significantly affects the evolution of virulence with fewer bacteria ingested selecting for highly virulent strains. These predictions can be tested in future experiments. This study highlights the usefulness of custom-designed mathematical models in the analysis and interpretation of empirical results from experimental evolution.