Using Bayesian Inference to Investigate the Influence of Environmental Factors on a Phytoplasma Disease

Abstract

Phytoplasma diseases cause major economic damage on crops worldwide. To draw inferences from such a system, joint estimation of dependencies and high flexibility in the model structure are required. Using Bayesian inference, the aim of this chapter was to infer the apple proliferation (AP) disease epidemiology in South Tyrol, Italy. The data consisted of (1) presence/absence of the AP vector Cacopsylla picta collected in 44 orchards in 2014; (2) prevalence of the AP pathogen "Candidatus Phytoplasma mali" in the vector population; and (3) AP symptomatic trees visually assessed in 2015. Generalized linear mixed models evaluated in a Bayesian framework were used to test species-environment relationships. The model results indicated that the occurrence of the AP vector and symptomatic plants are positively influenced by elevation and temperature and negatively by management. Vector and pathogen predictions in the disease symptoms model correlated negatively or not at all with the prevalence of AP symptoms occurrence. In conclusion, the model results suggest that the presence/absence of the AP vector alone may not be the only cause for disease occurrence. Considering factors such as phytoplasma transmission via root-bridges and specific management strategies, may help to improve inference and finally to optimize the existing pest management.