Esca is one of the most important fungal grapevine trunk diseases and, over the last decade, it has spread throughout the world. The primary fungal taxa contributing to esca are the tracheomycotic fungi, such as Phaeomoniella chlamydospora and Phaeoacremonium aleophilum (teleomorph = Togninia minima), and ligninolitic species, such as Fomitiporia mediterranea (Larignon & Dubos, 1997; Mugnai et al., 1999; Fischer, 2002).
Esca-inducing fungi can be spread by airborne inoculum and infect the vines through existing wounds (Eskalen & Gubler, 2001; Quaglia et al., 2009). For example, P. chlamydospora produces pycnidia or simple conidiophores on pruning wounds, wood cracks and crevices (Edwards et al., 2001a) and T. minima can produce inoculum in the form of ascospores (Rooney-Latham et al., 2005). The susceptibility of annual pruning wounds on grapevines to P. chlamydospora and P. aleophilum has been confirmed by some authors (Larignon & Dubos, 2000; Eskalen et al., 2007; Serra et al., 2008; Rolshausen et al., 2010; van Niekerk et al., 2011).
To better define the development of esca in vineyards, visual assessments have been conducted in many wine-growing regions. Disease assessment is complicated because foliar symptoms appear discontinuous: plants recorded as having symptoms one year (manifest esca) can be symptomless the next (hidden esca) (Mugnai et al., 1996; Serra et al., 1998). These fluctuating symptoms could also be influenced by exogenous factors such as rainfall and temperature (Surico et al., 2000a; Marchi et al., 2006).
Long-term assessments were used to create two-dimensional maps showing the plants with symptoms in space and time (Pollastro et al., 2000; Surico et al., 2000a; Marchi et al., 2006; Romanazzi et al., 2009). These maps were used to analyse spatial patterns to identify potential sources of primary contamination and evaluate the secondary spread of the disease. For example, external, airborne sources typically result in random distribution patterns, while internal or nearby sources typically produce aggregated patterns. In addition, aggregation along plant rows could indicate the spread of esca by human practices.
Two-dimensional maps based on vineyard observations only allow spatial patterns to be considered subjectively. Therefore, statistical and geostatistical analyses were conducted to extrapolate objective and measurable patterns of infection.
Surico et al. (2000b) analysed the distribution of grapevine esca symptoms in a vineyard using indices of dispersion, such as Morisita's index, Lloyd's index of patchiness (LIP), ordinary runs analysis and two forms of two-dimensional distance class analysis. The authors reported that, in half of the four examined vineyards, infected vines showed a random distribution, while in the remaining half, an aggregated pattern was observed. Aggregation was identified along rows in one case, a pattern that was attributed to differences in varietal susceptibility between adjacent rows, as each row consisted of a different cultivar; however, in the other case, aggregation was observed both along and across rows.
Ordinary runs analysis was also used by Edwards et al. (2001b) to evaluate the distribution patterns of vines with symptoms in two vineyards under study. The analysis showed that vines with symptoms were distributed at random in one site and were aggregated in the other.
Stefanini et al. (2000) attempted to explain the dynamics of symptom expression using a longitudinal model based on the probability that esca symptoms expressed in the year t were dependent on the symptoms expressed in the year t−1. They observed an association between a plant's vicinity and the expression of symptoms. In another study, Sofia et al. (2006) used two indices of dispersion (variance-to-mean ratio and Morisita's index) and a two-dimensional distance class analysis (2dclass software) to show that, in the observed vineyard, the infected vines were distributed at random.
Variation in the spatial patterns of grapevines with esca symptoms among vineyards could be attributed to different environments, which are often related to the vineyard's age. For example, in young vineyards, a random pattern of infection could be explained by primary inoculum (i.e. infected grafts or cuttings and airborne spores). Once primary infections are established, the spread of secondary inoculum (i.e. splash-borne conidia, cultural practices) could then produce an aggregated pattern. Data collection may also contribute to incongruent reports in the literature, as discontinuity in the appearance of symptoms precludes the evaluation of the true incidence and spread of affected plants. This is particularly true when symptom assessments begin after vineyards are mature.
For all of these reasons, this study investigated the onset of esca symptoms in a newly planted vineyard. The spread of symptoms was evaluated through space and time, beginning the year after planting and continuing over the next consecutive years. The analyses focused on identifying the process responsible for the secondary spread of esca. Different symptom distribution patterns were compared using hierarchical Bayesian spatiotemporal models, and the parameters were estimated via Monte Carlo Markov chain (MCMC) algorithms. Bayesian techniques combined with MCMC algorithms allowed for a unified inference approach. Indeed, once MCMC samples from the posterior distribution are drawn, the same procedure can be followed to answer almost every question. However, frequentist approaches are less flexible and often require asymptotic approximations or ad hoc procedures. As an example, based on the MCMC output presented in the results section, a method is proposed for assessing the contributions of different sources of variability to the spread of esca.