Spatial Bayesian Modeling Applied to the Surveys of Xylella fastidiosa in Alicante (Spain) and Apulia (Italy)

• Published in: Frontiers in plant science Vol. 11; p. 1204
• Main Authors: Cendoya, Martina, Martínez-Minaya, Joaquín, Dalmau, Vicente, Ferrer, Amparo, Saponari, Maria, Conesa, David, López-Quílez, Antonio, Vicent, Antonio
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 14-08-2020
• Subjects: hierarchical Bayesian models; integrated nested Laplace approximation; olive quick decline; Plant Science; species distribution models; stochastic partial differential equation; Xylella fastidiosa; hierarchical Bayesian models; integrated nested Laplace approximation; stochastic partial differential equation; Xylella fastidiosa; species distribution models; almond leaf scorch; olive quick decline
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2020.01204

The plant-pathogenic bacterium
was first reported in Europe in 2013, in the province of Lecce, Italy, where extensive areas were affected by the olive quick decline syndrome, caused by the subsp.
. In Alicante, Spain, almond leaf scorch, caused by
subsp.
, was detected in 2017. The effects of climatic and spatial factors on the geographic distribution of
in these two infested regions in Europe were studied. The presence/absence data of
in the official surveys were analyzed using Bayesian hierarchical models through the integrated nested Laplace approximation (INLA) methodology. Climatic covariates were obtained from the WorldClim v.2 database. A categorical variable was also included according to Purcell's minimum winter temperature thresholds for the risk of occurrence of Pierce's disease of grapevine, caused by
subsp.
. In Alicante, data were presented aggregated on a 1 km grid (lattice data), where the spatial effect was included in the model through a conditional autoregressive structure. In Lecce, data were observed at continuous locations occurring within a defined spatial domain (geostatistical data). Therefore, the spatial effect was included
the stochastic partial differential equation approach. In Alicante, the pathogen was detected in all four of Purcell's categories, illustrating the environmental plasticity of the subsp.
. Here, none of the climatic covariates were retained in the selected model. Only two of Purcell's categories were represented in Lecce. The mean diurnal range (
) and the mean temperature of the wettest quarter (
) were retained in the selected model, with a negative relationship with the presence of the pathogen. However, this may be due to the heterogeneous sampling distribution having a confounding effect with the climatic covariates. In both regions, the spatial structure had a strong influence on the models, but not the climatic covariates. Therefore, pathogen distribution was largely defined by the spatial relationship between geographic locations. This substantial contribution of the spatial effect in the models might indicate that the current extent of
in the study regions had arisen from a single focus or from several foci, which have been coalesced.