Spatial species distribution models often assume isotropy and stationarity, implying that spatial dependence is direction invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present. Despite this, the issue of non-stationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of barriers in the distribution of Xylella fastidiosa in the demarcated area in Alicante, Spain. Occurrence data from 2018 were analyzed through spatial Bayesian hierarchical models. The stationary model, illustrating a scenario without control interventions or geographical features, was compared with three non-stationary models: a model with mountains as physical barriers, and two models with a continuous and discontinuous perimeter barrier representing hypothetical control interventions. In the stationary model the posterior mean of the spatial range, as the distance where two observations are uncorrelated, was 4,030 m 95% CI (2,907, 5,564). This distance can be used to define the buffer zone in the demarcated area. The predicted probability of X. fastidiosa presence in the area outside the barrier was 0.46 with the stationary model, whereas it was reduced to 0.29 and 0.36 with the continuous and discontinuous barrier models, respectively. Differences between the discontinuous and continuous barrier models showed that breaks, where no control interventions were implemented, resulted in a higher predicted probability of X. fastidiosa presence in the areas with low sampling intensity. These results may help authorities prioritize the areas for surveillance and disease control.

Spatial models often assume isotropy and stationarity, implying that spatial dependence is direction invariant and uniform throughout the study area. However, these assumptions are violated when dispersal barriers are present in the form of geographical features or disease control interventions. Despite this, the issue of non-stationarity has been little explored in the context of plant health. The objective of this study was to evaluate the influence of different barriers in the distribution of the quarantine plant pathogenic bacterium Xylella fastidiosa in the demarcated area in Alicante, Spain. Occurrence data from the official surveys in 2018 were analyzed with four spatial Bayesian hierarchical models: i) a stationary model representing a scenario without any control interventions or geographical features; ii) a model with mountains as physical barriers; iii) a model with a continuous or iv) discontinuous perimeter barrier as control interventions surrounding the infested area. Barriers were assumed to be totally impermeable, so they should be interpreted as areas without host plants and in which it is not possible for infected vectors or propagating plant material to pass through. Inference and prediction were performed through the integrated nested Laplace approximation methodology and the stochastic partial differential equation approach. In the stationary model the posterior mean of the spatial range was 4,030.17 m 95% CI (2,907.41, 5,563.88), meaning that host plants that are closer to an infected plant than this distance would be at risk for X. fastidiosa. This distance can be used to define the buffer zone around the infested area in Alicante. In the non-stationary models, the posterior mean of the spatial range varied from 3,860.88 m 95% CI (2,918.61, 5,212.18) in the mountain barrier model to 6,141.08 m 95% CI (4,296.32, 9,042.99) in the continuous barrier model. Compared with the stationary model, the perimeter barrier models decreased the probability of X. fastidiosa presence in the area outside the barrier. Differences between the discontinuous and continuous barrier models showed that breaks in areas with low sampling intensity resulted in a higher probability of X. fastidiosa presence. These results may help authorities prioritize the areas for surveillance and implementation of control measures.