Dissemin is shutting down on January 1st, 2025

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American Phytopathological Society, Phytopathology, 5(112), p. 1036-1045, 2022

DOI: 10.1094/phyto-05-21-0218-r

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Modeling the Spatial Distribution ofXylella fastidiosa: A Nonstationary Approach with Dispersal Barriers

Journal article published in 2022 by Martina Cendoya ORCID, Ana Hubel, David Conesa ORCID, Antonio Vicent ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Data provided by SHERPA/RoMEO

Abstract

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 nonstationarity 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 nonstationary 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% credible interval (2,907 to 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. [Formula: see text] Copyright © 2022 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .