Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers
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...
| Autores principales: | , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
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American Phytopathological Society (APS)
2022
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/8174 https://apsjournals.apsnet.org/doi/full/10.1094/PHYTO-05-21-0218-R |
| _version_ | 1855492439338385408 |
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| author | Cendoya, Martina Hubel, Ana Conesa, David Vicent, Antonio |
| author_browse | Cendoya, Martina Conesa, David Hubel, Ana Vicent, Antonio |
| author_facet | Cendoya, Martina Hubel, Ana Conesa, David Vicent, Antonio |
| author_sort | Cendoya, Martina |
| collection | ReDivia |
| description | 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. |
| format | Artículo |
| id | ReDivia8174 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | American Phytopathological Society (APS) |
| publisherStr | American Phytopathological Society (APS) |
| record_format | dspace |
| spelling | ReDivia81742025-04-25T14:48:49Z Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers Cendoya, Martina Hubel, Ana Conesa, David Vicent, Antonio Almond leaf scorch Barriers Containment Eradication INLA Non-stationary models Stochastic partial differential equation H20 Plant diseases U10 Mathematical and statistical methods Disease control Xylella fastidiosa 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. 2022-06-03T08:36:35Z 2022-06-03T08:36:35Z 2022 article publishedVersion Cendoya, M., Hubel, A., Conesa, D. & Vicent, A. (2022). Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers. Phytopathology®, 112(5), 1036-1045. 0031-949X 1943-7684 (e-ISSN) http://hdl.handle.net/20.500.11939/8174 10.1094/PHYTO-05-21-0218-R# https://apsjournals.apsnet.org/doi/full/10.1094/PHYTO-05-21-0218-R en info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/E‐RTA2017-00004-C06-01 info:eu-repo/grantAgreement/EC/H2020/727987 info:eu-repo/grantAgreement/AEI/Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i/PID2019-106341GB-I00/ES/MODELIZACION BAYESIANA DE DATOS COMPLEJOS CORRELADOS This work was supported by FEDER INIA AEI-MCIN and Organización Interprofesional del Aceite de Oliva Espanol grant E-RTA 2017-00004-C06-01, the European Union’s Horizon 2020 Research and Innovation Programme (XF-ACTORS, “Xylella Fastidiosa Active Containment Through a Multidisciplinary-Oriented Research Strategy”) grant 72787, and FEDER AEI-MCIN grant PID2019-106341GB-I00. M. Cendoya holds an IVIA grant partially funded by the European Social Fund. Atribución-NoComercial-SinDerivadas 3.0 España http://creativecommons.org/licenses/by-nc-nd/3.0/es/ openAccess American Phytopathological Society (APS) electronico |
| spellingShingle | Almond leaf scorch Barriers Containment Eradication INLA Non-stationary models Stochastic partial differential equation H20 Plant diseases U10 Mathematical and statistical methods Disease control Xylella fastidiosa Cendoya, Martina Hubel, Ana Conesa, David Vicent, Antonio Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers |
| title | Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers |
| title_full | Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers |
| title_fullStr | Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers |
| title_full_unstemmed | Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers |
| title_short | Modeling the Spatial Distribution of Xylella fastidiosa: A Nonstationary Approach with Dispersal Barriers |
| title_sort | modeling the spatial distribution of xylella fastidiosa a nonstationary approach with dispersal barriers |
| topic | Almond leaf scorch Barriers Containment Eradication INLA Non-stationary models Stochastic partial differential equation H20 Plant diseases U10 Mathematical and statistical methods Disease control Xylella fastidiosa |
| url | http://hdl.handle.net/20.500.11939/8174 https://apsjournals.apsnet.org/doi/full/10.1094/PHYTO-05-21-0218-R |
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