Biology subjects

Vicent, A.

Publications and source records attributed to Vicent, A..

5 recordsLinked to original sources

bioRxiv2024

Developing epidemiological preparedness for a plant disease invasion: modelling citrus huanglongbingin the European Union

Huanglongbing (HLB; citrus greening) is the most damaging disease of citrus worldwide. While citrus production in the USA and Brazil have been affected for decades, HLB has not been detected in the European Union (EU). However, psyllid vectors have already invaded and spread in Portugal and Spain, and in 2023 the psyllid species known to vector HLB in the Americas was first reported within the EU. We develop a landscape-scale, epidemiological model, accounting for heterogeneous citrus cultivation and vector dispersal, as well as climate and disease management. We use our model to predict HLB dynamics following introduction into high-density citrus areas in Spain, assessing detection and control strategies. Even with significant visual surveillance, we predict any epidemic will be widespread on first detection, with eradication unlikely. Introducing increased inspection and roguing following first detection, particularly if coupled with intensive insecticide use, could potentially sustain citrus production for some time. However, this may require chemical application rates that are not permissible in the EU. Disease management strategies targeting asymptomatic infection will likely lead to more successful outcomes. Our work highlights modelling as a key component of developing epidemiological preparedness for a pathogen invasion that is, at least somewhat, predictable in advance.

ecology↗

bioRxiv2022

Inoculum and disease dynamics of citrus greasy spot caused by Zasmidium citri-griseum in sweet orange in Panama

Citrus greasy spot, caused by Zasmidium citri-griseum, is a disease characterized by inducing premature defoliation and a reduction in yield in different citrus species. Greasy spot is the most prevalent fungal disease in sweet orange in Panama. Nevertheless, no epidemiological information is available. In this study, the dynamics of the defoliation, inoculum production, airborne inoculum, and infection periods of Z. citri-griseum and their associations with environmental conditions were determined in Panama. The period from December to April was characterized by greater defoliation of trees, with the greatest amount of leaf litter being produced in January and February. The number of days until total leaf decomposition (DLD) was related to the number of rainy days >1mm (NRD), accumulated rainfall (AR), and average relative humidity (ARH). The number of ascospores released from leaf litter (ASCL) was related to DLD, NRD, AR, and average temperature (AT). The greatest amounts of airborne ascospores (AASC) of Z. citri-griseum occur during April and May, when the rainy season begins in Panama. Similarly, the highest incidence (INC) of greasy spot in the trap plants coincided with the months of the greatest availability of airborne ascospores. However, infections were also recorded during other times of the year. The AASC or INC data were fitted to Bayesian models including meteorological variables and an autoregressive temporal component, the latter being the most influential. The results obtained in this study will allow the development of more efficient and sustainable fungicide programs for greasy spot control in Panama.

pathology↗

bioRxiv2021

Barrier effects on the spatial distribution of Xylella fastidiosa in Alicante, Spain

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.

pathology↗

bioRxiv2020

Tracking the outbreak. An optimized delimiting survey strategy for Xylella fastidiosa.

O_LICurrent legislation enforces the implementation of intensive surveillance programs for quarantine plant pathogens. After an outbreak, surveys are implemented to delimit the geographic extent of the pathogen and execute disease control. The feasibility of control programs is highly dependent on budget availability, thus it is necessary to target and optimize surveillance strategies. C_LIO_LIA sequential adaptive delimiting survey involving a three-phase and a two-phase design with increasing spatial resolution was developed and implemented for the Xylella fastidiosa outbreak in Alicante, Spain. Inspection and sampling intensities were optimized using simulation-based methods and results were validated using Bayesian spatial models. C_LIO_LIThis strategy made it possible to sequence inspection and sampling considering different spatial resolutions, and to adapt the inspection and sampling intensity according to the information obtained in the previous, coarser, spatial resolution. C_LIO_LIThe proposed strategy was able to delimit efficiently the extent of Xf improving efficiency of the current in terms of survey efforts. From a methodological perspective, our approach provides new insights of alternative delimiting designs and new reference sampling intensity values. C_LI

pathology↗

bioRxiv2019

Modelling Inoculum Availability of Plurivorosphaerella nawae in Persimmon Leaf Litter with Bayesian Beta Regression

Circular leaf spot (CLS), caused by Plurivorosphaerella nawae, is a serious disease of persimmon (Diospyros kaki) inducing necrotic lesions on leaves, defoliation and fruit drop. Under Mediter-ranean conditions, P. nawae forms pseudothecia in the leaf litter during winter and ascospores are released in spring infecting susceptible leaves. Persimmon growers are advised to apply fungicides for CLS control during the period of inoculum availability, which was defined based on ascospore counts under the microscope. A model of inoculum availability of P. nawae was developed and evaluated as an alternative to ascospore counts. Leaf litter samples were collected weekly in LAlcudia from 2010 to 2015. Leaves were soaked, placed in a wind tunnel, and released ascospores of P. nawae were counted. Hierarchical Bayesian beta regression methods were used to fit the dynamics of ascospore production in the leaf litter. The selected model, having the lowest values of DIC, WAIC and LCPO, included accumulated degree days (ADD) and ADD taking into account the vapor pressure deficit (ADDvpd) as fixed effects, and year as a random effect. This model had a MAE of 0.042 and RMSE of 0.062. The beta regression model was evaluated in four orchards for different years from 2010 to 2015. Higher accuracy was obtained at the beginning and the end of the ascospore production period, which are the events of interest to schedule fungicide sprays for CLS control in Spain. This same modelling framework can be extended to other fungal plant pathogens whose inoculum dynamics are expressed as proportion data.

pathology↗