Suchergebnisse

Abstract
This report explores the integration of citizen science into the surveillance of insect pests, aiming to enhance statistically sound and risk‐based surveys. The study, conducted by researchers from Wageningen University, focuses on three primary objectives. First, it compiles a comprehensive inventory of citizen science initiatives across Europe, identifying 81 projects from 21 countries that contribute significant biodiversity data. Second, it develops a statistical methodology to estimate the probability of pest detection using opportunistic, presence‐only data from citizen science. This methodology is tested on three insect pests: Popillia japonica, Agrilus planipennis, and Anoplophora chinensis, demonstrating that citizen science can meaningfully complement official surveys. Third, the report provides guidelines for incorporating these findings into existing tools and methods used by the European Food Safety Authority (EFSA) and National Plant Protection Organizations (NPPOs). The results indicate that citizen science can increase the overall confidence in pest freedom and reduce the required sample sizes for official surveys. However, the study also highlights the spatial and temporal biases inherent in citizen science data and the need for further research to optimize its integration into pest surveillance frameworks.

Trabajo presentado en la 2nd European conference on Xylella fastidiosa (how research can support solutions), celebrada en Ajaccio el 29 y 30 de octubre de 2019. ; Understanding the dispersal of Xylella fastidiosais essential for effective management of the disease. In Puglia, Italy, surveillance is focused on buffer and containment zones, which have been established at the edge of the infected region with the aim of containing further spread. Success of this strategy will strongly depend on whether these zones are wide enough to form a barrier to long-distance dispersal of the bacterium. In this presentation, I will describe our progress towards estimating the dispersal range of Xylellain Puglia using a generic spatial epidemiological model adapted to the biology of the pathosystem. The model simulates the spread of the disease across a heterogeneous landscape depending on the location and timing of introduction, the distribution of host plants, the rate of infection growth in infected olive groves and both short-and long-distance dispersal. Long-distance dispersal seems to be a crucial feature of the Xylellaepidemic, causing rapid spread of the disease over large areas but in an unpredictable manner. To try to estimate long-distance dispersal, we use Approximate Bayesian Computation to calibrate the epidemiological model to observed detections in surveillance monitoring data from 2013 to 2018. I will present resultsfrom the model calibration, comparing long-distance dispersal estimates from models specified for different long-range dispersal mechanisms. This will inform discussion on the roles of mechanisms such as vehicle transport and wind dispersal in spreading Xylellaat regional scales. ; European Union Horizon 2020 grant agreement number 727987. ; Peer reviewed

Trabajo presentado en la 3rd European Conference on Xylella fastidiosa (Building knowledge, protecting plant health), celebrada online el 29 y 30 de abril de 2021. ; Understanding the dispersal of Xylella fastidiosa is essential for effective management of the disease. In Puglia, Italy, surveillance is focused on buffer and containment zones established at the edge of the infected region with the aim of containing further spread. Success of this strategy will strongly depend on whether these zones are wide enough to form a barrier to long distance dispersal of the bacterium. In this presentation, I will describe our progress towards estimating the dispersal range of Xylella in Puglia using a generic spatial epidemiological model adapted to the biology of the pathosystem. The model simulates the spread of the disease across a heterogeneous landscape depending on the location and timing of introduction, the distribution of host plants, the rate of infection growth in infected olive groves and both short and long distance dispersal. Long distance dispersal seems to be a crucial feature of the Xylella epidemic, causing rapid spread of the disease over large areas but in an unpredictable manner. To calibrate the model, we used Approximate Bayesian Computation to compare model simulations to Xylella surveillance data and remote sensing of severe damage. This allows us to contrast a simple spread scenario with more complex scenarios such as anisotropic dispersal in the direction of prevailing winds and spatial variation in disease transmission. In doing so we characterise the spread and estimate the year of introduction. Finally, I will discuss potential for using the model to simulate management strategies and new outbreaks in other regions, using the UK as a case study ; Support from XF-Actors & CURE-XF (EU H2020), EFSA, BRIGIT (BBSRC, Defra & Scottish Government) & Scottish Plant Health Centre.

The European Commission requested EFSA to facilitate the Member States in the planning and execution of their survey activities. In particular, EFSA is asked to provide scientific and technical guidelines in the context of the new plant health regime (Regulation (EU) 2016/2031), in which prevention and risk targeting are given an extra focus, and the European Commission co‐financing programme of the annual Member State survey activities for pests of EU relevance (Regulation (EU) No 652/2014). In order to address this mandate EFSA is requested to deliver by the end of 2019: (i) 47 pest survey cards that contain practical information required for preparing survey design; (ii) survey guidelines for 3 different pests that will be case studies to be developed in collaboration with the EU Member States; and, (iii) support to the Member States on the underpinning statistical methods and use of the EFSA WEB‐based tools RiBESS+ and SAMPELATOR to inform sampling strategy design, including sample size calculations. This technical report describes the methodological approach and the work‐plan EFSA will implement to deliver the requested outputs.

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution. ; Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain).

Abstract
In 2022, EFSA was mandated by the European Commission's Directorate‐General for Health and Food Safety (M‐2022‐00070) to provide technical assistance on the list of Union quarantine pests qualifying as priority pests, as specified in Article 6(2) of Regulation (EU) 2016/2031 on protective measures against plant pests. As part of Task C, EFSA conducted comprehensive expert knowledge elicitations for 46 candidate priority pests, focusing on the lag period, rate of expansion and impact on production (yield and quality losses) and the environment. This report details the methodology for assessing these candidate priority pests for which the EFSA outputs and supporting datasets were delivered to the European Commission's Joint Research Centre, to feed the Impact Indicator for Priority Pest (I2P2) model and complete the pest prioritisation ranking exercise.