Modeling the rejection probability in plant imports

Arthropods on imported plant products: Volumes predict general trends while contextual details enhance predictive power

Agricultural biosecurity interventions are aimed at minimizing introductions of harmful non-native organisms to new areas via agricultural trade. To prioritize such interventions, historical data on interceptions have been used to elucidate which factors determine the likelihood that a particular import is carrying a harmful organism. Here we use an interception data set of arthropod contaminants recorded on plant imports arriving in South Africa from 2005 to 2019, comprising 13,566 samples inspected for arthropod contaminants, of which 4902 were positive for the presence of at least one arthropod. We tested 29 predictor variables that have previously been used to explain variation in rates of detection and three variables describing possible sources of additional variation and grouped these into six mutually exclusive "factor classes." We used boosted regression trees as a non-parametric stochastic machine-learning method to build models for each factor class and interactions between them. We explored the influence of these variables with data split either randomly or chronologically. While we identified some specific patterns that could be explained post-hoc by historical events, only inspected volumes were reliably correlated with detection of arthropod contaminants across the whole data set. However, inspected volumes could not predict future interceptions of arthropods, which instead relied on contextual factors such as country, crop or year of import. This suggests that, although certain factors may be important in certain circumstances or for particular crops or commodities, there is little general predictive power in the current data. Instead, an idiographic approach would be most beneficial in biosecurity to ascertain the details of why a particular pest arrived on a particular pathway and how it might move (and be stopped) in future.

Guidance on quantitative pest risk assessment

This Guidance describes a two-phase approach for a fit-for-purpose method for the assessment of plant pest risk in the territory of the EU. Phase one consists of pest categorisation to determine whether the pest has the characteristics of a quarantine pest or those of a regulated non-quarantine pest for the area of the EU. Phase two consists of pest risk assessment, which may be requested by the risk managers following the pest categorisation results. This Guidance provides a template for pest categorisation and describes in detail the use of modelling and expert knowledge elicitation to conduct a pest risk assessment. The Guidance provides support and a framework for assessors to provide quantitative estimates, together with associated uncertainties, regarding the entry, establishment, spread and impact of plant pests in the EU. The Guidance allows the effectiveness of risk reducing options (RROs) to be quantitatively assessed as an integral part of the assessment framework. A list of RROs is provided. A two-tiered approach is proposed for the use of expert knowledge elicitation and modelling. Depending on data and resources available and the needs of risk managers, pest entry, establishment, spread and impact steps may be assessed directly, using weight of evidence and quantitative expert judgement (first tier), or they may be elaborated in substeps using quantitative models (second tier). An example of an application of the first tier approach is provided. Guidance is provided on how to derive models of appropriate complexity to conduct a second tier assessment. Each assessment is operationalised using Monte Carlo simulations that can compare scenarios for relevant factors, e.g. with or without RROs. This document provides guidance on how to compare scenarios to draw conclusions on the magnitude of pest risks and the effectiveness of RROs and on how to communicate assessment results.

Networks in plant epidemiology: from genes to landscapes, countries, and continents

There is increasing use of networks in ecology and epidemiology, but still relatively little application in phytopathology. Networks are sets of elements (nodes) connected in various ways by links (edges). Network analysis aims to understand system dynamics and outcomes in relation to network characteristics. Many existing natural, social, and technological networks have been shown to have small-world (local connectivity with short-cuts) and scale-free (presence of super-connected nodes) properties. In this review, we discuss how network concepts can be applied in plant pathology from the molecular to the landscape and global level. Wherever disease spread occurs not just because of passive/natural dispersion but also due to artificial movements, it makes sense to superimpose realistic models of the trade in plants on spatially explicit models of epidemic development. We provide an example of an emerging pathosystem (Phytophthora ramorum) where a theoretical network approach has proven particularly fruitful in analyzing the spread of disease in the UK plant trade. These studies can help in assessing the future threat posed by similar emerging pathogens. Networks have much potential in plant epidemiology and should become part of the standard curriculum.