A Suite of Models to Support the Quantitative Assessment of Spread in Pest Risk Analysis

Abstract

Pest Risk Analyses (PRAs) are conducted worldwide to decide whether and how exotic plant pests should be regulated to prevent invasion. There is an increasing demand for science-based risk mapping in PRA. Spread plays a key role in determining the potential distribution of pests, but there is no suitable spread modelling tool available for pest risk analysts. Existing models are species specific, biologically and technically complex, and data hungry. Here we present a set of four simple and generic spread models that can be parameterised with limited data. Simulations with these models generate maps of the potential expansion of an invasive species at continental scale. The models have one to three biological parameters. They differ in whether they treat spatial processes implicitly or explicitly, and in whether they consider pest density or pest presence/absence only. The four models represent four complementary perspectives on the process of invasion and, because they have different initial conditions, they can be considered as alternative scenarios. All models take into account habitat distribution and climate. We present an application of each of the four models to the western corn rootworm, Diabrotica virgifera virgifera, using historic data on its spread in Europe. Further tests as proof of concept were conducted with a broad range of taxa (insects, nematodes, plants, and plant pathogens). Pest risk analysts, the intended model users, found the model outputs to be generally credible and useful. The estimation of parameters from data requires insights into population dynamics theory, and this requires guidance. If used appropriately, these generic spread models provide a transparent and objective tool for evaluating the potential spread of pests in PRAs. Further work is needed to validate models, build familiarity in the user community and create a database of species parameters to help realize their potential in PRA practice.

Inventory and review of quantitative models for spread of plant pests for use in pest risk assessment for the EU territory

“…The potential benefits of quantitative modelling of spread and dispersal for pest risk assessment are clear, both in terms of dynamically predicting the region at risk of pest colonisation and in gaining greater understanding of the processes driving spread (Kehlenbeck et al, 2012;Truscott and Ferguson, 2012). Therefore it would be desirable for EFSA to make greater use of spread and dispersal modelling in EU Plant Health risk assessment.…”

“…EFSA Panel on Plant Health (PLH), 2010b(PLH), , 2011. A greater use of quantitative spread and dispersal models may improve EU Plant Health risk assessment, because in principle modelling can identify the regions where a pest can persist, dynamically model its expansion from existing populations or points of entry and estimate the impacted areas for any given time period (Kehlenbeck et al, 2012;Parry et al, 2013). Since the population dynamics of pest organisms are expected to be strongly influenced by climate, the distribution of host plants, land cover, cultivation practices and risk reduction options, changes in these factors are likely to have a large impact on pest distributions and spread rates.…”

“…Calonnec et al, 2008; Harwood et al, 2009;Meentemeyer et al, 2011) to very generic and host-and pest-independent (e.g. Brewster and Allen, 1997;Kehlenbeck et al, 2012). Both types of model can be important for risk assessment.…”

“…Previous attempts to do this have typically relied upon a priori definition of a small number of modelling strategies. For example Truscott and Ferguson (2012) and Kehlenbeck et al (2012) consider four model types based upon all combinations of two factors -whether or not the pest is modelled as occupancy (i.e. presence/absence) or as population density and whether the model only has a temporal component or also includes a spatial component.…”

“…Specific approaches can include a lot of biological detail and make accurate predictions for the focal system but can require more data than are available for most species (Bullock et al, 2008). Generic models can be used in sensitivity analyses to identify pest traits favouring spread under alternative conditions, though their applicability may be limited by the degree of biological complexity built into the model (Kehlenbeck et al, 2012). Furthermore, spread and dispersal models have been developed across very different spatial and temporal scales (Parry et al, 2013).…”