Pest risk analysis (PRA) provides the context for this paper. PRA is fundamental to plant biosecurity because it is primarily undertaken to assess the risks posed by plant pests that are not officially established in an area and to identify appropriate phytosanitary measures to prevent entry and establishment if the risk is unacceptable. Pest risk analyses that may affect international trade should follow international standards for phytosanitary measures (especially ISPM 11; FAO 2004) because they have been formulated by the International Plant Protection Convention and are recognised by the World Trade Organization. Although the international standards set out clearly what elements need to be assessed in order to evaluate the likelihood of entry and establishment together with the magnitude of spread and impacts, they do not provide clear guidance on the methods to be used in completing the PRA. As a result, a number of schemes have been created to assist pest risk analysts with the production of PRAs based on expert judgement and documented evidence. For example, the European and Mediterranean Plant Protection Organization (EPPO) provides a well known scheme to guide the production of PRAs through a series of questions that require answers in the form of a risk rating, an uncertainty score, and a written justification (EPPO 2011).

The EPPO PRA scheme has recently been enhanced by PRATIQUE, an EU funded research project (Baker et al. 2009), by providing several guidance documents and tools, for example, guidance for rating the level of risk (Schrader et al. 2012) and a computerised procedure for completing the PRA (Griessinger et al. 2012). Additional modules are available to help when it is important for pest risk analysts to quantify risk spatially or at least provide greater detail for particular components of the PRA. These include a decision support system (DSS) for mapping climatic suitability, summarised in Table 1 (Eyre et al. 2012) and a DSS for mapping endangered areas (Baker et al. 2012), the topic of this paper.

Maps provide an important method for visualising, summarising and communicating the risk posed by a pest in the PRA area that can be an officially defined country, part of a country or all or parts of several countries (FAO 2012). Within the PRA area, three different risk areas can be identified by pest risk analysts. These are: (i) the area of potential establishment, where it is likely that there is “perpetuation, for the foreseeable future, of a pest within an area after entry” (FAO 2012); (ii) the endangered area, where “ecological factors favour the establishment of a pest whose presence in the area will result in economically important loss” (FAO 2012) and (iii) the area at highest risk, where impacts are assessed as likely to be greatest, e.g. because particularly valuable or vulnerable hosts are growing in areas where abiotic and biotic factors are most suitable for the pest. Economic loss in the endangered area definition is considered to include environmental damage (FAO 2012). Although areas at risk can be described just by listing the geographical regions that are included, maps can convey a clearer message. Maps can also be deployed to help target eradication and containment actions in the event of an outbreak and set up an effective surveillance programme.

Although many PRAs already contain maps depicting components of pest risk that have been created without formal models and geographical information systems (GIS), most profit from such tools. Frequently, the PRA just includes a map of climatic suitability. Climatic suitability needs to be combined with factors such as host or habitat distribution firstly to obtain the area of potential establishment and secondly with impact related components, such as host or habitat vulnerability and value, to map the areas at highest risk. NAPPFAST provides a suite of interconnected models that can be used individually or collectively with tailored climatic data to map pest risk for North America (Magarey et al. 2007, 2011). Outputs from the PRATIQUE DSS for mapping climatic suitability (Eyre et al. 2012) and the area of potential establishment and highest risk (Baker et al. 2012) can be linked to models of spread (Kehlenbeck et al. 2012) and economic impact (Soliman et al. 2012) to map the dynamics of invasion and impact scenarios that illustrate possible endangered areas. The DSSs are independent of the models used and the area of concern, although the examples are provided for all or parts of Europe.

The PRATIQUE DSS described by Baker et al. (2012) focuses on identifying the area of potential establishment and the area at highest risk rather than endangered areas. This is because a map of the endangered area should show only where economically important loss is predicted to occur and this is very difficult given the uncertainty surrounding all pest invasions together with the need to predict pest population densities and relate these to poorly defined economic injury levels (Pedigo et al. 1986) while taking into account the effectiveness of pest management practices. Since the areas at highest risk from economic, environmental or social impacts can be mapped without modelling population densities in relation to economic thresholds it is therefore more practical to follow this approach not only to provide evidence supporting the PRA but also to help target actions following outbreaks and to design effective surveillance programmes and contingency plans.

Methods for combining maps of climatic suitability, host distribution, and host value with a simple mapping program (ABARES 2012) are summarised by Baker et al. (2012). The DSS has an introduction and four further stages, see Table 2.

In stage 1, the key factors that influence the endangered area are identified by using the biological, ecological and agronomic information in the pest risk assessment, the geographic data sets are assembled and, where appropriate, maps of the key factors are produced listing any significant assumptions. In stage 2, methods for combining these maps to identify the area of potential establishment and the area at highest risk from pest impacts are described, documenting any assumptions and combination rules utilised. When possible and appropriate, stage 3 can then be followed to show whether economic loss will occur in the area at highest risk and to identify the endangered area. As required, stage 4, provides techniques for producing a dynamic picture of the invasion process using a suite of spread models. Baker et al. (2012) illustrate the functioning of the DSS with two pests: the maize insect pest, Diabrotica virgifera virgifera, and the aquatic invasive alien plant, Eichhornia crassipes. For both these species, extensive information and maps are available on, e.g. climatic responses and host/habitat distribution, and there was ample time and resources for the analyses. A comprehensive description of the DSS is available in the project report (Baker et al. 2011).

In this paper, we apply the area mapping DSS to four case studies to determine the need for pest risk maps. We propose simple, quick analyses (i.e., shortcuts) to answer questions posed by the DSS and suggest these shortcuts could be particularly useful when risk maps are needed urgently, when an incursion threat seems imminent, or an outbreak has been detected. In addition, many plant health services have limited staff with skills in pest risk mapping and modelling and are faced with budget reductions. If used appropriately, the DSS can guide the production of exploratory pest risk maps created with relatively little time and resources. These exploratory analyses can still be helpful and, at minimum, can justify the need for a more detailed analysis and additional funding.

It is important to tailor efforts according to the priority for which pest risk mapping is needed to provide support for the PRA. Although strict rules cannot be set because maps provide other important functions, we have attempted to identify situations of high and low priority.

The risk maps used to support these PRAs were all created by using short cuts and none of them utilised all components of the PRATIQUE DSSs for climatic suitability analysis (Eyre et al. 2012) and mapping areas at highest risk (Baker et al. 2012) although detailed investigations of the current distribution of the pest and its climate responses were generally carried out.

Based on the rationale for shortcuts described above, Phytophthora austrocedrae and Phytophthora pityocampa can be considered to represent, respectively, low and high priorities for pest risk mapping. For Phytophthora austrocedrae, distribution maps of juniper for the whole country and for areas important for nature conservation were considered to be sufficient to show the area of potential establishment outside parks and gardens and the endangered area for environmental impacts, whereas even the potential for establishment of Thaumetopoea pityocampa is highly uncertain. The risk mapping priorities for Drosophila suzukii and Thaumatotibia leucotreta are intermediate. The area of potential establishment for both species was assessed with relatively simple methods based on climatic suitability analyses using, respectively, a simple phenology model and the difference between minimum and maximum winter temperatures with the distributions of the host crops primarily influencing the endangered areas and areas of highest risk.

The extent to which limited methods are appropriate to map risk is debatable because PRAs can only be validated when invasions occur. However, by ensuring that the literature has been searched comprehensively to uncover, for example, all that is known about a pest’s distribution, host range and climatic responses, greater reliance can be placed on the priority given and the methods used.

Short cuts and limited methods also generate greater uncertainty. Demonstrating uncertainty in maps remains a fundamental challenge (Venette et al. 2010) and so it is very important that pest risk analysts carefully document the uncertainties. For example, the Drosophila suzukii PRA (EPPO 2011) noted that, although the 250 degree days above a base of 10°C used in Figure 2 is required for development from egg to adult, a simple division of the annual degree days to obtain a map of the number of generations possible in an area creates uncertainty because: (a) an additional period is usually required by insects before adults are ready to oviposit, (b) considerable individual variation can be expected with overlapping generations occurring and (c) the grid cells both summarise and interpolate climate measured at weather stations, many locations within each grid cell will have different temperature accumulations. In addition, although the higher the degree day accumulation above 10°C, the greater the number of generations expected, the species cannot tolerate high temperatures if humidity is low and, in the southern Mediterranean areas, the species may survive only in irrigated crops. While such uncertainties influence the area at highest risk and the endangered area for Drosophila suzukii they do not fundamentally change the overall risk. For Thaumetopoea pityocampa, however, the uncertainties concerning winter solar radiation are so critical to the overwintering survival of PPM in southern England that the uncertainties do need further investigation.

Many other shortcuts are available in addition to the examples provided here. In fact the Drosophila suzukii PRA also included a visual examination of the global Köppen-Geiger climate zones (Kottek et al. 2006), hardiness zones (Magarey et al. 2008) and day-degree (Baker 2002) maps to help with the assessment. Regional maps of environmental zones, e.g. for Europe (Metzger et al. 2005), may help because they provide greater resolution than global maps. Tools that match the climate at locations that would be novel to the pest with those in the area where the pest is present, irrespective of a pest’s known climatic responses, can also be very useful. CLIMEX (Sutherst et al. 2007) provides an application for matching locations and regions that can exploit both weather station and gridded climatologies, e.g. CliMond (Kriticos et al. 2011).

The PRATIQUE DSSs for mapping the suitability of the climate for pest risk analysis (Eyre et al. 2012) and mapping areas at highest risk (Baker et al. 2012) already provide advice and examples for (a) when to map and when not to map, (b) what climate suitability model to use, (c) where to find other relevant spatial data and (d) how to combine other relevant spatial data with climatic suitability to create maps of potential establishment. Some of the issues that require further work are: (i) the representation of uncertainty to pest risk managers, (ii) the incorporation of climate and land use change in risk maps, (iii) linking maps of the area of highest risk with models of pest spread and impacts and (iv) exploring ways of mapping endangered areas. These challenges relate closely to the recommendations for improving pest risk maps identified by Venette et al. (2010).

This paper has focused on the additional challenges of identifying when pest risk mapping is a low and a high priority and relating this to an appropriate reduction or increase in the level of detail employed while ensuring that the uncertainties inherent in simplification are clearly demonstrated. We have shown that a number of approaches for simplifying the DSS and reducing the time taken to produce risk maps can be considered, e.g. (a) using previously published maps to help indicate risk, (b) deploying simpler models and (c) mapping key components for visual comparison without importing them all into a GIS, converting them to the same resolution and using GIS tools to highlight areas at high risk. However, the examples provided in this paper show that, to justify any shortcuts, it is always important to ensure that the literature is thoroughly searched for key information on, for example pest distribution, host/habitat range and climatic responses. In addition, any maps that have been generated from simplified approaches should be clearly documented so that the reader knows why these methods have been used and understands the uncertainties. The priorities for further research should also be indicated.

The future priorities for pest risk mapping DSSs include further testing and enhancements to address the challenges articulated in the roadmap provided by Venette et al. (2010) not only to assist pest risk mappers but also to guide policy makers when interpreting the maps produced. The identification of the situations that are priorities for detailed pest risk mapping with guidance on shortcuts relates closely to the increasing use of shorter PRA schemes that can be completed quickly, e.g. the Quick Scan PRA scheme of the Netherlands (Netherlands Plant Protection Service 2012), the Rapid PRA scheme of the UK (Fera 2012b) and the EPPO express PRA scheme (EPPO 2012).