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Background Development of a relatively simple growth modelling approach for plantation species that allows derivation of cardinal (base, optimum and ceiling) air temperatures for growth, whilst accounting for changes in organism size, would represent a considerable advance over existing models. Such an approach would provide insight into species phenology and, in an agronomic setting, allow growers to closely match species to sites. Here, a model is described that can be used to predict seasonal variation in growth and cardinal air temperatures from simple seasonal measurements at a single site. Methods The model was applied to data from an irrigated trial comprising two Eucalyptus species and three Eucalyptus crosses. Using measurements of mean daily air temperature data and stem volume, taken over a two year period, the model was fitted to the data and used to estimate cardinal air temperatures for the five species/crosses. Results The model predictions corresponded well to the actual data for all five species/crosses, with R 2 ranging from 0.993 to 0.999. The optimum air temperature, T o, for E. camaldulensis x E. globulus of 26.9°C significantly exceeded T o for the other four species/crosses, where T o ranged from 15.4 to 18.7°C. As T o for E. camaldulensis x E. globulus was close to the highest mean daily air temperature recorded at the study site, the air temperature modifier for this species was almost always sub-optimal and consequently this cross was not well matched to the site. In contrast, T o for the other four species/crosses were considerably closer to the mean air temperature of the site with T o for E. nitens most closely approximating the mean air temperature (15.4 vs. 13.0°C). Conclusion The described approach can be used to account for complex variation in seasonal growth patterns and provides insight into how well a species may be matched to a particular site. As climatic information is available at a range of scales (from local to global), this type of model is likely to be useful for producing maps that describe species growth and areas of optimal suitability. Published version

Economic globalization depends on the movement of people and goods between countries. As these exchanges increase, so does the potential for translocation of harmful pests, weeds, and pathogens capable of impacting our crops, livestock and natural resources (Hulme 2009), with concomitant impacts on global food security (Cook et al. 2011).

International audience

This paper describes a decision-support scheme (DSS) for mapping the area where economically important loss is likely to occur (the endangered area). It has been designed by the PRATIQUE project to help pest risk analysts address the numerous risk mapping challenges and decide on the most suitable methods to follow. The introduction to the DSS indicates the time and expertise that is needed, the data requirements and the situations when mapping the endangered areas is most useful. The DSS itself has four stages. In stage 1, the key factors that influence the endangered area are identified, the data 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, described elsewhere, provides techniques for producing a dynamic picture of the invasion process using a suite of spread models. To illustrate how the DSS functions, a maize pest, Diabrotica virgifera virgifera, and a freshwater invasive alien plant, Eichhornia crassipes, have been used as examples.

Benefit cost analysis is a tried and tested analytical framework that can clearly communicate likely net changes in social welfare from investment decisions to diverse stakeholder audiences. However, in a plant biosecurity context, it is often difficult to predict policy benefits over time due to complex biophysical interaction between invasive species and their hosts. In this paper, we demonstrate how benefit cost analysis remains highly relevant to biosecurity decision-makers using the example of a plant pathogen targeted for eradication from banana growing regions of Australia, banana bunchy top virus. We develop a partial budgeting approach using a stratified diffusion spread model to simulate the likely benefits of eradication to the banana industry over time relative to a status quo policy. Using Monte Carlo simulation to generate a range of possible future incursion scenarios, we predict that eradicating the disease will generate $12.5-23.6 million increased annual revenue for the banana industry. To reduce these benefits to zero would require a bunchy top re-establishment event three years in every four. Sensitivity analysis indicates that eradication benefits can be greatly improved through improvements in disease surveillance and incursion response.

The assessment of the suitability of the climate for pest establishment is an important part of pest risk analysis (PRA). This paper describes the work undertaken by the EU 7th Framework project PRATIQUE (Enhancements of Pest Risk Analysis Techniques) to develop guidance for this component of PRA. Firstly, there is a guide to rating the suitability of the climate in the PRA area using qualitative methods. Secondly, a Decision-support scheme (DSS) has been created to assist analysts in deciding whether to map climatic suitability, and to guide the selection of the most appropriate method from the large number available. The process of selecting a climatic mapping method is based on a review of the pest's climatic responses and distribution. A spreadsheet provides a comparison of the potential problems that can arise, depending on the mapping method and on the amount and quality of available data. Diagrams are provided to help choose the location data category that best represents the possible biases in the known distribution of the pest. A second spreadsheet provides general information on the differences and similarities of each method in terms of categories such as functionality, ease of use and quality assurance. A variety of data, tools and supporting documents are available as appendices to the DSS. All of the tools and guides are freely available online.

International standards for pest risk analysis (PRA) require assessors to define the endangered area: "the part of the PRA area where the presence of the pest will result in economically important loss." In some cases it may be appropriate to use a simple geographical description but maps generally provide a much better method for defining this area. However, mapping the endangered area is very challenging because it needs to be defined by combining maps of the area of potential establishment (based on climatic suitability and the distribution of hosts, habitats, soils, etc) with maps of factors that indicate where economically important loss is likely to occur. This presentation will describe progress made by the EU-funded PRATIQUE project in developing a decision support scheme (DSS) to guide risk assessors when mapping endangered areas highlighting the importance of clearly defining the map combination rules. The DSS shows how MCAS-S (The Multi-Criteria Analysis Shell for Spatial Decision Support) can be used to display, manipulate, and combine map layers. This software does not require GIS skills, and allows risk assessors to combine maps derived from either continuous or categorical variables using simple mathematical equations and risk matrices. The DSS is linked to the computerised EPPO PRA scheme and modules for mapping climatic suitability, quantifying economic impacts and modelling spread. The DSS is illustrated with a case study of the western corn rootworm (Diabrotica virgifera virgifera) in Europe.

Risk mapping techniques play a critical role in pest risk analysis (PRA), helping to identify areas at greatest risk, justify appropriate measures and communicate risk. However, there is no existing guide to best practice that (a) helps the risk analyst decide whether it is appropriate to expend the time and resources required to undertake the modelling required to develop a risk map, (b) demonstrates the implications of choosing different climatic modelling and mapping methods for pests with differing ecologies and for which there is varying amounts of information on their climatic responses and distribution, (c) describes how to perform each technique and interpret the results and (d) shows how maps of climatic suitability can be linked with other datasets to map endangered areas. This presentation will outline the progress made in tackling these issues by PRATIQUE, a European funded research project that is addressing the major challenges faced by pest risk analysts.

Executive Summary: HarvestChoice provides data, information and tools to support strategic investment decisions in agriculture. Most agricultural processes are strongly influenced by biotic stressors, such as insects and pathogens. However, available pest information has been lacking, for example global maps of where pests and diseases could occur have not been available. To address this deficiency, HarvestChoice has developed and implemented an approach to pest modeling aimed directly at supporting strategic decisions in agriculture. The HarvestChoice system includes methods, techniques and tools to both collect primary data on pest occurrence and to model potential global pest occurrence. In addition, a global team of recognized pest and climate experts has been assembled to aid in modeling, validation and peer-review. The pest modeling system developed by HarvestChoice differs from the more typical monitoring and prediction systems that support short- to medium-term objectives, such as responding to pest events. Rather, we support strategic decision-making by mapping not where pests occur, but where they might potentially occur. This approach allows us to answer hypothetical questions that were previously unanswerable. For example, we can provide broad information on what portion of the world’s crop area could be subject to a pest or what might happen if a new variety were planted in a previously un-cropped location. The end results of our efforts are maps of potential pest occurrence. These maps are produced by modeling a species’ response to factors such as temperature and moisture, while carefully keeping certain other factors exogenous (namely, those factors that might be decision variables for strategic planning). For example, the models are not conditional on the actual presence of a susceptible host, so our maps show the potential pest distribution assuming that susceptible hosts occur everywhere, avoiding confounding the host climate responses with those of the pest. The models combine geo-spatial climate data and information on the seasonal phenology of each pest to investigate potential growth, stress and persistence of pests on a global scale. Growth is measured by a “Growth Index,” higher values of which indicate a higher potential for that the pest’s population to grow at a given location. Potential growth is restricted by sub-optimal temperatures or moisture and the pest’s survival is limited by stresses, such as extremely cold or dry conditions. A final index, the “Ecoclimatic Index,” is calculated by considering the extent to which growth is possible during the favorable season and survival is limited by the stresses during the non-growth season at each location. The result of this calculation, the Ecoclimatic Index is a measure of the potential ability of the pest to persist and develop high population sizes at a location: that is, whether it can survive the extremes of summer, winter, dry and wet seasons. This Working Paper describes the HarvestChoice approach to pest modeling and places that work in the context of other pest mapping efforts.