Roger D. Magarey

Pest Risk Maps for Invasive Alien Species: A Roadmap for Improvement

Pest risk maps are powerful visual communication tools to describe where invasive alien species might arrive, establish, spread, or cause harmful impacts. These maps inform strategic and tactical pest management decisions, such as potential restrictions on international trade or the design of pest surveys and domestic quarantines. Diverse methods are available to create pest risk maps, and can potentially yield different depictions of risk for the same species. Inherent uncertainties about the biology of the invader, future climate conditions, and species interactions further complicate map interpretation. If multiple maps are available, risk managers must choose how to incorporate the various representations of risk into their decisionmaking process, and may make significant errors if they misunderstand what each map portrays. This article describes the need for pest risk maps, compares pest risk mapping methods, and recommends future research to improve such important decision-support tools.

NAPPFAST : An internet system for the weather-based mapping of plant pathogens

In recent years, the number of exotic pest introductions has increased rapidly as a result of increased volume of trade (22). The serious and sometimes irreparable ecological and economic damage of exotic pathogens, such as Cryphonectria parasitica, Ophiostoma novo-ulmi, and Phytophthora ramorum, the causal agents of chestnut blight, Dutch elm disease, and Sudden Oak Death, respectively, are amply documented (1,6,42). An estimate of annual losses for exotic plant pathogens is

A Simple Generic Infection Model for Foliar Fungal Plant Pathogens

21 billion dollars (32). The Plant Protection and Quarantine (PPQ) (Sidebar 1) division within the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service (USDA-APHIS) has the goal of safeguarding agriculture and natural resources from the risks associated with the entry, establishment, and spread of exotic pathogens. Two important components of the APHIS-PPQ mission are risk analysis and pest detection. A key goal of the risk analysis program is to identify exotic pest pathways and to assess the risks these exotic pests pose to plants and plant products as well as to the environment. Three types of risk assessments that evaluate the probability of the introduction and establishment of exotic plant pests are pathway analysis, organism pest risk assessment, and commodity risk assessment. The PPQ pest detection program and its state cooperators provide a continuum of pest surveillance, from offshore preclearance programs through port inspections, to surveys in rural and urban sites across the United States. The Center for Plant Health Science and Technology (CPHST) and the Cooperative Agricultural Pest Survey (CAPS) programs are instrumental in APHIS-PPQ’s pest detection programs. CAPS is responsible for supplying a means of detection, documentation, and rapid dissemination of information regarding the survey of regulated significant plant pests and weeds in the United States. The survey information gathered by CAPS is entered into a central database known as National Agricultural Pest Information System (NAPIS). CPHST, headquartered in Raleigh, NC, is a multi-program scientific support organization for PPQ. One way CPHST scientists help facilitate the APHIS-PPQ activities of risk analysis and pest detection is by mapping the potential introduction and establishment of exotic pathogens in the United States. These maps are the result of pathogen-specific information analyses, including climate, pathogen distribution, host distribution, and trade. Given its influence on pest phenology, reproduction, dispersion, and overwintering survival, climate is a critical component for the geographic assessment of potential pathogen distribution. A large number of climate-based risk mapping systems, such as CLIMEX, BIOCLIM, and GARP, have been used for pest risk analysis (3,10,38,44). Literature typically focuses on the development and/or evaluation of the best modeling techniques (10); however, often the quality of the inputs, including biological parameters, weather

Phylogenetic structure and host abundance drive disease pressure in communities

ABSTRACT In this study, a simple generic infection model was developed for predicting infection periods by fungal foliar pathogens. The model is designed primarily for use in forecasting pathogens that do not have extensive epidemiological data. Most existing infection models require a background epidemiological data set, usually including laboratory estimates of infection at multiple temperature and wetness combinations. The model developed in this study can use inputs based on subjective estimates of the cardinal temperatures and the wetness duration requirement. These inputs are available for many pathogens or may be estimated from related pathogens. The model uses a temperature response function which is scaled to the minimum and optimum values of the surface wetness duration requirement. The minimum wetness duration requirement (W(min)) is the number of hours required to produce 20% disease incidence or 5% disease severity on inoculated plant parts at a given temperature. The model was validated with published data from 53 controlled laboratory studies, each with at least four combinations of temperature and wetness. Validation yielded an average correlation coefficient of 0.83 and a root mean square error of 4.9 h, but there was uncertainty about the value of the input parameters for some pathogens. The value of W(min) varied from 1 to 48 h and was relatively uniform for species in the genera Cercospora, Alternaria, and Puccinia but less so for species of Phytophthora, Venturia, and Colletotrichum. Operationally, infection models may use hourly or daily weather inputs. In the case of the former, information also is required to estimate the critical dry-period interruption value, defined as the duration of a dry period at relative humidities <95% that will result in a 50% reduction in disease compared with a continuous wetness period. Pathogens were classified into three groups based on their critical dry-period interruption value. The infection model is being used to create risk maps of exotic pests for the U.S. Department of Agricultures Animal Plant Health and Inspection Service.

Evolutionary tools for phytosanitary risk analysis: phylogenetic signal as a predictor of host range of plant pests and pathogens

Pathogens play an important part in shaping the structure and dynamics of natural communities, because species are not affected by them equally. A shared goal of ecology and epidemiology is to predict when a species is most vulnerable to disease. A leading hypothesis asserts that the impact of disease should increase with host abundance, producing a ‘rare-species advantage’. However, the impact of a pathogen may be decoupled from host abundance, because most pathogens infect more than one species, leading to pathogen spillover onto closely related species. Here we show that the phylogenetic and ecological structure of the surrounding community can be important predictors of disease pressure. We found that the amount of tissue lost to disease increased with the relative abundance of a species across a grassland plant community, and that this rare-species advantage had an additional phylogenetic component: disease pressure was stronger on species with many close relatives. We used a global model of pathogen sharing as a function of relatedness between hosts, which provided a robust predictor of relative disease pressure at the local scale. In our grassland, the total amount of disease was most accurately explained not by the abundance of the focal host alone, but by the abundance of all species in the community weighted by their phylogenetic distance to the host. Furthermore, the model strongly predicted observed disease pressure for 44 novel host species we introduced experimentally to our study site, providing evidence for a mechanism to explain why phylogenetically rare species are more likely to become invasive when introduced. Our results demonstrate how the phylogenetic and ecological structure of communities can have a key role in disease dynamics, with implications for the maintenance of biodiversity, biotic resistance against introduced weeds, and the success of managed plants in agriculture and forestry.

Plant Biosecurity in the United states: Roles, Responsibilities, and Information Needs

Assessing risk from a novel pest or pathogen requires knowing which local plant species are susceptible. Empirical data on the local host range of novel pests are usually lacking, but we know that some pests are more likely to attack closely related plant species than species separated by greater evolutionary distance. We use the Global Pest and Disease Database, an internal database maintained by the United States Department of Agriculture Animal and Plant Health Inspection Service – Plant Protection and Quarantine Division (USDA APHIS‐PPQ), to evaluate the strength of the phylogenetic signal in host range for nine major groups of plant pests and pathogens. Eight of nine groups showed significant phylogenetic signal in host range. Additionally, pests and pathogens with more known hosts attacked a phylogenetically broader range of hosts. This suggests that easily obtained data – the number of known hosts and the phylogenetic distance between known hosts and other species of interest – can be used to predict which plant species are likely to be susceptible to a particular pest. This can facilitate rapid assessment of risk from novel pests and pathogens when empirical host range data are not yet available and guide efficient collection of empirical data for risk evaluation.

Developmental Database for Phenology Models: Related Insect and Mite Species have Similar Thermal Requirements

Plant biosecurity activities in the United States fall along a continuum ranging from offshore activities to the management of newly established exotic pests. For each step in the continuum, we examine the roles, responsibilities, and information needs of the Animal and Plant Health Inspection Service and other agencies involved in plant biosecurity. Both costs and information needs increase dramatically as a pest penetrates deeper into the continuum. To help meet these information needs, we propose a cyberinfrastructure for plant biosecurity to link phytosanitary agencies, researchers, and stakeholders, including industry and the public. The cyberinfrastructure should facilitate data collection, data integration, risk analysis, and reporting. We also emphasize the role of private industry in providing critical data for surveillance. We anticipate that this article will provide agricultural stakeholders, including scientists, with a better understanding of the information needs of phytosanitary organizations, and will ultimately lead to a more coordinated biosecurity effort.

The comparison of four dynamic systems-based software packages: Translation and sensitivity analysis

ABSTRACT Two values of thermal requirements, the lower developmental threshold (LDT), that is, the temperature at which development ceases, and the sum of effective temperatures, that is, day degrees above the LDT control the development of ectotherms and are used in phenology models to predict time at which the development of individual stages of a species will be completed. To assist in the rapid development of phenology models, we merged a previously published database of thermal requirements for insects, gathered by online search in CAB Abstracts, with independently collected data for insects and mites from original studies. The merged database comprises developmental times at various constant temperatures on 1,054 insect and mite species, many of them in several populations, mostly pests and their natural enemies, from all over the world. We show that closely related species share similar thermal requirements and therefore, for a species with unknown thermal requirements, the value of LDT and sum of effective temperatures of its most related species from the database can be used.

GLOBAL PLANT HARDINESS ZONES FOR PHYTOSANITARY RISK ANALYSIS

Abstract Dynamic model development for describing complex ecological systems continues to grow in popularity. For both academic research and project management, understanding the benefits and limitations of systems-based software could improve the accuracy of results and enlarge the user audience. A Surface Wetness Energy Balance (SWEB) model for canopy surface wetness has been translated into four software packages and their strengths and weaknesses were compared based on ‘novice’ user interpretations. We found expression-based models such as Simulink and GoldSim with Expressions were able to model the SWEB more accurately; however, stock and flow-based models such as STELLA, Madonna, and GoldSim with Flows provided the user a better conceptual understanding of the ecologic system. Although the original objective of this study was to identify an ‘appropriate’ software package for predicting canopy surface wetness using SWEB, our outcomes suggest that many factors must be considered by the stakeholders when selecting a model because the modeling software becomes part of the model and of the calibration process. These constraints may include user demographics, budget limitations, built-in sensitivity and optimization tools, and the preference of user friendliness vs. computational power. Furthermore, the multitude of closed proprietary software may present a disservice to the modeling community, creating model artifacts that originate somewhere deep inside the undocumented features of the software, and masking the underlying properties of the model.

Climatological Potential for Scirtothrips Dorsalis (Thysanoptera: Thripidae) Establishment in the United States

Plant hardiness zones are widely used for selection of perennial plants and for phytosanitary risk analysis. The most widely used definition of plant hardiness zones (United States Department of Agriculture National Arboretum) is based on average annual extreme minimum temperature. There is a need for a global plant hardiness map to standardize the comparison of zones for phytosanitary risk analysis. Two data sets were used to create global hardiness zones: i) Climate Research Unit (CRU) 1973-2002 monthly data set; and ii) the Daily Global Historical Climatology Network (GHCN). The CRU monthly data set was downscaled to five-minute resolution and a cubic spline was used to convert the monthly values into daily values. The GHCN data were subjected to a number of quality control measures prior to analysis. Least squares regression relationships were developed using GHCN and derived lowest average daily minimum temperature data and average annual extreme minimum temperatures. Error estimate statistics were calculated from the numerical difference between the estimated value for the grid and the station. The mean absolute error for annual extreme minimum temperature was 1.9oC (3.5oF) and 2/3 of the stations were classified into the correct zone.