Understanding trade pathways to target biosecurity surveillance

Increasing trends in global trade make it extremely difficult to prevent the entry of all potential invasive species (IS). Establishing early detection strategies thus becomes an important part of the continuum used to reduce the introduction of invasive species. One part necessary to ensure the success of these strategies is the determination of priority survey areas based on invasion pressure. We used a pathway-centred conceptual model of pest invasion to address these questions: what role does global trade play in invasion pressure of plant ecosystems and how could an understanding of this role be used to enhance early detection strategies? We concluded that the relative level of invasion pressure for destination ecosystems can be influenced by the intensity of pathway usage (import volume and frequency), the number and type of pathways with a similar destination, and the number of different ecological regions that serve as the source for imports to the same destination. As these factors increase, pressure typically intensifies because of increasing a) propagule pressure, b) likelihood of transporting pests with higher intrinsic invasion potential, and c) likelihood of transporting pests into ecosystems with higher invasibility. We used maritime containerized imports of live plants into the contiguous U.S. as a case study to illustrate the practical implications of the model to determine hotspot areas of relative invasion pressure for agricultural and forest ecosystems (two ecosystems with high potential invasibility). Our results illustrated the importance of how a pathway-centred model could be used to highlight potential target areas for early detection strategies for IS. Many of the hotspots in agricultural and forest ecosystems were within major U.S. metropolitan areas. Invasion ecologists can utilize pathway-centred conceptual models to a) better understand the role of human-mediated pathways in pest establishment, b) enhance current methodologies for IS risk analysis, and c) develop strategies for IS early detection-rapid response programs

Zonas globais de resistência às plantas para análise de risco fitossanitário

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.Zonas de resistência às plantas, definidas pelo " United States Department of Agriculture National Arboretum" com base na média anual das temperaturas mínimas extremas, são amplamente utilizadas para a seleção de plantas perenes e para a análise de risco fitossanitário. Há necessidade de um mapa global para padronizar a comparação de zonas nas análises de risco fitossanitário. Dois bancos de dados climatológicos foram utilizados para criar tais zonas globais de resistência às plantas: i) conjunto de dados mensais de 1973-2002 da " Climate Research Unit (CRU)" ; e ii) dados climatológicos diários da " Daily Global Historical Climatology Network (GHCN)" . Os dados mensais da CRU foram ajustados a uma escala reduzida de resolução de cinco minutos, e um ajuste cúbico foi empregado para converter os dados mensais para diários. Os dados da RDGH foram submetidos a várias medidas de controle de qualidade antes de serem empregados nas análises. Relações de regressão pelo método dos mínimos quadrados foram desenvolvidas usando dados da RDGH, resultando nos mais baixos valores médios diários de temperatura mínima e média anual das temperaturas mínimas extremas. Os erros estatísticos estimados foram calculados a partir da diferença numérica entre os valores estimados para a malha e os observados nas estações climatológicas. O erro médio absoluto para a temperatura mínima extrema anual foi 1,9ºC (3,5ºF), o que possibilitou a classificação de 2/3 das estações dentro das zonas corretas

Global plant hardiness zones for phytosanitary risk analysis Zonas globais de resistência às plantas para análise de risco fitossanitário

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.Zonas de resistência às plantas, definidas pelo " United States Department of Agriculture National Arboretum" com base na média anual das temperaturas mínimas extremas, são amplamente utilizadas para a seleção de plantas perenes e para a análise de risco fitossanitário. Há necessidade de um mapa global para padronizar a comparação de zonas nas análises de risco fitossanitário. Dois bancos de dados climatológicos foram utilizados para criar tais zonas globais de resistência às plantas: i) conjunto de dados mensais de 1973-2002 da " Climate Research Unit (CRU)" ; e ii) dados climatológicos diários da " Daily Global Historical Climatology Network (GHCN)" . Os dados mensais da CRU foram ajustados a uma escala reduzida de resolução de cinco minutos, e um ajuste cúbico foi empregado para converter os dados mensais para diários. Os dados da RDGH foram submetidos a várias medidas de controle de qualidade antes de serem empregados nas análises. Relações de regressão pelo método dos mínimos quadrados foram desenvolvidas usando dados da RDGH, resultando nos mais baixos valores médios diários de temperatura mínima e média anual das temperaturas mínimas extremas. Os erros estatísticos estimados foram calculados a partir da diferença numérica entre os valores estimados para a malha e os observados nas estações climatológicas. O erro médio absoluto para a temperatura mínima extrema anual foi 1,9ºC (3,5ºF), o que possibilitou a classificação de 2/3 das estações dentro das zonas corretas

Using a Network Model to Assess Risk of Forest Pest Spread via Recreational Travel

Long-distance dispersal pathways, which frequently relate to human activities, facilitate the spread of alien species. One pathway of concern in North America is the possible spread of forest pests in firewood carried by visitors to campgrounds or recreational facilities. We present a network model depicting the movement of campers and, by extension, potentially infested firewood. We constructed the model from US National Recreation Reservation Service data documenting more than seven million visitor reservations (including visitors from Canada) at campgrounds nationwide. This bi-directional model can be used to identify likely origin and destination locations for a camper-transported pest. To support broad-scale decision making, we used the model to generate summary maps for 48 US states and seven Canadian provinces that depict the most likely origins of campers traveling from outside the target state or province. The maps generally showed one of two basic spatial patterns of out-of-state (or out-of-province) origin risk. In the eastern United States, the riskiest out-of-state origin locations were usually found in a localized region restricted to portions of adjacent states. In the western United States, the riskiest out-of-state origin locations were typically associated with major urban areas located far from the state of interest. A few states and the Canadian provinces showed characteristics of both patterns. These model outputs can guide deployment of resources for surveillance, firewood inspections, or other activities. Significantly, the contrasting map patterns indicate that no single response strategy is appropriate for all states and provinces. If most out-of-state camper

Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly)

This study introduces a simple generic model, the Generic Pest Forecast System (GPFS), for simulating the relative populations of non-indigenous arthropod pests in space and time. The model was designed to calculate the population index or relative population using hourly weather data as influenced by evelopmental rate, high and low temperature mortalities and wet soil moisture mortality. Each module contains biological parameters derived from controlled experiments. The hourly weather data used for the model inputs were obtained from the National Center of Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) at a 38 km spatial resolution. A combination of spatial and site-specific temporal data was used to validate the GPFS models. The oriental fruit fly, Bactrocera dorsalis (Hendel), was selected as a case study for this research because it is climatically driven and a major pest of fruit production. Results from the GPFS model were compared with field B. dorsalis survey data in three locations: 1) Bangalore, India; 2) Hawaii, USA; and 3) Wuhan, China. The GPFS captured the initial outbreaks and major population peaks of B. dorsalis reasonably well, although agreement varied between sites. An index of agreement test indicated that GPFS model simulations matched with field B. dorsalis observation data with a range between 0.50 and 0.94 (1.0 as a perfect match). Of the three locations, Wuhan showed the highest match between the observed and simulated B. dorsalis populations, with indices of agreement of 0.85. The site-specific temporal comparisons implied that the GPFS model is informative for prediction of relative abundance. Spatial results from the GPFS model were also compared with 161 published observations of B. dorsalis distribution, mostly from East Asia. Since parameters for pupal overwintering and survival were unknown from the literature, these were inferred from the distribution data. The study showed that GPFS has promise for estimating suitable areas for B. dorsalis establishment and potentially other non-indigenous pests. It is concluded that calibrating prediction models with both spatial and sitespecific temporal data may provide more robust and reliable results than validations with either data set alone

Chapter 11 The Role of Surveillance Methods and Technologies in Plant Biosecurity

Countries design biosecurity systems to protect their animal, plant, and environmental resources from invasive alien species. Countries maintain biosecurity systems to safely manage trade and prevent the introduction of invasive pests (insects, diseases and weeds) through numerous pathways of entry. Plant biosecurity programmes seek to exclude exotic organisms from becoming established on agricultural crops, ornamental plants and “natural” areas. Without barriers for entry, invasive organisms can expand their range, colonize new territory and cause considerable economic and environmental damage (Magarey et al. 2009). Spatially, one country’s biosecurity efforts may be categorised as “pre-border”, “border” and “post-border” when describing that country’s attempts at minimising the movement of unwanted organisms. Countries collaborate internationally on a range of interrelated biosecurity activities to confront these exotic invasive species. Surveillance is a key component of that continuum. The International Plant Protection Convention (IPPC) defines surveillance as an official process which collects and records data on pest occurrence or absence by survey, monitoring or other procedures. The diverse purposes of surveillance include: • Promote early detection of pests to facilitate eradication or management; • Support trade by demonstrating areas of pest freedom or low pest prevalence; • Describe the distribution and prevalence of risk organisms already present; • Delimit the full extent of pest population following a detected incursion; • Measure the success of biosecurity systems; • Enable management and cost benefit decisions; • Develop a list of pests or hosts present in an area; • Monitor progress in a pest eradication campaign; • Enable reporting to other organisations. National Plant Protection Organisations (NPPO) and other regulatory agencies conduct different types of survey programmes to fulfil these needs. In addition, these Plant Protection agencies often rely on outreach to passively surveil partners who report pest detections. For example, in New Zealand most new pest detections are reported by industry, researchers, and the public via a toll-free telephone number (Froud et al. 2008). The success of plant protection programmes depends on the ability to detect pests. To conduct a survey, a large number of associated tools and technologies are required (Fig. 11.1). Some of the tools/technology involve statistics, GIS, data management and risk mapping, and will be discussed in this chapter. However, effective surveillance tools and technology are often lacking. When no effective insect trap or lure exists, officials must rely on visual surveys. Detecting plant diseases often presents an even greater challenge. The combination of high costs and inadequate technology leads to survey programmes that are less than optimal. As a result, pests frequently are introduced and become established before timely detection. With delay in discovery of invasive pests, the likelihood of eradication decreases while the cost of control/management/eradication increases dramatically. Figure 11.2 shows the hierarchy of surveillance activities and the flow of information. The flow of information starts at the point of collection in the field. From that point, the information is integrated and tailored to meet the needs of various end-users. For a fruit fly trapping example, regulatory officials collect, clean and compile survey data for managers to use to control fruit fly outbreaks (Chap. 15). For another application, industry collects survey data as part of the day-to-day commercial operations. This data is then used as a basis to run predictive models that can help industry understand the movement of emerging pests or pests of phytosanitary concern (Chap. 9). The same data might also be used by growers or regulatory officials to take action in support of surveillance or eradication. This chapter outlines types of survey operations and provides a review of survey design, information management, data integration, modelling, and GIS. Surveys may be structured around high-consequence target pests. Other surveys may focus on commodities and the survey of exotic pests that may be found associated with that commodity. Still other surveys may target high-risk areas. The USDA, APHIS PPQ Cooperative Agricultural Pest Survey (CAPS) serves as an example of a large surveillance programme that demonstrates various surveillance concepts in practise

Weighted means of intercept (<i>b</i>_•0) and slope (<i>b</i>_•1) from the study-unit regressions described in S2 Table, for each of five types of interaction type and measured impact.

<p>^aCoefficients are for the model <i>RI</i> = <i>b</i>_•0 + <i>b</i>_•1log₁₀(<i>PD</i> + 1), where <i>PD</i> is the phylogenetic distance in Ma from the most strongly affected host.</p><p>^bStandard errors for the weighted means are not available for disease—enemy development category because there was only one study unit (N), but were significant in the original study.</p><p>^cThe 95% confidence intervals are presented for the weighted mean slopes.</p><p>Weighted means of intercept (<i>b</i>_•0) and slope (<i>b</i>_•1) from the study-unit regressions described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0123758#pone.0123758.s004" target="_blank">S2 Table</a>, for each of five types of interaction type and measured impact.</p