NAPPFAST: An Internet System for the Weather-Based Mapping of Plant Pathogens

Potential Distribution of Invasive Boxwood Blight Pathogen (Calonectriapseudonaviculata) as Predicted by Process-Based and Correlative Models

Boxwood blight caused by Cps is an emerging disease that has had devastating impacts on Buxus spp. in the horticultural sector, landscapes, and native ecosystems. In this study, we produced a process-based climatic suitability model in the CLIMEX program and combined outputs of four different correlative modeling algorithms to generate an ensemble correlative model. All models were fit and validated using a presence record dataset comprised of Cps detections across its entire known invaded range. Evaluations of model performance provided validation of good model fit for all models. A consensus map of CLIMEX and ensemble correlative model predictions indicated that not-yet-invaded areas in eastern and southern Europe and in the southeastern, midwestern, and Pacific coast regions of North America are climatically suitable for Cps establishment. Most regions of the world where Buxus and its congeners are native are also at risk of establishment. These findings provide the first insights into Cps global invasion threat, suggesting that this invasive pathogen has the potential to significantly expand its range.

Performance and Profitability of Rain-Based Thresholds for Timing Fungicide Applications in Soybean Rust Control

Soybean rust (SBR), caused by the fungus Phakopsora pachyrhizi, is the most damaging disease of soybean in Brazil. Effective management is achieved by means of calendar-timed sprays of fungicide mixtures, which do not explicitly consider weather-associated disease risk. Two rain-based action thresholds of disease severity values (DSV50 and DSV80) were proposed and compared with two leaf wetness duration-temperature thresholds of daily values of infection probability (DVIP6 and DVIP9) and with a calendar program, with regard to performance and profitability. An unsprayed check treatment plot was included for calculating relative control. Disease severity and yield data were obtained from 29 experiments conducted at six sites across four states in Brazil during the 2012-13, 2014-15, and 2015-16 growing seasons, which represented different growing regions and climatic conditions. The less conservative rainfall action threshold (DSV80) resulted in fewer fungicide sprays compared with the other treatments, and the more conservative one (DSV50) resulted in fewer sprays than the DVIP thresholds. Yield was generally higher with the increase in spray number, but the economic analysis showed no significant differences in the risk of not offsetting the costs of fungicide sprays regardless of the system. Therefore, based on the simplicity and the profitability of the rain-based model, the system is a good candidate for incorporating into the management of SBR in soybean production fields in Brazil.

Thermal favorability for the Oidium caricae and Asperisporium caricae in areas of edaphoclimatic aptitude for the Carica papaya

Objective this study aimed to determine the thermal favorability for the oidium (Oidium caricae) and early blight (Asperisporium caricae) in areas of edaphoclimatic aptitude for the papaya (Carica papaya) in the Espírito Santo state, Brazil. The edaphoclimatic zoning was based on the overlapping of maps that characterize the average annual air temperature obtained by multiple linear regression, annual water deficiency calculated by the Thornthwaite and Matter method (1955) and favorable soil classes to the development of papaya. The results indicated that as regards the edaphoclimatic zoning for the papaya crop it was observed that 71.70% of the area is classified as apt for its development. In relation to agrometeorological favorability for the occurrence of fungal diseases, there was a favorability of 7.64% for the development without restrictions of the oidium and a predominance of 64,56% of favorability with thermal restriction. For the early blight fungus, it was observed that the zones of favorability without restriction correspond to 11.66% of the area apt for the papaya cultivation and that 55.13% of the area has favorability with restriction of humidity. The edaphoclimatic zoning for the papaya crop showed compatibility with the most productive areas of this crop in the state of Espírito Santo. The zoning of thermal favorability for the occurrence of papaya fungal diseases proved to be a valuable tool for studies of plant diseases, allowing the establishment of plans for the allocation of resistant varieties, in order to minimize the risks of loss of crop productivity due to the disease. This methodology presents potential to be used in other areas, cultures and phytopathological diseases.

Assessing Niche Shifts and Conservatism by Comparing the Native and Post-Invasion Niches of Major Forest Invasive Species

Invasive species experience biotic and abiotic conditions that may (or may not) resemble their native environment. We explored the methodology of determining climatic niches and compared the native and post-invasion niches of four invasive forest pests to determine if these species experienced shifts or changes in their new climatic niches. We used environmental principle components analysis (PCA-env) method to quantify climatic niche shifts, expansions, and temporal changes. Furthermore, we assessed the effect of variable selection in the delineation and comparison of niche space. We found that variable selection influenced the delineation and overlap of each niche, whereas the subset of climatic variables selected from the first two PCA-env axes explained more variance in environmental conditions than the complete set of climatic variables for all four species. Most focal species showed climatic niche shifts in their invasive range and had not yet fully occupied the available niche within the invaded range. Our species varied the proportion of niche overlap between the native and invasive ranges. By comparing native and invasive niches, we can help predict a species’ potential range expansion and invasion potential. Our results can guide monitoring and help inform management of these and other invasive species.

Role of Modelling in International Crop Research: Overview and Some Case Studies

Crop modelling has the potential to contribute to global food and nutrition security. This paper briefly examines the history of crop modelling by international crop research centres of the CGIAR (formerly Consultative Group on International Agricultural Research but now known simply as CGIAR), whose primary focus is on less developed countries. Basic principles of crop modelling building up to a Genotype × Environment × Management × Socioeconomic (G × E × M × S) paradigm, are explained. Modelling has contributed to better understanding of crop performance and yield gaps, better prediction of pest and insect outbreaks, and improving the efficiency of crop management including irrigation systems and optimization of planting dates. New developments include, for example, use of remote sensed data and mobile phone technology linked to crop management decision support models, data sharing in the new era of big data, and the use of genomic selection and crop simulation models linked to environmental data to help make crop breeding decisions. Socio-economic applications include foresight analysis of agricultural systems under global change scenarios, and the consequences of potential food system shocks are also described. These approaches are discussed in this paper which also calls for closer collaboration among disciplines in order to better serve the crop research and development communities by providing model based recommendations ranging from policy development at the level of governmental agencies to direct crop management support for resource poor farmers.

Modelling the impacts of pests and diseases on agricultural systems

The improvement and application of pest and disease models to analyse and predict yield losses including those due to climate change is still a challenge for the scientific community. Applied modelling of crop diseases and pests has mostly targeted the development of support capabilities to schedule scouting or pesticide applications. There is a need for research to both broaden the scope and evaluate the capabilities of pest and disease models. Key research questions not only involve the assessment of the potential effects of climate change on known pathosystems, but also on new pathogens which could alter the (still incompletely documented) impacts of pests and diseases on agricultural systems. Yield loss data collected in various current environments may no longer represent a adequate reference to develop tactical, decision-oriented, models for plant diseases and pests and their impacts, because of the ongoing changes in climate patterns. Process-based agricultural simulation modelling, on the other hand, appears to represent a viable methodology to estimate the impacts of these potential effects. A new generation of tools based on state-of-the-art knowledge and technologies is needed to allow systems analysis including key processes and their dynamics over appropriate suitable range of environmental variables. This paper offers a brief overview of the current state of development in coupling pest and disease models to crop models, and discusses technical and scientific challenges. We propose a five-stage roadmap to improve the simulation of the impacts caused by plant diseases and pests; i) improve the quality and availability of data for model inputs; ii) improve the quality and availability of data for model evaluation; iii) improve the integration with crop models; iv) improve the processes for model evaluation; and v) develop a community of plant pest and disease modelers.

Climate Suitability for Magnaporthe oryzae Triticum Pathotype in the United States

Wheat blast, caused by the Triticum pathotype of Magnaporthe oryzae, is an emerging disease considered to be a limiting factor to wheat production in various countries. Given the importance of wheat blast as a high-consequence plant disease, weather-based infection models were used to estimate the probabilities of M. oryzae Triticum establishment and wheat blast outbreaks in the United States. The models identified significant disease risk in some areas. With the threshold levels used, the models predicted that the climate was adequate for maintaining M. oryzae Triticum populations in 40% of winter wheat production areas of the United States. Disease outbreak threshold levels were only reached in 25% of the country. In Louisiana, Mississippi, and Florida, the probability of years suitable for outbreaks was greater than 70%. The models generated in this study should provide the foundation for more advanced models in the future, and the results reported could be used to prioritize research efforts regarding the biology of M. oryzae Triticum and the epidemiology of the wheat blast disease.

Emerging pests and diseases of South-east Asian cassava: a comprehensive evaluation of geographic priorities, management options and research needs

Cassava is a major staple, bio-energy and industrial crop in many parts of the developing world. In Southeast Asia, cassava is grown on >4 million ha by nearly 8 million (small-scale) farming households, under (climatic, biophysical) conditions that often prove unsuitable for many other crops. While SE Asian cassava has been virtually free of phytosanitary constraints for most of its history, a complex of invasive arthropod pests and plant diseases has recently come to affect local crops. We describe results from a region-wide monitoring effort in the 2014 dry season, covering 429 fields across five countries. We present geographic distribution and field-level incidence of the most prominent pest and disease invaders, introduce readily-available management options and research needs. Monitoring work reveals that several exotic mealybug and (red) mite species have effectively colonised SE Asia's main cassava-growing areas, occurring in respectively 70% and 54% of fields, at average field-level incidence of 27 ± 2% and 16 ± 2%. Cassava witches broom (CWB), a systemic phytoplasma disease, was reported from 64% of plots, at incidence levels of 32 ± 2%. Although all main pests and diseases are non-natives, we hypothesise that accelerating intensification of cropping systems, increased climate change and variability, and deficient crop husbandry are aggravating both organism activity and crop susceptibility. Future efforts need to consolidate local capacity to tackle current (and future) pest invaders, boost detection capacity, devise locally-appropriate integrated pest management (IPM) tactics, and transfer key concepts and technologies to SE Asia's cassava growers. Urgent action is needed to mobilise regional as well as international scientific support, to effectively tackle this phytosanitary emergency and thus safeguard the sustainability and profitability of one of Asia's key agricultural commodities. © 2016 Society of Chemical Industry.

Predictive Models for Tomato Spotted Wilt Virus Spread Dynamics, Considering Frankliniella occidentalis Specific Life Processes as Influenced by the Virus

Several models have been studied on predictive epidemics of arthropod vectored plant viruses in an attempt to bring understanding to the complex but specific relationship between the three cornered pathosystem (virus, vector and host plant), as well as their interactions with the environment. A large body of studies mainly focuses on weather based models as management tool for monitoring pests and diseases, with very few incorporating the contribution of vector's life processes in the disease dynamics, which is an essential aspect when mitigating virus incidences in a crop stand. In this study, we hypothesized that the multiplication and spread of tomato spotted wilt virus (TSWV) in a crop stand is strongly related to its influences on Frankliniella occidentalis preferential behavior and life expectancy. Model dynamics of important aspects in disease development within TSWV-F. occidentalis-host plant interactions were developed, focusing on F. occidentalis' life processes as influenced by TSWV. The results show that the influence of TSWV on F. occidentalis preferential behaviour leads to an estimated increase in relative acquisition rate of the virus, and up to 33% increase in transmission rate to healthy plants. Also, increased life expectancy; which relates to improved fitness, is dependent on the virus induced preferential behaviour, consequently promoting multiplication and spread of the virus in a crop stand. The development of vector-based models could further help in elucidating the role of tri-trophic interactions in agricultural disease systems. Use of the model to examine the components of the disease process could also boost our understanding on how specific epidemiological characteristics interact to cause diseases in crops. With this level of understanding we can efficiently develop more precise control strategies for the virus and the vector.

Range-expanding pests and pathogens in a warming world

Crop pests and pathogens (CPPs) present a growing threat to food security and ecosystem management. The interactions between plants and their natural enemies are influenced by environmental conditions and thus global warming and climate change could affect CPP ranges and impact. Observations of changing CPP distributions over the twentieth century suggest that growing agricultural production and trade have been most important in disseminating CPPs, but there is some evidence for a latitudinal bias in range shifts that indicates a global warming signal. Species distribution models using climatic variables as drivers suggest that ranges will shift latitudinally in the future. The rapid spread of the Colorado potato beetle across Eurasia illustrates the importance of evolutionary adaptation, host distribution, and migration patterns in affecting the predictions of climate-based species distribution models. Understanding species range shifts in the framework of ecological niche theory may help to direct future research needs.

Temperature-dependent development, cold tolerance, and potential distribution of Cricotopus lebetis (Diptera: Chironomidae), a tip miner of Hydrilla verticillata (Hydrocharitaceae)

A chironomid midge, Cricotopus lebetis (Sublette) (Diptera: Chironomidae), was discovered attacking the apical meristems of Hydrilla verticillata (L.f. Royle) in Crystal River, Citrus Co., Florida in 1992. The larvae mine the stems of H. verticillata and cause basal branching and stunting of the plant. Temperature-dependent development, cold tolerance, and the potential distribution of the midge were investigated. The results of the temperature-dependent development study showed that optimal temperatures for larval development were between 20 and 30°C, and these data were used to construct a map of the potential number of generations per year of C. lebetis in Florida. Data from the cold tolerance study, in conjunction with historical weather data, were used to generate a predicted distribution of C. lebetis in the United States. A distribution was also predicted using an ecological niche modeling approach by characterizing the climate at locations where C. lebetis is known to occur and then finding other locations with similar climate. The distributions predicted using the two modeling approaches were not significantly different and suggested that much of the southeastern United States was climatically suitable for C. lebetis.

A Coordinated Effort to Manage Soybean Rust in North America: A Success Story in Soybean Disease Monitoring

Existing crop monitoring programs determine the incidence and distribution of plant diseases and pathogens and assess the damage caused within a crop production region. These programs have traditionally used observed or predicted disease and pathogen data and environmental information to prescribe management practices that minimize crop loss. Monitoring programs are especially important for crops with broad geographic distribution or for diseases that can cause rapid and great economic losses. Successful monitoring programs have been developed for several plant diseases, including downy mildew of cucurbits, Fusarium head blight of wheat, potato late blight, and rusts of cereal crops. A recent example of a successful disease-monitoring program for an economically important crop is the soybean rust (SBR) monitoring effort within North America. SBR, caused by the fungus Phakopsora pachyrhizi, was first identified in the continental United States in November 2004. SBR causes moderate to severe yield losses globally. The fungus produces foliar lesions on soybean (Glycine max) and other legume hosts. P. pachyrhizi diverts nutrients from the host to its own growth and reproduction. The lesions also reduce photosynthetic area. Uredinia rupture the host epidermis and diminish stomatal regulation of transpiration to cause tissue desiccation and premature defoliation. Severe soybean yield losses can occur if plants defoliate during the mid-reproductive growth stages. The rapid response to the threat of SBR in North America resulted in an unprecedented amount of information dissemination and the development of a real-time, publicly available monitoring and prediction system known as the Soybean Rust-Pest Information Platform for Extension and Education (SBR-PIPE). The objectives of this article are (i) to highlight the successful response effort to SBR in North America, and (ii) to introduce researchers to the quantity and type of data generated by SBR-PIPE. Data from this system may now be used to answer questions about the biology, ecology, and epidemiology of an important pathogen and disease of soybean.

Bayesian analysis for inference of an emerging epidemic: citrus canker in urban landscapes

Outbreaks of infectious diseases require a rapid response from policy makers. The choice of an adequate level of response relies upon available knowledge of the spatial and temporal parameters governing pathogen spread, affecting, amongst others, the predicted severity of the epidemic. Yet, when a new pathogen is introduced into an alien environment, such information is often lacking or of no use, and epidemiological parameters must be estimated from the first observations of the epidemic. This poses a challenge to epidemiologists: how quickly can the parameters of an emerging disease be estimated? How soon can the future progress of the epidemic be reliably predicted? We investigate these issues using a unique, spatially and temporally resolved dataset for the invasion of a plant disease, Asiatic citrus canker in urban Miami. We use epidemiological models, Bayesian Markov-chain Monte Carlo, and advanced spatial statistical methods to analyse rates and extent of spread of the disease. A rich and complex epidemic behaviour is revealed. The spatial scale of spread is approximately constant over time and can be estimated rapidly with great precision (although the evidence for long-range transmission is inconclusive). In contrast, the rate of infection is characterised by strong monthly fluctuations that we associate with extreme weather events. Uninformed predictions from the early stages of the epidemic, assuming complete ignorance of the future environmental drivers, fail because of the unpredictable variability of the infection rate. Conversely, predictions improve dramatically if we assume prior knowledge of either the main environmental trend, or the main environmental events. A contrast emerges between the high detail attained by modelling in the spatiotemporal description of the epidemic and the bottleneck imposed on epidemic prediction by the limits of meteorological predictability. We argue that identifying such bottlenecks will be a fundamental step in future modelling of weather-driven epidemics.

Combining a climatic niche model of an invasive fungus with its host species distributions to identify risks to natural assets: Puccinia psidii Sensu Lato in Australia

Puccinia psidii sensu lato (s.l.) is an invasive rust fungus threatening a wide range of plant species in the family Myrtaceae. Originating from Central and South America, it has invaded mainland USA and Hawai'i, parts of Asia and Australia. We used CLIMEX to develop a semi-mechanistic global climatic niche model based on new data on the distribution and biology of P. psidii s.l. The model was validated using independent distribution data from recently invaded areas in Australia, China and Japan. We combined this model with distribution data of its potential Myrtaceae host plant species present in Australia to identify areas and ecosystems most at risk. Myrtaceaeous species richness, threatened Myrtaceae and eucalypt plantations within the climatically suitable envelope for P. psidii s.l in Australia were mapped. Globally the model identifies climatically suitable areas for P. psidii s.l. throughout the wet tropics and sub-tropics where moist conditions with moderate temperatures prevail, and also into some cool regions with a mild Mediterranean climate. In Australia, the map of species richness of Myrtaceae within the P. psidii s.l. climatic envelope shows areas where epidemics are hypothetically more likely to be frequent and severe. These hotspots for epidemics are along the eastern coast of New South Wales, including the Sydney Basin, in the Brisbane and Cairns areas in Queensland, and in the coastal region from the south of Bunbury to Esperance in Western Australia. This new climatic niche model for P. psidii s.l. indicates a higher degree of cold tolerance; and hence a potential range that extends into higher altitudes and latitudes than has been indicated previously. The methods demonstrated here provide some insight into the impacts an invasive species might have within its climatically suited range, and can help inform biosecurity policies regarding the management of its spread and protection of valued threatened assets.

Combining inferential and deductive approaches to estimate the potential geographical range of the invasive plant pathogen, Phytophthora ramorum

Phytophthora ramorum, an invasive plant pathogen of unknown origin, causes considerable and widespread damage in plant industries and natural ecosystems of the USA and Europe. Estimating the potential geographical range of P. ramorum has been complicated by a lack of biological and geographical data with which to calibrate climatic models. Previous attempts to do so, using either invaded range data or surrogate species approaches, have delivered varying results. A simulation model was developed using CLIMEX to estimate the global climate suitability patterns for establishment of P. ramorum. Growth requirements and stress response parameters were derived from ecophysiological laboratory observations and site-level transmission and disease factors related to climate data in the field. Geographical distribution data from the USA (California and Oregon) and Norway were reserved from model-fitting and used to validate the models. The model suggests that the invasion of P. ramorum in both North America and Europe is still in its infancy and that it is presently occupying a small fraction of its potential range. Phytophthora ramorum appears to be climatically suited to large areas of Africa, Australasia and South America, where it could cause biodiversity and economic losses in plant industries and natural ecosystems with susceptible hosts if introduced.

Estimation of leaf wetness duration requirements of foliar fungal pathogens with uncertain data-an application to Mycosphaerella nawae

Wetness of the host surface is a critical environmental factor for the development of foliar fungal diseases, but it is difficult to estimate the wetness durations required by pathogens for infection when only few experimental data are available. In this paper, we propose a method to estimate wetness duration requirements of foliar fungal pathogens when precise experimental data are not available. The proposed method is based on approximate Bayesian computation. It only requires lower and upper bounds of wetness duration requirements for one or fewer temperatures. We describe the method, show how to apply it to an infection model, and then present a case study on Mycosphaerella nawae, the causal agent of circular leaf spot of persimmon. In this example, the parameters of a simple infection model were estimated using experimental data found in the literature for the pathogen, and the model was applied to assess the risk in a Spanish area recently affected by the disease. The results showed that the probability of successful infection was higher than 0.5 for 32% of the on-site wetness durations recorded in the affected area. Results obtained with simulated data showed that our method was able to improve the estimation of wetness duration requirement. Given the flexibility of the proposed method, we expect it to become adopted for assessing the risk of introduction of exotic fungal plant pathogens.

Effects of the training dataset characteristics on the performance of nine species distribution models: application to Diabrotica virgifera virgifera

Many distribution models developed to predict the presence/absence of invasive alien species need to be fitted to a training dataset before practical use. The training dataset is characterized by the number of recorded presences/absences and by their geographical locations. The aim of this paper is to study the effect of the training dataset characteristics on model performance and to compare the relative importance of three factors influencing model predictive capability; size of training dataset, stage of the biological invasion, and choice of input variables. Nine models were assessed for their ability to predict the distribution of the western corn rootworm, Diabrotica virgifera virgifera, a major pest of corn in North America that has recently invaded Europe. Twenty-six training datasets of various sizes (from 10 to 428 presence records) corresponding to two different stages of invasion (1955 and 1980) and three sets of input bioclimatic variables (19 variables, six variables selected using information on insect biology, and three linear combinations of 19 variables derived from Principal Component Analysis) were considered. The models were fitted to each training dataset in turn and their performance was assessed using independent data from North America and Europe. The models were ranked according to the area under the Receiver Operating Characteristic curve and the likelihood ratio. Model performance was highly sensitive to the geographical area used for calibration; most of the models performed poorly when fitted to a restricted area corresponding to an early stage of the invasion. Our results also showed that Principal Component Analysis was useful in reducing the number of model input variables for the models that performed poorly with 19 input variables. DOMAIN, Environmental Distance, MAXENT, and Envelope Score were the most accurate models but all the models tested in this study led to a substantial rate of mis-classification.

Robustness of risk maps and survey networks to knowledge gaps about a new invasive pest

In pest risk assessment it is frequently necessary to make management decisions regarding emerging threats under severe uncertainty. Although risk maps provide useful decision support for invasive alien species, they rarely address knowledge gaps associated with the underlying risk model or how they may change the risk estimates. Failure to recognize uncertainty leads to risk-ignorant decisions and miscalculation of expected impacts as well as the costs required to minimize these impacts. Here we use the information gap concept to evaluate the robustness of risk maps to uncertainties in key assumptions about an invading organism. We generate risk maps with a spatial model of invasion that simulates potential entries of an invasive pest via international marine shipments, their spread through a landscape, and establishment on a susceptible host. In particular, we focus on the question of how much uncertainty in risk model assumptions can be tolerated before the risk map loses its value. We outline this approach with an example of a forest pest recently detected in North America, Sirex noctilio Fabricius. The results provide a spatial representation of the robustness of predictions of S. noctilio invasion risk to uncertainty and show major geographic hotspots where the consideration of uncertainty in model parameters may change management decisions about a new invasive pest. We then illustrate how the dependency between the extent of uncertainties and the degree of robustness of a risk map can be used to select a surveillance network design that is most robust to knowledge gaps about the pest.

Effect of temperature and moisture period on infection of Rhododendron 'Cunningham's White' by Phytophthora ramorum

We investigated the temperature and moisture conditions that allow Phytophthora ramorum to infect Rhododendron 'Cunningham's White'. Most experiments were performed with a single P. ramorum isolate from the NA1 clonal lineage. For whole plants incubated in dew chambers at 10 to 31 degrees C, the greatest proportion of diseased leaves, 77.5%, occurred at the optimum temperature of 20.5 degrees C. Disease occurred over the entire range of temperatures tested, although amounts of disease were minor at the temperature extremes. For whole plants exposed to varying dew periods at 20 degrees C and then incubated at 20 degrees C for 7 days, a dew period as short as 1 h resulted in a small amount of disease; however, at least 4 h of dew were required for >10% of the leaves to become diseased. Moisture periods of 24 and 48 h resulted in the greatest number of diseased leaves. In detached-leaf, temperature-gradient-plate experiments, incubation at 22 degrees C resulted in the greatest disease severity, followed by 18 degrees C and then 14 degrees C. In detached-leaf, moisture-tent experiments, a 1-h moisture period was sufficient to cause disease on 67 to 73% of leaves incubated for 7 days at 20 degrees C. A statistical model for disease development that combined the effects of temperature and moisture period was generated using nonlinear regression. Our results define temperature and moisture conditions which allow infection by P. ramorum on Cunningham's White rhododendron, and show that P. ramorum is able to infect this host over a wide range of temperatures and moisture levels. The results indicate that P. ramorum has the potential to become established in parts of the United States that are outside its current range.

Susceptibility to Phytophthora ramorum and Inoculum Production Potential of Some Common Eastern Forest Understory Plant Species

Twenty-five plant species (21 genera, 14 families), which comprise a portion of the understory in forests of the Eastern United States, were evaluated for susceptibility to infection by Phytophthora ramorum. The degree to which P. ramorum is able to form sporangia and chlamydospores was also assessed on these hosts. Seedlings were spray-inoculated with a mixture (4,000 sporangia/ml) of four P. ramorum isolates followed by incubation in a dew chamber at 20°C in darkness for 5 days. Percent infection on individual leaves/leaflets was assessed visually. Mean percent leaf area infected ranged from 0.7% for Smilax rotundifolia to 93.8% for Kalmia latifolia. Eight plant species tested developed significantly larger lesion areas than those found on susceptible control Rhododendron 'Cunningham's White'. Fourteen species in addition to the susceptible control exhibited infection of over 90% of their leaves. Sporangia production by P. ramorum varied considerably among plant species, ranging from 36 per cm² lesion area on Myrica pennsylvannica to 2,001 per cm² lesion area on Robinia pseudoacacia. Numbers of chlamydospores produced per 6-mm-diameter leaf disk incubated in a P. ramorum sporangia suspension ranged from 25 on Ilex verticillata to 493 on Rhus typhina. The results indicate that many common understory species in Eastern U.S. forests are susceptible to P. ramorum and capable of providing ample sources of inoculum (sporangia and chlamydospores) for forest epidemics should the pathogen be introduced and should temperature and moisture conditions exist that are conducive to disease development.

Winter Survival of the Soybean Rust Pathogen, Phakopsora pachyrhizi, in Florida

Soybean rust (SBR) survival and host availability (kudzu, Pueraria spp.) were assessed from November 2006 through April 2007 at six sites from the panhandle to southwest Florida. Micro loggers recorded both temperature and relative humidity hourly at each location. Periods of drought and cumulative hours below 0°C correlated with kudzu defoliation. Inoculum potential from detached kudzu leaves was evaluated in vitro under various temperature and relative humidity levels. Kudzu leaves with SBR kept at 4°C produced viable urediniospores with the highest germination at all moisture levels over time. Freezing temperatures (-4 and -20°C) drastically reduced spore germination. However, when leaves were incubated at low (<35%) relative humidity, inoculum potential was prolonged. Results from this study demonstrate that both temperature and relative humidity impact P. pachyrhizi in the field and in vitro, and that detached kudzu leaves have the potential to serve as an inoculum source in kudzu stands.

Spatio-temporal analysis of Xyleborus glabratus (Coleoptera: Curculionidae [corrected] Scolytinae) invasion in eastern U.S. forests

The non-native redbay ambrosia beetle, Xyleborus glabratus Eichhoff (Coleoptera: Curculionidae: Scolytinae), has recently emerged as a significant pest of southeastern U.S. coastal forests. Specifically, a fungal symbiont (Raffaelea sp.) of X. glabratus has caused mortality of redbay (Persea borbonia) and sassafras (Sassafras albidum) trees in the region; several other Lauraceae species also seem susceptible. Although the range of X. glabratus continues to expand rapidly, little is known about the species' biology and behavior. In turn, there has been no broad-scale assessment of the threat it poses to eastern U.S. forests. To provide a basic information framework, we performed analyses exploiting relevant spatio-temporal data available for X. glabratus. First, we mapped the densities of redbay and sassafras from forest inventory data. Second, we used climate matching to delineate potential geographic limits for X. glabratus. Third, we used county infestation data to estimate the rate of spread and modeled spread through time, incorporating host density as a weighting factor. Our results suggest that (1) key areas with high concentrations of redbay have yet to be invaded, but some are immediately threatened; (2) climatic conditions may serve to constrain X. glabratus to the southeastern U.S. coastal region; and (3) if unchecked, X. glabratus may spread throughout the range of redbay in <40 yr. Disruption of anthropogenic, long-distance dispersal could reduce the likelihood of this outcome.