Cryptic Infection and Systemic Colonization of Leguminous Crops by Verticillium dahliae, the Cause of Verticillium Wilt.

Verticillium dahliae, the cause of Verticillium wilt, is a widespread pathogen that affects many crops in California and throughout the world. Cover cropping with leguminous species is often integrated into a rotation scheme for its contribution to soil nitrogen, and can contribute to management of Verticillium wilt provided the chosen crop does not support development of V. dahliae. Seven cool season legumes (faba bean, bell bean, field pea, hairy vetch, common vetch, purple vetch, and woollypod vetch), and three warm season legumes (sesbania, sunn hemp, and black-eyed pea) were evaluated as hosts for reproductive growth of V. dahliae. All 10 legumes were colonized by V. dahliae, while remaining symptomless, when subjected to a root-dip inoculation. Similar results were obtained when plants were grown in infested potting soil, albeit with a lower frequency of infection than in root-dip assays. All tested legumes were also infected in field trials, with the exception of bell bean. Overall, warm season legumes sustained higher rates of infection than cool season legumes. Common vetch was the most extensively colonized of the cool season legumes. Based on the results of this study, legumes may not be an appropriate rotation crop in fields where Verticillium wilt is a problem.

Summary Measures of Predictive Power Associated with Logistic Regression Models of Disease Risk.

For an ordinary least-squares regression model, the coefficient of determination (R2) describes the proportion (or percentage) of variance of the response variable explained by the model, and is a widely accepted summary measure of predictive power. A number of R2-analogues are available as summary measures of predictive power associated with logistic regression models, including models of disease risk. Tjur's R2 and McFadden's R2 are of particular interest in this context. Both of these metrics have transparent derivations, which reveal that they apply to different aspects of model evaluation. Tjur's R2 is a measure of separation between (known) actual states (e.g., gold standard determinations of "healthy" or "diseased" status) whereas McFadden's R2 is a measure of separation between predicted states (e.g., forecasts of disease status based on models of disease risk). This clarifies their interpretation in the context of evaluation of logistic regression models of disease risk. In addition, versions of both Tjur's R2 and McFadden's R2 may be obtained from analyses of disease risk that are not preceded by logistic regression analysis. Tjur's R2 and McFadden's R2 are shown to be useful, distinct summary measures of predictive power for epidemiological models of disease risk.

A White Paper on Global Wheat Health Based on Scenario Development and Analysis.

Scenario analysis constitutes a useful approach to synthesize knowledge and derive hypotheses in the case of complex systems that are documented with mainly qualitative or very diverse information. In this article, a framework for scenario analysis is designed and then, applied to global wheat health within a timeframe from today to 2050. Scenario analysis entails the choice of settings, the definition of scenarios of change, and the analysis of outcomes of these scenarios in the chosen settings. Three idealized agrosystems, representing a large fraction of the global diversity of wheat-based agrosystems, are considered, which represent the settings of the analysis. Several components of global changes are considered in their consequences on global wheat health: climate change and climate variability, nitrogen fertilizer use, tillage, crop rotation, pesticide use, and the deployment of host plant resistances. Each idealized agrosystem is associated with a scenario of change that considers first, a production situation and its dynamics, and second, the impacts of the evolving production situation on the evolution of crop health. Crop health is represented by six functional groups of wheat pathogens: the pathogens associated with Fusarium head blight; biotrophic fungi, Septoria-like fungi, necrotrophic fungi, soilborne pathogens, and insect-transmitted viruses. The analysis of scenario outcomes is conducted along a risk-analytical pattern, which involves risk probabilities represented by categorized probability levels of disease epidemics, and risk magnitudes represented by categorized levels of crop losses resulting from these levels of epidemics within each production situation. The results from this scenario analysis suggest an overall increase of risk probabilities and magnitudes in the three idealized agrosystems. Changes in risk probability or magnitude however vary with the agrosystem and the functional groups of pathogens. We discuss the effects of global changes on the six functional groups, in terms of their epidemiology and of the crop losses they cause. Scenario analysis enables qualitative analysis of complex systems, such as plant pathosystems that are evolving in response to global changes, including climate change and technology shifts. It also provides a useful framework for quantitative simulation modeling analysis for plant disease epidemiology.

Resolution of Probabilistic Weather Forecasts with Application in Disease Management.

Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.

Spatiotemporal Patterns in the Airborne Dispersal of Spinach Downy Mildew.

Downy mildew is the most devastating disease threatening sustainable spinach production, particularly in the organic sector. The disease is caused by the biotrophic oomycete pathogen Peronospora effusa, and the disease results in yellow lesions that render the crop unmarketable. In this study, the levels of DNA from airborne spores of P. effusa were assessed near a field of susceptible plants in Salinas, CA during the winter months of 2013-14 and 2014/15 using rotating-arm impaction spore-trap samplers that were assessed with a species-specific quantitative polymerase chain reaction (qPCR) assay. Low levels of P. effusa DNA were detectable from December through February in both winters but increased during January in both years, in correlation with observed disease incidence; sharp peaks in P. effusa DNA detection were associated with the onset of disease incidence. The incidence of downy mildew in the susceptible field displayed logistic-like dynamics but with considerable interseason variation. Analysis of the area under the disease progress curves suggested that the 2013-14 epidemic was significantly more severe than the 2014-15 epidemic. Spatial analyses indicated that disease incidence was dependent within an average range of 5.6 m, approximately equivalent to the width of three planted beds in a typical production field. The spatial distribution of spores captured during an active epidemic most closely fit a power-law distribution but could also be fit with an exponential distribution. These studies revealed two important results in the epidemiology of spinach downy mildew in California. First, they demonstrated the potential of impaction spore-trap samplers linked with a qPCR assay for indicating periods of high disease risk, as well as the detection of long-distance dispersal of P. effusa spores. Second, at the scale of individual crops, a high degree of spatial aggregation in disease incidence was revealed.

A Synoptic Analysis of the Temporal and Spatial Aspects of Grapevine Leafroll Disease in a Historic Napa Vineyard and Experimental Vine Blocks.

Five Grapevine leafroll-associated virus 3 (GLRaV-3) epidemics were analyzed utilizing a standardized approach to robustly characterize the temporal and spatial parameters. Published data included in the analysis are from Spain, New Zealand, and Napa Valley, CA together with new data from a historic vineyard in Napa Valley, CA. Linear regression analyses of logit-transformed incidence data indicated a maximum average increase of 11% per year in disease incidence, with considerable variation among locations. Spatial analyses, including distribution fitting, examination of the effective sample size, and evaluation of the parameters of the binary power law fitted to variance data for disease incidence, indicated a high degree of consistency among the data sets. In all cases, except at very low disease incidence, a high degree of spatial aggregation was noted, with evidence that the degree of aggregation varied as a function of mean disease incidence. The polyetic dynamics of disease follow a logistic-like pattern over multiple seasons, consistent with limitation by inoculum availability (infected vines) at low incidence and limitation by disease-free vines at high incidence.

Season-Long Dynamics of Spinach Downy Mildew Determined by Spore Trapping and Disease Incidence.

Peronospora effusa is an obligate oomycete that causes downy mildew of spinach. Downy mildew threatens sustainable production of fresh market organic spinach in California, and routine fungicide sprays are often necessary for conventional production. In this study, airborne P. effusa spores were collected using rotating arm impaction spore trap samplers at four sites in the Salinas Valley between late January and early June in 2013 and 2014. Levels of P. effusa DNA were determined by a species-specific quantitative polymerase chain reaction assay. Peronospora effusa was detected prior to and during the growing season in both years. Nonlinear time series analyses on the data suggested that the within-season dynamics of P. effusa airborne inoculum are characterized by a mixture of chaotic, deterministic, and stochastic features, with successive data points somewhat predictable from the previous values in the series. Analyses of concentrations of airborne P. effusa suggest both an exponential increase in concentration over the course of the season and oscillations around the increasing average value that had season-specific periodicity around 30, 45, and 75 days, values that are close to whole multiples of the combined pathogen latent and infectious periods. Each unit increase in temperature was correlated with 1.7 to 6% increased odds of an increase in DNA copy numbers, while each unit decrease in wind speed was correlated with 4 to 12.7% increased odds of an increase in DNA copy numbers. Disease incidence was correlated with airborne P. effusa levels and weather variables, and a receiver operating characteristic curve analysis suggested that P. effusa DNA copy numbers determined from the spore traps nine days prior to disease rating could predict disease incidence.

Detection of Phytophthora ramorum in Nurseries and Forest Lands in California in 2004 to 2009.

From December 2004 through May 2009, samples were collected from California nurseries and wild lands to survey for Phytophthora ramorum and comply with federal regulations of nursery stock. Samples were prescreened by an enzyme-linked immunosorbent assay (ELISA) that detects Phytophthora spp. and tested by culture, P. ramorum-specific real-time polymerase chain reaction (PCR), and nested PCR. Yearly percentages of infected samples ranged from 0.6 to 2.3%. Camellia spp., Rhododendron spp., Magnolia spp., Pieris spp., and Laurus nobilis tested positive the most frequently in the nurseries and Lithocarpus densiflorus, Umbellularia californica, and Quercus agrifolia tested positive most often from wild lands. Of the 118,410 samples isolated onto PARP media, 0.8% was identified as P. ramorum. Of 115,056 samples tested by ELISA, 5.9% tested positive for Phytophthora spp. Of the 6,520 samples tested by PCR, 12.4% tested positive for P. ramorum. The false-negative, positive, and internal control failure rates of the assays are discussed. After removing the seasonal effect of sampling strategy, isolation of the pathogen into culture was found to be seasonally dependent whereas detectability by PCR and ELISA was not. To our knowledge, this is the first evaluation of a regulatory testing program for a plant pathogen on this scale using standardized assays.

The Evolution of a Process for Selecting and Prioritizing Plant Diseases for Recovery Plans.

One element of the cost of dealing with invasive species in the United States is the recovery from the arrival of exotic plant pathogens. We review the development of a process used to prioritize plant diseases for the federally mandated United State Department of Agriculture National Plant Disease Recovery System. A team of university, government, and industry scientists worked together over a 10-year period to develop a science-based objective approach to the challenge of effectively preparing for recovery plans from introduced pathogens, when the timing of the introduction of any single disease is unknown. Over time, the process transitioned from ad hoc, in which recovery plans were written when the relevant experts were able to do so, to a formally organized group-prioritization effort from which emerged the concept of generic recovery plan templates for groups of pathogens and diseases that have similar biological characteristics, and therefore, similar management approaches. Key characteristics for each template were determined through a multivariate analysis for 14 plant diseases for which a recovery plan already existed. The process was validated by a larger group of 15 plant pathologists, for which results were compared with those scored by 14 subject matter experts.

Information graphs for binary predictors.

Binary predictors are used in a wide range of crop protection decision-making applications. Such predictors provide a simple analytical apparatus for the formulation of evidence related to risk factors, for use in the process of Bayesian updating of probabilities of crop disease. For diagrammatic interpretation of diagnostic probabilities, the receiver operating characteristic is available. Here, we view binary predictors from the perspective of diagnostic information. After a brief introduction to the basic information theoretic concepts of entropy and expected mutual information, we use an example data set to provide diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating, and specific information. Our information graphs also illustrate correspondences between diagnostic information and diagnostic probabilities.

Information graphs for binary predictors.

Binary predictors are used in a wide range of crop protection decision making applications. Such predictors provide a simple analytical apparatus for the formulation of evidence related to risk factors, for use in the process of Bayesian updating of probabilities of crop disease. For diagrammatic interpretation of diagnostic probabilities, the receiver operating characteristic is available. Here, we view binary predictors from the perspective of diagnostic information. After a brief introduction to the basic information theoretic concepts of entropy and expected mutual information, we use an example data set to provide diagrammatic interpretations of expected mutual information, relative entropy, information inaccuracy, information updating and specific information. Our information graphs also illustrate correspondences between diagnostic information and diagnostic probabilities.

Perceptions of disease risk: from social construction of subjective judgments to rational decision making.

Many factors influence how people form risk perceptions. Farmers' perceptions of risk and levels of risk aversion impact on decision-making about such things as technology adoption and disease management practices. Irrespective of the underlying factors that affect risk perceptions, those perceptions can be summarized by variables capturing impact and uncertainty components of risk. We discuss a new framework that has the subjective probability of disease and the cost of decision errors as its central features, which might allow a better integration of social science and epidemiology, to the benefit of plant disease management. By focusing on the probability and cost (or impact) dimensions of risk, the framework integrates research from the social sciences, economics, decision theory, and epidemiology. In particular, we review some useful properties of expected regret and skill value, two measures of expected cost that are particularly useful in the evaluation of decision tools. We highlight decision-theoretic constraints on the usefulness of decision tools that may partly explain cases of failure of adoption. We extend this analysis by considering information-theoretic criteria that link model complexity and relative performance and which might explain why users reject forecasters that impose even moderate increases in the complexity of decision making despite improvements in performance or accept very simple decision tools that have relatively poor performance.

Risk factors for crop health under global change and agricultural shifts: a framework of analyses using rice in tropical and subtropical Asia as a model.

Plant disease epidemiology requires expansion of its current methodological and theoretical underpinnings in order to produce full contributions to global food security and global changes. Here, we outline a framework which we applied to farmers' field survey data set on rice diseases in the tropical and subtropical lowlands of Asia. Crop health risks arise from individual diseases, as well as their combinations in syndromes. Four key drivers of agricultural change were examined: labor, water, fertilizer, and land availability that translate into crop establishment method, water shortage, fertilizer input, and fallow period duration, respectively, as well as their combinations in production situations. Various statistical approaches, within a hierarchical structure, proceeding from higher levels of hierarchy (production situations and disease syndromes) to lower ones (individual components of production situations and individual diseases) were used. These analyses showed that (i) production situations, as wholes, represent very large risk factors (positive or negative) for occurrence of disease syndromes; (ii) production situations are strong risk factors for individual diseases; (iii) drivers of agricultural change represent strong risk factors of disease syndromes; and (iv) drivers of change, taken individually, represent small but significant risk factors for individual diseases. The latter analysis indicates that different diseases are positively or negatively associated with shifts in these drivers. We also report scenario analyses, in which drivers of agricultural change are varied in response to possible climate and global changes, generating predictions of shifts in rice health risks. The overall set of analyses emphasizes the need for large-scale ground data to define research priorities for plant protection in rapidly evolving contexts. They illustrate how a structured theoretical framework can be used to analyze emergent features of agronomic and socioecological systems. We suggest that the concept of "disease syndrome" can be borrowed in botanical epidemiology from public health to emphasize a holistic view of disease in shifting production situations in combination with the conventional, individual disease-centered perspective.

The basic reproduction number of plant pathogens: matrix approaches to complex dynamics.

The basic reproduction number, R0, is defined as the total number of infections arising from one newly infected individual introduced into a healthy (disease-free) host population. R0 is widely used in ecology and animal and human epidemiology, but has received far less attention in the plant pathology literature. Although the calculation of R0 in simple systems is straightforward, the calculation in complex situations is challenging. A very generic framework exists in the mathematical and biomathematical literature, which is difficult to interpret and apply in specific cases. In this paper we describe a special case of this general framework involving the use of matrix population models. Leading by example, we explain the existing mathematical literature on this subject in such a way that plant pathologists can apply the method for a wide range of pathosystems.

Relationships between disease incidence at two levels in a spatial hierarchy.

ABSTRACT Relationships between disease incidence measured at two levels in a spatial hierarchy are derived. These relationships are based on the properties of the binomial distribution, the beta-binomial distribution, and an empirical power-law relationship that relates observed variance to theoretical binomial variance of disease incidence. Data sets for demonstrating and testing these relationships are based on observations of the incidence of grape downy mildew, citrus tristeza, and citrus scab. Disease incidence at the higher of the two scales is shown to be an asymptotic function of incidence at the lower scale, the degree of aggregation at that scale, and the size of the sampling unit. For a random pattern, the relationship between incidence measured at two spatial scales does not depend on any unknown parameters. In that case, an equation for estimating an approximate variance of disease incidence at the lower of the two scales from incidence measurements made at the higher scale is derived for use in the context of sampling. It is further shown that the effect of aggregation of incidence at the lower of the two scales is to reduce the rate of increase of disease incidence at the higher scale.