Early detection surveillance for an emerging plant pathogen: a rule of thumb to predict prevalence at first discovery.

Emerging plant pathogens are a significant problem for conservation and food security. Surveillance is often instigated in an attempt to detect an invading epidemic before it gets out of control. Yet in practice many epidemics are not discovered until already at a high prevalence, partly due to a lack of quantitative understanding of how surveillance effort and the dynamics of an invading epidemic relate. We test a simple rule of thumb to determine, for a surveillance programme taking a fixed number of samples at regular intervals, the distribution of the prevalence an epidemic will have reached on first discovery (discovery-prevalence) and its expectation E(q*). We show that E(q*) = r/(N/Δ), i.e. simply the rate of epidemic growth divided by the rate of sampling; where r is the epidemic growth rate, N is the sample size and Δ is the time between sampling rounds. We demonstrate the robustness of this rule of thumb using spatio-temporal epidemic models as well as data from real epidemics. Our work supports the view that, for the purposes of early detection surveillance, simple models can provide useful insights in apparently complex systems. The insight can inform decisions on surveillance resource allocation in plant health and has potential applicability to invasive species generally.

Postharvest Quarantine Treatments for Diaphorina citri on Infested Curry Leaves.

Studies were conducted to evaluate treatments that reduce survival and attachment of Diaphorina citri nymphs on infested curry leaves (Bergera koenigii). Decontamination of curry leaves infested with D. citri in relation to disinfectant (none or Pro-San), temperature (0, 40, and 50°C), and treatment duration (0, 5, 10, and 20 min) was examined using a split-split plot design. Experiments were performed three times. Treatment duration did not significantly affect D. citri nymph survival or removal (P > 0.2). Temperature and disinfectant each significantly affected D. citri nymph survival and removal (P < 0.031). The interaction of temperature and disinfectant was significant with respect to nymph survival (P < 0.0001) but did not significantly affect removal (P = 0.4589). Tissue damage was significantly affected by temperature (P = 0.0056), duration (P = 0.0023), the interaction of temperature and duration (P = 0.0320), and the interaction of disinfectant, temperature, and duration (P = 0.0410). Of the treatments resulting in 100% D. citri nymph mortality on infested curry leaves, 40°C for 5 min with Pro-San was accompanied with the least proportion of curry leaf tissue damage (0.14 greater than untreated control, P = 0.25). Results from these studies may be useful in formulation of future regulatory policies regarding trade of citrus foliage, especially those used as condiments.

A generic risk-based surveying method for invading plant pathogens.

Invasive plant pathogens are increasing with international trade and travel, with damaging environmental and economic consequences. Recent examples include tree diseases such as sudden oak death in the Western United States and ash dieback in Europe. To control an invading pathogen it is crucial that newly infected sites are quickly detected so that measures can be implemented to control the epidemic. However, since sampling resources are often limited, not all locations can be inspected and locations must be prioritized for surveying. Existing approaches to achieve this are often species specific and rely on detailed data collection and parameterization, which is difficult, especially when new arrivals are unanticipated. Consequently regulatory sampling responses are often ad hoc and developed without due consideration of epidemiology, leading to the suboptimal deployment of expensive sampling resources. We introduce a flexible risk-based sampling method that is pathogen generic and enables available information to be utilized to develop epidemiologically informed sampling programs for virtually any biologically relevant plant pathogen. By targeting risk we aim to inform sampling schemes that identify high-impact locations that can be subsequently treated in order to reduce inoculum in the landscape. This "damage limitation" is often the initial management objective following the first discovery of a new invader. Risk at each location is determined by the product of the basic reproductive number (R0), as a measure of local epidemic size, and the probability of infection. We illustrate how the risk estimates can be used to prioritize a survey by weighting a random sample so that the highest-risk locations have the highest probability of selection. We demonstrate and test the method using a high-quality spatially and temporally resolved data set on Huanglongbing disease (HLB) in Florida, USA. We show that even when available epidemiological information is relatively minimal, the method has strong predictive value and can result in highly effective targeted surveying plans.

Presymptomatic Fibrous Root Decline in Citrus Trees Caused by Huanglongbing and Potential Interaction with Phytophthora spp.

Huanglongbing (HLB), associated with 'Candidatus Liberibacter asiaticus', was first detected in Florida in late 2005 and is now widely distributed throughout the commercial citrus-growing regions. In recent seasons, concurrent with freeze and drought episodes, symptomatic HLB-infected trees were much more affected by the extremes of temperature and moisture than trees without HLB. Symptoms exhibited by the stressed trees were excessive leaf loss and premature fruit drop even when HLB-infected trees were managed with good nutritional and irrigation practices recommended to support health of HLB-affected trees. This stress intolerance may be due to a loss of fibrous roots. To assess root status of HLB-infected trees on 'Swingle' citrumelo rootstock (Citrus paradisi × Poncirus trifoliata), blocks of 2,307 3-year-old 'Hamlin' orange trees and 2,693 4-year-old 'Valencia' orange trees were surveyed visually and with a real-time polymerase chain reaction (PCR) assay to determine 'Ca. L. asiaticus' infection status. The incidence of 'Ca. L. asiaticus'-infected trees (presymptomatic: 'Ca. L. asiaticus'+, visually negative; and symptomatic: 'Ca. L. asiaticus'+, visually positive) trees was 89% for the Hamlin block and 88% for the Valencia block. 'Ca. L. asiaticus'+ trees had 30 and 37% lower fibrous root mass density for presymptomatic and symptomatic trees, respectively, compared with 'Ca. L. asiaticus'- trees. In a second survey, 10- to 25-year-old Valencia trees on Swingle citrumelo or 'Carrizo' citrange (C. sinensis (L.) × P. trifoliata) rootstock were sampled within 3 to 6 months after identification of visual HLB status as symptomatic ('Ca. L. asiaticus'+, visually positive) or nonsymptomatic ('Ca. L. asiaticus'-, visually negative) in orchards located in the central ridge, south-central, and southwest flatwoods. Pairs of HLB symptomatic and nonsymptomatic trees were evaluated for PCR status, fibrous root mass density, and Phytophthora nicotianae propagules in the rhizosphere soil. 'Ca. L. asiaticus'+ trees had 27 to 40% lower fibrous root mass density and, in one location, higher P. nicotianae per root but Phytophthora populations per cubic centimeter of soil were high on both 'Ca. L. asiaticus'+ and 'Ca. L. asiaticus'- trees. Fibrous root loss from HLB damage interacted with P. nicotianae depending on orchard location and time of year.

An improved regulatory sampling method for mapping and representing plant disease from a limited number of samples.

A key challenge for plant pathologists is to develop efficient methods to describe spatial patterns of disease spread accurately from a limited number of samples. Knowledge of disease spread is essential for informing and justifying plant disease management measures. A mechanistic modelling approach is adopted for disease mapping which is based on disease dispersal gradients and consideration of host pattern. The method is extended to provide measures of uncertainty for the estimates of disease at each host location. In addition, improvements have been made to increase computational efficiency by better initialising the disease status of unsampled hosts and speeding up the optimisation process of the model parameters. These improvements facilitate the practical use of the method by providing information on: (a) mechanisms of pathogen dispersal, (b) distance and pattern of disease spread, and (c) prediction of infection probabilities for unsampled hosts. Two data sets of disease observations, Huanglongbing (HLB) of citrus and strawberry powdery mildew, were used to evaluate the performance of the new method for disease mapping. The result showed that our method gave better estimates of precision for unsampled hosts, compared to both the original method and spatial interpolation. This enables decision makers to understand the spatial aspects of disease processes, and thus formulate regulatory actions accordingly to enhance disease control.

Estimating the incidence of an epidemic when it is first discovered and the design of early detection monitoring.

The early detection of an invading epidemic is crucial for successful disease control. Although models have been used extensively to test control strategies following the first detection of an epidemic, few studies have addressed the issue of how to achieve early detection in the first place. Moreover, sampling theory has made great progress in understanding how to estimate the incidence or spatial distribution of an epidemic but how to sample for early detection has been largely ignored. Using a simple epidemic model we demonstrate a method to calculate the incidence of an epidemic when it is discovered for the first time (given a monitoring programme taking samples at regular intervals). We use the method to explore how the intensity and frequency of sampling influences early detection. In particular, we find that for epidemics characterised by high population growth rates it is most effective to spread sampling resources evenly in time. In addition we derive a useful approximation to our method which results in a simple equation capturing the relation between monitoring and epidemic dynamics. Not only does this provide valuable new insight but it provides a simple rule of thumb for the design of monitoring programmes in practice.

Transmission of Citrus leprosis virus C by Brevipalpus phoenicis (Geijskes) to Alternative Host Plants Found in Citrus Orchards.

The equivalent of US$75 million is spent each year in Brazil to control Brevipalpus phoenicis, a mite vector of Citrus leprosis virus C (CiLV-C). In this study, we investigated the possibility that hedgerows and windbreaks normally found in citrus orchards could host CiLV-C. Mites confined by an adhesive barrier were reared on sweet orange fruit with leprosis symptoms then were transferred to leaves of Hibiscus rosa-sinensis, Malvaviscus arboreus, Grevilea robusta, Bixa orellana, and Citrus sinensis. Ninety days post infestation, the descendant mites were transferred to Pera sweet orange plants to verify the transmissibility of the virus back to citrus. Nonviruliferous mites which had no feeding access to diseased tissue were used as controls. Local chlorotic or necrotic spots and ringspots, symptoms of leprosis disease, appeared in most plants tested. Results generated by reversetranscription polymerase chain reaction with primers specific for CiLV-C and by electron microscope analyses confirmed the susceptibility of these plants to CiLV-C.

A stochastic optimization method to estimate the spatial distribution of a pathogen from a sample.

Information on the spatial distribution of plant disease can be utilized to implement efficient and spatially targeted disease management interventions. We present a pathogen-generic method to estimate the spatial distribution of a plant pathogen using a stochastic optimization process which is epidemiologically motivated. Based on an initial sample, the method simulates the individual spread processes of a pathogen between patches of host to generate optimized spatial distribution maps. The method was tested on data sets of Huanglongbing of citrus and was compared with a kriging method from the field of geostatistics using the well-established kappa statistic to quantify map accuracy. Our method produced accurate maps of disease distribution with kappa values as high as 0.46 and was able to outperform the kriging method across a range of sample sizes based on the kappa statistic. As expected, map accuracy improved with sample size but there was a high amount of variation between different random sample placements (i.e., the spatial distribution of samples). This highlights the importance of sample placement on the ability to estimate the spatial distribution of a plant pathogen and we thus conclude that further research into sampling design and its effect on the ability to estimate disease distribution is necessary.

Some consequences of using the Horsfall-Barratt scale for hypothesis testing.

Comparing treatment effects by hypothesis testing is a common practice in plant pathology. Nearest percent estimates (NPEs) of disease severity were compared with Horsfall-Barratt (H-B) scale data to explore whether there was an effect of assessment method on hypothesis testing. A simulation model based on field-collected data using leaves with disease severity of 0 to 60% was used; the relationship between NPEs and actual severity was linear, a hyperbolic function described the relationship between the standard deviation of the rater mean NPE and actual disease, and a lognormal distribution was assumed to describe the frequency of NPEs of specific actual disease severities by raters. Results of the simulation showed standard deviations of mean NPEs were consistently similar to the original rater standard deviation from the field-collected data; however, the standard deviations of the H-B scale data deviated from that of the original rater standard deviation, particularly at 20 to 50% severity, over which H-B scale grade intervals are widest; thus, it is over this range that differences in hypothesis testing are most likely to occur. To explore this, two normally distributed, hypothetical severity populations were compared using a t test with NPEs and H-B midpoint data. NPE data had a higher probability to reject the null hypothesis (H0) when H0 was false but greater sample size increased the probability to reject H0 for both methods, with the H-B scale data requiring up to a 50% greater sample size to attain the same probability to reject the H0 as NPEs when H0 was false. The increase in sample size resolves the increased sample variance caused by inaccurate individual estimates due to H-B scale midpoint scaling. As expected, various population characteristics influenced the probability to reject H0, including the difference between the two severity distribution means, their variability, and the ability of the raters. Inaccurate raters showed a similar probability to reject H0 when H0 was false using either assessment method but average and accurate raters had a greater probability to reject H0 when H0 was false using NPEs compared with H-B scale data. Accurate raters had, on average, better resolving power for estimating disease compared with that offered by the H-B scale and, therefore, the resulting sample variability was more representative of the population when sample size was limiting. Thus, there are various circumstances under which H-B scale data has a greater risk of failing to reject H0 when H0 is false (a type II error) compared with NPEs.

The effect of landscape pattern on the optimal eradication zone of an invading epidemic.

A number of high profile eradication attempts on plant pathogens have recently been attempted in response to the increasing number of introductions of economically significant nonnative pathogen species. Eradication programs involve the removal of a large proportion of a host population and can thus lead to significant social and economic costs. In this paper we use a spatially explicit stochastic model to simulate an invading pathogen and show that it is possible to identify an optimal control radius, i.e., one that minimizes the total number of hosts removed during an eradication campaign that is effective in eradicating the pathogen. However, by simulating the epidemic and eradication processes in multiple landscapes, we demonstrate that the optimal radius depends critically on landscape pattern (i.e., the spatial configuration of hosts within the landscape). In particular, we find that the optimal radius, and also the number of host removals associated with it, increases with both the level of aggregation and the density of hosts in the landscape. The result is of practical significance and demonstrates that the location of an invading epidemic should be a key consideration in the design of future eradication strategies.

Wind Speed Effects on the Quantity of Xanthomonas citri subsp. citri Dispersed Downwind from Canopies of Grapefruit Trees Infected with Citrus Canker.

The epidemic of citrus canker (Xanthomonas citri subsp. citri) in Florida continues to expand since termination of the eradication program in 2006. Storms are known to be associated with disease spread, but little information exists on the interaction of fundamental physical and biological processes involved in dispersal of this bacterium. To investigate the role of wind speed in dispersal, wind/rain events were simulated using a fan to generate wind up to 19 m·s-1 and spray nozzles to simulate rain. Funnels at ground level and panels at 1.3 m height and distances up to 5 m downwind collected wind-driven splash. Greater wind speeds consistently dispersed more bacteria, measured by concentration (colony forming units [CFU] ml-1) or number sampled (bacteria flux density [BFD] = bacteria cm-2 min-1), from the canopy in the splash. The CFU ml-1 of X. citri subsp. citri collected by panels 1 m downwind at the highest wind speed was up to 41-fold greater than that collected at the lowest wind speed. BFD at the highest wind speed was up to 884-fold higher than that collected at the lowest wind speed. Both panels at distances >1 m and funnels at distances >0 m collected many-fold more X. citri subsp. citri at higher wind speeds compared to no wind (up to 1.4 × 103-fold greater CFU ml-1 and 1.8 × 105-fold the BFD). The resulting relationship between wind speed up to 19 m·s-1 and the mean CFU ml-1 collected by panel collectors downwind was linear and highly significant. Likewise, the mean CFU ml-1 collected from the funnel collectors had a linear relationship with wind speed. The relationship between wind speed and BFD collected by panels was generally similar to that described for CFU ml-1 of X. citri subsp. citri collected. However, BFD collected by funnels was too inconsistent to determine a meaningful relationship with increasing wind speed. The quantity of bacteria collected by panels declined with distance, and the relationship was described by an inverse power model (R2 = 0.94 to 1.00). At higher wind speeds, more bacteria were dispersed to all distances. Windborne inoculum in splash in subtropical wet environments is likely to be epidemiologically significant, as both rain intensity and high wind speed can interact to provide conditions conducive for dispersing large quantities of bacteria from canker-infected citrus trees. Disease and crop management aimed at reducing sources of inoculum and wind speeds in a grove should help minimize disease spread by windborne inoculum.

Optimal strategies for the eradication of asiatic citrus canker in heterogeneous host landscapes.

ABSTRACT The eradication of nonnative plant pathogens is a key challenge in plant disease epidemiology. Asiatic citrus canker is an economically significant disease of citrus caused by the bacterial plant pathogen Xanthomonas citri subsp. citri. The pathogen is a major exotic disease problem in many citrus producing areas of the world including the United States, Brazil, and Australia. Various eradication attempts have been made on the disease but have been associated with significant social and economic costs due to the necessary removal of large numbers of host trees. In this paper, a spatially explicit stochastic simulation model of Asiatic citrus canker is introduced that describes an epidemic of the disease in a heterogeneous host landscape. We show that an optimum eradication strategy can be determined that minimizes the adverse costs associated with eradication. In particular, we show how the optimum strategy and its total cost depend on the topological arrangement of the host landscape. We discuss the implications of the results for invading plant disease epidemics in general and for historical and future eradication attempts on Asiatic citrus canker.

Comparison of Assessment of Citrus Canker Foliar Symptoms by Experienced and Inexperienced Raters.

Citrus canker (Xanthomonas citri subsp. citri) is destructive in many citrus production regions in tropical and subtropical parts of the world. Assessment of canker symptoms is required for diverse reasons, including monitoring epidemics, evaluating the efficacy of control strategies, and disease response in breeding material. The objectives were to compare the ability of experienced and inexperienced raters at assessing citrus canker, to identify factors that affect the quality of the assessment, to determine common sources of error, and to discern how error is related to actual disease magnitude. Two-hundred digital leaf images (0 to 37% area infected) were assessed once by 28 raters, five of whom were experienced plant pathologists (PPs), and 23 who had no experience in disease severity assessment (NPPs). True disease (lesion number [LN], % necrotic area [%N], and % chlorotic+necrotic area [%CN]) was measured using image analysis on a leaf-by-leaf basis, and each parameter was estimated by the 28 raters. LN was neither severely over- nor underestimated, while %N was greatly overestimated, with a lesser tendency to overestimate %CN over the true severity range of these two symptom types. A linear relationship existed between estimate of the disease and true disease for all measures of severity. Data were heteroscedastic and error was not constant with increasing true disease. Agreement between rater estimates and true disease was measured with Lin's concordance correlation coefficient (ρc). LN showed greatest agreement (ρc = 0.88 to 0.99), followed by %CN (ρc = 0.80 to 0.95) and %N (ρc = 0.19 to 0.84). Greater lesion number resulted in overestimation of area infected for both %N and %CN. Overestimation was particularly noticeable at low disease severities. There was a linear relationship between log variance and log true disease for LN (r2 = 0.71), %N (r2 = 0.85), and %CN (r2 = 0.88), and raters tended to estimate disease above 10% to the nearest 5 or 10%. GLM analysis showed differences between PP and NPP groups in assessing disease. For LN, precision of assessment for both groups was similar (r2 > 0.92 and 0.94, respectively), but for estimates of %N and %CN, the PPs were more precise (%N and %CN, r2 = 0.61 and 0.73, respectively) compared to NPPs (%N and %CN, r2 = 0.45 and 0.58, respectively). Absolute error for mean LN was low. The absolute error of %N and %CN showed overestimation to approximately 8% area infected. Above 8%, absolute error increased, but comprised both over- and underestimation. For %N and %CN, relative error was almost exclusively positive and dramatic at severity <8% (up to approximately 600%), but at severity >10% it was relatively small. Error in rater estimates of canker severity is ubiquitous. Understanding these sources of error will aid in the development of both appropriate training and relevant rating aids.

Automated Image Analysis of the Severity of Foliar Citrus Canker Symptoms.

Citrus canker (caused by Xanthomonas citri subsp. citri) is a destructive disease, reducing yield and rendering fruit unfit for fresh sale. Accurate assessment of citrus canker severity and other diseases is needed for several purposes, including monitoring epidemics and evaluation of germplasm. We compared measurements of citrus canker severity (percent area infected) from automated image analysis to visual estimates by raters and true values using images from five leaf samples (65, 123, 50, 50, and 200 leaves; disease severity from 0 to 60%). Severity on leaves was measured by automated image analysis by (i) basing threshold values on a presample of leaves, or (ii) replacing healthy leaf color on a leaf-by-leaf basis before automating image analysis. Samples 1 to 4 were assessed by three trained plant pathologists, and sample 5 was assessed by an additional 25 raters. Healthy leaf area color replacement gave the most consistent agreement with the true severity data. Using color replacement, agreement with true values based on Lin's concordance correlation coefficient (ρc) was 0.93, 0.79, 0.71, 0.85, and 0.89 for each of the samples, respectively. The range and consistency of agreement was generally less good for automated thresholds based on a presample (ρc = 0.35-0.90) or visual raters (ρc = 0.30-0.94). The constituents of agreement (precision and accuracy) showed similar trends. No one rater or method was best for every leaf sample, but replacing healthy color in each leaf with a standard color before automation of image analysis improved agreement, and was relatively quick (20 s per image). The accuracy and precision of automated image analysis of citrus canker severity can be comparable to unaided, direct visual estimation by many raters.

Plant-pathogenic bacteria as biological weapons - real threats?

At present, much attention is being given to the potential of plant pathogens, including plant-pathogenic bacteria, as biological weapons/bioterror weapons. These two terms are sometimes used interchangeably and there is need for care in their application. It has been claimed that clandestine introduction of certain plant-pathogenic bacteria could cause such crop losses as to impact so significantly on a national economy and thus constitute a threat to national security. As a separate outcome, it is suggested that they could cause serious public alarm, perhaps constituting a source of terror. Legislation is now in place to regulate selected plant-pathogenic bacteria as potential weapons. However, we consider it highly doubtful that any plant-pathogenic bacterium has the requisite capabilities to justify such a classification. Even if they were so capable, the differentiation of pathogens into a special category with regulations that are even more restrictive than those currently applied in quarantine legislation of most jurisdictions offers no obvious benefit. Moreover, we believe that such regulations are disadvantageous insofar as they limit research on precisely those pathogens most in need of study. Whereas some human and animal pathogens may have potential as biological or bioterror weapons, we conclude that it is unlikely that any plant-pathogenic bacterium realistically falls into this category.

Constructing the effect of alternative intervention strategies on historic epidemics.

Data from historical epidemics provide a vital and sometimes under-used resource from which to devise strategies for future control of disease. Previous methods for retrospective analysis of epidemics, in which alternative interventions are compared, do not make full use of the information; by using only partial information on the historical trajectory, augmentation of control may lead to predictions of a paradoxical increase in disease. Here we introduce a novel statistical approach that takes full account of the available information in constructing the effect of alternative intervention strategies in historic epidemics. The key to the method lies in identifying a suitable mapping between the historic and notional outbreaks, under alternative control strategies. We do this by using the Sellke construction as a latent process linking epidemics. We illustrate the application of the method with two examples. First, using temporal data for the common human cold, we show the improvement under the new method in the precision of predictions for different control strategies. Second, we show the generality of the method for retrospective analysis of epidemics by applying it to a spatially extended arboreal epidemic in which we demonstrate the relative effectiveness of host culling strategies that differ in frequency and spatial extent. Some of the inferential and philosophical issues that arise are discussed along with the scope of potential application of the new method.

Visual Rating and the Use of Image Analysis for Assessing Different Symptoms of Citrus Canker on Grapefruit Leaves.

Citrus canker is caused by the bacterial pathogen Xanthomonas axonopodis pv. citri and infects several citrus species in wet tropical and subtropical citrus growing regions. Accurate, precise, and reproducible disease assessment is needed for monitoring epidemics and disease response in breeding material. The objective of this study was to assess reproducibility of image analysis (IA) for measuring severity of canker symptoms and to compare this to visual assessments made by three visual raters (VR1-3) for various symptom types (lesion numbers, % area necrotic, and % area necrotic+chlorotic), and to assess inter- and intra-VR reproducibility. Digital images of 210 citrus leaves with a range of symptom severity were assessed on two separate occasions. IA was more precise than VRs for all symptom types (inter-assessment correlation coefficients, r, for lesion numbers by IA = 0.99, by VRs = 0.89 to 0.94; for %, r for % area necrotic+chlorotic for IA = 0.97 and for VRs = 0.86 to 0.89; and r for % area necrotic for IA = 0.96 and for VRs = 0.74 to 0.85). Accuracy based on Lin's concordance coefficient also followed a similar pattern, with IA being most consistently accurate for all symptom types (bias correction factor, Cb = 0.99 to 1.00) compared to visual raters (Cb = 0.85 to 1.00). Lesion number was the most reproducible symptom assessment (Lin's concordance correlation coefficient, ρc, = 0.76 to 0.99), followed by % area necrotic+chlorotic (ρc = 0.85 to 0.97), and finally % area necrotic (ρc = 0.72 to 0.96). Based on the "true" value provided by IA, precision among VRs was reasonable for number of lesions per leaf (r = 0.88 to 0.94), slightly less precision for % area necrotic+chlorotic (r = 0.87 to 0.92), and poorest precision for % area necrotic (r = 0.77 to 0.83). Loss in accuracy was less, but showed a similar trend with counts of lesion numbers (Cb = 0.93 to 0.99) which was more consistently accurately reproduced by VRs than either % area necrotic (Cb = 0.85 to 0.99) or % area necrotic+chlorotic (Cb = 0.91 to 1.00). Thus, visual raters suffered losses in both precision and accuracy, with loss in precision estimating % area necrotic being the greatest. Indeed, only for % area necrotic was there a significant effect of rater (a two-way random effects model ANOVA returned a P < 0.001 and 0.016 for rater in assessments 1 and 2, respectively). VRs showed a marked preference for clustering of % area severity estimates, especially at severity >20% area (e.g., 25, 30, 35, 40, etc.), yet VRs were prepared to estimate disease of <1% area, and at 1% increments up to 20%. There was a linear relationship between actual disease (IA assessments) and VRs. IA appears to provide a highly reproducible way to assess canker-infected leaves for disease, but symptom characters (symptom heterogeneity, coalescence of lesions) could lead to discrepancies in results, and full automation of the system remains to be tested.

Characteristics of the Perception of Different Severity Measures of Citrus Canker and the Relationships Between the Various Symptom Types.

Citrus canker is a disease of citrus and is caused by the bacterial pathogen Xanthomonas citri subsp. citri. Ways of managing the disease are being sought, and accurate, precise, reproducible disease assessment is needed for monitoring epidemics. The objective of this study was to investigate the characteristics of visual assessment of citrus canker symptoms compared with actual disease measured using image analysis (IA). Images of 210 citrus leaves with a range of incidence and severity of citrus canker were assessed by three plant pathologists (VR1-3) and by IA. The number of lesions (L), % area necrotic (%AN), and % area necrotic+chlorotic (%ANC) were assessed. The best relationships were found between %AN and %ANC (r2 = 0.41 to 0.87), and the worst between L and %AN (r2 = 0.27 to 0.66). Bland-Altman plots showed various sources of rater error in assessments, including under- and over-estimation, proportional error, and heterogeneity of variation dependent on actual disease magnitude. There was a tendency to overestimate area diseased, but not lesion counts, and this tendency was pronounced at lower disease severity, with a leaf having more lesions tending to be assessed as having greater area infected compared with a leaf with fewer lesions but equal actual area infected. The rater estimations of disease were less accurate or precise with increasing actual disease severity as indicated by the fit of a normal probability density function-the incidence of extreme values increases with increasing actual disease. For example, for %ANC the kurtosis of the distribution ranged from 17.92 to 1.18, 0.51, and 0.22 in actual disease category ranges of 0 to 10, 11 to 20, 21 to 30, and 31 to 40% area infected, respectively. The log variance of the estimates plotted against log actual disease for all three raters over two assessment occasions gave a linear relationship for L, %AN, and %ANC (r2 = 0.74, 0.65, and 0.74, respectively). Training should improve the accuracy, precision, and reproducibility of raters, and knowledge of the characteristics of disease assessment should help develop and target the training more appropriately and address specific causes and sources of error.

Spatial pattern analysis of citrus canker-infected plantings in são paulo, Brazil, and augmentation of infection elicited by the asian leafminer.

ABSTRACT Eradication of Asiatic citrus canker (ACC) has become increasingly difficult over the last decade, following the introduction of the Asian leafminer into Brazil and Florida, which has led to changes in the eradication protocols. The present study, undertaken in Brazil, was aimed at characterizing the spatial patterns of ACC in commercial citrus plantings to gain better understanding of the dynamics of the disease subsequent to introduction of the leafminer. The spatial patterns of ACC were mapped in 326 commercial citrus plantings and statistically assessed at various spatial dimensions. The presence of "within-group" aggregation in each plot was examined via beta-binomial analysis for groups of trees parsed into three-by-three-tree quadrats. The relative intensity of aggregation was expressed as a binomial index of dispersion (D) and heterogeneity among plots expressed as the intracluster correlation coefficient, rho. The population of data sets was found to fall into three D categories, D < 1.3, 1.3 3.5. These categories then were related to other spatial characteristics. The binary form of Taylor's power law was used to assess the overdispersion of disease across plots and was highly significant. When the overall population of plots was parsed into D categories, the Taylor's R (2) improved in all cases. Although these methods assessed aggregation well, they do not give information on the number of foci or aggregations within each plot. Therefore, the number of foci per 1,000 trees was quantified and found to relate directly to the D categories. The lowest D category could be explained by a linear relationship of number of foci versus disease incidence, whereas the higher two categories were most easily explained by a generalized beta function for the same relationship. Spatial autocorrelation then was used to examine the spatial relationships "among groups" composed of three-by-three-tree quadrats and determine common distances between these groups of ACC-infected trees. Aggregation was found in >84% of cases at this spatial level and there was a direct relationship between increasing D category and increasing core cluster size, and aggregation at the among-group spatial hierarchy was generally stronger for the within-row than for the across-row orientation. Clusters of disease were estimated to average between 18 and 33 tree spaces apart, and the presence of multiple foci of infection was commonplace. The effectiveness of the eradication protocol of removing all "exposed" trees within 30 m surrounding each "ACC-infected tree" was examined, and the distance of subsequent infected trees beyond this 30-m zone from the original focal infected tree was measured for each plot. A frequency distribution was compiled over all plots to describe the distance that would have been needed to circumscribe all of these outliers as a theoretical alternative protocol to the 30-m eradication protocol. The frequency distribution was well described by a monomolecular model (R(2) = 0.98) and used to determine that 90, 95, and 99% of all newly infected trees occurred within 296, 396, and 623 m of prior-infected trees in commercial citrus plantings, respectively. These distances are very similar to previously reported distances determined for ACC in residential settings in Florida.

Temporal and Spatial Epidemiology of Phytophthora Root Rot in Fraser Fir Plantations.

In 1999, 19 plots of Fraser fir (Abies fraseri) with a disease focus were established in commercial plantings grown for Christmas tree production in the mountains of five western North Caro-lina counties. Progress of Phytophthora root rot caused by Phytophthora cinnamomi as estimated by mortality was followed in each plot over 3 to 4 years in an attempt to understand dispersal of inoculum. Slope, aspect, and field production age at the time plots were established were recorded. Rainfall estimated from National Weather Service stations each growing season also was recorded. The relationship of site parameters and rainfall to dispersal and disease was investigated. Disease incidence and mortality were assessed in June and September each year for 3 or 4 years depending on plot. Phytophthora root rot as estimated by mortality counts over time in a logistic regression model progressed in only five of 19 plots over 3 years. None of the site parameters correlated with mortality data, although slightly more disease was found in plots with a north aspect. Rainfall was below normal in the 3 years of the study and did not correlate with mortality in any year. Lack of disease progress in the majority of plots was attributed to drought conditions in the region. In the five plots where mortality increased over time, spatial analysis suggested an aggregated pattern of diseased plants. Aggregation was apparent but not very strong among nearest neighbors, but was considerably stronger among groups of trees within a local area. This aggregation within groups was stronger when larger group sizes were examined by beta-binomial analysis. A spatial analysis by distance indices method (SADIE) indicated the presence of secondary clusters occurring several meters away from the main focus. A stochastic model also was employed that indicated a combination of spatial processes were likely involved, specifically a tendency toward spread within a local area, but not necessarily to the nearest neighboring trees, combined with an influence of background inoculum that could not be accounted for within local areas and may have come from external sources. Thus, all sources of inoculum including infected planting stock, inoculum in soil, infected trees, and contaminated equipment were equally important in epidemics of Phytophthora root rot in Fraser fir and dispersal of P. cinnamomi.