Logistic regression modeling of prevalence of soybean sclerotinia stem rot in the north-central region of the United States

ABSTRACT Regional prevalence of soybean Sclerotinia stem rot (SSR), caused by Sclerotinia sclerotiorum, was modeled using tillage practices, soil texture, and weather variables (monthly air temperature and monthly precipitation from April to August) as inputs. Logistic regression was used to estimate the probability of stem rot prevalence with historical disease data from four states of the north-central region of the United States. Potential differences in disease prevalence between states in the region were addressed using regional indicator variables. Two models were developed: model I used spring (April) weather conditions and model II used summer (July and August) weather conditions as input variables. Both models had high explanatory power (78.5 and 77.8% for models I and II, respectively). To investigate the explanatory power of the models, each of the four states was divided into small geographic areas, and disease prevalence in each area was estimated using both models. The R(2) value of the regression analysis between observed and estimated SSR prevalence was 0.65 and 0.71 for models I and II, respectively. The same input variables were tested for their significance to explain the within-field SSR incidence by using Poisson regression analysis. Although all input variables were significant, only a small amount of variation of SSR incidence was explained, because R(2) of the regression analysis between observed and estimated SSR incidence was 0.065. Incorporation of available site-specific information (i.e., fungicide seed treatment, weed cultivation, and manure and fertilizer applications in a field) improved slightly the explained amount of SSR incidence (R(2) = 0.076). Predicted values of field incidence generally were overestimated in both models compared with the observed incidence. Our results suggest that preseason prediction of regional prevalence would be feasible. However, prediction of field incidence would not, and a different site-specific approach should be followed.     

Prevalence of sclerotinia stem rot of soybeans in the north-central United States in relation to tillage, climate, and latitudinal positions

ABSTRACT Since the early 1990s, Sclerotinia stem rot, caused by Sclerotinia sclerotiorum, has caused considerable damage to soybean production in the north-central United States. To determine the extent of its distribution and associated factors, investigations were conducted in 1995 and 1996 in Illinois, Iowa, Minnesota, Missouri, and Ohio. Investigations also were conducted in 1997 and 1998 in Iowa, Minnesota, and Missouri. In each state, soybean fields were randomly selected in collaboration with the National Agricultural Statistics Service. From each field, 20 soybean stems 20 cm long (from the base) in 1995 and 1996 and full-length stems in 1997 and 1998 were sampled in a zigzag pattern. During the 4-year period, stem samples were collected from 1,983 fields and assessed for the presence or absence of the disease. Of the five states, Sclerotinia stem rot was most prevalent in north-central Iowa and southern Minnesota. Sclerotinia stem rot was not detected in Missouri during the 4-year investigation period. The disease was most prevalent in 1996 and least prevalent in 1995. The prevalence of the disease was strongly related to cumulative departures from normal maximum and minimum temperatures in July and August. The disease was more prevalent when yearly temperatures were below normal than when they were above normal. In 1996, a year with a cooler-than-normal summer, the disease was detected farther south than in 1995. In both years, the prevalence of the disease was exponentially related to latitudinal positions of the fields (R(2) = 0.93 and 0.83 for 1995 and 1996, respectively) reflecting the effect of the north-south variations in temperature. During the 4-year period, there was no relationship between precipitation and the prevalence of the disease. The lack of relationship may suggest that there was no shortage of moisture since it is one of the primary factors for disease development. The prevalence of Sclerotinia stem rot was less in no-till than in minimum-till or conventional-till fields (P = 0.001 and 0.007, respectively) and greater in minimum-till than in conventional-till fields (P = 0.07). Fields that had Sclerotinia stem rot, however, did not differ in incidence of the disease regardless of the tillage system.     

A Bayesian approach to the analysis of environmental fate and behaviour data for pesticide registration

With the harmonisation of data requirements for pesticide registration under EC Directive 91/414 there is need for progress on the techniques used to analyse such data and so help make consistent the judgements applied by national regulatory authorities. This paper proposes a Bayesian technique for combining data from environmental fate and behaviour studies of pesticides in soil. The method uses expert knowledge, based on degradation and adsorption data, and logistic regression methods to form a prior probability distribution for the probability that a given compound leaches. Results from lysimeter experiments are used update the prior knowledge. Data for the compounds bentazone and triclopyr are used to illustrate the techniques. The advantages of the methodology and its implications for the pesticide registration procedure are discussed in the light of possible advances using modern Bayesian statistical techniques and mathematical models. © 1998 Society of Chemical Industry     

Evaluation of relationships between weather patterns and prevalence of sorghum ergot in the Texas panhandle

ABSTRACT Sorghum ergot caused by Claviceps africana was observed for the first time in the United States in Southern Texas in 1997. That year there was a widespread ergot epidemic in hybrid seed production fields in the Texas Panhandle. However, occurrence of the disease has been sparse during the past 3 years, easing fears that the hybrid seed industry in the region might be endangered. To determine whether climatic factors were associated with observed variations in prevalence of ergot, weather data (temperature, precipitation, and relative humidity) were collected from seven weather stations in the Texas Panhandle. Sorghum ergot prevalence data for the period 1997 to 2000 were collected from records of seed companies in the Panhandle and related to weather variables. Results showed that, in the southern section of the Panhandle, maximum temperature and precipitation between 1 and 15 August were associated (r(2) = 0.98, P = 0.001 and r(2) = 0.81, P = 0.0193, respectively) with variations in the prevalence of ergot during the 4-year period. In the northern section, only maximum temperature during 16 to 31 July was significantly associated (r(2) = 0.91, P = 0.0111) with disease prevalence. Over all, 1997 was wetter and cooler, during the 1 to 15 August period, than each of the subsequent 3 years. In addition to creating humid conditions for ergot development, precipitation was associated with suppression of maximum temperature, enhancing ergot-favorable temperature conditions. Examination of historic weather data for the region showed that there were many instances in the past where temperature depression was associated with a rise in cumulative precipitation, creating ergot-favorable conditions similar to those in 1997. Cross-spectral analysis was used to determine whether such association is periodic. Weather data from five of the seven locations in the region showed peaks of significant coherency ( alpha< 0.05) at 2 to 4 years and 7 to 10 years or greater, indicating the existence of a periodic cycle in the temperature-precipitation association. The results of the investigation suggested that association of precipitation with temperature depression is a primary factor in development of ergot in the Texas Panhandle, and such association has a periodic cycle.     

Use of bayesian methods to improve prediction of panicle and shoot blight severity of pistachio in california

ABSTRACT Panicle and shoot blight, caused by a Fusicoccum sp., is one of the major aboveground diseases of pistachio in California. The effects of temperature, number of continuous rainy days in April and May, irrigation system, and incidence of latent infection of the Fusicoccum sp. on severity of panicle and shoot blight of pistachio leaves and fruit have been quantified previously, using data collected from 1999 through 2001. A predictive model for leaves and another model for fruit with good explanatory power were generated. In 2003 and 2004, newly collected data were used to evaluate the two models with non-Bayesian and Bayesian methods. The 95% credible (i.e., confidence) intervals of initial (before modification with non-Bayesian and Bayesian methods) and updated parameter estimates were used to investigate their prognostic validity. In 2003, the non-Bayesian analysis resulted in all parameter estimates, with the exception of cumulative daily mean temperature from 1 June until harvest, having different 95% confidence intervals than the parameter estimates of the original models. In addition, the parameter estimates for drip irrigation for the leaf infection and the parameter estimates for drip irrigation and number of continuous rainy days in April and May for fruit infection were not statistically significant. With Bayesian methods, the reestimated model parameters had overlapping 95% credible intervals with the initial estimated parameters, except for the number of continuous rainy days in April and May. When the two sets of modified parameter estimates were used to predict disease severity, statistically significant (alpha = 0.05) differences between observed and predicted disease severities were found with non-Bayesian analysis for leaf infection in three locations and with Bayesian analysis for fruit infection in one orchard. The parameter estimates were modified again at the end of the 2004 season and were all statistically significant with both non-Bayesian and Bayesian methods. Both sets of parameter estimates gave predictions that were not significantly different from observed disease severity on leaves and fruit in all monitored orchards in 2004. In summary, Bayesian methods gave more consistent results when used to update parameter estimates with new information and yielded predictions not statistically different from observed disease severity in more cases than the non-Bayesian analysis.     

Bayesian analysis in plant pathology

ABSTRACT Bayesian methods are currently much discussed and applied in several disciplines from molecular biology to engineering. Bayesian inference is the process of fitting a probability model to a set of data and summarizing the results via probability distributions on the parameters of the model and unobserved quantities such as predictions for new observations. In this paper, after a short introduction of Bayesian inference, we present the basic features of Bayesian methodology using examples from sequencing genomic fragments and analyzing microarray gene-expressing levels, reconstructing disease maps, and designing experiments.     

Genetic Differentiation of Puccinia triticina Populations in Central Asia and the Caucasus

ABSTRACT Isolates of Puccinia triticina collected from common wheat in the Central Asia countries of Kazakhstan, Uzbekistan, Tajikistan, and Kyrgyzstan and the Caucasus countries of Azerbaijan, Georgia, and Armenia were tested for virulence to 20 isolines of Thatcher wheat with different leaf rust resistance genes and molecular genotype at 23 simple sequence repeat (SSR) loci. After clone correction within each country, 99 isolates were analyzed for measures of population diversity, variation at single SSR loci, and for genetic differentiation of virulence phenotypes and SSR genotypes. Isolates from Central Asia and the Caucasus were also compared with 16 P. triticina isolates collected from common wheat in North America that were representative of the virulence and molecular variation in this region and two isolates collected from durum wheat in France and the United States. Populations from the Caucasus, Uzbekistan, Tajikistan, and Kyrgyzstan were not significantly (P > 0.05) differentiated for SSR variation with F(st) and R(st) statistics. Populations from the Caucasus, Uzbekistan, Tajikistan, and Kyrgyzstan were significantly (P < 0.05) differentiated from the populations in South and North Kazakhstan for SSR variation. All populations from Central Asia and the Caucasus were significantly differentiated from the North American isolates and isolates from durum wheat for SSR variation and virulence phenotypes. There was a correlation between virulence phenotype and SSR genotype among individual isolates and at the population level. Mountain barriers may account for the differentiation of P. triticina geographic populations in Central Asia and the Caucasus.     

Neural network classification of tan spot and stagonospora blotch infection periods in a wheat field environment

ABSTRACT Tan spot and Stagonospora blotch of hard red spring wheat served as a model system for evaluating disease forecasts by artificial neural networks. Pathogen infection periods on susceptible wheat plants were measured in the field from 1993 to 1998, and incidence data were merged with 24-h summaries of accumulated growing degree days, temperature, relative humidity, precipitation, and leaf wetness duration. The resulting data set of 202 discrete periods was randomly assigned to 10 modeldevelopment or -validation (n = 50) data sets. Backpropagation neural networks, general regression neural networks, logistic regression, and parametric and nonparametric methods of discriminant analysis were chosen for comparison. Mean validation classification of tan spot incidence was between 71% for logistic regression and 76% for backpropagation models. No significant difference was found between methods of modeling tan spot infection periods. Mean validation prediction accuracy of Stagonospora blotch incidence was 86 and 81% for backpropagation and logistic regression, respectively. Prediction accuracies of other modeling methods were 相似文献   

Molecular Mapping of the rps1.a Recessive Gene for Resistance to Stripe Rust in BBA 2890 Barley

ABSTRACT Stripe rust, caused by Puccinia striiformis f. sp. hordei, is one of the most important diseases of barley in the south-central and western United States. Growing resistant cultivars is the best approach for controlling the disease. The barley genotype BBA 2890 has all-stage resistance against all races of P. striiformis f. sp. hordei (PSH) identified thus far in the United States. The resistance in BBA 2890 is controlled by a single recessive gene, rps1.a. The objectives of this study were to identify resistance gene analog polymorphism (RGAP) markers for the all-stage resistance gene rps1.a, to map the gene on a barley chromosome using chromosome-specific simple sequence repeat (SSR) markers, and to determine the presence or absence of the flanking RGAP markers for the gene in 24 barley genotypes. Seedlings of the parents and 200 F(8) recombinant inbred lines (RILs) were tested for resistance to pathogen races PSH-14, PSH-48, and PSH-54 in the greenhouse in 2005. Genomic DNA was extracted from the parents and 150 F(8) RILs. The RGAP technique was used to identify molecular markers for the rps1.a gene. Twelve primer pairs generating repeatable polymorphic bands were selected for genotyping the 150 F(8) RILs. A genetic linkage group was constructed for the resistance gene with 13 RGAP markers and four chromosome-specific SSR markers. The four SSR markers mapped the gene on the long arm of barley chromosome 3H. The closest RGAP marker for the resistant allele was within a genetic distance of 2.1 centimorgans (cM). The closest marker for the susceptible allele was 6.8 cM away from the locus. The two closest RGAP markers for the resistant allele detected polymorphisms in 67 and 71% of the 24 barley genotypes when used individually, and detected polymorphism in 88% of the genotypes when used in combination. This information should be useful in incorporating the resistance gene into barley cultivars and in pyramiding the gene with other resistance genes for superior stripe rust resistance.     

Probabilities for profitable fungicide use against gray leaf spot in hybrid maize

ABSTRACT Gray leaf spot, caused by the fungus Cercospora zeae-maydis, causes considerable yield losses in hybrid maize grown in the north-central United States and elsewhere. Nonchemical management tactics have not adequately prevented these losses. The probability of profitably using fungicide application as a management tool for gray leaf spot was evaluated in 10 field experiments under conditions of natural inoculum in Iowa. Gray leaf spot severity in untreated control plots ranged from 2.6 to 72.8% for the ear leaf and from 3.0 to 7.7 (1 to 9 scale) for whole-plot ratings. In each experiment, fungicide applications with propiconazole or mancozeb significantly reduced gray leaf spot severity. Fungicide treatment significantly (P 相似文献   

Calibration and verification of risk algorithms using logistic regression

The use of logistic regression is proposed as a method of verifying and calibrating disease risk algorithms. The logistic regression model calculates the log of the odds of a binary outcome as a function of a linear combination of predictors. The resulting model assumes a multiplicative (relative) relationship between the different risk factors. Computer programs for performing logistic regression produce both estimates and standard errors, thus permitting the evaluation of the importance of different predictive variables. The use of receiver operating characteristic (ROC) curves is also proposed as a means of comparing different algorithms. An example is presented using data on Sclerotinia stem rot in oil seed rape, caused bySclerotinia sclerotiorum.     

Rank regression for analyzing ordinal qualitative data for treatment comparison

ABSTRACT Ordinal qualitative data are often collected for phenotypical measurements in plant pathology and other biological sciences. Statistical methods, such as t tests or analysis of variance, are usually used to analyze ordinal data when comparing two groups or multiple groups. However, the underlying assumptions such as normality and homogeneous variances are often violated for qualitative data. To this end, we investigated an alternative methodology, rank regression, for analyzing the ordinal data. The rank-based methods are essentially based on pairwise comparisons and, therefore, can deal with qualitative data naturally. They require neither normality assumption nor data transformation. Apart from robustness against outliers and high efficiency, the rank regression can also incorporate covariate effects in the same way as the ordinary regression. By reanalyzing a data set from a wheat Fusarium crown rot study, we illustrated the use of the rank regression methodology and demonstrated that the rank regression models appear to be more appropriate and sensible for analyzing nonnormal data and data with outliers.     

Structure and Origin of Xanthomonas arboricola pv. pruni Populations Causing Bacterial Spot of Stone Fruit Trees in Western Europe

ABSTRACT Xanthomonas arboricola pv. pruni, the causal agent of bacterial spot on stone fruit, was found in 1995 in several orchards in southeastern France. We studied population genetics of this emerging pathogen in comparison with populations from the United States, where the disease was first described, and from Italy, where the disease has occurred since 1920. Four housekeeping genes (atpD, dnaK, efp, and glnA) and the intergenic transcribed spacer region were sequenced from a total of 3.9 kb of sequences, and fluorescent amplified fragment length polymorphism (FAFLP) analysis was performed. A collection of 64 X. arboricola pv. pruni strains, including 23 strains from France, was analyzed. The X. arboricola pv. pruni population had a low diversity because no sequence polymorphisms were observed. Population diversity revealed by FAFLP was lower for the West European population than for the American population. The same bacterial genotype was detected from five countries on three continents, a geographic distribution that can be explained by human-aided migration of bacteria. Our data support the hypothesis that the pathogen originated in the United States and subsequently has been disseminated to other stone-fruit-growing regions of the world. In France, emergence of this disease was due to a recent introduction of the most prevalent genotype of the bacterium found worldwide.     

Molecular mapping of the crown rust resistance gene rpc1 in barley

ABSTRACT Crown rust of barley, caused by Puccinia coronata var. hordei, occurs sporadically and sometimes may cause yield and quality reductions in the Great Plains region of the United States and Canada. The incompletely dominant resistance allele Rpc1 confers resistance to P. coronata in barley. Two generations, F(2) and F(2:3), developed from a cross between the resistant line Hor2596 (CIho 1243) and the susceptible line Bowman (PI 483237), were used in this study. Bulked segregant analysis combined with random amplified polymorphic DNA (RAPD) primers were used to identify molecular markers linked to Rpc1. DNA genotypes produced by 500 RAPD primers, 200 microsatellites (SSRs), and 71 restriction fragment length polymorphism (RFLP) probes were applied to map Rpc1. Of these, 15 RAPD primers identified polymorphisms between resistant and susceptible bulks, and 62 SSR markers and 32 RFLP markers identified polymorphisms between the resistant and susceptible parents. The polymorphic markers were applied to 97 F(2) individuals and F(2:3) families. These markers identified 112 polymorphisms and were used for primary linkage mapping to Rpc1 using Map Manager QT. Two RFLP and five SSR markers spanning the centromere on chromosome 3H and one RAPD marker (OPO08-700) were linked with Rpc1 and, thus, used to construct a 30-centimorgan (cM) linkage map containing the Rpc1 locus. The genetic distance between Rpc1 and the closest marker, RAPD OPO08-700, was 2.5 cM. The linked markers will be useful for incorporating this crown rust resistance gene into barley breeding lines.     

Neural networks that distinguish infection periods of wheat tan spot in an outdoor environment

ABSTRACT Tan spot of wheat, caused by Pyrenophora tritici-repentis, provided a model system for testing disease forecasts based on an artificial neural network. Infection periods for P. tritici-repentis on susceptible wheat cultivars were identified from a bioassay system that correlated tan spot incidence with crop growth stage and 24-h summaries of environmental data, including temperature, relative humidity, wind speed, wind direction, solar radiation, precipitation, and flat-plate resistance-type wetness sensors. The resulting data set consisted of 97 discrete periods, of which 32 were reserved for validation analysis. Neural networks with zero to nine processing elements were evaluated 20 times each to identify the model that most accurately predicted an infection event. The 200 models averaged 74 to 77% accuracy, depending on the number of processing elements and random initialization of coefficients. The most accurate model had five processing elements and correctly predicted 87% of the infection periods in the validation set. In comparison, stepwise logistic regression correctly predicted 69% of the validation cases, and multivariate discriminant analysis distinguished 50% of the validation cases. When wetness-sensor inputs were withheld from the models, both the neural network and logistic regression models declined 6% in prediction accuracy. Thus, neural networks were more accurate than statistical procedures, both with and without wetness-sensor inputs. These results demonstrate the applicability of neural networks to plant disease forecasting.     

Soil Chemical and Physical Properties Associated with Suppression of Take-all of Wheat by Trichoderma koningii

ABSTRACT Trichoderma koningii, originally isolated from a take-all-suppressive soil in Western Australia, has been shown to protect wheat against take-all disease and increase grain yield in field trials in Australia, China, and the United States. However, within a region, the level of protection provided by T. koningii can dramatically vary between field sites. We evaluated suppression of take-all by this fungus in eight silt loams from the Pacific Northwest of the United States and the influence of 21 abiotic soil parameters on biocontrol activity. While T. koningii significantly increased plant growth and reduced disease severity in all eight silt loams, the level of protection varied significantly among the soils. Disease suppression was not associated with the conduciveness of a soil to take-all, but rather to the supportiveness of a soil to biocontrol activity. Biocontrol activity was positively correlated with iron, nitrate-nitrogen, boron, copper, soluble magnesium, and percent clay, and negatively correlated with soil pH and available phosphorus. Principal component factor analysis using these eight variables resulted in a three-component solution that accounted for 95% of the variation in disease rating. Least squares regression analysis (R(2) = 0.992) identified a model that included nitrate-nitrogen, soil pH, copper, and soluble magnesium, and described the variance in take-all suppression by T. koningii. Potential applications of these results include amending soil or inoculants with beneficial factors that may be lacking in the target soil and customizing biocontrol treatments for sites that have parameters predictive of a favorable environment for disease suppression.     

Genetic Relatedness of African and United States Populations of Cercospora zeae-maydis

Two taxonomically identical but genetically distinct sibling species, designated groups I and II, of Cercospora zeae-maydis cause gray leaf spot of maize in the United States. Isolates of the gray leaf spot pathogen from Africa were compared with isolates from the United States by amplified fragment length polymorphism (AFLP) analysis and restriction digests of internal transcribed spacer (ITS) regions and 5.8S ribosomal DNA (rDNA), as well as by morphological and cultural characteristics. The isolates from Africa were morphologically indistinguishable from the U.S. isolates in both groups, but like isolates of group II, they grew more slowly and failed to produce detectable amounts of cercosporin in culture. Analysis of restriction fragments from the ITS and rDNA regions digested with five endonucleases indicated that all of the African isolates shared the profile of the C. zeae-maydis group II population from the eastern United States and, thus, are distinct from the group I population, which is more prevalent in the United States and other parts of the world. Cluster analysis of 85 AFLP loci confirmed that the African and U.S. group II populations were conspecific (greater than 97% average similarity) with limited variability. Among all group II isolates, only 8 of 57 AFLP loci were polymorphic, and none was specific to either population. Thus, although gray leaf spot was reported in the United States several decades prior to the first record in Africa, the relative age of the two populations on their respective continents could not be ascertained with confidence. The absence of C. zeae-maydis group I in our samples from four countries in the major maize-producing region of Africa as well as the greater AFLP haplotype diversity found in the African group II population, however, suggest that Africa was the source of C. zeae-maydis group II in the United States. The overall paucity of AFLP variation in this sibling species further suggests that its origin is recent or that the ancestral population experienced a severe bottleneck prior to secondary migration.     

Internal Transcribed Spacer Sequence-Based Phylogeny and Polymerase Chain Reaction-Restriction Fragment Length Polymorphism Differentiation of Tilletia walkeri and T. indica

ABSTRACT A polymerase chain reaction-restriction fragment length polymorphism assay to distinguish Tilleita walkeri, a rye grass bunt fungus that occurs in the southeastern United States and Oregon, from T. indica, the Karnal bunt fungus, is described. The internal transcribed spacer (ITS) region of the ribosomal DNA repeat unit was amplified and sequenced for isolates of T. indica, T. walkeri, T. horrida, and a number of other taxa in the genus Tilletia. A unique restriction digest site in the ITS1 region of T. walkeri was identified that distinguishes it from the other taxa in the genus. Phylogenetic analysis of the taxa based on ITS sequence data revealed a close relationship between T. indica and T. walkeri, but more distant relationships between these two species and other morphologically similar taxa.     

Worldwide Phylogeography of the Citrus Brown Spot Pathogen, Alternaria alternata

ABSTRACT Sixty-five isolates of Alternaria alternata were sampled from brown spot lesions on tangerines and mandarins (Citrus reticulata) and tangerine x grapefruit (C. reticulata x C. paradisi) hybrids in the United States, Colombia, Australia, Turkey, South Africa, and Israel to investigate the worldwide phylogeography of the fungus. Genetic variation was scored at 15 putative random amplified polymorphic DNA (RAPD) loci and 465 bp of an endo-polygalacturonase (endo-PG) gene was sequenced for each isolate. Cluster analysis of RAPD genotypes revealed significant differentiation between United State and Colombia isolates and Turkey, South Africa, Israel, and Australia isolates. Sequencing of endo-PG revealed 21 variable sites when the outgroup A. gaisen (AK-toxin-producing pathogen of Japanese pear) was included and 13 variable sites among the sampled isolates. Nucleotide substitutions at 10 of 13 variable sites represented silent mutations when endo-PG was translated in frame. Eight distinct endo-PG haplotypes were found among the sampled isolates and estimation of a phylogeny with endo-PG sequence data revealed three clades, each with strong bootstrap support. The most basal clade (clade 1) was inferred based on its similarity to the outgroup A. gaisen and consisted exclusively of pathogenic isolates from the United States and Colombia. Clade 2 consisted of pathogenic and nonpathogenic isolates from the United States, Australia, South Africa, and Israel and clade 3 contained pathogenic and nonpathogenic isolates from Australia, South Africa, Israel, and Turkey. Quantitative estimates of virulence (disease incidence) were obtained for isolates from the United States, Colombia, South Africa, Israel, and Turkey by spray inoculating detached citrus leaves and counting the number of lesions 24 h after inoculation. Large differences in virulence were detected among isolates within each location and isolates from the United States were significantly more virulent than isolates from other locations. Several isolates from Colombia, South Africa, Israel, and Turkey had low virulence and 8% of all isolates were nonpathogenic. All but one of the nonpathogenic isolates were found in clade 2 of the endo-PG phylogeny, which also included the most highly virulent isolates sampled.     

Genetic diversity of ‘<Emphasis Type="Italic">Cadidatus</Emphasis> Liberibacter solanacearum’ strains in the United States and Mexico revealed by simple sequence repeat markers

‘Candidatus Liberibacter solanacearum’ is associated with the Zebra Chip (ZC) disorder of potatoes. A panel of eight simple sequence repeat (SSR) markers was developed and used to genetically characterize ‘Ca. L. solanacearum’ strains obtained from ZC-affected potato plants in the United States and Mexico. The multilocus SSR markers in this study effectively differentiated genotypes and estimated genetic diversity of ‘Ca. L. solanacearum’ strains. Genotype assignment analyses identified two major lineages of ‘Ca. L. solanacearum’ in the North American populations while only one lineage type was identified in Mexican population. No clear genetic structure was found among haplotypes based on geographical proximity or host. The high resolution power of the SSR marker system developed in this study provides a useful tool for genotyping closely related strains and tracking sources of the pathogen. Genotype information combined with epidemiological data will advance knowledge of ZC disease and will facilitate development of effective disease management.