Genetic Diversity and Structure in Regional Cercospora beticola Populations from Beta vulgaris subsp. vulgaris Suggest Two Clusters of Separate Origin.

Cercospora leaf spot, caused by Cercospora beticola, is a highly destructive disease of Beta vulgaris subsp. vulgaris worldwide. C. beticola populations are usually characterized by high genetic diversity, but little is known of the relationships among populations from different production regions around the world. This information would be informative of population origin and potential pathways for pathogen movement. For the current study, the genetic diversity, differentiation, and relationships among 948 C. beticola isolates in 28 populations across eight geographic regions were investigated using 12 microsatellite markers. Genotypic diversity, as measured by Simpson's complement index, ranged from 0.18 to 1.00, while pairwise index of differentiation values ranged from 0.02 to 0.42, with the greatest differentiation detected between two New York populations. In these populations, evidence for recent expansion was detected. Assessment of population structure identified two major clusters: the first associated with New York, and the second with Canada, Chile, Eurasia, Hawaii, Michigan, North Dakota, and one population from New York. Inferences of gene flow among these regions suggested that the source for one cluster likely is Eurasia, whereas the source for the other cluster is not known. These results suggest a shared origin of C. beticola populations across regions, except for part of New York, where population divergence has occurred. These findings support the hypothesis that dispersal of C. beticola occurs over long distances.

Evaluation of App-Embedded Disease Scales for Aiding Visual Severity Estimation of Cercospora Leaf Spot of Table Beet.

Two diagrammatic ordinal scales are available in the Estimate app (2017 version) for Cercospora leaf spot (CLS) severity on table beet: 10% linear (linear-based diagrammatic scale [LIN]) and logarithmic based (Horsfall-Barratt [HB]). These allow for estimating severity data of four types depending on the system used. A group of 30 raters assigned percentage severity on 30 photographs of diseased table beet leaves during five rounds first without an aid and then using each of the four rating systems in Estimate. In two, the perceived ordinal score of the HB or LIN scale was assigned where severity of the subject fit best. HB2 and LIN2 involved a second choice of unitary severity within the perceived score interval. There was large variation in unaided ability of raters to estimate severity: 13% were accurate (Lin's concordance correlation [LCC] > 0.9), 23% were inaccurate (LCC < 0.7), and the remaining had moderate accuracy. Larger disparities between assigned and actual ordinal scores (mostly overestimates) occurred using the LIN compared with the HB. The LIN2 produced the most accurate estimates (Lin's concordance correlation coefficient, ρc = 0.96; generalized bias parameter, Cb = 0.99; Pearson's correlation coefficient r = 0.95) and the greatest interrater reliability (overall concordance correlation coefficient and intraclass correlation coefficient > 0.93). The two-step process using the 10% linear scale is recommended for severity estimates of CLS in table beet.

Estimate, a New iPad Application for Assessment of Plant Disease Severity Using Photographic Standard Area Diagrams.

Assessment of disease severity is a foundational component of plant pathology and essential for robust disease management. Researchers often estimate disease severity using standard area diagrams (SADs) that are reference images representing disease severity in percentage increments. SADs provide assessments of disease severity that are more accurate, precise, and reliable than other methods. Although specific SADs have been constructed for many plant diseases, they often depict severity in unrealistic black-and-white or grayscale illustrations. SADs are also usually printed, static references that can burden data collection in the field and require data to be transferred manually to a computer spreadsheet for manipulation. This data entry process and verification are prone to errors and require additional inputs of time and labor. We developed a new iPad application (app) called Estimate for researchers and crop managers for their use on a mobile device at the field-level for assessing plant disease severity in order to collect data or aid in treatment decisions. The app is a repository for digital, photographic SADs and offers savings in time for data collection and processing. Estimate allows users to select a disease from a prepopulated list and specify the reference disease images in either logarithmic or linear intervals. Data may be collected as the midpoint of an interval (ordinal) or as 1% increments (continuous). Users then select among photographic images by touching those that best match the observed disease severity on successive samples. Estimate allows data entry at the plant and leaf hierarchical levels within plots and subplots. Alternatively, data may be collected on single sampling units with an undefined experimental design (i.e., 1 to x leaves). The user may inspect and e-mail the final data in comma-separated values format for analysis using conventional spreadsheet software. Estimate was released with SADs for assessing the severity of Cercospora leaf spot in red and yellow table beet cultivars. A list of collaborators and up-to-date list of SADs included in Estimate is available at http://evade.pppmb.cals.cornell.edu/estimate/ . SADs for other diseases will be added to Estimate as they become available. Estimate is available for free download from iTunes ( https://itunes.apple.com/WebObjects/MZStore.woa/wa/viewSoftware?id=1193605571&mt=8 ) and is compatible with an iPad Air 2 or equivalent using iOS 9.0 or greater.

Cluster: A New Application for Spatial Analysis of Pixelated Data for Epiphytotics.

Spatial analysis of epiphytotics is essential to develop and test hypotheses about pathogen ecology, disease dynamics, and to optimize plant disease management strategies. Data collection for spatial analysis requires substantial investment in time to depict patterns in various frames and hierarchies. We developed a new approach for spatial analysis of pixelated data in digital imagery and incorporated the method in a stand-alone desktop application called Cluster. The user isolates target entities (clusters) by designating up to 24 pixel colors as nontargets and moves a threshold slider to visualize the targets. The app calculates the percent area occupied by targeted pixels, identifies the centroids of targeted clusters, and computes the relative compass angle of orientation for each cluster. Users can deselect anomalous clusters manually and/or automatically by specifying a size threshold value to exclude smaller targets from the analysis. Up to 1,000 stochastic simulations randomly place the centroids of each cluster in ranked order of size (largest to smallest) within each matrix while preserving their calculated angles of orientation for the long axes. A two-tailed probability t test compares the mean inter-cluster distances for the observed versus the values derived from randomly simulated maps. This is the basis for statistical testing of the null hypothesis that the clusters are randomly distributed within the frame of interest. These frames can assume any shape, from natural (e.g., leaf) to arbitrary (e.g., a rectangular or polygonal field). Cluster summarizes normalized attributes of clusters, including pixel number, axis length, axis width, compass orientation, and the length/width ratio, available to the user as a downloadable spreadsheet. Each simulated map may be saved as an image and inspected. Provided examples demonstrate the utility of Cluster to analyze patterns at various spatial scales in plant pathology and ecology and highlight the limitations, trade-offs, and considerations for the sensitivities of variables and the biological interpretations of results. The Cluster app is available as a free download for Apple computers at iTunes, with a link to a user guide website.

Leaf Doctor: A New Portable Application for Quantifying Plant Disease Severity.

An interactive, iterative smartphone application was used on color images to distinguish diseased from healthy plant tissues and calculate percentage of disease severity. The user touches the application's display screen to select up to eight different colors that represent healthy tissues. The user then moves a threshold slider until only the symptomatic tissues have been transformed into a blue hue. The pixelated image is then analyzed to calculate the disease percentage. This study reports the accuracy, precision, and robustness of Leaf Doctor using six different diseases with typical lesions of varying severity. Estimates of disease severity from Leaf Doctor were highly accurate (R2 ≥ 0.79; Cb ≥ 0.959) compared with estimates obtained from the discipline-standard, Assess. Precision was operationally defined as the ability of a rater to use Leaf Doctor and repeatedly obtain similar percentages of disease severity for the same image. Coefficients of variation were low (0.51 to 14.1%) across all disease datasets but a significant negative relationship was found between the coefficient of variation of estimates and mean disease severity. Other advantages of Leaf Doctor included comparatively less time for image processing, low cost, ease of use, ability to send results by e-mail, and the ability to create realistic standard area diagrams. Leaf Doctor is compatible with iPhone, iPad, and iPod touch and is optimized for iPhone 5. It is available as a free download at the iTunes Store.

Phytophthora morindae, a new species causing black flag disease on noni (Morinda citrifolia L) in Hawaii.

A homothallic, papillate Phytophthora species causing foliar and fruit blight of noni (Morinda citrifolia var. citrifolia) in Hawaii was identified. The asexual phase of this species is characterized by the production of umbellate sporangiophores and papillate sporangia that are ellipsoid and obpyriform with conspicuously tapered bases and possess caducous, medium to long pedicels. The sexual phase is characterized by the production of oogonia with tapered bases, small amphigynous antheridia and thick-walled, plerotic oospores. The morphology of the taxon does not match any of the valid 95 Phytophthora species described to date. Phylogenetic analysis based on sequences of the internal transcribed spacer rDNA region (ITS) and the translation elongation factor 1 alpha (EF-1 alpha) of this taxon and those from other Phytophthora species from GenBank and the Phytophthora database indicates that the new taxon is most closely related to species in ITS clade 10, including P. kernoviae, P. boehmeriae and the recently described P. gallica. The most closely related species is P. kernoviae, an invasive plant pathogen causing bleeding stem lesions on forest trees (beech, Fagus sylvatica) and foliar necrosis of ornamentals (rhododendron, pieris and magnolia) in the UK, and isolated in New Zealand from necrotic cherimoya shoots and fruits and soil. Although the morphological characters of the sexual phase of P. morindae and P. kernoviae are similar, the umbellate sporangiophores produced by the new taxon marks the main morphological distinction. In this paper we describe the morphological characteristics, the phylogenetic relationships and pathogenicity characteristics that support the description of this taxon as a new species with the proposed name Phytophthora morindae sp. nov.

Genetic structure of Cercospora beticola populations on Beta vulgaris in New York and Hawaii.

Cercospora leaf spot (CLS), caused by Cercospora beticola, is a major disease of Beta vulgaris worldwide. No sexual stage is known for C. beticola but in its asexual form it overwinters on infected plant debris as pseudostromata, and travels short distances by rain splash-dispersed conidiospores. Cercospora beticola infects a broad range of host species and may be seedborne. The relative contribution of these inoculum sources to CLS epidemics on table beet is not well understood. Pathogen isolates collected from table beet, Swiss chard and common lambsquarters in mixed-cropping farms and monoculture fields in New York and Hawaii, USA, were genotyped (n = 600) using 12 microsatellite markers. All isolates from CLS symptoms on lambsquarters were identified as C. chenopodii. Sympatric populations of C. beticola derived from Swiss chard and table beet were not genetically differentiated. Results suggested that local (within field) inoculum sources may be responsible for the initiation of CLS epidemics in mixed-cropping farms, whereas external sources of inoculum may be contributing to CLS epidemics in the monoculture fields in New York. New multiplex PCR assays were developed for mating-type determination for C. beticola. Implications of these findings for disease management are discussed.

Global genotype flow in Cercospora beticola populations confirmed through genotyping-by-sequencing.

Genotyping-by-sequencing (GBS) was conducted on 333 Cercospora isolates collected from Beta vulgaris (sugar beet, table beet and swiss chard) in the USA and Europe. Cercospora beticola was confirmed as the species predominantly isolated from leaves with Cercospora leaf spot (CLS) symptoms. However, C. cf. flagellaris also was detected at a frequency of 3% in two table beet fields in New York. Resolution of the spatial structure and identification of clonal lineages in C. beticola populations using genome-wide single nucleotide polymorphisms (SNPs) obtained from GBS was compared to genotyping using microsatellites. Varying distance thresholds (bitwise distance = 0, 1.854599 × 10-4, and 1.298 × 10-3) were used for delineation of clonal lineages in C. beticola populations. Results supported previous reports of long distance dispersal of C. beticola through genotype flow. The GBS-SNP data set provided higher resolution in discriminating clonal lineages; however, genotype identification was impacted by filtering parameters and the distance threshold at which the multi-locus genotypes (MLGs) were contracted to multi-locus lineages. The type of marker or different filtering strategies did not impact estimates of population differentiation and structure. Results emphasize the importance of robust filtering strategies and designation of distance thresholds for delineating clonal lineages in population genomics analyses that depend on individual assignment and identification of clonal lineages. Detection of recurrent clonal lineages shared between the USA and Europe, even in the relaxed-filtered SNP data set and with a conservative distance threshold for contraction of MLGs, provided strong evidence for global genotype flow in C. beticola populations. The implications of intercontinental migration in C. beticola populations for CLS management are discussed.