scholarly journals Spatial Pattern Analysis of Citrus Canker-Infected Plantings in São Paulo, Brazil, and Augmentation of Infection Elicited by the Asian Leafminer

2007 ◽  
Vol 97 (6) ◽  
pp. 674-683 ◽  
Author(s):  
T. R. Gottwald ◽  
R. B. Bassanezi ◽  
L. Amorim ◽  
A. Bergamin-Filho

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 β-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, ρ. The population of data sets was found to fall into three D categories, D < 1.3, 1.3 ≤ D = 3.5, and D > 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 β 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 (R2 = 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.

1999 ◽  
Vol 89 (5) ◽  
pp. 421-433 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden

Spatial pattern of the incidence of strawberry leaf blight, caused by Phomopsis obscurans, was quantified in commercial strawberry fields in Ohio using statistics for heterogeneity and spatial correlation. For each strawberry planting, two transects were randomly chosen and the proportion of leaflets (out of 15) and leaves (out of five) with leaf blight symptoms was determined from N = 49 to 106 (typically 75) evenly spaced sampling units, thus establishing a natural spatial hierarchy to compare patterns of disease. The beta-binomial distribution fitted the data better than the binomial in 92 and 26% of the 121 data sets over 2 years at the leaflet and leaf levels, respectively, based on a likelihood ratio test. Heterogeneity in individual data sets was measured with the index of dispersion (variance ratio), C(α) test, a standard normal-based test statistic, and estimated θ parameter of the beta-binomial. Using these indices, overdispersion was detected in approximately 94 and 36% of the data sets at the leaflet and leaf levels, respectively. Estimates of the slope from the binary power law were significantly (P < 0.01) greater than 1 and estimates of the intercept were significantly greater than 0 (P < 0.01) at both the leaflet and leaf levels for both years, indicating that degree of heterogeneity was a function of incidence. A covariance analysis indicated that cultivar, time, and commercial farm location of sampling had little influence on the degree of heterogeneity. The measures of heterogeneity indicated that there was a positive correlation of disease status of leaflets (or leaves) within sampling units. Measures of spatial association in disease incidence among sampling units were determined based on autocorrelation coefficients, runs analysis, and a new class of tests known as spatial analysis by distance indices (SADIE). In general, from 9 to 22% of the data sets had a significant nonrandom spatial arrangement of disease incidence among sampling units, depending on which test was used. When significant associations existed, the magnitude of the association was small but was about the same for leaflets and leaves. Comparing test results, SADIE analysis was found to be a viable alternative to spatial autocorrelation analysis and has the advantage of being an extension of heterogeneity analysis rather than a separate approach. Collectively, results showed that incidence of Phomopsis leaf blight was primarily characterized by small, loosely aggregated clusters of diseased leaflets, typically confined within the borders of the sampling units.


2003 ◽  
Vol 93 (12) ◽  
pp. 1543-1552 ◽  
Author(s):  
Sylvie Dallot ◽  
Tim Gottwald ◽  
Gérard Labonne ◽  
Jean-Bernard Quiot

The spatial pattern of Sharka disease, caused by Plum pox virus (PPV) strain M, was investigated in 18 peach plots located in two areas of southern France. PPV infections were monitored visually for each individual tree during one to three consecutive years. Point pattern and correlation-type approaches were undertaken using the binary data directly or after parsing them in contiguous quadrats of 4, 9, and 16 trees. Ordinary runs generally revealed a low but variable proportion of rows with adjacent symptomatic trees. Aggregation of disease incidence was indicated by the θ parameter of the beta-binomial distribution and related indices in 15 of the 18 plots tested for at least one assessment date of each. When aggregation was detected, it was indicated at all quadrat sizes and tended to be a function of disease incidence, as shown by the binary form of Taylor's power law. Spatial analysis by distance indices (SADIE) showed a nonrandom arrangement of quadrats with infected trees in 14 plots. The detection of patch clusters enclosing quadrats with above-average density of symptomatic trees, ellipsoidal in shape and generally extending from 4 to 14 trees within rows and from 4 to 10 trees perpendicular to the rows, could be interpreted as local areas of influence of PPV spread. Spatial patterns at the plot scale were often characterized by the occurrence of several clusters of infected trees located up to 90 m apart in the direction of the rows. When several time assessments were available, increasing clustering over time was generally evidenced by stronger values of the clustering index and by increasing patch cluster size. The combination of the different approaches revealed a wide range of spatial patterns of PPV-M, from no aggregation to high aggregation of symptomatic trees at all spatial scales investigated. Such patterns suggested that aphid transmission to neighboring trees occurred frequently but was not systematic. The mechanism of primary virus introduction, the age and structure of the orchards when infected, and the diversity of vector species probably had a strong influence on the secondary spread of the disease. This study provides a more complete understanding of PPV-M patterns which could help to improve targeting of removal of PPV-infected trees for more effective disease control.


Plant Disease ◽  
1999 ◽  
Vol 83 (11) ◽  
pp. 992-1000 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden

Spatial pattern of the incidence of strawberry leaf spot, caused by Mycosphaerella fragariae (Ramularia brunnea), was quantified on commercial strawberry farms in Ohio. For each planting of strawberry, one or two transects were randomly chosen, and the proportion of leaflets (out of 15) with leaf spot was determined from N = 29 to 87 evenly spaced sampling units. Based on a likelihood ratio test, the beta-binomial distribution described the frequency of diseased leaflets better than the binomial in 93% of the 59 data sets over 3 years. Estimates of mean incidence ranged from 0.0009 to 0.82, with a median of 0.05. Estimates of the beta-binomial aggregation parameter, θ, ranged from 0 to 1.06, with a median of 0.20. Moreover, the estimate of the slope of the binary power law, fitted to the variance data for the 59 data sets, was significantly (P < 0.01) greater than one, indicating that heterogeneity, and hence the pattern of disease incidence at the spatial scale of the sampling units or smaller, was dependent on mean incidence. Spatial autocorrelation and Spatial Analysis by Distance IndicEs (SADIE) analyses detected significant positive association of disease incidence among sampling units in approximately 40% of the data sets, indicating that disease clusters extended beyond the borders of the sampling units in these fields. Collectively, the results show that strawberry leaf spot was characterized by relatively tight clusters of disease (based on θ) that extended beyond the borders of the sampling units in a little under half of the data sets (based on correlations). The information on heterogeneity was used to develop fixed and sequential sampling curves to precisely estimate disease incidence. The sequential-estimation procedure was evaluated using statistical bootstrap methods and performed well over the range of disease incidences encountered.


Plant Disease ◽  
2006 ◽  
Vol 90 (11) ◽  
pp. 1433-1440 ◽  
Author(s):  
David H. Gent ◽  
Walter F. Mahaffee ◽  
William W. Turechek

The spatial heterogeneity of the incidence of hop cones with powdery mildew (Podosphaera macularis) was characterized from transect surveys of 41 commercial hop yards in Oregon and Washington from 2000 to 2005. The proportion of sampled cones with powdery mildew ( p) was recorded for each of 221 transects, where N = 60 sampling units of n = 25 cones assessed in each transect according to a cluster sampling strategy. Disease incidence ranged from 0 to 0.92 among all yards and dates. The binomial and beta-binomial frequency distributions were fit to the N sampling units in a transect using maximum likelihood. The estimation procedure converged for 74% of the data sets where p > 0, and a loglikelihood ratio test indicated that the beta-binomial distribution provided a better fit to the data than the binomial distribution for 46% of the data sets, indicating an aggregated pattern of disease. Similarly, the C(α) test indicated that 54% could be described by the beta-binomial distribution. The heterogeneity parameter of the beta-binomial distribution, θ, a measure of variation among sampling units, ranged from 0.01 to 0.20, with a mean of 0.037 and a median of 0.015. Estimates of the index of dispersion ranged from 0.79 to 7.78, with a mean of 1.81 and a median of 1.37, and were significantly greater than 1 for 54% of the data sets. The binary power law provided an excellent fit to the data, with slope and intercept parameters significantly greater than 1, which indicated that heterogeneity varied systematically with the incidence of infected cones. A covariance analysis indicated that the geographic location (region) of the yards and the type of hop cultivar had little effect on heterogeneity; however, the year of sampling significantly influenced the intercept and slope parameters of the binary power law. Significant spatial autocorrelation was detected in only 11% of the data sets, with estimates of first-order autocorrelation, r1, ranging from -0.30 to 0.70, with a mean of 0.06 and a median of 0.04; however, correlation was detected in only 20 and 16% of the data sets by median and ordinary runs analysis, respectively. Together, these analyses suggest that the incidence of powdery mildew on cones was slightly aggregated among plants, but patterns of aggregation larger than the sampling unit were rare (20% or less of data sets). Knowledge of the heterogeneity of diseased cones was used to construct fixed sampling curves to precisely estimate the incidence of powdery mildew on cones at varying disease intensities. Use of the sampling curves developed in this research should help to improve sampling methods for disease assessment and management decisions.


2010 ◽  
Vol 30 (1) ◽  
pp. 76-79 ◽  
Author(s):  
Rehan Sadiq Shaikh ◽  
Muhammad Amir ◽  
Ahmed Ijaz Masood ◽  
Asma Sohail ◽  
Habib-ur-Rehman Athar ◽  
...  

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee

Sequential sampling models for estimation and classification of the incidence of powdery mildew (caused by Podosphaera macularis) on hop (Humulus lupulus) cones were developed using parameter estimates of the binary power law derived from the analysis of 221 transect data sets (model construction data set) collected from 41 hop yards sampled in Oregon and Washington from 2000 to 2005. Stop lines, models that determine when sufficient information has been collected to estimate mean disease incidence and stop sampling, for sequential estimation were validated by bootstrap simulation using a subset of 21 model construction data sets and simulated sampling of an additional 13 model construction data sets. Achieved coefficient of variation (C) approached the prespecified C as the estimated disease incidence, [Formula: see text], increased, although achieving a C of 0.1 was not possible for data sets in which [Formula: see text] < 0.03 with the number of sampling units evaluated in this study. The 95% confidence interval of the median difference between [Formula: see text] of each yard (achieved by sequential sampling) and the true p of the original data set included 0 for all 21 data sets evaluated at levels of C of 0.1 and 0.2. For sequential classification, operating characteristic (OC) and average sample number (ASN) curves of the sequential sampling plans obtained by bootstrap analysis and simulated sampling were similar to the OC and ASN values determined by Monte Carlo simulation. Correct decisions of whether disease incidence was above or below prespecified thresholds (pt) were made for 84.6 or 100% of the data sets during simulated sampling when stop lines were determined assuming a binomial or beta-binomial distribution of disease incidence, respectively. However, the higher proportion of correct decisions obtained by assuming a beta-binomial distribution of disease incidence required, on average, sampling 3.9 more plants per sampling round to classify disease incidence compared with the binomial distribution. Use of these sequential sampling plans may aid growers in deciding the order in which to harvest hop yards to minimize the risk of a condition called “cone early maturity” caused by late-season infection of cones by P. macularis. Also, sequential sampling could aid in research efforts, such as efficacy trials, where many hop cones are assessed to determine disease incidence.


2021 ◽  
Vol 13 (9) ◽  
Author(s):  
Görkem Cenk Yeşilova ◽  
Andreu Ollé ◽  
Josep Maria Vergès

AbstractIn this manuscript, we present the first systematic refitting results of the small-scale Middle Pleistocene (MIS11) rock shelter site of La Cansaladeta. The lithic materials that have been recovered from the archaeological levels E and J were the main study materials. These levels were investigated regarding spatial pattern analysis and analyzed with auxiliary methods such as quantitative density mapping demonstration and technological analysis of the lithic clusters. Thus, the spatial patterns of the two levels were compared and discussed, in terms of connections, clusters, and movement of the lithic elements. Undoubtedly, the well preservation of the archaeological levels offered a great opportunity for the interpretation of the spatial patterns in a high-resolution perspective. La Cansaladeta has not been paid attention adequately so far may be due to the small dimension of the excavation surface or to the scarcity of faunal record. Our results show that small-scale sites without long-distance refit/conjoin connections can provide significant spatial information. Indeed, if the sites have very well-preserved archaeological levels, the absence of long connections can be supported by the auxiliary methods.


Citrus canker is the most devastating bacterial disease. In Pakistan, where canker is endemic, cultural practices and chemical control is vital module of integrated management system. But due to non-prudent use of chemicals wicked impacts start to appear on human health and environment there was need of some alternative management which should be eco-friendly and has no adverse effect for human. Therefore, our present study was based on screening of different cultivars of citrus and allelopathic management of citrus canker. The results revealed that Cara cara navel and kinnow both performed as moderately susceptible response in field condition than all other cultivars of citrus in screening experiment. Consequently, disease incidence was observed increasing by increasing the lesion area and these cultivars can be suggested as a source of resistance against canker pathogen. In allelopathic management we observed that ethanolic extracts were more efficient than aqueous extracts and their efficacy was also increasing by increasing the concentration. Ethanolic extracts of jatropha (13.33cm) followed by amaltas (12.5cm), Arjun (11.13cm), Bougenvilla (7.21cm) have great potency against the pathogen. So these ethanolic extracts can be used as good and alternative management of citrus canker disease.


2021 ◽  
Vol 14 (11) ◽  
pp. 2369-2382
Author(s):  
Monica Chiosa ◽  
Thomas B. Preußer ◽  
Gustavo Alonso

Data analysts often need to characterize a data stream as a first step to its further processing. Some of the initial insights to be gained include, e.g., the cardinality of the data set and its frequency distribution. Such information is typically extracted by using sketch algorithms, now widely employed to process very large data sets in manageable space and in a single pass over the data. Often, analysts need more than one parameter to characterize the stream. However, computing multiple sketches becomes expensive even when using high-end CPUs. Exploiting the increasing adoption of hardware accelerators, this paper proposes SKT , an FPGA-based accelerator that can compute several sketches along with basic statistics (average, max, min, etc.) in a single pass over the data. SKT has been designed to characterize a data set by calculating its cardinality, its second frequency moment, and its frequency distribution. The design processes data streams coming either from PCIe or TCP/IP, and it is built to fit emerging cloud service architectures, such as Microsoft's Catapult or Amazon's AQUA. The paper explores the trade-offs of designing sketch algorithms on a spatial architecture and how to combine several sketch algorithms into a single design. The empirical evaluation shows how SKT on an FPGA offers a significant performance gain over high-end, server-class CPUs.


2008 ◽  
Vol 211 (3-4) ◽  
pp. 403-410 ◽  
Author(s):  
Jun Chen ◽  
Masae Shiyomi ◽  
Yoshimichi Hori ◽  
Yasuo Yamamura

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