scholarly journals Spatiotemporal Spread of Abnormal Vertical Growth of Macadamia in Australia Informs Epidemiology

2020 ◽  
Vol 110 (7) ◽  
pp. 1294-1304
Author(s):  
Mohamed C. M. Zakeel ◽  
Andrew D. W. Geering ◽  
Olufemi A. Akinsanmi
Keyword(s):  
Binomial Distribution ◽  
Plant Hormone ◽  
Large Range ◽  
Disease Incidence ◽  
Infectious Agent ◽  
Point Pattern ◽  
Hormone Production ◽  
Vertical Growth ◽  
Rate Of Spread ◽  
Root Contact

Australian macadamia production is threatened by a disorder known as abnormal vertical growth (AVG), for which the etiology is unknown. AVG is characterized by vigorous upright growth and reduced lateral branching, flowering, and nut set that results in over 70% yield loss annually. Six commercial macadamia orchards were surveyed in 2012 and again in 2018 to examine spatiotemporal dynamics of the epidemic. Data were subjected to point-pattern and geostatistical analyses. AVG incidence in all orchards showed a better fit to the beta-binomial distribution than the binomial distribution. AVG incidence in the different orchards varied between 5 and 47% in 2012, and 13 and 55% in 2018 and the rate of spread was slow, averaging at about 2% increase in disease incidence per annum. Spatial patterns of AVG were highly aggregated on both survey years and spread was mainly between neighboring trees in a row or trees that were opposite to each other in different rows. Semivariograms showed large range values (approximately 15 to 120), indicating aggregation of AVG-affected trees beyond quadrat levels. Furthermore, clusters of disease were mainly at the edge of the orchard on the first survey date and the disease progressed toward the center of the orchard over time. It is concluded that AVG is caused by an infectious agent, and based on patterns of spread, we hypothesize that spread is facilitated by root grafting or root-to-root contact. Furthermore, a vascular-limited pathogen could be involved that modulates plant hormone production.

scholarly journals Spatial Heterogeneity of the Incidence of Powdery Mildew on Hop Cones

Plant Disease ◽  
2006 ◽  
Vol 90 (11) ◽  
pp. 1433-1440 ◽  
Author(s):  
David H. Gent ◽  
Walter F. Mahaffee ◽  
William W. Turechek
Keyword(s):  
Powdery Mildew ◽  
Spatial Heterogeneity ◽  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Disease Assessment ◽  
Cluster Sampling ◽  
Data Sets ◽  
Ratio Test ◽  
Frequency Distributions

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.

scholarly journals Sequential Sampling for Estimation and Classification of the Incidence of Hop Powdery Mildew II: Cone Sampling

Plant Disease ◽  
2007 ◽  
Vol 91 (8) ◽  
pp. 1013-1020 ◽  
Author(s):  
David H. Gent ◽  
William W. Turechek ◽  
Walter F. Mahaffee
Keyword(s):  
Powdery Mildew ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Model Construction ◽  
Data Sets ◽  
Data Set ◽  
Sampling Plans ◽  
Simulated Sampling

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.

Spatial Patterns of Microsclerotia of Verticillium dahliae in Soil and Verticillium Wilt of Cauliflower

1997 ◽  
Vol 87 (3) ◽  
pp. 325-331 ◽  
Author(s):  
C. L. Xiao ◽  
J. J. Hao ◽  
K. V. Subbarao
Keyword(s):  
Spatial Patterns ◽  
Verticillium Wilt ◽  
Verticillium Dahliae ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Soil Samples ◽  
Random Pattern ◽  
Parameter Estimates ◽  
Regular Pattern ◽  
Vascular Discoloration

The spatial patterns of microsclerotia of Verticillium dahliae in soil and wilt symptoms on cauliflower were determined at three sites in each of two fields in 1994 and 1995. Each site was an 8 × 8 grid divided into 64 contiguous quadrats (2 by 2 m each). Soil samples were collected to a depth of 15 cm with a probe (2.5 cm in diameter), and samples from four sites in each quadrat were bulked. Plants in each quadrat were cut transversely, and the number of plants with vascular discoloration and the number without discoloration were recorded. The soil was assayed for microsclerotia by the modified Anderson sampler technique. Lloyd's index of patchiness (LIP) was used as an indicator to evaluate the aggregation of microsclerotia in the field. Spatial autocorrelation and geostatistical analyses were also used to assess the autocorrelation of microsclerotia among quadrats. The LIP for microsclerotia was greater than 1, indicating aggregation of propagules; however, the degree of aggregation at most sites was not high. Significant autocorrelation within or across rows was detected in some spatial autocorrelograms of propagules, and anisotropic patterns were also detected in some oriented semivariograms from geostatistical analyses for microsclerotia, indicating the influence of bed preparation in the fields on pathogen distribution. The parameter estimates p and θ in the beta-binomial distribution and the index of dispersion (D) associated with the distribution were used to assess the aggregation of diseased plants at each site. A random pattern of wilt incidence was detected at 7 of 12 sites, and an aggregated pattern was detected at 5 of 12 sites. The degree of aggregation was not high. A regular pattern of wilt severity was detected at all sites. The high disease incidence (77 to 98%) observed at 11 of the 12 sites could be explained by high inoculum density.

scholarly journals Sampling for Plant Disease Incidence

1999 ◽  
Vol 89 (11) ◽  
pp. 1088-1103 ◽  
Author(s):  
L. V. Madden ◽  
G. Hughes
Keyword(s):  
Power Law ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Cluster Sampling ◽  
Diseased Plant ◽  
Ratio Test ◽  
Wide Range ◽  
Sequential Probability ◽  
The Relationship

Knowledge of the distribution of diseased plant units (such as leaves, plants, or roots) or of the relationship between the variance and mean incidence is essential to efficiently sample for diseased plant units. Cluster sampling, consisting of N sampling units of n individuals each, is needed to determine whether the binomial or beta-binomial distribution describes the data or to estimate parameters of the binary power law for disease incidence. The precision of estimated disease incidence can then be evaluated under a wide range of settings including the hierarchical sampling of groups of individuals, the various levels of spatial heterogeneity of disease, and the situation when all individuals are disease free. Precision, quantified with the standard error or the width of the confidence interval for incidence, is directly related to N and inversely related to the degree of heterogeneity (characterized by the intracluster correlation, ρ). Based on direct estimates of ρ (determined from the θ parameter of the beta-binomial distribution or from the observed variance) or a model predicting ρ as a function of incidence (derived from the binary power law), one can calculate, before a sampling bout, the value of N needed to achieve a desired level of precision. The value of N can also be determined during a sampling bout using sequential sampling methods, either to estimate incidence with desired precision or to test a hypothesis about true disease incidence. In the latter case, the sequential probability ratio test is shown here to be useful for classifying incidence relative to a hypothesized threshold when the data follows the beta-binomial distribution with either a fixed ρ or a ρ that depends on incidence.

scholarly journals Spatiotemporal Analysis of Spread of Infections by Verticillium dahliae Pathotypes Within a High Tree Density Olive Orchard in Southern Spain

2008 ◽  
Vol 98 (2) ◽  
pp. 167-180 ◽  
Author(s):  
J. A. Navas-Cortés ◽  
B. B. Landa ◽  
J. Mercado-Blanco ◽  
J. L. Trapero-Casas ◽  
D. Rodríguez-Jurado ◽  
...  
Keyword(s):  
Verticillium Dahliae ◽  
Binary Data ◽  
Disease Incidence ◽  
Spatial Association ◽  
Spatiotemporal Analysis ◽  
Southern Spain ◽  
Cotton Field ◽  
Point Pattern ◽  
Olive Orchard ◽  
Over Time

The development of Verticillium wilt epidemics in olive cv. Arbequina was studied from November 1999 to May 2003 in a drip-irrigated, nontillage orchard established in a soil without a history of the disease at Córdoba, southern Spain. Disease incidence measured at 1-month-intervals increased from 0.2 to 7.8% during this period. Verticillium dahliae infecting the trees was characterized as defoliating (D) or nondefoliating (ND) pathotypes by a specific, multiplex-polymerase chain reaction (PCR) assay. Of the symptomatic trees, 87.2 and 12.8% were infected by the D or ND pathotypes, respectively. Dynamics of disease incidence were described by a generalized logistic model with a multiple sigmoid pattern. In the fitted model, the infection rate was highest in the winter to spring period and decreased to minimum values in the summer to fall period. Binary data of disease incidence was analyzed for point pattern and spatial correlation, either directly or after parsing them in contiguous quadrats. Overall, ordinary runs analysis indicated a departure from randomness of disease within rows. The binomial index of dispersion, interclass correlation, and Taylor's power law for various quadrat sizes suggested aggregation of diseased trees within the quadrat sizes tested. Spatial analysis by distance indices showed a nonrandom arrangement of quadrats containing infected trees. Spatial pattern was characterized by the occurrence of several clusters of infected trees. Increasing clustering over time was generally suggested by stronger values of clustering index over time and by the increase in the size of patch clusters. Significant spatial association was found in the clustering of diseased trees over time across cropping seasons; however, clustering was significant only for infections by D V. dahliae, indicating that infections by the D pathotype were aggregated around initial infections. The number and size of clusters of D V. dahliae-infected trees increased over time. Microsatellite-primed PCR assays of a representative number of V. dahliae isolates from diseased trees indicated that the majority of infecting D isolates shared the fingerprinting profile with D V. dahliae isolated from soil of a naturally infested cotton field in close proximity to the orchard, suggesting that short distance dispersal of the pathogen from this soil to the olive orchard may have occurred.

scholarly journals Spatial Pattern of Strawberry Powdery Mildew (Podosphaera aphanis) and Airborne Inoculum

Plant Disease ◽  
2014 ◽  
Vol 98 (1) ◽  
pp. 43-54 ◽  
Author(s):  
H. Van der Heyden ◽  
M. Lefebvre ◽  
L. Roberge ◽  
L. Brodeur ◽  
O. Carisse
Keyword(s):  
Powdery Mildew ◽  
Power Law ◽  
Negative Binomial Distribution ◽  
Binomial Distribution ◽  
Negative Binomial ◽  
Disease Incidence ◽  
Data Sets ◽  
Incidence Data ◽  
Airborne Inoculum ◽  
The Relationship

The relationship between strawberry powdery mildew and airborne conidium concentration (ACC) of Podosphaera aphanis was studied using data collected from 2006 to 2009 in 15 fields, and spatial pattern was described using 2 years of airborne inoculum and disease incidence data collected in fields planted with the June-bearing strawberry (Fragaria × ananassa) cultivar Jewel. Disease incidence, expressed as the proportion of diseased leaflets, and ACC were monitored in fields divided into 3 × 8 grids containing 24 100 m2 quadrats. Variance-to-mean ratio, index of dispersion, negative binomial distribution, Poisson distribution, and binomial and beta-binomial distributions were used to characterize the level of spatial heterogeneity. The relationship between percent leaf area diseased and daily ACC was linear, while the relationship between ACC and disease incidence followed an exponential growth curve. The V/M ratios were significantly greater than 1 for 100 and 96% of the sampling dates for ACC sampled at 0.35 m from the ground (ACC0.35m) and for ACC sampled at 1.0 m from the ground (ACC1.0m), respectively. For disease incidence, the index of dispersion D was significantly greater than 1 for 79% of the sampling dates. The negative binomial distribution fitted 86% of the data sets for both ACC1.0m and ACC0.35m. For disease incidence data, the beta-binomial distribution provided a good fit of 75% of the data sets. Taylor's power law indicated that, for ACC at both sampling heights, heterogeneity increased with increasing mean ACC, whereas the binary form of the power law suggested that heterogeneity was not dependent on the mean for disease incidence. When the spatial location of each sampling location was taken into account, Spatial Analysis by Distance Indices showed low aggregation indices for both ACCs and disease incidence, and weak association between ACC and disease incidence. Based on these analyses, it was found that the distribution of strawberry powdery mildew was weakly aggregated. Although a higher level of heterogeneity was observed for airborne inoculum, the heterogeneity was low with no distinct foci, suggesting that epidemics are induced by well-distributed inoculum. This low level of heterogeneity allows mean airborne inoculum concentration to be estimated using only one sampler per field with an overall accuracy of at least 0.841. The results obtained in this study could be used to develop a sampling scheme that will improve strawberry powdery mildew risk estimation.

Estimation of Disease Incidence in Fish Populations

1983 ◽  
Vol 40 (12) ◽  
pp. 2194-2197 ◽  
Author(s):  
Donald D. Worlund ◽  
Gib Taylor
Keyword(s):  
Confidence Interval ◽  
Binomial Distribution ◽  
Pacific Salmon ◽  
Disease Incidence ◽  
Sampling Plan ◽  
Fish Populations ◽  
Interval Estimate ◽  
Large Populations ◽  
Juvenile Pacific Salmon

A method is described to estimate the disease incidence in large populations of fish where the material analyzed is composed of a number of pooled individuals. A procedure for calculating a confidence interval estimate is presented, and the bias of the estimate discussed. The methods were developed for estimating disease incidence in hatchery populations of juvenile Pacific salmon (Oncorhynchus spp.), but are applicable to any situation in which the sampling plan can be assumed to follow the binomial distribution.

scholarly journals Relationships Between Several Quadrat-Based Statistical Measures Used to Characterize Spatial Aspects of Disease Incidence Data

2000 ◽  
Vol 90 (6) ◽  
pp. 568-575 ◽  
Author(s):  
M. S. Ridout ◽  
X.-M. Xu
Keyword(s):  
Spatial Autocorrelation ◽  
Binomial Distribution ◽  
Disease Incidence ◽  
Intraclass Correlation ◽  
Quadrat Size ◽  
Incidence Data ◽  
Statistical Measures ◽  
Index Of Dispersion ◽  
Rate Of Increase ◽  
The Mean

This article investigates the relationships between various statistical measures that are used to summarize spatial aspects of disease incidence data. The focus is on quadrat data in which each plant in a quadrat is classified as diseased or healthy. We show that spatial autocorrelation plays a central role via the mean intraclass correlation, ρ, which is defined as the average correlation of the disease status of all pairs of plants within the quadrat. The value of ρ determines the variance of the number of infected plants in the quadrat and, if this variable follows a beta-binomial distribution, the heterogeneity parameter of the beta-binomial distribution is directly related to the mean intraclass correlation. We consider in detail a model in which the spatial autocorrelation depends only on the distance between the plants. For illustration, we consider a specific autocorrelation model that was derived from simulated data. We show that this model leads, approximately, to the binary form of the power law relating the variance of the number of infected plants per quadrat to the mean. Using an approximation technique, we then show how the index of dispersion is related to quadrat size and shape. The index of dispersion increases with quadrat size. The rate of increase is dependent on quadrat shape, but the effect of quadrat shape is small in comparison to the effect of quadrat size. Finally, we note that if the spatial autocorrelation depends on the relative orientation of the plants, as well as the distance between them, there are connections with distance class methods.

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