scholarly journals Spatial Pattern Analysis of Sharka Disease (Plum pox virus Strain M) in Peach Orchards of Southern France

2003 ◽  
Vol 93 (12) ◽  
pp. 1543-1552 ◽  
Author(s):  
Sylvie Dallot ◽  
Tim Gottwald ◽  
Gérard Labonne ◽  
Jean-Bernard Quiot
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Disease Incidence ◽  
Point Pattern ◽  
Plum Pox Virus ◽  
Spatial Pattern Analysis ◽  
Individual Tree ◽  
Southern France ◽  
Wide Range ◽  
Sharka Disease

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.

Plot Edge Bias in Forest Stand Growth Simulation Models

1974 ◽  
Vol 4 (4) ◽  
pp. 419-423 ◽  
Author(s):  
Robert A. Monserud ◽  
Alan R. Ek
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Simulation Models ◽  
Bias Reduction ◽  
Forest Stand ◽  
Tree Size ◽  
Individual Tree ◽  
Growth Simulation ◽  
Relative Simplicity ◽  
Stand Growth

The problem of edge bias arising in the computation of individual tree competition in stand growth simulation models is discussed. The problem arises from difficulty in characterizing tree size and spatial patterns beyond the edge of the simulation plot. Various methods for reducing this bias are reviewed along with related assumptions and major sources of error. Methods which involve shifting the simulation plot image to form a set of border plots are judged best on the basis of likely bias reduction and the relative simplicity of introduced spatial pattern periodicity.

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
Keyword(s):  
Frequency Distribution ◽  
Spatial Patterns ◽  
Disease Incidence ◽  
Citrus Canker ◽  
Data Sets ◽  
Spatial Pattern Analysis ◽  
Infected Tree ◽  
Spatial Dimensions ◽  
Spatial Level ◽  
Β Function

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.

scholarly journals Monitoring methods and spatial patterns of European canker disease in commercial orchards

2016 ◽  
Vol 69 ◽  
pp. 213-220 ◽  
Author(s):  
R.E. Campbell ◽  
S. Roy ◽  
T. Curnow ◽  
M. Walter
Keyword(s):  
Spatial Pattern ◽  
Disease Control ◽  
Spatial Patterns ◽  
Disease Incidence ◽  
Systematic Sampling ◽  
Monitoring Methods ◽  
Canker Disease ◽  
Single Row ◽  
Changes Over Time ◽  
Over Time

European canker (Neonectria ditissima) kills trees and decreases production in apple orchards To determine a level of disease control or the extent of its spread in commercial orchards efficient monitoring methods are required In this study we investigated two monitoring methods sampling a single row and systematic sampling of an orchard block The spatial pattern of disease within blocks and whether this changes over time was also investigated The accuracy of singlerow monitoring depended on the level of canker in the orchard and the patchiness of the distribution of infected trees However singlerow monitoring tracked changes over time in incidence severity and type of canker sufficiently well and was efficient The spatial patterns of disease incidence across the blocks were nonrandom but showed hotspots which did not change significantly over time

scholarly journals Dynamics of Lettuce Drop Incidence and Sclerotinia minor Inoculum Under Varied Crop Rotations

Plant Disease ◽  
2006 ◽  
Vol 90 (3) ◽  
pp. 269-278 ◽  
Author(s):  
J. J. Hao ◽  
K. V. Subbarao
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Field Experiments ◽  
Disease Incidence ◽  
Inoculum Density ◽  
Crop Rotations ◽  
Sclerotinia Minor ◽  
Progressive Decline ◽  
Fallow Period ◽  
Lettuce Drop

Field experiments were conducted to determine the population dynamics of Sclerotinia minor and incidence of lettuce drop at two sites during 1995 to 1998. Rotation treatments at the Spence site, which had a low density of inoculum (<7 sclerotia per 100 cm3 of soil) that was distributed randomly, included: continuous lettuce (LLL), lettuce rotated with broccoli (LBL), and lettuce followed by a fallow period (LFL). Treatments at the Hartnell site, which had a high density of inoculum (>7 sclerotia per 100 cm3 of soil) that was distributed uniformly, included: continuous lettuce (LLLL), alternate crops of broccoli and lettuce (BLBL), continuous broccoli or lettuce (BBLL), and fallow-lettuce-fallow-lettuce (FLFL). Under continuous lettuce cropping (LLLL) at the Hartnell site, a progressively aggregated spatial pattern of inoculum distribution developed, despite the initial uniform distribution of high inoculum density. In the fallow treatment (FLFL), the spatial pattern tended to be aggregated following a lettuce crop and less aggregated or random when left fallow. In contrast to these two treatments, treatments involving rotations with broccoli (BLBL and BBLL) exhibited consistently random spatial patterns of inoculum regardless of the crop in the field. The marginal increases in the number of sclerotia contributed by the few diseased lettuce plants were offset by the significant reductions in the number of sclerotia by the broccoli residue. Spatial patterns of disease incidence reflected the pattern of inoculum distribution in the soil at the Hartnell site. Higher inoculum density coupled with an aggregated distribution was associated with an aggregation in disease incidence. At Spence, this correlation was poor in most seasons because of progressive decline in the lettuce drop incidence and lack of treatment differences. In greenhouse experiments, the competence volume for S. minor sclerotia was quantified, which was calculated to be 25 3 for 100% infection and 200 cm3 for 50% infection. Thus, in 100 cm3 of soil, a minimum of four to five sclerotia are needed for 100% of infection, explaining the high correlation between inoculum density and disease incidence.

scholarly journals Spatial Pattern Analysis of Strawberry Leaf Blight in Perennial Production Systems

1999 ◽  
Vol 89 (5) ◽  
pp. 421-433 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden
Keyword(s):  
Spatial Pattern ◽  
Disease Incidence ◽  
Spatial Association ◽  
Disease Status ◽  
Leaf Blight ◽  
Data Sets ◽  
Ratio Test ◽  
Spatial Pattern Analysis ◽  
Test Statistic ◽  
Degree Of Heterogeneity

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.

scholarly journals Spatial Pattern Analysis and Sequential Sampling for the Incidence of Leaf Spot on Strawberry in Ohio

Plant Disease ◽  
1999 ◽  
Vol 83 (11) ◽  
pp. 992-1000 ◽  
Author(s):  
W. W. Turechek ◽  
L. V. Madden
Keyword(s):  
Spatial Pattern ◽  
Leaf Spot ◽  
Disease Incidence ◽  
Sequential Sampling ◽  
Positive Association ◽  
Estimation Procedure ◽  
Data Sets ◽  
Ratio Test ◽  
Spatial Pattern Analysis ◽  
Significant Positive Association

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.

scholarly journals Analysis of the Spatial Pattern of Strawberry Angular Leaf Spot in California Nursery Production

2017 ◽  
Vol 107 (10) ◽  
pp. 1243-1255 ◽  
Author(s):  
Christophe Gigot ◽  
William Turechek ◽  
Neil McRoberts
Keyword(s):  
Spatial Pattern ◽  
Leaf Spot ◽  
Management Practices ◽  
Disease Incidence ◽  
Spatial Association ◽  
Angular Leaf Spot ◽  
Disease Spread ◽  
Point Pattern ◽  
Common Disease ◽  
Nursery Production

In California, angular leaf spot (ALS) is a common disease in strawberry nursery production, and a major concern for nurseries wishing to export plants. As the spatial pattern of a disease can offer insight into pathogen source, mode of dissemination, and how current crop management practices affect epidemic development, an understanding of the spatial pattern of ALS would allow nursery growers to make informed decisions regarding disease management. Ninety-seven field assessments of disease incidence were performed at different nursery locations in 2014 and 2015 to quantify ALS spatial pattern under commercial conditions. Both point-pattern and geostatistical statistical procedures were used to analyze the data. The spatial pattern of ALS was characterized by a high degree of heterogeneity, as indicated by high median values of the beta-binomial distribution’s theta parameter (0.643), and the index of dispersion, D (4.218). The binary power law provided a robust description of the data with estimated slope and intercept parameters significantly greater than 1 and 0, respectively (P < 0.001). Spatial analysis by distance indices (SADIE) detected significant nonrandom spatial arrangements for 64% of the data sets. Analysis of directional disease spread showed a strong spatial association between sampling units along the same planting row. This suggests that recurrent crop operations during the growing season play a significant role in ALS spread and should be taken into account to improve disease control.

scholarly journals Type IV pilus shapes a "bubble-jet" pattern opposing spatial intermixing of two interacting bacterial populations

2021 ◽  
Author(s):  
Miaoxiao Wang ◽  
Xiaoli Chen ◽  
Yinyin Ma ◽  
David Johnson ◽  
Yong Nie ◽  
...  
Keyword(s):  
Spatial Pattern ◽  
Spatial Patterns ◽  
Pseudomonas Stutzeri ◽  
Community Level ◽  
Type Iv Pilus ◽  
Bacterial Populations ◽  
Type Iv ◽  
Wide Range ◽  
Genes Encoding ◽  
Different Populations

Microbes are social organisms that commonly live in sessile biofilms. Spatial patterns of populations within biofilms can be an important determinant of community-level properties. The best-studied characteristics of spatial patterns is spatial intermixing of different populations. The specific levels of spatial intermixing critically contribute to how the dynamics and functioning of such communities are governed. However, the precise factors that determine spatial patterns and intermixing remain unclear. Here, we investigated the spatial patterning and intermixing of an engineered synthetic consortium composed of two Pseudomonas stutzeri strains that degrade salicylate via metabolic cross-feeding. We found that the consortium self-organizes across space to form a previously unreported spatial pattern (referred to here as a "bubble-jet" pattern) that exhibits a low level of intermixing. Interestingly, when the genes encoding for type IV pili were deleted from both strains, a highly intermixed spatial pattern developed and increased the productivity of the entire community. The intermixed pattern was maintained in a robust manner across a wide range of initial ratios between the two strains. Our findings show that the type IV pilus plays a role in mitigating spatial intermixing of different populations in surface-attached microbial communities, with consequences for governing community-level properties. These insights provide tangible clues for the engineering of synthetic microbial systems that perform highly in spatially structured environments.

scholarly journals Multi-scale assessment of post-fire tree mortality models

2019 ◽  
Vol 28 (1) ◽  
pp. 46 ◽  
Author(s):  
Tucker J. Furniss ◽  
Andrew J. Larson ◽  
Van R. Kane ◽  
James A. Lutz
Keyword(s):  
Spatial Pattern ◽  
Sierra Nevada ◽  
Tree Mortality ◽  
Spatial Scales ◽  
Large Diameter ◽  
Fire Effects ◽  
Point Pattern ◽  
Individual Tree ◽  
Mortality Models ◽  
Rim Fire

Post-fire tree mortality models are vital tools used by forest land managers to predict fire effects, estimate delayed mortality and develop management prescriptions. We evaluated the performance of mortality models within the First Order Fire Effects Model (FOFEM) software, and compared their performance to locally-parameterised models based on five different forms. We evaluated all models at the individual tree and stand levels with a dataset comprising 34174 trees from a mixed-conifer forest in the Sierra Nevada, California that burned in the 2013 Rim Fire. We compared stand-level accuracy across a range of spatial scales, and we used point pattern analysis to test the accuracy with which mortality models predict post-fire tree spatial pattern. FOFEM under-predicted mortality for the three conifers, possibly because of the timing of the Rim Fire during a severe multi-year drought. Locally-parameterised models based on crown scorch were most accurate in predicting individual tree mortality, but tree diameter-based models were more accurate at the stand level for Abies concolor and large-diameter Pinus lambertiana, the most abundant trees in this forest. Stand-level accuracy was reduced by spatially correlated error at small spatial scales, but stabilised at scales ≥1ha. The predictive error of FOFEM generated inaccurate predictions of post-fire spatial pattern at small scales, and this error could be reduced by improving FOFEM model accuracy for small trees.

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