Spatial distribution and colonization pattern of Bemisia tabaci in tropical tomato crops

Abstract

BACKGROUND In precision integrated pest management, management tactics are implemented only where and when needed, by identifying the sites where the pest population has reached economic thresholds. Tomato, Solanum lycopersicum (Linn.), is a vegetable cultivated worldwide, but its production is reduced by insect pests such as the whitefly, Bemisia tabaci (Genn.). To improve management, there is a need to understand B. tabaci spatial dynamics in tomato fields, which will elucidate colonization patterns and may improve management of this pest. Thus, this study was conducted to assess the spatial autocorrelation, distribution, and colonization patterns of B. tabaci in 19 commercial tomato fields through the growing season. RESULTS A total of 69 isotropic variograms were fit for B. tabaci. The insect distribution was aggregated with a strong level of spatial dependence. Ranges of spatial dependence varied from 0.53 to 19.05 m and 0.5 to 20 m for adults and nymphs, respectively. Overall, densities of adults and nymphs were higher and reached the economic threshold mainly at the field edges. CONCLUSION Our results suggest a colonization pattern for B. tabaci starting at the edges and spreading inwards in to the tomato fields. This study can improve B. tabaci management in tomato fields, especially scouting and decision‐making to treat fields. Scouting for this pest should be directed to the field edges, with sample points at least 20 m apart from each other for independent insect counts. © 2020 Society of Chemical Industry

Spatial Distribution of Hunting Billbugs (Coleoptera: Curculionidae) in Sod Farms

“…Understanding spatial distributions helps to predict and manage pest populations by implementing accurate sampling plans and decision-making processes [25]. When using variograms to analyze the spatial distribution data, the range value of the variogram has a significant role for site-specific IPM efforts [42,55,56].…”

“…All models with evidence of spatial dependency have an additional parameter called "range". Range is the maximum distance between samples below which spatial autocorrelation is present [34,38], and the range value plays a critical role in determining the adequate sampling distance for an unbiased, independent sampling plan [6,11,15,25,39]. The nugget-to-sill ratio (C 0 /C 0 + C) and nugget were used to determine the degree of aggregation [40], where ratios <0.25, 0.25-0.75, and >0.75 indicated strong, moderate, and weak aggregation, respectively [11,[41][42][43].…”

“…Several geospatial methods, such as spatial analysis by distance indices (SADIE) and variograms, can be used to assess insect spatial distributions [14,20,[23][24][25]. Variograms are commonly used to analyze and model the spatial dependences among individuals in a population [14,25]. Spatial dependence (or spatial autocorrelation) can be used to define the sampling scales for independent samples and to quantify the spatial patterns of insect species [11,26].…”

Spatio‐temporal distribution of Anthonomus grandis grandis Boh. in tropical cotton fields

“…16,26 Linear variogram models indicate random distribution, whereas exponential, spherical or Gaussian models indicate aggregation or spatial dependence. 11,13 Therefore, following selection of the curvilinear models (exponential, spherical or Gaussian), SD was ensured, and digitalized maps containing interpolated data were built using ordinary kriging interpolation. Maps allowed to keep track of boll weevil infestation across seasons.…”

“…(1) develop site-specific sampling and control efforts; (2) predict pest movement; (3) improve insecticide-resistance management; (4) conserve biological control agents by precision targeting sprays for the infested areas; and (5) reduce the economic, social and environmental costs associated with pest control. 10,11,13 The spatial distribution of boll weevils has been investigated using mean-variance relationships 4,14 without considering within-field spatial density distribution, or has been based on pheromone-baited trap captures. 15 The most accurate approach is the use of geostatistics because the position of the samples in space is accounted for.…”