Spatial Distribution of Frankliniella schultzei (Thysanoptera: Thripidae) in Open-Field Yellow Melon, With Emphasis on the Role of Surrounding Vegetation as a Source of Initial Infestation

Abstract

Frankliniella schultzei (Trybom) is a serious pest of melon crops and is commonly found in the main producing areas of melon in Brazil (North and Northeast regions). This pest causes significant losses, damaging plants through feeding and tospovirus vectoring. Thus, the proper management of F. schultzei is crucial to prevent economic losses, and knowledge of the within-field distribution patterns of F. schultzei can be used to improve this pest management. This study aimed to determine the within-field distribution (through semivariogram modeling and kriging interpolation) and the factors associated with F. schultzei abundance in open-field yellow melon crops. We surveyed four yellow melon fields located in Formoso do Araguaia (Tocantins state, North Brazil) for thrips abundance in various crop stages (vegetative, flowering, and fruiting) in 2015 and 2016. Twelve models were fitted and it was determined that F. schultzei counts were strongly aggregated. The median spatial dependence was 4.79 m (range 3.55 to 8.02 m). The surface maps generated by kriging depicted an edge effect in fields 3 and 4. In addition, correlation analyses indicated that air temperature and presence of surrounding cucurbits are positively associated with F. schultzei abundance in yellow melon fields. Altogether, these insights can be combined for spatially based pest management, especially when the conditions (cucurbits in the surroundings and warmer periods) are favorable to F. schultzei.

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]. The average range value of the selected variograms in our study was 3.9 m (i.e., the cumulative mean of all three sites) for the larval S. venatus vestitus distributions and 5.4 m (the cumulative mean of all three sites) for the adult S. venatus vestitus distributions.…”

“…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]. After selection of the variograms, interpolated pest distribution maps of billbug infestations were generated to visually demonstrate the infestation hot spots in the fields using the kriging interpolation technique [11,[44][45][46].…”

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

“…Range values are the minimum distances over which the dependence among samples is maintained. 13,60 For sampling purposes, to determine infestation levels for triggering insecticide treatments, the minimum distance adopted to monitor the boll weevil population should be higher than the average range value of the variograms. 11,13 The range values in our study varied according to the cotton's phenological stage; the minimum estimated distance between samples should be more than 0.7 m and the maximum distance should be approximately 600 m. This recommendation should be incorporated in the future development of sampling plans for the pest and contribute to the reduction in the sampling effort while monitoring the pest.…”

Spatial–temporal distribution of Dalbulus maidis (Hemiptera: Cicadellidae) and factors affecting its abundance in Brazil corn

“…Through geostatistics, we can detect spatial variability and quantify correlations among sampled points based on semivariograms. 17,18 This information can be applied in interpolation algorithms to provide estimates of unsampled areas and build surface maps identifying places in greatest need of sampling and control measures. 14,18,19 Dalbulus maidis population dynamics may be affected by the wheatear conditions and surrounding vegetation.…”

“…17,18 This information can be applied in interpolation algorithms to provide estimates of unsampled areas and build surface maps identifying places in greatest need of sampling and control measures. 14,18,19 Dalbulus maidis population dynamics may be affected by the wheatear conditions and surrounding vegetation. This study aims to investigate within-field spatial distribution and the factors associated with D. maidis abundance in cornfields.…”