Unbiased estimation of greenhouse whitefly, <i>Trialeurodes vaporariorum</i>, mean density using yellow sticky trap in cherry tomato greenhouses

Kim, Jong-Kwan; Park, Jung-Joon; Park, Heungsun; Cho, Kijong

doi:10.1046/j.1570-7458.2001.00868.x

Cited by 25 publications

(20 citation statements)

References 22 publications

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“…Data from both direct observation and the beat cup methods were used to describe the intraÞeld spatial distribution of thrips in timothy. Following the procedure of Kim et al (2001), TaylorÕs power law (TPL; Taylor 1961) was calculated. Because TPL is a measure of dispersion, it should only be used to describe a population distribution when data are not autocorrelated.…”

Section: Methodsmentioning

confidence: 99%

Spatial Dependence, Dispersion, and Sequential Sampling ofAnaphothrips obscurus(Thysanoptera: Thripidae) in Timothy

Reisig

Godfrey

Marcum³

2011

Environ Entomol

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The spatial distribution and dispersion of Anaphothrips obscurus (Müller) (Thysanoptera: Thripidae) was examined with the goal of establishing a sequential sampling plan for this pest in timothy, Phleum pratense L. (Poaceae). Approximately 16 different California timothy fields were sampled twice yearly from 2006 to 2008 using direct observation and the beat cup method. For direct observation, the number of thrips on each leaf of the plant was counted. For the beat cup method, tillers were tapped into a cup and dislodged thrips were counted. Samples were separated by ≈3 m in 2006 and 2007 and exactly 3 m in 2008. Spatial autocorrelation of intrafield population distribution was tested for significance in 2008 using Moran's I, but autocorrelation was not detected. The population dispersion was assessed by Taylor's power law and was determined to be aggregated and density-dependent. Intraplant population dispersion and distribution for each year were also evaluated for adults, larvae, and total thrips. All lifestages were highly spatially dependent and more thrips were found near the top of the plant than the bottom. Direct observation proved to be a more accurate and precise method than the beat cup method, especially when thrips abundances were greater than one. However, the number of samples required to provide an accurate level of precision was unrealistic for both methods. A sequential sampling plan was evaluated, but was not practical for the beat cup method because few thrips were found using this method. Because there was no spatial autocorrelation at sampling distances of 3 m, samples can be taken at intervals at 3 m to obtain spatially independent population abundance estimates.

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Section: Methodsmentioning

confidence: 99%

Spatial Dependence, Dispersion, and Sequential Sampling ofAnaphothrips obscurus(Thysanoptera: Thripidae) in Timothy

Reisig

Godfrey

Marcum³

2011

Environ Entomol

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“…Ecologists and applied entomologists have recently begun to use two geostatistical techniques for describing the spatial distribution of insect populations (Schotzko and O'Keeffe 1989;Liebhold et al 1993;Midgarden et al 1993;Cho et al 2001;Kim et al 2001;Wright et al 2002). These two approaches are the variogram, which is a way to model spatial dependency, and kriging, which provides estimates of unrecorded locations (Isaaks and Srivastava 1989;Rossi et al 1992;Cressie 1993;Kitanidis 1997;Brandhorst-Hubbard et al 2001).…”

Section: Discussionmentioning

confidence: 99%

Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Park

Shin

Lee

et al. 2011

Ecological Research

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Outlier detection and cleaning procedures were evaluated to estimate mathematical restricted variogram models with discrete insect population count data. Because variogram modeling is significantly affected by outliers, methods to detect and clean outliers from data sets are critical for proper variogram modeling. In this study, we examined spatial data in the form of discrete measurements of insect counts on a rectangular grid. Two well-known insect pest population data were analyzed; one data set was the western flower thrips, Frankliniella occidentalis (Pergande) on greenhouse cucumbers and the other was the greenhouse whitefly, Trialeurodes vaporariorum (Westwood) on greenhouse cherry tomatoes. A spatial additive outlier model was constructed to detect outliers in both the isolated and patchy spatial distributions of outliers, and the outliers were cleaned with the neighboring median cleaner. To analyze the effect of outliers, we compared the relative nugget effects of data cleaned of outliers and data still containing outliers after transformation. In addition, the correlation coefficients between the actual and predicted values were compared using the leave-one-out crossvalidation method with data cleaned of outliers and non-cleaned data after unbiased back transformation. The outlier detection and cleaning procedure improved geostatistical analysis, particularly by reducing the nugget effect, which greatly impacts the prediction variance of kriging. Consequently, the outlier detection and cleaning procedures used here improved the results of geostatistical analysis with highly skewed and extremely fluctuating data, such as insect counts.

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“…A 30ϫ50-m (1,500 m 2 ) portion of each greenhouse was surveyed 4-6 times, at weekly intervals. The cherry tomato plants in all the greenhouses were grown using the modified ventral cordon system (Kim et al, 2001), which is used in Ϸ90% of the cherry tomato acreage in Korea. Horizontal support wires were positioned directly over the rows of plants at a height of 1.8-2.0 m. Initially, each plant was trained vertically along and around the support plastic twine, and then tied with plastic snap-on clips.…”

Section: Methodsmentioning

confidence: 99%

Development and validation of binomial sampling plans for estimating leafmine density of Liriomyza trifolii (Diptera: Agromyzidae) in greenhouse tomatoes

Lee

Park

Lee

et al. 2005

Appl. Entomol. Zool.

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The dynamics of leafmines developed by Liriomyza trifolii (Burgess) larvae in greenhouse tomatoes were studied in Korea during [2003][2004] in order to construct and validate binomial sampling plans. An empirical P T -m model, expressed as: ln(m)ϭaϩb ln[Ϫln(1ϪP T )], was used to relate between the proportion of infested leaves (P T ) and mean densities (m) at tally thresholds (T, the minimum number of leafmines present before a leaf is considered infested) of 1, 2, 3, 4 and 5 mines per leaf. To ensure the consistent selection of sample units, a sequence of reference pictures, with various levels of leafmine formation, was measured in advance by image analysis. The mines Ͻ0.4 cm 2 in area were excluded from counting in the greenhouses. There were no significant relationships between the total numbers of leafmines and individual mine areas. The binomial sampling plans were validated using resampling simulations with seven independent data sets. In an estimation of the density, the sampling precision (SE/mean) was found to increase with higher Ts; however, there were negligible improvements in the precision with TՆ3 mines per leaf. Using Tϭ3, over a wide range of mine densities, as few as 30 samples were necessary to achieve a precision of 0.30. In comparing binomial models with Tϭ3 and 5, using seven independent data, the model with Tϭ3 was a robust and relatively unbiased predictor of the mean density, whereas the Tϭ5 model was generally biased towards over-prediction of the mean density. The binomial sampling plans presented here should permit rapid estimation of the mine density and enhance development for a damage assessment program in greenhouse tomatoes.

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Unbiased estimation of greenhouse whitefly, Trialeurodes vaporariorum, mean density using yellow sticky trap in cherry tomato greenhouses

Cited by 25 publications

References 22 publications

Spatial Dependence, Dispersion, and Sequential Sampling ofAnaphothrips obscurus(Thysanoptera: Thripidae) in Timothy

Spatial Dependence, Dispersion, and Sequential Sampling ofAnaphothrips obscurus(Thysanoptera: Thripidae) in Timothy

Detecting and cleaning outliers for robust estimation of variogram models in insect count data

Development and validation of binomial sampling plans for estimating leafmine density of Liriomyza trifolii (Diptera: Agromyzidae) in greenhouse tomatoes

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