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1 August 2011 Examining the Spatial Distribution of Flower Thrips in Southern Highbush Blueberries by Utilizing Geostatistical Methods
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Abstract
Flower thrips (Frankliniella spp.) are one of the key pests of southern highbush blueberries (Vaccinium corymbosum L. x V. darrowii Camp), a high-value crop in Florida. Thrips' feeding and oviposition injury to flowers can result in fruit scarring that renders the fruit unmarketable. Flower thrips often form areas of high population, termed “hot spots”, in blueberry plantings. The objective of this study was to model thrips spatial distribution patterns with geostatistical techniques. Semivariogram models were used to determine optimum trap spacing and two commonly used interpolation methods, inverse distance weighting (IDW) and ordinary kriging (OK), were compared for their ability to model thrips spatial patterns. The experimental design consisted of a grid of 100 white sticky traps spaced at 15.24-m and 7.61-m intervals in 2008 and 2009, respectively. Thirty additional traps were placed randomly throughout the sampling area to collect information on distances shorter than the grid spacing. The semivariogram analysis indicated that, in most cases, spacing traps at least 28.8 m apart would result in spatially independent samples. Also, the 7.61-m grid spacing captured more of the thrips spatial variability than the 15.24-m grid spacing. IDW and OK produced maps with similar accuracy in both years, which indicates that thrips spatial distribution patterns, including “hot spots,” can be modeled using either interpolation method. Future studies can use this information to determine if the formation of “hot spots” can be predicted using flower density, temperature, and other environmental factors. If so, this development would allow growers to spot treat the “hot spots” rather than their entire field.
© 2011 Entomological Society of America
Elena M. Rhodes, Oscar E. Liburd and Sabine Grunwald "Examining the Spatial Distribution of Flower Thrips in Southern Highbush Blueberries by Utilizing Geostatistical Methods," Environmental Entomology 40(4), (1 August 2011). https://doi.org/10.1603/EN10312
Received: 21 December 2010; Accepted: 1 May 2011; Published: 1 August 2011
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