Identifying high‐density regions of pests within an orchard

Abstract

This paper proposes a statistical method for identifying high‐density regions of pests, so‐called hot spots, within an orchard. Our method uses scanning windows to search for clusters of high counts within the sampled data. The proposed method enables a localized alternative for treatment that could be faster, less costly, and more environmentally friendly. R code that implements the hot spot identification method is provided as online supplementary material. The method is illustrated through simulated examples and a real data on counts of cottony cushion scales from an orchard.

Characterizing the Spatiotemporal Distribution of Three Native Stink Bugs (Hemiptera: Pentatomidae) across an Agricultural Landscape

“…In addition to interpolations, trap sites were symbolized on maps in red if the local trap site cluster index was >1.5, or a significant aggregation, and in blue if the index was <−1.5, or a significant gap. Although SADIE cluster analysis identifies where significant aggregations occur in the landscape, this type of analysis does not indicate the number of individuals that form aggregations [36]. However, density data can be used to understand the magnitude of counts that make up significant aggregations, as well as display broader seasonal distribution patterns of the general population.…”