The Impact of Spatial Structure on the Accuracy of Contour Maps of Small Data Sets

Abstract

Spatial analysis of insect counts provides important information about how insect species respond to the heterogeneity of a given sampling space. Contour mapping is widely used to visualize spatial pest distribution patterns in anthropogenic environments, and in this study we outlined recommendations regarding semivariogram analysis of small data sets (N < 50). Second, we examined how contour maps based upon linear kriging were affected by the spatial structure of the given data set, as error estimation of contour maps appears to have received little attention in the entomological domain. We used weekly trap catches of the warehouse beetle, Trogoderma variabile, and the accuracy assessment was based upon data sets that had either a random spatial structure or were characterized by asymptotic spatial dependence. Asymptotic spatial dependence (typically described with a semivariogram analysis) means that trap catches at locations close to each other are more similar than trap catches at locations further apart. Trap catches were poorly predicted for data sets with a random spatial structure, while there was a significant correlation between observed and predicted trap catches for the spatially rearranged data sets. Therefore, for data sets with a random spatial structure we recommend visualization of the insect counts as scale-sized dots rather than as contour maps.