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A detailed knowledge on the spatial distribution of pests is crucial for predicting population outbreaks or developing control strategies and sustainable management plans. The diamondback moth,  Plutella xylostella , is one of the most destructive pests of cruciferous crops worldwide. Despite the abundant research on the species’s ecology, little is known about the spatio-temporal pattern of  P .  xylostella  in an agricultural landscape. Therefore, in this study, the spatial distribution of  P .  xylostella  was characterized to assess the effect of landscape elements in a fine-scale agricultural landscape by geostatistical analysis. The  P .  xylostella  adults captured by pheromone-baited traps showed a seasonal pattern of population fluctuation from October 2015 to September 2017, with a marked peak in spring, suggesting that mild temperatures, 15–25 °C, are favorable for  P .  xylostella . Geostatistics (GS) correlograms fitted with spherical and Gaussian models showed an aggregated distribution in 21 of the 47 cases interpolation contour maps. This result highlighted that spatial distribution of  P .  xylostella  was not limited to the  Brassica  vegetable field, but presence was the highest there. Nevertheless, population aggregations also showed a seasonal variation associated with the growing stage of host plants. GS model analysis showed higher abundances in cruciferous fields than in any other patches of the landscape, indicating a strong host plant dependency. We demonstrate that  Brassica  vegetables distribution and growth stage, have dominant impacts on the spatial distribution of  P .  xylostella  in a fine-scale landscape. This work clarified the spatio-temporal dynamic and distribution patterns of  P .  xylostella  in an agricultural landscape, and the distribution model developed by geostatistical analysis can provide a scientific basis for precise targeting and localized control of  P .  xylostella .

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Spatio-temporal distribution patterns of Plutella xylostella (Lepidoptera: Plutellidae) in a fine-scale agricultural landscape based on geostatistical analysis