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A cleptoparasitic mite, the Krombein’s hairy-footed mite, Chaetodactylus krombeini Baker (Acari, Chaetodactylidae) became a key pest that affects the maintenance and propagation of Osmia spp. (Hym., Megachilidae), thus disrupting orchard pollination in the United States. Although hypopi, the dispersal stages of C. krombeini, are known to disperse from nest to nest by hitchhiking on Osmia cornifrons adults, we observed that they might disperse in other ways too in commercial orchards. This study was conducted to elucidate the nest-to-nest dispersal mechanisms of C. krombeini hypopi. We tested three potential dispersal mechanisms of C. krombeini other than phoresy by O. cornifrons: (1) dispersal by walking from nest to entrances of nearby nests, (2) dispersal by walking from nest to nest through emergence holes made by parasitic wasps on nests, and (3) dispersal by being unloaded and uptaken to and from flowers by O. cornifrons. Results of this study showed that C. krombeini hypopi could disperse from a nest to nearby nests by walking through nest entrances and holes made by parasitic wasps of O. cornifrons. Although 0.06% of C. krombeini hypopi on blueberry flowers were picked up by O. cornifrons, they were not able to be unloaded to flowers from O. cornifrons and no hypopi could inhabit or survive on blueberry flowers. This indicated no or very low chance of C. krombeini hypopi dispersal via blueberry flowers. Based on our findings of C. krombeini dispersal ecology, development of C. krombeini control strategies are discussed in this article. [ABSTRACT FROM AUTHOR]

To develop spatial sampling plans for corn rootworm ( Diabrotica spp.) adults, their spatial distributions were characterized and economics of sampling plans were evaluated by comparing sampling costs between spatial and conventional (non-spatial) sampling plans. Semivariogram modelling and spatial by with distance indices showed that corn rootworm adults were significantly (P < 0.05) aggregated at peak population densities and any two samples were spatially correlated within approximately 45 m, with 39–90% of the variability explained by spatial dependence. Sampling costs for spatial sampling plans linearly increased as the sampling distance decreased and exponentially increased as the field size increased. Although sampling costs for non-spatial sampling plans were generally lower, spatial sampling plans could be more economical when the mean insect density became lower and the field size became smaller. This study demonstrated that spatial sampling plans could be optimized to minimize the sampling costs and maximize the spatial resolution. [ABSTRACT FROM AUTHOR]