Optimisation of Drop-Sheet Sampling in Macadamia (Macadamia integrifolia) Orchards: An Optimised Method for Monitoring Insect Ecology

Abstract

Understanding orchard arthropod populations is a critical element of making strategic decisions regarding pest management. Therefore, it is paramount for farmers and crop consultants to use appropriate survey methods that provide a representative sample of orchard insect ecology. Currently, macadamia (Macadamia integrifolia) industry arthropod sampling strategies are limited by the number of established methods available for use. Drop-sheet sampling has recently been introduced to the industry but requires optimisation for industry implementation. This study investigated how sheet size (1, 4, 9, 16 and 25 m²) affected arthropod diversity representation, sample time efficiency and required sampling effort. The method was then verified through a snapshot regional survey of the Gympie macadamia production region, conducted prior to flowering to establish baseline arthropod assemblages before seasonal increases in insect activity. It was found that as sheet size increased, so did the relative sample representation as well as the time taken to sample. Sheet sizes of 25 m² were the most effective sheet size overall for use in drop-sheet sampling and yielded the greatest species richness, community representation and sample completeness. Additionally, we found that minimum sample effort of five replicates was required for 25 m² sheet sizes. The regional field survey further improved on sample effort optimisation, where seven replicates were determined to be capable of providing representative samples from orchard blocks across independent sites. Using drop-sheeting in the pre-flowering survey of the Gympie macadamia production region recovered a wide range of arthropod orders, assemblages and rare species. Whereas validation on a wider spatial and temporal range is required, drop-sheeting is proving to be a useful tool for research and surveys on macadamia orchard insect ecology.