Customized and rapid observational delimiting surveys for plant pests based on transect data and scouting

We have created a complete, standard methodology for designing customized observation-based delimiting surveys for localized incursions of non-endemic plant pests. We propose using transects customized for the species and situation to do the following: Collect data for ∼120 detections Fit an exponential function to the histogram using an extreme values analysis peaks-over-threshold (EVA POT) technique Calculate a percentile-based boundary distance that accounts for two-dimensional (radial) spread Use field scouting for verification We call the approach “Delimitation via Transect Data and Scouting,” or DTDS. In simulations we compared DTDS plans to published survey plans for the Asian longhorned beetle (ALB; Anoplophora glabripennis (Motschulsky)), the fungus Phyllosticta citricarpa (Guigci), and the tomato brown rugose fruit virus (TBRFV; Tobamovirus). The EVA POT method avoided problems with highly skewed data, and ninety-ninth percentile boundary estimates from it that accounted for radial dispersal always contained the adventive populations. Based on sample sizes, time required, and success (containment) rates the DTDS surveys were efficient and effective. For ALB and TBRFV, DTDS surveys reduced sample sizes and durations by 77 percent or more. For P. citricarpa , the DTDS design greatly increased the sample area, but eliminated 6% (scenario 1) and 68% (scenario 2) failure rates in the original plan. The DTDS methodology synthesizes established techniques in a novel way, providing a standardized, clear, and often rapid process for designing customized, transect-based observation-based delimitation surveys that are both efficient and effective. The approach has clear management and financial benefits, and should facilitate quicker, improved decision making. Key messages DTDS is a customizable delimitation survey design method based on transect sampling and scouting Transects designed to produce ∼120 positives facilitate efficient sampling of incursion areas Boundaries can be accurately determined with uncertainty using extreme values analysis of the distance data Case studies demonstrated that using DTDS always contained incursions and often greatly reduced survey effort