Quantifying Dispersal of the Asian longhorned beetle (Anoplophora glabripennis, Coleoptera) with incomplete data and behavioral knowledge

Abstract

Eradication programs for invasive species can benefit from tools that delineate infestations and identify patterns of spread to guide eradication priorities and activities. However, identifying these patterns in cryptic organisms such the Asian longhorned beetle can be complicated by the sometimes conflicting needs of rapid eradication and research. Here, we describe the use of a simple approach based on tools and concepts used in graph theory to infer beetle movement, using infested tree records collected by the Asian Longhorned Beetle Eradication Program in Worcester, MA, the largest infestation yet found in the U.S. Analyses included two sets of assumptions about beetle dispersal (representing a gap in knowledge of beetle biology), and two data sets of varying completeness, which were combined to develop and compare four scenarios of beetle dispersal in Worcester, MA. Together, these four scenarios suggest that the shape of the beetle dispersal-distance probability curve or dispersal kernel is more sensitive to assumptions about the predilection of beetles to disperse than to the size and completeness of the infested tree database, though both impacted inferred patterns of dispersal. The four scenarios are used to produce empirical estimates of dispersal risk around the current infestation, which can inform eradication efforts while recognizing the limits of data availability in a rapidly evolving eradication program. These estimates of dispersal also highlight the importance of continuing to integrate data collection into eradication programs, and the need to expand our understanding of beetle behavior and biology, as the data shown suggest that differences in dispersal behavior could dictate different eradication strategies.