Genetic studies of aquatic biological invasions: closing the gap between research and management

Abstract

Recent years have seen a dramatic rise in the application of genetic methods to understand aquatic biological invasions. In part these methods have been adopted to address fundamental questions in biogeography, evolutionary biology, population ecology, and other fields. But it is also commonly suggested that genetic information has the potential to directly inform the management of aquatic invasions. Here I explore the potential promise of genetic approaches for informing management of aquatic invasive species, the degree to which that promise has been realized in terms of utilization of genetic information by managers and other decision-makers, and the likely limitations to the value of genetic methods (both in principle and in practice) and ways in which these limitations might be overcome. I consider a range of possible applications of genetic tools for management, including molecular detection and identification of cryptic invaders, source tracking and reconstruction of invasion history, and inference of population demographics. Retrospective assessment of the utility of such applications is based on both literature review and solicitation of expert opinion, and suggests that a number of hurdles likely often prevent genetic information from effectively informing decision-making. These include (1) limitations or misunderstandings of the resolution and certainty afforded by genetic analysis; (2) failure to engage decision-makers in problem formulation, research design and research implementation; and (3) complex relationships between basic research and management actions. While some of the obstacles considered are rooted in theoretical and practical limitations of genetic analysis, others are clearly associated with poor communication and insufficient engagement of potential end-users of genetic information. I consider possible avenues for overcoming these obstacles and for improving the applicability of genetic information for supporting management decisions.