Answers to some of the most important questions in wildlife science, conservation and management require reliable data on species’ presence, abundance, and distribution. DNA, extracted from hair, feaces, and other environmental sources (e.g. soil, water) is a valuable source of this information that does not require handling, capturing, or even observing individual animals. DNA-based detection methods promise a number of advances over traditional detection methods, based on visual identification of specimens using diagnostic morphological criteria. It is now widely considered that DNA-based methods of environmental monitoring can be undertaken with relatively high throughput and at low cost per sample, delivering substantial sensitivity benefits over traditional methods. However, many studies are undertaken without parallel development of robust quantitative frameworks that allow the probability of detection to be considered in order to derive clearly interpretable outcomes. For example, determining whether an invasive species is present or absent requires a probability estimate that encompasses both the sampling methodology and the DNA technique itself. The development and application of frameworks that deal explicitly with these probabilities will be essential if the promise of DNA surveillance as a standardised tool to address applied ecological questions is to be realised.

I will present an overview of DNA detection in the context of wildlife management using examples from my own research on invasive species. Particular focus will be on the development of a probabilistic framework for the design, implementation and interpretation of detection of environmental DNA (eDNA) methods to detect aquatic invasive species how this is critical for defensible decision-making.