Document Type

Journal Article

Publication Title

Biological Invasions




School of Science




Open Access funding enabled and organized by CAUL and its Member Institutions. There is no funding to declare.


Davis, R. A., Seddon, P. J., Craig, M. D., & Russell, J. C. (2023). A review of methods for detecting rats at low densities, with implications for surveillance. Biological Invasions, 25(12), 3773-3791.


Invasive rats are the biggest threat to island biodiversity world-wide. Though the ecological impacts of rats on insular biota are well documented, introduced rats present a difficult problem for detection and management. In recent decades, improved approaches have allowed for island-wide eradications of invasive rats on small-medium sized islands and suppression on large islands, although both these still represent a formidable logistical and financial challenge. A key aspect of eradication or suppression and ongoing management is the ability to detect the presence of rats, especially at low densities. Here we review recent developments in the field of rat surveillance and summarise current published literature to recommend practices and the factors to consider when developing a surveillance program for either eradication or suppression plans. Of 51 empirical studies covering 17 countries, 58% were from New Zealand. Although detecting rats at low density is extremely challenging, advances over the past 15 years, have significantly improved our ability to detect rats. Motion-sensored cameras and rodent detection dogs have greatly improved our ability to detect rats at low densities, with cameras consistently showing an ability to detect rats at lower densities than other techniques. Rodent detection dogs are also able to reliably detect even an individual rat, although there are challenges to their widespread adoption, particularly in developing countries, due to the cost and skills required for their training and maintenance. New monitoring devices, the use of eDNA and drones represent current and future innovations to improve detection.



Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.