Optimizing early-warning systems for invasive mammalian species: a multi-measure approach using people, cameras and farmed animals to detect raccoon dogs

The rapid spread of invasive alien species (IAS) is a major threat to biodiversity and ecosystem services worldwide.

To prevent IAS from establishing, early detection and rapid response is crucial; however, when abundance is low but risk of spread is high, IAS are often difficult to detect.

This can render IAS management ineffective, by delaying responses and wasting limited resources on inappropriate methods.

The raccoon dog is one of Europe’s most invasive alien mammals, having major impacts on local wildlife and posing threats to both human and animal health.

The long-term viability of raccoon dog management in Sweden is being challenged, because of persistent invasion pressure in the north and a new invasion threat emerging in the south.

This project will collaborate with the Swedish raccoon dog control program, to optimize raccoon dog detection using information from 15-years of GPS-tagged individuals tracked simultaneously with other monitoring methods (camera traps & public sightings) creating a rare field-based experiment.

We will 1) quantify the absolute & relative effectiveness of the three main monitoring methods under different conditions, and 2) develop an adaptive management tool that models the combined detection probability for any given set of methods, budgets and time constraints.

These results and tools will be directly implemented into current Swedish raccoon dog management, and be a showcase for early-warning IAS monitoring program design