Identifying dispersal events of red foxes (Vulpes vulpes) using early warning signals.

Abstract

Background: Many animals disperse to find their own territory, mates to reproduce or suitable environments to live. Dispersal can be described as a three-phase process consisting of two stationary phases (S₁ and S₂) at the beginning and the end of a dispersal event. These stationary phases are temporally separated by a transient phase (T), where the animal moves from S₁ to a new area S₂ in space. The net squared displacement (NSD) is a frequently used metric to identify these phases from animal tracking data.

Methods: We tested whether early warning signals (EWSs) on time series of the NSD, can be used to predict dispersal events. To identify EWSs we conducted a rolling window approach and evaluated the dispersal events by performing a spatial cluster analysis with the mechanistic range shift analysis (MRSA). We used data from 22 GPS-collared red foxes (Vulpes vulpes) as an example of a mammal species in which the juvenile (sub-) adult transition usually involves dispersal.

Results: Applying EWSs resulted in the identification of both transitions from S₁ to T and from T to S₂. For 10 individuals we detected EWSs. For 8 out of these 10 individuals (80%) we identified a spatial shift between S₁ and S₂ via a MRSA. Accordingly, for 8 out of 22 individuals (36%) we observed a transient phase (T) which led to a major and persistent transformation of red fox locations.

Conclusion: Even though the identification of dispersal events based on movement data is challenging using well known techniques such as state space models or the MRSA, our results suggested that EWS in combination with MRSA is appropriate to detect and identify dispersal events in radio-collared mammals. Thus, in the context of identifying dispersal events using EWSs we recommend to evaluate the existence of stationary and transient phases using the MSRA. The benefit of using EWSs is the calculation of the NSD and simple statistics (standard deviation, autocorrelation) and no requirement of high resolution tracking data. Additionally, transitions to the stationary or transient phase might be detected where home range calculations are not possible.