One current management need in the western US is for the identification and conservation of ungulate migration routes for pronghorn antelope (Antilocapra americana), mule deer (Odocoileus hemionus) and elk (Cervus canadensis) in 11 western states of the USA, secretarial order 3362. The dominant ungulate migration paradigm is that seasonal migrations are composed of stopover locations, where individuals rest and forage (low rate of movement areas), are interposed by travel corridors where individuals travel to their next stopover location (high rates of movement). Previous methods used to identify ungulate stopover locations are based on Brownian Bridge Movement Model’s utilized distributions of complete migration routes (BBMM, Sawyer et al. 2009) or deciphering migration trajectories based on individuals rate of movement and tortuosity (Jake et al. 2018). Although these methods have greatly advanced the study of movement behavior for mule and pronghorn, respectively, the methods assume that each individual will have stopover behavior, which was not always prevalent with these species in Idaho from GPS location data collected from 2003 to present. We introduce a more parsimonious methodology to identify ungulate migration stopovers based on rate of movement and the duration of movement rates. The methods developed are used to identify stopover locations across the state of Idaho for pronghorn antelope, mule deer, and elk.
Results/Conclusions
We apply these techniques to the states collection of over 4 million locations on these species to provide examples and comparisons to population level identification of stopovers for these iconic western ungulate species. For all species, we have found that population level seasonal migration stopovers where more accurately estimated by the accumulation of identified individual stopovers and were 27% more accurate for pronghorn, 18% more accurate for mule deer, and 14% more accurate for elk, when populations are determined by their winter location. Further, the method does not over-estimate stopover locations, meaning identify stopovers that are not used by the species in question. Although the technique is more parsimonious, it provides more explicit temporal behavior corresponding to specifically identified stopover locations that provides information on these species management and conservation in a state that has one of the highest population growth rates in the past decade. The implications of applying this new technique to ungulate conservation of seasonal migration will be discussed.