The secret acoustic world of leopards: A paired camera trap and bioacoustics survey facilitates individual identification of leopards via their roars

Abstract

Conservation requires accurate information about species occupancy, populations and behaviour. However, gathering these data for elusive, solitary species, such as leopards (Panthera pardus), is often challenging. Utilizing novel technologies that augment data collection by exploiting different species' traits could enable monitoring at larger spatiotemporal scales. Here, we conducted the first, large-scale (~450 km2) paired passive acoustic monitoring (n = 50) and camera trapping survey (n = 50), for large African carnivores, in Nyerere National Park, Tanzania. We tested whether leopards could be individually distinguished by their vocalizations. We identified individual leopards from camera trap images and then extracted their roaring bouts in the concurrent audio. We extracted leopard roar summary features and used 2-state Gaussian Hidden–Markov Models (HMMs) to model the temporal pattern of individual leopard roars. Using leopard roar summary features, individual vocal discrimination was achieved at a maximum accuracy of 46.6%. When using HMMs to evaluate the temporal pattern of a leopard's roar, individual identification was more successful, with an overall accuracy of 93.1% and macro-F1 score of 0.78. Our study shows that using multiple modes of technology, which record complementary data, can be used to discover species traits, such as, individual leopards can be identified from their vocalizations. Even though additional equipment, data management and analytical expertise are required, paired surveys are still a promising monitoring methodology which can exploit a wider variety of species traits, to monitor and inform species conservation more efficiently, than single technology studies alone.