Toward passive acoustic occupancy surveys of rare red uakari monkeys (Cacajao ucayalii): using automatic signal recognition and cluster analysis to organize signals?

We tested a workflow using a cluster analysis to develop a classifier to detect red uakari monkey calls using clustering and Hidden Markov Models, and Kaleidoscope viewer to review and discount false positives. We used recordings collected in a series of behavioral studies on a habituated group of red uakari monkeys on the Yavari-Miri River in the Peruvian Amazon as training data to develop the classifier and tested it on a passive acoustic survey at the same site where uakari distributions were known to us. We estimated detection probabilities for red uakaris and used an occupancy model to estimate habitat use to compare with use determined by behavioral research. We assessed a workflow for processing passive acoustic primate surveys that would eliminate false positives and demand minimal time and coding expertise from those implementing the analyses and reviewing audio recordings. These are key considerations in the design of landscape-scale PAM surveys for rare species.