Integrating automated acoustic vocalization data and point count surveys for estimation of bird abundance

Monitoring wildlife abundance across space and time is an essential task to study their population dynamics inform effective management. Acoustic recording units are a promising technology for efficiently monitoring bird populations communities. We present integrated modeling framework that combines high-quality but temporally sparse point count survey data with acoustic recordings. Using simulations, we compare the accuracy precision of estimates using differing amounts vocalizations obtained from clustering algorithm, data, subset manually validated vocalizations. also use our in case estimate Eastern Wood-Pewee (Contopus virens) Vermont, U.S.A. The simulation reveals combining via model improves compared models informed by either or alone. Combining only small number surveys yields without need validating any identified data. Within study, provided moderate support decline this region. Our approach dense few deliver reliable species manual identification prohibitively expensive large repeated surveys. proposed offers efficient alternative spatio-temporal regions when difficult obtain focused on rare low detection probability.