Using the ecological significance of animal vocalizations to improve inference in acoustic monitoring programs.

Recent bioacoustic advances have facilitated large-scale population monitoring for acoustically active species. Animal sounds, however, can convey substantial amounts of information underutilized in typical approaches to passive acoustic monitoring (PAM) that simply treat sounds as detections. We developed three methods of extracting additional ecological detail from acoustic data that can substantially enhance PAM programs for a broad range of acoustically active species using: 1) sex-specific vocalization frequency to inform multi-state occupancy models; 2) call rates (vocalizations/time) at occupied sites to characterize interactions with interspecific competitors and assess habitat quality; and 3) a novel and flexible multivariate approach to differentiate individuals based on vocal characteristics. To do so, we used data collected from acoustic surveys of a declining owl species and an invasive congeneric competitor that we conducted over 6,000 km(2) in California. Our novel application of multi-state occupancy models to acoustic data yielded reasonably precise estimates of breeding status occupancy rates that were more robust to false detections and captured known habitat associations more consistently than single-state occupancy models agnostic to sex. Call rate was related to the presence of a competitor but not habitat quality, and thus could constitute a useful behavioral metric for interactions that are challenging to detect in an occupancy framework. Quantifying multivariate distance between groups of vocalizations provided a rigorous means of discriminating individuals with >/=20 vocalizations, and a flexible tool for balancing Type I and II errors. Therefore, it appears possible to estimate site turnover and demographic rates, rather than just occupancy metrics, in PAM programs. These three methods can be applied individually or in concert and are likely generalizable to many acoustically active species. As such, they can provide substantial opportunities for improving inferences from PAM data and thus improve conservation outcomes. This article is protected by copyright. All rights reserved.