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

TL;DR

This paper introduces an integrated modeling framework that combines acoustic recordings and point count surveys to improve bird abundance estimates, reducing the need for manual vocalization validation and extensive surveys.

Contribution

The study presents a novel integrated approach that merges acoustic and point count data, enhancing accuracy and efficiency in bird population monitoring.

Findings

Combining acoustic and point count data improves abundance estimate accuracy.

Small number of point counts with acoustic data suffices for reliable estimates.

The method detects population trends without manual vocalization validation.

Abstract

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