Journal Article

A novel method for estimating avian roost sizes using passive acoustic recordings

Lee, Benjamin P. Y.‐H., Hoffmann‐Kuhnt, Matthias, Er, Kenneth B. H., Chitre, Mandar A., Soh, Malcolm C. K.

2024

Overview

Communal bird roosts serve as information centres and a means of thermoregulation for many species. While some communally roosting species are major pests and cause dis‐amenities, others are of conservation concern. Estimating the population of roosting birds can provide a useful proxy of population size and possibly a more reliable estimate than other sampling techniques. However, estimating these populations is challenging as some roosts are large and often occluded in foliage. Previous acoustic methods such as paired sampling, microphone arrays and use of call rate have been used to estimate bird abundances; however, these are less suited for estimating large roost populations where hundreds of individuals are calling in unison. To address this challenge, we explored using machine learning techniques to estimate a roost population of the Javan myna, Acridotheres javanicus, an invasive species in Singapore. While one may expect to use sound intensity to estimate roost sizes, it is affected by various factors such as distance to the recorder, local propagation conditions (e.g. buildings and trees), weather conditions, and noise from other sources. Here, we used a deep neural network to extract higher order statistics from the sound recordings and use those to help estimate roost sizes. Additionally, we validated our method using automated visual analysis with a dual‐camera setup and manual bird counts. Our estimated bird counts over time using our acoustic model matched the automated visual estimates and manual bird counts at a selected Javan myna roost, thus validating our approach. Our acoustic model estimated close to 400 individual mynas roosting in a single tree. Analyses of additional recordings of Javan myna roosts conducted on two separate occasions and at a different roost location using our acoustic model showed that our roost estimates over time also matched our automated visual estimates well. Practical implication: Our novel approach of estimating communal roost sizes can be achieved robustly using a simple portable acoustic recording system. Our method has multiple applications such as testing the efficacy of avian roost population control measures (e.g. roost tree pruning) and monitoring the populations of threatened bird species that roost communally. The Javan myna, Acridotheres javanicus, is an invasive communally roosting species in Singapore (left panel, photo taken by J.J. Harrison). We deployed a passive acoustic recorder (centre panel) and analysed their roost chorus by applying machine learning methods. Our estimated bird counts over time using our acoustic model matched the automated visual estimates well, thus validating our approach (right panel).

• Publisher: John Wiley & Sons, Inc,Wiley
• Subject: Acoustics / Acridotheres javanicus / Artificial neural networks / Automation / Bats / bird / Bird migration / Birds / Cameras / communal / deep neural network / Estimates / Foliage / Generalized linear models / Information centers / Introduced species / Invasive species / Javan myna / Machine learning / Neural networks / Pests / population / Population control / Population number / roost / Roosts / Sampling / Sampling methods / Sound intensity / Sound propagation / Sound recordings / Statistical analysis / Thermoregulation / Threatened species / Trees / Weather / Wildlife conservation