Automated audio recording as a means of surveying Tinamous (Tinamidae)
in the Peruvian Amazon
Abstract
1. The use of machine learning technologies to process large quantities
of remotely-collected audio data is a powerful emerging research tool in
ecology and conservation. 2. We applied these methods to a field study
of tinamou (Tinamidae) biology in Madre de Dios, Peru, a region expected
to have high levels of interspecies competition and niche partitioning
as a result of high tinamou alpha diversity. We used autonomous
recording units to gather environmental audio over a period of several
months at lowland rainforest sites in the Los Amigos Conservation
Concession and developed a Convolutional Neural Network-based data
processing pipeline to detect tinamou vocalizations in the dataset. 3.
The classified acoustic event data are comparable to similar metrics
derived from an ongoing camera trapping survey at the same site, and it
should be possible to combine the two datasets for future explorations
of the target species’ niche space parameters. 4. Here we provide an
overview of the methodology used in the data collection and processing
pipeline, offer general suggestions for processing large amounts of
environmental audio data, and demonstrate how data collected in this
manner can be used to answer questions about bird biology.