Automated audio recording as a means of surveying tinamous (Tinamidae) in the Peruvian Amazon

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. 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. 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. 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. 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. We used autonomous recording units to gather environmental audio over a period of several months at lowland rainforest sites in the southeastern Peruvian Amazon and developed a Convolutional Neural Network‐based data processing pipeline to detect tinamou (Tinamidae) vocalizations. The resulting occurrence dataset is 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.