loading page

Automated audio recording as a means of surveying Tinamous (Tinamidae) in the Peruvian Amazon
  • Reid Rumelt,
  • Arianna Basto,
  • Carla Mere Roncal
Reid Rumelt
Cornell University

Corresponding Author:[email protected]

Author Profile
Arianna Basto
Colorado State University
Author Profile
Carla Mere Roncal
University of Florida
Author Profile


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.
01 Apr 2021Submitted to Ecology and Evolution
02 Apr 2021Submission Checks Completed
02 Apr 2021Assigned to Editor
14 Apr 2021Reviewer(s) Assigned
26 Jul 2021Review(s) Completed, Editorial Evaluation Pending
27 Jul 2021Editorial Decision: Revise Minor
14 Aug 20211st Revision Received
17 Aug 2021Submission Checks Completed
17 Aug 2021Assigned to Editor
17 Aug 2021Review(s) Completed, Editorial Evaluation Pending
19 Aug 2021Editorial Decision: Accept