10.5061/DRYAD.N02V6WWXW
Rumelt, Reid
0000-0003-3551-0599
Cornell University
Basto, Arianna
Colorado State University
Mere Roncal, Carla
University of Florida
Automated audio recording as a means of surveying Tinamous (Tinamidae) in
the Peruvian Amazon
Dryad
dataset
2021
2022-09-21T00:00:00Z
2022-09-21T00:00:00Z
en
https://doi.org/10.22541/au.161840632.28174385/v1
4402762901 bytes
4
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
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.
This dataset has two components: training and testing data used to create
an acoustic detection model, and a csv containing detections in the survey
data. The acoustic dataset was derived from audio downloaded from the
Macaulay Library of Natural Sounds (https://macaulaylibrary.org) and
Xeno-Canto (http://www.xeno-canto.org) databases (S2) as well as from
exemplar cuts in the audio we collected in the field. Effort was taken to
ensure that the training examples covered the full breadth of the acoustic
parameter space of tinamous, including for the two species in this
study, C. soui and C. variegatus, that were observed to use distinct call
and song types in the survey audio. All audio was checked to ensure
correct assignment to species before use. Training and
testing datasets were subsets of this larger dataset. Survey audio was
collected using Swift recorders at sites in lowland Peruvian rainforest
from July-October 2019 and processed using a convolutional neural network
to obtain tinamou detections. The csv dataset includes all non-independent
detections; independent detections as defined in the manuscript are all
detections of a particular species separated from one another by more than
an hour.
Deployments are labeled from 1-4; each was about two weeks long, though
not every recorder recorded for the entire period due to battery issues.
Swift recorders are labeled from 1-10, but recorders were deployed to
different locations ("ct_code") in each deployment.