1. Data collection
Data collection was conducted under the auspices of the Amazon
Conservation Association at the Los Amigos Conservation Concession
(LACC), in the lowland rainforest of Madre de Dios, Peru. This site,
which protects ~145,000 ha of forest along the Río Los
Amigos basin, is one of the most biodiverse lowland rainforest sites in
the Amazon basin with close to 600 bird species, eleven of which are
tinamous in the genera Tinamus and Crypturellus (eBird,
2017; Table 1). The station’s biological diversity is due in part to its
diversity of terrestrial microhabitats, which include terra firme and
floodplain primary forest, secondary and edge forest, Guadua bamboo
stands, and Mauritia flexuosa palm swamps (Larsen et al., 2006). As
studies at this site (Mere Roncal et al., 2019) and elsewhere in the
Neotropics have demonstrated that tinamou species differ in their
specific habitat utilization characteristics (Guerta & Cintra, 2014),
LACC is an exemplary site for detecting tinamous across a variety of
habitat gradients.
Acoustic monitoring was conducted using ten SWIFT ARUs (Kahl et al.,
2019), provided by the Cornell Lab of Ornithology, from mid-July to
early October of 2019. This period overlaps with the latter half of the
dry season at LACC. The SWIFT units were deployed on rotating 14 day
deployment periods at terra firme and floodplain forest sites (Figure 1,
S1), 10 sites at a time, over three deployments from mid-July to late
August. A fourth deployment, duration 27 days, was conducted as a
follow-up at five of the 30 sites from late September to early October.
As the chosen sites are part of the station’s existing camera trap
system (approximately a 1 km2 grid located along the
edge of open trails), we were able to merge our detection set with
previously-collected site-level habitat data as well as to compare our
tinamou detection rates to those calculated using camera trap
detections. Recorders were tied to trees at a height of approximately
1.5 m from the ground with the microphone facing downwards. Each unit
was programmed to record for five hours a day, from 5:00 to 7:30 in the
morning and 16:00 to 18:30 in the afternoon to early evening, in order
to cover periods of high vocal activity for tinamous (Dias et al.,
2016). The SWIFT unit firmware allows for control of microphone gain and
sampling frequency; we set these values to -33 dB (the default) and 16
kHz, respectively. Setting the sampling frequency to 16 kHz is a
tradeoff that limits the acoustic frequency bandwidth to 0-8 kHz
(Landau, 1967) in exchange for smaller file sizes and lower power
demands than the default value of 32 kHz. The SWIFT firmware writes data
as 30 min-long WAV files (~58 MB). Each unit was
intended to collect data for the shorter of (a) the entire 14 or 27 day
recording period or (b) until battery power was exhausted. In practice,
battery life was always the limiting factor, with a mean
time-to-shutdown of 7.81 days (5.12 days for deployments 1-3 and 21.8
days for deployment 4). Due to supply limitations, we were forced to use
a different brand of battery for deployments 1-3 than for deployment 4,
which we suspect is at least partially responsible for the longer
per-recorder run times in the latter deployment. At the end of each
deployment period, all units were removed from the field, loaded with
fresh recording media and batteries, and deployed to their next assigned
site on the following day. All audio data was backed up to rugged solid
state storage media for transport out of the field.
Our chosen classification procedure is a type of supervised machine
learning, which requires a significant amount of training audio to
produce a working model (Kotsiantis et al., 2007). We used a set of
~3100 audio files of 2s duration (the typical phrase
length in tinamou calls) to train an initial classifier. These files
were coded as one of twelve classes: one class for each tinamou species,
and a “junk” class containing audio of other bird species, non-bird
organismal audio, and assorted environmental audio (Table 1). The
training 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.