DOI: 10.5061/dryad.rjdfn2zgt
title: Flexible use of visual and acoustic cues during roost finding in Spix’s disc-winged bat Thyroptera tricolor

description: The ability of an animal to detect environmental cues is crucial for its
 survival and fitness. In bats, sound certainly plays a significant role in
 the search for food, spatial navigation, and social communication. Yet,
 the efficiency of a bat’s echolocation could be limited by atmospheric
 attenuation and background clutter. In this context, sound can be
 complemented by other sensory modalities, like smell or vision. Spix’s
 disc-winged bat (Thyroptera tricolor) uses acoustic cues from other group
 members to locate the roost (tubular unfurled leaves of plants in the
 order Zingiberales). Our research focused on how individuals find a roost
 that has not been yet occupied, considering the urge to find a suitable
 leaf approximately every day, during nighttime or in daylight. We observed
 the process of roost finding in T. tricolor in a flight cage,
 manipulating the audio/visual sensory input available for each trial. A
 broadband noise was broadcast in order to mask echolocation, while
 experiments conducted at night significantly reduced the amount of light.
 We measured the time needed to locate the roost under these different
 conditions. Results show that with limited visual and acoustic cues,
 search time increases significantly. In contrast, bats seemed capable of
 using acoustic and visual cues in a similarly efficient manner, since
 roost search showed no strong differences in duration when bats could use
 only sound, only vision, or both senses at the same time. Our results show
 that non-acoustic inputs can still be an important source of information
 for finding critical resources in bats.
description: We captured individuals of <em>Thyroptera
 tricolor</em> in a secondary forest in southwestern Costa Rica. We
 conducted experiments in a flight cage (2.5 x 3.5 x 5.5 m) containing a
 roosting resource available for the bat (a furled leaf
 of <em>Heliconia sp. </em>or <em>Calathea
 luthea</em>). In daylight experiments, the flight cage was made of
 saran shade cloth while in night experiments it was made of a
 double-walled cloth to reduce the amount of light. Two Ultrasonic
 Omnidirectional Dynamic Speakers were positioned on both sides of the
 leaf, at 1 m distance. We tested the time needed to enter the roost under
 different treatments, that we named after the sensory cue available for
 the bats: "Vision" (a playback broadcasted a broadband noise
 masking the frequencies of <em>Thyroptera</em>'s
 echolocation,<em> </em>with daylight), "Sound and
 Vision" (no playbacks, with daylight), "Lessen input"
 (broadband noise, during nighttime), "Sound" (no playbacks,
 during nighttime). "Noise Control" was an additional treatment
 in which we broadcasted a sound not masking echolocation frequencies. We
 used that test as a control to check if bats' behaviour during the
 playback of the broadband noise was a response to a noisy environment or
 to effective acoustic masking. In the .csv file, you can see the times
 needed for each individual to reach the roost, in seconds, under each
 treatment. Maximum duration for each test was set at 300s.
 The data were then processed using a Bayesian regression to
 evaluate the effect of the different sensory inputs (categorical
 predictor) in the time required for the bat to enter the roost (response,
 modeled with a lognormal function), including individual as random effect
 (varying intercept). The regression models included the observations for
 individuals that were tested more than once in the same experimental
 condition. Regressions were run in Stan (Stan Development Team 2021)
 through the R package <em>brms</em> (Bürkner 2017; R Core Team
 2021). Effect sizes are presented as median posterior estimates and 95%
 credibility intervals as the highest posterior density interval. We
 compared the model with an intercept-only model (null model) using the
 Bayesian leave-one-out information criterion (LOOIC, Vehtari et al. 2017)
 with the R package <em>loo</em> (Vehtari et al. 2020). We
 conducted multiple comparisons of sensory input treatments (similar to
 post hoc tests in frequentist statistics) using the joint posterior
 distribution of the model parameters with the function hypothesis from the
 package <em>brms</em> (Bürkner 2017). Models were run on four
 chains for 5,000 iterations, following a warm-up of 2,500 iterations. The
 effective sample size was kept above 3,000 for all parameters. Performance
 was checked visually by plotting the trace and distribution of posterior
 estimates for all chains. We also plotted the autocorrelation of
 successive sampled values to evaluate the independence of posterior
 samples. A potential scale reduction factor was used to assess model
 convergence and kept below 1.05 for all parameter estimates.
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