DOI: 10.5061/dryad.mgqnk993n
title: A glimpse into the foraging and movement behavior of Nyctalus aviator: a complementary study by acoustic recording and GPS tracking

description: Species of open-space bats that are relatively large, such as bats from
 the genus Nyctalus, are considered as high-risk species for collisions
 with wind turbines. However, important information on their behavior and
 movement ecology, such as the locations and altitudes at which they
 forage, is still fragmentary, while crucial for their conservation in
 light of the increasing threat posed by progressing wind turbine
 construction. We adopted two different methods of microphone array
 recordings and GPS-tracking capturing data from different spatio-temporal
 scales in order to gain a complementary understanding of the echolocation
 and movement ecology of Nyctalus aviator, the largest open-space bat in
 Japan. Based on microphone-array recordings, we found that echolocation
 calls during natural foraging are adapted for fast-flight in open space
 optimal for aerial-hawing. In addition, we attached a GPS tag that can
 simultaneously monitor feeding buzz occurrence and confirmed that foraging
 occurred at 300 m altitude and that the flight altitude in mountainous
 areas is consistent with the turbine conflict zone. Thus, our acoustic GPS
 survey clearly identified N. aviator as a high-risk species in Japan.
description: Microphone-array
 recordings Microphone array recordings were conducted on a
 total of 10 days in August 2020 and 2021: August 4 and 5, 2020, June 26,
 27, 30, July 2, 28, 30, August 1 and 2, 2021 (total 1210 min), at Tokiwa
 Park in Asahikawa city, Hokkaido, Japan (N43°46'28.6"
 E142°21'27.1"),
 in the proximity of <em>N. aviator</em> roosts within hollows of <em>Ulmus
 davidiana</em>. The measurement was started around 21:00 in 2020 and
 around sunset (approximately 19:00) in 2021, and the recordings were made
 for about 2–3 hours for each day until the batteries ran
 out. A Y-shaped microphone array, consisting of four
 omnidirectional microphones (FG-23329-C05; Knowles Electronics, Itasca,
 IL), was placed at a height of approximately 1 m above the ground with the
 microphone tips pointing upwards. Based on the difference in arrival times
 between a central and three outer microphones, the 3D locations of flying
 bats were reconstructed using a custom made program in Matlab (Math Works,
 Natick, MA, USA). The detailed procedure of recording and calculation of
 the 3D positions is described in Fujioka et al 2011 and Mizuguchi et al 2022. The coordinates of the bats were calculated
 within a range of 47 m from the microphone array where the theoretical
 range error was less than 40 cm (corresponding to wing length of
 <em>N. aviator</em>). Those
 echolocation calls that were recorded by the central microphone with a
 sufficiently high signal to noise ratio were further analyzed. The end and
 start times of the pulses were automatically obtained based on a -15dB
 threshold from the peak power. Then, the inter pulse interval (IPI), which
 is the time between the beginning of a pulse and the end of the next
 pulse, the pulse duration, the minimum frequency, the bandwidth and the
 peak frequency were calculated using a custom-made program in Matlab,
 respectively. We identified feeding activity based on a characteristic
 sequence of echolocation calls termed “feeding buzz” from the
 spectrogram.     Acoustic-GPS logging
 We
 caught bats using mist nets close to a roosting tree (<em>Ulmus
 davidiana</em>) next to Asahikawa elementary school in eastern
 Asahikawa city, central Hokkaido, Japan (N43°46'10.0"
 E142°26'27.5")
 during sunset on July 29<sup>th</sup>,
 2021.
 Then, we carefully
 attached custom-made acoustic GPS data loggers (ArumoTech Corp., Kyoto,
 Japan, 2.4g) with a telemetry unit (PicoPip Ag337, Lotek, Canada, 0.3g)
 using Skin Bond (Osto-bond, Montreal Ostomy Inc., Canada) to the back of
 the bat. We held the bats
 for about 10 min to allow the glue to dry and released them on the
 roosting tree. The
 timers were set to start GPS logging every 5 seconds from 20:00 o’clock on
 the second day after the attachment until batteries ran out (approximately
 1 hour). It is possible to continuously record pulse emission timing with
 a high temporal resolution of 4 MHz sampling rate, because acoustic GPS
 loggers are designed to output a high voltage when the acoustic signal
 voltage exceeds a certain threshold. We
 identified feeding buzzes from the recorded IPI patterns based on
 observations from the microphone array recordings. In particular, pulse
 sequences with at least five pulses below an IPI threshold of 20 ms and a
 maximum duration of 100 ms were automatically classified as a feeding buzz
 (attack). The automatic classification of feeding buzzes corresponded to
 100% of those that we visually found in the IPI sequences. We identified
 the coordinates of the attack location by resampling GPS data from 5
 seconds to every second using linear completion, and searching for the
 timing closest to the end of a feeding buzz. The flight altitude was
 obtained by subtracting the ground altitude (the Geospatial Information
 Authority of Japan, https://www.gsi.go.jp/ENGLISH/index.html) from the absolute altitude above sea level recorded by the acoustic GPS logger. We identified habitat types (river, mountain, urban) from 'topographic' and 'satellite' maps provided in MATLAB.  Statistical Analysis All statistical analyses were performed in the R environment for statistical computing (R Core Team 2021) and its extended packages. We modeled the flight speed, altitude as well as the presence/absence of an attack, as a function of the habitat type (river, mountain, urban area) using linear modeling, generalized liner modeling [lme4, version 1.1-28, 29] and generalized linear mixed modeling using Template Model Builder [package glmmTMB_1.1.4, 30], respectively, to examine whether the tagged bat adapted its flight and foraging behavior to the respective habitat type. In general, we examined the quality of a model fit graphically using the functions in the DHARMa package [version 0.3.3.0, 31] We checked whether the model explained more variance than its respective null model by comparing them either via a parametric bootstrapping method [package pbkrtest_0.5.1, 32] or via a χ<sup>2</sup> test (function anova, R 2022). A χ<sup>2 </sup>type-II-Wald test [function Anova, package car_3.0.12, 33] was used to check the significance of the factor within the respective model. Bonferroni correction was applied to all pairwise post-hoc comparisons between the levels the factor (function lsmeans, package emmeans_1.8.0). In the residuals of the linear model for the flight speed, we detected heteroscedasticity which was most probably caused by the strong variance in the data and potentially also by the unbalanced number of data points between habitat categories. To correct the model, we subsampled the data by randomly selecting 76 data points from each habitat category. The residuals of the first model for flight altitude indicated temporal autocorrelation as well as heteroscedasticity. We corrected this model by randomly subsampling the data to a number of 40 data points per habitat type and applying a glmmTMB model with a Gaussian error distribution and the factor for habitat type as the dispersion parameter. Finally, we analyzed whether the attack probability differed between habitat types by modeling the presence and absence of an attack in a generalized model with binomial error distribution. For this model, we were able to use the full amount of data without any subsampling.
description: The [.csv] file is GPS coordinate data generated from the
 Acoustic-GPS logger, and the [.kml] file shows flight path from this
 [.csv] and can be opened using Google Earth. One [.xlsx] is the data of
 sonar emission timing (JST) measured by the acoustic-GPS data logger. The
 other [.xlsx] file shows all data of sound characteristics of sonar
 emission of the bats in this study.  The [.csv] and [.xlsx] files can be
 opened using Microsoft Excel.