Echolocation calls in Central American emballonurid bats: signal design and call frequency alternation

Abstract

In southern Central America, 10 species of emballonurid bats occur, which are all aerial insectivores: some hunt flying insects preferably away from vegetation in open space, others hunt in edge space near vegetation and one species forages mainly over water. We present a search call design of each species and link signal structure to foraging habitat. All emballonurid bats use a similar type of echolocation call that consists of a central, narrowband component and one or two short, frequency-modulated sweeps. All calls are multi-harmonic, generally with most energy concentrated in the second harmonic. The design of search calls is closely related to habitat type, in particular to distance of clutter. Emballonurid bats foraging in edge space near vegetation and over water used higher frequencies, shorter call durations and shorter pulse intervals compared with species mostly hunting in open, uncluttered habitats. Peak frequency correlated negatively with body size. Regular frequency alternation between subsequent calls was typical in the search sequences of four out of 10 species. We discuss several hypotheses regarding the possible role of this frequency alternation, including species identification and partitioning of acoustic channels. Furthermore, we propose a model of how frequency alternation could increase the maximum detection distance of obstacles by marking search calls with different frequencies.

Implications of sensory ecology for species coexistence: biased perception links predator diversity to prey size distribution

“…Whether a returning echo still is detectable in turn depends on the echo perception threshold (E min ), which we assumed to be at 20 dB SPL. Judging by field measurements of SL for aerial hawking bats (Holderied and von Helversen 2003;Surlykke and Kalko 2008), and data for E min (Kick 1982;Simmons et al 1992;Jung et al 2007) the chosen values represent a rather conservative estimate. However, our findings do not critically depend on the assumed magnitude of SL or the E min nor on the ''dynamic range'' between them (80 dB in our case).…”

“…We combined this knowledge to estimate prey perceptibility for a hypothetic community of bats that echolocate using different call frequencies. Several previous studies have estimated echolocation range and prey detectability as a function of call frequency for bats (e.g., Barclay and Brigham 1991;Waters et al 1995;Holderied and von Helversen 2003;Houston et al 2003;Jung et al 2007). We extended these in two ways: first, we aimed at realistically modeling the abiotic environmental conditions, i.e., by integrating the complex dynamical influences of atmospheric conditions and their interaction with call frequency on sound propagation and attenuation.…”

Mobility explains the response of aerial insectivorous bats to anthropogenic habitat change in the Neotropics

“…To ensure consistent species determination, criteria for the different species were established based on call characteristics (e.g. shape, duration, maximum, minimum and peak frequency) taken from existing literature (Jung et al, 2007(Jung et al, , 2009(Jung et al, , 2014Rydell et al, 2002) and from our own reference calls. Species-specific calls are shown in Fig.…”

Forest Structure and Composition Affect Bats in a Tropical Evergreen Broadleaf Forest

“…During classification, we defined a bat pass as a search-phase echolocation call sequence of ≥2 echolocation call pulses. To classify bat activity (i.e., passes) to family and genus post file-conversion and scrubbing, we used two methods: (1) automated identification using Kaleidoscope Pro 3 software and associated Neotropical and North American bat classifiers; and (2) visual identification of spectrograms [42][43][44][45][46][47][48] displayed in the Kaleidoscope Pro 3 Viewer. When classifying bat passes using automated and visual identification, differentiating among species' calls can be difficult; it is affected by the degree of clutter at sampling locations, direction the bat is pointing relative to the microphone when it emits a call, angle and direction of the detector microphone, call attenuation, Doppler shift, and similarity of call characteristics of different species [49][50][51][52].…”