Does similarity in call structure or foraging ecology explain interspecific information transfer in wild Myotis bats?

Abstract

Animals can gain important information by attending to the signals and cues of other animals in their environment, with acoustic information playing a major role in many taxa. Echolocation call sequences of bats contain information about the identity and behaviour of the sender which is perceptible to close-by receivers. Increasing evidence supports the communicative function of echolocation within species, yet data about its role for interspecific information transfer is scarce. Here, we asked which information bats extract from heterospecific echolocation calls during foraging. In three linked playback experiments, we tested in the flight room and field if foraging Myotis bats approached the foraging call sequences of conspecifics and four heterospecifics that were similar in acoustic call structure only (acoustic similarity hypothesis), in foraging ecology only (foraging similarity hypothesis), both, or none. Compared to the natural prey capture rate of 1.3 buzzes per minute of bat activity, our playbacks of foraging sequences with 23–40 buzzes/min simulated foraging patches with significantly higher profitability. In the flight room, M. capaccinii only approached call sequences of conspecifics and of the heterospecific M. daubentonii with similar acoustics and foraging ecology. In the field, M. capaccinii and M. daubentonii only showed a weak positive response to those two species. Our results confirm information transfer across species boundaries and highlight the importance of context on the studied behaviour, but cannot resolve whether information transfer in trawling Myotis is based on acoustic similarity only or on a combination of similarity in acoustics and foraging ecology.Significance statementAnimals transfer information, both voluntarily and inadvertently, and within and across species boundaries. In echolocating bats, acoustic call structure and foraging ecology are linked, making echolocation calls a rich source of information about species identity, ecology and activity of the sender, which receivers might exploit to find profitable foraging grounds. We tested in three lab and field experiments if information transfer occurs between bat species and if bats obtain information about ecology from echolocation calls. Myotis capaccinii/daubentonii bats approached call playbacks, but only those from con- and heterospecifics with similar call structure and foraging ecology, confirming interspecific information transfer. Reactions differed between lab and field, emphasising situation-dependent differences in animal behaviour, the importance of field research, and the need for further studies on the underlying mechanism of information transfer and the relative contributions of acoustic and ecological similarity.Electronic supplementary materialThe online version of this article (10.1007/s00265-017-2398-x) contains supplementary material, which is available to authorized users.

Weather conditions determine attenuation and speed of sound: Environmental limitations for monitoring and analyzing bat echolocation

“…To estimate the localization error caused by errors in c , I generated the call sequences that a microphone array would receive from a bat at different spatial positions, and then analyzed these sequences with a different c than used during generation. Specifically, I used cross‐correlation to calculated TOADs for symmetrical planar star‐shaped four‐microphone arrays with 60 cm (e.g., Goerlitz, ter Hofstede et al., ; Hügel et al., ; Lewanzik & Goerlitz, ) and 120 cm intermicrophone distance, a c of 338 m/s, and a grid (2 m spacing) of bat positions filling half a hemisphere above the array up to 20 m distance ( x = 0–20, y = −20 to 20, z = 2–20; Figure ). The half‐hemisphere left of the array ( x = −20 to 0) was omitted as it is identical to the right one ( x = 0–20) due to the array's symmetry.…”

“…The speed of sound (c) is the speed by which sounds propagate through the air (often approximated by 340 m/s). It is the fundamental physical sound parameter by which bats compute object range based on echo delay, as well as the fundamental parameter underlying the acoustic localization of echolocating bats and other soundproducing animals (reviewed in Blumstein et al, 2011), a method that is increasingly used to obtain bats' spatial positions based on the differences in arrival time of the same call on multiple microphones (e.g., Fujioka, Aihara, Sumiya, Aihara, & Hiryu, 2016;Goerlitz, ter Hofstede, Zeale, Jones, & Holderied, 2010;Hügel et al, 2017;Lewanzik & Goerlitz, 2018;Seibert, Koblitz, Denzinger, & Schnitzler, 2013;Surlykke, Pedersen, & Jakobsen, 2009).…”

Continued source level reduction during attack in the low‐amplitude bat Barbastella barbastellus prevents moth evasive flight

“…We used custom‐made software (field: see Hügel et al., ; flight room: TOADSuite, Peter Stilz) to calculate the three‐dimensional position of the bat for each recorded call based on time‐of‐arrival differences between the four array microphones. Bat positions were manually combined into flight trajectories and smoothed, using cubic smoothing splines (see Figure b for exemplary trajectory).…”

Acoustic localization of terrestrial wildlife: Current practices and future opportunities

“…The three remaining methods leveraged amplitude information to detect vocalizations, including an amplitude threshold within the frequency band of the target species (Simmons et al, 2008), a system that discarded extraneous low amplitude wind noise and used template matching to identify vocalizations in the remaining audio (Wang, Elson, Estrin, & Yao, 2003), and an amplitude‐detecting algorithm capable of adapting to continuously changing noise levels (Trifa et al, 2007). Curation methods for automated detectors included manually identifying calls that were not detected by automated detectors (e.g., Hügel et al, 2017) and removing false‐positive detections (e.g., Ali et al, 2007; Araya‐Salas et al, 2017). Another common manual curation step was excluding undesirable sounds from the target species, such as vocalizations with poor signal‐to‐noise ratios (e.g., Mennill et al, 2012; Papin et al, 2018; Sumiya et al, 2017) or vocalizations that were overlapped by the sounds of other species (e.g., Holderied, 2006; Krakauer et al, 2009).…”

“…Although the set of sounds to localize was detected automatically in many studies, even nominally automated detection methods often required human curation in practice. Curation methods included finding calls that were not detected by automated detectors (e.g., Hügel et al, 2017), and excluding detections that were false positives (e.g., Ali et al, 2007; Araya‐Salas et al, 2017), had poor signal‐to‐noise ratios (e.g., Mennill et al, 2012; Papin et al, 2018; Sumiya et al, 2017), or were overlapped by other vocalizations (e.g., Holderied, 2006; Krakauer et al, 2009). Designing an automated detector requires a priori knowledge of species acoustic properties and becomes more challenging as the number of species to be analyzed increases.…”