Bats and toothed whales such as porpoises have independently evolved the same solution for hunting prey when it is hard to see. Bats hunt in the dark with little light to allow them to see the insects they chase. Porpoises hunt in murky water where different ocean environments can quickly obscure fish from view. So, both bats and porpoises evolved to emit a beam of sound and then track their prey based on the echoes of that sound bouncing off the prey and other objects. This process is called echolocation.

A narrow beam of sound can help a porpoise or bat track distant prey. But as either animal closes in on its prey such a narrow sound beam can be a disadvantage because prey can easily escape to one side. Scientists recently found that bats can widen their sound beam as they close in on prey by changing the frequency—or pitch—of the signal they emit or by adjusting how they open their mouth.

Porpoises, by contrast, create their echolocation clicks by forcing air through a structure in their blowhole called the phonic lips. The sound is transmitted through a fatty structure on the front of their head known as the melon, which gives these animals their characteristic round-headed look, before being transmitted into the sea. Porpoises would also likely benefit from widening their echolocation beam as they approach prey, but it was not clear if and how they could do this.

Wisniewska et al. used 48 tightly spaced underwater microphones to record the clicks emitted by three captive porpoises as they approached a target or a fish. This revealed that in the last stage of their approach, the porpoises could triple the area their sound beam covered, giving them a ‘wide angle view’ as they closed in. This widening of the sound beam occurred during a very rapid series of echolocation signals called a buzz, which porpoises and bats perform at the end of a pursuit. Unlike bats, porpoises are able to continue to change the width of their sound beam throughout the buzz.

Wisniewska et al. also present a video that shows that the shape of the porpoise's melon changes rapidly during a buzz, which may explain the widening beam. Furthermore, images obtained using a technique called magnetic resonance imaging (MRI) revealed that a porpoise has a network of facial muscles that are capable of producing these beam-widening melon distortions.

As both bats and porpoises have evolved the capability to adjust the width of their sound beam, this ability is likely to be crucial for hunting effectively using echolocation.

Echolocation has evolved independently multiple times in mammals and birds (Kellogg et al., 1953; Griffin, 1958; Konishi and Knudsen, 1979; Griffin and Thompson, 1982), and allows these animals to orient under conditions of poor lighting. However, only toothed whales (henceforth ‘whales’) and laryngeally echolocating bats (henceforth ‘bats’) use echolocation to detect, locate, and track prey. In these groups, echolocation signals are primarily ultrasonic (>20 kHz) and are among the most intense biological sounds in water and in air (Madsen and Surlykke, 2013). These characteristics mean that in uncluttered spaces these predators can detect small prey many body-lengths ahead of them. Key to increasing the effective sensory range of biosonar is a directional sound beam (i.e., a narrow volume of forwardly ensonified space), which increases intensity along the acoustic axis and reduces ensonification of objects off-axis. It has recently been proposed that the advantages of a narrow sonar beam while in search of prey may have been the primary driver for the evolution of high frequency sonar signals in both whales and bats (Koblitz et al., 2012; Jakobsen et al., 2013; Madsen and Surlykke, 2013).

As whales and bats close in on prey, both are thought to concurrently decrease signal intensity and auditory sensitivity to partially compensate for reduced transmission loss (Hartley, 1992; Au and Benoit-Bird, 2003; Supin et al., 2004; Linnenschmidt et al., 2012; Madsen and Surlykke, 2013), while increasing the signal emission rate for faster updates on prey location (Griffin, 1958; Morozov et al., 1972; Au, 1993). Both groups terminate prey pursuits with a low intensity, high repetition rate sequence of echolocation signals called a buzz (see ref. [Madsen and Surlykke, 2013] for review). Signal production rates characteristic of buzzes have likely evolved in these echolocators to facilitate close range prey tracking. However, for a given sonar beam, effective beam diameter will decrease as the distance between predator and prey diminishes. Thus, while a directional sound beam enables longer detection range by restricting the acoustic field of view (FOV), it may be disadvantageous to the echolocator at short ranges, as moving prey may easily vanish at the periphery of the FOV. Directionality increases with aperture size (e.g., a bat's gape) and signal frequency (Au, 1993) and both factors appear to be exploited by some bats to modify their beam (Surlykke et al., 2009; Jakobsen and Surlykke, 2010).

Unlike bats, whales do not generate echolocation signals in the larynx nor do they emit them through the mouth or the nares (Ridgway et al., 1980; Cranford et al., 1996). Instead, whales have evolved specialized sound-producing structures, the phonic lips, located high in the blowhole (Figure 1). An echolocation click is generated by pneumatic actuation of the phonic lips as pressurized air is forced past them (Cranford et al., 1996). The resulting sound pulse propagates into the fatty melon and is transmitted into the water as a directional beam (Au et al., 1986). While the exact function of the melon is not known, its size and properties are expected to affect the radiation pattern of sound from the head (Varanasi et al., 1975; Aroyan et al., 1992; Harper et al., 2008). As the bulbous melon fills a large proportion of the forehead (Figure 1), the diameter of the head has come to be considered indicative of radiating aperture size (Au et al., 1999).

Figure 1

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Compared to bats, whales exhibit little flexibility in the frequency content of their echolocation signals (Madsen and Surlykke, 2013). We might therefore assume whales are capable of only small changes in beam directionality (Au et al., 1995) and thus be limited to a rather fixed FOV (Au et al., 1999). Yet, given that whales echolocate prey over much longer ranges than bats (Madsen and Surlykke, 2013) and are capable of making much narrower beams (Au et al., 1999; Surlykke et al., 2009; Koblitz et al., 2012; Jakobsen et al., 2013), such a constraint is hard to reconcile with the disadvantages associated with a fixed beamwidth during close tracking of prey. In bats, pursuit is often over quickly, often with less than 500 ms elapsing between prey detection and interception (Kalko, 1995). Prey pursuits by whales can last many seconds (Johnson et al., 2004) (Figure 2). Accordingly, we hypothesize that long buzzes in which the whale might follow its target from an uncluttered water column to a highly cluttered sea floor (Figure 2), and back again, may demand greater beam plasticity than is found in bats.

Figure 2

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Corroborating this hypothesis, most of the reported estimates of toothed whale beam patterns show relatively large variability (Evans, 1973; Au et al., 1986, 1987, 1995; Koblitz et al., 2012). Furthermore, a bottlenose dolphin was recently observed to steer, and modify the width of, its sonar beam when stationed on a bite plate and presented with targets displaced by large angles with respect to its body axis (Moore et al., 2008). The dolphin was proposed to use two mechanisms as means of beamwidth modulation: (i) phase shifting between two pairs of phonic lips dually actuated for generation of a single click and/or (ii) manipulation of the volume and geometry of the melon and the associated air-sacs. However, the latest experimental data suggest that dolphins use a single pair of phonic lips to produce echolocation signals (Au et al., 2012; Madsen et al., 2013; Finneran et al., 2014) and that the strong amplitude dependence of dolphin click spectra gives rise to a variable beam pattern, with more directional signals at higher source levels (Finneran et al., 2014). Hence, it remains unclear how and under what circumstances a bottlenose dolphin modulates the width of its sound beam, and whether other whale species, with more stereotyped sonar signals, such as porpoises, are capable of similar beamwidth changes.

In this study, we set out to test the hypothesis that whales can change their FOV adaptively during prey interception. To that end, we used two complementary experiments to record the beam pattern and signal frequency content of echolocation clicks from harbour porpoises (Phocoena phocoena) closing in on targets. Additionally, we obtained magnetic resonance imaging on a dead harbour porpoise to visualize the sound generating structures, the fatty melon and the associated musculature and present video demonstrating melon deformations in an actively echolocating harbour porpoise.

In experiment 1, three captive porpoises were recorded individually using a linear horizontal array of 8 hydrophones spaced 60 cm apart and submerged 75 cm below the water's surface, as the porpoises captured dead fish (Video 1). This setup (Figure 3A) allowed us to quantify the animals' beam patterns over long ranges as they approached and intercepted natural targets free-floating in the water column. A total of 16 trials were recorded for the three animals (5–6 trials/porpoise). Only trials in which the porpoises swam directly towards the centre of the array were analyzed, amounting to two trials for Freja and Eigil and one trial for Sif (a total of 75 clicks). Data from the two porpoises, Freja and Eigil, recorded during buzzes show that they could up to triple the width of their sonar beam as they approached prey (median difference of 7.85° for four trials: from a mean of 12.9° (6.6°–30.5°, with the large beamwidths produced at short target ranges, Figure 3B) during regular clicking (ICI >13 ms; [Wisniewska et al., 2012]) to 19.3° (10.2°–28.9°) during buzzes (ICI ≤13 ms; [Wisniewska et al., 2012]), Figure 3B). However, limitations in the linear array recordings prevented us from drawing strong conclusions about the exact extent of beam widening: the angular resolution was 4–12° and there was no way to precisely determine the vertical direction of the porpoise beam. Even though the video observations indicated that the vertical direction of the porpoise head was not changing with range to the array, this could not be adequately quantified.

Video 1

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Figure 3

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We therefore designed experiment two to measure the beamwidth at close target ranges using a non-uniform 2D hydrophone array. One of the three porpoises, Freja, was trained to approach a stationary target surrounded by a star-shaped array of 48 hydrophones in two configurations (Video 2). Initially, the hydrophones were arranged at increasing intervals away from the array center to cover the full extent of the beam at relatively long ranges while providing more resolution around the target at short ranges (Figures 3C, 4, Figure 4—figure supplement 1A). We then repeated the experiment using an array of more-tightly spaced hydrophones (Figures 3C, 4, Figure 4—figure supplement 1B) and suspended the target further in front of the array, to increase signal intensity resolution at short ranges.

Video 2

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Figure 4 with 1 supplement see all

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We recorded 123 trials with the long-range configuration and 93 trials with the short-range setup. Only trials in which the porpoise swam directly toward the target (within ±15° vertically and horizontally from the array's center) and repeatedly scanned its beam over the center hydrophone were analyzed. This resulted in 11 trials for the long-range- and 4 trials for the short-range array configuration. For each recorded click, the energy levels received at the hydrophones were fitted to the beam pattern of a circular piston (Au et al., 1987; Møhl et al., 2003). Only beamwidth estimates based on fits with R² > 0.8 (see Figure 4—figure supplement 1) were considered in the analysis, rendering a total of 34 and 458 clicks for the long-range- and short-range array, respectively. The results of this experiment show that the porpoise could almost double its beamwidth in degrees when switching to a buzz (ICI ≤13 ms; [Wisniewska et al., 2012]) at short target ranges (Figure 3D). That is, from a mean half-power beamwidth (i.e., the off-axis angle at which the sound energy has decreased by 3 dB relative to the on-axis energy) of 9.1° (6.7–12.3) to a maximum of 15.1° (mean = 11.0, min = 8.2). In this way the animal increased the ensonified area ahead of it by up to three times (200%) in the terminal buzz phase of its approach to a target, as indicated by the ratio of the area measured at a given target range to the area predicted for that range based on the median −3 dB beam angle calculated for long ranges (i.e., predicted area had the porpoise not switched to ‘wide-angle view’, Figure 5). A significant inverse relationship between the beamwidth of the clicks and distance they were emitted from the target was found for data sets from each experiment (Experiment 1: F = 56.9, R² = 0.44, p < 0.001; Experiment 2: F = 23.6, R² = 0.05, p < 0.001). Data from experiment two were also analyzed using ANOVA (see ‘Materials and methods’). The three clusters (sorted using beamwidth values) differed significantly with respect to distance to target (F = 5.21, p < 0.006). Specifically, clicks in the group with the greatest beamwidths were emitted significantly closer to the target than clicks in the group with the narrowest beamwidths (Tukey-Karmer HSD, p < 0.01).

Figure 5

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The timeline of these changes (Figure 6A,C) shows that the porpoise varied the width of its beam within the buzz independently of the inter-click intervals (ICIs), with both narrow and wide beam angles used at the lowest ICIs (Figure 6A,D). Consequently, the short- and long-range datasets have different distributions, but similar means, and the long-range clicks are better described by the mean beamwidth value (Figure 3D). The click centroid frequency (fc) dropped by about 1% at the start of a buzz (from a long-range median of 130.4 kHz (127–136.1) to a median of 127.7 kHz (124.1–134.3), see the color scale in Figure 6 and Figure 6—figure supplement 1).

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This negligible drop in frequency cannot explain the large changes in beamwidth (Figure 6—figure supplement 1; Spearman's correlation tests between centroid frequency and beamwidth: Experiment 1: p = 0.76, Experiment 2: p = 0.67). Rather, the animal must vary the size of the effective aperture, likely by effecting rapid muscular deformations of the melon (Video 3), the position of the phonic lips and/or the size and position of the associated air sacs. To visualize the musculature surrounding the melon, we obtained magnetic resonance imaging (MRI) on a dead juvenile harbour porpoise. The scanning images reveal nasal structures that include a complex, richly innervated (Huggenberger et al., 2009) network of facial muscles (Figure 1). These muscles are homologous to the muscles dedicated to facial expressions in primates, and should enable fast and subtle changes in the shape of nasal components and their associated air sacs (Huggenberger et al., 2009).

Video 3

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Here, we show that harbour porpoises can broaden their biosonar beams in the final phase of target approach (Figure 3), and, unlike echolocating bats, that they are also able to change their beamwidth within the terminal buzz (Figure 6). At its broadest, the beamwidth ranged from 15° in experiment two to more than 30° in experiment one. A number of factors may have contributed to this variability including: (i) differences in effective angular resolution (EAR) between the eight-hydrophone linear array and the 48-hydrophone star-shaped array, (ii) differences in the animals' behaviour when approaching a slowly sinking fish vs a stationary aluminum sphere, and, finally, (iii) potentially higher clutter originating from the large array behind the target and motivating the porpoise to use a narrower beam. Porpoises can adjust their buzz clicking rate to prey range differently when following a fish in open water than when tracking one in the cluttered and more restricted space close to the sea floor (Figure 2). The porpoise behaviour and experimental context seem the most likely explanation for the observed differences between the two experiments.

This is the first demonstration, to our knowledge, of a whale controlling its acoustic FOV while actively approaching a target. This control is achieved independently of spectral adjustments (Figure 6 and Figure 6—figure supplement 1), probably by changing the conformation of the melon (Figure 1; [Huggenberger et al., 2009]), the position of the phonic lips (Cranford et al., 2014) and the size and shape of the associated air sacs (Aroyan et al., 1992). Due to its heterogeneous structure (Norris and Harvey, 1974; Varanasi et al., 1975), the melon has long been considered an acoustic impedance matcher that minimizes the reflections and energy loss at the tissue–water interface (Norris and Harvey, 1974) and that provides directionality in the emitted click (Au et al., 1999, 2006). Porpoises have a complex facial musculature (Figure 1) with nervous innervation with 4.5 times more neurons than human facial muscles (Jacobs and Jensen, 1964), leading to the recent proposition that muscle induced deformations of the nasal soft structures such as the melon may provide means to change the FOV (Huggenberger et al., 2009). Our acoustic measurements and observations support that hypothesis by demonstrating that the melon and accessory structures apparently operate as the functional equivalent of an adjustable collimating lens of a flashlight. In other words, the porpoise's beam can be dynamically changed from spotlight to floodlight (and everything in between) to best suit the circumstances, offering unprecedented flexibility in control of the FOV in an echolocating animal that is unmatched in visual mammals (Land and Nilsson, 2012).

The porpoise's ability to change its FOV within a buzz (Figure 6) implies an even greater flexibility than recently documented in vespertilionid bats (Surlykke et al., 2009; Jakobsen and Surlykke, 2010; Jakobsen et al., 2013). While bats adjust their beams to the environment in which they are operating (Surlykke et al., 2009), and the task at hand (Jakobsen and Surlykke, 2010), changes in their FOV during the buzz are accounted for by a concurrent drop in signal frequency content. Thus in bats, changes in FOV, emission rate, and signal frequency content during the buzz appear to be tightly interconnected (Ratcliffe et al., 2013). Our results show that in whales these parameters are independently controlled (Figure 6 and Figure 6—figure supplement 1). Having a FOV that can be modulated independently of signal emission rate or frequency content (the latter often being dependent on the amplitude of the signal [Au et al., 1995; Finneran et al., 2014]) may be essential for managing flow of sensory information and optimizing long duration close-range prey tracking in acoustic scenes of varying complexity (Figure 2). The broad beam is advantageous to the porpoises at close range where it would reduce the likelihood of prey escaping perpendicularly to the approaching porpoise by vanishing from its acoustic FOV.

These findings support the hypothesis that porpoises dynamically control their acoustic FOV while tracking prey, and do so by altering the effective size of their radiating aperture. The mechanism underlying these adjustments may be muscle-induced phonic lips repositioning (Cranford et al., 2014), and melon and air sac deformations (Moore et al., 2008; Huggenberger et al., 2009). All toothed whales studied to date have similar facial musculature surrounding the melon (Cranford et al., 1996; Harper et al., 2008). All toothed whales then presumably have the ability to modify the melon's shape. Given the greater beam plasticity offered by this mechanism, compared to modulating the frequency content of clicks, we suggest that all toothed whales may be able to shape and modulate their beam this way.

Despite the independent evolution and very different means of sound generation and transmission, whales and bats have both evolved mechanisms to change their acoustic FOV while tracking prey. This suggests that beam plasticity has been a key driver in the evolution of echolocation, beyond simple orientation, for improved foraging success. Our results from these small toothed whales suggest that the demands of tracking moving prey over variable distances in complex acoustic environments have favored the evolution of a more sophisticated adjustment mechanism, in which pulse rate and beamwidth can be controlled independently. Compared to bats, the greater dynamic beam plasticity we have observed here likely reflects different sensory and ecological constraints. We propose that dynamic control of acoustic FOV in whales is a mechanism for the inclusion and exclusion of potential sensory information that allows these predators to quickly and repeatedly adjust to changes in habitat and prey trajectories.

All experiments were conducted in a semi-natural outdoor enclosure at Fjord&Bælt, situated in Kerteminde harbour, Denmark. The enclosure (approximately 34 × 17 m, natural sandy bottom at 3–5 m depth) is fenced off by a concrete wall alongshore, and nets on the two shorter ends. The net pen complex comprises two net-separated pools that allow for isolation of single animals for experimental work. All acoustic recordings in the present study were made in the smaller, 8 × 12 m net pen.

At the time the study was undertaken, the facility housed four harbour porpoises, three of which participated in the experiments: Freja (female, at Fjord&Bælt since April 1997, estimated to be 1–2 years old at arrival [Lockyer, 2003]), Eigil (male, at Fjord&Bælt since April 1997, estimated to be 1–2 years old at arrival [Lockyer, 2003]) and Sif (female, at Fjord&Bælt since July 2004, estimated to be 1-year-old at arrival [Lockyer, 2003]). All animals had extensive previous experience in various echolocation experiments, from being stationed at a target (e.g., [Beedholm and Miller, 2007; Koblitz et al., 2012; Linnenschmidt et al., 2012]) to free-swimming (e.g., [Verfuss et al., 2005; Beedholm and Miller, 2007; DeRuiter et al., 2009; Wisniewska et al., 2012]), as well as carrying a tag (DeRuiter et al., 2009; Wisniewska et al., 2012). The animals were trained to participate in the experiments using operant conditioning and positive reinforcement (Ramirez, 1999).

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Author details

1. Danuta M Wisniewska

   1. Zoophysiology, Department of Bioscience, Aarhus University, Aarhus, Denmark
   2. Marine Mammal Research, Department of Bioscience, Aarhus University, Roskilde, Denmark

   Contribution

   DMW, Designed the experiments, carried out the large array recordings for experiment two, conducted the live prey capture trials, analysed and interpreted the data, wrote the manuscript, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   danuta.wisniewska@bios.au.dk

   Competing interests

   The authors declare that no competing interests exist.

   "This ORCID iD identifies the author of this article:" 0000-0002-3599-7440

2. John M Ratcliffe

   1. Sound and Behaviour Group, Institute of Biology, University of Southern Denmark, Odense, Denmark
   2. Department of Biology, University of Toronto Mississauga, Mississauga, Canada

   Contribution

   JMR, Conducted experiment one, contributed to the design of experiments, wrote the manuscript, Conception and design, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Kristian Beedholm

   Zoophysiology, Department of Bioscience, Aarhus University, Aarhus, Denmark

   Contribution

   KB, Designed the experiments, wrote the data acquisition software, analysed and interpreted the data, revised the manuscript, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Christian B Christensen

   Zoophysiology, Department of Bioscience, Aarhus University, Aarhus, Denmark

   Contribution

   CBC, Analyzed the scan data and contributed to the data interpretation, revised the manuscript, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Mark Johnson

   Scottish Oceans Institute, University of St Andrews, St Andrews, Scotland

   Contribution

   MJ, Conducted the live prey capture trials, contributed analytical tools, contributed to interpretation of data and revised the manuscript, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Jens C Koblitz

   Animal Physiology, Institute for Neurobiology, University of Tübingen, Tübingen, Germany

   Contribution

   JCK, Conducted experiment one, analysed the data and revised the manuscript, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Magnus Wahlberg

   1. Sound and Behaviour Group, Institute of Biology, University of Southern Denmark, Odense, Denmark
   2. Marine Biological Research Centre, University of Southern Denmark, Kerteminde, Denmark
   3. Fjord and Belt, Kerteminde, Denmark

   Contribution

   MW, Contributed to the design of the experiments, conducted experiment one, analysed the data and revised the manuscript, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Peter T Madsen

   Zoophysiology, Department of Bioscience, Aarhus University, Aarhus, Denmark

   Contribution

   PTM, Designed the experiments, conducted the live prey capture trials, contributed to the analysis and interpretation of data, wrote the manuscript, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

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