1. Ecology
  2. Neuroscience
Download icon

Range-dependent flexibility in the acoustic field of view of echolocating porpoises (Phocoena phocoena)

  1. Danuta M Wisniewska Is a corresponding author
  2. John M Ratcliffe
  3. Kristian Beedholm
  4. Christian B Christensen
  5. Mark Johnson
  6. Jens C Koblitz
  7. Magnus Wahlberg
  8. Peter T Madsen
  1. Aarhus University, Denmark
  2. University of Southern Denmark, Denmark
  3. University of Toronto Mississauga, Canada
  4. University of St Andrews, Scotland
  5. University of Tübingen, Germany
  6. Fjord and Belt, Denmark
Research Article
Cited
12
Views
2,941
Comments
0
Cite as: eLife 2015;4:e05651 doi: 10.7554/eLife.05651

Abstract

Toothed whales use sonar to detect, locate, and track prey. They adjust emitted sound intensity, auditory sensitivity and click rate to target range, and terminate prey pursuits with high-repetition-rate, low-intensity buzzes. However, their narrow acoustic field of view (FOV) is considered stable throughout target approach, which could facilitate prey escape at close-range. Here, we show that, like some bats, harbour porpoises can broaden their biosonar beam during the terminal phase of attack but, unlike bats, maintain the ability to change beamwidth within this phase. Based on video, MRI, and acoustic-tag recordings, we propose this flexibility is modulated by the melon and implemented to accommodate dynamic spatial relationships with prey and acoustic complexity of surroundings. Despite independent evolution and different means of sound generation and transmission, whales and bats adaptively change their FOV, suggesting that beamwidth flexibility has been an important driver in the evolution of echolocation for prey tracking.

https://doi.org/10.7554/eLife.05651.001

eLife digest

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.

https://doi.org/10.7554/eLife.05651.002

Introduction

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).

Transverse MRI scans of a young harbour porpoise.

The scale bars indicate 5 cm (bar in B applies also to C and D). The caudal part of the melon (yellow, A) abuts against layers of connective tissue, muscles, and tendons (red, A) forming a dense theca, which, along with the skull and a collection of nasal air sacs, reflect the vibrations that originate in the phonic lips (light brown, A) into the melon (Aroyan et al., 1992; Cranford et al., 1996). The melon is under control of highly developed facial musculature (Harper et al., 2008; Huggenberger et al., 2009). The fibers and tendons of the muscles (Mu) associated with the melon (Me) lie at oblique angles relative to the frontal, transverse, and sagittal body planes (Harper et al., 2008; Huggenberger et al., 2009). The actions of these richly innervated muscles can change the three-dimensional shape and/or stiffness of the melon (Harper et al., 2008; Huggenberger et al., 2009), and thus likely adjust the properties of the emitted sound.

https://doi.org/10.7554/eLife.05651.003

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.

Long terminal phase of prey pursuit by an echolocating harbour porpoise.

(A) Echogram (see ‘Material and methods’: Live prey capture) displaying sonar clicks and echoes recorded by an acoustic tag attached to the animal just behind its blowhole (Johnson et al., 2004). y-axis (left) indicates time elapsed from emitted clicks to returning echoes, expressed also as target range (right). Clicks emitted at rates corresponding to inter-click intervals shorter than 3.3 ms time-window are displayed repeatedly. The color scale indicates signal energy from blue (faint) to red (intense). As pursuit proceeds from water column (0.7–1.1 m from surface, 2–2.5 m above sea floor) to sea bottom the immediate acoustic scene becomes more cluttered with complex bottom echoes shortly following fish echoes (from −9 s onwards). (B) Inter-click intervals color-coded for relative apparent output level (RAOL; [Wisniewska et al., 2012]) of signals as recorded by the tag. RAOL variation may stem from rapid head movements, source level adjustments, and/or beam directionality changes (with less energy reaching the tag from a narrow beam). On two occasions, when the fish escaped into the open space of the water column (at −16 s and −13 s), the porpoise increased its ICIs significantly, beyond the values considered as buzz (Wisniewska et al., 2012). However, when the fish escaped to similar distances while being at the bottom (and thus moving in arguably a more predictable way) the porpoise increased the ICIs only slightly, which might point to anticipatory acoustic tracking on the part of the echolocating animal.

https://doi.org/10.7554/eLife.05651.004

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.

Results

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
A representative trial from experiment one.

Video shows a porpoise capturing fish in front of the linear hydrophone array. Hydrophones were lowered to a depth of 75 cm along the short side of the pool. Prior to the experiment the blindfolded (i.e., wearing opaque silicone eyecups) porpoise was stationed at the opposite end of the pool. As freshly thawed fish were introduced approx. 3 m from the array, the animal was cued to perform the capture task. The porpoise did not roll throughout the approach. Thus, the observed beam changes could not result from the beam not being rotationally symmetric and the animal rolling consistently during the buzz.

https://doi.org/10.7554/eLife.05651.005
Porpoise biosonar beam widens at short target ranges.

3 dB beamwidth recorded in two experimental setups: (A, B) three harbour porpoises closing on prey and (C, D) a harbour porpoise approaching an aluminum sphere target. (A, C) show reconstructed porpoise locations for clicks fulfilling inclusion criteria in one trial per array configuration. Targets and their projections in the x–y plane are marked with dark-blue filled and open circles, respectively. For the small array recordings (light blue in C), the target was displaced outward to 0.4 m from the array to maintain high spatial resolution at short ranges. (B) Data collected using a horizontal array with effective angular resolution (EAR) of ∼12° at ranges of target interception (N = 75). Data points from Freja, the porpoise participating in experiment two, are represented with circles. (D) Data gathered with star-shaped arrays in two configurations: large (red in C), for long-range recordings (>1.3 m from array to sound source, squares, N = 34) and small (light blue in C), for greater resolution at short ranges (<2 m from array to sound source, circles, N = 458) (see Figure 4—figure supplement 1 for a detailed view of hydrophone spacing in the two array configurations). Hydrophone spacing provided EAR of ∼5° at the shortest ranges from the source examined. Color in (B) and (D) indicates inter-click intervals (ICI), with buzz starting at 13 ms (Wisniewska et al., 2012). Buzz- and regular-click datasets in (D), used at short- and long-ranges, respectively, have different distributions, but similar medians, because during buzzes the animal repeatedly changed its beamwidth (Figure 6). Beam of the long-range clicks varied less and is better approximated by the median.

https://doi.org/10.7554/eLife.05651.006

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
A representative trial from experiment two.

Video shows a blindfolded porpoise closing on an aluminum target in front of a 48-element hydrophone array. The sequence was recorded in a short-range trial, that is, with the array extending to 0.5 m on either side of the centre hydrophone and the target moved to 0.4 m from array centre. Only clicks recorded when the porpoise was <2 m away from the array, at an angle of <15° to its centre and with acoustic axis within 6 cm from the centre hydrophone were selected for the beamwidth analysis. The video's soundtrack was replaced with audio recording from the camera-synchronized DTAG-3 carried by the porpoise.

https://doi.org/10.7554/eLife.05651.007
Figure 4 with 1 supplement see all
−3 dB beamwidth variation with range.

Colored markers indicate beamwidth estimates based on the best fitting piston aperture, while the black vertical lines show the spread around the best fit (see lower panels in Figure 4—figure supplement 1). Data were gathered with star-shaped arrays in two configurations (Figure 3C and Figure 4—figure supplement 1): large (squares, N = 121) and small (circles, N = 745). Color indicates the least-square error associated with the fits. Only fits with error <0.2 were considered in the final analysis and presented in Figure 3. Distance from the sound source to the tip of the animal's rostrum was 17 cm.

https://doi.org/10.7554/eLife.05651.008

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).

Beam adjustments can triple the ensonified area.

(A) Approximate detection volume for a harbour porpoise tracking fish in a quiet environment, based on the active sonar equation (1) and source energy levels as measured. Fixed detection threshold (Kastelein et al., 1999) of 27 dB re 1 µPa2s and target strength of −36 dB for the Atlantic cod of 29–30 cm (Au et al., 2007) is assumed. Pattern of the outer beam (solid line cross section) is based on beamwidth estimates obtained at short target ranges. The inner, narrow beam (dashed line) is based on the directionalities measured at long range, representing predicted beam pattern had the porpoise not switched to ‘wide-angle view’. (B) Relative change in the size of ensonified area ahead of the porpoise as it approaches a target. Surface area was computed as base of a cone with height equal to target range and an opening angle corresponding to the measured −3 dB beam angle (measured area, solid line in A) or median −3 dB beam angle calculated for long ranges (>2 m; predicted area, dashed line in A). Color indicates inter-click intervals. Squares and circles mark data points obtained with the large (N = 34) and small (N = 458) array, respectively. The bold horizontal line indicates points where the measured and the predicted areas are equal.

https://doi.org/10.7554/eLife.05651.010

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).

Figure 6 with 1 supplement see all
Temporal variation in beamwidth within the terminal buzz.

Beamwidth changes in terminal phases of two trials (A, C) and their respective inter-click intervals (ICI; B, D). Color-coding represents centroid frequencies (fc) of signals. Dashed line in (A, C) corresponds to median beamwidth at long ranges (>2 m). The porpoise used different beamwidths whilst maintaining ICIs and vice versa. Both trials were recorded with the small star-shaped array (light blue in Figure 3C), but during the trial shown in (C, D) the porpoise was not blindfolded. Only data for clicks fulfilling inclusion criterion are presented.

https://doi.org/10.7554/eLife.05651.011

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
Harbour porpoises can manipulate their melon while producing clicks.

Video shows a harbour porpoise emitting echolocation click trains during a hearing test. It has been slowed down by a factor of two and synchronized with the output of a porpoise click detector. The porpoise depresses the melon as it switches to high repetition rate click trains. Conformation changes in the nasal complex can modulate the degree of sound collimation in the whale's forehead ([Harper et al., 2008; Moore et al., 2008; Huggenberger et al., 2009]) to change the field of view. Courtesy of Lee Miller.

https://doi.org/10.7554/eLife.05651.013

Discussion

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.

Materials and methods

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&B;ælt since April 1997, estimated to be 1–2 years old at arrival [Lockyer, 2003]), Eigil (male, at Fjord&B;ælt since April 1997, estimated to be 1–2 years old at arrival [Lockyer, 2003]) and Sif (female, at Fjord&B;ælt 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).

Experiment one

We recorded echolocation clicks from three blindfolded (i.e., wearing opaque silicone eyecups) porpoises as they swam alone in the 3–4 m deep net-pen across the long side of the pool to capture dead, freshly thawed fish and towards a horizontal linear array of 8 calibrated Reson TC4014 hydrophones spaced 60 cm apart (Figure 3A and Video 1). The array was deployed at a depth of 75 cm and the fish were introduced approx. 3 m away from its centre, giving the array an effective angular resolution of ∼12° at ranges of target interception (EAR = atan[0.6 m/3 m]). Signals were amplified and filtered using a custom-made conditioning box and then simultaneously A/D converted with 16-bit resolution at 500 kHz per channel (National Instruments PXI-6123, Austin, TX). All trials were monitored using a video camera (Profiline CTV7040, Abus, Germany) synchronized with the audio recordings.

Analyses were performed using Matlab (MathWorks, Natick, MA). We localized the animal at the time of each emission using hydrophone arrival-time differences (Madsen and Wahlberg, 2007). Porpoise positions were then verified with the synchronized videos. For each click, the time of maximum sound pressure on each hydrophone was identified, and the energy density of the signal was measured using a window of 30 µs before and 90 µs after the peak of the signal envelope. Such a window corresponds to the duration of a typical porpoise signal. Assuming spherical spreading, the apparent source level (ASL) (Madsen and Wahlberg, 2007; Finneran et al., 2014) was calculated from the recorded level (computed as energy flux density in a fixed window [peak-30 µs—peak +90 µs]) and the range. For a click to be included in the analysis its ratio of signal energy to the immediately preceding noise energy had to exceed 6 dB on all channels, and the maximum ASL should not have occurred on one of the outermost hydrophones. Radiation plots were created by plotting the ASLs against their respective angles relative to the estimated on-axis direction. First, the peak amplitude and angle were adjusted by interpolating between the peak ASL and the ASLs from the two neighbouring hydrophones using Lagrange interpolation (Menne and Hackbarth, 1986). All off-axis levels were then plotted as a function of the off-axis angle. The resulting transmission beam pattern was interpolated to a grid of 0.1°. The circular piston model was used to estimate the directivity index and −3 dB beamwidth of the beam pattern as described in (Møhl et al., 2003).

Experiment two

A single harbour porpoise (Freja) was trained to swim across the short side of the pool and close in on a 50.8 mm-diameter spherical aluminum target, suspended by a nylon line just in front (5–40 cm, depending on array configuration, see below and Figure 3) of the center of an array of 48 small (25 × 10 mm), custom-built hydrophones (Wisniewska et al., 2012) (Video 2). The hydrophones were attached to a mesh of 1 mm-diameter Dyneema string stretched over a 3 × 3.5 m (height by width) metal frame to form a grid of 5 × 5 cm squares. The hydrophones had a flat (±2 dB) frequency response between 100–160 kHz and were connected to a 48 channel conditioning box with 40 dB gain and a fourth order bandpass filter with −3 dB frequencies at 2 kHz and 200 kHz. The hydrophones were sampled continuously during the trials with 16-bit resolution at 500 kHz/channel by three synchronized National Instruments PXIE-6358 boards and streamed to disk, using custom made software developed in LabVIEW, National Instruments (Source code 1).

Differences in array sensitivity due to hydrophone arrangement and attachment were measured after each data collection and corrected during post-processing. The hydrophones were arranged in two star-shaped configurations (Figure 3C and Figure 4—figure supplement 1). To map the beam extent at long ranges (1.3–7 m), we used hydrophone spacing increasing toward the edge hydrophones 1.05–1.13 m from the centre. Consequently, along the vertical and horizontal axes the hydrophones were separated by: 5, 10, 15, 20, 25, and 30 cm. Along the diagonal axes the hydrophones were separated by 14.1, 14.1, 21.2, 28.3, and 35.4 cm. With hydrophones 14 cm apart, the effective angular resolution was ∼6° at 1.3 m from the array's centre. To maintain high spatial resolution at short ranges (≤2 m), we displaced the target outward to 0.4 m from the array and rearranged the hydrophones, resulting in an array extending out to 0.5 m with hydrophones separated by 5, 5, 5, 15, and 25 cm along the vertical and horizontal axes, and by 7.1, 7.1, 14.1, and 21.2 cm along the diagonal axes. Thus, a sound source at 0.55 m from the array (i.e., the shortest range examined) could have had its beamwidth measured to within ∼5°.

We pooled the two data sets together with a range overlap between 1.3–2 m. Data points acquired with the wider spacing when the animal was closer than 1.3 m were discarded, as were data acquired with the fine spacing when the porpoise was more than 2 m away. The porpoise was equipped with a DTAG-3 multi-sensor tag (Johnson and Tyack, 2003; Johnson et al., 2004; Wisniewska et al., 2012) attached with suction cups just behind the blowhole, to allow for measuring the range of the sound source to the target and the array from the difference in time-of-arrival of click–echo pairs. The tag sampled sound with 16-bit resolution at 500 kHz/channel and was synchronized with the array hydrophones (Wisniewska et al., 2012). In all but one (Figure 6C,D) of the analyzed trials, the porpoise was blindfolded with opaque eyecups.

All trials were monitored with a set of GoPro Hero 2 cameras (two on the heads of the trainers and one approximately 1.8 m behind the array; Eye of Mine Action Cameras, Carson, CA) synchronized with the DTAG-3 recordings.

The recorded trials were pre-screened for relatively straight approaches to the array using the videos. All subsequent sound analyses were performed using Matlab. Clicks from the study animal were identified in the DTAG-3 acoustic recordings using a supervised click detector. Spectral cues were used to eliminate occasional misdetections of echoes or signals from other porpoises in the neighbouring pen. Echograms were formed from the sounds recorded with the DTAG-3. Only trials with clear echoes from the target and the array were submitted to further analysis. Clicks from 13–21 key hydrophones of the large- and small star-shaped array were extracted using a supervised click detector with the synchronized timing of the clicks recorded on the animal as an input. Clicks from all the verified channels were then combined into a single template and used for automatic click detection on the remaining channels. For each click, we identified a subset of channels with peak received levels exceeding the rms noise level of the channel by at least 14 dB. We calculated click energy in a fixed window (peak-30 µs—peak +90 µs) on each of the selected channels, fitted a surface to the values using the Matlab ‘gridfit’ function with grid spacing of 0.5 cm and determined the location of the beam axis as the peak of the fitted surface (Figure 4—figure supplement 1, upper panels). We ran a series of computer simulations of our methods applied to virtual sources of known piston sizes to evaluate the influence of (i) an animal's bearing in azimuth and elevation and (ii) beam axis displacement relative to the centre of the array on the beamwidth measurement error. Based on the results of these simulations, we restricted our analysis to only include clicks emitted when the porpoise was swimming directly toward the target (within ±15° vertically and horizontally from the array's centre) and with beam axis within (i) ±12 cm from the array centre for the large array, (ii) ±8 cm for clicks recorded with the small array and produced up to 1 s before the start of buzz, and (iii) ±6 cm for clicks made 1 s before buzz and onwards until the end of the target approach. The estimated error was thereby limited to ≤±0.5°.

The simulations also verified that at these displacements the location of the beam axis could be estimated with an accuracy of ±1.5 cm at the ranges considered in this study. We used hydrophone arrival-time differences to compute the animal's bearing (azimuth and elevation) to the centre of the array.

For each click, we followed the method of Kyhn et al. (2010) to fit the energy levels received at the hydrophones to the beam pattern of a circular piston that fulfilled the following criteria: (i) it was at the same range to the array as the porpoise emitting the click, (ii) it was centered on the estimated beam axis and (iii) it transmitted the click recorded on the hydrophone closest to that axis. Given that the orientation of the porpoise relative to the array was constantly changing as the animal was approaching the target (Video 2), the beam pattern was assumed to be rotationally symmetrical around the acoustic axis. Furthermore, this assumption allowed us to utilize information from all hydrophones fulfilling the signal-to-noise ratio criterion to find the best fitting aperture. We carried out a Monte Carlo simulation using theoretical piston transducers with diameters of 1/3 to 3 times 8.3 cm (i.e., the best fitting vertical equivalent aperture in [Koblitz et al., 2012]) in 0.1 cm increments, and the circular piston model of Au et al. (1987). The diameter of the piston that matched the data best was found by means of a non-linear least-squares method (see Figure 4—figure supplement 1). Only fits with R2 > 0.8 (Figure 4) were kept for final analysis, from which the −3 dB beamwidth and the equivalent piston radius were extracted.

To examine the relative change in the size of ensonified area ahead of the porpoise as it approached a target, we computed the surface area as base of a cone with height equal to target range and an opening angle corresponding to (i) the measured −3 dB beam angle and (ii) median −3 dB beam angle calculated for long ranges.

We pooled beamwidth and distance to target data for all clicks from Experiment 1 for the three porpoises and ran a regression analysis on these data. We then pooled beamwidth and distance to target data for both array configurations from Experiment 2 and ran a regression analysis on these data as well. Additionally, we used hierarchical cluster analysis (centroid, non-standardized) to assign click beamwidth data from Experiment 2 to one of three clusters and compared these clusters using ANOVA with respect to click distance to target. All statistical analyses were carried out using JMP v. 11.2 (SAS Institute, Cary, NC, USA).

Live prey capture

To explore the acoustic scene experienced by a toothed whale tracking active prey, we deployed DTAG-3 tags (with the same recording settings as in experiment two) on porpoises involved in pursuit of small (∼15 cm), live trout in the pen complex of Fjord&Belt;, where the animals have access to a natural sandy bottom at 3–4 m depth. This unique setting approximates what this shallow-water predator might encounter in its natural surroundings. The tags recorded the whale's echolocation clicks as well as echoes from the fish and other objects and surfaces in the whale's surroundings (e.g., water surface and sea floor). The recorded sounds were used to form stack plots, or echograms, of sound envelopes synchronized to the outgoing click as in echosounder images (Johnson et al., 2004). These allowed us to follow movements of the echolocator and its prey in the environment (Figure 2). The time delay between the echolocation click and the echo, multiplied by one-half of the sound speed (1500 m/s was assumed) gives the distance to the target. Delays to the surface and bottom echoes approximate the animal's depth and altitude above the sea floor, respectively. Time delays between different echo groups represent their relative proximity.

Anatomy of a porpoise head

A video of melon deformations can be found online (Video 3). To visualize the anatomy of the head, a dead specimen was scanned in a 1.5T Siemens Avanto MRI system (Siemens Medical Solutions, Germany). A Flash 3D T1 weighted pulse-sequence with the following parameters was used: TR 14.8 ms, TE 3.38 ms, α = 15°, NEX = 3, spatial resolution = 0.64 × 0.64 × 0.75 mm. Following acquisition, segmentation and modeling were done using Amira 5.3.3 (Visualization Science Group, Germany).

References

  1. 1
  2. 2
    The sonar of dolphins
    1. WW Au
    (1993)
    New York: Springer-Verlag.
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
    Propagation of beluga echolocation signals
    1. WW Au
    2. RH Penner
    3. CW Turl
    (1987)
    Journal of the Acoustical Society of America 82:807–813.
    https://doi.org/10.1121/1.395278
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
    Listening in the dark: the acoustic orientation of bats and men
    1. DR Griffin
    (1958)
    New York: Cornell University Press.
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
    Animal eyes (edition 2)
    1. MF Land
    2. DE Nilsson
    (2012)
    Oxford University Press.
  34. 34
  35. 35
    Harbour porpoises (Phocoena phocoena) in the North Atlantic: biological parameters
    1. C Lockyer
    (2003)
    In: T Haug, G Desportes, GA Vikingsson, L Witting, editors. Harbour porpoises in the North Atlantic. Tromsø, Norway: North Atlantic Marine Mammal Commission Scientific Publications. pp. 71–89.
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
    Tracking frequency of the location signals of dolphins as a function of distance to the target
    1. VP Morozov
    2. AI Akopian
    3. VI Burdin
    4. KA Zaitseva
    5. YA Sokovykh
    (1972)
    Biofizika 17:139–145.
  43. 43
    Sound transmission in the porpoise head
    1. KS Norris
    2. GW Harvey
    (1974)
    Journal of the Acoustical Society of America 56:659–664.
    https://doi.org/10.1121/1.1903305
  44. 44
    Animal training: successful animal management through positive reinforcement
    1. K Ramirez
    (1999)
    Chicago, IL: Shedd Aquarium Society.
  45. 45
  46. 46
    Electromyographic and pressure events in the nasolaryngeal system of dolphins during sound production
    1. SH Ridgway
    2. DA Carder
    3. RF Green
    4. AS Gaunt
    5. SLL Gaunt
    6. WE Evans
    (1980)
    In: RG Busnel, JF Fish, editors. Animal sonar systems. New York: Plenum Press. pp. 239–250.
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51

Decision letter

  1. Russ Fernald
    Reviewing Editor; Stanford University, United States

eLife posts the editorial decision letter and author response on a selection of the published articles (subject to the approval of the authors). An edited version of the letter sent to the authors after peer review is shown, indicating the substantive concerns or comments; minor concerns are not usually shown. Reviewers have the opportunity to discuss the decision before the letter is sent (see review process). Similarly, the author response typically shows only responses to the major concerns raised by the reviewers.

Thank you for sending your work entitled “Range-dependent flexibility in the acoustic field of view of echolocating whales” for consideration at eLife. Your article has been favorably evaluated by Eve Marder (Senior editor), a Reviewing editor, and three reviewers.

The Reviewing editor and the reviewers discussed their comments before we reached this decision, and the Reviewing editor has assembled the following comments to help you prepare a revised submission.

Comments for authors:

1) Title: Suggestion to change the title for clarity. Possible title: “Range dependent flexibility in the acoustic field of view of echolocating porpoises.” You might include the species name in the title as well.

2) Abstract: The Abstract appears contradictory in stating that “porpoises… unlike echolocating bats, are able to change beamwidth within” the terminal phase of attack, but also that “both whales and bats have evolved mechanisms to change their FOV… for prey tracking.” You need to be more precise if you want to argue that they have converged but are different. In addition, you state “that harbor porpoises broaden their biosonar beam by >50% in the terminal phase”, but that is not a fair summary of the results, showing very little broadening on average. You should say “harbor porpoises can broaden their…”, to report the results objectively.

3) Text:

a) Please provide more information about the animals used including previous experience with echolocation experiments.

b) Explain why you used only a single animal for the second two experiments and analyzed only a small subsample of the data. Please be clearer about the possible effects of this selective process. Was the performance of their subject exceptional? Did they analyze all the analyzable click trains, or only a subset? Did their criterion reflect a behavioral difference (e.g., the data produced by one type of approach to the target being easier to analyze than another type of approach). Since this phenomenon has not been described previously would be inter2preted differently if the animals used a variety of strategies to home in on the target.

c) The MRI images of the structure of the melon and phonic lips are not described in sufficient detail. Please provide a brief description of how these images compare with previous images of this type from harbor porpoises. Is this the first time simulations of production have been done for the harbor porpoise, or were there others? Were they consistent?

d) There is a discrepancy between the text describing “a dramatic broadening of the beam” and the actual data showing almost no effect, when comparing the median beam width. However, is median the best measure? Figure 4 indicates a strong tendency to broaden the beam in the last phase. Wouldn't this be better described by another measure than median? The data are probably not Gaussian, but even so, the average (or “weight”) might illustrate the effect.

e) How were the data pooled to determine medians and distribution? According to array type? Distance? ICI?

f) Please present a more detailed data taking protocol (individual porpoise, distance between target and array, array type and deployment place) into consideration. Figure 5 shows the results from two very different trials suggesting that pooling the data might mask some of the effects. Figure 3–figure supplement 2 show many data at ca. 1.2 m range. Was there anything specific here? In addition, this figure (which should be included instead of e.g. Figure 2, which is not really used and mostly repeats the message from earlier similar echograms) suggests a very interesting pattern with broadest beam width at between 25 and 35 cm target range.

g) Did individual porpoises show the same results? How did the data from the two arrays compare for the porpoise investigated in both series?

h) What is portrayed in Figure 3–figure supplement 3? If the target was centered on the 0,0 point and the vertical axis was the Y-axis, it can be puzzled out but it should be easier. For example, show the location of the target, and say “bearing” and “azimuth” instead of showing two bearings.

i) At the end of the Introduction, the analysis provided does not directly address the hypothesis to be tested, but perhaps this can possibly be corrected by reanalysis of the existing data, and that this could be done in a reasonable time. The most important result described in the Abstract is that “porpoises broaden their sonar beam by >50%” while the results of experiment 2 state: “the porpoise could almost double its beamwidth at short target ranges (Figure 3D) from a median half-power beamwidth of 9° (6.6-11.8) to a maximum of 15° (median = 10.8, min = 7.4).” So, is the difference 50%, as the Abstract says, or 200% as the Results say? I assume that this does not refer to experiment 1, as the authors state “the relatively large hydrophone spacing in this experiment… prevented us from drawing strong conclusions about the exact extent of beam widening.” The comparison in the Results of experiment 2 is inexplicably between a median and a maximum. If you compare the two median values, 9° (6.6-11.8) to 10.8° (7.4-15), I am not at all convinced that there even is a significant difference. If the paper wants to reach conclusions about changes in beamwidth with distance to target or stages of capture, then it needs a valid statistical comparison of the two distributions. Since the goal is to test whether the porpoise changes FOV adaptively during prey interception, then I think the test should be organized using each approach as the basic unit of analysis. Figure 3D merges data from different approaches, and even totally different receiver set-ups. The actual analysis of adaptive changing of FOV during interception should only test for changes in FOV within each approach. The prediction should be that beamwidth expands as porpoise closes on prey.

j) Do porpoises change beamwidth independent of frequency by changing the configuration of the melon and air sacs? The paper several times cites Figure 5 as demonstrating this. While it shows two examples relating ICI, bandwidth, and center frequency in the context of time from start of buzz, this figure does not actually show a clear analysis that beamwidth is independent of frequency. This requires a statistical test of this point, perhaps against a physical model of how beamwidth would change with fc for a simple static transducer. A plot of beamwidth against fc would also help.

Specific linguistic concerns:

Introduction:

This is a bit picky, but beaked whales only decrease intensity as they switch from search to buzz; most of their closing on prey is not accompanied by decrease. There are very limited data on odontocete species with auditory gain control.

I think you need to walk the reader through the echogram. I think only a tiny minority would be able to link what you say in the text to what you see in Figure 2 without help. Where was this recorded? In the pool? What experiment? The figure is beautiful, but the data in this figure (actual distances to bottom and surface) are not used for anything in the Discussion.

Could the large increase in the experiment with linear array be due to the porpoise not focusing on the array (vertically) before closing in?

Results:

How did you control for beam axis in the vertical plane?

I suggest not reporting a difference from 9° to 18° as 99% difference. “100% different” indicate “no overlap”, and accordingly 99% would suggest “almost completely different”. Doubling the sonar beam width would be less confusing.

It is not easy to extract the real results when you provide a comparison between the median (at longer ranges) and the one maximum value at short range. If you want to “break records” it would be fairer to refer to a minimum value vs. a maximum value. Then the reader would know you are referring to the extremes of the dataset.

What does “better approximated” mean precisely?

Please explain which numbers you took to calculate the “at least three times increase”. Figure 5, lower part, seems to indicate that the beam can also be very broad before the buzz.

Discussion:

It is hard to see how the different array configurations could have created such a big difference. But a possible correlation to behavior/context could perhaps: either the freely floating fish with the linear array or the large clutter wall from the star-shaped array. It appears easy to test.

I think that the relation between wavelength and size of the lens/melon differs so radically that this analogy is probably not correct in terms of the physics.

I do not believe that the pupil and lens are not able to change FOV as much as you discuss here for echolocation.

Materials and methods:

How can you accurately interpolate to 0.1° if EAR is only good to 12°?

What does “just in front” mean? Few cm? mm? Figure 3 indicates that the target can be in two positions: very close to the center of the array and 1 (?) m in front of it? Figure 3, not clear.

How did arrangement of the array change the sensitivity of the hydrophones?

Is vertical axis tested?

The subsection headed “Live prey capture”: Is this dataset (and Figure 2) relevant for the study? Apparently, the data are not used for anything. The relevance would be obvious if both the array and the tag had recorded a trial in order to reveal possible correlation between changes in the echogram/acoustic scene and beam width, but that is not the case, as far as I can see.

Do you really think Atlantic cod is a good TS for porpoise prey? Most would be much smaller and lower TS.

Figure 5 is very interesting. The two trials are surprisingly different. Why is that? Were these recorded with the linear array or the star shaped? Probably star shaped, but what configuration?

Video 3: The video shows depression in the whole buzz phase. If that is a proxy for broadening the view, it suggests a constant broad beam in the buzz and not variation as suggested by Results and Discussion in this manuscript?

Please clarify what are the supplementary figures reporting and why.

https://doi.org/10.7554/eLife.05651.015

Author response

1) Title: Suggestion to change the title for clarity. Possible title:Range dependent flexibility in the acoustic field of view of echolocating porpoises.You might include the species name in the title as well.

Good point, thank you for this suggestion. The title has now been changed accordingly.

2) Abstract: The Abstract appears contradictory in stating thatporpoises… unlike echolocating bats, are able to change beamwidth withinthe terminal phase of attack, but also thatboth whales and bats have evolved mechanisms to change their FOV… for prey tracking.You need to be more precise if you want to argue that they have converged but are different. In addition, you statethat harbor porpoises broaden their biosonar beam by >50% in the terminal phase, but that is not a fair summary of the results, showing very little broadening on average. You should sayharbor porpoises can broaden their…, to report the results objectively.

Again, fair points, thank you. We have changed the Abstract accordingly.

3) Text:

a) Please provide more information about the animals used including previous experience with echolocation experiments.

Thank you for this suggestion. We have now added more information on the porpoises’ age, sex and experience in echolocation experiments.

b) Explain why you used only a single animal for the second two experiments and analyzed only a small subsample of the data. Please be clearer about the possible effects of this selective process. Was the performance of their subject exceptional? Did they analyze all the analyzable click trains, or only a subset? Did their criterion reflect a behavioral difference (e.g., the data produced by one type of approach to the target being easier to analyze than another type of approach). Since this phenomenon has not been described previously would be interpreted differently if the animals used a variety of strategies to home in on the target.

At the time the large-array experiments were conducted, Eigil, the male porpoise, was involved in an intensive training program for a psychophysical study using a “go-no go” procedure (see Linnenschmidt et al. 2012 and Linnenschmidt et al. 2013 for details). He was thus not available for our study.

Initially, we did, however, record both of the two females: Freja and Sif. Unfortunately, Sif became ill, and her training time had to be cut down and she never learned the task properly.

The 3 porpoises kept at Fjord & Bœlt also undergo daily husbandry training and are used in public presentations. Hence, training time and the amount of fish the animals were fed per day limited the number of porpoises and time assigned to our study.

With respect to the last point, we did analyze all analyzable click trains, using the standards we set for data quality. We feel our results can be extrapolated to harbor porpoises in general because, one, the results from the first experiment included all 3 porpoises (and all behaved similarly) and, two, these porpoises behave normally in the context of other echolocation parameters reported on from porpoises at other research centers and in the wild.

c) The MRI images of the structure of the melon and phonic lips are not described in sufficient detail. Please provide a brief description of how these images compare with previous images of this type from harbor porpoises. Is this the first time simulations of production have been done for the harbor porpoise, or were there others? Were they consistent?

We agree, and we have therefore expanded on the section that presents the scanning data. In that section we also compare in more detail with the one entirely anatomical study done on the porpoise head by Huggenberger and colleagues in 2009. Sound production has not been modelled in porpoises using scanning data, but interestingly Dr. Huggenberger concludes his anatomical paper by stating the following on the context of the musculature and the melon:

“Furthermore, the intermedius muscle (im) may pull the dorsal part of the melon caudally (Figure 9) and rostral parts of the anterointernus muscle (ai) may pull the melon terminus ventrally, thus changing its height. However, it would be a matter of speculation to decide how much such a change in shape can contribute to a potential modulation of the sonar beam.”

Here, we confirm for the first time that these muscles can modulate the shape of the melon, and that the beam width is indeed very adaptable in these animals. Thus, no one has previously modelled this, but this earlier study predicted that beams may be modulated by changing the melon confirmation. We have highlighted that fact in the revised manuscript.

d) There is a discrepancy between the text describinga dramatic broadening of the beamand the actual data showing almost no effect, when comparing the median beam width. However, is median the best measure? Figure 4 indicates a strong tendency to broaden the beam in the last phase. Wouldn't this be better described by another measure than median? The data are probably not Gaussian, but even so, the average (orweight) might illustrate the effect.

Agreed. We have now used mean, rather than median. We have also removed “dramatic” from the text (it appeared once in the Discussion).

e) How were the data pooled to determine medians and distribution? According to array type? Distance? ICI?

Data from the two experiments were examined separately. For the large star-shaped array in experiment two, we measured beamwidth for all clicks emitted at source ranges greater than 1.3 m from the array. Ranges ≤2m were examined with the small star-shaped array. These data were then pooled together (i.e. there was a range overlap of the two arrays at 1.3-2 m). For the summary statistics, data from the two experiments were divided into two long- and short-range clicks based on source-target range (≤2m vs >2m).

However, we agree that pooling by ICI is a better approach. For each experiment, we have, therefore, now divided the data into buzz clicks (inter-click intervals ≤13 ms) and regular clicks (inter-click intervals >13 ms; buzz onset threshold was based on ICI distributions examined in Wisniewska et al. 2012 for the same porpoises). The distributions of beamwidth in the two groups have been added to Figure 3.

f) Please present a more detailed data taking protocol (individual porpoise, distance between target and array, array type and deployment place) into consideration. Figure 5 shows the results from two very different trials suggesting that pooling the data might mask some of the effects. Figure 3–figure supplement 2 show many data at ca. 1.2 m range. Was there anything specific here? In addition, this figure (which should be included instead of e.g. Figure 2, which is not really used and mostly repeats the message from earlier similar echograms) suggests a very interesting pattern with broadest beam width at between 25 and 35 cm target range.

Trials with Freja, the porpoise used in experiment 2, were quite stereotyped; array location and distance between the target and the array did not change between the trials with a given array configuration. But in the trial presented in Figure 5C, D, the porpoise was not blindfolded. More information about the animals, target ranges and blindfolds has been added to the manuscript text and figure legends. See Materials and methods, second paragraph and Figures 3, 5.

Figure 3–figure supplement 2 has now been included in the manuscript as a standalone Figure 4. Toothed whales tend to initiate buzz when about a body length away from the intended target (and perhaps at even shorter ranges for stationary man-made targets (see e.g. Wisniewska et al. 2012)). Therefore, there will naturally be more clicks from that range onwards. While approaching a target, the animals scan their beam back and forth over the target, which, if the animal’s behaviour is very stereotyped, could lead to “click gaps” at some ranges. We are not sure why there is an accumulation of data points at 1.2 m, but we can speculate that that could have been the preferred range of transition to a buzz, during which the porpoise might have been more likely to focus its beam on the target. The fact that the data are from a single individual may have further exaggerated the effect.

Given that eLife does not limit the number of figures, we would like to keep Figure 2 in the manuscript. Please see our detailed justification below.

g) Did individual porpoises show the same results? How did the data from the two arrays compare for the porpoise investigated in both series?

Yes, there were some individual differences, but it is hard to address them with two trials per animal. The most extreme beamwidth values were all recorded from Eigil. Freja, the porpoise participating in the second experiment, broadened her beam up to a maximum of 24.3° when capturing fish in experiment one (as opposed to a maximum of 15.1° in experiment 2). We have now used separate symbols to represent data points from the different animals in experiment one.

h) What is portrayed in Figure 3–figure supplement 3? If the target was centered on the 0,0 point and the vertical axis was the Y-axis, it can be puzzled out but it should be easier. For example, show the location of the target, and saybearingandazimuthinstead of showing two bearings.

Good point, but given the comment, the figure has been removed altogether.

i) At the end of the Introduction, the analysis provided does not directly address the hypothesis to be tested, but perhaps this can possibly be corrected by reanalysis of the existing data, and that this could be done in a reasonable time. The most important result described in the Abstract is thatporpoises broaden their sonar beam by >50%while the results of experiment 2 state:the porpoise could almost double its beamwidth at short target ranges (Figure 3D) from a median half-power beamwidth of 9° (6.6-11.8) to a maximum of 15° (median = 10.8, min = 7.4).So, is the difference 50%, as the Abstract says, or 200% as the Results say? I assume that this does not refer to experiment 1, as the authors statethe relatively large hydrophone spacing in this experiment… prevented us from drawing strong conclusions about the exact extent of beam widening.The comparison in the Results of experiment 2 is inexplicably between a median and a maximum. If you compare the two median values, 9° (6.6-11.8) to 10.8° (7.4-15), I am not at all convinced that there even is a significant difference. If the paper wants to reach conclusions about changes in beamwidth with distance to target or stages of capture, then it needs a valid statistical comparison of the two distributions. Since the goal is to test whether the porpoise changes FOV adaptively during prey interception, then I think the test should be organized using each approach as the basic unit of analysis. Figure 3D merges data from different approaches, and even totally different receiver set-ups. The actual analysis of adaptive changing of FOV during interception should only test for changes in FOV within each approach. The prediction should be that beamwidth expands as porpoise closes on prey.

The 50% referred to the change in beamwidth, while the 200% referred to the change in the ensonified cross-sectional area. In the new version of the manuscript, we have refrained from using % and are now providing the difference in beamwidth in degrees instead.

With respect to the hypothesis to be tested, we understand this important point of critique. However, we cannot reanalyse our data to address it. As the reviewer her/himself points out, we used different setups for long- and short-range recordings to cover the full extent of the beam at relatively long ranges and provide the necessary resolution at short ranges. Doing what the reviewer asks would require a setup with twice as many hydrophones, which with our current equipment, is not possible, given that we have to sample at 500 kHz per channel. Recording using 48 hydrophones simultaneously at these sampling rates, we were limited by our ability to stream the data to the computer.

Furthermore, given our strict click selection criteria (necessary to provide a reliable estimate of the beamwidth), even with a larger/more hydrophone-dense array, we are not sure if we could address this comment. We are not certain if the trials would provide enough data points to run tests on a trial-by-trial basis.

Following the reviewers’ advice, we have, however, performed a regression and hierarchical cluster analysis on the available beamwidth vs. range data and found that the distributions are indeed statistically different. The statistical results have been incorporated into the manuscript. In brief, they show that for experiment one, beamwidth and distance to target are inversely related. That is, that as the animals approach their targets, they broaden their sonar beam. We tested this using regression analysis.

We found the same significant inverse relationship for experiment 2, but the r2 value was much lower. Thus, we ran a combination of cluster analysis and ANOVA. We found that those clicks with the greatest beamwidth were produced significantly closer to the target than those clicks produced furthest away.

j) Do porpoises change beamwidth independent of frequency by changing the configuration of the melon and air sacs? The paper several times cites Figure 5 as demonstrating this. While it shows two examples relating ICI, bandwidth, and center frequency in the context of time from start of buzz, this figure does not actually show a clear analysis that beamwidth is independent of frequency. This requires a statistical test of this point, perhaps against a physical model of how beamwidth would change with fc for a simple static transducer. A plot of beamwidth against fc would also help.

Thank you for this excellent suggestion. A plot of beamwidth against centroid frequency overlaying the results of a model of frequency-dependence of beamwidth for static circular piston transducers of different sizes has now been added to the manuscript (Figure 6–figure supplement 1). The plot shows that the variation of centroid frequencies measured in the two experiments could not explain the observed beamwidth changes. Even the best-fit models provided rather poor match to the data (RMSE=5.9 and SSE=2604 for experiment 1; RMSE=1.8 and SSE=1556 for experiment 2). To be sure, we ran exact Spearman's correlation tests of fc versus beamwidth, and found no significant relationship in the data from either experiment (experiment 1: P=0.76, experiment 2: P= 0.67). We have now added that information to the text.

Specific linguistic concerns:

Introduction:

This is a bit picky, but beaked whales only decrease intensity as they switch from search to buzz; most of their closing on prey is not accompanied by decrease. There are very limited data on odontocete species with auditory gain control.

We agree that it seems that odontocetes may show a whole spectrum of strategies of level adjustments to target range, dependent on the context of prey pursuit, and likely prey behaviour (see for example Wisniewska et al. 2014). While it was not our intention to state that all toothed whales and bats adjust their levels to fully compensate for the transmission loss, we can see how the sentence could be interpreted as such statement. We have therefore modified the sentence to clarify that.

I think you need to walk the reader through the echogram. I think only a tiny minority would be able to link what you say in the text to what you see in Figure 2 without help. Where was this recorded? In the pool? What experiment?

Good point. All the information can be found in one of the subsections of the Material and methods section, but we agree that that was not necessarily clear in the figure legend or the main text. A reference to the subsection has now been added to the figure legend.

Could the large increase in the experiment with linear array be due to the porpoise not focusing on the array (vertically) before closing in?

We have no detailed control over the vertical direction of the beam using the linear array. The porpoise was approaching the linear array throughout the recording, and from the video observations there was no obvious change in the porpoise swimming direction when being close to as compared to being far away from the array (with respect to either head angle or the depth of the porpoise itself). Any possible effects of the porpoise being slightly off axis vertically during the linear array recordings were ruled out by the subsequent planar array measurements. There is a new sentence included in the text to clarify this.

Results:

How did you control for beam axis in the vertical plane?

We have applied on-axis criteria that in a large number of papers seem to work convincingly (e.g. Jensen et al. 2009; Kyhn et al. 2010; Wahlberg et al. 2011; Jensen et al. 2013). But no, we could not control for the vertical direction of the beam using a horizontal linear array, which is why the planar arrays (experiment 2) were necessary to get detailed measurements on the beam broadening. We have included a sentence to clarify this in the new version.

I suggest not reporting a difference from 9° to 18° as 99% difference.100% differentindicateno overlap, and accordingly 99% would suggestalmost completely different. Doubling the sonar beam width would be less confusing.

We have now pooled the data based on their ICI, rather than range, and so the results are different. But we agree that perhaps % difference is not the best approach. We are now providing the actual difference in degrees instead.

It is not easy to extract the real results when you provide a comparison between the median (at longer ranges) and the one maximum value at short range. If you want tobreak recordsit would be fairer to refer to a minimum value vs. a maximum value. Then the reader would know you are referring to the extremes of the dataset.

We have now used mean, rather than median. The minimum and maximum values are provided in the brackets for comparisons.

What does “better approximated” mean precisely?

As suggested earlier on this review, we now use mean rather than median values. We also agree that “better approximated” is vague, and have replaced this with more precise language which reflects the fact that the long range click data are more evenly and tightly spread around the mean beamwidth value and thus better described by the mean than are the short range data.

Please explain which numbers you took to calculate theat least three times increase. Figure 5, lower part, seems to indicate that the beam can also be very broad before the buzz.

The sentence states “up to three times” and refers to the size of ensonified area ahead of the porpoise (which will depend on the beamwidth and the range to the target), rather than the beamwidth. We base that statement on the data presented in Figure 5 (Figure 4 in the previous version of the manuscript), where we show the ratio of area to area measured at a given target range to the area predicted for that range based on the median -3dB beam angle calculated for ranges >2m (i.e. predicted area had the porpoise not switched to “wide-angle view”).

Discussion:

It is hard to see how the different array configurations could have created such a big difference. But a possible correlation to behavior/context could perhaps: either the freely floating fish with the linear array or the large clutter wall from the star-shaped array. It appears easy to test.

We agree that the differences in array configurations are unlikely to generate such large differences in beamwidth as an artefact, and that it is the behaviour and/or context that likely have caused these differences. We have added a sentence pointing this out to the manuscript.

I think that the relation between wavelength and size of the lens/melon differs so radically that this analogy is probably not correct in terms of the physics.

We agree that the reviewers may be right, but argue that the analogy still has illustrative value (especially for those readers not familiar with whales/echolocation). We have, however, reworded this section to reflect that this analogy is speculative: specifically, we have removed the phrase “acoustic analogue” as this phrase did suggest an analogy argument based in physics.

I do not believe that the pupil and lens are not able to change FOV as much as you discuss here for echolocation.

A true, adjustable aperture to change the field of view can only be achieved in an optical system with at least two lenses, and no mammals or birds are equipped with two lenses to our knowledge. According to Professor Ronald Kröger at Lund University that we have consulted with on this topic, some shallow water fish have dorsal iris or skin flaps that seem to block out the sun when light adapted, while the upward direction is free in dark-adapted eyes. That is indeed an example of a change in FOV, but we have searched the literature and consulted people at the Lund vision group, and from that it is clear that a vertebrate generally cannot change its FOV, and no mammals have been shown to do it. What most animals can do is to move the FOV around by either moving the eyes or the head to effectively increase the search volume, but toothed whales can do that too by moving their head around. What we show here is that the acoustic FOV can be changed independent of head movements, which is not possible in mammalian and avian visual systems. We have clarified this point in the revised text.

Materials and methods:

How can you accurately interpolate to 0.1° if EAR is only good to 12°?

Details of the interpolation procedure have been included in the Materials and methods section. The 12° is only for the final clicks very close to the array – for the initial clicks further from the array the angular resolution were less than 4 degrees. This is now clarified in the beginning of the result section discussing the quality of the linear array data. It is true that in this experiment we have no control as to how accurate the interpolation is. We have data currently in press in Journal of Experimental Biology (Jensen et al.) using a transducer mimicking porpoise clicks and beam patterns and recorded with a linear array showing that with a similar array geometry and an angular resolution of 6° it is possible to achieve beam measurements with sub-degree precision, so using an interpolation step of 0.1° on the data of this manuscript does not seem that unrealistic.

What doesjust in frontmean? Few cm? mm? Figure 3 indicates that the target can be in two positions: very close to the center of the array and 1 (?) m in front of it? Figure 3, not clear.

5-40 cm, depending on array configuration. We agree that this was not clearly stated in the original manuscript. Thank you for pointing this out. The information has been added to the text and the legend of Figure 3. The projections of the targets on the x-y plane in Figure 3 a) and c) were meant to help the reader find the ranges of the targets to the arrays, but we agree that it may have been difficult to realize without appropriate guidance in the figure legend.

How did arrangement of the array change the sensitivity of the hydrophones?

We did not use the correct wording here. What we meant was the sensitivity at each point of the array, rather than hydrophone sensitivity. Sometimes hydrophones had to be replaced or switched around, especially when the array configuration was changed. We have clarified that in the revised version of the manuscript.

Is vertical axis tested?

Yes, the piston was fitted to all the hydrophones with peak levels exceeding the rms noise level of the channel by at least 14 dB (hydrophones marked with black filled circles in Figure 4–figure supplement 1).

The subsection headed “Live prey capture”: Is this dataset (and Figure 2) relevant for the study? Apparently, the data are not used for anything. The relevance would be obvious if both the array and the tag had recorded a trial in order to reveal possible correlation between changes in the echogram/acoustic scene and beam width, but that is not the case, as far as I can see.

We do feel that it conveys a visual representation of the echoic complexity faced by echolocating animals and as such it is important in our argument that the adaptable FOV shown here may have evolved to deal with this complexity. We now refer to it (Discussion, first and third paragraphs) more often in the manuscript and believe it has illustrative value (i.e. that it helps visual creatures like ourselves better appreciate how a complex environment might be interpreted using echoes). Our intention was therefore to use this figure to demonstrate that when buzzing in a natural context porpoises can move through acoustic scenes of different complexities. While echograms have been published for beaked whales chasing prey, no such literature currently exists for harbour porpoises.

We do of course have echograms from the star-shaped array recordings (we used them for range estimations). They show that in the pen the array constitutes an acoustically cluttered environment. But the clutter did not vary between trials, since we used the same setup every time. Also, the animals catching fish in experiment one were not tagged, and we therefore cannot examine the clutter they experienced as a comparison for the star-shaped array data.

Do you really think Atlantic cod is a good TS for porpoise prey? Most would be much smaller and lower TS.

Cod is the second-most common prey species found in the stomachs of harbour porpoises by-caught in the Danish Straits (see Sveegaard 2011 for a review). It is true that adult cod reach sizes that cannot be consumed by harbour porpoises (according to Andreasen (2009) since 94% of porpoise prey is smaller than 45 cm). However, the Atlantic cod subjects used by Au et al. (2007) were 29-30 cm long (we have now added this information to the figure legend), and Börjesson et al. (2003) reported the average length of cod found in porpoise stomachs to be 28 cm, so we feel that this is a relevant comparison.

Figure 5 is very interesting. The two trials are surprisingly different. Why is that? Were these recorded with the linear array or the star shaped? Probably star shaped, but what configuration?

Both trials were recorded with the small star-shaped array. During the trial shown in the lower two panels, the porpoise was not blindfolded, which could have contributed to the differences between the trials. This information has been added to the figure legend.

Video 3: The video shows depression in the whole buzz phase. If that is a proxy for broadening the view, it suggests a constant broad beam in the buzz and not variation as suggested by Results and Discussion in this manuscript?

The video is of a porpoise stationed at a constant range to a stationary target. That could explain a relatively constant beam angle and may further explain why most studies so far have not reported much beam variation.

Please clarify what are the supplementary figures reporting and why.

Following your recommendations, we have removed one of the figure supplements (formerly Figure 3–figure supplement 3), we included one of the figure supplements in the manuscript as an independent figure (formerly figure 3–figure supplement 2, now Figure 4) and we have added a new figure supplement (Figure 6–figure supplement 1).

Consequently, we now have 2 figure supplements:

Figure 4–figure supplement 1: illustrates the different steps of the piston fitting procedure of experiment 2. We feel that it aids the understanding of the results and their limitations.

Figure 6–figure supplement 1: plots beamwidth against centroid frequency and the results of a physical model of frequency-dependence of beamwidth for static circular piston transducers of different sizes. The plot shows that the variation of centroid frequencies measured in the two experiments could not explain the observed beamwidth changes, and thereby supports our hypothesis that the beamwidth changes resulted from changes in the size of the effective radiating aperture.

References:

Linnenschmidt M., Wahlberg M., Hansen J.D. 2013 The modulation rate transfer function of a harbour porpoise (Phocoena phocoena). Journal of Comparative Physiology A 199(2), 115-126.

Wisniewska, D.M., Johnson, M., Nachtigall, P.E. and Madsen, P.T. (2014). Buzzing during biosonar-based interception of prey in the delphinids Tursiops truncatus and Pseudorca crassidens. The Journal of Experimental Biology 217, 4279-4282

Jensen F.H., Bejder L., Wahlberg M., Madsen P.T. 2009 Biosonar adjustments to target range of echolocating bottlenose dolphins (Tursiops sp.) in the wild. The Journal of Experimental Biology 212, 1078-1086.

Wahlberg M., Jensen F.H., Soto N.A., Beedholm K., Bejder L., Oliveira C., Rasmussen M., Simon M., Villadsgaard A., Madsen P.T. 2011 Source parameters of echolocation clicks from wild bottlenose dolphins (Tursiops aduncus and Tursiops truncatus). The Journal of the Acoustical Society of America 130(4), 2263-2274.

Jensen F.H., Rocco A., Mansur R.M., Smith B.D., Janik V.M., Madsen P.T. 2013 Clicking in shallow rivers: short-range echolocation of Irrawaddy and Ganges River dolphins in a shallow, acoustically complex habitat. PloS one 8(4), e59284. (doi:10.1371/journal.pone.0059284).

Sveegaard, S. 2011. Spatial and temporal distribution of harbour porpoises in relation to their prey. In Department of Arctic Environment, National Environmental Research Institute. Aarhus University, Denmark, 128.

Andreasen, H. 2009. Marsvinets (Phocoena phocoena) rolle som prædator i de danske farvande. University of Copenhagen, Denmark.

Börjesson, P., Berggren, P., and Ganning, B. 2003. Diet of harbour porpoises in the Kattegat and Skagerrak Seas: Accounting for individual variation and sample size. Marine Mammal Science 19:38-58.

https://doi.org/10.7554/eLife.05651.016

Article and author information

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.
    ORCID icon 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.

Funding

Oticon Foundation Denmar

  • Danuta M Wisniewska
  • Christian B Christensen

Det Frie Forskningsråd

  • John M Ratcliffe
  • Mark Johnson
  • Peter T Madsen

National Instruments

  • Danuta M Wisniewska
  • Kristian Beedholm
  • Peter T Madsen

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We are grateful to JH Kristensen, JD Hansen, S Hansen, C Eriksson, M Dyndo, and L Jacobsen and the staff at Fjord&Belt for assistance with data collection, and H Lauridsen at Skejby University Hospital for help with MRI scanning. We thank NU Kristiansen, JS Jensen, M Dyndo, and K Ydesen for helping with construction of the recording setup, H-U Schnitzler (Universität Tübingen) for lending recording gear, and T Hurst (Woods Hole Oceanographic Institution) for providing the DTAG-3. WWL Au, AH Bass, MB Fenton, A Surlykke, and L Wiegrebe kindly provided comments that improved the manuscript. The animals are maintained by Fjord&Belt, Denmark, under permits no. SN 343/FY-0014 and 1996-3446-0021 from the Danish Forest and Nature Agency.

Ethics

Animal experimentation: The animals are maintained by Fjord&Belt, Denmark, under permits no. SN 343/FY-0014 from the Danish Ministry of Food, Agriculture and Fisheries, and 1996-3446-0021 from the Danish Forest and Nature Agency (under the Danish Ministry of the Environment). Their care and all experiments are in strict accordance with the recommendations of the Danish Ministry of Food, Agriculture and Fisheries (issuing the permit to keep the animals), the Danish Ministry of the Environment (permit for catching the animals) and the Danish Council for Experiments on Animals (always contacted for permits when appropriate—but in the case of this study such permit was not required).

Reviewing Editor

  1. Russ Fernald, Reviewing Editor, Stanford University, United States

Publication history

  1. Received: November 18, 2014
  2. Accepted: March 19, 2015
  3. Accepted Manuscript published: March 20, 2015 (version 1)
  4. Version of Record published: April 29, 2015 (version 2)

Copyright

© 2015, Wisniewska et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 2,941
    Page views
  • 485
    Downloads
  • 12
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

Comments

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

  1. Porpoises use a sophisticated sonar system to locate and track prey.

    1. Ecology
    M Teague O'Mara et al.
    Research Article