1pAB4 – Combining underwater photography and passive acoustics to monitor fish

Camille Pagniello – cpagniel@ucsd.edu
Gerald D’Spain – gdspain@ucsd.edu
Jules Jaffe – jjaffe@ucsd.edu
Ed Parnell – eparnell@ucsd.edu

Scripps Institution of Oceanography, University of California San Diego
La Jolla, CA 92093-0205, USA

Jack Butler – Jack.Butler@myfwc.com
2796 Overseas Hwy, Suite 119
Marathon, FL 33050

Ana Širović – asirovic@tamug.edu
Texas A&M University Galveston
P.O. Box 1675
Galveston, TX 77550

Popular version of paper 1pAB4 “Searching for the FishOASIS: Using passive acoustics and optical imaging to identify a chorusing species of fish”
Presented Monday afternoon, November 5, 2018
176th ASA Meeting, Victoria, Canada

Although over 120 marine protected areas (MPAs) have been established along the coast of southern California, it has been difficult to monitor their ability to quantify their effectiveness via the presence of target animals. Traditional monitoring methods, such as diver surveys, allow species to be identified, but are laborious and expensive, and heavily rely on good weather and a talented pool of scientific divers. Additionally, the diver’s presence is known to alter animal presence and behavior. As one alternative to aid and perhaps, in the long run, replace the divers, we explored the use of long-term, continuous, passive acoustic recorders to listen to the animals’ vocalizations.

Many marine animals produce sound. In shallow coastal waters, fish are often a dominant contributor. Aristotle was the first to note the “voice” of fish, yet only sporadic reports on fish sounds appeared over the next few millennia. Many of the over 30,000 species of fish that exist today are believed to produce sound; however, the acoustic behavior has been determined in less than 5% of these biologically and commercially important animals.

Towards the goal of both listening to the fish and identifying which species are vocalizing, we developed a Fish Optical and Acoustic Sensor Identification System (FishOASIS) (Figure 1). This portable, low-cost instrument couple’s a multi-element passive acoustic array with multiple cameras, thus allowing us to determine which fish are making which sound for a variety of species. In addition to detecting sporadic events such as fish spawning aggregations, this instrument also provides the ability to track individual fish within aggregations.

FishOASIS

Figure 1. A diver deploying FishOASIS in the kelp forest off La Jolla, CA.

Choruses (i.e., the simultaneous vocalization of animals) are often associated with fish spawning aggregations and, in our work, FishOASIS was successful in recording a low-frequency fish chorus in the kelp forest off La Jolla, CA (Figure 2).

Figure 2. Long-term spectral average (LTSA) of low-frequency fish chorus of unknown species on June 8, 2017 at 17:30:00. Color represents spectrum level, with red indicating highest pressure level.

The chorus starts half an hour before sunset and lasts about 3-4 hours almost every day from May to September. While individuals within the aggregation are dispersed over a large area (approx. 0.07 km2), the chorus’ spatial extent is fairly fixed over time. Species that could be producing this chorus include kelp bass (Paralabrax clathratus) and halfmoons (Medialuna californiensis) (Figure 3).

Figure 3. A halfmoon (Medialuna californiensis) in the kelp forest off La Jolla, CA.

FishOASIS has also been used to identify the sounds of barred sand bass (Paralabrax nebulifer), a popular species among recreational fishermen in the Southern California Bight (Figure 4).

Figure 4. Barred sand bass (Paralabrax nebulifer) call.

This article demonstrates that combining multiple cameras with multi-element passive acoustic arrays is a cost-effective method for monitoring sound-producing fish activities, diversity and biomass. This approach is minimally invasive and offers greater spatial and temporal coverage at significantly lower cost than traditional methods. As such, FishOASIS is a promising tool to collect the information required for the implementation of passive acoustics to monitor MPAs.

2pAB8 – Blind as a bat? Evidence suggests bats use vision to supplement echolocation in presence of ambient light

Kathryn A. McGowan – kmcgowan01@saintmarys.edu
Saint Mary’s College
Le Mans Hall, 149
Notre Dame, IN 46556

Presented Tuesday afternoon, November 6, 2018
176th ASA Meeting, Victoria, British Columbia

Bats use echolocation, or biological sonar, to make an auditory picture of their environment when foraging and avoiding obstacles in flight (1). To echolocate, bats emit a loud, high-pitched sound using their mouth or nose. The sound bounces off an object and returns to the bat as an echo, providing each individual with information about the object characteristics and location. While echolocation allows for the detection and discrimination of targets, the high-pitched frequency sounds that bats emit when echolocating provide a limited range of information (2). Despite being known for flying at night, some bats spend only a part of their time flying in complete darkness, suggesting that they may also rely on vision to supplement their echolocation in environments that have more light (2, 3). Previous studies have demonstrated that vision in bats influences flight behavior, which suggests bats may combine vision and echolocation to sense their environment (2). It is, therefore, accepted that bats are not blind, as the common phrase suggests, but little is known about how vision influences the way bats use echolocation.

Figure 1. Swarm of Brazilian free-tailed bats flying during daylight hours after emergence. Photo Credit – Dr. Laura Kloepper, 2018

The Brazilian free-tailed bat migrates annually from Mexico to form large maternal colonies in caves in the Southwestern United States (2). These bats forage for insects in flight and emerge from the cave in groups of thousands for nightly foraging. The bats return to the cave in the early hours of the morning, requiring them to navigate back to their complex cave environment across a vast, open landscape. This reentry occurs across periods of complete darkness as well as early morning hours when ambient light is present. This suggests that bats have the option of using both echolocation and visual cues to navigate their environment in hours of daylight. Our research addresses how bats change their echolocation calls from an open environment to the more complex cave edge environment, and how the presence of daylight may influence their level of echolocation when accomplishing this feat.

bat echolocation

Figure 2. Spectrogram image of a sequence of bat echolocation calls recorded at the cave environment.

Compared to the calls used over a vast landscape, bats at the cave edge used more complex calls that gathered more precise information about that environment. During hours of daylight, however, these calls collected less precise information than hours of darkness. As less information was gathered acoustically by bats during daylight hours, it is likely that bats are getting information from visual cues once daybreak occurs. This supplementing of vision for echolocation indicates that despite what the phrases say, bats are not blind.

Video 1. Bats emerging for foraging during early dusk.

  1. Moss, C. F., & Surlykke, A. 2010. Probing the natural scene by echolocation in bats. Frontiers in Behavioral Neuroscience 4: 33.
  2. Mistry, S. 1990. Characteristics of the visually guided escape response of the Mexican free-tailed bat Tadarida Brasiliensis Animal Behavior 39: 314-320.
  3. Davis, W.H., Barbour, R.W. 1965. The use of vision in flight by the bat Myotis sodalis. The American Midland Naturalist 74: 497–499.

1aAB7 – Drum fish spawning doesn’t miss a beat in the eye of a hurricane

Christopher R. Biggs – cbiggs@utexas.edu
Brad Erisman – berisman@utexas.edu

The University of Texas at Austin, Marine Science Institute
750 Channel View Drive,
Port Aransas, TX 78373

Popular version of paper 1aAB7
Presented Monday morning, November 5, 2018
176th ASA Meeting, Victoria, BC

Drum fish

Photo credit: Tyler Loughran

The location and frequency of spawning (reproduction) in fish has a direct effect on the abundance, stability, and resilience of a fish population. Major storm events, such as hurricanes, provide a natural experiment to test the ability of a fish population to withstand disturbances. Acoustic monitoring of Spotted Seatrout spawning revealed that these fish are extremely productive, spawning every day of the spawning season (April – September), including during a category 4 hurricane. These results illustrate the amazing resilience of estuarine fishes to intense disturbances and their potential to cope with projected increases in extreme weather events in the future.

Spotted Seatrout and many other species of “drum fish” make characteristic sounds during spawning (figure 1), which can be heard on underwater microphones, or hydrophones. This allows us to remotely monitor when fish spawn and how long they spawn for, which is especially helpful in murky water, where it is difficult to see. Seatrout spawning can be identified within the audio recordings by analyzing the intensity of the sound within the specific frequency range (250-500 Hz) of the Spotted Seatrout calls.

Figure 1. Recording of male Spotted Seatrout drumming sounds during spawning.

We monitored Spotted Seatrout spawning from April to September 2017 at 15 sites within the estuaries of South Texas, to see how changes in environmental conditions affected spawning. Our study also coincided with a category 4 hurricane. Hurricane Harvey made landfall 9 km east of Rockport, Texas on August 25, 2017 at 17:00 h CST. The eye of the storm was 28 km wide, maximum sustained winds were 59 m s-1 with gusts up to 65 m s-1, and the storm surge caused water levels to rise 3.8 meters above ground level.

The sound pressure level within the frequency range of seatrout spawning sounds peaked every evening between 20:00 and 21:00, indicating that spawning was occurring on a daily basis. During the hurricane wind-associated noise masked any potential spawning sounds, except at two stations that were directly in the path of the hurricane. When the eye of the storm was directly overhead those stations, wind-associated noise decreased, and spawning sounds were audible (figure 2). The time that spawning began shifted two hours earlier for five days after the storm, which may have been partly caused by the decrease in water temperature.

Figure 2. Spectrograms of recordings during Hurricane Harvey showing storm noise at 21:55 and seatrout chorusing at 22:25 within the 250-500 Hz bandwith (dotted lines).

Species that live and spawn in estuaries must deal with conditions that can change rapidly and unpredictably. It is important to understand how those changes impact spawning activity in order to maintain sustainable populations for the fishing industry. Further, understanding how fish respond to environmental disturbances in these environments may offer insight on how fish will respond to climate change and other human impacts elsewhere.

1pAB4 – Size Matters To Engineers, But Not To Bats

Rolf Müller – rolf.mueller@vt.edu
Bryan D. Todd

Popular version of paper 1pAB4, “Beamwidth in bat biosonar and man-made sonar”
Presented Monday, May 7, 2018, 1:30-3:50 PM, LAKESHORE B,
175th ASA Meeting, Minneapolis.

Bats and Navy engineers both use sonar systems. But do they worry about the same design features?

To find out, we have done an exhaustive review of both kinds of sonar systems, poring over the spec sheets of about two dozen engineered sonars for a variety of applications and using computer models to predict 151 functional characteristics of bat biosonar systems spanning eight different biological families. Crunching the numbers revealed profound differences between the way engineers approach sonar and the way bats do.

The most important finding from this analysis is related to a parameter called beamwidth. Beamwidth is a measure of the angle over which the emitted sonic power or receiver sensitivity is distributed. A small beamwidth implies a focused emission, where the sound energy is – ideally – concentrated with laser-like precision. But the ability to generate such a narrow beam is limited by the sonar system’s size: the larger the emitter is relative to the wavelength it uses, the finer the beam it can produce. Reviewing the design of man-made sonars indicates that beamwidth has clearly been the holy grail of sonar engineering — and in fact, the beamwidth of these systems hews closely to their theoretical minima.

bats

Some of the random emission baffles made from crumpled aluminum foil that served as a reference for the scatter seen in the bat beam width data.

But when it comes to beamwidth, tiny bats are at a significant disadvantage: even the largest bat ears are barely ten times the size of the animals’ ultrasonic wavelength, while engineered systems can exceed their wavelengths by 100 or 1000 times. Remarkably, our analysis showed that bats seem to disregard beamwidth entirely. In our data set, the bats’ beamwidth scattered widely towards larger values; the scatter was even larger than that for random cone shapes we created from crumpled aluminum foil. Clearly, the bats’ sonar systems are not optimized for beamwidth. But we know that they are incredible capable when it comes to navigating complex environments — which begs the question: what criteria are influencing their design?

We don’t know yet. But the bats’ superior performance demonstrates every night that giant sonar arrays with narrow beamwidths aren’t the only and certainly not the most efficient path to success: smaller, leaner solutions exist. And those solutions will be necessary for compact modern systems like autonomous underwater or aerial vehicles. To make sonar-based autonomy in natural environments a reality, engineers should let go of their fixation on size and look at the bats.

4aAB4 – Analysis of bats’ gaze and flight control based on the estimation of their echolocated points with time-domain acoustic simulation

Taito Banda – dmq1001@mail4.doshiha.ac.jp
Miwa Sumiya – miwa1804@gmail.com
Yuya Yamamoto – dmq1050@mail4.doshisha.ac.jp
Yasufumi Yamada – yasufumi.yamada@gmail.com
Faculty of Life and Medical Sciences, Doshisha UniversityKyotanabe, Kyoto, Japan

Yoshiki Nagatani – nagatani@ultrasonics.jp
Department of Electronics, Kobe City College of Technology, Kobe, Japan.

Hiroshi Araki – Araki.Hiroshi@ak.MitsubishiElectric.co.jp
Advanced Technology R&D Center, Mitsubishi Electric Corporation, Amagaski, Japan

Kohta I. Kobayasi – kkobayas@mail.doshisha.ac.jp
Shizuko Hiryu – shiryu@mail.doshisha.ac.jp
Faculty of Life and Medical Sciences, Doshisha University, Kyotanabe, Kyoto, Japan

Popular version of paper 4aAB4 “Analysis of bats’ gaze and flight control based on the estimation of their echolocated points with time-domain acoustic simulation.”
Presented Friday morning, December 7, 2017, 8:45-9:00 AM, Salon F/G/H
174th ASA in New Orleans

Bats broadcast ultrasound and listen to the echoes to understand surrounding information. It is called echolocation. By analyzing those echoes, i.e., arrival time of echoes, bats can detect the position of objects, shape or texture [1-3]. Contrary to the way people use visual information, bats use the sound for sensing the world. How is the world different between the two by sensing? Because both senses are completely different, we cannot imagine how bats see the world.

To address this question, we simulated the echoes arriving at the bats during obstacle-avoiding flight based on the behavioral data so that we could investigate how the surrounding objects were described acoustically.

First, we arranged microphone arrays (24 microphones) and two high-speed cameras in an experimental flight chamber (Figure 1) [4]. The timing, positions and directions of emitted ultrasound as well as the flight paths were measured. A small telemetry-microphone was attached on the back of the bat so that the intensity of emitted ultrasound could be recorded accurately [5]. The bat was forced to follow a S-shaped flight pattern to avoid the obstacle acrylic boards.

Based on those behavioral data, we simulated propagation of sounds with the measured strength and direction emitted at the position of the bat in the experiment, and we could obtain echoes reaching both left and right ears from the obstacles. By using interaural time difference of echoes, we could acoustically identify the echolocated points in the space for all emissions (square plots in Fig.2). We also investigated how the bats show spatial and temporal changes in the echolocated points in the space as they became familiar with the space (top and bottom panels).

We analyzed changes in the echolocated points by using this acoustic simulation, corresponding to which part of objects the bats intended to gaze at. In a comparison between before and after the habituation in the same obstacle layout, there are differences in the wideness of echolocated points on the objects. By flying the same layout repeatedly, false detection of objects was reduced, and their echolocating fields became narrower.

It is natural for animals to pay their attention toward objects adequately and adapt both flight and sensing controls cooperatively as they became familiar with the space. These finding suggests that our approach in this paper, i.e., acoustic simulation based on behavioral experiment is one of effective ways to visualize how the object groups are acoustically structured and represented in the space for bats by echolocation during flight. We believe that it might serve a tip to the question; “What is it like to see as a bat?”

ehcolocation
Figure 1 Diagram of bat flight experiment. Blue and red circles indicate microphones on the wall and the acrylic boards, respectively. Two high-speed video cameras are attached at the two corners of the room. Three acrylic boards are arranged to make bats follow S-shaped flight pattern to avoid the obstacles.

echolocation
Figure 2 Comparison of echolocated points between before and after space habituation. The measured positions where the bat emitted the sound are shown with circle plots meanwhile the calculated echolocated points are shown with square plots. Color variation from blue to red for both circle and square plots corresponds to temporal sequence of the flight. Sizes of circle and square plots correspond to the strength of emissions and their echoes from the obstacles at the bat, respectively.

References:
[1] Griffim D. R., Listning in the dark, Yle University, New Haven, CT, 1958

[2] Simmons J.A., Echolocation in bats: signal processing of echoes for target range, Science, vol. 171, pp.925-928., 1971

[3] Kick S. A., Target-Detection by the Echolocating Bat, Eptesicus fuscus, J Comp Physiol, A., vol. 145, pp.431-435, 1982

[4] Matsuta N, Hiryu S, Fujioka E, Yamada Y, Riquimaroux H, Watanabe Y., Adaptive beam-width control of echolocation sounds by CF-FM bats, Rhinolophus ferrumequinum nippon, during prey-capture flight, J Exp Biol., vol. 206, pp.1210-1218, 2013

[5] Hiryu S, Shiori Y, Hosokawa T, Riquimaroux H, Watanabe Y., On-board telemetry of emitted sounds from free-flying bats: compensation for velocity and distance stabilizes echo frequency and amplitude, J Comp Physiol A., vol. 194, pp.841-851, 2008