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The roles of nocturnal light and lunar phase in the diel migration of micronekton from a nearshore scattering layer were examined. Migration patterns were measured over six complete lunar cycles using moored upward‐looking echosounders while nocturnal surface irradiance was recorded. We hypothesized that animals would remain at a constant isolume at night despite changes in nocturnal illumination between nights. The scattering layer migrated closer to the surface during dark nights than during well‐lit ones. However, this movement was not enough to compensate for observed changes in light, and at night animals often remained at light levels higher than they experience at depth during the day. Light and lunar cycle were not completely coupled, allowing separation of the light and lunar phases. Contrary to the initial hypothesis, lunar phase accounted for substantially more of the variability in layer migration than surface irradiance, showing strong effects on the scattering layer’s depth and animal density within the layer. Changes in layer depth and animal density were amplified a small amount by variations in light level but were minimized by the seafloor in shallow areas. The horizontal component of the scattering layer’s migration was also affected by lunar phase, with animals remaining further offshore in deeper waters during nights near and during the full moon, even when these were not the nights with the highest light levels. These results suggest that moonlight may be a cue for an endogenous lunar rhythm in the process of diel migration rather than a direct cause.

An electronic simulated target apparatus was used in a two-experiment study to compare the target detection performance of an echolocating bottlenose dolphin with an optimal receiver. Random Gaussian noise with a relatively flat spectrum from 20 to 160 kHz was used as a masking source. Experiment I was conducted to establish a technique for estimating the echo energy-to-noise ratio,Ee/N, at the dolphin's threshold of detection. Dolphins typically vary the amplitude of their emitted signal over a large range making it difficult to estimateEe/N. In the first part of experiment I, the simulated echo was a double click, the pulses separated by 200 µs, with each pulse being a replica of the dolphin's transmitted signal. A staircase psychophysical procedure was used to obtain the detection threshold, and the echo energy-to-noise ratio based on the highest amplitude click emitted per trial, (Ee/N)max, was determined at each reversal point. The second echo type consisted of one of the animal's echolocation clicks, previously measured, digitized and stored in an erasable programmable read-only memory (EPROM). The electronic target simulator was modified so that every time the dolphin emitted an echolocation signal, the EPROM was triggered to produce two pulses separated by 200 µs. On any trial, the EPROM signal was played back at a fixed amplitude, regardless of the amplitude of the dolphin's emitted signal. TheEe/N obtained with the EPROM signal at threshold was found to be 2.9 dB lower than (Ee/N)max obtained with the normal phantom target. Therefore an estimate ofEe/N can be obtained by subtracting 2.9 dB from (Ee/N)max.

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders (3.81 cm o.d. and 12.7 cm length) was determined by Au and Pawloski [J. Comp. Physiol. A 170, 41–47 (1992)]. The dolphin was required to discriminate a standard target from comparison targets of differing wall thicknesses. Performance varied from 96% to 56% correct depending on the wall thickness of the comparison targets. The 75% correct threshold was determined to be wall thickness differences of −0.23 mm for comparison targets with thinner walls and +0.27 mm for comparison targets with thicker walls than the standard. The dolphin performance was unchanged in the presence of artificial broadband masking noise until the echo‐energy‐to‐noise ratio fell below approximately 15 dB. A counterpropagation artificial neural network was used to examine broadband echo features from the same cylinders. Features of the echoes were determined by passing them through a filter bank of constant‐Q filters. Echo features of the standard and each comparison target were analyzed in pairs by a neural network having two output nodes. Twenty echoes per target were used in the training set and 30 additional echoes per target were used in the test set. For the noise free condition, the network performed at a comparable level to the dolphin for Q values between 4 and 5. In the presence of noise, Q values between 7 and 8 were needed before the network could perform at a comparable level to the dolphin for echo‐energy‐to‐noise ratios of 10 and 15 dB. Discrimination cues seem to originate from a shift in the spectra of the thinner comparison targets to lower frequencies and to higher frequencies for the thicker comparison targets.

Backscatter measurements were made on a stationary Atlantic bottlenosed dolphin under controlled conditions. Three sets of measurements were made: (1) broadside aspect target strength as a function of frequency from 23 to 80 kHz; (2) relative target strength as a function of the polar angle about the animal using a short click signal having a peak frequency of 67 kHz; and (3) relative reflective strength of different portions of the animal’s body. The mean target strength at the broadside aspect decreased from −11 to −24 dB as the frequency increased from 23 to 45 kHz. As the frequency increased from 45 kHz, the target strength rose to a local maximum of −18 dB at 66 kHz and then decreased to −23 dB at 79 kHz. Maximum target strength was measured at the broadside aspect and exceeded the minimum (tail aspect) target strength by 21 dB. The target strength at the head aspect was 5 dB below that of the broadside aspect. Most acoustic energy was reflected from the area between the dorsal and pectoral fins, corresponding to the location of the dolphin’s lungs. © 1996 Acoustical Society of America.

The relationships between acoustic features of target echoes and the cognitive representations of the target formed by an echolocating dolphin will influence the ease with which the dolphin can recognize a target. A blindfolded Atlantic bottlenose dolphin (Tursiopstruncatus) learned to match aspect‐dependent three‐dimensional targets (such as a cube) at haphazard orientations, although with some difficulty. This task may have been difficult because aspect‐dependent targets produce different echoes at different orientations, which required the dolphin to have some capability for object constancy across changes in echo characteristics. Significant target‐related differences in echo amplitude, rms bandwidth, and distributions of interhighlight intervals were observed among echoes collected when the dolphin was performing the task. Targets could be classified using a combination of energy flux density and rms bandwidth by a linear discriminant analysis and a nearest centroid classifier. Neither statistical model could classify targets without amplitude information, but the highest accuracy required spectral information as well. This suggests that the dolphin recognized the targets using a multidimensional representation containing amplitude and spectral information and that dolphins can form stable representations of targets regardless of orientation based on varying sensory properties. © 1996 Acoustical Society of America.

The capability of an Atlantic bottlenose dolphin Tursiops truncatus to discriminate wall thickness differences of hollow cylinders by echolocation was studied. A standard cylinder of 6.35 mm wall thickness was compared with cylinders having wall thicknesses that differed from the standard by ± 0.2, ± 0.3, ± 0.4, and ± 0.8 mm. All cylinders had an O.D. of 37.85 mm, and a length of 12.7 cm. The dolphin was required to station in a hoop while the standard and comparison targets, separated by an angle of ± 11° from a center line, were simultaneously presented at a range of 8 m. The dolphin was required to echolocate and indicate the side of the standard target. Target location on each trial was randomized. Interpolation of the dolphin performance data indicated a wall thickness discrimination threshold (at the 75% correct response level) of -0.23 mm and +0.27 mm. Backscatter measurements suggest that if the dolphin used time domain echo cues, it may be able to detect time differences between two echo highlights to within approximately ± 500 ns. If frequency domain cues were used, the dolphin may be able to detect frequency shifts as small as 3 kHz in a broadband echo having a center frequency of approximately 110 kHz. Finally, if the dolphin used time-separation pitch (TSP) cues, it may be able to detect TSP differences of approximately 450 Hz.

The target detection range of two echolocating Atlantic Bottlenose dolphins as a function of target depth in Kaneohe Bay, Hawaii, was determined by Murchison [A. E. Murchison, Ph.D. dissertation, Univ. of Calif., Santa Cruz (1980)]. The threshold range decreased monotonically as the depth of the 6.35‐cm‐diam solid‐steel sphere increased and approached the bottom. For the target lying on the bottom, the 50% correct detection threshold detection range was approximately 70 m. The scattering strength of the bottom in Kaneohe Bay at approximately the same location of the Murchison’s experiment was recently measured using a simulated dolphin echolocation signal and a transducer tilted at the appropriate grazing angle. The bottom scattering strength along with the target strength of the 6.35‐cm sphere and the dolphin threshold range were incorporated into the generalized form of the sonar equation for a reverberation‐limited situation and a detection threshold of 4.0 dB was calculated. This detection threshold compared well with the 2.3 dB obtained in an experiment in which the dolphin was required to detect a target in the presence of a clutter screen.

Snapping shrimp are among the major contributors of biological noise in shallow bays, harbors, and inlets located in temperate and tropical waters. Snapping shrimp sounds can severely limit the use of underwater acoustics by humans and other animals such as dolphins, whales, and pinnepeds. They produce sounds by rapidly closing their frontal chela, or claws, snapping the two ends together to generate a loud click. The acoustics of the species Synalpheusparaneomeris was studied by measuring the sound produced by individual shrimp housed in a small cage located 1 m from an H‐52 broadband hydrophone. Ten clicks from 40 specimens were digitized at a 1‐MHz sample rate and the data stored on disk. Various acoustic parameters such as source level, spectral content, and center frequency were correlated with claw size and body length. Peak‐top‐peak source levels varied from 183 to 189 dB re: 1 μPa. A typical spectrum had a low‐frequency peak between 2 and 5 kHz and energy extending out to 200 kHz.

The capability of an echolocating dolphin to discriminate differences in the wall thickness of cylinders was previously modeled by a counterpropagation neural network using only spectral information from the echoes. In this study, both time and frequency information were used to model the dolphin discrimination capabilities. Echoes from the same cylinders were digitized using a broadband simulated dolphin sonar signal with the transducer mounted on the dolphin’s pen. The echoes were filtered by a bank of continuous constant‐Q digital filters and the energy from each filter was computed in time increments of 1/bandwidth. Echo features of the standard and each comparison target were analyzed in pairs by a counterpropagation neural network, a backpropagation neural network, and a model using Euclidean distance measures. The backpropagation network performed better than both the counterpropagation network, and the Euclidean model, using either spectral‐only features or combined temporal and spectral features. All models performed better using features containing both temporal and spectral information. The backpropagation network was able to perform better than the dolphins for noise‐free echoes with Q values as low as 2 and 3. For a Q of 2, only temporal information was available. However, with noisy data, the network required a Q of 8 in order to perform as well as the dolphin.

The echolocation transmission beam pattern of a false killer whale (Pseudorcacrassidens) was measured in the vertical and horizontal planes. A vertical array of seven broadband miniature hydrophones was used to measure the beam pattern in the vertical plane and a horizontal array of the same hydrophones was used in the horizontal plane. The measurements were performed in the open waters of Kaneohe Bay, Oahu, Hawaii, while the whale performed a target discrimination task. Four types of signals, characterized by their frequency spectra, were measured. Type‐1 signals had a single low‐frequency peak at 40±9 kHz and a low‐amplitude shoulder at high frequencies. Type‐2 signals had a bimodal frequency characteristic with a primary peak at 46±7 kHz and a secondary peak at 88±13 kHz. Type‐3 signals were also bimodal but with a primary peak at 100±7 kHz and a secondary peak at 49±9 kHz. Type‐4 signals had a single high‐frequency peak at 104±7 kHz. The center frequency of the signals were found to be linearly correlated to the peak‐to‐peak source level, increasing with increasing source level. The major axis of the vertical beam was directed slightly downward between 0 and −5°, in contrast to the +5 to 10° for Tursiops and Delphinapterus. The beam in the horizontal plane was directed forward between 0° and −5°. In both planes, the type‐1 signals had the broadest beam pattern, followed by the type‐2 and type‐3 signals, with the narrowest beam pattern being exhibited by the type‐4 signals. The beam pattern in the horizontal plane was much narrower than the beam pattern in the vertical plane.

Masked underwater pure‐tone thresholds were obtained for an Atlantic bottlenose dolphin using an up–down staircase method of stimulus presentation and a go/no‐go response paradigm. Two types of masking noise were used: a broadband noise and variable bandwidth noise with sharp low‐ and high‐frequency cutoffs. The animal’s critical ratio was measured at frequencies of 30, 60, 90, 100, 110, 120, and 140 kHz. For frequencies of 100 kHz and below, the critical ratios were similar to those measured by Johnson [J. Acoust. Soc. Am. 44, 965–967 (1968)]. The dolphin’s critical bandwidth at frequencies of 30, 60, and 120 kHz was measured with the variable bandwidth noise. The critical bandwidth was 10.4 dB (11 times) wider than the critical ratio at 30 kHz, 8.2 dB (6.6 times) wider at 60 kHz, and 3.5 dB (2.2 times) wider at 120 kHz.

The article discusses a methodology of detecting marine mammals developed by researchers at the Hawaii Institute of Marine Biology through a partnership with shipping company Young Brothers Ltd (Honolulu, Hawaii) that relies on a passive acoustic recording system attached to a commercial shipping barge. The system could be utilized for cetacean research, which faces challenges in data collection due to the wide and remote dispersal of whales and dolphins.

Echoes from four different cylinders having the same outer diameter (3.8 cm) and length (10.2 cm) were collected at 1‐deg increments from the broadside to the end aspect and used in a neural network recognition study. The water‐filled stainless‐steel, water‐filled aluminum, and foam‐filled aluminum cylinders had the same 0.64‐cm wall thickness. The fourth cylinder consisted of coral rock pebbles embedded in degassed epoxy. A dolphinlike sonar signal with a peak frequency of 120 kHz and a duration of approximation 50 μs was used in a monostatic echo collection system. Fifty echoes digitized at a 1‐MHz rate were collected for each target at each aspect. Time‐frequency representation of the echoes along with the envelope of the matched filter response were fed to two parallel independent multilayered feedforward backpropagation neural networks and the results were fused together in another feedforward network. The network was able to achieve better than 90% correct recognition of the echoes when trained with echoes that were separated in increments of 4 deg. Performance dropped to approximately 80% when the network was trained on echoes separated in increments of 10 deg.

The echolocation transmission beam pattern of a false killer whale (Pseudorcacrassidens) was measured in the vertical and horizontal planes. A vertical array of seven broadband miniature hydrophones was used to measure the beam pattern in the vertical plane and a horizontal array of the same hydrophones was used in the horizontal plane. The measurements were performed in the open waters of Kaneohe Bay, Oahu, Hawaii, with the whale performing a discrimination task. The width of the beams in both planes were similar to those of the Atlantic bottlenose dolphin (Tursiopstruncatus), and broader than those of the beluga whale (Delphinapterusleucas). The major axis of the vertical beam was directed slightly downwards between 0° and −5°, in contrast to the +5 to 10° for Tursiops and Delphinapterus. The beam in the horizontal plane was directed forward. Differences in the fatty structure of the melon of Pseudorca, Tursiops, and Delphinapterus could explain differences in the elevation angle of the vertical beam axis. a)At SAIC, Kailua, HI 96734.

The capability of an Atlantic bottlenose dolphin to discriminate wall thickness differences of hollow cylinders by echolocation was studied. A standard cylinder of 0.635‐cm wall thickness was compared with cylinders having wall thickness that differed from the standard by ±0.2, ±0.3, ±0.4, and ±0.8 mm. All cylinders had an o.d. of 3.81 cm and a length of 12.7 cm. The dolphin was required to station in a hoop while the standard and comparison targets, separated by an angle of ±11° from a center line, were simultaneously presented at a range of 8 m. The dolphin was required to echolocate and indicate on which side of the center line the standard target was located. Target location on each trial was randomized. The dolphin could discriminate wall thickness differences of −0.23 and +0.27 mm at the 75&percent; correct response threshold. Results of backscatter measurements suggest that if the dolphin used time domain echo cues, it may be able to detect difference in the time between two echo highlights to within approximately ±500 ns. If the dolphin used frequency domain cues, it may be able to detect frequency shifts as small as 3 kHz in broadband echoes having a center frequency of approximately 110 kHz.