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A Review and Inventory of Fixed Autonomous Recorders for Passive Acoustic Monitoring of Marine Mammals

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Fixed autonomous acoustic recording devices (autonomous recorders [ARs]) are defined as any electronic recording system that acquires and stores acoustic data internally (i.e., without a cable or radio link to transmit data to a receiving station), is deployed semi-permanently underwater (via a mooring, buoy, or attached to the sea floor), and must be retrieved to access the data. More than 30 ARs were reviewed. They varied greatly in capabilities and costs, from small, hand-deployable units for detecting dolphin and porpoise clicks in shallow water to larger units that can be deployed in deep water and can record at high-frequency bandwidths for over a year, but must be deployed from a large vessel. The capabilities and limitations of the systems reviewed herein are discussed in terms of their effectiveness in monitoring and studying marine mammals.
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Aquatic Mammals 2013, 39(2), 216-221, DOI 10.1578/AM.39.2.2013.216
Errata
Aquatic Mammals, 39(2), 2013, pp. 23-53
A Review and Inventory of Fixed Autonomous Recorders
for Passive Acoustic Monitoring of Marine Mammals
Renata S. Sousa-Lima, Thomas F. Norris, Julie N. Oswald, and Deborah P. Fernandes
Changes from incorrect and/or outdated informa-
tion are in brackets. Additional information and
any deletions from text or tables are indicated
below for each page with errata.
p. 23: Abstract, line 9: More than [40] ARs . . . .
pp. 26-28: Table 1
Acousonde: [Replaced the CAP and the B-Probe].
A-TAG: Add Ichikawa et al., 2011 to references.
AULS: DOSITS reference should be deleted and
Rountree et al., 2006; Rountree, 2008, 2011 added
to references.
AURAL-M2: Multi-Electronique (MTE) Inc. [Canada]
C-POD: Add Jefferson et al., 2002; Tregenza
et al., 2007; Brandt et al., 2011; Elliott et al.,
2011; Rayment et al., 2011; Castellote et al., 2012
to references.
HARP: Add S. Wiggins, pers. comm., 16 April
2013 to references.
PAL: Add Miksis-Olds et al., 2007, 2010, 2013;
Miksis-Olds & Parks, 2011 to references.
Additional instruments: EA-SDA14, Embedded
Acoustic recorder and SYLence, Low-power
sound recorder by RTSYS Marine Technologies,
France (L. Simon, pers. comm., 17 April 2013);
DAULS - Deepwater AULS (Rountree, 2011;
Rountree et al., 2012); and Deepwater versions of
SM2M Submersible and Ultrasonic by Wildlife
Acoustics, Inc., USA (S. Snyder, pers. comm.,
12 April 2013).
p. 29: In column 1, 2nd paragraph, line 25: (up to
61 h), uses a [secure digital card (SD)] as storage.
p. 37: In column 2, 2nd paragraph, line 12:
[AUSOMS-D, DASAR, and HARP; Wiggins
et al., 2012)].
p. 38: In column 1, line 3: Instruments like the
[AMAR (J. E. Moloney, pers. comm., 15 April
2013; Table 2) and] the HARP . . .; 2nd paragraph,
line 13: et al., 2010) [and from small boats (e.g.,
5m RHIB; S. Wiggins, pers. comm., 16 April
2013)].; 3rd paragraph, lines 9-10: downloads
to [compact flash]; line 12: requiring access to
[compact flash]; lines 14-15: delete sentence “The
hard drive runs for 6 s every 3 min when data
writing is occurring.” In column 2, line 2: little
over [175 d]; line 6: battery life to [115 d]; lines
7-9: delete “and the efficiency of shutting down
the hard drive between data writing sessions is
lost so that it runs continuously to record the data
flow,”; line 9-10: dropping battery life to [100 d
(H. Cheyne, pers. comm., 12 April 2013).]; 2nd
paragraph, line 1: delete first sentence that refers
to Table 3 which does not exist; line 4: The stan-
dard pop-up [compact flash stores 128 GB]; lines
7-9: delete sentence “The shift from hard drives
to high storage capacity flash cards will take care
of this limitation.”; line 9: The HARP, which can
sample at [320 kHz], has a much larger storage
capacity ([5TB with loss-less data compression
= 10 TB; Wiggins, pers. comm., 16 April 2013]),
than pop-ups; . . . .
p. 39: In column 2, 3rd paragraph, line 8: infor-
mation [about] the sounds; line 10: delete entire
sentence starting with “Nevertheless . . .” and
include C-PODs regularly deliver higher reso-
lution information on the frequency for some
classes of cetacean click than recording systems
achieve. C-PODs operate very wide acceptance
criteria as opposed to recording systems which,
by virtue of their profligate use of memory, are
forced to operate potentially overly narrow cri-
teria of acceptance of sounds (N. Tregenza, pers.
comm., 15 April 2013).
217 Errata
p. 40: In column 1, 3rd paragraph, line 8: The
[Acousonde]; line 10: that can sample up to [464]
kHz, includes [3D compass,] depth . . . .
p. 42: In column 1, lines 6-9: [of] 6,000 m[,
although beyond 2,500 m the acoustic release
may not work, and the user must rely on a timed
release for recovery.]
p. 44: In column 1, 5th paragraph, lines 12-13:
applications, [has been successfully deployed
in demanding tidal and river environments in
JASCO’s High-Flow-Low-Noise mooring frame
(J. E. Moloney, pers. comm., 15 April 2013),] and
also have localization . . . .
p. 48: In column 1, lines 1-5: delete the first
complete sentence starting with “Flash stor-
age . . . .” Acknowledgements section states that
Christopher W. Clark provided comments on this
paper, which is incorrect. Dr. Clark provided com-
ments on an earlier report to JIP (2009) which led
to this review.
Additional Literature Cited
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Horns Rev II offshore wind farm in the Danish North
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http://dx.doi.org/10.3354/meps08888
Castellote, M., Leeney, R. H., O’Corry-Crow, G.,
Lauhakangas, R., Kovacs, K. M., Lucey, W., . . . Belikov, R.
(2012). Monitoring white whales (Delphinapterus leucas)
with echolocation loggers. Polar Biology. http://dx.doi.
org/ 10.1007/s003000-012-1276-2.
Elliott, R. G., Dawson, S. M., & Henderson, S. D. (2011).
Acoustic monitoring of habitat use by bottlenose dol-
phins in Doubtful Sound, New Zealand. New Zealand
Journal Marine and Freshwater Research, 45(4), 637-
649. http://dx.doi.org/10.1080/00288330.2011.570351
Ichikawa, K., Akamatsu, T., Shinke, T, Adulyanukosol, K.,
& Arai, N. (2011). Callback response of dugongs to con-
specific chirp playbacks. The Journal of the Acoustical
Society of America, 129, 3623-3629. http://dx.doi.org/
10.1121/1.3586791
Jefferson, T. A., Hung, S. K., Law, L., Torey, M., &
Tregenza, N. J. (2002). Distribution and abundance of
finless porpoises in Hong Kong and adjacent waters
of China. Raffles Bulletin of Zoology Supplements, 10,
43-55.
Miksis-Olds, J. L., & Parks, S. E. (2011). Seasonal trends
in acoustic detection of ribbon seals (Histriophoca fas-
ciata) in the Bering Sea. Aquatic Mammals, 37(4), 464-
471. http://dx.doi.org/10.1578/AM.37.4.2011.464
Miksis-Olds, J. L., Nystuen, J. A., & Parks, S. E.,
(2010). Detecting marine mammals with an adaptive
sub-sampling recorder in the Bering Sea. Journal
of Applied Acoustics, 71, 1087-1092. http://dx.doi.
org/10.1016/j.apacoust.2010.05.010
Miksis-Olds, J. L., Donaghay, P. L., Miller, J. H., Tyack,
P. L., & Nystuen, J. (2007). Noise level correlates with
manatee use of foraging habitats. The Journal of the
Acoustical Society of America, 121, 3011-3020. http://
dx.doi.org/10.1121/1.2713555
Miksis-Olds, J. L., Stabeno, P. J., Napp, J. M., Pinchuk,
A. I., Nystuen, J. A., Warren, J. D., & Denes, S. L. (2013).
Ecosystem response to a temporary sea ice retreat in the
Bering Sea. Progress in Oceanography, 111, 38-51.
http://dx.doi.org/10.1016/j.pocean.2012.10.010
Rayment, W., Dawson, S., Scali, S., & Slooten, E. (2011).
Listening for a needle in a haystack: Passive acoustic
detection of dolphins at very low densities. Endangered
Species Research, 14, 149-156. http://dx.doi.org/10.3354/
esr00356
Rountree, R. A. (2008). Do you hear what I hear? Future
technological development—and needs—in passive
acoustics underwater observation. Marine Technology
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Rountree, R. A. (2011). Studies on soniferous fishes.
Retrieved 15 April 2013 from www.fishecology.org/
soniferous/soniferous.htm.
Rountree, R. A., Juanes, F., Goudey, C. A., & Ekstrom, K. E.
(2012). Is biological sound production important in the
deep sea? In A. N. Popper & A. Hawkins (Eds.), The
effects of noise on aquatic life (pp. 181-183). New York:
Springer Science+Business Media, LLC.
Rountree, R. A., Gilmore, R. G., Goudey, C. A., Hawkins,
A. D., Luczkovich, J., & Mann, D. A. (2006). Listening
to fish: Applications of passive acoustics to fisheries
science. Fisheries, 31(9), 433-446. http://dx.doi.org/
10.1577/1548-8446(2006)31[433:LTF]2.0.CO;2
Tregenza, N. J. C., Martin, A. R., & da Silva, V. (2007).
Click train characteristics in river dolphins in Brazil.
Proceedings of the Institute of Acoustics, 29(3).
Wiggins, S. M., MacDonald, M. A., & Hildebrand, J. A.
(2012). Beaked whale and dolphin tracking using a mul-
tichannel autonomous acoustic recorder. The Journal of
the Acoustical Society of America, 131, 156-163. http://
dx.doi.org/10.1121/1.3662076
218 Sousa-Lima et al.
Table 2. Only instruments for which there were changes (in brackets) are included. The first entry of Acousonde and for ARP (no longer available) should be omitted. The LARS (HF
and LF) and T-POD were superseded by DSG-Ocean and C-POD, respectively.
Instrument
Dimensions
Maximum
deployment
depth (m)
Maximum
deployment
time
Sampling
frequency
(Hz)
Power supply and
energy capacity
Data storage
Data format
Microprocessor
Examples of
species studied
AcousondeTM
3A (tag)
and
AcousondeTM
3B (tag)
22.1 × 3.2 cm [without
float]
[22.4 × 7.9 cm with float]
3.000 14 d [25 (low-power
channel)]
[464,000 (high-
frequency
channel)]
[A-cell lithium
battery]
[128 GB in 4 MicroSD
storage-card slots]
[64 GB in 2 MicroSD
storage-card slots]
2 acoustic channels,
MT files, depth,
tag temperature,
3-D acceleration/
tilt, ambient light
level, [3-axis
compass]
ARM9 with an ARM
vector floating point
(VFP) coprocessor
(www.arm.com)
208 MHz
[Balaenoptera
musculus,
Eschrichtius
robustus,
Megaptera
novaeangliae,
Mirounga
leonina, and
Stenella
attenuata]
AMAR G3 132.1 × 40.4 cm;
Diameter: 16.5 cm
250 (shallow
AMAR)
2,500 (deep
AMAR)
1 y dependent
on input channel
configuration,
duty cycle
settings, and
attached battery
packs
[16-bit channel
1- 687.5 Ksps, 8
24-bit channels:
1 to 120 Ksps,
standard, fitted
M8E hydrophone
specified for
1-150,000]
DC power from
battery pack (7 to
16 Vdc) or PoE
three standard
battery packs
available (short,
medium and
long)
Solid State storage.
256 GB, expandable to
1,796 GB
Acoustic data as
WAV formatted
files, non-acoustic
data as CSV files
NA [Pinnipeds,
baleen and
toothed whales,
delphinids,
porpoises, and
manatees]
A-[TAG]
(tag)
21 mm diameter and
108 mm length
200 75 d 55,000-
235,000
Lithium battery
cell
128 MB flash memory Click intensity,
timing, and the
difference in time
arrival between two
hydrophones
CPU
(PIC18F6620;
Microchip, Detroit,
MI, USA)
Neophocaena
phocaenoides
asiaeorientalis
[and Dugong
dugon]
AURAL-M2 With 16 batteries: 14.6 ×
90 cm and 20kg; with 64
batteries: 14.6 × 120 cm
and 32 kg; with 128
batteries: 14.6 × 178 cm
and 49 kg
300 1 y depending
on setting
parameters
[128-
32,768]
Alkaline D cell
or battery pack
Compact flash 1GB
or more and [640 GB
double hard drive kit]
WAV files,
temperature, and
depth
33 MIPS Dallas
DS89C450 Ultra
High Speed Flash
Microcontroller
Whales in the
St. Lawrence
River
219
Instrument
Dimensions
Maximum
deployment
depth (m)
Maximum
deployment
time
Sampling
frequency
(Hz)
Power supply and
energy capacity
Data storage
Data format
Microprocessor
Examples of
species studied
EAR 10.16 cm diameter by
60 cm long cylinder
[999] 1 y [2 - 80,000
(max)]
Alkaline battery
pack
[320 GB HDD] Binary files Persistor CF2
microprocessor, a
1 GB compact flash
card, a Persistor
BigIDEA IDE
adapter
Stenella
longirostris,
[M. novaeangliae,
Delphinapterus
leucas,
Balaenoptera
physalus, Orcinus
orca, Mesoplodon
sp., snapping
shrimp, and coral
reef fish]
HARP
[Standard seafloor package
is 1.5 m × 1.5 m × 1.5 m
but can be configured
into large trawl-proof
subsurface moorings or
small moorings deployed
by hand from a small
boat where the pressure
housings (22.9 cm
diameter × 81.3 cm long
tubes) are fastened to a line
connecting the flotation
and release/weight]
~ 7,000 [300 d at
200 kHz
continuous
recording and
loss-less data
compression]
[10,000-320,000] Alkaline or
lithium battery
pack
[16 × 320 GB HDD =
5 TB data storage × 2
compression = 10 TB
effective storage]
XWAV time series
files
32-bit, 20 MHz
microcontroller from
Motorola (www.
motorola.com)
[All baleen
whales, all
odontocetes,
and fish (i.e.,
bandwidth
from 10 to
160,000 Hz)]
OceanBase [11 cm diameter × 45 cm] [500] [Over 1 y] [200-96,000] Batteries SSD 128 GB
expandable to
1 TB or more
[WAV, flat] [RISC] Cetaceans, fish,
sea state, and
vessels
OceanPod [11 cm diameter × 45 cm] [2,000] [0.5 to 1 y
depending on
sampling rate]
[200-96,000] [Alkaline or
lithium battery]
[Four SD slots of any
memory size]
[WAV, flat] [RISC] Cetaceans, fish,
sea state, and
vessels
PAL 76.2 cm long and 15.2 cm
in diameter
[2,000] 1 y [0-100,000] Alkaline D cells 2 GB flash memory
card
Binary restored to
time series (sound
bites)
Tattletale Model 8 Orcinus orca,
dolphins
[manatees,
ribbon seals,
bearded seals,
walrus, bowhead
whales, and right
whales]
Errata
220
Instrument
Dimensions
Maximum
deployment
depth (m)
Maximum
deployment
time
Sampling
frequency
(Hz)
Power supply and
energy capacity
Data storage
Data format
Microprocessor
Examples of
species studied
Pop-up or
MARU
[Single sphere: 51 cm
high and 58 cm diameter]
[Double sphere:
110 cm high and
58 cm diameter]
[2,500
(acoustic
release limit);
6,000 (system
limit)]
[12-175 d
depending on
sampling rate;
12-370 d
depending on
sampling rate]
[2,000-
64,000]
[Alkaline D cells,
149 Wh]
[Alkaline D cells,
371 Wh]
[128 GB compact
flash]
[Binary fault-
tolerant format,
post processed to
AIFF]
[Tattletale Model 8
(Onset Computer
Corporation)]
[Balaenoptera
musculus,
B. physalus,
B. bonaerensis,
B. brydei,
M. novaeangliae,
Eubalaena
glaciali, and
Balaena
mysticetus]
RASP [C12] [9 × 25 cm] [250] [Several weeks] Up to 96,000 [Lithium primary
battery]
[32 GB microSD
memory]
[WAV and MP3] [Yamaha C24
recorder, with built-in
recording cycle (one
slot per 24 h)]
Whales and
dolphins
RASP
[C12 II]
[9 × 28 cm] [250] [Several weeks] Up to 96,000 [Lithium primary
battery]
[32 GB microSD
memory]
[WAV and MP3] [Modified Yamaha
W24 or Olympus
LS-3, with built-in
recording cycle (one
slot per 24 h or three
slots per 24 h)]
Whales and
dolphins
RUDARTM 17.8 cm; 36.4 kg or
45.5 kg with batteries
[500, 1,500,
or 3,500]
[Depends on
sample rate
chosen and
number of
battery packs]
[Selectable
sampling rates
from 4 to 750,000]
[Alkaline
batteries]
[250 GB to 1.5 TB
hard disks]
Up to 4 hydrophone
channels, WAV
[Sound Technology
ST500 mobile data
recorder and sound
level monitor]
Cetaceans
µRUDARTM [1 hydrophone: 29 cm long
× 10 cm diameter;
2 hydrophones: 37 cm
long × 10 cm diameter]
[250] Up to 61 h
depending on
sample rate
chosen
[44,100-96,000] Rechargeable
Li-Ion batteries
[64 GB SD flash
memory card]
[Up to 2
hydrophone
channels, WAV or
MP3]
[Zoom H1 digital
recorder]
Cetaceans
nRUDARTM [26 cm long × 8 cm
diameter]
[300] [Up to 26 h
depending on
sample rate and
battery chosen]
[44,100-96,000] [Single AA
alkaline or
lithium battery]
[32 GB SD flash
memory card]
[1 hydrophone,
WAV, or MP3]
[Zoom H1 digital
recorder]
[Cetaceans]
Sousa-Lima et al.
221
Instrument
Dimensions
Maximum
deployment
depth (m)
Maximum
deployment
time
Sampling
frequency
(Hz)
Power supply and
energy capacity
Data storage
Data format
Microprocessor
Examples of
species studied
SM2M
[Submersible]
16.5 cm diameter ×
79.4 cm long
150 [104 d
continuous
recording]
4,000-
96,000
LSD NiMH,
alkaline,
or lithium
manganese D-cell
batteries
128 GB with SDHC or
512 GB with SDXC
[WAV audio
with up to two
hydrophone
channels,
RMS peak and
ambient level log,
temperature log]
NA [Marine
mammals and
fish; noise
logging from
below sea state
0 up to 240 dB
SPL]
SM2M
Ultrasonic
16.5 cm diameter ×
79.4 cm long
150 [42 d
continuous
recording]
4,000-
384,000
LSD NiMH,
alkaline,
or lithium
manganese D-cell
batteries
128 GB with SDHC or
512 GB with SDXC
[WAV audio
with up to two
hydrophone
channels,
RMS peak and
ambient level log,
temperature log]
NA [Marine
mammals and
fish; noise
logging from
below sea state
0 up to 240 dB
SPL]
[SM2M
Deepwater]
16.5 cm diameter ×
148 cm long
1,500 208 d
(Submersible)
or 84 d
(Ultrasonic)
for continuous
recording
4,000-
96,000
LSD NiMH,
alkaline,
or lithium
manganese D-cell
batteries
128 GB with SDHC or
512 GB with SDXC
WAV audio with up
to two hydrophone
channels,
RMS peak and
ambient level log,
temperature log
NA Marine
mammals and
fish; noise
logging from
below sea state
0 up to 240 dB
SPL
[EA-SDA14] 12 cm diameter × 32 cm
length; 5 kg with
batteries
700 Over 1 y
depending
on setting
parameters
39,625-
2,5000,000
continuous
recording
Different
lithium batteries
configurations
6 to 18 D cell
cases; additional
24 D cells
rechargeable
external battery
packs
2,000 GB on hard
drive and/or 128 GB
SDHC and/or 600 GB
SSD
24 bits raw WAV
files on 4 channels
with nanoseconds
synchronization,
celerity,
temperature, depth,
orientation, GPS
time-stamped
32 bits ARM &
Digital Signal
Processor (DSP)
All odontocetes,
including sperm
whales (Physeter
macrocephalus)
and porpoises;
benthos
[SYLence] 12 cm diameter × 55 cm
length; 8 kg with
batteries
700 Over 1 y
depending
on setting
parameters
24,000-192,000
continuous
recording
Lithium D-cell
batteries
2,000 GB on hard
drive and/or 128 GB
16 or 24 bits
WAV files on
1-2 channels,
temperature,
pressure,
orientation
NA Odontocetes
Errata
... Marine scientist networks using PAM exist (Boyd et al. 2015), but the freshwater community is nascent, and the terrestrial community is often fragmented by taxa. Methodological differences are also striking: acoustic calibration and sound propagation modelling are advanced in aquatic studies (Wang et al. 2014) but seldom considered in terrestrial ones (but see Haupert et al. (2022) and Sousa-Lima et al. (2013)); artificial intelligence can increasingly identify species on land (Nieto-Mora et al. 2023), whereas most aquatic sounds are still challenging to identify (Looby, Erbe et al. 2023;Parsons et al. 2023). ...
... In oceans, taxonomically untargeted, long, and regular deployments, coupled to large detection ranges, concurrently sample many taxa (Lillis and Boebel 2018). Mutual sampling campaigns can share resources to mitigate potentially prohibitive equipment, power, storage, transportation and post-processing costs (Sousa-Lima et al. 2013). Emerging embedded-AI audio detectors may offer an alternative to continuous and broadband recording (Höchst et al. 2022), but whole soundscape recordings will remain essential for broader application. ...
Article
Full-text available
Aim The urgency for remote, reliable and scalable biodiversity monitoring amidst mounting human pressures on ecosystems has sparked worldwide interest in Passive Acoustic Monitoring (PAM), which can track life underwater and on land. However, we lack a unified methodology to report this sampling effort and a comprehensive overview of PAM coverage to gauge its potential as a global research and monitoring tool. To address this gap, we created the Worldwide Soundscapes project, a collaborative network and growing database comprising metadata from 416 datasets across all realms (terrestrial, marine, freshwater and subterranean). Location Worldwide, 12,343 sites, all ecosystem types. Time Period 1991 to present. Major Taxa Studied All soniferous taxa. Methods We synthesise sampling coverage across spatial, temporal and ecological scales using metadata describing sampling locations, deployment schedules, focal taxa and audio recording parameters. We explore global trends in biological, anthropogenic and geophysical sounds based on 168 selected recordings from 12 ecosystems across all realms. Results Terrestrial sampling is spatially denser (46 sites per million square kilometre—Mkm ² ) than aquatic sampling (0.3 and 1.8 sites/Mkm ² in oceans and fresh water) with only two subterranean datasets. Although diel and lunar cycles are well sampled across realms, only marine datasets (55%) comprehensively sample all seasons. Across the 12 ecosystems selected for exploring global acoustic trends, biological sounds showed contrasting diel patterns across ecosystems, declined with distance from the Equator, and were negatively correlated with anthropogenic sounds. Main Conclusions PAM can inform macroecological studies as well as global conservation and phenology syntheses, but representation can be improved by expanding terrestrial taxonomic scope, sampling coverage in the high seas and subterranean ecosystems, and spatio‐temporal replication in freshwater habitats. Overall, this worldwide PAM network holds promise to support cross‐realm biodiversity research and monitoring efforts.
... Different studies and fields collect drastically different data types, from time series of continuous sensor measurements to sequences of observed behaviors [33][34][35][36]. To apply the type of rhythmic analyses and measures presented here, the first step is identifying the events and intervals of interest to the research question under consideration [14]. ...
Article
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Rhythm is fundamental in many physical and biological systems. Rhythm is relevant to a broad range of phenomena across different fields, including animal bioacoustics, speech sciences, and music cognition. As a result, the interest in developing consistent quantitative measures for cross-disciplinary rhythmic analysis is growing. Two quantitative measures that can be directly applied to any temporal structure are the normalized pairwise variability index (nPVI) and rhythm ratios (rk). The nPVI summarizes the overall isochrony of a sequence, i.e., how regularly spaced a sequence’s events are, as a single value. Meanwhile, rk quantifies ratios between a sequence’s adjacent intervals and is often used for identifying rhythmic categories. Here, we show that these two rhythmic measures are fundamentally connected: the nPVI is a summary static of the rk values of a temporal sequence. This result offers a deeper understanding of how these measures are applied. It also opens the door for creating novel, custom measures to quantify rhythmic patterns based on a sequence’s rk distribution and compare rhythmic patterns across different domains. The explicit connection between nPVI and rk is one further step towards a common quantitative toolkit for rhythm research across disciplines.
... As vessel time and operator time is only required for their deployment and recovery, fixed recorders offer a costeffective option for PAM, where real-time detection is not required. Such fixed recorders have therefore become increasingly used (Sousa-Lima et al., 2013) to collect data on species' distributions (Mellinger et al., 2007), and their relative density (K€ usel et al., 2011;Marques et al., 2009). However, studies are limited by the time required to analyse such data, as processing is time-consuming and costly. ...
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Passive acoustic monitoring (PAM) is an increasingly popular tool to study vocalising species. The amount of data generated by PAM studies calls for robust automatic classifiers. Deep learning (DL) techniques have been proven effective in identifying acoustic signals in challenging datasets, but due to their black-box nature their underlying biases are hard to quantify. This study compares human analyst annotations, a multi-hypothesis tracking (MHT) click train classifier and a DL-based acoustic classifier to classify acoustic recordings based on the presence or absence of sperm whale (Physeter macrocephalus) click trains and study the temporal and spatial distributions of the Mediterranean sperm whale subpopulation around the Balearic Islands. The MHT and DL classifiers showed agreements with human labels of 85.7% and 85.0%, respectively, on data from sites they were trained on, but both saw a drop in performance when deployed on a new site. Agreement rates between classifiers surpassed those between human experts. Modeled seasonal and diel variations in sperm whale detections for both classifiers showed compatible results, revealing an increase in occurrence and diurnal activity during the summer and autumn months. This study highlights the strengths and limitations of two automatic classification algorithms to extract biologically useful information from large acoustic datasets.
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As climate change and anthropogenic activities continue to impact cetacean species, it becomes increasingly urgent to efficiently monitor cetacean populations. Continuing technological advances enable innovative research methodologies which broaden monitoring approaches. In our study, we utilized an autonomous wave glider equipped with acoustic and environmental sensors to assess delphinid species presence on the east Florida shelf and compared this approach with traditional marine mammal monitoring methods. Acoustic recordings were analyzed to detect delphinid presence along the glider track in conjunction with subsurface environmental variables such as temperature, salinity, current velocity, and chlorophyll-a concentration. Additionally, occurrences of soniferous fish and anthropogenic noise were also documented. These in-situ variables were incorporated into generalized additive models (GAMs) to identify predictors of delphinid presence. The top-performing GAM found that location, sound pressure level (SPL), temperature, and chlorophyll-a concentration explained 50.8% of the deviance in the dataset. The use of satellite environmental variables with the absence of acoustic variables found that location, derived current speed and heading, and chlorophyll-a explained 44.8% of deviance in the dataset. Our research reveals the explanatory power of acoustic variables, measurable with autonomous platforms such as wave gliders, in delphinid presence drivers and habitat characterization.
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Beaked whales ( Cetacea; Ziphiidae ), one of the most diverse families of cetaceans, can be identified by species‐specific, frequency‐modulated echolocation signals. Of the 24 known species of beaked whales, over half have been assigned a unique signal type. A novel echolocation pulse belonging to an unknown beaked whale species was recorded off West Africa (Beaked Whale of West Africa, BWWA), along the coast of the Democratic Republic of São Tomé and Príncipe. Bottom‐mounted autonomous acoustic recorders (sampling rate of 375 kHz) were deployed from October 2018 to August 2019 (294 recording days) at depths of 450–600 m. An automated detector‐classifier created to identify BWWA (per file Precision of 1.00; Recall of 1.00)‐guided manual validation. BWWA was present in all recording months and detected during local nighttime hours (98% of detections occurred during fully dark periods). BWWA had a 52.5 kHz median peak frequency, 55.4 kHz center frequency, 29.0 kHz −10 dB bandwidth, 843 μs duration, and 86 ms inter‐pulse interval (IPI). While species identification remains unsolved for BWWA, spectral similarities to unidentified signals in the Pacific Ocean, BWC, and in the Gulf of Mexico, BWG, find that all three signals can be characterized by longer pulse durations and shorter IPIs.
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Passive Acoustic Monitoring (PAM) provides a key ocean environmental monitoring approach as it enables estimation of parameters such as wind, rain, ocean water column depth, sea bed and sub bottom classification, using the prevailing ambient sound in the ocean, thereby protecting the marine ecosystem. PAM helps to determine Underwater Radiated Noise (URN) from ships and other human made noises, in order to reduce anthropogenic noise so that no harm is caused to marine life and their habitats. The Autonomous Passive Acoustic Monitoring system developed by National Institute of Ocean Technology (NIOT) plays a key role in tackling ecological issues based on acoustic data from the sea, and supporting sustainability across the blue economy. The PAM system of NIOT has been operational in the Indian seas since 2011 and the Arctic since 2015. NIOT has advanced this Ambient Noise Measurement System (ANMS) over the years to enhance data acquisition, storage, and processing; thereby extending PAM to a developmentally important tool for long term monitoring to promote ocean sustainability. The primary goal of the PAM by NIOT is to acquire ambient sound in the oceans for geo-acoustic, bioacoustic and acoustical oceanographic applications. With PAM, the blue economy can address ecological challenges to monitor marine biodiversity, assess the health of theecosystem, and assess human impacts to oceans. PAM provides a multi-scalar study of the ecosystems- from organisms and species to populations and communities, and soundscapes to measure biological (biophony), geophysical (geophony), and anthropogenic (anthrophony) sounds at any one time, and in any place.
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Marine energy converters can generate electricity from energetic ocean waves and water currents. Because sound is extensively used by marine animals, the radiated noise from these systems is of regulatory interest. However, the energetic nature of these locations poses challenges for performing accurate passive acoustic measurements, particularly with stationary platforms. The Drifting Acoustic Instrumentation SYstem (DAISY) is a modular hydrophone recording system purpose-built for marine energy environments. Using a flow shield in currents and mass–spring–damper suspension system in waves, we demonstrate that DAISYs can effectively minimize the masking effect of flow noise at frequencies down to 10 Hz. In addition, we show that groups of DAISYs can utilize time-delay-of-arrival post-processing to attribute radiated noise to a specific source. Consequently, DAISYs can rapidly measure radiated noise at all frequencies of interest for prototype marine energy converters. The resulting information from future operational deployments should support regulatory decision-making and allow technology developers to make design adjustments that minimize the potential for acoustic impacts as their systems are scaled up for utility-scale power generation.
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Calling blue and fin whales have been tracked using relative travel times and amplitudes from both direct and multipath arrivals to a seafloor array of seismometers. Calls of three fin whales swimming in the same general direction, but several kilometers apart, are believed to represent communication between the whales because of signature differences in call character, an alternating call pattern, and coordination of call and respiration times. Whale call tracks, call patterns, call character, and swimming speeds were examined during periods with and without the presence of noise. Noise sources included airguns, when the whales were subject to sound levels of up to 143 dB P-P (peak-to-peak) re: 1 pPa over the 10 to 60-Hz band, and transits of merchant ships, when the whales received continuous levels up to 106 dB rms re: I / • Pa over the 10 to 60-Hz band (115 dB P-P). Whale responses associated with these noises remain arguable. ¸ 1995 Acoustical Society of America.
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Pile driving during offshore windfarm construction goes along with considerable noise emissions that potentially harm marine mammals in the vicinity and may cause large scale disturbances. Information on the scale of such disturbances is limited. Therefore, assessment and evaluation of the effects of offshore construction on marine mammals is difficult. During summer 2008, 91 monopile foundations were driven into the seabed during construction of the offshore wind farm Horns Rev II in the Danish North Sea. We investigated the spatial and temporal scale of behavioural responses of harbour porpoises Phocoena phocoena to construction noise using passive acoustic monitoring devices (T-PODs) deployed in a gradient sampling design. Porpoise acoustic activity was reduced by 100% during 1 h after pile driving and stayed below normal levels for 24 to 72 h at a distance of 2.6 km from the construction site. This period gradually decreased with increasing distance. A negative effect was detectable out to a mean distance of 17.8 km. At 22 km it was no longer apparent, instead, porpoise activity temporarily increased. Out to a distance of 4.7 km, the recovery time was longer than most pauses between pile driving events. Consequently, porpoise activity and possibly abundance were reduced over the entire 5 mo construction period. The behavioural response of harbour porpoises to pile driving lasted much longer than previously reported. This information should be considered when planning future wind farm construction.
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Passive acoustic technologies are those technologies that enable us to listen to and record ambient underwater sounds. Such technologies have existed for decades; however, a major initiative to develop and promote their use in fisheries applications and as an important new tool for the census and exploration of marine life is now needed. Given the significant advancement in underwater tech- nologies, passive acoustic research promises to be an important new field in fisheries and related areas/disciplines. The ability to listen to fish and other marine life allows scientists to identify, record and study underwater animals, even in the absence of visual information. Coupling passive acoustics with conventional visual monitoring and sampling techniques provides a powerful new approach to undersea research. The Sea Grant College Program has recognized the great potential of passive acoustics for fisheries and related fields, and has taken a leadership role in supporting the development of innovative new research programs using this approach. (note, glossy print copies available from the authors or MIT SeaGrant)
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As an ecologist interested in observing the behavior of marine animals in their natural environment, there appears to be a disconnect between the technology industry and the needs of ecologists. Basically, ecologists often have to settle for technologies developed for scientists/users from other disciplines, getting what could be called "hand-me-down" technology …"hand-me-down" not in the sense of its being old or used, but rather just not quite what the ecologist wants. First they are often limited to a single observation modality. There are many types of observations I may want to make underwater in support of behavioral studies (e.g., spawning), ecological studies (e.g., census of populations or species assemblages), habitat mapping (e.g., percent coverage of a habitat type), etc. In nature there are many ways, or modalities, in which humans or other animals can observe their environment. Humans are highly biased towards visual observations that correspond to optic technologies. Another important way to make observations is through hearing, which translates to acoustic technology. Other ways to observe nature have rarely been translated into observational technologies: chemoreception (smell and taste), mechanoreception (complex of related senses, including equilibrium and balance, touch or tactile, "distance-touch", and hearing), electroreception (detection of electric fields), and magnetoreception (detection of magnetic fields). The development of observational technologies designed to emulate human sensory systems and observational strategies will become increasingly important in the coming decades and will greatly enhance our understanding of underwater ecosystems.
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Since May 1996, an array of autonomous hydrophone moorings has been continuously deployed in the eastern equatorial Pacific to provide long-term monitoring of seismic activity, including low-level volcanic signals, along the East Pacific Rise between 20°N and 20°S and the Galapagos Ridge. The instruments and moorings were designed to continuously record low-frequency acoustic energy in the SOFAR channel for extended periods and produce results comparable to those previously derived by using the U.S. Navy Sound Surveillance System (SOSUS) in the northeast Pacific. The technology and methodology developed for this experiment, including instrument design, mooring configuration, analysis software, location algorithms (with an analysis of errors), and a predicted error field, are described in detail. Volcanic activity is observed throughout the Pacific, along with seismicity along transform faults, subduction zones, and intraplate regions. Comparison data sets indicate detection thresholds and accuracy better than the land networks for open ocean areas and results comparable to, or better than, SOSUS. Volcanic seismicity along the fast spreading East Pacific Rise appears similar to documented examples in the northeast Pacific but with much shorter durations. One example from the intermediate spreading Galapagos Ridge is comparable to northeast Pacific examples, and several episodes of activity were observed in the Wilkes Transform Fault Zone. A site of continuing off-axis seismicity is located near 18°S and 116°W. Isolated intraplate earthquakes are observed throughout the study area. Earthquake information from this experiment and future observations will be provided through the World Wide Web and earthquake data centers.
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We studied the distribution and abundance of finless porpoises (Neophocaena phocaenoides) in Hong Kong and adjacent waters of China's Guangdong Province between September 1995 and November 2000. Vessel (50,194 km) and helicopter (2,696 km) surveys were used to assess distribution patterns, and estimates of abundance were calculated using line transect methods. Acoustic detection data from a towed porpoise click detector (POD) were used to make an estimate of the trackline detection probability [g(0)] for ship surveys, and surface and dive time data were used for correcting helicopter survey estimates. Porpoises occurred in Hong Kong and adjacent waters year-round, but showed evidence of seasonal movements, with porpoises largely vacating most of Hong Kong's southwestern waters in summer and autumn. Seasonal changes in overall abundance were also evident. The peak season within Hong Kong waters was spring, in which an estimated 152 porpoises inhabited territorial waters. The peak estimates for all areas combined (217 porpoises in spring and summer) can be viewed as a minimum estimate of the size of the local population. Examination of potential violations of line transect assumptions indicate that the techniques used were well-suited, with no evidence of serious biases. However, because the distribution clearly extends beyond the study area and the exact range limits are unknown, further work is needed to assess overall population size.
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The seasonal presence of ribbon seals (Histriophoca fasciata) on the central and southeastern Bering Sea shelf was determined from vocalizations recorded with a Passive Aquatic Listening (PAL) recorder at two sites along the 70-m isobath from 2007 to 2010. Ribbon seal vocalizations were identified as intense, stereotyped downsweeps, roars, and grunts. Acoustic detections were sea-sonal, with peak acoustic activity occurring in April at the southeastern site and May at the cen-tral shelf location. Ribbon seal acoustic presence was tightly coupled to sea ice presence, and onset of detection was associated with thicker, more extensive ice cover compared to the other Arctic pinnipeds (bearded seals [Erignathus barbatus] and walrus [Odobenus rosmarus]) detected in the region. Ribbon seal vocalizations were detected only when ice cover in the area exceeded 80%, suggesting that this species has a habitat prefer-ence or requirement for a more stable ice platform for some activities during the winter breeding season.
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Monitoring programmes for white whales (Delphinapterus leucas) have been called for repeatedly in recent years because this species is likely to be negatively impacted by climate change, but also because such a broadly dispersed, high trophic feeder can serve as an effective ecosystem sentinel. Arctic ecosystems are diffi-cult to monitor because of the extensive winter ice cover-age and extreme environmental conditions in addition to low human population densities. However, passive acoustic monitoring has proved to be a reliable method to remotely survey the presence of some marine mammals in the Arctic. In this study, we evaluate the potential use of echolocation loggers (T-POD and C-POD, Chelonia Ltd.) for remote monitoring of white whales. Captive experi-ments and open water surveys in three arctic/subarctic habitats (ice-noise-dominated environment, ice-free envi-ronment and low-turbidity waters) were used to document detection performance and to explore the use of logger angle and inter-click interval data to look at activity pat-terns and tidal influences on space use. When acoustic results were compared to concurrent visual observations, echolocation detection was only attributed to periods of white whale presence near the recorder deployment sites.