A scalable, satellite-transmitted data product for monitoring high-activity events in mobile aquatic animals

Abstract

A growing number of studies are using accelerometers to examine activity level patterns in aquatic animals. However, given the amount of data generated from accelerometers, most of these studies use loggers that archive acceleration data, thus requiring physical recovery of the loggers or acoustic transmission from within a receiver array to obtain the data. These limitations have restricted the duration of tracking (ranging from hours to days) and/or type of species studied (e.g., relatively sessile species or those returning to predictable areas). To address these logistical challenges, we present and test a satellite-transmitted metric for the remote monitoring of changes in activity, measured via a pop-off satellite archival tag (PSAT) with an integrated accelerometer. Along with depth, temperature, and irradiance for geolocation, the PSAT transmits activity data as a time-series (ATS) with a user-programmable resolution. ATS is a count of high-activity events, relative to overall activity/mobility during a summary period. An algorithm is used to identify the high-activity events from accelerometer data and reports the data as a count per time-series interval. Summary statistics describing the data used to identify high-activity events accompany the activity time-series. In this study, we first tested the ATS activity metric through simulating PSAT output from accelerometer data logger archives, comparing ATS to vectorial dynamic body acceleration. Next, we deployed PSATs with ATS under captive conditions with cobia (Rachycentron canadum). Lastly, we deployed seven pop-off satellite archival tags (PSATs) able to collect and transmit ATS in the wild on adult sandbar sharks (Carcharhinus plumbeus). In the captive trials, we identified both resting and non-resting behavior for species and used logistic regression to compare ATS values with observed activity levels. In captive cobia, ATS was a significant predictor of observed activity levels. For 30-day wild deployments on sandbar sharks, satellites received 57.4–73.2% of the transmitted activity data. Of these ATS datapoints, between 21.9 and 41.2% of records had a concurrent set of temperature, depth, and light measurements. These results suggest that ATS is a practical metric for remotely monitoring and transmitting relative high-activity data in large-bodied aquatic species with variable activity levels, under changing environmental conditions, and across broad spatiotemporal scales.

Full text

Skubeletal. Anim Biotelemetry (2020) 8:34
https://doi.org/10.1186/s40317-020-00220-0
METHODOLOGY
A scalable, satellite-transmitted data product
formonitoring high-activity events inmobile
aquatic animals
Rachel A. Skubel1* , Kenady Wilson2, Yannis P. Papastamatiou3, Hannah J. Verkamp4, James A. Sulikowski5,
Daniel Benetti6 and Neil Hammerschlag1,6
Abstract
A growing number of studies are using accelerometers to examine activity level patterns in aquatic animals. However,
given the amount of data generated from accelerometers, most of these studies use loggers that archive acceleration
data, thus requiring physical recovery of the loggers or acoustic transmission from within a receiver array to obtain
the data. These limitations have restricted the duration of tracking (ranging from hours to days) and/or type of species
studied (e.g., relatively sessile species or those returning to predictable areas). To address these logistical challenges,
we present and test a satellite-transmitted metric for the remote monitoring of changes in activity, measured via a
pop-off satellite archival tag (PSAT) with an integrated accelerometer. Along with depth, temperature, and irradiance
for geolocation, the PSAT transmits activity data as a time-series (ATS) with a user-programmable resolution. ATS is
a count of high-activity events, relative to overall activity/mobility during a summary period. An algorithm is used
to identify the high-activity events from accelerometer data and reports the data as a count per time-series interval.
Summary statistics describing the data used to identify high-activity events accompany the activity time-series. In this
study, we first tested the ATS activity metric through simulating PSAT output from accelerometer data logger archives,
comparing ATS to vectorial dynamic body acceleration. Next, we deployed PSATs with ATS under captive conditions
with cobia (Rachycentron canadum). Lastly, we deployed seven pop-off satellite archival tags (PSATs) able to collect
and transmit ATS in the wild on adult sandbar sharks (Carcharhinus plumbeus). In the captive trials, we identified both
resting and non-resting behavior for species and used logistic regression to compare ATS values with observed activ-
ity levels. In captive cobia, ATS was a significant predictor of observed activity levels. For 30-day wild deployments on
sandbar sharks, satellites received 57.4–73.2% of the transmitted activity data. Of these ATS datapoints, between 21.9
and 41.2% of records had a concurrent set of temperature, depth, and light measurements. These results suggest that
ATS is a practical metric for remotely monitoring and transmitting relative high-activity data in large-bodied aquatic
species with variable activity levels, under changing environmental conditions, and across broad spatiotemporal
scales.
Keywords: Biotelemetry, Biologging, Accelerometers, Activity, Sharks, Behavior, Activity levels, Satellite tags
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Background
Interpretation of animal movement patterns has been a
central focus of ecological studies and is a critical com-
ponent of modern conservation research [1, 2]. Given the
challenges of directly observing the movements and asso-
ciated behaviors of marine and freshwater animals under
natural conditions, researchers have used biologging and
Open Access
Animal Biotelemetry
*Correspondence: ras347@miami.edu
1 Abess Center for Ecosystem Science and Policy, University of Miami,
Coral Gables, FL, USA
Full list of author information is available at the end of the article
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Skubeletal. Anim Biotelemetry (2020) 8:34
biotelemetry tools to monitor activity remotely. ese
methods provide a glimpse into the animals’ behavior in
wild environments, without the burden of human pres-
ence for observation [3].
Researchers have been increasingly integrating mul-
tiple sensors into tracking tools to provide additional
information on how aquatic animals interact with their
environments. Common combinations include tri-axial
acceleration, temperature, and pressure (depth) sensors
(e.g., [4, 5]). Similar combinations have been used to
study where and when certain behaviors occur, such as
mating or feeding [6–8]; to investigate biological driv-
ers of movement patterns, such as circadian rhythmsor
behavioral thermoregulation[9, 10]; to identify impacts
of human activity, such as post-release fishing mortal-
ity [11, 12] or provisioning for dive tourism [13]; and
to measure field metabolic rates, infer thermal perfor-
mance, and measure activity levels and their responses to
environmental settings [14, 15].
In activity studies, accelerometers sample multiple
axes at high frequencies, often measuring and logging
at > 15Hz, and up to 500Hz [16–18]. e total amount of
raw data recorded is therefore too large for transmission
via satellite; as a result, researchers physically recover log-
ging devices to obtain their raw data, or logging devices
transmit their data from within an acoustic receiver array
[18–20]. Tag recovery is logistically difficult for wide-
ranging aquatic animals, such as elasmobranchs and large
teleost fishes that do not return to locations where their
recapture is predictable [21]. To maximize the probability
of retrieving the loggers or having the data transmitted to
an array, accelerometer applications are limited by track-
ing duration (e.g., from hours to days) and/or by the spe-
cies studied (e.g., less mobile species or those returning
to predictable areas) [19–23].
Study aims
Understanding how highly mobile or open-ocean ani-
mals respond to environmental variability, over multiple
months, can give researchers evidence of animals’, popu-
lations’, or species’ spatial and environmental preferences
[24, 25]. Garnering such evidence can contribute to con-
servation planning and management, such as assessing
climate change vulnerability or species use in protected
and unprotected areas [25–27]. Following previous
studies [28, 29] (Table1), we aim to address this area of
research by pairing a compressed metric of activity with
environmental data (depth and temperature) and loca-
tion data (geolocation). Specifically, we present a novel,
satellite-transmittable, acceleration-derived metric of
high-activity based on measurements obtained from
pop-off satellite archival tags (PSAT). PSATs transmit this
metric as an ‘activity time-series’ (ATS), which represents
a count of high-activity events per a time-series inter-
val, where an algorithm identifies high-activity events
from accelerometer data. ATS is paired with an hourly
measure of mobility (along x, y, and z-axes), and existing
time-series data products for depth and temperature. e
ATS-enabled PSAT can overcome the limited bandwidth
of satellite transmission via Argos by processing the raw
accelerometer data onboard the tag and only transmit-
ting the ATS time-series with concurrent summary sta-
tistics of the raw data. Accordingly, this study had three
primary objectives: (1) test the ATS data product under
captive conditions to verify that it is a reasonable metric
of high activity; (2) conduct wild deployments of ATS-
PSATs to test their utility for measuring and transmitting
ATS time-series data with corresponding mobility, depth,
temperature, and light levels in highly migratory species;
and (3) demonstrate the utility of the data obtained by
comparing the ATS data product against other traditional
accelerometer-derived measures of activity level (specifi-
cally, vectorial dynamic body acceleration, VeDBA).
Methods
PSAT tags
PSATs are positively buoyant devices that continuously
log sensor data for a predetermined length of time. e
tag then releases from the animal and floats at the sur-
face where it transmits data to a receiving satellite in the
Argos satellite network [30, 31]. ese data commonly
include temperature, depth, and light levels, which are
used to approximate tag location during the deployment
[32]. ese concurrent time-series of environmental
conditions contextualize the geospatial location of indi-
vidual animals. ere are two major drawbacks to data
transmission via the Argos network: message size and
satellite availability [33]. Data messages are limited in
size and must be transmitted at a very small bandwidth
(~ 32 bytes/message); this means that a researcher will
need more messages to transmit more data. e Argos
system comprises a network of polar-orbiting satellites;
the availability of these satellites can vary both spatially
and temporally. PSATs send messages to the satellites
without acknowledgment of receipt, and corruption of
messages is possible. To increase the likelihood that sat-
ellites receive the message correctly, manufacturers rec-
ommend sending each message multiple times. However,
if attempting to transmit an extensive amount of data
(e.g., three concurrent time-series), due to the above-
mentioned issues, there may be some gaps in the data.
To address this limitation, researchers can compress the
data’s dimensions, either by combining several data into
one metric [34] or by recording events based on a prede-
termined algorithm which incorporates several streams
of data [17, 28, 29], and/or compress the data temporally,
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Skubeletal. Anim Biotelemetry (2020) 8:34
Table 1 Accelerometer-derived metrics of activity and/or behaviour for marine animals, including the targeted activity or behavior, equipment used,
andexample study orstudies using themetric
ATS is included for reference
Metric Identication Target activity Equipment Frequency/timespan Species Ref
Calculated before data transmission (no device retrieval needed)
PrCA (prey catch attempt) Per-second change in accelera-
tion greater than a (running
average + threshold) value
Dive-foraging attempts via
rapid head and body move-
ments
Relay satellite tag One dive sampled and sum-
marized every 2.25 h at 16 Hz
for up to 338 days
Wedell (Leptonychotes weddel-
lii)41 and southern elephant
seals (Mirounga leonine)31
[28, 43]
KD (knockdown event) Abrupt switch of PSAT from
vertical to horizontal Beginning of a swimming bout,
mortality PSAT 2-h summary of 1-Hz
data/60 days Pacific halibut (Hippoglossus
stenolepis)[29, 44]
Tilt (g) Degree of PSAT tilt, from vertical
to horizontal via z-axis Swimming behavior (sustained
or saltatory), mortality PSAT 2-h summary of 1-Hz
data/60 days Pacific halibut (Hippoglossus
stenolepis)[29, 44]
ATS (activity time-series) Per-second instances of mobil-
ity exceeding a dynamic high
activity threshold
Relative high activity PSAT 75-s and 1-h summaries of 1-Hz
data/30 days Sandbar sharks (Carcharhinus
plumbeus)[This study]
G Vectorized acceleration along x,
y, and z-axes Diurnal activity PSAT 3-min summaries of 1.5625-Hz
data Pacific sailfish (Istiophorus
platypterus)[34]
Calculated before device retrieval
Burst acceleration events Exceeds manually defined
acceleration threshold Escape behaviors Accelerometer logger 500 Hz/18–45 h Red seabream (Pagrus major)
and yellowtail kingfish (Seriola
lalandi)
[45]
Calculated after device retrieval
Prey engulfment Differential of accelera-
tion > 1000 m/s Feeding (raptorial and suction) Accelerometer logger 200 and 333 Hz/timespan NA Harbor seal (Phoca vitulina) [19]
ODBA (overall dynamic
body acceleration) (Absolute acceleration along x,
y, and z-axes) – (static accel-
eration due to gravity)
Energy expenditure (as accel-
eration around a center of
mass)
Accelerometer logger Moderate-to-high frequency
(> 10 Hz) NA [46]
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Skubeletal. Anim Biotelemetry (2020) 8:34
by choosing a method to summarize data over a certain
period [29]. We combined both strategies by combining
three axes of acceleration into one metric, summarized
and transmitted as a time-series (described below).
e PSATs in this study record pressure (depth) to
1700 m (± 0.5 m resolution), temperature from − 40
to 60 °C (± 0.05 °C resolution), and light levels from
5 × 10−12 to 5 × 10−2Wcm−2, at 440nm resolution. e
devices’ total length x width measured 124 × 38 mm,
with a weight in air of 60g (Wildlife Computers). is
PSAT samples acceleration along the x, y, and z-axes (Ax,
Ay, and Az) at 8Hz for data processing and calculation
of ATS, and then archives the processed data, along with
raw sensor data every 1s for storage, which researchers
can access via download if they recover the tag.
e user chooses the time-series frequency and cor-
responding summary period span for MiniPAT tags. e
summary period is used to parse the data into the time-
series intervals, which the PSAT will transmit via satellite.
is also provides a way to calculate summary statistics
to describe the animal or environment over longer dura-
tions than the intervals themselves. In this study, the
shortest possible time-series interval (75s) was used to
calculate ATS; however, MiniPAT time-series can be
programmed for longer intervals. A longer period would
cause less frequent calculations, but would extend the
temporal coverage of the data. For example, a 75-s time-
series uses a 1-h summary period, and a 10-min time-
series uses an 8-h summary period. At the time of this
study, the tag could record and transmit 75-s time-series
data for activity, depth, and temperature, with additional
light-level data for approximately 1 month (Additional
file1: Text and Additional file1: Tables S1 and S2). All tag
conditions are set ahead of deployment using the Wildlife
Computers Tag Agent software.
We attached PSATs to the study animals via a tether to
an umbrella dart embedded in the dorsal musculature,
such that the tag trailed ~ 6cm off the animals. Tethers
comprised a stainless-steel cable sheathed in surgical tub-
ing and covered by heat-shrunk plastic tubing. We used
this attachment method so that the tags could detach
from the animal, float to the surface, and transmit their
data. Tags continuously transmit data through the Argos
satellite network until they deplete their batteries. We
note that most accelerometer experiments on fish usu-
ally affix the tag to the dorsal fin, permitting analysis for
tri-axial acceleration to measure fine-scale fish pitch, roll,
and tail-beat frequency. As the ATS-PSATs are tethered,
permitting tag rotation, our application captures the total
force exerted on the tag from fish movement. Accord-
ingly, the summary metric is axis-independent and does
not require differentiation among the x, y, and z-axes.
As such, ATS is not intended to provide information
on, or measure fine-scale fish pitch, roll, and tail-beat
frequency, nor on specific behaviors such as feeding or
hunting.
Activity metric
In this study, we broadly defined ‘activity’ as an animal’s
whole-body (locomotory activity) movement. We tested
a filtered metric of high activity that can be applied
across species and habitats and provide information
about an animal’s behavior without recovering the tag.
Wildlife Computers (WC) (Wildlife Computers, Red-
mond, WA, USA), in consultation with the authorship
team, developed the ATS metric and incorporated it into
a WC MiniPAT tag. WC similarly records and formats
all time-series data on their tags (e.g., at certain frequen-
cies and over certain time spans), so the ATS metric was
designed to operate within these parameters. After pre-
programmed release from the animal, the PSAT begins a
series of calculations (illustrated in Fig.1):
1. ‘Mobility’: Mobility is the row-wise mean of the
standard deviation (σ) of acceleration (Ax, Ay, and
Az are the x, y, and z-axes of acceleration), where σ
is calculated over a 3-s moving window on the 8-Hz
data that advances by 1-s increments, and then
recorded for every 1s:
2. ‘High activity’ (HA): for each summary period (e.g.,
1 h), the Mobility vector is centered to a mean of
0. Any Mobility values occurring in the tail of this
skewed distribution are identified as HA events.
Records in the ‘tail’ are isolated by a dynamic thresh-
old value, which is the absolute value of the mini-
mum Mobility value of the centered distribution:
3. ATS: the number of HA events during each 1-h sum-
mary period is counted and split into time-series
intervals (75 s). e count of HA events per 75-s
interval is then transmitted via satellite as a time-
series:
Transmitted time-series data for these tags include
the time-series data itself (ATS: high activity counts
every 75 s) and ‘Series Range’ data (Additional file 1:
TableS1). e Series Range data includes a set of met-
rics that describe the data used to calculate ATS over a
Mobility
i=
24
i=1σ(Axi +Ayi +Azi)
24 .
HAthreshold
i:i+3559 =


min

cent

ϕ(Mobility
i:i+3599

ATS
[i:i+74]=
75

i=1
Mi>HAthresholdi:i+74
.
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Skubeletal. Anim Biotelemetry (2020) 8:34
1-h summary period; Series Range includes the mean and
standard deviation of the Mobility vector that was used
to find the High Activity (HA) events over each succes-
sive 1h. e count of HA events (ATS) over the 1-h sum-
mary period is included with the ‘Series’ data.
e researcher can use ATS and its associated sum-
mary metrics (e.g., Mobility) to describe long-term and
short-term activity patterns. e hourly mean and SD of
Mobility provides a ‘baseline’ against which ATS events
are determined. For example, a 1-h record of a reef fish
swimming at a moderate, steady speed with no changes
in acceleration would cause low ATS values, moderate
1-h mean Mobility, and low 1-h SD of Mobility. If the reef
fish were to have several bouts of quicker swimming (e.g.,
evading a predator), there would be several instances of
higher ATS data-points during the 1-h summary period,
with higher SD in Mobility. Were this fish to rest on
the bottom with a few movements over the hour, mean
Mobility would be very low, although these few move-
ments would be reflected in the ATS values.
Design considerations We note that our metric is a
way to infer changes in activity from accelerometer data
on a PSAT. It is not reflective of ODBA or VeDBA, and
the inferences gained from it are also not equivalent to
those of ODBA or VeDBA (Table1). Rather, Mobility and
ATS provide a metric of relatively high activity and when
these active events occur, in a time-series format that cor-
responds to existing time-series metrics for temperature
and depth. Given the metric, and individual variation
both among and between species, the inference of a spe-
cific behavior is questionable and would likely not broadly
apply.
ATS simulation
To contextualize and differentiate ATS from prior met-
rics of activity, we calculated VeDBA and ATS on the
same set of archival data from tri-axial accelerometer
loggers. We used two archives from wild deployments of
accelerometer loggers, one at 50Hz from a nurse shark
(Ginglymostoma cirratum; OpenTag Motion OT3 Data-
logger, Loggerhead Instruments. Sarasota, FL, USA), and
one at 16Hz from a gray reef shark (Carcharhinus ambly-
rhynchos [9], ORI400-D3GT logger, Little Leonardo Co.,
Tokyo, Japan). Archives were sub-sampled to 8 Hz to
simulate data collected by ATS-PSATs. e sub-sampled
8-Hz data were then used to calculate ATS over 75-s
periods. After removing the static component of acceler-
ation from gravity using a Butterworth low-pass filter
over a 3-s window, we calculated VeDBA
(
VeDBA
=

A2
x+A2
y+A2
z ) at 8Hz (using the packages
“signal” and “tagtools” in R [35, 36]). We did not expect
that ATS would mirror VeDBA, but that relatively high-
activity events would occur at similar times. We
Fig. 1 A visual depiction of how the activity time-series (ATS) metric is calculated onboard the pop-off satellite tag (PSAT). (1) Tri-axial acceleration
values are sensed at 8 Hz, and (2) are summarized as a single mobility (M) value (the mean standard deviation of the sum of Ax, Ay, and Az over a
3-s moving window). (3) The distribution of M over a set summary period is centered at zero, and a threshold value for HA events is established (the
absolute value of the minimum of the centered distribution), such that (4) an M value greater than the threshold is considered a high-activity (HA)
event. (5) The number of HA events is recorded over predetermined time-series (every 75 s) for transmission as ATS
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Skubeletal. Anim Biotelemetry (2020) 8:34
identified relatively high-activity from VeDBA by apply-
ing a k-means clustering algorithm with four clusters
(using “stats” in R [37]), then visually compared VeDBA
clusters with simulated ATS. We did not conduct statisti-
cal tests, because we did not expect ATS and VeDBA to
have similarities in their time-series—rather, we expected
to see higher ATS values when there were sustained
‘spikes’ in VeDBA.
Captive trials
Animal tagging To test the performance of the ATS
algorithm for measuring burst-activity, we deployed the
tags on captive fish under both video and visual obser-
vation. We deployed tags on cobia (Rachycentron cana-
dum), which allowed us to test the performance of ATS
in a large, fast-moving teleost fish with heterogeneous
activity levels. We also deployed the tags on a relatively
slower moving fish with more homogenous activity lev-
els (dogfish sharks, Squalus acanthias). However, after
considering the video records, we deemed the small size
of the animals (57–66cm total length, TL) relative to the
tags insufficiently representative of wild applications. We
describe the tags’ data output is alongside that of cobia in
the supplementary electronic materials, however, did not
use these data for further analysis.
We deployed ATS-PSATs over 5 days on four mature
female cobia (103–112cm TL, weight 8.16–9.07kg) at the
University of Miami’s Experimental Hatchery (UMEH)
facility in Miami, FL, USA). e tank housing the cobia
was 20m in diameter and 1.8m in height and received
a constant influx of ultraviolet flow-through seawater fil-
tered down to 10μm. We programmed the tags to release
from the fish after 5days. We then recovered the tags
from the tanks so we could download the archived data
for a comparison of raw data with the transmitted ATS
product. We did not intend our captive deployments to
test the transmission of ATS; rather, we sought to use the
ATS archive for comparison with observed patterns in
activity level.
Video observation To record cobia activity patterns, we
mounted three GoPro cameras (two model HERO3 + and
one model HERO4, GoPro, San Mateo, CA) around
the tank (two downwards-facing, one lateral-facing).
Cameras were deployed for two, 2-h periods each day
(0900–1100 h, and 1500–1700h) to capture a breadth
of behaviors and activity levels based on research facility
staff’s prior knowledge (e.g., high activity associated with
feeding events). Using the video footage, we first visually
coded fish movements into 8 descriptive categories (Addi-
tional file1: TableS3), and then sorted these into one of
three activity levels, referred to as Activityobs: rest, cruis-
ing, and quicker swimming. “Rest” was identified as the
fish resting on the bottom of the tank; “cruising” as swim-
ming not preceded by acceleration, or swimming follow-
ing a ‘deceleration’; and “quicker” as swimming following
an acceleration. We assigned Activityobs for each 1s of the
video recording, for each fish, to correspond with the 1-s
frequency of PSAT archives.
Analysis ofcaptive trials To analyze the ability of ATS
to reflect a change in activity level, we further condensed
categories of Activityobs into two states: Resting and Not
Resting. We used Bayesian logistic regression models with
ATS as a predictor of ActivityObs using the ‘arm’ package
in R [38]. We also ran a multinomial model to see if ATS
could distinguish between additional behavior categories
(resting, cruising, and quicker swimming), using the ‘nnet’
package in R [39].
Wild deployments
To test the ability and utility of the ATS-enabled PSATs
to record and transmit ATS with corresponding environ-
mental data from highly migratory species, we deployed
seven ATS-PSATS on adult sandbar sharks (Carcharhi-
nus plumbeus): two off the coast of Miami (Florida, USA)
and five off the coast of Ocean City (Maryland, USA).
Our goal was to receive full triplets of time-series data to
match activity with depth and temperature data. In this
study, we use these data to confirm the potential of ATS
to monitor wild activity and do not infer beyond this. A
more formal analysis of activity related to the environ-
ment will be forthcoming. We note that using different
species for our wild and captive deployments was practi-
cal (i.e., having access to Cobia in a captive, observable
setting, versus having no access to sandbar sharks in a
captive setting) and did not interfere with the study goals;
a strength of ATS is its adaptive threshold for HA events,
rather than a pre-set threshold. e tags produced data
suitable for analysis so long as the species were suffi-
ciently large-bodied and varied in their activities. Rather
than additional assessment or validation of ATS itself, we
intended the wild deployments to test whether the ATS-
PSATs work in a field-setting and whether the tags can
collect activity data along with temperature and depth
time-series.
PSAT deployments Sharks in Miami were caught as part
of an ongoing survey using methods described in Calich
etal. [40], then briefly restrained for tagging and meas-
urement. Sharks in Maryland were caught using rod and
reel before tagging, measurement, and release. PSATs
were attached to the animals using a plastic umbrella dart
inserted into the dorsal musculature, using a stainless-
steel applicator. For Miami deployments, PSATS were
test tags provided by the manufacturer with known weak
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Skubeletal. Anim Biotelemetry (2020) 8:34
attachment points at the tag release mechanism, so while
we configured them for 30-day deployments, we expected
a premature release for these 2 tags. For Maryland deploy-
ments, we programmed all five PSATS for a 30-day deploy-
ment. Besides instrumentation, each animal was sexed
and measured for pre-caudal, fork, and total lengths [41].
Analysis ofwild deployments We estimated the move-
ment paths of the animals with the GPE3 state–space
modeling tool in the Wildlife Computers Data Portal.
GPE3 uses transmitted observations of irradiance (sun-
set and sunrise times), dive depth, and ambient surface
temperature data, in combination with a diffusion-based
movement model and known locations (from deployment
location and known Argos locations), to estimate the
most likely position of an animal at a given time. Before
using GPE3, we removed observations from after the
PSATs released from the animals (based on depth time-
series showing a rapid ascent and subsequent residency at
the surface) so that movement path calculation was only
based on data from when the PSAT was on the animal.
Results
ATS simulation
Using archived accelerometer data from wild deploy-
ments on a nurse shark (20min) and a gray reef shark
(6 h), we calculated ATS, and VeDBA (Fig. 2). Visual
examination showed similar timing for ATS (Fig. 2a,
c), and changes in VeDBA (Fig.2b, d). e reef shark’s
highest ATS values occurred within the first two hours
Fig. 2 A comparison of ATS and Mobility a, c with VeDBA b, d, calculated from two archived tri-axial accelerometry datasets. “High-Activity” (HA)
events based on mobility are indicated by black points, which are counted over 75-s periods to calculate ATS (orange line). In c, a gray line indicates
mean hourly mobility. VeDBA is colored by cluster, determined by a k-means clustering algorithm with a total of 4 clusters, to visualize different
activity levels. ATS and Mobility are not meant to replicate VeDBA, but rather to indicate relative high activity over time
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Skubeletal. Anim Biotelemetry (2020) 8:34
(Fig.2c, d), when VeDBA was most frequently switch-
ing from low to high values. For the remainder of the 6-h
time-series, ATS was lower, when VeDBA values were
lower and showed fewer instances of switching to a rela-
tively higher magnitude.
Captive deployments
PSAT deployments Of the four PSATs deployed on
cobia, three tags dislodged prematurely from the fish after
1, 1.5, and 4.5days, and one tag remained attached for the
full 5days. Video recording captured approximately 24
total hours of video for the tag that remained in place for
the full 5days (Additional file1: TableS4 and S5, describes
video recording durations for each tag).
ATS asapredictor ofactivity We observed some vari-
ability in observed activity during the intervals being
reported by ATS—for instance, between resting and cruis-
ing (Fig.3a), and between cruising and quicker swimming
(Fig.3b). e time-series nature of ATS allows it to be
adaptable throughout the deployment, which is evident
from the range of ATS values for each ActivityObs level. For
example, Fig.3 shows increased mobility for cobia over
two time periods; in Fig. 3a, the changes from ‘resting’
to prolonged durations of ‘cruising’ lead to the identifica-
tion of more ‘Active Events’ via ATS than for the changes
from ‘cruising’ to short durations of ‘quicker swimming’
in Fig.3b. Our logistic regression model suggested that
ATS was a significant predictor of Activityobs (coefficient
estimate 0.322, standard error 0.002, z-test value 151.5,
p value of the z statistic Pr ( >|z|) < 0.001; Additional
file1: FiguresS1–S3) for the cobia, with the odds ratio of
switching from resting to not resting when ATS increased
was 1.38. A pseudo-Chi-square test for goodness of fit fol-
lowing Matthiopoulos etal. [42] returned a value greater
than 0.05, indicating an acceptable model fit.
Wild deployments
PSAT deployment descriptions Miami We deployed
the ATS-PSATs in July 2018 on two adult female sandbar
sharks near Miami, FL (FL1 and FL2, Table2 and Fig.4).
Both of the sharks were on the fishing gear for less than
30min ahead of retrieval and tagging, and were in good
condition upon release. e PSATs used in these two
deployments released after 15 and 1days for the sharks
FL1 and FL2, respectively. As noted above, these tags were
test tags, so we anticipated the premature release. Because
of the short deployment duration, these tags transmitted
near-complete datasets while floating at the surface: A
shorter deployment resulted in fewer data collected and
therefore fewer data messages to be transmitted for a
complete dataset. Fewer data messages to be transmitted
resulted in greater opportunity to transmit each message
multiple times, and therefore increased the likelihood that
satellites would receive each message without corruption.
For these two tags, the majority (81.1 to 100%) of each
activity, temperature, and depth time-series were trans-
mitted and received; 61.7 (FL1) and 94.6% (FL2) contained
the full 75-s time-series triplet (of activity, temperature,
and depth). For ATS alone, 83.3 (FL1) and 94.6% (FL2) of
Fig. 3 For captive cobia CC2, 1-s mobility and Az, and 75-s ATS values over two 8-min periods. The gray bars indicate the count of ATS values, the
black line indicates Az, and circles indicate mobility values (the mean standard deviation of the sum of Ax, Ay, and Az over a 3-s window). The color
of each mobility data-point indicates which activity level was observed for that 1-s timepoint from video observation. Although one individual fish
is displayed here, these patterns were similar across all data
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Skubeletal. Anim Biotelemetry (2020) 8:34
both the time-series and range data were transmitted and
received.
Ocean City, MD We successfully deployed ATS-
PSATs on five sandbar sharks off the coast of Ocean
City, Maryland, USA, in August 2018 (MD1–MD5 in
Table2 and Fig.5). All sharks were in good condition
upon release. Tags remained on the sharks for their
pre-programmed 30-day duration. Each of the five tags
transmitted the majority of each 75-s activity, tempera-
ture, and depth time-series (56.4–72.1%). Of the time-
points covering the 30-day deployment, 22.2–41.2%
contained the full 75-s time-series triplet (of activity,
temperature, and depth). Additionally, 57.4–73.2% of
the hourly M records were transmitted and received
from MD sharks.
Depth and temperature trends To demonstrate the
‘triplet’ of measurements, we show the 75-s resolution
time-series for activity, temperature, depth, over the
entire deployment for sharks FL1 (Fig.6) near Florida,
and MD1 (Fig.7) near Maryland. Summary data for all 7
deployments (Table3) shows a higher mean temperature
over the deployments in FL (26.7 ± 2.2 °C for FL sharks
vs 22.5 ± 1.8°C for MD sharks) and a broader tempera-
ture range (12.3–30.8°C for FL sharks vs 10.4–26.1°C for
MD sharks). Depth range was broadest over the deploy-
ments in FL (0-213m for FL sharks vs 0.5-127m for MD
sharks). Mobility values had a similar range between
regions (28–63 for FL sharks vs 29–63 for MD sharks),
with higher mean mobility values for deployments in MD
(37.54 ± 8.98 for FL sharks vs 51.75 ± 11.8 for MD sharks).
Discussion
The simulated activity metric compared withVeDBA
As we anticipated, our simulation of ATS from acceler-
ometer data loggers reflected the timing of switches to
relatively high values in the VeDBA time-series (Fig.2).
Over the six hours of data from the reef shark, the ATS
time-series showed a decrease that mirrored decreasing
VeDBA values over the same time span.
Evaluations oftheactivity metric based oncaptive trials
For the captive trials, ATS was a significant predictor of
ActivityObs. e results of our logistic regression model
had an odds ratio greater than one, indicating that as
ATS increases, the switch from resting to not resting will
occur more often than not (e.g., 1.38 times more likely).
Our multinomial model’s results showed that ATS was
a good predictor of multiple activity levels, with the
transitions from both resting to cruising and resting to
Table 2 Animal size, sex, tagging location, deployment duration, and shark characteristics for the seven wild
Carcharhinus plumbeus tagged withPSATs
Lat latitude, Lon longitude, PCL pre-caudal length, TL total length
Tagging Shark characteristics Movement summary
ID Date Lat (DDs) Lon (DDs) Duration
(days) Sex PCL (cm) FL (cm) TL (cm) Distance (km) Speed (km/day)
FL1 2018-07-22 25.64 − 80.09 14 F 160 178 214 582.21 19.42
FL2 2018-07-15 25.80 − 80.08 < 2 F 161 177 217 – –
MD1 2018-09-19 38.22 − 75.03 30 M 105 115 152 241.41 7.91
MD2 2018-09-19 38.23 − 75.12 30 F 120 136 164 318.46 12.80
MD3 2018-09-19 38.22 − 75.12 30 F 122 132 163 789.12 26.05
MD4 2018-09-30 38.38 − 74.10 30 F 103 113 140 832.66 27.24
MD5 2018-10-04 38.25 − 74.08 30 F 105 115 142 260.34 8.59
Fig. 4 Tagging locations, tag release locations, and geolocations for
the two sandbar sharks tagged near Miami (Florida, USA). For shark
FL2, only the location of tagging was available
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quick swimming being significant. Cobias’ variability in
Activityobs likely explains the ability of the model to pre-
dict changes in their activity from ATS; we observed the
fish resting, cruising, and swimming quickly around their
tank, and displaying a significant change in activity dur-
ing feeding events. is suggests that ATS can identify
large changes in variable activity patterns. e detection
of this variability in cobia suggests that ATS could play
a role in detecting differences in activity among individ-
ual sharks, which researchers could relate to life history
characteristics (size, sex, reproductive stage) or environ-
mental conditions.
Our results are in line with other studies. Accelerom-
eters recording at 5Hz sufficed to capture swimming and
resting behaviors in lemon sharks (Negaprion brevirostris
[25]). In sailfish, satellite-transmitted metric of accel-
eration data (the standard deviation of g, where g is the
square root of the sum of acceleration over 3 min) was
successfully used to characterize general activity patterns
[26]. Despite a longer summary period of 3min, versus
the 75s in this study, the authors detected diel periodic-
ity in relative activity levels.
The activity metric inwild deployments
In FL, the short deployment duration enabled a high pro-
portion of data transmission and reception, providing
a detailed look at post-release behavior. For shark FL1,
linking the estimated movement path with the activity
data suggests a relatively low activity for the first 8–9days
of the track. During this time, the shark moved steadily
northwards, followed by periods of higher activity behav-
ior for the remaining 4days of the track (Fig.7a-b) when
the shark remained in a localized area (Fig.5).
In MD, the longer deployments provided a broader
perspective of activity levels, temperature, depth, and
spatial movements. For instance, shark MD1 moved
directly southwest for ~ 6 days after tag deployment,
heading towards the continental shelf (Fig. 5). As the
shark approached the edge of the shelf, there were more
clustered locations for ~ 14days. Next, the shark moved
back to the continental shelf, and then southwards for
the remainder of the 31-day track. ere were three time
periods of sustained higher Mobility and increased ATS
values during the track (Fig. 7a, b): post-release (Sept.
20), once the shark moved off the shelf, and when it con-
ducted a series of deeper dives in a localized area (Oct.
6–7 and Oct. 11–12; Fig.6d). Additionally, MD1 Mobility
values appeared higher at night than during the day prior.
Limitations
e time-series nature of ATS renders it low resolution
when compared with recovering a full archive of accel-
erometer data. As a result, fine-scale behaviors such as
burst acceleration events may be obscured if they occur
on very short timescales. Further, we could not account
for the influence of water flow on tag movement. is
Fig. 5 a Tagging locations, tag release locations, and geolocations for the five sandbar sharks tagged near Ocean City (Maryland, USA), and b
geolocations for shark MD1, with locations colored by date
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Skubeletal. Anim Biotelemetry (2020) 8:34
was most limiting in captive testing, as cobia were
smaller relative to the PSATs, compared with the sand-
bar sharks. Lastly, the results from the captive trials sug-
gest that while this metric is suitable for teleost fish with
variable levels of activity, benthic fish with homogenous
activity levels (e.g., smooth dogfish sharks) may not be
practical candidates.
e 1-s archived values of mobility from the cobia
(Fig.3) suggest some considerations for inference. First,
the summary period for ATS may have a lag effect
because the duration of an activity may not fully occur
within one time-series interval (Fig. 2a). Consequently,
the summary period and time-series interval should be
chosen wisely, ideally using prior knowledge of the study
species. Second, short durations of high-mobility values
did not appear to have a strong effect on ATS for cobia
(Fig.3b). However, the lag effect was not apparent in our
simulation of ATS on archived data from wild deploy-
ments of accelerometer loggers (Fig.2a, c); this may be
due to greater variation in activity levels observed for the
archived data (nurse sharks and gray reef sharks), such
that relatively high-activity was more pronounced for
those species for the cobia.
Lastly, in this study, our ATS-PSATS were limited to
1-month deployments for our choice of tag settings (e.g.,
75-s time-series intervals). For future ATS-PSAT deploy-
ments, developers have extended this recording period to
3months, with the accelerometer now able to sample at
10Hz.
Conclusions
In summary, we tested a novel satellite-transmitted met-
ric of activity in captive and wild settings, to approximate
coarse activity levels in free-ranging aquatic animals. is
metric is intended to measure relative changes in activity
levels over a sufficient length of time to capture variabil-
ity across a range of environmental conditions, which can
transmitted via satellite. is metric is not intended to
replace the high-resolution data collection and analysis
from recoverable devices which permit a more detailed
description of behavior and an absolute measure of activ-
ity level at a specific time point. In captive animals, the
Fig. 6 For shark FL1, a 75-s ATS (activity time-series), b 1-h Mobility, and c depth at 75-s frequency, and d mean mobility over 1 h, for the 30-day
deployment. Blue circles indicate time-series datapoints, and the thick white or black line represents a smoothed time-series using the loess
method at a 5% span. Shaded gray rectangles indicate sunset to sunrise (20:00 to 06:30)
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ATS, recorded as a 75-s time-series of acceleration at
8Hz, was used to predict visually observed behaviors in
cobia, a large teleost fish. Wild deployments in Maryland
and Florida (USA) produced a concurrent time-series
record of activity, temperature, and depth. is suggests
the potential for interpreting relative activity in the con-
text of an animals’ environment. ese data may also be
useful for studying the post-release recovery from fishery
Fig. 7 For shark MD1, a 75-s ATS (activity time-series), b 1-h Mobility, and c depth at 75-s frequency, and d mean mobility over 1 h, for the 30-day
deployment. Blue circles indicate time-series datapoints, and the thick white or black line represents a smoothed time-series using the loess
method at a 5% span. Shaded gray rectangles indicate sunset to sunrise (20:00 to 06:30)
Table 3 Temperature, depth, andactivity time-series (ATS), andmobility trends across all ve sharks, andall sharks
analyzed together, based on75-s transmitted values
SD indicates standard deviation, range indicates minimum to maximum values, and IQR indicates the interquartile range (25–75th percentiles). ATS is not given across
all sharks, as the values are calculated relative to the individual sharks’ mobility measurements within 75s. Mobility is recorded hourly
ID Temperature (°C) Depth (m) ATS Mobility
Mean ± SD Range Mean ± SD Range Mean ± SD Range Mean ± SD Range
FL1 27.02 ± 1.94 12.3–30.9 39.1 ± 30.5 0.5–213 3 ± 6 0–73 35.92 ± 7.01 28–63
FL2 25.45 ± 2.67 15.8–30.2 13.1 ± 21.7 0–142 4 ± 5 0–48 37.90 ± 9.75 31–50
All FL 26.71 ± 2.19 12.3–30.9 34.21 ± 30.77 0–213 – – 37.54 ± 8.98 28–63
MD1 21.77 ± 1.40 10.4–24.8 15.72 ± 7.98 1–82.5 3.78 ± 7.05 0–73 45.24 ± 7.27 35–63
MD2 23.96 ± 0.53 22.2–25.3 8.70 ± 3.75 1–19 3.02 ± 4.86 0–63 60.84 ± 7.78 30–63
MD3 22.11 ± 1.60 13.4–25.8 13.80 ± 11.10 0.5–91.5 4.49 ± 6.41 0–73 49.52 ± 11.56 31–63
MD4 20.63 ± 1.27 11.8–24.1 16.71 ± 13.00 0.5–127 3.10 ± 5.68 0–73 62 ± 2.28 50–63
MD5 24.10 ± 1.05 13.4–26.1 11.20 ± 4.66 1.5–46 5.67 ± 7.60 0–73 40.58 ± 9.74 34–44
All MD 22.54 ± 1.77 10.4–26.1 13.13 ± 9.36 0.5–127.0 – – 51.75 ± 11.8 29–63
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Skubeletal. Anim Biotelemetry (2020) 8:34
interactions over periods of weeks to months, depending
upon tag settings.
We particularly recommend this metric in settings
where researchers cannot feasibly retrieve biolog-
ging devices. e most successful research applications
would target animals that are both relatively large (e.g.,
fish > 1.5m total length) and undergo considerable vari-
ability in activity (e.g., from resting to moving, or from
slow to fast swimming speeds). Although the frequency
of the logged activity metric tested here (75s) is too low
to capture more fine-scale behaviors, we believe this met-
ric is measured at a sufficient frequency (8 Hz) ahead
of filtering to be a proxy for the distribution of general
activity level across time and space. e combination of
ATS, and environmental data over longer periods pro-
vides a unique opportunity for investigating the effects
of temperature on activity, diel activity patterns, activ-
ity patterns near habitat features (e.g., coral reefs ver-
sus pelagic areas), and/or comparisons of high-activity
events among individuals and species. is is the first
transmittable metric of continuous whole-body activ-
ity available on a PSAT-style tag, and our results suggest
that this activity metric could provide another dimension
(relatively high-activity) to studies of long-range aquatic
animal movements.
Supplementary information
Supplementary information accompanies this paper at https ://doi.
org/10.1186/s4031 7-020-00220 -0.
Additional le1: Text. Methodology for the deployment of PSATs on
dogfish sharks (Squalus acanthias). Figure S1. An example of camera out-
put used for activity level description and classification, of captive cobia a
and dogfish b. Figure S2. Frequency distribution of archival values from
captive deployments, for mobility (a, b) and activity time-series (ATS) (c, d),
for all animals of each species. Each panel includes mean, standard devia-
tion, and range. Figure S3. A logistic curve based on the results of our
binary logistic regression for ActivityObs ~ ATS. Teal circles represent obser-
vations of ATS, classified as either resting (0) or not resting (1). TableS1.
Data products generated by the Wildlife Computers miniPAT pop-off
satellite tag (PSAT). TableS2. Estimated deployment durations for the
Wildlife Computers miniPAT pop-off satellite tag (PSAT) incorporating the
ATS metric, depending on the length of the summary period (75 to 600 s).
Percentages represent the probability of receiving 1 message, and a triplet
of messages, sent 10, 20, and 30 times. TableS3. Description of observed
behavior states from video recordings of captive cobia and dogfish. States
were also observed in combination (e.g., quick swimming while rolling/
righting). TableS4. Animal characteristics for captive trials, and temporal
tag and video coverage of fish activity. Tag detachment was based on
visual observation of detachment where possible or estimated from
depth and activity time-series (ATS) records downloaded from tags, as the
point in time where depth and ATS remained constant. Captive dogfish
(Squalus acanthias) are indicated as CD, and captive cobia (Rachycentron
canadum) as CC. TableS5. The number of 1 s labeled visual observations
from video-recording of captive fish behavior, and the proportion of each
behavior (%) with respect to total observations for the species.
Abbreviations
PSAT: Pop-off satellite archival tag; miniPAT: Wildlife Computers-brand
PSAT; ATS: Activity time-series, a count of relatively high-activity events per
time-series interval; Mobility: Raw accelerometer measurements summarized
as the mean standard deviation of the sum of Ax, Ay, and Az; ActivityObs: Visu-
ally observed activity.
Acknowledgements
For their assistance in captive trials, we thank the staff and student volunteers
of the University of Miami Experimental Hatchery, particularly R. Hoenig, J.
Florentino, and S. Mathur, the University of Miami Shark Research and Conser-
vation Program, and the University of New England Department of Marine Sci-
ence. Thank you to Austin Gallagher from Beneath the Waves Inc and his team,
for deploying PSATS on sandbar sharks off Maryland. Thanks to Gaétan Richard
for the initial development of the activity algorithm and providing feedback
on our adaptation for use on a PSAT. We acknowledge that this research was
performed on ancestral Tequesta and Seminole territories.
Authors’ contributions
RS, NH, and YP conceived the ideas and designed methodology for testing
the algorithm; RS, HJV, and NH collected the data; RS analyzed the data and
led the writing of the manuscript, and KW adapted and integrated the ATS
algorithm for use on a PSAT, and assisted with data analysis for captive trials.
All authors contributed critically to the drafts. All authors read and approved
the final manuscript.
Funding
RS is supported by an NSERC PGS-D scholarship from the Government of
Canada, a UM Fellowship from the University of Miami, and a Guy Harvey
Scholarship from Florida Sea Grant and the Guy Harvey Ocean Foundation.
This study was supported by a University of Miami Provost Grant.
Availability of data and materials
The datasets and code produced during the current study are available from
the corresponding author on reasonable request.
Ethics approval and consent to participate
All applicable international, national, and institutional guidelines for the care
and use of animals were followed. All procedures in studies involving animals
were performed following ethical standards of the institution at which the
studies were conducted (the University of Miami Institutional Animal Care and
Use Committee (IACUC), Protocol Numbers [15–238], and the University of
New England IACUC Protocol Number 051518-001).
Consent for publication
Not applicable.
Competing interests
Authors declare no competing interests.
Author details
1 Abess Center for Ecosystem Science and Policy, University of Miami, Coral
Gables, FL, USA. 2 Wildlife Computers, Redmond, WA, USA. 3 Department
of Biological Sciences, Florida International University, North Miami, FL, USA.
4 School of Life Sciences, Arizona State University, Tempe, AZ, USA. 5 School
of Mathematical and Natural Sciences, Arizona State University, Glendale,
AZ, USA. 6 Rosenstiel School of Marine and Atmospheric Science, University
of Miami, Miami, FL, USA.
Received: 26 February 2020 Accepted: 17 October 2020
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