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Abstract Background Atlantic tarpon (Megalops atlanticus) are a highly migratory species ranging along continental and insular coastlines of the Atlantic Ocean. Due to their importance to regional recreational and sport fisheries, research has been focused on large-scale movement patterns of reproductively active adults in areas where they are of high economic value. As a consequence, geographically restricted focus on adults has left significant gaps in our understanding of tarpon biology and their movements, especially for juveniles in remote locations where they are common. Our study focused on small-scale patterns of movement and habitat use of juvenile tarpon using acoustic telemetry in a small bay in St. Thomas, US Virgin Islands. Results Four juvenile tarpon (80–95 cm FL) were tracked from September 2015 to February 2018, while an additional eight juveniles (61–94 cm FL) left the study area within 2 days after tagging and were not included in analysis. Four tarpon had > 78% residency and average activity space of 0.76 km2 (range 0.08–1.17 km2) within Brewers Bay (1.8 km2). Their vertical distribution was
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
Movement patterns ofjuvenile Atlantic
tarpon (Megalops atlanticus) inBrewers Bay, St.
Thomas, U.S. Virgin Islands
Mareike D. Duffing Romero1* , Jordan K. Matley1,2, Jiangang Luo3, Jerald S. Ault3, Simon J. Pittman4 and
Richard S. Nemeth1
Background: Atlantic tarpon (Megalops atlanticus) are a highly migratory species ranging along continental and
insular coastlines of the Atlantic Ocean. Due to their importance to regional recreational and sport fisheries, research
has been focused on large-scale movement patterns of reproductively active adults in areas where they are of high
economic value. As a consequence, geographically restricted focus on adults has left significant gaps in our under-
standing of tarpon biology and their movements, especially for juveniles in remote locations where they are common.
Our study focused on small-scale patterns of movement and habitat use of juvenile tarpon using acoustic telemetry
in a small bay in St. Thomas, US Virgin Islands.
Results: Four juvenile tarpon (80–95 cm FL) were tracked from September 2015 to February 2018, while an addi-
tional eight juveniles (61–94 cm FL) left the study area within 2 days after tagging and were not included in analysis.
Four tarpon had > 78% residency and average activity space of 0.76 km2 (range 0.08–1.17 km2) within Brewers Bay (1.8
km2). Their vertical distribution was < 18 m depth with occasional movements to deeper water. Activity was greater
during day compared to night, with peaks during crepuscular periods. During the day tarpon used different parts of
the bay with consistent overlap around the St. Thomas airport runway and at night tarpon typically remained in a
small shallow lagoon. However, when temperatures in the lagoon exceeded 30 °C, tarpon moved to cooler, deeper
waters outside the lagoon.
Conclusion: Our results, although limited to only four individuals, provide new baseline data on the movement ecol-
ogy of juvenile Atlantic tarpon. We showed that juvenile tarpon had high residency within a small bay and relatively
stable non-overlapping daytime home ranges, except when seasonally abundant food sources were present. Fine-
scale acoustic tracking showed the effects of environmental conditions (i.e., elevated seawater temperature) on tar-
pon movement and habitat use. These observations highlight the need for more extensive studies of juvenile tarpon
across a broader range of their distribution, and compare the similarities and differences in behavior among various
size classes of individuals from small juveniles to reproductively mature adults.
Keywords: Acoustic telemetry, Home range, Vertical movement, Diel movement, Environmental effects
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Tracking the movements and migrations of animals in the
aquatic environment provides insight into spatial and tem-
poral patterns of habitat use, trophic interactions, reproduc-
tive behavior, and behavioral responses to environmental
change [17]. Recent studies have shown that some highly
Open Access
Animal Biotelemetry
1 Center for Marine and Environmental Studies, University of the Virgin
Islands, 2 John Brewers, US Virgin Islands, St. Thomas 00803, USA
Full list of author information is available at the end of the article
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
migratory species can exhibit high site fidelity to discrete
nearshore areas between migratory events, whereas rela-
tively site-attached species can undergo repeated large-scale
migrations for reproduction [1, 810]. Integrating these
variable patterns of large-scale movements and small-scale
activity spaces are becoming increasingly important for
implementing ecosystem-based fisheries management,
understanding connectivity, and designing ecologically rel-
evant marine managed areas [5, 11, 12].
Atlantic tarpon (Megalops atlanticus) is a highly mobile
pelagic species that supports important recreational and
sport fisheries. Tarpon range across coastal areas, estuaries,
and rivers of the western and eastern Atlantic Ocean, the
Caribbean Islands, and the Gulf of Mexico [6, 13, 14]. Tar-
pon spend their larval stage as leptocephali in open ocean,
and as juveniles settle nearshore in tropical and subtropi-
cal estuarine, mangrove and lagoon habitats, where food
resources are high and predator pressures are low [1518].
Adult tarpon range in size from 90–250 cm fork length
(FL) and males reach sexual maturity at about 90cm while
females at 128cm FL [13, 1921]. Much of our knowledge
of tarpon movements and behaviors come from satellite
tracking and conventional anchor tag studies conducted in
Florida, southeast Atlantic, Gulf of Mexico, and the north-
western Caribbean (e.g., Mexico, Belize, Cuba) [6, 7, 13, 14,
22, 23]. ese studies have focused on large-scale move-
ments (> 500km) of large adult tarpon (> 130cm FL) that
support a valuable sport fishery. e focus on adult tarpon
over a limited geographic range leaves large gaps in our
understanding of tarpon biology and movement ecology,
especially in insular areas throughout the eastern Carib-
bean where they are common [13]. We applied acoustic
telemetry to quantify activity space, rates of movement,
vertical distribution and habitat use of juvenile tarpon
across diel and seasonal time scales. Additionally, we exam-
ined how environmental conditions (i.e., water tempera-
ture, dissolved oxygen) influenced their behavior.
Materials andmethods
Study site
Brewers Bay is located on the western end of St. omas,
U.S. Virgin Islands (18°2028N, 64°5840W) and is
bounded by a commercial airport runway and small
lagoon on the south, a sandy beach on the north-east-
ern shore, and a rocky headland and smaller bay (Per-
severance Bay) to the northwest (Fig.1). Brewers Bay is
1.8 km2 in area, ranges in depth between 0 and 33.1 m
(Fig.1), and has steep vertical slopes along the airport
runway and around the rocky headland. e bay is com-
posed of a variety of habitat types including sand, sea-
grass, patch reefs, fringing coral reefs, rocky reefs, and
rubble and reinforced concrete blocks (dolosse) around
the seaward slopes of the airport runway. e lagoon is
mostly soft muddy bottom with scattered rocks and dead
corals. It is partly enclosed by the airport runway with
the remaining shoreline composed of rocky reef or soft
sediments, and red mangroves (Rhizophora mangle).
Acoustic array
e acoustic monitoring system consisted of 45 omnidi-
rectional receivers (VR2W, 69 kHz, Innovasea Systems
Inc. (previously Vemco), Halifax, Nova Scotia, Canada)
that were moored, and spaced equally across Brewers Bay,
including eastern Perseverance Bay, and along the southern
side of the airport runway (Fig.1). Range testing of receiv-
ers [24] across the study site was conducted over four days
in June 2015, by placing receivers in depths ranging from
5 to 19m over different substrate types including shallow
and deep coral/rock and seagrass/sand [25]. Probabilities
of transmission were tested using three A69-1601 Inno-
vaSea transmitters V9-2H (151dB), V13-1H (153dB) and
V16-4H (158 dB) that transmitted every 60 s. Transmit-
ters were attached to mooring lines, connected to cinder
blocks, and suspended 1m above the bottom. A detection
probability of 70% for V13-1H transmitters was selected
providing high coverage throughout the study area with
estimated detection ranges of 101m in seagrass/sand and
120m in coral/rock substrates (Fig.1). Seawater tempera-
ture and dissolved oxygen (DO) were collected at several
stations in Brewers Bay using Hobo temperature loggers
(Onset Computer Corporation, Bourne, MA, USA) and
miniDot DO loggers (Precision Measurement Engineering
Inc, Vista, CA, USA) that were attached to acoustic receiver
moorings. Temperature loggers were deployed in August
2015, DO loggers were deployed in February 2016, and
both recorded data at 15-min intervals (Fig.1).
Fish capture
All capture and tagging methodology in Brewers Bay was
approved by the University of the Virgin Islands Institu-
tional Animal Care and Use Committee (IRB #747807-
1). Juvenile Atlantic tarpon were caught using hook and
line from a boat or dock between September 2015 and
November 2016. As each fish was reeled in, it was guided
alongside the boat or dock and into a floating cradle
constructed of PVC pipe, plastic mesh, and foam noo-
dles for buoyancy. Once in the cradle, the fish was held
under water, turned upside-down to induce tonic-immo-
bility, and the hook was removed from the mouth. Each
fish (n = 14) was measured for fork length (FL) and total
length (TL) to the nearest millimeter (mm). Acoustic
transmitters (either V13 [13mm × 36mm; n = 8] or V13P
[13mm × 46mm; n = 6; pressure tags that provide depth
data], 69kHz, Innovasea Inc, Halifax, NS, Canada) were
surgically implanted into the body cavity on the ventral
side of the fish [26]. e incision was closed with surgical
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
Fig. 1 Map of the Caribbean (a) and the island of St. Thomas in the US Virgin Islands (b) and study site in Brewers Bay (c) depicting bathymetry
and the acoustic array with station number and approximate range of 70% detection probability (circles). Detection ranges varied by habitat (deep
hard bottom = 115 m, deep soft bottom = 120 m, shallow soft and hard bottom = 101 m) based on range testing. Location of environmental
data logger stations shown as green dots (temperature) and red diamonds (dissolved oxygen). Yellow dots represent approximate location where
juvenile Atlantic tarpon were tagged and released.
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
staples and treated with antibacterial ointment (note:
antibacterial ointment is no longer used on incision and
sutures). Fish remained immersed in open seawater the
entire time so no general or local anesthetic was admin-
istered, which allowed for the release of fish shortly after
tagging and data collection were completed. Fish were
turned back over, the head was faced into the current to
increase ventilation, and after a few minutes of recovery,
fish were released at the capture location (Fig.1).
Data processing
Detections were downloaded from receivers every 3
months and analyzed using R Version 3.4.3 [27]. For each
tarpon (n = 14) the total number of detections, first/last
day detected, number of days between first and last day,
and total days detected were calculated. Detections for
each individual tarpon by receiver were plotted through
time to investigate the presence of dropped tags, dead indi-
viduals, and short-term residency. Of the 14 juvenile tar-
pon that were tagged, four (n = 4) individuals had at least
1month of tracking data to conduct spatial home range
analysis. ree of these tarpon were detected for 344–472
days and also had pressure transmitters, thus were used
to analyze monthly and seasonal trends in rates of move-
ment, activity space, and vertical distribution (Table1). Of
the remaining ten tarpon that were excluded from analy-
sis, eight (n = 8) were within the array two days or less and
had insufficient detections for analyses, and two (n = 2)
had either died or shed their tags (Table1).
Temporal data were examined for seasonal and diel pat-
terns. Seasons were defined as spring (March, April, May),
summer (June, July, August), fall (September, October,
November) and winter (December, January, February).
Crepuscular periods were calculated using astronomi-
cal twilight based on daily sunrise/sunsettime charts for
Charlotte Amalie, St. omas, U. S. Virgin Islands [28].
Specifically, dawn was defined as 1h before astronomi-
cal morning and + 1h after sunrise to account for seasonal
changes in daylength. Likewise, dusk was defined as 1h
before astronomical twilight to + 1h after sunset.Day and
night periods were the remaining hours between brack-
eted dawn and dusk, respectively.
Data analysis andstatistics
Residency index was calculated for the four fish used in
analyses by dividing total days each fish was detected
within the Brewers Bay array by number of days between
the first and last detection. Residency Index was defined
as the percentage of days spent within Brewers Bay array
for the duration of time that each fish was tracked.
Center of activity (COA) for juvenile tarpon (n = 4) was
calculated every 30 min using mean position (latitude
and longitude) of all detections during that time step [29].
Distance between COA relocation points and difference
in time between each relocation point were calculated
for each fish using ‘adehabitatLT’ package of R environ-
ment [30]. COA values were used to calculate rate of
Table 1 Summary data for Atlantic tarpon (M. atlanticus) caught and tracked in Brewers Bay acoustic array, including date caught
(mm/dd/yyyy), total length (TL), fork length (FL), total number of detections and total residency time detected in Brewers Bay
P acoustic pressure transmitter measured depth, n/a not applicable
a Fish used for Brewers Bay spatial analyses
b Transient sh not used in spatial analyses
c Fish died or shed tag
Fish ID Tag date TL (cm) FL (cm) Total
detections First day
detected Last day
detected Days between
rst and last
Total days
detected Residency
index (%)
36032a9/17/2015 109 95 12231 9/17/2015 10/19/2015 32 28 88
10980Pa6/17/2016 96 80 54564 6/18/2016 5/28/2017 344 330 96
10979Pa6/26/2016 112 95 106564 6/26/2016 7/1/2017 370 287 78
2966Pa10/25/2016 96 85 395606 10/26/2016 2/10/2018 472 464 99
36034c10/16/2015 90 78 326110 10/16/2015 7/11/2016 269 271 n/a
36036c10/21/2015 130 91.2 2017 10/22/2015 1/25/2016 95 74 n/a
59272b1/12/2016 86.4 76.1 10 1/12/2016 1/13/2016 1 1 n/a
36044b5/24/2016 70 61 5 5/24/2016 5/24/2016 1 1 n/a
36045b6/1/2016 130 91.2 14 6/1/2016 6/1/2016 1 1 n/a
2965Pb8/8/2016 96 85 70 8/9/2016 8/9/2016 1 1 n/a
2964Pb8/14/2016 100 80 11 8/14/2016 8/14/2016 1 1 n/a
36039b8/16/2016 100 94 10 8/16/2016 8/16/2016 1 1 n/a
2963Pb9/15/2016 92 77 660 9/16/2016 9/17/2016 2 2 n/a
24976b10/29/2016 96 83 212 11/8/2016 11/8/2016 1 1 n/a
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
movement (ROM) and activity space for individual fish,
and included minimum convex polygons (100% MCP)
and kernel utilization distributions (50% and 95% KUD).
Activity space, which incorporates MCP, 50% KUD and
95% KUD, was calculated using the ‘move’ and ‘adehabi-
tat’ package in R environment [30, 31]. MCPs provided
information on the extent of an individual’s range or
area used and included all outlying points that might be
the result of exploratory movement or periodic migra-
tion not part of their typical activity. KUDs highlight the
density of positions of an individual within the activity
space based on COAs (i.e., 50% KUD = high density, 95%
KUD = low density), as well as estimated error around
these positions [32, 33]. When necessary, a ‘land’ barrier
polygon was used to clip out the area of MCP and KUD
polygons that fell on land (‘rgeos’ package, [34]). e cal-
culated MCP and KUD (50% and 95%) activity spaces
were plotted in ArcGIS 10.6 for annual, monthly, and diel
periods. To calculate the degree of overlap in 50% and
95% KUD among individuals over diel and monthly time
periods, a home range (HR) percent overlap analyses was
applied using the ‘kerneloverlaphr’ function of the ‘ade-
habitatHR’ package [30, 35, 36]. e HR percent overlap
analyses calculates the proportion of animal a’s home
range that is overlapped by animal b’s home range [30, 35,
36]. e data output matrix provides indices of overlap
for all pairs of animals [35, 36]. Using the matrix output,
average and ranges in fish overlap values were calculated.
Repeated measures analyses of variance (RM-ANOVA)
was used to test for differences in KUD across monthly
and diel periods. All monthly analyses used data from
three (n = 3) tarpon that had average KUD activity space
representing each month (Table 1). Individual tarpon
were treated as random variables, and either monthly
or diel periods were treated for autocorrelation effects
(‘corAR1’) using the ‘lme’ function of the ‘nlme’ package
for R [37, 38]. To assess relationship between monthly
ROM and 50% KUD size, a linear regression was applied.
Rate of movement (ROM, m/s) was calculated by divid-
ing the distance between consecutive COA position val-
ues by the time difference between these consecutive
points. Kruskal–Wallis and Tukey post hoc tests were
used to test for differences in ROM between diel periods
and a two-way ANOVA was used to test for differences in
diel ROM across seasons. ROM provides a useful metric
for fish activity during diel periods and can also be used
as a proxy for feeding behavior [47].
Vertical distribution was calculated for tarpon tagged
with depth-enabled transmitters (n = 3, Table 1). Depth
measurements were binned into hourly and monthly
periods and boxplots were applied to elucidate their ver-
tical movement patterns. ANOVA and Tukey post hoc
tests were used to test for differences in vertical move-
ment across diel and monthly periods.
Environmental conditions and their relationship to
tarpon movement and habitat use were assessed for sea-
water temperature and DO. Daily average number of
detections, average temperature and average DO within
the lagoon and waters along the airport runway were
analyzed by applying a linear regression for the study
period (September 2015—February 2018).
Fourteen (n = 14) juvenile tarpon were captured and
acoustically tagged in Brewers Bay (average FL 83.7 cm,
range 61–95cm; Table1). Only four (n = 4) juvenile tarpon
provided a sufficient number of detections over a sufficient
duration (32–472days), and a residency index of 78–100%,
to be included in our spatial analysis (Table1). Eight (n = 8)
fish were detected for less than a week and had fewer than
1000 detections and upon assessment, it was determined
that the two remaining fish detected within the bay had
died or shed their tags within one day following release.
Activity space
e activity space of juvenile tarpon varied among indi-
viduals and through time. e average MCP for juve-
nile tarpon (n = 4) was 0.97 km2 (range 0.77–1.17 km2),
while the average 95% and 50% KUD was 0.76 km2 (range
0.49–0.99 km2) and 0.13 km2 (range 0.08–0.20 km2),
respectively (Table2; Fig.2). Comparison of mean day,
Table 2 Calculated home range size (km2) for each tarpon based on 50% and 95% Kernel utilization distribution (KUD) and 100%
minimum convex polygon (MCP); number of center of activity (COA) points that fell on land and total percentage of COA points on
land removed out of total COA points used for home range analyses
a In this case 100% MCP is smaller than 95% KUD based on how they are calculated (see “Materials and methods”)
Fish ID Total COA points MCP 100% area
(km2)KUD 95% area
(km2)KUD 50% area
(km2)COA points on
land Percentage
of COA points
2966 19,367 1.174 0.988 0.200 123 0.64
10979 11,888 1.055 0.619 0.075 163 1.37
10980 10,425 0.864 0.492 0.090 166 1.59
36032 887 0.767a0.938 0.149 7 0.79
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
Fig. 2 Activity space of juvenile tarpon (n = 4) based on yearlong 100 % MCP (black line in left panels), and 50% and 95% KUD for day (yellow/
orange left panels), night (blue/green right panels), and crepuscular (dawn = red, dusk = blue middle panels) time periods. The arrow represents an
example of a corridor between day and night activity space. See Fig. 1 for labels.
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
night and crepuscular activity spaces among juvenile tar-
pon for both 50% and 95% KUDs were not significantly
different across diel and crepuscular periods (50% KUD:
p = 0.07; 95% KUD: p = 0.44) and across months (50%
KUD: p = 0.78; 95% KUD: p = 0.29).
Analysis of daytime activity space overlap averaged
12% for 50% KUD and 42% for 95% KUD during the year
(Table3), with each tarpon showing distinct 50% KUD
core areas centered around northwest corner of runway
(ID#36032), around Black Point and deeper part of Brew-
ers Bay (ID#2966), in and around the lagoon and Range
Cay extending to shallow and deep parts of Brewers Bay
(ID#10979), and around the tip of runway (ID#10980)
(Fig.2, day). In April, however, overlap for 50% and 95%
KUD during daytime showed an increase to 20% and
63%, respectively (Table3). Excluding the month of April,
daytime 50% and 95% KUD overlap values declined from
12 to 2% and 42% to 23%, respectively (Table3). At night-
time, 50% KUD areas were centered in shallow Brewers
Bay, around the airport runway and particularly inside
the shallow lagoon, where juvenile tarpon went at night
(Fig. 2, night). Consistent use of these areas at night
tended to increase nighttime 50% and 95% KUD overlap
relative to daytime, except for April, when space overlap
decreased at night (Table3).
Rate ofmovement
Average ROM of juvenile tarpon was 0.07 m/s
(± 0.02 SD) and was significantly different among
diel periods (H = 12.4, P < 0.006). Post hoc com-
parisons between day (mean = 0.06 m/s ± 0.01
SD), night (mean = 0.05 m/s ± 0.01 SD),
dawn (mean = 0.09 m/s ± 0.01 SD) and dusk
(mean = 0.10 m/s ± 0.01 SD) showed a significant differ-
ence between dusk and nighttime periods only (Tukey
HSD: P < 0.01). Diel ROM also varied across seasons
(2-way ANOVA: F1,15 = 253.2, P < 0.0001). Most not able,
daytime ROM was significantly lower in winter com-
pared to other seasons (Tukey HSD: P < 0.001). During
all seasons, crepuscular ROM peaked between 04:00 to
05:00 and at 18:00 (Fig.3a). ROM was not significantly
different across months (mean = 0.07 m/s ± 0.01 SD), but
there was a strong relationship between monthly ROM
and 50% KUD (F = 34.07, P = 0.0001, R2 = 0.77) with the
highest rates for both metrics during the months of April,
June and September (Fig.3b).
Vertical movement
Vertical movement of juvenile tarpon with pressure trans-
mitters (n = 3) varied among time of the day (ANOVA:
F1,3 = 36,526, P < 0.0001) (Fig.4). Tarpon used more of the
water column during the day ranging between 2 to 13m
average depth and 16 to 27m maximum depth (Fig.4).
At night, tarpon stayed in shallower waters ranging from
0 to 5m average depth and 8 to 14m maximum depth
(Fig. 4; Additional file 1: Table S1). Nighttime vertical
movements were partly constrained when tarpon were in
lagoon (maximum depth 4m, Fig.2). During dawn and
dusk, average depth of tarpon ranged between 0 to 8m
(Fig.4; Additional file1: TableS1). Vertical distribution
across months showed no consistent patterns among the
three tarpon with depth transmitters.
Movement andenvironmental variability
Water temperature in Brewers Bay ranged from 25–28°C
in winter to 29–32 °C in late summer and early fall.
Inside the lagoon water temperature showed greater
fluctuations on a daily basis and had a greater range
(mean = 28.3°C ± 1.27 SD, range = 24.8–32.0°C) than in
the bay (mean = 28.1 °C ± 1.15 SD, range = 25.6–30.6 °C)
(Fig.5). Water temperature had a strong effect on tar-
pon movement and habitat use. We found a significant
negative relationship between number of tarpon detec-
tions and temperature in the lagoon at night (adjusted
R2 = 0.0.32; P < 0.001), but no relationship between fre-
quency of detections in the lagoon or around the runway
at other times of day (Fig.6). Juvenile tarpon were present
in the lagoon at night when temperature ranged between
26 and 28°C; however, once temperature reached 29°C
frequency of tarpon detections decreased rapidly and
Table 3 Summary of 50% and 95% Kernel utilization distribution (KUD) overlap for juvenile tarpon (n = 3) during day and night for full
year, May to March (April excluded) and only April
Month(s) Diel period KUD 50% area ± SE
(km2)KUD 50% area range
(km2) (%) KUD 95% area ± SE
(km2)KUD 95% area
range (km2)
All year Day 12% ± 6% 0–27 42% ± 12% 11–72
All year Night 20% ± 18% 0–99 51% ± 8% 28–99
May–March Day 2% ± 2% 0–6 23% ± 14% 8–55
May–March Night 19% ± 18% 0–99 44% ± 10% 14–99
April Day 20% ± 16% 1–55 63% ± 4% 42–74
April Night 10% ± 9% 0–32 33% ± 19% 11–81
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
stopped at about 30.5°C (Fig.6), indicating tarpon left
the lagoon. Water temperatures in the lagoon reached or
exceeded 30.5°C on 59 day of the study period compared
to only 4 days at the airport runway. Likewise, water tem-
peratures 26°C colder were recorded on 61 day in the
lagoon but only on 16 day along runway. At times of high
lagoon temperatures, juvenile tarpon left the lagoon and
had higher frequency of detections at night along the tip
and south side of the airport runway (i.e., stations 248,
249, 285, 251, 282; Fig. 1), where nighttime maximum
water temperatures were cooler (Figs.5, 6a). When water
temperatures in lagoon cooled to below 30.5°C, juvenile
tarpon returned to resting in lagoon at night (Fig.5).
Similar to water temperature, dissolved oxygen concen-
trations in the lagoon varied widely from 0.9 to 7.1mg/L
(mean = 4.7 ± 1.89 SD), but were more stable along
the airport runway (mean = 6.1 ± 1.9 SD, range 5.3–
6.6mg/L) (Fig.5). Based on detection frequencies, there
Fig. 3 a Diel ROM of juvenile tarpon by season of the year: Spring (March, April, May), Summer (June, July, August), Fall (September, October,
November) and Winter (December, January, February). b Relationship between average ROM and core activity space 50% KUD for four juvenile
tarpon during each month
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
was no significant relationship in number of detections
of tarpon at different levels of dissolved oxygen within
the lagoon or the runway (Fig.6b), indicating that tarpon
seemed to tolerate the low oxygen levels in the lagoon,
especially at night
To our knowledge, this study provided some of the first
data on small-scale three-dimensional movement pat-
terns of juvenile Atlantic tarpon (n = 4) by way of pas-
sive acoustic telemetry. e data can serve as a baseline
for juvenile tarpon movement ecology that can further
be examined and use for comparison to adult move-
ments or other regions [6, 22]. Although most juvenile
tarpon (n = 8) left the bay shortly after tagging and their
fate remained unknown, and two fish likely died or shed
their tags, the remaining four fish provided useful data
on the movement ecology of juvenile tarpon. Juvenile
tarpon were resident within the bay 78% to 99% of time,
but some transient behavior was observed for two of the
larger individuals (i.e., both were 95cm FL). One tarpon
(ID #10979) left the bay for nearly 2 months (October
and November) before returning to its home range
for another 7 months. e second tarpon (ID#3032)
remained within Brewers Bay for 1month before depart-
ing mid-October, but it was then detected at an acoustic
array 12km offshore in January. Interestingly, both tar-
pon departed in October when water temperature was
high. Seasonal movements, such as these, by Atlantic
tarpon and other coastal species have been attributed to
food availability, reproductive maturity (spawning aggre-
gations) and changes in environmental conditions (i.e.,
temperature, dissolved oxygen) [6, 13, 15, 16, 3941].
We found that juvenile tarpon had distinct daytime
50% KUDs, and core areas (0.07–0.20 km2) within
Brewers Bay that overlapped very little with the other
individuals for most of the year (< 2%). At night, tarpon
tended to move into or near a small, shallow lagoon in
Brewers Bay, which resulted in an increase in overlap
of 50% KUDs during most months. e spatial pat-
terns displayed by juvenile tarpon suggest habitat par-
titioning during daytime and sheltering and protection
from predation in a common area at night [6, 13, 17,
18, 42]. During April, however, daytime overlap in 50%
KUD area showed a tenfold increase, as they shifted
Fig. 4 Boxplot of daily distributions by hour of vertical movement of three juvenile tarpon during diel (day, night) and crepuscular (dawn, dusk)
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
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DungRomeroetal. Anim Biotelemetry (2021) 9:16
their activity space to similar areas within Brewers Bay.
ese changes in behavior and activity space coincided
with the arrival of schools of bait fish as well as nesting
seabirds that feed on these schools in the spring [43,
44]. When seabirds were present, we observed groups
of tarpon foraging on bait fish near the surface during
the spring months primarily in the middle of Brewers
Bay and near Black Point reef (Duffing Romero, M. and
Nemeth, R.S., pers. observations). is feeding strategy
is not uncommon for tarpon and other pelagic preda-
tors, which can increase their foraging success in the
presence of seabirds feeding on bait fish at the water
surface [15, 43, 45]. e areas of Brewers Bay where this
feeding behavior was observed corresponded to April
daytime activity space of tagged tarpon.
Adult tarpon tend to feed at sunset and continue
feeding into the night if there is enough food and avail-
able light for foraging [15, 46]. As with other species
[47], ROM was assessed as a proxy for feeding. Simi-
lar to adult tarpon, juveniles had the highest rates of
movement during dawn and dusk, which suggests
high feeding rates during crepuscular periods. How-
ever, this behavior may also indicate rapid movements
along migration pathways between nighttime and day-
time activity spaces [18, 4854]. ROM was significantly
slower at night than other time periods, which suggests
that juvenile tarpon were not feeding at this time. Fur-
ther research with improved experimental design will
help to distinguish differences between adult and juve-
nile behavioral states such as resting, foraging or trave-
ling [55].
Juvenile tarpon generally stayed less than 10 m
depth, but occasionally went to 25m or deeper, which
is also typical for adult tarpon [6, 13]. Many coastal
and pelagic fish, such as barracuda (Sphyraena barra-
cuda), white marlin (Kajikia albida), dolphinfish (Cory-
phaena. hippurus) and many species of tuna (unnus
spp) show similar vertical movement patterns, where
they spend the majority of time at shallow depths or
close to the surface and then make diel/seasonal deep
water movements [5658]. Adult tarpon show a variety
of vertical distributions that fall into four typical pat-
terns: (1) clear diel pattern shallow in day and deep at
night; (2) deep in day and shallow at night; (3) deep and
shallow at irregular intervals throughout diel period,
and (4) random vertical movements throughout diel
period [6]. Juvenile tarpon in Brewers Bay showed a
consistent diel vertical movement pattern that matched
pattern (2) where fish stayed shallow at night and
deeper during the day. At smaller sizes juvenile tarpon
Fig. 5 Water temperature (°C) and dissolved oxygen (mg/L) profiles for two locations in Brewers Bay: lagoon and airport runway (see Fig. 1) from
February 1 to December 31, 2016. Red horizontal line indicated 30.5 °C temperature threshold in lagoon. Green (lagoon) and black (airport runway)
horizontal line indicates location of juvenile tarpon (ID# 10979 and 10980) at night
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 11 of 14
DungRomeroetal. Anim Biotelemetry (2021) 9:16
Fig. 6 Day and night relationships between average number of tarpon detections and a water temperature (°C) and b dissolved oxygen (mg/L)
within the lagoon and along the airport runway
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Page 12 of 14
DungRomeroetal. Anim Biotelemetry (2021) 9:16
may select shallow, sheltered lagoon-type habitats, if
available, as a strategy against predation [14, 15]. In this
study, all four tarpon used the Brewers Bay lagoon con-
sistently throughout most of the year.
Environmental conditions influenced tarpon behavior
in Brewers Bay. Tarpon prefer water temperatures from
24 to 26°C in spring and fall and 28–30°C in summer
[6, 13, 23]. We found that juvenile tarpon avoided water
temperatures greater than 30 °C. For instance, tarpon
detection frequencies within the lagoon decreased at
temperatures above 29°C and they did not enter nor rest
in the lagoon at night when water temperature was higher
than 30.5°C, but instead moved to deeper water on the
south side of airport runway (Fig. 5). At this threshold
temperature, tarpon faced a trade-off of remaining in
higher temperatures within the protected lagoon or leav-
ing the lagoon for cooler, less protected waters around
the airport runway at night. Previous studies on barra-
cuda and bonefish (Albula vulpes) have shown that both
species move to deeper waters away from their home
range to avoid seasonal weather patterns and associ-
ated temperature fluctuations [15, 39, 59]. Adult tarpon
in Florida migrated farther northward on a daily basis as
sea surface temperatures increased and seemed to track
the 26°C isotherm from the Florida Keys to the south-
ern coast of Virginia from May to July, respectively [6].
Despite the effect of high water temperatures on tarpon
behavior, tarpon tolerated low dissolved oxygen concen-
trations in the lagoon, which is attributed to being facul-
tative air-breathers [13, 60].
To our knowledge, this acoustic telemetry study provided
some of the first information on juvenile tarpon move-
ment ecology including home range size, rates of move-
ment, vertical distribution, and habitat partitioning.
Although limited to only four fish, our results showed
high residency within a small bay and relatively stable
non-overlapping daytime home ranges, except when sea-
sonally abundant food sources were present. Fine-scale
acoustic tracking over multiple years showed the effects
of changing environmental conditions on juvenile tarpon
movement and habitat use. ese baseline observations
highlight the need for more extensive studies of juvenile
tarpon across a broader range of their distribution. In
addition to a larger sample size, we suggest including a
wider range of tarpon size classes, from small juveniles to
large reproductive adults, in future studies. Since tarpon
are highly mobile but also show resident behavior [6, 7,
13, 40], it is difficult to assess their larger-scale movement
patterns using an acoustic array limited to one bay. A bet-
ter approach, to facilitate tracking tarpon movements
over a broader geographic range, would be to tag tarpon
with both acoustic and satellite tags and place additional
receivers along the coastlines or use a regional network
within and among neighboring islands [9, 40, 61, 62].
COA: Center of activity; MCP: Minimum convex polygon; KUD: Kernel utiliza-
tion distribution; ROM: Rate of movement; HR: Home range.
Supplementary Information
The online version contains supplementary material available at https:// doi.
org/ 10. 1186/ s40317- 021- 00239-x.
Additional le1: TableS1. Summary data for vertical distribution (m) of
juvenile tarpon from box plot analysis.
We want to thank Damon Bo Green and Tyler S. Best for assisting in the field
to catch and tag many of the tarpon in this study. We thank Jonathan Jossart
for conducting detection range tests and assisting in the maintenance of the
Brewers Bay acoustic array in the beginning of the project. We thank master’s
students in Marine and Environmental Studies who helped download acoustic
receiver data in the field. We also want to thank the Center of Marine and
Environmental Science at the University of the Virgin Islands for providing the
facilities to complete this project. This is contribution # 212 of the University of
the Virgin Islands, Center for Marine and Environmental Studies.
Authors’ contributions
MDDR conducted most of the field work, data analyses/interpretation and
writing; JKM contributed to data management/analyses; RSN secured funding
for project and contributed to field work; JL, SJP, JKM, JSA and RSN contrib-
uted to data interpretation and writing of manuscript. All authors read and
approved the final manuscript.
Funding for this research was supported by VI-Established Program to Stimu-
late Competitive Research (VI-EPSCoR) through the NSF Grant #1355437.
Availability of data and materials
The datasets used and/or analyzed during the current study are available from
the corresponding author on reasonable request.
Ethics approval and consent to participate
All capture and tagging methodology on all fish in Brewers Bay was approved
by the University of the Virgin Islands Institutional Animal Care and Use Com-
mittee (IRB #747807-1).
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Center for Marine and Environmental Studies, University of the Virgin Islands,
2 John Brewers, US Virgin Islands, St. Thomas 00803, USA. 2 Great Lakes Institute
for Environmental Research, University of Windsor, 2990 Riverside Dr. W, Wind-
sor, ON N9C 1A2, Canada. 3 Department of Marine Ecosystems and Society,
University of Miami, 4600 Rickenbacker Causeway, Miami, FL 33149, USA.
4 Marine Conservation Research Group, School of Biological and Marine Sci-
ences, Marine Building, University of Plymouth, Plymouth PL4 8AA, UK.
Received: 24 July 2020 Accepted: 7 April 2021
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Page 13 of 14
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... With some subadults traveling hundreds of kilometers, our results highlight individual-level differences in subadult residence and dispersal behaviors. While the extent was not captured and individuals were <100 cm FL, Romero et al. (2021) Luo et al. 2020), our data provided no evidence for year-round adult residents in the Florida Keys. Overall, across all individuals and at the population level, our models that examined arrival/departures on a regular basis (rather than their first and last seasonal occurrence) indicated that M. atlanticus arrival/departure timing and occupancy were closely connected to time of year (month) and SST. ...
Atlantic tarpon Megalops atlanticus are important mesopredators in the western Atlantic Ocean, and the focus of a popular recreational fishery that targets them throughout their annual migration in the Gulf of Mexico and southeastern USA. Using 4 years of acoustic telemetry data, we quantified the seasonal variation in phenology of arrival and departure, and occupancy for subadult and adult M. atlanticus in the Florida Keys, USA. While detection profiles of subadult M. atlanticus (n = 11) varied in residency and dispersal patterns, all adult M. atlanticus detection profiles (n = 47) exhibited seasonal residency. The median spring−summer residence period of adult M. atlanticus ranged from 40 to 60 d, with a mean of 51 d across years. At the individual level, repeatability in the timing of arrival and duration were high across years, suggesting that photoperiod may be an important migratory cue. Further, the repeatability in the timing of arrival to the Florida Keys for individuals was not associated with sea surface temperature (SST). At the population level, residency corresponded with the spawning season, with the majority of adult M. atlanticus arriving in April once SST reached 26°C, and then departing in June (27−29°C). Highest occupancy probabilities for adult M. atlanticus occurred in May (26−28°C) and lowest between August and October. Large aggregations of M. atlanticus that occur during the spawning season (April−June) are potentially vulnerable to the effects of habitat degradation and angling related mortality and behavioral changes. These data on M. atlanticus phenology provide insights for implementing science-based strategic management plans.
... To assess Atlantic salmon smolt space use in Loch Lomond, we calculated their core (50%) and extended (90%) home ranges (kernel utilization distribution (KUDs)) using the adehabitathr package (Calenge 2006). A land barrier polygon was used to remove any portions of the KUDs that would overlap with land (Duffing Romero et al. 2021). In addition, trajectories of smolts were overlaid on a map of Loch Lomond using the plot.ltraj ...
Full-text available
The Atlantic salmon, Salmo salar Linnaeus 1758, is a charismatic, anadromous species that has faced dramatic declines throughout its range. There is currently a lack of information on the effect of free-standing bodies of water on a key life event, sea migration, for the species. This study extends our understanding in this area by combining acoustic telemetry with a correlated random walk model to try to examine potential morphological and behavioural factors that differentiate successful from unsuccessful migrants through Scotland’s largest lake. Consistent with other studies, we found that smolts experienced a high rate of mortality in the lake (~ 43%), with approximately 14% potentially predated upon by birds and 4% by Northern pike. Migration speed in the lake was slow (the mean minimum movement speed between centres of activity was 0.13 m/s), and pathways frequently deviated away from the outlet river. There was no evidence of a morphological or behavioural trait or migratory pathway that distinguished successful from unsuccessful smolts. This suggests that migration movement direction in the main body of Loch Lomond appeared to be random. This was further supported by the output of a correlated random walk model which closely resembled the pathway and migration speed and distance patterns displayed by successful migrants. However, once successful smolts came within ~2 km of the lake exit, a high proportion remained in this region prior to entering the River Leven. We suggest that this “goldilocks zone” is where directional cues become apparent to migrating fish. Future studies should combine random walk models with environmental variables to determine if external factors are driving the apparently random movement patterns exhibited by smolts in lakes.
Full-text available
Understanding large‐scale migratory behaviours, local movement patterns and population connectivity are critical to determining the natural processes and anthropogenic stressors that influence population dynamics and for developing effective conservation plans. Atlantic tarpon occur over a broad geographic range in the Atlantic Ocean where they support valuable subsistence, commercial and recreational fisheries. From 2001 through 2018, we deployed 292 satellite telemetry tags on Atlantic tarpon in coastal waters off three continents to document: (a) seasonal migrations and regional population connectivity; (b) freshwater and estuarine habitat utilization; (c) spawning locations; and (d) shark predation across the south‐eastern United States, Gulf of Mexico and northern Caribbean Sea. These results showed that some mature tarpon make long seasonal migrations over thousands of kilometres crossing state and national jurisdictional borders. Others showed more local movements and habitat use. The tag data also revealed potential spawning locations consistent with those inferred in other studies from observations of early life stage tarpon leptocephalus larvae. Our analyses indicated that shark predation mortality on released tarpon is higher than previously estimated, especially at ocean passes, river mouths and inlets to bays. To date, there has been no formal stock assessment of Atlantic tarpon, and regional fishery management plans do not exist. Our findings will provide critical input to these important efforts and assist the multinational community in the development of a stock‐wide management information system to support informed decision‐making for sustaining Atlantic tarpon fisheries.
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Extreme weather events (e.g., cyclones, floods, droughts) are capable of changing ecosystems and altering how animals obtain resources. Understanding the behavioural responses of animals being impacted by these natural events can help initiate and ameliorate conservation or management programs. This study investigated short- and long-term space-use of the critically endangered hawksbill sea turtle (Eretmochelys imbricata), as well as five species of fishes and stingrays, in response to two of the most destructive Caribbean hurricanes in known history – Irma and Maria, which were at their peak intensity when they passed the US Virgin Islands in September of 2017. Using passive acoustic telemetry in St. Thomas, US Virgin Islands, we show a variety of short-term behavioural patterns initiated across species to reduce exposure to the strong environmental conditions, such as moving to deeper habitats within the study area. Although short-term expansion of activity space was evident for several sea turtles, long-term impacts on space-use and body condition were limited. In contrast, southern stingrays (Hypanus americanus) left the study area shortly after the hurricanes, suggesting vulnerability stemming from altered habitat, prey availability, or temperature/oxygen profiles. This study shows the strong spatial resilience of several nearshore species despite exposure to two consecutive category 5 hurricanes.
Full-text available
Marine protected areas (MPA) that are created opportunistically must be evaluated in an ecological context to ensure that conservation goals and societal expectations are achievable. This study used acoustic telemetry to investigate movements of reef fish relative to the boundary of the Virgin Islands Coral Reef National Monument (VICRNM) in Coral Bay, U.S. Virgin Islands. Although created to enhance ecosystem protection, VICRNM boundaries were established purely on the basis of adjacency to public versus privately owned lands. Transmitters were implanted into a diversity of reef fish species representative of the local community whose movements were logged for one year on an array of acoustic-receivers that were positioned within, outside, and along the MPA boundary. Results indicate that the boundary has coincidentally aligned with a deep sandy area that does not cross through continuous reef or mangrove habitat. This acted as a natural barrier to movements of species such as Lutjanus griseus, Epinephelus guttatus, Cephalopholus cruentatus, Holocentrus rufus, and Sparisoma aurofrenatum. Other species were more mobile and were routinely detected outside VICRNM, especially at night, such as L. synagris, Haemulon plumierii, and H. sciurus. In addition to fish movements in relation to the VICRNM boundary, network analysis revealed several hotspots of concentrated fish activity including a reef promontory and bay mouths. Investment in enforcement of existing regulations to protect fish is warranted to realize the full potential of this MPA. Using these types of data, management actions in this and other MPAs can be focused on those species and locations that would experience the greatest benefit.
Full-text available
Within oligotrophic ecosystems, resource limitations coupled with interspecific variation in morphology, physiology, and life history traits may lead to niche partitioning among species. How generalist predators partition resources and their mechanisms, however, remain unclear across many ecosystems. We quantified niche partitioning among upper trophic level coastal and estuarine species: American alligators (Alligator mississippiensis), bull sharks (Carcharhinus leucas), common bottlenose dolphins (Tursiops truncatus), common snook (Centropomus undecimalis), and Atlantic tarpon (Megalops atlanticus) in the Shark River Estuary of the Florida Coastal Everglades, USA using acoustic telemetry, stable isotope analysis, and visual surveys, combined with published diet and life history demographic information. Spatial and isotopic niche overlap occurred among most species, with variability in partitioning among interspecific interactions. However, seasonal variability in habitat use, movements patterns, and trophic interactions may promote coexistence within this resource-limited estuary. Beyond guild-level niche partitioning, predators within the Shark River Estuary also exhibit partitioning within species through individual specializations and divergent phenotypes, which may lead to intraspecific variability in niche overlap with other predators. Niche differentiation expressed across multiple organizational levels (i.e., populations and communities) coupled with behavioral plasticity among predators in oligotrophic ecosystems may promote high species diversity despite resource limitations, which may be important when species respond to natural and human-driven environmental change.
Full-text available
Dolphinfish (Coryphaena hippurus), large pelagic predators and important fishery targets, frequently associate with floating debris or manmade fish aggregating devices (FADs). We tagged 8 dolphinfish with pressure-sensitive ultrasonic transmitters and actively tracked individuals continuously for up to 40 h to elucidate the vertical movement patterns and differences between FADassociated (FAD-A) and FAD-unassociated (FAD-U) fish. Four additional fish were equipped with acoustic transmitters and passively monitored for several days with receivers attached to FADs. When not associated with FADs, dolphinfish used the upper 75-100 m of the water column during the day and made descents up to 160 m during the night. In contrast, FAD-A fish generally stayed within the upper 10 m of the water column and tended to make deeper excursions during the day rather than at night. Water temperature data from expendable bathythermographs deployed during active tracking showed that fish only descended to depths where temperatures were ≤3°C cooler than the uniform-temperature surface layer. The use of vertical behavior to determine whether a dolphinfish is associated or not with a floating object opens the possibility for new, large-scale research aimed at investigating the role of floating objects in the ecosystem inhabited by this species and at assessing the impacts of FADs on its ecology. © 2016, National Marine Fisheries Service. All rights reserved.
Full-text available
Spawning aggregations of reef fish tend to be predictable in time and space. The extent of movement, residence time and seasonality of the aggregation can be difficult to determine, but are important for effective management. We utilized acoustic transmitters and a receiver array to track dog snapper Lutjanus jocu and Cubera snapper Lutjanus cyanopterus within a multi-species spawning aggregation site at the Grammanik Bank in the US Virgin Islands from June 2014 to September 2015. Acoustic detections showed that both species utilized spawning areas of 1.4 to 1.5 km2, centered at the shelf promontory. The aggregation area of L. cyanopterus was situated along the shelf edge; the L. jocu aggregation may have been displaced by L. cyanopterus as it occupied some of the inner shelf as well. Receivers along the shelf edge recorded the longest residence times during the hours of spawning (16:45 to 20:00 h), suggesting this is likely a spawning site for both species. L. cyanopterus aggregated monthly from May through November, with residence time peaking in August. L. jocu aggregated monthly throughout the year and residence time did not vary significantly by month. Each month, detections increased in the week before and the first week after the full moon, but then decreased to zero by the third week after the full moon. This study outlines the spatial and temporal dimensions of the spawning aggregation, which can be applied to the management and development of protected areas.
Understanding the nature of migratory behaviors within animal populations is critical to develop and refine conservation and management plans. However, tracking migratory marine animals across life stages and over multiple years is inherently difficult to achieve, especially for highly migratory species. In this paper, we explore the use of acoustic telemetry to characterize the spatial ecology of Atlantic tarpon (Megalops atlanticus), elucidate the ecology of this poorly studied species, and ultimately inform conservation and management. Using the data from twenty-two acoustically tagged Atlantic tarpon, we found a diversity of tarpon migratory patterns, including spatial and temporal overlap for some individuals. We also reveal fine scale movements within specific ecosystems, as well as a range of distributions and connectivity across coastal waters of the southeastern United States of America. For tarpon with tracking durations greater than one month (n = 13), we found heterogeneous space use and migratory connectivity with some tarpon remaining close to their capture location while others migrated hundreds of kilometers. In addition, we were able to identify a northern and southern limit for one migratory tarpon that had detections spanning over 365 days. We share analyses on Atlantic tarpon data, including model-driven approaches and network analysis, to investigate movement strategies and space use, which may be pertinent to other studies involving highly migratory species. The project was a collaborative effort involving several acoustic telemetry networks which enabled the monitoring of broad- and fine-scale movements for extended periods of time that would normally be difficult to achieve with other monitoring techniques. Although challenges exist with applying acoustic telemetry to monitor highly migratory species, we also discuss its value in enabling researchers to assess movements and space use beyond the focal species, such as cross-ecosystem comparisons and multi-species interactions.
Te Atlantic tarpon (Megalops atlanticus Valenciennes, 1847) is a visually-guided marine predator that may be active at any time in the 24-hr light-dark cycle despite dramatic changes in light intensity over the course of the day. To test the hypothesis that retinal sensitivity changes with time of day in M. atlanticus, possibly under the influence of a circadian clock, populations of fsh (three populations of 6 fsh per lighting treatment for two treatments = 36 fsh total) were exposed to different lighting treatments and sensitivity of the retina was measured periodically by electroretinography (ERG). Te intensity of light required to elicit a half-maximal ERG response was signifcantly greater during the day than the night in fsh held in 12L:12D light-dark cycles (LD). To determine whether this cycle of retinal sensitivity is driven by an internal timekeeping mechanism, ERG was performed at 4-hr intervals over the course of 24 hrs in constant darkness (DD). Sensitivity was signifcantly higher during subjective night than during subjective day, though the rhythm in DD was damped relative to the cycle in LD. Tese results show that retinal sensitivity is much higher at night than during the day in a light-dark cycle, and that this cycle of retinal sensitivity is driven at least in part by an internal biological clock. Such endogenous timekeeping mechanisms enhance survivability by allowing organisms to anticipate change in their external environments. Rhythms of retinal sensitivity are likely important for survival by supporting prey capture, predator avoidance, and reproduction, but they may be disrupted by abnormally-timed exposure to light, including artifcial light at night. © 2017 Rosenstiel School of Marine & Atmospheric Science of the University of Miami.