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This article was downloaded by: [71.46.64.222]On: 02 November 2014, At: 08:36Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registered office: Mortimer House,37-41 Mortimer Street, London W1T 3JH, UK
Marine and Coastal Fisheries: Dynamics, Management,and Ecosystem SciencePublication details, including instructions for authors and subscription information:http://www.tandfonline.com/loi/umcf20
Vertical and Horizontal Movements of Yellowfin Tuna inthe Gulf of MexicoJ. P. Hoolihana, R. J. D. Wellsb, J. Luoc, B. Faltermand, E. D. Princee & J. R. Rookerb
a Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marineand Atmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, Florida33149, USAb Department of Marine Biology, Texas A&M University–Galveston, 1001 Texas Clipper Road,Galveston, Texas 77553, USAc Rosenstiel School of Marine and Atmospheric Science, University of Miami, 4600Rickenbacker Causeway, Miami, Florida 33149, USAd Louisiana Department of Wildlife and Fisheries, 2021 Lakeshore Drive, Suite 220, NewOrleans, Louisiana 70122, USAe National Oceanic and Atmospheric Administration, National Marine Fisheries Service,Southeast Fisheries Science Center, Sustainable Fisheries Division, 75 Virginia Beach Drive,Miami, Florida 33149, USAPublished online: 28 Oct 2014.
To cite this article: J. P. Hoolihan, R. J. D. Wells, J. Luo, B. Falterman, E. D. Prince & J. R. Rooker (2014) Vertical andHorizontal Movements of Yellowfin Tuna in the Gulf of Mexico, Marine and Coastal Fisheries: Dynamics, Management, andEcosystem Science, 6:1, 211-222, DOI: 10.1080/19425120.2014.935900
To link to this article: http://dx.doi.org/10.1080/19425120.2014.935900
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Marine and Coastal Fisheries: Dynamics, Management, and Ecosystem Science 6:211–222, 2014C© American Fisheries Society 2014ISSN: 1942-5120 onlineDOI: 10.1080/19425120.2014.935900
ARTICLE
Vertical and Horizontal Movements of Yellowfin Tunain the Gulf of Mexico
J. P. Hoolihan*Cooperative Institute for Marine and Atmospheric Studies, Rosenstiel School for Marine andAtmospheric Science, University of Miami, 4600 Rickenbacker Causeway, Miami, Florida 33149, USA
R. J. D. WellsDepartment of Marine Biology, Texas A&M University–Galveston, 1001 Texas Clipper Road,Galveston, Texas 77553, USA
J. LuoRosenstiel School of Marine and Atmospheric Science, University of Miami,4600 Rickenbacker Causeway, Miami, Florida 33149, USA
B. FaltermanLouisiana Department of Wildlife and Fisheries, 2021 Lakeshore Drive, Suite 220, New Orleans,Louisiana 70122, USA
E. D. PrinceNational Oceanic and Atmospheric Administration, National Marine Fisheries Service,Southeast Fisheries Science Center, Sustainable Fisheries Division, 75 Virginia Beach Drive, Miami,Florida 33149, USA
J. R. RookerDepartment of Marine Biology, Texas A&M University–Galveston, 1001 Texas Clipper Road, Galveston,Texas 77553, USA
AbstractPop-up satellite archival tags (n = 31) were deployed on Yellowfin Tuna Thunnus albacares in the Gulf of Mexico
for periods ranging from 14 to 95 d. Differences in diel vertical behavior were assessed by comparing time spent attemperature relative to the surface temperature (�T). Pooled samples revealed that 31% of darkness hours, 20%of twilight hours, and 12% of daylight hours were spent in the uniform-temperature surface layer (i.e., �T = 0).Total time spent above 100 m was less during daylight (90.0%) than during darkness (99.8%), suggesting greaterexploration of deeper depths during daylight hours. Maximum depth visited ranged from 208 to 984 m, and minimumtemperature visited ranged from 5.4◦C to 11.8◦C. Only a small proportion of total time was spent at temperaturescolder than 8◦C below the surface temperature. Horizontal excursions for the majority of individuals were less than100 km from the point of release; however, three individuals moved distances of 411–1,124 km, suggesting that thisspecies has the capability to move relatively long distances within the Gulf of Mexico. The �T values are provided intabular format and serve as direct input variables for use in habitat standardization models.
Subject editor: Michelle Heupel, James Cook University, Queensland, Australia
*Corresponding author [email protected] January 7, 2014; accepted June 8, 2014
211
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212 HOOLIHAN ET AL.
The Yellowfin Tuna Thunnus albacares is a highly migra-tory pelagic fish that is distributed worldwide in most tropicaland subtropical areas. Economically, this species represents thenumber-one tropical tuna harvested by U.S. commercial fish-ermen in the Atlantic Ocean (NMFS 2013). The InternationalCommission for the Conservation of Atlantic Tunas (ICCAT) ismandated to manage the single Atlantic-wide Yellowfin Tunastock. This includes a responsibility to gather catch and effortdata, conduct stock assessments, determine stock abundance,and invoke management practices that conserve the stock. Un-certainties do occur in the stock assessment process, particu-larly in instances where catch data are deficient or where gearinteractions are poorly understood. Incorporating relevant bio-logical data into the stock assessment process can reduce theseuncertainties because catch itself is influenced by individualspecies’ behavior and habitat preferences. For example, a cor-relation between the temporal and spatial behavior of tunas andtheir vulnerability to longline and surface fishing gears has beendemonstrated (Boggs 1992; Bigelow and Maunder 2007).
Integration of vertical habitat preference and hook depth withcatch and effort statistics is useful for reducing uncertaintiesassociated with estimating relative abundance (Maunder et al.2006). Key factors influencing habitat use by Yellowfin Tunainclude environmental conditions, physiological traits, and for-aging behavior (Brill and Lutcavage 2001). Hinton and Nakano(1996) introduced a model incorporating behavioral constraintsinto the standardization of catch and effort statistics. Majorinput variables for the model include the proportions of timespent within each degree of water temperature relative to thesurface temperature (�T); �T is a major environmental factorgoverning the vertical habitat preference (i.e., depth distribu-tion) of tunas and tuna-like species (Hinton and Nakano 1996;Brill and Lutcavage 2001; Goodyear et al. 2008; Hoolihan et al.2011b).
One mechanism that allows monitoring of horizontal andvertical movements of marine species is electronic tagging(Sibert and Nielsen 2001; Nielsen et al. 2009). Previous elec-tronic tagging studies have indicated that Yellowfin Tuna preferto spend most of their time above the thermocline in the uniform-temperature surface layer (Block et al. 1997; Brill et al. 1999a;Schaefer et al. 2007; Weng et al. 2009). Pop-up satellite archivaltags (PSATs) offer certain advantages over other types of elec-tronic tags. The PSATs can be deployed for comparatively longperiods (months) extending well beyond the time it typicallytakes the fish to recover and resume normal behavior after cap-ture (Hoolihan et al. 2011a). The PSATs also have the advantageof being less dependent on fisheries than other tagging technolo-gies, which require recapture (Arnold and Dewar 2001); PSATsneed not be physically retrieved for data recovery. Instead, thePSAT detaches from the fish on a pre-programmed date andthen transfers the stored data via the Argos satellite system(Block et al. 1998). The objective of this study was to deployPSATs on Yellowfin Tuna in the Gulf of Mexico (GOM) to(1) investigate horizontal and vertical movement behavior and
(2) compile detailed habitat use information that can be appliedto spatiotemporal distributions and relative abundance estima-tions using habitat standardization modeling.
METHODSWe examined data that were collected from PSATs deployed
on Yellowfin Tuna (n = 31) in the northern GOM during2008–2011 (models MK10-PAT and Mini-PAT; Wildlife Com-puters, Redmond, Washington). Specimens were captured in theproximity of petroleum production platforms by using recre-ational fishing gear, and a 71- × 127-cm Frabill landing net(Plano Molding Company, Plano, Illinois) was then used to liftthe fish onboard for tagging. Net and fish were placed togetheron a “bean bag” chair; a wet cloth was used to subdue thefish by covering the eyes, and a hose running salt water wasplaced in the mouth for gill aeration. The PSAT tethers con-sisted of a medical-grade nylon anchor with toggles attached toapproximately 18 cm of 181.4-kg (400-lb) monofilament. An-chors were inserted in an anterolateral direction between thepterygiophores of the first or second dorsal fin. Tags were pro-grammed to collect depth (pressure), ambient temperature, andlight level data at 10- or 15-s intervals. The PSATs were pro-grammed for deployment periods of 90, 150, or 180 d (Table 1).Our analysis included Argos-transmitted summary data fromeither 1-h or 4-h time blocks programmed into the PSATs. Eachtime block included (1) the proportion of time spent within eachdepth bin (25-m resolution); (2) the proportion of time spentwithin each temperature bin (2◦C bin resolution); and (3) PSATdepth–temperature (PDT) profile messages. For cases in whichPSATs were physically recovered (n = 7), the full archived datarecorded at 10- or 15-s intervals were accessible.
Using methods described by Luo et al. (2006), the distribu-tion patterns of pooled data by hours of darkness, twilight, anddaylight were interpolated using linear methods to 1◦C temper-ature bins and 1-m depth bins. Luo et al. (2006) concluded thatthere is a very low rate of error associated with this method ofsimulation. These bin periods were determined by comparingthe time of sunrise and sunset at release and pop-up locations.A daylight bin was defined when the entire bin duration fellbetween 1 h after sunrise and 1 h before sunset. A darknessbin was defined when the entire bin duration fell between 1 hafter sunset and 1 h before sunrise. All bins that did not fitinto one of these two categories were classified as crepuscular(i.e., twilight). This was done to ensure that the daylight anddarkness categories were not compromised by bins falling intothe crepuscular periods. The PDT data for a depth of 1 m orless were used to calculate average daily surface temperature(T0). When surface temperatures were lacking, previous andsubsequent day records were used to linearly interpolate val-ues. Generated temperatures (GTs) per hour (n = 720) wereestimated for each depth bin using a random normal distribu-tion based on the PDT temperature range. The GT histogramwas then compared to the temperature (T) data histogram.
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TAB
LE
1.Su
mm
ary
ofta
ggin
gin
form
atio
nfo
r31
Yel
low
finT
una
that
wer
em
onito
red
with
pop-
upsa
telli
tear
chiv
alta
gs(P
SAT
s;W
ildlif
eC
ompu
ters
)in
the
Gul
fof
Mex
ico
(PA
T=
pop-
upar
chiv
altr
ansm
ittin
gta
g;D
AL
=da
ysat
liber
ty).
Tag
PSA
TFL
Rel
ease
Pop-
upD
AL
/day
sR
elea
seL
ocat
ion
ofD
ispl
acem
ent
No.
num
ber
vers
ion
(cm
)da
teda
tepr
ogra
mm
edlo
catio
nfir
sttr
ansm
issi
on(k
m)
106
A11
09M
K-1
098
Aug
6,20
08A
ug22
,200
816
/90
28◦ 3
9′ N,8
9◦ 45′ W
28◦ 3
5′ N,8
8◦ 90′ W
542
08A
0560
MK
-10
125
Aug
6,20
08N
ov4,
2008
90/9
028
◦ 39′ N
,89◦ 4
5′ W29
◦ 11′ N
,88◦ 1
9′ W14
63
08A
0561
abM
K-1
010
5A
ug6,
2008
Sep
7,20
0832
/90
28◦ 3
9′ N,8
9◦ 45′ W
28◦ 6
2′ N,8
9◦ 47′ W
264
06A
1118
aM
K-1
010
8A
ug6,
2008
Aug
29,2
008
23/9
028
◦ 39′ N
,89◦ 4
5′ W28
◦ 16′ N
,89◦ 1
1′ W43
506
A11
26M
K-1
011
0A
ug6,
2008
Sep
2,20
0827
/90
28◦ 3
9′ N,8
9◦ 45′ W
27◦ 9
′ N,8
9◦ 52′ W
144
608
A05
59bc
MK
-10
100
Aug
7,20
08A
ug14
,200
87/
9029
◦ 6′ N
,88◦ 9
′ W28
◦ 2′ N
,89◦ 1
7′ W15
77
08A
0562
bcM
K-1
010
5A
ug7,
2008
Sep
14,2
008
38/9
029
◦ 6′ N
,88◦ 9
′ W28
◦ 65′ N
,88◦ 8
9W91
808
A05
70a
MK
-10
110
Aug
7,20
08Se
p6,
2008
30/9
029
◦ 6′ N
,88◦ 9
′ W28
◦ 36′ N
,89◦ 4
8′ W15
69
08A
0563
acM
K-1
087
May
11,2
009
May
16,2
009
5/90
28◦ 3
9′ N,8
9◦ 45′ W
28◦ 5
5′ N,8
9◦ 28′ W
2410
06A
1110
MK
-10
109
Jul2
3,20
09O
ct25
,200
994
/180
28◦ 7
4′ N,8
8◦ 83′ W
27◦ 5
7′ N,9
1◦ 41′ W
285
1106
A11
29M
K-1
012
0Ju
l23,
2009
Sep
23,2
009
62/1
8028
◦ 74′ N
,88◦ 8
3′ W28
◦ 59′ N
,89◦ 9
7′ W11
312
06A
1111
MK
-10
116
Jul2
5,20
09A
ug13
,200
919
/180
28◦ 8
6′ N,8
8◦ 5′ W
29◦ 3
2′ N,8
7◦ 85′ W
5613
06A
1112
cM
K-1
011
6Ju
l25,
2009
Jul2
9,20
094/
180
28◦ 8
6′ N,8
8◦ 5′ W
29◦ 3
5′ N,8
6◦ 88′ W
126
1408
A05
65M
K-1
014
6Ju
l25,
2009
Aug
8,20
0914
/180
28◦ 8
6′ N,8
8◦ 5′ W
29◦ 2
3′ N,8
7◦ 79′ W
4815
08A
0568
acM
K-1
011
5Ju
l25,
2009
Jul3
1,20
096/
180
28◦ 8
6′ N,8
8◦ 5′ W
28◦ 7
7′ N,8
8◦ 6′ W
916
09P0
215
Min
i-PA
T14
7N
ov28
,200
9M
ar3,
2010
95/1
5028
◦ 74′ N
,88◦ 8
3′ W27
◦ 33′ N
,88◦ 9
4′ W0
1709
P021
7M
ini-
PAT
122
Nov
29,2
009
Dec
19,2
009
20/1
5028
◦ 83′ N
,88◦ 4
9′ W27
◦ 9′ N
,89◦ 6
6′ W22
418
09P0
225
Min
i-PA
T13
3N
ov30
,200
9D
ec26
,200
926
/150
28◦ 7
4′ N,8
8◦ 83′ W
27◦ 3
8′ N,8
8◦ 55′ W
154
1909
P022
6M
ini-
PAT
158
Nov
30,2
009
Dec
31,2
009
31/1
5028
◦ 74′ N
,88◦ 8
3′ W28
◦ 91′ N
,88◦ 3
7′ W48
2006
A03
54bc
MK
-10
112
Aug
6,20
10Se
p15
,201
040
/90
28◦ 2
6′ N,8
9◦ 2′ W
28◦ 3
9′ N,8
9◦ 42′ W
7021
06A
1119
MK
-10
115
Aug
6,20
10Se
p25
,201
050
/90
28◦ 2
9′ N,8
9◦ 2′ W
28◦ 4
9′ N,8
8◦ 59′ W
3922
08A
0566
MK
-10
106
Aug
6,20
10N
ov4,
2010
90/9
028
◦ 23′ N
,89◦ 2
3′ W27
◦ 34′ N
,94◦ 5
9′ W55
723
08A
0570
MK
-10
116
Aug
6,20
10N
ov4,
2010
90/9
028
◦ 26′ N
,89◦ 2
′ W18
◦ 42′ N
,85◦ 5
5′ W1,
128
2409
P022
3aM
ini-
PAT
114
Aug
7,20
10Se
p23
,201
047
/180
28◦ 2
7′ N,8
9◦ 2′ W
28◦ 5
0′ N,8
8◦ 44′ W
5225
09P0
227
Min
i-PA
T11
3A
ug7,
2010
Oct
31,2
010
85/1
8028
◦ 26′ N
,89◦ 2
′ W25
◦ 00′ N
,86◦ 1
6′ W41
126
09P0
216
Min
i-PA
T13
2M
ay8,
2011
May
26,2
011
18/1
8028
◦ 26′ N
,89◦ 3
4′ W28
◦ 13′ N
,89◦ 3
6′ W41
2709
P021
8M
ini-
PAT
119
May
8,20
11M
ay26
,201
118
/180
28◦ 2
6′ N,8
9◦ 34′ W
28◦ 2
9′ N,8
9◦ 58′ W
6528
09P0
219a
Min
i-PA
T11
0M
ay8,
2011
Jul3
,201
156
/180
28◦ 2
6′ N,8
9◦ 34′ W
28◦ 4
9′ N,8
8◦ 58′ W
7829
09P0
222
Min
i-PA
T11
2M
ay8,
2011
Jun
8,20
1131
/180
28◦ 2
7′ N,8
9◦ 34′ W
28◦ 2
2′ N,8
9◦ 54′ W
5430
09P0
233
Min
i-PA
T11
8M
ay8,
2011
May
23,2
011
15/1
8028
◦ 26′ N
,89◦ 2
7′ W28
◦ 10′ N
,89◦ 6
′ W70
3109
P029
6M
ini-
PAT
135
May
8,20
11Ju
n29
,201
152
/180
28◦ 2
7′ N,8
9◦ 34′ W
28◦ 2
9′ N,8
8◦ 51′ W
54
aT
hePS
AT
and
arch
ival
data
wer
ere
cove
red.
bPr
edat
ion
even
t.c
Exc
lude
dfr
oman
alys
es.
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214 HOOLIHAN ET AL.
Differences in numbers were corrected by removing valuesin positive bins and adding them randomly to the nega-tive bins. Values of �T were then calculated by subtract-ing the GT values from the T0 values, and we tabulatedthe proportion of time spent at decreasing temperatures witha 1◦C bin resolution. Proportions of records within succes-sively deeper layers of temperature relative to the surfacetemperature (�T) were assembled, and the mean �T val-ues were calculated for each distribution (Hoolihan et al.2012b).
WC-DAP and WC-GPE2 software (Wildlife Computers)were used for the initial processing of light-level geolocationdata, followed by a sea surface temperature-corrected Kalmanfilter (Nielsen et al. 2006; Lam et al. 2008). A custom bathymetryfilter was then used to relocate any points that were on land or inshallow water based on 2- × 2-min grid ETOPO2 bathymetrydata (National Geophysical Data Center 2006) and the dailymaximum depth from the tag. For each point where maximumdaily depth was greater than the bathymetric depth, we selectedall grid cells along the longitude where bathymetric depth wasgreater than the daily maximum depth within ± 1◦C of the pre-vious day’s latitude, and we then assigned a final location to asingle cell that was randomly selected from that group (Hoolihanand Luo 2007).
Horizontal movements were analyzed for spatial variationusing the kernel density estimators described by Worton (1995).Estimated values were cumulated based on highest to lowestareas of density whereby the 25% kernel area contour is re-flective of the highest observed densities and the 95% contoursrepresent up to 95% density areas (Hammerschlag et al. 2012).Values were plotted using Interactive Data Language software(www.ittvis.com).
RESULTSWe deployed PSATs on 31 Yellowfin Tuna in the GOM to
monitor vertical and horizontal habitat use. Of these, six PSATswere excluded from our analyses because the fish either died orthe PSAT remained attached for less than 10 d (Hoolihan et al.2011a). The remaining 25 fish included in our analyses rangedin size from 98 to 158 cm FL, with estimated weights rangingfrom 22.6 to 70.0 kg. Days at liberty (DAL) ranged from 14 to95 (mean = 45.2 DAL), while linear displacement between therelease and pop-up locations ranged from 0 to 1,128 km (mean= 146.0; Table 1).
Vertical BehaviorDifferences in diel vertical behavior of Yellowfin Tuna were
compared by pooling individual histogram sets derived fromperiods of daylight (n = 1,190), twilight (n = 768), and darkness(n = 939) and were illustrated by using the range and proportionsof time spent at �T for each period (Figure 1). The derivedpercentiles of mean �T for the pooled samples revealed thataround 12% of daylight hours, 20% of twilight hours, and 31%
of darkness hours were spent in the uniform-temperature surfacelayer (i.e., �T = 0; Table 2). The distributions for the percentilesof mean �T did exhibit variability.
The distributions for time (%) spent at depth during the day-time and nighttime were significantly different (χ2 = 35.4,df = 15, P < 0.001), showing that more time was spentnearer to the surface during darkness (Figure 2a). For exam-ple, 52.4% of time was recorded in the upper 20 m of thewater column at night compared with just 20.9% during day-light hours. The distributions of time at temperature were sig-nificantly different as well (χ2 = 35.8, df = 20, P < 0.05),corresponding to the differences in the time-at-depth distri-butions (Figure 2b). Total time spent above 100 m was less(90.0%) during daylight than during darkness (99.8%), suggest-ing more exploration of deeper depths during the daylight hours(Figure 2a).
Preference of Yellowfin Tuna for the uniform-temperaturesurface layer was obvious from the �T time-at-temperature dis-tributions (Table 2), although it did not preclude short-durationdives into deeper, colder strata. The maximum depths visited byindividuals from this group ranged from 208 to 984 m, and mini-mum temperatures visited ranged from 5.4◦C to 11.8◦C. Verticalmovements to depths below the thermocline were generally briefand occurred infrequently relative to vertical movements withinthe uniform-temperature surface layer. We questioned whetherthe longer monitoring periods in our study would provide a moredetailed account of actual Yellowfin Tuna habitat use in compar-ison with earlier short-term studies (Holland et al. 1990; Brillet al. 1999a). If true, we surmised that encountering evidence ofdeeper dives and lower minimum temperatures should increasewith the length of deployment. To confirm this, we comparednovel maximum daily depths and minimum temperatures withDAL (Figures 3, 4). The novel maximum daily depth (Dmax) in-creased, extending into deeper strata over time (i.e., DAL; Dmax
= 113.55·DAL0.376; r = 0.708, F = 85.2, df = 86, P < 0.0001;Figure 3). There was also a trend toward colder novel mini-mum daily temperatures over time (Dmax = 19.96·DAL−0.209;r = −0.678, F = 73.3, df = 87, P < 0.0001), reflect-ing the trend observed for increasing daily maximum depths(Figure 4). However, both the novel maximum daily depthsand minimum temperatures were highly variable over time(Figures 3, 4).
Mortality by predation was suspected for at least four Yel-lowfin Tuna in our study (Table 1) based on their altered verticalhabitat use (i.e., depth and temperature readings). For example,PSAT data from fish 3 (Table 1) indicated a radical change inbehavior after about 20 DAL (Figure 5). Maximum depths in-creased greatly, but ambient water temperature readings weremuch warmer than expected for the depths attained. Rather,they were more consistent with internal abdominal temperature,suggesting predation (e.g., shark) and ingestion of the PSAT.Depth and temperature data suggested that this PSAT (Figure 5)was regurgitated after about 30 DAL, allowing it to float to thesurface and transmit.
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TAB
LE
2.Pr
opor
tions
oftim
esp
enta
teac
hte
mpe
ratu
rere
lativ
eto
the
surf
ace
tem
pera
ture
(�T
)dur
ing
dark
ness
,tw
iligh
t,an
dda
ylig
htfo
r25
Yel
low
finT
una
that
wer
em
onito
red
with
pop-
upsa
telli
tear
chiv
alta
gs(P
SAT
s).P
ropo
rtio
nsw
ere
deri
ved
from
com
bine
dda
ta(a
rchi
vald
ata
reco
vere
dfr
omM
K-1
0PS
AT
s;m
ini-
PAT
seri
esda
ta;a
ndar
chiv
alda
tasi
mul
ated
from
tran
smitt
edM
K-1
0PS
AT
s).
Das
hes
indi
cate
zero
prob
abili
ty.D
ata
wer
eso
rted
inor
der
ofde
crea
sing
�T
(lef
tcol
umn)
;pro
port
ions
oftim
esp
enta
tth
eva
riou
ste
mpe
ratu
res
wer
eth
enca
lcul
ated
for
ara
nge
ofpe
rcen
tiles
.As
anex
ampl
e,fo
r75
%of
the
data
poin
tsdu
ring
dark
ness
,10.
6%w
ere
atsu
rfac
ete
mpe
ratu
re(�
T=
0,to
pro
w)
and
30.6
%w
ere
at2◦
Cbe
low
surf
ace
tem
pera
ture
(�T
=−2
,thi
rdro
w).
Perc
entil
eof
mea
n�
T
�T
0.01
00.
025
0.05
00.
100
0.25
00.
500
0.75
00.
900
0.95
00.
975
0.99
0M
ean
Dar
knes
s(n
=93
9)0
0.98
10.
683
0.40
50.
230
0.11
00.
064
0.10
60.
187
0.20
60.
217
0.22
30.
310
−10.
011
0.26
80.
463
0.43
30.
300
0.20
20.
164
0.16
10.
158
0.15
80.
157
0.22
5−2
0.00
90.
041
0.10
70.
254
0.39
40.
381
0.30
60.
270
0.26
10.
256
0.25
40.
230
−3—
0.00
40.
015
0.05
00.
110
0.19
30.
190
0.17
10.
167
0.16
40.
163
0.11
2−4
—0.
004
0.00
50.
019
0.04
90.
076
0.09
20.
085
0.08
40.
082
0.08
20.
052
−5—
—0.
004
0.00
90.
018
0.03
90.
056
0.05
10.
050
0.04
90.
049
0.03
0−6
——
0.00
20.
004
0.01
40.
027
0.04
10.
036
0.03
50.
035
0.03
40.
021
−7—
——
0.00
10.
003
0.01
10.
027
0.02
30.
022
0.02
20.
022
0.01
2−8
——
——
0.00
10.
005
0.01
00.
009
0.00
90.
009
0.00
90.
005
−9—
——
——
0.00
20.
006
0.00
50.
005
0.00
50.
005
0.00
2−1
0—
——
——
0.00
10.
002
0.00
20.
002
0.00
20.
002
0.00
1−1
1—
——
——
—0.
001
0.00
10.
001
0.00
10.
001
—−1
2—
——
——
——
——
——
—−1
3—
——
——
——
——
——
—−1
4—
——
——
——
——
——
—−1
5—
——
——
——
——
——
—T
wili
ght
(n=
768)
00.
475
0.29
50.
195
0.11
70.
060
0.03
40.
149
0.21
80.
232
0.24
50.
249
0.20
6−1
0.39
20.
523
0.45
50.
323
0.21
50.
140
0.12
50.
129
0.12
90.
129
0.12
90.
244
−20.
110
0.14
90.
276
0.44
80.
456
0.36
50.
272
0.24
40.
236
0.23
30.
230
0.27
4−3
0.02
20.
024
0.04
60.
065
0.14
30.
190
0.15
30.
142
0.13
90.
137
0.13
60.
109
−40.
001
0.00
70.
015
0.02
60.
064
0.09
80.
083
0.07
80.
078
0.07
60.
076
0.05
5−5
—0.
003
0.00
90.
014
0.02
70.
054
0.04
80.
044
0.04
40.
043
0.04
20.
030
−6—
0.00
10.
002
0.00
60.
021
0.05
00.
044
0.03
90.
038
0.03
70.
037
0.02
5−7
——
0.00
10.
001
0.00
70.
030
0.03
30.
028
0.02
80.
027
0.02
70.
016
−8—
——
—0.
005
0.01
90.
029
0.02
40.
024
0.02
30.
023
0.01
3−9
——
——
0.00
10.
010
0.02
00.
017
0.01
60.
016
0.01
60.
009
−10
——
——
—0.
006
0.01
70.
014
0.01
40.
013
0.01
30.
007
−11
——
——
—0.
002
0.01
20.
010
0.00
90.
009
0.00
90.
005
−12
——
——
—0.
001
0.00
70.
006
0.00
50.
005
0.00
50.
003
−13
——
——
—0.
001
0.00
60.
005
0.00
50.
004
0.00
40.
002
−14
——
——
——
0.00
30.
003
0.00
20.
002
0.00
20.
001
−15
——
——
——
——
——
——
215
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TAB
LE
2.C
ontin
ued.
Perc
entil
eof
mea
n�
T
�T
0.01
00.
025
0.05
00.
100
0.25
00.
500
0.75
00.
900
0.95
00.
975
0.99
0M
ean
Day
light
(n=
1,19
0)0
0.41
40.
226
0.14
20.
092
0.04
60.
027
0.02
10.
079
0.09
10.
097
0.10
10.
121
−10.
508
0.62
60.
445
0.30
60.
193
0.12
90.
095
0.09
60.
098
0.09
70.
097
0.24
4−2
0.06
20.
107
0.33
20.
432
0.39
30.
309
0.24
00.
211
0.20
50.
202
0.20
00.
245
−30.
015
0.03
50.
061
0.12
30.
224
0.21
20.
175
0.15
80.
154
0.15
30.
152
0.13
3−4
0.00
10.
002
0.00
60.
021
0.06
70.
105
0.09
00.
087
0.08
60.
087
0.08
60.
058
−5—
0.00
20.
005
0.01
00.
026
0.06
10.
055
0.05
50.
055
0.05
50.
055
0.03
4−6
—0.
001
0.00
40.
010
0.02
40.
050
0.05
20.
052
0.05
20.
052
0.05
20.
032
−7—
—0.
003
0.00
40.
011
0.03
40.
045
0.04
50.
045
0.04
50.
045
0.02
5−8
——
0.00
20.
002
0.00
90.
033
0.05
30.
051
0.05
00.
050
0.05
00.
027
−9—
——
0.00
10.
003
0.01
70.
045
0.04
40.
044
0.04
30.
043
0.02
2−1
0—
——
—0.
002
0.01
10.
037
0.03
70.
038
0.03
80.
038
0.01
8−1
1—
——
—0.
001
0.00
50.
029
0.02
80.
027
0.02
70.
027
0.01
3−1
2—
——
——
0.00
30.
024
0.02
20.
021
0.02
10.
021
0.01
0−1
3—
——
——
0.00
20.
021
0.01
90.
018
0.01
80.
018
0.00
9−1
4—
——
——
0.00
10.
019
0.01
70.
016
0.01
60.
016
0.00
8−1
5—
——
——
——
——
——
—
216
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MOVEMENTS OF YELLOWFIN TUNA 217
FIGURE 1. Proportions of time (log scale) spent at temperature relative to thesurface temperature (�T) during (a) daylight, (b) twilight, and (c) darkness,as estimated by using combined Argos-transmitted and archival data from 25Yellowfin Tuna that were monitored with pop-up satellite archival tags. Shadedbars denote the range of observations (shown by small gray dots). Red circlesdenote means ( ± 95% confidence interval).
Horizontal BehaviorThe kernel density estimated from horizontal tracking data
indicated that the highest densities of movement were con-fined to a relatively small area around the release locations(Figure 6). Three individuals (fish 23, 24, and 26; Table 1) un-dertook more lengthy excursions. Fish 23 moved 557 km to thewestern GOM; fish 24 moved 1,124 km in a southerly directioninto the Caribbean; and fish 26 moved southeasterly 411 kminto the area of the Loop Current. All three fish exhibited higherperiods at liberty (85–90 DAL). However, time was not the solefactor regulating long-distance movement, as many other taggedfish had comparable DAL periods and remained in relative prox-imity to their release locations (Table 1; Figure 6).
DISCUSSIONHabitat preference and vertical movement behavior of Yel-
lowfin Tuna are influenced by a variety of factors, includingphysiological capabilities, oceanographic features, environmen-tal conditions, and prey species behavior (Dizon and Brill 1979;Block et al. 1997; Brill and Lutcavage 2001). Yellowfin Tuna inthe GOM remained primarily within the uniform-temperaturesurface layer. This was similar to the findings of Weng et al.(2009) for six PSAT-monitored Yellowfin Tuna in the GOM andto the results of earlier ultrasonic tracking studies near Hawaii,California, French Polynesia, and the Comoros Islands (Careyand Olson 1982; Holland et al. 1990; Cayre and Marsac 1993;Block et al. 1997; Josse et al. 1998; Brill et al. 1999a). A notabledifference between PSAT and ultrasonic tracking studies is thatextremes in the maximum depth and minimum temperature usedare more likely to be detected over the longer deployment pe-riods associated with PSAT use (Figures 3, 4). Yellowfin Tunawere present in deeper, colder depths below the thermocline,albeit with less frequency and for shorter periods in compar-ison with vertical movement activities above the thermocline(Schaefer et al. 2011). Our study recorded Yellowfin Tuna asdeep as 984 m, with ambient temperatures as low as 5.4◦C.Studies based on implanted archival tagging of Yellowfin Tunain the eastern Pacific Ocean revealed that the fish are capable ofdiving to at least 1,600 m (Schaefer et al. 2014). Presumably, themain purpose of deep diving is associated with foraging activ-ity, which also allows them to prey upon deep-dwelling species.Deep diving can also be an advantage when used tactically toelude predators, such as sharks and billfishes.
Constraints on cardiac function are the primary reason whyYellowfin Tuna do not spend more time below the warmeruniform-temperature surface layer (Brill and Lutcavage 2001).Heart temperature in tunas and billfishes is directly affectedby changes in water temperature because the heart is on thewater side of their vascular countercurrent heat exchangers (Brillet al. 1999b; Galli et al. 2009). This fact supports prior evidencethat most tropical tunas and billfishes generally seek out thewarmest water available and is further supported by empiricaldata showing that Yellowfin Tuna, Blue Marlins Makaira ni-gricans, White Marlins Kajikia albida, and Sailfish Istiophorus
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218 HOOLIHAN ET AL.
60
50
40
30
20
10
0
40
35
30
25
20
15
10
5
010 10 12 14 16 18 20 22 24 26 28 3050 90 130 170 210 250 290
Center of depth bin (m)
Tim
e (%
)
Temperature (°C)
(a) (b)
FIGURE 2. Pooled percentage of time spent at (a) depth and (b) temperature for periods of darkness (black-shaded bars) and daylight (open bars) for 25 YellowfinTuna that were monitored with pop-up satellite archival tags. Interpolated 1-m depth bins were grouped (20-m bins) in this figure to conserve space.
platypterus usually spend only a small fraction of total time attemperature in waters colder than −8 �T (Goodyear et al. 2008;Hoolihan et al. 2011b, 2012a).
Because tunas and billfishes are sight feeders, their foragingactivities should be more prevalent during daylight hours. Thepresumption that most descents below the thermocline are as-sociated with foraging activity is supported by comparing thepercentiles of mean �T for darkness and daylight hours forYellowfin Tuna in the present study (Table 2). The percentilesplainly indicate an increased use of deeper, colder depths duringdaylight hours and more surface-oriented behavior during dark-ness, suggesting an opportunity to take advantage of warmertemperatures when foraging opportunities are low. Previousstudies have also reported greater deep-diving behavior duringdaylight hours for Yellowfin Tuna as well as billfishes, whichshare similar physiological traits (Holland et al. 1990; Blocket al. 1997; Brill et al. 1999a; Schaefer et al. 2007, 2011, 2014;Goodyear et al. 2008; Weng et al. 2009; Hoolihan et al. 2011b,2012a).
While ambient temperature is clearly a major factor limit-ing vertical movements of Yellowfin Tuna, other factors alsoaffect their use of vertical habitat. Brill (1996) pointed out sev-eral high-performance physiological traits of Yellowfin Tuna,including rapid rates of somatic growth, gonadal growth, di-gestion, and recovery from exhaustive exercise. Each of theseenergy demands requires oxygen delivery rates in excess ofthose needed for routine metabolic functions as well as thesustained propulsion that is mandatory for obligate ram ventila-tors, such as tunas and billfishes (Brill 1996). Therefore, thesespecies must adhere to environmental conditions (e.g., depths)that provide adequate levels of dissolved oxygen to maintainthese functions. Known areas with hypoxic conditions are as-sociated with limited vertical habitat use by tunas and tuna-likespecies (Prince and Goodyear 2006; Prince et al. 2010; Strammaet al. 2012). Although hypoxic conditions of varying severitydo form periodically along the continental shelf near the Mis-sissippi River’s outflow into the northern GOM (Rabalais et al.1996), there were no apparent indications that this phenomenonimpacted the behavior of Yellowfin Tuna in the present study.
Unfortunately, only 4 of the 31 deployed PSATs remained at-tached for the entire pre-programmed period (Table 2), and thosefour PSATs were programmed for 90 d. We concluded that atleast four of the premature releases in our study (Table 2) werethe result of predation (presumably sharks). Predation followedby predator ingestion of the PSAT was assumed when depthincreased greatly for extended periods, counter to the verticalbehavior we normally witnessed in Yellowfin Tuna. Concomi-tant to these depth changes, temperature readings were elevatedabove the values expected for a given depth; these temperatureswere relatively constant, suggesting that the PSAT was ingestedand therefore recorded the predator’s internal body temperature.In addition, light level measurements were negligible, suggest-ing that the PSAT was shielded from light after being ingested.When the PSAT reverted to recording temperature values thatwere plausible for the concurrent depth values and started trans-mitting data via the Argos system, the PSAT was presumed tohave been regurgitated. Predatory behavior resulting in PSATingestion has been reported in previous studies (Kerstetter et al.2004; Beguer-Pon et al. 2012; Lacroix 2014). Predation mayhave been responsible for the loss of other Yellowfin Tuna inour study, particularly when PSATs were at large for only a fewdays (Table 1).
All of our Yellowfin Tuna were captured in close proximityto petroleum production structures (i.e., platforms). In additionto their intended purpose, these structures function as fish ag-gregating devices (FADs). Foraging Yellowfin Tuna and theirprey are known to aggregate around FADs (Holland et al. 1990;Cayre 1991; Itano and Holland 2000). In turn, larger preda-tors that target Yellowfin Tuna (e.g., sharks and Blue Marlins)take advantage of this situation. In fact, shark predation on Yel-lowfin Tuna that were hooked and being retrieved near the GOMpetroleum production structures was a problem acknowledgedby local fishermen.
None of the PSATs with 150- or 180-d deployments func-tioned for full term. Early or premature release can result for avariety of reasons, including breakage of the tether component,breakage of the corrosive link pin, failure of the anchor mech-anism, failure of PSAT internal programming or hardware, and
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MOVEMENTS OF YELLOWFIN TUNA 219
FIGURE 3. Maximum depth reached by Yellowfin Tuna during time at liberty:(a) deepest dives recorded during individual time bins (gray dots), first recordsof each fish at the observed depth (red dots), and exponential fit to the firstoccurrence data (blue line); and (b) frequency distribution of the deepest divesobserved for each fish.
predation (Musyl et al. 2011). In many cases, it is difficult topinpoint any single reason for PSAT failure. In some instances,events such as predation can be ascertained by scrutinizing thetransmitted or stored archival data. Notification of pin breakageis provided by Argos transmission, although there is no wayto determine the exact cause of the breakage. Possible reasonsthat may contribute to metal fatigue and pin breakage include(1) behaviors exhibited by Yellowfin Tuna during the pursuit ofprey (i.e., rapid turning and jumping out of the water) and (2)misidentification of the PSAT as prey and subsequent attack onthe PSAT by other Yellowfin Tuna during a feeding frenzy.
Except for three individuals (Table 2; Figure 5), the Yel-lowfin Tuna in our study did not undertake long-distance hor-izontal movements. Conventional tagging studies indicate thatYellowfin Tuna are capable of long-distance movements, eventrans-Atlantic crossings beginning from the GOM (Ortiz 2001;Prince and Goodyear 2007). Detection of long-distance move-ments is more likely when tag deployment periods increase tothe point of encompassing seasonal changes that induce popu-lation migrations. None of the PSATs in our study exceeded 95DAL (Table 2); thus, the chances of revealing seasonal move-ments were lessened. Factors such as spawning activity, envi-ronmental conditions, and foraging opportunities may provide
FIGURE 4. Minimum temperature experienced by Yellowfin Tuna duringtime at liberty: (a) lowest temperature recorded during individual time bins(gray dots), first records of each fish at the observed temperature (red dots),and exponential fit to the first occurrence data (blue line); and (b) frequencydistribution of the lowest temperatures observed for each fish.
an incentive to remain in one locale for an extended period.The petroleum production platform FADs certainly contributeto more opportune foraging situations. The presence of nearly4,000 such structures in the northern GOM (NOAA 2012) mayentice Yellowfin Tuna to remain in a specific area for extendedperiods; this could explain why the Yellowfin Tuna in our studystayed within relative proximity to their release locations.
Yellowfin Tuna remain economically important in countriesthroughout their range, where the bulk of catches are attributedto commercial and artisanal fleets using longline and purse-seine gears. A 2011 ICCAT assessment of Atlantic YellowfinTuna based on production and age-structured models estimateda 76% probability that the stock was either (1) overfished or(2) overfished and undergoing overfishing (ICCAT 2011). Forstock assessment purposes, knowledge of hook depth and verti-cal habitat preference (i.e., distribution) of the catch (Hintonand Nakano 1996; Bigelow et al. 2006) are important con-siderations for reducing the uncertainties that are often asso-ciated with estimating fishing effort and relative abundancefrom longline catch rate indices. Longline hook depth hasbeen shown to affect species selectivity of longline sets (Boggs1992); however, there are difficulties in accurately estimatinghook depth (Rice et al. 2007). Habitat-based standardization
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220 HOOLIHAN ET AL.
FIGURE 5. Depth (black line), temperature (red line), and light level (blue line) profiles for a Yellowfin Tuna (pop-up satellite archival tag 08A0561; see Table 1),illustrating the change in vertical habitat use after a presumed shark predation event. Times of tag ingestion and subsequent regurgitation are indicated.
models were developed to improve the methodology for es-timating relative abundance, and standardization of historicalcatch and effort data, through use of detailed information onactual hook depth and vertical distribution of catch (Hinton andNakano 1996; Bigelow et al. 2002; Maunder et al. 2006). Wehave presented tabulated �T percentiles that can be used asdirect input variables for entry into habitat-based standardiza-tion models, such as the one described by Hinton and Nakano(1996). As such, the results of the present study representthe most comprehensive description to date for actual verticalhabitat use by Yellowfin Tuna in the Atlantic Ocean. How-ever, this information was obtained from a localized area of the
FIGURE 6. Kernel density estimates for horizontal habitat use by 25 YellowfinTuna that were monitored with pop-up satellite archival tags in the Gulf ofMexico.
northern GOM. Variation in oceanographic features can affectfish behavior (Rooker et al. 2012), so vertical habitat use byYellowfin Tuna in other areas of the Atlantic Ocean may differfrom that identified in the present study.
ACKNOWLEDGMENTSFunding was provided in part by a grant from the National
Oceanic and Atmospheric Administration (NOAA)–FisheriesCooperative Research Program. J. Hoolihan’s contribution wascarried out in part under the auspices of the Cooperative Insti-tute for Marine and Atmospheric Studies, a Cooperative Insti-tute of the University of Miami and the NOAA (CooperativeAgreement Number NA1RJ1266). The McDaniel CharitableFoundation provided logistical support for field work in Venice,Louisiana. The views expressed herein are those of the authorsand do not necessarily reflect the views of NOAA or any of itssubagencies. Reference to commercial products does not implyendorsement by the National Marine Fisheries Service or theauthors.
REFERENCESArnold, G., and H. Dewar. 2001. Electronic tags in marine fisheries research.
Pages 7–4 in J. R. Sibert and J. L. Nielsen, editors. Proceedings of thesymposium on tagging and tracking marine fish with electronic devices.Kluwer Academic, Dordrecht, The Netherlands. 468 pp.
Beguer-Pon, M., J. Benchetrit, M. Castonguay, K. Aarestrup, S. E. Campana,M. J. W. Stokesbury, and J. J. Dodson. 2012. Shark predation on migratingadult American Eels (Anguilla rostrata) in the Gulf of St. Lawrence. PloSONE [online serial] 7:e46830.
Bigelow, K. A., J. Hampton, and N. Miyabe. 2002. Application of a habitat-based model to estimate effective longline fishing effort and relative abun-dance of Pacific Bigeye Tuna (Thunnus obesus). Fisheries Oceanography11:143–55.
Bigelow, K. A., and M. N. Maunder. 2007. Does habitat or depth influence catchrates of pelagic species? Canadian Journal of Fisheries and Aquatic Sciences64:1581–1594.
Bigelow, K., M. K. Musyl, F. Poisson, and P. Kleiber. 2006. Pelagic longlinegear depth and shoaling. Fisheries Research 77:173–183.
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MOVEMENTS OF YELLOWFIN TUNA 221
Block, B. A., H. Dewar, C. Farwell, and E. D. Prince. 1998. A new satellitetechnology for tracking the movements of Atlantic Bluefin Tuna. Proceedingsof the National Academy of Sciences of the USA 95:9384–9389.
Block, B. A., J. E. Keen, B. Castillo, H. Dewar, E. V. Freund, D. J. Marcinek,R. W. Brill, and C. Farwell. 1997. Environmental preferences of YellowfinTuna (Thunnus albacares) at the northern extent of its range. Marine Biology130:119–132.
Boggs, C. H. 1992. Depth, capture time, and hooked longevity of longline-caught pelagic fish: timing bites of fish with chips. U.S. National MarineFisheries Service Fishery Bulletin 90:642–658.
Brill, R. W. 1996. Selective advantages conferred by the high performancephysiology of tunas, billfishes, and dolphin fish. Comparative Biochemistryand Physiology A 113:3–15.
Brill, R. W., B. A. Block, C. H. Boggs, K. A. Bigelow, E. V. Freund, and D. J.Marcinek. 1999a. Horizontal movements and depth distribution of large adultYellowfin Tuna (Thunnus albacares) near the Hawaiian Islands, recordedusing ultrasonic telemetry: implications for the physiological ecology ofpelagic fishes. Marine Biology 133:395–408.
Brill, R. W., T. E. Lowe, and K. L. Cousins. 1999b. How water temperaturereally limits the vertical movements of tunas and billfishes—it’s the heart,stupid. Pages 57–62 in K. Gamperl, A. Farrell, and D. MacKinlay, editors.Cardiovascular function in fishes. American Fisheries Society, Bethesda,Maryland.
Brill, R. W., and M. Lutcavage. 2001. Understanding environmental influenceson movements and depth distributions of tunas and billfishes can significantlyimprove population assessments. Pages 179–198 in G. R. Sedberry, editor.Island in the stream. American Fisheries Society, Symposium 25, Bethesda,Maryland.
Carey, F. G., and R. J. Olson. 1982. Sonic tracking experiments with tunas.International Commission for the Conservation of Atlantic Tunas (ICCAT)Collective Volume of Scientific Papers 17:458–466.
Cayre, P. 1991. Behaviour of Yellowfin Tuna (Thunnus albacares) and Skip-jack Tuna (Katsuwonus pelamis) around fish aggregating devices (FADs) inthe Comoros Islands as determined by ultrasonic tagging. Aquatic LivingResources 4:1–12.
Cayre, P., and F. Marsac. 1993. Modelling the Yellowfin Tuna (Thunnus al-bacares) vertical distribution using sonic tagging results and local environ-mental parameters. Aquatic Living Resources 6:1–14.
Dizon, A. E., and R. W. Brill. 1979. Thermoregulation in Yellowfin Tuna,Thunnus albacares. Physiological Zoology 52:581–593.
Galli, G. L. J., H. A. Shiels, and R. W. Brill. 2009. Temperature sensitivityof cardiac function in pelagic fishes with different vertical mobilities: Yel-lowfin Tuna (Thunnus albacares), Bigeye Tuna (Thunnus obesus), Mahimahi(Coryphaena hippurus), and Swordfish (Xiphias gladius). Physiological andBiochemical Zoology 82:280–290.
Goodyear, C. P., J. Luo, E. D. Prince, J. P. Hoolihan, D. Snodgrass, E. S. Orbesen,and J. E. Serafy. 2008. Vertical habitat use of Atlantic Blue Marlin (Makairanigricans): interaction with pelagic longline gear. Marine Ecology ProgressSeries 365:233–245.
Hammerschlag, N., A. J. Gallagher, J. Wester, J. Luo, and J. S. Ault. 2012.Don’t bite the hand that feeds: assessing ecological impacts of provisioningecotourism on an apex marine predator. Functional Ecology 26:567–576.
Hinton, M. G., and H. Nakano. 1996. Standardizing catch and effort statisticsusing physiological, ecological, or behavioral constraints and environmentaldata, with an application to Blue Marlin (Makaira nigricans) catch and effortdata from Japanese longline fisheries in the Pacific. Inter-American TropicalTuna Commission Bulletin 21:171–200.
Holland, K. N., R. W. Brill, and R. K. C. Chang. 1990. Horizontal and verticalmovements of Yellowfin and Bigeye Tuna associated with fish aggregatingdevices. U.S. National Marine Fisheries Service Fishery Bulletin 88:493–507.
Hoolihan, J. P., and J. Luo. 2007. Determining summer residence status andvertical habitat use of Sailfish (Istiophorus platypterus) in the Arabian Gulf.ICES Journal of Marine Science 64:1791–1799.
Hoolihan, J. P., J. Luo, F. J. Abascal, S. E. Campana, G. De Metrio, H. Dewar,M. L. Domeier, L. A. Howey, M. E. Lutcavage, M. K. Musyl, J. D. Neilson,
E. S. Orbesen, E. D. Prince, and J. R. Rooker. 2011a. Evaluating post-releasebehaviour modification in large pelagic fish deployed with pop-up satellitearchival tags. ICES Journal of Marine Science 68:880–889.
Hoolihan, J. P., J. Luo, C. P. Goodyear, E. S. Orbesen, and E. D. Prince. 2011b.Vertical habitat use of Sailfish (Istiophorus platypterus) in the Atlantic andeastern Pacific, derived from pop-up satellite archival tag data. FisheriesOceanography 20:192–205.
Hoolihan, J. P., J. Luo, and E. D. Prince. 2012a. Preliminary analysis of timespent at temperature relative to the surface temperature for White Marlinmonitored with pop-up satellite archival tags. International Commission forthe Conservation of Atlantic Tunas (ICCAT) Collective Volume of ScientificPapers 68:1585–1589.
Hoolihan, J. P., R. J. D. Wells, J. Luo, J. R. Rooker, and E. D. Prince. 2012b.Preliminary analysis of time spent at temperature relative to the surfacetemperature for Yellowfin Tuna monitored with pop-up satellite archivaltags in the Gulf of Mexico. International Commission for the Conserva-tion of Atlantic Tunas (ICCAT) Collective Volume of Scientific Papers 68:979–983.
ICCAT (International Commission for the Conservation of Atlantic Tunas).2011. Report of the 2011 ICCAT Yellowfin Tuna stock assessment. ICCAT,Madrid.
Itano, D. G., and K. N. Holland. 2000. Movement and vulnerability of Bigeye(Thunnus obesus) and Yellowfin tuna (Thunnus albacares) in relation to FADsand natural aggregation points. Aquatic Living Resources 13:213–223.
Josse, E., P. Bach, and L. Dagorn. 1998. Simultaneous observations of tunamovements and their prey by sonic tracking and acoustic surveys. Hydrobi-ologia 371/372:61–69.
Kerstetter, D. W., J. J. Polovina, and J. E. Graves. 2004. Evidence of sharkpredation and scavenging on fishes equipped with pop-up satellite archivaltags. U.S. National Marine Fisheries Service Fishery Bulletin 102:750–756.
Lacroix, G. L. 2014. Large pelagic predators could jeopardize the recoveryof endangered Atlantic Salmon. Canadian Journal of Fisheries and AquaticSciences 71:343–350.
Lam, C. H., A. Nielsen, and J. R. Sibert. 2008. Improving light- and temperature-based geolocation by unscented Kalman filtering. Fisheries Research 91:15–25.
Luo, J. G., E. D. Prince, C. P. Goodyear, B. E. Luckhurst, and J. E. Serafy.2006. Vertical habitat utilization by large pelagic animals: a quantitativeframework and numerical method for use with pop-up satellite tag data.Fisheries Oceanography 15:208–229.
Maunder, M. N., M. G. Hinton, K. A. Bigelow, and A. D. Langley. 2006.Developing indices of abundance using habitat data in a statistical framework.Bulletin of Marine Science 79:545–559.
Musyl, M. K., M. L. Domeier, N. Nasby-Lucas, R. W. Brill, L. M. McNaughton,J. Y. Swimmer, M. S. Lutcavage, S. G. Wilson, B. Galuardi, and J. B. Liddle.2011. Performance of pop-up satellite archival tags. Marine Ecology ProgressSeries 433:1–28.
National Geophysical Data Center. 2006. Two-minute gridded global re-lief data (ETOPO2 v2). National Oceanic and Atmospheric Administra-tion, National Geophysical Data Center, Boulder, Colorado. Available:www.ngdc.noaa.gov/mgg/fliers/06mgg01.html. (September 2014).
Nielsen, A., K. A. Bigelow, M. K. Musyl, and J. R. Sibert. 2006. Improv-ing light-based geolocation by including sea surface temperature. FisheriesOceanography 15:314–325.
Nielsen, J. L., H. Arrizabalaga, N. Frangoso, A. Hobday, M. Lutcavage, and J.Sibert, editors. 2009. Tagging and tracking of marine animals with electronicdevices. Springer, London.
NMFS (National Marine Fisheries Service). 2013. Fisheries of the United States2012. Page 135 in A. Lowther, editor. NMFS, Silver Spring, Maryland.
NOAA (National Oceanic and Atmospheric Administration). 2012. Oil andgas exploration. NOAA, Office of Ocean Exploration and Research.Available: //oceanexplorer.noaa.gov/explorations/06mexico/bacground/oil/media/platform 600.html. (November 2013).
Ortiz, M. 2001. Review of tag-releases and recapture for YellowfinTuna from the U.S. CTC program. International Commission for the
Dow
nloa
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by [
71.4
6.64
.222
] at
08:
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2 N
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222 HOOLIHAN ET AL.
Conservation of Atlantic Tunas (ICCAT) Collective Volume of ScientificPapers 52:215–221.
Prince, E. D., and C. P. Goodyear. 2006. Hypoxia-based habitat compression oftropical pelagic fishes. Fisheries Oceanography 15:451–464.
Prince, E. D., and C. P. Goodyear. 2007. Consequences of ocean scale hypoxiaconstrained habitat for tropical pelagic fishes. Gulf and Caribbean Research19:17–20.
Prince, E. D., J. Luo, C. P. Goodyear, J. P. Hoolihan, D. Snodgrass, E. S. Orbesen,J. E. Serafy, M. Ortiz, and M. J. Schirripa. 2010. Ocean scale hypoxia-basedhabitat compression of Atlantic istiophorid billfishes. Fisheries Oceanogra-phy 19:448–462.
Rabalais, N. N., R. E. Turner, Q. Dortch, W. J. Wiseman, and B. K. S. Gupta.1996. Nutrient changes in the Mississippi River and system responses on theadjacent continental shelf. Estuaries 19:386–407.
Rice, P. H., C. P. Goodyear, E. D. Prince, D. Snodgrass, and J. E. Serafy. 2007.Use of catenary geometry to estimate hook depth during near-surface pelagiclongline fishing: theory versus practice. North American Journal of FisheriesManagement 27:1148–1161.
Rooker, J. R., J. R. Simms, R. J. D. Wells, S. A. Holt, G. J. Holt, J. E. Graves,and N. B. Furey. 2012. Distribution and habitat associations of billfish andSwordfish larvae across mesoscale features in the Gulf of Mexico. PloS ONE[online serial] 7:e34180.
Schaefer, K. M., D. W. Fuller, and G. Aldana. 2014. Movements, behavior,and habitat utilization of Yellowfin Tuna (Thunnus albacares) in waters sur-
rounding the Revillagigedo Islands Archipelago Biosphere Reserve, Mexico.Fisheries Oceanography 23:65–82.
Schaefer, K. M., D. W. Fuller, and B. A. Block. 2007. Movements, behavior,and habitat utilization of Yellowfin Tuna (Thunnus albacares) in the north-eastern Pacific Ocean, ascertained through archival tag data. Marine Biology152:503–525.
Schaefer, K. M., D. W. Fuller, and B. A. Block. 2011. Movements, behav-ior and habitat utilization of Yellowfin Tuna (Thunnus albacares) in thePacific Ocean off Baja California, Mexico, deternined from archival tagdata analyses, including unscented Kalman filtering. Fisheries Research112:22–37.
Sibert, J. R., and J. L. Nielsen, editors. 2001. Electronic tagging and tracking inmarine fisheries. Kluwer, Dordrecht, The Netherlands.
Stramma, L., E. D. Prince, S. Schmidtko, J. Luo, J. P. Hoolihan, M. Visbeck,D. W. R. Wallace, P. Brandt, and A. Kortzinger. 2012. Expansion of oxygenminimum zones may reduce available habitat for tropical pelagic fishes.Nature Climate Change 2:33–37.
Weng, K. C., M. J. W. Stokesbury, A. M. Boustany, A. C. Seitz, S. L. H. Teo, S.K. Miller, and B. A. Block. 2009. Habitat and behaviour of Yellowfin TunaThunnus albacares in the Gulf of Mexico determined using pop-up satellitearchival tags. Journal of Fish Biology 74:1434–1449.
Worton, B. J. 1995. Using Monte Carlo simulation to evaluate kernel-based home range estimators. Journal of Wildlife Management 59:794–800.
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