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Marine and Coastal FisheriesDynamics, Management, and Ecosystem Science

ISSN: (Print) 1942-5120 (Online) Journal homepage: http://www.tandfonline.com/loi/umcf20

Vertical and Horizontal Movements of YellowfinTuna in the Gulf of Mexico

J. P. Hoolihan, R. J. D. Wells, J. Luo, B. Falterman, E. D. Prince & J. R. Rooker

To cite this article: J. P. Hoolihan, R. J. D. Wells, J. Luo, B. Falterman, E. D. Prince & J. R. Rooker(2014) Vertical and Horizontal Movements of Yellowfin Tuna in the Gulf of Mexico, Marine andCoastal Fisheries, 6:1, 211-222, DOI: 10.1080/19425120.2014.935900

To link to this article: http://dx.doi.org/10.1080/19425120.2014.935900

© American Fisheries Society 2014

Published online: 28 Oct 2014.

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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 john.hoolihan@noaa.govReceived January 7, 2014; accepted June 8, 2014

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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

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No.

num

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(cm

)da

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m)

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6,20

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,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

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abM

K-1

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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

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2008

Aug

29,2

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◦ 39′ N

,89◦ 4

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◦ 16′ N

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506

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2008

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27◦ 9

′ N,8

9◦ 52′ W

144

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9029

◦ 6′ N

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′ W28

◦ 2′ N

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2008

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◦ 65′ N

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9W91

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2008

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◦ 6′ N

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◦ 36′ N

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May

11,2

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May

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28◦ 5

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9◦ 28′ W

2410

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8◦ 83′ W

27◦ 5

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285

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2009

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◦ 74′ N

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◦ 59′ N

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312

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116

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4815

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2009

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916

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◦ 33′ N

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1709

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122

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◦ 9′ N

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418

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27◦ 3

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154

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158

Nov

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31,2

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◦ 91′ N

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112

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9◦ 42′ W

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115

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◦ 34′ N

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2010

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◦ 26′ N

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◦ 42′ N

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128

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2010

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◦ 26′ N

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◦ 00′ N

,86◦ 1

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126

09P0

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Min

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ay8,

2011

May

26,2

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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

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/180

28◦ 2

6′ N,8

9◦ 34′ W

28◦ 2

9′ N,8

9◦ 58′ W

6528

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Min

i-PA

T11

0M

ay8,

2011

Jul3

,201

156

/180

28◦ 2

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9◦ 34′ W

28◦ 4

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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

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◦ 26′ N

,89◦ 2

7′ W28

◦ 10′ N

,89◦ 6

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3109

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135

May

8,20

11Ju

n29

,201

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/180

28◦ 2

7′ N,8

9◦ 34′ W

28◦ 2

9′ N,8

8◦ 51′ W

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aT

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and

arch

ival

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t.c

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oman

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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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215

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TAB

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T

�T

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025

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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

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0.03

4−6

—0.

001

0.00

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010

0.02

40.

050

0.05

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0.05

20.

052

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021

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021

0.01

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0.00

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021

0.01

90.

018

0.01

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018

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60.

016

0.00

8−1

5—

——

——

——

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——

—

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.

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