Small Scale Vertical Behaviour of Juvenile Albacore in Relation to Their Biotic Environment in the Bay of Biscay

Small Scale Vertical Behaviour of JuvenileAlbacore in Relation to Their BioticEnvironment in the Bay of Biscay

Nicolas Goni, Igor Arregui, Ainhoa Lezama, Haritz Arrizabalagaand Gala Moreno

Abstract The goal of the present study is to analyze the small scale verticalbehaviour of juvenile albacore tuna (Thunnus alalunga) in relation to the abun-dance and distribution of their main prey, which has particular importance regard-ing catchability by surface fishing gears, such as trolling. A total of six juvenilealbacore were tracked in the south east Bay of Biscay in July and August 2005,using ultrasonic transmitters. Two echosounders working at 38 and 120 kHz onthe tracking vessel were used to collect data on the biotic environment (krill, smallpelagic fish and planktonic layers) between the surface and 200 m depth. These datawere echo-integrated in order to relate tuna vertical movements to food availability.The stomach contents of 97 albacore caught during the surveys were analyzed, thecomparison of prey occurrences respectively in the stomachs and on the echogramsshowed selectivity for blue whiting. However, the biotic factors considered in thisstudy had no significant influence on the depth of albacore, which possibly feedduring night-time in surface waters. The tracked albacore had a shallow depth dis-tribution and did not exhibit any regular deep-diving behaviour. A significant effectof time of day and body size on albacore depth was shown, all fish remaining deeperduring daytime, and smaller fish having a shallower vertical distribution.

Keywords Albacore · Behaviour · Ultrasonic telemetry · Echosounding · Prey ·Bay of Biscay · Atlantic

Introduction

Albacore (Thunnus alalunga) is a highly migratory tuna species, found in both trop-ical and temperate waters of the three oceans. It is considered the most pelagic of alltuna species (Bard, 1981) and the juveniles make large scale seasonal migrations

N. Goni (B)AZTI-Tecnalia, Marine Research Division, Herrera kaia portualdea z/g, 20110 Pasaia,Gipuzkoa, Spaine-mail: [email protected]

J.L. Nielsen et al. (eds.), Tagging and Tracking of Marine Animals with Electronic Devices,Reviews: Methods and Technologies in Fish Biology and Fisheries 9,DOI 10.1007/978-1-4020-9640-2 4, C© Springer Science+Business Media B.V. 2009

51

52 N. Goni et al.

between subtropical and temperate waters (Bard, 1981; Santiago, 2004). In theNorth Atlantic, juveniles make a trophic migration in summer, the period in whichthey show higher growth rates than during the rest of the year when in winteringgrounds (Santiago and Arrizabalaga, 2005). This migration occurs towards produc-tive zones in the Bay of Biscay and surrounding waters, where they are exploitedfrom June through October by surface gears, mainly Spanish trolling and baitboatfisheries, which delivered more than 50% of the total North Atlantic albacore catchin the last decade (ICCAT, 2007).

Knowledge on fish behaviour is highly important for a better understanding oftheir catchability (Freon and Misund, 1999), and the study of their vertical behaviourin relation to their biotic environment is particularly relevant regarding catchabil-ity by surface fishing gear. This is especially the case for trolling CPUE series,which are used as abundance indices for the North Atlantic albacore stock assess-ment (ICCAT, 2007). Possible biotic influences on albacore vertical behaviour couldbias these abundance indices, and need therefore to be identified.

Albacore is one of the the main commercial tuna species, with a particularlyhigh commercial importance in the North-East Atlantic (Santiago, 2004). However,little is known about albacore behaviour through tagging and/or tracking experi-ments (Childers et al., 2007; Laurs et al., 1977; Uosaki, 2004), compared with theinformation collected on Atlantic bluefin tuna (Thunnus thynnus) (Brill et al., 2002;Stokesbury et al., 2004; Wilson et al., 2005), Pacific bluefin tuna (Thunnus orien-talis) (Itoh et al., 2003; Kitagawa et al., 2004; Kitagawa et al., 2007; Marcinek et al.,2001), or to a lesser extent on yellowfin (Thunnus albacares) and bigeye tuna (Thun-nus obesus) (Brill et al., 1999; Dagorn et al., 2000a; Musyl et al., 2003; Schaefferand Fuller, 2005).

In most of these experiments on tuna behaviour, the movements of the tracked ortagged individuals have been mainly interpreted in relation to variations of abioticvariables, such as temperature, salinity or dissolved oxygen. In the case of alba-core, the horizontal movements of 17 juvenile (age-3 to age-5) albacore in theEastern North Pacific were related to sea surface temperatures between 15◦C and19◦C (Childers et al., 2007), which confirms the observations previously made byLaurs et al. (1977) in the same area on 84–87 cm individuals. As for their verti-cal movements, Childers et al. (2007) report a shallower distribution in summermonths related to shallow thermoclines associated with surface mixing, but withdives to waters with temperatures as low as 9◦C. This shallower distribution in sum-mer months is confirmed by Uosaki (2004), who also reports a distribution related toambient temperatures between 14◦C and 18◦C in summer months. Little interpreta-tion involving biotic environment has been given, either in experiments on albacoreor on other species, as most experiments did not consider or collect relevant obser-vations of tuna prey. However, in studies in which potential prey were observed andanalyzed in relation to the movements or habitat of tuna or large pelagic fish (Carey,1992; Josse et al., 1998; Bertrand et al., 2002; Domokos et al., 2007), prey distribu-tion had an important influence on the behaviour of tuna or large pelagic fish.

Tunas have high metabolic rates due to their obligate continuous activity(Graham and Laurs, 1982), and high standard metabolic rates compared to strictly

Sonic Tracking of Juvenile Albacore in Their Biotic Environment 53

poikilotherm fish species (Korsmeyer and Dewar, 2001). This metabolic rate maybe particularly high in juvenile individuals – i.e. in rapid growth phase – and inindividuals that perform long-distance seasonal migrations. For juvenile albacoreperforming a trophic migration from subtropical waters to the Bay of Biscay andsurrounding areas, we hypothesize that, due to their energetic needs, prey abundanceand distribution will have a significant influence on their behaviour.

The aim of this study is to analyze the small scale vertical behaviour of juvenilealbacore tuna, as inferred from ultrasonic telemetry, in relation to the abundanceand distribution of their main prey, as inferred from acoustic surveys. As this is thefirst tracking experiment on the North Atlantic albacore population, we also aimto compare the vertical distribution of albacore to that observed in other areas, anddiscuss implications regarding catchability by trolling gear.

Materials and Methods

Echosounding and Echointegration

Two Simrad EY60 split-beam scientific echosounders were used, working respec-tively at 38 and 120 kHz. The depth range of signal detection was from 5 to 400 m inthis experiment, and the detection angle of both echosounders was 7◦. The softwareSimrad was used to record echosounding data to a depth of 200 m. The use of twofrequencies allowed us to perform an echointegration of the recorded data, in orderto estimate the relative biomass of the prey groups observed.

Multi-frequency analysis uses the response of different targets to the differentoperative frequencies to distinguish between species. For instance the different fre-quency response of swim-bladdered fish from that of plankton or krill is well known(Madureira et al., 1993; Kang et al., 2002). In the present work two coarse groupsof prey were distinguished using multifrequency analysis: swim-bladdered fish andplanktonic organisms (including krill), this last group being further divided into krilland plankton. The energy scattered by a given target, at both 38 and 120 kHz, wascompared between acoustic samples, in order to create different filters suited for thediscrimination of a single prey group. The filters are applied to the echograms, toobtain virtual channels which contain the energy attributed only to the correspond-ing prey group.

For fish, the virtual channel was obtained by subtracting the 120 kHz channelfrom the 38 kHz channel. A constant value of –55 dB was then added to this newchannel, and a –55 dB threshold was applied during echointegration. The resultingchannel is a filter that was applied to the 38 kHz channel, leaving the fish detec-tions unaltered and subtracting the other prey groups. For krill, the virtual channelwas obtained by subtracting the 38 kHz channel from the 120 kHz channel. As forfish, a constant value of –55 dB was then added to this new channel, and a –55 dBthreshold was applied during echointegration. The resulting channel is a filter thatwas applied to the 120 kHz channel, separating krill echoes from other elements.For plankton, the virtual channels of fish and of krill were summed, and this sum

54 N. Goni et al.

divided by two in order to shift the minimal threshold from –160 dB to –80 dB. Theresult is then subtracted from the channel where the majority of the plankton appears(38 kHz channel in our case). A –55 dB threshold was applied during echointegra-tion. The resulting channel is a filter that was applied to the 38 kHz channel in orderto separate the echoes of plankton layers.

The echointegration was realized by shoals (krill and small pelagic fish), and bygroups within layers (krill, small pelagic fish, and plankton). Ten layers were con-sidered, nine layers of 10 m between 5 m and 95 m, and a single layer from 95 and200 m. The acoustic back-scattered energy by surface unit (sA, MacLennan et al.2002) of each layer was computed every 0.5 geo-referenced nautical mile, for fish,krill and plankton separately. For the echointegration by shoals, the following datawere computed for each shoal: position, time of detection, depth of the “backscattercentroid” (average of the latitude, longitude and depth of all the pixels in a shoal,weighted by the backscatter energy of each pixel), length, height, area of the ver-tical section, and volume reverberation index of the shoal (Sv, MacLennan et al.2002). Extracted shoal objects with a length smaller than two beam widths at depthwere rejected as being too small to be characterized adequately (Diner, 2001). Shoalobjects between the surface and 5 m depth were also rejected to avoid the risk ofconfusion with echoes generated by surface-related turbulence. The relative biomass(within each prey group) of each shoal was estimated as

biomass = area · 10Sv10

with area being the area of the vertical section of the shoal. The echointegration, or“EI by shoal” algorithm, implemented in Movies+ 3.4. software (Weill et al. 1993;Diner et al. 2006), was applied to the acoustic data.

Among fish shoals, the identification at the species level was not made auto-matically but visually through inspection of echo trace characteristics (Boyra et al.,2006). The species we focused on in the echograms were determined after stomachcontent analysis (see below). Mackerel (Scomber scombrus) shoals were character-ized by a higher reflected energy in the 120 kHz channel than on the 38 kHz channel,and a variable shape due to vertical movements within schools (Boyra, pers. comm.).Krill shoals were also characterized by a higher reflected energy in the 120 kHzchannel, but by a round shape. Anchovy (Engraulis encrasicolus) and sardine (Sar-dina pilchardus) shoals were both characterized by a high reflected energy (–50 to–25 dB) in the two channels, and were mostly located between 5 and 20 m depth.Blue whiting (Micromesistius poutassou) shoals appeared as consecutive small ele-ments, located below 60 m depth, with a higher reflected on the 38 kHz channel thanon the 120 kHz channel, this reflected energy being lower than for the previouslymentioned groups. Horse mackerel (Trachurus trachurus) shoals appeared as largeshoals located mostly below 30 m depth, with a higher reflection on the 38 kHzchannel than on the 120 kHz channel, this reflected energy being relatively low asfor blue whiting. Horse mackerel shoals appeared only rarely in the echograms,thus, they were not considered in the analysis of albacore behaviour. Other shoalsnot clearly fitting with any of the previously described characteristics were also


discarded. Discarded shoals represented 5.4% of the number of visualized shoals.The software Movies+ 3.4 was also used for the visualization of shoals.

Fishing Operations

Fishing operations were conducted from a 16 m commercial fishing boat (FVHandik, Pasaia) in the south east Bay of Biscay. The fishing gear used in this exper-iment was trolling lines. Individuals that were injured by the hooks or too small fortagging were kept on board and their stomachs stored for diet analysis. The stomachcontents of 97 non-tagged albacore were analyzed, with the weight and number ofindividuals of each identified prey species recorded.

Tagging and Tracking

The tracking equipment was a VEMCO VR28 system. Fish were tagged with V16and V22 ultrasonic transmitters with frequencies of 51 and 50 kHz, which min-imised interference with the echosounder. The tags were attached to the tunas usingtwo nylon tie-wraps that were implanted behind the second dorsal fin, as describedby Holland et al. (1990). A VH40 hydrophone, mounted within a VFIN (i.e. av-shaped fin that allows the hydrophone to be towed by the vessel at a uniformdepth), was used to detect the signals of the transmitters in each horizontal direction(bow, aft, starboard, and port). The V16 TP-5H transmitters were fitted with pressureand temperature sensors. V22 P-5XS (used because of their more powerful signal)and V16 P-4H transmitters were fitted with pressure sensors only. During tracksperformed using V22 P-5XS and V16 P-4H tags, temperature profiles of the watercolumn were measured using XBTs. Depending on the background noise, mainlyrelated to sea surface turbulence, the maximum distance between the tracked fishand the vessel was 300 m for V16P-4H transmitters (Girard et al., 2007) and upto 1000 m for V22 P-5XS transmitters (Moreno, unpublished data). Tracks wereterminated when the signal of the transmitter was lost.

Behaviour Analyses

The vertical behaviour of the tracked individuals was analyzed at two time-scales.By minute, the mean depth of the tracked individuals was related to the depth ofshoals of each prey group used, in order to look for immediate reactions of albacoreto the presence of prey. By hour, in order to look for influences of potential preyon the preferential depths of the tracked individuals, the mean depth of the trackedindividuals was related to the mean depth, mean relative biomass and hourly num-ber of shoals of each prey group used (anchovy/sardine, mackerel, blue whiting

56 N. Goni et al.

and krill), and to the biomass (as derived from the reflected energy by layers) andweighted mean depth of the biomass of krill, fish and plankton. The weighted meandepth of the biomass of krill, fish and plankton was defined as the average depth ofthe 10 layers, weighted by the relative back-scattered energy by surface unit (sA) ofeach layer.

Separate analyses were performed for diving periods. The start and end ofarbitrary diving periods were defined respectively as the first minute before a depthincrease greater than 25 m in three minutes, and as the last minute of a depthdecrease greater than 25 m in three minutes. The mean depth, maximum depth andduration of the dives were related to the depth and relative biomass of shoals of eachtaxonomic group considered. The water temperature was not taken into account, asthe measured temperatures during the diving periods observed in this study wereabove the minimal temperature threshold allowing thermoregulation (i.e. 11.5◦C) –according to the observations reported by Graham and Dickson (1981) in exper-imental tanks – except for one individual at 11.34◦C for a few seconds. Time atliberty, hour-of-day, and fork length of the tracked fish were considered likely toaffect their depth, and consequently considered in the analyses.

These relationships were analyzed using generalized additive models (GAMs),which are nonparametric generalizations of multiple linear regression techniques(Hastie, 1992). A Gaussian distribution was assumed for all random variables. Astepwise model selection procedure was adopted, where the explanatory variablescould either be modelled as absent, as a linear function or as a spline function.Rows with missing values were omitted before the stepwise model selection, so thatall models were based on the same observations. The model with lowest AkaikeInformation Criteria (AIC) was selected, following Hastie (1992). To assess thenon-spuriousness of each significant relationship encountered, the same model wastested on 1000 random re-samples of the original data; the relationship was consid-ered non-spurious when it was significant for fewer than 50 re-samples. The gen-eralized additive models were built stepwise using the R v. 2.6.0. (R DevelopmentCore Team 2007) statistical software (available online at http://www.r-project.org/)and the gam 0.98 package. The corresponding analyses of variance were performedusing the mgcv 1.3-29 package.

Results

Environment at the Time of the Tracking

During all tracks the weather was sunny or with light cloud cover, the wind speedwas less than force 4 on Beaufort scale (i.e. <20 km/h, with waves were smaller than1.5 m). The sea surface temperature varied between 22 and 23.9◦C. The inflectionpoint of the seasonal thermocline was between 12.6 and 25 m depth. The temper-ature below the thermocline was between 11.51 and 12.76◦C in the different XBTprofiles.


Albacore Prey in Relation to the Observed Shoals of Potential Prey

The main prey in stomachs of the albacore (52–82.6 cm fork-length) caught duringthis study were blue whiting (Micromesistius poutassou), krill (Meganyctiphanesnorvegica), and anchovy (Engraulis encrasicolus) (Table 1). Mackerel (Scomberscombrus), horse-mackerel (Trachurus trachurus), pink glass shrimp (Pasiphaeamultidentata), mantis shrimp larvae (Squillidae), decapod larvae (in megalopaphase), Henslow’s swimming crab (Polybius henslowi), flying squid (Todarodessagittatus), salps (Salpa sp.) and nematodes were minor prey (mean weightpercentage by stomach <5%).

The comparison between the importance (in mean weight percentage by stom-ach) of the main prey in the diet of albacore, and their temporal occurrence in therecorded echograms shows a prey selectivity for blue whiting – the most representedprey in the stomachs and the second least represented by hour in the echograms –versus krill and anchovy that occur more frequently in the echograms but in smalleramounts in the stomachs (Table 1).

Table 1 Frequency of occurrence (%F) and mean weight percentage by stomach (%MW) of iden-tified prey items in the stomachs of 97 albacore (Thunnus alalunga); frequency of occurrenceof prey shoals on echograms in percentage of minutes (%min) and of hours (%hour); mediandepth (minimum-maximum) of shoals on echograms. In the case of anchovy, the values given forechograms correspond to anchovy and sardine shoals

Prey %F %MW %min %hour depth

Blue whiting(Micromesistius poutassou)

41.2 53.81 1.569 8.78 98.5 m(61.1–158.9 m)

Anchovy(Engraulis encrasicolus)

9.3 11.28 1.809 21.62 6.8 m(4.1–28 m)

Mackerel(Scomber scombrus)

2.1 0.36 0.698 18.92 18.4 m(6.2–51.6 m)

Horse mackerel(Trachurus trachurus)

3.1 3.50 0.103 2.68 59.92 m(14.7–148.1 m)

Krill(Meganyctiphanes norvegica)

23.7 20.45 4.065 49.32 59.8 m(15.3–189 m)

Pink glass shrimp(Pasiphaea multidentata)

3.1 0.93 – – –

decapod larvae(megalopa phase)

5.2 3.18 – – –

Henslow’s swimming crab(Polybius henslowii)

1.0 0.15 – – –

Mantis shrimp(Squillidae)

3.1 2.34

Flying squid(Todarodes sagittatus)

1.0 0.77 – – –

Salp(Salpa sp.)

1.0 <0.01 – – –

Nematode 5.2 3.23 – – –

58 N. Goni et al.

Vertical Distribution of Krill and Fish Layers

An important horizontal heterogeneity in the reflected energy in each layer, as wellas in vertical distribution was observed. The reflected energy of a given layer couldvary geographically by three orders of magnitude. Vertically, the highest reflectedenergy appeared mainly in the shallowest layer, but in some locations deeper, downto the fifth layer (45–55 m). Despite this horizontal heterogeneity, a circadian patternin the vertical distribution of the reflected energy of fish and of krill was observed,

Fig. 1 Scatter plots of the weighted mean depth of the reflected energy along the time of the day(GMT time), for (a) krill and (b) fish


the weighted mean depth of the biomass of krill and fish being greater and morevariable during the day (from 6:00 AM to 8:00 PM) than during the night (from8:00 PM to 6:00 AM, Fig. 1). According to Kolmogorov-Smirnov tests, the verticaldistribution of the weighted mean depth of the biomass is significantly differentbetween these two periods for fish (p = 1.321∗10–14) and for krill (p = 1.913∗10–11).

Tracks

A total of six albacore were tracked in the south east Bay of Biscay (Fig. 2) betweenJuly 12th and August 6th 2005, the duration of each track was between 7.33 and48.5 h (Table 2). Tagged fish were between 68 and 85 cm in fork length. The track-ing area was between 43◦30’ – 44◦40’ N and 2◦00′ – 3◦40′ W (Fig. 2), the indi-viduals remaining offshore from the continental shelf (bottom depth always greaterthan 500 m) for all tracks. Their horizontal movements varied between localized dis-placements, as was the case for ALB54, and continuous straight courses, as was thecase for ALB81 that followed a northwesterly course for 45.5 h, or for ALB92 dur-ing the whole tracking. With exception of ALB54, which did not exhibit a directedhorizontal displacement, the tracked individuals had an overall westwards longitu-dinal displacement during tracking.

Vertical Behaviour of Albacore

Depth Profiles

The two smaller individuals (ALB52 and ALB73, respectively 59 and 69 cm fork-length) had the shallower vertical distribution in the water column, whereas thelarger one (ALB91, 85 cm fork-length) had the deeper vertical distribution (Table 2).ALB91 remained mostly below the seasonal thermocline (15–20 m depth in thistrack), at a median depth of 26.02 m. ALB54 (80 cm fork-length) remained mostlywithin the seasonal thermocline (17–23 m depth in this track), at a median depthof 20.6 m. The other individuals remained mostly above this thermocline. In thesix tracks, no apparent reaction to the prey shoals was observed (Figs. 3, 4, 5, 6),except during a short time-range (8:20 AM to 10:30 AM) in the first 24 h of trackingof ALB73, the depth of which coincided with the depth of all the mackerel shoalsdetected within this time-range. ALB92 (79 cm fork-length) exhibits a shallowervertical distribution during night-time than during daytime (Fig. 6), but this differ-ence is not visible on the depth profiles of the other individuals. Repeated divesduring daytime (8:30 AM to 11:30 AM) were observed for ALB81 (78 cm) in thelast 24 h of tracking (Fig. 5). Its first dive below 120 m corresponds to the shift inits horizontal direction. The other tracked individuals did not perform consecutivedeep dives as ALB81 did, and no such clear shift in their horizontal displacement

60 N. Goni et al.

was observed. The consecutive deep dives of ALB81 occurred in the most north-western part of the tracking area, north of 44◦20’N and west of 3◦20’W (Fig. 2),whereas no similar behaviour was observed in the rest of the tracking area, closer tothe continental shelf.

Fig. 2 (a) Map of the Bay of Biscay and localization of the tracking area. The limit of the conti-nental shelf is shown by the 200-, 500-, and 1000-m isobath bold lines. Horizontal movements of(b) ALB52 and ALB54 and of (c) ALB73, ALB81, ALB92 and ALB91. Circles represent the startpoints of each track


Tabl

e2

Sum

mar

yof

trac

ksof

six

juve

nile

alba

core

equi

pped

with

ultr

ason

icde

pth-

sens

itive

tran

smitt

ers.

Tem

pera

ture

sfo

rA

LB

52,

AL

B81

,A

LB

91an

dA

LB

92ar

ede

rive

dfr

omX

BT

profi

les.

The

rang

eis

prov

ided

inpa

rent

hese

s

Dis

plac

emen

tD

ate(

s)of

Tota

lbe

twee

nst

art

trac

k(Y

ear:

Dur

atio

nof

med

ian

mea

ndi

stan

cem

ean

and

end

poin

tsfis

hID

FL(c

m)

Tra

nsm

itter

2005

)tr

ack

dept

h(m

)te

mpe

ratu

re(◦ C

)co

vere

d(n

m)

spee

d(k

nots

)(n

m)

AL

B52

59V

16P

4H12

Jul

7h

20m

in12

.95

m(6

0.53

m)

20.3

3(1

1.84

–21.

97)

19.6

92.

68 (1.4

–4.7

)8.

18

AL

B54

80V

16T

P5H

13–1

4Ju

l14

h15

min

20.6

m(1

54.8

3m

)16

.58

(11.

68–2

2.02

)33

.51

2.35 (1

.0–6

.0)

0.64

AL

B73

69V

16T

P5H

25–2

7Ju

l41

h01

min

14.7

0m

(97.

58m

)20

.9 (11.

34–2

5.68

)11

4.49

2.79 (1

.2–5

.7)

26.3

9

AL

B81

78V

22P

5XS

1–3

Aug

48h

34m

in16

.57

m(1

65.0

2m

)20

.74

(11.

61–2

1.91

)16

2.11

3.34 (1

.4–6

.6)

69.5

6

AL

B91

85V

22P

5XS

4–5

Aug

17h

18m

in26

.02

m(4

6m

)15

.15

(12.

43–2

2.65

)43

.54

2.52 (1

.2–5

.4)

15.1

4

AL

B92

79V

22P

5XS

5–6

Aug

17h

28m

in18

.3m

(42.

3m

)18

.94

(12.

76–2

3.11

)47

.00

2.69 (1

.2–5

.2)

29.2

6

62 N. Goni et al.

Fig. 3 Depth profiles of ALB52 (upper panel ) and of ALB54 (lower panel ) and localization ofprey shoals: mackerel (open triangles) and krill (open circles). Circles of different sizes representkrill shoals of different biomass ranges

Influence of Tagging on Behaviour

According to the depth profiles, the vertical distributions of all tracked fish exceptALB91 appear deeper and/or more variable in the first minutes after tagging. Foreach tracked individual, the comparison of the vertical distribution during thefirst two hours of the tracking with the vertical distribution during the followingeight hours, showed a highly significant difference for all individuals (Table 3,Kolmogorov-Smirnov tests). The effect of tagging on fish behaviour is therefore


Fig. 4 Depth profile of ALB73 in the first 24 h of track (upper panel) and in the last 17 h (lowerpanel); localization of prey shoals : anchovy/sardine (open squares), blue whiting (black diamonds)mackerel (open triangles) and krill (open circles). For anchovy/sardine and krill, squares and cir-cles of different sizes represent shoals of different biomass ranges

significantly stronger during the two first hours than during the following eight;however, we cannot discard that a possible effect of tagging remains for longer thaneight hours.

Circadian Patterns

A significant effect of hour-of-day on albacore depth was shown by a GAM(p < 2.2∗10–16), the tracked fish remaining deeper during day-time than duringnight-time (Fig. 7). Although this effect seems visible only on the profile of ALB92,

64 N. Goni et al.

Fig. 5 Depth profile of ALB81 and localization of prey shoals (details given in Fig. 4) in the first24 h of track (upper panel) and in the last 24 h (lower panel)

the relationship is also significant in the generalized additive model when any of thesix individuals is removed from the analysis. Regarding albacore hourly depth, theeffect of hour on depth is not significant when ALB81 is not included, however,in that case the dataset is particularly small (100 values of depth), and somedaytime hours are represented by only one (ALB73) or two (ALB54 and ALB73)individuals.

For albacore speed a circadian pattern was also shown by a GAM (p < 2.2∗10–16),the highest values corresponding to the end of the morning, the lowest values tonight-time (Fig. 8). An important individual variability in speed was also observed.

Effect of Fish Size on Depth

As the dataset was limited to six albacore, body size was not considered a con-tinuous variable, rather it was considered a factor comprising four groups: FL1


Fig. 6 Depth profiles of ALB91 (upper panel) and of ALB92 (lower panel) and localization ofprey shoals (details given in Fig. 4)

Table 3 Median (MD) and standard error (S.E.) of the depth of the tracked albacore during the 2first hours of tracking (0–2 h) and during the following 8 h (2–10 h)

MD S.E. MD S.E. K. S. testFish ID (0–2 h) (0–2 h) (2–10 h) (2–10 h) (p-value)

ALB52 –10.77 7.38 –13.47 5.99 <2.2∗10–16

ALB54 –29.35 22.10 –20.06 3.24 <2.2∗10–16

ALB73 –15.69 7.70 –13.16 2.83 <2.2∗10–16

ALB81 –37.37 12.11 –18.18 9.31 <2.2∗10–16

ALB91 –21.46 3.57 –27.14 5.22 <2.2∗10–16

ALB92 –28.34 7.83 –9.13 4.74 <2.2∗10–16

66 N. Goni et al.

FL1

0.0 0.2 0.4 0.6 0.8 1.0time

40

part

ial f

or s

ize

–44

02

s(tim

e)

–4–8

FL2 FL3

size

FL4

Fig. 7 Generalized additive model of the depth of the tracked albacore (expressed in negative val-ues), as a function of size (four groups) and of the spline of time of the day (GMT time, expressedas a fraction of 24 h)

ALB52

0.4

0.0

–0.4

0.2

0.0

–0.2

0.0 0.2 0.4time

0.8 1.00.6

s(tim

e)pa

rtia

l for

fish

ID ALB54 ALB73 ALB81

fishID

ALB91 ALB92

Fig. 8 Generalized additive model of the speed of the tracked albacore as a function of the trackedindividual (six modalities) and of the spline of time of the day (GMT time, expressed as a fractionof 24 h)

(ALB52, 59 cm fork-length), FL2 (ALB73, 69 cm fork-length), FL3 (ALB54,ALB81 and ALB92, respectively 80, 78 and 79 cm fork-length) and FL4 (ALB91,85 cm fork-length). A significant effect of this factor was observed (p < 2.2∗10–16),with larger individuals remaining deeper (Fig. 7).


Vertical Behaviour of Albacore in Relation to Prey Shoals

Hour and fish size were considered in all GAMs relating albacore depth and bioticenvironment, in order to take their respective influences on albacore depth intoaccount. The GAMs of albacore hourly depth in function of the biomass, averagedepth and hourly number of shoals of anchovy/sardine, krill, mackerel and bluewhiting showed no significant relationship.

The GAM of albacore depth by minute as a function of the depth of the shoalsof the four identified prey groups showed a significant effect of the depth of krillshoals. However, the same model tested on 1000 random re-samples of the originaldata showed also a significant effect of the depth of krill shoals on 121 of them. Thisrelationship is thus considered spurious. No significant relationship was observedbetween the depth and relative biomass of prey shoals, and the depth and durationof the dives.

Vertical Behaviour of Albacore in Relation to the Depthand Biomass of Micronekton and Plankton

The GAM of albacore hourly depth as a function of the biomass and weighted meandepth of fish, krill and plankton shows significant influences of plankton biomassand of the weighted mean depth of krill biomass on albacore vertical behaviour(Table 4). However, the relationship with plankton biomass is not significant ifALB81 is removed from the analysis, and the relationship with the weighted meandepth of krill is not significant if ALB73 is removed from the analysis (Table 5), so

Table 4 ANOVAs of the generalized additive models of albacore hourly depth as a function ofsize, spline of hour [s(hour)], plankton biomass [P] and spline of the weighted mean depth of krillbiomass [s(DK)]. Degrees of freedom (df) for size and for plankton biomass, estimated degrees offreedom (edf) for spline parameters

Fish removed parameter df/edf F p-value

none size 2 24.62 7.51∗10–10

s(hour) 3.512 7.7926 7.797∗10–5

P 1 11.07 0.00113s(DK) 2.583 4.1213 0.007841

ALB52 size 2 21.42 1.03∗10–8

s(hour) 3.733 3.802 0.000506P 1 12.05 0.000714s(DK) 6.556 2.429 0.014109

ALB54 size 2 21.092 1.58∗10–8

s(hour) 3.855 3.891 0.00043P 1 9.454 0.00263s(DK) 8.229 2.905 0.00392

68 N. Goni et al.

Table 5 ANOVAs of the generalized additive models of albacore hourly depth as a function ofsize, spline of hour [s(hour)], plankton biomass [P] and spline of the weighted mean depth of krillbiomass [s(DK)]. Degrees of freedom (df) for size and plankton biomass, estimated degrees offreedom (edf) for spline parameters)

Fish removed parameter df/edf F p-value

ALB73 size 2 8.377 0.000445s(hour) 3.242 3.607 0.00172P 1 6.310 0.013683s(DK) 2.303 2.197 0.06085

ALB81 size 2 30.491 7.75∗10–11

s(hour) 1.000 3.010 0.0862P 1 0.511 0.476s(DK) 2.002 2.408 0.0426

ALB91 size 2 22.58 5.64∗10–6

s(hour) 3.634 4.469 9.12∗10–5

P 1 13.33 0.000388s(DK) 2.459 3.086 0.0118

ALB92 size 2 22.75 4.88∗10–9

s(hour) 4.609 3.600 0.000563P 1 10.17 0.00184s(DK) 7.548 2.535 0.010926

we cannot demonstrate a clear influence of the biotic environment on the verticalbehaviour of juvenile albacore in the Bay of Biscay.

Discussion

The vertical distribution of the tracked albacore was deeper during daytime and forlarger individuals, mean speed was also higher during daytime, fish did not showany particular diving pattern, and their vertical movements did not appear to beinfluenced by any of the biotic variables tested. The analysis of their diet and ofechogram data shows prey selectivity for blue whiting.

Circadian variation of the weighted mean depth of the biomass of krill and fishconfirms the circadian vertical movements of small pelagic fish and of krill. School-ing small pelagic fish commonly disperse at night-time and extend upwards to sur-face waters (Freon and Misund, 1999). Euphausiids generally exhibit a pronouncedcircadian vertical migration, rising to the surface layer at night-time to feed, thendescending at dawn to a depth where visual detection by predators is limited duringdaylight hours (Mauchline, 1980).

The lower swimming speed of albacore at night-time corroborates the observa-tions made by Laurs et al. (1977) on albacore in the eastern North Pacific. A deepervertical distribution of albacore during daytime was also observed in the easternNorth Pacific (Childers et al., 2007) and in the central Equatorial Pacific (Domokoset al. 2007), although the difference between day and night-time depth was much


higher in these two studies. In the western North Pacific, this vertical difference indistribution was observed only in winter months, however, these observations werebased on just a single individual (Uosaki, 2004). Among other tuna species, a sim-ilar circadian depth pattern has been observed for Pacific bluefin tuna (Kitagawaet al., 2007), Atlantic bluefin tuna (Wilson et al., 2005) and bigeye tuna (Musyl,2003).

Regarding the vertical behaviour of the tracked individuals, our observations con-trast with those made by Childers et al. (2007), who reported that albacore (3–5years of age) in the Eastern North Pacific had a pattern of repetitive deep diving (rou-tinely to depths of 250–300 m) during the day. In the western North Pacific (Uosaki,2004), a juvenile albacore (78 cm fork-length at release and 82.5 cm at recapture)implanted with an archival tag did not show any clear diurnal diving pattern in thesummer months. The absence of marked dives at dusk and dawn was also reported injuvenile Atlantic bluefin tunas (74–106 cm fork-length) tracked in the Gulf of Maine(Brill et al., 2002), while in juvenile Pacific bluefin tuna (49–151 cm fork-length)in the central North Pacific, deep dives at dusk and dawn were reported (Kitagawaet al., 2004). We suggest a different feeding behaviour for tunas on continental shelfareas, with feeding not necessarily related to diving. This fits with our observation ofrepeated deep dives only in the most offshore part of the tracking area, although thisobservation corresponds to a single individual (i.e. ALB81). These repeated deepdives, although not coinciding spatially with prey shoals, might be interpreted as aforaging behaviour according to Dagorn et al. (2000b), as they also coincided witha horizontal directional change. The absence of relationship between the depth andrelative biomass of prey shoals, and the depth and duration of the dives also dependson the way dives are defined – which is arbitrary in the present work – and on thenumber of observed dives – which is reduced in the present work.

In the Bay of Biscay, the absence of feeding behaviour of albacore at daytimein the presence of prey is also reported by baitboat fishermen, who frequentlyencounter juvenile albacore tunas that do not react to live bait. Regarding theabsence of relationship between albacore movements and their prey, our results alsocontrast with those reported by Domokos et al. (2007), who show that the verticaldistribution of albacore tuna seems to be strongly influenced by the availability offood. Nevertheless, their study is based on adult fish (93–95 cm fork-length) in asubtropical area, and the feeding behaviour of albacore is likely to differ betweenjuveniles and adults (Bard et al., 1998), adults feeding deeper in the mesopelagiczone. For bigeye and yellowfin tuna in French Polynesia, Josse et al. (1998) reportedan important influence of scattering layers (assimilated as prey) on the movements,during day-time as well as during night-time.

Tunas are highly opportunistic predators (Sund et al., 1981), thus, the importanceof blue whiting in the albacore diet during the tagging surveys suggests that this preyspecies may be particularly abundant and/or easy to catch in the south east Bay ofBiscay. However, blue whiting shoals were not particularly available to albacore, asthey were always observed below 60 m depth on the echograms, while albacore weremainly distributed within the upper 30 m. Thus, it is unlikely that an opportunisticpredator would select a deep-water species when other prey species are available in

70 N. Goni et al.

shallow waters. During other scientific surveys using the same echosounders, bluewhiting were caught at night by pelagic trawling at a depth of 13 m (Lezama, unpub-lished data) while no particular structure appeared on the echograms. This, togetherwith the observed shallower distribution of fish biomass at night-time (Fig. 1), sug-gests that juvenile albacore in the south east Bay of Biscay feed mainly at night,on blue whiting present in disaggregated structures in surface waters. This wouldexplain the dominance of blue whiting in their diet, and the absence of particularvertical behaviour induced by prey shoals. Feeding behaviour at night has also beenreported for bigeye and skipjack tuna (Schaefer and Fuller, 2005) as well as foryellowfin tuna (Schaefer, pers. comm.).

These results do need to be interpreted cautiously, as they are based on only sixindividuals, tracked for relatively short periods. An important point is that we donot know whether the tracked tunas were isolated or within schools, which can haveimplications on their foraging and swimming behaviour. This study also took placein a small and relatively marginal area compared to the summer geographical distri-bution of juvenile albacore in the Northeast Atlantic, and their feeding is probablydifferent in other areas, where stomach fullness is lower and blue whiting is notso predominant in their diet (Goni and Arrizabalaga, unpublished data). In spite ofthe inherent technical limitations, ultrasonic telemetry remains the most appropri-ate tracking technique to obtain simultaneous observations of the biotic environ-ment of tunas and gain knowledge about the reactions of tuna in the presence ofprey. These insights might be useful when interpreting electronic tagging data col-lected over broader areas (e.g. from archival tags) but without simultaneous obser-vations of the surrounding biotic environment, fine scale interactions can not bedetermined.

Conclusion

Overall, this study provides the first description of the vertical behaviour of juvenilealbacore tuna in the North East Atlantic, characterized as a shallow depth distri-bution and with an absence of repetitive deep diving behaviour. The vertical dis-tribution was deeper during daytime than during night-time, and was shallowerfor smaller individuals, although the number of observed individuals was limited.The studied albacore did not show any clear reaction to the presence of prey, theirdepth was not significantly influenced by the biotic factors considered, and we sug-gest juvenile albacore are likely to feed at night on disaggregated blue whitingpresent in surface waters. Prey distribution may therefore not affect significantlythe accessibility of albacore for surface fishing gear, such as trolling, in the southeast Bay of Biscay. However, we cannot confirm that trolling CPUE series are non-biased abundance indices, as trolling fishery occurs in a much wider area duringa longer period, in which albacore prey and feeding strategies may be different.Archival tagging along with the study of albacore feeding habits in more offshorezones would be interesting complementary tools for improving knowledge of thefeeding behaviour of albacore in a wider area, and therefore for more complete


insight into possible influences of this feeding behaviour on catchability by surfacefishing gears.

Acknowledgements We are grateful to Richard W. Brill for his valuable technical advice, toBeatriz Beldarrain for her invaluable help all along the surveys, to Luis Alberto Martin and to theHandik staff (specially Vicente) for their collaboration in the fishing operations and in the tracking,to Guillermo Boyra for his help in the analysis of echogram traces, and to the editor Alistair Hobdayand both anonymous referees for their comments on an earlier version of the manuscript.

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Small Scale Vertical Behaviour of Juvenile Albacore in Relation to Their Biotic Environment in the Bay of Biscay