Ppf

JI

ARA

KBESRI

1

ieC2daoM2

v2chebt

0h

Aquacultural Engineering 55 (2013) 32– 36

Contents lists available at SciVerse ScienceDirect

Aquacultural Engineering

j o ur nal hom ep age : www.elsev ier .com/ locate /aqua-onl ine

IT tagged individual Atlantic salmon registrered at static depthositions in a sea cage: Vertical size stratification and implicationsor fish sampling

onatan Nilsson ∗, Ole Folkedal, Jan Erik Fosseidengen, Lars Helge Stien, Frode Oppedalnstitute of Marine Research, Pb. 1870 Nordnes, 5817 Bergen, Norway

a r t i c l e i n f o

rticle history:eceived 4 July 2012ccepted 7 February 2013

eywords:ehaviornvironmentalmo salarepresentative sampling

a b s t r a c t

Individual dynamics within salmon sea cages are poorly understood. Large inter- and intra-individualvariations in swimming depth and higher average body weight deeper in the cage have been observed.Sampling of fish for inspection purposes and estimates of body weight distributions based on auto-matic measures at a limited depth interval may thus be skewed. The present study investigates how335 randomly PIT tagged Atlantic salmon of 3.4 ± 0.96 kg (bled weight, mean ± SD) swam near square PITantennas (0.6 m) fixed at either 5 or 9 m depth within a 14 m deep cage holding a total of 3750 individuals.The individual variation in registration frequency was large, with 76 individuals never registered and 12individuals registrered 30 times or more. Larger individuals were much overrepresented at 9 m depth,

ndividual variation resulting in 8.5% overestimation of average weight at this depth when repeated registrations were notaccounted for. No overrepresentation of any size class was found at 5 m depth. Half of the individualsregistered at 9 m depth were also registered at 5 m depth, and 82% of the individuals registered at 5 mdepth were also registered at 9 m depth. We interpret the result as fish of all sizes using a wide depthrange, but larger individuals spending more time at deeper water than smaller individuals. Therefore,size estimates at a limited depth interval should be avoided.

. Introduction

In the large groups of Atlantic salmon in aquaculture sea cages,ndividual variation in swimming depth has been denoted by sev-ral authors (Juell and Westerberg, 1993; Bégout Anras et al., 2000;ubitt et al., 2005, 2008a,b; Johansson et al., 2009; Føre et al.,011). Generally, it has been suggested that individual variationepends on factors such as sex, size, level of hunger, food avail-bility, predation pressure, environmental preferences, parasitismr competition from conspecifics (Woiwode and Adelman, 1992;agurran, 1993; Jobling, 1994; Juell et al., 1994; Johansson et al.,

009; Oppedal et al., 2011a).Johansson et al. (2009) found large between and within indi-

idual variations in swimming depth in caged salmon. Most of the3 studied individuals used a wide depth range, but there werelear differences in how much individuals moved vertically andow much time they spent at different depths in the cage. How-

ver, Johansson et al. (2009) did not find any clear relationshipsetween body size and swimming depth, besides an indica-ion of a negative relationship between growth and variation in

∗ Corresponding author. Tel.: +47 55238500.E-mail address: jonatan.nilsson@imr.no (J. Nilsson).

144-8609/$ – see front matter © 2013 Elsevier B.V. All rights reserved.ttp://dx.doi.org/10.1016/j.aquaeng.2013.02.001

© 2013 Elsevier B.V. All rights reserved.

temperature. Large variation in body size is usually found withingroups of caged salmon (e.g. Johansson et al., 2009). Boucher andPetrell (1999) found differences in swimming speed and conditionfactor between salmon at different depths, while they did not findbody weight differences between depths. Recently, Folkedal et al.(2012) estimated size of large sea-caged Atlantic salmon swimmingthrough size measure frames positioned at different depths, andfound a clear relationship between registration depth and averagesize. Individuals measured at 3 m depth was 15–25% smaller thanfish measured deeper in the school. The investigation by Folkedalet al. (2012) involved a vast amount of individual registrations; onaverage more than two registrations per individual over five days.Hence, it is probable that individuals frequently swimming at thedepth of statically positioned measure equipment are overrepre-sented in such measures, and the size of these individuals maydeviate from that of the average individual in the cage.

From the studies above we hypothezise that individual salmonmove between different depths, but that larger individuals spendmore time at greater depths than smaller individuals. If this is true,size estimates from samples of fish caught at a limited depth inter-

val or by use of equipment positioned at a fixed depth may resultin bias of population estimates.

In the present study we investigated how much the esti-mated weight distribution of caged salmon deviated from the true

ral Eng

drmovmnhP

2

2

tS1mwiwttFort

2

apadw4an5wtt

2

sd1fd4dhsSmsThtuct

J. Nilsson et al. / Aquacultu

istribution when the number of times an individual has beenecorded is controlled for (i.e. each individual is represented withaximum one value in the dataset) and when overrepresentation

f some individuals is not controlled for (each recording of an indi-idual is added to the dataset). The latter would be the case forost size estimate equipment used in cages, as they do not recog-

ize individuals. We randomly PIT-tagged 10% of a group of 3750arvest sized salmon which were recorded when they passed nearIT-antennas fixed at 5 and 9 m depth in a 14 m deep sea cage.

. Materials and methods

.1. Location and experimental design

The experiment was carried out from 2 to 16 February 2011 athe Institute of Marine Research cage-environment laboratory inolheim, Norway (61◦ N), in a square sea cage (12 m × 12 m and4 m depth; approximately 2000 m3). Two PIT antennas (customade, Trac ID Systems AS, Stavanger, Norway, www.trac-id.com)ere positioned by ropes at 5 and 9 m depth in the mid-radial hor-

zontal plane, approximately 3 m from the net wall. The antennasere shaped as frames of 0.6 m × 0.6 m size, and hung vertically

o allow horizontally swimming fish to pass through the antenna,hus increasing the area at which passing fish could be registered.ish were registered at a distance of approximately 20 cm or shorterutside the antenna and within all inside area. The time for eachegistration was recorded on a computer connected to the PIT sys-em.

.2. Environmental variables

Depth profiles of water temperature and salinity were sampledt 1–4 days intervals over the observation period with a verticallyrofiling CTD (SD204, SAIV AS, Bergen, Norway, www.saivas.no)t a reference point outside the cages from the surface to 15 mepth. The upper 2 m were characterized by cold brackish waterith an avearge temperature of 3 ◦C and salinity of 17 ppt. From 2 to

m average temperature and salinity increased gradually to 4.4 ◦Cnd 30 ppt, respectively. Below 4 m the water was more homoge-ous, with average temperature and salinity ranging from 4.5 to◦C and 32 to 33 ppt, respectively. The average oxygen saturationas around 80% in the whole depth range. The fish were exposed

o natural light conditions, with daylenght between 8 and 9 h overhe study period.

.3. Fish, tagging and PIT-registrations

Atlantic salmon of the Aquagen strain where transferred to theea cage in September 2009, and reared according to standard pro-uction routines at fish numbers and densities up to 9000 and7 kg m−3, respectively. On 3 January 2011, 3750 fish were withheldrom harvest and created the experimental group with stockingensity calculated as 6.5 kg m−3 at harvest on February 21. On the

and 5 January, the cage net was lifted up to approximately 2 mepths and splitted in two halves. With all fish crowded in onealf, 370 individuals (10% of the total group size) were randomlyampled, anaesthetized with metacain (60 mg l−1, FINQUEL vet.,canAqua AS, Årnes, Norway), weighed (3153 ± 838 g, mean ± S.D.),easured for fork length (62.3 ± 5.4 cm) and tagged with PIT (Pas-

ive Integrated Transponder, Glass tag Unique, 3.85 mm × 23 mm,rac-ID systems AS, Stavanger, Norway) inserted just behind theead on the dorsal side of the body. To facilitate recognition of

agged individuals the adipose fin was removed. Tagged individ-als were released into the other half of the splitted cage to haveontrol of the awakening from anaesthazia and to avoid recap-ure of the fish. When the tagging procedure was completed and

ineering 55 (2013) 32– 36 33

all fish had awakened from anaesthazia the cage net was loweredto its original depth. PIT registrations were recorded between 2and 16 February, except from 4 to 8 February when the anten-nas were inactive due to a technical problem. The fish were fedcommercial dry food in two meals daily, between 10:00 and 11:00and between 13:15 and 14:00, totally 0.45% of the biomass day−1.From 10 Febrary onwards the fish were starved as part of manage-ment practice prior to harvest, which occurred on 21 February. Atharvest the tagged individuals were again weighed (3430 ± 958 g,bled weight, mean ± S.D. Weight loss due to bleeding is normallyaround 3%) and fork length measured (63.9 ± 5.8 cm). After the ter-mination of the experiment, 35 tagged individuals could not bereliably identified and were excluded from the data set. The workwas conducted in accordance with the laws and regulations con-trolling experiments and procedures on live animals in Norwayfollowing the Norwegian Regulation on Animal Experimentation1996.

2.4. Data analysis

Fish weight were sorted into 250 g intervals to create figures ofsize distribution and to calculate average number of registrationsper individual for the different weight intervals. All statistical testswere made in the R software system Version 2.9.0 (Copyright 2009,The R Foundation for Statistical Computing, Vienna, Austria). Inorder to evaluate if intraindividual variation in registration affectedthe fish weight estimate, weight data from PIT-registered individ-uals were divided into two categories: The weight of each registeredfish represented once in the data set (OneReg) and the weight of aregistered individual being added to the data set each time it wasregistered (AllReg), as would be the case for most automatic mea-sure equipment such as size measure frames. These estimates werecompared with that of the whole tagged population (WholePop).Effect of fish weight on the average number of PIT registrationswas tested with generalized linear model (glm), with weight (250 gintervals) as a continuous variable. As the weight distribution wasGaussian (see Fig. 2, WholePop), number of individuals in each sizeclass differed and average number of registrations was thereforebased on unequal sample sizes for the different size classes. Dueto only one individual in each of the lowest and highest weightcategory, these two extremes were excluded from the statisticalanalysis. For the registration data from 9 m depth a polynomialmodel explained the data distribution significantly better than alinear function (F1,17 = 6.26, p < 0.05), and was therefore used todescribe the number of registrations as a function of weight. Dif-ferences in average weight between the weight estimation groupsWholePop, OneReg and AllReg at each depth were tested with glmwith group as a factor variable. All tests were two-tailed, and thelevel of significance set to 0.05.

3. 3. 1. Results

3.1. Individual variation in registration frequency

Of the 335 identified PIT-tagged individuals, 259 (77.3%) wereregistered once or more at 5 m (142) and/or 9 m (234) depth. Thetotal number of registrations for these individuals varied much,with 130 (38.8%) individuals with 5 registrations or less and 12individuals having 30 registrations or more (max 55). The 17 (5%percentile) most registered individuals constituted 25.7% of thetotal number of registrations. There was a significant effect of

weight on the average number of PIT registrations at 9 m depth(glm, polynomial model, F2,17 = 14.3, R2 = 0.63, p < 0.001), with num-ber of registrations increasing with weight up to a calculatedinflection point of 4838 g (Fig. 1). At 5 m depth, there was no effect of

34 J. Nilsson et al. / Aquacultural Engineering 55 (2013) 32– 36

Fig. 1. Average number of PIT-registrations per tagged fish for each 250-g weight category between 1250 and 6000 g. Weight category is based onbled weight, and the x-value indicates the upper limit of the weight interval,e.g. “2000” includes fish between 1750 and 2000 g. Open circles: 5 m depth;b9t

wp

3

(((mApO

3

ror

F(w

lack circles: 9 m depth. The line represents: Number of PIT-registrations at m = −5.063 + 0.00471 × weight − 0.0000004868 × weight2. Dotted lines indicatehe 95% confidence interval for the line.

eight on the number of PIT registrations (F1,18 = 0.051, R2 = 0.0028, > 0.1, Fig. 1).

.2. Effects of weight estimation method

At 5 m depth there was no effect of weight estimate methodF2,1051 = 0.0578, p = 0.94) with the average weight of AllReg3453 g), OneReg (3446 g) and WholePop (3430 g) being similarFig. 2A). At 9 m depth there was a significant effect of estimate

ethod (F2,2292 = 16.67, p < 0.0001) with the average weight ofllReg (3723 g) higher than that of OneReg (3482 g) (t = −3.54,

< 0.001) and that of WholePop (3430 g) (t = −5.01, p < 0.001), whileneReg did not differ from WholePop (t = 0.618, p = 0.54, Fig. 2B).

.3. Individual variation in multiple depth registrations

Individuals that were frequently registered at 5 m depth hadelatively few registrations at 9 m depth and vice versa (Fig. 3). Outf the 234 individuals registered at 9 m depth, 117 (50%) were neveregistered at 5 m depth (Fig. 3). In contrast, only 25 (18%) of the 142

ig. 2. Weight distribution of all tagged salmon (WholePop), registered individuals represOneReg), and registered individuals with weight represented for each time they were reeight (AllReg). (A) 5 m depth and (B) 9 m depth.

Fig. 3. Number of registrations at 5 and 9 m depth of the 259 individual fish thatwere registered. Data from the 76 individuals that were never registrered are notincluded in the figure.

individuals that were registered at 5 m depth were never registeredat 9 m depth (Fig. 3). A total of 76 fish remained unregistered at bothdepths.

4. Discussion

In the present study, there was a clear positive correlationbetween individual fish weight and frequency of registrations at9 m depth, while no such relationship was found at 5 m depth.Thus, sampling of fish for inspection purposes and estimates ofbody weight distributions based on automatic measures at a limiteddepth interval may not be representable for the whole fish group.The fact that the cumulative number of registrations (AllReg) gave ahigher average fish weight at 9 compared to 5 m depth demonstratea bias when using measure equipment such as measure frames andstereo cameras in a static position. When measuring in a verticalstatic position in a sea cage, the probability of multiple individualregistrations should be expected to increase with the time an indi-vidual spends at the specific depth. In other words, we interpret the

number of registrations of one individual at one depth to reflect thetime spent in the vicinity of the PIT-antenna, which in this case waspositively correlated with weight at 9 m. This suggests that largersalmon spend more time at a deeper position than the smaller, and

ented with weight data once for each depth independent of number of registrationsgistered, so that individuals with more registrations contribute more to the mean

ral Eng

ib

wtt9fiia

qdabqaooadss

otaallsseztesua(itdwawtd

tto2ifiesttprdi

sb

J. Nilsson et al. / Aquacultu

s consistent with the finding of size stratification with depth showny Folkedal et al. (2012).

Fish were registered at two depths only; 5 and 9 m. Therefore,e do not have any information about the size at depth shallower

han 5 m or deeper than 9 m, or between 5 and 9 m depth. The rela-ionship between weight and average number of registrations at

m depth was curved with an inflection point at 4.8 kg, with largersh having slightly less registrations. As the cage was 14 m deep it

s possible that the largest fish spent more time deeper than 9 mnd therefore were registered less often.

While the relationship between weight and registration fre-uency was clear at 9 m depth, no such pattern was found at 5 mepth. If average size increased linearily with depth one shouldssume fish of smaller size to be overrepresentated at 5 m depth,ut this was not the case. The lack of size-related registration fre-uency at 5 m may indicate that the size distribution did not follow

linear gradient, but that fish of all sizes spent a similar amountf time at this depth. However, although 5 m depth was close to anptimal position for size estimation purposes in this specific case,t other environmental conditions and fish sizes the pattern may beifferent. Predicting which depth that would give a representativeample should be very difficult as long as the mechanisms behindize stratification is not fully understood.

Of 335 tagged individuals, 152 (45%) were registered five timesr less, of which 76 individuals were never registered. PIT iden-ification with the tag still inside the fish at size measurementst the termination of the study revealed that the tags functioneds normal, and dysfunctional tags can therefore not explain theirow registration frequency. Thus, these individuals may have spentittle time at the recorded depths or at a distance (inside or out-ide) from the antenna in the radial horizontal plane. Within cages,almon typically display a torus shape school structure (e.g. Fernöt al., 1988; Oppedal et al., 2001, 2011a). Variations in both hori-ontal and vertical swimming trajectories has been noted betweenagged indivual Atlantic salmon (Bégout Anras et al., 2000; Cubittt al., 2005) which may lead to some individuals by chance rarelywimming near the antenna. It is also possible that some individ-als to a larger degree than others actively avoided the antenna,s a kind of novel object avoidance. Differences in personality traitKoolhaas et al., 1999) are known from a number of fish species,ncluding Atlantic salmon (Conrad et al., 2011), and the possibilityhat individual variation in registration frequency is partly due toifferent degree of neophobia cannot be ruled out. However, if thisas the main reason for higher registration frequency of larger fish

t 9 m depth one would expect a similar pattern at 5 m depth, whichas not the case. Therefore, the weight-registration frequency rela-

ionship is best explained by larger fish spending more time at 9 mepth than smaller fish.

A negative relationship between growth rate and variation inemperature as caused by change in swimming depth in a verticalemperature gradient was found by Johansson et al. (2009). In twother experiments with DST tagged Atlantic salmon submerged for2–42 days, fish with highest growth rates tended to swim deeper

n the submerged cages (Korsøen et al., 2012). Taking the presentnding of vertical size stratification, which is in line with Folkedalt al. (2012), into account, an interesting topic of further researchhould be whether individual strategies of environmental posi-ioning affects their growth and thus size, or whether size affectsheir environmental positioning. Studies similar to the present buterformed over a longer time span covering more variation in envi-onment and individual growth rate, preferentially with severalepths monitored, should give important information about the

nteraction between size, growth and environmental positioning.Individual depth-related thermal behavior have been demon-

trated to variable degree within groups of Atlantic salmon in cagesut with large intra- and inter-individual variations (Johansson

ineering 55 (2013) 32– 36 35

et al., 2009). Within the present study, a stable temperature withinthe depth layers of the antennaes (average variation <0.5 ◦C) mayintuitively not be a strong driver for preference. However, bothavoidances, preferences and bimodal depth distributions havebeen observed at group level at lesser temperature gradients (e.g.Oppedal et al., 2007), but no studies so far have investigated if suchslight environmental gradientss affects individual preferences forfish of different size. A hypothesis may be that the largest fish,in terms of size itself, will be able to occupy the slightly warmerwater in the deepest parts of the cages and force smaller individ-uals toward the colder surface. Previous observations showed thathigh stocking density forced more individuals to occupy unfavor-able high temperature (>17 ◦C) depth layers compared to normaldensity (Johansson et al., 2007, 2009; Oppedal et al., 2011b), butrevealed no clear effect of fish size on the rather few (N = 23) taggedindividuals (Johansson et al., 2009).

The present findings clearly show that sampling for fish sizewithin cages from one depth layer only, represent a potentiallylarge bias. Accordingly, sample results from present techniques forsize distributions exhibit huge variability (SINTEF, 2009). On aver-age the deviations were in the range of 5%, but spanning up to 40%.A new method that samples the whole cage volume by verticalprofiling would effectively minimize this bias.

This study is an important contribution to increase our under-standing of the preferences of salmon in sea cages as it focuses onindividual measurements. A better understanding of the individualcoping and motivational mechanisms that drive the behavior of fishin production environments (e.g. Sutterlin and Stevens, 1992; Juell,1995; Johansson et al., 2007, 2009) will broaden our ability to utilizebehavior as a welfare indicator (Dawkins, 2004) and manipulate theenvironment in order to meet the preferences of the fish.

Acknowledgements

The study was carried out within the KMB project EXACTUS(199788), Technologies, systems and operational procedures forhigh-level accuracy in biomass control in large cages financiallysupported by the Research Council of Norway and partners (seewww.sintef.no/Projectweb/EXACTUS/). We are grateful to the staffof Matre Aquaculture Research Station for their technical assis-tance, in particular Tone Vågseth, Stian Morken and the sea farmcrew. We would like to thank an anonymous referee for valuablecomments on the manuscript.

References

Bégout Anras, M.L., Kadri, S., Juell, J.E., Hansen, T., 2000. Measuring individual andgroup swimming behaviour under production densities: test of a 3D multiplefish acoustic positioning system in a sea cage. In: Moore, A., Russell, I. (Eds.),Advances in Fish Telemetry. CEFAS Lowesoft Publication, Lowestoft, Suffolk, pp.75–78.

Boucher, E., Petrell, R.J., 1999. Swimming speed and morphological features of mixedpopulations of early maturing and non-maturing fish. Aquacultural Engineering20, 21–35.

Conrad, J.L., Weinersmith, K.L., Brodin, T., Saltz, J.B., Sih, A., 2011. Behaviouralsyndromes in fishes: a review with implications for ecology and fisheries man-agement. Journal of Fish Biology 78, 395–435.

Cubitt, K.F., Churchill, S., Rowsell, D., Scruton, D.A., McKinley, R.S., 2005. 3-Dimensional positioning of salmon in commercial sea cages: assessment of atool for monitoring behaviour. In: Spedicato, M.T., Lembo, G., Marmulla, G. (Eds.),Acoustic Telemetry: Advances and Applications, Proceedings of the 5th Confer-ence on Fish Telemetry Held in Europe. Ustica, Italy, 913 June, 2003, Rome, pp.25–33, FAO/COISPA-FAO.

Cubitt, K.F., Winberg, S., Huntingford, F.A., Kadri, S., Crampton, V.O., Øverli, Ø., 2008a.Social hierarchies, growth and brain serotonin metabolism in Atlantic salmon(Salmo salar) kept under commercial rearing conditions. Physiology and Behav-

ior 94, 529–535.

Cubitt, K.F., Williams, H.T., Rowsell, D., McFarlane, W.J., Gosine, R.G., Butterworth,K.G., McKinley, R.S., 2008b. Development of an intelligent reasoning system todistinguish hunger states in Rainbow trout (Oncorhynchus mykiss). Computersand Electronics in Agriculture 62, 29–34.

3 al Eng

D

F

F

F

JJ

J

J

J

J

K

Sutterlin, A.M., Stevens, E.D., 1992. Thermal behaviour of rainbow trout and Arctic

6 J. Nilsson et al. / Aquacultur

awkins, M.S., 2004. Using behaviour to assess animal welfare. Animal Welfare 13,3–7.

ernö, A., Furevik, D., Huse, I., Bjordal, Å., 1988. A multiple approach to behaviourstudies of salmon reared in marine net pens. International Council for the Explo-ration of the Sea, Mariculture Committee, C.M., 1–15 (1988/F15).

øre, M., Alfredsen, J.A., Gronningsater, A., 2011. Development of two telemetry-based systems for monitoring the feeding behaviour of Atlantic salmon (Salmosalar L) in aquaculture sea-cages. Journal of Computers and Electronics in Agri-culture 76, 240–251.

olkedal, O., Stien, L.H., Nilsson, J., Torgersen, T., Fosseidengen, J.E., Oppedal, F., 2012.Sea caged Atlantic salmon display size-dependent swimming depth. AquaticLiving Resources 25, 143–149.

obling, M., 1994. Fish Bioenergetics. Chapman & Hall, London.ohansson, D., Juell, J.-E., Oppedal, F., Stiansen, J.-E., Ruohonen, K., 2007. The influ-

ence of the pycnocline and cage resistance on current flow, oxygen flux andswimming behaviour of Atlantic salmon (Salmo salar L.) in production cages.Aquaculture 265, 271–287.

ohansson, D., Ruohonen, K., Juell, J.-E., Oppedal, F., 2009. Swimming depth and ther-mal history of individual Atlantic salmon (Salmo salar L.) in production cagesunder different ambient temperature conditions. Aquaculture 290, 296–303.

uell, J.-E., 1995. The behaviour of Atlantic salmon in relation to efficient cage-rearing. Reviews in Fish Biology and Fisheries 5, 320–335.

uell, J.E., Westerberg, H., 1993. An ultrasonic telemetric system for automatic posi-tioning of individual fish used to track Atlantic salmon (Salmo salar L) in a seacage. Aquacultural Engineering 12, 1–18.

uell, J.E., Fernö, A., Furevik, D., Huse, I., 1994. Influence of hunger level and foodavailability on the spatial distribution of Atlantic salmon, Salmo salar L., in seacages. Aquaculture and Fisheries Management 25, 439–451.

oolhaas, J.M., Korte, S.M., De Boer, S.F., Van Der Vegt, B.J., Van Reenen, C.G., Hopster,H., De Jong, I.C., Ruis, M.A.W., Blokhuis, H.J., 1999. Coping styles in animals:

ineering 55 (2013) 32– 36

current status in behavior and stress–physiology. Neuroscience & BiobehavioralReviews 23, 925–935.

Korsøen, Ø.J., Dempster, T., Oppedal, F., Kristiansen, T.S., 2012. Individual variationin swimming depth and growth in Atlantic salmon (Salmo salar L.) subjected tosubmergence in sea-cages. Aquaculture, 142–151, 334–337.

Magurran, A.E., 1993. Individual differences and alternative behaviour. In:Pitcher, T.J. (Ed.), Behaviour of Teleost Fishes. Chapman & Hall, London, pp.441–477.

Oppedal, F., Juell, J.E., Taranger, G.L., Hansen, T., 2001. Artificial light and season affectvertical distribution and swimming behaviour of post-smolt Atlantic salmon insea cages. Journal of Fish Biology 58, 1570–1584.

Oppedal, F., Juell, J.-E., Johansson, D., 2007. Thermo and photoregulatory-behaviourinfluence swimming depth and schooling density of caged Atlantic salmon.Aquaculture 265, 70–81.

Oppedal, F., Dempster, T., Stien, L., 2011a. Environmental drivers of Atlantic salmonbehaviour in sea-cages: a review. Aquaculture 311, 1–18.

Oppedal, F., Vågseth, T., Dempster, T., Juell, J.-E., Johansson, D., 2011b. Stock-ing density affects seasonal variation in observed swimming density of cagedAtlantic salmon (Salmo salar L.) measured by echo sounders. Aquaculture 315,361–368.

SINTEF, 2009. Konsekvensanalyse – utredning rundt følgene av feilestimering avbiomasse i sjøbasert oppdrett. Oppdrag utført av SINTEF Fiskeri og havbruk påoppdrag fra Marine Harvest Norge AS, Norbit AS og Akvagroup ASA. Report SFHA096045. In Norwegian.

char in cages moored in stratified water. Aquaculture 102, 65–75.Woiwode, J.G., Adelman, I.R., 1992. Effects of starvation, oscillating temperatures,

and photoperiod on the critical thermal maxima of hybrid striped × white bass.Journal of Thermal Biology 17, 271–275.