Seasonal Variation in Energy

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Journal of Fish Biology (2001) 59, 380389doi:10.1006/jfbi.2001.1649, available online at http://www.idealibrary.com on

Seasonal variations in the energy density of fishes in theNorth Sea

J. P* J. R. G. H

*Danish Institute for Fisheries Research, North Sea Centre, P.O. Box 101, DK-9850Hirtshals, Denmark andFisheries Research Service, Marine Laboratory, Victoria Road,

Aberdeen, AB11 9DB, Scotland

(Received 30 September 2000, Accepted 2 May 2001)

The energy density (ED, kJ g1 wet mass) of saithe Pollachius virens, haddock Melanogrammusaeglefinus, whiting Merlangius merlangus, Norway pout Trisopterus esmarki, herring Clupeaharengus, sprat Sprattus sprattus, sandeel Ammodytes marinus and pearlsides MaurolicusMuelleri, from the North Sea, increased with total length, LT. However, there was not alwaysa significant (P>005) linear relationship between LT and ED. Seasonal differences in ED wereobvious in mature fish, while geographical differences were insignificant. For all species therewas a highly significant correlation (P


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in whiting fed natural prey is, however, affected by the energy density of the preyitems (Andersen, 1999). Using Andersens (1999) model, Pedersen (2000) foundthat the specific daily rations of whiting were significantly different between yearsand groups of whiting, depending on the energy density of their stomach

contents. Thus seasonal energy density data are also required for the applicationof the gastric evacuation model. However, information on the energy density ofpredator and their prey suitable for use in bioenergetics models is sparse.Although many data are available (Cummins & Wuycheck, 1971; Harris &Hislop, 1978; Hartman & Brandt, 1995) they can be misleading, because they donot take into account that there are changes in energy density offishes both withontogeny (Craig, 1977) and season (Kelso, 1973; Foltz & Norden, 1977; Hislopet al., 1991a). Therefore, if results obtained from models using energy data areto be accurate, determinations of energy density of predator and prey arerequired for seasons and fish size or age.

The present study is an attempt to expand former studies and to determine byseasons and fish size the energy density of eight species for which there arequantified predator-prey interactions in the North Sea.

MATERIALS AND METHODS

For determination of energy density, ungutted specimens of the piscivorous speciessaithe Pollachius virens (L.), whiting, haddock Melanogrammus aeglefinus (L.), and theirmain fish prey, herring Clupea harengus L., sprat Sprattus sprattus (L.), Norway poutTrisopterus esmarki (Nilsson), sandeel Ammodytes marinus (Raitt) and pearlsidesMaurolicus muelleri (Gmelin), were collected by research and commercial vessels in thenorthern and central North Sea during the period 19961998. The fishes were collected

quarterly, Q1=January, February or March; Q2=April, May or June; Q3=July, Augustor September; Q4= October, November or December. At sea, the fishes were sorted intolength classes (total length, LT), whose boundaries varied with species and LT (Table I).Each fish or batch offish in the case of small individuals was wrapped in a polythene bag,to minimize water loss, and stored in a domestic freezer at 20 C. To minimizedesiccation the fishes were processed within 6 months after capture. However, fishesshowing evidence of desiccation were not used.

All the saithe, whiting, herring, sprat, Norway pout and pearlsides were processed atthe Danish Institute for Fisheries Research (DIFRES), North Sea Centre, Hirtshals,Denmark. Prior to determination of energy density the samples were warmed at roomtemperature to a partially thawed state, which prevented loss of water and blood from thefishes. The fishes were roughly chopped and homogenized. Fishes >20 cm LT were

homogenized in a Tecator Mill 1094 homogenizer for 4 min and subsequently in a BuchiMixer B-400 for 15 s, while smaller specimens were processed first in a domestic foodblender and then in the Buchi mixer. Loss of water during processing was insignificantfor all size classs. Maximum loss (05% wet mass) was observed when fishes >20 cm LTwere chopped. For all size classes masses derived in this manner were therefore assumedto be reliable and not influence by loss of water.

Two samples of the homogenate weighing c. 20 g each were oven-dried to constantmass at 60 C (48 h) for the determination of dry mass (WD) content and afterwardsheated for 48 h at 600 C for determination of the ash mass. Two other samples wereoven-dried and two 1013 g WD subsamples were combusted to measure energy densitywith an IKA-Calorimeter C 7000. If the subsamples differed by >15%, the variation wasabove the precision of the calorimeter and a third subsample was combusted. The

average of the two to three subsamples was used for estimates of energy density forthose fish.The haddock and sandeel samples were processed at the Marine Laboratory,

Aberdeen, Scotland. Homogenates were prepared using procedures similar to those

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T I. Energy density (kJ g1 WW) of the common fish species in the North Sea by sizeand quarter (sample size in parentheses). Bold and underlined figures are from Hislopet al. (1991a) and Harris & Hislop (1978). Medio and ultimo refer to fish caught between

15 February and after 21 March, respectively

Species Size class(mm)

Quarter1 (medio) 1 (ultimo) 2 3 4

Saithe 200249 41 (4)Saithe 250299 47 (6)Saithe 300349 47 (10)Saithe 350399 42 (20) 45 (20) 48 (20) 49 (20)Saithe 400449 46 (20) 44 (18) 51 (20) 48 (20)Saithe 450499 50 (19) 40 (20) 53 (20) 48 (20)Saithe 500599 55 (9) 40 (9) 56 (10) 52 (10)Saithe 600699 53 (10) 35 (9) 68 (10) 62 (5)

Saithe 700799 50 (7) 48 (8) 60 (10)Saithe 800999 53 (10) 63 (10) 62 (2)Saithe 100011 999 53 (2) 60 (4)Haddock 100119 39 (20)Haddock 120149 36 (21) 33 (10) 42 (10) 36 (10)Haddock 150199 37 (22) 39 (10) 41 (10) 41 (30)Haddock 200249 43 (14) 43 (10) 42 (10) 49 (14)Haddock 250299 47 (11) 40 (10) 46 (18) 49 (12)Haddock 300349 47 (14) 45 (25) 50 (10) 52 (23)Haddock 350399 47 (14) 36 (5) 55 (11) 55 (18)Haddock 400449 45 (11) 38 (7) 49 (11) 51 (11)Haddock 450499 44 (5) 53 (2) 53 (2)

Haddock 500549 47 (1) 56 (5) 53 (1)Haddock 550599 36 (1)Herring 4049 41 (14)Herring 5059 42 (35)Herring 6079 39 (39)Herring 8099 46 45 (20) 46Herring 100119 47 (20) 46 (2) 44 (5) 46Herring 120149 44 (21) 45 (50) 52 (20) 63 (23)Herring 150199 44 (20) 44 (50) 101 (20) 71 (20)Herring 200249 65 (20) 57 (41) 110 (20) 85 (12)Herring 250299 85 49 (20) 119 (20) 88 (20)Herring 300349

Sprat 4393 67 (2)Sprat 104137 109 (3)Sprat 115125 115 (20)Sprat 110119 64 (6)Sprat 120129 58 (10)Sprat 130139 59 (10)Sprat 140149 56 (6)Pearlsides 4572 73 (50)Norway pout 4049 38 (50) 39 (20)Norway pout 5059 38 (50) 39 (20)Norway pout 6079 39 (20)Norway pout 8099 45 (1) 48 (50)

Norway pout 100119 62 (43) 40 (20) 42 (20) 57 (18) 52 (20)Norway pout 120149 64 (14) 41 (20) 44 (20) 63 (20) 70 (20)Norway pout 150199 47 (35) 40 (20) 57 (20) 70 (20)

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employed at DIFRES. However, although most fishes >20 cm were processed individ-ually smaller specimens had to be processed in batches (pooled samples) because theblender was unable to homogenize small quantities of tissue. One or more samples of thehomogenate, most weighing exactly 40 g, were taken for the determination ofWD andenergy density ED. Dry mass was determined by weighing the homogenate before andafter freeze-drying for 72 h in a HETO CD8 freeze dryer. Because the WD of the otherspecies had been determined by oven-drying, the two drying techniques were comparedusing replicate samples of homogenate from 20 haddock. No significant or consistentdifference was observed between the WD of the freeze-dried and oven-dried samples.After their WD had been determined, the freeze-dried samples were stored at 20 C forperiods ranging from weeks to months before their ED were determined by bombcalorimetry. To eliminate any water that may have been absorbed during storage thesamples were oven-dried for several hours at 60 C before calorimetry was undertaken.Spot checks indicated that WD were consistent and corresponded to those measuredimmediately after freeze-drying.

The calorimetry of haddock and sandeel was carried out at DIFRES and RowettResearch Institute (RRI), Aberdeen, Scotland, using an IKA C700 calorimeter and aGallenkamp adiabatic bomb calorimeter, respectively. Under normal circumstances twosamples per fish or per batch were used. However, if the ED, of the two samples differed

by >5%, which is the precision of the Gallenkamp calorimeter, an additional samples wasprocessed. Also, replicate samples were used to determine (a) whether freeze driedsamples had the same ED, irrespective of where they were analysed (i.e. DIFRES or RRI)and (b) whether freeze dried and oven-dried samples of the same fish returned similar ED.In neither case were significant differences detected.

For all eight species ED were initially expressed as kJ g1 WD and were subsequently

converted to wet mass (WW) by multiplying by (1((WWWD)W1

W ).

RESULTS

In general, the energy density (ED, kJ g1 WW) increased with increasing LT

and varied between quarters (Table I). These seasonal differences were obviousafter the fish had attained maturity. Norway pout and herring showed particu-larly pronounced differences in ED before and after spawning (note the ED of

T I. Continued

SpeciesSize class

(mm)Quarter

1 (medio) 1 (ultimo) 2 3 4

Whiting 5059 40 (40) 38 (4)Whiting 6079 40 (40) 37 (80)Whiting 8099 40 (2) 36 (40) 39 (50)Whiting 100119 41 (20) 38 (11) 38 (23) 38 (20)Whiting 120149 40 (20) 38 (46) 39 (21) 39 (20)Whiting 150199 43 (20) 39 (50) 47 (20) 42 (12)Whiting 200249 48 (20) 40 (35) 53 (10) 54 (3)Whiting 250299 50 (20) 43 (13) 54 (20) 51 (3)Sandeel 6069 40 37 (300)Sandeel 7079 46Sandeel 8099 44 42 (240)

Sandeel 100119 54 56 (80)Sandeel 120149 58 62 (80)Sandeel 150199 61

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Norway pout sampled early and late in Q1, i.e. pre-spawning and post-spawning). The ED of haddock did not differ significantly between geographical

areas, although in nearly every case the western values were higher than theeastern ones (Table II).Least-squared regression models were calculated by quarter (Q) with ED as the

dependent variable and LT (mid-point of each sampled length class) as theindependent variable. The relationships for saithe were: Q1, ED=349+0026 LT(n=6, r2=059, P>005); Q2, ED=370+0012 LT (n=9, r

2=023, P>005); Q3,ED=406+0025 LT (n=11, r

2=070, P005); Q2, ED=375+0006 LT (n=7, r

2=003, P>005); Q3,ED=358+0038 LT (n=10, r

2=084, P005). For saithe, significant relationships were found in Q3 and Q4,whereas haddock show significant relationships in Q3 only. For whiting,Norway pout, herring and sandeel none of the quarterly linear relationships wassignificant (P>005).

For all species, least-squared regression models were generated for eachquarter with ED as the dependent variable and the percent dry mass proportion[DS=100(WD W

1

W )] as the independent variable. For saithe (Fig. 1) and theother species the relationships were similar in all quarters. Therefore, for eachspecies the quarterly data were pooled and highly significantly correlationsbetween DS and energy density were established (Table III). Slopes of theregression lines were not significantly different between gadoids and sandeel(Table III). However, there were significant differences between gadoids andherring. Furthermore, the ash content did not change with fish size although theaverage ash content was significantly higher for gadoids than for herring, spratand pearlsides (Fig. 2). Therefore, the gadoid and sandeel data were combinedand a general relationship calculated between DS and ED for gadoids andsandeel: ED=31492+03459 DS (n=251, r

2=0908, P


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DISCUSSION

The energy density of all species varied considerably with size and season.Norway pout and herring showed particularly pronounced differences in energydensity between size and quarter (Table I). This is in accordance with Hislopet al. (1991a) who found that the energy density of a fish depends not only on itssize but also on season. The seasonal variations in energy density are generallyassociated with the reproductive and feeding cycles of the fish and tend to begreater amongst the larger, mature members of the population (Hislop et al.,1991a). Pronounced seasonal cycles are shown for cod Gadus morhua L.(Schwalme & Chouinard, 1999) and strong seasonal cycles in energy density are

typical for clupeoids (Hunter & Leong, 1981; Flath & Diana, 1985; F. Arrhenius& S. Hansson, pers. comm.). Large changes in energy density of Norway poutcan occur over a relatively short time period, i.e. a few months ( Table I), and in

230

7

DS

ED(kJg

1W

W)

14 16 18 20 22 24 26 28

3

4

5

6

F. 1. Relationship between percent dry mass (DS) and energy density. (ED) for saithe in Q1 (),Q2 (), Q3 () and Q4 ().

T III. Least-squared regression models for estimating energy density (kJ g1 WW)from percent dry mass proportion (DS)

Species r2 n Slope 95% CL Intercept 95% CL P

Saithe 0939 33 0305 0028 2205 0666


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sandeels (Hislop et al., 1991a). Further, energy density of European perch Percafluviatilis L. and yellow perch Perca flavescens Mitchill differs significantlyamong months (Craig, 1977; Henderson et al., 2000).

2.0

4.0

Species

Meanash(%)

San

deel

2.5

3.0

3.5

Pearlside

sSp

rat

H

errin

g

Had

dock

Norwayp

out

W

hiting

Saith

e

F. 2. Mean ash content (%) (95% CL) of eight North Sea fish species. (Sandeel data from N. G.Andersen.)

045

14

DS

ED(kJg1W

W)

15 20 25 30 35 40

12

10

8

6

4

2

F. 3. Relationships between percent dry mass (DS) and energy density (ED) for gadoids and sandeel ()and herring (). Regression lines () and 95% CL ( ) are indicated.

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Seasonal changes in energy density are not related only to the reproductioncycle; they can also be related to seasonal changes in food consumption and diet.Foltz & Norden (1977) found that smelt Osmerus mordax (L.) undergo a periodof energy storage prior to overwintering and spawning. Smelt apparently utilizestored energy during the winter as evidenced by low feeding levels and lossesfrom the gut and carcass, which parallel gonad growth. In yellow perch declinein energy density could be explained by the reduction in food consumption andallocation of energy to gonad development (Henderson et al., 2000). Duringboth overwintering and spawning food consumption may be reduced or cease,and fishes must rely in part on stored energy reserves to survive both events.Immediately after spawning energy reserves are commonly at a minimum and thefirst priority is to replenish energy reserves (Winkle et al., 1997). Knowledge ofthe seasonal dynamics in energy density is therefore important when energytransfer is quantified in predator-prey interactions.

Energy densities >10 kJ g1 WW were measured for sprat and herring>15 cm, whereas relatively low valeus (15 cm they reach sexualmaturity, and hence have high levels of energy density, at a much smaller length

than herring (Hislop et al., 1991a). However, in the North Sea there are threemain stocks of herring that spawn at different times and locations (Hulme, 1995).Further, the North Sea Skagerrak area comprises both immature autumn

085

14

Water content (%)

ED(kJg1W

W)

55 60 65 70 75 80

12

10

8

6

4

2

F. 4. Relationships between water content (%) and energy density (ED) for gadoids and sandeel () andherring (). Regression lines () and 95% CL ( ) are indicated.

387


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spawners from the North Sea and mature spring spawners from the Baltic andSkagerrak-Kattegat (Moksness & Fossum, 1991). Therefore, seasonal andgeographical fluctuations in energy density are likely to be complex even thoughthe geographical differences in energy density of haddock were insignificant.

Fluctuations in energy density due to for instance spawning and migration,imply that good geographical coverage and seasonal and size-specific energydensities are needed to quantify predator-prey interactions because the precisionof not only the bioenergetics models but also the gastric evacuation method forestimating food consumption relies on appropriate data on prey energy density.Fish size was not a good predictor of energy density due to the overwhelminginfluence of season. However, species-specific relationships between the percentdry mass proportion of the fish and energy density appear adequate forprediction of energy density of fish (Table III). Nevertheless, the present studyshows that there were insignificant differences between seasons (Fig. 1). The

insignificant diff

erences between species in the intercepts and slopes of theserelationships indicate generalized models can be established for genus or family.This is inconsistent with Hartman & Brandt (1995) who found that there arespecies-specific differences in the energy density relationship. However, despitethese apparent species-specific differences, they used a general model for describ-ing seasonal changes in energy content offishes. This model is very similar to thegeneral relationship established for gadoids and sandeel in the present study.However, the present herring model differs significantly from the clupeidaemodel established by Hartman & Brandt (1995). This could be due to the factthat their work is mainly based on freshwater species. Nevertheless, both thegeneral model and the herring model are adequate for prediction of energydensity of fishes. Therefore, data on energy density required for bioenergeticsand gastric evacuation models, can be obtained simply from the percent dry massproportion of fish sampled on appropriate time scales for all species and sizesconsidered. This is a time saving procedure in comparison with calorimetry orproximate analysis of each species and size class.

We thank D. Frandsen, M. Bell and W. MacDonald for help with bomb calorimetry.We also thank N. G. Andersen and J. Riis-Vestergaard for rewarding discussions, andtwo anonynous referees for valuable comments on the manuscript. B. Bhleco-ordinated collecting some of the saithe from Norwegian research vessels. The study

was supported financially by EU FAIR project CT-95-0604 (CORMA).

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Seasonal Variation in Energy