Growth trajectories of shell and soft tissue in bivalves: Seasonal variation in Mytilus edulis L.

J. Exp. Mar. Biol. Ecol., 1986, Vol. 96, pp. 103-113 Elsevier

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JEM 637

GROWTH

TRAJECTORIES SEASONAL

OF SHELL AND SOFT TISSUE IN BIVALVES:

VARIATION

IN MYTZLUS

EDULZS L.

THOMAS J. HILBISH department of l&logy, University of South Carolina, Co~~biu, SC 29208, U.S.A.

(Received 29 July 1985; revision received 10 December 1985; accepted 12 December 1985) Abstract: A commonly used method for examining productivity in marine bivalves and other species with skeletal structures is to employ regression statistics to adjust dry tissue weights to an individual of standard size (usually length). Temporal variation in adjusted weights are then taken to reflect changes in productivity, fecundity or condition. This procedure assumes that changes in the covariate (length) are trivial. Here I describe a method that utilizes the presence of growth checks to determine separately rates of shell and soft tissue growth in the mussel byte edulk L. The results indicate that rates of growth in shell and sofr tissue do not occur simultaneously; in this case shell growth precedes the growth of soft tissue. Uncoupled patterns of growth seriously affect the results obtained using tissue weights adjusted to a standard length. Seasonal variation in adjusted weight provided no indication of true levels of productivity. Key words: Myrilusedulis; growth trajectories; shell weight; soft tissue weight.

Growth rates have been measured in numerous populations of marine bivalves (Seed, 1976; Bayne & Worrall, 1980); using a wide variety of techniques (Crisp, 1984). Annual rates of growth can frequently be determined using age information from structural features of the shell (Lutz & Rhoads, 1980; Rodhouse et al., 1984a; Thompson, 1984) or from the analysis of size-frequency distributions (Bayne & Worrall, 1980). It is more difficult to obtain estimates of seasonal variation in growth as age isolation is frequently either not contained in the shell on less than an annual scale or is very labor intensive to retrieve. Size-frequency analysis can provide information on seasonal as well as annual growth rates (Incze et al., 1980; Rodhouse et al., 1984; Hilbish, 1985) but frequently size classes merge or recruitment is not sufficiently discrete to allow separation of age classes (Seed, 1976). A commonly used technique for obtaining estimates of seasonal variation in growth or producti~ty is to examine changes in the weight of animals statistic~ly adjusted to a standard size dimension, usually length (see Ansell, 1974a,b,c; Dare & Edwards, 1975; Hibbert, 1977; Grifliths & King, 1979; Bayne & Worrall, 1980; Sundet & Vahl, 1981; Barber & Blake, 1983; Rodhouse et al., 1984a, for examples). In bivalves, dry weight is usually regressed against shell length for several population samples and analysis of covariance used to estimate the mean weight and its variance for animals 0022-0981/86/SO3.50 0 1986 Elsevier Science Publishers B.V. (Biomedical Division)

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of a standard length (Crisp, 1984). The adjusted mean weight may then be plotted as a function of time and temporal variation in adjusted weight is frequently interpreted to reflect changes in productivity. This type of analysis has enjoyed a wide range of uses; a decline in adjusted weight has been taken to be an indication of nutrient stress (e.g., Koehn et al., 1980; Rodhouse et al., 1984a) or of spawning activity (e.g., Ansell, 1974a,b,c). If a decline in adjusted weight is independently shown to be coincident with spawning, the magnitude of weight loss has been used to estimate fecundity or the allocation of resources to reproduction (Bayne & Worrall, 1980; Kautsky, 1982b; Bayne et al., 1983 ; Rodhouse et al., 1984b; Thompson, 1984). Relative differences in adjusted weight have also been used as a general index of physiological condition (e.g., Barber & Blake, 1983). Here, an intertidal population of Myths edulis L. from eastern Long Island Sound was analyzed using the weight on length regression technique. This population exhibits large variation in adjusted weights that is similar to flesh weight cycles observed in other bivalve populations. Individuals used in this analysis establish a very evident demarcation in their shell during the winter, commonly called a growth check. A method is described that utilizes the presence of these growth checks to determine separately seasonal rates of growth in both sheil and soft tissue. The results show that shell and soft tissue exhibit different seasonal patterns of growth in this population and that uncoupled patterns of growth have serious ramifications for the interpretation of seasonal variation in tissue weights adjusted to a standard sized animal.

MATERIALS AND METHODS

Mussels were collected at low tide from an intertidal population of M. edulis in eastern Long Island Sound at w 6-wk intervals from January, 1982 to January, 1983. Fifty individuals, 15-20 mm in shell length were removed from the collection, dissected and soft tissues removed. The soft tissue was dried at 80 “C to a constant weight and measured to the nearest 0.1 mg. Shell length was measured to the nearest 0.1 mm with Vernier calipers. Dry tissue weight was regressed on shell length for each population sample and mean weights adjusted to a standard length of 17.4 mm (the grand mean in shell length) using analysis of covariance (ANCOVA, Sokal & Rohlf, 1981). Shell length at the time of the growth interruption was measured to the nearest 0.1 mm by taking the maximum distance between the umbo and the shell check. In the samples analyzed here, the growth check was readily apparent if present and more than one check was extremely rare. Growth checks observed in these samples were virtually identical to those described by Seed (1969; his Fig. 5). Shell length to the growth check is assumed to be the size of the individual during the previous January. Animals of the 15-20 mm size class collected in January showed no evidence of a growth check, while individuals from later samples exhibited evident shell discontinuities.

GROWTH TRAJECTORIES

IN MYTILUS

105

RESULTS

There were no significant differences among slopes of the weight/length regressions for the nine population samples (F = 1.7; df = 8,422; P > 0.05) while variation among adjusted means was highly significant (F = 141.9; df = 8,430; P -c 0.001). Mean tissue weights adjusted to a standard length of 17.4 mm and their standard errors are reported in Fig. 1. Between January and April, the adjusted mean tissue weights nearly doubled, followed by a sharp decline in adjusted weight between April and June. There was an increase in weight by July followed by a shallow decline during the fall months. This tissue weight cycle is similar to that observed in other populations of Mytilus (e.g., Dare & Edwards, 1975; Bayne & Worrall, 1980; Kautsky, 1982b; Rodhouse et al., 1984a). 0.05

0.04

5

0.03 l

.

z .6 ;

.

.

l

0.02

.

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FMAMJJASO

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Fig. 1. Dry weight of soft tissues adjuste.d by ANCOVA to a standard shell length of 17.4 mm for Mytilus edulis collected in 1982: SE of the adjusted mean are also plotted but are usually smaller than the symbol.

Growth checks were present in shells of animals collected between February and July while there were no individuals with growth checks in January. The sudden appearance of these checks between January and February indicates that they are reliable indicators of the onset of spring growth. In September, 24% of the sample did not have growth checks. It seems probable that these individuals were new recruits. Juvenile mussels recruit to populations in eastern Long Island Sound in July which would allow sufficient time for some individuals of the O-year class to grow to 15-20 mm (Hilbish, 1985). Individuals without growth checks were excluded from the analysis of the September sample. In samples collected after September, growth checks were much less discrete and could not be identified with confidence. Therefore, the analysis of growth trajectories was confined to samples collected between January and September of 1982. This

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J. HILBISH

was considered adequate because this includes the periods of rapid decline in adjusted weights that occurred between April and June and it includes the period from July to September during which animals in these populations spawn (Fell & Balsamo, 1985, unpubl. data). There are three types of data available for each sample; the average shell length and tissue weight at the time of collection and the average shell length during the previous January. The regression of dry weight (w) on shell length (L) for the January, 1982 sample was described by the equation w = uLb where a is the recalculated intercept (3 x 10P6) and b the common slope (3.137) from the ANCOVA. From the weight/length regression in January, the average tissue weight of the sample during the previous January can be estimated. From these data the net growth between January and the sample date of the collection for both shell length and soft tissue weight can be calculated. For example, in February the average length and weight of the sample was 17.5 mm and 0.0379 g. The average length at the growth check for this sample was 17.3 mm. Using the assumption that the growth check was established in January and referring to the regression line for January (Fig. 2) we estimate that a 17.3-mm animal averaged 0.0229 g in tissue weight. Therefore, between mid-January and the end of February the net shell growth is estimated to be 0.2 mm and the net tissue growth to be 0.0150 g. These values represent a 1% increase in length and a 65% increase in weight (Table I). A similar calculation can be made for the sample collected in April to give the net growth between January and April. It should, however, be noted that the net growth between January and April is composed of two growth vectors; the growth between January and February and then between February and April. These growth vectors can be estimated if we assume that growth during a given interval was proportionately the same for all samples. This assumption implies that the relative rate of growth is independent of initial size, at least over the size range being discussed here. For the April sample we would assume that the rate of growth experienced by this collection of individuals between January and February is the same (i.e., 1y0 for shell and 65% for weight) as was estimated previously for the February sample. The average length at the growth check of the April sample was 14.9 mm giving an estimated weight in January of 0.0142 g (Fig. 2). By February these animals are assumed to have grown to 15 mm (an increase of 1%) and to 0.0236 g (an increase of 65%). Between February and April the net growth vector must be sufficient to yield the observed bivariate mean of the population sample (17 mm and 0.0463 g). The February to April growth vector is, therefore, estimated to be a 16% increase in shell length and a 93% increase in weight (Table 1). The analysis of growth trajectories proceeds for each subsequent sample in the same manner, by calculating the average size at the time of collection and during the previous January and by assuming that intervening growth is given by the summation of previously estimated growth vectors. The results of this analysis are reported in Table I, which gives for each sample the mean shell length during the previous January (estimated from

GROWTH TRAJECTORIES

107

IN MYTILUS

Apr /

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.ol

S

noa

s .! .006 t?? 0 .OO4 DO3

Do2

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7

8

9

1

10

Shell Length

I

I

15

20

lmml

Fig. 2. Growth trajectories for samples of Mytirus edulti collected between January and September, 1982: solid lines are the regression of dry tissue weight on shell length; dashed lines represent growth trajectories between January, 1982 and other collection dates; see text for details.

growth checks) and the growth vectors for length and tissue weight. Growth vectors are reported as the per cent increase in size from the previous sample date. Note that the average size in January decreases for each successive sample. The sampling procedure constrains the average shell length at the time of collection to a mean of z 17.4 mm. Therefore, each successive sample includes smaller individuals from the cohort that was present in January, 1982.

THOMAS

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J. HILBISH

TABLE I Initial sizes and growth

trajectories. Growth vectors ( y0 change from previous

13 28 26 10 23 4

January February April June July September

date)

Length in January

Length

Weight

17.5 17.3 14.9 10.2 8.3 7.2

1.2 16.3 46.1 25.7 9.6

65.1 93.4 33.8 194.1 14.6

It should be noted that in samples collected after April, the average shell length in January was less than the size range (15-20 mm) used to generate the regression equation for January. Therefore, the confidence with which average dry weight can be extrapolated from the January regression equation progressively decreases in the June, July, and September samples. To determine the potential error in these extrapolations I measured the dry weight and shell length of an additional 79 individuals collected in January, 1982 that were 6-15 mm in shell length so that a regression equation could be generated that would represent a size range that included the extrapolated values (Table I). The recalculated regression equation was similar to that generated by the original ANCOVA (a = 4 x 10e6; b = 3.0422). The greatest extrapolation was to 7.2 mm for the average size at the growth check for the September sample (Table I) which gave an estimated dry weight of 0.00147 g. Using the recalculated regression equation an individual 7.2 mm in length is expected to weigh 0.00162 g. These estimates compare favorably dilfering only by 9% indicating that extrapolation beyond the size range represented in the original ANCOVA does not introduce much error to the dry weight estimated for a collection during the previous January. No attempt was made to correct for this minor source of error in extrapolated dry weights. Growth trajectories given in Table I are clearly heterogeneous; growth in shell and in soft tissue do not occur simultaneously. The greatest rate of shell growth was between April and June while the greatest increase in soft tissue was between June and July. Indeed, the correlation between the per cent increase in shell length and tissue weight for each sample interval was non-existent (r = 0.07, df = 3). In Fig. 2 the growth trajectories have been illustrated for the September collection and the variance in growth trajectories is apparent. If shell and soft tissue growth occurred simultaneously the growth trajectories given in Fig. 2 would have identical slopes; this is clearly not the case. The reconstructed growth history of the September sample is presented in Fig. 3. It is clear that the pattern of growth presented here bears little resemblance to the impression left by the analysis of dry weight adjusted to a standard length animal

GROWTH TRAJECTORIES

46

0.03-

cn

5 E F 8

-16 g 44

0.02-

F

3 42 3

Q, !! ‘_ t O.Ol6

109

IN MYTILUS

,e, JFMAMJJAS

z .lo 3

..1

Fig. 3. Reconstructed growth history for the September, 1982 sample ofMyfk shell length.

6

e&k-: 0, soft tissues; 0,

(Fig. 1). All growth vectors were positive (Fig. 2) indicating that there was no period over which the animals ever lost weight. Between April and June the analysis of adjusted tissue weights indicated a dramatic decline in weight while in fact the population experienced a 33% increase in weight over this period. This discrepancy is due to the temporal uncoupling of shell and tissue growth. During this interval the population experienced a rapid increase in shell length and a relatively modest increase in tissue weight (Fig. 2). This has the effect of moving the regression line to the right (see Fig. 2) which results in the apparent decline in weight of a standard size animal. There were several assumptions made in this analysis. The assumption of proportional growth cannot be substantiated with the data collected here. The maximum range in shell lengths used in this analysis was 7 to 18 mm; over this range it seems likely that relative growth rates are similar. The results are not, however, strongly dependent upon this assumption. All of the net growth measurements are positive indicating that between January and September the population never experienced a weight loss of the magnitude indicated by the adjusted weight analysis. The error in estimating growth vectors increases in later collections; the estimate of a final growth vector (e.g., July-September) is influenced by errors compiled by the summation of previously estimated vectors. The errors, however, cannot be substantial. Shell growth in this population was previously estimated using size-frequency analysis (Hilbish, 1985). A comparison of those results with changes in shell length given in Fig. 3 gives a very high degree of correlation (r = 0.974, P < 0.01, df = 4) indicating that the two methods yield similar estimates of growth rate. Therefore, whatever errors have been accumulated in this analysis are insufficient to affect seriously the results. It is also important to note that the analysis of growth trajectories need not rely on the presence of growth checks but could have been accomplished using animals that had been notched or otherwise marked in January.

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THOMASJ. HILBISH DISCUSSION

The analysis ofgrowth trajectories in M. edulis from Long Island Sound indicates that shell and soft tissue exhibit different seasonal patterns of growth. The greatest increase in soft tissue took place between June and July, coincident with maximal rates of energy gain (Hilbish & Koehn, 1985). The maximal rate of shell growth was in the sample interval between April and June (Figs. 2 and 3). The difference in seasonal patterns of shell and tissue growth were sufficiently uncoupled that there was no correlation between them (see p. 108). The analysis presented here only considers the period from January to September so it is possible that shell and soft tissue growth are more closely related during the remainder of the year. This, however, seems unlikely as mussels in this population appear to experience a negative energy balance in the fall (Hilbish & Koehn, 1985) with a concomitant loss of soft tissue (Koehn et al., 1980), while shell length, of course, cannot be lost. Kautsky (1982a) observed uncoupled growth in Baltic populations of Mytilus; soft tissue growth preceded shell growth in these populations. There are few other comparable data that estimate shell and soft tissue growth separately. Rodhouse et al. (1984a) measured seasonal variation in shell growth using size-frequency analysis and then determined rates of soft tissue growth by determining the ash-free dry weight of soft tissues from the modal size class. I have calculated relative changes in shell length and dry weight for each sample increment from the data presented in Rodhouse et al. (1984a, their Fig. 11). The August-September increment from their data was ignored since spawning during this interval resulted in a sharp decline in dry weight. The correlation between per cent change in shell length and per cent change in soft tissue weight was not significant (r = 0.590, df = 5). These data support the hypothesis that shell growth and soft tissue growth are uncoupled. An obvious mechanism by which shell and tissue growth may be uncoupled is if during the measurement interval there is a period of negative energy balance; soft tissues may decline in weight while shell cannot. This is also true if the loss of weight is due to spawning. For the Long Island Sound population neither of these situations appears to explain the absence of coupled growth. Measurements of energy balance in this population indicate a generally positive net energy gain from January to September (Hilbish & Koehn, 1985). The greatest discrepancies in relative rates of shell and tissue growth were between April and July, a period over which the net energy balance in this population is maximal (Hilbish & Koehn, 1985; unpubl. data). Weight loss due to spawning is not a complicating factor since spawning occurs in populations of mussels from eastern Long Island Sound in late summer (Fell & Balsamo, 1985; Newell & Hilbish, unpubl. data) while the greatest decline in adjusted weight was in the spring. Indeed, mussels gained weight throughout the period of spawning activity (Fig. 3). Therefore, it appears that shell and soft tissue growth in this population of M. edulis have different “schedules” and that uncoupled growth rates are not due to weight loss resulting either from negative energy balance or from spawning. Additional studies that

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determine rates of shell and tissue growth separately are needed to determine whether uncoupled growth is a general phenomenon in bivalve populations. A major ramification of uncoupled rates of shell and tissue growth is that serious errors may be made in the interpretation of temporal variation in mean dry weights that have been adjusted to a standard length. This type of analysis (Fig. 1) gives the impression that mussels in Long Island Sound suffer a rapid loss of weight between April and June. This apparent decline in weight is, however, due to the rapid change in the covariate, shell length. The interpretation of changes in adjusted weight usually includes the implicit assumption that shell length does not vary or that the effects of shell growth are removed by this type of analysis. Not only are these assumptions violated but seemingly small changes in shell length may have enormous impact upon adjusted weights. When growth rates are expressed as length and weight through time (Fig. 3) the effects of uncoupled growth can seem very minor but when expressed graphically as growth vectors (Fig. 2) these differences are quite large and may have a serious impact on adjusted weights. The greatest change in soft tissue weight was between June and July when animals increased in weight by 200% (Fig. 3). This increase in weight is not reflected in the seasonal variation in adjusted weights (Fig. 1) where the change between June and July was minor relative to the annual variation in adjusted weights. Uncoupled growth introduces serious limitations to the use of dry tissue weights adjusted to a standard size animal in estimating temporal variation in productivity, condition or the quantitative estimate of fecundity. For example, the data of Rodhouse et al. (1984a) documents continuous increase in both shell and ash-free dry weight for mussels cultivated in Killary Harbour, Ireland between May and August yet their analysis of adjusted mean ash-free dry weights shows a general decline in weight between June and August. Their data for adjusted weights indicated a general decline in dry weight over a period in which the population in reality increased their soft tissue weight by 400%. Clearly the analysis of adjusted weights provides no indication of the true levels of productivity. Most studies that have examined temporal variation in adjusted dry weights have used animals that were substantially larger than those used here. Rates of shell growth decline with increasing size in most species of bivalves (Seed, 1976), therefore by using large animals that have negligible shell growth the difficulties with a changing covariate (shell length) may be avoided. Unfortunately this is usually not practical and has not been the case in the majority of studies to date. Many studies on annual shell growth in M. edulis indicate that annual increases in shell length are nearly linear up to sizes ranging from 55 to 80 mm and are not negligible until animals are > 70 mm in length (Bayne & Worrall, 1980; Rodhouse et al., 1984a; Thompson, 1984). Shell growth may become asymptotic at much smaller sizes in other populations of Myths (Seed 1969, 1976) but it is commonly the case that animals with negligible shell growth are rare in the population (e.g. Kautsky, 1982a). Most studies have used animals in which shell growth cannot be considered negligible. Therefore, growth in shell length is a potential

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source of artefact in the analysis of dry weights adjusted to a standard length animal. These difficulties are not limited to the use of shell length as the covariate but are equally applicable to any other size measurement. While the use of adjusted means may add valuable insight to seasonal variation in productivity of mollusc populations, extreme caution should be used in the interpretation of this type of analysis.

ACKNOWLEDGEMENTS

I would like to thank Drs. R. Malouf, P. Rodhouse, B. Bayne, D. Wethey, and S. Woodin for their helpful comments on this analysis. Part of this work was supported by NSF grant DEB 7908862 to Dr. R. Koehn.

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LUTZ, R.A. & D.C. RHOADS, 1980. Growth patterns within the molluscan shell: an overview. In, Skeletal growth of aquatic organisms, edited by D.C. Rhoads & R. A. Lutz, Plenum Publ., New York, pp. 203-254. RODHOUSE,P.G., C.M. RODEN, M.P. HENSEY,T. MCMAHON, B. OTTWAY& T.H. RYAN, 1984a. Food resources, gametogenesis and growth of Myths edulis on the shore and in suspended culture: Killary Harbour, Ireland. J. Mar. Biol. Assoc. U.K., Vol. 64, pp. 513-529. RODHOUSE,P. G., C. M. RODEN, M. P. HENSEY& T. H. RYAN, 1984b. Resource allocation in Mytihs edulis on the shore and in suspended culture. Mar. Biol., Vol. 84, pp. 27-34. SEED, R., 1969. The ecology of Mytilus edulir L. (Lamellibranchiata) on exposed rocky shores. II. Growth and mortality. Oecologia (Berlin), Vol. 3, pp. 317-350. SEED, R., 1976. Ecology. In, Marine mussels: their ecology andphysiology, edited by B.L. Bayne, Cambridge University Press, Cambridge, pp. 13-66. SOKAL, R. S. & F.J. ROHLF, 1981. Biometry. W.H. Freeman, San Francisco 776 pp. SUNDET, J. H. & 0. VAHL, 1981. Seasonal changes in dry weight and biochemical composition of the tissues of sexually mature and immature Iceland scallops, Chlumys islandica. J. Mar. Biol. Assoc. U.K., Vol. 61, pp. 1001-1010. THOMPSON,R.J., 1984. Production, reproductive effort, reproductive value and reproductive cost in a population of the blue mussel Mytihu edulis from a subarctic environment. Mar. Ecol. Prog. Ser., Vol. 16, pp. 249-251.