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A model for the maintenance of genetic variability and its utilization in genetic improvement of oyster populations
Aquaculture, 15 (1978) 289-295 o Elsevier Scientific Publishing Company, Amsterdam - Printed in The Netherlands
289
Short Communication A MODEL FOR THE MAINTENANCE OF GENETIC VARIABILITY AND ITS UTILIZATION IN GENETIC IMPROVEMENT OF OYSTER POPULATIONS
S.M. SINGH Department (Canada)
of Zoology, The University of Western Ontario, London,
(Received 9 September
Ont. N6A 5B7
1977; revised 7 August 1978)
ABSTRACT Singh, SM., 1978. A model for the maintenance of genetic variability and its utilization in genetic improvement of oyster populations. Aquaculture, 15: 289-295. A large amount of variability for size is observed in oysters of the same age. A model is proposed for the maintenance of this variability in the population. It recognizes that: (1) growth rate has a strong genetic component; (2) the ability of an oyster to breed is a function of its size and not age; (3) most oysters are harvested and eliminated from the breeding population once they reach market size; and (4) there is a continuous mortality in the population. The time taken for oysters to reach spawning and harvesting sizes depends on the growth rate, and these thresholds for fast-growing and slow-growing individuals are realized at different times. Therefore, the breeding population has to meet two balancing restrictions and, in doing so, allows the fast- and slow-growing sub-populations to release gametes in equilibrium and maintain the variability generation after generation. The genetic improvement of a population by increasing growth could be achieved by using the model to eliminate slow-growing individuals at the right time. Selection for fast-growing oysters could also be accomplished by selecting fast-growing larvae that settle early, which may be particularly applicable in the hatcheries.
INTRODUCTION
The American oyster (Crussostrea uirginica Gmelin) is a very important species in the aquaculture of Maritime Canada. Most of the spat (young oysters) are produced in a limited number of places, as stable populations exist in the sheltered bays and estuaries where the water temperature increases to 20°C in summer and initiates spawning. In these cold waters, the growth rate is slow and oysters may take up to 7 years to reach market size. The purpose of this work was to develop a management model for the oyster populations of Maritime Canada to simulate the dynamic interactions and to allow for genetic improvement by selection of stocks.
MODEL
OF MAINTENANCE
OF VARIABILITY
IN OYSTERS
The model proposed relies on the following conditions: (1) Individuals grow at different rates and there is a strong genetic component for growth rate. (2) Animals become reproductively mature only when they reach a size ‘X’. (3) Animals larger than size ‘X’ breed every year as long as they stay in the population. (4) Animals which exceed size ‘Y’ are harvested and eliminated from the population. (5) There is a continuous mortality in the population, this mortality being higher for individuals faking a longer time to reach ‘Y’. It is also assumed that the growth rate is controlled by a single gene with two alleles in co-dominance, and the environment creates equal effects or no effects on the growth rate of the three genotypes. The model is introduced in Fig.1, with the frequency of the allele for fast growth (q) equal to 0.5. The three genotypes under panmixia will be produced in p2 : 2pq : q* proportions, q* being the fast growers, 2pq, intermediates and p*, slow growers. If spawning takes place at an assumed threshold size of ‘X’, only the fast-growing oysters (q*) are big enough to spawn after 1 year of growth. After the second year, individuals with an intermediate growth rate (2pq) would cross the threshold and be able to spawn along with the fast growing ones. By the third year, even the slow-growing individuals (p’) will be big enough to go through spawning along with the intermediate and fast-growing individuals. In the fourth year, the fast growing group will reach market size ‘Y’. They are fished out and removed from the population. The intermediates will reach market size by the fifth year and the slow growers by the sixth year. All oysters between ‘X’ and ‘Y’ release gametes every year. In the gametic pool the frequency for p_,= 0.5 and q = 0.5. After random mating they produce p* : 2pq : q* genotypes that become l-year-olds and the cycle continues. If one puts a different allele frequency through the model, it will maintain that gene frequency generation after generation. A disturbance in growth conditions and fishing pressures will change the original gene frequency and lead to the establishment of a new equilibrium, depending on the stability of new conditions experienced by the population. The polygenic nature of growth is accommodated by blending the three genotypes of Fig.1 to represent all combinations of contributors (alleles contributing to growth) and non-contributors (alleles not contributing to growth) as shown in Fig.2. The right hand side of the distribution represents a high concentration of contributors, and thus fast growers; the left hand side represents a low concentration of contributors, producing slow growers. The shape of these curves, however, may vary depending on the genetic constitution of growth genes in the population. These curves (Fig.2) represent the distribution of polygenes and are assumed to stay homoscedastic for each year-class.
291
6 ti_-Gometic
-+
Pool-.59:.5P
Larvae.-pz+2pq+q2
Gamft~c
pool
1
Larvae
Fig.1. (left). Model of maintenance of variability in overlapping generations assuming that the growth is controlled by a co-dominant gene with two alleles p and q. X = size threshold for sexual maturity; Y = threshold for market size (harvesting). Dotted areas represent breeding individuals. Fig.2. (right). Model to accommodate polygenic nature of growth rate. Note high concentratior of contributors in fast growers on the right-hand side of the distribution.
The distribution, as shown in the figure, moves along the size scale with time. Fast growers reach the spawning threshold (X) earlier than slow growers. A balance in the output of gametes with fast and slow growth rate gene combinations is brought about by the total number of individuals of different growth categories available for spawning every breeding season. As the phenotypic distribution of different age groups is not homoscedastic, due to differential growth rates, slow growers may stay longer in the breeding population. Continuous mortality regulates the proportion of individuals of different growth categories available for spawning in a breeding season. As slow growers are more exposed to mortality, since thay take a longer time to reach harvest size, the actual number of slow growers decreases with increasing age. The
experimental findings of Haley and Newkirk (1977) suggested that there was a higher mortality in the slow growers as compared to fast growers, and that it led to a balance in the gametic output of different growth gene combinations in every breeding season and maintained variability in the population. In summary of the illustrations in Figs 1 and 2, the fast growing genotypes start reproducing early, stay in the breeding population for a short time, reach market size, and are eliminated from the population. In contrast, the slow growing genotypes take a longer time to reach sexual maturity as well as marketing size. These may be expected to stay longer in the breeding population and release gametes every spawning season. The total gamete output by a given growing category of individuals is regulated by: (a) the length of time individuals stay in the breeding population, and (b) the number available for spawning. A form of balancing selection operates, which leads to a genetic equilibrium for the polymorphic systems associated with growth rate that is itself dependent on the demographic stability of the population. The occurrence of an equilibrium with the co-occurrence of a stable age distribution of the population has been suggested by Charlsworth (1970) and Charlsworth and Giesel (1972) in overlapping generations. APPLICATION
OF THE MODEL
Data from the oyster population of Maritime Canada (Fig.3) were applied to the model. They represent the northernmost distribution of the species. The water first reaches spawning temperature (20°C) in June. A single burst of spawning takes place and fertilized, fast-swimming larvae are produced at about the same time in all areas (Medcof, 1961). Larvae settle on hard surfaces and are collected by placing collectors in the spawning grounds. The collected spat are allowed to grow to market size. Most of the market size individuals of the natural population on the sea bed are regularly harvested by dredges and oyster tongs. The source of spat grown in this area is all or almost all from the natural populations. Some of the other features of this population are as follows: (1) There is a tremendous variation in growth rate of individuals of the same age (Medcof, 1961; Singh and Zouros, 1978). (2) Very high values of heritability for growth rate are observed at different stages of growth (Newkirk et al., 1977). Haley and Newkirk (1977) also suggest a strong genetic component to growth rate. (3) Most of the oysters spawn when they are about 1 inch (2.54 cm) long, and they normally spawn every year thereafter as long as they stay in the population. (4) Due to the restricted growing period, it may take up to 7 years for the oysters to reach market size. There is continuous mortality in the population. The mortality is higher in the slow-growers as compared to the fastgrowing genotypes (Haley and Newkirk, 1977). A survey of the population in May (prior to spawning) indicated that most
293
of the l-year-olds were too small for gonadal development and would not spawn. A large proportion of the 2- and 3-year-olds were large enough to spawn. This proportion may vary depending upon growth conditions. Almost all of the 4-7-year-olds were of spawning size. Very few were over 10 years old. As the market size oysters are continuously harvested and thus elimi- nated from the breeding population, the breeding population therefore included all individuals that were large enough to spawn, and had not reached market size. Random samples of 300 oysters from different spatfall years of the Malpeque Bay population were taken. The oysters were tagged, weighed and then placed in floating trays in the bay. Fig. 4 shows the actual weight distributions for five year-classes fitted to theoretical distributions. The l- and 2year-classes conformed to the lognormal distribution, when weights were grouped in classes of 0.5-g intervals (with 13 degrees of freedom, x2 = 10.32, p = 0.67 and x2 = 19.69, p = 0.103 for the l- and 2-year-olds, respectively). In the 3- or older year-classes, the weight distributions fitted a normal distribution, with p values ranging from 0.1 to 0.7. It was apparent from the data that more individuals grew to spawning size than reached market size. The data also suggested that there was a strong association between growth rates at different stages. Correlations involving
Gulf
of
St.Lawrence
Atlantic
Ocean
Fig.3. Map of the area showing oyster populations (0) in Maritime Canada; A represents the population studied. (P.E.I. = Prince Edward Island; N.B. = New Brunswick; N.S. = Nova Scotia.)
294
/’
0’
s.pyR&B *... *’ 5
/’
m f-Y\ *\
..-=,.
‘\
\,,,,,_,“‘..~‘~““‘.
P
\\_..,,,_,,, _.._.,_. E...-.....‘“.R\.~Harve:tlrx 11 I 15
30 Weight
...‘..,. 3
45
(9)
Fig.4. Actual weight distribution of various year-classes.
Note the increase in variance with
increasing age. weights at different stages were significant and positive with a range of 0.67 (for weight in early summer in the second year to weight in late summer of the third year) to 0.95 (for weight in early summer of the fifth year to weight in late summer of the fifth year). Haley and Newkirk (1977) followed the growth rates of early (16. --18 days after fertilization) and late (22. -24 days after fertilization) settled spat of this population and reported that early settled spat came from fast-growing larvae and stayed fast-growing l- and 2year-olds. The converse was true for the late settled spat which stayed slowgrowing all their lives. The early settled spat also showed low mortality compared with the late-set spat, when tested under different salinities and temperatures. It was concluded that the genetic predisposition for growth rate was realized during the larval period and stayed with the individual throughout its life, and would account for observed increasing variance in weight with age. The variability for growth genes of a given year-class, however, may not change as individuals grow with time. The differences between the proposed distribution of growth genes (Fig. 2) and the observed weights (Fig. 4) are: (1) the observed weight distribution in younger individuals (l- and 2-year-class) does not conform to a normal distribution; and (2) there is an increase in the variance of year classes with age. This is to be expected if there is a strong genetic predisposition to growth rate. These differences would not affect the applicability of the model to other populations if there was enough variation in the breeding population itself. PRACTICAL
IMPLICATIONS
The model is applicable to all oyster populations and in general can probably be applied to many organisms that are harvested on the basis of size and not age. Improvement for increased growth rate may be brought about
295
using the model to dispose of or select out the smaller oysters (slow growers) at the optimal time. If practised year after year, this will increase the proportion of genes for fast growth rate in the population, and will reduce the time presently taken by Maritime oysters to reach market size. It will, however, require a cooperative effort of all growers in the area to achieve significant improvement. Also a routine selection of early settled spats may be practised which will be most effective in hatcheries where spawning and fertilization are synchronous. Such selection has been found effective in improving oyster populations in the past. Natural selection yielded populations resistant to Malpeque disease (Needler and Logie, 1947), and against the halposporidian Minchinia nelsoni (MSX) disease (Haskin, 1972). ACKNOWLEDGEMENTS
I wish to thank Dr L.E. Haley for his help during the course of this investigation. Cooperation of the Fisheries Research Station at Ellerlie, P.E.I., Canada, is gratefully acknowledged. Financial support was provided by N.C.R. through a negotiated development grant. REFERENCES Charlsworth, B., 1970. Selection in populations with overlapping generations. I. The use of Malthusian parameters in population genetics. Theor. Popul. Biol., 352-370. Charlsworth, B. and Giesel, J.T., 1972. Selection in populations with overlapping generations. II. Relations between gene frequeecy and demographic variables. Am. Nat., 106: 338-401. Haley, L.E. and Newkirk, G.F., 1977. Selecting oysters for faster growth. Proc. 8 Annu. Meet., World Mariculture Society, pp. 557-566. Haskin, H.H., 1972. Disease resistant oyster program, Delaware Bay, 1965-72. Rep NMFS Prof. N-J-3-3-R under P. L. 88-309, Oct. 31,38 pp. Medcof, J.C., 1961. Oyster Farming in the Maritimes.Fish Res. Board Can., Bull. No. 131, 158 pp. Needler, A.W.H. and Logie, R.R., 1947. Serious mortalities in Prince Edward Island oysters caused by a contagious disease. Trans. R. Sot. Can. Sect. 5, 41: 73-89. Newkirk, G.F., Waugh, D.L., Haley, L.E. and Doyle, R.W., 1977. Genetics of larval and spat growth rate in oyster, Crassostren uirginica. Mar. Biol. 41: 49-52. Singh, S.M. and Zouros, E., 1978. Genetic variation associated with growth rate in the American oyster (Crassostrea uirginica). Evolution, 32. 342-353.
289
Short Communication A MODEL FOR THE MAINTENANCE OF GENETIC VARIABILITY AND ITS UTILIZATION IN GENETIC IMPROVEMENT OF OYSTER POPULATIONS
S.M. SINGH Department (Canada)
of Zoology, The University of Western Ontario, London,
(Received 9 September
Ont. N6A 5B7
1977; revised 7 August 1978)
ABSTRACT Singh, SM., 1978. A model for the maintenance of genetic variability and its utilization in genetic improvement of oyster populations. Aquaculture, 15: 289-295. A large amount of variability for size is observed in oysters of the same age. A model is proposed for the maintenance of this variability in the population. It recognizes that: (1) growth rate has a strong genetic component; (2) the ability of an oyster to breed is a function of its size and not age; (3) most oysters are harvested and eliminated from the breeding population once they reach market size; and (4) there is a continuous mortality in the population. The time taken for oysters to reach spawning and harvesting sizes depends on the growth rate, and these thresholds for fast-growing and slow-growing individuals are realized at different times. Therefore, the breeding population has to meet two balancing restrictions and, in doing so, allows the fast- and slow-growing sub-populations to release gametes in equilibrium and maintain the variability generation after generation. The genetic improvement of a population by increasing growth could be achieved by using the model to eliminate slow-growing individuals at the right time. Selection for fast-growing oysters could also be accomplished by selecting fast-growing larvae that settle early, which may be particularly applicable in the hatcheries.
INTRODUCTION
The American oyster (Crussostrea uirginica Gmelin) is a very important species in the aquaculture of Maritime Canada. Most of the spat (young oysters) are produced in a limited number of places, as stable populations exist in the sheltered bays and estuaries where the water temperature increases to 20°C in summer and initiates spawning. In these cold waters, the growth rate is slow and oysters may take up to 7 years to reach market size. The purpose of this work was to develop a management model for the oyster populations of Maritime Canada to simulate the dynamic interactions and to allow for genetic improvement by selection of stocks.
MODEL
OF MAINTENANCE
OF VARIABILITY
IN OYSTERS
The model proposed relies on the following conditions: (1) Individuals grow at different rates and there is a strong genetic component for growth rate. (2) Animals become reproductively mature only when they reach a size ‘X’. (3) Animals larger than size ‘X’ breed every year as long as they stay in the population. (4) Animals which exceed size ‘Y’ are harvested and eliminated from the population. (5) There is a continuous mortality in the population, this mortality being higher for individuals faking a longer time to reach ‘Y’. It is also assumed that the growth rate is controlled by a single gene with two alleles in co-dominance, and the environment creates equal effects or no effects on the growth rate of the three genotypes. The model is introduced in Fig.1, with the frequency of the allele for fast growth (q) equal to 0.5. The three genotypes under panmixia will be produced in p2 : 2pq : q* proportions, q* being the fast growers, 2pq, intermediates and p*, slow growers. If spawning takes place at an assumed threshold size of ‘X’, only the fast-growing oysters (q*) are big enough to spawn after 1 year of growth. After the second year, individuals with an intermediate growth rate (2pq) would cross the threshold and be able to spawn along with the fast growing ones. By the third year, even the slow-growing individuals (p’) will be big enough to go through spawning along with the intermediate and fast-growing individuals. In the fourth year, the fast growing group will reach market size ‘Y’. They are fished out and removed from the population. The intermediates will reach market size by the fifth year and the slow growers by the sixth year. All oysters between ‘X’ and ‘Y’ release gametes every year. In the gametic pool the frequency for p_,= 0.5 and q = 0.5. After random mating they produce p* : 2pq : q* genotypes that become l-year-olds and the cycle continues. If one puts a different allele frequency through the model, it will maintain that gene frequency generation after generation. A disturbance in growth conditions and fishing pressures will change the original gene frequency and lead to the establishment of a new equilibrium, depending on the stability of new conditions experienced by the population. The polygenic nature of growth is accommodated by blending the three genotypes of Fig.1 to represent all combinations of contributors (alleles contributing to growth) and non-contributors (alleles not contributing to growth) as shown in Fig.2. The right hand side of the distribution represents a high concentration of contributors, and thus fast growers; the left hand side represents a low concentration of contributors, producing slow growers. The shape of these curves, however, may vary depending on the genetic constitution of growth genes in the population. These curves (Fig.2) represent the distribution of polygenes and are assumed to stay homoscedastic for each year-class.
291
6 ti_-Gometic
-+
Pool-.59:.5P
Larvae.-pz+2pq+q2
Gamft~c
pool
1
Larvae
Fig.1. (left). Model of maintenance of variability in overlapping generations assuming that the growth is controlled by a co-dominant gene with two alleles p and q. X = size threshold for sexual maturity; Y = threshold for market size (harvesting). Dotted areas represent breeding individuals. Fig.2. (right). Model to accommodate polygenic nature of growth rate. Note high concentratior of contributors in fast growers on the right-hand side of the distribution.
The distribution, as shown in the figure, moves along the size scale with time. Fast growers reach the spawning threshold (X) earlier than slow growers. A balance in the output of gametes with fast and slow growth rate gene combinations is brought about by the total number of individuals of different growth categories available for spawning every breeding season. As the phenotypic distribution of different age groups is not homoscedastic, due to differential growth rates, slow growers may stay longer in the breeding population. Continuous mortality regulates the proportion of individuals of different growth categories available for spawning in a breeding season. As slow growers are more exposed to mortality, since thay take a longer time to reach harvest size, the actual number of slow growers decreases with increasing age. The
experimental findings of Haley and Newkirk (1977) suggested that there was a higher mortality in the slow growers as compared to fast growers, and that it led to a balance in the gametic output of different growth gene combinations in every breeding season and maintained variability in the population. In summary of the illustrations in Figs 1 and 2, the fast growing genotypes start reproducing early, stay in the breeding population for a short time, reach market size, and are eliminated from the population. In contrast, the slow growing genotypes take a longer time to reach sexual maturity as well as marketing size. These may be expected to stay longer in the breeding population and release gametes every spawning season. The total gamete output by a given growing category of individuals is regulated by: (a) the length of time individuals stay in the breeding population, and (b) the number available for spawning. A form of balancing selection operates, which leads to a genetic equilibrium for the polymorphic systems associated with growth rate that is itself dependent on the demographic stability of the population. The occurrence of an equilibrium with the co-occurrence of a stable age distribution of the population has been suggested by Charlsworth (1970) and Charlsworth and Giesel (1972) in overlapping generations. APPLICATION
OF THE MODEL
Data from the oyster population of Maritime Canada (Fig.3) were applied to the model. They represent the northernmost distribution of the species. The water first reaches spawning temperature (20°C) in June. A single burst of spawning takes place and fertilized, fast-swimming larvae are produced at about the same time in all areas (Medcof, 1961). Larvae settle on hard surfaces and are collected by placing collectors in the spawning grounds. The collected spat are allowed to grow to market size. Most of the market size individuals of the natural population on the sea bed are regularly harvested by dredges and oyster tongs. The source of spat grown in this area is all or almost all from the natural populations. Some of the other features of this population are as follows: (1) There is a tremendous variation in growth rate of individuals of the same age (Medcof, 1961; Singh and Zouros, 1978). (2) Very high values of heritability for growth rate are observed at different stages of growth (Newkirk et al., 1977). Haley and Newkirk (1977) also suggest a strong genetic component to growth rate. (3) Most of the oysters spawn when they are about 1 inch (2.54 cm) long, and they normally spawn every year thereafter as long as they stay in the population. (4) Due to the restricted growing period, it may take up to 7 years for the oysters to reach market size. There is continuous mortality in the population. The mortality is higher in the slow-growers as compared to the fastgrowing genotypes (Haley and Newkirk, 1977). A survey of the population in May (prior to spawning) indicated that most
293
of the l-year-olds were too small for gonadal development and would not spawn. A large proportion of the 2- and 3-year-olds were large enough to spawn. This proportion may vary depending upon growth conditions. Almost all of the 4-7-year-olds were of spawning size. Very few were over 10 years old. As the market size oysters are continuously harvested and thus elimi- nated from the breeding population, the breeding population therefore included all individuals that were large enough to spawn, and had not reached market size. Random samples of 300 oysters from different spatfall years of the Malpeque Bay population were taken. The oysters were tagged, weighed and then placed in floating trays in the bay. Fig. 4 shows the actual weight distributions for five year-classes fitted to theoretical distributions. The l- and 2year-classes conformed to the lognormal distribution, when weights were grouped in classes of 0.5-g intervals (with 13 degrees of freedom, x2 = 10.32, p = 0.67 and x2 = 19.69, p = 0.103 for the l- and 2-year-olds, respectively). In the 3- or older year-classes, the weight distributions fitted a normal distribution, with p values ranging from 0.1 to 0.7. It was apparent from the data that more individuals grew to spawning size than reached market size. The data also suggested that there was a strong association between growth rates at different stages. Correlations involving
Gulf
of
St.Lawrence
Atlantic
Ocean
Fig.3. Map of the area showing oyster populations (0) in Maritime Canada; A represents the population studied. (P.E.I. = Prince Edward Island; N.B. = New Brunswick; N.S. = Nova Scotia.)
294
/’
0’
s.pyR&B *... *’ 5
/’
m f-Y\ *\
..-=,.
‘\
\,,,,,_,“‘..~‘~““‘.
P
\\_..,,,_,,, _.._.,_. E...-.....‘“.R\.~Harve:tlrx 11 I 15
30 Weight
...‘..,. 3
45
(9)
Fig.4. Actual weight distribution of various year-classes.
Note the increase in variance with
increasing age. weights at different stages were significant and positive with a range of 0.67 (for weight in early summer in the second year to weight in late summer of the third year) to 0.95 (for weight in early summer of the fifth year to weight in late summer of the fifth year). Haley and Newkirk (1977) followed the growth rates of early (16. --18 days after fertilization) and late (22. -24 days after fertilization) settled spat of this population and reported that early settled spat came from fast-growing larvae and stayed fast-growing l- and 2year-olds. The converse was true for the late settled spat which stayed slowgrowing all their lives. The early settled spat also showed low mortality compared with the late-set spat, when tested under different salinities and temperatures. It was concluded that the genetic predisposition for growth rate was realized during the larval period and stayed with the individual throughout its life, and would account for observed increasing variance in weight with age. The variability for growth genes of a given year-class, however, may not change as individuals grow with time. The differences between the proposed distribution of growth genes (Fig. 2) and the observed weights (Fig. 4) are: (1) the observed weight distribution in younger individuals (l- and 2-year-class) does not conform to a normal distribution; and (2) there is an increase in the variance of year classes with age. This is to be expected if there is a strong genetic predisposition to growth rate. These differences would not affect the applicability of the model to other populations if there was enough variation in the breeding population itself. PRACTICAL
IMPLICATIONS
The model is applicable to all oyster populations and in general can probably be applied to many organisms that are harvested on the basis of size and not age. Improvement for increased growth rate may be brought about
295
using the model to dispose of or select out the smaller oysters (slow growers) at the optimal time. If practised year after year, this will increase the proportion of genes for fast growth rate in the population, and will reduce the time presently taken by Maritime oysters to reach market size. It will, however, require a cooperative effort of all growers in the area to achieve significant improvement. Also a routine selection of early settled spats may be practised which will be most effective in hatcheries where spawning and fertilization are synchronous. Such selection has been found effective in improving oyster populations in the past. Natural selection yielded populations resistant to Malpeque disease (Needler and Logie, 1947), and against the halposporidian Minchinia nelsoni (MSX) disease (Haskin, 1972). ACKNOWLEDGEMENTS
I wish to thank Dr L.E. Haley for his help during the course of this investigation. Cooperation of the Fisheries Research Station at Ellerlie, P.E.I., Canada, is gratefully acknowledged. Financial support was provided by N.C.R. through a negotiated development grant. REFERENCES Charlsworth, B., 1970. Selection in populations with overlapping generations. I. The use of Malthusian parameters in population genetics. Theor. Popul. Biol., 352-370. Charlsworth, B. and Giesel, J.T., 1972. Selection in populations with overlapping generations. II. Relations between gene frequeecy and demographic variables. Am. Nat., 106: 338-401. Haley, L.E. and Newkirk, G.F., 1977. Selecting oysters for faster growth. Proc. 8 Annu. Meet., World Mariculture Society, pp. 557-566. Haskin, H.H., 1972. Disease resistant oyster program, Delaware Bay, 1965-72. Rep NMFS Prof. N-J-3-3-R under P. L. 88-309, Oct. 31,38 pp. Medcof, J.C., 1961. Oyster Farming in the Maritimes.Fish Res. Board Can., Bull. No. 131, 158 pp. Needler, A.W.H. and Logie, R.R., 1947. Serious mortalities in Prince Edward Island oysters caused by a contagious disease. Trans. R. Sot. Can. Sect. 5, 41: 73-89. Newkirk, G.F., Waugh, D.L., Haley, L.E. and Doyle, R.W., 1977. Genetics of larval and spat growth rate in oyster, Crassostren uirginica. Mar. Biol. 41: 49-52. Singh, S.M. and Zouros, E., 1978. Genetic variation associated with growth rate in the American oyster (Crassostrea uirginica). Evolution, 32. 342-353.