The Effects Of Seed Size, Shell Bags, Crab Traps, And Netting On The Survival Of The Northern Hard Clam Mercenaria Mercenaria (Linne)

Seed size at planting is the dominant factor affecting hard clam survival to marketable size when field grow-out techniques are used. The use of plastic mesh nets, crab traps, and wire mesh bags (filled with oyster shells) alone or in combination can be used to increase survival of hard clams of ~ 6 to 8-mm shell height. These techniques do not provide sufficient protection for 2-mm seed. The combination of net + crab trap + shell bag was nearly twice as effective as the net alone when 10 to 14-mm seed was used and over five times as effective as the net alone when 6 to 8-mm seed were planted. Survival in excess of 50% slows the growth rate and yields higher percentages of submarketable, \u3c 25-mm thick (New York legal limit) clams. Local markets and dealers would accept all clams \u3e22 mm

Mercenaria Culture Using Stone Aggregate For Predator Protection

A low technology method utilizing hatchery-raised seed clams and field grow-out techniques is presented.This technique appears to be economically feasible and can be carried out by non-technical personnel with a minimum of training. The hatchery uses the Wells-Glancy (centrifuged, incubated seawater) method for raising food for the larval clams. The larvae set in 8 - 10 days and the seed are supplied with flowing seawater until they grow to 2 mm. The 2 mm seed were placed in nursery plots and protected from predation by a layer of gravel or crushed stone aggregate. Movement of the small clams was prevented by a system of baffles which enclosed and dissected the nursery areas. Field survival of a 1975 test group of 600,000 clams approached 75 % . Costs of raising the clams for the first year are included

Population Dynamics Model of the Hard Clam, Mercenaria Mercenaria: Development of the Age- and Length-Frequency Structure of the Population

An individual-based model was developed to simulate growth of the hard clam, Mercenaria mercenaria, in response to temperature, salinity and food supply conditions. Unique characteristics of the model are that: (1) length and tissue weight are related only by condition index, so that weight, up to a point, can vary independently of length, and (2) age is decoupled from length. Tissue weight changes result from the difference in assimilation and respiration. Changes in hard clam condition are determined from a standard length-weight relationship for average hard clam growth. Changes in hard clam length (growth) occur only when condition index is greater than zero, which happens when excess weight for a given length is attained. No change in length occurs if condition index is zero (mean case) or negative (less weight than expected at a given length). This model structure resolves limitations that accompany models used to simulate the growth and development of shellfish populations. The length-frequency distribution for a cohort was developed from the individual-based model through simulation of a suite of genotypes with varying physiological capabilities. Hard clam populations were then formed by the yearly concatenation of cohorts with partially independent trajectories that are produced by cohort- and population-based processes. Development and verification of the hard clam model was done using long-term data sets from Great South Bay, New York that have been collected by the Town of Islip, New York. The ability to separately track length and age in the simulations allowed derivation of a general mathematical relationship for describing age-length relationships in hard clam populations. The mathematical relationship, which is based on a twisted bivariate Gaussian distribution, reproduces the features of age-length distributions observed for hard clam populations. The parameters obtained from fitting the twisted bivariate Gaussian to simulated hard clam length-frequency distributions obtained for varying conditions yield insight into the growth and mortality processes and population-dependent processes, compensatory and otherwise, that structured the population. This in turn provides a basis for development of theoretical models of population age-length compositions. The twisted bivariate Gaussian also offers the possibility of rapidly and inexpensively developing age-length keys, used to convert length-based data to age-based data, by permitting a relatively few known age-length pairs to be expanded into the full age- and length-frequency structure of the population

Effects of the Fishery on the Northern Quahog (=Hard Clam, Mercenaria Mercenaria L.) Population in Great South Bay, New York: A Modeling Study

A numerical bioenergetics simulation model based on the physiological processes affecting individual clams across a range of phenotypes describing a cohort has been developed and applied to the conditions in Great South Bay, New York. The clam population is relatively sensitive to food and to a lesser extent to temperature within this system. The timing of temperature and food in tire spring, and more importantly in the fall, call increase population sensitivity beyond the effects of one factor operating alone. The effects of fishing on the stocks in proportion to the size structure present. and as directed fisheries oil various size classes (littleneck, cherrystone, chowder) was stimulated. Recruitment overfishing was responsible for the stock decline in the 1970s and 1980s, but the continued decline into the late 1990s and 2000s cannot be attributed to fishing alone. Recruit-per-adult declined after the mid 1990s. Modeled stock recovery times Under constant environmental conditions are oil order of 10-15 or more years depending oil the exploitation rate, Under base conditions a proportional fishery that removes approximately 25% of the stock, or a littleneck fishery that removes approxmately, 37.5% of that size class annually Would provide the best economic returns underconstant average environmental conditions. Slightly less harvest Would be desirable to avoid overfishing ill years of less than optimal environmental conditions

Mud Blister Worms and Oyster Aquaculture

The mud blister worm, Polydora websteri Hartman (Loosanoff and Engle 1943), burrows into the shells of bivalve mollusks, including Eastern oysters (Crassostrea virginica), sea scallops (Placopecten magellanicus) and blue mussels (Mytilus edulis). This report is for oyster producers interested in controlling mud blister worms, which when present in large numbers can reduce the value of oysters sold to the half-shell market. Although other species of blister-causing worms occur in several genera including Polydora, Pseudopolydora, and Boccardia, this report focuses specifically on Polydora websteri

Multiple Stable Reference Points in Oyster Populations: Biological Relationships for the Eastern Oyster (Crassostrea virginica) in Delaware Bay

In the first of two companion papers, a 54-yr time series for the oyster population in the New Jersey waters of Delaware Bay was analyzed to develop biological relationships necessary to evaluate maximum sustainable yield (MSY) reference points and to consider how multiple stable points affect reference point-based management. The time series encompassed two regime shifts, one circa 1970 that ushered in a 15-yr period of high abundance, and a second in 1985 that ushered in a 20-yr period of low abundance. The intervening and succeeding periods have the attributes of alternate stable states. The biological relationships between abundance, recruitment, and mortality were unusual in four ways. First, the broodstock-recruitment relationship at low abundance may have been driven more by the provision of settlement sites for larvae by the adults than by fecundity. Second, the natural mortality rate was temporally unstable and bore a nonlinear relationship to abundance. Third, combined high abundance and low mortality, though likely requiring favorable environmental conditions, seemed also to be a self-reinforcing phenomenon. As a consequence, the abundance-mortality relationship exhibited both compensatory and depensatory components. Fourth, the geographic distribution of the stock was intertwined with abundance and mortality, such that interrelationships were functions both of spatial organization and inherent population processes

Multiple Stable Reference Points in Oyster Populations: Implications for Reference Point-Based Management

In the second of two companion articles, a 54-year time series for the oyster population in the New Jersey waters of Delaware Bay is analyzed to examine how the presence of multiple stable states affects reference-point-based management. Multiple stable states are described by four types of reference points. Type I is the carrying capacity for the stable state: each has associated with it a type-II reference point wherein surplus production reaches a local maximum. Type-II reference points are separated by an intermediate surplus production low (type III). Two stable states establish a type-IV reference point, a point-of-no-return that impedes recovery to the higher stable state. The type-II to type-III differential in surplus production is a measure of the difficulty of rebuilding the population and the sensitivity of the population to collapse at high abundance. Surplus production projections show that the abundances defining the four types of reference points are relatively stable over a wide range of uncertainties in recruitment and mortality rates. The surplus production values associated with type-II and type-III reference points are much more uncertain. Thus, biomass goals are more easily established than fishing mortality rates for oyster populations