Printer Friendly

Annulus formation and growth of Atlantic surfclam (Spisula solidissima) along a latitudinal gradient in the western north atlantic ocean.

ABSTRACT Age data play an important role in the Atlantic surfclam [Spisula solidissima (Dillwyn, 1817)] stock assessment and their accuracy depends on understanding the periodicity and timing of annulus formation. In the United States, the surfclam stock area spans approximately 5[degrees] latitude in the northwest Atlantic. In recent years, surfclams at the southern end of this area have undergone changes in abundance, distribution, and growth, all likely due to increasing ocean temperature. To determine whether the periodicity or timing of annulus formation has also changed, shells from live surfclams were collected during every month of the year from three regions spanning 4[degrees] latitude: southern New England (northernmost), New Jersey (middle), and the Delmarva Peninsula (southernmost). Edge analysis and marginal increment analysis indicated a single annulus was formed each year in each region and by all age groups, verifying surfclams can still be aged reliably throughout the managed area. Some regional or age-specific variations were also noted. The model-derived peak times of annulus formation were mid-December for southern New England, late November for New Jersey, and early December for Delmarva. The youngest clams in New Jersey and Delmarva formed annuli about a month earlier than the older age classes. Shell lengths at age, estimated from chondrophore heights and fit to a von Bertalanffy growth model, indicated a latitudinal gradient in growth and asymptotic size. Surfclams grew slowest and to the smallest size in Delmarva [k = 0.217 (0.002 SE), [L.sub.[infinity]] = 153.0 (0.45 SE)] and fastest and to the largest size in southern New England [k = 0.298 (0.002 SE), [L.sub.[infinity]] = 162.0 (0.35 SE)]. Historic von Bertalanffy growth parameters, some unpublished, are also reviewed. There is some evidence of a decrease in k and since 1980, particularly in the New Jersey and Delmarva regions where fishing has been historically concentrated.

KEY WORDS: Spisula solidissima, annulus formation, age validation, von Bertalanffy model, geographic comparisons


Variation in annulus formation across the range of a single species has been noted in several studies. For instance, Atlantic sea scallops (Placopecten magellanicus) form annuli in summer over most of their range, but form them in winter at the range's northern edge (Chute et al. 2012). Atlantic cod (Gadus morhua) from the southern North Sea form their annuli in the summer into fall, but Atlantic cod from the colder Barents Sea form annuli in the winter into spring (Hoie et al. 2009). Reallocation of resources to the gonads during spawning, thermal stress, and food availability--all directly or indirectly linked to local oceanographic conditions--work in combination to influence when mineral deposition changes and an annulus is formed.

Differences in size and growth rate within a species' range are also seen frequently. The Patagonian scallop Zygochlamys patagonica reaches larger maximum size in one part of its Atlantic range, yet grows faster in another (Lomovasky et al. 2011). Both megrim Lepidorhombus whiffiagonis and haddock Melanogrammus aeglefinus have different growth patterns in the northern North Sea than they do in the eastern Atlantic approximately 1,290 km away (Macdonald et al. 2013). The cause in these cases also appears to be variability in localized oceanographic conditions influencing productivity.

Past studies have reported that Atlantic surfclams Spisula solidissima form one annulus per year in the fall, and thus annuli can be counted to determine the year class of the surfclam and final age can be assigned precisely relative to when the last annulus was formed (Almeida & Sheehan 1997). These studies used a variety of methods including tracking a single year class (Wagner 1984), seasonal increment analysis (Jones 1980), isotopic signatures (Jones et al. 1983), and mark recapture (Jones et al. 1978, Ropes 1985). Since then, age-based stock assessments (Loesch & Ropes 1977, Murawski & Serchuk 1979) have assumed surfclams form one annulus per year throughout their geographic range and among all age groups.

These age validation studies, while numerous and incorporating an array of methods, were done prior to indications that changing environmental conditions were affecting surfclams. In recent years, surfclams from the southern end of their range, the Delmarva (DMV) region, have been decreasing in size at age (Weinberg & Helser 1996, NEFSC 2010), declining in number, and shifting offshore (NEFSC 2013) likely in response to increasing water temperature (Weinberg 2005, Picariello 2006). Kim and Powell (2004) noted inshore clams from the DMV region had poorer body condition than those in cooler offshore waters. In recent years, localized temperature events in the DMV region have surpassed the lethal limit for surfclams (Weinberg 2005).

If conditions were changing at the southern end of the surfclam's range, could the periodicity or timing of annulus formation change as well? This study investigates whether all regions and sizes of surfclams could still be aged reliably and if changes in growth were occurring among regions or over time.

Shells from live surfclams from three regions along a latitudinal gradient were analyzed (Fig. 1): the Delmarva Peninsula (DMV, southern mid-Atlantic Bight, where there is evidence surfclams have been experiencing stress), New Jersey (NJ, central mid-Atlantic Bight, where fishing effort has historically been focused), and southern New England [SNE, at the conjunction of the Gulf of Maine and the mid-Atlantic Bight, where surfclam fishing effort has increased in recent years (NEFSC 2013)]. Samples were collected in these regions from every month of the year to establish the periodicity and timing of annulus deposition. Chondrophore height (CH)-shell length (SL) relationships for clams from each region were also determined, which allowed growth patterns to be examined.


Field Collection

All the surfclam shells used for this study were from living animals harvested by commercial vessels during normal fishing operations. The clams were shucked and their shells collected either by port agents who routinely sample surfclam landings, by clam fishermen during a trip, or at processing facilities the day the clams were processed. When fishermen provided the shells, precise locations were often available for the haul from which the clams were sampled. When the shells were collected by port agents or were from processing facilities, the location data were limited to the statistical area of the trip (Fig. 1).

Sampling began in August 2007 with the intent to collect at least 30 shells from each region during every month of the year; however, some months were missed during the winter of 2007 to 2008. After a hiatus, sampling resumed in 2011 to fill in the missing months and complete the annual cycle. Shells from 2,757 individual surfclams were collected. In some instances, two samples were collected during the same month. Also, some samples were obtained at the very beginning or end of the month. For these reasons, sample data were not plotted by month but by day of year collected for a realistic representation of the sample spacing.

Laboratory Processing

All shells were cleaned, and SL was measured if the shell was intact. Most of each valve was then snapped off, leaving only a small area of shell surrounding the chondrophore. A thin section of the chondrophore was removed by cutting into the umbo along the axis of growth with a pair of circular blades spaced 2 mm apart and removing the slice between the blades for analysis (Almeida & Sheehan 1997). Each chondrophore section was digitally imaged through a dissecting microscope at 4X to 7x magnification, and the image was evaluated by an experienced surfclam ager. Although a final age could be determined for almost all surfclams, about one-third of the chondrophore images were not used. This occurred when the image did not capture the individual's entire growth history, such as when the section was not cut exactly through the center of the umbo so the starting point for measuring increments was not reliable, or the section was broken. A total of 1,613 surfclams were analyzed for this study. Sample sizes of acceptable sections ranged from 8 to 64 shells per region and day of year (Table 1).

Annulus Definition and Measurement

As defined, an annulus is "any zone which is formed once a year ... which marks the end of a year of growth" (Almeida & Sheehan 1997). Looking at a surfclam chondrophore cross section, the shell material deposited consists of opaque zones and narrower hyaline zones which appear as light and dark bands, respectively, under reflected light. The light band is generally understood to be deposited during periods of fast growth and the dark band during times of slower growth. In the case of this report, "annulus" refers to a single dark zone, and "growth increment" refers to one light zone plus one dark zone that together constitute a year's growth (see Fig. 2).

For each of the 1,613 shells with an acceptable chondrophore image, the straight-line distance from the umbo to the distal edge of each annulus was measured using ImagePro imaging software (Fig. 2). For clams showing growth beyond the last annulus, the distance from the umbo to the growth edge was also recorded.

Age Validation

The periodicity and timing of annulus formation were analyzed in two ways. First, the percentage of surfclams in each sample actively forming an annulus (having no new light-colored shell growth at the shell margin) was plotted by day of the year (edge analysis; McBride & Richardson 2007). Second, the amount of new growth beyond the last annulus (marginal growth) was compared with the size of the previous growth increment for each clam. If the previous growth increment measured 10 mm and marginal growth was 5 mm, the new growth would be expressed as 50% of the previous increment. All surfclams that were actively forming an annulus were considered to have 0% growth. The mean percentage was then plotted by day of year (marginal increment analysis; Campana 2001). If the marginal growth exceeded the previous increment size, the marginal increment was capped at 120%. A generalized additive model (cyclic GAM, R package mgcv; Wood 2011) was fit to the sample means versus day of year for both edge and marginal increment analyses, and then used to predict values for each day of the year. If the smoothed edge analysis and marginal increment data plotted by day of year formed sinusoidal cycles with a frequency of 1 y (a qualitative, not model-based result), the assumption of true annuli was considered validated and the timing of annulus formation was estimated.

In addition to examining the data by regions, the data were grouped by age (three age groups per region) and region to test for effects of life stage. Age groups were chosen by sorting all the clams from each region by year class, then dividing them into three groups as even in size as possible without breaking up year classes. The break points between the three age groups are different for each region because the age frequencies vary from region to region.


It has been shown that growth increments within a chondrophore section correspond to those in the entire valve in number and relative position (Wagner 1984, Ropes 1985, Picariello 2006), but SL is the conventional measure of clam size used for growth analysis. Many of the shells collected for this study were broken and SL could not be measured accurately. They also represented the upper end of the size range disproportionately as they were caught with commercial gear designed to allow smaller clams to escape. As a result of these limitations, the multiple CH measurements for each shell were used to predict SL at age and the resulting SL were used to fit growth curves. Linear, GAM, quadratic, and cubic models were fit to the SL-CH data from the intact shells available (n = 1,295) to determine which would be the best predictor. A quadratic model was chosen by AIC score, and von Bertalanffy growth curves were fit to SL derived using the model. Because there was a CH value for each year of every clam's life, a complete, longitudinal series of SL could be estimated for each clam. A log-likelihood ratio test for von Bertalanffy curves (Kimura 1980) was applied to determine whether growth parameters from each separate region were significantly different than those from a curve estimated from combined data.

Historic von Bertalanffy parameters [L.sub.[infinity]] and k were available from several sources spanning over 30 y and are presented together with new estimates in the Discussion section. The sources were (1) the present study, (2) unpublished Northeast Fisheries Science Center survey age-length data from 1986, 2005, and 2008 (NJ and DMV regions only; SNE sample sizes were too small), and (3) Weinberg and Helser (1996) who analyzed clams from 1980 and 1989 to 1992.


Age Validation and Annulus Formation

Both edge analysis and marginal increment analysis indicated the surfclams used in this study produced true annuli, as the smoothed data formed a single curve when plotted over a year (Figs. 3 and 4). This held true across regions and age groups. The SNE surfclams formed annuli most synchronously, as at least one surfclam in the sample was in the process of annulus deposition in only 8 of the 12 monthly samples compared with 11 for the two other regions (Fig. 5). In terms of the timing of annulus formation, the model-derived peaks of annulus formation for all age groups combined were mid-December for SNE, late November for NJ, and early December for DMV. The youngest clams from NJ and DMV formed annuli about a month earlier than the older age groups. All the SNE surfclams had concluded annulus formation by the end of March, and all remained in the active growth phase until August. In contrast, at least one clam in the NJ and DMV samples was in the process of forming an annulus during every month of the year except April.

Growth Comparisons

Analysis of AIC values indicated the quadratic model

SL = [alpha] + [[beta].sub.1] X CH + [[beta].sub.2] X C[H.sup.2]

had the best fit to the CH-SL plots for each region as compared with linear, cubic, and GAM models (Fig. 6; regional parameters are listed in Table 2). All CH measurements were converted to SL and von Bertalanffy growth curves were fit to the data (Fig. 7). Results of the Kimura log-likelihood ratio test indicated the von Bertalanffy parameters differed significantly between the three regions (P < 0.001). The SNE surfclams grew fastest and reached the largest asymptotic size, whereas the DMV surfclams grew slowest and reached the smallest asymptotic size (Table 3).


Atlantic surfclams continue to produce a true annulus, once per year, throughout their managed range despite recent warming conditions; however, temperature is probably affecting the observed regional differences in annulus formation. The southern Gulf of Maine, which includes the SNE region, is considered a separate oceanographic region from the mid-Atlantic Bight, which includes the NJ and DMV regions (Townsend et al. 2006). The water masses defining these regions are different enough that the juncture is often considered a biogeographic boundary (Jennings et al. 2009). Although most of the SNE clams were collected from just south of Cape Cod, at the southern boundary of the Gulf of Maine, and some from just north, they were combined into one "northernmost" region, representative of a colder-water population. According to bottom-temperature data collected during the 1980 to 2013 NEFSC fall bottom trawl surveys (when water temperatures should be at or near maximum), the SNE region had a mean October temperature of 10.2[degrees]C, whereas the NJ and DMV regions had mean September temperatures of 16.0[degrees]C and 17.85[degrees]C, respectively.

The surfclams from the SNE region may show more synchronous growth and annulus formation because that region's temperatures are less variable throughout the year, whereas the mid-Atlantic experiences a wider annual range, higher maxima, and higher interannual variability (Frank et al. 1990, Mountain 2003, Fratantoni et al. 2013). According to bottom-temperature data collected during both the spring and fall NEFSC bottom trawl surveys from 1980 to 2013, the SNE region had an average range of 4.9[degrees]C from April to October, whereas the NJ and DMV regions had ranges of 10.7[degrees]C and 12.1[degrees]C, respectively, from March to September.

The primary spawning period for surfclams generally occurs between May and August; a secondary spawn is sometimes noted in the fall (Jones et al. 1978, Wagner 1984, Ivany et al. 2003, Jacobson et al. 2006). The data from this study indicate annulus formation peaked months after the reported onset of primary spawning. It would have been interesting to have reproductive index data to look at the temporal relationship between spawning and annulus deposition.

Age-specific differences in the timing of annulus formation may be indicated. In the NJ and DMV regions, the youngest surfclams may start to form annuli earlier than the two older groups, and finish forming them sooner. The smoothed marginal increment curve for the youngest clams in the DMV region is also flattened suggesting more of them may be in an active growth phase over a larger portion of the year, which was also noted by Jones et al. (1983).

Earlier studies have compared populations of surfclams from various depths (mostly expressed as "inshore" versus "offshore") within the same region and found their growth patterns often differ. Ambrose et al. (1980) hypothesized that inshore surfclams would grow faster due to increased food availability, but instead found growth to be positively correlated with depth. Increased food may be an advantage to the inshore clams at some times of year, but surfclams in shallow water habitats are subjected to more extreme temperatures (Ambrose et al. 1980). Jones et al. (1978) found that offshore surfclams live longer, and Wagner (1984) found offshore surfclams grew faster and to a larger size and had thinner annuli, meaning they were spending more time in the active growth phase. Marzec et al. (2010) looked at the condition of surfclams from DMV and SNE throughout their depth range and found, in general, the clams in the poorest condition were at the extremes of their depth range, whereas the healthiest were in the middle.

The results of this study probably reflect this variability to some degree. The samples came from the landings of multiple vessels, and there was often nothing more specific than a statistical area to locate where they were caught. Known locations of surfclam fishing within the various regions (Fig. 1) indicate fishing occurs consistently at depths of ~10-20 m in the SNE area and 30 40 m in NJ. This ensures the samples were likely not from widely ranging depths. Unfortunately, because the location of many of the shell samples could not be pinpointed, depth could not be used as a variable in modeling growth.

Another source of variability in this study is the samples were collected over several years: an initial period in 2007 to 2008 and another in 2011 to 2012, so any interannual differences in clams from the same region were treated as if they were occurring in a single year. To some extent this is a positive, because it increases the independence of the samples, making the model fits representative of recent, average conditions and not just one particular year. Plotting the data from the two collection periods separately was not useful for detecting year-to-year variation in annulus formation, as there were too few samples from the same month during the two collection periods.

A possible source of regional variation is genetic, as surfclams from different regions may represent separate populations with slightly divergent traits. Genetic differences among Spisula solidissima from various areas of the northwest Atlantic have been shown to be minimal, however, with surfclams from the Gulf of St. Lawrence in Canada the only regional population with a distinctive genetic makeup (Hare & Weinberg 2005). There is a subspecies of Atlantic surfclam Spisula solidissima similis found in coastal habitats south of Cape Cod, which is reported to have a reproductive cycle distinct from S. solidissima (Hare & Weinberg 2005). It is unlikely the samples used in this study contained S. solidissima similis, as they rarely get large enough to be captured by commercial surfclam gear (Walker & Heffernan 1994) and are found in very shallow water.

Comparing results of surfclam growth studies to date (Table 3, Figs. 8 and 9), there is some evidence of decreasing and k over time, especially in the southern regions. Using repeated measures of size at age per individual and converted CH for this study likely did not affect this result notably as the trend was apparent in earlier years. Picariello (2006) also generated CH SL conversion factors for use when only CH information was available. Picariello noted linear relationships between CH and shell height, and shell height and SL, then CH-SL was estimated using a two-step process. A quadratic model fit the CH SL relationships best, which makes sense because the linear distances recorded are a measure of a curved surface. The quadratic equation eliminated the middle step and hopefully increased the accuracy of the conversions.

Because the surfclam fishery was centered in the mid-Atlantic for many decades and has expanded to the north comparatively recently, there has been a longer period of fishing in DMV and NJ compared with the SNE region (Ropes 1982). There is the possibility that the apparent slower growth seen in the southern surfclams may be at least partially fishery induced, as the largest or fastest growing surfclams are preferentially removed from the population. The clams landed from the NJ region have not decreased in size since 1982 according to port sample length frequencies, and the landed DMV clams have always been smaller during the same period (NEFSC 2013). The size of harvested surfclams is controlled by settings on the dredge, so it is always possible to only harvest the largest clams. The LPUE for surfclams from both the NJ and DMV regions has fallen since around 2000 (NEFSC 2013), but this may be because there are fewer surfclams in total. The mean [L.sub.[infinity]] of surfclams in the mid-Atlantic caught by the NEFSC clam survey has also decreased somewhat over time and this may be caused by a combination of fishing pressure and temperature over time (Munroe et al. 2016).

To summarize, this analysis did not find evidence that the surfclams used for this study, from any region or age group, form more than one annulus per year. It also showed annulus formation was concentrated in the fall and early winter, but was not highly synchronous within regions. The analysis also supports the idea that current surfclam growth rates are not consistent between regions, with DMV surfclams showing the slowest growth and reaching the smallest size of the three. In conclusion, additional studies looking at the timing of annulus formation in relation to the timing of spawning, annulus formation in more localized populations over a single year, and the synchrony of annulus formation by depth would refine and complement the information presented here.


We extend our appreciation to everyone who collected surfclams and prepared shell samples for this study: Casey Macisso, Raul Martinez, George Mattingly, Joshua O'Connor, Joanne Pellegrino, Wayne Santos and Tom Slaughter; and the captains and crews of the F/V Betty C, F/V Captain Frank, F/V Christy, F/V John N and especially the F/V Lady Brittany. Special thanks to Larry Jacobson, Blanche Jackson, and Adriana Picariello for contributing their expertise, and to an anonymous reviewer for thoughtful and valuable suggestions.


Almeida, F. P. & T. F. Sheehan, editors. 1997. Age determination methods for northwest Atlantic species--an update of NOAA Technical Report NMFS 72, 1988 (Penttila, J. & L. M. Dery, editors). Web-based manual. Accessed October 30, 2016. Available at:

Ambrose, W. G., Jr., D. S. Jones & I. Thompson. 1980. Distance from shore and the growth rate of the suspension feeding bivalve, Spisula solidissima. Proc. Nalt. Shellfish. Assoc. 70:207-215.

Campana, S. E. 2001. Accuracy, precision, and quality control in age determination, including a review of the use and abuse of age validation methods. J. Fish Biol. 59:197-242.

Chute, A. S., S. C. Wainright & D. R. Hart. 2012. Timing of shell ring formation and patterns of shell growth in the sea scallop Placopecten magellanicus based on stable oxygen isotopes. J. Shellfish Res. 31:649-662.

Frank, K. T., R. I. Perry & K. F. Drinkwater. 1990. Predicted responses of northwest Atlantic invertebrate and fish stocks to CCK-induced climate change. Trans. Am. Fish. Soc. 119:353-365.

Fratantoni, P. S., T. Holzwarth-Davis, C. Bascunan & M. H. Taylor. 2013. Description of the 2012 Oceanographic Conditions on the Northeast U.S. Continental Shelf. Northeast Fisheries Science Center Reference Document 13-26. Woods Hole, MA: U.S. Department of Commerce, NOAA Fisheries, Northeast Fisheries Science Center. 40 pp.

Hare, M. P. & J. R. Weinberg. 2005. Phylogeography of surfclams, Spisula solidissima, in the western north Atlantic based on mitochondrial and nuclear DNA sequences. Mar. Biol. 146:707-716.

Hoie, H., R. S. Millner, S. McCully, K. H. Nedreaas, G. M. Pilling & J. Skadal. 2009. Latitudinal differences in the timing of otolith growth: a comparison between the Barents Sea and southern North Sea. Fish. Res. 96:319-322.

Ivany, L. C., B. H. Wilkinson & D. S. Jones. 2003. Using stable isotope data to resolve rate and duration of growth throughout ontogeny: an example from the surf clam, Spisula solidissima. Palaios 18: 126-137.

Jacobson, L., S. Sutherland, J. Burnett, M. Davidson, J. Harding, J. Normant, A. Picariello & E. Powell. 2006. Report from the Atlantic Surfclam (Spisula solidissima) Aging Workshop, November 7-9, 2005. Northeast Fisheries Science Center Reference Document 0612. Woods Hole, MA: U.S. Department of Commerce, NOAA Fisheries, Northeast Fisheries Science Center. 24 pp. Accessed June 5, 2015. Available at: crd0612/.

Jennings, R. M., T. M. Shank, L. S. Mullineaux & K. L. Halanych. 2009. Assessment of the Cape Cod phylogeographic break using the bamboo worm Clymenella torquata reveals the role of regional water masses in dispersal. J. Hered. 100:86-96.

Jones, D. S. 1980. Annual cycle of shell growth increment formation in two continental shelf bivalves and its paleontological significance. Paleobiology 6:331-340.

Jones, D. S., I. Thompson & W. Ambrose. 1978. Age and growth rate determinations for the Atlantic surf clam Spisula solidissima (Bivalvia: Mactracea) based on internal growth lines in shell cross-sections. Mar. Biol. 47:63-70.

Jones, D. S., D. F. Williams & M. A. Arthur. 1983. Growth history and ecology of the Atlantic surf clam, Spisula solidissima (Dillwyn), as revealed by stable isotopes and annual shell increments. J. Exp. Mar. Biol. Ecol. 73:225-242.

Kim, Y. & E. N. Powell. 2004. Surfclam histopathology survey along the Delmarva mortality line. J. Shellfish Res. 23:429-441.

Kimura, D. K. 1980. Likelihood methods for the von Bertalanffy growth curve. Fish Bull. 17:165-116.

Loesch, J. G. & J. W. Ropes. 1977. Assessment of surf clam stocks in nearshore waters along the Delmarva Peninsula and in the fishery south of Cape Henry. Proc. Natl. Shellfish. Assoc. 67:29-34.

Lomovasky, B. J., A. Baldoni, P. Ribiero, G. Alvarez, M. Lasta, S. Campodonico & O. Iribarne. 2011. Exploring the causes of differences in growth rate of the Patagonian scallop Zygochlamys patagonica along its commercial bed distribution in the SW Atlantic. J. Sea Res. 66:162-171.

Macdonald, P., C. H. Angus & C. T. Marshall. 2013. Spatial variation in life history characteristics of common megrim (Lepidorhombus whiffiagonis) on the Northern Shelf. J. Sea Res. 75:62-68.

Marzec, R. J., K. Yungkul & E. N. Powell. 2010. Geographical trends in weight and condition index of surfclams (Spisula solidissima) in the mid-Atlantic bight. J. Shellfish Res. 29:117-128.

McBride, R. S. & A. K. Richardson. 2007. Evidence of size-selective fishing mortality from an age and growth study of hogfish (Labridae: Lachnolaimus maximus), a hermaphroditic reef fish. Bull. Mar. Sci. 80:401-417.

Mountain, D. G. 2003. Variability in the properties of shelf water in the middle Atlantic Bight, 1977-1999. J. Geophys. Res. 108:3014.

Munroe, D. M., D. A. Narvaez, D. Hennen, L. Jacobson, R. Mann, E. E. Hofmann, E. N. Powell & J. M. Klinck. 2016. Fishing and bottom water temperature as drivers of change in maximum shell length in Atlantic surfclams (Spisula solidissima). Estuar. Coast. Shelf Sci. 170:112-122.

Murawski, S. A. & F. M. Serchuk. 1979. An Assessment of Offshore Surf Clam, Spisula solidissima, Populations off the Middle Atlantic Coast of the United States. Woods Hole, MA: Woods Hole Laboratory Reference Document 79-13. 38 pp. Accessed June 5, 2015. Available at: series/whlrd/whlrd7913.pdf.

Northeast Fisheries Science Center (NEFSC). 2010. 49th Northeast Regional Stock Assessment Workshop (49th SAW) Assessment Report. Northeast Fisheries Science Center Reference Document 10-03. Woods Hole, MA: U.S. Department of Commerce, NOAA Fisheries, Northeast Fisheries Science Center. 383 pp.

Northeast Fisheries Science Center (NEFSC). 2013. 56<h Northeast Regional Stock Assessment Workshop (56th SAW) Assessment Report. Northeast Fisheries Science Center Reference Document 13-10. U.S. Department of Commerce, NOAA Fisheries, Northeast Fisheries Science Center. 868 pp.

Picariello, A. 2006. The effects of climate change on the population ecology of the Atlantic surf clam, Spisula solidissima, in the Middle Atlantic Bight. MS thesis. School of Marine Science, College of William and Mary, Williamsburg, VA. 169 pp.

Ropes, J. W. 1982. The Atlantic coast surf clam fishery, 1965-1974. Mar. Fish. Rev. 44:1-14.

Ropes, J. W. 1985. Modern methods used to age oceanic bivalves. Nautilus 99:53-57.

Townsend, D. W., A. C. Thomas, L. M. Mayer, M. A. Thomas & J. A. Quinlan. 2006. Oceanography of the northwest Atlantic continental shelf. In: Robinson, A. R. & K. H. Brink, editors. The sea, Volume 14A. The global coastal ocean: interdisciplinary regional studies and syntheses. Cambridge, MA: Harvard University Press. 840 pp.

Wagner, E. S. 1984. Growth rate and annual shell structure patterns in a single year class of surf clams Spisula solidissima off Atlantic City, New Jersey. MS thesis, Rutgers University, New Brunswick, NJ. 161 pp.

Walker, R. L. & P. B. Heffernan. 1994. Age, growth rate, and size of the southern surfclam, Spisula solidissima similis (Say, 1822). J. Shellfish Res. 13:433-441.

Weinberg, J. R. 2005. Bathymetric shift in the distribution of Atlantic surfclams: response to wanner ocean temperature. ICES J. Mar. Sci. 26:1444-1453.

Weinberg, J. R. & T. E. Helser. 1996. Growth of the Atlantic surfclam, Spisula solidissima, from Georges Bank to the Delmarva Peninsula, USA. Mar. Biol. 126:663-674.

Wood, S. N. 2011. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. Roy. Slat. Soc. B Met 73:3-36.


Northeast Fisheries Science Center, National Marine Fisheries Service, 166 Water Street, Woods Hole, MA 02543

* Corresponding author. E-mail:

DOI: 10.2983/035.035.0402

The number of surfclams that were analyzed for this study by region,
year, and month.


                     January    February      March       April
2008                                                       52
2012                   22          20          29
Monthly total          22          20          29          52
Shells processed       612
  from SNE

                     January    February      March       April
2008                   44           8                      23
2012                                           19
Monthly total          44           8          19          23
Shells processed       481
  from NJ


                     January    February      March       April
2008                                                       28
2012                   29          22          29
Monthly total          29          22          29          28
Shells processed       520
  from DMV
Total shells          1,613


                       May        June        July       August

2007                                                       63
2008                   54          45          40          40
Monthly total          54          45          40          103
Shells processed
  from SNE

                       May        June        July       August

2007                                                       40
2008                                                       64
2011                                           21
2012                   19          15
Monthly total          19          15          21          104
Shells processed
  from NJ


                       May        June        July       August

2007                                                       57
2008                                           32
2012                   33          35
Monthly total          33          35          32          57
Shells processed
  from DMV
Total shells


                    September    October    November    December

2007                   33          42          46
2008                   64          37
2011                                                       25
Monthly total          97          79          46          25
Shells processed
  from SNE

                    September    October    November    December

2007                               43                      58
2008                   61          50
2011                                           16
Monthly total          61          93          16          58
Shells processed
  from NJ


                    September    October    November    December

2007                   32          35          52          43
2008                                           42          51
Monthly total          32          35          94          94
Shells processed
  from DMV
Total shells

Regional parameters and [R.sup.2] values for the quadratic equation SL
= [alpha] + [[beta].sub.1] x CH + [[beta].sub.2] x C[H.sup.2], used to
convert CH to SL for the surfclams collected in 2007 to 2012.

       [alpha]    [[beta].sub.1]   [[beta].sub.2]   [R.sup.2]

SNE     18.58          9.85            -0.14           0.78
NJ      20.52          9.93            -0.15           0.83
DMV     -7.05         12.30            -0.21           0.85

von Bertalanffy growth parameters [MATHEMATICAL EXPRESSION NOT
REPRODUCIBLE IN ASCII] from Weinberg and Helser (1996), unpublished
fishery-independent NEFSC clam survey surfclam samples from 1986,
2005, and 2008, and the present study (2007 to 2012).

                                   n          [L.sub.       [L.sub.
                                            [infinity]]   [infinity]]

1980 survey (from       SNE        97          166.5          2.12
  Weinberg & Helser     NJ        674          170.8          1.88
  1996)                 DMV       607          171.0          1.24
1986 survey             NJ        539          179.1          6.68
                        DMV       314          173.6          1.29
1989, 1992 surveys      SNE        96          165.4          2.76
  (from Weinberg &      NJ        774          163.7          1.87
  Helser 1996)          DMV       197          164.0          6.07
2005 survey             NJ        328          165.4          3.27
                        DMV       251          169.8          6.81
2008 survey             NJ        256          154.6          3.06
                        DMV       123          166.9          4.61
2007 to 2012 (present   SNE   6,324 (612)      162.0          0.35
  study: CH-SL          NJ    7,079 (481)      158.5          0.28
  converted             DMV   5,955 (520)      153.0          0.45
commercial samples)

                                                              SL range
                                 k        SE k    [t.sub.o]     (mm)

1980 survey (from       SNE     0.30     0.032       0.34        N/A
  Weinberg & Helser     NJ      0.25     0.012       0.01
  1996)                 DMV     0.26     0.012       0.13
1986 survey             NJ      0.15     0.020      -1.26      56-184
                        DMV     0.29     0.020       0.59      50-196
1989, 1992 surveys      SNE     0.31     0.031       0.88        N/A
  (from Weinberg &      NJ      0.22     0.012      -0.21
  Helser 1996)          DMV     0.18     0.030      -1.12
2005 survey             NJ      0.13     0.013      -1.67      46-184
                        DMV     0.12     0.018      -2.24      42-175
2008 survey             NJ      0.18     0.021      -1.17      40-189
                        DMV     0.17     0.020      -1.41      40-173
2007 to 2012 (present   SNE     0.30     0.002       0.38      113-188
  study: CH-SL          NJ      0.22     0.002       0.21      100-187
  converted             DMV     0.22     0.002       0.98      90-169
commercial samples)

Missing values for the SNE region are due to insufficient NEFSC survey
sample sizes. In the case of the present study, the total number of
converted CH measurements is expressed as n, and the number of whole
surfclam shells measured is in parentheses.


Please note: Some tables or figures were omitted from this article.
COPYRIGHT 2016 National Shellfisheries Association, Inc.
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2016 Gale, Cengage Learning. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Author:Chute, Antonie S.; McBride, Richard S.; Emery, Sarah J.; Robillard, Eric
Publication:Journal of Shellfish Research
Geographic Code:1U2NJ
Date:Dec 1, 2016
Previous Article:Comparative, large-scale field trials along the Maine Coast to assess management options to enhance populations of the commercially important...
Next Article:Mass mortalities affecting populations of the yellow clam Amarilladesma mactroides along its geographic range.

Terms of use | Privacy policy | Copyright © 2019 Farlex, Inc. | Feedback | For webmasters