Spisula Solidissima) Populations

Home , Atlantic surf clam, Mactridae, Pacific oyster, Placopecten magellanicus, Spisula sachalinensis, Spisula subtruncata

FISHERIES OCEANOGRAPHY Fish. Oceanogr. 22:3, 220–233, 2013

Underestimation of primary productivity on continental shelves: evidence from maximum size of extant surfclam (Spisula solidissima) populations

D.M. MUNROE,1,* E.N. POWELL,1 R. MANN,2 change on benthic secondary production and fishery J.M. KLINCK3 AND E.E. HOFMANN3 yield on the continental shelf. 1 Haskin Shellfish Research Laboratory, Rutgers University, 6959 Key words: benthic production, chlorophyll, clam Miller Ave, Port Norris, NJ, 08349, U.S.A feeding, filter feeder, individual-based model, spisula 2Virginia Institute of Marine Sciences, The College of William and Mary, Rt. 1208 Greate Road, Gloucester Point, VA, 23062-1346, U.S.A 3Department of Ocean, Earth and Atmospheric Sciences, Center INTRODUCTION for Coastal Physical Oceanography, Old Dominion University, 4111 Monarch Way, 3rd Floor, Norfolk, VA, 23529, U.S.A Atlantic surfclams (Spisula solidissima) are among the largest extant non-symbiotic clam species in the world and the largest mactrid bivalves living on continental ABSTRACT shelves. They are long-lived (maximum age >30 yr) and form dense aggregations along the extensive con- Atlantic surfclams (Spisula solidissima), among the larg- tinental shelf in the northwestern Atlantic Ocean in est extant non-symbiotic clam species in the world, sandy bottoms from southern Virginia to Georges live in dense aggregations along the Middle Atlantic Bank (Jacobson and Weinberg, 2006; NEFSC North- Bight (MAB) continental shelf. The food resources east Fisheries Science Center, 2010). With a biomass that support these populations are poorly understood. in this region greater than 850 9 103 metric tons, this An individual-based model that simulates the growth species is the basis of a major commercial fishery in the of post-settlement surfclams was used to investigate western North Atlantic Ocean (NEFSC Northeast the quantity of food needed to maintain existing surf- Fisheries Science Center, 2010). Maintenance of bio- clam populations along the MAB continental shelf. mass on this scale requires substantial food resources. Food inputs to the model were based on measured Distinct and rapid changes in climate are leading to near-bottom water-column chlorophyll concentra- shifts in primary production that have community- tions. Simulations showed that these water-column level effects (Keller et al., 2001; Prasad et al., 2010), food sources supported only 65% of the observed body making an understanding of food resources on the con- mass of a standard large surfclam (160-mm shell tinental shelf critical to management of fishery length). Additional simulations using benthic food resources and stability of large-scale ecosystems. sources to supplement water-column food sources by Since 1997, populations from southern inshore 20% generated surfclams that grew to observed size regions of the surfclam range have experienced signifi- and biomass and exhibited spawn timing consistent cant mortality events coincident with warm bottom with the known surfclam spawning season. The simu- water temperatures, reaching 21–24°C in September lation results suggest that measured water-column (Kim and Powell, 2004; Weinberg, 2005). Hence, surf- chlorophyll concentrations may underestimate the clams are potentially indicative of the influence of glo- food available to the continental shelf benthos. Large bal warming on secondary production and benthic continental shelf bivalves are an essential resource for community dynamics in this region. The resulting con- fisheries and higher trophic level consumers. Under- traction in population distribution has major implica- standing available and utilized food resources is impor- tions for the clam fishery. An effort is currently tant for predicting long-term impacts of climate underway that uses biological models in a cohesive framework with oceanographic and socio-economic *Correspondence. e-mail: [email protected] models to understand causes of declines in surfclam Received 2 March 2012 populations over the southern part of their range and Revised version accepted 14 November 2012 to make predictive management decisions regarding 220 doi:10.1111/fog.12016 © 2013 Blackwell Publishing Ltd. Gaps in understanding food resources of surfclams 221 biological and sociological goals of the fishery as both Figure 1. Individual surfclam model schematic. Schematic the clam and the fishery respond to climate change of processes included in the individual surfclam model, (McCay et al., 2011). A critical component to manag- adapted from Hofmann et al. (2006). Net production ing these biological responses is understanding food depends on temperature, clam weight and clam condition. resources and growth of individual clams in this region. Positive net production produces reproductive and somatic tissue, whereas negative net production causes resorption of A mathematical model is a useful tool for investi- reproductive tissue. gating the quantity of food needed to maintain existing surfclam populations along the Mid-Atlantic Bight (MAB) continental shelf. In this study, an individual-based model that simulates the growth of post-settlement surfclams was used to perform a series of simulations to compare growth of clams under vari- ous filtration, assimilation, and respiration rates, using three probable food sources. These simulations demonstrate that either the clam biological and energetic relationships used in the model are misunderstood, or the species is sustained by more abundant food than is documented by measurements of water-column plank- tonic food resources. Supplementation of pelagic food with benthic sources has been documented previously for many shallow water and intertidal filter-feeding macrobenthic bivalves (Coe, 1948; Sasaki, 1989; Emerson, 1990; De Jonge and Van Beuselom, 1992; Kamermans, 1994; Page and Lastra, 2003 Kang et al., oceanographic model, the Regional Ocean Modeling 2006; Yokoyama et al., 2009) and epibenthic bivalves System (ROMS; Shchepetkin and McWilliams, 2005; (Rhoads, 1973; Kiørboe et al., 1981; Winter, 1978; Haidvogel et al., 2008). Direct measurements of respi- Pernet et al., 2012). Fewer studies have shown evi- ration and filtration rates are not available for surfcl- dence for the inclusion of benthic food sources in diets ams. Consequently, we used a range of general of suspension-feeding benthos from deeper continental relationships covering the physiological capabilities of shelf habitats (Fry, 1988; Hobson et al., 1995). In the most bivalves: 10°C and 20°C respiration curves of following, we describe the simulation results and Powell and Stanton (1985) with a Q10 temperature discuss food sources that could potentially sustain response of 2 (Rueda and Smaal, 2004), and the high-gear surfclams, a high-biomass suspension-feeder, on the and low-gear filtration rate curves (we use high-gear scale of biomass that is currently observed on the and low-gear in reference to the pace of functioning of continental shelf of the Mid-Atlantic Bight. the two filtration rate curves described by Powell et al, 1992; the high-gear curve predicts filtration rates approximately three times that of the low-gear curve METHODS for a given shell length), with a modal temperature A series of simulations was performed using an individ- relationship well described for bivalves (Hofmann ual-based model, adapted from the model for hard et al., 2006; Flye-Sainte-Marie et al., 2007; Fulford clams, Mercenaria mercenaria, described by Hofmann et al., 2010) that has a temperature optimum at 18°C et al. (2006) to simulate growth of a surfclam (Spisula and cessation near 0°C and 24°C, consistent with solidissima). A schematic of the processes included in observed physiological responses (Marzec et al., 2010). the model is provided in Figure 1, the equations used Biological processes such as reproduction, growth rate are provided in Table 1, and a summary of simulation and maximum size integrate all physiological functions inputs is listed in Table 2. Simulations used a maximal specified in the model. Thus, in the absence of direct bivalve assimilation efficiency of 0.77 (Møhlenberg measurements for respiration and filtration, simulated and Kiørboe, 1981; Laing et al., 1987; Powell and Stan- reproductive behaviour, growth rates, and maximum ton, 1985; Reid et al., 2010; Ren et al., 2006) and an shell lengths, when verified against field-based observa- annual time series of bottom water temperatures from tions, offer strong support that the process rates, weight an area supporting growth of large (>160 mm) surfcl- dependencies, and temperature dependencies are ams (20–40 m depth off New Jersey in 2007). The tem- properly parameterized. In our study, spawning and perature time series was provided by a physical reproduction were verified against Ropes (1968) and

© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233. 222 Table 1. Summary of governing equations for calculation of changes in weight, condition and length and parameterizations used to represent the physiological processes determining growth and reproduction used in the individual model.

Equation Name Equation Deﬁnitions Reference Munroe D.M.

dW ¼ ðÞ ð ; Þ = = Weight dt A R W T WWweight (mg dry wt.) A Assimilation Hofmann et al. (2006) R(W,T) = Respiration ðÞ Condition CLðÞ¼; W WðÞt W0 L C(L,W) = condition index Hofmann et al. (2006) WmðÞL W0ðÞL

index W(t) = current weight deﬁned by weight al. et equation W0(L) = standard weight at length L Wm(L) = maximum weight at length L b Standard W0ðÞ¼L a0L 0 W0(L) = standard weight at length L Marzec et al. (2010) -6 Weight a0 = 5.84 9 10 b0 = 3.098 b Maximum WmðÞ¼L amL m Wm(L) = maximum weight at length L Marzec et al. (2010) -6 Weight am = 7.596 9 10 bm = 3.098 dL ¼ ðÞ = Change in dt gl C Lgl(C) rate of shell length increase (0.1) Modiﬁed from Hofmann length due L = Length et al. (2006)

© to positive 03BakelPbihn Ltd., Publishing Blackwell 2013 condition index ðÞ¼ CLðÞ;W = Rate of length gl C glmax glkþCLðÞ;W glmax maximum specific rate of increase in Hofmann et al. (2006) change length Verified against Ropes and glk = condition index when length increments Shepherd (1988); Weinberg are ½ maximum (0.2) (1998) C(L,W) = condition index Filtration* Filt = Flen 24 Tfac Filt = Filtration Flen = length dependency for filtration Tfac = temperature effect on filtration 2 Length- Flen ¼ af þ bf L þ cf L Flen = filtration rate as a function of length and Powell et al. (1992) dependent temperature ih Oceanogr. Fish. filtration af = 0.0744 for low gear curve and 1.199 for high gear curve bf = 0.0133 for low gear curve and 0.0121 for high gear curve 4 cf = 1.796 9 10 for low gear curve and , 9 5

22:3, 8.16 10 for high gear curve > ° ¼ : T Tf1 = Temperature For T 18 C: Tfac 0 51 tanh 0:5 Tfac effect of temperature on filtration Modified from Hofmann effect on T = Temperature et al. (2006) 220–233. TT ðÞT18 2 ° ¼ : f1 14 = filtration For T 18 C: Tfac 0 51 tanh 0:5 e Tf1 Maximum temperature for filtration Temperature cutoffs (24°C) parameterized to match © Table 1. (Continued) 03BakelPbihn Ltd., Publishing Blackwell 2013

Equation Name Equation Deﬁnitions Reference

observations in Marzec et al. (2010) Assimilation A ¼ FiltAEðÞ W FoodðÞ t A=Assimilation Hofmann et al. (2006) Filt = Filtration Weight dependency due to AE(W) = weight-dependent assimilation gill development from efficiency. This is included to account for Baker and Mann (1994); decreased efficiency of small juveniles during Cannuel and Beninger gill development. (2006) ih Oceanogr. Fish. Food(t) = One of the three food times series ÀÁÀÁ shown in Fig. 2. ðÞ¼ þ : þ W6 Assimilation AE W AE0 0 5AE1 1 tanh 12 AE(W) = weight-dependent assimilation Hofmann et al. (2006) efficiency efficiency, determines the fraction of available food that is assimilated. This is included to Maximum from Møhlenberg

, account for decreased efﬁciency of small and Kiørboe (1981) 22:3, juveniles during gill development. Weight dependency due to W=weight gill development from 220–233. AE0 = lowest AE (0.075); used for animals Baker and Mann (1994); W < 6g Cannuel and Beninger AE1 = 0.70; creates an increasing AE for animals (2006) W 6 g with maximum at 0.775

b c ðÞTT surfclams of resources food understanding in Gaps Respiration rate RWðÞ¼; T arW r e r 0 R(W,T) = Respiration (calories per day) Powell and Stanton (1985) W = Weight 1.498 1.759 ar = 10 for 10°C curve; 10 for 20°C curve br = 0.857 for 10°C curve; 0.914 for 20°C curve cr = 0.0693 = ÀÁ L 90 T Temperature Gsp ¼ Gsp1 þ Gsp2 Gsp1 0:51þ tanh T = 10 for 10°C curve; 20 for 20°C curve 20 0 Reproductive G = Reproductive efficiency, determines the Hofmann et al. (2006) ÀÁ L 150 sp efficiency þ 1 Gsp2 0:51þ tanh fraction of net production that goes into Verified against Ropes 10 reproductive tissue. Ranges from 50% at onset (1968) and Jones (1981) of maturity (30 mm) to 100% at 180 mm. Maturity at length 30 mm L = Length from Chintala and Grassle ÀÁ (1995) ¼ T5 Reproductive G GspCfac 0 5 1 G=Fraction of reproductive tissue fraction Gsp = Reproductive efficiency Cfac = Condition factor =

T Temperature 223 224 ..Munroe D.M. tal. et

Table 1. (Continued)

Equation Name Equation Deﬁnitions Reference

ST20CLðÞ;W Condition Cfac ¼ e Cfac = scaling factor that allows reproductive factor fraction to go to zero when condition is low which allows animal to preferentially recover somatic tissue when in poor condition ST = Spawning trigger C(L,W) = Condition index

03BakelPbihn Ltd., Publishing Blackwell 2013 trigger Spawning occurs when: G ST trigger (25%) for small animals (15 g) Malouf et al. (1991) or when: YrDay = 275 ST2 = Maximum spawning trigger (15%) for Maximum spawning trigger large animals (45 g) based on Sasaki (1982) and W = Weight Loesch and Evans (1994) G=Fraction of reproductive tissue Autumn spawn trigger from YrDay = Autumn spawn trigger Ropes (1968) and proprietary surfclam ﬁshery data

We use a predictor corrector scheme with a 4th order Milne predictor and a 4th order Hamming corrector. *Density-dependent overﬁltration is not included due to low clam densities [on the order of 0.2 m 2 for (NEFSC Northeast Fisheries Science Center, 2010) in the modeled

ih Oceanogr. Fish. populations]. , 22:3, 220–233. Gaps in understanding food resources of surfclams 225

Table 2. Summary of simulation inputs with simulation Science Center (2010). Simulated biology matched code name. observations, confirming the appropriateness of our parameterization of respiration and filtration, as well as Code Filtration Respiration Food the remainder of the physiology recorded in Table 1. 1.1.1 Low gear 20°C Chl Three possible food time series were used (Fig. 2). 1.2.1 Low gear 10°C Chl In a synthetic time series, water-column food was 1.1.2 Low gear 20°C Chl+Phaeo supplemented by benthic productivity. Two other 1.2.2 Low gear 10°C Chl+Phaeo time series were derived from near-bottom (1 m ° 1.1.3 Low gear 20 C Synthetic above bottom) food estimated from chlorophyll and ° 1.2.3 Low gear 10 C Synthetic phaeopigment concentrations obtained during MAR- 2.1.1 High gear 20°C Chl MAP surveys (O’Reilly and Zetlin, 1998). MARMAP 2.2.1 High gear 10°C Chl 2.1.2 High gear 20°C Chl+Phaeo surveys collected near-bottom water samples using 2.2.2 High gear 10°C Chl+Phaeo bottom-trip Niskin bottles and measured chlorophyll 2.1.3 High gear 20°C Synthetic a using in vitro pigment fluorescence (a detailed sam- 2.2.3 High gear 10°C Synthetic pling protocol can be found in O’Reilly and Zetlin, 1998). From the entire MARMAP data set (includes ‘Synthetic’ food denotes a derived food time series that fol- 78 cruises spanning 1977–1988), we extracted all lowed a seasonal cycle defined by a sine wave with peak tim- chlorophyll a and phaeopigment measurements taken ing and food levels derived from bottom water column plus ° sediment chlorophyll and phaeopigment values from LEO- nearest the bottom, within the boundary 37 Nto ° ° ° – 15 (Reimers et al., 2009). 42 N and 76 Wto71W, and in water depths of 10 20 m. The extracted measurements were summarized by calculating an average for each month (n = 7–20 Jones (1981), growth rate was verified against Ropes per month). Monthly averages were interpolated to and Shepherd (1988) and Weinberg (1998), and maxi- calculate daily measurements. Chlorophyll and phae- mum size was verified against Weinberg (1998) and opigment measurements were converted to available stock assessment data from NEFSC Northeast Fisheries food (mg L1) using a conversion factor of 0.088 mg

Figure 2. Food time series. Food time series used for simulations shown in Figure 3. Units are chlorophyll-based food concentration equivalents. Chlorophyll and chlorophyll + phaeopigment food was obtained from bottom-water samples (1 m above bottom) during MARMAP surveys (O’Reilly and Zetlin, 1998). The synthetic food time series peak at 1.2 mg L1 was the minimum peak concentration required to simulate a sufﬁciently large clam; higher peak values are justiﬁed from bottom water column plus sediment chlorophyll and phaeopigment values measured at LEO-15 (Reimers et al., 2009) but were not necessary for generation of realistically sized clams.

DW organic matter per lg chlorophyll (Hofmann is an adequate measure of available near-bottom et al., 2006; Powell et al., 1992). One time series con- food. tained food estimated from chlorophyll only (Chl), The synthetic food time series followed a seasonal another represented food estimated from chlorophyll cycle defined by a sine wave with peak timing and food plus phaeopigment (Chl+Phaeo) (Fig. 2). Phaeopig- levels derived from bottom water column and sediment is a breakdown product of chlorophyll and is ment chlorophyll and phaeopigment values measured generally considered a low-quality food source for fil- at LEO-15 (Reimers et al., 2009) (Fig. 2). A direct ter feeders (e.g., Page and Hubbard, 1987). Nonethe- comparison of chlorophyll concentration in water less, chlorophyll levels from MARMAP were too low samples taken 1 m above the bottom versus benthic to sustain large surfclams, therefore we added the and sediment-water interface samples showed benthic/ measured phaeopigment to supplement the available sediment concentrations of chlorophyll 50–250 times food, creating what is likely an optimistically-high the concentration measured in near-bottom water bottom-water food source. To validate the estimates samples, with peak benthic chlorophyll lagging behind of chlorophyll-derived food from the MARMAP data, peak water-column chlorophyll (Reimers et al., 2009). values of particulate organic nitrogen (PON) and Considering that detrital food sources are likely lower chlorophyll measured near-bottom simultaneously by quality food for surfclams and that their assimilation the SEEP program (Falkowski et al., 1988) were con- efficiency for detrital food is probably lower (Langdon verted to available food using conversion factors of and Newell, 1990), we increased the synthetic food by 0.1985 and 0.088, respectively (Wilson-Ormond a conservative 0.20 times (two orders of magnitude et al., 1997). PON and other constituent measures of lower than the lower range of observations by Reimers food (e.g., lipid, protein) have been shown to better et al., 2009) over the year relative to the observed bot- represent available bivalve food compared with chlo- tom water chlorophyll, resulting in a highly conserva- rophyll in many locations (Soniat et al., 1998; Wil- tive estimate of benthic food. This synthetic time son-Ormond et al., 1997; Hyun et al., 2001). series differs from the MARMAP time series in two Although data from the SEEP program are sparse in important ways. First, the peak food supply occurs later the region of our study, they suggest that chlorophyll in the spring after bottom water temperatures have

Figure 3. Simulated surfclam shell lengths. Legend codes follow the simulations listed in Table 2. Three-number code identifies (left) filtration rate (1, low-gear; 2, high gear), (middle) respiration curve (1, 20C; 2, 10C), and (right) food time series (1, chlorophyll only; 2, chlorophyll + phaeopigment; 3, synthetic food time series defined by a sine wave with peak timing and food levels derived from bottom water column plus sediment chlorophyll and phaeopigment values from LEO-15 (Reimers et al., 2009)). Dotted grey lines show bounds of von Bertalanffy growth functions from observations in Figure 3 of Weinberg (1998) [Note (i) that his density class E was not included here because it was recognized post-publication that animals within that density class came from a region that was experiencing thermal stress (Weinberg, 2005), and (ii) that age classes used in his calculations are 1 yr older than those used in our model simulations due to differences in the convention used for clam birthdays.]

© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233. Gaps in understanding food resources of surfclams 227 begun to warm, thus permitting an increase in clam fil- Figure 4. Zero scope for growth curves. Curves of zero scope tration rate when high food is available. This is consis- for growth for a 160-mm clam over a range of filtration fac- tent with the lag observed by Reimers et al. (2009). tors and assimilation efficiencies for the three food time ser- Secondly, the range of food concentration is expanded ies. Black solid curve shows food based on chlorophyll, black + over that supported by the MARMAP data. dotted line shows food based on chlorophyll phaeopigment, grey solid line shows the synthetic food time series. Positive scope for growth occurs above and to the right of RESULTS each isoline; negative scope for growth (loss of body mass) occurs below and to the left of each isoline. The dotted hori- None of the simulations using food concentrations zontal and vertical lines indicate the most optimistic filtra- derived from chlorophyll or chlorophyll + phaeopig- tion and assimilation efficiency plausible for an idealized ment resulted in generation of a realistically sized clam bivalve. in excess of 160-mm shell length within 20 yr (Fig. 3). Simulated maximum size closest to observed size [observed growth curves (Weinberg, 1998) are shown as the grey dotted lines in Fig. 3] from all simulations using chlorophyll- or chlorophyll + phaeopigment-derived food was approximately 20 mm smal- ler at 10 yr of age (160 mm versus 140 mm). This difference in length equates to a substantial difference in biomass of approximately 50 g, a 32% lower biomass than observed (weight in g = 8.3 9 105 9 length in mm2.85; from Marzec et al., 2010). Simulations were generated using high and low filtration rates, a range of respiration rates (as described previously), inclusion of phaeopigment in the measure of food supply, and a synthetic food data set. The combination of low-gear filtration rate, typical of many bivalves (Powell et al., 1992; Cranford and Hargrave, 1994), in combination with estimated food resources, mark the most plausible optimistic filtration and will not sustain clam growth. The high-gear filtration assimilation rates for an idealized bivalve based on rate in combination with chlorophyll or chloro- reviews of bivalve physiological rates (Laing et al., phyll + phaeopigment food sources supports clam 1987; Powell and Stanton, 1985; Reid et al., 2010; growth, but at a rate below field observations. Varying Ren et al., 2006; Powell et al., 1992). Simulations of the respiration curve only modestly changes achieved clam scope for growth using the synthetic food time maximum size; maximum size is predominantly a func- series including assumed benthic production provides tion of the filtration rate and food supply. A combina- the only curve that falls below the most optimistic fil- tion of the highest available food concentration tration and assimilation boundaries and thus this food justified from the MARMAP data set (the sum of chlo- time series can support a 160-mm clam. rophyll and phaeopigment measures) improves growth The wandering line in each plot of annual produc- because higher food is provided, but nevertheless fails tivity for a 160-mm-long simulated clam follows the to provide observed growth (Weinberg, 1998) despite clam’s scope for growth over time from January (1) to use of optimistic physiology (high filtration rate, low December (12) (Fig. 5). These plots highlight the respiration rate). On the other hand, simulations that temporal differences in surfclam performance between used the synthetic food time series provided sufficient the water column-based food sources (A and B) and food to simulate growth rate and maximum size of surf- benthic-supplemented food source (C). A large clam clams representative of observations, but only with the fed the food resources supplemented by benthic pro- most optimistic filtration rate relationship. duction spends more time (nearly 5 months) during Curves of zero scope for growth for a 160-mm clam the middle of the year with a positive scope for growth, over a range of filtration and assimilation rates for the whereas clams fed a food resource estimated from three food time series show positive scope for growth water-column chlorophyll or chlorophyll + phaeopig- above and to the right of the curve; negative scope for ment alone spend less time (1 and 3 months, respec- growth (loss of body mass) occurs below and to the left tively) during the late winter and early spring in the of it (Fig. 4). The dotted horizontal and vertical lines same physiological state.

Figure 5. Annual scope for growth. Annual time series of scope for growth for a simulated clam of 160-mm shell (a) length, fed each of the three food series. Isolines identify values of scope for growth (assimilation minus respiration) for a given level of food and temperature. Dotted curves identify regions of negative scope for growth; solid curves identify regions of positive scope for growth. The wandering line in each panel follows a clam’s scope for growth over time from January (1) indicated by the grey ﬁlled triangle, to December (12) indicated by the grey-ﬁlled circle; numbers along the line correspond to the middle of the indicated month. (a) Food based on chlorophyll; (b) food based on chlorophyll + phaeopigment; (c) synthetic food time series based on supplementation by benthic production.

Near-bottom measured values of chlorophyll and phaeopigment concentration are insufficient to sup- (b) port this mass if the metabolic energetics are properly formulated in the model. One possible alternate explanation for the failure of these food sources to sustain a body mass of large clams is misrepresenta- tion of the filtration and respiration rates. Powell et al. (1992) argued that the low-gear curve provided appropriate filtration rates for most bivalves. Exten- sive application of this formulation in bivalve modeling (Kobayashi et al., 1997; Powell et al., 1995) or the similar equation for hard clams (Doering and Oviatt, 1986; Hofmann et al., 2006) support this conclusion. The range of filtration rates measured for bivalves has been a source of controversy (Bayne, 2004; Petersen, 2004; Riisgard, 2001). These rates cover the range expressed by both the low-gear and high-gear (c) curves. Field-generated estimates of filtration in Placopecten magellanicus can periodically reach rates predicted by the high gear curve (Cranford and Har- grave, 1994); however, these rates are not sustained continuously. The use of the high-gear curve in the model likely overestimates surfclam filtration rate; thus underestimating required food supply. The model uses two standard respiration rates for molluscs. Justifying a further reduction in respiration rate would require that the activity level of surfclams fall below average among bivalves. A review of burrowing rates for a variety of clam species (Alexander et al., 1993) demonstrated that surfclams burrow rapidly in comparison with other species. Thus surfclams are at least as active, and likely more active, than the average bivalve (see by contrast Arctica islandica; Begum et al., 2009) and therefore a reduction in respiration rate DISCUSSION below that of the vast majority of bivalves is implausi- Surfclams are among the largest non-symbiont-bear- ble. Further, we added phaeopigment to chlorophyll as ing bivalves. Maintaining the body mass of a clam if it represents high-quality food, which very likely 160–180 mm in length requires a great deal of food. results in an inflated estimation of available water-

© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233. Gaps in understanding food resources of surfclams 229 column food. Even a simulated clam physiology with Stomach content analysis for the large Japanese clam an extraordinary scope for growth, using an optimisti- Pseudocardium sachalinense showed large amounts of cally high filtration rate and a minimal respiration rate, detritus and sediment-associated diatoms (Sasaki et al., fails to sustain an adult clam of realistically large size 2004). These observations indicate that the higher using an inflated observed near-bottom water-column metabolic demands of large clams are not satisfied rou- food (simulation number 2.2.2, Fig. 3). Our simula- tinely by pelagic production alone but require in addi- tions use average food values that may not account for tion bottom-associated food sources such as variability in water column food resources; this may resuspended detritus and sediment-associated benthic have consequences for clam growth. The distribution algae (Sasaki, 1989). Both wind- and tidally-driven of observed MARMAP values used to calculate the resuspension have been suggested to increase chloro- mean is skewed such that most observations fall below phyll and detrital concentrations in estuarine bottom the mean and a few are much higher than the mean. water (Roman and Tenore, 1978; Baillie and Welsh, Thus, in most years, available food will be at or below 1980; De Jonge and Van Beuselom, 1992). Similarly, the mean, and in rare years a higher food resource will the conditions in sandy bottom habitats occupied by be available to the clams. To evaluate the possible surfclams are appropriate for resuspension of benthic influence of the mean food value obtained from the algae. MARMAP program being an underestimate of food Comparison of water samples taken 1 m above the supply, clams with high filtration rates were fed with a bottom at LEO-15 (offshore of New Jersey, approxi- food time series equivalent to the mean food plus 1 SD mately 13 m depth) to benthic and sediment-water for 20 yr. These clams grow to observed shell lengths; interface samples showed sediment concentration of however, sustained food levels at 1 SD above the mean chlorophyll, phaeopigment, and TOC ranges from 50 for 20 yr are implausible; realistically clams will experi- to 250 times the concentration measured in near-bot- ence average or lower than average food often over that tom water samples (Reimers et al., 2009). Compari- period (unpublished data). Additionally, clams grown sons with similar results have been made in regions with mean + 1 SD food fail to spawn at the appropri- off the coast of North Carolina through Florida (Cah- ate time in the spring because of a mismatch in the tim- oon et al., 1994; Nelson et al., 1999). These studies ing of high food levels in the MARMAP data set demonstrate a considerably higher concentration of relative to physiologically relevant temperature. Con- food available in and on sediments of the continental sequently, the simulations described here strongly sug- shelf than is measured at 1 m above bottom. Even gest that observed near-bottom water-column food small resuspension rates of this material into the ben- resources measured on the continental shelf of the thic boundary layer would increase available food suf- Mid-Atlantic Bight are insufficient to support the ficiently to support the body mass of large surfclams. observed sizes of surfclams routinely encountered there. What is less clear is the importance of benthic versus Addition of benthic food sources is required to over- pelagic food resources over the range of depths that come the food deficit identified by the model; indeed, a surfclams live. In our simulations that used ‘synthetic’ synthetic food time series based on this requirement food, we used bottom-water chlorophyll and phaeo- produced clams of realistic sizes, indicating that food pigment values obtained during MARMAP surveys supply most likely includes benthic production. Studies (O’Reilly and Zetlin, 1998) with a benthic productiv- focused on shallow water and intertidal filter-feeding ity scaling factor derived from measurements made at benthic bivalves have noted the use of resuspended LEO-15 (Reimers et al., 2009). The MARMAP val- benthic resources in addition to pelagic production ues used were measured in water depths of 10-20 m, (Coe, 1948; Fry, 1988; Sasaki, 1989; Emerson, 1990; and LEO-15 observations were made in 13 m depth, De Jonge and Van Beuselom, 1992; Kamermans, 1994; thus these simulations represent growth at approxi- Hobson et al., 1995; Page and Lastra, 2003 Kang et al., mately 15 m depth. Surfclams inhabit depths from 2006; Yokoyama et al., 2009). The problem of inade- the intertidal to 60 m (Jacobson and Weinberg, quate food to sustain large-bodied clams has also been 2006), and it is likely that the primary productivity noted previously for surfclams from the Mid-Atlantic (both benthic and pelagic) varies over this range of continental shelf (Ambrose et al., 1980), for Spisula depths. Without observations with which to scale the sachalinensis from northern Japan (Sasaki et al., 2004), available food, it becomes difficult to predict the rela- and for modeled Manila clams Venerupis philippinarum tionship between bottom temperature, feeding and (Flye-Sainte-Marie et al., 2007). The related clam spe- growth, highlighting the importance of more empiri- cies Mactra veneriformis may gain upwards of 40% of its cal observations of bottom productivity over these food resources from benthic algae (Kasai et al., 2004). depths.

Inclusion of a component of benthic productivity is 2005) through their response to changes in tempera- required to explain the growth of large bivalves such ture and cumulative food supply. as surfclams on the Mid-Atlantic continental shelf. Are the temporal dynamics of benthic and water col- CONCLUSION umn different? One distinction between the MAR- MAP food time series that provided inadequate food Not unlike benthic bivalves from intertidal and and the synthetic time series that provided adequate coastal habitats (Coe, 1948; Fry, 1988; Sasaki, 1989; food (Fig. 2) is the offset in peak food availability, Emerson, 1990; De Jonge and Van Beuselom, 1992; with the latter time series providing the highest food Kamermans, 1994; Hobson et al., 1995; Page and La- values at times of more active clam feeding. Off the stra, 2003; Kang et al., 2006; Yokoyama et al., 2009), coast of New Jersey, elevated benthic chlorophyll lev- the maintenance of significant biomass of surfclams els were observed in April and May, with the remain- that exists along the continental shelf in the Mid- ing metrics (TOC, phaeopigment, POC and PON) Atlantic Bight requires more primary production than exhibiting little variation throughout the year (Rei- can be supplied by water-column food sources. The mers et al., 2009). This timing agrees with the differential between observed and estimated maxi- demands of the model for a food time series with peak mum individual size is substantial, with water-column food offset from the timing of the spring phytoplank- productivity alone failing to support at least one-third ton bloom. Overall benthic production often shows a of the observed body mass of a standard large animal, pattern divergent from that observed in the upper even after endowing them with highly optimistic water column (Cahoon and Cooke, 1992; Sarker et al., physiological capabilities. Large surfclams (150– 2009). When considered in concert, the combination 170 mm) support the bulk of the Mid-Atlantic Bight of water-column and benthic production creates a fishery. This is one of the largest shellfisheries world- more stable food supply throughout the year for wide, with landings upwards of 3 million bushels benthic consumers such as surfclams. Likewise, in (1 bu = 37 L) of clams annually (NEFSC Northeast examining diets of oysters and mussels in a Mediterra- Fisheries Science Center, 2010). Surfclams are bio- nean lagoon, Pernet et al. (2012) found that timing of mass dominants, yet their food resources are poorly benthic diatom blooms was important for shellfish understood. We suggest that resuspended benthic pro- growth rate and gamete production. duction is an important component of their diet; Abundant bivalve biomass along continental shelf however, few empirical data exist on which to verify habitats, such as surfclams on the Mid-Atlantic Bight, this hypothesis. Large bivalves are vulnerable to cli- Mactromeris polynyma (formerly Spisula polynyma)in mate-induced range shifts (Roy et al., 2001) and the Southeastern Bering Sea (Hughes and Bourne, therefore it is imperative to understand how these 1981) and Scotian Shelf (DFO, 2007) and Macoma clams are sustained to understand and predict the calcarea in the Chukchi Sea (Sirenko and Gagaev, ongoing impacts of climate change on the stock and 2007) may only be achieved by supplementation of to develop management options for the fishery into the clam’s diet by benthic productivity. 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