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
J.M. KLINCK3 AND E.E. HOFMANN3
1
Haskin Shellfish Research Laboratory, Rutgers University, 6959
Miller Ave, Port Norris, NJ, 08349, U.S.A
2
Virginia Institute of Marine Sciences, The College of William
and Mary, Rt. 1208 Greate Road, Gloucester Point, VA,
23062-1346, U.S.A
3
Department of Ocean, Earth and Atmospheric Sciences, Center
for Coastal Physical Oceanography, Old Dominion University,
4111 Monarch Way, 3rd Floor, Norfolk, VA, 23529, U.S.A
ABSTRACT
Atlantic surfclams (Spisula solidissima), among the largest extant non-symbiotic clam species in the world,
live in dense aggregations along the Middle Atlantic
Bight (MAB) continental shelf. The food resources
that support these populations are poorly understood.
An individual-based model that simulates the growth
of post-settlement surfclams was used to investigate
the quantity of food needed to maintain existing surfclam populations along the MAB continental shelf.
Food inputs to the model were based on measured
near-bottom water-column chlorophyll concentrations. Simulations showed that these water-column
food sources supported only 65% of the observed body
mass of a standard large surfclam (160-mm shell
length). Additional simulations using benthic food
sources to supplement water-column food sources by
20% generated surfclams that grew to observed size
and biomass and exhibited spawn timing consistent
with the known surfclam spawning season. The simulation results suggest that measured water-column
chlorophyll concentrations may underestimate the
food available to the continental shelf benthos. Large
continental shelf bivalves are an essential resource for
fisheries and higher trophic level consumers. Understanding available and utilized food resources is important for predicting long-term impacts of climate
*Correspondence. e-mail: [email protected]
Received 2 March 2012
Revised version accepted 14 November 2012
220
doi:10.1111/fog.12016
change on benthic secondary production and fishery
yield on the continental shelf.
Key words: benthic production, chlorophyll, clam
feeding, filter feeder, individual-based model, spisula
INTRODUCTION
Atlantic surfclams (Spisula solidissima) are among the
largest extant non-symbiotic clam species in the world
and the largest mactrid bivalves living on continental
shelves. They are long-lived (maximum age >30 yr)
and form dense aggregations along the extensive continental shelf in the northwestern Atlantic Ocean in
sandy bottoms from southern Virginia to Georges
Bank (Jacobson and Weinberg, 2006; NEFSC Northeast Fisheries Science Center, 2010). With a biomass
in this region greater than 850 9 103 metric tons, this
species is the basis of a major commercial fishery in the
western North Atlantic Ocean (NEFSC Northeast
Fisheries Science Center, 2010). Maintenance of biomass on this scale requires substantial food resources.
Distinct and rapid changes in climate are leading to
shifts in primary production that have communitylevel effects (Keller et al., 2001; Prasad et al., 2010),
making an understanding of food resources on the continental shelf critical to management of fishery
resources and stability of large-scale ecosystems.
Since 1997, populations from southern inshore
regions of the surfclam range have experienced significant mortality events coincident with warm bottom
water temperatures, reaching 21–24°C in September
(Kim and Powell, 2004; Weinberg, 2005). Hence, surfclams are potentially indicative of the influence of global warming on secondary production and benthic
community dynamics in this region. The resulting contraction in population distribution has major implications for the clam fishery. An effort is currently
underway that uses biological models in a cohesive
framework with oceanographic and socio-economic
models to understand causes of declines in surfclam
populations over the southern part of their range and
to make predictive management decisions regarding
© 2013 Blackwell Publishing Ltd.
Gaps in understanding food resources of surfclams
biological and sociological goals of the fishery as both
the clam and the fishery respond to climate change
(McCay et al., 2011). A critical component to managing these biological responses is understanding food
resources and growth of individual clams in this region.
A mathematical model is a useful tool for investigating 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 various 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 planktonic 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.,
2006; Yokoyama et al., 2009) and epibenthic bivalves
(Rhoads, 1973; Kiørboe et al., 1981; Winter, 1978;
Pernet et al., 2012). Fewer studies have shown evidence for the inclusion of benthic food sources in diets
of suspension-feeding benthos from deeper continental
shelf habitats (Fry, 1988; Hobson et al., 1995). In the
following, we describe the simulation results and
discuss food sources that could potentially sustain
surfclams, a high-biomass suspension-feeder, on the
scale of biomass that is currently observed on the
continental shelf of the Mid-Atlantic Bight.
METHODS
A series of simulations was performed using an individual-based model, adapted from the model for hard
clams, Mercenaria mercenaria, described by Hofmann
et al. (2006) to simulate growth of a surfclam (Spisula
solidissima). A schematic of the processes included in
the model is provided in Figure 1, the equations used
are provided in Table 1, and a summary of simulation
inputs is listed in Table 2. Simulations used a maximal
bivalve assimilation efficiency of 0.77 (Møhlenberg
and Kiørboe, 1981; Laing et al., 1987; Powell and Stanton, 1985; Reid et al., 2010; Ren et al., 2006) and an
annual time series of bottom water temperatures from
an area supporting growth of large (>160 mm) surfclams (20–40 m depth off New Jersey in 2007). The temperature time series was provided by a physical
221
Figure 1. Individual surfclam model schematic. Schematic
of processes included in the individual surfclam model,
adapted from Hofmann et al. (2006). Net production
depends on temperature, clam weight and clam condition.
Positive net production produces reproductive and somatic
tissue, whereas negative net production causes resorption of
reproductive tissue.
oceanographic model, the Regional Ocean Modeling
System (ROMS; Shchepetkin and McWilliams, 2005;
Haidvogel et al., 2008). Direct measurements of respiration and filtration rates are not available for surfclams. Consequently, we used a range of general
relationships covering the physiological capabilities of
most bivalves: 10°C and 20°C respiration curves of
Powell and Stanton (1985) with a Q10 temperature
response of 2 (Rueda and Smaal, 2004), and the high-gear
and low-gear filtration rate curves (we use high-gear
and low-gear in reference to the pace of functioning of
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
for a given shell length), with a modal temperature
relationship well described for bivalves (Hofmann
et al., 2006; Flye-Sainte-Marie et al., 2007; Fulford
et al., 2010) that has a temperature optimum at 18°C
and cessation near 0°C and 24°C, consistent with
observed physiological responses (Marzec et al., 2010).
Biological processes such as reproduction, growth rate
and maximum size integrate all physiological functions
specified in the model. Thus, in the absence of direct
measurements for respiration and filtration, simulated
reproductive behaviour, growth rates, and maximum
shell lengths, when verified against field-based observations, offer strong support that the process rates, weight
dependencies, and temperature dependencies are
properly parameterized. In our study, spawning and
reproduction were verified against Ropes (1968) and
© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233.
TT
For T > 18°C: Tfac ¼ 0:5 1 tanh 0:5 f1
T18 2
TT
For T 18°C: Tfac ¼ 0:5 1 tanh 0:5 f1 eð 14 Þ
Flen ¼ af þ bf L þ cf L2
Lengthdependent
filtration
Temperature
effect on
filtration
Filt = Flen 24 Tfac
Filtration*
CðL;W Þ
glðCÞ ¼ glmax glkþC
ðL;W Þ
¼ glðCÞL
Wm ðLÞ ¼ am Lbm
Maximum
Weight
dL
dt
W0 ðLÞ ¼ a0 Lb0
Standard
Weight
Change in
length due
to positive
condition
index
Rate of length
change
CðL; W Þ ¼ WmððtLÞ ÞW0 0 ðLÞ
Condition
index
W W ðLÞ
dW
dt
¼ ðA RðW; TÞÞW
Equation
Weight
Equation Name
glmax = maximum specific rate of increase in
length
glk = condition index when length increments
are ½ maximum (0.2)
C(L,W) = condition index
Filt = Filtration Flen = length dependency for
filtration Tfac = temperature effect on filtration
Flen = filtration rate as a function of length and
temperature
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
cf = 1.796 9 104 for low gear curve and
8.16 9 105 for high gear curve
Tfac = effect of temperature on filtration
T = Temperature
Tf1 = Maximum temperature for filtration
(24°C)
W = weight (mg dry wt.) A = Assimilation
R(W,T) = Respiration
C(L,W) = condition index
W(t) = current weight defined by weight
equation
W0(L) = standard weight at length L
Wm(L) = maximum weight at length L
W0(L) = standard weight at length L
a0 = 5.84 9 10-6
b0 = 3.098
Wm(L) = maximum weight at length L
am = 7.596 9 10-6
bm = 3.098
gl(C) = rate of shell length increase (0.1)
L = Length
Definitions
Modified from Hofmann
et al. (2006)
Temperature cutoffs
parameterized to match
Powell et al. (1992)
Hofmann et al. (2006)
Verified against Ropes and
Shepherd (1988); Weinberg
(1998)
Modified from Hofmann
et al. (2006)
Marzec et al. (2010)
Marzec et al. (2010)
Hofmann et al. (2006)
Hofmann et al. (2006)
Reference
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.
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D.M. Munroe et al.
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© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233.
Reproductive
fraction
G ¼ Gsp Cfac 0 T5
5
1
G = Fraction of reproductive tissue
Gsp = Reproductive efficiency
Cfac = Condition factor
T = Temperature
RðW; TÞ ¼ ar W br ecr ðTT0 Þ
Respiration rate
Reproductive
efficiency
AEðW Þ ¼ AE0 þ 0:5AE1 1 þ tanh W6
12
Assimilation
efficiency
L 90
Gsp ¼ Gsp1 þ Gsp2 Gsp1 0:5 1 þ tanh
20
L 150
þ 1 Gsp2 0:5 1 þ tanh
10
A = Assimilation
Filt = Filtration
AE(W) = weight-dependent assimilation
efficiency. This is included to account for
decreased efficiency of small juveniles during
gill development.
Food(t) = One of the three food times series
shown in Fig. 2.
AE(W) = weight-dependent assimilation
efficiency, determines the fraction of available
food that is assimilated. This is included to
account for decreased efficiency of small
juveniles during gill development.
W = weight
AE0 = lowest AE (0.075); used for animals
W<6g
AE1 = 0.70; creates an increasing AE for animals
W 6 g with maximum at 0.775
R(W,T) = Respiration (calories per day)
W = Weight
ar = 101.498 for 10°C curve; 101.759 for 20°C
curve
br = 0.857 for 10°C curve; 0.914 for 20°C curve
cr = 0.0693
T = Temperature
T0 = 10 for 10°C curve; 20 for 20°C curve
Gsp = Reproductive efficiency, determines the
fraction of net production that goes into
reproductive tissue. Ranges from 50% at onset
of maturity (30 mm) to 100% at 180 mm.
L = Length
A ¼ FiltAEðW ÞFoodðtÞ
Assimilation
Definitions
Equation
Equation Name
Table 1. (Continued)
Hofmann et al. (2006)
Verified against Ropes
(1968) and Jones (1981)
Maturity at length 30 mm
from Chintala and Grassle
(1995)
Powell and Stanton (1985)
Maximum from Møhlenberg
and Kiørboe (1981)
Weight dependency due to
gill development from
Baker and Mann (1994);
Cannuel and Beninger
(2006)
Hofmann et al. (2006)
observations in Marzec et al.
(2010)
Hofmann et al. (2006)
Weight dependency due to
gill development from
Baker and Mann (1994);
Cannuel and Beninger
(2006)
Reference
Gaps in understanding food resources of surfclams
223
Cfac = scaling factor that allows reproductive
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
ST = Spawning trigger ST1 = Maximum spawn
trigger (25%) for small animals (15 g)
ST2 = Maximum spawning trigger (15%) for
large animals (45 g)
W = Weight
G = Fraction of reproductive tissue
YrDay = Autumn spawn trigger
Cfac ¼ eST 20CðL;W Þ
ST ¼ ST 1 þ ððST 2 ST 1Þ0:5ð1 þ tanhðW 30Þ15ÞÞ
Spawning occurs when: G ST
or when: YrDay = 275
Condition
factor
Spawning
trigger
Hofmann et al. (2006);
Malouf et al. (1991)
Maximum spawning trigger
based on Sasaki (1982) and
Loesch and Evans (1994)
Autumn spawn trigger from
Ropes (1968) and
proprietary surfclam fishery
data
Reference
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 m2 for (NEFSC Northeast Fisheries Science Center, 2010) in the modeled
populations].
Definitions
Equation
Equation Name
Table 1. (Continued)
224
D.M. Munroe et al.
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Gaps in understanding food resources of surfclams
Table 2. Summary of simulation inputs with simulation
code name.
Code
Filtration
Respiration
1.1.1
1.2.1
1.1.2
1.2.2
1.1.3
1.2.3
2.1.1
2.2.1
2.1.2
2.2.2
2.1.3
2.2.3
Low gear
Low gear
Low gear
Low gear
Low gear
Low gear
High gear
High gear
High gear
High gear
High gear
High gear
20°C
10°C
20°C
10°C
20°C
10°C
20°C
10°C
20°C
10°C
20°C
10°C
Food
Chl
Chl
Chl+Phaeo
Chl+Phaeo
Synthetic
Synthetic
Chl
Chl
Chl+Phaeo
Chl+Phaeo
Synthetic
Synthetic
‘Synthetic’ food denotes a derived food time series that followed a seasonal cycle defined by a sine wave with peak timing and food levels derived from bottom water column plus
sediment chlorophyll and phaeopigment values from LEO15 (Reimers et al., 2009).
Jones (1981), growth rate was verified against Ropes
and Shepherd (1988) and Weinberg (1998), and maximum size was verified against Weinberg (1998) and
stock assessment data from NEFSC Northeast Fisheries
225
Science Center (2010). Simulated biology matched
observations, confirming the appropriateness of our
parameterization of respiration and filtration, as well as
the remainder of the physiology recorded in Table 1.
Three possible food time series were used (Fig. 2).
In a synthetic time series, water-column food was
supplemented by benthic productivity. Two other
time series were derived from near-bottom (1 m
above bottom) food estimated from chlorophyll and
phaeopigment concentrations obtained during MARMAP surveys (O’Reilly and Zetlin, 1998). MARMAP
surveys collected near-bottom water samples using
bottom-trip Niskin bottles and measured chlorophyll
a using in vitro pigment fluorescence (a detailed sampling protocol can be found in O’Reilly and Zetlin,
1998). From the entire MARMAP data set (includes
78 cruises spanning 1977–1988), we extracted all
chlorophyll a and phaeopigment measurements taken
nearest the bottom, within the boundary 37°N to
42°N and 76°W to 71°W, and in water depths of 10–
20 m. The extracted measurements were summarized
by calculating an average for each month (n = 7–20
per month). Monthly averages were interpolated to
calculate daily measurements. Chlorophyll and phaeopigment measurements were converted to available
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.
© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233.
226
D.M. Munroe et al.
DW organic matter per lg chlorophyll (Hofmann
et al., 2006; Powell et al., 1992). One time series contained food estimated from chlorophyll only (Chl),
another represented food estimated from chlorophyll
plus phaeopigment (Chl+Phaeo) (Fig. 2). Phaeopigment is a breakdown product of chlorophyll and is
generally considered a low-quality food source for filter feeders (e.g., Page and Hubbard, 1987). Nonetheless, chlorophyll levels from MARMAP were too low
to sustain large surfclams, therefore we added the
measured phaeopigment to supplement the available
food, creating what is likely an optimistically-high
bottom-water food source. To validate the estimates
of chlorophyll-derived food from the MARMAP data,
values of particulate organic nitrogen (PON) and
chlorophyll measured near-bottom simultaneously by
the SEEP program (Falkowski et al., 1988) were converted to available food using conversion factors of
0.1985 and 0.088, respectively (Wilson-Ormond
et al., 1997). PON and other constituent measures of
food (e.g., lipid, protein) have been shown to better
represent available bivalve food compared with chlorophyll in many locations (Soniat et al., 1998; Wilson-Ormond et al., 1997; Hyun et al., 2001).
Although data from the SEEP program are sparse in
the region of our study, they suggest that chlorophyll
is an adequate measure of available near-bottom
food.
The synthetic food time series followed a seasonal
cycle defined by a sine wave with peak timing and food
levels derived from bottom water column and sediment chlorophyll and phaeopigment values measured
at LEO-15 (Reimers et al., 2009) (Fig. 2). A direct
comparison of chlorophyll concentration in water
samples taken 1 m above the bottom versus benthic
and sediment-water interface samples showed benthic/
sediment concentrations of chlorophyll 50–250 times
the concentration measured in near-bottom water
samples, with peak benthic chlorophyll lagging behind
peak water-column chlorophyll (Reimers et al., 2009).
Considering that detrital food sources are likely lower
quality food for surfclams and that their assimilation
efficiency for detrital food is probably lower (Langdon
and Newell, 1990), we increased the synthetic food by
a conservative 0.20 times (two orders of magnitude
lower than the lower range of observations by Reimers
et al., 2009) over the year relative to the observed bottom water chlorophyll, resulting in a highly conservative estimate of benthic food. This synthetic time
series differs from the MARMAP time series in two
important ways. First, the peak food supply occurs later
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
begun to warm, thus permitting an increase in clam filtration rate when high food is available. This is consistent with the lag observed by Reimers et al. (2009).
Secondly, the range of food concentration is expanded
over that supported by the MARMAP data.
RESULTS
None of the simulations using food concentrations
derived from chlorophyll or chlorophyll + phaeopigment resulted in generation of a realistically sized clam
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 smaller 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,
will not sustain clam growth. The high-gear filtration
rate in combination with chlorophyll or chlorophyll + phaeopigment food sources supports clam
growth, but at a rate below field observations. Varying
the respiration curve only modestly changes achieved
maximum size; maximum size is predominantly a function of the filtration rate and food supply. A combination of the highest available food concentration
justified from the MARMAP data set (the sum of chlorophyll and phaeopigment measures) improves growth
because higher food is provided, but nevertheless fails
to provide observed growth (Weinberg, 1998) despite
use of optimistic physiology (high filtration rate, low
respiration rate). On the other hand, simulations that
used the synthetic food time series provided sufficient
food to simulate growth rate and maximum size of surfclams representative of observations, but only with the
most optimistic filtration rate relationship.
Curves of zero scope for growth for a 160-mm clam
over a range of filtration and assimilation rates for the
three food time series show positive scope for growth
above and to the right of the curve; negative scope for
growth (loss of body mass) occurs below and to the left
of it (Fig. 4). The dotted horizontal and vertical lines
227
Figure 4. Zero scope for growth curves. Curves of zero scope
for growth for a 160-mm clam over a range of filtration factors and assimilation efficiencies for the three food time series. Black solid curve shows food based on chlorophyll, black
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
each isoline; negative scope for growth (loss of body mass)
occurs below and to the left of each isoline. The dotted horizontal and vertical lines indicate the most optimistic filtration and assimilation efficiency plausible for an idealized
bivalve.
mark the most plausible optimistic filtration and
assimilation rates for an idealized bivalve based on
reviews of bivalve physiological rates (Laing et al.,
1987; Powell and Stanton, 1985; Reid et al., 2010;
Ren et al., 2006; Powell et al., 1992). Simulations of
clam scope for growth using the synthetic food time
series including assumed benthic production provides
the only curve that falls below the most optimistic filtration and assimilation boundaries and thus this food
time series can support a 160-mm clam.
The wandering line in each plot of annual productivity for a 160-mm-long simulated clam follows the
clam’s scope for growth over time from January (1) to
December (12) (Fig. 5). These plots highlight the
temporal differences in surfclam performance between
the water column-based food sources (A and B) and
benthic-supplemented food source (C). A large clam
fed the food resources supplemented by benthic production spends more time (nearly 5 months) during
the middle of the year with a positive scope for growth,
whereas clams fed a food resource estimated from
water-column chlorophyll or chlorophyll + phaeopigment alone spend less time (1 and 3 months, respectively) during the late winter and early spring in the
same physiological state.
© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233.
228
D.M. Munroe et al.
Figure 5. Annual scope for growth. Annual time series of
scope for growth for a simulated clam of 160-mm shell
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.
(a)
(b)
(c)
DISCUSSION
Surfclams are among the largest non-symbiont-bearing bivalves. Maintaining the body mass of a clam
160–180 mm in length requires a great deal of food.
Near-bottom measured values of chlorophyll and
phaeopigment concentration are insufficient to support 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 misrepresentation of the filtration and respiration rates. Powell
et al. (1992) argued that the low-gear curve provided
appropriate filtration rates for most bivalves. Extensive 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; Riisg
ard, 2001). These rates cover the
range expressed by both the low-gear and high-gear
curves. Field-generated estimates of filtration in
Placopecten magellanicus can periodically reach rates
predicted by the high gear curve (Cranford and Hargrave, 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
below that of the vast majority of bivalves is implausible. Further, we added phaeopigment to chlorophyll as
if it represents high-quality food, which very likely
results in an inflated estimation of available water-
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Gaps in understanding food resources of surfclams
column food. Even a simulated clam physiology with
an extraordinary scope for growth, using an optimistically high filtration rate and a minimal respiration rate,
fails to sustain an adult clam of realistically large size
using an inflated observed near-bottom water-column
food (simulation number 2.2.2, Fig. 3). Our simulations use average food values that may not account for
variability in water column food resources; this may
have consequences for clam growth. The distribution
of observed MARMAP values used to calculate the
mean is skewed such that most observations fall below
the mean and a few are much higher than the mean.
Thus, in most years, available food will be at or below
the mean, and in rare years a higher food resource will
be available to the clams. To evaluate the possible
influence of the mean food value obtained from the
MARMAP program being an underestimate of food
supply, clams with high filtration rates were fed with a
food time series equivalent to the mean food plus 1 SD
for 20 yr. These clams grow to observed shell lengths;
however, sustained food levels at 1 SD above the mean
for 20 yr are implausible; realistically clams will experience average or lower than average food often over that
period (unpublished data). Additionally, clams grown
with mean + 1 SD food fail to spawn at the appropriate time in the spring because of a mismatch in the timing of high food levels in the MARMAP data set
relative to physiologically relevant temperature. Consequently, the simulations described here strongly suggest that observed near-bottom water-column food
resources measured on the continental shelf of the
Mid-Atlantic Bight are insufficient to support the
observed sizes of surfclams routinely encountered there.
Addition of benthic food sources is required to overcome the food deficit identified by the model; indeed, a
synthetic food time series based on this requirement
produced clams of realistic sizes, indicating that food
supply most likely includes benthic production. Studies
focused on shallow water and intertidal filter-feeding
benthic bivalves have noted the use of resuspended
benthic resources in addition to pelagic production
(Coe, 1948; Fry, 1988; Sasaki, 1989; Emerson, 1990;
De Jonge and Van Beuselom, 1992; Kamermans, 1994;
Hobson et al., 1995; Page and Lastra, 2003 Kang et al.,
2006; Yokoyama et al., 2009). The problem of inadequate food to sustain large-bodied clams has also been
noted previously for surfclams from the Mid-Atlantic
continental shelf (Ambrose et al., 1980), for Spisula
sachalinensis from northern Japan (Sasaki et al., 2004),
and for modeled Manila clams Venerupis philippinarum
(Flye-Sainte-Marie et al., 2007). The related clam species Mactra veneriformis may gain upwards of 40% of its
food resources from benthic algae (Kasai et al., 2004).
229
Stomach content analysis for the large Japanese clam
Pseudocardium sachalinense showed large amounts of
detritus and sediment-associated diatoms (Sasaki et al.,
2004). These observations indicate that the higher
metabolic demands of large clams are not satisfied routinely by pelagic production alone but require in addition bottom-associated food sources such as
resuspended detritus and sediment-associated benthic
algae (Sasaki, 1989). Both wind- and tidally-driven
resuspension have been suggested to increase chlorophyll and detrital concentrations in estuarine bottom
water (Roman and Tenore, 1978; Baillie and Welsh,
1980; De Jonge and Van Beuselom, 1992). Similarly,
the conditions in sandy bottom habitats occupied by
surfclams are appropriate for resuspension of benthic
algae.
Comparison of water samples taken 1 m above the
bottom at LEO-15 (offshore of New Jersey, approximately 13 m depth) to benthic and sediment-water
interface samples showed sediment concentration of
chlorophyll, phaeopigment, and TOC ranges from 50
to 250 times the concentration measured in near-bottom water samples (Reimers et al., 2009). Comparisons with similar results have been made in regions
off the coast of North Carolina through Florida (Cahoon et al., 1994; Nelson et al., 1999). These studies
demonstrate a considerably higher concentration of
food available in and on sediments of the continental
shelf than is measured at 1 m above bottom. Even
small resuspension rates of this material into the benthic boundary layer would increase available food sufficiently to support the body mass of large surfclams.
What is less clear is the importance of benthic versus
pelagic food resources over the range of depths that
surfclams live. In our simulations that used ‘synthetic’
food, we used bottom-water chlorophyll and phaeopigment values obtained during MARMAP surveys
(O’Reilly and Zetlin, 1998) with a benthic productivity scaling factor derived from measurements made at
LEO-15 (Reimers et al., 2009). The MARMAP values used were measured in water depths of 10-20 m,
and LEO-15 observations were made in 13 m depth,
thus these simulations represent growth at approximately 15 m depth. Surfclams inhabit depths from
the intertidal to 60 m (Jacobson and Weinberg,
2006), and it is likely that the primary productivity
(both benthic and pelagic) varies over this range of
depths. Without observations with which to scale the
available food, it becomes difficult to predict the relationship between bottom temperature, feeding and
growth, highlighting the importance of more empirical observations of bottom productivity over these
depths.
© 2013 Blackwell Publishing Ltd., Fish. Oceanogr., 22:3, 220–233.
230
D.M. Munroe et al.
Inclusion of a component of benthic productivity is
required to explain the growth of large bivalves such
as surfclams on the Mid-Atlantic continental shelf.
Are the temporal dynamics of benthic and water column different? One distinction between the MARMAP food time series that provided inadequate food
and the synthetic time series that provided adequate
food (Fig. 2) is the offset in peak food availability,
with the latter time series providing the highest food
values at times of more active clam feeding. Off the
coast of New Jersey, elevated benthic chlorophyll levels were observed in April and May, with the remaining metrics (TOC, phaeopigment, POC and PON)
exhibiting little variation throughout the year (Reimers et al., 2009). This timing agrees with the
demands of the model for a food time series with peak
food offset from the timing of the spring phytoplankton bloom. Overall benthic production often shows a
pattern divergent from that observed in the upper
water column (Cahoon and Cooke, 1992; Sarker et al.,
2009). When considered in concert, the combination
of water-column and benthic production creates a
more stable food supply throughout the year for
benthic consumers such as surfclams. Likewise, in
examining diets of oysters and mussels in a Mediterranean lagoon, Pernet et al. (2012) found that timing of
benthic diatom blooms was important for shellfish
growth rate and gamete production.
Abundant bivalve biomass along continental shelf
habitats, such as surfclams on the Mid-Atlantic Bight,
Mactromeris polynyma (formerly Spisula polynyma) in
the Southeastern Bering Sea (Hughes and Bourne,
1981) and Scotian Shelf (DFO, 2007) and Macoma
calcarea in the Chukchi Sea (Sirenko and Gagaev,
2007) may only be achieved by supplementation of
the clam’s diet by benthic productivity. Dietary flexibility, the ability to utilize benthic productivity during
summer seasons when water column productivity is
depressed, either through filter-feeding on regularly
resuspended benthic material (surfclams and M. polynyma) or deposit-feeding (M. calcarea), may allow
these bivalves to achieve unusually high biomass in
broad continental shelf habitats in the northern hemisphere. These bivalves are trophically important; their
substantial biomass provides a critical resource supplying higher trophic levels, such as the Pacific walrus
(Sirenko and Gagaev, 2007; Ray et al., 2006), and supporting commercial fisheries (DFO, 2007; McCay
et al., 2011). Projection of climate change impacts on
higher trophic level production requires understanding
these large bivalves inhabiting continental shelves
worldwide, as sensitive indicators of changing climate
(Roy et al., 2001; Kim and Powell, 2004; Weinberg,
2005) through their response to changes in temperature and cumulative food supply.
CONCLUSION
Not unlike benthic bivalves from intertidal and
coastal habitats (Coe, 1948; Fry, 1988; Sasaki, 1989;
Emerson, 1990; De Jonge and Van Beuselom, 1992;
Kamermans, 1994; Hobson et al., 1995; Page and Lastra, 2003; Kang et al., 2006; Yokoyama et al., 2009),
the maintenance of significant biomass of surfclams
that exists along the continental shelf in the MidAtlantic Bight requires more primary production than
can be supplied by water-column food sources. The
differential between observed and estimated maximum individual size is substantial, with water-column
productivity alone failing to support at least one-third
of the observed body mass of a standard large animal,
even after endowing them with highly optimistic
physiological capabilities. Large surfclams (150–
170 mm) support the bulk of the Mid-Atlantic Bight
fishery. This is one of the largest shellfisheries worldwide, with landings upwards of 3 million bushels
(1 bu = 37 L) of clams annually (NEFSC Northeast
Fisheries Science Center, 2010). Surfclams are biomass dominants, yet their food resources are poorly
understood. We suggest that resuspended benthic production is an important component of their diet;
however, few empirical data exist on which to verify
this hypothesis. Large bivalves are vulnerable to climate-induced range shifts (Roy et al., 2001) and
therefore it is imperative to understand how these
clams are sustained to understand and predict the
ongoing impacts of climate change on the stock and
to develop management options for the fishery into
the future.
ACKNOWLEDGEMENTS
Thanks to D. Haidvogel for provision of bottom water
temperature data. Financial support was provided by
NSF Award GEO-0909484. Constructive feedback
was provided by two anonymous reviewers.
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