Journal of Marine Systems 141 (2015) 136–148

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Journal of Marine Systems

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Long-term dynamics in Atlantic surfclam (Spisula solidissima)populations: The role of bottom water temperature

Diego A. Narváez a,⁎, Daphne M. Munroe b, Eileen E. Hofmann a, John M. Klinck a, Eric N. Powell c,Roger Mann d, Enrique Curchitser e

a Center for Coastal Physical Oceanography, Old Dominion University, 4111 Monarch Way, 3rd Floor, Norfolk, VA 23508, United Statesb Haskin Shellfish Research Laboratory, Rutgers University, 6959 Miller Ave., Port Norris, NJ 08349, United Statesc Gulf Coast Research Laboratory, The University of Southern Mississippi, 703 East Beach Dr., Ocean Springs, MS 39564, United Statesd Virginia Institute of Marine Science, The College of William and Mary, Rt. 1208 Greate Road, Gloucester Point, VA 23062, United Statese Institute of Marine and Coastal Sciences, Rutgers University, 71 Dudley Rd., New Brunswick, NJ 08901, United States

⁎ Corresponding author. Tel.: +1 757 402 9797.E-mail address: diego@ccpo.odu.edu (D.A. Narváez).

http://dx.doi.org/10.1016/j.jmarsys.2014.08.0070924-7963/© 2014 Elsevier B.V. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Article history:Received 25 January 2014Received in revised form 30 July 2014Accepted 12 August 2014Available online 20 August 2014

Keywords:Atlantic surfclam modelPopulation dynamicsMiddle Atlantic BightOcean warmingROMS

The potential linkages between warming bottom temperatures and increased mortality and/or reduced growthof the Atlantic surfclam (Spisula solidissima) were investigated using a model that simulates the temperature-dependent growth of the post-settlement population at specific locations on the Middle Atlantic Bight (MAB)continental shelf. External forcing for the individual-based surfclam model is provided by a 50-year simulation(1958–2007) of bottom water temperature obtained from an implementation of the Regional Ocean ModelingSystem for the northwestern Atlantic. The simulations show that in years with above average bottom watertemperature (N2 °C above average), surfclam assimilation rate is significantly reduced as a result of thermalstress, which leads to starvation mortality and an overall decline in the surfclam population of 2–9%, mainly inthe inner shelf regions. Years with warmer bottom water temperatures were preceded by warm winters,which produced an earlier and longer summer season. These results suggest that the long-term observed declinein Atlantic surfclam populations on theMAB is a response to episodic warm years rather than a gradual warmingtrend in bottom water temperature, as previously suggested. These temperature driven population declines canpersist for several years and have the largest effect on older and larger animals, which are the target of thecommercial fishery.

© 2014 Elsevier B.V. All rights reserved.

1. Introduction

Marinefisheries are undergoing dramatic shifts in range distributionthat have been attributed to environmentally driven changes in habitat(Roessig et al., 2004 and references therein; Cheung et al., 2013).This redistribution potentially has substantial biological, ecological,and socio-economic impacts, especially for commercially importantfisheries (Cheung et al., 2010; Pinsky and Fogarty, 2012). Marine fisher-ies are ongoing as climate change continues to influence the distributionof the target organisms; yet little is known about how these exploitedorganisms adapt to such variability (Roessig et al., 2004).

The Atlantic surfclam (Spisula solidissima) provides an example of acommercially important species that is undergoing changes in itsrange distribution that have been attributed to increased bottomwater temperature driven by climate change (Jacobson and Weinberg,2006; Marzec et al., 2010; Weinberg, 2005; Weinberg et al., 2002).Atlantic surfclams are distributed in sandy bottoms along the continen-tal shelf from southern Virginia to Canada (Abbott, 1974), typically in

depths less than 50 m (Weinberg, 1993; Weinberg and Helser, 1996),where it is a biomass dominant (Ropes, 1978). In the 1940s a surfclamfishery was developed on the Middle Atlantic Bight (MAB, Fig. 1)continental shelf and it is now one of the most valuable of the U.S.commercial fisheries (NEFSC, 2010).

Temperatures of 16–22 °C are optimal for most surfclam adult andlarva physiological functions (Loosanoff and Davis, 1963; Munroeet al., 2013a; Savage, 1976; Snelgrove et al., 1998). Higher temperaturesnegatively impact surfclams (Clotteau and Dube, 1993; Marzec et al.,2010) and can be lethal (Goldberg and Walker, 1990; Saila and Pratt,1973). The temperature sensitivity of surfclams determines its cross-shelf distribution and likely determines the southern extent of itsrange (Weinberg, 2005; Weinberg et al., 2002; Weinberg et al., 2005).

Trends in the hydrographic properties of the MAB have beeninvestigated using datasets from cruises conducted as part of theNationalMarine Fisheries ServiceMarine ResourcesMonitoring, Assess-ment and Prediction (MARMAP) program,which occurred from1977 to1987 (Jossi and Benway, 2003; O'Reilly and Zetlin, 1998; Sherman,1980) and stock assessment surveys conducted by the National MarineFisheries Service (NMFS) since the early 1990s (described inWeinberg,2005). Analysis of hydrographic data collected from 1977 to 1999

Fig. 1. A)Map of theMiddle Atlantic Bight (MAB) region in the northwestern Atlantic continental shelf. The range distribution of the Atlantic surfclam (shading) and the locations of the 3transects (dels, deln and nj) where the individual-based surfclammodel was implemented (red circles) are shown. The numbers represent the locations in the inner (1), middle (2) andouter shelf (3). B) The distribution of the 50-year average bottom water temperature and C) its standard deviation obtained from the 1958–2007 hindcast simulation presented in Kangand Curchitser (2013). Bottom bathymetry (m) of the continental shelf is indicated (gray lines).

137D.A. Narváez et al. / Journal of Marine Systems 141 (2015) 136–148

suggested that the MAB shelf water (defined as water with salinity lessthan 34) was generally warmer (1 °C) and fresher (0.25) in the 1990sthan the previous decade (Mountain, 2003). Shelf water in the southernMAB was even warmer (2 °C) (Mountain, 2003). Similar trends in theMAB bottom water temperatures have been seen in the NMFS surveytemperature data (Nye et al., 2009; Weinberg, 2005).

Over the past decade, reduced Atlantic surfclam biomass has beenobserved between stock assessment surveys along the southern bound-ary of its range off the Delmarva Peninsula (Fig. 1), which has beenattributed to increased mortality (Weinberg, 2005; Weinberg et al.,2002). Corresponding observations of poor animal condition thatcoincided with exposure to warmer bottom temperatures led to thesuggestion that thermal stress produced increased surfclam mortality(Kim and Powell, 2004; Marzec et al., 2010). However, the relationshipbetweenwarming bottom temperatures and reduced biomass is correl-ative. The attribution of the decreased biomass to increased mortalityresulting from thermal stress is inferential.

A 50-year hindcast (1958–2007) of the circulation and waterproperties of the northwest Atlantic (including the MAB) is nowavailable (Kang and Curchitser, 2013). This hindcast solution providesestimates of bottom water temperature for the MAB at space and timescales of much higher resolution than is available from in situ observa-tions, and has the benefit of being produced by consistent and knowndynamics. The 50-year time series of bottomwater temperaturewas ex-tracted from the hindcast simulation for specific locations on the MABshelf where Atlantic surfclams are found (Fig. 1) and used as input toa model that simulates the growth of adult surfclams. The simulationresults provide insights into how warming bottom temperatures areaffecting the decline in Atlantic surfclam biomass.

The following section describes the hindcast simulation and theindividual-based surfclammodel. This is followed by simulation resultsshowing trends in Atlantic surfclam mortality from 1958 to 2007 atspecific locations on the MAB shelf. The discussion section puts theseresults into the context of what is known about surfclam populationdynamics in the region.

2. Methods

2.1. Models

2.1.1. Surfclam modelAn individual-based model (IBM) was developed for the Atlantic

surfclam following the structure used in a model developed for thehard clam (Mercenaria mercenaria) (Hofmann et al., 2006). A summaryof the equations and parameterizations used in the surfclam model isgiven in Munroe et al. (2013a) and in Table 1. The primary differencebetween the model used in this study and that used by Munroe et al.(2013a) is the inclusion of population mortality. A brief description ofthe model components that are relevant to this study is given below,followed by a description of the population mortality implementation.

The growth of a newly settled individual surfclam (N20 mm shelllength) that occurs in response to water temperature and food supplyis simulated. Themodel includes parameterizations for feeding, respira-tion, and somatic tissue and shell growth. A cohort is created by varyingphysiological and genetic characteristics over observed ranges(Hofmann et al., 2006). For this study, the mean assimilation efficiency(Table 1) was varied by±10%, which is within themeasured variabilityof bivalve assimilation efficiency (Han et al., 2008), assuming a normaldistribution. Respiration rate is assumed to follow a normal distributionandwas varied by±20% around amean rate (Table 1), which is consis-tent with variability in measured respiration rates within a bivalvecohort (Haure et al., 2003; Rueda and Smaal, 2004). The initial size ofsurfclams a few months after settlement is assumed to be normallydistributed about a mean shell length of 20 mm, with a range of18–22 mm shell length (Chintala and Grassle, 2001). Assimilationefficiency, respiration rate, and initial size are varied simultaneously toproduce a cohort with individuals that represent a range of phenotypes(Hofmann et al., 2006). A new cohort, which includes the weightedcontribution of genetic variability from individual surfclam (Hofmannet al., 2006), is added to the population in December of each year. Theaddition of the cohort in this manner allows the effects of temperature

Table 1Variables, equations and parameterizations used in surfclam individual-basedmodel. Additional details formodel equations and parameterizations are given inHofmann et al. (2006) andMunroe et al. (2013a).

Variable Governing equations Parameter definitions and units

Weight (W) (g dry wt) dWdt ¼ A−Rð ÞW A = Assimilation (g day−1)

R = Respiration (g day−1)(A − R) = Net production

Change in length due to positive condition index dLdt ¼ glmax C

glkþC L L = Length (mm)glmax = Maximum specific rate of increase in lengthglk = 0.2 (Condition index when length increments are ½ maximum)

Condition index (C) (nondimensional) C ¼ W tð Þ−W0Wm−W0

W(t) = Current weight at time tW0 = Standard weightWm = Maximum weight

W0 ¼ a0 Lb0

Wm ¼ am Lb0a0 = 5.84 × 10−6 (nondimensional)b0 = 3.098 (nondimensional)am = 7.596 × 10−6 (nondimensional)

Filtration (F) (L day−1) F = F(L)24 Tfac F(L) = Length-dependent filtration (L day−1)Tfac = Temperature effect on filtration (nondimensional)

F(L) = af + bf L + cf L2 af = −1.199*bf = 0.121*cf = 8.16 × 10−5** Values for high gear curve (see Munroe et al., 2013a)

Tfac ¼ 0:5 1− tanh T−Tf10:5

� �� �TN18�Cð Þ Tf1 = 24 °C (Maximum temperature for filtration)

Tfac ¼ 0:5 1− tanh T−Tf10:5

� �� �e−

T−1814ð Þ2 TN18�Cð Þ T = Temperature (°C)

Respiration (R) (g day−1) R W; Tð Þ ¼ arWbr ecr T−T0ð Þ ar = 57.37*br = 0.914*cr = 0.0693T0 = 20 °CT = Temperature (°C)* Values 20 °C curve (see Munroe et al., 2013a)

Assimilation efficiency (AE) (nondimesional) AE Wð Þ ¼ AEo þ 0:5AE1 1þ tanhW−612

� �� AEo = 0.075 (for animals W b 6 g)AE1 = 0.70 (for animals W ≥ 6 g)W = Weight (g)

Assimilation (A) (g day−1) A = F AE(W)Food(t) F = Filtration (L day−1)AE(W) = Weight-dependent assimilation efficiency (nondimensional)Food(t) = Food time series (g L−1)

Reproductive efficiency (Gsp)(nondimensional) Gsp ¼ Gsp1 þ 0:5 Gsp2− Gsp1� �

1þ tanh L−9020

� �� �� �þ 0:5 1− Gsp2

� �1þ tanh L−150

10

� �� �� � Gsp1 = 0.5 (Minimum fraction that goes to reproductive tissue)Gsp2 = 0.9 (Maximum fraction that goes to reproductive tissue)L = Length (mm)

Reproductive tissue (G) (nondimensional) G ¼ Gsp Cfac 0≤ T−55 ≤1

� �Gsp = Reproductive efficiency (nondimensional)Cfac = Condition factor (nodimensional)T = Temperature (°C)

Condition factor (Cfac) (nondimensional) Cfac = eST20C ST = Spawning trigger (nondimensional)C = Condition index (nondimensional)

Spawning trigger (ST) (nondimensional) ST ¼ ST1 þ 0:5 ST2−ST1ð Þ 1þ tanh W−305

� �� �ST1 = 0.25 (Maximum spawn trigger for small animals (15 g))ST2 = 0.15 (Maximum spawning trigger for large animals (45 g))W = Weight (gr)

Change in somatic tissue (dS) (g) dS = (Wt − Gt) − (Wt − 1 − Gt − 1) W = WeightG = Reproductive tissuet = current time stept − 1 = previous time step

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to be observed without the confounding effects of recruitment. Theinitial surfclam population density was specified as 1 ind m−2 for alllocations, which is consistent with observed densities on the MABshelf (NEFSC, 2010; 2013).

The total population mortality rate (M, day−1) is the sum of naturalmortality (Mn), fishing mortality (Mf) and deficit stress mortality (Mv).The population mortality is assumed to be proportional to the popula-tion number. Thus, the surfclam population declines exponentially at arate given by M (eMdt), which gives the fraction of the population thatis alive at the end of a time interval (0.2 day for this study). The fractionof the population that died in a time interval is given by 1 − eMdt.

Natural mortality (Mn) is set to a constant rate of 4 × 10−4 day−1

(0.15 year−1) based on observations reported in Weinberg (1999)and NEFSC (2010). Fishing was not included in the model, so fishingmortality (Mf) is zero.

Deficit stress mortality (Getz, 2011) occurs when environmentalconditions cause surfclams to lose somatic tissue (e.g. high temperatureor low food concentration). Somatic tissue is calculated as the differencebetween the weight gained and lost to reproduction (Table 1). A deficitstress (vi) is accumulated (Getz, 2011) when somatic tissue declines

between consecutive time intervals (dS, Table 1). A decline in somatictissue produces an increase in vi by the amount of the somatic tissuechange in the time interval (g-day). Surfclams recover slowly fromdeficit stress and this effect was included by reducing vi by 1 − eRdt,where the recovery rate (R) is 0.004 day−1.

The deficit stress mortality is calculated from

Mv ¼ 0:5Mv0 1þ tanhvi−Mv1

Mv2

� �ð1Þ

whereMv0 (0.0055 day−1) is themaximummortality rate due to deficitstress,Mv1 (1.5 g-day) is the stress at which the mortality rate is half ofthe maximum rate and Mv2 (0.2 g-day) controls the range of vi overwhich the mortality changes from zero to the maximum value. Thehyperbolic tangent function (tanh) allows the mortality to be lowuntil vi reaches a value near 1.45 g-day and then switch rapidly tohigh mortality when the stress exceeds 1.8 g-day. Larger and oldersurfclams have smaller scope-for-growth than smaller and youngersurfclams (Munroe et al., 2013a), so older surfclams accrue stressmore rapidly and are subject to higher mortality during stressful times.

139D.A. Narváez et al. / Journal of Marine Systems 141 (2015) 136–148

2.1.2. Bottom water temperature time seriesSimulated time series of bottom water temperature for 1958–2007

were obtained at nine sites along three transects on the MAB shelf(Fig. 1A) from an implementation of the Regional Ocean Model System(ROMS) for the northwest Atlantic (Kang and Curchitser, 2013). The cir-culationmodel domain is defined by a curvilinear grid with a horizontalresolution of ~7 km. Vertical processes are represented using sigmacoordinates with 40 vertical levels. The terrain-following coordinatesystem used for the circulation model gives variable vertical resolutionof the simulated temperature profiles, with fewer grid points in thebottom layers. For the locations used in this study (Fig. 1), the differencebetween the depth of simulated bottom temperature and the actualbottom depth (where the surfclams are found) is less than 1 m(Table 2), and any differences will be minimized by the well-mixedconditions in the deeper model layer.

2.2. Simulations

At each of the nine locations on theMAB shelf, surfclam growth wassimulated using the 50-year time series of bottomwater temperature asthe only environmental forcing of the population model. The surfclamsimulations were run for 100 years. The first 50 years were forcedwith an annual bottom water temperature climatology that wasconstructed by averaging the simulated temperatures for each day ofthe year. The bottom water climatology was repeated each year forthefirstfifty years to produce a stablemulti-cohort surfclampopulation.This simulation started on 1 Januarywith an initial surfclam shell lengthof 20 mm at all locations. The second 50 years was forced with thesimulated daily bottom water temperatures and these resultswere used to investigate surfclam temperature responses. The second50-year simulation started with the surfclam population structuregenerated by the simulation for the first 50 years.

The food time series input to the model is based on an annual foodsupply that follows a seasonal cycle (Munroe et al., 2013a). This foodsupply is repeated for each simulation year. This removes variability infood supply as a factor influencing surfclam growth and response toenvironmental conditions. An evaluation of the sensitivity of thesurfclam population to variability in food supply is given in Munroeet al. (2013a).

2.3. Model analysis

Correlations between surfclam mortality rate and the bottomwater temperature anomaly in September were calculated from thesimulation results. The September anomaly was used because this isthe time when bottom water temperatures tend to be the warmest.The September bottom water temperature anomaly was calculated bysubtracting the 50-year September average from the Septembermonthly-averaged bottom water temperature. A linear model was fit

Table 2Actual bottom depth, model bottom depth, the annual average bottomwater temperature (AVGsimulated bottom water temperature and surfclam density for the nine sites on the MAB conconfidence level). The sites are arranged north to south, from New Jersey (nj1, nj2, nj3), north

Region Bottom depth (m) Model depth (m) Temperature (°

nj1 — inner 22.7 22.4 11.44 ± 5.35nj2 — middle 36.3 35.7 10.09 ± 3.27nj3 — outer 63.0 62.0 10.01 ± 2.38deln1 — Inner 18.0 17.8 12.72 ± 5.58deln2 — middle 38.3 37.8 11.17 ± 3.50deln3 — outer 49.4 48.6 11.69 ± 2.97dels1 — inner 20.1 22.4 12.99 ± 5.87dels2 — middle 38.1 35.7 11.85 ± 3.77dels3 — outer 58.6 62.0 12.62 ± 2.84

to the yearly averaged bottom water temperature and the simulatedsurfclam population density (Table 2) to estimate trends in the modelresults.

The surfclam population model provides the age for each surfclam.This information was used to calculate the population age distributionand the 95% percentile was used as a metric of the stock age for eachsimulated year.

An aggregate estimated mortality rate (MR) that includes allmortality processes (natural, deficit stress) over a year was used as ametric to compare simulations. This mortality rate was calculated eachyear from the monthly population density as:

Ntþ1 ¼ Nte−MRt ð2Þ

whereNt+ 1 andNt are the surfclampopulation density at the beginningof December and the beginning of January in a given year, respectively,and t is the time interval (0.92 year). This mortality rate gives thetotal change in the surfclam population over a year resulting from allsources of mortality. This mortality rate allows year-to-year changesin population density to be compared.

3. Results

3.1. Spatial and temporal variability in bottom water temperature

The 50-year average bottomwater temperature shows an alongshelfgradientwith decreasing temperature from south to north (Fig. 1B). Theacross-shelf temperature gradient shows cooler waters occupying themid-shelf with warmer waters in the inner and outer shelf (Fig. 1B).The standard deviation for the 50-year averaged bottom temperatureis the largest, 5–6 °C, at the inner shelf sites (Fig. 1C, Table 2). Thestandard deviation at the mid and outer shelf sites is 2–3 °C (Fig. 1C,Table 2).

The temporal variability of monthly-averaged bottom watertemperature shows the expected seasonal progression at all simulationsites, with minimum temperatures occurring January to March, andmaximum temperatures in August to October (Fig. 2A–I). The innershelf sites show the largest amplitude of the seasonal cycle (Fig. 2A–C).

Bottomwater temperature showed large positive anomalies, 2–6 °C,in September 1974 at all simulation sites, which were particularlynoticeable at the middle (Fig. 2D–F) and outer (Fig. 2G–I) shelf sites.Positive bottom water temperature anomalies of 2–4 °C also occurredin 1986, 1995, 1999, and 2002 at these sites and at the inner shelfsites (Fig. 2A–C). However, the magnitude of the anomalies at theinner shelf sites is smaller because warmer temperatures are moretypical in these areas.

At the inner shelf sites, positive anomalies are more frequent in thesecond half of the simulated temperature time series (after 1982). Thelong-term linear trend of yearly averaged bottom water temperature

) and standard deviation (STD), and the 50-year linear trends and associated p-values fortinental shelf used in this study. Significant trends correspond to p-values ≤0.05 (≥95%ern Delaware (deln1, deln2, deln3), and southern Delaware (dels1, dels2, dels3).

C) AVG ± STD Trends

°C year−1 (p-value) Ind m−2 year−1 × 10−3 (p-value)

0.0173 (0.02) −4.3 (b0.01)0.0168 (0.02) −0.08 (0.78)0.0154 (0.05) 2.8 (b0.01)0.0147 (0.04) −7.1 (b0.01)0.0201 (0.04) 3.2 (0.02)0.0107 (0.39) 1.1 (0.22)0.0136 (0.07) −7.9 (b0.01)0.0162 (0.14) 3.3 (0.01)0.0044 (0.74) −0.4 (b0.01)

Fig. 2.Monthly-averaged bottom water temperature for the inner (A, B, C), middle (D, E, F) and outer (G, H, I) shelf sites (shown on Fig. 1). The panels are arranged from the inner (leftpanels) to outer (right panels) shelf sites along each transect, and fromnorth (upper panels) to south (bottompanels). The September bottom temperature anomaly is shown for each site(black line).

140 D.A. Narváez et al. / Journal of Marine Systems 141 (2015) 136–148

showed an increase of 0.0136 °C year−1 at the inshore southern site(dels1, Table 2) and 0.0173 °C year−1 at the inshore northern location(nj1, Table 2). These trends will produce temperature increases of0.7 °C to 0.9 °C over 50 years. The remaining locations also showedsimilar positive trends (Table 2).

During winter and early spring (January to April), temperatures atthe inner shelf locations are cold (5–9 °C) (Fig. 3 A,B) and vertically

Fig. 3.Vertical and time distribution of simulated temperature obtained from thehindcast simultransect. The two time periods bracket 1974 (A, B) and 1995 (C, D), which were characterized

homogeneous. In late spring and early summer (May to July) a thermo-cline develops which persists usually into September and October(Fig. 3A,B). This pattern is repeated with some variability in each year.Similarly, cold (5–10 °C) vertically homogeneous temperatures occurat the mid-shelf locations during winter and early spring (Fig. 3C,D). Athermocline forms in late spring and early summer as warming occurs,and deepens into the early fall. In 1974 and 1995 warm temperatures

ation at the inner (dels1, A–C) andmiddle (dels2, B–D) shelf sites along theDelaware Southby warmer than normal bottom water temperatures.

141D.A. Narváez et al. / Journal of Marine Systems 141 (2015) 136–148

occurred earlier (late April), extended deeper into the water column,and persisted longer into the fall at the inner and mid-shelf locations(Fig. 3). These two years were preceded by warmer winters (Fig. 4),which reduced the surface to bottom thermal difference in the spring.Cold winters, such as 1977 and 1996 (Fig. 4), are associated with adelay in the onset of warming in the following spring and summer atthe inner and mid-shelf locations (Fig. 3), In general, positive bottomwater temperature anomalies (Fig. 2) occurred in years with warmerwinters, warmwater distributed over thewater column, and prolongedwarming into late fall (Fig. 3).

3.2. Surfclam population density and mortality

The simulated surfclam population densities were consistently ator above 6 ind m−2 at the northern (nj, Fig. 5A) and outer shelf(deln3, dels3 in Fig. 5B,C) locations. The population reached stableconditions around 4–6 ind m−2. In the same locations, densitiesbelow 6 ind m−2 occurred in 1974 (Fig. 5A,B) and at the northerninner shelf sites in 1995 (nj1, Fig. 5A). Surfclam population densitiesbelow 6 ind m−2 also occurred throughout the 50-year simulation atthe inner and middle shelf sites along the middle and southern transects(deln1, deln2 and dels1 and dels2 in Fig. 5B,C). These locations hadepisodic population declines, with the two largest starting in 1974 and1995 (Table 3), which continued for 6–8 years and 4–5 years, respective-ly (Fig. 5B,C). Smaller amplitudedeclines occurred at the inner andmiddleshelf sites on the middle and southern transects (Fig. 5B,C). Prior to 1974these sites showed a small downward trend in surfclam density.

At the inner shelf sites surfclam population density declined~4–7 × 10−3 ind m−2 year−1 (Table 2). At the middle and outershelf locations the long-term trend in population density was positive,but less significant (1–3 × 10−3 ind m−2 year−1, Table 2).

Surfclam population mortality rates exceeded the natural back-ground mortality rate of 0.15 year−1 (NEFSC, 2010; Weinberg, 1999)at episodic intervals at the inner and mid-shelf sites on all threetransects (Fig. 6, Table 3) and at the outer shelf site on the middle

Fig. 4. Simulated average bottomwater temperature (BWT) for the winter (January, February,A) New Jersey (nj1), B) Delaware North (deln1), and C) Delaware South (dels1) transects.

transect (Fig. 6B). Mortality rates exceeded 0.2 year−1 (~20%) duringthe 1974 and 1995 population declines at all sites on the southern andmiddle shelf transects, and at the inner shelf sites on the northernmosttransect in 1995 (Fig. 6). The inner and mid-shelf surfclam populationsexperienced episodic mortality events throughout the 50-year simula-tion, which were coherent along the three transects (Fig. 6). Mortalityin the first part of the time series was characterized by intervals of5–6 years with mortality rates that were elevated relative to theobserved natural mortality rate of 0.15 years−1. After 1982 this patternchanged to abrupt events, lasting 2–3 years (Fig. 6), with rateschange of ~4–10% (Table 3).

Mortality has little effect on themean surfclampopulation age alongthe northern transect (Fig. 7A). The population remained between 16and 18 years old, except for a decrease in age at the inner shelf sitefollowing the 1995 mortality event. It took about 10 years for thispopulation to return to a mean age of 16 years. At the three sitesalong the middle transect, a decline in population age was observedduring the first part of the simulation (1958–1973) and a pronounceddrop in age following the 1974 mortality event (Fig. 7B). The outershelf population eventually returned to a mean age of about 19 years,but recovery took almost 20 years. The population at the mid-shelfsite was the most reduced in age, reaching a minimum age of12 years, following the 1974 mortality event (Fig. 7B). This populationeventually returned to a mean age of 16 years over about 15 years.The inner shelf population was more impacted by the 1995 mortalityevent and its mean age was reduced to 15 years at the end of simula-tion, a decrease of 3 years from the starting age in 1958. Surfclampopulations at the outer shelf site on the southernmost transect didnot experience changes in age over the 50-year simulation (Fig. 7C).Following the 1974 mortality event, the mean age of surfclams at themiddle and inner shelf sites decreased by about 4 to 5 years. Thepopulations recovered over about 8 years to a stable population withan age of about 16 years (Fig. 7C). Both populations were impacted bythe 1995 mortality event, and the inner shelf population reached amean age of 15 years at the end of the simulation, a reduction of

March) and summer (July, August, September) months at the inner shelf locations on the

Fig. 5. Simulated surfclampopulation densities obtained for the sites off A)New Jersey (nj1, nj2, nj3), B) DelawareNorth (deln1, deln2, deln3), and C)Delaware South (dels1, dels2, dels3).

142 D.A. Narváez et al. / Journal of Marine Systems 141 (2015) 136–148

3 years relative to its starting age (Fig. 7C). The reduction in agecombined with the decrease in population density over the 50-yearsimulations suggests that older (and larger) surfclams were removedfrom the population by the mortality events.

3.3. Temperature effects on surfclam growth processes

Correlations between relative mortality rate and the Septemberbottom water temperature anomalies were significant at the innershelf locations on all three transects (Fig. 8A–C). Correlations at themiddle and outer shelf sites on the middle transect (Fig. 8E,H) and themid-shelf site on the southern transect (Fig. 6F) were also significant.Correlations at the remaining sites were not significant. The sites withsignificant correlations suggest that periods of elevated temperaturehave significant effects on mortality.

Table 3Percent (%) change in surfclam mortality and density for the southern-most inner shelfsite (dels1) for the years when the September bottom water temperature average(BWT) exceeded the 50-year temperature average by more than 2 °C.

Year Mortality (%) Density (%) September BWT anomaly (°C)

1974 8.1 7.9 2.41983 6.9 5.4 2.21990 5.4 4.3 2.31995 10.9 8.8 2.52002 3.5 2.0 2.3

The effect of temperature on physiological processes was assessedwith simulations that used only the growth part of the individualsurfclam model; no mortality processes were included. The individual-based model was run for 50 years using the simulated bottom watertemperature time series as input. At the start of each simulation year,the length of the individual was set to an average value of 160 mm.This removes long-term trends in animal size which occur because ofthe lack of mortality processes. Simulations were run for a site withpronounced temperature effects (dels1) and a site with minimaltemperature influence (nj3).

The simulated net production, calculated as the difference betweenassimilation and respiration, shows marked differences at the twosites (Fig. 9). The net production at the northern site is consistentlyhigher than that at the southern inshore site, and shows larger changesin amplitude. Net production at the southern site was relativelyconstant with the largest decreases associated with the warm periodsin 1974, 1983 and 1995. Time series of individual growth processes atthe southern site for 1995 relative to the years just before and justafter (Fig. 10), provide insight into the effects of extended periods ofwarmer bottom temperature. As bottom temperature reached itsmaximum value of ~25 °C in mid-August (Fig. 10A), respiration ratesincreased about 33% relative to those in other summers (Fig. 10B) andassimilation rate decreased by 90% (Fig. 10C). The decrease in assimila-tion persisted for ~40 days, until bottom water temperature decreasedbelow 23 °C. The drop in assimilation rate reduced tissue weight(Fig. 10D) and condition index (Fig. 10E), resulting in surfclams at the

Fig. 6. Simulated surfclam mortality rates obtained for the sites off A) New Jersey (nj1, nj2, nj3), B) Delaware North (deln1, deln2, deln3), and C) Delaware South (dels1, dels2, dels3).

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end of the year that were in poorer condition than those that startedthe year. In years with cooler bottom water temperatures, such as1993, respiration rates are lower, assimilation is higher, and surfclamsare heavier and in better condition at the end of the year.

4. Discussion

4.1. Temperature effects and model limitations

The coolermid-shelfwaters seen in the simulated bottomwater tem-perature distribution, the cold pool (Bignami and Hopkins, 2003;Houghton et al., 1982; Jossi and Benway, 2003), are important instructuring the distribution of MAB benthic and demersal biota(Colvocoresses and Musick, 1983). Survey data with limited temporalresolution was used to infer a relationship between the observedreduction in surfclam biomass in the southern MAB and warming ofthe cold pool temperatures (Weinberg, 2005; Weinberg et al., 2002).The simulations provide a potential causal mechanism for the reducedbiomass. At bottom water temperatures above 23 °C, the simulatedassimilation rates are reduced because filtration rate essentially ceasesabove 24 °C (Hornstein, 2010; Munroe et al., 2013a).

Reduced assimilation rate and increased metabolic demand(respiration) reduce net production and hence simulated surfclamweight; an effect that remains for 4 months following the return ofbottom water temperatures and assimilation rates to normal values.The prolonged weight loss and corresponding decrease in conditionproduce higher mortality rates as a result of starvation, a conclusion

reached by Kim and Powell (2004) from their analysis of surfclamcondition along the Delmarva part of the MAB. These effects weremost pronounced for older surfclams.

The starvation mortality in the simulated surfclams was mostapparent at the inner shelf sites off the Delmarva Peninsula and netproduction for these sites was low. Observations show that surfclamsin this region are characterized by low condition index (Kim andPowell, 2004; Marzec et al., 2010;Weinberg et al., 2002). Low conditionindex can result from reduced food availability (e.g., Kraeuter et al.,2003; Powell et al., 2012; Smith et al., 2000), the presence of parasitesand diseases (e.g., Hine, 2002; Pérez-Camacho et al., 1997; Planaet al., 1996), thermal stress (e.g., Weinberg, 2005), and reducedfiltration rate as a consequence of suboptimal temperature or salinity(Flye-Sainte-Marie et al., 2007; Hofmann et al., 2006). In the simula-tions, thermal stress was the underlying cause for the low conditionbecause the other contributors were not included a part of the model.The reduction in simulated surfclam density from thermal stress wassignificant thereby suggesting thatwarming temperatures likely under-lie observed declines.

Direct evaluation of the simulation results by comparison withobservations would be desirable. However, comparisons between thesimulated surfclam population densities at specific locations withestimates of surfclam population biomass obtained from trawl surveysconducted over large areas of the MAB during the fishing season(Weinberg, 2005; Weinberg et al., 2002) are problematic. The surveydata are reported as mean surfclam kilograms per tow (kg tow−1),and are collected in several large strata that are distributed over the

Fig. 7. Simulated surfclam 95% age percentiles obtained for the sites off A) New Jersey (nj1, nj2, nj3), B) Delaware North (deln1, deln2, deln3), and C) Delaware South (dels1, dels2, dels3).

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surfclam fishery region on the MAB. The conversion from kg tow−1 toind m−2 is cumbersome given the different areas covered by thesurveys and changes in dredges for some surveys. The surfclam modelis applied at single points on the MAB shelf, which do not representthe large areas covered in the stock assessment surveys. Also, the cohortthat is added in each simulation year does not capture local and regionalvariations in recruitment that are included in the survey data. Thesurvey-derived biomass estimates are at 2–3-year intervals whichmiss years with episodic warming that the simulations suggest areimportant in regulating surfclam biomass. Hence, any correspondencebetween simulated and observed biomass is at best coincidental. Theobserved decline in surfclam biomass along the southern and inshorepart of its range observed from the surveys, however, does provide aqualitative comparison that is consistent with the simulation results.

The simulations show that large and old animals targeted by thefishery are more affected by warmer bottom temperatures. However,the fishing mortality rate has been well below the natural mortalityrate over the history of the fishery, though regional shifts in efforthave occurred in response to changes in the distribution of the stock(NEFSC, 2013). Nevertheless, the relative constancy of the fishingmortality rate limits the yearly influence on the stock and thusvariations in fishing pressure cannot explain rapid stock declines overa short period of years. The simulations show that warm years areepisodic and that the effect is not uniform over the MAB continentalshelf and this can be expected to introduce uncertainty into the settingof fishing quotas for the surfclam fishery.

4.2. Episodic versus long-term warming effects

The simulated bottom water temperature showed a small butconsistent warming trend in the southern inner shelf area of the MABof about 0.014 °C year−1, which agrees with warming trends suggestedin studies in the same area (Mountain, 2003;Nye et al., 2009;Weinberg,2005) and is consistent with the rates of global sea surface temperatureincrease (~0.013 °C year−1, Trenberth et al., 2007) and interior oceanwarming (0.00086 °C year−1, Levitus, 2005). Similar trends in seasurface warming have been observed in the northwestern AtlanticOcean between 1982 and 2006 (0–0.0096 °C yearr−1, Belkin, 2009).

The simulated warming trend and concurrent decrease in po-pulation densities along the southern boundary of surfclam's rangepotentially supports the suggestion based on observations that long-term warming is the cause. However, the simulations show thatepisodic warming events in the 50-year bottom water temperaturetime series have more of an effect on surfclam densities than the long-term warming trend. Years with early spring warming, delayedfall cooling, and summer bottom water temperatures above 23 °Coccur more frequently after 1982 in the simulated temperature timeseries. Similar trends in increasing length and intensity of the annualtemperature range have been reported for the sea surface temperaturein the northeast US continental shelf (Friedland and Hare, 2007). Yearswith longer warm seasons produced sharp declines in simulatedsurfclam densities. The recovery of the population was slow and formany episodes another warm summer occurred before the population

Fig. 8. Relationship between simulatedmortality rate and the September bottomwater temperature anomaly at the inner (A, B, C), middle (D, E, F) and outer (G, H, I) shelf sites along thenorthern (upper panels), middle (middle panels) and southern transects (bottom panels). The vertical line provides a reference for the 0 °C bottom water temperature anomaly. Theregression coefficient obtained from fitting an exponential relationship to the simulated data at each site is shown.

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density reached pre-decline densities. Episodic warm events separatedby insufficient recovery time would result in what appears as a long-term decline in surfclam abundances. Continued episodic warmingevents coupled with a long-termwarming trend will potentially reducesurfclam biomass and producewhat appears as a northward shift in thepopulation distribution. Local variability in the extent andmagnitude ofthe episodic warming events could also produce short-term and spatialfluctuations in surfclam catch (e.g., Baptista and Leitão, 2014).

4.3. Forcing mechanism for warm years

Identifying the underlying causes of the episodic warm years isbeyond the scope of this study, but the analysis of the simulated bottomwater temperature time series provides some insights. Summers that

Fig. 9. Simulated annual averaged net production obtained for the outer shelf site on the

were warmer than average were usually preceded by warm winters.As a result, the water column is warmer when the spring increase inwater temperature occurs and the seasonal stratification that developsis weaker than normal. Mixing by winds and tides can redistributethis heat throughout the water column, increasing the heat content ofdeeper waters especially in the shallow areas (Townsend et al., 2006).Tides do not explain the interannual variability in warming events, butvariability in wind strength and direction will modify the verticalmixing, stratification, and therefore heat content of the water column.The seasonal increase in the simulated water temperatures tended topersist longer during the warmer years, suggesting that atmosphericheating was extended.

Intrusions of warm-core rings onto the outer MAB shelf (Townsendet al., 2006), variability in the Gulf Stream location (Ezer et al., 2013;

northern transect (nj3) and the inner shelf site along the southern transect (dels1).

Fig. 10. Daily-averaged time series of simulated A) bottom water temperature obtained from the hindcast simulation and B) respiration rate, C) assimilation rate, D) weight, andE) condition index obtained from the individual-based surfclam model for 1995 (warm bottom water temperature) and the two years before (1993,1994) and after (1996,1997) thathad bottom water temperatures consistent with the 50-year average (Fig. 1B). The vertical line corresponds to 15 August. The x-axis represents day of the year starting in 1 January.

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Gawarkiewicz et al., 2012) and variability in the alongshelf transportfrom the Labrador Sea (Chapman et al., 1986; Shearman and Lentz,2010) could also potentially contribute to warmer temperatures overthe MAB during certain years. However, a recent study (Chen et al.,2014) examined an unusually warm sea surface temperature event inthe northeastern U.S. continental shelf waters that occurred duringthe winter-spring of 2011–2012. A heat budget analysis showed thatthe warming of the coastal ocean was determined by the localatmospheric heat flux resulting from a northward movement of theatmospheric jet stream (Chen et al., 2014). This warming event wasalso observed in deep waters (Chen et al., 2014). Comparison of thesimulated MAB water temperatures with those in Chen et al. (2014)for 1983–2007, shows that 3 of 4 warm years evident in the simulations(1983, 1995 and 2002) correspond to the warm sea surface tempera-tures observed in the MAB that are associated with a northwardmovement of the atmospheric jet stream. This comparison suggeststhat the change in location of the atmospheric jet stream is a possiblemechanism driving the warming water temperature in the MAB.

4.4. Fisheries redistribution in the northwestern Atlantic

The redistribution of surfclam populations in the MAB is consistentwith changes in the spatial distributions of other species in the sameregion between Cape Hatteras and the Gulf of Maine (Lucey and Nye,2010; Nye et al., 2009; Pinsky and Fogarty, 2012). Nye et al. (2009)used a 40-year time series based on spring trawl surveys in the MABto show northward shifts in the distribution of several marine fishspecies. The major change in distribution was found for stocks locatedin the southern MAB portion of the survey region (Nye et al., 2009).

The shift in fish distributions was followed by a northward shift inthe fisheries, but to a smaller extent, suggesting slow adaptation of the

fishing industry to shifts in the range of commercial species (Pinskyand Fogarty, 2012). The surfclam fishery has already shifted from thesouthern MAB to the region off New Jersey (NFSEC, 2010) in responseto declining abundances. Continued episodic warm events super-imposed on a long-term warming trend will continue the decline insurfclam abundances along the southern part of its range, requiringthe population and fishing industry to continue a northward migration.As the fishery moves new regulations and management policies willneed to be developed, which will affect the social systems that aredependent on the surfclam fishery (McCay and Creed, 1990; McCayet al., 2011a, 2011b).

5. Conclusions

Globally marine ecosystems are being impacted by the effects ofincreasing water temperatures (Cheung et al., 2013; Roessig et al.,2004). The northwestern Atlantic is one such affected region wherethe redistribution of commercially important species is being observed(e.g., Mills et al., 2013; Nye et al., 2009), one being the Atlantic surfclam(Weinberg, 2005). This study focused on the effects of warm bottomwater temperature on surfclam growth via its effect on filtration andconsequently scope for growth. Presumably, warming temperaturesaffect adult reproductive capacity and larval survival and recruitment.If warming continues, the downward trend in surfclam biomass is likelyto continue, unless northward range extension can counterweigh thetrend. Range extension, being dependent upon larval dispersal morethan adult physiological limitations (Guo et al., 2005; Holt et al.,2005), is likely to lag behind range contraction at the southern bound-ary and, thus, compression of surfclam's range is likely to continue.

The simulations presented in this study suggest that the long-termdecline in surfclam density is a response to episodic warm years instead

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of to a gradual increase in water temperatures. Increasing frequencyof episodes of warm bottom temperatures results in a trendtoward a population that consists of smaller and younger animals.Though extreme events are recognized as important component ofspecies' population dynamics (Boero, 1996; Munroe et al., 2013b;Taylor, 1934), often contributing to regime shifts (Collie et al., 2004;Manzano-Sarabia et al., 2008), rarely are environmentally-drivenepisodic mortality events included as part of management and quotasetting procedures, but incorporation into policy and regulations islikely to become critical for surfclam management as environmentalchange continues.

Acknowledgments

This research was supported by the National Science FoundationCoupled Natural and Human Systems grant number 0908939.

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