Oyster Mortality in Delaware Bay: Impacts and Recovery from Hurricane Irene and Tropical Storm Lee

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Estuarine, Coastal and Shelf Science 135 (2013) 209e219

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Estuarine, Coastal and Shelf Science

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Oyster mortality in Delaware Bay: Impacts and recovery from Hurricane Irene and Tropical Storm Lee

D. Munroe a,*, A. Tabatabai b, I. Burt a, D. Bushek a, E.N. Powell c, J. Wilkin b a Haskin Shellﬁsh Research Laboratory, Rutgers University, 6959 Miller Ave, Port Norris, NJ 08349, USA b Institute of Marine and Coastal Sciences, Rutgers University, 71 Dudley Road, New Brunswick, NJ, USA c Gulf Coast Research Laboratory, University of Southern Mississippi, 703 East Beach Drive, Ocean Springs, MS 39564, USA article info abstract

Article history: One predicted consequence of climate change is increasing variability of local weather extremes such as Received 6 June 2013 the frequency and intensity of storms. In August and September of 2011, Hurricane Irene and Tropical Accepted 16 October 2013 Storm Lee generated extreme flooding in the Delaware River watershed that produced prolonged bay- Available online 23 October 2013 wide low salinity and consequent historically-high mortalities for the oyster stock in the upper reaches of Delaware Bay. The dynamics, consequences, and projections for recovery from the anomalously high Keywords: oyster mortality that occurred as a consequence are reported using a combination of physical modeling, Crassostrea virginica field sampling, and metapopulation dynamics modeling. Monthly mortality of 10% and 55% on the upper freshet mortality bay beds (Arnolds and Hope Creek respectively) exceeded the longer-term average at those locations and < hurricane was associated with a continuous low salinity ( 7) exposure of greater than 20 days. Population recovery salinity projections based on metapopulation modeling suggests that recovery will take approximately 10 years fishery for the uppermost beds. Clear understanding of the circumstances leading to this high population-level impact on oysters is important because anticipated future conditions of increased storm frequency will intensify the challenge such events pose for the management of fishery and aquaculture resources, and the siting of restoration efforts. Ó 2013 Elsevier Ltd. All rights reserved.

1. Introduction Levinton et al., 2011; Bushek et al., 2012). Like many C. virginica stocks along the Atlantic and Gulf coasts of the US, Delaware Bay The Eastern oyster (Crassostrea virginica) is an important species oyster stocks suffer annual natural mortality ranging from <5% to both ecologically and economically. Ecologically, oysters provide 55% per year due to various mortality agents, with higher rates numerous functions including habitat creation and water filtration normally being disease-induced by Perkinsus marinus infection (Coen et al., 2007; zu Ermgassen et al., 2013). Economically, eastern (Dermo disease e Ford and Tripp, 1996; Bushek et al., 2012). For oysters continue to be an important fishery along the Atlantic coast estuaries like Delaware Bay, where a strong salinity gradient exists, of the United States (Mackenzie, 1996). The American fishery for the lower salinity (upper estuarine) regions become a refuge from C. virginica landed an estimated 18.2 million pounds of meats in disease mortality wherein oysters generally experience lower dis- 2010, worth $76.2 million US dollars (Lowther, 2011). The fishery in ease mortality (Bushek et al., 2012). Delaware Bay landed 94,470 bushels (1 bushel ¼ 37 L) of oysters in Salinity is a primary factor limiting oyster reef distributions 2011, worth approximately $4.2 million in dockside value (Powell within estuaries. Although frequent floods can prevent reef for- et al., 2012). mation (Galtsoff, 1964), oysters are remarkable in their ability to A trade-off exists for the eastern oyster such that in higher tolerate a wide range of salinity allowing them to inhabit most salinity environments, they experience faster growth but reduced regions of estuaries where mean or median salinity exceeds 5 survival due to disease and predation, whereas in lower salinity (Galtsoff, 1964; Castagna and Chanley, 1973). Additionally, oysters habitats, oysters grow more slowly (Kraeuter et al., 2007), but are able to withstand periods of unfavorable conditions (e.g., disease and predation pressure is lower (LaLa Peyre et al., 2003; exposure during low tide or episodic freshets) by closing their shells and shifting to anaerobic metabolism (Michaelidis et al., 2005). While this mechanism allows oysters to withstand other- * Corresponding author. wise intolerable conditions, anaerobiosis has limitations. Specif- E-mail address: [email protected] (D. Munroe). ically, when oysters are unable to open their shells, they are also

0272-7714/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.ecss.2013.10.011 210 D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 unable to flush out toxic metabolites that accumulate over time. Moreover, this ability is constrained by the amount of energy resources available to sustain glycolysis and to maintain muscular closure of the shells (de Zwaan and Wijsman, 1976). Thus, the duration that an oyster can withstand low salinity conditions, for example, depends upon metabolic demands and energy reserves. Prolonged exposure to freshwater can be an important source of mortality for oysters, particularly those in the upper reaches of estuaries where salinities are lower and other mortality agents such as disease and predation have less influence. In a partially mixed estuary, episodic, heavy precipitation events can temporarily displace the salinity gradient downbay thereby depressing salinity in the upbay regions to levels that are fatal for oysters. Examples of oyster mortality caused by prolonged exposure to low salinity due to freshets (Butler, 1952; May, 1972; Andrews, 1973; Burrell, 1977; Pollack et al., 2011; Levinton et al., 2011) and opening of flood control spillways (Gowanloch, 1950; Gunter, 1953; Dugas and Perret, 1975; LDWF, 2011) have been documented throughout much of the oyster’s range. Climate models predict that precipitation (Najjar et al., 2000; Hayhoe et al., 2007) and the frequency of extreme storm events (Voynova and Sharp, 2012; Wetz and Yoskowitz, 2013) will increase in the northeastern US (Karl et al., 1995; Allan and Soden, 2008). Voynova and Sharp (2012) show that half of the extreme storm events over the past century in the Delaware Estuary occurred within the last decade (2001e2011), Fig. 1. Location of the natural oyster reefs in Delaware Bay. Main map shows depth suggesting that incidence of extreme flooding is increasing in the (according to the color scale bar), location of selected oyster beds used in this study Delaware estuary. An increase in flooding would have important (black circles), and location of stations providing data for environmental comparison (white stars) in Delaware Bay. Shaded area in the top right inset shows the general consequences for oysters and the associated fishery in Delaware location of the fished oyster beds on the New Jersey portion of the Delaware Bay. Bay and other estuaries where oysters exist and are often the focus of restoration efforts (Beck et al., 2011; Kennedy et al., 2011; Wetz and Yoskowitz, 2013). increased mortality due to disease and predation, whereas oysters A combination of late winter snowfall early in the year, and from the upper bay have slower growth and lower disease mor- heavy spring precipitation followed by late summer storms made tality due to lower salinity upbay (Bushek et al., 2012). Oysters in 2011 the wettest year since 1895 in the Delaware Estuary water- Delaware Bay are an important fishery resource that has been shed (Elick, 2013). Two storms in quick succession (Hurricane Irene surveyed annually for management of the fishery since the early in late August and Tropical Storm Lee in early September) dumped 1950’s(Ford, 1997). half of the average annual cumulative precipitation into the Dela- ware Bay watershed (Elick, 2013), dropping salinity for a prolonged 2.1. Salinity exposure period across the natural oyster beds and causing high mortality in the upper bay oyster populations. Our intention is to define the rare A 3-dimensonal hydrodynamic model (ROMS; Regional Ocean environmental circumstances leading to this important impact to Modeling System) was used to calculate characteristics of estuarine the oyster stock (the stock we refer to here is the fished population), circulation in Delaware Bay in 2011. Shchepetkin and McWilliams thereby allowing better understanding of future conditions that (2005, 2009a) describe the ROMS computational kernel in detail, will be important to fishery and aquaculture management and and Haidvogel et al. (2008), as corrected by Shchepetkin and siting of restoration efforts. This paper describes (1) the charac- Mcwilliams (2009b), give an overview of ROMS features and ap- teristics of the spatial gradient in salinity across the oyster beds that plications. The configuration of ROMS for the Delaware Bay uses a resulted from these storms, (2) the oyster mortality that resulted curvilinear grid with horizontal resolution ranging from 200 m in from the low-salinity event, and (3) projections about expected the tidal river and upper bay (north of 39.7 latitude) to 2 km in the recovery windows of the most heavily impacted portions of the lower bay (near the bay mouth), and vertical resolution ranging oyster stock. from 0.03 to 6.2 m (20 levels in terrain-following vertical coordinates). The model grid and the configuration of freshwater 2. Methods inflows, air-sea fluxes, and tidal forcing follows that previously used and evaluated for Delaware Bay by Wang et al. (2012). We use a combination of physical modeling, field sampling, and Model results for salinity and temperature in our 2011 simula- metapopulation dynamics modeling to comprehensively study an tion were compared to observations at Ship John Shoal Light (Fig. 1) anomalously high oyster mortality event in Delaware Bay. The es- from a conductivity/temperature sensor 1.4 m below mean low low tuary is located along the Atlantic coast of the United States and is water. Station D (Fig. 1) was equipped with both surface and bottom characterized by a strong salinity gradient that establishes an moored conductivity/temperature sensors that were used to along-estuary gradient in oyster population parameters (Powell compare against surface and bottom simulations of salinity and et al., 2008). The location of the fishable oyster stock on the New temperature. Station D data were obtained from Robert Chant Jersey side of Delaware Bay is shown as the shaded area in the (pers. comm.,), and the Ship John Shoal data from the National Data upper right inset of Fig. 1; note that the area occupied by the Buoy Center (station ID: 8537121). Discreet monthly bottom fishable stock is only a portion of the entire salinity gradient in the salinity observations were also available for four oyster beds (Shell bay. Oysters from lower bay beds within this shaded region expe- Rock, Cohansey, Arnolds and Hope Creek, Fig. 1) from regular rience higher salinity and correspondingly higher growth and monitoring programs (I. Burt, pers comm.); these were also used to D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 211 compare with modeled bottom salinity. Delaware River discharge duration of low salinity exposure by counting the period of time data were obtained from United States Geological Survey’s National (number of days) that the bottom salinity at each bed location Water Information System (station ID: 01463500). These time se- remained continuously below 7. Counting began when salinity ries, and the corresponding model predictions, are shown in Fig. 2; dropped below 7 and ended when it exceeded 7, starting in January model predictions and the corresponding discreet salinity mea- 1 and ending December 31, and only the maximum count (longest surements at the oyster bed locations are shown in Fig. 3. The single exposure period) was retained for each bed location. The model performance in simulating these observations is summa- threshold salinity of 7 was chosen because previous research has rized in Table 1 using conventional skill metrics: root mean square demonstrated that oyster filtration (Loosanoff, 1958), growth error (RMSE), correlation (R), bias, and the overall Skill Score (Shumway, 1996), and overall health (Castagna and Chanley, 1973; introduced by Wilmott (1981; also see Warner et al., 2005) Heilmayer et al., 2008) can decrease at salinities below 7.

P 2.2. Oyster mortality jX X j2 skill ¼ 1 mod obs (1) P 2 þ Oyster mortality at the six bed locations included here was Xmod Xobs Xobs Xobs monitored during an ongoing field sampling program that is part of the annual oyster stock assessment (Powell et al., 2012; Bushek where X is the variable being compared, here salinity or tempera- et al., 2012). Monthly oyster mortality was estimated from 1 ture, from model results (Xmod) or observations (Xobs). Perfect bushel (35.2 L) samples taken monthly in October (2004e2012) by model agreement returns a Skill of 1, and complete disagreement dredge tow at each of the oyster bed sites shown in Fig. 1 (note that results in a Skill of zero. sampling at Hope Creek was initiated in 2010, therefore only 3 The tidal cycle dominates temperature and salinity variance, but years of data are available for this location). Oysters and boxes were it is processes at sub-tidal frequencies that are of primary signifi- collected randomly at each bed location using three replicate cance here. Tides are highly predictable phenomena in hydrody- dredge tows (1/3 bushel from each tow) to create the composite 1 namic modeling. As tides modulate much of the salinity variability bushel sample. From each 1 bushel sample, counts and sizes of all in an estuary, when model results and observational time-series live oysters, boxes (articulated oyster shells without oyster tissue both include high frequency tidal fluctuations, the two are inside) and gapers (open but intact shells with tissue inside) were strongly correlated. Therefore, to perform a more rigorous model obtained. Monthly mortality for each site was estimated as follows: assessment, the skill assessment is made using modal and observational time series after tidal and other high frequency variations boxes þ gapers were removed using a Lanczos low pass filter (Duchon, 1979) with a Morality ¼ (2) live oysters þ boxes þ gapers 60-h running window and a cutoff period of 31 h. Modeled bottom salinities for six oyster bed locations (Fig. 1, Mortality at each location downbay of Hope Creek in October Table 2) spanning the region of fished oyster beds were used to 2011 was compared against the remaining years in the mortality calculate the salinities experienced by oysters at those locations. time series (2004e2010 plus 2012) using a two-tailed t-test. The The summary calculations at each location estimated the maximum mortality monitoring program at Hope Creek began later than at

Fig. 2. Comparison of model results against observed salinity and temperature for surface water at Station D (2a), and bottom water at Station D (2b) and at Ship John Shoal Lighthouse (2c). The average monthly salinity at Ship John Shoal Lighthouse for the nine years preceding the ﬂood (2002e2010) is also shown (grey circles) with standard error. The shorter period of data collection in Station D at the bottom was due to instrument failure. The grey shaded area shows Delaware River discharge at Trenton, NJ (USGS station ID: 01463500). Periods of high discharge (i.e. Spring Freshet, Hurricane Irene and Tropical Storm Lee) are noted with vertical bars. A summary of model performance is shown in Table 1. 212 D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219

Hope Creek Model 15 Obs.

Arnolds 15

Cohansey 15

Shell Rock 15

0 Apr 1 May Jun Jul Aug Sep Oct Nov

Fig. 3. Comparison of model results (grey line) against observed salinity (circles) measured monthly in 2011 at four oyster bed locations as labeled on each panel. the other bed regions, meaning that comparisons of Hope Creek was parameterized to simulate a metapopulation containing four mortality in 2011 were limited to 2010 and 2012; therefore no populations of eastern oysters (Crassostrea virginica) from Delaware statistical comparison was attempted for this site (note that other Bay, connected to one another by larval dispersal. Population pa- biological observations exist for Hope Creek, such as abundance rameters used for these simulations can be found in Table 3; and size frequency for 2007e2012). The relationship between simulated populations correspond to surveyed populations such maximum duration of low-salinity exposure and October mortality that Population 1 corresponds to Hope Creek, Population 2 corre- was examined for each of the six locations. sponds to Arnolds, Population 3 corresponds to Cohansey, and Population 4 corresponds to Bennies. The model allows indepen- 2.3. Recovery projections dent control of populations within the metapopulation for parameters such as adult and juvenile mortality, local carrying An individual-based metapopulation model that includes oyster capacity, animal growth rate, and fecundity. Each population is population dynamics, larval dispersal, and spatially-explicit growth composed of multiple cohorts of oysters with the age, sex, and ﬁ rates was used to hindcast population dynamics preceding and genotype of each individual oyster being speci ed. Larvae are during the ﬂood and to forecast population recovery subsequently. created from randomly-selected parent pairs and larvae produced The Dynamic Population Genetics Engine (DyPoGEn) is a numerical in each population can remain within the source population (self- model that simulates metapopulation dynamics for marine pop- recruitment) or disperse to any of the other populations. Larval ulations; a full model description and comparison to Delaware Bay dispersal was parameterized following a transfer probability ob- oyster populations can be found in Munroe et al. (2012). The model tained from bio-physical model simulations of larval exchange using ROMS coupled to an oyster larval model that simulated growth as a function of food supply, temperature, and salinity and larval Table 1 Model predictive skill for 31-h low-passed temperature and salinity time series at Ship John Shoal and Station D (Fig. 1): Values are tabulated for Root mean square Table 2 error (RMSE), correlation (R), Bias and the overall Skill Score of Wilmott (1981) Model salinity statistics for each of the oyster bed locations, calculated for the month (Equation (1)). RMSE and Bias are in observation units. R and Skill are dimensionless. preceding hurricane Irene (June 20 through July 20).

Wilmott skill RMSE R Bias Minimum Maximum Mean Std. dev.

Temperature Station D, Surface 0.93 0.89 0.94 0.69 Hope Creek 1.1 13.6 7.0 2.8 Station D, Bottom 0.93 0.49 0.88 0.12 Arnolds 5.4 14.8 10.6 2.2 Ship John Shoal 1 1 1 0.9 Cohansey 8.7 18.7 14.2 2.2 Salinity Station D, Surface 0.97 1.23 0.97 0.6 Shell Rock 11.2 21.3 16.7 2.1 Station D, Bottom 0.91 1.85 0.91 0.92 Bennies 13.5 22.5 18.4 2.0 Ship John Shoal 0.96 1.22 0.94 0.06 New Beds 13.3 22.0 18.2 1.7 D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 213

Table 3 but positive at the bottom (0.92) indicating that the model was Population characteristics for each of the four populations during the decade 2000- slightly over-predicting bottom salinity. 2010 and larval transfer rates among populations. Unk, no data available. Low salinity exposure (maximum number of days that the Population 1 Population 2 Population 3 Population 4 bottom salinity remained continuously below 7) ranged from 1.5 Population characteristics of Delaware Bay oysters for the 2000s days at New Beds to 26 days at Hope Creek (Fig. 4). For all locations, Abundance (millions 492 395 868 197 this period of maximum low-salinity exposure began during Hur- a of oysters) ricane Irene (Aug. 28th) or shortly thereafter. Average Adult Mortality 8% 10% 16% 26% Fractiona,e Juvenile Mortality Unk.c 8% 23% 47% 3.2. Oyster mortality Fractiona,e Von Bertalanffy Growth Unk.c 0.175/110 0.26/125 0.23/140 In general, average mortality in October tends to increase from b Parameters (K/LN) upbay (Hope Creek) to downbay (New Beds) (Fig. 5, also Ashton- Fishing Rate 2% 2% 2% 4% Alcox et al., 2013a). Monthly (October) mortality in 2011 was d Larval transfer rates among populations distinctly different than that seen in other years, with mortality at Population 1 to: 11% 54% 27% 8% Hope Creek and Arnolds substantially higher than average. Mor- Population 2 to: 6% 56% 29% 9% ﬁ Population 3 to: 3% 40% 29% 28% tality at Arnolds in 2011 (8%) was signi cantly different from the 8- Population 4 to: 3% 19% 14% 64% year average (t ¼4.75, p ¼ 0.002, n ¼ 8), whereas no signiﬁcant

a 1 difference was observed at locations farther downbay (Fig. 5). From Powell et al. (2012);LN in mm, k in yr . b From Kraeuter et al. (2007). Limited years of sampling at Hope Creek prevented a statistical c Used approximated LN from stock assessment data and same Juvenile Mortality comparison, but mortality in 2011 at that location far exceeded the and K as Population 2. mortality in the year before or after. Mortality in 2011 fell below the d From Fig. 7einNarváez et al. (2012a). 8-year average at all remaining downbay locations (Fig. 5). e Fraction is equivalent to 1 e mt where m is the specific mortality rate and t is 1 year. The large differences observed in monthly mortality at Hope Creek and Arnolds beds compared to other years, corresponded vertical migratory behavior as a function of size, salinity, and with the low-salinity exposure experienced across the beds. Mor- temperature (Narváez et al., 2012a). Larval dispersal parameters tality in October remained relatively constant (w10%) across the can be found in Table 3. range of low-salinity exposures from one day (observed at Bennies) Simulations were run for 100 years to allow model stability through 21 days (seen at Arnolds) (Fig. 6). An increase in low- before and after the simulated flood. The first 50 years of the salinity exposure to 26 days (seen at Hope Creek) was correlated simulation used parameters following those observed in Delaware with a 45% increase in recent mortality in October (Fig. 6), Bay oyster beds from 2000 to 2010 (Powell et al., 2012). In simulation year 51, mortality rates were set to reflect those observed by Maximum time below 7−psu, August and September 2011, (day) the mortality monitoring program in 2011 (described in the previous section) thus capturing the biological effects of the flood. Population parameters were returned to those of the 2000s in 25 simulation year 52 through 100. This recreated the dynamics of the flood mortality and allowed projection of the time necessary for 39.45 20 populations most heavily impacted by the flood to recover population abundances and size structure, assuming population pa- 15 rameters returned to those observed during the decade preceding 39.4 the flood. 10 3. Results 39.35 5 3.1. Salinity exposure 0 Observations available for model comparison were nearly 39.3 continuous for Ship John Shoal Lighthouse (data span March through December), whilst observations at Station D were only available from June through mid-September (surface) and August 39.25 through mid-September (bottom) because of mooring movement. Model estimates of salinity and temperature at the three locations (Ship John Shoal Lighthouse, Station D surface and bottom) agreed well with observations (Fig. 2). Likewise, model estimates of 39.2 salinity at four oyster bed locations agree with discreet bottom sample measurements made at the oyster beds (Fig. 3). The model shows significant skill in capturing sub-tidal frequency variability 39.15 in salinity and temperature, both at the surface and the bottom; −75.55 −75.45 −75.35 −75.25 ranges for all comparison statistics (Table 1) indicate good model agreement with observed data. Model skill values ranged from 0.91 Fig. 4. Number of consecutive days that bottom salinity remained below 7 from to 1, RMSE ranged from 0.49 to 1.85, and correlations ranged from August through the end of September 2011 at the studied beds (shown with circles). As 0.88 to 1. Temperature bias values were all negative (range a result of higher river discharge, salinity at Hope Creek, Arnolds and Cohansey dropped below 7 on 8/28, 8/29, and 9/2 and remained below this threshold until 9/23, from 0.12 to 0.9), indicating that the model was slightly under- 9/17, and 9/16, respectively. Hope Creek, Arnolds and Cohansey beds experienced an predicting temperature. Salinity bias values at the surface were average salinity of 5.14, 8.14, and 11.35 respectively during the period leading up to slightly negative (0.6 at Station D and 0.06 at Ship John Shoal), Hurricane Irene and Tropical Storm Lee. 214 D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219

Alcox et al., 2013a, Fig. 10). The simulated population continued to recover linearly over a period of approximately 10 years, at which point the simulation with flood impact meets the non-flood simulation indicating that the population has recovered to pre- flood conditions. Deviations in abundance in the remaining three downbay populations are small and negligible; the flood-simulated populations track relatively closely with the non-flood simulation. Length frequency of the upbay population (Population 1) was also impacted by the flood. The length frequency shifted to small animals in the year after the flood (Fig. 9). This high frequency of small animals slowly shifted back to a length frequency typical of pre-flood conditions after approximately 10 years. Length frequencies in the remaining simulated populations stay relatively constant before, during and after the simulated flood; however the length frequencies for those populations are slightly larger than those from observed populations (Ashton-Alcox et al., 2013a, Fig. 5. Black circles show average oyster mortality in October from 2004 through 2012, Fig.10). This discrepancy is likely due to the lack of fishing mortality fi ¼ exclusive of 2011 (error bars show 95% con dence interval, n 8). Hope Creek mor- in the model, allowing population length frequency to slightly tality in 2010 and 2012 is shown with a black dash. Vertical white bars show oyster fi mortality for October 2011. Mortality at Arnolds in 2011 was significantly different than exceed that observed in the real shed populations. Oyster mor- the 8-year average (p ¼ 0.002), whereas no significant difference is observed at beds tality due to the flood was composed mostly of larger animals farther downbay. Statistical comparisons could not be made for Hope Creek due to the (Fig. 7); the simulation likewise implemented high mortality on short time series available. adult oysters during the flood, which resulted in the shift in length frequency. Slow growth rates in the upper bay (Table 3) slows the suggesting that a threshold may have existed in 2011 whereby exposure to low salinity water beyond 21 days led to extreme recovery of the population size structure. freshet mortality. The mortality event at Hope Creek in 2011 was strongly biased towards larger, market-size (>2.500 or 63.5 mm shell 4. Discussion height) oysters. Of the animals that died in 2011 in the upbay beds, more than 75% were larger than 2.5 inches in shell height (Powell 4.1. Storm events, freshets and mortality et al., 2012). This loss of large animals from the population caused a large shift in the population length frequencies towards Large freshet mortalities in oysters are rare, but have been smaller animals (Fig. 7). documented for C. virginica in a number of systems (Gowanloch, 1950; Butler, 1952; Gunter, 1953; May, 1972; Andrews, 1973; Dugas and Perret, 1975; Burrell, 1977; Levinton et al., 2011; LDWF, 3.3. Recovery projections 2011; Pollack et al., 2011). The impact of hurricanes and major storms on ecological and anthropogenic systems is often unique to Simulated impacts of the flood include a large drop in abun- the storm and depends on various storm characteristics (Mallin and dance of oysters in the upper estuary population (Fig. 8) and a shift Corbett, 2006). For example, storms that bring heavy precipitation in size frequency to smaller body sizes (Fig. 9) in simulation year 51 tend to have greater and longer-term impacts to downstream (corresponds to observations from 2011). Abundance of oysters in ecosystems (Paerl et al., 2001). 2011 brought record-setting the simulated upbay population (Population 1) decreased during amounts of freshwater to the Delaware Estuary watershed over the flood, dropping from about 350,000 animals pre-flood to less the course of the year, punctuated by two major storms (Hurricane than 200,000 animals afterwards (Fig. 8). In the simulation, the Irene and Tropical Storm Lee) in late August/early September that upper bay population began to recover abundance the year after the flood, in agreement with the recovery trajectory observed for that population from the most recent stock assessment (Ashton-

Fig. 7. Length frequencies of oysters from Hope Creek, 2008e2012. These data were collected as part of the annual stock assessment in Delaware Bay (Powell et al., 2012). Fig. 6. Oyster mortality in October 2011 at each bed location versus the duration of Sampling occurs in October/November. Grey lines are length frequencies before the low-salinity exposure (calculated as the maximum number of days in 2011 that salinity ﬂood; black solid and dotted lines are the following 2 years, respectively, after the remained continuously below 7). ﬂood. D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 215

storm impact is clearly reflected by the monthly mortality in October. With many oysters weakened by the freshet but not killed immediately, cumulative mortality on some beds in the lower salinity regions of the bay exceeded 70% by spring of 2012 because of overwinter and delayed freshet mortality (Ashton-Alcox et al., 2013a). The beds in higher salinity regions, on the other hand, experienced slightly reduced mortality after the flood because of lower salinity depressed disease pressure. Extreme freshwater mortality events, like this one, are generally observed in the summer when water temperatures remain high enough to maintain elevated oyster metabolic demands, thereby constraining the duration of time they can remain closed to avoid unfavorable conditions (Shumway and Koehn, 1982; Hailmayer et al., 2008; Le; La Peyre et al., 2009). In colder temperatures (<10 C, Loosanoff, 1958), oyster metabolism is low and oysters can effectively ‘hibernate’ through unsuitable conditions. During the period of interest here, August 1 e September 30 2011, bottom water temperatures (as calculated using ROMS) ranged from 18.5 C to 24.5 C at the six study locations, well above the 10 C threshold below which metabolism can be maintained at low levels for extended periods of time. Thus metabolic activity in oysters along the range of the fished oyster beds in the Delaware Estuary would have remained relatively high during the time of low-salinity exposure. At Hope Creek, bottom salinity remained below 7 for over 25 days and coincidentally, oysters suffered 55% mortality. In contrast, periodic pulsed freshwater events have been shown to enhance oyster productivity by keeping disease and predation in check (La Peyre et al., 2003, 2009); however, when the freshet occurs during the summer and is extreme, rapid, and extended, high mortality events such as the one described here can occur. Changing clima- tological characteristics including increased precipitation (Najjar et al., 2000; Hayhoe et al., 2007) and extreme storm event frequency (Voynova and Sharp, 2012) could create circumstances in which these large summer freshets are more common either through direct discharges into estuaries (as was the case for Hur- ricane Irene and Tropical Storm Lee) or through levee releases in flood controlled systems like those in Louisiana (LDWF, 2011). Fig. 8. Simulated oyster abundances for the four populations in the simulated metapopulation. Panels from upper to lower show the most upbay population, Population 1 These changes bring with them the potential to substantially alter (corresponds to Hope Creek); Population 2 (corresponds to Arnolds); Population 3 distributions and demographics of oyster beds, which are impor- (corresponds to Ship John); and Population 4 (corresponds to Bennies). Black line tant ecological and economic resources. shows the abundances in the absence of a flood, dotted grey line shows abundances when a flood happens in year 51. Vertical grey line denotes the flood year. caused prolonged freshening of the estuary (Elick, 2013). These extended periods of low salinity caused unforeseen mortality of market-size oysters in the lower salinity (upper bay) portions of the oyster stock. Prior to these storms, this region of the bay (Arnolds to Hope Creek) comprised about 34% of the oyster population managed as part of New Jersey’s commercial fishery. Monthly mortality of 55% was observed in the Hope Creek (lowest salinity) beds following extended exposure to salinity below 7. Monthly mortality in October was used in this analysis because it represents the short-term mortality at each bed that corresponds to the mortality during the period of interest here (post-flood). Monthly mortality estimates based on box-counts are a conservative estimate of the true mortality generated by this freshet because (1) box-count mortality estimates tend to underestimate the true population mortality rate (Mann et al., 2009; but see Ford et al., 2006 for Delaware Bay), and (2) the monthly estimate fails to capture the continued mortality of weakened oysters that occurs through the winter and into the following spring. Fig. 9. Length frequencies of oysters from simulated Population 1 (corresponds to Hope Creek). Heavy black line with square data points shows the length frequencies in Although October box-count measurements may underestimate year 51 (pre-flood). Length frequencies post-flood are shown with circles and grey the true longer-term mortality resulting from the freshet, the lines progressing from smallest circles and lightest grey immediately post-flood, to spatial (across population) and temporal (across years) scale of largest circles and darkest grey at year 60. 216 D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219

Fig. 10. Length frequencies (left column) and relative abundance (right column) of the four simulated populations (grey lines) and the corresponding observations from stock assessments of those same population (black lines). Length frequency plots include output and data from before the flood (2010), during the flood (2011) and after the flood (2012). Relative abundance plots show the temporal dynamics in abundance relative to the abundance at the first observation in the plot (relative to 2007 at Hope Creek, and relative to 2002 at Arnolds, Ship John and Bennies).

The stochastic nature of extreme storm events often means that and resiliency to extreme but infrequent events requires detailed detailed environmental monitoring for regions occupied by environmental observations that can help identify the conditions affected populations cannot be planned for prior to the event and and thresholds that generate unfavorable outcomes. We used a thus environmental data are serendipitous where they do exist physical oceanographic model (ROMS) to hindcast the environment (Greening et al., 2006). Unfortunately, understanding vulnerability at locations spanning impacted and non-impacted regions of the D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 217 oyster fishery during the time leading up to and during the freshet. animals was observed indicating that recovery of size frequency is The hindcast data were validated against observations from loca- underway (at the time of writing) as anticipated (Figs. 7 and 10). For tions near the oyster beds of interest here, demonstrating good fisheries management, an important consequence is the expectation agreement between model data and observations. Calculations that biomass recovers more rapidly than abundance in Delaware Bay using hindcast salinity allowed us to specifically characterize the following an extreme mortality-inducing storm event. Note that low-salinity exposure (continuous days below salinity of 7) at Pollack et al. (2011) describe the opposing case from Mission/Ara- Arnolds and Hope Creek where elevated oyster mortality was nsas Bay in south Texas where predictable high recruitment observed. These calculations showed that low-salinity exposure permitted a rapid recovery of abundance relative to biomass. greater than 20 days led to elevated oyster mortality. Notably, The overall time scale of recovery, however, is set by the time laboratory experiments with C. virginica showed that sustained required to recover abundance. In Delaware Bay, average larval summer freshet conditions lasting 21 days caused an approximate dispersion is biased downbay (Munroe et al., 2012; Narváez et al., 69% mortality of oysters (La Peyre et al., 2003), which agrees well 2012a,b). Thus, recruitment potential declines upbay. Long-term with the cumulative mortality (w70% e Ashton-Alcox et al., 2013a) time series of recruitment for Arnolds shows only a few significant observed from post-flood through the spring of 2012 at the low recruitment events post-1990 emphasizing the aperiodic and limited salinity beds. The mortality response observed at Hope Creek and nature of recruitment upbay. Model simulations used an average Arnolds in 2011 appeared to be generated as a threshold-type recruitment potential; thus model simulations likely biased up response whereby exposure to low salinity water beyond 21 days recruitment potential. Therefore, the predicted time course of re- led to extreme freshet mortality. This same duration of freshet does covery is more likely to be biased low than biased high because of the not lead to elevated mortality if it occurs at lower temperatures in reduced recruitment potential upbay; thus recovery of abundance spring or winter (La Peyre et al., 2003). Controlled experiments should lag the recovery of biomass for an extended period of time. allow definition of the biological response to isolated conditions; however, ecological systems have numerous interacting conditions. 4.3. The economic impact of freshets Here we provide results demonstrating a similar mortality response of oysters to freshet exposure on an ecological scale. The consequences of the sustained low-salinity conditions in the Delaware Estuary in 2011 were not restricted to ecological 4.2. Population dynamics and recovery impacts. The oyster beds in New Jersey sustain a fishery that is an important economic driver in the southern regions of the state. The The fished oyster stock in Delaware Bay has been monitored for entire fishery was temporarily suspended as a result of flooding over a half a century and thus the long-term decadal dynamics are that generated elevated coliform counts during and after the well documented (Powell et al., 2008). Oyster population abun- storms (August 27eSeptember 30) and the extreme oyster mor- dance has varied considerably during that time with relatively low tality observed in the upper bay regions resulted in complete abundances from 1950 through 1970, a consequence of high fishing closure of those beds (Hope Creek plus two others, Fishing Creek rates prior to 1953, followed by disease-driven mortality caused by and Liston Range, located near Hope Creek but not included in this the introduction of MSX disease. The period from 1970 through analysis) during the following two years (2012e2013). These beds 1985 had a relatively high stock abundance (about five times higher remain closed as of this writing. than the low abundance period). The current period, 1986 through The economic consequences of these closures warranted pro- today, has seen the stock return to a low abundance state that is jection of these impacts to help identify the expected window of mostly controlled by mortality due to Dermo disease as described population and fishery recovery. We used a population dynamics previously. Fishing mortality rate since the late 1950s has remained model (DyPoGEn) to evaluate the recovery potential for the range relatively low and constant throughout the shifts in abundance; of the fished beds. Simulated impacts of the flood on population thus disease mortality has tended to be the major driver of abun- characteristics include depression of size frequency and adult dance shifts over time. abundance in the upper bay population lasting for 10 years after the On the short term, the low salinity in 2011 helped to reduce flood (Figs. 8 and 9). Simulated recovery time after the flood de- Dermo disease mortality for the higher salinity populations (Powell pends on growth rates, larval dynamics, and general population et al., 2012); however, oyster abundance upbay underwent a large characteristics in the years following the flood. The simulations decline due to the extended period of low-salinity exposure. This assumed that post-flood population characteristics will reflect decline was biased towards larger size classes. Larger bivalves have those observed prior to the flood. Although a priori comparison of reduced scope for growth, particularly at low salinity where os- this assumption is impossible, the abundance estimates from the motic demands come with high metabolic costs (Shumway and 2012 stock assessment (Ashton-Alcox et al., 2013a) indicate that the Koehn, 1982). Thus, large animals have lower reserves relative to recovery trajectory predicted by model simulation matches well their body weight and metabolic demand, a factor that is exacer- with the observed one-year post-flood recovery trajectory in the bated by the low efficiency of anaerobic energy production Hope Creek population, lending some support to this assumption. (Collicutt and Hochachka, 1977; Hammen, 1980). Thus, one antici- These projections permit estimation of the economic value of pates that stress-induced mortality could favor larger bivalves (see past landings, and the cost of the impacts of the storms in 2011. The Munroe et al., 2013); that is the outcome that was observed in the economic value of landings can be estimated assuming (1) a typical Hope Creek region of Delaware Bay in 2011. dockside price per bushel for the Delaware Bay oyster of $45 (a In this way, the freshet imposed two important changes in value much higher than the national average due to the high population structure: reduced abundance and compressed size summer market quality which permits most of the catch to be sold frequency. These can be expected to recover on different time scales. in the half-shell trade), and (2) an economic multiplier of 6 (McCay Size frequency has the potential to recover relatively quickly et al., 1995) for converting dockside value to economic value for because smaller surviving animals can grow into larger sizes, seafood. The economic multiplier is a conservative estimate for whereas recovery of abundance is restricted to scales that follow the oysters. As an example, a bushel of Delaware Bay oysters contains rate of successful recruitment. This differential time scale of biomass approximately 238 market-sized oysters worth about 19¢ each. A versus abundance recovery is ongoing in Delaware Bay. In the 2012 market-sized oyster sells for about $1.50 on the half shell at many survey, one year post-flood, substantial growth of these smaller northeast restaurants, a factor of 7.9 above the dockside price. Over 218 D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 the four previous years, 2008e2011, the New Jersey oyster fishery Shellfish Research Laboratory, Rutgers University, Port Norris, New Jersey, averaged landings of 84,854 bushels, with a dockside value of p. 133. Ashton-Alcox, K.A., Powell, E.N., Hearon, J.A., Tomlin, C.S., Babb, R.M., 2013b. $3,818,430 and an annual economic value of $22,910,580. Lost Transplant monitoring for the New Jersey Delaware Bay oyster fishery. harvest in 2012 due to closures of the upper bay bed region (Hope J. Shellfish Res. 32, 459e469. Creek, Liston Range and Fishing Creek) resulting from high freshet Beck, M.W., Brumbaugh, R.D., Airoldi, L., Carranza, A., Coen, L.D., Crawford, C., Defeo, O., Edgar, G.J., Hancock, B., Kay, M.C., Lenihan, H.S., Luckenbach, M.W., mortality is approximately 11,665 bushels (this estimate of lost Toropova, C.L., Zhang, G., Guo, X., 2011. Oyster reefs at risk and recommenda- harvest is relative to catch in 2008e2011 and includes loss of tions for conservation, restoration, and management. Bioscience 61, 107e116. management strategies that support the fishery such as interme- Burrell, V.G., 1977. Mortalities of oysters and hard clams associated with heavy e runoff in the Santee River system, South Carolina in the spring of 1975. Proc. diate transplant Powell et al., 2012; Ashton-Alcox et al., 2013b). Nat. Shellfisheries Assoc. 67, 35e43. Assuming the approximately linear recovery of abundance pro- Bushek, D., Ford, S.E., Burt, I., 2012. Long-term patterns of an estuarine pathogen jected in our model (Fig. 8) over the ten-year recovery period, this along a salinity gradient. J. Mar. Res. 70, 225e251. Butler, P.A., 1952. Effect of Floodwaters on Oysters in Mississippi Sound in 1950, vol. lost harvest extrapolates to 65,538 bushels. Using the economic 31. U. S. Fish and Wildlife Service, p. 20. Research Report. multipliers above, this lost harvest translates to a loss of $2,949,210 Castagna, M., Chanley, P., 1973. Salinity tolerance of some marine bivalves from in dockside value and $17,695,260 loss to the regional economy. inshore and estuarine environments in Virginia waters on the western Mid- Atlantic coast. Malacologia 12, 47e96. Coen, L.D., Brumbaugh, R.D., Bushek, D., Grizzle, R., Luckenbach, M.W., Posey, M.H., 5. Conclusions Powers, S.P., Tolley, G.S., 2007. AS WE SEE IT: ecosystem services related to oyster restoration. Mar. Ecol. Prog. Ser. 341, 303e307. Collicutt, J., Hochachka, P., 1977. The anaerobic oyster heart e coupling of glucose Hurricane Irene and Tropical Storm Lee caused sustained low and aspartate fermentation. J. Comp. Phys. B Biochem. Syst. Environ. Physiol. salinity conditions in Delaware Bay that resulted in high oyster 115, 147e158. mortality that is likely to have a decadal-scale impact on the upbay de Zwaan, A., Wijsman, T.C.M., 1976. Anaerobic metabolism in Bivalvia (Mollusca): characteristics of anaerobic metabolism. Comp. Biochem. Physiol. Part B: Comp. beds of the Delaware Bay oyster population. This, and other recent Biochem. 54, 313e323. and unprecedented storm events (Malakoff, 2012), are evidence Duchon, C.E., 1979. Lanczos filtering in one and two dimensions. J. Appl. Meteorol. 18, that the ecological and economic consequences of increased storm 1016e1022. http://dx.doi.org/10.1175/1520-0450(1979)018<1016:LFIOAT>2.0.CO;2. fl frequency will be important to understand and predict so that Dugas, R.J., Perret, W.S., 1975. The effects of spring oodwaters on oyster populations in Louisiana. In: Proceedings of the 29th Annual Conference adequate adaptation strategies can be developed. Datasets from of the Southeastern Association of the Game and Fish Commission, long-term monitoring will be crucial to permit the rapid develop- pp. 208e214. fi ment of post-storm management measures and to allow impacts of Elick, J.M., 2013. The effect of abundant precipitation on coal re subsidence and its implications in Centralia, PA. Int. J. Coal Ecol. 105, 110e119. these rare events to be estimated and put in context. Multidisci- Ford, S.E., Cummings, M.J., Powell, E.N.P., 2006. Estimating mortality in natural plinary research strategies, such as the one described here where assemblages of oysters. Estuar. Coast. 29, 361e374. fi we use physical modeling to characterize the environment during a Ford, S.E., 1997. History and present status of molluscan shell sheries from Barnegat Bay to Delaware Bay. In: MacKenzie Jr., C.L., Burrell, Jr., V.G., . Rosenfield, A., biologically important event, increase the mechanistic under- Hobart, W.L. (Eds.), The History, Present Condition, and Future of the Molluscan standing of the underlying cause for observed impacts and provide Fisheries of North and Central America and Europe, vol. 1. U.S. Department of recovery trajectories that can be used for planning and manage- Commerce, Atlantic and Gulf Coasts, pp. 119e140. National Oceanic and At- mospheric Administration Technical Report, 127. ment of both preparation and recovery. Ford, S.E., Tripp, M.R., 1996. Diseases and defense mechanisms. In: Newell, R.I.E., Eble, A.F. (Eds.), The Eastern Oyster: Crassostrea Virginica. Maryland Sea Grant e Acknowledgments College, College Park, Maryland, USA, pp. 581 660. Galtsoff, P.S., 1964. The American oyster, Crassostrea virginica Gmelin. Fisheries Bulletin of the U.S. Fish Wildlife Ser. 64, 480. Comparison of the circulation model results was made possible Gowanloch, J.N., 1950. Fisheries effects on Bonnet Carré spillway opening. Louisiana e e by generous provision of salinity and temperature data by Robert Conserv. Rev. 2, 12 13, 24 25. Gunter, G., 1953. The Relationship of the Bonnet Carré Spillway to Oyster Beds in Chant and Maria Aristizabal (data collected as part of a National Mississippi Sound and the “Louisiana Marsh”, with a Report on the 1950 Science Foundation Grant OCE-0928567), and by National Oceanic Opening, vol. 3. Publications of the Institute of Marine Science, University of and Atmospheric Administration’s publicly available data at Ship Texas, pp. 17e71. Greening, H., Doering, P., Corbett, C., 2006. Hurricane impacts on coastal ecosys- John Shoal (station ID: 8537121). The annual Delaware Bay oyster tems. Estuar. Coast. 29, 877e879. fishery stock assessment is supported by the State of New Jersey, Heilmayer, O., Digialleonardo, J., Qian, L., Roesijadi, G., 2008. Stress tolerance of a the Delaware Bay oyster industry and Rutgers University. Sincere subtropical Crassostrea virginica population to the combined effects of tem- e fi perature and salinity. Estuarine. Coast. Shelf Sci. 79, 179 185. thanks to all Haskin Shell sh Research Laboratory staff, staff of the Haidvogel, D.B., Arango, H., Budgell, W.P., Cornuelle, B.D., Curchitser, E., Di New Jersey Department of Environmental Protection, and the Lorenzo, E., Fennel, K., Geyer, W.R., Hermann, A.J., Lanerolle, L., Levin, J., Delaware Bay oystermen who lend their assistance to this work. McWilliams, J.C., Miller, A.J., Moore, A.M., Powell, T.M., Shcheptkin, A.F., Sherwood, C.R., Signell, R.P., Warner, J.C., Wilkin, J., 2008. Ocean forecasting in Funding for recovery projection modeling was provided by the terrain-following coordinates: formulation and skill assessment of the regional National Science Foundation (OCE-6022642 and OCE-0622672) and ocean modeling system. J. Comput. Physiol. 227 (7), 3595e3624. by the Army Corps of Engineers under their Section 22 funding Hammen, C.S., 1980. Total energy metabolism of marine bivalve mollusks in authority, contract #W912BU-11-C-0004, through the Seaboard anaerobic and aerobic states. Comp. Biochem. Physiol. A Compar. Physiol. 67, 617e621. Fisheries Institute, in collaboration with the Sponsor, the South Hayhoe, K., Wake, C.P., Huntington, T.G., Luo, L., Schwartz, M.D., Sheffield, J.E., Wood, Jersey Port Corporation, a public agency of the State of New Jersey. Anderson, B., Bradbury, J., Degaetano, A., Troy, T., Wolfe, D., 2007. Past and We are grateful for the input of two anonymous reviewers who future changes in climate and hydrological indicators in the US Northeast. Clim. Dyn. 28, 381e407. provided great commentary for improvement of the manuscript. Karl, T.R., Knight, R.W., Plummer, N., 1995. Trends in high-frequency climate vari- We also extend our gratitude to Rosie Wilkin for haiku translation. ability in the twentieth century. Nature 337, 217e220. Kennedy, V.S., Breitburg, D.L., Christman, M.C., Luckenbach, M.W., Paynter, K., Kramer, J., Sellner, K.G., Keller, C., Mann, R., 2011. Lessons learned from efforts to References restore oyster populations in Maryland and Virginia, 1990 to 2007. J. Shell. Res. 30, 719e731. Allan, R.P., Soden, B.J., 2008. Atmospheric warming and the amplification of pre- Kraeuter, J.N., Ford, S., Cummings, M., 2007. Oyster growth analysis: a comparison of cipitation extremes. Science 321, 1481e1484. methods. J. Shellfish Res. 26, 479e491. Andrews, J.D., 1973. Effects of tropical storm Agnes on epifaunal invertebrates in La Peyre, M.K., Nickens, A.D., Volety, A.K., Tolley, G.S., La Peyre, J.F., 2003. Environ- Virginia estuaries. Chesapeake Sci. 14, 223e234. mental significance of freshets in reducing Perkinsus marinus infection in Ashton-Alcox, K.A., Bushek, D., Powell, E.N., 2013a. Report of the 2012 Stock Ass- eastern oysters Crassostrea virginica: potential management applications. Mar. sessment Workshop (15th SAW) New Jersey Delaware Bay Oyster Beds. Haskin Ecol. Prog. Series 248, 165e176. D. Munroe et al. / Estuarine, Coastal and Shelf Science 135 (2013) 209e219 219

La Peyre, M.K., Gossman, B., La Peyre, J.F., 2009. Defining optimal freshwater flow for Narváez, D.A., Klinck, J.M., Powell, E.N., Hofmann, E.E., Wilkin, J., Haidvogel, D., oyster production: effects of freshet rate and magnitude of change and duration 2012b. Circulation and behavior controls on dispersal of eastern oyster (Cras- on eastern oysters and Perkinsus marinus infection. Estuar. Coasts 32, 522e534. sostrea virginica) larvae in Delaware Bay. J. Mar. Res. 70, 411e440. Levinton, J., Doall, M., Ralston, D., Starke, A., Allam, B., 2011. Climate change, pre- Paerl, H.W., Bales, J.D., Ausley, J.W., Buzzelli, C.P., Crowder, L.B., Eby, L.B., Fear, J.M., cipitation and impacts on an estuarine refuge from disease. PLoS ONE 6 (4), Go, M., Peierls, B.L., Richardson, T.L., Ramus, J.S., 2001. Ecosystem impacts of e18849. http://dx.doi.org/10.1371/journal.pone.0018849. three sequential hurricanes (Dennis, Floyd, and Irene) on the United States’ Loosanoff, V.L., 1958. Some aspects of behaviour of oysters at different tempera- largest lagoonal estuary, Pamlico Sound, NC. Proc. Nat. Acad. Sci. 98, 5655e tures. Biol. Bull. 114, 57e70. 5660. Lowther, A. (Ed.), 2011. Fisheries of the United States 2010. National Oceanic and Pollack, J.B., Kim, H.C., Morgan, E.K., Montagna, P.A., 2011. Role of flood disturbance Atmospheric Administration, National Marine Fisheries Service, Silver Springs, in natural oyster (Crassostrea virginica) population maintenance in an estuary in Maryland, USA, p. 104. Current Fishery Statistics No. 2010. South Texas, USA. Estuar. Coast. 34, 187e197. LDWF, 2011. Oyster Stock Assessment of the Public Oyster Areas in Louisiana; Seed Powell, E.N., Ashton-Alcox, K.A., Bushek, D., 2012. Report of the 2012 Stock Assess- Grounds and Seed Reservations. Louisiana Department of Wildlife and Fish- ment Workshop (14th SAW) for the New Jersey Delaware Bay Oyster Beds. Haskin eries, Baton Rouge, LA, p. 92. Oyster Data Report Series No. 17. Shellfish Research Laboratory, Rutgers University, Port Norris, New Jersey, p. 155. Mackenzie, C.L., 1996. History of oystering in the United States and Canada, Powell, E.N., Ashton-Alcox, K.A., Kraeuter, J.N., Ford, S.E., Bushek, D., 2008. Long- featuring the eight greatest oyster estuaries. Mar. Fish. Rev. 58, 1e78. term trends in oyster population dynamics in Delaware Bay: regime shifts and May, E.B., 1972. The effect of floodwater on oysters in Mobile Bay. Proc. Nat. Shell. response to disease. J. Shell. Res. 27, 729e755. Assoc. 62, 67e71. Shchepetkin, A.F., McWilliams, J.C., 2005. The regional oceanic modeling system Malakoff, D., 2012. Scientists assess damage from Sandy’s deadly punch. Science (ROMS): a split-explicit, free-surface, topography-following-coordinate oceanic 338 (6108), 728e729. model. Ocean Model. 9, 347e404. Mallin, M.A., Corbett, C.A., 2006. How hurricane attributes determine the extent of Shchepetkin, A.F., McWilliams, J.C., 2009a. Computational kernel algorithms for environmental effects: multiple hurricanes and different coastal systems. fine-scale, multiprocess, longtime oceanic simulations, Handbook of numerical Estuar. Coast. 29, 1046e1061. analysis. Comput. Meth. Atmos. Oceans 14, 121e183. Mann, R., Southworth, M., Harding, J.M., Wesson, J.A., 2009. Population studies of Shchepetkin, A.F., Mcwilliams, J.C., 2009. Correction and commentary for “Ocean the native eastern oyster, Crassostrea virginica, (Gmelin, 1791) in the James forecasting in terrain-following coordinates: formulation and skill assessment River, Virginia, USA. J. Shell. Res. 28, 193e220. of the regional ocean modeling system” by Haidvogel et al., J. Comp. Phys. 227, McCay, B.J., Grant, G., Adelaja, A., 1995. The Status and Conditions of New Jersey’s pp. 3595e3624. J. Comput. Physiol. 228 (24), 8985e9000. Marine Fisheries and Seafood Industries. Report to the New Jersey Department Shumway, S.E., Koehn, R.K., 1982. Oxygen consumption in the American oyster of Agriculture. Rutgers University, New Brunswick, New Jersey, p. 157. Crassostrea virginica. Mar. Ecol. Prog. Ser. 9, 59e68. Michaelidis, B., Hass, D., Grieshaaber, M.K., 2005. Extracellular and intracellular Shumway, S.E., 1996. Natural environmental factors. In: Kennedy, V.S., Newell, R.I.E., acid-base status with regard to the energy metabolism in the oyster Crassostrea Eble, A.F. (Eds.), The Eastern Oyster Crassostrea Virginica. Maryland Sea Grant gigas during exposure to air. Physiol. Biochem. Zool. 78, 373e383. College, College Park, pp. 467e513. Munroe, D., Klinck, J., Hofmann, E., Powell, E.N., 2012. The role of larval dispersal in Voynova, Y.G., Sharp, J.H., 2012. Anomalous biogeochemical response to a flooding metapopulation gene flow: local population dynamics matter. J. Mar. Res. 70, event in the Delaware Estuary: a possible typology shift due to climate change. 441e467. Estuar. Coast. 35, 943e958. Munroe, D., Powell, E.N.P., Mann, R., Klinck, J., Hofmann, E., 2013. Underestimation Wang, Z., Haidvogel, D., Bushek, D., Ford, S., Hofmann, E.E., Powell, E.N., Wilkin, J., of primary productivity on continental shelves: evidence from maximum size 2012. Circulation and water properties and their relationship to the oyster of extant surfclam populations. Fisheries Oceanogr. 22, 220e233. http:// disease, MSX, in Delaware Bay. J. Mar. Res. 70, 279e308. dx.doi.org/10.1111/fog.12016. Warner, J.C., Geyer, W.R., Lerczak, J.A., 2005. Numerical modeling of an estuary: a Najjar, R.G., Walker, H.A., Anderson, P.J., Barron, E.J., Bord, R.J., Gibson, J.R., comprehensive skill assessment. J. Geophy. Res. 110, C05001. http://dx.doi.org/ Kennedy, V.S., Knight, C.G., Megonigal, J.P., O’Connor, R.E., Polsky, C.D., 10.1029/2004JC002691. Psuty, N.P., Richards, B.A., Sorenson, L.G., Steele, E.M., Swanson, R.S., 2000. The Wetz, M.S., Yoskowitz, D.W., 2013. An ‘extreme’ future for estuaries? Effects of extreme potential impacts of climate change on the mid-Atlantic coastal region. Climate climate events on estuarine water quality and ecology. Mar. Poll. Bull. 69, 7e18. Res. 14, 219e233. Wilmott, C.J., 1981. On the validation of models. Phys. Geograp. 2, 184e194. Narváez, D.A., Klinck, J.M., Powell, E.N., Hofmann, E.E., Wilkin, J., Haidvogel, D., zu Ermgassen, P.S.E., Spalding, M.D., Grizzle, R.E., Brumbaugh, R.D., 2013. Quanti- 2012a. Modeling the dispersal of eastern oyster (Crassostrea virginica) larvae in fying the loss of a marine ecosystem service: filtration by the Eastern oyster in Delaware Bay. J. Mar. Res. 70, 381e409. US estuaries. Estuar. Coast. 36, 36e43.