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Final Report Period covered by Report: 05/01/2013 – 9/27/2015

Sea Scallop Research NOAA Grant Number: NA13NMF4540017 Start Date: 5/01/2013

End Date: 9/27/2015

Project Title: Survey of persistent scallop aggregations and an examination of their influence on recruitment using the FVCOM oceanographic model

Investigators: Kevin D. E. Stokesbury, Ph.D. Bradley P. Harris, Ph.D. Changsheng Chen, Ph.D. Pingguo He, Ph.D Liuzhi Zhao, Ph.D. Address: School for Marine Science and Department of Environmental Technology Science University of Massachusetts Dartmouth Alaska Pacific University 200 Mill Road, Suite 325 4101 University Dr. Fairhaven, MA 02719 Anchorage, AK 99508 Email: [email protected] [email protected]

Amount: We were granted 101,933 lbs ($993,844) for research and compensation.

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Introduction

Project Objectives: We intensively video surveyed 13 persistent scallop aggregations on Georges Bank, 11 of which are in closed areas including: Nantucket Lightship, Closed Area I and Closed Area II Access Areas, and the Habitat Area of Particular Concern (HAPC) in the northern portion of Closed Area II. We then used the FVCOM to examine scallop zygote dispersion using these persistent aggregations as source beds. In the proposal we had stated two extreme recruitment events have been observed in the last decade, the first providing extreme recruitment in the Mid- Atlantic in 2003 (probably the 2001 year class) and the second in 2009 providing extreme recruitment in the central portion of the Gulf of Maine and along the northern edge of the great south channel and Georges Bank. However, in 2013-14 we observed a third extreme recruitment event and have added that to the analysis as well. The environmental conditions related to these three events were examined with the FVCOM. The working hypothesis was that specific environmental conditions enabled increased fertilization success producing a scallop larval “cloud” which was carried by currents to Mid-Atlantic, Gulf of Maine and Georges Bank. This research is based on Sinclair’s Member-Vagrant theory where the persistent aggregations allow closure of the life cycle and vagrant zygotes produce high abundance when environmental conditions favor settlement over suitable habitat (Sinclair 1988). Determining these dynamics is fundamental to understanding how the sea scallop resource rebuilds and sustains it’s abundance to support the fishery.

Methods

Video Survey:

Temporally persistent high scallop concentrations were identified previously by Harris (2011) using the annual video survey data from 1999 to 2010 (Figure 1). These aggregations were validated with input from the commercial scallop industry (for a complete description and references refer to Harris 2011).

The proposed stations were sampled between June and July 2013. Using a multi-stage systematic design, we conducted a high resolution video survey of 846 stations in 13 specific areas of Georges Bank (Figure 2). The stations were separated by approximately 1 km (Figure 2).

The SMAST sampling pyramid, supporting four cameras and eight lights, was deployed from a commercial fishing vessel (Stokesbury 2002, Stokesbury et al. 2004; Figure 3). A mobile studio including monitors, DVD recorders, DVRs and laptop computers for data entry and survey navigation (software integrated with the differential global positioning system) was assembled in the vessel’s wheelhouse. The vessel stopped at each pre-determined station and the pyramid was lowered to the sea floor. Two downward facing video cameras mounted on the sampling pyramid provide 2.84 m2 and 0.60 m2 quadrat images of the sea floor (Stokesbury 2002; Stokesbury et al. 2004). Another video camera, mounted parallel to the seafloor, provides a side profile of the quadrat area to aid in species identification. Lastly, a high-resolution digital still camera (12.3 megapixels) collects a single frame image of 1.06 m2 used to identify seed and juvenile scallops, verify species identification, habitat characteristics and sea scallop shell height measurements. Footage of the first quadrat is recorded and then the pyramid is raised so the sea floor can no longer 3 be seen. The vessel drifts approximately 50 m and the pyramid is lowered to the sea floor again to obtain a second quadrat; this is repeated four times. Sampling four quadrats at each station increases the sampled area to 11.36 m2.

Figure 1. Map showing the 13 persistent aggregations on Georges Bank, 11 are located with the closed areas overlaid on the high resolution substrate map (Harris and Stokesbury 2010; Harris 2011). These aggregations contained 20 to 30% of all scallops in Georges Bank between 2003 and 2009. Persistent aggregations are outlined in black with abbreviated names. Nantucket Shoals – North and –South (NS-N, NS-S), Asia Rip (AR), Nantucket Lightship (NL) – South (NL-S) and East (NL-E), Hambone (HB) –South (HB-S), -West (HB-W), East (HB-E), Northern Edge (NE), and Southeast Parts (SEP), -South (SEP-S). The Georges Bank groundfish Marine Protected Areas (NLCA), Closed Area I (CAI), and Closed Area II (CAII). The hashed areas are rotational scallop fishery access zones.

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Figure 2. Locations of the 846 stations sampled by the SMAST video survey from June to July 2013.

The camera view area was increased to account for scallops that lie on the edge of the image. This expansion was reviewed and accepted in the 50th SAW and is based on the average shell height of scallops in the area. The length and width of each image was increased by the mean shell height of measured scallops within the survey area using the equation:

(1) 퐸푥푝푎푛푑푒푑 푉푖푒푤 퐴푟푒푎 = (푙 + 푆퐻̅̅̅̅) × (푤 + 푆퐻̅̅̅̅) where l and w are quadrat length and width and 푆퐻̅̅̅̅ is mean shell height (O’Keefe et al. 2010).

Figure 3. SMAST Video survey pyramid including 8 lights, 3 DeepSea video cameras, a 12.3 megapixel digital still camera and the area of each quadrat.

Video footage of the sea floor was recorded on DVDs and onto a DVR. For each quadrat the time, depth, number of live and dead scallops, and latitude and longitude was recorded. After each survey the video footage was reviewed in the laboratory and a still image of each quadrat was digitized and saved. Within each quadrat, macroinvertebrates and fish were counted and the substrate was identified (Stokesbury 2002). When possible fish and macroinvertebrates were identified to species, otherwise animals were grouped into categories based on taxonomic orders. Counts were standardized to individuals m-2. Sponges, hydrozoa/bryozoa and sand dollars were recorded as present or absent within each quadrat.

Sediments were visually identified following the Wentworth particle grade scale from the video images, where the sediment particle size categories are based on a doubling or halving of the fixed 5 reference point of 1 mm; sand = 0.0625 to 2.0 mm, gravel = 2.0 to 256.0 mm and boulders > 256.0 mm (Lincoln et al. 1992). Gravel will be divided into two categories, granule/pebble = 2.0 to 64.0 mm and cobble = 64.0 to 256.0 mm (Lincoln et al. 1992). Shell debris was also identified.

Mean densities and standard errors of scallops were calculated using equations for a two-stage sampling design (Cochran 1977):

The mean of the total sample is: n  xi  (2) x    i1  n  where n is the number of stations and xi is the mean of the 4 quadrats at station i.

The SE of this 2-stage mean is calculated as:

1 2 (3) S.E.(x)  (s ) n n 2 2 where: s  (xi  x) /(n 1) . According to Cochran (1977) and Krebs (1989) this simplified version of the 2-stage variance is appropriate when the ratio of sample area to survey area (n/N) is small. In this case, thousands of square meters (n) are sampled compared with thousands of square kilometers (N) in the study areas. All calculations use number of scallops per square meter.

The absolute number of scallops in the survey areas is calculated by multiplying scallop density by the total area surveyed (Stokesbury 2002). Estimates of scallop meat weight in grams (w) were derived from shell height (mm) frequencies collected during each survey and shell height to meat weight regressions used in the 50th SAW. The mean meat weight for each 5 mm size bin was multiplied by the total number of scallops in the survey area to estimate the total biomass of scallop meats. Exploitable biomass was calculated using the commercial scallop dredge selectivity equation determined by Yochum & Dupaul (2008).

Scallop Concentration

The scallop concentration (Ca) in each survey station was calculated following Orensanz et al. (1998): 푄 2 ∑ 푛푖 퐶 = 푖=1 , 푎 푁 where Q = number of quadrats (4) at each station, ni is the number of scallops in quadrat i, and N is the total number of scallops at the station. Therefore, Ca gives the mean number of scallops experienced by each individual scallop in the four 2.84 m2 quadrats and thus has units of scallops per scallop (Orensanz et al. 1998). For simplicity we will give Ca in scallops. The Ca values 6 sampled each year were interpolated to a standard 1-km raster grid using Sibson’s Natural Neighbor method (Sibson 1981, Harris and Stokesbury 2010).

Habitat Conditions

The sediment characteristics were extracted from the Harris and Stokesbury (2010) maps of surficial sediment dominance, coarseness, heterogeneity and maximum type. In addition, we determined the degree of spatial correspondence between aggregations with the glacial-lag gravel outcrops mapped by Harris and Stokesbury (2010). Water depth (z) in the study area was mapped to the 1-km raster grid using 401,793 depth sounding records queried from the USA National Ocean Service data portal (www.ngdc.noaa.gov.html) with Sibson’s Natural Neighbor interpolation method (Sibson 1981). The benthic boundary shear stress (τ0s), sediment critical shear stress (τcr) and sediment stability (ξ) conditions in each aggregation based on procedures published in Harris and Stokesbury 2010 and Harris et al 2012.

FVCOM:

A joint research team at the University of Massachusetts-Dartmouth (UMASSD) and Woods Hole Oceanographic Institution (WHOI) has developed an integrated ocean model system for the Gulf of Maine (GoM) and Georges Bank (GB) region (Figure 4). The core of this model system is the Finite-Volume Coastal Ocean Model (FVCOM) developed by Chen et al. (2003) and upgraded by a team effort (Chen et al., 2006a-b, Chen et al., 2011). The integrated model system consists of six models: 1) WRF - the Weather Research and Forecast (WRF) model that is used to create the meteorological forcing (http: //fvcom.smast.umassd.edu/research_ project/NECOFS/index.html); 2) the regional FVCOM -an unstructured-grid Finite-Volume Coastal Ocean Model that features a non-overlapped triangular grid in the horizontal and a hybrid terrain-following coordinate in the vertical, and forced by tides at the open boundary, wind stress, heat flux, net precipitation minus evaporation at the surface, local runoff along the coast, and upstream flow at the Scotian shelf boundary; 3) FVCOM-SWAVE - an unstructured-grid version of the Simulating Wave Nearshore model (SWAN) (Qi et al., 2009); 4) FVCOM-SED - an unstructured-grid version of the USGS structured-grid community sediment model developed by Warner et al. (2008); 5) FVCOM-GBM - a generalized biological module coupled with FVCOM, which allow users to select either a pre- built biological model (such as a NPZ, NPZD, NPZDB, etc.) or construct their own biological model using the pre-refined pool of biological variables and parameterization functions; and 6) an IBM-based scallop population model coupled with FVCOM/FVCOM-SED/FVCOM-SWAVE (Tian et al., 2009a). The integrated model system also includes a non-hydrostatic version of FVCOM (FVCOM-NH) (Lai et al., 2010a, b), which allows us to examine the impact of vertical convection and high-frequency internal waves on the sediment re-suspension at the bottom, processes linked to the benthic dynamics.

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Figure 4. Integrated IBM-based scallop population model and NECOFS system.

The integrated model system has been validated for hindcast simulation for the period 1995-2010 through comparisons with water temperature, salinity, currents, and wave data recorded on moorings and buoys and measured by regional surveys. For scallop studies, two efforts have been made in the past three years. In 2006-2008, as a pilot study, we used a coupled FVCOM and IBM- based scallop population model to conduct a multi-year simulation from 1995 to 2005 to study the interannual variability of sea scallop larval dispersal and population connectivity (Tian et al. 2009b). The results suggest that dispersion and settlement of sea scallop larvae is significantly influenced by the interannual variability of subtidal currents over GB and in the GoM region. The sea scallop larvae were capable of long-distance dispersal from GB down to the Middle Atlantic Bight in the fall period of a year during which the southward shelf-break frontal current was well established. This process is directly related to the variability of the upstream physical conditions driven by remote forcing. A large portion of larvae, which were spawned on the northeast flank of GB, can settle in the Great South Channel (GSC) depending on the temporal and spatial variability of the local physical forcing.

The success of the IBM-based population model depends on the accuracy and reality of the physical model that provides the currents for the three-dimensional larval tracking. We conducted a model-drifter comparison to validate the accuracy and reality of the FVCOM-produced flow field from 1995 to present. A total of 684 drifters have been deployed in the GoM and GB region that returned useful trajectory data (Jim Manning, personnel communication). A non-parametric Kolmogorov-Smirnov test was used to judge “good” and “bad” comparisons (Van Sebille et al., 2009). The results show that 75% of drifters were in good comparison with the model-predicted 8 drifter trajectories. This validation experiment provides us with confidence in using the FVCOM- produced flow field to study the impact of physical processes on the interannual variability of sea scallop recruitment over GB/GSC.

Results and Discussion

Since 2003, two extreme recruitment events have occurred within the scallop stock assessment area (Figure 5). In 2003, about 12 billion recruits were observed in the Mid-Atlantic, while the total scallop population on Georges Bank and in the Mid-Atlantic was about 21 billion scallops (Stokesbury et al. 2004; Stokesbury et al 2011). In 2014, a recruit abundance of 31 billion scallops was observed on Georges Bank; the largest recruit abundance ever recorded and nine times more than the number of adult scallops. In 2009, close to half a billion recruit sized scallops were observed on several banks and ledges in the Gulf of Maine and northern Georges Bank (Figure 5). Scallop densities on the banks and ledges of the Gulf of Maine, totaling approximately 300 km2, ranged from 2.6 to 18.8 times higher than in high density areas on Georges Bank (Stokesbury et al. 2010). Less than 1% of the scallops had shell heights greater than 100 mm, suggesting a small adult biomass in these areas prior to 2009 (Stokesbury et al. 2010).

Figure 5. The number of recruits (scallops less than 75 mm shell height) observed by the SMAST scallop video survey in 2003, 2009, and 2014. Hatched marks identify portions of closed areas that are periodically open to scallop fishing.

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Video Survey:

(Note: for all the following Tables and Figures: Persistent aggregations are outlined in black with abbreviated names., Asia Rip (AR), Great South Channel (GSCN), Nantucket Shoals (MR) (these were formerly NS-N and NS-S), Nantucket Lightship (NL) – South (NL-S1 and 2) and East (NL- E), Hambone (HB) –South (HB-S), -West (HB-W), East (HB-E), Northern Edge (NE), and Southeast Parts (SEP), -Southern flank (SF). The Georges Bank groundfish Marine Protected Areas (NLCA), Closed Area I (CAI), and Closed Area II (CAII). The hashed areas are rotational scallop fishery access zones.)

The persistent aggregations of scallops were defined areas with a continuing number of large scallops between 2003 and 2010. The 2013 survey revealed that some of these areas had average small shell heights smaller than sexually mature scallops (<90 mm). This suggests that recruitment and or mortality events shift the population dynamics of these areas annually (Table 1). In total there were about 0.378 billion scallops in these areas while on average there are about 4 billion scallops on Georges Bank (between 2003 and 2012). The aggregations cover only 842 km2 while the Georges Bank scallop resource covers 28,000 km2. Thus about 10% of the abundance is within these areas which represent 3% of the scallop resource area (Table 1). This abundance equals 7000 mt of scallop meat weight (Table 2).

Table 1. Results from the persistent aggregation 2013 SMAST video large camera video data including quadrat area sampled, mean shell height of scallops observed (mm), number of scallop shell heights measured, mean number of scallops per m2, number of stations sampled, standard error, coefficients of variance, an estimate of the number of scallops in the area and mean depth (m).

Shell Height Millions Area mean n quad area per m2 stations SE CV% Area km2 scallops Depth GB AR 90.8 42 3.158 0.12 29 0.039 32.08 29 3.6 53 GB NE 105.9 2064 3.213 0.57 317 0.043 7.54 317 179.9 55 GSCN 85.0 310 3.137 0.48 62 0.084 17.50 62 29.6 49 HB 104.1 56 3.206 0.09 54 0.018 19.33 54 5.0 64 HBE 101.2 28 3.196 0.05 48 0.012 22.48 48 2.5 62 HBS 126.4 11 3.288 0.04 21 0.012 31.23 21 0.8 72 HBW 115.3 26 3.247 0.04 56 0.040 8.92 56 2.2 68 MR 67.3 116 3.074 0.21 50 0.034 16.17 50 10.5 54 NL 75.8 199 3.105 0.29 63 0.041 14.11 63 18.2 65 NLS_1 86.3 30 3.142 0.21 12 0.086 40.33 12 2.5 68 NLS_2 117.4 225 3.255 0.53 37 0.102 19.30 37 19.5 67 SEP 68.8 424 3.080 0.59 70 0.054 9.18 70 41.1 87 SF 51.9 710 3.020 2.74 23 0.331 12.06 23 63.1 84

Table 2. Results from the persistent aggregation 2013 SMAST video large camera video data estimates of total and exploitable biomass (MW = mean scallop meat weight (g) and total weight of scallops in millions of pounds (LBS) and metric tons (MT).

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Estimation of Total Biomass Estimation of Exploitable Biomass Area mean mwt mill lbs in mt SE mean mwt mill lbs in mt SE GB AR 21.0 0.16 75 24.0 37.4 0.11 50 16.1 GB NE 25.7 10.20 4627 348.8 35.6 8.06 3658 275.8 GSCN 15.9 1.04 471 82.5 27.1 0.50 229 40.1 HB 26.2 0.29 131 25.2 39.7 0.23 102 19.8 HBE 25.2 0.14 63 14.2 37.7 0.11 49 11.1 HBS 43.6 0.08 36 11.4 58.1 0.07 33 10.2 HBW 32.7 0.16 71 15.3 42.4 0.13 59 12.8 MR 9.0 0.21 94 15.3 22.0 0.08 34 5.5 NL 11.8 0.47 215 30.3 28.3 0.25 114 16.1 NLS_1 16.2 0.09 41 16.6 30.6 0.06 27 11.0 NLS_2 35.0 1.51 683 131.9 43.0 1.30 588 113.4 SEP 8.6 0.78 352 32.3 24.0 0.46 208 19.1 SF 3.4 0.47 212 25.6 20.8 0.13 58 7.0

Both the density and concentrations of scallops within the aggregations areas were patchy (Figures 6 and 7) with areas of high numbers and areas with no scallops at all. Scallops aggregate on a number of different scales from cm to 100 of kilometers and this demonstrates that pattern on the scale of km.

Figure 6. The density distribution of sea scallops within each of the persistent aggregations from the 2013 SMAST video large camera video data. 11

Figure 7. The concentration distribution of sea scallops within each of the persistent aggregations from the 2013 SMAST video large camera video data (details on the concentration calculation can be found in Harris 2011).

FVCOM - Environmental Factors:

We have examined the distributions of residual flow field and water temperature over Georges Bank (GB), Great South Channel (GSC) and Nantucket Shoal (NS) in June and July 2013 during which scallop surveys were carried out. In June, 2013, the residual flow field on GB was characterized by a well-defined clockwise circulation gyre, with an along-isobath velocity of ~20- 30 cm/s on the northern flank (Figure 8). A high-dense aggregation area of scallops in the northeast edge was located in the current bifurcated zone where upwelling was strong. The survey areas on the southern flank were located in the relatively colder water zone between tidal mixing and shelf- break front where the current was predominated by the along-isobath southwestward flow. The GSC area was the intersection region of the southward flow on the western slope, clockwise recirculation flow on the eastern slope, cyclonically turned flow across the channel in the northern GCS and westward flow on the southern shelf. They were all in the upwelling active zone.

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Figure 8. Distribution of the NECOFS-predicted residual velocity and water temperature averaged vertically through the water column above the 100 m or shallower for June, 2013.

Figure 9. Distribution of the NECOFS-predicted residual velocity and water temperature averaged vertically through the water column above the 100 m or shallower for July, 2013.

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In July, 2013, the flow over Georges Bank, GSC and NS was significantly intensified as the stratification increased (Figure 9). The flow pattern remained unchanged, but the flow intensification enhanced the upwelling in the current separation zone.

The model results show that in June, 2013, there was a strong divergence flow near the bottom at the northern edge and convergence flow towards the top of the bank over the region shallower than 40-m isobaths. It was clear that the divergence at the edge produced an upwelling, which brought the high nutrient water in the deep region onto GB. (Figure 10). The on-bank separated flow transported the nutrients to the area where the dense aggregation of scallop were. One also could trace the colder water on the southern flank back to the northeast edge, which suggested that the nutrients from the deep Gulf of Maine were upwelled onto GB and then transported to the southern flank where the scallop survey was conducted. This pattern remained unchanged in July, 2013, except that the water temperature increased by a few degrees as a result of surface heating.

Figure 10. The distributions of residual velocity and water temperature near the bottom over the eastern Georges Bank for June (left panel) and July (right panel), 2013, respectively.

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Figure 11. The distributions of residual velocity and water temperature near the bottom in the GSC, NS and western GB regions for June (left panel) and July (right panel), 2013, respectively.

In the GSC, NS and GB region, a southward flow existed near the bottom in the deep GSC region, which advects nutrients in the deep GCS region to the survey area (Figure 11). There were also noticeable upward flow on the western and eastern slope of the GCS, which also could bring nutrients onto NS and the western region of GB. The model results clearly show that the scallop survey areas in summer of 2013 were in the areas with the sufficient nutrient supplies from the deep Gulf of Maine.

Figures 12 through 17 show the habitat conditions calculated from the video survey observations that were combined with the data calculated from the FVCOM to produce the mean maximum, bi- weekly benthic boundary shear stresses (N m-2) (Figure 18), the sediment stability index (shear stress / critical shear stress) (Figure 19) in the Georges Bank scallop aggregations. The combination of sediment type and depth produce a stable substrate even in areas of high benthic boundary shear stress.

Scallop Larval Distribution

Scallop larvae were released from 13 aggregations. The potential number of zygotes released from these aggregations was estimated based on the density data (for example in the location of GB_AR, the density in AGR 2013 summary table is 0.12, there are 56 stations for AR in file AGR. Then the initial scallops would be 0.12 times 25 million for each of the 56 points). In an average spawn a female releases 50 million eggs and the sex ratio is 1:1 so 25 million seems a reasonable estimate assuming 100% fertilization success (i.e. extremely favorable conditions; MacDonald 1986).

The scallop spawning peak day is September 15 (Thompson et al. 2014). The deviation range for normal distribution function (Equation 4 in Tian et al., 2009 a) of scallop spawning is 7 days. The model was integrated begin August 1 for 13 aggregations in 2001, 2007 and 2012. The Scallop spawning is simulated at each time step of 2 minute with a daily output.

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Figure 12. Map showing the sediment coarseness in the Georges Bank scallop aggregations.

Figure 13. Map showing the dominant sediment type in the Georges Bank scallop aggregations.

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Figure 14. Map of Georges Bank sediment composition and associated critical shear stress. Dominant sediment precedes maximum sediment abbreviations which are in parentheses. Sediment compositions were noted as d50 (dmax). For example, S(C) refers to sand dominated sediments where the largest sediment type observed was cobble. The numbers are the associated sediment critical shear stress level (N m-2).

Figure 15. Map showing the sediment heterogeneity in the Georges Bank scallop aggregations.

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Figure 16. Map showing the largest sediment type in the Georges Bank scallop aggregations.

Figure 17. Map of the water depth (meters) in the Georges Bank scallop aggregations.

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Figure 18. Map showing the mean maximum, bi-weekly benthic boundary shear stresses (N m-2) in the Georges Bank scallop aggregations.

Figure 19. Map of the sediment stability index (shear stress / critical shear stress) in the Georges Bank scallop aggregations.

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Figure 20. The distribution of scallop larval settlement release from the 13 persistent aggregation locations on Georges Bank in 2001 based on the densities observed in 2013 compared to the locations where the 2003 extreme recruitment was observed (blocks represent areas of at least 10 recruited scallops in 2003 per station from the SMAST video survey).

Figure 20. The percent contribution of the 13 scallop persistent aggregations on Georges Bank from 2001 to areas of at least 10 recruited scallops in 2003 per station from the SMAST video survey. 20

Figure 22. The distribution of scallop larval settlement release from the 13 persistent aggregation locations on Georges Bank in 2007 based on the densities observed in 2013 compared to the locations where the 2009 extreme recruitment was observed (blocks represent areas of at least 10 recruited scallops in 2009 per station from the SMAST video survey).

Figure 20. The percent contribution of the 13 scallop persistent aggregations on Georges Bank from 2007 to areas of at least 10 recruited scallops in 2009 per station from the SMAST video survey. 21

Figure 24. The distribution of scallop larval settlement release from the 13 persistent aggregation locations on Georges Bank in 2012 based on the densities observed in 2013 compared to the locations where the 2014 extreme recruitment was observed (blocks represent areas of at least 10 recruited scallops in 2014 per station from the SMAST video survey).

Figure 20. The percent contribution of the 13 scallop persistent aggregations on Georges Bank from 2012 to areas of at least 10 recruited scallops in 2014 per station from the SMAST video survey. 22

Conclusions:

From this analysis it appears unlikely that scallop larvae from the Georges Bank persistent aggregations contributed to the extreme recruitment event observed in the Mid-Atlantic in 2003. The aggregations located in the Nantucket Lightship and Closed Area II appear to have contributed to the 2009 Gulf of Maine extreme recruitment event. The aggregations located in Closed Area II appear to have been the main source of scallop larvae for the 2014 Georges Bank recruitment. Interestingly the Southern Flank and Southeast parts seemed to contribute more than the larger aggregation in the Northern Edge. This suggests that placement in the appropriate currents is more important than abundance. This relates well to Sinclair’s member-vagrancy theory. Overall it appears that most of the larvae generated on Georges Bank remains on Georges Bank.

It is important to note that this is a theoretical approach to the stock-recruitment problem in scallops. We have assumed 100% fertilization success which is probably highly unlikely, further we assume no movement on the larvae to shift position in the water column and we make no predictions of larval mortality, which is likely extreme and variable with space as suggested by Cushing’s match-mismatch theory (Cushing 1981). Moving forward in this analysis the next step in examining connectivity will be to compare this result to other RSA funded work looking at genetic markers (Grabowski, and Harris 2014 RSA grant).

How the Priorities listed in the 2013 RSA RFP were addressed:

HIGHEST PRIORITY 1. An intensive industry-based survey of each of the access areas. This survey was conducted using commercial scallop fishing vessels with full participation of the fishing industry. The research provided a precise and accurate estimate of the highest density aggregations of scallop, spatial distribution, size distribution in three of the closed areas on Georges Bank. Abundance, distribution and biomass estimates, along with audited raw data of scallop counts and measurements for aggregations within Closed Area II were provided to the NEFSC and Scallop Plan Development Team (PDT) on time for inclusion in yearly management plan (emailed to D. Boelke NEFMC and D Hart NMFS on 4 August 2013). Emily Keiley presented the results of these surveys including estimates on the density and size frequency in CAII south, the northern aggregation in CAII (Tables 1 and 2, Figure 2) and the aggregations in CAI compared to previous years abundance (Table 3, Figure 3) to the Scallop PDT on 19th August 2013 in Falmouth, MA (presentation emailed to D. Boelke NEFMC 16 August 2013). These data were presented to the SARC-59 Sea scallop stock assessment NEFSC, Woods Hole, MA on 17 March 2014 by K Stokesbury. 3. An intensive industry-based survey of areas that may be candidate access areas in the future. This research increased our understanding between scallop adult spawning densities and recruitment. The FVCOM described the conditions and locations required for increased scallop recruitment. This could be used to direct resource surveys and candidate closed areas.

MEDIUM PRIORITY 23

1. Other resource surveys to expand/enhance survey coverage. This project surveyed scallop aggregations on Georges Bank on a high resolution scale (1-km grid) that has previously never been obtained.

OTHER PRIORITIES 1. Scallop biology, including studies aimed at understanding recruitment processes, growth and natural mortality. The survey and FVCOM modeling could lead to an understand recruitment processes. Data from this study will provide information on the small scale spatial distribution of adult scallops in high densities, which is critical to reproductive success and impossible to determine using dredge surveys. The fundamental biological question for scallops is “What adult population produces the next generation?” This proposal directly examined this question but as usual it lead to more questions. The next step would be to compare this result to other RSA funded work looking at genetic markers (Grabowski, and Harris 2014). 3. Habitat characterization research. The survey provided detailed habitat maps of the seafloor within the persistent scallop aggregations on Georges Bank. A list of the species observed for each aggregation is provide in Table 3

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Table. 3. Counts of animals from the large camera data set for the 13 persistent scallop aggregations observed in 2013.

AR GSCN HB HBE HBS HBW MR NE NL NLS_1 NLS_2 SEP SF Total scallops 45 372 64 32 11 28 126 2311 227 32 254 506 762 4770 seaStars 56 66 17 14 3 15 51 468 85 11 53 49 11 899 detritus 9 8 132 68 0 0 3 171 24 33 19 40 33 540 bHydra 0 4 17 16 38 97 2 151 3 0 64 20 0 412 hermitCrabs 3 2 21 1 7 27 1 51 9 5 21 24 0 172 euphausids 51 16 19 0 0 0 4 0 39 23 0 1 0 153 sandDollars 0 0 28 1 11 16 1 10 33 17 14 11 0 142 crabs 7 5 43 1 1 18 7 5 5 2 27 1 0 122 sponges 1 0 3 1 0 1 0 78 5 5 4 0 0 98 hake 0 0 22 5 2 5 1 0 28 6 22 2 1 94 skate 5 12 6 15 5 7 3 22 12 1 2 0 1 91 tunicate 0 0 2 0 1 3 0 67 1 0 0 2 0 76 ad 0 3 2 7 8 0 0 7 2 0 8 0 25 62 clams 0 0 0 0 0 0 0 43 0 0 0 0 0 43 anemone 0 0 11 2 13 12 0 0 0 0 0 0 0 38 sculpin 0 2 1 1 0 2 1 20 0 0 0 0 0 27 skateEggCase 0 0 1 2 8 1 0 10 1 0 0 0 0 23 ctenophores 0 0 0 0 0 0 0 0 0 0 0 7 15 22 flounder 0 0 0 2 0 0 0 13 2 0 0 1 1 19 moonsnailEggCase 2 0 1 1 1 0 2 0 2 1 6 0 0 16 buccinum 0 0 0 0 0 2 0 9 0 0 0 0 0 11 dogfish 1 0 2 1 1 1 0 0 3 0 1 0 0 10 oceanPout 0 2 2 0 0 0 4 0 0 0 1 0 0 9 mussels 0 7 0 1 0 0 0 1 0 0 0 0 0 9 moonsnail 0 0 1 2 1 1 0 3 1 0 0 0 0 9 silverHake 1 0 1 0 1 0 1 0 2 0 1 0 0 7 holes 0 0 1 0 0 0 0 0 2 0 0 1 1 5 haddock 0 0 0 0 0 0 0 4 0 0 0 0 0 4 unidentifiedFish 0 1 0 0 0 0 0 2 0 0 0 0 0 3 sandlance 0 2 0 0 0 0 0 0 1 0 0 0 0 3 cod 0 0 0 0 1 0 0 2 0 0 0 0 0 3 eel 0 0 1 0 0 0 0 0 0 0 1 0 0 2 urchin 0 0 0 0 0 0 0 1 0 0 1 0 0 2 filo 0 0 0 0 0 0 0 2 0 0 0 0 0 2 Sum of otherFish 0 0 0 0 0 0 0 0 1 0 0 0 0 1 herring 0 0 0 1 0 0 0 0 0 0 0 0 0 1 seaweed 0 0 0 0 0 0 0 0 0 0 1 0 0 1 mouse 0 0 0 0 0 0 0 0 0 0 0 1 0 1 lobster 0 0 0 0 0 0 1 0 0 0 0 0 0 1 25

Figure 20. The density distribution of sea anemones in 13 persistent scallop aggregations observed in 2013.

Figure 21. The density distribution of Bryozoa/Hydrozoa in 13 persistent scallop aggregations observed in 2013.

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Figure 22. The density distribution of Filograna in 13 persistent scallop aggregations observed in 2013.

Figure 23. The density distribution of crabs in 13 persistent scallop aggregations observed in 2013.

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Figure 24. The density distribution of hermit crabs in 13 persistent scallop aggregations observed in 2013.

4. Scallop and area management research. The survey provided information that could assist in actively managing spat collection and seeding of sea scallops.

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