Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 1 

# Environment and Resources 55 Wheeler Street, Cambridge, MA  Tel: 617 492 - 7100  www.abtassociates.com 

### Date November 18, 2009 To Tom Born, Josh Hall, and Erik Helm, U.S. Environmental Protection Agency From James Palardy, David Mitchell, and Elena Besedin, Abt Associates, Inc. Subject WA 2 - 09 Task 4 : Eco path simulations of Chesapeake Bay 

## 1 Introduction The operation of cooling water intake structures (CWIS) results in the impingement and entrainment (I & E) of aquatic organisms. The effects of these I & E losses may b e a substantial decrease in populations of commercial, recreational, and forage fish species (Blaber et al. 2000) . One way to estimate the value of these losses is to apply traditional single - species fisheries management techniques, such as the Thompson Bell model of fisheries yield (Ricker 1975) , to generate estimates of forgone fish productivity. However, these single - species approache s , based solely on species - specific demographic parameters, fail to consider the effects of ecological interactions among species ( e.g., predation, competition) that occur within food webs (May et al. 1979; NMFS 1999) . Consequently, such approaches cannot assess community -level effects of I & E on the overall productivity and community stability of the ecosystem (Link 2002a; Link 2002b; Leslie and McLeod 2007) . Thus, attempts to assess the effects of I & E reductions on fish stocks should not be based solely upon single - species stock estimates (Walters et al. 2005) , but should also include an ecosystem-based approach to help identify nonlinearities and potential non-intuitive effects that may result from changes to individual populations (Pauly et al. 2000; Christensen and Walters 2004) . This document describes the application of a common ecosystem-based management tool (Ecopath with Ecosim) to estimate effects of I & E reductions in the Chesapeake Bay. It describes methodology, parameter estimation, necessary background data, and presents results of the ecosystem response to I & E reductions on aquatic species. As such, the document contains the following sections:  The Chesapeake Bay Fisheries Ecosystem Model  Model Results  Model Limitations and Future directions  Key Findings 

## 2 The Chesapeake Bay Fisheries Ecosystem Model The Chesapeake Bay Fisheries Ecosystem Model (CBFEM) is an Ecopath with Ecosim (EwE) model that describes the trophic structure and biomass pools of aquatic organisms within Chesapeake Bay. Because it was constructed using EwE software, the CBFEM allows fisheries managers to quantitatively evaluate trophic interactions within Chesapeake Bay, thereby allowing modelers to estimate the outcome of ecosystem-based fisheries management policy options. For this assessment, I & E losses were input into the CBFEM, and community shifts were examined under various I & E scenarios. 

# memorandumAbt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 2



### 2.1 Model Background The EwE approach to fisheries models consists of two essential components: 1) Ecopath, which uses trophically -linked biomass pools to create a static, mass -balanced snapshot of the ecosystem, and 2) Ecosim, which estimates ecosystem dynamics through time (Christensen and Pauly 1992; Pauly et al. 2000; Christensen and Walters 2004) . 2.1.1 Ecopath Ecopath routines within the model are based upon the trophic interactions of biomass pools . These biomass p ool s may represent a single species, an ontogenetic age category within a species, or a group of species with similar ecological characteristics. To obtain accurate model output, all major components of biomass and major trophic pathways must be represented within these biomass pools. For each biomass pool , biomass accumulation, net migration, diet compositio n, and total fisheries catch must be known. Additionally, three of the following input parameters must be obtained: biomass, productivity per unit biomass, consumption per unit biomass, and ecotrophic efficiency (the fraction of production that is consumed or harvested within the ecosystem) . Ecopath then uses these input data to construct a quantitative network diagram of energy flow for the system for an arbitrary time period (generally 1 year) (Figure 1). For additional details , see Christensen and Pauly (1992) and Christensen and Walters (2004) . 2.1.2 Ecosim Using inputs from Ecopath, the Ecosim routines describe the interactions among ecosystem biomass pools through time , thereby facilitating policy exploration at the ecosystem level. Ecosim operates by: 1) transforming base Ecopath parameters into a system of differential equations that can be varied through

Figure 1: Flow diagram of biomass pools (circles) and trophic interactions (lines) in the CBFEM. Lines connecting two biomass pools indicate energy flow (i.e., predation/grazing) . Biomass inputs enter the lower half of the circle, while flows exiting do so on the upper half. The trophic level of each biomass pool is estimated by summing across food sources the product of food item trophic level and the proportion of the food item to the overall diet. Pools are arranged along the x - axis to minimize line overlap.Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 3



### time, 2) introducing a vulnerability parameter that expresses h ow predation mortality varies through time as a function of local carrying capacities, and 3) by parameterizing and ground -truthing these equations based on time - series analysis using a Monte Carlo optimization algorithm. Following optimization, Ecosim allows the modeler to vary all parameters through time, either individually, or in groups. Parameters may be altered gradually ( e.g., , a 10 -year phase - out of a particular fishery or fishing technique) or as one -time forcing functions (e.g., , a poor recruitment year for a particular species or group of species) to explore various policy scenarios. For additional details, see Pauly et al. (2000) and Christensen and Walters (2004) . 

### 2.2 Model Construction The CBFEM was first conceived in October 2001 (Sellner et al. 2001) , and has been under continuous development by the NOAA Chesapeake Bay Office , in collaboration with the Fisheries Center at the University of British Columbia , since then (Christensen et al. 2008) . The current model contains 45 biomass pools, representing all important components of aquatic biomass within th e Chesapeake Bay (Figure 1, Appendix 1). To obtain necessary Ecopath inputs, diet compositions were based upon advice from local experts (Sellner et al. 2001) , retrieved from FishBase (Froese and Pauly 2009) , or obtained from the literature (see Appendix A of Christensen et al. 2008 for a complete list of sources) . To tune the model’s parameters to time - series data, data f rom 92 time series are included in the model (Christensen et al. 2008) . Thes e time - series include catch monitoring, survey and stock assessment data , and extend from 1950 - 2002. Consequently, the model has been tuned to more than 50 years of data, and consequently is able to produce important time - series trends in individual fish biomass stocks (Christensen et al. 2008) . 

### 2.3 Model Parameterization for I & E Losses As designed, the CBFEM does not explicitly include stock reductions due to I & E . To incorporate these losses into the CBFEM, species - specific estimates of I & E for the Chesapeake Bay were extracted from regional estimates, then incorporated into the EwE model as a targeted f ishery of known size and biomass pool composition . All I & E fishery losses were treated as bycatch, and biomass was returned to the ecosystem via the detrital loop. After applying this fishery, Ecopath parameters were re -f it to historical time - series stock data with a Monte Carlo optimization procedure. 2.3.1 Estimating I & E losses To incorporate I & E losses into the CBFEM, estimates of lost biomass within the mid -Atlantic region were obtained from model output (Stratus Consulting, 2009). These regional - scale I & E estimates were converted to Chesapeake Bay I & E estimates by scaling regional losses by the proportion of regional CWIS average intake flow (AIF) occurring within Chesapeake Bay (27.75%) . After obtaining species - specific loss estimates due to I & E (67 species/groups ), these values were converted from total losses (in lbs) from the entire bay to kg/km2 

### [using 10,000 km2 

### as the area of the bay, consistent with estimates used in the construction of the CBFEM (Christensen et al. 2008) ] then apportioned into the 45 biomass pools within the CBFEM (Appendix 1) . First, species -level matches between CBFEM biomass pools and I & E model output ( 20 pools, 15 species) were identified . For species with multiple biomass pools, losses were apportioned as the proportion of biomass present in each pool . As such, young - of-year (YOY) biomass losses always represented less than 1% of total I & E biomass loss. Next, biomass pools representing multiple species were matched with I & E output from relevant species (6 pools, 33 species) . These matches were identified either according to group identities stated within CBFEM documentation (Christensen et al. 2008) , or by matching trophic interactions among biomass pools to the known feeding ecology of species with estimated I & E losses (Froese and Pauly 2009; Sea Around U s 2009) . For 17 biomass pools, no equivalent data could be extracted from I & E model output. Many of these biomass pools, however, represented groups of species for which I & E isAbt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 4



### not ecologically relevant (e.g., sea birds, detritus ), unlikely due to natural history (e.g., sandbar shark, adult oysters ), or of little demographic importance due to high reproductive rates and short life -cycles (e.g., zooplankton, phytoplankton, ctenophores, sea nettles). Together, the 26 biomass pools with matched I & E los ses accounted for 87% of all estimated I & E losses. To ensure relevant amounts of biomass were removed from the CBFEM at each timestep, remaining losses were apportioned relative to the size of biomass pools. 2.3.2 Generating the I & E fishery Estimates of I & E were implemented within the EwE model by creating a new fishery. Within the EwE framework, fisheries are classified by the biomass of all captures, broken down by target species. This captured biomass must in turn be distributed between landings (i.e., c atch that is kept, taken ashore and sold) and bycatch (i.e., catch that is returned to the ecosystem) (Christensen and Walters 2004) . Finally, the fate of the bycatch must be specified. The I & E “fishery” was defined as follows: the magnitude of the fishery was determined according to section 2.3.1 , with 100% of the catch classified as byc atch, and 100% of this bycatch returning to the ecosystem via the detrital loop (i.e., all I & E losses were classified as fatalities) . Consequently, I & E biomass was allowed to be recycled within through the ecosystem rather than being removed. 2.3.3 Reparama

terizing Ecopath The original CBFEM, tuned to fisheries data from 1950, is able to produce trends in fish biomass stocks (Christensen et al. 2008) . However, because the I & E ‘fishery’ was not included during model optimization simulations , we re - optimized basic Ecopath parameters to ensure their ability to predict the con sequences of reductions in I & E. To do this, we used a Monte Carlo optimization procedure to search relevant combinations of Ecopath parameter space (i.e., parameters that meet the assumptions of mass balance) that produced a better fit of model output to time - series data including I & E. Models were conditioned on fisheries data from 19 5 0 - 2002, including I & E data beginning in 1980, when AIF from CWIS were > 90% current values in the Chesapeake Bay (based on operational dates for power generating station s). During this time period, we assumed that I & E losses did not vary. 

### 2.4 Simulation Scenarios and Model Output We simulated four scenarios using the modified CBFEM. For all simulations, parameters remain ed c onstant unless noted, simulation began in 1950 (start of time - series data), and proceeded 40 years beyond available time - series data . These scenarios included:  Baseline: I & E remained at current levels throughout the simulation  Immediate I & E reduction: I & E is set to zero throughout the simulation  Gradual I & E reduction: I & E decreases at 2 % per year  I & E increase: I & E increases at 2% per year The value used for I & E reduction and increase (i.e., 2%) do es not represent any particular policy scenario. These I & E treatments were included to provide a useful estimate of the ecological effects of gradual changes in the magnitude of CWIS AIF in the Chesapeake Bay . Model output represents biomass within each pool on an annual basis , and was tested for community -level and biomass -pool changes 5, 10, 1 5, 25, and 40 years following the end of time - series data (i.e., years after 2002) . All statistics were calculated in R (R Development Core Team 2008) , and null hypotheses were rejected for  < 0.05. 2.4.1 Community

-leve l changes Model output was tested for changes to the Chesapeake Bay community by I & E treatment and through simulated time using two measures . First, the effects of I & E treatment and time on overall community structure were tested using permutational multivariate analysis of variance (PERMANOVA) on BrayCurtis distances (Anderson 2001; McArdle and Anderson 2001; Oksanen et al. 2008) . This procedureAbt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 5



### describes how multivariate variability in data can be attributed to various I & E treatments and the covariate of simulation time . Second, biomass pool data were used to generate values of Simpson’s Index of Diversity (D) : 

# 

 

###   S i i 

### p D 1 2 

### 1 where S is the number of biomass pools, and p i 

### is the proportionate biomas s of biomass pool i . Values of D range between 0 and 1, and represent the probability that two individuals sampled at random will be from different biomass pools (Magurran 2004) . Effects of I & E (factor) and time (covariate) on Simpson’s Index were tested using analysis of covariance (ANCOVA) (Gotelli and Ellison 2004) . Prior to analysis, the assumptions of ANCOVA were tested by visually inspecting plots of model residuals against predicted values , and testing for significant interaction terms between the covariate and main factor. 2.4.2 Changes in individual biomass pools To document changes in biomass pools through time, we standardized all reported output to the baseline simulation (to control for non -equilibrium conditions at the beginning of simulation), and reported changes as absolute and proportionate increases in biomass. In reco gnition of model variability and uncertainty, only differences that exceed an arbitrary (but minimal) threshold are reported. For proportionate biomass data, this threshold was set as a 0.25% deviation from baseline model runs. For absolute changes to biomass, the threshold was set at 1 kg/km2 

### . Because the surface area of suitable habitat for many fish stocks within the Chesapeake Bay is approximately 1 0 , 000 km2 

### (Christensen et al. 2008) , a change of 1 kg/km2 

### represents a bay-wide change of 1 0 metric tons , or approximately 2 2 , 000 lbs . 

## 3 Results 3.1 Model Inertia Due to residual model inertia from previous policy choices and fisheries pressure, biomass pools in the CBFEM were not in equilibrium at the end of time series data ( when I & E was altered in the model ) . Because ecological systems with major fisheries are highly dynamic, mo del inertia is not surprising, nor does it reduce the effectiveness of EwE in estimating average effects of I & E losses within the Chesapeake Bay. As a c onsequence of model inertia , biomass pools in the baseline simulation (as well as all I & E treatment simulations) did not approach equilibrium values for 20 years of simulation (Table 1, Appendix 2). Although the pool magnitude of plankton did not change appreciably, the magnitude of many finfish biomass pools at equilibrium was substantially different th an the magnitude of these same pools at the end of time series data . At equilibrium, several biomass pools had increases of >10% , including young of year and adult bluefish, catfish, and resident striped bass . Pools with decreases of >10% included alewife & herring, Atlantic croaker, hard clam, sandbar shark, spot, migratory striped bass and weakfish (Table 1, Appendix 2). 

### 3.2 Community- level changes The effect of simulation time on Simpson’s Index was statistically significant, explaining 50% of the variability in the data (Table 2). Conversely, I & E treatment had no detectable effect on Simpson’s Index (Table 2). Interestingly, despite substantial shifts in individual biomass pools through time, values of Simpson’s Index only ranged between 0.849 and 0.861. The minimal range observed suggests that the diversity of the Chesapeake Bay (as represented by the CBFEM) is stable through time (Magurran 2004) , and that the significance of simulation time in the statistical model would not be biologically relevant (Gotelli and Ellison 2004) .Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 6



### The stability in community diversity is supported by PERMANOVA analysis of overall community structure. Total changes within the ecosystem were minimal (Table 3), despite a detectable influence of simulation time on community structure. This suggests that large - scale shifts in the ecosystem do not occur, and that the overall ecosystem remains relatively stable. 

### 3.3 Changes to individual biomass pools Due to the substantial changes in biomass through simulation time attributable to model inertia (Table 1), all I & E treatment effects were standardized to changes from baseline biomass values. Because all demographic parameters within the model were identical, these changes can only be attributed to differences in the I & E treatment proscribed. 3.3.1 Relative biomass changes within pools Measured as a percent change from baseline data, the effects of reducing I & E on biomass pools within Chesapeake Bay are relatively small. The magnitude of these changes, however, are in direct proportion to the reduction of I & E mortality imposed by various simulations (Table 4). How ever, due to the indirect nature of food web interactions and time delays due to baseline model inertia, reducing I & E does not result in an increase in biomass for all species (Table 4). 3.3.1.1 Change with elimination of I & E 

### When all I & E losses were removed from the CBFEM, 16 of 45 biomass pools showed changes > 0.25% (relative to baseline data) after 40 years of simulation (Table 4). The fact that these changes are driven through trophic interaction is affirmed by the fluctuating directionality of change observed in some biomass pools. For example, 5 years following the cessation of I & E losses, Spot populations were 0.36% higher than expected under baseline data (Table 4). Over time, however, trophic interactions led to a decline in Spot populations relative to baseline data, with a decrease of 0.31% after 40 years of simulation. These changes through time appear to be a consequence of time lags in the accumulation of biomass in other pools which prey upon spot (e.g., bluefish, striped bass and weakfish) . Interestingly, the majority of commercially important fisheries species show population increases of > 0.25 % (Table 4). At simulation equilibrium, the largest increases occur for Weakfish, which experience a population increase of 2% (relative to baseline data) within 5 years of I & E closure, and a population increase in excess of 5% by 25 years (Table 4). Other commercially important species pools with detectable increases at model equilibrium include Bluefish (1.4%), Striped Bass [Migratory (1%) and Re sident (0.7%) populations], Atlantic Croaker (0.4%), white perch (0.3%), flatfish (0.7%) and various reef associated fish [such as smallmouth bass and tautog] (2.2%). These increases in biomass are offset in the model by decreases in the biomass of sea nettles ( -0.3%), littoral forage fish ( -0.25%), and bay anchovy ( -0.25%) (Table 4). The directionality of these changes suggests that as I & E decreases, there is an observable pattern of an increased biomass of fish at higher trophic levels, and a decrease i n the biomass of forage fish. 3.3.1.2 Change with reduced I & E 

### When simulated I & E losses decreased at a rate of 2% per year, the general direction of change in biomass pools relative to baseline data was as the same as when I & E losses were ended abruptly. However, the time required to observe these changes (beyond a threshold of 0.25%) increased substantially (Table 3). For example, after 5 years simulation, no biomass pools had changes in excess of 0.25% (compared with 14 pools when I & E was abruptly elimin ated), and only two pools passed the threshold after 15 years (15 pools when I & E eliminated). By 40 years of simulation, however, 10 biomass pools had observable differences (Table 3).Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 7



### When changes to biomass pools surpassed the threshold of 0.25%, the magnitude of these changes was substantially smaller in simulations when I & E was gradually reduced than when I & E was stopped immediately. On average, the magnitude of these proportionate changes were 48.0 ±5.6% smaller (Table 3). 3.3.1.3 Change with increased I & E 

### When I & E losses increased at a rate of 2% per year, the direction of change in biomass pools was exactly opposite of those changes observed when I & E losses decreased. Briefly, the vast majority of biomass pools showed appreciable losses over 40 ye ars of model simulation (Table 4). In many cases, the magnitude of proportionate decline exceeds the magnitude of increase that occurs for the same pools when I & E losses are ended. Not all pools exhibit decreases in mass with increases in I & E. Those po ols that increase include forage fish (0.27%), sea nettles (0.30%) and Non -Reef Associated fish (0.63%). 3.3.2 Absolute biomass change within pools Although the directionality of changes in absolute and proportional biomasses are (by definition) identical, an examination of absolute changes from baseline biomass data through time shows a slightly more nuanced description of changes within the CBFEM as a consequence of I & E. Overall, 30 of 46 biomass pools exhibited changes in excess of 1 kg/km2 

### in one or more I & E treatments. Due to the large size of Chesapeake Bay, even small changes in absolute biomass pools may reflect substantial changes in total biomass. Additionally, because most fisheries are managed on the basis of absolute fish population size, these results may be more immediately applicable to estimates of altered fish harvest as a consequence of changes in I & E. Generally, the largest changes in absolute biomass occurred in the largest biomass pools (Table 5). In some cases, these changes exceeded 100 kg/km2 

### (approximately 2.5 million pounds bay - wide) . As with observed changes to biomass proportions, the magnitude of changes in absolute biomass is not always straightforward due to the indirect nature of food web interactions and time delays due to b aseline model inertia (Table 5.) 3.3.2.1 Change with elimination of I & E 

### When I & E losses ended at the beginning of simulated data, changes in biomass relative to baseline data were rapid, and were clearly representative of some trophic pathways within the CBFEM (Figure 1, Table 5). For example, after I & E losses ceased, substantial, immediate and negative changes occurred within 5 years in the biomass pools of alewife and herring, littoral forage fish, bay anchovy, zooplankton, in - and epi -fauna, and submerged aquatic vegetation (SAV). All of these pools serve as important food sources for biomass pools in higher trophic levels (Figure 1). The largest change in biomass observed in any pool occurred in adult menhaden, which increased in biomass by a minimum of 8 5 kg/km2 

### relative to baseline data when I & E was eliminated (Table 5). These changes represent an increase of nearly 1000 tons in biomass throughout Chesapeake Bay. Although this increase does not represent a large proportional change in menhaden biomass, it may reflect an economically important increase at the scale of the Chesapeake. Similarly, substantial decreases were observed for the biomass pool of in - and epi -fauna, which decreased a minimum of 32 kg/km2 

### below baseline biomass (Table 5). Other biomass pools with large increases from baseline data include Atlantic croaker, bluefish, striped bass (both migratory and resident) and adult weakfish. Substantial declines in biomass were observed in littoral forage fish, zooplankton, and SAV (Table 5) . 3.3.2.2 Chan ge with reduced I & E 

### The effect of reducing I & E losses on biomass pools were qualitatively similar but quantitatively different than the effects of I & E elimination . These changes include increases in the biomass of high trophic level predatory fish such as weakfish and striped bass, and decreases in forage fish, zooplankton,Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 8



### SAV, as well as in - and epi -fauna. Although the magnitude of these changes increase through time with respect to baseline data, they never approach the magnitude of changes observe d shortly after I & E losses are eliminated. For example, after 5 years of simulation , the biomass of adult menhaden increase 6.5 kg/km2 

### when I & E is reduced, but decrease 105.3 kg/km2 

### when I & E is eliminated (Table 5) . Although the magnitude of difference in biomass between I & E treatments decreases with time, the productivity differences at higher trophic levels represent hundreds of tons of commercial fish available per year. For example, increases in striped bass [migratory and resident] and weakfish biomasses (19.54, 27.52 and 20.10 kg/km2 

### , respectively)) observed within 5 years of I & E cessation are not achieved after 40 years of I & E losses declining 2% per year (13.88, 9.61 and 18.63 kg/km2

### ) . 3.3.2.3 Change with increased I & E 

### When I & E losses slowly increase through time, their effect on the biomass of high trophic -level biomass pools is substantial. With respect to baseline data, increasing I & E losses led to large decreases in Weakfish ( - 32.45 kg/km2

### ), striped bass ( - 28.21 and - 21.18 kg/km2 

### for migratory and resident fish), bluefish ( - 13.93 kg/km2

### ) Atlantic croaker ( - 15.34 kg/km2

### ) and adult menhaden ( - 104.82 kg/km2

### ) after 40 years of model simulation. Additionally, the biomass pools with substantial increases, namely alewife & herring (9.76 kg/km2

### ), littoral forage fish (15.61 kg/km 2

### ), meso - and micro - zooplankton (19.41 and 13.12 kg/km2 

### , respectively), in - and epi -fauna (48.56 kg/km2

### ) and SAV (15.76 kg/km2

### ), suggest that the system was moving to a less diverse, lower-trophic level equilibrium (Pauly et al. 1998) . 

## 4 Model limitations and future directions Although EwE provides a very sophisticated modeling approach for estimating ecosystem level impacts of I & E losses, there are several limitations which mu st be noted. Some of these limitations are inherent to the model, and some are data limitations. To address some of these limitations, future work could explore incorporating spatially -explicit routines and increasing the number of biomass pools with age structure in the CBFEM. 

### 4.1 Structural Limitations All models use simplified assumptions to make predictions. One of the largest simplifying assumptions within the CBFEM is that the model deals with bay -wide averages, and is not spatially explicit. When dealing with point- source impacts such as I & E, however, such an assumption may underestimate impacts and/or mask potential nonlinearities within the system . For example, if spawning aggregations were to occur near high - volume CWIS, averaging the impact throughout the bay would be problematic because impacts would be highly localized. These spatially- structured effects could be explored in future work with the spatially-explicit Ecospace routine within the EwE model framework (Christensen et al. 2008) . A second major limitation inherent to the dynamics of biomass pools within the CBFEM is the lack of population structure for the majority of biomass pools within the model : currently, only 7 biomass pools incorporate population age structure . This simplifying assumption is likely to lead to underestimates of I & E impacts within the Chesapeake , because the majority of I & E losses occur early in the life -history of most species. Due to the nature of the mathematical functions driving EwE, however, the disproportionate effects of I & E losses on eggs and juvenile fish cannot be accur ately portrayed in the implicit population dynamics of the model. These nonlinear effects of I & E losses on age - structured populations can be seen in model output of the 7 biomass pools with age structure. These biomass pools, including Bluefish, Menhaden , Striped Bass, Weakfish, and White Perch, are over-represented among those pools with high proportionate and absolute changes in biomass (Table 4, Table 5). Consequently, addressing this limitation should be a key focus of future work on estimating I & E impacts using EwE. Two possibleAbt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 9



### routes to investigate would include using estimates of foregone yield to drive I & E losses within the model, or creating additional age - structured biomass pools within the model. 

### 4.2 Data - based limitations Like all models, the accuracy of CBFEM output is limited by the accuracy of its input data. In the case of the model simulations described above, the weakest data source is I & E inputs. First, unlike fish population estimates, and the catch of other fisheries, they are not bay- specific. Instead, they are derived from estimated regional -level I & E losses. These regional -level I & E losses are themselves treated as constant, despite being generated from data that vary in both space and time. As such, although I & E losses are likely to fluctuate through time with changes in fish populations naturally, they are unable to fluctuate in the model. Furthermore, there are several biomass pools, including oysters and other shellfish, that do not have estimates of I & E losses. Consequently, trophic discontinuities may occur within model simulations that are not present in the actual ecosystem. Other data-based limitations to CBFEM output include various assumptions about environmental policy. During simulation, it was assumed that fisheries -related mortality and other environmental forces (including nutrient availability, climate, water quality, etc.) remained constant. Since these forces can have substantial impacts on communities and the ecosystem goods and services they provide (Steneck and Carlton 2001; Ronnback et al. 2007) , model predictions should be considered qualitatively . 

## 5 Key findings The CBFEM provides a tool useful in estimating community -level effects of I & E losses within Chesapeake Bay , and as a case study for I & E losses more generally . Importantly, varying I & E treatment within the model did not lead to substantial community -level shifts within Chesapeake Bay. This suggests that the structure of the marine ecosystem within the bay is not significantly impacted by I & E losses. This lack of a community -level shift, however, does not suggest that quantitative estimates of the effects of I & E on the population structure of individual fish stocks are negligible . Indeed, I & E treatment led to substantial changes in the absolute biomass of several commercially and recreationally important species of fish, including striped bass, weakfish, menhaden, and bluefish. Moreover, changes were observed through several trophic levels as a consequence of changes in I & E loss rates. However, as a consequence of incomplete model inputs through time and structural limitations of the model itself, the magnitude of I & E impacts in CBFEM output are likely to underestimate its true effects within the Chesapeake , particularly on a local level . Consequently, population and species - specific fisheries models are likely to provide more accurate quantitative estimates of I & E effects on fish stocks and fisheries impacts . 

## 6 References Anderson MJ (2001) A new method for non -parametric multivariate analysis of variance. Austral Ecology 26: 32 -46 Blaber SJM, Cyrus DP, Albaret JJ, Ching CV, Day JW, Elliott M, Fonseca MS, Hoss DE, Orensanz J, Potter IC, Silvert W (2000) Effects of fishing on the structure and functioning of estuarine and nearshore ecosystems. ICES Journal of Marine Science 57: 590 - 602 Christensen V, Beattie A, Buchanan C, Ma H, Martell SJD , Latour RJ, Preikshot D, Sigrist M, Uphoff JH, Walters CJ, Wood RJ, Townsend H (2008) Fisheries ecosystem model of the Chesapeake Bay: Methodology, parameterization, and model exploration. Draft Technical Report for NOAA Technical Report Series. Revision 9 June 2008Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 10



### Christensen V, Pauly D (1992) Ecopath -II - a Software for Balancing Steady - State Ecosystem Models and Calculating Network Characteristics. Ecological Modelling 61: 169 - 185 Christensen V, Walters CJ (2004) Ecopath with Ecosim: methods, capabilities and limitations. Ecological Modelling 172: 109 - 139 Froese R, Pauly D (2009) Fishbase. Available at: www.fishbase.org (version 07/2009). Fisheries Centre, University of British Columbia, Vancouver, Canada Gotelli NJ, Ellison AM (2004) A Primer of Ecological Statistics. Sinauer Associates, Inc. , Sunderland, MA Leslie HM, McLeod KL (2007) Confronting the challenges of implementing marine ecosystem -based management. Frontiers in Ecology and the Environment 5: 540 - 54 8 Link JS (2002a) Ecological considerations in fisheries management: When does it matter? Fisheries 27: 10 - 17 Link JS (2002b) What does ecosystem-based fisheries management mean? Fisheries 27: 18 - 21 Magurran AE (2004) Measuring Biological Diversity. Blackw ell Science Ltd, Malden, MA May RM, Beddington JR, Clark CW, Holt SJ, Laws RM (1979) Management of Multispecies Fisheries. Science 205: 267 - 277 McArdle BH, Anderson MJ (2001) Fitting multivariate models to community data: A comment on distance -based redundancy analysis. Ecology 82: 290 - 297 NMFS (1999) Ecosystem-based fishery management. A report to Congres by the Ecosystems Principles Advisory Panel. National Marine Fisheries Service., U.S. Department of Commerce, Silver Spring, MD Oksanen J, Kindt R, Legendre P, O'Hara B, Simpson GL, Stevens MHH (2008) vegan: Community Ecology Package. R package vegan, version 1.15 - 1 Pauly D, Christensen V, Dalsgaard J, Froese R, Torres F (1998) Fishing Down Marine Food Webs. Science 279: 860 - 863 Pauly D, Christensen V, Walters C (2000) Ecopath, Ecosim, and Ecospace as tools for evaluating ecosystem impact of fisheries. Ices Journal of Marine Science 57: 697 -706 R Development Core Team (2008) R: A language and environment for statistical computing. R Foundation for Statistic al Computing, Vienna, Austria Ricker WE (1975) Computation and interpretation of biological statistics of fish populations. Fisheries Research Board of Canada, Bulletin 191 Ronnback P, Kautsky N, Pihl L, Troell M, Soderqvist T, Wennhage H (2007) Ecosystem goods and services from Swedish coastal habitats: Identification, valuation and implications of ecosystem shifts. Ambio 36: 534 - 544 Sea Around Us (2009) A global database on marine fisheries and ecosystems. Available at: www.seaaroundus.org . Fisheries Centre, University of British Columbia, Vancouver, Canada Sellner KG, Fisher N, Hager CH, Walter JF, Latour RJ (2001) Ecopath with Ecosim Workshop. Chesapeake Research Consortium Steneck RS, Carlton JT (2001) Human Alterations of Marine Communities: Students Beware! In: Bertness M, Gaines S, Hay M (eds) Marine Community Ecology. Sinauer Press, Sunderland, MA, pp 445 -468 Walters CJ, Christensen V, Martell SJ, Kitchell JF (2005) Possible ecosystem impacts of applying MS Y policies from single - species assessment. Ices Journal of Marine Science 62: 558 - 568Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 11



### Table 1 – Changes to biomass pools through time in baseline Ecopath model run. Biomass Percent Change in Biomass from 0 Years (kg/km2) Biomass Pool 0 Years 5 Years 10 Years 15 Years 25 Years 40 Years Alewife & Herring 5446.89 - 16.89 - 17.88 - 17.62 - 17.32 - 17.24 American Eel 3415.15 - 2.59 -4.46 -4.47 -4.49 -4.42 Atlantic Croaker 5309.96 - 33.70 -48.38 - 52.24 - 53.78 - 54.03 Bay Anchovy 3625.26 - 1.41 - 0.91 - 1.13 - 0.84 - 0.92 Benthic Algae 1515.39 - 3.01 -4.41 - 5.02 -4.83 - 5.42 Black Drum 616.64 4.40 3.14 0.77 - 2.73 -4.56 Blue Crab (Adult) 2657.88 3.09 8.35 8.24 10.20 6.57 Blue Crab (YOY) 1652.83 6.59 - 6.73 - 0.13 -4.40 0.77 Bluefish (Adult) 742.85 19.89 30.90 36. 04 40.20 41.46 Bluefish (YOY) 44.50 8.21 13.80 16.39 18.50 19.07 Catfish 1024.04 12.90 20.61 23.86 24.92 24.57 Ctenophores 3717.30 0.53 1.49 1.84 2.33 2.31 Detritus 1056.48 0.61 0.96 1.15 1.31 1.27 Gizzard Shad 2250.10 - 1.54 -4.92 - 8.04 - 11.53 - 13.09 Hard Clam 4849.21 - 11.46 - 20.42 - 21.72 - 23.27 - 22.73 Littoral Forage Fish 5557.62 0.33 1.65 2.74 2.70 2.74 Menhaden (0 - 1) 19894.93 0.75 0.93 0.83 0.87 0.73 Menhaden (Adult) 38970.09 1.95 0.86 0.29 - 0.21 - 0.53 Mesozooplankton 11117.92 0.43 1.42 1.89 2. 41 2.45 Microzooplankton 7081.41 0.97 1.85 2.27 2.68 2.62 Non- piscivorous Seabirds 1477.65 - 2.45 -4.92 - 3.51 - 3.13 - 2.90 Non- Reef Associated Fish 138.60 5.27 10.10 13.25 16.11 17.33 Other Elasmobranchs 581.26 - 1.88 - 3.41 - 3.80 - 3.10 - 1.48 Other Flatfish 178.81 4.17 8.95 11.66 13.06 13.32 Other In - and Epi - fauna 75407.17 2.98 4.94 5.33 5.59 5.89 Other Suspension Feeders 6853.62 - 5.08 - 6.72 - 8.30 - 8.25 - 7.95 Oyster (Adult) 163.10 - 0.77 - 2.29 - 3.41 - 5.82 - 9.26 Oyster (YOY) 63.26 - 1.16 - 2.54 -4.34 - 6.7 0 - 9.75 Phytoplankton 27236.39 0.31 0.24 0.35 0.30 0.31 Piscivorous Birds 337.54 0.90 1.62 2.13 2.78 3.17 Reef -Associated Fish 249.58 1.83 - 0.52 0.08 0.71 0.99 Sandbar Shark 120.46 - 29.32 - 50.32 - 60.53 - 67.83 - 69.80 SAV 9997.89 0.85 1.89 1.62 1.98 1.9 0 Sea Nettles 683.63 0.64 2.76 3.23 4.22 4.30 Spot 1973.11 - 23.58 - 30.93 - 32.57 - 34.12 - 34.49 Striped Bass (Migratory) 2995.25 - 15.99 - 14.15 - 15.10 - 16.25 - 16.49 Striped Bass (Resident) 2176.08 14.95 8.68 6.83 7.03 6.77 Striped Bass (YOY) 13.91 2.18 - 0.15 - 0.07 0.24 0.23 Summer Flounder 414.24 - 3.29 - 6.75 - 7.25 - 7.39 - 7.34 Weakfish (Adult) 790.75 - 3.86 - 3.73 - 2.94 - 3.03 - 3.03 Weakfish (YOY) 34.72 - 10.91 - 15.06 - 16.55 - 16.78 - 16.87 White Perch (Adult) 332.78 0.62 1.47 2.18 2.50 2.69 White Perch (YOY) 3.53 0.68 2.10 2.56 3.07 3.29 

### Note: Rapid changes from year 0 to year 5 indicates that the CBFEM was not in equilibrium at the end of time - series data .Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 12



### Table 3: Results of PERMANVOA testing the effects of time and I & E treatments on overall community structure of the Chesapeake Bay ecosystem estimated by Ecopath simulation. DF=degrees of freedom, MS = mean squares Factor DF MS (x10 -3

### ) * 

### F p Time 1 4.422 182.512 0.001 I & E 3 0.015 0.614 0.625 Error 159 0.024 Table 2: Results of ANCOVA testing the effects of time and I & E treatments on values of Simpson’s Diversity Index estimated from 40 years of annual Ecopath simulation output. DF = degrees of freedom, MS = mean s quares Factor DF MS F p Time 1 6.280 165.959 <0.001 I & E 3 0.001 0.026 0.994 Error 159 0.038Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 13



### Table 4: Proportionate change to biomass pools (with respect to baseline data) during Ecopath simulation by I & E treatment and time. C hanges > 0.25% from baseline data at each time step are noted.

Simulation Time 5 Years 10 Years 15 Years 25 Years 40 Years Biomass Pool / I & E Trt None Red. Inc. None Red. Inc. None Red. Inc. None Red. Inc. None Red. Inc. Init. Biomass (kg/km2) American Shad 0.30 0.40 0.56 - 0.56 0.01 Atlantic Croaker 1.03 0.8 7 0.61 0.44 - 0.36 0.42 0.28 - 0.63 5309.96 Bay Anchovy - 0.25 3625.26 Bluefish (Adult) 0.88 1.27 1.39 - 0.29 1.41 0.43 - 0.64 1.40 0.68 - 1.33 742.85 Bluefish (YOY) 0.52 0.60 0.62 0.62 - 0.29 0.60 0.30 - 0.61 44.50 Littoral Forage Fish - 0.28 - 0.26 - 0.26 - 0.25 0.27 5557.62 Menhaden (Adult) 0.27 - 0.27 38970.09 Non- Reef Associated Fish - 0.45 - 0.66 - 0.59 - 0.60 0.32 - 0.60 - 0.31 0.63 138.60 Other Flatfish 0.41 0.57 0.65 0.71 - 0.33 0.73 0 .35 - 0.69 178.81 Reef - Associated Fish 1.67 2.02 0.27 - 0.31 2.17 0.44 - 0.55 2.23 0.76 - 1.15 2.22 1.14 - 2.28 249.58 Sandbar Shark 0.50 0.63 0.69 - 0.28 0.70 0.34 - 0.61 120.46 Sea Nettles - 0.26 - 0.27 - 0.28 - 0.28 - 0.29 0.30 683.63 Spot 0 .36 - 0.30 - 0.31 1973.11 Striped Bass (Migratory) 0.78 1.14 1.11 - 0.28 1.05 0.38 - 0.57 1.06 0.55 - 1.13 2995.25 Striped Bass (Resident) 1.10 0.78 0.64 - 0.26 0.74 0.30 - 0.49 0.73 0.41 - 0.91 2176.08 Striped Bass (YOY) - 0.29 - 0. 27 13.91 Summer Flounder 0.27 414.24 Weakfish (Adult) 2.64 3.53 0.49 - 0.56 4.83 0.88 - 1.07 5.07 1.56 - 2.20 5.18 2.43 -4.23 790.75 Weakfish (YOY) 0.36 34.72 White Perch (Adult) 0.26 0.28 0.31 0 .31 0.31 - 0.35 332.78 

### I & E treatments: (1) None: No I & E losses, (2) Reduced: I & E losses decline 2% per year, (3) Increased: I & E losses increase 2% per yearAbt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 14



### Table 5 : Absolute change (in kg/km2

### ) to biomass pools during Ecopath simulation by I & E treatment and time . Differences > 1 kg/km2 

### from baseline data are noted .

Simulation Time 5 Years 10 Years 15 Years 25 Years 40 Years Initial Biomass Biomass Pool / I & E Trt None Red. Inc. None Red. Inc. None Red. Inc. None Red. Inc. None Red. Inc. (kg/km2) Alewife & Herring - 7.81 - 9.34 - 1.17 1.32 - 8.89 - 1.94 2.42 - 9.01 - 3.24 4.93 - 9.07 -4.75 9.76 5446.89 American Eel - 2.85 -4.82 -4.85 1.08 -4.60 - 1.59 2.34 -4.50 - 2.37 4.74 3415.15 Atlantic Croaker 36.24 2.00 - 2.09 23.93 3.58 -4.12 15.49 4.3 4 - 5.58 10.91 5.46 - 8.88 10.23 6.72 - 15.34 5309.96 Bay Anchovy - 5.23 - 7.47 - 8.51 - 1.42 1.70 - 8.91 - 2.69 3.86 - 9.07 -4.32 8.19 3625.26 Benthic Algae - 1.12 - 1.52 - 1.71 - 1.75 1.35 1515.39 Blue Crab (Adult) - 3.47 - 2.20 - 2.42 - 2.11 1.42 - 2.49 - 1.34 2.78 2657.88 Blue Crab (YOY) - 1.45 - 1.37 - 1.04 1.16 1652.83 Bluefish (Adult) 7.82 12.37 1.33 - 1.50 14.07 2.42 - 2.96 14.72 4.52 - 6.63 14.72 7.15 - 13.93 742.85 Ctenophores - 5.08 - 5.34 - 5.54 - 1.28 1.64 - 5.69 - 2.06 3.19 - 5.91 - 3.04 6.22 3717.30 Gizzard Shad 1.04 1.55 1.90 2.49 2.84 1.13 - 2.06 2250.10 Hard Clam 2.04 3.34 3.13 2.95 1.04 - 1.52 3.21 1.59 - 3.11 4849.21 Littoral Forage Fish - 13.31 - 16.01 - 1.95 2.19 - 14.70 - 3.16 3.93 - 14.64 - 5.21 7.91 - 14.49 - 7.63 15.61 5557.62 Menhaden (0 - 1) - 5.01 - 6.77 - 7.79 - 7.51 - 1.69 1.91 - 7.02 - 3.04 4.56 19894.93 Menhaden (Adult) 105.30 6.54 - 6.89 89.65 15.34 - 17.83 87.81 22.50 - 29.03 88.51 34.78 - 54.96 91.86 49.36 - 104.82 38970.09 Mesozooplankton - 15.76 - 1.22 1.30 - 1 6.47 - 2.64 3.09 - 16.94 -4.01 5.16 - 17.46 - 6.41 9.98 - 18.19 - 9.40 19.41 11117.92 Microzooplankton - 10.84 - 11.61 - 1.73 2.01 - 12.07 - 2.68 3.42 - 12.32 -4.37 6.70 - 12.69 - 6.48 13.12 7081.41 Non- Reef Associated Fish - 6.54 - 9.24 1.11 - 8.35 - 1.76 2.17 - 8.53 - 2.98 4.51 - 8.57 -4.43 9.09 138.60 Other Elasmobranchs - 1.23 581.26 Other Flatfish 1.10 1.29 1.43 1.48 - 1.40 178.81 Other In - and Epi - fauna - 89.11 - 5.58 5.83 -46.05 - 11.09 13.00 - 36.53 - 13.66 18.16 - 31.10 - 17.46 29.33 - 32.0 2 - 20.99 48.56 75407.17 Other Suspension Feeders - 1.73 - 3.98 - 5.10 1.01 - 5.89 - 1.66 2.42 - 5.80 - 2.68 5.10 6853.62 Reef - Associated Fish 4.23 5.03 5.43 1.11 - 1.38 5.60 1.92 - 2.88 5.60 2.88 - 5.74 249.58 SAV - 13.29 - 14.23 - 2.06 2.37 - 14.93 - 3.22 4.08 - 15.18 - 5.30 8.06 - 15.49 - 7.87 15.76 9997.89 Sea Nettles - 1.80 - 1.91 - 1.95 - 2.01 1.11 - 2.07 - 1.06 2.17 683.63 Spot 5.47 - 2.75 - 3.96 -4.07 - 1.22 1.21 1973.11 Striped Bass (Migratory) 19.54 1.03 - 1.07 29.25 3.46 - 3.92 28.32 5.84 - 7.2 3 26.28 9.48 - 14.34 26.40 13.88 - 28.21 2995.25 Striped Bass (Resident) 27.52 1.73 - 1.82 18.42 3.54 -4.13 14.99 4.65 - 6.12 17.19 7.03 - 11.38 17.02 9.61 - 21.18 2176.08 Summer Flounder 1.09 414.24 Weakfish (Adult) 20.10 1.38 - 1.45 26.90 3 .70 -4.24 37.03 6.72 - 8.24 38.90 11.98 - 16.87 39.73 18.63 - 32.45 790.75 White Perch (Adult) 1.05 1.06 1.05 - 1.19 332.78 

### I & E treatments: (1) None: No I & E losses , (2) Reduced: I & E losses decline 2% per year , (3) Increased: I & E losses increase 2% per yearAbt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 15



### Appendix 1: Matched CMFEM B iomass pools and species output from I & E model of biomass losses CBFEM Biomass Pool Species From I & E Model Alewife & Herring Alewife, Atlantic Herring, Blueback Herring, Herrings American S had American Shad Atlantic Croaker Atlantic Croaker Bay Anchovy Bay Anchovy Black Drum Black Drum Blue Crab (YOY) Blue Crab (Adult) Blue Crab, Other Commercial Crabs Bluefish (YOY) Bluefish (Adult) Bluefish Catfish Brown Bullhead, Bullhead spp., Channel Catfish Gizzard Shad Gizzard Shad Littoral Forage Fish Atlantic Silversides, Chain Pipefish, Northern Pipefish, Rainbow Smelt, Scup, Silver Perch, Silversides, Striped Mullet, Striped Killifish, Threespine Stickleback, Other Forage Fish Menhaden (0 - 1) Menhaden (Adult) Atlantic Menhaden, Menhaden Non-Reef Associated Fish Butterfish, Grubby, Red Drum, Red Hake, Sea Robin, Silver Hake, Spotted Sea Trout Other Flatfish Hogchoker, Windowpane, Winter Flounder Reef -Associated Fish Cunner, Gobies, S eaboard Goby, Smallmouth Bass, Tautog CBFEM Biomass Pool Species From I & E Model Spot Spot Striped Bass (YOY) Striped Bass (Resident) Striped Bass (Migratory) Striped Bass Summer Flounder Summer Flounder Weakfish (YOY) Weakfish (Adult) Weakfish White Perch (YOY) White Perch (Adult) White Perch American Eel Benthic Algae Ctenophores Detritus Hard Clam Mesozooplankton Microzooplankton Non-piscivorous Seabirds Other Elasmobranches Other In - and Epi -fauna Other Suspension Feeders Oyster (Adult) Oyster (YOY) Phytoplankton Piscivorous Birds Sandbar Shark SAV Sea Nettles Soft Clam No relevant I & E model output.Abt Associates Inc., May 19, 2010 Ecopath Simulations of Chesapeake Bay 16



### Appendix 2: B aseline Ecopath output. Absolute Biomass (kg/km2)

Biomass Pool 0 Years 5 Years 10 Years 15 Years 25 Years 40 Years Alewife & Herring 5446.89 4526.87 4473.24 4487.02 4503.71 4507.75 American Eel 3415.15 3326.62 3262.68 3262.61 3261.68 3264.27 American Shad 0.00 0.00 0.00 0.00 0.00 0.00 Atlantic Croaker 5309.96 3520.60 2741.02 2535.91 2454.10 2441.05 Bay Anchovy 3625.26 3574.18 3 592.37 3584.37 3594.96 3591.79 Benthic Algae 1515.39 1469.84 1448.52 1439.36 1442.15 1433.24 Black Drum 616.64 643.80 635.99 621.38 599.81 588.49 Blue Crab (Adult) 2657.88 2739.96 2879.70 2876.88 2928.86 2832.46 Blue Crab (YOY) 1652.83 1761.68 1541.56 1650.66 1580.06 1665.51 Bluefish (Adult) 742.85 890.59 972.38 1010.60 1041.50 1050.86 Bluefish (YOY) 44.50 48.15 50.64 51.79 52.73 52.98 Catfish 1024.04 1156.16 1235.12 1268.37 1279.26 1275.61 Ctenophores 3717.30 3736.93 3772.70 3785.87 3803.91 3803.28 Detritus 1056.48 1062.95 1066.63 1068.61 1070.33 1069.87 Gizzard Shad 2250.10 2215.43 2139.31 2069.10 1990.76 1955.54 Hard Clam 4849.21 4293.37 3859.02 3795.84 3720.58 3746.86 Littoral Forage Fish 5557.62 5576.21 5649.10 5709.64 5707.93 5709.68 Menhaden (0 - 1) 19894.93 20044.79 20080.77 20059.32 20067.64 20039.34 Menhaden (Adult) 38970.09 39730.51 39306.90 39081.75 38887.85 38762.45 Mesozooplankton 11117.92 11166.12 11275.82 11327.80 11385.95 11390.08 Microzooplankton 7081.41 7150.41 7212.41 7242.27 7271.47 7266.76 Non- piscivorous Seabirds 1477.65 1441.46 1404.92 1425.80 1431.34 1434.84 Non- Reef Associated Fish 138.60 145.91 152.60 156.96 160.93 162.63 Other Elasmobranchs 581.26 570.31 561.46 559.18 563.27 572.69 Other Flatfish 178.81 186.27 194. 82 199.67 202.17 202.63 Other In - and Epi - fauna 75407.17 77657.32 79135.06 79427.64 79620.69 79848.76 Other Suspension Feeders 6853.62 6505.20 6392.79 6284.79 6288.08 6308.75 Oyster (Adult) 163.10 161.84 159.36 157.53 153.60 148.00 Oyster (YOY) 63.26 6 2.53 61.65 60.52 59.03 57.10 Phytoplankton 27236.39 27320.23 27301.19 27330.57 27318.10 27320.53 Piscivorous Birds 337.54 340.57 343.00 344.72 346.91 348.25 Reef -Associated Fish 249.58 254.15 248.29 249.78 251.36 252.06 Sandbar Shark 120.46 85.14 59.85 47.55 38.76 36.38 SAV 9997.89 10083.33 10187.23 10160.17 10196.33 10187.70 Sea Nettles 683.63 688.00 702.47 705.67 712.44 713.04 Soft Clam 0.00 0.00 0.00 0.00 0.00 0.00 Spot 1973.11 1507.87 1362.81 1330.41 1299.89 1292.58 Striped Bass (Migratory) 299 5.25 2516.26 2571.57 2542.93 2508.47 2501.19 Striped Bass (Resident) 2176.08 2501.35 2364.94 2324.80 2328.97 2323.41 Striped Bass (YOY) 13.91 14.21 13.89 13.90 13.94 13.94 Summer Flounder 414.24 400.62 386.27 384.19 383.63 383.83 Weakfish (Adult) 790.7 5 760.20 761.26 767.50 766.78 766.77 Weakfish (YOY) 34.72 30.93 29.49 28.98 28.90 28.87 White Perch (Adult) 332.78 334.83 337.67 340.05 341.09 341.74 White Perch (YOY) 3.53 3.55 3.60 3.62 3.63 3.64 

### Note: Values for American Shad and Soft Clams are not errors. These stocks crashed in time series data, and although not formally extinct, are of minimal biomass during model simulation.