Ecological Modelling, Vol. 143 (1-2) (2001) pp. 71 - 94

PII: S0304-3800(01)00355-6

Modeling herbivorous consumer consumption in the Great Bay Estuary, New Hampshire

Pamela M. Behm * and Roelof M.J. Boumans

Modeling Herbivorous Consumer Consumption in the

Great Bay Estuary, New Hampshire

Pamela M. Behm and Roelof M.J. Boumans

Chesapeake Biological Laboratory, University of Maryland Center for Environmental Studies, PO Box 38, Solomons, MD 20688, USA

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Abstract

Herbivores play a vital role in eelgrass-based estuaries. They have been shown to alter plant productivity, distribution, and overall community structure. Epiphyte grazers are especially important as epiphytes compete with eelgrass for light and nutrients. For these reasons, an herbivorous consumer sector was constructed for a spatial ecosystem model developed for the Great Bay Estuary in New Hampshire (the Great Bay Model). There are three classifications used for consumers in the new sector: fast movers, slow movers, and sedentary. Defining this classification structure is the time needed to travel from one location to another. Criteria that determine movement include food availability and competition. Consumer ingestion is limited by metabolic activity, food preference, and food availability. Unit model results indicate that metabolic activity has a significant impact on consumption. Spatial results show an aggregation of consumers in areas of high food biomass. Travel time also appears to have a significant impact on the rate of growth of consumers. Future improvements to the model will incorporate bay competition between consumer groups as well as predator influence on herbivore behavior. The consumer sector should only be used as a framework for future development, pending available data.

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1. Introduction

The Great Bay Estuary in New Hampshire is historically known for its abundant eelgrass meadows. These meadows provide the largest spatial habitat distribution within Great Bay (Short 1992). A map of the estuary, showing the main waterways, is provided in Figure 1.

Eelgrass health is both a factor in and an indicator of the overall health of bays and estuaries (Short 1992). The production of eelgrass provides food, shelter, and nursery areas for many marine animals (Bach 1993, Connolly 1994). Eelgrass communities help stabilize bottom sediments and filter suspended sediments. Their leaves act as dampers and reduce water motion. Eelgrass meadows also act as a filter, removing dissolved nutrients, which are taken up by the leaves for growth (Short 1992). However, in the Great Bay Estuary, too many nutrients from wastewater effluent and fertilizers can produce algal blooms that shade and destroy eelgrass ecosystems (Short 1992).

Direct grazing of eelgrass can have a profound effect on eelgrass communities. Grazing of above-ground eelgrass biomass has been shown to alter plant productivity, distribution, and overall community structure (Thayer et al. 1984). There are claims that few species use living eelgrass as a direct food source and impact is minimal (Thayer et al. 1984). However, there is a surprising lack of studies assessing spatial and temporal variability in herbivorous impact to support these claims (Nienhuis 1986). Research has not exposed any recent studies to settle the debate regarding the importance of direct eelgrass grazing.

Eelgrass meadows support herbivores that feed on epiphytes, which compete with the eelgrass for light and nutrients (Hootsmans et al. 1985). Previous research has shown that epiphytes can reduce photosynthesis of eelgrass by 31% (Hootsmans et al. 1985). Both eelgrass and epiphytes are nutritious food sources containing organic matter easily assimilated by herbivores (van Montfrans et al. 1984). Therefore, when examining the productivity of eelgrass, it is essential to take into account the grazers that feed upon the light competitors as well as the herbivores that feed on the eelgrass itself.

A significant decline in eelgrass beds in the Great Bay Estuary was noticed in the late 1980’s. To understand what caused the decline (in order to prevent future declines), a spatial ecosystem-level model is under development for the portion of the Great Bay Estuary that is dedicated as a National Estuarine Research Reserve (Short et al. 1997). This is equivalent to the area shaded gray in Figure 1. A conceptual diagram of the unit model (now referred to as the Great Bay Model) is provided in Figure 2. Key sectors in the Great Bay Model include: eelgrass (above- and below-ground biomass), epiphytes, detritus, phytoplankton, suspended sediments, and herbivores. The Great Bay Model is formulated to simulate the annual as well as the spatial distribution of eelgrass habitats. The spatial simulation model is constructed of a spatial grid with each grid cell containing an entire functioning Great Bay Model (Short et al. 1997). Each grid cell is approximately 100 by 100 meters.

The five major food sources of the herbivorous consumers are the eelgrass shoots (above ground material), the eelgrass roots and rhizomes (below ground material), wrack (free-floating eelgrass litterfall), epiphytes, and phytoplankton. There are many consumers that feed on the various food sources. The original Great Bay Model only took into account a “general” consumer with constant preferences for food sources and a constant rate of travel. Research has shown that preference for various food sources may change based on food availability (Nienhuis et al. 1986). Further development of the consumers sector included examining the impact of varying food preference by available biomass as well as varying consumer travel time.

The purpose of this paper is to examine the impact travel time has on herbivorous consumption found within Great Bay as well as the associated impacts on herbivorous food sources. This investigation will aid in the further understanding of causes of eelgrass decline in Great Bay, specifically those that are consumption related. Therefore, only the consumers sector will be presented in this paper. Additional information on the Great Bay Model (including detailed descriptions of the other sectors) can be found at http://iee.umces.edu/GrBay.

2. Model Overview

The consumers unit sector was developed using STELLA^TM modeling software. The spatial model was run using the Spatial Modeling Environment (SME) software developed by Thomas Maxwell at the University of Maryland (http://iee.umces.edu/SME3/). The unit sector represents one cell of the spatial model.

There are three consumer “types” modeled in the consumers sector, classified by the time needed to travel from one cell to another. The three types are:

· fast moving consumers – travel from one cell to the next in about one day,

· slow moving consumers – travel from one cell to the next in about 10 days, and

· sedentary consumers – travel from one cell to the next in about 100 days.

A detailed ecological survey (Short 1992) provided information regarding the types of herbivores found in Great Bay. Fast Movers include waterfowl, amphipods, and crustaceans. Slow movers include snails and small fish. Sedentary consumers include clams, mussels, and oysters. The basic model structure followed by each type is shown in Figure 3.

3. Fast Moving Consumers Section

The fast moving consumers type model structure is provided in Figure 4. Equations for the fast moving consumers type is provided in Appendix A. As noted above, each type follows the same basic arrangement. The following subsections will discuss the equations in relation to the fast movers, but the same principles were applied to the other types.

3.1 General

The general subsection “sums up” all of the interactions for the fast consumer. The fast consumer (in units of kilograms per cell) is defined as:

CONSUMERS_1(t) = CONSUMERS_1(t - dt) + (cons1_in_X + cons_ingest_1 –cons_egest_1 - cons_mortality_1 - cons_respiration_1 - cons1_out_X) * dt

Where:

CONSUMERS_1 = fast moving consumers

cons1_in_X = consumers that moved into current cell;

cons_ingest_1 = amount of ingested material;

cons_egest_1 = amount of egested material;

cons_mortality_1 = consumer mortality;

cons_respiration_1 = consumer respiration;

cons1_out_X = consumers that moved out of current cell.

See Table 1 for parameter values and Appendix A for more detail. Respiration is a function of both metabolic activity and the rate of respiration:

cons_respiration_1 = CONSUMERS_1*Activity_1*•C_resp_rt.

Where:

Activity_1 =metabolic activity

•C_resp_rt = respiration rate

Metabolic activity is represented in the model as an index (ranging in value from zero to one, where one is the maximum activity rate). Metabolic activity by consumer type is shown in Figure 5. Previous studies have shown that metabolic activity is a function of water temperature and varies by consumer (Hochachka 1983, Vernberg 1983, Omori et al. 1984). For this model, metabolic activity was estimated for each consumer type through detailed discussions with experts of the ecology of Great Bay. However, more research regarding the metabolic activity for the consumer types used in this model will need to take place to obtain a better understanding of the metabolic activity of these unique consumer types.

Egestion is modeled as a constant proportion of consumption:

cons_egest_1 = cons_ingest_1*•C_egest_eff

Where:

C_egest_eff = egestion efficiency

Mortality is modeled as a constant proportion of fast consumers.

cons_mortality_1 = CONSUMERS_1*•C_mort_rt

Where:

C_mort_rt = mortality rate

Consumers moving into and out of the cell are only considered when running the model on a spatial scale. The basic equations defining spatial movement are provided below and discussed further in Section 3.3.

Consumers Moving Into Cell:

cons1_in_X = Cons1toE@W+Cons1toS@N+Cons1toW@E+Cons1toN@S

Where:

Cons1toE@W = Consumers moving into cell from western cell

Cons1toS@N = Consumers moving into cell from northern cell

Cons1toW@E = Consumers moving into cell from eastern cell

Cons1toN@S = Consumers moving into cell from southern cell

Consumers Moving Out of Cell:

cons1_out_X = Cons1toE+Cons1toN+Cons1toS+Cons1toW

Where:

Cons1toE = Consumers moving out of current cell into eastern cell

Cons1toN = Consumers moving out of current cell into northern cell

Cons1toS = Consumers moving out of current cell into southern cell

Cons1toW = Consumers moving out of current cell into western cell

Ingestion of food sources is discussed in detail in sections 3.2 and 3.4. The basic equation defining ingestion is:

cons_ingest_1 = Ingest_1

Where:

Ingest_1 = the total amount of food ingested by fast moving consumers

3.2 Food Availability

Food availability simply turns food sources “on and off”. This is done so that if the consumer does not have a preference for a particular food type, that food type will not be considered when allocating consumption.

3.3 Movement Criteria

The movement criteria subsection determines whether or not consumers will move into the surrounding cells. In STELLA, this is not a factor because the unit model represents only one cell. The basic equation used to calculate consumer movement (shown here for movement to the east cell) is:

Cons1toE = (Density1_X_E+Food1_X_E)*CONSUMERS_1

Where:

Cons1toE = consumers moving to east cell

Density1_X_E = fast moving consumer density in the east cell ratio to density in current cell

Food1_X_E = total amount of food sources in the east cell ratio to food in current cell

As can be seen in the above equation, the two factors determining movement out of the current cell are food density and competition. For example, if there are more “fast movers” in the current cell than in the east cell and if there is more food in the east cell, then fast movers will move into the east cell. The same conditions are used for movement to the north, south, and west cells. Note that this model does not have diagonal movement. Travel time dictates the speed at which consumers move into the next cell (values provided in Table 1).

3.4 Food Selection

The basic equation for determining consumption is as follows:

Ingest_1 = Cons_Ingest_Ep1+ Cons_ingest_Ph1+ Cons_ingest_RR1+ Cons_ingest_Sh1+ Cons_Ingest_Sw1+

Cons_ingest_Wr1

Where:

Cons_Ingest_Ep1 = amount of epiphytes consumed by fast movers

Cons_ingest_Ph1 = amount of phytoplankton consumed by fast movers

Cons_ingest_RR1 = amount of roots and rhizomes consumed by fast movers

Cons_ingest_Sh1 = amount of shoots consumed by fast movers

Cons_ingest_Wr1 = amount of wrack consumed by fast movers

The equation for the amount of epiphytes eaten is calculated by:

Cons_Ingest_Ep1 = Min (Epiphytes*Ep_need_1/Ep_need, (Ep_need_1/Need_Total_1)*Con_pot_ingest_1)

Where:

Epiphytes = total amount of epiphytes

Ep_need_1 = the total need for epiphytes by fast consumers

Ep_need = the total need for epiphytes by all consumers

Need_Total_1 = the total need for food by fast consumers

Con_pot_ingest_1 = the maximum total that fast consumers can ingest

Ingestion of the remaining food sources was derived in the same manner. “Need” is determined not only by whether or not the fast mover has a preference for a particular food source, but also by how much of a particular food source is available. For instance, if the fast consumer has a high preference for wrack, but there is not a lot of wrack available and they have a lower preference for shoots, but shoots are more abundant, the need for wrack might actually be lower than the need for shoots. This is shown by the following epiphytes “need” equation:

Ep_need_1 = (•Ep_Pref_1/pref_tot_1)+Ep_supply_1

Where:

•Ep_Pref_1 = the preference the fast movers have for epiphytes

pref_tot_1 = total preferences of fast consumers

Ep_supply_1 = ratio of epiphytes to the total food sources available to fast movers

Note that supply is calculated only if preference is greater than zero. Consumer preferences for all food sources are shown in Table 2. Preferences were determined by correspondence with various experts in the field of eelgrass communities and specifically, the Great Bay area. Several references also provided insight into the type of foods that various herbivores consume in eelgrass communities (Owen 1972, Fralick et al. 1974, Zimmerman et al. 1979, van Montfrans et al. 1982, Kitting 1984, Thayer et al. 1984, Hootsmans et al. 1985, and Percival et al. 1996).

The potential amount of food that could be consumed is determined by:

Con_pot_ingest_1 = MIN(Pot_food_1, (•Ingestion_rt * Activity_1 * CONSUMERS_1))

Where:

Pot_food_1 = total amount of food available to fast consumers

•Ingestion_rt = ingestion rate

Activity_1 = metabolic activity (described above in section 3.1)

The potential amount of food available to fast movers is calculated by:

Pot_food_1 = (EpiphytesEp_need_1/Ep_need)+ (PhytoplanktonPh_need_1/Ph_need)+ (RootsRhyRR_need_1/RR_need)+ (ShootsSh_need_1/Sh_need)+ (wrack*Wr_need_1/Wr_need)

Where:

Ep_need_1/Ep_need = this ratio (epiphytes used only as an example) distributes the total

amount of food available to all consumers so that:

Pot_food_1 + Pot_food_2 + Pot_food_3 =

Epiphytes+Phytoplankton+RootsRhy+Shoots+wrack

4. Model Evaluation

4.1 Calibration/Confirmation

Data on the relative amounts of consumer group activity in Great Bay is not readily available to calibrate the model at the present time. Data required includes: metabolic activity estimates (as discussed above), ingestion rates, group food preference distribution, and mortality rates. Estimates of parameter values currently used were made based on conversations with experts in the field as well as previous calibrations with available eelgrass data. As a result, this model is not confirmed for Great Bay and will be used only as a framework in the future.

5. Unit Model Simulations

The following subsections show results of the unit model run in STELLA^TM. Recall that the model in STELLA^TM is equivalent to one cell in the spatial model. There are no interactions between other cells and therefore, there is no movement into or out of the cell. The consumers sector was run jointly with the Great Bay Model. As mentioned above (Section 1), the purpose for development of the Great Bay Model is to better understand the causes/impacts of eelgrass depletion. Therefore, there were two scenarios modeled: a base scenario and an eelgrass depletion scenario. The base scenario has the same parameters and configurations found in Appendix A. The eelgrass depletion scenario was modeled by disabling eelgrass production to evaluate the impact of eelgrass loss on consumer behavior.

5.1 Base Run

The unit model was run for two calendar years at hourly timesteps using the parameters and configurations listed in Appendix A. Results are shown in Figures 6-11. All three consumer types gradually increase in biomass over the two years (Figure 6). Consumer biomass continues to increase for all three types until food sources decline, as shown in Figure 6a. Both slow moving and sedentary consumers peaked in amount ingested in the late fall (Figure 7) when shoots and roots are at a maximum, as can be seen in Figure 8. The amount of each food source ingested by fast moving consumers is shown in Figure 9. Slow moving consumer ingestion by food source peaks twice each year, in spring and in fall, as shown in Figure 10. This is when metabolic activity is at its peak, as discussed in Section 3.1. Ingestion of food sources by sedentary consumers is shown in Figure 11. Only food sources for which the consumer type has a preference are represented in Figures 9-11.

5.2 Depleted Eelgrass

An eelgrass depletion scenario was also simulated. This was done by assuming that there was no primary production of eelgrass. Results are shown in Figures 12-17. Interestingly, varying primary production between zero and the maximum value produced little difference in the biomass of consumers. This implies that there is enough eelgrass production (even at minimal primary production rates) to support herbivore consumption. As can be seen in Figure 12, consumers that had a high preference for any eelgrass component (shoots, roots and rhizomes, and wrack) did not grow nearly as much over the years as seen in the base run. The sedentary consumers, which mostly consume phytoplankton, did not experience as large a decline as the others. Consumers did not consume nearly as much during this simulation as shown in Figure 13. Depletion of eelgrass shoots caused depletion of roots and rhizomes, wrack, and epiphytes (Figure 14). As a result, once the eelgrass components were depleted, the only food source available to the consumers was phytoplankton (Figures 15, 16, and 17). Increased phytoplankton ingestion had a fairly significant impact on phytoplankton biomass, as seen when comparing Figures 8 and 14.

6. Spatial Dynamics

Spatial modeling results are shown in Figures 18-19. The spatial model is currently being run for one year. Results shown represent one cell chosen in an area of the bay where food biomass is readily abundant throughout the year. As can be seen in Figure 18, growth of fast moving consumers follows the same general pattern as food biomass for the particular cell, shown in Figure 19. Slow moving consumers increase more rapidly in the spring and fall, even though available food biomass is nearly identical to food available to fast consumers. There is a very slow increase in biomass of sedentary consumers as they are more confined to cells due to a slow travel time.

7. Discussion

The purpose of this analysis was to obtain a better understanding of the impacts herbivores can have on an eelgrass-based estuary. As can be seen from the results of this study, the amount of food ingested by each consumer type is dependent on several variables including preference, amount of food available, and metabolic activity. The results of the base run scenario suggest that with the current parameters and distributions, food abundance and metabolic activity have the greatest impact on consumption. In late fall and winter, fast and sedentary consumer consumption is at a maximum while slow consumers peak in ingestion in both spring and fall, as seen in Figure 7. Fast consumers ingest more shoots and roots and rhizomes than other food sources (Figure 9). Waterfowl dominate the fast consumer section due to a much larger biomass and primarily feed on the shoots and roots and rhizomes within this system (Thayer et al. 1984). As can be seen in Figure 8, shoots and roots and rhizomes constitute the major biomass of food sources. Therefore, with a high preference and high food abundance, ingestion will be at a maximum for these two food sources. Previous research has shown that the heaviest waterfowl grazing in eelgrass meadows primarily occurs during fall and winter (Thayer et al. 1984), which is accurately portrayed in this model. Ingestion by slow moving consumers seems to be far more dependent on metabolic activity as can be seen when comparing Figures 5 and 10. This implies that food is readily abundant to slow consumers and consumption is at the maximum that metabolic activity will allow. Sedentary consumer ingestion also appears to be more dependent on metabolic activity than food abundance (Figure 11). A possible explanation for consumption independence to food abundance is the relatively low ingestion rate.

The eelgrass depletion scenario was an attempt at determining the effects depleted food sources had on food selection and consumer growth. Eelgrass depletion in Great Bay in the 1980’s resulted from an outbreak of the “wasting disease” caused by the pathogenic organism Labyrinthula zosterae (Short et al. 1993). In this scenario, the depletion of eelgrass caused the mortality of epiphytes (Figure 14) resulting in a decline of consumers with a preference for both eelgrass and/or epiphytes. As all consumers had a preference for phytoplankton, consumers did not immediately leave the system or die from starvation. Rather, increase in biomass was greatly reduced over the year (Figure 12). As shown in Figures 15, 16 and 17, phytoplankton ingestion was dominant for all consumer groups.

Spatial modeling results provided further insight into consumer behavior in the bay. As shown in Figure 18, fast moving consumers increase in biomass throughout the year, which is consistent with the growth in food biomass (Figure 19). This validates the model constraints which force consumers to move from areas of low food abundance to areas of high abundance. In fact, consumer density seems to play a lesser role in determining consumer distribution as consumers aggregate in areas of high biomass. For waterfowl in particular, previous studies have shown that abundance is greater in areas of denser plant material (Thayer et al. 1984).

Fast-moving consumers have the highest travel times, so they can move to areas of greater food sources more rapidly, thereby making more food available to them than the slow or sedentary consumers. As shown in Figures 18 and 19, fast moving consumers increase in biomass at a much faster rate in the presence of a rapid increase in food biomass. Sedentary consumers, on the other hand, slowly increase in biomass throughout the year (Figure 18), enforcing the conclusion that travel time can significantly impact the growth of consumers throughout the year. These conclusions can easily be tested in Great Bay through an analysis of spatial distribution of herbivores (including observations of the cause of movement patterns).

Slow-moving consumers increase in biomass consistent with metabolic activity constraints shown in Figure 5. That is, during the spring and fall, when metabolic activity is at a maximum, slow-moving consumers experience the greatest increases in biomass. In fact, biomass tends to level off during the summer months as shown in Figure 18, regardless of available food biomass. This indicates that metabolic activity limits consumption rather than available food supply.

8. Conclusion

The results of this analysis should serve as an excellent guide for future field experiments. Several testable hypotheses have been formulated as a result of this analysis. First, results suggest that under normal conditions, food sources are readily abundant in eelgrass meadows to support herbivores. However, as seen in the eelgrass depletion scenario, depletion of food sources causes a rapid die-off of herbivores. This indicates that food availability only becomes the limiting factor in herbivore consumption during a die-off episode. Second, spatial analysis indicates that fast-moving consumers have the ability to move to food sources quicker than other types, therefore allowing for a rapid increase in biomass. As mentioned above, an analysis of spatial distribution of herbivores as well as an analysis of the cause of movement would provide more information to support this hypothesis. Finally, an analysis of the consequences of eelgrass depletion for herbivores would provide support for the eelgrass depletion scenario results.

There are several modifications that would improve the viability of this model. First, carnivores can have significant impacts on the feeding behavior of herbivores in several ways (Sogard et al. 1993). They introduce a stress, influence habitat selection, and can deplete herbivore populations. Second, competition between consumer types is currently not included in the model. Finally, the Great Bay Model is still a “work in progress.” As such, the consumers sector is only as valid as it relates to the structures in the other sectors determining food source magnitude.

In conclusion, the model framework provided in this paper provides an excellent basis for modeling consumer food consumption in an eelgrass-based estuary. Further data is needed to calibrate and confirm the model for Great Bay.

Acknowledgements

We would like to extend sincere gratitude to F. Short and D. Burdick, whose expertise with the Great Bay Estuary (as well as eelgrass dynamics) has been extremely useful. R. Costanza provided invaluable instruction in the area of model development. We would also like to thank three anonymous reviewers who provided excellent feedback for this paper.

References

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Connolly, R.M. 1994. The Role of Seagrass as Preferred Habitat for Juvenile Sillaginodes punctata (Cuv. and Val.) (Sillaginidae, Pisces): habitat selection for feeding? J. Exp. Mar. Biol. Ecol. 180: 39-47.

Fralick, R.A., K.W. Turgeon, and A.C. Mathieson. 1974. Destruction of Kelp Populations by Lacuna vincta (Montagu). The Nautilus. 88(4): 112-114.

Hochachka, P.W. 1983. The Mollusca, Volume 2, Environmental Biochemistry and Physiology. Academic Press, New York, NY.

Hootsmans, M.J.M. and J.E. Vermaat. 1985. The Effect of Periphyton-Grazing by Three Epifaunal Species on the Growth of Zostera marina L. Under Experimental Conditions. Aquat. Bot. 22: 83-88.

Kitting, C.L. 1984. Selectivity by Dense Populations of Small Invertebrates Foraging Among Seagrass Blade Surfaces. Estuaries. 7(4A): 276-288.

Nienhuis, P.H. and A.M. Groenendijk. 1986. Consumption of eelgrass (Zostera marina) by birds and invertebrates: an annual budget. Mar. Ecol. Prog. Ser. 29: 29-35.

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Sogard, S.M. and B.L. Olla. 1993. The Influence of Predator Presence on Utilization of Artificial Seagrass Habitats by Juvenile Walleye Pollock, Theragra chalcogramma. Env. Biol. Fish. 37: 57-65.

Thayer, G.W., K.A. Bjorndal, J.C. Ogden, S.L. Williams, and J.C. Zieman. 1984. Role of Larger Herbivores in Seagrass Communities. Estuaries. 7(4A): 351-376.

van Montfrans, J., R.J. Orth, and S.A. Vay. 1982. Preliminary Studies of Grazing by Bittium varium on Eelgrass Periphyton. Aquat. Bot. 14: 75-89.

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Figure 1. Great Bay Estuary showing important waterways.

Figure 2. Conceptual Great Bay Model, with major sectors noted.

General

Food Selection Food Availability

Movement Criteria

Figure 3. Conceptual structure of key components incorporated into each section of the consumers sector.

Figure 4. Fast Moving Consumers Section.

Table 1. Parameter values used to run the model.

Parameter	Symbol	Units	Value
Assimilation Efficiency	•C_egest_eff	No units	0.75
Mortality Rate	•C_mort_rt	Per hour	1e-6
Respiration Rate	•C_resp_rt	Per hour	1.25e-5
Ingestion Rate	•Ingestion_rt	Per hour	0.00125
Fast Movers Travel Time	•tt_1	Meters per day	10
Slow Movers Travel Time	•tt_2	Meters per day	1
Sedentary Travel Time	•tt_3	Meters per day	0.1

Table 2. Food Preference by food source and consumer type.

Food Source	Fast Moving	Slow Moving	Sedentary
Shoots	3	1	0
Roots and Rhizomes	3	2	2
Wrack	3	3	0
Epiphytes	1	4	0
Phytoplankton	0	1	8

Figure 5. Metabolic Activity expressed as a ratio from 0 –1, where Activity1 = fast moving consumers, Activity 2 = slow moving consumers and Activity 3 = sedentary activity.

Figure 6. Consumer biomass (kg/cell) over two years where consumers_1=fast movers, consumers_2=slow movers, and consumers_3=sedentary consumers.

Figure 6a. Decline in consumer biomass (kg/cell) only after decline in total food biomass (kg/cell).

Figure 7. Total Ingestion (kg/hour per cell) by consumer types where cons_ingest_1=ingestion by fast movers, cons_ingest_2=ingestion by slow movers, and cons_ingest_3=ingestion by sedentary consumers.

Figure 8. Food source biomass (kg/cell).

Figure 9. Ingestion (kg/hour per cell) of specific food sources by fast moving consumers.

Figure 10. Ingestion (kg/hour per cell) of specific food sources by slow moving consumers.

Figure 11. Ingestion (kg/hour per cell) of specific food sources by sedentary consumers.

Figure 12. Consumer biomass (kg/cell) with eelgrass depletion.

Figure 13. Eelgrass Depletion Scenario: total ingestion (kg/hour per cell) by consumer types where cons_ingest_1=ingestion by fast movers, cons_ingest_2=ingestion by slow movers, and cons_ingest_3=ingestion by sedentary consumers.

Figure 14. Eelgrass Depletion Scenario: food source biomass (kg/cell).

Figure 15. Eelgrass Depletion Scenario: ingestion (kg/hour/cell) of specific food sources by fast moving consumers.

Figure 16. Eelgrass Depletion Scenario: ingestion (kg/hour per cell) of specific food sources by slow moving consumers.

Figure 17. Eelgrass Depletion Scenario: ingestion (kg/hour per cell) of specific food sources by sedentary consumers.

Figure 18. Growth of consumers (in kilograms) in one cell of the spatial model.

Figure 19. Growth of available food biomass (in kilograms) in one cell of the spatial model.

Appendix A –Consumers Sector Equations

Using Fast Moving Consumers as an Example

Fast Moving Consumers

General Fast Moving Consumers

CONSUMERS_1(t) = CONSUMERS_1(t - dt) + (cons1_in_X + cons_ingest_1 - cons_egest_1 - cons_mortality_1 - cons_respiration_1 - cons1_out_X) * dt

INIT CONSUMERS_1 = •ic_consumer*•cell_size*.3

INFLOWS:

cons1_in_X = Cons1toE@W+Cons1toS@N+Cons1toW@E+Cons1toN@S

cons_ingest_1 = Ingest_1

OUTFLOWS:

cons_egest_1 = cons_ingest_1*•C_egest_eff

cons_mortality_1 = CONSUMERS_1*•C_mort_rt

cons_respiration_1 = CONSUMERS_1*Activity_1*•C_resp_rt

cons1_out_X = Cons1toE+Cons1toN+Cons1toS+Cons1toW

Activity_1 = ((H2O_Temp+10)^6)/((12^6)+((H2O_Temp+10)^6))

Consdens1 = CONSUMERS_1/•cell_size

•C_egest_eff = .75

•C_mort_rt = 1e-6

•C_resp_rt = 1.25e-5

•ic_consumer = •ic_mac_PhBio*.01

Food Available To Fast Consumers

OMtotBio_1 = Ep_Total_1+Ph_Total_1+RR_Total_1+Sh_Total_1+Wr_Total_1

Ep_Switch_1 = if •Ep_Pref_1>0 then 1 else 0

Ep_Total_1 = Epiphytes*Ep_Switch_1

Ph_Switch_1 = if •Ph_Pref_1>0 then 1 else 0

Ph_Total_1 = Phytoplankton*Ph_Switch_1

RR_switch_1 = if •RR_Pref_1>0 then 1 else 0

RR_Total_1 = RootsRhy*RR_switch_1

Sh_switch_1 = if •Sh_Pref_1>0 then 1 else 0

Sh_Total_1 = Shoots*Sh_Switch_1

Wr_Switch_1 = if •Wr_Pref_1>0 then 1 else 0

Wr_Total_1 = wrack*Wr_Switch_1

Movement Criteria

Cons1toE = if (Density1_X_E+Food1_X_E) > 1 then CONSUMERS_1 else (Density1_X_E+Food1_X_E)*CONSUMERS_1

Cons1toN = if (Density1_X_N+Food1_X_N) > 1 then CONSUMERS_1 else (Density1_X_N+Food1_X_N)*CONSUMERS_1

Cons1toS = if (Density1_X_S+Food1_X_S) > 1 then CONSUMERS_1 else (Density1_X_S+Food1_X_S)*CONSUMERS_1

Cons1toW = if (Density1_X_W+Food1_X_W) > 1 then CONSUMERS_1 else (Density1_X_W+Food1_X_W)*CONSUMERS_1

Density1_X_E = IF Consdens1 < Consdens1@E OR Consdens1 <1 THEN 0 ELSE Travel_time_1*((Consdens1-Consdens1@E)/Consdens1)

Density1_X_N = IF Consdens1 < Consdens1@N OR Consdens1 <1 THEN 0 ELSE Travel_time_1*((Consdens1-Consdens1@N)/Consdens1)

Density1_X_S = IF Consdens1 < Consdens1@S OR Consdens1 <1 THEN 0 ELSE Travel_time_1*((Consdens1-Consdens1@S)/Consdens1)

Density1_X_W = IF Consdens1 < Consdens1@W OR Consdens1 <1 THEN 0 ELSE Travel_time_1*((Consdens1-Consdens1@W)/Consdens1)

Food1_X_E = IF OMtotBio_1> OMtotBio_1@E OR OMtotBio_1 <1 THEN 0 ELSE (Travel_time_1)*(OMtotBio_1@E-OMtotBio_1)/OMtotBio_1

Food1_X_N = IF OMtotBio_1> OMtotBio_1@N OR OMtotBio_1 <1 THEN 0 ELSE (Travel_time_1)*(OMtotBio_1@N-OMtotBio_1)/OMtotBio_1

Food1_X_S = IF OMtotBio_1> OMtotBio_1@S OR OMtotBio_1 <1 THEN 0 ELSE (Travel_time_1)*(OMtotBio_1@S-OMtotBio_1)/OMtotBio_1

Food1_X_W = IF OMtotBio_1> OMtotBio_1@W OR OMtotBio_1 <1 THEN 0 ELSE (Travel_time_1)*(OMtotBio_1@W-OMtotBio_1)/OMtotBio_1

Travel_time_1 = •tt_1/24

•tt_1 = 96

Cons1toE@W = 0

Cons1toN@S = 0

Cons1toS@N = 0

Cons1toW@E = 0

Consdens1@E = 0

Consdens1@N = 0

Consdens1@S = 0

Consdens1@W = 0

OMtotBio_1@E = 0

OMtotBio_1@N = 0

OMtotBio_1@S = 0

OMtotBio_1@W = 0

Food Selection

Ingest_1= Cons_Ingest_Ep1+Cons_ingest_Ph1+Cons_ingest_RR1+Cons_ingest_Sh1 +Cons_ingest_Wr1

Cons_Ingest_Ep1 = Min(Epiphytes*Ep_need_1/Ep_need, (Ep_need_1/Need_Total_1)*Con_pot_ingest_1)

Cons_ingest_Ph1 = Min(Phytoplankton*Ph_need_1/Ph_need, (Ph_need_1/Need_Total_1)*Con_pot_ingest_1)

Cons_ingest_RR1 = Min(RootsRhy*RR_need_1/RR_need, (RR_need_1/Need_Total_1)*Con_pot_ingest_1)

Cons_ingest_Sh1 = Min(Shoots*Sh_need_1/Sh_need, (Sh_need_1/Need_Total_1)*Con_pot_ingest_1)

Cons_ingest_Wr1 = Min(Wrack*Wr_need_1/Wr_need, (Wr_need_1/Need_Total_1)*Con_pot_ingest_1)

Con_pot_ingest_1 = MIN(Pot_food_1, (•Ingestion_rt * Activity_1 * CONSUMERS_1))

Need_Total_1 = Ep_need_1+Ph_need_1+RR_need_1+Sh_need_1+Wr_need_1

Pot_food_1 = (Epiphytes*Ep_need_1/Ep_need)+ (Phytoplankton*Ph_need_1/Ph_need)+ (RootsRhy*RR_need_1/RR_need)+ (Shoots*Sh_need_1/Sh_need)+ (wrack*Wr_need_1/Wr_need)

pref_tot_1 = •Ep_Pref_1+•Ph_Pref_1+•RR_Pref_1+•Sh_Pref_1+•Wr_Pref_1

Ep_need_1 = (•Ep_Pref_1/pref_tot_1)+Ep_supply_1

Ep_supply_1 = if OMtotBio_1=0 then 0 else Ep_Switch_1*Epiphytes/OMtotBio_1

•Ep_Pref_1 = 1

Ph_need_1 = if •Ph_Pref_1=0 then 0 else (•Ph_Pref_1/pref_tot_1)+Ph_supply_1

Ph_supply_1 = if OMtotBio_1=0 then 0 else Ph_Switch_1*Phytoplankton/OMtotBio_1

•Ph_Pref_1 = 1

RR_need_1 = if •RR_Pref_1=0 then 0 else (•RR_Pref_1/pref_tot_1)+RR_supply_1

RR_supply_1 = if OMtotBio_1=0 then 0 else RR_switch_1*RootsRhy/OMtotBio_1

•RR_Pref_1 = 3

Sh_need_1 = Sh_supply_1+(•Sh_Pref_1/pref_tot_1)

Sh_supply_1 = if OMtotBio_1=0 then 0 else Sh_switch_1*Shoots/OMtotBio_1

•Sh_Pref_1 = 3

Wr_need_1 = if •Wr_Pref_1=0 then 0 else (•Wr_Pref_1/pref_tot_1)+Wr_supply_1

Wr_supply_1 = if OMtotBio_1=0 then 0 else Wr_Switch_1*wrack/OMtotBio_1

•Wr_Pref_1 = 3

•Ingestion_rt = 1.25e-3

Summation of Consumer Variables

Summation of Total Need

Ep_need = Ep_need_1+Ep_need_2+Ep_need_3

Ph_need = Ph_need_1+Ph_need_2+Ph_need_3

RR_need = RR_need_1+RR_need_2+RR_need_3

Sh_need = Sh_need_1+Sh_need_2+Sh_need_3

Wr_need = Wr_need_1+Wr_need_2+Wr_need_3

Summation of Consumer Ingestion

Cons_Ingest_Epi = Cons_Ingest_Ep1+Cons_Ingest_Ep2+Cons_Ingest_Ep3

Cons_Ingest_Ph = Cons_ingest_Ph1+Cons_ingest_Ph2+Cons_ingest_Ph3

Cons_Ingest_RR = Cons_ingest_RR1+Cons_ingest_RR2+Cons_ingest_RR3

Cons_Ingest_Sh = Cons_ingest_Sh1+Cons_ingest_Sh2+Cons_ingest_Sh3

Cons_Ingest_Wr = Cons_ingest_Wr1+Cons_ingest_Wr2+Cons_ingest_Wr3