Project Title: Modeling the effects of changes in turbidity on light available for submerged aquatic vegetation
Principal Investigator(s): Roger I. E. Newell, Raleigh R. Hood, Evamaria W. Koch, Raymond E. Grizzle.

Accomplishments
Scheduled Tasks:

1. Complete the development of our mathematical model in STELLA.
2. Test the predictions of light penetration and seagrass growth from our STELLA model at a hard clam aquaculture farm on the lower eastern shore of Chesapeake Bay where turbidities may have been reduced sufficiently by bivalve filtration to allow seagrass beds to become reestablished.
3. Publish manuscripts and refine the user interface of the model based on our preliminary demonstrations to resource managers. We will also make verbal and hands-on presentations to Chesapeake Bay resource managers, at scientific meetings, and distribute the model via the internet.

Progress on Tasks

1. Continued Model Development: Our model simulates seston concentration, water clarity and seagrass density as a function bivalve biomass and filtration, sedimentation and sediment resuspension. The core of the model consists of two equations, one of which describes the rate of change in seagrass biomass (B_sav):

   1) dB_sav /dt = u_m(1-e^(-I_avg/I_k))B_sav - rB_sav

   and a second which describes the rate of change of suspended seston concentration (S):

   2) dS/dt = [M(t_b-t_c) + w_sS - C_bSB_b]/Z_bot

   In (1) u_m is the maximum growth rate of seagrasses, I_avg is the average irradiance over the length of the shoot, I_k is the light saturation parameter for seagrass photosynthesis and r is a coefficient characterizing respiratory losses. In (2) M is the erosion rate, t_b and t_c are the bottom shear stress and the critical shear stress for resuspension, respectively, and w_s is the sinking rate of seston. In addition, in (2) C_b is the filtration rate of bivalves, B_b is the biomass of bivalves, and Z_bot is the distance from the water surface to the bottom. The model is cast in a vertically integrated form and with spatial units of m².

   In this model B_sav and S are dynamically modeled with B_sav determined in (1) by the balance between light-controlled growth and density-dependent respiration, and S is determined in (2) by the balance between sediment resuspension, sedimentation, and bivalve filtering. B_b is not dynamically modeled, i.e., the mass per unit area of bivalves is specified. Equation (2) feeds back on (1) through S, which partly determines I_avg in (1), and equation (1) feeds back on (2) through the wave attenuation of seagrasses on t_b.

   In order to calculate I_avg we include light attenuation due seston, K_s, self-shading by the seagrasses, K_sav, and all other light absorbing substances, K_x (i.e., water, dissolved matter and phytoplankton). And we split this attenuation vertically into two parts, attenuation above the shoots, K₁, which is due only to K_x and K_s:

   3) K₁ = K_x + K_s,

   and attenuation below the tips of the shoots, K₂, which also includes K_sav:

   4) K₂ = K_x + K_s + K_sav.

   For simplicity we assume that K_x is a constant. We estimate K_s = m₁S from seston concentration with m₁ derived from field and laboratory measurements. And we estimate K_sav = a_savB_sav using a seagrass biomass specific attenuation coefficient, asav, which is set to give realistic maximum seagrass densities. The incorporation of a self-shading effect in the model allows us to model maximum bed density as a function of depth and light attenuation in the overlying water column and vegetation. I_avg is calculated using a standard, non-spectral formula.

   For this application we assume that the critical bottom stress for resuspension, _{_c}, is a constant, and that the bottom stress, _{_b}, is determined primarily by waves:

   5) t_b = 1/2f_{w_}u²

   In 5) t_b is the maximum bottom shear stress due to waves, f_w is the wave friction factor, and u is the maximum wave-induced bottom velocity (Sanford 1994). In the model we calculate u as a function of a specified wave height, H, using standard (transitional) water depth-dependent formulae and we calculate f_w as a function of Reynold's number using Jonsson's diagram (Madsen 1976). The wave attenuation effects due to the presence of seagrasses are incorporated using a simple empirical model derived from direct observations from the Chesapeake Bay, which shows the degree to which waves are attenuated as a function of seagrass height and density. To our knowledge, this is the first time that the wave attenuation effects due to seagrasses have been incorporated into a shallow water model.

   This model has been implemented in STELLA and preliminary runs, with the seagrass model parameterized to simulate Ruppia maritima, give realistic seagrass depth distributions over the range of observed wave heights, i.e., equilibrium bed densities declining exponentially from about 2000 shoots m^-2 in shallow water, to 0 shoots m^-2 at 2 m depth. The model results also show that bivalve filtration can have a substantial effect on seston concentrations which, in turn, significantly influences the depth of distribution of the seagrass.

2. Obtain data to test the model at hard clam aquaculture farms.
   During the week of August 6 we spent 3 days in the field measuring over 3 tidal cycles changes in seston concentration (both using a Coulter Multisizer and by filtering water samples) and light attenuation over hard clam aquaculture beds. This was in cooperation with Dr. Mike Peirson, the manager of Cherrystone hard clam aquaculture cooperative on the lower eastern shore of Virginia on the Chesapeake Bay. Concurrently, we made the same measurements over an adjacent extensive bed of the seagrass Zostrea marina. The samples we collected still have to be fully processed and data analyzed. Once that is complete we can use these data to test our model predictions of the influence of bivalve feeding on light penetration and seagrass growth.
3. Disseminate Project Results
   We have made a start in disseminating the results of this work to the management and research community.

   Newell, R.I.E. Potential for N, P, and Sediment Removal by Oysters. Invited presentation at an "Exploratory meeting on nutrient/sediment removal by oysters" organized by EPA Chesapeake Bay Program. Annapolis MD March, 2001.

Difficulties Encountered
No major difficulties have been encountered so far in the development of this project. Because we only completed the field work on August 9, 2001 we still require some time to analyze those data. Once complete we can then test those actual filed data against predictions from the model.

Anticipated Success in Meeting Project Objectives in Scheduled Project Period
We have nearly met all the objectives we set out for this 24 month project. But as generally happens we still have several small aspects of the project that require completion. Based on our work plan we will have completed them all by February 2002.

Preliminary Results
After the June and October 2000 studies in the sea grass beds we were still uncertain about what was the minimum seagrass density necessary for wave attenuation. We performed 2 two-week deployments during which waves and seagrass density were measured. During the first deployment (Figure 1), although the seagrasses were present, their density was not high enough to lead to wave attenuation. During the second deployment (Figure 2), the first part of the time-series shows no wave attenuation but, when the seagrass density reaches values around 1,120 shoots m^-2, wave attenuation became evident during high energy/wave height events. This is the first time that it has been shown that wave attenuation is a function of seagrass density. The implications of this finding are many. For example, the reduction of seagrass density and not the entire loss of the vegetation may suffice to increase wave energy reaching the shorelines. Also, restoration projects need to restore seagrasses to a minimum density in order to effectively attenuate waves and consequently trap particles and increase nutrient loads in the sediments.

Tasks and activities for next reporting period

Tasks for the next reporting period

1. Complete the development of the predictive mathematical model incorporating all functional relationships and parameters derived from the field and laboratory studies. Carry out a full sensitivity analysis on the model.
2. Develop the user interface of the model. Make verbal presentations at scientific meetings and hands-on demonstrations of the model to resource managers. Make any needed improvements and refine the user interface.
3. Distribute the model via the internet.
4. Write paper describing the model and the results of the field research in scientific journals.

Work plan to accomplish tasks
We will complete the development and implementation of our mathematical model in STELLA.

Presentations and Dissemination of our Results
E.W. Koch and J.C. Stevenson have organized a special session at the Estuarine Federation Meeting to take place in St. Petersburg, FL entitled "Linking Seagrasses and Adjacent Plant and Animal Communities". This special session has the purpose of bringing together scientists interested in issues parallel to the link between oysters and seagrasses. The papers submitted include links between seagrasses and macroalgae, clam aquaculture and seagrasses and marshes and seagrasses. This session will also give us a good opportunity to disseminate the results of this CICEET study The following papers will be presented by our group:

Newell, R.I.E., E. Koch and R.R. Hood. Modeling influence of populations of suspension-feeding bivalves and seagrasses on suspended particulate load and consequent changes in light attenuation.

Hood, R.R. M. Wood, E. Koch and R.I.E. Newell. Modeling the interaction between wave-induced sediment resuspension, bivalve filtration and seagrass growth.

Koch, E.W. and M. Wood. Attenuation of wave height and period as a function of the vegetative state of a seagrass.

Concerns or difficulties
At this point we anticipate no major difficulties regarding the future progress of this project.

Expenditures
In no budget categories are expenditures exceeding estimates.