A REPORT SUBMITTED TO THE TAMPA BAY ESTUARY PROGRAM IN PARTIAL FULFILLMENT OF CONTRACT TBEP J.O.R. JOHANSSON ESTUARINE ECOLOGIST MAY 31, 2012

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1 APPLICATION OF A BIO-OPTICAL MODEL TO DETERMINE LIGHT AVAILABILITY (PAR) AT WATER DEPTHS REQUIRED TO REACH THE TAMPA BAY SEAGRASS RESTORATION GOAL, AND AT CURRENT DEPTHS OF DEEP TAMPA BAY SEAGRASS MEADOWS A REPORT SUBMITTED TO THE TAMPA BAY ESTUARY PROGRAM IN PARTIAL FULFILLMENT OF CONTRACT TBEP BY J.O.R. JOHANSSON ESTUARINE ECOLOGIST MAY 31, 2012

2 TABLE OF CONTENTS ACKNOWLEDGEMENTS... iv EXECUTIVE SUMMARY... v INTRODUCTION.. 1 METHODS Water Quality and Light Attenuation Data Sources. 2 Seagrass and Depth Data Sources. 3 Bio-Optical Model Description, Calibration and Verification 3 Partial Contribution to Total Kd by Water Quality Parameters. 4 Light Availability Calculations. 4 RESULTS Bio-Optical Model Verification... 4 Water Quality Parameters Required for Model Predictions 5 Light Availability at Bay Segment Specific Target Depths 5 Partial Contribution of Water Quality Parameters to Total Kd.. 6 Water Depth at the Current Deep Edges of Tampa Bay Seagrass Meadows... 6 Light Availability at the Deep Edge of Tampa Bay Seagrass Meadows.. 7 DISCUSSION AND CONCLUSION Apparent Light Requirements for Deep Tampa Bay Seagrass Meadows 7 Considerations of Seagrass Target Depths and Depths of Current Deep Seagrass Meadows 9 Considerations of Future Seagrass Expansion. 9 REFERENCES 11 ii

3 TABLE. 14 FIGURES iii

4 ACKNOWLEDGEMENTS Funding for this report was provided by the Tampa Bay Estuary Program. The overall support by Holly Greening and the technical support by Ed Sherwood is greatly appreciated. Janicki Environmental, Inc. kindly provided logistic and technical support. The assistance by Susan Janicki and Steven West (JEI) with data compilation and GIS illustrations is much appreciated. The following persons, and their respective agencies, provided water quality monitoring data and helpful information: Richard Boler and Joe Barron (EPC), Chris Anastasiou (FDEP), Paul Carlson, Grant McLaughlin and Laura Yarbro (FWC-FWRI), Greg Blanchard (MCNRD), Natashia Dickrell and Mark Flock (PCDEI), Kris Kaufman and Jaime Swindasz (SWFWMD). Their help is also much valued. Further, my former COT-BSG coworkers: Walt Avery, Kerry Hennenfent and John Pacowta are recognized for their dedicated and enthusiastic efforts in collecting and analyzing the COT data. Finally, the assistance, guidance, and review kindly provided by Dr. Charles Gallegos to facilitate the use of his bio-optical model for this project is greatly appreciated. iv

5 EXECUTIVE SUMMARY Recognizing the ecological importance of healthy and abundant seagrass meadows, the Tampa Bay Estuary Program and its partners in 1996 adopted a Tampa Bay seagrass restoration and protection goal of 15,380ha. Also adopted was a minimum bay-wide light availability target of 20.5%Io (surface irradiance) at bay segment specific depths required to reach the restoration goal (target depths). A management process to limit nitrogen loading to the system was developed and implemented to eventually increase the 1996 seagrass coverage of approximately 10,000ha to the adopted goal. Current seagrass expansion is progressing at a moderate long-term rate of about100ha per year. At that rate the bay-wide TBEP seagrass restoration goal may not be reached until A re-evaluation of the adopted seagrass management strategy was initiated in One aspect of the re-valuation process was to develop a calibrated and verified Tampa Bay specific bio-optical model to provide enhanced information on the light climate in the shallow waters where seagrass grow or could be expected to grow. The model was calibrated and verified using Tampa Bay water quality parameters (chlorophyll-a, turbidity [NTU] and color [CDOM]) and water column optical properties. Water quality information from eight Tampa Bay agencies, or joint agency projects, were used for model predictions of growing season (April through October) and annual water column light attenuation (Kd) for the major Tampa Bay segments and for smaller seagrass management areas (MA s). The partial contribution to the total Kd by each of the three water quality parameters required for model predictions was also estimated. Further, Kd coefficients were used for calculations of PAR light availability (%Io) at the deepest observed seagrass meadows along 56 fixed seagrass transects and also at depths required to achieve the seagrass restoration goal. Data analyses showed that the highest water column levels of chlorophyll-a and turbidity were generally found in the upper bay segments and in the northern section of Boca Ciega Bay. In contrast, the highest color levels were recorded for the Manatee River and for several MA s located close to the river. All three water quality parameters have near consistently higher values during the growing season. The areas with elevated water quality parameters have as expected high water column light attenuation. The contribution to the total Kd by turbidity is substantially greater than that for chlorophyll-a and color in the upper most bay segments, in Middle Tampa Bay segments 31 and 32, and in Boca Ciega Bay south. Turbidity accounts for about 40% of the total Kd in these areas. Color dominates the partial contribution in Lower Tampa Bay proper and south eastern MA s. In Manatee River, color attenuation accounts for nearly 45% of the total Kd. Light attenuation due to color is also dominant in the west central portion of OTB. Chlorophyll-a is not the dominant water quality component to the total Kd for any bay segment or MA; and generally contributes between 25% and 30% of the total light attenuation. The upper eastern Tampa Bay segments have the highest light availability at the respective target depths, with %Io ranging between 30% and 40%. Lower %Io than the established minimum 20.5% at target depths are generally found in the lower bay segments. Examination of the 56 fixed seagrass transects with a distinct deep seagrass edge found that about ten transects have a current deep edge which is near, or slightly exceeds, the established depth target. The deepest seagrass edge was identified at Egmont Key, at the v

6 mouth of the bay, at near -2.7mLMTL and the shallowest edge was located in the upper Hillsborough Bay at -0.17mLMTL. The average annual light availability at all deep seagrass edges examined is greater than the minimum 20.5%Io light target. The deep seagrass edge at Egmont Key receives the lowest average annual light level (23.0%Io) of any bay segment or MA. In contrast, the deep seagrass edges in the upper bay segments of Hillsborough Bay, and Middle Tampa Bay segments 31 and 32 receive above 45%Io; indicating that the upper bay deep seagrass meadows are established at higher apparent light conditions than the adopted minimum bay-wide light target. High apparent light requirements have also been reported from several other studies of seagrass growing in shallow eutrophic and turbid waters; and have partially been attributed to a competition for photosynthetically efficient light required both by the seagrass and the phytoplankton. Although, water column light attenuation is generally considered the major controlling factor; numerous physical, chemical and biological factors that are not directly linked to optical water quality parameters also affect seagrass growth and depth distribution. The current study did not aim to differentiate between water quality and non-water quality related effects on the apparent seagrass light requirement. However, a linear regression analysis between the maximum depth of the deep seagrass meadows and the respective water column light attenuation coefficient indicates that the latter can explain approximately 54% of the variation in depth of the deep seagrass meadows. The depth of the current deep seagrass meadows was on average, for all bay segments and MA s, shallower than the respective depth that would be required to achieve the seagrass restoration goal; suggesting that continued improvements in water quality and light climate may be required to accomplish the goal. A prediction of the potential water column light conditions that might be required to reach the goal was, however, not attempted. The value of such a prediction is uncertain because the seagrass depth distribution is not solely determined by water column light attenuation. The prediction would not account for undetermined and possible important physical, chemical and biological processes that also affect seagrass growth. With expected synergistic impacts between ongoing management actions to control local and nationwide water quality, and naturally occurring physical, chemical and biological processes that also affect seagrass growth; the ultimate water quality conditions that will be required to achieve the specific seagrass restoration goal for Tampa Bay are uncertain. Nevertheless, protecting light conditions at levels observed in this study will be important for continued success in achieving sustained Tampa Bay seagrass expansion. vi

7 INTRODUCTION Seagrass is a vital natural resource present in many shallow estuaries and coastal areas of the world, including Tampa Bay. Recognizing the importance of healthy and abundant seagrass meadows, the Tampa Bay Estuary Program (TBEP) and its partners adopted, in 1996, a Tampa Bay seagrass restoration and protection goal of 15,380ha. The goal was based on apparent seagrass coverage identified from ca.1950 aerial photography. A TBEP directed management process was developed and adopted for the purpose to eventually accomplish the seagrass restoration and protection goal. The selected management strategy, which has been thoroughly documented by Greening et al. (2011), links the management of nitrogen loading to Tampa Bay to water clarity and seagrass abundance. The two most relevant components of this strategy, for the purpose of this report, are summarized below: 1. The light requirement of deep Tampa Bay seagrass meadows was determined through field observations of Thalassia testudinum (turtle grass) meadows growing in Lower Tampa Bay (LTB; Dixon 2000). The selected deep meadows received approximately 20.5 percent of surface irradiance (%Io) on an annual average. The author emphasized, however, that the estimated 20.5%Io seagrass light requirement, did not account for additional potential light reductions caused by epiphytes, and should therefore be considered as a minimum light requirement for Tampa Bay seagrass. 2. Water clarity and light penetration targets were established for the four major bay segments that would allow a minimum of 20.5%Io to reach depths that were similar to the estimated deep extent of seagrass meadows in ca (Janicki et al. 1995; Janicki and Wade 1996). In this report, those depths will be referred to as seagrass target depths or the depths required reaching the Tampa Bay seagrass restoration goal. Target depths have recently also been estimated at several other Tampa Bay segments using a similar process (Janicki Environmental. Inc. 2011). Table 1 lists seagrass target depths by bay segments that were established from the ca mapped seagrass distribution. Seagrass expansion in Tampa Bay is currently progressing at an approximate average longterm rate of 100ha per year. At that modest rate, the bay-wide TBEP seagrass restoration goal would not be reached until 2050 (Greening et al. 2011). To address factors contributing to slow or stalled seagrass recovery in specific areas of the bay, the TBEP in accordance with its adaptive management strategy initiated a re-evaluation of the initial seagrass management strategy in 2007 (see TBEP 2006). One aspect of the re-valuation process, which is the focus of this report, was to develop a calibrated and verified Tampa Bay specific bio-optical model to provide enhanced information on the light climate in the shallow waters where seagrass grow or could be expected to grow. It should be emphasized that the optical model derived light information only addresses water column caused impacts to the light climate and does not account for potential physical, chemical and biological processes, not directly linked to water quality conditions, which also affect seagrass growth. Optical model calculations were used to estimate light attenuation coefficients for the major Tampa Bay segments and for 30 smaller seagrass management areas (MA s) that were defined during the re-evaluation process. In addition, light availability was determined at depths required to reach the Tampa Bay seagrass restoration goal and at the deepest 1

8 observed seagrass meadows in Tampa Bay segments and MA s over the current time period. Seagrass information from the 62 fixed seagrass transects monitored annually by the Tampa Bay Interagency Seagrass Monitoring Program under the direction of the TBEP assisted in the latter depth determinations (see Avery and Johansson 2010). METHODS Water Quality and Light Attenuation Data Sources: The water quality and light attenuation information examined in this project was specifically selected to characterize water quality and light availability conditions in the near-shore areas of Tampa Bay delineated by the 30 designated seagrass MA s (Figure 1). The individual agencies, or joint agency projects, contributing data to this project are identified in Table 1. Also identified in this table are the specific MA s and bay segments sampled by each agency or project, the respective period of data collection and the number of samples utilized in this study for model predictions of diffuse light attenuation coefficients (Kd s). Further, the geographical locations of samples used, specific to each agency or project, are shown in Figures 2 through 8. The majority of MA s have water quality information that includes the three water quality parameters (chlorophyll-a concentrations, turbidity [NTU] and color [CDOM]) required for bio-optical model predictions of Kd s. Color measurements that were reported as platinum cobalt units (PCU) were converted to CDOM absorption at 440nm using an equation provided by Gallegos (2005). A lack of water quality information precluded model predictions for MA13, MA14 and MA15, located in northern Boca Ciega Bay (BCBN), MA17 in Middle Tampa Bay segment MTB33, MA19 in MTB32 and Old Tampa Bay (OTB), and MA29 in Hillsborough Bay (HB). Although field measured Kd s are available for all BCBN MA s, and also for MA17 and MA19, these were not used for estimates of light availability at the target depths. They were excluded because the field measured Kd s were obtained at considerable shallower depths than the respective target depths. Spectral shift (narrowing) of the photosynthetic available radiation (PAR) spectrum with increasing depth could substantially weaken estimates of light availability at the target depths when calculated from shallow depth measured Kd s. Field measured Kd s available for the verification of model predicted Kd s are sparser than field measurements of the required three water quality parameters. Only 10 of the 30 MA s have available measurements. However, model verification for the majority of bay segments was possible by incorporating sample locations located in deeper water outside the MA delineation. Further, field measured Kd s for model verification were available for four of the seven agencies or projects. The agencies utilized both spherical (4π) and cosine (2π) LI-COR quantum sensors for these measurements. In addition, the three required water quality parameters, and model predicted Kd s and light availability calculations, were examined on both an annual and seasonal (April through October) seagrass growing season basis. Further, the relative percent contribution of the three water quality parameters to the total estimated Kd was also calculated. 2

9 Seagrass and Depth Data Sources: Recent information for 62 Tampa Bay seagrass transects (see Avery and Johansson 2010 and Figure 1) was examined and used to the greatest extent possible to determine the locations of the deepest extent of seagrass (also referred to as the deep edge) present along each transect. However, examination of recent Google Earth (GE) photos of the transect locations was also used to further enhance the determination of deep edge locations at several transects (see Figure 9). The deep edge locations appeared reasonably stable for most transects over the recent three to four years. The geographical position for each identified deep edge was determined from GE lat/lon coordinates. The water depth of the deep edge measured in meters (m) at local mean tide level (LMTL) was then obtained from the High resolution Bathymetry of Tampa Bay depth sounding data base (Hansen 2003). Note that the Tampa Bay seagrass target depths were developed from interpretation of mapped seagrass distribution (see Janicki et al. 1995; Janicki and Wade 1996) and differs from the method used to determine the depth of the current deep seagrass edges. Developing mapped distributions of current deep seagrass edges in a similar manner to the established targets was beyond the scope of this project. Bio-Optical Model Description, Calibration and Verification: The bio-optical model employed was developed by Dr. Charles Gallegos of the Smithsonian Environmental Research Center in Maryland. His model has been applied in numerous estuaries and coastal areas in the USA and elsewhere to evaluate water quality impacts on seagrass growth (see Biber et al for a detailed discussion of the Gallego s model). Dr. Gallegos has kindly provided assistance, guidance, and review to facilitate the use of his model for the Tampa Bay seagrass management re-evaluation strategy. Model calibration was performed by Dr. Gallegos using measurements of inherent and apparent optical properties of Tampa Bay waters provided by Dr. Chris Anastasiou and the City of Tampa Bay Study Group (see Johansson 2007; Anastasiou 2009). The optical properties used for the model calibration were measured in near-shore locations at the Kitchen area in HB, the Wolf Branch and North Shore Park areas in MTB and off the east shore of the Egmont Key in LTB (see Figure 1). Model verification was performed by the author by comparing field determined Kd s with model predicted Kd s. The model predicted Kd s were derived from near simultaneous field collections of the three water quality parameters required for model predictions and the field measurements of Kd. The comparison of field measured and model predicted Kd s was limited to light attenuation measurements most often conducted at water depths from 1.0m to 1.5m, and model predictions for the 1.25m depth, to ensure comparable depth influenced optical characteristics of the Kd estimates. A secondary verification of model predicted Kd s was conducted by comparing Kd s derived from the Gallegos model with Kd s predicted from Tampa Bay Secchi disk transparency measurements (see Janicki and Wade 1996), over the same time periods and bay segments. The latter method of Kd predictions is used by the TBEP in the annual Decision Matrix 3

10 evaluation of Tampa Bay chlorophyll-a concentrations and light availability levels (see Sherwood 2011). Partial Contribution to Total Kd by Water Quality Parameters: The partial contribution to the model predicted total Kd by each of the three water quality parameters required for model predictions was estimated on an annual and growing season basis for bay segments and MA s. For these calculations, the contribution by chlorophyll-a was adjusted to also include a 25% fraction of the turbidity value in order to account for turbidity associated with phytoplankton cells (Anastasiou and Gallegos personal communications; also see Biber et al. 2008). Consequently, the turbidity values were reduced by 25% when accounting for the total Kd turbidity fraction. Light Availability Calculations: Predicted Kd s from the three water quality parameters were also used to estimate the percent surface irradiance, or light availability, at depths required to reach the Tampa Bay seagrass restoration goal and at the current deep edges of the seagrass meadows. The percent surface irradiance (%Io) at depth was calculated using the Lambert-Beer law (see Gallegos 2001). RESULTS Bio-Optical Model Verification: The primary bio-optical model verification process, comparing the field determined and model predicted light attenuation coefficients was performed with the available information sorted on sampling agency or project basis, bay segment basis and MA basis (Figures 10 and, 11A and B). All three presentations show that the range of the field measured Kd s generally are greater than the range of the predicted Kd s. However, the median Kd values are generally very similar which suggests a strong agreement between measured and predicted Kd s. In a few MA s, with a low number of data points available for the comparison, the deviation between the medians is greater. The secondary verification, comparing the Gallegos model predicted Kd s with Kd s predicted from Tampa Bay Secchi disk transparency measurements on a bay segment basis (Figure 12), shows a generally strong agreement for all bay segment except OTB. For OTB, the median Kd calculated from Secchi disk measurements is substantially greater than the median predicted by the Gallegos model. Thus, calculations of estimated OTB light availability at depth using the Secchi disk - Kd relationship will in general be lower than estimates derived from the Gallego s model. 4

11 Water Quality Parameters Required for Model Predictions: The three water quality parameters required for the bio-optical model predictions of Kd show large variations among bay segments and MA s (Figures 13A and B, 14A and B, and 15A and B). The highest concentrations of chlorophyll-a and levels of turbidity are generally found in the upper bay segments and in the northern section of Boca Ciega Bay (BCBN). In contrast, the highest color levels occur in the Manatee River (MR) management area (MA9) and in several management areas located close to the MR. The variation in water quality values based on annualized and seasonal (April through October) measurements are also presented in these figures. All three water quality parameters have near consistently higher values during the seasonal growing period. Model predictions of water column light attenuation for bay segments and MA s with available water quality information required for model predictions also vary substantially among areas (Figures 16A and B). The areas with high water quality parameter values have as expected, the highest Kd s. Further, seasonal Kd s estimated for the growing season are generally higher than annual values. A lack of water quality information precluded model predictions of Kd and light availability calculations for BCBN (MA13, MA14 and MA15), MA17, MA19 and MA29. Light Availability at Bay Segment Specific Target Depths: The model predicted Kd s for bay segments and MA s with water quality information available for model predictions were used to calculate the percent surface irradiance available at the bay segment specific seagrass target depths (see Table 1) and at the corresponding target depth for the MA s (Figures 17A and B). Further, the predicted light availability at the target depths was examined on the annual and seasonal growing season basis, and was also compared to the established minimum Tampa Bay-wide light availability target of 20.5%Io. Although field measured Kd s are available for several areas with no data shown, these were not used for estimates of %Io at the target depths (see discussion above). Light availability at the respective target depths for bay segments and MA s, is, in most cases, lowest during the growing season. The variation between the two periods examined on a bay segment basis is generally not large; however, several MA s have considerably lower light availability during the growing season. The upper eastern Tampa Bay segments, HB and MacKay Bay (MKB), and the respective MA s, have the highest %Io at the target depths, with values ranging between 30% and 40%. Light availability lower than the established 20.5%Io minimum light target are generally found in lower bay segments (including BCBS) and their respective MA s. Also, the two MA s (23 and 24) in the uppermost section of OTB have considerably lower than target values during the growing season. Further, the lowest light availability at target depth, near 16%Io, was estimated for MA 10 in the most southern section of LTB. 5

12 Partial Contribution of Water Quality Parameters to Total Kd: The partial contribution to the total model predicted Kd by each of the three water quality parameters required for model predictions was estimated on the annual and seasonal basis for bay segments and MA s (Figures 18A and B). The contribution to the total Kd by turbidity is substantially greater than that for chlorophyll-a (adjusted for phytoplankton derived turbidity; see above) and color in the upper most bay segments of MKB, HB and OTB, and the respective MA s; and also in MTB31, MTB32 and BCBS. Turbidity accounts for about 40% of the total Kd in these areas. Color dominates the partial contribution to the total Kd in LTB proper and the south eastern bay segments of MR, BH, TCB. However, light attenuation due to color is also dominant in MA22, in the west central portion of OTB. In MR, color attenuation accounts for nearly 45% of the total Kd. Chlorophyll-a does not appear to be the dominant water quality component to the total Kd for any bay segment or MA. The chlorophyll-a contribution shows the least variation between areas; and generally contributes between 25% and 30% of the total light attenuation. Water Depth at the Current Deep Edges of Tampa Bay Seagrass Meadows: A search among the 62 Tampa Bay seagrass transects (see Figure 1) identified a distinct deep seagrass edge at 56 transects (Figure 19). An illustration of the deep seagrass edge located on the North Shore Park transect in MTB is shown in Figure 9. The distribution of transects by bay segments and MA s is shown in Figures 20A and B. It should be noted that many MA s only include one or two transects with a distinct deep seagrass edge. The water depths (mlmtl) of the deep seagrass edge at the 56 transects, and the respective target depth of the bay segment in which the transects are located, are compared in Figure 21A, and B. As noted above, the deep edges at seagrass transects were determined differently than the bay segment specific target depths. The majority of the 56 transects have current deep seagrass edges that are substantially shallower than the established bay segment specific seagrass depth target; only about ten transects have deep edges which are near, or slightly exceed, the target depth. The bay-wide distribution of the latter transects is, however, quite evenly distributed with two or three transects in each major bay segment, except for BCBS (Figure 21A). This bay segment does not have any transect with a deep edge closer than 0.8m of the 2.8m target depth. The deepest Tampa Bay seagrass edge was identified for the Egmont Key transect at a nearly -2.7mLMTL. A comparison of bay segment target depths and deep seagrass edge depths is shown in Figure 21B. Of all bay segments, the median depth (-0.8mLMTL) of the current HB deep seagrass edges appears closest to the respective bay segment target depth of -1.0mLMTL. Bay segment MTB32 has the greatest discrepancy between target depth (-2.0mLMTL) and the median deep seagrass edge depth (-0.6mLMTL), however, the range for the deep edge depths of the three transects included for this bay segment is large, varying from -0.50mLMTL to -2.0mLMTL. The geographical distribution of deep seagrass edge depths by MA is shown in Figure 21C. As expected, the maximum seagrass depth generally increases from the upper portions of the bay towards the Gulf of Mexico. 6

13 Light Availability at the Deep Edge of Tampa Bay Seagrass Meadows: The water depth at the 56 transects with an identified deep seagrass edge were averaged on a bay segment and MA basis. The depths and the field measured water quality information for the respective area of interest were then applied to the bio-optical model to predict the average water column light attenuation and the average light availability at the deep seagrass edges. Model calculations were made for all bay segments except BCBN and MTB32, and MA s located within these bay segments. These bay segments lacked color measurements required for Kd model predictions; however, field measured Kd s were available and used in lieu of model predicted Kd s. It appeared reasonable to use the field measured Kd s in these calculations, because the depths of the deep seagrass edges in these bay segments approximate the depths of the field measured Kd s, thus minimizing the potential error of PAR spectrum narrowing with depth. Percent surface irradiance at the deep seagrass edges was examined on an annual and growing season basis for bay segments and MA s (Figure 22A and B). Similar to the previous discussion of light availability at target depths (see Figure 17A and B), light availability at the deep seagrass edges is most often lowest during the growing season. However, variations between the two seasonal periods are generally not large, and therefore, the following discussion will be limited to annual information. The average annual light availability at the deep seagrass edges for all bay segments and MA s examined is greater than the minimum 20.5% light target. The transect located in MA11 (Egmont Key) at the mouth Tampa Bay receives the lowest average annual light levels (23.0%Io) of any bay segment or MA. In contrast, the deep seagrass edges in the upper bay segments of HB, MTB31 and MTB32 receive above 45% of surface irradiance. The deep edge of the transect located in MA2, in the upper north western area of HB, receives the highest %Io of all areas, near 82%. The geographical distribution of annual light availability at the deep seagrass edges by MA is shown in Figure 22C. A general pattern of decreasing %Io from the upper portions of the bay towards the Gulf of Mexico is evident. DISCUSSION AND CONCLUSION Apparent Light Requirements for Deep Tampa Bay Seagrass Meadows: The annual average percent surface irradiance at the current deep seagrass edges exceeds the 20.5% minimum light target for all bay segments and MA s (Figure 22A and B). The discrepancy between the light target and the calculated light availability is particularly large for the upper bay segments; areas that were not included in the 2002 study to establish the Tampa Bay-wide minimum light target. The deep seagrass meadows in these areas primarily consist of Halodule wrightii (see Avery and Johansson 2010); and they receive an estimated annual average %Io of 40% or higher. In contrast, the deep seagrass edges located in the lower bay segments, which generally are dominated by the 2002 study target species T. testudinum, receive lower annual average %Io. Specifically for LTB proper, the area where the light target was established, light availability at the deep meadows approximates the target at near 23%Io. Thus, the 20.5% bay-wide minimum seagrass light target appears 7

14 appropriate for lower bay segments, however, current light conditions at the deep meadows in the upper bay segments are much higher than the established target. The high percent surface irradiance levels present at the deep seagrass meadows in the upper bay segments suggests that factors other than light (see below) also contribute importantly to the depth distribution in these areas. Consequently, the reported high irradiance levels are most probably overestimates and not true measures of the compensation seagrass light requirement; and should be described as apparent light requirements. Still, it is possible that the shallow growing H. wrightii has a higher true light requirement than that established for the target species. To ascertain seagrass species specific true light requirements, and possible distributional patterns thereof, was beyond the aim of this project. High percent surface irradiance levels and high apparent seagrass light requirements, similar to those estimated for the upper Tampa Bay segments, have been reported in several other studies of seagrass growing in shallow eutrophic and turbid waters (Kenworthy and Fonseca 1996; Steward el al. 2005; Duarte et al, 2007; Gallegos et al. 2009); and also for deep seagrass meadows in Sarasota Bay by Dixon and Kirkpatrick (1995) and Tomasko (personal communication). The high apparent light requirement for seagrass in eutrophic waters has partially been attributed to a competition for photosynthetically efficient light required both by the seagrass and the phytoplankton (see Duarte et al. 2007). In eutrophic waters with high phytoplankton biomass, photosynthetically important light in the blue and red regions of the visible spectrum is rapidly absorbed in the water column. The light at depth, and available for seagrass growth, therefore, contains a greater proportion of light with wavelengths less efficient for photosynthesis. A relatively high percentage of surface irradiance would be required to sustain seagrass growth in eutrophic waters, such as the upper Tampa Bay segments, in order to compensate for important light quality losses occurring in the water column. In less eutrophic waters, such as LTB proper, proportionally less photosynthetically efficient light is absorbed with depth, and the seagrass meadows require a relatively lower percentage of surface irradiance to sustain growth. Water column light attenuation is often considered the major factor controlling the seagrass depth distribution. However, the apparent light requirement for the deep seagrass meadows, specifically in shallow and eutrophic systems, may also reflect numerous physical, chemical and biological factors that affect seagrass growth and depth distribution, and which may not be directly linked to optical water quality parameters (see Dixon 2000; Blakesley et al. 2001; Koch 2001; Fonseca et al. 2002; Lewis 2002; Steward el al. 2005; Duarte et al. 2007; Johansson et al. 2009, Carlson et al. 2011; Greening et al. 2011). These may include: climatic conditions, fresh-water inflow, salinity, water temperature, water and sediment dissolved oxygen conditions, wave action and other mechanical impacts, sediment composition, sediment erosion and accumulation, nutrient availability and potential toxicity, seagrass species composition, competition for space by benthic fauna and other flora, such as the attached green algae Caulerpa, shading from epiphytes and drifting macro-algae, grazing, disease and other factors. The current study did not aim to differentiate between water quality and non-water quality related effects on the apparent light requirement of the deep Tampa Bay seagrass meadows. 8

15 However, a visual comparison between the maximum depth of the deep seagrass meadows and the respective water column light attenuation coefficient for bay segments and MA s indicates a positive relationship between the two variables (see Figures 21C and 23). Further, a linear regression analysis of the two variables on an MA basis suggests that water column light attenuation can explain approximately 54% of the variation in depth of the deep seagrass meadows (Figure 24). A similar analysis by Virnstein et al. (2002) found a comparable value of about 52% for the Indian River Lagoon. Considerations of Seagrass Target Depths and Depths of Current Deep Seagrass Meadows: A distinct deep seagrass edge was identified at 56 of the 62 Tampa Bay seagrass transects. The average depth of the deep edges is for all bay segments and MA s shallower than the respective depths required to reach the Tampa Bay seagrass restoration goal, although several individual transects have deep edges that approximate, or exceed the target depth (see Figure 21A and B). It is likely that continued improvements in water quality and light climate will help extend seagrass growth to depths that eventually may be sufficient to reach the Tampa Bay seagrass restoration goal. However, recent seagrass coverage estimates by the Southwest Florida Water Management District (SWFWMD) from interpretations of aerial photography indicate that bay segments located in the lower portion of Tampa Bay, including MTB, LTB, BCB, TCB and Manatee River (MR), have seagrass coverage that are very near the historic ca coverage, or as for BCB and MR exceed the historic coverage (Kaufman personal communication). The high acreages reported in these areas, despite that the current deep seagrass meadows do not reach target depths, may be due to expansion of meadows in areas shallower than the target depth; and which were not present (see Janicki et al. 1995), or not identified, in the historic mapping effort. Considerations of Future Seagrass Expansion: It may be tempting to use the information gathered in this study to predict the potential water column light conditions that would be required for the seagrass to extend growth to the target depths. However, the value of such a prediction is uncertain because it would not account for undetermined and possible important physical, chemical and biological factors which, in addition to water column light attenuation, also affect seagrass growth and depth distribution (see above). Further, a prediction of future seagrass distribution based on current estimates of water column light conditions would assume that bay conditions and the apparent seagrass light requirement remains static over time. Long-term bay monitoring has conclusively shown that, especially the upper portions of Tampa Bay have over the last several decades transitioned from areas with high phytoplankton dominance to areas with increasing seagrass contributions to primary production (see Johansson 2005). These long-term changes are expected to continue as a result of recent local and nationwide efforts (e.g. reductions of atmospheric deposition from fossil fuel combustion; see Dennis and Arnold 2007) to further control eutrophication; and will most likely result in enhanced light conditions at depth and a progression of, particularly, the upper bay seagrass meadows to greater depths. Improved water quality conditions may also reduce epiphyte and macroalgae coverage; and further seagrass growth and depth expansion (see Dixon 2000; 9

16 Johansson 2002). In addition, seagrass meadows themselves contribute important positive feed-back functions by improving the light climate through reduced sediment resuspension, increased nutrient uptake, and other functions (see Koch 2001; Bos 2007: de Boer 2007; McGlarity et al. 2007; van derheide 2007). With a continued increase in area coverage of Tampa Bay seagrass (see Greening et al. 2011), the seagrass feed-back functions would be expected to become increasingly important. In consideration of expected synergistic impacts from local and nationwide actions to control water quality and difficult to predict natural feed-back functions; the ultimate water quality conditions that will be required to achieve the specific seagrass restoration goal for Tampa Bay are uncertain. Nevertheless, the success of ongoing and future Tampa Bay management actions to protect water quality and light conditions, and the possible utilization of additional means for seagrass restoration, are, and will be, important for achieving continued Tampa Bay seagrass expansion. 10

17 REFERENCES Anastasiou, C.J Characterization of the underwater light environment and its relevance to seagrass recovery and sustainability in Tampa Bay Florida. Ph.D. dissertation, College of Marine Science, University of South Florida. Avery, W.M. and J.O.R. Johansson Data Summary from the Tampa Bay Interagency Seagrass Monitoring Program through year Prepared by the City of Tampa Bay Study Group. Tampa Bay Estuary Program Tech. Publ p. Biber, P.D., C.L. Gallegos and W.J. Kenworthy Calibration of a bio-optical model in the North River, North Carolina (Albemarle Pamlico Sound): A tool to evaluate water quality impacts on seagrasses. Estuaries and Coasts 31: Blakesley, B., P. Hall, D. Berns, J Hyniova, M. Merello and R. Conroy Survey of the distribution of the marine slime mold Labyrinthula sp. in the seagrass Thalassia testudinum in the Tampa Bay Area, Fall 1999-Fall Prepared by the Florida Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission. Tampa Bay Estuary Program Tech. Publ Bos, A.R., T.J. Bouma, G.L.J. de Kort and M.M. van Katwijk Ecosystem engineering by annual intertidal seagrass beds: sediment accretion and modification. Estuarine Coastal Shelf Science 74: Carlson, P., L. Yarbro, A. Ritzmann, H. McKnight, A. Viaud, K. Almeida, C. Nosach, and P. Julian Characterization of the underwater light environment and its relevance to seagrass recovery and sustainability in Tampa Bay, Florida. Prepared by the Florida Wildlife Research Institute, Florida Fish and Wildlife Conservation Commission. Tampa Bay Estuary Program Tech. Publ de Boer, W.F Seagrass-sediment interactions, positive feedbacks and critical thresholds for occurrence: a review. Hydrobiologia 591:5-24. Dennis, R.L. and J. Arnold CMAQ UCD Atmospheric Deposition Estimates to Tampa Bay Watershed Sub-basins and Tampa Bay Waters. Submitted by USEPA, NERL, RTP, NC. Tampa Bay Estuary Program Tech. Publ p. Dixon, L.K Establishing light requirements for the seagrass Thalassia testudinum; An example from Tampa Bay, Florida, p In Bortone, S.A. (ed.), Seagrasses: Monitoring, Ecology, Physiology, and Management. CRC Press, Boca Raton, Florida. 318p. Dixon L.K. and G. Kirkpatrick Light attenuation with respect to seagrasses in Sarasota Bay, Florida. Submitted to Sarasota Bay National Estuary Program, Mote Marine Laboratory Technical Report Number p. Duarte, C.M., N. Marba, D. Krause-Jensen and M. Sanches-Camacho Testing the predictive power of seagrass depth limit models. Estuaries and Coasts 30:

18 Fonseca, M.S., B.D. Robbins, P.E. Whitfield, L. Wood and P. Clinton Evaluating the effects of offshore sandbars on seagrass recovery and restoration in Tampa Bay through ecological forecasting and hindcasting of exposure to waves. Tampa Bay Estuary Program Tech. Publ p. Gallegos, C.L Calculating optical water quality targets to restore and protect submerged aquatic vegetation: Overcoming problems in partitioning the diffuse attenuation coefficient for photosynthetically active radiation. Estuaries 24: Gallegos, C.L Optical water quality of a black river estuary: The Lower St. Johns River, Florida, USA. Estuarine, Coastal and Shelf Science 63: Gallegos, C.L., W.J. Kenworthy, P.D. Biber and B.S. Wolfe Underwater spectral energy distribution and seagrass depth limits along an optical water quality gradient, p In Lang, M.A. et al. (eds.), Proceedings of the Smithsonian Marine Science Symposium, Smithsonian Contributions to the Marine Sciences 38. Greening, H.S., L.M. Cross and E.T. Sherwood A multiscale approach to seagrass recovery in Tampa Bay, Florida. Ecological Restoration 29: Hansen, M High resolution Bathymetry of Tampa Bay. U.S. Geological Survey, St. Petersburg, FL. Janicki, A. and D. Wade Estimating critical nitrogen loads for the Tampa Bay estuary: An empirically based approach to setting management targets. Prepared by Coastal Environmental, Inc., St. Petersburg, Florida. Tampa Bay National Estuary Program Tech. Publ Janicki, A.J., D.L. Wade and D.E. Robinson Habitat protection and restoration targets for Tampa Bay. Prepared by Coastal Environmental, Inc., St. Petersburg, Florida. Tampa Bay National Estuary Program Tech. Publ Janicki Environmental, Inc Development of Numeric Nutrient Criteria for Boca Ciega Bay, Terra Ceia Bay, and Manatee River, Florida. Prepared for the Tampa Bay Estuary Program. Johansson, J.O.R Historical and current observations on macroalgae in the Hillsborough Bay Estuary (Tampa Bay), p In McGinty, M and C. Waznaik (eds.), Understanding the role of macroalgae in shallow estuaries. Workshop proceedings, January 10-11, 2002, Maryland Dept. Natural Resources. Johansson, J.O.R Shifts in phytoplankton, macroalgae, and seagrass with changes in nitrogen loading rates to Tampa Bay, p In: Treat, S. (ed.), Proceedings, Tampa Bay Area Scientific Symposium, BASIS4, October 2003, St. Petersburg, Fl. 295 p. 12

19 Johansson, J.O.R Near-shore water quality and seagrass relationships in the upper portions of Tampa Bay, p In Corbett, C. (ed.), Colored dissolved organic matter (CDOM) workshop summary. Punta Gorda, Florida May 29-30, Johansson, J.O.R., K.B. Hennenfent, W.M. Avery and J.J. Pacowta Restoration of seagrass habitat in Tampa Bay using large manatee grass (Syringodium filiforme) sod units and a discussion of planting site sediment elevation dynamics, p In Cooper, S.T. (ed.), Proceedings, Tampa Bay Area Scientific Symposium, BASIS5, October 2009, St. Petersburg, Fl. 538 p. Kenworthy, W.J., and M.S. Fonseca Light requirements of seagrasses Halodule wrightii and Syringodium filiforme derived from the relationship between diffuse light attenuation and maximum depth distribution. Estuaries 19: Koch, E.W Beyond light: Physical, geological and geochemical parameters as possible submerged aquatic vegetation habitat requirements. Estuaries 24:1-17. Lewis, R.R The importance of the long-shore bar system to the persistence and restoration of Tampa Bay seagrass meadows, p In Greening, H.S. (ed.), Seagrass Management, It s Not Just Nutrients! Proceedings of a symposium held August 22-24, 2000, St. Petersburg, FL. Tampa Bay Estuary Program. 246p. McGlathery, K.J., K. Sundbäck and I.C. Anderson Eutrophication in shallow coastal bays and lagoons: the role of plants in the coastal filter. Marine Ecology Progress Series 348:1-18. Sherwood, E.T Tampa Bay water quality assessment. Prepared by the Tampa Bay Estuary Program. Tampa Bay Estuary Program Tech. Publ Steward, J.S., R.W. Virnstein, L.J. Morris and E.F. Lowe Setting seagrass depth, coverage, and light targets for the Indian River Lagoon system, Florida. Estuaries 28: TBEP Seagrass restoration and protection master plan. Prepared by the Tampa Bay Estuary Program. van derheide, T., E.H. van Nes, G.W. Geerling, A.J.P. Smolders, T.J. Bouma and M.M. van Katwijk Positive feedbacks in seagrass ecosystems: Implications for success in conservation and restoration. Ecosystems 10: Virnstein, R.W., E.W. Carter, IV, L.J. Morris and J.D. Miller Utility of seagrass restoration indices based on area, depth and light, p In Greening, H.S. (ed.), Seagrass Management, It s Not Just Nutrients! Proceedings of a symposium held August 22-24, 2000, St. Petersburg, FL. Tampa Bay Estuary Program. 246p. 13

20 TABLE 14

21 Table 1. The table identifies individual local governmental research agencies, or joint agency projects, contributing data to this study. Also shown are the specific bay segments and MA s sampled by each agency or project, the seagrass target depth for the specific areas established from ca.1950s seagrass distribution, the respective period of data collection and the number of samples included in this study. For a key to abbreviations of agency or joint agency projects, see Figures 2 through 8. Abbreviations for bay segments are identified in the text. AGENCY BAY SEGMENT MANAGEMENT AREA TARGET DEPTH (LMTLm) PERIOD CASES COT HB HB HB MTB MTB Total 971 EPC BCBS BCBS HB HB HB HB LTB MKB MTB MTB MTB MTB OTB OTB OTB OTB OTB Total 1793 EPC_FWRI OTB OTB OTB OTB OTB Total 58 MCNRD BH 8a 1.8* LTB LTB MR MTB TCB 8b Total 2256 PCDEI BCBS** BCBN** BCBN** BCBN** BCBS** MTB33** OTB** OTB OTB OTB Total 555 PCEF OTB OTB OTB OTB Total 565 SWFWMD MTB Total 540 USF_TBEP HB LTB MTB MTB Total 118 Total 6856 * BH target depth assumed as 1.8m; the same as TCB. ** Predicted Kd at target depths was not calculated at these bay segments and MA s due to lack of color information. 15

22 FIGURES 16

23 MKB KITCHEN BCBN NORTH SHORE PARK WOLF BRANCH BCBS EGMONT KEY BH MR TCB Figure 1. Tampa Bay with locations of bay segments, seagrass management areas (MA s), other areas discussed and fixed seagrass transects (stars). 17

24 Figure 2. Locations sampled by the City of Tampa Bay Study Group (COT). 18

25 A B Figure 3A and B. Figure A shows locations sampled in a joint project by the Environmental Protection Commission of Hillsborough County (EPC) and the Florida Fish and Wildlife Research Institute (FWRI). Figure B shows monthly locations sampled by the EPC that fall within MA delineations. 19