# NUMERIC WATER QUALITY TARGETS FOR LEMON BAY, CHARLOTTE HARBOR AND ESTERO BAY, FLORIDA

Charlotte Harbor National Estuary Program Technical Report 06-3

November 20, 2006

1926 Victoria Avenue Fort Myers FL 33901 (239) 338-2556 www.CharlotteHarborNEP.orgThis document was written by Catherine A. Corbe tt, Charlotte Harbor National Estuary Program. Much of the text was taken direc tly or updated from the reference: Corbett, C. A. and J.A. Hale. 2006. Deve lopment of Water Quality Targets for Charlotte Harbor, Florida usi ng Seagrass Light Requirements. Florida Scientist. 69 (00S2): 36-50. in which Jason A. Hale, Charlotte Harbor Envi ronmental Center, provid ed significant aid. Any errors or omissions are solely the res ponsibility of Catherine A. Corbett.

The Charlotte Harbor National Estuary Program is a partnership of citizens, elected officials, resource managers and commerci al and recreational resource user s working to improve the water quality and ecological integrity of the greater Charlotte Harbor watershed. A cooperative decision-making process is used within the pr ogram to address diverse resource management concerns in the 4,400 square mile study area. Ma ny of these partners also financially support the Program, which, in turn, affords the Program oppor tunities to fund projects such as this. The entities that have financially support ed the program include the following:

U.S. Environmental Protection Agency Southwest Florida Water Management District South Florida Water Management District Florida Department of Environmental Protection Florida Coastal Zone Management Program Peace River/Manasota Regional Water Supply Authority Polk, Sarasota, Manatee, Lee, Ch arlotte, DeSoto, and Hardee Counties Cities of Sanibel, Cape Coral, Fort My ers, Punta Gorda, North Port, Venice, Fort Myers Beach, and Winter Haven and the Southwest Florida Regional Planning Council.

## Charlotte Harbor National Estuary Program

Policy Committee Mr. Tom Welborn, Co-Chair Mr. Jon Iglehart, Co-Chair Branch Chief, Wetlands, Coastal, & Water Quality South District Director U. S. Environmental Protection Agency, Region 4 Florida Department of Environmental Protection

CITIES Hon. Rue Berryman City of North Port

Hon. Yvonne Brookes City of Winter Haven

Mr. Martin Black City of Venice

Hon. Richard Ferreira City of Bonita Springs

Ms. Connie Jarvis City of Cape Coral

Hon. Andy Laurent City of Bartow

Hon. Don Massucco Town of Fort Myers Beach

Mr. Joe Ortolona City of Fort Myers

Hon. Dr. David Phelen City of Punta Gorda

Hon. Tom Rothman City of Sanibel COUNTIES Mr. Lex Albritton Hardee County

Hon. Adam Cummings Charlotte County

Hon. Patricia Glass Manatee County

Hon. Ray Judah Lee County

Mr. Jeffrey Spence Polk County

Hon. Jon Thaxton Sarasota County

Vacant Desoto County AGENCIES Hon. John Albion Southwest Florida Regional Planning Council

Mr. Bill Smith Florida Fish & Wildlife Conservation Commission

Brig. Gen. Rufus C. Lazzell (U.S. Army, ret.) Southwest Florida Water Management District

Mr. T. M. Gurr Central Florida Regional Planning Council

Ms. Rhonda Haag South Florida Water Management District

Management Committee Co-Chairs Mr. Bo Crum, Co-Chair Mr. Ken Heatherington, Co-Chair Section Chief, Wetlands, Coastal, & Water Quality Planning Director U. S. Environmental Protection Agency, Region 4 Southwest Florida Regional Planning Council

Technical Advisory Committee Co-Chairs Citizens Advisory Committee Officers Ms. Judy Ott, Co-Chair Mr. Stuart Stauss, Co-Chair Mr. Mike Gurr, Co-Chair Mr. Kayton Nedza, Co-Chair

Staff Dr. Lisa B. Beever , Director Ms. Catherine A. Corbett , Senior Scientist Ms. Elizabeth S. Donley, Esq. , Grants and Contracts Manager Ms. Maran Brainard Hilgendorf , Communications Manager Ms. Jaime Greenawalt Boswell , Environmental Scientist

# Table of Contents

Acknowledgeme nts....................................................................................................5

Executive Summary ...................................................................................................6

Introduc tion................................................................................................................8

Methods for Setting Wate r Quality Ta rgets.............................................................10 Regulatory..................................................................................................................... ............ 10 Historic Conditions ............................................................................................................ ....... 11 Reference Site ................................................................................................................. .......... 11 Resource-Based................................................................................................................. ........ 11



### Resource-Based Approach fo r Charlotte Harbor.....................................................12 Seagrass Light Requirements.................................................................................................... 13 Seagrass Depth Distribution Targets ........................................................................................ 14 Optical Model .................................................................................................................. ......... 18 The Target—Plane of Constant Attenuation............................................................................. 21



### Next Steps ................................................................................................................30 Water Quality Management Strategy Working Group ............................................................. 30 Further Refinements of Approach ............................................................................................ 31 Quality of Light............................................................................................................... .......... 32 Inherent and Apparent Optical Properties................................................................................. 35 Pollutant Load Management Strategies .................................................................................... 36 Implementation ................................................................................................................. ........ 37



### Conclusion ...............................................................................................................38

Literature Cited ........................................................................................................38

Appendix A ..............................................................................................................43 Cumulative Frequency Curves of Seagrass Coverage by Depth .............................................. 43



### Appendix B ..............................................................................................................50 3-D Graphs..................................................................................................................... ........... 50



### Appendix C ..............................................................................................................75 DEP Transect Data.............................................................................................................. ...... 75Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 5 

## Acknowledgements

Special thanks goes to Jason Hale who co-authored our article on this subject for the Charlotte Harbor special issue of the Florida Scientist . He provided invaluable insight and practical help throughout the effort and helped make this effort possible. This effort was also made possible by many other individuals who grea tly contributed their data, tim e and effort. Patrick Biber encouraged the use of an optical model for de veloping water quality targets a number of times before I started contemplating it. Ron Miller, David Tomasko, Ralph Montgomery and Charles Kovach provided valuable insight into partial light attenuation co mponents in Charlotte Harbor. Keith Kibbey, Judy Ott, James Evans, Connie Ja rvis, Philip Stevens, Patrick Casey, Peter Doering, Katie Fuhr, Dan Quick, Joanne Ver non, Kris Kaufman and other members of the Coastal Charlotte Harbor Monitoring Network s upplied water quality data and thoughtful input for this effort. Katie Fuhr, Celia Stearns, Stephanie Erickson, Jaime Greenawalt Boswell and Judy Ott provided transect deep edge measur ements, and Jaime Greenawalt Boswell provided seagrass species density and composition inform ation as well as other insightful comments. Robert Chamberlain and Peter Doering contribu ted invaluable input into seagrass light requirements and mechanisms into furthering th is work, while Kris Kaufman provided much needed insight with obtaining support for this effort. Tomma Barnes , Kris Kaufman, Dave Tomasko, Tim Lieberman and Frank Sargent su pplied seagrass areal coverage data. Tim Lieberman, Ping Wang, XinJian Chen and others pr ovided updated bathymetry data, while Jason Hale, Tim Walker, Tim Lieberman and Ravic N ijbroek helped with GIS work. Cambridge University Press graciously grante d permission to include 4 graphi cs (Figures 7, 8 and 9) from Kirk (1994). Finally, Roger Johansson, Ray Ku rz, John Ryan, Holly Greening and others too numerous to include provided support and enco uragement to continue and expand this work.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 6 

## Executive Summary

Charlotte Harbor has generally been considered one of Florida’s more unspoiled and productive estuaries because it is relati vely undeveloped compared to n earby estuaries. Indeed, the Charlotte Harbor estuary is the sole estuary within the federal National Estuary Program (NEP) designated as a “maintenance” NEP rather than a “restoration” NEP. In the Southwest Florida region, including Tampa, Saraso ta and Lemon Bays, research and restoration efforts have focused on seagrass areal coverage as an indica tion of estuarine condition, and as a result, Charlotte Harbor is generally cons idered to be fairly healthy. In much of the greater Charlotte Harbor region, there is little ev idence of significant changes in seagrass areal coverage since systematic mapping efforts began in 1988, and nut rient loads have not been documented as a threat to seagrass extent to date in Charlotte Harbor (except Lemon Bay [see Tomasko et al. 1999]). Previous research has demonstrated th at dissolved and suspended matter within the water column rather than chlorophyll a largely limits light availability for seagrass beds in Charlotte Harbor (McPherson and Miller 1987; McPherson and Mi ller 1994; Dixon and Kirkpatrick 1999) and that inter- annual seagrass covera ge changes are larg ely resultant from changes in freshwater inflow fr om Charlotte Harbor’s 3 major tr ibutaries (Corbett et al. 2005; Tomasko et al. 2005).

However, there is reason for concern for the long- term health of this es tuary as the southwest Florida region is one of the fast est growing areas in the U.S. In 1983 the Florida Department of Natural Resources (FDNR) documented a harbor-wide 29% de crease in seagrass extent in Charlotte Harbor, the majority of which was located in Pine Island Sound, Matlacha Pass and San Carlos Bay, from 1940s levels. A subsequent study (Corbett et al. 2005) do cumented a harbor-wide 6% loss from 1982 to 19 99, again mostly from the Lower Charlott e Harbor region. Gr eenawalt-Boswell et al. (2006) documented a decline in seagrass density throughout Charlotte Harbor and Lemon Bay from 1999-2005, whereas dec lines in density may be a pre-cursor to losses in areal coverage. In the early 1990s, the FDNR reported that the Caloosahatchee River had reached its nutrient loading limits, indicated by elevated chlorophyll a and depressed dissolved oxyg en levels, and water quality data collected in the tidal Peace River demonstrate that chlorophyll a levels exceeding 60-80 μg/L have occurred seasonally since monitoring began in 1976. Finally, a 2003 Charlotte Harbor NEP report determined significant increas ing trends in total su spended solids throughout the coastal Charlotte Harbor region and increasing trends in turbidity, nutr ients and others in the Lower Charlotte Harbor region. The creation of numeric water qua lity targets to serve as the basis for water quality restorati on and maintenance activities can se rve as a tool to stop these declining trends.

To offset future development and maintain curr ent water quality into the future, we propose the use of an optical model to establish water qua lity targets for color, turbidity and chlorophyll a specific to each estuarine segment that encomp asses the greater Charlotte Harbor estuarine complex including Lemon and Estero Bays. First we determined percent-light-at-depth targets required to achieve seagrass maximum depth distribut ion in each segment. Next we applied an optical model which describes total light attenuati on as the sum of three partial light attenuation components: color, chlorophyll a and non-algal suspended solids. This allowed us to backcalculate the contribution to total light attenuati on by each component in terms of concentration intercepts (e.g. μg/L chlorophyll). Finally, season al water quality data for each estuary segmentCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 7 were plotted with the intercep ts overlaid, producing a plane of constant attenuation given our percent-light-at-depth goal. It is this plane of constant attenuati on that is our proposed target. This plane allows the concentr ation for each component to assume any concentration between zero and its interecept; its value dependent on the concentrations of the other 2 components. This objective is in contrast to many water quality targets that set a discrete maximum for each specific analyte without regard to concentrations of other relevant c onstituents affecting the targeted outcome. Water quality data points loca ted outside of the plane of constant attenuation identify times and locations when water quality di d not meet our goals for that estuary segment. These water quality targets will be used by resource managers and NEP partners to hold the line on water quality in the greater Charlotte Harbor estuary, especially those parameters influencing water clarity and seagrass covera ge. Resource managers can in th e future determine if seagrass and water quality restoration ta rgets are appropriate and easily modify this approach to encompass such targets if so.

Subsequent to the approval of this document, the next step for these targets is to develop a Water Quality Management Strategy Working Group consisting of NEP Management Conference members. This group will take the lead role in determining whether segments within the Lemon Bay, Charlotte Harbor and Estero Bay are exceed ing the targets herein and resulting pollutant reduction management strategies. The Working Gr oup will also take the lead role in further refining the targets, such as whether seagrass re storation targets are appr opriate and refining the partial attenuation coefficients of the op tical model included in this approach.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 8 

## Introduction

In southwest Florida, substantial research a nd restoration efforts have focused on seagrass meadows as an environmental indicator for coasta l environmental conditions. Charlotte Harbor, FL is contiguous to the northwest with Le mon, Sarasota and Tampa Bays where seagrass management strategies focus on nutrient load and phytoplankton concen tration reductions. Charlotte Harbor confronts diffe rent issues, and phytoplankton concentrations do not have as large an influence on light attenuation in Char lotte Harbor as dissolv ed and suspended matter (McPherson and Miller 1987; Mc Pherson and Miller 1994; Dixon a nd Kirkpatrick 1999). Also, analyses of seagrass coverage data from the r ecent past demonstrate that seagrass coverage in Upper Charlotte Harbor is stable since mapping began in 1988 (Kurz et al. 1999; Corbett et al. 2005; Tomasko et al. 2005; Corbett 2006) and in both Upper a nd Lower Charlotte Harbor since 1982 (Corbett 2006). Seagrass management strategies in the Charlotte Harbor region have not focused on nutrient load reducti ons, and currently, resource ma nagers have not established restoration goals for seagrass coverage.

In Tampa Bay, where historical losses have been linked to both direct and indirect impacts, resource managers have set goals for restoring seagrass coverage to approximately ninety-five percent of the coverage present in 1950. Reduc tions in nitrogen loads since 1982 have led to reduced phytoplankton concentrations and increase d water clarity, a cascade of effects which has allowed increases in seagrass extent (Johansson 1991; Johanss on and Ries 1997; Lewis et al. 1998; Johansson and Greening 1999). Increases in seagrass cove rage is also a restoration objective for seagrass managers in Sarasota Bay, where recent incr eases (1988-1996) may be linked to decreased nitrogen loads to the bay by the City of Sara sota and Manatee and Sarasota counties (Kurz et al. 1999). In both Tampa and Sara sota Bays, water clarity and quantity of light reaching the tops of seagrass blades is rela ted to nitrogen loading and its effects on phytoplankton populations (cited in Tomasko et al. 2005); thus, seagrass restorat ion strategies in these areas have focused on nitrogen load reductions.

Lemon Bay, a comparatively small estuary that connects Sarasota Bay to the north to the Venice inlet and Charlotte Harbor in the south, is included within the larger Charlotte Harbor estuarine complex. It is very similar to Tampa and Sarasota Bays in that its water clarity is strongly tied to phytoplankton levels and nitrogen loads (Tomas ko et al. 2001). Phytoplankton biomass was calculated to contribute 12 to 39 % of light attenuation within th e water column with a mean of 29 %, and depth distribution of seagrasses in Lemon Bay is largely a factor of chlorophyll a concentrations (Tomasko et al. 2001). Seagrass mapping efforts have not documented trends in seagrass coverage in Lemon Bay since 1988 (T omasko et al. 2001; Tomasko et al. 2005). Nonetheless, estimated nitrogen loads to the bay have increased an estimated 59% from historical levels and are expected to increase further with future urbanization (Tomasko et al. 2001; Tomasko et al. 2005). Thus, seagrass mana gement strategies within Lemon Bay region also focus on nutrient load reductions.

South of Lemon and Sarasota Bays along the sout hwest Florida coast lies the Charlotte Harbor estuarine complex, which includes a number of in terconnected estuaries. The Charlotte HarborCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 9 watershed extends approximately 210 km (130 mi) from its northern headwaters of the Peace River to southern Estero Bay, and three large ri vers, the Peace, Myakka and Caloosahatchee, are the major sources of freshwater (Hammett 1990). Relative to Tampa, Sarasota and Lemon Bays to its northwest, Charlotte Harbor is strongly influenced by the fr eshwater inflows from its large watershed. One result of this large watershed is that the water clarity of the harbor is greatly influenced by dissolved and suspended matter, as opposed to the dominant influence of phytoplankton found in Tampa and Lemon Bays. Us ing data collected in Charlotte Harbor, McPherson and Miller (1987) f ound that non-chlorophyll suspended matter (including detritus, cellular material and minerals) accounts for an av erage of 72 % of light attenuation in the water column, color (dissolved organic matter) acco unts for 21% and phytoplankton chlorophyll for only 4%. In the 1994 study, McPherson and Miller found that light attenua tion and water quality analytes varied spatially with the highest light attenuation coefficients f ound near the mouths of freshwater inputs. They found color contribut ed 13-49% to total light attenuation, chlorophyll a contributed 16-18%, non-chlor ophyll suspended matter contribut ed 30-55% and seawater contributed 3-6%. Dixon and Kirkpatrick (1999) found that co lor, turbidity and chlorophyll accounted for 66%, 31% and 4% of light attenu ation, respectively. He nce, there are clear differences in the components of water column light attenuation between Charlotte Harbor and Tampa, Sarasota and Lemon Bays.

Six species of seagrass are found within the Charlotte Harbor and Lemon and Estero Bay regions: Halodule wrightii, Thalassia testudinum, Syringodium filiforme, Halophila englemanni, Halophila decipiens and Ruppia maritima. Despite the recent (1982-2004) st ability of seagrass meadows in the Charlotte Harbor region, there is historical evidence of catastrophic losses in Charlotte Harbor. Harris et al. (1983) documented a 29% har bor-wide decrease in seagrass coverage from 1940s to 1982, excluding Lemon and southern Estero Bays, and postulated some of this loss resulted from seagrasses receding from deeper depths because of decreasing water clarity resulting from hydrologi c changes and increased polluta nt loads. From 1982 to 1999, Charlotte Harbor as a whole dem onstrated a 6% decrease in seag rass extent, with 77% of that loss located in the Lower Charlotte Harbor regi on (Corbett et al. 2005) . Subsequently, from 1999 to 2003 seagrass areal extent displayed increas es in the Lower Charlotte Harbor region, and no significant trend was found in the Upper Ch arlotte Harbor region since 1982 (Corbett 2006). However, analyses of seagrass data on a smalle r scale indicate that se agrass densities have declined throughout Charlotte Harbor and Lemon Bay over the last 6 years (Greenawalt-Boswell et al. 2006). Declines in seagrass density may be a pre-cursor to la rger-scale losses such as areal coverage losses. Net losses of seagrass along the FDEP transects were also documented from 1999-2005, especially in the Upper Charlotte Harbor region (Greenawalt- Boswell et al. 2006). Although the cause of thinning seagrass meadow s is not known and may reflect natural variability, these trends are starti ng to point to the need to prot ect these resources before more large scale areal coverages losses are detectable.

In addition, in the early 1990s, the Florida Department of Natural Resources reported that the Caloosahatchee River had reached its nutrient load ing limits, indicated by elevated chlorophyll a and depressed dissolved oxygen levels, and water quality data collected in the tidal Peace River demonstrate that chlorophyll a levels exceeding 60-80 μg/L have occurred seasonally since monitoring began in 1976 (cited in Corbett et al . 2005). More recent analyses of water quality data demonstrate significant increases in total suspended solids (TSS) in the Lower Charlotte Harbor and Upper Charlotte Harb or regions and increasing turbid ity and nutrients in the LowerCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 10 Charlotte Harbor region (Janicki Environmenta l Inc. 2003). At the same time, the Charlotte Harbor region is facing rapid ur banization pressure along the co ast and more intensive landuse changes in its watershed, which may result in fu rther degradation of wate r quality. Increases in rainfall and freshwater inflows ove r the previous several decades ar e projected for the near future (Kelly, 2004), and in turn, pollutant loads that are strongly associated with stormwater runoff will also increase (Tomasko et al., 2005). Th ese trends do not bode well for the long-term maintenance of the region’s aquatic resources, an d thus, resource management strategies in this area need to focus on water quality improvement s and the long-term maintenance of seagrass

coverage.

This effort presents an optical model that looks at all components of light attenuation and can be used regardless of which component is the most influential to establish water clarity goals to maintain percent-light-at-depth requirements to achieve seagrass maximum depth distribution by segment within the Charlotte Harbor estuarine co mplex. The water clarity goals proposed herein are meant to maintain the present seagrass coverage and depth distribution into the future. This strategy is in line with the Charlotte Harbor NEP designation as a “maintenance” NEP rather than a “restoration” NEP. The Charlotte Ha rbor NEP has contracted to map 1950s seagrass coverage using methods comparable to current seag rass coverage maps. If this effort documents significant losses of seagrass areal coverage have occurred in the harbor from the 1950s, these water clarity targets can easily be modified to encompass restoration targ ets for those areas of losses.

These water clarity targets helps implement Qu antifiable Objective WQ-2 of the Charlotte Harbor NEP updated Comprehensive Conservati on and Management Plan (CCMP). This objective is as follows: WQ-2 : Develop and meet site specific alternativ e criteria which are protective of living resources for dissolved oxygen, chlorophyll a , turbidity/total suspended solids, salinity, and pesticides by 2015.



## Methods for Setting Water Quality Targets

There are several methods in which resource managers may determine numeric water quality targets. These include amongst others accepting wa ter quality standards or re gulations as targets; establishing a baseline or hist oric conditions to which conditions should be maintained or restored; identifying an appropriate reference s ite and accepting its water quality conditions as a target or developing resource-base d targets using the requirements of relevant living resources.



### Regulatory

The Florida Impaired Waters Rule (IWR) (C hapter 62-303, Florida Administrative Code) provides guidelines for chlorophyll a , dissolved oxygen, turbidity and transparency in surface waters. The IWR guidelines for these variables are as follows: 1. the annual average for chlorophyll a concentrations shall not exceed 11 ug/L and/or exceed historical values by 50% based on trend analysis;Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 11 2. dissolved oxygen concentrati ons shall not average less th an 5.0 mg/L in a 24-hour period and shall never be less than 4.0; 3. turbidity shall be less than 29 NTU above natura l background conditions; and 4. transparency shall not be reduced by more than 10% as compared to the natural background value.

Resource managers may accept these water quality st andards as numeric targets for the coastal Charlotte Harbor region. However, it should be noted that despite a nnual seasonal hypoxia in upper Charlotte Harbor bottom waters, significan t increasing trends in total suspended solids throughout coastal Charlotte Harbor and turbidity in Lower Charlotte Harbor, both with rates of change greater than or equal to 5% of the medi an value per year (Janicki Environmental Inc. 2003), these areas have not been designated impa ired under the IWR for these variables. Previous studies have documented that suspended matter, along with dissolved matter, constitute the dominant influences on the light available for seagrass beds in most areas of the coastal Charlotte Harbor region (McPherson and Mi ller 1987; McPherson and Miller 1994; Dixon and Kirkpatrick 1999), with the exception of Lemon Bay. It is th erefore possible that Florida water quality standards are not sufficiently protective of the Charlotte Harbor estuarine ecosystem.



### Historic Conditions

Resource managers may instead wish to identify th e water quality conditions of Charlotte Harbor and surrounding waterbodies in pre-development or a specified historic ti me period and use those conditions as numeric water quality targets. An issue with this method is determining scientifically-defensible baseline conditions if water quality data are rare, nonexistent or collected in a manner not comparable with current protocol s for the time period in question, which is often the case.



### Reference Site

An alternative course to follow in the determ ination of numeric water quality targets is to identify an appropriate reference site, a waterbod y with little to no anth ropogenic impacts that is physically, biologically and chemically similar to Charlotte Harbor and Lemon and Estero Bays. The water quality conditions of the reference site (s) could then be used as a goal for resource managers to emulate for the Charlotte Harbor region. Nevertheless, as each system in Florida is dynamic, it is difficult to identify si milar systems or systems with little anthropogeni c impacts.



### Resource-Based

Arguably the most successful approach to date fo r setting numeric water qua lity targets has been the resource-based approach. In the Tampa Bay area, for instance, resource managers established the goal of restori ng seagrass coverage to 95% of 1950’s level (except those areas impacted by the direct impacts of dredge and fill activities), resulting in a restoration target of 12,350 more acres of seagrass. Water clarity in Tampa Bay is related to phytoplankton levels and chlorophyll a concentrations (cited in Tomasko et al. 2005), and nitrogen has been shown to be the limiting nutrient (Johansson 1991). Theref ore, the focus of rest oring 1950’s level seagrass coverage is to reduce nutrients, specifically ni trogen, loading to the ba y (Greening and JanickiCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 12 2006). Approximately 8200 metric tons of nitrog en was estimated to enter Tampa Bay annually in the mid 1970s from point sources, atmospheri c deposition, nonpoint so urces, groundwater and fertilizer losses at 55%, 22% , 16%, 3% and 5%, respectively (Greening and Ja nicki 2006). Resource managers established a nitrogen load reduction goal that maintains existing conditions by reducing future nitrogen emissions to the Bay by ca. 7% by 2010 or 17 tons per year. Reductions in nitrogen loadings from point s ources (e.g., WWTP discharg es from Cities of Tampa, St. Petersburg and Clearwater and port f acilities) resulted in an estimated 60% reduction in nitrogen loading during the 1985-2003 period (c ited in Greening and Janicki 2006). As a result, there was an increase in seagrass c overage between 1982-1996 (Tomasko et al. 2005), and in 2004, baywide seagrass areal coverage was the highest observed si nce 1950 (Greening and Janicki 2006).

Resource Managers in southern Indian River La goon have also establishe d resource-based water quality targets using seagrass de pth distribution. They partitione d the southern Lagoon into 5 segments and determined the depth distribution of seagrass meadows in each segment. They determined that Jupiter Inlet had the deepest gr owing seagrass beds and the most stable. They then used the median deep edge s of Jupiter Inlet seagrass mea dows as their depth target (1.3 meters) and evaluated the water quality conditio ns in those areas of the Lagoon that included beds growing at least 1.3 m deep (Crean 2002). The median valu es of these regions were used to set water quality targets for salinity, color, turbid ity, dissolved oxygen, pH and photosynthetically active ra diation (PAR) (Crean 2002). 

## Resource-Based Approach for Charlotte Harbor

We propose using a resource-based approach to establish water quality targets for Charlotte Harbor and Lemon and Estero Bays. The ta rgets would be specific to each basin that encompasses this region. The Florida Fish and Wildlife Conservati on Commission-Fisheries Independent Monitoring Program and the Coastal Charlotte Harbor Mon itoring Network divide Charlotte Harbor into 12 hydrologic segments for their fisheries and water quality sampling programs (Figure 1). Using the methodology descri bed below, we developed water quality goals specific for each segment.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 13 Figure 1.



### Seagrass Light Requirements

Using published analyses, we estim ated percent-light-at-depth targ ets required to achieve the current estimated seagrass maximum depth distribut ion in each segment. The 25% PAR targetCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 14 used herein is specific to subsur face irradiance; that is, our goal is 25% incident light that has already passed through the air-water interface.

Numerous estimates for percent-light-at-depth requirements of seagrass exist, and depending on the species composition of a bed, these estimate s indicate a wide range of surface irradiance requirements reaching the deep edge of grass beds. For instance, Gallegos and Kenworthy (1996) cite a general range of 10-30 % for effort s documenting requirements of seagrass in other estuaries and 23-37% in Indi an River Lagoon, Florida for H. wrightii and S. filiforme

, specifically. Steward et al. ( 2005) recently estimated seagrass average annual maintenance light requirements as 33% of surface light at the median depth limit distribution (50 th percentile) and 20% at the maximum (95 th percentile). In more detail, this amounts to the following:

• Based upon 5-7 biennial seagrass areal coverage maps for the Lagoon and recent bathymetry data, the median (50th percen tile) and maximum (95t h percentile) depth limits for the 19 Indian River Lagoon segm ents are 0.8-1.8 meters and 1.2-2.8 meters, respectively;

• Based upon monthly PAR data from 1990-1999 the average annual subsurface light requirement at the median depth is 33±17% (34±17% in the growing season) and the minimum annual light requirement based on th e maximum depth limit (95th percentile) is 20±14%. The minimum growing season requireme nt (March to mid Sept) is essentially the same 20±13%. (See Steward et al. 2005 for more information).

Grasses in Charlotte Harbor require betw een 15-30% photosynthetical ly active radiation penetrating to depth (Dixon, 2000). Tomasko a nd Hall (1999) found an average 23% subsurface irradiance reaching all study sites but doc umented declines in productivity of T. testudinum during the study period. Of the species of seagrass in Charlotte Harbor, T. testudinum may have the highest light requirements. Greenawalt (2005) determined that S. filiforme is found in areas with generally lower per cent-light-at-depth than H. wrightii and T. testudinum , while Chamberlain theorizes that H. wrightii extends deeper and is found in areas with lower light conditions than T. testudinum (Chamberlain 2005). We propos e a percent-light-at-depth goal of 25 % subsurface irradiance, which is on the high end of the estimate in Dixon (2000), an estimate specific to Charlotte Harbor, and highe r than the annual average found in Tomasko and Hall (1999). Our 25% subsurface irradiance goal is also slightly higher than the 20.5% (Greening and Janicki 2006) of incident light target used in Tampa Bay’s restoration activities.



### Seagrass Depth Distribution Targets

Seagrass Transects

We calculated the annual mean maximum depth distribution of seagrass per segment based on the results of 50 fixed-transects monitored by the Florida Department of Environmental Protection Aquatic Preserves office throughout Ch arlotte Harbor and Lemon Bay since 1999 and 5 additional transects in Estero Bay established in 2002 (Figure 1). Each transect consists of a fixed line, determined by a compass heading and marked with PVC stakes, extending from the shoreward seagrass edge out to the deep edge of the meadow where seagrass was sparse or no longer existent. Program researchers collect depth measurements, seagrass species abundance (Braun-Blanquet cover scale [Bra un-Blanquet 1965]), blade length, sediment type and epiphyteCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 15 coverage and type at 50-meter intervals along each transect (or 10-meter intervals for transects shorter than 50 m) from shore to edge of bed (Staugler and Ott 2001). Depth measurements are adjusted to mean water depth by adjusting the tide level observed in the field to mean water based on the 12 National Oceanographic and Atmo spheric Administration (NOAA) tide stations located throughout the study area (Staugler and Ott 2001). There are several transects for each segment for each year; we used the greatest annual value of the segment’s mean maximum depths as the target depth. (The deep edge meas urements and in turn targets were updated since the Corbett and Hale (2006) effort.) There we re no comparable transect data for the Tidal Caloosahatchee River segment, so we used a goal of 1 meter based upon vertical scan hydroacoustic research by Chamberlain (2005).

Annual Mean Depth Distribution (meters)

Segment Number of DEP Transects 1999 2000 2001 2002 2003 2004 2005 Max Mean Depth Lemon Bay 4-6 1.5 1.5 1.5 1.5 1.7 1.6 1.7 1.7 Cape Haze/ Gasparilla 4-5 1.5 1.7 1.6 1.8 1.6 1.9 1.9 1.9 West Wall 3-5 1.3 1.4 1.2 1.2 0.9 1.3 1.3 1.4 East Wall 1-2 NA 1.2 1.0 1.2 1.3 1.3 1.4 1.4 Tidal Myakka River 3-5 NA 0.8 0.5 0.8 0.7 0.8 0.6 0.8 Tidal Peace River 2-4 0.9 0.9 0.8 0.9 1.0 0.7 0.9 1.0 Bokeelia 3-5 1.4 1.4 1.1 1.0 1.2 1.3 1.5 1.5 Pine Island Sound 8-11 1.6 1.6 1.6 1.6 1.7 1.9 1.7 1.9 Matlacha Pass 2-4 0.9 1.5 1.2 1.3 1.1 1.3 1.2 1.5 San Carlos Bay 3-6 1.6 1.6 1.7 1.8 2.0 1.9 1.6 2.0 Estero Bay 4-5 NA NA NA 1.0 1.0 1.0 0.8 1.0 Table 1. Annual mean seagrass depth distributio n by segment using FDEP transect data. The maximum deep edge for each segment was used as the preliminary depth target. The number of transects per year per segment varied slightly.

Comparison with Seagrass Maps and Bathymetry

We next quantified the seagrass areal covera ge per segment growing within the depth distribution targets from the DE P transect data. We overlai d the 2003 seagrass polygon maps produced by SFWMD and SWFWMD onto NO AA and SFWMD-updated bathymetry data converted to polygon files. We used the 2003 ma ps because 2003 is 1 of 2 years in which seagrass areal coverage data encompasses the entire coastal study area (see Corbett 2006 for discussion). The second dataset that encompasse s the entire area, the 199 9 dataset, represents decreased seagrass areal coverage , as there were losses in most segments from the previous mapping event (Corbett 2006). The 2003 dataset repres ents increased seagrass coverage in most seagrass segments (Corbett unpublished); the da ta were acquired for both Upper and Lower Charlotte Harbor regions on th e same day in January 2004.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 16 We created an intersect covera ge of each segment by combining the 2003 seagrass maps to the bathymetry data. This allowed us to determ ine the seagrass depth distribution per segment according to the seagrass polygon maps. We comp ared seagrass depth di stributions estimated from the intersect coverages to our depth distribution targ ets from the DEP transect data. If less than 95% of seagrass areal coverage per segment fell within these depth targets, we calculated a deeper depth target using the intersected seagrass map and ba thymetry coverages so that ≥ 95% of seagrass areal coverage shown by the maps occu rred shallower than our depth targets. Those segments with larger values for seagrass covera ge, such as Pine Island Sound and Matlacha Pass, we allowed a maximum of ~200 acres to fall deep er than our depth targ ets. The final depth targets are listed in Table 2. Cumulative fr equency curves of seagrass coverage by depth (Figure 2) for each segment are included in Appendix A.

Cumulative Frequency Seagrass Coverage by Depth Pine Island Sound

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Figure 2. Cumulative frequency curve of seagrass coverage by depth in meters for the Pine Island Sound segment. The target depth for this segment is 2.2 meters an d represented by the red

arrow.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 17 Segment 2003 Seagrass Coverage

(acres) Max Depth from DEP Transects

(meters) Depth Target from Intersected Seagrass and Bathymetry Coverages (meters) % Coverage

within target Final Depth Target (meters) Lemon Bay 2684* 1.7 2.0 99 2.0 Cape Haze/ Gasparilla 6,902 1.9 1.9 99 1.9 West Wall 1,680 1.4 1.4 100 1.4 East Wall 3,254 1.4 1.4 100 1.4 Tidal Myakka River 280 0.8 0.9 99 0.9 Tidal Peace River 246 1.0 0.9 100 1.0 Bokeelia 3,315 1.5 2.4 97 2.4 Pine Island Sound 27,837 1.9 2.2 99 2.2 Matlacha Pass 7,333 1.5 2.0 97 2.0 San Carlos Bay 5,114 2.0 2.2 96 2.2 Estero Bay 3,413 1.0 1.6 97 1.6 Tidal Caloosahatchee River 62 1.0** 1.2 72 1.2

by Chamberlain (2005). * Southern 2/3 Lemon Bay only. **Estimate based on vertical scan hydro-acoustic research

Table 2. Depth targets using DEP transect data a nd seagrass areal coverage and bathymetry data. Final depth targets are in the last column.

There were several issues with this last step. First, bathymetric data to the nearest tenth of a meter were not available for the northern 1/3 of the Lemon Bay segment at the time of this analysis. Second and perhaps the most importa nt issue is that the seagrass polygon maps produced by the Water Management Districts ma y not accurately represent the deep edge of seagrass beds. The 2003 maps were produced at a 1:12,000 scale, and whether photointerpreters can capture deep edges correctly, especially in darkly colored waters or steeply sloped areas, requires caution in using these data. Also, the sp atial accuracy of the seagrass maps may be off as much as 20 feet in some areas, while the bathym etry data may also add to these inaccuracies.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 18 

### Optical Model

Light Attenuation Coefficient

To calculate a water clarity target, we used the Lambert-Beer Law to determine our light extinction coefficient:

k = [ln(I o )-ln(I z )]/z

where k equals the light extinction co efficient at depth, I o is surface irradiance, I z is the irradiance taken at depth and z equals the depth in mete rs. This equation is more commonly shown as follows:

%light at depth/100 = e -k*z (1)

where, in our case, the percent light-at-depth is the estimated minimu m amount of subsurface incident light required by seagrasses, e is the base of the natural logarithm, k is the light attenuation coefficient (in m-1), and z equals the measured or estimated deep edge depth of seagrass distribution meters.

To use an example of the San Carlos Bay segmen t in the Lower Charlotte Harbor region (Table 2), inserting our percent- light-at-depth target of 25% PAR and depth of 2.2 meters, results in the following equation:

0.25 = e -k*2.2 (1a) ln(0.25) = ln( e -k*2.2 ) (1b) -1.4 = -k*2.2 (1c) k = 0.6

Partial Contributions to Light Attenuation

Light attenuation in the water column is cause d by scattering and absorption of light by water quality constituents (Kirk 1983). Light absorbing water column components can be grouped into 3 categories: water itself, dissolved yello w substances (gilvin) and particulates (tripton/phytoplankton) (Kirk 1994). Scattering itself does not re move light (photons) from the water column but impedes the vert ical penetration of light by increasing th e total pathlength it travels before it is absorbed (Kirk 1994). Thus, sc attering increases th e likelihood of light absorption by water column constituents (before it can reach seagrass beds), and it can cause some photons to be scattered back in an upwards direction. Scattering c onstituents of the water column include the particulates: mineral partic les from land or bottom sediments, phytoplankton, bacteria, dead cells or fragment s of cells, etc. (Kirk 1994).

Lorenzen (1972) partitioned absorption and sca ttering components of the water column into 4 parts: water, phytoplankton, dissolved matter an d non-chlorophyll suspended matter. This can be written as follows:Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 19 k = k 1 + k 2 + k 3 + k 4 (2)

where k 1 is the attenuation coe fficient of water, 0.0384 m -1 (Lorenzen 1972); k 2 is the attenuation coefficient of phytoplankton, [often approximated by multiplying 0.0138 m 2 mg -1 (Lorenzen 1972) by the concentration of chlorophyll a ]; k 3 is the attenuation coefficient of dissolved matter determined by absorptivity times concentration and k 4 is the attenuation co efficient of non-algal suspended matter, determined by absorptivity an d scatter times concentr ation (from McPherson and Miller 1987). This partitioning of light at tenuation components is an approximation as the distribution and availability of light is not a linear function of concentration (Kirk 1983).

For management purposes, light requirements of gra ss beds can be translated into concentrations of these constituents that meet the specified li ght availability target (Gallegos and Kenworthy 1996). To do this, we adapted an optical model derived by McPherson and Miller (1994: eq. 8) which describes total light attenuation as the su m of three partial light attenuation components: color, chlorophyll a and turbidity:

k = 0.014*C 2 + 0.062* C 3 + 0.049*C 4 + 0.30 (3)

where k equals the light extinction co efficient at depth, C 2 is water color in Pt-Co units, C 3 is turbidity in NTU and C 4 is chlorophyll a in micrograms per liter. The partial attenuation coefficients for color and chlorophyll a were derived by stepwise multiple regression analysis and multiplied by concentrations to yield the cont ribution of each to light attenuation, adjusted for sun angle (see McPherson and Miller (1994) for complete discussion). Total suspended solids and turbidity were tested by stepwise mu ltiple regression analysis as surrogates for nonalgal suspended matter. However, both analytes in cluded only part of the attenuation attributable to non-algal suspended matter, so this component was estimated by subtraction [field collected kk (water) -k (color) -k (chlorophyll) ] (see McPherson and Miller (1994) for complete discussion). Gallegos (1994) also uses turbidity in lieu of total suspended solids for the non-algal suspended matter component in his optical models due to its ease in measurements, superior analytical precision and use in many monitoring programs.

To determine the maximum concentration of each partial light attenuation component that meets a given rate of light attenuati on (calculated from the percent- light-at-depth target), set two components to zero and solve for the third. Conti nuing to use the San Carlos Bay segment as an example, we solved for color in the following case:

0.6 = 0.014*C 2 + 0.062* (0) + 0.049*(0) + 0.30 (3a) 0.3 = 0.014*C 2 (3b) C 2 = 24.0 Pt-Co

The resulting values we call our “intercepts”.

Table 3 lists the intercepts for each partial light attenuation component for given depths using the optical model above, and Table 4 lists our intercep ts given our seagrass depth and light targets.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 20 Depth (meters) k Color Intercept

(Pt-Co) Turbidity Intercept

(NTU) Chlorophyll

a Intercept (ug/L)

0.5 2.8 178.6 40.3 51.0 0.6 2.3 145.2 32.8 41.5 0.7 2.0 121.4 27.4 34.7 0.8 1.8 103.6 23.4 29.6 0.9 1.6 89.7 20.3 25.6 1.0 1.4 78.6 17.7 22.4 1.1 1.3 69.5 15.7 19.9 1.2 1.2 61.9 14.0 17.7 1.3 1.1 55.5 12.5 15.9 1.4 1.0 50.0 11.3 14.3 1.5 0.9 45.2 10.2 12.9 1.6 0.9 41.1 9.3 11.7 1.7 0.8 37.4 8.4 10.7 1.8 0.8 34.1 7.7 9.8 1.9 0.7 31.2 7.0 8.9 2.0 0.7 28.6 6.5 8.2 2.1 0.7 26.2 5.9 7.5 2.2 0.6 24.0 5.4 6.9 2.3 0.6 22.0 5.0 6.3 2.4 0.6 20.2 4.6 5.8 2.5 0.6 18.6 4.2 5.3 2.6 0.5 17.0 3.8 4.9 2.7 0.5 15.6 3.5 4.5 2.8 0.5 14.3 3.2 4.1 2.9 0.5 13.1 2.9 3.7

3.0 0.5 11.9 2.7 3.4 Table 3. Extinction Coefficients and Intercepts for each partial light attenuation component at specified depths calculated from Equation 3.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 21 Segment Final Depth Target (meters) k Color Intercept

(Pt-Co) Turbidity Intercept

(NTU) Chlorophyll

a Intercept (ug/L)

Lemon Bay 2.0 0.7 28.6 6.5 8.2 Cape Haze/ Gasparilla 1.9 0.7 31.2 7.0 8.9 West Wall 1.4 1.0 50.0 11.3 14.3 East Wall 1.4 1.0 50.0 11.3 14.3 Tidal Myakka River 0.9 1.6 89.7 20.3 25.6 Tidal Peace River 1.0 1.4 78.6 17.7 22.4 Bokeelia 2.4 0.6 20.2 4.6 5.8 Pine Island Sound 2.2 0.6 24.0 5.4 6.9 Matlacha Pass 2.0 0.7 28.6 6.5 8.2 San Carlos Bay 2.2 0.6 24.0 5.4 6.9 Estero Bay 1.6 0.9 41.1 9.3 11.7 Tidal Caloosahatchee River 1.2 1.2 61.9 14.0 17.7 Table 4. Seagrass Depth Targets wi th partial attenuation intercepts.



### The Target—Plane of Constant Attenuation

To describe water clarity with respect to seagrass depth limits, we overlaid the intercepts calculated above onto seasonal wate r quality data collected for coastal Charlotte Harbor and Estero and Lemon Bays. This produced a pl ane of constant attenuation for variable concentrations of the water qua lity parameters, given our per cent-light-at-depth goal, when combining the 3 components of the optical mode l. When reviewing only 2 components (see Figures 5 and 6 for examples), a line of constant atte nuation is produced. For the remainder of this document, we discuss the 3-D graphs and pl ane of constant attenuation. Water quality data points located above this plane id entify instances when water clarity did not meet our target.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 22 Bokeelia Dry Season

Figure 3. 3-D Scatterplot with the Bokeelia dry seas on water quality intercepts (Table 4) and the plane of constant attenuation are overlaid in red. Water qualit y data points located above the plane of constant attenuation identify instances wh en water clarity did not meet these targets.

Monthly water quality data from surface wate r samples collected between 2001 to 2006 were provided by the Coastal Charlotte Harbor Moni toring Network for 12 segments. Additional fixed station, monthly water quality data were provided by Lee County for the Pine Island Sound, Matlacha Pass, San Carlos Bay, Caloosahat chee River and Estero Bay segments; these data include the entire period of record for these segments collected by Lee County. Also, fixed station data for 12 stations within the West Wall, East Wall, Bokeelia, Tidal Peace River and Tidal Myakka River segments from the Southw est Florida Water Management District from January 1993 through December 2000 were also include d. All data were divided into “wet” and “dry” seasons, defined as data collected duri ng the months of July-O ctober and November-June, respectively. Salinity data analyses support this delineation (see Green awalt-Boswell et al. 2006).Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 23 Segment Period of Record Lemon Bay (southern) April 2001-March 2005 Gasparilla/Cape Haze April 2001-March 2006 West Wall Jan 1993-March 2006 East Wall Jan 1993-March 2007 Tidal Peace Jan 1993-March 2008 Tidal Myakka Jan 1993-March 2009 Bokeelia Feb 1993-July 2006 Matlacha Pass Dec 2000-June 2006 San Carlos Bay Dec 2000-June 2006 Pine Island Sound Dec 2000-July2006 Estero Bay Dec 2000-Aug 2006 Tidal Calooshatachee April 1999-July 2006 Table 5. Water Quality Data Period of Record by Segment

Immediately visible from the con centration intercepts listed in Table 4 is that some of these values are relatively high. For instance, the chlorophyll a values for multiple segments are higher than the Florida water qu ality standards for chlorophyll a of an annual average of 11 μg/L for marine and estuarine waters. These inter cepts are the maximum potential concentration of the analytes and are acceptable for meeting the percent-light-at-depth goal only when the concentrations for both the other 2 analytes are zero, an unlikely situation except when color is sufficiently high to limit phytopl ankton production. Excepting those periods when chlorophyll a concentrations are very low due to high color co ncentrations, the concentr ations for all 3 light attenuation components will be greater than zero, thereby requiring concentrations of all 3 components to be less than these intercepts to maintain the per cent-light-at-depth goal. The intercepts are a necessary step in developing th e resulting plane of constant attenuation, which denotes the acceptable concentratio n of a component of light a ttenuation in relation to the concentrations of the other components. This plane allows the concentration for each component to assume any concentration between zero a nd its maximum; its value dependent on the concentrations of the other 2 components. This objective is in contrast to many water quality targets that set a discrete maximum for each specif ic analyte without regard to concentrations of other relevant constituents affec ting the targeted outcome.

The results from plotting the derived planes of constant attenuation over water quality data collected in the recent pa st show that there are times and lo cations within each segment that current water quality would not meet the percent- light-at-depth goals propos ed in this effort. The locations in which these data were collecte d may support seagrass if, for instance, they are shallower than the depth target. Also, some data points on the scatterplots may represent locations in which data were collected that are deeper than our depth goal. However, both the seagrass light requirements and maximum seag rass depth distribution targets are reasonable goals based upon current observations. The light requirements for seagrass are based upon an average value, whereas the water quality data are point values.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 24 Multiplying the optical model part ial attenuation coefficients in Equation 3 above with color, chlorophyll a and turbidity concentrations collected monthly in th e Pine Island Sound segment by Lee County for the Coastal Charlotte Harb or Monitoring Networ k and fixed station monitoring from December 2000 through July 2006 dem onstrates that 26% of the collected data met the water clarity targets (N=443): 33% in the dry season (N=304) and 10% in wet season (N=139). In this segment, the seagrass depth targ et is 2.2 meters with an extinction coefficient target of 0.6. The mean extinction coefficient value was 0.8 ± 0.3 in the dry season and 1.3 ± 0.6 in the wet season. In comparison, 51% of the water quality data colle cted between 1993 and 2006 in the East Wall segment were equal to or le ss than the target extinction coefficient of 1.0 (N=470). The seagrass depth target for this segmen t is shallower than th at for Pine Island Sound at 1.4 meters. 66% of the data collected in the dry season (N=316) fell within the plane of constant attenuation, while 22% of the data collected in the we t season (N=154) did. The mean extinction coefficient for this segment was 1.0 ± 0.5 in the dry season and 2.03 ± 1.2 in the wet season. Table 6 lists the median, mean and standard deviation fo r color, chlorophyll a and turbidity concentrations, the attenuation due to th ese concentrations and th e percent of the total light extinction coefficient attributable to thes e concentrations for the water quality period of record (Table 5).

Nomographs

Lastly, for additional comparison purposes we could plot chlorophyll a and turbidity concentrations for specified depths and color values in terms of optical depth, k-z. Optical depth is the extinction coefficient multiplied by depth (in meters). Using the San Carlos Bay segment again as an example, we define our 25% subsurface irradiance at 2.2 meters as our target optical depth of 1.4 (see Light Attenuation Coefficient sec tion above for steps to calculate this). This step allows researchers to use any combination of depth (z) and attenuation coefficient (k) that equaled 1.4 (e.g., k = 0.7 and z = 2 m) to derive mu ltiple lines of constant attenuation. Thus, it allows us to review the results if we change either our 25% subsurface irradiance target or our depth targets for seagrass depth distribution.

For selected depths, 0.5, 0.75, 1.0, 1.5, 2.0 and 2.5 meters, we plotted our chlorophyll a and turbidity concentrations, given a range of color values in 10-step increments. This process allows one to determine a combinati on of the maximum color and chlorophyll a or color and turbidity concentrations for a depth of interest that meets our minimum li ght objective. Similar plots of turbidity and color give n a specified range of chlorophyll a concentrations or chlorophyll

a and color given a specified turbidity range could also be developed.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 25 Segment Chlorophyll

a (ug/L) Color (PCU) Turbidity (NTU) k color k turbidity k chlorophyll

a kd % color % turb % chla % intercept Lemon Bay Median 4.38 15.00 3.83 0.21 0.24 0.21 1.07 20.1 22.7 21.5 27.9

Mean 6.28 22.55 5.03 0.32 0.31 0.31 1.24 22.8 25.1 22.4 29.7 St Dev 6.56 28.43 3.80 0.40 0.24 0.32 0.72 13.2 13.3 10.9 11.8 Gasparilla/Cape Haze Median 4.00 25.00 2.77 0.35 0.17 0.20 1.06 31.1 15.1 19.0 28.2 Mean 7.09 36.40 3.62 0.51 0.22 0.35 1.38 32.1 17.4 22.0 28.5 St Dev 7.58 41.56 2.75 0.58 0.17 0.37 0.91 15.3 10.0 13.2 12.7 Tidal Myakka Median 5.87 68.00 3.20 0.95 0.20 0.29 1.90 51.1 10.2 16.5 15.8

Mean 8.68 101.08 3.91 1.42 0.24 0.43 2.38 52.8 12.0 18.4 16.8 St Dev 10.45 86.63 2.53 1.21 0.16 0.51 1.38 19.1 7.6 12.7 8.7 Tidal Peace Median 6.43 60.00 3.30 0.84 0.20 0.32 1.87 44.6 10.9 20.7 16.0

Mean 9.92 89.11 4.09 1.25 0.25 0.49 2.29 48.1 12.7 21.3 17.9 St Dev 12.13 79.09 2.97 1.11 0.18 0.59 1.35 20.2 7.4 14.3 10.0 West Wall Median 4.69 36.50 2.00 0.51 0.12 0.23 1.26 41.7 9.4 17.5 23.8

Mean 7.38 61.16 2.53 0.86 0.16 0.36 1.67 43.8 10.8 20.2 25.2 St Dev 9.19 64.31 2.03 0.90 0.13 0.45 1.16 18.3 7.0 13.1 13.1 East Wall Median 3.68 25.00 1.70 0.35 0.11 0.18 0.98 36.9 9.5 17.3 30.5 Mean 5.97 43.76 2.11 0.61 0.13 0.29 1.34 38.8 10.8 19.7 30.6 St Dev 7.14 50.18 2.19 0.70 0.14 0.35 0.95 17.3 6.7 12.3 14.2 Bokeelia Median 3.28 16.85 1.14 0.24 0.07 0.16 0.80 30.2 7.6 19.1 37.3 Mean 5.50 26.94 1.46 0.38 0.09 0.27 1.04 31.2 10.2 21.4 37.2 St Dev 7.58 27.81 1.24 0.39 0.08 0.37 0.69 15.1 7.8 11.9 15.7 Pine Island Sound Median 3.90 13.80 1.48 0.19 0.09 0.19 0.83 24.0 10.2 23.8 36.2 Mean 5.86 19.05 1.81 0.27 0.11 0.29 0.98 25.2 12.0 26.1 36.6 St Dev 5.52 16.40 1.47 0.23 0.09 0.27 0.48 12.4 7.6 13.3 14.0 Matlacha Pass Median 4.65 27.00 1.37 0.38 0.08 0.23 1.01 38.3 7.1 21.4 29.8

Mean 6.83 37.99 1.54 0.53 0.10 0.33 1.28 38.9 7.9 23.4 29.9 St Dev 6.84 30.35 0.82 0.42 0.05 0.33 0.74 12.2 3.3 12.3 12.8 San Carlos Bay Median 3.77 18.00 1.66 0.25 0.10 0.18 0.89 30.7 10.9 19.7 33.7

Mean 5.22 28.61 2.03 0.40 0.13 0.26 1.09 31.7 12.8 21.2 34.3 St Dev 4.82 29.02 1.37 0.41 0.08 0.24 0.62 15.6 8.0 11.2 14.1 Tidal Caloosahatchee Median 5.19 66.00 2.40 0.92 0.15 0.25 1.97 50.9 8.0 16.0 15.2

Mean 10.50 86.41 3.70 1.21 0.23 0.51 2.25 52.6 10.7 20.1 16.7 St Dev 17.08 58.06 3.79 0.81 0.24 0.84 1.22 18.7 8.7 16.4 7.8 Estero Bay Median 4.00 22.15 2.90 0.31 0.18 0.20 1.09 31.6 16.3 18.3 27.6

Mean 4.94 30.74 3.92 0.43 0.24 0.24 1.20 31.8 18.9 20.0 29.2 St Dev 4.53 26.33 3.72 0.37 0.23 0.22 0.54 16.3 10.0 11.1 11.2 Table 6. Median, mean and standard deviations for color, chlorophyll a and turbidity concentrations, the attenuation (k) due to thes e concentrations, total light attenuation usingCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 26 equation 3, and the percent of the total extinc tion attributable to each partial attenuation component, including the 0.3 intercept, fo r the water quality period of record.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 27 0 20 40 60 80 100 120 140 160 180 Color (Pt-Co) 0 10 20 30 40 50 60Chlorophylla(ug/L) Optical Depth, k-z equals 1.4

0.5 m 0.75 m 1 m 1.5 m 2 m 2.5 m

0 20 40 60 80 100 120 140 160 180 Color (Pt-Co) 0 10 20 30 40 50Turbidity (NTU) Optical Depth, k-z equals 1.4

0.5 m 0.75 m 1 m 1.5 m 2 m 2.5 m

Figure 4. Nomographs of Chlorophyll a Concentrations and Color and Turbidity and Color for Specified Depths. For a given depth, co mbinations of color and chlorophyll a concentrations can reach the values under the line and main tain our light attenuation target.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 28 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28

Chlorophyll a (ug/L) 0 2 4 6 8 10 12 14Turbidity (NTU)

0 20 40 60 80 100 120 140

Color (PCU) 0 2 4 6 8 10 12 14Turbidity (NTU)

Figure 5. Instances of dry seas on water quality which met (below line) or exceeded (above line) water quality goals for chlorophyll a and turbidity and color and turbidity in the Bokeelia segment.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 29 0 20 40 60 80 100 120 140

Chlorophyll a (ug/L) 0 1 2 3 4 5 6 7 8Turbidity (NTU)

0 20 40 60 80 100 120 140 160 180 200

Color (PCU) 0 1 2 3 4 5 6 7 8Turbidity (NTU)

Figure 6. Instances of wet season water quality which met (below line) or exceeded (above line) water quality goals for chlorophyll a and turbidity and color and turbidity in the Bokeelia segment.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 30 A comparison of wet and dry season water qua lity data from the each segment (Appendix B) suggests how water quality parameters differ by s eason. Generally, more water quality data fall within the plane of constant at tenuation in the dry season (December through June) than the wet. More than half of the data collected during th e wet season fall outside the plane of constant attenuation and would not meet minimum water clarity goals to provide 25% subsurface irradiance at the target maximum seagra ss depth distribution for this segment.

Nomographs of chlorophyll a and color as well as turbidity a nd color demonstrate that as color values rise, the concentrations of the other partial constituents must simultaneously decrease to meet our optical depth goal. The graphs also de monstrate that as depths increase from 0.5 to 2.5, the concentrations of all 3 partial constituents must then decrease. It follows then that the deeper our depth target, the lower the concentrations of the partial attenuation constituents must be to meet our light goal. 

## Next Steps

Water Quality Management Strategy Working Group

These water quality targets are a first step in maintaining wa ter quality of the Lemon Bay, Charlotte Harbor and Estero Bay regions. These ta rgets should be adapted over time as scientific knowledge of various parts of the approach is ga ined and implementation management strategies improved. These targets and the approach herein s hould be revisited systematically in the future.

As adaptive targets, an imperative first step in the implementation of these targets is the formation of a Working Group of scientists, re searchers and managers. This Working Group will meet regularly and will be ta sked with determining when segments are failing to meet the targets herein and developing pollutant load mana gement strategies to improve the water quality of those segments so that they meet these targets in the future. In this, it is expected that this Working Group will work similarly as the Tampa Bay Nitrogen Management Consortium and other groups in the implementation of the Tampa Bay water quality targets. The Working Group will also be tasked with developing further re finements of this approach including whether seagrass restoration goals are appropriate. The Charlotte Harbor Working Group will need to determine and fill knowledge gaps for the implemen tation of these targets, including determining regulatory, monitoring and management gaps. It is expected that the Working Group will encompass members of the NEP Technical Advi sory Committee, Management Committee and others as needed, and that the NEP Senior Sc ientist will staff the Working Group. Consultant services may be required to suppor t this process and shown in th e CHNEP workplan with partner funding. However, as in the other implementa tion steps for these targets and the NEP CCMP objectives, it is expected that Working Gr oup members will take on the lead role in implementation.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 31 

### Further Refineme nts of Approach

Other region-specific maximum depth distributio n goals could be developed. The plane of constant attenuation is based upon seagrass bed de pth goals derived from fixed transect data collected between 1999 and 2005 and seagrass ar eal coverage maps from 2003. The depth targets were created using the greatest a nnual average maximum seagrass deep edge per segment. A different strategy in the future coul d be to determine restoration depth targets based upon the 1950s seagrass maps currently being developed.

Light requirements of seagrasses within Charlotte Harbor could be refined as well. We used the estimate of 25% subsurface irradiance, but future research may document that more light is needed to be protective of seagrasses. Salin ity can affect seagrass photosynthesis (e.g., Torquemada et al. 2005), productivity (Tomas ko and Hall 1999) and abundance (Montague and Ley 1993). As both Tomasko and Hall (1999) and Dixon and Kirkpatrick (1999) cite salinity stress as possible reasons for reduced T. testudinum productivity in this area, seagrasses in Charlotte Harbor would benefit from research to determine ac tual light requirements based on environmental gradients such as salinity as well as water clarity.

The partial coefficients within this optical m odel could be refined in several ways. McPherson and Miller (1994) used water quality samples colle cted in Tampa Bay and Charlotte Harbor to derive the partial attenuation coe fficients used in this effort, and the model could be improved by including only data collected from Charlotte Harbor. Also, although other researchers have calculated partial light attenuation coefficients to support seagrass growth that differ from those in this effort (e.g., Gallegos and Kenworthy 1996), the coefficients used here are locally derived and the best available estimates for envi ronmental conditions in Charlotte Harbor.

The “non-algal suspended matter” partial light at tenuation coefficient is an important parameter to accurately estimate, as this component is generally responsible for over 50% of light attenuation in these areas (McPherson a nd Miller 1987; Dixon and Kirkpatrick 1999). Furthermore, the actual composition of “non- algal suspended matter”, represented by the turbidity term in McPherson and Miller (1994), will differ by segment and by season. Turbidity is a mixture of inorganic suspe nded matter, such as silt or cl ay, as well as plankton or other microscopic organisms (APHA 1985). Turbidity va lues in Charlotte Harbor are significantly different in dry and wet seas ons (Ott and Corbett 2005), and phytoplankton communities, which also differ from season to season and from region to region, will have a variable impact on light scattering and absorption (see Kirk 1994 and Je ffrey et al. 1997). However, McPherson and Miller (1987) suggested that re -suspended sediments may largely contribute to the non-algal suspended matter parameter in at least some of th e areas in Charlotte Harbor and later estimated its value by accounting for the contribution of chlorophyll to the difference in attenuation between filtered and unfiltered water samples (M cPherson and Miller 1994) . Therefore, while this effort uses the best available data appropr iate for the Charlotte Ha rbor region, management strategies incorporating our approach to setting water clarity goa ls should be prepared to estimate the “non-algal suspended matter” paramete r by both season and estuary segment.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 32 

### Quality of Light

These targets should be modified in the future to reflect the importance of the quality of light reaching seagrass beds in addition to the quantity of light. For example, the model can be modified to reflect the importance of the blue region of the visible spectrum for seagrass photosynthesis in lieu of our curren t 25% incident light target, that consists of all wavelengths between 400-700 nm.

Photosynthetically active radiation (PAR) consti tutes the 400-700 nm spectral range, while the ultraviolet and infrared spectral ranges have shorter and longer wavelengths than PAR, respectively. To become available for aquatic re sources, this light energy must first cross the airwater interface. Some of this light will be re flected at this interface, and the amount of light entering the water column will depend on sun a ngle, clouds, the roughness of the water surface and other conditions (Kirk 1994). Li ght will then be either scatte red or absorbed once it enters the water column (again scattering only increas es the pathlength of photons before they are absorbed unless the photons are scattered upwards). The 25% PAR target used herein is specific to subsurface irradiance; that is, our goal is 25% incident ligh t that has already passed through the air-water interface.

Absorption of light energy in the water column can essent ially be broken down into 4 components of the aquatic ecosystem: water, diss olved yellow pigments (gilvin), photosynthetic biota (phytoplankton and macrophytes) and inanimat e particulate matter (tripton) (Kirk 1994). Water itself absorbs light energy weakly in th e blue and green regions of the spectrum; it increases its absorption above 550 nm and stro ngly absorbs in the red region of the PAR spectrum (Kirk 1994). Gilvin, or dissolved organic matter, depending on its chemical composition, absorbs strongly in the blue and gr een spectrums, decreasing as wavelengths get longer until it absorbs weakly in the red region (K irk 1994). Gilvin is formed as plant tissue decomposes in soil or waterbodies by microbial actions or ultraviolet lig ht to ultimately carbon dioxide and inorganic forms of nitrogen, phosphor ous and sulfur. In the course of this decomposition process, a complex group of “humic substances” is formed, which vary in size from freely soluble compounds to insoluble macr omolecular aggregates (Kirk 1994). Much of the soluble humic substances from fresher waters particulates out when it comes into contact with higher salinity wate r, although a fraction remains in solu tion (cited in Kirk 1994). Humic substances in receiving waterbodi es arise not only indirectly fr om decomposing plant material leached from soils, but also decomposing phytoplankton and live aquatic plants, such as brown algal beds. The latter excrete phe nolic compounds that give rise to humic substances as a result of oxidation and other processes (Kirk 1994).

Tripton, inanimate particulate matter, does not abso rb light strongly but s catters intensely. Its absorption spectrum is similar to gilvin in that it absorbs strongly in the blue and green spectrums, decreasing as wavelengths get longer un til it absorbs weakly in the red region (Kirk 1994). Tripton is separate from the next absorp tion component and consists of freely floating particles from land or marine sediments or humic substances as well as humics bound to minerals.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 33 Figure 7. Total absorption spectrum of a mode l coastal waterbody with spectra of individual absorbing components. Chlorophyll a concentrations represent 1 μ g/L in this graphic. (Extracted from Kirk, J. T. O. 1994. 2 nd edition. Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press, New York, NY.).Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 34 Figure 8. Left graphic is the ab sorption spectra of chlorophylls a (—) and b (---) at concentrations of 10 μ g/ml. Graphic on right is ab sorption spectra of chlorophyll c 1 and c 2

. (Extracted from Kirk, J. T. O. 1994. 2 nd edition. Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press, New York, NY.).

The spectral absorption of light by photos ynthetic pigments-chl orophylls, carotenoids, biliproteins vary by the compositi on of pigments present. All photosynthetic plants, including seagrass, contain chlorophyll and carotenoid, while the red algae, blue-gr een algae and others contain biliprotein als o. The chlorophylls a , b

, c 1 and c 2 have 2 main spectral absorption bands: in the blue region less than 500 nm and w ithin the red region ca 625-670 nm (Kirk 1994). Absorption is very low, but not zero, in the mi ddle, green region of the spectrum. Carotenoids absorb at shorter wavelengths in the blue spectr al range. The spectral range of biliproteins vary but essentially has 2 peaks: a very strong peak at the higher visible wavelengths over 500 nm and a weaker peak in the ultraviolet range. Hi gher plants and green algae rely mainly on the chlorophylls for light harvesting. It is, thus, the 2 spectral re gions in which the chlorophylls absorb strongly—the blue less than 500 nm a nd the red ca 625-670 that are most important for seagrass photosynthesis. Previous research ha s documented that light quality in addition to quantity determined the maximum depth of subm erged aquatic vegetation distribution in lakes [i.e., Chambers and Prepas (1988) ci ted in McPherson and Miller 1994].Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 35 Figure 9. Comparison of spectral variations of the ve rtical attenuation coefficient for irradiance across the photosynthetic range in the following: a) unproductive oceanic waters, b) a productive (upwelling) oceanic water and c) an inland impoundment. (Extracted from Kirk,

J. T. O. 1994. 2 nd edition. Light and Photosynthesis in Aquatic Ecosystems. Cambridge University Press, New York, NY.).



### Inherent and Apparent Optical Properties

As sunlight enters a waterbody, it is either absorbed or scatte red when it interacts with the particulates and the dissolved mate rials within the water. When light is scattered, the direction of the light can be changed, and some light may be absorbed by particulates and changed into other forms or wavelengths of energy (e.g. fluores cence). These processes ch ange the intensity of light as a function of direction, th e light field. The light field is th e intensity of light as a function of direction, depth and wavele ngth. Since absorbance and scatte ring processes are a function of the wavelength of light, they change the spectr um of the light field. Thus, particulates and dissolved materials have spectral scattering a nd absorption characteristics that change the spectral light field. The spatiotemporal changes to the spectral light field within Charlotte Harbor and Lemon and Estero Bays are important with respect to the quality of light issue above. However, the measurement of scattering and ab sorption components of a waterbody, or inherent optical properties, is difficu lt and not usually undertaken in situ

. The scattering and absorption charac teristics of a waterbody are called Inherent Optical Properties of a waterbody. Their magnitudes depend only on the components of the waterbody and not on the geometric structure of the light field. Properties that depend on the inherent optical properties and the light field are called Apparent Optical Properties . For example, a cloud moving in front of the sun causes shadow ing and can immediately change the apparent optical properties of a waterbody, but not the inherent optical properties. The diffuse attenuation coefficient, K d , is an apparent optical proper ty; its value depends on depth, sun angle, sky conditions and shadowing by object s on the water surface. However, as depthCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 36 increases, the influence of surface illumination charac teristics decreases, and K d eventually approximates a value representing th e inherent optical properties.

The quantity and spectral quality of PAR in the water column affects photosynthesis of aquatic plants. The full spectrum of PAR from ca. 400- 700 nm is usually measured in the southwest Florida by either 2 pi or 4 pi Li cor instruments; the former in Upper Charlotte Harbor and Lemon Bay and the latter in Lower Char lotte Harbor and Estero Bay. The values derived from a 2 pi instrument reflects downward irradiance, while upward irradiance scattered from estuary bed sediments is additionally collected by 4 pi inst ruments. Both instrume nts also collect light reflected from vessel bottoms, and values can be in fluenced by the air-water interface, sun angle, waves and currents at the time of collection. Se cchi disk depth measurements are a separate method in southwest Florida of estimating water clarity. Secchi disk measurements are subject to the same errors as those listed above for the Licor instruments and several additional errors associated with the visual acuity and subjec tivity of the observer (P reisendorfer 1986). The human eye also does not detect the low and high ends of the PAR spectrum, less than 450 nm and above 650 nm (Johansson 2006). Thus, secchi disk measurements are of limited use in estimating the full spectrum of PAR; however, secchi disk measurements are inexpensive and easy. They are thus, widely used in field sa mpling and by volunteer groups, making secchi disk depth data widely available.

Downward irradiance diminishes with depth in an approximately exponential manner depending on wavelength and attenuation components of the water column; the rates of light attenuation for the different wavelengths are determined largel y by the absorption spectrum of the attenuation components (Kirk 1994). In clear unproductive , oceanic waters, water is the main light absorber; thus blue and green light penetrate d eeply, while red light is attenuated much more rapidly (see Fig. 7). In co astal waters that contain colo red dissolved organic matter and phytoplankton, the green wavelengths penetrate the most deeply, whereas in clear, productive areas, blue light is attenuated mo re strongly than green but not as strongly as red (the latter due to absorbance by water itself and the former due to absorbance by phytoplankton pigments) (Kirk 1994). In highly colored coastal waters infl uenced by major river discharge, blue light is attenuated as strongly as red light (Kirk 1994) . The contribution to total light attenuation by each partial attenuation component is not strictly a linear function of the concentrations of each component. However, simplification and approxi mation by linear equations, such as the optical model herein, is useful for management pur poses (Kirk 1983; McPherson and Miller 1994; Gallegos and Kenworthy 1996). There are on-going efforts to better approximate the spectral distribution of the absorbance and scattering partial component s in an optical model (e.g. Gallegos 1994), that can be used in the future in this region.



### Pollutant Load Management Strategies

Analysis of recently-collected water quality data s how that in all regions of the harbor, less than half of the water quality data meet the percent-lig ht-at-depth goals proposed by the NEP and even less data meet the goals during the wet seas on. Thus, load reductions for these analytes may be necessary for at minimum, the wet season that lasts July-November.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 37 One method in which pollutant load reduction goal s can be determined is to use existing landuse models. Sarasota County has created a polluta nt load model for the County which includes northern Lemon Bay. A landuse model (WMM) fo r the Caloosahatchee River and Estero Bay was created by CDM for the South Florida Water Management District. A WamView model for the Myakka River was completed by EPA in 2001 for the TMDL program. However, many of these models may not include runoff coefficients for dissolved matter and/or turbidity. Thus, spreadsheet models may be the preferred methods of determining preliminary load or yield estimates for each segment.

The Charlotte Harbor NEP hopes to better understa nd the role that landus e changes play with dissolved organic matter and concentrations in receiving water bodies . At present, in implementing these targets, the Charlotte Harbor NEP hopes to manage for chlorophyll a and turbidity and better understand color. However, there is anecdotal and other evidence supporting increasing trends in color concentrations in differe nt regions of Charlotte Harbor and Estero Bay. These trends may be a result of decreased ultraviolet radiatio n breakdown of color by decreases in residence times from an overall loss of natu ral wetlands, changes in pr ecipitation and flushing of sediments since monitoring of color concentra tions began, changes in landuse practices and/or other causes. As color is such a large influence on light availability to aq uatic resources in this region, resource managers need to question whether color can and should be managed. The program and its partners should design research and monitoring projects to shed some light on this issue.



### Implementation

It should be noted that the program itself re lies very heavily on its partners for the implementation of its objectives, including regu lation, research, education, monitoring and restoration. For instance, the pr ogram does not acquire conservati on lands, monitor water quality or submerged aquatic vegetation or establish Minimum Flows and Leve ls or Total Maximum Daily Loads rules, each of which are impera tive to the successful implementation of CCMP objectives. The success and streng th of the Charlotte Harbor NE P rests on the diligent support and enthusiastic participation of its partnering agencies, citizens, i ndustries, not-for-profit entities and others. The program itself acts more as a consensus-based facili tative and coordinating entity than a de facto restoration, monitoring or research en tity, with the exception of its central data compilation and warehouse role. The implem entation of the numeric water clarity targets will not differ in this regard. Thus, in the coming years, the program and its partners will need to review how resource management regulations, pl ans and policies of its member agencies and others implement these NEP water quality targets. A Water Quality Management Strategy Working Group will take the lead role in facilitating this.

There are many mechanisms in which the program will help its partners implement these numeric water quality targets, mostly by comp iling and analyzing data and information to support implementation. The program will use cons ultant services in workplan projects as necessary, Research and Restorati on Partners grants, leveraged gran ts and program staff projects to help partners with implementation. Others , such as the NEP Legislative Agenda, Research Needs Inventory and Restoration Plan are on-go ing efforts to inventory program and partnerCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 38 research and restoration needs that can be us ed to keep track of knowledge gaps for the implementation of these targets.



## Conclusion

The target light attenuation estimate presented he re is based upon current seagrass distribution, and the water clarity goals proposed in this effo rt are meant to mainta in the present seagrass coverage and depth distribution in to the future. However, recent analysis of water quality data has demonstrated significant increases in total suspended solids in Lower Charlotte Harbor and Upper Charlotte Harbor regions and increasing turbidity in the Lower Charlotte Harbor region (Janicki Environmental Inc. 2003). These wate r quality constituents, along with dissolved matter, constitute the dominant influences on the light available for seagrass beds in most areas of Charlotte Harbor. In addition, there is evidence that seagrass has receded from deeper depths from historic periods (Harris et al. 1983). Seagrass coverage in the Upper Charlotte Harbor region is stable since mapping began in 1988 (Kurz et al. 1999; Corbett et al. 2005; Tomasko et al. 2005; Corbett 2006) and in both Upper and Lowe r Charlotte Harbor since 1982 (Corbett 2006). Nonetheless, if seagrass depth distributio n is light limited (Di xon and Kirkpatrick 1999; Tomasko and Hall 1999), resource management strate gies in this area should focus on the longterm maintenance of present seagrass covera ge through the implementation of water clarity targets. If on-going research efforts determin e that seagrass meadows within Charlotte Harbor have indeed receded or catastrophic losses occurr ed since historic conditions, depth distribution goals reflecting restoration targets could be created from historic data. It should be noted then that water clarity would in turn need to improve to meet those restoration targets. Water quality that meets the goals derived in this report shoul d allow appropriate water clarity conditions for the maximum depth distribution of seagra ss meadows that curre ntly exist.



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Torquemada, Y. F., M. J. Durako, and J. L. Sanchez Lizano. 2005. E ffects of salinity and possible interactions with temperature and pH on growth and photosynthesis of Halophila johnsonii Eiseman. Marine Biology. 148:251-260.Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 43 

## Appendix A

Cumulative Frequency Curves of Seagrass Coverage by Depth

(Target Seagrass Deep edge is represented by red line)Charlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 44 Cumulative Frequency Seagrass Coverage by Depth Lemon Bay

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Cumulative Frequency Seagrass Coverage by Depth Cape Haze/Gasparilla

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass CoverageCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 45 Cumulative Frequency Seagrass Coverage by Depth Tidal Peace

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Cumulative Frequency Seagrass Coverage by Depth Tidal Myakka

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass CoverageCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 46 Cumulative Frequency Seagrass Coverage by Depth East Wall

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Cumulative Frequency Seagrass Coverage by Depth West Wall

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass CoverageCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 47 Cumulative Frequency Seagrass Coverage by Depth Bokeelia

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Cumulative Frequency Seagrass Coverage by Depth Estero Bay

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass CoverageCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 48 Cumulative Frequency Seagrass Coverage by Depth Pine Island Sound

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Cumulative Frequency Seagrass Coverage by Depth Matlacha Pass

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass CoverageCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 49 Cumulative Frequency Seagrass Coverage by Depth San Carlos Bay

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass Coverage

Cumulative Frequency Seagrass Coverage by Depth Tidal Caloosahatchee River

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5

Depth (m) 0 20 40 60 80 100Cumulative % of Seagrass CoverageCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 50 

## Appendix B

3-D Graphs

(Plane of constant at tenuation is in red)Charlotte Harbor National Estuary Program Numeric Water Quality Targets51Estero BayDrySeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets52Estero BayWet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets53Tidal Caloosahatchee Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets54Tidal Caloosahatchee Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets55Pine Island Sound Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets56Pine Island Sound Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets57Matlacha Pass DrySeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets58Matlacha Pass Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets59Bokeelia Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets60Bokeelia Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets61San Carlos Bay Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets62San Carlos BayWet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets63Gasparilla/Cape Haze Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets64Gasparilla/Cape Haze Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets65Lemon Bay Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets66Lemon BayWet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets67West Wall DrySeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets68West Wall Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets69East Wall Dry SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets70East Wall Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets71Tidal Myakka DrySeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets72Tidal Myakka Wet SeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets73Tidal Peace DrySeasonCharlotte Harbor National Estuary Program Numeric Water Quality Targets74Tidal Peace Wet SeasonCharlotte Harbor National Estuary Pr ogram Numeric Water Quality Targets 75 

## Appendix C

DEP Transect DataSegment Site Year Species BB Abundance Depth (m) Deepest Mean Depth Bokeelia PIS01 1999 Thalassia 3.0 0.7 Bokeelia PIS01 1999 Halodule 2.0 0.7 Bokeelia PIS02 1999 Thalassia 2.0 1.2 Bokeelia PIS03 1999 Thalassia 0.1 2.0 Bokeelia PIS06 1999 Thalassia 1.0 1.6 Mean (4) 1.4 Standard Deviation 0.5 Bokeelia PIS01 2000 Halodule 2.0 0.6 Bokeelia PIS02 2000 Halodule 1.0 1.0 Bokeelia PIS03 2000 Halodule 1.0 1.8 Bokeelia PIS06 2000 Thalassia 1.0 1.3 Bokeelia PIS07 2000 Halodule 0.5 2.3 Mean (5) 1.4 Standard Deviation 0.7 Bokeelia PIS01 2001 Halodule 0.5 0.4 Bokeelia PIS02 2001 Halodule 1.0 1.0 Bokeelia PIS03 2001 Halodule 0.5 1.4 Bokeelia PIS06 2001 Thalassia 3.0 0.5 Bokeelia PIS06 2001 Halodule 3.0 0.5 Bokeelia PIS07 2001 Halodule 0.5 2.1 Mean (5) 1.1 Standard Deviation 0.7 Bokeelia PIS02 2002 Thalassia 1.0 1.1 Bokeelia PIS03 2002 Halodule 0.1 1.5 Bokeelia PIS06 2002 Thalassia 2.0 0.5 Mean (3) 1.0 Standard Deviation 0.5 Bokeelia PIS01 2003 Thalassia 0.5 Bokeelia PIS01 2003 Halodule 1.0 0.5 Bokeelia PIS02 2003 Halodule 0.1 0.8 Bokeelia PIS03 2003 Halodule 0.1 1.7 Bokeelia PIS06 2003 Halodule 0.5 0.8 Bokeelia PIS07 2003 Halodule 0.1 2.3 Mean (5) 1.2 Standard Deviation 0.7 Bokeelia PIS01 2004 Halodule 0.1 0.5 Bokeelia PIS02 2004 Halodule 0.1 1.0 Bokeelia PIS03 2004 Halodule 0.1 2.0 Bokeelia PIS06 2004 Halodule 1.0 0.7 Bokeelia PIS07 2004 Halodule 2.3 Mean (5) 1.3 Standard Deviatio n 0.8 Bokeelia PIS01 2005 Thalassia 2.0 0.9 Bokeelia PIS02 2005 Halodule 0.1 1.3 Bokeelia PIS03 2005 Halodule 0.1 1.9 Bokeelia PIS06 2005 Halodule 0.5 0.9 Bokeelia PIS07 2005 Halodule 0.1 2.5 Mean (5) 1.5 1.5 Standard Deviation 0.7

East Wall MC03 2000 Halodule 1.0 1.2 East Wall MC02 2001 Halodule 0.5 0.7 East Wall MC03 2001 Halodule 0.1 1.2

Mean 1.0 Standard Deviation 0.4 East Wall MC03 2002 Halodule 0.5 1.3 East Wall MC02 2002 Halodule 0.1 1.2

Mean 1.2 Standard Deviation 0.0 East Wall MC02 2003 Halodule 0.1 1.5 East Wall MC03 2003 Halodule 0.1 1.1

Mean 1.3 Standard Deviation 0.3 East Wall MC03 2004 Halodule 0.1 1.3 East Wall MC02 2004 Halodule 0.1 1.3

Mean 1.3 Standard Deviation 0.0 East Wall MC03 2005 Halodule 1.3 East Wall MC02 2005 Halodule 0.1 1.6

Mean 1.4 1.4 Standard Deviation 0.2

Gasparilla Sound GAS02 1999 Thalassia 2.0 1.3 Gasparilla Sound GAS02 1999 Halodule 2.0 1.3 Gasparilla Sound GAS03 1999 Syringodium 2.0 1.6 Gasparilla Sound GAS04 1999 Halodule 1.0 1.9 Gasparilla Sound GAS05 1999 Thalassia 1.0 1.5 Gasparilla Sound GAS05 1999 Halodule 1.0 1.5 Gasparilla Sound PIS08 1999 Thalassia 0.5 1.3Segment Site Year Species BB Abundance Depth (m) Deepest Mean Depth Mean (5) 1.5 Standard Deviation 0.3 Gasparilla Sound GAS02 2000 Halodule 3.0 1.5 Gasparilla Sound GAS02 2000 Thalassia 1.0 1.5 Gasparilla Sound GAS03 2000 Syringodium 3.0 1.4 Gasparilla Sound GAS04 2000 Halodule 1.0 1.8 Gasparilla Sound GAS05 2000 Halodule 1.0 2.1 Mean (4) 1.7 Standard Deviation 0.3 Gasparilla Sound GAS02 2001 Thalassia 1.0 1.6 Gasparilla Sound GAS02 2001 Halodule 1.0 1.6 Gasparilla Sound GAS03 2001 Halodule 2.0 1.9 Gasparilla Sound GAS04 2001 Halodule 2.0 1.8 Gasparilla Sound GAS05 2001 Halodule 0.5 2.1 Gasparilla Sound PIS08 2001 Thalassia 0.5 0.7 Gasparilla Sound PIS08 2001 Halodule 2.0 0.7 Mean (5) 1.6 Standard Deviation 0.6 Gasparilla Sound PIS08 2002 Halodule 1.0 1.1 Gasparilla Sound GAS05 2002 Halodule 0.5 2.8 Gasparilla Sound GAS03 2002 Syringodium 2.0 1.8 Gasparilla Sound GAS02 2002 Thalassia 0.1 1.5 Gasparilla Sound GAS04 2002 Halodule 1.0 1.8 Mean (5) 1.8 Standard Deviation 0.6 Gasparilla Sound GAS05 2003 Halodule 0.1 1.8 Gasparilla Sound PIS08 2003 Thalassia 0.1 0.8 Gasparilla Sound GAS02 2003 Thalassia 1.0 1.5 Gasparilla Sound GAS03 2003 Halodule 2.0 1.9 Gasparilla Sound GAS04 2003 Halodule 0.5 2.0 Mean (5) 1.6 Standard Deviation 0.5 Gasparilla Sound GAS05 2004 Halodule 0.5 2.0 Gasparilla Sound GAS02 2004 Thalassia 0.1 1.6 Gasparilla Sound GAS03 2004 Halodule 0.5 1.9 Gasparilla Sound GAS04 2004 Halodule 2.0 Mean (4) 1.9 Standard Deviation 0.2 Gasparilla Sound GAS02 2005 Thalassia 0.1 1.7 Gasparilla Sound GAS04 2005 Halodule 0.5 2.2 Gasparilla Sound GAS05 2005 Halodule 1.0 1.9 Gasparilla Sound GAS03 2005 Halodule 1.0 1.6 Mean (4) 1.9 1.9 Standard Deviation 0.3

Lemon Bay GAS01 1999 Halodule 1.0 1.4 Lemon Bay ICW01 1999 Halodule 0.5 1.3 Lemon Bay ICW02 1999 Thalassia 0.1 1.8 Lemon Bay ICW03 1999 Halodule 0.5 1.6 Mean (4) 1.5 Standard Deviation 0.2 Lemon Bay ICW01 2000 Halodule 1.0 1.4 Lemon Bay ICW03 2000 Halodule 1.0 1.7 Lemon Bay ICW04 2000 Syringodium 5.0 1.2 Lemon Bay GAS01 2000 Halodule 3.0 1.5 Lemon Bay ICW05 2000 Syringodium 1.8 Mean (5) 1.5 Standard Deviation 0.2 Lemon Bay ICW01 2001 Halodule 0.5 1.2 Lemon Bay ICW04 2001 Syringodium 5.0 1.0 Lemon Bay GAS01 2001 Halodule 1.0 1.5 Lemon Bay ICW02 2001 Thalassia 0.1 1.8 Lemon Bay ICW03 2001 Halodule 0.5 1.6 Lemon Bay ICW05 2001 Thalassia 2.0 2.1 Mean (6) 1.5 Standard Deviation 0.4 Lemon Bay ICW02 2002 Halodule 0.5 1.7 Lemon Bay ICW02 2002 Thalassia 0.5 1.7 Lemon Bay ICW01 2002 Halodule 1.0 1.2 Lemon Bay GAS01 2002 Halodule 2.0 1.1 Lemon Bay ICW03 2002 Halodule 1.0 1.7 Lemon Bay ICW04 2002 Syringodium 1.4 Lemon Bay ICW05 2002 Thalassia 1.0 2.0 Mean (6) 1.5 Standard Deviation 0.3 Lemon Bay ICW01 2003 Halodule 0.1 1.4 Lemon Bay ICW02 2003 Halodule 0.1 1.8 Lemon Bay GAS01 2003 Halodule 0.1 1.5 Lemon Bay ICW05 2003 Thalassia 1.0 2.0Segment Site Year Species BB Abundance Depth (m) Deepest Mean Depth Mean (4) 1.7 Standard Deviation 0.3 Lemon Bay ICW04 2004 Halodule 0.1 1.0 Lemon Bay ICW04 2004 Syringodium 0.1 1.0 Lemon Bay GAS01 2004 Syringodium 0.5 1.4 Lemon Bay ICW05 2004 Thalassia 0.5 2.1 Lemon Bay ICW01 2004 Halodule 0.1 1.3 Lemon Bay ICW02 2004 Halodule 0.5 2.0 Lemon Bay ICW03 2004 Halodule 0.1 1.9 Mean (6) 1.6 Standard Deviation 0.5 Lemon Bay ICW01 2005 Halodule 0.5 1.2 Lemon Bay ICW02 2005 Halodule 1.6 Lemon Bay ICW03 2005 Halodule 0.1 2.1 Lemon Bay ICW04 2005 Halodule 0.5 1.0 Lemon Bay ICW05 2005 Halodule 0.1 2.5 Lemon Bay GAS01 2005 Syringodium 1.0 1.9 1.7 Mean (6) 1.7 Standard Deviation 0.6

Matlacha Pass MP02 1999 Halodule 1.0 1.0 Matlacha Pass MP01 1999 Halodule 0.5 0.8

Mean (2) 0.9 Standard Deviation 0.1

Matlacha Pass MP01 2000 Halodule 0.5 1.1 Matlacha Pass MP02 2000 Halodule 1.0 1.3 Matlacha Pass MP03 2000 Halodule 3.0 1.5 Matlacha Pass MP04 2000 Halodule 0.5 2.0 Matlacha Pass MP04 2000 Halophila 0.5 2.0

Mean (4) 1.5 Standard Deviation 0.4

Matlacha Pas s MP02 Halodul e 2.0 1.1 Matlacha Pass MP04 Halodule 1.0 1.5 Matlacha Pass MP01 Halodule 0.1 1.0

Mean (3) 1.2 Standard Deviation 0.3

Matlacha Pass MP02 Halodule 0.5 1.2 Matlacha Pass MP04 Halodule 0.5 1.6 Matlacha Pass MP03 Halodule 0.5 1.3 Matlacha Pass MP01 Thalassia 0.5 1.2

Mean (4) 1.3 Standard Deviation 0.2

Matlacha Pass MP03 Halodule 0.5 1.1 Matlacha Pass MP04 Halodule 0.5 1.2 Matlacha Pass MP02 Halodule 1.0 0.9 Matlacha Pass MP01 Thalassia 0.5 1.3

Mean (4) 1.1 Standard Deviation 0.1

Matlacha Pass MP01 Thalassia 0.1 1.3 Matlacha Pass MP02 Halodule 1.0 1.1 Matlacha Pass MP03 Halodule 1.0 1.5 Matlacha Pass MP04 Halodule 0.5 1.5

Mean (4) 1.3 Standard Deviation 0.2

Matlacha Pass MP01 Halodule 1.0 1.2 Matlacha Pass MP02 Halodule 0.5 1.2 Matlacha Pass MP03 Halodule 0.1 1.6 Matlacha Pass MP04 Halodule 1.0 0.9 1.5

Mean (4) 1.2 Standard Deviation 0.3

Myakka River MYR01 2000 Ruppia 0.1 0.5 Myakka River MYR02 2000 Halodule 0.1 1.0 Myakka River MYR03 2000 Halodule 0.1 0.9 Myakka River MYR04 2000 Halodule 0.1 1.1

Mean (4) 0.8 Standard Deviation 0.2

Myakka River MYR01 2001 Ruppia 0.5 0.2 Myakka River MYR05 2001 Halodule 1.0 0.7 Myakka River MYR04 2001 Halodule 2.0 0.7

Mean (3) 0.5 Standard Deviation 0.3

Myakka River MYR01 2002 Halodule 0.5 0.6 Myakka River MYR02 2002 Halodule 0.1 0.8 Myakka River MYR05 2002 Halodule 1.0 1.0 Myakka River MYR03 2002 Halodule 0.5 1.0 Myakka River MYR04 2002 Halodule 1.0 0.8

Mean (5) 0.8 Standard Deviation 0.2Segment Site Year Species BB Abundance Depth (m) Deepest Mean Depth Myakka River MYR02 2003 Halodule 0.5 0.5 Myakka River MYR03 2003 Halodule 0.1 0.7 Myakka River MYR04 2003 Halodule 0.1 0.8 Myakka River MYR05 2003 Halodule 0.1 0.9

Mean (4) 0.7 Standard Deviation 0.2

Myakka River MYR02 2004 Halodule 0.1 0.6 Myakka River MYR03 2004 Halodule 0.1 0.8 Myakka River MYR04 2004 Halodule 0.1 0.7 Myakka River MYR05 2004 Halodule 0.1 1.0

Mean (4) 0.8 Standard Deviation 0.2

Myakka River MYR05 2005 Halodule 0.5 1.0 Myakka River MYR01 2005 Halodule 0.5 0.2 Myakka River MYR02 2005 Halodule 0.5 0.3 Myakka River MYR03 2005 Halodule 0.1 1.0 Myakka River MYR04 2005 Halodule 0.5 0.6 0.8

Mean (5) 0.6 Standard Deviation 0.4

Peace River PR02 1999 Halodule 1.0 0.7 Peace River PR03 1999 Halodule 0.1 1.1 Peace River PR04 1999 Halodule 0.5 0.8 Peace River MC08 1999 Halodule 0.1 1.1

Mean (4) 0.9 Standard Deviation 0.2

Peace River PR03 2000 Halodule 0.5 0.7 Peace River MC08 2000 Halodule 0.1 1.1

Mean (2) 0.9 Standard Deviation 0.3

Peace River MC08 2001 Halodule 0.1 0.9 Peace River PR03 2001 Halodule 0.1 0.9 Peace River PR04 2001 Halodule 0.1 0.8

Mean (3) 0.8 Standard Deviation 0.1

Peace River PR03 2002 Halodule 0.5 1.0 Peace River MC08 2002 Halodule 0.1 0.9 Peace River PR04 2002 Halodule 0.5 0.8

Mean (3) 0.9 Standard Deviation 0.1

Peace River PR02 2003 Halodule 0.5 0.9 Peace River PR03 2003 Halodule 0.5 1.1 Peace River PR04 2003 Halodule 0.1 1.0

Mean (3) 1.0 Standard Deviation 0.1

Peace River PR03 2004 Halodule 0.5 0.7 Peace River PR04 2004 Halodule 0.1 0.9 Peace River MC08 2004 Halodule 0.5 0.6

Mean (3) 0.7 Standard Deviation 0.1

Peace River PR03 2005 Halodule 0.5 0.9 Peace River MC08 2005 Halodule 0.5 0.9 Peace River PR02 2005 Halodule 0.5 0.8 Peace River PR04 2005 Halodule 0.1 0.9 1.0

Mean (4) 0.9 Standard Deviation 0.0

Pine Island Sound PI08 1999 Thalassia 0.5 1.8 Pine Island Sound PI08 1999 Halodule 0.1 1.8 Pine Island Sound PI09 1999 Halodule 0.5 2.0 Pine Island Sound PI01 1999 Syringodium 2.0 2.3 Pine Island Sound PI02 1999 Halodule 0.5 1.6 Pine Island Sound PI04 1999 Thalassia 1.0 1.1 Pine Island Sound PI06 1999 Halodule 5.0 1.2 Pine Island Sound PI07 1999 Thalassia 0.5 1.6 Pine Island Sound PIS05 1999 Thalassia 0.5 1.5 Mean (8) 1.6 Standard Deviation 0.4 Pine Island Sound PI08 2000 Halodule 1.0 1.9 Pine Island Sound PI02 2000 Halodule 1.0 1.5 Pine Island Sound PI04 2000 Thalassia 1.0 1.0 Pine Island Sound PIS05 2000 Thalassia 1.0 1.4 Pine Island Sound PI03 2000 Thalassia 2.0 2.3 Pine Island Sound PI06 2000 Halodule 4.0 1.2 Pine Island Sound PI07 2000 Thalassia 1.0 1.3 Pine Island Sound PI09 2000 Halodule 1.0 2.0 Mean (8) 1.6 Standard Deviation 0.5 Pine Island Sound PI03 2001 Thalassia 1.0 2.3Segment Site Year Species BB Abundance Depth (m) Deepest Mean Depth Pine Island Sound PI06 2001 Halodule 1.0 1.3 Pine Island Sound PI01 2001 Syringodium 2.0 2.1 Pine Island Sound PI02 2001 Halodule 0.1 1.4 Pine Island Sound PI04 2001 Thalassia 0.5 0.9 Pine Island Sound PIS05 2001 Thalassia 1.0 1.0 Pine Island Sound PI07 2001 Thalassia 1.0 1.3 Pine Island Sound PI09 2001 Halodule 1.0 2.0 Pine Island Sound PI08 2001 Halodule 0.5 1.8 Pine Island Sound PI08 2001 Thalassia 0.1 1.8 Mean (9) 1.6 Standard Deviation 0.5 Pine Island Sound PI01 2002 Halodule 1.0 2.2 Pine Island Sound PI02 2002 Halodule 1.0 1.3 Pine Island Sound PI03 2002 Thalassia 0.5 2.4 Pine Island Sound PI08 2002 Thalassia 1.0 1.9 Pine Island Sound PIS05 2002 Thalassia 0.5 1.1 Pine Island Sound PI04 2002 Thalassia 1.0 1.0 Pine Island Sound PI06 2002 Halodule 2.0 0.9 Pine Island Sound PI07 2002 Thalassia 0.5 1.4 Pine Island Sound PI09 2002 Thalassia 0.5 2.3 Pine Island Sound PI09 2002 Halodule 1.0 2.3 Mean (9) 1.6 Standard Deviation 0.6 Pine Island Sound PI07 2003 Thalassia 2.0 1.2 Pine Island Sound PI07 2003 Halodule 0.5 1.2 Pine Island Sound PI08 2003 Halodule 0.1 2.1 Pine Island Sound PI09 2003 Thalassia 0.1 2.2 Pine Island Sound PI06 2003 Halodule 2.0 1.0 Pine Island Sound PI02 2003 Halodule 0.5 1.3 Pine Island Sound PI03 2003 Thalassia 0.5 2.5 Pine Island Sound PI04 2003 Halodule 0.1 1.5 Pine Island Sound PIS05 2003 Thalassia 0.5 1.1 Pine Island Sound PI01 2003 Syringodium 1.0 2.2 Pine Island Sound PI01 2003 Halodule 0.5 2.2 Mean (9) 1.7 Standard Deviation 0.6 Pine Island Sound PI07 2004 Halodule 0.5 1.6 Pine Island Sound PI07 2004 Thalassia 0.1 1.6 Pine Island Sound PIS05 2004 Thalassia 0.1 1.3 Pine Island Sound PI01 2004 Syringodium 0.5 2.3 Pine Island Sound PI03 2004 Thalassia 0.5 2.5 Pine Island Sound PI04 2004 Thalassia 0.1 1.6 Pine Island Sound PI06 2004 Halodule 2.0 1.1 Pine Island Sound PI10 2004 Halodule 0.5 2.5 Pine Island Sound PI11 2004 Halodule 0.1 1.6 Pine Island Sound PI09 2004 Halodule 0.1 2.5 Pine Island Sound PI02 2004 Halodule 0.1 1.4 Pine Island Sound PI08 2004 Thalassia 0.1 2.1 Pine Island Sound PI08 2004 Halodule 0.5 2.1 Mean (11) 1.9 Standard Deviation 0.5 Pine Island Sound PI08 2005 Thalassia 0.5 1.9 Pine Island Sound PI08 2005 Halodule 0.5 1.9 Pine Island Sound PI10 2005 Halodule 1.8 Pine Island Sound PI11 2005 Halodule 0.1 1.9 Pine Island Sound PI01 2005 Syringodium 2.0 2.2 Pine Island Sound PI03 2005 Thalassia 1.0 2.3 Pine Island Sound PIS05 2005 Thalassia 0.5 1.3 Pine Island Sound PI06 2005 Halodule 1.0 1.2 Pine Island Sound PI07 2005 Thalassia 0.1 1.6 Pine Island Sound PI09 2005 Halodule 0.5 2.2 Pine Island Sound PI02 2005 Halodule 0.5 1.5 Pine Island Sound PI04 2005 Halodule 1.2 1.9 Mean (11) 1.7 Standard Deviation 0.4

San Carlos Ba y SC01 1999 Thalassi a 1.0 1.9 San Carlos Bay SC01 1999 Syringodium 3.0 1.9 San Carlos Bay SC03 1999 Syringodium 1.0 1.5 San Carlos Bay SC02 1999 Syringodium 0.5 1.7 San Carlos Bay MP05 1999 Halodule 0.5 1.2 San Carlos Bay MP05 1999 Thalassia 0.5 1.2 San Carlos Bay MP05 1999 Halophila 0.5 1.2

Mean (4) 1.6 Standard Deviation 0.3

San Carlos Bay SC03 2000 Syringodium 2.0 1.7 San Carlos Bay SC03 2000 Thalassia 0.5 1.7 San Carlos Bay SC02 2000 Thalassia 0.5 1.8 San Carlos Bay MP05 2000 Halodule 2.0 1.2Segment Site Year Species BB Abundance Depth (m) Deepest Mean Depth San Carlos Bay MP05 2000 Thalassia 2.0 1.2

Mean (3) 1.6 Standard Deviation 0.3

San Carlos Bay MP05 2001 Halodule 1.0 1.6 San Carlos Bay SC03 2001 Thalassia 0.1 1.6 San Carlos Bay SC02 2001 Syringodium 0.5 1.7 San Carlos Bay SC01 2001 Halodule 1.0 1.9

Mean (4) 1.7 Standard Deviation 0.1

San Carlos Bay SC02 2002 Syringodium 0.1 1.9 San Carlos Bay SC02 2002 Halodule 0.1 1.9 San Carlos Bay SC01 2002 Halodule 0.5 1.9 San Carlos Bay MP05 2002 Halodule 0.5 1.5 San Carlos Bay SC03 2002 Thalassia 0.5 1.8 San Carlos Bay SC03 2002 Syringodium 1.0 1.8

Mean (4) 1.8 Standard Deviation 0.2

San Carlos Bay SC01 2003 Thalassia 0.5 2.0 San Carlos Bay SC02 2003 Thalassia 0.5 2.5 San Carlos Bay MP05 2003 Halodule 0.1 2.0 San Carlos Bay SC03 2003 Thalassia 0.5 1.7 San Carlos Bay SC03 2003 Syringodium 0.5 1.7

Mean (4) 2.0 Standard Deviation 0.3

San Carlos Bay SC04 2004 Syringodium 0.1 1.9 San Carlos Bay SC05 2004 Thalassia 0.1 1.2 San Carlos Bay SC01 2004 Thalassia 0.5 2.3 San Carlos Bay MP05 2004 Halodule 1.0 2.0 San Carlos Bay SC03 2004 Syringodium 0.1 2.2 San Carlos Bay SC02 2004 Thalassia 0.1 2.2

Mean (6) 1.9 Standard Deviation 0.4

San Carlos Bay SC04 2005 Thalassia 0.1 1.8 San Carlos Bay SC05 2005 Halodule 1.0 0.6 San Carlos Bay MP05 2005 Halodule 0.1 1.9 San Carlos Bay SC01 2005 Thalassia 0.1 1.9 San Carlos Bay SC03 2005 Thalassia 0.1 1.8 2.0

Mean (5) 1.6 Standard Deviation 0.5

West Wall MC06 1999 Halodule 1.0 1.0 West Wall MC07 1999 Halodule 0.1 1.0 West Wall MC04 1999 Halodule 0.1 1.3 West Wall MC05 1999 Halodule 0.5 1.8 Mean (4) 1.3 Standard Deviation 0.4 West Wall MC07 2000 Halodule 0.5 1.1 West Wall MC05 2000 Halodule 0.5 1.8 West Wall MC04 2000 Thalassia 2.0 1.5 Mean (3) 1.4 Standard Deviation 0.4 West Wall MC04 2001 Thalassia 0.1 1.7 West Wall MC05 2001 Halodule 0.5 1.4 West Wall MC01 2001 Halodule 1.0 0.7 West Wall MC06 2001 Halodule 0.5 1.4 West Wall MC07 2001 Halodule 0.5 0.7 Mean (5) 1.2 Standard Deviation 0.4 West Wall MC04 2002 Thalassia 0.1 1.1 West Wall MC05 2002 Halodule 0.5 1.8 West Wall MC01 2002 Halodule 0.1 1.0 West Wall MC06 2002 Halodule 0.1 1.3 West Wall MC07 2002 Halodule 1.0 0.5 Mean (5) 1.2 Standard Deviation 0.5 West Wall MC04 2003 Halodule 0.1 1.0 West Wall MC04 2003 Thalassia 0.1 1.0 West Wall MC05 2003 Halodule 0.1 1.2 West Wall MC06 2003 Halodule 0.5 1.3 West Wall MC01 2003 Halodule 0.1 0.5 West Wall MC07 2003 Halodule 0.5 0.6 Mean (5) 0.9 Standard Deviation 0.4 West Wall MC01 2004 Thalassia 0.1 0.7 West Wall MC01 2004 Halodule 0.1 0.7 West Wall MC07 2004 Halodule 0.5 1.1 West Wall MC05 2004 Halodule 0.1 1.6 West Wall MC06 2004 Halodule 0.5 1.4 West Wall MC04 2004 Halodule 0.5 1.6Segment Site Year Species BB Abundance Depth (m) Deepest Mean Depth Mean (5) 1.3 Standard Deviation 0.4 West Wall MC01 2005 Halodule 0.1 0.8 West Wall MC07 2005 Halodule 0.1 1.1 West Wall MC04 2005 Halodule 0.1 1.5 West Wall MC05 2005 Halodule 0.1 1.9 West Wall MC06 2005 Halodule 0.5 1.0 1.4 Mean (5) 1.3 Standard Deviation 0.4

Estero Bay EB01 2002 1.6 Estero Bay EB02 2002 0.8 Estero Bay EB03 2002 0.7 Estero Bay EB04 2002 0.9 Estero Bay EB05 2002 1.1

Mean 1.0 Standard Deviation 0.3 Estero Bay EB01 2003 1.0 Estero Bay EB02 2003 0.5 Estero Bay EB03 2003 1.2 Estero Bay EB04 2003 0.9 Estero Bay EB05 2003 0.8 Estero Bay EB01 2003 1.5 Estero Bay EB02 2003 0.9 Estero Bay EB03 2003 0.9 Estero Bay EB04 2003 1.1 Estero Bay EB05 2003 1.6

Mean 1.0 Standard Deviation 0.3 Estero Bay EB01 2004 0.4 Estero Bay EB02 2004 0.9 Estero Bay EB03 2004 1.4 Estero Bay EB04 2004 1.5 Estero Bay EB05 2004 1.4 Estero Bay EB01 2004 1.3 Estero Bay EB02 2004 0.5 Estero Bay EB04 2004 1.0 Estero Bay EB05 2004 0.7

Mean 1.0 Standard Deviation 0.4 Estero Bay EB01 2005 1.6 Estero Bay EB02 2005 0.5 Estero Bay EB03 2005 0.6 Estero Bay EB04 2005 0.8 Estero Bay EB05 2005 0.9 Estero Bay EB01 2005 1.4 Estero Bay EB02 2005 0.8 Estero Bay EB03 2005 0.8 Estero Bay EB04 2005 0.9 Estero Bay EB05 2005 0.2 1.0

Mean 0.8 Standard Deviation 0.4