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.org
This document was written by Catherine A. Corbett, Charlotte Harbor National Estuary Program. 

Much of the text was taken directly or updated from the reference: 

Corbett, C. A. and J.A. Hale. 2006. Development of Water Quality Targets for 

Charlotte Harbor, Florida using Seagrass Light Requirements. Florida 

Scientist. 69 (00S2): 36-50. 

in which Jason A. Hale, Charlotte Harbor Environmental Center, provided significant aid. Any 

errors or omissions are solely the responsibility of Catherine A. Corbett. 

The Charlotte Harbor National Estuary Program is a partnership of citizens, elected officials, 

resource managers and commercial and recreational resource users working to improve the water 

quality and ecological integrity of the greater Charlotte Harbor watershed. A cooperative 

decision-making process is used within the program to address diverse resource management 

concerns in the 4,400 square mile study area. Many of these partners also financially support the 

Program, which, in turn, affords the Program opportunities to fund projects such as this. The 

entities that have financially supported 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, Charlotte, DeSoto, and Hardee Counties 

Cities of Sanibel, Cape Coral, Fort Myers, 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 

Acknowledgements....................................................................................................5

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

Introduction................................................................................................................8

Methods for Setting Water Quality Targets.............................................................10

Regulatory................................................................................................................................. 10 

Historic Conditions ................................................................................................................... 11 

Reference Site ........................................................................................................................... 11 

Resource-Based......................................................................................................................... 11 

Resource-Based Approach for 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.................................................................................................................... 75 
Charlotte Harbor National Estuary Program 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 greatly contributed their data, time and effort. Patrick Biber 

encouraged the use of an optical model for developing 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 components in Charlotte Harbor. 

Keith Kibbey, Judy Ott, James Evans, Connie Jarvis, Philip Stevens, Patrick Casey, Peter 

Doering, Katie Fuhr, Dan Quick, Joanne Vernon, Kris Kaufman and other members of the 

Coastal Charlotte Harbor Monitoring Network supplied 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 measurements, and Jaime Greenawalt Boswell provided 

seagrass species density and composition information as well as other insightful comments. 

Robert Chamberlain and Peter Doering contributed invaluable input into seagrass light 

requirements and mechanisms into furthering this 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 supplied seagrass areal coverage data. Tim 

Lieberman, Ping Wang, XinJian Chen and others provided updated bathymetry data, while Jason 

Hale, Tim Walker, Tim Lieberman and Ravic Nijbroek helped with GIS work. Cambridge 

University Press graciously granted permission to include 4 graphics (Figures 7, 8 and 9) from 

Kirk (1994). Finally, Roger Johansson, Ray Kurz, John Ryan, Holly Greening and others too 

numerous to include provided support and encouragement to continue and expand this work. 
Charlotte Harbor National Estuary Program 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 relatively undeveloped compared to nearby 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, Sarasota and Lemon Bays, research and restoration efforts have 

focused on seagrass areal coverage as an indication of estuarine condition, and as a result, 

Charlotte Harbor is generally considered to be fairly healthy. In much of the greater Charlotte 

Harbor region, there is little evidence of significant changes in seagrass areal coverage since 

systematic mapping efforts began in 1988, and nutrient 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 that 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 Miller 1994; Dixon and 

Kirkpatrick 1999) and that inter-annual seagrass coverage changes are largely resultant from 

changes in freshwater inflow from Charlotte Harbor’s 3 major tributaries (Corbett et al. 2005; 

Tomasko et al. 2005). 

However, there is reason for concern for the long-term health of this estuary as the southwest 

Florida region is one of the fastest growing areas in the U.S. In 1983 the Florida Department of 

Natural Resources (FDNR) documented a harbor-wide 29% decrease 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) documented a harbor-wide 6% loss 

from 1982 to 1999, again mostly from the Lower Charlotte Harbor region. Greenawalt-Boswell et 

al. (2006) documented a decline in seagrass density throughout Charlotte Harbor and Lemon Bay 

from 1999-2005, whereas declines 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 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. Finally, a 2003 Charlotte 

Harbor NEP report determined significant increasing trends in total suspended solids throughout 

the coastal Charlotte Harbor region and increasing trends in turbidity, nutrients and others in the 

Lower Charlotte Harbor region. The creation of numeric water quality targets to serve as the 

basis for water quality restoration and maintenance activities can serve as a tool to stop these 

declining trends. 

To offset future development and maintain current water quality into the future, we propose the 

use of an optical model to establish water quality targets for color, turbidity and chlorophyll a

specific to each estuarine segment that encompasses 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 distribution in each segment. Next we applied an 

optical model which describes total light attenuation 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 attenuation by each component in terms of concentration 

intercepts (e.g. μg/L chlorophyll). Finally, seasonal water quality data for each estuary segment 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 7

were plotted with the intercepts overlaid, producing a plane of constant attenuation given our 

percent-light-at-depth goal. It is this plane of constant attenuation that is our proposed target. 

This plane allows the concentration 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 constituents affecting the 

targeted outcome. Water quality data points located outside of the plane of constant attenuation 

identify times and locations when water quality did 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 coverage. Resource managers can in the future determine if seagrass 

and water quality restoration targets 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 exceeding the targets herein and resulting pollutant 

reduction management strategies. The Working Group will also take the lead role in further 

refining the targets, such as whether seagrass restoration targets are appropriate and refining the 

partial attenuation coefficients of the optical model included in this approach. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 8

 

Introduction 

In southwest Florida, substantial research and restoration efforts have focused on seagrass 

meadows as an environmental indicator for coastal environmental conditions. Charlotte Harbor, 

FL is contiguous to the northwest with Lemon, Sarasota and Tampa Bays where seagrass 

management strategies focus on nutrient load and phytoplankton concentration reductions. 

Charlotte Harbor confronts different issues, and phytoplankton concentrations do not have as 

large an influence on light attenuation in Charlotte Harbor as dissolved and suspended matter 

(McPherson and Miller 1987; McPherson and Miller 1994; Dixon and Kirkpatrick 1999). Also, 

analyses of seagrass coverage data from the recent 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 and Lower Charlotte Harbor since 

1982 (Corbett 2006). Seagrass management strategies in the Charlotte Harbor region have not 

focused on nutrient load reductions, and currently, resource managers 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. Reductions in nitrogen loads since 1982 have led to 

reduced phytoplankton concentrations and increased water clarity, a cascade of effects which has 

allowed increases in seagrass extent (Johansson 1991; Johansson and Ries 1997; Lewis et al. 

1998; Johansson and Greening 1999). Increases in seagrass coverage is also a restoration 

objective for seagrass managers in Sarasota Bay, where recent increases (1988-1996) may be 

linked to decreased nitrogen loads to the bay by the City of Sarasota and Manatee and Sarasota 

counties (Kurz et al. 1999). In both Tampa and Sarasota Bays, water clarity and quantity of light 

reaching the tops of seagrass blades is related to nitrogen loading and its effects on 

phytoplankton populations (cited in Tomasko et al. 2005); thus, seagrass restoration 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 (Tomasko et al. 2001). Phytoplankton biomass was 

calculated to contribute 12 to 39 % of light attenuation within the 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 (Tomasko 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 management strategies within Lemon Bay region 

also focus on nutrient load reductions. 

South of Lemon and Sarasota Bays along the southwest Florida coast lies the Charlotte Harbor 

estuarine complex, which includes a number of interconnected estuaries. The Charlotte Harbor 
Charlotte Harbor National Estuary Program 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 rivers, 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 freshwater 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. Using data collected in Charlotte Harbor, 

McPherson and Miller (1987) found that non-chlorophyll suspended matter (including detritus, 

cellular material and minerals) accounts for an average of 72 % of light attenuation in the water 

column, color (dissolved organic matter) accounts for 21% and phytoplankton chlorophyll for 

only 4%. In the 1994 study, McPherson and Miller found that light attenuation and water quality 

analytes varied spatially with the highest light attenuation coefficients found near the mouths of 

freshwater inputs. They found color contributed 13-49% to total light attenuation, chlorophyll a

contributed 16-18%, non-chlorophyll suspended matter contributed 30-55% and seawater 

contributed 3-6%. Dixon and Kirkpatrick (1999) found that color, turbidity and chlorophyll 

accounted for 66%, 31% and 4% of light attenuation, respectively. Hence, 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) stability 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% harbor-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 hydrologic changes and increased pollutant loads. From 1982 to 1999, 

Charlotte Harbor as a whole demonstrated a 6% decrease in seagrass extent, with 77% of that 

loss located in the Lower Charlotte Harbor region (Corbett et al. 2005). Subsequently, from 

1999 to 2003 seagrass areal extent displayed increases in the Lower Charlotte Harbor region, and 

no significant trend was found in the Upper Charlotte Harbor region since 1982 (Corbett 2006). 

However, analyses of seagrass data on a smaller scale indicate that seagrass 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 larger-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 meadows is not known and may reflect natural 

variability, these trends are starting to point to the need to protect 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 loading 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 Harbor regions and increasing turbidity and nutrients in the Lower 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 10

Charlotte Harbor region (Janicki Environmental Inc. 2003). At the same time, the Charlotte 

Harbor region is facing rapid urbanization pressure along the coast and more intensive landuse 

changes in its watershed, which may result in further degradation of water quality. Increases in 

rainfall and freshwater inflows over the previous several decades are 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). These trends do not bode well for the long-term 

maintenance of the region’s aquatic resources, and thus, resource management strategies in this 

area need to focus on water quality improvements 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 complex. 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 Harbor NEP has contracted to map 1950s seagrass 

coverage using methods comparable to current seagrass 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 targets for those areas of 

losses. 

These water clarity targets helps implement Quantifiable Objective WQ-2 of the Charlotte 

Harbor NEP updated Comprehensive Conservation and Management Plan (CCMP). This 

objective is as follows: 

WQ-2: Develop and meet site specific alternative 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 water quality standards or regulations as targets; 

establishing a baseline or historic conditions to which conditions should be maintained or 

restored; identifying an appropriate reference site and accepting its water quality conditions as a 

target or developing resource-based targets using the requirements of relevant living resources. 

Regulatory 

The Florida Impaired Waters Rule (IWR) (Chapter 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 Program Numeric Water Quality Targets 11

2. dissolved oxygen concentrations shall not average less than 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 natural 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 standards as numeric targets for the coastal 

Charlotte Harbor region. However, it should be noted that despite annual seasonal hypoxia in 

upper Charlotte Harbor bottom waters, significant 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 median value per year (Janicki Environmental Inc. 

2003), these areas have not been designated impaired 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 Miller 1987; McPherson and Miller 1994; Dixon and 

Kirkpatrick 1999), with the exception of Lemon Bay. It is therefore 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 the water quality conditions of Charlotte Harbor 

and surrounding waterbodies in pre-development or a specified historic time 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 protocols for the time period in question, which is often 

the case. 

Reference Site 

An alternative course to follow in the determination of numeric water quality targets is to 

identify an appropriate reference site, a waterbody with little to no anthropogenic 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 similar systems or systems with little anthropogenic impacts. 

Resource-Based 

Arguably the most successful approach to date for setting numeric water quality targets has been 

the resource-based approach. In the Tampa Bay area, for instance, resource managers 

established the goal of restoring 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). Therefore, the focus of restoring 1950’s level seagrass 

coverage is to reduce nutrients, specifically nitrogen, loading to the bay (Greening and Janicki 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 12

2006). Approximately 8200 metric tons of nitrogen was estimated to enter Tampa Bay annually 

in the mid 1970s from point sources, atmospheric deposition, nonpoint sources, groundwater and 

fertilizer losses at 55%, 22%, 16%, 3% and 5%, respectively (Greening and Janicki 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 sources (e.g., WWTP discharges from Cities of 

Tampa, St. Petersburg and Clearwater and port facilities) resulted in an estimated 60% reduction 

in nitrogen loading during the 1985-2003 period (cited in Greening and Janicki 2006). As a 

result, there was an increase in seagrass coverage between 1982-1996 (Tomasko et al. 2005), and 

in 2004, baywide seagrass areal coverage was the highest observed since 1950 (Greening and 

Janicki 2006). 

Resource Managers in southern Indian River Lagoon have also established resource-based water 

quality targets using seagrass depth distribution. They partitioned 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 growing seagrass beds and the most stable. They 

then used the median deep edges of Jupiter Inlet seagrass meadows as their depth target (1.3 

meters) and evaluated the water quality conditions in those areas of the Lagoon that included 

beds growing at least 1.3 m deep (Crean 2002). The median values of these regions were used to 

set water quality targets for salinity, color, turbidity, dissolved oxygen, pH and 

photosynthetically active radiation (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 targets would be specific to each basin that 

encompasses this region. The Florida Fish and Wildlife Conservation Commission-Fisheries 

Independent Monitoring Program and the Coastal Charlotte Harbor Monitoring Network divide 

Charlotte Harbor into 12 hydrologic segments for their fisheries and water quality sampling 

programs (Figure 1). Using the methodology described below, we developed water quality goals 

specific for each segment. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 13

Figure 1. 

Seagrass Light Requirements 

Using published analyses, we estimated percent-light-at-depth targets required to achieve the 

current estimated seagrass maximum depth distribution in each segment. The 25% PAR target 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 14

used herein is specific to subsurface 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 estimates 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 efforts documenting requirements of seagrass in other 

estuaries and 23-37% in Indian 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 (50th percentile) and 

20% at the maximum (95th 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 percentile) and maximum (95th percentile) depth 

limits for the 19 Indian River Lagoon segments 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 the maximum depth limit (95th percentile) is 

20±14%. The minimum growing season requirement (March to mid Sept) is essentially 

the same 20±13%. (See Steward et al. 2005 for more information). 

Grasses in Charlotte Harbor require between 15-30% photosynthetically active radiation 

penetrating to depth (Dixon, 2000). Tomasko and Hall (1999) found an average 23% subsurface 

irradiance reaching all study sites but documented 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 percent-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 propose 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 higher 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 Charlotte 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 [Braun-Blanquet 1965]), blade length, sediment type and epiphyte 
Charlotte Harbor National Estuary Program 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 Atmospheric 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 measurements and in turn targets were updated since 

the Corbett and Hale (2006) effort.) There were 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 distribution 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 coverage per segment growing within the depth 

distribution targets from the DEP transect data. We overlaid the 2003 seagrass polygon maps 

produced by SFWMD and SWFWMD onto NOAA and SFWMD-updated bathymetry data 

converted to polygon files. We used the 2003 maps 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 encompasses the entire area, the 1999 dataset, represents 

decreased seagrass areal coverage, as there were losses in most segments from the previous 

mapping event (Corbett 2006). The 2003 dataset represents increased seagrass coverage in most 

seagrass segments (Corbett unpublished); the data were acquired for both Upper and Lower 

Charlotte Harbor regions on the same day in January 2004. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 16

We created an intersect coverage of each segment by combining the 2003 seagrass maps to the 

bathymetry data. This allowed us to determine the seagrass depth distribution per segment 

according to the seagrass polygon maps. We compared seagrass depth distributions estimated 

from the intersect coverages to our depth distribution targets 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 bathymetry coverages so that ≥95% 

of seagrass areal coverage shown by the maps occurred shallower than our depth targets. Those 

segments with larger values for seagrass coverage, such as Pine Island Sound and Matlacha Pass, 

we allowed a maximum of ~200 acres to fall deeper than our depth targets. The final depth 

targets are listed in Table 2. Cumulative frequency 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

100

Cumulative % 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 and represented by the red 

arrow. 
Charlotte Harbor National Estuary Program 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 and 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 important issue is that the seagrass polygon maps 

produced by the Water Management Districts may 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 spatial accuracy of the seagrass maps may be off 

as much as 20 feet in some areas, while the bathymetry data may also add to these inaccuracies. 
Charlotte Harbor National Estuary Program 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(Io)-ln(Iz)]/z 

where k equalsthe light extinction coefficient at depth, Io is surface irradiance, Iz is the irradiance 

taken at depth and z equals the depth in meters. 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 minimum 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 segment 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 caused 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 yellow substances (gilvin) and particulates 

(tripton/phytoplankton) (Kirk 1994). Scattering itself does not remove light (photons) from the 

water column but impedes the vertical penetration of light by increasing the total pathlength it 

travels before it is absorbed (Kirk 1994). Thus, scattering increases the 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 constituents of the water 

column include the particulates: mineral particles from land or bottom sediments, phytoplankton, 

bacteria, dead cells or fragments of cells, etc. (Kirk 1994). 

Lorenzen (1972) partitioned absorption and scattering components of the water column into 4 

parts: water, phytoplankton, dissolved matter and non-chlorophyll suspended matter. This can 

be written as follows: 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 19

 k = k1 + k2 + k3 + k4 (2) 

where k1 is the attenuation coefficient of water, 0.0384 m-1 (Lorenzen 1972); k2 is the attenuation 

coefficient of phytoplankton, [often approximated by multiplying 0.0138 m2

 mg

-1 (Lorenzen 

1972) by the concentration of chlorophyll a]; k3 is the attenuation coefficient of dissolved matter 

determined by absorptivity times concentration and k4 is the attenuation coefficient of non-algal 

suspended matter, determined by absorptivity and scatter times concentration (from McPherson 

and Miller 1987). This partitioning of light attenuation 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 grass beds can be translated into concentrations 

of these constituents that meet the specified light 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 sum of three partial light attenuation components: 

color, chlorophyll a and turbidity: 

k = 0.014*C2 + 0.062* C3 + 0.049*C4 + 0.30 (3) 

where k equalsthe light extinction coefficient at depth, C2 is water color in Pt-Co units, C3 is 

turbidity in NTU and C4 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 contribution 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 multiple regression analysis as surrogates for nonalgal suspended matter. However, both analytes included 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 attenuation (calculated from the percent-light-at-depth target), set two 

components to zero and solve for the third. Continuing to use the San Carlos Bay segment as an 

example, we solved for color in the following case: 

0.6 = 0.014*C2 + 0.062* (0) + 0.049*(0) + 0.30 (3a) 

0.3 = 0.014*C2 (3b) 

C2 = 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 intercepts given our seagrass depth and light targets. 
Charlotte Harbor National Estuary Program 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 Program 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 with 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 water quality data collected for coastal Charlotte Harbor and 

Estero and Lemon Bays. This produced a plane of constant attenuation for variable 

concentrations of the water quality parameters, given our percent-light-at-depth goal, when 

combining the 3 components of the optical model. When reviewing only 2 components (see 

Figures 5 and 6 for examples), a line of constant attenuation is produced. For the remainder of 

this document, we discuss the 3-D graphs and plane of constant attenuation. Water quality data 

points located above this plane identify instances when water clarity did not meet our target. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 22

Bokeelia Dry Season

Figure 3. 3-D Scatterplot with the Bokeelia dry season water quality intercepts (Table 4) and 

the plane of constant attenuation are overlaid in red. Water quality data points located above the 

plane of constant attenuation identify instances when water clarity did not meet these targets. 

Monthly water quality data from surface water samples collected between 2001 to 2006 were 

provided by the Coastal Charlotte Harbor Monitoring 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, Caloosahatchee 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 Southwest Florida Water Management District from 

January 1993 through December 2000 were also included. All data were divided into “wet” and 

“dry” seasons, defined as data collected during the months of July-October and November-June, 

respectively. Salinity data analyses support this delineation (see Greenawalt-Boswell et al. 

2006). 
Charlotte Harbor National Estuary Program 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 concentration 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 quality standards for chlorophyll a of an annual average of 11 μg/L 

for marine and estuarine waters. These intercepts 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 phytoplankton production. Excepting those periods when chlorophyll a

concentrations are very low due to high color concentrations, the concentrations 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 percent-light-at-depth goal. The 

intercepts are a necessary step in developing the resulting plane of constant attenuation, which 

denotes the acceptable concentration of a component of light attenuation in relation to the 

concentrations of the other components. This plane allows the concentration for each component 

to assume any concentration between zero and 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 specific analyte without regard to concentrations of 

other relevant constituents affecting the targeted outcome. 

 

The results from plotting the derived planes of constant attenuation over water quality data 

collected in the recent past show that there are times and locations within each segment that 

current water quality would not meet the percent-light-at-depth goals proposed in this effort. 

The locations in which these data were collected 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 seagrass 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 Program Numeric Water Quality Targets 24

Multiplying the optical model partial attenuation coefficients in Equation 3 above with color, 

chlorophyll a and turbidity concentrations collected monthly in the Pine Island Sound segment 

by Lee County for the Coastal Charlotte Harbor Monitoring Network and fixed station 

monitoring from December 2000 through July 2006 demonstrates 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 target 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 collected between 1993 and 

2006 in the East Wall segment were equal to or less than the target extinction coefficient of 1.0 

(N=470). The seagrass depth target for this segment is shallower than that 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 wet 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 for color, chlorophyll a and 

turbidity concentrations, the attenuation due to these concentrations and the percent of the total 

light extinction coefficient attributable to these 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 section 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 multiple 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 combination of the maximum color and chlorophyll a or color and 

turbidity concentrations for a depth of interest that meets our minimum light objective. Similar 

plots of turbidity and color given 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 Program 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 these concentrations, total light attenuation using 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 26

equation 3, and the percent of the total extinction attributable to each partial attenuation 

component, including the 0.3 intercept, for the water quality period of record. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 27

0 20 40 60 80 100 120 140 160 180

Color (Pt-Co)

0

10

20

30

40

50

60

Chlorophyll 

a (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

50

Turbidity (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, combinations of color and chlorophyll a concentrations can 

reach the values under the line and maintain our light attenuation target. 
Charlotte Harbor National Estuary Program 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

14

Turbidity (NTU)

0 20 40 60 80 100 120 140

Color (PCU)

0

2

4

6

8

10

12

14

Turbidity (NTU)

Figure 5. Instances of dry 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 Program 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

8

Turbidity (NTU)

0 20 40 60 80 100 120 140 160 180 200

Color (PCU)

0

1

2

3

4

5

6

7

8

Turbidity (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 Program Numeric Water Quality Targets 30

A comparison of wet and dry season water quality data from the each segment (Appendix B) 

suggests how water quality parameters differ by season. Generally, more water quality data fall 

within the plane of constant attenuation in the dry season (December through June) than the wet. 

More than half of the data collected during the 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 seagrass depth distribution for this segment. 

Nomographs of chlorophyll a and color as well as turbidity and 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 demonstrate 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 water quality of the Lemon Bay, 

Charlotte Harbor and Estero Bay regions. These targets should be adapted over time as scientific 

knowledge of various parts of the approach is gained and implementation management strategies 

improved. These targets and the approach herein should 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, researchers and managers. This Working Group 

will meet regularly and will be tasked with determining when segments are failing to meet the 

targets herein and developing pollutant load management 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 refinements 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 implementation of these targets, including determining 

regulatory, monitoring and management gaps. It is expected that the Working Group will 

encompass members of the NEP Technical Advisory Committee, Management Committee and 

others as needed, and that the NEP Senior Scientist will staff the Working Group. Consultant 

services may be required to support this process and shown in the CHNEP workplan with partner 

funding. However, as in the other implementation steps for these targets and the NEP CCMP 

objectives, it is expected that Working Group members will take on the lead role in 

implementation. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 31

Further Refinements of Approach 

Other region-specific maximum depth distribution goals could be developed. The plane of 

constant attenuation is based upon seagrass bed depth goals derived from fixed transect data 

collected between 1999 and 2005 and seagrass areal coverage maps from 2003. The depth 

targets were created using the greatest annual average maximum seagrass deep edge per 

segment. A different strategy in the future could 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. Salinity can affect seagrass photosynthesis (e.g., 

Torquemada et al. 2005), productivity (Tomasko 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 actual light requirements based on 

environmental gradients such as salinity as well as water clarity. 

The partial coefficients within this optical model could be refined in several ways. McPherson 

and Miller (1994) used water quality samples collected in Tampa Bay and Charlotte Harbor to 

derive the partial attenuation coefficients 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 environmental conditions in Charlotte Harbor. 

The “non-algal suspended matter” partial light attenuation coefficient is an important parameter 

to accurately estimate, as this component is generally responsible for over 50% of light 

attenuation in these areas (McPherson and 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 suspended matter, such as silt or clay, as well as plankton or other 

microscopic organisms (APHA 1985). Turbidity values in Charlotte Harbor are significantly 

different in dry and wet seasons (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 Jeffrey 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 the 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 (McPherson and Miller 1994). Therefore, while 

this effort uses the best available data appropriate for the Charlotte Harbor region, management 

strategies incorporating our approach to setting water clarity goals should be prepared to estimate 

the “non-algal suspended matter” parameter by both season and estuary segment. 
Charlotte Harbor National Estuary Program 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 current 25% incident light target, that consists of all wavelengths 

between 400-700 nm. 

Photosynthetically active radiation (PAR) constitutes 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 resources, this light energy must first cross the airwater interface. Some of this light will be reflected at this interface, and the amount of light 

entering the water column will depend on sun angle, clouds, the roughness of the water surface 

and other conditions (Kirk 1994). Light will then be either scattered or absorbed once it enters 

the water column (again scattering only increases 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 light that has already passed through 

the air-water interface. 

Absorption of light energy in the water column can essentially be broken down into 4 

components of the aquatic ecosystem: water, dissolved yellow pigments (gilvin), photosynthetic 

biota (phytoplankton and macrophytes) and inanimate particulate matter (tripton) (Kirk 1994). 

Water itself absorbs light energy weakly in the blue and green regions of the spectrum; it 

increases its absorption above 550 nm and strongly 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 green spectrums, decreasing as wavelengths get 

longer until it absorbs weakly in the red region (Kirk 1994). Gilvin is formed as plant tissue 

decomposes in soil or waterbodies by microbial actions or ultraviolet light to ultimately carbon 

dioxide and inorganic forms of nitrogen, phosphorous 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 macromolecular aggregates (Kirk 1994). Much of 

the soluble humic substances from fresher waters particulates out when it comes into contact 

with higher salinity water, although a fraction remains in solution (cited in Kirk 1994). Humic 

substances in receiving waterbodies arise not only indirectly from decomposing plant material 

leached from soils, but also decomposing phytoplankton and live aquatic plants, such as brown 

algal beds. The latter excrete phenolic compounds that give rise to humic substances as a result 

of oxidation and other processes (Kirk 1994). 

Tripton, inanimate particulate matter, does not absorb light strongly but scatters intensely. Its 

absorption spectrum is similar to gilvin in that it absorbs strongly in the blue and green 

spectrums, decreasing as wavelengths get longer until it absorbs weakly in the red region (Kirk 

1994). Tripton is separate from the next absorption 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 Program Numeric Water Quality Targets 33

Figure 7. Total absorption spectrum of a model coastal waterbody with spectra of individual 

absorbing components. Chlorophyll a concentrations represent 1 μg/L in this graphic. 

(Extracted from Kirk, J. T. O. 1994. 2nd edition. Light and Photosynthesis in Aquatic 

Ecosystems. Cambridge University Press, New York, NY.). 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 34

Figure 8. Left graphic is the absorption spectra of chlorophylls a (—) and b (---) at 

concentrations of 10 μg/ml. Graphic on right is absorption spectra of chlorophyll c1 and c2. 

(Extracted from Kirk, J. T. O. 1994. 2nd edition. Light and Photosynthesis in Aquatic 

Ecosystems. Cambridge University Press, New York, NY.). 

The spectral absorption of light by photosynthetic pigments-chlorophylls, carotenoids, 

biliproteins vary by the composition of pigments present. All photosynthetic plants, including 

seagrass, contain chlorophyll and carotenoid, while the red algae, blue-green algae and others 

contain biliprotein also. The chlorophylls a, b, c1 and c2 have 2 main spectral absorption bands: 

in the blue region less than 500 nm and within the red region ca 625-670 nm (Kirk 1994). 

Absorption is very low, but not zero, in the middle, green region of the spectrum. Carotenoids 

absorb at shorter wavelengths in the blue spectral 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. Higher plants and green algae rely mainly on the 

chlorophylls for light harvesting. It is, thus, the 2 spectral regions in which the chlorophylls 

absorb strongly—the blue less than 500 nm and the red ca 625-670 that are most important for 

seagrass photosynthesis. Previous research has documented that light quality in addition to 

quantity determined the maximum depth of submerged aquatic vegetation distribution in lakes 

[i.e., Chambers and Prepas (1988) cited in McPherson and Miller 1994]. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 35

Figure 9. Comparison of spectral 

variations of the vertical 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. 2nd 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 scattered when it interacts with the 

particulates and the dissolved materials 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. fluorescence). These processes change the intensity of 

light as a function of direction, the light field. The light field is the intensity of light as a function 

of direction, depth and wavelength. Since absorbance and scattering processes are a function of 

the wavelength of light, they change the spectrum of the light field. Thus, particulates and 

dissolved materials have spectral scattering and 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 absorption components of a waterbody, or inherent 

optical properties, is difficult and not usually undertaken in situ. 

The scattering and absorption characteristics 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 shadowing and can immediately change the apparent 

optical properties of a waterbody, but not the inherent optical properties. The 

diffuse attenuation coefficient, Kd, is an apparent optical property; its value depends on depth, 

sun angle, sky conditions and shadowing by objects on the water surface. However, as depth 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 36

increases, the influence of surface illumination characteristics decreases, and Kd eventually 

approximates a value representing the 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 Licor instruments; the former in Upper Charlotte Harbor and Lemon 

Bay and the latter in Lower Charlotte 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 instruments. Both instruments also collect light 

reflected from vessel bottoms, and values can be influenced by the air-water interface, sun angle, 

waves and currents at the time of collection. Secchi 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 subjectivity of the observer (Preisendorfer 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 sampling 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 largely 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 deeply, while red light is attenuated much more 

rapidly (see Fig. 7). In coastal waters that contain colored dissolved organic matter and 

phytoplankton, the green wavelengths penetrate the most deeply, whereas in clear, productive 

areas, blue light is attenuated more 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 influenced 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 approximation by linear equations, such as the optical 

model herein, is useful for management purposes (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 components 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 show that in all regions of the harbor, less than 

half of the water quality data meet the percent-light-at-depth goals proposed by the NEP and 

even less data meet the goals during the wet season. Thus, load reductions for these analytes 

may be necessary for at minimum, the wet season that lasts July-November. 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 37

One method in which pollutant load reduction goals can be determined is to use existing landuse 

models. Sarasota County has created a pollutant load model for the County which includes 

northern Lemon Bay. A landuse model (WMM) for 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 understand the role that landuse 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 different regions of Charlotte Harbor and Estero Bay. 

These trends may be a result of decreased ultraviolet radiation breakdown of color by decreases 

in residence times from an overall loss of natural wetlands, changes in precipitation and flushing 

of sediments since monitoring of color concentrations began, changes in landuse practices and/or 

other causes. As color is such a large influence on light availability to aquatic 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 relies very heavily on its partners for the 

implementation of its objectives, including regulation, research, education, monitoring and 

restoration. For instance, the program does not acquire conservation lands, monitor water quality 

or submerged aquatic vegetation or establish Minimum Flows and Levels or Total Maximum 

Daily Loads rules, each of which are imperative to the successful implementation of CCMP 

objectives. The success and strength of the Charlotte Harbor NEP rests on the diligent support 

and enthusiastic participation of its partnering agencies, citizens, industries, not-for-profit entities 

and others. The program itself acts more as a consensus-based facilitative and coordinating 

entity than a de facto restoration, monitoring or research entity, with the exception of its central 

data compilation and warehouse role. The implementation 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, plans 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 compiling and analyzing data and information to 

support implementation. The program will use consultant services in workplan projects as 

necessary, Research and Restoration Partners grants, leveraged grants and program staff projects 

to help partners with implementation. Others, such as the NEP Legislative Agenda, Research 

Needs Inventory and Restoration Plan are on-going efforts to inventory program and partner 
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 38

research and restoration needs that can be used to keep track of knowledge gaps for the 

implementation of these targets. 

Conclusion 

The target light attenuation estimate presented here is based upon current seagrass distribution, 

and the water clarity goals proposed in this effort are meant to maintain the present seagrass 

coverage and depth distribution into 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 water 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 Lower Charlotte Harbor since 1982 (Corbett 

2006). Nonetheless, if seagrass depth distribution is light limited (Dixon and Kirkpatrick 1999; 

Tomasko and Hall 1999), resource management strategies in this area should focus on the longterm maintenance of present seagrass coverage through the implementation of water clarity 

targets. If on-going research efforts determine that seagrass meadows within Charlotte Harbor 

have indeed receded or catastrophic losses occurred 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 should allow appropriate water clarity conditions for 

the maximum depth distribution of seagrass meadows that currently exist. 

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Charlotte Harbor National Estuary Program 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 Program 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

100

Cumulative % 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

100

Cumulative % of Seagrass Coverage
Charlotte Harbor National Estuary Program 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

100

Cumulative % 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

100

Cumulative % of Seagrass Coverage
Charlotte Harbor National Estuary Program 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

100

Cumulative % 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

100

Cumulative % of Seagrass Coverage
Charlotte Harbor National Estuary Program 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

100

Cumulative % 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

100

Cumulative % of Seagrass Coverage
Charlotte Harbor National Estuary Program 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

100

Cumulative % 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

100

Cumulative % of Seagrass Coverage
Charlotte Harbor National Estuary Program 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

100

Cumulative % 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

100

Cumulative % of Seagrass Coverage
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 50

Appendix B 

3-D Graphs 

(Plane of constant attenuation is in red)
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

51

Estero Bay Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

52

Estero Bay Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

53

Tidal Caloosahatchee Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

54

Tidal Caloosahatchee Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

55

Pine Island Sound Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

56

Pine Island Sound Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

57

Matlacha Pass Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

58

Matlacha Pass Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

59

Bokeelia Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

60

Bokeelia Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

61

San Carlos Bay Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

62

San Carlos Bay Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

63

Gasparilla/Cape Haze Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

64

Gasparilla/Cape Haze Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

65

Lemon Bay Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

66

Lemon Bay Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

67

West Wall Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

68

West Wall Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

69

East Wall Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

70

East Wall Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

71

Tidal Myakka Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

72

Tidal Myakka Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

73

Tidal Peace Dry Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targ

74

Tidal Peace Wet Season
Charlotte Harbor National Estuary Program Numeric Water Quality Targets 75

Appendix C 

DEP Transect Data 
Segment 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 Deviation 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 1.3
Segment 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 2.0
Segment 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 Pass MP02 Halodule 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 0.2
Segment 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 2.3
Segment 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 Bay SC01 1999 Thalassia 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 1.2
Segment 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 1.6
Segment 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