TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

Appendix A-4: FDEP’s Lake Vegetation Index (LVI) and Scientific 

Peer Review of the LVI 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

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Appendix A-4. FDEP’s Lake Vegetation Index (LVI) and Scientific Peer 

Review of the LVI 

Establishing Expectations for Aquatic Life Use—Lake Vegetation Index 

Following a method analogous to that described for the Stream Condition Index (SCI), FDEP 

examined the lower distribution of lake reference sites as the principal line of evidence for 

establishing aquatic life use support thresholds for the Lake Vegetation Index (LVI), in 

combination with the Biological Condition Gradient (BCG) approach. 

LVI Benchmark Site Approach 

FDEP evaluated data from existing sites to identify benchmark lakes that could be used to 

determine the appropriate threshold for the LVI. To be considered benchmark, the watershed 

scale landscape development intensity (LDI) index score had to be less than three, and the LDI 

of the 100 m stream corridor zone around the lake had to be less than two. FDEP biologists also 

examined aerial photos and conducted an onsite watershed survey to ensure that there were no 

adverse human influences not detected by the LDI, and performed a whole-lake habitat 

assessment. Candidate benchmark lakes were excluded if they had a history of adverse human 

activity (e.g., aquatic plant control, artificial fertilization) or current human activity (e.g., 

adjacent citrus groves). Appendix A-7 contains site information and taxa lists for the 30 

benchmark lakes used in this analysis, and Appendix A-8 contains maps, photos, and a summary 

of data for each of the verified benchmark lakes. 

FDEP’s consultant conducted statistical interval and equivalence tests with LVI data from these 

30 reference lakes to determine the lower bounds of the reference site distribution. As was 

described for the SCI, the intent was to identify a threshold for the LVI that balanced type I 

errors (falsely calling a reference site impaired) and type II errors (failing to detect that a site is 

truly impaired). See Appendix B-14 for statistical description and Appendix A-9 for full 

analysis. 

The analysis of the most recent LVIs at all 30 sites showed that the 2.5th percentile of reference 

data was in the range of 33-48 points, while the analysis of the two most recent visits at 15 lakes 

showed that the 2.5th percentile of reference data was in the range of 31-53 points (Table A4-1). 

The middle of this range was 46 points, representing an impairment threshold that balances Type 

I and Type II errors. In the proposed water quality threshold for the LVI, impairment will be 

determined by two site visits, so the threshold of 46 is closely aligned with the assessment 

methods. An impairment threshold of 46 would limit the percentage of reference sites that will 

be deemed impaired to 2.5%. 

Evaluation of Replicate Data: LVI 

The 15 benchmark sites with replicate data were analyzed to determine the variability that can 

occur in LVI scores at the same sampling location (Table A4-2). The mean difference in LVI 

scores from this sub-data set was 8.2, with a standard deviation of 8.3. The median difference 

was 4.3. 
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LVI Biological Condition Gradient Approach 

In a process analogous to that for the SCI BCG calibration, 20 Florida plant ecologists, botanists, 

and field lake managers, all with at least five years of experience, were involved in BCG 

calibration of the LVI. The experts examined taxa lists from 30 lakes throughout Florida that 

spanned the range of LVI scores (see Appendix A-10 for site information and taxa lists). Without 

any knowledge of the LVI scores, they reviewed the plant data and assigned each plant 

community a BCG score from 1 to 6, where 1 represents natural or native condition and 6 

represents a condition severely altered in structure and function from a natural condition. Experts 

independently assigned a BCG score to each lake, and then were able to discuss their scores and 

rationale, and could opt to change their scores based on arguments from other participants. At the 

conclusion of the workshop, FDEP regressed the mean BCG score given to each lake against the 

LVI score for that lake (Figure A4-1). 

FDEP established the LVI impairment threshold based primarily on the benchmark distribution. 

This analysis suggests that scores of 45 and below should represent impairment, and scores of 78 

and above should represent exceptional. 

Results from the LVI BCG workshop were also analyzed with a proportional odds logistic 

regression model (Guisan and Harrell 2000) to describe the relationship between a continuous 

variable (LVI scores) and a categorical variable (BCG categories). See Appendix B-13 for a full 

report of this analysis by Lester Yuan of U.S. EPA. This model is based on the cumulative 

probability of site being assigned to a given BCG tier (e.g., Tier 3) or to any higher quality BCG 

tier (Tiers 1 and 2). Thus, five parallel models are fit, modeling the probability of assignment to 

Tiers 5 to 1, Tiers 4 to 1, Tiers 3 to 1, Tiers 2 to 1, and Tier 1 only. Once these five models are 

fit, the probability of assignment to any single tier can be extracted from the model results. 

The mean predictions of a proportional odds logistic regression models are shown in 

Figure A4-2. Lines are color-coded and labeled by different tiers, and each line can be 

interpreted as the proportion of experts that assigned samples with the indicated LVI value to a 

particular tier. For example, approximately 45% of experts assigned a sample with the lowest 

LVI score to Tier 6 (brown line), while the remaining 55% of experts assigned the sample to Tier 

5 (purple line). In the figure, the solid circles represent the actual expert assignments recorded 

from the workshop for each LVI value. The size of the circle is proportional to the number of 

experts that assigned a sample to a particular tier, and the circles are color-coded by tier. There is 

some variability among experts in their assignment of BCG scores, but there is a clear central 

tendency at any given LVI score. 

The LVI range of approximately 50-58 corresponds with both a low probability of assignment to 

Tier 5 (i.e., impaired) and a low probability of assignment to Tier 2 (i.e., reference conditions). 

Thresholds selected in this range of values balance the probability of mistakenly assessing a 

degraded site as meeting aquatic life use goals with the probability of mistakenly assessing a 

reference site as impaired. Although more variable than the results for the SCI, the proportional 

odds results for the LVI were considered in relation to the lake reference site LVI distribution 

(discussed below). 
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Setting a LVI Impairment Threshold 

Weighing these multiple lines of evidence, the FDEP has determined that an LVI score of 46 

indicates that the designated use is being met, and a score of 45 is impaired. This impairment 

threshold is primarily supported by the lower distribution of verified reference site scores (46), 

which balances Type I and II errors and approaches the lower range suggested by the 

proportional odds BCG analysis (50). U.S. EPA is considering which approach to recommend 

for establishing a threshold: rely primarily on the reference site distribution with a low percentile 

threshold of 2.5 as suggested for the SCI, or consider the range suggested by the proportional 

odds modeling, which would suggest a threshold of 50 and would correspond to the 5th percentile 

of the reference condition distribution. 

The FDEP evaluated recent data for the individual metrics of the LVI to determine what range of 

plant attributes would be considered healthy by this impairment threshold. This analysis includes 

244 LVI samples collected since 2007 from a variety of sites, including sites located along a 

nutrient gradient and randomly chosen sites for the status network. Based upon the relationship 

described in Figure A4-1, the LVI values from this data set were subdivided into increments 

representing half-step BCG categories, and the individual metrics associated with each half step 

interval were averaged. The metric data bracketing BCG category 2 were averaged. Data within 

the range of the impairment threshold of 46 were also averaged to provide an example of the 

plant community condition that Florida’s LVI biological criterion will protect (Table A4-3). 

Note that although there are moderate differences between metrics associated with biological 

communities at BCG 2, and those near the range of the impairment threshold, the attributes 

associated with communities near the threshold are still considered to be indicative of healthy, 

well-balanced communities by the majority of the Florida lake experts who participated in the 

BCG exercise. 

Table A4-1. Results of Interval and Equivalence Tests Conducted on Reference Sites with Two 

LVI Results 

Note: Shown are site mean, impairment threshold, and range for threshold values defined at the 2.5th and 5th

percentile of reference sites (p < 0.05; n = 15 reference sites with two LVI values for each site.) 
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Table A4-2. Least-disturbed Benchmark Lake Sites with Replicate LVI Data 

Table A4-3. Average Values for Metrics at an LVI Score Equivalent to a Biological Condition 

Gradient of Category 2 and Average Values for Metrics near the LVI Score of Impairment 

Associated with FDEP’s Data Collection Effort Since 2007 (Consisting of 244 LVI samples)
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

A4-5 

Figure A4-1. The LVI regressed against the BCG scores developed “blindly” by a panel of lake 

experts. These data reflect updated LVI calculations from the 2007 calibration exercise. Fore et 

al. 2007b contains a previous analysis of these data. 

Figure A4-2. BCG tier assignments based on the LVI. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

Assessing the Biological Condition of Florida Lakes: Development of 

the Lake Vegetation Index (LVI) 

Assessing the Biological Condition of Florida Lakes: 

Development of the Lake Vegetation Index (LVI) 

Final Report 

Prepared for: 

Florida Department of Environmental Protection 

Twin Towers Office Building 

2600 Blair Stone Road 

Tallahassee, FL 32399-2400 

Leska S. Fore*, Russel Frydenborg**, Nijole Wellendorf**, Julie Espy**, 

Tom Frick**, David Whiting**, Joy Jackson**, and Jessica Patronis** 

* Statistical Design 

136 NW 40th St. 

Seattle, WA 98107 

** Florida Department of Environmental Protection 

2600 Blair Stone Rd. 

Tallahassee, FL 32399-2400 

December, 2007 
ii

This project and the preparation of this report was funded by a Section 319 

Nonpoint Source Management Program Implementation grant from the U.S. 

Environmental Protection Agency through an agreement with the Nonpoint 

Source Management Section of the Florida Department of Environmental 

Protection. The cost of the project was $54,600, which was provided entirely 

by the U.S. Environmental Protection Agency. 
iii

TABLE OF CONTENTS 

Table of Contents.......................................................................................................................iii 

List of Tables .............................................................................................................................. v 

List of Figures............................................................................................................................ iv 

List of Appendices ..................................................................................................................... vi 

Acknowledgments..................................................................................................................... vii 

Foreword..................................................................................................................................viii 

Abstract....................................................................................................................................... 1 

Introduction................................................................................................................................. 3 

Methods....................................................................................................................................... 5 

Study area................................................................................................................................ 5 

Lake sampling......................................................................................................................... 6 

Quantifying human disturbance.............................................................................................. 8 

Metric development and testing............................................................................................ 11 

Sensitive and tolerant taxa evaluation................................................................................... 14 

Lake Vegetation Index (LVI) development and testing ....................................................... 15 

Expectations for statistical correlation.................................................................................. 18 

Results....................................................................................................................................... 20 

Human disturbance gradient ................................................................................................. 20 

Metric selection..................................................................................................................... 21 

Sensitive and tolerant taxa .................................................................................................... 31 

Evaluation of the Lake Vegetation Index (LVI)................................................................... 33 

Correlation of LVI with human disturbance and natural features ................................... 34 

Variability analysis of alternative lake sampling protocols ............................................. 35 

Metric scoring for the LVI ................................................................................................ 40 

Validation of the LVI with independent data .................................................................... 41 

Description of outliers ...................................................................................................... 42
iv

Comparison of spring and summer LVI values................................................................. 44 

Discussion................................................................................................................................. 46 

Human disturbance gradient ................................................................................................. 46 

Biological indicators ............................................................................................................. 47 

Statistical precision of the Lake Vegetation Index (LVI)..................................................... 48 

Recommendations..................................................................................................................... 50 

Conclusions............................................................................................................................... 53 

References................................................................................................................................. 54 
v

LIST OF TABLES 

Table 1. Scoring rules for HDG.....................................................................................................10 

Table 2. Examples of LDI coefficient values ................................................................................11 

Table 3. Coefficient of conservatism (CC) scoring criteria ..........................................................13 

Table 4. Correlation for HDG and its component measures..........................................................20 

Table 5. Comparison of water chemistry measures for porous and non-porous soils ...................21 

Table 6. Candidate plant metrics and their correlation with HDG, WQ index, LDI, and habitat 

index...............................................................................................................................................23 

Table 7. Plant taxa most frequently identified as dominant ..........................................................25 

Table 8. Plant taxa that were significantly associated with low or high HDG ..............................32 

Table 9. Correlation between LVI, measures of disturbance, and natural features.......................34 

Table 10. Coefficients from multiple regression for LVI and its component metrics...................35 

Table 11. Cumulative number of taxa for 12 lake sections ...........................................................36 

Table 12. Variance estimates and number of detectable categories for LVI .................................37 

Table 13. Correlation between LVI, its component metrics and HDG for different sampling 

methods..........................................................................................................................................39 

Table 14. Correlation between LVI and measures of disturbance for different numbers of 

replicate LVI samples ....................................................................................................................40 

Table 15. Metric scoring rules for LVI..........................................................................................41 

Table 16. Correlation between LVI and measures of disturbance for the validation data set .......42 

Table 17. Lakes with higher or lower LVI values than predicted by HDG...................................43 
vi

LIST OF FIGURES 

Figure 1. Diagram showing 12 lake sampling sections ...................................................................7 

Figure 2. Detail of lake section sampling methods..........................................................................8 

Figure 3. LVI plant metrics plotted against HDG..........................................................................27 

Figure 4. LVI plant metrics plotted against the WQ index............................................................28 

Figure 5. LVI plant metrics plotted against the habitat index........................................................29 

Figure 6. LVI plant metrics plotted against LDI............................................................................30 

Figure 7. Plant CC value plotted against the average HDG for lakes in which taxon was found .33 

Figure 8. Species accumulation curve for lake sections ................................................................36 

Figure 9. Number of categories that LVI could detect for different sampling protocols ..............38 

Figure 10. Distribution of LVI values............................................................................................39 

Figure 11. LVI plotted against HDG for the validation data set....................................................42 

Figure 12. LVI plotted against HDG with outliers indicated.........................................................43 

Figure 13. LVI for repeat visits during spring and summer for 15 lake sites................................45 

Figure 14. Diagram showing recommended lake sampling for LVI .............................................50 

LIST OF APPENDICES 

Appendix 1. Aquatic plant attributes 

Appendix 2. EPA’s wetland plant attributes 

Appendix 3. Results of testing for tolerance and sensitivity 

Appendix 4. LVI values by district 

Appendix 5. Additional tables 

Appendix 6. LVI validation and calibration for 2005–2006 sampling 

Appendix 7. Biological condition gradient to define biocriteria for LVI 
vii

ACKNOWLEDGMENTS 

Florida Department of Environmental Protection (DEP) biologists provided comments and 

guidance throughout the development and testing of the lake vegetation index. Data organization 

and retrievals, explanation of Florida DEP field and laboratory methods, and general discussions 

of plant biology and lake management were provided by E. McCarron, D. Miller, A. O’Neal, and

A. Wheeler. E. McCarron and D. Cobb provided administrative support and a regulatory 

perspective. Biologists throughout Florida collected and identified macrophyte samples, 

including L. Banks, F. Butera, T. Deck, D. Denson, J. Espy, K. Espy, S. Evans, T. Frick, R. 

Frydenborg, S. Gerardi, M. Heyn, J. Jackson, T. Kallemyen, P. Morgan, P. O’Conner, A. O'Neal, 

E. Pluchino, D. Ray, J. Richardson, B. Rutter, D. Scharr, E. Springer, C. Swanson, M. 

Szafraniec, R. Taylor, M. Thompson, F. Walton, N. Wellendorf, and A. Wheeler. J. Tobe, D. 

Hall, and J. Burkhalter helped with plant identifications. We thank the Florida DEP Integrated 

Water Resource Monitoring program for data used to calculate the human disturbance gradient. 
viii

FOREWORD 

The original version of this document was completed in 2005. Two appendices were added in 

2007 that respond specifically to recommendations made in the 2005 version of this report. 

Appendix 6 provides additional validation of the association between the biological condition of 

lake macrophytes and independent measures of human disturbance using data collected in 2005–

2006. Scoring rules for combining metrics in the lake vegetation index (LVI) were also amended 

slightly for this more recent, larger data set. In addition, new estimates of variance for the LVI 

were calculated for multiple years of sampling. Appendix 7 describes the development of 

biological criteria as part of Florida’s water quality standards for lakes. Recent guidance from the 

U.S. Environmental Protection Agency was used to define thresholds for impaired and 

exceptional lake condition based on the advice of a panel of regional experts. 
1

ABSTRACT 

The Florida Department of Environmental Protection (DEP) is required under the Clean Water 

Act to assess the biological condition of its streams, rivers and lakes. We developed a 

multimetric index, the Lake Vegetation Index (LVI), to assess the biological condition of aquatic 

plant communities in Florida lakes. For the development and testing of the LVI, aquatic plants in 

95 lakes were sampled by boat during 2000–03. To validate the results for the LVI, data from an 

additional 63 lakes were collected in 2004; these data included 15 lakes with repeat visits for 

spring and summer. A total of 48 candidate metrics based on measures of community structure, 

taxa richness, and percent of total taxa were calculated and tested against independent measures 

of human disturbance. An additional 17 metrics derived from plant information from a national 

database were also evaluated. To test metrics, we developed a human disturbance gradient 

(HDG) that summarized measures of water chemistry, habitat condition, intensity of land use in a 

100 m buffer around the lake, and hydrologic modifications. A total of 10 metrics met the 

targeted values for correlation with HDG; of these ten, four were not redundant with each other 

and were included in the LVI: percent native taxa, percent invasive taxa, percent sensitive taxa, 

and the average tolerance value of the taxon present over the largest area. 

Tolerant and sensitive taxa were defined based on designations made by 10 expert 

botanists working independently to define coefficient of conservatism (CC) scores for wetland 

(not lake) plants in Florida. Metrics derived from CC scores were highly correlated with HDG. 

In contrast, relatively few individual taxa were significantly associated with HDG: only 29 out of 

404 taxa showed significant preferences. Rare taxa were partially to blame for weak results, 

more than half of the taxa were found in less than 5 lakes. 

Lakes were divied into 12 pie-shaped sections. Plant data were collected from each 

section and taxa lists from the 12 sections were kept separate. Using the replicate data, we 

compared the ability of different sampling protocols to detect differences in lake condition. 

Based on LVI calculated and averaged from four lake sections, LVI could detect five categories 

of biological condition. 
2

LVI was highly correlated with HDG and other independent measures of human 

disturbance for both the development and the validation data sets (-0.68 and -0.72, Spearman’s 

r). For the 15 lakes with repeat visits, LVI values for repeat visits within the year were more 

variable than for repeat visits on the same-day; however, neither spring nor summer LVI values 

were consistently higher across lakes. We conclude that LVI is a reliable indicator of lake 

condition and has sufficient statistical precision to detect multiple levels of biological condition. 

Given the relatively small data sets available for this study (<200 site-visits), we recommend that 

future studies be designed to evaluate the influence of season on metric values, to determine 

whether regional adjustments are needed to metric scoring, and to assess the annual variability of 

LVI. 
3

INTRODUCTION 

A primary objective of the federal Clean Water Act (CWA) is “to restore and maintain the 

chemical, physical, and biological integrity of the Nation’s waters.” Under the CWA, each state 

must develop water quality standards for all its surface waters. Water quality standards include 

designated uses assigned to a water body, water quality criteria to protect the uses, and an 

antidegradation policy (Ransel, 1995; Karr et al., 2000). Until the late 1980s, most states used 

primarily chemical criteria to assess surface waters. A shift occurred when resource managers 

realized that chemical criteria alone often fail to protect aquatic life uses (Karr and Chu, 1999). 

At that time, EPA recommended that states adopt biological criteria for the protection of water 

resources (Karr, 1991). 

The state of Florida recognizes the importance of biological monitoring of water 

resources and has developed sampling protocols to assess the condition of streams, lakes, and 

wetlands based on their biological assemblages (Barbour et al., 1996; Gerritsen and White, 1997; 

McCarron and Frydenborg, 1997; Fore, 2004; Lane et al., 2004; Reiss and Brown, 2005). Florida 

Department of Environmental Protection (DEP) uses bioassessments derived from these 

protocols to define acceptable conditions for the support of aquatic life uses. Within the context 

of the CWA, bioassessments can be used to define impairment, evaluate best management 

practices, develop targets for management plans or restoration, or identify exceptional resources 

for protection (Yoder and Rankin, 1998; Karr and Yoder, 2004). 

Compared to rivers, streams, and wetlands, relatively less work has been done to develop 

biological monitoring tools for lakes (USEPA, 2002a, 2002b; but see Whittier et al., 2002 and 

Harig and Bain, 1998 for lake indicator development). While most states have biological 

assessment programs in place for rivers and streams, Florida is one of only nine states with lake 

or reservoir bioassessment programs in place and one of only three that is developing numeric 

biocriteria for lakes (Gerritsen and White, 1997; USEPA, 2003). In many states, lakes may 

represent a smaller proportion of surface waters; however, with more than 7700 lakes greater 

than 10 acres in size, lakes represent a significant natural resource in Florida. 
4

Although lakes are not wetlands, they share many of the same habitats and taxa within a 

particular region. The U.S. Environmental Protection Agency (EPA) has recently supported 

numerous research efforts related to the development of bioassessment and biocriteria for 

wetlands (USEPA, 2002b). Resources developed for wetland assessment were borrowed and 

applied for this study of lakes. For example, extensive literature surveys have documented the 

current science for wetland monitoring at the national level (Adamus et al., 2001) and 

specifically for Florida (Doherty et al., 2000). Documents published by EPA summarize the 

aquatic plant metrics that have been successfully applied in wetlands throughout the U.S., and 

this information was very helpful in identifying potential metrics for this study (USEPA, 2002c). 

Similar lists of candidate metrics for aquatic plants in lakes have not been developed (Gerritsen 

et al., 1998). Another source for potential metrics was Ohio EPA which has tested several types 

of plant metrics for inclusion in their vegetation IBIs for wetlands (Mack, 2004). Within Florida, 

the multimetric indices developed for isolated depressional herbaceous wetlands by Lane et al. 

(2004) and for isolated depressional forested wetlands by Reiss and Brown (2005) included 

additional metrics that were tested in this study for lakes. Finally, a study by Cohen et al. (2004) 

used the professional experience of ten botanists to define aquatic plant tolerance and sensitivity 

to disturbance in wetlands. We used these designations to define sensitive and tolerant plant taxa. 

The purpose of this study was to develop a monitoring and assessment tool for lakes 

based on aquatic plant sampling. Though used extensively in wetland monitoring, aquatic plants 

are rarely selected as indicators of lake condition (but see Nichols et al., 2000 for a Wisconsin 

index). Aquatic plants provide an effective endpoint for monitoring lake condition for several 

reasons: 1) a wide array of plants with a variety of life history strategies are represented in 

Florida lakes, 2) much is known about the specific preferences and tolerances of many of these 

plants, and 3) collecting and identifying plants in the field is relatively straightforward, which 

means that laboratory costs are minimal. The goals of this study were to identify potential 

metrics for aquatic plants, test them against an independent gradient of human disturbance, 

combine the metrics into a lake vegetation index (LVI), and determine the most efficient method 

for collecting plant data to calculate the index. 
5

METHODS 

Study area 

The state of Florida can be divided into three geographic regions based on watershed drainage 

patterns: the northern panhandle, the southern peninsula, and a transition region known as the 

northeast. These three regions were used to develop and test a multimetric index for invertebrates 

because different species assemblages were associated with these three geographic areas 

(Barbour et al., 1996; Fore, 2004). Sufficient data did not exist for lake plants to perform a 

similar test for associations between geographic areas and plant species assemblages; 

consequently, we used the geographic areas derived from watershed drainage patterns to test for 

regional differences in metric values. 

 The middle and lower Suwannee basin provides a natural demarcation between the 

panhandle and peninsula, with the northeast region straddling the upper Suwannee northeast of 

the Cody escarpment (White, 1970). Terrestrial vegetation communities in the panhandle 

generally consist of mixed pine/oak/hickory forests (Pinus spp., Quercus spp., Carya spp.), 

longleaf pine forests (Pinus palustris), hardwood forests with beech/magnolia climax community 

(Fagus grandiflora/Magnolia grandiflora), and swamp hardwood forests of cypress (Taxodium

spp.) or tupelo (Nyssa spp.), interspersed by a mosaic of pine plantations, cropland (e.g., corn, 

soy beans, peanuts), and pasture (SWCS, 1989; Fernald and Purdum, 1992). The panhandle is 

less densely populated by humans than the other areas. 

 The peninsula has a sandy highland ridge extending down its center almost to Lake 

Okeechobee. The elevation of the central ridge is approximately 150 to 200 ft. Terrestrial 

vegetation communities on the ridge of the peninsula consist of longleaf pine/turkey oak forests 

(Pinus palustris/Quercus laevis), on flat areas are slash pine (Pinus eliottii) or loblolly pine 

(Pinus teada) with palmetto/gallberry understory (Serenoa repens/Ilex glabra), and in 

depressional areas are marsh/wet prairies (maidencane, pickerel weed), and hardwood wetlands 

of sweetbay (Magnolia virginiana), cypress (Taxodium spp.), and ash (Fraxinus spp.; SWCS, 

1989). The dominant land use is pasture, cropland (e.g., watermelons, nursery products, 
6

tomatoes), and urban areas (Fernald and Purdum, 1992). Dense population centers are located at 

Tampa and Orlando. 

The northeast region includes portions of the Okeefenokee Swamp, parts of the upper 

Suwannee drainage, the Black Creek drainage, and the Sea Island flatwoods. Plant communities 

consist of longleaf pine/turkey oak (Pinus palustris/Quercus laevi) on the sandy uplands, 

hardwood wetlands of cypress (Taxodium spp.), tupelo (Nyssa spp.), and loblolly bay (Gordonia 

lasianthus), pine flatwoods, and marsh (FNAI, 1990). Jacksonville is the only major population 

center. 

Lake sampling 

For this study, lakes were defined as fresh water bodies with >= 2 acres of open water of 

sufficient depth and size to require a boat for sampling. Two different data sets were used to 

develop and validate the LVI. The first data set included data from 95 lakes that were selected to 

represent a broad range of site conditions and human influence across the state. These lakes were 

used to test metrics and develop the LVI. Lakes were sampled by Florida DEP during August–

November, 2000–2003. Of the 95 lakes, 17 were located in the panhandle region, 74 in the 

peninsula, and 4 in the northeast. Lake surface area was known for 80 lakes and ranged from 8–

3500 acres, with a mean area of 343 acres. Lakes were not selected randomly, nor were they 

selected to ensure coverage in all ecoregions. Consequently some geographic areas were not 

included. 

The second data set included data from 63 additional lakes, and these data were used to 

validate the correlation between LVI and independent measures of human disturbance. These 

lakes were sampled during March–September, 2004; 34 lakes were located in the panhandle and 

29 in the peninsula. Of these lakes, 15 small lakes from the panhandle were sampled during 

spring and summer and were used to test for seasonal differences in LVI. 

Aquatic macrophytes include aquatic plants large enough to be easily seen by the unaided 

eye, as well as some larger algae such as Nitella and Chara. Aquatic macrophytes grow in water 

or wet areas and may be rooted in the sediment or floating on the water’s surface. Most aquatic 

macrophytes are vascular plants and include herbaceous species as well as trees and shrubs. 
7

Each lake was sampled 12 times by dividing the lake into 12 approximately wedgeshaped sections, depending on the shape of the lake (Figure 1). Within each section, two methods 

were used to identify plants: 1) from the boat, plants were identified, using either binoculars or 

the unaided eye, while boating slowly along the shore, and 2) plants were also identified within a 

5 m belt transect. The transect was perpendicular to the shore, from the mean high water mark 

towards the center of the lake. For the belt transect, visible plants were identified and submersed 

plants were also sampled with a standard frotus, a device used to collect underwater plants. The 

frotus was deployed 5 times along each 5 m belt transect (Figure 2). Lakes sampled before the 

fall of 2003 used only the drive-by method (35 of the 95 lakes in the development data set), 

while lakes sampled during or after fall 2003 used both methods. Plants identified using the two 

above methods were combined into a single taxa list, one for each of the 12 sections. 

The plant judged to have the greatest areal extent, determined visually, within each of the 

12 lake sections was denoted as “dominant,” while all others were recorded as “present.” If two 

taxa were more abundant than the other plants present, they were noted as “co-dominant.” If the 

degree of dominance was not readily determined, all plants in the sampling unit would simply be 

marked “present.” Plants were identified to the lowest taxonomic level possible, typically 

species. Unknown species were placed on ice and sent to an expert for identification. 

Figure 1. Diagram showing the method used to divide a typical lake into 

12 sections for replicate sampling. 

11 12 1 2 

3 

4

7 6 5

8 

9 

10 
8

Emergent Zone

Submersed Zone

Frodus deployment

5 m Belt Transect

Drive-by

Figure 2. Detail of sampling methods used to identify plant taxa 

within a lake section. 

Quantifying human disturbance 

Karr and colleagues describe five factors to summarize the ways in which humans alter and 

degrade rivers and streams (Karr et al., 1986; Karr et al., 2000). The five factors are flow regime, 

physical habitat structure, water quality, energy source, and biological interactions. For this study 

data were available to evaluate three of these factors (water quality, physical habitat structure, 

and flow regime), as well as human disturbance at the watershed scale.

To evaluate lake water chemistry, DEP biologists measured conductivity, total Kjeldahl 

nitrogen (TKN), nitrites/nitrates (NOx), total phosphorus (TP), and algal growth potential (AGP). 

We summarized information from these five measures into a water quality (WQ) index by 

converting values for each measure into unit-less scores and then averaging the scores. To 

convert to unit-less scores, we used the percentiles from a statewide data set (Integrated Water 

Resource Monitoring [IWRM] Cycle 1, 2000-2003) to define expectations. For the IWRM Cycle 

1 study, water chemistry data were collected from ~1100 randomly chosen lakes. If the observed 
9

value from the LVI index development data set was less than the 10th percentile value observed 

for the statewide IWRM data set, the lake-visit received a score of 1 for that chemistry measure; 

if less than the 20th percentile the lake-visit scored a 2; and so on. We repeated this process for 

each of the five chemical measures. After scoring each of the measures, we averaged the unitless scores (ignoring missing data) for all the chemical measures to define the water quality 

(WQ) index. For the 95 lakes in the index development data set, 10–25 had missing values for 

one or more of the chemical measures. To test whether missing values contributed to the 

correlation (or lack of correlation) between the WQ index and other measures of disturbance, 

correlation was also tested for sites without missing data. 

For each lake, Florida DEP biologists also evaluated habitat condition by assigning 

numeric scores to qualitative descriptions of vegetation quality, stormwater inputs, bottom 

substrate, lakeside human alterations, upland buffer zone, and watershed land use (DEP protocol 

FT-3200). These scores are summed to yield a single value, the habitat index. Sufficient 

information was not available to develop a similar index for hydrologic condition. Instead, each 

lake was assigned a score of 0 if no hydrologic modification was observed or 1 if the lake was 

impounded or its hydrology artificially controlled. 

Human land use around each lake was derived from aerial photos of 1995 land use 

coverages and a 100 m buffer area around the lake defined. Land use within a 100 m buffer area 

around the lake was summarized using an index developed to estimate the intensity of human 

land use based on nonrenewable energy flow (Brown and Vivas, 2004). The landscape 

development intensity (LDI) index was calculated as the percentage area within the catchment of 

a particular type of land use multiplied by the coefficient of energy use associated with that land 

use, summed over all land use types found in the catchment (Table 1). 

= ∑( ) LDI i %* LULDI i . 

Where, 

LDIi = the nonrenewable energy land use for land use i, and 

%LUi = the percentage of land area in the catchment with land use i. 

To define the human disturbance gradient (HDG), we converted the four measures of 

human disturbance (the water quality index, the habitat index, the measure of hydrologic 
10

condition, and the LDI) to unit-less scores and summed the scores to define HDG values for each 

lake-visit. Three of the measures had scores of 0, 1, or 2 indicating low, moderate or high levels 

of human influence. One measure, hydrologic condition, only had scores of 0 or 1 (Table 2). 

HDG ranged from the minimum value of 0 to the maximum of 7 for the 95 lakes in the 

development data set. Each of the eight categories of HDG was represented by 6–18 lakes with 

the extreme values (HDG = 0, 6, or 7) having the fewest lakes. 

Table 1. Description of land use and the coefficient value used to calculate the LDI. Higher 

values indicate greater intensity of human land use. 

Land use LDI value

Natural Open water 1.00 

Pine Plantation 1.58 

Woodland Pasture 2.02 

Pasture 2.77 

Recreational / Open Space (Low-intensity) 2.77 

Low Intensity Pasture (with livestock) 3.41 

Citrus 3.68 

High Intensity Pasture (with livestock) 3.74 

Row crops 4.54 

Single Family Residential (Low-density) 6.79 

Recreational / Open Space (High-intensity) 6.92 

High Intensity Agriculture 7.00 

Single Family Residential (Med-density) 7.47 

Single Family Residential (High-density) 7.55 

Low Intensity Highway 7.81 

Low Intensity Commercial 8.00 

Institutional 8.07 

High Intensity Highway 8.28 

Industrial 8.32 

Low Intensity Multi-family residential 8.66 

High intensity commercial 9.18 

High Intensity Multi-family residential 9.19 

Low Intensity Central Business District 9.42 

High Intensity Central Business District 10.00 
11

Table 2. Scoring rules for measures used to calculate the human disturbance gradient (HDG). 

HDG is the sum of the scores. 

Measure 0 1 2 

WQ index <3.5 3.5–5.9 >=6 

Habitat index >65 45–65 <45 

Hydrologic condition Not impounded Impounded — 

LDI <2 2–4 >4 

Metric development and testing 

Candidate metrics in six general categories were tested for correlation with HDG using only the 

data from the development data set of 95 lakes. Only taxa presence was recorded within each 

lake section; therefore, no information regarding extent of cover was collected. One exception to 

this was the designation of one or two plants as “dominant” or “codominant” if they had the 

greatest areal cover. Only 64 of the 95 lakes had information on dominant plants in at least one 

lake section; thus, 31 lakes lacked sufficient information to calculate metrics related to 

dominance. 

For this initial phase of index development, taxa lists from all 12 sections were combined 

to create a single composite plant sample for each lake. Aquatic plant metrics were calculated 

from the combined data with one value for each metric per lake. Data were combined from the 

12 sections to ensure the strongest signal for metric testing. After metrics were selected, 

alternative sampling protocols based on varying numbers of lake sections were compared using 

the final index (see “LVI development and testing” below). Most metrics were calculated as both 

the total number of taxa representing a specific group (e.g., native plants) and the percentage of 

total taxa that the taxa of interest represented (e.g., number of native taxa/total number of taxa). 

Coefficient of conservatism scores (CC) were used to calculate several metrics. CC 

scores were defined by a panel of 10 expert botanists working independently, and CC scores 
12

from each expert were averaged to derive a single CC score for each taxon. The CC scores were 

developed for depressional marshes in Florida, not for lakes (Cohen et al., 2004). 

Community structure – The total number of taxa found is expected to decline as human 

disturbance eliminates habitat, changes water chemistry, and interrupts the natural hydroperiod. 

Although some studies have documented a decline in the number of wetland taxa as disturbance 

increases (Findlay and Houlahan,1997; Lopez et al., 2002), this metric is not typically chosen for 

wetland monitoring (USEPA, 2002c). The number of plant guilds summarized the number of 

different types of plants present at a lake, e.g., forb/herb, graminoid, shrub, tree, or vine. The 

number of plant guilds is expected to decline with disturbance. We expect more tolerant plants to 

dominate the assemblage as disturbance increases. Dominant C of C was defined as the CC score 

for the one or two plants that covered the greatest area. 

Nativity – Native taxa are those whose natural range included Florida at the time of European 

contact (1500 AD). Exotic taxa are species introduced to Florida from a natural range outside of 

Florida. Definitions of invasive taxa were taken from lists developed by the Florida Exotic Pest 

Plant Council (FLEPPC). FLEPPC defines Category I invasives as “exotics that are altering 

native plant communities by displacing native species, changing community structures or 

ecological functions, or hybridizing with natives” (FLEPPC, 2003). Category II invasives are 

defined as “exotics that have increased in abundance or frequency but have not yet altered 

Florida plant communities to the extent shown by Category I species.” For 95 lakes in the 

development data set, 17 taxa were listed as Category I and 7 as Category II. (See Appendix 1 

for plant attributes.) 

Threatened and endangered species – Four endangered species (Xyris isoetifolia, Hypericum

lissophloeus, Salix eriocephala, and S. floridana) and one threatened species were found 

(Drosera intermedia). Five species did not provide enough range of values to define a metric and 

no metric was tested for this attribute. 

Tolerance/Sensitivity – Coefficient of conservatism (CC) scores were available for 240 of the 

404 taxa found. CC scores are most typically used to calculate a ‘floristic quality index (FQI)’ 

(Lopez and Fennessy, 2002). FQI was calculated as the average CC value multiplied by the 

square root of the total number of plant taxa. The simple mean of the CC scores has also been 
13

reported and was tested here. Lane et al. (2004) found that mean CC score was significantly 

correlated with LDI for herbaceous wetlands in Florida. We calculated the total number of 

sensitive and tolerant taxa by defining taxa with a CC score > 7 to be sensitive and CC < 3 to be 

tolerant (Table 3). Percent sensitive and tolerant taxa were selected as metrics for both the 

herbaceous and forested wetland indices in Florida (Lane et al., 2004; Reiss and Brown, 2005). 

Duration – Lakes with less human disturbance are expected to have a greater number of 

perennial taxa or a higher relative proportion of perennial taxa than annual taxa. Annual taxa 

represent more opportunistic taxa which tend to be associated with human disturbance. Ohio 

EPA uses the ratio of annual to perennial taxa in several of its vegetation indices for wetlands 

(Mack, 2004). 

Table 3. Coefficient of conservatism (CC) scoring criteria (after Cohen et at., 2004; Andreas 

1995) and designations used to define a plant as tolerant or sensitive for this study. 

CC 

score 

Sensitive or 

Tolerant 

Criteria 

0 T Alien taxa and native taxa that are opportunistic invaders 

1–3 T Widespread taxa that are found in a variety of communities, 

including disturbed sites 

4–6 Neither Taxa that display fidelity to a particular community, but tolerate 

moderate disturbance 

7–8 S Taxa that are typical of well-established communities, which have 

sustained only minor disturbances 

9–10 S Taxa that exhibit high degrees of fidelity to a narrow set of 

ecological conditions 

Wetland status – Although all sampling locations were defined as lakes and not wetlands, we 

tested metrics related to wetland status because extensive lists have been developed for many 

plants and because wetland status may provide an indicator of hydrologic alteration or other 

types of human disturbance in lakes (USEPA, 2002c). Species defined as ‘obligate wetland’ or 

‘facultative wetland’ plants are considered to be adapted to life in anaerobic soils (USACE, 

1987); facultative species are equally likely to occur in wetlands or non-wetlands; and upland 

species are expected not to occur in wetlands. 
14

Growth form – This category included several aspects of plant growth. Metrics were calculated 

for herbaceous and woody taxa and for emergent, floating, and submersed taxa. Fern and 

gymnosperm taxa were also summarized. The previous metrics were also calculated for native 

taxa only. In addition, candidate metrics based on native forbs+herbs, graminoids, nonvascular 

plants, vines, shrubs, subshrubs and trees were tested. Nichols et al. (2000) suggest that the 

relative frequency of submersed taxa may be greater than floating or emergent taxa when water 

quality is degraded. Emergent species may also tolerate greater wave action. Other studies 

suggest that emergent species may increase with an increase in nutrients, while submersed taxa 

decline (Doherty et al., 2000). 

Dicot/monocot – Flowering plants (angiosperms) are divided into two groups depending on the 

number of cotyledons found in the embryo. The cotyledons are the seed leaves produced by the 

embryo. In Ohio, native dicots decline with increasing disturbance, and this metric is included in 

four out of five Ohio wetland indices (Mack, 2004). 

Additional metrics derived from the national wetland database – In addition, several metrics 

were derived from a national database of plant characteristics developed for wetland plants 

(Adamus and Gonyaw, 2000). Information in this database was derived from published, peerreviewed studies. Development of this database was funded by EPA in response to requests from 

state agencies involved in monitoring wetlands. The attributes were derived from literature 

surveys for each taxon and summarized information regarding general sensitivity or tolerance, 

tolerance to nutrients, nitrogen or phosphorus, and sensitivity or tolerance to flooding, sediment, 

and salinity (Appendix 2). Of the 404 taxa found in the 95 lakes, 137 were listed in the EPA 

database. 

Sensitive and tolerant taxa evaluation 

We tested the association of individual plant taxa with the HDG using a 2 x 2 contingency table 

analysis (χ2

, Yates correction, α = 0.05). Lakes with an HDG of 0, 1, or 2 were defined as ‘good’ 

lakes (N = 36), and the remaining 59 lakes with HDG from 3–7 as the ‘poor’ lakes. An HDG < 3 

meant that a lake could have moderate disturbance indicated for two measures or high 

disturbance for a single indicator. This statistical approach tests whether occurrence of an 

individual plant taxon depended on the level of human disturbance. 
15

We also calculated the average HDG value for all the lakes at which a particular taxon 

was found and compared this empirical value for sensitivity (or tolerance) to the CC values 

derived from expert judgment. This approach was not intended as a potential metric, but as a test 

of the CC scores for the lakes data set. 

Lake Vegetation Index (LVI) development and testing 

Candidate metrics were considered for inclusion in the LVI if they were significantly correlated 

with the HDG (Spearman’s r >= 0.4 or <= –0.4), the correlation was in the predicted direction, 

and the metric was not redundant with another metric. 

After selecting metrics to be included in the LVI, additional tasks remained before 

finalizing the details of index calculation. First, we tested for correlation between LVI and lake 

surface area, latitude and longitude. Second, we used data from the 12 lake sections to compare 

alternative sampling protocols in order to determine the most efficient method for collecting 

plant data from a lake. From that analysis we defined the final protocol for the LVI to be based 

on the average of 4 replicate LVI values from 4 lake sections. Using this version of the LVI, we 

tested that the patterns of correlation observed in the original 95 lakes were valid, using an 

independent data set of 63 lakes to test for correlation between LVI and HDG. Finally, we 

evaluated seasonal differences for 15 lakes with LVI sampling during both spring and summer. 

There were numerous choices in how to calculate the LVI from the 12 taxa lists for each 

lake visit. We used three versions of the LVI for different aspects of index testing and 

development and denoted the different versions with suffixes. The three versions of LVI were 

calculated as: 

LVI_1x – one sample from each of 12 sections, N = 12 per lake-visit, 

LVI_2x – one sample equals the combination of data from two sections from opposite 

sides of the lake, N = 6 per lake-visit, and 

LVI_12x – one sample per lake derived from the combination of data from all 12 lake 

sections, N = 1 per lake-visit. 

Additional versions of the index could be calculated, for example, by combining data from 3 or 4 

lake sections. We did not pursue these other combinations because results for these three 
16

versions were so similar. Furthermore, we were interested in the smallest amount of sampling in 

order to minimize field effort. 

For all three versions of the LVI, the index was calculated by first transforming metrics 

into unit-less scores on the basis of their 5th and 95th percentiles. Leaving out the upper and lower 

5% of metric values eliminates extremely high or low values that may not be typical of 

minimally or extremely disturbed sites. The 95th percentile value was assigned a score of 10 (for 

metrics that declined with disturbance such as percent native taxa) and the 5th percentile value 

was assigned a score of 0. For metrics that increased with disturbance, the scores were reversed 

for the 5th and 95th percentiles. After transformation, metric scores ranged from 0–10. The LVI 

was the sum of the four metrics multiplied by a constant to adjust the range of LVI to a 0–100 

scale. A scale from 0–100 was selected for convenience with the intention of keeping the same 

scale for all Florida multimetric indices (Hughes et al., 1998). 

We used non-parametric correlation to test association between LVI_12x and lake 

surface area, latitude, and longitude. Because LVI_12X was significantly correlated with latitude 

as well as HDG, we used multiple regression to evaluate the relationship between LVI_12x and 

these independent variables. After confirming that human disturbance was the primary correlate 

for both LVI_12x and its component metrics, we next addressed the question of how many lake 

sections should be sampled. We evaluated the different versions of the index using two criteria: 

1) LVI correlation with human disturbance and 2) the number of categories of biological 

condition that each version of the index could detect. 

We estimated within-lake variability of the different versions of LVI using an ANOVA 

model. We used lake as the main factor and repeat samples within each lake were defined as 

replicates and used to calculate the mean squared error (MSE). This estimate of variance was 

used to calculate a 90% confidence interval for different versions of LVI (Zar, 1984). The 

confidence interval was calculated as: 
17

LVI ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

± 645.1*

2

n

s , 

where s2 = variance estimated from ANOVA (mean squared error), and 

n = number of samples taken from the lake. 

The number of categories of biological condition that LVI can reliably detect was 

obtained by dividing the possible range of the index (0–100) by the confidence interval. Thus, 

the confidence interval defined the level of precision of the index by representing how different 

LVI would have to be during a subsequent lake-visit to conclude that a statistically significant 

change had occurred in lake condition. That confidence interval was also used to define how 

many non-overlapping categories of biological condition could be defined over the potential 

range of LVI. To compare the different versions of LVI, we looked at the number of categories 

of biological condition each version could reliably detect for different numbers of replicate 

samples. 

We used z-values from the normal distribution to calculate 90% confidence limits for 

LVI for two reasons. First, the distribution of multimetric indices are known to approximate the 

normal distribution in that they are unimodal and symmetric (Fore et al., 1994). Second, for 

small sample sizes, the t-distribution is appropriate for calculating confidence limits because 

variance may be underestimated; however, for large sample sizes (df > 30) the two distributions 

converge. For this data set, data from 95 lakes provided an adequate sample size to apply the 

normal distribution. An additional concern when estimating variance is that the full range of 

expected values are represented. LVI values for this data set ranged from 0–100, which included 

all possible values for the index. 

To validate the results based on the development data set of 95 lakes, an additional 63 

lakes were sampled in 2004. LVI, HDG, the WQ index, the habitat index and LDI were 

calculated for these additional lakes and tested for correlation. Fifteen of these lakes were 

sampled twice during 2004, once during spring (March–April) and again during the summer 

(July–August). 
18

Expectations for statistical correlation 

The statistical significance of a correlation coefficient (r) is a function of the sample size, such 

that for larger sample sizes a smaller correlation coefficient will be significant. For large data 

sets, e.g., N = 100, a correlation coefficient of 0.17 will be statistically significant (alpha = 0.05, 

1-sided test). Such a small correlation coefficient may be statistically significant but biologically 

not very meaningful. Consequently, before correlation testing is complete, a scientist should 

consider the underlying meaning of correlation coefficients and what values represent biological 

significance. 

Based on these considerations, we selected specific values for correlation coefficients that 

would represent a meaningful association depending on which relationship was being tested. We 

used a correlation coefficient for metrics > 0.4 to define a significant relationship with HDG, 

then further evaluated each metric by looking at scatter plots. Thus, for metric selection, a higher 

standard was set than simple statistical significance. For metric correlation with the HDG, we 

anticipated that the variability associated with HDG and the inherent difficulty involved in fully 

assessing human influence would mean that high correlation coefficients (e.g., > 0.7) would be 

unlikely. On the other hand, correlation coefficients < 0.4, though they may be statistically 

significant, tend to be unconvincing when graphed. 

Using graphs, we tested to be sure that the lack of correlation was not due to good metric 

values in sites where human disturbance was known to be high. In contrast, we tolerated poor 

metric values in sites with no known disturbance because the HDG does not include many 

potential sources of degradation (e.g., herbicides). 

To test for metric redundancy, we first screened metrics to determine whether a metric 

pair’s correlation was greater than 0.8. We expected metrics to be highly correlated with each 

other because they were initially selected for their correlation with the same underlying measure, 

the HDG. When selecting metrics for the LVI, we wanted to be sure that the metrics were not 

redundant in that they were derived from the same information (species). If a metric pair was 

highly correlated, we evaluated the metrics to determine whether the same taxa were used in 

calculation of both metrics. When metrics were derived from redundant information, one metric 
19

in each pair (or group) was selected on the basis of its correlation with HDG, biological meaning, 

and sensitivity to degradation. 

When testing for correlation between biological measures (LVI and its component 

metrics) and physical measures (lake surface area, latitude, and longitude), we were less tolerant 

of correlation. For this analysis, any statistical significant correlation was considered because we 

were concerned that underlying natural features or processes could bias the biological assessment 

of LVI. We wanted to be sure that smaller lakes, for example, did not consistently score lower 

for LVI for the same level of human disturbance. Thus, in different testing situations, different 

values for the correlation coefficient were selected to indicate a significant association before 

testing. 
20

RESULTS 

A total of 404 taxa were identified from the 95 lakes in the index development data set. Most 

plants were identified to species, in some cases plants were identified to genus (e.g., Bidens, 

Cyperus, Ludwigia, Panicum, Xyris), and in a very few cases to family. The number of taxa 

identified in a lake based on the combination of data from all 12 sections ranged from 13–57, 

with an average of 34 taxa. The number of taxa found in a single lake section (1 of 12) ranged 

from 1–44 with an average of 15 taxa (Kell-Air and Karick Lakes had the lowest value of one 

taxon in a section and Lake Juliana had 44 taxa in one section). 

Human disturbance gradient 

The HDG was highly correlated with its component measures, indicating that HDG effectively 

integrated site condition for all three component measures (Table 4). Overall, each individual 

measure of human disturbance was more highly correlated with the HDG than with other 

measures, suggesting that the HDG was a better measure of general human disturbance. 

Hydrologic condition was not tested for correlation because it had only two possible values. The 

WQ and habitat indices were also highly correlated with each other, as were the habitat index 

and the LDI. In contrast, the WQ index was not significantly correlated with LDI, indicating that 

land use was not a good predictor of water quality condition for this data set. When only sites 

with no missing values for water quality variables were tested for correlation, values for the 

correlation coefficient changed little (< 0.04). 

Table 4. Correlation for measures of human disturbance and the HDG, only significant 

correlations are shown (Spearman’s r, p < 0.01). Index values were tested for correlation using 

their original values, not the scores (0, 1, 2) used to calculate the HDG. 

HDG WQ index Habitat index LDI 

HDG 0.62 -0.87 0.73

WQ index 0.62 -0.46

Habitat index -0.87 -0.46 -0.69

LDI 0.73 -0.69
21

Because the WQ index and LDI were not highly correlated, we tested whether soil type 

could explain the lack of correlation (Florida GIS coverage STATSGO soils). Some soils are 

more porous than others and for lakes in porous soils, chemicals or pollutants may run into the 

soil rather than run off the surface and into the lake. Lakes surrounded by non-porous soils may 

have higher levels of chemicals. We used a nonparametric two-sample test to check for 

differences in the WQ index, conductivity, NH3, TKN, NOx, TP, orthophosphates (OP), chloride, 

and sulfate. Conductivity, TP, and TKN were significantly higher in lakes with non-porous soils 

(Mann-Whitney U test, 1-sided, p < 0.05; Table 5). NOx was significant, but not in the direction 

predicted, values were lower in lakes with non-porous soil. Though statistically significant, the 

differences between levels of chemical measures in porous and non-porous soils were probably 

not large enough to explain the lack of correlation between the WQ index and LDI. 

Table 5. Water quality measure, and statistics from Mann-Whitney U test including rank sums 

for non-porous (NP) and porous (P) soils, U-statistic, z-statistic, sample size (N), and p-value. 

Significantly different measures are marked with an *. 

WQ measure Rank Sum NP Rank Sum P U Z N (NP) N (P) p 

WQ index 1901.0 2285.0 959.0 0.49 40 51 0.31 

* Conductivity 2070.5 2024.5 749.5 2.03 40 50 0.02 

* TKN 1662.0 1824.0 648.0 1.77 35 48 0.04 

NOX 1294.0 2361.0 591.0 -2.63 37 48 0.00 

* TP 1972.5 1855.5 630.5 2.57 38 49 0.01 

AGP 1273.5 1576.5 678.5 -0.20 34 41 0.42 

OP 564.0 517.0 264.0 0.00 24 22 0.50 

NH3_N 1624.5 2203.5 883.5 -0.41 38 49 0.34 

Chloride 371.0 532.0 181.0 0.70 16 26 0.24 

Sulfate 390.0 513.0 162.0 1.19 16 26 0.12 

Metric selection 

Many candidate metrics were tested and relatively few were strongly correlated with HDG or 

consistently correlated with the other measures of human disturbance. Of the 65 metrics tested 

for correlation with HDG, only 10 had an r-value > 0.4 (or < –0.4; Table 6). Because many of 

these metrics were redundant, a total of four metrics were selected for the final multimetric 

index. In general, if a candidate metric was correlated with HDG when calculated as number of 
22

taxa, it was also correlated with HDG when calculated as percentage of total taxa. In addition, if 

a candidate metric was correlated with HDG, it was typically correlated with at least two of the 

other measures of disturbance. 

Overall, the metrics that measured the numbers of native and exotic taxa and metrics 

derived from CC values were the most consistently associated with human disturbance. Within 

specific metric categories, results for individual metrics tended to be similar. Of the three metrics 

related to community structure, only dominant C of C (defined as the CC score for the one or 

two plants that covered the greatest area) was significantly correlated with HDG and was 

included in the index. A total of 63 taxa were identified as dominant in at least one lake section. 

Of these 63 taxa, about 1/3 were identified only once and about 1/3 were identified >10 times as 

dominant (Table 7). Total number of taxa and the total number of growth forms were not 

correlated with HDG. 

Of the four metrics related to nativity, all were significantly correlated with HDG, but the 

three based on exotic taxa were redundant. Category I and II invasives represented a subset of 

the exotic taxa. We retained percent invasive taxa for the LVI because invasive taxa represent a 

significant economic concern and an objective threat to native plant assemblages. We calculated 

this metric as a percentage of total taxa rather than total number of taxa because this form of the 

metric was less affected by geographic factors such as latitude. We also included percent native 

taxa in the index, which was only significantly associated with HDG when calculated as a 

percentage of total taxa. 
23

Table 6. Candidate plant metrics and their correlation with HDG, the WQ index, LDI, and the 

habitat index. Only Spearman’s correlations >0.40 are shown ( p < 0.001). The sample size is 

shown for all metrics except dominant C of C for which N ranged from 57–65. Most metrics 

were calculated as both the total number of taxa and the percentage of total taxa (left vs. right 

side of table); exceptions to this were metrics in the category “Community structure” which 

could only be calculated in one way. The source of information for each metric is listed. Similar 

metrics calculated from the national EPA database are also shown (Adamus and Gonyaw, 2000). 

Metrics selected for the final index are marked (“*”). 

Number of taxa Percent of total taxa 

HDG WQ LDI Habitat HDG WQ LDI Habitat Source 

N = 95 95 93 90 95 95 93 90 

Community structure 

Total taxa – – – – FDEP 

No. of plant guilds – – – – FDEP 

* Dominant C of C -0.52 -0.43 0.49 – – – – FDEP 

Nativity 

* Native -0.56 -0.46 -0.53 0.64 FDEP 

Exotic 0.55 0.50 0.45 -0.48 0.63 0.51 0.61 -0.65 FDEP 

Category 1 0.56 0.44 0.48 -0.50 0.58 0.43 0.58 -0.58 FLEPPC 

* Categories 1 & 2 0.59 0.51 0.49 -0.53 0.62 0.48 0.60 -0.62 FLEPPC 

Tolerance 

FQI Score -0.52 -0.32 -0.54 0.57 – – – – FDEP 

Average CC -0.67 -0.49 -0.59 0.63 – – – – CC 

* Sensitive (CC > 7) -0.40 -0.41 0.46 -0.49 -0.44 -0.41 0.48 CC 

Tolerant (CC < 3) 0.48 0.67 0.42 0.60 -0.59 CC 

V. Tolerant (CC < 2) 0.51 0.40 -0.40 0.65 0.41 0.59 -0.59 CC 

Duration 

Perennial FDEP 

Annual FDEP 

Annual:Perennial ratio FDEP 

Native A:P ratio FDEP 

Native perennials FDEP 

Native annuals FDEP 

Wetland status 

Obligate wetland FDEP 

Obligate + facultative FDEP 

Upland FDEP 

Native obligate wetland FDEP 

Native facult. wetland FDEP 

Native upland FDEP 

Growth form 

Herbaceous FDEP 

Woody FDEP 

Emergent FDEP 

Floating FDEP 

Submersed FDEP 

Fern FDEP 

Gymnosperm FDEP 
24

Number of taxa Percent of total taxa 

HDG WQ LDI Habitat HDG WQ LDI Habitat Source 

N = 95 95 93 90 95 95 93 90 

Native herbaceous FDEP 

Native woody -0.48 FDEP 

Native emergent FDEP 

Native floating FDEP 

Native submersed FDEP 

Native fern FDEP 

Native gymnosperm FDEP 

Native forbs + herbs FDEP 

Native graminoids FDEP 

Native vines FDEP 

Native shrubs -0.44 -0.52 0.48 -0.41 FDEP 

Native subshrubs FDEP 

Native tree FDEP 

Dicot/monocot 

Dicot FDEP 

Monocot FDEP 

Native dicot -0.40 FDEP 

Native monocot FDEP 

EPA database 

Sensitive EPA 

Tolerant EPA 

V. Tolerant EPA 

Nutrient tolerant EPA 

V. Nutrient tolerant EPA 

N tolerant EPA 

P sensitive EPA 

P tolerant -0.41 EPA 

Flood sensitive -0.40 EPA 

Flood tolerant -0.43 EPA 

V. Flood tolerant EPA 

Sediment sensitive 0.40 EPA 

Sediment tolerant -0.40 0.43 -0.41 -0.40 0.41 EPA 

V. Sediment tolerant EPA 

Salinity sensitive EPA 

Salinity tolerant EPA 

V. salinity tolerant EPA 
25

Table 7. List of plant taxa that were most frequently identified as the dominant (or co-dominant) 

taxon in a lake section. Shown are the number of times the taxon was named dominant (or codominant) out of 786 occasions when dominant taxa were noted and CC score for that taxon. 

Taxon Number of times CC score

Panicum repens 169 0

Panicum hemitomon 164 5.82

Typha domingensis 37 0.59

Nuphar luteum 32 4.64

Typha latifolia 32 1.6

Mayaca fluviatilis 27 8.45

Hydrilla verticillata 26 0

Panicum 20 

Taxodium ascendens 19 7.21

Ludwigia octovalvis 17 4.09

Nymphaea odorata 17 7.18

Vallisneria americana 17 7.28

Eichhornia crassipes 16 0

Cladium jamaicense 12 9.04

Fuirena scirpoidea 12 6.5

Hypericum fasciculatum 12 7.27

Hypericum lissophloeus 11 

 

Of the five metrics related to tolerance and sensitivity, all were significantly correlated 

with HDG. FQI score and average CC represented a general measure of overall tolerance, while 

the number (or percentage) of tolerant or sensitive taxa represented opposite ends of the 

spectrum. The general metrics that summarized sensitivity (or tolerance) of the entire plant 

assemblage were redundant with the more specific metrics that measured either tolerance or 

sensitivity. Of these, we selected the more specific metric, percentage sensitive taxa, for 

inclusion in the index. Other studies have found that metrics related to sensitive or intolerant taxa 

are the first to register changes in an assemblage when human disturbance is introduced into new 

locations (Karr and Chu, 1999). We did not include percent tolerant taxa in the index because 

many of the same plant taxa were also included in the invasive or exotic metrics. 

Other candidate metrics related to duration of life cycle, wetland status, and number of 

cotyledons were not correlated with HDG. Similarly, candidate metrics based on growth form or 
26

type were not correlated with HDG, with the exception of one metric, number of native shrub 

taxa, which was significantly correlated with HDG, LDI and the habitat index. This metric was 

not included in the final index for two reasons. First, very few shrub taxa are typically found at a 

lake and, second, of these few, some are indicators of disturbed conditions (e.g., Baccharis and 

Sambucus). 

The candidate metrics derived from the EPA database failed to correlate predictably or 

consistently with HDG or other measures of disturbance. The sediment sensitive and sediment 

tolerant metrics were significantly correlated with HDG but in the opposite direction predicted, 

and so were not retained for the index. 

Of the metrics correlated with HDG, r-values were generally higher when calculated as 

percentage of total taxa rather than as number of taxa, probably because the total number of taxa 

varied as a function of other natural features and calculation based on a percentage of total taxa 

controlled for that source of variability. 

The four metrics included in the LVI were all significantly correlated with each other, as 

expected, but correlation coefficients were not high enough to warrant concerns regarding 

redundancy of metrics. Correlation coefficients were < 0.8 (or > -0.8) for all metrics. More 

importantly, the metrics were based on different sets of taxa with minimal overlap. 

The four metrics selected for inclusion in the LVI, percent native taxa, percent invasive 

taxa, percent sensitive taxa and dominant C of C, were correlated with HDG and its component 

measures (Figures 3–6). Both percent invasive taxa and dominant C of C showed regional 

differences in the panhandle and peninsula, and metric scores were adjusted for percent invasive 

taxa by calculating the 5th and 95th percentiles separately for each region. Metric scoring for 

dominant C of C was not adjusted for region, but should be reconsidered as more data become 

available. Dominant C of C had three lakes with values that were outliers, that is, dominant C of 

C was high in three lakes that also had high values for HDG. For these lakes, dominant plants 

with high CC scores were Panicum hemitomon (CC = 5.82) and Vallisneria americana (CC = 

7.28). 
27

HDG

% Native taxa

-1 0 1 2 3 4 5 6 7 8

10%

30%

50%

70%

90%

HDG

% Invasive taxa

-1 0 1 2 3 4 5 6 7 8

0%

20%

40%

60%

HDG

% Sensitive taxa

-1 0 1 2 3 4 5 6 7 8

0%

20%

40%

60%

HDG

Dominant C of C

-1 0 1 2 3 4 5 6 7 8

0

2

4

6

8

Peninsula

Panhandle

Figure 3. Metrics selected as components of LVI were strongly correlated with the human 

disturbance gradient (HDG). Percent invasive taxa and dominant C of C differed slightly 

in the peninsula and panhandle for the same values of HDG. Each point represents a 

single lake; lines are least fit regression lines for each geographic area. Four lakes from 

the NE region are not shown. 
28

1 2 3 4 5 6 7 8 9 10

WQ index

10%

30%

50%

70%

90%

% Native taxa

1 2 3 4 5 6 7 8 9 10

WQ index

0%

20%

40%

60%

% Invasive taxa

1 2 3 4 5 6 7 8 9 10

WQ index

0%

10%

20%

30%

40%

50%

60%

% Sensitive taxa

1 2 3 4 5 6 7 8 9 10

WQ index

-1

0

1

2

3

4

5

6

7

8

Dominant C of C

Figure 4. LVI metrics were highly correlated with the water quality index. Each point 

represents a single lake. 
29

0 20 40 60 80 100

Habitat index

10%

30%

50%

70%

90%

% Native taxa

0 20 40 60 80 100

Habitat index

0%

20%

40%

60%

% Invasive taxa

0 20 40 60 80 100

Habitat index

0%

20%

40%

60%

% Sensitive taxa

0 20 40 60 80 100

Habitat index

0

2

4

6

8

Dominant C of C

Figure 5. LVI metrics were highly correlated with the habitat index. Each point 

represents a single lake. 
30

0123456789

LDI

10%

30%

50%

70%

90%

% Native taxa

0123456789

LDI

0%

20%

40%

60%

% Invasive taxa

0123456789

LDI

0%

20%

40%

60%

% Sensitive taxa

012345678

LDI

0

2

4

6

8

Dominant C of C

Figure 6. LVI metrics were highly correlated with the LDI index. Each point represents a 

single lake. 
31

Sensitive and tolerant taxa 

Of the 404 taxa found in 95 lakes, 62 were defined as sensitive (CC score > 7) and 47 as tolerant 

(CC < 3) based on professional judgment (Cohen et al., 2004). Not all these assignments could 

be tested using the data from 95 lakes because so many taxa were rare. More than half of the taxa 

occurred in < 5 lakes (248 out of 404, or 61%). Of the 62 taxa defined as sensitive, 35 (56%) 

occurred in < 5 lakes; of the 47 tolerant taxa, 19 (40%) occurred in < 5 lakes. These taxa 

occurred in too few lakes to be tested for their association with HDG. 

Of the taxa that occurred frequently enough to test, relatively few (29 of 404, or 7%) 

showed an association with HDG that was statistically significant (χ2

, p < 0.05). Nine sensitive 

taxa and 11 tolerant taxa were significantly associated with disturbance (Table 8; see Appendix 3 

for all taxa). One species, Vallisneria americana, was defined as sensitive on the basis of its CC 

score, but was significantly associated with more disturbed lakes for this data set indicating that 

this taxon may be incorrectly defined for lakes. In general, most of the other taxa that showed a 

statistically significant association with HDG agreed with their CC scores in terms of their 

preference for undisturbed or disturbed conditions. 

Although statistically significant, the preferences for ‘good’ or ‘poor’ lakes were not 

strong in several cases. For example, Cephalanthus occidentalis showed a significant preference 

with 25 out of 51 occurrences in ‘good’ sites although the preference represented only 49% of its 

occurrences. From a statistical point of view, the association may be better than chance (which 

was equal to 38% of occurrences at good sites); however, from a biological point of view, 51% 

of its occurrences in ‘poor’ sites does not indicate a sensitive species. For this reason, a similar 

analysis of macroinvertebrates in streams set the criteria for defining sensitive taxa much higher 

(87% of occurrences in good sites) than statistical significance (Fore, 2004). 

We continued to use the sensitive and tolerant taxa lists derived from the panel of expert 

botanists rather than define a list based on the results of this statistical analysis for two reasons. 

First, most of the taxa were too rare to test with these data. In addition, the statistical analysis for 

these data yielded too few taxa to define reliable metrics for sensitive or tolerant taxa. 
32

Table 8. Taxa that were significantly associated with low or high HDG (χ2, p < 0.05). Shown are 

taxon name, CC value, whether the taxon was identified as sensitive or tolerant based on CC 

value, number of lakes in which taxon was found out of 95 lakes, number of occurrences in 

‘good’ lakes (HDG < 3) and ‘bad’ lakes (HDG >= 3), proportion of total occurrences in good 

lakes, and whether the direction of taxon preference agreed with the CC value. 

Taxon CC Sens/Tol # Occur # Good # Bad %Good Agree 

Vallisneria americana 7.28 S 18 2 16 0.11 no 

Cephalanthus occidentalis 7.27 S 51 25 26 0.49 yes 

Cladium jamaicense 9.04 S 22 15 7 0.68 yes 

Decodon verticillatus 7.8 S 12 10 2 0.83 yes 

Hypericum fasciculatum 7.27 S 13 10 3 0.77 yes 

Lyonia lucida 7.06 S 8 7 1 0.88 yes 

Mayaca fluviatilis 8.45 S 19 12 7 0.63 yes 

Nymphaea odorata 7.18 S 43 22 21 0.51 yes 

Triadenum virginicum 8.16 S 17 11 6 0.65 yes 

Alternanthera philoxeroides 0 T 49 10 39 0.20 yes 

Brachiaria mutica 0 T 18 2 16 0.11 yes 

Colocasia esculenta 0 T 40 7 33 0.18 yes 

Cyperus surinamensis 2.03 T 14 1 13 0.07 yes 

Hydrilla verticillata 0 T 19 1 18 0.05 yes 

Ludwigia peruviana 0.62 T 42 10 32 0.24 yes 

Mikania scandens 1.95 T 65 16 49 0.25 yes 

Panicum repens 0 T 73 23 50 0.32 yes 

Pistia stratiotes 0 T 15 1 14 0.07 yes 

Sapium sebiferum 0 T 21 3 18 0.14 yes 

Schinus terebinthifolius 0 T 34 6 28 0.18 yes 

Ludwigia alata 5.85 4 4 0 1.00 unk 

Nymphoides aquatica 6.09 17 13 4 0.76 unk 

Utricularia purpurea 6.5 7 6 1 0.86 unk 

Cabomba caroliniana 5.07 9 7 2 0.78 unk 

Cyperus odoratus 4.25 27 3 24 0.11 unk 

Lachnanthes caroliana 3.76 24 16 8 0.67 unk 

Scirpus cyperinus na 4 4 0 1.00 unk 

Solidago na 17 12 5 0.71 unk 

Utricularia na 18 11 7 0.61 unk 
33

1234567

Taxon average HDG

0

2

4

6

8

10

Taxon CC

Figure 7. Plant CC value declined as the average HDG for the lakes at which a taxon was 

found increased (Spearman’s r = -0.42, r2 = 0.18, p < 0.01, N = 123 taxa). Shown are 

results for taxa with >4 occurrences in 95 lakes. 

Cohen et al. (2004) describe another approach for comparing field data with CC scores. 

For each taxon, they calculated the average LDI value for all the sites at which the taxon was 

found. We followed their approach using HDG instead of LDI. We averaged the HDG values for 

each lake in which a particular taxon was found. We compared the average HDG values for each 

taxon with the CC score for each taxon and found that they were significantly correlated 

(Spearman’s r = –0.42, N = 123 taxa, p < 0.01). Although statistically significant, the agreement 

between the CC values based on expert opinion and the average HDG value at all sites in which 

a taxon was found was not close (r2

 = 0.18; Figure 7). 

Evaluation of the Lake Vegetation Index (LVI) 

The following sections describe results for 1) correlation analysis of LVI with human 

disturbance and other natural geographic features; 2) selection of the most efficient lake 

sampling protocol; 3) validation of LVI using an independent data set; 4) description of lakes 

with higher or lower LVI values than predicted by the HDG (outliers); and 5) a comparison of 
34

LVI values for lakes sampled during spring and summer. We compared different versions of LVI 

using different numbers of lake sections and selected a final protocol based on LVI calculated 

from 4 lake sections from opposite sides of the lake. 

Correlation of LVI with human disturbance and natural features 

For this initial testing of LVI, plant data from all lake sections were combined to obtain a single 

LVI for each lake (LVI_12x). We anticipated that correlations with natural features might be 

subtle and our goal was to use the most accurate LVI for this testing. (Subsequent analyses based 

on smaller numbers of lake sections revealed index values differed little according to the number 

of sections used in calculations.) LVI_12x was highly correlated with HDG and was more highly 

correlated with HDG than other measures of human disturbance indicating the value of the HDG 

as an integrated measure of disparate types of human disturbance (Table 9; Appendix 4). 

LVI_12x was not correlated with lake size (surface area), which meant that adjustments to metric 

scoring based on lake size were not necessary. LVI_12x was not significantly correlated with 

longitude, but was correlated with latitude, probably because the HDG was also correlated with 

latitude. 

Table 9. Correlation coefficients for LVI_12x and measures of human disturbance, lake area, 

latitude and longitude. Non-significant correlations indicated by parentheses (Spearman’s r, p < 

0.01). 

HDG WQ index Habitat index LDI Area Latitude Longitude 

N = 95 87 90 93 76 95 95 

LVI_12x -0.68 -0.46 0.69 -0.62 (0.03) 0.35 (0.06) 

The significant correlation between LVI_12x and latitude triggered a more detailed 

analysis between these variables to ensure that human disturbance was the primary cause of 

changes in LVI values and not spurious correlation with other natural features. We used multiple 

regression to test for significant associations between LVI_12x and HDG, lake surface area, and 

latitude (the independent variables) simultaneously. We also tested each of LVI’s component 

metrics separately for association with the same independent measures to identify any 

confounding relationships among the metrics. 
35

LVI_12x was significantly correlated with HDG but not with area or latitude when all 

three were included in a multiple regression model. Of the four metrics, only percent invasive 

taxa was significantly associated with both latitude and HDG; the other three were only 

significantly associated with HDG (Table 10). Adjustments to the metric scoring for panhandle 

and peninsula areas helped to resolve the underlying correlation with geographic location for the 

final LVI. 

Multiple regression assumes that the independent variables (area, latitude, and longitude) 

are independent and not correlated. For this analysis, HDG and latitude were significantly 

correlated (Pearson’s r = 0.3, p < 0.01). Correlation among independent variables in a multiple 

regression model can result in unstable solutions or inconsistent results. Correlation among 

independent variables is difficult to avoid in regional surveys such as these because human land 

use typically follows geographic gradients. Nonetheless, the consistent high correlation between 

LVI_12x and its component metrics with HDG and consistent exclusion of latitude and area 

from the model solution supports the conclusion that HDG was the primary source of variance in 

LVI. 

Table 10. Standardized regression coefficients from multiple regression. Each row represents a 

different statistical test for each biological measure. Column variables were entered in each 

model and only coefficients for significant predictors are shown. Only 76 lakes had information 

for lake surface area. 

Biological 

measure 

N HDG Latitude Area 

LVI_12x 76 -0.68 

% Native taxa 76 -0.50 

% Invasive taxa 76 0.49 -0.28 

% Sensitive taxa 76 -0.50 

Dominant C of C 50 -0.62 

Variability analysis of alternative lake sampling protocols 

As expected, the total number of species increased as the number of lake sections increased from 

1–12 (Figure 8). The percentage of the total taxa collected increased steadily, but an obvious 

asymptote was not achieved. About half the taxa found in all 12 sections were found within a 
36

single section when results were averaged across all 100 lake-visits (95 lakes with 5 lakes visited 

twice; Table 11). For eight sections, 92% of the taxa (on average) were found. Results from this 

analysis of species accumulation failed to identify an obvious asymptote for a particular number 

of sections that could be used to define the minimum sampling effort for lake assessment and 

LVI calculation. Therefore, we looked at the variability of LVI derived from different sampling 

protocols to define how many lake sections should be sampled during each visit. 

Table 11. The cumulative number of sections sampled per lake-visit, the proportion of the total 

taxa found for a given number of sections (averaged across all lake-visits), and the number of 

lake-visits that attained the maximum number of taxa found in all 12 sections for each number of 

sections sampled. 

Number of sections 1 2 3 4 5 6 7 8 9 10 11 12 

Mean of prop. of taxa 0.49 0.62 0.70 0.76 0.81 0.86 0.89 0.92 0.95 0.96 0.98 1.00

Number achieve max 0 0 0 2 5 6 9 13 21 41 62 100 

1 2 3 4 5 6 7 8 9 10 11 12

Number of samples

20%

40%

60%

80%

100%

Percent of species accumulated

 Mean 

 Min-Max 

Figure 8. Number of species increased as the number of lake sections sampled increased 

from 1–12. Averaged across all lakes, 49% of the taxa found in all 12 sections were 

found in one section. For two sections, 62% of the total taxa were collected. 
37

Variance estimates for LVI were derived from ANOVA with lake as the main factor and 

replicate samples in a lake used to calculate the within-lake variability (mean squared error). We 

calculated variance for two versions of the LVI: 1) treating each of the 12 lake sections as 

individual replicates (LVI_1x), and 2) combining data from two lake sections on opposite sides 

of the lake, which yielded a total of 6 replicates per lake (LVI_2x). Within lake variance for 

LVI_1x was ~50% greater than variance calculated for LVI_2x (Table 12). We used the variance 

estimates for both versions of LVI to calculate confidence intervals. We divided the possible 

range of the LVI (0–100) by the confidence interval to obtain the number of categories of 

biological condition that each version of the index could detect. 

If data were collected from a single lake section and LVI_1x calculated, 2.9 categories of 

biological condition could be detected. If two replicate samples of this type were collected, 4.1 

categories could be detected. For LVI_2x, if a single sample were collected (representing a 

composite of data from two lake sections), 3.6 categories could be detected; if two samples of 

this type were collected, 5.1 categories of condition could be detected. The two different 

sampling approaches yielded similar levels of precision for an equivalent number of lake 

sections (Figure 9). Two composite samples equals four single samples in terms of field effort, 

and the two field methods yielded a similar number of categories: 5.7 vs. 5.1 (for LVI_1x vs. 

LVI_2x). 

Table 12. Two versions of the LVI, their variance estimates (MSE) for within lake sampling, and 

the number of categories of biological condition that could be detected for 1–6 replicate samples. 

Results are shown for two versions of the LVI calculated for 1) single section samples (LVI_1x) 

and 2) composite samples derived from two lake sections (LVI_2x). 

Index version Variance Number of replicate samples 

 1 2 3 4 5 6 

LVI_1x 112.33 2.87 4.06 4.97 5.74 6.41 7.02

LVI_2x 70.20 3.63 5.14 6.29 7.27 8.12 8.90
38

Number of replicate samples

0 1 2 3 4 5 6 7 8 9 10

1

2

3

4

5

6

 LVI_1x

 LVI_2x

Figure 9. The number of categories of biological condition that can be reliably detected 

increased as the number of replicate samples within each lake increased from 1 to 6. 

Shown are results for two versions of the LVI based on samples from a single section 

(LVI_1x) and composite samples composed of two lake sections (LVI_2x). 

In addition to evaluating the statistical precision, we also tested whether LVI derived 

from more lake sections was more highly correlated with HDG. In fact, correlation coefficients 

were very similar for the three versions of LVI and for its component metrics (Table 13). Thus, 

correlation with HDG did not improve with larger sample sizes (i.e., greater field effort), even 

though the single sections had only half as many taxa as the composite samples based on data 

from 12 lake sections. These results demonstrate that an exhaustive sample of all the plants in the 

lake is not needed and that a consistent sample of a small subset of the plants present will 

provide a reliable assessment of the plant assemblage. Based on these results, we selected the 

LVI_1x version of the index for lake assessment. LVI_1x had a unimodal and fairly symmetric 

distribution for the lakes in this data set (Figure 10). 
39

Table 13. Correlation with HDG for LVI and its component metrics for three different sampling 

methods (Spearman’s r, p < 0.01). 

Sampling 

method 

LVI % Native taxa % Invasive taxa % Sensitive taxa Dominant C of C 

N = 95 95 95 95 62 

1x -0.70 -0.62 0.60 -0.49 -0.48 

2x -0.70 -0.59 0.61 -0.48 -0.48 

12x -0.68 -0.56 0.62 -0.49 -0.48 

-10 10 30 50 70 90 110

LVI_1x

0

40

80

120

160

200

Number of observations

Figure 10. Distribution of LVI_1x for 12 replicate samples from 95 lakes. Curved line 

represents the normal distribution. 

We used replicate samples of LVI_1x to test whether index values were more variable in 

larger lakes than smaller lakes and found that they were not. In contrast, within-lake variance of 

LVI_1x was higher in lakes with more human disturbance as measured by HDG (Spearman’s r = 

0.38). 

We also tested whether replicate sampling improved the accuracy of the lake assessment 

for LVI_1x by averaging different numbers of lake replicates and correlating the average index 

values with measures of human disturbance. The greatest improvement in correlation occurred 
40

for one vs. two lake sections; improvements were incrementally smaller as more lake sections 

were added to calculate the average LVI (Table 14). These results indicate that four replicate 

samples of LVI_1x were sufficient to reliably assess a lake. 

Table 14. Correlation between LVI_1x and measures of disturbance for different numbers of 

replicate samples within each lake (Spearman’s r, N = 95, p < 0.01). 

Number of reps HDG WQ index Habitat index LDI 

1 -0.60 -0.45 0.65 -0.40

2 -0.65 -0.46 0.70 -0.49

3 -0.66 -0.44 0.70 -0.53

4 -0.67 -0.46 0.69 -0.54

5 -0.66 -0.46 0.69 -0.54

6 -0.66 -0.48 0.68 -0.54

7 -0.67 -0.48 0.68 -0.55

8 -0.68 -0.49 0.68 -0.57

9 -0.69 -0.49 0.68 -0.58

10 -0.69 -0.50 0.68 -0.58

11 -0.70 -0.50 0.69 -0.59

12 -0.69 -0.51 0.68 -0.58

Metric scoring for the LVI 

Percent invasive taxa differed slightly in metric values for similar values of HDG in the 

peninsula and panhandle regions of the state. Metric scores were adjusted to ensure that an 

equivalent level of human disturbance would translate into a similar LVI value independent of 

location in the state (Table 15; see also Appendix 6). Several lakes had no information available 

to calculate dominant C of C. For those lakes, we used the average of three (rather than four) 

metric scores to calculate LVI. 
41

Table 15. Metric scoring rules derived from the 5th and 95th percentiles. For percentage of taxa 

metrics, N = 1196 (12 sections in 100 lake-visits – 4 missing sections), but for Dominant C of C, 

N = 551. For dominant C of C, lakes in the northeast were scored as panhandle lakes. (See 

Appendix 6 for more recent scoring rules.) 

Metric 5th %tile 95th %tile Scoring rule

% Native taxa 43% 95% (x – 43)/52 

% Invasive taxa 0% 39% 

 Panhandle 0% 27% 1 – (x/27) 

 Peninsula 0% 40% 1 – (x/40) 

% Sensitive taxa 0% 41% x/41 

Dominant C of C 0 7.8 x/7.8 

Validation of the LVI with independent data 

LVI_1x calculated for the 63 independent lakes in the validation data set was highly correlated 

with HDG (Table 16). Lakes in this data set provided a good test of the LVI because the lakes 

represented a broad range of site conditions and included all possible values of HDG from 0–7 

(Figure 11). LVI was also highly correlated with the habitat index and LDI; correlation was not 

as high with the WQ index. These results were similar to those observed for the development 

data set (see Table 9 above; see App. 6 for more recent validation). 

The distribution of LVI values vs. HDG does not clearly illustrate the 5 non-overlapping 

categories of biological condition that the variance analysis predicted. The 5 categories of 

biological condition represent detectable levels of change for an individual lake. Figure 11 plots 

LVI values from different lakes with similar values for HDG. In other words, the figure 

introduces additional sources of variability for LVI associated with the type of disturbance or the 

intensity of disturbance at a particular lake. Because the “true” value of human disturbance 

cannot be accurately measured, lakes with approximately the same level of disturbance are 

combined for similar values of HDG. 
42

Table 16. Correlation between LVI_1x and HDG, WQ index, habitat index, and LDI for the 

validation data set. All correlations were significant (Spearman’s r, p < 0.01, N = 63). 

HDG WQ index Habitat index LDI 

LVI_1x (average of 4 reps) -0.72 -0.34 0.59 -0.78

01234567

HDG

0

20

40

60

80

100

LVI_1x (average of 4 reps)

 Median 

 25%-75% 

 Non-Outlier Range 

 Outliers

Figure 11. For the validation data set, LVI declined significantly as human disturbance 

increased (Spearman’s r = -0.73, p << 0.01 N = 63). LVI for each lake was calculated as 

the average LVI from four lake sections. 

Description of outliers 

Six lakes in the index development data set of 95 lakes had lower LVI values than predicted on 

the basis of their association with HDG and three lakes had a higher LVI value (Figure 12). For 

the six lakes that fell below the 90% confidence bound, HDG ranged from 0–5, which 

represented almost the entire range of possible values (Table 17). These lakes may have other 

causes of degradation not captured in the HDG used for this study. They may also share some 

natural feature that lowers the expectation for LVI; several of these lakes were located in the 

southernmost part of the state. Two of the three lakes with higher LVI than expected were 

located in the northeast district. 
43

012345678

HDG

0

20

40

60

80

100

INDX_1x

Figure 12. LVI declined as human disturbance increased. The regression line (solid) and 

the 90% confidence bounds (dotted) are shown, each point represents one lake (N = 95 

lakes). Six lakes had lower LVI values than predicted by the HDG (open circles) and 

three lakes had an LVI slightly higher than predicted (open triangles). 

Table 17. Lakes with higher or lower LVI values than expected based on HDG. Shown are the 

nickname, STORET, observed difference from expectation, region, district, LVI, HDG, WQ 

index, LDI and habitat index for each lake. 

Nickname STORET Diff Region District LVI HDG WQ 

index 

Hab 

index

LDI

L84-BANANA 27584418154127 Lower Peninsula SW 18.7 4 8.6 50.7 3.3

LKLECLARE 28063598232193 Lower Peninsula SW 29.3 2 3.0 67.1 5.1

L51PEAST 24040806 Lower Peninsula SW 26.8 3 5.0 6.2

FRANTST 21020056 Higher Panhandle 

East 

NE 69.4 5 5.2 12.9 7.2

LKHALPATOK 28010594 Lower Peninsula SE 47.9 0 2.4 66.4 1.4

LAKEIDA 28010500 Lower Everglades SE 9.6 5 5.6 35.0 6.6

LITTLE REF 26010636 Higher Peninsula S 87.8 2 89.3

TANKLAKE 20030151 Higher Peninsula NE 96.8 1 4.2 82.9 1.1

L52PWALDEN 24040170 Lower Peninsula SW 9.1 3 6.0 3.1
44

Comparison of spring and summer LVI values 

Some lakes had different LVI values in spring vs. summer, but higher LVI values were not 

consistently associated with one season. LVI for five lakes were higher in spring, five lakes were 

higher in summer, and five lakes were similar during both seasons (Figure 13). Because LVI was 

higher in some lakes in spring and lower in others, no consistent seasonal association could be 

identified and corrected. We could not discern from these data why some lakes were higher in 

spring and others lower. 

The observed differences may have occurred because some plants are easier to identify in 

the spring due to the presence of flowers or seeds. Looking at the complete list of taxa for all 15 

lakes, the number of individual plants that were not identified beyond genus was 8% during the 

spring vs. 11% during the summer, a relatively small difference. Data from such a small data set 

may be difficult to interpret; for example, 5 of the 15 sites had no invasive taxa recorded for 

either season. 
45

SR1-SL2004SR1-SL2025SR1-SL2028SR1-SL2039SR1-SL2059SR1-SL2082SR1-SL2087SR1-SL2090SR1-SL2094SR1-SL2111SR1-SL2117SR1-SL2136SR1-SL2153SR1-SL2156SR1-SL2171

10

30

50

70

90

LVI_1x

 Spring 

 Summer

Figure 13. LVI for repeat visits to 15 lakes during the spring (closed circles) and summer 

(open triangles) of 2004. Each point represents the average LVI from four lake sections. 

Each lake-visit had three replicate samples of four sections each (4 x 3 = 12 lake 

sections). 
46

DISCUSSION 

When selecting biological measures as indicators of water body condition, two criteria must be 

considered. First, the indicator must show a consistent and meaningful association with an 

independent measure of human disturbance (Karr and Chu, 1999). Second, the indicator must be 

sufficiently precise to detect changes in site condition should a change occur in order to be 

protective of the resource (Fore, 2003). Metric selection for the LVI was relatively 

straightforward because so few candidate metrics were reliably correlated with HDG. Those 

metrics were also highly correlated with the other independent measures of disturbance. The LVI 

was also precise enough to reliably detect five categories of biological condition based on 

sampling 4 of 12 possible lake sections. 

The LVI was highly correlated with HDG, more so than its component metrics, which 

illustrates one of the advantages of a multimetric index: the index provides a more integrative 

assessment of biological condition than a single metric. For multimetric indices, precision 

typically increases with more metrics, and 7–10 metrics are preferred. Nonetheless, the precision 

for LVI was quite high with only four metrics. LVI derived from four replicate lake sections had 

sufficient statistical precision to reliably detect five categories of biological condition. Thus, the 

LVI was both highly correlated with an independent measure of human disturbance and was very 

precise for detecting changes in lake condition. 

Human disturbance gradient 

Human activities influence and degrade aquatic systems in multiple ways including the addition 

of chemicals and nutrients to the water, destruction and loss of habitat, alteration of natural flow 

patterns, changes in the way energy moves through the system, and addition or loss of species 

that change how species interact (Karr et al., 1986; Karr and Chu, 1999; Karr et al., 2000). 

Information was available from routine lake sampling to characterize water chemistry, habitat 

condition, hydrologic alteration, and the intensity of human land use around the lake. When 

developing a human disturbance gradient, the hope is that different measures of human 

disturbance will be highly correlated and contribute equally to the final HDG. 
47

For this study, only the WQ index and LDI failed to correlate with each other, although 

both were highly correlated with the final HDG. To test whether the lack of correlation was 

associated with different types of soils, we compared the WQ index for lakes surrounded by 

porous soils to those with non-porous soils. The expectation was that porous soils would have 

lower WQ index values because nutrients and chemicals would percolate through the soil and 

become trapped before reaching the lake. The WQ index values did not differ according to soil 

type although three of the five component measures included in the WQ index were higher in 

non-porous soils. Conductivity, total phosphorus, and total Kjeldahl nitrogen were significantly 

higher in non-porous soils; however, the differences in values for the two soil types were 

relatively small and likely could not explain the lack of correlation between LDI and the WQ 

index. An alternative explanation for the lack of correlation between these two measures of 

disturbance may be that different types of human activity influence water chemistry in different 

ways. For example, agriculture may increase nutrients while urban development does not. 

The primary benefit derived from using the HDG to test metrics was the opportunity to 

select metrics that showed a consistent change across a broad range of possible site conditions. 

An added benefit associated with the HDG was in setting consistent metric expectations for 

different areas of the state. By adjusting metric scores by region according to HDG, the final LVI 

had the same relative meaning independent of geographic location. 

Biological indicators 

Sampling and analysis methods for lake bioassessment are less mature than those for streams or 

wetlands; consequently, a short list of best metrics has yet to be defined for aquatic plants. 

Therefore, we approached metric testing as a somewhat exploratory exercise and identified a 

long list of potential metrics. Although the list of metrics was long, 48 metrics plus an additional 

17 metrics derived from the national database (Adamus and Gonyaw, 2000), surprisingly few 

correlated with HDG. In fact, from a similar long list of candidate metrics tested for wetlands, 

most of the same metrics were selected based on their correlation with LDI (Lane et al., 2004; 

Reiss and Brown, 2005). 

For LVI, percent native taxa, percent invasive taxa, FQI, percent sensitive taxa, and 

percent tolerant taxa were highly correlated with HDG. Similarly for forested wetlands, native 
48

perennial, exotic, FQI, sensitive, and tolerant species were all highly correlated with LDI and 

selected for the wetland condition index (WCI, Reiss and Brown, 2005). For herbaceous 

wetlands, nearly identical metrics were selected for the WCI: percent exotic taxa, average CC, 

percent sensitive taxa, and percent tolerant taxa (Lane et al., 2004). All of these metrics were also 

significantly associated with disturbance in lakes for this study. One additional metric, 

annual/perennial ratio, was significant for wetlands but not for lakes. Lane et al. (2004) noted 

that although the species composition of wetlands varied between regions, the metrics that were 

significantly correlated with LDI did not. Results were similar for lakes in this study, and the 

same metrics were consistently correlated with HDG across the state. 

Metric selection for LVI was simple because metrics that were correlated with HDG were 

also highly correlated with the WQ index, the habitat index, and LDI. Most other candidate 

metrics failed to correlate with HDG or its component metrics. Thus, the patterns of metric 

correlation were easy to interpret. 

One goal of this study was to test the plant CC designations based on professional 

experience with field data and the HDG. A fair assessment of the expert designations of plant 

taxa could not be done with this data set, primarily because plants tended to be rare (61% of the 

plants occurred in < 5 lakes). Only 29 taxa (7%) were significantly associated with HDG. 

Because the data were sparse for so many taxa and the lists of sensitive and tolerant taxa derived 

from the analysis were also very short, we elected to retain the current designations as sensitive 

or tolerant based on expert definitions of CC values. Empirical data did support the CC 

designations; average HDG values for the lakes in which each taxon occurred were highly 

correlated with CC values, indicating that professional judgment was supported by field data. 

Statistical precision of the Lake Vegetation Index (LVI) 

The most important characteristic of a biological indicator should be its correlation with an 

independent measure of human disturbance; second in importance is its statistical precision. A 

statistically precise indicator that fails to indicate disturbance is meaningless for assessment. A 

third important characteristic of an indicator is the cost associated with sampling, that is, the 

level of sampling effort needed to obtain a reasonably precise measure of resource condition. 
49

We expected that correlation with HDG would be higher for LVI derived from all 12 lake 

sections combined (LVI_12x) rather than for LVI based on smaller samples of one or two 

sections (LVI_1x, 2x), particularly because the percentage of the plants found in a single lake 

section was only about half the number found in all 12 sections. In fact, correlation with HDG 

was very similar for the different versions of LVI. These results indicated that a smaller sampling 

effort (i.e., fewer lake sections) would not sacrifice the accuracy of LVI for assessing lake 

condition. In addition, these results illustrated the fact that it is not necessary to sample all the 

taxa present in order to obtain a reliable assessment of site condition. 

In contrast, the number of sampling sections used to calculate LVI had a large influence 

on the variability of LVI. One goal of this study was to identify a less intensive sampling 

protocol (i.e., less than 12 sections) in order to reduce the time required for routine field 

assessments. We compared the statistical precision of LVI based on single lake sections and LVI 

based on the composite of two lake sections. We summarized the relative statistical precision of 

the different methods in terms of the number of categories of biological condition that each 

version of the index could reliably detect. We found that collecting plant information from 12 

lake sections was not necessary because data from four replicate samples (using data from 4 of 

the 12 sections) could detect five categories of biological condition. 

Land use can vary in both type and intensity around a lake’s perimeter. In order to 

capture this difference, we defined the lake sampling protocol to be based on samples from four 

sides of the lake. To avoid bias in choosing which lake sections to sample, the starting section is 

randomly selected and the subsequent three sections located in each quadrant around the lake. 
50

RECOMMENDATIONS 

• Define a minimum, standard sampling effort to consist of LVI calculated from four 

quadrants of a lake. 

For the development data set, lakes were “oversampled” in order to determine the appropriate 

sampling effort needed to characterize a lake’s aquatic plant assemblage. Rather than collecting 

data from 12 separate sections of a lake, data from four lake sections was adequate to detect five 

categories of biological condition. This represents approximately one-third of the current 

sampling effort. 

To avoid bias in lake sampling, the first replicate sample should be randomly selected 

from sections 1, 2, or 3. Section 1 is designated as the first section located clockwise from the 

north, and subsequent sections are numbered in a clockwise direction. If section 1 is randomly 

selected, the three additional replicates would form a cross-pattern as seen in Figure 14. 

Figure 14. Diagram showing 12 lake sections and locations for the four replicate samples 

(marked with triangles). 

N

1 

4 

7 

10 
51

• Develop guidelines to identify the dominant taxon within a lake section. 

Data for several lakes did not include information on dominant taxon. Because dominant C of C 

is needed to calculate LVI, each lake sample should include this information. Criteria should be 

specified for defining dominance and co-dominance and future lake training should emphasize 

the best way to estimate this metric. LVI based on 3 metrics was somewhat more variable than 

the index based on 4 metrics; therefore, simply excluding this metric when dominance is difficult 

to determine would not be the preferred solution. 

Data from different seasons indicate that the dominant taxon can change during the year 

even in the absence of changes in human influence. When different taxa dominate on opposite 

sides of the lake, simple averaging is the best approach for calculating this metric. 

• Evaluate other sources of temporal variability for LVI. 

Estimates of the number of categories of biological condition that the LVI can detect were based 

on same-day sampling. Data were not available to estimate the influence of annual variability. 

Thus, the variance may be underestimated and the number of categories of biological condition 

exaggerated because not all sources of natural variability (e.g., annual or seasonal differences) 

could be adequately assessed or included for LVI. (See Appendix 6 for more recent estimates of 

annual variability.) 

To estimate annual variability, a subset of 20–30 lakes should be resampled. Lakes 

selected for resampling should represent a range of conditions (from low to high HDG values). 

In addition, lakes should be selected for which little or no change in human influence is expected 

between sampling visits. Human influence may be high, but it should be consistent from one 

year to the next so that natural variability can be measured. Estimates of annual variance are 

needed to define the sampling design for trend monitoring and to determine the amount of 

change that can be detected over time (Larsen et al., 1995; Urquhart et al., 1998). 

Repeat lake visits for 15 lakes sampled during the same year indicated that LVI differed 

according to season for some lakes, but the direction of the difference was not consistent across 

lakes. One approach to avoid differences associated with seasons would be to restrict sampling to 

a single season, e.g., spring, when many taxa are blooming and more easily identifiable. 
52

However, for the LVI to be adopted as a useful management tool, it must be available for much 

of the year. Consequently, a better approach would be to continue to carefully evaluate the 

influence of seasonal differences on LVI values, particularly for the peninsula which had no 

repeat visits in this data set. 

• Evaluate metric scoring rules as more data become available. 

Metric scoring for percent invasive taxa was adjusted to reflect lower metric values in the 

peninsula region of the state. For the northeast region, metric differences could not be evaluated 

because only four lakes were sampled. The three state regions were based on riverine drainage 

patterns, and other variables, e.g., latitude or freeze-line, may be more appropriate for setting 

metric expectations for plants in lakes. For this study, lakes in the far southern section of the 

state (26–27o latitude) had somewhat lower LVI values than predicted by HDG and lakes in this 

area should be assessed for consistency as more data become available. 

• Reevaluate sensitive and tolerant designations of plant taxa. 

For this study, 95 lakes did not provide sufficient data to define a list of sensitive and tolerant 

taxa for lake plants, primarily because most plants were somewhat rare (61% of taxa occurred in 

< 5 lakes). Consequently, we used sensitive and tolerant designations based on the CC values 

derived from expert opinion. One concern is that these values were developed for wetlands, not 

specifically for lakes. 

For wetlands, expert opinion and field data showed a stronger correlation than for lakes. 

When CC values were compared to the average HDG for lakes (LDI for wetlands) in which each 

taxon was found, agreement was much better for wetlands than lakes (r2 = 0.18 for lakes vs. 0.54 

for wetlands; Cohen et al., 2004). Although the percent sensitive and tolerant taxa metrics were 

highly correlated with HDG as currently defined, a larger data set (~300–400 lakes), would allow 

a better list of sensitive and tolerant taxa to be defined specifically for lakes. 
53

CONCLUSIONS 

The different types of plants found in Florida lakes were strongly associated with lake condition 

and the level of human disturbance observed around the lake perimeter. Ten metrics were highly 

correlated with independent measures of human disturbance, and four were selected for inclusion 

in the final Lake Vegetation Index (% native taxa, % invasive taxa, % sensitive taxa, and 

dominant C of C). Based on an independent set of lakes not used for the original development or 

testing of metrics, the LVI was highly correlated with measures of disturbance related to water 

chemistry, habitat condition and land use intensity. LVI values derived from repeat samples 

within a lake varied little, and four replicate samples within a lake would be sufficient to detect 

five categories of biological condition. 

Additional work remains to be done as more lake data become available, but changes to 

the LVI will be minimal. Tolerant and sensitive taxa designations based on professional 

judgment need to be confirmed for specific taxa using field data and some designations for 

individual taxa may change. Small adjustments in the metric scoring rules may be needed for 

southern and northeastern lakes. Better estimates of within-year and annual variability of LVI 

values are also needed to define the sensitivity of LVI through time. 

The primary goal of this study was to test the feasibility of using plants as biological 

indicators. Results reported here confirm that simple measures of the plant assemblage can 

provide a reliable and meaningful biological assessment of lake condition. 
54

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APPENDIX 1. AQUATIC PLANT ATTRIBUTES Shown are lake plant taxa names sorted alphabetically by phylum, class, and order (not shown), taxonomic famil

is listed as a category 1 or 2 invasive by the Florida Exotic Pest Plant Council, whether the plant is herbaceous or (GR_1: F/H = forb/herb, G = graminoid, NV = nonvascular, SH = shrub, SSH = subshrub, T = tree, V = vine; GR

= floating, O = other, S = submersed), whether the plant is listed as threatened or endangered, reproductive groupduration of life cycle (A = annual, B = biennial, P = perennial), nativity, and CC score. 

Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Acer rubrum Aceraceae W T O A_dicot FACW P Agalinis filifolia Scrophulariaceae H F/H E A_dicot Upland A Agalinis linifolia Scrophulariaceae H F/H E A_dicot OBL P Agarista populifolia Ericaceae W SH O A_dicot OBL P Alternanthera philoxeroides Amaranthaceae Cat2 H F/H E A_dicot OBL P Amorpha fruticosa Leguminosae H SH O A_dicot FACW P Ampelopsis arborea Vitaceae W V O A_dicot FAC P Amphicarpum muhlenbergianum Poaceae H G E A_Mono FACW P Andropogon Poaceae H G E A_Mono P Andropogon glomeratus Poaceae H G E A_Mono FACW P Andropogon gyrans stenophyllus Poaceae H G E A_Mono FAC P Andropogon virginicus Poaceae H G E A_Mono FAC P Annona glabra Annonaceae W T O A_dicot OBL P Arundinaria Poaceae W SSH E A_Mono FACW P Arundinaria gigantea Poaceae W SSH E A_Mono FACW P Arundo donax Poaceae H SSH E A_Mono FAC P Aster Asteraceae A_dicot Aster carolinianus Asteraceae H SSH E A_dicot OBL P Aster elliottii Asteraceae H F/H E A_dicot OBL P Aster pilosus Asteraceae H F/H E A_dicot FAC P Aster subulatus Asteraceae H F/H E A_dicot OBL P Aster tortifolius Asteraceae H F/H A_dicot P Axonopus Poaceae H G E A_Mono Azolla caroliniana Azollaceae H F/H F Fern OBL A Baccharis Asteraceae A_dicot P Baccharis angustifolia Asteraceae SH O A_dicot OBL P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Baccharis glomeruliflora Asteraceae W SH O A_dicot FAC P Baccharis halimifolia Asteraceae W SH O A_dicot FAC P Bacopa caroliniana Scrophulariaceae H F/H E A_dicot OBL P Bacopa monnieri Scrophulariaceae H F/H E A_dicot OBL P Bambusa Poaceae W A_Mono Bidens Asteraceae H F/H E A_dicot Bidens alba Asteraceae H F/H E A_dicot FAC AP Bidens laevis Asteraceae H F/H E A_dicot OBL AP Bidens mitis Asteraceae H F/H E A_dicot OBL A Bidens pilosa Asteraceae H F/H E A_dicot FACW A Blechnum Blechnaceae E Fern Blechnum serrulatum Blechnaceae H F/H E Fern FACW P Boehmeria cylindrica Urticaceae H F/H E A_dicot OBL P Brachiaria Poaceae H G E A_Mono FACW Brachiaria mutica Poaceae Cat1 H G E A_Mono FACW P Brasenia schreberi Nymphaeaceae H F/H F A_dicot OBL P Cabomba caroliniana Cabombaceae H F/H S A_dicot OBL P Callistemon viminale Myrtaceae W SH O A_dicot Callitriche Callitrichaceae A_dicot Canna Cannaceae E A_Mono Canna flaccida Cannaceae H F/H E A_Mono OBL P Carex Cyperaceae H G E A_Mono Carya aquatica Juglandaceae W T O A_dicot OBL P Cassia Leguminosae H F/H E A_dicot Casuarina Casuarinaceae W A_dicot Casuarina equisetifolia Casuarinaceae Cat1 W T O A_dicot Upland P Catalpa Bignoniaceae A_dicot Centella asiatica Umbelliferae H F/H E A_dicot FACW P Cephalanthus occidentalis Rubiaceae W SH O A_dicot OBL P Ceratophyllum demersum Ceratophyllaceae H F/H S A_dicot OBL P Ceratopteris thalictroides Parkeriaceae H F/H E Fern OBL AP Chamaecyparis thyoides Cupressaceae W T O Gymno OBL P Chara Characeae NV S Algae Chasmanthium laxum Poaceae H G E A_Mono FACW P Cicuta maculata Umbelliferae H F/H E A_dicot OBL PB Cinnamomum camphora Lauraceae Cat1 W T O A_dicot Upland P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Cladium jamaicense Cyperaceae H G E A_Mono OBL P Coelorachis Poaceae Colocasia esculenta Araceae Cat1 H F/H E A_Mono OBL P Commelina Commelinaceae H F/H E A_Mono FACW Commelina diffusa Commelinaceae H F/H E A_Mono FACW AP Commelina erecta Commelinaceae H F/H E A_Mono FACW P Conoclinium coelestinum Asteraceae H F/H E A_dicot FAC P Coreopsis gladiata Asteraceae H F/H E A_dicot FACW P Cortaderia selloana Poaceae G A_Mono P Crinum americanum Liliaceae H F/H E A_Mono OBL P Crotalaria pallida Fabaceae W SSH E A_dicot P Crotalaria spectabilis Fabaceae F/H E A_dicot A Cyperaceae Cyperaceae Cyperus Cyperaceae H G E A_Mono Cyperus alternifolius Cyperaceae Cat2 H G E A_Mono OBL P Cyperus articulatus Cyperaceae H G E A_Mono OBL P Cyperus croceus Cyperaceae H G E A_Mono FAC AP Cyperus difformis Cyperaceae H G E A_Mono OBL A Cyperus distinctus Cyperaceae H G E A_Mono OBL P Cyperus erythrorhizos Cyperaceae H G E A_Mono OBL AP Cyperus esculentus Cyperaceae H G E A_Mono FAC P Cyperus haspan Cyperaceae H G E A_Mono OBL P Cyperus lecontei Cyperaceae H G E A_Mono FACW P Cyperus ligularis Cyperaceae H G E A_Mono FACW P Cyperus odoratus Cyperaceae H G E A_Mono FACW AP Cyperus papyrus Cyperaceae H G E A_Mono OBL P Cyperus polystachyos Cyperaceae H G E A_Mono FACW AP Cyperus retrorsus Cyperaceae H G E A_Mono FAC P Cyperus rotundus Cyperaceae H G E A_Mono FAC P Cyperus surinamensis Cyperaceae H G E A_Mono FACW P Cyrilla racemiflora Cyrillaceae W T O A_dicot FAC P Decodon verticillatus Lythraceae W SH O A_dicot OBL P Dichromena colorata Cyperaceae H G E A_Mono FACW P Diodia virginiana Rubiaceae H F/H E A_dicot FACW AP Dioscorea bulbifera Dioscoreaceae Cat1 V O A_Mono P Diospyros virginiana Ebenaceae W T O A_dicot FAC P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Drosera Droseraceae H A_dicot Drosera capillaris Droseraceae H F/H E A_dicot FACW AP Drosera intermedia Droseraceae H F/H E T A_dicot OBL P Echinochloa Poaceae H A_Mono Echinochloa crusgalli Poaceae H G E A_Mono FACW A Echinochloa walteri Poaceae H G E A_Mono FACW A Eclipta prostrata Asteraceae H F/H E A_dicot FACW AP Eichhornia crassipes Pontederiaceae Cat1 H F/H F A_Mono OBL P Eleocharis Cyperaceae H G E A_Mono OBL Eleocharis baldwinii Cyperaceae H G E A_Mono OBL P Eleocharis cellulosa Cyperaceae H G E A_Mono OBL P Eleocharis elongata Cyperaceae H G E A_Mono OBL P Eleocharis equisetoides Cyperaceae H G E A_Mono OBL P Eleocharis flavescens Cyperaceae H G E A_Mono OBL P Eleocharis interstincta Cyperaceae H G E A_Mono OBL P Eleocharis montana Cyperaceae H G E A_Mono OBL P Eleocharis robbinsii Cyperaceae H G E A_Mono OBL P Eleocharis vivipara Cyperaceae H G E A_Mono OBL A Eragrostis Poaceae E A_Mono Erechtites hieracifolia Asteraceae H F/H E A_dicot FAC A Erianthus giganteus Poaceae H G E A_Mono OBL P Erigeron quercifolius Asteraceae H F/H E A_dicot FAC A Eriocaulon Eriocaulaceae H F/H E A_Mono OBL PB Eriocaulon decangulare Eriocaulaceae H F/H E A_Mono OBL P Eupatorium Asteraceae H F/H E A_dicot P Eupatorium capillifolium Asteraceae H F/H E A_dicot Upland P Eupatorium leptophyllum Asteraceae H F/H E A_dicot OBL P Eupatorium mikanioides Asteraceae H F/H E A_dicot FACW P Eustachys petraea Poaceae H G E A_Mono FAC P Euthamia tenuifolia tenuifolia Asteraceae H F/H E A_dicot FAC P Ficus aurea Moraceae W V O A_dicot FAC P Fimbristylis castanea Cyperaceae H G E A_Mono OBL P Fraxinus americana Oleaceae W T O A_dicot Upland P Fraxinus caroliniana Oleaceae W T O A_dicot OBL P Froelichia floridana Amaranthaceae H F/H O A_dicot A Fuirena Cyperaceae H G E A_Mono 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Fuirena breviseta Cyperaceae H G E A_Mono OBL P Fuirena pumila Cyperaceae H G E A_Mono OBL A Fuirena scirpoidea Cyperaceae H G E A_Mono OBL P Fuirena squarrosa Cyperaceae H G E A_Mono OBL P Gleditsia triacanthus Fabaceae W T O A_dicot FACW P Gordonia lasianthus Theaceae W T O A_dicot FACW P Habenaria repens Orchidaceae H F/H E A_Mono FACW P Hedyotis Rubiaceae Hibiscus coccineus Malvaceae H F/H E A_dicot OBL P Hibiscus furcellatus Malvaceae SSH E A_dicot P Hibiscus grandiflorus Malvaceae H F/H O A_dicot OBL P Hydrilla verticillata Hydrocharitaceae Cat1 H F/H S A_Mono OBL P Hydrocotyle Umbelliferae A_dicot Hydrocotyle umbellata Umbelliferae H F/H E A_dicot FACW P Hydrolea corymbosa Hydrophyllaceae H F/H E A_dicot OBL P Hydrolea quadrivalvis Hydrophyllaceae H F/H E A_dicot OBL P Hypericum Clusiaceae A_dicot Hypericum cistifolium Clusiaceae W SH O A_dicot FACW P Hypericum fasciculatum Clusiaceae W SH O A_dicot OBL P Hypericum hypericoides Clusiaceae W SH O A_dicot FAC P Hypericum lissophloeus Clusiaceae W SH O E A_dicot OBL P Hyptis alata Lamiaceae H F/H E A_dicot FACW P Ilex cassine Aquifoliaceae W T O A_dicot OBL P Ilex coriacea Aquifoliaceae W T O A_dicot FACW P Ilex glabra Aquifoliaceae W SH O A_dicot FACW P Ilex myrtifolia Aquifoliaceae W SH O A_dicot OBL P Ilex vomitoria Aquifoliaceae W T O A_dicot FAC P Indigofera hirsuta Leguminosae H F/H E A_dicot A Ipomoea sagittata Convolvulaceae H V O A_dicot P Iris hexagona Iridaceae H F/H E A_Mono OBL P Iris virginica Iridaceae H F/H E A_Mono OBL P Itea virginica Saxifragaceae W SH O A_dicot OBL P Iva microcephala Asteraceae H F/H E A_dicot FACW A Juncus Juncaceae H G A_Mono Juncus effusus Juncaceae H G E A_Mono OBL P Juncus marginatus Juncaceae H G E A_Mono FACW P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Juncus megacephalus Juncaceae H G E A_Mono OBL P Juncus scirpoides Juncaceae H G E A_Mono OBL P Justicia Acanthaceae A_dicot Kosteletzkya Malvaceae A_dicot Kosteletzkya virginica Malvaceae H F/H E A_dicot OBL P Lachnanthes caroliana Haemodoraceae H F/H E A_Mono FAC P Lachnocaulon anceps Eriocaulaceae H F/H E A_Mono FACW P Lachnocaulon minus Eriocaulaceae H F/H E A_Mono OBL P Leersia hexandra Poaceae H G E A_Mono OBL P Lemna Lemnaceae H F/H F A_Mono OBL Lemna minor Lemnaceae H F/H F A_Mono OBL P Leucothoe racemosa Ericaceae W SH O A_dicot FACW P Liatris Asteraceae Ligustrum sinense Oleaceae Cat1 W SH O A_dicot FAC P Limnobium spongia Hydrocharitaceae H F/H F A_Mono OBL P Limnophila sessiliflora Scrophulariaceae Cat2 H F/H E A_dicot OBL P Lindernia Scrophulariaceae A_dicot Liquidambar styraciflua Hamamelidaceae W T O A_dicot FACW P Liriodendron tulipifera Magnoliaceae W T O A_dicot FAC P Ludwigia Onagraceae H F/H E A_dicot Ludwigia alata Onagraceae H F/H E A_dicot OBL P Ludwigia alternifolia Onagraceae H F/H E A_dicot OBL P Ludwigia arcuata Onagraceae H F/H E A_dicot OBL P Ludwigia decurrens Onagraceae H F/H E A_dicot OBL AP Ludwigia grandiflora Onagraceae H SSH E A_dicot OBL P Ludwigia leptocarpa Onagraceae H F/H E A_dicot OBL AP Ludwigia linifolia Onagraceae H F/H E A_dicot OBL P Ludwigia octovalvis Onagraceae H F/H E A_dicot OBL P Ludwigia peruviana Onagraceae H F/H E A_dicot OBL P Ludwigia repens Onagraceae H F/H E A_dicot OBL P Ludwigia sphaerocarpa Onagraceae H F/H E A_dicot OBL P Ludwigia suffruticosa Onagraceae H F/H E A_dicot FACW P Luziola fluitans Poaceae H G E A_Mono OBL P Lycopodiella alopecuroides Lycopodiaceae W SSH E Fern OBL P Lycopus Lamiaceae H F/H E A_dicot P Lycopus amplectens Lamiaceae H F/H E A_dicot OBL P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Lycopus virginicus Lamiaceae H F/H E A_dicot OBL P Lygodium microphyllum Schizaeaceae Cat1 H V O Fern P Lyonia Ericaceae A_dicot Lyonia lucida Ericaceae W SH O A_dicot FACW P Magnolia grandiflora Magnoliaceae W T O A_dicot FAC P Magnolia virginiana Magnoliaceae W T O A_dicot FACW P Mayaca fluviatilis Mayacaceae H F/H S A_Mono OBL P Melaleuca Myrtaceae W T O A_dicot Melaleuca quinquenervia Myrtaceae Cat1 W T O A_dicot FAC P Melia azedarach Meliaceae Cat1 W T O A_dicot P Micranthemum glomeratum Scrophulariaceae H F/H E A_dicot OBL P Micranthemum umbrosum Scrophulariaceae H F/H E A_dicot OBL AP Mikania scandens Asteraceae H V O A_dicot FACW P Mimosa pigra Leguminosae Cat1 W T O A_dicot FAC P Muhlenbergia capillaris Poaceae H G E A_Mono Upland P Musa Musaceae E A_Mono Musa sapientum Musaceae T O A_Mono Upland P Myrica cerifera Myricaceae W SH O A_dicot FAC P Myriophyllum Haloragaceae H F/H S A_dicot OBL P Myriophyllum aquaticum Haloragaceae H F/H S A_dicot OBL P Myriophyllum heterophyllum Haloragaceae H F/H S A_dicot OBL P Najas gracillima Najadaceae H F/H S A_Mono OBL A Najas guadalupensis Najadaceae H F/H S A_Mono OBL A Najas minor Najadaceae H F/H S A_Mono OBL A Nelumbo Nelumbonaceae H A_dicot Nelumbo lutea Nelumbonaceae H F/H F A_dicot OBL P Nephrolepis Polypodiaceae Nitella Characeae NV S Algae Nuphar luteum Nymphaeaceae H F/H F A_dicot OBL P Nymphaea Nymphaeaceae H A_dicot Nymphaea mexicana Nymphaeaceae H F/H F A_dicot OBL P Nymphaea odorata Nymphaeaceae H F/H F A_dicot OBL P Nymphoides aquatica Menyanthaceae H F/H F A_dicot OBL P Nyssa sylvatica biflora Cornaceae W T O A_dicot OBL P Osmunda Osmundaceae E Fern Osmunda cinnamomea Osmundaceae H F/H E Fern FACW P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Osmunda regalis Osmundaceae H F/H E Fern OBL P Ostrya virginiana Betulaceae W T O A_dicot Upland P Panicum Poaceae H G E A_Mono Panicum dichotomiflorum Poaceae H G E A_Mono FACW A Panicum hemitomon Poaceae H G E A_Mono OBL P Panicum repens Poaceae Cat1 H G E A_Mono FACW P Panicum rigidulum Poaceae H G E A_Mono FACW P Panicum rigidulum pubesens Poaceae H G E A_Mono OBL P Panicum scabriusculum Poaceae H G E A_Mono OBL P Panicum tenerum Poaceae H G E A_Mono OBL P Panicum virgatum Poaceae H G E A_Mono FACW P Paspalidium geminatum Poaceae H G E A_Mono OBL P Paspalum Poaceae H G E A_Mono Paspalum distichum Poaceae H G E A_Mono OBL P Paspalum floridanum Poaceae H G E A_Mono FACW P Paspalum repens Poaceae H G E A_Mono OBL A Paspalum urvillei Poaceae H G E A_Mono FAC P Peltandra Araceae H F/H E A_Mono OBL P Peltandra virginica Araceae H F/H E A_Mono OBL P Pennisetum purpureum Poaceae Cat1 H G E A_Mono FAC P Persea borbonia Lauraceae W T O A_dicot FACW P Persea borbonia humilis Lauraceae W SH O A_dicot P Persea palustris Lauraceae W T O A_dicot OBL P Phanopyrum gymnocarpon Poaceae H G A_Mono OBL P Phragmites australis Poaceae H F/H E A_Mono OBL P Phyla nodiflora Verbenaceae H F/H E A_dicot FAC P Pinus Pinaceae W T O Gymno Pinus clausa Pinaceae W T O Gymno FACW P Pinus elliottii Pinaceae W T O Gymno P Pinus palustris Pinaceae W T O Gymno P Pistia stratiotes Araceae Cat1 H F/H F A_Mono OBL P Pluchea Asteraceae H F/H E A_dicot FACW Pluchea foetida Asteraceae H F/H E A_dicot FACW P Pluchea odorata Asteraceae H F/H E A_dicot FACW AP Pluchea rosea Asteraceae H F/H E A_dicot FACW P Poaceae Poaceae 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Polygonum Polygonaceae H F/H E A_dicot Polygonum densiflorum Polygonaceae H F/H E A_dicot OBL AP Polygonum hydropiperoides Polygonaceae H F/H E A_dicot OBL P Polygonum punctatum Polygonaceae H F/H E A_dicot OBL AP Polypogon monspeliensis Poaceae H G E A_Mono FAC A Pontederia cordata Pontederiaceae H F/H E A_Mono OBL P Pontederia rotundifolia Pontederiaceae H F/H E A_Mono P Potamogeton diversifolius Potamogetonaceae H F/H S A_Mono OBL P Potamogeton illinoensis Potamogetonaceae H F/H S A_Mono OBL P Potamogeton pectinatus Potamogetonaceae H F/H S A_Mono OBL P Potamogeton pusillus Potamogetonaceae H F/H S A_Mono OBL P Quercus Fagaceae W T O A_dicot P Quercus laurifolia Fagaceae W T O A_dicot FACW P Quercus nigra Fagaceae W T O A_dicot FACW P Rhexia Melastomataceae H F/H E A_dicot FACW P Rhexia cubensis Melastomataceae H F/H E A_dicot FACW P Rhexia mariana Melastomataceae H F/H E A_dicot FACW P Rhexia nashii Melastomataceae H F/H E A_dicot FACW P Rhexia virginica Melastomataceae H F/H E A_dicot FACW P Rhus copallinum Anacardiaceae W SH O A_dicot P Rhynchospora Cyperaceae H G E A_Mono Rhynchospora baldwinii Cyperaceae H G E A_Mono OBL AP Rhynchospora cephalantha Cyperaceae H G E A_Mono OBL P Rhynchospora fascicularis Cyperaceae H G E A_Mono FACW P Rhynchospora globularis Cyperaceae H G E A_Mono FACW AP Rhynchospora inundata Cyperaceae H G E A_Mono OBL P Rhynchospora microcarpa Cyperaceae H G E A_Mono OBL P Rhynchospora microcephala Cyperaceae H G E A_Mono FACW P Rhynchospora nitens Cyperaceae H G E A_Mono OBL AP Rhynchospora scirpoides Cyperaceae H G E A_Mono OBL A Rhynchospora tracyi Cyperaceae H G E A_Mono OBL P Rubus Rosaceae H SH O A_dicot P Rubus argutus Rosaceae H SH O A_dicot FAC P Rubus trivialis Rosaceae H SH O A_dicot FAC P Sabal palmetto Palmae W T O A_Mono FAC P Sabatia grandiflora Gentianaceae H F/H E A_dicot FACW A 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Saccharum Poaceae H G E A_Mono Sacciolepis striata Poaceae H G E A_Mono OBL P Sagittaria Alismataceae H F/H A_Mono OBL P Sagittaria graminea Alismataceae H F/H E A_Mono OBL P Sagittaria kurziana Alismataceae H F/H S A_Mono OBL P Sagittaria lancifolia Alismataceae H F/H E A_Mono OBL P Sagittaria latifolia Alismataceae H F/H E A_Mono OBL P Sagittaria subulata Alismataceae H F/H E A_Mono OBL P Salix Salicaceae W A_dicot Salix babylonica Salicaceae W T O A_dicot OBL P Salix caroliniana Salicaceae W T O A_dicot OBL P Salix eriocephala Salicaceae W T O E A_dicot FACW P Salix floridana Salicaceae W T O E A_dicot FACW P Salvinia minima Salviniaceae H F/H F Fern OBL AP Sambucus Caprifoliaceae A_dicot Sambucus canadensis Caprifoliaceae W SH O A_dicot FAC P Sapium sebiferum Euphorbiaceae Cat1 W T O A_dicot FAC P Sarcostemma clausum Asclepiadaceae H V O A_dicot FACW P Saururus cernuus Saururaceae H F/H E A_dicot OBL P Schinus terebinthifolius Anacardiaceae Cat1 W T O A_dicot FAC P Schizachyrium Poaceae A_Mono Scirpus Cyperaceae H G E A_Mono OBL P Scirpus americanus Cyperaceae H G E A_Mono OBL P Scirpus californicus Cyperaceae H G E A_Mono OBL P Scirpus cubensis Cyperaceae H G E A_Mono OBL P Scirpus cyperinus Cyperaceae H G E A_Mono OBL P Scirpus pungens Cyperaceae H G E A_Mono OBL P Scirpus validus Cyperaceae H G E A_Mono OBL P Scleria Cyperaceae H G E A_Mono Serenoa repens Palmae W SH O A_Mono Upland P Sesbania Fabaceae A_dicot Sesbania herbacea Fabaceae H F/H E A_dicot FAC A Sesbania punicea Fabaceae Cat2 W SH O A_dicot FAC P Setaria magna Poaceae H G E A_Mono FACW A Setaria parviflora Poaceae H G E A_Mono FAC P Smilax Smilacaceae V O A_Mono P 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Smilax laurifolia Smilacaceae V O A_Mono FACW P Smilax pumila Smilacaceae V O A_Mono P Solidago Asteraceae H F/H E A_dicot P Solidago fistulosa Asteraceae H F/H E A_dicot FACW P Solidago leavenworthii Asteraceae H F/H E A_dicot FAC P Solidago stricta Asteraceae H F/H E A_dicot FACW P Spartina alterniflora Poaceae H G E A_Mono OBL P Spartina bakeri Poaceae H G E A_Mono FACW P Spirodela polyrhiza Lemnaceae H F/H F A_Mono OBL P Taxodium ascendens Taxodiaceae W T O Gymno OBL P Taxodium distichum Taxodiaceae W T O Gymno OBL P Thalia geniculata Marantaceae H F/H E A_Mono OBL P Thelypteris Polypodiaceae E Fern Thelypteris interrupta Polypodiaceae H F/H E Fern FACW P Toxicodendron Anacardiaceae A_dicot Toxicodendron radicans Anacardiaceae H V O A_dicot FAC P Toxicodendron vernix Anacardiaceae W T O A_dicot OBL P Tradescantia ohiensis Commelinaceae H F/H E A_Mono FAC P Triadenum virginicum Clusiaceae H F/H E A_dicot OBL P Typha Typhaceae H E A_Mono P Typha domingensis Typhaceae H F/H E A_Mono OBL P Typha latifolia Typhaceae H F/H E A_Mono OBL P Ulmus americana Ulmaceae W T O A_dicot FACW P Urena lobata Malvaceae Cat2 H SSH O A_dicot Upland P Utricularia Lentibulariaceae H S Utricularia cornuta Lentibulariaceae H F/H S A_dicot OBL AP Utricularia floridana Lentibulariaceae H F/H S A_dicot OBL P Utricularia foliosa Lentibulariaceae H F/H S A_dicot OBL P Utricularia gibba Lentibulariaceae H F/H S A_dicot OBL AP Utricularia inflata Lentibulariaceae H F/H S A_dicot OBL P Utricularia olivacea Lentibulariaceae H F/H S A_dicot OBL AP Utricularia purpurea Lentibulariaceae H F/H S A_dicot OBL AP Utricularia radiata Lentibulariaceae H F/H S A_dicot OBL AP Utricularia subulata Lentibulariaceae H F/H S A_dicot OBL AP Vallisneria americana Hydrocharitaceae H F/H S A_Mono OBL P Verbena bonariensis Verbenaceae H F/H E A_dicot FAC APB 
Taxon Family FLEPPC H/W GR_1 GR_2 T&E Repro Wetland Duration Vigna luteola Fabaceae W V O A_dicot FACW P Vitaceae Vitaceae Vitis Vitaceae H V O A_dicot Vitis rotundifolia Vitaceae H V O A_dicot FAC P Websteria confervoides Cyperaceae H G S A_Mono OBL P Wedelia trilobata Asteraceae Cat2 H F/H E A_dicot FAC AP Wolffiella Lemnaceae H F/H F A_Mono OBL P Woodwardia areolata Polypodiaceae H F/H E Fern OBL P Woodwardia virginica Polypodiaceae H F/H E Fern FACW P Xanthosoma sagittifolium Araceae Cat2 H F/H E A_Mono FACW P Xyris Xyridaceae H F/H E A_Mono OBL P Xyris baldwiniana Xyridaceae H F/H E A_Mono OBL P Xyris fimbriata Xyridaceae H F/H E A_Mono OBL P Xyris isoetifolia Xyridaceae H F/H E E A_Mono OBL P Xyris smalliana Xyridaceae H F/H E A_Mono OBL P Zanthoxylum Rutaceae Zizania aquatica Poaceae H G E A_Mono OBL A Zizaniopsis miliacea Poaceae H G E A_Mono OBL P 
69

APPENDIX 2. EPA’S WETLAND PLANT ATTRIBUTES 

Florida taxon name, the taxon’s general tolerance to pollution, nutrients, N, P, flooding, 

sediment, or salinity based on a national wetland database (Adamus and Gonyaw, 2000) (T = 

tolerant, M = moderately tolerant, V = very tolerant, S = sensitive). 

Taxon GenPoll Nutrients N P Flooding Sediment Salinity

Acer rubrum T M T T T S 

Amorpha fruticosa M 

Andropogon virginicus T M M S 

Andropogon M 

Arundinaria gigantea M M S 

Aster pilosus T M M S 

Aster S 

Baccharis halimifolia M M T 

Bacopa monnieri T 

Bidens laevis M M M 

Bidens T 

Boehmeria cylindrica M M M S 

Brasenia schreberi M M M S 

Callitriche T T S 

Carex T T S S 

Carya aquatica T 

Catalpa M 

Cephalanthus occidentalis M M T S 

Ceratophyllum demersum M M T T VT S 

Chamaecyparis thyoides T M S 

Chara S T 

Cicuta maculata T M M S 

Cladium jamaicense T T T 

Conoclinium coelestinum M M S 

Cyperus erythrorhizos M 

Cyperus esculentus T M M S 

Cyperus odoratus M 

Cyperus rotundus T 

Decodon verticillatus M M M S 

Diospyros virginiana T M T T S 

Drosera intermedia S 

Echinochloa crusgalli T M M S 

Echinochloa walteri M 

Eichhornia crassipes M T T VT S 

Eleocharis elongata T 

Eleocharis interstincta T T T 

Eleocharis robbinsii M T 

Eleocharis T M 

Erechtites hieracifolia M M S 

Eupatorium S 

Fraxinus americana M M M S 
70

Taxon GenPoll Nutrients N P Flooding Sediment Salinity

Fraxinus caroliniana T 

Gordonia lasianthus M 

Hydrilla verticillata T S 

Hydrocotyle umbellata S M M S 

Hypericum T S 

Ilex glabra M M S 

Iris virginica M 

Itea virginica M M S 

Juncus effusus T M M M 

Juncus marginatus M M M S 

Juncus S 

Leersia hexandra T 

Lemna minor M M T T T S 

Lemna T M T 

Leucothoe racemosa M M S 

Liquidambar styraciflua M T S 

Liriodendron tulipifera M M T S 

Ludwigia alternifolia M M M S 

Lycopus virginicus M M M S 

Lyonia lucida M M S 

Magnolia grandiflora M 

Magnolia virginiana M M S 

Melaleuca quinquenervia T 

Mikania scandens M T S 

Mimosa pigra T 

Myrica cerifera M M S 

Myriophyllum aquaticum VT T T S 

Myriophyllum 

heterophyllum 

S T 

Najas gracillima M 

Najas guadalupensis M 

Najas minor VT M T S 

Nelumbo lutea T M S 

Nuphar luteum M T 

Nymphaea odorata M M M S 

Osmunda cinnamomea M M M S 

Osmunda regalis M M M S 

Ostrya virginiana M M M S 

Panicum dichotomiflorum M 

Panicum hemitomon T 

Panicum virgatum M M M M 

Panicum T S 

Peltandra virginica M M T T M S 

Persea borbonia M 

Phragmites australis T VT T M S M 

Pinus elliottii S 

Polygonum densiflorum VT 

Polygonum 

hydropiperoides 

M M T S 
71

Taxon GenPoll Nutrients N P Flooding Sediment Salinity

Polygonum punctatum M M M M 

Pontederia cordata M M M S 

Potamogeton diversifolius M 

Potamogeton pectinatus T T T T T T 

Potamogeton pusillus T 

Quercus laurifolia M 

Quercus nigra T M T S 

Rhexia mariana M M S 

Rhexia virginica M M S 

Rhus copallinum S 

Rhynchospora inundata M M S 

Rubus S 

Sagittaria graminea M 

Sagittaria lancifolia T T 

Sagittaria latifolia M M M S 

Sagittaria M M S 

Salix babylonica VT M M S 

Salix eriocephala M 

Salix VT 

Sambucus canadensis M M M S S 

Sapium sebiferum VT 

Saururus cernuus M M M S 

Scirpus americanus M M T T 

Scirpus cyperinus M M M S 

Scirpus pungens M 

Scirpus validus M T M 

Solidago M 

Spartina alterniflora M T T T 

Spirodela polyrhiza T M T S 

Taxodium distichum M T S 

Toxicodendron vernix M M S 

Tradescantia ohiensis M 

Triadenum virginicum M M M S 

Typha domingensis T T T 

Typha latifolia T M T T T S 

Typha M T T M S 

Ulmus americana M M T S 

Utricularia cornuta T M S 

Utricularia gibba M 

Utricularia inflata M T S 

Utricularia purpurea M T S 

Utricularia subulata T 

Vallisneria americana M M T S S 

Vitis rotundifolia M M S 

Woodwardia areolata M M M S 

Woodwardia virginica S M M S 

Xyris smalliana T 

Xyris S 

Zizania aquatica M M T S 
APPENDIX 3. RESULTS OF TESTING FOR TOLERANCE AND SENSITIVITY 

List of lake taxa that occurred in >= 4 lakes (out of 95). Shown for each taxon are CC value, 

whether the taxon was defined as a sensitive or tolerant taxa on the basis of CC scores, number 

of lakes in which taxon was found, number of occurrences that were in minimally disturbed lakes 

(HDG < 3), whether the association with HDG was significant (χ2

, * p < 0.05, * * p < 0.01), and 

whether the association was in the direction predicted. 

Taxon CC Tol/Sens # Occur # Good Significant Correct 

Bidens laevis 7.19 S 7 5 

Blechnum serrulatum 7.15 S 13 6 

Cephalanthus occidentalis 7.27 S 51 25 * yes 

Cladium jamaicense 9.04 S 22 15 * * yes 

Crinum americanum 8.67 S 5 2 

Decodon verticillatus 7.80 S 12 10 * * yes 

Eleocharis cellulosa 7.80 S 13 3 

Eleocharis interstincta 7.80 S 14 4 

Gordonia lasianthus 9.03 S 11 7 

Hypericum fasciculatum 7.27 S 13 10 * * yes 

Ilex cassine 7.66 S 37 18 

Itea virginica 7.09 S 9 6 

Lyonia lucida 7.06 S 8 7 * * yes 

Magnolia virginiana 9.44 S 42 18 

Mayaca fluviatilis 8.45 S 19 12 * yes 

Nitella 7.28 S 11 2 

Nymphaea odorata 7.18 S 43 22 * yes 

Nyssa sylvatica biflora 9.04 S 30 13 

Osmunda regalis 8.04 S 7 4 

Persea palustris 8.31 S 10 4 

Potamogeton diversifolius 7.15 S 4 1 

Rhynchospora inundata 7.25 S 8 5 

Saururus cernuus 7.33 S 8 4 

Serenoa repens 7.03 S 4 3 

Taxodium ascendens 7.21 S 39 17 

Taxodium distichum 7.21 S 34 9 

Thalia geniculata 7.12 S 5 1 

Triadenum virginicum 8.16 S 17 11 * yes 

Vallisneria americana 7.28 S 18 2 * no 

Woodwardia areolata 7.68 S 4 3 

Alternanthera philoxeroides 0.00 T 49 10 * * yes 

Azolla caroliniana 1.81 T 6 2 

Brachiaria mutica 0 T 18 2 * yes 

Centella asiatica 1.92 T 11 6 

Colocasia esculenta 0 T 40 7 * * yes 

Cyperus alternifolius 1.11 T 9 1 

Cyperus polystachyos 1.56 T 9 3 

Cyperus surinamensis 2.03 T 14 1 * yes 
73

Taxon CC Tol/Sens # Occur # Good Significant Correct 

Eichhornia crassipes 0.00 T 25 10 

Eleocharis baldwinii 2.82 T 50 23 

Eupatorium capillifolium 0.83 T 59 20 

Hydrilla verticillata 0.00 T 19 1 * * yes 

Hydrocotyle umbellata 1.92 T 62 20 

Ludwigia peruviana 0.62 T 42 10 * yes 

Melaleuca quinquenervia 0.00 T 20 6 

Mikania scandens 1.95 T 65 16 * * yes 

Myriophyllum aquaticum 0.98 T 5 0 

Panicum repens 0 T 73 23 * yes 

Pistia stratiotes 0 T 15 1 * yes 

Salix caroliniana 2.95 T 61 19 

Salvinia minima 2.03 T 34 9 

Sambucus canadensis 1.48 T 12 1 

Sapium sebiferum 0 T 21 3 * yes 

Schinus terebinthifolius 0.00 T 34 6 * * yes 

Typha domingensis 0.59 T 15 4 

Typha latifolia 1.60 T 55 16 

Vitis rotundifolia 1.18 T 16 6 

Wedelia trilobata 0 T 9 1 

Acer rubrum 4.65 67 24 

Andropogon na 11 7 

Andropogon glomeratus 3.90 20 8 

Andropogon virginicus 3.44 9 5 

Aster na 4 2 

Aster carolinianus na 6 2 

Aster subulatus 5.74 5 3 

Baccharis na 6 2 

Baccharis glomeruliflora 6.12 16 6 

Bacopa caroliniana 5.31 9 6 

Bacopa monnieri 4.49 11 3 

Bidens na 25 10 

Bidens mitis 6.31 5 4 

Blechnum na 7 2 

Boehmeria cylindrica 5.91 19 8 

Cabomba caroliniana 5.07 9 7 * unknown 

Canna flaccida 6.75 7 1 

Casuarina equisetifolia 0 4 0 

Ceratophyllum demersum 4.16 7 1 

Chara 3.90 15 2 

Cicuta maculata na 5 2 

Cyperus na 23 5 

Cyperus haspan 5.68 17 7 

Cyperus lecontei na 8 3 

Cyperus odoratus 4.25 27 3 * * unknown 

Cyrilla racemiflora 5.20 14 8 

Echinochloa na 5 0 

Echinochloa walteri 3.36 5 3 

Eleocharis na 9 3 
74

Taxon CC Tol/Sens # Occur # Good Significant Correct 

Eriocaulon na 7 5 

Eupatorium na 14 6 

Eupatorium leptophyllum 4.94 4 2 

Fuirena na 7 2 

Fuirena scirpoidea 6.50 21 9 

Habenaria repens 4.58 6 4 

Hydrocotyle na 17 5 

Hypericum na 6 5 

Juncus na 7 5 

Juncus effusus 3.25 12 6 

Juncus marginatus 3.65 5 0 

Juncus megacephalus 5.70 4 3 

Lachnanthes caroliana 3.76 24 16 * * unknown 

Leersia hexandra 5.61 8 5 

Lemna minor 3.77 19 6 

Limnobium spongia 4.79 5 3 

Liquidambar styraciflua 5.56 13 7 

Ludwigia na 25 10 

Ludwigia alata 5.85 4 4 * yes 

Ludwigia arcuata 5.32 6 3 

Ludwigia grandiflora na 7 2 

Ludwigia leptocarpa 3.47 25 5 

Ludwigia octovalvis 4.09 39 10 

Ludwigia repens 5.20 9 4 

Ludwigia suffruticosa 6.23 6 5 

Luziola fluitans 4.79 10 6 

Lycopus na 4 2 

Lycopus virginicus na 5 3 

Magnolia grandiflora na 8 6 

Melaleuca na 7 0 

Micranthemum umbrosum 5.66 7 2 

Musa na 5 0 

Myrica cerifera 3.82 76 28 

Myriophyllum heterophyllum 4.77 4 3 

Najas guadalupensis 5.07 7 2 

Nelumbo lutea 6.26 8 3 

Nuphar luteum 4.64 63 25 

Nymphoides aquatica 6.09 17 13 * * yes 

Osmunda cinnamomea 6.44 9 4 

Panicum na 5 3 

Panicum hemitomon 5.82 70 28 

Paspalidium geminatum 6.36 9 1 

Paspalum na 4 2 

Paspalum repens 6.69 6 1 

Phragmites australis 4.39 15 4 

Pinus elliottii 4.21 26 13 

Pluchea na 7 2 

Pluchea foetida 6.65 5 3 

Pluchea odorata 4.96 8 3 
75

Taxon CC Tol/Sens # Occur # Good Significant Correct 

Pluchea rosea 5.45 8 5 

Polygonum na 12 4 

Polygonum densiflorum 5.32 21 5 

Polygonum hydropiperoides 4.02 23 9 

Polygonum punctatum 4.02 6 2 

Pontederia cordata 5.38 64 25 

Potamogeton illinoensis 6.64 8 1 

Quercus laurifolia 5.14 9 1 

Quercus nigra 4.14 4 1 

Rhynchospora na 10 5 

Rhynchospora nitens 5.20 5 2 

Rubus na 4 3 

Sabal palmetto 4.85 9 2 

Sacciolepis striata 5.35 40 15 

Sagittaria lancifolia 4.96 44 16 

Sagittaria latifolia 6.50 14 4 

Sagittaria subulata na 4 2 

Salix na 10 3 

Sambucus na 7 1 

Sarcostemma clausum 3.81 9 2 

Scirpus californicus 6.01 10 3 

Scirpus cubensis 3.77 15 3 

Scirpus cyperinus na 4 4 * unknown 

Scirpus validus 5.55 10 1 

Sesbania na 5 2 

Solidago na 17 12 * * unknown 

Spartina bakeri 5.98 7 2 

Thelypteris na 4 3 

Typha na 9 3 

Utricularia na 18 11 * unknown 

Utricularia floridana 6.34 6 3 

Utricularia gibba 6.37 10 4 

Utricularia purpurea 6.50 7 6 * yes 

Vitis na 4 2 

Woodwardia virginica 6.50 11 6 

Xyris na 26 13 

Zizaniopsis miliacea 6.21 5 2 
APPENDIX 4. LVI VALUES BY DISTRICT 

Lake district, water body name, nick name, county, HDG, and LVI (average of 12 replicates) for 

the 95 lakes used to develop and test the LVI. 

District Waterbody name Nick name County STORET HDG LVI_1x

Central Russell Lake OSCEO78 Osceola 26010239 2 61.81

Central Holden Lake ORANG159 Orange 26010884 5 23.07

Central Lake Barton LakeBarton Orange 20010890 5 35.50

Central Lake Rexford REXFORDUNK Orange 26010012 5 47.16

Central Downey Lake ORANG44 Orange 20010319 3 63.91

Central Halfmoon Lake HALFMN*REF Marion 20020463 1 74.47

Central Winona Lake LKWINONA Lake 20020470 4 32.05

Central Davis Lake LKDAVIS Lake 20030148 3 62.69

Central Lake Norris LKNORRIS Lake 20010355 3 64.00

Central Hammond Lake HAMMONDREF Lake 20020014 1 80.22

Central Sellers Lake SELLCTREF Lake 20020496 0 84.42

Central Grasshopper Lake GRAHOP*REF Lake 20030913 1 90.97

Northeast Butler Lake UNION8 Union 21030015 2 59.52

Northeast Swift Creek Pond SFPNDCTREF Union 21030066 0 81.17

Northeast Louise Lake SUWAN4 Suwannee 21020016 3 46.97

Northeast Lake Ida LKIDA Putnam 20020116 4 42.71

Northeast Lake Fanny LKFANNY Putnam 20020100 1 74.08

Northeast Lake Fanny LKFANNY Putnam 20020100 1 89.79

Northeast Cherry Lake CHERYNEUNK Madison 21010039 2 53.57

Northeast Cherry Lake CHERYNEUNK Madison 21010039 2 61.60

Northeast Francis Lake FRANTST Madison 21020056 5 69.36

Northeast Lake Mystic MYSTICREF Madison 22040025 1 82.11

Northeast Dead Lake CRESTUDY4 Flagler 20030461 3 49.76

Northeast Gore Lake GOREREF Flagler 20030339 2 74.60

Northeast Tank Lake TANKLAKE Flagler 20030151 1 96.84

Northeast Watertown Lake WATERTWNLK Columbia 21010206 3 49.65

Northeast Magnolia Lake MAGNOLREF Clay 20030545 1 72.00

Northeast Rowell Lake ROWELLCT Bradford 21030110 4 38.97

Northeast Lake Altho LKALTHO Alachua 21030047 2 77.47

Northwest Rattlesnake Lake RSNAKEREF Washington 32030099 0 69.45

Northwest Porter Lake PORTERREF Washington 32030097 0 82.83

Northwest Porter Lake PORTERREF Washington 32030097 1 84.80

Northwest Otter Lake OTTER*REF Wakulla 22020090 1 68.79

Northwest Bear Lake BEARTST Santa Rosa 33030057 4 51.47

Northwest Kell-Air Lake KELLALK Okaloosa 32010050 7 14.63

Northwest Karick Lake KARICKTST Okaloosa 33040042 5 65.32

Northwest Lake Munson LKMUNSON Leon 23010165 6 22.98

Northwest Lake Victor LKVICTOR Holmes 32010085 5 37.78

Northwest Sand Hammock Pond SANDPOND Holmes 32010038 2 78.47

Northwest Lake Cassidy CASSCTREF Holmes 32020104 0 80.73

Northwest Stone Lake LKSTONE Escambia 33020097 3 34.99

Northwest Court Martial Lake CORTMARREF Bay 32030098 1 71.78
77

District Waterbody name Nick name County STORET HDG LVI_1x

South Lake Verona LKVERONMID Highlands 26010339FTM 4 28.75

South Lake Pioneer LKPIONEER Highlands 26010651FTM 7 30.48

South Lake Tulane LKTULANMID Highlands 26010340FTM 5 31.25

South Lake Carrie LKCARRIE Highlands 26010585 7 36.78

South Lake Denton LKDENTON Highlands 26010648 4 39.52

South Lake Viola VIOLCTREF Highlands 26010605 7 41.41

South June In Winter Lake HIGHL73 Highlands 26010559 6 47.56

South Lake Placid LKPLACIDNO Highlands 26010650FTM 4 48.91

South Wolf Lake WOLFCTREF Highlands 26010613 4 50.55

South Lake Little Bonnet LTLBONNET Highlands 26010326 3 50.95

South Lake Olivia LKOLIVIA Highlands 26010652FTM 6 51.05

South Red Beach Lake REDBCHREF Highlands 26010638 4 53.63

South Lake Apthorpe APTHORPREF Highlands 26010630 3 57.85

South Persimmon Lake HIGHL11 Highlands 26010303 5 58.12

South Annie Lake ANNMIDREF Highlands 26010310 0 81.23

South Submarine Lake SUBMARIREF Highlands 26010640 2 83.72

South Little Lake LITTLE REF Highlands 26010636 2 87.82

South Lake Yellowhammer LKYELHAM Charlotte 25010080FTM 3 41.53

South Lake Webb LKWEBB Charlotte 25010079 2 61.66

Southeast Lake At Tozour Rd. LKTOZOUR St Lucie 28010596 4 38.79

Southeast Lake Eden LKEDEN St Lucie 28010595 1 52.13

Southeast Lake Ida Pb LAKEIDA Palmbeach 28010500 5 9.61

Southeast Lake Osborne LKOSBORNE Palmbeach 28010500 7 11.82

Southeast Lake Clark LKCLARK Palmbeach 28010400 6 13.43

Southeast Wellington Lake LKWELNGTON Palmbeach 28010600 6 41.58

Southeast Lake Halpatioke LKHALPATOK Martin 28010594 0 47.91

Southeast Quiet Waters Lake N LKQUIETH2O Broward 28030600 2 46.49

Southwest Lake Deaton L58P Sumter UNKNOWNL58P 1 54.21

Southwest Banana Lake L84-BANANA Polk 27584418154127 4 18.74

Southwest Lake Conine CONINETST Polk 25020131 5 31.26

Southwest Lake Lena L74-LENA Polk 28040038148367 6 42.36

Southwest Lake Mattie MATTIE1REF Polk 23010406 2 43.98

Southwest Lake Elbert LKELBERT Polk 28013598142314 5 44.95

Southwest Lake Rochelle SWDLL1027 Polk 14580 4 46.00

Southwest Lake Daisy LKDAISY Polk 27594708139333 5 49.41

Southwest Lake Juliana JULIANREF Polk 23010427 5 49.47

Southwest Lake Martha LKMARTHA Polk 25020198 5 49.71

Southwest Lake Shipp LKSHIPP Polk 25020355 7 49.99

Southwest Lake Martha LKMARTHA Polk 25020198 5 51.62

Southwest Lake Easy L53PEASY Polk 25023002 1 54.88

Southwest Lake Annie LAKE ANNIE Polk 27594380813618 2 55.66

Southwest East Lake L51PEAST Pasco 24040806 3 26.78

Southwest Lake Como L61PCOMO Pasco 24040805 6 51.83

Southwest Lake Walden L52PWALDEN Hillsborough 24040170 3 9.12

Southwest Lake Weeks WEEKSREF Hillsborough 24030082 5 23.96

Southwest Lake Norbert LKNORBERT Hillsborough 28091278228372 4 25.04

Southwest Lake Bellows LKBELLOS Hillsborough 24030127 5 29.08

Southwest Lake Le Clare LKLECLARE Hillsborough 28063598232193 2 29.33

Southwest Thonotosassa Lake THONOTST Hillsborough 24030022 5 30.55
78

District Waterbody name Nick name County STORET HDG LVI_1x

Southwest White Trout Lake L62P Hillsborough UNKNOWNL62P 3 40.48

Southwest Lake Williams LKWILLIAMS Hillsborough 28055458236093 4 51.66

Southwest Lake Brooker L59PBRKR Hillsborough 24040172 4 53.85

Southwest Lake Alice ALICEPREF Hillsborough 24040019 2 68.19

Southwest Lake Rogers L63P Hillsborough UNKNOWNL63P 1 71.24

Southwest Spring Lake L65P Hernando UNKNOWNL65P 4 46.37

Southwest Lake Lindsey LKLINDSY Hernando 23010439 2 66.78

Southwest Lake Tooke TOOKEPREF Hernando 24040015 2 67.21

Southwest Lake Tooke TOOKEPREF Hernando 24040015 1 74.54
79

APPENDIX 5. ADDITIONAL TABLES 

App. Table 5.1. Details for Table 4 including N, Spearman’s r, and associated p level. 

Disturbance measures N r p-level 

HDG & WQ index 91 0.62 0.00 

HDG & LDI 98 0.73 0.00 

HDG & Habitat index 95 -0.87 0.00 

WQ index & HDG 91 0.62 0.00 

WQ index & LDI 91 0.19 0.07 

WQ index & Habitat index 87 -0.46 0.00 

LDI & HDG 98 0.73 0.00 

LDI & WQ index 91 0.19 0.07 

LDI & Habitat index 93 -0.69 0.00 

Habitat index & HDG 95 -0.87 0.00 

Habitat index & WQ index 87 -0.46 0.00 

Habitat index & LDI 93 -0.69 0.00 
80

App. Table 5.2. Candidate plant metrics and their correlation with HDG, the WQ index, LDI, 

and the habitat index, detail from Table 6. The sample size is shown for all metrics except 

dominant C of C for which N ranged from 57–65. Most metrics were calculated as both the total 

number of taxa and the percentage of total taxa (left vs. right side of table); exceptions to this 

were metrics in the category “Community structure” which could only be calculated in one way. 

 Number of taxa Percent of total taxa 

HDG WQ LDI Hab HDG WQ LDI Hab 

N = 95 95 93 90 95 95 93 90 

Community structure 

Total taxa -0.05 0.04 -0.17 0.16 - - - - 

No. of plant guilds 0.25 0.15 0.07 -0.15 - - - - 

Dominant C of C -0.52 -0.43 0.49 - - - - 

Nativity 

Native -0.24 -0.13 -0.31 0.35 -0.56 -0.46 -0.53 0.64 

Invasive 0.55 0.50 0.45 -0.48 0.63 0.51 0.61 -0.65 

Category 1 0.56 0.44 0.48 -0.50 0.58 0.43 0.58 -0.58 

Categories 1 & 2 0.59 0.51 0.49 -0.53 0.62 0.48 0.60 -0.62 

Tolerance 

FQI SCORE -0.52 -0.32 -0.54 0.57 - - - - 

Average C of C -0.67 -0.49 -0.59 0.63 - - - - 

Sensitive (CC>7) -0.40 -0.31 -0.41 0.46 -0.49 -0.44 -0.41 0.48 

Tolerant (CC<3) 0.48 0.39 0.35 -0.37 0.67 0.42 0.60 -0.59 

V. Tolerant (CC<2) 0.51 0.40 0.38 -0.40 0.65 0.41 0.59 -0.59 

Duration 

Perennial -0.07 -0.01 -0.16 0.18 -0.08 -0.15 -0.01 0.15 

Annual -0.15 0.12 -0.31 0.26 -0.16 0.09 -0.32 0.25 

Annual:Perennial ratio 0.11 0.18 0.10 0.02 -0.12 0.14 -0.27 0.21 

Native perennials -0.24 -0.16 -0.32 0.35 -0.07 -0.07 -0.08 0.07 

Native annuals 0.00 0.16 -0.05 0.14 0.16 0.24 0.14 -0.04 

Wetland status 

Obligate wetland -0.06 0.11 -0.15 0.17 -0.02 0.23 0.00 0.04 

Obl. & Facult. -0.03 0.08 -0.14 0.17 0.07 0.20 0.00 0.07 

Upland 0.07 -0.10 0.01 0.04 0.09 -0.14 0.04 -0.01 

Native obl. Wetland -0.20 -0.03 -0.26 0.31 0.01 0.23 0.09 -0.06 

Native facult wetland -0.03 -0.09 -0.11 0.17 0.23 -0.01 0.15 -0.12 

Native upland 0.00 -0.15 -0.08 0.13 0.12 -0.11 0.04 -0.02 

Growth form 

Herbaceous 0.02 0.09 -0.07 0.12 0.14 0.18 0.24 -0.11 

Woody -0.18 -0.11 -0.31 0.21 -0.18 -0.21 -0.25 0.15 

Emergent 0.01 0.07 -0.09 0.14 0.18 0.15 0.14 -0.09 

Floating -0.04 0.20 -0.05 0.04 -0.03 0.18 0.06 -0.03 

Submersed 0.05 -0.18 0.19 -0.07 0.05 -0.19 0.23 -0.09 

Fern -0.08 0.16 -0.27 0.21 -0.06 0.14 -0.23 0.19 

Gymnosperm 0.07 -0.06 0.01 0.00 0.06 -0.12 0.05 -0.03 

Native herbaceous -0.14 -0.07 -0.17 0.29 0.13 0.11 0.29 -0.10 

Native woody -0.32 -0.14 -0.48 0.37 -0.13 -0.08 -0.29 0.13 

Native emergent -0.14 -0.03 -0.21 0.31 0.11 0.17 0.14 -0.02 

Native floating -0.28 -0.16 -0.14 0.19 -0.22 -0.17 -0.02 0.09 
81

 Number of taxa Percent of total taxa 

HDG WQ LDI Hab HDG WQ LDI Hab 

N = 95 95 93 90 95 95 93 90 

Native submersed 0.01 -0.28 0.25 -0.03 0.09 -0.21 0.36 -0.13 

Native fern -0.25 -0.07 -0.38 0.34 -0.22 -0.08 -0.35 0.31 

Native gymnosperm 0.06 -0.07 0.02 0.02 0.16 -0.01 0.14 -0.14 

Native forbs & herbs -0.17 -0.08 -0.17 0.27 0.02 0.06 0.24 -0.07 

Native graminoids -0.13 -0.12 -0.14 0.29 -0.01 -0.07 0.00 0.13 

Native vines 0.23 0.11 0.11 -0.10 0.37 0.17 0.27 -0.26 

Native shrubs -0.44 -0.31 -0.52 0.48 -0.37 -0.30 -0.41 0.34 

Native subshrubs -0.14 0.19 -0.37 0.16 -0.13 0.19 -0.36 0.14 

Native tree -0.18 -0.03 -0.37 0.21 0.01 0.05 -0.17 -0.03 

Dicot/Monocot 

Annual dicot -0.17 -0.02 -0.28 0.25 -0.30 -0.19 -0.29 0.24 

Monocot 0.14 0.13 0.05 -0.01 0.32 0.22 0.35 -0.28 

Native annual dicot -0.31 -0.15 -0.40 0.39 -0.16 -0.06 -0.20 0.16 

Native annual monocot -0.10 -0.07 -0.13 0.21 0.13 0.09 0.18 -0.13 

EPA database 

Sensitive 0.15 0.05 0.12 0.02 0.24 0.03 0.25 -0.08 

Tolerant -0.02 0.09 -0.15 0.11 0.01 0.15 -0.05 -0.03 

V. Tolerant 0.17 0.22 -0.10 -0.08 0.24 0.23 0.04 -0.21 

Nutrient tolerant -0.08 0.06 -0.25 0.17 -0.14 -0.01 -0.24 0.09 

V. Nutrient tolerant -0.10 -0.09 -0.12 0.03 -0.07 -0.07 -0.06 -0.06 

N tolerant 0.18 0.26 -0.09 -0.17 0.28 0.29 0.08 -0.38 

P sensitive 0.20 -0.02 0.28 -0.14 0.20 -0.03 0.29 -0.14 

P tolerant 0.21 0.32 -0.04 -0.21 0.29 0.36 0.09 -0.41 

Flood sensitive -0.33 -0.31 -0.40 0.32 -0.31 -0.33 -0.35 0.30 

Flood tolerant 0.19 0.18 0.12 -0.11 0.38 0.22 0.39 -0.43 

V. Flood tolerant 0.28 0.38 0.23 -0.28 0.31 0.36 0.32 -0.38 

Sediment senstive 0.40 0.24 0.28 -0.34 0.37 0.22 0.28 -0.34 

Sediment tolerant -0.40 -0.27 -0.39 0.43 -0.41 -0.30 -0.40 0.41 

V. Sediment tolerant -0.13 0.23 -0.28 0.20 -0.13 0.23 -0.28 0.20 

Salinity sensitive -0.04 0.07 -0.20 0.13 -0.07 0.01 -0.16 0.08 

Salinity tolerant 0.16 0.14 -0.03 -0.04 0.19 0.17 0.03 -0.10 

V. salinity tolerant 0.07 0.14 -0.08 -0.03 0.08 0.14 -0.07 -0.04 

App. Table 5.3. Spearman’s correlation coefficients for plant metrics included in LVI. Shown are 

correlations for metric values calculated from the combined data from all 12 lake sections. N = 

95 for % native taxa, % invasive taxa and % sensitive taxa; N = 62 for dominant C of C. 

% Native taxa % Invasive taxa % Sensitive taxa Dominant C of C 

% Native taxa -0.72 0.46 0.40 

% Invasive taxa -0.72 -0.48 -0.48 

% Sensitive taxa 0.46 -0.48 0.43 

Dominant C of C 0.40 -0.48 0.43
82

APPENDIX 6. LVI VALIDATION AND CALIBRATION FOR 2005–2006 

SAMPLING 

LVI was initially developed from data collected during 2000–2003. The association 

between measures of macrophyte condition and human disturbance was validated with 

additional data collected in 2004. The final report from that study was completed in 2005. 

This appendix used data collected during 2005–2006 to document the association 

between LVI and human disturbance for a new data set, calibrate metric scoring for 

regional differences, and evaluate the annual variability of LVI. 

Methods 

Data set 

During 2005–2006, 167 lakes were sampled and some were visited more than once. For 

validation testing, only the most recent visits were used from each lake. For metric 

calibration, metric values from the most recent visits to the 167 lakes were used as well 

as data from 88 of the original lakes sampled in 2002–2003 (10 lakes sampled in 2000 

were excluded because sampling methods changed). For variability analysis, 31 lakes had 

2–3 repeat visits during 2005–2006. Some lakes had visits before 2005, but protocols for 

defining and calculating some of the metrics changed during that time; therefore, earlier 

LVI values were not entirely comparable. Of the 31 lakes, 11 lakes had two visits during 

2005 and one visit during 2006; the remaining lakes had two visits either during one year 

(2005) or during both years. 

Data collected during 2005-2006 included three modifications to metric 

calculation: C of C scores were added for some species that were missing during the 

initial calibration; native status was added for plants identified to genus, where 

appropriate; and Cephalanthus occidentalis, Nymphaea odorata, and Vallisneria 

americana were removed from the list of sensitive taxa based on the original analysis 

(see Table 8 main document). 
83

Data analysis 

The WQ index and habitat index were calculated as described in the main document but 

two changes were made to the calculation of HDG. Information related to hydrology was 

not included in the HDG because impoundment was difficult to measure reliably and, 

even when known, failed to influence metric values as predicted. The second change 

related to treatment of missing values for the WQ and habitat indexes (all lakes had LDI 

values). Previously, missing values were assumed to show no impairment and scored as 

0. For the analysis described here, when only two of the three components of HDG were 

available, the sum of the scored values was multiplied by 3/2 to make values more 

comparable to lakes for which all three component values of the HDG were available. In 

this way the value for the third, missing component was inferred from the other two. If 

both the WQ and habitat indices were missing, HDG was not calculated. Four lakes were 

excluded from HDG comparisons due to missing data. 

For all lake visits, four of 12 lake sections were measured. For 75% of the visits in 

2005, sections from opposite sides of the lake were combined before calculating metrics 

and LVI. For all other visits, the four lake sections were kept separate, and metrics and 

LVI were calculated for all four. The final LVI value for a lake-visit was the average of 

the LVI values for the sections. All comparisons and analyses of both LVI and its 

component metrics used the average value for each lake-visit. 

Metric scoring rules were derived (as previously) from the 5th and 95th percentiles. 

The original analysis had relatively few lakes from the southern part of the state; 

consequently, ecological differences in metric values due to latitude could not be fully 

evaluated. The new data had more southern sites and scoring rules for metrics were 

updated to make LVI values more comparable across the state. Northern and southern 

lakes were divided according to climatological zones defined by the U.S. Department of 

Agriculture (Fernald and Purdum, 1998; Lane, 2000). The line between the USDA’s 

north central and south central divisions falls near 28.2o

 latitude and approximates the 

frost line. Along this line, Pasco, Sumter, Lake, and Orange counties are in the northern 
84

region, Pinellas, Hillsborough, Polk, and Osceola counties are in the southern region, 

while Brevard county is divided by the 28.35° latitude line. 

Variance of LVI was evaluated in two ways. To compare sources of variance, a 

balanced ANOVA design is preferred, that is, a design for which all year and lake 

combinations have an LVI value. For this analysis, only 11 lakes had the same pattern of 

visits (2 visits in 2005 and 1 in 2006), and these lakes were used. In contrast, to estimate 

variance of LVI, a larger sample size was preferred; therefore, all the lakes visits during 

2005–2006 were used. 

Components of variance analysis was used to partition the variance of LVI and its 

four metrics according to different sources of variability. Sources of variance included 

different lakes, different visits within a year, different years, and lake x year interaction 

(additional differences that cannot be explained by lake and year alone). An ANOVA 

model was used to derive estimates of variance, one model each for LVI and the four 

metrics. The relative percentages for each source of variance were compared to identify 

which source contributed the greatest portion to the overall variability of LVI; a similar 

comparison was done for each of the four metrics. 

In the original analysis, variance of LVI was calculated for repeat visits on the 

same day. For this study, repeat visits during 2005 and 2006 to 31 lakes were used to 

estimate variance of LVI. With lake as the main factor, all repeat visits were used to 

calculate the mean squared error (MSE). In other words, different years were not treated 

as a factor. The estimate of variance was used to calculate a 90% confidence interval as: 

LVI ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

± 645.1*

2

n

s , 

where s2 = variance estimated from ANOVA (mean squared error), and 

n = number of lake visits. 
85

App. Figure 6.1. Lake locations for the original data collected in 2000–2003 (open circles) and 

the more recent data used here that were collected in 2005–2006 (closed circles). The line 

dividing northern and southern lakes is shown. 
86

Results 

Validation testing 

LVI and its component metrics were, again, highly correlated with HDG, the WQ index, 

and the habitat index (App. Table 6.1). Correlation values for metrics were slightly higher 

for the more recent data set, perhaps due to the adjustment made to the HDG scoring for 

missing values or adjustments made to metric calculations. Correlation between LVI and 

HDG was nearly identical to earlier results for the 2004 validation testing (Spearman’s r 

= –0.71 vs. –0.70 (development data set; Table 13 main document) and –0.72 (first 

validation data set; Table 16). Correlation between LVI and the WQ index was higher for 

this data set (–0.58 vs. –0.34) but lower for LDI (–0.60 vs. –0.78). 

0123456

HDG

20%

40%

60%

80%

100%

% Native taxa

0123456

HDG

0%

10%

20%

30%

40%

50%

60%

% Invasive taxa

0123456

HDG

0%

10%

20%

30%

40%

% Sensitive taxa

0123456

HDG

0

2

4

6

8

Dominant C of C

 Median 

 25%-75% 

 Non-Outlier Range 

 Outliers

 Extremes

App. Figure 6.2. Percent native and sensitive taxa and dominant C of C declined as human 

disturbance increased. Percent invasive taxa increased with disturbance (N = 164 except for 

dominant C of C for which N = 118). 
87

As seen in the original analysis (see Table 4 main document), all measures of human 

disturbance were significantly correlated with each other (App. Table 6.2). LDI and the 

WQ index again had the weakest association. 

App. Table 6.1. Spearman’s correlation values for LVI (original version developed in 

2005 and with modified 2007 scoring described below) and its metrics (2005–2006 data) 

with measures of disturbance. All values significant (p < 0.01). N represents most recent 

lake visit; number of lakes for dominant C of C lower due to missing data. Last column 

shows correlation values from the original analysis (see Table 13). 

HDG N LDI N Habitat 

index (%)

N WQ index N HDG 

(original 

analysis) 

LVI (2005) -0.71 164 -0.60 167 0.70 98 -0.58 162 -0.70 

LVI (2007) -0.72 164 -0.60 167 0.72 98 -0.58 162 

% Native taxa -0.68 164 -0.54 167 0.74 98 -0.58 162 -0.59 

% Invasive taxa 0.70 164 0.60 167 -0.70 98 0.58 162 0.61 

% Sensitive taxa -0.61 164 -0.52 167 0.51 98 -0.47 162 -0.48 

Dominant C of C -0.53 118 -0.41 121 0.55 86 -0.44 116 -0.48 

App. Table 6.2. Spearman’s correlation of disturbance measures. All values significant 

(p < 0.01). N varied from 96-164. For comparison, correlation values from the original 

analysis are shown below (see Table 4). 

HDG N LDI N Habitat 

index (%)

N 

LDI 0.84 164 

Habitat index (%) -0.89 98 -0.75 98 

WQ index 0.64 162 0.33 162 -0.59 96

Original analysis: 

HDG LDI Habitat 

index (%)

LDI 0.73 

Habitat index (%) -0.87 -0.69 

WQ index 0.62 -0.46
88

Regional adjustment of metric scoring 

All four metrics showed regional differences in that northern lakes had values indicating 

better biological condition than southern lakes for the same level of human disturbance 

(App. Figure 6.3). Dominant C of C showed the greatest difference between regions. Two 

metrics, percent sensitive taxa and dominant C of C, were adjusted for regional 

differences by using separate scoring rules for lakes in the north and south (App. Table 

6.3). Percent native and invasive taxa were not adjusted because the differences in these 

metric values represent real differences in biological condition. Lower values for these 

two metrics are expected in southern lakes because many invasive exotic plants are from 

tropical climates and are able to persist and displace native plants in the absence of 

freezing temperatures. Thus, southern lakes are more vulnerable to this type of biological 

change associated with human disturbance. 

These adjustments to metric scoring had only a small effect on the LVI, and the 

two versions were very similar in their correlation with disturbance measures (see App. 

Table 6.1 above). 

App. Table 6.3. Metric scoring rules derived from the 5th and 95th percentiles using the 

most recent visit from each lake including lakes from the original development data set. 

Metric scores less than 0 are set equal to 0; scores greater than 1 are set equal to 1. 

Metric N 5th %tile 95th %tile Scoring rule

% Native taxa 256 54% 98% (x – 54)/44 

% Invasive taxa 256 0% 36% 1 – (x/36) 

% Sensitive taxa 

North 154 0% 37% x/37 

South 102 0% 24% x/24 

Dominant C of C 

North 111 0 7.2 x/7.2 

South 67 0 5.8 x/5.8 
89

HDG

% Native taxa

-1 0 1 2 3 4 5 6 7

10%

30%

50%

70%

90%

110%

HDG

% Invasive taxa

-1 0 1 2 3 4 5 6 7

0%

10%

20%

30%

40%

50%

60%

70%

HDG

% Sensitive taxa

-1 0 1 2 3 4 5 6 7

0%

10%

20%

30%

40%

50%

60%

HDG

Dominant C of C

-1 0 1 2 3 4 5 6 7

-1

0

1

2

3

4

5

6

7

8

9

North

South

App. Figure 6.3. LVI metrics plotted against the HDG with northern and southern lakes 

distinguished. Regression lines are shown for each group of lakes (N=252 for all except 

dominant C of C for which N=178). 
90

Overall, the LVI declined consistently as human disturbance increased (App. 

Figure 6.4). Much of the overlap in categories defined by HDG was due to the inaccuracy 

of measurements of human disturbance; therefore, this method was not used to define the 

number of categories of biological condition that LVI could detect. Instead, repeat visits 

to the same lake were used to determine the natural variability of LVI values and estimate 

precision of the index. 

 Median 

 25%-75% 

 Non-Outlier Range 

 Outliers

Extremes 0123456

HDG

0

20

40

60

80

100

LVI (2007)

App. Figure 6.4. LVI declined as human disturbance increased for lakes sampled in 

2005–2006 (Spearman’s r = 0.72, p < 0.01). N: 164 (total), 23 (HDG = 0), 30 (HDG = 1), 

14 (HDG = 2), 22 (HDG = 3), 47 (HDG = 4), 13 (HDG = 5), and 15 (HDG = 6). 
91

Variability analysis 

Repeat visits to 11 lakes were used to evaluate the precision of LVI. Comparisons 

through time assumed that the influence of human disturbance at each lake changed very 

little during the sampling period and that differences in LVI values were due to natural 

variability. LVI values for the 11 lakes ranged from 25–94. Excluding the LVI variance 

due to differences in lakes (which is the type of variance we hope to detect), within year 

variability represented the largest component of variance for most of the indicators (App. 

Figure 6.5). Differences associated with annual variability were much smaller in 

comparison. The most variable metric continued to be dominant C of C even though the 

metric value was missing for only one lake-visit. 

The variance, 90% confidence interval, and number of categories of biological 

condition that LVI could detect were derived from same-day repeat visits in the original 

analysis. For an LVI value calculated as the average of values from four lake sections, the 

variance would be equal to the within-lake variance divided by four, or 28.0 (=112.33/4; 

see Table 12, main document). The 90% confidence interval derived from that estimate 

of variance was 17.4 (LVI +/- 8.7). Dividing the confidence interval into 100 (the range 

of values possible for LVI) yielded an estimate of 5.7 categories of biological condition 

for a single lake visit. 

LVI variance derived from repeat visits in 2005 and 2006 was 53.7 (App. Table 

6.4), approximately twice the estimate observed for same-day sampling (28.0). This 

estimate of variance also includes differences associated with sampling crews because the 

same-day sampling of sections was done by a single field crew while the repeat visits in 

2005 and 2006 could have been done by different field crews. The 90% confidence 

interval for this variance estimate was 24.1 (LVI +/- 12) for a single lake visit (n = 1). 

Dividing into 100 yielded an estimate of 4.2 categories of biological condition for repeat 

visits across years. For two visits, variance decreased to 14.0 (=28/2) and the 90% 

confidence interval was 17.0 (LVI +/- 8.5), which yielded 5.9 categories of biological 

condition. 
92

App. Table 6.4. ANOVA results for LVI for repeat visits to lakes (STORET sites). 

Effect df MS df MS F p

STORET Random 33 1020.8 41 53.663 19.02209 0.00

 Within year

 Lake x Year

 Year

 Lake

LVI

% Native taxa

% Invasive taxa

% Sensitive taxa

Dom C of C

0

20

40

60

80

100

% of Total variance

App. Figure 6.5. Relative contributions of different sources of variance for LVI and its 

four metrics. Differences associated with lakes represented the greatest percentage of 

total variance for LVI and all four metrics. Repeat visits within a year accounted for the 

next largest percentage of variance for LVI and three metrics (lake x year interaction was 

next greatest for % sensitive taxa). Variability due to differences in years contributed a 

relatively small percentage of the total variance for LVI and its four metrics. 
93

Conclusions 

Recent data for macrophyte metrics and the LVI show a very similar pattern to earlier 

testing: LVI and its component metrics were highly correlated with independent 

measures of human disturbance, including landscape level disturbance (LDI), measures 

of water chemistry (WQ index), and physical habitat condition (habitat index). 

Differences in metric values were associated with climatological differences in 

location. Southern lakes are more vulnerable to invasion by exotics from tropical 

climates. Lack of freezing temperatures allows these plants to obtain a firmer foothold. 

For this reason percent native and percent exotic taxa were not adjusted during the metric 

scoring process. Percent sensitive taxa and dominant C of C were adjusted because many 

of the sensitive plants with high C of C values are not found in southern lakes; therefore, 

expectations for these metrics should be lower and are now reflected in the scoring rules 

for these metrics. 

The variance estimate of LVI derived from repeat visits to the same lake during 

two years was approximately twice the variance estimate derived from same-day repeat 

visits for the original analysis. Although variance estimates derived from different data 

sets should be compared cautiously, results suggest that about the half the variability 

observed for LVI was due to natural differences associated with different lake sections 

and half was due to changes through time. Precision could be increased by increasing the 

number of lake visits, but for the current sampling protocol the LVI is capable of 

detecting approximately 4 levels of biological condition. Two repeat visits to a lake 

would increase the precision of LVI and support the distinction of 6 levels of biological 

condition. Managers can use the LVI to distinguish lakes with exceptional macrophyte 

communities as well as those whose degraded condition fails to support the expected 

assemblage of plants. 

References 

Fernald, E. A. and E. D. Purdum. 1998. Water Resources Atlas of Florida. Tallahassee, FL. 

Florida State University Institute of Science and Public Affairs. 

Lane, C. R. 2000. Proposed Wetland Regions for Florida Freshwater Wetlands. Final Report to 

Florida DEP. Available: www.dep.state.fl.us/labs/library/index.htm. 
94

APPENDIX 7: BIOLOGICAL CONDITION GRADIENT TO DEFINE 

BIOCRITERIA FOR LVI 

Background 

The regulatory context for the management of surface waters is derived from the Clean 

Water Act (CWA; EPA, 2005; Davies and Jackson, 2006). Within this framework, states 

and tribes must adopt water quality standards to protect their waters. Water quality 

standards (WQS) include three parts: 1) designated uses, 2) numeric and narrative criteria 

that protect the uses, and 3) antidegradation policies to prevent deterioration of highquality waters. WQS define the water quality goals for a water body by designating the 

use(s) and setting criteria necessary to protect the use(s). States are required to report to 

Congress the water bodies that are failing to support their designated uses. 

Florida DEP intends to use biological criteria along with physical and chemical 

criteria to determine whether lakes are meeting their designated uses, particularly uses 

related to aquatic life support. Designated aquatic life uses represent a state’s biological 

goals for its water bodies. In 2001, the National Research Council recommended tiering

designated uses to improve the decision-making related to setting water quality standards 

(NRC, 2001). The NRC found the CWA’s goals to be too broad to provide the 

operational definition of designated uses needed to support aquatic life. For example, 

rather than stating that a water body needs to be “fishable,” the designated use should 

specifically describe the expected fish assemblage (e.g., cold water fishery, warm water 

fishery, or salmon, trout, bass, etc.). In response to the NRC’s recommendations, EPA 

has developed guidance for developing tiered aquatic life uses (TALUs), which are 

bioassessment-based statements of expected biological condition in specific water bodies 

that allow more precise and measurable definitions of designated aquatic life uses. Thus, 

the TALU approach is designed to support the implementation of biocriteria, and the 

scientific knowledge of aquatic ecology that they represent, into state water quality 

standards. Because FDEP’s intends ultimately to adopt criteria based on this tiered 

aquatic life use concept, three categories are proposed for the LVI. Subject to future rule 

making decisions, those three categories may be conceptually interpreted as 
95

“exceptional” (Category 1), “healthy” (Category 2), and “impaired” (Category 3). The 

approach described here for defining biocriteria for lakes closely parallels the methods 

used to define biocriteria for macroinvertebrate assemblages in streams (Fore et al., 2007) 

Biological Condition Gradient 

Within the context of defining TALUs for surface waters, EPA recommends that states 

use a Biological Condition Gradient (BCG) to illustrate how ecological attributes change 

in response to increasing levels of human disturbance (Davies and Jackson, 2006). The 

BCG is a conceptual model that assigns the relative health of aquatic communities into 

one of six categories. The BCG is based on fundamental ecological principles and has 

been extensively tested and verified by aquatic biologists throughout the U.S. 

The BCG utilizes ten biological attributes of aquatic systems that respond 

predictably to increasing pollution and human disturbance: 

I. Historically documented, sensitive, long-lived or regionally endemic taxa 

II. Sensitive and rare taxa 

III. Sensitive but ubiquitous taxa 

IV. Taxa of intermediate tolerance 

V. Tolerant taxa 

VI. Non-native taxa 

VII. Organism condition 

VIII. Ecosystem functions 

IX. Spatial and temporal extent of detrimental effects 

X. Ecosystem connectance. 

The gradient represented by the BCG is divided into six levels (tiers) of condition that 

were defined during a series of national workshops with experienced aquatic biologists 

from across the U.S. (Davies and Jackson, 2006). The six tiers are described as: 

1. Natural or native condition 

2. Minimal changes in structure of the biotic community and minimal changes in 

ecosystem function 

3. Evident changes in structure of the biotic community and minimal changes in 

ecosystem function 
96

4. Moderate changes in structure of the biotic community with minimal changes in 

ecosystem function 

5. Major changes in structure of the biotic community and moderate changes in 

ecosystem function 

6. Severe changes in structure of the biotic community and major loss of ecosystem 

function. 

Davies and Jackson (2006) recommend that regional biological experts adapt the 

conceptual tiers described by the BCG to local conditions using data from a regional 

monitoring program. This was the approach adopted by Florida DEP to define biocriteria 

for the LVI. 

Methods 

A panel of 20 experienced plant ecologists, all with more than five years of experience, 

was convened as part of a workshop to develop a BCG for Florida lakes (App. Table 7.1). 

Participants were asked to apply and calibrate the general BCG model to macrophyte data 

collected from Florida lakes. Results from the workshop were used to define biological 

thresholds (biocriteria) for the LVI. 

Prior to the workshop, experts were provided with background literature on the 

BCG concept (Davies and Jackson, 2006; Stoddard et al., 2006) and a description from 

DEP of the workshop process and the LVI sampling method. Experts were informed that 

they would be asked during the workshop to review macrophyte data from individual 

lakes and assign each sample a score from 1 (best) to 6 (worst), signifying their 

assessment of the biological condition represented by the plant assemblage. 

The experts were given taxa lists and metric values for each lake. Data were 

provided for 15 lakes in the north and 15 in the south; experts were told whether lakes 

were located in the north or south. Experts were not given LVI index values or any 

physical, chemical, or habitat descriptions of the sites. 

Lake visits were selected primarily from the 2005-2006 data, with additional lake 

visits from the southern region as needed. Within each region, the samples with LVI 

scores closest to the 10th, 20th, 30th, 40th, 50th, 60th, 70th, 80th, and 90th percentiles of LVI 
97

scores for that region were chosen (nine visits; “LVI %iles” in App. Table 7.2). Then one 

lake visit was randomly selected from each of HDG levels 1-6 for each region (six visits; 

“HDG cat” in App. Table 7.2). 

During the workshop, experts were given the opportunity to discuss their rationale 

for scoring a lake, and, if they desired, to change their score for a lake based on their 

assessment from the discussions (Delphi method). Scoring sheets were provided to each 

expert for recording both initial and final BCG scores. Experts were requested to indicate 

what relevance they ascribed to their BCG scores. More specifically, they were asked to 

record the maximum BCG score that they believed did not meet the interim goals of the 

Clean Water Act (impaired site; does not meet designated use). In other words, lakes 

below a selected tier score would be considered “impaired.” Experts were also asked to 

record the minimum score they believed represented an exceptional condition. 

Before the TALU guidance was developed, many states used an approach based 

on reference site condition to develop biocriteria (EPA, 2006). Typically percentiles of 

index values for references sites are used to define thresholds. A buffer derived from the 

variability of reference site index values may also be used to define the lowest index 

value that indicates departure from reference condition, or impairment. Although not 

used to define thresholds for the LVI, LVI statistics for reference lakes are reported here 

because this method has been widely used before the more recent recommendations from 

EPA to use the BCG approach. 
98

App. Table 7.1. Workshop participants, their affiliation and years of experience working 

with plants. Three participants had <5 years of experience and their scores were not 

included in threshold calculations. 

Expert Affiliation Years of Experience 

Loran Anderson FSU Emeritus >60 

Erica Anderson DEP - Bureau Aquatic Plant Management >5 

David Hall Independent consultant 47 

Jess Van Dyke DEP - Bureau Aquatic Plant Management 37 

Joe Hinkle DEP - Bureau Aquatic Plant Management 33 

Jeff Schardt DEP - Bureau Aquatic Plant Management 31 

John Rodgers DEP - Tampa 30 

Jackie Smith DEP - Bureau Aquatic Plant Management 25 

Robbie Lovesrand DEP - Floral City 20 

Ed Harris DEP - Orlando 20 

Katherine Gilbert DEP - Tallahassee 20 

John Tobe ERC, Inc 20 

Terry Sullivan DEP - Floral City 20 

Jeff Holland Reedy Creek Improvement District 17 

David Demmi DEP - Bureau Aquatic Plant Management 15 

Christine Keenan DEP - Tallahassee 15 

Nathalie Visscher DEP - Orlando 8 

Peggy Morgan DEP - Tampa 5 

Julie Espy City of Tallahassee 5 

Dana Denson DEP - Orlando 5 

Kelli Gladding DEP - Orlando 4 

Danielle Sobczak DEP - Bartow 2 

Marissa Rodriguez DEP - Orlando 1.5 
99

App. Table 7.2. Lake visits used in the BCG workshop. Shown are station nickname, 

STORET number, macrophyte ID, date of sampling, LVI, HDG, LDI and the method 

used to select the lake. 

BCG 

ID Station Nickname STORET 

Macro

ID Date LVI HDG LDI 

Selection 

method 

N01 LKTHOMAS 24040132 3861 10/27/2005 48 2 3.8 LVI %iles 

N02 PETTYGULF 22020129 4867 10/20/2005 33 4 4.4 HDG cat 

N03 CHERYNEUNK 21010039 5123 10/13/2005 69 3 5.9 HDG cat 

N04 PALSTCTREF 19010044 5201 10/11/2005 94 1 1.0 HDG cat 

N05 ORANG161 26010887 5282 11/2/2005 41 5 6.4 LVI %iles 

N06 LKSAUNDERS 20020166 5341 10/27/2005 63 5 4.6 HDG cat 

N07 MARINERLK 20020165 5361 10/25/2005 82 2 3.8 HDG cat 

N08 RSNAKEREF 32030099 5381 11/10/2005 82 0 1.7 LVI %iles 

N09 LKSTANLEY 33040029 5383 10/26/2005 85 3 7.1 LVI %iles 

N10 NW3SL2065 29664 6061 7/26/2006 78 2 2.6 LVI %iles 

N11 LKSTANLEY 33040029 6241 11/6/2006 68 3 7.1 LVI %iles 

N12 HIAWATHA 22020042 6364 10/19/2006 85 1 2.2 LVI %iles 

N13 LONG POND 32020131 6409 10/16/2006 60 3 2.7 LVI %iles 

N14 LKFRANCES 21020123 6410 11/15/2006 68 6 7.0 HDG cat 

N15 BAKER15 21010011 6419 11/1/2006 87 1 2.0 LVI %iles 

S01 LKYELHAM 25010080FTM 502 8/13/2002 38 3 1.9 HDG cat 

S02 LKPLACIDNO 26010650FTM 546 8/20/2002 54 3 3.8 LVI %iles 

S03 LKVERONMID 26010339FTM 921 10/27/2003 36 4 6.6 LVI %iles 

S04 CYPRESSLK1 26010075 5621 5/10/2006 41 0 1.8 LVI %iles 

S05 SAR616NL SAR616NL 6322 11/1/2006 63 2 1.1 HDG cat 

S06 LKHUNTER 24030148 6323 10/19/2006 63 5 6.8 HDG cat 

S07 HIL540NL HIL540NL 6324 9/28/2006 49 1.5 1.9 LVI %iles 

S08 SW3SL2006 30724 6367 8/8/2006 22 4 4.2 HDG cat 

S09 GENTNEREF 26010988 6373 10/16/2006 66 1 1.9 HDG cat 

S10 SW3SL2013 30726 6376 9/5/2006 18 6 7.3 HDG cat 

S11 SW3SL2082 30736 6392 8/22/2006 31 4 5.5 LVI %iles 

S12 SW3SL2114 30745 6400 8/15/2006 72 4.5 3.3 LVI %iles 

S13 SW3SL2170 30748 6401 9/6/2006 12 6 8.2 LVI %iles 

S14 SW3SL2102 30741 6403 8/24/2006 41 5 7.7 LVI %iles 

S15 LKEDEN 28010595 6426 11/8/2006 42 2 1.0 LVI %iles 
100

Results 

Each expert assigned a BCG to all of the 30 lakes (App. Table 7.3). After reviewing the 

data, most experts selected tiers 1 and 2 as exceptional aquatic life use (16 out of 19; 1 

with no response; App. Table 7.4). Most experts agreed that tiers 5 and 6 indicated 

impairment or failure to support aquatic life use (13 out of 20). Seven experts thought the 

line for impairment should be higher: two identified tier 3 and five identified tier 4 as 

failing to support aquatic life use. 

BCG scores from the experts were averaged to define thresholds for LVI. For the 

definition of the impairment line, an average of 4.6 was calculated from the experts’ 

definitions. We rounded this value to the nearest half category (4.5) because BCG scores 

were not considered in increments of 0.1, and a value of 4.6 implied an accuracy that was 

not supported by the discussion. For the definition of the exceptional category, a value of 

2.0 was calculated from the experts definitions. To translate these thresholds into LVI 

values, a regression equation was used (App. Figure 7.1). 

A BCG score of 4.5 corresponded to an LVI value of 37 as the impairment 

threshold. Thus, based on the recommendations of 20 regional experts, LVI values from 

0-37 are proposed as Category 3 (provisionally interpreted as “impaired”). A BCG score 

of 2.0 corresponded to an LVI value of 78 as the threshold between Categories 2 and 1 

(provisionally interpreted as “healthy” and “exceptional”). 

 
App. Table 7.3. Lake visit, BCG scores for each expert, median BCG for each lake, and LVI. Lake E01 E02 E03 E04 E05 E06 E07 E09 E10 E12 E13 E14 E15 E17 E18 E19 E20 E21 E22 E23 Mean

BCG 

M

N01 4 4 4 4 5 5 4 3 5 3 4 5 5 4 4 4 5 4 4 4 4.2 N02 3 5 6 5 6 5 5 5 5 5 5 5 6 5 5 5 5 5 5 5 5.1 N03 4 3 3 3 3 3 3 3 4 3 3 4 4 3 3 3 4 1 3 3 3.2 N04 1 1 1 1 1 2 1 1 1 2 1 4 2 2 2 1 2 2 2 2 1.6 N05 5 5 5 4 4 4 4 3 4 4 4 2 4 3 3 4 4 3 3 3 3.8 N06 3 3 4 3 4 4 4 3 3 3 4 3 4 3 3 3 3 4 3 3.4 N07 2 1 1 1 1 2 1 1 1 1 1 3 1 1 2 1 4 2 1 1 1.5 N08 2 1 1 3 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 1 1.3 N09 2 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 2 1.9 N10 3 2 2 2 2 3 2 2 2 3 2 2 3 2 3 2 3 2 2 3 2.4 N11 3 2 1 2 2 2 2 2 2 2 2 2 2 2 3 2 3 2 2 2 2.1 N12 1 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 3 1 1 1 1.2 N13 2 3 2 3 2 3 3 4 3 3 3 3 3 2 3 3 1 3 3 3 2.8 N14 3 4 3 4 3 4 4 3 4 3 3 3 4 3 4 3 3 4 3 3 3.4 N15 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 4 2 1 1 1.3 S01 5 5 5 5 5 5 5 4 5 5 5 4 5 5 5 4 2 5 4 5 4.7 S02 3 3 3 4 3 3 3 4 3 3 4 3 3 4 4 3 5 3 4 3 3.4 S03 5 5 4 5 5 5 5 5 5 5 5 4 4 5 5 5 4 4 4 5 4.7 S04 4 3 4 4 4 4 4 5 5 5 4 5 5 4 4 5 5 2 4 4 4.2 S05 3 3 3 2 2 3 3 2 2 2 3 2 2 2 2 3 5 2 2 2 2.5 S06 3 3 3 4 2 4 4 3 3 3 4 3 3 3 3 3 2 3 2 3 3.1 S07 4 3 4 4 4 4 4 5 4 5 4 4 6 5 4 5 3 3 5 4 4.2 S08 5 5 5 5 5 5 5 4 5 4 5 5 4 5 5 5 5 5 5 4 4.8 S09 3 2 3 2 3 2 3 4 3 3 3 2 3 3 3 3 5 2 3 3 2.9 S10 5 6 6 5 5 5 5 3 6 5 5 5 5 4 5 5 5 5 4 4 4.9 S11 5 5 6 4 5 5 5 5 4 4 5 5 5 5 5 5 4 4 4 4 4.7 S12 4 3 3 3 3 3 4 3 3 4 3 3 4 4 3 3 3 4 2 3 3.3 

S13 5 6 6 5 5 5 5 6 5 6 5 6 6 6 6 5 5 5 5 6 5.5 

S14 4 4 4 4 4 4 4 5 4 4 4 4 4 4 4 4 5 4 4 4 4.1 S15 3 4 4 3 4 3 4 4 4 4 3 3 3 4 4 3 5 4 5 3 3.7 
App. Table 7.4: Experts’ selection of threshold values of BCG that indicate exceptional 

condition and impaired condition. 

Expert Exceptional 

Impaired/ 

Does Not Meet

E01 1 5 

E02 2 5 

E03 2 5 

E04 1 4 

E05 2 5 

E06 1 4 

E07 2 5 

E09 2 5 

E10 3 5 

E12 2 3 

E13 2 3 

E14 3 6 

E15 2 4 

E17 ND 5 

E18 2 4 

E19 2 5 

E20 2 5 

E21 2 4 

E22 2 5 

E23 3 5 

MEDIAN = 2 5 

AVERAGE = 2 4.6 
103

0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0

Expert's Biological Condition Gradient score (mean)

0

10

20

30

40

50

60

70

80

90

100

LVI

App. Figure 7.1. LVI values were highly correlated with the BCG scores assigned by the 

experts (Pearson’s r = 0.89). Shown are the regression line (solid black) and threshold 

values for impaired lakes (LVI = 37; red dashed lines) and exceptional lakes (LVI=78; 

green solid line). 

Reference sites 

Initially, 37 reference lakes with HDG = 0 were identified from the 2005–2006 validation data 

set. Recall that HDG = 0 does not mean no human disturbance, but minimal human disturbance. 

This initial list of lakes was evaluated by biologists who had sampled the lakes and were familiar 

with local conditions. If any lake could not be confidently labeled as "minimally disturbed," it 

was excluded from the reference set based on the best professional judgment of the biologist. 

This process yielded a total of 22 reference lakes for calculating statistics for the LVI (App. 

Table 7.5). Notably, only two reference lakes could be identified for the southern section of the 

state. 

Paralleling an approach used by other states, a buffer of LVI values below the 10th

percentile was used to represent the natural variability associated with field sampling that could 

be due to water level fluctuation, weather, or time of sampling. Half the 90% confidence interval 
104

(12.0) was subtracted from the 10th percentile of LVI as: 66–12.0 = 54 (App. Table 7.6; 

Appendix 6). Values below 54 would be considered impaired using this approach. This value 

(LVI = 54) for the impairment line is 17 points higher than the threshold derived from the BCG 

approach (LVI = 37). 

In contrast, the higher threshold for the LVI value (dividing “exceptional” from “healthy” 

lakes) based on the reference site approach was very close to the value from the BCG workshop. 

Subtracting half the 90% confidence interval from the 90th percentile for LVI as: 92–12.0 = 80, 

yielded a value only 2 points higher than the LVI threshold derived from the BCG workshop. 

App. Table 7.5. Reference lake STORET, nickname, region, date of sampling LVI, and LDI. 

STORET Station Nickname Region Date LVI LDI

26010301 NONAREF North 11/1/2005 93 1.6 

27144 SJ6SL2036 North 9/16/2005 92 1.0 

29671 NW3SL2086 North 8/1/2006 92 1.1 

27141 SJ6SL2020 North 9/2/2005 92 1.0 

27150 SJ6SL2052 North 9/2/2005 91 1.1 

32020104 CASSCTREF North 11/1/2006 89 1.0 

32010045 DUNFORDREF North 10/12/2005 86 1.0 

26010300 MUDTST North 12/7/2005 85 1.2 

26762 NW2SL2126 North 8/4/2005 84 1.8 

14441 NWC-LL1002 North 10/26/2005 83 1.6 

20020496 SELLCTREF North 10/18/2006 82 1.8 

26742 NW2SL2025 North 7/7/2005 79 1.4 

32030097 PORTERREF North 11/1/2005 78 1.5 

26010310 ANNMIDREF South 11/3/2005 78 1.7 

26753 NW2SL2086 North 7/28/2005 75 1.6 

27166 SJ6SL2098 North 9/1/2005 74 1.7 

20010080 BUCKLKONF North 6/1/2005 73 2.0 

32030099 RSNAKEREF North 10/31/2006 70 1.7 

26769 NW2SL2158 North 7/27/2005 67 1.6 

26010988 GENTNEREF South 10/16/2006 66 1.9 

22100 NW1-LL2007 North 11/2/2006 60 1.4 

29655 NW3SL2045 North 7/11/2006 43 1.3 
105

Biocriteria for Lakes 

The above analyses support the establishment of three categories of aquatic life use for Florida 

lakes based on macrophyte sampling. The categories of biological condition were described as 

Category 1 (“exceptional”, with LVI = 78–100), Category 2 (“healthy”, with LVI = 38–77), and 

Category 3 (“impaired”, with LVI = 0–37). Narrative descriptions of these categories were 

derived from the ranges of metric values associated with each LVI category (App. Table 7.7) 

For regulatory decisions, two separate, temporally independent site-visits resulting in a 

Category 3 evaluation are recommended to list a lake as failing to support aquatic life use. If two 

lake visits were routine, the precision of LVI would support the designation of additional 

categories. 

App. Table 7.6. Statistics and percentile values for LVI reference sites. 

Statistic Value 

N 22 

Standard deviation 12.3 

Minimum 43 

10th percentile 66 

25th percentile 73 

Mean 79 

Median 80 

75th percentile 89 

90th percentile 92 

Maximum 93 

App. Table 7.7. Proposed aquatic life use categories, corresponding LVI values, and narrative 

descriptions of biological conditions typically found for that category. 

Aquatic life use 

category 

LVI 

Range 

Description 

Category 1 78–100 Nearly every macrophyte present is a species native to Florida, invasive 

taxa typically not found. About 30% of taxa present are identified as 

sensitive to disturbance and most taxa have C of C values >5. 

Category 2 38–77 About 85% of macrophyte taxa are native to Florida; invasive taxa 

present. Sensitive taxa have declined to about 15% and dominant C of C 

values average about 5. 

Category 3 0–37 About 70% of macrophyte taxa are native to Florida. Invasive taxa may 

represent up to 1/3 of total taxa. Less that 10% of the taxa are sensitive 

and C of C values of most taxa are <4. 
106

References

Davies, S.P. and S.K. Jackson. 2006. The biological condition gradient: a descriptive model for 

interpreting change in aquatic systems. Ecological Applications 16(4):1251-1266. 

Fore, L. S., R. Frydenborg, D. Miller, T. Frick, D. Whiting, J. Espy, and L. Wolfe. 2007. Development 

and Testing of Biomonitoring Tools for Macroinvertebrates in Florida Streams (Stream Condition 

Index and Biorecon). Final Report to FDEP. Available: 

ftp://ftp.dep.state.fl.us/pub/labs/assessment/sopdoc/sci_final.pdf. 

National Research Council (NRC). 2001. Assessing the TMDL approach to water quality management. 

Water Science and Technology Board, Division on Earth and Life Studies. National Academy 

Press, Washington, DC. 

Stoddard, J.L., D.P. Larsen, C.P. Hawkins, R.K. Johnson, and R.H. Norris. 2006. Setting expectations for 

the ecological condition of streams: the concept of reference condition. Ecological Applications 

16(4):1267-1276. 

U.S. Environmental Protection Agency (EPA). 2005. Draft. Use of biological information to better define 

designated aquatic life uses in state and tribal water quality standards: tiered aquatic life uses. 

Washington, D. C.: Office of Science and Technology. 

———. 2006. Best Practices for Identifying Reference Condition in Mid-Atlantic Streams. Washington, 

D. C.: Office of Environmental Information. EPA-260-F-06-002. 

Available: http://www.epa.gov/bioindicators/html/publications.html. 
TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters

Peer Reviews of the Florida Lake Vegetation Index and FDEP 

Responses 
Peer Reviews and Responses for LVI 

FDEP Standards and Assessment Section 1 

Peer Reviews of the Florida Lake Vegetation Index 

and FDEP Responses 

The Florida Department of Environmental Protection (FDEP) and its consultant, Leska Fore, 

developed the Lake Vegetation Index (LVI) in 2005 and further validated and calibrated the tool 

in 2007. Dr. Roger Bachmann and colleagues at the University of Florida submitted comments 

on the 2005 index development as part of the Triennial Review public comment period, and 

they later submitted additional comments on the 2007 Biological Condition Gradient (BCG) 

workshop and its role in setting thresholds for the LVI. FDEP responded in writing to those 

comments in October 2008 and revised them July 1, 2009. 

In October 2008, FDEP sought reviews from limnologists and scientists familiar with biocriteria. 

Some of the limnologists we contacted were potential reviewers that Dr. Bachmann 

recommended. Five scientists agreed to review the final LVI report that Leska Fore produced. 

FDEP only received reviews from three reviewers, despite several reminders. FDEP asked 

reviewers to evaluate the main body of the LVI report (2005 index development) and two 

appendices that described the 2007 validation and the 2007 BCG workshop results. 

Unfortunately, only one of the three reviewers evaluated the appendices, and the other two 

did not provide written comments on the appendices. Since we did not offer compensation for 

peer reviewer services, we did not press for completion. 

We asked reviewers to consider the following question, “Is the Florida Lake Vegetation Index a 

scientifically defensible and relevant tool for management and determination of impairment of 

plant communities in Florida lakes? Please specifically address 1) the Human Disturbance 

Gradient, 2) the Lake Vegetation Index and component metrics, and 3) the development of 

management thresholds.” 

FDEP responded to comments of Bachmann and colleagues in writing, but did not respond to 

the three other peer reviewers. Below we summarize and briefly respond to the major 

comments of those three reviewers. 

Clarity of report: There were many clarifying questions and comments regarding the lack of 

clarity in the LVI report. The reviewers had questions about methods or suggested that more 

detail should be provided in the report to explain decisions made or methods used. FDEP has 

not yet rewritten the report to improve its clarity. In our estimation, most of these comments 

are clarifying in nature and if the items were clarified, the reviewers would not be concerned. 

Circularity of Habitat Assessment and Water Quality in the Human Disturbance Gradient: A 

common concern among all reviewers was that aspects of the Lake Habitat Assessment and 
Peer Reviews and Responses for LVI 

FDEP Standards and Assessment Section 2 

water quality parameters in the Human Disturbance Gradient (HDG) were natural in origin and 

did not necessarily represent human disturbance. Bachmann et al. expressed that concern and 

FDEP responded to it in writing. The most recent validation of the LVI showed that it is almost 

as highly correlated with the LDI (Spearman’s r = -0.60) as with the full HDG (Spearman’s r = -

0.72), suggesting that the index is responding convincingly to a robust and objective measure of 

human disturbance (in this case, the intensity of anthropogenic landscape alterations). It 

should be noted that the HDG was only used to select metrics; the independent BCG exercise 

were used to determine whether or not the index had validity in assessing the response of 

valued ecological attributes (the plant community) to human influences. 

Lack of smaller scale regions in the LVI: A couple reviewers suggested that the LVI should have 

been calibrated separately for the 47 lake regions within Florida. Lake regions in Florida were 

based on water chemistry and did not consider vegetation communities. There are simply not 

enough lakes in each of the 47 lake regions to develop and test an index for each. The LVI 

development and validation analyses showed that some metrics responded differently in the 

southern than in the northern part of the state, so scoring for those metrics was adjusted 

accordingly. 

Adequacy of 100 m buffer for LDI: One reviewer thought that the landscape development 

intensity index (LDI) was an appropriate measure of human disturbance, but questioned 

whether the 100 m buffer LDI was sufficient to capture disturbance in the watershed. FDEP 

recently calculated LDI in the 100 m buffer and within the watershed for 358 lakes. Correlation 

analysis shows a high correlation between the LDI of the 100 m buffer and watershed, and 

similar correlations between the two versions of the LDI and the LVI (Table 1). Although there 

can be water quality disturbances within the watershed that may not be captures in the 100 m 

buffer, macrophytes are also heavily influenced by physical disturbance of the shoreline and 

within the lake, which is better captured by the near-field buffer zone. 

Table 1. Correlations between the LVI, LDI of the 100 m buffer around a lake, and the 

watershed LDI, for 358 lakes. 

100m_LDI Shed_LDI

100m_LDI 1

Shed_LDI 0.697352 1

LVI -0.4901 -0.53308
Peer Reviews and Responses for LVI 

FDEP Standards and Assessment Section 3 

Table of Contents 

1. Bachmann, Canfield, and Hoyer comments on 2005 LVI development, September 11, 2008 

2. FDEP responses to Bachmann et al. comments, October 31, 2009 (with some additions) 

3. Havens comments on 2005 LVI development, November 20, 2008 

4. Trebitz comments on full LVI report, December 3, 2008 

5. Fennessy comments on 2005 LVI development, February 2, 2009 

6. Bachmann, Canfield, and Hoyer comments on BCG workshop, May 27, 2009
Peer Reviews and Responses for LVI 

FDEP Standards and Assessment Section 4 
Peer Reviews and Responses for LVI 

FDEP Standards and Assessment Section 5 
1

September 11, 2008 

To: 

Hearing Officer 

Florida Department of Environmental Protection 

From: 

Roger W. Bachmann, Research Professor of Limnology 

Daniel E. Canfield, Jr., Professor of Limnology and Director of Florida LAKEWATCH Mark V. 

Hoyer, Assistant Director, Research Programs/Services Limnology, Florida LAKEWATCH 

School of Forest Resources and Conservation, University of Florida 

We are submitting a comment as a part of the triennial review of state surface water quality 

standards. In particular it is our opinion that the proposed Lake Vegetation Index should not be 

used for the classification of lakes as impaired by human actions. There are flaws in the design of 

the study developing the Lake Vegetation Index such that the results cannot be used to separate 

out natural factors that determine the vegetation quality in a lake from human actions. We have 

reached this conclusion through a detailed critique of the report on which the LVI is based and on 

our own analyses of the same data used to establish the LVI. A detailed report follows. Some of 

our conclusions for rejecting the use of the Lake Vegetation Index at this time are given below. 

Findings of fact 

The Human Development Gradient (HDG) used to link the Lake Vegetation Index to human 

actions is not a valid indicator of human actions, since it includes natural factors not related to 

human activities. 

The Landscape Development Intensity index (LDI) used to link the Lake Vegetation Index to 

human actions is not related to water quality in Florida lakes based on a statistical analysis of data 

from a large sample of Florida lakes. 

The experimental design did not take into account known natural factors that influence the 

distribution of aquatic plant species. In particular pH was found to be more important than the 

LDI in explaining the variance in the Lake Vegetation Index. 

The experimental design did not adequately take into account known natural variations in lake 

characteristics across the state of Florida 

The results of the study were biased by the nonrandom method used to select the lakes examined. 

The study supporting the Lake Vegetation Index has not been peer-reviewed by independent 

scientists knowledgeable about aquatic biology and limnology and having no connection with the 

Florida DEP.
2

A critique of the Lake Vegetation Index: Why it does not measure 

human impairment of Florida lakes 

Roger W. Bachmann, Research Professor in Limnology 

Daniel E. Canfield, Jr., Professor of Limnology and Director of Florida LAKEWATCH 

Mark V. Hoyer, Assistant Director, Research Programs/Services Limnology, Florida 

LAKEWATCH 

School of Forest Resources and Conservation, University of Florida 

September 11, 2008 

 The lake vegetation index (LVI) has been 

proposed by the Florida Department of Environmental 

Protection (FDEP) as a measure to identify lakes that 

are impaired by human activities. It is our 

understanding that if a lake has a LVI score in a 

certain range, it will be placed on a list of impaired 

waters and entered into a planning process for 

remediation. The justification for the index is based 

on several years of sampling on Florida lakes by the 

FDEP with the goal of establishing the LVI. Leska 

Fore, an outside consultant, carried out the data 

analyses and prepared a report (Fore 2007) outlining 

how she developed the LVI and how she related it to 

human disturbance. 

 The use of the taxonomic composition of the 

aquatic macrophytes in the littoral of a lake to 

determine human impairment is unique to the science 

of limnology. As far as we know this relationship has 

never been published in the peer-reviewed scientific 

literature. This means that the science has never been 

reviewed by independent scientists who have no 

relation to the authors or sponsoring agency. This 

kind of review is the gold standard in science. For 

example new findings in medicine are not applied 

until they have been published in the literature. The 

objective of our study is to provide such a review to 

determine if the conclusions by Fore (2007) are 

justified by the data. 

Background

 The search for a biological measure that 

indicates that a lake is impaired due to human actions 

is difficult in the state of Florida, for there is a large 

natural diversity of lakes types. There are differences 

in soils, groundwater quality, hydrology and other 

natural factors from one part of the state to another 

reflecting the diverse geological history of different 

regions of the state. To classify lakes, the state has 

been divided into 47 lake regions, with each region 

representing a cluster of lakes with similar chemical 

and biological characteristics (Griffith et al. 1997). 

Some lake regions contain lakes that are naturally 

high in nutrients and biological productivity while 

other regions have lakes with low nutrient 

concentrations and low biological productivity. 

Likewise some regions have lakes with low pH values 

and low alkalinities and other regions are 

characterized by high pH values and high alkalinities. 

The characteristics of these lakes are determined by 

the soils, geology, and hydrology of the state and are 

independent of human activities. The result is that 

prior to European settlement some of the lakes were 

high in plant nutrients, had massive growths of aquatic 

plants, algal blooms, and low water clarity. By our 

current standards these might be considered as lakes 

impaired for swimming, boating, and water supply; 

however the causes are natural and not due to human 

actions. Likewise some of the lakes naturally should 

have had low nutrient concentrations, low biological 

productivity, clear water, and white sand beaches. 

This again reflects natural factors and not the absence 

of human impacts. 

 The problem is determining if a lake that does 

not meet our requirements for some desired use is 

naturally that way or is suffering from human impacts. 

The approach of the study analyzed by Fore (2007) 

was to find one or more aquatic plant metrics that 

would indicate human impairment as opposed to 

natural impairment. Lakes were selected that had 

varying degrees of human development. An index 

called the human development gradient (HDG) was 

used as a measure of human impact and was 

correlated against a long list of possible aquatic plant 

metrics to find the ones with the highest correlations. 
3

The result was the lake vegetation index (LVI). The 

correlation between the HDG and the LVI was the 

basis for concluding that the LVI was a measure of 

human impairment. The implicit assumption was that 

human actions were responsible for the differences in 

the LVI scores and that absent those human actions 

the LVI scores of different lakes would all be about 

the same. 

Data sources

 Russel Frydenborg and Nijole Wellendorf of 

the FDEP were helpful in providing us with 

spreadsheets containing most of the data used by Fore 

(2007) in her analyses for the LVI. We also obtained 

similar data on 352 lakes collected by the FDEP in a 

study designed to use benthic macroinvertebrates to 

calculate a lake condition index (LCI) as an indicator 

of human impairment. The data for the initial LVI 

studies conducted prior to 2004 contained data on the 

pH of each lake sampled, while later data sets did not 

include this variable. This was most likely because 

pH was not used in the calculations of the HDG. 

When we found that pH might play a significant role 

in determining the LVI, we requested pH data on the 

other lakes sampled by the FDEP. In the time 

available they were able to provide pH data for some 

but not all of the additional lakes. The analyses 

reported on here are based on the 164 lakes with pH 

and the other data available. We used the JMP 

statistical package to run statistical correlations. To 

show the degree of relationship between the two 

variables an R2 score is shown. This is the square of 

the traditional Pearson coefficient of correlation and 

represents the fraction of the variance of the 

dependent variable that is explained by the 

independent variable. For example in Figure 1 the R2

of 0.38 for the first graph indicates that the LDI 

explains 38 % of the variance in the score for the 

adverse watershed land use. 

Definition of variables

 Fore (2007) presents many measurements and 

observational scores that are used to calculate the lake 

vegetation index and the human disturbance gradient. 

As an aid to the reader we have outlined them in Table 

1, and in the following section we have defined the 

metrics and discussed their application to the 

validation of the LVI as a measure of human 

disturbance. 

Lake Vegetation Index (LVI). This index is 

calculated from a standardized assessment of the 

littoral aquatic plants in a lake. It is based on four 

metrics, the percent native taxa, percent invasive taxa, 

percent sensitive taxa, and the average tolerance value 

of the taxon present over the largest area. The 

tolerance value applied was a Coefficient of 

Conservatism (CC) score that was developed for most 

of the plant species found in depressional wetlands in 

Florida and applied to the littoral vegetation of lakes. 

The scores for individual species, which range from 0 

to 10, were developed from the opinions of a group of 

botanists. In this system, plants with higher scores 

were considered the most desirable. Plants with a 

score of 7 or greater were considered to be sensitive. 

 The final LVI score is obtained through a 

complex series of manipulations of these four metrics 

and can range from 0 to 100. Lakes with low values 

of the LVI are considered to be impaired and in need 

of remedial action. In the study of Fore (2007) the 

LVI was usually taken as the dependent variable in 

statistical tests of various metrics. The correlation of 

LVI with the Human disturbance gradient was used by 

Fore (2007) to establish the LVI as a measure of 

human impairment for a lake. 

Human Disturbance Gradient (HDG). This metric 

is intended to be an independent measure of human 

activities that might influence the quality of a lake and 

originally was made up of four different measures that 

summarized water chemistry, habitat conditions, 

intensity of land use in a 100-m buffer around the 

lake, and hydrologic conditions. The hydrologic 

condition measure was not included in the final 

version of the HDG calculation (Fore 2007). 

Water Quality Index (WQ). The WQ is based on 

measurements of specific conductance, total Kjeldahl 

nitrogen, nitrates/nitrites, total phosphorus, and algal 

growth potential. It is one of the three components of 

the HDG. As discussed previously the basic water 

chemistry of Florida lakes depends on natural factors, 

so that it is not valid to assume that it is an 

independent measure of human disturbance. While 

there can be cases where human disturbance 

influences water chemistry, it cannot be assumed in 

advance. It will be shown later that there is no 

statistically significant correlation between a measure 

of human activities in a 100-m wide band around a 

Florida lake (LDI) and a range of measures of water 

quality in the adjacent lake, indicating that natural 

process are more important in determining water 

quality. 

Landscape Development Intensity index (LDI). 

The LDI is calculated from an examination of the land 

uses in a 100-m wide band around a lake. The 

percentage area of each land use in the band is 

multiplied times a coefficient of energy use for that 

land use, and the sum of the products is taken as the 

LDI. The coefficients range from 1 for natural open 

water to 10 for high intensity central business district 
4

Table 1. Metrics used by the Florida Department of 

Environmental Protection to calculate the lake vegetation 

index (LVI) and the Human disturbance gradient (HDG) as 

described by Fore (2007). Indentations show metrics used 

to calculate the metric above it in the list. As discussed in 

the text items in italics are not considered by the authors to 

be independent measures of human impact on a lake 

because they are determined by natural processes unless 

demonstrated otherwise. Also Vegetation quality is an 

observational score of the same metrics that make up the 

LVI and thus is not an independent variable indicating 

human impacts. 

_______________________________________ 

Lake Vegetation Index (LVI) 

 Percent native taxa 

 Percent invasive taxa 

 Percent sensitive taxa 

 Average coefficient of conservatism (CC) 

 for the dominant and codominant plants 

Human Disturbance Gradient (HDG) 

Water Quality Index (WQ) 

 Specific conductance 

 Total Kjeldahl nitrogen 

 Nitrates/nitrites 

 Total phosphorus 

 Algal growth potential 

 Landscape Development Intensity index (LDI) 

Habitat Assessment (HA) 

 Stormwater inputs 

 Lakeside adverse human 

 alterations 

 Upland buffer zone 

 Adverse watershed land use 

 Secchi depth 

 Bottom substrate quality 

 Vegetation quality

_______________________________________ 

uses. The higher the value for the LDI the greater is 

the inferred impact on the lake. In our opinion this is 

the only metric in the study that is based on actual 

measurements of human activities that might 

influence the adjacent lake. 

Habitat Assessment (HA). The HA is made up of 

the qualitative scores of seven different variables that 

are intended to assess the quality of the lake habitat. 

In the field a qualitative score ranging from 20 to 1 is 

assigned to each metric with 20 being the “optimal 

score” and then ranging down through “suboptimal”, 

“marginal” and “poor”. 

The following are the seven metrics used to calculate 

the HA. 

Stormwater inputs. The optimal score reflects 

stormwater that enters the system via sheetflow over 

non-cultivated and/or natural vegetation and ranges 

down to “Much direct input of stormwater (ditches, 

pipes, cultivated vegetation>51%) and no or 

ineffective BMPs in place” for the poor score. 

Lakeside adverse human alterations. The optimal 

score involves very few man-made structures, roads, 

or other disturbance adjacent to the lake (<10%). On 

the other end of the scale the lakeside is highly 

developed or disturbed (>70% of lakeside affected). 

Upland buffer zone. The highest scores reflect 

“Expected native vegetation between uplands and 

littoral zone, greater than 90% of shoreline with >18 

m buffer”. The lowest scores reflect, 29% of 

shoreline with >18 m buffer. 

Adverse watershed land use. This metric scores the 

“potential effects from adverse human land uses, 

based on a continuum of amount and type, with least 

to most adverse as follows: “Native Vegetation, 

Silviculture, Pasture or Citrus, Low density 

Residential, Row Crops, Commercial, High Density 

Residential, Urban, Industrial.”

Secchi. This scores the Secchi disk transparency with 

the highest scores for readings > 3 m and grading 

down with readings of 0.1 m. 

 In our opinion this is not an independent 

measure of human impact. The Secchi disk 

transparency is determined by natural factors such as 

lake trophic state and humic acid concentration (water 

color). While there can be cases where human 

disturbance influences water transparency, it cannot 

be assumed in advance. 

Bottom substrate quality. The composition of the 

bottom substrate is scored. On the optimal end of the 

scale the substrate is a “diverse mixture of sand, 

detritus, with small amounts of CPOM/mud/muck. 

SAV may be present. [CPOM stands for coarse 

particulate organic matter and SAV for submersed 

aquatic vegetation] On the poor end of the scale we 

have “thick deposits of CPOM, or fine detritus and 

anaerobic muck/mud/silt, or algal growth or nuisance 

plants (Hydrilla) on the bottom”. 

 In our opinion this is not an independent 

measure of human impact. The bottom substrate of a 

lake is determined by natural factors such as the 

regional soils and past geological history of the lake as 

well as the lake trophic state. While there can be 

cases where human disturbance influences bottom 

substrate composition, it cannot be assumed in 

advance. 

Vegetation quality. The vegetation quality is scored 

as optimal when it is “Diverse, expected native 

vegetation (emergent or submersed), less than 5% 

nuisance taxa.” It ranges down to poor with 

increasing amounts of nuisance vegetation. The 

poorest scores are for “lake choked (>40%) with 

nuisance macrophytes (duckweed, hyacinth, etc.) or 
5

algal mats, or few plants present (e.g. plants 

removed)”.

 In our opinion this is not an independent 

measure of human impact. The vegetation of a lake 

can be determined by natural factors such as lake 

morphometry, herbivores, and the lake trophic state. 

While there can be cases where human disturbance 

influences lake vegetation composition, it cannot be 

assumed in advance. Further the vegetation quality 

metric is a description of the supposed effects of 

human disturbance that is being estimated by the lake 

vegetation index (LVI) itself. It will be shown later 

that the vegetation quality metric is highly correlated 

with the LVI. This means that a qualified field person 

can do a good job of estimating what the 

quantitatively determined LVI will be for a particular 

lake. It is not an independent measure of human 

impact that can be used to establish the use of the LVI 

as a measure of human impairment. 

FINDINGS OF FACT 

Results

 The above analysis of the metrics that make 

up the HDG shows that several of them (e.g. specific 

conductance, total Kjeldahl nitrogen, nitrates/nitrites, 

total phosphorus, algal growth potential, Secchi, 

bottom substrate quality, and vegetation quality) are 

not independent measures of human impact on a lake. 

For this reason the HDG contains several 

measurements that cannot be used to establish a causal 

relationship between human impacts and the LVI. 

The landscape development intensity index (LDI) is 

the only independent quantitative measure of human 

disturbance adjacent to a lake. The observation scores 

on stormwater inputs, lakeside adverse human 

alterations, upland buffer zones, and adverse 

watershed land use would be qualitative indications of 

human impacts as well. 

 The plots shown in Figure 1 show that the 

LDI is correlated to the observational scores of 

stormwater inputs, lakeside adverse human alterations, 

upland buffer zones, and adverse watershed land use, 

so it seems reasonable to use the LDI score as a 

measure of human actions that might influence a lake 

in place of the HDG. The relationships between the 

LDI and the observational scores show that the trained 

field observers of the FDEP are capable of estimating 

the LDI of a lake before carrying out the detailed 

measurements. 

 The data set for the lake maroinvertebrate 

study contain data on the LDI and several measures of 

water quality and the maroinvertebrate-based lake 

condition index (LCI) for 352 Florida lakes. Plots of 

Figure 1. Relationship between measured values of the 

Landscape development intensity index (LDI), and the 

observational scores for adverse watershed land use, 

lakeside adverse human alteration, stormwater inputs, and 

upland buffer zone 

those data (Fig. 2) show that the LDI explains only a 

very small fraction of the variance in the measures of 

water quality (e.g. total phosphorus, Kjeldahl 

nitrogen) in the lakes studied. It also does not explain 

the variance in the LCI, so this bioindicator is no 

longer considered as a valid indicator of lakes 

impaired by human actions. This lack of correlation 

between the LDI and several water quality measures 

also agrees with the report of Fore (2007) who found 

no significant correlation between the LDI and the 

water quality index (WQ) for the lakes in the LVI 

study. This means that the LDI is not a good measure 

of human activities that influence water quality in 

Florida lakes. In this study it would negate the theory 

that the human activities measured by the LDI change 

the water quality which in turn influences the 

taxonomic composition of the littoral plants as 

measured by the LVI. It also calls into question the 

use of the LDI in other studies to relate human 

activities to biological responses. 

 The lake vegetation index (LVI), a 

quantitative measure of some aspects of the littoral 

plant community, is related to the observation score 

on vegetation quality (Fig. 3). As mentioned 

previously this is not surprising in that it is estimating 

several of the same factors as are used to calculate the 

LVI. It is a tribute to the skills of the trained field 

observers of the FDEP that they can look at a lake and 

estimate a metric that requires a significant time and 

effort in field and laboratory work to calculate. 
6

Figure 2. Plots of the landscape development intensity 

index against total phosphorus, total Kjeldahl nitrogen, 

chlorophyll, Secchi disk depth, macroinvertebrate-based 

lake condition index (LCI), and turbidity. These data are 

from 352 lakes studied to develop the LCI. 

Figure 3. The lake vegetation quality score as estimated by 

a field observer and the lake vegetation index (LDI) as 

calculated from plant identification and plant 

characteristics. 

 The LDI does explain about 27 % of the 

variance in the measures of LVI in the test lakes (Fig. 

4), which might indicate that the LVI is related to 

human activities. The problem is that the pH explains 

even more of the variance (34 %) than the LDI. 

Figure 4. The lake vegetation index (LVI) plotted against 

the pH of the lake water and the landscape development 

intensity index (LDI). 

 A more detailed look at pH (Fig. 5) shows 

that pH is correlated with several other plant metrics 

including the % native plants, % sensitive plants, % 

invasive plants, and % Category I and II invasive 

plants. It is also correlated with the coefficient of 

conservatism (CC) of the dominant and codominant 

plants in a lake. In general the lakes on the acidic end 

of the scale are more likely to have higher percentages 

of native and sensitive plants and to be dominated by 

plants with narrow ecological requirements (high CC 

score) while more alkaline lakes are more likely to 

have invasive plants and be dominated by plants able 

to tolerate a broader range of ecological conditions 

(lower CC scores). 

 Aquatic botanists have known for a long time 

that pH can influence the distribution of aquatic 

plants. In his classic study of the aquatic plants of 

Minnesota Moyle (1945) found that each plant species 

had its own pH preference, and he went on to set up a 

plant classification based on the preferred pH and 

alkalinity for each species. Moyle (1945) pointed out 

that Iversen (1929) had previously shown for the lakes 

of Denmark that the distribution of aquatic plants 

could be related to the pH of their waters. Hoyer et al. 

(1996) in their study of Florida freshwater plants 

surveyed the aquatic plants of over 322 Florida lakes 

and noted that each species had its own range of 

preferred chemical conditions including pH. These 

findings were in agreement with Beal (1977) who 

made extensive surveys of North Carolina lakes and 

found that the distributions of many species of aquatic 

plants coincided with the distributions of pH and other 

chemical factors. 

 The pH of lakes is generally determined by 

the chemical composition (balance between acidic and 

basic ions) of the inflows from precipitation, 
7

Figure 5. Plots of lake pH versus several measures of the 

composition of the littoral aquatic plant community 

including the lake vegetation index (LVI), the percent 

native taxa, percent invasive taxa, percent sensitive taxa, 

percent category I and II invasive taxa, and the coefficient 

of conservatism (CC) of the dominant and codominant plant 

taxa. 

groundwater and surface inlets and is closely related 

to the surrounding geology and soils (Stumm and 

Morgan 1995). In the Florida lake regions study 

(Griffith et al 1997) pH was found to differ from one 

lake region to another. There was a tendency for lake 

regions in the southeast to have higher pH values than 

those in the northwest regions, but there was 

considerable variation at the same latitude as well. 

 Separating the effects of pH versus human 

activities in determining the LVI is complicated by the 

fact that in the data set used for the LVI study there is 

a significant relationship between the pH of a lake and 

the amount of human development as measured by the 

LDI (Fig. 6). One possibility is that the effects of 

human development somehow increase the pH, 

though there are no data to support this contention. 

Since the LDI was not important in determining 

several other water quality measures (Fig. 2), it seems 

unlikely that it would raise pH values. 

 We might be able to sort out the effects of 

human activities versus pH on LVI if our sample of 

lakes included some lakes with high values for LDI at 

Figure 6. Plots of pH versus the landscape development 

intensity index (LDI) for the lakes selected for the LVI 

study and the lakes selected for the prior macroinvertebrate 

LCI study. 

low pH and low LDI values at a high pH. These 

combinations are rare on non-existent in the LVI 

study lakes (Fig. 6), but they are present in the sample 

of macroinvertebrate study lakes also presented in 

Figure 6. This indicates that such lakes do exist in 

Florida, but they were not included in the LVI study. 

 The above analysis leads to the question of 

how the study lakes were selected. Fore (2007) 

indicated that the sample did not include all lake 

regions and FDEP personnel have indicated that a 

random process was not used to select the lakes in the 

LVI study. Presumably they wanted to develop their 

lake vegetation index by selecting a sample of lakes 

with a wide variation in human impacts and a wide 

variation in lake vegetation. The problem with this 

procedure is illustrated by the following example. 

 A psychologist had a hypothesis that on 

average winners of Olympic bronze medals (3rd place) 

were happier than those who won silver medals (2nd

place). He tested this by obtaining video clips of all 

the silver and bronze medal winners at the 1992 

Olympics in Barcelona taken at the time they learned 

their rankings. He then selected a sample of the clips 

for use by a panel of experts to judge the relative 

happiness of each medal winner as they appeared in 

the videos. The psychologist could then determine if 

the bronze medal winners had a higher average 

happiness score than the winners of the silver medals 

(they did). The critical part of the experimental 

design is the method he used to select the sample to be 

viewed by the panel. If when he made the decision 

whether to use a particular video clip in his sample he 

knew what kind of medal the athlete had won, the 

sample would be biased. He might have a 

subconscious tendency to select happier bronze 

medalists and less happy silver medalists. In that case 

the results would be suspect. He did use the proper 

way to select his sample by using a random method to 

pick his athletes like using a table of random numbers 

or picking names out of a hat. In this way he was able 
8

to eliminate any possible bias on his part that might 

influence the result. 

 The selection of the lakes used in the LVI 

study is subject to possible subconscious biases as 

shown in the above example. The data have shown 

that a skilled FDEP field scientist can look at a Florida 

lake and estimate the likely LDI score (Fig 1) and at 

the same time estimate the likely LVI value (Fig 3). 

There could be a tendency to subconsciously or 

consciously select lakes with a lot of human activity 

and vegetation dominated by exotics and plants 

tolerant of a wide range of conditions and then select 

other lakes that had little or no human development 

and a predominance of native plants. Since pH was 

closely related to LVI scores, the differences between 

the plots of pH and LDI in the lakes chosen for the 

LVI study versus the same plots for the lakes used in 

the macroinvertebrates studies (Fig. 6) would seem to 

indicate that this was the case for the LVI study. 

 The end result is that the correlation between 

human actions and the LVI score is most likely a 

statistical artifact that derives from the use of a 

selective lake sample rather than a random sample. 

For this reason the results of this study cannot be used 

to support the use of the LVI as a measure of human 

impairment in Florida lakes. It should not be added to 

the DEP regulations. 

References

Beal, E. O. 1977. A manual of marsh and aquatic 

vascular plants of North Carolina with habitat 

data. Technical Bulletin No. 247. North 

Carolina Agricultural Experiment Station, 

Raleigh NC. 

Fore, L. 2007. Assessing the biological condition of 

Florida lakes: Development of the lake 

vegetation index. Florida Department of 

Environmental Protection. 

http://www.dep.state.fl.us/labs/library/ 

Griffith, G.E., D.E. Canfield, Jr., C.A. Horsburgh, & 

J.M. Omernik, 1997. Lake Regions of Florida. 

EPA/R-97/127. U.S. Environmental Protection 

Agency, National Health and Environmental 

Effects Research Laboratory, Corvallis, OR. 

89pp. 

http://aquat1.ifas.ufl.edu/education/misc_pdfs/L

ake_regions.pdf 

Hoyer, M. V., Canfield, D. E., Jr., C. A. Horsburgh, 

and K. Brown. 1996. Florida freshwater plants 

– A handbook of common aquatic plants in 

Florida lakes. University of Florida Institute of 

Food and Agricultural Sciences. SP 186 

Iversen, J. 1929. Studien über die pH-Verhältnisse 

dänischer Gewässer and ihren Einfluss auf die 

Hydrophyten-Vegetation. Bot. Tidskrift. 

40:277-326. 

Moyle, J. B. 1945. Some chemical factors influencing 

the distribution of aquatic plants in Minnesota. 

American Midland naturalist. 34:402-420. 

Stumm, W., and J.J. Morgan. 1995. Aquatic 

Chemistry: Chemical Equilibria and Rates in 

Natural Waters. Wiley. NY. 
DEP Responses to LVI comments from Bachmann et al. 1 of 7 

DEP Standards and Assessment Section Response to comments dated 

September 11, 2008, from Dr. Roger Bachmann and colleagues concerning 

the proposed inclusion of the Lake Vegetation Index in Triennial Review 

(Original response dated October 31, 2008; some additional figures in current version) 

Below are responses to the points made in Bachmann et al.’s “Finding of Fact” summary 

on page 1 of his comments. 

Comment 1: “The Human [Disturbance] Gradient (HDG) used to link the Lake Vegetation 

Index to human actions is not a valid indicator of human actions, since it includes natural 

factors not related to human activities.”

DEP Response: Bachmann et al. correctly point out components of the HDG that can 

be influenced by both human activity and natural factors. It is impossible to completely 

distinguish between the contributions of humans and nature, so we used these elements 

to be inclusive of potential human impacts. For the water quality index, nutrients are 

naturally occurring in FL lakes, but it is well known that human disturbance also 

increases nutrient levels. Conductivity can also be naturally high where water contacts 

limestone, but typically reflects ions (e.g., calcium, chloride, sulfate) associated with 

human disturbance (urban and agricultural runoff). 

 We were also concerned that some of the habitat assessment component 

measures were natural drivers, and we tested them separately. The three components 

Bachmann et al. noted as potentially influenced by nature (Secchi depth, bottom 

substrate quality, vegetation quality) were also found to have the lowest correlations with 

the HDG (Spearmans r = -0.21, -0.28, -0.51 vs. Spearmans r = -0.65 to -0.79 for the four 

other components) in an analysis conducted by Leska Fore in 2005 (Memo RE: 

Evaluation of the lake HDG after excluding the habitat index, dated August 8, 2005), 

indicating that they were not the driving influences of the HA scores on the HDG and 

therefore did not greatly influence the resulting biological index. 

 The LDI, an assessment method developed by the University of Florida Center 

for Wetlands, was entirely a measure of human disturbance. 

Comment 2: “The Landscape Development Intensity index (LDI) used to link the Lake 

Vegetation Index to human actions is not related to water quality in Florida lakes based 

on a statistical analysis of data from a large sample of Florida lakes.”

DEP Response: The components of the HDG were intended to complement each other 

to form the strongest HDG we could design because we know that no one measurement 

alone can quantify human disturbance. It is not surprising or problematic that the LDI 

does not correlate well with selected water measurements in Dr. Bachmann’s analysis. 
DEP Responses to LVI comments from Bachmann et al. 2 of 7 

The LDI estimates the level of human activity in the surrounding landscape, which 

affects aquatic systems through means other than water chemistry. Human 

development changes community structure as exotics are introduced, it changes flow 

regime as more run-off goes to the lake, it changes the types of biological interactions as 

pesticides eliminate sensitive pollinators and herbicides destroy native vegetation, it 

changes habitat structure when soils are compacted or eroded, and it changes the 

energy source available to plants as canopy cover is removed and nutrients are added. 

Summarizing human influence is notoriously difficult. For this reason we 

considered various modes of disturbance when we developed the HDG. The water 

quality index was correlated with the habitat index which measures human disturbance 

at a scale closer to the lake. Thus, human influence at the lake’s edge may be a better 

predictor of changes in water quality. 

Comment 3: “The experimental design did not take into account known natural factors 

that influence the distribution of aquatic plant species. In particular pH was found to be 

more important than the LDI in explaining the variance in the Lake Vegetation Index.”

DEP Response: Bachmann et al. state that there are lakes in Florida with very low pH 

and high human disturbance but that we did not include those lakes in the LVI 

development studies. As evidence of this omission, they compare the relationship 

between pH and the Landscape Development Intensity index (LDI) from LVI study lakes 

(206 of approximately 360 lake samples, not all data readily available) and 

macroinvertebrate study lakes (341 lake samples), and point out that there are samples 

of low pH (<5) and high LDI (>6) in the macroinvertebrate study set but not in the LVI 

study set. Further investigation showed that the 2 lakes with LDI = 8.1 and pH 4.2-4.4 

were Lake Submarine and Little Lake on a military base in Highlands county. The LDI is 

high because the land use is considered Institutional/Military and gets a high energy use 

coefficient, but the lakes are actually minimally disturbed and surrounded by pine 

forests. That example shows that the LDI needs to be supplemented with other 

observations and that the points in the upper left corner of Figure 6(b) in Bachmann et 

al. are erroneous. Below is a graph of pH vs. LDI for 860 randomly selected lake 

samples statewide from the full first cycle of the Status Monitoring Network. It shows 

that Florida is, in fact, lacking lakes of low pH and high LDI, and no type of lake was 

purposefully excluded from the LVI analysis. 
DEP Responses to LVI comments from Bachmann et al. 3 of 7 

IWRM Cycle 1 - 2000-2003

860 Statewide Randomly Selected Lake Samples

0

1

2

3

4

5

6

7

8

9

10

2 4 6 8 10 12

pH

LDI

Figure 1. Human activities, measured by the Landscape Development Intensity index, 

are positively related to pH levels in lakes (N=860 samples statewide, probabalistic 

sampling Status Network, 2000-2003). 

It is clear from Figure 1 above and Figure 6 in Bachmann et al. that there is a 

wide range of natural pH values (at low LDI). While some plant species may favor one 

pH regime or another, the LVI does not assess the presence or absence of any one 

species of plant or any one community type; it assesses whatever taxa are present as 

native, invasive exotic, specialists, or generalists. There is no evidence that the taxa on 

the Florida Exotic Pest Plant Council list of invasive exotic plants (which informs the % 

invasive exotic metric in the LVI) prefer lakes of high pH over lakes of low pH. Similarly, 

there is no evidence that all taxa deemed sensitive in the LVI prefer low pH. 

While there is a natural range in pH values in Florida’s lakes, human activities 

can artificially increase the pH of lakes through various mechanisms. Through one 

avenue, stormwater run-off events contribute ions (Ca, Mg, SO4, etc) from the soil which 

then raise pH in receiving waters. A second aspect relates to leaf litter in lakes. 

Development clears vegetation from lake shores, reducing the amount of vegetation 

falling into lakes to reduce tannin which increase pH. Furthermore, excess nutrients 

from human disturbance increase phytoplankton production with causes more carbon 

fixation and an increase in pH, as evidenced in various lake fertilization experiments 

(eg., Schindler et al. 1971, O’Brien and deNoyelles 1974, Wetzel 2001). 

Also note that the gradient of pH is confounded with a gradient of human 

disturbance. Human disturbance increases in a general pattern from north to south 

throughout the state. Acidity is higher in northern lakes due to tannins and other natural 

biogeographic features. It is important to note that we adjusted metric scores to reflect 

these natural differences. 

Bachmann’s assertion that pH explains the variance in the LVI better than the 

LDI is not true. The LVI is negatively correlated to the LDI regardless of the pH of the 

lake (Figure 2), whereas the relationship between the LVI and pH is driven by a set of 

low pH lakes in minimally disturbed condition (Figure 3). 
DEP Responses to LVI comments from Bachmann et al. 4 of 7 

Figure 2. The Lake Vegetation Index responds to human disturbance regardless of pH 

level (N=172 samples collected 2005-2008). 

Figure 3. The perceived relationship between pH and the LVI is driven by undisturbed 

low pH sites, without which there is no relationship. When linear trendlines are drawn for 

LDI categories, for LDI < 2 R2 = 0.16, all others R2 < 0.06 (data source same as Figure 

2). 
DEP Responses to LVI comments from Bachmann et al. 5 of 7 

Comment 4: “The experimental design did not adequately take into account known 

natural variations in lake characteristics across the state of Florida.”

DEP Response: A wide variety of minimally disturbed lakes were included in the study, 

located in the majority of the lake physiographic regions identified by Griffith et al. DEP 

is confident that these minimally disturbed lakes included the range of naturally occurring 

variables expected to influence lake plants (color, pH, conductivity, oxygen regime, soils, 

etc.). Also included in the data set were lakes subject to a variety of human influences 

which could also affect these parameters. 

Comment 5: “The results of the study were biased by the nonrandom method used to 

select the lakes examined.”

DEP Response: The 95 lakes used for the initial development of the LVI were chosen 

by DEP Biologists to represent the full range of conditions in lakes around the state, as 

correctly noted by Bachmann et al, “Presumably they wanted to develop their lake 

vegetation index by selecting a sample of lakes with a wide variation in human impacts 

and a wide variation in lake vegetation.” However, all of the 63 lakes in the initial 

validation dataset of 2005 completely consisted of lakes randomly chosen as part of 

Florida’s Status Network, a probabilistic sampling network that follows EPA’s EMAP 

design. 

The second validation dataset of 2007 contained 167 lakes; 104 of those lakes were 

randomly chosen for the Status Network, 29 lakes were chosen to represent conditions 

across the disturbance gradient and around the state, and the remaining lakes were 

sampled for other purposes. Both validation datasets showed strong relationships 

between the LVI and the HDG. Even if the initial lake selection was somehow biased, 

73% of the lakes used for the two validations of this tool were indeed randomly chosen. 

The two validations conclusively showed that the LVI was primarily responding to human 

disturbance when using data from probabilistic surveys. 

Comment 6: “The study supporting the Lake Vegetation Index has not been peerreviewed by independent scientists knowledgeable about aquatic biology and limnology 

and having no connection with the Florida DEP.”

DEP Response: We received suggestions from Dr. Bachmann and his colleagues of

appropriate independent reviewers. DEP is contacting several authors of peer-reviewed 

journal articles about vegetation indices of biotic integrity, as well as limnologists 

specializing in plant ecology, for reviews of the LVI. 

Below are responses to other statements within the text of the Bachmann et al. 

comments: 

P. 2. “The use of the taxonomic composition of the aquatic macrophytes in the littoral of 

a lake to determine human impairment is unique to the science of limnology. As far as 

we know this relationship has never been published in the peer-reviewed scientific 

literature.”
DEP Responses to LVI comments from Bachmann et al. 6 of 7 

Several authors have published results relating macrophyte community composition to 

human influence in wetlands and shoreline habitats of lakes in CO, PA, MN, WI, OH, 

and IN. 

Fennessy, S., M. Gernes, J. Mack, and D. H. Wardrop. 2002. Methods for evaluating 

wetland condition. # 10 Using vegetation to assess environmental conditions in 

wetlands. EPA-822-R-02-020. U.S. Environmental Protection Agency. Office of 

Water, Washington, D.C. 

http://www.epa.gov/waterscience/criteria/wetlands/10Vegetation.pdf

* Lopez, R.D., C.B. Davis, and M.S. Fennessy. 2002. Ecological relationships between 

landscape change and plant guilds in depressional wetlands. Landscape Ecology 

17(1): 43-56.

* Lopez, R.D. and M.S. Fennessy. 2002. Using the floristic quality assessment index 

(FQAI) in wetlands as a biological indicator of landscape condition along a 

gradient of human influence. Ecological Applications 12(2): 487-497. 

* Nichols, S., S. Weber, and B. Shaw. 2000. A proposed aquatic plant community biotic 

index for Wisconsin lakes. Environmental Management 26:491-502. 

* Mack, J. 2006. Landscape as predictor of wetland condition: an evaluation of the 

Landscape Development Index (LDI) with a large reference wetland dataset from 

Ohio. Environmental Monitoring and Assessment 120: 221-41. 

Mack, J. J., N. H. Avdis, E. C. Braig IV, and D. L. Johnson. Application of a vegetationbased index of biotic integrity for Lake Erie coastal marshes in Ohio. Aquatic 

Ecosystem Health and Management 11: 91-104. 

Miller, S.J., D.H. Wardrop, W.M. Mahaney, and R.P. Brooks. 2006. A plant-based index 

of biological integrity (IBI) for headwater wetlands in Pennsylvania. Ecological 

Indicators 6 (2):290-312.

Rocchio, J. 2006. Vegetation Index of Biotic Integrity for Southern Rocky Mountain Fens, 

Wet Meadows, and Riparian Shrublands: Phase 1 Final Report to Colorado 

Department of National Resources, Denver CO. 

Rothrock, P.E., T.P. Simon, P.M. Stewart. 2008. Development, calibration, and 

validation of a littoral zone plant index of biotic integrity (PIBI) for lacustrine 

wetlands. Ecological Indicators 8: 79-88. 

* Cited in references of LVI Final report. 

P. 3. “In the study of Fore (2007) the LVI was usually taken as the dependent variable in 

statistical tests of various metrics.” 

This statement is misleading and implies that the LVI was tested against its component 

metrics, which would be a circular test. We use the term “metric” to mean a measure of 

biological condition, e.g., the percentage of native plant taxa. The authors are perhaps 

referring to HDG which they incorrectly term a metric. 

“The correlation of LVI with the Human disturbance gradient was used by Fore (2007) to 

establish the LVI as a measure of human impairment for a lake.” 

This statement is not exactly true. We tested metrics for correlation with independent 

measures of human disturbance. Metrics with consistent and predictable responses 

were combined into an index, LVI, and tested with a new data set. 

P. 8. “The end result is that the correlation between human actions and the LVI score is 

most likely a statistical artifact that derives from the use of a selective lake sample rather 

than a random sample.”
DEP Responses to LVI comments from Bachmann et al. 7 of 7 

DEP disagrees that correlation with LVI is a statistical artifact. Repeat testing for metrics 

(1 data set) and LVI (2 additional data sets) were all consistent. Most of the sites were 

randomly selected (see Comment #5 above). 

As a final demonstration that the LVI is fundamentally linked to nationally accepted 

ecological principles, the index was calibrated using the EPA Biological Condition 

Gradient (BCG; see Appendix 7 of Fore’s 2007 report). For this exercise, 20 

experienced Florida lakes biologists independently scored 30 data sets of actual lake 

plant lists according to the definitions established in the BCG document. These 

scientists had NO knowledge of the lakes' water chemistry, location (other than north or 

south), or LVI score. They scored the lakes based on the plant community structure and 

how far it deviated from an undisturbed condition. The very high correlation between the 

BCG scores and the LVI scores (see App. Figure 7.1 of Fore 2007 and below) provides 

convincing evidence that the index is very suitable for determining plant community 

degradation, and can be used as Biocriteria. 

Mean BCG Score vs. LVI for 30 Workshop Lakes

y = -16.469x + 110.7

R

2

 = 0.8869

0

10

20

30

40

50

60

70

80

90

100

1 2 3 4 5 6

Mean Biocondition Gradient Score Lake Vegetation Index

References Cited 

O’Brien, W.J. & F. DeNoyelles, Jr. 1974. Relationship between nutrient concentration, 

phytoplankton density, and zooplankton density in nutrient enriched experimental ponds. 

Hydrobiologia 44:105-125. 

Schindler, D.W., F.A.J. Armstrong, S.K. Holmgren, & G.J. Brunskill. 1971. Eutrophication of Lake 

227, Experimental Lakes Area, Northwestern Ontario, by addition of phosphate and 

nitrate. J. Fish. Res. Bd. Canada 28: 1763-1782. 

Wetzel, R.G. 2001. Limnology: Lake and River Ecosystems, 3rd ed. Academic Press, San Diego. 

1006 pp. 
1 

Florida Sea Grant College Program Building 803 McCarty Drive

A statewide university program for P O Box 110400 

Coastal Research, Education & Extension Gainesville, FL 32611-0400 U.S.A. 

(352) 392-5870

FAX (352) 392-5113

khavens@ufl.edu 

www.flseagrant.org

November 20, 2008 

Nia Wellendorf 

Florida Department of Environmental Protection 

Standards and Assessment Section 

2600 Blairstone Rd, MS 6511

Tallahassee, FL 32399 

Ms. Wellendorf: 

The following are my comments and suggestions regarding the FDEP document entitled ‘assessing the 

biological condition of Florida lakes: development of the lake vegetation index.’ These comments are based 

on my own professional experience and should not be construed as representing the viewpoint of the 

University of Florida, the UF Institute for Food and Agricultural Sciences, or Florida Sea Grant. I speak only 

for myself in providing this expert review. 

I decided to not read the comments provided by Dr. Bachmann or the FDEP responses to those comments, and 

instead provide this independent assessment of the report. I hope the comments are helpful to the FDEP in 

supporting efforts to use science-based information to protect and sustain Florida aquatic resources. This 

project is a good starting point in regard to using aquatic vegetation as an index of disturbance, but as noted 

below, there are some major issues to be resolved in future work and before this index is implemented. 

The following comments are in order of occurrence of related text / figures / tables in the report. 

Page 8 – it is noteworthy that under ‘quantifying human disturbance’ the FDEP measures water quality 

attributes that vary widely among Florida lakes and to a large extent are influenced both by natural and human 

factors. 

Page 8 – it is unclear why ‘percentiles from a lake-wide dataset’ would be used to standardize the nutrient 

data, vs. standardizing by lake region, as is being done in the development of numeric nutrient criteria. 

Page 9 – similar to water quality, there is tremendous natural variation among Florida lakes in regard to 

vegetation density and diversity (vegetation quality) and bottom substrate type. This variation is not uniquely 

linked with the actions of humans. 

Page 9 – in my experience, hydrology is one of the major drivers of aquatic vegetation diversity and water 

quality in Florida lakes, especially those which periodically experience low water during droughts and high 

through-flow during wet periods. Yet this critical driver can take on values of only 0 or 1. 
Page 18 – while I agree with the authors that it is important to consider more than just statistical significance 

when conducting correlation-based analyses, identifying p > 0.4 is hardly what I would consider ecologically 

meaningful. When p = 0.4, just 20% of the data variability has been explained. 

Page 19 – the comments about ‘concern that underlying natural features or processes could bias the biological 

assessment’ could equally apply to the HDG and its associated metrics.

Page 20 – it is surprising to see that LDI does not correlate with LVI. As I understand it, the LVI is being 

developed as a tool that can be used in a comprehensive analysis of how water quality and associated flora and 

fauna have been impacted by humans. However, of the metrics used to derive the HDG, all could be 

associated with natural variation. The one attribute expected to reflect human impacts is LDI and it does not 

correlate with water quality. 

Page 21 – one of the most serious problems that I see in this study is incorporating a metric called ‘habitat 

quality’ into the multi-metric HDG, which subsequently is examined in relation to the LVI. Because habitat 

index is driven in part by a survey of the conditions of plants on the lake (and sediments, which are related to 

the plants), and LVI is a quantitative measure of the plants on / in the lake, of course the HDG and LVI are 

correlated. It suggests to me that results like those shown in Figures 3, 7 and 11 may just reflect spurious 

correlations because the X and Y axes contain measurements of the same variables using different methods of 

observation. 

Page 26 – here in the third full paragraph the authors note that there are not ‘concerns’ about redundancy of 

metrics in the LVI because correlation coefficients are below 0.8. This is not consistent with using 0.4 as a 

cutoff for including attributes earlier, as noted in the comment regarding page 18 above. 

Page 27 – but more broadly – one of the underlying issues is that we do not know what factors lead to greater 

development of certain regions of the state and subsequent greater potential impacts on the associated water 

resources. Some of the most pristine areas of Florida occur where there are nutrient-poor sandy soils, and 

logically those areas would not be conducive to development such as agriculture. Other areas with richer soils 

may have historically been a greater focus for development, and at the same time, have contained water 

resources with higher natural levels of nutrients. It is very difficult to tease this out except by finding regional 

‘reference sites,’ an approach discussed by the nutrient criteria panel but not explicitly done here.

Page 28 – after a reasoned statement about needing to look at relationships from the standpoint of what is 

ecologically important vs. just statistically significant, I am surprised to see the phrase ‘highly correlated’ in 

the legend of Figure 4, where one of the panels looks like a scatter-shot of data points. 

Page 29 – similar comment about the next figure, and here I reiterate the concern that the Y and X axes plot 

metrics that have spurious correlation. 

Page 30 – same comment as on page 28 – the lower right hand figure is a scatter shot. 

Page 53 – again, to reiterate, the primary conclusion that ‘the different types of plants found in Florida lakes 

were strongly associated with lake conditions and the level of human disturbance observed around the lake 

perimeter’ is misleading because the HDG incorporates a metric that involves observing and categorizing 

condition based on the plants, and this metric is a substantive component of HDG because a very important 

metric, dealing with hydrology, is relegated to a 0 or 1 scoring system. 
Review of the lake vegetation index, human disturbance index, and development 

of management thresholds, as presented in the Fore et al. report, “Assessing the 

biological condition of Florida lakes: development of the Lake Vegetation Index 

(LVI)”.

The review guidelines ask us to address the technical merit of the lake vegetation 

index, the human disturbance index against which it is calibrated, and the development 

of management thresholds. I address each of these in turn below. I also provide some 

suggestions for making the report itself more user-friendly. This evaluation is based on 

my 15+ years of professional experience with wetlands and shallow lakes. While a 

good part of that experience has been gained during my career with the U.S. EPA, my 

comments should not to be construed as any official viewpoint of the EPA. 

Human disturbance gradient

 The report develops the human disturbance index as a composite of a measure 

of landscape development intensity (LDI), habitat condition, and water chemistry. 

 The LDI was taken directly from a peer-reviewed publication (Brown and Vivas 

2005) that demonstrates strong correlations to nutrient loading and rapid condition 

assessment scores for wetlands across the state of Florida. Furthermore, the LDI is 

conceptually quite similar to an agricultural intensity index that was successfully used as 

the disturbance gradient in a large biotic indicator development for wetlands along the 

coast of the Great Lakes (see J. of Great Lakes Res. 2007 volume 33 special issue 3). 

Both the scientific basis and the geographic applicability for the landscape development 

index seem sound to me. I do wonder, however, about summarizing landuse only in a 

100 m buffer around the lakes. The report cites a study that finds 100 m buffers 

adequate for the flat Florida terrain, but it is not hard to envision development that might 

preserve a shoreline buffer yet impact the watershed beyond in ways which are then 

delivered to the lake via streams or groundwater. Could the assumption that the 

condition of the 100 m buffer adequately reflects the condition of the whole watershed 

be evaluated with a subset of your lakes? [As a side note, if you evaluated the buffers 

from 1995-era aerial photographs, presumably by digitizing the lake outline in a GIS 

and then using a GIS buffering tool, can’t these digitized polygons be used to infer the 

missing lake surface areas?]. 

 Combining a variety of water chemistry endpoints into an overall index is also an 

established practice in the literature. The data set used in this report is from >1000 

randomly-selected lakes, which is a large sample size collected with an appropriate 

experimental design. I am concerned, however, about scoring water quality based 

simply on the distribution percentiles without any inspection of biological relevance. 

What if the lakes sampled are not well distributed across the disturbance gradient? As 

a hypothetical example, if 80% of the lakes were in “natural” condition, then 80% of the 

range in the index would be “used up” describing the natural variability, while only a 

small portion of the index’s range would actually be relevant to disturbed systems. 

Since the report does not present the water quality data and I am not personally familiar 

with Florida lakes and wetlands, I don’t know whether mismatch between the 
percentiles of the water quality distribution and the actual condition gradient is actually 

an issue, but I would encourage the authors to address this possibility by showing the 

range of values that their scoring criteria is based on and by verifying that it 

appropriately covers the disturbance gradient. Might an inappropriate distribution of 

percentiles be the reason for the unexpectedly low correlation between LDI and water 

quality? At the least, I’d like to see how the nutrient distribution compares to some 

simple benchmarks such as those commonly used to classify systems as oligo-, meso-, 

or eutrophic. If further analyses do reveal a concern about the water quality gradient, 

then a more biologically relevant scoring system needs to be devised. 

 The habitat condition index incorporates aspects of vegetation structure both 

directly and indirectly through the measures of substrate, which can be heavily 

influenced by plants via their detrital inputs and role in stilling water movement and 

encouraging sediment deposition. The habitat condition index is therefore not 

biologically independent of the lake vegetation index, and it is circular to evaluate the 

latter against the former. However (If I am understanding correctly) the habitat data 

was collected in a different study and is independent from the lake vegetation index as 

a data set. It is entirely appropriate therefore that values of the habitat condition index 

be compared to values of the LVI (e.g., via correlation), but the habitat index values 

should not be used in developing the LVI (e.g., metric selection, scoring). 

 Given my concerns with the water quality index and habitat condition index, I feel 

the authors should focus on landscape development intensity alone as the disturbance 

gradient against which the vegetation index is evaluated, but from Table 6, it appears 

this would result in little change in that index. 

Lake vegetation index

 Both using vegetation as a condition indicator and the approach to indicator 

development taken in this study are scientifically well established. There is a 

considerable literature on macrophytes as indicators of the condition of aquatic systems 

(the part of the introduction summarizing this literature could be expanded to better 

reflect this), and this study follows an established approach to indicator development –

find the sensitive metrics from a fairly large list of candidates using some sort of 

external disturbance gradient, score and combine them, validate on an independent 

data set, evaluate spatial and temporal variability, examine the sample size needed, 

etc. 

 Scoring an index separately for water bodies in different regions is an accepted 

practice, but ought to be done in a way that is readily “traced” in the final index rather 

than being “buried” in a less obvious step. So I’d rather see you attach different 

expectations to scores for panhandle vs. peninsula lakes than compute the 5'th and 

95'th percentile separately for these regions prior to scoring. The end result may be the 

same, but the fact that scores were adjusted by region is less likely to get lost. 

 There are a few details of how the data for the LVI are obtained that I found 

insufficiently well described. In boat-based sampling, it can be difficult to tell where the 

shore line is if the emergent vegetation is dense. Did you only record plants actually 

growing in the water, or did you also record plants growing along wet shorelines? 

Similarly, it is not clear how field crews did the searching - was it an exhaustive search 

or was it somehow time limited, and was the boat simply driven parallel to shore (at 
some distance or depth contour) or did crews make an effort to cover the entire 

vegetated zone (which could be narrow or wide depending on how steep-sided the lake 

is)? Any of these protocols is scientifically defensible if consistently applied, but the 

methods details ought to be clearly stated. 

 I did not find the discussion of interpreting differences between spring and 

summer data particularly helpful. Managers would probably prefer a concrete 

recommendation on what the index period ought to be, even if the growing season is 

long in Florida. Plants being in flower to aid field identification is a reasonable criteria 

for the index period. 

 As in other studies, the approach to index development taken in this report is 

empirical and driven by the data at hand, so I don’t think it appropriate to criticize the 

types of metrics that were considered. However I do think the authors might want to 

more explicitly acknowledge the limitations of presence/absence data. For example, 

I’m not surprised that avg. coefficient-of-conservatism of the plants present were not 

strongly correlated to the lake disturbance levels, since sensitive plant taxa can persist 

for some time even in conditions under which they do not thrive, and taxa that come to 

dominate under eutrophication and disturbance may already be present in less 

disturbed systems. Simple presence/absence will not capture such subtleties. 

 I strongly encourage you to retain the dominant C of C metric as part of your LVI, 

as this is the only metric that has any connection to plant structure. C of C doesn’t itself 

measure structure, but the fact that the dominant species is identified means that shifts 

in C of C can parallel shifts towards more invasive species, denser cover, greater areal 

extent, etc. Personally, I’d like to see a vegetation index that more explicitly reflects the 

ecosystem role of aquatic plants (as substrate, shelter, detritus source, modulator of 

physical habitat, etc.), instead of considering only floristics. But plant composition 

certainly is accepted as an biotic condition indicator, so the sorts of vegetation metrics 

considered depend on the goals of the project. I do wonder if there is some way to get 

at plant abundance or areal extent from the data you already have. For example, you 

might experiment with using the number of lake segments in which a taxa was found as 

an index of abundance rather than simply tallying presence across multiple segments. 

Development of management thresholds

We were asked to evaluate “the development of management thresholds” but 

there is actually not much on this in the report, and what is there is hidden in an 

appendix that does not use “management thresholds” as a term. This aspect of the 

study should be made much more prominent! 

 Threshold values for the three LVI categories (App. Table 7.7) appear to have 

been derived by averaging the BCG score that each expert thought divided exceptional 

from good and good from impaired conditions, and recording the corresponding LVI 

value on a regression of LVI vs. BCG. The report mentions that a distribution-based 

approach to selecting thresholds (identifying the 10'th and 90'th percentile LVI and 

making an adjustment for background variability) gave a fairly similar “exceptional” vs. 

“healthy” threshold but a much higher “healthy” vs. “impaired” threshold. I agree with 

using the BCG-based rather than distribution-based approach because the latter 

doesn’t consider whether the levels selected are relevant to biological condition (the 
same issue I raised earlier with the water quality data) whereas referring to the BCG 

score explicitly does so. 

 I do wonder why so few lakes in the set the experts evaluated fell into the 

impaired category. Perhaps most of the lakes were in fact in pretty good condition, but 

in that case the scale used leaves little room across which to distribute the bad sites 

(only 4.5 to 6) even though there may be a tremendous range in their degree of 

impairment. Again, I don’t know Florida very well, but still I’d be surprised if only 23 

percent of lakes in the state were impaired based on their vegetation (7 out of 30 in 

App. Fig. 7.1). I encourage you to re-evaluate whether the scale reflects the full suite of 

lakes out there and provides sufficient resolution among degrees of impairment. 

 Also, I wonder why only three tiers of condition were selected. Since the text 

argues that the LVI can reliably distinguish 4 categories of condition, it seems you are 

throwing information away by only proposing 3 condition classes. Why not 

“exceptional”, “healthy”, “slightly impaired”, and “greatly impaired”? Managers 

appreciate having interim benchmarks, and being able to say that a system has been 

improved from “greatly” to only “slightly” impaired will be desirable in situations where 

attaining “healthy” is difficult or as an interim milestone on the way to recovery.

Report organization

Re-organizing the report structure would make understanding the vegetation 

index much more straightforward. The current, chronological presentation means that 

important aspects of the study (like modifications to the scoring, comparison of the 

index to a lake quality rating based on expert consensus, and discussion of how the 

index could be used in a biological condition gradient) are relegated to appendices. 

This makes it hard to understand how all the pieces fit together and risks some 

important pieces of information (like scoring adjustments) simply being missed. I would 

suggest the report be re-organized around index evaluation tasks, and that only 

supporting data be in the appendices. For example, the methods, results, and 

discussion concerning how many lake segments to sample should all appear together 

under one section instead of being scattered about. The report should end with an 

expanded recommendation section that stands completely on its own for someone who 

just want to know what data to collect and how to compute the LVI without diving in to 

how it was developed and evaluated. Reorganized in this fashion, the report could 

actually serve as a “how-to” document; in its present form it needs a separate guidance 

document for practitioners. 

 I found the various ways of combining data from lake segments hard to follow. I 

understand the value of testing whether sampling effort can be reduced, but the way 

the LVI_1x, LVI_2x, and LVI_12x are described on p. 15 makes it sound like you still 

need data from 12 lake sections, just averaged in different sequences. Later on, the 

report shows data from averaging 4 lake sections (e.g., Fig. 13), which does not match 

any of the earlier combinations. And in Appendix 6, yet another averaging scheme is 

presented. I’m not disagreeing with your conclusion that averaging 4 sections is a 

suitable compromise between metric performance and field effort – but make it more 

straightforward to follow your evaluation process! 

The acronyms were hard to keep track of, especially since the letters “L” and “D” 

changed meanings across them. Write out the full terms on a regular basis. 
I have never heard of a “frotus” and therefore do not find it to be standard, as p. 

7 of the report states. 

On p. 17, the report states that “an additional concern when estimating variance 

is that the full range of expected values are represented” and goes on to say the LVI 

meets that criteria. But you made it so by defining the range of scores based on the 

distribution of your data, so LVI covers that range by definition. This is not an issue 

scientifically, but the language is disingenuous. 

 And speaking of language, there is some generally repetitive text and awkward 

phrasing scattered throughout the report. If this report itself will be widely disseminated 

(rather than some subsequent guidance document) then a thorough editorial review 

would be desirable. 

– Anett Trebitz, 3 Dec. 2008
1

Florida Department of Environmental Protection 

Assessing the Biological Condition of Florida Lakes: Development of the Lake 

Vegetation Index

Review by S. Fennessy 

Professor, Department of Biology 

Kenyon College 

Gambier, OH 

The Florida DEP (FDEP) has developed a multimetric index, the Lake Vegetation Index 

(LVI), to assess the ecological condition of the aquatic plant communities in Florida 

lakes. This review will comment on both the human disturbance gradient developed to 

test the LVI, and the LVI itself. 

In general, the FDEP has used a sound approach in developing these indexes (this 

approach is outlined by the USPA, http://www.epa.gov/waterscience/biocriteria/). There 

are, however, several issues that should be resolved before the final LVI can be used with 

the level of confidence that the FDEP desires. These are outlined below: 

1. The Human Disturbance Gradient, HDG 

There are major issues with the HDG as presented that call to question its use as a 

reliable indicator of human influence. As will be discussed below, the HDG should be 

more clearly explained and independently validated before it can be used to evaluate lake 

condition. This would greatly increase confidence in using it to test against the LVI. 

Some of the methods are not clear as presented liming the ability to make comments on 

the HDG. For instance, on page 9 it states that DEP biologists evaluated habitat 

condition of the lake by observing hydrological and physical variables. Were these the 

same biologists who collected plant data? If so, this potentially compromises the 

independence of the two measures. There is also no information provided as to how the 

variables that make up the habitat condition score were evaluated (e.g., “qualitative 

descriptions of vegetation quality (does this not somehow score the same information as 

the LVI?), stormwater inputs, bottom substrate (what about it?), etc.). In their scoring, 
2

the sensitivity of these very qualitative measures, collected over a range of spatial scales, 

is likely diminished by combining them, although it is hard to judge without seeing more 

detail. 

The LDI is an excellent method to calculate an integrated measure of the intensity of 

human land use around a site, a factor that is critical in determining the ecological 

condition of aquatic sites. Some studies have found quite good correlations between the 

LDI and site conditions, however, other studies have found the opposite. For example, in 

a recent study Mack (2006) found that the LDI is simply not very good at indicating the 

condition of any particular wetland. In a similar study by Fennessy et al. (2007) it was 

reported that the LDI tended to either under- or over- estimate measures of ecological 

condition. This certainly does not preclude the use of the LDI, it simply indicates that its 

accuracy in predicting condition must be demonstrated. The data presented in Figure 6 

is not complete enough to determine this (see below). 

The four categories of stressors that were selected for the HDG appear quite reasonable, 

however, the calculation of the HDG by combining scores (as for the water quality 

metric), then ranking them, then combining them again (i.e., summing all four) is 

problematic since the influence of the different stressors cannot be assessed. How were 

the scoring rules for the HG measures derived (pg. 11)? Were these derived by 

subdividing the range of scores for each measure? Using these scoring rules and then 

summing them for the final scores means that the HDG values are not transparent, 

particularly if they have not been validated in an independent test. 

The Lake Vegetation Index, LVI 

The final LVI was derived from a list of candidate metrics that were tested against the 

HDG. Like the HDG, some of the methods used were not presented clearly. For 

instanced on page 12 it states that the dominant C of C value was “defined as the CC 

score for the one or two plants that covered the greatest area”. How were these estimates 

made? How was it decided whether one or two plants should be included in this 
3

calculation? If two species were included, are their C of C scores averaged? Without 

this information it is difficult to assess the usefulness of the metric (and calls to question 

its texting). This issue also appears on page 22, when the metric is selected as one of four 

to be included in the index. 

The tolerance/sensitivity metrics described here typically respond quite strongly to 

human disturbance, however it states on page 12 that CC scores were available for only 

240 of the 404 taxa found. Were the remaining 164 species for which CC scores were 

not available ignored in the calculation? If so, this point should be made explicit. It 

would also be useful to know whether there are plans to develop CC scores for the 

remaining species. Doing so would increase the reliability of the metrics based on CC 

scores. 

Page 13: The wetland indicator status of plants has not been shown to be an indicator of 

disturbance such as hydrologic alteration as stated here. Indicator status is a measure of 

the fidelity of a species to wetlands, regardless of their condition. For example, Typha 

latifolia is an obligate species, but show a wide range of tolerance to disturbance. 

Page 14: The additional metrics that were derived from the national wetland database 

should be described here. Perhaps a table would do. 

Page 15: For the field protocols described here, it might be worthwhile to combine all 

the vegetation data from the four replicate lake sections and then calculate an LVI score 

based on this „master species list‟. Since the LVI is meant to indicate the condition of the 

lake, it is more inclusive to pool the data and then calculate the LVI instead of calculating 

the LVI for each section and taking the average. In addition, three variations on 

calculating the LVI are shown here, but the results in using these three are not clearly 

presented making it difficult to judge their relative merits. 

Page 16: The data analysis on LVI_12, latitude and HDG should be presented here. 
4

Page 18: The third paragraph on this page is an important part of the analysis that 

requires more detail. If a graph shows that the lack of correlation was due to poor (low) 

metric values in a site where disturbance was know to be high, what is the fate of that 

metric (or point)? What threshold of „lack of correlation” was needed in order for the 

metric to be eliminated as described here? Likewise, it states that poor metric values in 

sites with little disturbance were „tolerated‟. This paragraph gives the impression that 

metrics were cut based on an a priori knowledge of a site in order to increase the 

correlation between these variables, and not due to the more objective measures being 

tested here. As written, this appears to bias the results. This paragraph should be 

clarified, and at least one example presented of a graph and how it was used to make this 

type of decision. 

Page 20: Results for the HDG are presented here. Line 10 says that the HDG integrates 

site condition for the three component measures, while four measures have been 

discussed and are presented in Table 4. It also states that each measure of human 

disturbance was more highly correlated with the HDG than with the other measures, 

suggesting that the HDG is a better measure of human disturbance. This is not a valid 

conclusion based on the analysis as presented. It is statistically invalid to test the 

components of the HDG against the HDG itself; these are not independent data. It is not 

surprising that the disturbance measures and the HDG are correlated since the measures 

are part of the HDG. This analysis should be discarded. 

Figure 3 – 6: No details of the statistical analysis of the relationship between the LVI 

metrics and the HDG are presented here! This severely limits the evaluation of the 

metrics. Regression equations, r-squared values and p values should be included. 

Page 32: The first sentence on this page states that 62 species were defined as sensitive 

and 47 as tolerant based on „best professional judgment”. Does this mean that each 

species was defined as such on a case by case basis (based on BPJ), or were the 

thresholds for sensitive and tolerant taxa defined, then the CC scores for each species 

used to categorize them? As written this is not clear. 
5

Page 40: Again, there is confusion here about the field methods used. The proceeding 

few pages have described why the LVI_1x is being used as the basis for sampling, yet 

here it states that four replicate samples of LVI-1x are sufficient to assess a lake. As 

described on page 15, should this not be described as LVI_4x (see also Figure 14)? 

Terms need to be clarified in both of these sections. Page 15 refers to “one sample from 

each of the 12 sections”, I interpret this as data from one transect in one section. What 

does the “four replicate samples of LVI_1x” at the top of page 40 refer to? 

The analysis presented on validation on pages 41and 42 is very clear. 

In sum, the authors have done an extensive amount of work in developing these indexes, 

but missing information in many sections makes a thorough evaluation of the approach 

difficult. I would recommend revising the analysis and report to more thoroughly 

document the work completed to date, including a more clearly defined rationale for the 

metrics and how they were scored and combined into the HDG and LVI. At that time 

another peer review could be undertaken. 

Citations: 

Fennessy, M. S., J. J. Mack, M. Sullivan, M. Knapp, and M. Micacchion. 2007. 

Assessment of Wetlands in the Cuyahoga River Watershed of Northeastern Ohio. Ohio 

EPA Technical Report WET/2007-4. Ohio Environmental Protection Agency, Wetlands 

Ecology Unit, Division of Surface Water, Columbus, OH. 

Mack, J. J. 2006. Landscape as a predictor of wetland condition: an evaluation of the 

Landscape Development Index (LDI) with a large reference wetland dataset from Ohio. 

Environmental Monitoring and Assessment 120:221-241. 
1

A critique of the Biological Condition Gradient to define biocriteria for 

LVI (Appendix 7 of the LVI study)

Roger W. Bachmann, Research Professor of Limnology

Daniel E. Canfield, Jr., Professor of Limnology and Director of Florida LAKEWATCH 

Mark V. Hoyer, Assistant Director, Research Programs/Services Limnology, Florida 

LAKEWATCH

School of Forest Resources and Conservation, University of Florida

May 27, 2009

Appendix 7 of the Lake Vegetation Index 

(LVI) report of Fore (2007) describes how an expert 

panel of plant ecologists looked at lists of aquatic 

plants and metrics from 30 Florida lakes and ranked 

them from best (1) to worst (6). Collaboratively they 

came up with a mean score that was called the 

Expert’s Biological Condition Gradient Score. This 

was compared with the LVI scores for the 30 lakes in 

the sample. Further the same panel formed opinions 

on the ranges of LVI values that would define 

“exceptional”, “healthy”, and “impaired” lakes. These 

are proposed for regulatory use by the state.

We have reviewed the appendix, made some data 

analyses, and concluded the following:

1. The investigators misapplied the conceptual model 

of Davis and Jackson (2006) for the Biological 

Condition Gradient (BCG) they claimed to be 

following, so they were never able to come up with an 

objective measure of the BCG in any of the lakes 

studied.

2. They ignored the documented natural variations in 

Florida lakes (Griffith et al. 1997) that result in 

different natural plant communities in different lake 

regions in the state.

3. They chose as the reference aquatic plant 

community to represent the natural state for all Florida 

lakes the assemblage of aquatic macrophytes found in 

acidic lakes with mean pH values of about 4.9. Lakes 

with pH values above about 6.6 were placed in 

categories of healthy or impaired regardless of 

quantified human impacts.

4. They did not establish any quantitative relationship 

between aquatic plant communities and any humancaused pollution in Florida lakes.

5. The process used to determine the lake health 

categories did not follow the scientific method of 

proposing hypotheses and then testing them through 

data collection and analysis. There is no scientific 

basis for the use of the LVI to determine which 

Florida lakes are impaired by human activities.

Background

Davis and Jackson (2006) recently published 

a conceptual model called the Biological Condition 

Gradient (BCG) that illustrates how ecological 

attributes change in response to increasing levels of 

human disturbance. The concept has its roots in 

classical stream pollution biology with descriptions of 

what happens when untreated sewage is introduced 

into streams. Through intensive research over the past 

100 years it was found that as the concentration of 

pollutants increased in a stream, the biological 

community structure in the stream changed from a 

“native or natural condition” through a series of steps 

to “severe changes in structure of the biotic 

community and major loss of ecosystem function”. 

Once a firm relationship is established between the 

changes in the biological community and the degree 

of stress, samples of community structure have been 

used to estimate the degree of departure from the 

natural condition. The concept is illustrated in Figure 

1 (from Davis and Jackson (2006)). 

The vertical axis represents the numerical 

scores (tiers) for the BCG and ranges from 1 to 6. The 

text within the graph represents partial descriptions of 

the six tiers. Once a firm relationship is established 

between the changes in the biological community and 

the degree of stress, samples of community structure 

have been used to estimate the degree that the 

biological community and ecosystem depart from the 

natural condition. The horizontal axis indicates the 

degree of stress on the stream such as the metals, 
2

concentrations of dissolved oxygen, ammonia, heavy 

BOD, etc.

In order to apply the BCG model as illustrated 

in Figure 1, there are at least three essential conditions 

that have to be met.

Figure 1. The theoretical concept of Davis and Jackson 

(2006, Fig 2) showing the stages of change in biological 

condition in response to an increasing stressor gradient. 

1. Knowledge of natural communities. We need to 

know about the structure and function of the 

communities in their “native or natural condition”. 

For example for the streams within one state we might 

have to distinguish between the natural biotic 

communities and ecosystems found in cold, soft 

water, headwaters versus those found in warm, hard 

water, lowland streams. Often it is possible to use the 

biological conditions upstream of the effluent pipe as 

the indicator of natural conditions. For lakes detailed 

information is needed on the natural communities of 

the different types of lakes in the state. For example 

in using an LVI in Florida we need to distinguish 

between plant communities favoring acidic, soft water 

lakes and those found in alkaline, hard water lakes.

2. Human-cause stressors. Quantitative information 

is needed on the human-caused stressors that are 

influencing the biotic communities in question and the 

range of their values.

3. Establish link between stressors and ecosystem 

variables. In order to establish the BCG gradient and 

replicate Figure 1, quantitative information is needed 

on the ways that the biotic communities and 

ecosystems change in response to different levels of 

the stressors. The BCG vertical scale would be based 

on knowledge that includes in part information on the 

species structure of various plant and animal 

communities, the trophic structure of the ecosystem, 

balances between predators and prey, and ecosystem 

functions like primary production, community 

respiration, and biochemical transformations. 

Once a reliable relationship is established between the 

BCG and the important stressors, then the BCG 

concept can be considered for regulatory purposes.

The analysis reported in Appendix 7 fails to 

follow the important parts of the BCG concept as 

outlined by Davis and Jackson (2006). The “Experts’ 

Biological Condition Gradient score (mean)” as given 

in Figure 7.1 of Appendix 7 (my Figure 2) does not 

meet the necessary conditions for the BCG as 

discussed in Davis and Jackson (2006) and 

recommended by the EPA.

Figure 2. Comparison of LVI scores for 30 lakes as found 

by the use of a formula and expert’s Biological Condition 

Gradient scores (mean) as determined by a panel of plant 

ecologists after examining lists of plants and metrics from 

30 lakes. This is taken from Appendix 7 of Fore (2007).

1. Natural conditions. The LVI study and Appendix 

7 did not take into account the natural variability 

among Florida lakes that are related to local geology 

and hydrology. The lakes region study (Griffith et al. 

1997) delineated 47 different lake regions in the state 

that had natural differences in plant nutrients, trophic 

states, pH, alkalinity and other physical, chemical, and 

biological characteristics. Instead there is only one 

formula used to calculate the LVI score for all lakes in 

the state, and the plant ecologists did not take the lake 

regions into account in estimating their BCG scores. 

They did know whether the lakes were from the 

northern or southern halves of the state, but this 

separation is too crude to represent the complexity of 

the various lake region types and in the end lake 

location was not used in setting the categories.

The importance of the failure to recognize that 

different lake regions are going to have different plant 

metrics in their natural state is illustrated by the 

relationship between pH and the LVI and the metrics 

used in its calculation. Figure 3 was produced for our 
3

comments on the main body of the report (Bachmann 

et al. 2009).

Figure 3. Plots of lake pH versus several measures of the 

composition of the littoral aquatic plant community 

including the lake vegetation index (LVI), the percent 

native taxa, percent invasive taxa, percent sensitive taxa, 

percent category I and II invasive taxa, and the coefficient 

of conservatism (CC) of the dominant and codominant 

plant.

Figure 3 illustrates the fact that the LVI score 

and the component metrics are significantly related to 

the pH of the lakes. The most acidic lakes (lowest pH 

values) have the highest (best) LVI scores and the best 

scores for the component metrics. This is a reflection 

of the ecological requirements of the aquatic plant 

species found in Florida lakes under natural conditions 

(Hoyer et al. 1996). The pH factor is also dominant in 

the lakes used in the BCG exercise for Appendix 7. In 

Figure 4A we have plotted the available pH values for 

the lakes used in the experts’ estimation of BCG 

scores. 

Note that pH plays the dominant role in 

determining the experts’ opinion of whether the 

biological condition was “best” or “worst”. It says that 

the macrophyte communities of the lakes with low pH 

values were more likely to be judged by the plant 

ecologists as representing the ” natural or native 

condition” for Florida lakes, while the macrophyte 

Figure 4A-D. The BCG score as estimated by a panel of 

plant ecologists versus the measured values for A. the pH, 

B. landscape development intensity index, C. total Kjeldahl 

nitrogen, and D. total phosphorus concentrations for the 

same lakes.

communities of the lakes with high pH values were 

more likely to be judged as representing lakes with 

“severe changes in structure of the biotic community 

and major loss of ecosystem function”.

Since the pH of Florida lake waters is 

determined for the most part by natural factors such as 

the geology, soils, and hydrology of a lakes watershed 

(Griffith et al. 1997), it represents a natural factor 

rather than a human caused stressor. This means that 

one must first take into account pH and perhaps other 

natural factors in order to determine the natural 

condition of a lake. 

Gradients in stressors. The study presented in 

Appendix 7 does not present a gradient in change in 

the biological condition in response to a gradient in 

change of any human-caused stressors. In fact the 

appendix does not mention any stressors at all that 

could be causing changes in biological conditions. 

The study in the appendix fails this essential 

component of the BCG concept. We have taken their 

data for the lakes used in the expert panel exercise and 

looked for relationships between the “Experts’ 

Biological Condition Gradient score (mean)” and total 

phosphorus, total Kjeldahl nitrogen, and the 

Landscape Development Intensity (Figure 4B-D).

There is no significant relationship between 

the total phosphorus concentration and the BCG 

scores and the total Kjeldahl nitrogen accounts for at 

most 10 % of the variance indicating that the BCG 
4

scores have no relationship with the concentrations of 

the two major plant nutrients in lakes. Further in this 

analysis there is no reliable relationship between the 

BCG scores and the landscape development intensity, 

a supposed measure of human activities within 100 

meters of the lakeshore. We have previously shown 

that there is no correlation between the landscape 

development intensity index and concentrations of 

plant nutrients in Florida lakes. In a stepwise multiple 

regression analysis, after pH is taken into account (70 

% of the variance), none of the other three variables 

have any statistically significant effect. 

Quantitative information on biological condition. 

The expert panel of plant ecologists had no 

quantitative information on the ecosystem functions of 

the respective lakes in order to base their opinions on 

their BCG score. Their scores were more in the form 

of an untested hypothesis about the biological 

conditions in the lakes presented to them. The 

hypotheses were never tested. This aspect of the BCG 

concept was not met as well. There is no scientific 

basis for the BCG scores presented.

In summary the exercise in Appendix 7 does 

not represent a proper application of the BCG concept. 

The mean BCG scores in the study simply reflected 

the opinions of the plant ecologists. They had no data 

available on community structure or ecosystem 

function. They were merely expressing their opinions. 

In terms of the scientific method, we are still at the 

hypothesis stage. The hypothesis has not been tested 

in a scientific manner, so we cannot draw any 

conclusions from the study about the relationship 

between the LVI and any water pollution. There is no 

scientific justification to use the LVI to determine if a 

lake is impaired or failing to support aquatic life.

What is the explanation for the good agreements 

between the experts’ mean BCG scores and the 

LVI? 

In our review of the main report we noted that 

DEP field biologists made estimates of “vegetation 

quality” for each lake based on field observations of 

the aquatic plants. These estimates correlated well 

with the final measured LVI for that lake (Figure 5).

In the study reported in Appendix 7 the experts were 

asked “to review macrophyte data from individual 

lakes and assign each sample a score from 1 (best) to 

6 (worst), signifying their assessment of the biological 

condition represented by the plant assemblage.” It is 

reasonable to expect that the panel of experts would 

do much better than the field staff considering that 

they had the actual species lists and metrics to work 

with. They were familiar with the methods of 

calculating the LVI, the role of native versus nonnative plants, and the role of tolerant versus sensitive 

plants in the calculation of the LVI. Further they were 

able to discuss their opinions and change their scores 

as a result of their discussions. 

Figure 5. The lake vegetation quality score (0=worst, 

20=best) as estimated by a field observer and the lake 

vegetation index (LVI) as calculated from plant 

identification and plant characteristics.

Lastly, since the samples were rather evenly 

distributed among possible LVI scores, a simple 

ranking of the lakes from their best to worst would 

correlate well with the actual LVI scores. Taking all 

this into account it is most likely that the two axes of 

the graph were estimating the same thing and had 

nothing to do with a gradient in human impairments of 

the lakes. 

It follows from all this that there is no basis to 

use the relationships in Figure 2 and/or the LVI to 

categorize lakes as being healthy or impaired. Its 

application would lead to erroneous categorization of 

the lakes with high pH lakes being falsely classified as 

impaired lakes with a resultant waste of effort to 

“restore” them to health.

Biocriteria for Lakes

The purpose of the exercise described in 

Appendix 7 was to place any Florida lake into one of 

three categories of aquatic life use based on LVI 

scores. “Category 1 (“exceptional”, with LVI = 78-

100), Category 2 (“healthy”, with LVI = 38-77), and 

Category 3 (“impaired”, with LVI = 0-37). We 

looked at the data for the lakes used in the Appendix 7 

exercise and sorted them into the 3 categories based 

on their LVI scores. We then looked at the average 

values for pH, total phosphorus, and total Kjeldahl 

nitrogen for the lakes in the 3 categories (Figure 6).
5

Figure 6. Starting with the lakes examined by the panel of 

plant ecologists for the Appendix 7 study, the LVI scores 

for those lakes were used to sort them into the three 

categories proposed in this study. The values for pH, total 

phosphorus and total Kjeldahl nitrogen are plotted by 

category, and the mean values are given in parentheses.

From Figure 6 the lakes placed in Category 1 

(exceptional) were acid lakes with a mean pH of 4.9, 

while the lakes in the other two categories were found 

in waters with higher pH values. The mean value for 

the category 1 lakes was significantly different from 

the other two means. This reflects the fact that the 

underlying assumption of the expert opinions used to 

set up this system of classification is that the aquatic 

plant fauna of acidic lakes represents the natural 

conditions for all lake in the state. There were no 

significant differences between the plant nutrient 

concentrations of phosphorus and nitrogen among the 

categories of lakes. The proposed system of 

categories bears no relationship to plant nutrients, so 

that it would be not a valid basis for setting nutrient 

standards for lakes.

Conclusions

1. The investigators misapplied the conceptual model 

for the Biological Condition Gradient (BCG) they 

claimed to be following, so they were never able to 

come up with an objective measure of the BCG in any 

of the lakes studied.

2. They ignored the documented natural variations in 

Florida lakes that result in different natural plant 

communities in different lake regions in the state.

3. They chose as their reference plant community to 

represent the natural state for all Florida lakes the 

assemblage of aquatic macrophytes found in acidic 

lakes with pH values of about 4.9. Lakes with pH 

values above about 7 were placed in categories of 

healthy or impaired regardless of quantified human 

impacts.

4. They did not establish any quantitative relationship 

between aquatic plant communities and any humancaused pollution in Florida lakes.

5. The approach used in Appendix 7 to set the rules 

for determining lake categories with LVI scores 

bypasses the scientific data that had been colleted on a 

large sample of Florida lakes over several years for 

this study. No use was made of the data on lake 

regions to determine expected natural conditions, 

water quality, human impacts, and designated uses of 

the lakes in question. Instead the categories are based 

on the opinions of a selected group of plant ecologists 

from lists of plants and plant metrics. This process 

does not follow the scientific method of proposing 

hypotheses and then testing them through data 

collection and analysis. There is no scientific basis 

for the use of the LVI to determine which Florida 

lakes are impaired by human activities.

References

Bachmann, R. W., D. E. Canfield, Jr., and M. V. 

Hoyer. 2008. A critique of the Lake Vegetation 

Index: Why it does not measure human 

impairment of Florida lakes. Submitted to the 

Florida Department of Environmental 

Protection, Sept. 11, 2008

Fore, L. 2007. Assessing the biological condition of 

Florida lakes: Development of the lake 

vegetation index. Florida Department of 

Environmental Protection. 

http://www.dep.state.fl.us/labs/library/

Griffith, G.E., D.E. Canfield, Jr., C.A. Horsburgh, & 

J.M. Omernik, 1997. Lake Regions of Florida. 

EPA/R-97/127. U.S. Environmental Protection 

Agency, National Health and Environmental 

Effects Research Laboratory, Corvallis, OR. 

89pp. 

http://aquat1.ifas.ufl.edu/education/misc_pdfs/L

ake_regions.pdf

Hoyer, M. V., Canfield, D. E., Jr., C. A. Horsburgh, 

and K. Brown. 1996. Florida freshwater plants 

– A handbook of common aquatic plants in 

Florida lakes. University of Florida Institute of 

Food and Agricultural Sciences. SP 186