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



## Appendix A-4: FDEP’s La ke Vegetation Index (LVI) and Scientific Peer Review of the LVITSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



### A4-1 

## Appendix A-4. FDEP’s Lake Vegetati on Index (LVI) and Scientific Peer Review of the LVI
Establishing Expectations for Aquati c 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 referen ce 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 C ondition Gradient (BCG) approach.



### LVI Benchmark Site Approach FDEP evaluated data from existing sites to iden tify benchmark lakes th at 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 conducte d an onsite watershed survey to ensure that there were no adverse human influences not detected by th e LDI, and performed a whole-lake habitat assessment. Candidate benchmark lakes were excl uded if they had a history of adverse human activity (e.g., aquatic plant cont rol, 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 A ppendix A-8 contains maps, photos, and a summary of data for each of the verified benchmark lakes.

FDEP’s consultant conducted stat istical interval and e quivalence 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 identif y 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 stat istical description and Appendix A-9 for full analysis. The analysis of the most recent LVIs at all 30 sites showed that the 2.5 th 

### percentile of reference data was in the range of 33-48 point s, while the analysis of the two most recent visits at 15 lakes showed that the 2.5 th 

### percentile of reference data was in the range of 31-53 points (Table A4-1). The middle of this range was 46 points, representi ng an impairment threshold that balances Type I and Type II errors. In the proposed water qua lity threshold for the LVI, impairment will be determined by two site visits, so the threshold of 46 is closely aligne d with the assessment methods. An impairment threshold of 46 would lim it the percentage of re ference 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 locat ion (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.TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



### A4-2 

### LVI Biological Condition Gradient Approach In a process analogous to that fo r the SCI BCG calibration, 20 Flor ida plant ecologists, botanists, and field lake managers, all with at least fi ve years of experience, were involved in BCG calibration of the LVI. The experts examined ta xa 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 review ed 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 al tered in structure and function from a natural condition. Experts independently assigned a BCG score to each lake, a nd 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 m ean 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 belo w 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 Ha rrell 2000) to describe the re lationship 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 ar e 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 singl e tier can be extracted from the model results.
The mean predictions of a proportional odds lo gistic 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 w ith the indicated LVI value to a particular tier. For example, a pproximately 45% of experts assi gned a sample with the lowest LVI score to Tier 6 (brown line), while the remain ing 55% of experts assigned the sample to Tier 5 (purple line). In the figure, th e solid circles represent the actua l expert assignments recorded from the workshop for each LVI value. The size of the circle is proportio nal to the number of experts that assigned a sample to a particular tier, and the circle s 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 ba lance 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 th e SCI, the proportional odds results for the LVI were considered in relation to the lake referenc e site LVI distribution (discussed below).TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



### A4-3 

### Setting a LVI Impairment Threshold Weighing these multiple lines of evidence, the FD EP 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 distri bution of verified refere nce site scores (46), which balances Type I and II errors and a pproaches the lower range suggested by the proportional odds BCG analysis (5 0). U.S. EPA is considering which approach to recommend for establishing a threshold: rely primarily on the reference site distributi on with a low percentile threshold of 2.5 as suggested for the SCI, or consider the range sugge sted by the proportional odds modeling, which would suggest a thresh old of 50 and would correspond to the 5 th 

### percentile of the reference c ondition distribution.

The FDEP evaluated recent data for the individual metrics of the LVI to determine what range of plant attributes would be consid ered healthy by this impairment threshold. This analysis includes 244 LVI samples collected since 20 07 from a variety of sites, including sites located along a nutrient gradient and randomly chosen sites fo r the status network. Based upon the relationship described in Figure A4-1, the LVI values from th is data set were subdivided into increments representing half-step BCG categor ies, and the individual metrics a ssociated with each half step interval were averaged. The metric data bracke ting BCG category 2 were averaged. Data within the range of the impairment thre shold of 46 were also averaged to provide an example of the plant community condition that Florida’s LVI bi ological criterion will protect (Table A4-3). Note that although there are mode rate differences between metric s associated with biological communities at BCG 2, and those near the range of the impairment threshold, the attributes associated with communities near the threshold ar e 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 Equivale nce Tests Conducted on Refe rence Sites with Two LVI Results

Note: Shown are site mean, impairment threshold, and range for threshold values defined at the 2.5 th and 5 th percentile of reference sites (p < 0.05; n = 15 reference sites with two LVI values for each site.)TSD for U.S. EPA’s Proposed Numeric Nutrient Criteria for FL Inland Surface Fresh Waters



### A4-4 Table A4-2. Least-disturbe d 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 Aver age Values for Metrics near the LVI Score of Impairment Associated with FDEP’s Data Collection E ffort 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 ag ainst the BCG scores developed “blindly” by a panel of lake experts. These data reflect upda ted 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 Fr ydenborg**, Nijole Welle ndorf**, Julie Espy**,

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

* Statistical Design

136 NW 40 th 

### 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 Impl ementation grant from the U.S. Environmental Protection Agency thr ough an agreement with the Nonpoint Source Management Section of the Fl orida Department of Environmental Protection. The cost of the project was $54,600, which wa s 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 di sturbance 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 La ke 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 conserva tism (CC) scoring criteria ..........................................................13

Table 4. Correlation for HDG a nd 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 corr elation with HDG, WQ index, LDI, and habitat index...............................................................................................................................................23
Table 7. Plant taxa most freque ntly 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 regre ssion 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 compone nt 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 lowe r 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 metric s plotted against HDG..........................................................................27
Figure 4. LVI plant metrics plot ted against the WQ index............................................................28
Figure 5. LVI plant metrics plot ted against the habitat index........................................................29
Figure 6. LVI plant metric s plotted against LDI............................................................................30
Figure 7. Plant CC value plotted against the av erage 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 coul d 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 recomm ended 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 Protec tion (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 pr ovided by E. McCarron, D. Miller, A. O’Neal, and


### A. Wheeler

. 

### E. McCarron and D. Cobb provided ad ministrative support and a regulatory perspective. Biologists thr oughout Florida collected and identified macrophyte samples, including L. Banks, F. Butera, T. Deck, D. De nson, 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 co mpleted 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 la ke 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 sampli ng. Appendix 7 describes the development of biological criteria as part of Fl orida’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 advi ce of a panel of regional experts.

### 1 ABSTRACT

The Florida Department of Environmental Prot ection (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 developm ent 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 cal culated 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 metric s, we developed a huma n disturbance gradient (HDG) that summarized measures of water chemis try, habitat condition, intensity of land use in a 100 m buffer around the lake, and hydrologic modi fications. A total of 10 metrics met the targeted values for correlation with HDG; of thes e ten, four were not re dundant with each other and were included in the LVI: pe rcent native taxa, percent invasive taxa, percent sensitive taxa, and the average tolerance value of th e taxon present over th e largest area.

Tolerant and sensitive taxa were define d based on designations made by 10 expert botanists working independently to define coeffi cient of conservatism (CC) scores for wetland (not lake) plants in Florida. Metrics derived from CC scores were hi ghly correlated with HDG. In contrast, relatively few indi vidual taxa were significantly asso ciated with HDG: only 29 out of 404 taxa showed significant preferences. Rare ta xa 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 sec tions. 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 prot ocols 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 statisti cal precision to detect multiple levels of biological condition. Given the relatively small data sets available fo r this study (<200 site-vis its), we recommend that future studies be designed to evaluate the infl uence of season on metric values, to determine whether regional adjustments are needed to metric scoring, and to assess th e 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 th e Nation’s waters.” Under the CWA, each state must develop water quality standards for all it s surface waters. Water quality standards include designated uses assigned to a water body, water qu ality 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 impor tance 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 de fine impairment, evaluate best management practices, develop targets for management plans or restoration, or identify exceptional resources for protection (Yoder and Ranki n, 1998; Karr and Yoder, 2004).

Compared to rivers, streams, and wetlands, rela tively 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 developm ent). 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 (Gerrits en 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, th ey share many of the same habitats and taxa within a particular region. The U.S. Environmental Pr otection Agency (EPA) has recently supported
numerous research efforts related to the de velopment of bioassessment and biocriteria for wetlands (USEPA, 2002b). Resources develope d for wetland assessment were borrowed and applied for this study of lakes. For example, ex tensive literature surv eys 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 successfu lly applied in wetlands throughout the U.S., and this information was very helpful in identifyi ng potential metrics for this study (USEPA, 2002c). Similar lists of candidate metrics for aquatic plan ts in lakes have not been developed (Gerritsen et al., 1998). Another source for potential metrics was Ohio EPA wh ich has tested several types of plant metrics for inclusion in their vegetati on 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 st udy 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 exte nsively in wetland monitoring, aquatic plants are rarely selected as indicator s of lake condition (but see Ni chols 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 plan ts with a variety of life histor y strategies are represented in Florida lakes, 2) much is known about the specif ic preferences and tolera nces of many of these plants, and 3) collecting and iden tifying 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 divi ded 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 a ssociated with these three geographic areas (Barbour et al., 1996; Fore, 2004). Su fficient data did not exist for lake plants to perform a similar test for associations between geogr aphic 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 provi des a natural demarcation between the panhandle and peninsula, with the northeast re gion 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 huma ns than the other areas.

The peninsula has a sandy highland ridge extending down its cente r almost to Lake Okeechobee. The elevation of the central ridge is approximately 150 to 200 ft. Terrestrial vegetation communities on the ridge of the peninsul a 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/ga llberry 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 Pur dum, 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 sa mpling. Two different data sets were used to develop and validate the LVI. The first data set in cluded data from 95 lakes that were selected to represent a broad range of site conditions and hum an 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 lo cated in the panhandle region, 74 in the peninsula, and 4 in the northeast. Lake surf ace 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 ge ographic areas were not
included.

The second data set included data from 63 additi onal lakes, and these data were used to validate the correlation between LVI and independ ent 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 la rge 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 he rbaceous species as well as trees and shrubs.

### 7 Each lake was sampled 12 times by dividi ng the lake into 12 approximately wedgeshaped sections, depending on the shape of the la ke (Figure 1). Within each section, two methods were used to identify plants: 1) from the boat, pl ants were identified, using either binoculars or the unaided eye, while boating slowly along the shor e, 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 transe ct, 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 ta xa list, one for each of the 12 sections.

The plant judged to have the grea test 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 pr esent, they were noted as “co-dominant.” If the degree of dominance was not readily determined, a ll plants in the sampling unit would simply be marked “present.” Plants were identified to the lowest taxonomic le vel possible, typically species. Unknown species were placed on ice and sent to an expe rt for identification.

Figure 1. Diagram showing the method used to divide a typi cal lake into 12 sections for replicate sampling. 11 12 1 2

3

4

5 6 7 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 f actors are flow regime, physical habitat structure, water quality, energy source, and biologica l interactions. For this study
data were available to evaluate three of these factors (water quality, phy sical habitat structure, and flow regime), as well as human disturbance at the watershed scale.

To evaluate lake water chemistry, DEP biol ogists measured conductivity, total Kjeldahl nitrogen (TKN), nitrites/nitrates (NO x 

### ), 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 aver aging the scores. To convert to unit-less scores, we used the percentil es 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 developm ent data set was less than the 10 th 

### 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 20 th 

### percentile the lake-v isit scored a 2; and so on. We repeated this process for
each of the five chemical measures. After scor ing each of the measures, we averaged the unitless scores (ignoring missing data) for all the chem ical measures to define the water quality (WQ) index. For the 95 lakes in the index deve lopment data set, 10–25 had missing values for
one or more of the chemical measures. To te st whether missing values contributed to the correlation (or lack of correlation) between the WQ index and other measures of disturbance,
correlation was also tested fo r 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 bu ffer 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 si milar index for hydrologic condition. Instead, each lake was assigned a score of 0 if no hydrologic m odification was observed or 1 if the lake was impounded or its hydrology ar tificially 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). 

# ( ) ∑ = i i 

## LULDI LDI %*

. Where,
LDI i 

### = the nonrenewable ener gy land use for land use i , and
%LU i 

### = 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 i ndex, the measure of hydrologic

### 10 condition, and the LDI) to unit-less scores and su mmed 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 measur e, 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 categ ories 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 coeffici ent 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 ca lculate 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 genera l categories were tested for co rrelation 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 regardi ng extent of cover was co llected. 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 pl ants 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, ta xa 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 fo r metric testing. After metrics were selected, alternative sampling protocols based on varying nu mbers 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 specifi c 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) we re used to calculate several metrics. CC scores were defined by a panel of 10 expert botanists working indepe ndently, 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 Flor ida, 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 wate r 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,199 7; 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 presen t at a lake, e.g., forb/herb, grami noid, 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 in cluded Florida at the time of European contact (1500 AD). Exotic taxa ar e species introduced to Florida from a natural range outside of
Florida. Definitions of invasive taxa were taken from lists deve loped by the Florida Exotic Pest Plant Council (FLEPPC). FLEPPC defines Category I invasives as “exotics that are altering native plant communities by displacing native sp ecies, changing community structures or ecological functions, or hybridizing with na tives” (FLEPPC, 2003). Cate gory II invasives are
defined as “exotics that have increased in a bundance or frequency but have not yet altered Florida plant communities to the extent show n 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) sc ores were available for 240 of the 404 taxa found. CC scores are most typically used to calculate a ‘florist ic quality index (FQI)’ (Lopez and Fennessy, 2002). FQI was calculate d 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 tolera nt taxa were selected as metrics for both the
herbaceous and forested wetland indices in Flor ida (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 rela tive 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 vegeta tion indices for wetlands (Mack, 2004).

Table 3. Coefficient of conservatism (CC) scor ing criteria (after Cohen et at., 2004; Andreas 1995) and designations used to define a plan t as tolerant or sensitive for this study.

CC

score Sensitive or Tolerant Criteria

0 T Alien taxa and native taxa th at 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 de fined 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 hydrologi c 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 e qually likely to occur in wetl ands 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 pr evious 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 di vided into two groups depending on the number of cotyledons found in the embryo. The co tyledons are the seed leaves produced by the embryo. In Ohio, native dicots decline with increasi ng 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 plan t 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 sens itivity or tolerance, tolerance to nutrients, nitrogen or phosphorus, an d sensitivity or tolera nce 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 i ndividual plant taxa with the HDG using a 2 x 2 contingency table analysis ( χ 2 

### , 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 dist urbance indicated for two measures or high disturbance for a single indicator . This statistical approach te sts 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 cal culation. 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 dete rmine the most efficient method for collecting plant data from a lake. From that analysis we de fined the final protocol for the LVI to be based on the average of 4 replicate LVI values from 4 la ke sections. Using this version of the LVI, we tested that the patterns of co rrelation observed in the original 95 lakes were valid, using an independent data set of 63 lakes to test fo r correlation between LVI and HDG. Finally, we
evaluated seasonal differences for 15 lakes w ith LVI sampling during both spring and summer.

There were numerous choices in how to calcula te 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 differe nt versions with suffixes. Th e 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 calcula ted, 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 inde x was calculated by first transforming metrics
into unit-less scores on the basis of their 5 th 

### and 95 th 

### percentiles. Leaving out the upper and lower 5% of metric values eliminates extremely hi gh or low values that may not be typical of minimally or extremely disturbed sites. The 95 th 

### percentile value was assigned a score of 10 (for
metrics that declined with disturbance such as percent native taxa) and the 5 th 

### percentile value was assigned a score of 0. For metrics that increase d with disturbance, the scores were reversed
for the 5 th 

### and 95 th 

### percentiles. After transformation, me tric scores ranged from 0–10. The LVI was the sum of the four metrics multiplied by a constant to adjust th e range of LVI to a 0–100
scale. A scale from 0–100 was selected for conve nience with the intention of keeping the same
scale for all Florida multimetric indices (Hughes et al., 1998).

We used non-parametric correlation to te st 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. Af ter 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 diffe rent 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 vari ability of the different vers ions of LVI using an ANOVA
model. We used lake as the main factor and re peat samples within each lake were defined as replicates and used to calculate the mean squa red 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 s 2 

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

n = number of samples taken from the lake.

The number of categories of biological c ondition that LVI can re liably 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 in dex by representing how different LVI would have to be during a subsequent lake-v isit to conclude that a statistically significant change had occurred in lake condition. That conf idence interval was also used to define how many non-overlapping categories of biological c ondition 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 vers ion could reliably detect for di fferent 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 mu ltimetric 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 larg e 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 es timating variance is that the full range of expected values are represented. LVI values fo r 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 (M arch–April) and agai n 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 correlatio n 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 coefficien t may be statistically significant but biologically not very meaningful. Consequently, before corre lation testing is comple te, a scientist should consider the underlying meaning of correlation coeffi cients 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 significan t relationship with HDG,
then further evaluated each metr ic by looking at scatter plots. T hus, for metric selection, a higher standard was set than simple st atistical significance. For metr ic correlation with the HDG, we anticipated that the variability associated with HDG and the inhe rent difficulty involved in fully assessing human influence would mean that hi gh correlation coefficients (e.g., > 0.7) would be unlikely. On the other hand, corre lation coefficients < 0.4, though they may be statistically significant, tend to be un convincing when graphed.

Using graphs, we tested to be sure that the lack of corre lation was not due to good metric
values in sites where human disturbance was kn own to be high. In contrast, we tolerated poor
metric values in sites with no known dist urbance because the HDG does not include many potential sources of degrad ation (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 exp ected metrics to be highly correlated with each other because they were initially selected for th eir 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 th e 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 basi s of its correlation with HDG, biological meaning, and sensitivity to degradation.

When testing for correlation between biol ogical measures (LVI and its component metrics) and physical measures (lake surface area, la titude, and longitude), we were less tolerant of correlation. For this analysis, any statistical significant correla tion was considered because we were concerned that underlying na tural features or processes coul d 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 testin g situations, different
values for the correlation coefficient were se lected to indicate a significant association before testing

.

### 20 RESULTS

A total of 404 taxa were identified from the 95 la kes 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 highl y correlated with the HDG than with other measures, suggesting that the HDG was a bette r measure of general human disturbance. Hydrologic condition was not tested for correlati on 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 variable s 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, NH 3 

### , TKN, NO x 

### , TP, orthophosphates (OP), chloride, and sulfate. Conductivity, TP, and TKN were signi ficantly higher in lakes with non-porous soils (Mann-Whitney U test, 1-sided, p < 0.05; Table 5). NO x 

### was significant, but not in the direction predicted, values were lower in lakes with non-po rous soil. Though statistically significant, the
differences between levels of chemical measur es in porous and non-porous soils were probably not large enough to explain th e lack of correla tion 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 si ze (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 relativ ely few were strongly co rrelated 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 corr elated with HDG when calculated as number of

### 22 taxa, it was also correlated with HDG when calcula ted 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 numbe rs of native and exotic taxa and metrics
derived from CC values were the most consisten tly 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 iden tified 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 a nd the total number of growth forms were not
correlated with HDG.

Of the four metrics related to nativity, all were significantly correla ted with HDG, but the
three based on exotic taxa were redundant. Category I and II inva sives represented a subset of the exotic taxa. We retained percent invasive ta xa for the LVI because invasive taxa represent a significant economic concern and an objective threat to native pl ant assemblages. We calculated
this metric as a percentage of total taxa rather than total numbe r 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 corr elation 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 metr ics 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 databa se are also shown (Adamus and Gonyaw, 2000). Metrics selected for the fina l 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 ta xon 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 se nsitive taxa represented opposite ends of the spectrum. The general metrics that summarized se nsitivity (or tolerance) of the entire plant assemblage were redundant with the more specifi c 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 assembla ge when human disturbance is introduced into new locations (Karr and Chu, 1999). We did not includ e percent tolerant taxa in the index because many of the same plant taxa were also incl uded in the invasive or exotic metrics.

Other candidate metrics related to duration of life cycle, we tland status, and number of cotyledons were not correlated with HDG. Simila rly, 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 fo r two reasons. First, very few sh rub 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 da tabase failed to correlate predictably or consistently with HDG or other measures of di sturbance. 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 a nd calculation based on a percentage of total taxa
controlled for that s ource 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 differe nt 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 invasi ve taxa and dominant C of C showed regional differences in the panhandle and peninsula, and me tric scores were adjust ed for percent invasive taxa by calculating the 5 th 

### and 95 th 

### percentiles separate ly 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 va lues that were outliers, that is, dominant C of C was high in three lakes that also had high va lues 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%

HDGDominant 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 LV I were strongly correl ated with the human disturbance gradient (HDG). Pe rcent 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 8Dominant 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 8Dominant C of C



### Figure 5. LVI metrics were highly correlate d 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 8Dominant C of C



### Figure 6. LVI metrics were highly correlated w ith 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 test ed 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 th at was statistically significant ( χ 2 

### , p < 0.05). Nine sensitive taxa and 11 tolerant taxa were significantly associ ated with disturbance (T able 8; see Appendix 3 for all taxa). One species, Vallisneria americana , was defined as sensitiv e 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 associa tion with HDG agreed with their CC scores in terms of their preference for undisturbed or disturbed conditions.

Although statistically significan t, the preferences for ‘good’ or ‘poor’ lakes were not strong in several cases. For example, Cephalanthus occidentalis showed a signifi cant preference
with 25 out of 51 occurrences in ‘good’ sites although the preference represented only 49% of its occurrences. From a statistical poi nt 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 indicat e 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 signifi cance (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 resu lts of this statistical analysis for two reasons. First, most of the taxa were too rare to test with these data. In addition, th e statistical analysis for these data yielded too few taxa to define reli able metrics for sensitive or tolerant taxa.

### 32 Table 8. Taxa that were significantl y associated with low or high HDG ( χ 2 

### , p < 0.05). Shown are taxon name, CC value, whether the taxon was iden tified 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 ‘b ad’ 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 10Taxon CC



### Figure 7. Plant CC value declined as the aver age HDG for the lakes at which a taxon was found increased (Spearman’s r = -0.42, r 2 

### = 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 compari ng field data with CC scores. For each taxon, they calculated the average LDI va lue 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 f ound 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 a nd the average HDG value at all sites in which a taxon was found was not close (r 2 

### = 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 ge ographic 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 (ou tliers); and 5) a comparison of

### 34 LVI values for lakes sampled during spring and su mmer. We compared different versions of LVI using different numbers of lake sections and se lected 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 inde x values differed little according to the number of sections used in calculations.) LVI_12x wa s highly correlated with HDG and was more highly
correlated with HDG than other measures of hum an disturbance indicating the value of the HDG as an integrated measure of disparate types of human disturbance (T able 9; Appendix 4). LVI_12x was not correlated with la ke 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, pr obably 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 correl ations indicated by pa rentheses (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 th at 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 variab les) simultaneously. We also te sted 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 th e metric scoring for panhandle and peninsula areas helped to resolve the underl ying 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 inde pendent variables in a multiple regression model can result in unstable solutions or inconsis tent results. Correlation among independent variables is difficult to avoid in re gional surveys such as these because human land use typically follows geographic gradients. Noneth eless, 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 th at HDG was the primary source of variance in
LVI.

Table 10. Standardized regression coefficients from multiple regression. Ea ch row represents a different statistical test for each biological measure. Column variables were entered in each model and only coefficients for significant predic tors 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 altern ative lake sampling protocols
As expected, the total number of species increase d 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 iden tify 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 vari ability 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 la ke-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 th e within-lake variability (mean squared error). We
calculated variance for two versions of the LVI: 1) treating each of th e 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 replicat es 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 inte rval to obtain the num ber of categories of biological condition that each versio n of the index could detect.

If data were collected from a single lake section and LVI_1x calcula ted, 2.9 categories of biological condition could be detected. If two rep licate 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 c ondition could be detected. The two different sampling approaches yielded similar levels of precision for an equiva lent 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 categorie s: 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 fr om 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

### 38Number 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 sample s 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 prec ision, we also tested whether LVI derived
from more lake sections was mo re 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 assessmen t. 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 co mponent 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 200Number of observations



### Figure 10. Distribution of LVI_1x for 12 replic ate 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 indicat e 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 distur bance would translate into a si milar LVI value independent of location in the state (Table 15; see also Appendi x 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 5 th 

### and 95 th 

### percentiles. For pe rcentage 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 5 th %tile 95 th %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 pr ovided 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 LD I; 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 conditi on that the variance analysis predicted. The 5 categories of biological condition represent detect able 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 vari ability for LVI associated with the type of disturbance or the intensity of disturbance at a particular lake . Because the “true” valu e 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 100LVI_1x (average of 4 reps)

Median 25%-75% Non-Outlier Range Outliers



### Figure 11. For the validation data set, LVI d eclined 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 developmen t data set of 95 lakes had lowe r LVI values than predicted on
the basis of their association with HDG and thr ee 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 no t 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 th ree lakes with higher LV I than expected were
located in the northeast district.

### 43 012345678 HDG 0 20 40 60 80 100INDX_1x



### Figure 12. LVI declined as human disturbance increased. The regressi on line (solid) and the 90% confidence bounds (dotted) are shown, e ach point represents one lake (N = 95 lakes). Six lakes had lower LVI values th an predicted by the HDG (open circles) and three lakes had an LVI slightly highe r than predicted (open triangles).

Table 17. Lakes with higher or lower LVI valu es 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 spri ng vs. summer, but higher LVI values were not consistently associated with one season. LVI for fi ve 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 othe rs, no consistent seasonal association could be
identified and corrected. We coul d 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 we re not identified beyond genus was 8% during the spring vs. 11% during the summer, a relatively sma ll difference. Data from such a small data set may be difficult to interpret; fo r example, 5 of the 15 sites had no invasive taxa recorded for
either season.

### 45SR1-SL2004SR1-SL2025SR1-SL2028SR1-SL2039SR1-SL2059SR1-SL2082SR1-SL2087SR1-SL2090SR1-SL2094SR1-SL2111SR1-SL2117SR1-SL2136SR1-SL2153SR1-SL2156SR1-SL2171 

### 10 30 50 70 90LVI_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 LV I from four lake sections. Each lake-visit had three replicate samples of four secti ons each (4 x 3 = 12 lake sections).

### 46 DISCUSSION

When selecting biological measures as indicato rs of water body condition, two criteria must be considered. First, the indicator must show a c onsistent and meaningful association with an independent measure of human di sturbance (Karr and Chu, 1999). S econd, 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 metr ics were reliably correlated with HDG. Those metrics were also highly correlated with the othe r independent measures of disturbance. The LVI was also precise enough to reliably detect fi ve categories of biol ogical 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 singl e metric. For multimetric indices, precision typically increases with more metrics, and 7–10 metrics are pref erred. Nonetheless, the precision for LVI was quite high with only f our metrics. LVI derived from f our replicate lake sections had sufficient statistical precision to reliably detect five categories of biological condition. Thus, the LVI was both highly correlated with an independ ent 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, destructio n and loss of habitat, alteration of natural flow patterns, changes in the way en ergy moves through the system, a nd 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, a nd the intensity of human la nd use around the lake. When developing a human disturbance gradient, the ho pe 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 fa iled 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 meas ures included in the WQ index were higher in non-porous soils. Conductivity, tota l phosphorus, and total Kjeldahl nitrogen were significantly higher in non-porous soils; however, the differe nces 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 metr ic scores by region according to HDG, the final LVI had the same relative meaning i ndependent of geographic location.

Biological indicators

Sampling and analysis methods for lake bioassessme nt are less mature than those for streams or wetlands; consequently, a short lis t of best metrics has yet to be defined for aquatic plants. Therefore, we approached metric testing as a somewhat exploratory ex ercise 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 l ong 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 inva sive taxa, FQI, percent sensitive taxa, and percent tolerant taxa were highl y correlated with HDG. Similarl y for forested wetlands, native

### 48 perennial, exotic, FQI, sensitive, and tolerant species were all highly co rrelated with LDI and selected for the wetland condition index (W CI, 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 (L ane 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 w ith LDI did not. Results were simila r for lakes in this study, and the same metrics were consistently co rrelated 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 co mponent 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, primar ily 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 a nd the lists of sensitive and tolerant taxa derived from the analysis were also very short, we elec ted 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 lake s in which each taxon occurred were highly correlated with CC values, indicating that prof essional judgment was supported by field data.

Statistical precision of the Lake Vegetation Index (LVI)

The most important characteristi c of a biological indicator shoul d be its correlation with an independent measure of human di sturbance; second in importance is its statistical precision. A statistically precise indicator th at 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 r easonably 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 pe rcentage 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 LV I. These results indicated that a smaller sampling effort (i.e., fewer lake sections) would not sacr ifice the accuracy of LVI for assessing lake condition. In addition, these results illustrated the f act that it is not necessary to sample all the taxa present in order to obtain a re liable 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 orde r 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 biologi cal 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 fromfour 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 intensit y around a lake’s perimeter. In order to capture this difference, we defined the lake samp ling 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 se ctions 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 “overs ampled” in order to determine the appropriate sampling effort needed to charac terize 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 represen ts approximately one-third of the current sampling effort.

To avoid bias in lake sampling, the first re plicate sample should be randomly selected from sections 1, 2, or 3. Section 1 is designated as the first sec tion located clockwise from the north, and subsequent sections ar e numbered in a clockwise dire ction. 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 an d locations for the four replicate samples (marked with triangles). 

## N

1

4

7 10

### 51 • Develop guidelines to identify the do minant taxon within a lake section.

Data for several lakes did not include inform ation on dominant taxon. Because dominant C of C is needed to calculate LVI, each lake sample s hould include this information. Criteria should be specified for defining dominance and co-dominan ce and future lake training should emphasize the best way to estimate this metric. LVI base d 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 influe nce. When different taxa dominate on opposite sides of the lake, simple averaging is the best approach for calculating this metric.

• Evaluate other sources of tem poral variability for LVI.
Estimates of the number of categories of biologica l 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 th e 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 LV I. (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 rang e 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 (L arsen et al., 1995; Ur quhart et al., 1998).

Repeat lake visits for 15 lakes sampled duri ng the same year indicated that LVI differed according to season for some lakes, but the direct ion 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 ar e 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 bette r approach would be to conti nue 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 ad justed to reflect lower metric values in the peninsula region of the state. Fo r the northeast region, metric diffe rences 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 th is study, lakes in the far southern section of the state (26–27 o 

### latitude) had somewhat lower LVI values than predicted by HDG and lakes in this
area should be assessed for consistenc y as more data become available.

• Reevaluate sensitive and tolerant designations of plant taxa.
For this study, 95 lakes did not provide sufficient da ta 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 a nd 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 s howed a stronger correlation than for lakes. When CC values were compared to the averag e HDG for lakes (LDI for wetlands) in which each taxon was found, agreement was much better for wetlands than lakes (r 2 

### = 0.18 for lakes vs. 0.54 for wetlands; Cohen et al., 2004). Although the percen t 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 la kes were strongly associat ed with lake condition
and the level of human disturbance observed arou nd the lake perimeter. Ten metrics were highly correlated with independent measures of human dist urbance, 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 intens ity. LVI values derived from repeat samples within a lake varied little, and four replicate sa mples within a lake would be sufficient to detect five categories of biological condition.

Additional work remains to be done as more la ke data become available, but changes to
the LVI will be minimal. Tolerant and sensit ive 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 th e sensitivity of LVI through time.

The primary goal of this study was to test th e feasibility of using plants as biological indicators. Results reported here confirm that simple measures of the plant assemblage can provide a reliable and meaningful biol ogical assessment of lake condition.

### 54 REFERENCES

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AQUATIC PLANT ATTRIBUTESShown are lake plant taxa names sorted alphabetically by phylum, class, and order (not shown), taxonomic family, whether the speciesis listed as a category 1 or 2 invasiveby the Florida Exotic Pest Plant Council,whether the plant is herbaceous or woody, growth form(GR_1: F/H = forb/herb, G = graminoid, NV= nonvascular, SH = shrub, SSH = subshrub, T= tree, V = vine; GR_2: E = emergent, F= floating, O = other, S = submersed), whether the plant is listed as threatenedor endangered, reproductive group, wetland status,duration of life cycle (A = annual, B = biennial, P = perennial), nativity, and CC score.Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CAcer rubrumAceraceaeW T O A_dicot FACW P N 4.65Agalinis filifoliaScrophulariaceaeH F/H E A_dicot Upland A N 6.69Agalinis linifoliaScrophulariaceaeH F/H E A_dicot OBL P N 7.04Agarista populifoliaEricaceaeW SH O A_dicot OBL P NAlternanthera philoxeroidesAmaranthaceaeCat2 H F/H E A_dicot OBL P E 0Amorpha fruticosaLeguminosaeH SH O A_dicot FACW P NAmpelopsis arboreaVitaceaeW V O A_dicot FAC P N 3.25Amphicarpum muhlenbergianumPoaceaeH G E A_Mono FACW P N 5.7AndropogonPoaceaeH G E A_Mono P NAndropogon glomeratusPoaceaeH G E A_Mono FACW P N 3.9Andropogon gyrans stenophyllusPoaceaeH G E A_Mono FAC P NAndropogon virginicusPoaceaeH G E A_Mono FAC P N 3.44Annona glabraAnnonaceaeW T O A_dicot OBL P NArundinariaPoaceaeW SSH E A_Mono FACW P NArundinaria giganteaPoaceaeW SSH E A_Mono FACW P NArundo donaxPoaceaeH SSH E A_Mono FAC P EAsterAsteraceaeA_dicot NAster carolinianusAsteraceaeH SSH E A_dicot OBL P NAster elliottiiAsteraceaeH F/H E A_dicot OBL P N 6.76Aster pilosusAsteraceaeH F/H E A_dicot FAC P NAster subulatusAsteraceaeH F/H E A_dicot OBL P N 5.74Aster tortifoliusAsteraceaeH F/H A_dicot P NAxonopusPoaceaeH G E A_MonoAzolla carolinianaAzollaceaeH F/H F Fern OBL A N 1.81BaccharisAsteraceaeA_dicot P NBaccharis angustifoliaAsteraceaeSH O A_dicot OBL P N58Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CBaccharis glomerulifloraAsteraceaeW SH O A_dicot FAC P N 6.12Baccharis halimifoliaAsteraceaeW SH O A_dicot FAC P N 2.53Bacopa carolinianaScrophulariaceaeH F/H E A_dicot OBL P N 5.31Bacopa monnieriScrophulariaceaeH F/H E A_dicot OBL P N 4.49BambusaPoaceaeW A_Mono EBidensAsteraceaeH F/H E A_dicotBidens albaAsteraceaeH F/H E A_dicot FAC AP NBidens laevisAsteraceaeH F/H E A_dicot OBL AP N 7.19Bidens mitisAsteraceaeH F/H E A_dicot OBL A N 6.31Bidens pilosaAsteraceaeH F/H E A_dicot FACW A EBlechnumBlechnaceaeE FernBlechnum serrulatumBlechnaceaeH F/H E Fern FACW P N 7.15Boehmeria cylindricaUrticaceaeH F/H E A_dicot OBL P N 5.91BrachiariaPoaceaeH G E A_Mono FACWBrachiaria muticaPoaceaeCat1 H G E A_Mono FACW P E 0Brasenia schreberiNymphaeaceaeH F/H F A_dicot OBL P N 8.79Cabomba carolinianaCabombaceaeH F/H S A_dicot OBL P N 5.07Callistemon viminaleMyrtaceaeW SH O A_dicot ECallitricheCallitrichaceaeA_dicotCannaCannaceaeE A_MonoCanna flaccidaCannaceaeH F/H E A_Mono OBL P N 6.75CarexCyperaceaeH G E A_MonoCarya aquaticaJuglandaceaeW T O A_dicot OBL P NCassiaLeguminosaeH F/H E A_dicot 1.46CasuarinaCasuarinaceaeW A_dicotCasuarina equisetifoliaCasuarinaceaeCat1 W T O A_dicot Upland P E 0CatalpaBignoniaceaeA_dicotCentella asiaticaUmbelliferaeH F/H E A_dicot FACW P N 1.92Cephalanthus occidentalisRubiaceaeW SH O A_dicot OBL P N 7.27Ceratophyllum demersumCeratophyllaceaeH F/H S A_dicot OBL P N 4.16Ceratopteris thalictroidesParkeriaceaeH F/H E Fern OBL AP E 2.93Chamaecyparis thyoidesCupressaceaeW T O Gymno OBL P NCharaCharaceaeNV S Algae N 3.9Chasmanthium laxumPoaceaeH G E A_Mono FACW P N 7.37Cicuta maculataUmbelliferaeH F/H E A_dicot OBL PB NCinnamomum camphoraLauraceaeCat1 WT O A_dicot Upland P E 059Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CCladium jamaicenseCyperaceaeH G E A_Mono OBL P N 9.04CoelorachisPoaceaeColocasia esculentaAraceaeCat1 H F/H E A_Mono OBL P E 0CommelinaCommelinaceaeH F/H E A_Mono FACWCommelina diffusaCommelinaceaeH F/H E A_Mono FACW AP E 2.02Commelina erectaCommelinaceaeH F/H E A_Mono FACW P NConoclinium coelestinumAsteraceaeH F/H E A_dicot FAC P N 4.37Coreopsis gladiataAsteraceaeH F/H E A_dicot FACW P NCortaderia selloanaPoaceaeG A_Mono P ECrinum americanumLiliaceaeH F/H E A_Mono OBL P N 8.67Crotalaria pallidaFabaceaeW SSH E A_dicot P ECrotalaria spectabilisFabaceaeF/H E A_dicot A ECyperaceaeCyperaceaeCyperusCyperaceaeH G E A_MonoCyperus alternifoliusCyperaceaeCat2 H G E A_Mono OBL P E 1.11Cyperus articulatusCyperaceaeH G E A_Mono OBL P N 6.64Cyperus croceusCyperaceaeH G E A_Mono FAC AP N 1.3Cyperus difformisCyperaceaeH G E A_Mono OBL A ECyperus distinctusCyperaceaeH G E A_Mono OBL P N 5Cyperus erythrorhizosCyperaceaeH G E A_Mono OBL AP NCyperus esculentusCyperaceaeH G E A_Mono FAC P ECyperus haspanCyperaceaeH G E A_Mono OBL P N 5.68Cyperus leconteiCyperaceaeH G E A_Mono FACW P NCyperus ligularisCyperaceaeH G E A_Mono FACW P NCyperus odoratusCyperaceaeH G E A_Mono FACW AP N 4.25Cyperus papyrusCyperaceaeH G E A_Mono OBL P ECyperus polystachyosCyperaceaeH G E A_Mono FACW AP N 1.56Cyperus retrorsusCyperaceaeH G E A_Mono FAC P N 1.79Cyperus rotundusCyperaceaeH G E A_Mono FAC P ECyperus surinamensisCyperaceaeH G E A_Mono FACW P N 2.03Cyrilla racemifloraCyrillaceaeW T O A_dicot FAC P N 5.2Decodon verticillatusLythraceaeW SH O A_dicot OBL P N 7.8Dichromena colorataCyperaceaeH G E A_Mono FACW P N 6.18Diodia virginianaRubiaceaeH F/H E A_dicot FACW AP N 4.96Dioscorea bulbiferaDioscoreaceaeCat1 V O A_Mono P E 0Diospyros virginianaEbenaceaeWT O A_dicot FACP N 5.7660Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CDroseraDroseraceaeH A_dicotDrosera capillarisDroseraceaeH F/H E A_dicot FACW AP N 7.09Drosera intermediaDroseraceaeH F/H E T A_dicot OBL P N 8.23EchinochloaPoaceaeH A_MonoEchinochloa crusgalliPoaceaeH G E A_Mono FACW A E 0.22Echinochloa walteriPoaceaeH G E A_Mono FACW A N 3.36Eclipta prostrataAsteraceaeH F/H E A_dicot FACW AP N 3.22Eichhornia crassipesPontederiaceaeCat1 H F/H F A_Mono OBL P E 0EleocharisCyperaceaeH G E A_Mono OBL NEleocharis baldwiniiCyperaceaeH G E A_Mono OBL P N 2.82Eleocharis cellulosaCyperaceaeH G E A_Mono OBL P N 7.8Eleocharis elongataCyperaceaeH G E A_Mono OBL P N 6.97Eleocharis equisetoidesCyperaceaeH G E A_Mono OBL P N 9.1Eleocharis flavescensCyperaceaeH G E A_Mono OBL P NEleocharis interstinctaCyperaceaeH G E A_Mono OBL P N 7.8Eleocharis montanaCyperaceaeH G E A_Mono OBL P NEleocharis robbinsiiCyperaceaeH G E A_Mono OBL P NEleocharis viviparaCyperaceaeH G E A_Mono OBL A N 3.81EragrostisPoaceaeE A_MonoErechtites hieracifoliaAsteraceaeH F/H E A_dicot FAC A N 1.37Erianthus giganteusPoaceaeH G E A_Mono OBL P N 6.34Erigeron quercifoliusAsteraceaeH F/H E A_dicot FAC A N 3.31EriocaulonEriocaulaceaeH F/H E A_Mono OBL PB NEriocaulon decangulareEriocaulaceaeH F/H E A_Mono OBL P N 7.5EupatoriumAsteraceaeH F/H E A_dicot P NEupatorium capillifoliumAsteraceaeH F/H E A_dicot Upland P N 0.83Eupatorium leptophyllumAsteraceaeH F/H E A_dicot OBL P N 4.94Eupatorium mikanioidesAsteraceaeH F/H E A_dicot FACW P NEustachys petraeaPoaceaeH G E A_Mono FAC P NEuthamia tenuifolia tenuifoliaAsteraceaeH F/H E A_dicot FAC P NFicus aureaMoraceaeW V O A_dicot FAC P N 3.38Fimbristylis castaneaCyperaceaeH G E A_Mono OBL P NFraxinus americanaOleaceaeW T O A_dicot Upland P NFraxinus carolinianaOleaceaeW T O A_dicot OBL P NFroelichia floridanaAmaranthaceaeH F/H O A_dicot A NFuirenaCyperaceaeHG E A_Mono61Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CFuirena brevisetaCyperaceaeH G E A_Mono OBL P N 7.6Fuirena pumilaCyperaceaeH G E A_Mono OBL A N 5.92Fuirena scirpoideaCyperaceaeH G E A_Mono OBL P N 6.5Fuirena squarrosaCyperaceaeH G E A_Mono OBL P NGleditsia triacanthusFabaceaeW T O A_dicot FACW P NGordonia lasianthusTheaceaeW T O A_dicot FACW P N 9.03Habenaria repensOrchidaceaeH F/H E A_Mono FACW P N 4.58HedyotisRubiaceaeHibiscus coccineusMalvaceaeH F/H E A_dicot OBL P NHibiscus furcellatusMalvaceaeSSH E A_dicot P NHibiscus grandiflorusMalvaceaeH F/H O A_dicot OBL P N 6.86Hydrilla verticillataHydrocharitaceaeCat1 H F/H S A_Mono OBL P E 0HydrocotyleUmbelliferaeA_dicotHydrocotyle umbellataUmbelliferaeH F/H E A_dicot FACW P N 1.92Hydrolea corymbosaHydrophyllaceaeH F/H E A_dicot OBL P N 5.85Hydrolea quadrivalvisHydrophyllaceaeH F/H E A_dicot OBL P NHypericumClusiaceaeA_dicot NHypericum cistifoliumClusiaceaeW SH O A_dicot FACW P N 6.32Hypericum fasciculatumClusiaceaeW SH O A_dicot OBL P N 7.27Hypericum hypericoidesClusiaceaeW SH O A_dicot FAC P N 5.44Hypericum lissophloeusClusiaceaeW SH O E A_dicot OBL P NHyptis alataLamiaceaeH F/H E A_dicot FACW P N 4.58Ilex cassineAquifoliaceaeW T O A_dicot OBL P N 7.66Ilex coriaceaAquifoliaceaeW T O A_dicot FACW P NIlex glabraAquifoliaceaeW SH O A_dicot FACW P N 5.85Ilex myrtifoliaAquifoliaceaeW SH O A_dicot OBL P NIlex vomitoriaAquifoliaceaeW T O A_dicot FAC P NIndigofera hirsutaLeguminosaeH F/H E A_dicot A EIpomoea sagittataConvolvulaceaeH V O A_dicot P N 6.42Iris hexagonaIridaceaeH F/H E A_Mono OBL P N 6.97Iris virginicaIridaceaeH F/H E A_Mono OBL P NItea virginicaSaxifragaceaeW SH O A_dicot OBL P N 7.09Iva microcephalaAsteraceaeH F/H E A_dicot FACW A N 4.68JuncusJuncaceaeH G A_Mono NJuncus effususJuncaceaeH G E A_Mono OBL P N 3.25Juncus marginatusJuncaceaeH G E A_MonoFACW P N 3.6562Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CJuncus megacephalusJuncaceaeH G E A_Mono OBL P N 5.7Juncus scirpoidesJuncaceaeH G E A_Mono OBL P N 4.33JusticiaAcanthaceaeA_dicotKosteletzkyaMalvaceaeA_dicotKosteletzkya virginicaMalvaceaeH F/H E A_dicot OBL P N 7.49Lachnanthes carolianaHaemodoraceaeH F/H E A_Mono FAC P N 3.76Lachnocaulon ancepsEriocaulaceaeH F/H E A_Mono FACW P N 7.15Lachnocaulon minusEriocaulaceaeH F/H E A_Mono OBL P N 7.97Leersia hexandraPoaceaeH G E A_Mono OBL P N 5.61LemnaLemnaceaeH F/H F A_Mono OBLLemna minorLemnaceaeH F/H F A_Mono OBL P N 3.77Leucothoe racemosaEricaceaeW SH O A_dicot FACW P N 9.44LiatrisAsteraceaeLigustrum sinenseOleaceaeCat1 W SH O A_dicot FAC P E 0Limnobium spongiaHydrocharitaceaeH F/H F A_Mono OBL P N 4.79Limnophila sessilifloraScrophulariaceaeCat2 H F/H E A_dicot OBL P E 0LinderniaScrophulariaceaeA_dicotLiquidambar styracifluaHamamelidaceaeW T O A_dicot FACW P N 5.56Liriodendron tulipiferaMagnoliaceaeW T O A_dicot FAC P NLudwigiaOnagraceaeH F/H E A_dicotLudwigia alataOnagraceaeH F/H E A_dicot OBL P N 5.85Ludwigia alternifoliaOnagraceaeH F/H E A_dicot OBL P N 6.24Ludwigia arcuataOnagraceaeH F/H E A_dicot OBL P N 5.32Ludwigia decurrensOnagraceaeH F/H E A_dicot OBL AP N 6.76Ludwigia grandifloraOnagraceaeH SSH E A_dicot OBL P ELudwigia leptocarpaOnagraceaeH F/H E A_dicot OBL AP N 3.47Ludwigia linifoliaOnagraceaeH F/H E A_dicot OBL P N 7.04Ludwigia octovalvisOnagraceaeH F/H E A_dicot OBL P N 4.09Ludwigia peruvianaOnagraceaeH F/H E A_dicot OBL P E 0.62Ludwigia repensOnagraceaeH F/H E A_dicot OBL P N 5.2Ludwigia sphaerocarpaOnagraceaeH F/H E A_dicot OBL P NLudwigia suffruticosaOnagraceaeH F/H E A_dicot FACW P N 6.23Luziola fluitansPoaceaeH G E A_Mono OBL P N 4.79Lycopodiella alopecuroidesLycopodiaceaeW SSH E Fern OBL P NLycopusLamiaceaeH F/H E A_dicot P NLycopus amplectensLamiaceaeH F/H EA_dicot OBL P N63Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CLycopus virginicusLamiaceaeH F/H E A_dicot OBL P NLygodium microphyllumSchizaeaceaeCat1 H V O Fern P E 0LyoniaEricaceaeA_dicotLyonia lucidaEricaceaeW SH O A_dicot FACW P N 7.06Magnolia grandifloraMagnoliaceaeW T O A_dicot FAC P NMagnolia virginianaMagnoliaceaeW T O A_dicot FACW P N 9.44Mayaca fluviatilisMayacaceaeH F/H S A_Mono OBL P N 8.45MelaleucaMyrtaceaeW T O A_dicotMelaleuca quinquenerviaMyrtaceaeCat1 W T O A_dicot FAC P E 0Melia azedarachMeliaceaeCat1 W T O A_dicot P E 0Micranthemum glomeratumScrophulariaceaeH F/H E A_dicot OBL P N 5.85Micranthemum umbrosumScrophulariaceaeH F/H E A_dicot OBL AP N 5.66Mikania scandensAsteraceaeH V O A_dicot FACW P N 1.95Mimosa pigraLeguminosaeCat1 W T O A_dicot FAC P E 0Muhlenbergia capillarisPoaceaeH G E A_Mono Upland P NMusaMusaceaeE A_MonoMusa sapientumMusaceaeT O A_Mono Upland P EMyrica ceriferaMyricaceaeW SH O A_dicot FAC P N 3.82MyriophyllumHaloragaceaeH F/H S A_dicot OBL PMyriophyllum aquaticumHaloragaceaeH F/H S A_dicot OBL P E 0.98Myriophyllum heterophyllumHaloragaceaeH F/H S A_dicot OBL P N 4.77Najas gracillimaNajadaceaeH F/H S A_Mono OBL A NNajas guadalupensisNajadaceaeH F/H S A_Mono OBL A N 5.07Najas minorNajadaceaeH F/H S A_Mono OBL A E 3.64NelumboNelumbonaceaeH A_dicotNelumbo luteaNelumbonaceaeH F/H F A_dicot OBL P N 6.26NephrolepisPolypodiaceaeNitellaCharaceaeNV S Algae N 7.28Nuphar luteumNymphaeaceaeH F/H F A_dicot OBL P N 4.64NymphaeaNymphaeaceaeH A_dicotNymphaea mexicanaNymphaeaceaeH F/H F A_dicot OBL P N 8.61Nymphaea odorataNymphaeaceaeH F/H F A_dicot OBL P N 7.18Nymphoides aquaticaMenyanthaceaeH F/H F A_dicot OBL P N 6.09Nyssa sylvatica bifloraCornaceaeW T O A_dicot OBL P N 9.04OsmundaOsmundaceaeE FernOsmunda cinnamomeaOsmundaceaeHF/H E Fern FACWP N 6.4464Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of COsmunda regalisOsmundaceaeH F/H E Fern OBL P N 8.04Ostrya virginianaBetulaceaeW T O A_dicot Upland P NPanicumPoaceaeH G E A_MonoPanicum dichotomiflorumPoaceaeH G E A_Mono FACW A N 4.96Panicum hemitomonPoaceaeH G E A_Mono OBL P N 5.82Panicum repensPoaceaeCat1 H G E A_Mono FACW P E 0Panicum rigidulumPoaceaeH G E A_Mono FACW P N 5.47Panicum rigidulum pubesensPoaceaeH G E A_Mono OBL P NPanicum scabriusculumPoaceaeH G E A_Mono OBL P N 7.15Panicum tenerumPoaceaeH G E A_Mono OBL P N 8.67Panicum virgatumPoaceaeH G E A_Mono FACW P N 5.44Paspalidium geminatumPoaceaeH G E A_Mono OBL P N 6.36PaspalumPoaceaeH G E A_MonoPaspalum distichumPoaceaeH G E A_Mono OBL P N 5.54Paspalum floridanumPoaceaeH G E A_Mono FACW P N 6.11Paspalum repensPoaceaeH G E A_Mono OBL A N 6.69Paspalum urvilleiPoaceaeH G E A_Mono FAC P E 0PeltandraAraceaeH F/H E A_Mono OBL P N 7.31Peltandra virginicaAraceaeH F/H E A_Mono OBL P N 7.31Pennisetum purpureumPoaceaeCat1 H G E A_Mono FAC P E 0Persea borboniaLauraceaeW T O A_dicot FACW P N 8.02Persea borbonia humilisLauraceaeW SH O A_dicot P NPersea palustrisLauraceaeW T O A_dicot OBL P N 8.31Phanopyrum gymnocarponPoaceaeH G A_Mono OBL P NPhragmites australisPoaceaeH F/H E A_Mono OBL P E 4.39Phyla nodifloraVerbenaceaeH F/H E A_dicot FAC P N 1.92PinusPinaceaeW T O GymnoPinus clausaPinaceaeW T O Gymno FACW P NPinus elliottiiPinaceaeW T O Gymno P N 4.21Pinus palustrisPinaceaeW T O Gymno P N 4.77Pistia stratiotesAraceaeCat1 H F/H F A_Mono OBL P E 0PlucheaAsteraceaeH F/H E A_dicot FACW NPluchea foetidaAsteraceaeH F/H E A_dicot FACW P N 6.65Pluchea odorataAsteraceaeH F/H E A_dicot FACW AP N 4.96Pluchea roseaAsteraceaeH F/H E A_dicot FACW P N 5.45PoaceaePoaceae65Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CPolygonumPolygonaceaeH F/H E A_dicotPolygonum densiflorumPolygonaceaeH F/H E A_dicot OBL AP N 5.32Polygonum hydropiperoidesPolygonaceaeH F/H E A_dicot OBL P N 4.02Polygonum punctatumPolygonaceaeH F/H E A_dicot OBL AP N 4.02Polypogon monspeliensisPoaceaeH G E A_Mono FAC A EPontederia cordataPontederiaceaeH F/H E A_Mono OBL P N 5.38Pontederia rotundifoliaPontederiaceaeH F/H E A_Mono P EPotamogeton diversifoliusPotamogetonaceaeH F/H S A_Mono OBL P N 7.15Potamogeton illinoensisPotamogetonaceaeH F/H S A_Mono OBL P N 6.64Potamogeton pectinatusPotamogetonaceaeH F/H S A_Mono OBL P N 7.8Potamogeton pusillusPotamogetonaceaeH F/H S A_Mono OBL P N 7.8QuercusFagaceaeW T O A_dicot P NQuercus laurifoliaFagaceaeW T O A_dicot FACW P N 5.14Quercus nigraFagaceaeW T O A_dicot FACW P N 4.14RhexiaMelastomataceaeH F/H E A_dicot FACW P NRhexia cubensisMelastomataceaeH F/H E A_dicot FACW P N 7.22Rhexia marianaMelastomataceaeH F/H E A_dicot FACW P N 5.5Rhexia nashiiMelastomataceaeH F/H E A_dicot FACW P N 7.8Rhexia virginicaMelastomataceaeH F/H E A_dicot FACW P NRhus copallinumAnacardiaceaeW SH O A_dicot P N 3.65RhynchosporaCyperaceaeH G E A_Mono NRhynchospora baldwiniiCyperaceaeH G E A_Mono OBL AP NRhynchospora cephalanthaCyperaceaeH G E A_Mono OBL P N 6.19Rhynchospora fascicularisCyperaceaeH G E A_Mono FACW P N 5.92Rhynchospora globularisCyperaceaeH G E A_Mono FACW AP NRhynchospora inundataCyperaceaeH G E A_Mono OBL P N 7.25Rhynchospora microcarpaCyperaceaeH G E A_Mono OBL P N 5.29Rhynchospora microcephalaCyperaceaeH G E A_Mono FACW P N 6.5Rhynchospora nitensCyperaceaeH G E A_Mono OBL AP N 5.2Rhynchospora scirpoidesCyperaceaeH G E A_Mono OBL A NRhynchospora tracyiCyperaceaeH G E A_Mono OBL P N 9.03RubusRosaceaeH SH O A_dicot P NRubus argutusRosaceaeH SH O A_dicot FAC P N 3.56Rubus trivialisRosaceaeH SH O A_dicot FAC P N 2.6Sabal palmettoPalmaeW T O A_Mono FAC P N 4.85Sabatia grandifloraGentianaceaeHF/H E A_dicot FACWA N 7.0966Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CSaccharumPoaceaeH G E A_MonoSacciolepis striataPoaceaeH G E A_Mono OBL P N 5.35SagittariaAlismataceaeH F/H A_Mono OBL P NSagittaria gramineaAlismataceaeH F/H E A_Mono OBL P N 5.53Sagittaria kurzianaAlismataceaeH F/H S A_Mono OBL P N 9.75Sagittaria lancifoliaAlismataceaeH F/H E A_Mono OBL P N 4.96Sagittaria latifoliaAlismataceaeH F/H E A_Mono OBL P N 6.5Sagittaria subulataAlismataceaeH F/H E A_Mono OBL P NSalixSalicaceaeW A_dicotSalix babylonicaSalicaceaeW T O A_dicot OBL P ESalix carolinianaSalicaceaeW T O A_dicot OBL P N 2.95Salix eriocephalaSalicaceaeW T O E A_dicot FACW P NSalix floridanaSalicaceaeW T O E A_dicot FACW P NSalvinia minimaSalviniaceaeH F/H F Fern OBL AP E 2.03SambucusCaprifoliaceaeA_dicotSambucus canadensisCaprifoliaceaeW SH O A_dicot FAC P N 1.48Sapium sebiferumEuphorbiaceaeCat1 W T O A_dicot FAC P E 0Sarcostemma clausumAsclepiadaceaeH V O A_dicot FACW P N 3.81Saururus cernuusSaururaceaeH F/H E A_dicot OBL P N 7.33Schinus terebinthifoliusAnacardiaceaeCat1 W T O A_dicot FAC P E 0SchizachyriumPoaceaeA_MonoScirpusCyperaceaeH G E A_Mono OBL PScirpus americanusCyperaceaeH G E A_Mono OBL P N 6.5Scirpus californicusCyperaceaeH G E A_Mono OBL P N 6.01Scirpus cubensisCyperaceaeH G E A_Mono OBL P E 3.77Scirpus cyperinusCyperaceaeH G E A_Mono OBL P NScirpus pungensCyperaceaeH G E A_Mono OBL P NScirpus validusCyperaceaeH G E A_Mono OBL P N 5.55ScleriaCyperaceaeH G E A_MonoSerenoa repensPalmaeW SH O A_Mono Upland P N 7.03SesbaniaFabaceaeA_dicotSesbania herbaceaFabaceaeH F/H E A_dicot FAC A N 1.5Sesbania puniceaFabaceaeCat2 W SH O A_dicot FAC P E 0Setaria magnaPoaceaeH G E A_Mono FACW A NSetaria parvifloraPoaceaeH G E A_Mono FAC P N 3.4SmilaxSmilacaceaeV O A_MonoP N67Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CSmilax laurifoliaSmilacaceaeV O A_Mono FACW P NSmilax pumilaSmilacaceaeV O A_Mono P N 6.01SolidagoAsteraceaeH F/H E A_dicot P NSolidago fistulosaAsteraceaeH F/H E A_dicot FACW P N 4.49Solidago leavenworthiiAsteraceaeH F/H E A_dicot FAC P NSolidago strictaAsteraceaeH F/H E A_dicot FACW P N 5.49Spartina alternifloraPoaceaeH G E A_Mono OBL P N 7.94Spartina bakeriPoaceaeH G E A_Mono FACW P N 5.98Spirodela polyrhizaLemnaceaeH F/H F A_Mono OBL P N 2.95Taxodium ascendensTaxodiaceaeW T O Gymno OBL P N 7.21Taxodium distichumTaxodiaceaeW T O Gymno OBL P N 7.21Thalia geniculataMarantaceaeH F/H E A_Mono OBL P N 7.12ThelypterisPolypodiaceaeE FernThelypteris interruptaPolypodiaceaeH F/H E Fern FACW P N 6.74ToxicodendronAnacardiaceaeA_dicotToxicodendron radicansAnacardiaceaeH V O A_dicot FAC P N 1.44Toxicodendron vernixAnacardiaceaeW T O A_dicot OBL P NTradescantia ohiensisCommelinaceaeH F/H E A_Mono FAC P NTriadenum virginicumClusiaceaeH F/H E A_dicot OBL P N 8.16TyphaTyphaceaeH E A_Mono P NTypha domingensisTyphaceaeH F/H E A_Mono OBL P N 0.59Typha latifoliaTyphaceaeH F/H E A_Mono OBL P N 1.6Ulmus americanaUlmaceaeW T O A_dicot FACW P N 7.68Urena lobataMalvaceaeCat2 H SSH O A_dicot Upland P E 0UtriculariaLentibulariaceaeH SUtricularia cornutaLentibulariaceaeH F/H S A_dicot OBL AP N 7.46Utricularia floridanaLentibulariaceaeH F/H S A_dicot OBL P N 6.34Utricularia foliosaLentibulariaceaeH F/H S A_dicot OBL P N 6.44Utricularia gibbaLentibulariaceaeH F/H S A_dicot OBL AP N 6.37Utricularia inflataLentibulariaceaeH F/H S A_dicot OBL P N 5.85Utricularia olivaceaLentibulariaceaeH F/H S A_dicot OBL AP NUtricularia purpureaLentibulariaceaeH F/H S A_dicot OBL AP N 6.5Utricularia radiataLentibulariaceaeH F/H S A_dicot OBL AP N 6.01Utricularia subulataLentibulariaceaeH F/H S A_dicot OBL AP N 7.23Vallisneria americanaHydrocharitaceaeH F/H S A_Mono OBL P N 7.28Verbena bonariensisVerbenaceaeHF/H E A_dicot FAC APB E 0.5668Taxon FamilyFLEPPC H/W GR_1 GR_2 T&ERepro Wetland Duration Nativity C of CVigna luteolaFabaceaeW V O A_dicot FACW P NVitaceaeVitaceaeVitisVitaceaeH V O A_dicotVitis rotundifoliaVitaceaeH V O A_dicot FAC P N 1.18Websteria confervoidesCyperaceaeH G S A_Mono OBL P NWedelia trilobataAsteraceaeCat2 H F/H E A_dicot FAC AP E 0WolffiellaLemnaceaeH F/H F A_Mono OBL P N 4.37Woodwardia areolataPolypodiaceaeH F/H E Fern OBL P N 7.68Woodwardia virginicaPolypodiaceaeH F/H E Fern FACW P N 6.5Xanthosoma sagittifoliumAraceaeCat2 H F/H E A_Mono FACW P E 0XyrisXyridaceaeH F/H E A_Mono OBL P NXyris baldwinianaXyridaceaeH F/H E A_Mono OBL P N 6.97Xyris fimbriataXyridaceaeH F/H E A_Mono OBL P N 7.08Xyris isoetifoliaXyridaceaeH F/H E E A_Mono OBL P NXyris smallianaXyridaceaeH F/H E A_Mono OBL P N 7.8ZanthoxylumRutaceaeZizania aquaticaPoaceaeH G E A_Mono OBL A N 6.69Zizaniopsis miliaceaPoaceaeH G E A_Mono OBL P N 6.21

### 69 APPENDIX 2. EPA’S WETLAND PLANT ATTRIBUTES

Florida taxon name, the taxon’s general tolerance to pollutio n, 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 associa tion with HDG was significant ( χ 2 

### , * 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, count y, 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 Northwes t 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 Wellingt on 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 Southwe st 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 Brooke r 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 thei r 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 ra nged 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 struct ure” 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 -0.0 8 -0.17 0.27 0.02 0.06 0.24 -0.07 Native graminoids -0.13 -0.1 2 -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 -0.0 9 -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 th e 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 a nd 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 cha nged). For variability analysis, 31 lakes had 2–3 repeat visits during 2005–2006. Some lakes had visits be fore 2005, but protocols for defining and calculating some of the metrics ch anged 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 lake s had two visits eith er during one year (2005) or during both years.

Data collected during 2005-2006 include d 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 sensitiv e 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 th e WQ and habitat inde xes (all lakes had LDI values). Previously, missing values were assu med to show no impairment and scored as 0. For the analysis described here, when onl y 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 com ponent 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 se parate, 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 comp arisons and analyses of both LVI and its component metrics used the averag e value for each lake-visit.

Metric scoring rules were deri ved (as previously) from the 5 th 

### and 95 th 

### 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 28.2 o 

### latitude and approximates the frost line. Along this line, Pasco, Sumter, La ke, and Orange counties are in the northern

### 84 region, Pinellas, Hillsborough, Polk, and Osceo la counties are in the southern region, while Brevard county is divide d by the 28.35° latitude line.

Variance of LVI was evaluated in two wa ys. 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 an alysis, only 11 lakes had the same pattern of visits (2 visits in 2005 and 1 in 2006), and thes e lakes were used. In contrast, to estimate variance of LVI, a larger sample size was pr eferred; 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 ca nnot be explained by lake and year alone). An ANOVA
model was used to derive estimates of va riance, one model each for LVI and the four metrics. The relative percentage s 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 LV I was calculated for repeat visits on the same day. For this study, repeat visits dur ing 2005 and 2006 to 31 lakes were used to
estimate variance of LVI. With lake as the ma in factor, all repeat visits were used to calculate the mean squared error (MSE). In ot her 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 s 2 

### = 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 so uthern lakes is shown.

### 86 Results

Validation testing
LVI and its component metrics were, again, hi ghly 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 metr ic calculations. Correlation between LVI and HDG was nearly identical to earlier result s for the 2004 validation testing (Spearman’s r

= –0.71 vs. –0.70 (development data set; Tabl e 13 main document) and –0.72 (first validation data set; Table 16). Correlation be tween 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 8Dominant 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 Tabl e 4 main document), all measures of human disturbance were significantly correlated w ith 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 descri bed 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 compar ison, 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 condit ion than southern lakes for the same level of human disturbance (App. Figure 6.3). Dominant C of C showed th e greatest difference between regions. Two metrics, percent sensitive taxa and domina nt C of C, were adjusted for regional differences by using separate scoring rules for lakes in th e 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 s outhern lakes because many invasi ve exotic plants are from tropical climates and are able to persist a nd 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 co rrelation with disturba nce measures (see App. Table 6.1 above).

App. Table 6.3. Metric scoring rules derived from the 5 th 

### and 95 th 

### percentiles using the most recent visit from each lake including la kes 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 5 th %tile 95 th %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%

HDGDominant 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 categorie s defined by HDG was due to the inaccuracy of measurements of human disturbance; theref ore, this method was not used to define the number of categories of biological condition th at 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 100LVI (2007)



### App. Figure 6.4. LVI declined as human dist urbance 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 hu man disturbance at each lake changed very little during the sampling period and that differe nces 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 (whi ch is the type of variance we hope to detect), within year variability represented the larg est component of variance for most of the indicators (App. Figure 6.5). Differences associated with a nnual variability were much smaller in comparison. The most variable metric conti nued 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 re peat visits in the original analysis. For an LVI value calculated as the aver age of values from four lake sections, the variance would be equal to the within-lak e variance divided by four, or 28.0 (=112.33/4; see Table 12, main document). The 90% confid ence interval derived from that estimate of variance was 17.4 (LVI +/- 8.7). Dividing th e confidence interval into 100 (the range of values possible for LVI) yielded an estim ate 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 si ngle field crew while the repeat visits in 2005 and 2006 could have been done by differe nt 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 cat egories of biological condition for repeat visits across years. For tw o visits, variance decreased to 14.0 (=28/2) and the 90% confidence interval was 17.0 (LVI +/- 8.5), which yielded 5.9 cate gories of biological
condition.

### 92 App. Table 6.4. ANOVA results for LVI for rep eat 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 LakeLVI% Native taxa% Invasive taxa% Sensitive taxaDom C of C 

### 0 20 40 60 80 100% of Total variance



### App. Figure 6.5. Relative contributions of differe nt sources of variance for LVI and its four metrics. Differences associated with lakes represented the gr eatest 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). Variabili ty 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 LV I show a very similar pattern to earlier testing: LVI and its component metrics were highly correlated with independent measures of human disturbance, including la ndscape level disturbance (LDI), measures of water chemistry (WQ index), and physical habitat condition (habitat index).

Differences in metric values were associ ated with climatological differences in
location. Southern lakes are more vulnera ble 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 lowe r 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 varian ce estimate derived from same-day repeat visits for the original analysis. Although vari ance 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 diffe rent lake sections and half was due to changes through time. Pr ecision could be increa sed by increasing the number of lake visits, but for the curren t sampling protocol th e LVI is capable of detecting approximately 4 levels of biologi cal 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 di stinguish 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 Jack son, 2006). Within this framework, states and tribes must adopt water quality standard s to protect their waters. Water quality standards (WQS) include three parts: 1) desi gnated uses, 2) numeric and narrative criteria that protect the uses, and 3) antidegradati on policies to prevent deterioration of highquality waters. WQS define the water quali ty goals for a water body by designating the use(s) and setting criteria necessa ry to protect the use(s). Stat es are required to report to Congress the water bodies that are faili ng to support their designated uses.

Florida DEP intends to use biological cr iteria along with physical and chemical
criteria to determine whether lakes are mee ting their designated uses, particularly uses related to aquatic life support. Designated aquatic life us es 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 need ed 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 asse mblage (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 m easurable definitions of designated aquatic life uses. Thus, the TALU approach is designed to support th e implementation of biocriteria, and the scientific knowledge of aqua tic ecology that they represen t, into state water quality
standards. Because FDEP’s intends ultimatel y to adopt criteria based on this tiered aquatic life use concept, three categories are pr oposed for the LVI. S ubject to future rule making decisions, those three categories may be conceptually interpreted as

### 95 “exceptional” (Category 1), “healthy” (Cate gory 2), and “impaired” (Category 3). The approach described here for defining biocrite ria for lakes closely parallels the methods
used to define biocriteria fo r 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 Grad ient (BCG) to illustrate how ecological attributes change in response to increasing le vels 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 pollu tion 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 divide d into six levels (tie rs) of condition that were defined during a series of national wo rkshops 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 bi otic community and minimal changes in ecosystem function
3. Evident changes in structure of the bi otic community and minimal changes in ecosystem function

### 96 4. Moderate changes in structure of the bi otic community with minimal changes in ecosystem function
5. Major changes in structure of the bio tic community and moderate changes in ecosystem function
6. Severe changes in structure of the bio tic 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 lo cal conditions using data from a regional monitoring program. This was the approach ad opted 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 deve lop a BCG for Florida lakes (App. Table 7.1). Participants were asked to apply and calibra te 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 provi ded with background literature on the BCG concept (Davies and Jack son, 2006; Stoddard et al., 2006) and a description from DEP of the workshop process and the LVI sa mpling 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 th e south; experts were told whether lakes were located in the north or south. Expert s 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 10 th 

### , 20 th 

### , 30 th 

### , 40 th 

### , 50 th 

### , 60 th 

### , 70 th 

### , 80 th 

### , and 90 th 

### percentiles of LVI

### 97 scores for that region were chosen (nine vis its; “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 sc ores. More specifically, they were asked to
record the maximum BCG score th at they believed did not me et 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 consider ed “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 bioc riteria (EPA, 2006). Typi cally percentiles of
index values for references sites are used to define thresholds. A buffer derived from the variability of reference site index values ma y also be used to define the lowest index value that indicates departure from refe rence condition, or impairment. Although not used to define thresholds for the LVI, LVI st atistics 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 a ffiliation and years of experience working with plants. Three participants had <5 year s 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 11/1/200 6 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 expe rts agreed that tiers 5 and 6 indicated impairment or failure to support aquatic life use (13 out of 20). Se ven experts thought the line for impairment should be higher: two iden tified 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 near est 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 definitio n 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 “imp aired”). 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”).101App. Table 7.3. Lake visit, BCG scores for eachexpert, median BCG for each lake, and LVI.LakeE01E02E03E04E05E06E07E09E10E12E13E14E15E17E18E19E20E21E22E23MeanBCGMedianBCGLVIN014 4 4 4 5 5 4 3 5 3 4 5 5 4 4 4 5 4 4 44.24 48N023 5 6 5 6 5 5 5 5 5 5 5 6 5 5 5 5 5 5 55.15 33N034 3 3 3 3 3 3 3 4 3 3 4 4 3 3 3 4 1 3 33.23 69N041 1 1 1 1 2 1 1 1 2 1 4 2 2 2 1 2 2 2 21.62 94N055 5 5 4 4 4 4 3 4 4 4 2 4 3 3 4 4 3 3 33.84 41N063 3 4 3 4 4 4 3 3 3 4 3 4 3 3 3 3 4 33.43 63N072 1 1 1 1 2 1 1 1 1 1 3 1 1 2 1 4 2 1 11.51 82N082 1 1 3 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 11.31 82N092 2 1 2 2 2 2 1 2 2 2 2 2 2 2 2 1 2 2 21.92 85N103 2 2 2 2 3 2 2 2 3 2 2 3 2 3 2 3 2 2 32.42 78N113 2 1 2 2 2 2 2 2 2 2 2 2 2 3 2 3 2 2 22.12 68N121 1 1 1 1 2 1 1 1 1 1 1 1 1 1 1 3 1 1 11.21 85N132 3 2 3 2 3 3 4 3 3 3 3 3 2 3 3 1 3 3 32.83 60N143 4 3 4 3 4 4 3 4 3 3 3 4 3 4 3 3 4 3 33.43 68N151 1 1 1 1 1 1 1 1 1 1 1 1 1 2 1 4 2 1 11.31 87S015 5 5 5 5 5 5 4 5 5 5 4 5 5 5 4 2 5 4 54.75 38S023 3 3 4 3 3 3 4 3 3 4 3 3 4 4 3 5 3 4 33.43 54S035 5 4 5 5 5 5 5 5 5 5 4 4 5 5 5 4 4 4 54.75 36S044 3 4 4 4 4 4 5 5 5 4 5 5 4 4 5 5 2 4 44.24 41S053 3 3 2 2 3 3 2 2 2 3 2 2 2 2 3 5 2 2 22.52 63S063 3 3 4 2 4 4 3 3 3 4 3 3 3 3 3 2 3 2 33.13 63S074 3 4 4 4 4 4 5 4 5 4 4 6 5 4 5 3 3 5 44.24 49S085 5 5 5 5 5 5 4 5 4 5 5 4 5 5 5 5 5 5 44.85 22S093 2 3 2 3 2 3 4 3 3 3 2 3 3 3 3 5 2 3 32.93 66S105 6 6 5 5 5 5 3 6 5 5 5 5 4 5 5 5 5 4 44.95 18S115 5 6 4 5 5 5 5 4 4 5 5 5 5 5 5 4 4 4 44.75 31S124 3 3 3 3 3 4 3 3 4 3 3 4 4 3 3 3 4 2 33.33 72S135 6 6 5 5 5 5 656 5 6 6 6 6 5 5 5 5 65.55 12S144 4 4 4 4 4 4 5 4 4 4 4 4 4 4 4 5 4 4 44.14 41S153 4 4 3 4 3 4 4 4 4 3 3 3 4 4 3 5 4 5 33.74 42

### App. Table 7.4: Experts’ selection of threshol d 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 100LVI



### 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 da shed lines) and excep tional lakes (LVI=78; green solid line).

Reference sites
Initially, 37 reference lakes with HDG = 0 we re 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 c onfidently labeled as "minimally disturbed," it
was excluded from the reference set based on th e best professional judgment of the biologist. This process yielded a total of 22 reference la kes for calculating statistics for the LVI (App. Table 7.5). Notably, only two referen ce lakes could be identified fo r the southern section of the

state.

Paralleling an approach used by other st ates, a buffer of LVI values below the 10 th 

### percentile was used to represent the natural vari ability associated with field sampling that could be due to water level fluctuation, weather, or tim e of sampling. Half the 90% confidence interval

### 104 (12.0) was subtracted from the 10 th 

### 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 wa s very close to the value from the BCG workshop. Subtracting half the 90% conf idence interval from the 90 th 

### 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 No rth 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 No rth 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 conditi on were described as Category 1 (“exceptional”, with LVI = 78–100), Ca tegory 2 (“healthy”, with LVI = 38–77), and Category 3 (“impaired”, with LVI = 0–37). Narra tive descriptions of these categories were derived from the ranges of metric values a ssociated with each LVI category (App. Table 7.7)

For regulatory decisions, two separate, temporal ly independent site-visits resulting in a Category 3 evaluation are recommende d 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 90 th percentile 92 Maximum 93



### App. Table 7.7. Proposed aquatic life use categor ies, 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 ta xa 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, Divisi on on Earth and Life Studies. National Academy Press, Washington, DC. Stoddard, J.L., D.P. Larsen, C.P. Hawkins, R.K. John son, 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 C ondition 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 La ke Vegetation Index and FDEP ResponsesPeer 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 andPeer 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.

Ad equacy 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

- 0.53308Peer 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, 2009Peer Reviews and Responses for LVI

FDEP Standards and Assessment Section 4Peer 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 DE P.

### 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, As sistant 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 h ow 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.

Ba ckground 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 R 2 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 R 2 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 poo r 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 t o 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 (3 rd place) were happier than those who won silver medals (2 nd 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 measuremen t 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 la ke. 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

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 mechanism s. Through one avenue, stormwater run-off events contribute ions (Ca, Mg, SO 4 , 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 be tter 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

Fi gure 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 R 2 = 0.16, all others R 2 < 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 characte ristics 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 vegeta tion.” 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 . E ven 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 peer

reviewed 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 v arious 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 correlat ion 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.

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, 3 rd 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 call ed ‘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 statistica lly 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 lak es 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 inde x 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 Florid a 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 re adily “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 appendi x 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 wit h 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 whe n 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 instanc e, 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 the se 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 L DI 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 aquati c 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 certa inly does not preclude the use of the LDI, it simply indicates that its

accura cy 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 c alculation 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 wer e 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

sc ore 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 i ssue 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 ig nored 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 scor e 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 de tail. If a graph shows that the lack of correlation was due to poor (low)
metric values in a site whe re 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 elimi nated as described here? Likewise, it states that poor metric values in
sites with litt le 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
correla tion 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 ea ch 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 c larified 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. Hoye r, 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 pane l 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

wa s 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 ha ve 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 ab le 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 s tate. 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 plac ed 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 human

caused 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 act ivities.

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 com munity 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 t he 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 Jacks on (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 c ondition”. 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 Florid a 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 eco system, 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

co ncept 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 Fig ure 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 Bio logical 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 ta ke 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, t rophic 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 i n 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 calcu lation. 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 com ponent 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 dev elopment 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 biot ic 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 facto r 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 prese nt 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 appe ndix 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 Kjel dahl 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 t hat 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 hum an 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, a fter 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 te sted. 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 hypothesi s 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 failin g 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 o n 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 ha d the actual species lists and metrics to work with. They were familiar with the methods of calculating the LVI, the role of native versus non

native plants, and the role of tolerant versus sensitive plants in the calculation of the LVI. Further they wer e 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 iden tification 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 t o 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 fo r 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 Kjelda hl 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 i n 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 th e 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 use d 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 nitrog en 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 i n 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 human

caused pollu tion 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 impa ired 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 Pr otection, 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. Canfie ld, 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