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Biological indicators of wetland health

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Title:
Biological indicators of wetland health comparing qualitative and quantitative vegetation measures with anuran measures
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English
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Gonzalez, Shannon M
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University of South Florida
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Subjects / Keywords:
wellfield
reproductive success
tadpole
amphibian
biological monitoring
Dissertations, Academic -- Biology -- Masters -- USF   ( lcsh )
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government publication (state, provincial, terriorial, dependent)   ( marcgt )
bibliography   ( marcgt )
theses   ( marcgt )
non-fiction   ( marcgt )

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Summary:
ABSTRACT: Understanding wetland responses to human perturbations is essential to the effective management of Florida's surface and ground water resources. Southwest Florida Water Management District (SWFWMD) Rules (Chapter 40D-2.301(c) FAC) prohibit adverse environmental effects to wetlands, fish and wildlife caused by groundwater withdrawal. Numerous studies have documented the responses of biological attributes across taxa and regions to human disturbance. Biological assessment can provide information about ecological condition. Based on long-term monitoring conducted by the SWFWMD, the anthropogenic changes observed on the Starkey Wellfield are attributed to groundwater withdrawal. Biological indicators are species, species assemblages, or communities whose presence, abundance, and condition are indicative of a particular set of environmental conditions. Monitoring early indicators of ecosystem stress may shorten response time by shifting attention to the relatively quick response of sensitive species. Species used to assess biological condition should be abundant and tractable elements of the system that provide an early, diagnosis. Regulatory requirements within 40D-2 F.A.C. dictate an extensive analysis be conducted twice yearly on wetlands within all wellfields. This quantitative analysis provides information on the wetland plant community through the collection of eighteen categorized vegetative and physical variables. Because of the size of the area in which monitoring is required and the large number of wetlands, a rapid qualitative monitoring method was developed using vegetation and physical variables to classify wetlands into one of three categories based on their perceived health. Wetland plants have many characteristics suited to assessments of biological condition including their diversity, taxonomy, distribution, relative immobility, well developed sampling protocols, and, for herbaceous species, their moderate sensitivity to disturbance (U.S. EPA 2002, Doherty et al. 2000). Because amphibians occupy both aquatic and terrestrial habitats in their life history, have physiological adaptations and specific microhabitat requirements, they are considered to be extremely sensitive to environmental perturbations and excellent barometers of the health of the aquatic and terrestrial habitats in which they reside (Vitt et al. 1990, Wake 1998, Blaustein 1994, Blaustein et al. 1994). The purpose of my study was to 1) compare a qualitative method of wetland vegetation monitoring to a quantitative method, 2) document the reproductive success of anurans, and 3) compare anuran reproductive success to the vegetation monitoring results on the J. B. Starkey Wellfield (SWF). The results are published in chapters, with each chapter addressing one of the topics stated above. The results show a rapid, qualitative measure of wetland health is useful for the determination of severely affected wetlands. The anuran reproductive success reflected similar results. The results show that wetlands can be categorized based solely on amphibian reproductive success variables. The anuran categorization, qualitative vegetative categorization, and quantitative vegetative categorization overlap on the high and low success wetlands. The low degree of overlap observed in the intermediate category could be attributed to fish predation in a wetland otherwise suited for amphibian reproduction, natural variability in the two years of anuran data collected or lag time inherent in vegetative monitoring. Strong correlative evidence suggests hydroperiod regulates anuran reproductive success on the J. B. Starkey Wellfield. The average length of inundation was correlated with the number of tadpoles captured per unit effort and the number of tadpole species captured per year (R=0.73, p< .01; R=0.70, p< .05). The average Julian date of inundation was negatively correlated with the same two tadpole variables (R=0.81, p< .01, R=0.78, p< .01). The Julian date of inundation at which breeding attempts stopped and no tadpoles were observed was weeks within the published breeding season for many species. I detected a correlation between the number of species calling in each wetland and the number of tadpole species captured per year (R=0.87, p< .001) suggesting call censuses may be used at this site to estimate anuran reproductive success if enough well-timed observations are made. These findings will allow resource managers and regulators to evaluate and possibly refine land management practices, including existing monitoring methods, and water policy to meet the needs of resident amphibians at the J.B. Starkey Wellfield.
Thesis:
Thesis (M.S.)--University of South Florida, 2004.
Bibliography:
Includes bibliographical references.
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Mode of access: World Wide Web.
Statement of Responsibility:
by Shannon M. Gonzalez.
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Title from PDF of title page.
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Document formatted into pages; contains 147 pages.

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usfldc doi - E14-SFE0000259
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Biological Indicators of Wetland Health: Comparing Qualitative and Quantitative Vegetation Measures with Anuran Measures by Shannon M. Gonzalez A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of Biology College of Arts and Sciences University of South Florida Co-Major Professor: Earl D. McCoy, Ph.D. Co-Major Professor: Henry R. Mushinsky, Ph.D. Peter D. Stiling, Ph.D. Date of Approval: April 9, 2004 Keywords: Amphibian, Tadpole, Biological Monitoring, Reproductive Success, Wellfield Copyright 2004, Shannon M. Gonzalez

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Acknowledgements I would like to express my gratitude to Br ian Halstead, Kris Raymond, Neal Halstead, Pablo Delis, Lee Walton and Jason Lancaster fo r field assistance; R onn Altig for selected tadpole confirmation; Doug Durbin and St eve Godley for their encouragement and thoughtful comments on experimental design and sampling techniques. I thank Lou Anne Perkins for document preparation assi stance, Brian Halstead and Robin Moore for their comments on earlier drafts of this document, Diane Willis and Dan Schmutz for providing monitoring reports and quantitativ e vegetation data. I thank Pasco County Staff for vehicular site access and assistan ce with public relations during weekend and evening sampling. I thank Jennifer Gonzalez for understanding and support of my many long evenings and weekends through this effort. Biological Research Associates provided sampling equipment a nd software. This study was partially funded by the Southwest Florida Water Management Di strict under Agreement Number 01CON000124 and certified by the Institutional Animal Ca re and Use Committee under protocol number 1857.

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i Table of Contents List of Tables iii List of Figures v Abstract viii Chapter One – Vegetative Measures of Wetland Health: A Comparison of Two Methods 1 Introduction 1 Methods 4 Study Sites 4 Qualitative Analysis 8 Quantitative Analysis 9 Statistics 11 Results 13 R-mode Analysis 13 Q-mode Analysis 15 Discussion 26 Literature Cited 29 Chapter Two – Amphibian Measures of We tland Health: An Evaluation of Anuran Reproductive Success 33 Introduction 33 Methods 36 Study Sites 36 Anuran Breeding Male Census 41 Quantitative Larva Sampling 42 Measurement of Environmental Factors 47 Statistics 48 Results 50 Discussion 65 Literature Cited 73 Chapter Three – Biological Measures of Wetland Health: Comparing Vegetation and Anurans as Indicator 79 Introduction 79

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ii Table of Contents (Continued) Methods 82 Study Sites 82 Qualitative Analysis of Vegetation 87 Anuran Breeding Male Census 90 Quantitative Larva Sampling 92 Measurement of Environmental Factors 96 Statistics 97 Results 99 Discussion 118 Conclusions and Recommendations 126 Literature 128

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iii List of Tables Table 1.1 Sampling Locations and Physical Characteristics 8 Table 1.2 Wetland Assessmen t Procedure Variables 10 Table 1.3 Correlations between Wetland Assessment Procedure Variables and Vegetative Health Rating, Inundati on Length, and Julian Date of Inundation 14 Table 1.4 Descriptive Statistics of Wetland Assessment Procedure Variables Categorized by Vegetative Health Rating 16 Table 1.5 Descriptive Statistics of Wetland Assessment Procedure Variables Categorized by Two-Category Alternate Vegetative Health Rating 23 Table 1.6 Descriptive Statistics of Wetland Assessment Procedure Variables Categorized by Three-Category Alternate Vegetative Health Rating 25 Table 2.1 Sampling Locations and Monitoring Methods 40 Table 2.2 Frog Species Known with a Range that Includes Pasco County, Florida 45 Table 2.3 2001 Calling Ma le Summary Table 51 Table 2.4 2002 Calling Ma le Summary Table 52 Table 2.5 2001 Quantitative Ta dpole Sampling Summary 53 Table 2.6 2002 Quantitative Ta dpole Sampling Summary 54 Table 3.1 Sampling Locations and Monitoring Methods 86 Table 3.2 Wetland Assessmen t Procedure Variables 90 Table 3.3 Frog Species Known with a Range that Includes Pasco County, Florida 94 Table 3.4 2001 Calling Ma le Summary Table 101 Table 3.5 2002 Calling Ma le Summary Table 102

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iv List of Tables (Continued) Table 3.6 2001 Quantitative Ta dpole Sampling Summary 104 Table 3.7 2002 Quantitative Ta dpole Sampling Summary 106 Table 3.8 Tadpole Descriptive Sta tistics Categorized by Vegetative Health Rating 109 Table 3.9 Comparison of Altern ate Wetland Health Ratings 117

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v List of Figures Figure 1.1 Location Map 6 Figure 1.2 Site Locations and Land Use Map 7 Figure 1.3 Box and Whisker Plot of Quantitative Vegetation Scores Categorized By Sampling Event 15 Figure 1.4 Box and Whisker Plot of Average Quantitative Variables Categorized By Vegetative Health Rating 17 Figure 1.5 Cluster Analysis Using We tland Assessment Procedure Variables 18 Figure 1.6 NMDS Plot of All Quantitative Variables 19 Figure 1.7 NMDS Plot of Average Quantitative Variables 20 Figure 1.8 Cluster Analysis Using Soils and Hydrologic Variables 21 Figure 1.9 NMDS Plot Using Soils and Hydrologic Variables 21 Figure 1.10 Box and Whisker Plot of Alternate Wetland Classifications 24 Figure 2.1 Location Map 37 Figure 2.2 Site Locations and Land Use Map 39 Figure 2.3 Cluster Analysis Using a Vari ety of Anuran and Predator Variables 57 Figure 2.4 NMDS Plot Created Using Seven Anuran Variables 58 Figure 2.5 NMDS Plot Created Usin g Twenty-Five Anuran and Predator Variables 59 Figure 2.6 NMDS Plot Using Nine Anuran and Predator Variables 60

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vi List of Figures (Continued) Figure 2.7 Spearman Rank Correlati ons Between Hydroperiod Variables and Tadpole Variables in 2001 and 2002 62 Figure 2.8 Spearman Rank Correlation Between Average Number of Species Heard Calling and the Average Number of Tadpole Species Captured in 2001 and 2002 63 Figure 2.9 Box and Whisker Plot Using Average Number of Tadpoles Captured Per Unit Effort for Unsuccessful vs. Successful Anuran Reproductive Success Categories 64 Figure 2.10 Box and Whisker Plot Using Average Number of Tadpoles Species Captured Per Unit Effort for Unsu ccessful vs. Successful Anuran Reproduction Wetlands 64 Figure 3.1 Location Map 84 Figure 3.2 Site Locations and Land Use Map 85 Figure 3.3 Spearman Rank Correla tion Between Average WAP Scores and Tadpole Variables 108 Figure 3.4 Comparison of Vegetatio n and Anuran Cluster Analyses 110 Figure 3.5 NMDS Comparison of Ve getative and Anuran Indicators 112 Figure 3.6 Spearman Rank Correlation Between Average Length of Inundation and Number of Tadpol es Captured Per Unit Effort in 2001 and 2002 113 Figure 3.7 Spearman Rank Correlation Between Average Length of Inundation and Number of Tadpole Species Captured Each Year in 2001 and 2002 113 Figure 3.8 Spearman Rank Correlati on Between Average Julian Date of Inundation and Number of Ta dpoles Captured Per Unit Each Year in 2001 and 2002 114 Figure 3.9 Spearman Rank Correlati on Between Average Julian Date of Inundation and Number of Tadpole Species Captured Each Year in 2001 and 2002 114

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vii List of Figures (Continued) Figure 3.10 Spearman Rank Correla tion Between Average Number of Species Heard Calling and the Average Number of Tadpole Species Captured in 2001 and 2002 115 Figure 3.11 Box and Whisker Comp arison of Vegetation and Anurans Indicators 117

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viii Biological Indicators of Wetland Health: Comparing Qualitative and Quantitative Vegetation Measures with Anuran Measures Shannon Gonzalez ABSTRACT Understanding wetland responses to human pert urbations is essential to the effective management of Florida’s surface and ground water resources. Southwest Florida Water Management District (SWFWMD) Rules (C hapter 40D-2.301(c) FAC) prohibit adverse environmental effects to wetla nds, fish and wildlife caused by groundwater withdrawal. Numerous studies have documented the responses of biological attributes across taxa and regions to human disturbance. Biological assessment can provide information about ecological condition. Based on long-term monitoring conducted by the SWFWMD, the anthropogenic changes observed on the Starke y Wellfield are attributed to groundwater withdrawal. Biological indicators are species, species assemblages, or communities whose presence, abundance, and condition are indicative of a pa rticular set of envi ronmental conditions. Monitoring early indicators of ecosystem stress may shorten response time by shifting attention to the relatively quick response of sensitive species. Species used to assess biological condition should be abundant and tract able elements of the system that provide an early, diagnosis.

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ix Regulatory requirements within 40D-2 F.A.C. dictate an extensive analysis be conducted twice yearly on wetlands within all wellfields. This quantitative analysis provides information on the wetland plant community through the collection of eighteen categorized vegetative and physical variables. Because of the size of the area in which monitoring is required and the large number of wetlands, a rapid qualitative monitoring method was developed using vege tation and physical variables to classify wetlands into one of three categories based on their perceived health. Wetland plants have many characteristics su ited to assessments of biological condition including their diversity, taxonomy, dist ribution, relative immobility, well developed sampling protocols, and, for herbaceous species their moderate sensitivity to disturbance (U.S. EPA 2002, Doherty et al. 2000). Because amphibians occupy both aquatic and terrestrial habitats in their life history, have physiological adaptations and specific microhabitat requirements, they are considered to be extremely sensitive to environmental perturbations a nd excellent barometers of th e health of the aquatic and terrestrial habitats in which they re side (Vitt et al. 1990, Wake 1998, Blaustein 1994, Blaustein et al. 1994). The purpose of my study was to 1) compar e a qualitative method of wetland vegetation monitoring to a quantitative method, 2) docum ent the reproductive success of anurans, and 3) compare anuran reproductive success to the vegetation monitoring results on the J.B. Starkey Wellfield (SWF). The results are published in chapters, with each chapter addressing one of the topics stated above.

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x The results show a rapid, qualitative measure of wetland health is useful for the determination of severely a ffected wetlands. The anuran reproductive success reflected similar results. The results show that wetlands can be categorized based solely on amphibian reproductive success variables. The anuran categorization, qualitative vegetative categorization, and quantitative vegetative categor ization overlap on the high and low success wetlands. The low degree of overlap observed in the intermediate category could be attributed to fish preda tion in a wetland otherwise suited for amphibian reproduction, natural variability in the two year s of anuran data collected or lag time inherent in vegetative monitoring. Strong correlative evidence suggests hydroperiod regulates anuran reproductive success on the J. B. Starkey Wellfield. The average length of inundation was correlated with the number of tadpoles captured per unit effort and the number of tadpole species captured per ye ar (R=0.73, p<.01; R=0.70, p<.05). The average Julian date of inunda tion was negatively correlated with the same two tadpole variables (R=0.81, p<.01, R=0.78, p<.01). Th e Julian date of inundation at which breeding attempts stopped and no tadpoles were observed was weeks within the published breeding season for many species. I detected a correlation between the number of species calling in each wetland and the number of tadpole species captured per year (R=0.87, p<.001) suggesting call censuses may be used at this site to estimate anuran reproductive success if enough well-timed obser vations are made. These findings will allow resource managers and regulators to ev aluate and possibly refine land management practices, including existing monitoring met hods, and water policy to meet the needs of resident amphibians at the J.B. Starkey Wellfield.

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1 CHAPTER 1 – Vegetative Measures of Wetland Health: A Comparison of Two Methods Introduction Understanding wetland responses to human pert urbations is essential to the effective management of Florida’s surface and gr ound water resources (Doherty et. al. 2000, USEPA 1987, 1989). Southwest Florida Water Ma nagement District (SWFWMD) Rules (Chapter 40D-2.301(c) FAC) prohi bit adverse environmental eff ects to wetlands, fish and wildlife caused by groundwater withdrawal. Measurement of environmental effects is currently conducted by SWFWMD and indepe ndent environmental consultants through numerous quantitative and qualita tive assessments on over 2,900 square kilometers of land in the northern Tampa Bay Area (Rochow 1994). Because it is impractical to measure all aspects of the ecosystem to de tect anthropogenic change or measure wetland function, recent focus has been on determining if certain attributes may reflect the overall biological integrity of certai n systems (Doherty et al. 2000). Biological assessment can provide inform ation about ecological condition (Karr 1991). Numerous studies have documented the responses of biological attributes across taxa and regions to human disturbance (Doherty et al 2000). Biological indicators are species, species assemblages, or communities whos e presence, abundance, and condition are indicative of a particular se t of environmental conditions (Adamus 1996). For instance, botanists and ecologists

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2 have been developing ways to use plants as indicators of local conditions for many years (Goslee et al. 1997). On a lands cape scale, plant distribution is a function of climate, but on the local scale, distribution is primarily determined by local environmental factors (Billings 1952). Vegetation characteristics i ndicate the presence of wetlands and their boundaries and are used as a basis for many classification sche mes (U.S. EPA 2002). Wetland plants have many characteristics su ited to biological assessments of condition including their diversity, established ta xonomy, distribution, relative immobility, welldeveloped sampling protocol s, and, for herbaceous species, their sensitivity to disturbance (U.S. EPA 2002, Dohe rty et al. 2000). Because plant communities represent a diverse assemblage of species with differi ng requirements, adaptations, tolerances and life histories, community composition can reflec t the biological integrity of a wetland. Rapid and profound effects on forested wetland vegetation may be brought on by changes in hydroperiod (length of time that soils are saturated during a year) (Ewel 1990). Chronically reduced periods of inundation in North Florida cypress wetlands resulted in poor tree regeneration, an increase in shrubs a nd hardwood density and an increase in fire potential (Marois and Ewel 1983, Harris and Vi ckers 1984). Changes in hydroperiod can be a natural response to changing climate or response to an anthropogenic influence. Some anthropogenic activities, such as clearcutting wetland or adjacent upland vegetation, directly affect we tland hydroperiod (Riekerk and Ko rhnak 2000). Such is the case when agricultural features (i.e. ditche s, ponds, etc.) are created to increase the agricultural potential of the landscape. Many anthropogenic changes are generally revealed through site reviews or interpreta tion of aerial photography. Other changes are

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3 induced more subtly and are initially more difficult to detect. For instance, drawdown effects on wetland plant community compos ition are well documented (Sonenshein and Hofstetter 1990, Edwards and Denton 1993, Roch ow 1994, Ormiston et al. 1995), despite the lack of immediate or short-term physical evidence of alteration to the landscape. Wetland drawdown from high capacity groundwater withdrawals is realized by lowering the potentiometric surface of the Floridan aquifer, which in turn lowers the surficial aquifer level, and finally the level of inundation in wetlands (Brown 1984, also see Stewart 1968, Cherry et al. 1970, Parker 1975, Hutchinson 1984, SWFWMD 1996). Regulatory requirements within 40D-2 F.A.C. dictate an extensive analysis be conducted twice yearly on wetlands within all groundwater supply wellfields. Because of the size of the area in which monitoring is require d and the large number of wetlands, a rapid qualitative monitoring method was deve loped (SWFWMD 1996, 1999, Rochow 1998). This qualitative method, or Vege tative Health Rating (VHR), was a supplement to a quantitative analysis and provide s a more rapid method to measure the health of a larger set of wetlands than could othe rwise be monitored by more rigorous quantitative methods (SWFWMD 1999). The purpose of this study was to examine the quantitative monitoring method currently used and compare the re sults to the qualitative VHR method using multivariate analysis. This multivariate an alysis was conducted using three years (2000, 2001 and 2002) of vegetative monitoring data in 12 wetlands that exhibit differing levels of groundwater withdrawal influence.

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4 Methods Study Sites Starkey Wellfield (SWF) is located in Pasco County, Florid a, approximately 28.20 North Latitude and 82.50 West Longitude (Figure 1.1). The site consists of approximately 3,237 hectares, portions of whic h were donated to or purchased by the Southwest Florida Water Management Distri ct (SWFWMD), since1975. The rectangular parcel is bounded by the Suncoast Parkway (to ll highway) to the east, the Anclote River to the south, residential development to th e west and a combination of residential development, the Pithlachasco tee River, and another 3200-hectare state-owned preserve to the north (Figure 1.2). The habitat at SWF is a matrix of sand-pine dominated sandhill and lakes throughout the topographically high er western third of the site, while the topographically lower eastern two-thirds of th e site are characterized by pine flatwoods and cypress wetlands. Currently, SWF is maintained for multiple uses, including wildlife habitat, low-intensity recreation (i.e., hiking, biking and backpack camping) and groundwater pumping. The SWFWMD manages the land and assists with the monitoring of groundwater pumping effects. Groundwater withdrawal monitori ng is reported according to water-years beginning October and ending the following September. During the two study wateryears (October 1, 2001September 30, 2002), groundwater pumping averaged 11.2 million gallons per day.

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5 Cypress wetlands occur frequently in the s outheastern coastal plain and are typically found scattered throughout the pine flatw oods of Florida (Ewel 1990). The typical hydrologic pattern for cypr ess wetlands in this area is inundation upon the onset of summer rains followed by a slow drying beginning in the fall (Mitsch 1984) and occasionally shorter periods of inundation in the winter (Berryman and Henigar 2000). Twelve cypress wetlands were chosen, in coordination with the SWFWMD. Each study wetland had similar surrounding ha bitat, and met criteria for minimum size (>0.2 hectare) and depth of historic in undation (>0.3 meter) (Table 1.1, Figure 1.2).

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6 Figure 1.1 J.B. Starkey Wellfield Location Map

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7 Figure 1.2 J.B. Starkey Wellfield Site Locations and Land Use Map

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8 Table 1.1 Sampling Locations and Physical Characteristics # MONITORING ID LATITUDE/ LONGITUDE VEGETATIVE HEALTH RATING SIZE (HECTARES) 1 S-87 28.24501N/82.59625W Green 1.30 2 S-95 28.24473N/82.60283W Blue 8.67 3 S-97 28.23936N/82.59667W Blue 1.21 4 S-94 28.24566N/82.59286W Green 36.90 5 S-68 28.23825N/82.57515W Blue 2.17 6 S-106 28.24647N/82.58296W Blue 8.06 7 S-10 28.23955N/82.64303W Green 6.61 8 S-96 28.23871N/82.60947W Blue 0.95 9 C 28.25725N/82.60318W Green 1.67 10 Z 28.25561N/82.63597W Green 2.74 11 S-44 28.24678N/82.60641W Red 1.09 12 S-30 28.25055N/82.62383W Red 92.26 *Wetlands S-87, C and Z were monitored by the SWFWMD. Wetlands S-95, S92, S-94, S-106, S-10, S-96 and S-44 were monitored by an independent consultant. Monitoring was conducted by both the SWFWMD and the independent consultant on Wetlands S-68 and S-30. Qualitative Analysis The original qualitative analysis me thod (SWFWMD 1999) included quantitative categorical variables that measured many of the same characters as the current quantitative method discu ssed below, and ultimately used three color-coded categories to describe each wetland by the le vel of anthropogenic change exhibited at the time of the monitoring event. After collecting several quantitative variables, the wetlands were rated on a 1-5 scale without a logarithm of any kind. This subjective decision rendered the evaluation qualitative, regardless of the quantitative data collected. Over time, the quantitative variables were replaced with re viewer comments and notes only; however, the three-color system remained. A Vegetative Health Rating of blue was assigned to a wetland having vegetation, hydrology and soils indicative of natural, healthy cypress wetland. A VHR of green was

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9 assigned to a wetland in which moderate anthropogenic changes were observed. Anthropogenic changes were observed in vegetative composition and zonation, hydrologic indicators, soil subsidence or ot her abnormal characteristics noted by the researcher. A VHR of red was assigned to a wetland in which severe anthropogenic changes were observed. These changes include severe tree fall or death, upland species encroachment, changes in or elimination of zonation, severe so il oxidation or soil subsidence and biological evidence of hydrope riod reduction. The assignment of color categories was conducted one time in the Sp ring of 2001. The wetlands used in the analysis had not changed color category for at least one-year prior to or upon completion of the study (T. Rochow personal communicati on). The static Vegetative Health Rating allows for a comparison of the Vegetative Health Rating with the Quantitative Analysis during the three study years. Quantitative Analysis Because of the regulatory requirements of the SWFWMD Water Use Permit, an Environmental Management Plan (EMP) wa s developed for SWF and other Northern Tampa Bay regional wellfields (Tampa Ba y Water 2000). One requirement of the EMP is a specific monitoring method known as the Wetland Assessment Procedure (WAP) be used twice yearly. The WAP consists of eigh teen variables scored on a 1-3 scale (Table 1.2) measured on half-point increments. A score of one represents a wetland character that has been severely affected, while three represents an unaffected character or natural

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10 condition. Quantitative data from the spring a nd fall from three years were analyzed for the study. Table 1.2 Wetland Assessmen t Procedure Variables VARIABLE DESCRIPTION Groundcover Deep Zone Composition Percent cover of hydrophytic herbaceous vegetation Transitional Zone Composition Percent cover of hydrophytic herbaceous vegetation in transitional zone Species Zonation Current zonation vs. expected in an unaffected system Weedy Groundcover Percent cover of weedy herbaceous vegetation in the entire wetland1 Shrub Composition Percent cover of hydrophytic woody species with a diameter at breast height (dbh) of < 4cm and < 1.0 m total height Species Zonation Current zonation vs. expected in an unaffected system Weedy Shrubs Percent cover of weedy woody species with a dbh of < 4cm and < 1.0 m total height1 Vines Zonation Current zonation vs. expected in an unaffected system Canopy Composition Percent cover of appropriate woody species with a dbh of > 4cm and > 1.0 m total height Zonation Current zonation vs. expected in an unaffected system Tree Health Stress Percent of wetland-appropriate trees that exhibit signs of stress Leaning Percent of wetland-appropriate trees that are leaning Dead Percent of wetland-appropriate trees that are dead Soils D X 7/8 Presence and severity of soil subsidence at a fixed location near the edge of the wetland D X 1/2 Presence and severity of soil subsidence at a fixed location near the center of the wetland NP – 3 Presence and severity of soil subsidence at a fixed location where the ground elevation is approximately 3 inches below the Normal Pool elevation NP – 12 Presence and severity of soil subsidence at a fixed location where the ground elevation is approximately 12 inches below the Normal Pool elevation Hydrology Current water level indicators Presence and level of biological indicators of hydrology (i.e. moss collars, lichens, stain lines, etc.) A list of potential “weedy” species is provided to the reviewer. Hydrologic data were obtained from the SWFW MD or the environmental consultant that monitors wetlands at SWF on behalf of the wellfield operator, Tampa Bay Water. The Julian date of inundation was calculated us ing the first date of continuous inundation after the residual inundation from the previous ye ar was no longer present. For instance,

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11 if a wetland was inundated continuously fro m July 2000 to January 2001, followed by a dry period that lasted until August 2001, th e calculation of the 2001 Julian date of inundation used the August date even though it was not the first time that calendar year that inundation was present. In addition, wh en an anomalous rain event left a wetland inundated for a brief period (<2 weeks) in the dry season, this inundation was disregarded. Statistics Both R and Q-mode analyses were used. Rmode analysis was conducted to determine the relationships among variab les. Spearman Rank Correlation was used to examine the relationships between each quantitative variable and the VHR, the average Julian date of inundation, and the average length of inunda tion for each wetland. In addition, simple descriptive statistics were used to examine the variability in scores between year and season. Q-mode analysis was conducted to illustrate the relationships among wetlands and compare the three VHR groups. I used descriptive statistics to compare the average score within each VHR category. I used hierarchical cluster analysis and nonmetric multidimensional scaling (an ordination technique) to elucidate patterns and categorize wetlands according to quantitative data. Cluster analysis provides a dendrogram that illustrates the relationships of wetlands to each other in a nested fashion. I used Un weighted Pair-Group Average (UPGA) as the linkage method because, during the clustering, this linkage method preserves the original properties of the space in the dissimilarity matrix (Statistica for Windows 1995, McCune

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12 and Grace 2002). I used the Euclidean dist ance measure because of its compatibility with UPGA. Ordination provides a graphical summary of complex relationships and extracts a dominant pattern from an infinite number of possible patterns (McCune and Grace 2002). Nonmetric multidimensional scaling (NMDS) is considered the most generally effective ordination technique for ecological communi ty data and is recommended as the ordination method of choice unless a specific goal requires another method (McCune and Grace 2002). I used the dissimilarity matrix derived from the cluster analysis to conduct an NMDS. First, eighteen variables are used in the multivariate analysis, and in the second the average of the eighteen variables is used. In a third NMDS, the soil and hydrologic indicator variables are used in a separate analysis. Finally, descriptive statistics are used in a confirmatory manner to illustrate the difference between the qualitative vegetative categories and a proposed alternate grouping based on the NMDS results. In some cases, data were not recorded on the soils and hydrologic indicators of each site, presumably because of the depth of inundati on. Notes indicating the reviewer suggestion to use values from a previous event (i.e., us e Spring 2001 score) were present for specific variables on some data sheets. In any case, when notes were taken referring to a past event, the instructions were followed. In a few cases, the reference was omitted, not legible, or referred to an event that had a re ference to another previ ous (third) event. In these cases, the data were considered unrelia ble, and not included. All missing values

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13 were substituted by means for the multivariate analysis, but ignored when calculating the average score. For two wetlands, both the SWFWMD and an inde pendent consultant collected data. In a preliminary analysis, th e duplicate measurements were included. The duplicate wetlands fell into similar categories in the cluster and the NMDS analysis. Only the SWFWMD data were used for th is analysis to avoid pseudoreplication. Results R-mode analysis Table 1.3 presents the correlations among th e average quantitative variables over the six events and the VHR, average length of inund ation and average Julia n date of inundation. The average score (average of all eighteen variables) is highly correlated (R-value 0.812, p = <0.001) with the VHR. In addition, eight other variables are significantly correlated (p = <0.05). If I increase p to 0.10, then 10 of the 18 parameters exhibit a significant correlation with the VHR. In contrast, only two variables (canopy zonation and biological indicators of hydrol ogy) were significantly correlated (p = <0.05) with the average length of inundation. The number of significant correlations doubled when changing the acceptable p to 0.10 (canopy composition and canopy death), but none of the additional correlations were stronger th an 0.55 (p = <0.10). Finally, each of the variables was examined for a relationship w ith the average Julian date of inundation. Two variables (canopy composition and biol ogical indicators of hydrology) were negatively correlated (p = <0.05). Two othe rs (average score and canopy composition) were significant at p = 0.10.

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14 Table 1.3 Correlations between Wetland Assessment Procedure Variables and Vegetative Health Rating, Inundati on Length, and Julian Date of Inundation Correlation with VHR Correlation with Inundation Length Correlation with Julian date of inundation R p-level R p-level R p-level Average Score .812218 .001329 .433566 .159106 -.524476 .080019 Groundcover Deep Composition .603720 .037648 .187560 .559402 -.320415 .309922 Transitional Composition .301370 .341130 .466712 .126123 -.123970 .701082 Species Zonation .671486 .016796 .429588 .163399 -.471843 .121455 Weedy Groundcover .682932 .014375 .244558 .443641 -.447729 .144408 Shrubs Composition .074187 .818764 -.352121 .261243 .179582 .576520 Species Zonation .703824 .010634 .411982 .183271 -.408461 .187417 Weedy Shrubs -.034711 .914717 -.413053 .182022 .137684 .669587 Vines Species Zonation .496711 .100438 .395244 .203498 -.116493 .718443 Canopy Composition .701239 .011052 .5549181 .064405 -.520057 .083061 Zonation .756004 .004445 .709575 .0099745 -.748996 .005056 Stress .408528 .187338 -.014135 .965224 .056050 .861449 Leaning .510740 .089729 .100968 .754870 -.055074 .865014 Death .775797 .003019 .508611 .091303 -.463734 .128886 Soils Edge Subsidence .383173 .218897 .354646 .258000 -.361739 .247922 Center Subsidence .368509 .238521 .049740 .877996 -.284228 .370604 Subsidence at location 3” below NP .724131 .007743 .486791 .108505 -.472682 .120702 Subsidence at location12” below NP .521330 .082176 .168322 .602099 -.356643 .255138 Hydrology Current biological indicators .842279 .000586 .814847 .001244 -.772517 .003227 Bold numbers are significant to <.05. The mean of all study wetland scores rang ed from 2.38 to 2.58 over three years. The mean of the fall event (conducted at the end of the rainy season) was approximately 0.2 higher than the spring event each year. Fi gure 1.3 illustrates the similarities between the average scores of each event.

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15 Figure 1.3 Box and Whisker Plot of Quantitative Vegetation Scores Categorized by Sampling Event Q-mode analysis Table 1.4 presents descriptive statistics for each of the three VHR categories using the average quantitative score for each wetland. All groups reached their maximum score over the study period in 2002. The mean scor e for all Red wetlands ranged from 1.63 to 1.98 reaching a peak in Spring 2002. Green wetlands had a mean score of 2.34 to 2.58. Blue wetlands mean score ranged from 2.70 to 2.83. Both Green and Blue wetlands reached their maximum score in Fall 2002.

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16 Table 1.4 Descriptive Statistics of Wetland Assessment Procedure Variables Categorized by Vegetative Health Rating N Mean Confid. -95.000% Confid. 95.000 Min Max Range Variance Std. Dev. Std. Error Skewness Std.Err. Skewness Kurtosis Std.Err. Kurtosis Spring 2000 2 1.63 -0.726 3.976 1.44 1.81 0.37 0.068 0.262 0.185 ----Fall 2000 2 1.67 0.272 3.068 1.56 1.78 0.22 0.024 0.156 0.110 ----Spring 2001 2 1.71 0.439 2.981 1.61 1.81 0.20 0.020 0.141 0.100 ----Fall 2001 2 1.84 1.136 2.534 1.78 1.89 0.11 0.006 0.078 0.055 ----Spring 2002 2 1.98 0.074 3.886 1.83 2.13 0.30 0.045 0.212 0.150 ----RED Fall 2002 2 1.93 -2.713 6.563 1.56 2.29 0.73 0.266 0.516 0.365 ----Spring 2000 5 2.48 1.908 3.048 1.80 2.89 1.09 0.211 0.459 0.205 0.927 0.913 -0.739 2 Fall 2000 5 2.46 1.992 2.932 2.17 2.92 0.75 0.143 0.379 0.169 0.637 0.913 -3.141 2 Spring 2001 5 2.35 2.153 2.547 2.20 2.61 0.41 0.025 0.159 0.071 1.410 0.913 2.088 2 Fall 2001 5 2.33 2.117 2.547 2.19 2.61 0.42 0.030 0.173 0.077 1.351 0.913 1.145 2 Spring 2002 5 2.36 2.059 2.661 2.00 2.64 0.64 0.059 0.242 0.108 -0.660 0.913 0.506 2 GREEN Fall 2002 5 2.58 2.248 2.920 2.21 2.83 0.62 0.073 0.270 0.121 -0.742 0.913 -1.828 2 Spring 2000 5 2.76 2.651 2.861 2.61 2.83 0.22 0.007 0.084 0.038 -1.845 0.913 3.934 2 Fall 2000 5 2.83 2.681 2.987 2.67 3.00 0.33 0.015 0.123 0.055 0.041 0.913 0.197 2 Spring 2001 5 2.70 2.501 2.908 2.56 2.94 0.38 0.026 0.162 0.073 0.948 0.913 -0.986 2 Fall 2001 5 2.70 2.488 2.916 2.56 2.94 0.38 0.030 0.173 0.077 0.782 0.913 -1.993 2 Spring 2002 5 2.73 2.597 2.863 2.61 2.87 0.26 0.011 0.107 0.048 0.427 0.913 -1.767 2 BLUE Fall 2002 5 2.84 2.727 2.953 2.79 3.00 0.21 0.008 0.0941 0.041 2.050 0.913 4.232 2

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17 Figure 1.4 Box and Whisker Plot of Average Quantitative Variables Categorized by Vegetative Health Rating The mean average score of Red wetlands over all events was significantly different from the Green wetlands and the Blue wetlands. The st andard deviations of the mean scores of Green wetlands and Blue wetla nds overlapped (Figure 1.4). A visual representation of wetland dissimilarity is presented through a cluster analysis. Two alternative cluster analyses are offered. Both were constructed using Euclidean distance measures and Unweighted Pair-G roup Averages linkage method. The first (Figure 1.5A) was constructed using all eighteen quantitative variables for each of the six monitoring events. The second alternative (Figure 1.5B) was constructed using only the

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18 average of the eighteen variables for each of the six events. The distance matrix derived from the cluster analyses were then used in constructing an NMDS plot. Figure 1.5 Cluster Analysis Using Wetland Assessment Procedure Variables Figure 1.5A Cluster analysis using all quantitative variables, unweighted pair-group average linkage method and Euclidean distance measures. Figure 1.5B Cluster analysis using average quantitative variables, unweighted pair-group average linkage method and Euclidean distance measures. Figure 1.5A was created using all eighteen vari ables collected for each year and season. The figure illustrates the difference between S-44, S-30, S-87, and all other wetlands. All relationships are separated by a minimum of five distance units. Figure 1.5B was created using the averages of all eighteen variables collected each season and year. The dendrogram provides ev idence that S-44 and S-30 form a separate cluster, and S-87, C, and S-10 form a separate cluster; how ever, the relationship between

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19 these two groups and the remaining wetlands is because of the potential rotation of each axis. All relationships are separate d by a maximum of one distance unit. Figure 1.6 NMDS plot of all quantitativ e variables for spring and fall 2000, 2001 and 2002. Figure 1.6 was constructed using the distance matrix derived from Figure 1.5A. Fivehundred twenty-five iterations were performed. The final configura tion stress value was .052, alienation value was .074, DStarr: Raw stress was .799 and D-Hat: Raw stress was .394. In general, the red wetlands fall to the le ft and the blue wetlands to the right. The green wetlands span the center. S-10 S-87 C S-94 Z S-106 S-97 S-96 S-95 S-68(DD) S-30(U) S-44

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20 Figure 1.7 NMDS plot of average quan titative variables for spring and fall 2000, 2001 and 2002. Figure 1.7 was created using the distance matrix derived from Figure 1.5B. Fourhundred forty iterations were performed. Th e final configurati on stress value was .028, alienation value was .048, D-St ar: Raw stress was .333 and D-Hat: Raw stress was .112. The red wetlands fall on the right of the NMDS plot and the blue wetlands fall on the left of the plot. Green wetlands are concentrated in the center except S-94 which is tightly clustered with the blue wetlands.

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21 Figure 1.8 Cluster analysis using soils a nd hydrologic variables, unweighted pairgroup average linkage method and Euclidean distance measures. Figure 1.9 NMDS plot using soils and hy drologic variables for spring and fall 2000, 2001 and 2002. S-10 C S-94 Z S-87 S-106 S-97 S-96 S-95 S-68 S-30 S-44 Figure 1.8 was created using the soils and hy drologic variables collected each season and year. Two clusters area apparent wetlands within the cluster containing five wetlands

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22 (S-10, S-87, S-44, S-95, and S-30) are not as tigh tly associated as those within the other cluster. Figure 1.9 was created from the distance ma trix derived from Figure 1.8. Four-hundred forty-seven iterations were performed. The final config uration stress value was .073, alienation value was .104, D-St ar: Raw stress was 1.58 and D-Hat: Raw stress was .757. The distribution of wetlands on this NMDS pl ot is more uniform relative to Figure 1.6 and 1.7. The red wetlands remain removed from other wetlands to the right. Blue and green wetlands are scattered on the left and S-95 is near the cent er with S-87 and S-10. Table 1.4 and Figures 1.4-1.9 indicate the VHR assigned to the wetlands do not reflect the results of the multivariate analysis, but some overlap of the Green and Blue categories is evident. Although the VHR is used to indicate three categories of anthropogenic change, the evidence suggests that a two-color VHR would more accurately depict the quantitative data that were collected between 2000 and 2002, or perhaps a reshuffling of the wetlands to form three different categor ies. Based upon the evidence presented here and the statement of caution by Karr and Chu (1999) warning against the preoccupation with anthropogenic disturbance gradients, I pr opose an alternative to the original threecolor categories. Tables 1.5 and 1.6 provide the revised version of the descriptive statistics presented in Table 1.4. The revision involved simply adjusting the VHR categories to match the multivariate analysis and reclassifying the wetlands based upon a two-color scheme (Table 1.5, Figure 1.10A) a nd a revised three-color scheme (Table 1.6, Figure 1.10B).

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23 Table 1.5 Descriptive Statistics of Wetland Assessment Procedure Variables Categorized by Two-Category Alternate Vegetative Health Rating N Mean Confid. -95.000% Confid. 95.000 Min Max Range Variance Std. Dev. Std. Error Skewness Std.Err. Skewness Kurtosis Std.Err. Kurtosis Spring 2000 4 1.82 1.310 2.325 1.44 2.22 0.78 0.102 0.319 0.159 0.235 1.014 1.526 2.617 Fall 2000 4 1.93 1.433 2.422 1.56 2.20 0.64 0.097 0.311 0.155 -0.407 1.014 -3.615 2.617 Spring 2001 4 2.02 1.432 2.608 1.61 2.38 0.77 0.136 0.369 0.185 -0.184 1.014 -4.270 2.617 Fall 2001 4 2.07 1.619 2.521 1.78 2.39 0.61 0.080 0.284 0.142 0.173 1.014 -3.660 2.617 Spring 2002 4 2.06 1.756 2.364 1.83 2.28 0.45 0.037 0.191 0.096 -0.133 1.014 -0.615 2.617 RED Fall 2002 4 2.11 1.515 2.710 1.56 2.39 0.83 0.141 0.376 0.188 -1.771 1.014 3.277 2.617 Spring 2000 8 2.77 2.694 2.843 2.61 2.89 0.28 0.008 0.089 0.031 -0.776 0.752 0.357 1.481 Fall 2000 8 2.76 2.553 2.974 2.19 3.00 0.81 0.063 0.252 0.089 -2.034 0.752 4.718 1.481 Spring 2001 8 2.58 2.371 2.779 2.20 2.94 0.74 0.059 0.243 0.086 -0.242 0.752 -0.246 1.481 Fall 2001 8 2.57 2.357 2.783 2.19 2.94 0.75 0.065 0.255 0.090 -0.222 0.752 -0.457 1.481 Spring 2002 8 2.65 2.515 2.778 2.38 2.87 0.49 0.025 0.157 0.056 -0.312 0.752 -0.008 1.481 BLUE/GREEN Fall 2002 8 2.82 2.745 2.885 2.71 3.00 0.29 0.007 0.083 0.030 1.666 0.752 4.240 1.481

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24 Figure 1.10 Box and Whisker Plots of Alternate Wetland Classifications Figure 1.10A Box and Whisker Plot of Average Quantitative Variables Categorized by Vegetative Health Rating. Figure 1.10B Box and Whisker Plot of Average Quantitative Variables Categorized by Vegetative Health Rating. Figure 1.10A illustrates an alte rnative classification scheme based on two categories. The Red group includes wetlands S-10, S-30, S-44, and S-87; the Blue/Green group includes wetlands C, Z, S68, S-94, S-95, S-96, S-97, and S106. Upon reclassification, the mean of average scores for Red wetla nds is 2.00 and the new Green/Blue wetland category is 2.69. The standard deviations no longer overlap, confirming that these groups are discrete. The New Red group includes wetlands S-30, a nd S-44; the New Green group includes wetlands C, S-10, and S-87; the New Blue group includes Z, S-68, S-94, S-95, S-96, S97, and S-106. Figure 1.12 illustrates a second alternative classification scheme. The mean score of the New Red class is 1.79; Gr een is 2.27, and Blue is 2.73. The standard deviations do not overlap, confirming th at these three groups are discrete.

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25 Table 1.6 Descriptive Statistics of Wetland Assessment Procedure Variables Categorized by Three-Category Alternate Vegetative Health Rating N Mean Confid. -95.000% Confid. 95.000 Min Max Range Variance Std. Dev. Std. Error Skewness Std.Err. Skewness Kurtosis Std.Err. Kurtosis Spring 2000 2 1.63 -0.726 3.976 1.44 1.81 0.370 0.068 0.262 0.185 ----Fall 2000 2 1.67 0.272 3.068 1.56 1.78 0.220 0.024 0.156 0.110 ----Spring 2001 2 1.71 0.439 2.981 1.61 1.81 0.200 0.020 0.141 0.100 ----Fall 2001 2 1.84 1.136 2.534 1.78 1.89 0.110 0.006 0.078 0.055 ----Spring 2002 2 1.98 0.074 3.886 1.83 2.13 0.300 0.045 0.212 0.150 ----RED Fall 2002 2 1.93 -2.713 6.563 1.56 2.29 0.730 0.266 0.516 0.365 ----Spring 2000 3 2.23 1.149 3.311 1.80 2.67 0.870 0.189 0.435 0.251 0.103 1.225 --Fall 2000 3 2.19 2.149 2.225 2.17 2.20 0.030 0.000 0.015 0.009 -0.935 1.225 --Spring 2001 3 2.29 2.063 2.511 2.20 2.38 0.180 0.008 0.090 0.052 0.331 1.225 --Fall 2001 3 2.27 1.999 2.535 2.19 2.39 0.200 0.012 0.108 0.062 1.583 1.225 --Spring 2002 3 2.22 1.731 2.709 2.00 2.38 0.380 0.039 0.197 0.114 -1.244 1.225 --GREEN Fall 2002 3 2.46 1.736 3.184 2.21 2.78 0.570 0.085 0.291 0.168 1.019 1.225 --Spring 2000 7 2.78 2.703 2.862 2.61 2.89 0.280 0.007 0.086 0.032 -1.399 0.794 3.401 1.587 Fall 2000 7 2.85 2.748 2.943 2.67 3.00 0.330 0.011 0.106 0.040 -0.306 0.794 0.492 1.587 Spring 2001 7 2.63 2.438 2.819 2.28 2.94 0.660 0.042 0.206 0.78 -0.200 0.794 1.120 1.587 Fall 2001 7 2.62 2.421 2.827 2.25 2.94 0.690 0.048 0.220 0.083 -0.279 0.794 0.887 1.587 Spring 2002 7 2.68 2.570 2.799 2.50 2.87 0.370 0.015 0.124 0.047 0.201 0.794 -0.182 1.587 BLUE Fall 2002 7 2.82 2.738 2.902 2.71 3.00 0.290 0.008 0.089 0.034 0.149 0.794 3.563 1.587

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26 Discussion From 1995 – 2020, the public supply water dema nd within the Southwest Florida Water Management District is expected to increase thirty-nine percent from 459.4 million gallons per day (mgd) to 640.2 mgd. The agri cultural demand during the same temporal and spatial scale is separate and also is expected to increase from 587.8 to 710.7mgd. Much of the current water supply is provide d through groundwater withdrawal. Because of “known resource impacts throughout much of the planning region caused by existing water withdrawals”, groundwater was not incl uded as a potential source for the increased demand (Southwest Florida Water Management District 2001). Even at the current level of groundwater withdrawal, signs of ecosystem stress are present. Thus, it is imperative to have accurate and rapid means of monitoring and the ability to adjust management and policy to ensure habitat is availabl e for the natural flora and fauna. The purpose of this study was to examine the quantitative monitoring method currently used and compare the results to the qualitativ e VHR method using multivariate analysis. Although the data did not reflect the original three VHR color categories presented, they appeared to have correctly characterized the highest scoring and lowe st scoring wetlands. Two of the Green wetlands (S-87 and S-10) sometimes fell into the Red category and three of the Green wetlands (C, Z, and S-94) sometimes fell into the Blue category, but when long-term results are considered, only S-94 and Z were mischaracterized. Mischaracterization of the wetlands in the m oderately anthropogenically influenced green VHR category could reflect the small sample size, or the scale on which each of the

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27 variables was measured. A 1-3 scale with ha lf-point increments (which were seldom used) offers a maximum of 7 possible scores fo r each variable. If the researcher were able to evaluate vegetation on the same scale in .1 increments, more variability could be expressed. It is also possible that because many of the variables are correlated with one another, expected variability is lost and sc ores tend toward a bimodal distribution. The relatively rapid VHR assessment may be conducted on many more wetlands in a shorter time period than the more rigorous quantitative method. The increase in the number of wetlands that are monitored as a re sult of the VHR, coupled with the ability of the VHR to characterize the highest and lowest scoring wetlands make the VHR a useful tool. A more detailed analysis of wetlands that fall into the moderate or low VHR categories may be warranted, but if a wetland consistently maintains a high VHR, then further resources need not be unnecessarily expended on detailed monitoring. The appropriate criteria for measuring wetland health must include physical/chemical as well as biological conditions because ther e are sampling limitations, including a lag in response time inherent in measuring biolog ical conditions (U.S. EPA 2002, Tiner 1991). The quantitative method used in this study in cluded five non-vegetative variables, but these variables were the most often overlooke d or omitted by the researcher, presumably because the indicators were inundated at the time of the site visit. Soil indicators may also have a lag in response time to changes in hydrology. Although a pattern is evident in the groups of wetlands when examining only the vegetation and hydr ologic information, it may be more informative to measure other variables as well. When the physical

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28 variables were partitioned and used in a mu ltivariate analysis without vegetation, only wetlands that had evidence of long-term an thropogenic change (S-44 and S-30) were segregated from all others, but this pattern had similarities to the analysis measuring change in vegetation. The reason for the s imilarity was likely the variables included in the physical variable analysis measure long -term changes. Soil oxidation and subsidence in a seasonally-inundated wetland must be preceded by hydrologic changes, such as inundation timing or length, that would affect vegetation and wildlife habitat more immediately. Wetland plants are unreliable as sole indicat ors of change in hydrologic regime or nutrient status (Tiner 1991). Rather, soil biogeochemistry and physical characteristics need to be used in concert with vegetation to avoid lag times in plant response to hydrologic alteration. The effort to coll ect additional soil variables would undoubtedly increase the sensitivity of the analysis, but could also prove cost prohibitive or difficult because of training costs or additional personne l. Also, many of the physical variables currently measured may have a greater lag time than the vegetation. I suggest a focus on more rapidly responding biolog ical indicators. Cost asid e, other factors such as invertebrate colonization, overall wildlife u tilization or amphibian reproductive success could serve to further distinguish between gr oups of wetlands or provide a similar picture without the lag time inherent in vegetation monitoring.

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29 Literature Cited Abraham, W.G. and D.C. Hartnett. 1990. Pi ne Flatwoods and Dry Prairies. Pp. 103-149 In: Myers, R.L. and J.L. Ewel (eds.). Ecosystems of Florida. University of Central Florida Press, Orlando, 765 pp. Adamus, P.R. 1996. Bioindicators for Assessing Ec ological Integrity of Prairie Wetlands, U.S. Environmental Protection Agency National Health and Environmental Effects Research Laboratory, West ern Ecology Division, Corvallis, OR. Berryman and Henigar. 2000. J.B. Star key Wellfield and No rth Pasco Wellfield Ecological/Hydrological Monitoring Program. Annual Monitoring Report October 1, 1998 – September 30, 1999. Water Year 1999. Final Report. Billings, W.D. 1952. The environmental co mplex in relation to plant growth and distribution. Quarterly Review of Biology 27:251-265. Brinson, M.M. 1993. Changes in the func tioning of wetlands along environmental gradients. Wetlands 13:65-74. Brown, S. L. 1984. The role of wetlands in the Green Swamp. Pp. 405-415 in K.C. Ewel and H.T. Odum (eds.), Cypress Sw amps. University of Florida Press, Gainesville, Florida. 472 pp. Cherry, R.N., J.W. Stewart and J.A. Mann. 1970. General hydrology of the middle gulf area, Florida. U.S. Geological Survey Water Resources Inves tigations Report No. 87-4188. Doherty, S.J., M. Cohen, C. Lane, and J. Surdick. 2000. Biological criteria for inland freshwater wetlands in Florida: A review of scientific and t echnical litera ture (19901999). Report to United States Environm ental Protection Agency, Biological Assessment of Wetlands Workgroup by Univer sity of Florida Center for Wetlands, Gainesville. Edwards, L.D., and S.R. Denton. 1993. Cr oss Bar Ranch Wellfield Ecological Monitoring Report. Biological Resear ch Assosciates, Inc., Tampa, FL. Ewel, C.E. 1990. Swamps. Pp. 281-323 In : Myers, R.L. and J.L. Ewel (eds.). Ecosystems of Florida. University of Central Florida Press, Orlando, 765 pp. Goslee, S.C., R.P. Brooks and C.A. Cole. 1997. Plants as indicators of wetland water source. Plant Ecology. 131:199-206.

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30 Harris, L. D. and C. R. Vickers. 1984. Some faunal community characteristics of cypress ponds and the changes induced by perturbations. Pp. 171-185 In : K. C. Ewel and H. T. Odum (eds.). Cypress Swamps. University Presses of Florida, Gainesville, 472 pages. Hutchinson, C.B. 1984. Hydrogeology of well field areas near Tampa, Florida. Phase 2development and documentati on of a quasi-three dimensional finite difference model for simulation of steady state ground water flow. U.S. Geological Survey Open File Report No. 81-630. Johnston, C. A. 1994. Cumulative effects to wetlands. Wetlands 14: 49-55. Karr, J.R. 1991. Biological Integrity: A Lo ng-Neglected Aspect of Water Resource Management. Ecological Applications 1(1): 66-84. Ludwig, J.A., and J.F. Reynolds. 1988. Statistical Ecology: A Primer on Methods and Computing John Wiley and Sons, New York Marois, K.C., and K.C. Ewel. 1983. Natural and management-related variation in cypress domes. Forestry Science 29:627-640. McCune, B., and J. B. Grace. 2002. Analysis of ecological communities. MjM Software Design. Gleneden Beach, OR. Ormiston, B.G., S. Cook, K. Watson and C. Reas. 1995. Annual Comprehensive Report: Ecological and Hydrological Monitoring of the Cypress Creek Wellfield and Vicinity, Pasco County, Florida. Envir onmental Science and Engineering, Inc., Tampa, FL. Parker, G.G. 1975. Water and water probl ems in the Southwest Florida Water Management District and some possible solutions. Water Resources Bulletin 11:1-20 Potvin, C. 1993. Distribution-free and robust sta tistical methods: Viab le alternatives to parametric statistics? Ecology 74:1617-1628 Riekerk, H., and L.V. Korhnak. 2000. The hy drology of cypress wetlands in Florida pine flatwoods. Wetlands 20(3): 448-460. Rochow, T.F. and P. Rhinesmith. 1992. The effects of ground water withdrawal and urbanization on the health of isolated fre shwater wetlands in Southwest Florida. Florida Scientist 55 (Supplement 1): 17. Rochow, T.F. 1994. The effects of water table level changes in the Northern Tampa Bay Region. Southwest Florida Water Mana mgement District, Brooksville, FL.

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31 Rochow, T.F. 1998. The effects of water leve l changes on freshwater marsh and cypress wetlands in the Northern Tampa Ba y Region. Southwest Florida Water Management District Technical Report 1998-1. Brooksville, FL. Sonenshein, R.S. and R.H. Hofstetter. 1990. Vegetative changes in a wetland in the vicinity of a well field, Dade County, Florida. US Geological Survey WaterResources Investigations Report 89-4155. Southwest Florida Water Management Di strict. 1996. Northern Tampa Bay water resources assessment project. Brooksville, FL. Southwest Florida Water Manage ment District. 1999. Establishmen t of recovery levels in the northern Tampa Bay area. Brooksville, FL. Southwest Florida Water Management Dist rict. 2001. Regional Water Supply Plan. Brooksville, FL. Statistica for windows (Volume III): Statistic s II. 1995. Statsoft Inc., Tulsa, OK. Stewart, J.W. 1968. Hydrologic effects of pumping from the Floridan Aquifer in northwest Hillsborough, northeast Pinella s, and southwest Pasco counties, Florida. U.S. Geological Survey Open File Report No. 68-005. Tampa Bay Water. 2000. Environmental Ma nagement Plan for the Tampa Bay Water Central System wellfields. Clearwater, Florida. Tiner, R.W. 1991. The concept of a hydrophyte for wetland identification. BioScience 41(4): 236-247. US Environmental Protection Agency. 1987. Su rface water monitoring: A framework for change. USEPA, Office of Water and Offi ce of Policy, Planning and Evaluation, Washington, D.C. (NTIS #PB94-178670). US Environmental Protection Agency. 1989. Wetlands and 401 certification: Opportunities and guidelines for States and eligible Indian tribes. USEPA, Office of Water, Washington, D.C. U.S. Environmental Protection Agency. 2002. Methods for evaluating wetland condition: Introduction to wetland biological assessment. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-014. U.S. Environmental Protection Agency. 2002. Methods for evaluating wetland condition: Study design s for monitoring wetlands. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-015.

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32 U.S. Environmental Protection Agency. 2002. Methods for evaluating wetland condition developing metrics and indexes of biological integrity. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-016. U.S. Environmental Protection Agency. 2002. Methods for evaluating wetland condition: Using vegetation to assess environmental conditions in wetlands. Office of Water, U.S. Environmental Pr otection Agency, Washington D. C. EPA822-R-02-020.

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33 CHAPTER 2 – Amphibian Measures of Wetland Health: An Evaluation of Anuran Reproductive Success Introduction Wetlands provide many ecosystem functi ons including primary production, water attenuation, biochemical tran sfer and storage, decom position, and wildlife habitat (Richardson 1994). The interactions of flor a and fauna with the physical environment provide the functions that are important to the overall landscape (U.S. EPA 2002a). When the interaction of organisms and the e nvironment is altered, the functions of the ecosystem may be disrupted (U.S. EPA 2002a). Because it is impractical to measure all aspects of the ecosystem to detect anthropogenic influen ce or measure wetland function, recent focus has been on determining if certain attributes may reflect the overall biological integrity of certain systems. Nu merous studies have doc umented the responses of biological attributes acro ss diverse taxa and regions to human disturbance (Doherty et al. 2000). Sensitive species are usually effect ed first during times of environmental stress (Odum 1992). Currently, much debate exists over which sensitive species, or species assemblages, are used appropriately as biolog ical indicators. Biological indicators are species, species assemblages, or communitie s whose presence, abundance, and condition are indicative of a particular set of envir onmental conditions (Adamus 1996). Monitoring early indicators of ecosystem stress may shorte n response time by shifting attention to the relatively quick response of sensitive species (Rapport 1992). Species used as indicators

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34 should be abundant and tractable elements of the system that provide an early diagnosis (Rapport 1992, Welsh and Ollivier 1998). Becau se of their biphasic life history, physiological adaptations and specific microhabitat requirements, amphibians are sensitive to environmental perturbations and excellent barometers of the health of the aquatic and terrestrial hab itats in which they reside (Vitt et al. 1990, Wake 1998, Blaustein et al. 1994). Physical and che mical conditions in a wetland are known to influence amphibian assemblages (Lehtin en et al. 1999, Pierce 1985, Sadinski and Dunson 1992, and Skelly 1996); however, given their diversity and ecological requirements, no consensus exists on what c onditions are “suitable” for survival and reproduction of amphibians. Although much attention has been aimed at iden tifying large scale, even global, threats to anurans (Wake 1998), evidence exists that lo cal populations are in decline because of changes in their habitats. Many detrimental habitat alterations are associated with increased human influence and urbanization (D elis et al. 1996). During the five-year period ending in 2000 the human population of Florida increased by approximately 1.7 million. Similar increases are projected for each of the next five-year periods until the year 2025 (U.S. Census Bureau 2000). Accordingly, the human population may increase from 14.2 million in 1995 to 20.7 million in 2025. Landscape-level land use practices can have both direct and indi rect effects on wetland habita ts and amphibian populations (Lehtinen et al. 1999). Some influences are la rge in scale and extrem ely visible such as habitat destruction in the form of convers ion from native land-cover to agriculture or development, but others may occur without la rge-scale topographic or land cover changes

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35 (Dodd 1997). A more subtle type of habitat destruction may affect the availability or suitability of amphibian breeding habitat. Breeding habitats may be modified to the extent that they become unsuitable for many species as a result of pollution, introduced species, vegetative composition changes, a ltered hydrologic regimes, or other anthropogenic alterations (Johnson et al. 2002). Hydrologic alterations such as ditching of wetlands to enhance drainage can have a large effect on anuran use of wetland sites because of alterations to hydroperiod or sp ecies interactions (Vickers et al. 1985). Groundwater withdrawal is another type of hy drologic alteration that may have effects on amphibian breeding habitat. Wetland dr awdown is realized by lowering the potentiometric surface of the aquifer, which in turn lowers the surficial aquifer level and finally the level of inundati on in wetlands (Brown 1984, also see Stewart 1968, Cherry et al. 1970, Parker 1975, Hutchinson 1984, SWFWMD 1996). Draw down effects on wetland plant community composition are well documented (Sonenshein and Hofstetter 1990, Edwards and Denton 1993, Rochow 1994, Or miston et al. 1995), but may leave no immediate physical evidence of alteration to the landscape. Groundwater withdrawal from the Edwards Aquifer in Texas did not alter land cover of the region, but it was determined, the withdrawal could lead to th e loss of aquatic biota including amphibians (U.S. Fish and Wildlife Service 1984, Chippindale et al. 1993). Alterations in the availability of breeding habitat even on a small scale may have longterm or large-scale effects on amphibian populations. Many aut hors have discussed pond-breeding amphibian populations in te rms of metapopulations (Levins 1969, Gill

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36 1978, Berven and Grudzien 1990, Sinsch 1992, Hecnar and M’Closkey 1996, Semlitsch and Bodie 1998, and Skelly et al. 1999) whose viability is dependent on a balance of subpopulation colonization and extinction. With in the context of a metapopulation, local habitat perturbations that a lter breeding habitat even on a relatively small scale could have long-term negative effects on the regional population (Johnson et al. 2002). The purpose of this study was to (1) docum ent amphibian reproductive success, and (2) identify potential causative factors in a nuran reproductive success or failure among wetlands on the J.B. Starkey wellfield (SWF). I used multivariate analysis to describe the gradient of anthropogenic change in and cate gorize a selection of cypress wetlands. The goal of my study was to provide data for resource managers to evaluate and possibly refine land management practices, includi ng existing monitoring methods. Methods Study Sites Starkey Wellfield (SWF) is 3,237 hectares located in Pasco County, Florida, approximately 28.20 North Latitude and 82.50 West Longitude (Figure 2.1). The rectangular parcel is bounded by the Suncoast Parkway (toll highway) to the east, the Anclote River to the south, residential deve lopment to the west and a combination of residential development, the Pithlachascot ee River, and another 3200-hectare state-owned preserve to the North (Figure 2.2). The ha bitat at SWF is a ma trix of sand-pine dominated sandhill and lakes throughout the topo graphically higher western third of the

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37 site, while the topographically lower eastern tw o-thirds of the site are characterized by pine flatwoods and cypress wetlands. Figure 2.1 J.B. Starkey Wellfield Location Map

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38 Currently, SWF is maintained for multiple uses, including wildlife habitat, low-intensity recreation (i.e., hiking, biking and backpack camping) and groundwater pumping. The SWFWMD manages the land and assists with the monitoring of groundwater pumping effects. Groundwater withdrawal monitori ng is reported according to water-years beginning October and ending the following September. During the two study wateryears (October 1, 2001September 30, 2002), groundwater pumping averaged 11.2 million gallons per day (mgd). Cypress wetlands occur frequently in the s outheastern coastal plain and are typically found scattered throughout the pine flatw oods of Florida (Ewel 1990). The typical hydrologic pattern for cypr ess wetlands in west-central Florida is inundation upon the onset of summer rains followed by a slow drying beginning in the fall (Mitsch 1984) and occasionally shorter periods of inundation in the winter (Berryman and Henigar 2000). Twelve cypress wetlands were chosen, in coordination with the SWFWMD, for the anuran reproductive success analysis. Each study wetland had similar surrounding habitat, and met criteria for minimum size (> 0.2 hectare) and depth of historic inundation (>0.3 meter). An additional 14 wetlands were chosen for a breeding male census (Table 2.1, Figure 2.2).

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39 Figure 2.2 J.B. Starkey Wellfield Site Locations and Land Use Map

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40 Table 2.1 Sampling Locations and Monitoring Methods # MONITORING ID LATITUDE/ LONGITUDE SAMPLING METHODS 1 S-87 28.24501N/82.59625W Tadpole Monitoring Site, Traps and Net 2 S-95 28.24473N/82.60283W Tadpole Monitoring Site, Traps and Net 3 S-97 28.23936N/82.59667W Tadpole Monitoring Site, Traps and Net 4 S-94 28.24566N/82.59286W Tadpole Monitoring Site, Net 5 S-68 28.23825N/82.57515W Tadpole Monitoring Site, Traps and Net 6 S-106 28.24647N/82.58296W Tadpole Monitoring Site, Net 7 S-10 28.23955N/82.64303W Tadpole Monitoring Site, Net 8 S-96 28.23871N/82.60947W Tadpole Monitoring Site, Traps and Net 9 C 28.25725N/82.60318W Tadpole Monitoring Site, Net 10 Z 28.25561N/82.63597W Tadpole Monitoring Site, Net 11 S-44 28.24678N/82.60641W Tadpole Monitoring Site, Net 12 S-30 28.25055N/82.62383W Tadpole Monitoring Site, Net 13 S-18 28.24219N/82.63418W Call Census Only 14 S-12 28.24212N/82.64040W Call Census Only 15 S-13 28.24480N/82.63851W Call Census Only 16 S-63 28.24850N/82.58331W Call Census Only 17 S-27 28.25561N/82.63597W Call Census Only 18 S-24t 28.25187N/82.63857W Call Census Only 19 S-20 28.24509N/82.63346W Call Census Only 20 S-76 28.24829N/82.55843W Call Census Only 21 S-73 28.24613N/82.56585W Call Census Only 22 S-75 28.25091N/82.56259W Call Census Only 23 S-67 28.23770N/82.57811W Call Census Only 24 S-89 28.23898N/82.56568W Call Census Only 25 S-35 28.23742N/82.61278W Call Census Only 26 S-48 28.24117N/82.60011W Call Census Only In all, the two-year study included a frog call census of 26 wetlands on SWF and a more detailed tadpole census of 12 of the 26. The wetlands were sampled 12 times for breeding males and 13 times for larvae be tween July 2001 and December 2002. The number of wetlands chosen for the study re presents the maximum number of wetlands possibly visited by one individual in one nigh t (call-collection) or three sequential days (tadpole collection). Site se lection was based on ecological habitat type, geographic position of the wetlands and specific requests of the SWFWMD. Sites were visited for tadpole census every three weeks during the peak-breeding season to reduce the possibility of missing an entire breeding cycle while reducing the potential disturbance to

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41 the community. The 15 September event 2001 was rescheduled for one week after a tropical storm. Anuran Breeding Male Census Anuran surveys at breeding ponds are particul arly effective in estimating species richness or comparing breeding attempts across site s (Scott and Woodward 1994). Surveys were conducted in accordance with the North Am erican Amphibian Monitoring Program (NAAMP) guidelines (http://www.mp2-pwrc.us gs.gov/naamp/protocol/). Surveys began immediately upon inundation of any of the 26 study wetlands. Because of quickly changing summer weather patterns in Florida, ideal conditions (warm ambient temperature with high humidity or light rain) were not present at each wetland on every evening. Surveys were conducted when th ese conditions were present in the late afternoon or forecast for the evening. If a survey was begun, all wetlands were visited unless weather conditions or lack of vehicula r access prohibited data collection. Survey results were reported for evenings in which a ll wetlands were visited. In 2001, I chose a route that allowed all wetlands to be visited between 30 minutes after sunset and 0100 hours. This route was followed during all 12 surveys. Thus, each wetland was visited approximately the same time afte r sunset during each survey. Data were collected during nine surveys in 2001 and eleven surveys in 2002. Each wetland was visited for 3 minutes and the number of calling males of each species was recorded. The size of the chorus recorded was the maximum number of individuals of

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42 each species heard during the 3-minute obser vation. Calling activity was measured in size categories. The categories were base d on the NAAMP, but refined to reflect six categories as follows: 1-10 calling males, 11-25 calling males, 26-50 calling males, 51100 calling males, 101-500 callin g males and greater than 500 calling males. I also recorded the date, time, current weathe r conditions (ambient temperature and observations regarding clouds and precip itation) and weather conditions over the preceding 24 hours. If no water was present at the permanently marked center of the wetland, the observation was limited to 1 mi nute provided no calling males of any species were recorded. During four of the call events each year, sampling could not be completed because of impassible roads or in tense thunderstorms. Thus, data from five nights in 2001 and seven in 2002 are reported herein. Quantitative Larva Sampling Collection and identification of larvae is difficult for many reasons. Thus, tadpole sampling efforts must be well planned to obt ain meaningful data without affecting the population. Injury or death of individual ta dpoles may occur as a result of excessive handling (P. Delis personal communication). Identification often requires magnification of mouthparts which is difficult or impossible w ith living specimens in the field. Even in the laboratory, many tadpole species have been incorrectly identified (McDairmid and Altig 2000). Under normal conditions, tadpol e densities drop rapidly throughout the larval period and samples shoul d be taken when the larvae are approximately the same age (McDairmid and Altig 2000). Tadpole census events were scheduled for three

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43 sequential days to minimize changes in densities during a single sampling event and each event was separated by three weeks to minimiz e disturbance to the site and population. Larva microhabitat is often species-speci fic (McDairmid and Altig 2000), and each microhabitat must be sa mpled with equal intensity (Heyer et al. 1994). Only one species expected on the SWF ( Scaphiophus holbrookii ) has a mean metamo rphosis time of less than 30 days (Wright 1932) (Table 2.2). The three weeks between sampling was short enough to be confident that no species had comp leted the larval stage between sampling. Twelve wetlands were monitored for larvae. All wetlands chosen were greater than 0.2 hectare and had biological i ndicators that demonstrated hi storic normal seasonal water levels of at least 0.3 meters in depth. Data were collected during six sampling periods in 2001 and seven in 2002. A standardized sampling effort was applied at each site during the two years to estimate relative sizes of tadpole population as they advanced toward meta morphosis. Tadpoles were identified to species (Altig et al. 1999 ) during active and passive larval sampling methods, as described below. The active sampling method was based on The Florida Department of Environmental Protection (DEP) Habitat Asse ssment Standard Operating Pr ocedures (2001) commonly used for rapid bioassessment (macroinvertebrates and fish) of streams and rivers. The method required making a number of one-me ter dip net sweeps in each microhabitat proportional to the fraction of the total area of the wetland that each microhabitat covers.

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44 For instance, if there are two microhabitats present in a wetland (water column 25% and edge vegetation 75%) and 20 sweeps to be us ed in each wetland, then 5 water column sweeps and 15 edge-vegetation sweeps are required. The available microhabitat (acreage and percentage) changed with fluctuating water levels. Vegetation within each wetland was generally homogeneous, and microhabitat was base d upon depth of water and presence/absence of vegetation.

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45 Table 2.2 Frog Species known with a range that includes Pasco County, Florida. Commom NameScientific NameLarval Period*Breeding Time* oak toad Bufo quercicus 33-44 daysApril to October southern toad Bufo terrestris 35-55 daysMarch to October Florida cricket frog Acris gryllus 50-90 daysthroughout the year green treefrog Hyla cinerea 55-63 daysMarch to October pinewoods treefrog Hyla femoralis 35-65 daysMarch to October barking treefrog Hyla gratiosa 41-65 daysMarch to August squirrel treefrog Hyla squirella 40-60 daysMarch to October little grass frog Pseudacris ocularis 45-70 daysthroughout the year Florida chorus frog Pseudacris nigrita 40-60 daysthroughout the year eastern narrowmouth toad Gastrophryne carolinensis 23-67 daysApril to October eastern spadefoot Scaphiopus holobrooki 14-30 daysApril to October Florida gopher frog Rana capito 85-106 daysFebruary to October bullfrog Rana catesbeiana 365-730 daysFebruary to October pig frog Rana grylio 365-730 daysApril to October southern leopard frog Rana utricularia 67-86 daysthroughout the year giant toad Bufo marinus 45-50 dayslate spring to summer greenhouse frog Eleutherodactylus planirostris no larval periodApril to September Cuban treefrog Osteopilus septentrionalis 21-28 daysMay to October 1.Ashton,R.E.andP.S.Ashton.1998. HandbookofReptilesandAmphibiansofFlorida : PartThree,TheAmphibains. Windward, Miami. 2.Conant,R.AndJ.T.Collins,1998. AFieldGuidetoReptilesandAmphibians:EasternandCentralNorthAmerica .ThePeterson Guide Services. Houghton Miflin, Boston. 3. Wright, A.H., 1932. Life Histories of the Frogs of Okeifinokee Swamp, Georgia. The Macmillan Company, New York. Larval period and breeding time is listed as published in the references below. A passive sampling effort was a pplied at selected sites by us ing two sizes of funnel traps that allowed sampling of different micr ohabitats within each wetland. Ten unbaited minnow traps (60X30X30 cm) were used for sampling relatively deep areas in the wetland. Traps were used in wetlands that had water over 0.3 meters in depth in 2001.

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46 Wetlands that met the water depth criteria only after the passing of Tropical Storm Gabrielle (14 Septem ber 2001) were not sampled with large traps. Calling activity indicated that there were few calling males in the wetlands that did not have water until mid-September. Traps were used in the same wetla nds both years regardless of water levels. A total of 10 traps were placed in e ach wetland scattered over approximately 2 hectares. The funnel traps are constructed of 3-mm black plastic Vexar netting (DuPont De Memours & Co., Model N o. 5-59-V-360-BABK) stretched over welded frames with funnel entrances at each end (Godley et al. 1981). The funnel entrance was modified to maintain it at approximately 5 centimeters in diameter. Traps were placed in each wetland for approximately 24 hours. Sampling time of approximately 24 hours is recommended (U.S. EPA 2002e) because the a llotted time is sufficient to allow for acclimation to disturbance caused by the trap placement and yet short enough to reduce mortality from the variety of vertebrate and invertebrate predators that coexist with the tadpoles. Tadpoles were removed from traps by hand and identified to species (Altig et al. 1999) before release. In cas es where field identification wa s not possible, a sample of tadpoles were collected and preserved for late r identification. All aquatic animals caught in the traps or dipnets were identified. Twenty sweeps, using D-Frame dip nets (Wildco model number 486-E80; 12 X16 1/16 mesh), were used on every wetland during each sampling event in 2002. If a small pool of water was the only inundation in a wetla nd, then the number of dip net sweeps was reduced to eliminate sampling the same area more than once.

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47 Some of the collection methods were refined before the beginning of sampling in 2002 within the framework of the protocol used in 2001. Refinements include documenting each unit of sampling effort (trap or dipnet sweep ) separately rather than pooling data by wetland. For those wetlands in which traps we re not used, ten dipne t sweeps were added in 2002 to obtain consistency of units of sampling effort in each wetland. In 2002, I classified tadpoles into one of th ree Gosner Stage Categories. The categories used were similar to those proposed Mc Dairmid and Altig (2000) for use in developmental and ecological studies. The categories were adopted to allow measurement of tadpole development progress in the field with minimal harm to the individual. Category 1 is egg or larval maturation prior to hind limb bud development (Gosner Stages 1-25). Category 2 is subse quent to hind limb bud development, but prior to front limb bud development (Gosner Stages 26-40), and Category 3 is development beyond front limb development (Gosner Stages 41-46). Measurement of Environmental Factors During each sample period water surface temperature and pH were collected with a Corning Checkmate II pH meter at the edge (<3 meters from the waters edge) and the center. The minimum and maximum temperature between events and a current thermometer reading were collected at the wetland bottom adjacent the staff gauge (Sper Scientific, model num ber 736690, min/max thermometer) to provide a record of the

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48 fluctuation of water temperature between ev ents and the difference between the surface and the bottom temperatures at the sampling time. The staff gauge reading was taken during each sampling period during 2001. In two wetlands it was noted that the staff gauge placement was not in the deepest location of the wetland and consequently indi cated the wetland was dry when water was actually present elsewhere in the wetland. To correct this problem, I installed a staff gauge at more appropriate locations in wetland S-87 and S-44. The new staff gauges were used for the 2002 events. Some gauges were installed to measure inundation in terms of a known vertical coordinate system (NGVD) and some were installed to measure the level of inundation above the wetland bottom. The measurements in NGVD were converted to depth of inundation measurements for all an alyses. The size of each wetland was measured using the (GIS) Florida Land Us e Cover and Forms Cl assification System (FLUCFCS) map obtained from SWFWMD. Statistics Each wetland was considered a Sampling Unit (SU). Data for each SU were analyzed each year of the study individually, and also pooled for a two-year analysis. Nonparametric statistic s are sometimes suggested when sa mple sizes are small or the underlying distribution of the data is unknow n (Potvin and Roff 1999). Although there is debate about the reason to us e nonparametric statistics (validity vs. efficiency) or the situations that warrant their use (non-norm al distribution, small sample size, high

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49 kurtosis) nonparametric statistics were used throughout this analysis because of their wide acceptance in ecological literature, and their simplicity and efficiency in revealing patterns within the data (S tewart-Oaten 1995). Three types of data analysis were conducte d. The first was an R-mode analysis using Spearman Rank Correlation Coefficient was conducted to deter mine the relationship between tadpole variables (capture rate per un it effort and average number of species per year) and timing of inundation, length of i nundation and size of wetland. Temperature and pH were not used in this analysis becaus e of their consistency among sites, given the dates and times of the measurements. Simple descriptive statistics were used to examine the variability between year and event. I used Spearman Rank Correlation index to explore the relationship between the number of species heard calling and the number of tadpole species captured in each wetland. Second, a Q-mode analysis was conducted to illustrate the relations hip between wetlands based on anuran and predator variables. The an alysis consisted of a cluster analysis and a non-metric multidimensiona l scaling (NMDS) analysis that provided a visual illustration of the dissimilarity among wetlands. I used hierarchical cluster analysis and ordination to elucidate patterns and categorize wetlands using quantitative data. The cluster analysis provided a dendrogram that illustrated the relationships of wetlands to each other in a nested fashion. I used Unweighted Pair-Group Average (UPGA) as the linkage method because, during the clustering, this linkage method preserves the original

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50 properties of the space in the dissimilarity matrix (Statistica for Windows 1995, McCune and Grace 2002). I used Euclidean distance measure because of its compatibility with UPGA. Ordination is a way of graphically summarizi ng complex relationships and extracting a dominant pattern from an infinite number of possible patterns (McCune and Grace 2002). Nonmetric multidimensional scaling (NMDS) is considered the most generally effective ordination technique for ecological communi ty data and is recommended as the ordination method of choice unless a specific goal requires another method (McCune and Grace 2002). I used the dissimilarity matrix derived from the cluster analysis to conduct an NMDS. The third type of data analysis was box and whisker plots used to confirm patterns observed throughout the Q and R-mode analys es. Average number of tadpoles captured per unit effort and average number of tadpole species captured per year were used to confirm one distinct gro up of wetlands had successful anuran reproduction and a separate group of wetlands did not have successful anuran reproduction. Results The numbers of species calling in each wetland per event and the average size category for the choruses detected are presented in Tables 2.3 and 2.4. Peak calling activity was observed on 21 July 2001 during the first year of the study and 13 July 2002 during the second year of the study. The number of i ndividuals per unit effort and the number of

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51 taxa captured during each sampling event for ta dpoles and all predators are presented in Tables 2.5 and 2.6. Tadpole abundances generall y peaked two to four weeks after calling activity. Invertebrate abundances fluctuated throughout the year. Fish presence and abundance increased steadily in 2001 until the wetlands began to dry, and throughout sampling in 2002 when most wetlands remained inundated. Table 2.3 2001 Calling Male Summary Table. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 Sampling Event #5 11 July 2001 14 July 2001 21 July 2001 15 August 2001 04 September 2001 Wetland # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category C 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-10 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-12 6 1.33 1 4.00 1 4.50 2 1.50 1 1.00 S-13 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-18 2 3.00 1 4.00 2 3.00 2 3.50 2 1.50 S-20 2 2.5 0 N/A 0 N/A 3 1.50 0 N/A S-24 5 2.60 0 N/A 2 1.00 1 3.00 3 1.33 S-27 0 N/A 0 N/A 1 1.00 0 N/A 0 N/A S-30 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-35 1 5.00 0 N/A 0 N/A 3 2.67 1 2.00 S-44 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-48 7 2.57 4 2.75 6 2.17 1 1.00 0 N/A S-63 2 5.00 1 2.00 0 N/A 1 1.00 1 1.00 S-67 2 1.50 1 6.00 0 N/A 1 5.00 0 N/A S-68 4 3.00 4 2.00 3 1.67 1 3.00 2 1.50 S-73 2 3.00 0 N/A 6 2.83 3 2.00 0 N/A S-75 0 N/A 0 N/A 3 2.00 4 1.50 2 1.50 S-76 4 2.00 4 2.00 4 2.75 3 1.33 2 1.50 S-87 6 2.67 0 N/A 5 1.60 1 1.00 3 1.33 S-89 5 2.60 5 1.60 4 3.00 1 3.00 2 2.00 S-94 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-95 2 2.50 2 1.00 3 2.67 2 1.50 0 N/A S-96 6 2.17 4 2.00 6 1.50 2 2.00 2 1.50 S-97 5 3.80 4 3.00 4 1.50 3 2.00 1 2.00 S-106 0 N/A 0 0.00 1 1.00 1 1.00 0 N/A Z 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Total number of species calling and the average size category of each chorus illustrated by date and wetland number. Size cate gories are as follows: 1 (1-10 calling males), 2 (11-25 calling males), 3 (26-50 calling males), 4 (51-100 calling males), 5 (100-500 calling males), and 6 (greater than 500 calling males).

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52 Table 2.4 2002 Calling Male Summary Table. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 Sampling Event #5 Sampling Event #6 Sampling Event #7 25 June 2002 01 July 2002 09 July 2002 13 July 2002 30 July 2002 15 August 2002 25 August 2002 Wetland # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category C 0 N/A 0 N/A 0 N/A 4 4.75 3 4.33 2 1.00 3 1.00 S-10 0 N/A 0 N/A 0 N/A 2 3.50 0 N/A 0 N/A 1 1.00 S-12 5 3.60 2 4.50 2 3.50 4 3.00 3 1.67 2 1.00 0 N/A S-13 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-18 5 4.20 2 3.50 3 3.33 4 3.00 3 2.67 3 2.67 2 1.50 S-20 0 N/A 0 N/A 0 N/A 6 2.00 2 2.00 3 1.33 3 1.33 S-24 0 N/A 0 N/A 0 N/A 7 4.43 4 2.75 2 1.00 2 1.00 S-27 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-30 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-35 0 N/A 0 N/A 1 3.00 4 4.50 6 3.67 3 2.33 3 1.33 S-44 0 N/A 0 N/A 0 N/A 0 N/A 1 2.00 1 1.00 0 N/A S-48 0 N/A 4 4.50 4 2.00 5 2.60 3 5.33 3 1.33 2 1.50 S-63 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-67 0 N/A 8 1.63 0 N/A 0 N/A 1 3.00 2 1.50 2 2.50 S-68 0 N/A 8 2.63 4 4.25 5 3.60 3 4.67 2 1.50 3 1.33 S-73 1 1.00 4 1.75 2 4.00 4 3.50 N/A N/A N/A S-75 0 N/A 4 4.00 2 1.50 4 1.50 4 2.00 2 1.00 2 1.00 S-76 0 N/A 5 1.60 5 2.40 5 1.80 4 1.25 4 1.25 4 1.50 S-87 0 N/A 3 1.67 2 1.00 5 3.40 4 3.50 3 1.67 2 2.00 S-89 0 N/A 2 1.00 5 3.60 5 3.40 4 3.50 2 1.50 1 2.00 S-94 0 N/A 0 N/A 0 N/A 0 N/A 6 3.00 1 6.00 2 2.00 S-95 0 N/A 0 N/A 0 N/A 6 4.67 4 5.25 4 1.75 2 1.50 S-96 0 N/A 7 3.29 0 N/A 3 3.00 3 4.33 4 1.75 4 1.00 S-97 0 N/A 4 3.25 1 1.00 5 3.60 3 5.00 2 2.00 2 2.00 S-106 0 N/A 0 N/A 0 N/A 6 3.67 5 4.20 4 2.00 1 1.00 Z 0 N/A 0 N/A 0 N/A 0 N/A 4 2.25 3 1.67 2 2.00 Total number of species calling and the average size category of each chorus is illustrated by date and wetland number. Size c ategories are as follows: 1 (1-10 calling males), 2 (11-25 calling males), 3 (26-50 calling males), 4 (51-100 calling males), 5 (100-500 calling males), and 6 (greater than 500 calling males).

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53 Table 2.5 2001 Quantitative Sampling Summary. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 04 August 2001 25 August 2001 22 September 2001 13 October 2001 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other C 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .05(1) 1.05(4) 0(0) 0(0) .30(2) 1.7(4) 0(0) 0(0) S-10 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1.60(3) 0(0) 0(0) 0(0) .75(4) 0(0) 0(0) S-30 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .70 0(0) 0(0) 0(0) .90 0(0) 0(0) S-44 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1.70 0(0) 0(0) 0(0) 1.55 0(0) 0(0) S-68 2.46(2) 3.23(7) .10(1) 0(0) .60(3) .67(3) 1.23(3) 0(0) 23.00(2) 1.17(6) .70(2) 0(0) 0(0) .77(4) 1.13(2) 0(0) S-87 .80(2) 1.67(5) 0 .07(1) 2.50(3) .87(6) 0(0) .03(1) .33(3) 1.90(5) 0(0) 0(0) .23(2) 1.63(4) 0(0) 0(0) S-94 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1.90(3) 0(0) 0(0) 0(0) .55(4) 0(0) 0(0) S-95 1.53(2) 1.17(6) 0(0) 0(0) 9.37(5) .80(5) 0(0) 0(0) 1.10(2) 2.00(1) 0(0) 0(0) .23(2) 1.70(5) 2.37(1) 0(0) S-96 2.33(3) .90(5) 0(0) 0(0) 4.60(4) 1.47(5) 0(0) 0(0) 1.20(4) .97(6) 0(0) 0(0) .37(3) 1.70(5) 0(0) .07(1) S-97 6.90(5) .80(4) 0(0) 0(0) 10.13(4) 1.77(5) 0(0) 0(0) .90(4) 1.53(5) 0(0) 0(0) .26(2) 1.60(5) 0(0) 0(0) S-106 15.5(2) .80(1) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .60(1) 3.55(5) 0(0) 0(0) 1.10(1) .65(4) 0(0) 0(0) Z 4.45(3) .75(2) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .50(1) 3.15(4) 0(0) 0(0) .05(1) 2.00(3) 0(0) 0(0) Sampling Event #5 Sampling Event #6 10 November 2001 14 December 2001 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other C 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) S-10 0(0) 3.60(4) 0(0) 0(0) 0(0) 23.00(1) 0(0) 0(0) S-30 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) S-44 0(0) 2.70(4) 0(0) 0(0) 0(0) 30.00(2) 0(0) 0(0) S-68 .20(1) 1.2(6) 4.10(2) 0(0) 0(0) 0(0) 0(0) 0(0) S-87 0.1(3) 1.80(5) 0(0) 0.03(1) .37(2) 1.47(6) 0(0) .03(1) S-94 0(0) 3.50(3) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) S-95 .30(1) 2.10(6) 0.87(1) 0(0) .55(1) 2.05(4) .55(1) 0(0) S-96 .25(2) 2.4(5) 0(0) 0(0) 4.55(2) 3.10(4) 0(0) 0(0) S-97 .93(1) 4.10(5) 0(0) 0.07(1) 20.20(1) 11.20(5) 0(0) 0(0) S-106 1.10(3) 4.95(6) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) Z 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) Each cell represents the total number of individuals captured per unit effort followed by the number of groups (tadpole species predator family) captured in each wetland sampled during each event.

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54 Table 2.6 2002 Quantitative Ta dpole Sampling Summary. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 20 July 2002 10 August 2002 30 August 2002 19 September 2002 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other C .10(2) 1.40(3) 0(0) 0(0) 1.93(7) 3.63(5) .43(1) 0(0) 1.60(6) 2.80(7) 1.87(3) 0(0) .70(2) 1.17(5) 1.53(2) 0(0) S-10 0(0) 0(0) 0(0) 0(0) .03(1) 3.10(7) 0(0) 0(0) .20(1) 2.67(7) 0(0) 0(0) .47(3) 1.80(4) 0(0) 0(0) S-30 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .70(3) 0(0) 0(0) 0(0) 2.07(5) 0(0) 0(0) S-44 0(0) 0(0) 0(0) 0(0) 0(0) 2.33(5) 0(0) 0(0) .40(2) 7.2(6) 0(0) 0(0) 0(0) 8.43(7) 0(0) 0(0) S-68 1.27(3) 2.73(5) .03(1) 0(0) .47(1) 4.07(6) .53(3) 0(0) .27(2) 1.07(3) .40(2) 0(0) .07(2) 1.93(5) .60(2) 0(0) S-87 5.37(3) 5.20(7) 0(0) 0(0) 2.77(3) 3.83(7) 0(0) 0(0) 1.07(1) 3.87(7) 0(0) 0(0) .20(1) 2.13(7) 0(0) 0(0) S-94 0(0) 0(0) 0(0) 0(0) .23(1) 3.70(7) 0(0) 0(0) .60(2) 2.23(6) 0(0) 0(0) .27(2) 3.67(7) 1.50(1) 0(0) S-95 3.70(2) 3.43(5) 0(0) 0(0) 2.77(3) 7.93(8) 0(0) 0(0) 1.00(3) 4.30(6) .70(1) 0(0) .83(4) 2.73(5) 2.16(1) 0(0) S-96 1.97(4) 5.33(6) 0(0) 0(0) 3.63(4) 4.70(7) 0(0) 0(0) 2.77(5) 5.87(8) 0(0) 0(0) .83(3) 2.83(6) 0(0) .03(1) S-97 1.67(3) 3.83(7) 0(0) 0(0) 2.70(4) 5.60(8) 0(0) .03(1) 1.97(5) 4.70(7) 0(0) 0(0) .43(3) 2.40(8) .06(1) .03(1) S-106 .93(2) 1.97(5) 0(0) 0(0) 2.67(4) 1.40(4) 0(0) 0(0) 2.00(4) 2.70(7) 0(0) 0(0) .77(3) 3.9(8) .17(1) 0(0) Z 0(0) 0(0) 0(0) 0(0) 7.00(6) 3.23(6) 0(0) 0(0) 5.17(4) 2.23(5) 0(0) 0(0) 2.43(5) 2.10(6) .07(1) 0(0) Sampling Event #5 Sampling Event #6 Sampling Event #7 11 October 2002 01 November 2002 27 November 2002 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other C .07(2) 1.07(5) 3.03(2) 0(0) .03(1) 1.17(6) 2.10(2) 0(0) .03(1) .77(4) 10.50(2) 0(0) S-10 0(0) 1.00(4) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) S-30 0(0) 1.20(5) 0(0) 0(0) 0(0) 8.30(7) 0(0) 0(0) 0(0) 5.53(6) 0(0) 0(0) S-44 .07(2) 5.33(6) 0(0) 0(0) 0(0) 2.33(6) 0(0) 0(0) 0(0) 4.73(4) 0(0) 0(0) S-68 0(0) 1.2(4) 1.36(3) 0(0) 0(0) .67(4) 1.53(3) 0(0) 0(0) .63(4) 1.93(2) 0(0) S-87 .37(1) 2.87(8) 0(0) 0(0) .67(1) 3.10(8) 0(0) 0(0) 1.56(2) 3.93(5) 0(0) 0(0) S-94 0(0) 3.30(7) 1.70(1) 0(0) .23(1) 3.97(7) 3.73(1) 0(0) .07(1) 3.17(5) 2.93(1) 0(0) S-95 .10(2) 3.60(8) 5.03(1) 0(0) .13(1) 2.00(6) 3.70(1) 0(0) 0(0) 2.50(6) 17.97(1) 0(0) S-96 .33(4) 3.37(9) 0(0) 0(0) .17(3) 2.8(6) 0(0) 0(0) .17(1) 2.5(5) 0(0) 0(0) S-97 .10(2) 3.27(7) .03(1) 0(0) .43(2) 2.77(7) 1.13(1) 0(0) .43(1) 1.3(5) 2.27(1) 0(0) S-106 0(0) 2.8(5) 2.03(1) 0(0) 0(0) 1.93(6) 4.23(1) 0(0) .03(1) 3.00(7) 4.20(1) 0(0) Z .37(4) 2.97(6) 2.77(2) 0(0) .13(1) 2.77(5) 4.87(2) 0(0) .13(1) 1.93(6) .07(1) 0(0) Each cell represents the total number of individuals captured per unit effort followed by the number of groups (tadpole species predator family) captured in each wetland sampled during each event.

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55 Figure 2.3 provides four alternate dendrograms that illustrate clusters based on various tadpole and predator species and individual abundances. Variables used in the creation of Figure 2.3A include the average number of tadpoles captured per unit effort (Year 1, Year 2, and Overall Average), the number of species of tadpoles captured per event (Year 1, Year 2, and Overall Average), and the average number of species captured per year. The cluster analysis illustrates that there are at least two distinct groups. Figure 2.3B was created using 25 variables. The variables were individuals per unit effort and taxa per event for tadpoles, inverteb rate predators, fish and other vertebrate predators (Year 1, Year 2 and Overall Average), and the average number of tadpole taxa in each wetland per year. This analysis does not clearly illustrate disc rete clusters. Two wetlands were separated very early in the process (S-97 and S-30), followed by a group of two (S-44 and S-10). It is not clear what the relative similarity is between these early departures or their relative proximity to th e remaining group. Although the NMDS was sufficient to reduce ambiguity among cluste rs, Ward’s Linkage Method was used in a cluster analysis to illustrate the differences in cluster analyses given different linkage methods. The resultant matrix for the two cl uster analyses was identical and thus, only one NMDS was provided. Figure 2.3C was created using the same data as Figure 2.3B, but the Linkage Method was changed from Unweighted Pair Group Average to Ward’s. Although the dissimilarity matrices were identical, and thus the NMDS plots were identical, the presentation of the

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56 clusters in the dendrogram is much different. The differences in Figures 2.3B and 2.3C illustrate the problem with using a single cluster analysis to categorize sampling units. Multiple cluster analyses or ordination s hould be used to clarify clusters. Nine variables were used to create the cluste r analysis in Figure 2.3D. The figure was created using the average number of individuals per unit effort and species per event over two years and the number of tadpole species pe r year. This cluster analysis provides a comprehensive illustration of the data that were collected over the two-year study. Similar to previous cluster analyses, the four wetlands with the fewest numbers and species of tadpoles fall into a group while the remaining wetlands fall into a separate group. The separation based solely on tadpole va riables in Figure 2.3A and the similarity to the separation illustrated in Figure 2.3D is notable because tadpole variables comprise only three of nine variables used in Figure 2.3D and seven of the 25 variables in Figures 2.3B and 2.3C.

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57 Figure 2.3 Cluster analysis using a variet y of anuran and predator variables. 2.3A. Cluster analysis created using seven anuran variables, Unweighted Pair Group Average Linkage Method, and Euclidean distance measure. 2.3C. Cluster analysis created using twenty-five anuran and anuran predator variables, Wards Linkage Method, and a Euclidean distance dissimilarity matrix. 2.3B. Cluster analysis created using twenty-five anuran and anuran predator variables, Unweighted Pair Group Average Linkage Method, and a Euclidean distance dissimilarity matrix. 2.3D. Cluster analysis created using nine anuran and anuran predator variables, Unweighted Pair Group Average Linkage Method, and a Euclidean distance dissimilarity matrix.

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58 Figure 2.4 NMDS plot created using se ven anuran variables and a Euclidean distance dissimilarity matrix. (S tress .0166, Alienati on .0287, D-Hat Raw Stress .0396, D-Star Raw Stress .1188). Figure 2.4 plot clears up the ambiguity from th e cluster analyses presented in Figure 2.3. Variables used in the creation of Figure 2.4 include the average number of tadpoles captured per unit effort (year 1, year 2 and overall average), the number of species of tadpoles captured per event (year 1, year 2 and overall number average), and the number of species captured per year. It is eviden t that four wetlands (S -10, S-30, S-44, and S-94) are separate from the remaining wetlands. Examination of the tadpole summary tables (Tables 2.5 and 2.6) show that the separation of these four wetlands is because of their lack of tadpole numbers and diversity. Wetlands C and Z appear tightly grouped and separate from the remaining wetlands. Ex amination of the tadpole summary tables

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59 suggests that these wetlands are separate because of fluctuation between low tadpole number and diversity in Year 1 and high tadpole numbers and diversity in Year 2. Figure 2.5 NMDS plot created using twenty-five anuran and predator variables and a Euclidean distance dissimilarity matrix (Stress .0382, Alienation .0574, D-Hat Raw Stress .2098, D-St ar Raw Stress .4746). The NMDS using 25 anuran and anuran predat or variables (Figure 2.5) provides a twodimensional view of the relative similarity of wetlands based upon tadpole and predator variables. The groups are similar to those pr esented in the NMDS plot using only tadpole variables (Figure 2.4); however, an intermedia te group appears to be distinct in Figure 2.5.

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60 Figure 2.6 NMDS plot using nine anuran and predator variables and a Euclidean distance dissimilarity matrix. ( 329 iterations, Stress .0199, Alienation .0321, D-Hat Raw Stress .0576, DStar Raw Stress .1476). Clear separation of four wetla nds is apparent in Figure 2.6, which was created using a Euclidean Distance dissimilarity matrix that was constructed using nine variables. Overall individuals per unit effort and overa ll taxa per wetland for tadpoles, invertebrate predators, fish and other vert ebrate predators, and average number of tadpole species per year. The four wetlands that lie on the left in Figure 2.6 had low tadpole abundances in both years. All other wetlands had vari able species presence and abundances considerably higher than S-10, S-30, S-44 a nd S-94. Figures 2.7A through 2.7D provide correlative evidence that th e species richness and abundances captured are non-random. There are strong positive correlations between the average length of inundation and the

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61 number of individual tadpoles captured per unit effort and the number of species of tadpoles captured per year (R= .73 p <.01; R= .70, p <.05). There is also a strong negative correlation between th e Julian date of inundation and the number of individual tadpoles captured per unit effort and the numbe r of species of tadpoles captured per year (R= -.81, p <.01; R= -.78, p <.01) Wetland size was not significantly correlated with any tadpole variable tested. The number of anuran species heard calling was highly correlated with the number of tadpole species captured (Figure 2.8)

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62 Figure 2.7 Spearman Rank Correlations be tween hydroperiod variables and tadpole variables in 2001 and 2002. Figure 2.7A Spearman Rank Correlation between average length of inundation and number of tadpoles captured per unit effort (Spearman R= .73, p < .01). Figure 2.7B Spearman Rank Correlation between average length of inundation and number of tadpole species captured each year (Spearman R= .70, p < .05). Figure 2.7C. Spearman Rank Correlation between average Julian Date of inundation and number of tadpoles captured per unit effort (Spearman R= -.81, p < .01). Figure 2.7D Spearman Rank Correlation between average Julian Date of inundation and number of tadpole species captured each (Spearman R= -.78, p < .01).

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63 Figure 2.8 Spearman Rank Correlation betw een average number of species heard calling and the average number of tadpole species captured in 2001 and 2002 (Spearman R= .87, p < .001). Based on the evidence provided in the cluste r and NMDS analysis and the correlative evidence, the wetlands were separated into a successful reproduction group (S-10, S-30, S-44, and S-94) and an unsuccessful re production group (C, Z, S-68, S-87, S-95, S-97, and S-106). Box and whisker plots were cr eated for the average number of tadpoles captured per unit effort (Figure 2.9) and the average number of tadpole species captured per year (Figure 2.10) separated by success group.

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64 Figure 2.9 Box and Whisker plot using av erage number of tadpoles captured per unit effort for unsuccessful vs. successful anuran reproductive success categories. Figure 2.10 Box and Whisker plot using aver age number of tadpoles species captured per unit effort for unsuccessful vs. su ccessful anuran reproductive success categories.

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65 Discussion Amphibians have characteristics of a good indicator taxon. They are sensitive to perturbations in aquatic and terrestrial environments becau se of their biphasic life-cycle, physiological adaptations, and specifi c microhabitat (Vitt et al. 1990, Wake 1991, Blaustien et al. 1994, Welsh and Ollivier 1998). Despite these characteristics, it is difficult to separate natural population variability from true population decline. For a variety of reasons, especially extreme va riability in population sizes (e.g., Berven 1990, Dodd 1992), anuran studies need to be planned as long-term efforts at multiple locations. Using anurans as a surrogate for wetland health may be misleading without some knowledge about what is happening in nearby wetlands (U.S. EPA 2002). This study provides data on 12 wetlands over two years and evidence that there are two groups of wetlands based on amphibian reprodu ctive success. The first group includes wetlands S-10, S-30, S-44 and S-94). This group had no calling activity in 2001 and little activity in 2002. No tadpoles were captured in any of the four wetlands in 2001 and few were captured in 2002. The average capture rate for each wetland within this group over two years was less than 0.10 individual per unit effort and less than 2 species per year. The second group consists of wetlands C, Z, S-68, S-87, S-95, S-96, S-97 and S-106. This group had relatively large breeding chorus es, a capture rate of over 0.39 individual per unit effort (6 of 8 wetlands had a capt ure rate of over 1.03) and over 4 species per year. These two groupings were confirmed using box and whisker plots for average number of tadpoles captured per unit effort and number of tadpole species captured per

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66 year. Various alternate groupings were teste d, including adding an intermediate success group and moving C, Z, and S-68 individuall y or in combination from the successful group to the unsuccessful group. All variations other than those presented in Figures 2.9 and 2.10 diminished the significance of the se paration. The clear separation of wetlands based solely on larval amphibian capture rates provides evidence for amphibian reproduction as an indicator of wetland health on the SWF. The differences in categorization between wetlands can be attributed to the variability in abundance and richness for tadpoles, invertebrate predators, fish, and other vertebrate predators each year. Although no other sta tistically significant success groupings were observed in the two study years, some pattern s began to merge that could be significant over a longer study period. A group of four wetlands (S -10, S-30, S-44, and S-94) re mained a distinct group regardless of separation or combination of study years. These four wetlands had relatively low individual capture rates and species richness for tadpoles, invertebrates, fish and other vertebrate predators. No changes in categorization of these wetlands would be expected unless the hydroperiod imp roved. A group of four wetlands (S-95, S96, S-97, and S-106) were distinct and distan t from the low success group in all cluster and NMDS analysis. These three wetlands ha d relatively high individual tadpole capture rates, tadpole species richness, invertebrate predator capt ure rates, and relatively low individual fish capture rates and fish richness in both years. No changes in categorization of these wetlands would be expected unless the hydroperiod length or timing was altered.

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67 The remaining four wetlands (C, Z, S-68, a nd S-87) fluctuated between high and low tadpole numbers and species depending on year or sampling event. Wetlands C and Z had a relatively low capture rate and tadpole sp ecies richness in one year and a relatively high capture rate and species richness in the other year resulting in intermediate results overall. Wetlands S-68 and S-87 actually had capture rates and species richness intermediate to the high and low success ca tegories in both years. Wetland S-68 had relatively high capture rates during the first sampling event each year, but the number decreased sharply during subsequent events resulting in overall intermediate numbers each year. Wetland S-68 was relatively near the Cross Cypress branch of the Anclote River and flooding of the river routinely cont ributed to the water level in the wetland. Fish capture rates and richness in wetland S-68 increased sharply after the first event of both years and included regular captures of known voracious tadpole predators in the family Centrarchidae. River overflow may not affect vegetation composition, but could have detrimental effects on the anurans by supplying a constant source of fish predators. Wetland S-87 also had intermed iate capture rate and tadpol e richness; however, no fish were captured either year. The same method of sampling We tlands S-68 and S-87 produced an average of 1.31 individual fish per unit effort in we tland S-68 and none in S87. Water depth over 1 meter and fallen trees made samplin g Wetland S-87 difficult and could have reduced the capture rate Two species of salamander, of Amphiuma means and Siren lacertian were captured in low numbers in wetland S-87 and nowhere else on the site. The presence of these predators could have also reduced the tadpole capture rate.

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68 Strong correlative evidence suggests the hydr operiod of wetlands contributed to capture rates and species richness within wetlands on the SWF and confirms the conclusions of Paton and Crouch III (2002). Figure 2.7 shows wetlands with an average inundation length of less than 90 days had a tadpole capt ure rate of less than 0.10 individuals per unit effort; Wetlands with an average inundation leng th of less than 80 days had an average of less than 2 species per year captured. Als o, in wetlands not inundated before day 235 (August 23), individual capture rates were le ss than 0.10 and an average of less than less than 2 species were captured over the study. The average hydroperiod of the high success wetlands was relatively long (>120 days) and be gan early in the year (before 26 July). The timing of the inundation in relation to repr oduction is especially notable because the published breeding season for most of the frogs in central Florida extends to October and the maximum larval period for 14 of the 17 sp ecies is 90 days or less (Table 2.2). One explanation for the correlation between anuran success and length of inundation is that a longer period of inundation allows for a suite of asynchronous individuals and species to call, breed, and go through me tamorphosis. That is, several large breeding events over several weeks could all pr oduce new metamorphs. A shorter inundation period would allow fewer successful breeding events th roughout the season because some tadpoles may not have time to comple te metamorphosis. No sign ificant correlation existed between wetland size, water depth, water temper ature, or water pH and tadpole capture rate or species richness.

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69 Human activities are known to be detrimental to the natural amphibian biota (Duellman and Trueb 1986). The two most dramatic eco logical trends of the past century are anthropogenic changes in biotic diversity and alterations to the structure and function of natural systems (Vitousek 1997). To preserve the functions of the natural systems on the SWF, and extensive monitoring protocol is followed. Monitoring efforts on the J. B. Starkey Wellfield produce valuable data that may be used to predict the occurrence and reproductive success of the natural amphibian community. Maintenance and protection of biological diversity are best accomp lished when ecologists and natural resource managers coordinate their efforts (Semlitsch 2000). The monitoring data should be evaluated in a timely manner to allow land managers to make necessary management adjustments. The primary challenge with an indicator species is to separa te natural population fluctuations from fluctuations occurring be cause of anthropogenic change (Welsh and Ollivier 1998, Penchman and Wilbur 1994). In this study, I illustrate changes in the categorization of wetlands based upon variation in tadpol e abundance and richness over two years. If results from only one year were examined as opposed to both years individually and the average of both, diff erent conclusions could be reached. Intermediate anuran reproductiv e success in wetlands C and Z was, in part, because of differential success over two years. Whether the differential success is part of a natural fluctuation or anthropogenic change is unknown.

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70 Categorization of wetlands into two groups would provide a statistically strong result. Such a limited categorization strategy, however, could usher in unforeseen problems. If management decisions are made on the basi s of the two-category system, then the wetlands are deemed either successful or unsuccessful at providing amphibian-breeding habitat. The danger in this strategy is that rehabilitation of an unsuccessful wetland may be perceived to be much more difficult and/or costly than rehabilitation of an intermediate wetland, when in reality, such is not the case. A limited categorization strategy would likely result in neglect and fu rther deterioration of unsuccessful wetlands. Furthermore, because amphibian populations exist as metapopulations, a change in the success of a small number of seemingly isolat ed ponds for a short time period could have far-reaching detrimental effects on the am phibians across the landscape. Changes in management strategies should account for na tural variability and focus on prevention of long-term reductions in hydroperiod. Adding an intermediate category, even if it is statistically insignificant, may assist land managers in identifying potential problem s before they become reproductively unsuccessful. However, categorization of wetlands into three groups and allowing potentially natural fluctuations in reproductive success to be deemed overall intermediate success could lead to erroneous conclusions. Actions taken as a result of such erroneous conclusions to correct perceived problem s in reproductive success could be costly, unnecessary, or even detrimental to the long -term success of amphibian assemblage.

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71 Determining the value of seas onal wetlands to anuran and ot her vertebrate populations is the focus of much contemporary research (Gibbs 1993 and 2000, Johnston 1994). For example, Gibbs (1993) reported that sma ll wetlands play a greater role in the metapopulation dynamics of certa in taxa of wetland animals than the modest area covered by such small wetlands might imply. In South Carolina, wetlands that retain water for about eight to ten months each year tended to have more anuran species than wetlands with longer or shor ter hydroperiods (Snodgrass et al. 2000a). In general, wetlands that retain water for long periods of time tend to support fish, which, in turn, tend to reduce the number of amphibian species breeding in them (Moler 1992). Factors other than wetland size and length of inunda tion may influence amphibian populations as well. In Montana, amphibian species rich ness declined as wetland isolation and road density increased, regardless of the spatial scale used for the evaluation (Lehtinen et al. 1999). Habitat fragmentation, road density, habitat quality, or other landscape-scale urbanization affects are not relevant to the SWF at this time on the scale this study was conducted. The habitat is we ll managed and generally characterized by native land cover. Wetland size, depth, water temperature and pH ha d no significant relationship with anuran reproductive success. It is lik ely that the anurans are responding to the length and timing of inundation. Thus, hydrop eriod may be used to predict the anuran success on the SWF. Source of inundation c ould also be important in determining amphibian success because of different predat or contributions or water chemistry from the contributing water source.

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72 Using amphibian success to supplement the ongoing vegetative and hydroperiod monitoring would provide the greatest protection from discounting important anthropogenic changes and provide a basis for understanding their natural population fluctuations. Comparing the results of ve getative monitoring with anuran reproductive success may provide the sensitivity to measure small changes that could indicate negative anthropogenic influence or recovery from such negative influences.

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73 Literature Cited Adamus, P.R. 1996. Bioindicators for Assessing Ecological Integrity of Prairie Wetlands, U.S. Environmental Protection Agency National Health and Environmental Effects Research Laboratory, West ern Ecology Division, Corvallis, OR. Alford, R. A. and S. J. Richards. 1999. Gl obal amphibian declines: a problem in applied ecology. Annual Review of Ecology and Systematics 30: 133-165. Altig R., R.W. McDairmid, K.A. Nichils, a nd P.C. Utach. 1999. Tadpoles of the United States and Canada: A tutorial and Key. U.S. Geological Survey. http://www.pwrc.usgs.gov Berven, K. A. 1990. Factors affecting populatio n fluctuations in larval and adult stages of the wood frog ( Rana sylvatica ). Ecology 71: 1599-1608. Berven, K. A., and T. A. Grudzein. 1990. Dispersal in the wood frog (Rana sylvatica): implications in genetic population structure. Evolution 44: 2054-2056. Blaustein, A. R., D. B. Wake, and W. P. Sousa. 1994. Amphibian declines: judging stability, persistence, and susceptibility of populations to local and global extinctions. Conservation Biology 8: 60-71. Brown, S. L. 1984. The role of wetlands in the Green Swamp. Pp. 405-415 in K.C. Ewel and H.T. Odum (eds.), Cypress Sw amps. University of Florida Press, Gainsville, Florida. 472 pp. Cherry, R.N., J.W. Stewart and J.A. Mann. 1970. General hydrology of the middle gulf area, Florida. U.S. Geological Survey Water Resources Inves tigations Report No. 87-4188. Chippindale, P.T., A.H. Price, and D.M. H illis. 1998. Systematic status of the San Marcos salamander, Eurycea nana (Caudata Plethodontidae): Arlington, Texas, Department of Biology Universi ty of Texas, Copeia 4:1046-1049. Delis, P. R., H. R. Mushinsky, and E. D. McCoy. 1996. Decline of some west-central Florida anuran populations in re sponse to habitat degradation. Biodiversity and Conservation 5:1579-1595. Dodd, C. K. 1992. Biological diversity of a temporary pond herpetofauna in north Florida sandhills. Biodiversity and Conservation 1: 125-142.

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74 Dodd, C. K. 1997. Imperiled Amphibi ans: A historical perspective In Aquatic Fauna in Peril: The Southeastern Perspective. G.W. Benz and D.E. Collins (eds.). Lenz Design & Communications Inc. Doherty, S.J., M. Cohen, C. Lane, and J. Surdick. 2000. Biological criteria for inland freshwater wetlands in Florida: A review of scientific and technical literature (1990-1999). Report to United States Environmental Protection Agency, Biological Assessment of Wetlands Workgroup by University of Florida Center for Wetlands, Gainesville. Duellman, W. E., and L. Trueb. 1986. Biology of Amphibians McGraw-Hill Book Co., New York. Edwards, L.D., and S.R. Denton. 1993. Cr oss Bar Ranch Wellfield Ecological Monitoring Report. Biological Research Associates, Inc., Tampa, FL. Ewel, C.E. 1990. Swamps. Pp. 281-323 In : Myers, R.L. and J.L. Ewel (eds.). Ecosystems of Florida. University of Central Florida Press, Orlando, 765 pp. Florida Department of Environmental Protection. 2001 Draft Habitat Assessment Standard Operating Procedur e. FDEP SOP: FS 7000 Gibbs, J. P. 1993. Importance of small wetla nds for the persistence of local populations of wetland-associated animals. Wetlands 13: 25-31. Gibbs, J. P. 2000. Monitoring populations. Pp. 213-252 in L. Boitani and T. K. Fuller (eds.), Research techniques in animal eco logy. Columbia University, New York, 442 pp. Gill, D. E. 1978. The metapopulation ecology of the red-spotted newt, Notophthalamus viridescens (Rafinesque). Ecological Monographs 48: 145-166. Godley, J.S., R.W. McDairmid, and G. T. Bancroft. 1981. Large scale operations management test of use of the white amur for control of problem aquatic plants, report 1: Baseline studies, Volume V. The Herpetofauna of Lake Conway, Florida Technical report A-78-2, U.S. Army Engineer Waterways Experiment Station, CE, Vicksburg, Mississippi. Hecnar, S. J., and R. T. M’Closkey. 1996. Regional dynamics and the status of amphibians. Ecology 77: 2091-2097. Heyer, W.R., M.A. Donnelly, R.W. McDairmid, L.A.C. Hayek, and M.S. Foster, eds. 1994. Measuring and monitoring biological diversity. Standard methods for amphibians Biological Diversity Handbook Series. Smithsonian Institution Press, Washington, D.C.

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75 Hutchinson, C.B. 1984. Hydrogeology of well field areas near Tampa, Florida. Phase 2development and documentati on of a quasi-three dimensional finite difference model for simulation of steady state groundw ater flow. U.S. Geological Survey Open File Report No. 81-630. Johnson, C.M., L.B. Johnson, C. Richards, and V. Beasely. 2002. Predicting the occurrence of amphibians: An assessm ent of multiple–scale models. Pp. 157-170 In : Scott, J.M., P.J. Heglund, M.L. Morr ison, J.B. Haufler, M.G. Raphael, W.A. Wall and F.B. Samson (eds.). Predicting species occurrences – issues of accuracy and scale. Island Press, Washington, 868 pages. Johnston, C. A. 1994. Cumulative effects to wetlands. Wetlands 14: 49-55. Lehtinen, R. M., S. M. Galatowitsch, and J. R, Tester. 1999. Cons equences of habitat loss and fragmentation for wetland amphibian species. Wetlands 19: 1-12. Levins, R. 1969. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bulletin of the Entomological Society of America 15:237-240. McCune, B., and J. B. Grace. 2002. Analysis of ecological communities. MjM Software Design. Gleneden Beach, OR. McDairmid, R.W., and R. Altig. 2000. Tadpoles: The biology of anuran larvae The University of Chicago Press, Chicago Moler, Paul E. (ed). 1992. Rare and Endangered Biota of Florida. Volume III: Amphibians and Reptiles. University Press of Florida, Tallahassee. Odum, E. P. 1992. Great ideas in ecology for the 1990’s. Bioscience 42:542-545. Ormiston, B.G., S. Cook, K. Watson and C. Reas. 1995. Annual Comprehensive Report: Ecological and Hydrological Monitoring of the Cypress Creek Wellfield and Vicinity, Pasco County, Florida. Envir onmental Science and Engineering, Inc., Tampa, FL. Parker, G.G. 1975. Water and water probl ems in the Southwest Florida Water Management District and some possible solutions. Water Resources Bulletin 11:1-20 Paton, P.W.C., and W.B. Crouch III. 2002. Using the phenology of pond-breeding amphibians to develop conservation strategies. Conservation Biology 16(1): 194204.

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76 Potvin, C., and D. A. Roff. 1993. Distribution -free and robust statisti cal methods: viable alternatives to parametric statistics? Ecology 74(6):1617-1628. Pechmann, J.H.K. and H.M. Wilbur. 1994. Putting declining amphibian populations in perspective: Natural fluctuations and human impacts. Herpetologica 50: 65-84. Pierce, T.K. 1985. Acid tolerance in amphibians. Bio s cience 35:239-243. Rapport, D. J. 1992. Evaluating ecosystem health. Journal of Aquatic Ecosystem Health 1:15-24. Richardson, C.J. 1994. Ecological functions a nd human values in wetlands: A framework for assessing forestry impacts. Wetlands 14(1): 1-9. Rochow, T.F. 1994. The effects of water table level changes in the northern Tampa Bay region. Southwest Florida Water Manageme nt District Technical Report 1994-1, Brooksville, FL. Sadinski, W.J., and W.A. Dunson. 1992. A mu ltilevel study of effects of low pH on amphibians of temporary ponds. Journal of Herpetology 26:413-422. Scott, N. J., and B. D. Woodward. 1994. Surveys at breeding ponds. Pp. 118-125. In W. R. Heyer, M. A. Donnelly, R. W. McDiarmid, L.-A. C. Hayek, and M. S. Foster (Eds.), Measuring and monitoring biolog ical diversity: standard methods for amphibians. Smithsonian Institution Press, Washington, D.C. Semlitsch, R. D. 2000. Principles for mana gement of aquatic-breeding amphibians. Journal of Wildlife Management 64: 615-631. Semlitsch, R. D., and J. R. Bodie 1998. Are small, isolated wetlands expendable? Conservation Biology 12: 1129-1133. Sinsch, U. 1992. Structure and dynamic of a natterjack toad metapopulation ( Bufo calamita ). Oecologia 112:42-47. Skelly, D. K.. 1996. Pond drying, predators and the distribution of Pseudacris tadpoles. Copeia 1996:599-605. Skelly, D. K., E. E. Werner, and S. A. Cortwright. 1999. Long-term distributional dynamics of a Michigan amphibian assemblage. Ecology 80:2326-2337. Snodgrass, J. W., M.J. Komoroski, A. L. Brya n, Jr., and J. Burger. 2000a. Relationships among isolated wetland size, hydroperi od, and amphibian species richness: implications for wetlands regulations. Conservation Biology 14: 414-419.

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77 Snodgrass, J. W., A. L. Bryan, Jr., and J. Burger. 2000b. Development of expectations of larval amphibian assemblage structur e in southeastern depression wetlands. Ecological Applications 10: 1219-1229. Southwest Florida Water Management Dist rict. 1996. Northern Tampa Bay Water Resource Assessment Project, Volumes 1 and 2, March 1996. Sonenshein, R.S. and R.H. Hofstetter. 1990. Vegetative changes in a wetland in the vicinity of a well field, Dade County, Florida. US Geological Survey WaterResources Investigations Report 89-4155. Statistica for windows (Volume III): Statistic s II. 1995. Statsoft Inc., Tulsa, OK. Stewart-Oaten, A. 1995. Rules and judgme nts in statistics three examples. Ecology 76:2001-2009. Stewart, J.W. 1968. Hydrologic effects of pumping from the Floridan Aquifer in northwest Hillsborough, northeast Pinella s, and southwest Pasco counties, Florida. U.S. Geological Survey Open File Report No. 68-005. U.S. EPA. 2002a. Methods for evaluating wetland c ondition: Introduction to wetland biological assessment. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-014. U.S. EPA. 2002b. Methods for evaluating wetland condition: Study designs for monitoring wetlands. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-015. U.S. EPA. 2002c. Methods for evaluating wetland condition developing metrics and indexes of biological integrity. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-016. U.S. EPA. 2002d. Methods for evaluating wetland condition: Using vegetation to assess environmental conditions in wetlands. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-020. U.S. EPA. 2002e. Methods for evaluating wetland condition: Using amphibians in bioassessments of wetlands. Office of Water, U.S. Environmental Protection Agency, Washington D. C. EPA-822-R-02-022. U.S. Fish and Wildlife Service. 1984. San Ma rcos River recovery plan for San Marcos River endangered and threatened species – San Marcos gambusia ( Gambusia georgei ) Hubbs and Peden, fountain darter ( Etheostome fonticola ) ( Jordan and Gilbert ) Sam Marcos salamander ( Eurycea nana ) ( Bishop ) and Texas wild-rice ( Zizania texana ) Hitchcock: U.S. Fish and Wildlife Service, 109 pp.

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78 Vickers, C.R., L.D. Harris, and B.F. Swinde l. 1985. Changes in herpetofauna resulting from ditching of cypress ponds in Coastal Plains flatwoods. Forest Ecology and Management 11:17-29. Vitousek, P.M., H.A. Mooney, J. Lubchenc o, J.M. Melillo. 1997. Human domination of the earth’s ecosystem. Science 277:494. Vitt, L., J. P. Caldwell, H. M. Wilbur, and D. C. Smith. 1990. Amphibians as harbingers of decay. Bioscience 40:418. Wake, D.B. 1991. Declining amphibian populations. Science 253:860. Wake, D. B. 1998. Action on Amphibians. Trends in Ecology and Evolution 13:379380. Welsh, H. H., and L.M. Ollivier. 1998. Str eam amphibians as indi cators of ecosystem stress: A case study from California’s redwoods. Ecological Applications 8(4):1118-1132. Wright, A. H., 1932. Life-histories of the frogs of the Okefinokee Swamp, Georgia. Macmillan Company, New York, NY.

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79 Chapter 3 – Biological Measures of Wetland Health: Comparing Vegetation and Anurans as Indicators Introduction Wetlands provide many ecosystem functi ons including primary production, water attenuation, biochemical tran sfer and storage, decom position, and wildlife habitat (Richardson 1994). The interactions of flor a and fauna with the physical environment provide the functions that are important to the overall landscape (U.S. EPA 2002a). When the interaction of organisms and the e nvironment is disrupted, the functions of the ecosystem may be diminished (U.S. EPA 2002a). Because it is impractical to measure all aspects of the ecosystem to detect anthropogenic change or measure wetland function, recent focus has been on determining if certain attributes may reflect the overall biological integrity of certain systems. Nu merous studies have doc umented the responses of biological attributes acro ss diverse taxa and regions to human disturbance (Doherty et al. 2000). Sensitive species are usually affect ed first during times of environmental stress (Odum 1992). Currently, much debate exists over which sensitive species, or species assemblages, are used appropriately as biolog ical indicators. Biological indicators are species, species assemblages, or communitie s whose presence, abundance, and condition are indicative of a particular set of envir onmental conditions (Adamus 1996). Monitoring early indicators of ecosystem stress may shorte n response time by shifting attention to the relatively quick response of sensitive species (Rapport 1992). Species used as indicators

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80 should be abundant and tractable elements of the system that provide an early, holistic diagnosis (Rapport 1992, Welsh and Ollivier 1998). Because of their biphasic life history, physio logical adaptations and specific microhabitat requirements, amphibians are considered to be extremely sensitive to environmental perturbations and excellent barome ters of the health of the aquatic and terrestrial habitats in which they reside (Vitt et al. 1990, Wake 1998, Blaustein et al. 1994). Physical and chemical conditions in a wetland are known to exert great influence on amphibian assemblages (Lehtinen et al. 1999, Pierce 1985, Sadinski and Dunson 1992, and Skelly 1996); however, given the diverse taxa and of ten-specific requirements and responses of amphibians, no consensus exists on what conditions are “suitable” for survival and reproduction. While much attention has been aimed at identifying large scale, or even global, threats to anurans (Wake 1998), evidence exists that lo cal populations are in decline because of changes in their habitats. Many detrimental habitat alterations are associated with increased human influence and urbanization (D elis et al. 1996). During the five-year period ending in 2000 the human population of Florida increased by approximately 1.7 million. Similar increases are projected for each of the next five-year periods until the year 2025 (U.S. Census Bureau 2000). Accordingly, the human population may increase from 14.2 million in 1995 to 20.7 million in 2025. Landscape-level land use practices can have both direct and indi rect effects on wetland habita ts and amphibian populations (Lehtinen et al. 1999). Some influences are la rge in scale and extrem ely visible such as

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81 habitat destruction in the form of convers ion from native land-cover to agriculture or development, but others may occur without la rge-scale topographic or land cover changes (Dodd 1997). A more subtle type of habitat destruction may affect the availability or suitability of amphibian breeding habitat. Breeding habitats may be modified to the extent that they become unsuitable for many species as a result of pollution, introduced species, vegetative composition changes, a ltered hydrologic regimes, or other anthropogenic alterations (Johnson et al. 2002). Hydrologic alterations such as ditching of wetlands to enhance drainage can have a large effect on anuran use of wetland sites because of alterations to hydroperiod or sp ecies interactions (Vickers et al. 1985). Other forms of hydrologic alteration also may have effects on amphibian breeding habitat. Wetland drawdown is realized by lowering the potentiometric surface of the floridan aquifer, which in turn lowers the su rficial aquifer level a nd finally the level of inundation in wetlands (Brown 1984, also s ee Stewart 1968, Cherry et al. 1970, Parker 1975, Hutchinson 1984, SWFWMD 1996). Draw down effects on wetland plant community composition are well documented (Sonenshein and Hofstetter 1990, Edwards and Denton 1993, Rochow 1994, Ormiston et al 1995), but leave no immediate physical evidence of alteration to the landscape. Groundwater withdrawal from the Edwards Aquifer in Texas did not alter land cover of the region, but it was determined, the withdrawal could lead to the loss of aquatic biota including amphibians (U.S. Fish and Wildlife Service 1984, Chippindale et al. 1993).

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82 Alterations in the availability of breeding habitat even on a small scale may have longterm or large-scale effects on amphibian populations. Many aut hors have discussed pond-breeding amphibian populations in te rms of metapopulations (Levins 1969, Gill 1978, Berven and Grudzien 1990, Sinsch 1992, Hecnar and M’Closkey 1996, Semlitsch and Bodie 1998, and Skelly et al. 1999) whose viability is dependent on a balance of subpopulation colonization and extinction. Within the parameters of a metapopulation, local habitat perturbations th at alter breeding habitat on a relatively small scale could have long-term negative effects on the regional population (Johnson et al. 2002). My study was designed to (1) compare anuran reproductive success to a vegetative method of wetland monitoring, and (2) id entify differences in wetland health classification among wetlands on the J.B. Starkey Wellfield (SWF). The results will allow land resource managers and regulators to evaluate and possibly refine land management practices, including existing m onitoring methods, and water policy to suit the natural fauna of the SWF. Methods Study Sites J.B. Starkey Wellfield (SWF) is located in Pasco County, Florid a, approximately 28.20 North Latitude and 82.50 West Longitude (Figure 3.1). The site consists of approximately 3,237 hectares, portions of whic h were donated to or purchased by the Southwest Florida Water Management District (SWFWMD), since 1975. The

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83 rectangular parcel is bounded by the Suncoast Parkway (toll highway) to the east, the Anclote River to the south, residential deve lopment to the west and a combination of residential development, the Pithlachascot ee River, and another 3200-hectare state-owned preserve to the north (Figure 3.2). The habita t at SWF is a matrix of sand-pine dominated sandhill and lakes throughout the topographically higher western third of the site, while the topographically lower eastern two-thirds of the site are characterized by pine flatwoods and cypress wetlands. Currently, SWF is maintained for multiple uses, including wildlife habitat, low-intensity recreation (i.e., hiking, biking and backpack camping) and groundwater pumping. The SWFWMD manages the land and assists with the monitoring of groundwater pumping effects. Groundwater withdrawal monitori ng is reported according to water-years beginning October and ending the following September. During the two study wateryears (October 1, 2001September 30, 2002), groundwater pumping averaged 11.2 million gallons per day. Cypress wetlands occur frequently in the s outheastern coastal plain and are typically found scattered throughout the pine flatw oods of Florida (Ewel 1990). The typical hydrologic pattern for cypr ess wetlands in this area is inundation upon the onset of summer rains followed by a slow drying beginning in the fall (Mitsch 1984) and occasionally shorter periods of inundation in the winter (Berryman and Henigar 2000). Twelve cypress wetlands were chosen, in coordination with the SWFWMD, for the vegetative analysis and the anuran reproductive success analysis. Each study wetland

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84 had similar surrounding habitat, and met criteria for minimum size (>0.2 hectare) and depth of historic inundation (> 0.3 meter). An additional 14 wetlands were chosen for a breeding male census (Table 3.1, Figure 3.2). Figure 3.1 J.B. Starkey Wellfield Location Map

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85 Figure 3.2 J.B. Starkey Wellfield Site Locations and Land Use Map

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86 Table 3.1 Sampling Locations and Monitoring Methods # MONITORING ID LATITUDE/ LONGITUDE SAMPLING METHODS 1 S-87 28.24501N/82.59625W Tadpole Monitoring Site, Traps and Net 2 S-95 28.24473N/82.60283W Tadpole Monitoring Site, Traps and Net 3 S-97 28.23936N/82.59667W Tadpole Monitoring Site, Traps and Net 4 S-94 28.24566N/82.59286W Tadpole Monitoring Site, Net 5 S-68 28.23825N/82.57515W Tadpole Monitoring Site, Traps and Net 6 S-106 28.24647N/82.58296W Tadpole Monitoring Site, Net 7 S-10 28.23955N/82.64303W Tadpole Monitoring Site, Net 8 S-96 28.23871N/82.60947W Tadpole Monitoring Site, Traps and Net 9 C 28.25725N/82.60318W Tadpole Monitoring Site, Net 10 Z 28.25561N/82.63597W Tadpole Monitoring Site, Net 11 S-44 28.24678N/82.60641W Tadpole Monitoring Site, Net 12 S-30 28.25055N/82.62383W Tadpole Monitoring Site, Net 13 S-18 28.24219N/82.63418W Call Census Only 14 S-12 28.24212N/82.64040W Call Census Only 15 S-13 28.24480N/82.63851W Call Census Only 16 S-63 28.24850N/82.58331W Call Census Only 17 S-27 28.25561N/82.63597W Call Census Only 18 S-24t 28.25187N/82.63857W Call Census Only 19 S-20 28.24509N/82.63346W Call Census Only 20 S-76 28.24829N/82.55843W Call Census Only 21 S-73 28.24613N/82.56585W Call Census Only 22 S-75 28.25091N/82.56259W Call Census Only 23 S-67 28.23770N/82.57811W Call Census Only 24 S-89 28.23898N/82.56568W Call Census Only 25 S-35 28.23742N/82.61278W Call Census Only 26 S-48 28.24117N/82.60011W Call Census Only In all, the two-year study included a fro g call census of 26 wetlands on SWF and a periodic tadpole census of 12 of the 26. The wetlands were sampled for breeding males 12 times and 13 times for larvae between July 2001 and December 2002. The number of wetlands chosen for the study represents the maximum number of wetlands possibly visited by one individual in one night (call-collection) or three sequential days (tadpole collection). Site selection wa s based on habitat type, geogra phic location of the wetlands, exhibited level of anthropogenic degradati on and specific requests of the SWFWMD (see

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87 SWFWMD, 1996, Hancock et al. 1999, and Ro chow 1998). Land managers estimated the level of anthropogenic change exhibited in each wetland and wetlands were placed in one of three categories described below. Site s were visited for tadpole census every three weeks during the peak-breeding season to eliminate the possibility of missing an entire breeding cycle and, at the same time, min imize the potential disturbance to the community. The 15 September event 2001 was re scheduled for one week after a tropical storm. Analysis of Vegetation Prior to this study, land managers categorized each of the study wetlands qualitatively using the Vegetative Health Rating (VHR). The VHR method (See Wetland Evaluation Method in Rochow 1998) incl udes quantitative categorical variables that measure vegetative composition and physical variab les to ultimately pr oduce three color-coded categories that reflect the level of anthropo genic change. Based on scores for several quantitative variables, the researcher rated the wetland on a 1-5 scale without a logarithm of any kind. Over time, the quantitative variab les were replaced with reviewer comments and notes only. Each of the twelve study wetlands was assigned a color representing the VHR of the wetland by a long-term land manager. A VHR of blue was assigned to a wetland having vegetation, hydrology and soils indicative of a natural, healthy cypress wetland. A VHR of green was assigned to a wetland in wh ich moderate anthropogenic changes were

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88 observed. Anthropogenic changes were observed in vegetative composition and zonation, hydrologic indicators, soil subsid ence or other abnormal characteristics noted by the researcher. A VHR of red was assigned to a wetland in which severe anthropogenic changes were observed. Such changes include severe tree fall or death, upland species encroachment, changes in or elimination of zonation, severe soil oxidation or soil subsidence and biological evidence of hydroperiod reduction. The assignment of color categories was most recently conducted in the spring of 2001. The wetlands used in this study had not changed color category for at least one-year prior to, or upon completion of the study (T. Rochow persona l communication). The stable Vegetative Health Rating allows for a comparison of the Vegetative Health Rating with the results of our study. Because of the regulatory requirements of the SWFWMD Water Use Permit, an Environmental Management Plan (EMP) wa s developed for SWF and other Northern Tampa Bay regional wellfields. One require ment of the EMP is a specific monitoring method known as the Wetland Assessment Proced ure (WAP) be used twice yearly. The WAP consists of eighteen va riables scored on a 1-3 scale (Table 3.2) measured on halfpoint increments, although fractions were rare ly used. A score of one represents a wetland character that has been severely affected, while three represents an unaffected character or natural condition. Quantitative data from the spring and fall from 2001 and 2002 years were analyzed for our study.

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89 Hydrologic data were obtained from the SWFW MD or the environmental consultant that monitors wetlands at SWF on behalf of the wellfield operator, Tampa Bay Water. The Julian date of inundation was calculated us ing the first date of continuous inundation after the residual inundation from the previous ye ar was no longer present. For instance, if a wetland was inundated continuously fro m July 2000 to January 2001, followed by a dry period that lasted until August 2001, th e calculation of the 2001 Julian date of inundation used the August date even though it was not the first time that year that inundation was present. In addition, when an anomalous rain event left a wetland inundated for a brief period (<2 weeks) in the dry season, this inundation was disregarded.

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90 Table 3.2 Wetland Assessmen t Procedure Variables VARIABLE DESCRIPTION Groundcover Deep Zone Composition Percent cover of hydrophytic herbaceous vegetation Transitional Zone Composition Percent cover of hydrophytic herbaceous vegetation in transitional zone Species Zonation Current zonation vs. expected in an unaffected system Weedy Groundcover Percent cover of weedy herb aceous vegetation in the entire wetland1 Shrub Composition Percent cover of hydrophytic woody species with a diameter at breast height (dbh) of < 4cm and > 1.0 m total height Species Zonation Current zonation vs. expected in an unaffected system Weedy Shrubs Percent cover of weedy woody species with a dbh of < 4cm and >1.0 m total height1 Vines Zonation Current zonation vs. expected in an unaffected system Canopy Composition Percent cover of appropriate woody species with a dbh of > 4cm and > 1.0 m total height Zonation Current zonation vs. expected in an unaffected system Tree Health Stress Percent of wetland-appropriate trees that exhibit signs of stress Leaning Percent of wetland-appropriate trees that are leaning Dead Percent of wetland-appropriate trees that are dead Soils D X 7/8 Presence and severity of soil subsidence at a fixed location near the edge of the wetland D X 1/2 Presence and severity of soil subsidence at a fixed location near the center of the wetland NP – 3 Presence and severity of soil subsidence at a fixed location where the ground elevation is approximately 3 inches below the Normal Pool elevation NP – 12 Presence and severity of soil subsidence at a fixed location where the ground elevation is approximately 12 inches below the Normal Pool elevation Hydrology Current water level indicators Presence and level of biological indicators of hydrology (i.e. moss collars, lichens, stain lines, etc.) 1 – A list of potential “weedy” species is provided to the reviewer. Anuran Breeding Male Census Anuran surveys at breeding ponds are particul arly effective in estimating species richness or comparing breeding attempts across site s (Scott and Woodward 1994). Surveys were conducted in accordance with the North Am erican Amphibian Monitoring Program (NAAMP) guidelines (http://www.mp2-pwrc.us gs.gov/naamp/protocol/). Surveys began

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91 immediately upon inundation of any of the 26 study wetlands. Because of quickly changing summer weather patterns in Florida, ideal conditions (warm ambient temperature with high humidity or light rain) were not present at each wetland on every evening. Surveys were conducted when th ese conditions were present in the late afternoon or forecast for the evening. If a survey was begun, all wetlands were visited unless weather conditions or lack of vehicula r access prohibited data collection. Survey results were reported for evenings in which a ll wetlands were visited. In 2001, I chose a route that allowed all wetlands to be visited between 30 minutes after sunset and 0100 hours. This route was followed during all 12 surveys. Thus, each wetland was visited approximately the same time afte r sunset during each survey. Data were collected during nine surveys in 2001 and eleven surveys in 2002. Each wetland was visited for 3 minutes and the number of calling males of each species was recorded. The size of the chorus recorded was the maximum number of individuals of each species heard during the 3-minute obser vation. Calling activity was measured in size categories. The categories were base d on the NAAMP, but refined to reflect six categories as follows: 1-10 calling males, 11-25 calling males, 26-50 calling males, 51100 calling males, 101-500 callin g males and greater than 500 calling males. I also recorded the date, time, current weathe r conditions (ambient temperature and observations regarding clouds and precip itation) and weather conditions over the preceding 24 hours. If no water was present at the permanently marked center of the wetland, the observation was limited to 1 mi nute provided no calling males of any species were recorded. During four of the call events each year, sampling could not be

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92 completed because of impassible roads or in tense thunderstorms. Thus, data from five nights in 2001 and seven in 2002 are reported herein. Quantitative Larva Sampling Collection and identification of larvae is difficult for many reasons. Thus, tadpole sampling efforts must be well planned to obt ain meaningful data without affecting the population. Injury or death of individual ta dpoles may occur as a result of excessive handling (P. Delis personal communication). Identification often requires magnification of mouthparts which is difficult or impossible w ith living specimens in the field. Even in the laboratory, many tadpole species have been incorrectly identified (McDairmid and Altig 2000). Under normal conditions, tadpol e densities drop rapidly throughout the larval period and samples shoul d be taken when the larvae are approximately the same age (McDairmid and Altig 2000). Tadpole census events were scheduled for three sequential days to minimize changes in densities during a single sampling event and each event was separated by three weeks to minimiz e disturbance to the site and population. Larva microhabitat is often species-speci fic (McDairmid and Altig 2000), and each microhabitat must be sa mpled with equal intensity (Heyer et al. 1994). Only one species expected on the SWF ( Scaphiophus holbrookii ) has a mean metamo rphosis time of less than 30 days (Wright 1932) (Table 3.3). The three weeks between sampling was short enough to be confident that no species had comp leted the larval stage between sampling.

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93 Twelve wetlands were monitored for larvae. All wetlands chosen were greater than 0.2 hectare and had biological i ndicators that demonstrated hi storic normal seasonal water levels of at least 0.3 meters in depth. Data were collected during six sampling periods in 2001 and seven in 2002. A standardized sampling effort was applied at each site during the two years to estimate relative sizes of tadpole population as they advanced toward meta morphosis. Tadpoles were identified to species (Altig et al. 1999 ) during active and passive larval sampling methods, as described below. The active sampling method was based on The Florida Department of Environmental Protection (DEP) Habitat Asse ssment Standard Operating Pr ocedures (2001) commonly used for rapid bioassessment (macroinvertebrates and fish) of streams and rivers. The method required making a number of one-me ter dip net sweeps in each microhabitat proportional to the fraction of the total area of the wetland that each microhabitat covers. For instance, if there are two microhabitats present in a wetland (water column 25% and edge vegetation 75%) and 20 sweeps to be us ed in each wetland, then 5 water column sweeps and 15 edge-vegetation sweeps are required. The available microhabitat (acreage and percentage) changed with fluctuating water levels. Vegetation within each wetland was generally homogeneous, and microhabitat was base d upon depth of water and presence/absence of vegetation

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94 Table 3.3 Frog Species known with a range that includes Pasco County, Florida. Commom NameScientific NameLarval Period*Breeding Time* oak toad Bufo quercicus 33-44 daysApril to October southern toad Bufo terrestris 35-55 daysMarch to October Florida cricket frog Acris gryllus 50-90 daysthroughout the year green treefrog Hyla cinerea 55-63 daysMarch to October pinewoods treefrog Hyla femoralis 35-65 daysMarch to October barking treefrog Hyla gratiosa 41-65 daysMarch to August squirrel treefrog Hyla squirella 40-60 daysMarch to October little grass frog Pseudacris ocularis 45-70 daysthroughout the year Florida chorus frog Pseudacris nigrita 40-60 daysthroughout the year eastern narrowmouth toad Gastrophryne carolinensis 23-67 daysApril to October eastern spadefoot Scaphiopus holobrooki 14-30 daysApril to October Florida gopher frog Rana capito 85-106 daysFebruary to October bullfrog Rana catesbeiana 365-730 daysFebruary to October pig frog Rana grylio 365-730 daysApril to October southern leopard frog Rana utricularia 67-86 daysthroughout the year giant toad Bufo marinus 45-50 dayslate spring to summer greenhouse frog Eleutherodactylus p lanirostris no larval periodApril to September Cuban treefrog Osteopilus septentrionalis 21-28 daysMay to October 1.Ashton,R.E.andP.S.Ashton.1998. H andbookof R eptilesandAmphibiansof F lorida : P ar t Three,The Amphibains. Windward, Miami. 2.Conant,R.AndJ.T.Collins,1998. A F ieldGuideto R eptilesandAmphibians: E asternandCentra l NorthAmerica The Peterson Guide Services. Houghton Miflin, Boston. 3. Wright, A.H., 1932. Life Histories of the Frogs of Okeifinokee Swamp, Georgia. The Macmillan Company, New Yo r *Larval period and breeding time is listed as publsihed in the references below. A passive sampling effort was a pplied at selected sites by us ing two sizes of funnel traps that allowed sampling of different micr ohabitats within each wetland. Ten large (60X30X30 cm) unbaited minnow traps were used for sampling relatively deep areas in

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95 the wetland. Large traps were used in we tlands that consistent ly had water over 0.3 meters in depth. Wetlands that met the wa ter depth criteria only after the passing of Tropical Storm Gabrielle (14 Se ptember 2001) were not sample d with large traps. Calling activity indicated that there were few calling males in the wetlands that did not have water until mid-September. Traps were used in the same wetlands both years regardless of water levels. A total of 10 traps were placed in each wetland scattered over approximately 2 hectares. The funnel traps are constructed of 3-mm black plastic Vexar netting (DuPont De Memours & Co., Mode l No. 5-59-V-360-BABK) stretched over welded frames with funnel entrances at each end (Godley et al. 1981). The funnel entrance was approximately 5 centimeters in diameter. Traps were placed in each wetland for approximately 24 hour s (EPA 2002e) because the allotted time is sufficient to allow for acclimation to disturbance caused by the trap placement and yet short enough to reduce mortality from the variety of vertebrate and invertebrate predators that coexist with the tadpoles. Tadpoles we re removed from traps by hand and identified to species (Altig et al. 1999) before release. In cases where field identification was not possible, a sample of tadpoles was collected and preser ved for later identific ation. All aquatic animals caught in the traps or dipnets were identified. Twenty sweeps, using D-Frame dip nets (Wildco model number 486-E80; 12 X16 1/16 mesh), were used on every wetland during each sampling event in 2002, provided sufficient water was present. When a sma ll pool of water was the only inundation in a wetland, the number of dip net sweeps was re duced to eliminate sampling the same area

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96 more than once. Ten dipnet sweeps were added in 2002 to the wetlands that were not trapped to standardize the e ffort to sample each wetland. Some of the collection methods used in 2001 were refined in 2002. Refinements include documenting each unit of sampling effort (trap or dipnet sweep) separately rather than pooling data by wetland. For those wetlands in which large funnel traps were not used, ten dipnet sweeps were added in 2002. In 2002, I classified tadpoles into one of three Gosner Stage Categories. The categorie s used were similar to those proposed McDairmid and Altig (2000) for use in deve lopmental and ecological studies. The categories were adopted to allow measurem ent of tadpole development progress in the field with minimal harm to the individual. Gosner Stage Category 1 is egg or larval maturation prior to hind limb bud developmen t (Gosner Stages 1-25). Gosner Stage Category 2 is subsequent to hind limb bud development, but prior to front limb bud development (Gosner Stages 26-40), and Go sner Stage Category 3 is development beyond front limb development (Gosner Stages 41-46). Measurement of Environmental Factors Water surface temperature and pH were measur ed with a Corning Checkmate II pH meter with temperature capabilities at the time of sampling at the edge (<3 meters from the waters edge) and the center of each we tland. The minimum and maximum temperature between sampling events and a current temperature reading was collected at the wetland bottom adjacent the staff gauge (Spe r Scientific, model number 736690, min/max

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97 thermometer) to provide a record of the fluctuation of water temperature between events and the difference between the surface and the bottom temperatures at the sampling time. The staff gauge reading was taken during each sampling period during 2001. In two wetlands it was noted that the staff gauge placement was not in the deepest location of the wetland and consequently indi cated the wetland was dry when water was actually present elsewhere in the wetland. To correct this problem, I installed a staff gauge at more appropriate locations in wetland S-87 and S-44. The new staff gauges were used for the 2002 events. Some gauges were installed to measure inundation in terms of a known vertical coordinate system (NGVD) and some were installed to measure the level of inundation above the wetland bottom. The measurements in NGVD were converted to depth of inundation measuremen ts for all analyses. Statistics Each wetland was considered a Sampling Unit (SU). Data for each SU were analyzed each year of the study individually, and also pooled for a two-year analysis. Nonparametric statistic s are sometimes suggested when sa mple sizes are small or the underlying distributions of the data are unknown (Potvin and Roff 1999). Although debate exists about the proper use of nonpar ametric statistics (validity vs. efficiency) and/or the situations that warrant their us e (non-normal distribution, small sample size, high kurtosis) nonparametric sta tistics were used throughout this analysis because of their

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98 wide acceptance in ecological literature, and their simplicity and efficiency in revealing patterns within the data (S tewart-Oaten 1995). Three types of data analysis were conducte d. The first was an R-mode analysis using Spearman Rank Correlation Coefficient was conducted to deter mine the relationship between tadpole variables (capture rate per un it effort and average species per year) and WAP, timing of inundation, length of inundati on and size of wetland. Temperature and pH were not used in this analysis because of their consistency among sites, given the dates and times of the measurements. Simple descriptive statistics were used to examine the variability between year and event. We used Spearman Rank Correlation index to explore the relationship between the number of species heard calling and the number of tadpole species captured in each wetland. Second, a Q-mode analysis was conducted to illustrate the relations hip between wetlands and compare the three VHR groups. The anal ysis consisted of de scriptive statistics comparing each VHR category, followed by a cluster analysis and a non-metric multidimensional scaling (NMDS) analysis that provided a visual illustration of the dissimilarity among wetlands. We used hierarchical cluster analysis and ordination to elucidate patterns and categorize wetlands using quantitative data. The cluster analysis provided a dendrogram that illustrated the relationships of wetlands to each other in a nested fashion. We used Unweighted Pair-Group Average (UPGA) as the linkage method because, during the cluste ring, this linkage method preserves the

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99 original properties of the space in the di ssimilarity matrix (Statistica for Windows 1995, McCune and Grace 2002). We used Eu clidean distance measure because of its compatibility with UPGA. Ordination is a way of graphically summarizi ng complex relationships and extracting a dominant pattern from an infinite number of possible patterns (McCune and Grace 2002). Nonmetric multidimensional scaling (NMDS) is considered the most generally effective ordination technique for ecological communi ty data and is recommended as the ordination method of choice unless a specific goal requires another method (McCune and Grace 2002). We used the dissimilarity matrix derived from the cluster analysis to conduct an NMDS. Third, box and whisker plots were used to confirm patterns observed throughout the Q and R-mode analyses. Average number of tadpoles captured per unit effort and average number of tadpole species captured per year were used to confirm one distinct group of wetlands had successful anur an reproduction and a separate group of wetlands did not have successful anuran reproduction. Results The numbers of species calling in each wetland per event and the average size category for the choruses detected are presented in Tables 3.4 and 3.5. The mean of all average size categories in each VHR category excludes the wetlands that had no calling frogs,

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100 whereas the average number of species per VHR category includes the wetlands with no calling frogs. A consistent difference in the number of calling species and chorus size is evident between VHR categories in most events over both years. Blue wetlands consistently had more species and larger choruses and Red wetla nds consistently had fewest species and smallest choruses.

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101 Table 3.4 2001 Calling Male Summary Table. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 Sampling Event #5 11 July 2001 14 July 2001 21 July 2001 15 August 2001 04 September 2001 Wetland # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category S-27 0 N/A 0 N/A 1 1.00 0 N/A 0 N/A S-68 4 3.00 4 2.00 3 1.67 1 3.00 2 1.50 S-73 2 3.00 0 N/A 6 2.83 3 2.00 0 N/A S-75 0 N/A 0 N/A 3 2.00 4 1.50 2 1.50 S-76 4 2.00 4 2.00 4 2.75 3 1.33 2 1.50 S-89 5 2.60 5 1.60 4 3.00 1 3.00 2 2.00 S-95 2 2.50 2 1.00 3 2.67 2 1.50 0 N/A S-96 6 2.17 4 2.00 6 1.50 2 2.00 2 1.50 S-97 5 3.80 4 3.00 4 1.50 3 2.00 1 2.00 Blue S-106 0 N/A 0 0.00 1 1.00 1 1.00 0 N/A Average 2.80 2.72 2.30 1.66 3.50 1.99 2.00 1.93 1.10 1.67 C 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Z 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-10 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-87 6 2.67 0 N/A 5 1.60 1 1.00 3 1.33 Green S-94 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Average 1.20 2.67 0.00 N/A 1.00 1.60 .20 1.00 .60 1.33 S-20 2 2.5 0 N/A 0 N/A 3 1.50 0 N/A S-30 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Red S-44 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Average .67 2.50 0.00 N/A 0.00 N/A 1.00 1.50 0.00 N/A S-12 6 1.33 1 4.00 1 4.50 2 1.50 1 1.00 S-13 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-18 2 3.00 1 4.00 2 3.00 2 3.50 2 1.50 S-24 5 2.60 0 N/A 2 1.00 1 3.00 3 1.33 S-35 1 5.00 0 N/A 0 N/A 3 2.67 1 2.00 S-48 7 2.57 4 2.75 6 2.17 1 1.00 0 N/A S-63 2 5.00 1 2.00 0 N/A 1 1.00 1 1.00 Unclassified S-67 2 1.50 1 6.00 0 N/A 1 5.00 0 N/A Average 3.13 3.00 1.00 3.75 1.38 2.67 1.38 2.52 1.00 1.37 Total number of species calling and the average size category of each chorus illustrated by date and Vegetative Health Rating. Size categories are as follows: 1 (1-10 calling males), 2 (11-25 calling males), 3 (26-50 calling males), 4 (51-100 calling males), 5 (100500 calling males), and 6 (greater than 500 calling males). Blue, Green and Red categories refer to Vegetative Health Rating described in the Qualitative Analysis of Vegetation Section.

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102 Table 3.5 2002 Calling Male Summary Table. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 Sampling Event #5 Sampling Event #6 Sampling Event #7 25 June 2002 01 July 2002 09 July 2002 13 July 2002 30 July 2002 15 August 2002 25 August 2002 Wetland # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category # of Species Avg. size Category S 27 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S 68 0 N/A 8 2.63 4 4.25 5 3.60 3 4.67 2 1.50 3 1.33 S 73 1 1.00 4 1.75 2 4.00 4 3.50 N/A N/A N/A S 75 0 N/A 4 4.00 2 1.50 4 1.50 4 2.00 2 1.00 2 1.00 S 76 0 N/A 5 1.60 5 2.40 5 1.80 4 1.25 4 1.25 4 1.50 S 89 0 N/A 2 1.00 5 3.60 5 3.40 4 3.50 2 1.50 1 2.00 S 95 0 N/A 0 N/A 0 N/A 6 4.67 4 5.25 4 1.75 2 1.50 S 96 0 N/A 7 3.29 0 N/A 3 3.00 3 4.33 4 1.75 4 1.00 S 97 0 N/A 4 3.25 1 1.00 5 3.60 3 5.00 2 2.00 2 2.00 Blue S 106 0 N/A 0 N/A 0 N/A 6 3.67 5 4.20 4 2.00 1 1.00 Average .10 1.00 3.40 2.50 1.90 2.79 4.30 3.19 3.33 3.78 2.67 1.78 2.11 1.42 C 0 N/A 0 N/A 0 N/A 4 4.75 3 4.33 2 1.00 3 1.00 Z 0 N/A 0 N/A 0 N/A 0 N/A 4 2.25 3 1.67 2 2.00 S 10 0 N/A 0 N/A 0 N/A 2 3.50 0 N/A 0 N/A 1 1.00 S 87 0 N/A 3 1.67 2 1.00 5 3.40 4 3.50 3 1.67 2 2.00 Green S 94 0 N/A 0 N/A 0 N/A 0 N/A 6 3.00 1 6.00 2 2.00 Average 0 N/A .60 1.67 .40 1.00 2.20 3.88 3.40 3.27 1.80 2.59 2.00 1.60 S-20 0 N/A 0 N/A 0 N/A 6 2.00 2 2.00 3 1.33 3 1.33 S-30 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Red S-44 0 N/A 0 N/A 0 N/A 0 N/A 1 2.00 1 1.00 0 N/A Average 0 N/A 0 N/A 0 N/A 2.00 2.00 1.00 2.00 1.33 1.17 1.00 1.33 S-12 5 3.60 2 4.50 2 3.50 4 3.00 3 1.67 2 1.00 0 N/A S-13 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A S-18 5 4.20 2 3.50 3 3.33 4 3.00 3 2.67 3 2.67 2 1.50 S-24 0 N/A 0 N/A 0 N/A 7 4.43 4 2.75 2 1.00 2 1.00 S-35 0 N/A 0 N/A 1 3.00 4 4.50 6 3.67 3 2.33 3 1.33 S-48 0 N/A 4 4.50 4 2.00 5 2.60 3 5.33 3 1.33 2 1.50 S-63 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A 0 N/A Unclassified S-67 0 N/A 8 1.63 0 N/A 0 N/A 1 3.00 2 1.50 2 2.50 Average 1.25 .975 2.0 3.53 1.25 2.96 3.00 3.51 2.50 3.18 1.88 1.64 1.38 1.57 Total number of species calling and the average size category of each chorus is illustrated by date and Vegetative Health Ratin g. Size categories are as follows: 1 (1-10 calling males), 2 (11-25 calling males), 3 (26-50 calling males), 4 (51-100 calling males), 5 (100-500 calling males), and 6 (greater than 500 calling m ales). Blue, Green and Red categories refer to Vegetative Health Rating described in the Qualitative Analysis of Vegetation Section.

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103 The number of individuals per unit effort a nd the number of taxa captured during each sampling event for tadpoles and all predat ors are presented in Tables 3.6 and 3.7. Tadpoles, invertebrates, and non-fish vertebrate predators were captured at a consistently higher rate in Blue wetlands than either Green or Red wetlands over both years. Fish were captured at a slightly higher rate in Blue wetlands consistently in Year 1, however in Year 2, were captured at a rate higher in Green wetlands than Blue wetlands in several events. The tables are organized by VHR.

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104 Table 3.6 2001 Quantitative Tadpo le Sampling Summary. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 04 August 2001 25 August 2001 22 September 2001 13 October 2001 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other S-68 2.46(2) 3.23(7) .10(1) 0(0) .60(3) .67(3) 1.23(3) 0(0) 23.00(2) 1.17(6) .70(2) 0(0) 0(0) .77(4) 1.13(2) 0(0) S-96 2.33(3) .90(5) 0(0) 0(0) 4.60(4) 1.47(5) 0(0) 0(0) 1.20(4) .97(6) 0(0) 0(0) .37(3) 1.70(5) 0(0) .07(1) S-97 6.90(5) .80(4) 0(0) 0(0) 10.13(4) 1.77(5) 0(0) 0(0) .90(4) 1.53(5) 0(0) 0(0) .26(2) 1.60(5) 0(0) 0(0) S-95 1.53(2) 1.17(6) 0(0) 0(0) 9.37(5) .80(5) 0(0) 0(0) 1.10(2) 2.00(1) 0(0) 0(0) .23(2) 1.70(5) 2.37(1) 0(0) Blue S-106 15.5(2) .80(1) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .60(1) 3.55(5) 0(0) 0(0) 1.10(1) .65(4) 0(0) 0(0) Totals 2.96(7) 1.38 .02 0 8.23(5) .94 .25 0 5.36(4) 1.84 .14 0 .39(4) 1.28 .70 .01 C 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .05(1) 1.05(4) 0(0) 0(0) .30(2) 1.7(4) 0(0) 0(0) Z 4.45(3) .75(2) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .50(1) 3.15(4) 0(0) 0(0) .05(1) 2.00(3) 0(0) 0(0) S-10 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1.60(3) 0(0) 0(0) 0(0) .75(4) 0(0) 0(0) S-94 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1.90(3) 0(0) 0(0) 0(0) .55(4) 0(0) 0(0) Green S-87 .80(2) 1.67(5) 0 .07(1) 2.50(3) .87(6) 0(0) .03(1) .33(3) 1.90(5) 0(0) 0(0) .23(2) 1.63(4) 0(0) 0(0) Totals 1.05(4) .48 0 .01 .50(3) .17 0 .01 .18(3) 1.92 0 0 .12(4) 1.33 0 0 S-30 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) .70 0(0) 0(0) 0(0) .90 0(0) 0(0) R ed S-44 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 1.70 0(0) 0(0) 0(0) 1.55 0(0) 0(0) Totals 0(0) 0 0 0 0(0) 0 0 0 0(0) 1.20 0 0 0(0) 1.23 0 0 Each cell represents the total number of individuals captured per unit effort followed by the number of groups (species, family etc.) captured in each wetland sampled during each event. Blue, Green and Red categories refer to Vegetative Health Rating described in the Qualitative Analysis of Vegetation Section.

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105 Table 3.6 Continued Sampling Event #5 Sampling Event #6 10 November 2001 14 December 2001 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other S-68 .20(1) 1.2(6) 4.10(2) 0(0) 0(0) 0(0) 0(0) 0(0) S-96 .25(2) 2.4(5) 0(0) 0(0) 4.55(2) 3.10(4) 0(0) 0(0) S-97 .93(1) 4.10(5) 0(0) 0.07(1) 20.20(1) 11.20(5) 0(0) 0(0) S-95 .30(1) 2.10(6) 0.87(1) 0(0) .55(1) 2.05(4) .55(1) 0(0) Blue S-106 1.10(3) 4.95(6) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) Totals .56(4) 2.95 .99 .01 5.06(2) 3.27 .11 0 C 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) Z 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) S-10 0(0) 3.60(4) 0(0) 0(0) 0(0) 23.00(1) 0(0) 0(0) S-94 0(0) 3.50(3) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) Green S-87 0.1(3) 1.80(5) 0(0) 0.03(1) .37(2) 1.47(6) 0(0) .03(1) Totals .02(3) 1.78 0 .01 .074(2) 4.89 0 .01 S-30 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) R ed S-44 0(0) 2.70(4) 0(0) 0(0) 0(0) 30.00(2) 0(0) 0(0) Totals 0(0) 1.35 0 0 0 15 0 0

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106 Table 3.7 2002 Quantitative Tadpo le Sampling Summary. Sampling Event #1 Sampling Event #2 Sampling Event #3 Sampling Event #4 20 July 2002 10 August 2002 30 August 2002 19 September 2002 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other S-68 1.27(3) 2.73(5) .03(1) 0(0) .47(1) 4.07(6) .53(3) 0(0) .27(2) 1.07(3) .40(2) 0(0) .07(2) 1.93(5) .60(2) 0(0) S-96 1.97(4) 5.33(6) 0(0) 0(0) 3.63(4) 4.70(7) 0(0) 0(0) 2.77(5) 5.87(8) 0(0) 0(0) .83(3) 2.83(6) 0(0) .03(1) S-97 1.67(3) 3.83(7) 0(0) 0(0) 2.70(4) 5.60(8) 0(0) .03(1) 1.97(5) 4.70(7) 0(0) 0(0) .43(3) 2.40(8) .06(1) .03(1) S-95 3.70(2) 3.43(5) 0(0) 0(0) 2.77(3) 7.93(8) 0(0) 0(0) 1.00(3) 4.30(6) .70(1) 0(0) .83(4) 2.73(5) 2.16(1) 0(0) Blue S-106 .93(2) 1.97(5) 0(0) 0(0) 2.67(4) 1.40(4) 0(0) 0(0) 2.00(4) 2.70(7) 0(0) 0(0) .77(3) 3.9(8) .17(1) 0(0) Totals 1.91(5) 3.46(7) .01(1) 0(0) 2.45(4) 4.74(8) .11(3) .01(1) 1.60(6) 3.72(8) .22(2) 0(0) .59(5) 2.76(8) .60(2) .01(2) C .10(2) 1.40(3) 0(0) 0(0) 1.93(7) 3.63(5) .43(1) 0(0) 1.60(6) 2.80(7) 1.87(3) 0(0) .70(2) 1.17(5) 1.53(2) 0(0) Z 0(0) 0(0) 0(0) 0(0) 7.00(6) 3.23(6) 0(0) 0(0) 5.17(4) 2.23(5) 0(0) 0(0) 2.43(5) 2.10(6) .07(1) 0(0) S-10 0(0) 0(0) 0(0) 0(0) .03(1) 3.10(7) 0(0) 0(0) .20(1) 2.67(7) 0(0) 0(0) .47(3) 1.80(4) 0(0) 0(0) S-94 0(0) 0(0) 0(0) 0(0) .23(1) 3.7(7) 0(0) 0(0) .60(2) 2.23(6) 0(0) 0(0) .27(2) 3.67(7) 1.50(1) 0(0) Green S-87 5.37(3) 5.20(7) 0(0) 0(0) 2.77(3) 3.83(7) 0(0) 0(0) 1.07(1) 3.87(7) 0(0) 0(0) .20(1) 2.13(7) 0(0) 0(0) Totals 1.25(4) 1.32(7) 0(0) 0(0) 2.39(8) 3.50(7) .09(1) 0(0) 1.73(6) 2.76(7) .37(3) 0(0) .81(5) 2.17(7) .62(2) 0(0) S-30 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 14(3) 0(0) 0(0) 0(0) 2.07(5) 0(0) 0(0) Re d S-44 0(0) 0(0) 0(0) 0(0) 0(0) 2.33(5) 0(0) 0(0) .40(2) 144(6) 0(0) 0(0) 0(0) 8.43(7) 0(0) 0(0) Totals 0(0) 0(0) 0(0) 0(0) 0(0) 1.17(5) 0(0) 0(0) .20(2) 158(6) 0(0) 0(0) 0(0) 5.25(7) 0(0) 0(0) Each cell represents the total number of individuals captured per unit effort followed by the number of groups (species of tadp oles, family of all other groups) captured in each wetland sampled during each event. Blue, Green and Red categories refer to Vegetative Health Rating described in the Qualitative Analysis of Vegetati on Section.

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107 Table 3.7 Continued. Sampling Event #5 Sampling Event #6 Sampling Event #7 11 October 2002 01 November 2002 27 November 2002 Wetland Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other Tadpoles Inverts Fish Other S-68 0(0) 1.2(4) 1.36(3) 0(0) 0(0) .67(4) 1.53(3) 0(0) 0(0) .63(4) 1.93(2) 0(0) S-96 .33(4) 3.37(9) 0(0) 0(0) .17(3) 2.8(6) 0(0) 0(0) .17(1) 2.5(5) 0(0) 0(0) S-97 .10(2) 3.27(7) .03(1) 0(0) .43(2) 2.77(7) 1.13(1) 0(0) .43(1) 1.3(5) 2.27(1) 0(0) S-95 .10(2) 3.60(8) 5.03(1) 0(0) .13(1) 2.00(6) 3.70(1) 0(0) 0(0) 2.50(6) 17.97(1) 0(0) Blue S-106 0(0) 84(5) 61(1) 0(0) 0(0) 58(6) 127(1) 0(0) 1(1) 90(7) 4.20(1) 0(0) Totals .11(4) 2.84(9) 1.76(3) 0(0) .15(4) 2.03(7) 2.12(3) 0(0) .13(1) 1.99(7) 5.27(2) 0(0) C .07(2) 1.07(5) 3.03(2) 0(0) .03(1) 1.17(6) 2.10(2) 0(0) .03(1) .77(4) 10.50(2) 0(0) Z .37(4) 2.97(6) 2.77(2) 0(0) .13(1) 2.77(5) 4.87(2) 0(0) .13(1) 1.93(6) .07(1) 0(0) S-10 0(0) 1.00(4) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) 0(0) S-94 0(0) 3.30(7) 1.70(1) 0(0) .23(1) 3.97(7) 3.73(1) 0(0) .07(1) 3.17(5) 2.93(1) 0(0) Green S-87 .37(1) 2.87(8) 0(0) 0(0) .67(1) 3.10(8) 0(0) 0(0) 1.56(2) 3.93(5) 0(0) 0(0) Totals .16(4) 2.24(8) 1.50(2) 0(0) .213(1) 2.2(8) 2.14(2) 0(0) .36(2) 1.96(6) 2.85(2) 0(0) S-30 0(0) 1.20(5) 0(0) 0(0) 0(0) 8.30(7) 0(0) 0(0) 0(0) 5.53(6) 0(0) 0(0) R ed S-44 .07(2) 5.33(6) 0(0) 0(0) 0(0) 2.33(6) 0(0) 0(0) 0(0) 4.73(4) 0(0) 0(0) Totals .03(2) 3.27(6) 0(0) 0(0) 0(0) 5.32(7) 0(0) 0(0) 0(0) 5.13(6) 0(0) 0(0)

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108 Figures 3.3A and 3.3B illustrate the relati onship between the WAP and the number of tadpoles captured per unit effort (Figure 3.A) and the number of tadpole species captured per year (Figure 3.3B). Table 3.8 provides desc riptive statistics for six tadpole variables separated by VHR. Included in the table and separated by VHR are number of tadpoles captured per unit effort in 2001 and 2002, numbe r of tadpole species captured per event in 2001 and 2002, the average tadpoles captured per unit effort over both years and the average number of species captured per wetland over both years. Figure 3.3 Spearman Rank Correlations Between Average WAP Scores and Tadpole Variable Figure 3.3A Spearman Rank Correlation between average WAP score and number of tadpoles captured per unit effort (Spearman R= 0.71, p <.05). Average Vegetation Score 1.41.61.82.02.22.42.62.83.0 Average Number of Tadpoles per Unit Effort -1 0 1 2 3 4 5 S-30 S-44 C S-94 S-10 Z S-87 S-97 S-96 S-95 S-68 S-106 Figure 3.3B Spearman Rank Correlation between average WAP score and number of tadpole species captured per year (Spearman R= .53, p <.10). Average Vegetation Score 1.41.61.82.02.22.42.62.83.0 Average Number of Tadpole Species per Year 0 2 4 6 S-30 S-44 C S-94 S-10 Z S-87 S-97 S-96 S-95 S-106 S-68

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109 Table 3.8 Tadpole Descriptive Statistics Categorized by Ve getative Health Rating. N Mean Confid. -95 % Confid. 95 % Min Max Range Variance Std. Dev. Std. Error Skewness Std.Err. Skewness Kurtosis Std.Err. Kurtosis TP #/effort 2001 2 0 --0 0 0 0 0 0 ----TP spp./event 2001 2 0 --0 0 0 0 0 0 ----TP #/effort 2002 2 0.029 -0.334 0.392 0 0.057 0.057 0.002 0.040 0.029 ----TP spp./event 2002 2 0.143 -1.672 1.958 0 0.286 0.286 0.041 0.202 0.143 ----Avg TP #/effort 2 0.014 -0.167 0.196 0 0.029 0.029 0.000 0.020 0.014 ----Avg TP spp./effort 2 0.071 -0.836 0.979 0 0.143 0.143 0.010 0.101 0.071 ----RED Avg spp./wetland 2 0.750 -8.780 10.280 0 1.500 1.500 1.125 1.061 0.750 ----TP #/effort 2001 5 0.323 -0.196 0.841 0.000 0.833 0.833 0.175 0.418 0.187 0.635 0.913 -3.079 2 TP spp./event 2001 5 0.767 -0.514 2.047 0.000 2.500 2.500 1.064 1.031 0.461 1.628 0.913 2.738 2 TP #/effort 2002 5 0.903 -0.114 1.921 0.173 2.084 1.912 0.672 0.819 0.366 0.721 0.913 -0.924 2 TP spp./event 2002 5 1.764 0.873 2.655 1.250 2.857 1.607 0.515 0.718 0.321 1.132 0.913 -0.358 2 Avg TP #/effort 5 0.613 -0.146 1.372 0.086 1.459 1.373 0.373 0.611 0.273 0.691 0.913 -1.735 2 Avg TP spp./effort 5 1.265 0.528 2.003 0.625 1.893 1.268 0.353 0.594 0.266 -0.349 0.913 -2.931 2 GREEN Avg spp./wetland 5 3.900 1.144 6.656 1.500 6.000 4.500 4.925 2.219 0.992 -0.494 0.913 -3.165 2 TP #/effort 2001 5 2.916 0.159 5.674 0.582 6.553 5.972 4.932 2.221 0.993 1.318 0.913 2.546 2 TP spp./event 2001 5 2.067 0.966 3.168 1.000 3.000 2.000 0.786 0.887 0.397 -0.205 0.913 -2.563 2 TP #/effort 2002 5 1.082 0.525 1.638 0.337 1.451 1.114 0.201 0.448 0.200 -1.565 0.913 2.288 2 TP spp./event 2002 5 2.543 1.756 3.330 1.429 3.000 1.571 0.402 0.634 0.284 -2.038 0.913 4.349 2 Avg TP #/effort 5 1.999 0.446 3.552 0.459 3.946 3.487 1.565 1.251 0.560 0.789 0.913 2.201 2 Avg TP spp./effort 5 2.305 1.472 3.138 1.381 3.000 1.619 0.450 0.671 0.300 -0.593 0.913 -1.504 2 BLUE Avg spp./wetland 5 6.000 5.240 6.760 5.000 6.500 1.500 0.375 0.612 0.274 -1.361 0.913 2.000 2

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110 Figure 3.4 Comparison of vegetatio n and anuran cluster analyses Figure 3.4A Cluster analysis using average quantitative variables, unweighted pair-group average linkage method and Euclidean distance measures. Figure 3.4B Cluster analysis created using nine anuran and anuran predator variables, Unweighted Pair Group Average Linkage Method, and a Euclidean distance dissimilarity matrix. Figure 3.4 provides a comparison of cluster an alyses using vegetation (3.4A) and anuran measures (3.4B) as indicators. Figure 3.4A has three clusters, while Figure 3.4B clearly illustrates two clusters. Figure 3.4A was created using the averages of all eighteen variables collected each season and year. The dendrogram provides ev idence that S-44 and S-30 form a separate cluster, and S-87, C, and S-10 form a separate cluster; how ever, the relationship between these two groups and the remaining wetlands is because of the potential rotation of each axis. All relationships are separate d by a maximum of one distance unit. Nine variables were used to create the cluste r analysis in Figure 3.4B. The figure was created using the average number of individuals per unit effort, the average taxa (species

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111 for tadpoles and non-fish vertebrate predators, and family for invertebrate predators and fish) per event over two years, and the number of tadpole species per year. This cluster analysis provides a comprehensive illustration of the data that were collected over the two-year study. Similar to other cluster analyses (not reported), the four wetlands with the fewest numbers and species of tadpoles fall into a group while the remaining wetlands fall into a separate group. Th is consistency is notable becau se tadpole variables comprise only three of nine variables used in analysis used to create Figure 3.4B. The NMDS plots in Figure 3.5 illustrates the relationships between the clusters observed in Figure 3.4. Figure 3.5A illustrates the distance between Wetla nds S-30, S-44, and all other wetlands. Wetlands C, S 10 and S-87 are intermediate, and all other wetlands (Z, S68, S-94, S-95, S-96, S-97, and S106) are separate to the left. Figure 3.5A was created using the distance matrix derived from Figure 3.4A. Four-hundred forty iterations were performed. The final configuration stre ss value was .028, aliena tion value was .048, DStar: Raw stress was .333 and D-Hat: Raw stress was .112. In Figure 3.5B, it is evident that four we tlands (S-10, S-30, S-44, a nd S-94) are separate from the remaining wetlands. Examination of the tadpole summary tables (Tables 3.6 and 3.7) show that the separation of these four wetlands reflects their lack of tadpole numbers and diversity. Figure 3.5B was creat ed using the distance matrix derived from Figure 3.4B. Three hundred twenty-nine iterations were performed The final configuration stress valu e was .0199, alienation value was .0321, D-Hat Row Stress .0576, and D-Star Row Stress .1476.

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112 Figure 3.5 NMDS Comparison of Vegetative and Anuran Indicators Figure 3.5A NMDS plot of average quantitative variables for spring and fall 2000, 2001 and 2002. Figure 3.5B NMDS plot created using nine anuran and anuran predator variables and a Euclidean distance dissimilarity matrix. S-10 C S-94 Z S-87 S-106 S-97 S-96 S-95 S-68 S-30 S-44 Figures 3.6 and 3.7 illustrate the strong corre lations between length of inundation and number and species of tadpoles captured. We tlands with less than 90 days of inundation had fewer species and individuals captured the wetlands inundated longer than 90 days. Conversely, the Julian date of inundation was negatively correlated with both the number of species and number of individuals captured (Figures 3.8 and 3.9). Wetlands that were not inundated before day 235 (August 23) had fewer individuals and species.

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113 Figure 3.6 Spearman Rank Correlation betw een average length of inundation in 2001 and 2002 and number of tadpoles captu red per unit effort in 2001 and 2002 (Spearman R=.73, p <.01). Blue, Gr een and Red categories refer to Vegetative Health Rating described in the Analysis of Vegetation Section. Average Length of Inundation (Days) 20406080100120140160180200 Average Number of Tapoles per Unit Effort 0 1 2 3 4 5 S-30 S-44 C S-94 S-10 Z S-87 S-97 S-96 S-95 S-68 S-106 Figure 3.7 Spearman Rank Correlation between average length of inundation in 2001 and 2002 and number of tadpole specie s captured each year in 2001 and 2002 (Spearman R=.70, p <.05). Blue, Gr een and Red categories refer to Vegetative Health Rating described in the Analysis of Vegetation Section. Average Length of Inundation (Days) 20406080100120140160180200 Average Number of Tadpole Species per Year 0 2 4 6 8 10 S-30 S-44 C S-94 S-10 Z S-87 S-97 S-96S-95 S-68 S-106

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114 Figure 3.8 Spearman Rank Correlation betw een average Julian Date of inundation in 2001 and 2002 and number of tadpoles captured per unit effort each year in 2001 and 2002 (Spearman R=-.81, p <.01). Blue, Green and Red categories refer to Vegetative Health Rating described in the Analysis of Vegetation Section. Average Julian Date of Inundation 180200220240260280300 Number of Tadpoles per Unit Effort -1 0 1 2 3 4 5 S-30 S-44CS-94 S-10 Z S-87 S-97 S-96 S-95 S-68 S-106 Figure 3.9 Spearman Rank Correlation betw een average Julian Date of inundation in 2001 and 2002 and number of tadpole species captured each year in 2001 and 2002 (Spearman R= -.78, p <.01). Blue, Green and Red categories refer to Vegetative Health Rating described in the Analysis of Vegetation. Average Julian Date of Inundation 180200220240260280300 Average Number of Ta dpole Species per Year 0 2 4 6 S-30 S-44 C S-94 S-10 Z S-87 S-97 S-96S-95 S-68 S-106

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115 Figure 3.10 Spearman Rank Correlation betw een average number of species heard calling and the average number of tadpole species captured in 2001 and 2002 (Spearman R=.87, p <.001). Blue, Gr een and Red categories refer to Vegetative Health Rating described in the Analysis of Vegetation Section. Average Number of Species Documented Calling per Year 0246810 Average Number of Tadpole Species Captured per Year 0 2 4 6 8 S-30 S-44 C S-94 S-10 Z S-87 S-97 S-96 S-95 S-68 S-106 Figure 3.10 illustrates the correlation between the number of species heard calling and the number of tadpole species captured per year. S imilar to other analyses presented in this study, these data are separated into a gr oup of four wetlands with low numbers and a group of eight wetlands with high numbers. Based on these data, I present Figure 3.11. Figure 3.11 illustrates two ne w wetland success categories based on anuran variables (Figures 3.11B and 3.11D) and two new wetla nd success categories based on vegetative variables (Figure 3.11A and 3.11C). Table 3.9 is a comparison of the alternate success categories with the original VHR presented in the Analysis of Vegetation Section and Table 3.1.

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116 Both Red wetlands were judged to be unsu ccessful in terms of reproductive success and scored distinctly lower by vegetative measure. Similarly, four of the five Blue wetlands were judged to be successful anuran reprodu ction wetlands and all fi ve scored distinctly higher than other wetlands vegetatively. The few differences in the new classification of wetlands lie within the intermediate category.

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117 Figure 3.11 Box and Whisker Comparison of Vegetation and Anuran Indicators Figure 3.11A Box and Whisker Plot of Average Quantitative Variables for two-category reclassification. Figure 3.11C Box and Whisker Plot of Average Quantitative Variables for threecategory reclassification. Figure 3.11B Box and Whisker plot using average number of tadpoles species captured per unit effort for two-category reclassification. Figure 3.11D Box and Whisker plot using average number of tadpoles species captured per unit effort for three-category reclassification. Table 3.9 Comparison of Alternate Wetland Health Ratings ID Original VHR Alternate ThreeCategory Vegetative Alternate ThreeCategory Anuran Alternate TwoCategory Vegetative Alternate TwoCategory Anuran S-30 Red Red Red Red Red S-44 Red Red Red Red Red S-10 Green Green Red Red Red S-87 Green Green Green Red Blue S-94 Green Blue Red Blue Red C Green Green Green Blue Blue Z Green Blue Green Blue Blue S-68 Blue Blue Green Blue Blue S-95 Blue Blue Blue Blue Blue S-96 Blue Blue Blue Blue Blue S-97 Blue Blue Blue Blue Blue S-106 Blue Blue Blue Blue Blue

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118 Discussion Amphibians have characteristics of a good indicator taxon. They are sensitive to perturbations in aquatic and terrestrial environments becau se of their biphasic life-cycle, physiological adaptations, and specifi c microhabitat (Vitt et al. 1990, Wake 1990, Blaustien 1994, Welsh and Ollivier 1998). Despite these characteristics, it is difficult to separate natural population vari ability from true population decline. For a variety of reasons, especially extreme variability in population sizes (e.g., Berven 1990, Dodd 1992), anuran studies need to be planned as long-term efforts at multiple locations. Using anurans as a surrogate for wetland health may be misleading without some knowledge about nearby wetlands (EPA 2002). Similarly, plants are useful as biological indicators because of their established sa mpling protocols and taxonomy, immobility, ubiquitous presence and sensitivity to disturbance. There is an inherent lag time in the response of established plan ts to anthropogenic change. This study provides data on 12 wetlands during two years. NMDS, cluster analysis, and descriptive statistics provide evidence that there are two groups of wetlands based on amphibian reproductive success. The first group includes all Red wetlands (S-30 and S44) and two Green wetlands (S-10 and S-94) This group had no calling activity in 2001 and little activity in 2002. No tadpoles were ca ptured in any of the four wetlands in 2001 and few were captured in 2002. The average capture rate for each wetland within this group over two years was less than 0.10 individua l per unit effort and less than 2 species per year. The second group consists of th ree Green wetlands (S-87, C, and Z) and all Blue wetlands (S-68, S-95, S-96, S-97, and S-106). This group had relatively large

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119 breeding choruses, a capture rate of 0.39 or more individual per unit effort (6 of 8 wetlands had a capture rate of over 1.03) and more than 4 species per year. These groupings were confirmed using box and wh isker plots for average number of tadpole species captured per year. Various altern ate groupings were tested, however, all diminished the significance of the separati on. The clear separation of wetlands based solely on larval amphibian capture rates pr ovides evidence for amphibian reproduction as an indicator of wetland health on the SWF. Although this two-category classification scheme is statistically valid, it may be beneficial to add an intermediate class to assist land managers. This study did not provide evid ence that a statistically valid intermediate category exists using Anuran measures. Usin g only anurans, there was some distinction between three categories with minimal overlap, and with more data the three categories may become valid. The group of four wetlands with relatively low reproductive success both years (S-10, S30, S-44, and S-94) remains a distinct group whether using a two-category or threecategory reclassification. These four wetlands had relatively low individual capture rates and species richness for tadpoles, invertebrates, fish and other vertebrate predators. Using vegetation and a three-category classi fication system, only two wetlands warrant a Red rating. A group of four wetlands (S95, S-96, S-97, and S-106) were di stinct and distant from the low success group in the cluster and NMDS analyses. These three wetlands had relatively high individual tadpole capture rate s, tadpole species richness, invertebrate

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120 predator capture rates, and relatively low indi vidual fish capture rate s and fish richness in both years. All of these wetlands were also categorized as Blue in a three-category vegetative scheme. Also, wetla nds S-68 and Z were classified as Blue in the threecategory vegetative measure, but Green when using the anuran measure. There is evidence to explain these mis classification. Wetland S-68 ha d relatively high capture rates during the first sampling event each year, but the number dropped sharply during subsequent events resulting in overall intermediate numbers each year. Wetland S-68 was the closest wetland to the Cross Cypress br anch of the Anclote River and flooding of the river routinely contributed to the water level in the wetland. Fish capture rates and richness in wetland S-68 increased sharply afte r the first event of both years and included regular captures of known voracious tadpole predat ors in the family Centrarchidae. River overflow may not affect vegetation compositi on, but could have de trimental effects on the anurans by supplying a constant source of fish predators. Wetland S-94 was classified originally as Green, and in the alternate three-category schemes Red and Blue when using anurans and vegetation, respectively. This wetland provides an excellent example of the sensitivity of anurans as indicators and the lag time inherent in using vegetation as indicators. The anurans responded immediately to the hydroperiod as illustrated by the lack of ca lling activity (Figure 3.10) and the correlations between tadpole numbers and inundation (Figur es 3.6 – 3.9). The vegetation has not yet responded, yielding high WAP scores.

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121 Wetland S-87 also had intermed iate capture rate and tadpol e richness, however, no fish were captured either year. The same method of sampling We tlands S-68 and S-87 produced an average of 1.31 individual fish per unit effort in we tland S-68 and none in S87. Water depth over 1 meter and fallen trees made samplin g Wetland S-87 difficult and could have reduced the capture rate. A few Amphiuma means and Siren lacertina were captured in wetland S-87 and nowhere else on the site. The presence of these predators could have also reduced the tadpole capture rate. There is obviously soil subsidence at the edges of Wetland S-87 increasing tree stress and treefall, but the hydroperiod remains adequate for frog reproduction. Strong correlative evidence suggests the hydr operiod of wetlands contributed to capture rates and species richness within wetlands on the SWF. This evidence was most apparent in numbers of the high and low success gr oups. Figures 3.6 and 3.7 show wetlands with an average inundation length of less than 90 da ys had a tadpole capture rate of less than 0.10 individuals per unit effort; Wetlands with an average inundation length of less than 80 days had an average of less than 2 species per year captured. Similarly, Figures 3.8 and 3.9 illustrate that in wetlands not inunda ted before day 235 (August 23), individual capture rates were less than 0.10 and an aver age of less than less than 2 species were captured over the study. The average hydroperiod of the high success wetlands was relatively long (>120 days) and began early in the year (before day 207). The timing of the inundation in relation to reproduction is especially notable because the published breeding season for most of the frogs in ce ntral Florida extends to October and the maximum larval period for 14 of the 17 specie s is 90 days or less (Table 3.3). One

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122 explanation for the correlation between anuran success and length of inundation is that a longer period of inundation allows for a suite of asynchronous individuals and species to call, breed, and go through me tamorphosis. That is, several large breeding events over several weeks could all pr oduce new metamorphs. A shorter inundation period would allow fewer successful breeding events th roughout the season because some tadpoles may not have time to comp lete metamorphosis. No significant correlation existed between wetland size and tadpole capture rate or species richness. A significant correlation did exist between quantitative vegetation score and tadpole capture rate and richness (Figur es 3.3A and 3.3B). Because changes in hydroperiod are known to affect vegetati on composition and zonation, however, we suggest that vegetation is not regulating a nuran success, but both vegetation and anurans are responding to the hydroperiod. Human activities are known to be detrimental to the natural biota (Duellman and Trueb 1986). The two most dramatic ecological tre nds of the past century are anthropogenic changes in biotic diversity a nd alterations to the structure and function of natural systems (Vitousek 1997). To preserve the functions of the natural systems on the SWF, and extensive monitoring protocol is followed. Monitoring efforts on the J. B. Starkey Wellfield produce valuable data that may be used to predict the occurrence and reproductive success of the natural amphibian community.

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123 Maintenance and protection of biological diversity are best accomplished when ecologists and natural resource managers coordinate their efforts (S emlitsch 2000). The monitoring data should be evaluated in a timely manner to allow land managers to make necessary management strategy adjustments. The primary challenge with an indicator species is to separa te natural population fluctuations from fluctuations occurring be cause of anthropogenic change (Welsh and Ollivier 1998, Penchman and Wil bur 1994). In this study, I see changes in the categorization of wetlands based upon variation in tadpol e abundance and richness over two years. If results from only one year were examined as opposed to both years individually and the average of both, diff erent conclusions could be reached. Intermediate anuran reproduc tive success in wetlands C, Z, and S-87 was, in part, because of differential success over two years. Whether the differential success is part of a natural fluctuation or anthropogenic change is unknown. Further examination of the length and dates of historic inundation could offer insight into this question. Categorization of wetlands into two groups a nd elimination of the intermediate category would provide a statistically strong result. Such a limited categorization strategy, however, could usher in unforeseen problems. If management decisions are made on the basis of the two-category system, then the wetlands are deemed either successful or unsuccessful at providing amphibian -breeding habitat. The danger in this strategy is that rehabilitation of an unsuccessful wetland may be perceived to be much more difficult and/or costly than rehabilitation of an interm ediate wetland, when in reality, such is not

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124 the case. A limited categorization strategy would likely result in neglect and further deterioration of unsuccessful wetlands. Fu rthermore, because amphibian populations exist as metapopulations, a change in the succ ess of a small number of seemingly isolated ponds for a short time period could have fa r-reaching detrimental effects on the amphibians across the landscape. Changes in management strategies should account for natural variability and focus on prevention of long-term reductions in hydroperiod. Categorization of wetlands into three groups and allowing pot entially natural fluctuations in reproductive success to be deemed over all intermediate succ ess could lead to erroneous conclusions. Actions taken as a result of such erroneous conclusions to correct perceived problems in reproductive success could be costly, unnecessary, or even detrimental to the long-term success of amphibi an assemblage. Thus, it may be useful to measure vegetation and reproductive success of anurans. Monitoring these two measures separately allows the sensitivity of the anurans to alert managers of a problem, but the vegetation measures provide the long-term rela tively consistent measure to prevent snap decisions made because of sensitivity of anurans. Determining the value of seas onal wetlands to anuran and ot her vertebrate populations is the focus of much contemporary research (Gibbs 1993 and 2000, Johnston 1994). For example, Gibbs (1993) reported that sma ll wetlands play a greater role in the metapopulation dynamics of certa in taxa of wetland animals than the modest area covered by such small wetlands might imply. In South Carolina, wetlands that retain water for about eight to ten months each year tended to have more anuran species than

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125 wetlands with longer or shorter hydroper iods (Snodgrass et al. 2000). In general, wetlands that retain water for long periods of time tend to support fish, which, in turn, tend to reduce the number of amphibian species breeding in them (Moler 1992). Factors other than wetland size and length of inunda tion may influence amphibian populations as well. In Montana, amphibian species rich ness declined as wetland isolation and road density increased, regardless of the spatial scale used for the evaluation (Lehtinen et al. 1999). Habitat fragmentation, road density, habitat quality, or other landscape-scale urbanization affects are not relevant to the SWF at this time. The habitat is well managed and generally characterized by native land co ver. Wetland size, depth, water temperature and pH had no significant re lationship with anuran repr oductive success. The positive correlation between healthy, natural wetland vegetation was significant, but we do not believe it is causative. It is more likely th at the vegetation and anurans are responding to the same variables in which case length and timing of inundation could be used to predict the vegetative and anuran success on the SWF. Source of inundation could also be important in determining amphibian success be cause of different pr edator contributions or water chemistry from the contributing water source. We conclude that amphibians are excellent indicators of wetland health on the SWF. Using amphibian success to supplement the ongoing vegetative and hydroperiod monitoring would provide a time-sensitive measure to complement the more stable current measures. Using both measures separa tely would provide the greatest protection from discounting important anthropogenic change s and provide a basis for understanding natural population fluctua tions in anurans.

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126 Conclusions & Recommendations The rapid, qualitative measure of wetland health used by long-term land managers accurately predicts the highest and lowest quantitative scores produced by a more intensive quantitative vegetation measure. Using standard sampling techniques pres ented in Heyer et al. (1994), anuran variables, such as individuals captured per unit effort and species captured per year, can be used to separate wetland breeding habitats into two success categories. Long-term studies may elucidat e more categories not apparent in this study. Information on the wetland (size, hydrop eriod, water chemistry, etc) and the community (vegetation, predators, etc.) should be collected to provide potential causative factors. Multivariable analysis should be used if categorization of wetlands is the goal. Distilling the data into a single value such as a diversity index masks potentially important information. A natural (non-augmented) hydroperiod of greater than 120 days is recommended as a minimum value. Other studies have shown that a hydroperiod of 240 to 300 days provides a more diverse amphibian community (Snodgrass et al., 2000a, Snodgrass et al., 2000b, Paton and Crouch III 2002). A natural (non-augmented) hydroperiod be ginning before mid-July is ideal and before mid-August is essential for anuran reproductive success.

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127 If a natural (non-augmented) hydroperiod is not possible in the foreseeable future, then augmentation should be explored in the form of a controlled experiment. The augmented wetland(s) should mirror similar nearby wetlands with similar surrounding habitat. Success in both augmented and non-augm ented wetlands should be documented with methods similar to this study. B ecause of the differences between surface water and groundwater chemis try, detailed water qualit y parameters should be collected on both control a nd experimental wetlands. Anuran call census should be performed on a subset of wetlands on all wellfields within the district. Periodic active tadpole sampling can be us ed to spot-check the correlation between calling activity and reproductive success. Wetlands exhibiting low reproductive succe ss should be sampled and examined more closely to evaluate the reproductive success and determine if anthropogenic change or natural community interactions is causative. A long-term study focusing on anurans could be used to examine the natural variability in the moderate-success category.

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128 Literature Cited Adamus, P.R. 1996. Bioindicators for Assessing Ecological Integrity of Prairie Wetlands, U.S. Environmental Protection Agency National Health and Environmental Effects Research Laboratory, West ern Ecology Division, Corvallis, OR. Alford, R. A. and S. J. Richards. 1999. Gl obal amphibian declines: a problem in applied ecology. Annual Review of Ecology and Systematics 30: 133-165. Altig R., R.W. McDairmid, K.A. Nichils, a nd P.C. Utach. 1999. Tadpoles of the United States and Canada: A tutorial and Key. U.S. Geological Survey. http://www.pwrc.usgs.gov Baringa, M. 1990. Where have all the froggies gone? Science 247:1033-1034. Barnwell, M.E., P. M. Elliott, D. L. Fr eeman, and C. A. Gates. 1998. Resource Monitoring Program Report: Natural Systems. Southwest Florida Water Management District, 201 pp. Berven, K. A. 1990. Factors affecting populatio n fluctuations in larval and adult stages of the wood frog ( Rana sylvatica ). Ecology 71: 1599-1608. Berven, K. A., and T. A. Grudzein. 1990. Dispersal in the wood frog (Rana sylvatica): implications in genetic population structure. Evolution 44: 2054-2056. Billings, W.D. 1952. The environmental co mplex in relation to plant growth and distribution. Quarterly Review of Biology 27:251-265. Blaustein, A. R. and D. B. Wake. 1990. Declining amphibian populations: A global phenomenon? Trends in Ecology and Evolution 5: 203-204. Blaustein, A. R., D. B. Wake, and W. P. Sousa. 1994. Amphibian declines: judging stability, persistence, and susceptibility of populations to local and global extinctions. Conservation Biology 8: 60-71. Brinson, M.M. 1993. Changes in the func tioning of wetlands along environmental gradients. Wetlands 13:65-74. Brown, S. L. 1984. The role of wetlands in the Green Swamp. Pp. 405-415 in K.C. Ewel and H.T. Odum (eds.), Cypress Sw amps. University of Florida Press, Gainsville, Florida. 472 pp.

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129 Campbell, H.W. and S. P. Christman. 1982. Field techniques for herpetofaunal community analysis. Pp. 193-200. In N.J. Scott, Jr. (ed.), Herpetological Communities U.S. Department of the Inte rior, Fish and Wildlife Service, Wildlife Research Report 13. Chapin III, F.S., B.H. Walker, R.J. Hobbs, D.U. Hooper, J.H. Lawton, O.E. Sala, D. Tilman. 1997. Biotic control over the functioning of ecosystems. Science 277:500-504. Cherry, R.N., J.W. Stewart and J.A. Mann. 1970. General hydrology of the middle gulf area, Florida. U.S. Geological Survey Water Resources Inves tigations Report No. 87-4188. Chippindale, P.T., A.H. Price, and D.M. H illis. 1998. Systematic status of the San Marcos salamander, Eurycea nana (Caudata Plethodontidae): Arlington, Texas, Department of Biology Universi ty of Texas, Copeia 4:1046-1049. Delis, P. R., H. R. Mushinsky, and E. D. McCoy. 1996. Decline of some west-central Florida anuran populations in re sponse to habitat degradation. Biodiversity and Conservation 5:1579-1595. Dodd, C. K. 1992. Biological diversity of a temporary pond herpetofauna in north Florida sandhills. Biodiversity and Conservation 1: 125-142. Dodd, C. K. 1997. Imperiled Amphibi ans: A historical perspective In Aquatic Fauna in Peril: The Southeastern Perspective. G.W. Benz and D.E. Collins (eds.). Lenz Design & Communications Inc. Dodd, C. K., and B. S. Cade. 1998. Move ment patterns and the conservation of amphibians in small, temporary wetlands. Conservation Biology 12: 331-339. Doherty, S.J., M. Cohen, C. Lane, and J. Surdick. 2000. Biological criteria for inland freshwater wetlands in Florida: A review of scientific and technical literature (1990-1999). Report to United States Environmental Protection Agency, Biological Assessment of Wetlands Workgroup by University of Florida Center for Wetlands, Gainesville. Duellman, W. E., and L. Trueb. 1986. Biology of Amphibians McGraw-Hill Book Co., New York. Edwards, L.D., and S.R. Denton. 1993. Cr oss Bar Ranch Wellfield Ecological Monitoring Report. Biological Research Associates, Inc., Tampa, FL. Ewel, C.E. 1990. Swamps. Pp. 281-323 In : Myers, R.L. and J.L. Ewel (eds.). Ecosystems of Florida. University of Central Florida Press, Orlando, 765 pp.

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130 Florida Department of Environmental Protection. 2001 Draft Habitat Assessment Standard Operating Procedur e. FDEP SOP: FS 7000 Gibbs, J. P. 1993. Importance of small wetla nds for the persistence of local populations of wetland-associated animals. Wetlands 13: 25-31. Gibbs, J. P. 2000. Monitoring populations. Pp. 213-252 in L. Boitani and T. K. Fuller (eds.), Research techniques in animal eco logy. Columbia University, New York, 442 pp. Gill, D. E. 1978. The metapopulation ecology of the red-spotted newt, Notophthalamus viridescens (Rafinesque). Ecological Monographs 48: 145-166. Godley, J.S., R.W. McDairmid, and G. T. Bancroft. 1981. Large scale operations management test of use of the white amur for control of problem aquatic plants, report 1: Baseline studies, Volume V. The Herpetofauna of Lake Conway, Florida Technical report A-78-2, U.S. Army Engineer Waterways Experiment Station, CE, Vicksburg, Mississippi. Goslee, S.C., R.P. Brooks and C.A. Cole. 1997. Plants as indicators of wetland water source. Plant Ecology 131:199-206. Hancock, M.C., R.J. Basso, and D.L. Ellison. 1999. Establishment of Recovery Levles in the Northern Tampa Bay area. Hy drologic Evaluation Section, Resource Conservation and Development Depa rtment. December 1999. SWFWMD. Harris, L. D. and C. R. Vickers. 1984. Some faunal community characteristics of cypress ponds and the changes induced by perturbations. Pp. 171-185 In : K. C. Ewel and H. T. Odum (eds.). Cypress Swamps. University Presses of Florida, Gainesville, 472 pages. Hecnar, S. J., and R. T. M’Closkey. 1996. Regional dynamics and the status of amphibians. Ecology 77: 2091-2097. Herbeck, L. A. and D. R. Larsen. 1999. Plet hodontid salamander res ponse to silviculture practices in the Missouri Ozark Forests. Conservation Biology 13: 623-632. Heyer, W.R., M.A. Donnelly, R.W. McDairmid, L.A.C. Hayek, and M.S. Foster, eds. 1994. Measuring and monitoring biological diversity. Standard methods for amphibians Biological Diversity Handbook Series. Smithsonian Institution Press, Washington, D.C.

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131 Hutchinson, C.B. 1984. Hydrogeology of well field areas near Tampa, Florida. Phase 2development and documentati on of a quasi-three dimensional finite difference model for simulation of steady state groundw ater flow. U.S. Geological Survey Open File Report No. 81-630. Johnson, C.M., L.B. Johnson, C. Richards, and V. Beasely. 2002. Predicting the occurrence of amphibians: An assessm ent of multiple–scale models. Pp. 157-170 In : Scott, J.M., P.J. Heglund, M.L. Morr ison, J.B. Haufler, M.G. Raphael, W.A. Wall and F.B. Samson (eds.). Predicting species occurrences – issues of accuracy and scale. Island Press, Washington, 868 pages. Johnston, C. A. 1994. Cumulative effects to wetlands. Wetlands 14: 49-55. Karr, J.R. 1991. Biological Integrity: A Lo ng-Neglected Aspect of Water Resource Management. Ecological Applications 1(1): 66-84. Karr, J. R. and E. W. Chu. 1999. Restoring life in running waters: Better biological monitoring. Island Press, Washington, D.C. Lehtinen, R. M., S. M. Galatowitsch, and J. R, Tester. 1999. Cons equences of habitat loss and fragmentation for wetland amphibian species. Wetlands 19: 1-12. Levins, R. 1969. Some demographic and genetic consequences of environmental heterogeneity for biological control. Bulletin of the Entomological Society of America 15:237-240. Ludwig, J.A., and J.F. Reynolds. 1988. Statistical Ecology: A Primer on Methods and Computing John Wiley and Sons, New York Magee, T. K., T. L. Ernst, M. E. Kentul a and K. A. Dwire. 1999. Floristic comparison of freshwater wetlands in an urbanizing environment. Wetlands 19(3): 517-534. Marois, K.C., and K.C. Ewel. 1983. Natural and management-related variation in cypress domes. Forestry Science 29:627-640. McCune, B., and J. B. Grace. 2002. Analysis of ecological communities. MjM Software Design. Gleneden Beach, OR. McDairmid, R.W., and R. Altig. 2000. Tadpoles: The biology of anuran larvae The University of Chicago Press, Chicago Mitsch, W. J. 1984. Seasonal patterns of cypress dome in Florida. Pp. 25-33 In : K. C. Ewel and H. T. Odum (eds.). Cypress Sw amps. University Presses of Florida, Gainesville, 472 pages.

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ABSTRACT: Understanding wetland responses to human perturbations is essential to the effective management of Florida's surface and ground water resources. Southwest Florida Water Management District (SWFWMD) Rules (Chapter 40D-2.301(c) FAC) prohibit adverse environmental effects to wetlands, fish and wildlife caused by groundwater withdrawal. Numerous studies have documented the responses of biological attributes across taxa and regions to human disturbance. Biological assessment can provide information about ecological condition. Based on long-term monitoring conducted by the SWFWMD, the anthropogenic changes observed on the Starkey Wellfield are attributed to groundwater withdrawal. Biological indicators are species, species assemblages, or communities whose presence, abundance, and condition are indicative of a particular set of environmental conditions. Monitoring early indicators of ecosystem stress may shorten response time by shifting attention to the relatively quick response of sensitive species. Species used to assess biological condition should be abundant and tractable elements of the system that provide an early, diagnosis. Regulatory requirements within 40D-2 F.A.C. dictate an extensive analysis be conducted twice yearly on wetlands within all wellfields. This quantitative analysis provides information on the wetland plant community through the collection of eighteen categorized vegetative and physical variables. Because of the size of the area in which monitoring is required and the large number of wetlands, a rapid qualitative monitoring method was developed using vegetation and physical variables to classify wetlands into one of three categories based on their perceived health. Wetland plants have many characteristics suited to assessments of biological condition including their diversity, taxonomy, distribution, relative immobility, well developed sampling protocols, and, for herbaceous species, their moderate sensitivity to disturbance (U.S. EPA 2002, Doherty et al. 2000). Because amphibians occupy both aquatic and terrestrial habitats in their life history, have physiological adaptations and specific microhabitat requirements, they are considered to be extremely sensitive to environmental perturbations and excellent barometers of the health of the aquatic and terrestrial habitats in which they reside (Vitt et al. 1990, Wake 1998, Blaustein 1994, Blaustein et al. 1994). The purpose of my study was to 1) compare a qualitative method of wetland vegetation monitoring to a quantitative method, 2) document the reproductive success of anurans, and 3) compare anuran reproductive success to the vegetation monitoring results on the J. B. Starkey Wellfield (SWF). The results are published in chapters, with each chapter addressing one of the topics stated above. The results show a rapid, qualitative measure of wetland health is useful for the determination of severely affected wetlands. The anuran reproductive success reflected similar results. The results show that wetlands can be categorized based solely on amphibian reproductive success variables. The anuran categorization, qualitative vegetative categorization, and quantitative vegetative categorization overlap on the high and low success wetlands. The low degree of overlap observed in the intermediate category could be attributed to fish predation in a wetland otherwise suited for amphibian reproduction, natural variability in the two years of anuran data collected or lag time inherent in vegetative monitoring. Strong correlative evidence suggests hydroperiod regulates anuran reproductive success on the J. B. Starkey Wellfield. The average length of inundation was correlated with the number of tadpoles captured per unit effort and the number of tadpole species captured per year (R=0.73, p< .01; R=0.70, p< .05). The average Julian date of inundation was negatively correlated with the same two tadpole variables (R=0.81, p< .01, R=0.78, p< .01). The Julian date of inundation at which breeding attempts stopped and no tadpoles were observed was weeks within the published breeding season for many species. I detected a correlation between the number of species calling in each wetland and the number of tadpole species captured per year (R=0.87, p< .001) suggesting call censuses may be used at this site to estimate anuran reproductive success if enough well-timed observations are made. These findings will allow resource managers and regulators to evaluate and possibly refine land management practices, including existing monitoring methods, and water policy to meet the needs of resident amphibians at the J.B. Starkey Wellfield.
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