AQUATIC MACROINVERTEBRATE DIVERSITY AND
WATER QUALITY OF URBAN LAKES
by
CRAIG F. WOLF, B.A.
A THESIS
IN
BIOLOGY
Submitted to the Graduate Faculty of Texas Tech University in Partial Fulfillment of the Requirements for the Degree of MASTER OF SCIENCE
May, 1996 ACKNOWLEDGMENTS
I am very thankful for the support and inspiration that so many people have provided me over the years. Foremost, I would like to thank Dr. Daryl Moorhead for his sincere friendship and enduring patience that has guided me throughout the years, and for the many opportunities he has provided me to broaden my horizons. Secondly, I wish to thank Drs. Michael Willig, Tony MoUhagen, and John Zak for serving on my committee and for their invaluable services that have guided me throughout my studies. I also would like to thank Dr. John Bums for his everlasting friendship and belief in me.
There were many people that assisted me throughout my project. First, I would like to thank Max Westerfield and Shane Davis, because without their joint interest in urban lakes, there would have been many long nights in the lab. Furthermore, I would like to thank Brad Thomhill and the staff of the Environmental Sciences Laboratory, Texas Tech University, for the analysis of the nutrient data. I thank Dr. Robert Sites (University of Missouri) for the identification and confirmation of invertebrate species, Dianne Hall, for her assistance in collecting invertebrates, and Michele Secrest, for her friendship and moral support. I especially thank the Moorhead family for their continuous support and friendship over the years. Finally, I am very grateful for my family. My mother, father, and sister have provided the endless love and encouragement I needed to achieve my goals. Their perseverance over the past few years has been a major part of my inspiration. Financial support for this research was funded by the Office of Research Services and Ecology Program, Department of Biological Sciences, Texas Tech University.
u TABLE OF CONTENTS
ACKNOWLEDGMENTS ii ABSTRACT v
LIST OF TABLES vii
LIST OF FIGURES viii CHAPTER
I. INTRODUCTION 1 Surface Water Quality 1 Playas of the Southern High Plains 2 Objectives 3 Literature Cited 4 II. EVALUATING WATER QUALITY CHARACTERISTICS IN URBAN LAKES 5 Introduction 5 Urban Lakes of the Southern High Plains 5 Surface Water Quality 6 Objectives 7 Materials and Methods 8 Study Site 8 Water Quality Sampling 9 Water Quality Analyses 10 Data Analyses 12 Results 14 Analysis of Variance 14 Principal Components Analysis 15 Pearson-Product Moment Correlation 16 Discussion 17 Literature Cited 21 III. AQUATIC MACROINVERTEBRATE DIVERSITY AND COMMUNITY COMPOSITION IN URBAN LAKES 37 Introduction 37 Macroinvertebrate Species Diversity 37 Objectives 39 Materials and Methods 39 Study Site 39 Macroinvertebrate Sampling 39 iii Macroinvertebrate Community Analyses 40
Results 44 Richness and Abundances for Individual Lakes 44 Diversity for Individual Lakes 45 Richness and Abundances for Groups of Lakes 46 Community Similarity 46 Principal Components Analysis 48 Discussion 49 Literature Cited 53 IV. RELATIONSHIPS BETWEEN AQUATIC MACROINVERTEBRATE DIVERSITY AND WATER QUALITY CHARACTERISTICS . OF URBAN LAKES 73 Introduction 73 Species - Environment Relationships 73 Objectives 74 Materials and Methods 74 Results 76 Discussion 77 Literature Cited 82 APPENDICES A. WATER QUALITY DATA 89 B. AQUATIC MACROINVERTEBRATE DATA 99
IV ABSTRACT
Macroinvertebrate species diversity and community composition are important themes in aquatic ecology, and are often used to evaluate environmental stress resulting from a variety of anthropogenic disturbances. On the Southern High Plains of Texas, urban areas have incorporated lakes into their stormwater and surface-water management systems. Eight urban lakes were selected to include a range of physical and biological features (i.e., lake size, relative potential for nonpoint source pollution, and presence of aquatic vegetation) representative of urban lakes in Lubbock, Texas. These lakes were categorized into three a priori groups based on the above characteristics. I evaluated 16 physicochemical attributes on a monthly basis, from February 1993 to April 1994, and found that 12 of the attributes contributed significantly to differences among groups of lakes during the study.
Macroinvertebrate community composition was sampled on six different dates in each lake to capture seasonal patterns in species diversity. No significant differences in species diversity (Fisher's log series a) existed among groups of lakes in the summer or for the combined seasons data, although significant differences did occur in the spring and fall. Groups of lakes that were significantly different represent the extremes in habitat complexity and invertebrate community composition. Community composition of group 1 and 3 lakes were dominated by three to four families of invertebrates, whereas in group 2 lakes, over 60% of species abundance was attributed to one species.
Mantel's nonparametric test found a significant association between matrices based on water quality similarities and macroinvertebrate similarities during the fall sampling period. Furthermore, stepwise multiple regression using invertebrate species abundances as the dependent variable and water quality characteristics as the independent variables found significant relationships between corixid, notonectid, chironomid, and cladoceran abundances and salinity, total phosphorus, dissolved oxygen, total organic carbon, and ammonia. These attributes accounted for 33-76% of the variation observed in the abundances of these species. Of the water quality characteristics found to be significant predictors of species abundances, salinity, ammonia, and total organic carbon were correlated significantly to areal extent of multiple family housing and commercial land-use surrounding each lake. These results suggest that land-use may indirectly influence macroinvertebrate community composition of urban lakes.
VI LIST OF TABLES
2.1 Characteristics and locations of selected urban lakes in Lubbock, Texas 23 2.2 List of water quality characteristics measured for each selected lake in Lubbock, Texas 24 2.3 Results for two-way Analysis of Variance comparing groups of lakes with respect to physicochemical characteristics 25 2.4 Mean water quality characteristics from urban stormwater runoff and selected urban lakes in Lubbock, Texas 27 3.1 Systematic list of aquatic invertebrate taxa collected from selected urban lakes in Lubbock, Texas 56 4.1 List of dependent and independent variables used in Stepwise Multiple Regression 85 4.2 Results of Mantel Matrix Randomization test to determine whether there are significant positive relationships between water quality and macroinvertebrate similarity matrices 86 4.3 Results of Stepwise Multiple Regression evaluating the relationships between invertebrate abundances and physicochemical characteristics 87 A. 1 Analytical methods outlined by EPA and standard methods with detection-limits 90 A.2 Results of physicochemical analyses performed on selected urban lakes in Lubbock, Texas 91 A.3 Results of nitrogen, phosphorus, and carbon analyses performed on selected urban lakes in Lubbock, Texas 94 A.4 Descriptive statistics of physicochemical characteristics for selected urban lakes in Lubbock, Texas 97 A.5 Descriptive statistics of nitrogen, phosphorus, and carbon characteristics for selected urban lakes in Lubbock, Texas 98 B. 1 Abundance of aquatic invertebrate taxa from selected urban lakes in Lubbock, Texas 100
B .2 Fisher's log series a diversity index for each group of lakes and individual lakes in each of three seasons and for combined seasons 103
Vll LIST OF FIGURES
2.1 Map of Lubbock, Texas, identifying selected urban lakes and their drainage patterns 28 2.2 Total area of each land-use category that suppUes direct runoff to selected urban lakes in Lubbock, Texas 29 2.3 Seasonal patterns of specific conductance for selected urban lakes in Lubbock, Texas 30 2.4 Seasonal patterns of temperature for selected urban lakes in Lubbock, Texas 31 2.5 Principal Components Analysis based on monthly physicochemical characteristics for groups of lakes 32 2.6 Seasonal patterns of PC 1 from Principal Components Analysis of physicochemical characteristics for each lake .: 33 2.7 Seasonal patterns of PC 2 from Principal Components Analysis of physicochemical characteristics for each lake 34 2.8 Seasonal patterns of PC 3 from Principal Components Analysis of physicochemical characteristics for each lake 35 2.9 Pearson-Product Moment Correlation correlogram for physicochemical characteristics, precipitation, and land-use categories associated with selected urban lakes in Lubbock, Texas 36 3.1 Taxa richness and total numbers of aquatic invertebrates collected from each lake, for all sampling dates combined 58 3.2 Relative abundance of invertebrates collected from Rushland, Higinbotham, and Wendover lakes (Group 1) 59 3.3 Relative abundance of invertebrates collected from Maxey, Leroy Elmore, and Buster Long lakes (Group 2) 60 3.4 Relative abundance of invertebrates collected from Jack Stevens and Quaker & Brownfield lakes (Group 3) 61 3.5 Rank abundance plots of aquatic invertebrates for selected urban lakes in Lubbock, Texas 62 3.6 Fisher's log series a diversity index for each lake in each of three seasons and for combined data 63 3.7 Fisher's log series a diversity index for each group of lakes in each of three seasons and for combined data 64
Vlll 3.8 Relative abundance of invertebrates for each group of lakes 65
3.9 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) based on Jaccard's dissimilarity index for presence-absence invertebrate taxa 66
3.10 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) based on Ochai's dissimilarity index for presence-absence invertebrate taxa 67 3.11 Seasonal and overall dendrograms from cluster analysis based on Euclidean distance for invertebrate abundances 68 3.12 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) based on Cosine dissimilarity index for invertebrate abundances 69 3.13 Dendrograms from Cluster Analysis (UPGMA) based on Jaccard's and Ochai's dissimilarity indices for presence-absence of invertebrate families 70 3.14 Dendrograms from Cluster Analysis (UPGMA) based on Euclidean distance and Cosine indices for invertebrate family abundances 71 3.15 Principal Components Analysis based on invertebrate families of selected urban lakes in Lubbock, Texas 72
IX CHAPTER I
INTRODUCTION
Surface Water Ouality Since the 1960s, deteriorating quality of urban waters has been a primary concern of federal and state agencies. A variety of problems are presented by point and nonpoint source pollution, including deleterious effects on aquatic and wetland biota (Mason 1991). Traditional water quality studies typically have focused on point source pollution, because point sources are easily identified. However, current research initiatives by federal and state agencies are increasingly directed toward the study of nonpoint source pollution, such as evaluating the effects of land-use on surface water quality (EPA 1983; Ennis 1994).
Nonpoint source pollution, particularly stormwater runoff, has the potential for depositing a wide range of contaminants into urban impoundments. For instance, a surface water management system in Florida linked 85% of metals, 90% of oxygen demand material, >50% of nutrients, and 99% of suspended solids entering surface water systems to stormwater runoff (Livingston & Cox 1989). Sources can be traced to surrounding land-use, chemical applications to vegetation, and sediment release of toxic compounds into the water. Pollutants from these sources may represent both short-term and long-term environmental perturbations that affect the ecology of urban impoundments (Wanielista & Yousef 1993). However, a general understanding of the interaction between basic water quality attributes and biological components of urban impoundments is necessary to assess the effects of these perturbations. Much of the traditional research on water quality focuses on physicochemical characteristics, but recent research has taken more of an interdisciplinary approach by including the relationships between water quality and biodiversity (Gower et al. 1994: Tate & Heiny 1995). Currently, local, state, and federal agencies routinely monitor approximately 30 pollutants commonly found in surface waters, although this number is small in comparison to the approximately 1,500 known contaminants (Mason 1991). Biological monitoring of surface waters has gained popularity with the increase in new and unsuspected pollutants. Unlike chemical analyses, biological monitoring is not limited to immediate conditions of the environment or single contaminants, but integrates information about past disturbances and effects of multiple factors. A species survival depends in part, on its environment. If physical, chemical, and nutritional resources do not meet the minimum requirements of a species, then displacement from the habitat is inevitable (Abel 1989). However, if environmental conditions meet species-specific requirements, then biotic interactions, such as competition and predation, may also affect community composition. If changes in species diversity and population abundances result from either direct or indirect environmental stressors, then changes in biota may be used to elucidate changes in the environment. In this context, indicator species are those which, by their presence or abundance, provide some indication of the prevailing environmental conditions (Hellawell 1978).
Playas of the Southern High Plains On the Southern High Plains of Texas, federal and state conservation efforts have been directed toward the preservation of wetland habitats and the reintroduction of native grasses through the Conservation Reserve Program (CRP). This region contains between 25,000 and 30,000 playas, dominating the wetland and wildlife habitats of the region (Haukos & Smith 1992). Playas are shallow, ephemeral pools in largely isolated watersheds that capture approximately 88% of the precipitation, which subsequently is lost through ground water seepage and evaporation (Bolen et al. 1989). Playas provide critical habitat for more than 115 species of birds (including 20 species of waterfowl), 10 species of mammals, 14 species of amphibians, and 60 taxa of macroinvertebrates (Sublette &
1 Sublette 1967; Haukos & Smith 1992; Neck & Schramm 1992; Smith 1993). The importance of playas as centers of biodiversity extend beyond the local level, because they provide nesting and wintering habitats for more than 2 million waterfowl that use the central migratory flyway (US Fish & Wildlife Service 1988).
Urban areas of the Southern High Plains have incorporated the use of modified playas to receive stormwater and urban runoff. Once a playa has been deepened to permanently hold water, it is more appropriately called an urban lake. The urban lakes located within Lubbock, Texas, also host a wide range of recreational activities and serve as areas of urban biodiversity. Current research efforts within Lubbock focus on the quality of stormwater runoff entering urban lakes (Ennis 1994). However, the relationships between water quality and aquatic biodiversity have not been examined. A study relating community composition of aquatic invertebrates to water quality of urban lakes would increase our understanding of the basic ecology of these systems, providing insight to the biotic and abiotic mechanisms which influence species diversity.
Objectives The three primary objectives of this study were (1) to assess water quality of urban lakes (Chapter II), (2) to determine aquatic macroinvertebrate diversity and community composition of these lakes (Chapter IE), and (3) to evaluate the relationships between water quality and invertebrate community composition and abundances of numerically dominant taxa (Chapter IV). Literature Cited Abel, P.D. 1989. Water pollution biology. EUis Horwood Limited, Chichchester, England.
Bolen, E.G., L.M. Smith, and H.L. Schramm, Jr. 1989. Playa lakes: Prairie wetlands of the Southern High Plains. Bioscience. 39:615-623. EPA. 1983. Results of the nationwide urban runoff program. Vol. I. Water Planning Division, United States Printing Office, Washington, D.C. Ennis, T.E. 1994. City of Lubbock stormwater NPDES permit data analysis. Master's Thesis, Texas Tech University, Lubbock, Texas. Gower, A.M., G."Myers, M. Kent, and M.E. Foulkes. 1994. Relationships between macroinvertebrate communities and environmental variables in metal-contaminated streams in south-west England. Freshwater Biology. 32:199-221. Haukos, D.A., and L.M. Smith. 1992. Ecology of playa lakes. Waterfowl management handbook leaflet 13.3.7, Office of Information Transfer, US Fish and Wildlife Service, Ft. Collins, Colorado. Hellawell, J.M. 1978. Biological surveillance of rivers: A biological monitoring handbook. Water Research Centre. Stevenage, England. Livingston, E., and J. Cox. 1989. Florida development manual. Vol. I, Florida Department of Environmental Regulation, Tallahassee, Florida.
Mason, C.F. 1991. Biology of freshwater pollution. 2^^^ edition. Longman Scientific & Technical, England. Neck, R.W., and H.L. Schramm, Jr. 1992. Freshwater molluscs of selected playa lakes of the Southern High Plains of Texas. The Southwestern Naturalist. 37:205-209. Sublette, I.E., and M.S. Sublette. 1967. The limnology of playa lakes on the Llano Estacado, New Mexico and Texas. The Southwestern Naturalist. 12:369-406. Smith, C.L. 1993. Water boatmen (Hemiptera: Corixidae) faunas in the playa lakes of the Southern High Plains. Master's Thesis, Texas Tech University, Lubbock, Texas. Tate, CM., and J.S. Heiny. 1995. The ordination of benthic invertebrate communities in ' the South Platte River Basin in relation to environmental factors. Freshwater Biology. 33:439-454. US Fish and Wildlife Service. 1988. Playa lakes region waterfowl habitat concept plan, category 24 of the North American waterfowl management plan. US Fish and Wildlife Service, Albuquerque, New Mexico. Wanielista, P.E., and Y.A. Yousef. 1993. Stormwater management. John Wiley & Sons, New York, New York. CHAPTER II
EVALUATING WATER QUALITY CHARACTERISTICS
IN URBAN LAKES
Introduction Urban Lakes of the Southern High Plains
Urban areas of the Southern High Plains have frequently modified playa basins to receive stormwater and urban runoff Many of these lakes have been dredged to increase storage capacity. This also decreases surface area to volume ratios, thereby decreasing relative evaporation losses. Once a playa has been modified to permanently hold water, it is more appropriately identified as an urban lake. The removal of the characteristic clay lining increases groundwater recharge because of higher infiltration rates (Chen et al. 1988). During the period of 1981 to 1987, groundwater levels beneath many of the urban lakes in Lubbock, Texas, increased by as much as 14 m due to increased infiltration (Chen et al. 1988). However, increased infiltration rates may also enhance the potential for groundwater contamination with surface water pollutants. Urban lakes in Lubbock may receive 89% of all surface water runoff within their watersheds due to the lack of an alternative stormwater removal system and impervious urban surfaces (e.g., asphalt roads and parking lots, and cement sidewalks). These shallow lakes act as a sink for urban drainage; water enters a lake and remains until it evaporates, infiltrates, is pumped to another location, or overflows into another lake. The City of Lubbock has incorporated the use of 32 modified playa basins, within a roadway system that channels urban runoff into the lakes. As a consequence, these lakes may be classified as primary, secondary, or tertiary basins, based on hydrological connections. For instance, a primary basin receives urban runoff directly from the surrounding watershed, whereas a secondary basin also will receive inputs from the overflow of an adjoining primary basin (Fig. 2.1). Consequently, water quality in these lakes is determined by surface runoff from the surrounding watershed and overflow from adjacent lakes.
Surface Water Ouality
Since the 1960s, quality of urban waters have declined, in part, because of point and nonpoint source pollution. During the period between 1974 and 1988, approximately $200 billion was spent for the management of point source pollution from sewage and industrial waste systems in the United States (Novotny & Bendoricchio 1989). In contrast, funding to alleviate nonpoint source pollution (i.e., stormwater runoff) remained relatively low and has been supported primarily by regional and state agencies (Wanielista & Yousef 1993). Nonpoint source pollution, such as delivered by stormwater runoff, has the potential for depositing a wide range of chemical and biological contaminants into urban impoundments. Typically, urban runoff contains high levels of suspended solids and organic matter that increase oxygen demand of recipient waters. As a result, aquatic organisms, such as fish and macroinvertebrates, may become oxygen-stressed, increasing mortality rates of sensitive species (Mason 1991). Bioaccumulation and biomagnification of chemical contaminants also are known to occur in aquatic plants and wetland fauna (US Fish & Wildlife Service 1989). Both chemical and biological contaminants in urban environments can be traced to waterfowl, pets, vehicle emissions, chemical applications to vegetation, erosional deposition and sediment release of toxic compounds, all of which differ as functions of surrounding land-use (i.e., residential, commercial or agricultural). Also associated with land-use patterns is the extent of impervious surface areas (Porcella & Sorensen 1980; Jones & Clark 1987). Rimer and Nissen (1978) have shown that chemical oxygen demand, suspended solids, and total phosphorus concentrations in urban impoundments are correlated positively with the extent of impervious coverage. Other factors which influence the type and quantity of pollutants entering surface water impoundments include the time of year in which the runoff occurs, elapsed time between inputs, and conditions prior to the runoff event, including human activities within the watershed (Wells et al. 1975).
In urban areas of the United States, research has focused on the characterization and quantification of contaminants entering surface water impoundments, and stormwater runoff appears to account for the bulk of many inputs. For instance, a surface water management system in Florida has shown that 85% of metals, 90% of oxygen demand material, over 50% of nutrients, and 99% of suspended solids enters surface water systems from stormwater runoff (Livingston & Cox 1989). Similar findings have been reported for Lubbock, where chemical oxygen demand, total solids, and suspended solids in surface water runoff entering lakes have exceeded standards for raw sewage (Thompson et al. 1974; Wells et al. 1975). Enhanced oxygen demand increases the risk of damage to biotic components of these ecosystems. Suspended solids reduce storage capacity due to increased sedimentation, as well as the depth of the euphotic zone. Although urban lakes have received considerable attention concerning qualitative aspects of water characteristics, few quantitative studies describe temporal pattems of water quality and the influence of surrounding land-use on these systems.
Objectives The objectives of the water quality assessment portion of this study were (1) to evaluate if water quality attributes differ among lakes, (2) to evaluate if seasonal dynamics in water quality characteristics were similar among urban lakes, and (3) to evaluate relationships between water quality of urban lakes and areal extent of land-use categories in urban watersheds. Information obtained from this portion of the study will be used in
7 conjunction with studies of macroinvertebrate communities (Chapter HI) to identify possible bioindicators of water quality, as well as to determine possible relationships between water quality characteristics and diversity characteristics of macroinvertebrate assemblages (Chapter IV).
Materials and Methods Study Site
This study focused on 8 of the 32 urban lakes located within Lubbock, Texas, 10r52' N latitude 33°35' W longitude (Table 2.1). Urban lakes were selected to include a representative range of physical and biological characteristics, such as watershed size, relative potential for nonpoint source pollution, and presence of aquatic vegetation. Most lakes receive direct runoff from residential and surrounding park areas (Fig. 2.2). Others receive runoff from commercial areas and parking lots, and a few receive runoff from agricultural areas and vacant lots. The extent of vegetation in the littoral zone of urban lakes is highly variable, ranging from no vegetation to extensive macrophyte growth. Attributes of selected lakes, including location, total area of direct surface water runoff, potential inputs from adjacent lakes, and vegetation, were used to construct an a priori classification of urban lakes (Table 2.1). The first group of urban lakes included Rushland, Higinbotham, and Wendover; these are medium sized, primary basins, that receive direct runoff from residential or park areas. Wendover also receives agricultural runoff During the summer, these lakes contain littoral vegetation, such as pink smartweed {Persicaria pennsylvanica) and dock weed {Rumex crispus). The second group of lakes, Maxey, Leroy Elmore, and Buster Long, contain the largest basins and generally receive more runoff from commercial areas, as well as overflow from adjacent lakes. No littoral vegetation is present in these lakes. The last group, Quaker & Brownfield and Jack Stevens, contain the smallest lakes and receive
8 runoff primarily from residential, park, or commercial areas. These lakes contain an extensive growth of cattails (Typha sp.) and pondweed (Potamogeton sp.) in the littoral zone.
Water Oualitv Sampling
The sampling and evaluation of standard water quality characteristics began in February 1993, and continued on a monthly basis through March 1994 (Table 2.2). To expedite sampling, Rushland, Higinbotham, Wendover, and Quaker & Brownfield were sampled during the 3^^ week of each month, whereas the remaining lakes were sampled during the 4th week. Because these systems are shallow, well mixed, and exhibit little spatial variation in water quality (Mollhagen, personal communication), a single sampling location, away from direct surface water inputs, was selected in each lake for all sampling. For each lake, grab samples were taken 0.2 m below the surface in approximately 1.2 m of water, and placed in four, 1-liter glass jars. To reduce the possibility of contamination, sample containers were prewashed in the laboratory and rinsed three times with lake water prior to sampling. Once samples were obtained, they were placed on ice for transportation to the Environmental Science Laboratory (ESL), Department of Civil Engineering, Texas Tech University. Two sample containers were designated for nutrient analyses; one was preserved with 2 mL of concentrated H2SO4 for nitrogen and phosphorus analyses, the other received 2 mL of concentrated HCL for carbon analyses (APHA 1992). Samples for nutrient analyses were refrigerated (4° C) at the ESL until examination. The remaining two containers were refrigerated (4° C) until oxygen demand, alkalinity and hardness analyses were performed (same day as collected). Personnel of the ESL performed nitrogen and phosphorus analyses, whereas the investigator performed all in situ analyses as well as determinations of oxygen demand, carbon, conductivity, alkalinity and hardness at the ESL. Water Oualitv Analysps
Temperature and pH were analyzed in situ, using a Coming Model 90 portable pH unit. At each lake, three measurements were made at a depth of 10 cm, approximately 1 m apart. The unit was double endpoint calibrated at pH 4 and pH 10 just prior to sampling. Dissolved oxygen concentrations were performed in the laboratory with a YSI model 58 dissolved oxygen meter, immediately upon return from field collection. For this analysis, a 300 mL dark bottle was used to collect water, following grab sample procedures outlined above, and transported to the ESL for analysis within 45 minutes of collection (APHA 1992). The oxygen meter was calibrated by holding the probe 1 inch above oxygen- saturated water and adjusting the meter to read 88.5% saturation, based on standard temperature (25° C) and atmospheric pressure of Lubbock.
Analyses for alkalinity and hardness involved standard titrimetric methods outlined in APHA (1992). Alkalinity was determined by a potentiometric titration to a endpoint of pH 4.3 using 0.02 N standard HCl acid solution. Hardness was determined by a colorimetric method using EDTA titrant as follows. The sample was diluted 1:1 with deionized water; 1 mL of ammonium chloride buffer solution and 2 drops of calgamite indicators were added; the resulting solution was titrated to the endpoint color change of blue. Each analysis was replicated to verify the precision of the procedure. Analysis of chemical oxygen demand (COD) also utilized a colorimetric method, boiling 2 mL of sample in excess potassium dichromate sulfuric acid solution (Hach Company) for 2 hours. The sample was cooled to room temperature and placed in a Hach DR 2000 direct-reading spectrophotometer to compare the colorimetric change of dichromate between two replicate samples and a control vial. The degree of color change accompanying oxidation of 95-100% of available organic and inorganic matter indicates the COD of the sample (APHA 1992).
10 A standard 5-day biological oxygen demand (BOD) technique was used to quantify the biochemical oxidation of organic matter in each sample. Because biological oxygen demand is related to chemical oxygen demand, COD was used to estimate a range of sample dilutions for BOD analyses (APHA 1992). In this approach, repHcates of three sample dilutions are prepared in graduated cylinders and placed in separate 300 mL dissolved oxygen bottles. Standard microbial inoculent and nutrients that support microbial growth are added to each bottle (Hach Company), which is filled subsequently with oxygen-saturated deionized water and incubated at 20° C for 5 days. At the end of the incubation period, dissolved oxygen is measured with a dissolved oxygen meter and only the samples exhibiting more than 2.0 mg L'l oxygen depletion are used to calculate BOD (APHA 1992).
Analysis of total organic and inorganic carbon was performed using a Shimadzu Total Organic Carbon Analyzer (TOC-5000). This method involves heating the sample in the presence of oxygen to oxidize organic carbon to CO2. The CO2 concentration was then measured by a Nondispersive Infrared Analyzer. To eliminate interference of inorganic carbon in the determination of total organic carbon, samples were acidified with concentrated HCl to pH < 2, thereby converting inorganic carbon to CO2. The sample was then injected, by the analyzer, into a chamber containing phosphoric acid-coated beads, where the CO2 content was measured. Organic carbon is not oxidized under these conditions, so only the inorganic carbon is measured (APHA 1992). Total carbon is calculated as the sum of organic and inorganic carbon. Equipment failure from December 1993 to March 1994 compromised the integrity of total, organic and inorganic carbon data, because analyses were performed on samples stored beyond suggested holding times. This may influence interpretation of subsequent statistical analyses.
11 Conductivity was measured with a YSI Model 35 Conductance Meter; the probe was placed directly into a 100 mL sample that was stirred continuously until a stable reading was obtained. Results were standardized to 25° C by the meter.
Total Kjeldahl Nitrogen, ammonia, nitrates-nitrites, total phosphorus, and ortho- phosphate were analyzed by personnel of the ESL with a Bran Luebbe Technicon Random Access Automated Chemistry System (TRAACS) 800. EPA (1974) and standard methods (APHA 1992), including detection limits, are outlined in Appendix A.l.
Data Analyses
Many problems commonly interfere with the interpretation and analysis of water quality data (Wolman 1971). For example, (1) techniques and sensitivities of analytical methods may change throughout a study; (2) correlations of variables with limnologic and hydrologic behavior rarely are available, so it is difficult to partition variance; and (3) causal explanation requires a knowledge of human activities, hydrologic processes, and land-use patterns. Other factors that contribute to analytical difficulties include missing data, method-detection-limits (left-censored data), non-normal distribution of parameters, strong temporal variation in physicochemical characteristics, and correlation among parameters (Lettenmaier 1988; Newman et al. 1989; Willig 1994). Consequently, these factors make statistical comparisons difficult. The first objective of this portion of the study was to evaluate if water quality attributes differed among groups of lakes. Two-way ANOVAs were used to test for differences among the group means over time with respect to each water quality characteristic (SPSS 1990). Water quality characteristics were log-transformed (ln[x-i-l]) to meet two of the basic assumptions of ANOVA, i.e., homoscedasticity and normality. However, problems associated with left-censored data for nitrogen and phosphorus assays could not be circumvented by data transformation or substitution, because detection limits
12 changed frequently for each attribute even though the analytic method remained the same (Appendix A. 1). As a consequence, many of the nutrient values used in statistical analyses were at different detection limits, complicating statistical analysis and the interpretation of results.
To limit experiment-wise error rate of each two-way ANOVA, the overall P value for each model was compared to Bonferroni's Sequential a level to determine if that variable contributed significantly to differences among groups of lakes. Bonferroni's Sequential Adjustment maintains experiment-wise error rate of a collection of k tests by adjusting the a level for each analysis (Rice 1989). If the overall P value was significant at the Bonferroni's level, and the two-way interaction term (group x date) also was significant (a = 0.05), then Scheffe a posteriori contrasts were used to make all possible comparisons within each month to determine which groups were significantly different. Conversely, if the overall P value was significant at the Bonferroni's level, and the two-way interaction term was non-significant, then the source of variation due to group or date effects was used to evaluate mean differences.
Principal Components Analysis (PCA) was used to further illustrate the seasonal patterns observed in water quality characteristics among both groups of lakes and individual lakes. PCA reduces the complexity of addressing many different characteristics simultaneously and presents the data in an more parsimonious way (SPSS 1990). Principal components scores for individual lakes were plotted against sampling date to illustrate temporal pattems of water quality. This could reveal possible relationships between water quality and nonpoint source pollution of individual lakes as related to land- use pattems, because differences in water quality as related to land-use might be seasonal. To assess possible relationships between areal extent of land-use and water quality,
Pearson-Product Moment Correlation was used. Urban land-use was divided into four categories: single family housing, multiple family housing, commercial, or parks and
13 vacant lots (Fig. 2.2). Total surface area of each land-use category that drained directly into each lake was calculated from a USGS topographical map. A grid was overlaid on the map to quantify total land-use area. Only the surface area which exhibited direct surface water mnoff into each lake, based on topographical relationships, was used in the analysis. This approach did not consider possible inputs from storm sewers into Maxey and Leroy Elmore lakes, as well as the effect of overflow drainage from primary or secondary lakes.
Results Analysis of Variance
Results of physicochemical analyses are listed in Appendices A.2 and A.3; descriptive statistics (mean ± standard deviation, minimum and maximum) are given in Appendices A.4 and A.5. Two-way Analysis of Variance showed that water quality characteristics were significantly different among groups of lakes (Table 2.3). The only physicochemical attributes that did not contribute to overall differences were chemical oxygen demand, nitrate-nitrite, total carbon, and total organic carbon. Physicochemical characteristics that had a significant two-way interaction term (group x date) included conductivity, dissolved oxygen, pH, temperature, inorganic carbon, total phosphoms, and ortho-phosphoms, which suggests that these attributes differentially changed among groups of lakes during the study. Nonetheless, the Scheffe a posteriori test was only able to detect specific differences between groups 1 and 2, and groups 1 and 3, for total phosphoms and ortho-phosphoms during the month of July. The inability of Scheffe tests to determine where other differences occurred is probably the result of the conservative nature of this test. For example, from August to October, specific conductance for Rushland, Higinbotham, and Wendover lakes (group 1) was approximately 2 times greater than other lakes (Fig. 2.3). This pattem was not a result of equipment malfunction, because it was not a one-time occurrence. Also, Quaker & Brownfield lake was analyzed
14 concurrently with Rushland, Higinbotham, and Wendover lakes, but showed lower levels of conductance. Even though this suite of analyses only addressed intra-group variation, seasonal pattems of some physicochemical attributes indicate that inter-group variability may be high. For example, during the first sampling period, water temperature varied between 5° and 14° C for the group 1 lakes: Rushland, Higinbotham, and Wendover (Fig. 2.4). This is a result of sampling one lake a day for three consecutive days instead of sampling all three lakes on one day.
Other water quality attributes that showed consistent mean group differences for all sampling dates included alkalinity, biological oxygen demand, hardness, total Kjeldahl nitrogen, and ammonia (Table 2.3). Also, the source of variation due to date had a significant effect on the mean differences of these attributes. This indicates that mean differences among groups of lakes, with respect to water quality attributes, are the same during each sampling period, however, their magnitude changes seasonally.
Principal Components Analysis Principal Components Analysis (PCA) using group means of water quality characteristics did not identify a perceivable arrangement of lakes based on a priori groupings when PC axes were plotted against each other (Fig. 2.5). The first factor score loaded positively with alkalinity and hardness, and negatively with temperature and biological oxygen demand, accounting for 31% of the variation observed among groups of lakes. When PC 2 was plotted against PC 3, group 1 was slightly separated from groups 2 and 3, based on lower inorganic carbon (loaded negatively on PC 2) and total carbon content (loaded positively on PC 3; Fig. 2.5). In the same plot, groups 2 and 3 were separated based on lower chemical oxygen demand (loaded positively on PC 2) and dissolved oxygen concentration (loaded negatively on PC 3). The second and third PC axes accounted for 16% and 14% of the observed variation among groups of lakes,
15 respectively. A total of 61% of the variation observed in lakes was explained by all three PC axes.
To assess whether individual lakes exhibited similar temporal changes in water quality characteristics, a second PCA was performed using either the mean value of an attribute, when multiple observations were made, or single observations for each lake. Factor scores then were plotted over time to elucidate trends (Figs. 2.6, 2.7, 2.8). Principal Components Analysis reduced the data to include the following attributes in the first 3 factor scores: temperature, alkalinity and hardness (PC 1); chemical oxygen demand and total organic carbon (PC 2); pH, dissolved oxygen, inorganic carbon, and total carbon (PC 3). Together, the first three PC scores accounted for 55% of the variation observed among lakes. All lakes exhibited similar pattems in alkalinity and hardness (Fig 2.6). However, there was more variability in chemical oxygen demand and carbon content (Figs. 2.7, 2.8). Patterns of water quality identified by plots of PC scores with date appear to be seasonal, with possible short-term responses to weather conditions (rainfall events, temperature, increased evaporation rates). Some of the variation observed between lakes within a single month may also reflect changes in weather conditions between the 3rd and 4th weeks of each month, because lakes were divided into two sampling groups (previously discussed).
Pearson-Product Moment Correlation Results of the Pearson-Product Moment Correlation analyses confirmed that relationships among water quality characteristics were consistent with results from Principal Components Analysis. For example, temperature was highly correlated with many variables, including alkalinity, hardness, dissolved oxygen, and biological oxygen demand (Fig. 2.9). In part, these correlations were expected because the solubility of oxygen is related to temperature (Wetzel 1983). The seasonal trend in alkalinity and
16 hardness (PC 1; Fig. 2.6) may be a result of higher CO2 fluxes associated with increased metabolism during the summer, which reduces CaC03 availabiHty. An increase in dead organic matter during the winter may also increase carbonate solubility at that time (Wetzel
1983). Therefore, alkalinity and hardness are inversely correlated with temperature. The amount of rainfall between monthly sampling periods was significantly correlated with many of the water quality attributes (Fig. 2.9), but the effect of rainfall in the week prior to each sampling date showed less correlation. A possible explanation is that water quality is more closely related to inputs received over the longer time scale (monthly), because of slow response times. This study did not consider daily inputs, such as those related to lawn watering or well-water additions that are sometimes made to maintain a consistent water level during the summer months.
With few exceptions, areal extent of land-use categories was not significantly correlated with water quality variables. However, the area of multiple family housing was negatively correlated with conductivity (P < 0.01), chemical oxygen demand (P < 0.05), total carbon (P < 0.05), and total organic carbon (P < 0.05), and positively correlated with ammonia (P < 0.01) and nitrate-nitrite (P < 0.01). Commercial land-use area was negatively correlated with conductivity (P < 0.05), and positively correlated with ammonia and nitrate-nitrite (P < 0.05). Although correlation coefficients do not infer cause-effect relationships, the increased quantities of nitrates and ammonia in multiple family housing and commercial land-use areas could be a result of large areas covered by asphalt. These areas collect atmospheric dry-fall and soil particles that are washed into lakes by urban and stormwater drainage (Rimer et al. 1978).
Discussion
Even though urban lakes of Lubbock, Texas, share a common origin, similar morphology, and hydrological inputs primarily from urban mnoff and stormwater
17 drainage, significant differences exist in their water quahty characteristics. Some differences are directly correlated to pattems of land-use within their watersheds, while others apparently result from other factors. For example, an increase in specific conductance during the summer months for Rushland, Higinbotham, and Wendover lakes is likely a result of evaporative concentration of dissolved ions due to high ambient temperatures and windy conditions common to the Southem High Plains. Also, increased ammonia levels during the fall-winter months may result from both watershed features and increased fecal inputs from migratory waterfowl during this period.
Trends in water quality characteristics identified by PCA support the results from the two-way ANOVA. Physicochemical attributes that loaded heavily in the first three factor scores of PCA (e.g., alkalinity, hardness, biological oxygen demand, chemical oxygen demand, total carbon, and total organic carbon), had non-significant interaction terms in the two-way ANOVA. These results show that the magnitude of change in each attribute was consistent among lakes throughout the sampling period. Changes in magnitude of these characteristics may be tied to mnoff events, watershed features, and biological activities within lakes, that affect all lakes in roughly the same way. Urban lakes are sinks for allochthonous inputs transported by surface water mnoff The initial flow of water entering lakes via stormwater or urban mnoff typically contains the highest quantities of organic carbon, nitrate-nitrite nitrogen, ortho-phosphates, heavy metals, and suspended particles (Wanielista & Yousef 1993). Concentrations of these constituents may be expected to vary greatly over time with local weather conditions, such as temperature, drought, dust storms, and sporadic thunderstorms (Daniel et al. 1978); conditions usual for Lubbock. Inputs of this nature probably have a strong short-term influence on water quality of urban lakes, producing transient peaks in nutrient concentrations and oxygen demand, as observed during this study. In urban impoundments, 40 to 80% of the total annual oxygen demand results from stomiwater
18 mnoff (Daniel et al. 1978). The National Urban Runoff Program also has shown that urban mnoff in residential areas, when compared to commercial areas, contained higher concentrations of Total Kjeldhal nitrogen, ammonia, nitrate-nitrite, ortho-phosphate, and total phosphate (EPA 1983). These higher nutrient concentrations are attributed to organic matter inputs and the extensive and intensive use of fertilizers in residential areas. These allochthonous inputs lead to increased chemical and biological oxygen demand in the receiving waters (EPA 1983). In Lubbock, similar results have been reported for stormwater mnoff in residential-commercial areas (Ennis 1994). However, when compared to residential-commercial areas of the NURP study, areas in Lubbock contain higher levels of COD, BOD, TKN, nitrate-nitrite, and total phosphoms (Ennis 1994). Results from this study, with respect to nitrogen and phosphoms, are consistent with the Lubbock Stormwater NPDES data (Table 2.4). However, BOD and COD are considerably lower in these urban lakes than in stormwater mnoff, suggesting that inputs of oxygen demanding material are diluted and undergo rapid oxidation in lakes.
Water quality characteristics that were negatively correlated with the areal extent of multiple family housing and commercial land-use (i.e., conductivity, chemical oxygen demand, total carbon, and total organic carbon) suggest that these areas may provide lakes with less organic matter and fertilizer inputs than single family housing or park areas. These two land-use categories are characterized as having more impervious surface area than does single family housing, and thus, are less likely to have inputs of organic matter from vegetation. In addition, these two land-use categories are more likely to be cleaned by street sweepers, further reducing inputs of organic matter from the watershed (Daniel 1978; Wanielista & Yousef 1993). The percent area of multiple family housing is smaller than that of other land-use types, so inputs may be minimal compared to other land-use types. In conclusion, significant differences existed among groups of lakes with respect to many physicochemical characteristics, such as temperature, pH, conductivity, dissolved
19 oxygen, total Kjeldahl nitrogen, and total phosphoms. Urban lakes exhibited similar seasonal changes with respect to alkalinity, temperature, chemical oxygen demand, total carbon, and dissolved oxygen, which appeared to be controlled by sporadic allochthonous inputs from stormwater and urban mnoff Results of this study are consistent with the results of the Lubbock Stormwater NPDES Permit study (Ennis 1994), supporting the conclusion that water quality of urban lakes is largely controlled by surface water mnoff.
20 Literature Cited Abel, P.D. 1989. Water pollution biology. ElUs Horwood Limited, Chichchester, England.
APHA (American Public Health Association). 1992. Standard methods for the examination of water and waste water. 18^^ edition. American Public Health Association, Washington D.C.
Chen, Y.C., K.A. Rainwater, R.H. Ramsey, and M.J. Dvoracek. 1988. Economic potential for development of increasing groundwater storage beneath a High Plains municipality, Lubbock, Texas. Interim report, Texas Tech University, Lubbock, Texas.
Daniel, T.C., R.C. Wendt, and J.G. Konrad. 1978. Nonpoint pollution: Runoff in urban areas. Wisconsin Department of Natural Resources, Madison, Wisconsin. Ennis, T.E. 1994. City of Lubbock stormwater NPDES permit data analysis. Master's Thesis, Texas Tech University, Lubbock, Texas. EPA. 1974. Methods for chemical analysis of water and wastes. Environmental Protection Agency, Water Quality Office, Analytical Quality Control Laboratory, Cincinnati, Ohio. EPA. 1983. Results of the nationwide urban mnoff program. Vol. I, Water Planning Division, United States Printing Office, Washington, D.C. Jones, R., and C.C. Clark. 1987. Impact of watershed urbanization on stream insect communities. Water Resources Bulletin. 23:1047-1055. Lettenmaier, D.P. 1988. Multivariate nonparametric tests for trend in water quality. Water Resources Bulletin. 24:505-512. Livingston, E., and J. Cox. 1989. Florida development manual. Vol. I, Florida Department of Environmental Regulation, Tallahassee, Florida.
Mason, C.F. 1991. Biology of freshwater pollution. 2^^ edition. Longman Scientific & Technical, England. Newman, M.C., P.M. Dixon, B.B. Looney, and J.E. Pinder. 1989. Estimating mean and variance for environmental samples with below detection limit observations. Water Resources Bulletin. 25:905-915. Novotny, V., and G. Bendoricchio. 1989. Water quality: Linking nonpoint pollution and deterioration. Water Environment and Technology. Nov. :400-407. Porcella, D.B., and D.L. Sorensen. 1980. Characteristics of nonpoint source urban runoff and its effects on stream ecosystems. Corvallis Environmental Research Laboratory, Office of Research and Development (US EPA), CorvaUis, Oregon.
Rice, W.R. 1989. Analyzing tables of statistical tests. Evolution. 43:223-225.
21 Rimer, A.E., and J.A. Nissen. 1978. Characterization and impact of stormwater mnoff from various land cover types. Water Pollution Control Federation. 50:252-264.
SPSS. 1990. Statistical package for the social sciences. SPSS Inc. Chicago, Illinois. Thompson, G.B., D.M. Wells, R.M. Sweazy, and B.J. Clabom. 1974. Variation of urban mnoff quality and quantity with duration and intensity of storms. Interim Report, Water Resources Center, Texas Tech University, Lubbock, Texas. US Fish and Wildlife Service. 1989. Atrazine hazards to fish, wildlife, and invertebrates: A synoptic review. US Fish and Wildlife Service, Denver, Colorado. Wanielista, P.E., and Y.A. Yousef 1993. Stormwater management. John Wiley & Sons, New York, New York.
Wells, D.M., R.M. Sweazy, B.J. Clabom, and R.H. Ramsey. 1975. Variation of urban mnoff quality and quantity with duration and intensity of storms: Phase III. Vol. 4, Project Summary. Final report of the office of Water Research and Technology, Water Resources Center, Texas Tech University, Lubbock, Texas.
Wetzel, R.G. 1983. Limnology. 2"^ edition. Saunders College Publishing, Philadelphia, Pennsylvania. Willig, M.R. 1994. Statistical approaches to data analysis in wildlife ecotoxicology (pp. 489-495). In Wildlife toxicology and population modeling: Integrated studies of agroecosystems. (Kendall, R.J., and T.E. Lacher Eds.). CRC Press, Boca Raton, Florida. Wolman, M.G. 1971. The nation's rivers. Science. 174:40-42.
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Physicochemical Parameters Alkalinity Total Kjeldahl Nitrogen Biological Oxygen Demand Ammonia Chemical Oxygen Demand Nitrate / Nitrite Conductivity Total Phosphorus Dissolved Oxygen Ortho Phosphorus Hardness Total Carbon pH Total Organic Carbon Temperature Total Inorganic Carbon 24 i.7 (^'9U c CM O CO o © o ^ o o oof:: ^«-***^-X-^4«-^***** do S^ O . •^H o -a V O rr; (D ^ ^ OH > (U ON-2 (U —' c o o rr •x-**4t^*^e-*-5e-^*-3«-*-x- OH b -^ ' o oo o » • oo d> d> ^ ^•3 s V V 43 "g CO I-^ 1—1 ^^ oo® o O o o o ® •5«-**-X--3«-**-X-*-5«-**-X-i«- C|^ cu <^ oo d d d c o B S^ o V V V V U P § S c/3 on ^ ^ ^1 ^ "^ 00 '—1 _j SP (u c/5 i 28S *-X-*-3«-'X-^-X-*'X--X--X--X-** VH O o I dd dd6 ^^ o (U V H «+H ^ Js cn en en •I—> en OS OS 13-;^ CO en • "^ ON on 5 S OS en t-H Ui O 1o >'a Q " en OS (U OS on P o 1-n o o o o P OH o o o o o *****-x-****-x-*-x-* •^ O o • d d d 4=! V V V dV OJ -^ o TH y-i o O O lO o o o O •5«-*-H-*****^f**^-3f-3f o • o • o • o• V d en r- en U-i en 1—( »—I O «n 0000000000000000000000000000 VO o O o o Zi7:'^z,ZiZiZ.:z.:^:Zi7:ZiZiZi o o o o o 73 C O o o o r-. ^*-x-***-x-^*-x-^^-x--x- • • • o o o o o o V V H o •i-H O OS en (N (N (N O VO -H O O O 0\ oooooooooooooooooooooooooooo -^ CO d d d> d d> ZiZi'i^ZiZiZiZiZiZiZi'iZ.Zi'^'Z o (J CO O o o W)-0 o o •3f**4f^*-5«-**^4«-*** ,00 4 .02 4 .00 1 o ^ d o o o V d V V TO O 13 O oooooooooooooooooocooooooooo t:^ 43 23 2 01 7 03 5 ,80 2 73 o o o o o (U P a •I—> a a o (U en OS OS ^ o 13 -^ -I—> en ^ - 'si- OS U "^ "-H CO P^ ^ en tH S- ^v- "U1H^ OS - Ti en Q ^Os -^ O ^ X §43^?^5!t^ B ^ BB 4=: 00 K> OH OH CO P D P p P '-I t-H o B o CO > O 1^ O'-H^ O PQ •—> IJH M o on O -^ O SO ^ (N O SO 43 ^ O -H (Sj CNj r-; P C i> oo en d d d CO so c CO -in 13 CO -r! C^ 00 ^^ OS oo en p OS OS O so ^ en 73 'U U >' (U oo ^H en O O O o so in (N 13 73 C CO 13 O P o o 00 r- o •*—> O en ^ in !Z1 P -C2O p o 1^ O O OS so so .J-H p OO (N in CN in r^ -^ d d so o .1—1 -I—on> ..—I S-H 00 OS on (D 1—H W (N on CO X (U I H 27 28 200-, Single family housing [^ Commercial Multiple family housing Q Park/vacant lot 150- CO CO 100- 50- 3 Maxe y Elmor e wnfiel d e r Lon g Steven s ushlan d endove r •ut c< ^ on 2 ^4 iginbotha m ro y P en K PQ CO Quake r & ; S Fig. 2.2. Total area of each land-use category that supplies direct runoff to selected urban lakes in Lubbock, Texas. 29 4.0-1 3.0- Group 1 • Rushland 2.0- Higinbotham Wendover 1.0- 0.0 T—I—I—I—I—I—I—r 1—I—r 4.0 C/D O -P 3.0- Group 2 "• Maxey o p 2.0- Leroy Elmore CO o Buster Long p 73 P 1.0- O U 0.0 T—I—I—I—I—I—I—I—I—I—I—I—r 4.0 • 3.0- Group 3 ' Quaker & Brownfield 2.0- Jack Stevens 1.0- 0.0' T—I—I—I—r 1—r T—I—I—I—I—I—T > O C X3 >= >, g\ C3 ^ S 3 to D, ti o Fig. 2.3. Seasonal pattems of specific conductance for selected urban lakes in Lubbock, Texas. 30 30.0° - 25.0° - 20.0° - Group 1 • Rushland 15.0°- Higinbotham -D Wendover 10.0°- 5.0°- 0.0= T—I—I—I—I—I—I—r I I I 1 r 30.0° -I 25.0° -I U °(U 20.0° -| Group 2 VH "• Maxey p I 15.0° H Lery Elmore OH Buster Long p § 10.0°-| H 5.0°-I 0.0°' T—\—I—I—I—I—I—I—I—T T—T 30.0° -[ 25.0°- Group 3 20.0° - • Quaker & Brownfield 15.0°- Jack Stevens 10.0°- 5.0°- 0.0° ' I I I I I I I I I I I I I I ON S < Fig. 2.4. Seasonal pattems of temperature for selected urban lakes in Lubbock, Texas. 31 3- 2- 1- ^ CP OD u 0- •i • OH -1 - D -2- -| -3 -1 0 3 PC 2 3- 2- 1- 0- •° O D "^ 1- O D 2- D °« • 3- 1 •1 01 1 1 1 PC 3 3- 2- O 1- u 0- 0 Of ° CLH -1- o • -2- • -3- 1 1 1 1 1 1 -3 0 PC 3 Fig. 2.5. Principal Components Analysis based on monthly physicochemical characteristics for groups of lakes; solid circles = group 1; open circles = group 2; squares = group 3. 32 Rushland Higinbotham 2' Wendover Quaker & Brownfield 1' u 0 Cu -1 -2 T r T en 60 a. o c X3 rt c o > C3 U ON 3 Maxey Leroy Elmore Buster Long •— Jack Stevens o c x> u CO OJ Q —' UM Month Fig. 2.6. Seasonal patterns of PC 1 from Principal Components Analysis of physicochemical characteristics for each lake. 33 Rushland Higinbotham 3 Wendover • - Quaker & Brownfield 2' (N 1 u 0 0^ -1 -2 -3 I c3 c — 3 > o < < 00 O o Z u OH Q Maxey Leroy Elmore 3 Buster Long Jack Stevens 2 (N 1 u 0 y ^ v^~:---v:^^ cu -1 ^•^ ..^^'- "a,-- ^:.^.^r-: : ~: -2' ••••C-' '\ .'"' -3' v en 1 1 1 1 1 1 1 1 1 1 r X) ta^-c — Boa,tj>H = -'= Month Fig. 2.7. Seasonal patterns of PC 2 from Principal Components Analysis of physicochemical characteristics for each lake. 34 Rushland Higinbotham Wendover 3 Quaker & Brownfield 2 1 ::-.c CO U 0 -1 -2 -3 T r T- en C 50 > ON 3 o o CcO x(>U 3 oo u LIH X3 o Q >—> < Maxey Leroy Elmore Buster Long Jack Stevens 3 2' 1 en U 0 Cu -1 • -2' -3' m c ^^ ftO O, o > o c X) CO 3 3 u lU CO u < —s 00 o 1—> X) < o z Q uu UH Month a *«• s: s Fig. 2.8. Seasonal patterns of PC 3 from Principal Components Analysis of physicochemical characteristics for each lake. 3 35 Alkalinity Biological oxygen demand Chemical oxygen demand Conductivity Dissolved oxygen Hardness Inorganic carbon Ammonia Nitrate-nitrite pH Ortho-phosphorus Total Carbon Temperature Total Kjeldahl nitrogen Total organic carbon Total phosphorus Precipitation 1 week Precipitation 1 month Single family housing Multiple family housing Commercial Park P < 0.01 P < 0.05 P > 0.05 s 3a Fig. 2.9. Pearson-Product Moment Correlation correlogram for physicochemical characteristics, precipitation, and land-use categories associated with selected urban lakes in Lubbock, Texas. 36 CHAPTER m AQUATIC MACROINVERTEBRATE DIVERSITY AND COMMUNITY COMPOSITION IN URBAN LAKES Introduction Macroinvertebrate Species Diversity Macroinvertebrate species diversity and community composition are important topics in aquatic ecology, and are often used to evaluate environmental stress resulting from urbanization and industrialization (Caims & Pratt 1993). Factors that affect macroinvertebrate diversity in lentic systems may be categorized into three groups: (1) environmental conditions, which consist of abiotic characteristics (e.g., temperature, photoperiod, oxygen, hydrology) (Ward 1992); (2) habitat attributes, such as vegetative complexity (Merritt & Cummins 1984); and (3) species interactions, such as competition, predation, and mutualism, which define trophic relationships within the community (Ward 1992). Aquatic invertebrate diversity is a result of interactions between all factors. However, the first two groups of factors may play more dominant roles in structuring aquatic invertebrate communities when human activities alter habitat quality (Ward 1992). Aquatic insects have been used extensively as indicators of environmental status in lotic and lentic systems (Gower et al. 1994; France 1995; Tate & Heiny 1995). They possess many of the desired characteristics required of indicator species, such as relative ease of identification, limited mobility and relatively long life cycles. These features lend themselves to temporal and spatial analysis of community composition (Abel 1989). To date, most macroinvertebrate research conducted in lentic ecosystems, has focused on benthic community composition as a function of lake productivity or trophic structure (Macan 1949; Kullberg 1992; France 1995; Hanson & Butler 1994). However, a few 37 studies have examined the composition of littoral invertebrate communities as a function of water quality characteristics (Hershey 1985; Hammer et al. 1990; Rasmussen 1993; Wollheim & Lovvom 1995). For example, Macan (1949) related corixid community composition to calcium and organic matter content of water, and vegetation complexity within lakes. Certain corixid taxa were limited to oligotrophic lakes lacking higher plants and having low calcium content. As the level of organic matter increased within the lake, corixid community composition also changed (Macan 1955). Jansson (1977) observed similar pattems in corixid communities, and attributed them to species-specific differences in oxygen and food requirements, and generally discounted interspecific competition as a major influence on community composition. Other aquatic insects (e.g., Haliplus beetles), show similar relationships between community composition and productivity (Seeger 1971), suggesting the general importance of environmental factors in structuring communities. Considerable research has focused on aquatic macroinvertebrate species diversity and community composition of ephemeral lakes (playas) in the Southern High Plains (Sublette & Sublette 1967; Merickel & Wangberg 1981; Smith 1993). Over sixty species of aquatic macroinvertebrates have been identified in rural habitats. However, little attention has been given to macroinvertebrates in urban lakes. Due to physical and morphological differences between rural playas and urban lakes in this region, invertebrate community composition may differ, especially with regard to species that require alternating wet and dry periods or cannot survive fish predation. Other factors that may differentially affect macroinvertebrate community composition in playas and urban lakes include surface water quality, habitat complexity (i.e., presence and absence of littoral vegetation), trophic status (i.e., oligotrophy versus eutrophy), and hydrological connections (drainage pattem). 38 Obiectives The objectives of the macroinvertebrate samphng portion of the present study were to assess macroinvertebrate diversity and community composition of lakes, and if they varied as a function of a priori groupings of lakes (Chapter II). Second, to assess whether differences in aquatic macroinvertebrate diversity or community composition were correlated with differences in water quality characteristics (Chapter IV). Evaluating possible relationships between community composition and water quality may elucidate the mechanisms that control macroinvertebrate species diversity in urban lakes, as well as provide insight to possible effects of urban land-use on aquatic macroinvertebrate community composition. Materials and Methods Study Site This study focused on 8 of 32 lakes that occur within the City of Lubbock, Texas (10r52' N latitude, 33°35' W longitude). Lakes were selected to include a range of physical characteristics representative of urban lakes, such as basin size, relative potential for nonpoint source pollution, and presence of aquatic vegetation, and were categorized into three groups based on these attributes (Table 2.1). A detailed description of the lakes used in this study is contained in Chapter II. Macroinvertebrate Sampling Macroinvertebrates were collected from each lake on six dates beginning in March and concluding in December, 1993. The sampling regime was structured to capture seasonal components (spring, March and April; summer, June, July, and August; fall, November-December) of macroinvertebrate community composition. A uniform, stratified sampling regime was used to collect invertebrates from the pleustonic, nektonic, and 39 emergent vegetation areas of the littoral zone in each lake. Invertebrate collections were limited to waters < 1.2 m deep (maximum depth for chest-waders). Benthic invertebrates were collected with three Ekman grabs and washed on a 500 |im sieve. Nektonic invertebrates were sampled less than 10 cm above the sediments with 20, 5-m sweeps using a 500 jim mesh aquatic D-net. Pleustonic invertebrates were sampled with 20, 5-m sweeps using a 500 jim mesh aquatic D-net at the air-water interface. When submerged or emergent macrophytes were present, epiphytic invertebrates were sampled with 20, 5-m sweeps, through the vegetated zone, using a 500 |xm mesh aquatic D-net. Five of the 20 nekton, pleuston, and vegetation samples were collected from each of the north, south, east, and west quadrats of each lake (approximately 90° arcs). The nekton and pleuston samples were uniformly spaced along the littoral circumference of each quadrat. If vegetation was not present in a quadrat, then vegetation samples were divided between the nektonic and pleustonic samples, so that a total of 15 samples were taken from each quadrat. Invertebrates were separated from organic matter, initially preserved in 95% ethanol, and later transferred into 80% ethanol for storage prior to identification. Invertebrates were separated based on morphotypes and identified to the generic level. Because individuals were not identified to the species level, the term "taxa" is used to represent distinct morphotypes. Voucher specimens identified by Dr. Robert Sites (University of Missouri) aided in these identifications. Macroinvertebrate Communitv Analyses Characterization of macroinvertebrate communities began with estimations of species richness and evenness for each lake. Species richness is the total number of taxa collected, whereas evenness depends on the relative abundance of individuals representing each taxa (Begon et al. 1990). Individual lake data were combined to produce overall 40 richness and evenness values for each lake group. Also, invertebrates were grouped according to familial level or a higher taxonomic level (i.e., order, class) and used in community similarity measures and Principal Components Analyses. The grouping of taxa by family or order minimized problems associated with the occurrence of few individuals representing a single taxa in community similarity measures. It also allows one to assess whether the taxonomic level of identification affects the resolution of community analyses. Henceforth, the term "familial" refers to the grouping of a taxa into a higher taxonomic level in this study. The assortment of diversity measures in the ecological literature often leads to difficulty in choosing the appropriate index. Taylor et al. (1976) examined a variety of diversity measures and found that Fisher's log series a was the most effective at discriminating between communities with small differences in diversity. For this reason, I used the Fisher's log series a index to elucidate differences in macroinvertebrate species diversity with respect to groups of lakes and among individual lakes. Fisher's log series a is sensitive to both species richness and species evenness, although it weights species richness more heavily; values range from zero to infinity (Magurran 1988). It is also robust to deviations from log series distributions and to variations in the total number of individuals sampled (Magurran 1988). Fisher's log series index (Taylor et al. 1976) is defined by: a = N{\-x)l X where N is the total number of individuals and x is a parameter estimated by an iterative procedure. More specifically, x is defined as the ratio of species richness (5) to total number of individuals (N) and can be estimated from: ^/Ar = [(i-.v)-'][-ln(l-.v)]. 41 Fisher's log series diversity values for both lake groups and individual lakes were considered to be statistically different if the 95% confidence limits of a did not overlap. Even though this method drastically increases experiment-wise error rate, results can still be used heuristically. To assess whether observed species distributions adhered to the log series a model, expected species distributions were created using S and N values for each lake and their estimated x and a values (Magurran 1988). Chi-square goodness-of-fit tests were used to compare observed versus expected species distributions for each lake, and to evaluate whether the observed distribution was significantly different from the expected log series a model (Sokal & Rohlf 1995). Both species diversity and richness indices utilize numerical data such that two systems could exhibit the same species richness and diversity, but their community composition could be completely different (Begon et al. 1990). Community composition reflects species richness and relative abundances but retains a taxonomic component. To evaluate the relationships among urban lakes based on macroinvertebrate community composition, four widely used community similarity measures were selected (Euclidean distance. Cosine, Jaccard, Ochai). The Euclidean distance and Cosine similarity measures are based on species abundance data, but differ in the degree to which they are sensitive to species abundance. The Euclidean distance measure (Ej^) is defined as: E^,=Jl{x,-x,J ^j^ .,,=, where x-. and x-, are the abundances of the/"species (s) for lake; and k, respectively. This ij ik measure emphasizes differences in species abundances between lakes, and ranges from zero to infinity. The Cosine measure (C^J is defined by the following equation: 42 (C )= -^'=i^-^ty'^/<:) Cjk ranges from zero to V2, and places greater importance on the relative abundances of species (Ludwig & Reynolds 1988). Jaccard and Ochai similarity measures are based on presence-absence data and differ in the manner in which shared species are considered. The Jaccard index (Jj^) is given by: /.. = " ^'' a + b + c where (7^^) ranges from zero to one, and a is the number of species in common to lakes / and k, b is the number of species that occur in 7 but not k, and c is the number of species that occur in k but not7 (Ludwig & Reynolds 1988). The Ochai index (O^^) is the binary form of the Cosine index and is given by the following equation: o. = " ^'^ -Ja + b-^a + c and (O J also ranges from zero to one (Ludwig & Reynolds 1988). The Jaccard, Ochai and Cosine similarity values were transformed into dissimilarity values. An eight by eight matrix was created for each measure and clustered subsequently with a unweighted pair-group method average algorithm (UPGMA) using taxa-level and familial-level data, separately (SPSS 1992). PCA was used to identify macroinvertebrate families that contributed the most unique variation observed in urban lakes. PCA is a multivariate technique that is used to identify a subset of factors that can be used to illustrate relationships in large data sets (SPSS 1992). The first three PC scores for each lake were plotted in pair-wise 43 combinations to distinguish urban lakes containing invertebrate families contributing to unique variation in taxa abundances. The eigen value for each factor was used to determine the percent variation attributed to each family. Results Richness and Abundances for Individual Lakes Over 10,500 individuals representing at least 94 taxa and 45 families of aquatic invertebrates were collected during the study (Table 3.1, Appendix B.l). Between 29-43% of the total recorded species were collected in the following lakes: Rushland (33%), Higinbotham (32%), Wendover (38%), Quaker & Brownfield (43%), and Maxey (29%) (Fig. 3.1). Jack Stevens contained 63% of the total recorded species (Fig. 3.1), whereas Leroy Elmore and Buster Long lakes were relatively species-poor, containing 16% and 19% of the total species collected, respectively. Although taxa richness was similar in five lakes (see above), relative abundances of invertebrates were highest in three lakes. Over 70% of the total number of individuals collected were from the Rushland (19%), Wendover (21%), and Jack Stevens lakes (30%). The remaining individuals were evenly distributed among Higinbotham (6%), Quaker & Brownfield (9%), Maxey (7%), and Buster Long (7%). Leroy Elmore contributed less than 1% of the total numbers of individuals (Fig. 3.1). Abundance data revealed that four families of insects (Corixidae, Notonectidae, Coenagrionidae, and Chironomidae) and two groups of Crustacea (Palaemonidae and Cladocera) dominated invertebrate communities. In Rushland and Wendover lakes, the Notonectidae (backswimmers) and Corixidae (water boatmen) accounted for 38% and 44% of the total number of individuals, respectively (Fig. 3.2). In Quaker & Brownfield and Maxey lakes, a freshwater shrimp (Palaemonetes kadiakensis) was the dominant species, accounting for 33% and 61% of the relative abundances of invertebrates collected from 44 each lake, respectively (Fig. 3.4, 3.5). Similarly, a crustacean (cladocera) accounted for 62% of the total numbers of individuals collected from Buster Long lake (Fig. 3.3). In Leroy Elmore and Higinbotham lakes, the Chironomidae accounted for 71% and 28% of the relative abundances, respectively (Fig. 3.2, 3.3). Lastly, a damselfly (Coenagrionidae) accounted for 26% of the total numbers of individuals collected in Jack Stevens lake (Fig. 3.4). Rank abundance plots illustrated similar pattems in the distributions of macroinvertebrate taxa between Rushland, Wendover, Quaker & Brownfield, and Jack Stevens (Fig. 3.5). These lakes were relatively taxa rich, containing between 30 and 59 taxa (Fig. 3.1). For all lakes, except Buster Long, distributions of total macroinvertebrate taxa (combined seasons) adhered to the log series model (Fig. 3.5). The observed taxa distribution was significantly different (P = 0.029) from the expected log series distribution only for Buster Long lake. Diversity for Individual Lakes Fisher's log series a was calculated for each lake based on macroinvertebrates collected in the spring, summer, and fall, as well as for combined seasons data (Fig. 3.6, Appendix B.2). Jack Stevens lake consistently had the highest Fisher's a throughout all seasons and was statistically different from all lakes, except Quaker & Brownfield during the spring. Fisher's log series a could not be calculated for Higinbotham and Leroy Elmore lakes during the spring, because species abundances were evenly distributed among the few species collected. For the summer, invertebrate diversity of Buster Long lake was significantly different from that in Quaker & Brownfield and Jack Stevens lakes. For the fall, invertebrate diversity of Leroy Elmore and Buster Long lakes were each significantly different from the diversity within Rushland, Higinbotham, Quaker & Brownfield, and Jack Stevens lakes. When data were combined for all seasons, the invertebrate di\'ersit> in 45 Buster Long lake was significantly different from the diversity in Jack Stevens and Quaker & Brownfield lakes. Richness and Abundances for Groups of Lakes Data were pooled within a priori groups of lakes to address whether macroinvertebrate richness, abundance, and diversity differed among lakes, as classified by physical attributes. The first group of lakes shared 27 taxa, which accounted for 47% of the total number of invertebrates collected during this study. Group 2 lakes shared 12 taxa, which accounted for 14% of the total number of invertebrates collected. Lastly, group 3 lakes shared 26 taxa, accounting for 39% of the total number of invertebrates collected. No significant differences existed between a priori groups with respect to species diversity for the combined seasons and for the summer (Fig. 3.7, Appendix B.l). However, species diversity of group 2 was statistically different from that of group 3 during the spring, and from that of groups 1 and 3 during the fall (Fig. 3.7). Four groups of invertebrate taxa were numerically dominant in lake groups 1 and 2 (Fig. 3.8). In group 1 lakes, the Notonectidae (33%), Corixidae (24%), Coenagrionidae (15%), and Chironomidae (12%) dominated invertebrate abundances, while in group 2 lakes, the Crustacea (32%), Palaemonidae (28%), Corixidae (20%), and Chironomidae (12%) were dominant. In contrast, macroinvertebrates in group 3 lakes were more evenly distributed among: Baetidae (13%), Gastropoda (12%), Chironomidae (12%), Palaemonidae (8%), Corixidae (7%), Libellulidae (6%) and Crustacea (5%), although the Coenagrionidae (23%) were the most numerous. Community Similarity Cluster analysis, based on Jaccard's similarity measure using combined seasons data, identified two clusters of lakes: (1) Rushland, Jack Stevens, Wendover, Maxey, 46 Higinbotham, and Quaker & Brownfield lakes, and (2) Buster Long and Leroy Elmore lake (Fig. 3.9). In contrast, cluster analysis based on Ochai's measure identified Rushland and Jack Stevens lakes as the most similar, sharing 26 of the 64 taxa collected in both lakes, while Maxey and Wendover were the next most similar, sharing 18 of the 45 species collected in both lakes (Fig. 3.10). Maxey and Wendover lakes were most similar to Higinbotham lake, and eventually clustered with Rushland, Jack Stevens and Quaker & Brownfield to form one large cluster. Pattems of association produced by cluster analysis based on Jaccard and Ochai measures were similar throughout all seasons, and actually showed few differences. Dendrograms based on the Jaccard and Ochai indices for combined seasons data were strongly influenced by the macroinvertebrate taxa collected during the summer, probably because approximately 86% of all individuals were collected during this time period for each lake. Similarities between the summer and combined seasons were also observed in the Euclidean distance and Cosine dendrograms (Figs. 3.11, 3.12). In contrast to the similar dendrograms based on Jaccard and Ochai measures, the Euclidean distance and Cosine measures produced disparate patterns using the overall data. The Euclidean distance measure appears to have more difficulty in distinguishing between lakes because it clustered Maxey, Leroy Elmore, Wendover, Rushland, Quaker & Brownfield and Jack Stevens into one cluster, while Buster Long and Higinbotham lakes were outliers (Fig. 3.11). Cluster analysis using Cosine dissimilarity measures of overall abundance data, identified two distinct clusters of lakes with a single outlier (Fig. 3.12). The first cluster consisted of Quaker & Brownfield, Maxey, Wendover, and Leroy Elmore lakes, with Quaker & Brownfield and Maxey being the most similar based on the proportion of taxa within each site. The second group consisted of Rushland, Jack Stevens, and Higinbotham lakes, with Rushland and Jack Stevens being the most similar. Buster Long lake was the most unique, based on taxa abundances. 47 Cluster analysis, based on Jaccard's and Ochai's dissimilarity matrices using the familial level data, provided comparable results to those based on finer-scale taxonomic data (Fig. 3.13). Ochai's dissimilarity measure identified two major clusters of urban lakes. In the larger cluster, Rushland and Jack Stevens were the most similar, and later combined with Higinbotham. Wendover, Maxey and Quaker & Brownfield were also part of this cluster. A second cluster consisted of Leroy Elmore and Buster Long, with these being the most dissimilar in comparison to the remaining lakes based on famiUal composition. Cluster analysis using Jaccard's dissimilarity measure provided essentially the same results as the Ochai's method. The Euclidean and Cosine dissimilarity dendrograms, using familial data, were nearly identical and differed little from the dendrograms based on finer-scale taxonomic resolution (Fig. 3.14). The only exception to this pattem was the switching of Wendover and Higinbotham lakes between major clusters in the Cosine cluster analysis using the overall data (compare Fig. 3.12 to Fig. 3.14). Buster Long lake was still identified as the most dissimilar with its zooplankton-dominated community. Principal Components Analysis PCA distinguished three lakes based on relative abundances of familial groups. The first three factor scores accounted for 75% of the variation observed in abundances of famihal groups (Fig 3.15). PC 1 was positively loaded with densities of gastropods, baetid mayflies, annelids, and aeshnid dragonflies, thus separating Jack Stevens from the other lakes. The first PC accounted for 44% of the variation observed in macroinvertebrate familial abundances of urban lakes. PC 2 was positively loaded with densities of chrysomelid beetles and gerrids, distinguishing Quaker & Brownfield lake. The second PC accounted for 16% of the variation observed in familial abundances. PC 3 was 48 positively loaded with notonectids and corixids, distinguishing Wendover lake from the others, and accounted for 15% of the observed variation. Discussion Although Fisher's log series a showed no significant differences in macroinvertebrate diversity among groups of lakes with respect to the combined seasons or summer data, there were significant differences in the spring and fall (Fig. 3.7). During the spring, group 2 lakes were significantly different from group 3 lakes, and in the fall, group 2 lakes were significantly different from both groups 1 and 2. The combined seasons data for each group reflects the influence of the summer sampling period in the estimation of the overall Fisher's log series a, because during this period, taxa richness reached a maximum. Even though overall diversity was not significantly different among groups of lakes for the combined seasons data, there were noticeable differences in community composition for each group. The aquatic macroinvertebrate communities of group 1 lakes were dominated by corixids, notonectids, coenagrionid damselflies, and chironomids, whereas invertebrate communities in group 2 lakes were dominated by Crustacea (cladocera, copepods, and a fresh water shrimp [Palaemonetes kadiakensis]). Lakes in group 3 contained 73 of the 94 taxa collected, representing all major families of invertebrates, with exception of the Collembola. However, the Coenagrionidae was the only dominant taxon in this group of lakes, and accounted for 22% of the total individuals. The remaining individuals were more evenly distributed among the rest of the families. Differences among groups of lakes with respect to invertebrate composition appears to be related to vegetation. Although diversity was not statistically different among groups of lakes for the combined seasons, analyses of individual lakes showed Jack Stevens to be significantly different from Buster Long and Leroy Elmore lakes (Fig. 3.6). There also was a seasonal 49 component to macroinvertebrate diversity. In the spring. Jack Stevens was statistically different from all lakes, except Quaker & Brownfield. However, during the summer and fall sampling periods, macroinvertebrate diversity of Jack Stevens lake was only statistically different from Buster Long and Leroy Elmore lakes. Jack Stevens, Buster Long and Leroy Elmore lakes represented the extremes in habitat quality observed in urban lakes for this study; Jack Stevens is a shallow, taxa-rich lake, supporting an extensive emergent and submerged macrophyte community, while Buster Long lake is a steep- sloping basin with no littoral vegetation, but high phytoplankton populations (Brownlow 1994). This may account for why the macroinvertebrate community at Jack Stevens is dominated by insects while Buster Long is dominated by zooplankton. Scheffer (1990) proposed that there are two basic types of shallow freshwater lakes: one with clear water dominated by macrophytes and the other with turbid waters dominated by phytoplankton. The macrophyte-dominated community is characteristic of low to intermediate nutrient levels and low turbidity. As nutrient levels increase, there is an associated increase in phytoplankton. Increased chlorophyll concentrations and suspended solids, combined with constant wind mixing, increase turbidity. Once turbidity increases, the euphotic zone decreases in depth, inhibiting the growth of submerged and emergent vegetation (Jupp & Spence 1977; Hough et al. 1989; Scheffer 1990). In lentic systems, changes in macroinvertebrate communities accompany changes in plant communities. Wollheim and Lovvom (1995) found that in shallow lakes of the Wyoming High Plains, a decrease in macrophyte habitat corresponded to a major reduction in aquatic invertebrate biomass. However, this loss of biomass was offset by an increase in zooplankton biomass. Similarly, in urban lakes of Lubbock, Texas, lack of macrophytes and high phytoplankton populations corresponded to increased numbers of zooplankton and reduced numbers of insect taxa. The higher diversity and abundances of other invertebrates found in group 3 lakes is Hkely a result of greater macrophyte dominance. Wollheim and 50 Lovvom (1995) also found that littoral macrophyte habitats supported a greater diversity and abundance of aquatic invertebrates. This greater abundance of invertebrates occurs because macrophytes increase niche space, provide stmctural support (Rosine 1955; Rooke 1984), provide higher food quality (Carpenter & Lodge 1986), and cover from predators (Crowder & Cooper 1982). A further discussion of habitat and water quality correlations with macroinvertebrate abundances is found in Chapter IV. Of the community similarity measures utilized in cluster analysis, the Cosine similarity measure appeared to represent the most faithful pattem of lake associations because of its finer resolution in clustering lakes based on their shared taxa. Also, cluster analysis based on community similarity measures does not support the a priori assumption that each group of lakes has an unique invertebrate community. Moreover, the level of taxonomic classification had little impact on the clustering of lakes; only Wendover and Higinbotham switched positions between the familial and taxa-level dendrograms. Cluster analysis using Cosine's dissimilarity measure identified two groups of lakes based on their macroinvertebrate familial composition. The first group was composed of Quaker & Brownfield, Maxey, Higinbotham, and Leroy Elmore lakes, while the second group consisted of Rushland, Jack Stevens, and Wendover lakes (Fig. 3.14). Again, the macroinvertebrate community composition of Buster Long was considerably different from all other lakes, primarily consisting of cladocera and copepods. Differences associated with these two main clusters are linked to a few macroinvertebrate taxa. Rushland, Jack Stevens and Wendover lakes contained relatively greater numbers of Notonectidae (backswimmers) Baetidae (mayflies), Caenidae (mayflies), Coenagrionidae (damselflies), and Chironomidae (midges). In contrast, Quaker & Brownfield, Maxey, Leroy Elmore, and Higinbotham lakes contained relatively greater numbers of individuals representing the Palaemonidae (freshwater shrimp), Corixidae (water boatmen), and Gerridae (water striders) families. 51 The physical connectedness of lakes, described in Chapter II, provided little insight to macroinvertebrate species composition of lakes. Lakes with physical connections only shared about 33% of the total species collected. However, the freshwater shrimp {Palaemonetes kadiakensis) was only found in Higinbotham, Quaker & Brownfield and Maxey, which suggests that the physical connectedness of these lakes may influence the distribution of this species (Fig. 2.1). Principal Components Analysis also provided little insight to differences in lakes, based on macroinvertebrate species composition, although it did identify invertebrate families providing the most unique variation to these lakes. PC 1 and PC 2 separated both of the most vegetated lakes from the others, based on the abundance of gastropods, baetid mayflies, annelids, aeshnid dragonflies, chrysomelid beetles and gerrids. PC 3 separated Wendover lake based on corixids and notonectids collected during August and November. While PC 1 and 2 may reflect habitat diversity provided by vegetation, the reason for large numbers of corixids and notonectids in Wendover is unclear. Perhaps, the development of seasonal, emergent vegetation may be a key factor in stmcturing corixid and notonectid communities in this lake. In summary, richness of urban lakes ranged between 15 to 59 taxa, with most lakes containing an average of 32 taxa. However, richness and diversity were low for Maxey, Buster Long and Leroy Elmore lakes because a few taxa accounted for > 60% of the individuals collected in each of these lakes. Jack Stevens was the only lake that consistently had high richness. The presence of aquatic macrophytes in group 3 lakes was associated with greater taxa richness and diversity, although community composition differed. Development of annual wetland vegetation in group 1 lakes may influence species diversity and abundance, and the lack of vegetation in group 2 lakes appears to be associated with a shift in macroinvertebrate community composition from an insect dominated community to one dominated by Crustacea. 52 Literature Cited Abel, P.D. 1989. 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Macroinvertebrate standing crop in littoral regions of allochthonous detritus accumulation: ImpUcations for forest management. Biological Conservation. 75:35-39. Gower, A.M., G. Myers, M. Kent, and M.E. Foulkes. 1994. Relationships between macroinvertebrate communities and environmental variables in metal-contaminated streams in south-west England. Freshwater Biology. 32:199-221. Hammer, U.T., J.S. Sheard, and J. Kranabetter. 1990. Distribution and abundance of littoral benthic fauna in Canadian prairie saline lakes. Hydrobiologia. 197:173-192. Hanson, M.A., and M.G. Butler. 1994. Responses of food web manipulation in a shallow waterfowl lake. Hydrobiologia. 279/280:457-466. Hershey, A.E. 1985. Littoral chironomid communities in an arctic Alaskan lake. Holarctic Ecology. 8:39-48. Hough, R.A., M.D. Fornwall, B.J. Negele, R.L. Thompson, and D.A. Putt. 1989. Plant community dynamics in a chain of lakes: Principal factors in the decline of rooted macrophytes with eutrophication. 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Merickel, F.W., and J.K. Wangberg. 1981. Species composition and diversity of macroinvertebrates in two playa lakes on the Southem High Plains, Texas. The Southwestern Naturalist. 26:153-158. Merritt, R.W., and K.W. Cummins. 1984. An introduction to the aquatic insects of North America. 2^^^ edition. Kendall/Hunt Company, Dubuque, Iowa. Rasmussen, J.B. 1993. Pattems in the size stmcture of httoral zone macroinvertebrate communities. Canadian Journal of Aquatic Sciences. 50:2192-2207. Rooke, B.J. 1984. The invertebrate fauna of four macrophytes in a lotic system. Freshwater Biology. 14:507-513. Rosine, W.N. 1955. The distribution of invertebrates on submerged aquatic plant surfaces in Muskee Lake, Colorado. Ecology. 36:308-314. Seeger, W. 1971. Die biotopwhal bei Halipliden, zugleich ein Beitrag zum problem der syntopischen (sympatrschens str.) Arten (Halipidae; Coleoptera). Archiv fur Hydrobiologie. 69:155-199. Scheffer, M. 1990. Multiplicity of stable states in freshwater systems. Hydrobiologia. 200/201:475-486. Smith, C.L. 1993. Water boatmen (Hemiptera: Corixidae) faunas in the playa lakes of the Southem High Plains of Texas. Master's Thesis, Texas Tech University, Lubbock, Texas. Sokal, R.R., and F.J. Rohlf 1995. Biometry: The principles and practice of statistics in biological research. 3^^ edition. W.H. Freeman & Co., New York, New York. SPSS. 1990. Statistical package for the social sciences. SPSS Inc. Chicago, Illinois. Sublette, J.E., and M.S. Sublette. 1967. The limnology of playa lakes on the Uano Estacado, New Mexico and Texas. The Southwestern Naturalist. 12:369-406. Tate CM andJS Heiny. 1995. The ordination of benthic invertebrate communities in the South Platte River Basin in relation to environmental factors. Freshwater Biology. 33:439-454. 54 Taylor, L.R., R.A. Kempton, and LP. Woiwod. 1976. Diversity and the log series model. Journal of Animal Ecology. 45:255-271. Ward, J.V. 1992. Aquatic insect ecology: Biology and habitat. John Wiley & Sons, New York, New York. Wollheim, W.M., and J.R. Lovvorn. 1995. SaUnity effects on macroinvertebrate assemblages and waterbird food webs in shallow lakes of the Wyoming High Plains. Hydrobiologia. 310:207-223. 55 Table 3.1. Systematic list of aquatic invertebrate taxa collected from selected urban lakes in Lubbock, Texas. ANNELIDA ARTHROPODA (continued) Hirudinea Insecta Gnathobdellida Odonata Hirudinidae * Aeshnidae sp. 1 Anax sp. sp. 2 Gynacantha sp. PharyngobdelUda Libellulidae Erpobdellidae * Belonia sp. sp. 1 Erythrodiplax sp. Orthemis sp. MOLLUSCA Pachydiplax sp. Gastropoda Perithemis sp. Plathemis sp. B asommatophora Tramea sp. Lymnaeidae B Coenagrionidae sp. 1 Enallagma sp. Physidae 6 Hemiptera sp. 1 sp. 2 Hydrometridae £ sp. 3 Hydrometra martini sp. 4 Macroveliidae £ Planorbidae B Macrovelia sp. sp. 1 Gerridae sp. 2 Gerris marginatus Rhematobates sp. Belostomatidae ARTHROPODA Belostoma flumineum Cmstacea Nepidae £ Cladocera Y Ranatra nigra sp. 1 Corixidae Eucopepoda Y Corisella edulis Calenoid sp. Corisella tarsalis Cyclopoid sp. Ramphocorixa sp. Ostracoda Y Sigara altemata sp. 1 Notonectidae sp. 2 Buenoa sp. 1 Decopoda Buenoa sp. 2 Palaemonidae Notonecta undulata Palaemonetes kadiakensis MesoveUidae Insecta Mesovelia mulsanti Collembola Hebridae £ Isotomidae Hebrus sp. sp. 1 Saldidae Ephemeroptera sp. 1 Baetidae Saldula pallipes Callibaetis sp. Saldula sp. 2 Caenidae Caenis punctatus 56 Table 3.1. Continued. ARTHROPODA ARTHROPODA (continued) Insecta Insecta Coleoptera Coleoptera Gyrinidae § Salpingidae § Dineutus sp. Limnebius sp. Haliplidae Georyssidae § Haliplus sp. 1 Georyssus sp. Haliplus sp. 2 Chrysomelidae Peltodytes sp. Disonycha sp. Dytiscidae Donacia sp. Brachyvatus sp. sp. 1 Copelatus sp. Diptera Laccophilus fasciatus Tipulidae¥ Laccophilus proximus sp. 1 Liodessus sp. Culicidae ¥ Neobidessus sp. Culex sp. Thermonectus sp. Chaoboridae ¥ Uvarus sp. Chaoborus sp. Sphaeridae § Psychodidae ¥ Sphaerius sp. Pericoma sp. Hydrophilidae Ceratopogonidae ¥ sp. 1 Berosus sp. 1 Chironomidae Berosus sp. 2 adult sp. 1 Berosus sp. 3 adult sp. 2 Berosus sp. 4 adult sp. 3 Berosus sp. 5 larvae spp. Enochrus sp. Stratiomyidae Helophorus sp. Ondontomyia sp. Hydrochous sp. Tabanidae¥ Hydrophilus triangularis Tabanussp. Laccobius sp. Ephydridae ¥ Paracymus sp. sp. 1 Tropistemus lateralis Muscidae ¥ Staphylinidae § sp. 1 Micaralymma sp. Stenus sp. 1 Stenus sp. 2 * Families were combined to fomi the Annelida group for familial data analyses, Y Taxa in these orders were combined to form the Cmstacea group for familial data analyses. Gastropoda group for familial data analyses. B Families were combined to form the Coleoptera (other) group for familial data analyses. § Families were combined to form the Diptera (other) group for familial data analyses. ¥ Families were combined to form the Hemiptera (other) group for familial data analyses. £ Families were combined to form the 57 Fig. 3.1. Taxa richness and total numbers of aquatic invertebrates collected from each lake, for all sampling dates combined. 58 50 n Rushland 40- 30- 20- 10- 0' T—I—r T—r 50' Higinbotham 40- o 30- Wendover 40' 30' 20' 10' 0' T—I—I—r 4) U 4) 1> O 1) (L) U ^rtcCcdcticacg ca c^ c^ cd cd _^ _^ T3 T3 ^ "O "O "O "P T3 -o i2 -c -g '5 'c "c 3 'c S 't "-S .>< S = 2 o -5. ?^ ;^ •« S •£ > y^ E 3 U 3 ^ C/3 ca C3 Q. E o • 1 ((LD> < Fig. 3.2. Relative abundance of invertebrates collected from Rushland, Higinbotham, and Wendover lakes (Group 1). 59 61 I Maxey 60.7 40- 30- 20- 10- T—r-T T-T—r-T—r I 71 \ Leroy Elmore 71.1 40- o c 30- 20- > 10- 0 T-T- I I r 1—I—r I Jff, 62-1 H 62.1 Buster Long 40- 30- 20 10-1 0 T—r T—r ^ T—I—I—I—I—r T—i—I—r-T 1—I—r 1^ 4> 4> 4) Fig. 3.3. Relative abundance of invertebrates collected from Maxey, Leroy Elmore, and Buster Long lakes (Group 2). 60 50-1 Quaker & Brownfield 40- 30- 20- oo (U 10- O c 03 '^rf I T I f TT I I T I I PT C 50-1 c3 D y^c^ Stevens > 4—> 40- 13 30- 20- 10- cacdca4)4)4)4i4)4)4) 4) 4) 4J 4) 4> 4) 4>T34)cap3c3ci3cacac3 4) 4) 4) 4) I. ca cd ca ca ca cd 4) T3 "O T3 T3 -O T3 ca cd ca ca S 'P -S •— -n ••- •— -^ T•—3 T.—3 T.X3 ."O_ .JS3 -TS1 -s^ 2 U o 4) o C3 •—' 4) a, ° I ^ « £ ca o 4°) Z S o T" J^ '^ .3 " U E 4) 4) "o •* U Q U "^ U Fig. 3.4. Relative abundance of invertebrates collected from Jack Stevens and Quaker & Brownfield lakes (Group 3). 61 lOOOn 1000-1 P = 0.877 P = 0.611 100 100- 10 10- 1 1 Rushland Higinbotham 0.1- T 1 1- 1 0.1 1 \ 1 1 1000- uuu- P = 0.602 P = 0.329 • 100- 100- •• t •• 10' 10- • ••• •••• 1' •••••••••• 1- • •••••••••••••••• • o Wendover Quaker & Brownfield 73 0.1' 1 1 1 1 u.i —1 1 1 1 1 1000' 1000-I P = 0.068 P = 0.633 100' 100- 10' 10 1- n Maxey Leroy Elmore 0.1 r 0.1 1 1 1 1 0 10 20 30 40 1000- 1000- P = 0.029 P = 0.331 100- 100- Ns 10- 10- 1- 1- Buster Long Jack Stevens 0.1 1 1 1 1 0.1 —I 1 1 1 1 1 0 10 20 30 40 0 10 20 30 40 50 60 Taxa Sequence Fig 3 5 Rank abundance plots of aquatic invertebrates for selected urban lakes in Lubbock Texas The P values are for chi-square goodness-of-fit tests, comparing observed'abundance distributions to expected log series distributions (note the scale difference for Jack Stevens). 62 12-, 10- Spring 8- b 6- T ab 4- a a 2- T a 1 T T • T 0 2 1 1 ^ I 12-, 10 - Summer ab 8- ab ab ab T ab ab 6- T T 1 4- T 1 I T a C/3 i 1 2- 1 T • •—( ' I • 1 o Tc/3: 12-, 10- Fall oo 8- 6^ a a a a 4- T T 1 T ab ab 2- 1 T b^ 1 0- i I 1— 14-, 12 - Combined ac T abc 10- abc abc abc 8- ab T T 1 T T 6- 1 1 1 T T b 4- 1 1 T 2- 1 _L 0 1 "1— 1^ "T" B Ui bD t/3 a > C 13 O O c •t—I «^ o o > c Ui 4—1 B bX) C/3 PQ 3 o o PQ 03 Ui 1) Fig. 3.6 Fisher's log series a diversity index for each lake in each of three seasons and for combined data. Vertical bars represent 95% confidence interval, and the same letters denote lakes that are indistinguishable. 63 14 12 Spring 10 ab 8 T 6 4 a 1 2 T 0 1— 14 12 Summer 10 a T 8 T T 1 6 i 1/) 1 (U 4 .^^ UI 0- 1 1 1 (N en OH OH 3 3 3 O O O oUi ou oUi Fig. 3.7. Fisher's log series a diversity index for each group of lakes in each of three seasons and for combined data. Vertical bars represent 95% confidence interval, and the same letters denote groups that are indistinguishable. 64 £ 30- 3 20- J loH 01^3 Fig. 3.8. Relative abundance of invertebrates for each group of lakes. 65 Rescaled Dissimilarity 0 5 10 15 20 25 Spring I- -4- 1 1 -I- -I Quaker & Brownfield Maxey 3 Higinbotham Leroy Elmore Buster Long Jack Stevens Rushland Wendover Summer Higinbotham Maxey Wendover Rushland Quaker & Brownfield Jack Stevens Leroy Elmore Buster Long Fall Rushland Quaker & Brownfield Zh Maxey Jack Stevens Higinbotham Leroy Elmore Wendover Buster Long Overall Rushland Higinbotham 3 Wendover Maxey 3 Quaker & Brownfield Jack Stevens Leroy Elmore Buster Long Fig 3 9 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) based on Jaccard's dissimilarity index for presence-absence of mvertebrate taxa. 66 Reseated Dissimilarity 0 10 15 20 25 Spring 1 1— -4- Quaker & Brownfield Maxey 3 Higinbotham 1 Leroy Elmore Buster Long Jack Stevens Rushland Wendover Summer Higinbotham Maxey Wendover Rushland Quaker & Brownfield Jack Stevens Buster Long Leroy Elmore Fall Rushland Quaker & Brownfield 3 Maxey Jack Stevens Higinbotham Leroy Elmore Wendover Buster Long Overall Rushland Jack Stevens Wendover Maxey Higinbotham Quaker & Brownfield Buster Long Leroy Elmore Fig 3 10 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) based on Ochai's dissimilarity index for presence-absence mvertebrate taxa. 67 Reseated Dissimilarity 0 10 15 25 Spring 20 1 1 -f- Wendover Leroy Elmore Rushland Higinbotham Quaker & Brownfield Maxey Jack Stevens Buster Long Summer Leroy Elmore Buster Long Wendover Quaker & Brownfield Maxey Rushland Higinbotham Jack Stevens Fall Higinbotham Leroy Elmore 3 Quaker & Brownfield Maxey Buster Long Rushland Jack Stevens Wendover Overall Maxey Leroy Elmore Wendover Rushland Quaker & Brownfield Jack Stevens Buster Long Higinbotham Fig 3 11 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) based on Euclidean distance index for invertebrate abundances. 68 Reseated Dissimilarity 0 10 15 20 25 Spring Quaker & Brownfield + + Maxey 3 Rushland Leroy Elmore Higinbotham Buster Long Jack Stevens Wendover Summer Quaker & Brownfield Maxey 3 Wendover Leroy Ehnore Buster Laig Rushland Higinbotham Jack Stevens Z} Fall Quaker & Brownfield Maxey 3 Jack Stevens Rushland Wendover 3 Higinbotham Leroy Elmore Buster Long Overall Quaker & Brownfield Maxey 3 Wendover Leroy Elmore Rushland Jack Stevens Higinbotham Buster Laig Fiff 3 12 Seasonal and overall dendrograms from Cluster Analysis (UPGMA) blfed on Costne dLimilarity index for invertebrate abundances. 69 Rescaled Dissimilarity 0 10 15 20 25 1 1 -4- Rushland Jack Stevens z\- Higinbotham Wendover Maxey Quaker & Brownfield Leroy Elmore Buster Long Rushland Jack Stevens 3 Higinbotham Wendover Maxey Quaker & Brownfield Leroy Elmore Buster Long Fig 3 13 Dendrograms from Cluster Analysis (UPGMA) based on Jaccard's (top) and Ochai's (bottom) dissimilarity indices for presence- absence of invertebrate families. 70 Rescaled Dissimilarity 0 10 15 20 25 Higinbotham Leroy Elmore 3 Quaker & Brownfield Maxey 3 Buster Long Rushland Jack Stevens Wendover Quaker & Brownfield Maxey 3 Higinbotham Leroy Elmore Rushland Jack Stevens Wendover Buster Long Fig. 3.14. Dendrograms from Cluster Analysis (UPGMA) based on Euclidean distance (top) and Cosine (bottom) indices for invertebrate family abundances. 71 3 -| 2 - 1 - 1—H u ^ 0 - '6V^ -1 - ~i r -2 -1 0 1 PC 2 3 -, 2 1 -1 U OH 0 - %.V' 1 - n I r •1 0 1 PC 3 3 -| 2 - u 5 PH 0 - 2i8 6 7 0 1 PC 3 Fig. 3.15. Principal Components Analysis based on invertebrate families of selected urban lakes in Lubbock, Texas: 1, Rushland; 2, Higinbotham; 3, Wendover; 4, Quaker & Brownfield; 5, Maxey; 6, Leroy Elmore; 7, Buster Long; and 8, Jack Stevens. 72 CHAPTER IV RELATIONSHIPS BETWEEN AQUATIC MACROINVERTEBRATE DIVERSITY AND WATER QUALITY CHARACTERISTICS OF URBAN LAKES Introduction Species - Environment Relationships The influence of physicochemical attributes on the stmcture and composition of invertebrate communities has been a dominant theme in aquatic ecology (Macan 1963; Hynes 1970; Rosenberg & Resh 1993). Geomorphological features, water chemistry, and disturbance regimes influence spatial pattems of invertebrate community composition in lotic systems (Moss et al. 1987; Meyer et al. 1988; Corkum 1989; Death 1995). In lentic systems, trophic status (i.e., oligotrophic versus eutrophic), as well as physicochemical characteristics, are primary factors influencing invertebrate community composition (Rasmussen & Kalff 1987; Growns et al. 1992; Wollheim & Lovvom 1995). In many studies, diversity and community similarity measures have been used in conjunction with multivariate analyses to evaluate relationships between macroinvertebrate diversity and water chemistry (Dyer 1978; Pedersen & Perkins 1986; Gower et al. 1994; Death 1995; Tate & Heiny 1995). For example, Gower et al. (1994) found that alkalinity, pH, dissolved organic matter, and algal cover were significandy correlated to abundances of aquatic macroinvertebrates in European streams. In Chapter II, I demonstrated that significant differences existed among playas, with regard to several physicochemical characteristics. Moreover, some of these physicochemical attributes were significandy correlated to surrounding land-use pattems. In Chapter ffl, I demonstrated that invertebrate community composition varied greatly among urban lakes, although diversity was relatively similar for all lakes. In three lakes. 73 Crustacea dominated invertebrate abundances, whereas insects were more dominant in the other lakes. Therefore, it remains to more fully explore the relationships between aquatic invertebrate community composition and water quaUty characteristics. Objectives The objectives of this portion of the study were (1) to evaluate if similarities in macroinvertebrate community composition (Chapter HI) were correlated with similarities in water quality of urban lakes (Chapter II), and (2) to evaluate the relationships between numerically dominant invertebrates and physicochemical characteristics of these lakes. Relating invertebrate diversity to water quality may provide insight to the mechanisms that influence diversity in urban lakes of Lubbock, Texas. Materials and Methods The collection of physicochemical and aquatic macroinvertebrate data is outlined in Chapters II and III, respectively. In this chapter. Mantel's nonparametric test (Manly 1986) and a suite of stepwise multiple regression models (SPSS 1990) were used to evaluate whether differences in water chemistry are correlated with differences in aquatic macroinvertebrate community composition. The Mantel nonparametric test was used to ascertain the relationships between matrices of species similarity and water quality similarity among lakes. Because macroinvertebrates were collected on six different sampling dates (March, April, June, July, August, and November), only physicochemical data for these six dates were used in the analyses. Mantel's test evaluates the cortelation between two independent matrices and ascertains whether the association is stronger than one would expect by chance alone (Smouse et al. 1986). Two similarity matrices were constmcted for each sampling date, one characterizing pair-wise differences in macroinvertebrate composition (based on 74 abundances of individual taxa) and the other representing pair-wise differences in water chemistry. Because water chemistry attributes were measured differently (i.e., mg L'l, mmho, ° C), data were transformed into Z scores. The Cosine similarity measure then was used to constmct each matrix (SPSS 1990). The Mantel procedure randomly modifies one matrix to generate a random distribution of Z test statistic values for comparison with the observed Z value (Manly 1985). If the matrices are unrelated, then the observed Z value will be typical of the randomized values. A positive correlation between the matrices is indicated when the observed Z value is larger than the randomized values, suggesting a significant relationship between macroinvertebrate community composition and water quality (Manly 1985). Stepwise multiple regression was used to explore relationships between species abundances and physicochemical characteristics of urban lakes (Sokal & Rohlf 1995). To eliminate problems associated with rare species in statistical analyses, only species that accounted for more than 1% of the total number of individuals collected for all lakes were used in regressions (Table 4.1). Also, to reduce the influence of autocorrelation among independent variables in the regression equations, only those variables a priori considered to be biologically most relevant were used in the analyses (Table 4.1). It is important to note that standardized regression coefficients do not indicate the strength of the relationships between physicochemical characteristics and invertebrate abundances, they only assess whether the relationship is positive or negative (Sokal & Rohlf 1995). Another common misperception of stepwise multiple regression is that it can provide insight into the causal relationships between invertebrate abundances and environmental attributes. At best, stepwise multiple regression may identify a subset of environmental variables that can be considered the most parsimonious explanation for the variation observed in invertebrate abundances (Norris & Georges 1993). 75 Results Mantel's nonparametric test found a significant positive relationship between differences in physicochemical attributes and invertebrate community composition during November, although no significant relationships occurred during the other sampling periods (Table 4.2). Stepwise multiple regression identified eight physicochemical characteristics that were significandy related to abundances of eleven species (Table 4.3). Physicochemical characteristics that were entered into three or more regression models include: conductivity (6), total phosphoms (6), dissolved oxygen (4), and total organic carbon (3); alkalinity, biological oxygen demand, pH, and ammonia were each entered into one regression. Of the eleven multiple regression models, five were able to account for more than 40% of the variation observed in species abundances. For adult chironomids (midges), total phosphoms, alkalinity, and conductivity were significant variables, accounting for 76% of the variation observed in abundances. Similarly, for chironomid larvae, dissolved oxygen, conductivity, pH, and total phosphoms accounted for 47% of the variation observed in abundances. For two species of Notonectids, Buenoa sp. 1 and 2, conductivity, total organic carbon, and total phosphoms were significant variables, accounting for 40 and 58% of the variation in abundances, respectively. For one species of Corixidae, Corisella edulis, conductivity, total phosphoms, and biological oxygen demand accounted for 42% of the variation observed in species abundances. For the remaining species, Enallagma sp., Ramphocorixa sp., Mesovelia mulsanti, Tramea sp., Planorbidae sp. 1, and Cladocera sp., stepwise multiple regression accounted for 16 to 32% of the variation observed in their abundances (Table 4.3). Stepwise multiple regression found no significant relationships between physicochemical characteristics and the abundances of Callibaetis sp., Palaemonetes kadiakensis, Corisella tarsalis, and Sigara altemata. 76 Discussion Although urban lakes were highly variable with respect to water quality (Chapter II), these differences were not significantly con-elated to differences in aquatic macroinvertebrate community composition during the spring and summer sampling periods (Chapter III). However, individual species abundances were significantly correlated to conductivity, total phosphoms, dissolved oxygen, total organic carbon, alkalinity, pH, ammonia, and biological oxygen demand. Many of these attributes previously have been shown to influence aquatic invertebrate community composition (Effler et al. 1990; Hanmier et al. 1990; Gower et al. 1994; Malmqvist & Eriksson 1995; Wollheim & Lovvorn 1995). In shallow lakes of semi-arid regions of Wyoming, USA, invertebrate species richness has been reported to decline as salinity increased (Wollheim & Lovvom 1995). Also reported is a decrease in benthic and epiphytic invertebrate biomass with increasing salinity, concurrent with increased zooplankton biomass. Loss of benthic and epiphytic biomass may be indirectly related to salinity, because increased salinity is associated with decreased macrophyte growth. Wollheim and Lovvom (1995) found that habitats with emergent vegetation supported the greatest invertebrate biomass regardless of salinity, because macrophytes provided high quality foods (Carpenter & Lodge 1986) and cover from vertebrate predators (Crowder & Cooper 1982). In this study, salinity was not measured directly, but specific conductance may be used as a surrogate, because it is proportional to the concentration of major ions that contribute to more than 99% of total salinity (Rodhe 1949). Therefore, the positive correlation found between conductivity and notonectid, corixid, and chironomid abundances in this study, suggests that these taxa may be more tolerant to changes in overall ionic concentrations. Even though these urban lakes are not tmly saline, specific conductance was significantly higher (P < 0.001; Chapter II) in lakes containing the highest 77 corixid and notonectid abundances. Smith (1993) found that several species of corixids; Corisella edulis, C. tarsalis, Sigra altemata, and Ramphocorixa acuminata, prefen-ed vegetated or feedlot lakes, while two species, Trichocorixa reticulata, and T. verticalis, were limited to saline lakes. Hammer et al. (1990) also reported Trichocorixa verticalis to occur over a salinity range of 3 to 60 %^, but was most abundant in lakes of 28 to 60 ^^ salinity. Other species of corixids were limited by about 28 %^ salinity. Of these species, only Trichocorixa reticulata and T. verticalis were not collected in Lubbock lakes. This suggests broad tolerance to key environmental variables, which seems reasonable because most physicochemical characteristics of urban lakes have a seasonal component and are susceptible to sudden shifts. In the prairie lakes of southem Saskatchewan, Canada, Hammer et al. (1990) found that four of the 23 collected species of Chironomidae were numerically abundant (2548 m"2 to 12645 m"2) and each of these four were restricted to lakes of differing salinity, ranging from 6 to 75 ^^. In the present study, chironomid abundances showed a positive relationship to conductivity, although total phosphoms (positive) and dissolved oxygen (negative) individually accounted for more of the observed variation. Chironomid larvae were not identified to genus in the present study, because of the difficulty with identification, so these relationships address only the entire chironomid community. Oxygen depletion is another common phenomenon associated with lentic ecosystems, especially in the profiindal zone of eutrophic lakes (Wetzel 1983). In Lubbock lakes, dissolved oxygen ranged between 14.35 and 1.87 mg L"!, the latter value approaching a stressful level for some invertebrates (i.e., mayflies, dragonflies, and damselflies; Merritt & Cummins 1984). In the present study, however, abundances of four species were correlated negatively to dissolved oxygen concentrations, indicating that abundances were higher at lower dissolved oxygen concentrations. One species group, the chironomid larvae, have enhanced oxygen uptake and storage, due to the presence of 78 hemoglobin-like molecules. Therefore, they may be better able to withstand oxygen depletion. The level of eutrophication has long been used as a predictor of species distributions and abundances in lentic ecosystems (Macan 1963; Saether 1979). Growns et al. (1992) found that invertebrate species richness was significandy higher in mesotrophic lakes of westem Australia, including large numbers of rare species. In more eutrophic lakes, species richness declined, but the abundances of cosmopolitan species (i.e., chironomids, notonectids, and corixids) increased dramatically. Nutrient enrichment (i.e., phosphoms) of these systems created a shift from a macrophyte-dominated community in meso-eutrophic lakes, to a phytoplankton-dominated community in hyper-eutrophic lakes. Saether (1979) identified 15 different chironomid assemblages associated with lake trophic status, and found that these assemblages were highly correlated with mean total phosphoms and chlorophyll a concentrations. In the present study, total phosphoms concentrations were correlated positively with chironomid species abundances, and accounted for 68% of the variation observed in their abundances. Other nutrients significantly correlated with species abundances included total organic carbon and ammonia. Ammonia is a primary challenge to the health of freshwater ecosystems because of its toxicity to aquatic biota (Effler et al. 1990; Richards et al. 1993). Concentrations of ammonia reflect the level of primary production in a lake, because the predominant source of ammonia is the decomposition of phytoplankton and detritus (Effler et al. 1990). In urban lakes of Lubbock, Texas, ammonia concentrations were elevated throughout the winter sampling periods, possibly as a result of decomposition or inputs of wastes from waterfowl that are abundant during this season (see Appendix A.3). In other lentic systems, aquatic biota were found to be a quantitatively minor source of ammonia in comparison to that generated by bacterial decomposition (Wetzel 1983). However, this 79 may not be the case for urban lakes in Lubbock, Texas, because they host thousands of migratory waterfowl during the winter. Richards et al. (1993) found that ammonia concentrations exhibited the strongest influence on community composition, decreasing species richness in streams of central Michigan. In the present study, ammonia was significantly correlated (positive) to abundances of cladocera. Over 76% of the cladocera collected during this study were from Buster Long lake, which contained higher levels of ammonia than other lakes, and is dominated by large phytoplankton populations (Brownlow 1994). Therefore, the relationship between ammonia and cladoceran abundances may be indirect, mediated by phytoplankton responses. To evaluate possible associations between surrounding land-use and macroinvertebrate diversity in urban lakes, physicochemical characteristics entered into regressions were correlated to land-use attributes using Pearson-Product Moment Correlation (SPSS 1990). Conductivity and ammonia levels of urban lakes were significantly correlated to areas of multiple family housing (P < 0.01) and commercial land- use (P < 0.05) surrounding each lake (see Fig. 2.9; Chapter II). Total organic carbon also was correlated to area of multiple family housing (P < 0.05). These results suggest that these two land-use categories may have an indirect affect on distributions of notonectids, corixids, chironomids, and cladocerans, all of which showed significant relationships to conductivity and ammonia levels. These three attributes (conductivity, ammonia, and total organic carbon), as well as two others (chemical oxygen demand and total carbon), were the only attributes correlated to land-use characteristics. In conclusion, even though urban lakes exhibited similar macroinvertebrate species diversity, invertebrate community composition and taxon dominance differed greatly. Abundances of many ubiquitous species could be reasonably predicted given specific physicochemical attributes. Some of these attributes also were highly correlated with multiple family housing and commercial land-use pattems surrounding each lake. These 80 results suggest that land-use may affect macroinvertebrate community composition in urban lakes. The present study found invertebrate abundances to be significantly correlated to total phosphoms, ammonia and total organic carbon. However, their influences on abundances and community composition may be indirect via effects on primary production and subsequent decay of organic matter. In urban lakes, primary productivity is limited by light availability, due to turbidity caused by constant wind mixing and the resuspension of inorganic sediment loads (Brownlow 1994). This phenomenon may be responsible for some phytoplankton-dominated communities {sensu, Scheffer 1990). Therefore, abundances or compositional responses of macroinvertebrate communities induced by nutrient concentrations may be secondary to habitat-related factors. 81 Literature Cited Brownlow, C. 1994. Trophic state assessment of an urban lake in Lubbock, Texas. Master's thesis, Texas Tech University, Lubbock, Texas. Carpenter, S.R., and D.M. Lodge. 1986. Effects of submersed macrophytes on ecosystem processes. 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Hydrobiologia. 310:207-223. 84 Table 4.1. List of dependent and independent variables used in Stepwise Multiple Regression. Taxa listed represent > 1% of the total individuals collected from selected urban lakes. Dependent Variables Abundances Independent Variables Enallagma sp. 1678 Alkalinity Chironomidae larvae spp. 1076 Biological Oxygen Demand Buenoa sp. 2 1036 Conductivity Callibaetis sp. 111 Dissolved Oxygen Palaemonetes kadiakensis 762 Hardness Corisella tarsalis 709 pH Sigra altemata 654 Temperature Buenoa sp. 1 650 Total Kjeldhal Nitrogen Cladocera sp. 598 Ammonia Planorbidae sp. 1 392 Total Phosphoms Mesovelia mulsanti 342 Total Organic Carbon Corisella edulis 261 Tramea sp. 200 Ramphocorixa sp. 122 Chironomidae adults sp. 1 110 85 Table 4.2. Results for the Mantel Matrix Randomization test to determine whether there are significant positive relationships between water quality and macroinvertebrate similarity matrices. Statistics include: Z test statistic, and associated P value. Bolded value represents significance at a = 0.05. Statistics Sampling Period Z P-value March 1.093 0.304 April 1.805 0.228 June 5.266 0.504 July 8.154 0.684 August 6.440 0.460 November 4.886 0.048 86 ON '^ 00 CN cn o r^ cn (N O O CN cn c^ cn 00 ON O ^ 00 cn 00 Tt cn vo o in «n in vo ON •^ in 0\ CO 00 o cn o r-- o O o CN in r- o 00 CM o\ o vo 10 00 00 (N v£) vo ^ ^ VO in CN r-H 2 2^ 3 CM -^ CN >n »n '-H cn Tt vo r- r^ cs cn Tt 'sf d d (D d> di CD c5 <6 d d d d !/3 •-H ^ ._j3 b o —' ^ r^ I-- ^ 00 O ON —I ^ in CN C O c2 > -<—> O CN in vo vo O O cn o O O ^ ^ 00 o 00^0 ON cn CN Tl" cn CN ^ cn o ON 00 NO 00 ^ cn O in r- o o ON —I in 00 ^ ON ^ CN O O 1^ cn »n «a^ 8 >^ >n cn cn «n CN (N r- o in cn 00 PQ ON vq (N ^_ in -n- cn cn vq d> (5 ^ d d d wn d d d 'si^ d d d d CN CD d> s? b ?^ X a > S '-I C/D c t B s o >-. 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Analytical methods outlined by EPA and standard methods with detection limits. Analysis Methods Detection Limits (mg/L) Alkalinity 2320* Biochernical Oygen Demand 5210* Total Carbon 5310* 0.4 Inorganic Carbon 5310* 0.4 Organic Carbon 5310* 0.4 Chemical Oxygen Demand 5220* Hardness 2340* Total Kjeldahl Nitrogen 351.2 0.04 ,0.4 Ammonia 350.1 0.03 , 0.2, 0.4 Nitrates/Nitrites 353.1 0.02 ,0.2 Total Phosphoms 365.4 0.1, 0.2, 0.4 Ortho-Phosphate 365.4 0.04 , 0.4, 0.05 * Denotes standard methods (APHA 1992), otherwise EPA methods (EPA 1974). 90 ^52[P^r^;2!l^r^5Q9S 92 '-* NO 2- c» rt r^ r- ON «n — ON t^ O ^t O O m — ON — — r- ffi ^. ^'^H'>'nw-lqoNpcnoolnpr<^OTtc^^^c^|r>;ln^ cu '^'^'^2<^<^^'=fNt--^O\0N0N0d0NC:rNdONON0dONt~-^(»CXD0dONON^ (U tu r ca U TT OJ oo NO CN «n 00 00 CN cs r-; ON ON /^ —;p-^*n«nNq >. 4—> _> c/3 '^—» O •<:tc^cNON(noN^oooNO»noNNONoo)t—cooNTtT—O'^NO'—ir^r-'^ro r<^;»n oi m ON o J2 rncocnc^NqinNqc^jr<3Tfc^in^c^r^(N'^inNqc^joirncNTtcNcscNCNO^ 3 6 OOOO—''-I-HOO oo OC^IOOO— --<—'OOOOOOOOO-^ — o o C 6 o U Q d o ooooooooooooooooooooooooooooooo O &0 p w-3iopu^»n«optn TtooTfcNooio-^cou^Tt-—iocir---c~-Tt-fnrj-ON>nc)Oc<^ oo>n^^oor--<»ooooNor-'^r--or-->nONr<^r--NO'^r--'-'rnO'^OND'*r--fNr^t----^ 'c o u^ONONONoo OS >> B B B B fi 6 6 6 c3 cd C3 CO cd cd rj cd X cd l-l u u •£ 5 -S I •4—» X (1) b CU TD t3 -o TO *0 "O 'O "O *© x: •S •£ O X C C C, C o o o o o o o o o o > > > > > > CTJ a CcJ c cd 3 XI XJ X5 X X X X o X o o o o o o o .3 ^ c c c: c c c c c X c c c x: X G X c '0'0'0'0'a'OT3"OT3 2 2 2 2 s2 c3 cfl cj cS CO 2:2 c c c c/3 c/l CO c/5 c/3 c/5 c/5 5b"5b'5b'Sb'5b'S)'5b"Sb'5b*5b 5b OO (u o cnON-t-cncMcnc4 D. 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ON Tf cn U E Tf so — 00 ON od r- Tf r~ r-I so NC3 o ON C7N-- cJ od r-^' Tf d O o Tf CN NO O d Tf, l/^ T-H t— oo NO «n ON so CM —' ON Tf CM NO 13 if i o in . cn O ON p '' Tf • cn ci o NO in d in o +1 +1 +1 0+01 in _^Tf, o cn CM Tf , Tf ON c/3 O bi) <50 cn 0C3 r- \o Tf +1 Tf cn r~ r~ o o so ^ o ^ '-' 'se n d r-^< r- —i Tf -?.c<^ d r^ ffl E —I d od 1—1 1—t r~~ oo ci d NO cn ^ , . -« d ^ Cl — CN d c/3 o .1-H •t—> C/3 .1-tdH •I—> C/3 > •1-H +-pu» , •-H VoH C/) < 97 cn in p. : s6 so cn m +1 +1 so s6 d o p O P so Tf ':\ in C' oo O oo 00: p r-. in d <^l +1 Tf d oo d «3 r-. o. d ON d m d d> ^ d +in1 cn in d o> •^. : d cn 00: d -"-^ ""^ ci d ^ CI od cn in c/3 ON 00 cn so Tf 'Tl cn ^;-•• in ^ r-. NO d Tf d cn r-, in +1 jt! oo cn vo cn ON so cn r-- Tf Tf 1-H r— :d^d +1 +cn1 d . —"-. c4 cn r; O o d OO od ^ vO^ : (s) I—< cn d r-. cr^ cn Ov ^ d as'c:> O: fsj ^ d (s) T—t d :3 cn vq -o NO vO Tf oo .' in B ON in +1 CJ od NO:,. m so in Scnr^. ON| TH c^ +1 so OO cn d o in +1 in r- d in in '- cn <-• cnl c/3 un in d (S -^ cn d c"n" so d ON d od vo +1 cn -^, wn ^ ON VH d d cn cn ^El r~ ^ p ON cn d '* cn d d^ cn d C/3 d d d CJ d d^ d .T-H d d d d' +1:3; ^ cn O C/3 d d +1 ^ ^ d d +1 cn O '^f d d "UH d +1 +' :2; ^ cn s^. +1:2; ^ cn O '^. +1 cn cn O ^. d d <^ B cn z^. cn O ^. ,d d o d d c> 3^. d d o d d CD UH IciH El d d o Cd d o d o o d o c: p , o +1 +1 d Tf ^d so p o cn T- r~- cn o ^ d p o o o cn fk) t—I TT d d d d ^ d oq ^ d P d +1 »-H Tf d <^ ci d d <=> -^ d r- o d -^t ^ d f^ CJ o d d O C/3 o +1 s:^ ^ +1 p (>'l O cn O Nq o d CM O P Tf p <^. d d d O +1 d +1 S:i rsl cn O NO d d 98 APPENDIX B AQUATIC MACROEsTVERTEBRATE DATA 99 Table B.l. Abundance of aquatic invertebrate taxa from selected urban lakes in Lubbock, Texas: 1, Rushland; 2, Higinbotham; 3, Wendover; 4, Quaker and Brownfield; 5, Maxey; 6, Leroy Elmore; 7, Buster Long; 8, Jack Stevens. Aquatic Invertebrates Lake 1 2 3 4 5 6 7 8 Hirundinidae sp. 1 0 0 0 0 0 a 0 16 Hirundinidae sp. 2 0 0 0 b b 0 0 5 Erpobdellidae sp. 0 0 0 L-...::: 0 0 0 9 Lymnaeidae sp. 0 d 0 9 b b 0 0 Physidae sp. 1 13 0 0 52 0 . .L.,- 0 13 Physidae sp. 2 0 0 0 0 0 • • 0 "" 0 3 Physidae sp. 3 0 0 0 1 0 0 0 0 Physidae sp. 4 b 6 0 1 0 0 1 0 Planorbidae sp. 1 7 0 0 0 0 0 0 385 Planorbidae sp. 2 3 0 0 0 0 0 0 11 Cladocera sp7: 0 0 0 0 19 0 453 126 Copepoda sp. 1 0 0 0 0 0 0 0 28 Copepoda sp. 2 0 0 0 0 0 0 0 36 Ostracoda sp. 1 0 0 0 0 0 0 0 16 Ostracoda sp. 2 0 0 0 0 0 0 0 1 Palaemonetes kadiakensis 0 29 0 322 411 0 0 0 Isotomidae sp. 0 0 37 0 0 0 0 0 Callibaetis sp. 139 53 18 32 18 0 32 485 Caenis punctatus 42 0 0 0 0 0 1 7 Anax sp. 0 0 0 0 0 0 0 23 Gynacantha sp. 0 0 0 1 0 0 0 10 Belonia sp. 0 0 0 0 0 0 0 35 Erythrodiplax sp. 0 0 0 0 0 0 1 10 Orthemis sp. 0 0 0 0 1 0 0 2 Pachydiplax sp. 0 0 0 1 0 0 0 1 Perithemis sp. 1 0 0 5 0 0 0 0 Plathemis sp. 0 0 0 0 0 0 0 2 Tramea sp. 0 0 0 0 0 0 0 200 Enallagma sp. 576 43 126 107 5 3 1 817 Hydrometra martini 0 0 1 2 0 0 0 0 Macrovelia sp. 0 0 0 0 1 0 I 0 Gerris marginatus 13 4 39 15 17 0 0 12 Rhematobates sp. 0 0 0 25 0 0 0 0 Belostoma flumineum 3 4 6 0 0 0 0 9 Ranatra nigra 0 0 0 6 0 0 0 3 Corisella edulis 23 66 85 4 61 0 20 2 Corisella tarsalis 2 66 461 0 6 0 164 10 Ramphocorixa sp. 0 1 88 0 33 0 0 0 Sigara altemata 0 17 361 16 5 6 0 249 100 Table B.l. Continued. Aquatic Invertebrates Lake 1 7 8 Buenoa sp. 1 402 4 233 1 8 0 0 2 Buenoa spv2 367 2 589 0 2 0 0 76 Notonecta undulata 0 0 1 b 0 0 0 5 Mesovelia imdsanti 36 50 66 75 1 0 0 114 Hebrus sp. 0 1 0 0 0 b 0 0 Saldidae sp. 1 2 5 5 5 1 2 31 Saldula pallipes 0 0 2 0 0 0 0 0 Saldula sp. 2 1 2 0 3 2 0 0 1 Dineutus sp. 4 b i 0 0 b 0 0 Haliplus sp. 1 2 0 1 0 1 0 0 1 Haliplus sp. 2 0 0 b 0 1 0 0 6 Peltodytes sp. 0 0 0 0 0 0 0 1 Brachyvatus sp. 0 0 3 0 0 2 2 10 Copelatus sp. 0 0 0 0 0 1 0 0 Laccophilus fasciatus 1 2 0 0 0 0 0 0 Laccophilus proximus 0 3 2 1 1 0 0 2 Liodessus sp. 0 0 4 2 0 0 0 0 Neobidessus sp. 3 43 4 2 2 2 2 2 Thermonectus sp. 0 0 1 0 0 1 0 1 Uvarus sp. 0 0 1 0 0 0 0 0 Sphaerius sp. 0 6 0 0 0 0 0 0 Berosus sp. 1 19 0 1 0 0 2 0 7 Berosus sp. 2 0 2 0 0 0 0 0 0 Berosus sp. 3 0 0 1 0 0 0 0 0 Berosus sp. 4 0 0 0 0 0 0 0 1 Berosus sp. 5 0 0 0 0 0 0 0 1 Enochrus sp. 1 0 0 0 0 0 0 3 Helophorus sp. 12 8 8 3 1 1 1 2 Hydrochous sp. 0 0 0 0 0 0 0 1 Hydrophilus triangularis 0 0 0 1 0 0 0 1 Laccobius sp. 8 0 14 4 1 0 0 5 Paracymus sp. 0 12 0 0 0 0 0 0 Tropistemus lateralis 1 61 0 1 0 0 0 25 Micaralymma sp. 0 0 0 0 0 1 0 0 Stenus sp. 1 0 1 0 0 0 0 1 0 Stenus sp. 2 0 0 0 0 1 0 0 0 Limnebius sp. 0 0 1 4 0 0 0 0 Georyssus sp. 0 0 0 3 0 0 I 0 Chrysomelidae sp. 1 0 0 0 0 0 0 0 101 Table B.l. Continued. Aquatic Invertebrates Lake 1 2 3 4 5 6 7 8 Disonycha sp. . , 0 / '.. 1 : 0 A ; 0 , 0 Donacia sp. 0 0 4 6 1 6 0 1 Tipulidaesp. v- o; 0 0 ;'; 1 V-;o 0 0 3 Culex sp. 2 0 0 0 0 0 0 2 Chaoborus^^pl.^^ : ^ - \-'' " 0 0 0 - 0 ; I 0 0" 0 Pericoma sp. 0 0 0 0 1 0 0 0 feratopogonidae sp. 0 . 0 0 0 0 ' 0 0 "2 Chironomidae adults sp. 1 57 25 15 3 0 5 4 1 Chironomidae adults sp. 2 . , 28 1 8 -' 2 0 ' 3 0 0 Chironomidae adults sp. 3 6 0 3 1 0 0 2 1 Chironomidae larvae spp. 229. 161 "51 239 / 71' 46 41 229 Ondontomyia sp. 0 1 3 21 0 0 0 45 ITabariussp. - ' " '" T \ : 0 . 0 , 1 0 ; 0 0 0 Ephydridae sp. 0 0 0 1 0 0 0 0 Muscidae sp. : ;, : 0 : , 1 ; 0 0 0 0 0 102 Si o cd 00 O c<^ O in NO ^ Tt Tt ^ T-H OJ cn r^ »n cn ON r- in in ON cn 00 cn o »n ^ 00 in OS NO NO (N (N (N cs oi oi ^ c/3 CN CN CN ed 13 -a D c •(—' .T-H ^ t^ ^ cn ON in ^ cn 00 Tt ON in "Tj- in ON 00 ON Tt (N c^ .1-H d cn 00 p ;-l ON ^ O O cn •t—' cd '^ in -1 (N -^ CN ^ ri C/3 c/3 d d .-H +1 +1 +1 +1 +1+1 +1+1 +1 +1 +1 13 cd in NO t^ (N ON o in 00 cn CN 'a (N cn Tt ON ON CN ON '-t Tt r^ NO (N p r- 01 MD cn cn > cn cn '-^ —1 ^ cno ino in cn -^ I-H O Ui c/3 O NO r-H Tt OO Tt NO O cn Tt 00 r- Id cd r- in 00 ^^ (S| ^H ^H ^ 1-H in oo »n Tt (U r--1—1 00 NO '-I r-_ ^ VH CN CN oi O —; c^i —^ CN oi CN ^ +1 +1 +1 .1-H PH +1 +1 +1 +1 +1 +1 +1 13 > NO cn cn cn NO NO 00 cn (N r~ .s 00 ON oo -^ T^ 00 •*—< so ^H oo ^^ c/3 so ^^ p —I cn 00 p cn cn NO c/3 CN Tt in in 0C5 in ^ cn ^ r^ od X) X 13 13 C3N "^ NO NO in Tt Tt NO in NO in ;§ a 00 NO vq (N (N ON 3 c/3 ^ ^^ d oi '-^ oi c/3 d ;-i o +1 +1 +1 * +1 +1 +1 +1 (U c/3 ON in NO Id cd (N cn cn in 00 CN in (N T-i in CN (U Tt in Tt cn (N c/3 ON »n in ^ r~^ oi vd c/3 O • 1-H ;-H o c/3 I CJ c/3 C/3 (U .2 .1-H l-l 13 o (U Si P c/3 c/3 a ;-i .^H cd CQ c/3 S o . o c/3 (N ~S-i ^. ^ >. w'dUH OH cd ^ •X- 103 PERMISSION TO COPY In presenting this thesis in partial fulfillment of the requirements for a master's degree at Texas Tech University or Texas Tech University Health Sciences Center, I agree that the Library and my major department shall make it freely available for research purposes. 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