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Assessing the effectiveness of the roaring branch bmp retrofit using macroinvertebrate bioassessment
h [electronic resource] /
by James Banning.
[Tampa, Fla] :
b University of South Florida,
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Thesis (M.S.)--University of South Florida, 2010.
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ABSTRACT: Using benthic macroinvertebrates to measure stream health has been widely used and accepted around the world. Macroinvertebrates are resident monitors of chronic impairment in a stream since they are relatively sessile and most commonly respond to disturbance by drift but can recolonize a restored stream reach very quickly. This study tested the effectiveness of macroinvertebrate metrics developed through the Rapid Bioassessment Protocol (RBP) to detect changes in stream integrity as the result of placement of a best management practice (BMP), installed on a tributary of Roaring Branch, located in Columbus, Georgia. The BMP was designed to attenuate flow to reduce sediment suspension and downstream deposition. A sampling protocol derived from the Georgia Ecoregions Project was implemented to evaluate the macroinvertebrate community, located downstream of the BMP, and downstream of the confluence with Roaring Branch, both before and after the BMP installation. The resulting metrics were compared to a reference condition described for subecoregion 65c, sandhills-lower piedmont. A dramatic improvement or increase of macroinvertebrate populations suggests an improvement in water quality (via reduction in fine sediment deposition) due to improved physical habitat conditions for indicators (Trichoptera) of healthier streams. The results of this study suggests further restoration activities should continue and that re-evaluation of the sampling protocol should take into account a larger subsample size of benthic macroinvertebrates than currently recommended by the RBP.
Advisor: James A. Gore, Ph.D.
x Geography & Env Sci & Policy
t USF Electronic Theses and Dissertations.
Assessing the Effectiveness of the Roaring Branch BMP Retrofit Using Macroinvertebrate Bioassessment by James L Banning A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science Department of En vironmental Science, Policy & Geography College of Arts and Sciences University of South Florida St. Petersburg Major Professor: James Gore P h.D. James Krest P h.D. William Birkhead P h.D. Barnali Dixon P h.D. Date of Approval: February 1 20 10 Keywords: stream health, biological monitoring, sediment, best management practice, non point source pollution RBP, rapid bioassessment Copyright 2 010 James L. Banning
i Table of Contents List of Tables ................................ ................................ ................................ .............. ii List of Figures ................................ ................................ ................................ ........... i v Abstract ................................ ................................ ................................ ..................... v i Introduc .. . 1 B i o m o n i t o r i n g ................................ ................................ ................................ 4 B e s t m a n a g e m e n t P r a c t i c e s ................................ ................................ .......... 8 G e o r g i a E c o r e g i o n s P r o j e c t ................................ ................................ ......... 1 4 R o a r i n g B r a n c h P r o j e c t ................................ ................................ ............... 3 7 41 55 60 75 86
ii List of Tables Table 1 W In dex 6 Table 2 Descriptive statistics for D ominant B enthic Macroinvertebrat e Taxa and Calculated Ind ices .. . 9 Table 3 Descriptions of Georgia Ecoregions. Data for E levation and Slope Represent the R ange for 1.5 Standard Deviations from the M 17 Table 4 Modified Ande rson Level II Land use classifications U 2 1 Table 5 Land use Measures Used in Selecting C andidate R eference S 2 6 Table 6 Water Chemistry/Quality Parameters Measured a .27 Table 7 Summary of Metrics Used in C haracterizing E cological 29 Table 8 Stream Rating Based on Numeric Ranking .. 37 Table 9 Characteristic Reference Stream Landuse, Habitat, and Chemistry Data for Subecoregion 65c 43
iii Table 10 Discriminating Invertebrate Metrics for Subecoregion 65c Sand Hills Index 65c 43 Table 1 1 Prioritized Li st of Habitat T ypes . ... .. 49 Table 12 Metrics for 65 c Sandhills Lower Piedmont . .. 51 Table 13 Stream Ratings for Subecoregion 65c Sand Hills 51 Table 14 Example of Metric Value Calculation for 65 . . .. 52 Table 15 Description of Numeric Ranking for Subecoregion 65c 53 Table 16 Index for Stream Health Rating 53 Table 17 Discriminating Invertebrate Metrics for Subecoregion 65c Sand Hills Index 65c 54 Table 1 8 R oaring Branch Tributary Just Downstream of the BMP .. 56 Table 1 9 Roaring Branch Just Downstream of the Confluence ... . 58 Table 20 Summer 2008 Metric Scores 59 Table A 1 Prioritized L ist of H abitat T ypes for Sampling and S ample Reallocation for the Modified 20 jab M ethod . .. .. 89
iv List of Figures Figure 1 Macroinvertebrate Mean Total Number of Individuals 1 1 Figure 2 Composite Biometric Scores in Buck Creek and 11 F igure 3 Weracoba Creek BMP 1 3 Figure 4 15 Figure 5 Ecoregions of Georgia ... 31 Figure 6 Referen ce Sites of the Georgia Ecoregion Project .... 36 Figure 7 Impaired Sites of the Georgia Ecoregion Project 36 Figure 8 Map of Lakes (Impounds) of the Chattahoochee River in Georgia ... 38 Figure 9 Map of Roaring Branch Meeting Lake Oliver 39 Figure 10 Roaring Branch BMP Design 40 Figure 11 65C Sandhills 41 Figure 12 Subecoregion 65 c Typical Reference Stream 42
v Figure 13 Subecoregion 65 c Typical Impaired Stream 42 Figure 1 4 Discriminating Index Characteristic between Reference and Impaired Streams for Subecoregion 65c 45 Figure 1 5 Sampling Sites of the Tributary & Roaring Branch 4 7 Figure 1 6 Sample Area Immediately Downstream of BMP 4 8 Figure 1 7 S ample Area of the Roaring Branch, D ownstream o f the confluence 48 Figure 1 8 Composite Sampling Tray 50
vi A s s e s s i n g t h e E f f e c t i v e n e s s o f t h e R o a r i n g B r a n c h B M P R e t r o f i t U s i n g M a c r o i n v e r t e b r a t e A s s e s s m e n t J a m e s L B a n n i n g A B S T R A C T Using benthic macroinvertebrates to measure stream health has been widely used and accepted around the world. Macroinvertebrates are resident monitors of chronic impairment in a stream since they are relatively sessile and most commonly respond to disturbance by drift but can recolonize a restored stream reach very quickly. T his study tested the effectiveness of macroinvertebrate metrics developed through the Rapid Bioassessment Protocol (RBP) to d etect changes in stream integrity as the result of placement of a best management practice (BMP), installed on a tributary of Roaring Branch, located in Columbus, Georgia. The BMP was designed to attenuate flow to reduce sediment suspension and downstream deposition. A sampling protocol derived from the Georgia Ecoregions Project was implemented to evaluate the macroinvertebrate community, located downstream of the BMP, and downstream of the confluence with Roaring Branch, both before and after the BMP inst allation. The resulting metrics were compared to a reference condition described for subecoregion 65c, sandhills lower p iedmont A dramatic improvement or increase of macroinvertebrate populations suggests an improvement in water quality (via reduction in fine sediment deposition) due to improved physical habitat conditions for indicators (Trichoptera) of healthier streams. The results of this stud y suggests further restoration activities should continue and that re evaluation of the sampling
vii protocol shoul d take into account a larger subsample size of benthic macroinvertebrates than currently recommended by the RBP.
1 Introduction T he construction o f large metropolitan areas and exponential human population growth has led to degradation of fresh su rface water supplies. This issue has become a critical concern around the world because the availability of fresh surface water is limited. Most, if not all, of the lakes and streams around the world have been directly or indirectly impacted negatively by some type of anthropogenic activity (Brown 2003). Some of the root causes of freshwater degradation are from the construction of dams, introduction of raw sewage, runoff from impervious surfaces, and from general mismanagement (Magnuson et al 1995). A fe w key methods have been developed to determine the extent of pollution of lakes and streams around the world. Barbour et al. (1999) assert that the Rapid Bioassessment Protocol (RBP) a method of comparing biological metrics to reference conditions on an e coregional or subecoregional basis to evaluate stream integrity, is sufficiently robust to evaluate the impacts of non point sources of pollution ( sensu Section 319 of the Clean Water Act [CWA]), those streams which require allocation of total maximum dail by Section 303(d) of the Clean Water Act), multiple stressors, or any other mechanical damage to stream habitat or condition. The RBP has been endorsed by the United States Environmental Protection A gency and has been recommend ed to each state for integration into their water monitoring and assessment programs through the CWA Section 319(h) program. The state of Georgia, through a research program known as the Georgia Ecoregions Project (Gore et al. 2005), has begun to incorpor ate this protocol into the Department of Natural Resources (GA DNR) monitoring program. The purpose of the research described in this thesis was to evaluate the success of a newly proposed and implemented Best Management Practice (BMP), a structure designe d to control
2 sediment loading in a small stream, an unnamed tributary of Roaring Branch, in urban Columbus, Georgia, utilizing the RBP. Th is study tested the RBP on a stream that was designated to be in potential non compliance with TMDL regulations in the reflected by an improvement in the metric scores defined for the subecoregion in which Columbus, Georgia, is located. Secondarily, the results of this research would also be further evidence of the robustness of the RBP as an effective tool in evaluating changes in the integrity of wadeable streams in Georgia. An early method of water quality assessment was introduced in Germany, when Kolkwitz and Mars s on developed the first known biotic inde x based up on the concept of saprobity (Kolkwitz and Marsson 1908, Kolkwitz 1950) The saprobian system is the assessment of organic pollution which could be the result of the contamination of streams by raw sewage. The saprobian system designates three dis tinct zones that are ranked from a zone of severe impairment to a less impacted zone. Each zone is distinguished by a specific range of oxygen saturation, organic matter load products of septic decay, and products of mineralization. Biologically the comp osition of each zone provides optimal conditions for certain species or communities of organisms that are considered to be indicators for the impairment of each zone (Kolkwitz 1950). The downfall s of this metho d are that it is not easily adaptable and it c annot be applied globally to a multitude of streams that have varying conditions of impairment other than organic contamination ( Goodknight 1973 ). Historically, rather than a biological system of analysis, t he most commonly accepted method used to determi ne water quality has been that of chemical analysis. This method examines a suite of concentrations of total suspended solids, dissolved oxygen, nitrates, phosphorus, and heavy metals as well as organic loads and other physicochemical conditions One prob lem of using this type of analysis is that by just taking a single water sample it becomes a To accurately assess the ambient water quality of a stream numerous samples would need to be taken over a long period of time to properly depict the level of disturbance This may be
3 laborious and not cost effective when completing an ecological study of stream health. Another common practice when making a chemical analysis of a stream is to test for bacterial concentrations such as fecal coliform. Numerous studies have been completed and published that incorporated fecal coliform levels in relation to stream health (Geldreich and Kenner 1969, Maul and Block 1983, Vowell 2001). The pitfall of determining stream health using just fecal coliform concentrations is that it is restricted to analysis of contamination by organics Fecal coliform studies are also very laborious and expensive especially when many samples are taken from a large number of streams. Anot her tool that has been commonly used to evaluate stream health is the measure of biod iversity (Egloff and Brakel 1973, Wilhm 1967 ) Two main concepts are involved when using biodiversity as a measurement tool ; species richness and species evenness. Species richness measures the number of species in a community ; the more species evenness is a measure of the proportion of each species within the community; greater evenness indicating greater ecological stability (Pielou 1975). T here are two main ind ices that are frequently used to calculate biodiversity. Simpson Diversity Index is based upon the probability that two individuals randomly selected from a sample will belong to the same species ( Simpson 1949) The limitation to using the presence or absence of rare species does not influence a significant change in the index. called the Shannon Wiener or Shannon Weaver index ) also uses species richness and evenness as a measure but balances the contribution of the two components (Shannon and Weaver 1949) Since the data are log transformed with values increas ing with either addition of rare species or by greater species evenness some ecologists feel that the downfall to this index is there is insufficient numerical separation in values between impaired and pristine conditions for statistical analysis For example a stream with a Shannon Wiener diversity of 3 .1 has much greater diversity than a stream with an index va lue of 2. 9 However, it is difficult to communicate this log based change to the public and to policy makers In
4 addition, diversity indices do not take into account the complete change in species composition that often results when extreme contamination might occur. For example, a sample from a community of five mayfly species, known to be found only in the cleanest streams, will have the same Simpson or Shannon diversity as a sample containing five species of dipterans, known to be found only in the mo st organically contaminated streams. Because of their inability to distinguish indicator species, t he use of biodiversity indexes should not be relied on as the only evaluation technique As a result, the most recent attempts to rapidly assess the integri ty of stream ecosystems are various indices of bioassessment (e.g. Canton 1991, Lenat 1993, Barbour et al. 1999, Gore et al. 2004) Currently the most widely used bioassessment tool s are biotic indices that take in to account some combination of parameter s such as total number of individuals, functional feeding groups, preferred habitats, or pollution tolerance values among different species within a community. Bio monitoring The earliest research in the United States was performed by S tephen Forbes He was an entomologist for the state of Illinois and a professor of zoology and entomology at Illinois State University. His paper titled The Lake as a Microcosm w as used in the development of the field of ecology and the introduction o f the ecosystem concept (Forbes 1887) Forbes examined the interactions between different species, and interactions within different species assemblages and also how organisms with in an ecosystem can affect the overall dynamics of an ecosy stem. The basis of our current theories of food chains, food webs, and keystone species are considered to be derived from For lotic ecosystems, the was solidified by the work of Vannote et al. (1980 ) in the introduction of the River Continuum Concept The River Continuum Concept ( or RCC ) proposes that streams will display a changing suite of biotic community structures between the
5 upstream headwaters and the estuaries downstream The RCC established th e notion of multiple trophic levels of functional feeding groups and could demonstrate that streams of different sizes in different regions can have very dissimilar assemblages of benthic macroinvertebrates as influenced by various natural conditions of or ganic energy input, stream gradient, and fate of energy consumed within the system (Vannote et al. 1980). Although not strictly used to monitor stream health, the RCC demonstrated the utility of the functional organization of stream communities in assessi ng stream condition. employed in the assessment of water quality of streams but the bioassessment process was lacking a specific design or method to make these assessments. One of the pioneers of rapid bioassessment in the United S tates was William Hilsenhoff. His work was based on streams in the state of Wisconsin and he was macroinvertebrates as a measure o f stream condition Hilsenhoff initial biotic index ( Hilsenhoff 1977 ) evaluated the health (water quality) of Wi sconsin streams using arthropod distributions to create a numerical index employing tolerance values as a measure. These tolerance values sepa rated o rganisms i nto four distinct categories of pollution tolerance: sensitive semi sensitive semi tolerant and tolerant to pollution (Hilsenhoff 1977) In most healthy streams it is expected that one would fi nd macroinvertebrates that are both sensitive to and tolerant to organic pollution with no particular species or group of organisms dictating the entire composition With increased organic pollution dissolved oxygen levels within the stream ar e expected to fluctuate or decline and there will be fewer pollution sensitive organisms to be found ; those m acroinvertebrates that can tolerate lower oxygen levels will become more predominant ( Hilsenhoff 1977) This biotic index (commonly called the HBI) provided a sturdy foundation and framework for rapid bioassessment because it introduced the protocol for smaller sample sizes which allows less time in sorting, organizing, and identifying ( Hilsenhoff 1977). The protocols for sampling were based on a temporal pro tocol of grabbing samples in a finite amount of time. Hilsenhoff concluded that 10
6 minutes of sampling would be sufficient to evaluate the overall stream health using arthropods as a measure ( Hilsenhoff 1977) Hilsenhoff developed an improved biotic inde x with tolerance values that ranged from 0 to 5 with a value of five indicating a high tolerance to organic pollution (Hilsenhoff 1982) Hilsenhoff incorporated tolerance values for insects, isopods and amphipods which are easily collected in most wadeab le streams, and being less mobile than fish they cannot retreat far when a disturbance occurs. This improved biotic index s till incorporated the temporal protocol but it included more species f ro m various orders ( e.g. Trichoptera ). Hilsenhoff did express some problems with this new improved index such as availability of keys for many species, incomplete tolerance values, and possible correction fact ors for current and temperature (Hilsenhoff 1982) In 1979 the Wisconsin Department of Natural Resources in stituted the Hilsenhoff Biotic Index (HBI) to use in the assessment of stream quality as part of non point source stream pollution monitoring ( Hilsenhoff 1982 ) In 1987 Hilsenhoff amended his biotic index again making improvements such as expand ing the tolerance values from 0 to 10 ( T able 1 ) for greater precision and adding tolerance value for many other species ( Hilsenhoff 1987 ). In 1988 Hilsenhoff created the FBI or Family Biotic Index for in field evaluation of stream health This protocol was design ed for experienced biologists to evaluate the health of streams and it could be utilized to complete a stream health evaluation rather quickly (Hilsenhoff 1988). This study became the model for some of the sampling protocols that are currently standard in some regions. Table 1 Water Quality Classifications f ( Hilsenhoff 1987 ) Biotic Index Value Quality Degree of Organic Pollution 0.00 3.50 Excellent No Apparent Organic Pollution 3.51 4.50 Very good Slight Organic Po llution 4.51 5.50 Good Some Organic Pollution 5.51 6.50 Fair Fairly Significant Organic Pollution 6.51 7.50 Fairly Poor Significant Organic Pollution 7.51 8.50 Poor Very Significant Organic Pollution 8.51 10.00 Very Poor Severe Pollution
7 Karr (1981) presented the concept of using fish communities to assess biotic integrity. A few years later t he results of these fish based studies produced the Index of Biotic Integrity (IBI) which was a multi metric index that was used as an evaluation to ol to assess stream health in a multi tude of streams located in the m idwest ern United States ( Karr 1991) The IBI was adopted by a la r ge majority of the riverine scientific community assuming fish communities had distinct advantages for biomonitoring. L ife cycle information wa s obtainable and for most geographical regions, communities of fish usually ha d representatives from many different trophic levels In addition, fish we re somewhat easy to identify in the field almost all streams contain some specie s of fish and fish community surveys were easy to relate to the fishable swimmable mandate of the Clean Water Act of 1977 (Karr 1981) This biotic index was widely applied to the bioassessment of streams across the United States and eventually an IBI in dex was created to evaluate stream health using macroinvertebrates as a measure ( Karr 1991 ). This index involving macroinvertebrates was based upon the work of Hilsenhoff state agencies, namely Florida and Vermont adopted legal biological criteria using b ioassessment and monitoring criteria (Karr 1991) in order to assess the health of streams and the potential impacts of non point source pollution derived f rom anthropogenic activities. By 1989 a universal sampling protocol for macroinvertebrate collection was adopted by the Environmental Protection Agency (EPA) : the Rapid Bioassessment Protocol (RBP) ( Plafkin et al 1989 ) a finite number of samples could be taken relatively quickly using a d frame type net in which a finite number of individuals (100) would be extracted and indentified to the family level (Plafkin et al. 1989). Another term that was commonly applied to this sampling finite amount of samples taken from the field could be used to assess the macroinvertebrate community structure with a reasonable amount of confidence. The RBP was designed to be very flexible so that sampling procedure could be altered or tailored to suit the needs of specific states or regions because of the differences in ecosystem structure and community diversity The s functionality is intended
8 to provide a rudimentary base method for individual state s that do not have any established sampling pro cedures and it also may be used to enhance sampling methods that might already exist for a particular state or region (Barbour et al. 1999). Many of these bioassessment techniques and biotic indices have been applied to test the usefulness and validity of the installation of best management practices (BMPs) to control various impairments of stream ecosystems around the world. Best Management Practices Best Management Practices, or BMPs, have been long used as tool s to mitigate non point sources of pollu tion (Ice 2004) and can be methods or devices that reduce a pollutant or control flow to reduce runoff, attenuate flow or filter flow. BMPs can be as simple as a buffer strip or as complicated as ultraviolet light used to kill bacteria. Under Section 319 of the 1972 Clean Water Act BMPs were introduced to control non point sources of pollution such as sediment loading, fecal coliform and runoff from silvicuture practices ( Vowell 2001 ). Many projects around the world have incorporated means to reduce non point source contamination in streams. This project evaluated an innovative design for a BMP which function ed to reduce flash flood events by stabilizing the flow during precipitation events. There are many previous example s o f typical BMP stud ies O ne such study consist s of the implementation of a BMP on the Upper Thames River a watershed in s outhern Ontario, Canada that experienc ed ecosystem degradation resulting from farming practices (Yates et al. 2007). BMPs consisted of drainage tiles that were installed in drainage basins near farmland The tiles were designed to reduce nutrient and bacterial contamination from entering the basins by re directing the flow of some of the runoff from precipitation or irrigation This redi rected flow wa s s low er so it g a ve the bacterial processes time to utilize some of the nutrients The BMP s effectiveness was evaluated using water quality data and benthic macroinvertebrates to determine
9 if the BMP was creating a positive change in water q uality In this particular project the richness and evenness of macroinvertebrates were measured usin g B enthic Macroinvertebrate Composition (BMI) and the Family Biotic Index. A sample set was collected and calculated for a summer and fall with a total of 32 individual samples ( Table 2 ) (Yates et al. 2007). The c omparison of the summer and fall samples indicated that both the stream water quality and the invertebrate community changed significantly with the season The flaw in this study acknowledged by the authors, was that the macroinvertebrate community change could have occurred from annual turnover of benthic species from summer to winter communities and they should have compared a fall sample to a subsequent fall sample. I n addition to the installe d BMPs there were governme nt funded conservation programs that could have led to increased environmental awareness and improved management by farmers that could have also explained the visible improvement of the streams. Table 2 Descriptive for domina nt benthic macroinvertebrate taxa and calculated indices, based on summer and fall samples for 32 sampled basins ( from Yates et al. 2007) Another example in which bioassessmen t of lotic f auna was used to evaluate a BMP is in the case of the implementation of a BMP in Kentucky to control runoff from a confined animal feeding operation (CAFO) A traditional BMP that has been applied to waste produced from C A FOs is to pump the waste into a l agoon and route the lagoon effluent through constructed wetlands so that BMI Community and Indices Mean Median Min Max SD Chironomidae 0.3 0.32 0.1 0.86 0.228 Oligochaeta 0.08 0 .13 0 0.6 0.14 Sphaeriidae 0.02 0.09 0 0.62 0.138 Family Biotic Index 5.83 5.92 2.78 7.7 0.0891 Richness (Family Level) 18.5 18 10 25 4.02 Relative Density 51.7 75 10.1 393 76.8
10 decomposition can reduce nutrient levels. The effluent may then be applied to neighboring fields as a fertilizer that contains reduced levels of n itrogen and p hosphorus. The field wil l still possess high nutrient loads but should reduce the loading to nearby stream s much better than pumping the effluent directly into the streams ( Jack et al. 2006) Macroinvertebrate communities were sampled from ek) to test whether the BMP was effective The macroinvertebrate sampling protocols were from the Kentucky Division of Water (KDOW) and were derived from the Bioassessment protocol. The KDOW methods required sampling of preferred macroinverteb rate habitats such as snags, leaf packs, and undercut banks (KDOW 1993). T hree d frame n et samples were taken in each location resulting in a total of six samples for the upstream and downstream area combined for each stream The KDOW protocol recommend e d the analysis of four macroinvertebrate evaluation metrics which are : E phmeoptera/Plecoptera /Trichoptera (EPT) index, the total number of individuals (TNI), tax onomic richness (TR), and the percentage of the five most common taxa (PCD5) in the total sa mple and the HBI which was transformed to the KIBI or Kentucky Index of Biological Integrity (Jack et al. 2006). The s e data were then log transformed and an ANOVA ( analysis of variance) was used to examine relationships between the metrics The results o f the study (figures 1 and 2 ) showed that the CAFO effluent passing through the BMP had no significant harm on the biological community. The BMP was successfully reducing the nitrogen and phosphorus levels prior to introduction into the stream through runo ff Jack ( et al. 2006 ) indicated that one major downfall to the study was that no pre impact dat a had been taken before the BMPs were installed so the initial condition and health of the stream is unknown and had to be assumed. Using a method such as BACI (Before/After Control/Impact) would have help ed determine the level of impairment before the installation of the BMP so it could be compared to the post BMP installation condition This method would better determine if there ha d been any type of positive or negative change in the health of the stream following the installation of the BMP.
11 Figure 1 Macroinvertebrate Mean Total Number of Individuals (TNI) ( adapted from Jack et al. 2006) Figure 2 B ranch ( adapted from Jack et al. 2006). Weracoba Creek an urban stream in Columbus, Georgia, was found to be in violation of the Clean Water Act water quality standards because fecal coliform bacteria l levels exceed ed the Total Maximum Daily Loa d (TMDL) set forth by the EPA This was a major concern for the city of Columbus because Weracoba Creek feeds into the Chattahoochee River which supplies the city with drinking 0 5 10 15 20 25 Buck Up Buck Down Mays Up Mays Down Invertebrate Count Individuals 0 10 20 30 40 50 60 EPT Total Taxa KIBI PCD HBI Biometric Scores Buck Up Buck Down Mays Up Mays Down
12 water. The Columbus Water Works installed a BMP pre treatment structure to co ntrol coliform levels. The BMP uses pretreatment in the form of an attenuation structure to reduce sand, oils, grease and trash. U ltraviolet light (UV ) is applied continuously to the water passing through the BMP during both wet and dry weather conditio n s to decrease bacterial levels During dry weather the flow is attenuated and divert ed through the UV beds only As the flow increases the water passes through a compressed media filter prior to UV exposure With a further increase in flow approxima tely one third of the w ater passes through the UV beds ; the rest is either diverted through the compressed media filter or topples over the attenuation structure ( Figure 3 ) The compressed media filter further remove s debris and particulates ( Oij 20 10 )
13 Figure 3 Weracoba Creek BMP (WWETCO QAPP 2007 ) Plan View In Weracoba Creek, b ioassessment of macroinvertebrates was performed to evaluate the effectiveness of this BMP using a specific R BP that was designed Resources [called the Georgia Ecoregion Project] ( Gore et al. 2004 ) Samples upstream and downstream of the BMP were acquired before and after the installation. Specific metrics created for the Georgia Ecoregion Project were applied to the evaluation of this stream. The bioassessment of the benthic macroinvertebrates in this study demonstrated that there was an improvement in stream water quality after the installation of the BMP (Oij 200 9)
14 In general t he impl emen tation of BMPs have created a synergy between the need to control non point source pollution and creat problem (Ice 2004) The examples above demonstrate that macroinvertebrates ha ve been used successfull y to assess stream health and to analyze the effectiveness of very different types of BMPs installed on streams. Georgia Ecoregions Project (GEP) The protocol that was utilized during this project is derived from the Georgia Ecoregions Protocol (GEP). In 1996 the state of Georgia Department of Natural Resources (GDNR ) decided to create a sampling protocol unique to the state of Georgia which was based upon the Rapid Bioassessment Protocol. This project was designed in four separate phases which w ere completed in a five year period by Dr. James Gore and grad students at Columbus State University (Gore et al. 200 7 ) The GD NR also made some recommendations of preferable sampl ing protocols to be implemented : sub sample sizes of 100 individuals (to ke ep in line with previous protocols) and the use of metrics that minimized or eliminated taxonomic identification of chironomids. Chironomids are generally rather small and require some prep work and practice to identify them correctly and this procedure i s rather time consuming in the lab. However, the creators of the RBP (Barbour et al. 1999) recommended that a subsample of at least 200 random individuals would need to be used to assess stream health (James A. Gore : personal communication) as in the guide Bioassessment Protocol. Also chironomids would have to be included into the metrics because they are dominant in macroinvertebrate communities in southeastern streams, especially south of the Southeast Fall Line (Figure 4) (Epler 2 001).
15 Figure 4 Geologic Map of Georgia Depicting the Fall Line (USGS) The P hase 1 goal was to determine the Georgia Ecoregion/Subecoregion Delineation and Refere nce Site Selection. This p hase was a useful, general
16 purpose, geographical framew ork that categorized large sections of Georgia into logical units of similar geology, physiography, soils, vegetation, land use/land cover, and water quality ( Figure 4 ). This analysis included the state of Georgia and the area of any catchments shared wi th the neighboring states of Tennessee, Alabama, North Carolina, and Florida, covering an area of 153,169 km 2 South Carolina did not share any catchments of the size evaluated in this study. This area lies across five ecoregions as described by Omernik (1987): (a) the Blue Ridge Mountains, (b) the Ridge and Valley, (c) the Cumberland Plateau, (d) the Piedmont, (e) the Southeastern Plains, and (f) the Coastal Plains. These ecoregions categorize the major differences found in topography, physiography, cli mate, elevation, hydrology, vegetation, wildlife, land use, and surface geology as reflected by soils across Georgia (see Table 3, Descriptions of Georgia Ecoregions). Each of these ecoregions has been further divided into sub ecoregions, reflecting highe r resolution changes in these variables. The sub ecoregions divide the state into 28 areas, ranging in size from 290 to 31,590 km 2 (see figure 5 Sub ecoregions of Georgia). Some of these sub ecoregions were excluded from the GEP study area. The flood p lain ecoregions (65p & 75i) were removed from the study area because they contained a limited number of streams of the size of interest (see discussion of catchment size below). Sub ecoregion 75g, the Okefenokee Swamps did have enough streams of the appr opriate size and could have been evaluated. However, because the sub ecoregion exists almost entirely within a national wildlife refuge there was no need to find reference sites for determining the amount of human impact on streams within the refuge. Als o, because the swamp is a unique landscape within Georgia, reference sites are not needed for comparison to other streams in different sub ecoregions. For these reasons, this sub ecoregion was also excluded from the study area.
17 Table 3 Descript ions of Georgia Ecoregions. Data for elevation and slope represent the range for 1.5 Standard Deviations from the mean (Gore et al 2005) Ecoregion Code Ecoregion Name Geology Elevation (Ft) Slope (degrees) Drainage Pattern Principle Land use / Vegetation Climate 45 Pied mont Metamorphic 346 1335 0 28 Dendritic Mixed Forest, Silviculture, & Urban Mesic Xeric 65 South eastern Plain Sedimentary (Cretaceous Miocene) 110 517 0 16 Dendritic Agriculture, Pine Forest, & Silviculture Mesic Xeric 66 Blue Ridge Mountains Me tamorphic 920 3114 8 60 Dendritic Hardwood Forest Mesic Submesic 67 Ridge & Valley Sedimentary (Paleozoic) 600 1031 0 33 Trellis Hardwood Forest, Agriculture Mesic Submesic 68 Cumber land Plateau Sedimentary (Paleozoic) 1074 2108 0 50 Trellis Hardwoo d Forest, Agriculture Mesic Submesic 75 Southern Coastal Plain Sedimentary (Pliocene Pleistocene) 0 206 0 4.5 Dendritic Pine Forest, Agriculture, Silviculture Mari time The focus of P hase 2 was based up on developing land use judgment criteria for c andidate reference sites ( Figure 5 ), characterization of resident biota
18 inhabiting those reference sites, determining the best attainable reference conditions representative of each ecoregion and to collect ing and analyz ing chemical and biological water qu ality samples at reference sites that are representative ecor egions across the state. Gore et al (2005) have described the process in great detail; much of it being repeated here. To conduct a statewide analysis of all wadeable streams, data had to be acquired that was both spatially expansive, inexpensive, and relatively detailed. Since the degrees of each type of stream impairment that occur in Georgia are unknown, the data w ere chosen to cover the widest array of potential impacts feasible. This i ncluded agricultural, silvicultural, and urban impacts, as well as road density, and road crossings. Data were also included on riparian conditions, point sources of pollution, and hydrologic impacts like in stream impoundments. In order to capture as m uch detail as possible, the highe st resolution data available were used (Gore et al. 2005) The largest scale data available for the entire state was the 1:12,000 scale digital orthogonal quarter quadrangles (DOQQ) images. Although this would have been the most detailed information available for each catchment, it was both cost (>$15,000) and memory prohibitive for statewide coverage (greater than 163 Gb). In addition, there was no way to rapidly analyze these data on a statewide scale. However, a larg e assortment of both vector and raster data were available at 1:24,000 scale. Because of its cost and ease of availability, the GEP chose to do as much analysis as possible at this scale. However, data at that scale were provided only for counties, with the data projected into whichever Universal Transverse Mercator (UTM) zone covers the majority of that county. Each of the datasets downloaded (from the sources described below), were first decompressed, then imported from an ArcInfo export file or Spatia l Data Transfer System (SDTS) format, then merged with other county datasets in the same projection, and finally changed to a geographic, or unprojected, format. Digital Elevation Model (DEM) data were used to both analyze stream order and to delineate catchment boundaries. These were then joined together into coverage for each Georgia County and made available over the internet (Georgia
19 GIS Clearinghouse 2000, USGS 1979). An ArcInfo eoo=exchange f ile for each county was downloaded, decompressed, and i mported into ArcView. Each of these coverages was then joined with other coverages within the same UTM zone. Using the GIS to portray the county boundaries, the major north south hydrologic divide between Atlantic and Gulf drainages (as shown by the Hydr ologic Cataloging Units (CUs)), and the division between the UTM zones, those counties that lay in one drainage, but which had the projection of w hat was predomina n t of the other, were selected. T he DEMs of these counties were converted into the predomina nt projection for that drainage of which that county was a part (e.g. counties that were part of the Gulf drainage which was mostly in UTM zone 16, but were projected in UTM zone 17, were then converted to UTM 16). This was done using Spatial Tools 3.3 co mmand for Grid Warp (Hooge 1998). Each of these converted DEMs was then joined to the original DEM set in the other UTM projection to produce two DEM sets (one for each UTM projection) that were divided along hydrologic as opposed to geographic boundaries (Gore et al. 2005) These DEM sets were then supplemented with additional individual DEM quarter quads downloaded from the GIS Data Depot. These data were only and Tarboton 1999). The data values were then changed from with the appropriate DEM set. Reference sites were needed by sub ecoregion to assess the appropriateness of usin g these regions as areas for stream comparison s Glenn National Health and Environmental Effects Research Laboratory (NHEERL) in Corvallis, OR, provided the data on sub ecoreg ions (Griffith 1999, 2000). An initial draft of the sub ecoregions was used at the beginning of the study that consisted of 28 sub ecoregions. Sub ecoregions were delineated in a manner similar to that described by Omernik (1987), but at a higher resoluti on. The sub ecoregions were delineated on fine scale differences in climate, physiography, soils, surficial geology, vegetation, land use, and water chemistry (Gallant et al 1989).
20 Hydrographic data were used to both delineate cat chments for analysis and comparison as well as measuring the amount of anthropogenic hydrologic impact within each catchment. Land cover data provided most of the information that was used to determine the amount of impact found in both catchments and r iparian zones. EPA Region 4 provided a draft version of the product of the Multi Resolution Land Characteristics Consortium (MRLC). These data have since been published as the National Land Cover Data, 1992 (NLCD92) (Vogelmann et al 2001). These data w ere produced by analyzing two sets of Landsat 5 Thematic Mapper satellite data (leaves on and leaves off), collected between 1991 and 1993. Accuracy of the data was assessed at randomly chosen pixels and groups of pixels by comparing the classifications a ssigned in the NLCD to those determined by photo interpretation. For EPA Region 4 the overall accuracy was 81% for the level II classifications, with the urban classes being the least accurate (down to 23% for commercial/industrial), and the forest classe s being the most accurate (up to 100% for mixed forest). Since many of the errors occurred within an Anderson Level I classification (i.e. confusing pasture with row crops and vice a versa), accuracy improved to 83% when comparing urban vs. agricultural v s. forested. (Vogelmann et al 2001, USGS 2001a) Unfortunately, this accuracy assessment was also in doubt, since the classifications by photo interpretation were not without error, and the photos that were used were two years older than the satellite da ta, so some of the land uses may have changed in the interim. The NLCD92 data were correct in all cases except in one low intensity residential area (with many large older trees), where approximately a third of the area was classified as mixed forest An other measure of the amount of human impact on a catchment wa s road density in the catchment. Unfortunately, such data sets were only available at that time for a limited number of quadrangles in Georgia; The Georgia DOT produced a 1:24,000 scale statewid highway base map, which has been revised using 1993 Digital Ortho Photo Quarter Quads (DOQQs). This dataset was not independently assessed for accuracy, although the DOT did perform internal quality control (GA DOT 1997).
21 These data did not cover any parts of the study area outside of Georgia nor did they cover the road networks found on the two large military posts in the state, Ft Benning and Ft Stewart. Even though these data would have been usef ul, the gaps in coverage were fixed using the same method used to supplement the hydrographic data described earlier. Since catchment wide land use was not necessarily correlated with the occurrence of point sources of pollution, data on these point sou rces were also needed to ensure they were excluded as possible reference sites. The EPA has produced a nationwide set of data and a set of GIS analysis tools designed to be used with these data in the Better Assessment Science Integrating point and Nonpoi nt Sources (BASINS) program. The data available for point sources database for CONUS (USEPA 1998a), EPA/OW Permit Compliance System (PCS) for CONUS (a national computerized management information system that automates entry, updating, and retrieval of National Pollutant Discharge Elimination System (NPDES) data) (USEPA 1998b), and USEPA Toxic Release Inventory (TRI) of industrial manufacturing facilities in the United States (USEPA 1998c). The accuracy of the data in each of these datasets was assessed by comparing the location information (latitude & longitude) to other positional data Table 4 Modified Anderson Level II Land use classifications used in NLCD92 (Gore et al. 2005) Category Code Description Water 11 Open Water 12 Perennial Ice/Snow Developed 21 Low Intensity Residential, 30 80 percent of cover is man made, single family housing 22 High Intensity Residential, 80 to100 percent of cover is man made, apartment complexes 23 Commercial/Industrial/Transportation Barren 31 Bare Rock/Sand/Clay, Perennially barren areas of bedrock, scarps, talus, slides 32 Quarries/Strip Mines/Gravel Pits
22 3 Transitional Forested Upland, sparse vegetative cover, in cludes forest clearcuts Forested Upland 41 Deciduous Forest, 75 percent or more of the tree species shed foliage 42 Evergreen Forest, 75 percent or more of the tree species maintain their leaves all year 43 Mixed Forest, neither deciduous nor evergre en species represent more than 75 percent Shrubland 51 Shrubland, shrub canopy accounts for 25 100 percent of the cover Non Natural Woody 61 Orchards/Vineyards/Other Herbaceous Upland Natural/Semi natural Vegetation 71 Grasslands/Herbaceous, not int ensively managed, but are utilized for grazing Herbaceous Planted/Cultivated 81 Pasture/Hay 82 Row Crops, such as corn, soybeans, vegetables, tobacco, and cotton 83 Small Grains, such as wheat, barley, oats, rice 84 Fallow 85 Urban/Recreational G rasses, includes parks, lawns, and golf courses Wetlands 91 Woody Wetlands 92 Emergent Herbaceous Wetlands for the facility in the same data set (state for PCS, county for IFD, or zip code for TRI). Any facilities without agreement between the pos itional data were excluded from the dataset (EPA 1998a, 1998b, 1998c). The steps used to choose candidate reference sites were: (1) delineating areas to be compared, (2) measuring the amount of land use impact in each of those areas, and then (3) choosin g the least impacted sites to be candidates. Because stream biota v ary longitudinally, any system of comparing streams will have to account for this natural variation (Stanford 1996; Allen 1997). The GEP chose to control for this variation by studying a single size class of stream catchments. Often in the past, stream order was used to describe relative stream size (Allen 1997) and, initially, they (Gore et al. 2005) used stream order to delineate study catchments. In choosing which size stream to focus upon, a
23 balance was struck between using catchments that were small enough to fit within sub ecoregions and have wadeable streams and catchments that were large enough to have perennial streams that would be flowing, even during drought conditions. Fourt h order streams were chosen as an appropriate size to study. In addition, large second and third order streams with a total catchment length of more than eight kilometers and small fifth order streams with catchment lengths of less than eight kilometer s w ere also included, since they had roughly the same catchment area as most fourth order catchments. With the stream order theme laid over the sub ecoregion of interest and the stream orders of interest highlighted, stream selection and catchment delineatio n w as used to do the catchment delineation. It catchment to be delineated. ArcView then selected every other cell that produced flo w that eventually went through this pour point based on the flow d irection grid created earlier. When pour points were chosen, if an in stream impoundment existed immediately upstream, or one downstream caused that pour point to be inundated, then a pour p oint was selected up steam of the impoundment if it still provided an overall catchment length of over eight kilometers. Hughes et al (1986) recommended selecting candidate catchments based on catchment area and annual discharge as opposed to stream ord er. They also recommended that the sites to be compared differ by less than an order of catchment magnitude. Based on these recommendations, the delineated catchments were reassessed. Using the Analytical Tools Interface for Landscape Assessments (ATtIL A) ArcView extension (see details below), areas for all of the delineated catchments were calculated. The range of areas for each sub ecoregion was examined to ensure that all the catchments were within a single order of magnitude. Catchments that were to o large were either split into smaller catchments or simply redelineated with the pour point of the catchment moved upstream. The next step was to measure the relative amounts of human impact on each catchment. Each of these measurements were made using an extension for
24 (Ebert and Wade 1999). ATtILA was designed specifically to analyze landscape data, including the NLCD 92, in terms of discrete areas such as polygon themes of counti es, ecoregions, or catchments. Input was provided as themes of land use data and the resulting output data were appended to the attribute table of the polygonal area theme being analyzed Inputs used included a theme of all the delineated catchments in a sub ecoregion, a raster land use theme (NLCD 92), function then determined the total area and percentage of cover for each of the major land use categories (see table 4, Modified Anderson Level II Land use classifications used in NLCD92) within each catchment. Areas that had been categorized as barren/transitional (land use code 33) include d clear cuts, transitions between forest and agriculture, and areas disturb ed temporarily by natural causes like fire or flood (USGS 2001b). Since the amount of agricultural land was relatively stable and the amount o f land that is allowed to under go natural disturbances was small compared to the amount of land clear cut in Geor gia, it was assumed that all transitional areas were clear cuts. The amount of clear cuts was then used as a surrogate for estimating the relative amount of silviculture within a catchment. al areas and percentages of land use for three buffers of different width along all the streams within a catchment. Buffers were also calculated at various pixel distances, providing buffers with widths of 10 to 15m, 40 to 45 m and 130 to 135 m respective ly. number of stream/road crossings within a catchment. Road density was calculated as the total road distance (in kilometers) within a catchment divided by the area of the catchment (in square kilometers). Stream crossings by roads were calculated both as a number and as a density (number of crossings per kilometer of stream).
25 The next step was to decide which catchments were the least impacted based on the measurements made. An iterative approach was used to develop a selection method, starting by analyzing the results of possible methods against the raw data, then by comparing predictions against a sample of ground truthed streams, and finally to a validation of the method in se veral different sub ecoregions. The initial attempt at ranking was done by assessing a single combined impact measurement at a time, and then ranking all catchments based on that measurement. Another scoring system was attempted to overcome the deficie ncies of simple combined ranking. Scores were assigned based on which quartile the catchment fell into for a particular landuse or impact measurement, to account for the aggregate distribution of the data, ranging between one and four. T his scoring syste m tended to produce many tie scores because of its relatively low number of possible scores (27). T he riparian data that were not used for initially scoring catchments (data for the 45 m and 135 m buffers) were used to break ties. These data were scored as the other measurements were scored and then combined with all the original scores to produce a larger compound score. If this larger score failed to resolve a tie, then the total percentage of land in non anthropogenic use (a combination of all forest, w etland, and shrubland) was used to break the deadlock. Since this selection method worked well when comparing the results to the raw data, it was then compared to actual streams to see how well it predicted relative amounts of impact.
26 Table 5 Land use measures used in selecting candidate reference sites (Gore et al. 2005) Primary Selection Measures Catchment Wide % Urban % Total Agriculture % Barren Road Density Density of Road/Stream Crossings Impoundment Density 15 m Ripari an Buffer % Urban % Total Agriculture % Barren Tie Breaking Selection Measures 45 m Riparian Buffer % Urban % Total Agriculture % Barren 135 m Riparian Buffer % Urban % Total Agriculture % Barren Using spreadsheets of land use created for each of the sub ecoregions, distributional scores were calculated for each measurement, and then summed, and these summed scores sorted the sites. The GEP decided upon a goal of sampling five candidate reference sites in each sub ecoregion, or five percen t in the larger sub ecoregions. With the spreadsheets already sorted by summed score, the possible candidate sites were easily selected by assigning grades of one to the top five or five percent to be primary catchments to be ground truthed and sampled if relatively unimpaired. The next five or five percent were assigned a grade of two to serve as alternates for any of the primary sites that were inaccessible or impaired. The physical, chemical and biological condition of 87 candidate reference sites a cross the state were characterized between 6 October, 2000 and 6 March,
27 2001. Characterizations were pe r formed using the following procedures, as (CSU 2000): 1. Benthic macroinver Method (CSU 2000), with the macroinvertebrates being identified to the lowest practicable taxonomic level. 2. Water chemistry was measured for the parameters in table 10 (water chemistry parameters meas ured) both in situ using a Hydrolab H 20 probe and by taking water grab samples that were later tested in the lab. 3. The streams physical properties were recorded. These included a stream cross section, velocity, substrate size using a modified Wolman Pe bble Count, and observations of degree of shading and presence of oils, impacting land uses, bank erosion, and types of deposits. 4. Rapid Bioassessment Protocol habitat assessment method s and forms. Table 6 Water chemistry/quality parameters measured at sites (Gore et al. 2005) Parameter Measured Type of Sample Taken Method / Instrumentation Used Range of Detection Ammonia (mg/l as N) Grab Sample EPA Method #350.3 0.0 3 to 1400 NH3 N/L Nitrate (as N) Grab Sample EPA Method #353.3 0.01 to 1.0mg NO3 N/L Total Phosphorus (mg/l as P) Grab Sample EPA Method #365.3 0.01 to 1.2 mg P/L Copper (mg/l) Grab Sample EPA Method #220.1 low detection limit is 0.1ppm Iron (mg/l) Gra b Sample EPA Method #236.1 low detection limit is 0.1ppm
28 The determination of whether the water quality of the candidate reference sites was impaired was made by comparing the water chemistry data against national standards (for those parameters where they exist), and by comparing them to published data on water quality in other streams in the region. To compare the ecological integrity of the proposed reference sites, the benthic macroinvertebrate species data were analyzed following the metho d described in Rothrock et al (1998). A Composite Normalized Metric (CNM) was calculated by dividing each separate metric score by the largest score so metric scores would vary between zero and one, and then summing all of the metric scores into a single score for comparison between sites. The reciprocal of the metrics that become smaller with increased ecological integrity was used to calculate the CNM, so a higher CNM score is indicative of higher biologic integrity. Two sets of metrics were used in t his comparison as shown in table 11. The metric suggested by Rothrock et al (1998) was based on a general set of metrics Manganese (mg/l) Grab Sample EPA Method #243.1 low detection limit is 0.1ppm Zinc (mg/l) Grab Sample EPA Method #289.1 low detection limit is 0.1ppm Conductivity (mS/cm) In situ Measurement HydroL ab H 20 probe 1 to 100 mS/cm Dissolved Oxygen (%) In situ Measurement HydroLab H 20 probe 0 to 100 % Dissolved Oxygen (mg/l) In situ Measurement HydroLab H 20 probe 0.2 to 18.8 mg/L PH In situ Measurement HydroLab H 20 probe 0 to 14 units Turbidity (NT U) In situ Measurement HydroLab H 20 probe 5 to 1000 NTU Water Temperature (C) In situ Measurement HydroLab H 20 probe 5 to 50 o C Alkalinity (mg/l as CaCO3) Grab Sample EPA Method #310.1 All concentration ranges of alkalinity Hardness (mg/l as CaCO3) G rab Sample EPA Method #130.2 All concentration ranges of hardness
29 recommended in the EPA Rapid Bioassessment Protocol (Plafkin et al 1989). The other set of metrics was chosen by Stribling et al ( 1998) for assessing ecological integrity of non coastal plain streams in Maryland as part of the Maryland Biological Stream Survey (MBSS). Table 7 Summary of metrics used in characterizing ecological integrity from Rothrock et al (1998) and Stribling et al (1998). (Gore et al. 2005) Metric Ecological Relevance Rothrock MBSS Taxa Richness The richness of the community indicates biodiversity of ecosystems and is used as a quantitative measure of stream water and habitat quality. Taxa richness gener ally decreases as a stream ecosystem degrades. EPT Richness The richness of the intolerant insect orders Ephemeroptera (mayflies), Plecoptera (stoneflies), and Trichoptera (caddisflies) can indicate stream condition, since they tend to become scarcer wit h increasing disturbance. Rothrock et al. (1998) Total Abundanc e Total number of organisms sampled can have variable response to stream impairment, but will generally decrease as a stream ecosystem degrades. EPT Abundanc e The number of the these general ly intolerant insects can indicate stream condition, since the organisms themselves also tend to become more scarce with increasing levels of disturbance Percent Dominant Taxa The proportion of the entire community composed of the most abundant taxa can be used to examine community balance. A community dominated by relatively few taxa is indicative of stress. Biotic Index An index based on the tolerance of different organisms to pollution and stress. Tolerance values were based on values developed by Lenat (1993), or given in the RBP manual (Barbour et al. 1999) EPT : Chironomid The ratio of EPT to Chironomids (midges). Decreasing ratios indicate stress since Chironomids tend to increase with increasing organic enrichment. Shredders: Total Measure of distribution among functional feeding groups, shredders will decrease due to riparian zone impacts and can be indicators of toxins. Scrapers to Filterers Shifts in functional feeding group between the scrapers who increase with increasing diatom abun dance and filterers who increase with increasing filamentous algae, indicate an over abundance of certain food sources.
30 MBSS (Stribling et al. 1998) Number of Ephemer optera taxa The richness of mayfly taxa indicates the ability of a stream to support the se intolerant insects. Organic enrichment and excess fine sediment will often reduce the diversity of mayflies. Number of Diptera taxa Diptera as an order are relatively diverse and Dipterans are variable in their tolerance to stress. However, a high d iversity of Diptera taxa generally suggests good water and habitat quality. Percent Ephemer optera The dominance of the community by mayflies can indicate the relative success of these pollution intolerant individuals in sustaining reproduction. Stresses will reduce the abundance of mayflies relative to others. Number of Intolerant Taxa Intolerant taxa are the first to be eliminated by perturbations, since they are often specialists with specialized habitat or water quality requirements. Taxa with tole rance ratings from 0 to 3 were considered intolerant. Percent Tolerant As perturbation increases, tolerant individuals (tolerance values 7 10) tend to predominate in the sample. Intolerant individuals become less abundant as stress increases, leading to more individuals in tolerant taxa. Percent Tanytarsini of Chironomi d The tribe Tanytarsini is a relatively intolerant group of midges. The degree to which they represent the total number of midges indicates the general sensitivity of the midge assemb lage. A high percentage of Tanytarsini among the midges may indicate lower levels of anthropogenic stress. Percent Collectors Abundance of detritivores typically decreases with increased disturbance. This ecological response may be a food web effect, where organic material becomes scarce or unsuitable with increased perturbation. Using the final selection method of ranking catchments based on the distributions of land use and then choosing the top five or five percent of catchments in a sub ecoreg ion (Figure 5) as possible candidate reference sites, 157 out of the 2158 possible catchments were chosen as primary possible candidates and another 157 were chosen as alternates. Over half of these sites were not sampled because they appeared un satisfact ory, but other catchments appeared superior, they were not representative of the region, and some of the catchments were dry due to drought. Twenty two of these primary sites were visibly impaired, with six of these impaired due to obvious recent changes in land use (mostly forest clear cuts).
31 Figure 5 Ecoregions of Georgia (Gore et al. 2005) Drinking Water Regulations (USEPA 1999; National primary drinking wate r regulations 2001) for those chemical parameters that we measured with only three exceptions. The standards that were exceeded are the 0.3 mg/L standard for iron, which was exceeded in 12 instances, the 5 NTU standard for turbidity which was exceeded in 39 instances, and the 0.05 mg/L standard for manganese which was exceeded in only one instance. As a result, Gore et al. (2005) concluded that the Geographic Information System ( GIS ) approach was an unbiased method to designate reference conditions. Pha se 3 of th e GEP focused upon evaluation of impaired streams ( Figure 7 ) in comparison to reference characteristics in each of the major ecoregions and subecoregions in Georgia. Specific activities included the identification of a suite of impaired sites in each of the ecoregions and distinctive sube coregions (identified in Phase T wo), which were sampled, using Rapid Bioassessment techniques, for physical, chemical, and biological condition.
32 Macroinvertebrates were collected by the means of a D frame net usi ng the twenty jab method (Georgia Bioassessment Protocol). A net mesh size of 595 600 microns was used. Macroinvertebrates were sampled in all habitats including: fast and slow riffles, undercut banks, leaf material, snags, and sandy bottoms. The sampli ng procedure started at the zero meter mark and continued upstream to reduce habitat disturbance (Columbus State University 2000). Water samples for laboratory analytical chemistry were collected according to procedures in the Quality Assurance Project Pla n (QAPP) (Columbus State University 2000) and were labeled, preserved, and chilled until returned to the lab. Macroinvertebrate samples were transferred to one liter bottles, labeled, and preserved in 95% ethyl alcohol. From each macroinvertebrate sampl e, a 200 organism subsample was randomly selected. The s ample was evenly spread upon a C aton gridded screen (Caton 1991) Sample squares were chosen using a random number sheet. Each grid square was che cked for organisms until all were removed. At leas t four grids were taken from each sample and then continuously selected until 200 organisms we re collected. Once subsampling was complete, macroinvertebrates were identified to the lowest possible taxonomic level. Based on the raw macroinvertebrate data a multimetric analysis calculated by Ecological Data Application System (EDAS) (MS Access 2000; Tetra Tech Inc. 2001 a ) was used to assess stream condition. Metrics were selected from the following categories of biological information: richness, composit ion, tolerance/intolerance, and habit/trophic measures, so that each category was represented when possible. Metrics were grouped into candidate indices for each ecoregion (Gore et al 2005 ). The following protocol was used for developing ecoregion based multimetric indices and subsequent classification systems. All data were entered, quality checked, and metrics calculated using EDAS (MS Access 2000; Tetra Tech Inc. 2001 a ). Statistica correlation and box and whisker correlation was used to determine redundancy among metrics. If metrics were too similar, one was
33 eliminated to avoid describing the same ecological characteristic multiple times. Box and whiskers plots were use d to demonstrate the ability of different indices to discriminate between stream conditions. Desirable indices showed a complete separation in box and whisker plots ( i.e. no overlap of interquartile ranges) between reference and impaired conditions (see Gore et al 2005 ), for an expanded description of this procedure). First, candidate metrics were selected from each biological category, when possible, and calculated in EDAS. Metric data were exported to Excel spreadsheets and the discrimination efficie ncy and the percentile distribution for each metric were determined. The discrimination efficiency (DE) was determined as follows (MDEQ 2003). For metrics that increase with stress: DE = ( number of impaired sites > the 75 th percentile of references sites ) / total number of impaired sites For metrics that decrease with stress: DE = ( number of impaired sites < the 25 th percentile of references sites ) / total number of impaired sites Next, metric data were exported to Statistica Once in Statistic a reference and impaired conditions were compared using box and whisker plots and Pe correlation (Gotellii and Ellison 2004 ). Metrics that revealed low discrimination ability in box and whiskers plots were not considered for candidate indices. correlation of greater than 0.90 or less than 0.90, one was automatically eliminated from candidate metrics because of redundancy with other metrics. Metrics with a value of 0.80 to 0. 90 or 0.80 to 0.90 were considered as candidate metrics if the relationship was not similar in relation to other metrics. If candidate
34 metrics have a parallel linear relationship, their relationship was considered to be co dependent and thus the informa tion provided by that metric did not provide additional discrimination. When metrics with linear relationships were encountered, one was eliminated. Once undesirable metrics were eliminated, final candidate metric scores were standardized to a 100 point scale (MDEQ 2003). From final candidate metric scores, several candidate indices were selected, each including four to seven metrics. Metrics were selected to represent each structural and behavioral category, to discriminate between reference and stre ssed conditions, and to produce unique information for each index. Each index was compared using the discrimination efficiency and box and whisker plots. The discrimination efficiency and box and whisker plots revealed whether or not each candidate index discriminated between reference and impaired conditions. The index with the greatest discrimination ability was selected (MDEQ 2003). Selection of the final indices considered the metric selection criteria and chemical and physical data. Any other sele ction criteria were based on best professional judgment. The ideal index had a box and whisker plot with good discrimination efficiency, little or no overlap between reference and impaired conditions, allowed detection of stream impairment, and ranked rel ative severity of impairment. The final analytical product was the formulation of a numeric rating system for wadeable streams in the state of Georgia, in the context of ecoregional or subecoregional differences. Each stream classification level had a multimetric index that designated a specific impairment condition. Using multimetric indices and abiotic data, streams were grouped into levels of impairment and were each given a numeric assessment of 1 to 5 (1 5 (Table 8) A total of 111 reference sites (Figure 6) and 184 impaired sites (Figure 7) was sampled and considered in developing the numerical indices of stream health for each region. Benthic indices were developed for each ecoregion and subecoregion. In ecoregion 75, additional indices were created by grouping tidal and nontidal streams in subecoregion 75j (including one tidal stream from subecoregion 75f).
35 By grouping tidal and nontidal streams, the indices were found to have higher d iscrimination efficiencies. During the development of the numerical index, each stream received an index score. The index score was the average of all standardized metric values used in the index. Each stream also was ranked, described, and rated. A stream received a ranking between 1 and 5, which corresponded with a narrative subecoregion level indices had higher discrimination efficiencies than ecoregion level indices. Subecoregions with smaller catchment areas tended to have higher discrimination ef ficiencies than subecoregions with larger catchment areas. Indices for ecoregions in the piedmont and mountain areas (45, 65, 66, 67, and 68) tended to contain metrics from all functional and structural categories, especially richness. For the Southeaste rn Plains (Ecoregion 65) and the Southern Coastal Plains (Ecoregion 75), indices were developed primarily from metrics in the composition category and rarely from richness category. The objectives of Phase 4 were the verification and validation of the num erical scoring system ( Table 3 ) as well as development of a defensible system for applying the numerical scoring system to evaluate the health of other streams in Georgia. The G eorgia E coregion P rotocol established reference conditions and impaired condit ions rather than assigning single streams or sets of streams as points of comparison (Gore et al 200 7 ). In addition a framework for the application of bioassessment to various regulatory activities (such as TMDLs or other CWA Â§ 303(d) requirements) was dev eloped (Gore et al. 2005)
36 Figure 6 Reference Sites of the Georgia Ecoregion Project (Gore et al. 2005) Figure 7 Impaired Sites of the Georgia Ecoregion Project (Gore et al. 2005)
37 Table 8 Stream Rating B ased on Numeric Ranking (Gore et al. 2005) Numeric Ranking Stream Health Rating Management Decision 1 A Continue periodic monitoring to detect change baseline reference condition 2 3 B Frequent monitoring critical to detect change in ecological status, lower range especially 4 C Freq uent monitoring necessary to determine remediation needs and if remediation has been successful 5 Roaring Branch Project In Columbus, Geo r gia the Columbus Water Works (CWW) identified a sediment l oading problem on Roaring Branch (the focus of this study) an ur ban stream which exceeded TMDL standards. CWW contracted with an engineering consulting firm, Wet Weather Engineering and Technology LLC ( WWETCO ) to design a BMP for Roaring Branch ( Figure 7 ) in an attempt to reduce the problem of sediment l oading into Lake Oliver (Fig ure 8 ) The BMP was designed as a control structure that attenuates flow to remove some suspended sediment and to reduce flows in the downstream reaches to reduce erosion and downstream transport. Previous studies have linked ma croinvertebrate assemblages to the amount of sediment loading in streams ( Wentsel et al. 1977, Lenat et al. 1981, Giesy and Hoke 1989). Therefore, it was decided to utilize the RBP and criteria for
38 references streams, created by the Georgia Ecoregions Proj ect, as tools to demonstrate the effectiveness of the BMP The objective was to determine if the BMP control of sediment would be sufficient to alter macroinvertebrate community structure to the extent that the health condition of the stream could be demon strably improved. Fig ure 8 Map of Lakes (Impounds) of the Chattahoochee River in Georgia (University of Georgia
39 Figure 9 Map of Roaring Branch Meeting Lake Oliver (Columbus Water Works CWW) Roaring Branch input into Lake Oliver
40 Fig ure 10 Roaring Branc h BMP Design WWETCO (QAPP from WWETCO 2007 ) ( The above figure is demonstrating how the flow control bladder can control flow passage, drainage, and storage of water from the retrofitted pond to the tributary of Roaring Branch.)
41 Materials a nd Methods Roaring Branch is a small urban stream in Columbus, Georgia located in the 65c Sandhills ( Figure 11 ) subecoregion (Gore et al. 2005) This stream is a djacent to ecoregion 45b ( S outhern O uter P iedmont), but after inspection, it was determined t hat Roaring B ranch had most similar characteristics to a Sand Hills stream The typical reference stream for 65 c subecoregion can be depicted by figure 12 and the typical impaired stream can be depicted by figure 13. Figure 11 (adapted from Gore et al. 2005)
42 Figure 12 Subecoregion 65 c Typical Reference Stream Figure 13 Subecoregion 65 c Typical Impaired Stream
43 The Sand Hills ecoregion forms a narrow, rolling to hilly, highly disse cted coastal plain belt stretching across the state of Georgia, from Augusta to Columbus. The region is composed primarily of Cretaceous and some Eocene age marine sands and clays deposited over the crystalline and metamorphic rocks of the Piedmont (Ecoreg ion 45). Many of the droughty, low nutrient soils were formed from thick beds of sand, although soils in some areas contain more loamy and clayey horizons. On the drier sites, turkey oak and longleaf pine are dominant, while shortleaf loblolly pine forests and other oak pine forests are common throughout the region (Gore et al. 2005) Gore et al. (2005) produced the following criteria for the Sand Hills subecoregion. Table 9 Characteristic Reference Stream Landuse, Habitat, and Chemistry Data for Subecor egion 65c Sand Hills (Gore et al. 2005) Catchment Landuse Parameter Mean Median Range % Natural 72.5 72.2 65.4 77.5 % Agriculture 7.1 8.4 0 13.1 % Silviculture 15.3 15.3 9.0 21.1 % Urban 5.1 5.2 3.0 7.3 Habitat Total Habitat Score (20 0) 164.4 164.0 159 170 Epifaunal Substrate (20) 15.6 16.0 13 18 Pool Substrate Characterization (20) 13.8 15.0 9 16 Pool Variability (20) 14.8 16.0 10 16 Sediment Deposition (20) 17.0 17.0 16 18 Channel Flow Status (20) 19.0 19.0 19 Channel Alt eration (20) 18.4 19.0 17 19 Channel Sinuosity (20) 11.8 13.0 9 15 Bank Stability (L) (10) 8.8 9.0 8 9 Bank Stability (R) (10) 9.2 9.0 8 10 Vegetative Protection (L) (10) 8.4 8.0 8 9 Vegetative Protection (R) (10) 8.4 8.0 8 9 Riparian Vegetativ e Width (L) (10) 9.4 10.0 8 10 Riparian Vegetative Width (R) (10) 9.8 10.0 9 10 In Stream % Silt/Clay 37.0 12.0 0 22.8 % Sand 87.0 95.7 63.0 100.0
44 Habitat (Substrate) % Gravel 1.1 0 0 4.3 % Cobble 0 0 0 % Boulder 0 0 0 % Bedrock 0 0 0 Chemi stry (in situ) Specific Conductivity (mS/cm) 0.020 0.015 0.003 0.049 Dissolved Oxygen (mg/l) 11.3 11.7 10.3 12.5 pH (SU) 5.1 5.1 4.3 6.2 Turbidity (NTU) 2.3 1.1 0 6.9 Chemistry (laboratory) Alkalinity (mg/l as CaCO 3 ) 1.8 0 0 8.2 Total Hardness (mg/l as CaCO 3 ) 9.8 10.3 5.5 18.0 Ammonia (mg/l as N) 0.054 0.052 BD 0.07 Nitrate Nitrite (mg/l as N) 0.18 0.11 0.07 0.47 Total Phosphorous (mg/l as P) BD BD BD Copper (mg/l) BD BD BD Iron (mg/l) 0.54 0.54 BD 0.92 Manganese (mg/l) BD BD BD Zinc (mg/l) BD BD BD BD = Below Detection Table 10 Discriminating Invertebrate Metrics for Subecoregion 65c Sand Hills (Gore et al. 2005) Index 65c Metric Metric Category % Trichoptera Composition Tolerant Taxa Tolerance/Intolerance Intolerant Taxa % Scra per Functional Feeding Group Clinger Taxa Habit
45 Figure 14 Discriminating Index Characteristic between Reference and Impaired Streams for Subecoregion 65c Sand Hills (Gore et al. 2005) The sampling scheme to assess the effectiveness of the Roaring Branch BMP utilized an adapted version of the Rapid Bioassessment Protocol (Gore et al 2005). Sampling was completed in winter 2007, summer 2008, and winter 2008 in order to create a Before/After Control/Impact (BACI) comparison. T wo sets o f samples were collected during each index period, from a site on the downstream side of the BMP and from a site on the actual Roaring Branch which was about one kilometer downstream of the BMP location ( Fig ure 9 ). In most cases, when performing a BACI des ign, an upstream (control) sample is collected but in this particular case, the stream runs underground just upstream of the location of the BMP. The downstream sample, on the main stem of Roaring Branch, served as the comparison. The sampling s ite that was immediately downstream of the BMP was a very small tributary adjacent to a parking lot which had a few very
46 large drains that feed into the tributary. The tributary also contained a high concentration of red clay ( Fig ure 10 ) and large rocks th at seemed out of place. The rocks were most likely placed to slow the flow and trap erosional material during high discharge events but because the streambed is composed largely of clay, the flow simply cut around the structures. The comparison sampling s ite, on the actual Roaring Branch downstream of the confluence ( Fig ure 11 ), was more heterogeneous, containing substrates of sand, gravel and cobble, without much clay in its composition. Sampling was completed using the 20 jab method (Appendix A) in whic h a prioritized list of habitats were used to determine the type of samples that were to be collected. This prioritized list was influenced by the gradient of the stream to be sampled. Roaring Branch was not considered to be a high gradient stream; but the high gradient priority table was used because it best cha ra cterized most sandhill streams ( Table 11 ; adapted from Gore et al. 2005) If a certain sample type was not contained in sufficient numbers over the reach sampled, the sample was reallocated to the top of the list until all 20 jabs had been completed.
47 Fig ure 1 5 Sampling Sites of the Tributary & Roaring Branch ( Created by James Banning using GIS) Sample Area Immediately Downstream of BMP S ample Area Roaring Bra nch downstream of confluence
48 Fig ure 13 Sample Area Immediately Downstream of BMP Fig ure 14 Sample Area ownstream of the C onfluence
49 Table 11 Prioritized list of habitat types for sampling and sample reallocatio n for the modified 20 jab method. ( adapted from Gore et al. 2005) HIGH GRADIENT STREAMS Priority Habitat Type 1 Fast Riffle 2 Slow Riffle 3 Snags 4 Undercut Banks/Rootwads 5 Leaf Packs 6 Sand 7 Macrophytes (if any) The collecting technique [or Standard Operating Procedure (SOP)] for in situ sampling (Appendix A) employed the use of a d frame net with a mesh size of 595 to 600 microns. Macroinvertebrates were sampled in all habitats including: fast and slow riffles, undercut banks, leaf mate rial, snags, and sandy bottoms. Sampling was completed from downstream to upstream to reduce the chance of contamination using a technique in which the collector stands just downstream of the sampling area and employs his/her foot to disturb about a one s quare meter macroinvertebrates in the downstream net The material collected was then deposited into a sampling bucket with a 550 micron mesh bottom to allow the water to drain while keep ing the collected materials In between each grab, the net was inspected for macroin vertebrates, especially clingers such as caddis flies, which were removed by hand and added to the sampled mat erial. Once all twenty samples had been collected the material was then placed into containers and preserved with 95% ethanol. Once the composited material was taken to the lab, the ethanol was drained and each sample was recharged with fresh ethanol to reduce the chance of decomposition. The lab technique for s ubsampling the composite (Appendix B) is much more time consuming than in situ sampling. Since samples from a single site were contained in several bottles, these bottled samples were composited together and rinsed with tap water to remove as much ethanol as possible. The entire
50 composite was then placed into a plastic tray and the large organic debris was rinsed off and removed. The next step was to place the composite into a one tenth square meter tray that is numbered as a 5x6 grid which splits the samp le up into 30 equal squares ( Fig ure 12 ) (see Caton 1991). A random number generator (www.random.org) was then utilized to randomly pick which square would be chosen (without replacement) to be sorted. The material from the square was placed in Petri dishes and, with the use of a dissecting m icroscope, the macroinvertebrates were removed and placed into test tubes containing ethanol for later identification. Once the target of 200 individuals had been reached no further squares were extracted from the tray. T he 200 individuals from the sample were separated according to order and identified to the lowest possible taxon, following accepted procedures (Covich and Thorp 2001, Merritt and Cummins 2009, see Appendix C ). In the case of the identification of chiro nomids the process is more time consuming capsule using a scalpel. The head and body were then placed on a glass slide using CMC 10 mounting medium. The slides were then placed in a fume hood for a minimu m of 24 hours to dry and clear. After the slides were dry, individuals were identified to the lowest possible taxonomic level (Epler 1995), using a co mpound microscope with video attachments (to increase magnification and resolution). Figure 12 Composite Sampling Tray
51 The results from sampling and identification were then compared to a table of metrics constructed for 65c Sandhills in the Georgia Ecoregions Project (Gore et al. 2005) (Table 12 ) and a composite metric score for each site was produced according to the following protocol The median value for each metric was divided into the proportion or percentage of individuals found in each sample (e.g. Tolerant Taxa: 14/10= 1.40) as they related to the reference (unimpaired) condition for the metric in question. This score was then multiplied by one fifth of the maximum reference metric score in the ecoregion. In the case of subecoregion 65c, the maximum or highest index score was 92 (Table 13 ) so each metric was multiplied by 18.4 (Table 14 ). If the adjusted metric score for any value was more than 18.4, the value was changed to 18.4 to represent the maximum value for the metric in that ecoregion. Table 12 Metrics & Distribution of Scores for 65 c Sandhills Lower Piedmont ( Gore et al. 2005) Metr ics DE Minimum Percentile n = 5 Maximum 5th 25th 50th 75th 95th % Trichoptera 0.7 4.3 4.5 5.1 8.8 13.7 23.8 26.3 Tolerant Taxa 0.8 3.0 3.8 7.0 10.0 11.0 11.8 12.0 Intolerant Taxa 0.8 3.0 3.4 5.0 5.0 9.0 10.6 11.0 % Scraper 0.9 4.0 5.0 10.8 11.3 23 .6 27.1 28.0 Clinger Taxa 0.6 10.0 10.2 11.0 12.0 15.0 16.6 17.0 Table 13 Stream Ratings for Subecoregion 65c Sand Hills (Gore et al 2005) StationID Condition Index Score Numeric Ranking Narrative Description Stream Rating HH25 Reference 92 1 very good A HH24 Reference 65 2 Good A 65c 40 Impaired 63 2 Good A 65c 3 Impaired 62 2 Good A 65c 80 Reference 59 3 Fair B 65c 89 Reference 58 3 Fair B 65c 8 Impaired 55 3 Fair B 65c 12 Impaired 52 3 Fair B HH26 Reference 47 3 Fair B
52 65c 88 Impaired 35 3 Fair B 65c 5 Impaired 34 3 Fair B 65c 38 Impaired 26 4 Poor C 65c 92 Impaired 25 4 Poor C 65c 48 Impaired 24 4 Poor C 65c 4 Impaired 11 5 very poor C Table 14 Example of Metric Value Calculation for 65 C Metric Median Value # Matrix x 18.4 Score % Trichoptera 8.8 41.67 4.735 87.12818182 18.4 Tolerant Taxa 10 14 1.400 25.76 18.4 Intolerant Taxa 5 1 0.200 3.68 3.68 % Scraper 11.3 8.33 0.737 13.56389381 13.56 CLG 12 7 0.583 10.73333333 10.73 Total 64.77 If the required 200 individuals were not obtaine d during subsampling, the actual number of individuals was divided by 200. This value was then multiplied by the total metric score ( e.g. 50/200= .25, .25x64.77= 16.19 ) to calculate a total metric score and adjusting for the small number o f individuals found. These results were then compared to the assessment table ( Table 15 ) for th e ecoregion 65c to determine current health conditions. Positive changes in the ranking of the macroinvertebrate metrics w ere assumed to indicate an improvement in stream health. Once the stream rating was determined, a management decision or policy was to be recommended to improve the current condition (Table 16 ). The results of stream health rating also demonstrated, with a reasonable amount of confidence, whet her there ha d been an improvement in water quality in the stream since the installation of the BM P. If there was no change then it was conclude d that the BMP was not working effectively to alleviate the issue of sediment loading .
53 Table 15 Description of Numeric Ranking for Subecoregion 65c Sand Hills. n=all reference and imp aired sites in subecoregion 65c (Gore et al. 2005) Index Score Numeric Ranking Percentile n = 15 73 and above 1 Above 95 th 61 72 2 Below 95 th Above 75th 30 60 3 Below 75 th A bove 25th 20 29 4 Below 25 th Above 5th 19 and below 5 Below 5 th Table 16 Index for Stream Health Rating (Gore et al. 2005) Numeric Ranking Stream Health Rating Management Decision 1 A Continue periodic monitoring to detect change baseline referen ce condition 2 3 B Frequent monitoring critical to detect change in ecological status, lower range especially 4 C Frequent monitoring necessary to determine remediation needs and if remediation has been successful 5 The macroinvertebrate t axon omic i dentification r esults can be found in the table in Appendix D. Based up on the raw macroinvertebrate data, a multimetric analysis calculated by Ecological Data Application System (EDAS) (MS Access 2000; Tetra Tech Inc. 2001a) was used to assess stream condition ( Jessup and Gerritsen 2000 ) Metrics were selected from the following categories of biological
54 information specific to subecoregion 65c : richness, composition, tolerance/intolerance, and habit/trophic measures, so that each category was repres ented when possible (Table 1 7 ). Table 1 7 Discriminating Invertebrate Metrics for Subecoregion 65c Sand Hills Index 65c (Gore et al. 2005) Metric Metric Category % Trichoptera Composition Tolerant Taxa Tolerance/Intolerance Intolerant Taxa % Scrap er Functional Feeding Group Clinger Taxa Habit
55 Results Using the specific metrics prescribed for subecoregion 65c, the pre BMP installation condition s (scores) of the stream, based upon sampl e s collected during the index period w ere as follows: Immediately Downstrea m of BMP S ite Winter 2007 : Total Metric Score: 11.34 Category 5 Stream Health Rating: Low C Actual Roaring Branch Just Downstream of the Confluence Winter 2007: Total Metric Score: 23.83 Category 4 Stream Health Rating: High C Thus, prior to installation of the BMP, the tributary was a very low class C according to tables 15 and 16 indicating the need for frequent monitoring necessary to determine remediation needs and if remediation has been successfu l. The site at Roaring Branch downstream of the confluence containing the BMP was conservatively a high class C stream, which also needs frequent monitoring critical to detect change in ecological status. If the operations of the BMP were successful there should be an improvement in metric scores which reflected improv ed water quality and habitat conditions. Using the specific metrics prescribed for subecoregion 65c, described above, this study determined that the post BMP condition s of the stream s sampled during the index period were as follows: Immediately Downstream of BMP site Winter 2008: Total Metric Score: 69
56 Category 2 Stream Health Rating: Low A Actual Roaring Branch Just Downstream of the Confluence Winter 2008 : Total Metric Sc ore: 48.17 Category 3 Stream Health Rating: Avg. B Although considerably improved, metric scores evaluate the stream condition to be at the borderline between class A and class B streams, indicating that management strategies should include less fre quent, but continued sampling, in order to be certain that the health of the stream is being sustained. At each site there has been an improvement in total metric score (Table 1 8 ) and changes within each individual score. This improvement is indicated i n at least four of the five indicators for the tributary site. Table 1 8 Roaring Branch Just Downstream of the BMP Metric Category Metric Roaring Branch Roaring Branch just downstream just downstream of the BMP of the BMP BEFORE AFTER Composit ion % Trichoptera Metric Score = 4.16 Metric Score = 18.4 #of Tolerant Taxa Tolerance/Intolerance Metric Score = 4.41 Metric Score = 18.4 # of Intolerant Taxa
57 There is a substantial change in community composition immediately downstream of the BMP The density of macroinvert e brates increased at least one hundred fold ; only 48 individuals were recovered from the entire composite sample prior to installation of the BMP while the winter 2008 sample contained 200 individuals collected from six random squares (approximately 30cm 2 ) out of the 30 available or 20%, on the one tenth square meter sampling tray. T here was also a marked increase in Trichoptera which are generally more intolerant to impairment The table of comparisons for the Roaring Branch comparison site demonstrates a slight improvement in the total metric score and stream health rating (Table 1 9 ) This improvement occurred in three of the five metrics utilized ; the most significant change was in the increase of the number of individuals in the sample. There were no scrapers (snails) found in the random squares giving that metric a score of zero. P rior to BMP installation, the entire one tenth square meter tray was processed yielding only 105 individuals. From the post BMP sample more than 200 individuals were extrac ted from approximately 7 (approximately 35 cm 2 ) random squares out of the 30 available or 25% of the one square meter sample tray Metric Score = .88 Metric Score = 18.4 Functional Feeding Group % Scrapers Metric Score = .04 Metric Score = 0 Habitat # of Clinger Taxa Metric Score = 1.85 Metric Score = 13.8 11.34 69 5 2 C A
58 Table 1 9 Roaring Branch Just Downstream of the Confluence Metric Category Metric Roaring Branch Roaring Branch Just downstream Just downstream of the Confluence of the Confluence BEFORE AFTER Composition % Trichoptera Metric Score = 9.66 Metric Score = 18.4 # of Tolerant Taxa Metric Score = 8.7 Metric Score = 18.4 Tolerance/Intolerance # of Intoleran t Taxa Metric Score = 3.86 Metric Score = 3.68 Functional Feeding Group % Scrapers Metric Score = 0 Metric Score = 0 Habitat # of Clinger Taxa Metric Score = 1.61 Metric Score = 7.667 4 3 C B 23.83 48.15 The Georgia Ecoregion Project's metrics for invertebrate populations are based up on a winter index period but summer sample s were also collected in September of 2008, approximately 9 months after the BMP's installation (Table 20 )
59 Table 20 Summer 2008 Metric Scor es Metric Category Metric Roaring Branch Roaring Branch Tributary just downstream Just downstream of the tributary Summer 2008 Summer 2008 After After Composition % Trichoptera Metric Score = 18.4 Metric Score = 18.4 # of Tolerant Taxa Tolerance/Intolerance Metric Score = 18.4 Metric Score = 18.4 # of Intolerant Taxa Metric Score = 11.04 Metric Score = 3.68 Functional Feeding Group % Scrapers Metric Score = 12.52 Metric Score = 13.56 Habitat # of Clinger Taxa Metric Score = 13.8 Metric Score = 10.73 74.16 64.77 1 2 A A
60 Discussion It has been demonstrated in many case studies that the evaluation of benthic macroinvertebrate communities possess es advantages in the determination of lotic ecosystem he alth M acroinvertebrates are relatively sessile and can be used to assess temporal change and cumulative effects in a specific location (Murtaugh 1996) and they also have a wide range of sensitivities or are regionally and taxonomically v ariable. T olerance values have been established for numerous taxa of macroinvertebrates with values ranging from one to ten ; ten being extremely tolerant to pollution (Hilsenhoff 1988). T olerance values can then be totaled or averaged to evaluate the over all health of a river or a stream. Also, macroinvertebrates are excellent indicators of stream health because they have been known to recolonize a once disturbed stream in a relatively brief period of time, sometimes within two to three weeks (Gore and Mil ner 1990). This quick re colonization allows the health of the stream to be assessed almost immediately following a disturbance or restoration and rehabilitation activity. Due to anthropogenic changes in land use near streams, lakes and rivers, many aquat ic sediments have been demonstrated to be contaminated with organic and inorganic chemicals that can be harmful to the flora and fauna that reside in a stream or riparian zone. Roaring Branch was designated to be in potential non compliance with TMDL regul ations in the state Georgia due to the high suspended sediment loading and possible suspension of trace heavy metals. Wentsel and others (1977) studied the effects of heavy metals contained within sediment on larval chironomids. The studies reported that t he chironomids actively avoided sediments with high levels of heavy metals and most likely migrated from highly contaminated area s to less contaminated area s in order to mature Wensel and others (1977) also claim that a measure of toxicity in the water co lumn can show
61 little or no relationship to what is buried and contained within the sediment conversely Giesy and Hoke (1989) evaluated sediment toxicity in 30 samples of Detroit River sediment in lab bioassays and its affect upon algae, fish and two spec ies of benthic macroinvertebrates (the midge, Chironomus tentans, and the burrowing mayfly Hexagenia sp .). Hexagenia spends the majority of its life span within sediment and is very sensitive to metal toxicity and organic contaminants (Schloesser 1988). Their results, from in situ sampling and bioassays, concluded that metals such as copper can influence growth, restrict colonization, or even cause mortality. In the case of Chironomus tentans there was a distinct reduction (up to 30%) in growth (size) by adults. Reynolds and Ferrington (2001) examined the frequency and severity of mouthpart deformities of larval chironomids in a lake reservoir system in Kansas. The focus of the study was trace metals (zinc, cadmium, and lead) in the sediment and if they w ere causing the high number of mouthpart deformities of chironomids in Empire Lake reservoir. The results demonstrated no definitive answer but there was an increased level of deformity in the subfamily Chironominae which builds sediment based cases. The c onclusion was that an unknown agent in the sediment was causing or enhancing these mouthpart deformities. Most toxins can become inert when trapped in the streambed, but, when a physical disturbance occurs, these toxins can be released backed into the str eam flow and affect macroinvertebrates (Allan 1995). Limiting suspended sediments should obviously be linked to reducing the probability of contamination of organic and inorganic pollutants. There are numerous suspended sediment studies similar to Roaring Branch that relate the composition of benthic macroinvertebrate communities to the concentration and distribution of fine sediment pollution. These studies indicate that the greatest peril to macroinvertebrates is from the loss of habitat and the potentia l f or loss of primary production. Lenat and others (1981) conducted a study of two upper piedmont streams to determine the effect of sediment introduction, from nearby road construction projects, to the lotic communities. Before they sample d for invertebra tes they took limited physiochemical samples th i e r results
62 suggest ed that p H, temperature and dissolved oxygen were not affecting the benthic macroinvertebrate population. However, areas of streams subject to disturbance resulting from an over abundance of suspended sediment had a much lower macroinvertebrate density than undisturbed areas due to the loss of available habitat area. The y concluded that, d uring periods of low flow the sand substrate can be a suitable area for small grazers that re colonize and reproduce quickly, but during high flows a sand based substrate is not suitable for most macroinvertebrates except burrowers. Richards and Bacon (1994) conducted a study in Bear Creek Valley, Idaho, that linked increasing fine sediment to a change i n benthic macroinvertebrate populations. Many macroinvertebrates utilized the hyperheos region during transition between seasons, high flows, or during other disturbances. Since the hyperheos is dependent upon a larger substrate size to create an interstit ial space, and when fine sediment becomes suspended it fills these voids and reduces habitat, they concluded that fine sediment had reduced available habitat for macroinvertebrates which in turn limited secondary production within the stream. Gray and War d (1982) examined the effects of sediment release from the Guernsey Reservoir, located on the North Platte River in southeastern Wyoming, upon benthic macroinvertebrates downstream. They found a pronounced alteration of densities of various benthic macroi nvertebrates, including a large increase in oligochaete density with a concurrent decline in chironomid density. Gray and Ward suggested that the changes in macroinvertebrate composition were due to the elevated nutrient levels which resulted in increased alga l blooms ( Cladoph o ra ) that trapped some of the suspended sediment and creat ed some new microhabitats. Another possible reason for the composition change was the abrupt changes in ambient instream temperature during the silt releases. With the dam in p lace and functioning normal ly the temperature in the stream remain ed somewhat predictable, according to seasonality. A combination of temperature change from the hypolimnetic dam release (possibly affecting macroinvertebrate seasonal emergence cycles) and a loss of potential habitat were probably responsible for the increase in more pollution tolerant species of macroinvertebrates.
63 Similarly, Doeg and Koehn (1994) evaluated the effects of desilting of a small weir in Melbourne, Australia. Macroinvertebrat e sampling was conducted before and after the release of sediment, approximately two kilometers downstream of the weir. The increased suspended sediment resulted in a reduction in the average total numbers of individuals (TNI) and a reduction of species ri chness. The released silt reduced gravel permeability (interstitial space), reduced habitat availability and blocked sunlight which impaired the photosynthetic process, usually resulting in less dissolved oxygen. Doeg and Koehn reported that the water stay there was a distinct smell of hydrogen disulphide, which also suggested low dissolved oxygen conditions within the silt. Relyea and others (2000), using the Snake River Basin, Idaho as their data source, created a model that predicted suspended sediment loading and compared these predictions to actual sediment concentration values and the composition of benthic macroinvertebrate communities from the same areas. The model predicted macro invertebrate populations as a function of the volume of suspended sediment concentrations. The re was a direct negative correlation between inorganic sediment and populations of benthic macroinvertebrates inferring that the increase of these fine suspended sediments reduce d potential habitat locations along with a reducing light penetration vital for primary production. A reduction in primary production resulted in a reduction in fine particulate organic matter (F POM ) which is the basis for the lotic ecosys tem food web (Vannote et al. 1980) In New Zealand, Quinn and others (1992) evaluated benthic macroinvertebrate community composition upstream and downstream of alluvial gold mines on six streams on the w est c oast of the South Island. The areas downstr eam of the mines had very high turbidity with benthic communities of very low density and taxonomic richness. The upstream samples had consistently Zealand river system. The f indings of this study demonstrated that suspended sediment (clay) from the mining process was extremely detrimental to the benthic
64 macroinvertebrates because of reduced bed permeability (a fouled hyperheos), reduced interstitial dissolved oxygen, and avoid ance reactions of invertebrates (i.e., increased drift) Drift is a mechanism/behavior that is commonly used by stream ecology is the practice of the fauna allowing the stream f low to transport them downstream (there is upstream movement that requires swimming or gripping onto the substrate to move). Not all drift is intentional though. Increased fine sediment can greatly reduce the surface area of stable materials (rocks, logs e tc.) for attachment by benthic macroinvertebrates. movement can be slowed or greatly reduced by an increase in fine sediment substrate (sand). They concluded that many common riffle insects (i.e. caddis flies) Culp and others (1985) also reported on responses of macroinvertebrates when fine sediment was introduced into slow flowing riffle s ystems. The project area was located on a small creek in British Columbia and consisted of an upstream control and a before/after impact scenario. The results suggested that the sediment deposited in the interstitial area affected the species Paraleptophle bia a mayfly that resides in the substrate interstices to a depth of six centimeters. There were five more dominant taxa in the same location that seemed relatively unaffected, except for a small increase in drift, probably due to a reduction in attachmen t point surface area. Ryan (1991) reviewed the effects of sediment on streams in New Zealand. He concluded that suspended sediment affected benthic macroinvertebrates by interfering with feeding, causing mortality by suffocation, results of toxicity from heavy metals, reduced attraction for grazers when sediment is trapped by periphyton, increased drift, covering food supplies, and filling interstitial pores; all resulting in a total change in community productivity. When fine sediment is introduced into the system it does not eliminat e all the macroinvertebrates it just
65 change can alter the food web w hich in turns changes the productivity of the area. Lalor and others (2004) evaluated the Cahaba watershed in North central Alabama to control erosion in the etated buffers near the riparian zones of construction locations. Areas without erosion control measures were also evaluated as controls and points of comparison. Macroinvertebrate composition was evaluated using three different methods; the Hilsenhoff Bio tic Index, a variation of the EPT index (a count of the number of individuals or taxa represented by the Ephmeoptera, Plecoptera, and Trichoptera) (Lenat 1993), and the Sorenson Index (Sorenson 1948) (a statistic used for comparing the similarity of two sa mples). In this case, all indices indicated a decline in the composition of the macroinvertebrate assemblage in the areas without the BMPs. The re was a decline in richness and overall population density, correlated with high sedimentation. The macroinverte brate population shifted to those species more tolerant of low dissolved oxygen such as certain species of chironomids and oligachaetes. This shift probably occurred due to an increase in organic material and nutrients in the stream. An overabundance of o rganics in a stream can result in a high b iochemical oxygen demand (BOD a quantitative expression of ) High BOD in streams cause s the dissolved oxygen (DO) content of the water to drop which results in macroinvertebrate mortality, drift or retreat into the hyperheos. All of the studies mentioned above illustrate that there is a relationship between suspended sediment and the health of stream biota W ith increased sediment loading, there is a decreas e in stream benthic macroinvertebrate density and diversity indexes ; thereby, indicating a decline in overall stream health as was demonstrated in the Roaring Branch project There have been many published studies in which counts of benthic macroinvertebr ates and their accompanying tolerance values were used to create biotic indexes similar to the one created for the Georgia Ecoregions Project to determine potential changes in stream health. Beck (1955) proposed the idea of
66 using a simple index from zero ( polluted) to forty (pristine) to evaluate stream conditions. These values were based upon assigning the designations, class 1 (intolerant) and class 2 (tolerant) to all organisms in a typical sample. The calculation to provide the score was: 2 ( ln class 1) + ( ln class 2) = Biotic Index Beck proposed that using a relatively simple biotic index of this nature would assist in information transfer to law makers and the public because, up until that time, most reports of ecosystem health were based on complex c hemistry and physics, reported in terms highly technical and difficult for the layman to interpret. Since that time, there have been a number of indices proposed to simplify reporting and interpretation of stream health and analysis of the success of resto ration or rehabilitation of streams from disturbance. Wallace and others (1996) utilized the North Carolina Biotic Index (NCBI) and the Ephmeoptera/Plecoptera/Trichoptera (EPT) Index to evaluate macroinvertebrate/stream recovery after an insecticide was ap plied seasonally for a three year period. The NCBI is a biological index that was created ( Lenat 1993) exclusively for North Carolina Division of Environmental Managemen t. The Division had a data set of over 2000 stream macroinvertebrate samples divided i nto five water quality ratings A piece wise regression was applied to broaden this range on a scale from one to ten. Species tolerances were then derived from the dominant species from each stream category. Similar to other indices, tolerance values range d from one, being intolerant, to ten, being tolerant to pollution. Classification criteria were adjusted for both season and ecoregion, but no corrections were required for stream size (Lenat 1993). The EPT way to measure a s the orders of macroinvertebrates that are highly sensitive to pollution In this case, the total number of Ephmeoptera Plecoptera and Trichoptera taxa is divided by the total number of midges (Chironomidae) (Weber 1973) As the EPT v alue increases, the The streams evaluated by Wallace and others (1996) were located in western North Carolina, in the Coweeta basin. They created an artificial an application of an insecti cide, which could be manipulated and controlled within similar habitats and between very similar streams. Wallace and
67 his associates also sampled a reference stream that did not receive an application of the insecticide. Their conclusion was that both meth ods for calculating a biotic index were successful but that the EPT method was very easy, inexpensive, and displayed a notable ability to track secondary production which is generally a more labor intensive and costly procedure. Using both methods of calcu lating a biotic induced event occurred. So, the measured recovery rate was almost constant using both indexing methods. Lenat and Crawford (1994) studied the effects of land u se on three streams located in North Carolina. All three streams had similar characteristics and channel capacity but land use differed. One was located within a forested area, a second was near agricultural lands and the third was located in an urban area Benthic macroinvertebrate samples were collected using a qualitative kick net combined with a standardized quantitative technique, consisting of aquatic sweep nets and fine mesh samplers. The analysis was focused upon species richness, abundance, unique taxa, EPT, and the HBI. The results from the metrics of richness, biotic index, and unique taxa ranked the forest as the the agricultural stream, second, and the urban stream ranked as poor or impaired Comparison of the dominant macroinvertebrate species at each site showed very little overlap. In this case, the biotic index was utilized to determine that land use is an important factor controlling the structure of the aquatic communities Although one of the most common approach es to bioassessment of stream health, the EPT index may not be appropriate for streams of the southeastern United States. Kaller and Hartman (2003) conducted a study on seven streams Monongahela National Forest in Pendleton and Pocohantas counties of east central West Virginia. The results clearly demonstrated a negative relationship between EPT taxa richness and small substrate size. This result (and those of similar studies across the United States) demonstrates th at substrate size analysis has more wei ght or utility tha n just evaluating health using just an index such as the EPT in the e specially south of the F all L ine where most of the sand based streams exist. (The Fall Line is a geological boundary about 35 kilometers wide
68 running across Georgia northeastward from Columbus to Augusta. I t separates Upper Coastal Plain sedimentary rocks to the south from Piedmont crystalline rocks to the north .) Kaller and Hartmen discovered a fine sediment based threshold that dramatically reduced macroinvertebr ate diversity and abundance. The evidence suggest ed t hat a threshold level of fine sediment ( < 0.25 mm) will lead to declines in macroinvertebrate community diversity. Nerbonne and Vondracik (2001) collected benthic macroinvertebrates using The RBP III pro tocol (Plafkin et al. commonly used in farming practices. These BMPs include alternative tilling methods (chisel plow, ridge till etc.) and riparian buffers which filter sediment from agricultural runoff. Thes e BMPs were intended to reduce agricultural soil loss while at the same time allowing land to remain in production. In this study they found no statistical differences among macroinvertebrate assemblages from the different BMP sampling areas. Nerbonne and Vondarcik did admit that using the RBP III and only using the first 100 random individuals encountered in subsamples, may have underestimate d site quality by underestimating taxa richness, but relative comparisons among sites should not have be en biased They suggested that, p erhaps a larger sample size of 200 300 individuals would have created greater separation between the index scores of the reference and BMP applied streams. Yates and others (2007) evaluated farm based BMPs (drainage tiles) on the Upper Thames River in Ontario, Canada. Macroinvertebrate samples were collected using three minute traveling kicks with a D frame net. In this method samples we re collected continuously for a period of three minutes. The samples were then composited into a gridded pan and cells were randomly selected until 300 individuals were found. The Hilsenhoff family biotic index (FBI) was applied (Hilsenhoff 1988) along with a measure of the habitat quality using the RBP (Barbour et al. 1999). The results of the stud y demonstrated that higher quality habitat was strongly related to lower values of sediment stressors (Yates et al 2007) and that the sampling method derived from the RBP was easy and effective in evaluating the health of the streams.
69 Jack and o thers (20 06) utilized a modified version of the RBP prescribed by the Kentucky Division of Water called the KDOW protocol. B enthic macroinvertebrates were sampled from six riffles upstream and six riffles downstream of a BMP that was designed to eliminate a suspe cted source of impairment which was a confined animal feeding operation (CAFO). The grabs were seperat ed into two categories, upstream and downstream and evaluated usi ng five different biotic metrics : t otal r ichness, n umber of individuals, family level bi otic index ( FBI ) e venness and EPT. Each of the five metrics used contributes between 1 and 5 points to the total score in the Kentucky Index of Biotic Integrity (KIBI) The total IBI scores can be tallied to d etermine the range from 5 (least integrity) t o 25 (greatest integrity). The results reported from the study showed no negative impact of macroinvertebrate populations from the CAFO. The upstream and downstream macroinvertebrate populations were very similar in richness and in TNI. Since the R B P has been demonstrated to effectively evaluate stream condition without the cost of physicochemical evaluation (Barbour et al. 1999) t his protocol was employed for this research During this project, the Georgia Ecoregions Sampling Protocol (adapted from the R apid Bioassessment Protocol (RBP)) was used to collect benthic macroinvertebrate samples. This field sampling was relatively easy and it was completed in under a few hours for each sampling period. The RBP does not require any expensive or complicated coll ection tools, just a D frame net, a mesh bottom sampling bucket, sample collection bottles and ethanol to preserve the samples. As previously detailed, the RBP has been used to evaluate Best Management Practices numerous times (Nerbonne and Vondracek 2001 Yates et al. 2007, Jack et al. 2006) and has been demonstrated to be successful tool in determining relative stream health. The RBP has been demonstrated to be one of be st sampling technique s developed for wadeable streams. Another advantage is that usin g the RBP to monitor a stream is relatively cost effective (in comparison to chemical analysis) and is limited only by budgets that dictate how long monitoring may occur.
70 In the case of the Roaring Branch project, a BMP was installed to reduce sediment t ransport. The results from the study are typical of previously reported improvements ( e.g. Jack et al. 2006 Yates et al. 2007 ) that show that both sampling sites had improved macroinvertebrate metric scores after placement of the BMP structure, meaning th at there were more intolerant taxa, greater percent of Trichoptera, or an overall increase of diversity (better metric scores across the board) in the post BMP samples compared to the pre BMP samples. Another improvement in macroinvertebrate community stru cture at the sampling site just downstream of the BMP,was an increase in number of individuals in the sample, from 48 in the entire 2007 (pre BMP) composite sample to an estimated 1000 individuals in the 2008 (post BMP) composite sample. The data presented in tables 1 8 and 1 9 indicate that the BMP has improved biotic conditions in the stream. This improvement was substantiat ed by showing that summer sampling T able 20 ) which was done approximately 9 months after the construction of the BMP also improve d from the pre BMP condition. Even though some of the individual species assemblages from the s ummer sampling were very different from the winter samples, the metric scores indicated a significant improvement in stream health in regards to macroinvertebrates. T he w inter data illustrate that a fter placement and operation of the BMP for a year, there was an increase in the health index score to category 3, with the health rating improving from C to a marginal A just downstream of the BMP Based on metric scores (Table 14 ) it was suggested that even though there was a dramatic improvement in benthic macroinvertebrate population s and community composition further monitoring should be part of the plan. (Banning presentation at Columbus Water Works Dec. 8, 2008) E ven class A streams which are classified as minimally or marginally impaired (close to the reference condition), must be continually monitored (albeit with a longer sampling interval; say every two or three years) to assure that the stream health is being sustained. In this case, for example the improved streams scored at marginal levels to be considered representative of category two (A) streams. There is no doubt that significant improvement has been detected. However, the margin of analytical
71 error (Je ssup and Gerritson et al. 2000) can be slightly misleading by placing the stream condition score as a high value in category t hree or an even higher value in category two Ultimately, the addition of new taxa (some will take a year or more to colonize) co uld result in even higher metric scores, moving the stream solidly into category two or in the low values of category one ; both indicating a stream health rating of A. Even though the RBP is a n established sampling protocol, it will likely require impr ovement in various protocols to be more accurate in assessing stream health. For example, Rai (2003) concluded that a sample size of 300 individuals would better characterize the health of a stream than the 200 individuals prescribed by the RBP. In the ca se of application of the protocol in the state of Georgia, 300 individuals were shown to create a wider separation between the metric scores of the reference and impaired streams thus reducing the amount of error in ecoregions that have overlaps between th e reference and impaired stream metric statistics Because the RBP is currently based upon 200 individuals, a subsample of 200 was utilized in this project to calculate the metric scores. However, a subsample of 300 appears to be more appropriate based on (2003) work. An extra 100 individuals would not tak e that much longer to identify even if they consisted primarily of chironomids. Williams (2004) demonstrated that the increase in sensitivity of the analysis more than compensates for the cost of greater taxonomic resolution. A few recommendations were made to WWETCO LLC, who was contracted emplaced damming up the stream should be removed. These large rocks stop or slow the stre am flow and, when a precipitation event occurs, the flow takes a path of least resistance around the instream objects causing erosion of the clay banks and increased clay siltation suspension and deposition Second gravel should be added to the substrate to bolster it against erosion.,I t has been demonstrated that adding gravel can be productive in creating a riffle habitat for more intolerant species such as caddisflies along with increasing overall productivity, density and species diversit y (Gortz 1998, Moerke et al. 2004)
72 Gravel also creates streambed stability, a substrate macroinvertebrates can attach their life lines to, an interstitial area for benthic macroinvertebrates to colonize in, a safe place to retreat under or behind during h igh flows, and a place for primary production to begin. Thus, a second recommendation is to create several patches of medium and large gravel to create some natural riffles, previously shown to increase productivity, especially at lower flows (Gore et al. 1998). It has been demonstrated that substrate size and sand influence the composition of macroinvertebrate communities in a stream. Over three decades ago, Hynes (1970) and Hart (1978) suggested addin g of cobble and gravel to increase habitat and produc tivity of a stream. Williams and Mundie (1978) examined the effects of varying flows over several different sizes of substrate and the densities of macroinvertebrates colonizing each substrate type. Larger gravel had a higher biomass (TNI) and more diversi ty than the medium or small gravel and sand. These rougher substrates provide more interstitial space for colonization and attachment points for filter feeders. As in the Roaring Branch project many studies have been completed in stream restoration proj ects using benthic macroinvertebrates as a tool to assess stream health. Gortz (1998) compared macroinvertebrate populations before and after restoration on the River Esrom in Denmark. The restoration consisted of adding gravel, boulders, and flow concen trators (constricting flow to increase velocity) to mimic some reference sites found in other locations of the stream that were less anthropogenically altered. The results from macroinvertebrate sampling demonstrated that the community structure and biomas s increased while there were not any differences found in the richness or evenness of the communities in the restored sites. Species from the reference areas of the stream seem to have immigrated to the new restored areas. The restoration project ha d impr oved the physical properties and the heterogeneity of the substrate on a former sandy bottom to such a degree that a positive change in the macroinvertebrate community ha d taken place (Gortz 1998) Muotka and others (2002) evaluated the effectiveness of r estoration in multiple river systems in Finland which were previously used by the logging
73 industry to transport logs. They conducted a BACI comparison, as well as comparisons to macroinvertebrate community data from reference streams in the area. The resul ts of sampling demonstrated an increase of benthic macroinvertebrates from four functional feeding (FFG) groups: scrapers, collector gatherers, filters, and predators. Only the shredder FFG was reduced after the acroinvertebrate communities were still impoverished in terms of (total numbers, evenness and richness) when compared to the reference state. Thus, it seems that physical restoration does take more than a few years. Similarly, Moerke and others (2004) examined the restoration of Juday Creek, in Indiana, and its impacts on benthic macroinvertebrate communities. During the study three reaches (two restored and one unrestore d) were designated for monitoring a fter construction was completed. The two restored reaches were both the same length ( 400m ) and were designed to include meandering channels, gravel and cobble substrate, abundant large woody debris, and a moderate canopy. Using a BACI evaluation it was apparent that benthic macroinvertebrate diversity increased a hundred fold, but Moerke and others (2004) suggest that a better indicator of improved macroinvertebrate health would be TNI or a measure of secondary production rather than diversity. The results from the installation of the BMP on Roaring Branch validated the fact the H A hypothesis in which an improvement in the metric scores defined for the subecoregion 65 c sandhi lls. Therefore the H 0 hypothesis was rejected that there would be no improvement in the metric scores after the BMP installation Water resources (lakes, streams, and great rivers) are very limited on this to protect the unimpaired streams and use human ingenuity to attempt to restore the ones we have damaged with our activities. This study utilized a sampling protocol (RBP) that has been demonstrated to be a successful tool in evaluating stream health, us ing macroinvertebrates which have several advantages when assessing water quality.
74 The problem of fine suspended sedimentation in streams has been well documented and been studied extensively in many rivers and streams around the world. I believe this stud y has demonstrated the value of the RBP as an easily applied tool for urban streams with similar issues of fine sedimentation.
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86 A ppe ndices
87 Appendix A Standard Operating Procedures (SOPs) for data collection, analysis, and quality control SOP Number: FLD (GERS) 001 Title: Macroinvertebrate Collection in wadeable streams: Aquatic Dip Net 20 Jab Me thod (Modified to GAEPD Multihabitat Method strict assignment approach ) Date of Last Revision: October 3, 2000 Equipment/Materials: standard aquatic dip net, D frame, no. 30 mesh (595 m openings), 0.3 meter width (~1 foot), sieve bucket, no. 30 me sh (595 m openings), wash bucket, 95 percent ethanol, sample containers, forceps, field notebook, pencils, first aid kit References: None Procedures: Habitat: snags, submerged macrophytes, banks, riffles, soft sediment/sandy bottom substrate Area: 20 jabs, each 1 m in length
88 Appendix A (continued) Mesh size: No. 30 mesh (595 m openings) Index Period: Fall/Winter (Sept Dec) 1. The sample reach should extend to a 100 meter instream segment habitat having no major tributaries in the asses sment area. Sampling should be conducted at least 100 meters upstream of any road or bridge crossing to minimize the effects on stream velocity, depth, and overall habitat. If objective is to assess overall watershed conditions, which include bridge/road stressors, sampling should be targeted to a reach downstream of bridge crossings at least 100 meters. 2. Sampling is conducted from downstream to upstream by jabbing the D frame net into productive and stable habitats 20 times. A single jab consist s of forcefully thrusting the net into a productive habitat for a linear distance of 1 meter. 3. Different types of habitat should be sampled according to the following guidelines. Unique habitat types (i.e., those consisting of less than 5 percen t of stable habitat within the sampling reach) should not be sampled. Following are specific sampling techniques for different productive and stable habitats: Riffles, Snags, Soft sediment/Sandy bottom, Banks and root mats, Leaf Packs, and Submerged Macro phytes.
89 Appendix A (continued) Table A 1 Prioritized list of habitat types for sampling and sample reallocation for the modified 20 jab method. (Gore et al 2005.) HIGH GRADIENT STREAMS Priority Habitat Type Number of Samples 1 Fast Riffle 3 2 Sl ow Riffle 3 3 Snags 5 4 Undercut Banks/Rootwads 3 5 Leaf Packs 3 6 Sand 3 7 Macrophytes (if any) 3 LOW GRADIENT STREAMS Priority Habitat Type Number of Samples 1 Woody debris/Snags 8 2 Undercut Banks/Rootwads 6 3 Leaf Packs 3 4 Sand 3 5 Macro phytes (if any) 3
90 Appendix A (continued) 4. The collected sample is washed by running clean stream water through the net 2 3 times; transfer the sample to the sieve bucket. Samples should be cleaned and transferred to the sieve bucket at leas t every five jabs, more if necessary. Do not let the net become so clogged with debris that it results in the diversion of water around the net rather than through the net. If clogging occurs, discard the sample in the net and redo that portion of the sa mple in a different location. 5. As the sample is added to the sieve bucket, it should be further washed to remove fines. Mix the sample by hand while sieving, remove large debris from the sample after rinsing and inspecting for organisms; place any organisms back into the sieve bucket. Do not attempt to inspect small debris. 6. Transfer the sample from the sieve bucket to prelabelled sample container(s) and preserve in 95 percent ethanol cut to approximately 70% with 1/4 1/5 volume of stream water Forceps may be needed to remove organisms from the sieve s creen and dipnet. 7. Field notes should be taken on the overall habitat condition (in addition to habitat assessment), weather, observations on condition of the macroinvertebrate and fish commun ities, and other wildlife observed. Notes on the stable habitats Once the sample are collected and brought back into the lab, laboratory and subsampling must be completed along with identification of individuals mostly to the genus or species level (C hironomidae are identified a little further).
91 Appendix B Laboratory Sorting and Subsampling SOP Number: LAB (GERS) 014 Date of Last Revision: March 22, 2001 Equipment/Materials: sample log in sheet, standardized gr idded screen (595 micron screen, 30 squares, each 6 cm 2 ), white plastic holding tray for gridded screen, 6 cm scoop, 6 cm 2 metal dividing frame, forceps, white plastic or enamel pan (6" x 9") for sorting, specimen vials with caps or stoppers, sample labels standard laboratory bench sheets, dissecting microscope for organism identification with magnification of 10 40x, fiber optics light source, compound microscope with phase contrast for identification of mounted organisms, 70 percent ethanol for storage o f specimens, appropriate taxonomic keys, taxonomy validation notebook References: Caton, L.W. (1991) Procedures: All samples should be logged in on receipt by laboratory by recording sample name/number, project name/number, number of containers per samp le, date sample collected, and date received at laboratory.
92 Appendix B (continued) Mechanics of subsampling To facilitate processing and identification, a randomized 100 200 or 300 organism subsample is sorted and preserved separately from the rem aining sample. Documentation for the level of effort, or proportion of sample processed 7 and A14 8). 1. All primary samples should be sorted in a single laboratory to enhance quality control. 2. Thoroughly rinse sample in a No. 30 mesh (595 remove preservative and fine sediment. Any large organic material (whole leaves, twigs, algal or macrophyte mats) not removed in the field should be rinsed, visually inspected, and disca rded. If the samples have been preserved in alcohol, it will be necessary to soak the sample contents in water for about 15 minutes to hydrate the benthic organisms, preventing them from floating on the water surface during sorting. If the sample was sto red in more than one container, the contents of all containers for a given sample should be combined at this time. 3. A standardized gridded screen designed by Larry Caton, OR DEQ (Caton, 1991) contains 30 clearly marked squares, each square is a uniform 6 cm 2 ; the gridded screen fits into another slightly larger tray so that water may be
93 Appendix B (continued) added to the sample to allow for even distribution. Place the gridded screen inside the tray and pour the sample onto the screen. Add enough water to spread the sample evenly over the screen then lift the screen out of the tray, the sample contents will settle onto the screen. Samples too large to be effectively sorted in a single pan may be thoroughly mixed in a cont ainer with some water, and half of the homogenized sample placed in each of two gridded pans. 4. Note presence of large or obviously abundant organisms on the back page of the taxonomic bench sheet and in the sorting notebook. Do not remove as part of th e subsample. Also note the type of sample, collection date, project number, station number, log number, and any comments regarding the sample (e.g., description of organic material in sample -sand, fine organics), and sorting time in the sort notebook. 5. Use a random numbers table to select four numbers corresponding to squares within the gridded pan. Remove all material (organisms and debris) from the four grid squares. Any organism that is lying over a line separating two grids is considered to be on the grid containing its head. In those instances where it may not be possible to determine the location of the head (worms for instance), the organism is considered to be in the grid containing most of its body. The material is placed into a shallow whit e pan for sorting and small amount of water is added to facilitate sorting and focused light is used to illuminate the sorting pan; magnification is not used in the sorting process. Between each subsample, be careful not to
94 Appendix B (continued) d isturb the subsample pan (this will cause a redistribution of specimens and could possibly change the probability of selection). If the density of organisms is high enough that many more than the targeted number of organisms are contained in the four gri ds (e.g., totaling greater than approximately 150 for a 100 organism subsample), transfer the contents of the grids to a second gridded pan. Randomly select grids for this second level of sorting as was done for the first level of sorting. Beginning with the first four selected grids, remove material and place into a white pan to sort. Remove and count all the macroinvertebrates in the pan. If the density of organisms is high enough that many more than the targeted number of organisms are contained in th e four grids, transfer the contents of the grids to a third gridded pan and continue as before. If targeted subsample amount 20 percent organisms are found, the subsample is done. If less than the targeted amount 20 percent organisms are found, conti nue randomly selecting and sorting grids one at a time until the targeted subsample 10 percent organisms are found. If picking through the entire next grid is likely to result in a subsample of greater than + 20 percent of the targeted number of organi sms, then that single grid may be subsampled in the same manner as before to decrease the likelihood of exceeding 20 percent of the targeted amount of organisms. (That is, spread the contents of the last grid into another gridded pan. Pick grids one at a time until the desired number is reached.) The total number of grids for each subsorting level should be noted on the laboratory bench sheet. Each grid selected for sorting must be sorted in its entirety.
95 Appendix B (continued) 6. If the sample is l arge enough to be distributed onto two or more screens, each grid square should have a unique number such that all grid squares in all screens have an equal probability at being selected for sorting. For example, if the sample is distributed between two g rids with 30 squares each, random numbers are selected in the range 1 60, corresponding to the 60 grid squares. 7. Save the sorted debris residue in a separate container. Add a label that includes the words "sorted residue" in addition to all prior sampl e label information and preserve in 70 percent ethanol. Save the remaining unsorted sample debris residue in a separate container labeled "sample residue"; this container should include the original sample label. Length of storage and archival is determi ned by the laboratory or benthic section supervisor. 7. Place specimens sorted as the subsample (100 200 300 organism) into glass vials, and preserve in 70 percent ethanol. Organisms from the sorting process will be segregated into separate vials accord ing to the categories: midges, worms, insects, molluscs, and crustaceans to be further identified later (See step 1 of identification). Label the vials inside with the serial code, station descriptor (sample ID code and collection date), taxonomic group, sorter, and vial number if more than one vial used. If more than one vial is needed, each should be labeled separately and numbered (e.g., 1 of 2, 2 of 2). For convenience in reading the labels inside the vials, insert the labels left edge first.
96 Ap pendix B (continued) 9. Midges (Chironomidae), that will be identified past family level, should be mounted on slides in cytoseal or other appropriate medium (e.g., Euperal or CMC 10); slides should be labeled with the site identifier, date collected, an d a space for the first initial and last name of the taxonomist responsible for identifying them. Worms (Oligochaeta), if further identification is necessary, may also be mounted on slides and should be appropriately labeled as with the midges. 10. Any en tire sample sorted that falls below the designated subsample size 20% is excluded from further analyses. If a processed sample falls above the targeted amount by more than 20 percent, the entire sample is identified and then subsampled electronically in order to fall into the proper range for analyses. Identification 1. Identification is to the lowest practical level (generally genus or species) by a qualified taxonomist using a dissecting microscope for most organisms. Midges (Family Chironomidae ) and oligochaetes (Families Tubificidae and Naididae) are mounted on slides in an appropriate medium and identified using a compound microscope. Each taxon found in a sample is recorded and enumerated on the laboratory bench sheet. Any difficulties
97 Appendix B (continued) encountered during identification (e.g., missing gills) are noted on these sheets. Each identification is followed with a Taxonomic Certainty Rating (1 5) with 1 being most certain and 5 being uncertain; any rating 3 5 must be explained (i.e., missing legs, gills etc.). Record the taxonomic references used in the identification process 2. Labels with specific taxa and first initial and last name of the taxonomist are added to the vials of specimens by the taxonomist Individual specimens may be extracted from the sample to be included in a reference collection or to be verified by a second taxonomist (see Step 2 under QC). The taxonomist initials slides. A separate label may be added to slides to include the taxa (taxon) name(s) for use in a voucher or reference collection. 3. For archival, the specimen vials, grouped by station and date, will be placed in jars with a small amount of denatured 70 percent ethanol, and tightly capped. The ethanol level in these j ars will be examined periodically, and replenished as needed, before ethanol loss from the specimen vials takes place. A stick on label will be placed on the outside of the jar indicating: sample identifier, date, denatured 70 percent ethanol used as pres ervative
98 Appendix C Taxonomic References Covich A. P. and J. H. Thorp. 2001. Ecology and Classification of North American Freshwater Invertebrates, Second Edition Academic P ress, San Diego, CA Epler, J.H. 1995. Identification Manual for the Larval Chironomidae (Diptera) of Florida. Revised edition FL Dept. Environ. Protection, Tallahassee, FL. 317 pp Merritt R. W. and K. W. Cummins. 2009. An Introduction to the Aquatic Insects of North America Kemdall/Hunt Pub. Co. Dubuque, IA
99 Appendix D Taxonomic Identification Results Upper= Just downstream of the BMP Lower = Actual Roaring Branch Just Downstream of the Confluence 2007 Upper Winter 2007 # Order Family Genus Species Tol erance FFG T/I Habit 3 Decapoda Cambarinae Procambarus 9 GC T SWM 1 Basommatophora Physidae Physell a 8 SC T SPR 2 Veneroida Corbicudulae Corbicula f luminae 6.3 FC T SPR 15 Odonata Coanargrionidae Chromagrio n 9 PR T CLB 1 Diptera Stratiomyidae Stratomia 7 CG T SPR 3 Trichoptera Hydropsychidae Hydropsyche 4.5 FC I CLG 1 Trichoptera Hydropsychidae Chematopsyche 5 FC T CLG 3 Diptera Chironomidae Ortho c ladius/Cric o o tpus Complex 7 CG T BRW 12 Dipter a Chironomidae Polypedilum c onvictum grp. 5.3 CG T CLG 3 Diptera Chironomidae Polypedilum t ritum 6.7 CG T CLG 2 Diptera Chironomidae Polypedilum s calaenum grp. 8.7 CG T CLG 2 Diptera Chironomidae Hudsonim y ia 6.4 PR T SPR 48 Low er Winter 2007 # Order Family Genus Species Tolerance FFG T/I Habit 2 Veneroida Corbicudulae Corbicula fluminae 6.3 FC T SPR 1 Oligochaeta 8 T BRW 38 Trichoptera Hydropsychidae Hydropsyche 4.5 FC I CLG 25 Trichoptera Hydropsychi dae Chematopsyche 5 FC T CLG 11 Diptera Chironomidae Ortho c ladius/Cricotpus comple x 7 CG T BRW 21 Diptera Chironomidae Hudsonim y ia 6.4 PR T PR 3 Diptera Chironomidae Rhe o tanytarsus 6.4 FC T CLG 2 Diptera Chironomidae Ablabesemyia m allochi 7.6 O M T SRW 2 Diptera Chironomidae Polypedilim a viceps 4 CG I CLG 105
100 2008 Upper Winter 2008 # Order Family Genus Species Tolerance FFG T/I Habit 1 Veneroida Corbicudulae Corbicula fluminae 6.3 FC T SPR 2 Odonata Co anargrionidae Chromagrion 9 PR T CLB 33 Trichoptera Hydropsychidae Hydropsyche 4.5 FC I CLG 26 Trichoptera Hydropsychidae Chematopsyche 5 FC T CLG 24 Diptera Chironomidae Orthocladius/Cricotpus complex 7 CG T BRW 17 Diptera Chironomidae Hudsoni m y ia 6.4 PR T PR 26 Diptera Chironomidae Rheotanytarsus 6.4 FC T CLG 15 Diptera Chironomidae Polypedilum a viceps 4 CG I CLG 2 Diptera Chironomidae Labrundia p ilosella 6 PR T SPR 1 Diptera Chironomidae Psectrocladius 3.8 CG I SPR 1 Diptera Chiron omidae Dicrotendipes 7.9 CG I BRW 1 Diptera Chironomidae Polypedilum Sp A 5.6 CG T CLG 14 Diptera Chironomidae Polypedilum c onvictum grp 5.3 CG T CLG 4 Diptera Chironomidae Polypedilum t ritum 6.7 CG T CLG 2 Diptera Chironomidae Nanocladius 7.2 CG I SPR 1 Diptera Chironomidae Hydrobaenus 9.6 CG I SPR 5 Diptera Chironomidae Polypedilum s calaenum grp 8.7 CG T CLG 2 Diptera Chironomidae Cor y noneura 6.2 CG T SPR 1 Diptera Chironomidae Polypedilum h alterale grp 7.2 CG T CLG 178 Upper Winter 2008 # Order Family Genus Species Tolerance FFG T/I Habit 1 Veneroida Corbicudulae Corbicula fluminae 6.3 FC T SPR 2 Odonata Coanargrionidae Chromagrion 9 PR T CLB 33 Trichoptera Hydropsychidae Hydropsyche 4.5 FC I CL G 26 Trichoptera Hydropsychidae Chematopsyche 5 FC T CLG 24 Diptera Chironomidae Orthocladius/Cricotpus complex 7 CG T BRW 17 Diptera Chironomidae Hudsonim y ia 6.4 PR T PR 26 Diptera Chironomidae Rheotanytarsus 6.4 FC T CLG 15 Diptera Chironomid ae Polypedilum a viceps 4 CG I CLG 2 Diptera Chironomidae Labrundia p ilosella 6 PR T SPR 1 Diptera Chironomidae Psectrocladius 3.8 CG I SPR 1 Diptera Chironomidae Dicrotendipes 7.9 CG I BRW 1 Diptera Chironomidae Polypedilum Sp A 5.6 CG T CLG 14 D iptera Chironomidae Polypedilum c onvictum grp 5.3 CG T CLG 4 Diptera Chironomidae Polypedilum t ritum 6.7 CG T CLG 2 Diptera Chironomidae Nanocladius 7.2 CG I SPR 1 Diptera Chironomidae Hydrobaenus 9.6 CG I SPR 5 Diptera Chironomidae Polypedilum s c alaenum grp 8.7 CG T CLG 2 Diptera Chironomidae Cor y noneura 6.2 CG T SPR 1 Diptera Chironomidae Polypedilum h alterale grp 7.2 CG T CLG 178
101 Summer 2008 Upper Summer 2008 # Order Family Genus Species Tolerance FFG T/ I Habit 2 Oligochaeta 8 CG T BRW 1 Veneroida Corbicudulae Corbicula fluminae 6.3 FC T SPR 35 Trichoptera Hydropsychidae Hydropsyche 4.5 FC I CLG 5 Basommatophora Physidae Physelle 8 SC T SPR 1 Diptera Tipulidae Tipula 7.7 SH T SPR 1 Basom matophora Planorbidae Helisoma 8 SC T SPR 4 Odonata Coanargrionida e Chromagrion 9 PR T CLB 1 Diptera Chironomidae Corynoneura 6.2 CG T SPR 3 Diptera Chironomidae Orthoc ladius/Cri cotpus Complex 7 CG T BRW 21 Diptera Chironomidae Hudsonim y ia 6.4 PR T PR 12 Diptera Chironomidae Labrundinia 6 PR T SPR 9 Diptera Chironomidae Dicrotendipes 7.9 CG I BRW 5 Diptera Chironomidae Polypedilum s calaenum grp 8.7 CG T CLG 5 Diptera Chironomidae Rheotanytarsus 6.4 FC T CLG 4 Diptera Chironomidae Po lypedilum c onvictum grp 5.3 CG T CLG 1 Diptera Chironomidae Polypedilum a viceps 4 CG I CLG 2 Diptera Chironomidae Polypedilum t ritum 6.7 CG T CLG 1 Diptera Chironomidae Tanytarsus sp. B 6.7 CG T CLG 1 Diptera Chironomidae Glypt otendipes sp. B 8.5 C G T BRW 1 Diptera Chironomidae Microtendipes 6.2 CG T CLG 1 Diptera Chironomidae Cryptochironom us 7.4 PR T SPR 39 Diptera Chironomidae Chironomus 9.8 CG T BRW 1 Diptera Chironomidae Stelechomyia 7 CG T BRW 13 Diptera Chironomidae Polypedilum h alterale grp 7.2 CG T CLG 169 Lower Summer 2008 # Order Family Genus Species Tolerance FFG T/I Habit 3 Oligochaeta 8 CG T BRW 2 Veneroida Corbicudulae Corbicula f luminae 6.3 FC T SPR 9 Basommatophora Physidae Ph ysell a 8 SC T SPR 75 Trichoptera Hydropsychidae Hydropsyche 4.5 FC I CLG 6 Basommatophora Planorbidae Helisoma 8 SC T SPR 10 Odonata Coanargrionida e Chromagrion 9 PR T CLB 11 Diptera Chironomidae Ortho c ladius/ Cricotpus Complex 7 CG T BRW 22 D iptera Chironomidae Hudsonim y ia 6.4 PR T PR 21 Diptera Chironomidae Labrundinia 6 PR T SPR 10 Diptera Chironomidae Polypedilum s calaenum grp 8.7 CG T CLG
102 3 Diptera Chironomidae Rheotanytars us 6.4 FC T CLG 1 Diptera Chironomidae Polypedilum c onvi ctum grp 5.3 CG T CLG 3 Diptera Chironomidae Chironomus 9.8 CG T BRW 1 Diptera Chironomidae Phaeno p sect ra 6.8 SCR/C G T CLG 3 Diptera Chironomidae Polypedilum h alterale grp 7.2 CG T CLG 180