Comparing abiotic and biotic parameters when assessing streams within a geologically diverse area (Bartow County, GA)

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1 Comparing abiotic and biotic parameters when assessing streams within a geologically diverse area (Bartow County, GA) Joseph M. Dirnberger, William Ensign, Heather Sutton, and Donald McGarey Department of Biological and Physical Sciences Kennesaw State University Kennesaw, GA ABSTRACT During the summer of 2000, we sampled twenty-two stream sites in Bartow County (Georgia) for fish, macro-invertebrates, water quality, habitat condition, and bacterial indicators as part of a county-wide watershed assessment. Three major geological regions occur within the county (to the west rocks are mainly limestone, to the north shales and sandstones, and to the east and southeast harder metamorphic rocks). Because the county is so geologically diverse yet all sites are within the same drainage basin, this data set provides a rare opportunity to examine the influence of watershed geology within a relatively small geographic area on parameters and metrics traditionally used to assess anthropogenic impacts. Principal component analysis (PCA) and other comparisons indicate that most biotic and abiotic parameters over all sites are related to one another in ways expected among sites that vary due to anthropogenic impacts. Multimetrics developed for fish and for invertebrates reflected trends along the primary PCA 'water quality' factor (turbidity, suspended solids, and BOD), whereas site geology did not reflect trends across this factor. As expected dissolved ions (as well as alkalinity and ph) were strongly related to watershed geology, but other less obvious water quality parameters such as nitrate were also associated with geologic location. Most individual metrics that are traditionally used to assess invertebrate and fish communities did not appear to be influenced by geology, but rather by anthropogenic habitat and water changes. In addition, differences in taxonomic composition of invertebrate communities were associated with watershed geology. Any given invertebrate community within the western limestone region was most similar (based on similarity indices of species composition) to other communities within this same region. Communities within the other two regions were not similar to western communities, but not distinctly different from each other. Because water chemistry between streams of western limestone and streams of northern shale-sandstone are more similar, geological influences on invertebrate species distribution may reflect physical stream characteristics (such as streambed morphology and substrate characteristics) rather than water chemistry. While underlying geology strongly affected many water quality parameters, biotic measures were relatively independent of these water quality effects, indicating biotic parameters are reliable in assessing degradation in a geologically diverse basin.

2 KEYWORDS macroinvertebrates, geology, ecoregions, watershed assessment INTRODUCTION Natural physiographic and historical influences have long been recognized to affect ecological communities over large (landscape) scales (e.g. Whittaker, 1970; Dayton and Tegner, 1984). These effects potentially confound differences in biotic communities caused by human impacts (e.g. Barbour et al., 1992), and are a problem when assessing watersheds because determination of such differences is a major goal. To minimize these geographic effects, it is typical for biotic data to be compared within defined regions (e.g. ecoregions ; Omernik, 1987). The underlying assumption of this concept is that there exists distinct geographical groupings of biotic communities that differ in fundamental ecological processes (Omernik et al., 1997). One problem with using the ecoregion concept in assessing aquatic communities is that ecoregions are traditionally delineated by soil units, potential natural vegetation, land surface form, and land use (Omernik, 1987). Terrestrial plants are known to respond to soil characteristics (Cain, 1944) that are determined in part by surface geology. However, for communities such as aquatic macroinvertebrates in streams, other factors, such as geologic ones influencing channel morphology and substrate characteristics, may be important. A second potential problem is assessing areas that straddle ecoregions. The efficiency of aquatic invertebrate dispersal and colonization has long been noted (Hynes, 1963; Maquire, 1963) and such movement may greatly influence species composition of biotic communities (e.g. Sale 1977: Connor and Simberloff, 1979). Streams and watersheds do not respect ecoregion boundaries so that colonization is even more likely where streams cross from one region to the next because of the tendency of invertebrates to disperse by downstream drift. Adding to this problem in streams, dissolved and suspended matter and bed substrate are easily transported so that water and bed characteristics in one region may be more similar to those in an upstream region. Availability of credible reference sites (typically considered as the sites with best attainable conditions) is often limited in areas where history of human activity is extensive. Researchers may be faced with the decision of choosing a nearby reference site in an adjacent region that may share species and physical characteristics or one a much greater distance within the same ecoregion. A watershed assessment of Bartow County in the summer of 2000 (Dirnberger et al., 2001) provides a way to investigate the usefulness of the ecoregion concept assessing watersheds over relatively small scales. The county straddles several ecoregions, major geological units, and physiographic provinces. The central question addressed in this paper is whether macroinvertebrate taxonomic composition reflects ecoregion boundaries over the scale investigated in this study (Bartow County). If so, macroinvertebrate descriptors should be more similar among sites within regions than across regions.

3 Alternatively, patterns in macroinvertebrate taxonomic composition reflect colonization (historical) influences across the study area or factors not considered in delineations of ecoregions. Metrics based on taxonomic data should also reflect difference in fundamental ecological function among ecoregions, but because sites within Bartow County vary in degree of human perturbation, potential differences in ecologic function due to ecoregion are confounded with impacts on ecological integrity (measured by metrics). However, if patterns in taxonomic composition are independent of metrics used to assess integrity, then taxonomic composition presumably reflects some aspects of basic ecosystem function. This paper will also examine patterns in commonly used macroinvertebrate metrics among sites relative to ecoregions, perturbation, and taxonomic similarity. METHODS During the summer of 2000, we sampled twenty-two stream sites in Bartow County (Georgia) for fish, macro-invertebrates, water quality, habitat condition, and bacterial indicators as part of a county-wide watershed assessment. Three major rock types occur within the county (Figure 1A), the first two are considered to be a geological part of the Ridge and Valley Province, while the third contains geologically similar rock of the Blue Ridge and Northern Piedmont Provinces (McConnell and Abrams, 1984). To the west rocks are mainly limestone and dolomite, mostly of the Upper Cambrian and Lower Ordivician Knox Group. A wedge of shales and sandstones extends southward from the north central to northeastern county line, nearly bisecting the county. While shales and sandstones predominate (Cambrian Rome Formation and to a lesser extent the Cambrian Conasauga Group), the area also contains some limestone and dolomite (Cambrian Shady Dolomite and part of the Cambrian Conasauga Group). Crystalline metamorphic rocks of the Blue Ridge and Northern Piedmont Provinces extend along the east and south central (Blue Ridge) and southeast (Northern Piedmont) areas of the county, and include quartzite, granite, gneiss, and schist. A thin band of quartzite extends along the eastern edge of the Valley and Ridge groups at the boundary with Blue Ridge groups. A physiographic map (Figure 1B), places the limestone/dolomite and shale/sandstone geologies in the Great Valley physiographic region. Most of the Piedmont and Blue Ridge geologies in Bartow County are combined in the Cherokee Upland of the Piedmont (Georgia DNR, 2001). An ecoregion map (Figure 1C), divides the county into three Level III Ecoregions: Ridge and Valley, Blue Ridge, and Piedmont (Griffith et al., 1998). In this map, the Blue Ridge geologic areas in the south central part of the county are included in the Piedmont

4 Figure 1. A. Geological regions based on dominant rock types. B. Physiographic regions. C. Ecoregions (dark lines indicate Ecoregion III level boundaries; 67g and 67f are Level IV Ecoregion, Southern Shale Valley and Southern Limestone/Dolomite Valleys and Low Rolling Hills, respectively). A shale and sandstone limestone and dolomite crystalline metamorphic B Cherokee Uplands C Great Valley Cherokee Upland Ridge and Valley Blue Ridge Piedmont

5 Ecoregion. The Ridge and Valley areas in Bartow County are further divided into two Level IV Ecoregion along boundaries defined apparently by soil maps. This map differs from the geological map in that a small patch of Southern Shale Valley (lying within Southern Limestone/Dolomite Valleys and Low Rolling Hills ) is located to the south of the geological area in which shale is most predominant. Streams in Bartow County feed into one of two rivers. The four streams crossing the northern border of the county flow into the Coosawattee River and then into the Oostanaula. The other streams in this study flow into the Etowah River (running east to west across the county; Figure 4) where it comes together with the Oostanaula to form the Coosa River in Rome, Georgia. Protocols for sampling benthic macroinvertebrates are based on the U.S. EPA guidelines (Barbour, et al, 1999). Within a 100-m representative reach, a composite sample was taken from individual sampling spots in the riffles and runs. A 1-m2 area of the streambed was sampled using a 1 m kick net by vigorously disturbing one square meter upstream of the net. For each site, cobble areas in 2 riffles and 2 runs were sampled. Two to four additional areas were sampled, depending on the presence of additional habitat diversity (snags, vegetated banks, sand, submerged macrophytes, and bedrock). Large debris was removed after rinsing and inspecting it for organisms and all organisms found and small debris were preserved in 10% formalin for sorting and organism identification in the laboratory. In most cases, organisms were identified to species. Physical habitat and fish communities were also assessed at the sites. Sites were sampled for water quality and microbial analyses on four dates (2 rain events and 2 dry). Methodologies for these samplings were based on standard procedures for watershed assessments (see Dirnberger et al., 2001). For the macroinvertebrate data, a series of metrics (a metric is an attribute of the sampled biological community that should reflect ecological condition) were chosen (Table 1) from a list of potential metrics (U.S. EPA, 1999; Barbour et al., 1992), based in part on metrics that are minimally redundant, that are representative of different fundamental community, population, and physiological processes, and that are not sensitive to known (understood) taxonomic anomalies from the Bartow County data set (see Dirnberger et al., 2001). The purpose of a multimetric is to provide a single value for each stream site that reflects the ecological integrity of the invertebrate community at that site based on several different measures. Community similarity was estimated based on Jaccard s, Sorennsen s, and Baroni-Urbani and Buser s indices, which consider species presence and absence among samples from two communities (Krebs 1999). Sorensen s coefficient weighs species matches (species present at both sites) more heavily than mismatches (species present at only one site). Baroni-Urbani and Buser s coefficient includes in its calculation negative matches (species that are not present at either site being compared). Single linkage cluster analysis (using the highest Jaccard Coefficients in comparisons of each site to others) was used to examine relative degree to which each site is similar to all other sites.

6 Principle component analyses (PCA) were used as an exploratory tool to describe general relationships among sites for multiple parameters (Table 1), allowing numerous intercorrelated variables to be condensed into fewer dimensions (factors). Separate analyses were performed for habitat and invertebrate metric data sets. Component loadings >0.7 or <-0.7 were used to describe each factor. From parameters measured in the physical habitat assessment, those parameters that are expected to be most affected by geology were selected for PCA (e.g. cobble size rather than woody). Table 1 - Parameters used in principle component analyses. Asterisks for the invertebrate data set indicate parameters used in calculating the invertebrate multimetric. Habitat parameters Slope Stream width Depth (average) Depth (coefficient of variation) Velocity (average) Velocity(coefficient of variation) Silt Sand Gravel Pebble Boulder Bedrock Invertebrate metrics Taxa richness* Ephemeroptera, Plecoptera, Trichoptera, (EPT) Index % Contribution of Dominant Taxon* North Carolina Biotic Index (NCBI)* % Shredders % EPT Percent Chironomidae Ratio of EPT to Chironomidae* Hydropsychidae/Trichoptera Ratio of scrapers to filterers* (SC/FC) Ratio of EPT taxa to all macroinvertebrate taxa* RESULTS Based on taxonomic composition of macroinvertebrate communities, some sites were most similar to sites within the same ecoregion, while other sites were more similar to sites in other ecoregions (as estimated for each site as the site with the highest similarity coefficient to itself; Figure 2). The most common similarities across ecoregion boundaries occurred between Blue Ridge sites and three Ridge and Valley sites in the north central part of the county. Also, for Raccoon Creek which crosses from Piedmont to Ridge and Valley between upper and lower sampling sites (the only stream in the study with sites that extensively straddle a Level III ecoregion boundary), taxonomic composition at the lower site was not similar to other Ridge and Valley sites (and tended to be more similar to Piedmont sites). All three similarity indices yielded comparable results (with the only difference among the 3 coefficients for the 22 site comparisons being 4 of the comparisons using the Baroni-Urbani and Buser coefficient).

7 Figure 2. Similarity of invertebrate taxonomic composition among sites. Arrows point to the site that is most similar to the site from which the arrow originates. Open circles are Blue Ridge sites, squares are Piedmont sites, and triangles are Ridge and Valley sites (solid triangles indicate sites in mixed shale/sandstone and limestone/dolomite, inverted solid triangles indicate sites in shale/sandstone dominated areas). 10 km Cluster analysis indicates that groups of sites with greatest similarities do not clearly reflect ecoregion boundaries. Of the three clusters with highest similarity (>0.414; indicated by brackets in Figure 3), one cluster consists entirely of Ridge and Valley Ecoregion sites. The other two clusters are a mix of Ridge and Valley sites with sites from Blue Ridge or Piedmont Ecoregions (the one Piedmont site is actually located on the boundary of Blue Ridge and Piedmont Ecoregions). Most sites south of the Etowah were not similar to those on the north side, but neither were they very similar to one another.

8 Figure 3. Cluster analysis among sites based on taxonomic similarity. Open circles are Blue Ridge sites, squares are Piedmont sites, and triangles are Ridge and Valley sites (solid triangles indicate sites in mixed shale/sandstone and limestone/dolomite, inverted solid triangles indicates sites in shale/sandstone dominated areas). Brackets enclose sites that are most similar. Site: 0.46 L Tom's Nancy U Pettit Connesena Cedar U Two Run U Stamp L Two Run Little Pine Salacoa U Pumpkin Pine Log L Stamp U Tom's Rowland Ward U Raccoon L Pettit L Raccoon L Pumpkin L Euharlee U Euharlee In contrast, water chemistry differed markedly between the Blue Ridge-Piedmont Ecoregions and the Ridge and Valley Ecoregion. Total dissolved ions (Figure 4) and alkalinity of stream waters are much higher at all Ridge and Valley stream sites (more than twice as high), as are nitrates at most Ridge and Valley sites. For example, in one stream (Raccoon Creek), conductivity increased from 60 µs where it leaves the Piedmonts to 260 µs less than 4 km downstream in Ridge and Valley. Limestones of the Ridge and Valley would be expected to disproportionately contribute dissolved ions (Spock, 1953) affecting conductivity and alkalinity. Nitrate concentration in groundwater from Ridge and Valley wells in Bartow County (Donahue, 2001) are similar to concentrations found in streams within this region. Other water quality parameters such as total suspended solids, turbidity, and biological oxygen demand tended to be related to levels of human impact (Dirnberger et al., 2001).

9 Figure 4. Conductivity as a function of sites and hydrology. Dashed lines are isopleths estimated from mean conductivity over 4 dates at each site (all Piedmont and Blue Ridge sites were less than 118 µs and all Ridge and Valley sites were greater than 236 µs). Open circles are Blue Ridge sites, squares are Piedmont sites, and triangles are Ridge and Valley sites (solid triangles indicate sites in mixed shale/sandstone and limestone/dolomite, inverted solid triangles indicate sites in shale/sandstone dominated areas). 200 µs 100 µs Cedar Little Pine Log Pine Log Salacoa 300 µs L. Tom s U. Tom s Connesena Etowah L. Two Run U. Two Run U Pettit Nancy Rowland U. Stamp L. Stamp L. Euharlee L Pettit L. Raccoon U. Euharlee 200 µs 100 µs U. Raccoon Ward L. Pumpkinvine U. Pumpkinvine 10 km Diffferences in geologically related measurements of physical habitat are less distinct between the Piedmont / Blue Ridge Ecoregions and the Ridge and Valley than are water quality parameters. Principle component analysis of sites based on geologically sensitive habitat measurements suggest some differences between limestone sites of the Ridge and Valley and crystalline metamorphic sites (Piedmont and Blue Ridge), particularly when comparing Factor 1 (slope, width-depth) and Factor 3 (pebble). Comparison of all three factors indicates overlap among these regions. Shale sites of the Ridge and Valley do not fall cleanly into either of the other regions but instead tend to be intermediate (Figure 5).

10 Figure 5. PCA based on geologic-influenced physical habitat parameters. Factor 2: Sand (-), Cobble (+), Boulder (+) Factor 1: Slope (-), Width (+), Depth (+) Factor 3: Pebble (-) Factor 1: Slope (-), Width (+), Depth (+) Factor 3: Pebble (-) Factor 2: Sand (-), Cobble (+), Boulder (+) Piedmont Blue Ridge Ridge and Valley Ridge and Valley Mixed Ridge and Valley Shale Ridge and Valley/Piedmont

11 Figure 6. PCA based on invertebrate metrics. Factor 2: EPT/Chironomid Ratio (+), Shredder/Filterer Ratio (-) Factor 3: Chironomid Ratio (+) Factor 3: Chironomid Ratio (+) Factor 1: NCBI (-), Taxa Richness (+) Factor 1: Taxa Richness, EPT (+), NCBI (-) Factor 2: EPT/Chironomid Ratio (+), Shredder/Filterer Ratio (-) Piedmont Blue Ridge Ridge and Valley Ridge and Valley Mixed Ridge and Valley Shale Ridge and Valley/Piedmont

12 Ecoregion and geological influences are not apparent in analysis of macroinvertebrate metrics, but instead reflect anthropogenic impact. Unlike the results of the principle component analysis of physical habitat (Figure 5), there were not apparent differences in invertebrate metrics among limestone sites of the Ridge and Valley, crystalline metamorphic sites (Piedmont and Blue Ridge), and Shale sites of the Ridge and Valley (Figure 6). While this analysis must be interpreted with caution (PCA of metrics is a statistical analysis on parameters that have themselves been statistically processed), lack of trends among regions suggests that ecoregion does not greatly influence the ability of the metrics to assess anthropogenic impacts. Instead, general relationships among metrics over all the sites are related in ways expected due to human impact (with the exception of chironomid-based metrics and the shredder metric). Taxa Richness, EPT metrics, ratio of scrapers to filterers, and Habitat Score tend to be positively correlated with one another and negatively correlated with percent contribution, North Carolina Biotic Index and ratio of Hydropsychidae to Trichoptera (Table 2) as predicted by ecological theory and empirical relationships found in other studies (Barbour et al., 1999). The multimetric estimating invertebrate community ecological integrity was positively related to physical habitat condition (r=0.37; Figure 7) and rankings of sites based on the invertebrate multimetric were comparable to those based on fish community and water quality data (Dirnberger et al., 2001). Figure 7. Invertebrate multimetric as a function of habitat score based on all physical habitat parameters. Open circles are Blue Ridge sites, squares are Piedmont sites, and triangles are Ridge and Valley sites (solid triangles indicate sites in mixed shale/sandstone and limestone/dolomite, inverted solid triangles indicates sites in shale/sandstone dominated areas) U Stamp U Tom's Pine Log U Raccoon Connesena Cedar Ward L Stamp L Raccoon Little Pine Nancy U Pettit L Two Run L Tom's 60 Salacoa U Two Run U Euharlee L Euharlee L Pettit 40 U Pumpkin Rowland 20 L Pumpkin Physical Habitat Score

13 Table 2. Correlation coefficients (r) of metrics among 22 sites. Habitat (%) Taxa Richness EPT Index % Contribution NCBI % Shredders % EPT % Chironomid EPT / Chironomid Hydropsychi dae/trichopt SC/FC EPT/taxa Richness Multimetric Habitat (%) 1.00 Taxa Richness EPT Index % Contribution NCBI % Shredders % EPT % Chironomidae EPT/Chironomidae Hydropsychidae/Trich SC/FC EPT/taxa Richness Multimetric Patterns in metrics among sites did not reflect patterns in taxonomic composition. Correlations among sites based on a ranking of sites by each metric indicate that a site s highest correlation coefficient with another site was rarely the same site that it was most similar to based on taxonomic composition (only 1 of 22 comparisons; Figures 8 and 2), but instead to sites with similar impacts often in other ecoregions and geologies. DISCUSSION The necessity of using the ecoregion concept to compare biotic data is based on the concept that fundamental ecological processes differ among regions (i.e. sites within ecoregions have similar ecosystem behaviors ; Omernik, et al., 1997). However, it is not clearly understood how these processes may differ, but instead assumed that similar lands should produce similar waterbodies (Barbour, et al. 1999). Species composition, in part, should affect fundamental ecological function. While different players (species) can be cast in similar ecological roles, similar groups with many of the same players should be indicative of ecosystems that function similarly. Analysis of taxonomic composition over the area sampled in this study does not support the concept that ecological function is more similar within ecoregions than to sites in adjacent regions. Similarities in taxonomic composition among many sites across some ecoregion boundaries as observed in this study may be the result of several factors.

14 Figure 8. Similarity in metrics among sites based on highest positive correlation coefficient of invertebrate metrics. Open circles are Blue Ridge sites, squares are Piedmont sites, and triangles are Ridge and Valley sites (solid triangles indicate sites in mixed shale/sandstone and limestone/dolomite, inverted solid triangles indicates sites in shale/sandstone dominated areas). 10 km Similarities of sites across ecoregions could be due to factors influencing taxonomic composition that are not considered in delineation of ecoregions. Ions dissolving from rock link geology with terrestrial vegetation through soils, yielding a convenient degree of congruency when developing ecoregion maps from multiple factors. But the tendency of greater similarity in taxonomic composition of aquatic invertebrates at shale sites to metamorphic sites in other ecoregions, rather than to limestone sites in their same ecoregion could reflect aspects of geology that have little influence on distribution of terrestrial vegetation upon which ecoregions are delineated (in part). In aquatic systems, Bunn et al. (1987) noted taxonomic differences were strongly correlated with cation concentration across two Australian watersheds but apart from reflecting geology, a causal explanation is unlikely. Possible effects of ions on stream invertebrates include

15 changes in benthic algal communities (Leland and Porter, 2000) and buffering capability of stream water to acid precipitation (Ventura and Harper, 1996). However, Cannan and Armitage (1999) found distinct differences in taxonomic composition among three geological regions along the River Frome despite little difference in water chemistry (chemistry differed little because groundwater from the most upstream geology contributed most of the water to this system). Unfortunately, geological changes along the River Frome occurred sequentially along the stream making it difficult to differentiate geological influences from longitudinal factors such as stream size, gradient, and degree of biological processing (e.g. Vannote et al., 1980). Streams in the present study were of similar order, but abrupt differences among sites in channel water ion concentration (conductivity and alkalinity) among sites was not reflected in aquatic invertebrate taxonomic composition. The tendency for shale sites to be intermediate between limestone and metamorphic geological areas in terms of geologically influenced physical habitat characteristics could account for similarities among shale sites and metamorphic sites in taxonomic composition, despite differences in water chemistry. Johnson et al. (1995) attributed channel features such as bank-full width to geological factors. Erosion of nearby geologic units certainly influences streambed substrate characteristics, and the effects of substrate characteristic on invertebrate communities are well documented (e.g. Waters, 1995). However, the number of sites in this study is insufficient to link specific aspects of geologically influenced stream characteristics to differences among invertebrate communities. Another, more commonly debated aspect not emphasized in the delineation of ecoregion is watershed boundary (Omernik and Bailey, 1997; Griffith et al., 1999). In this study, the shale-dominated streams are three of only four streams that are located within the Coosawattee River watershed. While this could account for dissimilarity in taxonomic composition of these streams from other Ridge and Valley sites, it would be difficult to use a watershed explanation to account for the similarity of these sites to more distant metamorphic sites within the Etowah River watershed. Furthermore, watershed boundaries potentially have minimal effects for aquatic invertebrates given the ability of most aquatic invertebrates to disperse as flying adults or by resting stages resistant to desiccation (Hynes, 1963; Maquire, 1963). The capability of invertebrates to colonize other stream systems in of itself may contribute to the blurring of ecoregion boundaries at the spatial scale in this study. Shale sites tend to lie geographically between Ridge and Valley limestone sites and Blue Ridge sites, and observed similarity in taxonomic composition to both would be expected due to dispersal among nearby streams. These basins lie side by side or with upper reaches head to head. In contrast, basins with their downstream mouths more or less facing each other (those that flow south into the Etowah River to those that flow north into the Etowah River) tend not to be similar (with one exception). Because many aquatic insects fly upstream as adults to lay eggs (the number of aquatic insect species with terrestrial adult stages, most of which fly, is an order of magnitude greater than those with aquatic adult stages; Hutchinson, 1981), movement of individuals between basins would be more likely in basins whose headwaters border each other. Movement between basins on

16 opposite sides of the Etowah River (and its impoundment, Lake Allatoona, upstream to the east) may be limited because the size and regulation of the river is a significant barrier to invertebrates that are adapted to shallow, swift streams. Even though taxonomic composition did not reflect ecoregions, taxonomic similarity is not necessarily equivalent to similarity in ecological function. Ecosystem function is influenced by such factors as relative abundances of individuals among species (not examined in the presence/absence similarity analyses performed in this study) and the distribution of individuals and species within groups based on feeding strategies and trophic position. Such data are used to calculate metrics. While the ecoregion concept presumes differences in fundamental ecological function among regions, it is difficult to use metrics to detect such differences. This is because metrics are designed to identify differences in ecologic integrity, and the concept of ecologic integrity is that it is an indication of impacts on ecologic function. Variation in the level of impact among sites might confound natural ecoregional differences, though in this study no major differences could be attributed to ecoregion but instead to levels of perturbation. This pattern of metrics among sites is not apparent in patterns of similarity based on taxonomic composition. This supports the idea that taxonomic composition at least in part is indicative of ecological function independent of impact, and, if so, ecological function does not differ according to ecoregion. Metrics appear to be a reliable assessment tool across regions within Bartow County as they are designed to do within ecoregions. Whether observable lack of ecoregion difference in metrics in Bartow County is due completely to variation in impact among sites or is also influenced by possible effects of dispersal and geology would require comparisons of reference (minimally disturbed) sites over a comparable spatial scale. Minimally disturbed sites in Bartow County are rare, as they typically are elsewhere. Other studies have noted ecoregional differences in taxonomic composition over reference sites across much broader spatial scales (for invertebrates over France, Charvet et al., 2000; for fish over Ohio, Griffith, et al., 1999), but at these scales dispersal is less likely to influence regional ecosystem processes and geological effects are more likely to be overwhelmed by major differences in topography and land use. CONCLUSIONS Taxonomic composition among sites was frequently similar to sites in other ecoregions and largely independent of patterns in measures of ecological integrity (metrics). While it is not possible with this data set to clearly sort out the effect on metrics due to ecoregion from differences due to human perturbation, spatial patterns in taxonomic composition suggest ecosystem functions are not fundamentally different across ecoregion boundaries on the scale examined here. If so, multiple reference sites are not necessary to compare with other sites within multiple ecoregions at this scale. However, other geographical considerations (dispersal distances and geology) may be warranted when considering the appropriate scale and area over which biotic data are to be compared.

17 ACKNOWLEDGEMENTS We are grateful to the members of the A.L. Burruss Institute of Public Service for managing this study, especially Carol Pierannunzi and Christy Storey. Funding for this study was provided by Bartow County, Georgia and we are especially grateful for the insight of Gene Camp, Superintendent of the Bartow County Water Department, and County Commissioner Clarence Brown. For assistance we thank fellow faculty Ralph Rascati and Mark Patterson, and students Tim Pugh, Erin Feichtner, Calley Brewer, Kevin Thomas, Jacob Nickerson, Mike McDuffie, Jessica Bowman, and Christy Hardy. For identification of invertebrates, we thank Wendell Pennington of Pennington & Associates. This study would not have been possible without the dedicated work of Harry McGinnis who brought together all those who participated. REFERENCES Barbour MT, JL Plafkin, BP Bradley, CG Graves, RW Wisseman. (1992) Evaluation of EPA s rapid bioassessment benthic metrics: metric redundancy and variability among reference stream sites. Environmental Toxicology and Chemistry 11: Barbour MT, J Gerritsen, BD Snyder, JB Stribling. (1999) Rapid Bioassessment Protocols For Use in Streams and Wadeable Rivers: Periphyton, Benthic Macroinvertebrates, and Fish. U.S. EPA Publication EPA 841-B , Second Edition. Bunn SE, DH Edward, NR Loneragan. (1986) Spatial and temporal variation in the macroinvertebrate fauna of streams of the northern jarrah forest, Western Australia: community structure. Freshwater Biology 16: Cain SA Foundations of Plant Geography. Hafner Press, New York. Cannan CE, PD Armitage. (1999) The influence of catchment geology on the longitudinal distribution of macroinvertebrate assemblages in a groundwater dominated river. Hydrol. Process. 13: Charvet S, B Statzner, P Usseglio-Polatera. B Dumonts. (2000) Traits of benthic macroinvertebrates in semi-natural French streams: an initial application to biomonitoring in Europe. Freshwater Biology 43: Connor EF, D Simberloff. (1979) The assembly of species communities: chance or competition? Ecology 60: Dayton PK, MJ Tegner. (1984) The importance of scale in community ecology: A kelp forest example with terrestrial analogs. In PW Price, CN Slobodehikoff, WS Gaud

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