Biological integrity in urban streams: Toward resolving multiple dimensions of urbanization

Landscape and Urban Planning 79 (2007) 110–123

Biological integrity in urban streams: Toward resolving multiple dimensions of urbanization B. Michael Walton a,∗ , Mark Salling b,1 , James Wyles b,2 , Julie Wolin a,3 b

a Department of Biological, Geological, and Environmental Sciences, Cleveland State University, Cleveland, OH 44115, United States Northeast Ohio Data and Information Service, Levin College of Urban Affairs, Cleveland State University, Cleveland, OH 44115, United States

Received 7 December 2004; received in revised form 12 September 2005; accepted 21 October 2005 Available online 30 January 2006

Abstract Most studies of urban streams have relied on single variables to characterize the degree of urbanization, which may not reflect interactions among features of urban landscapes adequately. We report on an approach to the characterization of urbanization effects on streams that used principal components analysis and multiple regression to explore the combined, interactive effects of land use/land cover, human population demography, and stream habitat quality on an index of biological integrity (IBI) of fish communities. Applied to a substantially urbanized region in northeast OH, USA, the analysis demonstrated the interactive nature of urbanization effects. Urban land use and stream habitat quality were significant predictors of IBI, but were no better than and, in some cases, poorer predictors than other gradients and interactions among gradients. High integrity sites were characterized by low forest cover and high grassland cover at sub-catchment scale, but high forest cover within a 500 m radius local zone of the sample point, conditions often found in protected parklands in the region. The analysis also indicated that variability in stream habitat quality was unrelated to landscape or demographic features, a result we attribute to the interaction between the geological and urbanization histories of the region. © 2005 Elsevier B.V. All rights reserved. Keywords: Biological integrity; Land use; Urban streams

1. Introduction Urbanization poses vexing challenges to the ecological sustainability and restoration of stream ecosystems. Stream habitat and biota in urban settings are often profoundly degraded in comparison to natural or less-impacted rural conditions (e.g., Klein, 1979; Steedman, 1988; Schuler, 1994; May et al., 1997; Boward et al., 1999; Morley and Karr, 2002; Morse et al., 2003; Miltner et al., 2004), even at modest amounts of urban development (Weaver and Garman, 1994; Booth and Jackson, 1997). Given these impacts and the accelerating pace of urbanization (Cohen, 2003), there has been great interest in describing ∗

Corresponding author. Tel.: +1 216 687 3979; fax: 1 216 687 6972. E-mail addresses: [email protected] (B.M. Walton), [email protected] (M. Salling), [email protected] (J. Wyles), [email protected] (J. Wolin). 1 Tel.: +1 216 687 3716; fax: +1 216 687 5068. 2 Tel.: +1 216 687 2209; fax: +1 216 687 5068. 3 Tel.: +1 216 687 3505; fax: +1 216 687 6972. 0169-2046/$ – see front matter © 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.landurbplan.2005.10.004

quantitative relationships among the intensity of urbanization, constraints on stream recovery, and potential thresholds of degradation imposed by urban development, e.g., reviews by Paul and Meyer (2001) and Allan (2004). However, considerable variability surrounds these general biological integrity–urbanization relationships, so that equivalent levels of urbanization can be associated with a wide range of biological indicator scores (Wang et al., 1997; Klauda et al., 1998). For example, streams sites in urban regions of Ohio can vary by more than four-fold in biological integrity within the same level of up-stream urban land use (Yoder et al., 2000; Miltner et al., 2004). Further, slopes and thresholds of urbanization effects differ among urban regions (Yoder et al., 1999, 2000; Coles et al., 2004). Some of this variability is surely attributable to identifiable, allied stressors affecting stream sites, such as point-source pollutants and combined or sanitary sewer outfalls that may exert effects in addition to the generalized impacts of urbanization (Yoder et al., 2000; Miltner et al., 2004). However, signifi-

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cant fractions may also be associated with complex interactions among features of urbanized landscapes and the effects of urbanization within regionally specific contexts of geology, climate, or history of development and anthropogenic disturbance (Paul and Meyer, 2001; Allan, 2004). Such complexity is unlikely to be captured by any single measure of urbanization (Booth and Jackson, 1997; Yoder et al., 2000; Allan, 2004; Booth et al., 2004; Coles et al., 2004). Hence, a method for quantifying urban effects on streams that integrates multiple dimensions of urbanization and interactions among these factors is desirable. In this report, we describe such an approach and illustrate its application with an analysis of small stream sites in a highly urbanized region of northeastern OH, USA. Earlier studies have examined urbanization–biological integrity relationships in NE Ohio using single indicators of urbanization (e.g., Yoder et al., 2000), thereby facilitating comparisons of the efficacy of our approach with that of more typical analyses. The current analysis integrates the influences of three sets of variables characterizing the urban environment: major land use/land cover features; human population and housing density; stream habitat quality. We compared the relative impacts of these variables on measures of biological integrity based on fish communities to address the following questions: (1) Are multivariate descriptors of urbanization better predictors of biological integrity than a single variable measure of urban effects, e.g., percent urban land, population, or housing density? (2) Does a multivariate approach provide useful, additional insight into effects of urbanization not revealed by single measures of urbanization, e.g., spatial interactions among variables that mitigate or exacerbate the general effects of urbanization, or spatial interactions that represent regionally specific patterns of urban development? (3) Do the impacts of urbanization and the interactions among landscape gradients differ with spatial scale? (4) What are the relative contributions of in-stream habitat and landscape-level variables in determining biological integrity in an urban setting? To accomplish this, we used principal components analysis to generate statistically independent combinations of the original land cover/population demographic variables. Patterns of interaction among the original variables were interpreted from correlations and the magnitude and direction of factor loadings on the principal components. Multiple linear regression was used to test the value of the multivariate components for predicting biological integrity. The question of scale was addressed by comparing the relative influences of stream habitat and landscape-level effects on biological integrity, and by comparing analyses that aggregated the predictor variables at different scales, i.e., the catchment of the biological sampling point versus 500 m radius “local zone” surrounding the biological sampling point. 2. Study area The analysis described herein focuses on small stream catchments, for the most part 20–52 km2 drainage area, tributary to the Cuyahoga River in the Cleveland–Akron metropolitan area, as well as a few small streams tributary to Lake Erie within the

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same geographic area. The Cuyahoga River consists of 1963 km of stream miles draining 2100 km2 in northeastern OH, USA. The catchment lies within the Erie/Ontario Lake Plain Ecoregion and parts of three different physiographic provinces: the Allegheny Plateau, till plains, and lake plains. The Cuyahoga River watershed is one of the most densely populated, urbanized, and industrialized regions of Ohio. The basin accounts for 2% of total state land, but 17% of Ohio’s population, approximately 1.9 million people (921 people km−2 ) (Rybka et al., 2001). While the upper reaches are dominated by agriculture, the middle and lower reaches are heavily influenced by urbanization. Two of Ohio’s major cities, Akron and Cleveland, occur within the mid- and lower segments, respectively. The river is characterized by an unusual, U-shaped morphology, formed during the last glacial recession through the merging of several formerly separate drainages. Hence, the Cuyahoga flows southward from its upper reaches, then changes direction near Akron, OH, and flows northward to its terminus at Lake Erie in Cleveland, OH (Fig. 1). Because of this shape, eastward urban and sub-urban expansion is encroaching upon headwater regions that are currently dominated by forest and agriculture. The region has a long history of urbanization, accelerated by completion of the Ohio and Erie Canal in 1825 (Cockrell, 1992). Subsequently, the Cleveland–Akron corridor became one of the major commercial–industrial centers of North America through the first half of the 20th century, with petroleum, steel, rubber, and manufacturing among the major industries (Cockrell, 1992; Rybka et al., 2001). The Cuyahoga Valley National Park, established in 1974, protects 134 km2 of the basin between Akron and Cleveland. However, even this parkland has a history of substantial disturbance, including agricultural, commercial, and industrial uses, as well as many contemporary impacts within and along its boundaries (Cockrell, 1992). Additional environmental challenges arise from economic and infrastructural decline and population loss from city-centers and older, inner-ring suburbs (e.g., leaky sanitary sewers and inadequate stormwater controls), and out-migration to the urban fringe (e.g., mobilization of sediments from new home construction) (Bier, 1993, 2001). 3. Methods 3.1. Biological integrity and stream habitat data Biological integrity and stream habitat data were extracted from a statewide database maintained by the Ohio Environmental Protection Agency (OEPA). These data have served as the basis for previously published analyses regarding the Cuyahoga and other Ohio watersheds (Yoder et al., 1999, 2000; Miltner et al., 2004) and are a central component of the statewide program for water quality assessment. Biological data consist of multimetric indices of biological integrity (IBI) and well-being (modified index of well-being, MIWB) based upon fish communities. The IBI is an aggregate index based upon 12 sub-metrics characterizing the taxonomic composition, trophic structure, abundance, and condition of the fish community (Karr, 1981; Karr et al., 1986), as modified for Ohio streams and rivers (Ohio

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B.M. Walton et al. / Landscape and Urban Planning 79 (2007) 110–123

Fig. 1. (A) Map of Cuyahoga River basin showing locations of IBI sample points. (B) (Inset) Example of IBI sample point catchment and 500 m local zone delineations. Land cover categories are shown for local zones.

EPA, 1987, 1989; Yoder and Rankin, 1995). The MIWB is an index that incorporates measures of abundance, biomass, and diversity (Gammon, 1976, 1980; Hughes and Gammon, 1987), as modified to increase sensitivity to conditions particular to Ohio’s streams and rivers (Ohio EPA, 1987, 1989; Yoder and Rankin, 1995). Stream habitat data were also extracted from the statewide database as the Qualitative Habitat Evaluation Index (QHEI). The QHEI is a qualitative assessment of major features

of stream habitats that influence the potential for healthy fish communities (Rankin, 1989, 1995). 3.2. Land use and census data Land use data were extracted from a statewide land cover inventory of Ohio produced by the Ohio Department of Natural Resources, based upon Landsat Thematic Mapper Data collected

B.M. Walton et al. / Landscape and Urban Planning 79 (2007) 110–123

in September and October 1994 (Schaal and Motsch, 1997), the approximate mid-point of the time frame for the biological data used here. The data were classified into seven land cover categories of urban, agriculture/open urban areas, shrub/scrub, wooded, open water, non-forested wetlands, and barren. Population density and housing density data were obtained from the U.S. Census Bureau, 2000 Census of Population and Housing, Summary File 1, 2001. 3.3. Delineation of sample point catchments The basic spatial unit of analysis in this study was the catchment area for each of the biological sampling points. The sample-point catchment area was defined using digital elevation models (DEM), a vector hydrography database, and sample point locations. The DEM used here was extracted from the 1:24,000-scale seamless US Geological Service (USGS) National Elevation Dataset (NED) which was developed by merging USGS’s highest resolution, best quality elevation data available (NED is accessible on-line at http://gisdata.usgs.net/ned/default.asp). To improve accuracy of stream and catchment delineation, we used a vector hydrography database, the Valley Stream Segment (VST Rivers) file, to adjust the DEM. The VST file is based upon a 1:100,000 base map from the National Hydrography Dataset (NHD) from the USGS and the U.S. Environmental Protection Agency (USEPA). The two main sources for information for this dataset are USGS digital line graphs and the USEPA Reach File Version 3 (http://nhd.usgs.gov/). To improve catchment delineation, raster cells were adjusted in elevation at or near the VST vector layer streams, thereby improving stream channel results. The adjusted raster elevation values were then used to create a new vector-based stream network which included only the streams recognized by the original hydrography stream layer but were precisely located in reference to the DEM and the slope, flow direction, and related information necessary to delineate catchment areas up-stream and up-slope from the sample points. GIS surface analysis tools were used to create catchment area polygons. Fig. 1 illustrates the points, VST Rivers hydrography layer, the revised DEM-based hydrography network, and the catchment polygons in a portion of the study area. In addition to the sample point catchments, we also delineated 500 m radius local zone sub-polygons for each sample point. These were defined by inscribing a 500 m radius around the sample point within the boundaries of the original catchment area (Fig. 1). Population and housing unit counts are available at the census block level. Because catchments and local zones split census blocks and block groups, these census data were estimated by areal interpolation, specifically area apportionment. This method apportions the data based on the relative areas of the block or block group that are contained in each part split by catchments or zones. Geographic (polygon) boundary files in computerized GIS database structure for census blocks and block groups are available from the Census Bureau’s Topologically Integrated Geographically Encoded Referenced (TIGER) database.

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3.4. Statistical procedures The biological, land use, population demographic, and stream habitat variables used in this study are listed in Table 1. Biological data (IBI and MIWB) served as dependent variables in these analyses. Also, IBI was decomposed into its 12 sub-metrics, and these also were analyzed as dependent variables. QHEI served as both a dependent and an independent variable. QHEI was the dependent variable in regressions evaluating land use and demographic gradients as predictors of stream habitat quality, whereas QHEI was entered as an independent variable in regressions seeking to predict IBI, IBI sub-metrics, or MIWB. All variables were evaluated for conformance to normality and transformed, if necessary, using appropriate transformations. Indices, counts, and density measures were transformed according to log10 X or log10 (X + 1), whereas proportion variables were arcsin square-root transformed. Pearson product moment correlations and linear regression analyses were used to explore urbanization–stream quality relationships based upon “typical”, non-multivariate characterization of urbanization, e.g., percent urban land use and population or housing density. To explore the contributions of landscape gradients in addition to urban land use and to assess the potential for interactions among landscape and demographic gradients, we employed principal components and multiple linear regression analyses. Principal components analyses reduced the dimensionality among the predictor variables and produced gradients, i.e., principal components, which were statistically independent of one another, thereby eliminating the problem of collinearity within multiple regression analyses. Principal components were obtained for the combined land use/land cover and population demography dataset. Distance of the sample point from stream terminus and area of the sample-point catchments were also included within these data since these two spatial variables were correlated with several of the landscape and demographic features. Only components with eigenvalues > 1 were retained for subsequent analyses. Principal components were interpreted based upon magnitude of factor loadings and inspection of bivariate plots of components against the original variables. The predictive value of principal components for biological integrity or habitat quality was assessed using stepwise multiple regression (forward and backward selection procedures, P = .05 for entry or removal from the model). R2 -change was used to assess and rank the proportional contribution of each significant predictor to overall variance explained by regression models. Julian date of the biological sample was entered into these analyses to adjust for temporal changes in biological integrity. All statistical procedures were conducted using SPSS for Windows, Version 11. 4. Results 4.1. Single indices of urbanization The index of biological integrity was significantly correlated with variables that have been commonly used as measures of

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Table 1 List and summary statistics for variables used in the current analysis Variable

Abbreviated name

N

Sub-catchment scale Mean ± S.E.M.

Biological/habitat variables IBI MIWB IBI sub-metrics 1. Number of native species 2. Number of darter species 3. Number of headwater species 4. Number of cyprinid species 5. Number of sensitive species 6. Percent of tolerant species 7. Percent omnivores 8. Percent insectivores 9. Percent pioneer species 10. Number of individuals 11. Percent simple lithophiles 12. Percent of individuals with deformities, eroded fins, lesions, or tumors QHEI

Minimum–maximum

29.48 ± .6 5.54 ± .12

227 227

500 m radius local zone Mean ± S.E.M.

Minimum–maximum

12.00–50.00 0–9.02

227 10.89 1.30 .97 4.04 .23 59.70 26.55 27.21 33.04 731.89 30.76 1.00

Land use/demographic variables Percent urban Percent open grassland/parkland/agricultural fields Percent shrub/scrub Percent non-forested wetland Percent open water Percent forested Percent barren Population density (km−2 ) Housing density (km−2 ) Catchment area (km2 ) Distance from terminus (km)

± ± ± ± ± ± ± ± ± ± ± ±

.29 .10 .06 .14 .04 1.76 1.37 1.55 1.48 56.86 1.27 .14

165

61.66 ± .89

Urban Grass

227 227

16.12 ± 1.51 37.75 ± 1.91

Shrub Wetland Water Forest Barren Pop House Area Distance

227 227 227 227 227 227 227 227 227

8.73 9.00 .43 27.87 .07 5130.20 2329.72 5.06 7.97

± ± ± ± ± ± ± ± ±

0–24 0–6 0–6 0–9 0–3 0–100 0–100 0–92.31 0–100 39–6523 0–82.98 0–15.43

25.00–86.50 18.42 ± 1.60 23.84 ± 1.42

0–100 0–100

1.08 .96 .12 1.95 .02 1157.28 544.61 1.12 .69

0–86.13 0–100 0–18.81 0–100 0–3.16 0–133772.57 0–71217.05 .10–84.50 0–46.67

3.67 4.93 .11 43.98 .05 326.73 39.92

± ± ± ± ± ± ±

.35 .52 .05 1.69 .03 29.14 4.31

0–99.23 0–94.45 0–28.33 0–58.15 0–10.16 0–100 0–6.06 0–2320.92 0–378

Also listed are shortened variable names used in subsequent tables.

urbanization, e.g., percent urban land use, population density, and housing density, although the magnitudes of these correlations were generally low, and several other land use variables were more strongly correlated with IBI than urban land use,

e.g., grassland cover (Table 2). Percent urban land use and population density were also found to be significant predictors, in combination with QHEI, of IBI by multiple linear regression analysis, although only population density retained statistical

Table 2 Pearson product moment correlations for variables aggregated at sub-catchment scale MIWB IBI MIWB QHEI Barren Grass Shrub Urban Water Wetland Forest Pop House Distance * ** ***

QHEI

.570*** .161*

.05 > P > .01. .01 > P > .001. P < .001.

.153*

Barren .093 .078 .015

Grass .312*** .069 −.026 .049

Shrub

Urban

−.016 .086 −.044 .007 −.227**

−.153**

−.037 .046 .063 −.329*** −.059

Water .263*** .119* −.034 .135* .123* −.097 −.115*

Wetland

Forest

Pop

House

Distance

Area

.156**

−.180**

−.152**

−.216**

−.338***

.340*** .162** .040 .422*** .067 −.113* −.082 .396*** −.047 .355*** .101 −.080 −.158**

.221** .035 −.074 −.208** .329*** −.117* .092

−.138* .041 .084 −.384*** −.351*** −.286*** .024 −.322***

.087 −.021 .055 −.283*** −.203** .308*** −.035 −.139* .281***

−.173** −.081 .124* −.016 .137* .112* −.012 −.072 −.092 .148**

−.034 −.086 −.053 −.173** .213** −.028 −.012 .062 −.010 .072 .087

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Table 3 Multiple linear regressions relating common measures of “urbanization” (percent urban land use, housing density, and population density) to IBI Variable

R2

Regression statistics Coefficient

Standard error

P-value

Regressions excluding distance from terminus and catchment area Y-intercept 1.086 .141 <.001 Percent urban land use −.069 .031 .026 QHEI .238 .107 .028

.055

Y-intercept Housing density QHEI

1.024 −.022 .294

.235 .017 .129

<.001 .213 .024

.068

Y-intercept Population density QHEI

1.181 −.039 .220

.193 .014 .014

<.001 .005 .040

.073

Regressions including distance from terminus and catchment area Y-intercept 1.028 .187 <.001 Percent urban land use −.049 .030 .102 QHEI .202 .101 .046 Distance from terminus −.074 .020 <.001 Catchment area .026 .008 .001

.184

Y-intercept .923 Housing density −.016 QHEI .279 Distance from terminus −.084 Catchment area .027

.224 .016 .119 .024 .010

<.001 .321 .022 .001 .005

.225

Y-intercept 1.085 Population density −.041 QHEI .191 Distance from terminus −.066 Catchment area .032

.184 .013 .098 .020 .003

<.001 .002 .054 .001 <.001

.219

Regressions are presented both with and without the spatial co-factors, distance from terminus, and catchment area. Statistically significant P-values are shown in bold.

significance as a predictor of IBI in regressions that included two spatial covariates of IBI, distance from terminus and catchment area (Table 3). 4.2. Multivariate indices of urbanization: sub-catchment scale analyses Principal components based upon variables aggregated at the sample point sub-catchment scale yielded four components with eigenvalues > 1, which accounted for 67.5% of total variation in the dataset. Factor loadings for the components are shown in Table 4, where principal components are labeled SPC1, SPC2, etc., to indicate that they are based upon sub-catchment scale data. SPC1 was most strongly influenced by catchment area with lesser loadings associated with housing density, percent barren, and percent open water cover. SPC2 increased with percent wetland and shrub/scrubland cover. SPC3 described a contrast between percent forest and percent grass/park/field cover (Fig. 2). SPC4 was strongly, positively correlated with urban land cover. No component was correlated significantly with QHEI (Table 4), whereas two components, SPC1 and SPC3, were correlated significantly with IBI. Three principal components (1, 3,

Fig. 2. Percent forest cover (closed circles) and percent grassland cover (open circles), arcsin-square root transformed, as a function of the third principal component at sample point sub-catchment scale (SPC3).

and 4) were significant predictors of IBI according to stepwise, multiple linear regression (Table 5). IBI increased with SPC1, but declined with SPC3 and SPC4. Multiple regression also indicated that IBI increased significantly with QHEI and Julian date (Table 5). The modified index of well-being was also significantly related to principal components and IBI. MIWB also increased with SPC1, as well as SPC2 (Table 5). As in the case for IBI, MIWB increased with QHEI and Julian day. The multiple regression relating principal components to IBI had a substantially higher coefficient of determination (R2 ) than any of the regressions relating principal components to submetrics of the IBI (Table 5). Nevertheless, each of the principal components was a significant predictor of at least two of the sub-metrics of the IBI. SPC3 showed the highest number of significant coefficients (it was a predictor of 6 of 12 sub-metrics, range of P-values = .017 to <.001; Table 5). This component was negatively related, generally, to sub-metrics representative of species richness and community composition (e.g., number of native species, number of darter species, and number of sensitive species) and was positively related to a measure of fish condition, the proportion of individuals with deformities, eroded fins, lesions, or tumors (Table 5). SPC2 was a significant predictor of 5 of 12 sub-metrics, despite failing to emerge as a significant predictor of overall IBI. SPC2 was positively related to two measures of community composition (metrics of native and darter species), two measures of trophic structure (percent omnivores and insectivores), and was negatively related to percent lithophile species. SPC1 was positively related to 4 of 12 submetrics which were indicative of abundance or species richness and community composition (e.g., number of native species, number of darter species, and number of insectivores). SPC4 was negatively related to two sub-metrics (percent insectivores and number of individuals). QHEI was a significant predictor of 7 of 12 sub-metrics, which were largely measures of species richness and community composition. Number of darter species increased during the time period within this dataset, while proportions of tolerant species, omnivores, and individuals with deformities, eroded fins, lesions, and tumors declined (Table 5).

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Table 4 Factor loadings, percent of total variance, and correlation with QHEI and IBI for principal components obtained from sub-catchment scale analysis Original variables

Principal components SPC1

SPC2

SPC3

SPC4

Barren Grass/parkland Shrub/scrub Urban Water Wetland Forest Population density Housing density Catchment area Distance from terminus

.616 – – – .597 – .185 .312 .777 .894 −.178

– −.292 .787 – .132 .757 −.458 −.283 −.350 −.132 .365

– −.766 – – −.151 – .706 .558 .404 .141 .414

.142 −.320 – .938 −.234 – −.381 .450 .144 – –

Percent of total variance

26.7

16.1

13.5

11.2

−.030 (.700) .035 (.595)

.001 (.991) −.369 (<.001)

Correlation with QHEI Correlation with IBI

.024 (.761) .353 (<.001)

.034 (.666) .112 (.093)

Only loadings that were significant at P < .01 are shown. Pearson product moment correlations with IBI and QHEI are shown with P-values in parentheses (statistically significant correlations are shown in bold).

4.3. Multivariate indices of urbanization: effects of scale The addition of variables describing land use/land cover within the 500 m radius neighborhood of the biological sampling point resulted in identification of eight principal components with eigenvalues > 1, accounting for 80% of the total variance within the dataset. Factor loadings for this analysis are shown in Table 6, where principal components are labeled “LPC” to indicate that 500 m local zone data were included in their calculation. LPC1 was strongly related to population and housing density at both 500 m local zone and the overall sample sub-

catchment scales. LPC1 also increased with urban land use at both scales. LPC2 was most strongly related to basin area at the sub-catchment scale. LPC3 was strongly related to wetland cover at both the sub-catchment and 500 m local scale. LPC4 was principally a descriptor of shrub/scrub cover at the two scales. LPC5 decreased strongly with increasing forest cover in the 500 m zone, but increased significantly with increasing grass/park/field cover and urban land cover in the 500 m local zone. LPC6 was essentially a descriptor of barren lands at both scales. LPC7 was strongly related to open water cover at the 500 m scale, and to a lesser extent to water at the sub-catchment scale. LPC8 described a contrast between

Table 5 Multiple regression results at sub-catchment scale relating IBI, MIWB, and sub-metrics of IBI to principal components describing land use/land cover and demographic data, QHEI, and Julian day Dependent variables

Multimetric indices IBI MIWB IBI sub-metrics 1. Number of native species 2. Number of darter species 3. Number of headwater species 4. Number of cyprinid species 5. Number of sensitive species 6. Percent tolerant species 7. Percent omnivores 8. Percent insectivores 9. Percent pioneer species 10. Number of individuals 11. Percent simple lithophiles 12. Percent of individuals with deformities, eroded fins, lesions, and tumors

R2

Y-intercept

Regression coefficients for independent variables

QHEI

Julian day

SPC1

SPC2

SPC3

SPC4

.39 −59.528 (<.001) .17 −57.996 (.002)

.034 (<.001) .034 (.005)

ns .033 (.010)

−.039 (<.001) ns

−.018 (.030) ns

.322 (<.001) .382 (.006)

11.666 (<.001) 11.218 (.002)

.22 .015 (.957) .30 −57.535 (.035) .13 −.525 (.079) .13 −.546 (.085) .17 −.800 (.001) .05 105.988 (.004) .12 110.476 (<.001) .18 .499 (<.001) .03 1.439 (.001) .12 2.632 (<.001) .07 −.223 (.552) .12 23.080 (.005)

.048 (.001) .051 (.003) ns ns ns ns ns .061 (.004) ns .121 (.001) ns ns

.051 (.001) .063 (.001) ns ns ns ns .050 (.010) .096 (<.001) ns ns −.053 (.008) ns

−.035 (.017) −.061 (.001) −.065 (<.001) −.049 (.003) −.049 (<.001) ns ns ns ns ns ns .018 (.002)

ns ns ns ns ns ns ns −.051 (.024) ns −.121 (.002) ns ns

.573 (<.001) 1.050 (<.001) .434 (.010) .671 (<.001) .488 (<.001) ns ns ns −.486 (.044) ns .434 (.040) ns

ns 10.800 (.040) ns ns ns −20.292 (.004) −21.239 (<.001) ns ns ns ns −4.445 (.005)

Statistically significant coefficients are shown with P-values in parentheses; variables excluded by stepwise selection procedure are indicated by ns (not significant).

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Table 6 Factor loadings, percent of total variance, and correlation with QHEI and IBI for principal components obtained from analysis incorporating variables aggregated at 500 m radius local zone scale Original variables

Sub-catchment scale Barren Grass/parkland Shrub/scrub Urban Water Wetland Forest Housing density Population density Catchment area Distance from terminus Local scale Barren at 500 m Grass/parkland at 500 m Shrub/scrub at 500 m Urban at 500 m Water at 500 m Wetland at 500 m Forest at 500 m Housing density at 500 m Population density at 500 m

Principal components LPC1

LPC2

LPC3

LPC4

LPC5

LPC6

LPC7

LPC8

– −.328 – .549 – – – .550 .896 – –

.361 .205 −.159 −.325 .316 – .472 .729 .209 .920 −.228

– −.362 .235 .152 .213 .852 −.241 – – – −.145

– – .841 – −.141 .148 −.192 – – – .553

– – – – – – – – – – .194

.783 −.173 – .403 – – – – – – –

.142 .177 – −.209 .643 – – – – – .378

– −.706 – −.212 −.157 – .783 – .144 – .268

–

–

– – .238 −.430 – .786 – −.150 –

–

–

.213 .202 −.222 .316 .205 – .247 –

– −.157 – – .808 .251 – – –

– −.211 – .230 – – −.147 – –

11.2

5.4

4.8

−.005 (.953) .031 (.639)

.056 (.479) −.319 (<.001)

.426 – .544 – – – .871 .912

Percent of total variance

21.7

14.6

Correlation with QHEI Correlation with IBI

−.019 (.807) −.195 (.003)

−.012 (.875) .290 (<.001)

.073 (.350) .216 (.001)

.156 .816 −.267 – .213 – – –

.680 –

.795 .190

– – −.953 – –

– – – – – – –

9.3

6.6

6.2

−.122 (.120) −.132 (.048)

−.106 (.178) −.265 (<.001)

.392

.067 (.394) .036 (.592)

Only loadings that were significant at P < .01 are shown. Pearson product moment correlations with IBI and QHEI are shown with P-values in parentheses. Statistically significant correlations are shown in bold.

grass/park/field cover and forest cover at the sub-catchment scale. As in the analysis conducted at the sub-catchment scale exclusively, none of these components were correlated with QHEI (Table 6). Six of the eight components (LPC1, LPC2, LPC3, LPC4, LPC5, and LPC8) were correlated significantly with IBI (Table 6), but only five of these (LPC4 excluded) were predictive of IBI based upon stepwise multiple regression analysis (Table 7). LPC2 and LPC3 were predictive of MIWB (Table 7). IBI and MIWB also increased with QHEI and Julian date. Once again, the multiple regression predicting IBI had a substantially higher coefficient of determination (R2 = .44) than regressions for each of the IBI sub-metrics (range of R2 = .09–.35), although each of the sub-metrics showed a significant regression with at least one of the principal components. In general, sub-metrics of the IBI responded to the same gradients as did overall IBI. However, LPC4, which did not emerge as a significant predictor of IBI, was associated with 6 of 12 sub-metrics. LPC4 was negatively associated with measures of species richness and community composition and trophic structure, but positively associated with percent of individuals with deformities, eroded fins, lesions, and tumors (Table 7). LPC7, which also showed no predictive value for IBI overall, was positively associated with the proportion of insectivores in samples.

5. Discussion 5.1. Are multivariate measures more informative than univariate measures of urbanization? Previous studies of the Cuyahoga basin have found significant, negative relationships between biological integrity, as measured by fish and invertebrate community indices, and urbanization, as indexed using univariate measures of urbanization, e.g., percent urban land use, population density, and housing unit density (Yoder et al., 1999, 2000). Similarly, we found that a fish-IBI declined with percent urban land use and population density. However, housing unit density was a poor predictor of IBI in the current study (Tables 2 and 3). The earlier studies were based on a smaller, subset of the data analyzed here, and, even in those studies, biological integrity was generally insensitive to housing density above the relatively low density of 250 housing units/km2 . Although several authors have indicated that single measures of urbanization are unlikely to be sufficient for assessing the ecological health of urban streams (Booth and Jackson, 1997; Yoder et al., 2000; Morley and Karr, 2002; Allan, 2004; Booth et al., 2004; Coles et al., 2004), no previous study has conducted a comparison of the efficacy of univariate and multivariate approaches for the same dataset. Perhaps one useful criterion for evaluat-

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Dependent variables

Multimetric indices IBI MIWB IBI sub-metrics 1. Number of native species 2. Number of darter species 3. Number of headwater species 4. Number of cyprinid species 5. Number of sensitive species 6. Percent tolerant species 7. Percent omnivores 8. Percent insectivores 9. Percent pioneer species 10. Number of individuals 11. Percent simple lithophiles 12. Percent of individuals with deformities, eroded fins, lesions, and tumors

R2

Y-intercept

Regression coefficients for independent variables

QHEI

LPC1

LPC2

LPC3

LPC4

LPC5

.44 −47.625 (<.001) .17 −46.357 (.014)

−.025 (.004) ns

.031 (<.001) .027 (.026)

.027 (.001) .037 (.004)

ns ns

−.029 (<.001) ns ns ns

ns ns

−.038 (<.001) .232 (.007) −.025 (.042) .311 (.023)

9.399 (<.001) 8.995 (.013)

.21 .35 .29 .17 .24 .11 .17 .19 .09 .16 .12 .16

ns −.058 (<.001) ns ns −.025 (.037) ns ns ns ns ns ns .016 (.007)

.032 (.024) .043 (.013) ns ns ns ns −.040 (.027) .045 (.034) ns .124 (.001) ns −.012 (.026)

.047 (.001) .068 (<.001) ns ns ns −.082 (.001) ns .093 (<.001) ns ns ns ns

ns ns −.072 (<.001) −.043 (.007) −.029 (.016) ns .066 (.001) .062 (.006) .067 (.003) ns −.065 (.001) .016 (.004)

−.037 (.008) −.062 (<.001) −.045 (.001) ns −.042 (<.001) ns ns ns ns −.075 (.037) −.036 (.035) ns

ns ns ns ns ns ns ns .050 (.012) ns ns ns ns

−.041 (.001) −.072 (<.001) −.062 (<.001) −.048 (.002) −.036 (.001) ns ns ns ns −.130 (.001) −.042 (.020) ns

ns ns ns ns ns −16.998 (.015) −17.196 (.001) ns ns ns ns −3.475 (.025)

.131 (.638) −1.215 (.001) −.344 (.212) −.423 (.166) −.673 (.003) 88.917 (.014) 89.532 (.001) .503 (<.001) 1.513 (<.001) 2.647 (<.001) .534 (<.001) 18.054 (.025)

LPC6 LPC7

Julian day

ns ns ns ns ns ns ns ns ns ns ns ns

Statistically significant coefficients are shown with P-values in parentheses; variables excluded by stepwise selection procedure are indicated by ns (not significant).

LPC8

.509 (.001) .838 (<.001) .338 (.030) .605 (.001) .417 (.001) ns ns ns −.526 (.028) ns ns ns

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Table 7 Multiple regression results relating IBI, MIWB, and sub-metrics of IBI to principal components incorporating local zone scale aggregation

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Fig. 3. Plot of observed IBI as a function of predicted IBI values derived from multiple regression relating stream habitat quality (QHEI) and principal components combining land use and demographic variables to IBI. Symbols denote pentile groupings of residuals derived from the multiple regression. Stippled region indicates boundary region for warm water habitat (WWH) use attainment. Sites to the right of arrow labeled “max” all show IBIs sufficient for WWH attainment; sites to the left of “min” arrow failed to exceed the WWH boundary.

ing such comparisons is the relative performance of methods for identifying critical management thresholds. For example, univariate analyses of the Cuyahoga basin have indicated a threshold of 8% urban land use, above which fish communities failed to achieve Ohio EPA’s warm water habitat (WWH) use attainment criterion (Yoder et al., 1999, 2000). The current analysis indicated that sites for which the multivariate combination of land use, population, housing, and stream habitat data predicted IBIs greater than 41 all had observed IBIs exceeding the minimum value required for attainment of the warm water habitat use criterion (Fig. 3). The average percent urban land use for this group of sites, 6.5 ± 2.3% (N = 7), was indistinguishable from the 8% value based on percent urban land use alone. Seventy-four percent of sites with predicted IBIs lower than 41 failed to meet WWH use attainment. No sites with predicted IBIs below 26 achieved WWH status and the average percent urban land use for this group was 24.6 ± 4.4% (N = 50). In an analysis of streams in the Columbus, OH area, Miltner et al. (2004) report a similar upper threshold of percent urban land use (27.1%), above which stream sites failed to achieve WWH status. Hence, the multivariate approach used here identified management and assessment thresholds largely equivalent to previous analyses based on univariate approaches. However, the multivariate approach revealed interactions among landscape and demographic variables that could not be assessed with a single measure of urbanization. In particular, the importance of urban land use recedes in multivariate analyses, where other gradients and interactions among gradients emerge as more important predictors. At the sub-catchment scale, the fourth prin-

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cipal component (SPC4) most strongly represented urban land use. Although this component emerged as a significant predictor of IBI (Table 5), SPC4 accounted for only 1.8% of variance in IBI. In comparison, SPC3, which described a counter-gradient of forest versus grassland cover, accounted for 9.1% of variance in IBI. When 500 m local zone land use/land cover was entered into the analysis, urban land use receded even farther into the background. Although urban land use at sub-catchment or 500 m scale loaded significantly on several principal components, loadings were relatively low (Table 6). In the local zone analysis, urban land use made its strongest contribution to LPC1, which was even more strongly related to population and housing density (Table 6). However, this component had predicted IBI only weakly, accounting for only about 3.3% of variance in biological integrity. Components 1, 2, 3, 5, and 8 describing other aspects of land use/land cover, including percent forest, wetland, and grasslands in the sub-catchment and within the 500 m local zone, all accounted for more variance in IBI (5.6–6.5%, accounting for 24% variance in total). What accounts for the reduction in the influence of urban land use within these analyses? Certainly one important factor is that much of the area is either heavily urbanized or at least sub-urbanized to some degree, so that the effects of urbanization are pervasive but the gradient of urbanization is relatively small. Mean urban land use among the sites used in this study is relatively high (16%), even though many of the sites are outside the urban core or are found within parklands or forested ravines. This level of urbanization has been associated with substantial, and perhaps irreversible, biological degradation (Steedman, 1988; Booth and Jackson, 1997; Yoder et al., 1999). It is also likely that these analyses reflect the long history of anthropogenic disturbance within the region. Stream biota can reflect the historical legacy of past stressors and land uses long after those factors have changed (Harding et al., 1998). Northeast Ohio has been a center for commerce and industry since early in the 19th century, when development of the region was accelerated substantially with the establishment of the Ohio and Erie Canal (Cockrell, 1992). Indeed, fish in many of the region’s streams had shown evidence of substantial decline for decades prior to the timeframe of the current study (Trautman, 1981). Another advantage of the multivariate approach is that the separate effects of landscape gradients can be quantified, and may provide insight into particular stressor sources. For example, an examination of the patterns of covariation in Table 7 suggests that several gradients had separate and independent effects on different sub-metrics of the IBI. LPC5, an interaction between forest and grassland cover at the local zone scale, and LPC8, which indexed the forest–grassland interaction at the sub-catchment scale, were particularly important determinants of fish community species composition, as indicated by their effects on the number of native species, the number of darter species, and the number of headwater species. QHEI was also predictive of essentially the same suite of IBI sub-metrics. Thus, species composition within Cuyahoga tributaries is influenced by factors that, apparently, act independently at three differ-

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ent scales, i.e., sub-catchment, local zone, and stream channel. LPC1, largely a measure of population and housing density, and LPC4, an index of shrubby vegetation, were strong and independent predictors of DELT anomalies (deformities, eroded fins, lesions, and tumors) and both were also predictive of the number of sensitive species. Perhaps these axes provide a landscape signature indicative of heavily urbanized, disturbed, or industrialized sites impaired by particularly damaging pollutants. Lands surrounding industrial sites, for example, are often associated with shrubby vegetation near the stream corridor (B.M. Walton, unpublished data). 5.2. Are there interactive and scale effects among land use/demographic gradients? Our findings reinforce the notion that the mix and spatial juxtaposition of land uses within an urbanized basin are important determinants of biological integrity of streams (Wang et al., 2003). For example, the principal components that emerged as most important in explaining variability in IBI in both the subcatchment (SPC3, 9.1% of total variance in IBI) and the local zone analyses (LPC8, 6.5% of total variance in IBI) were components describing a spatial counter-gradient in forested and open, grassland cover (Fig. 2). Further, the nature of land use effects changed profoundly with spatial scale and proximity to the biological sampling point. In particular, the polarity of forest cover effects on biological integrity changed between sub-catchment and local zone scales. Whereas high forest cover within the sub-catchment overall was associated with low IBI, high forest cover within the local zone was associated with high IBI (Fig. 4; Table 7). As illustrated in Fig. 1, forest cover within the 500 m local zone often

includes riparian vegetation. Hence, the positive effect on biological integrity at this scale is consistent with general findings that riparian vegetation can buffer upland effects (Steedman, 1988; Horner et al., 1997; May et al., 1997). On the other hand, the negative impact of forest cover at the sub-catchment scale seems counter-intuitive at first glance. However, these findings are interpretable in light of current and historical patterns of land use in northeast Ohio. Many sites characterized as having high forest cover at the sub-catchment scale are also associated with high population and housing density, as well as relatively high urban land use (Fig. 4). This combination of factors characterizes older, inner-ring suburbs in the region. In these neighborhoods, the canopies of large street trees overhang houses, streets, and other impervious surfaces, and wooded parks are interspersed within densely populated residential areas (Clapham, 2003). These suburbs have a long history of urban impact on local streams. By 1900, wealthy industrialists and merchants were leaving an increasingly industrialized city-center of Cleveland to establish new sub-urban neighborhoods just beyond the city limits (Cigliano, 1991). Outmigration from the city center and from older inner-ring suburbs has continued and, in fact, has accelerated in recent decades (Bier, 1993, 2001). Within these older urban/sub-urban areas, population loss and economic decline are associated with ageing and inadequate waste water management infrastructure (Bier, 2001). In addition, the counter-gradient of forest versus grasslands at the sub-catchment scale, in combination with the positive effect of local zone forest cover on IBI, defines a landscape signature indicative of high biological integrity for the region. The sites with highest biological integrity in our dataset were those characterized by high open grassland cover (>40%) and low

Fig. 4. Characteristic land use and population densities associated with quartiles of IBI. Top panels (A) and (B) illustrate land use categories distributed among IBI quartiles at sub-catchment and 500 m local zone scales, respectively. Bottom panels illustrate population densities associated with IBI quartiles at: (C) sub-catchment and (D) 500 m local zone scales. IBI quartiles are arranged from lowest (quartile 1) to highest IBI score (quartile 4). Bars show means ± 1 standard error of the mean.

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forest cover (<20%) at the sub-catchment scale, but high forest cover (>60%) in the local zone (Fig. 4). Sites with this landscape signature (N = 33, or 14.5% of all sites) had a mean IBI of 37.52 ± 1.41, significantly higher than that of other sites within the dataset (28.11 ± .61, two-tailed t-test, t = 5.916, P < .001). This nexus of land cover values was most often associated with areas beyond the urban core where forest cover is largely within riparian strips adjacent to open parkland and/or agricultural fields. Within cities and suburbs, similar landscapes are often found in protected and managed areas, including an extensive network of regional parks and the Cuyahoga Valley National Park. Such “parkland” sites were further characterized by somewhat lower percent urban land use within the sub-catchment (parkland, 10.65 ± 2.22%, versus non-parkland, 17.05 ± 1.72%, t = 2.280, P = .025), but substantially lower percent urban land use within the 500 m local zone than sites lacking this land cover signature (parkland, 4.00 ± 1.50%, versus non-parkland, 20.87 ± 1.80%, t = 7.221, P <.001; Fig. 4). However, parkland and non-parkland sites were similar in mean QHEI (parkland, 61.91 ± 2.71, versus non-parkland, 61.61 ± .91, t = .127, P = .899). 5.3. What are the relative influences of stream habitat and landscape variables as predictors of biological integrity? Our measure of stream habitat quality in these analyses, the Qualitative Habitat Evaluation Index, was designed and calibrated as a measure of the potential for stream habitat to support healthy, native fish communities (Rankin, 1989). Hence, this variable was expected to covary significantly with IBI and MIWB. Indeed, QHEI was a significant predictor of IBI, MIWB, and a majority of the IBI sub-metrics. In this regard, the current findings are congruent with previous studies demonstrating that fish community health is associated with habitat quality (Schlosser, 1982; Roth et al., 1996). However, QHEI accounted for considerably less variability in IBI among sites than the landscape-level variables overall. For the sub-catchment level analysis, QHEI accounted for 4.8% of total variation in IBI, whereas the principal components describing landscape and demographic features combined to account for 19.5% of variance in IBI. In the analysis including variables describing the 500 m radius local zone, the landscape components combined to explain 27.3% of the variance in IBI, in comparison to 2.6% attributable to QHEI alone. Moreover, several landscape/demographic components explained more variability singly than did QHEI. For example, LPC8 alone explained three-fold more variance in IBI (8.6%) than did QHEI. Roth et al. (1996) also reported that habitat quality was no better as a predictor of fish community health than features of land cover. Overall, these findings emphasize the combined importance of conditions both within and beyond the stream channel as determinants of biological quality of streams (Booth and Jackson, 1997). We also found that QHEI was unrelated to any of the land cover or demographic variables, either alone or in combination as principal components, or when the land cover or demographic variables were aggregated at sub-catchment or local-zone scales.

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Given the links between landscape features and stream morphology, hydrology, and stream habitat quality that have been documented in a variety of studies (Richards and Host, 1994; Roth et al., 1996; MacRae and DeAndrea, 1999), this finding is noteworthy, but it is not unique to the current analyses. Wang et al. (1997, 2003) reported little or no correlation between habitat quality variables and effective impervious surface cover among urban streams in Wisconsin. How then is it possible for stream habitat quality to vary independently of land use/land cover and demography, while biological integrity covaries significantly with both stream habitat quality and landscape-level variables? We suggest that the resolution of this apparent paradox lies in the interaction of the geological and urbanization histories of the region. In many cases, streams in northeast Ohio lie within ravines, often quite deeply incised, that were formed by the retreat of the last glaciation (White and Totten, 1982). During early settlement of the region, the deeply incised terrain made transportation and communication difficult, isolated settlements, and the steep, unstable hillsides were largely unavailable for building, cultivation, or pasture land (Cockrell, 1992). Many of these ravines formed the template for parklands, including the Cuyahoga Valley National Park. Hence, these areas preserved natural features precisely because they were not useful for other purposes. Thus, the ravines, and associated parks, have provided some degree of protection from the worst effects of urbanization on the physical features of streams. Nevertheless, biological degradation may proceed inexorably through a variety of urbanization effects that degrade biota but have lesser impacts on stream habitat (Allan, 2004), including stressors that short-circuit the riparian zone, e.g., sewer outfalls, thermal heat island effects, and atmospheric deposition. Further, stream biodiversity can reflect the impacts of devastating pulse events that may not necessarily have discernable long-term effects on physical habitats. One local example is a large fire in a scrap tire yard in 1981 that released tens of thousands of liters of petroleum derivatives into the headwaters of a small stream that was otherwise largely contained within the Cuyahoga Valley National Park (Cockrell, 1992). From this perspective, complex interactions among landscape features, the historical legacy of settlement patterns, urbanization, and disturbance, all interacting with local geology, may overwhelm the effects of contemporary habitat conditions. 6. Conclusions While our findings are consistent with previous studies indicating that urban land use has a negative association with biological integrity of streams (Klein, 1979; Steedman, 1988; Roth et al., 1996; Dreher, 1997; May et al., 1997; Boward et al., 1999; Yoder et al., 2000; Morse et al., 2003; Roy et al., 2003; Miltner et al., 2004), our analyses also demonstrate that spatial interactions with other aspects of the urban landscape are important determinants of variability in stream biota. In fact, our results suggest that in regions with long histories of urban development such as northeast Ohio, other axes of landscape variability may emerge as even stronger predictors of variability in biological quality among stream sites. Further, multiple landscape features

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may have interactive effects on biological integrity varying both in magnitude and direction with spatial scale, e.g., forest cover in the current case. Our analyses emphasize that the influence of urbanization on streams is shaped by regional geological and historical contexts. Within the Cuyahoga River basin, unstable ravines of glacial origin have impeded agricultural and urban development in some stream reaches, thereby preserving natural features of riparian zones, stream habitats, and in some cases, biological integrity. As a result, a land use signature indicative of parklands with forest cover adjacent to streams is an even better predictor of high biological integrity than is the quality of in-stream habitat. Thus, our findings also indicate that efforts for preserving or improving biological integrity within urbanized streams must place at least as much emphasis on management beyond, as well as within, the stream channel. Acknowledgements We thank Stuart Schwartz, former Director of the Center for Environmental Sciences, Technology, and Policy (CESTP) at Cleveland State University, and his staff for their administrative and data management assistance on this project. We thank Elizabeth Whippo-Cline for her assistance with early stages of the project and Lester Stumpe of the Northeast Ohio Regional Sewer District for his advice and support. The project has also benefited from the work of the following graduate student assistants: Shawn Bleiler, Sonya Steckler, and Cari-Ann Hickerson. This publication was financed in part through a grant from the Ohio Environmental Protection Agency and the United States Environmental Protection Agency, under the provisions of Section 319(h) of the U.S. Clean Water Act. References Allan, J.D., 2004. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu. Rev. Ecol. Evol. Syst. 35, 257–284. Bier, T., 1993. Cuyahoga County Outmigration. Housing Policy Research Program. Levin College of Urban Affairs, Cleveland State University. Bier, T., 2001. Moving Up, Filtering Down: Metropolitan Housing Dynamics and Public Policy. The Brookings Institution, Center on Urban and Metropolitan Policy. Booth, D.B., Jackson, C.R., 1997. Urbanization of aquatic systems: degradation thresholds, stormwater detection, and the limits of mitigation. J. Am. Water Resour. Assoc. 33, 1077–1090. Booth, D.B., Karr, J.R., Schauman, S., Konrad, C.P., Morley, S.A., Larson, M.G., Burges, S.J., 2004. Reviving urban streams: land use, hydrology, biology, and human behavior. J. Am. Water Resour. Assoc. 40, 1351–1364. Boward, D., Kayzak, P., Stranko, S., Hurd, M., Prochaska, T., 1999. From the Mountains to the Sea: The State of Maryland’s Freshwater Streams. Maryland Department of Natural Resources, Monitoring and Non-tidal Assessment Division, Annapolis, MD, EPA 903-R-99-023. Cigliano, J., 1991. Showplace of America: Cleveland’s Euclid Avenue, 1850–1910. The Kent State University Press, Kent, OH. Clapham, W.B., 2003. Continuum-based classification of remotely sensed imagery to describe urban sprawl on a watershed scale. Remote Sens. Environ. 86, 322–340. Cockrell, R., 1992. A Green Shrouded Miracle: The Administrative History of Cuyahoga Valley National Recreation Area, Ohio., http://www.cr.nps.gov/history/online%5Fbooks/Cuyahoga/.

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National Conference on Tools for Urban Water Resource Management and Protection, Chicago, IL. B. Michael Walton is an associate professor in the Department of Biological, Geological and Environmental Sciences (BGES) at Cleveland State University (CSU). His research focuses on species interactions within streams and riparian ecotone communities, with emphasis on the effects of urbanization on the relationship between species diversity and ecological function. He teaches courses in ecology, environmental science, conservation biology, and physiology, and he is the Director of a U.S. National Science Foundation Research Experiences for Undergraduates program in urban stream ecology. He received a BS in zoology and a MS in biology from the George Washington University and a PhD in evolutionary biology from the University of Chicago. Mark Salling directs the Northern Ohio Data & Information Service (NODIS), an applied research center in the Maxine Goodman Levin College of Urban Affairs at CSU. The center provides data dissemination, demographic analysis, and applications in urban and geographic information systems (GIS). Mark is active in GIS, demography, and urban planning applications locally and nationally. He is the State of Ohio’s liaison to the Census Bureau on redistricting data, chairs the Cleveland region’s Census statistical areas committee, and serves on the Council of the state’s Ohio Geographically Referenced Information Program (OGRIP). Mark has taught courses on geographic information systems, urban spatial systems, statistical and computer methods, and demography. He received his PhD in geography from Kent State University. James Wyles is a research associate-senior geographic information systems (GIS) specialist and project manager for NODIS. He received an MS in geology from the University of Akron and is a certified GIS professional (GISP). Jim develops and manages GIS projects within the college and for community clients. Jim also provides and develops materials for training the GIS user community. As an adjunct faculty member, he teaches students in introductory and advanced mapping and GIS courses, including cartography and graphics, introduction to GIS, and GIS capstone. Julie Wolin is an associate professor in BGES at CSU. Her research interests involve natural variation in freshwater/wetland ecosystems and the impact of human activities on them. She utilizes stream benthos (macroinvertebrates, periphyton, and diatoms) as bioindicators and a variety of techniques, including quantitative environmental reconstruction to estimate present conditions, natural variability, identify disturbances, and determine rates of change and recovery in aquatic ecosystems. She holds BS and MS in biology (aquatic ecosystems) from Eastern Michigan University and a PhD in natural resources from the University of Michigan.