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Biological Integrity in Urban Streams: Toward Resolving
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Multiple Dimensions of Urbanization
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B. Michael Walton*a,
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Mark Sallingb,
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James Wylesb,
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Julie Wolina,
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Cleveland State University, Cleveland OH 44115;
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b
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State University, Cleveland, OH 44115;
Department of Biological, Geological, and Environmental Sciences,
Northeast Ohio Data and Information Center, Levin College of Urban Affairs, Cleveland
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November 19, 2004
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*Corresponding author
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Running head: Biological Integrity in Urban Streams
Biological Integrity in Urban Streams
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Abstract
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Most studies of urban streams have relied on single variables to characterize the degree of
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urbanization, which may not reflect interactions among features of urban landscapes
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adequately. We report on an approach to the characterization of urbanization effects on
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streams that used principal components analysis and multiple regression to explore the
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combined, interactive effects of land use/land cover, human population demography, and
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stream habitat quality on an index of biological integrity (IBI) of fish communities.
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Applied to a substantially urbanized region in northeast Ohio, USA, the analysis
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demonstrated the interactive nature of urbanization effects. Urban land use and stream
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habitat quality were significant predictors of IBI, but were no better than and, in some
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cases, poorer predictors than other gradients and interactions among gradients. High
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integrity sites were characterized by low forest cover and high grassland cover at sub-
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catchment scale, but high forest cover within a 500 m radius local zone of the sample
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point, conditions often found in protected parklands in the region. The analysis also
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indicated that variability in stream habitat quality was unrelated to landscape or
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demographic features, a result we attribute to the interaction between the geological and
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urbanization histories of the region.
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Keywords: biological integrity, fish, land use, urban streams, water quality
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1. Introduction
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Urbanization poses vexing challenges to the ecological sustainability and restoration of
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stream ecosystems. Stream habitat and biota in urban settings are often profoundly
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degraded in comparison to natural or less-impacted rural conditions, e.g., (Klein, 1979;
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Steedman, 1988; Schuler, 1994; May, et al., 1997; Boward, et al., 1999; Morley and Karr,
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2002; Morse, et al., 2003; Miltner, et al., 2004), even at modest amounts urban
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development (Weaver and Garman, 1994; Booth and Jackson, 1997). Given these
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impacts and the accelerating pace of urbanization (Cohen, 2003), there has been great
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interest in describing quantitative relationships among the intensity of urbanization,
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constraints on stream recovery, and potential thresholds of degradation imposed by urban
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development, e.g, reviews by Paul and Meyer (2001) and Allan (2004).
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However, considerable variability surrounds these general biological integrity-
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urbanization relationships, so that equivalent levels of urbanization can be associated
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with a wide range of biological indicator scores (Wang, et al., 1997; Klauda, et al., 1998).
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For example, streams sites in urban regions of Ohio can vary by more than four-fold in
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biological integrity within the same level of upstream urban land use (Yoder, et al., 2000;
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Miltner, et al., 2004). Further, slopes and thresholds of urbanization effects differ among
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urban regions (Yoder, et al., 1999, 2000; Coles, et al., 2004).
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Some of this variability is surely attributable to identifiable, allied stressors
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affecting stream sites, such as point-source pollutants and combined or sanitary sewer
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outfalls that may exert effects in addition to the generalized impacts of urbanization
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(Yoder, et al., 2000; Miltner, et al., 2004). However, significant fractions may also be
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associated with complex interactions among features of urbanized landscapes and the
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effects of urbanization within regionally-specific contexts of geology, climate, or history
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of development and anthropogenic disturbance (Allan, 2004). Such complexity is
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unlikely to be captured by any single measure of urbanization (Booth and Jackson, 1997;
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Yoder, et al., 2000; Allan, 2004; Coles, et al., 2004).
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Hence, a method for quantifying urban effects on streams that integrates multiple
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dimensions of urbanization and interactions among these factors is desirable. In this
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report, we describe such an approach and illustrate its application with an analysis of
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small stream sites in a highly urbanized region of northeastern Ohio, USA. The region
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has figured prominently in past analyses using single indicators of urbanization, e.g.,
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Yoder et al. (2000), thereby facilitating comparisons with those earlier studies. The
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analysis integrates the influences of three sets of variables characterizing the urban
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environment: major land use / land cover features; human population and housing
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density; and stream habitat quality. We compared the relative impacts of these variables
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on measures of biological integrity based on fish communities to address the following
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questions: (1) Are multivariate descriptors of urbanization better predictors of biological
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integrity than a single variable measure of urban effects, e.g., % urban land, population or
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housing density?; (2) Does a multivariate approach provide useful, additional insight into
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effects of urbanization not revealed by single measures of urbanization, e.g., spatial
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interactions among variables that mitigate or exacerbate the general effects of
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urbanization, or spatial interactions that represent regionally-specific patterns of urban
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development?; (3) Do the impacts of urbanization and the interactions among landscape
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gradients differ with spatial scale?; and (4) Are stream habitat or landscape-level
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variables better predictors of biological integrity?
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To accomplish this, we used principal components analysis to generate
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statistically-independent combinations of the original land cover/population demographic
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variables. Patterns of interaction among the original variables were interpreted from
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correlations and the magnitude and direction of factor loadings on the principal
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components. Multiple linear regression was used to test the value of the multivariate
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components for predicting biological integrity. The question of scale was addressed by
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comparing the relative influences of stream habitat and landscape-level effects on
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biological integrity, and by comparing analyses that aggregated the predictor variables at
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different scales, i.e., the catchment of the biological sampling point vs. 500 m radius
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“local zone” surrounding the biological sampling point.
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2. Study Area
The analysis described herein focuses on small stream catchments, for the most
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part 20-52 km2 drainage area, tributary to the Cuyahoga River in the Cleveland-Akron
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metropolitan area, as well as a few small streams tributary to Lake Erie within the same
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geographic area. The Cuyahoga River consists of 1963 km of stream miles draining 2100
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km2 in northeastern Ohio, USA. The catchment lies within the Erie/Ontario Lake Plain
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Ecoregion and parts of three different physiographic provinces: the Allegheny Plateau,
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till plains, and lake plains.
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The Cuyahoga River watershed is one of the most densely populated, urbanized,
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and industrialized regions of Ohio. The basin accounts for 2% of total state land, but 17%
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of Ohio’s population, approximately 1.9 million people (921 people km-2) (Rybka et al.,
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2001). While the upper reaches are dominated by agriculture, the middle and lower
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reaches are heavily influenced by urbanization. Two of Ohio’s major cities, Akron and
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Cleveland, occur within the mid- and lower segments, respectively. The river is
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characterized by an unusual, U-shaped morphology, formed during the last glacial
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recession through the merging of several formerly separate drainages. Hence, the
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Cuyahoga flows southward from its upper reaches then changes direction near Akron,
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Ohio and flows northward to its terminus at Lake Erie in Cleveland, Ohio (Fig. 1).
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Because of this shape, eastward urban and suburban expansion is encroaching upon
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headwater regions that are currently dominated by forest and agriculture.
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The region has a long history of urbanization, accelerated by completion of the
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Ohio and Erie Canal in 1825 (Cockrell, 1992). Subsequently, the Cleveland-Akron
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corridor became one of the major commercial-industrial centers of North America
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through the first half of the 20th century, with petroleum, steel, rubber, and manufacturing
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among the major industries (Cockrell, 1992; Rybka et al., 2001). The Cuyahoga Valley
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National Park, established in 1974, protects 134 km2 of the basin between Akron and
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Cleveland. However, even this parkland has a history of substantial disturbance,
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including agricultural, commercial, and industrial uses, as well as many contemporary
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impacts within and along its boundaries (Cockrell, 1992). Additional environmental
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challenges arise from economic and infrastructural decline and population loss from city-
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centers and older, inner-ring suburbs, and out-migration to the urban fringe (Bier, 1993,
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2001).
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3. Methods
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3.1 Biological Integrity and Stream Habitat Data
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Biological integrity and stream habitat data were extracted from a statewide
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database maintained by the Ohio Environmental Protection Agency (OEPA). These data
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have served as the basis for previously published analyses regarding the Cuyahoga and
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other Ohio watersheds (Yoder, et al., 1999, 2000; Miltner, et al., 2004) and are central
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component of the state-wide program for water quality assessment a. Biological data
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consisted of multimetric indices of biological integrity (IBI) and well-being (modified
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index of well-being, MIWB) based upon fish communities. The IBI is an aggregate index
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based upon 12 sub-metrics characterizing the taxonomic composition, trophic structure,
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abundance, and condition of the fish community (Karr, 1981; Karr, et al., 1986), as
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modified for Ohio streams and rivers (Ohio EPA, 1987b, 1989b; Yoder and Rankin,
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1995). The MIWB is an index that incorporates measures of abundance, biomass, and
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diversity (Gammon, 1976; Gammon, 1980; Hughes and Gammon, 1987), as modified to
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increase sensitivity to conditions particular to Ohio’s streams and rivers (Ohio EPA,
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1987b, 1989b; Yoder and Rankin, 1995). Stream habitat data were also extracted from
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the statewide database as the Qualitative Habitat Evaluation Index (QHEI). The QHEI is
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a qualitative assessment of major features of stream habitats presumed to influence the
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potential for healthy fish communities (Rankin, 1989, 1995).
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3.2 Land Use and Census Data
Land use data were extracted from a statewide land cover inventory of Ohio
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produced by the Ohio Department of Natural Resources, based upon Landsat Thematic
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Mapper Data collected in September and October 1994 (Schaal and Motsch, 1997), the
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approximate mid-point of the time frame for the biological data used here. The data were
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classified into seven land cover categories of urban, agriculture/open urban areas,
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shrub/scrub, wooded, open water, non-forested wetlands and barren. Population density
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and housing density data were obtained from the U.S. Census Bureau, 2000 Census of
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Population and Housing, Summary File 1, 2001. Age of housing data were extracted from
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the U.S. Census Bureau, 2000 Census of Population and Housing, Summary File 3, 2002.
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3.3 Delineation of sample point catchments
The basic spatial unit of analysis in this study was the catchment area for each of
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the biological sampling points. The sample-point catchment area was defined using
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digital elevation models (DEM), a vector hydrography database, and sample point
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locations. The DEM used here was extracted from the 1:24,000-scale seamless US
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Geological Service (USGS) National Elevation Dataset (NED) which was developed by
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merging USGS’s highest resolution, best quality elevation data available (NED is
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accessible on-line at gisdata.usgs.net/ned/default.asp).
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To improve accuracy of stream and catchment delineation, we used a vector
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hydrography database, the Valley Stream Segment (VST Rivers) file, to adjust the DEM.
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The VST file is based upon a 1:100,000 base map from the National Hydrography
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Dataset (NHD) from the USGS and the U.S. Environmental Protection Agency (USEPA).
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The two main sources for information for this dataset are USGS digital line graphs and
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the USEPA Reach File Version 3 (http://nhd.usgs.gov/). To a improve catchment
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delineation, raster cells were adjusted in elevation at or near the VST vector layer
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streams, thereby improving stream channel results. The adjusted raster elevation values
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were then used to create a new vector-based stream network which included only the
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streams recognized by the original hydrography stream layer but were precisely located
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in reference to the DEM and the slope, flow direction, and related information necessary
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to delineate catchment areas up stream and up slope from the sample points.
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GIS surface analysis tools were used to create catchment area polygons. Figure 1
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illustrates the points, VST Rivers hydrography layer, the revised DEM-based
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hydrography network, and the catchment polygons in a portion of the study area. In
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addition to the sample point catchments, we also delineated 500 m radius local zone sub-
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polygons for each sample point. These were defined by inscribing a 500 m radius around
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the sample point within the boundaries of the original catchment area (Fig. 1).
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Population and housing unit counts are available at the census block level.
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Because catchments and local zones split census blocks and block groups, these census
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data were estimated by areal interpolation, specifically area apportionment. This method
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apportions the data based on the relative areas of the block or block group that are
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contained in each part split by catchments or zones. Geographic (polygon) boundary files
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in computerized GIS database structure for census blocks and block groups are available
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from the Census Bureau's Topologically Integrated Geographically Encoded Referenced
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(TIGER) database.
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3.4 Statistical Procedures
The biological, land use, population demographic and stream habitat variables
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used in this study are listed in Table 1. Biological data (IBI and MIWB) served as
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dependent variables in these analyses. Also, IBI was decomposed into its 12 sub-metrics,
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and these also were analyzed as dependent variables. QHEI served as both a dependent
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and independent variable. QHEI was the dependent variable in regressions evaluating
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land use and demographic gradients as predictors of stream habitat quality, whereas
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QHEI was entered as an independent variable in regressions seeking to predict IBI, IBI
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sub-metrics, or MIWB.
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All variables were evaluated for conformance to normality and transformed, if
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necessary, using appropriate transformations. Indices, counts, and density measures were
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transformed according to log10X or log10(X+1), whereas proportion variables were arcsin
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square-root transformed. Pearson-product moment correlations and linear regression
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analyses were used to explore urbanization-stream quality relationships urbanization, as
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measured using % urban land use and population or housing density.
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To explore the contributions of landscape gradients in addition to urban land use
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and to assess the potential for interactions among landscape and demographic gradients,
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we employed principal components and multiple linear regression analyses. Principal
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components analyses reduced the dimensionality among the independent variables and
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produced gradients, i.e., principal components, which were independent and appropriate
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for multiple regression analysis. Principal components were obtained for the land
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use/land cover and population demography dataset. Distance of the sample point from
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stream terminus and area of the sample-point catchments were also included within these
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data since these two spatial variables were correlated with several of the landscape and
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demographic features. Only components with eigenvalues > 1 were retained for
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subsequent analyses. Principal components were interpreted based upon magnitude of
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factor loadings and inspection of bivariate plots of components against the original
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variables.
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The predictive value of principal components for biological integrity or habitat
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quality was assessed using stepwise multiple regression (forward and backward selection
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procedures, P=0.05 for entry or removal from the model). R2-change was used to assess
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and rank the proportional contribution of each significant predictor to overall variance
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explained by regression models. Julian date of the biological sample was entered into
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these analyses to adjust for temporal changes in biological integrity. All statistical
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procedures were conducted using SPSS for Windows, Version 11.
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4. Results
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4.1 Single Indices of Urbanization
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The index of biological integrity (IBI) was significantly correlated with variables
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that have been commonly used as measures of urbanization, e.g., % urban land use and
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population density, although the magnitudes of these correlations were generally low, and
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several other land use variables were more strongly correlated with IBI than urban land
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use, e.g., grassland cover (Table 2). Percent urban land use and population density were
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also found to be significant predictors, in combination with QHEI, of IBI by multiple
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linear regression analysis, although only population density retained statistical
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significance as a predictor of IBI in regressions that included two spatial covariates of
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IBI, distance from terminus and catchment area (Table 3).
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4.2 Multivariate Indices of Urbanization: Sub-catchment Scale Analyses
Principal components based upon variables aggregated at the sample point subcatchment scale yielded four components with eigenvalues > 1, which accounted for
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67.5% of total variation in the data set. Factor loadings for the components are shown in
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Table 4, where principal components are labeled SP1, SPC2, etc., to indicate that they are
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based upon sub-catchment scale data. SPC1 was most strongly influenced by catchment
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area with lesser loadings associated with housing density, % barren, and % open water
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cover. SPC2 increased with % wetland and shrub/scrubland cover. SPC3 described a
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contrast between % forest and % grass/park/field cover (Fig. 2). SPC4 was strongly,
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positively correlated with urban land cover.
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No component was correlated significantly with QHEI (Table 4), whereas two
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components, SPC1 and SPC3, were correlated significantly with IBI. Three principal
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components (1, 3, and 4) were significant predictors IBI according to stepwise, multiple
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linear regression (Table 5). IBI increased with SPC1, but declined with SPC3 and SPC4.
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Multiple regression also indicated that IBI increased significantly with QHEI and julian
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day (Table 5). The modified index of well-being (MIWB) was also significantly related
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to principal components and IBI. MIWB also increased with SPC1, as well as SPC2
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(Table 5). As in the case for IBI, MIWB increased with QHEI and julian day.
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The multiple regression relating principal components to IBI had a substantially
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higher coefficient of determination (R2) than any of the regressions relating principal
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components to submetrics of the IBI (Table 5). Nevertheless, each of the principal
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components was a significant predictor of at least two of the sub-metrics of the IBI. SPC3
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showed the highest number of significant coefficients (it was a predictor of 6 of 12 sub-
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metrics, range of P-values = 0.017 - <.001, Table 5). This component was negatively
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related, generally, to sub-metrics representative of species richness and community
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composition (e.g., number of native species, number of darter species, number of
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sensitive species) and was positively related to a measure of fish condition, the proportion
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of individuals with deformities, eroded fins, lesions, or tumors (Table 5). SPC2 was a
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significant predictor of 5 of 12 submetrics, despite failing to emerge as a significant
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predictor of overall IBI. SPC2 was positively related to two measures of community
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composition (metrics of native and darter species), two measures of trophic structure (%
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omnivores and insectivores), and was negatively related to % lithophile species. SPC1
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was positively related to 4 of 12 submetrics which were indicative of abundance or
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species richness and community composition (e.g., number of native species, number of
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darter species, number of insectivores). SPC4 was negatively related to 2 submetrics (%
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insectivores and number of individuals). QHEI was a significant predictor of 7 of 12
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submetrics, which were largely measures of species richness and community
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composition. Number of darter species increased during the time period within this data
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set, while proportions of tolerant species, omnivores, and individuals with deformities,
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eroded fins, lesions, and tumors declined (Table 5)
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4.3 Multivariate Indices of Urbanization: Effects of Scale
The addition of variables describing land use/land cover within the 500 m radius
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neighborhood of the biological sampling point resulted in identification of eight principal
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components with eigenvalues > 1, accounting for 80% of the total variance within the
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dataset. Factor loadings for this analysis are shown in Table 6, where principal
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components are labeled “LPC” to indicate that 500 m local zone data were included in
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their calculation.
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LPC1 was strongly related to population and housing density at both 500 m local
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zone and the overall sample sub-catchment scales. LPC1 also increased with urban land
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use at both scales. LPC2 was most strongly related to basin area at the sub-catchment
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scale. LPC3 was strongly related to wetland cover at both the sub-catchment and 500 m
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local scale. LPC4 was principally a descriptor of shrub/scrub cover at the two scales.
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LPC5 decreased strongly with increasing forest cover in the 500 m zone, but increased
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significantly with increasing grass/park/field cover and urban land cover in the 500 m
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local zone. LPC6 was essentially a descriptor of barren lands at both scales. LPC7 was
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strongly related to open water cover at the 500 m scale, and to a lesser extent to water at
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the sub-catchment scale. LPC8 described a contrast between grass/park/field cover and
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forest cover at the sub-catchment scale.
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As in the analysis conducted at the sub-catchment scale exclusively, none of these
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components were correlated with QHEI (Table 6). Six of the eight components (LPC1,
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LPC2, LPC3, LPC4, LPC5, and LPC8) were correlated significantly with IBI (Table 6),
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but only five of these (LPC4 excluded) was predictive of IBI based upon stepwise
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multiple regression analysis (Table 7). LPC2 and LPC3 were predictive of MIWB (Table
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7). IBI and MIWB also increased with QHEI and julian day.
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Once again, the multiple regression predicting IBI had a substantially higher
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coefficient of determinantion (R2 = .44) than regressions for each of the IBI sub-metrics
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(range of R2 = .09 - .35), although each of the sub-metrics showed a significant
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regression with at least one of the principal components. In general, sub-metrics of the
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IBI responded to the same gradients as did overall IBI. However, LPC4, which did not
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emerge as a significant predictor of IBI, was associated 6 of 12 submetrics. LPC4 was
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negatively associated with measures of species richness and community composition and
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trophic structure, but positively associated with percent of individuals with deformities,
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eroded fins, lesions and tumors (Table 7). LPC7, which also showed no predictive value
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for IBI overall, was positively associated with the proportion of insectivores in samples.
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5. Discussion
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5.1 Are multivariate measures more informative than univariate measures of
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urbanization?
Several authors have indicated that single measures of urbanization are unlikely to
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be sufficient for assessing the ecological health of urban streams (Booth and Jackson,
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1997; Karr and Chu, 1999; Yoder, et al., 2000; Morley and Karr, 2002), although no
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previous study has conducted a comparison of the relative power of bivariate and
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multivariate approaches for the same dataset. Previous analyses for northeast Ohio based
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upon a subset of the data used here found significant, negative relationships between
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urban land use and biological integrity based upon fish and invertebrate community
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quality and that a threshold for significant degradation of fish communities, as measured
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by IBI, occurred at 8% urban land use within the Cuyahoga River basin (Yoder, et al.,
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1999, 2000). Similarly, we found that IBI declined with % urban land use or population
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density (Tables 2 and 3, Fig. 3). Further, sites for which the multivariate combination of
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land use, population, housing, and stream habitat data predicted IBIs greater than 41 all
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had observed IBIs exceeding the minimum value required for attainment of Ohio EPA
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warm water habitat (WWH) use criterion (Fig. 4). The average % urban land use for this
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group of sites, 6.5% ± 2.3% (N= 7) was essentially indistinguishable from the 8%
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threshold value based on % urban land use alone. The majority of sites with predicted
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IBIs lower than 41 failed to meet WWH use attainment and showed poor IBIs overall. No
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sites with predicted IBIs below 26 achieved WWH status and the average % urban land
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use for this group was 24.6% ± 4.4% (N = 50). In an analysis of streams in the Columbus,
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Ohio area, Miltner et al. (2004) report a similar upper threshold of % urban land use
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(27.1%), above which stream sites failed to achieve WWH status.
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Hence, the multivariate approach used here identified management and
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assessment thresholds largely equivalent to previous analyses based on bivariate
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approaches. However, the multivariate approach revealed interactions among landscape
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and demographic variables that could not be assessed with a single measure of
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urbanization. In particular, the importance of urban land use recedes in multivariate
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analyses, where other gradients and interactions among gradients emerge as more
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important predictors. At the sub-catchment scale, the fourth principal component (SPC4)
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most strongly represented urban land use. Although this component emerged as a
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significant predictor of IBI (Table 5), SPC4 accounted for only 1.8% of variance in IBI.
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In comparison, SPC3, which described a counter-gradient of forest versus grassland
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cover, accounted for 9.1% of variance in IBI.
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When 500 m local zone land use/land cover was entered into the analysis, urban
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land use receded even farther into the background. Although urban land use at sub-
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catchment or 500 m scale loaded significantly on several principal components, loadings
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were relatively low (Table 6). In the local zone analysis, urban land use made its
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strongest contribution to LPC1, which was even more strongly related to population and
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housing density (Table 6). However, this component had predicted IBI only weakly,
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accounting for only about 3.3% of variance in biological integrity. Components 1, 2, 3, 5,
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and 8 describing other aspects of land use/land cover, including percent forest, wetland,
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and grasslands in the sub-catchment and within the 500 m local zone, all accounted for
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more variance in IBI (5.6-6.5%, accounting for 24% variance in total).
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What accounts for the reduction in the influence of urban land use within these
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analyses? Certainly one important factor is that much of the area is either heavily
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urbanized or at least suburbanized to some degree, so that the effects of urbanization are
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pervasive but the gradient of urbanization is relatively small. Mean urban land use
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among the sites used in this study is relatively high (16%), even though many of the sites
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are outside the urban core or are found within parklands or forested ravines. This level of
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urbanization has been associated with substantial, and perhaps irreversible, biological
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degradation (Steedman 1988, Booth and Jackson 1997, Yoder et al.1999).
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It is also likely that these analyses reflect the long history of anthropogenic
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disturbance within the region. Stream biota can reflect the historical legacy of past
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stressors and land uses long after those factors have changed (Harding, et al., 1998).
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Northeast Ohio has been a center for commerce and industry since early in the 19th
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century, when development of the region was accelerated substantially with the
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establishment of the Ohio and Erie Canal (Cockrell, 1992). Indeed, fish in many of the
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region’s streams had shown evidence of substantial decline for decades prior to the
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timeframe of the current study (Trautman, 1981).
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5.2 Are there interactive and scale effects among land use/demographic gradients?
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Our findings reinforce the notion that the mix and spatial juxtaposition of land
392
uses within an urbanized basin are important determinants of biological integrity of
393
streams (Wang, et al., 2003). For example, the principal components that emerged as
394
most important in explaining variability in IBI in both the sub-catchment (SPC3, 9.1 % of
395
total variance in IBI) and the local zone analyses (LPC8, 6.5 % of total variance in IBI)
396
were components describing a spatial counter-gradient in forested and open, grassland
397
cover (Fig. 2).
398
Further, the nature of land use effects changed profoundly with spatial scale and
399
proximity to the biological sampling point. In particular, the polarity of forest cover
400
effects on biological integrity changed between sub-catchment and local zone scales.
401
Whereas high forest cover within the sub-catchment overall was associated with low IBI
402
value, high forest cover within the local zone was associated with high IBI (Fig. 3, Table
403
7). Since forest cover in the local zone may represent riparian vegetation in large part,
404
the positive effect on biological integrity at this scale is consistent with general findings
405
that riparian vegetation can buffer upland effects (Steedman, 1988; Horner, et al., 1997;
406
May, et al., 1997). On the other hand, the negative impact of forest cover at the sub-
407
catchment scale seems counterintuitive at first glance.
408
However, these findings are interpretable in light of current and historical patterns
409
of land use in northeast Ohio. Many sites characterized as having high forest cover at the
410
sub-catchment scale are also associated with high population and housing density, as well
411
as relatively high urban land use (Fig. 3). This combination of factors characterizes
412
older, inner-ring suburbs in the region. In these neighborhoods, the canopies of large
Biological Integrity in Urban Streams
Walton et al. -19
413
street trees overhang houses, streets and other impervious surfaces, and wooded parks are
414
interspersed within densely populated residential areas. These suburbs have a long history
415
of urban impact on local streams. By 1900, wealthy industrialists and merchants were
416
leaving an increasingly industrialized city-center of Cleveland to establish new suburban
417
neighborhoods just beyond the city limits (Cigliano, 1991). Out-migration from the city
418
center and from older inner-ring suburbs has continued and, in fact, has accelerated in
419
recent decades (Bier 1993, 2001). Within these older urban/suburban areas, population
420
loss and economic decline are associated with ageing and inadequate waste water
421
management infrastructure (Bier, 2001).
422
In addition, the counter-gradient of forest vs. grasslands at the sub-catchment
423
scale, in combination with the positive effect of local zone forest cover on IBI, defines a
424
landscape signature indicative of high biological integrity for the region. The sites with
425
highest biological integrity in our dataset were those characterized by high open
426
grassland cover and low forest cover at the sub-catchment scale, but high forest cover in
427
the local zone (Fig. 3). This nexus of land cover categories is most often found in areas
428
beyond the urban core where forest cover is associated with riparian strips adjacent to
429
open parkland and/or agricultural fields. Within cities and suburbs, similar landscapes are
430
found in protected and managed areas, including an extensive network of regional parks
431
and the Cuyahoga Valley National Park.
432
433
5.3 Are stream habitat or landscape variables better predictors of biological integrity?
434
Our measure of stream habitat quality in these analyses, the Qualitative Habitat
435
Evaluation Index (QHEI), was designed and calibrated as a measure of the potential for
Biological Integrity in Urban Streams
Walton et al. -20
436
stream habitat to support healthy, native fish communities (Rankin, 1989). Hence, this
437
variable was expected to covary significantly with IBI and MIWB. Indeed, QHEI was a
438
significant predictor of IBI, MIWB, and a majority of the IBI sub-metrics. In this regard,
439
the current findings are congruent with previous studies demonstrating that fish
440
community health is associated with habitat quality (Schlosser, 1982; Roth, et al., 1996).
441
However, QHEI was a poorer predictor of IBI than were landscape variables
442
overall. For the sub-catchment level analysis, QHEI accounted for 4.8 % of total variation
443
in IBI, whereas the principal components describing landscape and demographic features
444
combined to account for 19.5% of variance in IBI. In the analysis including variables
445
describing the 500 m radius local zone, the landscape components combined to explain
446
27.3% of the variance in IBI, in comparison to 2.6% attributable to QHEI alone.
447
Moreover, several landscape/demographic components explained more variability singly
448
than did QHEI. For example, LPC 8 alone explained 3-fold more variance in IBI (8.6%)
449
than did QHEI. Roth et al. (1996) also reported than habitat quality was no better as a
450
predictor of fish community health than features of land cover. Overall, these finding
451
emphasize the combined importance of both stream channel and conditions within the
452
uplands as determinants of biotic quality of streams (Booth and Jackson, 1997).
453
We also found that QHEI was unrelated to any of the land cover or demographic
454
variables, either alone or in combination as principal components, or when the land cover
455
or demographic variables were aggregated at sub-catchment or local-zone scales. Given
456
the links between landscape features and stream morphology, hydrology, and stream
457
habitat quality that have been documented in a variety of studies (Richards and Host,
458
1994; Roth, et al., 1996; MacRae and DeAndrea, 1999), this finding is noteworthy, but it
Biological Integrity in Urban Streams
Walton et al. -21
459
is not unique to the current analysis. Wang et al. (1997, 2003) reported little or no
460
correlation between habitat quality variables and effective impervious surface cover
461
among urban streams in Wisconsin. How then is it possible for stream habitat quality to
462
vary independently of land use/land cover and demography, while biological integrity
463
covaries significantly with both stream habitat quality and landscape level variables?
464
We suggest that the resolution of this apparent paradox lies in the interaction of
465
the geological and urbanization histories of the region. In many cases, streams in
466
northeast Ohio lie within ravines, often quite deeply incised, that were formed by the
467
retreat of the last glaciation (White and Totten, 1982). During early settlement of the
468
region, the deeply incised terrain made transportation and communication difficult,
469
isolated settlements, and the steep, unstable hillsides were largely unavailable for
470
building, cultivation or pasture land (Cockrell, 1992). Many of these ravines formed the
471
template for city and suburban parklands, including the Cuyahoga Valley National Park.
472
Hence, these areas preserved natural features precisely because they were not useful for
473
other purposes. Thus, the ravines, and associated parks, have provided some degree
474
protection from the worst effects of urbanization on the physical features of streams.
475
Nevertheless, biological degradation may proceed inexorably through a variety of
476
urbanization effects that degrade biota but have lesser impacts on stream habitat (Allan,
477
2004), including stressors that short-circuit the riparian zone, e.g., sewer outfalls, thermal
478
heat island effects, and atmospheric deposition. Further, stream biodiversity can reflect
479
the impacts of devastating pulse events that may not necessarily have discernable long
480
term effects on physical habitats. One local example is a large fire in a scrap tire yard in
481
1981 that released tens of thousands of liters of petroleum derivatives into the headwaters
Biological Integrity in Urban Streams
Walton et al. -22
482
of a small stream that was otherwise largely contained within the Cuyahoga National
483
Park (Cockrell, 1992). In these geological and historical contexts, contemporary
484
assessments of stream habitat for this region may provide only limited guidance
485
regarding the potential for streams to support healthy biological communities.
486
487
488
6. Conclusions
While our findings are consistent with previous studies indicating that urban land
489
use has a negative association with biological integrity of streams (Klein, 1979;
490
Steedman, 1988; Roth, et al., 1996; Dreher, 1997; May, et al., 1997; Boward, et al., 1999;
491
Yoder, et al., 2000; Morse, et al., 2003; Roy, et al., 2003; Miltner, et al., 2004), this
492
analysis also demonstrates that spatial interactions with other aspects of the urban
493
landscape are important determinants of variability in stream biota. In fact, our results
494
suggest that in regions with long histories of urban development such as northeast Ohio,
495
other axes of landscape variability may emerge as even stronger predictors of variability
496
in biological quality among stream sites. Further, multiple landscape features may have
497
interactive effects on biological integrity which may vary both in magnitude and direction
498
with spatial scale, e.g., forest cover in the current case.
499
Our analysis also emphasizes that the influence of urbanization on streams is
500
shaped by regional geological and historical contexts. Within the Cuyahoga River basin,
501
unstable ravines of glacial origin have impeded agricultural and urban development in
502
some stream reaches, thereby preserving natural features of riparian zones and stream
503
habitats, but not necessarily biological integrity. Rather, in northeastern Ohio, land use
Biological Integrity in Urban Streams
Walton et al. -23
504
signatures indicative of parklands are better predictors of biological integrity of fish
505
communities than measures of stream habitat quality.
506
507
Acknowledgements
508
We a thank Stuart Schwartz, Director of the Center for Environmental Sciences,
509
Technology, and Policy (CESTP) at Cleveland State University, and his staff for their
510
administrative and data management assistance on this project. We thank Elizabeth
511
Whippo-Cline for her assistance with early stages of the project and Lester Stumpe of the
512
Northeast Ohio Regional Sewer District for his advice and support. The project has also
513
benefited from the work of the following graduate student assistants: Shawn Bleiler,
514
Sonya Steckler and Cari-Ann Hickerson. This project was financed through a grant from
515
the Ohio Environmental Protection Agency and the United States Environmental
516
Protection Agency, under the provisions of Section 319(h) of the U.S. Clean Water Act,
517
and through the U.S.E.P.A. National Risk Management Laboratory.
518
519
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Biological Integrity in Urban Streams
Walton et al. -29
639
Table 1. List and summary statistics for variables used in the current analysis. Also listed are shortened variable
names used in subsequent tables.
Sub-Catchment Scale
500 m Radius Local Zone
Abbreviated
Variable
N Mean  S.E.M. Min - Max Mean  S.E.M. Min - Max
Name
Biological/Habitat
Variables
IBI
227
12.00 – 50.00
29.48  .6
MIWB
227
0 – 9.02
5.54  .12
IBI Sub-metrics
227
1. Number of native species
0 – 24
10.89  .29
2. Number of darter species
0–6
1.30  .10
3. Number of headwater
0–6
.97  .06
species
4. Number of cyprinid
0–9
4.04  .14
species
5. Number of sensitive
0–3
.23  .04
species
6. % of tolerant species
0 – 100
59.70  1.76
7. % omnivores
0 – 100
26.55  1.37
8. % insectivores
0 – 92.31
27.21  1.55
9. % pioneer species
0 – 100
33.04  1.48
10. Number of individuals
39
- 6523
731.89  56.86
11. % simple lithophiles
0 – 82.98
30.76  1.27
12. % of individuals with
deformities, eroded fins,
0 – 15.43
1.00  .14
lesions, or tumors
QHEI
165 61.66  .89
25.00 – 86.50
Land Use/Demographic
Variables
% Urban
Urban
227 16.12  1.51
0-100
0 – 99.23
18.42  1.60
% Open Grassland /
Parkland / Agricultural
Grass
227 37.75  1.91
0-100
0 – 94.45
23.84  1.42
Fields
% Shrub / Scrub
Shrub
227 8.73  1.08
0-86.13
0 – 28.33
3.67  .35
% Non-forested Wetland
Wetland 227
0-100
0 – 58.15
9.00  .96
4.93  .52
% Open Water
Water
227
0-18.81
0 – 10.16
.43  .12
.11  .05
% Forested
Forest
227 27.87  1.95
0-100
0 – 100
43.98  1.69
% Barren
Barren
227
0-3.16
0 – 6.06
.07  .02
.05  .03
Population Density (per
5130.20 
Pop
227
0 – 133772.57 326.73  29.14 0 – 2320.92
km2)
1157.28
2329.72 
Housing Density (per km2)
House
227
0 – 71217.05 39.92  4.31
0 - 378
544.61
Catchment Area (km2)
Area
227 5.06  1.12
.10 – 84.50
Distance from Terminus
Distance 227
0-46.67
7.97  .69
(km)
Biological Integrity in Urban Streams
Walton et al. -30
Table 2. Pearson product moment correlations for variables aggregated at sub-catchment scale. * .05>P>.01, **.01>P>.001, *** P<.001
IBI
MIWB
QHEI
Barren
Grass
Shrub
Urban
Water
Wetland
Forest
Pop
House
Distance
MIWB
.570***
QHEI
.161*
.153*
Barren
.093
.078
.015
Grass
.312***
.069
-.026
.049
Shrub
-.016
.086
-.044
.007
-.227**
Urban
-.153**
-.037
.046
.063
-.329***
-.059
Water
.263***
.119*
-.034
.135*
.123*
-.097
-.115*
Wetland
.156**
.221**
.035
-.074
-.208**
.329***
-.117*
.092
Forest
-.180**
-.138*
.041
.084
-.384***
-.351***
-.286***
.024
-.322***
Pop
-.152**
.087
-.021
.055
-.283***
-.203**
.308***
-.035
-.139*
.281***
House
-.064
.046
-.025
-.107
-.235***
-.209**
.404***
-.087
-.185**
-.034
.315***
Distance
-.338***
-.034
-.086
-.053
-.173**
.213**
-.028
-.012
.062
-.010
.072
-.097
Area
.340***
.162**
.040
.422***
.067
-.113*
-.082
.396***
-.047
.355***
.101
.816***
-.158**
Biological Integrity in Urban Streams
Walton et al. -31
Table 3. Multiple linear regressions relating common measures of “urbanization” (%
urban land use, housing density, population density) to IBI. Regressions are presented
both with and without the spatial co-factors, distance from terminus and catchment area.
Regression Statistics
Variable
Coefficient
Std. Error
P-value
R2
Regressions excluding distance from terminus and catchment area
Y-intercept
1.086
.141
<.001
.055
% Urban Land Use
-.069
.031
.026
QHEI
.238
.107
.028
Y-intercept
Housing Density
QHEI
1.024
-.022
.294
.235
.017
.129
<.001
.213
.024
.068
Y-intercept
1.181
.193
<.001
Population Density
.039
.014
.005
QHEI
.220
.014
.040
Regressions including distance from terminus and catchment area
Y-intercept
1.028
.187
<.001
% Urban Land Use
-.049
.030
.102
QHEI
.202
.101
.046
Distance from Terminus
-.074
.020
<.001
Catchment Area
.026
.008
.001
.073
Y-intercept
Housing Density
QHEI
Distance from Terminus
Catchment Area
.923
-.016
.279
-.084
.027
.224
.016
.119
.024
.010
<.001
.321
.022
.001
.005
.225
Y-intercept
Population Density
QHEI
Distance from Terminus
Catchment Area
1.085
-.041
.191
-.066
.032
.184
.013
.098
.020
.003
<.001
.002
.002
.001
<.001
.219
.184
Biological Integrity in Urban Streams
Walton et al. -32
Table 4. Factor loadings, % of total variance, and correlation with QHEI and IBI for
principal components obtained from sub-catchment scale analysis. Only loadings that
were significant at P < 0.01 are shown. Pearson product-moment correlations with IBI
and QHEI are shown with P-values in parentheses.
Principal Components
SPC1
SPC2
SPC3
SPC4
Original Variables
Barren
.616
----.142
Grass/Parkland
---.292
-.766
-.320
Shrub/Scrub
--.787
----Urban
------.938
Water
.597
.132
-.151
-.234
Wetland
--.757
----Forest
.185
-.458
.706
-.381
Population Density
.312
-.283
.558
.450
Housing Density
.777
-.350
.404
.144
Catchment Area
.894
-.132
.141
--Distance from Terminus
-.178
.365
.414
--% of Total Variance
Correlation with QHEI
Correlation with IBI
26.7
16.1
13.5
11.2
.024 (.761)
.353 (<.001)
-.030 (.700)
.035 (.595)
.001 (.991)
-.369 (<.001)
.034 (.666)
.112 (.093)
Biological Integrity in Urban Streams
Walton et al. -33
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. Coefficients shown with P-values in parentheses; variables excluded by stepwise
selection procedure are indicated by ns.
Dependent Variables
Regression Coefficients for Independent Variables
SPC
SPC
SPC
SPC
Multimetric Indices
R2
Y-intercept
QHEI
Julian Day
1
2
3
4
-59.528
.034
-.039
-.018
.322
11.666
IBI
.39
ns
(<.001)
(<.001)
(<.001)
(.030)
(<.001)
(<.001)
-57.996
.034
.033
.382
11.218
MIWB
.17
ns
ns
(.002)
(.005)
(.010)
(.006)
(.002)
IBI Sub-Metrics
.015
.048
.051
-.035
.573
1. Number of native species
.22
ns
ns
(.957)
(.001)
(.001)
(.017)
(<.001)
-57.535
.051
.063
-.061
1.050
10.800
2. Number of darter species
.30
ns
(.035)
(.003)
(.001)
(.001)
(<.001)
(.040)
-.525
-.065
.434
3. Number of headwater species
.13
ns
ns
ns
ns
(.079)
(<.001)
(.010)
-.546
-.049
.671
4. Number of cyprinid species
.13
ns
ns
ns
ns
(.085)
(.003)
(<.001)
-.800
-.049
.488
5. Number of sensitive species
.17
ns
ns
ns
ns
(.001)
(<.001)
(<.001)
105.988
-20.292
6. % tolerant species
.05
ns
ns
ns
ns
ns
(.004)
(.004)
110.476
.050
-21.239
7. % omnivores
.12
ns
ns
ns
ns
(<.001)
(.010)
(<.001)
.499
.061
.096
-.051
8. % insectivores
.18
ns
ns
ns
(<.001)
(.004)
(<.001)
(.024)
1.439
-.486
9. % pioneer species
.03
ns
ns
ns
ns
ns
(.001)
(.044)
2.632
.121
-.121
10. Number of individuals
.12
ns
ns
ns
ns
(<.001)
(.001)
(.002)
-.223
-.053
.434
11. % simple lithophiles
.07
ns
ns
ns
ns
(.552)
(.008)
(.040)
12. % of individuals with deformities, eroded
23.080
.018
-4.445
.12
ns
ns
ns
ns
fins, lesions, and tumors
(.005)
(.002)
(.005)
Biological Integrity in Urban Streams
Walton et al. -34
Table 6. Factor loadings, % 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. Only loadings that were significant at P < 0.01 are shown. Pearson product-moment correlations with IBI and QHEI
are shown with P-values in parentheses.
Principal Components
Original Variables
LPC1
LPC2
LPC3
LPC4
LPC5
LPC6
LPC7
LPC8
Sub-Catchment Scale
Barren
--.361
------.783
.142
--Grass/Parkland
-.328
.205
-.362
-----.173
.177
-.706
Shrub/Scrub
---.159
.235
.841
--------Urban
.549
-.325
.152
----.403
-.209
-.212
Water
--.316
.213
-.141
----.643
-.157
Wetland
----.852
.148
--------Forest
--.472
-.241
-.192
------.783
Housing Density
.550
.729
------------Population Density
.896
.209
----------.144
Catchment Area
--.920
------------Distance from Terminus
---.228
-.145
.553
.194
--.378
.268
Local Scale
Barren @ 500 m
----------.795
----Grass/Parkland @ 500 m
.426
.213
--.156
.680
.190
-.157
-.211
Shrub/Scrub @ 500 m
--.202
.238
.816
--------Urban @ 500 m
.544
-.222
-.430
-.267
.392
----.230
Water @ 500 m
--.316
--------.808
--Wetland @ 500 m
--.205
.786
.213
----.251
--Forest @ 500 m
---------.953
-----.147
Housing Density @ 500 m
.871
.247
-.150
----------Population Density @ 500 m
.912
--------------% of Total Variance
21.7
14.6
11.2
9.3
6.6
6.2
5.4
4.8
Correlation with QHEI
-.019 (.807)
-.012 (.875)
.073 (.350)
-.122 (.120)
-.106 (.178)
.067 (.394)
-.005 (.953)
.056 (.479)
Correlation with IBI
-.195(.003)
.290(<.001)
.216(.001)
-.132(.048)
-.265(<.001)
.036(.592)
.031(.639)
-.319(<.001)
Biological Integrity in Urban Streams
Walton et al. -35
Table 7. Multiple regression results relating IBI, MIWB, and sub-metrics of IBI to principal components incorporating local zone scale aggregation. Coefficients
shown with P-values in parentheses; variables excluded by stepwise selection procedure are indicated by ns.
Dependent Variables
Regression Coefficients for Independent Variables
YLPC
LPC
LPC
LPC
LPC
LPC
Julian
2
Multimetric Indices
R
LPC 6 LPC 7
QHEI
intercept
1
2
3
4
5
8
Day
-47.625
-.025
.031
.027
-.029
-.038
.232
9.399
IBI
.44
ns
ns
ns
(<.001)
(.004)
(<.001)
(.001)
(<.001)
(<.001)
(.007)
(<.001)
-46.357
.027
.037
-.025
.311
8.995
MIWB
.17
ns
ns
ns
ns
ns
(.014)
(.026)
(.004)
(.042)
(.023)
(.013)
IBI Sub-Metrics
.131
.032
.047
-.037
-.041
.509
1. Number of native species
.21
ns
ns
ns
ns
ns
(.638)
(.024)
(.001)
(.008)
(.001)
(.001)
-1.215
-.058
.043
.068
-.062
-.072
.838
2. Number of darter species
.35
ns
ns
ns
ns
(.001)
(<.001)
(.013)
(<.001)
(<.001)
(<.001) (<.001)
3. Number of headwater
-.344
-.072
-.045
-.062
.338
.29
ns
ns
ns
ns
ns
ns
species
(.212)
(<.001)
(.001)
(<.001)
(.030)
-.423
-.043
-.048
.605
4. Number of cyprinid species
.17
ns
ns
ns
ns
ns
ns
ns
(.166)
(.007)
(.002)
(.001)
-.673
-.025
-.029
-.042
-.036
.417
5. Number of sensitive species
.24
ns
ns
ns
ns
ns
(.003)
(.037)
(.016)
(<.001)
(.001)
(.001)
88.917
-.082
-16.998
6. % tolerant species
.11
ns
ns
ns
ns
ns
ns
ns
ns
(.014)
(.001)
(.015)
89.532
-.040
.066
-17.196
7. % omnivores
.17
ns
ns
ns
ns
ns
ns
ns
(.001)
(.027)
(.001)
(.001)
.503
.045
.093
.062
.050
8. % insectivores
.19
ns
ns
ns
ns
ns
ns
(<.001)
(.034)
(<.001)
(.006)
(.012)
1.513
.067
-.526
9. % pioneer species
.09
ns
ns
ns
ns
ns
ns
ns
ns
(<.001)
(.003)
(.028)
2.647
.124
-.075
-.130
10. Number of individuals
.16
ns
ns
ns
ns
ns
ns
ns
(<.001)
(.001)
(.037)
(.001)
.534
-.065
-.036
-.042
11. % simple lithophiles
.12
ns
ns
ns
ns
ns
ns
ns
(<.001)
(.001)
(.035)
(.020)
12. % of individuals with
18.054
.016
-.012
.016
-3.475
deformities, eroded fins,
.16
ns
ns
ns
ns
ns
ns
(.025)
(.007)
(.026)
(.004)
(.025)
lesions, and tumors
Biological Integrity in Urban Streams
Walton et al. -36
Figure Captions
Fig. 1. A. Map of Cuyahoga River basin showing locations of IBI sample points. B. (Insert)
Example of IBI sample point catchment and 500 m local zone delineations. Land cover
categories are shown for local zones.
Fig. 2. Percent forest cover (closed circles) and percent grassland cover (open circles),
arcsin-square root transformed, as a function the third principal component at sample point
sub-catchment scale (SPC3).
Fig. 3. 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 subcatchment and local zone scales, respectively. Bottom panels illustrate population densities
associated with IBI quartiles at (C) sub-catchment and (D) local zone scales. IBI quartiles
are arranged from lowest (quartile 1) through highest IBI score (quartile 4). Bars show
means ± 1 standard error of the mean.
Fig. 4. 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.
Biological Integrity in Urban Streams
Walton et al. -37
A.
Lake Erie
LAKE
LAKE
GEAUGA
GEAUGA
Cleveland
Cleveland
Cuyahoga
Cuyahoga River
River Basin
Basin
CUYAHOGA
CUYAHOGA
MEDINA
MEDINA
PORTAGE
PORTAGE
Akron
Akron
Legend
IBI Sample
Stream
Cuyahoga Watershed
0
SUMMIT
SUMMIT
5
10
kilometers
County
B.
0
500
1,000
meters
Legend
Land Use
IBI Sample
Stream
IBI Catchment
500 Meter Local Zone
Urban
Agriculture/ Open Urban Area
Shrub/ Scrub
Wooded
Open Water
Non Forested Wetlands
Biological Integrity in Urban Streams
Walton et al. -38
Biological Integrity in Urban Streams
Walton et al. -39
Biological Integrity in Urban Streams
Walton et al. -40
min
60
max
54
OBSERVED IBI
48
42
36
30
24
18

  






 
   
  

   



 

 
  


 
 
    
  
  



 
      

    



 

 
    

 

 



18
24
30
36


 





42
48
PREDICTED IBI
54
60