333

American Fisheries Society Symposium 41:333–359, 2005© 2005 by the American Fisheries Society

A Comparison of the Influences of Urbanization inContrasting Environmental Settings on Stream Benthic Algal

Assemblages

MARINA POTAPOVA

Patrick Center for Environmental Research, the Academy of Natural Sciences1900 Benjamin Franklin Parkway, Philadelphia, Pennsylvania 19103, USA

JAMES F. COLES

U.S. Geological Survey, c/o USEPA New England, Suite 1100 (HBS)1 Congress Street, Boston, Massachusetts 02114, USA

ELISE M. P. GIDDINGS

U.S. Geological Survey, 3916 Sunset Ridge Road, Raleigh, North Carolina 27607, USA

HUMBERT ZAPPIA

1162 Rock Cliff Drive, Martinsburg, West Virginia 25401, USA

Abstract.—Patterns of stream benthic algal assemblages along urbanization gradients were investi-gated in three metropolitan areas—Boston (BOS), Massachusetts; Birmingham (BIR), Alabama;and Salt Lake City (SLC), Utah. An index of urban intensity derived from socioeconomic, infrastruc-ture, and land-use characteristics was used as a measure of urbanization. Of the various attributes ofthe algal assemblages, species composition changed along gradients of urban intensity in a moreconsistent manner than biomass or diversity. In urban streams, the relative abundance of pollution-tolerant species was often higher than in less affected streams. Shifts in assemblage composition wereassociated primarily with increased levels of conductivity, nutrients, and alterations in physicalhabitat. Water mineralization and nutrients were the most important determinants of assemblagecomposition in the BOS and SLC study areas; flow regime and grazers were key factors in the BIRstudy area. Species composition of algal assemblages differed significantly among geographic regions,and no particular algal taxa were found to be universal indicators of urbanization. Patterns in algalbiomass and diversity along urban gradients varied among study areas, depending on local environ-mental conditions and habitat alteration. Biomass and diversity increased with urbanization in theBOS area, apparently because of increased nutrients, light, and flow stability in urban streams,which often are regulated by dams. Biomass and diversity decreased with urbanization in the BIRstudy area because of intensive fish grazing and less stable flow regime. In the SLC study area,correlations between algal biomass, diversity, and urban intensity were positive but weak. Thus, algalresponses to urbanization differed considerably among the three study areas. We concluded that thewide range of responses of benthic algae to urbanization implied that tools for stream bioassessmentmust be region specific.

* Corresponding author: potapova@acnatsci.org

Introduction

Streams go through hydrological and water qualitychanges as they become part of the urban landscape

(Paul and Meyer 2001). Alterations of stream habitatinevitably lead to transformations in stream biota, in-cluding algal assemblages. Although the presence ofharmful chemicals in streams or the disappearance ofimportant fish species attracts attention, changes inalgal assemblages usually do not cause public concern

334 POTAPOVA ET AL.

unless excessive amounts of algae diminish the estheticvalue of streams, clog water supplies, or cause second-ary water pollution. Algae are sensitive, however, towater chemistry and habitat disturbance and, thus,have a long history of being used in water qualitymonitoring (Lowe and Pan 1996). Algae also play animportant role in the function of stream ecosystems.Algae are a source of organic matter and provide habi-tat for other organisms, such as nonphotosyntheticbacteria, protists, invertebrates, and fish. The crucialrole in stream ecosystems and excellent indicator prop-erties of algae make them an important component ofenvironmental studies to assess the effects of humanactivities on stream health.

Relations among lotic benthic algal assemblagesand various environmental factors associated withwater pollution and landscape alteration have beenstudied intensively (Stevenson et al. 1996), but onlya few studies have focused specifically on the effectsof urbanization. Algal vegetation in running watershas been studied in many urban areas, includingtemperate zones (e.g., Whitton 1984; Sabater et al.1987; Lobo et al. 1995; Vis et al. 1998; Winter andDuthie 1998; Fukushima 1999; Siva et al. 2001;Sonneman et al. 2001) and tropics (Nather Khan1991; Wu 1999). Studies in individual urban streamsor individual metropolitan areas generally have beencarried out to determine which attributes of algalassemblages are indicative of human influences foruse in future monitoring programs. Many of thesedescribed changes in taxonomic composition of as-semblages in flowing waters influenced by munici-pal or industrial wastes when compared to less affectedsites. Some also reported declining diversity of algaein severely polluted urban rivers (Whitton 1984;Nather Khan 1991) and increased algal biomass (Tay-lor et al. 2004). It is not clear, however, whetherstream algal assemblages are affected by urbanizationin a similar way in different geographic areas. If theyare, then it should be possible to identify the at-tributes of assemblages that can be used as universalindicators of the effects of urbanization. Urban areasdiffer, however, in their environmental settings andhistory of development. Climate, geology, and hu-man influences are considered to be ultimate envi-ronmental factors that determine biomass andcomposition of algal assemblages in streams (Biggs1990; Stevenson 1997). These factors may modifythe patterns of algal assemblage variations along ur-banization gradients. To understand to what degreethe responses of stream algal assemblages to urban-ization vary geographically, it is necessary to conduct

studies based on a common approach and method-ology in various urban areas.

We studied stream algal assemblages in three met-ropolitan areas of contrasting climate and geography.This investigation was part of the Urban Land UseGradient (ULUG) study conducted in 2000 as partof the U.S. Geological Survey’s (USGS) National Wa-ter-Quality Assessment (NAWQA) Program (Tate etal. 2005; this volume). Our goals were to (1) de-scribe relations between urbanization and attributesof stream algal assemblages, such as biomass, diver-sity, and species composition; and (2) determinewhether patterns of algal assemblages along urban-ization gradients were different in three metropoli-tan areas. Uniform responses of particular attributesof algal assemblages to urbanization indicate the pos-sibility of using these attributes as universalbioassessment tools in urban streams. Conversely,large differences among patterns of algal assemblagesin three metropolitan areas indicate the overridinginfluence of local environmental and historical con-ditions and the need to develop region-specificbioassessment tools.

Methods

Site Selection

The study was conducted in three diverse metro-politan areas in terms of climate and geography—Boston (BOS), Massachusetts, representing the cooland humid Northeast; Birmingham (BIR), Alabama,representing the warm and humid Southeast; andSalt Lake City (SLC), Utah, representing the aridWest. In each area, 30 stream sites were selected alonggradients of urban intensity quantified by using themultimetric urban intensity index (UII). The UIIcombined several land-cover, population, socioeco-nomic, and infrastructure variables (McMahon andCuffney 2000). In each study area, a group of can-didate basins was established to represent a gradientfrom low (background) to high urban intensitywithin a relatively homogeneous environmental set-ting, and several variables characterizing human in-fluences and natural variability were calculated forthese basins by using geographic information system(GIS) programs. Variables selected to derive the UIIwere different among the three study areas; in eachcase, however, they correlated with population den-sity and did not correlate with basin area. The vari-ables were combined to calculate UII values usingformulas from McMahon and Cuffney (2000). The

335A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

calculated values then were adjusted to range from 0to 100. The final set of 30 sites was selected in eachstudy area with the goal of minimizing variability insoils, geology, ecoregion, physiographic province, cli-mate, drainage area, and topography, which influ-ence the physical, chemical, and biological character-istics of streams. Finally, the UII was recalculated sothe values ranged from 0 to 100 in each set of 30sites. Greater details of the process of index develop-ment, site selection, and lists of variables used to de-rive the UII in each study area are provided in Tate etal. (2005).

All 30 sites in the BOS study are within or adja-cent to ecological subsection 221Ai, Gulf of MaineCoastal Plain, of the U.S. Forest Service ecological unit221A, Southern New England Coastal Hills and Plain(Keys et al. 1995), which corresponds to the U.S.Environmental Protection Agency (USEPA) level IIIecoregion, Northeastern Coastal Zone (Omernik1995). The BIR study sites are within the USEPAlevel IV ecoregion, Southern Limestone/Dolomite Val-leys and Low Rolling Hills. The final number of sam-pling sites in BIR area was 27 because of logisticalproblems. Streams sampled in the SLC originate inthe Wasatch Mountain range in the USEPA level IIIecoregion, Wasatch and Uinta Mountains, but theurban areas mostly are in lower parts of stream drain-age basins in the Central Basin and Range ecoregion.Only 13 perennial streams were available for study inthis area because of the dry climate and numerouswater diversions. One to three sites were selected ineach of the stream basins in the SLC study area for atotal number of 30 sites.

Sample Collection and Laboratory Analyses

Benthic algae were sampled as described by Porter etal. (1993). At each site, two quantitative samples werecollected—one from hard substrates, mostly rocks lo-cated in a riffle or run (richest-targeted habitat or RTHsample), and the other from soft sediment in a deposi-tional area (depositional-targeted habitat or DTHsample). Richest-targeted habitat samples were col-lected by scraping algae from cobble-size stones se-lected from five riffle areas of the sampling reach. Thescraped area was determined by the foil-templatemethod. Depositional-targeted habitat samples werecollected from five depositional areas in the same sam-pling reach by pressing an inverted petri dish into thesediment and sliding a spatula under the petri dish toisolate the upper layer of sediment. Additionally, amultihabitat qualitative sample (QMH) was collected

from various substrates in the sampling reach.Subsamples of RTH samples were taken for determi-nations of chlorophyll a. The other samples and re-maining portion of RTH samples were preserved witha 5% solution of formalin for identification and enu-meration of algae. Algal samples were collected in low-flow conditions from August 1 to September 1, 2000,in the BOS study area; from June 6 to June 12, 2000,in the BIR study area, and from July 17 to August 9,2000, in the SLC study area.

Algae were identified and enumerated followingprotocols in Charles et al. (2002) by taxonomic spe-cialists in the Phycology Section of the Patrick Centerfor Environmental Research, Academy of Natural Sci-ences, Philadelphia, and in the Department of Zool-ogy at Michigan State University. Algae were identifiedand enumerated using the Palmer-Maloney countingcell. Diatoms were identified from permanent slides;only the total number of live and dead diatoms wasrecorded using the Palmer-Maloney counting cell. Celldensity was expressed as number of cells/cm2 and cellbiovolume as mm3/cm2. Chlorophyll a (mg/m2) wasmeasured at the USGS Water Quality Laboratory inLakewood, Colorado, using standard methods (Ararand Collins 1997).

Invertebrates and fish were sampled concurrentlywith algae. The data used in this study include abso-lute abundance of invertebrate scrapers in all threestudy areas and abundance of herbivorous fish in theBIR study area. Herbivorous fish were absent in theBOS and SLC study areas.

Water quality samples were collected in August2000 in the BOS area, in May 2000 in the BIR area,and in July 2000 in the SLC area. Dissolved oxygen,alkalinity, pH, and conductivity were measured in thefield according to Shelton (1994). Nutrient concen-trations, major ions, and pesticides were analyzed atthe USGS Water Quality Laboratory in Lakewood,Colorado, following methods described in Fishman(1993). Water quality characteristics that were used asexplanatory environmental variables for this investi-gation are listed in Table 1.

Physical habitat characteristics were measuredaccording to Fitzpatrick et al. (1998) close to the timeof algal sampling. Physical habitat characteristics weremeasured at 11 equally spaced transects along the sam-pling reaches and consisted of measurements of cur-rent velocity, discharge, channel morphology, bedsubstrate, and canopy cover. Means and coefficientsof variation (CV) were calculated from these data tocharacterize general habitat conditions of the reaches.Channel shape index was calculated as W/D D/Dmax,

336 POTAPOVA ET AL.

TABLE 1. Environmental characteristics used as explanatory variables in the analyses of algal data. Physical habitat variableswere derived from measurements made at 11 transects at each sampling site. Abbreviations are given for some variables shownin tables and graphs.

Variable group and description Abbreviation

Basin-scale characteristicsUrban intensity index UIIBasin area (km2)1999 population density (people/km2) PopulationRoad density (km/km2)Number of dams (number/100km2)Land use/land cover (% of basin area)

Water and wetlandsUrbanBarrenForestShrub landHerbaceous upland/grassland GrasslandHerbaceous planted/cultivated CultivatedWetlands

Elevation (m)Sampling siteMean in basinRange in basin Relief

Percentage of the basin area with slope less than 1%Percentage of the basin area with slope less than 1% at elevations above average UplandsPercentage of the basin area with slope less than 1% at elevations below averageStream order

Physical habitat characteristicsSegment sinuosityReach sinuositySurface-water gradient (m/km) GradientPercentage of reach area as poolsPercentage of reach area as rifflesPercentage of reach area as runsWetted width (m)Coefficient of variation of wetted widthMaximum wetted width (m)Bank-full width (m)Coefficient of variation of bank-full widthMaximum bank-full width (m)Wetted depth (m)Coefficient of variation of wetted depthMaximum wetted depth (m)Bank-full depth (m)Coefficient of variation of bank-full depthMaximum bank-full depth (m)Depth at the sampling location (m)Wetted width–depth ratioCoefficient of variation of wetted width–depth ratioBank-full width–depth ratioCoefficient of variation of bank-full width–depth ratioChannel shape indexCoefficient of variation of channel shape index

337A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

TABLE 1. Continued.

Variable group and description Abbreviation

Discharge (m3/s)Coefficient of variation of dischargeMean flow velocity (m/s)Coefficient of variation of flow velocityMaximum flow velocity (m/s)Flow velocity at the sampling location (m/s)Shear stressFroude numberStream power (W/m)Flow-stability indexMean dominant substrate sizeCoefficient of variation of dominant substrate sizeSilt occurrencePercentage of nonporous substratePercentage of fine sediments % finePercentage of silt-clay size particles % silt + clayPercentage of sand size particles % sandPercentage of gravel size particles % gravelPercentage of cobble size particles % gravelPercentage of boulder size particlesPercentage of silt-gravel size particles % silt + gravelPercentage of gravel-cobble size particlesPercentage of cobble-boulder size particlesEmbeddedness (%)Manning’s channel roughnessChannel relative roughnessHydrological channel radiusCanopy closure (%)Coefficient of variation of canopy closureLight intensity (photosynthetically active radiation, mmol s–1 m–2) Light

Hydrological (stage-related) characteristicsVariation of stage valuesSkew of stage valuesNumber of time periods when stage rises by at least 0.03 m/h periodr1Number of time periods when stage rises by at least 0.09 m/h periodr3Number of time periods when stage rises by at least 0.15 m/h periodr5Number of time periods when stage rises by at least 0.21 m/h periodr7Number of time periods when stage rises by at least 0.27 m/h periodr9Number of time periods when stage falls by at least 0.03 m/h periodf1Number of time periods when stage falls by at least 0.09 m/h periodf3Number of time periods when stage falls by at least 0.15 m/h periodf5Number of time periods when stage falls by at least 0.21 m/h periodf7Number of time periods when stage falls by at least 0.27 m/h periodf9Maximum duration of low stage pulses with discharge < 5th percentile as low flow (h) Mxl5Maximum duration of low stage pulses with discharge < 10th percentile as low flow (h) Mxl10Maximum duration of low stage pulses with discharge < 25th percentile as low flow (h) Mxl25Median duration of low stage pulses with discharge < 5th percentile as low flow (h) Mdl5Median duration of low stage pulses with discharge < 10th percentile as low flow (h) Mdl10Median duration of low stage pulses with discharge < 25th percentile as low flow (h) Mdl25

338 POTAPOVA ET AL.

TABLE 1. Continued.

Variable group and description Abbreviation

Maximum duration of high stage pulses with discharge > 75th percentile as high flow (h) Mxh75Maximum duration of high stage pulses with discharge > 90th percentile as high flow (h) Mxh90Maximum duration of high stage pulses with discharge > 95th percentile as high flow (h) Mxh95Median duration of high stage pulses with discharge > 75th percentile as high flow (h) Mdh75Median duration of high stage pulses with discharge > 90th percentile as high flow (h) Mdh90Median duration of high stage pulses with discharge > 95th percentile as high flow (h) Mdh95

Water chemical and physical characteristicsPhosphorus as total phosphorus (mg/L as P) TPPhosphorus as orthophosphate, dissolved (mg/L as P) PO

4

Nitrogen as total nitrogen (mg/L as N) TNNitrogen as total organic nitrogen + dissolved ammonia (mg/L as N) TKNNitrogen as nitrite + nitrate nitrogen, dissolved (mg/L as N) NO

2 + NO

3

Nitrogen as ammonia, dissolved (mg/L as N) NH4

Conductivity (mS/cm) CondAlkalinity (mg/L as CaCO

3)

pH (standard units)Sulfate, dissolved (mg/L) SO

4

Chloride, dissolved (mg/L) ClCalcium, dissolved (mg/L) CaMagnesium, dissolved (mg/L) MgSodium, dissolved (mg/L) NaPotassium, dissolved (mg/L) KFluoride, dissolved (mg/L) FIron, dissolved (mg/L) FeSilica as silica dioxide, dissolved (mg/L) SiO

2

Residue on evaporation at 180oC (mg/L)Total herbicide concentration (mg/L) HerbConcNumber of herbicide detections per sample HerbHitsTotal pesticide concentration (mg/L)Number of pesticide detectionsOxygen, dissolved (mg/L) O

2

Water temperature (°C)Turbidity (NTU)

GrazersAbundance of invertebrate scrapers (number/m2) ScrapersAbundance of herbivorous fish largescale stoneroller (in BIR, number/reach) Stoneroller

where W is wetted channel width, D is mean waterdepth, and Dmax is maximum water depth. Smallervalues of the shape index generally indicate more pool-like conditions, whereas larger values indicate moreriffle-like conditions. The flow-stability index charac-terized high-flow events and was calculated as D

lf/D

bf,

where Dlf is depth of water at low flow, and D

bf is

bank-full depth. Current velocity, water depth, andlight intensity also were measured at the sampling lo-cation (except light intensity in the BIR study area).

Mean light intensity (photosynthetically active radia-tion, PAR) measured near the stream bottom withlight meters was used in the analyses. More detailedinformation on the physical habitat characterizationcan be found in Short et al. (2005, this volume) andin Fitzpatrick et al. (1998).

Stream stage (water level) was recorded at 15-min intervals at each site using USGS gauges or stagetransducers (McMahon et al. 2003). Summary hy-drological variables were calculated from the recorded

339A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

stage data and used in our analyses as measures of flowstability. Watershed-scale land-use, population, infra-structure, and socioeconomic variables in each water-shed were derived from GIS data. All environmentalvariables used in this study are listed in Table 1.

Data Analysis

Algal biomass was characterized by the amount ofchlorophyll a in RTH samples and the total algalbiovolume in RTH and DTH samples. Relations be-tween algal biomass and environmental variables wereevaluated by using correlation analysis (Pearson’s r).The number of environmental variables in our dataset was higher (>100) than the number of observa-tions in each of the regional data sets (27–30). Thus,some correlations may be spurious, caused by chancealone. To avoid overinterpretation, we sought com-binations of a few variables that could explain biom-ass variability. This was accomplished by stepwiseforward selection of variables in multivariate regres-sion analysis. Initially, we selected 10–12 variablesrepresenting different classes, such as nutrients (TPand TN), major ion chemistry (conductivity and al-kalinity), pH, light, sediment size, channel morphol-ogy, flow variability, and abundance of grazers. Thesevariables were chosen so that they were not highlyintercorrelated with each other but had significantcorrelation with algal biomass. The criteria for vari-able inclusion in the model was F > 0.05, and forexclusion, F < 0.1. Resulting models had one to threevariables that explained observed biomass variabilityin the most parsimonious way. The low number ofvariables is reasonable for small data sets such as ours.Total algal biovolume and concentration of nutri-ents, ions, alkalinity, and conductivity were log-trans-formed prior to analyses. Correlation and multipleregression procedures were conducted using SPSSfor Windows, version 11.0.

Algal diversity was estimated by species richness,which is the total number of species in a sample, andShannon-Wiener diversity index, which is a functionof both species richness and evenness of species distri-bution. Both metrics are commonly used in waterquality bioassessment (Stevenson and Bahls 1999).We also calculated relative abundance of algal taxathat are currently considered as possibly endemic.Trends in algal diversity along urban gradient wereevaluated by calculating Kendall’s correlations betweenthese diversity measures and UII.

To evaluate patterns in taxonomic compositionof algal assemblages and their relations with stream

environment, we used nonmetric multidimensionalscaling (NMS, Kruskal 1964; Mather 1976).Nonmetric multidimensional scaling is a nonpara-metric iterative technique using ranked distances, andthis technique is used increasingly in ecological stud-ies (McCune and Mefford 1999). Nonmetric multi-dimensional scaling solution is based on minimizingstress, which is a measure of poorness of fit betweenthe ordination matrix and original data matrix. Torun NMS, we used the “slow-and-thorough” autopi-lot mode available in PC-ORD 4.0 (McCune andMefford 1999) and the quantitative Sørensen (Bray-Curtis) distance measure. Three-dimensional solu-tions were optimal in all NMS runs. Four algal datasets from each study area were used, representingsamples from two habitats (RTH and DTH) andincluding either diatom proportions or all algal taxaproportions. Species relative abundance was squareroot transformed. We rotated ordinations to load thevariable UII on a single (horizontal) axis and illus-trated relations between algal assemblages and envi-ronment with the joint plots. These plots showedthe positions of taxa and selected environmental vari-ables most strongly correlated with ordination axesand related to different aspects of stream environ-ment (e.g., hydrology, substrate composition, lightregime, ionic composition, nutrients). Variance ex-plained by ordination was expressed by the coeffi-cient of determination (r2) between Euclideandistances in the ordination space and Sørensen dis-tances in the original species spaces. Algal taxa shownin the ordination plots are those that had the highestcorrelations with ordination axes and were found inat least three samples in each data set. The relativeimportance of environmental variables in explainingassemblage composition patterns was estimated bythe sum of squared correlations between a variableand all ordination axes (Σr2).

Kendall’s correlations between relative abun-dance of most abundant (reaching 15% or 20%)algal taxa and UII were calculated to identify whichtaxa were associated with urban gradient in eachstudy area. Autecological characteristics of algal taxa,such as trophic categories, relations to water mineralcontent and pollution tolerance, were obtained fromthe works of Sládecek (1973), Lange-Bertalot(1979), and van Dam et al. (1994). These sourcesare useful for characterizing the autecology of cos-mopolitan taxa; however, metrics calculated fromthese characteristics, such as percentage of pollution-sensitive, pollution-tolerant, oligo- and polysaprobic(tolerance to organic pollution), and oligo- and

340 POTAPOVA ET AL.

eutraphentic (tolerance to nutrient enrichment) taxa,must be considered with caution. These sources pro-vide no autecological information for many algal taxafound in American streams.

Results

Habitat Characteristics

The three study areas differed in climate, topography,and geology, and therefore, in water chemistry andflow regime (Table 2). Water-mineral content, as mea-sured by conductivity, was generally higher in theSLC area than in the other two study areas because ofthe arid climate. In the BIR area, conductivity and pHwere relatively high because of the prevalence of car-bonate bedrock. In the BOS area, conductivity andpH were lowest among three areas because of the preva-lence of noncarbonate bedrock and the cool, humidclimate. Conductivity was significantly positively cor-related with the UII in all three study areas (Table 2).Water pH tended to be higher in urbanized streams inall three study areas, but we found no significant cor-relation between pH and the UII (Table 2).

Streams in the SLC area had the steepest chan-nels, whereas streams in the BIR area had the lowestgradients and flow velocities. Flow stability, as quanti-fied by the flow-stability index derived from channelmorphology, increased with urbanization in the BOSstudy area, and showed no pattern in the BIR andSLC areas (Table 2).

Streams in the BIR area had, in general, a higherproportion of sand in the bed sediment than streams inthe BOS and SLC areas (Table 2). In BOS and SLC,proportions of sand were low in the nonurban streamsbut increased along gradients of urbanization. Some ofthe high-gradient nonurban stream sites in the SLCarea had very rough channels with numerous boulders.

The dominant natural land-cover type in the twoeastern study areas was forest; in the SLC study area,the dominant natural land-cover type was shrub land(Table 2). Cultivated land cover was present in allthree study areas, but it was significantly negativelycorrelated with the UII only in the BIR area. In corre-spondence with this pattern, nutrient concentrationsincreased with urbanization in the BOS and SLC ar-eas, but not in the BIR area. Median nutrient concen-trations were comparable in all study areas. One sitewith exceptionally high phosphorus and ionic (NaCl)content was in the BIR area. Concentrations of herbi-cides were higher in urbanized streams than in lessurbanized streams, but significant positive correlations

between herbicide concentrations and UII were foundonly in the BIR and SLC areas.

Biomass of Benthic Algae in Streams

Regional differences.—Median algal biovolumewas highest in the BIR study area and lowest in theBOS study area (Figure 1A). Total algal biovolumewas consistently higher in DTH samples than in RTHsamples in all three areas, although the difference inbiovolume between DTH and RTH samples was notlarge in the SLC area. In the BOS and BIR study areas,algal biovolume was approximately one order of mag-nitude higher in DTH samples than in RTH samples.Median chlorophyll a was lowest in the BOS area andhighest in the SLC area (Figure 1B). A chlorophyll-aconcentration of 200 mg/m2, commonly considered anuisance level (Biggs 1996; Dodds et al. 1998), oc-curred in only two BIR streams of relatively low urbanintensity (UII = 16.5–16.8) and in five SLC streamsof moderate to high urban intensity (UII = 45.5–71.3). Values between 50 and 200 mg/m2, whichalso can be regarded as a nuisance level (Biggs 1996),were observed at nine sites of varying degrees of ur-banization in the SLC area, at seven sites in the BIRarea, and at three sites of rather high urban intensity(UII = 62.7–92.6) in the BOS area.

Boston study area.—In the BOS area, algal biomassincreased along the urban intensity gradient. Positivecorrelation between the UII and total algal biovolumewas significant for RTH samples (r = 0.53, P < 0.01)but not significant for DTH samples (r = 0.12). Chlo-rophyll a concentration in RTH samples also was sig-nificantly positively correlated with the UII (r = 0.48, P< 0.01). Diatoms were the most abundant algae inDTH samples and the second most abundant algae inRTH samples (Figure 2A, B). Red algae constitutedmost of the algal biomass in RTH samples (Figure 2A).

In the BOS study area, total algal biovolume inRTH samples was significantly positively correlatedwith nutrients (all measured forms of phosphorus andnitrogen, r = 0.36–0.50, P < 0.05), alkalinity (r =0.45, P < 0.05), conductivity (r = 0.52, P < 0.01),water depth (r = 0.47, P < 0.05), flow-stability index(r = 0.57, P < 0.01), proportion of fine sediments (r =0.53, P < 0.01), number of dams (r = 0.44, P < 0.05),and light intensity (r = 0.44, P < 0.05). Total algalbiovolume was significantly negatively correlated withchannel width (r = –0.36, P < 0.05). The significanceof these correlations, however, must be regarded withcaution. The high number of tested variables increasedthe possibility of a spurious correlation.

341A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

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342 POTAPOVA ET AL.

For the stepwise multiple regression analysis, thefollowing variables were selected initially as possible in-dependent variables: TP, TN, alkalinity, wetted chan-nel width, flow-stability index, percentage of finesediments, current velocity and depth at the samplinglocation, light, and abundance of invertebrate scrapers.Variables finally chosen by the stepwise forward-selec-tion procedure as those best predicting total algalbiovolume on rocks were flow stability and TN (Table3). Final variables in the chlorophyll-a model were lightand TP (Table 3). Total algal biovolume in DTHsamples could not be predicted from measured envi-ronmental factors. Although the relatively low numberof observations in our data sets did not allow us to createhighly predictive models, results of the selection of vari-ables indicated that the main factors associated withincreased algal biomass on rocks in urban streams of theBOS area were nutrients, light, and flow stability.

Birmingham study area.—In the BIR study area,algal biovolume was lower in streams at the high end of

the urban gradient (Figure 2C, D). Negative correla-tion between the UII and total algal biovolume wassignificant for DTH samples (r = –0.53, P < 0.01) butnot significant for RTH samples (r = –0.33). No trendwas detected in chlorophyll a along the urban gradient.Correlation analysis showed that algal biovolume waspositively correlated (P < 0.05) with a number of vari-ables that had decreasing values along the urban gradi-ent, including segment sinuosity (r = 0.43, P < 0.05 inRTH samples and r = 0.44, P < 0.05 in DTH samples),turbidity (r = 0.42, P < 0.05 in RTH samples and r =0.39, P < 0.05 in DTH samples), concentration ofsuspended solids (r = 0.42, P < 0.05 in both RTH andDTH samples), percentage of gravel in the sediment (r= 0.40, P < 0.05 in RTH samples), and variation indischarge (r = 0.46, P < 0.05 in RTH samples), and wasnegatively correlated with variables that had increasingvalues along the urban gradient, such as surface watergradient (r = –0.41, P < 0.05 in DTH samples) andchannel width (r = –0.42, P < 0.05 in RTH samples

1

10

100

1,000

106

107

108

109

1010

1011

1012

Boston Salt Lake

City

Birmingham

A B

Bio

vo

lum

e,

µm

3/c

m2

Ch

loro

ph

yll

a,

mg

/m2

EXPLANATION

Value above 90th percentile

90th percentile

75th percentileMedian

25th percentile

10th percentile

Value below 10th percentile

RTH samples

DTH samples

Boston Salt Lake

City

Birmingham

FIGURE 1. Box plots of the (A) total algal biovolume and (B) chlorophyll a in streams in three study areas—Boston n =30; Birmingham n = 27; and Salt Lake City n = 30 for RTH samples, n = 28 for DTH samples.

343A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

and r = –0.51, P < 0.01 in DTH samples). The stron-gest correlations occurred between algal biovolume andabundance of the herbivorous fish, the largescalestoneroller Campostoma oligolepis (r = –0.50, P < 0.01in RTH samples and r = –0.77, P < 0.01 in DTHsamples). Stepwise forward selection of variables in

multiple regression analysis confirmed that abundanceof largescale stoneroller most likely explained the de-crease in algal biovolume in the BIR streams (Table 3).Potential explanatory variables tested in the multipleregression model were TP, TN, alkalinity, channel width,flow stability, canopy closure, turbidity, percentage of

FIGURE 2. Algal biovolume in streams in (A, B) Boston (BOS); (C, D) Birmingham (BIR); and (E, F) Salt Lake City(SLC); A, C, E—hard substrata (RTH samples); B, D, F—soft sediments (DTH samples). The order of the sampling sitesis by increasing UII.

RTH DTH

0 1 5 7 14 19 23 37 38 40 57 60 63 87 93 1 4 6 10 15 20 28 38 39 53 57 62 73 87 100

0 1 5 7 14 19 23 37 38 40 57 60 63 87 93 1 4 6 10 15 20 28 38 39 53 57 62 73 87 100

0 2 5 8 13 16 17 31 41 45 50 64 72 100 1 3 7 11 16 16 26 34 41 46 58 65 74

0 2 5 8 13 16 17 31 41 45 50 64 72 100 1 3 7 11 16 16 26 34 41 46 58 65 74

0 4 31 41 48 51 59 67 71 72 76 77 81 88 90 1 15 36 46 49 58 63 71 71 72 76 79 85 89 100

0 4 31 41 48 51 59 67 71 72 76 77 88 90 1 36 46 49 58 63 71 71 72 76 79 85 89 100

Bio

volu

me,

µm

3 /cm

2

Urban intensity index

A B

C D

E F

no

dat

a

no

dat

a

BOS

BIR

BOS

BIR

SLC SLC

0

3x109

5x109

diatoms

green algae

cyanobacteria

red algae

0

6x109

12x109

1x1012

0

1x1012

2x1012

0

15x109

30x109

45x109

0

15x109

30x109

0

1x109

2x109

3x10917x109

18x109

344 POTAPOVA ET AL.

fine-grained sediments, abundance of largescalestoneroller, stream depth, and current velocity at thesampling site. The final model for algal biovolume inRTH samples included only largescale stonerollerabundance as an explanatory variable, and the modelfor DTH samples includes abundance of largescalestoneroller and alkalinity. Diatoms and red algae mostoften dominated algal assemblages in this area, show-ing no particular pattern of relative abundance alongthe urban gradient (Figure 2C, D).

Salt Lake City study area.—No pattern was ob-served in algal biomass along the urban gradient instreams of the SLC study area. The filamentous greenalga Cladophora glomerata occasionally proliferatedin streams with various degrees of urban intensity(Figure 2E, F). Weak positive correlations (r = 0.37–0.53, P < 0.05) were identified between algal biom-ass variables (total algal biovolume and chlorophylla) and several variables representing stream widthand flatness of the watershed estimated as percent-age of watershed area with slopes less than 1%.Biovolume in RTH samples was negatively associ-ated with hydrologic variables related to flow stabil-ity, but the number of sites with hydrologic datawere too low to ascertain the significance of thesecorrelations. Stepwise multiple regression analysis didnot reveal any relations between algal biomass andenvironmental factors except for a tendency towardhigher biomass in wider streams and streams withflatter watersheds (Table 3). Variables initially con-

sidered for inclusion in the models were TP, TN,alkalinity, light, percentage of fine-grained sediment,stream width, stream depth, and current velocity atthe sampling site, flow-stability index, percentage ofthe watershed with slope less than 1%, and abun-dance of invertebrate scrapers. Only stream depth,width, and percentage of the watershed with slopeless than 1% were selected by the stepwise proce-dure for the final equations (Table 3).

Algal Diversity

The diversity of stream algal assemblages differed amongthe three study areas (Figure 3). The total number ofspecies in a sample (species richness) was higher in theBIR study area compared with the other two areas (Fig-ure 3A). Total number of algal species (gamma diver-sity) reported for the BIR data set also was higher (358taxa) than for the BOS (278 taxa) and SLC (291 taxa)data sets. The Shannon-Wiener diversity index, how-ever, was similar in all study areas. This index was some-what higher in soft-sediment samples in the BOS area(Figure 3B), which can be explained by the absence offilamentous green algae. Evenness usually is low whenthere is an abundant growth of filamentous algae andhigh when filamentous algae are absent.

The strongest positive correlation between diver-sity of algal assemblages and urban gradient occurredin streams of the BOS study area (Tables 4, 5). In theSLC area, algal assemblages in DTH and QMH

TABLE 3. Results of the stepwise multiple regression analysis with three algal biomass characteristics (biovolume of algae inRTH and DTH samples, and chlorophyll a) as dependent variables. Explanatory variables were selected by the stepwiseforward procedure as best predictors of algal biomass. No environmental variables were selected as predictors in two data sets—algal biovolume of DTH samples in the BOS study area and chlorophyll a in the BIR study area.

AdjustedDependent variable Model r2 model r2 Explanatory variables Beta r2

BostonTotal algal biovolume, RTH 0.44 0.40 Flow-stability index 0.47 0.33

Log TN 0.35 0.11Chlorophyll a 0.72 0.70 Light 0.69 0.62

Log TP 0.33 0.10Birmingham

Total algal biovolume, RTH 0.25 0.22 Stoneroller abundance –0.51 0.25Total algal biovolume, DTH 0.67 0.56 Stoneroller abundance –0.77 0.58

Alkalinity 0.31 0.09

Salt Lake CityTotal algal biovolume, RTH 0.16 0.13 Stream width 0.16Total algal biovolume, DTH 0.42 0.37 Stream width 1.12 0.29

Stream depth –0.68 0.13Chlorophyll a 0.19 0.16 Basin area with slope < 1% 0.43 0.19

345A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

samples also had higher diversity in urban rivers. Inthe BIR area, only species richness in DTH sampleswas lower in urban streams than in nonurban streams.

Taxa with the highest number of occurrences inall study areas generally were cosmopolitan diatoms,such as Achnanthidium minutissimum, Cocconeisplacentula var. lineata, Navicula cryptotenella, andGomphonema parvulum, which are common in tem-perate rivers. There were, however, obvious regionaldifferences in the taxonomic composition of algal as-

semblages. In the BOS study area, the list of taxa withthe highest occurrences included a number ofoligohalobous (salt intolerant) taxa, such as Tabellariaflocculosa, Achnanthidium rivulare, and Eunotia incisa.Conversely, in the BIR and SLC study areas, diatomsknown to prefer high water-mineral content were com-mon, including Rhoicosphenia abbreviata, Achnan-thidium deflexum, Nitzschia paleacea, and N. dissipata.Besides these widely distributed taxa, some specieswith limited geographic distribution were found. The

Nu

mb

er

of

alg

al ta

xa p

er

sa

mp

le

0

20

40

60

80

100

Sh

an

no

n-W

ien

er

div

ers

ity in

de

x

0

1

2

3

4

5

6

A B

Boston Salt Lake

City

Birmingham

EXPLANATION

Value above 90th percentile

90th percentile

75th percentileMedian

25th percentile

10th percentile

Value below 10th percentile

RTH samples

DTH samples

QMH samples

Boston Salt Lake

City

Birmingham

FIGURE 3. Median values and ranges of (A) total species richness and (B) Shannon-Wiener diversity index in streams ofthree study areas: Boston n = 30; Birmingham n = 27 for RTH and DTH samples and n = 17 for QMH samples; and SaltLake City n = 30 for RTH samples, n = 28 for DTH samples, and n = 27 for QMH samples.

TABLE 4. Kendall rank correlation between the Shannon-Wiener diversity index of algal assemblages and the urbanintensity index in the three study areas. **, P < 0.01, *, P < 0.05.

All algal taxa Diatoms onlyStudy area RTH DTH RTH DTH

Boston (n = 30) 0.59** 0.26* 0.63** 0.30*Birmingham (n = 27) –0.05 –0.09 0.03 –0.24Salt Lake City (n = 30 for RTH; n = 28 for DTH) 0.24 0.05 0.24 0.42**

346 POTAPOVA ET AL.

highest number of rare and yet undescribed taxa wasfound in the BIR area. At least six diatom species, onegreen alga, and one chrysophyte were undescribedspecies, currently found only in the southeasternUnited States. Correlations (Kendall’s tau), however,between the UII and relative abundance (RTH andDTH samples) and occurrence (RTH, DTH, andQMH samples) of these possibly endemic taxa in theBIR data set were not significant.

In the SLC study area, one diatom species (Nav-icula sp.) was found that is new to science and possiblyendemic in the western United States. Its occurrenceand abundance did not correlate with UII. No speciesof limited distribution were found in the BOS area.

Trends in Taxonomic Composition

Boston study area.—Algal taxa reaching the high-est (20% in at least one sample) relative abundance ofcells in the RTH samples collected in streams of theBOS study area were the diatoms Achnanthidiumminutissimum, A. rivulare, Cocconeis placentula var.lineata, Gomphonema parvulum; filamentous cyano-bacteria Homoeothrix simplex, Calothrix fusca,Phormidium formosum, P. aeruginosum, Lyngbyahieronymusii, Lyngbya sp.; and some red algae not iden-tifiable because of the absence of reproductive struc-tures in the summer. Among these taxa, only relativeabundances of Achnanthidium minutissimum andCalothrix fusca were negatively correlated with UII(Kendall’s tau, P < 0.05); eutraphentic Gomphonemaparvulum was positively correlated with UII. In theDTH samples, the most abundant species (a relativeabundance of 15% at least once) were the diatomsAchnanthidium minutissimum, A. rivulare, Cocconeisplacentula var. lineata, Fragilaria capucina var. rumpens,Tabellaria flocculosa, Staurosira construens var. venter,Psammothidium subatomoides, Sellaphora seminulum,Brachysira microcephala, and Melosira varians. Amongthese, only Achnanthidium minutissimum andoligotraphentic Psammothidium subatomoides were

negatively correlated (Kendall’s tau, P < 0.05) withthe UII, and no taxa showed significant positive corre-lations with the UII.

In the BOS study area, NMS ordinations of bothdata sets (diatoms and all algal taxa) from the RTHsamples produced similar results (Table 6). Variancein species data explained by all-taxa ordination was87%, and variance explained by diatom ordinationwas 91%. Both ordinations had strong correlationswith the UII. The axis aligned with the UII explained52% of the variation in the all-taxa and 57% in thediatom data set. Variables strongly correlated with theUII-aligned ordination axis included alkalinity, con-ductivity, road density, TN, wetted channel width-to-depth ratio, shape index, and channel relativeroughness (Table 6). Variables that showed strong as-sociation with algal assemblage composition in the BOSstudy area, but not strongly correlated with the UII,were TP, number of herbicide detections, and somevariables characterizing flow regime (Table 6; Figure4A). Eutraphentic and pollution-tolerant diatoms(Navicula minima, N. gregaria, Sellaphora seminulum)were positioned at the high end of the urban gradient.Some species in the left side of the ordination diagram,corresponding to the low end of the urban gradient,were oligotraphentic taxa (Brachysira microcephala andB. serians). Abundance of filamentous cyanobacteria,such as Lyngbya bergei and Calothrix fusca, was lowerin urban streams.

Ordination of DTH samples indicated similar en-vironmental gradients explaining variation in algal as-semblages as ordination of RTH samples, although taxathat had the highest ordination scores were differentbetween the two ordinations (compare Figure 4A, B).Diatoms positioned at the high end of the urban gradi-ent were mostly eutraphentic, pollution-tolerant, andhalophilic species (Navicula lanceolata, Rhoicospheniaabbreviata, Nitzschia bremensis). The all-taxa and dia-tom ordinations extracted 85% and 91% variability inspecies data, respectively. After rotation, the first axisaligned with UII extracted 28% and 33% of the varia-

TABLE 5. Kendall rank correlation coefficients between species richness of algal assemblages and the urban intensity indexin the three study areas. **, P < 0.01, *, P < 0.05.

All algal taxa Diatoms onlyStudy area RTH DTH QMH RTH DTH QMH

Boston (n = 30) 0.66** 0.27* 0.30* 0.67** 0.28* 0.30*Birmingham (n = 27 for RTH and DTH;

n = 17 for QMH) –0.02 –0.29* –0.31 –0.04 –0.32* –0.33Salt Lake City (n = 30 for RTH; n = 28

for DTH; n = 27 for QMH) 0.21 0.47** 0.35* 0.22 0.44** 0.39*

347A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

tion in all-taxa and diatom data sets, respectively. Simi-lar to the RTH ordination, the variation of algal assem-blage along urban gradient was mostly associated withwater-mineral content, nitrogen content, and channelmorphology (Table 7). Variation in assemblages notdirectly related to urban intensity gradient was mostlyassociated with several characteristics of flow regime inboth diatom and all-taxa ordinations, with silt occur-rence in the all-taxa ordination, and with percentage ofriffles in the diatom ordination.

Birmingham study area.—In the BIR study area,the most abundant taxa in both RTH and DTH

samples were diatoms Achnanthidium minutissimumand A. deflexum; filamentous cyanobacteria Homoe-othrix simplex, Phormidium autumnale, and unidenti-fied Leptolyngbia; green alga Scenedesmus acutus; andthe chantransia stage of red algae. Among these, onlyrelative abundance of Scenedesmus acutus increased sig-nificantly with urbanization.

Ordinations of RTH samples in the all-taxa anddiatom data sets showed that algal assemblages weremore related to physical habitat characteristics and thepresence of grazers than to the urban gradient (Table8). The all-taxa ordination extracted more variability

TABLE 6. Correlations of environmental variables and NMS ordination axes for the RTH 30-sample data set for the Bostonstudy area. Ordinations were rotated to maximize loadings of the first axis on the urban intensity index. Only environmentalvariables with highest multiple r2 (Σr2) are shown. Significant correlations (P < 0.05) are in bold. Abbreviations for variablesand variable units are in Table 1.

All taxa Diatoms onlyEnvironmental variable axis 1 axis 2 axis 3 Σr2 axis 1 axis 2 axis 3 Σr2

Alkalinity 0.72 –0.20 0.18 0.59 0.81 0.02 0.38 0.79Wetted width–depth ratio –0.66 0.08 –0.27 0.52 –0.72 0.02 –0.22 0.57Channel shape index –0.62 0.09 –0.29 0.48 –0.66 0.09 –0.23 0.49Urban intensity index 0.67 0.00 0.07 0.45 0.83 –0.03 0.17 0.71Conductivity 0.63 –0.13 0.18 0.44 0.83 –0.02 0.42 0.87Road density 0.64 0.02 0.10 0.42 0.74 –0.11 0.11 0.56Coefficient of variation of

bank-full width–depth 0.49 –0.33 0.23 0.40 0.53 –0.05 0.34 0.40TN 0.50 –0.33 0.18 0.39 0.74 –0.06 0.49 0.79Dissolved oxygen –0.51 0.28 –0.20 0.38 –0.54 0.11 –0.10 0.31Channel relative roughness –0.58 0.12 –0.15 0.37 –0.70 –0.08 –0.18 0.54NO

2+NO

30.49 –0.32 0.15 0.36 0.71 0.01 0.49 0.74

TKN 0.50 –0.25 0.23 0.36 0.64 –0.11 0.30 0.51SO

40.55 –0.09 –0.23 0.36 0.62 0.44 0.11 0.59

Mean flow velocity 0.50 –0.28 –0.15 0.35 0.50 0.43 0.29 0.52Maximum flow velocity 0.47 –0.27 –0.21 0.34 0.51 0.41 0.31 0.52Mxh90 0.29 0.18 0.47 0.34 0.28 –0.18 0.25 0.17TP 0.39 –0.38 0.18 0.33 0.67 0.00 0.51 0.71Number of herbicide detections 0.36 –0.41 0.19 0.33 –0.17 –0.03 –0.15 0.05Population 0.56 0.04 0.04 0.32 0.71 –0.10 0.03 0.51Forest –0.52 –0.21 0.01 0.31 –0.44 0.08 0.11 0.21Segment sinuosity –0.52 0.18 –0.02 0.31 –0.51 –0.03 –0.15 0.29Coefficient of variation of

bank-full width 0.40 –0.21 0.31 0.31 0.19 –0.20 0.22 0.12Bank-full width–depth ratio –0.51 0.05 –0.19 0.30 –0.60 0.07 –0.16 0.39Mxl5 –0.33 0.42 –0.10 0.29 –0.44 0.02 –0.36 0.32Water temperature 0.42 –0.26 0.20 0.28 0.47 0.20 0.18 0.29Wetted depth 0.51 –0.06 0.14 0.28 0.66 –0.17 0.01 0.46Discharge 0.36 –0.34 –0.17 0.27 0.50 0.16 0.09 0.28% silt+gravel 0.47 –0.07 0.21 0.26 0.45 –0.27 –0.13 0.29Wetted width –0.41 –0.05 –0.29 0.25 –0.29 0.00 –0.25 0.14% sand 0.41 –0.24 0.11 0.24 0.45 –0.18 –0.11 0.25Maximum wetted depth 0.46 –0.05 0.10 0.23 0.63 –0.18 –0.01 0.42

348 POTAPOVA ET AL.

teristics and the UII-aligned axis indicated that streamswere more flashy in urban settings than in nonurbansettings and that this factor could cause shifts in thecomposition of algal assemblages along the urban gra-dient. Abundance of invertebrate scrapers and her-bivorous fish were important factors in ordination ofthe all-taxa data set but not in the diatom data set(Table 8), indicating that grazers may exert a selectivepressure on particular nondiatom taxa, most probablymacroscopic algae. Abundance of the largescalestoneroller was positively correlated and abundance ofinvertebrate scrapers was negatively correlated withthe UII-aligned axis; therefore, these variables couldbe associated with algal assemblage composition alongthe urban gradient. Other variables that had strongcorrelations with ordination but were not associatedwith urbanization gradient were several characteristicsof channel morphology and canopy closure.

Ordination of DTH samples indicated that as-semblages in soft sediments were more strongly re-lated to the urban intensity gradient than assemblageson rocks (Table 9). Ordinations of the all-taxa anddiatom data sets accounted for 83% and 75% of vari-ability in species data, respectively. The axis alignedwith the UII extracted 36% and 39% of the variationof the all-taxa and diatom ordinations, respectively.The UII variable in both ordinations was associatedwith the highest amount of variability in the speciesdata compared to all other measured variables (Table9). Other variables that had highest correlations withordination but were not strongly aligned with theUII-aligned axis were characteristics of flow regime,channel morphology and sediment composition, con-centration and number of herbicide detections, wa-ter-mineral content, and nitrogen content (Table 9;Figure 5B). Algal taxa at the high end of the urbangradient were common pollution-tolerant species, suchas Navicula veneta and Scenedesmus acutus, whereassome of those at the low end were taxa of limitedgeographic distribution (Surirella stalagma, N.repentina) that could be indicators of natural condi-tions in the BIR study area.

Salt Lake City study area.—The taxa with the high-est relative abundances in streams of the SLC study areawere the diatoms Achnanthidium minutissimum, Am-phora pediculus, Rhoicosphenia abbreviata, and Nitzschiainconspicua; filamentous cyanobacteria Homoeothrixjanthina, Phormidium autumnale, and Calothrixparietina; an unidentified coccoid cyanobacterium; andthe chantransia stage of red algae. Phormidium autumnalehad a significant negative correlation (Kendall’s tau, P <0.05) with the UII in the RTH sample data set. Two

1

0

-1

Axis 1

HerbHitsTP

Cond

Alkalinity

UIILyngbya bergei

Navicula minima

Brachysiraserians

Synedra parasitica

Sellaphora seminulum

Navicula gregaria

Cymbella naviculiformis

Gomphonema parvulius

Brachysira microcephala

Phormidium formosum

Calothrixfusca

Cocconeis fluviatlilis

Wetted width/depthratio

-1 0 1

Flowvelocity

Cond

TN

Silt occurrence

UII

Phormidium formosum

Anabaena oscillatoriae

Cavinula cocconeiformis

Nitzschia bremensis

Melosira varians

Lemnicola hungarica

Staurosirella leptostauron

Navicula lanceolata

Karayevia laterostrata

Cyclotella bodanica var.

lemanica

Brachysira microcephala Rhoicosphenia abbreviata

Mayamaea atomus

Planothidium peragalli

-1 0 1

1

0

-1

A

B

Axi

s 2

Axi

s 2

Axis 1

FIGURE 4. Results of nonmetric multidimensional scal-ing ordinations for (A) RTH and (B) DTH samples from 30streams in the Boston study area. Joint plots of species andenvironmental variables were obtained by rotating ordina-tions to align the UII variable with the first ordination axis.Abbreviations for variables are listed in Table 1.

of species data (86%) than the diatom ordination(77%). The ordination axis aligned with the UII ex-tracted only a low amount of variation of species data—21% in the all-taxa data set and 19% in the diatomdata set. The relatively short line for the UII (Figure5A), corresponding to its low correlation with ordina-tion (Table 8), indicated that this variable was notassociated with considerable variation in the assem-blage composition. Variation of algal assemblages alongthe gradient of urban intensity was associated mostlywith hydrological variables, such as frequency of streamlevel (stage) above or below particular values (Table8). Positive correlation between stage-related charac-

349A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

diatoms commonly associated with sandy sediments,Amphora pediculus and Nitzschia inconspicua, correlatedpositively with the UII.

Algal assemblages on hard substrates in urbanstreams of the SLC area were apparently influencedby the increased concentrations of ions and nutrients(Table 10). Both NMS ordinations of the all-taxa anddiatom data sets extracted a high percentage of vari-ability—87% and 84%, respectively. The ordinationaxis aligned with the UII extracted 43% of the vari-

ability in the all-taxa data set and 41% in the diatomdata set. Several variables related to water-mineral con-tent, TP, and the percentage of fine-grained sedimentswere positively correlated with the UII-aligned axis(Table 10). Water-mineral content could be consid-ered the most probable cause of the shift in the algalassemblage composition along the urban gradient inthe SLC. Algal taxa at the high end of the urban gra-dient were diatoms commonly found in waters of highionic strength, such as Diatoma moniliformis and

TABLE 7. Correlations of environmental variables and NMS ordination axes for the DTH 30-sample data set for the Bostonstudy area. Ordinations were rotated to maximize loadings of the first axis on the urban intensity index. Only environmentalvariables with highest multiple r2 (Σr2) are shown. Significant correlations (P < 0.05) are in bold. Abbreviations for variablesand variable units are in Table 1.

All taxa Diatoms onlyEnvironmental variable axis 1 axis 2 axis 3 Σr2 axis 1 axis 2 axis 3 Σr2

NO2 + NO

30.60 0.28 0.30 0.53 0.55 0.12 –0.16 0.34

Conductivity 0.66 0.07 0.30 0.52 0.64 0.06 –0.15 0.43SO

40.66 –0.22 –0.13 0.49 0.51 0.20 0.37 0.43

TN 0.59 0.19 0.31 0.48 0.61 0.11 –0.22 0.43Mean flow velocity 0.46 –0.48 0.03 0.45 0.52 0.46 0.29 0.57Maximum flow velocity 0.49 –0.45 0.05 0.45 0.50 0.41 0.33 0.52Alkalinity 0.62 0.15 0.15 0.43 0.71 0.25 –0.15 0.59Mxh95 –0.33 0.15 0.53 0.41 –0.09 –0.14 –0.48 0.26Dissolved oxygen –0.58 0.14 0.05 0.37 –0.54 –0.04 0.12 0.31Coefficient of variation of

discharge –0.47 0.35 0.02 0.35 –0.23 –0.18 0.00 0.09Channel relative roughness –0.50 0.28 –0.10 0.33 –0.62 0.01 0.09 0.39Coefficient of variation of flow

velocity –0.34 0.46 –0.06 0.33 –0.26 –0.46 –0.22 0.33TP 0.48 0.17 0.25 0.32 0.51 0.11 –0.13 0.29Discharge 0.41 –0.37 –0.06 0.31 0.47 0.09 0.18 0.26Wetted width–depth ratio –0.52 0.15 –0.13 0.31 –0.68 –0.03 0.11 0.48Urban intensity index 0.55 –0.02 0.00 0.30 0.67 –0.02 0.00 0.45Silt occurrence –0.27 0.44 –0.15 0.29 –0.34 –0.24 –0.07 0.02Bank-full width–depth ratio –0.50 0.15 –0.10 0.28 –0.54 0.03 0.17 0.32Channel shape index –0.49 0.09 –0.14 0.27 –0.64 –0.03 0.13 0.43Forest –0.50 –0.04 0.05 0.25 –0.58 0.09 –0.01 0.34Road density 0.46 0.06 0.02 0.22 0.64 –0.04 –0.05 0.41TKN 0.43 0.10 0.13 0.21 0.61 0.10 –0.28 0.46Population density 0.45 –0.04 –0.05 0.20 0.59 –0.12 –0.01 0.37Wetted depth 0.42 –0.11 –0.01 0.19 0.57 –0.21 –0.11 0.39Maximum wetted depth 0.39 –0.10 0.01 0.16 0.54 –0.25 –0.08 0.36Coefficient of variation of

bank-full width–depth 0.20 –0.02 0.32 0.14 0.54 0.27 –0.15 0.38% silt + gravel 0.31 –0.04 –0.05 0.10 0.53 –0.19 –0.24 0.37% sand 0.28 –0.10 –0.05 0.09 0.53 –0.11 –0.14 0.31% gravel + boulder –0.29 0.06 0.04 0.09 –0.51 0.19 0.23 0.35Percentage of reach area as riffles 0.09 –0.11 –0.26 0.09 0.21 0.50 0.40 0.45% fine 0.27 –0.05 –0.08 0.08 0.53 –0.19 –0.18 0.35Number of herbicide detections 0.24 –0.01 0.12 0.07 0.40 0.41 –0.18 0.36

350 POTAPOVA ET AL.

Encyonema auerswaldii (Figure 6A). Taxa at the lowend of the urban gradient, such as Diatoma mesodonand Homoeothrix janthina, were typical inhabitants ofcold, oligotrophic, and fast-flowing streams.

Ordination of DTH samples showed weaker re-lations of algal assemblages to the urban gradient in

the SLC area (Table 11). The all-taxa and diatom or-dinations extracted 84% and 85% of variance, re-spectively. After rotation, the first ordination axisaligned with the UII extracted 22% and 31% of vari-ance, respectively. The all-taxa ordination was relatedmostly to light conditions, canopy closure, stream size

TABLE 8. Correlations of environmental variables and NMS ordination axes for the RTH 27-sample data set from theBirmingham study area. Ordinations were rotated to maximize loadings of the first axis on the urban intensity index. Onlyenvironmental variables with highest multiple r2 (Σr2) are shown. Significant correlations (P < 0.05) are in bold. Abbreviationsfor variables and variable units are in Table 1.

All taxa Diatoms onlyEnvironmental variable axis 1 axis 2 axis 3 Σr2 axis 1 axis 2 axis 3 Σr2

Coefficient of variation of canopyclosure 0.20 0.04 –0.64 0.45 0.09 0.04 0.02 0.01

Canopy closure –0.10 –0.05 0.65 0.44 –0.07 0.06 0.02 0.01Dissolved oxygen –0.07 –0.56 –0.28 0.39 –0.32 0.08 0.30 0.20% silt + clay –0.51 0.02 –0.34 0.38 –0.20 –0.29 0.10 0.13Scrapers –0.41 0.40 –0.24 0.38 –0.09 –0.35 –0.36 0.26Mxl5 0.61 0.04 –0.08 0.37 0.37 0.44 0.07 0.33Wetted depth –0.48 0.32 –0.13 0.35 –0.03 –0.27 –0.17 0.10Stoneroller 0.44 –0.37 –0.10 0.34 0.33 0.25 0.14 0.19Channel relative roughness 0.39 –0.36 0.13 0.30 0.25 0.06 0.28 0.14Flow-stability index –0.32 0.25 –0.33 0.28 –0.27 –0.32 0.08 0.18Maximum wetted depth –0.46 0.20 –0.11 0.27 –0.13 –0.26 –0.08 0.09Periodr7 0.50 0.07 0.09 0.27 0.53 0.38 –0.05 0.43Periodr9 0.49 0.06 0.10 0.26 0.50 0.37 –0.04 0.38Periodr5 0.48 0.09 0.08 0.25 0.56 0.36 –0.05 0.45Segment sinuosity –0.23 0.43 –0.05 0.24 –0.10 –0.36 –0.06 0.14Periodf9 0.45 0.03 0.09 0.21 0.46 0.37 –0.04 0.35Periodr3 0.43 0.01 0.15 0.21 0.59 0.36 –0.09 0.48Mean basin elevation 0.29 –0.35 0.05 0.21 –0.40 0.30 0.02 0.25Periodf7 0.43 0.01 0.09 0.19 0.46 0.34 –0.05 0.33Periodf3 0.42 0.01 0.11 0.19 0.52 0.31 –0.06 0.37Silt occurrence 0.15 0.36 0.20 0.19 0.17 –0.10 –0.49 0.28Periodf1 0.10 0.31 0.18 0.14 0.49 0.16 –0.47 0.48Coefficient of variation of

bank-full width–depth ratio 0.20 –0.01 0.32 0.14 –0.16 0.13 0.66 0.47Periodr1 0.04 0.32 0.16 0.13 0.41 0.11 –0.45 0.38Percentage of reach area as riffles –0.01 –0.33 0.00 0.11 –0.02 0.34 0.51 0.38Urban intensity index 0.29 –0.01 –0.05 0.09 0.52 0.01 0.00 0.27Basin area 0.07 0.24 0.18 0.09 0.47 –0.02 –0.42 0.39Periodf5 –0.13 –0.03 –0.26 0.08 0.49 0.33 –0.06 0.35Coefficient of variation of

channel shape index 0.17 –0.19 0.09 0.08 0.03 0.12 0.54 0.30Uplands –0.25 –0.12 –0.08 0.08 –0.04 –0.16 0.51 0.28Road density 0.22 –0.03 –0.15 0.07 0.54 –0.03 –0.03 0.29Variation in wetted width 0.22 –0.10 –0.01 0.06 –0.15 0.07 0.62 0.42Bank-full width 0.19 –0.13 0.00 0.05 0.56 –0.03 0.06 0.32Total herbicide concentration 0.21 –0.07 0.03 0.05 0.55 0.06 0.01 0.30Coefficient of variation of

bank-full width 0.03 –0.14 –0.07 0.02 –0.14 –0.02 0.60 0.38

351A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGSA

xis

2

Segmentsinuosity

Scrapers

Stoneroller

UII

Planothidium granum

Karayevia clevei

Mayamaea atomus

Achnanthidium exiguum

unidentif ied cryptophyte

Nitzschia paleacea

Scenedesmus acutus

Navicula viridulacalcis

Cyclotella meneghiniana

Navicula reichardtiana

Encyonema prostratum

Surirella angusta

% silt+clay

O2

Mxl5

1

0

-1-1 0 1

Mxh75

Gradient

HerbHits

UII

Chantransia stage of red

algae

Planothidium granum

Navicula reichardtiana

Homoeothrix simplex

Navicula submuralis

Scenedesmus acutus

Navicula veneta

Fallacia omissa

Mxh95

Periodf3

Planothidium biporomum

Surirellastalagma

Nitzschia lorenziana

Navicula repentina

1

0

-1

-1 0 1

A

B

Axi

s 2

Axis 1

Axis 1

FIGURE 5. Results of nonmetric multidimensional scal-ing ordinations for (A) RTH and (B) DTH samples from 27streams in the Birmingham study area. Joint plots of speciesand environmental variables were obtained by rotating ordi-nations to align the UII variable with the first ordination axis.Abbreviations for variables are listed in Table 1.

(as stream order, basin area and discharge, stream depthand width), and some chemical characteristics, such asF, pH, and NH

4. The variables most closely aligned

with the UII-axis were those related to stream size andcanopy closure (negative correlation) and residue onevaporation (positive correlation). Light reaching thestream bottom had a strong relation to algal assemblagestructure. Light conditions generally were worse in theurban streams, despite less canopy closure, because ofincreased suspended sediments. Some cyanobacteria(Calothrix parietina, Homoeothrix janthina) and thediatom Cocconeis neodiminuta, which often is associ-ated with sandy sediments, were positioned at the high

end of the urban gradient (Figure 6B). Although thisanalysis did not indicate very strong relations betweenalgal assemblages and stream environment, the higherconcentration of dissolved solids (measured as residueon evaporation and concentrations of Na, K, Cl, SO

4,

and SiO2) was a likely factor related to the assemblage

patterns revealed by this ordination. In the diatom or-dination, variables related to stream size and residue onevaporation were the most important factors, but con-centration of nutrients (TKN, TP, SiO

2) and some ions

(Na, K, Cl, SO4) and sediment size also had high corre-

lations with ordination axes.Percentages of pollution-sensitive, oligosaprobous,

and oligotraphentic diatoms mostly decreased, whilepercentages of pollution-tolerant, α-mesosparobous +polysaprobous, and eutraphentic diatoms increasedwith urbanization (Table 12). This pattern was mostpronounced in the BOS study area, less obvious in theSLC area, and weakest in the BIR area. Percentages ofpollution-tolerant and saprophilous taxa were alwayshigher in urban streams, although other metricsshowed less consistency.

Discussion

The results of this study indicate that patterns inalgal assemblages along urban intensity gradients varyregionally. Of the various attributes of algal assem-blages tested, only proportions of taxa tolerant togeneral and organic pollution consistently tended tobe higher in urban streams than in nonurban streams,although correlations of these attributes with UIIwere not always statistically significant. Algal biom-ass and diversity increased or decreased along theurban gradient depending on natural factors andhuman influences.

Our work demonstrates that climate and geologyimposed ultimate constraints on the structure of algalassemblages in streams as predicted by the conceptualmodels of Biggs (1990) and Stevenson (1997). Loweralgal biomass in the BOS study area in comparison toalgal biomass in the SLC and BIR areas was in agree-ment with a generally observed pattern of lower bio-mass levels in streams of higher latitudes where climateis harsher and nutrients are lower compared to streamsof lower latitudes (Dodds et al. 1998). Higher speciesdiversity in the BIR area in comparison to diversity inthe BOS and SLC areas confirmed previous observa-tions that highest diversity of stream algae in the UnitedStates occurs in the warm, humid Southeast (Potapovaand Charles 2004). The higher proportion ofoligohalobous algal taxa in the BOS area compared to

352 POTAPOVA ET AL.

the dominance of halophilic species in the two otherstudy areas reflected differences in the bedrock com-position and, therefore, in water mineralization amongthe study areas. Biotic interactions, such as the pres-

ence of grazers, also influenced algal assemblages. Inthe BIR study area, the herbivorous largescalestoneroller was able to tolerate urban-related habitatdisturbance (Meador et al. 2005, this volume). The

TABLE 9. Correlations of environmental variables and NMS ordination axes for the DTH 27-sample data set from theBirmingham study area. Ordinations were rotated to maximize loadings of the first axis on the urban intensity index. Onlyenvironmental variables with highest multiple r2 (Σr2) are shown. Significant correlations (P < 0.05) are in bold. Abbreviationsfor variables and variable units are in Table 1.

All taxa Diatoms onlyEnvironmental variable axis 1 axis 2 axis 3 Σr2 axis 1 axis 2 axis 3 Σr2

Urban intensity index 0.65 –0.02 –0.37 0.55 0.71 –0.03 0.00 0.51Forest –0.62 0.11 0.38 0.53 –0.63 0.15 0.10 0.43Periodf3 0.51 –0.13 –0.42 0.45 0.66 0.04 –0.07 0.44Periodf5 0.50 –0.14 –0.42 0.44 0.65 0.08 –0.05 0.43Number of herbicide detections 0.46 –0.47 0.00 0.44 0.28 –0.46 –0.22 0.34Periodr5 0.52 –0.12 –0.38 0.43 0.65 0.11 –0.13 0.45Periodr3 0.50 –0.18 –0.37 0.42 0.64 0.02 –0.16 0.44Periodf9 0.46 –0.15 –0.40 0.40 0.62 0.12 –0.07 0.40Periodf7 0.47 –0.14 –0.40 0.40 0.63 0.11 –0.04 0.40Periodr9 0.47 –0.15 –0.38 0.40 0.62 0.12 –0.09 0.40Periodr7 0.48 –0.12 –0.39 0.39 0.63 0.12 –0.11 0.42Mean basin elevation –0.29 0.33 –0.42 0.36 0.13 –0.52 0.54 0.58Mxh95 –0.56 0.07 0.19 0.35 –0.53 0.09 0.15 0.31% cobbles –0.34 –0.17 –0.41 0.32 0.13 –0.13 0.02 0.04Stoneroller 0.22 0.18 –0.48 0.31 0.63 0.23 0.27 0.52Mg 0.51 –0.17 –0.04 0.29 0.36 –0.08 –0.08 0.14Relief –0.26 0.36 –0.30 0.28 0.08 0.45 0.24 0.26Cultivated –0.31 –0.33 0.27 0.28 –0.46 –0.23 –0.12 0.27Mxl5 0.12 0.07 –0.50 0.26 0.46 0.15 –0.07 0.24Segment sinuosity –0.05 0.07 0.51 0.26 –0.35 –0.26 –0.26 0.26Mxh75 –0.02 –0.50 –0.05 0.25 –0.07 –0.08 –0.04 0.01% gravel 0.13 0.22 0.43 0.25 –0.41 –0.01 –0.04 0.17Bank-full depth –0.03 0.07 –0.48 0.24 0.53 –0.02 –0.09 0.28Surface-water gradient 0.21 0.41 –0.09 0.22 0.21 0.22 0.41 0.26% silt + gravel –0.16 0.02 0.44 0.22 –0.58 0.21 –0.34 0.49Total herbicide concentration 0.27 –0.17 –0.34 0.21 0.48 –0.30 –0.18 0.36Flow-stability index 0.01 0.15 0.43 0.21 –0.43 –0.24 0.00 0.24Bank-full width 0.27 –0.05 –0.35 0.20 0.60 –0.18 –0.13 0.41Wetted width 0.31 –0.08 –0.31 0.20 0.49 –0.29 –0.24 0.38NO

3 + NO

20.04 –0.12 0.43 0.20 –0.24 –0.41 –0.12 0.24

Scrapers –0.32 0.06 0.28 0.18 –0.46 0.01 –0.34 0.32Discharge 0.16 0.08 0.36 0.16 –0.04 –0.52 –0.02 0.27Wetted width–depth ratio 0.29 –0.03 –0.22 0.13 0.43 –0.14 0.21 0.24TN 0.10 –0.08 0.31 0.12 –0.05 –0.50 –0.19 0.28Basin area 0.09 –0.20 –0.23 0.10 0.25 –0.06 –0.52 0.33Residue on evaporation 0.26 –0.10 0.13 0.09 0.24 –0.44 –0.39 0.40Uplands 0.03 0.07 0.30 0.09 –0.02 –0.48 0.05 0.23Conductivity 0.21 –0.06 0.08 0.05 0.23 –0.41 –0.37 0.35Periodf1 0.17 –0.03 –0.04 0.03 0.19 –0.10 –0.50 0.30Ca 0.10 –0.10 0.11 0.03 0.05 –0.44 –0.20 0.23Dissolved oxygen 0.03 0.02 0.13 0.02 0.02 –0.06 0.48 0.23

353A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

presence of this grazer was not only associated withreduced algal biomass, but also with shifts in taxo-nomic composition and diversity of algae.

Nonuniform responses of stream algal assemblagesto urbanization reflected the regional differences inhuman influences. The gradient of urban intensity in

TABLE 10. Correlations of environmental variables and NMS ordination axes for the RTH 30-sample data set from the SaltLake City study area. Ordinations were rotated to maximize loadings of the first axis on the urban intensity index. Onlyenvironmental variables with highest multiple r2 (Σr2) are shown. Significant correlations (P < 0.05) are in bold. Abbreviationsfor variables and variable units are in Table 1.

All taxa Diatoms onlyEnvironmental variable axis 1 axis 2 axis 3 Σr2 axis 1 axis 2 axis 3 Σr2

Na 0.63 0.05 0.22 0.44 0.48 –0.02 0.15 0.26Cl 0.64 0.20 0.06 0.44 0.51 0.04 0.11 0.27SiO

20.59 0.21 0.13 0.40 0.30 –0.06 –0.07 0.10

Residue on evaporation 0.58 0.24 –0.01 0.39 0.55 0.15 –0.13 0.34Conductivity 0.58 0.20 –0.02 0.37 0.58 0.22 –0.07 0.39K 0.52 –0.06 0.28 0.36 0.40 –0.02 0.37 0.30% gravel + boulders –0.55 –0.18 –0.05 0.34 –0.57 –0.10 0.19 0.37% silt + gravel 0.55 0.18 0.05 0.34 0.57 0.10 –0.19 0.37Mg 0.51 0.24 –0.13 0.34 0.57 0.31 –0.17 0.45F 0.47 –0.21 0.25 0.32 0.21 –0.28 0.49 0.36% sand 0.55 0.15 –0.05 0.32 0.49 –0.02 –0.12 0.25Mean dominant substrate size –0.54 –0.17 0.02 0.32 –0.63 –0.05 0.21 0.44Alkalinity 0.38 0.34 –0.25 0.32 0.54 0.44 –0.33 0.60% fine 0.54 0.17 –0.01 0.32 0.49 0.11 –0.15 0.27Coefficient of variation of flow

velocity –0.28 –0.48 –0.10 0.32 –0.28 –0.53 0.37 0.49Grassland –0.45 0.27 –0.16 0.30 0.03 0.49 –0.03 0.24Coefficient of variation of

bank-full width 0.39 0.37 0.01 0.29 –0.61 –0.24 0.30 0.52Ca 0.33 0.33 –0.26 0.28 0.51 0.40 –0.42 0.60Bank-full width–depth ratio –0.26 –0.42 –0.19 0.28 –0.39 0.21 0.17 0.23TP 0.49 0.00 0.17 0.27 0.53 0.00 0.14 0.31Fe –0.16 –0.37 0.34 0.27 0.26 –0.27 0.03 0.14Channel shape index –0.34 –0.36 –0.16 0.27 –0.48 –0.35 0.48 0.59Wetted width–depth ratio –0.30 –0.40 –0.11 0.26 –0.41 –0.51 0.56 0.74Segment sinuosity 0.28 0.28 0.33 0.26 0.54 0.28 –0.12 0.39Urban intensity index 0.50 0.06 –0.07 0.26 0.54 0.00 0.00 0.29Embeddedness 0.26 0.43 0.00 0.25 0.61 0.50 –0.33 0.73Coefficient of variation of

discharge –0.24 –0.43 –0.08 0.25 –0.31 –0.65 0.47 0.73Depth at sampling location 0.16 0.46 –0.22 0.25 0.09 0.34 –0.13 0.14Channel relative roughness –0.37 –0.33 0.08 0.25 –0.36 –0.55 0.32 0.53TKN 0.25 –0.18 0.21 0.14 0.38 –0.15 0.38 0.31Manning’s channel roughness –0.15 –0.34 –0.04 0.14 –0.37 –0.27 0.25 0.27Maximum wetted depth 0.08 0.35 0.00 0.13 –0.08 0.50 –0.24 0.32Wetted depth 0.08 0.36 0.00 0.13 –0.06 0.50 –0.24 0.32% silt + clay 0.32 0.05 –0.10 0.12 0.44 0.03 –0.35 0.32Number of herbicide detections 0.16 –0.24 0.14 0.10 0.38 –0.13 0.42 0.33Velocity at sampling location 0.10 0.29 0.06 0.10 –0.04 0.39 –0.38 0.30Flow velocity –0.04 0.25 0.18 0.09 –0.27 0.33 –0.34 0.30Maximum flow velocity –0.09 0.22 0.15 0.08 –0.30 0.28 –0.34 0.29Silt occurrence 0.20 0.09 –0.07 0.05 0.53 0.09 0.32 0.39

354 POTAPOVA ET AL.

related to urban intensity. In the BIR area, patterns ofalgal composition along the urban gradient were relatedmore to physical habitat characteristics and the pres-ence of grazers. Taxonomic composition of algal assem-blages in the BOS study area showed a clear patternalong the conductivity gradient, which was collinearwith the gradient of urban intensity. The process of siteselection for the BOS area ensured homogeneity of natu-ral conditions of the data set, meaning that increasedconductivity at the urban sites was caused primarily byhuman influence. Thus, the ability of algae to indicatewater-mineral content may be exploited successfully tomonitor water mineralization in affected streams of theBOS area. The composition of algal assemblages alsowas related to water-mineral content in the SLC studyarea. However, urban development was concentratedin lower elevations, while nonurban sampling was con-ducted in higher elevations. Water-mineral content natu-rally increases downstream; therefore, we could not besure that an increase in water-mineral content at theurban sites was a result of human influence alone. Abun-dance of halophilic diatoms on hard substrates of SLCurban streams can be considered a sign of human influ-ences with caution only.

We found that while algal assemblages in the BOSand SLC study areas had higher biomass and diversityin urban streams than in less affected streams, assem-blages in the BIR streams exhibited opposite patterns.Evidently, algae in the nonurban BOS and SLC streamswere nutrient-limited, and increases in nutrient con-centrations in urban streams were at least partly associ-ated with higher algal abundance and diversity. Patternsin species composition of algal assemblages also wereclosely associated with nutrients. The strongest correla-tions occurred in the BOS area where nutrient concen-trations increased significantly with urbanization. Inthe SLC and BIR study areas, even in the absence ofstrong correlations between nutrient concentrations andurban intensity, most algal taxa occurring at the upperend of the urban gradient were eutraphentic diatoms.The composition of benthic algae has potential as anindicator of nutrient concentrations in running waters(Kelly and Whitton 1995; Pan et al. 1996; Winter andDuthie 2000; Soininen and Niemelä 2002), but de-tailed, regional-scale studies are needed to create reliablealgae-based monitoring tools. In particular, many spe-cies typical of nutrient-rich waters are broadly distrib-uted and well known, whereas oligotraphentic speciesoften are characteristic of more limited geographic areasand are not sufficiently studied (Potapova and Charles2004). This may explain why, in this study, we some-times did not observe decreases in relative abundance of

Axis 1

Variation in flowvelocity

Depth atsamplinglocation

Cond

ClUII

Homoeothrix janthina

Diatoma mesodon

Phormidium autumnale

Achnanthidium deflexum

Navicula shroeteri var. escambia

Diatoma moniliformis

Amphora copulata

Gyrosigmaattenuatum

1

0

-1

-1 0 1

Light

Stream order

UII

Homoeothrix janthina

unidentif ied coccoid

cyanobacterium

Phormidium autumnale

Anabaena sp.

Navicula cincta

Calothrix parietina

Cocconeis neodiminuta

Fragilaria vaucheriae

Caloneis bacillum

Oedogonium sp.

Nitzschia recta

Sellaphora pupula

Residue on evaporation

Bankfull depth

1

0

-1

-1 0 1

A

B

Axis 1

Axi

s 2

Axi

s 2

Encyonemaauerswaldii

FIGURE 6. Results of nonmetric multidimensional scal-ing ordinations for (A) 30 RTH and (B) 28 DTH samplesfrom streams in the Salt Lake City study area. Joint plots ofspecies and environmental variables were obtained by rotatingordinations to align the UII variable with the first ordinationaxis. Abbreviations for variables are listed in Table 1.

the BOS and SLC study areas was associated mostlywith the transition from natural landscapes (forest andshrub land) to urban areas. In the BIR area, not onlynatural landscapes, but also percentages of agricul-tural land decreased along an urban gradient. In agree-ment with this pattern, concentrations of nutrientsand major ions significantly increased along gradientsof urban intensity in the BOS and SLC study areas,but not in the BIR area. Presumably, agricultural in-puts were replaced by urban inputs in BIR. Taxo-nomic composition of the stream algal assemblageschanged especially along the urban gradient in theBOS and SLC study areas where water chemistry was

355A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

pollution-sensitive, oligosaprobic, and oligotraphenticspecies along urban gradients when using metrics basedon European studies.

The three study areas in this investigation also dif-fered in the hydrological modifications to urban streamsthat affect patterns of flow regime along urban gradi-

ents. Dams in the watersheds of urban streams in theBOS study area stabilized discharges. Stable flow com-monly promotes accumulation of algal biomass in streams(Biggs 1996), a pattern that we observed in the urbanstreams of the BOS study area. In contrast, urban streamsin the BIR study area had more variable flow patterns.

TABLE 11. Correlations of environmental variables and NMS ordination axes for the DTH 28-sample data set from the SaltLake City study area. Ordinations were rotated to maximize loadings of the first axis on the urban intensity index. Onlyenvironmental variables with highest multiple r2 (Σr2) are shown. Significant correlations (P < 0.05) are in bold. Abbreviationsfor variables are in Table 1.

All taxa Diatoms onlyEnvironmental variable axis 1 axis 2 axis 3 Σr2 axis 1 axis 2 axis 3 Σr2

Light –0.48 –0.48 0.34 0.57 –0.41 0.39 0.05 0.32Stream order –0.66 0.03 –0.16 0.47 –0.72 –0.28 0.53 0.87Grassland –0.65 0.11 0.16 0.46 –0.56 0.00 0.37 0.44F 0.43 –0.05 –0.42 0.36 0.45 0.36 –0.32 0.43Basin area –0.56 –0.03 –0.18 0.35 –0.60 –0.15 0.44 0.57pH –0.22 0.06 –0.53 0.33 –0.29 –0.11 0.29 0.18Canopy closure –0.51 –0.18 0.12 0.30 –0.19 –0.10 0.52 0.31Discharge –0.45 –0.04 –0.29 0.29 –0.56 –0.11 0.45 0.53NH

4–0.09 –0.30 0.43 0.28 0.14 0.28 –0.07 0.10

Uplands –0.12 0.45 –0.24 0.27 0.10 0.13 0.20 0.07Bank-full width –0.44 0.06 –0.25 0.25 –0.59 0.01 0.25 0.41Wetted width –0.42 0.01 –0.27 0.25 –0.59 –0.02 0.27 0.43Bank-full depth –0.45 0.12 –0.01 0.22 –0.11 0.39 0.07 0.16TKN –0.03 –0.20 –0.37 0.18 0.16 0.74 0.01 0.57Na 0.37 0.08 –0.32 0.25 0.65 0.30 –0.08 0.52K 0.16 –0.12 –0.39 0.19 0.39 0.58 –0.04 0.49Cl 0.30 0.10 –0.32 0.20 0.62 0.31 0.01 0.48Residue on evaporation 0.43 0.15 –0.10 0.22 0.67 –0.09 –0.16 0.48Velocity at sampling location –0.30 0.19 –0.26 0.19 –0.30 –0.20 0.50 0.38SiO

20.42 –0.05 –0.02 0.17 0.62 0.02 –0.21 0.43

Relative channel roughness 0.37 0.05 0.16 0.17 0.13 –0.01 –0.54 0.31Coefficieint of variation of flow

velocity 0.23 0.06 0.32 0.16 0.26 0.16 –0.47 0.31SO

40.38 0.08 –0.05 0.15 0.51 –0.24 –0.20 0.36

Embeddedness –0.36 –0.09 0.12 0.15 0.14 0.14 0.53 0.32Wetted depth –0.37 –0.09 0.02 0.15 –0.39 –0.11 0.36 0.29Coefficient of variation of

discharge 0.25 0.14 0.23 0.14 0.32 0.25 –0.41 0.33Conductivity 0.26 0.20 –0.19 0.14 0.56 0.07 0.01 0.32TP 0.06 –0.23 –0.28 0.13 0.34 0.57 0.05 0.45Maximum wetted depth –0.36 –0.06 0.01 0.13 –0.40 –0.13 0.35 0.30Flow velocity –0.20 –0.14 –0.23 0.11 –0.44 –0.27 0.27 0.34Water temperature 0.01 –0.17 –0.21 0.07 0.19 0.58 0.16 0.40Number of herbicide detections 0.22 0.04 –0.14 0.07 0.35 0.40 –0.30 0.37Maximum flow velocity –0.11 –0.13 –0.19 0.06 –0.43 –0.32 0.13 0.30% silt + gravel 0.19 –0.09 0.08 0.05 0.50 0.21 0.10 0.31% gravel + boulder –0.18 0.09 –0.08 0.05 –0.50 –0.21 –0.11 0.31Mean dominant substrate size –0.11 0.01 –0.18 0.04 0.52 –0.20 –0.13 0.32Urban intensity index 0.18 0.00 –0.01 0.03 0.54 0.00 0.00 0.29

356 POTAPOVA ET AL.

Higher disturbance levels associated with stream flashi-ness may lower algal biomass and diversity and affectspecies membership (Biggs et al. 1998), as was observedin the BIR urban streams. Flow variability, measured asdischarge or stage variability, may affect algal assem-blages at different temporal scales (Clausen and Biggs1997; Matthaei et al. 2003). Flow variability in thedays or weeks preceding algal sampling often deter-mines at which successional stage the algal assemblageswill be found and which life forms will dominate. Long-term flow variability, measured over seasons and years,and current velocity may control the local species pool,favoring species best adapted for a particular hydrologi-cal stream type. In our study, streams were sampledwithin relatively short time periods in each study areaand during periods of stable flow; thus, short-term flowconditions were not considered as explanatory variablesin our analyses. Flow regime was quantified by usinglong-term stage observations and channel morphology.Stage-related variables, such as the number of periodswhen stage rose above or fell below specific levels, char-acterized urban streams in the BIR area as having a lessstable flow regime than the background streams. Thisflow variability apparently was reflected in the compo-sition of algal assemblages. The correlative nature of thisstudy, however, only allows us to hypothesize about thecausal relations between long-term flow regime and thecomposition of algal assemblages.

Our study differed fundamentally from otherstudies of algal assemblages in urban streams in thatwe used the multimetric index of urban intensity basedon several infrastructure, socioeconomic, and land-usecharacteristics. Studying relations between biologicalassemblages and a general measure of human influ-ences could be enlightening for several reasons. Vari-ous stressors associated with human influence mayhave a cumulative effect on biota. A single disturbance-

quantifying index, such as the UII, may be used in-stead of the multiple characteristics of impairment oncestrong relations have been established among such anindex, individual stressors, and biological assemblages.Our analyses demonstrated, however, that individualenvironmental factors associated with urbanizationmore often than the UII showed strong relations toalgal assemblage composition, biomass, and diversity.In almost all data sets in this study, a number of proxi-mate environmental factors had higher correlationswith the NMS ordination axes than with the UII.Similarly, Sonneman et al. (2001) found that diatomcommunities in Australian urban streams were morestrongly related to water chemistry than to a gradientof urban intensity, quantified as the proportion ofimpervious surface in the watershed.

Results of this study imply that no universal algae-based indicators can be used successfully in bioassess-ment across different geographic zones and environ-mental settings. The relative abundance of some taxa,recognized worldwide as reliable indicators of nutrient-rich and generally polluted or unpolluted waters, wasthe single attribute of algal assemblages showing themost consistent response to urbanization in this study.Our gradient analyses demonstrated, however, that alarge amount of additional information on the relationsbetween various habitat variables and algal assemblagesin each area could be explored and then used inbioassessment. For instance, algal assemblages in ourstudy showed distinct responses to many physical habi-tat characteristics. Further observational and experimen-tal studies may clarify the significance of these responsesand their usefulness for stream monitoring.

The design of this pilot study allowed us tocompare patterns of biota along urban gradients indifferent environmental settings, but not to developmetrics for immediate use in bioassessment. Such

TABLE 12. Kendall rank correlation coefficients between selected autecological diatom metrics and the urban intensity indexin the three study areas. **, P < 0.01;*, P < 0.05.

Metrics Boston Birmingham Salt Lake CityRTH DTH RTH DTH RTH DTHn = 30 n = 30 n = 27 n = 27 n = 30 n = 28

Pollution-sensitivea –0.50** –0.35** 0.08 –0.08 –0.17 –0.25Pollution-toleranta 0.55** 0.27* 0.18 0.12 0.11 0.14Oligosaprobousb 0.38** –0.15 –0.17 –0.17 –0.30* 0.01α-meso + polysaprobousb 0.66** 0.33** 0.18 0.11 0.44** 0.41**Oligotraphenticb 0.12 –0.18 –0.15 –0.32* –0.39** –0.16Eutraphenticb 0.42** 0.35** 0.13 –0.01 0.21 –0.06

a From Lange-Bertalot (1979)b From van Dam et al. (1994)

357A COMPARISON OF THE INFLUENCES OF URBANIZATION IN CONTRASTING ENVIRONMENTAL SETTINGS

work requires not only exploratory, but also confir-matory analyses and larger data sets specifically col-lected to investigate the effects of particular stressorson stream algal assemblages. Our work illustrated,however, the importance of developing regional-scalemethods of bioassessment that are based on the de-tailed investigations of biological community re-sponses to specific stresses associated with humaninfluences.

Acknowledgments

Frank Acker, Kalina Manoylov, Eduardo Morales, LontMarr, Jackie White-Reimer, and Diane Winter identi-fied and enumerated algae. Cathy Tate, David Mueller,David Litke, Gerard McMahon, Tom Cuffney,Michael Meador, Peter Ruhl, Stephen Moulton, andTerry Short provided environmental information.Robert Gray, Julie Hambrook Berkman, DonaldCharles, Karin Ponader, and an anonymous reviewerprovided helpful comments on earlier drafts of themanuscript. The use of firm, trade, or brand names inthis article is for identification purposes only and doesnot constitute endorsement by the U.S. GeologicalSurvey.

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