spatial patterns and ecological determinants of benthic algal assemblages in mid-atlantic streams,...

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460 J. Phycol. 35, 460–468 (1999) SPATIAL PATTERNS AND ECOLOGICAL DETERMINANTS OF BENTHIC ALGAL ASSEMBLAGES IN MID-ATLANTIC STREAMS, USA 1 Yangdong Pan 2 Environmental Sciences and Resources, Portland State University, Portland, Oregon 97207 R. Jan Stevenson Department of Biology, University of Louisville, Louisville, Kentucky 40292 Brian H. Hill National Exposure Research Laboratory, U.S. Environmental Protection Agency, Cincinnati, Ohio 45244 and Philip R. Kaufmann and Alan T. Herlihy Department of Fisheries and Wildlife, Oregon State University, c/o U.S. Environmental Protection Agency, Corvallis, Oregon 97333 We attempted to identify spatial patterns and de- terminants for benthic algal assemblages in Mid-At- lantic streams. Periphyton, water chemistry, stream physical habitat, riparian conditions, and land cov- er/use in watersheds were characterized at 89 ran- domly selected stream sites in the Mid-Atlantic re- gion. Cluster analysis (TWINSPAN) partitioned all sites into six groups on the basis of diatom species composition. Stepwise discriminant function analy- sis indicated that these diatom groups can be best separated by watershed land cover/use (percentage forest cover), water temperature, and riparian con- ditions (riparian agricultural activities). However, the diatom-based stream classification did not cor- respond to Omernik’s ecoregional classification. Al- gal biomass measured as chl a can be related to nu- trients in habitats where other factors do not con- strain accumulation. A regression tree model indi- cated that chl a concentrations in the Mid-Atlantic streams can be best predicted by conductivity, stream slope, total phosphorus, total nitrogen, and riparian canopy coverage. Our data suggest that broad spatial patterns of benthic diatom assemblag- es can be predicted both by coarse-scale factors, such as land cover/use in watersheds, and by site- specific factors, such as riparian conditions. How- ever, algal biomass measured as chl a was less pre- dictable using a simple regression approach. The re- gression tree model was effective for showing that ecological determinants of chl a were hierarchical in the Mid-Atlantic streams. Key index words: ecoregion; gradient; nutrients; pe- riphyton; regression tree model Abbreviations: TN, total nitrogen; TP, total phos- phorus; TWINSPAN, two-way indicator species anal- ysis 1 Received 27 May 1998. Accepted 12 January 1999. 2 Author for reprint requests; e-mail [email protected] Hierarchy theory predicts that complex ecological systems can be decomposed into relatively isolated levels in which ecological processes operate at rela- tively distinct temporal and spatial scales (O’Neill et al. 1989). Frissell et al. (1986) proposed a hierar- chical view of stream habitats that included micro- habitat, channel unit, reach, and watershed/basin. Elements and processes forming and controlling habitats are a function of basin, geomorphology, and riparian processes, linked across scales (Gregory et al. 1991, Church 1992). This scale-explicit view of stream ecosystems provides a conceptual model for better understanding the distribution patterns of bi- ota and associated scale-dependent determinants in streams. Biggs and Gerbeaux (1993) showed that pe- riphyton development in New Zealand streams might be regulated by both a basin-scale factor (ge- ology) and a local-scale variable (velocity). Richards et al. (1997) demonstrated that reach-scale proper- ties were highly predictive of macroinvertebrate spe- cies traits. Roth et al. (1996) reported that fish as- semblages were significantly correlated with basin- scale factors, such as land use. Ecoregional classification can serve as a conve- nient starting point for analysis of scale-dependent ecological constraints (Omernik 1995). Several com- ponents, such as geology, soils, land use, land sur- face form, and potential natural vegetation, are used to divide large, complex landscapes into ecoregions, which are regions with relative homogeneity in eco- logical systems and relationships between organisms and their environments (Omernik 1987). These fac- tors should regulate the abiotic stressors in streams that directly or indirectly affect the physiology, re- production, and species composition of benthic or- ganisms, such as algae in streams (Stevenson 1997). Ecoregions can serve as a spatial framework that would facilitate establishment of scale-explicit water quality standards for stream bioassessment (Larsen et al. 1986, Hughes and Larsen 1988).

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Page 1: SPATIAL PATTERNS AND ECOLOGICAL DETERMINANTS OF BENTHIC ALGAL ASSEMBLAGES IN MID-ATLANTIC STREAMS, USA

460

J. Phycol. 35, 460–468 (1999)

SPATIAL PATTERNS AND ECOLOGICAL DETERMINANTS OF BENTHIC ALGALASSEMBLAGES IN MID-ATLANTIC STREAMS, USA1

Yangdong Pan2

Environmental Sciences and Resources, Portland State University, Portland, Oregon 97207

R. Jan StevensonDepartment of Biology, University of Louisville, Louisville, Kentucky 40292

Brian H. HillNational Exposure Research Laboratory, U.S. Environmental Protection Agency, Cincinnati, Ohio 45244

and

Philip R. Kaufmann and Alan T. HerlihyDepartment of Fisheries and Wildlife, Oregon State University, c/o U.S. Environmental Protection Agency, Corvallis, Oregon 97333

We attempted to identify spatial patterns and de-terminants for benthic algal assemblages in Mid-At-lantic streams. Periphyton, water chemistry, streamphysical habitat, riparian conditions, and land cov-er/use in watersheds were characterized at 89 ran-domly selected stream sites in the Mid-Atlantic re-gion. Cluster analysis (TWINSPAN) partitioned allsites into six groups on the basis of diatom speciescomposition. Stepwise discriminant function analy-sis indicated that these diatom groups can be bestseparated by watershed land cover/use (percentageforest cover), water temperature, and riparian con-ditions (riparian agricultural activities). However,the diatom-based stream classification did not cor-respond to Omernik’s ecoregional classification. Al-gal biomass measured as chl a can be related to nu-trients in habitats where other factors do not con-strain accumulation. A regression tree model indi-cated that chl a concentrations in the Mid-Atlanticstreams can be best predicted by conductivity,stream slope, total phosphorus, total nitrogen, andriparian canopy coverage. Our data suggest thatbroad spatial patterns of benthic diatom assemblag-es can be predicted both by coarse-scale factors,such as land cover/use in watersheds, and by site-specific factors, such as riparian conditions. How-ever, algal biomass measured as chl a was less pre-dictable using a simple regression approach. The re-gression tree model was effective for showing thatecological determinants of chl a were hierarchical inthe Mid-Atlantic streams.

Key index words: ecoregion; gradient; nutrients; pe-riphyton; regression tree model

Abbreviations: TN, total nitrogen; TP, total phos-phorus; TWINSPAN, two-way indicator species anal-ysis

1 Received 27 May 1998. Accepted 12 January 1999.2 Author for reprint requests; e-mail [email protected]

Hierarchy theory predicts that complex ecologicalsystems can be decomposed into relatively isolatedlevels in which ecological processes operate at rela-tively distinct temporal and spatial scales (O’Neill etal. 1989). Frissell et al. (1986) proposed a hierar-chical view of stream habitats that included micro-habitat, channel unit, reach, and watershed/basin.Elements and processes forming and controllinghabitats are a function of basin, geomorphology,and riparian processes, linked across scales (Gregoryet al. 1991, Church 1992). This scale-explicit view ofstream ecosystems provides a conceptual model forbetter understanding the distribution patterns of bi-ota and associated scale-dependent determinants instreams. Biggs and Gerbeaux (1993) showed that pe-riphyton development in New Zealand streamsmight be regulated by both a basin-scale factor (ge-ology) and a local-scale variable (velocity). Richardset al. (1997) demonstrated that reach-scale proper-ties were highly predictive of macroinvertebrate spe-cies traits. Roth et al. (1996) reported that fish as-semblages were significantly correlated with basin-scale factors, such as land use.

Ecoregional classification can serve as a conve-nient starting point for analysis of scale-dependentecological constraints (Omernik 1995). Several com-ponents, such as geology, soils, land use, land sur-face form, and potential natural vegetation, are usedto divide large, complex landscapes into ecoregions,which are regions with relative homogeneity in eco-logical systems and relationships between organismsand their environments (Omernik 1987). These fac-tors should regulate the abiotic stressors in streamsthat directly or indirectly affect the physiology, re-production, and species composition of benthic or-ganisms, such as algae in streams (Stevenson 1997).Ecoregions can serve as a spatial framework thatwould facilitate establishment of scale-explicit waterquality standards for stream bioassessment (Larsenet al. 1986, Hughes and Larsen 1988).

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461ALGAL PATTERNS IN STREAMS

FIG. 1. a. A USA map showingthe location of the Mid-Atlanticregion. b. A map of the Mid-At-lantic region showing samplinglocations. The solid lines areecoregional boundaries. Thedashed lines are state boundaries.

This study was aimed at identifying spatial pat-terns and ecological determinants of benthic algalassemblages in Mid-Atlantic streams of the easternUnited States (Fig. 1). The Mid-Atlantic region is amosaic of diverse landscapes. The spatial heteroge-neity in the region is largely a function of geology,topography, climate, land cover/use, and naturalvegetation, and thus the region has been delineatedinto several ecoregions (Woods et al. 1996). Ourfirst objective was to identify spatial patterns of dia-tom species assemblages. We used two-way indicatorspecies analysis (TWINSPAN), a hierarchical classi-fication method (Hill et al. 1975), to determinewhether benthic diatom assemblages distributed asdiscrete clusters in this region with complex land-scapes. Benthic algae are sensitive and integrative toenvironmental variation in both streams and water-sheds, and thus their species composition should re-flect major environmental patterns in this region

(Pan et al. 1996). The second objective was to de-termine whether environmental factors were signif-icantly different among spatial clusters of diatom as-semblages and how spatial patterns of benthic dia-tom assemblages corresponded to ecoregions, an ex-isting spatial framework. Finally, we wanted todetermine relationships between algal biomass mea-sured as chl a and environmental factors. We ex-pected that the regional pattern of benthic algaemight reflect coarse scale factors, such as geology,and that other factors, such as shading, becomemore important at site-specific levels.

MATERIALS AND METHODS

Study area. The Mid-Atlantic region covers portions of five ma-jor river drainage systems (Susquehanna, Potomac, Allegheny,Monongahela, and Kanawha). For purposes of this analysis, wesubdivided the region into seven ecoregions on the basis of ag-gregations of the ecoregion classification of Woods et al. (1996)and Omernik (1987). These ecoregions are Central, Northern,

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462 YANGDONG PAN ET AL.

TABLE 1. Characterization of major ecoregions in the Mid-Atlantic area (modified from Woods et al. 1996). CP, coastal plain; AP,Appalachian Plateau.

Ecoregion Major characterization

Blue mountain ridge A narrow forested and dissected ridge with high elevation. Bedrock typically is composed of Cambrian andPrecambrian metamorphosed sedimentary and complex gneissic and plutonic rocks. Ultisols (Hapludults-Hapludalfs-Kandiudults) and Inceptisols (Dystrochrepts-Eutrochrepts).

Piedmont/CP Middle Atlantic Coastal plain and northern Piedmont consisting of low rounded hills, irregular plains, andopen valleys. Ultisols. Coastal Plain is underlain by young unconsolidated sediments. Piedmont is underlainby ancient crystalline metamorphic and plutonic rocks.

Northern AP A part of plateau with high hills and low mountains. Sandstones, shales, siltstone, conglomerate, and coal.Soils are low in nutrients. Northern hardwood forest.

Central AP High, dissected and rugged plateau. Similar rock types as in Northern AP. Ultisols (Hapludults; Fragiudalfs-Dystrochrepts) and Inceptisols (Dystrochrepts-Fragiochrepts-Haplaquepts).

Western AP Low, dissected, and unglaciated plateau. Alfisols (Hapludults-Fragiudalfs; Hapludalfs-Hapludults-Dystrochrepts)or Ultisols (Hapludults-Fragiudalfs-Dystrochrepts). Appalachian oak forest.

Ridge High, steep, forested ridges typically are composed of more resistant sandstones.Valley Limestone and shale formations are common in the valley bottoms.

and Western Appalachian Plateaus; Valley; Ridge; Piedmont/Coastal Plains; and Blue Mountain Ridge (Table 1).

Stream sites in the region were selected using a systematic, ran-domized sampling design (Herlihy et al. 1998). The stream pop-ulation in the region was defined on the basis of digitized versionsof the U.S. Geological Survey’s (USGS’s) 1:100,000-scale topo-graphic maps. To get a more equitable distribution of streamsizes, sample probabilities were set so that roughly equal numbersof first-, second-, and third-order streams would be sampled. Atotal of 89 sites were sampled for periphyton and water chemistryfrom late April to early July in 1993 and 1994 (Fig. 1).

Sampling design. A study reach (403 mean channel width),ranging in length from 150 to 500 m, was sampled in each streamsite. Eleven cross-section transects were set up in each study reachby dividing the reach into 10 equal-length intervals (includes atransect at the start and end of each reach). Periphyton sampleswere collected from each of the 11 transects and combined intoeither a depositional or an erosional habitat composite sample.Transects with no visible water movement were defined as depo-sitional habitat, and those with visible water movement were con-sidered erosional habitat. At each transect, periphyton was col-lected from a 12-cm2 area of streambed using a 1.5-cm-long pieceof 3.9-cm-diameter PVC pipe as a template. For fine substrate,periphyton was suctioned into a 60-mL syringe; in coarser sub-strate, periphyton was scraped off with a toothbrush and rinsedwith stream water. Composite periphyton samples were then pre-served with 37% formalin. The end result was one composite peri-phtyon sample for erosional habitats and one composite samplefor depositional habitats for each stream site. However, deposi-tional habitats were absent in a large number of stream sites.Using 49 sites with both depositional and erosional data from thesame study, Pan et al. (1996) showed that diatom assemblages indepositional and erosional habitats had similar responses to waterchemistry. For this reason, periphyton samples from erosionalhabitats only were used in the present analysis.

Periphyton analysis. An aliquot of algal suspension was used tomake a water-mounted slide for enumerating nondiatom algaeand living diatoms. At least 300 algal cells with protoplasm werecounted at 10003 magnification. Nondiatom algae were identi-fied only to genus. Another aliquot from the same sample wasacid-cleaned and mounted in HYRAXt to identify and enumeratediatom species (Patrick and Reimer 1966). A minimum of 500diatom valves were counted at 10003 magnification. Diatom tax-onomy followed mainly Krammer and Lange-Bertalot (1986,1988, 1991a, b) and Patrick and Reimer (1966, 1975). Chloro-phyll a and ash free dry mass (AFDM) were measured followingstandard procedures (American Public Health Association 1992).The siltation index (Bahls 1993) was calculated as a summationof the relative abundance of all motile diatoms (defined as dia-toms with raphes in both valves). Motile diatoms, such as Nitzschiaand Surirella, are often abundant in streams dominated by unsta-ble substrates and thus can reflect sedimentation in streams.

Land cover/use in watersheds. Watershed boundaries above thesampled sites were delineated on 1:250,000-scale USGS topo-graphic maps and digitized into a Geographic Information Sys-tem (GIS), from which watershed areas were calculated. Landuse/land cover for each study watershed was calculated by clip-ping out USGS land use/land cover data layers using ARC/INFOsoftware. Watershed slope (percentage) was calculated as the dif-ference in elevation between the sample site and the highestpoint in the watershed divided by the distance between them.Elevation in each stream site was determined using the contourlines on 1:24,000-scale USGS maps.

Water chemistry. Stream water samples were collected by syringe(60 mL) and cubitainer (4 L) at a flowing portion near the mid-dle of each stream. The syringe samples were sealed with a leuer-lock syringe valve to prevent gas exchange. Within 48 to 72 h ofcollection, water from the syringe samples was analyzed for closedheadspace measurements of pH, dissolved inorganic carbon(DIC), and monomeric aluminum. The cubitainer samples werepartitioned into five aliquots for chemical analyses of major basecations and anions. These aliquots were processed (e.g. filtrationor acid preservation) according to the requirement for differentchemical analyses (see Pan et al. 1996: table 1). Base cations weredetermined by atomic absorption, and anions were measured byion chromatography. Both dissolved organic carbon (DOC) andDIC were determined by a carbon analyzer. The DIC analyseswere done by injecting the sample directly from the sample sy-ringe into a Dohrman carbon analyzer. In the analyzer the sampleis acidified, and the resulting CO2 is purged from the sample byN2 gas and measured by an infrared spectrophotometer. Totalnitrogen (TN) and total phosphorus (TP) were analyzed by thepersulfate oxidation and colorimetry method. Detailed informa-tion on the analytical procedures used for each of the analysescan be found in U.S. Environmental Protection Agency (1987).

Physical habitats. Riparian vegetative cover over the stream wascharacterized as the mean of 44 midchannel measurements, foureach at the 11 cross-section transects (facing in four directions),using a convex spherical densiometer (Lemmon 1957). At eachof the transects, the presence and proximity of 11 categories ofstreamside human influences were estimated (i.e. row crops, pas-ture, dams and revetments, buildings, pavement, roadways, pipes,landfill or trash, parks/lawns, logging operations, and mining ac-tivities). A proximity-weighted disturbance index was calculatedby averaging the tallys of these 11 specified types of disturbancethat were observed at each of 22 locations on the stream reach.On the left and right sides of the reach at 11 transects, the prox-imity of each disturbance type was specified and then weightedaccording to whether it was observed within the stream or on itsactive bank (weight 5 1.67), observed within a 10 3 100-mstreamside riparian plot (weight 5 1.00), or observed outside theriparian plot (weight 5 0.67). Scores of the disturbance indexranged from 0 to 5 in this data set, reflecting a range from lowto high riparian disturbance. Stream water temperature was mea-

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463ALGAL PATTERNS IN STREAMS

sured at the same time and location as the water samples with ahandheld thermometer.

In-stream habitat characterization included thalweg depth,mean wetted width, mean reach cross section, width/depth,slope, residual pool area, and surficial substrates. We did a lon-gitudinal survey of maximum (thalweg) depth at 100 equallyspaced points and wetted width at 21 equally spaced locationsalong the sample reaches; channel depth and substrate were pro-filed at 11 equally spaced cross sections at transect locations oneach stream reach. Reach mean values for thalweg depth, wettedwidth, and width/depth ratio were calculated from these mea-surements. Stream reach slope (percentage) was calculated as thearithmetic mean of 10 water surface gradient measurementsmade using handheld clinometer siting between sequential pairsof the 11 equally spaced transects. Combined with mean reachslope, thalweg depth profiles were used to calculate residual meandepth (residual pool vertical profile area), a flow-independentindex of pool volume by which residual pools are defined as areasin a stream that would contain water at zero discharge becauseof the damming effect of its downstream riffle crest (Kaufmann1987, Robison and Kaufmann 1994). Substrate size and embed-dedness were evaluated using a systematic selection and size clas-sification of five substrate particles from each of 11 transects ineach study reach. Detailed field methods and calculation can befound in Kaufmann and Robison (1994).

Data analysis. Two-way indicator species analysis (TWINSPAN),a hierarchical classification method (Hill et al. 1975), was used toclassify all sites into groups on the basis of their diatom speciescomposition. It starts with all sites as one group and repeatedlydivides it into two smaller groups on the basis of overall differ-ences (e.g. top down; van Tongeren 1995). Attributes that dis-criminate TWINSPAN groups were identified using a stepwise dis-criminant function analysis (SAS 1985).

A regression tree model was used as an exploratory techniqueto identify sources of variability in algal biomass measured as chla. The regression tree model provides an alternative to linear andadditive models for regression problems. The tree-based modelcan screen variables (both numeric and categorical), automati-cally handle interactions between predictor variables, and illus-trate a hierarchical relationship among predictors (Venables andRipley 1994). The regression tree is developed on the basis of thebinary partitioning, which recursively splits the data until sub-groups become homogeneous or contain ,5 observations(MathSoft 1997). The end point of the tree model is predictedvalues of the responsive variable (e.g. chl a) along with hierar-chically organized predictor variables. The model fitness is mea-sured by deviance, the square root of residuals (Brieman et al.1984). The regression tree model was developed using S-Plus Ver-sion 4 statistical software (MathSoft 1997).

Overall differences in species assemblages and environmentalvariables among TWINSPAN groups were assessed using analysisof variance (ANOVA; Zar 1984). If the ANOVA shows a significantdifference among the groups (P , 0.05), a multiple comparisontest (Tukey test) was used to test specific group differences (Zar1984). Prior to analyses, all environmental variables were log-transformed except pH and proportional variables. The propor-tional variables (e.g. land cover/use and species relative abun-dance), which often form a binomial distribution, were trans-formed with an arcsine square-root function (Zar 1984).

RESULTS

Regional environmental patterns. The land cover inthe region was dominated by forests (71.5%), fol-lowed by agriculture (24.4%), urban (2.9%), mining(0.6%), and other (0.6%). Among the sample sites,the average watershed size was 23.3 km2. Streamchannels were generally well canopied, averaging83.4% bankside and 76.1% midchannel ripariancanopy. Of 89 sampled sites, 46% were first-orderstreams, and the rest were second- and third-order

streams in about equal proportions. Mean wettedstream width varied from 0.3 to 17 m with an aver-age of 5.1 m. The ratio of wetted width to depthranged from a high of 50 to a low of 8 with an av-erage of 22. Average channel sinuosity and slopewere 1.2% and 2.4%, respectively. However, slopevaried from a high of 41% to a low of 0.1% amongthe 89 sites. Average composition of stream sub-strates consisted of cobbles (19.5%), coarse gravel(16.2%), fine gravel (13.9%), silt/clay/muck (13%),boulders (11.4%), and bedrock (5.1%).

Spatial patterns of diatom species assemblages. Dia-toms were the most important algal group in theMid-Atlantic streams. Of 243 algal taxa identified,diatoms had the highest taxa richness (194), fol-lowed by Chlorophyta (18) and Cyanobacteria (12).Other groups (Chrysophyta, Dinophyta, Engleno-phyta, and Rhodophyta) were present but with lowtaxa richness. Diatoms were also the most abundant,having an average of 68% of total algal abundanceon the basis of cell density. Other abundant groupswere Cyanobacteria (20%) and Chlorophyta(10.7%). Because of their high richness, abundance,and established taxonomy, only diatoms were in-cluded for further analyses.

Spatial patterns of diatom assemblages were evi-dent. TWINSPAN partitioned all sites into sixgroups on the basis of diatom species composition.Achnanthes minutissima Kutz. was abundant in allgroups (average relative abundance . 20%) (Fig.2). Group I was characterized by acidophilic diatomspecies. Eunotia exigua (Breb. ex Kutz.) Rabh. com-prised about 15% of diatom abundance, which wassignificantly higher than any other group (P ,0.05). The relative abundance of A. deflexa v. alpestriswas significantly higher in group II than in groupsI, IV, and VI (P , 0.05). A. deflexa v. alpestris, A.minutissima, A. lanceolata (Breb.) Grun., and Gom-phonema angustatum (Kutz.) Rabh. were dominant ingroup III. In group IV, the relative abundance of N.minima Grun. was significantly higher than othergroups (P , 0.05). G. angustatum and N. lanceolata(Ag.) Ehr. were common in groups V and VI, re-spectively. Differences among the TWINSPANgroups can also be demonstrated by the siltation in-dex, which in groups IV, V, and VI was significantlyhigher than in groups I and II (P , 0.05; Fig. 2).

Environmental characteristics among diatom groups.Stepwise discriminant function analysis identifiedthat four variables (percentage forest cover, watertemperature, DOC, and riparian agricultural distur-bance) can best discriminate TWINSPAN groups(Table 2). Forest coverage as a percentage of water-sheds was significantly higher in groups I, II, and IIIthan in group IV (,40%; P , 0.05; Fig. 3). Watertemperature in group III was significantly lowerthan in groups II, IV, V, and VI (P , 0.05; Fig. 4a).The DOC concentrations in group IV were signifi-cantly higher than in groups II and III (Fig. 4b).Riparian agricultural disturbance in groups V and

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464 YANGDONG PAN ET AL.

FIG. 2. Comparison of select-ed common diatom species andSiltation Index (mean 1 SE)among TWINSPAN groups in theMid-Atlantic streams. Differentletters in the figure indicate sig-nificant differences among theTWINSPAN groups at P,0.05.

FIG 3. Comparison of land cover/use as a proportion of wa-tersheds amoung TWINSPAN groups in the Mid-Atlantic streams.Different letters in the figure indicate significant differencesamong the TWINSPAN groups at P,0.05.

TABLE 2. Stepwise discriminant function analysis of environmen-tal variables. A total of 31 variables of land cover/use in water-sheds, physical habitats, riparian conditions, and water chemistrywas used to identify environmental variables that can best sepa-rate TWINSPAN groups.

Step Variable entered Partial r2Wilks’

lambdaF-

statistics P-value

1234

5

Percentage of forestWater temperatureDissolved organic carbonRiparian agriculatural

disturbanceTotal riparian disturbance

0.320.230.210.15

0.12

0.680.530.410.35

0.31

7.544.934.292.87

2.09

0.00010.00050.0020.02

0.08

VI was significantly higher than in groups I, II, andIII (Fig. 4c).

Classification of streams on the basis of diatomsdid not correspond well with the ecoregional clas-sification. Diatom groups corresponded to theecoregions with dense human settlement and inten-sive land use. About 64% of the group IV sites werelocated in the Piedmont/Coastal Plains ecoregion,whereas 89% of group VI sites were from the West-ern Appalachian and Valley ecoregions (Table 3).However, the overall mismatch between TWINSPANgroups and ecoregions was high. For example, thesites in groups I, III, and V did not correspond toany particular ecoregions (Table 3).

Algal biomass. Chlorophyll a was positively corre-lated with TP in the region (r 5 0.29, P 5 0.04, n5 89). At the ecoregional level, correlation coeffi-cients between chl a and TP ranged from a low of20.08 (Northern Appalachian) to a high of 0.69(Piedmont/Coastal Plains) (Table 4). The CentralAppalachian ecoregion showed a better correlationbetween chl a and TN (r 5 0.49, P 5 0.03, n 5 17).

A regression tree model indicated that variabilityin chl a concentrations in the Mid-Atlantic streamscan be attributed to conductivity, stream slope, TP,

TN, and riparian canopy coverage (Fig. 5). Themodel predicted that chl a can be as high as 45.0mg·cm22 if conductivity is greater than 266 mS·cm21

and TP is greater than 18.4 mg·L21. Chlorophyll acan be as low as 3.1 mg·cm22 if stream slope is great-er than 6.6% among streams with conductivity lessthan 266 mS·cm21. The terminal group membershipof the regression tree model did not correspond toecoregion divisions.

DISCUSSION

Spatial patterns of benthic diatoms and ecoregions. Pe-riphyton, especially diatoms, has been used as anindicator of environmental conditions, such as waterquality and habitat conditions in streams (Kutka andRichards 1996, Pan et al. 1996, Stevenson and Pan1999). Stresses to water and habitat quality instreams might be contributed primarily by human

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465ALGAL PATTERNS IN STREAMS

FIG. 4. Comparison of selected environmental variables(mean 1 SE) among TWINSPAN groups in the Mid-Atlanticstreams. Different letters in the figure indicate significant differ-ences among the TWINSPAN groups at P,0.05

TABLE 3. Correspondence between TWINSPAN and ecoregionalclassification. AP, Appalachian Plateau ecoregion. The numbersare sampled stream sites.

Ecoregion

TWINSPANgroups

I II III IV V VI Total

Central APNorthern APWestern APPiedmont/Coastal

Plains

242

1

1016

2

321

2

111

7

12

1

8

179

20

12ValleyRidgeBlue Ridge

32

143

21

1 212

81

15106

Total 14 27 11 11 8 18

TABLE 4. Pearson correlation coefficients among AFDM, chl a,total phosphorus (TP), and total nitrogen (TN). AP, AppalachianPlateau ecoregion.

Region/Ecoregions (n) AFDM chl a TP

Central AP (17) chl aTPTN

0.430.12

20.110.130.49* 0.43

Northern AP (9) chl aTPTN

0.6520.4820.04

20.080.28 0.54

Western AP (20) chl aTPTN

0.74**0.240.32

0.300.34 0.51*

Piedmont/CoastalPlains (12)

chl aTPTN

0.64*0.380.11

0.69*0.33 0.49

Ridge (10) chl aTPTN

0.490.530.42

0.330.07 0.79*

Valley (15) chl aTPTN

0.70*0.330.44

0.120.20 0.61*

Mid-Atlantic region(89)

chl aTPTN

0.59**0.170.17

0.29*0.19 0.54**

* 0.001 , P , 0.05. ** P , 0.001.

disturbance in watersheds and riparian zones (Greg-ory et al. 1991, Biggs 1995). Johnson et al. (1997)showed that water chemistry in streams was largelya function of landscape characteristics, such as landcover/use in midwestern agricultural stream ecosys-tems. Our data showed that benthic diatom assem-blages can be divided into six groups, which can bebest separated by both coarse-scale factors, such asland cover/use in watersheds, and site-specific fac-tors, such as riparian conditions.

However, correspondence between diatom spatialpatterns and ecoregional classification was not evi-dent, indicating stronger control by more local geo-morphic and disturbance factors (e.g. watershedsand riparian conditions). Several studies showedthat spatial patterns of aquatic biota and chemistrybecame more apparent under the ecoregionalframework (see the review by Hughes and Larsen1988). Such a correspondence has been most evi-dent for fish, which are larger-bodied organisms that

might respond to larger-scale factors (Roth et al.1996, Allen et al. 1999). For example, Larsen et al.(1986) reported that spatial patterns of fish showeddistinct ecoregional differences in Ohio streams.However, Whittier et al. (1988) found no clear cor-respondence between periphyton spatial patternsand ecoregional classification in their study ofOregon streams. Stevenson (1997) suggested thatbenthic algal species composition and distributionpatterns in streams might be predicted by multi-scaled environmental factors. He classified these fac-tors as ultimate, intermediate, and proximate. Theultimate factors, such as climate, geology, and landuse, might operate at the watershed or regionalscales and set up constraints on local biotic inter-actions in streams. Unlike the proximate factors,such as pH and nutrients, the ultimate factors oftenexert indirect effects on algae in streams (see theconceptual model in Stevenson 1997). Thus, the in-

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466 YANGDONG PAN ET AL.

FIG. 5. A regression tree mod-el showing best predictors of chl aconcentrations and hierarchicalrelationship among these predic-tors in the Mid-Atlantic streams.The number at each terminal ofthe tree model is predicted meanchl a concentrations.

direct effects of ultimate and intermediate factorson algae might be more complex than the directalgal responses to proximate factors.

Correspondence between diatom spatial patternsand their ecological determinants under the ecore-gional framework might be enhanced by using afunctional group approach. Ecoregional delineationis based largely on coarse scale and qualitative fea-tures (Woods et al. 1996). Thus, comparable scalesbetween ecoregions and aquatic biota should beused to best illustrate correspondence between thespatial pattern of biota and their ecoregional deter-minants (O’Neill et al. 1986). One approach is toaggregate biota on the basis of species traits (func-tional groups) instead of taxonomic identities. Poffand Allan (1995) reported that functional organi-zation of fish, derived from fish species’ habitat, tro-phic, morphological, and tolerance characteristics,showed strong association with hydrological vari-ability in streams. Richards et al. (1997) showed thatlife history and behavioral attributes of aquatic in-sects had strong relationships to local environmen-tal conditions, such as physical habitat features. Theuse of species traits to elucidate algal assemblagesand environmental gradients has been proposed formarine intertidal systems (Dethier 1994) andstreams (Steinman et al. 1992). Steneck and Dethier(1994) suggested that species attributes, oftenshared by taxonomically distant species, were highlyconvergent at a functional group level for benthicmarine algae and thus that algal assemblages at thefunctional level were more temporally stable andpredictable than at the species level. Our data in-

dicated that differences among TWINSPAN groupscan also be illustrated by the siltation index, an ag-gregation of motile diatoms. Motile diatoms, such asNitzschia and Navicula, are abundant in streamsdominated by unstable substrates and thus can re-flect sedimentation in streams (Pringle 1990, Bahls1993). The species traits, identified on the basis ofsound ecological mechanisms (e.g. the strategiestheory of Grime 1979), might enhance the effective-ness of biological indicators in environmental mon-itoring and assessment.

Algal biomass and nutrient interaction: A hierarchicalorganization? Algal–nutrient interactions in streamsare complex. Large-scale studies often fail to showstrong relationships between algal biomass and nu-trients in streams (Leland 1995). Multiple ecologicalprocesses operating at different spatial and tempo-ral scales certainly contribute to this complexity(Stevenson 1997). New approaches that recognizescale-dependent constraints are required to revealpatterns and processes at broad scales (O’Neill et al.1989). Biggs (1995) suggested that large regionalfactors, such as climate, geology, and land use,should be integrated with local processes to studyalgal–nutrient interaction.

Ecological determinants of algal biomass mea-sured as chl a were hierarchically organized in theMid-Atlantic streams. At the regional level, chl atended to increase with TP. However, association be-tween chl a and TP was weak (r 5 0.29). Similarlyweak or weaker correlations have been observed inother broad-scale studies. Leland (1995) reportedno correlation between AFDM and nutrients in the

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467ALGAL PATTERNS IN STREAMS

Yakima River Basin. Chlorophyll a and nutrient in-teraction is both spatially and temporally dynamicin streams. Low-frequency sampling used by surveyssuch as ours might not sufficiently characterize suchdynamics, thus resulting in a poor correlation be-tween algal biomass and nutrients.

Spatial heterogeneity (e.g. ecoregional differ-ence) in the chl a–nutrient interaction might alsoaccount for the weak correlation between chl a andTP at the regional level. At the ecoregional level, astronger association between chl a and TP wasfound in the Piedmont/Coastal Plains ecoregion,one of dense human settlement and intensive landuse. Although ranges of TP were similar in otherecoregions, no strong correlations were seen be-tween chl a and TP in the Valley and Western Ap-palachian ecoregions, both of which have similarlevels of human activity (Woods et al. 1996). Theregression tree model indicated that variability ofchl a concentrations in the Mid-Atlantic streams canbe attributed to multiple factors, such as conductiv-ity, stream slope, TP, TN, and riparian canopy cov-erage. Large-scale factors, such as geology, might setup templates for local biotic interaction. For exam-ple, basin geology contributes significantly to waterchemistry in streams (Johnson et al. 1997). In fre-quently disturbed streams, flow-related disturbancecan often account for much more variance in algalbiomass than do nutrients (Fisher and Grimm1988). In some Mid-Atlantic streams, the effects ofTP on chl a were dependent on conductivity andstream slope. Within similar ranges of conductivity(,266 mS·cm21) and stream slope (,6.6%), TP ef-fects on chl a also were dependent on either TN ormidchannel canopy cover. Thus, prediction of algalbiomass, such as chl a, in streams should be madeconditionally.

In summary, ecological determinants of lotic algalassemblages in the Mid-Atlantic region with com-plex landscapes can be hierarchically organized.Spatial patterns of benthic diatom assemblages wereevident and best predicted by both coarse-scale fac-tors, such as land cover/use in watersheds, and site-specific factors, such as riparian conditions. Omer-nik’s ecoregional classification, based on coarse-scale factors only, did not correspond well to thediatom-based stream classification. Algal biomassmeasured as chl a was less predictable using a simpleregression approach. The regression tree model waseffective for showing that ecological determinants ofchl a were hierarchical in the Mid-Atlantic streams.

This research was funded by the U.S. Environmental ProtectionAgency through a cooperative agreement with Oregon State Uni-versity (CR821738) and the University of Louisville. It has beensubjected to the agency’s peer and administrative review and ap-proved for publication. Mention of trade names or commercialproducts does not constitute endorsement or recommendationfor use. The comments of Alan D. Steinman, C. David McIntire,and an anonymous reviewer greatly improved the quality of thismanuscript. We thank Patti Grace-Jarrett for counting periphyton

and J. M. Omernik for providing information on ecoregional di-visions in the Mid-Atlantic region and discussion on the ecoregionconcept. We thank Victoria Rodgers for her help with the EMAPdatabase and Patti Haggerty for making the site map.

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