Plant Ecology138: 1–16, 1998.© 1998Kluwer Academic Publishers. Printed in the Netherlands.

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Structure of herbaceous plant assemblages in a forested riparianlandscape

Jonathan Lyon∗ & Cynthia L. Sagers632 SCEN, Department of Biological Sciences, University of Arkansas, Fayetteville, AR 72701, USA (∗Currentaddress: Natural Science Department, Edgewood College, 855 Woodrow Street, Madison, WI 53711, USA)

Received 12 March 1997; accepted in revised form 7 April 1998

Key words:Ecotone, Gradient analysis, Riparian vegetation, Species richness, Vascular plants

Abstract

We assessed patterns of herbaceous and woody species richness, plant-environment interactions, and correspon-dence between the herb and tree layer in a riparian landscape (the Ozark National Scenic Riverways, Missouri,USA). A total of 269 herb and 70 tree species were identified on 94 sample plots. Gradient analysis revealed thatenvironmental variables and vegetation were influenced by a strong elevation gradient. However, high variabilityin environmental variables (pH, elevation, slope, sand, clay, organic matter) indicated a high level of substrateheterogeneity across the riparian landscape. We were unable to predict the composition of the herb understoryfrom the canopy trees with any detailed accuracy and no clear characterization of herb species assemblages wasfound using cluster analysis or ecological land type (ELT) classifications. Canonical correspondence analysis(CCA) results for both tree and herb plots showed that elevation (height above river) and pH were the dominantenvironmental gradients influencing vegetation patterns on the first CCA axis while soil particle size exhibited thestrongest correlation with the second CCA axis. Secondary gradients of importance included slope, soil containercapacity, and organic matter. No significant linear or quadratic correlation was found between elevation and herbor woody species richness. Environmental variables alone or in combination, were weak predictors of herb andwoody species richness, despite the patterns observed in the gradient analysis and the correlations observed inthe CCA results. Ecotonal analysis showed that the herb layer exhibited a high species replacement rate at thelower elevations most susceptible to flooding (0–3 m). Above the flooding zone, there was more or less continuousspecies replacement, suggesting the presence of a gradual ecotone/ecocline. The tree layer exhibited much strongerdiscontinuities than the herb layer in the lower elevations along the height gradient (0–10 m). Recognizing thelimitations of classification techniques for riparian herb assemblages and the importance of scale and heterogeneityin vegetation layers is especially important in light of mandates to preserve, protect, and manage for plant diversity.

Introduction

The ecological importance of riparian forest ecosys-tems and their constituent flora has received grow-ing attention in the last decade (Forman & Godron1986; Ellenberg 1988; Knopf et al. 1988; Petts 1989;Naiman et al. 1993; Malanson 1993). Riparian plantcommunities have been shown to be species rich andhighly variable in species composition in both tem-perate and tropical systems (Salo et al. 1986; Dayet al. 1988; Hughes 1988; Schoonmaker & McKee1988; Baker 1990; Tabacchi et al. 1990; Gregory et

al. 1991; Nilsson 1992; Alcaraz et al. 1997; Pollock etal. 1998). Riparian corridors have also been linked tospecies flows and exchanges across ecotonal and eco-clinal boundaries, including influencing longitudinalpatterns of species distributions (Nilsson et al. 1991;Tabacchi 1994; Johansson et al. 1996), serving as refu-gia for vernal herb species (Bratton et al. 1994), andaffecting exotic plant invasions (Pysek & Prach 1993;Planty-Tabacchi et al. 1996).

Based on their ecological importance, understand-ing the underlying factors influencing vegetation pat-terns in riparian systems is critical for modeling vege-

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tation change in these systems and for the managementof diversity in the broader riparian landscape. Thereis expanding knowledge of the structure and compo-sition of riparian vegetation and the role of flooding(Swanson et al. 1988; Ward 1989; Naiman & Decamps1990; Gregory et al. 1991; Auble et al. 1994; Ben-dix 1994; Nilsson et al. 1997; Toner & Keddy 1997).However, there is less information on the patterns ofplant species diversity from the river’s edge beyondthe zone of flooding influence and the influence of en-vironmental gradients in this zone. Furthermore, muchof the vegetation research in riparian systems has fo-cused exclusively either on woody vegetation (Hermy& Stieperaere 1981; Hupp & Osterkamp 1985; Curry& Slater 1986; Dollar et al. 1992; Ware et al. 1992;McKenney et al. 1995; Scott et al. 1996) or onherb vegetation (Bell 1974; Menges & Waller 1983;Menges 1986; Bratton et al. 1994). Despite a histori-cal precedent (Gleason 1926; Lippmaa 1939; Weaver1960), fewer studies have focused on the interactionsbetween different vegetation layers and the influenceof environmental gradients on species richness andcomposition in riparian systems (McCune & Antos1981; Dunn & Stearns 1987; Nilsson et al. 1989;Gilliam et al. 1995; Sagers & Lyon 1997).

The composition and spatial distribution of herbassemblages in riparian forests can be strongly influ-enced by a host of factors, including geomorphology(Hupp 1986, 1992; Nilsson et al. 1991, 1994; McKen-ney et al. 1995), flooding frequency (Nilsson et al.1997), elevation (Menges 1986), the composition ofdifferent vegetation layers (McCune & Antos 1981;Hardin & Wistendahl 1983; Beatty 1984; Dunn &Stearns 1987), microtopography (Titus 1990; Shafferet al. 1992), seasonal change (Bratton 1976a) and soildepth (Bratton 1976b). Furthermore, many of the en-vironmental gradients influencing vegetation patterns,both at the micro- and macro-scale, may interact ina non-hierarchical manner, adding complexity to theinterpretation of species distributions and species as-sociations (Day et al. 1988; Baker 1989; Bendix 1994;Short & Hestbeck 1995; Stohlgren et al. 1997).

Despite a growing literature of quantitative studieson herb-environment and herb-forest layer interac-tions, the spatial context of many of the most detailedriparian studies has been limited to the geolittoral zoneand focused on ecological interactions between ripar-ian zones and river channels (Nilsson 1983; Menges1986; Nilsson et al. 1989; Roberts & Ludwig 1991;Decamps 1993; Nilsson et al. 1994). However, ripar-ian vegetation may be linked to vegetation patterns in

adjacent communities and the surrounding landscape,especially landscapes with pronounced gradients suchas elevation. Thus, in order to identify these linkages,the composition and distribution of riparian vegetationneeds to be assessed in terms of the interactions be-tween plant assemblages and/or communities in andadjacent to the flood-influenced riparian corridor andenvironmental gradients.

In this paper, we focus on herb community compo-sition and its relationship with environmental variablesacross a riparian landscape. The study was conductedin a protected riparian landscape in southeast Mis-souri, USA, the Ozark National Scenic Riverways(ONSR). Given the complexity of riparian systemsin the region, in this study we defined riparian for-est as the forest vegetation extending from the river’sedge upslope beyond the elevation of periodic floodingand maximum floodstage into the upland forest veg-etation. Including upland vegetation adjacent to theflood-impacted riparian corridor allowed us to take abroader view of vegetation patterns in the riparian for-est landscape. This study is complementary to otherinvestigations we have conducted on forest vegetationin the Ozark Highlands (Sagers & Lyon 1997).

The study site is located more or less on the borderbetween the central and southern forest regions (Eyre1980) with vegetation generally classified as eitheroak-hickory or oak-pine forest. Little detailed inves-tigation of riparian herb communities (Redfearn et al.1969; Witherspoon 1971) or forest communities (Dol-lar et al. 1992; Ware et al. 1992) has been conductedin the Ozarks. There has been much suggestion aboutthe high degree of herb species richness occurring inthe Ozarks (Braun 1950; Steyermark 1959). However,little quantitative evidence has been amassed that hasverified high species richness, assessed the spatial pat-terns of species richness in riparian landscapes, and/orexplained some of the key environmental variablesinfluencing herb vegetation patterns.

Our main goal in this study was to assess the pat-terns of herb and woody species richness in an Ozarkriparian landscape in an attempt to isolate the fac-tors that control the composition and structure of herbvegetation. We address five main questions: (1) whatenvironmental variables are influencing compositionpatterns of herb vegetation in the ONSR; (2) can theherb layer be effectively classified; (3) is the compo-sition of the herb layer correlated or coupled with thecomposition of the tree layer; and (4) is herb speciesrichness linked to elevation in the riparian zone; and(5) are there differences in ecotonal boundaries in the

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herb and tree layers? The management implications ofour results are also discussed.

Study area

The study was conducted within the Ozark NationalScenic Riverways (ONSR), a forest corridor enclosinga 161 km stretch of the Current River and a 55 kmstretch of the Jacks Fork River in southwest Mis-souri, USA (Figure 1). The ONSR was establishedin 1964 to protect a 26 306 ha tract located in por-tions of Dent, Shannon, Carter, and Texas counties,Missouri. The site is located on the Salem Plateau ofthe Ozark Plateaus physiographic province (Fenneman1938), a region underlaid predominantly by Ordovi-cian age cherty dolomite and cherty limestone, withsome smaller areas containing sandstone and shale(Branson 1944). The soils in the region are residualand most belong to the Clarksville-Coulstone asso-ciation (Krusekopf 1963; Gilbert 1971). Streambedelevations range from 280 m to 118 m in the CurrentRiver and from 268 m to 174 m in the Jacks ForkRiver. Most of the ONSR region has a karst drainagesystem; as much as 60% of the two rivers’ flow is fromkarst springs (Barks 1978). The Jacks Fork watershedcovers about 1046 km2. The Current River watershedis substantially larger (9560 km2) with only a smallproportion of that watershed (26 306 ha) protectedwithin the boundaries of the ONSR. Typical yearlyflood levels on both the Jacks Fork and Current River’srange from 2–3 m above baseflow; a typical 25 to 50year flood reaches 4–6 m above baseflow (Jacobson &Primm 1994).

The Ozark Plateaus in general, and the SalemPlateau in particular, have been a continuous land areasince the end of the Paleozoic (Branson 1944; Stey-ermark 1959). Because the Ozark region has neverbeen glaciated, it has been open for plant migrationsince the Tertiary. However, much of the riparian land-scape of the ONSR has been disturbed since Europeansettlement (around 1800). Clearing for cattle pasturewas the predominant land-use until around 1880, whenlarge-scale timber exploitation was initiated (Jacobson& Primm 1994). The forests of the Ozarks experi-enced dramatic anthropogenic disturbances from 1890to 1920 in the form of large-scale and indiscriminateclearcutting, agricultural clearings, burning, and graz-ing (Stevens 1991). During this period, essentially allthe forest cover in the ONSR was cut over. The ex-isting secondary forests have been broadly classified

as oak-pine and oak-hickory (Braun 1950, Eyre 1980)but specific forest assemblages range from wet bot-tomland to mesic mid-slope to more xeric upland. TheONSR is located in the most heavily forested region inMissouri (Spencer et al. 1992).

Methods

Vegetation sampling

In August 1994, and between June and August 1995,we established transects at 35 locations along theCurrent and Jacks Fork Rivers. Transects are the rec-ommended sampling method where communities arethought to be strongly influenced by an environmen-tal gradient (Barbour et al. 1987). Study sites werechosen from among locations accessible by secondaryroads or foot trails that were separated by approxi-mately 4.8 river km. Transects began at the river’sedge and continued upslope to a point where the for-est canopy was dominated by oak (Quercussp.) andhickory (Caryasp.) species. Along each transect, weestablished 10× 20 m plots at 20 m intervals. Plotdimensions were determined from species area curves.Thus, plots were not specifically located in definedfluvial geomorphic surfaces. We did not want to biasour sampling design towards preconceived vegetationzones. The design chosen allowed us to test for thepresence of discrete vegetation assemblages while alsoproviding a high level of sampling resolution along thetransect (Stohlgren et al. 1997).

A total of 135− 5 × 10 m plots were sampledduring the survey. Each transect was categorized bycurrent land use practice (i.e., secondary forest, camp-ground, or old field). Thirty-one transects were locatedin secondary forest sites. Only plots (n = 94) withwoody vegetation> 1 cm dbh are presented in thispaper. Plants were classified as trees, shrubs, or herbsbased on stem diameter and height. All woody plants≥1 cm in diameter at 1.3 m in height were catego-rized as trees and measured. Each stem within theplot was identified to species and its diameter wasrecorded. Voucher specimens were deposited in theUniversity of Arkansas herbarium in Fayetteville, AR.Nomenclature follows Steyermark (1968).

Non-tree vegetation was segregated into two heightclasses: 0.1–1.3 m and 0–0.1 m in height. This vegeta-tion was analyzed using the cover classes outlined byDaubenmire (1959). In the 0.1–1.3 m layer, the per-cent cover of each species was estimated from within

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Figure 1. Map of the location and boundaries of the Ozark National Scenic Riverways (ONSR). The Current and Jacks Fork Rivers are shownas are the major roads in the region.

four – 3.1 m2 circular plots placed regularly withinthe larger plot. In the 0–0.1m layer, the percent coverof each species was estimated from within 10–0.1 m2

rectangular plots placed regularly within the largerplot. The percent cover of each species was calculatedfor the plot as the mean cover recorded from the sub-plots. The frequency of each shrub and herb specieswas calculated as the percentage occurrence amongthe total number of subplots. In order to combine datafor those herb species found in both height layers intoa single matrix, the average of the mean cover of eachspecies from each height class was calculated and usedin the combined matrix. Species fidelity was definedas the number of plots of occurrence for each speciesdivided by the total number of plots (94).

Physical attributes and soils

Each study plot was characterized by slope, aspect,and elevation (plot height above river at baseflow).Fluvial or upland landform type was noted for eachplot using the ecological land type (ELT) classificationscheme of Miller (1981). The slope and aspect of eachplot were measured with a clinometer and compass,respectively. Plot elevation (c) was calculated from anangle (a) and distance (b) between one observer atthe edge of the plot and another at the river’s edgeas: c = (sina)b. Sampling occurred during the dri-est months of the year (June–August) when the rivers

were at or near baseflow. Floodstage information wastaken from Jacobson & Primm (1994).

We collected soil samples at a depth of 10 cm from3 locations chosen haphazardly within each 10× 20 mplot. Soil samples were collected into polyvinyl bagsand stored at 0◦C until they could be processed. Bulksoil samples were air-dried and passed through a 2 mmsieve to separate fine and coarse soil fractions. Thetotal sample weight, and the weight of the smallersize fraction were recorded to calculate the percent-age of total sample< 2 mm. All subsequent analyseswere performed on the fine fraction. Soil pH was mea-sured using 0.01 M CaCl2 following the methods ofMcLean (1982). pH was measured with a high perfor-mance combination probe read with a Corning pH/ion350 meter. Soil texture was measured using a methodmodified slightly from Bouyoucos (1951). Eighteen gof air-dried, fine soil was dissolved in a 0.1 m sodiumhexametaphosphate (HMP) solution by mixing andallowing to soak overnight. Twelve h later the sus-pension was transferred to a 500 ml sedimentationcylinder and mixed thoroughly. Hydrometer readingswere taken 40 s and 120 min after mixing with a stan-dard hydrometer (ASTM no. 152 H with Bouyoucosscale in g/L). Hydrometer readings were correctedfor deviations from normal room temperature. Theproportions in the soil of sand, clay and silt werecalculated from these readings following Bouyoucos(1951).

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Herbaceous species

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Figure 2. Comparison of herbaceous species (top) and woody species (bottom) fidelity on 94 sample plots in the ONSR.

Container capacity or the water content of a sat-urated soil after it has been allowed to drain, wasdetermined following the methods of Cassel & Nielsen(1986). Container capacity was calculated as the dif-ference between the post-, and pre-wetting weightsdivided by the post-wetting weight of the sample.Organic content was determined following Lim &Jackson (1982). Air-dried, fine soil was added to aporcelain crucible, weighed, and placed into a muf-fle furnace. The furnace temperature was increasedgradually to about 900◦C and held there for 15 min.The crucible was cooled, and the sample re-weighed.Loss on ignition at this temperature includes water ofconstitution, organic matter, and some soluble volatilesalts.

Vegetation analysis, ordinations, and classifications

To evaluate relationships between vegetation and en-vironmental variables, we conducted two series ofanalyses. First, we performed a basic gradient analysisusing elevation (plot height above river) as the de-pendent variable and environmental variables as inde-pendent variables. Second, we performed multivariateanalyses, namely detrended correspondence analysis(DCA) and canonical correspondence analysis (CCA)ordinations on herb and tree species-environmentalvariable matrices using the programs PC-ORD (Mc-Cune & Mefford 1995) and CANOCO 3.10 (ter Braak1990). Mean cover values for herb species, and im-

portance values for tree species (relative dominance[100% max]+ relative density [100% max]) wereused in the ordination analyses. Soil and environmen-tal variables were transformed, when necessary, tomeet the assumptions of normality. The DCA proce-dure used segment detrending, nonlinear rescaling ofaxes, and rare species downweighting (Hill & Gauch1980). The CCA procedure involved linear combina-tion of variables for site scores, no transformation ofspecies abundance matrices, and the use of a MonteCarlo permutation test to test the significance of thefirst axis eigenvalue (ter Braak 1990). In all CCA or-dinations performed, the Monte Carlo test indicatedthat the eigenvalues for the first axis were significant.Given the influence of noisy environmental data onCCA (McCune 1997), CCA was used and interpretedin the limited context of describing plant communityvariation with respect to the limited set of measuredenvironmental variables in the study.

Classification of herb species assemblages wasconducted using cluster analysis (McCune & Mef-ford 1995). Relative Euclidean distance was used asa distance measure and Ward’s method was used forgroup linkage. To evaluate the variation in herb vege-tation across the elevation gradient and relative to thecluster analysis and ELT classification results, meanDCA axis 1 scores were determined for each clus-ter and ELT and plotted (y axis) versus elevation (xaxis) with the respective standard deviations for eachaxis. Total species richness was defined at the sum

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Figure 3. Comparison of the relationship between four key soil variables (organic matter, pH, container capacity, fines) with elevation in theONSR. Each point represents mean values for each plot (94 plots total).

of species across sub-sample plots within each largersample plot. Species fidelity was defined as the num-ber of plots where a species was found out of the totalplot number. Significance is reported at theα = 0.05level, unless otherwise noted.

To test if there was any relationship between theherb and tree layer, cluster analysis groupings werecompared using a kappa statistic (Siegel & Castellan1988). An 8× 8 contingency table was created basedon the eight clusters found via cluster analysis for boththe herb and tree layer. Cells contained the numberof common plots found between each pairing of herband tree layer clusters. The null hypothesis was thatthere was overlap in clusters between layers (i.e., acoupling of vegetation clusters between the herb andtree layers). Any significant result would result in a re-

jection of the null hypothesis (i.e., layer clusters werenot coupled).

The presence of ecotonal features in the vegeta-tion data were determined by the use of differentialDCA profiles using DCA scores (Hobbs 1986). DCAgraphical profiles were created by plotting sample plotscores from the first axis of DCA ordinations versusplot elevation above river. In a DCA graphical pro-file, the steeper the profile slope, the more abruptthe change in the composition of vegetation, and themore abrupt the ecotone. A ‘moving window’ algo-rithm (8 frames) was used to smooth the data alongthe elevation transect.

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Table 1. Comparison of the non-transformed means (± 1 SD), medians, andranges of the 10 environmental variables analyzed in the ONSR.

Variables∗ Units Mean Median Range

Low High

Slope deg 20.7 (13.5) 16.0 0.0 65.0

Aspect deg – – – –

Elevation m 8.8 (10.0) 4.4 0.1 44.1

pH pH 6.1 (0.9) 6.2 3.5 7.4

Fines % 60.2 (12.3) 57.0 1.2 99.9

Container capacity % 33.9 (6.8) 33.6 20.2 52.5

Sand % 33.8 (24.0) 27.8 0.0 97.2

Silt % 8.1 (2.3) 5.6 0.0 36.1

Clay % 58.1 (30.3) 66.7 0.0 99.4

Organic matter % LOI 8.3 (3.1) 6.8 0.6 43.1

∗Variables are defined as follows: Slope= slope (◦) through the vegetationplot; Aspect = aspect of plot (◦); Elevation= height above river of vegetationplot (m); pH= soil pH of top 10 cm of soil; Fines= % of total sample<2 mm dia; Container capacity of soil samples (%); Sand= % sand in soil;Silt=% silt in soil; Clay=% clay in soil; Organic matter content (%) in top10 cm of soil determined by LOI (loss on ignition).

Results

Species richness and fidelity

A total of 269 herb species representing 65 familieswere identified in the vegetation sampling. Becausethe sampling was conducted in late summer, the herbtotals do not include a limited number of springephemeral species. A total of 70 woody species (over-story and understory trees, and woody shrubs) werealso identified. A summary of the overall fidelity ofthe herb and woody species encountered in the studyis presented in Figure 2. The herb species plot in Fig-ure 2 shows that the majority of the species sampledwere uncommon; cumulative totals of species fidelityshow that 39.4% of the species were found on a sin-gle sample site, 56.9% on 2 sample sites, 68.1% on3 sample sites, and 73.3% on 4 sample sites. The 42plant species that had fidelity values>10% accountedfor only 15.6% of the total number of species encoun-tered. The influence of rarer species on the overalllevel of herb species richness in the ONSR cannot beoverstated. The plot of woody species richness in Fig-ure 2 shows a more equitable distribution of speciesacross plots with 41.5% of the species found on 4 plotsor less. The 25 woody species that had fidelity values> 10% accounted for 35.7% of the total number ofspecies.

Gradient analysis

The vegetation plots used in this study were locatedacross a wide range of physical and soil chemicalgradients, the most obvious being elevation. Table 1contains a summary of the means (± 1 SD), medians,and ranges of the 10 environmental variables analyzedin the study. The wide ranges and high SD’s depictedin Table 1 illustrate both the existence of strong gradi-ents and a high level of substrate heterogeneity acrossthe riparian landscape in the ONSR. Gradient analysiswas employed to detect spatial patterns in environmen-tal variables and vegetation with elevation. Figure 3shows a composite plot of elevation versus four of thekey soil environmental variables measured. Signifi-cant correlations between elevation and organic matter(r = 0.50) and container capacity (r = 0.490) werefound (second-order polynomials). Significant linearcorrelations were also observed for both pH (r =0.48) and fines (r = 0.25). However, the relativelylow r values indicate a high level of variability acrossthe elevation gradient.

Agglomerative cluster analysis produced 8 clustersat a dissimilarity value of 0.6. Figure 4 summarizes therelationship between plot elevation and the classifica-tion results from both the cluster analysis and the ELTdesignation. Figure 4 shows a high level of overlap be-tween clusters and ELT designations in both elevation(x axis) and DCA axis 1 scores (y axis). These resultsindicate that the composition and structure of the herb

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Table 2. Comparison of eigenvalues, correlations, and species-environmentcorrelations between environmental variables and CCA ordination axes forherbaceous species and tree species (in parentheses). All CCA correlationslisted below are ‘intraset correlations’ as described by ter Braak (1986).

CCA AXES

1 2

herb layer tree layer herb layer tree layer

Eigenvalue 0.533 0.553 0.517 0.322

Variables∗

Slope 0.523 0.465 0.073 0.199

Aspect −0.102 −0.063 −0.147 −0.128

Elevation 0.791 0.789 0.153 0.338

pH −0.708 −0.757 0.132 −0.243

Fines −0.144 0.391 −0.905 −0.801

Container capacity 0.466 0.461 0.054 −0.003

Sand −0.190 −0.450 0.191 0.118

Silt 0.090 0.436 −0.258 −0.175

Clay 0.289 0.278 0.157 0.145

Organic matter 0.339 0.423 −0.176 −0.200

∗For description of variables see the bottom of Table 1.

layer was not well correlated with elevation or eitherof the two chosen classification schemes.

Vegetation-environment relationships

Canonical correspondence analysis (CCA) was per-formed for all herb species on 94 plots with 10 en-vironmental variables. The eigenvalues for the firsttwo CCA axes (λ1 = 0.553 andλ2 = 0.517) in-dicate acceptable levels of separation of plot scoresalong the measured environmental gradients. The pro-portion of variance in the species matrix explained bythe environmental matrix was 23.4%. While CCA maybe sensitive to noise in environmental data (McCume1997), the results provide corroboration of the gradientanalysis and classification results. An overall compar-ison of the ‘intraset correlations’ (ter Braak 1986) ofthe 10 environmental variables with the first 2 CCAaxes are given in Table 2.

For the herb layer, Table 2 indicates that pH (r =−0.71) and elevation (r = 0.79) were the dominantenvironmental gradients influencing vegetation pat-terns on the first CCA axis and fines exhibited thestrongest correlation (r = −0.91) with the secondCCA axis. Secondary gradients of importance in-cluded slope, container capacity, and OM. Segregationof species along the noted gradients was also observed,with species typically found in moist, streamside envi-

ronments located in the upper left quadrant, includingMelothria pendulaL., Acalypha rhomboideaRaf. var.rhomboidea, Amorpha fruticosaL., Cuscuta com-pactaJuss., andCephalanthus occidentalisL. Speciesadapted to drier and more acidic conditions werefound on the far right of the first CCA axis, includingAster anomolusEngelm. andCunila origanoides(L.)Britt.

Table 2 also contains the correlations between thefirst 2 CCA axes and the environmental variables mea-sured for the tree layer. The results in Table 2 showthat the environmental gradients influencing the treelayer closely parallel the gradients influencing the herblayer, with a few exceptions: fines, sand, and siltshowed stronger correlations with the first CCA axisin the tree layer than with the herb layer. Overall, bothwoody and herb species appeared to be responding tothe same dominant environmental variables.

Species richness and elevation

To determine if either herb and/or woody species rich-ness were correlated with elevation, total herb andwoody species richness were plotted, respectively, ver-sus the main gradient in the study area, elevation.The two plots are shown in Figure 4. The major-ity of the vegetation sampling plots were located atlower elevations (0–15 m) above the river. A second

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Figure 4. Comparison of herb vegetation composition with plot elevation in the ONSR. Mean DCA axis 1 scores (± 1 SD) and mean elevations(± 1 SD) are shown for:A – vegetation groupings based on cluster analysis; andB – vegetation groupings based on ecological land type (ELT)designation.

order polynomial regression of elevation and woodyspecies richness provided the best fit (p = 0.003;df 1,90; F= 6.92; r2 = 0.185). However, the lowlevel of variance explained by the regression indi-cates other, non-elevation factors were contributing towoody species richness. Regression of elevation andherb species richness showed no significant relation-ship (p = 0.812; df 1,88;F = 0.26; r2 = 0.001).Smoothing of species richness data using running av-erages of either height and/or species richness didnot alter these results. Overall, elevation showed littlerelationship with herb or woody species richness.

To test if herb species richness could be explainedin terms of total herb cover (i.e., sampling effectsensuPalmer 1991), linear regression analysis between totalherb species richness and total herb cover was con-ducted (both variables were normally distributed). Apositive, significant (P < 0.001) relationship (df 1,88;F = 31.14; r2 = 0.266) was found between herbrichness and cover. However, the low amount of vari-ance explained, indicates that the level of cover alonecannot entirely explain the observed patterns of herbspecies richness. Despite these results, a relativelystrong and highly significant (P < 0.001) positivecorrelation was found between total species richnessand the number of rare species (r = 0.58). Thus, morespecies rich sites tended to have greater numbers ofrare species. Woody species richness was weakly cor-

related with herb species richness (r = 0.25; P =0.098).

Relationships between richness and environmentalgradients

Table 3 contains the results of correlation analysisbetween herb species richness, cover, rare species,and woody species richness with the 10 environmentalvariables in this study. Overall, few significant cor-relations were observed: herb species richness wassignificantly correlated with slope, fines, and organicmatter; herb species cover was correlated with fines,silt, and organic matter; and rare herbs were corre-lated with only elevation and fines. Woody speciesrichness was signficantly correlated with organic mat-ter, slope, container capacity, and elevation. Basedon the Pearson correlation coefficients in Table 3,the environmental variables (gradients) alone or incombination, provided little explanatory power in pre-dicting patterns of herb and woody species richness,despite the correlations observed in the gradient analy-sis (Figure 3) and the CCA results (Table 2). However,some differences in environmental correlates betweenvegetation categories were observed.

Herb species richness was only weakly correlatedwith the first two axes of the CCA ordination for herbspecies (r = 0.13 andr = 0.06 for the first and secondCCA axes, respectively). Herb species richness alsoexhibited a weak correlation with the first two axes

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Woody species Herbaceous species

Figure 5. Comparison of total woody (left) and herbaceous (right) species richness per sample plot versus plot elevation above the river.

of the tree CCA ordination (r = 0.15 andr = 0.26,respectively). Overlays of herb species richness (perplot) on the herb and tree CCA biplot and DCA plot(results not presented) indicated no distinct patterns or‘zones’ of herb species richness, with the exceptionof three sites with very low species richness in plotsfound on gravel bars.

Herbaceous-tree layer interactions

The species assemblages determined by cluster analy-sis for the two layers were not coupled with oneanother (kappa statisticχ2 = 131.12; df= 42; P =0.004). These results indicate that there was little cor-respondence between vegetation composition in theherb and tree layers.

Ecotonal analysis

Figure 6 is a plot of DCA axis 1 score profiles forboth the tree and herb layers. The steeper slopesin the profile represent more abrupt, discontinuouschanges in species composition along the height gra-dient. Figure 6 indicates clear differences between thetwo vegetation layers; the herb layer exhibited a highrate of species replacement at the lowest elevationscorresponding to the gravel bars that are most prone toyearly flooding (0–3 m above baseflow). Thus, someof the low elevation patterns can be linked to fluvialgeomorphology (especially gravel bars). Above theflooding zone, the relatively smooth DCA profile indi-cates a more or less continuous replacement of species,

suggesting the presence of a gradual ecotone/ecoclinethrough elevations prone to 25–50 year floods (4–6 mabove baseflow) and beyond the maximum recordedfloodstage (9.4 m).

The tree layer exhibited much stronger disconti-nuities than the herb layer along the lower elevationsin the height gradient (0–10m). The DCA profiles re-veal a differential topographic response of the herband tree species along the pronounced elevation gradi-ent, despite evidence from both the gradient analysisand CCA results that both vegetation layers were in-fluenced by similar environmental variables (Table 2;Figure 3).

Discussion

Species richness, fidelity, and elevation

The tree (n = 70) and herb species (n = 269) richnessobserved in this study is substantially higher than re-sults from other investigations of temperate vegetationbased on similar scale studies (Brewer 1980; Rogers1980, 1981; Parker & Leopold 1983; Robertson et al.1984; Dunn & Stearns 1987; Parker 1989; Dollar etal. 1992 ) but on par with some other studies in thefloodplain forests of the southern USA (Gemborys &Hodgkins 1971; Robertson et al. 1978 ) and in Swe-den (Nilsson 1983; Nilsson et al. 1989; Nilsson etal. 1994). The observed low fidelity of the majorityof herb species in the present study, however, does

11

Table 3. Comparison of Pearson correlations of herbaceous species rich-ness, herb cover, rare herb species, and woody species richness with 10environmental variables (transformed) on 94 plots in the ONSR.

Variable∗ Herbaceous species Woody species

Richness Cover Rare richness

Slope 0.236 −0.003 0.082 0.300

Aspect −0.075 −0.090 0.049 −0.059

Elevation −0.019 −0.228 −0.136 0.279

pH 0.105 0.171 0.094 −0.115

Fines 0.204 0.422 −0.136 0.151

Container capacity 0.144 0.089 −0.095 0.281

Sand 0.024 −0.182 0.070 0.003

Silt 0.052 0.260 −0.044 −0.021

Clay −0.002 0.023 0.013 0.086

Organic matter 0.244 0.245 −0.042 0.309

∗For description of variables see the bottom of Table 1.

coincide with the results of other herb studies (Rogers1980; Bratton et al. 1994; Nilsson et al. 1994).

The lack of any strong relationship between ele-vation and herb species richness in the ONSR is instark contrast to the results of other riparian studiesthat have indicated an increase in species richnesswith elevation (Bell & del Moral 1977; Robertson etal. 1978; Frye & Quinn 1979; Menges 1986). Whilethere was high variability in species richness at lowelevations and in the flood prone plots in the currentstudy, no pronounced patterns of increasing richnesswith elevation were observed. Furthermore, no strongcorrelations between herb or woody species richnessand any of the environmental variables measured wereobserved. The observed equability of species rich-ness with elevation is likely the result of numerousinteracting factors, including substrate heterogeneityin space (Ward & Stanford 1983) and time (Fowler1988), complex fertility gradients (Day et al. 1988),limited impact of flooding at low elevations, asexualreproduction as a hedge against many of the floodingepisodes/ disturbances, and spatial mass effect main-taining sink populations of species on non-optimalmicrosites (Shmida & Wilson 1985).

Vegetation-environment interactions

Integrating vegetation analyses with environmentaland physiographic variables can provide a more robustbasis for classification and characterization than vege-tation analyses alone (Rowe 1984; Hix 1988; Palmer1993). The gradient analysis and CCA results in thisstudy indicate that both trees and herb species are

influenced by, and sorting out along, pronounced land-scape scale gradients, namely elevation both withinand beyond the zone of direct fluvial influence, pH,and soil particle size. Herb communities secondarilyare responding to slope and soil container capacity.However, the high variability among environmentalvariables, indicates that substrate heterogeneity maybe an important micro-scale influence on vegetationin the ONSR and that the choice of measurementand/or mapping scale is an important consideration inclassifying riparian vegetation.

Spatial heterogeneity in substrate conditions canhave important consequences on population dynamicsand biodiversity (Pulliam 1988). Riparian floodplainsoils are often highly variable in nutrient content andtexture (Peterson & Rolfe 1982) and soils and soilparent materials have been shown to have a stronginfluence on vegetation type and species distributionsin the Ozarks (Read 1952; Autry 1988; Dollar et al.1992; Ware et al. 1992) and in other riparian systems(Ward & Stanford 1983; Nilsson et al. 1989). Soils inthe Ozarks are old, residual soils that are unglaciatedand have developedin situ (Krusekopf 1963). Basedon the varied array of soil types, pronounced soilchemical and physical gradients, complex macro- andmicrotopography, interacting modes of disturbance,and the amount of time that the Ozarks have beenunder a continuous vegetation cover, the level of sub-strate heterogeneity in the Ozarks is likely to be highlydeveloped. Thus, the observation that herb and woodyspecies richness was continuous across the entire ele-vation gradient should not be surprising. Furthermore,

12

0

100

200

300

400

500

600

DC

A a

xis

1 sc

ores

0 3 6 9 12 15 18 21 24 27

Height above river - m

Herb layer

Tree layeryearly floods

25-50 year floods

maximum floodstage

Figure 6. DCA score profiles (ecotone analysis) for herb and woody species layers plotted versus elevation. The DCA values are based onan eight-point moving weighted average to reduce variation. The elevations corresponding to typical yearly floods, 25–50 year floods, andmaximum floodstage are also noted on the figure.

although light intensity beneath the canopy was not avariable measured in this study, it may be contributingto the observed variability in herb vegetation along thegradient from the river upslope.

Vegetation layers and ecotones

Different vegetation layers have been shown to re-spond differently to a variety of gradients (Bell 1974;Rogers 1980, 1981; Ehrenfeld & Gulik 1981; McCune& Antos 1981; Dunn & Stearns 1987; Gilliam et al.1995). The lack of any significant coupling of the treeand herb layer assemblages in this study (based oncluster analysis), indicates that the different vegeta-tion layers are responding differently to the underlyinginfluence of strong elevation and pH gradients in theONSR. While these gradients have been shown to beimportant in other riparian forest studies in and aroundthe Ozark region (Nigh et al. 1985; Pallardy et al.1988; Dollar et al. 1992; Ware et al. 1992), all the stud-ies cited excluded analysis of the herb layer and thusdid not evaluate the entire complexity of the riparianlandscape (O’Neill et al. 1986). Thus, inference of treelayer results to the entire plant biota is not appropriateand should be avoided.

The presence of ecotones and their manifestationare influenced by a host of factors, including edaphicconditions, geomorphology, disturbance, and climate

(Risser 1990; van der Maarel 1990; Gosz 1993). Theresults of our ecotonal analysis (DCA profiles) indi-cate distinct differences in ecotonal distribution andstructure between the tree and herb layer in the ONSR.There are high rates of species replacement in the treelayer with numerous discontinuities observed, espe-cially at the lower, flood-influenced elevations. Theherb layer, on the other hand, exhibited high speciesreplacement rates and sharp ecotonal boundaries onlyat the lowest elevations (0-3 m). Thus, although theCCA revealed the two vegetation layers were respond-ing to the same overall gradients, herbs and woodyvegetation were responding at different positions inthe landscape and at different scales.

Succession

Succession is also influencing the composition andstructure of vegetation in the ONSR and our resultsshould be interpreted within a successional context.The forests of the ONSR are essentially all secondaryforests that are products of large-scale, indiscriminateclearcutting that occurred at the turn of the century(Jacobson & Primm 1994). Secondary succession isoften driven by interspecific differences in resourceuptake and tolerance (Connell & Slayter 1977; Can-ham et al. 1994). Thus, the existence of strongpH, elevation, and soil particle size gradients in the

13

ONSR is providing the edaphic backdrop for succes-sional change. Successional influences are affectingboth vegetation layers. While it is possible to iden-tify successional influences in the tree layer, such asrecruitment of late-successional species in establishedstands, identifying and predicting successional influ-ences is more problematic for the herb layer. Severalstudies have indicated that the recovery of late suc-cessional herbs often lags behind tree species aftermajor anthropogenic disturbances such as clearcut-ting (MacLean & Wien 1977; Brewer 1980; Duffy& Meier 1992; Duffy 1993), although the length ofthe lag period has been debated (Johnson et al. 1993).Ascertaining successional influences on herb layervegetation is complicated by several factors, includinglimited information on their life history and demog-raphy (Thompson 1980; Bierzychudek 1982), theircapacity for asexual reproduction (the presence of agiven species may be more an artifact of micro-scaledisturbance history than a general indicator of a suc-cessional sere), and that spring and summer herbs canexhibit differential responses to disturbance (Moore &Vankat 1986).

Implications for management of biodiversity

Given the observed separation of species composi-tion and structure among the tree and herb layers, theconsequences of these results for biodiversity manage-ment (i.e., plant diversity) become non-trivial issues.The inability to effectively predict the composition ofthe herb understory from the woody species (based oncluster analysis) or ELT designations indicates prob-lems linking classification techniques and/or large-scale map units with more intensive sampling resultssuch as those generated in the current study. Recog-nizing the importance of scale and heterogeneity invegetation layers is especially important in light ofmandates to preserve, protect, and manage for plantdiversity. In the ONSR, herb species outnumber treespecies nearly four to one. Hence, effective manage-ment strategies need to encompass the herb layer andidentify the biotic and abiotic conditions that main-tain the high level of species richness. Identifyingzones of species richness and effective monitoring ofvegetation change will require comprehensive sam-pling on a landscape scale, rather than focusing ontraditional classification of the flood-influenced ripar-ian zone. The ability to elucidate key environmentalgradients influencing vegetation patterns is a neces-sary component in predicting vegetation change and

in the preservation of the rich plant diversity in thesecomplex riparian landscapes.

Acknowledgements

We thank D. Coutourier, C. Crownover, A. Delp, H.Hubbard, B. Madison, D. Moore, J. Sharma and A.Spann for their help with data collection. B. Hin-tertheur and Drs. E. E. Dale, Jr. and E. Smith identifiedmany unknown plant specimens. Dr. R. D. Evans pro-vided advice on experimental design. The ArkansasWater Resources Center and the Department of Bi-ological Sciences, University of Arkansas providedlogistical support. We appreciate the efforts of the Na-tional Park Service, and the Ozark National ScenicRiverways for their support of this work. This workwas funded by the Department of the Interior/NationalPark Service Subagreements Nos. 4 & 9 to Coopera-tive Agreement No. CA 7150–4–0001.

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