Prepared in Cooperation with the National Park Service

Significance of Headwater Streams and Perennial Springs 

in Ecological Monitoring in Shenandoah National Park

By Craig D. Snyder, James R. Webb, John A. Young, and Zane B. Johnson

Open-File Report 2013–1178

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

SALLY JEWELL, Secretary

U.S. Geological Survey

Suzette M. Kimball, Acting Director

U.S. Geological Survey, Reston, Virginia: 2013 

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Suggested citation:

Snyder, C.D., Webb, J.R., Young, J.A., and Johnson, Z.B., 2013, Significance of headwater streams and perennial 

springs in ecological monitoring in Shenandoah National Park: U.S. Geological Survey Open-File Report 2013-

1178, 46 p.

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endorsement by the U.S. Government. 

Although this information product is in the public domain, permission must be secured from the individual copyright 

owners to reproduce any copyrighted material contained within this report.
iii

Contents

Abstract ..................................................................................................................................................................... 1

Introduction ............................................................................................................................................................... 2

Objectives ................................................................................................................................................................. 6

Purpose and Scope................................................................................................................................................... 6

Procedures................................................................................................................................................................ 6

General Design ..................................................................................................................................................... 6

Landscape Analyses and Site Selection................................................................................................................ 7

Sampling Methods............................................................................................................................................... 14

Data Analysis ...................................................................................................................................................... 15

Assessment of Physical Habitat Characterisitcs ..................................................................................................... 17

Assessment of Macroinvertebrate Communities..................................................................................................... 18

Multivariate Analysis of Assemblage Composition .............................................................................................. 18

Univariate Analysis of Community-Structure Metrics........................................................................................... 24

Assessment of Water Chemistry Patterns............................................................................................................... 27

Relations Between Water Chemistry and Macroinvertebrate Communities ............................................................ 31

Significance of Headwater Streams and Perennial Springs .................................................................................... 32

Monitoring Implications ........................................................................................................................................... 36

Acknowledgments................................................................................................................................................... 37

References Cited .................................................................................................................................................... 38

Appendix. Description of macroinvertebrate taxa collected at 54 sites in Shenandoah National Park 

in 2007 and 2008. .................................................................................................................................................. 42

Figures

1. Graph showing distribution of stream sizes in Shenandoah National Park ....................................................... 4

 2. Graph showing flow patterns for the Rapidan River near Culpepper, Virginia................................................... 9

3. Map showing sample site locations in Shenandoah National Park ............................................................... 13

4. Graph showing a comparison of local habitat characteristics among sampling site types .............................. 18

5. Graph showing a comparison of mean taxonomic and trophic composition of benthic 

macroinvertebrate communities among sampling-site types........................................................................... 19

6. Graph showing a comparison of the number and proportion of rare taxa among sampling-site types ............ 20

7. Graph showing non-metric multidimensional scaling ordination plot of macroinvertebrate communities

from the 54 sites in Shenandoah National Park that were sampled in 2007 and 2008.................................. 21

8. Graph showing the interaction between stream type and underlying bedrock geology on non-metric

multidimensional scaling (NMS) axis two ordination score ............................................................................. 22

9. Graph showing the relation between nonmetric multidimensional scaling axis-2 ordination scores and

base-flow discharge........................................................................................................................................ 23

10 Graph showing the interaction between underlying bedrock geology and site elevation on nonmetric

multidimensional scaling axis-1 scores derived from ordinations .................................................................. 24

11. Graph showing a comparisons of the distributions of benthic macroinvertebrate metric values 

between long-term data collected at 24 long-term ecological monitoring sites over the 
iv

20-year period from 1987 to 2006 and data collected from 9 long-term ecological monitoring 

sites, 23 headwater stream sites with 100-hectare watersheds, and 22 perennial spring sites in 

2007 and 2008 ................................................................................................................................................ 25

12. Graph showing the relationship between fall and spring season water chemistry measurements................. 28

13. Graph showing comparisons of historical water-chemistry measures from larger, long-term 

water-chemistry monitoring sites (Shenandoah Watershed Study, SWAS) with 

100-hectare-watershed stream and perennial spring sites .......................................................................... 30

Tables

1. Description of twenty-three 100-hectare stream sites in Shenandoah National Park sampled in 2007..........8

2. Description of 34 perennial-spring sites in Shenandoah National Park sampled in 2008.............................10

3. Description of 9 long-term ecological monitoring (LTEM) and 14 Shenandoah Watershed Assessment 

(SWAS) sites sampled in Shenandoah National Park in 2007. ....................................................................12

4. Description of the 15 macroinvertebrate community metrics evaluated for this study and the expected 

response of each metric to stress. ...............................................................................................................16

5. Taxa showing strong correlations (Pearson correlation r > 0.40) with NMS axis ordination scores. ............21

6. Results of interactive stepwise general linear modeling used to assess the effects of study design 

variables on macroinvertebrate metrics. ......................................................................................................26

 7. Comparisons of least-square means among the three stream types for 14 macroinvertebrate 

community metrics found to differ among the three stream types (LTEM, 100-hectare, and springs)..........27

8. Results of interactive stepwise general linear modeling used to assess the effects of study design 

variables on water-chemistry parameters. ...................................................................................................29

9. Comparisons of least-square means among the three stream types for two water- chemistry 

parameters found to differ among stream types (SWAS, 100-hectare, and springs). ..................................29

10. Correlations between water chemistry and biological measures in streams in Shenandoah 

National Park. ..............................................................................................................................................32
v 

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in.)

millimeter (mm) 0.03937 inch (in.)

meter (m) 3.281 foot (ft) 

kilometer (km) 0.6214 mile (mi)

kilometer (km) 0.5400 mile, nautical (nmi) 

meter (m) 1.094 yard (yd) 

Area

square meter (m2) 0.0002471 acre 

hectare (ha) 2.471 acre

hectare (ha) 0.003861 square mile (mi2) 

square kilometer (km2) 0.3861 square mile (mi2)

Volume

liter (L) 33.82 ounce, fluid (fl. oz)

liter (L) 2.113 pint (pt)

liter (L) 1.057 quart (qt)

liter (L) 0.2642 gallon (gal)

liter (L) 61.02 cubic inch (in3) 

Flow rate

cubic meter per second (m3/s) 35.31 cubic foot per second (ft3/s)

Mass

gram (g) 0.03527 ounce, avoirdupois (oz)

Temperature in degrees Celsius (°C) may be converted to degrees Fahrenheit (°F) as follows:

°F=(1.8×°C)+32

Temperature in degrees Fahrenheit (°F) may be converted to degrees Celsius (°C) as follows:

°C=(°F-32)/1.8

Vertical coordinate information is referenced to North American Vertical Datum of 1988 (NAVD 88).

Horizontal coordinate information is referenced to the North American Datum of 1983 (NAD 83).

Specific conductance is given in microsiemens per centimeter at 25 degrees Celsius (μS/cm at 25°C).

Concentrations of chemical constituents in water are given in microequivalents per liter (μeq/L).
1 

Significance of Headwater Streams and Perennial Springs 

in Ecological Monitoring in Shenandoah National Park

By Craig D. Snyder,1 James R. Webb,2 John A. Young,1 and Zane B. Johnson3

Abstract

Shenandoah National Park has been monitoring water chemistry and benthic macroinvertebrates 

in stream ecosystems since 1979. These monitoring efforts were designed to assess the status and trends 

in stream condition associated with atmospheric deposition (acid rain) and changes in forest health due 

to gypsy moth infestations. The primary objective of the present research was to determine whether the 

current long-term macroinvertebrate and water-quality monitoring program in Shenandoah National 

Park was failing to capture important information on the status and trends in stream condition by not 

sufficiently representing smaller, headwater streams. The current benthic-macroinvertebrate and waterchemistry sampling designs do not include routine collection of data from streams with contributing 

watershed areas smaller than 100 hectares, even though these small streams represent the overwhelming 

proportion of total stream length in the park. In this study, we sampled headwater sites, including 

headwater stream reaches (contributing watershed area approximately 100 hectares (ha) and perennial 

springs, in the park for aquatic macroinvertebrates and water chemistry and compared the results with 

current and historical data collected at long-term ecological monitoring (LTEM) sites on larger streams 

routinely sampled as part of ongoing monitoring efforts. The larger purpose of the study was to inform 

ongoing efforts by park managers to evaluate the effectiveness and efficiency of the current aquatic 

monitoring program in light of other potential stressors (for example, climate change) and limited 

resources. 

Our results revealed several important findings that could influence management decisions 

regarding long-term monitoring of park streams. First, we found that biological indicators of stream 

condition at headwater sites and perennial springs generally were more indicative of lower habitat 

quality and were more spatially variable than those observed at sites on routinely monitored larger 

streams. We hypothesized that poorer stream condition observed in smaller streams was due to stream 

drying that occurs more frequently in headwater areas. We also found that biological and waterchemistry measures responded differently to landscape drivers. Variation in most biological endpoints 

was driven primarily by stream size and was only secondarily associated with bedrock geology. In 

contrast, water chemistry showed essentially the opposite pattern, with underlying geology explaining 

much of the variation and stream size being of secondary importance. Therefore, expanding the LTEM 

program to include headwater areas would yield substantially different biological information, whereas 

broad inferences regarding spatial patterns in water chemistry would probably not change. 

 

1 U.S. Geological Survey, Leetown Science Center, Aquatic Ecology Branch, Kearneysville, WV

2 Department of Environmental Sciences, University of Virginia, Charlottesville, VA

3 Department of Biology, Lake Erie College, Painesville, OH
2 

Although significant differences in community composition were observed among streams of 

different sizes, no taxa were unique to headwater sites. All taxa collected at the 45 headwater sites also 

had been collected at one or more LTEM sites during one or more years. This observation indicates that

headwater sites in the park may be structured by biotic nestedness; consequently, focusing management 

efforts on preserving the species pool at the larger LTEM sites would likely result in the protection of 

most taxa parkwide. Finally, linkages (correlations) between water chemistry and biological measures of 

stream condition were significantly stronger when assessed at the LTEM sites than when assessed at the 

springs or headwater sites, indicating that conditions at downstream sites may be better indicators of 

water-quality trends. 

Introduction

Headwater streams commonly account for more than 75 percent of the total stream channel 

length in drainage basins (for example, Benda and others, 2004). In addition, headwater streams are 

critical sites for carbon and nutrient processing, and consequently are important sources of water, 

nutrients, and species for downstream reaches (Wallace and others, 1997; Peterson and others, 2001). 

Moreover, headwater sites represent preferred or obligate habitat for some aquatic species (Clarke and 

others, 2008), and provide dispersal corridors and refugia from natural and anthropogenic disturbances 

for many others (Covich and others, 2006; Meyer and others, 2007). Despite their importance, however, 

headwaters are often underrepresented or ignored altogether in biomonitoring and assessment programs, 

and the implications of failing to incorporate headwater sites into monitoring efforts have not been 

adequately investigated. 

Water resources in Shenandoah National Park (SHEN) include about 90 perennial streams, more 

than 50 of which support reproducing populations of native brook trout (Salvelinus fontinalis) (National 

Park Service, 1998). In addition, 89 springs have been mapped, and many more are known to exist in 

the park but have not been mapped (Dekay, 1972). These aquatic habitats together with their associated 

riparian areas constitute unique and important resources in SHEN, providing irreplaceable habitat for 

many aquatic and terrestrial species and contributing greatly to the visitor experience of Shenandoah 

National Park. 

Water resources in SHEN are threatened by numerous natural and anthropogenic stressors 

operating at both local and regional scales. Local stressors include wildfires, ice storms, road salts, 

water withdrawls, and visitor use. Because of the park’s topographic setting at the top of the watershed, 

however, the primary stressors to aquatic resources are regional in scale and include atmospheric 

deposition (especially acid-forming compounds of sulfur and nitrogen, as well as mercury), ozone 

contamination, forest defoliation by invasive pests (for example, gypsy moth and hemlock wooly 

adelgid), and climate change (National Park Service, 1998). 

Long-term stream monitoring in SHEN began in 1979 with the Shenandoah Watershed Study 

(SWAS) program, a cooperative undertaking of the park and the University of Virginia. The initial 

objectives of the SWAS program centered on characterizing and understanding changes in water 

chemistry associated with acidic deposition that was known to be occurring in the eastern United States. 

Over time, the SWAS program has addressed additional issues that challenge watershed ecosystems in 

SHEN. The current SWAS watershed data-collection system involves 14 primary study watersheds, 

including a combination of routine discharge gaging; quarterly and weekly water-quality sampling; and 

high-frequency episodic, or stormflow, sampling. In addition, a number of extensive stream-quality 

surveys have been conducted throughout SHEN in support of various research and monitoring 

objectives (Cosby and others, 2006).
3 

In 1982, the fisheries monitoring program began and was designed primarily to inform park 

management of the status of the brook trout fishery in the park. The objectives were expanded in 1995 

to link the fisheries program with the SWAS program and to provide a biological response indicator of 

acidic deposition in the park. Consistent with the design of the SWAS program, the fisheries monitoring 

program was stratified by major bedrock type and ultimately included a set of primary sites (N = 41 

sites on 18 streams) sampled annually and another set of secondary sites (N = 87 sites on 71 streams) 

sampled approximately once every 6 years (Wofford and Demarest, 2011). 

In 1984, SHEN was designated a prototype long-term ecological monitoring (LTEM) park. The 

broad objective of the national LTEM program was to monitor a set of ecological indicators that would 

characterize the status of and trends in ecosystem condition. Consequently, the selected biological 

indicators focused on those ecosystem components perceived by managers to be most vulnerable to 

environmental stressors. At the time of the program’s initiation in SHEN, park managers were 

particularly concerned about the impact of the gypsy moth (Lymantria dispar) and acidic deposition on 

natural resources. The aquatic component of the program focused on benthic macroinvertebrates as a 

bioindicator of stream condition, and a panel sampling design was implemented that roughly 

corresponded to that of the fisheries program, with 28 primary sites sampled annually (currently every 

other year) and another 83 sites sampled on a rotational basis every 3 to 9 years (Wofford and others, 

2011). 

Taken together, the long-term stream-monitoring effort in SHEN constitutes one of the most 

holistic (water chemistry, stream habitat, fish and benthic macroinvertebrates) and spatially and 

temporally comprehensive biomonitoring programs in the country. However, the sampling design of the 

stream-monitoring program, including water-quality, fish, and macroinvertebrate components, is 

skewed toward larger stream sites. For example, all 14 primary stream-monitoring sites included in the 

SWAS program are located on second- and third-order streams whose average contributing basin areas 

range in size from 230 to 2,370 hectares (ha) (mean = 1,161 ha). Similarly, only 15 (13.5 percent) of the 

111 macroinvertebrate sample sites had contributing basin areas smaller than 100 ha (fig. 1A). In 

addition, although more than 68 percent of the total stream length in the park is first order, only 32 

percent of the 111 macroinvertebrate sample sites (n = 36) are located in first-order streams (fig. 1B). 

Moreover, this estimate is based on stream channels determined from blue lines on U.S. Geological 

Survey (USGS) 7.5-minute topographic maps, which may underrepresent small streams (Hughes and 

Omernick, 1983; Leopold, 1994). By using a 15-meter (m) digital elevation map (DEM), we modeled 

stream channels park-wide using a flow-accumulation algorithm and a stream-origination area of 10 ha. 

We found that the “blue-line method” underestimates the total stream length in the park by about 30 

percent (fig. 1C), indicating that small headwater streams are even more abundant than previously 

thought. Although it is likely that a substantial proportion of this additional length determined from 

flow-accumulation models represents intermittent streams, all 40 of the spring sites (see “General 

Design” in the Procedures section of this report, below) sampled in this study had drainage areas smaller

than 10 ha and yet contained water year round, and most had flowing water (that is, they were perennial) 

throughout the year. Therefore, it is clear that small headwater streams dominate the aquatic landscape 

in SHEN but are underrepresented in the park’s long-term monitoring efforts. 
4 

Figure 1. Description of stream sizes in Shenandoah National Park. (A) shows the distribution of long-term 

ecological monitoring (LTEM) sites among basin area categories(<,less than;>, greater than);(B) compares the 

total stream length to the number of LTEM sampling sites of each stream order (determined from blue lines on 

U.S. Geological Survey 7.5-minute topographic maps);and (C) compares the total stream length computed by the 

"blue line" method to the total computed by flow accumulation modeling. 
5 

Failing to adequately represent headwater streams is a pervasive problem in biomonitoring that 

is not unique to SHEN. Although there are numerous examples of intensive investigation and 

monitoring of selected small watersheds, few attempts have been made to conduct systematic sampling 

of headwater streams over large areas (Bishop and others, 2008); consequently, the ability of monitoring 

programs to provide statistically valid inferences about trends in ecological condition over large areas 

could be compromised. Underrepresentation of headwater streams in aquatic monitoring programs is 

due in large part to the numerous logistical challenges associated with routine sampling of these smaller 

streams at the top of the watershed. For example, as already mentioned, the resolution of available maps 

is typically insufficient to capture the location and extent of many headwater stream channels. 

Moreover, even less is typically known about the upstream limits of perennial flow in mapped channels 

within a basin (Rivenbark and others, 2004). In addition, headwater areas are commonly more remote

than downstream areas, thereby increasing the sampling effort (for example, the time required to reach 

sampling sites) and monitoring costs. 

The focus of stream monitoring on downstream areas has also been justified on theoretical 

grounds. In particular, the linear view of stream systems exemplified by the River Continuum Concept 

(RCC; Vannote and others, 1980) has provided conceptual support for using downstream sites to 

evaluate the cumulative condition of watersheds (for example., Cole and others; 2003; Megan and 

others, 2007). In addition to practical considerations including accessibility and perennial flow, this 

theoretical concept of downstream sites as integrators of ecological condition was a primary 

consideration for selecting downstream sites for the long-term monitoring efforts in SHEN. The RCC 

argues that predictable changes in geomorphology lead to gradual changes in energy sources and, 

ultimately, aquatic-community structure. Although the RCC has enjoyed substantial empirical support, 

recent evidence indicates that stream systems often may be more complicated, displaying discontinuities 

and a “patchy” nature influenced by the geometry of stream networks (Townsend, 1989; Benda, 2004; 

Grant and others, 2007). These alternative models indicate that the ecological condition of headwater 

areas may be less strongly correlated with the condition of downstream areas than previously thought. 

These recent developments have led SHEN managers and science advisors to question whether 

the current focus of SHEN long-term monitoring programs should be adjusted or expanded to 

incorporate more headwater sites. The SHEN Water Resources Scoping Report (WRSR) (Vana-Miller 

and Weeks, 2004), a review of current and historical research and monitoring of aquatic resources in 

SHEN, concludes that research on small-stream habitats is necessary in order to fully understand and 

protect water resources in the park. Specifically, the WRSR called for water-quality sampling at upper 

stream reaches rather than only at the lower elevation stream reaches routinely sampled through the 

existing programs. Moreover, additional information presented in the Assessment of Air Quality and 

Related Values in Shenandoah National Park (Sullivan and others, 2003) indicates that among all 

surface waters in SHEN, the upper headwater reaches, which represent the largest fraction of total 

stream length in the park, exhibit the greatest range in chemical and biological properties and the most 

extreme degree of current impairment and sensitivity to future impairment. The WRSR further 

recognized that springs and seeps are unique aquatic habitats, supporting biological communities that 

may be different from downstream habitats, including numerous species endemic to the park, and that 

these habitats may serve as biological refugia during periods of climate change. Although various 

sources of information concerning SHEN springs are available (for example, Dekay, 1972; Young and 

others, 2009; Plummer and others, 2000), more information is needed to inventory and characterize 

spring habitats. The WRSR recommended further characterization of baseline physical, chemical, and 

biological conditions in order to provide an informed basis for management and long-term monitoring. 

This study was designed to address these data gaps.
6 

Objectives

The goal of this study was to characterize the physical, chemical, and biological characteristics 

of headwater streams and small springs in SHEN. Expanding knowledge of these characteristics will fill 

a critical information gap regarding the character and importance of headwater areas in the park, and 

ultimately will inform managers about whether it would be useful to expand the water-resource 

inventory and monitoring programs in SHEN from the lower stream reaches in the park to include 

aquatic habitats farther upstream in the headwater portion of individual watersheds. The specific 

objectives were to--

(1) determine the relations between landscape setting, as defined by topography and drainage area, and 

the physical, chemical, and biological characteristics of headwater streams and springs in SHEN; 

and 

(2) determine the extent to which the current water-resources monitoring program (which is focused on 

larger, lower elevation stream sites with larger drainage basins) is representative of the range of 

aquatic-habitat conditions in the park. 

Purpose and Scope

This report compares water quality, benthic-macroinvertebrate community composition and 

structure, and physical-habitat characteristics between headwater streams and larger stream sites that are 

routinely sampled as part of existing monitoring programs. We sampled 23 headwater sites that drain 

approximately 100 hectares, 22 perennial spring sites, and 9 long-term ecological monitoring (LTEM) 

sites in the park. We also conducted retrospective analyses of long-term data (1987–2006) collected 

from more than 100 larger stream sites in the park. In addition, three alternative monitoring scenarios 

are evaluated in the context of the study results. 

Procedures

General Design

This study was conducted through a collaborative effort between the USGS and the University 

of Virginia (UVA). The USGS coordinated and conducted landscape analyses and biological sampling, 

and the UVA coordinated and conducted water-chemistry sampling. Data analysis and reporting aspects 

of the study were shared. Biological endpoints focused on aspects of aquatic-macroinvertebrate 

assemblages, and water-chemistry endpoints focused on characteristics related to nutrients and acidic 

deposition. Aquatic macroinvertebrates were selected for two reasons. First, measures of the structure 

and composition of aquatic-macroinvertebrate assemblages have been shown to be strongly tied to the 

physical and chemical conditions of the site and surrounding watershed (Hynes, 1975). Second, the 

current water-resources monitoring program in SHEN uses aquatic macroinvertebrates to assess the 

status and trends in aquatic habitats (Wofford and others, 2011). Water chemistry was selected because 

of the well-established relations between water chemistry and the distribution and productivity of fish 

and other aquatic biota. 

The project was conducted over a 4-year period (2007-10) that included 2 years of field 

sampling, 1 year of laboratory analysis, and 1 year of data analysis and reporting. Project objectives 

were met through the collection and integration of detailed water-chemistry, habitat, and biological data 

collected from headwater stream reaches at sites draining watersheds approximately 100 ha in size

(referred to in this report as “100-ha stream sites” or “headwater stream sites”) and from perennial 
7 

springs, and by comparing characteristics observed in these smaller stream habitats to those measured at 

larger sites representative of the currently existing program. Historical water-chemistry and benthicmacroinvertebrate data collected over the last 20 years (1987-2006) at SWAS (water chemistry) and 

LTEM (aquatic macroinvertebrates and stream habitat) sites were also used to provide long-term 

context. 

Landscape Analyses and Site Selection

We conducted landscape analyses to use as the basis for site selection. Analyses involved using 

geographic information system (GIS) tools to (1) identify all possible headwater stream and spring 

sampling sites, (2) characterize the landscape setting at all possible sites, and (3) assign identified sites 

to classes or positions along important landscape gradients. Subsequently, we used a stratified random 

approach to select sites for sampling. This general approach ensured that sampled sites reflected the 

range of landscape settings present in the park, and that the studyfindings could be extrapolated to the 

larger park landscape. 

Potential headwater streams were identified by using a combination of existing data and terrain 

modeling. An existing stream-network map originally digitized by National Park Service (NPS) 

personnel from the blue lines (indicating streams) on 1:24,000-scale USGS topographic maps (Daniel

Hurlbert, Shenandoah National Park, written commun., 2009). Because these maps may not show all 

headwater streams, however, flow-accumulation modeling was used to construct stream networks 

throughout the park to identify first-order stream reaches that were not present on the existing stream 

layer. A 15-m-resolution DEM developed by Young and others (2009) was used for hydrologic 

modeling. Analysis routines available in the ArcHydro extension of ArcGIS also were used (Maidment, 

2002). Because it was expected that many of the first-order streams identified with flow modeling 

would likely be intermittent, a minimum watershed area was selected on the basis of the distribution of 

watershed areas of known permanent streams observed through previous studies (Bulger and others, 

1995), and only streams identified from the flow modeling that exceeded the minimum threshold were 

selected. Initially, a threshold of 50 ha was used, but it was found during field-validation surveys in July 

2006 (under base-flow conditions) that the water in many of the sites draining 50-ha watersheds dried 

up completely. Consequently, we selected 100-ha basin areas for potential headwater stream sites. With

these methods, 147 potential headwater stream sites with 100-ha (+/- 3 ha) watersheds were initially 

identified. 

The list of potential headwater stream sites was parsed further on the basis of two other 

landscape factors. First, we included only stream sites for which the entire 100-ha watershed upstream 

from the site was within the park boundaries; second, we limited the list to sites at which more than 85 

percent of the watershed was underlain by a single type of bedrock geology (siliciclastic, granitic, or 

basaltic). These assessments were accomplished by overlaying the 147 potential site locations on digital 

maps of park boundaries and bedrock geology (Morgan and others, 2004). We further characterized the 

landscape setting of potential headwater sites by overlaying site locations on digital maps that depict 

terrain (Young and others, 2009) so that we could determine elevations at site locations. We classified 

site elevations into three strata: low, 1,100–1,600 feet (335-488 meters); medium, 1,900–2,500 feet 

(579-762 meters), and high, >2,900 feet (>884 meters). Ultimately, twenty-three 100-ha headwater sites 

were selected for aquatic-macroinvertebrate and water-chemistry sampling from the final list stratified 

by geology and elevation (table 1). No high-elevation sites were in the siliciclastic bedrock geologic 

type, and only two potential stream sites were in the high-elevation granitic geologic type (table 1).
8 

Table 1. Description of twenty-three 100-hectare stream sites in Shenandoah National Park sampled in 2007.

Site Coordinates2

Geol

Strata

Elev

Strata

Site

No.1 Watershed Dom.Geol(%)

Elev

(m) Latitude Longitude

Siliciclastic

Low

251

279

281

Gap Run

Paine Run

Paine Run

100

100

100

460

489

472

38.32469

38.20508

38.19772

-78.63886

-78.77981

-78.77297

Med

280

285

287

Moorman’s

Meadow Run

Meadow Run

100

100

100

668

619

600

38.20211

38.17519

38.16306

-78.74711

-78.78958

-78.78942

High

Granitic

Low

152

179

192

Smith Creek

Thorton River

Dry Run

100

100

100

467

390

475

38.80800

38.64314

38.58433

-78.19311

-78.28106

-78.31714

Med

181

182

227

Dry Run (North fork)

Hazel River

Staunton River

100

100

100

654

705

632

38.62342

38.62011

38.45822

-78.35417

-78.29336

-78.40028

High

183

224

Hughes River

Staunton River

100

100

911

911

38.61694

38.46769

-78.33492

-78.41906

Basaltic

Low

148

161

260

SF Shenandoah

Dry Run (East fork)

Ivy Creek

100

100

100

350

468

423

38.88772

38.76592

38.27931

-78.19931

-78.32378

-78.63447

Med

165

240

257

Jeremy’s Run

South River

Ivy Creek

100

100

100

624

718

693

38.74586

38.37864

38.30100

-78.31278

-78.50900

-78.61878

High

195

216

217

Whiteoak Canyon

Naked Creek

Rapidan River

84

100

100

973

919

921

38.57931

38.50358

38.50214

-78.37233

-78.44967

-78.43731

1

Site numbers were established for this study and were not used in previous studies. 

2

Site coordinates are reported in decimal degrees and are referenced to the North American Datum of 1983. 

Similar methods were used to select perennial spring sites for sampling. Existing data on the 

location of springs (Dekay, 1972; Plummer and others, 2000; and USGS topographic maps) were used 

to define potential spring sites for sampling. Where necessary, spring locations were digitized from 

paper sources (Dekay, 1972; USGS topographic maps), and all spring locations were merged into a 

single dataset. A total of 83 springs were identified from existing data. A GIS was then used to overlay 

spring-site locations on geology (Morgan and others, 2004) and terrain (Young and others, 2009) layers 

to determine underlying geology and elevation for each spring. Only springs with perennial flow were 

selected. Consequently, in July 2007 (under base-flow conditions), we visited spring sites to verify their 

presence and to determine their appropriateness for collecting water samples for water-chemistry 

analysis (water present) and benthic macroinvertebrates (flowing water present). Flow records from the 

USGS streamgage on the Rapidan River (streamgage 01667500) were used to estimate overall flow 

patterns in the park. Because flow during the months leading up to July 2007 was unusually low (fig. 2), 

it is likely that those sites observed to have flowing water in July 2007 are indeed perennial over a large 

range of weather patterns. However, these sites could be dry during prolonged droughts. 
9 

Figure 2. Mean monthly flows for the Rapidan River near Culpepper, Virginia (U.S. Geological Survey 

streamgage 01667500). Box plots show the distribution of long-term monthly averages (1930-2010), and lines depict

montly average flows for the 2007 and 2008 sampling years. For box plots, horizontal black lines within boxes depict

medians, upper and lower limits of the box define 50 percent of the values (25th and 75th percentile), whiskers define

90 percent of the values (95th and 5th percentiles), and Individual points represent outliers to the distribution.

Ultimately, 34 perennial-spring sites were selected for sampling and were stratified by geology 

and elevation (table 2). Because spring sites were concentrated at high elevations, they were not initially 

classified by elevation. However, sites selected for intensive sampling (benthic macroinvertebrates and 

water chemistry) spanned the range in elevation observed within each geologic type. Both benthicmacroinvertebrate and water-chemistry samples were collected from 22 sites, and only water-chemistry 

samples were collected from an additional 12 sites. 
10

Table 2. Description of 34 perennial-spring sites in Shenandoah National Park sampled in 2008.

Site Coordinates3

Geol

Strata

Site

No.1 Spring Name Source2 Elev(m) Latitude Longitude ElementsSampled4

Siliciclastic

8

3023

4004

58

3025

62

4003

3023B

Sawmill Spring (west side)

Blackrock Springs

Blackrock Springs (South)

Pinefield Hut

Pond Ridge Spring

Backrock Hut

Cold Springs Hollow

Blackrock Springs (Lower)

NPS

Topo

GPS

NPS

Dekay

NPS

GPS

GPS

479

594

615

741

773

796

678

561

38.10900

38.20812

38.20705

38.29083 

38.15632

38.21445

38.18089

38.26392

-78.82674

-78.75215

-78.75207

-78.64577

-78.76613

-78.74300

-78.78853

-78.75311

BM, WC

BM, WC

BM,WC

BM, WC

BM, WC

BM,WC

WC

WC

Granitic

1898

1883

1865

1858

3004

50

1876

47

4002

1912

3011

3009

3013

1864

Old Rag Shelter

Indian Run Shelter

Dickey Ridge Spring

Byrds Nest #3 Spring

Little Hogback East Weir

Bearfence Hut

Hogback Spring #5

Rock Spring Hut

Old Rag E Ridge Trail

Swift Run Gap #27

Shaver Hollow Shelter

Hazel Mountain Overlook

Powwow Grounds West Weir

Dean Mountain Gap

NPS

NPS

NPS

NPS

Dekay

NPS

NSP

NPS

GPS

NPS

Topo

Dekay

Dekay

Dekay

620

698

706

929

943

943

991

1,018

665

766

862

940

1,054

890

38.55445

38.82752

38.83349

38.63659

38.75589

38.44399

38.75898

38.55415

38.55772

38.35444

38.61828

38.64429

38.58746

38.38912

-78.32956

-78.16572

-78.17584

-78.32017

-78.26933

-78.47043

-78.27234

-78.40868

-78.30085

-78.54893

-78.35368

-78.31509

-78.38658

-78.51361

BM,WC

BM,WC

BM,WC

BM,WC

BM,WC

BM,WC

BM,WC

BM,WC

WC

WC

WC

WC

WC

WC

Basaltic

3001

39

196

1895

1884

1886

3019

3014

3008

53

1870

4001

Fox Hollow Spring (west weir)

Gravel Springs Hut

Ivey Creek Maintenance hut

Matthews Arm Spring

Ivy Creek Shelter Spring

Lewis Mountain Spring #25

Colvin Spring Weir

Par Springs East Weir

Beahms Gap Spring #2

Hightop Hut

Furnace Spring

Head of Jeremy’s Run

Dekay

NPS

NPS

NPS

NPS

NPS

Dekay

Dekay

Dekay

NPS

Dekay

NPS

547

759

791

789

890

952

999

1,047

744

956

1,037

786

38.87663

38.76416

38.25070

38.75845

38.26496

38.43317

38.49220

38.58496

38.69724

38.33330

38.59736

38.75864

-78.20517

-78.23342

-78.68282

-78.30028

-78.65433

-78.48232

-78.44899

-78.38127

-78.32248

-78.55941

-78.37951

-78.30027

BM,WC

BM,WC

BM, WC

BM,WC

BM,WC

BM,WC

BM,WC

BM,WC

WC

WC

WC

WC

1

Site numbers were established for this study and do not correspond to number used by others for same site location.

2

Source – NPS = map provided by NPS (Dan Hurlbert, SHEN, oral communication, 2009); Dekay = Sites digitized from 

maps in Dekay (1972); Topo = Spring sites digitized from USGS topographic quadrangles; and GPS = sites located by field 

crews during reconnaissance for this study.

3

Site coordinates are reported in decimal degrees and are references to the North American Datum of 1983.

4

BM, benthic macroinvertebrates; WC, water chemistry.

Although long-term data exist for many LTEM sites, benthic-macroinvertebrate sampling at

LTEM sites was not scheduled for 2007 when smaller streams were sampled as part of this study. 

Therefore, nine LTEM sites were also sampled so that benthic-macroinvertebrate assemblage measures 

could be compared between LTEM and smaller streams within the same study year, and to ensure that 

the study year was not unusual. The nine sites were selected from the 111 LTEM sites on the basis of 

several criteria. First, we wanted to equally represent the three major geologic types underlying 
11

watersheds in the park. As with the 100-ha stream sites, only LTEM sites with geologically 

homogeneous watersheds (that is, > 85 percent of the watershed area is underlain by a single type) were 

selected. Second, the selection of LTEM sites was limited to those with relatively small drainage areas 

(that is, < 600 ha) to ensure that inferences regarding “stream type” (surrogate for stream size) effects 

would be conservative. In other words, if significant effects of stream type were observed when 

comparing springs and 100-ha stream sites to relatively small LTEM sites, then we could be confident 

that the chemical and biological condition of small headwater sites was not being represented by the 

current LTEM sampling design. Third, with the three sites within each geologic type class, an attempt 

was made to represent variation in elevation. However, meaningful variation in elevation occurred only 

within the basaltic geology type (that is, within a geology type, LTEM sites tended to be distributed 

within a fairly uniform elevation). Finally, LTEM sites would ideally represent the park spatially (east

to west and north to south). 

Water chemistry was also measured at the 14 primary SWAS sites as part of the annual SWAS 

sampling program. As with the LTEM sites for macroinvertebrates, water-chemistry measurements 

from the larger watershed SWAS sites would allow for the evaluation of water-chemistry differences 

between small-watershed headwater sites and larger watershed sites within the same study year. The 

nine LTEM sites and 14 SWAS sites selected for sampling are described in table 3. 

In total, we sampled 80 sites, including twenty-three 100-ha stream sites, 34 spring sites, 9 

LTEM stream sites, and 14 SWAS stream sites. We sampled benthic macroinvertebrates at all 100-ha 

stream sites and LTEM sites, but only 22 of the 34 spring sites. Water chemistry was measured at all 80 

sites. Sample sites were defined as 50-m reach lengths for both 100-ha and spring sites. LTEM sites 

were 100 m in length. We used a 50-m sample reach for the headwater and perennial spring sites 

because a smaller reach provided at least three riffle-pool sequences whereas the 100-m reach was 

required to provide a similar number of geomorphic units in the larger stream sites. Sample site 

locations are depicted in figure 3, and all site locations are attributed in NPSTORET.
12

Table 3. Description of 9 long-term ecological monitoring (LTEM) and 14 Shenandoah Watershed Assessment 

(SWAS) sites sampled in Shenandoah National Park in 2007.

Site Coordinates2

Geol

Strata

Site

No.1 Stream Name LTEM/SWAS

Dom.

Geol(%)

Basin Area 

(ha)

Elev

(m) Latitude Longitude

Siliciclastic

3F105

3L300

3L302

DR01

PAIN

VT36

VT53

WOR1

Lower Lewis Run

Lower Paine Run

Twomile Run

Deep Run

Paine Run

Meadow Run

Two mile Run

Whiteoak Run

LTEM

LTEM

LTEM

SWAS

SWAS

SWAS

SWAS

SWAS

100

100

100

99.2

100

100

100

100

257

175

144

346

1271

874

555

512

460

562

525

412

425

468

371

447

38.29647

38.20911

38.31117

38.27968

38.19862

38.15868

38.33392

38.25083

-78.72722

-78.75297

-78.64953

-78.76361

-78.79346

-78.80584

-78.67114

-78.74886

Granitic

1L307

2L308

2L302

NFDR

STAN

VT58

VT62

Lands Run

N. F. Dry Run

Hazel River

N.F. Dry Run

Staunton River

Brokenback Run

Hazel River

LTEM

LTEM

LTEM

SWAS

SWAS

SWAS

SWAS

92.3

100

100

100

100

94.4

100

172

220

274

235

1068

990

1126

524

507

682

486

309

329

341

38.82783

38.63097

38.63003

38.63372

38.44449

38.57052

38.61605

-78.18850

-78.35886

-78.29464

-78.35773

-78.37085

-78.30444

-78.26388

Basaltic

1L313

2L304

LIM1

PINE

VT51

VT61

VT66

VT75

Jeremy’s Run

Rose River

Whiteoak Canyon

Piney River

Jeremy’s Run

N.F. Thorton

Rose River

Whiteoak Canyon

LTEM

LTEM

LTEM

SWAS

SWAS

SWAS

SWAS

SWAS

100

100

93.6

69.2

70.5

71

87.1

83.8

560

229

234

1240

2203

1898

2360

1395

543

863

953

364

286

333

341

349

38.74886

38.52083

38.57736

38.70178

38.71555

38.69286

38.51541

38.54092

-78.32381

-78.42217

-78.37117

-78.26776

-78.38144

-78.27403

-78.36624

-78.35024

1

Site numbers are derived from the Shenandoah National Park long-term database.

2

Site coordinates are reported in decimal degrees and are references to the North American Datum of 1983. 
13

Figure 3. Map of Shenandoah National Park depicting sample site locations. 
14

Sampling Methods

Water samples were collected for chemical analyses at all 71 sites (twenty-three 100-ha stream, 

34 perennial spring, and 14 SWAS) during two seasons: early spring (April 7–June 6), when streamflow 

is typically high, and late summer to early fall (September 29--November 2), when streamflow is 

usually low (that is, base flow). Spring-season sampling for 100-ha stream and LTEM sites occurred in 

2007, whereas spring-season sampling for spring sites occurred in 2008. All late summer to early fall 

sampling occurred in 2007. For each sample interval, water samples were collected and transported to 

UVA for laboratory determination of pH, acid-neutralizing capacity (ANC), conductivity, and the 

concentrations of major cations (Ca+2, Mg+2, K+1, and Na+1) and anions (SO4

-2, NO3-1, Cl-1). Water 

samples were collected in prewashed 500-milliliter (mL) collection bottles and maintained on ice until 

delivery to the SWAS program lab at UVA. Sample preparation and analyses were conducted following 

methods appropriate for low-ionic-strength water and by methods described in SWAS standard 

operation procedures for laboratory analysis and quality assurance (Webb and others, 2011).

Benthic macroinvertebrate (BMI) samples were collected in the spring of 2007 at the twentythree 100-ha stream sites and at the nine LTEM sites, and in the spring of 2008 at the 22 spring sites. 

The timing of BMI sampling was coincident with spring-season water-chemistry sampling. Sampling 

protocols were consistent with those used at LTEM sites for more than 20 years (Moeykens and 

Voshell, 2002; Wofford and others, 2011) except that perennial springs and 100-ha stream sites were 

sampled with a Surber sampler and LTEM sites were sampled with a Portable Invertebrate Box Sampler 

(PIBS). Both samplers were equipped with a 350-micrometer collecting net. At each site, three samples 

were collected at random locations within riffles areas, preserved with 10-percent formalin, and returned 

to the USGS Science Center in Leetown, West Virginia, for sorting and identification. 

Macroinvertebrates were picked from samples under 10× magnification, represerved in 70-percent

ethanol, and stored for later identification. Macroinvertebrates were identified to the “lowest practical 

taxonomic level,” which is genus level for all insects (except Chironomidae (family level)), family level 

for all molluscs, and class level for worms. These protocols are consistent with the taxonomic resolution 

used in the SHEN LTEM program (Moeykens and Voshell, 2002; Wofford and others, 2011). We used 

Invertebrate Data Analysis System software (IDAS; Cuffney and Brightbill, 2010) to resolve taxonomic 

ambiguities common to BMI data (Cuffney and others, 2007). In addition, we created a voucher 

collection that includes at least one representative of each taxon collected. Voucher specimens were 

processed (preservation and labeling) according to NPS standards. 

We conducted habitat assessments in the spring season at the same time as the biological 

sampling, except for flow measurements, which were made during the late summer base-flow season. 

For 100-ha and spring sites, we measured channel depth and substrate size at 10 points along the 

longitudinal profile of the 50-m stream reach (that is, every 5 m). For larger LTEM sites, the 10 

transects were located every 10 m along the 100-m study reach and three measurements were made at 

each transect location to account for the larger drainage area and greater heterogeneity of these reaches. 

At each sample point, we measured depth to the nearest centimeter with a meter stick and classified the 

dominant substrate as either silt/detritus (nonmineral, < 6.4 millimeters (mm)), sand (mineral, < 6.4 

mm), gravel (mineral, 6.4-50 mm), cobble (mineral, 50-200 mm), boulder (mineral, > 200 mm), or 

bedrock. Substrate classes were coded numerically (1--6, silt--bedrock) to calculate mean substrate size 

(Bain and Stevenson, 1999). 

We estimated flow in 100-ha and spring sites using the salt-dilution method (Hongve, 1987). 

This method is designed for assessing discharge in small streams with low flow where more traditional 

methods are not reliable. Flow at LTEM sites was measured with a Marsh McBirney Flo-Mate 2000 

electromagnetic flow meter.
15

Data Analysis

The main study objectives were met by comparing measures of BMI community composition 

and structure and water chemistry between the larger, long-term ecological monitoring (SWAS and 

LTEM) sites and the 100-ha headwater sites and perennial springs. These site classifications represent a 

gradient in stream size and are hereafter collectively referred to as “stream types.” 

We summarized BMI community data in two ways. First, we ordinated taxon abundance data 

(Log10Abundance +1) from all 54 sites where BMIs were collected by using nonmetric 

multidimensional scaling (McCune and Grace, 2002) to explore broad patterns in community 

composition among stream types. For these anlayses, we removed rare taxa (that is, taxa that occurred at 

fewer than 25 percent of the sites (n = 13) prior to ordination. We used the resulting axis scores in 

further analyses (see below). Second, we computed 15 univariate summary statistics from the BMI 

taxon abundance matrix for the 54 sites. These summary statistics are widely used to infer stream 

condition on the basis of community structure and were recommended (Jeb Wofford, Shenandoah 

National Park, oral communication, 2008) for use in previous analyses. Distributions of all BMI 

measures were evaluated for normality prior to comparisons and modeling, and transformations were 

applied where appropriate. The 15 univariate metrics are described in table 4. 

For water chemistry, we directly compared pH, ANC, conductivity, and the concentrations of the 

three anions sulfate (SO4

-2), nitrate (NO3-1), and chloride (Cl-1). In addition, the concentrations of the 

four main base cations sodium (Na+1), potassium (K+1), magnesium (Mg+2), and calcium (Ca+2) were 

added to obtain the sum of base cations (SBC) for comparison among stream types. Prior to analyses, 

the distribution of each water-chemistry parameter was evaluated for normality. No transformations 

were required for pH and ANC. The other five parameters were log10-transformed prior to comparisons 

and modeling. 

We used General Linear Modeling (GLM) to assess the influence of landscape setting on BMI 

community attributes (that is, NMS ordination axis scores and the 15 univariate summary statistics) and 

water-chemistry parameters. The landscape-setting variables included stream type, geology, and site 

elevation as main effects. Stream type (perennial spring, 100-ha stream, larger LTEM or SWAS stream 

site) and geology (siliciclastic, granitic, or basaltic) were class variables and elevation was a continuous 

variable. We also evaluated all two-way interactions (stream type by geology, stream type by elevation, 

geology by elevation). As our primary interest in these tests was to explore the relative strength of the 

relations of numerous response variables to these predictors, we believed it to be unnecessary to use a 

multiple-test correction such as Bonferroni to adjust significance levels. We also used least-square 

means computed from GLM models for comparisons among stream types. 

An interactive backward selection process was used for model building. We began with the full 

model (that is, all three main effects and all three two-way interaction terms). We removed 

nonsignificant predictors one at a time beginning with the interaction terms. For interaction terms that 

were found to be significant predictors (p < 0.10), we plotted the interaction to make sure the relation 

was not unduly influenced by outliers or points with high leverage. After all interaction terms were 

evaluated, we used the same procedure to evaluate main effects. This approach allowed us to evaluate 

the effects of stream type after accounting for the effects of other potentially important landscape 

drivers, and allowed us to evaluate the importance of stream type relative to these other predictors using 

partial coefficients of determination (R2

). 

The effects of “local” habitat factors (that is, stream depth, base-flow discharge, and substrateparticle size) on biological and water-chemistry response variables were evaluated. Specifically, 

because it is well established that local habitat is commonly highly correlated with stream size (Frissell 

and others, 1986), we were interested in evaluating the relative importance of depth, discharge, and 
16

substrate size as proximate causes of differences observed among stream types. For this assessment, we 

substituted mean depth, base-flow discharge, and mean substrate size, one at a time, for stream type and 

evaluated changes in overall model coefficient of determination (R2

). 

Table 4. Description of the 15 macroinvertebrate community metrics evaluated for this study and the expected 

response of each metric to stress. [* indicates metric was ArcSin square-root transformed prior to analyses.] 

Metric Definition Response

Richness Total number of taxa Decrease

EPT-r Number of taxa in orders Ephemeroptera, Plecoptera, Trichoptera Decrease

EPT% Percent abundance of individuals of orders Ephemeroptera, Plecoptera, and 

Trichopera

Decrease

E%* Percent abundance of individuals in order Ephemeroptera Decrease

Hydro:T%* Percent abundance of caddis flies (Trichoptera) in family Hydropsychidae 

(Trichopera)

Increase

Leuctra:P%* Percent abundance of stoneflies (Plecoptera) represented by low pH 

tolerant genus Leuctra

Increase

Dom5 Relative abundance (%) of the five most abundant taxa Increase

SimpsonD Simpson diversity index (1-D). Measure of richness weighted by evenness Decrease

PTV Pollution tolerance value. Weighted sum of total taxa by taxon-specific 

tolerance values (Klemm and others, 2002)

Increase

Intol% Percent abundance of macroinvertebrates with tolerance values < 2 Decrease

Scraper%* Percent abundance of functional feeding group containing “scrapers” 

(Merritt and Cummins, 1996)

Decrease

Shredders% Percent abundance of functional feeding group containing “shredders” 

(Merritt and Cummins, 1996)

Decrease

Hapto% Haptobenthos. Percent abundance of macroinvertebrates requiring clean, 

firm, coarse substrates (“crawlers” + “clingers”) (Merritt and Cummins, 

1996)

Decrease

Chiro%* Percent abundance of indivuals from family Chironomidae Increase

SCI Stream condition index. Multimetric index comprised of eight individual 

metrics developed for highland streams in Virginia (Burton and Gerritsen, 

2003)

Decrease
17

Assessment of Physical Habitat Characteristics

Two landscape factors, geologic class and elevation, were design variables in the study. That is, 

we ensured through the stratified site-selection process that there were sites within all three stream types 

representative of broad patterns in geology and elevation. Moreover, a third landscape factor, drainage 

area, was explicitly accounted for by stream type itself (that is, drainage area or stream size was the 

defining basis for distinguishing stream types). However, local habitat characteristics were random 

variables. 

We examined the distributions of base-flow discharge, mean depth, and mean substrate size 

among sites within stream types to determine whether local habitat characteristics were different among 

stream types. Not surprisingly given large differences in drainage area, we found large differences in 

discharge with about an order of magnitude difference in means among stream types (fig. 4). Discharge 

was highly correlated with stream type (Pearson r = 0.87). Mean depth was also significantly higher at

LTEM sites than at either 100-ha stream sites or spring sites, and substrate was dominated by cobble at

both LTEM and stream sites. In contrast, mean substrate size at spring sites was between sand and 

gravel (fig. 4). However, both mean substrate and mean depth were also highly correlated with stream 

type (Pearson r = 0.64 and 0.76, respectively), indicating the large influence of drainage area on local 

habitat. 

In contrast to stream type, correlations between local habitat and the other two design factors 

were weak. Elevation showed a moderate negative correlation with substrate size (r = -0.49), but very 

weak correlations with depth (r = -0.05) and discharge (r = -0.29). Geology showed weak correlations 

with depth (r = -0.15), substrate size (r = 0.01), and discharge (r = -0.06). 
18

Figure 4. Comparison of local habitat characteristics (discharge, depth and substrate) among sampling site types 

(LTEM, long-term ecological monitoring; headwater, stream with 100-hectare watershed; spring, perennial spring). 

For box plots, black lines within boxes depict medians, the upper and lower limits of boxes define 50 percent of the 

values (25th-75 percentile), whiskers define 90 percent of values, and points represent outliers to distributions.

Assessment of Macroinvertebrate Communities

Multivariate Analysis of Assemblage Composition

We identified 120 unique taxa from 70,994 specimens collected from the 54 sites (nine LTEM, 

twenty-three 100-ha stream, and 22 spring sites) sampled in 2007 and 2008 (see appendix A). Many 

taxa collected from springs and 100-ha headwater sites were not collected from the nine LTEM sites in 

2007. However, no unique taxa were collected from these smaller sites that had not been collected 

historically from one or more LTEM sites (that is, over a 20-year period and 111 sites that currently 

compose the long-term database). Nevertheless, we found substantial differences in taxonomic 

composition among stream types. Springs supported fewer maylies (Ephemeroptera) and stoneflies 

(Plecoptera), but more caddis flies (Trichoptera) and non-insect taxa than either 100-ha stream or LTEM 

sites (fig. 5). In addition, 100-ha stream sites had fewer mayflies and more non-insect taxa than LTEM 

sites. In general, the proportion of mayflies increased with stream size and the proportion of non-insect 
19

taxa decreased with stream size. Trophic composition also differed, with specimens from spring sites 

composed of fewer collector and scraper taxa and more predators than the 100-ha and larger LTEM 

sites. The 100-ha and LTEM sites were almost identical in terms of trophic structure (fig. 5).

Figure 5. Comparisons of mean taxonomic (top panel) and trophic (bottom panel) composition of benthic 

macroinvertebrate communities among sampling-site types. Site type: LTEM, long-term ecological monitoring; 

headwater, stream with 100-hectare watershed; spring, perennial springs.

In addition to taxonomic and trophic composition, we found large differences in patterns of taxa 

rarity among stream types. The number and proportion of rare taxa decreased with stream size (fig. 6). 

For example, we found fewer rare taxa at LTEM sites than at 100-ha stream sites, and fewer rare taxa at

100-ha stream sites than at spring sites. We observed this pattern despite the fact that overall taxon 

richness increased with stream size--that is, total richness increased with stream size (see report section 

“Univariate Analysis of Community Structure Metrics,” below). 
20

Figure 6. Comparison of the number and proportion of rare taxa (mean+/- 1standard deviation) among samplingsite types: LTEM, long-term ecological monitoring; headwater, stream with 100-hectare watersheds; spring, 

perennial spring. Rare-taxa thresholds were defined on the basis of occurrence patterns observed at the 111 longterm monitoring sites between 1985 and 2007: (A) P1 = 0.10 (taxa occurred at fewer than 10 percent of the sites); (B) 

P1 = 0.25 (taxa occurred at fewer than 25 percent of the sites).

We further assessed differences in macroinvertebrate assemblage composition by using 

ordination. The most parsimonious ordination was a two-factor model (Stress = 17.68, Stability = 

0.00001, p = 0.004) that explained 83.1 percent of the total variation in macroinvertebrate community 

structure. The NMS-2 factor explained far more variation in community structure (65.8 percent) than the 

NMS-1 factor (17.3 percent). The individual taxa that correlated with each NMS axis are shown in table 

5. 

The ordination reveals two important patterns. First, in terms of community composition, 

perennial spring and 100-ha stream sites are distinctly different from communities at larger LTEM sites. 

All three stream types are distinguished along the NMS-2 (vertical) axis, with scores progressively 

increasing from spring sites to 100-ha stream sites to LTEM sites (fig. 7). In addition, 100-ha stream 

sites tend to have lower NMS-1 scores than either LTEM or spring sites (fig. 7). The second important 

pattern is that variation in site scores is higher for these headwater streams and springs than for the 

LTEM sites. The range of both NMS-1 and NMS-2 site scores is greatest for spring sites, and 100-ha 

sites exhibit more variation in NMS-1 scores than LTEM sites, especially along the NMS-1 axis. 
21

Table 5. Taxa showing strong correlations (Pearson correlation r > 0.40) with NMS axis ordination scores. [Taxa showing positive and negative correlations for each NMS axis are shown separately along with their correlation 

coefficient in parentheses.] 

NMS-1 NMS-2

Positive Negative Positive Negative

Nigronia (0.524)

Pycnopsyche (0.438)

Lepidostoma (0.434)

Tallaperla (0.427)

Agnetina (-0.451)

Peltoperla (-0.522)

Probezzia (0.737)

Epeorus (0.703)

Haploperla (0.674) 

Polycentropus (0.669)

Acroneuria (0.619)

Ephemerella (0.602)

Oulimnius (0.579)

Stenonema (0.563)

Ectopria (0.543)

Pteronarcys (0.529)

Psephenus (0.514)

Baetis (0.509)

Isoperla (0.491)

Optioservus (0.457)

Glossosoma (0.432)

Hydropsyche (0.420)

Drunella (0.416)

Promoresia (0.410)

Antocha (0.408)

Leucrocuta (0.406)

Forcipomyia (-0.728)

Molophilus (-0.701)

Gastropoda (-0.668)

Oligochaeta (-0.638)

Pseudolimnophila (-0.617)

Limonia (-0.617)

Rhyacophila (-0.563)

Hexatoma (-0.555)

Lepidostoma (-0.498)

Isotomidae (-0.494)

Pedicia (-0.494)

Hydrobius (-0.493)

Crangonyx (-0.446)

Sphaeriidae (-0.403)

Odontomyia (-0.402)

Figure 7. Nonmetric multidimensional scaling (NMS) ordination plot of macroinvertebrate communities from the

54 sites in Shenandoah National Park that were sampled in 2007 and 2008. Black symbols refer to the nine longterm ecological monitoring (LTEM) sites; gray symbols refer to the stream sites with 100-hectare headwater

watersheds; and open symbols refer to the 22 perennial spring sites.
22

We found that landscape factors (that is, the design variables) were highly predictive of NMS-2 

scores. Specifically, there was a significant interaction between stream type and geology that, along 

with their main effects, explained more than 85 percent of the total variation in NMS-2 scores (fig. 8, 

inset). In general, NMS-2 site scores were substantially lower for spring sites than for either 100-ha 

stream or LTEM sites, and scores were independent of underlying geology (fig. 8). In contrast, NMS-2 

scores for 100-ha headwater streams and LTEM sites depended, to a small extent, on geology, with 

lower scores for siliciclastic geology (fig. 8). Elevation was not an important predictor of NMS-2 scores. 

Figure 8. Interaction between stream type and underlying bedrock geology on non-metric multidimensional 

scaling (NMS) axis two ordination scores (see figure 7). Inset table shows analysis of variance results of interactive

stepwise general linear modeling: DF =degrees of freedom;MS Error= mean square error. The coefficient of

determination (R2) for the model was 0.855.

Because physical habitat characteristics (that is., depth, flow, and substrate size) were highly 

correlated with stream type (see discussion of physical habitat above), we substituted mean depth, baseflow discharge, and mean substrate-particle size for stream type in the model (one variable at a time) to 

determine the most important local factor. We found that substituting discharge for stream type 

produced an almost identical model, with only a slight reduction in model strength (that is, 5.4 percent 

less variation in NMS-2 scores explained by discharge than by stream type (fig. 9)). In contrast, 

substituting depth or substrate size into the model resulted in large reductions in model strength ranging 

from 31.9 (substrate size) to 50.1 (depth) percent (fig. 9). These results indicate that the effect of stream 

type on that fraction of assemblage variation associated with NMS-2 is likely associated primarily with 

flow. 
23

Figure 9. Relation between nonmetric multidimensional scaling (NMS) axis-2 ordination scores (see figure 7) and

base-flow discharge. Inset table shows the model coefficient of determination (R2 ) for relation between NMS-2 

scores and stream-site type; and the changes in model R2 associated with substituting discharge,mean depth, and

mean substrate size for stream-site type in the general linear model.

Landscape predictor variables explained substantially less variation (approximately 34 percent) 

in NMS-1 scores, (which themselves explained only 17.3 percent of the total variation in 

macroinvertebrate community structure (fig. 10). Stream type was not an important predictor of NMS-1 

scores, but both elevation and geology, as well as the interaction between the two, were significant 

predictors (fig. 10). NMS-1 scores showed a strong negative relation with elevation, but only for sites 

draining siliciclastic bedrock geology. NMS-1 scores showed a weaker negative relation with elevation 

for basaltic sites and were relatively insensitive to elevation in the granitic geologic type (fig. 10). 
24

Figure 10. Interaction between underlying bedrock geology and site elevation on nonmetric multidimensional 

scaling (NMS) axis-1scores derived from ordinations (see figure 7). Inset table shows analysis of variance results

of interactive stepwise general linear modeling; DF =degrees of freedom; MS Error= mean square error. 

Coefficient of determination (R2 ) for the model was 0.336.

Univariate Analysis of Community-Structure Metrics

We computed benthic-macroinvertebrate community metric values (table 4) for samples 

collected from all 54 sites (nine LTEM, twenty-three 100-ha stream, and 22 spring sites) in 2007-08. In 

addition, we computed metric values for samples collected at 24 LTEM sites with long-term data (> 10 

years) in order to examine the 2007-08 values with a long-term perspective (fig. 11). 

Comparisons of the distributions of metric values among stream types have two important 

results. First, patterns observed at the nine LTEM sites in 2007 generally were similar to those observed 

during historical sampling in the park. Mean metric values observed at the nine LTEM sites in 2007 

were comparable to long-term means, and the distribution of metric values incorporated a significant 

fraction of the range of values observed for the larger set of LTEM sites and sample years for most 

metrics (fig. 11). The second result is that smaller streams commonly exhibited more extreme measures 

than the larger LTEM sites. Specifically, for 100-ha stream sites, and especially for spring sites, we 

typically observed values for a single sample year that had never been observed over a 20-year period at 

the 24 LTEM sites (fig. 11). 
25

Figure 11. Comparisons of the distributions of benthic macroinvertebrate metric values between long-term data 

(historical) collected at 24 long-term ecological monitoring sites over the 20-year period from 1987 to 2006 (box 

plots) and data collected from 9 long-term ecological monitoring (LTEM) sites (black symbols), 23 headwater 

stream sites with 100-hectare watersheds (gray symbols), and 22 perennial spring sites (open symbols) in 2007

and 2008. For box plots, the horizontal black line depicts the median,the upper and lower limits of the box

represent 50 percent of the observations (25th- 75th percentile), whiskers represent 90 percent of the

observations (5th- 95th percentile), and remaining values are depicted as points. See table 4 for description of

individual metrics.
26

Stream type was an important predictor of univariate as well as multivariate metric scores, 

indicating that differences in macroinvertebrate composition among stream types (see report section 

“Multivariate Assessment of Macroinvertebrate Assemblage Composition,” above) translated to 

differences in univariate metrics and indices commonly used to infer stream conditon. Stream type was 

a significant predictor of scores for 14 of the 15 metrics evaluated, with only the Leuctra% metric 

failing to show a significant relation with stream type (table 6). Moreover, stream type was substantially 

more important than either geology or elevation for all metrics except DOM5 (table 6). In addition, the 

effect of stream type was more strongly related to base-flow discharge than to either mean depth or 

mean substrate size, just as we observed for NMS-2 scores. Of the 14 metrics that had significant 

relations with stream type, the overall model fit (R2) improved by substituting flow for stream type for 5 

metrics. For the remaining 9 metrics, the goodness of fit decreased slightly, although the decrease was 

less than that observed when either depth or substrate size was substituted for stream type (table 6). 

Table 6. Results of interactive stepwise general linear modeling used to assess the effects of study design 

variables on macroinvertebrate metrics. [Table reports partial coefficients of determinations (partial R2

) for design variables found to be statistically significant 

predictors (p < 0.05) for each metric, and overall model R2

, and change in model R2 associated with substituting flow, depth, 

and substrate size for stream type. NI = “Not interpretable” (main effects not interpretable when interactions are significant; 

NA = “not applicable (stream type not a signficant predictor)].

Partial R2 Change in Model R2

Metric Stream

Type Geol Elev Type xGeol

Type x

Elev

Ele x

Geol

Model

R2 Flow Depth Substrate

Richness

EPT-r 

EPT%

E%Hydro:T%

Leuctra:P%

Dom5

SimpsonD

PTV

Intol%

Scraper%

Shredder%

Hapto%

Chiro%

SCI

0.211

0.475

0.385

0.559

NI

0.226

0.285

0.511

0.525

NI

NI

NI

0.172

0.437

0.083

0.047

0.093

NI

NI

0.225

0.117

NI

NI

NI

0.108

0.033

0.058

0.040

NI

NI

0.141

0.109

0.151

0.133

0.100

0.125

0.352

0.562

0.385

0.652

0.500

0.235

0.451

0.402

0.511

0.525

0.581

0.363

0.464

0.280

0.470

-0.066

-0.042

0.020

-0.017

-0.049

NA

-0.040

-0.099

0.041

0.030

-0.006

0.044

-0.112

-0.161

0.044

-0.105

-0.207

-0.160

-0.254

-0.269

NA

-0.157

-0.231

-0.230

-0.198

-0.281

-0.096

-0.164

-0.168

-0.185

-0.070

-0.249

-0.143

-0.164

-0.209

NA

-0.11

-0.205

-0.247

-0.277

-0.228

-0.155

-0.116

-0.165

-0.253

Geology was also an important predictor of univariate metrics and was included in the “best” 

model for 12 of the 15 metrics evaluated. However, the observed relations between geology and metric 

scores were much weaker than those observed for stream type (table 6). Elevation was a significant 

predictor for only four univariate metrics, and the strength of the associations tended to be weaker than 

those observed for stream type or geology (table 6).

In order to more fully evaluate differences, we also compared least-square means (that is, mean 

metric score after accounting for effects of other predictors in the model) among the three stream types 

for those 12 metrics for which stream type was a significant predictor. We found that means at spring 

sites differed significantly (p < 0.05) from those at LTEM sites for all but 2 metrics (Shredder% and 

Hapto%), whereas means at 100-ha sites differed from those at LTEM sites for only 2 of the 14 metrics 

(E% and Hydro:T%) (table 7). In addition, for all but 1 of the 14 metrics, the scores indicated that 
27

stream condition was “better” at LTEM sites than at headwater stream and spring sites. Specifically, 

metric values were higher at LTEM sites for Richness, EPT-r, EPT%, E%, SimpsonsD, Intol%, 

Scraper%, Shredder%, and SCI (table 7), all metrics that are expected to decline with stress. In contrast, 

metric values were lower at LTEM sites for DOM5, PTV, and Chiro% (table 7), all metrics that are 

expected to increase with stress (table 7). The metric Hydro:T% was the only metric that indicated that 

stream condition may be poorer at LTEM sites than at 100-ha and spring sites on the basis of expected 

responses to perturbation (table 7). 

Table 7. Comparisons of least-square means among the three stream types for 14 macroinvertebrate community 

metrics found to differ among the three stream types (LTEM, 100-hectare, and springs). [Table reports means for each stream type along with results of Tukey’s Multiple Range test to evaluate the statistical 

signicance of pairwise differences in means between types.] 

Means Tukey’s Range Test Results (p-values)

Metric LTEM 100-

hectare Spring LTEM=100-hectare LTEM=Spring Spring=100-hectare

Richness

EPT-r 

EPT%

E%1

Hydro:T%1

DOM5

SimponsD

PTV

Intol%

Scraper%1

Shredder%1

Hapto%

Chiro%

SCI

34.91

21.96

58.90

25.67

49.75

76.33

0.83

2.68

61.18

12.29

21.66

48.71

20.37

72.42

34.79

20.45

47.41

15.95

23.01

75.75

0.80

3.44

49.54

11.55

22.80

43.47

32.03

68.42

26.60

10.55

27.19

1.58

5.52

85.60

0.67

4.94

24.44

1.76

18.25

34.71

40.42

53.41

0.994

0.703

0.166

0.042

0.027

0.980

0.751

0.084

0.103

0.977

0.981

0.624

0.121

0.509

0.022

0.00002

0.00003

0.00001

0.00003

0.011

0.0009

0.00001

0.00001

0.0005

0.829

0.077

0.003

0.00002

0.002

0.00001

0.0002

0.00001

0.005

0.0003

0.0003

0.00001

0.00001

0.00003

0.571

0.180

0.148

0.00001

1

Metric values transformed using ArcSin square-root transformation to normalize data prior to testing. However, backtransformed least square means are shown for comparison.

Assessment of Water Chemistry Patterns

Spatial patterns in seven water-chemistry parameters at 71 sites (fourteen SWAS sites, thirtyfour spring sites, and twenty-three 100-ha stream sites) over two seasons (spring and fall). Initially, we 

tested for the effect of season and found significant differences for four of seven parameters (twosample T-test, p < 0.0005 for all). ANC, SBC, and Cl ̅1 measurements were greater in fall, and SO4

measurements were greater in spring (fig. 12). That is, seasonal differences in water chemistry did not 

seem to be influenced by stream type except for SO4, which showed little difference between seasons at 

100-ha and LTEM sites, but which showed higher values in the spring season at spring sites (fig. 12). 

Therefore, for all remaining analyses, only spring-season data were used for all parameters except SO4, 

for which we assessed both seasons separately. 
28

Figure 12. Relationship between fall and spring season water chemistry measurements. Symbol color depicts

stream type: black symbol, long-term ecological monitoring (LTEM) site; gray symbol, stream site with 100-hectare

watershed;open symbol, perennial spring site. The 45 degree line indicates equal values between seasons;

values below the line indicate fall measurements were higher and values above the line indicate spring

measurements were higher. Except for pH and conductivity,values are shown in microequivalents per liter;pH is

shown in standard units, or the log of the hydrogen-ion concentration; conductivity is shown microsiemens per

centimeter at 25 degrees Celsius.

In contrast to macroinvertebrate measures, water-chemistry parameters were much less

influenced by stream type. Stream type was an important predictor for only two of the eight parameters. 

Specifically, stream type explained 6.5 percent of the variation in pH and 10.9 percent of the variation in 

spring-season SO4 concentration (table 8). 
29

Table 8. Results of interactive stepwise general linear modeling used to assess the effects of study design 

variables on water-chemistry parameters. [Results reported are for spring-season data only, except for SO4, for which results are reported for both seasons. Table 

reports partial coefficients of determination (partial R2

) for design variables found to be statistically significant predictors (p

< 0.05) for each metric, and overall model R2

. 

Partial R2

Metric Type Geol Elev Type x

Geol

Type x

Elev

Ele x

Geol

Model

R2

ANC

pH

Conductivity1

SBC1

NO3

1

Cl1

SO4 (Spring)1

SO4 (Fall)1

0.065

0.109

0.590

0.708

0.447

0.616

0.124

0.176

0.213

0.169

0.361

0.590

0.773

0.447

0.616

0.485

0.176

0.323

0.169

1

Metric values log-transformed to normalize prior to testing.

Underlying geology, however, was an important predictor of all eight parameters and explained 

substantially more variation, ranging from 12.4 percent for NO3 to 70.8 percent for pH (table 8), than 

stream type. Mean pH was significantly lower at spring sites than at 100-ha stream and larger SWAS 

sites (table 9). Mean spring-season SO4 concentration was significantly lower at spring sites than at 

SWAS sites, although differences between 100-ha sites and SWAS sites were not significant (table 9). 

Table 9. Comparisons of least-square means among the three stream types for two water- chemistry parameters 

found to differ among stream types (SWAS, 100-hectare, and springs).

[Table reports means for each stream type along with results of Tukey’s Multiple Range test to evaluate the statistical 

signicance of pairwise differences in means between types.] 

Means Tukey Range Test Results (p-values)

Metric SWAS 100-

hectare Spring SWAS=100-hectare SWAS=Spring Spring=100-hectare

pH

SO4 (Spring)

6.484

77.90

6.420

53.58

6.098

40.72

0.77130

0.19199

0.00008

0.00443

0.00174

0.38618

The strong association between water-chemistry measures and bedrock geology is not 

unexpected. In fact, a prior understanding of these relations was the primary basis for stratifying site 

selection when the SWAS program was developed (Cosby and others, 2006; Wofford and others, 2011). 

Unlike biological measures, mean values of most water-chemistry parameters did not depend on stream 

type and, for those that did (pH and spring-season sulfate concentration), differences in means among

stream types were relatively modest. The general relation between geology and water-chemistry values 

did not depend on stream type, either (that is, there was no significant interaction between stream type 

and geology). However, a closer examination of these relations indicates that although mean values did 

not differ significantly among stream types for most parameters, variation in water-chemistry 

measurements was substantially greater for the 100-ha stream sites and especially for the spring sites 

than for the larger SWAS sites (fig. 13). 
30

Figure 13. Comparisons of historical water-chemistry measures from larger, long-term water-chemistry

monitoring sites (Shenandoah Watershed Study, SWAS) with 100-hectare-watershed stream (Headwater) and

perennial spring (Spring) sites. Data for all parameters are for spring season only except for sulfates, where

both spring and fall seasons are shown. Comparisons are made at all sites as well as subsets of sites based on

geology. Data for SWAS sites were derived from quarterly samples collected from 1993 to 2009 at 14 sites

(see table 3), and are shown with box plots. For box plots, the horizontal black line in the box depicts the

median, the upper and lower limits of boxes represent 50 percent of observations (25th -751h percentile), 

whiskers represent 90 percent of observations (5 1h - 95th percentile), and remaining values are depicted as

points. Headwaters and spring data were derived from sampling in 2007 and 2008 as part of this study (see

tables 1 and 2). Individual values are shown as black circles and mean values are shown as "X" symbols. 

Except for pH and conductivity, values are shown in microequilvalents per liter; pH is shown in standard units, or

the log of hydrogen-ion concentration; conductivity is shown in microsiemens per centimeter at 25 degrees

Celcius.
31

Specifically, for most-water chemistry parameters, the distributions of values measured at the 

thirty-four spring sites and the twenty-three 100-ha stream sites included measurements that were both 

lower and higher than values in the long-term database for the larger SWAS sites (fig. 13). More 

extreme values were observed at 100-ha stream and spring sites despite the fact that the dataset for the 

larger SWAS sites contained significantly more observations (N = 238 compared to 23 for 100-ha 

stream sites and 22 for spring sites) measured over many more sites and years (14 SWAS sites over a 

17-year period). Moreover, this pattern of more extreme values for 100-ha and spring sites was 

consistent within bedrock geology types as well (fig. 13). Therefore, with the exception of nitrate, for 

which zero concentration values were relatively common for all three stream types, both the highest and 

lowest values were observed at the headwater or spring sites. From a water-chemistry perspective, then, 

headwater stream and spring sites appear to be both more vulnerable and more resistant to atmospheric 

deposition. 

The relation between all seven water-chemistry parameters and geology was essentially the same 

for all three stream types (fig. 13). That is, the relative differences in means among geologic types were 

consistent irrespective of stream type. These results indicate that, in contrast to biological measures, 

water-chemistry values and, consequently, inferences regarding the status and trends in water chemistry 

would be similar no matter how the sampling effort was distributed among stream types. 

Relations Between Water Chemistry and Macroinvertebrate Communities

Given that one of the primary considerations that has driven stream monitoring in SHEN has 

been concern over changes in streamwater chemistry associated with regional stressors (for example, 

atmospheric deposition and defoliation due to gypsy moths) and their effects on biological communities, 

we examined the relative strength of correlations (Spearman’s rank correlation, r) between waterchemistry and macroinvertebrate-community summary measures for each of the three stream types 

(table 10). For these analyses, we only used four of the seven water-chemistry variables because pH was 

highly correlated with ANC, SBC, and conductivity, and, of these four measures, pH is the most 

biologically relevant. In general, correlations between water-chemistry and biological measures were 

stronger at the larger LTEM sites than at either spring or 100-ha stream sites. Specifically, of the 17 

biological measures, LTEM sites showed the highest correlation for 12 measures for pH, 10 measures 

for sulfate, 11 measures for nitrate, and 12 measures for chloride (table 10). Moreover, in nearly all of 

these cases, correlations were relatively strong (r > 0.5). In contrast, 100-ha stream sites showed the 

strongest correlation for no more than seven biological measures (for sulfate) and spring sites showed 

the strongest correlation for only four biological measures (for nitrate and chloride); in most of these 

cases, however, even the strongest correlations were relatively weak (r<0.5). 
32

Table 10. Correlations between water chemistry and biological measures in streams in Shenandoah National 

Park. [Table reports Spearman’s rank correlation coefficients (r) within each stream type. Values in bold type indicate the stream 

type (SP = spring site; ST = 100-ha stream site; LT = long-term monitoring or LTEM site) with the strongest correlation for a 

water-chemistry/biological-metric comparison. Biological metrics include all 15 univariate metrics as well as the two sets of 

uncorrelated measures of community composition derived from nonmetric multidimensional scaling ordination.] 

pH Sulfate Nitrate Chloride

Bio Metric SP ST LT SP ST LT SP ST LT SP ST LT

Richness

EPT-r 

EPT%

E%

Hydro:T%

Leuctra:P%

Dom5

SimpsonD

PTV

Intol%

Scraper%

Shredder%

Hapto%

Chiro%

SCI

NMS-1 

NMS-2

0.46

0.42

0.23

0.41

-0.27

0.00

-0.25

0.04

-0.21

0.33

0.34

0.00

0.00

-0.20

0.22

-0.07

0.08

0.04

0.03

0.06

0.32

0.20

-0.25

-0.40

0.24

0.02

0.02

0.03

-0.40

0.07

-0.36

0.21

0.09

0.50

0.80

0.82

-0.20

0.53

0.38

-0.10

-0.93

0.68

0.02

0.18

0.78

-0.55

-0.58

-0.68

0.68

-0.68

0.00

-0.47

-0.36

-0.04

0.00

0.12

-0.23

0.06

-0.26

-0.22

-0.10

0.03

0.07

0.07

0.29

-0.19

0.16

0.09

-0.47

-0.30

0.08

-0.04

-0.37

0.53

0.52

-0.37

0.17

-0.24

-0.52

0.08

0.37

0.35

-0.37

0.32

-0.35

-0.63

-0.61

0.60

-0.65

-0.02

0.15

0.43

-0.12

-0.38

0.15

-0.62

0.72

0.73

0.77

-0.17

0.87

0.07

0.24

0.18

0.08

-0.16

-0.31

-0.29

0.20

0.11

0.18

0.06

-0.34

0.11

0.01

-0.30

0.19

-0.43

-0.26

-0.05

-0.03

-0.16

-0.05

-0.21

-0.36

-0.21

-0.04

0.26

-0.17

0.16

0.01

0.17

0.13

-0.17

-0.14

0.25

0.90

0.87

0.30

0.60

0.00

0.90

-0.50

0.10

-0.40

0.10

-0.10

0.00

0.00

-0.80

0.30

-0.50

-0.60

0.31

0.22

0.16

0.22

-0.10

0.02

-0.51

0.36

-0.20

0.19

0.51

-0.06

-0.06

-0.01

0.24

-0.07

0.06

-0.03

0.14

0.01

0.16

0.01

0.16

0.00

0.02

0.04

-0.06

-0.22

-0.17

0.19

0.10

-0.13

0.31

0.06

0.83

0.85

-0.40

0.63

-0.02

-0.03

-0.70

0.18

0.15

0.08

0.68

-0.78

-0.75

-0.92

0.32

-0.78

-0.33

Significance of Headwater Streams and Perennial Springs

The primary objective of this research was to determine whether the current focus of the longterm macroinvertebrate and water-quality monitoring program in SHEN was failing to capture important 

information on the status of and trends in stream condition by not sufficiently representing smaller, 

headwater streams. The SWAS water-quality monitoring program does not routinely obtain data for the 

first-order streams in SHEN. Only 13.5 percent of 111 streams sites that have been sampled as part of 

the macroinvertebrate LTEM program drain watersheds smaller than 100 ha, and no perennial springs 

are included. Moreover, only 2 of the 30 sites (6.6 percent) that are routinely sampled (that is, fixed 

sampling sites) have drainage areas smaller than 100 ha. The program relies largely on measures of 

aquatic-macroinvertebrate asssemblage structure (a bioindicator of stream condition) and waterchemistry parameters related primarily to acidic deposition to infer the status of and trends in stream 

condition. In 2007 and 2008, we sampled thirty-four perennial springs (twenty-two perennial springs for 

macroinvertebrates), twenty-three 100-ha headwater sites, and nine LTEM sites. We also used longterm stream macroinvertebrate and water-chemistry data from a larger number of LTEM and associated 

SWAS sites (24 for macroinvertebrates and 14 for water chemistry) to address the study objective.

Our findings indicate that biological indicators of stream condition and water-chemistry 

parameters respond differently to landscape drivers. Variation in most biological endpoints was driven 

primarily by stream size (that is, stream type) and was only secondarily associated with bedrock 

geology. In contrast, water chemistry essentially showed the opposite pattern, with underlying geology 

explaining much of the variation and stream type being of secondary importance. Elevation was not a 

particularly important driver of either biological or water-chemistry measures. Therefore, a major 
33

conclusion of our study is that expanding the LTEM program to include headwater areas would yield 

substantially different biological information, whereas broad inferences regarding spatial patterns or 

temporal trends in water chemistry would probably not change. 

We found that biological measures of stream condition were higher at larger LTEM sites than at 

sites in headwater areas indicating stream condition was “better” at LTEM sites. Lower index scores 

were due mainly to lower taxon richness and higher proportion of relatively tolerant taxa at headwater 

sites. The data should not be interpreted as reduced stream condition, however, because it is well 

established that macroinvertebrate diversity increases with stream size from headwaters to mid-reach 

stream sizes (Clarke and others, 2008, and references therein); consequently, lower condition scores are 

expected at smaller sites even in the absence anthropogenic stressors. Individual metrics and composite 

indices are commonly adjusted for watershed size to facilitate comparison among streams of different 

sizes (Klemm and others, 2003). These adjustments are typically based on regional relations between 

biological metrics or indices and stream size (usually basin area) in reference condition. However, data 

from small headwater sites have not typically been available or have been severely underrepresented in 

establishing these baseline relations. Consequently, direct comparisons of condition measures between 

headwater areas and sites farther downstream should proceed with caution. 

A primary explanation for the positive relation between stream size and macroinvertebrate 

diversity and abundance is related to the frequency of natural-disturbance events, the relative ability of 

component taxa to recover from natural disturbances, and how these factors vary across stream size 

(Vinson and Hawkins, 1998). Stream drying associated with drought is a particularly important 

disturbance to headwater communities (Clarke and others, 2010). However, because there is a paucity of 

long-term flow records for headwater streams, little information is available about the frequency of 

stream drying across a gradient in stream size or basin area. Nevertheless, it is intuitive that stream 

drying would be more frequent in perennial headwater areas than in downstream reaches, and it is clear 

that stream drying has a dramatic effect on macroinvertebrate communities that can persist for many 

years (Feminella, 1996; Boulton, 2003). Moreover, we found stream discharge to correlate more 

strongly with stream-type differences in benthic-macroinvertebrate community composition and 

structure than either depth or substrate size, indicating that headwater sites may be more vulnerable to 

flow-based disturbance. 

Recent research indicates that the ability of taxa to recover from stream drying may be reduced 

for headwater communities as a result of the spatial geometry of stream networks. Specifically, within a 

drainage network, headwater streams tend to be farther apart from each other than larger order streams 

(Grant and others, 2007), thereby requiring a longer distance for colonists to disperse in order to 

repopulate denuded headwater streams. This point is emphasized when aquatic-insect dispersal 

distances are considered. For example, Griffith and others (1998) found that mean maximal dispersal 

distances for adult Trichoptera and Plecoptera in four West Virginia headwater streams was less than 

half the distance among adjacent headwater streams for most species. Hughes (2007) also found limited 

dispersal of aquatic insects among headwater streams and argued that local extinctions would likely be 

“final” in many instances. These observations have important implications for biomonitoring. 

Specifically, macroinvertebrate-community composition and structure measures would be more variable 

over time because they would be more likely to be in some state of recovery from past natural 

disturbances. As a result, it may be more difficult to distinguish the effects of natural disturbances from 

the effects of anthropogenic disturbances in headwater streams. 

Another important finding of this study was that although we observed significant differences in 

community composition among stream types based on taxon abundances, we found no taxa that were 

unique to headwater sites in SHEN. All taxa collected at the 45 headwater sites also had been collected 
34

at one or more LTEM sites during one or more sampling years. This observation indicates that

headwater sites in SHEN are structured by biotic nestedness (Cutler, 1994)--that is, the identity of 

component taxa in the relatively species-poor headwater communities were a subset of the taxa of the 

more taxonomically diverse LTEM communities. Evidence for biotic-nestedness structure in headwater 

communities has been reported by others (Malmqvist and Hoffsten, 2000; Heino and others, 2005; 

Monaghan and others, 2005). This pattern indicates that focusing management efforts on preserving the 

species pool at larger LTEM sites may result in protection of most taxa parkwide. 

As already noted, we did not see significant differences in water-chemistry-parameter means 

among stream types, and the acid/base chemistry of both springs and 100-ha sites reflected the same 

general relations with defined bedrock classes as the larger SWAS program sites. However, spatial 

variability was substantially greater at headwater sites than at larger stream sites for all water-chemistry 

parameters evaluated. Specifically, parameter values observed at springs and headwater sites tended to 

include observations that were both lower and higher than values observed in the SWAS site long-term 

database (resulting in comparable means). This finding was unexpected because headwater sampling 

was limited to a single year whereas SWAS measurements included more than 20 years of data. 

However, recent research by others has shown similar patterns. For example, Temnerud and Bishop 

(2005) found that the range of values for dissolved organic carbon (DOC), pH, and alkalinity observed 

at 49 headwater sites in two basins was equal to or greater than the range observed in all of northern 

Sweden in a national survey of hundreds of sites. Similarly, Wolock and others (1997) identified a 

basin-area threshold of approximately 300 ha in the Neversink River watershed in New York, below 

which variation in water-chemistry parameters (pH, ANC, DOC, and SBC ) was substantially greater

than at sites with watershed areas greater than 300 ha. 

In general, headwater sites are believed to be more sensitive to atmospheric deposition than 

larger sites because they have steeper slopes and more rapid runoff (Sullivan and others, 2008), leading 

to reduced subsurface contact time of rainfall with buffering soils and rock (Wolock and others, 1997). 

In contrast, recent systematic surveys of headwater areas (Temnerud and Bishop, 2005; this study) 

indicate that individual headwater sites may be highly sensitive or relatively tolerant of environmental 

gradients such as acid deposition relative to larger stream sites. These two statements are not necessarily 

contradictory, however. It may be possible that inaccuracies in the bedrock geology maps could lead to 

erroneous inferences in water chemistry –geology relations. Specifically, there are strong indications 

that the coarse spatial grain of bedrock geology maps miss some important features that occur over 

small spatial scales. For example, it has been observed that basaltic outcrops occur within several of the 

watersheds classified as underlain by siliciclastic bedrock (Richard Webb, University of Virginia, 

personal communication, 2007). In this case, the basaltic outcrops could have a substantial influence on 

water chemistry, leading to water-chemistry values indicative of a relatively tolerant site (for example, 

high ANC, high pH, low SO4). The locations and spatial extent of such small-scale features are not 

typically well known and consequently usually not accounted for in analyses (including our analysis 

here). 

We suspect that these inaccuracies in geology maps would tend to have a larger impact on sites 

within smaller watersheds because they have stronger aquatic-terrestrial linkages than larger watersheds 

(Lowe and Likens, 2005). Larger sites are also vulnerable to such inaccuracies, but to a smaller extent. 

In other words, smaller catchments almost certainly are more sensitive to acid deposition than larger 

catchments if bedrock geology is compositionally the same. In addition, for some parameters such as 

chloride concentration, local factors such as proximity to roads (and consequently winter road salts) 

were likely highly influential on variability. Again, these factors would affect both small and large 

streams, but would have a larger effect on smaller streams. 
35

An alternative explanation of greater spatial variability in water chemistry at 100-ha streams and 

especially perennial spring sites relates to the relative contributions of groundwater and surface water to 

streamflow. Specifically, though groundwater is an important contributor to streams of all sizes in 

SHEN (Dekay, 1972), the relative contribution of groundwater is greatest in perennial springs and least 

in larger streams. In fact, many of the perennial springs sampled in this study had virtually no surface 

drainage area; therefore, all or nearly all of the flow was derived from groundwater. Consequently, 

water chemistry in these springs may be more likely to be driven by unmeasured factors related to 

characteristics of the underlying aquifer. Specifically, the boundaries of the underlying aquifer may not 

correspond to the watershed boundaries (Winter et al. 1998), and the length and complexity of flow 

paths from groundwater to surface water may be highly variable. Consequently, it is difficult to 

determine with any certainty the size and underlying geology that influences these systems. For 

example, unlike for the 100-ha streams and larger SWAS sites, for which we computed the amount of 

each bedrock geology type in the watershed above the sample site, for perennial springs we noted only 

the bedrock geology directly beneath the site. It is possible that the underlying aquifer expands widely 

and a different geology type other than the one directly beneath the spring is more influential. Perhaps 

even more important, the depth of the underlying aquifer, and consequently the age of groundwater, 

might also be an important driver of spatial variation in water chemistry (Mulholland, 1993). 

These findings have important implications for long-term monitoring for atmospheric-deposition 

effects on streamwater quality. Expanding the SWAS program to include headwater sites might 

confound rather than explain parkwide spatial patterns in water quality because of the introduction of 

“unimportant” variance (such as inaccurate geology) or the incomplete understanding of 

groundwater/surface-water interactions. Or, because of the greater spatial variation observed, 

substantially more effort (at higher cost) would be required to effectively characterize natural variability 

in water chemistry in headwater streams parkwide. Alternatively, a relatively small number of 

headwater sites might be added to the SWAS program, either to include the areas of the park that are 

most sensitive to water-chemistry changes associated with acid deposition, or to characterize responses 

to gradients that vary locally, such as road-salt effects on Clˉ1 concentrations and associated biological 

effects.

Finally, the linkages (correlations) between water chemistry and biological measures of stream 

condition were significantly stronger at larger LTEM sites than at the springs or 100-ha sites. This 

observation is expected given the disproportionately large role of flow disturbance (especially stream 

drying) on headwater communities. Specifically, because larger streams are less likely to become dry, 

the benthic communities associated with them are more strongly tied to water chemistry than those 

associated with springs or smaller headwater streams. In contrast, because headwater communities 

respond more frequently to natural disturbances, their relation with water chemistry is obscured. In 

addition, the finding of fewer sensitive taxa (indicated by lower EPT-r, lower EPT%, lower %Intol, and 

higher PTV) in headwater areas than in larger streams (also potentially as a result of more frequent 

natural disturbances in headwater areas) could also mask correlations with water chemistry. Therefore, 

maintaining the current focus on larger, integrative sites would be likely be most effective with respect 

to tracking and understanding the biological effects of, or recovery from, anthropogenic disturbance 

gradients. 
36

Monitoring Implications

In this study, we sampled headwater sites in SHEN for aquatic macroinvertebrates and water 

chemistry and compared the results with current and historical data collected at larger stream sites 

routinely sampled as part of the LTEM and SWAS programs. The purpose of the study was to inform 

ongoing efforts by SHEN management to evaluate the current long-term monitoring program. 

Specifically, our primary objective was to determine whether the current focus of the long-term 

monitoring program in SHEN is failing to capture important information on the status of and trends in 

stream condition by not sufficiently representing smaller, headwater streams. Here we discuss the 

implications of our results in terms of three possible monitoring scenarios. Although SHEN 

management may consider alternative objectives, placing our results in this context of these three 

scenarios should be informative. 

Scenario 1: Monitor stream condition as it relates to regional stressors documented to be important at 

SHEN (specifically, atmospheric deposition and gypsy moths) and their effects on water quality. This is 

essentially the original monitoring objective cited when the LTEM and SWAS programs were initiated 

(Wofford and Demerest, 2011; Wofford and others, 2011). 

Expanding the current monitoring program to include headwater sites is not likely to yield 

substantial additional information useful for tracking the effects of regional stressors such as acid 

deposition. The acid/base chemistry of smaller watersheds reflected the same general relations with 

defined bedrock classes as that of larger watersheds and, therefore, additional water-chemistry 

information from smaller watersheds would contribute little to our understanding of spatial patterns or 

temporal trends in water chemistry. Moreover, variation in water-chemistry measurements was greater 

at headwater sites than at sites on the larger streams, Therefore, this added variation would be more 

likely to obscure than to reveal important relations. In addition, relations between water chemistry and 

biological indicators would be weaker if headwater sites were included in the monitoring program, 

likely in large part as a result of the influence of natural-disturbance events on headwater communities. 

On the other hand, including water-chemistry sampling at headwater sites known to have low 

ANC or pH or high SO4 or NO3 concentration could be valuable in terms of identifying “early-warning” 

sites. Because this would be a nonrandom selection of sites, however, it may not be appropriate to use 

these data in the parkwide assessment of status and trends. In the context of this scenario, biological 

sampling at headwater sites would be valuable only if the effects of natural disturbance could be 

accounted for, which would necessitate the costly instrumentation and maintenance of a number of 

headwater sites with streamflow gages. 

Scenario 2: In addition to scenario 1, expand the scope of the current monitoring program to include 

monitoring for the effects of local stressors such as road and trails, development within the park, ice 

storms, forest fires, patchy distribution of forest health (for example, hemlock woolly adelgid), and 

others. 

In the context of this scenario, effectively documenting changes in stream condition associated 

with local stressors would be difficult in the absence of program expansion to headwater areas because 

much of the development is located in the headwaters. Because this additional monitoring would be 

extremely costly, however, the use of targeted research rather than monitoring to evaluate these types of 

local effects could be considered. 
37

Scenario 3: In addition to scenario 1, expand the scope of the current monitoring program to include 

monitoring for the effects of climate change. 

Our evaluation of this scenario is similar to our evaluation of scenario 1. For example, as with 

acid deposition, small watersheds may be more vulnerable to climate change, and acid-impacted 

headwater sites may be particularly vulnerable to climate change (Durance and Ormerod, 2007). 

Therefore, headwater areas may represent sentinel sites for climate change effects. However, as is also 

the case with monitoring for acid-deposition effects, biological information would be useful only if we 

could account for effects of natural flow-disturbance patterns on stream communities. Unlike acid 

deposition, however, climate change is actually predicted to increase the frequency and severity of flowdisturbance events such as droughts and floods. Therefore, in this scenario, flow disturbance is not 

merely a covariate that needs to be accounted for, but a response of interest. In some regions and 

ecosystem types, changes in disturbance patterns may be a more ecologically consequential effect of 

climate change in the nearer term than warming temperatures. Stream drying and flooding events may 

become more frequent or their timing may become more detrimental to the health of stream 

communities than gradually increasing temperatures. Therefore, having long-term monitoring sites in 

highly sensitive headwater areas could be extremely valuable.

Establishing a relatively small number of additional headwater sites for intensive monitoring, 

including continuous flow and temperature measurements, may be an effective compromise to costly 

monitoring of headwater sites throughout the park. Locating additional headwater sites within basins 

that are already more intensively monitored, such as Paine Run, Piney River, and Staunton River, would 

be a logical expansion of the current LTEM and SWAS programs. This approach provides limited 

replication and consequently it may not be possible to extrapolate findings to infer park-wide trends. 

However, with careful site selection, SHEN could be viewed as a sentinel site for effects of climate 

change in the eastern United States. 

We also believe that additional research is needed that is designed to assess the importance of 

flow variation on bioassessment measures of stream condition. In particular, the temporal pattern of 

flow-disturbance events (floods and droughts) is likely an important determinant of annual variation in 

stream-community structure and composition. Therefore, the ability to account for the effects of these 

natural disturbances may substantially increase bioassessment precision and improve the power to detect 

change due to anthropogenic stressors (Snyder and others, in press). This is especially important for 

monitoring programs designed to be early-warning indicators of ecological decline, an explicit goal of 

the National Park Service Vital Signs Monitoring Program (Fancy and others, 2009). 

Acknowledgments 

The authors thank the following people for their assistance in collecting and processing samples: 

Mary Mandt Rockey, David Weller, and Marcus Springmann of the U.S. Geological Survey; and, 

Spencer Ingram, Shane Spitzer, Ben Sutphen, Zach Lucy and Suzanne Maben of the University of 

Virginia. We are also grateful to John Karish, James Atkinson, Jeb Wofford, Alan Williams, and James 

Schaberl of the Shenandoah National Park Natural Resource Management staff for assisting with project 

development and providing logistical support. Thanks also to Nathaniel Hitt of the U.S. Geological 

Survey and Amy Villamagna of Virginia Tech University for reviewing the manuscript. This study was 

conducted as part of the U.S. Geological Survey’s Natural Resource Preservation Program.
38

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42

Appendix A. Description of macroinvertebrate taxa collected at 54 sites in Shenandoah National Park 

in 2007 and 2008. 

[Appendix contains taxonomic descriptions as well as taxon-specific ecological characteristics required to compute benthic-macroinvertebrate summary statistics 

described in table 4 of this report.] 

TAXON1 Phylum Class Order Family Genus FUNC

GRP2 TV3 HABIT4

5060 PLATYHELMINTHES TURBELLARIA TRICLADIDA PLANARIIDAE CG 8 SP

6000 NEMATODA CG 8 BU

D2 ANNELIDA OLIGOCHAETA CG 8 BU

E08101 ARTHROPODA CRUSTACEA ISOPODA ASELLIDAE Caecidotea CG 8 SP

E09300 ARTHROPODA CRUSTACEA AMPHIPODA Crangonyx CG 6 CR

E0A004 ARTHROPODA CRUSTACEA DECAPODA CAMBARIDAE Cambarus GN 5 GN

E10001 ARTHROPODA INSECTA PLECOPTERA PTERONARCYIDAE Pteronarcys SH 1 CR

E10100 ARTHROPODA INSECTA PLECOPTERA PELTOPERLIDAE Peltoperla SH 1 CR

E10101 ARTHROPODA INSECTA PLECOPTERA PELTOPERLIDAE Tallaperla SH 1 CR

E10210 ARTHROPODA INSECTA PLECOPTERA NEMOURIDAE Amphinemura SH 3 CR

E10301 ARTHROPODA INSECTA PLECOPTERA PERLIDAE Paragnetina PR 2 CR

E10302 ARTHROPODA INSECTA PLECOPTERA PERLIDAE Agnetina PR 0 CR

E10313 ARTHROPODA INSECTA PLECOPTERA PERLIDAE Acroneuria PR 2 CR

E10314 ARTHROPODA INSECTA PLECOPTERA PERLIDAE Eccoptura PR 3 CR

E10315 ARTHROPODA INSECTA PLECOPTERA PERLIDAE Perlesta PR 5 CR

E10401 ARTHROPODA INSECTA PLECOPTERA PERLODIDAE Yugus PR 0 CR

E10404 ARTHROPODA INSECTA PLECOPTERA PERLODIDAE Remenus PR 0 CR

E10411 ARTHROPODA INSECTA PLECOPTERA PERLODIDAE Isoperla PR 2 CR

E10511 ARTHROPODA INSECTA PLECOPTERA CHLOROPERLIDAE Haploperla PR 1 CR

E10512 ARTHROPODA INSECTA PLECOPTERA CHLOROPERLIDAE Sweltsa PR 0 CR

E10700 ARTHROPODA INSECTA PLECOPTERA LEUCTRIDAE Leuctra SH 0 CR

E10801 ARTHROPODA INSECTA PLECOPTERA CAPNIIDAE Paracapnia SH 1 CR

E11000 ARTHROPODA INSECTA EPHEMEROPTERA EPHEMERIDAE Ephemera CG 4 BU

E11303 ARTHROPODA INSECTA EPHEMEROPTERA EPHEMERELLIDAE Drunella SC 0 CR
43

Appendix A (continued).

TAXA1 Phylum Class Order Family Genus FUNC

GRP2 TV3 HABIT4

E11304 ARTHROPODA INSECTA EPHEMEROPTERA EPHEMERELLIDAE Ephemerella CG 2 CR

E11305 ARTHROPODA INSECTA EPHEMEROPTERA EPHEMERELLIDAE Eurylophella CG 4 CR

E11500 ARTHROPODA INSECTA EPHEMEROPTERA AMELETIDAE Ameletus SC 1 CG

E11601 ARTHROPODA INSECTA EPHEMEROPTERA LEPTOPHLEBIIDAE Paraleptophlebia CG 1 CR

E11603 ARTHROPODA INSECTA EPHEMEROPTERA LEPTOPHLEBIIDAE Habrophlebia CG 1 CR

E11604 ARTHROPODA INSECTA EPHEMEROPTERA LEPTOPHLEBIIDAE Habrophlebiodes SC 1 CR

E11701 ARTHROPODA INSECTA EPHEMEROPTERA BAETIDAE Baetis CG 5 CG

E11707 ARTHROPODA INSECTA EPHEMEROPTERA BAETIDAE Acerpenna CG 5 CG

E1170Z ARTHROPODA INSECTA EPHEMEROPTERA BAETIDAE Baetis (complex) CG 5 CG

E11900 ARTHROPODA INSECTA EPHEMEROPTERA HEPTAGENIIDAE Stenonema SC 4 CG

E11901 ARTHROPODA INSECTA EPHEMEROPTERA HEPTAGENIIDAE Stenacron CG 4 CG

E11902 ARTHROPODA INSECTA EPHEMEROPTERA HEPTAGENIIDAE Epeorus CG 1 CG

E11903 ARTHROPODA INSECTA EPHEMEROPTERA HEPTAGENIIDAE Cinygmula SC 1 CG

E11904 ARTHROPODA INSECTA EPHEMEROPTERA HEPTAGENIIDAE Leucrocuta SC 2 CG

E11905 ARTHROPODA INSECTA EPHEMEROPTERA HEPTAGENIIDAE Heptagenia SC 3 CG

E11D04 ARTHROPODA INSECTA EPHEMEROPTERA ISONYCHIIDAE Isonychia CF 3 CG

E12000 ARTHROPODA INSECTA ODONATA ANISOPTERA Cordulegaster PR 3 BU

E12029 ARTHROPODA INSECTA ODONATA ANISOPTERA Lanthus PR 1 BU

E12053 ARTHROPODA INSECTA ODONATA ANISOPTERA Somatochlora PR 8 CL

E13400 ARTHROPODA INSECTA HEMIPTERA VELIIDAE Microvelia PR 8 SK

E14000 ARTHROPODA INSECTA MEGALOPTERA SIALIDAE Sialis PR 7 SP

E14111 ARTHROPODA INSECTA MEGALOPTERA CORYDALIDAE Nigronia PR 4 CR

E16012 ARTHROPODA INSECTA TRICHOPTERA HYDROPTILIDAE Hydroptila MP 6 CR

E16210 ARTHROPODA INSECTA TRICHOPTERA HYDROPSYCHIDAE Hydropsyche CF 6 CG

E16211 ARTHROPODA INSECTA TRICHOPTERA HYDROPSYCHIDAE Cheumatopsyche CF 6 CG

E16220 ARTHROPODA INSECTA TRICHOPTERA HYDROPSYCHIDAE Diplectrona CF 2 CG

E16230 ARTHROPODA INSECTA TRICHOPTERA HYDROPSYCHIDAE Parapsyche CF 2 CG
44

Appendix A (continued).

TAXA1 Phylum Class Order Family Genus FUNC

GRP2 TV3 HABIT4

E16300 ARTHROPODA INSECTA TRICHOPTERA RHYACOPHILIDAE Rhyacophila PR 1 CR

E16400 ARTHROPODA INSECTA TRICHOPTERA PHILOPOTAMIDAE Chimarra CF 3 CG

E16410 ARTHROPODA INSECTA TRICHOPTERA PHILOPOTAMIDAE Wormaldia CF 1 CG

E16411 ARTHROPODA INSECTA TRICHOPTERA PHILOPOTAMIDAE Dolophilodes CF 1 CG

E16501 ARTHROPODA INSECTA TRICHOPTERA PSYCHOMYIIDAE Lype SC 4 CG

E167 ARTHROPODA INSECTA TRICHOPTERA LEPTOCERIDAE CG 4 CR

E16A00 ARTHROPODA INSECTA TRICHOPTERA ODONTOCERIDAE Psilotreta SC 0 CG

E16C00 ARTHROPODA INSECTA TRICHOPTERA BRACHYCENTRIDAE Micrasema SH 1 CG

E16C01 ARTHROPODA INSECTA TRICHOPTERA BRACHYCENTRIDAE Brachycentrus CF 1 CG

E16E00 ARTHROPODA INSECTA TRICHOPTERA LEPIDOSTOMATIDAE Lepidostoma SH 1 CR

E16F00 ARTHROPODA INSECTA TRICHOPTERA GLOSSOSOMATIDAE Glossosoma SC 0 CG

E16F10 ARTHROPODA INSECTA TRICHOPTERA GLOSSOSOMATIDAE Agapetus SC 0 CG

E16G10 ARTHROPODA INSECTA TRICHOPTERA LIMNEPHILIDAE Pycnopsyche SH 4 CR

E16H01 ARTHROPODA INSECTA TRICHOPTERA POLYCENTROPODIDAE Neureclipsis CF 4 CG

E16H02 ARTHROPODA INSECTA TRICHOPTERA POLYCENTROPODIDAE Nyctiophylax PR 3 SP

E16H03 ARTHROPODA INSECTA TRICHOPTERA POLYCENTROPODIDAE Polycentropus PR 4 CG

E16H04 ARTHROPODA INSECTA TRICHOPTERA POLYCENTROPODIDAE Cernotina PR 4 SP

E16I00 ARTHROPODA INSECTA TRICHOPTERA MOLANNIDAE Molanna SC 6 SP

E16J00 ARTHROPODA INSECTA TRICHOPTERA SERICOSTOMATIDAE Fattigia SH 1 SP

E16M00 ARTHROPODA INSECTA TRICHOPTERA UENOIDAE Neophylax SC 2 CG

E17 ARTHROPODA INSECTA LEPIDOPTERA 8 

E18220 ARTHROPODA INSECTA COLEOPTERA DYTISCIDAE Hydroporus PR 6 GN

E18400 ARTHROPODA INSECTA COLEOPTERA HYDROPHILIDAE Tropisternus PR 7 GN

E18460 ARTHROPODA INSECTA COLEOPTERA HYDROPHILIDAE Hydrobius SH 8 GN

E18700 ARTHROPODA INSECTA COLEOPTERA PSEPHENIDAE Psephenus SC 4 CG

E18701 ARTHROPODA INSECTA COLEOPTERA PSEPHENIDAE Ectopria SC 4 CG

E18900 ARTHROPODA INSECTA COLEOPTERA ELMIDAE Stenelmis SC 5 CG
45

Appendix A (continued).

TAXA1 Phylum Class Order Family Genus FUNC

GRP2 TV3 HABIT4

E18904 ARTHROPODA INSECTA COLEOPTERA ELMIDAE Optioservus SC 5 CG

E18906 ARTHROPODA INSECTA COLEOPTERA ELMIDAE Promoresia SC 2 CG

E18907 ARTHROPODA INSECTA COLEOPTERA ELMIDAE Oulimnius SC 2 CG

E18D ARTHROPODA INSECTA COLEOPTERA STAPHYLINIDAE PR 5 CG

E19000 ARTHROPODA INSECTA DIPTERA BLEPHARICERIDAE Blepharicera SC 0 CG

E19200 ARTHROPODA INSECTA DIPTERA TIPULIDAE Tipula SH 5 BU

E19205 ARTHROPODA INSECTA DIPTERA TIPULIDAE Brachypremna SH 4 BU

E19210 ARTHROPODA INSECTA DIPTERA TIPULIDAE Antocha CG 3 CG

E19211 ARTHROPODA INSECTA DIPTERA TIPULIDAE Limonia SH 6 BU

E19220 ARTHROPODA INSECTA DIPTERA TIPULIDAE Dicranota PR 3 CR

E19221 ARTHROPODA INSECTA DIPTERA TIPULIDAE Pedicia PR 4 BU

E19230 ARTHROPODA INSECTA DIPTERA TIPULIDAE Hexatoma PR 3 CR

E19231 ARTHROPODA INSECTA DIPTERA TIPULIDAE Limnophila PR 4 BU

E19232 ARTHROPODA INSECTA DIPTERA TIPULIDAE Pseudolimnophila PR 7 BU

E19234 ARTHROPODA INSECTA DIPTERA TIPULIDAE Pilaria PR 3 BU

E19235 ARTHROPODA INSECTA DIPTERA TIPULIDAE Dactylolabis PR 3 BU

E19241 ARTHROPODA INSECTA DIPTERA TIPULIDAE Molophilus CG 3 BU

E19242 ARTHROPODA INSECTA DIPTERA TIPULIDAE Ormosia CG 4 BU

E19243 ARTHROPODA INSECTA DIPTERA TIPULIDAE Erioptera CG 3 BU

E19311 ARTHROPODA INSECTA DIPTERA PSYCHODIDAE Pericoma CG 10 BU

E19500 ARTHROPODA INSECTA DIPTERA DIXIDAE Dixa CG 3 CR

E19700 ARTHROPODA INSECTA DIPTERA SIMULIIDAE Prosimulium CF 4 CG

E19720 ARTHROPODA INSECTA DIPTERA SIMULIIDAE Simulium CF 6 CG

E198 ARTHROPODA INSECTA DIPTERA CHIRONOMIDAE CG 6 BU

E19901 ARTHROPODA INSECTA DIPTERA CERATOPOGONIDAE Forcipomyia SC 6 SP

E19922 ARTHROPODA INSECTA DIPTERA CERATOPOGONIDAE Bezzia PR 6 BU

E19925 ARTHROPODA INSECTA DIPTERA CERATOPOGONIDAE Ceratopogon PR 6 BU
46

Appendix A (continued).

TAXA1 Phylum Class Order Family Genus FUNC

GRP2 TV3 HABIT4

E19926 ARTHROPODA INSECTA DIPTERA CERATOPOGONIDAE Probezzia PR 6 BU

E19A10 ARTHROPODA INSECTA DIPTERA STRATIOMYIIDAE Odontomyia CG 10 SP

E19B00 ARTHROPODA INSECTA DIPTERA TABANIDAE Chrysops CG 7 BU

E19J00 ARTHROPODA INSECTA DIPTERA EMPIDIDAE Hemerodromia PR 6 CR

E19J01 ARTHROPODA INSECTA DIPTERA EMPIDIDAE Chelifera PR 6 SP

E19J10 ARTHROPODA INSECTA DIPTERA EMPIDIDAE Clinocera PR 6 CG

E19J30 ARTHROPODA INSECTA DIPTERA EMPIDIDAE Oreogeton PR 6 SP

E19K ARTHROPODA INSECTA DIPTERA MUSCIDAE PR 8 SP

E1A0 ARTHROPODA INSECTA COLLEMBOLA PODURIDAE CG 7 SK

E1A2 ARTHROPODA INSECTA COLLEMBOLA ISOTOMIDAE CG 7 SK

E1A3 ARTHROPODA INSECTA COLLEMBOLA ENTOMOBRYIDAE CG 7 SK

E1A4 ARTHROPODA INSECTA COLLEMBOLA ONYCHIURIDAE CG 7 SK

E20 ARTHROPODA ARACHNIDA ACARI PR 6 CR

F0 MOLLUSCA GASTROPODA SC 8 

F110 MOLLUSCA BIVALVIA VENEROIDA SPHAERIIDAE CF 8 BU

1

Taxa Codes were derived from those contained in Shenandoah National Park long-term monitoring database provided to U.S. Geological Survey in Microsoft 

Access file “USGS_All_Stream_Data_2010”; table “W_AIzdd_Taxa.” 

2

Functional groups were derived from those contained in Shenandoah National Park long-term monitoring database provided to U.S. Geological Survey in 

Microsoft Access file “USGS_All_Stream_Data_2010” ; table “W_AIzdd_Taxa.” Functional groups are as follows: CF=Collector filterer; CG=Collectorgatherer; GN=Generalist feeder; MP=Macrophyte piercer; PR=Predator; SC=Scraper; and SH=shredder. These characteristics were used to compute community

metrics %Shredders and %Scrapers (see table 4).

3

Tolerance values were derived from those contained in Shenandoah National Park long-term monitoring database provided to U.S. Geological Survey in 

Microsoft Access file “USGS_All_Stream_Data_2010” ; table “W_AIzdd_Taxa.”. These values were used to compute community pollution tolerance values (see 

table 4). 

4

Taxon-specific “habits” were derived from those contained in Shenandoah National Park long-term monitoring database provided to U.S. Geological Survey in 

Microsoft Access file “USGS_All_Stream_Data_2010” ; table “W_AIzdd_Taxa.” Habit codes are as follows: BU=Burrowers; CG=Clingers; CL=Climbers; 

DV=Divers; GN=Generalist; SK=Skaters; and SP=Sprawlers. These characteristics were used to compute the community metric “%Hapto (Haptobenthos), 

which is the sum of crawlers and clingers (see table 4).