a reference-based framework for evaluating the ...

A REFERENCE-BASED FRAMEWORK FOR EVALUATING THE ECOLOGICAL CONDITION OF STREAM NETWORKS IN SMALL WATERSHEDS Author(s): Richard D. Rheinhardt, Mark M. Brinson, Robert R. Christian, Kevin H. Miller, and Greg F. Meyer Source: Wetlands, 27(3):524-542. 2007. Published By: The Society of Wetland Scientists DOI: http://dx.doi.org/10.1672/0277-5212(2007)27[524:ARFFET]2.0.CO;2 URL: http://www.bioone.org/doi/full/10.1672/0277-5212%282007%2927%5B524%3AARFFET %5D2.0.CO%3B2

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WETLANDS, Vol. 27, No. 3, September 2007, pp. 524–542 ’ 2007, The Society of Wetland Scientists

A REFERENCE-BASED FRAMEWORK FOR EVALUATING THE ECOLOGICAL CONDITION OF STREAM NETWORKS IN SMALL WATERSHEDS Richard D. Rheinhardt1, Mark M. Brinson1, Robert R. Christian1, Kevin H. Miller2,3, and Greg F. Meyer3 1 Department of Biology East Carolina University Greenville, North Carolina, USA 27858 2

North Carolina Ecosystem Enhancement Program Washington, North Carolina, USA 27889 3

Coastal Resources Management Program East Carolina University Greenville, North Carolina, USA 27858

Abstract: Nine field indicators were identified for evaluating the hydrologic, biogeochemical, and/or habitat functioning of stream channels, riparian zones, or both. We ranked condition from ‘‘relatively unaltered’’ to ‘‘severely altered’’ for each of the identified indicators based on the range of conditions actually encountered among reference sites in the Coastal Plain of North Carolina, USA. The rankings provided a framework for developing a narrative used for scoring condition of the indicators at the scale of a reach (100-m-long 3 60-m-wide segment). Reach condition was then derived by aggregating indicator scores, which were weighted by the number of functions with which each indicator was affiliated. Watershed-scale assessments were conducted by sampling randomly chosen first- to fourthorder reaches within stream networks at the density of approximately one 100-m reach per 1.0 km2 of watershed drainage area. We used the association between indicators and hydrologic, biogeochemical, and habitat functions to obtain aggregated, weighted scores for channel and riparian zone condition. We used both aggregated network scores and mean indicator scores to compare condition among stream networks. At a reach scale, scores of indicators suggest strategies for restoration. At the watershed scale, aggregate scores showed differences among stream networks that could be used to prioritize restoration efforts and monitor change over time. Key Words: Assessment, coastal plain, functions, indicators, reference framework, restoration, riparian

INTRODUCTION

watersheds (Brinson 1993a, Rheinhardt et al. 2005). Riparian ecosystems are highly interconnected in that stresses or alterations that occur in one part of a stream network affect other parts of the network (Naiman and Latterell 2005, Wipfli et al. 2007), primarily in the downstream direction. Thus, assessments or monitoring of stream networks should be capable of characterizing both stream channel and riparian zone condition at multiple spatial scales from a single reach to the stream network. For small watersheds (, 100 km2), an unbiased method should include all riparian ecosystems in the network, from intermittent headwater reaches to those of perennial mainstem reaches. Various bioassessment designs have been developed for evaluating and monitoring stream network condition (Plafkin et al. 1989, Barbour and Stribling 1994). One approach is to compare the loading of target chemicals relative to some established allow-

Freshwater streams and their riparian zones (riparian ecosystems) are universally recognized as an important natural resource in need of protection, management, monitoring, and restoration. Riparian ecosystems provide essential services, including fresh drinking water, fish and wildlife habitat, and recreational opportunities. The capacity of a riparian ecosystem to provide these services depends on its condition, which is influenced by both the condition of its channel and its riparian zone. The two parts, channel and riparian zone, are inexorably linked ecologically; alterations to one affect the other (NRC 2002). However, both are, in turn, affected by the condition of the landscape in which they are embedded (Brooks et al. 2004). This is particularly true for headwater reaches, which interface most directly with upland activities and constitute the majority (70%–90%) of the stream length in 524

Rheinhardt et al., FRAMEWORK FOR EVALUATING STREAM NETWORKS able total maximum daily load (TMDL) of pollutants (http://www.epa.gov/owow/tmdl/overviewfs. html). Another approach is to compare the composition of invertebrate or fish samples with benchmarks developed with data from relatively unaltered reference stream reaches (i.e., the Index of Biological Integrity (IBI) approach (Karr 1987, U.S. EPA 2005) (http://www.epa.gov/bioindicators/html/ ibi-hist.html). Both the TMDL and IBI approaches are useful when calibrated against appropriate reference data. However, neither approach was intended to assess the condition of riparian zones, nor were they designed to explicitly identify the source of water quality problems. Further, neither approach was designed to work in intermittent, headwater streams; rather they were designed for more-downriver, perennial (i.e., wadeable) streams. Other assessment protocols measure the condition of riverine wetlands in a watershed by using a random or probabilistic sampling approach to assign assessment locations (Stevens and Olson 2004). However, because riparian wetlands associated with the lowest-order reaches (first- to secondorder) are usually restricted to channels or to narrow floodplains only a few meters wide, such wetlands are too small to be mapped on an NWI or similarscale maps (Brooks et al. 1999, Wardrop et al. 2007). This means that randomly assigned assessments limited to wetland portions of headwater riparian zones would miss most (70%–90%) of the headwater reaches in a stream network. Given the myriad of current assessment approaches designed to evaluate stream or riparian condition (but not both together) of perennial streams, there remains a need to develop a condition assessment procedure that can be used for both headwater and higher-order riparian ecosystems. The assessment procedure also should be capable of measuring condition and diagnosing problems to effectively track net change over time due to improvement (from restoration) and degradation (from continuing alterations). Such an assessment should not only integrate stream channel and riparian zone condition along selected stream segments (Gregory et al. 1991), but it should also be able to allow one to use results of condition at the reach scale to seamlessly estimate the condition of stream networks at the watershed scale. This paper provides one approach for doing this. Description of Study Area The inner coastal plain of North Carolina is primarily a topographically subdued landscape consisting of a mixture of agricultural, silvicultural,

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and urban/suburban watersheds. Extensive flats of little relief occur at the topographic divides between watersheds, many of which have been converted to pine silviculture. These precipitation-driven flats are so poorly drained that a large portion of them are wetlands, except where drained by ditches. Many streams begin in or near the edge of these flats and flow through agricultural areas before reaching alluvial valleys through which flow third-order and higher streams with active floodplains (where they have not been channelized). The agricultural areas, located between the flats and alluvial bottoms, are drained by a network of first- to third-order channels and field ditches (Daniels et al. 1999). Even many channelized, higher-order streams are forested on their historic floodplains because their soils remain too wet to farm. Regardless of adjacent land use, high spoil banks are often found on one or both sides of channelized higher-order streams and at least one bank is kept clear of vegetation to provide access for channel maintenance. In urban areas, low-order streams generally have been truncated by development and converted to trapezoidal concrete channels or piped underground. Most higher-order streams are deeply incised due to increased runoff from impervious surfaces in their watersheds. In most older urban areas, stormwater drains export water directly into channels. In some developments, stormwater-detention basins are improperly installed (pers. obs.), making them ineffective for their designed use. According to the classification developed for wetlands by Cowardin et al. (1979), channels of low-order coastal plain streams would be called intermittent riverine, comprised of unconsolidated sand or mud. The adjacent wetlands would be classified as palustrine, usually dominated by forest or scrub-shrub, depending on successional status. From a hydrogeomorphic (HGM) perspective (sensu Brinson 1993b), headwater wetlands would be classified as riverine headwater (with appropriate subclasses, see following). Headwater riparian ecosystems include both channels and adjacent riparian zones, which together constitute an inter-dependant ecological unit. For most headwater sections of stream networks, both narrow floodplain wetlands and adjacent non-wetlands (uplands) are critical components of stream ecosystems. In cases where streams have been channelized and/or where fill material has been deposited in floodplains, wetlands may no longer exist except along channel banks where ground water is intercepted. Therefore, in developing assessment protocols for the channel/wetland/upland riparian ecosystem complex, we included both

526 non-wetlands and former wetlands in our definition of riparian zone. Excluding adjacent uplands from our definition of riparian zone would have omitted a critical component of riparian ecosystem functioning (NRC 2002). Exclusion would have also omitted many altered sections, leading to a biased evaluation of stream network condition. Because adjacent land-uses influence riparian condition, we also classified stream segments according to whether they occurred either primarily in an urban/suburban setting or in a rural setting (see Rheinhardt et al. 2005 for definitions). The urban versus rural differentiation was necessary because stresses imposed upon riparian ecosystems in the two types of settings differ. For example, urban watersheds have more impervious surface than rural ones. This results in stormwater being carried more rapidly to channels in urban areas, making urban stream flows more flashy and energetic than rural flows, particularly in headwater parts of stream networks where low-order streams have been converted to drainage pipes (Paul and Meyer 2001). This increased flashiness causes a suite of other modifications to urban streams that are not as apparent or common in rural streams, including bank erosion and channel incision. By applying different assessment protocols for urban and rural stream segments, we were able to calibrate indicators of condition typical for urban segments in ways that would not have been very sensitive in rural segments. Stream size is another factor influencing our classification of coastal plain streams. Low- (firstand second-) order streams differ hydrologically from higher- (third- and fourth-) order streams in both rural and urban landscapes. All low-order streams have intermittent flow, are primarily driven by ground-water discharge, and receive surficial ground water that has passed through organic-rich surface soils and root zones (except where streams are channelized) (Brinson et al. 2006). Higher-order streams have intermittent to perennial flow, are also supplied with ground water and, under unaltered conditions, generate sufficient peak flows to put them in regular contact with their floodplains. Given the previously described hydrologic differences among reach types, protocols for determining riparian condition were developed for four types of riparian ecosystems (Rheinhardt et al. 2005, 2007): low- and high-order rural streams and low- and high-order urban streams. For each type of riparian ecosystem, we identified reference conditions (sensu Smith et al. 1995, Brinson and Rheinhardt 1996) that encompassed the range of conditions actually encountered for stream reaches in the inner coastal

WETLANDS, Volume 27, No. 3, 2007 plain from relatively unaltered to severely altered. This was the reference framework used to calibrate indicator scores. Thus, standards were based on characteristics of conditions encountered in the field, which ensured that standards for individual indicators were scaled to stay within bounds of natural and human-induced sources of variation. METHODS Six stream networks were evaluated to show how stream network-scale assessments could be applied: four in the Tar drainage basin, one in the Neuse basin, and one in the Lumber basin (Figure 1). The near-stream land-uses of the Crisp, Cow, and Bear/ Moss Neck stream networks were almost entirely rural; the near-stream land-use types of the Hendricks, Green Mill, and Stoney networks were a mixture of rural and urban uses. Forest condition ranged from recently cut to mature stands in both agricultural and urban areas. However, beaver activity has eliminated the canopy in many secondto fourth-order sections of streams in both agricultural and urban areas, leaving only open water with numerous snags and downed wood (Bason 2004). Statistical Design for Stream Network Sampling Random points along streams were chosen, with each point defining the center of a 100-m-long section of stream, hereafter called a reach. A reach of 100 m in a natural coastal plain stream is generally a uniform ecological unit in that there are no riffle/pool sequences or sinuosity that varies in a pattern longer than 100 m. Random sampling was the best basis for inferring the distribution of riparian conditions within a stream network. In addition, the information could be used to compare the aggregate condition of stream reaches among stream networks. The sampling density of random points was chosen to be approximately one point per 1.0 km2 of watershed drainage area. With an observed drainage density of approximately 1 km per 1.0 km2 of watershed area, sampling density was approximately one point per 1.0 km of stream length. Since the assessment method evaluated a 100-m reach, approximately 10% of total stream network length was assessed in each watershed. To assign random points for assessment, a GIS algorithm available from the Web page of Environmental Systems Research Institute (ESRI) was used (http://arcscripts.esri.com/details.asp?dbid510296). The algorithm uses an Avenue script that places random points on line shapefiles derived from USGS 1:24,000 scale maps (7.5-minute quadrangle

Rheinhardt et al., FRAMEWORK FOR EVALUATING STREAM NETWORKS

Figure 1.

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Location of sampled stream networks in six watersheds in coastal plain North Carolina.

series). The algorithm treated the GIS lines representing streams as a continuous resource. It did this by generating n random integers between 0 and 109, changed each into a percentage (random integer / 107), and located sampling points as a percentage to seven decimal places of the total continuous stream length. Because the number of sampling points n was approximately one point per km stream length, we would randomly choose approximately 100 points for a stream 100 km long (stream networks lengths in our watersheds were 35–90 km long). In this example, the algorithm would randomly choose 100 points among 109 possible points, each located approximately 0.1 mm apart. Because 0.1 mm apart is too fine to be measured in the field, the streams were effectively treated as a linear resource for sampling purposes (Stevens and Olsen 2004). This was desirable in that we wanted to be able to use data from sampled reaches to make inferences on the condition of the stream networks as a proportion of stream length (sensu Herlihy et al. 2000). In field-verifying the stream network, we found that the USGS 1:24,000 hydrographic layer omitted many headwater intermittent streams and included

many ditches that were never part of the natural stream network. Although those ditches contributed to the hydrologic and biogeochemical condition of their stream networks, they were not assessed because they were never streams, and thus would not be considered candidates for restoration. However, sources of pollution from agricultural ditches upstream were evaluated as part of assessing reach condition under the indicator, ‘‘Pollution affecting stream’’ (Table 1). In contrast, ditched reaches of once natural streams were assessed because they were a part, albeit an altered part, of the original stream network. To correct for inadequacies in the hydrographic layer from which sample reaches would be drawn, we tried several approaches for modifying the layer so that the sampled stream networks would better represent the true stream networks. Because a final determination would be made in the field as to whether a randomly selected reach was or was not a part of the natural stream network, we decided that we would adopt an approach (which follows) that would tend toward adding ephemeral channels to the stream networks rather than excluding true streams.

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Table 1. Indicators adopted for the riparian assessment protocol and examples of rationale for adoption. Each indicator is scored between 0 and 100 based on measurements and narrative descriptions, but calibrations of indicators differed among the four reach types. Detailed rationale is provided in Rheinhardt et al. (2005, 2007). ‘‘Stream bank stability’’ was not assessed in rural low-order reach type. Associated Function Indicator Near-stream cover (0–3 m)

Rationale

Vegetation nearest the stream channel assimilates nutrients through plant growth, contributes organic matter to the channel, and stabilizes banks to reduce sediments from entering stream. Leaves provide material for shredder biota (Cuffney 1988) and contribute dissolved organic matter for microbial food webs (Sabater et al. 1993). Large down wood provides instream structural habitat complexity (Gregory et al. 1991). Riparian zone Forested riparian zones contribute to infiltration of cover (0–30 m) precipitation, groundwater storage, and evapotranspiration (Gilliam 1994, Gregory et al. 1991, Snyder et al. 2003); assimilate nutrients; and provide habitat structure. Instream woody Wood in channel creates riffle and pool sequences that structure dissipate energy of flowing water and store water in pools during low flows (Harman et al. 1986, Brinson et al. 2006), affects biogeochemistry by providing a surface for microbial activity and a potential source of DOC, and provides structural habitat complexity for epifauna and epiphytes (Thorp et al. 1985) and for fish and invertebrates (Seastedt et al. 1989). Sediment regime Excessive sediment may transport phosphorus and heavy metals to channel (Cooper and Gilliam 1987, Hupp et al. 1993; Noe and Hupp 2005) and reduce water transparency, thus suppressing primary production of epiphytic algae (Jacobs et al. 1990). Channel-riparian Overbank flow dissipates energy, thus reducing channel zone connection incision and bank erosion (Arcement and Schneider 1989, Benke et al. 2000). Storage of water in floodplains reduces downstream flood peaks. Storage increases residence times of water for nutrient assimilation. Channelization eliminates contact of surficial ground water with the organic rich surface horizons of the soil, thus reducing the potential for denitrification (Bohlke and Denver 1995). Hydrologic connections allow movement of biota between channel and riparian zone. Pollution from roadside ditches and ditches draining Pollution agricultural fields, both upstream and within a reach, affecting degrades instream habitat and interferes with normal stream biogeochemical cycling (Jordan et al. 1993, Lowrance et al. 1984, Peterjohn and Correll 1984). Habitat is degraded by nutrient or chemical additions (Vargo et al. 1998). Impervious surfaces and channelized tributaries increase flashiness of flow and may lead to channel incision, bank erosion and channel scour, and a decrease in groundwater discharge.

Channel

Riparian Zone

Biogeochemistry Habitat quality

Hydrology Biogeochemistry Habitat quality


Biogeochemistry




Rheinhardt et al., FRAMEWORK FOR EVALUATING STREAM NETWORKS Table 1.

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Continued. Associated Function

Indicator Factors affecting riparian zone

Habitat quality of riparian zone Stream bank stability

Rationale

Channel

Alterations, ranging from conversion to impervious surface to filling with spoil to pollution, interfere with hydrology, biogeochemistry, and habitat quality by contributing excess water and/or nutrients (primarily nitrogen and phosphorus) and toxic pollutants to riparian zones (Brinson et al. 1984, Cooper and Gilliam 1987, Jordan et al. 1993). Forest species composition, forest age, and 3-D structure contribute to nesting, foraging, and denning opportunities that are otherwise absent in altered forests (Harmon et al. 1986, Walker and Smith 1997, Simberloff et al. 2005). Degree of bank erosion, when excessive, increases suspended sediments downstream and degrades aquatic habitat when excessive. (Not used for rural low-order streams because of difficulty in assessing.)

Several approaches for extending the stream network upstream were tried, and each was verified in the field to determine which one worked best. We first tried to digitize additional streams from NRCS county soil survey maps manually, but this proved too time-consuming (digital stream network data are not presently available for most county soil surveys in NC). We also tried using digital elevation models (DEMs) constructed using high-resolution LiDAR data available from the NC Floodplain Mapping Program by processing the data using ArcGIS 9 and a geospatial hydrologic modeling extension (HECGeoHMS) developed by the U.S. Army Corps of Engineers (http://www.hec.usace.army.mil/software/ hec%2Dgeohms/). Despite manipulation of model parameters (primarily the flow-initiation threshold), we were unable to replicate the true stream network reasonably. At low values of the flow-initiation threshold, many streams were generated by the model that did not exist. Raising the threshold reduced the number of nonexistent streams added, but also failed to retain all of the true streams not already identified on topographic maps. A suitable intermediate threshold could not be found that would prevent the addition of nonexistent streams without removing streams known to exist. The flat topography of the coastal plain is probably the main reason that this approach failed to identify the true stream network reliably. The third approach tried, and the one adopted, was to amend stream segments on existing topographic maps manually. Most unmapped streams observed in previous surveys had occurred in topographic, linear depressions (visible on topographic maps as a crenulation, or ‘‘draw’’). From

Riparian Zone Hydrology Biogeochemistry Habitat quality

Habitat quality

Biogeochemistry Habitat quality

this observation, and from previously collected slope data for headwater streams (Rheinhardt et al. 1998, Brinson et al. 2006), we developed rules for extending streams headward and removing ditches manually on maps, based on topography. For a linear depression to indicate the presence of an intermittent or perennial stream, it had to have the following attributes: 1) two or more topographic contours showing a v-shaped deflection of less than 90u from the general trend of the contour line, 2) a slope of greater than 0.5%, and 3) a downstream connection to a mapped stream not more than two stream orders higher than the added stream (e.g., first-order added streams could connect to a first-, second-, or third-order stream but not to a fourthor higher-order stream). This third connection rule was developed to avoid adding tributary streams where the higher-order stream would likely have lowered the water table enough to prevent groundwater-driven flow to the tributary. Ground-truthing showed that these criteria worked well in identifying unmapped streams (with few additional streams being missed) and in eliminating field ditches that were never streams. Reach segments identified on the topographic maps as impoundments were also manually eliminated from the stream network because the protocols were not designed to evaluate them. Once the modified data layer was completed for each stream network, a random point algorithm was run to generate random points along streams in the network. Because we suspected that some of the random points would not fall on a true stream, an additional (replacement) set of random points was also generated to be added sequentially to the

530 original sample array upon rejection of initial sample points. The first replacement random point was used to replace the first point rejected, the second replacement point was used to replace the second rejected point, and so on. Points on beaver-impounded floodplains were not replaced, but if the point was on an un-impounded reach within 50 m of an impounded floodplain reach, the point was moved upstream or downstream until it was 50 m from the impounded floodplain. This prevented any portion of the 100m-long reaches from being on an impounded floodplain. In contrast, reaches with beaver-impounded channels (but not floodplains) were assessed, but some indicators were not measured (explained in next section). In both cases, the call as to whether a floodplain was or was not flooded by beaver had to be made in the field. Replacements were also made if the random point was not located on a true stream (i.e., if located on an ephemeral draw, in a field ditch that was never a stream, or on a culverted or piped section) (see Rheinhardt et al. 2005 for detailed replacement criteria and protocol). The number of points rejected for not being a true stream was tallied, by stream network and by reason for rejection, to calculate the true length of the stream network. For example, if the sampled network were 100 km long and 10 of the 100 initially assigned random points (10%) were rejected (and replaced) for being ephemeral draws, field ditches, and culverts, then the true stream network length would be adjusted to represent 91 km in length (based on 10 points rejected divided by 100 points initially visited + 10 additional points sampled as replacement points). By determining the length of the true stream network, assessment results could be related to the proportion of the network affected. Reach Assessments Riparian assessments were designed to be applied at the scale of a reach, defined as a 100-m-long section of stream with a 30-m-wide riparian zone on each side of the channel. Thirty meters was decided upon as a reasonable width to define the outer boundary of the riparian zone because mature trees in the coastal plain generally grow to 30 m in height. A 30-m-tall tree within 30 m of the channel would have a better than 50% chance of falling, at least partially, into the riparian zone and some chance of contributing wood (detritus) to the channel when it falls (closer trees would have a better chance). Indicators of reach condition characterized some facet of hydrologic, biogeochemical, and/or habitat

WETLANDS, Volume 27, No. 3, 2007 functioning and was related to channel condition, riparian zone condition, or both (Table 1, Figure 2). For each reach, 8–9 condition indicators were evaluated and scored. Indicators were combined to reflect logical contributions toward ecosystem functioning (hydrologic, biogeochemical, and habitat) of channel or riparian condition. Except for the indicator ‘‘Sediment regime,’’ indicators contributed to the condition of more than one function (Figure 2), but the rationale for assignment to a given function differed by function (Table 1). As an example, wood in stream channels creates riffle and pool sequences that dissipate the energy of flowing water and store water in pools during low flows. Thus, the condition of ‘‘Instream woody structure’’ affects hydrologic functions. However, wood in channels also affects biogeochemistry by providing surfaces for microbial activity and by trapping detritus and releasing dissolved organic carbon during decomposition. These processes slowly release dissolved organic carbon into streams over long periods to supply energy for detritus food webs and for denitrification. No attempt was made to weight the contribution of indicators to specific functions, mainly because a technical basis is lacking for doing so. However, some indicators carry more weight than others simply because they were logically linked to more than one function and each function varied in the number of indicators associated with it (Figure 2). As an example of the former case, ‘‘ChannelRiparian Zone Connection’’ carried the greatest weight overall because it directly influenced all functions of both stream channel and riparian zone. In the latter case, for stream channels, the biogeochemistry function had six indicators that were averaged to obtain its function score, while the hydrology function had only three indicators assigned to it. Consequently, each stream biogeochemistry indicator carried only half the weight of a stream hydrologic indicator. The same indicator names (Table 1) were used for assessing conditions in all four riparian types, but the narrative for each indicator was written to represent the range of conditions observed among the array of reference reaches from relatively ‘‘unaltered’’ to ‘‘severely altered’’ within each riparian type (condition categories are defined in detail following). As an exception, the ‘‘Stream bank stability’’ indicator was not measured in the rural low-order riparian type because hydrologic energy is often too low to overwhelm the binding capacity of streamside vegetation, even herbaceous vegetation. However, ‘‘Stream bank stability’’ was measured in the urban low-order type because runoff from


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Figure 2. Aggregation of indicators that characterize functions and the condition of channels and riparian zones. For each randomly chosen reach, function scores for riparian zone and stream channel reflect the mean of the indicators attributed to them (sorted by color), with the exception that the indicator ‘‘Stream bank stability’’ was not measured in rural low-order reaches. The three function scores are averaged for channel and riparian zone separately to obtain condition scores for each and then graphed. Dashed lines in the graph compartmentalize reaches by composite condition score (mean of riparian condition and channel condition) within the following condition categories: severely altered (0–29) in lower left, altered (30–59), somewhat altered (60–89), and relatively unaltered (90–100) in upper right. Percent of reaches in each condition category is equivalent to percent stream length represented by the category.

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Figure 3. Narrative for scoring the indicator ‘‘Instream woody structure,’’ provided as an example. Supplementary criteria (a–d) are used to specify in more detail the condition categories. Scoring criteria for this indicator are based on data from an array of rural low-order reference reaches ranging from relatively unaltered to severely altered. (See Rheinhardt et al. (2005) for condition narratives for all indicators and assessment protocol for all riparian types.)

impervious surfaces in urban watersheds causes flashy hydrodynamics that destabilize banks and cause excessive erosion. ‘‘Stream bank stability’’ was also used to measure bank condition in high-order riparian types (both urban and rural) because larger streams can generate sufficient hydraulic energy to cause bank erosion problems if the streams are unusually flashy and/or stream banks are inadequately vegetated. Bank condition is important because excessively eroding banks contribute sediment to channels that detrimentally affects biogeochemistry and aquatic habitat. Because stream banks naturally erode, reference conditions were used to determine bank conditions in relation to the degree of natural variation in erosion. Of the 295 points sampled, 18 (5%) had floodplains impounded by beaver, with all impoundments along third- or fourth-order streams. Although reaches with beaver-impounded floodplains were explicitly excluded from the assessment procedure for all classes, reaches that had only their channels impounded (and not their floodplains) were assessed. There were 14 reaches (4%) where only channels were impounded. In these reaches, three indicators of channel condition were not evaluated: ‘‘Sediment regime,’’ ‘‘Channel-riparian zone connection,’’ and ‘‘Stream bank stability’’ because fluvial indicators were either under water where they could not be observed or channel processes were modified

to such a degree that the indicators were not appropriate for measuring condition. All indicators of riparian condition were calibrated for each riparian type such that the scoring range (0–100) was grouped into four condition categories: relatively unaltered (90 or 100), somewhat altered (80, 70, or 60), altered (50, 40, or 30), and severely altered (20, 10, or 0) (Figure 3). Within each condition category, a narrative defined the condition category, the various field criteria used for scoring that category, and in many cases, the specific guidance for scoring. For example, the narrative defined not only what constituted a ‘‘somewhat altered’’ condition for each indicator, but within the somewhat altered category (60, 70, 80), it defined what specific conditions warranted a score of 70 as opposed to 60 or 80. Stream Network Assessments Natural resource professionals from three consulting companies were trained to apply the assessment methods to the four riparian types. These consultants were contracted by the North Carolina Ecosystem Enhancement Program to assess reaches in one or more assigned watersheds and to provide the raw field data to the authors for analysis. Training involved one day of classroom instruction and two days of field training at various reference


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sites ranging from relatively unaltered to severely altered. Several days were also spent accompanying field crews during the first several days of field sampling to ensure better consistency among crews in scoring indicators. Two hundred ninety-five (295) reaches in the six stream networks were randomly assigned for sampling. The 6 sampled stream networks ranged in size from 23 km2 to 78 km2, while the initial stream network length ranged from 37 km to 91 km. Following training, field crews (usually composed of two persons) were able to sample 6–8 reaches per day (including travel) or about 0.5 h per reach once at the site. Indicator Analysis. A matrix was generated of indicator scores for each reach. Indicators were then combined and averaged following the logic in Figure 2 to compare reaches by hydrologic, biogeochemical, and habitat functions for both the stream channel and the adjacent riparian zones. The three resulting function scores for stream channel and for riparian zone were then averaged to obtain a mean condition score for riparian zone and stream channel (Figure 2). The riparian and stream channel condition scores were averaged to obtain a composite condition score for each reach. Because all indicator scores ranged between 0 and 100, riparian and stream channel condition scores and composite condition scores for each reach likewise ranged between 0 and 100. Stream Network Analysis. Individual indicator scores were averaged for each stream network to compare networks at the basic level of indicators. Stream networks were evaluated relative to channel condition and riparian zone condition by graphing channel condition on the x-axis and riparian condition on the y-axis, as outlined in Figure 2. This provided a distribution of channel and riparian condition scores for all reaches in a watershed. Averaging riparian and channel condition scores provided a composite function score (one number per reach) that enabled a comparison of reaches within watersheds. A mean composite function score was then calculated for each stream network by averaging the mean of all composite functions scores in the network. Like reach scores, all mean indicator scores for the stream network, mean function scores, and mean composite condition scores ranged between 0 and 100 (severely altered to relatively unaltered). RESULTS AND DISCUSSION Random points were evenly distributed throughout the stream networks, although a few

Figure 4. Location of random points in the stream network of Cow Swamp. The density of sampled points was 1 point per 1 km2 of drainage basin area. Only points shown in solid were sampled.

small catchments were missed. Figure 4 shows the coverage of random, alternate, and sampled points on the Cow stream network, as an example of coverage. There was only one reach in all four networks for which it was not possible to gain access, representing 0.3% of random points. Of the 295 reaches randomly sampled in all networks, 66 low-order reaches were initially rejected (and alternate points chosen) for not meeting our definition of being an intermittent or perennial stream: 30 for being ephemeral, 18 for being culverted or piped (all in urban reaches), and 18 for being ditches that were not formerly streams (all in rural reaches). To calculate the true length of each stream network, we used the proportion of points rejected for not being on a true stream relative to the total number of points visited (n 5 361), which included beaver-impounded floodplains (not sampled, but not replaced) and reaches rejected for being non-stream (replaced by alternate points). After accounting for the proportion of non-streams in the network, the adjusted stream network lengths ranged from 15–70 km (Table 2). The Cow and Crisp watersheds were entirely rural. The Bear/Moss Neck watershed had only one urban reach; the rest were rural. Green Mill, Hendricks, and Stoney consisted of a mixture of urban and rural reaches, ranging from 29%–61% urban. The proportion of low-order reaches ranged from 64%–78% (Table 2), with a lower proportion

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Table 2. Network lengths and percent of reaches, by network type. Adjusted stream length was determined by accounting for the proportion of the sampled population in the network that did not meet criteria defining a true stream. % Reach Type Major River Basin

Watershed Cow Crisp Green Mill Hendricks Stoney Bear/Moss Neck Mean %

Tar Tar Tar Tar Neuse Lumber

Urban Adjusted Stream Rural Low Low Watershed Order Area (km2) Length (km) Order 44.5 45.9 34.4 22.9 77.5 73.7

40.2 37.8 29.7 23.7 73.5 62.6

77.3 77.8 52.9 39.1 42.9 62.5 58.7

of low-order reaches generally associated with urban reaches. This result was likely due to the conversion of headwater reaches to culverts and pipes during urbanization (i.e., the total length of headwater reaches available for sampling was truncated by development). Condition Scores Indicator scores ranged widely from a low of 6 for channel-riparian zone connection in rural high-order streams of two watersheds to occasional scores above 90 for Hendricks, Stoney, and Bear/Moss Neck (one stream only) (Table 3). Streams in Cow and Crisp have received extensive channelization, a practice that tends to result in low scoring of channel-riparian zone connection, factors affecting riparian zone, and habitat quality of riparian zone. Graphical representations of stream network condition show how each stream network varied with respect to its distribution of reach conditions (Figure 5). Each reach in a stream network is represented by a point on its watershed graph, with its position based on its stream channel and riparian

0.0 0.0 14.7 26.1 23.4 1.4 10.9

Rural High Urban Order High Order 13.6 22.2 0.0 0.0 15.6 30.6 13.7

0.0 0.0 32.4 34.8 5.2 0.0 12.1

Beaver Impounded 9.1 0.0 0.0 0.0 13.0 5.6 4.6

zone condition scores, derived as outlined in Figure 2. The dashed lines compartmentalize composite condition scores of reaches by the same condition categories defined for indicators (Figure 3), ranging from relatively unaltered reaches in the upper right of the graph to severely altered reaches in the lower left. Somewhat altered and altered groups of reaches are in the middle two regions. Because assessment reaches were randomly selected, the percent of reaches in each composite condition category within a stream network is equivalent to the percent stream length in the category. For example, 45% of Cow’s stream network length was severely altered (Figure 5). This corresponds to about 20 km of stream length (Table 2). The graphs in Figure 5 show that most points are clustered along a slope from lower left to upper right. This suggests that channel condition and riparian condition are positively correlated. This is due partly to the fact that many of the same indicators are used to describe different aspects of channel and riparian condition (Figure 2) and partly because human alterations tend to affect

Table 3. Mean function scores and mean composite condition scores, by watershed. Composite condition scores are derived from the average of all function scores within a reach or from the average of stream channel condition and riparian zone condition scores (Figure 2). Relatively unaltered watersheds would score 90 or greater for all mean composite condition scores. STREAM CHANNEL

Cow Crisp Green Mill Hendricks Stoney Bear/Moss Neck

RIPARIAN ZONE

Hydrology

Biogeochemistry

Habitat

Hydrology

Biogeochemistry

40 35 55 63 64 48

39 35 54 63 63 46

40 35 58 63 66 49

31 20 51 63 72 42

31 20 51 63 72 42

Mean Composite FuncHabitat tion Scores 31 21 44 63 69 39

35 28 52 62 67 44


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Figure 5. Condition of six sampled watersheds. Each point on the graphs represents a sampled reach. Channel and riparian conditions for each reach were derived from averaging function scores for channel and riparian zone as described in Figure 2. Dashed lines compartmentalize reaches by condition category, based on composite function scores, ranging from relatively unaltered (upper right) to severely altered (lower left). Percent of reaches for each condition category is equivalent to percent stream length in the category. Cow, Stoney, and Bear/Moss Neck have beaver-impounded reaches and so sum of condition categories is , 100%.

536 more than one indicator at a time. For example, stream channelization not only lowers the ‘‘Channel-riparian zone connection’’ score but also reduces the score for five other indicators of condition: ‘‘Instream woody structure’’ (because large down wood is often removed from channelized streams), ‘‘Stream bank stability’’ (because banks are destabilized due to removal of trees along banks), ‘‘Riparian zone cover’’ and ‘‘Habitat quality of riparian zone’’ (because riparian forest buffer is usually removed or degraded), and ‘‘Pollution affecting stream’’ (because sediment and other pollutants are transported to streams due to concomitant changes in adjacent land-use practices). The scores of all these indicators affect the condition scores for both the channel and riparian zone. Figure 5 can also be used to contrast riparian conditions among stream networks. This is illustrated by comparing Stoney, in which 57% of reaches were relatively unaltered and somewhat altered, with Crisp, in which 2% were in these categories. This type of information can be used by resource agencies to guide decisions on allocating resources for restoration projects among watersheds. A goal of stream network management would be to inhibit further reach degradation and restore altered reaches in a watershed so that a subsequent assessment of stream networks would show more reaches positioned toward the upper right corner of the graphs. Function Scores Society places value on functions provided by ecosystems, so comparing scores for functioning is another logical way to compare reaches and stream networks. Just as each indicator was related to some aspect of channel and/or riparian zone condition, each indicator can also be related to the condition of one or more functions of channels and/or riparian zones (Table 1, Figure 2). In the studied stream networks, overall ecosystem functioning differed among watersheds (Table 3). Functions in Crisp scored lower than those for the other stream networks, while Stoney’s function scores were highest. For evaluating functions or tracking changes in functioning over time, a goal of some natural resource agencies, these data provide a means for doing so. Indicator Scores Although comparing mean composite function scores (last column, Table 3) provides a broad-scale

WETLANDS, Volume 27, No. 3, 2007 overview of stream network condition, it lacks the detail provided by examining indicator scores (Table 4). While some indicators tend to co-vary, each indicator provides a different insight into what is in need of improvement along a given reach or in a given stream network. For example, widespread channelization in rural watersheds would be reflected in low mean indicator scores for ‘‘Channelriparian zone connection’’ and ‘‘Sediment regime,’’ particularly for higher-order rural reaches. Desnagging (removal of large wood from channels) and other channel maintenance practices (e.g., deepening an already channelized stream) are usually intense in channelized reaches as well, and this would be reflected in a low score for ‘‘Instream woody structure.’’ Using the Bear/Moss Neck network as an example, the higher ‘‘Instream woody structure’’ score reflects less frequent channel maintenance of higher-order channels in that network. Indeed, we noted widespread lack of channel maintenance in our reconnaissance of the Bear/Moss Neck network, where much sediment had accumulated in channel bottoms. Due to excessive sedimentation and lack of maintenance, narrow, meandering channels had formed in the bottom of the larger channel, many small trees and shrubs grew along the small floodplains within the channel, and large down wood was common. While indicator scores not only suggest what is wrong with a reach or network, they also provide insight into the type of improvements (restoration) needed. For example, even though the Stoney stream network had a moderately high mean composite condition score of 67 (Table 3), it had low mean scores for a number of indicators in its urban reaches (Table 4): ‘‘Sediment regime’’ (33– 39), ‘‘Pollution affecting stream’’ (24–50), and ‘‘Habitat quality of riparian zone’’ (38–49). Restoration of condition could be directed toward reducing sediment input (e.g., building additional detention basins and maintaining and repairing old ones), abating pollution from stormwater drains (again, detaining runoff), and improving riparian habitat quality (restoring forested buffers where possible). These mean indicator scores could provide guidance for general types of restoration opportunities. However, restoration activities themselves would be targeted toward specific sites where these indicators have low scores. Technically, there tend to be greater opportunities for improving channel-riparian zone connections in rural watersheds than in partially urban ones, particularly in channelized low-order streams flanked by cropland. In contrast, opportunities may be constrained in higher-order streams because


537

Table 4. Mean indicator scores, by watershed and riparian type. The number of reaches sampled within a type influences the resulting scores for watersheds. There was only one urban low-order reach in Bear/Moss Neck, so generalizations about its urban reaches are not meaningful. Green Mill, Hendricks, and Stoney are partially urban watersheds. Habitat Factors Pollution Affecting Quality of Stream ChannelInstream Riparian Bank Zone Near-Stream Woody Sediment Riparian Zone Affecting Riparian Riparian Stability Zone Zone Stream Connection Structure Regime Cover Cover Cow Rural Low Order Urban Low Order Rural High Order Urban High Order Mean Crisp Rural Low Order Urban Low Order Rural High Order Urban High Order Mean Green Mill Rural Low Order Urban Low Order Rural High Order Urban High Order Mean Hendricks Rural Low Order Urban Low Order Rural High Order Urban High Order Mean Stoney Rural Low Order Urban Low Order Rural High Order

37

42

48

33

28

48

32

33

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

41

39

25

22

6

57

13

38

45

NA

NA

NA

NA

NA

NA

NA

NA

NA

38

41

45

31

25

49

29

34

45

25

30

37

34

11

43

19

20

NA

NA

NA

NA

NA

NA

NA

NA

NA

NA

51

62

65

24

6

51

14

31

44

NA

NA

NA

NA

NA

NA

NA

NA

NA

31

37

44

32

10

45

17

22

44

44

46

82

48

45

76

41

35

NA

80

89

63

33

34

52

34

25

44

NA

NA

NA

NA

NA

NA

NA

NA

NA

69

77

64

28

59

47

43

21

45

71

79

66

33

43

54

38

25

45

45

49

89

87

94

88

92

44

NA

80

87

35

45

46

60

44

23

72

NA

NA

NA

NA

NA

NA

NA

NA

NA

74

89

49

30

38

43

34

23

41

64

73

61

57

62

65

59

31

25

61

62

79

57

73

64

85

64

NA

72

78

41

39

51

50

62

38

49

86

86

76

56

73

71

93

88

68

538 Table 4.

WETLANDS, Volume 27, No. 3, 2007 Continued. Habitat Factors Pollution Affecting Quality of Stream ChannelInstream Riparian Bank Zone Near-Stream Woody Sediment Riparian Zone Affecting Riparian Riparian Stability Zone Zone Stream Connection Structure Regime Cover Cover

Urban High Order Mean Bear/Moss Neck Rural Low Order Urban Low Order Rural High Order Urban High Order Mean

88

89

50

33

58

24

64

49

60

70

72

66

51

67

59

79

60

57

37

43

52

35

38

47

46

29

NA

94

95

90

10

60

45

85

10

60

53

54

61

22

35

57

40

38

75

NA

NA

NA

NA

NA

NA

NA

NA

NA

43

47

55

30

37

50

44

32

74

county agricultural drainage districts would likely resist reconnecting channels with their floodplains. However, restoring channel-riparian connections in lower-order streams would likely increase dynamic water-storage capacity in the upper parts of watersheds, thereby reducing the need for the intensive management now necessary in channelized, higherorder streams. Even in these intensely managed reaches, hydrologic reconnections between stream and riparian zone may be possible without harming cropland. Where hydrologic reconnections are not possible, water from tributary channels (most of which are also channelized and connected to the main stems via culverts) could be shunted to former floodplains of higher-order streams. In this way, former floodplains could process nutrient-rich waters of tributary channels before transport further downstream. Because the conditions of many indicators tend to co-vary, it might be tempting to use fewer indicators to explain condition. However, such redundancy can be useful to resource managers because 1) one or more indicators may be difficult to measure under certain field conditions and so redundancy could compensate for ambiguous measurements, and 2) the various indicators provide insight into what factors are in need of restoration, thus providing achievable goals for restoration. Application and Limitations of Approach It is our experience that qualified professionals will agree on what constitutes a ‘‘good’’ reach,

a highly degraded reach, and some intermediate condition. Consensus is much more difficult for individual indicators, as they often lead to a logic of ‘‘more or bigger is better’’ rather than anchoring indicator narratives to the actual condition of sites in the field. The approach outlined in this paper provides the framework for developing a way to evaluate the condition of an individual reach, and from reach data, evaluate stream networks based on the condition of a suite of diagnostic indicators. The key to this sequence is that the evaluation of each indicator condition is based on the range of indicator conditions that occur among a suite of real sites (reference sites); hence, it is a referencebased approach. This is not a trivial distinction. Failure to tie assessment to reference may lead to restoration projects that further degrade natural resources and the allocation of large sums of money that would be better spent restoring buffers over greater lengths of stream. A common tendency is to design channels of coastal plain streams to resemble the ‘‘pattern, profile, and dimension’’ of channels formed and maintained by fluvial processes under higher gradient conditions. In contrast, vegetation plays a dominant role in shaping channel morphology in low-gradient coastal plain streams. In such cases, construction designs based on higher-gradient Piedmont streams is inappropriate. Such designs may lead to more incised channels and further loss of channel/floodplain connections, the unnecessary removal of forested buffers to achieve sinuosity patterns, and the introduction of excessive sediment to streams during construction.

Rheinhardt et al., FRAMEWORK FOR EVALUATING STREAM NETWORKS The condition of indicators described in this paper and their logical connection with ecosystem functioning, as described in Table 1, is based on a very large body of research (see Rheinhardt et al. (2005) for a summary of research connecting indicators to functions). Our own recent studies in the Neuse River basin on headwater streams provide further support for relationships among indicators and ecosystem functioning (Brinson et al. 2006). Testing of assessment methods may be based on other indicators of integrity, such as benthic invertebrate composition, but have not been attempted here due largely to the difficulty of applying an Index of Biotic Integrity (IBI) to intermittent coastal plain streams in the southern USA (Davis et al. 2003). The need for corroboration in the present case is partly alleviated by two factors: 1) the large array of research studies that have demonstrated the negative effects on aquatic condition of buffer removal and stream channelization (Lowrance et al. 1984, Peterjohn and Correll 1984, Cooper 1990, Jordan et al. 1993, Correll 1997), and 2) the use of a reference framework that provides a context for ranking condition of sites based on the degree of alteration, in the present case from severely altered to relatively unaltered. In addition to reliance on earlier research to support our choice of reference sites, we developed a relationship between organic matter standing stocks and land-cover types in the riparian zone (Brinson et al. 2006). This array of reference sites spanned the range of riparian zone biomass from herbaceous plant cover (the most degraded or severely altered condition, including deeply excavated channels) to mature forest (the least altered condition, including natural stream channels). This reference framework of field sites provided us with a gradient of condition in which indicators could likewise be arranged. By developing indicators from sites within the geographic region of interest, they represent the ranges of natural and human-induced sources of variation. The reference sites are useful also for training of practitioners new to the assessment method. For any stream network, its condition could be improved by enhancing the condition of one or more indicators without improving the condition of all indicators simultaneously. Such a strategy might prove effective where there are impediments, social or financial, to restoring all reaches to a relatively unaltered condition. For example, in places where restoring channel morphology would not be possible, buffers could be restored along headwater riparian zones. Such an enhancement strategy could prove cost-effective by simply purchasing conservation easements along streams. This practice would

539

improve all three functions (hydrology, biogeochemistry, habitat), primarily in riparian zones, although conservation easements could also improve habitat and biogeochemical functions in channels through improvements in near-stream cover. Comparisons of stream network condition are strongest if watersheds are similar in their distribution of rural vs. urban riparian types. This is because calibrations for indicator scoring differ between urban and rural reaches. For example, potential scores for both near stream and riparian zone cover are compressed in urban assessments simply because there are more types of highly degraded categories within the 0 to 100 range (Rheinhardt et al. 2005, 2007). Regardless of such differences, stream networks can be compared relative to their potential (least altered condition) because indicator scores of reaches for all four riparian types (low- and highorder of rural and urban types) are calibrated against the range of conditions in the population of reference reaches identified for each type. Comparison of indicator scores (Table 4) is another approach for comparing networks. This comparison focuses attention on the specific components of the stream networks that are degraded, rather than the overall condition of the networks. A high frequency of low scores could provide information for identifying prescriptions for restoration. Just as human-induced alterations tend to degrade more than one aspect of stream-riparian ecosystems, as reflected in the correlation among indicator scores, restoration activities may also serve to improve several indicators simultaneously. In other words, components of stream-riparian ecosystems are highly integrated, as suggested by the positive correlations between channel condition and riparian condition (Figure 5). The site-scale assessment approach described in this study qualifies as a Level 2 ‘‘rapid assessment’’ (requires a field visit and less than 0.5 day, excluding travel) in contrast with Level 1 (uses remote sensing, with no field visit) and Level 3 (requires more than a day with intensive data collection) (Brooks et al. 2004, Fennessy et al. 2004, Fennessy et al. 2007). When applied to individual sites, Level-2 assessments provide insufficient detail to serve as structural design criteria for restoration projects, although they would be useful in providing measurable objectives for the types of restorations or enhancements that should be attempted. In this way, such ‘‘Level-2’’ data generally have been used by regulatory personnel to evaluate mitigation alternatives or characterize pre- or post-project conditions. The approach outlined here uses the

540


same procedure for individual stream reaches (at a site scale) as it does in aggregate to characterize condition at a larger scale (a small watershed scale). At both scales, the approach provides useful information for diagnosing problems and offering potential and measurable solutions. The random sampling approach outlined here was not designed to identify reference sites for calibrating indicator scores because few relatively unaltered reaches would be encountered using that approach (see for example Cow, Crisp, and Green Mill watersheds, Figure 5). Because reach conditions are not randomly distributed, we chose to search systematically for sites that would encompass the range of possible conditions from relatively unaltered to severely altered. Before developing our assessment protocols, we examined more than 150 rural low-order reaches to obtain what we considered to be sufficient information about variation in condition as a preliminary step necessary for ranking headwater reaches, identifying appropriate indicators, and calibrating them. Once a set of reference sites had been identified, the identification of appropriate indicators and their calibration was relatively straightforward because the data represented real sites and calibrations could be defended based on the ranking of condition among those reference sites. The assessment protocol presented here was designed to characterize reaches in a manner that would estimate ecosystem condition and rank reaches and stream networks relative to one another, thus offering guidance for viable restoration options. Although the protocols were designed to assess reaches of 100 m, many of the indicators can be used to evaluate longer or shorter reaches. They also can be applied to assessing pre-and post-project conditions and used to monitor the success of specific restoration sites. Thus, this approach provides an adaptable and useful tool for diagnosing problems and offering solutions to correct problems with riparian ecosystems at both reach and watershed scales. ACKNOWLEDGMENTS Jane Almon (EarthTech, Inc.), Mike Schlegel (KCI Associates), and Melissa Ruiz and Amber Coleman (Blue Land Water Infrastructure) led teams that assessed all the randomly chosen reaches and provided field data in a timely manner. Chris Bason and Emma Hardison (East Carolina University) helped assess a subsample of the sites. Mac Haup (North Carolina Ecosystem Enhancement Program) and the previously mentioned participants provided invaluable suggestions for

improvements to field data sheets, and especially to protocol narratives. The manuscript also benefited from the input of two anonymous reviewers. This research was supported by a grant from the North Carolina Ecosystem Enhancement Program (http:// www.nceep.net) provided by the U.S. Environmental Protection Agency (USEPA) state grant program. Progress on this project benefited from our participation with the Atlantic Slope Consortium (http://www.asc.psu.edu), which received funding from USEPA’s Science to Achieve Results (STAR) Estuarine and Great Lakes (EaGLe) program through Penn State University, US EPA Agreement (R-82868401). Although the research described in this article has been funded wholly or in part by the USEPA, it has not been subjected to the Agency’s policy review and therefore does not necessarily reflect the views of the Agency and no official endorsement should be inferred. LITERATURE CITED Arcement, G. J., Jr. and V. R. Schneider. 1989. Guide for selecting Manning’s roughness coefficients for natural channels and flood plains. U.S. Geological Survey Water-Supply Paper 2339. Barbour, M. T. and J. B. Stribling. 1994. A technique for assessing stream habitat structure. p. 156–78. In Conference proceedings, Riparian ecosystems in the humid U.S.: functions, values and management. National Association of Conservation Districts, Washington, DC, USA. Bason, C. W. 2004. Effects of beaver impoundments on stream water quality and floodplain vegetation in the inner coastal plain of North Carolina. M.S. Thesis. East Carolina University, Greenville, NC, USA. Benke, A. C., I. Chaubey, G. M. Ward, and E. L. Dunn. 2000. Flood pulse dynamics of an unregulated river floodplain in the southeastern U.S. coastal plain. Ecology 81:2730–41. Bohlke, J. K. and J. M. Denver. 1995. Combined use of groundwater dating, chemical, and isotopic analyses to resolve the history and fate of nitrate contamination in two agricultural watersheds, Atlantic coastal-plain, Maryland. Water Resources Research 31:2319–39. Brinson, M. M. 1993a. Changes in the functioning of wetlands along environmental gradients. Wetlands 13:65–74. Brinson, M. M. 1993b. A Hydrogeomorphic Classification for Wetlands. U.S. Army Corps of Engineers, Waterways Experiment Station, Vicksburg, MS, USA.Technical Report WRPDE-4. Brinson, M. M., H. D. Bradshaw, and E. S. Kane. 1984. Nutrient assimilative capacity of an alluvial floodplain swamp. Journal of Applied Ecology 21:1041–57. Brinson, M. M., K. Miller, R. D. Rheinhardt, R. R. Christian, G. Meyer, and J. O’Neal. 2006. Developing reference data to identify and calibrate indicators of riparian ecosystem condition in rural coastal plain landscapes in coastal North Carolina. A Report to the Ecosystem Enhancement Program. North Carolina Department of Environment and Natural Resources, Raleigh, NC, USA. Source: (http://www.nceep.net/pages/ resources.htm). Brinson, M. M. and R. D. Rheinhardt. 1996. The role of reference wetlands in functional assessment and mitigation. Ecological Applications 16:69–76. Brooks, R. P, D. H. Wardrop, and J. A. Bishop. 2004. Assessing wetland condition on a watershed basis in the Mid-Atlantic

Rheinhardt et al., FRAMEWORK FOR EVALUATING STREAM NETWORKS region using synoptic land-cover maps. Environmental Monitoring and Assessment 94:9–22. Brooks, R. T., D. H. Wardrop, and J. K. Perot. 1999. Development and Application of Assessment Protocols for Determining the Ecological Condition of Wetlands in the Juniata River Watershed. U.S. Environmental Protection Agency, National Health and Environmental Effects Laboratory, Western Ecology Division, Corvallis, OR, USA. EPA/ 600/R-98/181. Cooper, A. B. 1990. Nitrate depletion in the riparian zone and stream channel of a small headwater catchment. Hydrobiologia 202:13–26. Cooper, J. R. and J. W. Gilliam. 1987. Phosphorus redistribution from cultivated fields into riparian areas. Soil Science Society of America Journal 51:1600–4. Correll, D. L. 1997. Buffer zones and water quality protection: general principles. p. 7–20. In N. E. Haycock, T. P. Burt, K. W. T. Goulding, and G. Pinay (eds.) Buffer Zones: Their Processes and Potential in Water Protection. Proceedings of the International Conference on Buffer Zones. September 1996. Quest Environmental, Harpenden, England. Cowardin, L. M., V. Carter, F. C. Golet, and E. T. LaRoe. 1979. Classification of Wetlands and Deepwater Habitats of the United States. U.S. Fish and Wildlife Service, Office of Biological Services, Washington, DC, USA. FWS/OBS79/31. Cuffney, T. F. 1988. Input, movement and exchange of organic matter within a subtropical coastal blackwater river-floodplain system. Freshwater Biology 19:305–20. Daniels, R. B., S. W. Buol, H. J. Kleiss, and C. A. Ditzler. 1999. Soil Systems in North Carolina. North Carolina State University, Raleigh, NC, USA. Technical Bulletin 314. Davis, S., S. W. Golladay, G. Vellidis, and C. M. Pringle. 2003. Macroinvertebrate biomonitoring in intermittent coastal plain streams impacted by animal agriculture. Journal of Environmental Quality 32:1036–43. Fennessy, M. S., A. D. Jacobs, and M. E. Kentula. 2004. Review of rapid methods for assessing wetland condition. U.S. Environmental Protection Agency, Washington, DC, USA. EPA/620/R-04/009. Fennessy, M. S., A. D. Jacobs, and M. E. Kentula. 2007. An evaluation of rapid methods for assessing the ecological condition of wetlands. Wetlands 27:543–60. Gilliam, J. W. 1994. Riparian wetlands and water quality. Journal of Environmental Quality 23:896–900. Gregory, S. V., F. J. Swanson, W. A. McKee, and K. W. Cummins. 1991. An ecosystem perspective of riparian zones. BioScience 41:540–51. Harmon, M. E., J. F. Franklin, F. J. Swanson, P. Sollins, S. V. Gregory, J. D. Lattin, N. H. Anderson, S. P. Cline, N. G. Aumen, J. R. Sedell, G. W. Lienkaemper, K. Cromack Jr., and W. Cummins. 1986. Ecology of coarse woody debris in temperate ecosystems. Advances in Ecological Research 15:133–302. Herlihy, A. T., D. P. Larsen, S. G. Paulsen, N. S. Urquhart, and B. J. Rosenbaum. 2000. Designing a spatially balanced, randomized site selection process for regional stream surveys: the EMAP Mid-Atlantic pilot study. Environmental Monitoring and Assessment 63:95–113. Hupp, C. R., M. D. Woodside, and T. M. Yanosky. 1993. Sediment and trace element trapping in a forested wetland, Chickahominy River, Virginia. Wetlands 13:95–104. Jacobs, J. M., M. R. Seeley, E. B. Welch, and R. R. Horner. 1990. Responses of periphyton to changes in current velocity, suspended sediment, and phosphorus concentration. Freshwater Biology 42:215–32. Jordan, T. E., D. L. Correll, and D. E. Weller. 1993. Nutrient interception by a riparian forest receiving inputs from adjacent cropland. Journal of Environmental Quality 22:467–73. Karr, J. R. 1987. Biological monitoring and environmental assessment: a conceptual framework. Environmental Management 11:249–56. Lowrance, R. R., R. L. Todd, J. Fail, O. Hendrickson, R. Leonard, and L. E. Asmussen. 1984. Riparian forests as

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nutrient filters in agricultural watersheds. BioScience 34:374–77. Naiman, R. J. and J. J. Latterell. 2005. Principles for linking fish habitat to fisheries management and conservation. Journal of Fish Biology 67:166–85. Noe, G. B. and C. R. Hupp. 2005. Carbon, nitrogen, and phosphorus accumulation in floodplains of the Atlantic coastal plain rivers, USA. Ecological Applications 15:1178–2290. NRC (National Research Council). 2002. Riparian Areas: Functions and Strategies for Management. National Academy Press, Washington, DC, USA. Source: http://books.nap.edu/ books/0309082951/html/index.html Paul, M. J. and J. L. Meyer. 2001. Streams in the urban landscape. Annual Review of Ecology and Systematics 32:333–65. Peterjohn, W. T. and D. L. Correll. 1984. Nutrient dynamics in an agricultural watershed: observations on the role of a riparian forest. Ecology 65:1466–75. Plafkin, J. L., M. T. Barbour, K. D. Porter, S. K. Gross, and R. M. Hughes. 1989. Rapid bioassessment protocols for use in streams and rivers: benthic macroinvertebrates and fish. U.S. Environmental Protection Agency, Office of Water Regulations and Standards, Washington, DC, USA. EPA 440-4-89001. Rheinhardt, R., M. Brinson, R. P. Brooks, M. McKenneyEasterling, J. Rubbo, J. Hite, and B. Armstrong. 2007. Development of a reference-based method for identifying and scoring indicators of condition for coastal plain riparian reaches Ecological Indicators 7:339–61. Rheinhardt, R., M. Brinson, R. Christian, K. Miller, G. Meyer, C. Bason, and E. Hardison. 2005. Applying Ecological Assessments to Planning Stream Restorations in Coastal Plain North Carolina Report to the Ecosystem Enhancement Program. North Carolina Department of Environment and Natural Resources, Raleigh, North Carolina, USA. (http:// www.nceep.net/pages/resources.htm). Rheinhardt, R. D., M. C. Rheinhardt, M. M. Brinson, and K. E. Faser. 1998. Forested wetlands of low order streams in the inner coastal plain of NC, USA. Wetlands 18:365–78. Sabater, F., J. L. Meyer, and R. T. Edwards. 1993. Longitudinal patterns for dissolved organic-carbon concentration and suspended bacterial density along a backwater river. Biogeochemistry 21:73–93. Seastedt, T. R., M. V. Reddy, and S. P. Cline. 1989. Microarthropods in decaying wood from temperate coniferous and deciduous forests. Pedobiologia 33:69–77. Simberloff, D., I. M. Parker, and P. N. Windle. 2005. Introduced species policy, management, and future research needs. Frontiers of Ecology and Environment 3:12–20. Smith, R. D., A. Ammann, C. Bartoldus, and M. Brinson. 1995. An approach for assessing wetland functions using hydrogeomorphic classification, reference wetlands, and functional indices. U.S. Army Corps Engineers, Waterways Experiment Station Wetland Research Program, Vicksburg, MS, USA. Technical Report WRP-DE-9. Source: http://www.wes.army. mil/el/wetlands/wlpubs.html Snyder, C. D., J. A. Young, and R. Villella. 2003. Influences of upland and riparian land use patterns on stream biotic integrity. Landscape Ecology 18:647–64. Stevens, D. L., Jr. and A. R. Olsen. 2004. Spatially-balanced sampling of natural resources. Journal of American Statistical Association, 99:262–78. Source: http://www.epa.gov/ NHEERL/arm/bibliography.htm. Thorp, J. H., E. M. McEwan, M. Flynn, and F. R. Hauer. 1985. Invertebrate colonization of submerged wood in a cypresstupelo swamp and blackwater stream. American Midland Naturalist 113:56–68. U. S. EPA 2005. Use of biological information to tier designated aquatic life uses in state and tribal water quality standards. U.S. Environmental Protection Agency, Office of Water, Washington, DC, USA. Vargo, S. M., R. K. Neely, and S. M. Kirkwood. 1998. Emergent plant decomposition and sedimentation: response to sediments

542 varying in texture, phosphorus content and frequency of deposition. Environmental and Experimental Botany 40:43– 58. Walker, L. R. and S. D. Smith. 1997. Community response to plant invasion. p. 69–86. In J. O. Luken and J. W. Thieret (eds.) Assessment and Management of Plant Invasions. Springer, New York, NY, USA. Wardrop, D. H., M. E. Kentula, D. L. Stevens, Jr., S. F. Jensen, and R. P. Brooks. 2007. Assessment of wetland condition: an

WETLANDS, Volume 27, No. 3, 2007 example from the Upper Juniata watershed in Pennsylvania, USA. Wetlands 27:416–31. Wipfli, M. S., J. S. Richardson, and R. J. Naiman. 2007. Ecological linkages between headwaters and downstream ecosystems: transport of organic matter, invertebrates, and wood down headwater channels. Journal of the American Water Resources Association 43:72–85. Manuscript received 23 December 2005; accepted 27 March 2007.