EXAMINING LAND USE INFLUENCES ON STREAM HABITATS AND MACROINVERTEBRATES: A GIS APPROACH

WATER RESOURCES BULLETINVOL. 30, NO.4 AMERICAN WATERRESOURCES ASSOCIATION AUGUST 1994

EXAMINING LAND USE INFLUENCES ON STREAM HABITATSAND MACROINVERTEBRATES: A GIS APPROACH'

Carl Richards and George Host2

ABSTRACT: Geographic Information Systems (GIS) were used toassess the relationships between land use patterns and the physi-Cal habitat and macroinvertebrate fauna of streams within similarsized watersheds. Eleven second or third order watersheds rangingfrom highly urbanized to heavily forested were selected along LakeSuperior's North Shore. Land use patterns within the watershedswere quantffied using readily available digital land use/land coverinformation, with a minimum mapping resolution of 16 ha. Physi-cal habitat features, describing substrate characteristics andstream morphology, were characterized at sample points withineach stream. Principle component and correlation analyses wereused to identify relationships between macroinvertebrates andstream physical habitat, and between habitat and land use pat-terns. Substrate characteristics and presence of coarse woodydebris were found to have the strongest correlations with macrein-vertebrate assemblage richness and composition. Agricultural andurban land use was correlated with substrate characteristics. Algalabundance, associated with macroinvertebrate compositional differ-ences, was correlated with housing density and non-forest land cov-ers. The use of readily available spatial data, even at this relativelycoarse scale, provides a means to detect the primary relationshipsbetween land use and stream habitat quality; finer-resolution GISdatabases are needed to assess more subtle influences, such asthose due to riparian conditions.(KEY TERMS: stream ecology; land use; watershed assessment;GIS; risk analysis; nonpoint source pollution.)

INTRODUCTION

Stream assemblages are integrally linked to in-stream physical and chemical characteristics and forthis reason are useful environmental indicators. In-stream conditions (e.g., substrate composition, chan-nel morphology, presence of woody debris), in turn,are modified by a variety of factors operating at thewatershed scale, such as land use practices andriparian conditions. The relative strength of the rela-tionships between watershed characteristics and in-

stream variables can have important implications forunderstanding ecological linkages between streamsand terrestrial systems, as well as for identifying con-sequences of watershed management practices. Usedpredictively, these relationships could be useful indetermining habitat quality for fisheries or otherstream biota and potentially for determining appro-priate mitigation measures over large geographicareas without the necessity for extensive and costlyfield surveys.

Several in-stream characteristics are influenced bymanagement activities that occur at the watershed orlandscape scale. Forestry, agriculture, and urbaniza-tion modify watershed cover characteristics thatinfluence runoff patterns, which in turn determinethe timing and quantity of flow within channels. Flowalteration can have significant effects on stream plantand animal communities (Menzel et at., 1984; Bain etat., 1988; Statzner et al., 1988). Forestry and agricul-ture also influence the supply of sediment to streams,altering habitat characteristics, and significantlyimpacting fish and macroinvertebrate populations(Lenat, 1984; Everest et at., 1987; Swanson et at.,1987). Other large-scale modifications of vegetativecharacteristics, such as fire, can alter nutrientdynamics within streams (Minshall et at., 1989) andhave impacts on stream macroinvertebrate assem-blages that persist for several years (Richards andMinshall, 1992).

Vegetative cover along stream courses also plays animportant role in streams. Organic material fromriparian areas serves as the dominant energy sourcein forested stream ecosystems (Cummins, 1974), andwoody debris provides an important structural ele-ment to stream channels. Woody debris serves as an

'Paper No. 93125 of the Water Resources Bulletin. Discussions are open until April 1, 1995.2Research Associates, Natural Resources Research Institute, University of Minnesota, 5013 Miller Trunk Hwy., Duluth, Minnesota 55811.

729 WATER RESOURCES BULLETIN

Richards and Host

important site for invertebrate attachment andproduction (Benke et al., 1984), it can play a largerole in the ability of a stream channel to retainallochthonous material (Bilby, 1981), and it canincrease the diversity of microhabitats within streamchannels available for fish and invertebrates (O'Con-nor, 1991).

Stream macroinvertebrates have been used exten-sively for biomonitoring of numerous environmentalstresses (Rosenberg and Resh, 1993). They are sensi-tive to watershed conditions and exhibit sufficientstability in assemblage structure over time to makethem useful as long-term monitors of stream health(Richards and Minshall, 1992). As with fish, macroin-vertebrates exhibit strong responses to physical habi-tat characteristics.

Although there is growing awareness of the impor-tance of watershed and landscape-scale influences onstreams (Franklin, 1992; Naiman, 1992), the tools toexamine these influences are still in their infancy.The spatial analysis of landscape patterns has beenapplied only recently to assess stream habitat qualityand biological assemblages. This delay was due inpart to the difficulty of quantifying spatial informa-tion, such as the amounts and distribution of land usetypes within a watershed or along stream reaches.However, the increased availability of digital spatialinformation, coupled with increased capabilities of,and access to, geographic information systems (GIS),makes spatial analysis a much more feasible analyti-cal method. For example, Osborne and Wiley (1988)used a GIS to assess water quality in streams, andJohnston et al. (1988, 1990) used GIS to assess theimportance of wetland position within a watershedon stream water quality. The objective of the presentstudy was to use a GIS-based analysis of watershed-scale land use patterns to quantify critical compo-nents of stream habitat features and their subsequentinfluence on stream macroinvertebrate assemblagecomposition.

Study Area

We selected eleven second- or third-order streamsalong 45 km of Lake Superior's North Shore (Fig-ure 1). All streams drained directly into Lake Superi-or. Sample points for habitat evaluation andmacroinvertebrate collections were established a fewkilometers above the stream mouth on each stream.The watersheds above the sample points comprise avariety of land use conditions, ranging from highly-

WATER RESOURCES BULLETIN 730

urbanized regions within the city limits of Duluth,Minnesota, to heavily-forested watersheds in thenortheastern portion of the study area. Approximate-ly half of the North Shore of Lake Superior is in pub-lic ownership. The dominant surficial geology of theregion is bedrock, clay and silt deposits, and sand andgravel (Johnston et al., 1991). Prior to European set-tlement, the North Shore drainage basin was domi-nated by aspen/birch and pine stands. Pine forests inthe region were largely converted to aspen/birchforests by post-settlement logging that occurred in thelate 1800s and early 1900s. Land use and populationwithin the region have remained relatively stableover the last two decades.

Habitat Evaluation

We assessed physical habitat along a 200 m longreach at each site during baseflow conditions. Wemeasured habitat variables that are frequently incor-porated in stream habitat surveys (e.g., Osborne etal., 1991). Most measurements estimated physicalconditions within two general categories: substratecondition or channel morphology (Table 1). Substratemeasurements included estimates of both dominantparticle sizes on the stream bottom and an estimate ofthe proportion of fines in the substrate matrix. Chan-nel morphology was described using dimensionalchannel measurements (e.g., width, depth) as well asmeasurements with incorporate longitudinalattributes (e.g., percent pool, channel sinuosity). Wealso recorded the degree of stream surface shading;this parameter directly indicates the amount of lightavailable for photosynthesis and indirectly estimatesriparian vegetative development. An index to algaldevelopment was included since algae can alter bothhabitat and food supply for stream invertebrates.

Macroinvertebrate Collections

Macroinvertebrates were collected during October1989 by agitating a known area (0.09 m2) in front of aD frame kick net. Four samples were taken in ero-sional habitats within the 200 m stream reach. Sam-ples were immediately preserved in 70 percent ethylalcohol. In the laboratory, macroinvertebrates werehand sorted from samples with the aid of a dissectionmicroscope and enumerated. Invertebrates were iden-tified to genus whenever possible although some taxawere identified only to family (e.g., early instar chi-ronomidae). Arthropod taxa accounted for over 95 per-cent of invertebrates at the sites and were used for allanalyses. Taxa were categorized into taxonomic and

MATERIALS AND METHODS

Figure 1. Location and Land Use/Land Cover Characteristics of the Study Watersheds. Inset to Minnesota map shows location of study watersheds along the Lake SuperiorNorth Shore and Duluth city limits. Detail maps a), b), and c) show land use patterns within study watersheds. Inset to a) shows land use within 100 m buffer.

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Richards and Host

TABLE 1. Physical Habitat Characteristics Assessed at Each of the Sites.

Characteristic

Substrate Composition

Embeddedness (percent)

Riffle, Run, Pool (percent)

Shading (percent)

Maximum Pool Depth (m)

SinuosityWidth (m)

Depth (m)

Flood Width (m)

Flood Depth (m)

Woody Debris

Algae

Method of Assessment

Visual examination of riffles5

Visual estimation in riffles5

Sum of lineal distance for each category per 200 m reach

Visual estimate5

Depth of deepest pool in stream reach

Visual examination of 200 m reach5

Average stream width at wetted perimeter at three transects

Average depth at three points along three transects

Maximum width at highest water mark

Depth at highest water mark

Ranked in terms of relative abundance, 1 = little or none present,2 = accumulations presents in portions of reach, 3 = accumulationsin more than one portion of reach, 4 = high abundance

Ranked in terms of abundance: 1 = little or no filamentous growths visible,2 = some filamentous growth obvious, 3 = moderate filamentous growths,4 = extensive filamentous growth

5Following Platts et al., 1983.

functional feeding groups according to Merritt andCummins (1984).

Differences in assemblage structure among thesites were examined using principal component anal-ysis (PCA) (Wilkinson, 1990). Species abundance datawere log-transformed and centered prior to analysis(Pielou, 1984). Species that accounted for less than 2percent of the total number of arthropods wereremoved from the data prior to analysis to decreasethe influence of rare species in the ordination (Gauch,1982). To determine which habitat variables had thestrongest relationships with macroinvertebrate distri-bution, Pearson correlations were calculated betweenthe major axes of the PCA ordination and physicalhabitat variables.

Spatial Analysis

A vector-based GIS was used to quantify land usecharacteristics within the 11 watersheds (ESRI,1992). Several readily available data sources wereused to construct the geographic data layers. Astream reach data layer was developed using hydro-graphic information from 1:100,000 USGS DigitalLine Graph (DLG) files (U.S. Geological Survey,1985). DLG transportation files were used to map pri-mary and secondary roads within the area. A land usedata layer for the study area was derived from1:250,000 Land use/Land cover digital data (LUDA)(U.S. Geological Survey, 1990). These data consist of

an Anderson Level II land use inventory at a 16 hamapping resolution (4 ha resolution for urban landuse categories (Anderson et al., 1976).

Watersheds above each sample point were delineat-ed by hand from 1:24,000 USGS topographic quadran-gles. By overlaying the watershed boundaries on topof the regional LUDA database, we were able to quan-tify land use within each watershed. Land use wasquantified at two scales upstream of the sample point:within the entire watershed and within a 100 mbuffer on either side of the stream. We used a buffer-ing technique within the GIS environment to create aseparate buffer data set (Osborne and Wiley, 1988;Johnson and Richards, 1992), and we quantified landuse within these buffers. We also determined linknumbers, defined as the number of stream conflu-ences upstream of the sample point. Lastly, we usedinformation from the 1:24,000 topographic maps tocalculate housing densities as the number ofdwellings per unit area within each watershed.

To determine the relationship between land use orother watershed-scale variables and stream habitat,Pearson correlations (Wilkinson, 1990) were calculat-ed between landscape variables and those physicalhabitat variables that significantly correlated withmacroinvertebrate assemblage composition and struc-ture. This approach allowed us to assess both land-scape-habitat and habitat-community relationships.


Examining Land Use Influences on Stream Habitats and Macroinvertebrates: A GIS Approach

RESULTS N Oci 0o .- o '- '- .- o o ci o Co o ci to

Physical Habitat toNCoNO0Substrate particle size did not vary greatly among ci ci to ci 0 ci Ci to 0 0) 0 Ci

the sites (Table 2). All substrates were mostly cobbleand large gravel. However, embeddedness, the degree to

to which the dominant particles are surrounded byfine inorganic sediments, did vary considerablyamong the sites, ranging from less than 10 percent inWest Split Rock River to 60 percent in East Branch to c co toKnife River. Most sites had some woody debris pre-sent and were shaded by more than 30 percent. Runhabitat was dominant at most sites. Other morpholog-ical characteristics (pool depth, sinuosity, width, and to to o '-toc0citocici0lcito00)0ciCodepth) of the stream channels exhibited considerablevariation among sites (Table 2).

Macroinuertebrate Assemblages o o 0 0 o to cij cjto ci ci to Co to ,4 to ci 4 ,-4 ,4

A total of 62 arthropod taxa were identified from jthe streams. The total number of taxa (species rich- o o o o o ci to t- Nness) within individual streams ranged from 12 to 36(Table 3). At each site, over half of the taxa were inthe orders Ephemeroptera, Plecoptera, and Tn- .ooooochoptera (EPT taxa). The predominant functional to Co to to

feeding group within the insect communities weregatherers, although most were Chironomidae. Howev-er, the relative proportion of gatherers varied consid- to to to N

N ci Co to ci 'i '-4 ci ci '-4 0 '-4 -4 Co '-4erably among sites. Shredders were, on average, the .least numerous of the functional feeding groups. Fil-terers, scrapers, and predators varied from less than5percentoftheassemblagetogreaterthan30per-cent.

Assembl age Structure and Physical Habitat to ocCCocitocito0CoCo'CiCo0'-ltoRelationships

The principal component ordination of sites Co

accounted for 75.3 percent of the variation in the to to Co ci (0 C to ci 40 0 'macroinvertebrate data in the first four axes. Axis I,which described 35.1 percent of the variation in theinvertebrate assemblages, differentiated between CO toChester Creek, Tischer Creek, and East Branch Knife -River along one end of the ordination gradient andKnife River and West Split Rock River on the other(Figure 2). The greatest difference among streams onAxis I was related to species richness (Table 3). TheKnife River and West Split Rock River had the high- . .-.est species richness values; Chester Creek, Lester . . .River, and East Branch Knife River had the lowest Ivalues. 'gg'g


Richards and Host

Co -4 O -4N U '-4 '-4 '-4 -.4 '-4 -4 1'

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Figure 2. Principal Component Ordination of Sample Sites.

Results of the correlation analysis of Axis I withhabitat variables suggest that substrate heterogene-ity in the form of availability of hard substratesincluding woody debris was the strongest factor influ-encing richness patterns. The habitat variables thathad significant correlations with Axis I were embed-dedness, substrate size, and the amount of woodydebris (Table 4).

The second PCA axis described 17.1 percent of thevariation in the invertebrate data. This axis differen-tiated the Amity and Lester rivers from Silver Creek,Stewart River, Encampment River, and East SplitRock River (Figure 2). All of these sites had interme-diate species richness values, indicating that composi-tional differences determined stream position alongthis axis (Table 3). The predominant compositionaldifference among these streams was the percentage offilterers. Amity River and Lester River had the high-est proportions of filterers C> 30 percent) among thestreams, whereas the Stewart, Encampment, andEast Split Rock rivers had the lowest (< 4 percent)proportions. In terms of physical habitat, algal abun-dance and stream width were significantly correlatedwith Axis II (Table 4).

The third and fourth PCA axes (12.8 percent and10.3 percent variation, respectively) exhibited no dis-tinct relationships with the major assemblage charac-teristics shown in Table 3. No significant correlationswere observed between the Axis III and any physicalhabitat variables. Axis IV was related to the amountof run habitat, the degree of shading, and sinuosity(Table 4).

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Land Use

While most of the watersheds along the NorthShore consist primarily of deciduous and mixed (conif-erous+deciduous) forest, several watersheds had sig-nificant proportions of urban and agricultural land(Table 5, Figure 1). The Tischer watershed was themost highly urbanized, with approximately half of itsarea classified as urban. The Chester watershed had18 percent total area in the urban category. The EastBranch Knife and Amity watersheds had the mostagricultural land, with 23 percent and 13 percent oftheir areas in this category, respectively. The Amitywatershed also differed in that 58 percent of its areawas under deciduous forest, whereas the forests ofmost other watersheds were primarily mixed forest.

The relative proportions of land use within the 100m stream buffers were very similar to those summa-rized for the entire watershed. Most buffer valueswere within 5 percent of the watershed values. Thewetlands category was an exception: since riparianzones are often wetlands, proportionately more wet-lands (7-20 percent increase) occurred within the

stream buffers in three of the streams. Because therewas so little difference in land use between the twomethods, further analyses were conducted at thewatershed scale only.

Physical Habitat and Watershed Relationships

Five of the eight habitat variables correlating withmacroinvertebrate assemblage composition also hadsignificant (p < 0.05) correlations with watershed-scale variables (Table 6). Of the three habitatvariables that most strongly influenced PCA Axis I(Table 4), embeddedness (p < 0.05) and substrate size(p < 0.10) had significant correlations with watershedvariables. Embeddedness increased with increasingagriculture, and substrate size decreased withincreasing urban development. Woody debris was notcorrelated with any watershed variables. Algal abun-dance, which influenced PCA Axis II (Table 4), had asignificant positive correlation (p < 0.05) with housingdensity and a negative correlation with the proportionof mixed forest. Stream width, which also influenced

TABLE 4. Habitat Variables That Had Significant Correlations with at Least Oneof the First Four Axes of the Principal Component Ordination.

Variable Axis 1 Axis 2 Axis 3 Axis 4

Embeddedness —0.23 0.12 0.16

Substrate 0.61* 0.35 0.18 —0.15

Run (percent) —0.12 —0.01 0.16 0.65*

Shade (percent) —0.13 —0.36 —0.41 0.69*

Sinuosity 0.16 —0.48 —0.22 0.62*

Woody Debris 0.62* —0.33 —0.22 —0.37

Stream Width 0.14 _0.59** —0.13 —0.19

Algae —0.51 0.64* 0.01 —0.18

*p <0.05.<0.10.

TABLE 5. Percent Land Cover Within Anderson Level II Categories Summarized at Watershed Scale.

Deciduous Mixed Lakes andUrban Agricultural Forest Forest Wetlands

East Split Rock 0 0 0 92 8West Split Rock 0 0 0 99 1

Encampment 0 2 0 85 12

Silver Creek 0 0 0 84 15

Stewart 0 6 0 84 10

East Branch Knife 0 23 0 77 0Knife 0 1 0 98 1Amity 0 13 58 25 4Chester 18 2 0 75 5Lester 1 8 7 72 13Tischer 49 0 1 46 4


Richards and Host

TABLE 6. Correlations Between Watershed Variables and the Eight Habitat VariablesThat Had Strongest Influence on Macroinvertebrate Distributions.

HabitatWatershed Variable

House LinkVariable Agriculture Deciduous Mixed Wetland Urban Density Number

Embeddedness 0.63* 0.06 —0.49 —0.37 0.47 0.45 —0.28Substrate —0.45 —0.01 0.46 0.28 .0.55** —0.45 0.45Run (percent) 0.60** —0.06 0.27 —0.05 .0.6l* —0.23 —0.04Shade (percent) —0.37 0.31 —0.42 —0.12 0.49 0.67* —0.30Sinuosity —0.25 0.15 —0.24 -0.45 0.46 0.18 —0.26Woody Debris —0.35 0.25 0.03 —0.12 —0.13 —0.13 0.03Stream Width —0.39 —0.21 0.19 0.45 —0.19 —0.05 0.60Algae 0.29 0.42 —0.49 0.56** 0.73* —0.36

p <0.05.<0.10.

Axis II, was significantly correlated with link number,an indicator of watershed size.

No significant correlations were observed betweenthe proportions of deciduous forest or wetlands andthe habitat variables. However, these land use typesgenerally constituted a relatively small proportion ofthe watersheds (Table 5).

DISCUSSION

The relationships found in this study suggest thatthe use of relatively coarse landscape data has someutility for predicting major patterns of macroinverte-brate assemblage composition among streams. Suchpredictions could be based upon the relationshipsbetween watershed variables and physical habitatcharacteristics that are described in this paper andwhich strongly influence macroinvertebrate assem-blages. Since there are strong causal linkagesbetween land use practices and stream habitat condi-tions, such as increased sediment and nutrient inputsresulting from agricultural practices, we should beable to use watershed-scale variables as predictors ofstream habitat quality. We intend to test this hypoth-esis in subsequent phases of this research program.

We found that substrate characteristics, influencedby the presence of fine sedimentary materials thatembedded large particles and the amount of woodydebris in stream channels, had the strongest influ-ence on the presence or absence of macroinvertebratespecies. Species richness was substantially reduced instreams with high proportions of fine substrate. Fineparticles (typically < 2mm in diameter) clog intersti-tial spaces within the substrate matrix in bothsurface and hyporheic zones (Richards and Bacon,1994) and effectively decrease water flow through thesubstrate. In addition, these particles also reduce


substrate heterogeneity. As a result, substrate charac-teristics are one of the most important determinantsof stream invertebrate distributions in streams (seeMinshall, 1984). Other studies have found that sub-strate characteristics account for large portions of thevariance in macroinvertebrate assemblages amongstreams (Wright et al., 1984; Milner, 1987; Faith andNorris, 1989; Quinn and Hickey, 1990; Richardset al., 1993). As noted in the current study, agricul-ture and urban development can contribute to finesediment accumulation in streams and reducemacroinvertebrate species richness (Lenat et al.,1981; Sloane-Richey et al., 1981; Lenat, 1984).

Similarly, agricultural and urban land uses havebeen associated with increased nutrient runoff (Smartet al., 1981; Osborne and Wiley, 1988) and are likelyto be related tO the patterns of algal abundance notedin this study. Streams along the northwestern shoreof Lake Superior are nutrient-limited and exhibitincreased algal growth with increased nutrient supply(Nan Allen, University of Minnesota, unpublisheddata). Plafkin et al. (1989) found that filterer abun-dance can be used to indicate algal abundance. Ourstudy suggests that increased algal abundance leadsto shifts in assemblage composition, particularly largeincreases in filterer abundance.

Although we found woody debris to be an impor-tant physical variable in this study and the generalimportance of woody debris in streams has been notedin numerous studies (Bilby 1981; Benke et al., 1984;Dolloff, 1986; Bisson et al., 1987; O'Connor, 1991), wewere unable to predict abundance of woody debris inthe stream channels with the LUDA land use data.This important limitation to predicting habitat char-acteristics in these streams may lie in the spatialscale at which woody debris enters streams. Riparianzones in close proximity to the stream channel supplythe majority of woody material, with riparian zones asnarrow as 3 or 4 m providing significant amounts of


debris. Furthermore, silvicultural activities or otherlocalized land uses can alter the rates at which debrisenters streams. The LUDA data do not have sufficientspatial resolution to estimate these activities or depictriparian composition along the stream margins. Inaddition, this lack of resolution may also have influ-enced our inability to predict other morphologicalcharacteristics, such as sinuosity, that can be stronglyinfluenced by the presence of large woody debris.Higher resolution GIS databases may be required toprovide sufficient resolution to quantify fine-scaleaspects of stream physical habitat.

An understanding of scale effects is critical forexamining ecological phenomena (Wiens, 1989).Among streams, the spatial resolution of LUDA (16ha minimum mapping unit) appears to be sufficientfor predicting major water quality trends amongstreams (Omernik et al., 1981; Osborne and Wiley1988). Geological parent materials define much of thevariation in potential vegetative cover (Host and Pre-gitzer, 1992) and dominant land use categories amongwatersheds. However, the appropriate spatial scalesfor defining other critical stream morphological char-acteristics may be much smaller (Frissell et al., 1986).Consequently, prediction of some potentially signifi-cant trends in macroinvertebrate assemblage compo-sition may require considerably more spatialresolution. Nonetheless, we found significant differ-ences in macroinvertebrate assemblage compositionbased on this relatively coarse approach to quantify-ing land use patterns and recommend further work todevelop the predictive abilities of these relationships.

The use of GIS technology for examining variationin macroinvertebrate assemblages is promising.Existing databases, particularly the USGS DLG data(U.S. Geological Survey, 1985), provide powerful toolsfor basemap development that can be used in conjunc-tion with an array of other landscape data. Theseapproaches will be most useful when used over largegeographic scales where conventional analyses requir-ing extensive ground-based data acquisition are notfeasible. Climatic changes that alter vegetative coverin a region, regional forestry initiatives that influenceland cover, and socio-economic changes that result inaltered regional land use practices that impact sur-face water quality are examples of environmental con-cerns that may benefit from these analyses. Asgovernment and commercial geographic databasesbecome more readily available, the ability to identifyrelationships across scales and consequently predictstream assemblage composition from watershed-scaleattributes will facilitate the assessment of ecologicalrisk associated with land use changes.

ACKNOWLEDGMENTS

Paul Tucker and Nancy Kirsch assisted with invertebrate labo-ratory analysis. Lucinda Johnson provided valuable insights todata analysis and development, and Connie Host assisted in mapcreation. Chris Robinson, Dan Brenneman, and three anonymousreviewers provided helpful comments on the manuscript. Fundingfor this study was provided by the Center for Water and the Envi-ronment of the Natural Resources Research Institute. This is Cen-ter for Water and the Environment Contribution number 130 andNatural Resources Geographic Information System LaboratoryPublication No. 30.

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EXAMINING LAND USE INFLUENCES ON STREAM HABITATS AND MACROINVERTEBRATES: A GIS APPROACH

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