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Land-use proximity as a basis for assessing stream water quality in

New York State (USA)

Christopher P. Tran a,*, Robert W. Bode b, Alexander J. Smith b, Gary S. Kleppel a

a Department of Biological Sciences, State University of New York at Albany, 1400 Washington Avenue, Albany, NY 12222, United Statesb New York State Department of Environmental Conservation, Stream Biomonitoring Unit, Albany NY 12233, United States

1. Introduction

Although the negative impacts of human land-use activities on

surface waterquality and aquatic ecosystems are well documented

(Limburg and Schmidt, 1990; Woodcock et al., 2006, etc.), the

importance of proximity of urbanization and agriculture to water

bodies continues to be a topic of discussion. Numerous studies

have suggested that surface water quality is affected by the land

cover characteristics found within the watershed drainage areas of

streams (Limburg and Schmidt, 1990; Jones et al., 1999; Bis et al.,2000; Riva-Murray et al., 2002; Woodcock et al., 2006). However,

other studies have suggested that clear influences of land-use

impacts on water quality commonly occur within a shorter

distance of the receiving body of water (Barling and Moore, 1994;

Storey and Cowley, 1997; Harding et al., 1998).

Theobjectives of this study were to (1) compare the influence of

far-field land-use, encompassing a watershed drainage area, to a

near-field, 200-m bufferon each side of the streamin an attempt to

determine which proximity of land-use has the largest impact on

water quality, and (2) incorporate the EPA’s Rapid Habitat

Assessment Protocol (Barbour et al., 1999) to characterize the

riparian and channel characteristics of a stream which influence

water quality, but are often overlooked in New York State’s

monitoring protocols. Accurate habitat assessments could add a

new dimension of understanding to the biological and chemical

data that is collected in New York State.

2. Methods

2.1. Study area

Twenty-nine sampling sites were chosen in eastern (upper &

lower Hudson Valley, Champlain Valley) New York State (Fig. 1) on

the basis of the presence of three land cover types. Land cover

information was derived from National Land Cover Data (NLCD

2001) at a 30-m resolution. The percentage of each land cover type

was determined on two different zones of influence using ArcGIS

version 9.0. The 200-m buffer was chosen as a comparison to the

Ecological Indicators 10 (2010) 727–733

A R T I C L E I N F O

Article history:

Received 26 November 2008Received in revised form 8 December 2009

Accepted 10 December 2009

Keywords:

Water quality

Land-use

Habitat assessment

A B S T R A C T

Theinfluence of theproximity of urbanization and agriculture to streamwater quality is often difficult to

quantify. The objectives of this study were to (1) compare the influence of far-field land-use,

encompassing a watershed drainage area, to a near-field, 200-m buffer on each side of the stream in an

attempt to determine on which zone of influence land-use has the largest impact on water quality, and

(2) incorporate the EPA’s Rapid Habitat Assessment Protocol (Barbour et al., 1999) to characterize the

riparian and channel characteristics of a stream that influence water quality, which can improve New

York State’s monitoring protocols. Impacts were assessed through biological, chemical, and physical-

habitat data from 29 streams located within a variety of land-use categories. Land-use was identified

through USGS National Land Cover Data (NLCD). Principal components analysis (PCA) indicated that

land-useand waterquality variables wereassociated withnon-point source contaminants (e.g. nutrients

and specific conductance). Using Spearman’s rank correlation coefficient, significant relationships

between all three land-use types andstream water quality were determined at the200-m buffer zone of

influence. At the watershed zone of influence, water quality indicators did not correlate significantly

with land cover type. DO and BAP values within the 200-m buffer zone varied inversely with the

percentageof urban-landcover. The stronger correlationbetweenland coverand stream waterquality at

the 200-m proximity than that of the watershed suggests that the presence of a riparian buffer zone

between streams and agricultural and urban areas is a significant factor in reducing contamination fromnon-point source loading.

Published by Elsevier Ltd.

* Corresponding author. Tel.: +1 845 340 7846.

E-mail addresses: [email protected], [email protected] (C.P. Tran).

Contents lists available at ScienceDirect

Ecological Indicators

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o l i n d

1470-160X/$ – see front matter. Published by Elsevier Ltd.

doi:10.1016/j.ecolind.2009.12.002

mailto:[email protected]


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watershed drainage area on the basis of findings in previous

research regarding the distance that land-use affects water quality(Hachmoller 1991; Houlahan and Findlay, 2004; Woodcock et al.,

2006, etc.), as well as for the accuracy it provides when using GIS

data at a resolution of 30 m.

Land cover was first determined on the far-field by calculating

the percentage of urban,agricultural, and forested land cover types

within each stream’s watershed drainage area. The near-field

consisted of land-use within a 200-m buffer around existing

stream boundaries. This buffer encompassed the entire length of

thestream upstream of thesampling site. At some points located at

a greater distance upstream, it is possible that in stream processes

may have reset water quality variables before they reached the

sampling point downstream. However, in many cases non-point-

sources during storm events may cause the loading of contami-

nants in amounts that would exceed probable levels of natural

assimilative capacity, thus it was necessary to include all land-use

upstream from sampling points.

Urban (low-intensity developed, medium-intensity developed,

high-intensity-developed, open space developed) sites were those

which had >20% total area of impervious surface (watershed;

N = 7, 200 m; N = 11). The agricultural (pasture, cropland) group

consisted of sites with >25% total area of farmlands (watershed;

N = 11, 200 m; N = 10). Sites which consisted<20% urban area, and

<25% agricultural area, but consisted of >40% forested land were

considered forested (deciduous, evergreen, mixed) sites (water-

shed; N = 10, 200 m; N = 8). Miscellaneous land cover types that

didnot fall into theforest, agriculture,or urban categories were not

included in analyses. Therefore, land-use percentages for each site

may not add up to one-hundred percent (Table 1).

2.2. Aquatic invertebrate communities

Invertebrate data for this project were provided by the

Department of Environmental Conservation (D.E.C.) Stream

Biomonitoring Unit. Benthic macroinvertebrates were sampled

in accordance with the Rotating Integrated Basin Studies (RIBS)

Ambient Water Quality Sampling Program. This data set wascollected from each of the 29 stream sites between 2001 and

2005. Benthic macroinvertebrates were collected using a kick net

during a 5-min traveling kick over a 5-m transect within a riffle

area. Samples were sieved in the field using a U.S. no. 30 standard

sieve, transferred to quart jars, and preserved in 95% aqueous

ethanol. In the laboratory, the alcohol was removed with a U.S.

no. 60 sieve. A random sub-sample was collected from each

sample and placed in a 90-mm petri dish. The sub-sample was

examined under a dissecting microscope and the first 100

organisms >1.5 mm encountered were identified to the lowest

possible taxonomic level, usually genus or species (Bode et al.,

2002).

Fig. 1. D.E.C. RIBS sampling locations in New York State used in the study of the

impacts of land-use on stream water quality.

Table 1Drainage areas and % land cover for the 29 sampling sites used in this study.

Sampling site Drainage area (km2) % Land-use watershed % Land-use 200-m buffer

Forest Urban Ag. Forest Urban Ag.

Alplaus Kill 138.0 55 15 26 29 29 16

Anthony Kill 162.0 25 23 17 11 22 27

Ausable River 145.3 73 12 16 62 5 9

Black Creek 97.9 65 0 33 47 8 34

Canajoharie Creek 177.8 36 1 62 24 27 15

Canopus Creek 41.1 93 3 1 79 13 4

Cascade Brook 279.7 95 0 3 73 10 2

Claverack Creek 442.0 64 4 26 56 18 14

Dwaas Kill 50.4 40 31 25 47 14 8

Fox Creek 271.5 67 0 30 14 7 66

Lisha Kill 45.4 28 61 6 19 34 5

Little Hoosic River 193.4 93 1 6 44 11 42

Lower Esopus Creek 1079.5 99 0 1 65 24 5

Mettawee River 462.8 68 1 27 49 23 23

Minisceongo Creek 48.7 51 39 4 33 47 8

Ninemile Creek 179.0 54 1 43 43 0 29

Oriskany Creek 374.7 41 2 54 42 20 28

Otsquago Creek 161.3 34 1 65 33 15 38

Poultney River 170.9 69 1 27 27 6 44

Reall Creek 23.4 95 1 0 41 19 26

Saranac River 1615.3 96 0 2 23 60 1

Saw Mill River 65.4 85 0 4 10 89 0

Schroon River 1440.2 37 61 1 50 18 7

Spar Kill 13.5 93 0 1 28 46 1

Sprout Creek 141.3 44 55 0 30 23 36

Starch Factory Brook 17.9 62 5 26 41 19 26

Upper Hudson River 2188.6 55 20 24 78 16 0

White Creek 125.6 92 0 0 57 5 36

West Brook 23.9 93 0 1 60 35 1

C.P. Tran et al./ Ecological Indicators 10 (2010) 727–733728

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Five macroinvertebrate community indices were calculated for

each sample: (1) Species Richness (SPP; total number of species

found), (2) Hilsenhoff Biotic Index (HBI, an indicator of organic

pollution) (Hilsenhoff, 1987), (3) Percent Model Affinity (PMA,

compared the sample community to what is considered a

regionally ideal invertebrate community and provided an analysis

of similarity between the two) (Novak and Bode, 1992), (4)

Ephemeroptera, Plecoptera, and Trichoptera taxa (EPT, number of

pollution intolerant species) (Lenat, 1993), (5) Biological Assess-

ment Profile (BAP, overall integrity of the biotic community was

determined by normalizing the four biotic indices previously

described to a common scale ranging from 1 to 10 and taking the

mean). Sites with high BAP scores (on a scale of 1–10) are

considered to have good water quality (Bode et al., 2002).

2.3. Water and sediment quality

Water quality samples were collected and analyzed at an

interval of 1–2 times per month from April to November of the

years 2001–2005. Mean values were used in statistical analyses.

Water temperature, pH, dissolved oxygen (DO) and conductivity

were measured with a Hydrolab Surveyer 4 multi-parameter

sensor at a depth of approximately 1 m. Water was collected for

the measurement of nutrients (nitrite (NO2), nitrate (NO3), andtotal phosphorus (TP)), total suspended solids (TSS) and fecal

coliform counts with a flow-orienting, depth integrating sampling

nozzle on a 1 L plastic bottle at multiple vertical transects across

the stream. Analyses were performed by a NYSDEC contract

laboratory using U.S. Environmental Protection Agency (USEPA)

methods for water quality analysis.

2.4. Rapid habitat assessment

Habitat assessments including detailed observation and esti-

mation of the condition of riparian habitat and stream channel at

each site were performed in June and July of 2006. Sites were

scored using the EPA Rapid Bioassessment Protocol for Use in

Streams and Wadeable Rivers Habitat Assessment Field Data Sheet(Barbour et al., 1999). Each of 10 habitat categories were scored

from 0 (poor) to 20 (optimal), giving a possible total score of 200.

The categories were: (1) epifaunal substrate/available cover, (2)

pool substrate characterization, (3) pool variability, (4) sediment

deposition, (5) channel flow status, (6) channel alteration, (7)

channel sinuosity, (8) bank stability, (9) vegetative protection, (10)

riparian vegetative zone. Since the invertebrate and water

sampling was conducted at an earlier date than the habitat

assessment, current habitat was compared to photographs taken at

the time of invertebrate and water sampling to ensure that no

significant changes in land-use occurred between the two time

periods. Detailed field reconnaissance and observations were made

in order to give consideration to the possible presence of piped

point-sources (i.e. waste water treatment effluent & storm water),which could add significant levels of contamination at the sites.

2.5. Statistical analyses

Principal components analysis (PCA) was used to identify key

biological, chemical, and physical sources of variation among the

sampling sites. Relationships among strongly loaded variables

were quantified using Spearman’s rank correlation coefficient in

order to account for the possible presence of non-normal

distribution (Helsel and Hirsch, 2002). Breakpoint regression

was used to explore a potential threshold in the correlation

between forested area andhabitat score within the200-mbuffer. A

one-way analysis of variance (ANOVA) was used to test the null

hypothesis that communities representing the three land cover

types do not differ in habitat quality, invertebrate community

composition,and water chemistryon eitherzone of influence. Sites

were placed into forest, urban, or agricultural groups based upon

what was considered the dominant land cover type present.

Tukey’s multiple comparison test identified between-group

differences in the data set. The three groups (forest, agriculture,

urban) were compared against one another for each water/habitat

quality parameter, for a total of 30 comparisons. Results of this

analysis are presented with thestatistics F (comparison of variance

between-group factors to variance within-group factors), d.f.

(degrees of freedom), and p (probability that the factor has no

effect).

3. Results

3.1. Comparison of 200-m buffer and watershed zone of influence

A PCA performed on stream water quality data served as a

means to group several potentially correlated variables into fewer

sets of principal components which are uncorrelated with one

another. The first principal component accounts for the largest

amount of variability in the data, and each successive principal

component accounts for less. Results indicate that the water

quality variables that make up a principal component axis share acommon influence. The first two principal components explained

60% of the variability in the data set ( Fig. 2). Habitat score and BAP

hada strong negative loading on principal component(PC) 1, while

TSS, NO3, conductivity, and TP were positively loaded on this axis.

The water quality indicators which were positively loaded on PC 1

are commonly associated with urban and agricultural develop-

ment, suggesting that this axis may be an indicator of alteration

from the natural habitat of streams. The negative loadings of

habitat score and BAP further indicate the influence of the

alteration of habitat and water quality.

The frequency with which land-use types correlated with water

quality differed depending on the zone of proximity (Table 2). The

results of the Spearman’s rank correlation coefficient analysis

indicate that at the far-field (watershed) zone of influence landcover was not significantlycorrelated with water quality variables,

including stream invertebrate community structure. Correlations

were more frequent within the near-field (200-m buffer), with a

significant relationship occurring for decreases in DO concentra-

tions with % urban-land cover (Table 2). Urban areas were also

significantly inversely correlated with BAP. DO concentrations

were positively correlated with the presence of agriculture. The

presence of forested area within the 200-m buffer was correlated

with indicators of good water quality. This was observed with

increases in habitat score, as well as decreases in conductivity, TSS,

Fig. 2. Ordination of principle components of water quality indicators used in this

study, dominated by non-point source loading.

C.P. Tran et al./ Ecological Indicators 10 (2010) 727–733 729

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TP, and turbidity. The relationship between forest area and habitat

score is observed in Fig. 3. Breakpoint regression suggests that a

threshold exists at approximately 42% forested area (N = 29,

breakpoint 41.95, r = 0.83, 68% variance explained, p < 0.05), at

which point habitat score does not significantly increase with

forested area. Regression models performed separately on data

points below this threshold (N = 14, r = 0.38, p < 0.05) were

stronger than those above it (N = 15, r = 0.01, p< 0.05). A difference

in observed relationships between forest area and conductivity can

be found in Fig. 4.ANOVA identified significant differences between land cover

types in TP, TSS, DO and turbidity within the 200-m buffer (TP,

F = 4.44, d.f. = 2, p< 0.05; TSS, F = 4.03, d.f. = 2, p < 0.05; DO,

F = 3.58, d.f. = 2, p< 0.05; turbidity, F = 3.87, d.f. = 2, p< 0.05).

Tukey’s test indicated that TP concentrations of streams buffered

by forests were lower than in streams in urban buffer areas (Fig. 5).

Forested areas also had lower values than agricultural areas in TSS

concentrations (Fig. 6) and turbidity (mean difference = 8.29,

d.f. = 2, p < 0.05). Measurements of DO at urban and agricultural

sites were significantly different (mean difference = 2.02, d.f. = 2,

p< 0.05). ANOVA for land cover type on the watershed zones of

influence revealed significant differences between groups only in

temperature (F = 2.24, d.f. = 2, p < 0.05). A significant difference in

temperature was observed between the forest and agriculturalgroups using Tukey’s test (mean difference = 4.40, d.f. = 2,

p< 0.05).

Table 2

A comparison of significant correlations between landcover typeand water quality

indicators within the200-m zoneof influencebasedon Spearman’s rankcorrelation

coefficient (r ) ( p<0.05).

Land cover category Water quality parameter r -Value N

Forest Habitat score 0.52 29

Conductivity À0.53 22

TSS À0.64 23

TP À0.46 22

TurbidityÀ

0.53 23Agriculture DO 0.53 22

Urban BAP À0.44 29

DO À0.47 22

Fig. 3. The results of breakpoint regression suggest that a threshold exists at

approximately42% forest cover (breakpoint 41.95, r = 0.83, 68%variance explained,

p < 0.05), at which point habitat score does not significantly increase with % forest

cover.

Fig. 4. Conductivity concentrations decrease with forest area within the 200-m

buffer (A), but shows little correlation on the watershed scale (B).

Fig. 5. Box plots displaying the significant difference in median total phosphorus

measurements between the forest and urban groups (mean difference = .038,

d.f. = 2, p < 0.05).


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3.2. Rapid habitat assessment

Total habitat scores for the 29 sampling sites ranged from 84 to

180 out of a possible 200. There was a significant relationship

between the rapid habitat scores and the measured water quality

indicators that was consistent with the physical-habitat char-

acteristics observedwithinthe 200-m buffer. Many of the land-use

characteristics that influenced water quality within the 200-m

buffer were visually evident while performing the rapid habitat

assessment. Five of the strongest correlations between habitatassessment metrics and biological and chemical variables are

displayed in Table 3. Sites located in a channel which has been

physically altered from its natural state (i.e. cement banks,

straightened channel, etc.) reflected increased TSS, nitrite and

conductivity values. Results suggest that macroinvertebrate

communities are highly dependent on natural channel character-

istics, as EPT and BAP values were higher in channels that were not

significantly altered by human activity.

4. Discussion

4.1. Influence of the 200-m zone of influence on water quality

The proximity at which land-use influences water quality has

been studied in several different ways with varying results

(Limburg and Schmidt, 1990; Sponseller et al., 2001; Woodcock

et al., 2006, etc.). Results suggest that for this particular study area

and catchment size, there was a stronger correlation among water

and habitat quality characteristics and land-use characteristics

within the 200-m buffer zone than over the entire watershed.

Urbanization had the strongest negative influence over water

quality in this study.

Since surface water contamination is highly dependent on

storm water runoff, it is not surprising that contaminants located

in close proximity are more likely to reach water bodies than those

located at a further distance within the watershed. Phillips and

Hanchar (1996) estimated that the Hudson River Basin, where

many of the sites in our study are located, receives 20–30 inches of

storm water runoff annually from impervious surfaces. It was

observed during field surveys that the urban sites in this study had

very little or no vegetated riparian zone, therefore increasing the

probability that concentrations of these contaminants increased

after storm events. The contaminants and increased temperaturein this runoff likely contributed to low DO values and a sharp

decrease in BAP values within the 200-m buffer analyses. Low DO

values have traditionally been associated with the elimination of

sensitive taxa from streams (Closs and Lake, 1994). Sponseller et

al. (2001) found that in a comparison between catchment and local

proximity land-use, the strongest relationships with temperature,

EPT richness, and overall invertebrate diversity occurred within a

200-m buffer. It is also possible that the drop-off of invertebrate

communities in this study may have been a result of increases in

sedimentation after storm events. Sediment deposition has been

known to cause shiftsin invertebratecommunities from tolerant to

intolerant species by reducing the amount of available habitat,

which in most cases is rocky substrate (Lenat et al., 1981).

It is important to note that in some instances contaminantloading can be independent of land-use proximity, and may not be

a direct reflection of adjacent land-use. It is possible that storm

water runoff, as well as effluent from waste water treatmentplants

can be piped past the forested areas and directly into streams.

However, the presence of piped infrastructure was not observed at

the sampling sites of this study during field reconnaissance. It was

also taken into account that this piped point source infrastructure

could be present upstream from the area that was surveyed during

thehabitat assessment. Piped infrastructure didnot appearto have

a significant influence on water quality in the areas that were

observed directly and there is not sufficient evidence of contami-

nant loading through piped infrastructure in areas that were not

observed. This suggests that water quality in this study is linked to

non-point source loading in storm water runoff, particularly inareas with impervious surface, and is being offset by the ability of

riparian zones to act as a buffer.

Several of the urban sites in this study displayed signs of

channel alteration and a loss of natural sinuosity, typically

consisting of straightened channels and the removal of natural

woody debris and bank vegetation (Wall et al., 1998). This trend

was observed at sites urbanized within the 200-m buffer such as

Anthony Kill and Canojoharie Creek, where the natural stream

banks had been replaced by cement walls. These channelized

streams displayed poor water quality and decreased ability to

support a diversity of biota. Results of the habitat assessment

suggest that high conductivity and TSS values, as well as decreased

BAP, EPT, PMA, and HBI values were evident in streams with

channels altered from their natural states. It is possible that

Fig. 6. Box plots displaying significant difference in total suspended solids count

between the forest and agriculture groups. High variability is displayed within the

urban and agriculture groups, suggesting that there are a variety of sub-groups

within each land cover type (mean difference = 11.3, d.f. = 2, p < 0.05).

Table 3

Significant correlations between habitat scoring categories and water quality

parameters based on Spearman’s rank correlation coefficient (r ) ( p<0.05, N =29).

Habitat scoring category Water quality parameter r -Value

Channel alteration BAP 0.38

HBI À0.60

EPT 0.47

PMA 0.52

Conductivity À0.65

Nitrite À0.45

TSS À0.45

Channel sinuosity Fecal coliform À0.68

Nitrite À0.57

Vegetative protection Fe cal colifor m À0.55

Pool substrate PMA 0.49

Characterization Conductivity À0.54

Nitrite À0.60

TSS À0.51


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channels that lose their natural form and integrity have a

decreased capacity to assimilate contaminants through natural

processes. The alteration of stream channels and hydrologic

regime is a trend that is not always reflective of land-use within

the broad spectrum of a watershed, but is more likely to be evident

within the immediate stream channel and the habitat directly

adjacent to it (200-m buffer width). This suggests that for water

quality assessment, an attempt to increase focus on local habitat

assessment may be very effective in understanding stream

ecosystem functioningand detecting negative water quality issues.

Agricultural land-use was significantly different from forested

land-use within the 200-m buffer, particularly through the

contribution of TSS which has been known to transport various

pathogens and contaminants (Wall et al., 1998). Total suspended

solid levelswere highlyvariablein theagricultural areas withinthe

200-m buffer (Fig. 4), due in part to the variety of agricultural

types. It was observed that sites that were forested within the 200-

m buffer had significant decreases in TSS, most likely due to the

bank stability and erosion prevention that is provided by the

rooting of vegetation (Rosgen, 2001).

It seems counterintuitive that DO levels were positively

correlated with the presence of agriculture in this study. One

possible explanation for this observation is that many of the

agricultural sites in this study maintained a flow regime thatconsisted of either riffle/run or riffle/pool sequences. Particularly in

the shallow stream types where there is less vertical stratification

of the water column, a relatively turbulent flow regime consisting

of riffles where the water surface is broken by flow can provide

adequate aeration and mixing of atmospheric oxygen in the water

column (Matthews and Berg, 1997). It is possible that the presence

of riffle sequences at the agricultural sites resulted in the high

oxygen levels which were observed.

4.2. Influence of the watershed zone of influence on water quality

Land-use assessed on the watershed zone of influence did not

have a significant influence over water quality indicators in this

study. One possible explanation for the difference seen betweenzones of influence is that although a watershed can be

predominantly forested, there may still be some urban or

agricultural activity that is influencing water quality within a

few hundred meters of the stream. Five of the sites in this study;

West Brook,Saranac River,Spar Kill, Minisceongo Creek,and Lower

Esopus Creek were initially thought to have been dominated by

forested area based on land cover within their respective water-

sheds, but were determined to be highly urbanized when land

cover was calculated within a 200-m buffer of the streams

(Table 1). A similar trend was noted by Sponseller et al. (2001),who

observed that one of their south-western Virginia sampling sites

was >90% forested within its catchment, but only $75% forested

within a 200-m buffer of the stream. This difference in forested

area can have a strong influence over water quality, and furthersuggests that attempting a single-zone of influence approach to

studying land-use may yield inaccurate results.

It is possible that the distance over which land-use impacts

water quality may be highly dependent upon the size of the

streams under study. Woodcock et al. (2006) found that

invertebrate communities in Adirondack (NY) headwater streams

were more significantly influenced by land-use within the entire

watershed catchment than within smaller patches located near-

site. However, it is likely that in the smaller drainage areas of

headwater streams (<25 km2), land-use is located in relatively

close proximity to the water and is thus more likely to have an

influence over water quality and ecosystem functioning. A greater

percentage of the land cover in the larger watersheds used in our

study (most>

25 km

2

) is located a larger distance away from the

actual stream, therefore reducing the possibility that it will have a

greater influence on water quality than land-use that is in closer

proximity.

4.3. Rapid habitat assessment as a tool for water quality analysis

The results of the rapid habitat assessment further suggest the

importance of including a study of the near-field when viewing the

impacts of land-use on water quality. The scoring system’s

negative correlations with conductivity, TSS, and turbidity are

consistent with results of land-use within the 200-m buffer. PCA

results also display habitat score as negatively associated with

indicators of poor water quality, indicating that the rapid habitat

assessment may be used as an accurate and efficient predictor of

how land-use within the near-field is influencing water quality.

The rapid habitat assessment is not intended to be used as a stand

alone metric for the assessment of stream habitat or water quality.

Stream water quality assessments which include biological and

chemical sampling, which is the common practice in NYS, can

benefit from incorporating the rapid habitat assessment because it

can add an important dimension of data that can make

biomonitoring data more multi faceted, while adding little cost

to projects.

The habitat assessment in this study proved to be highlydependent on the presence of an adequate riparian area. The dense

bank vegetation of the riparian zone prevented excess sediment

deposition due to bank erosion, while also preventing contami-

nants from entering the streams during runoff events (Storey and

Cowley, 1997). While conducting a similar habitat assessment,

Roth et al. (1996) also found that sites that were lacking riparian

cover displayed lower habitat scores. Therefore, it is not surprising

that the lowest habitat scores in our study came from sites located

in urban areas with poor water quality.

The Rapid Habitat Assessment Protocol allows the sampler to

examine themost current physical conditionof a stream, andwhen

combined with current protocols for biological and chemical

sampling, can provide a well rounded and efficient assessment of

overall stream integrity. Some contradiction exists as to whetherhabitat should be assessed at a single section of a stream, or as an

average of multiple sections (Rabeni, 2000). In our study, all

biological and chemical samples were taken using protocols for

rapid assessment from a single transect. Therefore, in the interest

of efficiency and consistency of data, a single habitat assessment

for each site taken in the same location as the biological and

chemical samples was the most logical procedure.

This protocol can be performed in approximately 5–10 min per

site. A noticeable drawback to this method is that it is subjective in

nature, possibly allowing a margin of error between different

samplers. However, Hannaford et al. (1997) found that pre-

assessment training of the observer does in fact reduce variability

in results. Proper training, in addition to the simplicity of the ten

categories included in this assessment should ensure that scoresobtained by various samplers would most likely be within a similar

range.

5. Conclusions

The results of this study demonstrate the importance of

considering the proximity of land-use in stream water quality

assessment. Urbanization and agriculture significantly influenced

stream water quality, particularly through the removal of riparian

habitat and the alteration of stream channels that accompanies

these land-uses. The understanding of this important link between

land-use and stream water quality is critical to the management of

healthy ecosystems. However, it should be understood that

although the preservation or restoration of riparian habitat can


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effectively reduce non-point source pollution, it will not eliminate

all water quality issues resulting from urban and agricultural

development. Although not evident in this study, there can be

cases where a significant amount of contaminants are being piped

into streams. In these instances, a wide riparian buffer alone

cannotcounteract decreases in water quality. Until both point-and

non-point-sources of contamination are addressed along with the

alteration of hydrologic regime, streams are not likely to maintain

integrity.

This study suggests that near-field development is more

strongly correlated than far-field development with water quality

in streams and that the preservation of a high quality riparian zone

directly adjacent to a stream can provide considerable protection

from the loading of non-point source contaminants, particularly in

areas with a high presence of impervious surfaces. Results suggest

that a rapid habitat assessment (i.e. Barbour et al., 1999) could add

important data to water quality monitoring programs regarding

the physical characteristics of streams, and may increase the

understanding of biological and chemical processes.

Acknowledgments

The authors would like to thank the NYSDEC StreamBiomonitoring Unit for providing data and continuous assistance

with the development of this study; Douglas Burns and Karen

Murray of the USGS, Monika Calef of SUNY at Albany, and Tim

Mihuc of the Lake Champlain Research Institute for providing

assistance with development, statistical analyses, and editing of

this manuscript.

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