Development of an Approach for Integrating Components of

the U.S. Geological Survey Biomonitoring of Environmental

Status and Trends (BEST) and National Stream Quantity

Accounting Network (NASQAN) Programs for Large U.S.

Rivers

Scientific Investigations Report 2005—5083

U.S. Department of the Interior

U.S. Geological Survey
Cover: Map of the conterminous United States showing waterways, river basins and BEST stations sampled in 1995 and 1997-98.

BEST, Biomonitoring of Environmental Status and Trends program; NASQAN, National Stream Quantity Accounting Network;

NAWQA, National Water-Quality Assessment program.
Development of an Approach for

Integrating Components of the U.S.

Geological Survey Biomonitoring of

Environmental Status and Trends (BEST)

and National Stream Quantity Accounting

Network (NASQAN) Programs for Large

U.S. Rivers

By Nancy J. Bauch, Christopher J. Schmitt, and Charles G. Crawford

Scientific Investigations Report 2005—5083

U.S. Department of the Interior

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

Gale A. Norton, Secretary

U.S. Geological Survey

Charles G. Groat, Director

U.S. Geological Survey, Reston, Virginia: 2005

For more information about the USGS and its products:

Telephone: 1-888-ASK-USGS

World Wide Web: http://www.usgs.gov/

Any use of trade, product, or firm names in this publication is for descriptive purposes only and does not imply 

endorsement by the U.S. Government.

Although this report is in the public domain, permission must be secured from the individual copyright owners to 

reproduce any copyrighted materials contained within this report.

Suggested citation:

Bauch, N.J., Schmitt, C.J., and Crawford, C.G., 2005, Development of an approach for integrating components of 

the U.S. Geological Survey Biomonitoring of Environmental Status and Trends (BEST) and National Stream Quantity 

Accounting Network (NASQAN) programs for large U.S. rivers: U.S. Geological Survey, Columbia Environmental 

Research Center, Columbia, Mo., Scientific Investigations Report 2005—5083, 53 p. 
Contents iii

Contents

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

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

Background ................................................................................................................................................... 2

BEST Program ...................................................................................................................................... 3

NASQAN Program ............................................................................................................................... 3

BEST Program Implementation Projects ......................................................................................... 4

Potential Complementarity of BEST and NASQAN Programs...................................................... 6

Integration of BEST and NASQAN Data ................................................................................................... 7

Problem Statement.............................................................................................................................. 8

Purpose and Approach....................................................................................................................... 9

Literature Review................................................................................................................................. 9

Sources of Water-Quality Data ....................................................................................................... 10

Data Summarization and Analysis Techniques............................................................................. 14

Water-Quality Data................................................................................................................... 14

Fish Data..................................................................................................................................... 16

Integration of Water-Quality and Fish Data.......................................................................... 17

Data Refinements and Restrictions for Analysis.......................................................................... 17

Water-Quality Data Refinements ........................................................................................... 17

Fish Data Refinements ............................................................................................................. 29

Results. ......................................................................................................................................................... 31

Stable Isotopes ........................................................................................................................................... 40

Discussion and Recommendations ......................................................................................................... 44

Acknowledgments...................................................................................................................................... 48

Literature Cited............................................................................................................................................ 48

Figures

Figure 1. Map of the conterminous United States showing the waterways, river basins, and BEST 

stations sampled in 1995 and 1997-98. ..................................................................................... 5

Figure 2. Cumulative relative frequency of copper concentrations above U.S. Environmental ProFigure 3. Relation between mean total PCB concentrations and mean trophic position, by fish 

species in the Columbia River basin; and meant total organochlorine pesticide concentection Agency freshwater criterion ...................................................................................... 31

trations and mean trophic position in the Rio Grande basin. ............................................. 43

Tables

Table 1. Methods incorporated into the 1995 and 1997 BEST large rivers projects......................... 6

Table 2. Fish health-assessment indicators and stable isotope, trace element, organochlorine 

pesticide, and PCB parameter list for fish collected at BEST stations in the Columbia 

Table 3. Fish-collection stations of the BEST program in the Columbia River and Rio Grande 

Table 4. Water-quality parameters of interest with water-quality guidelines for the protection of 

River and Rio Grande basins, 1997-98 ...................................................................................... 7

basins, 1997-98 ........................................................................................................................... 11

freshwater aquatic life.............................................................................................................. 12
iv Approach for Integrating BEST and NASQAN

Table 5. NASQAN water-quality stations in the Columbia River and Rio Grande basins, 1995-99...13

Table 6. Minimum sample criteria for calculation of time-weighted concentrations for selected 

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality 

parameters for selected NASQAN water-quality stations in the Columbia River and Rio 

Table 8. Time-weighted mean, median, and percentile concentrations of selected water-quality 

parameters for selected NASQAN water-quality stations in the Columbia River and Rio 

Table 9. Concentration of last water sample before date of fish collection for selected NASQAN 

Table 10. Frequencies-of-detection of selected water-quality parameters for selected NASQAN 

water-quality stations in the Columbia River and Rio Grande basins for the 18 months 

Table 11. Time-weighted geometric mean concentrations of water-quality parameters of interest 

for selected NASQAN water-quality stations in the Rio Grande basin for the 18 months 

Table 12. Minimum, median, and maximum estimates of total trace-element toxicity for selected 

Table 13. Kendall’s tau correlation matrix of frequencies-of-detection of trace elements in water 

for the 18 months prior to the date of fish collection and trace-element concentrations 

in carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande 

Table 14. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of 

interest and mean fish-health observation indicators for male carp at selected NASQAN 

Table 15. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of 

interest and median fish-health measurement indicators for male and female carp at 

of fish at selected NASQAN and BEST stations in the Columbia River and Rio Grande 

Table 17. Kendall’s tau correlation matrix of mean trophic positions and mean total organics 

concentrations for fish species at selected NASQAN and BEST stations in the Columbia 

NASQAN water-quality stations ............................................................................................. 15

Grande basins ............................................................................................................................ 18

Grande basins for water year 1997......................................................................................... 24

water-quality stations in the Columbia River and Rio Grande basins............................... 28

prior to the date of fish collection........................................................................................... 29

prior to the date of fish collection........................................................................................... 30

NASQAN water-quality stations in the Columbia River and Rio Grande basins for the 18 

months prior to the date of fish collection............................................................................. 32

basins........................................................................................................................................... 33

and BEST stations in the Columbia River and Rio Grande basins. .................................... 34

selected NASQAN and BEST stations in the Columbia River and Rio Grande basins... 35

Table 16. Mean values and ranges of δ15N of POM and C:N POM in water and ranges of δ15N 

basins, water year 1997. ........................................................................................................... 41

River and Rio Grande basins, water year 1997. .................................................................... 42
Cnversion Factors v

Conversion Factors

Multiply By To obtain

Length

centimeter (cm) 0.3937 inch (in)

millimeter (mm) 0.03937 inch (in)

meter (m) 3.281 foot (ft)

kilometer (km) 0.6214 mile (mi)

Area

square kilometer (km2) 0.3861 square mile (mi)

cubic kilometer (km3) 0.2399 cubic mile (mi3)

Volume

milliliter (mL) 0.03381 fluid ounce (fl. oz)

liter (L) 1.057 quart (qt)

liter (L) 0.2642 gallon (gal)

cubic meter (cm3) 0.0002642 million gallons (Mgal)

Mass

kilogram (kg) 2.205 pound, avoirdupois (lb)

Concentration

microgram per gram (μg/g) = part per million (ppm; 106)

nanogram per gram (ng/g) = part per billion (ppb; 109

)

picogram per gram (pg/g) = part per trillion (pptr; 1012)

milligram per millimeter (mg/mL) = part per thousand (ppt; 103

)

milligram per liter (mg/L) = part per million (ppm; 106

)

microgram per liter (μg/L) = part per billion (ppb; 109

)

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

°F=(1.8×°C)+32

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

°C=(°F-32)/1.8
v

iApproachforIntegratingBESTandNASQAN
Development of an Approach for Integrating Components

of the U.S. Geological Survey Biomonitoring of

Environmental Status and Trends (BEST) and National

Stream Quantity Accounting Network (NASQAN)

Programs for Large U.S. Rivers

By Nancy J. Bauch1, Christopher J. Schmitt

2

, and Charles G. Crawford3

Abstract

A national-scale framework for monitoring environmental contaminants in fish and effects of contaminant exposure 

on fish in large U.S. rivers has been proposed by the Biomonitoring of Environmental Status and Trends (BEST) Program 

of the U.S. Geological Survey (USGS). The framework 

shares many features and objectives with the USGS National 

Stream Quantity Accounting Network (NASQAN) Program, 

which monitors water quality in large U.S. river basins–those 

with drainage areas of 250,000 to 1,200,000 square miles at 

their most downstream stations. Because the two programs 

appear to be complementary, this study was initiated in 2001 

to investigate alternative techniques for summarizing and integrating the water-quality data with the fish-contaminant and 

fish-health data, and to provide recommendations to the BEST 

program for future integrated studies.

Test data sets from BEST and NASQAN stations in 

the Columbia River and Rio Grande basins (CRB and RGB, 

respectively) with fish data for 1997-98 and water-quality 

data for water years 1995-98 were compiled. Water-quality 

data for field properties, trace elements, and water-soluble 

1

U.S. Geological Survey, Water Resources Discipline, Colorado Water 

Science Center, P.O. Box 25046, MS-415, Denver Federal Center, Denver, CO 

2

U.S. Geological Survey, Biological Resources Discipline, Columbia Environmental Research Center, 4200 New Haven Rd., Columbia, MO 65201

3

U.S. Geological Survey, Water Resources Discipline, Indiana Water Science Center, 5957 Lakeside Blvd., Indianapolis, IN 46278

pesticides were summarized as time-weighted concentrations, 

concentrations in the most recent water sample prior to fish 

collection, frequency-of-detection, and total toxicity estimates, 

and were compared to guidelines for the protection of freshwater aquatic life. Individual and total concentrations of contaminants in fish, mean values of fish-health observation indicators, and median values of fish-health measurement indicators 

were computed by station, species, and gender. Most analyses 

of fish data were restricted to common carp (Cyprinus carpio), 

the most widely distributed species. Where appropriate, fish 

data were further summarized by age and gonadal stage. The 

ratio of the stable isotopes 15N and 14N (δ15N) of particulate 

organic matter in water and fish were summarized as means 

and ranges. Relations between water-quality and fish data 

were investigated with Kendall’s tau correlation coefficient 

and the Kruskal-Wallis test to illustrate the types of analyses 

that could be performed on such data sets.

Only a small number of stations in each basin met minimum sample criteria, and the resulting test data sets contained 

too few observations for in-depth analysis. The analyses 

and results are reported to illustrate how data from the two 

programs might be combined and how results of the integrated 

data can be interpreted. Examples include documentation 

of statistically significant positive correlations (p ≤ 0.05) 

between the following NASQAN and BEST variables (respectively): atrazine with external lesions; atrazine and zinc with 

external anomalies; arsenic, copper, and total trace elements 

(sum of arsenic, chromium, copper, and zinc concentrations) 

with ethoxyresorufin O-deethylase activity; specific conductance, copper, and total trace elements with percent of tissue 

occupied by macrophage aggregates; total trace elements 

80225
2 Approach for Integrating BEST and NASQAN

with splenosomatic index and condition factor; copper with 

condition factor; and arsenic, copper, total trace elements, and 

zinc with health-assessment index. Other significant correlations between NASQAN and BEST variables included trophic 

position (δ15N of fish minus δ15N of particulate organic matter 

in water) with total organochlorine pesticides and total PCBs 

in fish. In-depth analyses using these techniques could be 

conducted using larger data sets.

Recommendations to the BEST Program pertain to the 

quantity of water-quality and fish data, additional water-quality parameters to measure, summarization and integration 

techniques, the use of guidelines for the protection of freshwater aquatic life, substitution methods for censored data, use of 

water and fish trace-element and δ15N data, and additional data 

(suspended- and streambed-sediment) parameters that could 

be included in future integrated studies. Data sets combined 

and summarized in the manner described here would represent 

a comprehensive assessment of fish exposure to contaminants 

and the effects of exposure on the fish.

Introduction

The U.S. Geological Survey (USGS) Biomonitoring 

of Environmental Status and Trends (BEST) Program has 

initiated testing of a national-scale framework for monitoring 

environmental contaminants and their effects in U.S. rivers. In 

this framework, fish are collected periodically from selected 

sites on large rivers (nominally those with drainage areas 

greater than 20,000 square miles at their most downstream stations) and analyzed for a suite of accumulative environmental 

contaminants (such as hydrophobic organic chemicals, metals, 

and metalloids) and for biological indicators of exposure to 

contaminants and their effects. The BEST program conducted 

studies in the Mississippi River Basin (MRB), Columbia River 

Basin (CRB), and Rio Grande Basin (RGB) to evaluate this 

proposed suite of chemical and biological methods (Schmitt 

and others 1999a, 2004; Schmitt, 2002; Hinck and others 

2004), and similar investigations are underway in other basins. 

The 1995 MRB study (Schmitt, 2002) included stations in two 

USGS National Water-Quality Assessment (NAWQA) Program study units lying within the basin. The CRB and RGB 

studies, initiated in 1997 (Schmitt and others, 2004; Hinck 

and others, 2004), included stations from the USGS National 

Stream Quantity Accounting Network (NASQAN) Program, 

two of which were also NAWQA stations.

The NASQAN program monitors water quality in five 

large (250,000 to 1,200,000 square miles) U.S. river systems—the Colorado, Columbia, Mississippi, Rio Grande, and 

Yukon—by regularly measuring the concentrations of dissolved and suspended substances. NASQAN analytes include 

hydrophyllic contaminants (such as water-soluble pesticides, 

major ions, and selected trace elements) as well as nutrients, 

carbon, and other parameters. Because quantifying mass flux 

is an important NASQAN objective, continuous discharge 

measurements are also obtained. 

The proposed national-scale component of the BEST 

program shares many features and objectives with NASQAN, 

and the programs appear to be complementary. The BEST 

program studies in the CRB and RGB therefore incorporated 

NASQAN stations as sampling locations to evaluate the compatibility of the two programs. The study reported here was 

initiated in 2001 to investigate techniques for summarizing 

and integrating fish-contaminant data from the BEST program for 1997-98 and water-quality data from the NASQAN 

program for water years (WYs) 1995-98. (Note: A water year 

is the time period from October 1 of the year preceding the 

designated water year through September 30 of the designated 

water year; for example, WY 1995 is October 1, 1994 through 

September 30, 1995.). Test data sets containing selected 

BEST and NASQAN data for the CRB and RGB were used as 

examples for summarizing and integrating monitoring data for 

large river basins in the U.S. and to provide recommendations 

to the BEST and NASQAN programs to enhance the existing 

complementary relationships between the two programs. As 

such, the study illustrates opportunities for information sharing among USGS programs. 

Background

The 1995 and 1997-98 BEST studies in the MRB 

(Schmitt, 2002), CRB (Hinck and others, 2004), and RGB 

(Schmitt and others, 2004) evolved from the fish component 

of the National Contaminant Biomonitoring Program (NCBP) 

(Schmitt and others, 1999c). The NCBP originated as part of 

the National Pesticide Monitoring Program (NPMP), a multiagency effort in which the U.S. Fish and Wildlife Service 

(USFWS) participated by periodically collecting and analyzing avian wildlife, starlings, and freshwater fish (Johnson and 

others, 1967; Bunck and others, 1987; Schmitt and Bunck, 

1995). Fish collection stations were located at key points in 

major U.S. rivers and in the open waters of the Great Lakes, 

with the objective of characterizing geographic trends in the 

concentrations of toxins that accumulate in fish and, thereby, 

threaten both fish and fish-eating wildlife. The fish component of the NCBP was revised several times during its 20-year 

history; prior to its last implementation during 1986-87, 115 

stations were sampled every other year (Schmitt and Bunck, 

1995; Schmitt and others, 1999c). From each station, a total 

of three composite samples of two species (a piscivore and 

a benthivore) was collected and analyzed for whole-body 

concentrations of organochlorine chemicals (pesticides and 

industrial chemicals), heavy metals, and metalloids.
Background 3

BEST Program

Bio-accumulative contaminants in large rivers are still 

perceived to be important. Nevertheless, by the mid-1980s it 

was apparent that contaminant threats represented by contemporary-use pesticides (that is, herbicides and short-lived 

insecticides) and other chemicals not amenable to monitoring 

through chemical analysis of animal carcasses were not being 

addressed by the NCBP. Consequently, planning was initiated 

for an expanded program that would include biological components and which would be targeted towards habitats, lands, 

and species of greater concern to the USFWS. This expanded 

program—the BEST program—was initiated in 1992 and was 

transferred to the USGS in 1996 after several years of development and pilot studies (BEST, 1996; Schmitt, 2002).

The BEST program documents spatial and temporal 

trends in the exposure of organisms and ecosystems to contaminants and the effects of exposure on selected organisms 

(BEST, 1996). This is accomplished through the application 

of chemical and biological methods spanning several levels of 

biological organization (such as cellular, organ, organism) and 

through the incorporation of information from other programs 

and sources. Similar weight-of-evidence approaches underlie other environmental monitoring programs (for example, 

Hirsch and others, 1988). The suite of BEST program 

methods includes chemical analysis of persistent accumulative contaminants in fish and biological indicators (biomarkers) of chemical exposure and the effects of such exposure 

(BEST, 1996; Schmitt and Dethloff, 2000). Some biomarkers 

integrate the cumulative effects of multiple contaminants and 

other environmental stressors (Adams, 1990). 

NASQAN Program

The NASQAN program also spans multiple decades and 

was designed to provide general information on status and 

trends of contaminants in large watersheds. NASQAN originally sought to account for the quantity and quality of water 

moving within the U.S., document temporal and geographic 

trends in water quality, and provide a database against which 

to evaluate future changes in water quality and quantity. First 

implemented in 1970, by 1978 NASQAN was sampling more 

than 500 fixed (long-term) stations. NASQAN stations were 

situated at the lowermost points in hydrologic accounting 

units, typically at the terminus or confluence of major waterways. As such, some stations were originally located at or 

near NCBP sites (Smith and others, 1988). At each station, 

discharge was measured and water samples were collected 

monthly and analyzed for a suite of water-quality constituents 

including suspended sediments, nutrients, total and fecal coliform bacteria, and major and trace elements.

The NASQAN program was reviewed by the USGS 

several times. In general, findings relative to temporal and 

geographic trends were deemed interesting and informative; 

however, like the NCBP, the program itself was perceived as 

somewhat deficient because of its inability to assess causation with respect to observed water-quality changes and a 

lack of focus on specific contemporary water-quality issues. 

Additional technical problems identified through internal 

and external reviews included the questionable suitability 

of monthly sampling for estimating mass flux of chemical 

constituents; the tenuous connection between conditions at the 

lowest points in sub-basins, where samples were collected, to 

upstream conditions; and a lack of analytical quality control 

for some analytes, most notably trace metals (Windom and 

others, 1991; Hooper and others, 1996). A major re-design of 

the program was consequently initiated in 1993 and completed 

in 1995. The redesigned program, initially called NASQAN II 

(henceforth known as NASQAN), was phased into operation 

in the mid-1990s (Hooper and others, 1996). 

For the first five years (1996-2001) after the major 

redesign, NASQAN focused on the four largest river systems 

of the conterminous states—the Colorado, Columbia, Mississippi, and Rio Grande (Hooper and others, 1996; U.S. Geological Survey, 1997, 2003). The program sought to estimate the 

mass flux or loads of materials from and through these large 

river systems. Specifically, NASQAN was designed to characterize large (20,000 to 70,000 square miles) sub-basins of 

the four river systems, determine regional source areas for dissolved and suspended water constituents, and assess the effects 

of human activities on the concentrations and amounts of these 

materials (U.S. Geological Survey, 1997). Other intended uses 

of NASQAN data included documenting long-term trends in 

constituent fluxes and concentrations; calculating constituent 

loads to receiving waters; providing a framework for more 

detailed assessments of water-quality conditions and their 

causes; and, together with information from the NAWQA program (Hirsch and others, 1988), evaluating the influences of 

land use practices and water quality in lower-order basins on 

conditions in large rivers using water-quality models (Hooper 

and others, 1996; U.S. Geological Survey, 1997).

To achieve these objectives, NASQAN stations located 

at the downstream limits of sub-basins within the four main 

basins [current (2005) total n = 27] are sampled at varying, 

flow-dependent intervals, with the sampling frequency determined independently for each station. Typically, stations are 

sampled 6-15 times annually with the most intensive sampling conducted during high-flow periods, when variability 

is characteristically greatest. NASQAN water samples are 

analyzed for about 100 dissolved and 30 suspended constituents. These include carbon (dissolved and suspended organic 

carbon, dissolved inorganic carbon), major ions (calcium, 

chloride, sulfate, and others), nutrients (total and dissolved 

forms of nitrogen and phosphorus), hydrophyllic pesticides 

(insecticides and herbicides, such as carbofuran and atrazine, 

respectively), field properties (discharge, water temperature, 

dissolved oxygen, pH, specific conductivity), suspended sedi-
4 Approach for Integrating BEST and NASQAN

ment, and suspended and dissolved trace elements (arsenic, 

lead, zinc, and others). Discharge is monitored continuously 

at all sites.

The NASQAN program began a special five-year phase 

of study in 2001. Resources were redirected to an intensive 

sampling program in the Yukon River basin, and sampling 

in the Colorado and Columbia River basins was significantly 

decreased to only one and two stations, respectively. Sampling in the MRB and RGB remained unchanged from that of 

1996-2001 (U.S. Geological Survey, 2003). 

BEST Program Implementation Projects

The BEST program conducted studies designed to test its 

aquatic ecosystem methods in large rivers. The studies were 

implemented cooperatively with other USGS water-monitoring programs. Historic NCBP fish-collection stations in the 

MRB (n = 33) were sampled in late 1995 (Schmitt and others, 

1995), and NCBP sites in the CRB (n = 10) and RGB (n = 5) 

were sampled in 1997-98 (Hinck and others, 2004; Schmitt 

and others, 2004; fig. 1). These studies were designed to 

provide contemporary information on the distribution and 

abundance of bioaccumulable contaminants and to test the feasibility of incorporating biological indicators into a large-scale 

monitoring program. More specifically, the objectives of the 

projects were to: (1) field-test, evaluate, and optimize the suite 

of aquatic indicators selected for use in the BEST program; 

(2) document and characterize the geographic distribution of 

chemical contaminants and their effects on fish and wildlife at 

selected sites in the large rivers of the MRB, CRB, and RGB; 

(3) compare the findings for bioaccumulable contaminants 

with those of previous NCBP fish collections; and (4) evaluate and demonstrate the technical and logistic feasibility of 

implementing the BEST program through partnerships with 

USGS science centers, cooperative research units, universities, and other monitoring programs and U.S. Department 

of the Interior agencies. To achieve the latter objective, the 

MRB study (Schmitt, 2002) included sites on smaller rivers 

and streams in the Eastern Iowa Basins (n = 5) and Mississippi 

Embayment (n = 8) study units of the NAWQA Program. The 

1997-98 projects included five NASQAN sites in each of the 

CRB (Hinck and others, 2004) and RGB (Schmitt and others, 

2004); two in the CRB were concurrent NAWQA stations.

The specific methods incorporated into the BEST studies included analyses of composite samples of whole fish for 

organochlorine chemical residues (pesticides and industrial 

chemicals) and elemental contaminants (metals and metalloids) following a sampling protocol derived from the NCBP 

(Schmitt and others, 1999c); toxicity testing of fish carcass 

extracts with the H4IIE rat hepatoma cell bioassay (Tillitt 

and others, 1991); and additional indicators of fish health and 

condition (table 1). These biological response (fish-health 

assessment) indicators (tables 1 and 2) were chosen to represent chemical responses spanning a wide range of biological 

organization and chemical specificity (Schmitt and Dethloff, 

2000). These range from biochemical responses to individual 

contaminants or groups of structurally similar contaminants 

to general indicators of fish health that respond to many 

contaminants, individually and in combination, as well as to 

other environmental factors (Adams, 1990). Other programs, 

including NAWQA, include indicators of even higher-level 

effects, such as the taxonomic composition of the algal, benthic macroinvertebrate, and fish communities. Further information on the biological methods and the rationale for their 

inclusion is provided elsewhere (BEST, 1996; Schmitt and 

others, 1999b; Schmitt and Dethloff, 2000; Schmitt, 2002). 

Chemical residue and elemental contaminant concentrations in fish reflect concentrations in water and diet to varying 

degrees. For hydrophobic organic chemicals, it is generally 

accepted that concentrations in fish are at (or at least tending 

toward) equilibrium or steady−state with respect to the water 

(Hamelink and others, 1971). From the earliest days of the 

NPMP (predecessor of the NCBP), it was tacitly assumed that 

fish collected from nodal points in major drainages integrate 

broad expanses of space and time with respect to accumulative contaminants. The dynamics of uptake and elimination 

are highly variable, however, and the expanse of space and 

time over which the measurements integrate is consequently 

also variable. In addition, it is presumed that observed differences in the concentrations of accumulative contaminants in 

fish (among locations, time periods, or both) reflect differing 

exposure concentrations, durations, or both. In fact, such 

differences may at least partly reflect population and ecosystem factors such as longevity and trophic dynamics, the latter 

including productivity, food chain length, and so on (Kiriluk 

and others, 1995; Cabana and Rasmussen, 1996; Kendall, 

1998). From the environmental manager’s perspective, it is 

important to differentiate between increases or decreases in 

the concentrations of toxins that are attributable to changing 

environmental fluxes, which can at least potentially be regulated, from those caused by changing ecological conditions, 

which are much more difficult (if not impossible) to control. 

For this reason, the ratio of the stable isotopes 15N:14N (δ15N) 

in organisms, which can be used as an indicator of trophic 

position (Cabana and Rasmussen, 1996; Kendall, 1998), was 

incorporated into the 1995 and 1997-98 BEST projects as a 

corollary variable. Similar measurements have been made on 

samples of various media collected under the auspices of the 

NASQAN and NAWQA programs (Kendall and others, 1999). 

By accounting for trophic differences, it may be possible to 

more precisely determine temporal and geographic trends in 

environmental contaminant concentrations.
98

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Figure 1. Map of the conterminous United States showing the waterways, river basins, and BEST stations sampled in 1995 and 1997-98. BEST, Biomonitoring of Environmental Status and Trends program; NASQAN, National Stream Quantity Accounting Network; NAWQA, National Water-Quality Assessment program. See Tables 3 and 5 for station descriptions and sampling dates of stations used in this study. 

Background 5
6 Approach for Integrating BEST and NASQAN

Table 1. Methods incorporated into the 1995 and 1997 BEST large rivers projects. 

Method Description 

Tissue(s) 

examined 

Primary 

reference(s) Sensitivity 

Histopathology Microscopic examination for Liver, gill, Overall organism Hinton and others (1992); 

the presence of lesions; can gonads, spleen, health and Hinton (1993); Goodbred 

provide early indication of and kidney contaminants and others (1997) 

chemical exposure 

Ethoxyresorufin O- Enzyme induction by planar Liver PCBs; Pohl and Fouts (1980); 

deethylase (EROD) hydrocarbons chlorinated Kennedy and Jones (1994); 

activity dioxins and Whyte and others (2000) 

furans; PAHs 

Lysozyme activity A disease resistance factor that Blood plasma Overall organism Blazer and others (1994) 

can be suppressed in the health 

presence of contaminants 

Macrophage Macrophages are important in Spleen Multiple Blazer and others (1994); 

aggregate analysis the immune system, serving as contaminants Blazer and others (1997) 

a first line of defense for the including PAHs 

organism and as an antigen and metals 

processing cell 

H4IIE bioassay A screening tool to determine Whole fish PCBs; Tillitt and others (1991); 

the presence of certain classes (composite chlorinated Whyte and others (2004) 

of planar halogenated samples) dioxins and 

compounds furans 

Vitellogenin A precursor of egg yolk, Blood plasma Endocrine- Denslow and others (1999) 

normally synthesized in the modulating 

liver of female fish substances 

Steroid hormones Determine reproductive health Blood plasma Endorcrine- Guillette and others (1994); 

(estradiol and and status modulating Goodbred and others 

testosterone) substances (1997) 

Chemical analyses Organochlorine chemical Whole fish Specific analytes Schmitt and others 

residues and elemental (composite (1999c) 

contaminants samples) 

Somatic indices The relative mass of some Gonads, spleen, Overall organism Grady and others (1992) 

organs is often indicative of liver health 

chemical exposure 

Necropsy-based fish Visual assessment of All Overall organism Goede (1988, 1996); 

health external/internal anomalies (for health Adams and others (1993); 

assessment example, lesions, parasites, Adams (1990) 

tumors), which may indicate 

contaminant-related stress 

Potential Complementarity of BEST and

NASQAN Programs

The large rivers monitoring component of the BEST 

program and the water monitoring conducted by the NASQAN 

program appear to complement each other. BEST yields 

information on the concentrations of persistent hydrophobic 

organic chemicals, mercury, selenium, and other accumulative contaminants and on the cumulative effects of exposure 

to these and other substances. NASQAN yields information 

on concentrations of water-soluble contaminants to which the 

fish have been exposed, thereby providing a possible explanation for observed biological findings and observations. The 

BEST program identified the analysis of hydrophyllic contaminants in water as a desirable complement to its suite of 

organism-based methods for monitoring in aquatic habitats 

(BEST, 1996). Within sub-basins of the NASQAN basins, the 

NAWQA program also produces information on contaminants 

in water, sediment, and biota, as well as on land use and land 
Integration of BEST and NASQAN Data 7

Table 2. Fish health-assessment indicators and stable isotope, trace element, organochlorine pesticide, and 

PCB parameter list for fish collected at BEST stations in the Columbia River and Rio Grande basins, 1997-98. 

[BHC, benzene hexachloride; %, percent; δ15N, ratio of 15N to 14N; EROD, ethoxyresorufin-O-deethylase; g, gram; mm, millimeter; PCBs, 

polychlorinated biphenyls; TCDD, 2,7,3,8-tetrachlorodibenzo-p-dioxin. Fish health-assessment indicators were determined for individual fish. δ15

N, 

trace elements, organochlorine pesticides, and PCBs were determined for composite fish samples. See table 1 for specific tissues that were examined] 

Fish health assessment indicators

1 Stable isotopes and 

trace elements 

Organochlorine chemical 

residues 

Field observations Field or laboratory 

measurements Stable isotope: alpha-BHC 

Bile color Atresia (%) δ15N beta-BHC 

Body surface EROD activity Trace elements: delta-BHC 

External lesions Condition factor Aluminum gamma-BHC 

Left and right eye condition Gonadosomatic index Arsenic cis-Chlordane 

Aggregated fin condition Gonad stage (histopathology) Barium trans-Chlordane 

Gallbladder (bile) fullness Gonad weight (to 0.01 g) Boron cis-Nonachlor 

Left and right gill condition Health-assessment index Beryllium trans-Nonachlor 

Gonad (gender, condition) Hepatosomatic index Cadmium Oxychlordane 

Gonadal stage (visual) Length (mm) Chromium Dieldrin 

Kidney condition Liver weight (to 0.1 g) Copper Endrin 

Liver condition Macrophage aggregates Iron Heptachlor epoxide 

Extent of mesenteric fat Tissue occupied by 

macrophage aggregates (%) Lead Mirex 

Opercle condition Spleen weight (to 0.002 g) Mercury Toxaphene 

Pseudobranch condition Splenosomatic index Magnesium o,p’-DDD 

Spine sample collection Vitellogenin Manganese o,p’-DDE 

Spleen condition Weight (g) Molybdenum o,p’-DDT 

Intersex Nickel p,p’-DDD 

Selenium p,p’-DDE 

Strontium p,p’-DDT 

Vanadium Hexachlorobenzene 

Zinc Total PCBs 

Percent moisture TCDD-equivalents 

Percent lipid 

1

See Schmitt and Dethloff (2000) for description of fish health-assessment indicators. 

cover; and the relations among these variables. Thus, the Integration of BEST and NASQAN Data

NASQAN and NAWQA programs provide valuable contemporaneous information for the analysis and interpretation 

of the biological information beyond the chemical analyses The objective of this study was to evaluate alternative 

acquired by BEST, and BEST provides information on the techniques for summarizing and integrating data from the cumulative effects of exposure in fish to selected chemicals BEST and NASQAN programs that monitor contaminants and and other stressors measured by NASQAN and NAWQA. their effects on fish in large rivers. Interfacing the informa-
8 Approach for Integrating BEST and NASQAN

tion generated by water programs, NASQAN in particular, 

with that of the BEST program presents some challenges. 

Although the programs share similar overall goals related 

to contaminant monitoring (including the documentation of 

spatial and temporal trends), their specific objectives are quite 

different. Each program also has particular data issues (such 

as censoring and sampling frequency) that could affect data 

integration. This latter factor and others will be discussed in 

the evaluation of alternatives for manipulating, summarizing, 

and packaging the data generated by the programs to better 

complement each other.

Problem Statement

The condensation, summarization, and reporting of 

water-quality measurements made at different spatial and 

temporal scales has been addressed from several perspectives 

and are well represented in the literature (for example, Hirsch 

and others, 1982; Helsel and Hirsch, 1992; Olsen and others, 1999). There are spatial as well as temporal aggregation 

problems to be resolved. Spatially, the problem may be stated 

simply as, “To what extent do the measurements made on fish 

and water represent the same point, segment, or reach of the 

river being considered?” This question is largely beyond the 

scope of the present investigation. Although we may refer in 

passing to station locations and the degree to which BEST/

NCBP, NASQAN, and (perhaps) NAWQA stations coincide, we assumed for this investigation that the measurements 

based on fish and water were collected from the same “place” 

or site—however this was or will be defined. It is important 

to note that the topic of fixed station versus probability-based 

sampling is the focus of substantial debate among water-quality monitoring entities (for example, Olsen and others, 1999). 

This study therefore focused on the “what” and “when” 

components of the question—temporal issues and the overlap or complementarity of the programs and their respective 

endpoints.

Censoring is common and problematic for water constituents. Censored data comprise measured values below the 

minimum reporting level (MRL) that are reported as less-than 

(<) concentrations for an analyte or an analyte that is undetected; they are also known as non-detects. The percentage 

of non-detects is often high for constituents such as trace 

elements and seasonally applied pesticides. In addition, the 

distributional properties of water-quality variables often make 

them difficult to analyze by traditional parametric statistical 

methods. Consequently, water data are often reported and 

tested non-parametrically; for a given time period, the median, 

quartiles, minimum, and maximum are computed and compared, along with incidence (percentage of samples above the 

reporting level). Where sufficient uncensored observations are 

present, log-transformed values may be analyzed by parametric methods and reported as geometric means and standard 

errors (for example, Goodbred and others, 1997). A number 

of alternatives are available for dealing with censored data, 

ranging from very simplistic (replacement of censored values 

with zeros or other constants; adding constants; replacement 

with 50% of the censoring level) to complex and computerintensive (probability-based computation of replacement 

values and others). 

The NASQAN data present an additional challenge. 

Because of the program’s specific objective related to the estimation of mass flux, the measurements are weighted towards 

the seasons with greatest concentration and flux variability, 

normally the high-flow seasons. However, the concentrations 

of hydrophobic chemicals and elemental contaminants in fish 

are concentration-dependent, not flux-dependent; and the 

biological response variables measured by the BEST program 

are generally dose-dependent. In the context of water quality, dose is defined in terms of concentration and duration of 

exposure. Consequently, the NASQAN data also must be 

aggregated in a manner that removes temporal bias before 

they can be combined with the BEST data. Goodbred and 

others (1997) used time-weighted annual geometric means to 

describe hydrophyllic pesticide concentrations for comparison with reproductive biomarkers in fish. They noted that the 

pattern of temporal and geographic variation differs among 

chemicals, but that seasonal patterns tend to repeat annually 

at a site; and that time-weighted annual means were therefore 

good indicators of central tendency. Goodbred and others 

(1997) also noted that the most relevant measure of exposure 

of fish to dissolved constituents is difficult to determine, which 

suggests that a less proscribed approach may be more appropriate.

Pollutant concentrations in water and tissue are reported 

differently, at least by some investigators. Liquid-phase 

concentrations of water constituents are typically reported in 

units of mass per volume, such as micrograms per liter (μg/L). 

Concentrations of suspended constituents may be reported 

this way or in units of mass [mass/mass, dry weight basis; 

for example, micrograms per gram (μg/g)]. Tissue concentrations of inorganic contaminants (metals, metalloids) are 

generally measured from dried samples and reported in units 

of mass/mass; however, they may be reported as either dry 

weight or wet weight concentrations, the latter after adjusting 

for moisture content (reported as percent). Organic chemical 

residue concentrations are measured in a lipid extract (mass/

lipid mass) and back calculated to wet-weight concentrations 

(mass/mass) based on gravimetrically determined lipid content 

(percent).

Statistically, the data describing contaminant concentrations in fish and other organisms share some problems with 

water data but also are plagued by some of their own. Chemical residue and elemental contaminant data for fish are generally less temporally dynamic than water concentrations and 

biomarkers, and samples are typically collected and analyzed 

less frequently. Owing to the expense of analysis and high 

inter-individual variation, composite samples are commonly 

analyzed by many programs (including BEST and NAWQA). 
9

The number of composite samples usually is small compared 

to the sample sizes characteristic of water-monitoring programs. As is true for water data, concentrations of elemental 

and organic contaminants in fish are often distributed non-normally, and censored values are common. Also like water data, 

the extremes, central tendencies, variability, and incidence are 

typically analyzed and reported. Both parametric and nonparametric statistical procedures are used regularly.

The biological response (fish-health assessment) indicators used in the BEST program (table 2) vary greatly in 

response times and persistence (Adams, 1990). Among the 

BEST indicators, some (ovotestes in adult male fish, for 

example) indicate exposure to chemicals at specific points in 

the early development of individual organisms (Tyler and others, 1998), and may therefore reflect previous conditions that 

might or might not be reflected by contemporaneous water 

data. Other indicators (such as reproductive hormones) are 

ephemeral and extremely sensitive to environmental stimuli, 

and therefore can reflect both the conditions (chemical and 

other) to which the organism was exposed at or shortly before 

the time of capture (Tyler and others, 1998). However, the 

biological endpoints may also reflect the effects of longerlived chemicals stored within the organisms, chemicals 

transferred maternally, the manifestation of early developmental effects, or combinations of these causal agents. Other 

biological indicators are intermediate, cumulative, or both. 

For example, liver cancer, as manifested by grossly visible and 

histopathologically diagnosed tumors, may represent both a 

historic event (a mutation caused by exposure to a genotoxin, 

the so-called initiation phase of chemical carcinogenesis) 

followed by subsequent promotion of the initial mutation. 

The promoting agent might or might not be the same as the 

initiator, depending on the chemical or chemicals involved. 

Consequently, and as noted by Goodbred and others (1997), 

the most appropriate summary statistics for one variable or 

group of variables in either the fish or the water data set may 

not be relevant for other variables without some formidable 

(and untestable) assumptions. 

Purpose and Approach

The objectives of this report are to (1) describe alternative techniques for summarizing and integrating data from the 

BEST and NASQAN programs; (2) illustrate, through the use 

of test data sets, results of the summarization and integration 

techniques; and (3) provide recommendations to the BEST 

program for future studies. Because the focus of this study 

and report is on describing and illustrating techniques for summarizing and integrating NASQAN and BEST data using test 

data sets, results are presented for illustration purposes only 

and are not in-depth analyses. The analyses show how data 

from two monitoring programs can be integrated and how the 

results can be interpreted.

Integration of BEST and NASQAN Data

Given the difficulties identified in the foregoing section, 

this study was conducted iteratively following three steps. 

First, the pertinent literature was examined, and techniques 

used in similar situations were summarized. Because the issue 

has been addressed by the NAWQA program to some extent 

(for example, Goodbred and others, 1997) scientists familiar 

with NASQAN and(or) NAWQA were consulted. Next, test 

data sets based on BEST and NASQAN studies in the CRB 

and RGB were assembled for comparisons of alternative summarization and integration techniques. The data sets included 

biological variables based on individual fish (biomarkers, 

age, and others) and composite samples (concentrations of 

organochlorine and elemental contaminants, and δ15N) from 

the BEST program, and concentrations of pertinent measured 

parameters in water from the NASQAN program for stations 

in the two basins for the period of record preceding the date 

of fish collection at each station. Because only selected trace 

elements and δ15N are common to both programs and their 

respective media (fish and water), these pairs were examined 

as part of the process of summarizing and integrating the data 

sets. The utility of δ15N as a corollary variable for normalizing 

among sites also was investigated. As the last step, alternative summarization and integration techniques were illustrated 

using the test data sets. Results from ongoing BEST synthesis 

activities (Schmitt and Dethloff, 2000; Whyte and others, 

2000) investigating the influence of chemical and other factors 

on the biological variables for fish were also utilized.

Literature Review

As noted in the ”Problem Statement” section, waterquality data are commonly censored. Statistical treatment of 

censored water-quality data has been discussed by Helsel and 

Gilliom (1986), Gilliom and Helsel (1986), and Helsel (1990). 

Applicable techniques from these sources include simple 

substitutions and estimation/replacement procedures. In addition, water-quality data are typically distributed non-normally. 

Both conditions make the use of parametric statistical methods problematic. Accordingly, Helsel (1987) reported on the 

advantages of using nonparametric procedures.

Trace elements and organic compounds in fish have been 

investigated in almost all NAWQA study units (http://water.

usgs.gov/nawqa/bib.html). Many investigations concentrated 

on the occurrence and distribution of trace elements, organic 

compounds, or both in fish [see, for example, Deacon and 

Stephens (1998), Frenzel (2000), Knight and Powell (2001), 

and Chambers (2002)]. Investigations also have examined 

the relations between land use and contaminants in fish [for 

example, Long and others (2000) and Gebler (2000)]; and 

trace metals and fish tissue [for example, Goldstein and others 

(1996) and Goldstein and DeWeese (1999)]. Riva-Murray and 

others (2003) determined trends in concentrations of polychlorinated biphenyls in fish tissue.
10 Approach for Integrating BEST and NASQAN

Numerous studies have examined differences in biota 

or fish-tissue data collected at different sites or regions and 

illustrate some common summarization issues. For example, 

Brown (1998b) investigated concentrations of chlorinated 

organic compounds in Asiatic clams (Corbicula fluminea) 

and fish tissue in California. Tissue data were normalized by 

lipid content, and one-half the reporting level was substituted 

for less-than concentrations in tissue for use in statistical 

tests. Bilger and others (1999) compared local, regional, and 

national concentrations of organochlorine chemical residues in 

fish tissue to concentrations in the Lower Susquehanna River 

Basin. In this investigation, concentrations of total DDT (sum 

of o,p’-DDD, o,p’-DDE, o,p’-DDT, p,p’-DDD, p,p’-DDE, and 

p,p’-DDT) and total chlordane (sum of cis-chlordane, transchlordane, cis-nonachlor, trans-nonachlor, and oxychlordane) 

were also computed using a value of one-half the MRL for 

less-than concentrations. One-half the reporting level has 

also been used in the analysis of elemental contaminant and 

organochlorine residue data from NCBP and BEST studies 

(Schmitt, 2002; Schmitt and others, 1999c; 2004; Hinck and 

others, 2004).

Studies integrating water-quality (water-chemistry and 

bed-sediment) and fish data have been described by a number 

of authors. Cuffney and others (2000) and Deacon and others 

(1999) determined the level of impairment of stream sites by 

assessing or characterizing physical, chemical, and biological conditions with multimetric indices and water-quality 

measurements. Multimetric indices developed by Cuffney 

and others (2000) included metals enrichment in bed sediment; non-pesticide agricultural intensity index; and pesticide 

contamination in filtered water, suspended sediment, bed 

sediment, and fish tissue. Deacon and others (1999) examined 

nine measures of water quality, which included information 

on nutrients in the water column, specific conductance, trace 

elements in streambed sediment, pesticides in fish tissue, fish 

communities, macroinvertebrate richness and composition, 

and measures of stream habitat, such as stream modification 

and bank erosion. Both studies developed fish-impairment 

or fish-community degradation indices based on the sum and 

percentages of tolerant individuals, omnivores, non-native 

species, and external anomalies. Fish-community metrics also 

were used by Brown (1998a) and May and Brown (2000) to 

characterize fish communities and their relations to environmental variables. Machala and others (2001) investigated biochemical responses to environmental mixtures of contaminants 

in the liver of chub (Leuciscus cephalus) by matching biochemical response data with bed-sediment contaminant data. 

Investigations of endocrine-disrupting chemicals in surface 

waters and reproductive biomarkers in fish have been reported 

by Bevans and others (1996), Goodbred and others (1997), 

and Smith (1998, 2000). Bevans and others (1996) examined 

occurrences and differences in measured chemical parameters 

that included organochlorines and semivolatile industrial 

compounds in the water column, bottom sediment, and tissues of common carp (Cyprinus carpio, henceforth carp) and 

reproductive biomarkers such as steroid hormone concentrations and gonadal histology at five sites in southern Nevada. 

Differences among groups of data were examined with 

nonparametric tests, including a chi-squared approximation 

of the Kruskal-Wallis test and the Duncan multiple-range test. 

No statistical analysis was conducted to relate water-quality 

parameters to fish data in this study. Goodbred and others 

(1997) studied 647 carp from 25 sites in the U.S. ANOVA, 

analysis of covariance, and Tukey’s range test were used to test 

for significant differences among endocrine biomarkers in carp 

within and between various regions of the country. Pearson’s 

product-moment correlation analysis (r) was used to test for 

relations between contaminant groups (dissolved pesticides in 

water and contaminant groups in fish tissue and bed sediment) 

and between contaminant groups and biomarkers in male and 

female carp. Additional patterns in bed sediment and fish-tissue contaminants among sites were examined with principal 

component analysis. Relations between contaminant groups 

and biomarkers were examined by stepwise multiple regression analysis. In these analyses, pesticide concentrations were 

computed as time-weighted annual mean concentrations, all 

values for contaminant groups and endocrine biomarkers were 

log10-transformed prior to correlation analysis, and data for 

all sites from all regions were combined prior to the analyzes 

(Goodbred and others, 1997). Censored or less-than values 

were treated as zero concentrations. Smith (1998, 2000) used 

correlation analyses (Pearson’s r and Spearman’s rho) and 

partial regression to compare and analyze endocrine biomarkers, sediment residues, fish-tissue residues, histopathology, 

and ancillary data for selected fish species in the Hudson River 

of New York. Correlation analyses were not conducted with 

water-quality data, however, because of the question of which 

summary statistic (time-weighted concentrations, monthly 

averages, latest concentrations prior to fish collection, and so 

on) best represented exposure of the fish (Stephen Smith, U.S. 

Geological Survey, personal communication, 2002).

Sources of Water-Quality Data

As a first step in evaluating methods for combining 

results of the USGS BEST and NASQAN programs, the locations of the BEST program fish-collection stations in the CRB 

and RGB (table 3) were compared to locations of NASQAN 

water-quality stations identified from the NASQAN database 

(http://water.usgs.gov/nasqan/data/finaldata.html). Two other 

USGS water-quality databases were also searched for possible stations: (1) NAWQA Data Warehouse (http://infotrek.

er.usgs.gov/servlet/page?_pageid=543&_dad=portal30&_

schema=PORTAL30) and (2) National Water Information 

System (NWIS) (http://waterdata.usgs.gov/usa/nwis/qw). If a 

BEST station and a water-quality station were found to be colocated, the availability of water-quality data was determined 

for the parameters of interest (table 4), and all available data 

collected after the NASQAN re-design in 1995 and prior to the 
Integration of BEST and NASQAN Data 11

Table 3. Fish-collection stations of the BEST program in the Columbia River and Rio Grande basins, 1997-98. 

[USGS-NWIS, U.S. Geological Survey National Water Information System; --, no corresponding NWIS water-quality station] 

BEST 

station 

number 

Nominal station 

location 

(latitude, longitude) 

River Nearest city or feature Sampling date(s) st

USGS-NWIS

ation number 

Columbia River Basin 

41 -- Snake Hagerman, Idaho 10/2/1997 42°47’36.21”N, 

114°56’18.10”W 

42 -- Snake Lewiston, Idaho 9/24-25/1997 46°24’54.28”N, 

117°02’03.49”W 

43 -- Salmon Riggins, Idaho 9/30/1997 45°35’43.42”N, 

116°16’55.00”W 

44 -- Yakima Granger, Washington 10/15-16/1997 46°20’49.31”N, 

120°12’27.03”W 

45 -- Willamette Oregon City, Oregon 11/21/1997, 11/24/1997 45°19’0..47”N, 

122°39’57.50”W 

46 -- Columbia Cascade Locks, Oregon 11/18/1997, 11/20/1997 45°41’23.11”N, 

121°51’00.41”W 

96 13353200 Snake Ice Harbor Dam, Washington 10/8-9/1997 46°41’51.68”N, 

118°53’07.88”W 

97 -- Columbia Pasco, Washington 10/10-11/1997 46°31’49.22”N, 

119°16’42.07”W 

98 -- Columbia Grand Coulee, Washington 11/6-7/1997 47°57’44.85”N, 

118°58’53.84”W 

117 -- Flathead Creston, Montana 10/31/1997, 11/1/1997 48°09’01.09”N, 

114°11’29.71”W 

501 14246900 Columbia Beaver Army Terminal, Oregon 3/31/1998, 4/1/1998 46°10’57.86”N, 

123°04’13.87”W 

502 14128910 Columbia Warrendale, Oregon 11/25-26/1997 45°38’00.82”N, 

121°58’42.57”W 

503 12472900 Columbia Vernita Bridge, Washington 10/13-14/1997 46°37’28.40”N, 

119°51’31.45”W 

504 12400520 Columbia Northport, Washington 11/3-4/1997 48°58’21.70”N, 

117°38’48.92”W 

505 14211720 Willamette Portland, Oregon 11/14/1997, 11/17/1997 45°33’04.51”N, 

122°41’43.74”W 

506 -- Columbia Marine Park - Vancouver, 

Washington 

4/2/1998 45°35’44.21”N, 

122°32’13.61”W 

Rio Grande Basin 

16 -- Rio Grande Mission, Texas 12/2-3/1997 26°09’28.74”N, 

98°20’02.82”W 

63 -- Rio Grande Elephant Butte Reservoir, New 

Mexico 

10/22-24/1997 33°12’48.55”N, 

107°13’27.26”W 

64 -- Rio Grande Alamosa, Colorado 9/23-24/1997, 10/20-

21/1997 

37°25’06.42”N, 

105°46’48.48”W 

65 -- Pecos Red Bluff Lake, Texas 1/21-22/1998 32°00’00.00”N, 

103°58’56.28”W 

511 08480400 Arroyo 

Colorado Harlingen, Texas 9/30/1997, 10/2-3/1997 26°11’44.28”N, 

97°36’20.52”W 

512 08475000 Rio Grande Brownsville, Texas 10/28/1997, 11/24-

25/1997 

25°52’12.96”N, 

97°27’06.30”W 

513 08461300 Rio Grande Falcon Dam, Texas 11/18-19/1997 20°08’06.66”N, 

99°08’06.42”W 

514 08450900 Rio Grande Amistad Dam, Texas 11/4-5/1997 29°26’49.06”N, 

101°03’22.44”W 

515 08377200 Rio Grande Foster Ranch - Langtry, Texas 11/6/1997 29°46’40.91”N, 

101°45’13.22”W 

516 08364000 Rio Grande El Paso, Texas 10/28-29/1997 31°47’55.00”N, 

106°32’25.08”W 
12 Approach for Integrating BEST and NASQAN

Table 4. Water-quality parameters of interest with water-quality guidelines for the protection of freshwater aquatic life. 

[USGS-NWIS, U.S. Geological Survey National Water Information System; MRL, minimum reporting level; --, no criterion established; Cr, chromium; CRB, 

Columbia River basin; RGB, Rio Grande basin; EPTC, s-ethyl dipropylthiocarbamate. Major ions and trace elements are in the dissolved form: sample water 

was passed through a 0.45 micrometer filter prior to analysis. Pesticides are in the dissolved form: sample water was passed through a 0.7 micrometer filter prior 

to analysis. Freshwater criteria for aquatic-life protection are criteria continuous concentrations in the dissolved phase from the U.S. Environmental Protection 

Agency (2002), unless otherwise noted] 

USGS-NWIS 

Parameter 

code 

Parameter name Units MRL Freshwater criterion for protection 

of aquatic life 

Field parameters and major ions 

Flow (mean daily streamflow) cubic feet per second 1 --

00061 Instantaneous discharge cubic feet per second 1 --

00010 Water temperature degrees Celsius 0.1 --

00400 pH standard units 0.1 6.5-9.0

microsiemens per 

00095 Specific conductance centimeter at 25 degrees 1 --

Celsius

70300 Total dissolved solids milligrams per liter 1 --

00940 Chloride, dissolved milligrams per liter 0.1 230 

Trace elements 

01000 Arsenic, dissolved micrograms per liter 1 1

150 

01030 Chromium, dissolved micrograms per liter 1 Cr (III), 274; Cr (VI), 10.582 

01040 Copper, dissolved micrograms per liter 1 31.4-10 (CRB), 314-29 (RGB) 

01145 Selenium, dissolved micrograms per liter 1 4.61 

01090 Zinc, dissolved micrograms per liter 1 318-94 (CRB), 3180-380 (RGB) 

Pesticides 

46342 Alachlor, dissolved micrograms per liter 0.002 --

39632 Atrazine, dissolved micrograms per liter 0.001 412

82680 Carbaryl, dissolved5 micrograms per liter 0.003 60.20

38933 Chlorpyrifos, dissolved micrograms per liter 0.004 0.041

82682 Dacthal (DCPA), dissolved micrograms per liter 0.002 --

39572 Diazinon, dissolved micrograms per liter 0.002 7

0.08

82668 EPTC, dissolved8 micrograms per liter 0.002 --

39532 Malathion, dissolved5 micrograms per liter 0.005 0.1

39415 Metolachlor, dissolved micrograms per liter 0.002 97.8

82630 Metribuzin, dissolved8 micrograms per liter 0.004 9

1.0

82679 Propanil, dissolved10 micrograms per liter 0.004 --

04035 Simazine, dissolved micrograms per liter 0.005 610 

82678 Triallate, dissolved8 micrograms per liter 0.001 9

0.24 

1

This recommended water-quality criterion was derived from data for arsenic (III) but is applied here to total arsenic in the dissolved phase. 2

The freshwater criterion for this metal is expressed as a function of hardness (mg/L) in water. The value given here corresponds to a hardness of 100 mg/L. See U.S. 

Environmental Protection Agency (2002) for calculating criteria based on other hardness values. 3

Criteria have been adjusted for hardness and reflect different hardness among the samples. 4

Draft chronic criterion, from U.S. Environmental Protection Agency (2001). 5

Rio Grande basin only. 6

Criterion for the protection of freshwater aquatic life is a Canadian Water Quality Guideline from Canadian Council of Ministers of the Environment (1999). 7

Criterion for the protection of freshwater aquatic life is from International Joint Commission Canada and United States (1978). 8

Columbia River basin only. 9

Interim criterion for the protection of freshwater aquatic life is a Canadian Water Quality from Canadian Council of Ministers of the Environment (1999). 10

Rio Grande basin only. All concentrations but one were less than the minimum reporting level. This parameter has been excluded from the calculations of time-weighted 

concentrations for the Rio Grande basin 
Integration of BEST and NASQAN Data 13

Table 5. NASQAN water-quality stations in the Columbia River and Rio Grande basins, 1995-99. 

[USGS-NWIS, U.S. Geological Survey National Water Information System; na, not applicable] 

USGS- NWIS 

station 

number 

Parameters

3 Period of record1 Number of 

samples

Stat 2 ion name 

Columbia River Basin 

12400520 Columbia River at Northport, Washington 11/25/1995-10/7/1997 28 Field parameters, major ions, 

trace elements, pesticides 

12472900 Columbia River at Vernita Bridge near 

Priest Rapids Dam, Washington 1/17/1996-8/12/1997 15 Field parameters, major ions, trace elements, pesticides 

13353200 Snake River at Burbank, Washington 10/13/1995-9/18/1997 31 Field parameters, major ions, 

trace elements, pesticides 

14128910 Columbia River at Warrendale, 

Washington 10/21/1996-10/29/1997 16 Field parameters, major ions, trace elements, pesticides 

14211720 Willamette River at Portland, Oregon 10/23/1995-10/30/1997 29 Field parameters, major ions, 

trace elements, pesticides 

14246900 Columbia River at Beaver Army Terminal 

near Quincy, Oregon 

10/24/1995-3/9/1998 33 Field parameters, major ions, 

trace elements, pesticides 

Rio Grande Basin 

08364000 Rio Grande at El Paso, Texas 11/2/1995-9/10/1997 24 Field parameters, major ions, 

trace elements, pesticides 

08374200 Rio Grande below Rio Conchos near 

Presido, Texas 4/7/1999-9/28/1999 na na 

08377200 Rio Grande at Foster Ranch near Langtry, 

Texas 10/25/1995-9/10/1997 24 Field parameters, major ions, trace elements, pesticides 

08447410 Pecos River near Langtry, Texas 2/7/1995-9/9/1997 27 Field parameters, major ions, 

trace elements, pesticides 

08450900 Rio Grande below Amistad Dam, near Del 

Rio, Texas 5/28/1996-9/11/1997 11 Field parameters, major ions, trace elements, pesticides 

08459200 Rio Grande below Laredo, Texas 1/28/1998-9/22/1999 na na 

08461300 Rio Grande below Falcon Dam, Texas 10/19/1995-9/3/1997 10 Field parameters, major ions, 

trace elements, pesticides 

08470400 Arroyo Colorado at Harlingen, Texas 3/20/1996-9/10/1997 20 Field parameters, major ions, 

trace elements, pesticides 

08475000 Rio Grande near Brownsville, Texas 10/17/1995-10/11/1997 17 Field parameters, major ions, 

trace elements, pesticides 1

Does not include sample dates, if any, after time of fish collection. 2

Only includes samples collected prior to the time of fish collection. 3

Only considers parameters listed in table 4. 

date of fish collection (table 3) were retrieved. The water- location for this study and are described using the NASQAN 

quality parameters included in this study were selected from name throughout this report.

more extensive data sets because they were either toxicologically significant to fish or were illustrative of the types of data NASQAN.—Water-quality data for all six stations in 

to be encountered in this type of integration. the CRB and six of eight RGB stations were available for 

Fish sampling at BEST Station 96, Snake River at Ice periods of record prior to fish collection; data included field 

Harbor Dam, Washington (in the CRB) occurred at the his- parameters, major ions, trace elements, and pesticides (table 

toric NCBP collection site (table 3) rather than at the nearby 5). Water-quality-data collection at two NASQAN stations in 

NASQAN site Snake River at Burbank, Washington (table 5). the RGB—Rio Grande below Laredo, Texas, and Rio Grande 

These stations were nevertheless considered to be in the same below Rio Conchos near Presidio, Texas—did not begin until 

1998 and 1999, respectively, so these stations were excluded 
14 Approach for Integrating BEST and NASQAN

from the study. The remaining 12 stations, six in each of 

the two basins (table 3), were included in this evaluation. 

The NASQAN station Pecos River near Langtry, Texas, was 

excluded from consideration because fish were not collected 

at this site. Information about the NASQAN program in the 

CRB and RGB is available in Kelly and Hooper (1998) and 

Lurry and others (1998). 

NAWQA.—Four NAWQA study units—Upper Snake 

River Basin, Willamette River Basin, Central Columbia Plateau-Yakima River Basin, and Northern Rockies Intermontane 

Basins—are in the CRB and one—Rio Grande Valley—is in 

the RGB. Using the latitude/longitude of the BEST fish-collection stations, the NAQWA Data Warehouse was searched 

for the presence of water-quality stations in each study unit at 

or near the BEST fish-collection stations. When stations did 

coincide, water-quality data for the years immediately prior to 

the date of fish collection were inventoried.

For some of the NAWQA study units, the collection of 

water-quality data did not coincide with the test WYs of 1995-

98 or the test time period of the 18 months prior to the date 

of fish collection at a station. For other NAWQA study units, 

available water-quality data for WYs 1995-98 were of limited 

quantity and could not be used in this evaluation. For example, only one NAWQA water-quality sample was available for 

the station Snake River near Hagerman, Idaho (BEST fish-collection station 41) for WYs 1995-98. Two NAWQA stations, 

Willamette River at Portland, Oregon, and Rio Grande at El 

Paso, Texas, were also NASQAN stations (table 3). For each 

of these stations, all available water-quality data were joint 

NAWQA and NASQAN data, and no additional data were 

available in the NAWQA Data Warehouse. Because of this 

and the limited quantity of NAWQA data for the test years, 

data specific to the NAWQA program were not used.

Other Data Sources.—The USGS-NWIS was searched 

for water-quality stations and data from Colorado, Idaho, 

Montana, New Mexico, Oregon, Texas, and Washington. 

Most of the resulting water-quality stations located near the 

fish-collection stations did not have adequate water-quality 

data for WYs 1995-98; data were either absent entirely or 

insufficient for statistical analysis. For example, the waterquality station Little Salmon River at Riggins, Idaho, is near 

BEST fish-collection Station 43, Salmon River near Riggins, 

Idaho (table 3). Water-quality data for the Little Salmon River 

were collected bimonthly or quarterly in WY 1995, but no 

data were collected in WYs 1996-97. At one water-quality 

station in the CRB and at three in the RGB that were located 

near fish-collection stations, periodic (monthly, bimonthly, or 

quarterly) data for field parameters, major ions, or both were 

available for WYs 1995-98. Data sets for these four stations 

were not as extensive as those for the NASQAN stations in 

terms of sampling frequency and trace-element and pesticide 

data, however, and these four stations were excluded from the 

study. For this reason and others stated in this and the previous paragraph, water-quality data from sources other than the 

NASQAN program were not used.

Data Summarization and Analysis Techniques

Water-Quality Data

Various methods for summarizing the water-quality data 

were considered, including flow- and time-weighted concentrations [the latter used by Goodbred and others (1997)], 

concentration of the last water sample before fish collection, 

frequency-of-detection or incidence, and total (combined) 

toxic units for trace elements (Wildhaber and Schmitt, 1996). 

Because an important consideration in this study is the length 

of time fish were exposed to contaminants in water, timeweighted concentrations were deemed more appropriate than 

flow-weighted concentrations for use in statistical correlations 

with fish-contaminant data. Conversely, the use of loads (for 

example, zinc loads in a stream) and streamflow to back calculate water-quality concentrations were determined inappropriate for this study. 

Time-weighted concentrations for the water-quality parameters of interest first were determined for the 12 

NASQAN stations in the CRB and RGB using techniques 

described in Larson and others (2004). Three separate timeweighted concentrations were computed for each station: (1) 

All available data (full period of record or FPR) were used to 

compute mean, median, and 90th percentile concentrations, 

(2) Data for WY 1997 (10/1/96-9/30/97) were used to compute mean, median, and 90th percentile concentrations, and (3) 

Natural-log-transformed data for WY 1997 were used to compute geometric means. Data for the FPR also could have been 

used to compute geometric means, but preliminary analyses 

indicated that these did not differ substantially from the WY 

1997 means. For those computations involving the FPR for a 

station, a sampling year was defined as 12 consecutive months 

of data, beginning with the first date of sample collection. For 

all stations except Columbia River at Beaver Army Terminal, 

water-quality data for the 12 months preceding the collection 

of fish closely matched WY 1997; thus, water-quality data for 

WY 1997 were used to represent the 12 months preceding fish 

collection. This process yielded a common period of record 

for all stations. 

Minimum sample criteria (drainage area and number of 

samples per year) for a station had to be met in order to compute time-weighted concentrations. In using the methods of 

Larson and others (2004) (table 6), smaller time gaps between 

samples and a greater number of samples overall are required 

for streams in smaller basins than for those in larger basins 

because runoff events and subsequent pulses of pesticides and 

other constituents can occur very quickly in streams of smaller 
Integration of BEST and NASQAN Data 15

Table 6. Minimum sample criteria for calculation 

of time-weighted concentrations for selected 

NASQAN water-quality stations using techniques 

in Larson and others (2004). 

[mi

2

, square miles; <, less than; �, greater than or equal to] 

Required number of 

samples per year

Dra 1 inage area class (mi

2

) 

Small, <500 16 

Medium, �500 and <5,000 12 

Large, �5,000 and <50,000 10 

Huge, �50,000 8 

1

Time-weighted concentrations were not calculated if any two 

samples within a year were more 120 days apart. 

basins due to their flashy nature. The maximum time gap 

between samples was 120 days. The criteria used were subjective and, as originally developed, were designed with the 

goal of increasing the accuracy of annual percentage and mean 

percentile concentrations of pesticides while retaining as many 

stations in the investigation as possible (Larson and Gilliom, 

2001; Larson and others, 2004).

Time-weighted mean and percentile concentrations were 

calculated by weighting each concentration according to the 

amount of the time that it represented the parameter concentration in the stream. Specifically, the weights were computed as 

the amount of time extending from one-half the time interval 

between a value and the preceding value and one-half the 

time interval extending from the value to the subsequent value 

divided by the total time being considered. The Statistical 

Analysis System (SAS Institute, 1998) Proc Univariate procedure was used to compute the means and percentiles from the 

weighted values. Less-than values were set to MRLs (table 4) 

for these computations. For computations of means, if fewer 

than 15 percent of the data were less-than values, the censored 

values were replaced by one-half the MRL (U.S. Environmental Protection Agency, 2000). If less-than values composed 

15-50 percent of the data and there were at least 20 observations, then the log regression (LR) method (Gilliom and Helsel, 1986; Helsel and Gilliom, 1986) was used to estimate the 

mean. Otherwise, the mean returned by the Univariate procedure was considered to be a less-than value. For example, the 

mean of 3, 4, 5, and <4 was considered to be <4. For percentile concentrations, if more than p percent of the sample data 

were less-than values and there were at least 20 observations 

in the sample with at least 10 values above the MRL, the LR 

procedure was used to estimate the pth percentile. Otherwise, 

the percentile computed by the Univariate procedure was used 

and considered to be less-than at that value (Larson and others, 

2004). 

Time-weighted geometric mean concentrations for the 18 

months prior to the date of fish collection were determined for 

the six RGB stations using less complex techniques than those 

described by Larson and others (2004). Concentration data 

for the parameters of interest for each station were natural-log 

transformed and time-weighted using the methods described in 

the previous paragraph. The resulting time-weighted natural-log concentrations were summed over the 18-month time 

period, and the sum was untransformed to determine the timeweighted geometric mean. Data censored at the MRL were set 

at the MRL or at one-half the MRL. Use of the MRL would 

overestimate exposure of fish to water-borne contaminants. 

Data censored at concentrations greater than the MRL were 

omitted from the calculations. The 18-month time period 

was selected to yield a common length of time that fish were 

exposed to contaminants. Using lengths of time longer than 

18 months would have caused some stations to be eliminated 

from the analyses. 

Concentrations of the last water sample prior to the date 

of fish collection were retrieved for selected water-quality 

parameters for each station in the CRB and RGB. For many 

parameters, the last sample concentration for most or all 

stations was a censored value. Frequency-of-detection was 

calculated for the 18 months prior to the date of fish collection 

at a station for each parameter of interest. For each station, the 

frequency was determined by counting the number of samples 

in which the parameter was detected at a concentration greater 

than or equal to the MRL during the 18 months prior to the 

date of fish collection, then dividing by the total number of 

samples collected during the 18 months. Samples that were 

censored above the MRL for each parameter were excluded 

from frequency-of-detection calculations. Frequency-ofdetection of total trace elements at a station was determined 

by counting the number of times each trace element (arsenic, 

chromium, copper, and zinc) was detected at a concentration 

greater than the MRL (1.0 μg/L), summing this number, and 

then dividing by the total number of trace-element samples at 

the station.

Water-quality guidelines for the protection of aquatic life 

have been established for most of the trace elements and pesticides of interest (table 4). Guidelines have not been established for the pesticides alachlor, Dacthal® (DCPA), S-ethyl 

dipropylthiocarbamate (EPTC), or propanil. Sources of the 

guidelines include the U.S. Environmental Protection Agency 

(USEPA) (2001, 2002), Canadian Council of Ministers of 

the Environment (1999), and International Joint Commission Canada and United States (IJC) (1978). For the USEPA 

guidelines, a criterion continuous concentration (CCC) is 

an estimate of the highest concentration of a parameter in 

surface water to which an aquatic community can be exposed 

indefinitely without resulting in an unacceptable effect (U.S. 

Environmental Protection Agency, 2002). The USEPA is 

revising current aquatic life criteria for copper, iron, lead, 

selenium, and silver and is developing new aquatic life criteria 

for diazinon, nonylphenol, methyl tert-butyl ether (MTBE), 

and manganese (U.S. Environmental Protection Agency, 

2002). For atrazine, draft chronic and acute criteria have been 

established (U.S. Environmental Protection Agency, 2001). A 

chronic criterion is the average concentration of a parameter 
16 Approach for Integrating BEST and NASQAN

to which aquatic organisms can be exposed for four days once 

every three years without injurious effects. An acute criterion 

is the average concentration over one hour to which aquatic 

organisms can be exposed once every three years without injurious effects. For the trace-element and pesticide parameters 

of interest, guidelines for the protection of aquatic life were 

compared to in-stream concentrations for each station in the 

two basins. Concentrations greater than the guidelines were 

identified. 

Water-quality criteria for certain metals [cadmium, 

chromium (III), copper, lead, nickel, and zinc] are hardnessdependent. Accordingly, CCCs for copper and zinc, two of 

the trace elements of interest, were adjusted for hardness (as 

CaCO3) and are listed in table 4 with a range of values that 

reflect different hardness among the samples. Because metals 

are less toxic when hardness is high, a hardness of 400 milligrams per liter (mg/L) was used in the adjustment equation 

when the hardness for a sample was greater than 400 mg/L 

(U.S. Environmental Protection Agency, 2002). By doing this, 

the resulting adjusted criteria are more protective of aquatic 

life than if the actual hardness had been used in the adjustment 

equation. For chromium, CCCs have been established for 

chromium (III) and chromium (VI). The valence states of the 

chromium concentrations in this study were not distinguished, 

but the CCC of chromium (VI), which is not hardness-dependent, was used because this is the more toxic form. As such, 

the chromium criterion is also more protective of aquatic life 

because not all chromium in water is chromium (VI).

A cumulative relative frequency diagram was developed 

to assess how often the CCC was being exceeded for each 

station’s FPR when the CCC for a trace element or pesticide 

was exceeded in more than two samples for a station. The 

frequency-of-exceedence value is an additional measure of 

the degree to which fish have been exposed to a particular 

contaminant.

Water-quality guidelines for the protection of aquatic life 

are focused on the effects of individual contaminants rather 

than on mixtures of contaminants. To account for the toxicity 

of contaminant mixtures, toxic units were calculated (Wildhaber and Schmitt, 1996). A toxic unit is defined here as the 

ratio of the concentration of a contaminant in a water sample 

to the CCC of the contaminant. For this study, the toxic unit 

for each trace element of interest (arsenic, chromium, copper, selenium, and zinc) in a water sample was determined 

individually and then summed to determine a total trace-element toxicity estimate for that sample. Finally, minimum, 

median, and maximum total trace-element toxicity estimates 

were determined for each station using all water samples at 

the station. When data for the individual trace elements were 

censored, the censored value was used in the calculations of 

toxic units. Censored values greater than 2 μg/L were omitted 

from the calculations. 

Fish Data

Fish collected in the CRB and RGB as part of the BEST 

program were examined and analyzed for health-assessment 

indicators, δ15N, trace-element and organic contaminants, and 

total PCBs (table 2) (BEST, 2001; Hinck and others, 2004; 

Schmitt and others, 2004). The health-assessment indicators 

were observed or measured in individual fish whereas δ15N, 

elemental contaminants, organochlorine chemical residues, 

and 2,7,3,8-tetrachlorodibenzo-p-dioxin equivalents from the 

H4IIE bioassay (henceforth TCDD-EQ) were measured in 

composite fish samples (by species and gender) (Schmitt and 

others, 1999a and 1999b). Inorganic data for composite samples included dry- and wet-weight elemental concentrations 

and percent water (table 2). Organic data included concentrations of organochlorine pesticides, total PCBs, TCDD-EQ, and 

percent lipid (table 2). Total organochlorine pesticide concentrations for each sample were calculated as the sum of all 

concentrations for parameters in the “Organochlorine chemical 

residues” column of table 2 except for total PCBs and TCDDEQ. Total DDT concentrations were calculated as the sum of 

o,p’-DDD, o,p’-DDE, o,p’-DDT, p,p’-DDD, p,p’-DDE, and 

p,p’-DDT. Both total organochlorine pesticide and total DDT 

concentrations were determined for each species and gender 

of fish. For concentrations reported as less than the reporting 

levels, both zero and one-half the reporting level were used as 

substitutions for the less-than concentrations.

Mean values for the fish-health observation indicators 

were determined for each species and gender of fish at a station by converting descriptive nomenclature to presence or 

absence, which was represented by zero for normal and one 

for abnormal (tumors, lesions, parasites, and others). Measured fish-health indicators were summarized with mean and 

median values for each species and gender of fish at a station 

and then further summarized by age. Three indicators—the 

gonadosomatic index (GSI) in male and female fish, and 

atresia and vitellogenin (vtg, an egg yolk precursor) in female 

fish—were also summarized by gonadal stage because these 

indicators change over the reproductive cycle (Schmitt, 2002).

Final Data Set Composition.—The procedures described 

in the preceding section yielded a water-quality data set of 

5,046 data points from the NASQAN program. The data 

set for fish from the BEST program comprised 10,946 data 

points representing individual fish for health assessment endpoints and 3,136 data points representing composite samples 

for trace elements, organochlorine pesticides, TCDD-EQ, 

and PCBs. These data sets spanned six stations in each of 

the CRB and RGB for WYs 1995-98, with all 12 stations 

included in both programs. As discussed previously, two 

of the NASQAN stations concurrently were stations for the 

NAWQA program, and all available water-quality data for the 

two stations were shared. No other NAWQA stations in study 

units comprised by the CRB and RGB contained adequate 

water-quality data for the period preceding collection of the 
17

fish. Three NASQAN stations, all in the RGB, had to be 

eliminated. Water-quality data collection occurred after fish 

collection at two stations, and no fish were collected at the 3rd

station. Other USGS water-quality stations in the CRB and 

RGG basins did not contain adequate water-quality data for 

the period preceding fish collection at the remaining BEST 

stations. 

Integration of Water-Quality and Fish Data

Potential relations between in-stream water-quality data 

and fish-contaminant and health-assessment data in the CRB 

and RGB were investigated in a two-step process. First, the 

fish data were graphed against time-weighted water-quality 

concentrations, concentrations of the last water sample before 

the date of fish collection, frequencies-of-detection in water, 

and total trace-element toxicity estimates. Data were not 

transformed for these analyses. Next, the strength of the associations between fish data and water-quality data were quantified by correlation analysis, specifically through the calculation of Kendall’s tau using the SPLUS 2000 statistical package 

(Insightful Corporation, Seattle, Washington). Kendall’s tau 

is a nonparametric rank-based procedure that measures the 

strength of the linear or nonlinear monotonic relation between 

two variables. It is resistant to outliers, and its large sample 

approximation produces significance-test p-values very near 

exact values, even for small sample sizes (Helsel and Hirsch, 

1992). Other correlation analyses that were considered were 

Pearson’s product-moment correlation and Spearman’s rho. 

Pearson’s correlation is used under the assumption that both 

variables are normally distributed. With small sample sizes, 

normally distributed data is unlikely, even if the data are 

transformed by procedures such as log or polynomials. The 

data sets used in this study were too small to have normal 

distributions. In contrast, Goodbred and others (1997) were 

able to use Pearson’s product-moment correlation because 

their data set was large. Like Kendall’s tau, Spearman’s rho 

is a rank-based procedure. Both measure the same correlation 

but use different scales. With Spearman’s rho, differences 

between data ranked further apart are given more weight than 

data ranked closer together whereas Kendall’s tau weights 

all data equally (Helsel and Hirsch, 1992). In addition, large 

sample and rank approximations for Spearman’s rho do not 

produce significance-test p-values near exact values for small 

sample sizes (Helsel and Hirsch, 1992). Because of the small 

sample sizes in the test data sets, Kendall’s tau was selected. 

A significance level of p ≤ 0.05 was used for all correlation 

analyses.

Integration of BEST and NASQAN Data

Data Refinements and Restrictions for Analysis

Water-Quality Data Refinements

Time-weighted mean, median, and percentile concentrations for the water-quality parameters of interest were 

determined for one or two separate years for nine of the 12 

NASQAN stations (table 7) using techniques described in Larson and others (2004). Data spanning the FPR for each station 

were used. These concentrations could not determined for 

three stations—Willamette River (BEST Station 505) in the 

CRB and Rio Grande at Falcon Dam (Station 513) and Arroyo 

Colorado (Station 511) in the RGB—because the minimum 

sample criteria for drainage area, number of samples per year, 

or both were not met. Many of the remaining nine stations 

with calculated time-weighted concentrations had different 

periods of record for the water-quality data. Some stations 

had enough data for the FPR to determine time-weighted 

concentrations for two years, but others only had data for one 

year of computations. When two years of data were available, 

time-weighted concentrations for each year were determined. 

Otherwise, time-weighted concentrations for one year were 

determined. Time-weighted mean, median, and percentile 

concentrations were also computed for seven stations using 

WY 1997 as time (table 8). These concentrations could not 

be determined for five stations—Willamette River (Station 

505) in the CRB; and the Rio Grande below Amistad Dam 

(Station 514) and Falcon Dam (Station 513), Rio Grande near 

Brownsville (Station 512), and Arroyo Colorado (Station 511) 

in the RGB—because the minimum sample criteria could not 

be met. The only RGB stations with sufficient data for the calculation of time-weighted concentrations for WY 1997 were 

Rio Grande at El Paso (Station 516) and Foster Ranch (Station 

515) (table 8). In addition, some water-quality parameters of 

interest could not used because of minimum sample criteria 

restrictions. 

Summarization options for time-weighted concentrations 

from stations with multiple years of data were examined. For 

example, we examined whether the time-weighted mean or 

percentile concentration for one time period (for example, 

1/1/96-12/30/97) could be averaged with the mean or percentile concentration for a second time period (1/1/97-10/7/97) 

to yield a single time-weighted value for the FPR (1/1/96-

10/7/97; dates for the station Columbia River at Northport) 

(table 7). We determined that time-weighted mean concentrations representing more than one time period could be 

averaged together to obtain a time-weighted mean for the FPR 

provided that the individual time periods have the same number of samples. For time periods with different numbers of 

samples and for time-weighted percentiles, the mean value for 

the averages of the individual years does not equal the mean 

for the FPR. This issue proved to be irrelevant, however; most 

Text continues on page 27
18 Approach for Integrating BEST and NASQAN

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected NASQAN waterquality stations in the Columbia River and Rio Grande basins for each station’s full period of record.—Continued 

[Dates in parentheses after station name refer to period of record in table 5; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm,

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and percentile

concentrations were determined using techniques described in Larson and others (2004)]

Number of Time-weighted concentration 

Mean 

Median 

(50th 

percentile) 

90th 

percentile 

Sa

year

mple Begin 

date 

Number 

of 

Number 

months samples 

of le

values

ss-than 

Water-quality parameter 

(units) 

Columbia River Basin 

Columbia River at Northport, Washington (11/25/1995-10/7/1997) 

Atrazine (�g/L) 1 1/1/1996 11 15 13 <0.001 <0.001 0.002 

Atrazine (�g/L) 2 1/1/1997 8 11 9 <0.001 <0.001 0.003 

Chloride (mg/L) 1 1/1/1996 11 16 0 0.7 0.8 0.8 

Chloride (mg/L) 2 1/1/1997 7 10 0 0.7 0.7 1 

Chromium (�g/L) 1 1/1/1996 11 16 16 <1 <1 <1 

Chromium (�g/L) 2 1/1/1997 8 11 5 <1.128 1 2 

Copper (�g/L) 1 1/1/1996 11 16 4 <1.268 1 2 

Copper (�g/L) 2 1/1/1997 8 11 5 <1.1 <1 1 

Dacthal (�g/L) 1 1/1/1996 11 15 15 <0.002 <0.002 <0.002 

Dacthal (�g/L) 2 1/1/1997 8 11 9 <0.002 <0.002 0.002 

pH (standard units) 1 1/1/1996 11 16 0 8.1 8.1 8.2 

pH (standard units) 2 1/1/1997 7 10 0 8.2 8.2 8.2 

Simazine (�g/L) 1 1/1/1996 11 15 14 <0.005 <0.005 <0.005 

Simazine (�g/L) 2 1/1/1997 8 11 11 <0.005 <0.005 <0.005 

Specific conductance (�S/cm) 1 1/1/1996 11 16 0 139 138 152 

Specific conductance (�S/cm) 2 1/1/1997 7 10 0 133 125 149 

Total dissolved solids (mg/L) 1 1/1/1996 11 16 0 80 80 88 

Total dissolved solids (mg/L) 2 1/1/1997 8 11 0 77 72 88 

Water temperature (°C) 1 1/1/1996 11 16 0 8.8 6.8 15.5 

Water temperature (°C) 2 1/1/1997 7 10 0 10.2 13 17.6 

Zinc (�g/L) 1 1/1/1996 11 16 0 4 3 6 

Zinc (�g/L) 2 1/1/1997 8 11 0 2 2 3 

Columbia River at Vernita Bridge near Priest Rapids Dam, Washington (1/17/1996-8/12/1997) 

Arsenic (�g/L) 1 5/1/1996 8 8 8 <1 <1 <1 

Atrazine (�g/L) 1 5/1/1996 8 8 6 <0.001 <0.001 0.002 

Chloride (mg/L) 1 5/1/1996 8 8 0 1.0 0.9 1.5 

Chlorpyrifos (�g/L) 1 5/1/1996 8 8 7 <0.004 <0.004 0.005 

Chromium (�g/L) 1 5/1/1996 8 8 7 <1.02 <1 1 

Copper (�g/L) 1 5/1/1996 8 8 3 <1.382 1 3 

Dacthal (�g/L) 1 5/1/1996 8 8 7 <0.002 <0.002 <0.002 

EPTC (�g/L) 1 5/1/1996 8 8 7 <0.002 <0.002 0.003 
Integration of BEST and NASQAN Data 19

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected NASQAN waterquality stations in the Columbia River and Rio Grande basins for each station’s full period of record.—Continued 

[Dates in parentheses after station name refer to period of record in table 5; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and percentile 

concentrations were determined using techniques described in Larson and others (2004)] 

Number of 

less-than 

values 

Time-weighted concentration 

Mean 

Median 

(50th 

percentile) 

90th 

percentile 

Begin 

date 

Number 

of 

months 

Number 

of 

samples 

Water-quality parameter 

(units) 

Sa

year

mple 

Metolachlor (�g/L) 1 5/1/1996 8 8 8 <0.002 <0.002 <0.002 

pH (standard units) 1 5/1/1996 8 8 0 7.9 8 8 

Simazine (�g/L) 1 5/1/1996 8 8 8 <0.005 <0.005 <0.005 

Specific conductance (�S/cm) 1 5/1/1996 8 8 0 140 135 157 

Total dissolved solids (mg/L) 1 5/1/1996 8 8 0 88 88 100 

Triallate (�g/L) 1 5/1/1996 8 8 7 <0.001 <0.001 0.005 

Water temperature (°C) 1 5/1/1996 8 8 0 10.2 9.5 19.0 

Zinc (�g/L) 1 5/1/1996 8 8 2 <2.17 2 4 

Snake River at Burbank, Washington (10/13/1995-9/18/1997) 

Alachlor (�g/L) 1 10/1/1995 11 15 10 <0.002 <0.002 0.004 

Alachlor (�g/L) 2 10/1/1996 10 14 9 <0.003 <0.002 0.004 

Arsenic (�g/L) 1 10/1/1995 10 14 0 2 2 3 

Arsenic (�g/L) 2 10/1/1996 10 14 1 2 2 3 

Atrazine (�g/L) 1 10/1/1995 11 15 2 <0.005 0.004 0.008 

Atrazine (�g/L) 2 10/1/1996 10 14 0 0.006 0.005 0.008 

Chloride (mg/L) 1 10/1/1995 12 17 0 5.7 5.2 9.7 

Chloride (mg/L) 2 10/1/1996 10 14 0 7.1 6.8 12 

Chromium (�g/L) 1 10/1/1995 10 15 12 <1.752 <5 5 

Chromium (�g/L) 2 10/1/1996 10 14 9 <1.099 <1 1 

Copper (�g/L) 1 10/1/1995 10 14 8 <1.113 <1 1 

Copper (�g/L) 2 10/1/1996 10 14 9 <1.193 <1 2 

Dacthal (�g/L) 1 10/1/1995 11 15 5 <0.002 0.002 0.003 

Dacthal (�g/L) 2 10/1/1996 10 14 6 <0.002 <0.002 0.003 

EPTC (�g/L) 1 10/1/1995 11 15 8 <0.003 <0.002 0.007 

EPTC (�g/L) 2 10/1/1996 10 14 6 <0.003 <0.002 0.009 

Metolachlor (�g/L) 1 10/1/1995 11 15 7 <0.004 0.002 0.007 

Metolachlor (�g/L) 2 10/1/1996 10 14 6 <0.004 0.002 0.006 

Metribuzin (�g/L) 1 10/1/1995 11 15 14 <0.004 <0.004 <0.004 

Metribuzin (�g/L) 2 10/1/1996 10 14 12 <0.004 <0.004 <0.004 

pH (standard units) 1 10/1/1995 12 17 0 7.9 7.9 8.1 

pH (standard units) 2 10/1/1996 10 14 0 7.9 8 8.1 

Simazine (�g/L) 1 10/1/1995 11 15 13 <0.005 <0.005 <0.005 

Simazine (�g/L) 2 10/1/1996 10 14 13 <0.005 <0.005 <0.005 

Specific conductance (�S/cm) 1 10/1/1995 12 17 0 177 171 265 

Specific conductance (�S/cm) 2 10/1/1996 10 14 0 218 217 325 
20 Approach for Integrating BEST and NASQAN

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected NASQAN waterquality stations in the Columbia River and Rio Grande basins for each station’s full period of record.—Continued 

[Dates in parentheses after station name refer to period of record in table 5; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and percentile 

concentrations were determined using techniques described in Larson and others (2004)] 

Number of Time-weighted concentration 

Mean 

Median 

(50th 

percentile) 

90th 

percentile 

Sample Begin 

date 

Number 

of 

Number 

months samples 

of less-than 

year 

values 

Water-quality parameter 

(units) 

Total dissolved solids (mg/L) 1 10/1/1995 12 17 0 113 110 159 

Total dissolved solids (mg/L) 2 10/1/1996 10 13 0 132 130 192 

Triallate (�g/L) 1 10/1/1995 11 15 9 <0.004 <0.001 0.008 

Triallate (�g/L) 2 10/1/1996 10 14 8 <0.003 <0.001 0.008 

Water temperature (°C) 1 10/1/1995 12 17 0 12.0 11.5 21 

Water temperature (°C) 2 10/1/1996 10 14 0 11.7 12.5 20 

Zinc (�g/L) 1 10/1/1995 10 14 11 <1 <3 3 

Zinc (�g/L) 2 10/1/1996 10 14 11 <1 <1 1 

Columbia River at Warrendale, Washington (10/21/1996-10/29/1997) 

Alachlor (�g/L) 1 10/1/1996 9 15 12 <0.002 <0.002 <0.002 

Arsenic (�g/L) 1 10/1/1996 9 15 4 <1.035 1 1 

Atrazine (�g/L) 1 10/1/1996 9 15 1 0.003 0.004 0.005 

Chloride (mg/L) 1 10/1/1996 9 15 0 2.619 2.2 5.4 

Chlorpyrifos (�g/L) 1 10/1/1996 9 15 13 <0.004 <0.004 0.005 

Chromium (�g/L) 1 10/1/1996 9 15 9 <1.035 <1 1 

Copper (�g/L) 1 10/1/1996 9 15 2 <1.365 1 2 

Dacthal (�g/L) 1 10/1/1996 9 15 7 <0.002 <0.002 0.002 

EPTC (�g/L) 1 10/1/1996 9 15 7 <0.003 <0.002 0.004 

Metolachlor (�g/L) 1 10/1/1996 9 15 8 <0.002 <0.002 0.004 

Metribuzin (�g/L) 1 10/1/1996 9 15 12 <0.004 <0.004 0.005 

pH (standard units) 1 10/1/1996 9 15 0 7.9 7.9 8.1 

Simazine (�g/L) 1 10/1/1996 9 15 11 <0.005 <0.005 0.005 

Specific conductance (�S/cm) 1 10/1/1996 9 15 0 142 142 191 

Total dissolved solids (mg/L) 1 10/1/1996 9 15 0 92 94 126 

Triallate (�g/L) 1 10/1/1996 9 15 11 <0.003 <0.001 0.014 

Water temperature (°C) 1 10/1/1996 9 15 0 11.8 12.1 21.4 

Zinc (�g/L) 1 10/1/1996 9 15 6 <1.601 <1 4 

Columbia River at Beaver Army Terminal near Quincy, Oregon (10/24/1995-3/9/1998) 

Arsenic (�g/L) 1 11/1/1995 10 12 11 <1 <1 <1 

Arsenic (�g/L) 2 11/1/1996 8 14 6 <1.039 1 1 

Atrazine (�g/L) 1 11/1/1995 11 14 2 0.01 0.007 0.023 

Atrazine (�g/L) 2 11/1/1996 8 14 1 0.016 0.005 0.078 

Chloride (mg/L) 1 11/1/1995 10 12 0 2.9 3.1 3.6 

Chloride (mg/L) 2 11/1/1996 8 14 0 3.5 3 6.7 

Chlorpyrifos (�g/L) 1 11/1/1995 11 14 13 <0.004 <0.004 <0.004 
Integration of BEST and NASQAN Data 21

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected NASQAN waterquality stations in the Columbia River and Rio Grande basins for each station’s full period of record.—Continued 

[Dates in parentheses after station name refer to period of record in table 5; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and percentile 

concentrations were determined using techniques described in Larson and others (2004)] 

Number of Time-weighted concentration 

Mean 

Median 

(50th 

percentile) 

90th 

percentile 

Sample Begin 

date 

Number 

of 

Number 

months samples 

of less-than 

year 

values 

Water-quality parameter 

(units) 

Chlorpyrifos (�g/L) 2 11/1/1996 8 14 11 <0.004 <0.004 0.004 

Chromium (�g/L) 1 11/1/1995 11 13 12 <1 <1 <1 

Chromium (�g/L) 2 11/1/1996 8 14 9 <1.088 <1 1 

Copper (�g/L) 1 11/1/1995 11 13 2 <1.597 2 2 

Copper (�g/L) 2 11/1/1996 8 14 2 1 1 2 

Dacthal (�g/L) 1 11/1/1995 11 14 9 <0.002 <0.002 0.002 

Dacthal (�g/L) 2 11/1/1996 8 14 6 <0.002 <0.002 0.002 

EPTC (�g/L) 1 11/1/1995 11 14 8 <0.002 <0.002 0.004 

EPTC (�g/L) 2 11/1/1996 8 14 7 <0.002 <0.002 0.004 

Metolachlor (�g/L) 1 11/1/1995 11 14 4 <0.004 0.003 0.007 

Metolachlor (�g/L) 2 11/1/1996 8 14 3 0.006 <0.004 0.023 

Metribuzin (�g/L) 1 11/1/1995 11 14 12 <0.004 <0.004 <0.004 

Metribuzin (�g/L) 2 11/1/1996 8 14 10 <0.006 0.004 0.013 

pH (standard units) 1 11/1/1995 11 13 0 7.7 7.6 8.1 

pH (standard units) 2 11/1/1996 8 14 0 7.7 7.8 8 

Simazine (�g/L) 1 11/1/1995 11 14 3 <0.007 0.005 0.013 

Simazine (�g/L) 2 11/1/1996 8 14 6 <0.009 0.005 0.034 

Specific conductance (�S/cm) 1 11/1/1995 11 13 0 121 123 136 

Specific conductance (�S/cm) 2 11/1/1996 8 14 0 130 125 183 

Total dissolved solids (mg/L) 1 11/1/1995 10 12 0 77 77 84 

Total dissolved solids (mg/L) 2 11/1/1996 8 14 0 86 79 123 

Triallate (�g/L) 1 11/1/1995 11 14 10 <0.002 <0.001 0.005 

Triallate (�g/L) 2 11/1/1996 8 14 8 <0.003 <0.001 0.011 

Water temperature (°C) 1 11/1/1995 11 13 0 12.2 10.3 20.5 

Water temperature (°C) 2 11/1/1996 8 14 0 12.7 11.5 21.7 

Zinc (�g/L) 1 11/1/1995 11 13 3 <1.659 2 3 

Zinc (�g/L) 2 11/1/1996 8 14 7 <1.642 1 3 

Rio Grande Basin 

Rio Grande at El Paso, Texas (11/2/1995-9/10/1997) 

Arsenic (�g/L) 2 11/1/1996 12 12 0 3 3 4 

Atrazine (�g/L) 2 11/1/1996 12 12 4 <0.002 0.003 0.004 

Carbaryl (�g/L) 2 11/1/1996 12 12 11 <0.004 <0.008 <0.008 

Chloride (mg/L) 1 11/1/1995 12 13 0 142 120 310 

Chloride (mg/L) 2 11/1/1996 12 12 0 170 98.1 400 

Chlorpyrifos (�g/L) 2 11/1/1996 12 12 7 <0.005 <0.004 0.004 
22 Approach for Integrating BEST and NASQAN

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected NASQAN waterquality stations in the Columbia River and Rio Grande basins for each station’s full period of record.—Continued 

[Dates in parentheses after station name refer to period of record in table 5; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and percentile 

concentrations were determined using techniques described in Larson and others (2004)] 

Number of Time-weighted concentration 

Mean 

Median 

(50th 

percentile) 

90th 

percentile 

Sample Begin 

date 

Number 

of 

Number 

months samples 

of less-than 

year 

values 

Water-quality parameter 

(units) 

Chromium (�g/L) 2 11/1/1996 12 12 3 <1.895 2 3 

Copper (�g/L) 2 11/1/1996 12 12 4 <1.592 <2 2 

Dacthal (�g/L) 2 11/1/1996 12 12 1 0.003 0.003 0.004 

Diazinon (�g/L) 2 11/1/1996 12 12 9 <0.002 <0.005 0.005 

Malathion (�g/L) 2 11/1/1996 12 12 11 <0.007 <0.005 <0.005 

Metolachlor (�g/L) 2 11/1/1996 12 12 2 <0.004 0.004 0.006 

pH (standard units) 1 11/1/1995 11 12 0 8.3 8.3 8.5 

pH (standard units) 2 11/1/1996 12 12 0 8.4 8.5 8.6 

Selenium (�g/L) 2 11/1/1996 12 12 12 <1 <1 <1 

Simazine (�g/L) 2 11/1/1996 12 12 4 <0.008 0.008 0.012 

Specific conductance (�S/cm) 1 11/1/1995 11 12 0 1290 1110 2300 

Specific conductance (�S/cm) 2 11/1/1996 12 12 0 1450 1060 2660 

Total dissolved solids (mg/L) 1 11/1/1995 12 13 0 832 698 1510 

Total dissolved solids (mg/L) 2 11/1/1996 12 12 0 926 656 1740 

Water temperature (°C) 1 11/1/1995 11 12 0 16.0 16.5 24 

Water temperature (°C) 2 11/1/1996 12 12 0 16.6 16 25 

Zinc (�g/L) 2 11/1/1996 12 12 1 3 2 5 

Rio Grande at Foster Ranch near Langtry, Texas (10/25/1995-9/10/1997) 

Arsenic (�g/L) 2 11/1/1996 8 11 1 <1.89 2 3 

Atrazine (�g/L) 2 11/1/1996 8 11 7 <0.001 <0.001 0.003 

Chloride (mg/L) 2 11/1/1996 8 11 0 142 120 250 

Chromium (�g/L) 2 11/1/1996 8 11 2 <1.759 2 2 

Copper (�g/L) 2 11/1/1996 8 11 5 <1.388 <1 2 

Dacthal (�g/L) 2 11/1/1996 8 11 8 <0.002 <0.002 0.002 

Diazinon (�g/L) 2 11/1/1996 8 11 9 <0.003 <0.002 0.008 

Metolachlor (�g/L) 2 11/1/1996 8 11 11 <0.002 <0.002 <0.002 

pH (standard units) 2 11/1/1996 8 11 0 7.8 7.8 8.1 

Selenium (�g/L) 2 11/1/1996 8 11 5 <1.095 1 2 

Simazine (�g/L) 2 11/1/1996 8 11 10 <0.005 <0.005 0.005 

Specific conductance (�S/cm) 2 11/1/1996 8 11 0 1280 1230 1790 

Total dissolved solids (mg/L) 2 11/1/1996 8 11 0 850 804 1140 

Water temperature (°C) 2 11/1/1996 8 11 0 20.0 19 27.5 

Zinc (�g/L) 2 11/1/1996 8 11 6 <1.719 1 3 
Integration of BEST and NASQAN Data 23

Table 7. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected NASQAN waterquality stations in the Columbia River and Rio Grande basins for each station’s full period of record.—Continued 

[Dates in parentheses after station name refer to period of record in table 5; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and percentile 

concentrations were determined using techniques described in Larson and others (2004)] 

Number of Time-weighted concentration 

Mean 

Median 

(50th 

percentile) 

90th 

percentile 

Begin 

date 

Number 

of 

months 

Number 

of 

samples 

Water-quality parameter 

(units) 

Sample 

year 

less-than 

values 

Rio Grande below Amistad Dam, near Del Rio, Texas (5/28/1996-9/11/1997) 

Arsenic (�g/L) 1 6/1/1996 8 8 0 3 2 5 

Atrazine (�g/L) 1 6/1/1996 8 8 2 <0.003 0.003 0.004 

Chloride (mg/L) 1 6/1/1996 8 8 0 174 161 200 

Chromium (�g/L) 1 6/1/1996 8 8 1 <1.574 1 3 

Copper (�g/L) 1 6/1/1996 8 8 2 <1.186 1 2 

Dacthal (�g/L) 1 6/1/1996 8 8 7 <0.002 <0.002 <0.002 

Diazinon (�g/L) 1 6/1/1996 8 8 7 <0.004 <0.002 0.01 

pH (standard units) 1 6/1/1996 8 8 0 7.8 7.8 7.9 

Simazine (�g/L) 1 6/1/1996 8 8 6 <0.004 <0.005 0.005 

Specific conductance (�S/cm) 1 6/1/1996 8 8 0 1270 1210 1360 

Total dissolved solids (mg/L) 1 6/1/1996 8 8 0 846 798 862 

Water temperature (°C) 1 6/1/1996 8 8 0 17.9 17.5 25 

Zinc (�g/L) 1 6/1/1996 8 8 1 3 2 6 

Rio Grande near Brownsville, Texas (10/17/1995-10/11/1997) 

Arsenic (�g/L) 1 3/1/1996 9 11 0 4 3 7 

Atrazine (�g/L) 1 3/1/1996 9 11 0 0.013 0.009 0.027 

Carbaryl (�g/L) 1 3/1/1996 9 11 11 <0.003 <0.003 <0.003 

Chloride (mg/L) 1 3/1/1996 9 11 0 216 210 280 

Chlorpyrifos (�g/L) 1 3/1/1996 9 11 9 <0.005 <0.004 0.004 

Chromium (�g/L) 1 3/1/1996 9 11 3 <1.245 1 2 

Copper (�g/L) 1 3/1/1996 9 11 0 2 2 3 

Dacthal (�g/L) 1 3/1/1996 9 11 6 <0.005 0.003 0.021 

Diazinon (�g/L) 1 3/1/1996 9 11 10 <0.003 <0.002 0.005 

Malathion (�g/L) 1 3/1/1996 9 11 11 <0.005 <0.005 <0.005 

Metolachlor (�g/L) 1 3/1/1996 9 11 7 <0.003 <0.002 0.004 

pH (standard units) 1 3/1/1996 9 11 0 8.2 8.2 8.2 

Simazine (�g/L) 1 3/1/1996 9 11 10 <0.005 <0.005 0.005 

Specific conductance (�S/cm) 1 3/1/1996 9 11 0 1600 1590 1810 

Total dissolved solids (mg/L) 1 3/1/1996 9 11 0 956 938 1140 

Water temperature (°C) 1 3/1/1996 9 11 0 24.3 24 30.5 

Zinc (�g/L) 1 3/1/1996 9 11 0 3 2 4 
24 Approach for Integrating BEST and NASQAN

Table 8. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected 

NASQAN water-quality stations in the Columbia River and Rio Grande basins for water year 1997.—Continued 

[water year 1997, October 1, 1996 through September 30, 1997; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and 

percentile concentrations were determined using techniques described in Larson and others (2004)] 

Time-weighted concentration 

Geometric 

Mean 

Median 

(50th 

percentile) 

Number of 

90th 

percentile 

Water-quality parameter 

(units) 

Number 

of 

months 

Number 

of 

samples 

less-than 

values Mean 

Columbia River Basin 

Columbia River at Northport, Washington

Atrazine (�g/L) 9 12 10 <0.001 <0.001 <0.001 0.003 

Chloride (mg/L) 8 11 0 0.7 0.7 0.8 1 

Chromium (�g/L) 9 12 7 <1.068 <1.057 <1 1.5 

Copper (�g/L) 9 12 6 <1.1 <1.084 <1 1.3 

Dacthal (�g/L) 9 12 11 <0.002 <0.002 <0.002 <0.002 

pH (standard units) 8 11 0 8.2 8.2 8.2 8.2 

Simazine (�g/L) 9 12 12 <0.005 <0.005 <0.005 <0.005 

Specific conductance (�S/cm) 8 11 0 136 136 138 149 

Total dissolved solids (mg/L) 9 12 0 79 78 77 88 

Water temperature (°C) 8 11 0 9.1 7.4 6.9 17.6 

Zinc (�g/L) 9 12 0 2.942 2.815 3 4 

Columbia River at Vernita Bridge near Priest Rapids Dam, Washington 

Arsenic (�g/L) 9 11 10 <1.038 <1.031 <1 <1 

Atrazine (�g/L) 9 11 7 <0.001 <0.001 <0.001 0.002 

Chloride (mg/L) 9 11 0 1.0 0.9 0.9 1.5 

Chlorpyrifos (�g/L) 9 11 10 <0.004 <0.004 <0.004 <0.004 

Chromium (�g/L) 9 11 8 <1.038 <1.034 <1 1.2 

Copper (�g/L) 9 11 5 <1.125 <1.107 <1 1.5 

Dacthal (�g/L) 9 11 7 <0.002 <0.002 <0.002 0.002 

EPTC (�g/L) 9 11 8 <0.003 <0.002 <0.002 0.003 

Metolachlor (�g/L) 9 11 10 <0.002 <0.002 <0.002 <0.002 

pH (standard units) 9 11 0 7.9 7.9 8.0 8.1 

Simazine (�g/L) 9 11 10 <0.005 <0.005 <0.005 <0.005 

Specific conductance (�S/cm) 9 11 0 135 134 135 152 

Total dissolved solids (mg/L) 9 11 0 85 84 86 100 

Triallate (�g/L) 9 11 10 <0.001 <0.001 <0.001 <0.001 

Water temperature (°C) 9 11 0 10.6 8.8 10.8 19.0 

Zinc (�g/L) 9 11 1 <2.547 <2.162 2 4 

Snake River at Burbank, Washington 

Alachlor (�g/L) 10 14 9 <0.003 <0.002 <0.002 0.004 

Arsenic (�g/L) 10 14 1 2.064 1.972 2 3 

Atrazine (�g/L) 10 14 0 0.006 0.005 0.005 0.008 

Chloride (mg/L) 10 14 0 7.1 6.1 6.8 12.0 
Integration of BEST and NASQAN Data 25

Table 8. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected 

NASQAN water-quality stations in the Columbia River and Rio Grande basins for water year 1997.—Continued 

[water year 1997, October 1, 1996 through September 30, 1997; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and 

percentile concentrations were determined using techniques described in Larson and others (2004)] 

Time-weighted concentration 

Geometric 

Mean 

Median 

(50th 

percentile) 

Number of 

90th 

percentile 

Water-quality parameter 

(units) 

Number 

of 

months 

Number 

of 

samples 

less-than 

values Mean 

Chromium (�g/L) 10 14 9 <1.099 <1.074 <1 1.2 

Copper (�g/L) 10 14 9 <1.193 <1.155 <1 2 

Dacthal (�g/L) 10 14 6 <0.002 <0.002 <0.002 0.003 

EPTC (�g/L) 10 14 6 <0.003 <0.003 <0.002 0.009 

Metolachlor (�g/L) 10 14 6 <0.004 <0.004 0.002 0.006 

Metribuzin (�g/L) 10 14 12 <0.004 <0.004 <0.004 <0.004 

pH (standard units) 10 14 0 7.9 7.9 8.0 8.1 

Simazine (�g/L) 10 14 13 <0.005 <0.005 <0.005 <0.005 

Specific conductance (�S/cm) 10 14 0 218 201 217 325 

Total dissolved solids (mg/L) 10 13 0 132 123 130 192 

Triallate (�g/L) 10 14 8 <0.003 <0.002 <0.001 0.008 

Water temperature (°C) 10 14 0 11.7 10.0 12.5 20.0 

Zinc (�g/L) 10 14 11 <1.065 <1.042 <1 1 

Columbia River at Warrendale, Washington 

Alachlor (�g/L) 9 15 12 <0.002 <0.002 <0.002 <0.002 

Arsenic (�g/L) 9 15 4 <1.035 <1.03 1 1.1 

Atrazine (�g/L) 9 15 1 0.003 0.004 0.004 0.005 

Chloride (mg/L) 9 15 0 2.6 2.4 2.2 5.4 

Chlorpyrifos (�g/L) 9 15 13 <0.004 <0.004 <0.004 0.005 

Chromium (�g/L) 9 15 9 <1.035 <1.034 <1 1.1 

Copper (�g/L) 9 15 2 <1.365 <1.297 1.2 2 

Dacthal (�g/L) 9 15 7 <0.002 <0.002 <0.002 0.002 

EPTC (�g/L) 9 15 7 <0.003 <0.002 <0.002 0.004 

Metolachlor (�g/L) 9 15 8 <0.002 <0.002 <0.002 0.004 

Metribuzin (�g/L) 9 15 12 <0.004 <0.004 <0.004 0.005 

pH (standard units) 9 15 0 7.9 7.9 7.9 8.1 

Simazine (�g/L) 9 15 11 <0.005 <0.004 <0.005 0.005 

Specific conductance (�S/cm) 9 15 0 142 139 142 191 

Total dissolved solids (mg/L) 9 15 0 92 91 94 126 

Triallate (�g/L) 9 15 11 <0.003 <0.002 <0.001 0.014 

Water temperature (°C) 9 15 0 11.8 9.9 12.1 21.4 

Zinc (�g/L) 9 15 6 <1.601 <1.376 <1 3.7 
26 Approach for Integrating BEST and NASQAN

Table 8. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected 

NASQAN water-quality stations in the Columbia River and Rio Grande basins for water year 1997.—Continued 

[water year 1997, October 1, 1996 through September 30, 1997; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and 

percentile concentrations were determined using techniques described in Larson and others (2004)] 

Time-weighted concentration 

Geometric 

Mean 

Median 

(50th 

percentile) 

Number of 

90th 

percentile 

Water-quality parameter 

(units) 

Number 

of 

months 

Number 

of 

samples 

less-than 

values Mean 

Columbia River at Beaver Army Terminal near Quincy, Oregon 

Arsenic (�g/L) 9 15 7 <1.039 <1.035 1 1.1 

Atrazine (�g/L) 9 15 1 0.014 0.008 0.005 0.078 

Chloride (mg/L) 9 15 0 3.5 3.3 3.1 6.7 

Chlorpyrifos (�g/L) 9 15 12 <0.004 <0.004 <0.004 0.004 

Chromium (�g/L) 9 15 10 <1.088 <1.067 <1 1.2 

Copper (�g/L) 9 15 3 <1.177 <1.156 1 1.6 

Dacthal (�g/L) 9 15 7 <0.002 <0.002 <0.002 0.002 

EPTC (�g/L) 9 15 8 <0.002 <0.002 <0.002 0.004 

Metolachlor (�g/L) 9 15 3 0.006 0.005 0.004 0.023 

Metribuzin (�g/L) 9 15 11 <0.006 <0.005 <0.004 0.013 

pH (standard units) 9 15 0 7.7 7.7 7.8 8 

Simazine (�g/L) 9 15 6 <0.008 <0.005 0.005 0.034 

Specific conductance (�S/cm) 9 15 0 131 129 130 183 

Total dissolved solids (mg/L) 9 15 0 87 86 84 123 

Triallate (�g/L) 9 15 9 <0.003 <0.002 <0.001 0.011 

Water temperature (°C) 9 15 0 12.1 10.5 11.5 21.7 

Zinc (�g/L) 9 15 7 <1.564 <1.377 1 3 

Rio Grande Basin 

Rio Grande at El Paso, Texas 

Arsenic (�g/L) 12 12 0 3.107 3.022 3 4 

Atrazine (�g/L) 12 12 3 <0.003 <0.003 0.003 0.004 

Carbaryl (�g/L) 12 12 10 <0.004 <0.003 <0.003 0.005 

Chloride (mg/L) 12 12 0 176 143 98 400 

Chlorpyrifos (�g/L) 12 12 7 <0.005 <0.004 <0.004 0.004 

Chromium (�g/L) 12 12 3 <1.807 <1.73 2 2.1 

Copper (�g/L) 12 12 4 <1.622 <1.478 <2 2 

Dacthal (�g/L) 12 12 1 0.003 0.004 0.003 0.006 

Diazinon (�g/L) 12 12 8 <0.002 <0.002 <0.002 0.004 

Malathion (�g/L) 12 12 10 <0.009 <0.007 <0.005 0.026 

Metolachlor (�g/L) 12 12 2 <0.005 <0.004 0.004 0.008 

pH (standard units) 12 12 0 8.5 8.5 8.5 8.6 

Selenium (�g/L) 12 12 12 <1 <1 <1 <1 

Simazine (�g/L) 12 12 3 <0.007 <0.007 0.008 0.012 

Specific conductance (�S/cm) 12 12 0 1470 1350 1060 2660 
Integration of BEST and NASQAN Data 27

Table 8. Time-weighted mean, median, and percentile concentrations of selected water-quality parameters for selected 

NASQAN water-quality stations in the Columbia River and Rio Grande basins for water year 1997.—Continued 

[water year 1997, October 1, 1996 through September 30, 1997; �g/L, micrograms per liter; <, less than; mg/L, milligrams per liter; �S/cm, 

microsiemens per centimeter at 25 degrees Celsius; °C, degrees Celsius; EPTC, s-ethyl dipropylthiocarbamate. Time-weighted mean, median, and 

percentile concentrations were determined using techniques described in Larson and others (2004)] 

Time-weighted concentration 

Geometric 

Mean 

Median 

(50th 

percentile) 

Number of 

90th 

percentile 

Water-quality parameter 

(units) 

Number 

of 

months 

Number 

of 

samples 

less-than 

values Mean 

Total dissolved solids (mg/L) 12 12 0 942 852 656 1740 

Water temperature (°C) 12 12 0 15.9 14.7 13.5 25.0 

Zinc (�g/L) 12 12 1 2.832 2.37 2.1 5 

Rio Grande at Foster Ranch near Langtry, Texas 

Arsenic (�g/L) 8 11 1 <1.788 <1.664 <2 2.9 

Atrazine (�g/L) 8 11 7 <0.001 <0.001 <0.001 0.003 

Chloride (mg/L) 8 11 0 153 124 133 250 

Chromium (�g/L) 8 11 2 <1.844 <1.74 2 2.4 

Copper (�g/L) 8 11 5 <1.388 <1.303 <1 2.1 

Dacthal (�g/L) 8 11 8 <0.002 <0.002 <0.002 0.002 

Diazinon (�g/L) 8 11 9 <0.004 <0.003 <0.002 0.008 

Metolachlor (�g/L) 8 11 11 <0.002 <0.002 <0.002 <0.002 

pH (standard units) 8 11 0 7.9 7.9 7.8 8.1 

Selenium (�g/L) 8 11 5 <1.095 <.081 <1 1.5 

Simazine (�g/L) 8 11 10 <0.005 <0.005 <0.005 0.005 

Specific conductance (�S/cm) 8 11 0 1330 1250 1250 1790 

Total dissolved solids (mg/L) 8 11 0 884 842 815 1140 

Water temperature (°C) 8 11 0 18.5 16.9 18.0 27.5 

Zinc (�g/L) 8 11 6 <1.889 <1.667 1.4 3 

mean and median time-weighted concentrations representing 

multiple years of CRB and RGB water quality were reported 

as less-than concentrations, which could not be condensed 

into single values. Because an exact concentration value is not 

known for a less-than concentration, these concentrations cannot be averaged. Substitution of zero or one-half the MRL for 

censored values was also considered but subsequently rejected 

because the percentage of replaced observations would have 

exceeded the recommended maximum (15%) for substitution (U.S. Environmental Protection Agency, 2000). In such 

instances, time-weighted concentrations for WY 1997 (table 

8) were combined with the fish data for correlation analyses.

Specifically, we used the 90th percentile time-weighted 

concentrations for WY 1997, as determined using the techniques described by Larson and others (2004), in the CRB correlation analyses. Although the 90th percentile concentrations 

overestimate exposure of fish to water-borne contaminants 

as compared to mean or median concentrations, these values 

were used to facilitate computations in this example because 

of the large amount of censored data. The 90th percentile 

values for specific conductance, arsenic, atrazine, copper, 

total trace elements (sum of 90th percentile concentrations 

for arsenic, chromium, copper, and zinc), and zinc were used. 

Two parameters of interest, chloride and total dissolved solids, 

were highly correlated (p ≤ 0.05) with specific conductance; 

therefore, we used only specific conductance. The 90th percentile concentrations of chromium were excluded because all 

concentrations were very similar (±0.1 μg/L). Selenium was 

excluded because most or all concentrations were less than the 

reporting level. 

Only specific conductance could be used in the CRB 

correlation analyses for the last water sample before the date 

of fish collection, (table 9). All other parameters of interest 

had at least one concentration that was less than the respective 

MRL for the four stations with appropriate fish data, resulting 

in insufficient data for analysis. Frequencies-of-detection of 

arsenic, atrazine, chromium, copper, EPTC, total trace elements, and zinc in the CRB for the 18 months prior to the date 

of fish collection also were used in the correlation analyses 
28 Approach for Integrating BEST and NASQAN

Table 9. Concentration of last water sample before date of fish collection for selected NASQAN water-quality 

stations in the Columbia River and Rio Grande basins. 

[�S/cm, microsiemens per centimeter at 25 degrees Celsius; �g/L, micrograms per liter; <, less than; E, estimated. Table only includes 

stations with carp] 

Specific 

conductance 

(μS/cm) 

Arsenic 

(μg/L) 

Chromium 

(μg/L) 

Copper 

(μg/L) 

Zinc 

(μg/L) 

Atrazine 

(μg/L) Station name 

Columbia River Basin 

Columbia River at Vernita Bridge near 

Priest Rapids Dam, Washington 122 <1.0 1.0 <1.0 1.8 <0.001 

Snake River at Burbank, Washington 217 2.3 <1.0 1.5 1.0 0.007 

Columbia River at Warrendale, Washington 165 1.0 1.3 1.2 <1.0 E 0.004 

Columbia River at Beaver Army Terminal 

near Quincy, Oregon 

144 <1.0 1.5 <1.0 2.2 0.012 

Rio Grande Basin 

Rio Grande at El Paso, Texas 1040 3.4 1.2 2.0 2.7 <0.001 

Rio Grande at Foster Ranch near Langtry, 

Texas 1090 2.2 <1.0 <1.0 <1.0 <0.001 

Rio Grande below Amistad Dam, near Del 

Rio, Texas 1220 2.9 1.4 <1.0 <1.0 <0.001 

Rio Grande below Falcon Dam, Texas 1130 2.9 <1.0 1.3 5.8 0.007 

Arroyo Colorado at Harlingen, Texas 4760 5.1 <2.0 3.0 4.4 0.252 

Rio Grande near Brownsville, Texas 819 3.4 1.8 1.5 2.3 0.224 

(table 10). Selenium was not detected in the CRB during this 

period (table 10). 

Concentrations of most constituents were greater in the 

RGB than in the CRB. Nevertheless, and as discussed previously, time-weighted concentrations could not be determined 

for most RGB stations when the data requirements of Larson 

and others (2004) were applied. Consequently, we relaxed the 

data requirements by eliminating minimum sample criteria for 

drainage area and number of samples per year and used data 

for all six stations. Time-weighted geometric mean concentrations of arsenic, copper, zinc, total trace elements (the 

sum of arsenic, chromium, copper, and zinc concentrations), 

and atrazine were determined for the 18 months prior to the 

date of fish collection for each station (table 11). The timeweighted geometric mean concentration for chromium was 

not determined because almost 37 percent of the data comprised censored values. As in the CRB, most or all selenium 

concentrations in the RGB were less than the reporting level, 

and selenium was therefore excluded from the calculations for 

total trace elements. 

Other RGB water-quality parameters used in the correlation analyses included the last sample values of specific 

conductance and arsenic, and frequencies-of-detection of 

atrazine, chromium, copper, total trace elements, and zinc for 

the 18 months prior to the date of fish collection (tables 9 and 

10). Eighteen-month frequencies-of-detection of arsenic and 

selenium were excluded because arsenic was detected in every 

sample from five of six stations and selenium was not detected 

at three of six stations (table 10). 

Trace-element and pesticide data for the two basins were 

also compared to guidelines for the protection of freshwater 

aquatic life (table 4). Trace-element concentrations greater 

than USEPA hardness-adjusted trace element CCCs only 

occurred for copper in four (13.8%) samples, all from Willamette River (Station 505) in the CRB. The copper concentrations and related hardness-adjusted CCCs (in parentheses) 

were 3.0 (1.4), 2.0 (1.8), 3.0 (1.9), and 4.0 (2.7) μg/L. For 

two other samples, the copper concentration and harnessadjusted CCC for each were nearly the same at 2.0 and 2.03 

μg/L, respectively. Pesticide concentrations greater than or 

equal to the USEPA or IJC pesticide aquatic life CCCs only 

occurred in the RGB. These included three stations and three 

pesticides: Arroyo Colorado (Station 511), diazinon (0.11 and 

0.105 μg/L) and malathion (0.84 μg/L); Rio Grande below 

Falcon Dam (Station 513), chlorpyrifos (0.061 μg/L); and Rio 

Grande at Foster Ranch (Station 515), diazinon (0.16 μg/L). 

A cumulative relative frequency diagram was developed 

for copper at Station 505 over the FPR (fig. 2) to measure the 

degree to which fish were exposed to concentrations greater 

than or equal to relevant aquatic life criteria. This was the 
Integration of BEST and NASQAN Data 29

Table 10. Frequencies-of-detection of selected water-quality parameters for selected NASQAN water-quality stations in the 

Columbia River and Rio Grande basins for the 18 months prior to the date of fish collection. 

[%, percent; EPTC, s-ethyl dipropylthiocarbamate; --, no data. Total trace elements are arsenic, chromium, copper, and zinc] 

18-month frequency-of-detection (%) 

Station name Total 

Arsenic Chromium Copper Selenium trace Zinc Atrazine EPTC 

elements 

Columbia River Basin 

Columbia River at Northport, Washington 4.76 28.6 61.9 0.00 47.6 100.0 14.3 0.00 

Columbia River at Vernita Bridge near 7.14 21.4 64.3 0.00 44.6 85.7 28.6 21.4 Priest Rapids Dam, Washington 

Snake River at Burbank, Washington 95.7 29.2 39.1 0.00 43.0 21.7 95.7 65.2 

Columbia River at Warrendale, 

Washington 75.0 43.8 87.5 0.00 67.2 56.2 93.8 50.0 

Willamette River at Portland, Oregon 0.00 5.56 50.0 0.00 36.1 88.9 100.0 35.3 

Columbia River at Beaver Army Terminal 

near Quincy, Oregon 

50.0 38.9 72.2 0.00 57.7 55.6 94.4 38.9 

Rio Grande Basin 

Rio Grande at El Paso, Texas 100.0 76.9 83.3 0.00 88.7 92.9 75.0 --

Rio Grande at Foster Ranch near Langtry, 94.4 72.2 66.7 61.1 73.2 61.1 27.8 -- Texas 

Rio Grande below Amistad Dam, near Del 

Rio, Texas 100.0 90.0 70.0 0.00 82.5 70.0 70.0 --

Rio Grande below Falcon Dam, Texas 100.0 25.0 75.0 12.5 75.0 100.0 75.0 --

Arroyo Colorado at Harlingen, Texas 100.0 90.9 100.0 94.7 98.4 100.0 100.0 --

Rio Grande near Brownsville, Texas 100.0 64.3 92.9 0.00 87.5 92.9 100.0 --

only station at which trace-element or pesticide concentrations 

met or exceeded the respective CCC in more than two samples. The cumulative relative frequency of copper concentrations greater than or equal to the copper CCC was about 25 

percent (fig. 2). 

The final water components used in the correlation analyses were minimum, median, and maximum estimates of total 

trace-element toxicity (toxic units; Wildhaber and Schmitt, 

1996) for the 18 months prior to the date of fish collection 

(table 12). Toxic units data for all stations in both basins were 

used.

Fish Data Refinements

Use of the fish-contaminant data and health-assessment 

indicators was restricted in this study to subsets of data. As a 

first restriction, only data for male carp in the CRB and male 

and female carp in the RGB were used. In the CRB, male carp 

were collected at the most (four of six) stations. All other species and female carp were collected at three or fewer stations, 

which was insufficient for the analyses. For male carp in the 

CRB, data were also insufficient to further divide by gonadal 

stage and age, so the correlation analyses were conducted 

using all stages and ages combined. In the RGB, carp was the 

only species collected at all six stations. Male carp of stage−3 

(all ages) and stage−3, age−2 were included in the analyses. 

Data for female carp were similarly restricted to stage−2. No 
30 Approach for Integrating BEST and NASQAN

Table 11. Time-weighted geometric mean concentrations of water-quality parameters of interest for selected 

NASQAN water-quality stations in the Rio Grande basin for the 18 months prior to the date of fish collection. 

[ug/L, micrograms per liter; --, no data. Dates in parentheses after station name refer to the 18-month time-period prior to the data of fish 

collection. Total trace elements are arsenic, chromium, copper, and zinc] 

Water-quality parameter Number of samples 

Number of less-than 

values 

Geometric 

Mean1 

(μg/L) 

Geometric 

Mean2 

(μg/L) 

Rio Grande at El Paso, Texas (4/29/1996-10/28/1997) 

Atrazine 16 4 0.003 0.002 

Arsenic 14 0 3.02 --

Copper 13 2 1.67 1.53 

Total trace elements 53 6 10.0 9.48 

Zinc 14 1 2.99 2.86 

Rio Grande at Foster Ranch near Langtry, Texas (5/7/1996-11/6/1997) 

Arsenic 18 0 2.06 --

Copper 18 6 1.44 1.04 

Total trace elements 71 18 7.23 6.49 

Zinc 18 7 1.84 1.44 

Rio Grande below Amistad Dam, near Del Rio, Texas (5/5/1996-11/4/1997) 

Atrazine 10 3 0.002 0.002 

Arsenic 11 0 2.57 --

Copper 11 3 1.16 0.925 

Total trace elements 44 7 7.32 6.89 

Zinc 11 3 2.12 1.67 

Rio Grande below Falcon Dam, Texas (5/19/1996-11/18/1997) 

Atrazine 8 2 0.004 0.004 

Arsenic 8 0 2.82 --

Copper 8 2 1.16 1.02 

Total trace elements 32 8 9.01 8.49 

Zinc 8 0 3.39 --

Arroyo Colorado at Harlingen, Texas (3/31/1996-9/30/1997) 

Atrazine 19 0 0.336 --

Arsenic 19 0 6.46 --

Copper 15 0 4.42 --

Total trace elements 63 1 20.76 18.47 

Zinc 18 0 6.80 --

Rio Grande near Brownsville, Texas (4/29/1996-10/28/1997) 

Atrazine 14 0 0.012 --

Arsenic 14 0 4.38 --

Copper 14 1 1.55 1.38 

Total trace elements 56 7 9.94 9.56 

Zinc 14 1 2.28 2.02 1

Minimum reporting level substituted for less-than concentrations. No substitution needed for arsenic. 2

One-half the minimum reporting level substituted for less-than concentrations. No substitution needed for arsenic.
Results 31

Cumulative relative

frequency (percent) 100 

80 

60 

40 

20 

0 

0 1 2 3 4 5 

Copper concentration (micrograms per liter) 

Freshwater ctierion 

for protection of 

aquatic life = 2 ug/L 

Figure 2. Cumulative relative frequency of copper concentrations above U.S. Environmental Protection Agency freshwater criterion 

continuous concentration for protection of aquatic life, Willamette River at Portland, Columbia River basin, September 1995-October 

1997 (U.S. Environmental Protection Agency, 1999).

other stages or ages of male or female carp were present at all 

six stations.

Elemental contaminant data for fish were first restricted 

to the elements of interest for water: arsenic, chromium, copper, selenium, and zinc. Correlation analyses were performed 

using dry-weight concentrations of arsenic, chromium, copper, 

and zinc in male carp in the CRB, and chromium, copper, and 

zinc in male and female carp in the RGB. Correlation analyses using concentrations of total organochlorine pesticides in 

fish also were limited to male carp in the CRB and male and 

female carp in the RGB. Analyses using total DDT concentrations were limited to female carp in the RGB because most 

of the total organochlorine pesticides in male carp were total 

DDT. Total PCBs were detected only in fish from the CRB. 

In the correlation analyses, both zero and one-half the reporting level were substituted for censored values of trace elements and organic contaminants in fish.

The fish health-assessment indicators examined in this 

study included both observations and measurements (table 2). 

Two observation indicators (external lesions and fin anomalies) were selected as examples for evaluation. Both were 

included in analyses for the CRB whereas only fin anomalies 

were included for the RGB. For each observation indicator, 

species-station mean values were used for correlation analyses because most median values were zero. Some observation indicators not selected also could have been studied in 

the same manner; others had mean values of zero for many 

stations and were therefore eliminated from consideration. 

Median values of the fish-health measurement indicators were 

used in the correlation analyses. 

For the CRB, correlation analyses were conducted using 

the fish-health observation and measurement indicators for 

male carp (all stages and ages combined). For the RGB, 

correlation analyses using the observation indicators were 

performed for male carp with all stages and ages combined 

whereas analyses using the measurement indicators were performed separately for male and female carp and were further 

separated by stage for both genders and also by age for males. 

The observation indicators for male carp by stage and age and 

for female carp in the RGB also could have been summarized 

in the same manner as the male carp with all stages and ages 

combined. 

In general we did not compare data among stations in the 

CRB or RGB or compare the two basins; such comparisons 

are presented elsewhere (Schmitt and others, 2004; Hinck 

and others, 2004) and are beyond the scope of this investigation. The exception is stable isotope data, which are compared 

among sites and basins. 

Results

Only a small number of stations in each basin (four 

in CRB, five to six in RGB) met the requirements for data 

integration; the test data sets contained too few observations for in-depth analysis and the power of the statistical test 

was low. The results of the analyses reported here are therefore for illustration purposes to show how a larger data set 

from the two programs could be combined and interpreted in 

larger studies.

As noted in the “Integration of Water-Quality and Fish 

Data” section, Kendall’s tau is based on the ranks of data 

rather than actual values. For this study, the ranking of most 

water-quality and fish data did not change when different 

substitution values were used for less-than concentrations, 

and there was no effect on Kendall’s tau. Ranked values did 

change when different substitution values were used for lessthan concentrations in the calculation of the total trace-element 

geometric mean concentrations in water and total organochlorine concentrations in female carp in the RBG. Substitution 

values are included in the discussion of correlation results for 

these two parameters. 

Arsenic, chromium, copper, and zinc concentrations 

in male carp from the CRB were not significantly cor-
32 Approach for Integrating BEST and NASQAN

Table 12. Minimum, median, and maximum estimates of total trace-element toxicity for selected 

NASQAN water-quality stations in the Columbia River and Rio Grande basins for the 18 months prior to 

the date of fish collection. 

[ug/L, micrograms per liter. Total trace element is arsenic, chromium, copper, selenium, and zinc] 

Station name 

Minimum 

Total trace-element toxicity estimate (μg/L) 

Median Maximum 

Columbia River Basin 

Columbia River at Northport, Washington 0.493 0.577 0.749 

Columbia River at Vernita Bridge near Priest Rapids 

Dam, Washington 0.498 0.546 0.896 

Snake River at Burbank, Washington 0.447 0.584 0.983 

Columbia River at Warrendale, Washington 0.475 0.588 1.04 

Willamette River at Portland, Oregon 0.717 0.789 2.23 

Columbia River at Beaver Army Terminal near 

Quincy, Oregon 0.497 0.610 0.905 

Rio Grande Basin 

Rio Grande at El Paso, Texas 0.250 0.490 0.614 

Rio Grande at Foster Ranch near Langtry, Texas 0.365 0.479 0.690 

Rio Grande below Amistad Dam, near Del Rio, Texas 0.377 0.442 0.613 

Rio Grande below Falcon Dam, Texas 0.388 0.406 0.494 

Arroyo Colorado at Harlingen, Texas 0.352 0.709 1.05 

Rio Grande near Brownsville, Texas 0.374 0.440 0.576 

related (p ≥ 0.05) with frequencies of detection of these 

elements in water for the 18 months prior to the date of fish 

collection (table 13). Similarly, chromium, copper, and zinc 

concentrations in male and female carp from the RGB were 

also not significantly correlated (p ≥ 0.05) with the 18-month 

detection frequencies of these elements in water (table 13). 

Similar to previously reported findings (Hinck and others, 

2004), correlations between concentrations of total organochlorine pesticides and total PCBs in fish with fish-health 

observation indicators (external lesions and fin anomalies) in 

CRB male carp were not statistically significant (p ≥ 0.05) 

(table 14). Most of the remaining correlations between waterquality data and external lesions and fin anomalies were also 

not statistically significant (p ≥ 0.05) (table 14). However, 

in the CRB significant positive correlations (p ≤ 0.05) were 

detected between atrazine in water (time-weighted 90th 

percentile concentration) and external lesions and between 

zinc in water (time-weighted 90th percentile concentration 

and 18-month frequency of detection) and fin anomalies 

(table 14). A value of 1.00 for Kendall’s tau in the atrazine 

relation (table 14) indicates that successive samples of both 

variables increased consistently and did not oscillate. An 

equally significant negative correlation (Kendall’s tau = -1.0, 

p ≤ 0.05) was detected between the 18-month frequency of 

detection for atrazine and fin anomalies in the CRB (table 14). 

This result is opposite of our expectation, which was increasing fin anomalies with atrazine detections, and may be a result 

of small sample size; with few samples, the chance of spurious 

correlations increases because statistical power is low. 

In the RGB, concentrations of total organochlorine 

pesticides in fish and fin anomalies were not significantly 

correlated (p ≥ 0.05) for male carp (table 14). Correlations 

also were not significant (p ≥ 0.05) between four categories of 

water-quality data (time-weighted geometric mean and percentile concentrations, last-sample concentrations, and detection 

frequencies in water) and fin anomalies for male carp. 

Results of the Kendall’s tau correlation analyses between 

fish-contaminant and water-quality data and fish-health 

measurement indicators are shown in table 15. Use of the 

indicator GSI, which is related to gonadal stage, was restricted 

to stage−3, age−2 male carp in the RGB because this was 

the only category for male or female carp with sufficient 

data to summarize by stage. In the CRB, statistically significant correlations (p ≤ 0.05) between fish-contaminant and 
Results 33

Table 13. Kendall’s tau correlation matrix of frequencies-of-detection of trace elements in water for the 18 months 

prior to the date of fish collection and trace-element concentrations in carp at selected NASQAN and BEST stations 

in the Columbia River and Rio Grande basins. 

[p, p-value; --, no data. Significance level of correlation test was p�0.05. Number of stations: Columbia River basin, 4; Rio Grande basin, 6] 

Arsenic in 

carp 

Chromium in 

carp 

Copper in 

carp 

Zinc in carp Water-quality parameter 

tau (p) tau (p) tau (p) tau (p) 

Columbia River Basin 

Male 

Arsenic, 18-month frequency-of-detection 0.33 (0.50) -- -- --

Chromium, 18-month frequency-of-detection -- -0.33 (0.50) -- -- 

Copper, 18-month frequency-of-detection -- -- -0.33 (0.50) -- 

Zinc, 18-month frequency-of-detection -- -- -- 0.33 (0.50) 

Rio Grande Basin 

Male 

Chromium, 18-month frequency-of-detection -- -0.07 (0.85) -- -- 

Copper, 18-month frequency-of-detection -- -- -0.33 (0.35) -- 

Zinc, 18-month frequency-of-detection -- -- -- -0.20 (0.56) 

Female 

Chromium, 18-month frequency-of-detection -- -0.20 (0.57) -- -- 

Copper, 18-month frequency-of-detection -- -- -0.47 (0.19) -- 

Zinc, 18-month frequency-of-detection -- -- -- 0.07 (0.85) 

water-quality data and fish-health measurement indicators in 

male carp (all stages and ages) were detected between a few 

parameters and the splenosomatic index (SSI) and condition factor (CF) (table 15). Statistically significant negative 

correlations (p ≤ 0.05) occurred between the concentration of 

total PCBs and SSI and CF in male carp. SSI and CF were 

positively correlated (p ≤ 0.05) with the time-weighted 90th 

percentile concentration of total trace elements and maximum 

estimate of total trace-element toxicity in water. The maximum total toxicity estimates probably overestimate exposure 

of fish to water-borne contaminants, as do the time-weighted 

90th percentile concentrations. The SSI can vary depending 

on a number of factors, including species, gender, age, gonad 

development, nonspecific stressors, and water quality. Acute, 

nonspecific stressors and chronic exposure to a number of 

chemical contaminants can decrease SSI whereas infection can 

cause spleen enlargement, which increases SSI (Schmitt and 

Dethloff, 2000). Condition factor varies directly with nutrition but can also increase or decrease in response to chemical 

exposure (Schmitt and Dethloff, 2000). Because SSI and 

CF are non-specific indicators and can increase or decrease 

as a result of contaminant exposure, they are typically used 

together with other data in a weight-of-evidence assessment 

(for example, Schmitt and others, 2004). 

Statistically significant negative correlations (p ≤ 0.05) 

also occurred between the 18-month detection frequencies of 

copper and total trace elements in water and the percent of 

tissue occupied by macrophage aggregates in CRB carp (table 

15). Macrophage aggregates, which occur in the spleen, 

kidney, and liver, are immune system biomarkers (Schmitt and 

Dethloff, 2000). Macrophage aggregate numbers have generally been reported to increase with exposure to contaminants, 

whereas only a few studies have reported decreasing numbers 

or no significant contaminant effect. The negative relation 

between the 18-month detection frequencies of copper and 

total trace elements with percent of tissue occupied by macrophage aggregates is therefore counter-intuitive; further study 

would be required to determine if exposure to copper or other 

trace elements can reduce aggregate density. 

For male carp in the RGB, a statistically significant 

positive correlation (p ≤ 0.05) was documented between the 

last-sample concentration of arsenic and ethoxyresorufin 

O-deethylase (EROD) activity (table 15). EROD activity 

can be influenced by water temperature, gonadal stage, age, 

and a variety of chemicals and chemical mixtures including 

organic, organometallic, and metallic compounds (Whyte 

and others, 2000). EROD activity tends to increase following 

exposure to certain planar hydrocarbons (Schmitt and Dethloff, 2000; Whyte and others, 2000). For correlations between 

18-month time-weighted geometric mean concentrations 

of total trace elements in water and all fish-health measurement indicators for male carp, Kendall’s tau differed when 

Text continues on page 40
34 Approach for Integrating BEST and NASQAN

Table 14. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of interest and mean fishhealth observation indicators for male carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande 

basins. 

[EL, external lesions; FINS, fin anomalies; p, p-value; PCBs, polychlorinated biphenyls; --, no data; EPTC, s-ethyl dipropylthiocarbamate. All 

concentrations in water are time-weighted concentrations. Correlations that are significant at p�0.05 are shown in bold. Total trace elements for 

18-month geometric mean concentrations in water are arsenic, chromium, copper, and zinc. Total trace elements for 18-month frequencies-ofdetection in water are arsenic, chromium, copper, selenium, and zinc. Number of stations: Columbia River basin, 4; Rio Grande basin, 6] 

Fish-contaminant and water-quality parameters 

Rio Grande 

Basin 

FINS 

tau (p) 

Mean fish-health observation indicator 

Columbia River Basin 

EL FINS 

tau (p) tau (p) 

Total organochlorine pesticides concentration in fish -0.33 (0.50) 0.00 (1.00) 0.27 (0.44) 

Total PCBs concentration in fish 0.00 (1.00) 0.33 (0.50) --

Arsenic, 90th percentile concentration in water 0.50 (0.28) -0.83 (0.07) --

Arsenic, 18-month geometric mean concentration in water -- -- 0.27 (0.44) 

Arsenic, concentration of last sample in water before fish collection -- -- 0.20 (0.55) 

Arsenic, 18-month frequency-of-detection in water 0.33 (0.50) -0.67 (0.17) --

Atrazine, 90th percentile concentration in water 1.00 (0.04) -0.67 (0.17) --

Atrazine, 18-month frequency-of-detection in water 0.67 (0.17) -1.00 (0.04) 0.13 (0.69) 

Chromium, 18-month frequency-of-detection in water 0.33 (0.50) 0.00 (1.00) 0.13 (0.70) 

Copper, 90th percentile concentration in water 0.33 (0.50) -0.67 (0.17) --

Copper, 18-month geometric mean concentration in water

 1 -- -- 0.53 (0.13) 

Copper, 18-month frequency-of-detection in water 0.00 (1.00) 0.33 (0.50) 0.27 (0.44) 

EPTC, 18-month frequency-of-detection in water 0.33 (0.50) -0.67 (0.17) --

Specific conductance, 90th percentile concentration in water 0.33 (0.50) -0.67 (0.17) --

Specific conductance, concentration of last sample in water before 

fish collection 0.33 (0.50) -0.67 (0.17) -0.13 (0.70)

Total trace elements, 90th percentile concentration in water 0.00 (1.00) -0.33 (0.50) --

Total trace elements, 18-month geometric mean concentration in -- -- 0.27 (0.44) water

 1

Total trace-elements, 18-month frequency-of-detection in water 0.00 (1.00) 0.33 (0.50) 0.13 (0.70)

Total trace-element toxicity estimate for water, minimum -0.33 (0.50) 0.67 (0.17) -0.40 (0.25)

Total trace-element toxicity estimate for water, median 0.67 (0.17) -0.33 (0.50) 0.40 (0.25)

Total trace-element toxicity estimate for water, maximum 0.00 (1.00) -0.33 (0.50) 0.53 (0.13)

Zinc, 90th percentile concentration in water -0.67 (0.17) 1.00 (0.04) --

Zinc, 18-month geometric mean concentration in water

 1 -- -- 0.00 (1.00)

Zinc, concentration of last sample in water before fish collection -- -- -0.07 (0.85)

Zinc, 18-month frequency-of-detection in water -0.67 (0.17) 1.00 (0.04) -0.07 (0.85)

1

Minimum reporting level substituted for less-than concentrations.
Table 15. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of interest and median fish-health measurement indicators for male and female 

carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande basins.—Continued

[EROD, ethoxyresorofin-O-deethylase; MA, macrophage aggregate; % Tis, percent of tissue occupied by macrophage aggregates; GSI, gonadosomatic index; SSI, splenosomatic index; CF, conditionfactor; HAI, health-assessment index; p, p-value; --, no data; PCBs, polychlorinated biphenyls; EPTC, s-ethyl dipropylthiocarbamate. All concentrations in water are time-weighted concentrations.

Correlations that are significant at p�0.05 are shown in bold. Total trace elements are arsenic, chromium, copper, and zinc. Number of stations: Columbia River basin, 4; Rio Grande basin, 6, except for 5

stations for SSI and male carp (all stages and ages) and geometric mean concentrations for male carp (stage 3, age 2)] 

Median fish-health measurement indicator 

Fish-contaminant and water-quality parameters EROD MA % Tis GSI1 SSI CF HAI

tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) 

Columbia River Basin 

Male carp, all stages and ages

Total organochlorine pesticides concentration in fish -- 0.50 (0.28) 0.67 (0.17) -- -0.67 (0.17) -0.67 (0.17) -0.17 (0.72)

Total PCBs concentration in fish -- 0.17 (0.72) 0.33 (0.50) -- -1.00 (0.04) -1.00 (0.04) -0.50 (0.28)

Arsenic, 90th percentile concentration in water -- 0.00 (1.00) 0.17 (0.72) -- 0.50 (0.28) 0.50 (0.28) 0.00 (1.00)

Arsenic, 18-month frequency-of-detection in water -- 0.17 (0.72) 0.00 (1.00) -- 0.67 (0.17) 0.67 (0.17) 0.17 (0.72)

Atrazine, 90th percentile concentration in water -- -0.50 (0.28) 0.00 (1.00) -- 0.00 (1.00) 0.00 (1.00) -0.50 (0.28)

Atrazine, 18-month frequency-of-detection in water -- -0.17 (0.72) 0.33 (0.50) -- 0.33 (0.50) 0.33 (0.50) -0.17 (0.72)

Chromium, 18-month frequency-of-detection in water -- -0.50 (0.28) -0.67 (0.17) -- 0.67 (0.17) 0.67 (0.17) 0.17 (0.72)

Copper, 90th percentile concentration in water -- 0.17 (0.72) 0.67 (0.17) -- 0.00 (1.00) 0.00 (1.00) -0.50 (0.28)

Copper, 18-month frequency-of-detection in water -- -0.50 (0.28) -1.00 (0.04) -- 0.33 (0.50) 0.33 (0.50) 0.17 (0.72)

EPTC, 18-month frequency-of-detection in water -- 0.17 (0.72) 0.00 (1.00) -- 0.67 (0.17) 0.67 (0.17) 0.17 (0.72)

Specific conductance, 90th percentile concentration in water -- 0.17 (0.72) 0.00 (1.00) -- 0.67 (0.17) 0.67 (0.17) 0.17 (0.72)

Specific conductance, concentration of last sample in water before fish

-- 0.17 (0.72) 0.00 (1.00) -- 0.67 (0.17) 0.67 (0.17) 0.17 (0.72) collection

Total trace elements, 90th percentile concentration in water -- -0.17 (0.72) -0.33 (0.50) -- 1.00 (0.04) 1.00 (0.04) 0.50 (0.28)

Total trace-elements, 18-month frequency-of-detection in water -- -0.50 (0.28) -1.00 (0.04) -- 0.33 (0.50) 0.33 (0.50) 0.17 (0.72)

Total trace-element toxicity estimate for water, minimum -- -0.17 (0.72) 0.00 (1.00) -- -0.67 (0.17) -0.67 (0.17) -0.17 (0.72)

Total trace-element toxicity estimate for water, median -- -0.83 (0.07) -0.33 (0.50) -- 0.33 (0.50) 0.33 (0.50) -0.17 (0.72)

Total trace-element toxicity estimate for water, maximum -- -0.17 (0.72) -0.33 (0.50) -- 1.00 (0.04) 1.00 (0.04) 0.50 (0.28)

Zinc, 90th percentile concentration in water -- 0.17 (0.72) -0.33 (0.50) -- -0.33 (0.50) -0.33 (0.50) 0.17 (0.72)

Zinc, 18-month frequency-of-detection in water -- 0.17 (0.72) -0.33 (0.50) -- -0.33 (0.50) -0.33 (0.50) 0.17 (0.72)

Results35
3

6

Table 15. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of interest and median fish-health measurement indicators for male and female 

carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande basins.—Continued

[EROD, ethoxyresorofin-O-deethylase; MA, macrophage aggregate; % Tis, percent of tissue occupied by macrophage aggregates; GSI, gonadosomatic index; SSI, splenosomatic index; CF, conditionfactor; HAI, health-assessment index; p, p-value; --, no data; PCBs, polychlorinated biphenyls; EPTC, s-ethyl dipropylthiocarbamate. All concentrations in water are time-weighted concentrations.

Correlations that are significant at p�0.05 are shown in bold. Total trace elements are arsenic, chromium, copper, and zinc. Number of stations: Columbia River basin, 4; Rio Grande basin, 6, except for 5

stations for SSI and male carp (all stages and ages) and geometric mean concentrations for male carp (stage 3, age 2)] 

Median fish-health measurement indicator 

Fish-contaminant and water-quality parameters EROD MA % Tis GSI1 SSI CF HAI 

tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) 

Rio Grande Basin

Male carp, all stages and ages

Total organochlorine pesticides concentration in fish 0.33 (0.35) -0.20 (0.57) -0.20 (0.57) -- 0.07 (0.85) -0.07 (0.85) 0.13 (0.70) 

Arsenic, 18-month geometric mean concentration in water 0.40 (0.25) 0.20 (0.57) 0.20 (0.57) -- 0.00 (1.00) -0.47 (0.19) 0.13 (0.70) 

Arsenic, concentration of last sample in water before fish collection 0.87 (0.01) 0.07 (0.85) 0.07 (0.85) -- 0.07 (0.85) -0.47 (0.17) 0.00 (1.00) 

Atrazine, 18-month frequency-of-detection in water 0.40 (0.23) 0.20 (0.56) 0.20 (0.56) -- 0.00 (1.00) -0.47 (0.17) 0.13 (0.69) 

Chromium, 18-month frequency-of-detection in water 0.13 (0.70) -0.33 (0.35) -0.33 (0.35) -- -0.20 (0.62) -0.20 (0.57) -0.13 (0.70) 

Copper, 18-month geometric mean concentration in water2 0.27 (0.44) 0.33 (0.35) 0.33 (0.35 -- 0.00 (1.00) -0.60 (0.09) 0.27 (0.44) 

Copper, 18-month frequency-of-detection in water 0.40 (0.25) 0.20 (0.57) 0.20 (0.57) -- 0.00 (1.00) -0.47 (0.19) 0.13 (0.70) 

Specific conductance, concentration of last sample in water before fishcollection 0.20 (0.57) -0.60 (0.09) -0.60 (0.09) -- -0.07 (0.85) 0.33 (0.35) 0.00 (1.00) 

Total trace elements, 18-month geometric mean concentration in water2 0.53 (0.13) 0.07 (0.85) 0.07 (0.85) -- 0.20 (0.62) -0.33 (0.35) 0.00 (1.00) 

Total trace elements, 18-month geometric mean concentration, in water3 0.40 (0.25) 0.20 (0.57) 0.20 (0.57) -- 0.00 (1.00) -0.47 (0.19) 0.13 (0.70) 

Total trace-elements, 18-month frequency-of-detection in water 0.67 (0.06) -0.07 (0.85) -0.07 (0.85) -- 0.20 (0.62) -0.47 (0.19) -0.13 (0.70) 

Total trace-element toxicity estimate for water, minimum -0.40 (0.25) -0.20 (0.57) -0.20 (0.57) -- 0.00 (1.00) 0.47 (0.19) -0.13 (0.70) 

Total trace-element toxicity estimate for water, median 0.13 (0.70) -0.07 (0.85) -0.07 (0.85) -- -0.20 (0.62) -0.47 (0.19) 0.13 (0.70) 

Total trace-element toxicity estimate for water, maximum 0.00 (1.00) 0.07 (0.85) 0.07 (0.85) -- -0.40 (0.33) -0.33 (0.35) 0.27 (0.44) 

Zinc, 18-month geometric mean concentration in water2 0.27 (0.44) -0.20 (0.57) -0.20 (0.57) -- 0.20 (0.62) -0.07 (0.85) 0.13 (0.70) 

Zinc, 18-month frequency-of-detection in water 0.13 (0.69) -0.20 (0.56) -0.20 (0.56) -- 0.20 (0.60) -0.07 (0.85) 0.13 (0.69) 

Male carp, stage 3 

Arsenic, 18-month geometric mean concentration in water 0.73 (0.04) 0.33 (0.35) 0.47 (0.19) -- 0.07 (0.85) -0.60 (0.09) 0.20 (0.56) 

Arsenic, concentration of last sample in water before fish collection 0.87 (0.01) 0.20 (0.56) 0.33 (0.33) -- 0.20 (0.56) -0.60 (0.08) 0.07 (0.84) 

Atrazine, 18-month frequency-of-detection in water 0.60 (0.08) 0.33 (0.33) 0.47 (0.17) -- -0.07 (0.85) -0.47 (0.17) 0.20 (0.55) 

Chromium, 18-month frequency-of-detection in water 0.47 (0.19) -0.20 (0.57) -0.07 (0.85) -- 0.07 (0.85) -0.33 (0.35) -0.07 (0.85) 

ApproachforIntegratingBESTandNASQAN
Table 15. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of interest and median fish-health measurement indicators for male and female 

carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande basins.—Continued

[EROD, ethoxyresorofin-O-deethylase; MA, macrophage aggregate; % Tis, percent of tissue occupied by macrophage aggregates; GSI, gonadosomatic index; SSI, splenosomatic index; CF, conditionfactor; HAI, health-assessment index; p, p-value; --, no data; PCBs, polychlorinated biphenyls; EPTC, s-ethyl dipropylthiocarbamate. All concentrations in water are time-weighted concentrations.

Correlations that are significant at p�0.05 are shown in bold. Total trace elements are arsenic, chromium, copper, and zinc. Number of stations: Columbia River basin, 4; Rio Grande basin, 6, except for 5

stations for SSI and male carp (all stages and ages) and geometric mean concentrations for male carp (stage 3, age 2)] 

Median fish-health measurement indicator 

Fish-contaminant and water-quality parameters EROD MA % Tis GSI1 SSI 

tau (p)

CF HAI

tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) 

Copper, 18-month geometric mean concentration in water2 0.60 (0.09) 0.47 (0.19) 0.60 (0.09) -- -0.07 (0.85) -0.73 (0.04) 0.33 (0.33)

Copper, 18-month frequency-of-detection in water 0.73 (0.04) 0.33 (0.35) 0.47 (0.19) -- 0.07 (0.85) -0.60 (0.09) 0.20 (0.56)

Specific conductance, concentration of last sample in water before fish 0.20 (0.57) -0.47 (0.19) -0.33 (0.35) -- 0.07 (0.85) 0.20 (0.57) 0.07 (0.84) collection

Total trace elements, 18-month geometric mean concentration in water2 0.87 (0.01) 0.20 (0.57) 0.33 (0.35) -- 0.20 (0.57) -0.47 (0.19) 0.07 (0.85)

Total trace elements, 18-month geometric mean concentration, in water3 0.73 (0.04) 0.33 (0.35) 0.47 (0.19) -- -0.60 (0.09) 0.07 (0.85) 0.20 (0.56)

Total trace-elements, 18-month frequency-of-detection in water 1.00 (<0.01) 0.07 (0.85) 0.20 (0.57) -- 0.33 (0.35) -0.60 (0.09) -0.07 (0.85)

Total trace-element toxicity estimate for water, minimum -0.47 (0.19) -0.07 (0.85) -0.20 (0.57) -- -0.07 (0.85) 0.60 (0.09) -0.20 (0.56)

Total trace-element toxicity estimate for water, median 0.47 (0.19) 0.07 (0.85) 0.20 (0.57) -- 0.07 (0.85) -0.60 (0.09) 0.20 (0.56)

Total trace-element toxicity estimate for water, maximum 0.33 (0.35) 0.20 (0.57) 0.33 (0.35) -- -0.07 (0.85) -0.47 (0.19) 0.33 (0.33)

Zinc, 18-month geometric mean concentration in water2 0.60 (0.09) -0.07 (0.85) 0.07 (0.85) -- 0.20 (0.57) -0.20 (0.57) 0.20 (0.56)

Zinc, 18-month frequency-of-detection in water 0.47 (0.17) -0.07 (0.85) 0.07 (0.85) -- 0.20 (0.56) -0.20 (0.56) 0.20 (0.55)

Male carp, stage 3, age 2

Arsenic, 18-month geometric mean concentration in water 0.60 (0.14) 0.00 (1.00) 0.40 (0.33) -0.20 (0.62) 0.20 (0.62) -0.20 (0.62) 0.50 (0.21) 

Arsenic, concentration of last sample in water before fish collection 0.80 (0.04) 0.00 (1.00) 0.40 (0.30) 0.00 (1.00) 0.40 (0.30) 0.00 (1.00) 0.40 (0.29) 

Atrazine, 18-month frequency-of-detection in water 0.50 (0.21) -0.10 (0.80) 0.30 (0.45) -0.10 (0.80) 0.10 (0.80) -0.10 (0.80) 0.60 (0.12) 

Chromium, 18-month frequency-of-detection in water 0.20 (0.62) 0.00 (1.00) 0.00 (1.00) 0.20 (0.62) 0.20 (0.62) 0.20 (0.62) -0.50 (0.21) 

Copper, 18-month geometric mean concentration in water2 0.40 (0.33) 0.20 (0.62) 0.60 (0.14) -0.40 (0.33) 0.00 (1.00) -0.40 (0.33) 0.10 (0.80) 

Copper, 18-month frequency-of-detection in water 0.60 (0.14) 0.00 (1.00) 0.40 (0.33) -0.20 (0.62) 0.20 (0.62) -0.20 (0.62) 0.50 (0.21) 

Specific conductance, concentration of last sample in water before fishcollection 

-0.20 (0.62) -0.40 (0.33) -0.80 (0.05) 0.60 (0.14) 0.20 (0.62) 0.60 (0.14) -0.10 (0.80) 

Total trace elements, 18-month geometric mean concentration in water2 0.80 (0.05) -0.20 (0.62) 0.20 (0.62) 0.00 (1.00) 0.40 (0.33) 0.00 (1.00) 0.50 (0.21) 

Total trace elements, 18-month geometric mean concentration, in water3 0.60 (0.14) 0.00 (1.00) 0.40 (0.33) -0.20 (0.62) 0.20 (0.62) -0.20 (0.62) 0.50 (0.21) 

Total trace-elements, 18-month frequency-of-detection in water 1.00 (0.01) 0.00 (1.00) 0.40 (0.33) 0.20 (0.62) 0.60 (0.14) 0.20 (0.62) 0.30 (0.45) 

Results37
3

8

Table 15. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of interest and median fish-health measurement indicators for male and female 

carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande basins.—Continued

[EROD, ethoxyresorofin-O-deethylase; MA, macrophage aggregate; % Tis, percent of tissue occupied by macrophage aggregates; GSI, gonadosomatic index; SSI, splenosomatic index; CF, conditionfactor; HAI, health-assessment index; p, p-value; --, no data; PCBs, polychlorinated biphenyls; EPTC, s-ethyl dipropylthiocarbamate. All concentrations in water are time-weighted concentrations.

Correlations that are significant at p�0.05 are shown in bold. Total trace elements are arsenic, chromium, copper, and zinc. Number of stations: Columbia River basin, 4; Rio Grande basin, 6, except for 5

stations for SSI and male carp (all stages and ages) and geometric mean concentrations for male carp (stage 3, age 2)] 

Median fish-health measurement indicator 

Fish-contaminant and water-quality parameters EROD MA % Tis GSI1 SSI CF HAI 

tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) 

Total trace-element toxicity estimate for water, minimum -0.40 (0.33) -0.60 (0.14) -0.60 (0.14) 0.40 (0.33) 0.00 (1.00) 0.40 (0.33) 0.30 (0.45) 

Total trace-element toxicity estimate for water, median 0.20 (0.62) 0.40 (0.33) 0.40 (0.33) -0.20 (0.62) 0.20 (0.62) -0.20 (0.62) -0.50 (0.21) 

Total trace-element toxicity estimate for water, maximum 0.00 (1.00) 0.60 (0.14) 0.20 (0.62) -0.40 (0.33) 0.00 (1.00) -0.40 (0.33) -0.70 (0.08) 

Zinc, 18-month geometric mean concentration in water 2 0.40 (0.33) -0.60 (0.14) -0.20 (0.62) 0.40 (0.33) 0.40 (0.33) 0.40 (0.33) 0.90 (0.02) 

Zinc, 18-month frequency-of-detection in water 0.30 (0.45) -0.50 (0.21) -0.10 (0.80) 0.30 (0.45) 0.30 (0.45) 0.30 (0.45) 0.90 (0.02) 

Female carp, all stages and ages

Total organochlorine pesticides concentration in fish4 0.07 (0.85) 0.07 (0.85) 0.07 (0.85) -- -0.33 (0.35) -0.20 (0.57) 0.20 (0.56) 

Total organochlorine pesticides concentration in fish3 -0.07 (0.85) -0.07 (0.85) -0.07 (0.85) -- -0.20 (0.57) -0.33 (0.35) 0.33 (0.33) 

Arsenic, 18-month geometric mean concentration in water 0.47 (0.19) 0.20 (0.57) 0.20 (0.57) -- -0.47 (0.19) -0.33 (0.35) -0.20 (0.56) 

Arsenic, concentration of last sample in water before fish collection 0.47 (0.17) 0.20 (0.56) 0.20 (0.56) -- -0.33 (0.33) -0.20 (0.56) -0.20 (0.56) 

Atrazine, 18-month frequency-of-detection in water 0.33 (0.33) 0.20 (0.56) 0.20 (0.56) -- -0.47 (0.17) -0.20 (0.56) -0.20 (0.55) 

Chromium, 18-month frequency-of-detection in water 0.47 (0.19) -0.07 (0.85) -0.07 (0.85) -- 0.07 (0.85) -0.07 (0.85) -0.07 (0.85) 

Copper, 18-month geometric mean concentration in water2 0.33 (0.35) 0.33 (0.35) 0.33 (0.35) -- -0.60 (0.09) -0.47 (0.19) 0.20 (0.56) 

Copper, 18-month frequency-of-detection in water 0.47 (0.19) 0.20 (0.57) 0.20 (0.57) -- -0.47 (0.19) -0.33 (0.35) -0.20 (0.56) 

Specific conductance, concentration of last sample in water before fishcollection 0.20 (0.58) -0.33 (0.35) -0.33 (0.35) -- 0.07 (0.85) 0.20 (0.57) 0.20 (0.56) 

Total trace elements, 18-month geometric mean concentration in water2 0.33 (0.35) 0.07 (0.85) 0.07 (0.85) -- -0.33 (0.35) -0.20 (0.57) -0.07 (0.85) 

Total trace elements, 18-month geometric mean concentration, in water3 0.47 (0.19) 0.20 (0.57) 0.20 (0.57) -- -0.47 (0.19) -0.33 (0.35) -0.20 (0.56) 

Total trace-elements, 18-month frequency-of-detection in water 0.47 (0.19) 0.20 (0.57) 0.20 (0.57) -- -0.20 (0.57) -0.07 (0.85) -0.20 (0.56) 

Zinc, 18-month geometric mean concentration in water2 0.07 (0.85) -0.20 (0.57) -0.20 (0.57) -- -0.33 (0.35) 0.07 (0.85) 0.20 (0.56)

Zinc, 18-month frequency-of-detection in water 0.07 (0.85) -0.20 (0.56) -0.20 (0.56) -- -0.33 (0.34) 0.07 (0.85) 0.13 (0.69) 

Female carp, stage 2 

Arsenic, 18-month geometric mean concentration in water 0.47 (0.19) 0.20 (0.57) 0.07 (0.85) -- -0.47 (0.19) -0.33 (0.35) -0.73 (0.04) 

Arsenic, concentration of last sample in water before fish collection 0.47 (0.17) 0.20 (0.56) -0.07 (0.85) -- -0.33 (0.33) -0.20 (0.56) -0.87 (0.01) 

ApproachforIntegratingBESTandNASQAN
Table 15. Kendall’s tau correlation matrix of fish-contaminant and water-quality parameters of interest and median fish-health measurement indicators for male and female 

carp at selected NASQAN and BEST stations in the Columbia River and Rio Grande basins.—Continued

[EROD, ethoxyresorofin-O-deethylase; MA, macrophage aggregate; % Tis, percent of tissue occupied by macrophage aggregates; GSI, gonadosomatic index; SSI, splenosomatic index; CF, conditionfactor; HAI, health-assessment index; p, p-value; --, no data; PCBs, polychlorinated biphenyls; EPTC, s-ethyl dipropylthiocarbamate. All concentrations in water are time-weighted concentrations.

Correlations that are significant at p�0.05 are shown in bold. Total trace elements are arsenic, chromium, copper, and zinc. Number of stations: Columbia River basin, 4; Rio Grande basin, 6, except for 5

stations for SSI and male carp (all stages and ages) and geometric mean concentrations for male carp (stage 3, age 2)] 

Median fish-health measurement indicator 

Fish-contaminant and water-quality parameters EROD MA % Tis GSI1 SSI CF HAI

tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) tau (p) 

Atrazine, 18-month frequency-of-detection in water 0.33 (0.33) 0.20 (0.56) 0.07 (0.85) -- -0.47 (0.17) -0.20 (0.56) -0.60 (0.08) 

Chromium, 18-month frequency-of-detection in water 0.47 (0.19) -0.07 (0.85) -0.20 (0.57) -- 0.07 (0.85) -0.07 (0.85) -0.47 (0.19) 

Copper, 18-month geometric mean concentration in water2 0.33 (0.35) 0.33 (0.35) 0.20 (0.57) -- -0.60 (0.09) -0.47 (0.19) -0.60 (0.09) 

Copper, 18-month frequency-of-detection in water 0.47 (0.19) 0.20 (0.57) 0.07 (0.85) -- -0.47 (0.19) -0.33 (0.35) -0.73 (0.04) 

Specific conductance, concentration of last sample in water before fishcollection 0.20 (0.57) -0.33 (0.35) -0.60 (0.09) -- 0.07 (0.85) 0.20 (0.57) -0.20 (0.57) 

Total trace elements, 18-month geometric mean concentration in water2 0.33 (0.35) 0.07 (0.85) -0.07 (0.85) -- -0.33 (0.35) -0.20 (0.57) -0.87 (0.01) 

Total trace elements, 18-month geometric mean concentration, in water3 0.47 (0.19) 0.20 (0.57) 0.07 (0.85) -- -0.47 (0.19) -0.33 (0.35) -0.73 (0.04) 

Total trace-elements, 18-month frequency-of-detection in water 0.47 (0.19) 0.20 (0.57) 0.07 (0.85) -- -0.20 (0.57) -0.07 (0.85) -1.00 (<0.01) 

Zinc, 18-month geometric mean concentration in water2 0.07 (0.85) -0.20 (0.57) -0.33 (0.35) -- -0.33 (0.35) 0.07 (0.85) -0.60 (0.09) 

Zinc, 18-month frequency-of-detection in water 0.07 (0.85) -0.20 (0.56) -0.33 (0.33) -- -0.33 (0.33) 0.07 (0.85) -0.47 (0.17) 

1Immature gonad stage.2Minimum reporting level substituted for less-than concentrations.3One-half the minimum reporting level substituted for less-than concentrations.4Zero substituted for less-than concentrations.

Results39
40 Approach for Integrating BEST and NASQAN

either zero or one-half the reporting level was substituted for 

censored trace-element concentrations, but the correlations 

were nevertheless statistically insignificant (p ≥ 0.05, table 

15). 

Statistically significant positive correlations (p ≤ 0.05) for 

stage−3 RGB male carp were detected between five categories of water-quality data (18-month time-weighted geometric mean concentrations of arsenic and total trace elements, 

last-sample arsenic concentration, and 18-month detection 

frequencies of copper and total trace elements in water) and 

EROD activity (table 15). Kendall’s tau changed with use of 

the different substitution values for total trace-element concentrations but nevertheless remained significant (p ≤ 0.05). 

A significant negative correlation (p ≤ 0.05) was detected 

between the 18-month time-weighted geometric mean concentration of copper and CF.

In stage−3, age−2 male carp from the RGB, EROD 

activity was positively correlated (p ≤ 0.05) with the lastsample arsenic concentration and with the 18-month timeweighted geometric mean concentration (MRL substitution) 

and detection frequency of total trace elements in water (table 

15). Significant positive correlations (p ≤ 0.05) were also 

detected between 18-month time-weighted geometric mean 

concentration and detection frequency of zinc in water and the 

health-assessment index (HAI) (table 15). The HAI is based 

on the fish-health observation indicators. Each structure or 

organ observed (for example, eye or gill) is given a numeric 

value, and these numbers are summed to yield a single HAI 

value for each fish. Higher HAI values indicate greater 

cumulative stress (Schmitt and Dethloff, 2000). A statistically significant negative correlation (p ≤ 0.05) was detected 

between last sample specific conductance and the percent of 

spleen tissue occupied by macrophage aggregates. As with the 

negative associations involving copper and other trace elements and percent of tissue occupied macrophage aggregates 

noted for the CRB, documentation of the negative association 

between specific conductance and percent of tissue occupied 

by the aggregates in the RGB would require further investigation. 

Among the twelve BEST stations included in our analyses, vtg was detected in at least one male carp from three 

stations in the CRB (Vernita Bridge, Snake River at Burbank, 

and Warrendale) and three in the RGB (Foster Ranch, Amistad 

Dam, and Arroyo Colorado). However, correlation analyses 

relating fish-contaminant or water-quality data to vtg concentrations in male carp were not conducted because most vtg 

concentrations were less than the detection limit. 

No statistically significant correlations were detected for 

female carp (all stages and ages) from the RGB (table 15). 

Values of Kendall’s tau differed slightly when substitution 

values were used in the calculations of total organochlorine 

pesticide concentrations in the fish and time-weighted geometric mean concentrations of total trace elements in water, 

but the overall results (statistically insignificant correlations) 

did not change. For stage−2 female carp, the last-sample 

arsenic concentration, 18-month time-weighted geometric 

mean concentrations of arsenic and copper, and 18-month 

detection frequencies of copper and total trace elements in 

water were negatively correlated (p ≤ 0.05) with the HAI. 

These results are contrary to expectations; HAI should 

increase (get worse) with exposure to chemical contaminants. Because so many factors and combinations of factors 

can affect fish health, the negative correlations between the 

constituents in water and HAI may be reflecting the effects of 

other chemicals or factors on fish health or may be the result 

of the small sample size. 

Stable Isotopes

The final techniques evaluated for integrating the BEST 

and NASQAN data sets were the examination of stable 

nitrogen isotopes in fish and particulate organic matter (POM) 

in water, and ratios of carbon to nitrogen (C:N) in the POM. 

In biota, the ratio of the abundance of naturally occurring 

stable (non-radioactive) isotopes of nitrogen (15N:14N, or δ15N) 

reflects nitrogen sources to the ecosystem and trophic relations 

within the ecosystem (Schmitt and Dethloff, 2000). The δ15N 

of POM in water reflects the relative contributions of terrestrial and in-stream sources of nitrogen in the POM. These 

various sources often have distinctive isotopic compositions 

and also distinctive C:N values (Kendall and others, 2001). As 

part of the BEST and NASQAN sampling efforts in the CRB 

and RGB, fish were analyzed for δ15N and POM samples were 

analyzed for δ15N and C:N (atomic). 

Correlation analyses of δ15N of POM in water and δ15N of 

fish were examined using Kendall’s tau correlation coefficient. 

The Kruskal-Wallis test was used in comparisons of the isotope data. The latter test is an analysis of variance, nonparametric rank-based procedure that is appropriate for small data 

sets. Other analysis of variance procedures, such as ANOVA, 

could not be used because the assumption of normality could 

not be met.

Mean values and ranges of δ15N and C:N in POM in 

water in the CRB and RGB are shown in table 16. The δ15N 

values for POM were significantly (p ≤ 0.05) lower in the 

CRB than in the RGB, and most C:N values in POM were 

greater in the CRB. Based on the δ15N and C:N values, POM 

sources in both basins included plankton, fresh terrestrial plant 

material, aquatic plants, and soil organic matter (Kendall and 

others, 2001). The POM values of δ15N reflected the sources 

of nitrogen—low values for mineral sources and high values 

for animal sources. Relative to the other sites in their respective basins, animal nitrogen inputs were small at the Northport 

and Vernita Bridge stations in the CRB and at Amistad Dam 

in the RGB, as reflected by relatively low mean δ15N of POM 

(table 16). In contrast, animal nitrogen was evident at the 

Falcon Dam, Arroyo Colorado, and Brownsville stations in the 

RGB, where mean δ15N of POM was greater (table 16). 
4

1

Table 16. Mean values and ranges of δ15 N of POM and C:N POM in water and ranges of δ15 N of fish at selected NASQAN and BEST stations in the Columbia River and Rio Grande basins, water year 1997. [water year 1997, October 1, 1996 through September 30, 1997; POM, particulate organic matter; C, carbon; N, nitrogen; ‰, permil (parts per thousand); at., atomic; --, no data. Values for δ15 N mean and 

C:N for water are modified from Kendall et al. 2001; δ15 N data for fish are from Carol Kendall, U.S. Geological Survey, unpublished data]

Water 

3

Fish 

Station δ15N range (‰) δ15N range (‰) δ15N of POM δ15N of POM C:N POM C:N POM Mean trophic position1 Bottom mean (‰) range (‰) mean (at.) range (at.) Piscivores feeders2 

Bottom

feeders2 Piscivores3 

Columbia River Basin 

Columbia River at Northport, Washington -0.8 -3.1 – 3.8 19.4 8.4 – 39.7 6.1 – 7.2 8.0 – 8.3 7.4 9.0 

Columbia River at Vernita Bridge near Priest 0.4 -1.6 – 4.7 7.0 5.4 – 9.2 10.1 – 10.2 11.8 – 12.6 9.8 11.8 Rapids Dam, Washington 

Snake River at Burbank, Washington 3.2 0.4 – 5.4 9.2 6.8 – 14.3 12.0 – 12.2 15.1 – 15.2 9.2 11.9 

Columbia River at Warrendale, Washington 3.8 1.8 – 6.4 12.5 7.5 – 16.9 9.1 – 10.0 13.2 – 13.6 5.8 9.6 

Willamette River at Portland, Oregon 2.7 0.5 – 5.6 9.8 7.5 – 16.0 11.0 – 11.5 14.0 – 14.4 8.6 11.5 

Columbia River at Beaver Army Terminalnear Quincy, Oregon 3.1 -0.6 – 4.8 9.4 7.3 – 12.4 10.8 – 11.1 13.2 7.9 10.1 

Rio Grande Basin 

Rio Grande at El Paso, Texas 7.9 5.0 – 10.4 7.6 6.9 – 8.2 13.6 – 14.5 13.8 – 14.2 6.1 6.1 

Rio Grande at Foster Ranch near Langtry, 5.1 3.2 – 7.9 7.8 5.7 – 9.8 13.4 – 14.0 -- 8.6 -- Texas

Rio Grande below Amistad Dam, near Del Rio, Texas 2.0 1.1 – 3.0 5.6 3.2 – 8.0 15.9 – 17.2 17.5 – 19.0 14.5 16.3 

Rio Grande below Falcon Dam, Texas 9.6 8.8 – 10.2 7.5 6.0 – 9.5 15.1 – 15.2 18.0 – 18.1 5.5 8.5 Arroyo Colorado at Harlingen, Texas 12.0 8.6 – 14.4 11.4 7.1 – 30.2 14.8 – 15.7 16.4 – 17.6 3.2 5.0 

Rio Grande near Brownsville, Texas 8.4 5.0 – 14.0 9.2 6.1 – 16.6 12.1 – 13.4 17.7 – 17.9 4.3 9.4 

1Mean δ15 N of fish minus mean δ15 N of POM in water.2Common carp (Cyprinus carpio) and largescale sucker (Catostomus macrocheilus).3Largemouth bass (Micropterus salmoides), smallmouth bass (Micropterus dolomieui), rainbow trout (Oncorhynchus mykiss), channel catfish (Ictalurus punctatus), and northern pikeminnow

(Ptychocheilus oregonensis).

StableIsotopes
42 Approach for Integrating BEST and NASQAN

Table 17. Kendall’s tau correlation matrix of mean trophic positions and mean total organics 

concentrations for fish species at selected NASQAN and BEST stations in the Columbia River 

and Rio Grande basins, water year 1997. 

[water year 1997, October 1, 1996 through September 30, 1997; OCs, organochlorine pesticides; PCBs, 

polychlorinated biphenyls; p, p-value; -- no data. Correlations that are significant at p�0.05 are shown in bold. 

Number of data pairs: Columbia River basin, 15; Rio Grande basin, 12] 

Mean total 

Mean trophic positions per basin OCs DDT 

tau (p) 

Mean total 

tau (p) 

Mean total 

PCBs 

tau (p) 

Mean trophic positions, Columbia River basin 0.15 (0.43) 0.16 (0.40) 0.50 (0.01) 

Mean trophic positions, Rio Grande basin -0.55 (0.01) -0.55 (0.01) --

The spread of δ15N in benthivorous fish [carp and largescale sucker (Catostomus macrocheilus)] and piscivorous 

fish [largemouth bass (Micropterus salmoides), smallmouth 

bass (Micropterus dolomieui), rainbow trout (Oncorhynchus 

mykiss), channel catfish (Ictalurus punctatus), and northern 

pikeminnow (Ptychocheilus oregonensis)] are also shown 

in table 16. The δ15N of benthivores and piscivores were 

significantly (p ≤ 0.05) lower in the CRB than in the RGB, 

reflecting the significantly (p ≤ 0.05) lower values of δ15N of 

POM and Kjeldahl nitrogen (organic plus ammonia nitrogen) 

in waters of the CRB and greater nitrogen enrichment in the 

RGB. However, when these data were normalized by subtracting the mean δ15N of POM in water from the mean δ15N of 

fish, the resulting mean trophic positions (mean δ15N of fish 

minus mean δ15N of POM in water) of the benthivorous fish 

did not differ significantly between basins, as measured by 

the Kruskal-Wallis test. Trophic positions of the piscivores 

(adjusted δ15N) also did not differ significantly between basins 

(table 16). The trophic position of the nominal piscivores 

was significantly (p ≤ 0.05) higher than that of the nominal 

benthivores in the CRB, but not in the RGB. These results 

indicate that there is considerably more dietary overlap among 

the fishes sampled in RGB than in those from the CRB.

The strengths of the associations between mean trophic 

position and mean concentrations of bio-accumulative contaminants (total organochlorine pesticides, total DDT, or total 

PCBs) for each species at a station in the CRB and RGB were 

examined using Kendall’s tau. Statistically significant correlations (p ≤ 0.05) were documented between mean trophic 

position and mean total PCB concentrations in CRB fish, and 

between mean trophic position and mean concentrations of 

total organochlorine pesticides and total DDT in RGB fish 

(table 17). Values of Kendall’s tau were only moderate (about 

0.50), however, and reflected the influence of outliers in the 

data (fig. 3). When mean trophic positions greater than 11.0 

were omitted from the CRB analysis, Kendall’s tau increased 

to 0.80, and the positive correlation between mean trophic 

positions and mean total PCB concentrations in fish became 

stronger (p ≤ 0.01). However, it is important to note that there 

was no a priori reason to eliminate these means from consideration, other than the fact that the adjusted δ15N values were 

larger than most. For the RGB, the elimination of mean total 

organochlorine and mean total DDT concentrations in fish 

greater than 1.0 part per million (equivalent to 1 microgram 

per gram) resulted in a Kendall’s tau of –0.42 and no significant relation (p = 0.09) between these contaminants and mean 

trophic positions. Similar results were obtained when one-half 

the reporting level of the bio-accumulative contaminants was 

substituted for censored values. In contrast to the outliers 

eliminated from the CRB analysis, the values excluded from 

the RGB analysis all represented samples from the Arroyo 

Colorado that reflected relatively localized sources of contamination.

As illustrated by the situation described in the previous 

paragraph, analyses of correlations between mean trophic 

position and bio-accumulative contaminants in fish on a basinwide scale are probably not very relevant. Significant correlations (positive or negative) between δ15N of fish and any 

contaminants across all stations in a basin are not necessarily 

expected unless the correlation is a result of a large-scale 

pollutant; for example, atmospheric mercury. Instead, the 

expectation is that δ15N and bio-accumulative contaminants in 

fish will co-vary at a station; that is, some of the differences 

between fish exposed to the same concentrations of a bioaccumulable contaminant can be explained by differences in 

trophic position, as measured by δ15N. The examination of 

the different taxa present at a station is probably more relevant 

than the basin-wide analyses.

As noted by Schmitt and Dethloff (2000), δ15N of POM 

in water may be useful for interpreting the results of reproductive biomarkers. Among all BEST stations sampled in 1997-

98 (n=26), vtg was detected in at least one male carp from 

four stations in the CRB (Vernita Bridge, Warrendale, Snake 

River at Burbank, and Willamette at Oregon City) and six in 

the RGB (Alamosa, Elephant Butte, Foster Ranch, Mission, 

Amistad Dam, and Arroyo Colorado in the RGB). Concentrations were especially high (mid-vitellogenic female range) in 

two carp from Arroyo Colorado in the RGB (Schmitt and others, 2004), and several vtg concentrations in male carp from 

Warrendale in the CRB (Hinck and others, 2004) were greater 

than most other concentrations. These two stations also had 

the highest δ15N of POM in water in their respective basins. 

The elevated POM values indicate inputs of animal sources 

of nitrogen such as livestock or sewage, which may also be 
4

3

1.41.21.00.80.60.40.20.0 

Largescale sucker,Northport Largescale sucker, Carp, Snake Vernita Smallmouth bass,

Carp, Beaver Warrendale 

Warrendale Beaver 

Largescale sucker,Willamette

Smallmouth bass,

Carp, Vernita

Northern pikeminnow,Warrendale

Snake 

Northern pikeminnow,Beaver 

Northern pikeminnow,

Willamette

Vernita

Largemouth bass,

Smallmouth bass,

Columbia River Basin

Carp,

Rainbow trout,

Northport 

5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 

Mean trophic position 

4.00.0 

2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 

Mean trophic position 

Figure 3. Relation between mean total PCB concentrations and mean trophic position, by fish species, in the Columbia River basin; and mean total organochlorine pesticide concentrations and mean trophic position in the Rio Grande basin. Figure 3. Relation between mean total PCB concentrations and mean trophic position, by fish species, in the Columbia River basin; and mean total organochlorine pesticide concentrations and mean trophic position in the Rio Grande basin.

3.0 2.01.0

s edi c i t s ep e nirol hc onagrol at oT

) thgiewt ew, noillimr ep s trap(

Rio Grande Basin

Carp, Arroyo 

Channel catfish, Arroyo 

Carp, Brownsville 

Carp, Falcon Carp, El Paso

Channel catfish, El Paso

Largemouth bass, Falcon

Carp, Foster

Largemouth bass, Brownsville Carp, Amistad

Largemouth bass, Amistad

Smallmouth bass,Amistad 

StableIsotopes

s BCPl atoT

) thgiewt ew, noillimr ep s trap(
44 Approach for Integrating BEST and NASQAN

sources of endocrine-modulating chemicals [for example 

natural and synthetic estrogens, alkylphenol polyethoxylates 

(APEs), and pharmaceuticals] to aquatic systems (Schmitt and 

Dethloff, 2000; Folmar and others, 1996; Tyler and others, 

1998; Kolpin and others, 2002). Conversely, Vernita Bridge 

(CRB) and Amistad Dam (RGB) had the lowest δ15N of POM 

in water in their respective basins, but vtg was nevertheless 

detected in male carp from both stations. At these two stations, other or additional factors may be involved, including 

contaminants not included in our analysis.

Discussion and Recommendations

This study was initiated in 2001 to investigate alternative 

techniques for summarizing and integrating water-quality data 

of the NASQAN program with fish-contaminant and fishhealth data of the BEST program. Test data sets from both 

programs for stations in the CRB and RGB with water-quality 

data for WYs 1995-98 and fish data for 1997-98 were used in 

the study. Integration of the two data sets through the calculation of Kendall’s tau correlation coefficient and the KruskalWallis test showed that statistically significant correlations (p 

≤ 0.05) occurred between NASQAN water-quality data and 

BEST fish data in both basins. The following parameters were 

found to be significantly correlated with one or more other 

parameters: arsenic, atrazine, copper, specific conductance, 

total trace elements and their toxicity estimate, and zinc in 

water; total organochlorine pesticides and total PCBs in fish; 

external lesions and fin anomalies; EROD activity, percent 

of tissue occupied by macrophage aggregates, splenosomatic 

index, condition factor, and health-assessment index; and 

trophic position (δ15N of fish minus δ15N of POM in water). A 

few significant negative correlations, including those between 

water-quality data and fish-health observation and measurement indicators for both basins and between trophic position 

and fish-contaminant data for the RGB, were counter-intuitive 

in a biological sense. For these results, the small sample sizes 

may have resulted in spurious correlations, or water-quality 

parameters may have mimicked the effects of other parameters 

or factors.

Results presented in this report have value to the BEST 

program. We specifically address issues related to the quantity 

of water-quality and fish data, water-quality parameters and 

summarization and integration techniques, guidelines for the 

protection of freshwater aquatic life, substitutions for censored data, use of water and fish trace element and δ15N data, 

and additional parameters that could be included in future 

studies. Of the 25 BEST stations sampled in the CRB and 

RGB, only 12 were near NASQAN stations that had sufficient 

water-quality data for the study. Because only six stations in 

each basin were investigated in this study and not all of these 

stations could be used in correlation analyses, the power of the 

statistical tests was low and the analyses were conducted for 

illustrative purposes. With the inclusion of additional stations, 

the power of the statistical tests would increase. If integration 

of the programs and their data is to become an objective, collection of fish and water data should be conducted at as many 

stations in common as possible. Prior to station selection for 

the BEST program, the NASQAN data should be examined 

for parameters of interest to the BEST program, sampling 

frequency and period of record for the parameters of interest, 

and streamflow data. This also would pertain to NAWQA stations that might be selected by the BEST program. The period 

of record for the water-quality data for either the NASQAN or 

NAWQA stations should run through the time fish are collected. 

As stated in the ”Background” section of this report, 

BEST sampled NAWQA stations in the MRB during 1995; 

however, these stations could not be included in this study 

because most water-quality data for the NAWQA stations 

were collected after the fish sampling had occurred. The 

NAWQA program could represent a source of water-quality data for future integrated studies because data have been 

collected continuously at some stations (long-term trend sites) 

since the 1990s. Nevertheless, a potential drawback to the 

use of NAWQA water-quality data for integration with BEST 

fish data is the lack of trace-element data for surface-water 

samples. In the 1990s, only a few NAWQA study units, such 

as the Upper Colorado River Basin and the Northern Rockies 

Intermontane Basins, included trace elements in surface-water 

analyses. The NAWQA program currently analyses trace elements in ground water, streambed sediment, and fish and clam 

tissue but not in surface water. In our study, the trace elements 

arsenic, chromium, copper, and zinc in water were important 

components of the correlation analyses. In addition, these are 

potentially toxic to aquatic organisms at environmental concentrations which, with the exception of arsenic, do not tend 

to bioaccumulate. Therefore, their measurement in surface 

water is an important monitoring and assessment component. 

Nevertheless, it is likely that these data will continue to be 

available only for NASQAN stations. 

An important consideration in our study was the amount 

of time that fish were potentially exposed to water-borne 

contaminants. Time-weighted concentrations were therefore 

determined to be more appropriate than flow-weighted concentrations. Time-weighted concentrations of the parameters 

of interest first were determined for a station’s FPR and for 

WY 1997 using techniques described by Larson and others 

(2004). Time-weighted concentrations for some stations could 

not be determined because minimum sample criteria could not 

be met. Many of the time-weighted mean, geometric mean, 

and median concentrations of the parameters of interest in 

water that could be determined were less than the parameters’ 

MRL. Because of this, the use of time-weighted 90th percentile concentrations was examined and is believed to be an 

adequate statistical representation for concentrations in water. 

Although the 90th percentile concentration may overestimate 

the long-term (that is, chronic) exposure of fish to contami-
Discussion and Recommendations 45

nants, it might represent a reasonable estimate of short-term 

(acute) exposure. 

Data for WY 1997 were used to evaluate the use of a 

common period of record for all stations. This was unsatisfactory because it further reduced the data available. At some 

stations additional water-quality data were collected between 

the end of WY 1997 and the date of fish collection; restricting 

water-quality data to WY 1997 eliminated this data. An alternative would have been the use of water-quality data for the 12 

months prior to the date of fish collection for all stations. This 

approach should be considered in future studies.

Time-weighted concentrations also were determined 

using less restrictive data requirements than those needed for 

use of techniques in Larson and others (2004). By ignoring 

minimum sample criteria for drainage area and number of 

samples per year, we could determine time-weighted geometric mean concentrations of the parameters of interest for all 

six stations in the RGB for the 18 months prior to the date of 

fish collection. This technique was similar to the method used 

by Goodbred and others (1997) to determine time-weighted 

concentrations and is an alternative technique when minimum 

sample criteria eliminate stations from the computations.

Selenium, one of the trace-element parameters of interest, was either not detected or was detected at concentrations 

above the MRL in only a few samples for most stations. 

Detections greater than the MRL were only common at the 

Arroyo Colorado station in the RGB. Geologic and irrigation 

conditions in the Arroyo Colorado watershed were appropriate 

for selenium to occur in the water column. Except for situations such as this, the nation-wide detection of selenium in 

water at most stations may be too low to include in integrated 

studies.

For this study, some correlations involving concentrations 

of arsenic, specific conductance, and zinc in the last water 

sample prior to fish collection were statistically significant (p 

≤ 0.05). The validity of using the last-sample concentration 

as a summarization technique for water-quality data would 

depend on the appropriateness of this technique for bioexposure. With a short (1− or 2−month) time lapse between the 

last water-quality sample and fish collection, the last-sample 

concentration would reflect, at best, acute exposure of fish to 

water-borne contaminants rather than chronic or long-term 

exposure. 

Frequency-of-detection for the 18 months prior to the 

data of fish collection also was used as a summarization 

technique for the water-quality parameters of interest. Some 

correlations between frequencies-of-detection and the fish 

data were statistically significant (p ≤ 0.05). In our study, 

the frequency-of-detection of a parameter of interest was 

determined by counting the number of samples in which the 

concentration of the parameter was at or above the MRL and 

dividing this number by the total number of samples. As such, 

the detection frequency depends on both sampling design 

and analytical sensitivity. Also, there can be some instances 

where frequency-of-detection is large and concentrations are 

low or frequency-of-detection is small and concentrations are 

elevated. For example, it is possible for the detection frequency of atrazine at a station to be 70 percent for the period 

of interest, but concentrations might only range between 0.005 

and 0.01 μg/L. Such concentrations might be too low to affect 

fish, but this would not be reflected in the frequency-of-detection. Conversely, a frequency-of-detection of 6 percent would 

not adequately represent exposure if concentrations in a few 

samples were greater than criteria for the protection of aquatic 

life. Consequently, frequency-of-detection is not a valid summarization technique for use in integrated studies.

Guidelines for the protection of aquatic life were used 

as an additional technique for integrating NASQAN waterquality data and BEST fish data. Based on comparisons of 

trace-element and pesticide concentrations in water for each 

station to guidelines for the protection of freshwater aquatic 

life, water quality in the RGB was more impaired than water 

quality in the CRB in terms of the pesticides chlorpyrifos, 

diazinon, and malathion. Fish-health data at those stations 

with chlorpyrifos, diazinon, and malathion concentrations 

greater than or equal to aquatic life criteria could be examined 

more closely if additional information were available on toxic 

effects of each pesticide to particular fish species rather than to 

all aquatic life. If one species is more susceptible to a chlorpyrifos concentration of 1 μg/L, for example, than another 

species, this information possibly could be used as an additional tool for examining fish health at stations with elevated 

concentrations of chlorpyrifos. A similar approach could be 

used for exceedences of trace-element criteria concentrations 

and estimates of total trace-element toxicity. An additional 

approach was used for copper concentrations in water. A 

cumulative relative frequency diagram was developed for one 

station to illustrate the extent to which fish at that station had 

been exposed to copper concentrations greater than or equal to 

the aquatic life criterion.

When censored or less-than concentrations were indicated for water-quality or fish data, the MRL and one-half 

the MRL or one-half the MRL and zero, respectively, were 

substituted for the less-than concentrations. For most parameters, the ranking of data did not change with substitution, and 

the results for Kendall’s tau remained unchanged. However, 

ranks did change for the geometric mean total trace-element 

concentrations in water and total organochlorine concentrations in female carp in the RGB when different substitution 

values were used. As a result, correlations of total trace-element concentrations in water with fin anomalies and HAI 

scores and of total organochlorine pesticide concentrations in 

female carp with HAI scores also changed. For bioexposure, 

though, one-half the reporting level or an imputed value (for 

example, Helsel, 1990) would probably be more appropriate than zero for fish, which would imply no exposure, or the 

MRL for water, which may overestimate exposure. In each 

instance where Kendall’s tau changed with substitution, the 

significance of the correlations remained the same. 

As noted in the “Purpose and Approach” section, only 

selected trace elements and δ15N were common to both the 

NASQAN and BEST programs and the respective water and 
46 Approach for Integrating BEST and NASQAN

fish media. As a test of integrating the two data sets, correlations between occurrences of four trace elements (arsenic, chromium, copper, and zinc) in water were paired with 

corresponding concentrations in fish. However, and as noted 

previously, trace-element occurrences in water may not be 

appropriate for use in a situation such as this because, with the 

exception of arsenic, these elements do not tend to accumulate 

in fish. Nevertheless, all four can be toxic to fish at environmentally relevant concentrations, and it is therefore important 

to account for them in an integrated assessment. Trace-element concentrations are generally greater and less temporally 

variable in streambed sediments than in water, and correlations 

between concentrations of trace elements in sediments and 

fish may therefore represent a better technique for examining 

bioexposure. Streambed sediments are not collected as part 

of the NASQAN program but are collected and analyzed for 

trace elements as part of the NAWQA program. Analyses of 

correlations between concentrations of most trace-element 

concentrations in water and fish could be excluded from future 

integrated studies.

δ15N was used as a corollary variable for normalizing 

among CRB and RGB stations. δ15N of POM in water was 

compared between the two basins and between individual 

stations as an indicator of nitrogen sources. The trophic positions of benthivorous and piscivorous species were compared 

between the two basins and within a basin, as were trophic 

positions and concentrations of bio-accumulative organic contaminants in fish. As a result of these analyses, we feel that the 

latter comparison of trophic positions and bio-accumulative 

contaminants in fish between basins could be excluded from 

future investigations because relations between the two groups 

should only occur if it reflects a large-scale pollutant such 

as atmospheric mercury. Finally, δ15N POM values in water 

indicative of animal-derived nitrogen inputs co-occurred with 

reproductive biomarker effects in both basins. Overall, results 

of the analyses of δ15N of POM in water and fish indicate that 

δ15N generally was useful for comparing stations between the 

two basins and within each basin, and the use of δ15N should 

continue. 

This study was conducted to evaluate the feasibility of 

combining fish-contaminant and fish-health data of the BEST 

program with water-quality data of the NASQAN program. 

As such, the concentrations of the fish and water contaminants investigated at the small number of stations included 

in the study do not span the range likely to be represented by 

a larger-scale study. The range of fish-health variables was 

similarly narrow. These narrow ranges and small numbers of 

stations undoubtedly contributed to the results (or lack thereof) 

reported here. Nevertheless, they represent suitable examples 

of the types of data to be encountered and problems inherent 

in their analysis. Moreover, and as noted elsewhere (Schmitt, 

2002), studies such as these are exclusively exploratory, not 

explanatory. Correlations quantify associations between 

measured variables rather than determine cause and effect; 

regardless of the number of samples and variables and statistical tools available, only carefully planned and controlled field 

and laboratory research can identify cause and effect relations. 

The foundation of biomarker-based monitoring is the understanding of the factors that influence the biomarkers based 

on such research; for example, interpretation of biomarker 

findings is based more on knowledge of the biomarkers than 

on empirical correlations. Such correlations typically generate 

more questions than answers but may suggest testable hypotheses to be evaluated through subsequent laboratory and more 

focused field studies. Moreover, simple correlations are inherently deceptive because many variables are inter-correlated 

and cannot be controlled or otherwise accounted for. Finally, 

the notion of biological responses rising or falling monotonically with the concentration of one or more contaminants in 

fish collected over broad expanses of time and space is grossly 

simplistic. Curvilinear and asymptotic relations on environmental gradients are common, and many variables are interrelated. Given these factors, results of investigations such as 

ours should be considered exploratory, with results used as a 

starting point for additional focused laboratory or field studies 

to better define causation. 

In the process of integrating the fish and water data we 

have identified substantial previously recognized and unrecognized difficulties. Nevertheless, data sets combined and 

summarized in the manner described here would represent a 

comprehensive assessment of fish exposure to contaminants 

and the effects of exposure on fish irrespective of the program 

or programs from which the data originate. We offer the following additional recommendations and comments to guide 

and improve future integrated assessments:

1. Analytical methods. The NASQAN water 

samples were analyzed at the USGS National Water 

Quality Laboratory. The five trace elements used 

in this study all had MRLs of 1.0 μg/L or greater. 

More recent NASQAN samples have been analyzed 

using laboratory methods with lower reporting 

levels. Currently (2005), all five trace elements 

have MRLs less than 1.0 μg/L: arsenic, 0.2 μg/L; 

chromium, 0.8 μg/L; copper and selenium, 0.4 μg/L; 

and zinc 0.6 μg/L. With lower reporting levels the 

number of censored observations should be reduced, 

which may alleviate some of the problems arising 

from the use of censored data we identified.

2. Sampling frequency. Water was sampled too 

infrequently at some stations to accurately characterize time-weighted concentrations; more frequent 

sampling at these stations may have allowed the 

determination of the time-weighted concentrations. 

However, it is difficult to make an overall recommendation for an adequate sampling frequency. 

Such a recommendation would depend in a large 

part on the time period being examined and how 

concentrations vary at a station. Monthly sampling 

during low-flow conditions and more frequent 
Discussion and Recommendations 47

sampling during high-flow conditions, as is done at 

many NASQAN and NAWQA stations, over a long 

enough time period could represent the range of 

conditions that typically occur over a hydrograph. 

Even this may not be adequate for determining timeweighted concentrations for pesticides and trace elements if they are episodic, however. For documenting waterborne exposure of fish to pesticides and 

elevated aqueous concentrations, it is important to 

sample water during the first flush of pesticides following application. Because the timing of the flush 

cannot be predicted and determining the time to 

sample is difficult, the degree to which fish exposure 

is represented by time-weighted concentrations is 

questionable. Conversely, trace-element concentrations are typically greater during low-flow conditions, and more frequent sampling during high-flow 

conditions may not be needed. Although costly and 

time consuming, a period of over-sampling could 

be used as a starting point for determining sampling 

frequency at a station or possibly one nearby. At 

present there are few stations in the United States 

that are over-sampled, especially on large rivers.

3. Station density. A simplified recommendation 

for the density of stations per basin is also difficult 

to make. For an integrated study, the density of 

stations would largely depend on data availability and the homogeneity of the stations across the 

basin, other factors being equal. Assuming that data 

availability was adequate for the stations in question, similarities or differences between the stations 

would determine the station density. If a basin is 

relatively uniform with respect to such factors as 

type and source(s) of contaminants, contaminant 

concentrations, land use, physical conditions (temperature, pH, and so on), and stream modification, 

few stations would be needed and they could be 

distributed more-or-less uniformly throughout the 

basin. Conversely, a heterogeneous basin would 

require more stations. Sampling density and distribution could be different in a basin with urban areas 

and areas with different agricultural crops than in 

an agricultural basin with a single crop or in a basin 

with a number of mining areas. 

4. Estimates of total trace-element toxicity. Toxic 

units were used in this report to estimate total traceelement toxicity, the combined toxicity of multiple 

trace elements in water. These estimates were easy 

to compute and use in correlation analysis, but interpretation of the results is more difficult. Although 

fish at any site are likely to be exposed to a mixture 

of contaminants, interactions among them can vary. 

Mixtures can be less than, equal to, or greater than 

the sum of the toxicities of the individual contaminants. Moreover, there is considerable debate within 

the scientific community about how mixtures of 

contaminants interact to affect aquatic biota; for 

example, how multiple pesticides or a mixture of 

pesticides and trace elements interact to affect fish. 

As noted by Wildhaber and Schmitt (1996), an 

estimate such as total trace-element toxicity, which 

assumes strictly additive toxicity, should be used 

cautiously and only as a comparative tool.

5. Correlations between parameters. Results presented in this report indicate that some correlations 

will have limited value in future integrated studies. 

For example, because they do not bioaccumulate in 

fish, correlations between concentrations of most 

trace elements in water and fish could be excluded. 

The exceptions would be arsenic, selenium, and 

mercury, with the caveat that a high degree of 

analytical sensitivity is necessary for their inclusion 

to avoid problems associated with large numbers 

of censored values. Correlations between trophic 

conditions (as reflected by δ15N) and bio-accumulative contaminants in fish could also be excluded for 

most contaminants because relations between the 

two groups are not necessarily expected unless they 

describe a large-scale pollutant such as atmospheric 

mercury. 

6. Passive accumulative samplers. An alternative 

or additional method for determining the amount 

of contamination in water is the use of sampling 

devices that are broadly defined as passive accumulative samplers. These include the semipermeable 

membrane device (SPMD) for hydrophobic organic 

compounds (Huckins and others, 1993), the polar 

organic chemical integrative sampler (POCIS) for 

hydrophilic organic compounds (Alvarez and others, 

2004), and the stabilized liquid membrane device 

(SLMD) for labile metals (Brumbaugh and others, 

1999). Passive accumulators are typically placed 

in the water for a predetermined period, generally 

about a month. Upon removal, the contents are 

analyzed for contaminants and time-weighted mean 

concentrations can be readily determined. Passive 

accumulators eliminate many of the previously 

discussed problems associated with water sampling; 

very low aqueous concentrations of potential analytes are concentrated by the samplers, and sampling 

spans the range of concentrations that occurred over 

the full range of hydrologic conditions present during the deployment period. Although passive accumulators sample contaminants such as hydrophobic 

chemicals and metals that may also be accumulated 
48 Approach for Integrating BEST and NASQAN

by fish, passive accumulators typically accumulate greater concentrations and larger numbers of 

chemicals because they lack metabolic and excretory capacity. Concentrations in passive accumulators may therefore represent short-term exposure 

more accurately than concentrations in fish. In an 

integrated study, passive accumulators could be a 

valuable tool for determining contaminant concentrations in water and thereby documenting exposure. 

Passive accumulators do not sequester all chemicals 

equally well, however; for example, the SMLD can 

accurately gage exposure to waterborne lead, zinc, 

cadmium, copper, and similar metals, but is less well 

suited for bioaccumulative elemental contaminants 

such as arsenic, selenium, and mercury. Consequently, a battery of passive accumulators would 

be required to completely characterize waterborne 

exposure (see, for example, Petty and others, 2004), 

and fish would still have to be analyzed for bioaccumulative elemental contaminants and biomarkers 

to fully gage both exposure and effects. Additional 

information on passive accumulators is presented 

by Huckins and others (1993), Ellis and others 

(1995), Capel (1996), Brumbaugh and others (1999), 

McCarthy and Gale (2001), Alvarez and others 

(2004), and Petty and others (2004).

7. Additional measurements and parameters.

Additional water-quality parameters, such as contaminant concentrations in suspended and streambed 

sediments and elemental ratios, may be of value in 

future integrated assessments. Questions that could 

be addressed with suspended-sediment data include 

the following: Are suspended-sediment contaminant concentrations related to streambed-sediment 

concentrations? And are either suspended-sediment 

or streambed-sediment concentrations related to 

fish-contaminant concentrations or fish health? For 

these questions, suspended-sediment data could 

also be used as a proxy for erosion and runoff that 

may yield substances harmful to fish. Elemental 

ratios (based on molar concentrations) could also be 

examined in some biotic and abiotic sample matrices to account for the effects of geochemical and 

biochemical processes on measured concentrations 

and to reduce otherwise unexplained variation (see, 

for example, Settle and Patterson, 1980; Schmitt 

and Finger, 1987). Benthic macroinvertebrates 

represent an important route of contaminant transfer 

from sediments to fish and other biota in rivers and 

streams; contaminant concentrations in streambed 

sediments or benthic macroinvertebrates could 

therefore be used to assess bioexposure. Sedimentquality guidelines, although not enforceable, also 

provide a means for further evaluating streambedsediment data. In depositional environments such 

as reservoirs, information on historic sediment and 

water quality can also be obtained from sediment 

cores (for example, Van Metre and others, 1997; 

Greve and others, 2001). Examination of sediments 

could include determining the degree to which 

contaminant concentrations are correlated with concentrations in fish and with fish-health indicators. 

It is important to note that the issues we identified 

for summarizing water-quality data for combination 

with fish data also apply for integration with streambed-sediment data because sediment is also typically 

sampled less frequently than water.

Acknowledgments

This study was supported by the USGS-Biomonitoring of 

Environmental Status and Trends (BEST) program as part of 

the development of the Large River Monitoring Network. The 

authors greatly appreciate the advice and assistance provided 

by T. Bartish, R. DeWeese, D. Druliner, A. Greve, C. Kendall, 

D. Mueller, S. Porter, W. Battaglin, D. Helsel, C. Kendall, D. 

Tillitt, J. Hinck, and N. Spahr during the conduct of the study. 

W. Battaglin, R. DeWeese, and D. Tillitt also reviewed earlier 

versions of this report and made numerous helpful suggestions. R. Lipkin managed the production of the report. 

Literature Cited

Adams, S.M., 1990, Status and use of biological indicators for 

evaluating the effects of stress on fish, in Adams, S.M., ed., 

Biological indicators of stress in fish, American Fisheries 

Society Symposium 8: Bethesda, Md., American Fisheries 

Society, p. 1-8.

Adams, S.M., Brown, A.M., and Goede, R.W., 1993, A quantitative health assessment index for rapid evaluation of fish 

condition in the field: Transactions of the American Fisheries Society, v. 122, p. 63-73.

Alvarez, D.A., Petty, J.D., Huckins, J.M., Jones-Lepp, T.L., 

Getting, D.T., Goddard, J.P., Manahan, S.E., 2004, Development of a passive, in situ, integrative sampler for hydrophyllic organic contaminants in aquatic environments: Environmental Toxicology and Chemistry, v. 23, p. 1640-1648.
Literature Cited 49

Bevans, H.E., Goodbred, S.L., Miesner, J.F., Watkins, S.A., 

Gross, T.S., Denslow, N.D., and Schoeb, T., 1996, Synthetic 

organic compounds and carp endocrinology and histology in 

Las Vegas Wash and Las Vegas and Callville Bays of Lake 

Mead, Nevada, 1992 and 1995: U.S. Geological Survey 

Water-Resources Investigations Report 96-4266, 12 p.

Bilger, M.D., Brightbill, R.A., and Campbell, H.L., 1999, 

Occurrence of organochlorine compounds in whole fish 

tissue from streams of the Lower Susquehanna River Basin, 

Pennsylvania and Maryland, 1992: U.S. Geological Survey 

Water-Resources Investigations Report 99-4065, 17 p.

Biomonitoring of Environmental Status and Trends Program 

(BEST), 1996, Summary report from a workshop on selection of tier 1 bioassessment methods: National Biological 

Service, Information and Technology Report 7, 55 p.

Biomonitoring of Environmental Status and Trends Program 

(BEST), 2001, Contaminants and biomarkers in fish in the 

Columbia and Rio Grande River Basins: U.S. Geological 

Survey Biomonitoring of Environmental Status and Trends 

(BEST) Program, accessed October 22, 2001, at URL 

(http://www.best.usgs.gov/colrio.htm).

Blazer, V.S., Facey, D.E., Fournie, J.W., Courtney, L.A., and 

Summers, J.K., 1994, Macrophage aggregates as indicators of environmental stress, in Stolen, J.S., and Fletcher, 

T.C., Modulators of fish immune responses, volume 1: Fair 

Haven, N.J., SOS Publications, p. 169-186.

Blazer, V.S., Fournie, J.W., and Weeks-Perkins, B.A., 1997, 

Macrophage aggregates—Biomarker for immune function 

in fishes?, in Dwyer, F.J., Doane, T.R., and Hinman, M.L., 

eds., Environmental toxicology and risk assessment—Modeling and risk assessment, vol 6: American Society for Testing and Materials, ASTM Special Publication 1317.

Brown, L.R., 1998a, Assemblages of fishes and their associations with environmental variables, lower San Joaquin River 

drainage, California: U.S. Geological Survey Open-File 

Report 98-77, 20 p.

Brown, L.R., 1998b, Concentrations of chlorinated organic 

compounds in biota and bed sediment in streams of the 

lower San Joaquin River drainage, California: U.S. Geological Survey Open-File Report 98-171, 22 p.

Brumbaugh, W.G., Petty, J.D., Huckins, J.N., and Manahan, 

S.F., 1999, Development of a passive accumulator integrative sampler for labile metals in water, in Morganwalp, 

D.W., and Buxton, H.T., eds., U.S. Geological Survey 

Toxic Substances Hydrology Program--Proceedings of the 

technical meeting, Charleston, S.C., March 8-12, 1999, 

Volume 1 of 3—Contamination from hard-rock mining: 

U.S. Geological Survey Water-Resources Investigations 

Report 99-4018A, p. 93-98, accessed February 8, 1991, at 

URL (http://toxics.usgs.gov/pubs/wri99-4018/Volume1/sectionA/1212_Brumbaugh/index/html).

Bunck, C.M., Prouty, R.M., and Krynitsky, A.J., 1987, 

Residues of organochlorine pesticides and polychlorinated 

biphenyls in starlings (Sturnus vulgaris) from the continental United States, 1982: Environmental Monitoring and 

Assessment, v. 8, p. 59-75.

Cabana, G., and Rasmussen, J.B., 1996, Comparison of 

aquatic food chains using nitrogen isotopes: Proceedings of 

the National Academy of Sciences of the United States of 

America, v. 93, no. 20, p. 10844-10847.

Canadian Council of Ministers of the Environment, 1999, 

Canadian water-quality guidelines for the protection of 

aquatic life: Canadian Council of Ministers of the Environment, accessed September 25, 2002, at URL (http://www.

ccme.ca./publications/pubs_updates.html#102).

Capel, P., 1996, Measurement of hydrophobic organic chemicals using semi-permeable membrane devices (SPMD’s), 

in NASQAN II, redesign plan for the National Stream 

Quantity Accounting Network: Reston, Va., U.S. Geological 

Survey, Water Resources Division, p. 142-145.

Chambers, A., 2002, Trace elements and organic compounds 

in streambed and fish tissue of the New England Coastal 

Basins: U.S. Geological Survey Water-Resources Investigations Report 02-4179, 30 p.

Cuffney, T.F., Meador, M.R., Porter, S.D., and Gurtz, M.E., 

2000, Responses of physical, chemical, and biological 

indicators of water quality to a gradient of agricultural land 

use in the Yakima River Basin, Washington: Environmental 

Monitoring and Assessment, v. 64, p. 259-270.

Deacon, J.R., Mize, S.V., and Spahr, N.E., 1999, Characterization of selected biological, chemical, and physical 

conditions at fixed sites in the Upper Colorado River 

Basin, Colorado, 1995-98: U.S. Geological Survey WaterResources Investigations Report 99-4181, 71 p.

Deacon, J.R., and Stephens, V.C., 1998, Trace elements in 

streambed sediment and fish liver at selected sites in the 

Upper Colorado River Basin, Colorado, 1995-96: U.S. 

Geological Survey Water-Resources Investigations Report 

98-4124, 19 p.

Denslow, N.D., Chow, M.C., Kroll, K.J., and Green, L., 1999, 

Vitellogenin as a biomarker of exposure for estrogen or 

estrogen mimics: Ecotoxicology, v. 8, p. 385-398.

Ellis, G.S., Huckins, J.N., Rostad, C.E., Schmitt, C.J., Petty, 

J.D., and McCarthy, P., 1995, Evaluation of lipid-containing 

semipermeable membrane devices and gas chromatography-negative chemical ionization-mass spectrometry for 

monitoring organochlorine chemical residues in large rivers: 

Environmental Toxicology and Chemistry, v. 14, p. 1875-

1884.
50 Approach for Integrating BEST and NASQAN

Folmar, L.C., Denslow, N.D., Rao, V., Chow, M., Crain, D.A., 

Enblom, J., Marcino, J., and Guillette, L.J., Jr., 1996, Vitellogenin induction and reduced serum testosterone concentrations in feral male carp (Cyprinus carpio) captured near a 

major metropolitan sewage treatment plant: Environmental 

Health Perspectives, v. 104, no. 10, p. 1096-1101. 

Frenzel, S.A., 2000, Selected organic compounds and trace 

elements in streambed sediments and fish tissues, Cook 

Inlet Basin, Alaska: U.S. Geological Survey WaterResources Investigations Report 00-4004, 39 p.

Gebler, J.B., 2000, Organochlorine compounds in streambed 

sediment and in biological tissue from streams and their 

relations to land use, central Arizona: U.S. Geological Survey Water-Resources Investigations Report 00-4041, 21 p.

Gilliom, R.J., and Helsel, D.R., 1986, Estimation of distributional parameters for censored trace level water quality data, 

1, Estimation techniques: Water Resources Research, v. 22, 

no. 2, p. 135-146.

Goede, R.W., 1988, Fish health/condition assessment procedures, Part 2, A color atlas of necropsy classificiation 

categories: Utah Division of Wildlife Resources, Fisheries 

Experiment Station.

Goede, R.W., 1996, Fish health/condition assessment procedures: Logan, Utah, Utah Division of Wildlife Resources, 

Fisheries Experiment Station.

Goldstein, R.M., Brigham, M.E., and Stauffer, J.C., 1996, 

Comparison of mercury concentrations in liver, muscle, 

whole bodies, and composites of fish from the Red River of 

the North: Canadian Journal of Fisheries and Aquatic Sciences, v. 53, p. 244-252.

Goldstein, R.M., and DeWeese, L.R., 1999, Comparison of 

trace element concentrations in tissue of common carp and 

implications for monitoring: Journal of the American Water 

Resources Association, v. 35, p. 1133-1140. 

Goodbred, S.L., Gilliom, R.J., Gross, T.S., Denslow, N.P., 

Bryant, W.L., and Schoeb, T.R., 1997, Reconnaissance of 

17β-estradiol, 11-ketotestosterone, and gonad hisopathology 

in common carp of United States streams—Potential for 

contaminant-induced endocrine disruption: U.S. Geological 

Survey Open-File Report 96-627, 47 p.

Grady, A.W., McLaughlin, R.M., Caldwell, C.W., Schmitt, 

C.J., and Stalling, D.L., 1992, Flow cytometry, morphometry and histopathology as biomarkers of benzo[a]pyrene 

exposure in brown bullheads (Amieurus nebulosus): Journal 

of Applied Toxicology, v. 12, p. 165-177.

Greve, A.I., Spahr, N.E., Van Metre, P.C., and Wilson, J.T., 

2001, Identification of water-quality trends using sediment 

cores from Dillon Reservoir, Summit County, Colorado: 

U.S. Geological Survey Water-Resources Investigations 

Report 01-4022, 33 p. 

Guillette, L.R., Jr., Gross, T.S., Masson, G.R., Matter, J.M., 

Percival, H.F., and Woodward, A.R., 1994, Developmental abnormalities of the gonads of juvenile alligators from 

contaminated and control lakes in Florida: Environmental 

Health Perspectives, v. 102, p. 680-688.

Hamelink, J.L., Waybrandt, R.C., and Ball, R.C., 1971, A

proposal—Exchange equilibria control the degree to which 

chlorinated hydrocarbons are biologically magnified in 

lentic environments: Transactions of the American Fisheries 

Society, v. 100, p. 207-214.

Helsel, D.R., 1987, Advantages of nonparametric procedures 

for analysis of water quality data: Journal of Hydrological 

Sciences, v. 32, p. 179-190.

Helsel, D.R., 1990, Less than obvious—Statistical treatment of 

data below the detection limit: Environmental Science and 

Technology, v. 24, no. 12, p. 1766-1774.

Helsel, D.R., and Gilliom, R.J., 1986, Estimation of distributional parameters for censored trace level water quality data, 

2, Verification and applications: Water Resources Research, 

v. 22, no. 2, p. 147-155.

Helsel, D.R., and Hirsch, R.M., 1992, Statistical methods in 

water resources, Elsevier Studies in Environmental Science 

No. 49: Amsterdam, The Netherlands, Elsevier Science 

B.V., 529 p. 

Hinck, J.E., Schmitt, C.J., Bartish, T.M., Denslow, N.D., 

Blazer, V.S., Anderson, P.J., Coyle, J.J., Dethloff, G.M., and 

Tillitt, D.E., 2004, Biomonitoring of Environmental Status 

and Trends (BEST) Program— Environmental contaminants and their effects on fish in the Columbia River Basin: 

U.S. Geological Survey Scientific Investigations Report 

2004-5154, 125 p.

Hinton, D.E., 1993, Toxicological histopathology of fishes: 

a systemic approach and overview, in Couch, J.A., and 

Fournie, J.W., eds., Pathobiology of marine and estuarine 

organisms: Boca Raton, Fla., CRC Press.

Hinton, D.E., Baumann, P.C., Gardner, G.R., Hawkins, W.E., 

Hendricks, J.D., Murchelano, R.A., and Okihiro, M.S., 

1992, Histopathological biomarkers, in Huggett, R.J., 

Kimerle, R.A., Mehrle, P.M., and Bergman, H.L., eds., Biomarkers: Chelsea, Mich., Lewis Publishers, p. 155-210.

Hirsch, R.M., Alley, M., and Wilber, W.G., 1988, Concepts 

for a National Water-Quality Assessment Program: U.S. 

Geological Survey Circular 1021, 42 p.
51

Hirsch, R.M., Slack, J.R., and Smith, R.A., 1982, Techniques 

of temporal trend analysis for monthly water quality data: 

Water Resources Research, v. 18, p. 107-121.

Hooper, R., Goolsby, D., McKenzie, S., and Rickert, D., 1996, 

National program framework, in NASQAN II, redesign 

plan for the National Stream Quantity Accounting Network: 

Reston, Va., U.S. Geological Survey, Water Resources Division, p. 2-28.

Huckins, J.N., Manuweera, G.K., Petty, J.D., Mackay, D., and 

Lebo, J.A., 1993, Lipid-containing semipermeable membrane device for monitoring organic contaminants in water: 

Environmental Science and Technology, v. 27, p. 2489-

2496.

International Joint Commission Canada and United States, 

1978, Agreement between Canada and the United States of 

America on Great Lakes Water Quality, 1978: International 

Joint Commission Canada and United States, accessed September 25, 2002, at URL (http://www.ijc.org/agree/quality.

html#ann1).

Johnson, R.E., Carver, T.D., and Dustman, E.H., 1967, Indicator species near top of food chain chosen for assessment of 

pesticide base levels in fish and wildlife—clams, oysters, 

and sediment chosen for estuaries: Pesticide Monitoring 

Journal, v. 1, p. 7-13.

Kelly, V.J., and Hooper, R.P., 1998, Monitoring the water 

quality of the Nation’s large rivers, Columbia River Basin 

NASQAN Program: U.S. Geological Survey Fact Sheet FS083-98, accessed October 24, 2001, at URL (http://water.

usgs.gov/nasqan/progdocs/factsheets/clmbfact.html).

Kendall, C., 1998, Tracing nitrogen sources and cycling 

in catchments, in Kendall, C., and McDonnell, J.J., eds, 

Isotope tracers in catchment hydrology: New York, N.Y., 

Elsevier Science, p. 519-576.

Kendall, C., Battaglin, W., Cabana, G., Chang, C.C., Silva, 

S.T., Porter, S.D., Goolsby, D.A., Campbell, D.H., Hooper, 

R.P., and Schmitt, C.J., 1999, Isotopic tracing of nitrogen 

sources and cycling in the Mississippi River Basin, in

Morganwalp, D.W., and Buxton, H.T., eds., U.S. Geological 

Survey Toxic Substances Hydrology Program--Proceedings of the technical meeting, Charleston, S.C., March 

8-12, 1999, Volume 2 of 3—Contamination of hydrologic 

systems and related ecosystems: U.S. Geological Survey 

Water-Resources Investigations Report 99-4018B, p. 339-

344, accessed February 8, 2001, at URL (http://toxics.usgs.

gov/pubs/wri99-4018/Volume2/sectionC/2414_Kendall/

index.html).

Kendall, C., Silva, S.R., and Kelly, V.J., 2001, Carbon and 

nitrogen isotopic compositions of particulate organic matter 

in four large river systems across the United States: Hydrological Processes, v. 15, p. 1301-1346.

Literature Cited

Kennedy, S.W., and Jones, S.P., 1994, Simultaneous measurement of cytochrome P4501A catalytic activity and total 

protein concentration with a fluorescence plate reader: 

Analytical Biochemistry, v. 222, p. 217-223.

Kiriluk, R.M., Servos, R.R., Whittle, D.M., Cabana, G., and 

Rasmussen, J.B., 1995, Using ratios of stable nitrogen 

and carbon isotopes to charaterize the biomagnification of 

DDE, mirex, and PCB in a Lake Ontario pelagic food chain: 

Canadian Journal of Fisheries and Aquatic Sciences, v. 52, 

p. 2660-2674.

Knight, R.R., and Powell, J.R., 2001, Occurrence and distribution of organochlorine pesticides, polychlorinated 

biphenyls, and trace elements in fish tissue in the Lower 

Tennessee River Basin, 1980-98: U.S. Geological Survey 

Water-Resources Investigations Report 01-4184, 32 p.

Kolpin, D.W., Furlong, E.T., Meyer, M.T., Thurman, E.M., 

Zaugg, S.D., Barber, L.B., and Buxton, H.T., 2002, Pharmaceuticals, hormones, and other organic wastewater contaminants in U.S. streams, 1999-2000—A national reconnaissance: Environmental Science and Technology, v. 36, no. 6, 

p. 1202-1211.

Larson, S.J., Crawford, C.G., and Gilliom, R.J., 2004, 

Development and application of watershed regression for 

pesticides (WARP) for estimating atrazine concentration 

distributions in streams: U.S. Geological Survey WaterResources Investigations Report 03-4047, 68 p.

Larson, S.J., and Gilliom, R.J., 2001, Regression models for 

estimating herbicide concentrations in U.S. streams from 

watershed characteristics: Journal of the American Water 

Resources Association, v. 37, no. 5, p. 1349-1368.

Long, G.R., Chang, M., and Kennen, J.G., 2000, Trace elements and organochlorine compounds in bed sediment and 

fish tissue at selected sites in New Jersey streams—Sources 

and effects: U.S. Geological Survey Water-Resources Investigations Report 99-4235, 29 p.

Lurry, D.L., Reutter, D.C., and Wells, F.C., 1998, Monitoring 

the water quality of the Nation’s large rivers, Rio Grande 

NASQAN Program: U.S. Geological Survey Fact Sheet FS083-98, accessed January 29, 2001, at URL (http://water.

usgs.gov/nasqan/progdocs/factsheets/riogfact/engl.html).

Machala, M., Dusek, L., Hilscherova, K., Kubinova, R., 

Jurajda, P., Neca, J., Ulrich, R., Gelnar, M., Studnickova, 

Z., and Holoubek, I., 2001, Determination and multivariate 

statistical analysis of biochemical responses to environmental contaminants in feral freshwater fish Leuciscus cephalus

L.: Environmental Toxicology and Chemistry, v. 20, no. 5, 

p. 1141-1148.
52 Approach for Integrating BEST and NASQAN

May, J.T., and Brown, L.B., 2000, Fish community structure in 

relation to environmental variables within the Sacramento 

River Basin and implication for the greater Central Valley, 

California: U.S. Geological Survey Open-File Report 00-

247, 19 p.

McCarthy, K.A., and Gale, R.W., 2001, Evaluation of persistent hydrophobic organic compounds in the Columbia River 

Basin using semipermeable-membrane devices: Hydrological Processes, v. 15, p. 1271-1283.

Olsen, A.R., Sedransk, J., Edwards, D., Gotway, C.A., Liggett, 

W., Rathbun, S., Reckhow, K.H., and Young, L.J., 1999, 

Statistical issues for monitoring ecological and natural 

resources in the United States: Environmental Monitoring 

and Assessment, v. 54, p. 1-45.

Petty, J.D., Huckins, J.N., Alvarez, D.A., Brumbaugh, W.G., 

Cranor, W.L., Gale, R.W., Rastall, A.C., Jones-Lepp, T.L., 

Leiker, T.J., Rostad, C.E., and Furlong, E.T., 2004, A holistic passive integrative sampling approach for assessing the 

presence and potential impacts of waterborne environmental 

contaminants: Chemosphere, v. 54, p. 695-705.

Pohl, R.J., and Fouts, J.R., 1980, A rapid method for assaying 

the metabolism of 7-ethoxyresorufin by microsomal subcellular fractions: Analytical Biochemistry, v. 107, p. 150-155.

Riva-Murray, K., Brightbill, R.A., and Bilger, M.D., 2003, 

Trends in concentrations of polychlorinated biphenyls in 

fish tissue from selected sites in the Delaware River Basin 

in New Jersey, New York, and Pennsylvania, 1969-98: U.S. 

Geological Survey Water-Resources Investigations Report 

01-4066, 20 p.

Schmitt, C.J., ed., 2002, Biomonitoring of Environmental 

Status and Trends (BEST) Program—Environmental contaminants and their effects on fish in the Mississippi River 

Basin: Columbia, Mo., U.S. Geological Survey, Biological 

Resources Division, Biological Science Report USGS/BRD/

BSR 2002-0004, 217 p.

Schmitt, C.J., Bartish, T.M., Blazer, V.S., Gross, T.S., Tillitt, 

D.E., Bryant, W.L., and DeWeese, L.R., 1999a, Biomonitoring of Environmental Status and Trends (BEST) Program—

Contaminants and their effects in fish from the Mississippi, 

Columbia, and Rio Grande Basins, in Morganwalp, D.W., 

and Buxton, H.T., eds., U.S. Geological Survey Toxic Substances Hydrology Program--Proceedings of the technical 

meeting, Charleston, S.C., March 8-12, 1999, Volume 2 of 

3—Contamination of hydrologic systems and related ecosystems: U.S. Geological Survey Water-Resources Investigations Report 99-4018B, p. 437-446, accessed February 8, 

2001, at URL (http://toxics.usgs.gov/pubs/wri99-4018/Volume2/sectionD/2507_Schmitt/index.html).

Schmitt, C.J., Blazer, V.S., Dethloff, G.M., Tillit, D.E., Gross, 

T.S., Bryant, W.L., Jr., DeWeese, L.R., Smith, S.B., Goede, 

R.W., Bartish, T.M., and Kubiak, T.J., 1999b, Biomonitoring of Environmental Status and Trends (BEST) Program—Field procedures for assessing the exposure of fish 

to environmental contaminants: U.S. Geological Survey, 

Biological Resources Division, Information and Technology 

Report USGS/BRD/ITR-1999-0007.

Schmitt, C.J., and Bunck, C.M., 1995, Environmental contaminants in fish and wildlife, in LaRoe, E.T., Farris, G.S., 

Puckett, C.E., Doran, P.D., and Mac, M.J., eds., Our living 

resources—A report to the Nation on the distribution, abundance, and health of U.S. plants, animals, and ecosystems: 

Washington, D.C., National Biological Service, p. 413-416.

Schmitt, C.J., and Dethloff, G., eds., 2000, Biomonitoring 

of Environmental Status and Trends (BEST) Program—

Selected methods for monitoring chemical contaminants 

and their effects in aquatic ecosystems: Columbia, Mo., 

U.S. Geological Survey, Biological Resources Division, 

Information and Technology Report 2000-0005, 81 p.

Schmitt, C.J., Dethloff, G.M., Hinck, J.E., Bartish, T.M., 

Blazer, V.S., Coyle, J.J., Denslow, N.D., and Tillitt, D.E., 

2004, Biomonitoring of Environmental Status and Trends 

(BEST) Program—Environmental contaminants and their 

effects on fish in the Rio Grande Basin: U.S. Geological 

Survey Scientific Investigations Report 2004-5108, 117 p.

Schmitt, C.J., and Finger, S.E., 1987, The effects of sample 

preparation on the measured concentrations of eight elements in the edible tissues of fish contaminated by lead 

mining: Archives of Environmental Contamination and 

Toxicology, v. 16, p. 185-207.

Schmitt, C.J., Zajicek, J.L., May, T.W., and Cowman, D.F., 

1999c, National Contaminant Biomonitoring Program—

Concentrations of organochlorine chemical residues and 

elemental contaminants in U.S. freshwater fish, 1976-1986: 

Reviews in Environmental Contamination and Toxicology, 

v. 162, p. 43-104.

Settle, D.M., and Patterson, C.C., 1980, Lead in albacore—

Guide to lead pollution in Americans: Science, v. 207, p. 

1167-1176.

Smith, R.A., Alexander, R.B., and Schmitt, C.J., 1988, Lead 

concentrations in fish in relation to concentrations and 

sources in major U.S. freshwater rivers: Preprinted extended 

abstracts of the Annual meeting, American Chemical Society, June 22, 1988, Toronto, Ontario, Canada.

Smith, S.B., 1998, Investigations of endocrine disruption in 

aquatic systems associated with the National Water Quality 

Assessment (NAWQA) Program: U.S. Geological Survey 

Fact Sheet FS-081-98, accessed July 10, 2001, at URL 

(http://water.usgs.gov/pubs/FS/FS-081-98).
53

Smith, S.B., 2000, Altered endocrine biomarkers in selected 

fish species in the Hudson River, New York: U.S. Geological Survey Fact Sheet FS-113-00, 2 p.

SAS (Statistical Analysis System) Institute, 1998, SAS/STAT 

user’s guide, version 8: Cary, N.C., SAS Institute, 3848 p.

Tillitt, D.E., Giesy, J.P., and Ankley, G.T., 1991, Characterization of the H4IIE rat hepatoma cell bioassay as a tool for 

assessing toxic potency of planar halogenated hydrocarbons 

in environmental samples: Environmental Science and 

Technology, v. 25, p. 87-92.

Tyler, C.R., Jobling, S., and Sumpter, J.P., 1998, Endocrine 

disruption in wildlife—A critical review of the evidence: 

Critical Reviews in Toxicology, v. 28, p. 319-361.

U.S. Environmental Protection Agency, 2000, Guidance 

for data quality assessment—Practical methods for data 

analysis: U.S. Environmental Protection Agency, Office of 

Environmental Information Report EPA/600/R-96/084.

U.S. Environmental Protection Agency, 2001, Aquatic life, 

atrazine (draft): U.S. Environmental Protection Agency, 

Office of Water Fact Sheet EPA/600/R-96/084, accessed 

September 25, 2002, at URL (http://www.epa.gov/waterscience/criteria/atrazine/atrazinefacts.html).

U.S. Environmental Protection Agency, 2002, National recommended water quality criteria—2002: U.S. Environmental 

Protection Agency, Office of Water Document EPA-822-R02-047, accessed September 18, 2003, at URL (http://www.

epa.gov/waterscience/standards/wqcriteria.html).

U.S. Geological Survey, 1997, NASQAN—A program to 

monitor the water quality of large rivers: U.S. Geological 

Survey Fact Sheet FS-055-097, 6 p.

U.S. Geological Survey, 2003, NASQAN program description, 

accessed May 30, 2004, at URL (http://www.water.usgs.

gov/nasqan/producs/index.html).

Van Metre, P.C., Callender, E., and Fuller, C.C., 1997, Historical trends in organochlorine compounds in river basins identified using sediment cores from reservoirs: Environmental 

Science and Technology, v. 31, no. 8, p. 2339-2344.

Whyte, J.J., Jung, R.E., Schmitt, C.J., and Tillitt, D.E., 2000, 

Ethoxyresorufin-O-deethylase (EROD) activity in fish as a 

biomarker of chemical exposure: Critical Reviews in Toxicology, v. 30, p. 347-570.

Whyte, J.J., Schmitt, C.J., and Tillitt, D.E., 2004, The H4IIE 

cell bioassay as an indicator of dioxin-like chemicals in 

wildlife and the environment: Critical Reviews in Toxicology, v. 30, no. 4, p. 347-350.

Literature Cited

Wildhaber, M.L., and Schmitt, C.J., 1996, Estimating aquatic 

toxicity as determined through laboratory tests of Great 

Lakes sediments containing complex mixtures of environmental contaminants: Environmental Monitoring and 

Assessment, v. 41, p. 255-289.

Windom, H.L., Byrd, J.T., Smith, R.G., Jr., and Huan, F., 

1991, Inadequacy of NASQAN data for assessing metal 

trends in the nation’s rivers: Environmental Science and 

Technology, v. 25, p. 1137-1142.
Bauch, N.J., Schmitt, C.J., and Crawford, C.G.—Development of an Approach for Integrating Components of the U.S. Geological Survey Biomonitoring of 

Environmental Status and Trends (BEST) and National Stream Quantity Accounting Network (NASQAN) Programs for Large U.S. Rivers—Scientific 

Investigations Report 2005—5083

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