Regression Model for Explaining and Predicting 

Concentrations of Dieldrin in Whole Fish from 

United States Streams

Scientific Investigations Report 2006–5020

U.S. Department of the Interior

U.S. Geological Survey



National Water-Quality Assessment Program
Cover: Map shows the predicted dieldrin concentrations in whole fish (with 6.2 percent lipid content, the national 

average for sampled whole fish) from streams throughout the conterminous United States (see figure 7B).
Regression Model for Explaining and 

Predicting Concentrations of Dieldrin in 

Whole Fish from United States Streams

By Lisa H. Nowell, Charles G. Crawford, Naomi Nakagaki, 

Gail P. Thelin, and David M. Wolock

National Water-Quality Assessment Program

Scientific Investigations Report 2006–5020

Sacramento, California

2006
U.S. Department of the Interior

Gale A. Norton, Secretary

U.S. Geological Survey

P. Patrick Leahy, Acting Director

U.S. Geological Survey, Reston, Virginia: 2006

For Sale by U.S. Geological Survey, Information Services

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Denver, CO 80225-0286

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imply endosement 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 reference:

Nowell, L.H.; Crawford, C.G.; Nakagaki, Naomi; Thelin, G.P.; and Wolock, D.M., 2006, Regression 

model for explaining and predicting concentrations of dieldrin in whole fish from United States streams: 

U.S. Geological Survey Scientific Investigations Report 2006–5020, 30 p. 
Contents

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

Introduction........................................................................................................................................................................ 1

Background ............................................................................................................................................................... 1 

Purpose and Scope .................................................................................................................................................. 2

Acknowledgments.................................................................................................................................................... 3

Methods.............................................................................................................................................................................. 3

Dieldrin Data Used for Model Development ........................................................................................................ 3

Sample Collection............................................................................................................................................. 3

Sample Work-Up and Analysis....................................................................................................................... 3

Database Decisions for Model Development.............................................................................................. 4

Explanatory Variables .............................................................................................................................................. 5

Fish Sample Characteristics........................................................................................................................... 5

Past Sources of Aldrin and Dieldrin .............................................................................................................. 8

Other Watershed Characteristics.................................................................................................................. 9

Development of Regression Model ..................................................................................................................... 11

Regression Models......................................................................................................................................... 11

Transformation of Response and Explanatory Variables......................................................................... 12

Selection of Explanatory Variables.............................................................................................................. 12

Analysis of Model Fit...................................................................................................................................... 13

Model Validation............................................................................................................................................. 13

Extrapolation to Unmonitored Streams............................................................................................................... 14

Data Sources for Unmonitored Streams Nationwide............................................................................... 14

Lipid Content.................................................................................................................................................... 14

Uncertainty Analysis ...................................................................................................................................... 15

Predicting the Potential Effects of Fish Consumption on Human Health and Wildlife................................ 17

Potential Effects on Human Health.............................................................................................................. 17

Potential Effects on Fish-Eating Wildlife..................................................................................................... 17

Results and Discussion.................................................................................................................................................. 17

Regression Model for Dieldrin in Whole Fish ............................................................................................ 17

Analysis of Significant Explanatory Variables ........................................................................................... 19

Extrapolation to Unmonitored Streams....................................................................................................... 20

Significance to Human Health...................................................................................................................... 20

Significance to Fish-Eating Wildlife............................................................................................................. 23

Uncertainty in Model Predictions................................................................................................................ 23

Summary and Conclusions............................................................................................................................................ 26

References....................................................................................................................................................................... 27

iii
Figures

1. Map showing estimated historical use of aldrin plus dieldrin in agriculture (1966) in the 

conterminous United States.................................................................. 8

2. Map showing urban areas in the United States shown superimposed on the 

relative hazard of subterranean termite infestations .................................... 10

3. Graphs showing residual error developed from calibration data, and residual error 

developed from validation data ............................................................ 14

4. Graphs showing hypothetical regressions for which the response variable is 

log-transformed .............................................................................. 16

5. Map showing measured dieldrin concentrations in fish at NAWQA sites, excluding 

sites with 

the highest and lowest 10 percent of lipid levels .......................................... 18

6. Graph showing Effect of adding explanatory variables to the regression model for 

dieldrin in whole fish on the model’s pseudo-R2 and scale values ..................... 19

7. Map showing predicted dieldrin concentrations in whole fish for U.S. streams at 

(A) 4.2 percent lipid content (largemouth bass) and (B) 6.2 percent lipid 

content (all fish taxa) ........................................................................ 21

8. Maps showing probability of streams having whole-fish samples that exceed 

(A) 5 μg/kg and (B) 25 μg/kg .................................................................. 23

9. Map showing probability of streams having whole-fish concentrations 

greater than 120 μg/kg ........................................................................ 25 

Tables

1. The 648 NAWQA stream sites used in model development, by major physiographic 

division and land use in the basin ......................................................... 4

2. Potential explanatory variables considered in developing the regression model for 

dieldrin in whole fish ........................................................................ 6

3. Explanatory variables and coefficients for the regression model for dieldrin in 

whole fish....................................................................................... 18

iv

NOTE TO USGS USERS: , Use of hectare (ha) as an alternative name for square hectometer 

(hm2) is restricted to the measurement of small land or water areas. Use of liter (L) as a 

special name for cubic decimeter (dm3) is restricted to the measurement of liquids and 

gases. No prefix other than milli should be used with liter. Metric ton (t) as a name for 

megagram (Mg) should be restricted to commercial usage, and no prefixes should be 

used with it.

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

°F = (1.8×°C)+32. 

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

°C = (°F-32)/1.8. 
Abbreviations, Acronyms, and Initialisms

(Additional information given in parentheses) 

AIC, Akaike Information Criterion

Cest, estimated dieldrin concentration

Cg, guideline concentration

DEM, digital elevation model

EPA, U.S. Environmental Protection Agency

FWS, Fish and Wildlife Service

GIRAS, Geographic Information Retrieval and Analysis System

HG, hydrologic groups

LULC, Land Use and Land Cover

NAWQA, National Water-Quality Assessment (USGS)

NCBP, National Contaminant Biomonitoring Program (FWS)

p, level of significance

Pexc, probability of exceedance

pR2, pseudo-R2

PI, prediction interval

PRISM, Parameter-elevation Regressions on Independent Slopes Model

R2, coefficient of multiple determination

SDest, standard error of the estimated logarithm of concentration

STATSGO, State Soil Geographic (database; National Resources Convervation Service)

SV, screening value

TIGER, Topologically Integrated Geographic Encoding and Referencing

TOPMODEL, topography model

USDA, U.S. Department of Agriculture

USGS, U.S. Geological Survey

Units of measurement

km, kilometer

lb/mi2, pound per square mile 

m, meter

mi, mile

μg/kg, microgram per kilogram

μg/L, microgram per liter

v
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Abstract

A regression model was developed for estimating dieldrin 

concentrations in whole fish from United States streams using 

watershed characteristics, including estimates of past agricultural use intensity. Dieldrin concentrations were measured 

in composite whole-fish samples collected at about 650 sites 

nationwide during 1992–2001, as part of the U.S. Geological 

Survey’s National Water-Quality Assessment Program. 

Important explanatory variables in the model include (1) fish 

lipid content; (2) the estimated historical agricultural use 

intensity of aldrin and dieldrin (which was calculated from 

regional application rates developed using U.S. Department 

of Agriculture data on pesticide use by farmers in 1966 

and harvested acreage from the 1964 and 1969 Census of 

Agriculture); (3) a weighted termite-urban score (which 

represents past use for termite control and was developed 

from estimates of urban land use in the basin, combined with 

the national distribution of subterranean termite density); and 

(4) forested land in the basin (where past aldrin and dieldrin 

use was expected to be minimal). Other explanatory variables 

considered in developing the model included surrogates for 

past urban use (such as population density and percentage 

urban land use in the basin), taxon of the fish sampled, various 

watershed characteristics (including soil properties, hydrologic 

parameters, climatic variables, and agricultural management 

practices), and region of the country. Tobit regression methods 

were used for developing the model because about 60 percent 

of the dieldrin concentration values in the model-development 

data were reported as less than a detection threshold, resulting 

in censored data. The model explains 64 percent of the variability in dieldrin concentrations measured in whole fish. This 

model was used to predict dieldrin concentrations in whole 

fish in unmonitored streams throughout the United States using the U.S. Environmental Protection Agency’s River Reach 

File 1.

Introduction

Background

The organochlorine pesticide dieldrin is still frequently 

detected in soil, sediment, biota, and air in the United States 

(Nowell and others, 1999; Schmitt and others, 1999; Wong 

and others, 2000), even though its uses were discontinued 

15–30 years ago. Dieldrin residues in the environment may 

originate from past application of either aldrin, which degrades 

readily in the environment to dieldrin, or dieldrin itself. Aldrin 

and dieldrin (like DDT) are both organochlorine pesticides that 

were once used extensively, but were discontinued because of 

their persistence, tendency to bioaccumulate, carcinogenicity, 

and hazard to wildlife. Aldrin and dieldrin were applied in 

agriculture in the United States until the early 1970s, mainly 

on corn, but also on fruits and nuts, other vegetables, tobacco, 

and cotton. They also were used in nonagricultural applications—especially in termite control, which continued until at 

least the late 1980s. Dieldrin, like many organochlorine pesticides, is hydrophobic (low in water solubility, with a tendency 

to sorb to soil and sediment and to partition to organic matter) 

and resistant to degradation in the environment.

Regression Model for Explaining and 

Predicting Concentrations of Dieldrin in 

Whole Fish from United States Streams

By Lisa H. Nowell, Charles G. Crawford, Naomi Nakagaki, Gail P. Thelin, and David 

M. Wolock
Three national studies have monitored dieldrin and 

other organochlorine pesticides in whole freshwater fish from 

rivers and streams across the United States. These are the 

Fish and Wildlife Service’s (FWS) National Contaminant 

Biomonitoring Program (NCBP, 1969–1986), the U.S. 

Environmental Protection Agency’s (EPA) National Study 

of Chemical Residues in Fish (1986–1987), and the U.S. 

Geological Survey’s (USGS) National Water-Quality 

Assessment (NAWQA) Program (1992–2001). Evidence 

from these programs indicates that, nationally, organochlorine 

pesticide levels have declined since the use of these compounds was discontinued (Schmitt and others, 1999; Fuhrer 

and others, 1999), with the biggest declines occurring during 

the 1970s. Dieldrin residues in fish were highly variable during the 1970s, but were markedly lower after 1976 than before 

1976; residues measured by the NCBP Program continued to 

decline during the 1980s (Schmitt and others, 1999; Nowell 

and others, 1999). NAWQA sampling data during 1992–2001 

indicates the continued presence of dieldrin at low levels in 

fish and bed sediment from many streams in the United States 

(Wong and others, 2000; Nowell and Crawford, 2003)—dieldrin was detected in whole fish at over 50 percent of agricultural and urban streams, and in over 40 percent of streams 

in mixed land use areas (Nowell and Crawford, 2003). This 

suggests that dieldrin-contaminated soil is still being carried 

into receiving waters from both agricultural and urban land use 

settings.

Organochlorine pesticides have been shown to adversely 

affect the survival of various organisms (including aquatic 

invertebrates, fish, birds, and mammals) in laboratory tests; 

to disrupt the reproduction of fish and birds in the field; and 

to accumulate to high levels in fish-eating mammals (U.S. 

Environmental Protection Agency, 1975; Nowell and others, 1999). In addition, organochlorine pesticides (including 

dieldrin) induce the monooxygenase enzymes that hydroxylate testosterone (Haake and others, 1987), and they have 

been associated with endocrine and reproductive changes 

and immunosuppressive effects (Amdur and others, 1991). 

The biological significance of the low-level organochlorine 

residues detected in the environment today is more difficult 

to assess. Although the linkage between body burdens and 

effects in fish is not well understood, body burdens provide at 

least a direct indicator of exposure (Black and others, 2000). 

Organochlorine pesticide contamination in the field also has 

been associated with fish kills (Hunt and Linn, 1970; Madhun 

and Freed, 1990) and fish diseases (Myers and others, 1993). 

A few organochlorine pesticides (especially DDT and chlordane, and to a lesser extent dieldrin) are the subject 

of fish consumption advisories issued by the states for protection of human health from eating fish contaminated with 

bioaccumulative pollutants (U.S. Environmental Protection 

Agency, 2005).

Given the continued detection of low-level residues of 

organochlorine pesticides in fish, plus the uncertainty as to 

their potential effects on humans and wildlife from fish 

consumption, there is a need to ascertain the geographic extent 

and the degree of present-day exposure of aquatic populations 

to organochlorine pesticides. However, because of the high 

cost of monitoring and analysis, combined with decreasing 

interest in organochlorine pesticides as residues decline nationally, it is unlikely that substantial investment in monitoring 

will be made, except in areas where there is a strong probability of concern. An alternative to widespread direct monitoring 

is to predict concentrations using empirical regression models 

that relate measured concentrations in fish to watershed characteristics and estimates of past use.

A few prior studies have investigated the relationship 

between large-scale land use practices and the presence of 

organochlorine pesticides in sediment or biota (Truhlar and 

Reed, 1976; Munn and Gruber, 1997; Black and others, 2000). 

Black and others (2000) used logistic regression to develop 

predictive models for estimating the probability of detecting specific organochlorine pesticides in fish (sculpins) from 

streams in the Willamette and Puget Sound Basins of the 

Pacific Northwest. This regional-scale study predicted presence or absence, rather than specific concentration estimates. 

A number of studies have used linear regression or logistic 

regression models to predict concentrations of dissolved pesticides in stream water (Krueger and Tornqvist, 1996; Battaglin 

and Goolsby, 1997, 1998; Larson and Gilliom, 2001; Larson 

and others, 2004). Predictor variables in these models included 

pesticide use in the watershed, physical and chemical properties of the pesticides, and (or) various watershed characteristics. We are unaware of any studies that have developed 

models relating organochlorine pesticide concentrations in fish 

to land use and other watershed characteristics on a national 

scale.

Purpose and Scope

Assessment of risk to humans or wildlife from pesticides 

in the environment requires evaluation of both exposure and 

effects. The purpose of this report is to investigate the suitability and effectiveness of a regression model for predicting, 

and explaining variability in, dieldrin concentrations in whole 

fish from United States streams on a national scale. Specific 

objectives are to:

• Develop a regression model to estimate dieldrin concentrations in whole fish on the basis of national data on 

watershed characteristics, including estimates of historical 

pesticide use;

• Assess how much variability in pesticide concentrations in 

whole fish can be explained by factors in the regression 

model;

• Predict dieldrin concentrations in whole fish in streams 

across the United States, as a function of lipid (fat) content, from the regression model on the basis of watershed 

characteristics and estimates of historical pesticide use.

2 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
This model was developed using NAWQA data on 

dieldrin in whole fish at 648 stream sites sampled during 

1992–2001. These stream sites are located in most parts of 

the country and represent a variety of land uses. Ultimately, 

the method described in this report will be applied to other 

organochlorine pesticides detected in fish by NAWQA.

In this report, the following terms are used: 

Concentrations measured at NAWQA sites are “observed 

concentrations.” Application of the regression model using 

a given set of explanatory variables produces an “estimate” 

or “estimated concentration” for a site (in contrast to an 

observed, or measured, concentration). When the model is 

applied to an unmonitored site, the result is a “prediction” or 

“predicted concentration.” Finally, when predictions are made 

for unmonitored sites over a wide geographic area, this builds 

a “spatial extrapolation.”

Acknowledgments

The NAWQA data used in this study are the product of 

work by many people at the USGS who contributed to the 

study design, data collection, sample processing and analysis, 

and geographic information system (GIS) data analysis in the 

NAWQA Program. The authors are indebted to them all—especially to NAWQA study unit personnel, who developed 

the environmental framework and planned and conducted 

sampling for their study units, and provided basin-specific 

expertise for national land use classification and data analysis. 

The authors thank Larry R. Brown and David K. Mueller of 

the U.S. Geological Survey for their very helpful technical reviews of this report. The survreg procedure for the S language 

written by Terry M. Therneau of the Mayo Clinic was used to 

fit the regression model developed in this study. The authors 

are grateful to Michael Haverty (U.S. Dept. of Agriculture, 

Forest Service), Nan-Yao Su (University of Florida), and W. 

David Woodson (U.S. Geological Survey) for their assistance 

in obtaining termite distribution data and maps.

Methods

The methods used for this study included the use of 

appropriate data on dieldrin concentrations in whole fish in 

model development; estimating the past use of aldrin and dieldrin in agriculture; developing surrogates for past use of aldrin 

and dieldrin in urban settings; obtaining ancillary data on 

watershed characteristics from nationally available databases; 

developing a regression model to estimate dieldrin concentrations in whole fish; and using the model to predict dieldrin 

concentrations in whole fish in unmonitored streams across the 

United States.

Dieldrin Data Used for Model Development

Data on dieldrin in whole fish collected as part of the 

USGS’s NAWQA Program were used for model development. The NAWQA Program collected samples from rivers 

and streams in 51 study areas (study units) across the United 

States. The study units are major hydrologic basins, which 

collectively encompass about 60 percent of the population and 

water use in the conterminous United States (Hirsch and others, 1988; Fuhrer and others, 1999).

The NAWQA program sampled whole fish (as well as 

bed sediment) at NAWQA sites to identify important hydrophobic contaminants, including dieldrin, and to determine 

their geographic distribution within the basin. Typically, fish 

sampling sites within a NAWQA study unit included the following types of sites: mixed land-use sites representing the 

large streams in the study unit, including major nodes in the 

drainage system; sites representing the principal environmental settings (land areas characterized by a unique combination 

of natural and anthropogenic factors, such as row crop cultivation on glacial-till soils); additional sites with known contamination (although point sources generally were avoided); and 

reference sites (generally forest or rangeland) where minimal 

occurrence of organochlorine compounds was expected.

Sample Collection

The NAWQA study design called for collecting at least 

one tissue sample (and one bed sediment sample) at each site. 

Whole-fish data were collected at 700 NAWQA sites. The distribution of sites does not quite cover the entire United States 

because clams were collected instead of fish at some sites 

(especially in the southeast), and appropriate fish and clam 

taxa were not available at some sites.

One fish taxon generally was sampled at each site, 

although multiple taxa were collected at about 10 percent of 

sites. Taxa were selected from a National Target Taxa List, 

and the same taxon was sampled at as many sites as possible 

within the study unit. Fish samples were composites of 5–8 

individual whole fish, all from a single taxon (Crawford and 

Luoma, 1994).

Sample Work-Up and Analysis

Details on sample processing and analysis of organochlorine compounds in whole fish are published elsewhere (Leiker 

and others, 1995). Briefly, composite whole-fish samples were 

homogenized and a subsample was dried, Soxhlet-extracted 

with dichloromethane, and an aliquot was removed for lipid 

determination. The extract was cleaned up by gel permeation 

chromatography, solvent-exchanged to hexane, fractionated 

Methods 3
using alumina/silica adsorption chromatography, then analyzed by gas chromatography with electron capture detection. 

Target analytes and their method detection limits in fish are 

listed at http://ca.water.usgs.gov/pnsp/rep/bst/table1.html.

Database Decisions for Model Development

Whole-fish data were collected at 700 NAWQA sites 

nationally. For model development, data for the Cook Inlet 

Basin (Alaska) and the Island of Oahu (Hawaii) study units, 

plus a few sites with watersheds extending into Canada or 

Mexico, were excluded because national ancillary databases 

are available only for the conterminous United States. Dieldrin 

data for the remaining 648 NAWQA sites were used in model 

development. A single fish sample from each site was used. At 

most sites, only one fish sample was collected. For sites where 

multiple fish samples were collected, preference was given to 

the fish sample(s) collected on the same date, or as close as 

possible in time, to bed sediment sampled at the same sites. 

(The availability of coincident sediment data was a priority 

because dieldrin concentrations in bed sediment were considered as potential explanatory variables in alternative regression 

models not discussed in this report.) If multiple fish samples 

were collected on the same date, preference was given to common carp samples (if available) or otherwise to the taxon with 

the highest lipid content.

In summary, the national data set used to develop the 

dieldrin fish model contains data for 648 sites, each with one 

composite fish sample collected during 1992–2001. The data 

represent 59 different fish taxa, of which the most common are 

common carp (29 percent of fish samples) and white sucker 

(26 percent). The 648 streams used in model development 

represent a variety of land use and 

environmental settings (table 1).

Table 1. The 648 NAWQA stream sites used in model development, by major physiographic division and land use in the basin.

[Major physiographic division was determined by clipping the 

national physiographic region coverage (Fenneman and Johnson, 

1946) to the basin boundaries for each site. National land use 

classifications were based on the enhanced 1992 National Land 

Cover Data Set (unpublished data described in Nakagaki and 

Wolock, 2005) as follows: agricultural sites contained >50% agricultural land use and ≤5% urban land use in the watershed; 

urban sites contained >25% urban and ≤25% agricultural land 

use; undeveloped sites contained ≤25% agricultural and ≤5% 

urban land use; mixed sites were all other combinations of agricultural, urban, and undeveloped land. Land cover classifications 

used to designate sites as agricultural included pasture/hay, row 

crops, small grains, fallow, orchards/vineyards/other. Land cover 

classifications used to designate sites as urban included low intensity residential, high intensity residential, commercial/industrial/

transportation, urban/recreational grasses, and forested residential. 

—, none; %, percent]

Physiographic 

division

Agricultural land 

use (sites)

Mixed 

land use 

(sites)

Undeveloped

land use 

(sites)

Urban 

land use 

(sites)

Total 

sites

Appalachian 

Highland

16 82 57 28 183

Atlantic Plain 51 32 10 9 102

Interior Highlands

4 5 8 — 17

Interior Plains 79 49 21 9 158

Intermontane 

Plateaus

18 11 15 2 46

Laurentian 

Upland

1 1 10 — 12

Pacific Mountain System

8 9 20 10 47

Rocky Mountain System

— 16 63 4 83

Total sites 177 205 204 62 648

4 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Explanatory Variables

The dieldrin fish model developed in this study is an 

empirical regression model. A large number of variables that 

could reasonably affect, or indicate an influence on, the use 

or transport of aldrin and (or) dieldrin were considered as 

potential explanatory variables for estimating dieldrin concentrations in fish. Statistical procedures were used to select from 

among the potential variables on the basis of their ability to 

reproduce the observed dieldrin concentrations in fish.

Table 2 lists the potential explanatory variables considered for inclusion in the regression model. Values of source 

and watershed variables for individual fish sampling sites 

were determined by applying basin boundaries for that site to 

various national ancillary data sets. Some potential explanatory variables were based on characteristics of the fish sample; 

most potential variables were based on watershed characteristics, including estimates of (or surrogates for) the use of aldrin 

and dieldrin in various applications. For ease of identification, 

potential explanatory variables are denoted in bold in the following section. 

Fish Sample Characteristics

Three types of explanatory variables are based on characteristics of the fish sample.

• Fish lipid content: This is a measured parameter. Dieldrin 

concentrations in whole fish were significantly correlated 

with fish lipid content (p ≤ 0.0001), although lipid content 

explained a relatively small percentage of the variance 

(R2 = 0.12). This occurs because the sites sampled represented a wide variety of source conditions, ranging from 

reference streams (where pesticide use is expected to have 

been low) to streams in heavily agricultural or urbanized 

areas with histories of high pesticide use. Differences in 

source conditions (and therefore exposure) are likely to 

overwhelm differences in fish lipid content. Conversely, 

lipid content would be a confounding variable when 

investigating the relationship between dieldrin concentrations and source conditions. Therefore, fish lipid content 

was considered as a potential explanatory variable in the 

regression model. Because lipid content was retained 

as a variable in the final dieldrin model, prediction for 

unmonitored streams using the model therefore requires 

the assumption of a specified lipid content (see the 

next section “Extrapolation to Unmonitored Streams”). 

Alternatively, the model could have been developed for 

lipid-normalized dieldrin concentrations. However, the 

latter approach would have the following drawbacks: (1) 

it would reduce the variability explained by the model by 

ignoring the role of lipid content; and (2) the response 

variable then would be in units of micrograms of dieldrin 

per gram of lipid, whereas most fish data are expressed on 

a wet weight basis.

• Taxon dummy variables: Differences in contaminant residues between different species of fish have been widely 

reported in the literature. These have been attributed in 

part to differences in age, body size, lipid content, feeding 

habitat, and trophic state of the different species; but in 

many cases, there was no clear-cut or single explanation 

for the observed differences (Nowell and others, 1999). 

The NAWQA fish data set contained multiple species 

of fish. Although the model includes lipid content as an 

explanatory variable, this may not be sufficient to account 

for differences in contaminant levels among species. 

Therefore, a dummy variable was created for each of 

the eight most commonly sampled taxa (for example, 

common carp), and these eight dummy variables were 

considered as potential explanatory variables. Each taxon 

dummy variable has a value of either “1” (if the sample 

belongs to that taxon) or “0” (if it does not). 

• Time: The elapsed time from 1966 (which represents peak 

historical agricultural use of aldrin and dieldrin) to the 

sampling year was considered as a potential explanatory 

variable. Because dieldrin trends in fish are declining 

nationally (Schmitt and others, 1999; Nowell and others, 1999), it is possible that samples collected earlier in 

the ten-year NAWQA sampling period (1992–2001) may 

contain higher levels than those collected later. Therefore, 

elapsed time was considered as a potential explanatory 

variable.

Methods 5
Phase I—Source Variables

Past agricultural use—from combining county pesticide use data (1966) with mapped agricultural land from 1970s GIRAS land use and land cover 

data, at the 100-m resolution.

• Aldrin + dieldrin use intensity in agriculture, as amount applied per basin area (pounds per square mile).

• Aldrin + dieldrin use intensity in agriculture, as amount applied per area of agricultural land in basin (pounds per square mile).

Land use—from 1970s GIRAS land use and land cover data (polygons converted to 100-m resolution grid cells).

 • Total agricultural land (percentage of basin area)—sum of cropland and pasture, orchards, groves, vineyards, nurseries, ornamental horticultural 

areas, confined feeding operations, other agricultural land.

• Cropland and pasture (percentage of basin area).

• Orchards, groves, vineyards, nurseries, ornamental horticultural areas (percentage of basin area).

• Total urban land (percentage of basin area)—sum of residential, commercial and services, industrial, transportation, communication, utilities, mixed 

urban and built-up land, other urban or built-up land.

• Residential land (percentage of basin area).

• Commercial-industrial land (percentage of basin area)—sum of commercial and services, industrial.

• Transportation land (percentage of basin area)—sum of transportation, communication, and utilities.

• Total forested land (percentage of basin area)—sum of deciduous, evergreen, and mixed forest land, and forested wetland.

• Total undeveloped land (percentage of basin area)—sum of deciduous, evergreen, and mixed forest land; forested wetland; nonforested wetland; herbaceous, shrub and brush, and mixed rangeland; herbaceous, shrub and brush, wet, and mixed tundra; dry salt flats; beaches; sandy areas other than 

beaches; bare exposed rock; bare ground; mixed barren land; streams and canals; lakes; reservoirs; perennial snowfields; glaciers.

Other surrogates for past urban use

• Mean 1990 population density (people per square kilometer)—from 1990 Census Population and Housing at the block group level processed at the 

1-km resolution.

• Weighted termite-urban score, 1970s—urban land use (1970s GIRAS) weighted by termite density zone at the 100-m resolution.

• Weighted termite-urban score, adjusted to include new residential data from 1990s—urban land use (1970s–1990s GIRAS) weighted by termite 

density zone at the 100-m resolution.

• High termite-urban score, 1970s—urban land use (1970s GIRAS) in the very heavy plus moderate-to-heavy zones of termite growth at the 100-m 

resolution.

• High termite-urban score, adjusted to include new residential data from 1990s—urban land use (1970s–1990s GIRAS) in the very heavy plus moderate-to-heavy zones of termite growth at the 100-m resolution.

Characteristics of Sample

• Lipid content (percent)

• Elapsed time since 1966 (years)

Phase II—Taxon Dummy Variables

Taxon

• White sucker

• Common carp

• Largemouth bass

• Largescale sucker

• Longear sunfish

• Rock bass

• Brown trout

• Sculpins

Phase III—Watershed Characteristics

Physical watershed characteristics—delineated from sources ranging in scale from 1:24,000 to 1:100,000.

• Basin area (square kilometers).

• Mean basin slope (percent)—derived from National Elevation Data (NED) at 100-m resolution.

• Mean basin aspect (degrees)—derived from NED at 100-m resolution.

• Site elevation (meters)—derived from NED at 100-m resolution.

• Mean basin elevation (meters)—derived from NED at 100-m resolution.

• Road density (kilometers per square kilometer)—derived from Census Bureau 2000 Topologically Integrated Geographic Encoding and Referencing 

(TIGER) roads.

Table 2. Potential explanatory variables considered in developing the regression model for dieldrin in whole fish. 

[Data sources are cited in text. km, kilometer; lb/mi2, pound per square mile; m, meter]

6 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Soil Properties—from the State Soil Geographic (STATSGO) data (polygons converted to 1-km resolution grid cells) unless noted otherwise.

• Mean available water capacity (inches per inch).

• Mean sand, silt, and clay composition (percent).

• Mean percent of soils classified as soil hydrologic groups A and B (indicator of well-drained soil).

• Mean percent of soils classified as soil hydrologic groups C, D, or C/D (indicator of poorly drained soil).

• Mean soil organic matter content (percent).

• Mean soil permeability (inchs per hour).

• Mean R factor from the Universal Soil Loss Equation, mean annual rainfall erosivity, 1971–2000 (from NOAA station data analyzed and interpolated 

to 1-km cells).

• Mean K factor for the uppermost soil horizon from the Universal Soil Loss Equation, soil erodability.

Hydrologic parameters

• Dunne overland flow—percentage of stream flow derived from runoff resulting from precipitation on saturated soil, generated from TOPMODEL 

(Wolock, 1993).

• Horton overland flow—percentage of stream flow derived from runoff resulting from precipitation exceeding soil infiltration rate, generated from 

TOPMODEL (Wolock, 1993).

• Soil contact time—Time required for precipitation to travel through subsurface flow paths before entering stream (years).

• Topographic wetness index, expressed as “ln(m)” (natural log of meters), derived from a 1-km resolution representation of the U.S. Geological 

Survey’s Digital Elevation Model (U.S. Geological Survey, 1993).

• Mean annual runoff, 1951–1980 (millimeters).

Weather and Climate—estimated using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) (Daly and others, 1997).

• Mean annual precipitation, 1961–1990 (millimeters).

• Mean annual temperature, 1961–1990 ( degrees Celsius).

• Mean annual potential evapotranspiration—estimated using the PRISM temperature with the Hamon PET equation (Hamon, 1961).

Agricultural Management Practices—from 1992 Natural Resources Inventory (NRI) and agricultural land classifications from the National Land 

Cover Dataset 1992.

• Irrigation (percentage of agricultural land in watershed).

• Artificial drainage (percentage of agricultural land in watershed).

• Conservation tillage (percentage of agricultural land in watershed).

Phase IV—Regional Dummy Variables

USDA Farm Production Region—Each site was assigned to whichever region contained the largest percentage of the basin area.

Appalachians

Corn Belt

Delta

Lake States

Mountain States

Northeast

Northern Plains

Pacific States

Southern Plains

Southeast

Table 2. Potential explanatory variables considered in developing the regression model for dieldrin in whole fish—Continued. 

[Data sources are cited in text. km, kilometer; lb/mi2, pound per square mile; m, meter]

Methods 7
Past Sources of Aldrin and Dieldrin

Several variables representing past sources of aldrin and 

dieldrin to the basin were considered. Because it is impossible to distinguish dieldrin residues that originated from 

application of aldrin from those originally applied as dieldrin, 

agricultural use estimates were calculated for the sum of aldrin 

plus dieldrin. Because quantitative estimates of urban pesticide 

use are not available, various urban-use surrogates were used 

as potential explanatory variables, as described below.

• Agricultural use intensity: The historical agricultural use 

intensity of aldrin plus dieldrin in the basin was estimated 

from regional application rates developed using U.S. 

Department of Agriculture (USDA) data on pesticide use 

by farmers in 1966, and harvested crop acreage from the 

1964 and 1969 Census of Agriculture (Gail P. Thelin, 

U.S. Geological Survey, written commun., July 13, 2004). 

Briefly, regional pesticide-use-by-crop coefficients were 

developed for aldrin and dieldrin on 13 crops in each of 

the 10 USDA farm production regions using national 

and regional estimates of pesticide use in 1966 (Eichers and others, 1970) and total harvested crop acreage 

from the 1964 or 1969 (depending on the crop) Census 

of Agriculture (Economic Research Service, 1999). Next, 

these regional pesticide-use-by-crop coefficients were 

used to calculate use estimates for aldrin plus dieldrin in 

all counties in the conterminous United States that have 

agricultural land. The resulting estimated historical use 

of aldrin plus dieldrin in agriculture is shown in figure 

1 for the conterminous United States. Finally, historical 

agricultural estimates of aldrin and dieldrin use within 

the basin (watershed) were calculated for each sampling 

site by combining the basin boundaries for the site with 

county-based use estimates of pesticide use and 1970s 

Geographic Information Retrieval and Analysis System 

(GIRAS) land-use information for the basin (described 

further in the next paragraph). Two agricultural use intensity variables were calculated and considered in model 

development: one was computed per area of agricultural 

land in the basin and one per total basin area.

0.001 – 0.337

none

0.338 – 0.951

0.952 – 2.705

2.706 – 6.261

6.262

Pounds of active ingredient per square mile

EXPLANATION

Figure 1. Estimated historical use of aldrin plus dieldrin in agriculture (1966) in the conterminous United 

States (from Gail P. Thelin, U.S. Geological Survey, unpub. data, 2005). 

8 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
• Other agricultural source variables: The general agricultural source variables considered were the percentage of 

cropland and pasture in the basin, and the percentage of 

total agricultural land in the basin (including cropland, 

pasture, orchards, vineyards, and miscellaneous agriculture), both about 1970. These percentages were derived 

from the USGS’s Land Use and Land Cover (LULC) data, 

which are based on aerial photography taken in the 1970s 

to mid-1980s (Fegeas and others, 1983; data from U.S. 

Geological Survey, 1998, as enhanced by Price and others, 2003). The LULC data are also known as Geographic 

Information Retrieval and Analysis System (GIRAS) data, 

which refers to the digital file storage format and software 

system used to develop the LULC data set (Mitchell and 

others, 1977). (In the remainder of this report, this data 

set will be referred to as the GIRAS data set.) Polygons 

of land use and land cover in the GIRAS data set were 

converted to grid cells at the 100-m resolution. (In this 

report, the term “grid” refers to a raster-based geodata set 

consisting of rows and columns of cells, each of which 

is referenced by its geographic x- and y-coordinates and 

has an associated data value.) The land use and land cover 

categories are based on a classification system developed 

by Anderson and others (1976).

• Weighted termite-urban score: Aldrin and dieldrin were 

used in subterranean termite control through the late 

1980s (this use was voluntarily canceled by industry in 

1987). Although EPA published national-scale estimates 

of pesticides used in subterranean termite control in 1980 

and 1985 (U.S. Environmental Protection Agency, 1983; 

Esworthy, 1987), historical data on termiticide use at the 

county or even regional scale are not available. Therefore, a weighted termite-urban score was developed as 

a surrogate variable to represent the relative probability 

of past aldrin and dieldrin use in subterranean termite 

control. This score was calculated from the percentage of 

urban land use in the basin (1970s GIRAS data) and the 

relative hazard of subterranean termite infestations (Beal 

and others, 1994). Combined, these factors are expected 

to reflect the extent of past termiticide use, because 

termiticides were most likely applied to buildings in parts 

of the country where termites occurred. The national 

distributions of these two factors are shown in figure 2. 

The surrogate variable was calculated as follows: the 

urban land (as a percentage of basin area) located within 

each of four zones of termite density (none-to-slight, 

slight-to-moderate, moderate-to-heavy, and very heavy) 

was determined; the percentage of urban land within each 

zone was multiplied by a weighting factor for that zone (1 

for none-to-slight, 2 for slight-to-moderate, 3 for moderate-to-heavy, and 4 for very heavy); and the four weighted 

percentages were summed to get a weighted termiteurban score. Alternative types of termite-urban scores 

were tried, including summing urban land in only the two 

highest zones of termite growth, and (or) calculating a 

termite-urban score using a version of the 1970s GIRAS 

land use data in which new residential areas were reclassified using 1990 census information (Hitt, 1994). However, 

the weighted termite-urban score that was based on the 

original 1970s GIRAS data had the strongest correlation 

with the response variable.

• Other urban source variables: Besides termite control, 

aldrin and dieldrin were used in other urban applications 

such as moth proofing, wood preservation, and control of 

disease vectors (such as mosquitoes) (U.S. Environmental 

Protection Agency, 1992a). Because quantitative use data 

are not available for such urban applications, two general 

urban-use surrogate variables were considered in model 

development: the percentage of urban land use in the 

basin and population density (people per square kilometer). The percentage of urban land use in the basin (which 

included residential, commercial, industrial, transportation, communication, and utilities) was determined from 

1970s GIRAS data, as described above for agricultural 

land use variables. Population density data were derived 

from a 100-m resolution grid of the census block group 

boundaries and population counts (Price, 2003) derived 

from the 1990 Census of Population and Housing data 

files (U.S. Bureau of the Census, 1991). 

• Minimal use variables: Additional source variables considered were the percentage of total forested land and 

the percentage of total undeveloped land in the basin, 

both about 1970, which each represent areas where past 

use of aldrin and dieldrin is expected to be minimal. Total 

forested land included deciduous, evergreen, and mixed 

forest land, and forested wetland. Total undeveloped land 

included total forested land, rangeland (herbaceous, shrub 

and brush, and mixed), water (streams, canals, lakes, reservoirs), nonforested wetland, tundra (herbaceous, shrub 

and brush, mixed, wet), barren land, beaches, other sandy 

areas, dry salt flats, bare rock, glaciers, and perennial 

snowfields. These land use percentages were determined 

from the 1970s GIRAS land use data.

Other Watershed Characteristics

Numerous watershed-based explanatory variables were 

considered, including soil characteristics, hydrologic parameters, climate variables, and agricultural management practices 

in the basin. Only those variables that were available for the 

entire conterminous United States were used. In addition, 

regional dummy variables were used to represent any regional 

differences not accounted for by the source- and watershedbased variables considered in the model.

Methods 9
• Physical watershed characteristics included watershed 

drainage area (square kilometers), site elevation

(meters), mean basin elevation (meters), mean watershed slope (fraction), mean watershed aspect (degrees), 

and mean road density (kilometer per square kilometer). 

Mean basin elevation, slope, and aspect were computed 

from USGS digital elevation model data (U.S. Geological Survey, 2000). Road density was derived from Census 

Bureau 2000 Topologically Integrated Geographic Encoding and Referencing (TIGER) roads (Geolytics, Inc., 

2004).

• Soil characteristics included mean values within the basin 

of soil organic matter content (percent by weight); clay, 

silt, and sand content (percent) in the soil; soil permeability (inches per hour); and available water capacity

(inches per inch). In addition, two soil type variables, 

well-drained and poorly drained (percent), were based 

on soil hydrologic groups (HG) A, B, C, and D (U.S. 

Department of Agriculture, 1999) by combining them into 

two groups (HGA+HGB and HGC+HGD) to differentiate the watersheds in terms of soil type. Higher values of 

HGA+HGB imply that soils in the watershed are generally well drained, with a relatively low potential for surface runoff, whereas higher values of HGC+HGD imply 

poorer drainage and generally more surface runoff. The 

previous soil characteristics were based on national 1-km 

resolution geodata sets produced by Wolock (1997) from 

data in the State Soil Geographic (STATSGO) database 

(National Resources Conservation Service, 1994). Two 

additional soil-related variables were the mean R factor

(rainfall erosivity) and mean K factor (soil erodibility) 

values for the uppermost soil horizon from the Universal 

Soil Loss Equation. Mean R factors were estimated from 

mean annual (1971–2000) R factor values at the 1-km 

Termite zones

EXPLANATION

Very heavy

Moderate to heavy

Slight to moderate

None to slight

NAWQA study unit boundary

Urban area

Figure 2. Urban areas in the United States (U.S. Geological Survey, 1986) shown superimposed on the relative hazard of subterranean termite 

infestations (Beal and others, 1994). [For urban areas to be visible on the map, the 100-m resolution urban areas were generalized to the 3-km 

resolution; only those 3-km grid cells consisting of at least 10 percent urban land use are colored on the map. km, kilometer; m, meter] 

10 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
grid cell resolution. The mean annual R factor values 

were estimated by the Illinois State Water Survey and 

interpolated to a 2.5 min resolution grid by the Spatial 

Climate Analysis Service at Oregon State University 

using the Parameter-elevation Regressions on Independent 

Slopes Model (PRISM) methodology (Greg Johnson, U.S. 

Department of Agriculture, written commun., May 12, 

2003). K factor values were averaged for each State Soil 

Geographic (STATSGO) soil map unit for the uppermost 

soil horizon, and then spatially represented in a 1-km 

resolution grid (David M. Wolock, U.S. Geological Survey, written commun., May 9, 2003).

• Hydrologic parameters included percent Dunne overland 

flow (the percentage of total streamflow contributed by 

overland flow resulting from precipitation falling on 

saturated soil) and percent Horton overland flow (the 

percentage of total streamflow contributed by overland 

flow resulting from precipitation falling at a rate exceeding the infiltration rate of the soil), topographic wetness 

index (natural log of meters), index of subsurface flow 

contact time (days) and 1951–1980 mean annual runoff

(inches). The Dunne and Horton overland flow parameters 

at the 5-km resolution (Wolock, 2003a,b) were generated using a national-scale application of the topography 

model, TOPMODEL rainfall-runoff model (Wolock, 

1993). The topographic wetness index was derived from 

the U.S. Geological Survey’s Digital Elevation Model 

(U.S. Geological Survey, 1993; Verdin and Greenlee, 

1996) at the 1-km resolution using the single flow direction algorithm method developed by Wolock and McCabe 

(1995). Subsurface contact time was developed from the 

100-m resolution Digital Elevation Model (U.S. Geological Survey, 1993) combined with soil characteristics 

(Wolock, 1997) using methods described in Wolock and 

others (1997). Mean annual runoff was created from 

Gebert and others (1987).

• Climatic variables include mean annual temperature

(degrees Celsius) and precipitation (millimeters) (1961–

1990), and potential evapotranspiration (millimeters). 

Mean annual temperature and precipitation are from climate data developed by the Spatial Climate Analysis Service at Oregon State University (Daly and others, 2002) 

using PRISM, which is an analytical model that uses point 

measurements and a Digital Elevation Model (DEM) to 

generate gridded estimates of monthly and annual precipitation, as well as other climatic parameters. Potential 

evapotranspiration was estimated using temperature data 

from PRISM with Hamon’s equation for potential evapotranspiration (Hamon, 1961). All climatic data variables 

were processed as grid cells at the 1-km resolution.

• Agricultural management practice variables included the 

mean percentage of the basin under selected agricultural 

management practices. These variables were derived from 

a digital map of the basin overlaid with a national geodata 

set of agricultural practices integrated with mapped 

agricultural land. The selected agricultural practices 

consisted of conservation tillage (Wieczorek, 2004a), 

irrigation practices (Wieczorek, 2004b,c,d), and artificial 

drainage (Wieczorek, 2004e,f). The data sources used by 

Wieczorek in these publications were the 1992 National 

Resources Inventory (U.S. Department of Agriculture, 

1995) and the U.S. Geological Survey’s National Land 

Cover Dataset 1992 (Vogelman and others, 2001).

• Ten regional dummy variables were used, which correspond to the ten USDA farm production regions, as 

defined for 1971 agricultural pesticide use (Andrilenas, 

1974). Each site was assigned to one primary region, 

defined as the region containing the largest percentage of 

the watershed area for that site. Each region is represented 

by a 1/0 dummy variable; for example, a site with most of 

its basin in the Corn Belt region will have a value of “1” 

assigned to the ‘Corn Belt’ dummy variable and a value of 

“0” assigned to the nine remaining regional dummy variables. These regional dummy variables were considered 

in case there were regional influences on pesticide use or 

transport that were not represented by the pesticide use 

estimates and surrogates and the watershed characteristics 

previously added to the model.

Development of Regression Model

The response variable in the regression model is the logtransformed concentration of dieldrin in one whole-fish composite sample from each NAWQA site. Because each sample 

is a composite of 5–8 individual fish from a given site, the 

measured concentration represents an approximate “average” 

for that site. Explanatory variables were tested in four phases: 

those representing (phase I) sources of the pesticide, fish lipid 

content, and time; (phase II) taxon of the fish sample; (phase 

III) characteristics of the watershed; and (phase IV) geographic region. Statistical methods were used for selecting and 

transforming variables (as needed) for inclusion in the model 

and in estimating coefficients of the regression equation.

Regression Models

Many of the dieldrin concentration values used as the response variable (62 percent of samples) were reported as less 

than a detection threshold (that is, dieldrin was not detected), 

resulting in censored data. With censored data, conventional 

least-squares regression methods will yield biased and inconsistent estimates (Judge and others, 1985, p. 780). Therefore, 

Tobit regression methods (also called maximum likelihood 

methods) were used for developing the model. These can be 

used to obtain parameter estimates of a censored linear model 

when the regression residual errors are independent, identically and normally distributed, and with a mean of zero and 

finite variance (Maddala, 1983). Maximum likelihood methods 

Methods 11
implemented in the survreg procedure (Therneau, 1999) in the 

statistical analysis program S-PLUS (Insightful Corporation, 

1999) were used to estimate the parameters of the regression 

models.

Measures of goodness of fit used in conventional least 

squares analysis—such as the standard deviation of the 

residual error, or the coefficient of multiple determination 

(R2)—cannot be computed for the Tobit regression model. The 

standard deviation of the residual error is alternatively referred 

to as the scale parameter in maximum likelihood estimation. 

Estimates of the scale parameter from the maximum likelihood 

procedure provide only asymptotically unbiased estimates of 

the standard deviation of the residual error when estimated 

from sample data (Aitkin, 1981). These estimates, on average, underestimate the true standard deviation. The bias is a 

function of the sample size and the degree of censoring. In 

this report, biased estimates of the standard deviation of the 

residual error are referred to as “scale” in tables and figures. 

As an alternative to a conventional R2, a pseudo-R2 (pR2) 

suitable for use in Tobit regression model was calculated using 

the method of Laitila (1993). As with conventional R2, this pR2

ranges from 0 to 1 and is an estimate of the proportion of the 

variation in the response variable explained by the regression 

model (“0” indicates no variation is explained; “1” indicates 

all variation is explained).

Transformation of Response and Explanatory 

Variables

The maximum likelihood methods used for estimating 

the parameters of the models require several assumptions. The 

relation between the variables must be linear in the parameters, and the residual error must be identically and normally 

distributed. Departures from these assumptions can result in 

estimates of model coefficients with considerable error. One 

means of addressing departures from model assumptions is 

through transformations of the response and (or) explanatory variables. Various transformations were considered to 

minimize departures from the assumptions of the maximum 

likelihood methods used. For the response variable, a logarithmic transformation was used. For explanatory variables, the 

logarithmic, square-root, and square transformations of each 

variable were considered, as well as the untransformed 

value. (For variables that have zero as a possible value, 1 

was added to variable values before applying the logarithmic 

transformation.)

Because the response variable is a logarithmic transformation, concentrations predicted by the model (after retransformation) are the median concentrations expected for sites 

that have a given set of explanatory values, rather than mean 

concentrations. For example, for 10 basins with identical 

explanatory variables, the model will predict the same dieldrin 

concentration for all 10 basins. In actuality, the true concentrations are expected to be less than the predicted value at half of 

the 10 basins and greater than the predicted value at the other 

half. Thus, the “median” response refers to the distribution of 

concentrations among sites. In this study, predicted concentrations were not adjusted for transformation bias (Bradu and 

Mundlak, 1970; Duan, 1983) because estimates of site medians were considered appropriate for the study objectives.

Selection of Explanatory Variables

Potential explanatory variables were considered and 

added to the model in four phases, as described below.

• Phase I: Source variables, lipid content, time—Estimates 

of historical pesticide use in agriculture, various surrogates for agricultural and urban pesticide use, and 

surrogates for minimal use were added to represent past 

pesticide use in the basin. In addition, fish lipid content 

(a measured parameter) was included in phase I because 

of its significant correlation with dieldrin residues in fish 

(p ≤ 0.0001). Elapsed time was also considered during 

this phase. These variables were considered first because 

they were expected to be the most important in terms of 

explaining the variance in concentrations among sites.

• Phase II: Taxon—Dummy variables representing the most 

commonly sampled fish taxa were considered as explanatory variables, in case lipid content (added to the model 

during phase I) did not completely account for differences 

in contaminant bioaccumulation among fish taxa.

• Phase III: Watershed variables—Additional watershed 

characteristics (soil properties, hydrologic parameters, 

climate variables, and agricultural management practices) were considered. These variables have potential to 

affect contaminant fate and transport and, therefore, may 

contribute to differences in contaminant concentrations 

measured in fish from different watersheds, once differences between sources and fish taxa have been taken into 

account.

• Phase IV: Region—Dummy variables representing USDA 

farm production regions were considered in case there 

were regional differences not accounted for by source 

estimates and watershed characteristics.

During each phase, a stepwise procedure that was based 

on the Akaike Information Criterion (AIC) was applied 

(Venables and Ripley, 1999, p. 186–188). This procedure is 

similar to stepwise regression, except that the AIC was used to 

select variables for inclusion in the model. AIC is a function of 

the maximized likelihood and the number of parameters in the 

model; it balances model goodness of fit with the number of 

parameters needed to achieve that fit. AIC attempts to quantify 

the concept of model parsimony, in that simpler models are 

favored over complex ones unless a complex one substantially 

improves the fit. During each phase of model development, 

a stepwise Akaike-based procedure (stepAIC) was used that 

computed the AIC for multiple (alternative) models and 

selected the one that best described the response variable data. 

Candidate variables not selected by the stepAIC procedure 

12 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
were eliminated, and the stepAIC procedure was rerun until 

no further variables were eliminated from the model. This process, described below in more detail for phase I, was repeated 

during each of the four phases of model development.

To select variables during phase I: (1) The stepAIC 

procedure was run to identify candidate phase I variables that 

best describe the response variable data. (2) For each variable 

identified as important by the stepAIC procedure, that variable and three alternate transformations (the logarithm, square, 

and square root of the variable) were each plotted against the 

response variable and examined to see if the relation was approximately linear, and if the variance was relatively constant across the range of values of the variable. The stepAIC 

procedure also was run on all four versions of the variable 

(untransformed and the three transformations) to determine 

whether transformation(s) improved the fit. For each untransformed explanatory variable that was selected by the stepAIC 

procedure, the following was performed: (a) if the relationship 

between the response variable and that untransformed explanatory variable showed non-normality or nonconstant variance, 

but the problem was corrected by one or more transformations of that explanatory variable, then that transformation 

was substituted for the original, untransformed variable in 

the model; (b) if the relationship between the response variable and that untransformed explanatory variable was highly 

skewed, showed non-normality or nonconstant variance, and 

these problems were not improved by transformation, then that 

variable was dropped from consideration in the model. (3) The 

stepAIC procedure was run again on the revised list of candidate variables. (4) Any variables that were not selected by the 

stepAIC procedure were eliminated and the stepAIC procedure was run again. (5) Step 4 was repeated until no further 

phase I variables were eliminated by the stepAIC procedure. 

(6) Any variables that were selected by the stepAIC procedure, 

but had p-values greater than 0.05, were dropped from the 

model. Prior to this step, some variables with p-values greater 

than 0.05 may have been selected, and some variables with pvalues less than 0.05 excluded, by the stepAIC procedure because this procedure relies on AIC (not on p-values) to select 

variables. During this step, any variables with p-values greater 

than 0.05 were dropped to simplify the model and to limit the 

overall number of variables in the model. (7) Occasionally the 

stepAIC procedure selected two or more redundant variables, 

such as two transformations of the same variable, or two variables that represented the same or overlapping ancillary data 

(such as total forested land and total undeveloped land, or two 

termite-urban score variables calculated in different ways); 

in such cases, the variable with the strongest correlation was 

selected, and the redundant variable(s) were eliminated. At 

this point, phase I ended and phase II began.

Beginning with phase II, each phase started with the 

best model from the preceding phase, and then the new set of 

variables was added and tested. The seven steps listed above 

were repeated during each of phases II through IV (except that 

dummy variables in phases II and IV were not transformed). 

During the later stages of model development, sometimes 

variables selected during previous phases were no longer 

retained (selected) by the stepAIC procedure; that is, addition 

of new explanatory variables to the model caused some previously selected variables to be eliminated from the model.

After phase IV, a subsampling technique was used to reduce the number of variables and to eliminate overfitting. The 

AIC-based selection procedure was run on a random subset of 

50 percent of the dieldrin data, and a record was made of any 

variables dropped from the model (that is, no longer selected 

by stepAIC). This random subsampling procedure was repeated 50 times, and any variables that were routinely dropped 

(that is, dropped in at least 20 percent of replicate runs) were 

eliminated from the model. This subsampling approach was 

repeated with the abbreviated model, and again variables were 

eliminated if they were dropped in 20 percent or more of the 

replicates. The subsampling approach was repeated until no 

variables were routinely dropped. The subsampling routines 

reduced the final dieldrin model from 13 to 6 variables.

Analysis of Model Fit

Condition indices and variance decomposition proportions were used to detect colinearity among variables. One 

variable was eliminated from the dieldrin model because of 

colinearity. Diagnostics for censored regression (Escobar 

and Meeker, 1992) available in the survreg procedure in the 

S-PLUS program (Insightful Corporation, 1999) were used to 

identify influential observations and to aid in variable selection. These diagnostics included case-weight perturbations 

(a measure of the effect that dropping an observation has on 

the inference), response perturbations (a measure of the effect that errors in the observed response variable has on the 

inference), shape parameter perturbations (a measure of the 

effect that unequal variances of the response variable have on 

the inference), deviance residuals (a residual transformed to a 

log-likelihood scale—the sum of the squared deviance residuals totals to the deviance), and leverage (a measure of how 

much influence an observation has on the overall regression). 

For individual observations identified as high values for any 

of these diagnostics, the regression was rerun excluding this 

observation to determine the effect of the observation on the 

regression; however, in all cases, the Akaike selection procedure retained (that is, selected) all variables in the regression 

model, there was no change in the sign of the coefficients, and 

the pR2 and scale values did not change markedly. Therefore, 

all observations were retained for model development.

Model Validation

The final model was cross-validated as follows: The 

NAWQA fish data used in model development were randomly 

divided into two subsets, a calibration data set (containing 70 

percent of the data) and a validation data set (30 percent of 

the data). The model was first fit to the calibration data subset, 

Methods 13
NATURAL LOG (OBSERVED DIELDRIN 

CONCENTRATION) – NATURAL LOG 

(PREDICTED DIELDRIN CONCENTRATION)

4

2

0

– 2

– 4

CALIBRATION

DATA

VALIDATION

DATA

90th percentile

EXPLANATION

10th percentile

Median

75th percentile

25th percentile

4

2

0

– 2

– 4

CALIBRATION

DATA

VALIDATION

DATA

90th percentile

EXPLANATION

10th percentile

Median

75th percentile

25th percentile

NATURAL LOG (OBSERVED DIELDRIN 

CONCENTRATION) – NATURAL LOG 

(PREDICTED DIELDRIN CONCENTRATION)

Figure 3. (A) Residual error developed from calibration data. 

Dashed lines are reference lines at y values of 1 and –1. Dieldrin 

concentrations are in units of micrograms per kilogram, wet weight.

Figure 3. (B) Continued. Residual error developed from validation data. 

and predicted values and residuals were computed for both the 

calibration data and validation data subsets. Then the process 

was reversed, and the model was fit to the validation data 

subset. There was little difference in the predicted and residual 

errors obtained from the two fitted models that were based on 

the different data subsets (fig. 3).

Extrapolation to Unmonitored Streams

The regression model was used to predict the dieldrin 

concentration in whole fish in all streams in the conterminous 

United States, using national data sources to estimate values 

of model variables for unmonitored streams. Unlike the other 

explanatory variables, lipid content is not available from a 

nationwide data set; therefore, the extrapolation must assume a 

specified lipid content value that is appropriate to the objectives of the extrapolation.

Data Sources for Unmonitored Streams 

Nationwide

The U.S. streams were defined by the EPA River Reach 

File 1 (Nolan and others, 2003), which includes over 600,000 

mi of streams and more than 60,000 watersheds, at a scale 

of approximately 1:500,000. For every watershed, values of 

the explanatory variables required by the regression models 

were computed from nationwide data sources using geospatial analysis tools. This step required delineation of the entire 

watershed area draining to the downstream end of each stream 

reach and then computing area-weighted average values for 

each explanatory variable. The regression model then was 

used to predict the concentration of dieldrin in whole fish for 

the stream at the outlet of each watershed.

Lipid Content

Nationwide extrapolations were made at two different values for fish lipid content: (a) 4.2 percent and (b) 6.2 

percent. These correspond to the average lipid content of (a) 

whole-body largemouth bass and (b) all whole fish that were 

sampled by the NCBP (1969–1986, from U.S. Geological 

Survey, 2004) and the NAWQA Program (1992–2001) 

combined, respectively. The 4.2 percent value is intended 

to represent the average lipid content of whole largemouth 

bass (a nationally distributed sport fish that is popular with 

recreational fishers), and the 6.2 percent value is intended to 

represent the average lipid content of all whole fish, in United 

States streams.

14 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Uncertainty Analysis

A regression model provides an estimate of the response 

variable conditional for a given a set of values of the explanatory variables. If the form of the regression model reasonably 

describes the relation between the response and explanatory 

variables and the sample data are representative of the population of all data, then the model can be used to predict values 

of the response variable for additional sets of explanatory variable values not included among the sample data. In this study, 

each prediction represents the median, or “mid-point” estimate 

of the response variable (logarithm of dieldrin concentration 

in whole fish) for all unmonitored sites with an identical set 

of explanatory variable values. Thus, for a group of sites with 

identical explanatory variable values, there is a 50 percent 

probability that the measured concentration at any site exceeds 

the predicted concentration for that site; the actual value is 

expected to be lower than the model prediction at 50 percent 

of these sites and higher than the predicted concentration at 

50 percent of these sites. Moreover, because the model was 

developed using measured concentrations in composite wholefish samples, the model predictions also represent composite 

whole-fish samples. Finally, because the response variable is 

the logarithm of dieldrin concentration, the antilogarithm of 

the estimate from the regression model must be computed to 

obtain the median estimate of dieldrin concentration.

If the assumptions of the maximum likelihood method 

used to fit the model parameters pertaining to the residual 

error distribution are valid, then uncertainty in the estimated 

response variable values can be assessed by calculating prediction intervals. Upper and lower prediction limits are constructed around the estimated concentration from the model error 

(scale). Specifically, a prediction interval (PI) is computed as 

follows:

(1)

where log(Cest) is the logarithm of the estimated dieldrin concentration, t(α/2) is the point on student’s t-distribution with 

a probability of exceedance of α/2, and SDest is the standard 

error of the estimated logarithm of concentration.

This means of assessing uncertainty is only approximate 

because it doesn’t fully take into account the effect of censoring of the observed concentration data. A prediction interval 

represents the likelihood that the actual unknown concentration for an individual unmonitored stream falls within a 

specified interval of the predicted concentration. For a 90th 

percentile prediction interval, for example, only 10 percent of 

measured concentrations in fish samples (with the appropriate 

lipid content) collected from a given stream would be expected 

to fall outside that interval—5 percent would fall below the 

lower limit, and 5 percent above the upper limit. (That is, there 

is only a 5 percent probability that the measured concentration 

at any site exceeds the upper 90th percentile prediction interval limit and a 95 percent probability that it exceeds the lower 

prediction interval limit.) The wider the prediction interval, the 

greater the uncertainty in the estimated concentration.

Sometimes it is useful to be able to determine the probability that an estimated concentration exceeds a certain concentration, such as a guideline for protection of human health 

or wildlife. This probability is a function of the estimated 

concentration for a site and the overall model error, and it can 

be approximated by algebraically rearranging the formula for 

computing prediction interval limits (equation 1). The probability of exceedance (Pexc) is thus equal to

P

C C

SD exc

g est

est

=

 −  

 log( ) log( )

(2)

where Cg is the guideline concentration. This concept is illustrated in figure 4. Figure 4A shows a hypothetical regression line that represents the estimated values of the response 

variable for the explanatory variable shown on the x-axis. For 

four selected values of the explanatory variable, figure 4A also 

shows the error distribution about the regression line and the 

90th percentile prediction interval limits. When the regression 

residual error is normally distributed, the error distribution 

about an estimated value of the response variable follows the 

student’s t-distribution, as shown. The probability that the estimated response value will exceed the guideline is thus represented by the area under the error distribution curve above the 

point where the guideline intersects the curve. In the example 

shown in figure 4, the exceedance probabilities for the four selected values of the explanatory variable are (from left to right) 

0.02, 2.3, 31, and 84 percent. (Figure 4 is a 2-dimensional 

representation of a 3-dimensional figure. In three dimensions, 

the error distribution curve would be perpendicular to the paper and the prediction interval limits would lie on the curve.) 

Figure 4B shows the same regression and error relation after 

antilogarithms have been computed. Once this has been done, 

the error distribution around the regression line is no longer 

symmetric, nor does it have constant variance. Because the 

error distribution is no longer symmetric, the magnitude of the 

uncertainty is greater above the estimated concentration than it 

is below it. Likewise, because the error distribution no longer 

has constant variance, the magnitude of the uncertainty about 

the estimated dieldrin concentrations is much greater for larger 

concentrations than for smaller ones.

In this report, for both human health and wildlife guidelines, the probability that an unmonitored stream would exceed 

a given guideline was calculated and the national distribution 

of probabilities was mapped for unmonitored streams across 

the United States. This entailed assuming a default lipid content appropriate for each type of guideline, as discussed further 

in the next section.

PI = ( ) C S est e ± × D t st































 log

α

2

Methods 15
−2.0

0 10 20 30 40 50

0 10 20

EXPLANATORY VARIABLE

30 40 50

−1.5

−1.0

−0.5

0.0

0.5

1.0

1.5

LOGARITHM OF RESPONSE VARIABLE

A

0

2

4

6

8

10

12

14

RESPONSE VARIABLE

Regression line B

Error distribution

90th percentile prediction interval limit

Guideline

Figure 4. (A) A hypothetical regression for which the response variable is log-transformed, showing the regression line; the error distribution 

and the upper and lower 90th percentile prediction interval limits for four selected estimated values of the response variable; and a hypothetical 

guideline value. (B) Same as A, after taking the antilogarithm of the response variable.

16 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Predicting the Potential Effects of Fish 

Consumption on Human Health and Wildlife

Potential Effects on Human Health

Potential effects on human health from fish consumption 

were assessed by (1) assuming a lipid content of 4.2 percent, 

and then (2) comparing predicted dieldrin concentrations in 

whole fish for unmonitored streams across the United States 

with the EPA recommended screening values for dieldrin in 

edible fish (U.S. Environmental Protection Agency, 2000). As 

noted above, the lipid content of 4.2 percent was chosen because it is the average lipid content of whole-body largemouth 

bass sampled by the NCBP and NAWQA Programs. This 

choice of taxon was considered appropriate from a human 

health perspective because largemouth bass is a common sport 

fish with a national distribution. This choice of tissue type 

(whole-body fish) is not optimal from a human health perspective because people tend to consume only the edible portion 

(fillets)—however, it is necessary because the model was 

developed from whole-body fish data, and it cannot be used 

to predict concentrations in fish fillets. Therefore, comparison 

with human-health guidelines (EPA screening values) is considered a screening-level assessment (discussed further in the 

section on “Significance to Human Health”).

The EPA screening value for a contaminant in edible 

fish tissue is defined as the concentration of that contaminant 

in edible fish or shellfish that is of public health concern and 

that is used as a threshold value against which tissue residue 

levels of that contaminant in edible fish and shellfish can be 

compared (U.S. Environmental Protection Agency, 2000). For 

potential carcinogens such as dieldrin, EPA uses a conservative (protective) model of carcinogenesis that assumes there is 

no safe level of a carcinogen; instead, EPA calculates a screening value that corresponds to a maximum acceptable cancer 

risk (such as 1 in 100,000). If the maximum acceptable cancer 

risk decreases, the allowable dieldrin concentration also will 

decrease accordingly. The EPA screening values (SV) can be 

adjusted for different acceptable risk levels, as for example:

SV ( : 1 10, ) 000 = × 10 SV ( : 1 100, ) 000 (3)

where SV(1:10,000) is the screening value at a maximum acceptable cancer risk of 1 in 10,000, and SV(1:100,000) is the screening 

value at a cancer risk of 1 in 100,000.

For dieldrin, the screening value is 25 μg/kg for a cancer 

risk level of 1 in 10,000, and 2.5 μg/kg for a cancer risk 

level of 1 in 100,000. Both of these screening values (at the 

1:10,000 and 1:100,000 cancer risk levels) are used in the 

extrapolation for human health (see “Significance to Human 

Health”).

Potential Effects on Fish-Eating Wildlife

Potential effects on fish-eating wildlife were assessed 

by (1) assuming a default lipid content of 6.2 percent and (2) 

comparing predicted dieldrin concentrations in whole fish 

for unmonitored streams across the U.S. with the New York 

guideline for protection of fish-eating wildlife (Newell and 

others, 1987). As noted above, the default lipid content of 6.2 

percent was chosen because it is the average lipid content of 

all whole fish sampled by the NCBP and NAWQA Programs. 

This assumption is appropriate because wildlife will eat a 

variety of fish taxa and they tend to consume the whole fish. 

The New York wildlife guideline of 120 μg/kg is intended to 

protect fish-eating birds and mammals from adverse effects 

other than cancer, such as mortality, reproductive impairment, 

and organ damage (Newell and others, 1987).

Dieldrin is a potential carcinogen (U.S. Environmental 

Protection Agency, 1992b), and the 120-μg/kg guideline does 

not address the potential incidence of cancer in wildlife populations from dieldrin exposure. Although Newell and others 

(1987) also determined a cancer risk guideline for dieldrin, 

this guideline assumes a maximum acceptable cancer risk of 

1 in 100—that is, it assumes that a 1:100 cancer risk would 

result in virtually no reduction in a population from contaminant-induced cancer, an assumption that could not be fully 

justified at the time (Newell and others, 1987). Because it is 

not clear what effect this level of contamination and cancer 

risk would have on wildlife populations, the New York cancerrisk guideline was not used as a benchmark in this report.

Results and Discussion

The model development data set contains data for 59 

different fish taxa at 648 stream sites. Site locations and 

measured dieldrin concentrations in whole fish are shown in 

figure 5. However, sites with the highest and lowest 10 percent 

of lipid levels were excluded from figure 5 so that the range of 

lipid levels shown (2.3–10.4 percent, for n = 514 sites) more 

closely approximates the lipid levels used in the nationwide 

extrapolations.

Regression Model for Dieldrin in Whole Fish

The explanatory variables included in the dieldrin fish 

model, as well as their transformations and coefficients, are 

summarized in table 3 .

Results and Discussion 17
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Streams

Agricultural Urban Mixed land

use

Undeveloped

5.0

5.0–25.0

25.1–120

120

Mean concentration, in Mg/kg

EXPLANATION

Figure 5. Measured dieldrin concentrations in fish at NAWQA sites, excluding sites with 

the highest and lowest 10 percent of lipid levels. The 514 sites plotted have 2.3–10.4 

percent lipid content. μg/kg, microgram per kilogram.

Explanatory variable Transformation Coefficient p-value

Fish lipid content (%) Logarithmic 1.0542 1.32E–21

Aldrin–dieldrin use in agriculture, per basin area (lb/mi2) — 0.0733 7.91E–26

Forest land (% of basin) Square root –0.0829 5.18E–16

Urban-termite score Logarithmic(x + 1) 0.5406 2.91E–27

Organic matter content of basin soils (average %) Logarithmic –0.4838 2.94E–10

Southern Plains region Binary group variable (1/0) 0.2714 0.0146

(Intercept) — –0.1674 0.132

Table 3. Explanatory variables and coefficients for the regression model for dieldrin in whole fish. 

[—, none; lb/mi2

, pound per square mile]

18 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Analysis of Significant Explanatory Variables

The first four and most important explanatory variables in 

the model have reasonable relations to dieldrin concentrations 

in fish, in terms of their relative importance and the direction 

of their influence. The measured lipid content is positively 

related to dieldrin concentration in fish, which is consistent 

with the tendency of organochlorine pesticides to accumulate 

in fat tissue. Coefficients are positive for agricultural use of 

aldrin and dieldrin and the urban-termite score (representing 

historical use in agriculture and termite control, respectively), 

and negative for the variable representing forested land in the 

basin (where use would have been minimal). Together these 

four variables explain 58 percent of the variability in dieldrin 

concentrations measured in fish (fig. 6).

The addition of two more variables, soil organic matter and the Southern Plains regional variable, made a modest 

improvement in model performance. The final model explains 

64 percent of the variability in dieldrin concentrations in 

whole fish, and the scale parameter (standard deviation of the 

residual error) for the final regression is 0.485 (fig. 6). The last 

two variables are significantly, though weakly, correlated with 

the response variable. Because these variables explain relatively little of the variability, possible reasons for their influence 

on the response variable are speculative. The soil organic matter variable has a negative coefficient, and multiple explanations are possible. High values for soil organic matter may be 

associated with either or both (1) reduced transport of contaminated soil to streams and (or) (2) a higher organic carbon 

content in stream sediment, which would tend to have a higher 

fraction of dieldrin sorbed to sediment and a correspondingly 

lower fraction in water, which could in turn decrease uptake by 

fish. The first possible explanation is supported by the observation that, in data from the STATSGO database for NAWQA 

fish sampling sites, mean soil organic matter is positively correlated with mean soil permeability. In support of the 

second possible explanation, the logarithm of the dieldrin 

concentration measured in fish is also negatively correlated 

(p < 0.05) with the logarithm of the measured organic carbon 

content of streambed sediment collected at the same sites.

The Southern Plains regional variable has a positive 

coefficient, indicating that without this variable, the regression 

model would tend to underestimate dieldrin concentrations in 

fish in the Southern Plains region (Texas and Oklahoma). The 

reason for this underestimation is not known. However, in this 

region, aldrin and dieldrin were used in agriculture (especially 

on cotton), as well as in urban applications (the eastern parts 

of Texas and Oklahoma are in the zone of heaviest termite 

density). Of the NAWQA sites sampled in the Southern Plains 

region, the highest dieldrin residues in fish were from the 

Trinity River Basin study unit in eastern Texas—especially at 

sites in large basins with mixed (agricultural and urban) land 

use—whereas dieldrin was low or not detected at strictly 

agricultural sites in this study unit. Although not conclusive, 

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1 2 3 4 5 6

NUMBER OF EXPLANATORY VARIABLES ADDED

PSEUDO-R2

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

SCALE

pR2

Scale

Figure 6. Effect of adding explanatory variables to the regression model for dieldrin 

in whole fish on the model’s pseudo-R2 and scale values.

Results and Discussion 19
this suggests that urban use (rather than agricultural use) of 

aldrin and dieldrin in this area may have been underestimated 

by the use terms in the regression model. In fact, most urban 

sites in the Trinity River Basin are located in the moderateto-heavy zone of termite density, but very near (just outside) 

the boundary of the very-heavy termite zone (fig. 2); so, it is 

possible that the weighted termite-urban score may under-represent past termiticide use in these urban areas. Because this 

regional variable explains very little of the variability (about 1 

percent), the apparent regional bias is slight and the explanation for it is speculative.

Extrapolation to Unmonitored Streams

The regression model developed here can be used for 

spatial extrapolation to streams that have not yet been assessed 

by direct measurements, because the model is based on certain 

characteristics of each hydrologic system, such as estimated 

historical pesticide use and soil characteristics within the 

basin, for which data or estimates are available for the entire 

nation. This approach to extrapolation is fundamental to 

extending the targeted local and regional studies of NAWQA 

to a comprehensive national assessment (Gilliom and others, 

2006).

The results of spatial extrapolation to all United States 

streams are shown in figure 7, which maps the dieldrin concentration in composite whole fish predicted by the model, 

for whole fish with a lipid content of (A) 4.2 percent and (B) 

6.2 percent. As noted previously, lipid content is not available from a nationwide data set, as are the other explanatory 

variables, and therefore default lipid values have been assumed 

that are appropriate to the objectives of the extrapolations. 

These maps show the spatial distribution of the best or most 

likely concentrations of dieldrin in composite whole-fish 

samples (for each of the specified lipid contents).

In figure 7, unmonitored streams are color coded to correspond to different concentration ranges for the predicted 

dieldrin concentration in whole fish (at the lipid content specified in the figure). A single set of color-coded dieldrin concentration ranges is used in both maps, and the boundary concentrations defining these ranges correspond to specific dieldrin 

guidelines for either human health or wildlife. The highest 

concentration category (>120 μg/kg) is defined by the New 

York guideline for protection of fish-eating wildlife (Newell 

and others, 1987). The lowest category consists of streams predicted to have dieldrin whole-fish residues below the reporting 

limit (<5 μg/kg), and the two middle categories are defined 

in relation to EPA screening values for consumption of edible fish or shellfish (U.S. Environmental Protection Agency, 

2000). Specifically, the 5–25 μg/kg category is defined at the 

lower end by the reporting limit (5 μg/kg), which is associated 

with a cancer risk of 2 in 100,000 (or 1:50,000). (Note that the 

EPA recommended screening value at a 1:100,000 cancer risk 

is 2.5 μg/kg, which is lower than NAWQA was able to reliably 

quantitate in fish.) For the 25–120 μg/kg category, all sites 

have predicted values greater than the EPA screening value 

at a 1:10,000 cancer risk (25 μg/kg). These predictions are 

discussed further below in relation to potential significance to 

human health and wildlife.

Because the response variable is a logarithmic transformation, concentrations predicted by the model (after retransformation) are median concentrations expected for a given set 

of explanatory values. This means that, for sites with the identical predicted dieldrin concentration, the actual concentrations 

are expected to be greater than the predicted value (for a given 

lipid content) at half of the sites, and lower than predicted 

at half of the sites. In figure 7, therefore, actual concentrations (for composite whole-fish samples) at half the sites are 

expected to exceed the predicted values. Uncertainty in model 

predictions is discussed below in the context of human health 

and wildlife guidelines.

Because fish lipid content (a measured parameter) is a 

variable in the model and has a positive coefficient, predicted 

dieldrin concentrations will increase as fish lipid content 

increases. This is illustrated by comparing predictions for 

6.2 percent lipid (fig. 7B) with predictions for 4.2 percent lipid 

(fig. 7A). The extrapolation at 6.2 percent lipid (fig. 7B) has 

fewer blue streams, and more yellow, orange, and red streams 

than the extrapolation at 4.2 percent lipid (fig. 7A). Whole fish 

with a lipid content greater than 6.2 percent would be predicted to have still higher dieldrin concentrations than those 

shown in figure 7B, and whole fish with a lipid content less 

than 4.2 percent would be expected to have lower concentrations than those shown in figure 7A. As a point of reference, 

the average whole-fish lipid content in NAWQA and NCBP 

fish samples for some additional species are lake trout, 

15 percent; channel catfish, 7.5 percent; common carp, 6.5 

percent; smallmouth buffalo, 6.5 percent; white sucker, 5.8 

percent; yellow perch, 4.3 percent; sculpins, 3.8 percent; and 

bluegill, 3.1 percent.

Significance to Human Health

NAWQA fish data were not collected with the intention 

of assessing potential effects on human health from consumption of contaminated fish. Instead, NAWQA’s primary fish 

sampling objective was to assess the occurrence and geographic distribution of hydrophobic contaminants in streams, 

and the fish sampling methods were designed accordingly. 

Two aspects of the NAWQA study design detract from the 

20 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Figure 7. Predicted dieldrin concentrations in whole fish for U.S. streams at (A) 4.2 percent lipid 

content (largemouth bass) and (B) 6.2 percent lipid content (all fish taxa). 

Extrapolated concentration of dieldrin, 

in Mg/kg

5

5.0–25.0

25.1–120

120

EXPLANATION

B

A

Results and Discussion 21
usefulness of NAWQA fish data in the assessment of human 

exposure and health risk. First, NAWQA analyzed whole-body 

residues, whereas people generally consume only the edible 

portion (fillet or muscle tissue), and second, many of the fish 

taxa collected are bottom feeders that are not commonly consumed by people. Nonetheless, careful data analysis will allow 

some inference with respect to potential human health effects.

It is not necessarily straightforward to predict how residues of organochlorine pesticides will compare in a wholebody bottom feeder versus a game-fish fillet, because two 

factors (fish taxon and fish tissue type) have changed. In its 

National Study of Chemical Residues in Fish, EPA measured 

bioaccumulative chemicals in whole-body bottom-feeders 

and game-fish fillets, and found that the relative magnitude 

of residues in each depended on the specific chemical being 

measured (U.S. Environmental Protection Agency, 1992b). 

Of the organochlorine pesticide chemicals measured, some 

(including dieldrin, oxychlordane, and DDE) were present in 

roughly the same average concentrations in game-fish fillets 

as in whole-body bottom-feeders, whereas others (including 

chlordane, nonachlor, and heptachlor epoxide) had higher 

average concentrations in whole-body bottom feeders than in 

game-fish fillets.

These findings suggest that, with caveats, NAWQA 

whole-fish data can be compared with EPA screening values 

for edible fish consumption in a screening-level assessment—

such as in deciding whether and where to conduct future dieldrin monitoring in game-fish fillets. For example, in streams 

where dieldrin residues in whole fish are found (or predicted) 

to be high, there exists some potential for concentrations in 

edible game fish to be high as well. Therefore, if such streams 

are frequented by sport or subsistence fishermen, this suggests 

that additional sampling and analysis of game-fish fillets from 

these streams may be warranted. Conversely, where dieldrin 

residues in whole fish are low or undetected, concentrations in 

edible fish tissue are also likely to be low—allowing for differences that are due to species and (or) lipid content. 

To assess relevance to human health, the regression model was used to predict dieldrin concentrations in whole fish, 

assuming a 4.2 percent lipid content (to represent the average 

lipid content of whole-body largemouth bass), in unmonitored 

streams. As noted previously, the 4.2 percent lipid value pertains to whole-body largemouth bass, whereas the EPA screening values for fish consumption apply to the edible portion of 

the fish, which tends to have a lower lipid content. Data from 

EPA’s National Study for Chemical Residues in Fish indicate 

that lipid levels in bass fillets average 0.84 percent, or about 

one fifth the content of whole-body largemouth bass sampled 

by the NCBP and NAWQA programs (U.S. Environmental 

Protection Agency, 1992b). Therefore, the comparison shown 

in figure 7A represents a worst-case assessment at the 4.2 percent lipid level from the perspective of human health. For fish 

with a higher lipid level, such as lake trout (with an average 

whole-body lipid content of 15 percent), whole-body dieldrin 

concentrations would likely be much higher than the wholebody concentrations calculated at the 4.2 percent lipid level 

(and shown in fig. 7A). We cannot make specific predictions 

about dieldrin concentrations in edible fish tissues for largemouth bass, lake trout, or any other taxon because the data 

required to make such predictions were not collected. 

In figure 7A, dieldrin concentrations in whole fish from 

the dark-blue streams are predicted to be less than the reporting limit (5 μg/kg) for the method NAWQA used to analyze 

for dieldrin in fish. In edible fish tissue, a concentration less 

than 5 μg/kg would correspond to a cancer risk level of less 

than 1 in 50,000. Because dieldrin concentrations in edible 

fish are expected to be even lower than those in whole fish, the 

dark blue streams are likely to have median dieldrin concentrations in edible fish tissue that are below the EPA screening 

value at 1:50,000 cancer risk. 

In figure 7A, streams with predicted dieldrin concentrations greater than 25 μg/kg (red and orange shaded streams) 

are mostly concentrated in the Corn Belt, with scattered 

patches in Texas, the southeast, urbanized parts of the northeast, and California. If measured in edible fish tissue, dieldrin concentrations greater than 25 μg/kg would exceed the 

EPA recommended screening value for 1 in 10,000 cancer 

risk, and concentrations in the range of 5–25 μg/kg (yellow 

shaded streams) would exceed the EPA screening value for 

1 in 50,000 cancer risk. Exactly what constitutes an acceptable maximum level of cancer risk is not clearly defined by 

EPA. Many state and EPA programs use a cancer risk level of 

between 1 in 100,000 and 1 in 1,000,000. Certainly, cancer 

risks exceeding 1:10,000 are higher than the maximum acceptable cancer risk levels used by most state and EPA programs 

(Nowell and Resek, 1994). Again, this comparison must be 

qualified because predicted dieldrin concentrations in whole 

fish probably overestimate concentrations in edible fish. In 

streams where whole-fish dieldrin concentrations are predicted 

by the model to exceed EPA screening values at 1:10,000 

(red and orange streams) or 1:50,000 (yellow streams), it is 

not clear that concentrations in edible fish also would exceed 

screening values. However, for such streams that are actively 

fished, the extrapolation results can be used to guide future 

monitoring and assessment priorities for measuring dieldrin in 

the edible tissues of game fish.

22 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
Significance to Fish-Eating Wildlife

The extrapolation shown in figure 7B assumes a fish 

lipid content of 6.2 percent lipid (to represent the average 

lipid content of all whole fish, regardless of species). Because 

wildlife consume many fish species and typically eat the entire 

fish, it is appropriate to directly compare model predictions at 

6.2 percent lipid with whole-fish guidelines for protection of 

wildlife.

The New York wildlife guideline used in this analysis 

(120 μg/kg dieldrin) is intended to protect fish-eating birds and 

mammals from adverse effects other than cancer, as noted previously. Relatively few streams are predicted to have median 

concentrations that exceed this dieldrin guideline (mapped in 

red in fig. 7B) and these few are in the Corn Belt, where past 

use of aldrin and dieldrin in agriculture was highest.

Uncertainty in Model Predictions

In the preceding model predictions, it is important to 

consider uncertainty, especially when comparing predicted 

concentrations with human-health or aquatic-life guidelines. 

In figures 7A and B, measured fish samples (at the appropriate 

lipid content) will have a 50 percent probability of exceeding 

the predicted concentration. For example, even for dark blue 

streams in figure 7A, where the predicted median concentration is <5 μg/kg, this means that 50 percent of streams are 

expected to have whole-fish concentrations greater than this 

predicted concentration, and some may in fact be greater than 

EPA screening values of 2.5 or even 25 μg/kg, which are associated with cancer risk levels of 1:100,000 and 1:10,000, 

respectively.

The maps in figures 8A and 8B and 9 help put this 

uncertainty into perspective by showing the probability 

of streams having whole-fish concentrations that exceed a 

5, 186,010 mi

5–25, 186,001 mi

25–50, 164,778 mi

50–75, 68,070 mi

75, 45,078 mi

Probability class, with river miles

EXPLANATION

A

Figure 8. Probability of streams having whole-fish samples that exceed (A) 5 μg/kg (which 

is the reporting level for dieldrin in whole fish and also corresponds to an EPA screening 

value at a cancer risk of 1:50,000) and (B) 25 μg/kg (which corresponds to the EPA screening value at a cancer risk of 1:10,000). In both maps, whole-fish samples are assumed to 

have 4.2 percent lipid content (the mean value for largemouth bass sampled by the NCBP 

and NAWQA programs combined). The EPA screening values apply to edible fish tissue. 

mi, mile; μg/kg, microgram per kilogram. 

Results and Discussion 23
specific guideline concentration. The maps in figure 8A and 

8B assume a lipid content of 4.2 percent and relate to human 

health guidelines, whereas the map in figure 9 assumes a lipid 

content of 6.2 percent and relates to potential effects on fisheating wildlife.

The maps in figure 8 show the probability of streams 

having whole-fish concentrations (at 4.2 percent lipid content) 

that exceed 5 μg/kg (fig. 8A) and 25 μg/kg for dieldrin (fig. 

8B). For dark blue streams in figure 8A, whole-fish samples 

(with 4.2 percent lipid) have less than a 5 percent probability 

of exceeding 5 μg/kg; in other words, fewer than 1 out of 20 

dark blue streams are expected to have whole-fish dieldrin 

concentrations greater than 5 μg/kg. In edible fish, a 5-μg/kg 

concentration would correspond to a maximum acceptable 

cancer risk of 1 in 50,000. Dark blue streams account for 

only 29 percent of total stream miles in the United States. 

The remaining 71 percent of total stream miles have at least 

a 5 percent probability of whole-fish concentrations exceeding 5 μg/kg. Of these streams, whole-fish concentrations are 

expected to exceed 5 μg/kg in more than 1 out of 20 light blue 

streams (>5 percent probability), more than 1 out of 4 yellow 

streams (>25 percent probability), more than half of orange 

streams (>50 percent probability), and more than 3 out of 4 

red streams (>75 percent probability). Orange and red streams, 

which have at least a 50 percent probability of whole-fish 

concentrations exceeding 5 μg/kg, make up 17 percent of total 

river miles and are located mostly in the Corn Belt, parts of 

Texas and California, and scattered urban areas.

Far fewer streams are likely to have whole-fish samples 

(at 4.2 percent lipid) with dieldrin concentrations exceeding 

25 μg/kg, as shown in figure 8B. In edible fish tissue, a 

concentration of 25 μg/kg would correspond to the EPA 

screening value at a cancer risk of 1:10,000. Here, most 

streams (75 percent of stream miles) are shaded dark blue and 

have less than a 5 percent probability of whole-fish concentrations exceeding 25 μg/kg. Of the remaining streams, more than 

1 out of 20 light blue streams (>5 percent probability), more 

than 1 out of 4 yellow streams (>25 percent probability), more 

than half of orange streams (>50 percent probability), and 

more than 3 out of 4 red streams (>75 percent probability) are 

expected to have whole-fish concentrations that exceed 

25 μg/kg. Orange and red streams account for only about 

2 percent of total stream miles, and they are located mostly 

in the Corn Belt. Again, these maps show the probability of 

whole-fish samples exceeding the predicted concentrations—

edible fish tissue is likely to have lower lipid levels and lower 

dieldrin concentrations than in the whole fish.

Figure 8. Continued.

24 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams

5, 485,836 mi

5–25, 121,717 mi

25–50, 26,449 mi

50–75, 13,317 mi

75, 2,618 mi

Probability class, with river miles

EXPLANATION

B
Figure 9 relates to potential effects on fish-eating wildlife. This map shows the probability of streams that have 

whole-fish concentrations (at 6.2 percent lipid content) greater 

than the New York wildlife guideline of 120 μg/kg. Most 

streams (about 94 percent of total stream miles) are shaded 

dark blue and have less than a 5 percent probability of wholefish samples (at 6.2 percent lipid) exceeding 120 μg/kg—in 

other words, fewer than 1 out of 20 dark blue streams are 

expected to exceed the wildlife guideline. More than 1 out of 

20 light blue streams (> 5 percent probability), more than 1 

out of 4 yellow streams (>25 percent probability), more than 

half of orange streams (>50 percent probability), and 3 out of 

4 red streams (>75 percent probability) are expected to exceed 

the New York wildlife guideline. Orange and red streams—for 

the most part located in the Corn Belt—account for less than 

1 percent of total stream miles. Fish with a lipid content 

greater than 6.2 percent would likely have higher dieldrin 

concentrations than those predicted by the model; conversely, 

fish with a lower lipid content would likely have lower dieldrin 

concentrations than predicted by the model.

Although dieldrin residues in fish have declined substantially since the 1960s (Schmitt and others, 1999; Nowell and 

others, 1999), the recent NAWQA data and results of these 

extrapolations suggest that detectable residues are present in 

some streams across the country at levels sufficient to be of 

potential human health or wildlife concern. A number of fish 

consumption advisories for dieldrin have been rescinded in recent years, consistent with the national declining trends. As of 

2004, there were still 22 active fish consumption advisories for 

dieldrin affecting seven states within the United States (U.S. 

Environmental Protection Agency, 2005). Models such as the 

one described here for dieldrin may assist in planning future 

monitoring efforts to support the setting or rescinding of fish 

consumption advisories.

5, 609,715 mi

5–25, 33,290 mi

25–50, 6,305 mi

50–75, 602 mi

75, 25 mi

Probability class, with river miles

EXPLANATION

Figure 9. Probability of streams having whole-fish concentrations greater than 

120 μg/kg, which is the New York criterion for protection of fish-eating wildlife (noncancer 

effects) from Newell and others (1987). Whole-fish samples are assumed to have 6.2 percent lipid content (the mean value for all whole fish sampled by the NCBP and NAWQA 

programs combined). mi, mile; μg/kg, microgram per kilogram.

Results and Discussion 25
Summary and Conclusions

A regression model was developed for estimation of 

dieldrin concentrations in whole fish in United States streams, 

using nationally available data on watershed characteristics 

and estimates of (or surrogates for) the past uses of aldrin and 

dieldrin. The model also included lipid content (a measured 

parameter) as an explanatory variable. The model accounts 

for 64 percent of the variability in dieldrin concentrations in 

whole fish from the 648 streams used for model development. 

Four variables were the most important and together accounted for 58 percent of the variability. Three of these variables 

have positive coefficients: lipid content, the estimated agricultural use intensity of aldrin and dieldrin per basin area, and a 

weighted termite-urban score (which is a surrogate for past use 

of aldrin and dieldrin in termite control). The fourth important 

variable, total forested land (where past use was probably 

minimal), has a negative coefficient.

The regression model performed well when cross-validation procedures were conducted, in which the NAWQA fish 

data were randomly divided into two subsets (calibration and 

validation data sets); the model was fit to both data sets, and 

there was little difference between predicted and residual errors obtained from the two fitted models that were based on 

the different data subsets.

The regression model was used to predict the median 

dieldrin concentrations in whole fish from unmonitored 

streams across the United States. Because lipid content is 

an explanatory variable in the model, these predictions were 

made by assuming a specific lipid content value. Two nationwide spatial extrapolations were done—at lipid content values 

of 4.2 and 6.2 percent lipid—to assess potential effects on 

human health and wildlife, respectively, from consumption 

of dieldrin-contaminated fish. These lipid values represent 

the average lipid content of whole-body largemouth bass (4.2 

percent) and all species of whole fish (6.2 percent) sampled by 

the NCBP and NAWQA programs, combined.

The extrapolation at 4.2 percent lipid was used to predict 

dieldrin concentrations in whole-body largemouth bass, which 

is a nationally distributed sport fish popular with recreational 

fishers, in unmonitored streams across the U.S. For fish with a 

higher lipid content (such as lake trout, which has an average 

lipid content of 15 percent), whole-fish dieldrin concentrations are likely to be much higher than those predicted for a 

4.2 percent lipid content. On the other hand, people generally 

consume only the edible fish tissue (fillets), which is expected 

to have a lower lipid content and lower dieldrin concentrations 

than in the whole fish. The model should not be used to predict 

specific dieldrin concentrations in edible fish tissue. However, 

it can be a useful screening tool in identifying streams where 

dieldrin contamination may be of potential human health concern. For streams that are actively fished and where predicted 

whole-body dieldrin concentrations are greater than EPA 

screening values for human consumption of edible fish, it may 

be worthwhile to monitor dieldrin residues in game-fish fillets 

from these streams. A substantial fraction of streams have at 

least a 5 percent probability (1 in 20) of whole-fish concentrations exceeding EPA screening values for edible fish consumption that are based on potential carcinogenic effects of 

dieldrin—for 71 percent of total stream miles, 1 in 20 streams 

are expected to have whole-fish concentrations greater than 5 

μg/kg (which has a cancer risk of 1:50,000); and for about 25 

percent of total stream miles, 1 in 20 streams are expected to 

have whole-fish concentrations greater than 25 μg/kg (which 

has a cancer risk of 1:10,000). The streams with the highest 

probability of exceeding EPA screening values are located primarily in the Corn Belt, where aldrin and dieldrin were used 

in the past on corn, and in scattered areas across the United 

States, especially in the urbanized northeast, the southeast, 

Texas, and California.

The extrapolation at 6.2 percent lipid was used to predict 

dieldrin concentrations in whole fish from unmonitored 

streams across the United States, to represent fish that may be 

consumed by wildlife. Predicted concentrations were compared with the fish flesh criterion for protection of fish-eating 

wildlife (protective of noncancer effects) from the State of 

New York. Relatively few streams—located predominantly 

in the Corn Belt—were predicted to have median dieldrin 

concentrations that exceeded the New York wildlife guideline 

of 120 μg/kg. For most unmonitored streams in the United 

States (94 percent of total stream miles), fewer than 1 out of 

20 streams are expected to exceed the wildlife guideline for 

dieldrin.

26 Regression Model for Explaining and Predicting Concentrations of Dieldrin in Whole Fish from United States Streams
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—U.S. Geological Survey Scientific Investigations Report 2006–5020