Stressor Identification (SI) at 

Contaminated Sites: Upper 

Arkansas River, Colorado 

National Center for Environmental Assessment 

Office of Research and Development, Washington DC 20460 

EPA/600/R-08/029 | January 2011 | www.epa.gov/ncea 
[This page intentionally left blank.] 
EPA/600/R-08/029

January 2011

Stressor Identification (SI) at 

Contaminated Sites: Upper 

Arkansas River, Colorado

National Center for Environmental Assessment

Office of Research and Development

U.S. Environmental Protection Agency

Cincinnati, OH 45268
ii

NOTICE

The document was produced by the U.S. Environmental Protection Agency’s 

Office of Research and Development. It has been subjected to the Agency’s peer and 

administrative review and has been approved for publication as an EPA document. 

Mention of trade names or commercial products does not constitute endorsement or 

recommendation for use.

ABSTRACT

During the performance of an ecological risk assessment, it is often unclear 

whether observed impairments are due to the contaminants of concern, to other 

contaminants or to other factors such as habitat disruption. In 2000, the U.S. 

Environmental Protection Agency developed a methodology for determining the causes 

of biological impairments termed Stressor Identification (SI), and the utility of the 

methodology is being demonstrated in the context of case studies for the Clean Water 

Act. The Agency has not applied this methodology to a waste site assessment. The 

purpose of this project was to apply the SI process at a terrestrial contaminated site to 

shed light on its utility in such an environment. The site chosen is in a highly 

mineralized area of the Colorado Rocky Mountains, consisting of the 500-year 

floodplain and adjacent irrigated lands of the Upper Arkansas River from the confluence 

of California Gulch to approximately 11 miles downstream. Impairments evaluated were 

barren areas in the floodplain (reduced vegetation), and reduced plant growth and plant 

species richness in meadows irrigated with water from the Upper Arkansas River. After 

a number of candidate causes were considered, the various lines of evidence support 

the interaction of elevated levels of extrinsic metal with decreased pH as the cause of 

the barren areas in the floodplain. Similar evaluation of reduced plant growth and plant 

species richness in the irrigated meadows leads to the conclusion that elevated levels of 

extrinsic metal is the cause. Aspects of the assessment process that may differ 

between aquatic and terrestrial systems include the critical variables that are measured,

degree of development of bioassessment criteria, spatial heterogeneity and linearity of 

physico-chemical factors, and management practices. This project demonstrates the 

usefulness of the SI methodology for terrestrial systems.

Preferred citation:

Kravitz, M. 2010. Stressor Identification (SI) at Contaminated Sites: Upper Arkansas River, Colorado. 

U.S. Environmental Protection Agency, Office of Research and Development, National Center for 

Environmental Assessment, Cincinnati, OH. EPA/600/R-08/029.

Cover photo:

United States Geological Survey 

(http://toxics.usgs.gov/photo_gallery/photos/upper_ark/upper_ark_river_lg.jpg). A view of the Arkansas 

River, Colorado.
iii

TABLE OF CONTENTS

Page

LIST OF TABLES............................................................................................................ v

LIST OF FIGURES........................................................................................................ viii

LIST OF ABBREVIATIONS............................................................................................. x

AUTHORS, CONTRIBUTORS AND REVIEWERS.........................................................xi

ACKNOWLEDGMENTS................................................................................................. xii

1. STEP 1: DEFINE THE CASE .................................................................................. 1

1.1. INTRODUCTION.............................................................................................. 1

1.1.1. Site History ........................................................................................... 2

1.1.2. Riparian Areas...................................................................................... 6

1.1.3. Irrigated Meadows................................................................................ 7

1.2. EVIDENCE OF IMPAIRMENT.......................................................................... 7

1.2.1. Impairment 1: Barren Areas in the Arkansas River Floodplain ............. 7

1.2.2. Impairment 2: Reduced Plant Growth and Reduced Plant 

Species Richness in Irrigated Meadows............................................. 11

1.2.3. Impairment 3: Effects on Livestock..................................................... 11

1.3. REGULATORY AUTHORITY......................................................................... 12

2. STEP 2: LIST CANDIDATE CAUSES ................................................................... 13

2.1. IMPAIRMENTS 1 AND 2 ................................................................................ 13

3. IMPAIRMENTS 1 AND 2: EVALUATE DATA AND IDENTIFY PROBABLE 

CAUSES................................................................................................................. 16

3.1. IMPAIRMENT 1: ARKANSAS RIVER FLOODPLAIN BARREN AREAS........ 16

Step 3: Evaluate Data from the Case ............................................................ 16

A. Fluvial Mine-Waste Deposit Areas............................................... 16

B. Comparison of Barren Areas with Nearby Vegetated Areas........ 19

C. Field and Greenhouse—Manipulation of Exposure ..................... 21

Step 4: Evaluate Data from Elsewhere.......................................................... 32

Step 5: Identify Probable Cause.................................................................... 34

A. Characterize Causes: Eliminate................................................... 34

B. Characterize Causes: Strength of Evidence ................................ 38

C. Characterize Cause: Probable Cause ......................................... 44

3.2. IMPAIRMENT 2: REDUCED PLANT GROWTH AND PLANT SPECIES 

RICHNESS IN IRRIGATED MEADOWS........................................................ 45

Step 3: Evaluate Data from the Case ............................................................ 45

A. Soil Characterizations and Laboratory Phytotoxicity Testing ....... 45

B. Statistical Analysis of Laboratory Phytotoxicity Tests .................. 50

C. Soil and Vegetation Data in Irrigated Meadow Areas: Field 

Studies......................................................................................... 59
iv

TABLE OF CONTENTS cont.

Step 4: Evaluate Data from Elsewhere........................................................... 71

A. Verification of Prediction .............................................................. 71

B. Comparison to Phytotoxicity Screening Levels............................ 72

C. Indicator Species......................................................................... 72

Step 5: Identify Probable Cause.................................................................... 76

A. Characterize Causes: Eliminate................................................... 76

B. Characterize Causes: Strength of Evidence ................................ 79

C. Characterize Cause: Probable Cause ......................................... 83

4. LESSONS LEARNED............................................................................................. 85

4.1. INTRODUCTION............................................................................................ 85

4.2. CRITICAL VARIABLES .................................................................................. 85

4.3. BIOASSESSMENT CRITERIA ARE NOT WELL DEVELOPED FOR 

PLANTS ......................................................................................................... 86

4.4. SPATIAL SCALE ISSUES.............................................................................. 88

4.5. MULTIDIMENSIONAL.................................................................................... 89

4.6. HUMAN MANAGEMENT................................................................................ 89

4.7. SOIL MICROBIAL PROCESSES ................................................................... 89

4.8. BINARY DISTRIBUTION OF PHYTOTOXICITY............................................ 90

4.8.1. Statistical Analysis of Vegetation Data ............................................... 90

4.9. REMOTE SENSING....................................................................................... 91

4.10.CONCLUSION................................................................................................ 91

5. REFERENCES....................................................................................................... 92

APPENDIX A: DESCRIPTION OF DOWNSTREAM REACHES ALONG THE 

UPPER ARKANSAS RIVER FLOODPLAIN.......................................... 99

APPENDIX B: BOX-AND-WHISKER PLOTS............................................................. 101
v

LIST OF TABLES 

No. Title Page

1. Summary of Vegetation Coverage of Fluvial Mine-Waste Deposits in the 

Upper Arkansas River Floodplain....................................................................... 18

2. Comparison of Vegetation Coverage and Average Metal Concentration in 

Fluvial Mine-Waste Deposits .............................................................................. 18

3. Concentration Ranges of Selected Elements from Eleven Barren Fluvial 

Tailings Deposits and Two Nearby Vegetated Area Background Soil 

Samples from the Upper Arkansas River Floodplain and Mean 

Background Concentrations for Soils in the Western United States................... 20

4. Total Metals and pH of Amended Fluvial Mine-Waste Deposits Along the 

Upper Arkansas River, Leadville, CO, After Adding a Mixture of Municipal 

Biosolids and Agricultural Limestone in 1998 ..................................................... 22

5. Emergence and Growth of Rye Grass in a Greenhouse in Soil Collected 

from the Field...................................................................................................... 23

6. Metal Concentration (mg/kg dry wt) in Aboveground Tissue of Ryegrass 

Grown in a Greenhouse in Plots Containing (1) Amended Fluvial MineWaste Material Collected from the Field, (2) Soils from a UUC and a CVA 

in 1999 and 2000................................................................................................ 24

7. Means (± SE) of Cd, Pb and Zn (mg/kg dry wt) in Aboveground Tissue of 

Plant Samples Collected in 2000 and 2001 from UUC, CVA and Amended 

Areas in Leadville, CO........................................................................................ 25

8. Plant Families and Number of Species Identified at the UUC, CVA and an 

Amended Fluvial Mine-Waste Deposit, Two Years after Amendment 

Addition and Seeding with a Mixture of Native Plant Species ............................ 26

9. Characterization of Fluvial Mine-Waste Deposits Collected from a Deposit 

on the Bank of the Arkansas River and Composted Biosolids from Summit 

County, CO......................................................................................................... 27

10. Metal-Chelating Cation Exchange Resin-Extractable Metal Concentrations 

(mg/kg dry wt) as Affected by Lime, Lime Plus Organic Matter and Organic 

Matter Soil Amendments .................................................................................... 28

11. Metal Concentrations (mg/kg dry wt) in Aboveground Plant Tissue from 

Geyer Willow as Affected by Lime, Lime Plus Organic Matter and Organic 

Matter Soil Amendments Incorporated into Fluvial Mine-Waste Deposits .......... 29

12. Trends in Willow Tissue Metal Concentrations and Growth as a Result of 

Lime and Organic Matter Soil Amendments Incorporated into Fluvial Mine 

Waste Deposits .................................................................................................. 29
vi

LIST OF TABLES cont.

No. Title Page

13. Soil pH, 1.0 M KCl Exchangeable Metal Concentrations (mg/kg dry wt), 

and Available Phosphorus (P) Concentrations as Affected by Lime, 

Organic Matter (OM), Lime Plus Organic Matter (LOM) and Triple-SuperPhosphate (TSP) Soil Amendments ................................................................... 30

14. Survival and Growth Indices from Field Plots for Geyer Willow and Grass 

Species—Tufted Hairgrass and Creeping Bentgrass—as Affected by 

Lime, Organic Matter (OM), Lime Plus Organic Matter (LOM) and TripleSuper-Phosphate (TSP) Soil Amendments ........................................................ 31

15. Results of Metal Analyses, pH Measurements and Echinochloa crusgalli

Emergence Performed on Soils Collected Along a Transect Perpendicular 

to a Barren Area Boundary on the Clark Fork River ........................................... 33

16. Comparison of Soil Concentrations of Metals and Arsenic Observed in the 

Floodplain of the Upper Arkansas River to Published Phytotoxicity 

Thresholds.......................................................................................................... 34

17. Considerations for Determining Whether or not Candidate Causes Should 

be Eliminated for Impairment 1........................................................................... 35

18. Strength of Evidence for Specific Causes and Considerations for Barren 

Areas in the Upper Arkansas River Floodplain................................................... 39

19. Range of Element Concentrations, pH and Total Organic Carbon Found at 

the 126 Phase I Soil Sampling Stations.............................................................. 47

21. Site-Specific Laboratory Data............................................................................. 51

22. Correlation Among Candidate Measurement Endpoints..................................... 53

23. Posterior Estimates of the Model Parameters .................................................... 55

24. Distribution of Pi.................................................................................................. 56

25. Weighted-Average Total Metals (g/m3), pH and Organic Carbon (g/kg) to a 

Depth of 30 cm at the Reference (TP) and Impaired Meadow Sites................... 61

26. Plant Species, Percent Ground Cover and Plant Metal Concentrations 

(mg/kg dry wt) from Reference and Impaired Meadow Sites.............................. 63

27. Total Soil Metal Concentrations (mg/kg dry wt) Observed in Impaired 

Meadow on E. Seppi Property............................................................................ 64

28. Comparison of Soil Concentrations (mg/kg dry wt) of Metals and Arsenic 

Observed in Irrigated Meadows of the Upper Arkansas River to Published 

Thresholds for Phytotoxicity................................................................................ 75
vii

LIST OF TABLES cont.

No. Title Page

29. Considerations for Determining Whether or not Candidate Causes Should 

be Eliminated for Impairment 2........................................................................... 77

30. Strength of Evidence for Specific Causes and Considerations for Impaired 

Irrigated Meadows .............................................................................................. 80
viii

LIST OF FIGURES 

No. Title Page

1. Location of the 11-Mile Reach of the Arkansas River........................................... 3

2. Study Area Terrain Map ....................................................................................... 4

3. The 500-Year Floodplain and Adjacent Irrigated Meadows Area of the 

Upper Arkansas River .......................................................................................... 5

4. Fluvial Mine-Waste Deposit Locations in Reaches 1 and 2 Along the 

Arkansas River Floodplain.................................................................................... 8

5. Fluvial Mine-Waste Deposit Locations in Reaches 2 and 3 Along the 

Arkansas River Floodplain.................................................................................... 9

6. Fluvial Mine-Waste Deposit Locations in Reach 3 Along the Arkansas 

River Floodplain.................................................................................................. 10

7. Conceptual Model for Seven Candidate Causes for Impairments of Plant 

Growth and Plant Species Richness .................................................................. 14

8. Phase I and Phase II Soil Sampling Locations ................................................... 46

9. Distribution of Zinc and Soil pH in the 11-Mile Reach of the Arkansas 

River Valley and Sources of Irrigation Water...................................................... 48

10. Distribution of Pi for Zinc and Copper Models for the Seven Upper 

Arkansas River Valley Sites Considered to be Nonphytotoxic............................ 57

11. Distribution of Pi for Zinc and Copper Models for the 13 Upper Arkansas 

River Valley Sites Considered to be Phytotoxic.................................................. 58

12. Study Site Locations........................................................................................... 60

13. Soil and Plant Sampling Locations ..................................................................... 65

14. Plant Aboveground Biomass Versus Soil Total Zinc........................................... 67

15. Plant Aboveground Biomass Versus Soil Total Zinc........................................... 67

16. Plant Aboveground Biomass Versus Soil Ammonium Bicarbonate DTPA 

Extractable Zinc.................................................................................................. 68

17. Plant Species Richness Versus Soil Total Zinc .................................................. 69

18. Plant Species Richness Versus Soil Ammonium Bicarbonate DTPA 

Extractable Zinc.................................................................................................. 70

19. Zinc Concentration in Above-Ground Plant Parts of Grasses and GrassLike Species Versus Soil Total Zinc ................................................................... 71

20. Zinc Concentration in Above-Ground Plant Parts of Grasses and GrassLike Species Versus Soil Ammonium Bicarbonate DTPA Extractable Zinc........ 71
ix

LIST OF FIGURES cont.

No. Title Page

21. Forage Yield Sampling Locations....................................................................... 73

22. Comparison of Measured Forage Yield (g/m2) with Estimated Soil Zinc 

Concentrations ................................................................................................... 74
x

LIST OF ABBREVIATIONS 

ASTM American Society for Testing and Materials

AVIRIS Airborne Visible and Infra-Red Imaging Spectrometer

BA Smith ranch

BLM Biotic Ligand Model

CaCO3 lime

CEC cation-exchange capacity

CV coefficient of variation

CVA contaminated vegetated area

DIC deviance information criterion

DTPA diethylenetriaminepentaacetate

GLIM generalized linear model

lpH laboratory measured pH

NPL National Priority List

NRCS Natural Resources Conservation Service

RI/FS Remedial Investigation/Feasibility Study

RipES Riparian Evaluation System

SB Seppi ranch

SC Seppi ranch

SD Seppi ranch

SI stressor identification

TCLP toxicity characteristic leaching procedure

TKN total Kjeldahl-N 

TP Tennessee Park

TSP triple-super-phosphate

U.S. EPA U.S. Environmental Protection Agency

UAR Upper Arkansas River

USFWS U.S. Fish and Wildlife Service

USGS U.S. Geological Survey

UUC upstream uncontaminated reference site
xi

AUTHORS, CONTRIBUTORS AND REVIEWERS

AUTHORS

Michael Kravitz

U.S. Environmental Protection Agency

Office of Research and Development

National Center for Environmental Assessment

Cincinnati, OH 45268

CONTRIBUTING AUTHORS

David Eskew

Oneida Total Integrated Enterprises

Oak Ridge, TN 37830

William Warren-Hicks

EcoStat, Inc.

Mebane, NC 27302-0425

Larry Kapustka

Ecological Planning and Toxicology, Inc.

Corvallis, OR 97333-9540

REVIEWERS

Anne Fairbrother

U.S. Environmental Protection Agency

Office of Research and Development

National Health and Environmental Effects Research Laboratory

Corvallis, OR 97339

(Presently at Exponent, Bellevue, WA 98007) 

Sharon K. Taylor

U.S. Fish & Wildlife Service

Environmental Contaminants Division

Carlsbad, CA 92011

EXTERNAL REVIEWERS

Rebecca A. Efroymson

Oak Ridge National Laboratory

Environmental Sciences Division

Oak Ridge, TN 37831
xii

AUTHORS, CONTRIBUTORS AND REVIEWERS cont.

Roman Lanno

Ohio State University

Columbus, OH 43210

Charles A. Menzie

Exponent Engineering and Scientific Consulting

Alexandria, VA 22314

ACKNOWLEDGMENTS

Dale Hoff, EPA Region 8 (presently at EPA’s National Health and Environment Effects 

Research Laboratory, ORD), and Dan Wall, U.S. Fish and Wildlife Service Denver, CO 

(presently at EPA Region 8) provided data and valuable feedback in the preparation of 

this document. I would also like to acknowledge the following scientists from EPA’s 

National Center for Environmental Assessment (NCEA), ORD for their contributions. 

Kate Schofield prepared the original draft of the conceptual model that appears in this 

document. David Farrar, Jeff Swartout and Michael Griffith assisted with some of the 

statistical analyses. Glenn Suter and Michael Troyer provided useful comments and 

insight throughout the development of this project.
1 

1. STEP 1: DEFINE THE CASE

1.1. INTRODUCTION 

With the development of biological survey and bioassessment methods, it is 

increasingly common for ecological risk assessments for contaminated sites to include 

descriptions of the actual biological impairment. However, whether those observed 

impairments are due to the contaminants of concern, to other contaminants or to other 

factors such as habitat disruption is often unclear. Without a formal, defensible method 

to determine causes, the use of bioassessment results in baseline risk assessments 

may be challenged. The United States Environmental Protection Agency (U.S. EPA or 

the Agency) has developed a methodology for determining the causes of biological 

impairments termed Stressor Identification (SI; U.S. EPA, 2000), and the utility of the 

methodology is being demonstrated in the context of case studies for the Clean Water 

Act. While the methodology is being applied by consultants in waste site assessments, 

the Agency has not addressed this application. The purpose of this project is to apply 

the SI process at a terrestrial contaminated site to shed light on its utility in such an 

environment. Although there is no a priori reason that the methodology for performing a 

causal assessment should differ between aquatic and terrestrial systems, there are

inherent differences between these environments. Aspects of the assessment process 

that may differ between aquatic and terrestrial systems include the critical variables that 

are measured, degree of development of bioassessment criteria, spatial heterogeneity 

and linearity of physico-chemical factors, and management practices. This causal 

analysis was performed using the procedure in the SI Guidance Document (U.S. EPA, 

2000), and updated with information from U.S. EPA’s current procedure for causal 

analysis at http://www.epa.gov/caddis. Another terrestrial case study, focusing on an oil 

field in California, is also available (see www.epa.gov/caddis, U.S. EPA, 2008).

The SI process follows five steps that conclude with the identification of a 

probable cause. These steps are (1) Define the case, (2) List the candidate causes, 

(3) Evaluate data from the case, (4) Evaluate data from elsewhere and (5) Identify 

probable cause. The first two steps are Chapters 1 and 2, respectively. Steps 3, 4 and 

5 are contained within Chapter 3. The lessons learned from performing this case study 

are summarized in Chapter 4.

The site chosen for this case study is located in a highly mineralized area of the 

Colorado Rocky Mountains, approximately 100 miles southwest of Denver, CO (see 

Figure 1). It is comprised of the 500-year floodplain and adjacent irrigated lands of the 

Upper Arkansas River from the confluence of California Gulch to approximately 11 miles 

downstream (see Figures 2 and 3). Beyond this point, the Arkansas River is constricted 

into a canyon. Part of the 11 miles overlaps the California Gulch National Priority List 

(NPL) Site. The elevation at the confluence of California Gulch with the Arkansas River 

is 9515 feet above mean sea level. The impairments evaluated in this study were 

barren areas in the floodplain (reduced vegetation), and reduced plant growth and plant 

species richness in meadows irrigated with water from the Upper Arkansas River. 

Following consideration of a number of candidate causes and lines of evidence, the 

probable cause of each impairment was determined through a strength of evidence 
2 

analysis that uses all the evidence generated in the analysis phases to examine the 

credibility of likely candidate causes. The causal considerations for the strength of 

evidence analyses use three types of evidence: case-specific evidence, evidence from 

other situations or biological knowledge, and evidence based on multiple lines of 

evidence.

1.1.1. Site History

The site is located in a highly mineralized area of the Colorado Rocky Mountains. 

The most prominent minerals in the ore deposits include iron (Fe), manganese (Mn), 

zinc (Zn), lead (Pb), copper (Cu), and small amounts of gold (Au) and silver (Ag) 

(U.S. EPA, 2004a). Mining, mineral processing and smelting activities have produced 

gold, silver, lead, manganese and zinc for more than 130 years. Mining began in the 

Leadville area in 1859 when prospectors working channels of Arkansas River tributaries 

discovered gold at the mouth of California Gulch. Initial activities consisted only of 

small-scale placer mining until 1868 when the first gold ore veins were discovered along 

California Gulch. However, by 1872, problems with water transportation and labor 

made ore removal so difficult that most miners left the area. In 1874, silver-bearing lead 

carbonate was discovered and mining in the Leadville district boomed (U.S. EPA, 

2004a).

Extensive deposits of lead, silver and gold ores associated with fissure veins 

were discovered and mined. Zinc and manganese, which were of little value in the early 

days, later were mined extensively. As surface veins diminished, miners tunneled 

deeper into the mountains. As underground mines were established, groundwater had 

to be pumped out continuously. In 1889, the Yak Tunnel was constructed to drain water 

from the mines. Through the years, the Yak Tunnel was extended more than three 

miles eastward to drain other underground mines. Until 1992, untreated water from the 

Yak Tunnel drained directly into California Gulch (U.S. EPA, 2004a; U.S. EPA Region 8 

and USFWS, 2003).

As mines were developed, waste rock was excavated along with the ore. The 

waste rock was placed near the mine entrance, and the ore was transported to the mill. 

At the mill, ores were crushed and separated into metallic concentrates and waste 

products by physical processes. The metallic concentrates were then shipped 

elsewhere or further processed at a smelter in the area. Forty-four different smelters 

are known to have operated in the California Gulch area at various times. Because of 

the crude methodology, the tailings from these operations had relatively high residual 

metal content and were coarse sand to gravel-sized particles. The lack of area for 

storage of milling wastes led to the release of these wastes directly into tributary 

drainages to California Gulch as a method for disposal. This resulted in downstream 

movement of mine waste during periods of normal flow and large transport events 

during flooding (U.S. EPA, 2004a; U.S. EPA Region 8 and USFWS, 2003). Mining 

activity occurred in most of the watersheds in the region, both upstream and 

downstream of the California Gulch confluence with the Arkansas River, but the 

greatest concentration of mining activity took place in the California Gulch watershed.
3

FIGURE 1. Location of the 11-Mile Reach of the Arkansas River

Note: Figure after CDM, 1997.
4

FIGURE 2. Study Area Terrain Map
California Gulch

Note: Figure after U.S. EPA, 2004a 

FIGURE 3. The 500-Year Floodplain and Adjacent Irrigated Meadows 

Area (OU11) of the Upper Arkansas River

5
6 

1.1.2. Riparian Areas

Releases of mine tailings into the upper Arkansas River floodplain have occurred 

and continue to occur as a result of releases from sources within the California Gulch 

drainage area in Leadville, CO (USFWS, 2002). Fluvial mine tailing deposits are found 

at 153 sites along the floodplain of the upper Arkansas River. They cover an area of 

2,829,911 ft2 (64 acres) and have an estimated volume of 2,698,514 ft3 (see Figures 4 

through 6).

An upstream area referred to as Reach 0 was evaluated as a relatively 

less-impacted reference area. The 11-mile reach of the upper Arkansas River 

floodplain was divided into four sub-reaches (Reach 1, 2, 3 and 4) based on the 

characteristics of the channel and of the fluvial mine-waste deposits (USFWS, 2002).

Reach 0. The upstream reference area, Reach 0, includes portions of 

Tennessee Creek and the east fork of the Arkansas River upstream from their 

confluence, and the Arkansas River extending downstream to just upstream of the 

confluence of California Gulch (see uppermost portion of Figure 4). Mining also 

occurred in the watersheds of both Tennessee Creek and the east fork of the Arkansas 

River. Consequently, Reach 0 has been influenced by mining activity. The primary 

mining activity in the Tennessee Creek drainage occurred in St. Kevin’s Gulch, but there 

are also other abandoned mines in the Tennessee Creek drainage. Although mining 

has occurred at numerous locations in the east fork Arkansas River drainage, the 

primary mining influence has likely been the discharge of water from the Leadville Mine 

Drainage Tunnel. The upstream reach (Reach 0) was characterized as a point of 

comparison to establish baseline conditions for the 11-mile reach, with the realization 

that mine wastes have affected this area. Despite the mining influences upstream of 

California Gulch, no fluvial mine-waste deposits were found within Reach 0 

(Keammerer, 1987).

Vegetation in Reach 0 is dominated by a riparian shrub community consisting 

primarily of willow species, interspersed with open water wetlands and grasses. The 

uplands are dominated by herbaceous riparian vegetation consisting of sedges, rushes 

and mesic grasses representative of moist soils. These areas are interspersed with 

upland grasses (CDOW, 1988).

Downstream Reaches. Fluvial deposits in the first few miles downstream of 

California Gulch appear to be older, coarser mine wastes than those found further 

downstream in the 11-mile reach (see Reach 1 in Figure 4). Approximately three miles 

downstream of California Gulch, Lake Fork Creek joins the Arkansas River. The flow 

from Lake Fork Creek is augmented by large volumes of water diverted from the 

western slope of the Rocky Mountains. The dilution effects of the augmented flow are 

significant, resulting in reductions of metal concentrations in the Arkansas River. Water 

quality continues to improve downstream as the flow from more tributaries dilutes the 

contaminated water of the Arkansas River (USFWS, 2002). 
7 

For the next several miles downstream from the Lake Fork Creek confluence, the 

floodplain broadens (see Reach 2 in Figure 4). The volume of tailings deposits per 

stream length is less than that upstream of Lake Fork, probably due to the increased 

flow capacity of the channel in this area, which would reduce the frequency of overbank 

flow conditions. Further downstream, however, the number of deposits increases as the 

channel becomes shallower, making the creek more susceptible to overbank flow (see 

Reach 3 in Figures 5 and 6). Over the remaining length of the 11-mile reach, the 

floodplain generally narrows, resulting in increased flow velocity and less deposition of 

mine waste sediments (see Reach 4 in Figure 6). Only a few, small deposits are 

present along this section of the 11-mile reach (USFWS, 2002). Details on the 

vegetation of the downstream reaches of the upper Arkansas River floodplain are 

contained in Appendix A.

1.1.3. Irrigated Meadows

Soils suitable for irrigated pasture and hay production are found just outside the 

500-year floodplain of the Upper Arkansas River in several locations along the upper, 

middle and lower parts of the 11-mile reach (Fletcher, 1975). Areas of Newfork gravelly 

sandy loam and Rosane loam typically are found in association with each other. Small 

areas of marsh are included in the areas of Newfork and Rosane soils. Several areas 

of Newfork-Marsh-Rosane soil association have been irrigated with water from 

California Gulch, the Upper Arkansas River and/or from other tributaries. Nearly all 

areas of Rosane loam soil are flood irrigated and used as pasture grazed by cattle. 

Newfork gravelly sandy loam soil occurs on low terraces in Lake County. Most areas of 

this soil are in irrigated meadow that is cut for hay and grazed early and late in the 

growing season. The marsh soil areas are of little grazing value and are used by 

wildlife for food and protection.

1.2. EVIDENCE OF IMPAIRMENT

1.2.1. Impairment 1: Barren Areas in the Arkansas River Floodplain

A total of 153 separate fluvial mine waste deposits with an overall area of 

64 acres have been delineated in the 500-year floodplain of the upper Arkansas River 

along the 11-mile reach (USFWS, 2002). Growth of vegetation on these deposits is 

impaired. The vegetative cover is less than 10% on 42 of the deposits, between 

10−50% on 84 deposits, and greater than 50% on 27 deposits (Table 1 in Chapter 3). 

Outside the fluvial mine waste deposits, the vegetative cover, biomass and number of 

plant species on riparian (floodplain) soils below the confluence of California Gulch are 

equal to or greater than those upstream of California Gulch (USFWS, 2002).
8 

FIGURE 4. Fluvial Mine-Waste Deposit Locations in Reaches 1 and 2 

Along the Arkansas River Floodplain. The confluence of Lake Fork Creek 

and the Arkansas River marks the boundary between Reach 1 (upstream, 

or North) and Reach 2 (downstream, or South). Reach 0, the upstream 

reference area, is North of California Gulch

Note: Figure after U.S. EPA, 2004a.
FIGURE 5. Fluvial Mine-Waste Deposit Locations in Reaches 2 and 3 

Along the Arkansas River Floodplain. The highway (24) crossing the 

Arkansas River marks the boundary between Reach 2 (upstream, or 

North) and Reach 3 (downstream, or South)

Note: Figure after U.S. EPA, 2004a.

9
FIGURE 6. Fluvial Mine-Waste Deposit Locations in Reach 3 Along the 

Arkansas River Floodplain. Reach 4 begins just downstream, or South, of 

the confluence of Spring Creek and the Arkansas River where the 

floodplain narrows

Note: Figure after U.S. EPA, 2004a. 

10
11

1.2.2. Impairment 2: Reduced Plant Growth and Reduced Plant Species Richness 

in Irrigated Meadows

Plant growth in the irrigated meadows has also been affected. One ranch shows 

evidence of severely chlorotic plants and barren sections of ground (Levy et al., 1992; 

Swyers, 1990). Aerial photographs of the most affected meadows show areas of barren 

ground intermingled with vegetated areas. The affected area is approximately 

354 contiguous acres. Levy et al. (1992) stated that forage yield in the affected irrigated 

meadow has decreased from 4.48 megagrams/hectare (Mg/ha) (3990 lbs/acre) in 1874 

to 1.68 Mg/ha (1496 lbs/acre) in the early 1990s. Swyers (1990) quoted Dr. Bernard 

Smith, a local veterinarian and rancher, as stating that ranchers had noted decreases in 

hay yield after mining and smelting became widespread in the late 1880s. Swyers 

(1990) conducted an investigation of the extent of metal contamination in the soils of a 

large irrigated meadow on the property of E. Seppi. He reported barren areas that were 

located near an old irrigation ditch. He also reported an area described as “a barren, 

gray and white, crusted wasteland.” This area, however, was located in the flood basin 

near the Arkansas River and may be in a fluvial mine waste deposit area. 

Differences in plant species richness in irrigated pastures above and below the 

confluence of California Gulch with the Arkansas River have also been reported (Levy 

et al., 1992). Nine plant species were observed in an irrigated meadow on a similar soil 

type located upstream of the California Gulch confluence. This upstream meadow had 

received relatively “clean” irrigation water from Tennessee Creek. In contrast, at three 

locations in irrigated meadows just downstream of the California Gulch confluence, only 

three or four plant species were observed at each sampling location. Similar low plant 

species abundance (four species) was observed in a nonirrigated, riparian, floodplain 

area used for grazing, just downstream of the California Gulch confluence (Levy et al., 

1992). Several of the plant species at the upstream location were also present in at 

least one of the downstream locations, but three species, Alpine timothy (Phleum 

alpinium), dandelion (Taraxacum spp.) and clover (Trifolium spp.), were not reported at 

any of the four downstream locations.

1.2.3. Impairment 3: Effects on Livestock

Area ranchers recognized the poor health of livestock raised downstream from 

the California Gulch confluence. The most affected areas were the ranches of the 

Seppi and Smith families, located from the California Gulch confluence to 5.5 miles 

downstream. Livestock grazed on these irrigated meadows suffered from swelling of 

the joints, coat discoloration, and scanty weight gain (Levy et al., 1989). Young colts 

and calves were most likely to develop symptoms of trace metal toxicity through 

ingestion of contaminated forages, but would recover within a few weeks when moved 

to uncontaminated meadows upstream from California Gulch (Levy et al., 1992). Due to 

a paucity of more specific data on this impairment, effects on cattle are not evaluated 

further in this report.
12

1.3. REGULATORY AUTHORITY

The Colorado Water Quality Control Act of 1981, CRS 1973, 25-8-101 et seq. 

(“Act”) designates stream classifications and water quality standards for state waters of 

the Arkansas River Basin. It also contains provisions for establishing basic standards, 

and an antidegradation rule. The basic regulations establish a system for classification 

of state waters according to “the beneficial uses for which they are suitable or are to 

become suitable, and for assigning specific numerical water quality standards according 

to such classifications” (CDPHE, 2004).

Streams and water bodies throughout Colorado have been designated as “High 

Quality—Class 1” and “High Quality—Class 2” where evidence indicates that the 

requirements of Section 3.1.13(1)(e) of the basic regulations contained in the Act have 

been met. The Arkansas River Basin is designated as High Quality—Class 2; however, 

California Gulch was determined to be one of the most degraded streams in Colorado. 

As a result, the State of Colorado Water Quality Commission has adopted only a limited 

set of numeric water quality standards for compliance within the California Gulch. 

Under the State’s Mined Land Program, mining discharge to California Gulch is to be 

eliminated. Goals have been established for cleanup of the site to allow for eventual 

agricultural redesignation.

For existing mining, 40 CFR, Section 440—ore mining and dressing—regulates 

the runoff from mining sites, provides guidelines for reestablishing vegetation on 

exposed soils, and provides guidelines for minimization of runoff. A Metal Mining 

Stormwater Management Plan is required for any operations that may result in direct 

discharge or stormwater runoff from a mining site. This plan must establish procedures 

to determine that both state and federal water quality standards are met.
13

2. STEP 2: LIST CANDIDATE CAUSES

2.1. IMPAIRMENTS 1 AND 2

Seven candidate causes for the observed impairments of plant growth and plant 

species richness in the riparian and irrigated meadows were considered. (Cause #7 is 

only applicable to the irrigated meadows.) The candidate causes are shown in terms of 

a conceptual model (see Figure 7).

1. Increased concentrations of extrinsic metals in the soil may result in 

increased levels of soluble metals and their transport throughout the 

environment. Increased metals in solution may also become bioavailable and 

lead to the increased uptake of metals by plants, resulting in phytotoxicity. Mine 

wastes and/or smelter emissions may have resulted in metal concentrations high 

enough to be phytotoxic, even at neutral pH levels.

2. Interaction of decreased pH and increased extrinsic metals in the soil from 

mining operations may result in increased levels of metals in soil solution 

(Arienzo, 2005). Increased metals in solution may lead to increased uptake of 

metals by plants, resulting in phytotoxicity. Solubility of many metals is increased 

at lower pH levels. Increased soil metal concentrations may interact with 

reduced pH levels due to acid mine drainage or smelter emissions (which are 

also acidic), resulting in bioavailable metal concentrations that are phytotoxic.

3. Interaction of decreased pH and intrinsic (background) metals in the soil may 

result in increased levels of metals in soil solution, causing phytotoxicity. 

Solubility of many metals is increased at lower pH levels. The upper Arkansas 

River area is highly mineralized. Soils derived from these parent materials may 

have high background concentrations of metals. Acid mine drainage or smelter 

emissions could result in decreased pH levels that could result in phytotoxicity at 

background soil metal concentrations. Soil pH levels below 5.5 result in 

increased solubility of aluminum (Al) and Mn that can be toxic to plants at 

background concentrations (Rengel, 2002). (Below pH 5.5, Mn oxides solubilize 

and release Mn2+ into soil solution. At soil pH levels <4.2, Al ions dominate the 

soil solution. At soil pH levels <3.8, Fe2+ becomes the dominant ion.)

4. Decreased soil organic matter results in higher metal bioavailability. Soil 

organic matter binds metal cations, removes them from solution and reduces 

metal uptake (Arienzo, 2005). Decreased soil organic matter, either in mine 

waste deposits or resulting from agricultural tillage, could result in reduced soil 

microbial activity and potentially reduced mycorrhizal colonization of plant roots. 

Mycorrhizal colonization of plant roots enhances plant growth by increasing 

uptake of phosphorus. The low organic matter content in mine waste deposits 

could result in the increased uptake of metals by plants and phytotoxicity. There 

also is evidence that mycorrhizae may reduce metal uptake due to immobilization 

of metals in fungal cell walls (Punshon et al., 2005). Agricultural tillage over a 
14

 

FIGURE 7. Conceptual Model for Seven Candidate Causes for Impairments of Plant Growth and Plant 

Species Richness

Candidate causes are identified by number.

↑ metal 

solubility

source

proximate 

stressor response

KEY

additional steps in 

causal pathway

geology

smelter 

emissions

California 

Gulch

other mine 

sources

hay 

production

grazing/

mowing

↑ mine waste

(solid and dissolved)

↓ pH

↑ metal uptake 

and direct toxicity ↓ mycorrhizae ↓ root growth

↓ nutrient uptake ↓ water uptake

↑ metals in soil ↓ organic matter ↑ soil compaction

↑ herbicides

↓ plant growth

↓ plant species richness

1 4 5

7

6

2 & 3
15

period of years results in more rapid oxidation of soil organic matter and also 

results in lower organic matter. Lowered organic matter content could result in 

increased metal bioavailability and phytotoxicity.

5. Increased soil compaction can reduce plant growth. Cattle grazing and, to a

lesser extent, hay production, particularly when soils are saturated, can result in 

soil compaction. Cattle grazing in a montane riparian ecosystem in northern 

Colorado caused a decrease in infiltration rates and an increase in bulk density 

at 5−10 cm and 10−15 cm depths, though conditions returned to predisturbed 

values within 1 year after grazing events (Wheeler et al., 2002). Normal farming 

operations such as tilling or harvesting can also cause soil compaction (USDA, 

1996). Lastly, soil compaction can arise from differences in soil origin or type, 

e.g., deposited fines. Soil compaction reduces the permeability of the soil 

surface layers, resulting in reduced water availability, reduced nutrient movement 

and reduced availability of oxygen to roots (USDA, 1996). This may reduce root 

growth and thus, lower the availability of nutrients for plant growth. Decreased 

root growth due to soil compaction would reduce water availability for plant 

growth (Unger and Kaspar, 1994).

6. Increased grazing and mowing (for hay production) can affect plant growth or 

species richness (Hickman et al., 2004; Williams et al., 2007). Cattle 

preferentially graze certain plant species (Uresk, 1986), which may reduce their 

abundance. Some plant species are more able to withstand grazing pressure, 

which allows them to increase in abundance in grazed areas (e.g., Kentucky 

bluegrass, Poa pratensis [Schulz and Leininger, 1990]). Similar effects may also 

occur as a result of mowing.

7. Increased herbicide usage to enhance hay production may affect plant growth 

or species composition (Tomkins and Grant, 1977). Repeated applications of 

selective herbicides over a period of years could eliminate some plant species 

and preferentially select for others. High application rates of persistent 

herbicides can also reduce plant growth. This candidate cause is only relevant in 

the irrigated meadows and hence was considered for Impairment 2.
16

3. IMPAIRMENTS 1 AND 2: EVALUATE DATA AND IDENTIFY PROBABLE CAUSES

3.1. IMPAIRMENT 1: ARKANSAS RIVER FLOODPLAIN BARREN AREAS

Data on soil metal concentrations, pH, organic matter content and plant growth 

were used to evaluate associations among the candidate causes and areas of impaired 

plant growth in the Arkansas River floodplain. Information from reports published in 

peer-reviewed journals, U.S. Geological Survey (USGS) reports, Remedial 

Investigation/Feasibility Study (RI/FS) reports, graduate theses and undergraduate 

research reports were used.

Step 3: Evaluate Data from the Case

Several investigations provide evidence on the potential causes of impaired plant 

growth in the upper Arkansas River floodplain. At the request of EPA, URS (1997, 

1998) identified and selected mine-waste deposits from landowner’s input, as well as 

from observations of tailing material on cut banks, dead vegetation and/or metal salts on 

the soil surface. The relative vegetation cover of the mine-waste deposits was 

evaluated on a qualitative scale of 1 to 3 and compared with average metal 

concentrations along the 11-mile reach of the Upper Arkansas River (USFWS, 2003). 

The significance of trends in metal concentrations with distance from the confluence of 

California Gulch was tested statistically. Walton-Day et al. (2000) compared 

concentrations of metals in 11 barren fluvial mine waste deposits to concentrations in 

two nearby vegetated areas. A number of studies that performed experimental 

manipulation of exposure in the lab and field provided particularly strong evidence for 

determining the cause of impaired plant growth (Brown et al., 2005; Fisher, 1999; Fisher 

et al, 2000). Brown et al. (2005) added lime and municipal biosolids to a depth of 20 cm 

into fluvial mine-waste deposits at four sites, and examined resulting metals 

concentrations, pH, organic carbon, total Kjeldahl-N (TKN), and phosphorus. Properties 

of the amended deposits were compared to those from the unamended deposits, an 

upstream uncontaminated reference site (UUC), and a contaminated vegetated area 

(CVA). Brown et al. also conducted phytotoxicity tests using rye grass emergence and 

early growth protocols, measured above-ground plant tissue concentrations, and 

evaluated plant species diversity. Fisher (1999; Fisher et al., 2000) conducted 

greenhouse studies with fluvial mine-waste tailing from the Arkansas River floodplain 

amended with lime, biosolids or lime plus biosolids with Geyer willow cuttings. Fisher 

(1999) also conducted field experiments on effects of lime, biosolids and phosphorus on 

establishment of plant cover (tufted hairgrass and creeping bentgrass) and Geyer 

willow. The results of the above studies are combined to evaluate the strength of 

evidence.

A. Fluvial Mine-Waste Deposit Areas

U.S. EPA charged the Superfund Technical Assessment and Response Team 

with locating and characterizing fluvial mine-waste deposits along 10 miles of the 
17

Arkansas River, with the confluence of California Gulch and the Arkansas River being 

the northern boundary (URS, 1997, 1998). The study focused on fluvial mine-waste 

deposits within 100 feet of the river. Data were collected on location, depth and area of 

each deposit. Samples were analyzed for total metal concentrations. Using these data, 

location information was paired with aerial photographs obtained from geographic 

information system (GIS) to obtain a more accurate estimate of the area of the deposits.

Mine-waste deposits were identified from landowner’s input, as well as from 

observations of tailing material on cut banks, dead vegetation and/or metal salts on the 

soil surface. Some deposits were observed but not studied because they were small, 

had vegetation cover and/or were away from the riverbank (URS, 1997, 1998).

The relative vegetation cover of the mine-waste deposits was evaluated on a 

qualitative scale of 1 to 3. Mine-waste deposits with >50% vegetative cover were rated 

as good and assigned a rating of 1, deposits with 10−50% vegetative cover were rated 

as fair and assigned a rating of 2 and deposits with <10% vegetative cover were rated 

as poor and assigned a rating of 3 (see Tables 1 and 2).

Reach 1. Twenty-four mine-waste deposits were selected for study in Reach 1. 

Among these deposits, there was a total of approximately 887,000 ft3 of mine waste, 

covering a surface area of approximately 785,364 ft2

. The average depth of the 

deposits was 1.1 ft; no deposits averaging greater than 2.0 ft in depth were found. Total 

metal concentrations in the deposits averaged 177 mg/kg for cadmium (Cd), 446 mg/kg 

for Cu, 4228 mg/kg for Pb and 7271 mg/kg for Zn (see Table 2).

Reach 2. Thirty-five mine-waste deposits were identified and sampled in 

Reach 2 (URS, 1998). Among these deposits, there was a total of approximately 

233,389 ft3 of mine waste, covering a surface area of approximately 405,936 ft2. The 

average depth of the deposits was 0.6 ft, and no deposits averaging greater than 1.5 ft 

in depth were found. Total metal concentrations in the deposits averaged 153 mg/kg for 

Cd, 200 mg/kg for Cu, 3266 mg/kg for Pb and 3438 mg/kg for Zn (see Table 2).

Reach 3. Ninety-four mine-waste deposits were identified and sampled in 

Reach-3 (URS, 1998). Among these deposits, there was a total of approximately 

1,578,311 ft3 of mine-waste, covering a surface area of approximately 1,639,612 ft2 in 

Reach 3. The average depth of the deposits was 1 foot. Seven deposits averaging 

over 2 ft in depth were found, with one deposit having an average depth of 3 ft. Total 

metal concentrations in the deposits averaged 129 mg/kg for Cd, 258 mg/kg for Cu, 

3059 mg/kg for Pb and 4926 mg/kg for Zn (see Table 2).

In Reach 3 (not in other reaches), pH values were reported for several of the 

fluvial mine-waste deposits. The pH values in the fluvial deposits in Reach 3 ranged 

from 1.26−5.80 (USFWS, 2002). 
18

TABLE 1. Summary of Vegetation Coverage of Fluvial Mine-Waste 

Deposits in the Upper Arkansas River Floodplain

Reach Distancea

Vegetative Cover Evaluation

(Categories defined by percentage of minewaste deposit with vegetative cover) Total

Poor <10% Fair 10−50% Good >50%

1 (1.8 miles) 14 (58)b 9 (38) 1 (4) 24

2 (1.8−5.6 miles) 2 (6) 19 (54) 14 (40) 35

3 (5.6−9.5 miles) 26 (28) 56 (60) 12 (13) 94

a

Arkansas River miles downstream from confluence with California Gulch.

b

Percent of fluvial mine-waste deposits in each reach.

Table adapted from USFWS (2002).

TABLE 2. Comparison of Vegetation Coverage and Average Metal 

Concentration in Fluvial Mine-Waste Deposits

Reach # Avg Veg 

Class*

Avg Cd

mg/kg dry wt

Avg Cu

mg/kg dry wt 

Avg Pb

mg/kg dry wt

Avg Zn

mg/kg dry wt

0 NA 3.3 29.9 238 428

1 2.54 177 446 4228 7271

2 1.66 153 200 3266 3438

3 2.14 129 258 3059 4926

*Vegetation coverage class: good (1, >50%); fair (2, 10−50%); poor (3, <10%).

Table adapted from USFWS (2002).

Reach 4. Small areas of fluvial mine-waste deposits occurred along Reach 4, 

but there were no data available to characterize the physical or chemical characteristics 

of these deposits.

Summary. Average metal concentrations in the fluvial mine-waste deposits 

declined with distance from the confluence of California Gulch with the Arkansas River. 

The concentrations furthest downstream in Reach 3 were still highly elevated in 

comparison to the upstream floodplain soil concentrations (see Table 2). The average 

concentrations of Zn and Cu in the fluvial mine-waste deposits declined from Reach 1 to 

Reach 2 and increased in Reach 3, although they were still lower than in Reach 1. The 
19

decline in concentration with distance from the California Gulch was significant only for 

Cu. Cu concentrations in Reach 1 were significantly greater than in Reach 2 or Reach 

3, and there was no significant difference in Cu concentration between Reaches 2 and 3 

(p < 0.05; General Linear Model Regression, Tukey’s Studentized Range [HSD] Test for 

average Cu concentration using data in U.S. Fish and Wildlife Service [USFWS, 2002]).

The evaluation of vegetative cover of the mine-waste deposits suggested that 

vegetative cover improved with distance from the confluence of California Gulch (see 

Table 1; also note Avg Veg Class in Table 2). Only 4% of the mine-waste deposits had 

good vegetative cover within Reach 1, 40% in Reach 2 and 13% in Reach 3. The 

rating, however, is subjective, and interpretation of these data on a quantitative basis is 

complicated by the fact that the definition of a mine-waste deposit was based at least in 

part on the presence of dead vegetation or lack of vegetation. Also, some fluvial 

mine-waste deposits were not included in the URS study because they were too small 

or had good vegetative cover. This situation suggests that the selection of the sites 

included in this evaluation may have been biased toward poor vegetation. The 

apparent (qualitative) improvement in vegetative cover of mine-waste deposits with 

distance from the confluence of California Gulch was correlated with a decrease in 

metal concentrations. This decrease was statistically significant only for Cu.

B. Comparison of Barren Areas with Nearby Vegetated Areas

In a study on the potential effects of fluvial mine waste deposits on ground water 

and surface water, Walton-Day et al. (2000) compared concentrations of 13 metals in 

11 fluvial mine waste deposits to concentrations in two nearby vegetated areas (see 

Table 3). Sampling locations were along a 3-mile reach of the east side of the Arkansas 

River floodplain, south of Leadville. The 3-mile reach sampled by Walton-Day et al. 

(2000) corresponded approximately to Reach 3 shown in Figure 5. Samples were 

collected from the 6−12 inch (15−30 cm) depth interval. Of 13 samples analyzed for 

metals, 11 were collected from areas where elevated metal concentrations were 

anticipated, based on lack of vegetation and the presence of iron-oxide staining or other 

indicators. Examples of other indicators of mine waste include the presence of pyrite or 

the odor of sulfur. The two additional samples were collected on or topographically 

above the floodplain in apparently uncontaminated areas. These two samples provided 

an estimate of background metal concentrations. The soil sample locations in the 

vegetated areas were within 200 m of the Arkansas River channel and within the same 

3-mile reach.

Elevated concentrations of arsenic (As), Ag, Cd, Cu, Fe, mercury (Hg), Pb and 

Zn were observed in the barren fluvial mine-waste deposits, when compared to 

concentrations in nearby vegetated areas in the floodplain (see Table 3). Elevated 

metal concentrations were found in all 11 areas that had been selected based on lack of 

vegetation. In contrast, the metal concentrations in the vegetated area soils were much 

closer to concentrations typically found in the western United States (Schacklette and 

Boerngen, 1984). Nonetheless, the observed metal concentration ranges for As, Cd, 

Mn, Pb and Zn were still higher than the background concentrations typically found in 

the western United States.
20

TABLE 3. Concentration Ranges of Selected Elements from Eleven 

Barren Fluvial Tailings Deposits and Two Nearby Vegetated Area 

Background Soil Samples from the Upper Arkansas River Floodplain 

and Mean Background Concentrations for Soils in the Western United 

States

Element 

(concentration units 

in dry wt)

Western

United Statesa

Arkansas River

Floodplain,

Vegetated Areasb

Arkansas River

Fluvial Tailings 

Areas Devoid of 

Vegetationc

Iron (percent) 2.1 1.9−2.6 4.7−30

Arsenic (mg/kg) 5.5 10−10 85−440

Cadmium (mg/kg) -- <2−5 7−91

Chromium (mg/kg) 41 23−50 6−48

Cobalt (mg/kg) 7.1 7−11 3−8

Copper (mg/kg) 21 31−34 37−390

Lead (mg/kg) 17 180−200 1300−6500

Manganese (mg/kg) 380 450−610 120−720

Mercury (mg/kg) 0.05 0.07−0.6 0.2−2.5

Nickel (mg/kg) 15 9−18 2−11

Selenium (mg/kg) 0.23 0.1−0.5 0.4−2.0

Silver (mg/kg) -- <2−<2 8−51

Zinc (mg/kg) 55 130−230 1100−12,000

a

Table 2 in Shacklette and Boerngen (1984). b

n = 2. c

n = 11.

Table adapted from Walton-Day et al. (2000).

Although Walton-Day et al. (2000) did not report pH values for the fluvial 

mine-waste deposits, the pH values were likely acidic. Shallow ground water in 

four wells in a 12-acre area (see Mine Waste Deposit IDs NB, NG, NH and NI in 
21

Figures 5 and 6) of fluvial mine-waste deposits had pH values lower than 3 

(Walton-Day et al., 2000). In contrast, shallow groundwater pH values from wells 

outside the area ranged from 6−7. Thus, acidic drainage from the fluvial deposits 

apparently has caused acidification of ground water. Pyrite and some of its weathering 

products were identified in the fluvial mine-waste deposit samples. In a separate study 

(summarized by USFWS, 2002), pH values in the N group of fluvial deposits ranged 

from 2.1−4.5. The pH values in the barren fluvial mine-waste deposits examined by 

Walton-Day et al. (2000) probably were similar.

C. Field and Greenhouse—Manipulation of Exposure

Brown et al. (2005) Field Experiment with Lime/Biosolids Amendment—Soil 

Properties. Brown et al. (2005) conducted field experiments to characterize fluvial 

mine waste deposits in the Arkansas River floodplain. Lime and municipal biosolids 

(224 kg/ha each) from the Denver wastewater treatment facility were incorporated to a 

depth of 20 cm into fluvial mine-waste deposits at four sites in the C, M and R groups of 

deposits in the summer of 1998. The unamended fluvial deposits lacked plant cover. 

The C group fluvial deposits were located just above the confluence of Lake Fork Creek 

in Reach 1 as described by USFWS (2002) (see Figure 4). The M and R groups were 

located in Reach 3 (see Figure 6). Brown et al. (2005) also characterized soil from a 

contaminated vegetated area (CVA) and an upstream uncontaminated reference site 

(UUC). The amended areas were seeded with a native seed mix with a composition of 

species that was similar to the vegetation at the UUC site (Sally Brown, personal 

communication to David Eskew, August 15, 2005).

Soil concentrations of Cd, Pb and Zn observed by Brown et al. (2005) in the UUC 

area were typical of other background samples in the Arkansas River floodplain, and 

were similar to background concentrations reported by Walton-Day et al. (2000) in two 

vegetated downstream areas (see Tables 3 and 4). Average concentrations of Cd, Pb 

and Zn in the fluvial mine waste deposits were highly elevated, but the highest 

concentrations were observed at the CVA. The CVA supported a dense vegetative 

stand of grasses and willows despite high metal concentrations. Plant cover was 

established on all the amended areas. Plant sample collection was limited to two of the 

amended areas, because the property owners harvested hay from the experimental 

plots and allowed cattle to graze the area.

Adding lime and biosolids increased soil pH and organic carbon. Lime addition 

raised the pH of the fluvial mine waste deposits from 3.5 to approximately 6.7 (see 

Table 4) (Brown et al., 2005). Total organic carbon increased from a range of 

17−26 g/kg before treatment, to 42−76 g/kg following treatment. In comparison, the 

organic matter concentration at the UUC site was 106 g/kg. Amendment also was

successful in reducing soluble concentrations of Cd, Pb and Zn. This was determined 

by four methods: water extraction, exchangeable, weak acid-extractable, and toxicity 

characteristic leaching procedure (TCLP). TCLP concentrations were reduced by 

approximately an order of magnitude in fluvial mine-waste deposits where lime was 

added.
22

TABLE 4. Total Metals (mg/kg dry wt) and pH of Amended Fluvial MineWaste Deposits (1−4) Along the Upper Arkansas River, Leadville, CO, 

After Adding a Mixture of Municipal Biosolids (224 Mg/ha) and 

Agricultural Limestone (224 Mg/ha) in 1998

Area Cd

mg/kg

Pb

mg/kg

Zn

mg/kg pHa

1 9.5 ± 2.9b 2490 ± 640 1950 ± 610 6.75

2 24.3 ± 12 2730 ± 370 2520 ± 580 6.7

3 16.9 ± 9.1 1560 ± 285 1440 ± 310 6.65

4 12.1 ± 2.8 1390 ± 610 1400 ± 425 6.84

Unamended 

Mine-waste

15.9 ± 3.1 3170 ± 490 1730 ± 350 3.53

UUC 2.3 ± 0.1 100 ± 12 210 ± 40 7.05

CVA 27 ± 5.7 3450 ± 2050 3400 ± 1415 7.47

a

Before amendment, pH in all fluvial mine-waste deposits averaged 3.44. b

Means ± SE for samples collected annually from 1998−2000.

Table adapted from Brown et al. (2005).

Also included are an UUC and a CVA, and unamended fluvial mine-waste material.

Addition of lime and biosolids also affected other soil properties that could 

potentially affect plant growth. Total Kjeldahl-N (TKN) increased from a range of 

1−1.6 g/kg in the untreated fluvial mine-waste deposits to 4.6 to 8.9 g/kg in the 

amended areas. The TKN concentration was 4.5 g/kg in the UUC soil and 2.9 g/kg in 

the CVA soil. Available phosphorus concentrations increased greatly also as a result of 

the lime and biosolids addition. Before amendment, the available phosphorus in the 

fluvial mine-waste deposits ranged from 1−12 mg/kg, and after amendment 

concentrations ranged from 333−433 mg/kg. Available phosphorus in the UUC soil was 

23 mg/kg. Both the unamended deposits and UUC soil would be considered potentially 

deficient in available phosphorus for plant growth (Pote et al., 1996). Brown et al. 

(2005) did not report available phosphorus concentrations in the CVA soil.

Brown et al. (2005) Lab Experiments with Lime/Biosolids Amendment. 

Brown et al. (2005) conducted phytotoxicity tests using rye grass emergence and early 

growth following protocols established by the American Society for Testing and 

Materials (ASTM, 2003). Plant emergence, shoot length, biomass and tissue metal 

concentrations were determined. No emergence occurred in unamended fluvial mine 

waste deposit samples with a pH of 3.5 (see Table 5) (Brown et al., 2005). After liming 
23

to pH 6.7 and adding biosolids, however, rye grass emergence in fluvial mine-waste 

material was similar to that of the laboratory control soil. Similar emergence rates were 

observed in soil collected from the UUC and CVA areas. The elevated Cd, Pb and Zn 

concentrations in the CVA soil and the amended mine-waste deposits did not inhibit rye 

grass emergence when compared to the laboratory control soil or the UUC site soil.

TABLE 5. Emergence and Growth of Rye Grass in a Greenhouse in 

Soil Collected from the Field

Soil Source Emergence

%

Aboveground Biomass

mg dry wt/pot

Unamended Fluvial 

Deposits

0 0

Amended Fluvial Deposits 71.4−88 84

CVA Similar to laboratory control* 12.8

UUC Similar to laboratory control* 24.4

Laboratory Control Soil 87 42

*Numerical data were not presented.

Data from Brown et al. (2005).

Rye grass shoot length was comparable among the four treatments (Brown et al., 

2005). Aboveground biomass accumulation in the amended tailing was about twice that 

observed in the lab control soils and 6.5 times that in the CVA soil (see Table 5). 

Growth in both the UUC soil and CVA soil was reduced compared to that of the 

laboratory control soil. However, Brown et al. (2005) did not present data allowing 

statistical evaluation of the aboveground biomass accumulation. The increased 

aboveground growth in the amended tailings may have been due to additions of N in the 

biosolids. Total Kjeldahl-N increased approximately 4-fold from 1−1.6 g/kg to 

4.6−8.9 g/kg after biosolid amendment of fluvial mine-waste material. Measured NH4-N 

and NO3-N were over an order of magnitude higher in the amended fluvial mine-waste 

material, when compared to the UUC, CVA or unamended fluvial mine-waste material 

(Brown et al., 2005).

Metal concentrations in aboveground tissue of rye grass grown in a greenhouse 

in pots containing amended fluvial mine-waste material collected from the field were 

determined one and two years (1999 and 2000) after amendment incorporation (see 

Table 6) (Brown et al., 2005). Metal concentrations in rye grass tissue in soils collected 

from the UUC and CVA sites were also tested. Metal concentrations—Cu, Pb, Mn and 

Zn—were clearly elevated in rye grass grown in amended fluvial mine-waste material 
24

TABLE 6. Metal Concentration (mg/kg dry wt) in Aboveground Tissue of 

Ryegrass Grown in a Greenhouse in Plots Containing (1) Amended 

Fluvial Mine-Waste Material Collected from the Field, (2) Soils from a 

UUC and a CVA in 1999 and 2000 (one and two years after amendment 

incorporation)a

Location, Year Cd Cu Pb Mn Zn

Amended Tailings

1999 3.4 ± 1.4b 75 ± 31 313 ± 247 2028 ± 1800 670 ± 290

2000 1.9 ± 0.5 49.3 ± 5.8 88 ± 37 360 ± 113 385 ± 96

UUC

1999 1.5 20 17 100 150

2000 0.5 12.5 3.3 34.2 62.5

CVA 

1999 8.9 30.8 343 161 920

2000 4.1 28 390 200 630

a

Plants in the unamended fluvial tailings did not germinate.

b

Means followed by SE (n = 6).

Table adapted from Brown et al. (2005).

when compared to UUC site soil (see Table 6). Elevated levels of Pb and Zn also were 

observed in rye grass plants grown in soil from the CVA site. The enhanced growth of 

rye grass seedlings in the amended fluvial mine-waste material suggests that these 

concentrations were not phytotoxic at pH 6.7.

Brown et al. (2005) Field Experiment with Lime/Biosolids Amendment—Plant 

Tissue Concentrations. Aboveground plant tissue metal concentrations were also 

measured in plants growing in the field in 2000 and 2001 (Brown et al., 2005). Plant 

tissue Zn concentrations from the amended fluvial mine-waste plots were elevated 

relative to those at the UUC area in both years (see Table 7). Tissue Zn concentrations 

were intermediate at the CVA site. Tissue Pb concentrations were elevated in plants 

from the amended fluvial mine-waste area in 2000, but were much lower in 2001. Plant 

tissue Cd concentrations were somewhat higher in plants from the amended fluvial 

mine-waste and CVA sites than the UUC site in both years. No plant biomass data 

were reported from the field plots. Therefore, it is not possible to draw conclusions 

about whether the elevated plant tissue concentrations were phytotoxic.
25

TABLE 7. Means (± SE) of Cd, Pb and Zn (mg/kg dry wt) in Aboveground 

Tissue of Plant Samples Collected in 2000 and 2001 from UUC, CVA and 

Amended Areas in Leadville, COa

Location, Year Cd

mg/kg

Pb

mg/kg

Zn

mg/kg

UUC

2000b 0.1 ± 0.1 1.4 ± 0.7 23 ± 7.6

2001c 0.07 ± 0.03 0.3 ± 0.15 8.8 ± 1.4

CVA 

2000 0.6 ± 0.2 3.3 ± 3.3 77 ± 12

2001 1.2 ± 0.4 2.2 ± 0.8 69 ± 20

Amended Tailings

2000 4.5 ± 2.4 400 ± 282 546 ± 264

2001 0.9 ± 0.3 3.1 ± 0.6 121 ± 42

a

Each collection included a range of plant species, potentially including bluegrass (Poa compressa L.), 

tufted hairgrass [Deschampsia cespitosa (L.) P. Beauv.], horsetail (Equisetum arvense L.) and crested 

wheatgrass [Agropyron cristatum (L.) Gaertn]. b

In 2000, n > 2. c

In 2001, n > 7.

Table adapted from Brown et al. (2005).

Brown et al. (2005) Field Experiment with Lime/Biosolids Amendment—Plant 

Species Diversity. Brown et al. (2005) evaluated the plant communities at the UUC, 

CVA and an amended fluvial mine-waste deposit. Fourteen 20 × 20-cm quadrants were 

randomly placed in one of the amended deposits and both (contaminated and 

uncontaminated) reference areas (CVA and UUC). Greater plant species biodiversity 

was observed at the CVA reference area than at the UUC reference area (see Table 8). 

A total of 27 plant species from 12 different families were identified at the CVA in 

comparison to 16 species from 8 different families at the UUC. The amended fluvial 

mine-waste deposit was disturbed by the incorporation of lime and biosolids two years 

before plant species observations were made. The amended deposit was also seeded 

with a mixture of native plant species. Therefore, a species biodiversity comparison 

between the amended deposit and the undisturbed CVA and UUC areas cannot be 

justified. More grass species (Poacae) were observed in the amended deposit than at 

either undisturbed reference area, but only four families were identified. The family 

Brassicaceae, found at the amended deposit and CVA but not the UUC, is known to 

contain a number of hyperaccumulators of metals (Baker et al., 1994).
26

TABLE 8. Plant Families and Number of Species Identified at the UUC, 

CVA and an Amended Fluvial Mine-Waste Deposit, Two Years after 

Amendment Addition and Seeding with a Mixture of Native Plant Species

Plant Family UUC CVA Amended Plots

Poaceae 4 4 14

Rosaceae 2 2 0

Asteraceae 5 6 0

Ranunculaceae 1 1 0

Scrophulariaceae 1 4 0

Fabaceae 1 2 0

Campanulaceae 1 1 0

Onagraceae 1 0 0

Cistaceae 0 1 0

Polygalaceae 0 1 1

Boraginaceae 0 1 1

Brassicaceae 0 3 3

Gentianaceae 0 1 0

Data from Brown et al. (2005).

Fisher Lab and Field Experiments with Amendments. Fisher (1999; Fisher et 

al., 2000) conducted greenhouse studies with fluvial mine-waste tailings from the 

Arkansas River floodplain amended with lime, biosolids or lime plus biosolids with 

Geyer willow cuttings (Salix geyeriana Anderson). Fisher (1999) also conducted field 

experiments on effects of lime, biosolids and phosphorus on establishment of plant 

c L. Beauv.] and creeping bentgrass 

[Agrostis stolonifera L.]) and establishment of Geyer willow. Willows are a key species 

for ecosystem function in riparian areas. However, they can readily colonize metalenriched soils too (Punshon, 1996). Willows and other shrubs account for 40 −50% of 

the annual primary production on undisturbed soils near the field site (NRCS, 1997).

over (tufted hairgrass [Deschampsia caespitosa
27

Fisher Lab Experiments. Fisher (1999; Fisher et al., 2000) collected material 

from a fluvial mine-waste deposit on the east bank of the upper Arkansas River 

approximately 8 km south of Leadville, CO (39.20 latitude, 106.35 longitude). Table 9 

summarizes characteristics of the tailings material. The tailings were amended with 

lime, organic matter, or lime plus organic matter. Lime, added at the rate of 1 kg lime 

(CaCO3) per 32.5 kg dry weight tailings, increased its pH from 3.8−7.3. The organic 

matter source was a composted biosolids product from Summit County, CO (see 

Table 9). Organic matter was added at a rate of 1 kg per 32.5 kg tailings. Tailings 

material was placed in 15-cm diameter polyvinyl chloride tubes to a depth of 60 cm over 

a 20-cm layer of sand. Willow cuttings, pruned to a length of 90 cm, were placed in the 

tailings and sand material to a depth of 70 cm. Plant material was harvested after 

4 months.

TABLE 9. Characterization of Fluvial Mine-Waste Deposits Collected 

from a Deposit on the Bank of the Arkansas River and Composted 

Biosolids (Summit-GrowTM) from Summit County, CO

Parameter Units Mine Tailing Biosolidsa

Texture - Sandy Loam NAb

pH - 4.0 6.2

ECc Siemen 1.4 2.2

Cd mg/kg dry wt 11.0 1.6

Cu mg/kg dry wt 413.5 69.0

Pb mg/kg dry wt 3062.8 35.0

Zn mg/kg dry wt 1470.0 NA

Organic Matter % 3.0 35.7

SMP Buffer Capacity Mg/ha 46.95 NA

Total Sulfur % 0.95 0.29

Pyritic Sulfur % 0.78 NA

Acid: Base Potentiald - 24.4 NA

a

Characterization performed by ACZ Laboratories, Steamboat Springs, CO.

b

NA = Not applicable; not analyzed.

c

Electrical conductivity.

d

Calculated as: 31.25 (% Pyritic S) = Mg CaCO3 per 907.2 Mg of material.

Table adapted from Fisher et al. (2000).
28

Increasing the pH by adding lime greatly increased plant growth, but organic 

matter alone had no significant effect. Adding lime alone increased growth of willow 

cuttings by 12-fold from 0.7 g/container in unamended tailings to 8.2 g/container. 

Adding organic matter alone increased willow growth to 1.7 g/container, but the 

increase was not statistically significant (p > 0.05). Similarly, adding organic matter plus 

lime did not significantly increase plant growth (8.8 g/container) over addition of lime 

alone.

Plant available metal concentrations were estimated by shaking a soil 

suspension with metal-chelating cation exchange resin for 24 hours. The resin was 

contained in a mesh bag and was subsequently extracted with 0.12 M hydrochloric acid 

to recover metals for analysis. Liming significantly (p < 0.05) decreased the chelating 

cation exchange resin-extractable concentrations of Cd, Cu, Pb and Zn in the tailings 

material (see Table 10). Amendment with organic matter also significantly reduced 

extractable concentrations of Cu, Pb and Zn; however, no significant reduction in 

extractable Cd was observed.

TABLE 10. Metal-Chelating Cation Exchange Resin-Extractable Metal 

Concentrations (mg/kg dry wt) as Affected by Lime, Lime Plus Organic 

Matter and Organic Matter Soil Amendments

Treatment Cd Cu Pb Zn

Lime 1.29ba 1.13c 7.97c 33.62c

Lime Plus Organic Matter 1.44b 1.45c 6.99c 43.81c

Organic Matter 2.62a 5.57b 32.19b 252.93b

Controlb 3.15a 6.89a 51.37a 326.23a

a

Different lower case letters in columns indicates significant differences among means at p < 0.05.

b

Unamended fluvial mine-waste material collected from the Arkansas River floodplain.

Table adapted from Fisher et al. (2000). 

Aboveground plant tissue concentrations of Zn, Cd, Cu and Pb were significantly 

lower in plants grown in tailings amended with lime (see Table 11). Aboveground plant 

tissue concentrations of Cd, Cu and Pb were significantly lower in plants grown in 

tailings amended with organic matter, but the plant Zn concentrations were not 

significantly affected. The response of plant Zn concentrations to liming and organic 

matter addition compared to the respective responses in plant growth suggest that 

elevated Zn limited plant growth in mine tailings (see Table 12).
29

TABLE 11. Metal Concentrations (mg/kg dry wt) in Aboveground Plant 

Tissue from Geyer Willow (Salix geyeriana Andersson) as Affected by 

Lime, Lime Plus Organic Matter and Organic Matter Soil Amendments 

Incorporated into Fluvial Mine-Waste Deposits

Treatment Cd Cu Pb Zn

Lime 23.75abca 8.88bc 8.05b 1130.0b

Lime Plus Organic Matter 29.69ab 8.99bc 7.03b 1233.8b

Organic Matter 24.45abc 9.57b 5.47b 2847.0a

Controlb 28.70ab 24.90a 28.52a 2328.0a

a

Different lower case letters in columns indicate significant differences in means at p < 0.05.

b

Unamended fluvial mine-waste material collected from the Arkansas River floodplain.

Table adapted from Fisher et al. (2000).

TABLE 12. Trends in Willow Tissue Metal Concentrations and Growth as 

a Result of Lime and Organic Matter Soil Amendments Incorporated into 

Fluvial Mine Waste Deposits. (Lack of a trend is denoted by “−”.)

Treatment Zn Cd Cu Pb Plant Growth

Lime ↓ ↓ ↓ ↓ ↑

Organic Matter − ↓ ↓ ↓ −

Data from Table 11 and Fisher et al. (2000).

Fisher Field Experiment. Fisher (1999) also conducted a field experiment on 

the effects of lime, organic matter and triple-super-phosphate amendments on in situ

revegetation of fluvial mine tailing deposits. Lime was added at a rate of 200 Mg/ha, 

and organic matter at a rate of 100 Mg/ha. These materials were incorporated to a 

depth of 60 cm. Triple-super-phosphate was incorporated at a rate of 120 kg 

phosphorus/ha. Specifically, he tested the effects of lime, organic matter, lime plus 

organic matter, and triple-super-phosphate soil amendments on the establishment of 

Geyer willow cuttings and establishment of plant cover in plots seeded with a mixture of 

6 L. Beauv) and 33% creeping bentgrass 

(Agrostis stolonifera L.). Because this experiment was planned as a multi-year study, 

the plants could not be sacrificed after the first season to determine growth. As 

surrogate measurements of growth, willow cuttings were assessed for survival, number 

7% tufted hair-grass (Deschampsia caespitosa
30

of leaders and total leader length (sum of all leader lengths on each cutting). 

Establishment of grass cover was evaluated by counting the number of individual plants 

and estimating the percent plant cover within a 10-cm diameter quadrant at each 

sample point.

Liming was effective in neutralizing acidity (Fisher, 1999). Soil pH increased 

from 4.83 to approximately 7.0 (see Table 13). Exchangeable concentrations (1.0 M 

KCl) of Cd, Cu, Pb and Zn were significantly reduced in treatments that received lime, 

but organic matter addition had no significant effect. Addition of triple-super-phosphate 

did not increase the available phosphorus in the soil samples.

TABLE 13. Soil pH, 1.0 M KCl Exchangeable Metal Concentrations 

(mg/kg dry wt), and Available Phosphorus (P) Concentrations as Affected 

by Lime, Organic Matter (OM), Lime Plus Organic Matter (LOM) and 

Triple-Super-Phosphate (TSP) Soil Amendments

Treatment pH

KCl Exchangeable Available

Cd Cu Pb Zn P

Controla 4.83bb 23.48ac 16.83a 292.90a 1992.25a 2.32a

Control + TSP 4.39a 23.41a 30.25a 329.30a 2214.75a 3.45a

OM 4.71ab 20.20a 20.43a 279.59a 2016.75a 4.42a

OM + TSP 4.74ab 24.06a 15.72a 233.48a 2361.50a 4.62a

Lime 7.05c 9.09b 0.68b 2.25b 71.38b 5.53ad

Lime + TSP 7.01c 8.50b 0.63b 1.23b 46.06b 6.48ad

LOM 6.95c 8.32b 0.54b 0.93b 27.14b 8.83ad

LOM + TSP 7.01c 9.59b 0.61b 2.29b 68.06b 6.86ad

a

Unamended fluvial mine-waste material collected from the Arkansas River floodplain. b

Different lower case letters in columns indicate significant differences among means at p < 0.05. c

Statistical comparisons were based on transformed values, but data are reported as nontransformed 

means for ease of interpretation. d

Statistical comparisons for available phosphorus were separated for lime and nonlime treatments 

because analysis procedures were different.

Table adapted from Fisher (1999).
31

Survival of Geyer willow was 24% greater in treatments that received lime (see 

Table 14) (Fisher, 1999). Lime addition was the only variable that significantly 

increased the number of leaders per cutting. Liming also increased total leader length 

per surviving cutting. Organic matter or triple-super-phosphate addition alone did not 

significantly affect survival or leader growth, but addition of organic matter or triplesuper-phosphate to limed treatments resulted in a significant increase in total leader 

length. The density of grasses increased from 35/m2 to approximately 1100/m2 in 

treatments that received lime. Similarly, plant cover increased from 0.3% to 

approximately 3.8%. Plant establishment and growth in the first year after planting was 

probably limited by the brief growing season (approximately 6 weeks) at the altitude of 

the study location.

TABLE 14. Survival and Growth Indices from Field Plots for Geyer 

Willow (Salix geyeriana Andersson) and Grass Species—Tufted 

Hairgrass (Deschampsia caespitosa L. Beauv.) and Creeping Bentgrass 

(Agrostis stolonifera L.)—as Affected by Lime, Organic Matter (OM), Lime 

Plus Organic Matter (LOM) and Triple-Super-Phosphate (TSP) Soil 

Amendments

Treatment

Willows Grasses

Survival

(%)

Number of 

Leaders

(#/cutting)

Total Leader 

Length

(mm/cutting)

Density

(plants/m2

) 

Cover

(%)

Controla 50.0bcdb 1.90ab 158.17a 35.03a 0.28a

Control + TSP 32.5d 1.33a 101.90a 0.00a 0.0a

OM 45.0bcd 1.54ab 138.32a 6.37a 0.3a

OM + TSP 42.5cd 1.70ab 183.92a 31.85a 0.05a

Lime 60.0abc 2.11bc 215.86ab 1050.89b 5.18b

Lime + TSP 57.5abc 2.58bc 280.27bc 1445.76b 4.13b

LOM 80.0a 2.84c 370.21c 1108.21b 2.53b

LOM + TSP 67.5ab 2.84c 332.13c 901.21b 3.43b

a

Unamended fluvial mine-waste material collected from the Arkansas River floodplain. b

Different lower cases in columns indicate significant differences among means at p < 0.05.

Table adapted from Fisher (1999).
32

Step 4: Evaluate Data from Elsewhere

Similar conditions to those occurring in OU 11 in the floodplain of the upper 

Arkansas River (see Figure 3) also occur along the Clark Fork River near the city of 

Deer Lodge, MT. Large-scale mining and smelting operations occurred upstream from 

1880 to 1980 in Butte and Anaconda, MT. Large quantities of mine waste from the 

mining and smelting operations have moved into the river and have been deposited 

throughout the floodplain of the Clark Fork River. Barren areas devoid of vegetation 

occur frequently in the floodplain along the Clark Fork River. Soils or tailings at the sites 

contain acid producing materials and are elevated in metal and As concentrations. 

Amendments were added so that the soil reached a target pH level of 7. At some sites, 

organic matter amendments also were added. The calcium carbonate amendments 

(ground limestone or industrial waste) have successfully raised pH levels and are still 

effective in several test areas after 6−19 years. There are indications that once 

vegetation is established on the waste, the plant root mass complexes the metal ions 

and renders them less bioavailable. This permits further root proliferation into 

nonamended areas and initiates a self-perpetuating cycle. Over a period of years, 

successional changes in vegetation have been observed in several areas (Neuman et 

al., 2005).

Detailed phytotoxicity studies of the fluvial mine-waste deposits along the 

floodplain of the Clark Fork River also demonstrated that decreased pH and increased 

extrinsic metals were invariably associated with barren areas (Rader et al., 1997; Brown 

et al., 2005). Studies along the Clark Fork River showed that vegetated areas could 

have metal concentrations equal to or higher than those found in barren areas. Soils 

adjacent to the barren areas had higher, often near neutral pH values, but did not inhibit 

emergence of Echinochloa crusgalli (barnyard grass) seeds in 5-day emergence tests, 

despite the high metal concentrations. Rader et al. (1997) collected samples along a 

7.6-m transect starting from a vegetated area and extending into one of the barren 

areas. Soil samples from within the barren areas were acidic, with pH values between 

4.4 and 4.9, in contrast to more neutral pH values, between 5.8 and 6.2, in the 

vegetated area (see Table 15). Emergence of E. crusgalli decreased from over 80% 

(versus 90% in control soil) in soil from the vegetated area, to 7% in soil collected just 

1.5 m away within the barren area. Concentrations of Cu, Cd, Pb and Zn were 

comparable along the transect.

Similar results also were reported for samples collected from seven barren areas 

and compared to paired sample points selected at random within vegetated areas less 

than 20 m away. Liming the acidic mine-waste deposit material to near pH 7 improved 

emergence of E. crusgalli to control levels, but root growth was still inhibited 

(Rader et al., 1997). As observed by Brown et al. (2005), areas with high metal 

concentrations but near neutral pH values can support healthy plant communities.

Metal and As concentrations observed in the fluvial mine-waste deposit areas in 

the upper Arkansas River floodplain, along with soil concentrations that are considered 

to be potentially phytotoxic, are summarized in Table 16. The chemicals listed in the 
33

TABLE 15. Results of Metal Analyses, pH Measurements and 

Echinochloa crusgalli Emergence Performed on Soils Collected Along a 

Transect Perpendicular to a Barren Area Boundary on the Clark Fork 

River

Distance 

Along 

Transect (m)a

Locationb

Acid-Extracted (mg/kg dry wt)

pH Cu Zn As Cd Pb Emergencec

0 Outside 6.2 3970 2190 433 12 368 86

1.5 Outside 5.8 4640 2140 293 13 256 81

3.0 Outside 6.1 3440 1670 292 8 262 78

4.6 Inside 4.4 2880 1380 272 5 270 7

6.1 Inside 4.9 5040 2340 233 8 260 0

7.6 Inside 4.8 6050 2070 210 8 213 0

a

Samples were collected at 1.5-m intervals along a 7.6-m transect drawn across and perpendicular to the 

barren area boundary. b

Location refers to whether the sample was collected outside or inside the barren area.

c

Emergence data were estimated from Figure 3 in Rader et al. (1997).

table are those found at elevated concentrations in the barren fluvial mine-waste 

deposits (Walton-Day et al., 2000). Concentrations of Cd, Pb and Zn reported by 

Brown et al. (2005) in the CVA, a vegetated area with high metal concentrations within 

the floodplain that supported a dense growth of a diverse native plant community, are 

also listed for comparison. The upper end of the concentrations reported by 

Walton-Day et al. (2000) for Ag, As, Cd, Cu, Pb and Zn were all well above the critical 

soil concentrations where phytotoxicity may be possible (Alloway, 1990; Efroymson et 

al., 1997; Kapustka et al., 2004). The upper end of the reported Pb and Zn 

concentration ranges exceed the upper end of the critical ranges by 16 and 30-fold, 

respectively. The reported concentration ranges of several chemicals are sufficient 

reason to expect that fluvial mine-waste deposits could be toxic to plants. Soil 

concentrations in the CVA, however, also exceeded the soil critical concentrations for 

Cd, Pb and Zn, but this area supported a dense growth of willows and grasses. The 

plant community was more diverse than that found at the UUC area, where soil 

concentrations of metals were near background levels (Brown et al., 2005).
34

TABLE 16. Comparison of Soil Concentrations of Metals and Arsenic 

Observed in the Floodplain of the Upper Arkansas River to Published 

Phytotoxicity Thresholdsa

Chemical

Concentrations Observed Phytotoxicity Thresholds

Walton-Day et al. 

(2000)b

Brown et 

al. (2005)c

Alloway 

(1990)d

Kapustka et 

al. (2004)

Efroymson 

et al. (1997)

Ag 8−51 nde 2 nd 2

As 85−440 nd 20−50 31 nd

Cd 7−91 27 3−8 28 4

Cu 37−390 nd 60−125 95 100

Fe 4.7−30 nd nd nd nd

Hg 0.2−2.5 nd 0.3−5 nd 0.3

Pb 1300−6500 3450 100−400 210 50

Zn 1100−12,000 3400 70−400 130 50

a

Concentrations are in mg/kg dry wt except for Fe, which is in percent.

b

Range of concentrations reported by Walton-Day et al. (2000) from 13 barren areas selected as fluvial 

mine-waste deposits in the Upper Arkansas River floodplain. c

Concentrations reported by Brown et al. (2005) from a contaminated vegetated area adjacent to a barren 

area of fluvial mine-waste deposit. d

Critical range of soil concentrations above which toxicity is considered to be possible. Data from 

Kabata-Pendias and Pendias (1984). e

nd = no data on this chemical.

Step 5: Identify Probable Cause

 Step 5—identifying the probable cause—is the last step in the Stressor 

Identification (SI) process. Based on available evidence organized in Steps 3 and 4, the 

most probable cause(s) is distinguished from a set of less probable causes. Step 5 

consists of three components discussed below.

A. Characterize Causes: Eliminate

 

Considerations for determining whether or not candidate causes should be 

eliminated are summarized in this section and in Table 17. Table 17 addresses four 

questions for each candidate cause. These questions are:
35

TABLE 17. Considerations for Determining Whether or not Candidate 

Causes Should be Eliminated for Impairment 1

Candidate Cause Impairments 

Occur Same 

Place as 

Exposure?

Exposure 

Increased Over 

Reference 

Site?

Gradient of 

Recovery at 

Reduced 

Exposure?

Exposure 

Pathway 

Complete?

Candidate 

Causes 

Remaining

1. Increased 

extrinsic metals

Yes Yes/Noa ? Yes Yes

2. Decreased pH 

with increased 

extrinsic metals

Yes Yes Yes Yes Yes

3. Decreased pH 

with intrinsic 

(background) 

metals

Yes Yes Yes Yes Yes

4. Decreased 

organic matter

Yes Yes No Yes No

5. Soil compaction NEb No NE NE No

6. Grazing and 

mowing

Yes No No Yes No

a

Soil concentrations of extrinsic metals were increased in the impaired areas in comparison to an 

upstream uncontaminated background site, but were not increased in comparison to a nearby 

contaminated vegetated site where good plant growth and species diversity was observed. b

NE = no evidence.

• Do impairments occur in the same place as exposure?

• Is exposure increased over that at the reference site?

Is there a gradient of recovery with reduced exposure?

•

•

Is the exposure pathway complete?

If any of the answers for a candidate cause are clearly “no,” then that cause can be 

eliminated. Otherwise, including cases where insufficient data are available, the 

candidate cause is carried forward to the strength of evidence analysis (i.e., the 

candidate cause is not eliminated, or is remaining).

1. Increased Concentrations of Extrinsic Metals in the Soil. Total plant 

cover was approximately 52% in Reach 0, upstream of the confluence of California 

Gulch with the Arkansas River. Soil metal concentrations in Reach 0 were roughly 

comparable to background soil concentrations typical of the western United States 
36

(Schacklette and Boerngen, 1984). The vegetation cover values for Reach 0 also 

reflect land use impacts of cattle and horses grazing at a number of the sample 

locations. Downstream of the confluence of California Gulch, numerous areas were 

identified as fluvial mine-waste deposits that had less than 50% vegetative cover, and 

many additional areas had less than 10% vegetative cover (see Tables 1 and 2). The 

concentrations of Cd, Cu, Pb and Zn in the fluvial mine-waste deposits were elevated in 

comparison to average soil concentrations upstream of the confluence with California 

Gulch (see Table 2). Areas with poor vegetative cover were associated with elevated 

metal concentrations. 

The evaluation of vegetative cover of the mine-waste deposits suggested that 

vegetative cover improved with distance from the confluence of California Gulch (see 

Table 1). The rating, however, is subjective, and interpretation of the data on a 

quantitative basis is complicated by the fact that the definition of a fluvial mine-waste 

deposit was based at least in part on the presence of dead vegetation or lack of 

vegetation. The apparent (qualitative) improvement in vegetative cover of mine-waste 

deposits with distance from the confluence of California Gulch was correlated with a 

decrease in metal concentrations. This decrease was statistically significant only 

for Cu.

Brown et al. (2005) also found that concentrations of Cd, Pb and Zn were 

elevated in four barren fluvial mine-waste deposits in comparison to the UUC upstream 

reference area (see Table 4). However, even higher Cd, Pb and Zn concentrations 

were found in another location, CVA, in the Arkansas River floodplain, which supported 

a dense vegetative stand with grasses and willows. Based on this evidence, elevated 

metal concentrations were frequently associated with barren areas in the Arkansas 

River floodplain, but elevated metals alone were not a sufficient cause to prevent 

establishment of vegetative cover.

There are very limited data that can be used to evaluate whether the pathway for 

exposure of plants to metals in the fluvial mine-waste deposits is complete. Intuitively, 

because plant roots would extend into the mine-waste deposit during growth, the 

exposure pathway should be complete. Interpretation, however, is complicated by the 

fact that these areas were selected on the basis of lack of vegetation, and seeds 

planted in the fluvial mine-waste deposits failed to germinate (Brown et al., 2005; 

Fisher, 1999). Therefore, no plant tissue was available to analyze for metal uptake. 

The data of Fisher (1999) for Geyer willow cuttings grown in a greenhouse in 

unamended mine-waste deposit material is the only applicable data found. The plant 

tissue concentrations of Cu, Pb and Zn were elevated in unamended mine-waste 

deposit material in comparison to cuttings growing in mine-waste amended with lime. 

This provides evidence that the pathway for exposure of plants to elevated metal 

concentrations in the fluvial mine-waste deposits is complete.

Increased concentrations of extrinsic metals in the fluvial mine-waste deposits 

have been frequently observed in the field in association with impaired plant growth. 

There is evidence suggesting that a gradient of declining metal concentrations coincides 
37

with an improvement in the vegetation cover of fluvial mine-waste deposits. Therefore, 

Candidate Cause #1 (increased extrinsic metals) could not be eliminated.

2. Interaction of Decreased pH and Increased Extrinsic Metals in the Soil. 

Low soil pH, in combination with increased extrinsic metal concentrations, occurred 

consistently in fluvial mine-waste deposits in association with reduced plant growth, 

both in the field and in greenhouse studies (Brown et al., 2005; Fisher, 1999; URS, 

1997, 1998; USFWS, 2002; Walton-Day et al., 2000). Amending the fluvial mine-waste 

deposits with lime and biosolids to increase the soil pH from 3.5 to 6.7 was 

accompanied by an increase in rye grass emergence (Brown et al., 2005). These 

amendments also reduced soluble and/or extractable concentrations of Cd, Pb and Zn. 

Aboveground tissue concentrations of Cd, Cu, Pb and Zn in plants grown in amended 

fluvial mine-waste material, however, were still elevated when compared to plants from 

an upstream uncontaminated reference soil, both in greenhouse and field experiments. 

Also, plant tissue metal concentrations in plants from the CVA were elevated in 

comparison to those from UUC.

Fisher (1999) reported that adding lime to raise the pH of fluvial mine-waste 

deposits from 4.8 to 7.0 decreased exchangeable/extractable metal concentrations and 

increased growth of willow cuttings, both in greenhouse and field experiments. Adding 

lime also reduced plant tissue concentrations of Cd, Cu, Pb and Zn.

Observations of decreased soluble and/or extractable metal concentrations after 

adding lime, coupled with decreased aboveground plant tissue concentrations, are 

consistent with the impairment of plant growth being caused by the interaction of low pH 

with elevated extrinsic metals. Therefore, Candidate Cause #2 (interaction of 

decreased pH with extrinsic metals) could not be eliminated.

3. Interaction of Decreased pH and Intrinsic (Background) Metals in the 

Soil. Interaction of decreased pH and intrinsic (background) metals in the soil may 

result in increased levels of metals in solution, causing phytotoxicity (see 

Section 2.1 #3). However, in the barren areas of the UAR Floodplain, low pH values 

were invariably associated with high extrinsic metal concentrations.

4. Decreased Soil Organic Matter Results in Decreased Plant Growth. Total 

organic carbon in barren fluvial mine-waste deposits was reported to range from 17−26 

g/kg, in comparison to 106 g/kg at the vegetated UUC site (Brown et al., 2005). No 

other quantitative data on organic carbon content of the fluvial mine-waste deposits 

were found. No mycorrhizae data were available. Fisher (1999), however, reported 

that adding organic matter alone at a rate of 225 kg/ha did not significantly increase 

growth of willow cuttings in either greenhouse or field experiments. Adding organic 

matter together with lime also did not further increase plant growth over lime alone. 

Because adding organic matter did not improve willow growth, either with or 

without lime, Candidate Cause #4 (decreased soil organic matter) was eliminated 

from further consideration.
38

5. Soil Compaction Reduces the Permeability of the Soil Surface Layers 

and Root Growth. Soil compaction is a common problem that restricts plant growth. 

Soil compaction is particularly severe under the saturated soil conditions that 

predominate in the upper Arkansas River area. Grazing cattle can compact soil 

(Wheeler et al., 2002). However, grazing occurs both upstream and downstream of the 

confluence of California Gulch with the Arkansas River (see below), and barren areas 

were not reported upstream. Soil compaction can also arise from differences in soil 

origin or type, e.g., deposited fines, but evidence of substantially differing soil types 

above and below the confluence of California Gulch is lacking. Soil compaction does 

not appear to be a cause of the barren areas.

6. Grazing by Cattle and Mowing (for hay production) can Affect Plant 

Growth or Species Richness. The available data do not suggest that grazing or 

mowing were notably different for the study area—which includes the floodplain barren 

areas—compared to surrounding areas. According to data from 1997 and 2002 Cattle 

and Calves Inventories (USDA, 2004), the study area county (Lake) contained 1858 and 

287 head of cattle in 1997 and 2002, respectively, compared to 12,803 and 5964 head 

of cattle (1997 and 2002) in the county (Eagle) north of the study area and 11,167 and 

6590 head of cattle (1997 and 2002) in the county (Chaffee) south of the study area. 

Data on mowing per se is not available, but according to harvested hay data from a 

2002 census (USDA, 2004), 220 acres, yielding 141 tons, was harvested from the study 

area county (Lake), compared to 6391 acres, yielding 7021 tons in Eagle County, and 

7198 acres in Chaffee County. Based on the above data, there does not appear to be 

evidence for a relationship between either grazing by cattle or mowing and plant growth 

or species richness. Because cattle grazing and mowing occur both upstream and 

downstream, they are not likely to be causes of Impairment 1.

B. Characterize Causes: Strength of Evidence

Strength of evidence analysis uses all the evidence generated in the analysis 

phase to examine the credibility of the remaining candidate causes. The causal 

considerations for the strength of evidence analysis use three types of evidence: 

case-specific evidence, evidence from other situations or biological knowledge, and 

evidence based on multiple lines of evidence. All evidence was evaluated for 

consistency with the hypothesized causes, and coherency where necessary (i.e., to 

explain inconsistent evidence). The results of the strength of evidence analysis are 

summarized in Table 18. In the table, each line of evidence is given a score as follows:

NE = no evidence.

+ + + = convincing evidence as cause.

+ + = strong evidence as cause.

+ = weak evidence as cause.

0 = unclear.

– = weak evidence as not cause.

– – = strong evidence as not cause.

– – – = convincing evidence as not cause.
39

TABLE 18. Strength of Evidence for 

Arkansas River Floodplain

Specific Causes and Considerations for Barren Areas in the Upper 

Consideration

Extrinsic Metals Extrinsic Metals with Decreased pH Intrinsic Metals with Decreased pH

Results Score Results Score Results Score

Case-Specific 

Evidence

Spatial cooccurrenceIncompatible: Area with 

elevated extrinsic metals 

but neutral pH values 

downstream of California 

Gulch (CVA) supported 

dense diverse vegetation, 

compared with relatively 

uncontaminated site above 

California Gulch (UUC) (3.1 

Step 3 C).

– – – Compatible: Barren 

areas—only found 

downstream of California 

Gulch—had low pH values 

and elevated extrinsic 

metals (3.1 Step 3 B).

+ Incompatible: In the 

barren areas, low pH 

values were invariably 

associated with high 

extrinsic metal 

concentrations (3.1 

Step 5 A).

– – – 

Temporality Compatible: Barren areas 

were not observed prior to 

mining activity in California 

Gulch.

+ Compatible: Barren areas 

were not observed prior to 

mining activity in California 

Gulch.

+ 

Consistency of Inconsistent: Areas with – Consistent: Barren areas + 

association elevated extrinsic metals 

supported dense diverse 

vegetation (3.1 Step 3 C).

had low pH values and 

elevated levels of extrinsic 

metals (3.1 Step 3 B).

Biological Moderate: There was + + No evidence. Data for pH NE 

gradient evidence for decreased 

metal concentrations and 

improved vegetation with 

distance from California 

Gulch (3.1 Step 3 A).

was available only in 

Reach 3.

Complete Increased extrinsic metal + + Increased extrinsic metal + + 

exposure concentrations were concentrations were 

pathway observed in plants both in 

the field and greenhouse 

(3.1 Step 3 C).

observed in plants both in 

the field and greenhouse 

(3.1 Step 3 C).
40

TABLE 18 cont.

Consideration

Extrinsic Metals Extrinsic Metals with Decreased pH Intrinsic Metals with Decreased pH

Results Score Results Score Results Score

Case-Specific 

Evidence cont.

Experiment Inconcordant: Field and 

greenhouse studies with 

lime to raise pH to 

neutrality allowed 

vegetation to establish, 

although soil metal 

concentrations were not 

changed (3.1 Step 3 C).

– Concordant: Field and 

greenhouse studies with 

lime to raise pH to 

neutrality allowed 

vegetation to establish. 

Extractable soil metal 

concentrations and plant 

tissue metal 

concentrations were 

reduced by liming (3.1 

Step 3 C).

+ + + No evidence. NE

Information From 

Other Situations 

or Biological 

Knowledge

Mechanism Plausible: Plants are 

sensitive to elevated 

metal concentrations 

(2.1).

+ Plausible: Plants are 

sensitive to elevated metal 

concentrations and metal 

solubility increases when 

pH declines (2.1).

+ Plausible: Plants are 

sensitive to soluble 

concentrations of intrinsic 

metals (Al, Mn and Fe) 

that occur in acidic soils 

(2.1).

+ 

StressorresponseConcordant: 

Concentrations of 

extrinsic metals in soil are 

sufficient to potentially 

cause phytotoxicity (3.2 

Step 4)

+ Concordant: pH values 

were low enough to make 

metals bioavailable, and 

concentrations of extrinsic 

metals in soil are sufficient 

to be potentially phytotoxic 

(3.2 Step 4).

+ No evidence. NE

Consistency of 

association 

Inconsistent: Many areas 

with high concentrations 

of extrinsic metals 

supported vegetation in 

the Clark Fork River

floodplain (3.2 Step 4).

– Invariant: Barren areas in 

the Clark Fork River 

floodplain consistently had 

high extrinsic metal 

concentrations and low pH 

(3.2 Step 4).

+ + + No evidence. NE
TABLE 18 cont.

Consideration

Extrinsic Metals Extrinsic Metals with Decreased pH Intrinsic Metals with Decreased pH

Results Score Results Score Results Score

Information From 

Other Situations 

or Biological 

Knowledge cont.

Specificity of 

cause

Other causes result in 

barren areas.

0 Other causes result in 

barren areas.

0 Other causes result in 

barren areas.

0 

Analogy No evidence NE No evidence NE A number of metals in 

well-drained organic soils 

are differentially leached 

at an accelerated rate as 

precipitation pH 

decreases (i.e., acid 

deposition) (Hanson et 

al., 1982). Soil pH levels 

below 5.5 result in 

increased solubility of Al 

and Mn that can be toxic 

to plants at background 

concentrations (Rengel, 

2002) (2.1).

+ 

41
TABLE 18 cont.

Consideration

Extrinsic Metals Extrinsic Metals with Decreased pH Intrinsic Metals with Decreased pH

Results Score Results Score Results Score

Information From 

other Situations 

or Biological 

Knowledge cont.

Experiment Inconcordant:

- In soil samples from a 

7.2 m transect extending 

from a vegetated area 

into a barren area, 

emergence of E. 

crusgalli decreased from 

over 80% in the 

vegetated area to 7% 

within the barren area. 

Concentrations of metals 

were high, and 

comparable, along the 

entire transect, but pH 

was low in the barren 

area.

- Liming acidic, metalscontaminated mine-waste 

samples from a barren 

area to near pH 7 

improved emergence of 

E. crusgalli to control 

levels (root growth was 

still inhibited) (3.1 Step 

4).

– – – Concordant:

- In soil samples from a 

7.2 m transect extending 

from a vegetated area 

into a barren area, 

emergence of E. crusgalli

decreased from over 80% 

in the vegetated area to 

7% within the barren 

area. Concentrations of 

metals were high, and 

comparable, along the 

entire transect, but pH 

was low in the barren 

area.

- Liming acidic, metalscontaminated mine-waste 

samples from a barren 

area to near pH 7 

improved emergence of E. 

crusgalli to control levels 

(root growth was still 

inhibited) (3.1 Step 4).

+ + + No evidence NE

Predictive 

Performance

No evidence NE No evidence NE No evidence NE

42
TABLE 18 cont.

Consideration

Extrinsic Metals Extrinsic Metals with Decreased pH Intrinsic Metals with Decreased pH

Results Score Results Score Results Score

Considerations 

Based on 

Multiple Lines of 

Evidence

Consistency of 

Evidence

Not consistent: Dense 

diverse plant 

communities exist and 

seeds emerge in areas 

with high metal 

concentrations and near 

neutral pH values.

– – – All lines of evidence are 

consistent with this 

candidate cause.

+ + + Consistency of evidence 

is limited. Low pH levels 

can be toxic to plants at 

background metal 

concentrations, but in the 

study area, low pH 

values were invariably 

associated with high 

extrinsic metal 

concentrations.

– 

Coherence of 

evidence

Inconsistency may be 

explained by low 

bioavailability of metals at 

neutral pH.

+ 

43
44

Increased extrinsic metal concentrations were observed in both well vegetated 

areas with a diverse collection of plant species and in barren areas in the floodplain of 

the 11-mile reach of the Upper Arkansas River. Soil pH values below 5.5 combined 

with increased extrinsic metals were invariably observed in the barren areas, or areas 

with impaired vegetation. Similar observations were reported in the Clark Fork River 

floodplain downstream from the Anaconda mining area in Montana. Barren areas there 

were also observed only in conjunction with both low pH values and increased extrinsic 

metal concentrations. Increased extrinsic metal concentrations alone were not sufficient 

to cause impairment of plant cover and species diversity. The extrinsic metals 

associated with the fluvial mine-waste deposits are all potentially phytotoxic (the upper 

end of the concentration range exceeded phytotoxicity screening levels). The 

bioavailability and potential phytotoxicity of these metals depends on pH. Increased 

plant tissue metal concentrations were also observed with increased soil metal 

concentrations.

The evidence presented in Table 18 is consistent with increased extrinsic metal 

concentrations interacting with pH values below 5.5 as the cause of barren areas in the 

Arkansas River floodplain. Soil pH values as low as 3.5 were observed in the barren 

areas. At these pH levels Al, Mn and Fe can all become soluble in soil, bioavailable to 

plants and phytotoxic at concentrations measured in background soil. Therefore, the 

combination of intrinsic metals and decreased pH from mining and smelting also could 

have caused phytotoxicity. In the barren areas of the Arkansas River floodplain, 

however, low pH values were invariably associated with high extrinsic metal 

concentrations.

C. Characterize Cause: Probable Cause

After three candidate causes were eliminated in Step 5A, three candidate causes 

remained. These remaining scenarios were then evaluated on a strength of evidence 

basis. These scenarios were #1, increased extrinsic metals; #2, interaction of extrinsic 

metals with decreased pH; and #3, interaction of decreased pH with intrinsic metals. 

Experimental manipulation of exposure in both the lab and field was a particularly strong 

line of evidence for determining the cause of impaired plant growth in the upper 

Arkansas River floodplain.

The evidence for Candidate Cause #1, increased extrinsic metal concentrations, 

was inconsistent. Both in the upper Arkansas River floodplain and in the Clark Fork 

River floodplain, areas with dense vegetation and a high diversity of plant species 

occurred where metal concentrations were as high as, and in many cases higher than, 

those observed in the barren areas. The areas with vegetation consistently had pH 

values ranging from 5.8−7.6. Barren areas, in contrast, consistently had pH values 

below 5.5. Adding lime to the material in the barren fluvial mine-waste deposit materials 

to raise the pH to near neutrality allowed seed emergence and permitted vegetative 

cover to become established despite high metal concentrations.

The evidence supporting Candidate Cause #2, impairment of plant growth due to 

interaction of elevated levels of extrinsic metal with decreased pH, was consistent 
45

throughout the lines of evidence. The strength of association, spatial co-occurrence, 

plausible stressor response, and experimental lines of evidence strongly supported this 

candidate cause. The quality of the data are adequate for this conclusion, and 

confidence is high (see Table 18).

Site-specific evidence for Candidate Cause #3, interaction of low pH with intrinsic 

metals such as Al, Mn and Fe, was limited. In the barren areas, low pH values were 

invariably associated with high extrinsic metal concentrations.

3.2. IMPAIRMENT 2: REDUCED PLANT GROWTH AND PLANT SPECIES 

RICHNESS IN IRRIGATED MEADOWS

Soil and vegetation data and the results of laboratory phytotoxicity tests were 

used to evaluate associations among the candidate causes and reduced plant growth 

and plant species richness in the irrigated meadows areas. Information from reports 

published in peer-reviewed journals, USGS reports, RI/FS reports, graduate theses and 

undergraduate research reports were used.

Step 3: Evaluate Data from the Case

Several investigations provide evidence on the potential causes of the reduced 

forage yields and reductions in plant species richness in the irrigated meadows areas. 

Levy et al. (1992) examined the depth profile of metals concentrations at an upstream 

site in comparison to several irrigated meadow sites just below the confluence of 

California Gulch and the Arkansas River. Swyers (1990) investigated the soil metals 

concentrations in an irrigated meadow where chlorosis and barren areas had been 

observed on the Seppi Ranch, the first ranch below the confluence of California Gulch. 

Keammerer (1987) collected plant data and soils samples from 40 locations along the 

Arkansas River, Tennessee Creek, and in California Gulch. U.S. EPA Region 8 and 

USFWS (2003) collected soil samples from 120 locations over the 11-mile reach and 

determined soil chemical properties and conducted laboratory phytotoxicity tests using 

soil samples collected from a subset of 20 of these locations. The USDA Natural 

Resources Conservation Service (NRCS, 2001) collected forage samples from 

16 locations in the irrigated pastures area. The results of these studies are combined to 

evaluate the strength of evidence.

A. Soil Characterizations and Laboratory Phytotoxicity Testing

U.S. EPA Region 8 and USFWS (2003) conducted a study designed to 

characterize the soil properties and develop a method to predict phytotoxicity along the 

11-mile reach of the Arkansas River (see Figure 8). In Phase I of this study, soil 

samples (15-cm depth) were collected from 126 stations at systematic locations 

throughout the riparian zone and the irrigated meadows (U.S. EPA Region 8 and 

USFWS, 2003). These samples were analyzed for 23 metals, pH, total organic carbon 

and nutrients (N, phosphorus and Ca). In Phase II of this study, a subset of 

20 sampling stations were selected for more intensive studies. The Phase II sampling 

locations were selected to be representative of the range of soil characteristics 

observed in Phase I. Statistical analysis of laboratory phytotoxicity tests on Phase II 
46

FIGURE 8. Phase I and Phase II Soil Sampling Locations (U.S. EPA 

Region 8 and USFWS, 2003)
47

samples (see Section 3.2 Step 3 B) indicated that Zn and Cu concentrations, in that 

order, appear to be reasonable measures of exposure for investigating the relationship 

between soil properties and phytotoxicity in the upper Arkansas River valley.

A wide range of soil characteristics were found among the 126 soil sampling 

stations (see Table 19). The distribution of Zn and pH is shown in Figure 9. As seen in 

Figure 9, the highest concentrations of Zn occurred in the irrigated meadows area about 

1.5 miles below California Gulch. The area of highest Zn concentrations was also 

characterized by near-neutral pH values (range: 6.1−7.1). Other areas with Zn 

concentrations up to 2599 mg/kg occur further downstream, with some isolated “hot 

spots” of Zn (concentrations up to 5562 mg/kg) up to 5 miles downstream.

TABLE 19. Range of Element Concentrations, pH and Total Organic 

Carbon Found at the 126 Phase I Soil Sampling Stations

Parameter Units in 

dry wt

Concentration

Minimum Maximum Average

Al mg/kg 926 15,700 7965

As mg/kg 1.5 270 31

Cd mg/kg 0.11 73 10

Ca mg/kg 593 204,000 19,311

Cu mg/kg 7.0 709 86

Fe mg/kg 3040 72,100 20,027

Pb mg/kg 4.2 24,200 1075

Mn mg/kg 20 4910 770

Hg mg/kg 0.03 122 2.2

Ag mg/kg 0.05 64 5.7

Zn mg/kg 56 13,500 1133

pH – 3.3 9.0 6.6

Total Organic Carbon g/kg 0.48 444 50

Ca = calcium.
48 

FIGURE 9. Distribution of Zinc and Soil pH in the 11-Mile Reach of the 

Arkansas River Valley and Sources of Irrigation Water
49

In Phase II sampling, soil samples collected at 20 stations were taken to a 

laboratory where the ability of the soils to support the growth of plants was measured 

(EP&T, 2002). Standard artificial soil consisting of 70% sand, 20% kaolinite and 10% 

peat moss, adjusted to pH 7 with calcium carbonate, was used as laboratory control 

soil. Three species of plants were tested: alfalfa (Medicago sativum), tall wheatgrass 

(Elymus elongatum) and yarrow (Achillea millefolium L. var. occidentalis). For each 

species, up to 26 indices of growth and phytotoxicity were measured (23 for alfalfa, 22 

for wheatgrass and 26 for yarrow). A phytotoxicity score combining information from the 

indices of growth/phytotoxicity for each species was developed and assigned to each of 

the 20 stations (U.S. EPA Region 8 and USFWS, 2003). The phytotoxicity score 

ranged from 1−4. The score is interpreted as shown in Table 20.

TABLE 20. Scheme for Scoring Results of Laboratory Phytotoxicity Tests

Magnitude of Endpoint 

Response (% of control) Phytotoxicity Score Description

>90% 0 Nonphytotoxic

>75−90% 0.5 Mildly phytotoxic

>50−75% 1.0 Moderately phytotoxic

>25−50% 2.0 Highly phytotoxic

0−25% 4.0 Severely phytotoxic

EPA Region 8 and USFWS (2003) developed a site-specific model using 

principal components analysis based on the results of the laboratory phytotoxicity test to 

predict the potential distribution of phytotoxic conditions in the field. Because most of 

the mining-related metals observed in the soils were highly correlated with each other, 

the set of metal concentration values were reduced to a smaller number of variables by 

principal components analysis. Based on data for Zn, Cd, Cu and Pb at the 20 Phase II 

sampling sites, the first principal component for metals in bulk soil explained a large 

fraction (93.1%) of the total variability in the soil concentrations of mining-related metals. 

Thus, only the first principal component was used to develop site-specific models. The 

bulk soil variables giving the best prediction of laboratory mean phytotoxicity score were 

the first principal component for metals, pH and Ca concentration. The best-fit model 

was

MPS = 2.07 + 0.025•PC1 – 0.235•pH + 0.00001•Ca (Adj R2 = 0.69) (1)

where

MPS = Mean Phytotoxicity Score

PC1 = First principal component based on log-soil concentrations of metals in 

bulk soil
50

pH = Bulk soil pH

Ca = Concentration of calcium in bulk soil

U.S. EPA Region 8 and USFWS (2003) concluded that:

...the primary contributors to phytotoxicity in the upper portions of the 

11-mile reach area are usually metals or metals plus pH, while the lower 

portions of the reach include a number of stations where calcium is the 

main driver. Those stations for which calcium was the primary contributor 

to phytotoxicity may represent locations where over-liming has occurred.

An alternative interpretation, however, is that the high Ca concentrations 

observed are also a result of mining activity. The Zn, Pb and Ag deposits mined in the 

Leadville area are found as bedded replacement deposits in limestone, in particular the 

Leadville Dolomite. The lowest 20 to 30 ft of Leadville Limestone is a very sandy 

dolomite that is finely crystalline, gray and thought to be equivalent to the Gilman 

Sandstone Member of Leadville. This unit is useful for recognizing the 

Chaffee-Leadville contact. It is overlain by unnamed gray limestone typical of Leadville 

(USGS, 2005). Although the geology of the Leadville district is complex, the majority of 

the metal production came from manto deposits in the Leadville dolomite that followed 

pre-existing paleokarst features (Maslyn, 1996). The characteristic paleokarst caves 

are described as “upper portion is calcite lined, the middle is filled with stratified 

dolomite sand and the lower portion is mineralized (in part with detrital sulfide 

fragments).” Thus, it is possible that the high Ca concentrations observed in some of 

the irrigated meadows soils are due to dolomitic limestone transported to regions of 

California Gulch and Upper Arkansas River from which irrigation water was obtained.

B. Statistical Analysis of Laboratory Phytotoxicity Tests

A Microsoft® Office Excel file containing the site-specific laboratory 

measurements and phytotoxicity scores discussed in U.S. EPA Region 8 and USFWS 

(2003) was obtained from U.S. EPA Region 8 (EP&T, 2002). Data included 

concentrations of As, Cd, Cu, Pb, Hg and Zn. Measurements of field pH and laboratory 

pH (lpH) also were available for each sample (see Table 21). Phytotoxicity scores and 

dry weight per sample were available for alfalfa, wheatgrass and yarrow.

The raw data indicated that the phytotoxicity score could be simplified into a 

binary variable, representing nontoxic sites (assigned a value of zero) or toxic sites 

(assigned a value of one). The binary value simplifies statistical analysis of the data 

and reduces uncertainty in the classification of toxicity at each site. Photographs 

showing all 5 replicate pots for each test plant species, taken just before harvest, were 

obtained from U.S. EPA Region 8. To develop a binary classification, the photographs 

and the raw laboratory toxicity test results for each plant species were examined and 

contrasted with control plants grown in the standard artificial test soil. Based on visual 

evaluation of the photographs, each soil sample was classified as either nontoxic or 

toxic for each of the three test plant species. From this visual assessment, it seemed 
51

TABLE 21. Site-Specific Laboratory Data

Site Area As Cd Cu Pb Hg Zn pH lpH Alfalfa Wheatgrass Yarrow Score

HAY-17 Meadow 9.7 2.6 18.0 91.6 0.05 98.2 5.38 6.92 0.194 0.290 0.267 0.18

SEP-02 Meadow 23.9 4.2 26.4 572.0 0.11 674.0 6.88 7.57 0.179 0.279 0.147 0.33

MOY-08 Riparian 216.0 19.5 113.0 5120.0 66.10 2150.0 7.73 8.71 0.116 0.291 0.119 0.39

STL-11 Meadow 6.9 2.0 18.7 119.0 0.06 99.9 8.44 9.40 0.143 0.261 0.085 0.48

SEP-09 Meadow 25.1 9.6 43.8 761.0 0.16 579.0 5.50 6.50 0.096 0.337 0.149 0.61

MOY-04 Meadow 4.5 0.8 14.3 20.1 0.07 79.1 7.51 8.58 0.142 0.247 0.037 0.80

HAY-47 Riparian 18.8 14.4 36.8 565.0 0.20 744.0 8.25 9.39 0.160 0.230 0.038 0.82

SEP-08 Meadow 17.4 9.3 86.7 368.0 0.14 966.0 6.07 6.66 0.097 0.192 0.071 0.90

HAY-23 Riparian 12.3 4.6 56.4 242.0 0.18 594.0 5.16 5.49 0.073 0.325 0.066 0.96

HAY-08 Riparian 11.3 3.1 20.7 126.0 0.06 131.0 8.69 9.86 0.101 0.258 0.022 1.01

HAY-14 Meadow 19.9 17.5 119.0 10.6 0.37 1960.0 6.34 6.54 0.069 0.176 0.028 1.32

HAY-26 Riparian 36.0 13.2 117.0 751.0 0.59 1880.0 5.81 7.43 0.076 0.170 0.031 1.35

DOC-17 Riparian 95.7 8.6 164.0 3520.0 4.80 1400.0 5.69 6.38 0.077 0.208 0.016 1.38

SEP-01 Riparian 19.8 22.2 222.0 367.0 0.14 2920.0 5.56 5.78 0.071 0.176 0.028 1.39

SCO-08 Meadow 3.9 2.8 11.9 43.9 0.05 468.0 7.82 7.61 0.000 0.539 0.050 1.48

DOC-04 Meadow 293.0 113.0 806.0 56,200.0 2.40 17,100.0 6.40 6.84 0.056 0.239 0.014 1.49

HAY-15 Riparian 34.8 6.9 89.8 70.6 0.41 843.0 5.24 5.88 0.056 0.212 0.015 1.59

HAY-48 Riparian 125.0 24.8 350.0 4360.0 2.30 2920.0 4.83 4.86 0.062 0.124 0.015 1.63

DOC-02 Meadow 273.0 48.1 687.0 22,000.0 4.50 11,800.0 6.70 6.44 0.019 0.093 0.008 2.23

LCOS-02 Riparian 153.0 8.8 173.0 4940.0 4.80 1430.0 3.52 3.11 0.000 0.003 0.000 2.32

Units are mg/kg dry wt for metals, standard units for pH, and g dry wt for plant species.
52

reasonable to interpret those sites with an average phytotoxicity score (for the 23−26 

growth/phytotoxicity indices for the three species) of 0.82 and smaller as nontoxic. 

Similarly, sites with a larger score were interpreted as toxic. The phytotoxicity score of 

0.82 selected by this process as the binary break point between nontoxic and toxic falls 

between the designations of mildly phytotoxic and moderately phytotoxic (see 

Table 20). The binary assignment of toxicity described here resulted in 13 “toxic” sites 

and 7 “nontoxic” sites.

Box-and-whisker plots were generated for each of the measurement endpoints 

using data from the 20 sites (see Appendix B). The plots were developed using the 

data in the original units and natural logarithm transformed data. The box-and-whisker 

plots display the distribution of each available laboratory and field endpoint for the toxic 

and nontoxic site data (i.e., the nontoxic site data consist of 7 values, and the toxic site 

data consist of 13 values for each endpoint). Examining the graphics (see Appendix B) 

indicated the following:

• Of the metals, a clear difference in the distribution of Zn and Cu between the 

toxic and nontoxic sites is evident. Some separation among the toxic and 

nontoxic site distributions is evident based on As data, but little separation is 

evident for Pb or Hg data. 

• For all metals, log transformation of the data reduces the effect of high extreme 

values. This also normalized the data and increased the separation among the 

toxic and nontoxic site distributions.

• Little overlap among the toxic and nontoxic site distributions based on lpH is 

evident. However, a much larger degree of overlap is evident from distributions 

based on field measured pH.

• The distributions of the alfalfa, wheatgrass and yarrow average dry weight values 

(per plant) are dissimilar among the toxic and nontoxic sites.

• Cd distributions among the toxic and nontoxic sites have a large overlap.

Based on the box-and-whisker plots, Zn and Cu appear to be reasonable 

measures of exposure for investigating the relationship between soil properties and 

phytotoxicity in the Upper Arkansas River Valley. The separation between toxic and 

nontoxic site distributions based on laboratory pH suggests that the laboratory pH 

influenced toxicity.

Development of a Generalized Linear (GLIM) Model of Phytotoxicity. Various 

methods and approaches are available for building phytotoxicity models. In this project, 

Bayesian inference and graphical evidence are used to select a final model. Initially, 

the correlation among the available measurement endpoints was examined (see 

Table 22). A high (r > 0.8) linear correlation is found among many of the metals (e.g., 

Zn and Cu, As and Cu). The high correlation among the metals presents a number of 

problems from a model-building perspective, including the following:
53

TABLE 22. Correlation Among Candidate Measurement Endpoints

ln(Zn) ln(Pb) ln(Hg) Lab pH ln(Cu) ln(Cd) ln(As)

ln(Zn) 1.0 0.7 0.7 −0.5 0.9 0.9 0.8

ln(Pb) 0.7 1.0 0.7 −0.3 0.8 0.7 0.9

ln(Hg) 0.7 0.7 1.0 −0.3 0.7 0.6 0.9

lab pH −0.5 −0.3 −0.3 1.0 −0.6 −0.3 −0.4

ln(Cu) 0.9 0.8 0.7 −0.6 1.0 0.9 0.9

ln(Cd) 0.9 0.7 0.6 −0.3 0.9 1.0 0.8

ln(As) 0.8 0.9 0.9 −0.4 0.9 0.8 1.0

• High correlation among the measurement endpoints significantly reduces the 

ability to distinguish the relative contribution of each endpoint to site toxicity. 

Therefore, statistical approaches for selecting from among a set of candidate 

predictor variables (e.g., stepwise variable selection procedures) are 

compromised. The outputs from these procedures can be used as general 

guidance, but should not be used exclusively to select the final model.

• Nonlinear models may have convergence issues under conditions of high 

correlation. This is particularly true when building multiple parameter models.

• The signs (positive or negative) on the resulting model parameters may not 

reflect biological reality. For example, we expect higher metal concentrations to 

be associated with greater toxicity, therefore, the sign on the model parameter 

should be positive. Multi-parameter models under conditions of high correlation 

frequently result in inverted parameter signs. Therefore, the predictive ability of 

the models on future data sets is compromised.

Mindful of the above issues, we developed models based on (1) a conceptual 

model of the relationships between toxicity, metals concentration, and metals solubility 

and bioavailability, and (2) support available through the examination of graphics. A 

complete discussion of the relationship between phytotoxicity and metals concentrations 

is available in the previous section of this report. Box-and-whisker plots are presented 

in Appendix B and discussed earlier in this section. In addition, we considered the 

findings of other researchers (see EP&T, 2002). Relative to the other metals, Zn and 

Cu were shown to be reasonable predictors of site toxicity (see Appendix B). 

Laboratory pH was shown to be superior to field pH, and Cd was shown to be a poor 

indicator of toxicity. A model consisting of a metals concentration and pH as predictor 
54

variables is consistent with the conceptual understanding of metals toxicity presented in 

the previous section.

Based on toxicological evidence, and the results of assessments by earlier 

investigators, two models were selected for detailed study. The final models were 

developed using the Bayesian software WinBugs (Lunn et al., 2000). Advantages of the

Bayesian approach and WinBugs software, in the present study, include the following: 

• The sampling routines used in the procedure result in informative graphical 

presentations of the model outputs.

• The Bayesian framework allows specification of a probability model matching the 

biological and toxicological concepts.

• The resulting model can be used as an aid for examining future analyses of fieldbased toxicity data.

For the simple models examined below, a standard classical statistics approach 

would result in very similar estimates of the model parameters. However, the flexible 

probability model used in the Bayesian approach is difficult to solve using classical 

statistics, and the graphical outputs are difficult to create.

A generalized linear model (GLIM) is described in the following text. For each 

site (i), a binary measure of toxicity (yi) is assigned. The distribution of y is assumed to 

be Bernoulli (Evans et al., 2000) with random parameter P. Pi is the probability of 

toxicity at site i: 

yi ~ Bernoulli (Pi) (2)

In the second step, P is linked to the stressor concentrations using a logistic function:

logit(Pi) = β0 + β1 ln(Ci ) + β2 lpH (3)

Consistent with the exploratory statistical analyses (see above), C is the 

concentration of either Cu or Zn at site i. Therefore, there are two competing models, 

one based on Zn and one based on Cu. Laboratory measured pH was selected as the 

covariable based on the evidence discussed above. The random parameters β0, β1 and 

β2 are assumed to be normally distributed.1

 While these models are not the only 

available models, they should provide reasonable estimations of site-specific toxicity. 

Additional models could be explored in future studies.

 

1 In the Bayesian framework, the model parameters are assumed to be random and assigned a 

distribution. The final sufficient statistics of these distributions (termed a posterior estimate) are 

generated from a function of the data (termed a likelihood function) and prior information on the random 

parameters. In this study, prior distributions on the model parameters did not exist, so noninformative 

prior distributions are used. The noninformative prior distributions have no effect on the final result. 

Model predictions utilize the posterior estimates of the model parameters.
55

The Cu and Zn model parameter estimates are shown in Table 23.2 The signs

on the parameters match the toxicological expectations. As larger metal concentration 

results in increased toxicity and a smaller pH results in increased toxicity, WinBugs 

produces a measure of fit called the deviance information criterion (DIC), with smaller 

values indicating a better fit. The models had almost identical values (DIC Cu = 23.7; 

DIC Zn = 22.9), indicating that the models had very similar fits. Notice that the 

coefficients of variation (CVs) of the Cu model parameters are larger than those found 

in the Zn model, indicating increased uncertainty in the parameter distributions. In both 

models, β0 has the largest CV of all model parameters, indicating relatively high 

uncertainty in the general toxicity at all sites. The CV of β1 in the Zn model is much 

smaller than the CV of β1 in the Cu model. This finding indicates that across all sites, 

the relationship between toxicity and Zn is stronger than the relationship between Cu 

and toxicity.

TABLE 23. Posterior Estimates of the Model Parameters

Parameter

Zinc Model Copper Model

Median Std. CV Median Std. CV

β0 12.53 11.75 0.94 8.93 11.1 1.24

β1 9.80 0.61 0.06 1.14 0.78 0.68

β2 −8.67 5.09 0.59 −6.46 4.82 0.75

Std. = standard deviation.

The median and standard deviation of Pi at each site based on the Zn and Cu 

models are shown in Table 24. The distribution of Pi for each site is shown in 

Figures 10 and 11. Figure 10 displays the distribution of Pi for the seven nontoxic sites. 

Figure 11 displays the distribution of Pi for the toxic sites. When examining the figures, 

one should note the relative height and breadth of each distribution. Tall and narrow 

distributions indicate a relatively small amount of uncertainty in the probability of a toxic 

effect. Those sites with a low and broad distribution of Pi have a higher degree of 

uncertainty in the probability of site-specific toxicity. One should also note the position 

(on the abscissa) of the peak of each distribution. Those sites with a low expected 

toxicity are shifted to the left. Those sites with relatively high expected toxicity are 

shifted to the right. Examination of the table and figures provides the following findings:

 

2 Winbugs uses a sampling procedure to solve for the parameter estimates. Extreme estimates of the 

parameters can occur. To minimize the effect of these values, a median is used to represent the center 

of the distribution.
56

TABLE 24. Distribution of Pi

Site Area

Zn Model Cu Model Zn

mg/kg

Cu

mg/kg Lab pH

Median Pi Std Median Pi Std

HAY-17 Meadow 0.37 0.26 0.42 0.23 98.2 18.0 6.92

SEP-02 Meadow 0.55 0.16 0.40 0.17 674.0 26.4 7.57

MOY-08 Riparian 0.48 0.26 0.59 0.26 2150.0 113.0 8.71

STL-11 Meadow 0.04 0.11 0.11 0.13 99.9 18.7 9.40

SEP-09 Meadow 0.78 0.15 0.74 0.16 579.0 43.8 6.50

MOY-04 Meadow 0.08 0.14 0.14 0.14 79.1 14.3 8.58

HAY-47 Riparian 0.18 0.20 0.21 0.18 744.0 36.8 9.39

SEP-08 Meadow 0.82 0.12 0.84 0.11 966.0 86.7 6.66

HAY-23 Riparian 0.76 0.16 0.79 0.15 594.0 56.4 5.49

HAY-08 Riparian 0.04 0.11 0.09 0.14 131.0 20.7 9.86

HAY-14 Meadow 0.90 0.01 0.89 0.10 1960.0 119.0 6.54

HAY-26 Riparian 0.99 0.04 0.99 0.04 1880.0 117.0 7.43

DOC-17 Riparian 0.90 0.10 0.93 0.09 1400.0 164.0 6.38

SEP-01 Riparian 0.99 0.06 0.99 0.06 2920.0 222.0 5.78

SCO-08 Meadow 0.47 0.17 0.21 0.19 468.0 11.9 7.61

DOC-04 Meadow 0.97 0.12 0.98 0.12 17,100.0 806.0 6.84

HAY-15 Riparian 0.92 0.11 0.92 0.10 843.0 89.8 5.88

HAY-48 Riparian 0.97 0.06 0.97 0.06 2920.0 350.0 4.86

DOC-02 Meadow 0.98 0.09 0.98 0.09 11,800.0 687.0 6.44

LCOS-02 Riparian 0.99 0.06 0.99 0.06 1430.0 173.0 3.11

Std. = standard deviation.
57

FIGURE 10. Distribution of Pi for Zinc and Copper Models for the Seven 

Upper Arkansas River Valley Sites Considered to be Nonphytotoxic

3-Parameter Zn and Lab pH model

Density

0

1

2

3

4

5

6

7

8

9

10

Probability of a Toxic Effect

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

3-Parameter Cu and Lab pH model

Density

0

1

2

3

4

5

Probability of a Toxic Effect

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
58

FIGURE 11. Distribution of Pi for Zinc and Copper Models for the 13 

Upper Arkansas River Valley Sites Considered to be Phytotoxic

3-Parameter Zn and Lab pH model

Density

0

10

20

30

40

Probability of a Toxic Effect

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

3-Parameter Cu and Lab pH model

Density

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Probability of a Toxic Effect

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
59

• Sites STL-11 and MOY-04 have a very low probability of toxicity. These sites 

also have low metals concentrations. A third site, HAY-47, also has a low 

probability of toxicity, but the distribution of toxicity is relatively broad indicating 

some uncertainty in the expected toxicity at this site.

• Site SEP-09 was interpreted as a nontoxic site (phytotoxicity score <0.82). 

However, based on either model the expected probability of toxicity is high (Zn 

model Pi = 0.78, Cu model Pi = 0.74). This site has moderately high metals 

concentrations at a pH of 6.5.

• The distributions of Pi for nontoxic sites are similar for both models. However, the 

distributions associated with the Cu model are more uncertain than their 

respective distributions generated by the Zn model. The Zn model is found to 

have a smaller model prediction error than the Cu model.

• Of the toxic sites, both models indicate that site HAY-08 may be misclassified. 

The median Pi at this site indicates a small chance the site is toxic.

• The Cu model and Zn model treat site SCO-08 somewhat differently. The Cu 

model indicates a low chance of toxicity (Pi = 0.21) and the Zn model indicates a 

moderate chance of toxicity (Pi = 0.47). However, the distribution of Pi based on 

each model is relatively broad, indicating uncertainty in the model predictions. 

The Zn model estimate of toxicity is more consistent with the original 

interpretation of toxicity at this site than the Cu model.

Overall, the Zn model is shown to be moderately superior to the Cu model for 

predicting site phytotoxicity, although both models seem viable.

C. Soil and Vegetation Data in Irrigated Meadow Areas: Field Studies

Plant growth is impaired in the irrigated meadows below California Gulch. On 

one ranch, severely chlorotic plants and barren sections of ground have been observed 

and forage yield in the affected meadow has decreased from 4.48 Mg/ha (3990 

lbs/acre) in 1874 to 1.68 Mg/ha (1496 lbs/acre) in the early 1990s (Levy et al., 1992). 

Levy et al. (1992) collected soil samples at six depth intervals to a total depth of 30 cm 

from two ranches just below the confluence of California Gulch with the Arkansas River. 

This sampling was replicated six times at each study site. Replicates for the reference 

meadow (slope 5%) were collected along a 200-m transect from the summit to the toe 

position. Replicates for all other locations (0−3% slope) were sampled within a 5-m 

radius of the initial excavation. The reference site used in this study for background 

metal levels in the Newfork-Marsh-Rosane Association soil series was located in 

Tennessee Park (TP), upstream from the East Fork of the Arkansas River (see 

Figure 12). The other four study sites were selected to represent areas of distinct 

vegetation, landscape position and irrigation history located on the Seppi (SB, SC, SD) 

or Smith (BA) ranches. The reference site received “clean” irrigation water from East 

Tennessee Creek. Water from California Gulch was used to irrigate meadows on local 

ranches from 1874 until the 1920s (Levy et al., 1992) before the detrimental effects on 

animal health and forage quality in the meadows were noted in 1939. Since the 1920s, 
60

FIGURE 12. Study Site Locations (Levy et al., 1992)
61

the meadows have been irrigated with water diverted from the Arkansas River above 

and below California Gulch.

Weighted-average soil concentrations of Cd, Cu, Pb and Zn were elevated at all 

four locations sampled in the impaired meadows compared to the upstream reference 

site (see Table 25). The highest concentrations of Pb and Zn were found at a depth of 

15 cm below the soil surface at sites BA and SB (Levy et al., 1992). This finding 

correlated with the presence of a 10-cm layer of sand uncovered during the sampling. 

The sand layer is not described in the Chafee-Lake Area Soil Survey, and probably 

washed into the valley from hydraulic mining activities. It is said that at one time the 

sands blanketed this area of the Upper Eastern Arkansas Valley (Levy et al., 1992).

TABLE 25. Weighted-Average Total Metals (g/m3), pH and Organic 

Carbon (g/kg) to a Depth of 30 cm at the Reference (TP) and Impaired 

Meadow Sites (SB, SC, SD, BA)

Parameter

Location

TP SB SC SD BA

Cd 4 15 9 14 39

Cu 16 162 103 81 431

Pb 82 870 301 308 18,000

Zn 62 1400 706 699 7650

pH* 4.5−4.8 6.1−7.0 4.7−5.3 2.8−4.6 5.6−5.9

Organic C 9−288 58−243 19−304 53−403 18−183

*Soil pH values and organic C concentrations are the range of values observed over the six soil depth 

intervals sampled at each site. 

Adapted from Levy et al. (1992).

Neither soil pH values nor soil organic C concentrations were consistently 

different between the upstream reference site and the downstream sites in the impaired 

meadows (see Table 25). The soil pH was acidic at the upstream reference site and at 

two of the impaired meadow sites. In contrast, pH at the impaired meadow sites SB 

and BA were more alkaline than the upstream reference site. The range of organic 

C concentrations was similar to the upstream reference site at two of the impaired 

meadow sites (SB and SC), lower at one impaired meadow site (BA) and higher at the 

fourth impaired meadow site (SD). The impaired meadow site SD was in a peat bog; at 

this site there was no indication of a hydraulically deposited sediment layer, although 
62

the site has been irrigated with water from California Gulch. Differences in pH and 

organic carbon are not consistent with either of these parameters being the primary 

cause of impairment in the downstream irrigated meadows areas.

Differences in plant species richness in irrigated pastures above and below the 

confluence of California Gulch with the Arkansas River have also been reported (Levy 

et al., 1992). Nine plant species were observed in an irrigated meadow on a similar soil 

type located upstream of the California Gulch (see Table 26). This upstream meadow 

had received relatively “clean” irrigation water from Tennessee Creek. In contrast, at 

four locations in irrigated meadows just downstream of California Gulch, only three or 

four plant species were observed at each sampling location. Several of the plant 

species observed at the upstream location also were observed in at least one of the 

downstream locations, but three species―Alpine timothy (Phleum alpinum), dandelion 

(Taraxacum spp.), and clover (Trifolium spp.)―were not reported at any of the four 

impaired meadow locations. However, the sampling method at the reference site 

differed from the method employed at the four study sites. Samples were collected 

along a 200-m transect from the summit to the toe of a slope at the reference site but all 

samples were collected within a 5-m radius at the impaired meadow sites. Therefore, a 

wider range of potential habitats was sampled at the reference site. As a result, no 

conclusion can be supported based on these limited observations.

Above-ground plant metal (Cd, Cu, Pb or Zn) concentrations were not elevated 

greatly at sites SB and SC relative to the upstream reference site (see Table 26). The 

concentration of Zn, however, clearly was elevated over the reference site at impaired 

meadow sites SD and BA. Cd, Cu and Pb also clearly were elevated at site BA.

Swyers (1990) conducted an investigation of the extent of metal contamination in 

the soils of a large irrigated meadow on the property of E. Seppi. He collected soil 

samples from 36 locations along a series of transects spaced approximately 91 m apart. 

Soil metal concentrations were highly variable, but elevated concentrations frequently 

were observed (see Table 27). Swyers (1990) reported barren areas with exceptionally 

high metal concentrations at several points near an old irrigation ditch. He described 

sampling point M5 as an area of extremely sparse growth just south of the old irrigation 

ditch. Soil concentrations at M5 were 113 mg/kg Cd; 177 mg/kg Cu; 1096 mg/kg Pb; 

and 8722 mg/kg Zn. He also reported an area described as “a barren, gray and white, 

crusted wasteland.” This area, however, was located in the floodplain near the 

Arkansas River and may be in a fluvial mine-waste deposit area. For this reason, the 

results from this sampling site were omitted from Table 27. The results described in the 

impaired meadow on the Seppi property are consistent with elevated metal 

concentrations as the cause of sparse plant growth. 
63

TABLE 26. Plant Species, Percent Ground Cover and Plant Metal 

Concentrations (mg/kg dry wt) from Reference and Impaired Meadow Sites

Location Species Common 

Name

Percent 

Cover Cu Cd Pb Zn

Reference Achillea lanulosa Yarrow 10 7.9 2.23 1.85 30

Carex spp. Sedge, 

sloughgrass

15 6.4 0.55 0.92 27

Juncus spp. Rush, wiregrass 7 6.1 0.77 0.25 35

Pedicularis spp. Lousewort 1 9.4 0.63 0.98 42

Phleum alpinum Alpine timothy 8 4.2 0.11 0.74 39

Poa spp. Bluegrass 3 6.3 0.22 0.81 26

Potentilla spp. Cinquefoil 20 6.4 1.52 1.39 52

Taraxacum spp. Dandelion 15 12.5 2.01 1.45 47

Trifolium spp. Clover 10 9.8 0.35 1.3 109

SB Agropyron spp. Wheatgrass 60 8.1 0.27 1.06 61

Poa spp. Bluegrass 30 4.5 0.25 1.20 28

Potentilla spp. Cinquefoil 10 6.2 0.89 2.79 100

SC Carex spp. Sedge, 

sloughgrass

4 8.3 0.98 1.27 74

Juncus spp. Rush, wiregrass 8 6.7 1.25 0.52 150

Muhlenbergia 

spp.

Muhly 88 5.3 0.84 1.16 102

SD Carex spp. Sedge, 

sloughgrass

44 13.3 3.04 1.76 263

Juncus spp. Rush, wiregrass 45 4.5 3.55 0.81 306

Pedicularis spp. Lousewort 1 4.4 3.80 3.31 634

Salix spp. Willow shrub 10 4.4 13.2 2.24 588

BA Achillea lanulosa Yarrow 10 11.2 7.27 52.0 517

Carex spp. Sedge, 

sloughgrass

62 21.1 2.12 26.4 593

Iris missouriensis Iris 1 4.7 21.0 23.4 403

Juncus spp. Rush, wiregrass 2 6.8 3.5 12.7 443

Poa spp. Bluegrass 25 11.6 1.9 43.1 570

Adapted from Levy et al. (1992).
64

TABLE 27. Total Soil Metal Concentrations (mg/kg dry wt) Observed in 

Impaired Meadow on E. Seppi Property

Metal Concentration Range Mean (± SE)

Cd 5−113 19 ± 4

Cu 21−409 106 ± 12

Pb 46−1112 292 ± 41

Zn 294−8722 1842 ± 311

Data from Swyers (1990).

Keammerer (1987) conducted a survey of plant growth and species composition 

at 40 locations, including 13 locations upstream of the confluence of California Gulch, 

4 locations along California Gulch and 23 locations in irrigated meadows along the 

Arkansas River Valley within the 11-mile study reach. Two of these samples were 

collected 2.5 miles upstream near the reference location sampled by Levy et al. (1992); 

another was collected 2.3 miles upstream in the floodplain of the east fork of the 

Arkansas River. These three sampling locations are not illustrated. However, the other 

37 sampling locations are shown in Figure 13.

Keammerer (1987) collected soil samples (0−15 cm) at the same locations. The 

soil samples were analyzed for total and plant-available (ammonium bicarbonate 

diethylenetriaminepentaacetate [DTPA] extractable) As, Cd, Cu, Pb and Zn 

concentrations. Extracting with the chelate, DTPA, is a standard method for estimating 

bioavailable trace element concentrations in soils (Soltanpour, 1985). Above-ground 

plant tissue samples also were analyzed for As, Cd, Cu, Pb and Zn. Data on planttissue metal concentrations were grouped by plant type, such as grasses and forbs. In 

this study, grass-like species such as sedges and reeds were included with the true 

grass species for metal analysis.

The Keammerer (1987) data are difficult to interpret for several reasons. 

Because the study was unpublished, copies are not available and few methodological 

details are known. For example, the basis for selecting the sampling locations, the size 

of the plots sampled for plant species richness, and date of sample collection are not 

reported. The Upper Arkansas River Valley is located at approximately 9500 ft 

elevation and has a frost-free growing season of 30−40 days. Thus, sampling date can 

make a large difference in plant biomass. The areas sampled are known to be grazed 

by cattle, but little information is available on grazing intensity. As summarized in 

USFWS (2002), Keammerer (1987) reported that grazing ranged from 0−35% forage 

utilization within the upstream locations. However, no similar information was provided 

for locations below California Gulch.
65

FIGURE 13. Soil and Plant Sampling Locations (Keammerer, 1987)
66

Factors other than grazing could account for the differences in plant growth at a 

given location and time. The sample locations within the irrigated meadows areas 

include some areas of higher ground that have not been irrigated due to the topography. 

Due to the limited rainfall in this area, plant growth could well be limited by available 

moisture. Plant growth may also be affected by agricultural practices such as 

fertilization and herbicide use, and these applications may differ among the ranches in 

the study area. Therefore, a range of plant growth rates may be expected even in the 

absence of phytotoxicity. However, if phytotoxic conditions are present, the upper limit 

of plant growth could be limited.

Several of the samples from areas just below California Gulch are west of the 

Arkansas River. The soils on these ranches have soil total metal concentrations that 

are typical of the background in the Arkansas River Valley. Therefore, plotting the plant 

growth and species data against distance from the confluence of California Gulch is not 

meaningful. Instead, the plant biomass data were plotted versus soil total Zn 

concentration and against DTPA-extractable soil Zn. Similar plots of plant growth and 

species richness versus concentrations of As, Cd, Cu or Pb could also be prepared. 

Statistical analysis of the laboratory plant growth tests suggested that Zn was the 

strongest determinant of phytotoxicity. In addition, concentrations of these elements are 

highly correlated with Zn concentration (see Table 21). For this reason, only figures for 

Zn concentrations are presented in this report.

In the present case study, as in other situations, a number of different stressors 

can account for a given biological response such as differences in plant growth. 

Quantile regression is a technique that can be used to help describe stressor-response 

relationships (see data analysis section at http://www.epa.gov/caddis). It models the 

relationship between a specified conditional quantile (or percentile) of a dependent 

(response) variable and one or more independent (explanatory) variables (Cade and 

Noon, 2003). As with mean regression, the relationship is often assumed to be a 

straight line.

In this case study, quantile regression is used to estimate the location of the 

upper boundary of a scatter plot (e.g., the 95th percentile line). An assumption for using 

this upper boundary is that the wedge shape often observed in scatter plots of biological 

metrics results from the effects of other stressors co-occurring with the modeled 

stressor that cause additional negative effects on the biological response. Hence, it is 

assumed that an upper quantile line represents the best possible performance of the 

plants given the concentration of the metal.

Figure 14 shows a possible relationship between maximum plant biomass and 

total soil Zn concentration, i.e., decreasing maximum plant biomass with increasing Zn. 

Plant biomass at the two locations with the highest soil Zn concentrations was 

dominated by plant species that are known to be tolerant of high soil metal 

concentrations. If these two points are treated as outliers, the relationship between 

maximum plant biomass and soil total Zn concentration is strengthened but is still not 

significant (SAS quantile regression [QUANTREG Procedure] at 0.9 and 0.95 quantiles 

[Figure 15] tested with Likelihood Ratio). 
67

 

FIGURE 14. Plant Aboveground Biomass Versus Soil Total Zinc (all data 

points)

Data from Keammerer (1987).

Plant Biomass vs Zinc

0

50

100

150

200

250

300

350

400

450

0 5000 10000 15000 20000

Zinc mg/kg

Plant Biomass g/m2

FIGURE 15. Plant Aboveground Biomass Versus Soil Total Zinc (minus 

two outliers; see text)

Data from Keammerer (1987).

Zinc mg/kg

Plant Biomass g/m2

0.95 Quantile Fit

0 2000 4000 6000

100

200

300

400
68

At the location where Zn concentration was highest (LV22; 17,400 mg/kg), four 

plant species were reported (Keammerer, 1987). In terms of plant dry weight, 

Deschampsia caespitosa (tufted hairgrass) accounted for 58%, Carex spp. (sedge, 

sloughgrass) accounted for 15%, Juncus arcticus spp. Ater (wiregrass) accounted for 

14% and Phelum pratense (timothy) accounted for 13%. Keammerer (1987) reported 

three plant species as present at the site with the second highest Zn concentration 

(LV03; 12,550 mg/kg); but only one species, Carex aquatilis, accounted for 100% of the 

plant dry weight. Both tufted hairgrass and Carex species are considered indicator 

plants for metal contamination (Brown et al., 1988; Cooper and Emerick, 1989).

A plot of plant biomass versus soil DTPA-extractable Zn also shows a trend of 

decreasing maximum plant biomass with increasing DTPA-Zn (see Figure 16). This 

trend is significant (SAS quantile regression [QUANTREG Procedure] at 0.95 quantile 

tested with Likelihood Ratio, p = 0.0281). These results offer at least a partial 

explanation for the relatively high plant biomass observed at the highest bulk soil Zn 

concentrations. The points with the highest bulk soil Zn values had lower DTPA-Zn 

concentrations (LVO3, 341 mg/kg; LV22, 544 m/kg) than some points with lower bulk 

soil Zn values. As was the case for the bulk soil concentrations of Zn, the relationship 

between maximum plant biomass and DTPA-Zn is complicated by the limited number of 

samples at high DTPA-Zn concentrations and the variability exhibited at lower 

concentrations of DTPA-Zn.

FIGURE 16. Plant Aboveground Biomass Versus Soil Ammonium 

Bicarbonate DTPA Extractable Zinc

Data from Keammerer (1987).

DTPA-Extractable Zinc mg/kg

Plant Biomass g/m2

0 200 400 600 800

100

200

300

400 0.95 Quantile Fit
69

The number of plant species - or species richness - observed by Keammerer 

(1987) at low soil Zn concentrations was highly variable (see Figure 17). Up to nine 

plant species were observed at four locations with soil Zn concentrations up to 1185 

mg/kg soil. The number of plant species observed at each sampling point ranged from 

2−9 at soil Zn concentrations up to 1500 mg/kg and from 1−5 at higher concentrations. 

Interpretation of the number of species observed at high soil Zn concentrations is 

difficult because there were only a few sample points. Nonetheless, the observations 

are consistent with a decline in maximum species richness with increasing soil Zn 

concentrations. This trend is significant (SAS quantile regression [QUANTREG 

Procedure] at 0.9 and 0.95 quantiles tested with Likelihood Ratio, p = 0.0031 and 

0.0029 respectively).

FIGURE 17. Plant Species Richness Versus Soil Total Zinc

Data from Keammerer (1987). Plant Species Richness

Zinc mg/kg

0 5000 10000

2

4

6

8

15000

0.95 Quantile Fit

Figure 18 presents a plot of the number of plant species—or species richness—

observed by Keammerer (1987) against DTPA-Zn concentration. The number of plant 

species observed was highly variable, but all four locations with nine species observed 

occurred at DTPA-Zn concentrations at or below 274 mg/kg. Interpretation of the 

number of species observed at high DTPA-Zn concentrations is difficult because there 

were only a few sample points. Nonetheless, the observations are consistent with a 

decline in maximum species richness with increasing DTPA-Zn concentrations. This 

trend is significant (SAS quantile regression [QUANTREG Procedure] at 0.9 quantile 

tested with Likelihood Ratio, p = 0.0135).
70

FIGURE 18. Plant Species Richness Versus Soil Ammonium Bicarbonate 

DTPA Extractable Zinc

Data from Keammerer (1987).

Plant Species Richness

DTPA-Extractable Zinc mg/kg

0 200 400 600

2

4

6

8

800

0.9 Quantile Fit

The exposure pathway is complete, as demonstrated by increasing Zn 

concentrations in the above-ground biomass of grasses and grass-like species with 

increasing soil total Zn and with increasing DTPA-Zn (see Figures 19 and 20). Although 

plants can accumulate metals in vacuoles without expressing toxic effects (Kapustka et 

al., 2004), the increasing above-ground plant concentrations demonstrate that the Zn is 

in a biologically available form and is taken up and translocated to the stems and 

leaves. Grasses and grass-like species are the dominant plant types present in the 

native vegetation in the irrigated meadows, and no forbs were observed at several of 

the sampling points. Therefore, only the data for grasses and grass-like species were 

plotted. The station with the highest DTPA-Zn concentration (924 mg/kg) appears to be 

an anomaly in that the above-ground grass Zn concentration was only 158 mg/kg. This 

station consisted almost exclusively of Western wheatgrass, Agropyron smithii. 
71

FIGURE 19. Zinc Concentration in Above-Ground Plant Parts of Grasses 

and Grass-Like Species Versus Soil Total Zinc

Data from Keammerer (1987).

Plant Zinc vs Soil Zinc

0

100

200

300

400

500

600

700

0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000

Soil Zinc Concentration mg/kg

Above-Ground Grass Zinc Concentration 

mg/kg

FIGURE 20. Zinc Concentration in Above-Ground Plant Parts of Grasses 

and Grass-Like Species Versus Soil Ammonium Bicarbonate DTPA 

Extractable Zinc

Data from Keammerer (1987).

Plant Zinc vs DTPA Soil Zinc

0

100

200

300

400

500

600

700

0 100 200 300 400 500 600 700 800 900 1000

DTPA Extractable Soil Zinc mg/kg

Aboveground Grass Zinc Concentration 

mg/kg

Step 4: Evaluate Data from Elsewhere

A. Verification of Prediction

Forage yield samples (n = 16) were collected by the Lake County USDA Natural 

Resources Conservation Service agent on August 11, 2000 (NRCS, 2001). The sample 
72

points were randomly selected, and only the current year’s plant growth was harvested. 

These forage yield data were collected independently from other sampling efforts and 

thus allow testing of the prediction that plant growth along the 11-mile reach is limited by 

elevated concentrations of extrinsic metals. Although the Natural Resources 

Conservation Service (NRCS, 2001) did not collect soil samples, the U.S. EPA Phase I 

data set was used to estimate the soil Zn concentration at the forage sampling points. 

One forage data point was excluded because it was too far from the nearest Phase I 

sampling point. Locations of the forage sampling points are shown in Figure 21. No 

information was provided in the NRCS (2001) about grazing intensity, irrigation or any 

other factor that might affect plant growth. Forage yield declined with increasing 

estimated levels of soil Zn (see Figure 22). This trend is significant (SAS quantile 

regression [QUANTREG Procedure] at 0.95 quantile tested with Likelihood Ratio, 

p < 0.0001). The limited number of sampling points in the highest Zn concentration 

areas makes interpretation difficult. Nonetheless, the lower yields observed at the 

highest Zn concentrations are consistent with Zn (or other correlated extrinsic metals) 

phytotoxicity.

B. Comparison to Phytotoxicity Screening Levels

Metal and As concentrations in irrigated meadows areas, along with soil 

concentrations considered to be potentially phytotoxic, are summarized in Table 28. 

The elements listed in Table 28 are those that are found at elevated concentrations in 

the irrigated meadows areas (Levy et al., 1992; Swyers, 1990; U.S. EPA Region 8 and 

USFWS, 2003). The upper end of the observed total concentrations for Ag, As, Cd, Cu, 

Pb and Zn were all well above the concentrations capable of causing phytotoxicity 

(Alloway, 1990; Efroymson et al., 1997; Kapustka et al., 2004). The upper end of the 

reported concentration ranges for Pb and Zn exceed the upper end of the critical ranges 

by 60- and 34-fold, respectively. The reported concentration ranges of several 

elements support the hypothesis that meadows irrigated with water from California 

Gulch are toxic to plants.

C. Indicator Species

Plant biomass at the two locations with the highest total soil concentrations of Zn 

was dominated by plant species that are known to be resistant to metal contamination 

and are considered indicator species. The plant biomass at the LV22 location with the 

highest Zn concentrations in the Keammerer (1987) study was dominated by 

Deschampsia caespitosa (tufted hairgrass) and Carex spp. At LV03, with the second 

highest soil total Zn concentration, Carex aquatilis accounted for 100% of the plant 

biomass. Both tufted hairgrass and Carex species are considered indicator plants for 

metal contamination.
73

FIGURE 21. Forage Yield Sampling Locations (NRCS, 2001)
74

FIGURE 22. Comparison of Measured Forage Yield (g/m2

) with Estimated 

Soil Zinc Concentrations (mg/kg dry wt)*

*Forage yield data were from random locations in the upper Arkansas River valley 

collected by the USDA NRCS (2001). Soil zinc concentrations were estimated 

independently based on the nearest soil sample from the U.S. EPA Region 8 Phase I 

study (U.S. EPA Region 8 and USFWS, 2003).

Estimated Soil Zinc mg/kg

Forage Yield g/m2

0.95 Quantile Fit

0 2000 3000 4000

200

400

600

800

1000

0
75

TABLE 28. Comparison of Soil Concentrations (mg/kg dry wt) of Metals 

and Arsenic Observed in Irrigated Meadows of the Upper Arkansas River 

to Published Thresholds for Phytotoxicitya

Chemical

Concentrations Observed Phytotoxicity Thresholds

Levy et al. 

(1992)b

Swyers 

(1990)c

U.S. EPA 

Region 8 

and 

USFWS 

(2003)d

Alloway 

(1990)e

Kapustka 

et al. 

(2004)

Efroymson 

et al. 

(1997)

Ag ndf nd 0.05−64 2 nd 2

As nd nd 1.5−270 20−50 31

Cd 9−39 5−113 0.11−73 3−8 28 4

Cu 81−431 21−409 7−709 60−125 95 100

Hg nd nd 0.03−122 0.3−5 nd 0.3

Pb 301−18,000 46−1112 4.2−24,200 100−400 210 50

Zn 699−7650 294−8722 56−13,500 70−400 130 50

a

Concentration units are mg/kg.

b

Range of concentrations reported by Levy et al. (1992) from the impaired meadows area. 

c

Concentrations reported by Swyers (1990) from an impaired meadow area.

d

Concentration range reported by U.S. EPA in the 11-mile reach. e

Critical range of soil concentrations above which toxicity is considered to be possible. Data from 

Kabata-Pendias and Pendias (1984). f

nd = no data on this element. 

Tufted hairgrass is a metals-tolerant species and occurs on acidic or pyritic mine 

spoils at high elevations throughout the western United States (Brown et al., 1988). 

This grass also is good to excellent forage for all types of livestock and wildlife, and is 

commonly planted on mine spoil sites as a restoration technique (Morris et al., 1950; 

Stubbendieck et al., 1992). Tufted hairgrass also was the dominant plant species at 

other sampling locations where high concentrations of metals were not present. Thus, 

the presence of tufted hairgrass alone is not sufficient to reliably indicate metal toxicity. 

Because tufted hairgrass is seeded in high altitude meadows as a desirable forage 

grass, its distributions also are affected by agricultural management of the meadows.

Some herbaceous plants are quite sensitive to heavy metals and other 

contaminants. Thus, contamination of soils with metals can alter plant species 

composition and decrease species richness, canopy coverage and net annual 

productivity of wetland communities (e.g., Cooper and Emerick, 1989; Olson, 1979). 
76

Based on studies of eight Colorado wetlands exposed to varying degrees of heavy 

metal-contaminated runoff, Cooper and Emerick (1989) noted

Subalpine fen wetlands in the Colorado Front Range that have less than 

three vascular plant species growing in the main part of the wetland (not 

the edges) and have less than 50 percent total canopy coverage and less 

than 100 g/m2 total annual primary production, are likely to indicate impact 

from heavy metal toxicity. An exception is areas that are flooded or have 

ponded water for much of the growing season.

Forbs (herbaceous dicots in the Cooper and Emerick [1989] study) seemed particularly 

uncommon in polluted wetlands. These authors noted no species that occurred only at 

contaminated sites, but found that the sedges Carex aquatilis, C. utricularia and/or 

C. scopulorum dominated those areas (Cooper and Emerick, 1989).

Step 5: Identify Probable Cause

Step 5—identifying the probable cause—is the last step in the Stressor 

Identification (SI) process. Based on available evidence organized in Steps 3 and 4, the 

most probable cause(s) is distinguished from a set of less probable causes. Step 5 

consists of three components discussed below.

A. Characterize Causes: Eliminate

Considerations for determining whether or not candidate causes should be 

eliminated are summarized in this section and in Table 29. Table 29 addresses four 

questions for each candidate cause. These questions are:

• Do impairments occur in the same place as exposure?

• Is exposure increased over that at the reference site?

• Is there a gradient of recovery with reduced exposure?

• Is the exposure pathway complete?

If any of the answers for a candidate cause are clearly “no,” then that cause can 

be eliminated. Otherwise, the candidate cause is carried forward to the strength of 

evidence analysis (i.e., the candidate cause is not eliminated, or is remaining).

1. Increased Concentrations of Extrinsic Metals in the Soil. Concentrations 

of As, Cd, Cu, Pb and Zn were higher in the impaired meadows that were irrigated with 

water from California Gulch from 1874 until the 1920s, compared to either upstream 

meadows or meadows further downstream, which were irrigated with water from other 

sources. Forage yield in the affected meadow has decreased from 4.48 Mg/ha (3990 

lbs/acre) in 1874 to 1.68 Mg/ha (1496 lbs/acre) in the early 1990s (Levy et al., 1992). 

Swyers (1990) quoted Dr. Bernard Smith, a local veterinarian and rancher, as stating 

that ranchers had noted decreases in hay yield after mining and smelting became 
77

 

a

NE = no evidence.

widespread in the late 1880s. Statistical models based on soil concentrations of either 

Zn or Cu both provided good fits with distribution of phytotoxicity in laboratory tests

(EP&T, 2002; U.S. EPA Region 8 and USFWS, 2003). Although the data were variable 

and there were few sampling points at the highest Zn concentrations, plots of plant 

biomass and plant species richness versus either total soil Zn concentration or DTPAextractable Zn were consistent with decreasing maximum plant biomass and species 

richness with increasing Zn concentrations (Keammerer, 1987). Intermediate levels of 

plant biomass observed at the highest concentrations of total soil Zn were dominated by 

plant species that are considered to be indicators of metal contamination (Brown et al., 

1988; Cooper and Emerick, 1989). Forage yields based on data collected 

independently by the USDA NRCS (2001) declined with increasing estimated 

concentrations of soil Zn. Because increased concentrations of extrinsic metals 

consistently were observed in association with decreased plant growth, decreased plant 

species richness and presence of metal-tolerant indicator species, Candidate Cause #1 

(increased extrinsic metals) cannot be eliminated.

TABLE 29. Considerations for Determining Whether or not Candidate 

Causes Should be Eliminated for Impairment 2

Candidate Cause Impairments 

Occur Same 

Place as 

Exposure?

Exposure 

Increased over 

Reference 

Site?

Gradient of 

Recovery at 

Reduced 

Exposure?

Exposure 

Pathway 

Complete?

Candidate 

Causes 

Remaining

1. Increased 

extrinsic metals

Yes Yes Yes Yes Yes

2. Decreased pH 

with increased 

extrinsic metals

NEa Yes Yes Yes Yes

3. Decreased pH 

with intrinsic 

(background) 

metals

No No No No No

4. Decreased 

organic matter

No No No No No

5. Soil compaction NE No No Yes No

6. Grazing and

mowing

Yes No NE Yes No

7. Herbicides NE No No NE No
78

2. Interaction of Decreased pH and Increased Extrinsic Metals in the Soil. 

No data are available on change in soil pH levels over time in the irrigated meadows. 

Data on upstream pH levels are very limited (Levy et al., 1992). Statistical analyses of 

laboratory phytotoxicity tests indicate that low soil pH is a contributing factor to 

phytotoxicity (U.S. EPA Region 8 and USFWS, 2003). Based on the laboratory 

phytotoxicity testing, Candidate Cause #2 (interaction of decreased pH and increased 

extrinsic metals) cannot be eliminated.

3. Interaction of Decreased pH and Intrinsic (Background) Metals in the 

Soil. Interaction of decreased pH and intrinsic (background) metals in the soil may 

result in increased levels of metals in solution, causing phytotoxicity (see 

Section 2.1 #3).

The pH values found in areas where impairment was observed ranged from 

5.2−7.1 (see Figure 9). The pH value in an unimpaired, upstream meadow was lower 

than the pH values for the impaired meadow areas (Levy et al., 1992). Based on the 

absence of colocation of decreased pH with the observed impairment, Candidate 

Cause #3 (interaction of decreased pH with intrinsic metals) was eliminated from 

further consideration. 

4. Decreased Soil Organic Matter Results in Higher Metal Bioavailability. 

Data presented by Levy et al. (1992) showed no consistent difference in soil organic 

carbon between an upstream meadow and the impaired meadows. Statistical analyses 

also did not reveal a significant correlation between soil organic carbon and 

phytotoxicity in laboratory tests. Based on the absence of colocation of decreased 

soil organic carbon with the observed impairment, Candidate Cause #4 

(decreased soil organic matter) was eliminated from further consideration. 

5. Soil Compaction Reduces the Permeability of the Soil Surface Layers 

and Root Growth. Soil compaction is a common problem that restricts plant growth. 

Soil compaction is particularly severe under the saturated soil conditions that 

predominate in the upper Arkansas River area. Grazing cattle can compact soil 

(Wheeler et al., 2002). However, grazing occurs both upstream and downstream of the 

confluence of California Gulch with the Arkansas River (see below), and chlorosis and 

barren areas in irrigated meadows were not reported upstream of California Gulch. Soil 

compaction can also arise from differences in soil origin or type, e.g., deposited fines, 

but evidence supporting this is lacking. Soil compaction does not appear to be a 

cause of impairment in the irrigated meadows.

6. Grazing by Cattle and Mowing (for Hay Production) can Affect Plant 

Growth or Species Richness. The available data do not suggest that grazing or 

mowing were different for the study area—which includes the impaired meadows—

compared to surrounding areas. According to data from 1997 and 2002 Cattle and 

Calves Inventories (USDA, 2004), the study area county (Lake) contained 1858 and 

287 head of cattle in 1997 and 2002, respectively, compared to 12,803 and 5964 head 

of cattle (1997 and 2002) in the county (Eagle) north of the study area and 11,167 and 
79

6590 head of cattle (1997 and 2002) in the county (Chaffee) south of the study area. 

Data on mowing per se is not available, but according to harvested hay data from a 

2002 census (USDA, 2004), 220 acres, yielding 141 tons, was harvested from the study 

area county (Lake), compared to 6391 acres, yielding 7021 tons in Eagle County, and 

7198 acres in Chaffee County. Based on the above data, there does not appear to be 

evidence for a relationship between either grazing by cattle or mowing and plant growth 

or species richness. Because cattle grazing and mowing occur both upstream and 

downstream, they are not likely to be causes of the impairment.

7. Increased Herbicide Usage to Enhance Hay Production May Reduce 

Plant Growth or Species Richness. Selective herbicides sometimes are used to favor 

the growth of grass species at the expense of other plant species. Repeated 

applications of selective herbicides over a period of years could eliminate or reduce the 

growth of some plant species. The limited available data do not suggest an increased 

use of herbicides in the study area—which includes the impaired meadows—compared 

to surrounding areas. According to data from a 1997 census (USDA, 2004), herbicides 

were applied at 3 farms in the study area county (Lake) compared to 33 and 24 farms, 

respectively, in counties north (Eagle) and south (Chaffee) of the study area. The data 

were similar for the 2002 census, where herbicides were applied to 2 (Lake), 30 (Eagle) 

and 7 (Chaffee) farms. Hence, there does not appear to be evidence for a relationship 

between herbicide usage and plant growth or species richness. Because herbicide 

usage for hay production occurs both upstream and downstream, it is not likely 

to be a cause of the impairment.

B. Characterize Causes: Strength of Evidence

Strength of evidence analysis uses all the evidence generated in the analysis 

phase to examine the credibility of the remaining candidate causes. The causal 

considerations for the strength of evidence analysis use three types of evidence: casespecific evidence, evidence from other situations or biological knowledge, and evidence 

based on multiple lines of evidence. All evidence was evaluated for consistency or 

coherence with the hypothesized causes. The results of the strength of evidence 

analysis are summarized in Table 30. In the table, each line of evidence is given a 

score as follows:

NE = no evidence.

+ + + = convincing evidence as cause.

+ + = strong evidence as cause.

+ = weak evidence as cause.

0 = unclear.

– = weak evidence as not cause.

– – = strong evidence as not cause.

– – – = convincing evidence as not cause.
80

TABLE 30. Strength of Evidence for Specific Causes and 

Considerations for Impaired Irrigated Meadows

Consideration

Extrinsic Metals Extrinsic Metals with 

Decreased pH

Results Score Results Score

Case-Specific 

Evidence

Spatial cooccurrenceCompatible: Concentrations 

of extrinsic metals were 

consistently elevated in the 

impaired meadows 

downstream of California 

Gulch (3.2 Step 3 A, C). 

Concentrations of metals 

were elevated at impaired 

meadow sites on two ranches 

that received irrigation water 

from California Gulch, 

compared to an upstream 

reference site (3.2 Step 3 C). 

+ Incompatible: pH 

values in most of the 

impaired areas were 

near neutral (3.2 

Step 3 A).

– – 

Temporality Compatible: Decreased 

forage yields were reported 

after mining and smelting 

became widespread in the 

area (3.2 Step 3 C).

+ NE NE

Consistency 

of association

Consistent: Decreased plant 

biomass and species 

richness was observed 

consistently with increased 

Zn (3.2 Step 3 C).

+ + Inconsistent: 

Impaired plant 

growth occurred in 

areas with nearneutral pH (3.2 Step 

3 C).

– 

Biological 

Gradient

Moderate: There is a general 

trend towards less plant 

biomass and species 

richness with increasing Zn. 

However, intermediate plant 

biomass occurred at two 

locations where Zn 

concentrations were highest, 

due to the dominance of 

metals-tolerant indicator 

species at those locations 

(3.2 Step 3 C).

+ + Inconsistent: Areas 

of impaired 

vegetation often had 

near-neutral or 

alkaline pH values 

(3.2 Step 3 C).

0 

Complete 

exposure 

pathway

Complete: Increasing Zn 

concentrations were found in 

plants with increasing soil 

total Zn and with increasing 

DTPA-Zn (3.2 Step 3 C).

+ + NE 0 
81

TABLE 30 cont.

Consideration

Extrinsic Metals Extrinsic Metals with 

Decreased pH

Results Score Results Score

Case-Specific 

Evidence cont.

Experiment Concordant: Phytotoxicity 

tests and follow-up models 

indicate that Zn or Cu 

concentrations are 

reasonable predictors of site 

phytotoxicity (3.2 Step 3 A; 

B).

+ + Concordant: 

Phytotoxicity tests 

and follow-up 

models indicate that 

Zn or Cu 

concentrations are 

reasonable 

predictors of site 

phytotoxicity. Low 

soil pH is a 

contributing factor 

(3.2 Step 3 A; B).

+ 

Information 

From Other 

Situations or 

Biological 

Knowledge

Mechanism Plausible: Ag, As, Cd, Cu, 

Hg, Pb, Zn are known to 

cause the types of effects 

seen in the meadows (2.1).

+ Plausible: Plants are 

sensitive to elevated 

metal 

concentrations and 

metal solubility 

increases when pH 

declines (2.1).

+ 

StressorresponseConcordant: Ag, As, Cd, Cu, 

Hg, Pb, Zn were present at 

concentrations that exceed 

phytotoxicity screening 

thresholds (3.2 Step 4 B).

+ Inconcordant: 

Increased extrinsic 

metals alone was 

sufficient to cause 

impairment (3.2 

Step 4 B). 

– 

Consistency 

of association 

Consistent: Contamination 

of soils with metals altered 

plant species composition, 

and decreased species 

richness, canopy coverage 

and net annual productivity 

of subalpine wetland 

communities (3.2 Step 4 C).

+ + Inconsistent: 

Reported 

concentration 

ranges of several 

elements, alone, 

were sufficient to 

cause impairment 

(3.2 Step 4 B).

– 

Specificity of 

cause

Many other stressors can 

impair vegetation; 

nonetheless, locations with 

the highest Zn 

concentrations were 

associated with few plant 

species, two of which are 

indicator species for metal 

contamination (3.2 Step 3 C; 

3.2 Step 4 C).

+ Many other 

stressors can impair 

vegetation.

0 
82

TABLE 30 cont.

Consideration

Extrinsic Metals Extrinsic Metals with 

Decreased pH

Results Score Results Score

Information 

From Other 

Situations or 

Biological 

Knowledge 

cont.

Analogy Dominance of tolerant 

species including 

Deschampsia caespitosa

and Carex species at metalcontaminated sites has been 

reported in many other 

locations (3.2 Step 4 C).

+ NE 

Experiment NE NE 

Predictive 

Performance

Confirmed: Forage yield 

measured at random 

locations declined with 

estimated concentrations of 

Zn (3.2 Step 4 A).

+ + NE 

Considerations 

Based on 

Multiple Lines 

of Evidence

Consistency 

of Evidence

All lines of evidence are 

consistent with increased 

levels of extrinsic metals as 

a cause of impaired 

vegetation.

+ + + Some lines of 

evidence were 

consistent with 

decreased pH as a 

contributing cause, 

but neutral to 

alkaline areas were 

also impaired.

+ 

Coherence of 

evidence

 Inconsistency may 

be explained by the 

fact that 

concentrations of 

metals greatly 

exceeded 

concentrations 

known to be 

phytotoxic and 

would be expected 

to cause toxicity 

even at circumneutral pHs.

+ 

Impaired areas of irrigated meadows occur on the Seppi and Smith ranches just 

below the confluence of California Gulch. Plant growth in these areas was impaired 

relative to upstream reaches and to meadows further downstream. Elevated 

concentrations of extrinsic metals were consistently observed in the impaired areas 

(Levy et al., 1992; Swyers, 1990; U.S. EPA Region 8 and USFWS, 2003). A layer of 

buried sand with elevated levels of extrinsic metals was reported in several locations in 

the meadows on the Seppi ranch (Levy et al., 1992; Swyers, 1990). Soil pH values in 

the impaired meadows on the Seppi ranch were slightly acidic: this condition is 

consistent with increased availability of extrinsic metals to plants. However, the pH 

levels were not acidic enough to be consistent with known effects of acidity on solubility 
83

of Al, Fe and Mn, intrinsic metals that are phytotoxic at low pH. The highest 

concentrations of metals were observed on the Smith ranch, accompanied by neutral to 

slightly alkaline pH. In general, plant biomass and species richness declined with 

increasing concentrations of Zn and other extrinsic metals (see Figures 14 through 17). 

However, intermediate plant biomass occurred at the two locations where Zn 

concentrations were highest (see Figure 14). The plant biomass at these two locations 

was dominated by Deschampsia caespitosa, Carex aquatilis and Carex spp. These 

plant species can tolerate high concentrations of metals and are considered to be 

indicator species for metal contamination.

The results of laboratory phytotoxicity tests were consistent with increased 

concentrations of extrinsic metals as the cause of impaired plant growth in the irrigated 

meadows on the Seppi and Smith ranches (U.S. EPA Region 8 and USFWS, 2003). 

Soil samples collected at 20 representative locations were tested in the laboratory using 

alfalfa, yarrow and wheat grass (EP&T, 2002). The bulk soil variable giving the best 

prediction of laboratory mean phytotoxicity score was the first principal component, 

which was dominated by metals, pH and Ca concentration.

Additional examination of the laboratory phytotoxicity test results was performed 

by dividing the data into a binary variable: phytotoxic versus nontoxic locations. 

Bayesian models developed for Zn and Cu had almost identical DIC values (DIC Cu = 

23.7; DIC Zn = 22.9). This result shows that the models had very similar fits. The CVs 

of the Cu model parameters were larger than those found in the Zn model, indicating 

greater uncertainty in the parameter distributions. The CV of β1 in the Zn model is much 

smaller than the CV of β1 in the Cu model. This finding indicates that, across all sites, 

the relationship between toxicity and Zn is stronger than the relationship between Cu 

and toxicity.

We used forage yield data collected independently by the USDA NRCS (2001) to 

test the prediction that increased concentrations of extrinsic metals caused decreased 

plant growth. Although metal concentrations were not measured at the locations of the 

forage sample collection, we used the data from 126 sample locations (U.S. EPA 

Region 8 and USFWS, 2003) to estimate Zn concentrations at the forage-yield 

locations. As predicted, the upper limit of forage yield declined monotonically as the 

estimated concentration of Zn increased (see Figure 21). High variability at low Zn 

concentrations and relatively few observations decrease the strength of this evidence. 

The relationship between forage yield and Zn concentrations was consistent with the 

predictions from models of laboratory studies.

C. Characterize Cause: Probable Cause

After five candidate causes were deemed improbable, two candidate causes 

remained. These were compared on the basis of strength of evidence. The two 

candidate causes were: #1 (increased extrinsic metals) and #2 (interaction of extrinsic 

metals with decreased pH).
84

The evidence supporting Candidate Cause #1 (impairment of plant growth due to 

elevated levels of extrinsic metal) was consistent throughout the lines of evidence. The 

strength of association, spatial co-occurrence, plausible stressor response and 

experimental lines of evidence strongly supported this candidate cause. The quality of 

the data is adequate for this conclusion, and confidence is high (see Table 30).

In contrast, the evidence for Candidate Cause #2 (interaction of decreased pH 

with increased extrinsic metal concentrations) was inconsistent. Impaired plant growth 

occurred in meadow areas with near-neutral to alkaline pH values. Hence, it appears 

that increased extrinsic metals alone were sufficient to cause impairment of plant growth 

in the irrigated meadows.
85

4. LESSONS LEARNED

4.1. INTRODUCTION

The Stressor Identification methodology (U.S. EPA, 2000) has been 

demonstrated in the context of case studies for the Clean Water Act. The present case 

study applies the SI methodology at a terrestrial contaminated site, the highly 

mineralized area of the Colorado Rocky Mountains, to shed light on its utility in such an 

environment. This section summarizes lessons learned during application of the SI 

process to a terrestrial hazardous waste site relative to application at aquatic sites. It 

also highlights what, if any, aspects of the SI process (as described in U.S. EPA, 2000) 

are different, and provides suggestions for further development of the SI process at 

terrestrial sites.

The greatest difference between aquatic and terrestrial systems is the 

uncertainty associated with measuring environmental parameters. In aquatic systems, 

a certain degree of homogeneity can be assumed for water column exposures at a local 

level due to the mixing effects of water currents. Sediments are less homogeneous, but 

soil systems present a difficult problem with the great heterogeneity in environmental 

parameters, both horizontally and vertically within the soil profile. Such is the case in 

this study.

4.2. CRITICAL VARIABLES

Dominant soil chemical/physical properties known to affect the bioavailability of 

contaminants are soil pH, organic carbon, cation-exchange capacity (CEC), clay 

content, and reactive iron, aluminum and manganese oxides (Basta et al., 2005; 

Fairbrother et al., 2007). These parameters are very important factors affecting the 

bioavailability of both intrinsic and extrinsic metals to plants. Concentrations of Al, Mn 

and Fe present in all soils can be phytotoxic at low pH. Conversely, extrinsic metals 

that have been introduced by human activity may not be phytotoxic even at elevated 

concentrations if the pH is high, because the solubility of many metals is reduced at 

high pH levels. The contaminated, vegetated area in the Upper Arkansas River (UAR) 

illustrates this, with its abundant, diverse vegetation despite As, Cu, Cd, Pb and Zn 

levels that were as high as those in barren fluvial mine-waste deposit areas. The critical 

difference was the difference in pH. Thus, pH must be known in order to determine if 

impairment is due to elevated metal concentrations.

Soil compaction can reduce plant growth. In the present study, the known 

causes of soil compaction did not appear to be associated with impaired areas. 

Nonetheless, measurements of soil compaction would lend more credibility to the 

analysis.

In the UAR case study, organic matter appeared to be less critical than soil pH. 

This may not be true in other cases, and both soil organic matter and CEC are 
86

important determinants of how metals “operate” on plants. Brown et al. (1998) suggests 

that the effects of added organic matter are more important after a period of aging. (To 

date, there are no models available that incorporate an aging term linked to chemical 

bioavailability.) When designing investigations of terrestrial sites, these soil parameters 

should be included. If the investigator is working with available information and data for 

these parameters are not available, then these data gaps should be pointed out in an 

uncertainty section.

Although metals and low pH are the cause of the barren areas surrounding mine 

tailing deposits, low nutrient levels may be a contributing factor. TKN ranged from 

1−1.6 g/kg in untreated fluvial mine-waste deposits, compared to 4.5 g/kg in the upland 

reference and 2.9 g/kg in the contaminated vegetated area soils (Brown et al., 2005). 

Available phosphorus concentrations ranged from 1−12 mg/kg in the untreated fluvial 

mine-waste deposits, compared to 23 mg/kg in the upland reference soil (though both of 

these soils would be considered potentially deficient in available phosphorus for plant 

growth). The addition of lime and biosolids to fluvial mine-waste deposits (that resulted 

in the establishment of plant cover) increased soil pH, but also increased TKN and 

phosphorus concentrations (Brown et al., 2005). In other field experiments (Fisher, 

1999), adding triple-super-phosphate (TSP) alone did not increase the available 

phosphorus in soil samples and did not significantly affect survival or leader growth of 

willow cuttings. However, the addition of TSP to limed treatments resulted in a 

significant increase in total leader length. This indicates that phosphorus may have 

been a factor contributing to decreased plant growth. Elucidation of the contribution of 

nutrients to the effects of metals would require more and better nutrient data.

It is essential that critical soil chemical/physical characteristics are measured in 

association with chemical levels at terrestrial sites to establish the effects of these 

modifying factors on bioavailability. In aquatic systems, the Biotic Ligand Model (BLM) 

can be used to examine the effects of environmental modifying factors (i.e., pH, water 

hardness) on metal speciation and subsequent binding to biotic ligand (fish gill). BLMs 

exist for Cu and Ag in aquatic systems (U.S. EPA, 2003), and a few examples exist for 

terrestrial systems (Thakali et al., 2006a,b; Koster et al., 2006). Also, regression 

equations, similar to the one developed in this document, have been developed to 

describe the effects of soil properties on bioavailability and toxicity. These regressiontype models are somewhat limited in their range of application over different soils, but 

are useful in getting a rough idea of the most important factors determining metal 

bioavailability at a given site (reviewed by Smolders et al., 2009). Use of Path Analysis, 

another statistical tool, can augment regression analysis and provide a measure of the 

relative contribution of specific soil properties in modifying metal bioavailability and 

toxicity (Dayton et al., 2006).

4.3. BIOASSESSMENT CRITERIA ARE NOT WELL DEVELOPED FOR PLANTS

Bioassessment criteria for terrestrial systems are not well developed. For plants, 

the Riparian Evaluation System (RipES) has been developed, but the criteria are very 

general (U.S. EPA, 2004b). Basically the RipES method evaluates vegetation as 
87

present or absent, and indicates that Deschampsia caespitosa (tufted hairgrass) is an 

indicator of metal contamination. There is also some limited information for indicator 

species for metal contamination in wetlands (U.S. EPA, 1990).

Forbs, and probably legumes in particular, may be uniquely sensitive to metal 

contamination, whereas grasses generally tend to be more resistant (U.S. EPA Region 

8 and USFWS, 2003). This generality is similar to the situation in streams, where the 

Ephemeroptera (mayflies), Plecoptera (stoneflies) and Trichoptera (caddisflies) (EPT) 

species are intolerant of pollutants and will not be found in polluted waters (Carlisle and 

Clements, 1999). The greater the pollution, the lower the species richness expected, as 

only a few species are pollutant tolerant. The metal resistant grass species, D.

caespitosa, is, however, a palatable, nutritious forage grass for both domestic animals 

and wildlife species (Morris et al., 1950; Stubbendieck et al., 1992). Carex aquatilis, a 

wetland plant recognized as an indicator species for metal contamination, is also 

considered a good source of forage. In the presence of high soil metal concentrations 

these species were observed to be dominant, almost monocultures. Thus, the 

biodiversity of the habitat became very limited. However, without data on metal uptake 

by (and toxicity to) domestic animals/wildlife species, it is not clear that these plant 

species are undesirable or incapable of supporting a healthy ecosystem.

There is a potential physiological basis for the tendency for grasses to be more 

metal tolerant. There are two basic strategies among plant families for uptake of Fe and 

other metal micronutrients (Welch, 1995). Dicots and nongrass monocots, reduce 

Fe(III)-chelates to Fe(II) outside the cell membrane in the apoplasm prior to uptake. 

Actual uptake of Fe(II) into the cytoplasm is driven by active export of H+ ions out of the 

plant cell. The membrane potential generated by H+ export drives uptake of divalent 

cations (Ca2+, Fe2+, Zn2+, Mn2+, Cu2+ and Ni2+) across the plasmalemma. Divalent 

cation uptake is thought to be mediated by inward directed divalent cation channels. In 

contrast, grasses have not been shown to reduce Fe(III)-chelates. Also, these plants do 

not acidify the rhizosphere in response to Fe-deficient conditions. Instead, these plants 

apparently depend on specific transport proteins in the plasmalemma with high 

specificity Fe(III)-phytometallophores (aka phytosiderophores). These transport 

proteins specifically bind the Fe(III)-phytometallophores and transport the intact 

complex across the plasmalemma. This type of mechanism could drive the uptake of 

other micronutrient metals (e.g., Zn2+, Mn2+, Cu2+ and Ni2+) phytometallophore chelates 

into plant cells. These apparent fundamentally different multivalent metal uptake 

mechanisms could help explain why grasses are apparently less sensitive to metal 

toxicity than dicots and nongrass monocots. That is, the nongrass plant species acidify 

the rhizosphere, which would increase metal solubility near the root surface, and 

divalent ions are taken up through relatively nonspecific cation channels. Both of these 

factors would increase indiscriminant uptake of potentially toxic metals. This concept, 

however, does not offer an explanation for the metal tolerance of C. aquatilis, which is a 

sedge species, not a grass species.

Loss of legumes from the plant community could also be a critical parameter. 

Biological nitrogen fixation by legume symbiosis provides critical inputs of nitrogen to 
88

soil in a form needed for plant growth. Alfalfa was more sensitive to the phytotoxic 

effects of the soils from the UAR valley in laboratory studies (EP&T, 2002). Also, 

Siemer (1999) reported that clovers were abundant in the irrigated meadows of the UAR 

valley on ranches where metal concentrations were not elevated. In contrast, few 

legumes were found in areas with elevated metal concentrations (Keammerer, 1987). 

In general nodulation is more affected than host plant growth (Crowley and Alvey, 

2002). Loss of legumes and biological nitrogen fixation could affect overall plant 

community growth.

Additional research on plant species metal tolerance, may help identify shifts in 

plant species composition that are indicative of specific stressors. Systematic 

evaluation of existing data from metal contaminated sites may define patterns in species 

shifts that are indicative of metal contamination.

4.4. SPATIAL SCALE ISSUES

Small scale changes in soil characteristics and moisture availability can confound 

interpretation of evidence of impairment. The plant species composition varies greatly 

between wetland and upland ecosystems. Heterogeneity in soil characteristics can 

affect plant growth and species composition. The effects of these factors need to be 

separated from effects due to stressors when assessing the cause of an impairment.

Wetlands are the transition from the aquatic ecosystems to purely upland 

terrestrial ecosystems. The plant species composition differs for the wetland areas in 

contrast to the completely upland areas. This is particularly problematic in the UAR 

valley due to its semi-arid climate. Near the river, wetlands predominated and the soil is 

saturated most of the year. Along a river-to-upland transect, much of the first bench 

level that was included in the study was irrigated. This situation limited differences in 

moisture availability. Flood irrigation could not reach the few elevated areas within the 

meadows. These elevated, upland areas were very different in plant species 

composition. In the application of SI principles to terrestrial ecosystems, it is important 

to understand that plant community species composition is strongly influenced by the 

hydrology of the site.

Soil is very heterogeneous, and soil properties can change greatly over a 

distance of a few meters. This can result in changes in plant growth and species 

composition that are unrelated to any changes in anthropogenic stressors. This is 

reflected in the large variability in plant growth that was observed at low metal 

concentrations in the UAR. These variations can result from differences in water 

availability, soil fertility and agricultural management practices. One method to control 

for differences caused by soil heterogeneity is to obtain available information on soil 

properties. In the UAR we first consulted the Chafee-Lake Area Soil Survey (Fletcher, 

1975) to check that the areas included in the irrigated meadows areas were of a similar 

soil type, to limit the possibility that differences in plant growth and species 

compositions were due to natural soil differences as opposed to introduced stressors.
89

4.5. MULTIDIMENSIONAL

The SI process for streams assumes that the systems are linear, with upstream 

versus downstream being very clear delineations. This concept may not work very well 

for most terrestrial systems. Where the contaminants are transported by stream 

processes, such as the fluvial mine-waste deposit areas in the UAR case study, the 

upstream versus downstream comparison can work. In the irrigated meadows areas, 

the source of the irrigation water became very important. In this case study, we 

addressed this problem by plotting plant growth and plant species richness data as a 

function of measured soil metal concentrations, rather than as a function of distance 

from the assumed source.

The distribution of contaminants as a function of soil depth is also important to 

consider. This was illustrated by the data of Levy et al. (1992) that indicated the highest 

concentrations of Pb and Zn were found 15 cm below the soil surface at two sites. We 

also had access to data from the UAR site from soil samples collected from a depth of 

5 cm. These data would have missed the highest concentrations observed by Levy et 

al. (1992) and could have resulted in incorrect assessment of the cause of impairment. 

Soil sampling for terrestrial SI should occur over a range of soil depths appropriate to 

the plant community being examined. For example grasses tend to have shallow root 

systems, and soil samples from the top 30 cm of soil should be adequate. Many shrub 

and tree species have deeper roots systems.

 

4.6. HUMAN MANAGEMENT

Plant species distribution and plant growth can be strongly affected by 

agricultural management practices. In the UAR area, the resistant plant species 

D. caespitosa—tufted hairgrass—may have been purposely planted in the areas of high 

metal concentrations to provide some forage yield in the metal contaminated meadows. 

D. caespitosa is a palatable forage grass and is grazed readily by wildlife. Fertilization, 

mowing practices, grazing, irrigation and herbicide use all can affect plant growth. 

Information on these factors should be collected to allow factors that affect plant growth 

and species composition to be properly evaluated.

4.7. SOIL MICROBIAL PROCESSES

Soil microbial processes can affect plant growth. Mycorrhizal symbioses, both 

ectomycorrhizal and endomycorrhizal, can affect nutrient uptake and metal uptake by 

plants. Mycorrhizal fungal hyphae greatly increase the absorptive surface area of plant 

roots interacting with the soil. Mycorrhizae also can reduce metal uptake in plants by 

immobilizing metals in fungal cell walls (Punshon et al., 2005).

Biological nitrogen fixation is a key process in many nutrient-poor terrestrial 

areas. As evidenced by the greater phytotoxic effects with alfalfa observed in the 

laboratory, legumes may be more sensitive to metal phytotoxicity (U.S. EPA Region 8 

and USFWS, 2003). Observations by a local agronomist suggest that legumes, clover 
90

in particular, are abundant on ranches without high metal concentrations; in the samples 

reported by Keammerer (1987) there were few legumes present. Both nodulation and 

nitrogen fixation are more sensitive to high metal concentrations and lower pH than is 

legume growth (Crowely and Alvey, 2002). Additional data presented by Brown et al. 

(2005) indicate that nitrification was inhibited in the barren fluvial mine-waste deposits. 

This effect was ameliorated when lime was added to raise the soil pH. The form of 

nitrogen present in the soil also affects plant species composition.

Although methods for assessing effects of soil microbes on plant growth are not 

highly developed, researchers should keep these processes in mind when designing 

investigations.

Nitrogen deficiency can be diagnosed rapidly over large areas using remote 

sensing data. This would provide a rapid method for assessing nitrogen nutrition of 

plants. Legumes can readily be analyzed for differences in 15N natural abundance 

variation as a measure of their dependence on biological nitrogen fixation versus soil 

nitrogen (Eskew et al., 1992). This in turn may provide an indication of the amount of 

bioavailable nitrogen in the soil.

4.8. BINARY DISTRIBUTION OF PHYTOTOXICITY

Reducing the evaluation of phytotoxicity to a simple binary distribution of toxic 

versus nontoxic allowed the statistical analysis to distinguish more subtle differences in 

the correlations between soil chemical concentrations and observed phytotoxicity. The 

variability inherent in continuous measures of phytotoxicity obscured the relationship 

between soil metal concentrations and phytotoxicity.

4.8.1. Statistical Analysis of Vegetation Data

Various follow-up statistical analyses can be used to provide additional information:

• Exploration of laboratory models with additional predictor variables, including 

combinations of metals such as Zn, Cu and As. 

• Examination of the role of pH in estimating toxicity. Examination of the relative 

change in the log-likelihood statistic based on pH alone (called a slice function) 

may provide useful insights into the relative contribution of multiple metals at 

fixed pH intervals.

• Exploration of models for plant species presence/absence based on field 

observations. 

• Comparative analysis of laboratory-derived models and field-derived models. 

Any differing relationships found in the lab and field data could be modeled and 

explored.

• Investigation of statistical methods for overcoming the effect of correlation among 

candidate predictor variables.
91

• Examination of additional response variables measured in the laboratory and 

field, such as plant dry weight or root growth.

4.9. REMOTE SENSING

Remote sensing techniques can be a powerful tool for obtaining fine-scale spatial 

data on plant abundance and near-surface concentrations of metals. These data would 

be limited to surface soil metal concentrations. Remote sensing data produced by the 

USGS using information from National Aeronautics and Space Administration's Airborne 

Visible and Infra-Red Imaging Spectrometer (AVIRIS) are available for the area 

immediately around Leadville (Swayze et al., 1998). Combining the AVIRIS 

hyperspectral data with other available remote sensing data may allow development of 

maps that correlate soil metal concentrations with plant stress levels. Remote sensing 

data also could be used to evaluate grazing intensity or land use practices.

4.10. CONCLUSION

There is no a priori reason that the methodology for performing a causal 

assessment should differ between aquatic and terrestrial systems. Yet, there are 

inherent differences between these environments. Aspects of the assessment process 

that may differ between aquatic and terrestrial systems include the critical variables that 

are measured, degree of development of bioassessment criteria, spatial heterogeneity 

and linearity of physico-chemical factors, and management practices. The causal 

analysis described here would have yielded conclusions with a higher degree of 

certainty if more data were available, e.g., data on grazing intensity and agricultural 

practices. Nonetheless, this exercise, along with the causal assessment for kit foxes on 

the Elk Hills, CA (U.S. EPA, 2008), demonstrates the usefulness of the Stressor 

Identification methodology for terrestrial systems. Additional case studies should shed 

light on whether the SI methodology is sufficiently robust to be applied to a greater 

variety of terrestrial systems having other potential stressors such as organic 

contaminants.
92

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99

APPENDIX A

DESCRIPTION OF DOWNSTREAM REACHES ALONG THE UPPER ARKANSAS 

RIVER FLOODPLAIN

Reach 1. This reach, about 8850 feet in length, extends from California Gulch to 

the confluence of Lake Fork Creek and contains numerous fluvial mine-waste deposits 

(see Figure 4). In the lower 3600 feet of this reach, before the confluence of Lake Fork 

Creek, there are numerous irrigation ditches that may have diverted water containing 

elevated concentrations of metals to the floodplain and adjacent meadows.

In 1997, the Natural Resources Conservation Service (NRCS) completed 

vegetation mapping of the 11-mile reach of the Arkansas River basin. The assessment 

was designed to map vegetation communities adjacent to the river. Five major plant 

community types were identified along the 11-mile reach (NRCS, 1997). Three of these 

community types were mapped within Reach 1 including meadow, subirrigated and 

riparian subirrigated community types. Vegetation in Reach 1 is dominated by a 

riparian shrub community consisting primarily of willow species. It is interspersed with

open water wetlands and herbaceous riparian vegetation consisting of sedges and 

rushes representative of saturated soils. Areas of unvegetated fluvial mine-waste 

deposits and unvegetated sandbars occur throughout. The uplands are dominated by 

herbaceous riparian vegetation consisting of sedges, rushes, and mesic grasses 

representative of moist soils. These areas are interspersed with upland grasses. 

Agricultural activities influence the composition and productivity of vegetation in 

Reach 1 (CDOW, 1988). 

Reach 2. This reach, about 18,000 feet in length, extends from the confluence of 

Lake Fork Creek downstream to the Highway 24 bridge across the Arkansas River (see 

Figures 4 and 5). Tributaries in Reach 2 include Lake Fork Creek, Halfmoon Creek, 

Iowa Gulch and Thompson Gulch (see Figure 8). One large agricultural diversion ditch 

(Derry Ditch No. 1) collects water on the western side of the river and conveys the water 

to the floodplain. Other agricultural irrigation ditches are also present.

Vegetation mapping conducted by NRCS (1997) identified five plant community 

types along Reach 2: wet meadow, subirrigated, riparian subirrigated, irrigated pasture 

and upland. Vegetation in the upper half of Reach 2 is dominated by riparian shrub 

community consisting of willow species, and herbaceous vegetation consisting of 

sedges and rushes, interspersed with areas of open standing water. The area contains 

unvegetated mine-waste deposits and unvegetated sandbars. The lower half of 

Reach 2 is dominated by riparian herbaceous vegetation consisting primarily of sedges 

and rushes indicative of saturated soils. The uplands are dominated by herbaceous 

riparian vegetation consisting of sedges, rushes and mesic grasses representative of 

moist soils. The area is interspersed with unvegetated fluvial mine-waste deposits and 

unvegetated sandbars (CDOW, 1988).
100

Reach 3. This reach, about 19,000 ft in length, extends from the Highway 24 

bridge across the Arkansas River to a short distance downstream of County Road 55 

(Spring Creek) (see Figures 5 and 6). Tributaries in Reach 3 include Empire Gulch, Dry 

Union Gulch, drainage from the Mount Massive Lakes area located downstream of Dry 

Union Gulch, and Spring Creek (see Figure 8). 

Vegetation mapping identified four plant community types along Reach 3: wet 

meadow, subirrigated, riparian subirrigated, and upland (NRCS, 1997). Vegetation in 

Reach 3 is dominated by riparian herbaceous vegetation consisting primarily of sedges 

and rushes indicative of saturated soils with areas of open standing water. The area is 

interspersed with riparian shrub vegetation consisting of willow species. There are large 

areas of unvegetated fluvial mine-waste deposits and unvegetated sandbars (CDOW, 

1988).

Reach 4. This reach, about 9400 ft in length, extends from the narrows below 

County Road 55 downstream to approximately 700 ft above the confluence of Two-Bit 

Gulch. At the downstream end of Reach 4, the channel enters a bedrock canyon (see 

Figure 6).

Vegetation mapping identified three plant community types along Reach 4: wet 

meadow, subirrigated, and riparian subirrigated (NRCS, 1997). Vegetation in Reach 4 

is dominated by riparian herbaceous vegetation consisting primarily of sedges and 

rushes characteristic of waterlogged soils. The reach is interspersed with riparian shrub 

vegetation, small areas of unvegetated fluvial mine-waste deposits and small areas of 

unvegetated sandbar (CDOW, 1988).
101

APPENDIX B

BOX-AND-WHISKER PLOTS

Box-and-whisker plots for measurement endpoints using laboratory phytotoxicity 

test data (site-specific laboratory measurements and phytotoxicity scores) from 20 sites 

(see Section 3.2 Step 3 B). The small square represents the mean of the toxic or 

nontoxic site data.
102

Arsenic

Non-toxic Toxic

0

100

200

300

Concentration (ppm)

Arsenic

Non-toxic Toxic

1

2

3

4

5

6

Log Concentration (ppm)
103

Cadmium

Non-toxic Toxic

0

25

50

75

100

125

Concentration (ppm)

Cadmium

Non-toxic Toxic

-1

0

1

2

3

4

5

Log Concentration (ppm)
104

Copper

Non-toxic Toxic

2

3

4

5

6

7

Log Concentration (ppm)

Copper

Non-toxic Toxic

0

200

400

600

800

1000

Concentration (ppm)
105

Lead

Non-toxic Toxic

0

10000

20000

30000

40000

50000

60000

Concentration (ppm)

Lead

Non-toxic Toxic

2

4

6

8

10

12

Log Concentration (ppm)
106

Mercury

Non-toxic Toxic

0

20

40

60

80

Concentration (ppm)

Mercury Mercury

Non-toxic Toxic

0

20

40

60

80

Concentration (ppm)

Mercury

Mercury

Non-toxic Toxic

-4

-2

0

2

4

6

Log Concentration (ppm)

Mercury Mercury

Non-toxic Toxic

-4

-2

0

2

4

6

Log Concentration (ppm)

Mercury
107

Zinc

Non-toxic Toxic

0

5000

10000

15000

20000

Concentration (ppm)

Zinc

Non-toxic Toxic

4

5

6

7

8

9

10

Log Concentration (ppm)
108

pH

Non-toxic Toxic

3

4

5

6

7

8

9

pH units

pH

Non-toxic Toxic

3

4

5

6

7

8

9

pH units

lpH

Non-toxic Toxic

2

4

6

8

10

pH units
109

Wheatgrass

Alfalfa

Non-toxic Toxic

0

0.05

0.10

0.15

0.20

Total Dry Wt. (grams)

Non-toxic Toxic

0

0.1

0.2

0.3

0.4

0.5

0.6

Total Dry Wt. (grams)

Non-toxic Toxic

0

0.1

0.2

0.3

0.4

0.5

0.6

Total Dry Wt. (grams)
110

Yarrow

Non-toxic Toxic

0

0.05

0.10

0.15

0.20

0.25

0.30

Total Dry Wt. (grams)