Plants at Native and Altered Sites, 

San Juan Basin, New Mexico

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1134-D

Biogeochemical Variability of 

Plants at Native and Altered Sites, 

San Juan Basin, New Mexico

By L. P. GOUGH and R. C. SEVERSON

GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

GEOLOGICAL SURVEY PROFESSIONAL PAPER 1134-D

Regional background concentrations of elements 

in native plants and a discussion of soil-plant 

relationships are given for an area of 

increasing energy development

UNITED STATES GOVERNMENT PRINTING OFFICE, WASHINGTON: 1981
UNITED STATES DEPARTMENT OF THE INTERIOR 

JAMES G. WATT, Secretary

GEOLOGICAL SURVEY 

Doyle G. Frederick, A cting Director

Library of Congress Cataloging in Publication Data

Gough, L. P.

Biogeochemical variability of plants at native and altered sites, San Juan Basin, New Mexico.

(Geochemical survey of the western energy regions)

(Geological Survey Professional Paper 1134-D)

Supt. of Docs, no.: I 19.16:1134-D

Bibliography: p. 25

1. Botany—New Mexico—Variation. 2. Botany-San Juan River watershed (Colo.-Utah)

3. Plant-soil relationships—New Mexico. 4. Plant-soil relationships—San Juan River watershed (Colo.-Utah)

5. Plants-Chemical analysis. 6. Soils—New Mexico. 7. Soils—San Juan River watershed (Colo.-Utah)

8. Coal mines and mining—Environmental aspects—New Mexico. 9. Reclamation of land—Environmental

aspects—New Mexico. 10. San Juan River watershed (Colo.-Utah) 

I. Severson, R. C. (Ronald Charles), 1945- II. Title. III. Series. IV. Series: Geological Survey Professional

Paper 1134-D. 

QK176.G68 581.9789 81-607946 AACR2

For sale by the Superintendent of Documents, U.S. Government Printing Office 

Washington, D.C. 20402
CONTENTS

Abstract ............................................

Introduction ........................................

Location of the study area ......................

Physical features of the study area ..-...-....-..

Reasons for the study ............................

Acknowledgments ..............................

Methods .---...----.-....-..........................

Field ..............................................

Study 1 .------.--.-.----........._...........-.

Study 2 ........................................

Study 3 ........................................

Laboratory ......................................

Soil samples ......................................

Study design and statistical analysis of data ------

Results and discussion --------....................

Study 1, broad area most likely to be affected by 

energy development -------.....--.-......-.

Ecological notes on the species examined ----

Page

1

1

1

2

3

3

4

4

4

4

5

6

6

7

9

9

9

Results and discussion — Continued 

Study 1 — Continued 

Ecological notes — Continued

Galleta ................................................ 9

Fourwing saltbush ---......-..---.--.-..--.....-.---.. 9

Broom snakeweed ---—------------------------------ 9

Regional biogeochemical patterns --------------------- 9

Study 2, element content of plants growing on the

Sheppard, Shiprock, and Doak soil series ------------ 11

Study overview -..----.—........—....-...---...--.-.- 11

Geographical distance-related variability and baselines - 12 

Study 3, San Juan mine -----------—-.-.---.-.---....---.. 16

Plant-element concentration comparisons -------------- 16

Soil-plant element relations ..--.-....---.--------.---.- 18

Geographical distance-related variability -------------- 22

Element toxicity and deficiency considerations -------- 23

Conclusions ------------.---------..-----...--....----..-.-... 24

References cited .------------.-----.-----.--.—--..--.-...... 25

ILLUSTRATIONS

Page

FIGURE 1. Photograph showing an area in the San Juan Basin dominated by fourwing saltbush .--------------.------------..-.---. 2

2. Map showing geology and location of the study areas within the San Juan Basin --------------------..----------..-.---. 4

3. Map showing the soil and plant sampling locations and the nested-cell design of Study 1 -------------------------------- 5

4. Map showing the barbell-cluster sampling locations of Study 2 ..---.-.--.--.-.-----.----.--------------------------------- 5

5. Photograph showing galleta grass in an area in the San Juan Basin dominated by big sagebrush and

blue gramagrass. ---.-.---.-...--.-----.--.--.--.---...--.-..----..--------------.------------------------------------------- 6

6. Diagram showing the unbalanced, nested, analysis-of-variance design used in Study 2 ---------------------------------- 6

7. Photograph of the San Juan mine study site (Study 3) --.-..---.-------------.---------------------.------------------------ 7

8. Maps showing element concentrations of galleta grass at 25 sampling locations in Study 1:

8A. Manganese ---------.-----.-----------.-------.------.---------------.--.---------------------------------------------- 12

8 B. Molybdenum - — — .—-..-- — — - — - — - — — — — . — . ——-- — -- — - — — - — — - — — — — ---- 12

8C. Nickel——- — -——---———---———--——————-———-——————- — — -——-—- 12

8Z>. Uranium ..............................................................................—... — — — .... — .... — .-. 12

8E. Iron .................................................................................................................... 13

8F. Selenium .............................................................................................................. 13

9. Diagram contrasting the concentrations of 11 selected elements in fourwing saltbush sampled at the San Juan mine with

samples from throughout the San Juan Basin ...-.....-...-.--.-....---.--.------------.---------.-------------------- 18

10. Bar graphs contrasting the average values for the extractable and total concentrations of copper, iron, manganese, and

sodium, and the total concentrations of aluminum and uranium in native soils and mine soils ---------------------- 20

11. Bar graphs contrasting the average values for extractable and total concentrations of cobalt and lead in native soils and

mine soils -----...-----...-.-.....--....--....---...-.-....-----.----..------------------.--------------------------------- 21

12. Bar graphs contrasting the average values for extractable and total concentrations of boron and total concentration of

arsenic in native soils and mine soils ---------.-----..-----------.-------.----------------------,,.-----.-------------- 21

in
IV CONTENTS

TABLES

Page

TABLE 1. Analytical methods and their lower limits of determination for the plant materials sampled ---------------------------- 7

2. Summary statistics for the element content of galleta, broom snakeweed, and fourwing saltbush (Study 1) ............ 10

3. Detection ratios for elements in galleta and broom snakeweed (Study 2) ................................................ 13

4. Variation in and summary statistics for element concentrations in dry material of galleta growing in the Sheppard,

Shiprock, and Doak soil association, San Juan County (Study 2) ------------------------------------------------------ 14

5. Variation in and summary statistics for element concentrations in the dry material of broom snakeweed growing in the

Sheppard, Shiprock, and Doak soil association, San Juan County (Study 2) ................-............-.--.......— 15

6. Statistical comparison between barbell units of the average element concentrations in galleta and broom snakeweed

(Study 2) ................................................................................................................ 16

7. Variation in and summary statistics for element concentrations of fourwing saltbush growing over spoil at the San Juan

mine (Study 3) .......................................................................................................... 17

8. Variation in and summary statistics for element concentrations in alkali sacaton growing over spoil at the San Juan mine

(Study 3) ................................................................................................................ 19

9. Saline-sodic characteristics of San Juan mine soils (Study 3) -------------------------------------------------------------- 23
GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

BIOGEOCHEMICAL VARIABILITY OF NATIVE AND ALTERED SITES,

SAN JUAN BASIN, NEW MEXICO

By LARRY P. COUGH ai id R. C. SEVERSON

ABSTRACT

The San Juan Basin is becoming a major energy resource region. 

The anticipated increase in strip mining for coal can be expected to 

alter the geochemical and biogeochemical environment, because 

such activities destroy the native vegetation communities, rearrange the rock strata, and disrupt natural soil development. This 

study investigated the variability in the biogeochemistry of native 

plant species at both undisturbed and altered sites and assessed the 

importance of the observed differences. Three studies are involved 

in this investigation: Study 1, the biogeochemical variability of 

native species found at sites throughout that part of the basin underlain by economically recoverable coal; Study 2, the biogeochemical 

variability of native species growing on soils considered favorable 

for use in the topsoiling of spoil areas; and Study 3, the biogeochemical variability of native species on rehabilitated sites at the San Juan 

coal mine.

Summary statistics for concentrations of 35 elements (and ash 

yield) are reported in Study 1 for galleta grass, broom snakeweed, 

and fourwing saltbush. The concentrations of manganese, molybdenum, nickel, and uranium (and possibly iron and selenium) in 

galleta show regional patterns, with the highest values generally 

found in the south-central region and western edge of the study area. 

Differences in the concentration of elements between species was 

generally subtle (less than a factor of two) except for the following: 

ash yield of saltbush was two times that of the other plants; boron in 

snakeweed and saltbush was four times greater than in galleta; iron 

in galleta was two times greater than in saltbush; and, calcium, 

magnesium, potassium, phosphorus, and sulfur were generally 

highest in saltbush.

Summary statistics (including the 95-percent expected range) for 

concentrations of 35 elements (and ash yield) are reported from 

Study 2 for galleta and broom snakeweed growing on the Sheppard, 

Shiprock, and Doak soil association. Significant regional (greater 

than 10 km) variation for aluminum, iron, sulfur, vanadium, and 

zirconium in galleta are reported; however, for most elements, a 

significant proportion of the variation in the data was measured 

locally (less than 0.1 km). This variation indicates that samples of 

galleta and snakeweed taken more than 10 km apart vary, in their 

element composition, little more than plants sampled as close 

together as 0.1 km.

The concentrations of 35 elements (and ash yield) in alkali sacaton 

and fourwing saltbush, which were collected on a rehabilitation plot 

at the San Juan mine (Study 3), are compared with those of control 

samples of similar material from native sites from throughout the

' >an Juan Basin. Concentrations of aluminum, arsenic, boron, 

(obalt, copper, fluorine, iron, lead, manganese, sodium, and urani am in samples of saltbush growing over spoil generally exceed the 

13vels of these elements in control samples. For many elements, 

< oncentrations in mine samples are from two to five times higher 

1 han concentrations in the control samples. Sodium concentrations 

i n saltbush, however, were 100 times higher in mine samples than in 

< ontrol samples. This high concentration reflects a corresponding 

'. 00-fold increase in the extractable sodium levels in spoil material 

{ s compared to C-horizon control samples. Sampled plants from the 

i line area, spaced relatively close together (5 m (meters) or less), 

1 ary greatly in their element compositions, apparently in response 

1 a the heterogenous composition and element availability of the 

i line soils. Topsoiling to a depth of 20 cm (centimeters) does little to 

i meliorate the uptake of elements from spoil by saltbush.

INTRODUCTION 

LOCATION OF THE STUDY AREA

The study area is in the San Juan Basin of northvestern New Mexico and was confined to the area

' :ontaining strippable coal deposits. Specifically, this

s a 38,000 km2 (square kilometer) area, approxinately bounded by 35°-37° N latitude and 107°-109°

'V longitude, that includes parts of McKinley, Rio

. ^.rriba, Sandoval, and San Juan Counties. The Dakota

! Jandstone, the Menefee Formation of the Mesaverde

jroup, and the Kirtland Shale and Fruitland Forma-

;ion\ all of Late Cretaceous aage (Dane and Bachman,

'. 965), contain coal; however, the thickest and most

< iconomically recoverable coal seams are in the Kirtiand Shale and Fruitland Formation (Shomaker and

i ithers, 1971). These authors stated that within the San

, luan Basin, the Kirtland Shale and Fruitland Formaion contain more than 90 percent of the strippable

i ;oal deposits, which are defined as having less than 76

Although we refer to the coal deposits of the Kirtland Shale and Fruitland Formation, 

ve realize that the most significant deposits are in the Fruitland Formation.
GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

m of overburden. Fassett and Hinds (1971) estimated 

that the Kirtland Shale and Fruitland Formation 

have a total reserve, within New Mexico, of about 180 

billion (1.8 * 10 11 ) t (metric tons), and about 11 billion t 

are overlain by less than 150 m of overburden. The 

Navajo coal field, in the Kirtland Shale and Fruitland 

Formation, is southwest of Farmington, within the 

Navajo Indian Reservation, and is the location of Utah 

International Inc.'s Navajo mine, the largest coal strip 

mine in the State. South of the Navajo mine there are 

two vast leases of near-surface coal nearing development. The coal from the Navajo field is predominantly 

used onsite by the 2,000 M W(e) (megawatts of electricity) Four Corners powerplant. Another mine-mouth 

operation is north west of Farmington in the Fruitland 

coal field. Here Western Coal Co.'s San Juan mine 

supplies coal to the 328 MW(e) San Juan generating 

station. A recent estimate for coal production in San 

Juan County, for 1980 and 1990, is 12.5 and 18.9 million t, respectively (U.S. Department of Energy, 1980). 

All of this coal is low in sulfur (less than 0.7 percent), is 

subbituminous, and yields 8,500 to 10,500 BTU per 

pound.

The west, southwest, and south parts of the basin 

contain deposits of strippable coal (to the east the coal 

is too deep and dips too steeply to be strip mined), and 

other parts of the basin contain vast reserves of oil and 

natural gas (Barnes and Arnold, 1951) and uranium 

(Hilpert, 1969). This region, therefore, is extremely 

rich in energy resources, and the development of these 

resources is accelerating as demand for domestically 

produced fuels increases. Energy-related impacts on 

this region will include mining, conversion, and transport of coal; mining and processing of uranium ore; 

extraction and refining of oil and gas; and ultimately, 

the gasification and perhaps liquification of coal. By 

the year 2000, therefore, hundreds of square kilometers of land in the San Juan Basin will have been 

altered by activities associated with energy development.

PHYSICAL FEATURES OF THE STUDY AREA

The San Juan Basin is in the southeastern part of the 

Colorado Plateaus physiographic province (Fenneman, 

1931). Drainage of the moderately to highly dissected 

basin is toward the west via the perennial San Juan 

River and its tributaries. The southern part of the 

basin is drained by the intermittent Chaco River and 

its tributaries, which flow north to the San Juan River. 

The elevation of that part of the basin underlain by 

Cretaceous rocks ranges from about 1,500 to 2,200 m 

and is characterized by broad plains occasionally 

broken by abrupt basaltic dikes and hogbacks (fig. 1)

FIGURE 1. — Typical landscape in the southeastern part of the San 

Juan Basin showing broad plains (foreground) that are interrupted by occasional volcanic necks and dikes (background). 

Galleta grass was found interspersed among the dominant fourwing saltbush. Soils were of the Turley series (Typic Torriorthent, 

fine loamy, mixed (calcareous), mesic), having developed in 

alluvium.

but more commonly by sandstone-, shale-, and clinkercapped buttes and mesas. Unstable sand dunes, rolling hills, badlands, pediment surfaces and dry arroyos 

are also common landscape features. The climate profile given by U.S. Department of the Interior (1976) for 

the region essentially covered by the Study 2 area is 

summarized as follows: winds are generally from the 

southeast in winter and from west to east in spring and 

summer; the highest sustained velocities are in the 

summer. Precipitation is from 14 to 21 cm annually 

depending on elevation and position relative to the 

rain-shadow effects of the Chuska Mountains on the 

west, and season (summer is wetter than winter; 

spring and fall are dry); evaporation is 130 cm annually at Farmington. Monthly maximum mean temperatures (F) are (U.S. Department of the Interior, 

1976, p. 5): January, 29; February, 35; March, 41; 

April, 51; May, 59; June, 70; July, 76; August, 73; 

September, 66; October, 54; November, 40; and December, 30.

The vegetation of the San Juan Basin is characteristic of North American continental desert regions 

whose summer temperatures are moderated by a relatively high elevation and winter temperatures by relatively low latitude. Soil types strongly influence both 

the areal distribution of plants and the floristic composition of the vegetation. For example, shallow soils 

that develop over shale have very low permeability, 

are high in clay, and are usually saline. This condition
BIOGEOCHEMICAL VARIABILITY OF PL; .NTS, SAN JUAN BASIN, NEW MEXICO

results in essentially barren areas. Deep sandy soils, 

however, are permeable and support a diverse vegetation. Grazing pressure (in places severe), aspect 

(direction of slope), and local edaphic and topographic 

extremes also contribute to a complex vegetation mosaic. Depending on individual sites, therefore, any of 

the following species may dominate:

Grasses

Bluteloua gracilis ...................... blue grama

Hilariajamesii .............................. galleta

Muhlenbergia torreyi .................. ring muhly

Oryzopsis hymenoides .......... indian ricegrass

Sporobolus airoides ................ alkali sacaton

S. cryptandrus ...................... sand dropseed

Shrubs and Trees

Artemisia tridentata .............. big sagebrush

Artiplex canescens .............. four wing saltbush

A. confertifolia ............................ shadscale

Ephedra torreyana ..................................

Torrey ephedra (jointfir)

Gutierrezia sarothrae .......... broom snake weed

Juniperus spp. ------------------------------ juniper

Pinus edulis .................................. pinon

Sarcobatus vermiculatus .............. grease wood

Chrysothamnus nauseosus .......... rabbitbrush

REASONS FOR THE STUDY

Lands altered as a result of the activities of strip 

mining must now be rehabilitated according to specific guidelines and regulations. In New Mexico, 

rehabilitation of mined lands and rehabilitation 

research (Aldon and Springfield, 1975) began in 1972 

with the passage of the New Mexico Coal Surface 

Mining Act. The establishment of long-term plant 

cover and eventual ecosystem stabilization is the first 

concern of rehabilitation. Initially, plot studies are 

conducted on different plant species and ecotypes for 

their ability to successfully germinate, grow, and reproduce under the sometimes severe arid conditions 

that characterize sections of the San Juan Basin 

(Aldon and Springfield, 1975). Once these plants are 

identified and cover is achieved on favorable sites, 

work shifts to specific mine sites having peculiar 

overburden characteristics that severely affect, for 

one reason or another, the establishment of desirable 

species. Such studies seek to identify specific properties that make altered substrates either toxic or deficient in some essential property for normal plant 

growth (Gould and others, 1975, 1977). This type of 

work leads to new methods that ameliorate the effects 

of the undesirable property. We have attempted, in 

this and similar studies (Gough, Severson, and McNeal,

.979), to provide background concentration levels for

a large number of elements in native-plant species

ikely to be utilized in rehabilitation work. Problems

; issociated with geochemical alterations in soils, spoils,

and rehabilitation species were discussed by Erdman

11978) and through time may prove just as troublei .ome as the problems associated with achieving accepable plant cover.

This study had the following objectives: (1) To evallate the natural spatial variability in the element con-

,ent of selected native species growing over the Cre-

,aceous coal deposits of the Mesaverde Group and the

^ruitland Formation. This is the area most likely to be

'iirectly affected by the surface mining of coal. (2) To

' evaluate the natural spatial variability in the element

content of selected native species growing in Shep-

)ard, Shiprock, and Doak soils. From the data presi ;nted by Maker, Folks, Anderson, and Link (1973) and

Maker, Keetch, and Anderson (1973), these substrates

ire inferred to be the most desirable native soils for

stockpiling and respreading over rehabilitation sites.

Because the Fruitland Formation coals are of greatest

nterest, our sampling sites were restricted to approp-

•iate soil types that have developed over this forma- 

,ion. (3) To evaluate the variability in the element

content of rehabilitation species growing on revege- 

,ated mine sites and to compare the biogeochemistry

i >f rehabilitation plants to native plants.

The results from this study include: (1) observed 

^anges and calculated baseline-concentration ranges 

?or as many as 35 elements in four native-plant species; 

2) an assessment of varying distance increments at 

A^hich significant proportions of the total variability 

)ccur in the concentration of elements in two nativeslant species growing in a defined soil association; (3) a 

comparison of the element content of plants growing 

)n native and geochemically altered sites; (4) an 

issessment of the forage quality of selected species 

growing on native and altered sites; and (5) a discussion of soil-plant element relations at both native and 

dtered sites. 

We use the format of Severson and Gough (1981) and

•efer to that part of the study dealing with native

)lants collected in the broad region most likely to be 

affected by energy development as Study 1; reference

o work on plants growing on the Sheppard, Shiprock, 

and Doak soil series is labeled Study 2; and reference

o studies at the San Juan mine is called Study 3.

ACKNOWLEDGMENTS

We appreciate the diverse assistance from many 

ndividuals that made this study possible. The plant sam-
GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

pies were washed by J. J. Dickson and M. P. Pantea, 

and were ashed and chemically analyzed by B. L. Bolton, T. F. Harms, K. E. Koran, C. S. E. Papp, S. E. 

Prelipp, and Michele Tuttle. R. W. Alien, manager of 

the Western Coal Co., kindly gave us access to the San 

Juan mine, and W. L. Gould, D. J. Leman, and J. A. 

Ferraiuolo of New Mexico State University allowed us 

to collect samples from their San Juan mine rehabilitation plots. W. L. Gould also reviewed this manuscript and shared with us unpublished information. 

The orientation to the region given us by Monte 

McClendon of the U.S. Bureau of Land Management 

proved invaluable. We acknowledge with gratitude 

the cooperation of Peter MacDonald and the Navajo 

Tribal Council for giving us sampling privileges on 

the Navajo Nation lands in New Mexico.

METHODS 

FIELD

In July and August 1977 we sampled various grasses 

and shrubs, along with supporting A and C horizons of 

soil, at sites throughout the western, southwestern, 

and southern parts of the San Juan Basin in northwestern New Mexico. The grasses included the leaves, 

culms, and inflorescences of alkali sacaton (Sporobolus 

airoidese (Torr.) Torr.) and the entire plant (leaves, 

culms, inflorescences, rhizomes, and roots) of galleta 

grass (Hilaria jamesii (Torr.) Benth.). The shrub 

material consisted of the terminal 10-20 cm of stems 

with accompanying leaves of fourwing saltbush (Atriplex canescens (Pursh) Nutt.) and the whole aboveground parts (stems, leaves, and inflorescences) of 

broom snakeweed (Gutierreziasarothrae (Pursh) Britt. 

& Rusby). Fourwing saltbush is dioecious, and our 

collections are a mixture of both male and female 

shrubs. None of the samples from female shrubs 

included the seeds because on some shrubs the seeds 

were so abundant that, had they been included, they 

would have dominated the sample. Except for the galleta samples, which consisted of numerous individuals 

commonly collected from a 10 or more square meter 

area, the samples were taken from one plant. Voucher 

specimens of each species are stored in the herbarium 

of the U.S. Geological Survey in Denver, Colo. Three 

separate studies were conducted, Study 1, Study 2, 

and Study 3.

STUDY 1

Galleta, snakeweed, and saltbush were collected 

throughout that part of the basin most likely to be 

directly affected by the surface extraction of coal. This 

is the area underlain by the subbituminous coalbearing Kirtland Shale and Fruitland Formation and 

by the undifferentiated Mesaverde Group (Dane and 

Bachman, 1965; Fassett and Hinds, 1971), all of Late 

Cretaceous age (fig. 2). Within this area, 48 sites for 

plant and soil sampling were randomly selected according to a five-level, unbalanced, nested-cell, analysis-ofvariance design (Severson and Gough, 1981). This 

design utilized the strategic placement of eight cells, 

50-km on a side, on the coal-bearing region. Within 

each 50-km cell all four 25-km cells were sampled. The 

design was unbalanced below the 25-km cell level so 

that only a few 5-km and 1-km cells were sampled in 

each 25-km cell. Figure 3 shows the location of each 

randomly selected site within the nested-cell design. 

Because of the absence of of appropriate plant species 

at many of the Study 1 sites, an analysis of variance of 

the plant element-content data was not performed. 

The following numbers of samples for each species 

were collected: galleta, 25; snakeweed, 18; and saltbush, 10 (fig. 3). These samples are independent from 

one another because they were collected at sites chosen 

in a random selection process.

STUDY 2

The Kirtland Shale and Fruitland Formation occur 

as a narrow crescent within the center of the San Juan

Uppermost Cretaceous to 

Eocene rocks

FIGURE 2. — Location of the three study areas within the San Juan 

Basin. Geology modified from Dane and Bachman (1965). Kkf, 

Kirtland Shale and Fruitland Formation; Klmv, Lewis Shale and 

Mesaverde Group; all of Late Cretaceous age. Units listed in order 

of increasing age.
BIOGEOCHEMICAL VARIABILITY OF PL/.NTS, SAN JUAN BASIN, NEW MEXICO

109° 108° 107°

1 COLORADO

EXPLANATION 

°) 9 i[arrainston O Soil, 47 samples

Galleta, 25 samples

O Broom snakeweed, 

18 samples

C Fourwing saltbush, 

10 samples

35°

FIGURE 3. — Soil and plant sampling locations for Study 1 within 

the San Juan coal region with the unbalanced, nested, analysis-ofvariance sampling grid superimposed.

Basin (figs. 2 and 4). Most of the economically recoverable coal is in the western half of this crescent, which 

extends for about 150 km from near the Colorado-New 

Mexico State Line in the north to near Cuba, N. Mex. 

in the southeast. Within this region, the Sheppard, 

Shiprock, and Doak soil series possesses physical 

(Maker, Folks, Anderson, and Link, 1973, and Maker, 

Keetch, and Anderson, 1973) and chemical (Severson 

and Gough, 1981) features favorable for native plant 

growth. Therefore, we consider these soils to be desirable for stockpiling for use in the rehabilitation of 

strip-mined lands. These soils are developed on stable 

(nondune) sands. This study, therefore, was confined 

to an examination of the element content of two common plants that occur on the major soil series within 

the area of outcrop of the Kirtland Shale and Fruitland Formation (fig. 5). Galleta and broom snakeweed 

were collected according to an unbalanced, nested, 

analysis-of-variance design of the "barbell-cluster" 

type described by Tidball and Ebens (1976). We 

divided the western part of the Kirtland Shale and 

Fruitland Formation into approximate 50-km segments and randomly located one barbell cluster within 

each of the three segments (fig. 4). The orientation of 

the major axis of the barbell depended on the width of 

the geologic unit studied and the extent of the Sheppard, Shiprock, and Doak soil series.

FIGURE 4. — Positions of the three barbell-cluster sampling areas 

for Study 2, San Juan County.

Each barbell cluster, like the one shown in Figure 6, 

consisted of a 10-km main axis. At each end of the main 

axis, a 5-km axis was oriented in a randomly selected 

direction. The next two smaller axes (1 and 0.1 km) 

were positioned at the ends of the 5-km axis in a like 

manner. Because of the unbalanced nature of the 

design, only 10 out of a possible 16 sites per barbell 

were targeted for sampling. Out of a total of 30 randomly selected sites we were able to collect 30 galleta 

samples and 27 fourwing saltbush samples. Unbalancing the design serves to economize field and laboratory expenses with little sacrifice in the reliability of

;he estimates of the variance components (Tidball,

L976; Miesch, 1976).

STUDY 3

The San Juan coal mine, which is operated by the 

Western Coal Co., is 23 km west-northwest of Farmington in San Juan County, N. Mex. (fig. 4). Subbituminous coal is mined from the Kirtland Shale and 

Fruitland Formation and supplied to the San Juan 

generating station nearby. The study site was in an 

area that had been mined, regraded, topsoiled (to a 

depth of about 20 cm), and seeded with fourwing saltbush and alkali sacaton in 1974. The rehabilitation site 

was not fertilized; however, straw mulch was crimped 

into the soil, and the area was periodically irrigated 

until the desired species became established (fig. 7).

In order to estimate local (less than 100-m) variation 

in the element content of reclamation species, we utilized a nested, unbalanced, analysis-of-variance design 

of the barbell-cluster type discussed above under 

Study 2. A single barbell was used and consisted of a
.^ - *" ^ j»-* , , S£

V

GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

NESTED DESIGN

Barbells (n=3)

FIGURE 5. — Galleta grass stand in a big sagebrush-blue gramagrass dominated area of Study 2 that received little grazing pressure (markings on rod are in decimeter increments). Soils (Doak 

series, Typic Haplargid, fine loamy, mixed, mesic) in this area 

were characterized as being well drained.

main axis of 100 m; at the ends of the main axis were 

positioned the centers of two 25-m axes, and at the ends 

of the 25-m axes were positioned the centers of 5-m 

axes. By unbalancing the design we collected six out of 

a possible eight sites. As in Study 2, the orientation of 

each axis was determined by a random compass 

orientation.

LABORATORY

The sample preparation of the plant material for 

analysis from all three studies was the same. The samples were dried in a forced-air oven at 35°C and 

ground in a stainless steel mill to pass a 1.3-mm (millimeter) screen, and the homogenized ground material 

was either ashed by dry ignition at 500°C for 24 hours 

or by wet digestion (Harms, 1976). Because of excessive soil and dust contamination, samples of galleta, 

alkali sacaton, and snakeweed were washed prior to 

being dried and ground. This process consisted of 

many tap-water rinses (until the rinse water was free

10 km

5 km

1 km

0.1 km

B C D E G H /

BARBELL-CLUSTER

10 km

0.1 km

FIGURE 6. —Diagram of the unbalanced, nested, analysis-of-variance 

design used in Study 2. Sample sites are indicated by letters A 

through J.

of visible suspended and settled material) followed by 

a single distilled water rinse. Ten percent of the samples were selected at random for splitting of the 

ground material and the analytical sequence of the 

entire suite of samples (plus splits) from all three studies was randomized. The analysis of blind splits provided a measure of laboratory precision, whereas the 

analysis of the samples in a randomized sequence 

insured that possible analytical bias would be converted to random error (Miesch, 1976). All element 

analyses were performed in the Denver laboratories of 

the U.S. Geological Survey by the methods listed in 

table 1.

SOIL SAMPLES

Chapter C of this series (Severson and Gough, 1981) 

and Crock and Severson (1980) detailed the sampling 

and analytical methods for soil materials in the San 

Juan Basin studies. A brief summary of these methods 

will aid the reader in following the discussions on 

soil-plant element relations in subsequent sections.

Samples of A- and C-horizon soils were collected 

throughout the basin in conjunction with the sampling
BIOGEOCHEMICAL VARIABILITY OF PI ANTS, SAN JUAN BASIN, NEW MEXICO

TABLE 1. — Analytical methods and their approximate lower limits 

of determination for the plant materials sampled

[All values are reported on an ash-weight basis, except where noted; LLD, lower limit of 

determination; ES, plate-reader emission spectrography; AA, flame atomic-absorption 

spectroscopy; SIE, selective ion electrode; FL-AA, flameless atomic-absorption spectroscopy; COLR, colorimetric; FLUR, fluorometric; TURB, turbidimetric]

Element

Aluminum ——

Arsenic 1 ——

Barium ————

Boron ————

Cadml tun ——

Calcium ———

Chromium ——

Cobalt ————

Copper ————

Europium ——

Fluorine 1 —

Lanthlum —— 

Lead —————

Lithium ———

Manganese —

Analytical 

method

ES

AA

ES

ES

I? e?

AA

AA

ES

AA

AA

ES

SIE

ES 

ES

AA

ES

Approximate 

LLD (ppm)

200

.05

4.4

10

.4

100

1

1

1

2

1

9.2 

4.4

4

2

Element

Mercury1- ————

Molybdenum ————

Nickel ———————

Niobium ——————

Pot as slum ————

Scandium —————

Selenium1 ————

Sodium ——————

Strontium ————

Sulfur (total) 1

Tl tanl urn —————

Vanadium ————— 

Ytterbium ————

Yttrium ——————

Zl r conl urn ————

Analytical 

method

FL-AA

ES

ES

ES

AA

ES

FLUR

AA

ES

TURB

ES

ES 

ES

ES

ES

Approximate 

LLD (ppm)

0.01

2

2

9.2

100

2

.01

25

1

100

90

2 

.92

2

4.4

Analyses determined on dry material not ash.

2 Upper limit of determination.

FIGURE 7. — Site at the San Juan mine (Study 3) where plant and 

mine-soil samples were collected. Irrigation (pipe in middle 

ground) was used initially to establish vegetation in an area that 

was regraded and then topsoiled to a depth of about 20 cm. Isolated fourwing saltbush individuals and crimped straw containing alkali sacaton in seeded rows are visible.

of the plant material. At the San Juan mine, replaced 

topsoil and the underlying mine soil (to a depth of 50 

cm) were also collected. In every instance, the soil 

samples were collected with a bucket auger adjacent 

to the plant (or group of plants) sampled.

In the laboratory, the samples were dried at ambient 

temperature, disaggregated, and sieved (10 mesh). 

Splits of the minus 2-mm material were further 

ground to minus-100 mesh and used for all totalelement determinations. The disaggregated but unground minus 2-mm material was used for the extractable element determinations.

The extractable concentrations (in parts per million) of cadmium, cobalt, copper, iron, manganese, 

nickel, lead, and zinc were measured following the use 

of the chelate DTPA (0.005 M (molar) diethylenetriaminepentaacetic acid at a pH of 7.3) (Lindsay and 

Norvell, 1978). A water saturation extract was used 

for determining soluble concentrations (meq/L) (milliequivalents per liter) of calcium, magnesium, potassium, sodium, chloride, and sulfate, and a sodiumacetate extract was also used for exchangeable concentrations (meq/lOOg) (milliequivalents per 100 

grams) of calcium, magnesium, potassium and sodium. 

Concentrations (in parts per million) of boron were 

determined in a hot-water extract. Standard methods 

(or calculations) were also used to determine the following additional soil chemical and physical properties: pH, cation-exchange-capacity (CEC), exchangeable-sodium-percentage (ESP), sodium-adsorptionratio (SAR), specific conductance, and the relative 

amounts of sand, silt, and clay. A number of different 

methods were also used to determine the total concentration of the following elements and compounds in 

soil: aluminum, arsenic, boron, barium, beryllium, 

carbon, carbonate carbon, organic carbon, calcium, 

cobalt, chromium, copper, erbium, iron, germanium, 

mercury, potassium, lanthanum, lithium, magnesium, 

manganese, molybdenum, sodium, niobium, nickel, 

lead, rubidium, sulfur, scandium, silicon, tin, strontium, thorium, titanium, uranium, vanadium, yttrium, 

ytterbium, zinc, and zirconium.

STUDY DESIGN AND STATISTICAL 

ANALYSIS OF DATA

Frequency distributions for the element content of 

the plants studied were found to be strongly positively 

skewed, particularly the minor-essential and trace 

elements. A logarithmic transformation of such data 

adjusts the distribution curves so that they are more 

nearly normal. Therefore, statistical tests requiring 

the frequency distributions of the data to be normal 

are satisfied by this log transformation. Miesch (1976) 

gave additional information on the advantages of 

working with log-transformed geochemical data.

Because our data were transformed to logarithms, 

our summary statistics are reported as the geometric
8 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

mean (GM) and geometric deviation (GD). For those 

elements that had censored values, the GM and GD 

were estimated using the technique of Cohen (1959). In 

Study 1, which did not utilize analysis of variance 

(AoV), the GD was calculated as the square of the 

logarithmic standard deviation. In Study 2 and Study 

3, however, for those elements without censuring, the 

GD was calculated from the total variation as estimated by AoV, which accounts for the effects of the 

nested design. Total variation, as estimated by the 

square of the logarithmic standard deviation, is always 

smaller than total variation as estimated by the nested 

AoV design. For this study we have chosen to present 

the largest estimate of the GD; therefore, where 

wholly numeric data sets are involved (no censoring), 

and when an AoV calculation was made, the GD calculated from the AoV-estimated total variance is given.

Because the AoV procedure requires completely 

numeric data sets, all censored data were substituted 

with a real value equal to 0.7 times the lower limit of 

determination in ash, and 1.3 times the upper limit of 

determination. These multipliers are arbitrary fractions of the limit of determinations, and we assumed 

they would result in valid AoV results as long as censoring did not exceed one-third of the total number of 

values. If an element possessed more than one-third 

censoring, it was dropped from the study. The analysis 

of the data was performed on a computer using the 

U.S. Geological Survey's STATPAC library (VanTrump and Miesch, 1977).

Plant-material concentration data, reported by the 

analyst to be on an ash-weight basis, were converted to 

a dry-weight basis prior to statistical analysis. This 

conversion was done because of the interest in dryweight data by agronomists and others involved with 

reclamation studies.

The sampling designs used in this study have the 

advantage of maximizing biogeochemical information obtained from a limited sampling. Detailed discussions of the utility of the unbalanced, nested, 

analysis-of-variance design in geochemical studies 

were given by Tourtelot and Miesch (1975), Tidball

(1976), Tidball and Ebens (1976), and Severson and 

Tidball (1979) and for biogeochemical studies by 

Erdman and others (1976) and Erdman and Gough

(1977). The basic purpose of these AoV designs is to 

partition the total variation measured into at least two 

parts: a natural-variation component (using distancerelated increments) and an analytical-error component. If the analytical error is significantly large for

an element, relative to the natural variation, then it 

may not be economically feasibile to map the natural 

variation in the data. Many more samples would need 

to be collected or a more precise analytical method 

would have to be utilized in order to reduce this analytical variation to an acceptable level. However, if analytical variation is significantly small relative to natural variation, then it may be possible to construct a 

map showing this natural variation as differences in 

element concentration at some distance increment 

where there is a significant variance component. If 

natural variation is significant at all distance increments, then a map is impractical; the grand mean is a 

better estimate of a baseline value.

The Study 2 and Study 3 sampling designs permitted an estimation of the variation in the element content of the plants sampled at varying distance increments and, for Study 2, the variation due to analytical 

methods. The following statistical model was used in 

Study 2 to partition the variance, and it follows the 

hierarchial or "nested" case discussed in mathematical detail by Miesch (1976):

where the total observed logarithmic variance in the 

study area, for a given element concentration in either 

galleta grass or broom snakeweed, is represented by 

the term s2log x and is the sum of the estimates of six 

sources of variation. The factor s2 represents variability due to differences between the three barbell clusters at distances greater than 10 km (in these types of 

studies, called the "regional" component); s2, represents 

differences at 5-10 km; s2 represents differences at 1-5 

km; s2B represents differences at 0.1-1 km; s2 represents 

differences at less than 0.1 km (or the "local" component); and the last term, s2 , is composed of variation 

from sample preparation and analysis. The partitioning of the variance in Study 3 was similar:

a f) y'

where the total observed logarithmic variance, in 

either fourwing saltbush or alkali sacaton, is represented by the term s2lQgx and is the sum of three components: s2, variability at scales of 25-100 m; s|, variability at scales of 5-25 m; and, s2 , variability at scales 

greater than 5 m plus all other sources of variation 

including sample preparation and analysis.
BIOGEOCHEMICAL VARIABILITY OF PI ANTS, SAN JUAN BASIN, NEW MEXICO 9

RESULTS AND DISCUSSION

STUDY 1, BROAD AREAS MOST LIKELY TO BE 

AFFECTED BY ENERGY DEVELOPMENT

ECOLOGICAL NOTES ON THE SPECIES EXAMINED

GALLETA

The physiological ecology, taxonomy, and rangeland importance of galleta (H. jamesii) has been 

reviewed by West (1972). Galleta is a coarse perennial 

grass that revegetates predominantly by an extensive 

rhizome system. West (1972, p. 29) noted that "Bouteloua gracilis [blue grama] is probably the only grass of 

greater abundance and importance than H. jemesii 

throughout northern Arizona and New Mexico." He 

further stated that, although perhaps not preferred 

forage, galleta is often essentially the only grass available on many ranges and does make up a considerable 

proportion of the diet of cattle and sheep. Because of its 

economic importance, its resistance to drought, and its 

general hardiness once established, galleta appears to 

be an ideal candidate for use in mined-site rehabilitation. The only factor that prevents the general use of 

galleta, however, is the lack of an economical source of 

viable seed. W. L. Gould (written commun., 1980) 

reported that the seeds ripen gradually, from one end 

of the flower spike to the other, and upon ripening the 

seeds readily shatter. Consequently, only part of the 

seed crop can be harvested by combining a native 

stand. Further, the timing of the combining is critical 

because much of the seed shatters within a few days. 

High germination of seeds will occur in the field, however, if the palea and lemma are removed from the 

caryopsis during threshing and processing.

FOURWING SALTBUSH

This shrub, also called "chamiza" in New Mexico, is 

one of the most widely distributed western-American 

species of the Goosefoot Family. In the San Juan Basin 

it occurs in dry, moderately saline or alkaline sites 

where soil permeability is somewhat restricted. It 

occurs usually as scattered shrubs; however, extensive 

stands are found where it dominates the vegetationtype. Fourwing saltbush is a highly desirable desertshrub browse species for the reasons given by Dayton 

(1931, p. 28): "Its importance is due to its abundance, 

accessibility, size, agreeable saline taste, evergreen 

habit, high percentage of useableness — leaves, stems, 

flowers, and fruits all being edible — and tremendous

root development enabling great tolerance of drought, 

ability to withstand low temperature, copious production of fattening, highly palatable seed, and high 

nutritiousness." Apparently cattle and sheep are more 

apt to preferentially browse on fourwing saltbush 

than either horses, deer, or rabbits (Elmore, 1976). 

These features, plus characteristics that enable the 

shrub to germinate, establish itself as a seedling, and 

grow on rather unfavorable raw spoil (Aldon and 

Springfield, 1973), have made it a highly desirable 

species for use in land rehabilitation.

BROOM SNAKEWEED

Another common name for this desert half-shrub is 

"turpentine weed" — an apt description, because this 

member of the Composite Family emits a very pungent, resinous odor. Broom snakeweed is usually found 

in well-drained loamy soils, and if abundant is considered an indication of intensive overgrazing. This is 

because the plant itself is avoided and has no forage 

value but proliferates at the expense of the loss of the 

more palatable species. It has little value as a rehabilitation species; however, snakeweed will colonize rehabilitated areas. Therefore, it was sampled throughout 

the basin (Study 1) and on the Sheppard, Shiprock, 

and Doak soil series (Study 2) because of its widespread occurrence and its potential importance as an 

interloper.

REGIONAL BIOGEOCHEMICAL PATTERNS

Table 2 gives the summary statistics for the 35 elements (and ash yield) in three plant species collected. 

Differences in the GM and observed range for each 

plant indicate variability in the uptake of a number of 

elements. For example, the following general observations are made: elements highest in galleta are 

aluminum, arsenic, cobalt, iron, manganese, mercury, 

titanium, vanadium, ytterbium, and yttrium; elements 

lowest in galleta are boron cadmium, calcium, copper, 

magnesium, potassium, scandium, selenium, strontium, and sulfur. Elements highest in snakeweed are 

copper, lead, lithium, and sodium; elements (and ash) 

lowest in snakeweed are ash, chromium, and nickel; 

elements (and ash) highest in fourwing saltbush are 

ash, boron, nickel, phosphorus, potassium, selenium, 

strontium, and zinc; elements lowest in fourwing saltbush are aluminum, arsenic, barium, cobalt, iron, 

lead, mercury, titanium, uranium, vanadium, ytterbium, yttrium, and zirocnium. Most of these differen-
10 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

TABLE 2. — Summary statistics for the element content of dry 

material ofgalleta, broom snakeweed, andfourwing saltbushfrom 

the San Juan Basin (Study 1)

[G, galleta; S, broom snakeweed; F, fourwing saltbush; ppm, parts per million; <, less than; 

>, more than; leaders (--), mean nodata; ratio is the proportion of the number of analyses 

having values above the lower limit of determination to the total number of analyses]

Element or Geometric Geometric Observed 

ash Material Ratio mean deviation range

Ash (percent) ——— G 25:25 6.8 1.24 4.0 - 10 

S 18:18 5.9 1.22 4.3 - 9.1 

F 10:10 12 1.22 8.4 - 16

Aluminum (percent) G 25:25 .097 1.75 .025 - .26 

S 18:18 .083 1.76 .023 - .19 

F 10:10 .053 1.49 .030 - .098

Arsenic (ppm) ——— G 25:25 .16 1.43 .10 - .30 

S 18:18 .13 1.53 .050 - .20 

F 6:10 ^Oll 1 1.19 <.050 - .10

S 18:18 44 1.93 8.8 - 98 

F 10:10 18 1.44 10 - 30

S 13:18 J 24 1 1.48 12 - >36 

F 9:10 J 27 ! 1.46 17 - >70

Cadmium (ppm) ——— G 24:25 1 .064 1 1.78 <.025 - .26 

S 18:18 .23 1.65 .10 - .73 

F 10:10 .11 1.27 .048 - .32

Calcium (percent)- G 25:25 .35 1.34 .23 - .65 

S 18:18 1.0 1.40 .56 - 1.7 

F 10:10 1.4 1.30 1.0 - 2.7

Manganese (ppm) —— G 25:25 85 1.76 22 - 200 

S 18:18 50 1.29 28 - 83 

F 10:10 71 1.60 29 - 160

Mercury (ppm) ——— G 25:25 .25 1.44 .15 - .50 

S 18:18 .19 1.51 .10 - .45 

F 10:10 .11 1.61 .050 - .20

Chromium (ppra) ——— G 25:25 1.0 1.38 .56 - 1.8 

S 18:18 .85 1.32 .47 - 1.5 

F 10:10 1.1 1.32 .81 - 1.9

S 18: 18 .18 1.86 .051 - .35 

F 9:10 '.14 h.44 <.085 - .25

S 18:18 8.1 1.42 4.6 - 17 

K 10:10 4.7 1.28 3.3 - 7.0

Europium (ppm) ——— G 14:25 1 .17 l l.Sb <.080 - .46 

S 9:18 ! .13 ! 2.35 <.086 - .40 

F 4:10 '.24 ^.Sl <.17 - .61

Fluorine (ppm) ——— G 25:25 8.5 1.33 4.0 - 14 

S 18:18 9.4 1.36 6.0 - 14 

K 10:10 6.2 1.71 3.0 - 15

S 18:18 420 1.60 190 - 820 

F 10:10 220 1.73 87 - 430

Lanthanum (ppm) —— G 20:25 'l.3 l i.98 <.59 - 3.8 

S 15:18 'l.l '1.91 <.47 - 2.4 

F 5:10 '1.2 1 2.7<) <1.0 - 4.8

S 18:18 1.1 1.59 .58 - 3.0 

F 6:10 !.52 'l.37 <.41 - .78

Lithium (ppm) ———— G 21:25 '.36 'l.42 <.22 - .77 

S 18:18 .63 1.73 .26 - 1.4 

K 5:10 ! .45 'l.58 <.42 - .92

Magnesium (percent) G 25:25 .063 1.57 .027 - .14 

S 18:18 .14 1.40 .067 - .36 

F 10:10 .58 1.31 .36 - .78 

Molybdenum (ppm)— G 321:21 .51 1.50 .25 - 1.2 

S 18:18 .54 1.27 .38 - .99 

F 10:10 .49 1.64 .24 - 1.5

TABLE 2. — Continued

Element or Geometric Geometric Observed 

ash Material Ratio mean deviation range

Nickel (ppm) ———— G 25:25 .83 1.33 .50 - 1.8 

S 18:18 .66 1.38 .39 - 1.4 

F 10:10 1.2 1.81 .50 - 3.4

S 14:18 1 .41 1 1.96 <.22 - 1.2 

F 4:10 J .41 J3.23 <.36 - 2.2

Phosphorus (percent) G 25:25 .056 1.52 .016 - .13 

S 18:18 .062 1.33 .042 - .10 

F 10:10 .091 1.53 .059 - .17

Potassium (percent) G 25:25 .36 1.84 .096 - 1.1 

S 18:18 .74 1.48 .37 - 2.0 

F 10:10 3.0 1.56 1.0 - 4.6

Scandium (ppm) ——— G 22:25 ^25 1 l.« <.13 - .51 

S 18:18 .26 1.41 .13 - .61 

F 8:10 ^30 1 1.55 <.19 - .59

Selenium (ppm) ——— G 25:25 .12 1.62 .060 - .45 

S 18:18 .27 1.84 .080 - 1.2 

F 10:10 .81 3.07 .15 - 4.5

Sodium (ppm) ———— G 25:25 77 1.64 35 - 180 

S 18:18 110 1.19 84 - 150 

F 10:10 72 1.48 26 - 100

Strontium (ppm) —— G 25:25 24 1.29 17 - 43 

S 18:18 74 1.57 40 - 150 

F 10:10 87 1.70 48 - 240

Sulfur (total) ——— G 25:25 .099 1.38 .055 - .22 

(percent) S 18:18 .11 1.41 .055 - .24 

F 10:10 .30 1.39 .15 - .54

Titanium (ppm) —— G 25:25 46 2.01 8.6 - 170 

S 18:18 31 2.06 5.5 - 75 

F 10:10 10 2.06 3.5 - 29

Uranium (ppm) ——— G 420:20 .075 1.32 .040 - .12 

S 17:18 1 .11 1 1.66 <.029 - .21 

F 4:10 ^044 ^.Se <.034 - .072

Vanadium (ppm) ——— G 25:25 1.3 1.47 .63 - 2.7 

S 18:18 1.1 1.63 .49 - 2.1 

F 10:10 .70 1.50 .40 - 1.2

Ytterbium (ppm) —— G 19:25 1 .OS3 1 1.61 <.056 - .29 

S 14:18 1 .068 J 1.39 <.047 - .13 

F 0:10 — — <.080 - <.13

Yttrium (ppm) ——— G 25:25 .83 1.80 .19 - 3.7 

S 18:18 .68 1.61 .26 - 1.4 

F 7:10 1 .32 1 1.87 <.22 - .72

Zinc (ppm) —————— G 25:25 13 1.44 8.4 - 37 

S 18:18 17 1.34 9.6 - 27 

F 10:10 19 2.37 6.8 - 77

Zirconium (ppm) —— G 25:25 7.5 2.15 1.5 - 20 

S 18:18 5.2 2.10 1.2 - 23 

F 9:10 1 1.7 1 2.06 <.52 - 3.4

The technique of Cohen (1959) was used to calculate the mean and 

deviation because there were one or more concentration values outside of the 

limits of determination of the analytical method used.

Due to contamination, one sample was eliminated from the calculation 

of the summary statistics — see Study 1 results section on "Regional 

biogeochemical patterns".

•'Four of the original 25 samples were eliminated from the calculation 

of the summary statistics because they possessed unusually high molybdenum 

concentrations — see Study 1 results section on "Regional biogeochemical 

patterns".

Five of the original 25 samples were eliminated from the calculation 

of the summary statistics because they possessed unusually high uranium 

concentrations — see Study 1 results section on "Regional biogeochemical 

patterns".
BIOGEOCHEMICAL VARIABILITY OF PL^ NTS, SAN JUAN BASIN, NEW MEXICO 11

ces are subtle (less than a factor of two), and may not be 

significant; however a few are large. For example, ash 

yield of saltbush is two times that of the other plants; 

boron in snakeweed and saltbush is four times greater 

than in galleta; iron in galleta is two times the levels 

found in saltbush; lead is much higher in galleta and 

snakeweed when compared to saltbush; the major 

essential elements calcium, magnesium, potassium, 

phosporus, and sulfur are generally highest in saltbush; and, selenium content of saltbush is four times 

greater than the levels in galleta or snakeweed.

The distribution of galleta samples in the San Juan 

Basin study area enabled us to define some very general regional patterns. Such patterns cannot be 

defined for snakeweed or fourwing saltbush because 

only 18 and 10 samples of each, respectively, were 

collected. The regional patterns for galleta are based 

on observed differences in concentrations at different 

sampling sites: the significance of the differences was 

not tested by AoV because too few of the 48 sites in 

Study 1 contained galleta (fig. 3). Of the elements 

examined in galleta, four (manganese, molybdenum, 

nickel, and uranium) have discernible trends (fig. 8 A8D), and two others (iron and selenium) show possible 

trends (fig. SE and 8F).

The frequency distributions of these six elements 

were separated into two or three groups, each representing approximately equal proportions of the total 

number of samples. For example, the manganese data 

(fig. 8-4) is displayed using three classes, each accounting for approximately one-third of the total number of 

samples.

These figures show that the concentration of manganese, nickel, and probably iron are highest in the 

south-central part of that portion of the San Juan 

Basin examined. Selenium in galleta showed a similar 

pattern except that two samples west of Farmington 

were also high. The molybdenum and uranium data 

are unique because the samples can be separated into 

two populations. The frequency distribution in figure 

8B shows that 21 of 25 samples of galleta had molybdenum concentrations between 0.23 and 1.1 ppm 

(parts per million). The other four samples, all from 

the far western edge of the study area, had unusually 

high concentrations of between 4.5 and 64 ppm. (Reanalysis of these samples confirmed the high values.) 

Figure 8D shows that 20 of 25 samples of galleta have 

uranium concentrations ranging from 0.04 to 0.12 

ppm, and the remaining samples have slightly higher 

concentrations of from 0.16 to 0.19 ppm. Four of these 

samples high in uranium were collected west and 

southwest of Farmington.

A comparison of the plant-element data to our maps 

of regional soil-element trands (Severson and Gough, 

1981) shows poor correspondence. However, the west-

(rn side of the study area was very high in total A1 orizon molybdenum — this is the area of our highest 

recorded concentrations of molybdenum in galleta. 

r. 'he pH of the soil at these high-molybdenum sites, 

1 owever, was not unusually high, suggesting that 

i vailable molybdenum might not be any greater than 

i t other sites. The concentration of all other elements 

i i galleta from these high-molybdenum sites was also 

i ot unusual. We conclude that, under certain as-yeti ndefined conditions, molybdenum concentrations in 

ralleta probably reflect total soil molybdenum, and 

1 hat galleta is capable of concentrating molybdenum 

i n its tissues to levels far above those found in fourwing 

s altbush, snakeweed, and probably alkali sacaton (see 

i Iso the molybdenum discussion in the section on 

' Element toxicity and deficiency considerations").

Product-moment correlation coefficients were de1 ermined for the log concentration of elements in gal13ta and broom snakeweed samples, each collected at 

Ihe same 15 sites (fig. 3) throughout the basin. All 

s amples were considered independent because each 

£ ite was originally selected at random. Only four elements showed significant correlations: boron, 

i = 0.593; molybdenum, r = 0.624; selenium, r = 0.842; 

;,nd zinc, r = 0.562. We may assume that the uptake 

i nechanisms for these four elements are probably very 

i imilar for galleta and snakeweed. Regional patterns 

i n the element content of snakeweed throughout the 

1 lasin were not apparent. Lack of regional patterns 

'vas also true for molybdenum and selenium, even 

1 hough these elements showed patterns in galleta, and 

1 heir concentrations in the two species were signifii :antly correlated.

STUDY 2, ELEMENT CONTENT OF PLANTS

GROWING ON THE SHEPPARD, 

SHIPROCK, AND DOAK SOIL SERIES

STUDY OVERVIEW

This study concentrated on the area underlain by 

Ihe Kirtland Shale and Fruitland Formation. The 

( oals in these geologic units have the greatest potential 

] or recovery using surface-mining methods. The native 

i oils of this region vary as to their quality as media for 

; )lant growth. The Sheppard, Shiprock, and Doak soil 

i eries is a soil group that could be retrieved from the 

; trea to be mined and stockpiled for future replacement.

We sampled galleta and broom snakeweed, as well 

; is A- and C-horizons of the Sheppard, Shiprock, and 

Doak soil series, using the procedure detailed in the 

'Methods" section for Study 2. The samples were colected at random points defined by a barbell cluster. 

Three barbell clusters were positioned at random 

x>ints within the study area (fig. 4). The purpose of the 

study was to estimate at what distance increment most
12 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

37°

36°

35'

109° 108°

I COLORADO 

NEW"MEXICO

28 36 36 

O . 3 O

Farmington 10—

u

z

3-

109° 108° 107°

I I I

— 1_

I I

42 87 130 180 220

MANGANESE, IN 

PARTS PER MILLION

A. Manganese (dry-weight basis) in galleta

109° 108°

NEW"MEXICO~~ COLORADO _ _ 

48

o

r i ,.

0.5 0.7 0.91J2 

NICKEL, IN 

PARTS PER MILLION

0 10 20 30 40 50 KILOMETERS

I I I I I I

35'

i i 

R S ^

O O O O 0

MOLYBDENUM, IN 

PARTS PER MILLION

GM 0.51 PPM 

3D 1.50

B. Molybdenum (dry-weight basis) in galleta 

108° 107°

C. Nickel (dry-weight basis) in galleta D. Uranium (dry-weight basis) in galleta

FIGURES (above and facing page). — Concentrations of manganese, molybdenum, nickel, uranium, iron, and selenium in galleta grass at 

25 sampling locations from Study 1, expressed by symbols as concentration classes and by the geometric mean (GM) and geometric 

deviation (GD). The numbers above the frequency diagram are the percentages of the samples in each class. The class boundaries are 

indicated by vertical dashed lines. The sites marked with triangles (molybdenum and uranium) possessed concentrations (noted in 

parentheses) considered outside the normal range.

of the areal variability in the element content of a plant 

species occurred. This information was used to determine whether or not regional element patterns in 

plants were present, and to calculate biogeochemical 

baselines where appropriate.

GEOGRAPHICAL DISTANCE-RELATED ELEMENT 

VARIABILITY AND BASELINES

Samples of galleta grass were collected at all 30 

randomly selected sites and broom snakeweed at 27 of
BIOGEOCHEMICAL VARIABILITY OF PL^ NTS, SAN JUAN BASIN, NEW MEXICO 13

109° 108° 107° 109° 107°

i i i i i i

0 290 460 640 730 9901200 

IRON, IN PARTS PER MILLION

o 

riSELENIUM, IN PARTS PER MILLIO

E. Iron (dry-weight basis) In galleta F. Selenium (dry-weight basis) in galleta

FIGURE 8. — ( ontinued

30 sites. The absence of three samples of snakeweed 

from the unbalanced AoV design made the total 

number of samples for each barbell unequal (table 3). 

The F test for estimating significant differences between the barbells was not materially affected because 

the absence of the three samples did not appreciably 

change the coefficients used to estimate the mean 

square. Also, nine of the 35 elements analyzed required 

the replacement of censored values prior to the analysis of variance. Table 3 lists these elements and gives 

the replacement ratio for each of the three barbells 

and for each of the two plant species. For reference

TABLE 3. — Detection ratios for elements in the ash of galleta and 

broom snakeweed (Study #)

[Ratios are the number of values above the lower limit of determination to the number of 

samples analyzed; ratios are based on each of the three barbell units (fig. 4)]

Broom snakewead

Element Barbell 1 Barbell 2 Barbell 3 Barbell 1 Barbell 2 Barbell 3

Boron* —— —

Europium —

LanthanumLithium ——

Molybdenum

Niobium ——

Scandium —

Uranium ——

Ytterbium10

6

9

9

10

9

10

9

9

10

10

10

10

10

10

10

10

10

10

7

10

10

10

8

10

10

10

10

10

10

10

10

10

10

10

10

10

7

2

3

9

7

9

10

6

10

10

10

10

10

10

10

10

10

8

6

8

8

8

6

8

8

8

8

8

8

8

8

8

8

8

Q

9

10

10

10

10

8

10

10

8

10

10

10

10

10

10

10

10

10

9:9

5:9

7:9

9:9

9:9

8:9

9:9

9:9

4:9

1 Ratios for the number of values below the upper limit of determination 

to the number of samples analyzed.

I urposes the barbell locations have been numbered 

f rom north to south (fig. 4).

Tables 4 and 5 give the results of the analysis of 

irariance for element concentrations in galleta and 

i nakeweed and the summary statistics (GM, GD, 

< observed range, and the expected 95-percent range) 

: or each element. The expected 95-percent range is the 

'baseline" as first proposed by Tidball and Ebens 

(1976) and is calculated as a concentration range 

>racketed by the GM/GD2 to the GM-GD2 . Tidball and 

Ebens (1976, p. 300) explained that the "*** 95-percent 

< xpected range is broader than a 95-percent confi- 

< ience interval *** [it] is broader because it accounts 

1 or the variability among individual samples as meai ured by the standard deviation; the confidence inter1 ral reflects the indeterminacy in the mean of the dis1 ribution and is measured by the standard error of the 

i nean."

The proportion of the total logic variance that is 

€ xplained by each of the five distance increments, plus 

t he proportion of the variability explained as analyti- 

(al error, are given for 35 elements plus ash yield 

(tables 4 and 5). The three distance increments of most 

i nterest are greater than 10 km (regional component 

(r variability between barbell clusters), less than 0.1 

1 :m (local component or variability at a distance of less 

1 han 100 m), and the component associated with vari- 

{bility due to analytical error (precision). When the 

•< .nalytical error exceeded 50 percent of the total vari-
14 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

TABLE 4. — Variation in and summary statistics for the element concentrations in dry material ofgalleta growing in the Shepard, Shiprock

and Doak soil association, San Juan County (Study 2)

[Except as noted, the geometric deviation was calculated from the total variance; *, component of variance was tested as significant at the 0.05 probability level for the specified 

geographic distance increment; ratio, the proportion of the number of analyses having values above the lower limit of determination to the total number of analyses. >, greater than; <, 

less than; ppm, parts per million]

Analysis of variance

Element 

or ash

Ash —— — —— ——

Al umi num —— ——

Arsenic — — ——

Barium — —— ——

Boron —— —— ——

Cadml urn ----- -—

Calcium —— ———

Chromium —— ——

Cobalt —— — ——

Copper — —— ——

Europium ————

Fluorine —— ——

Iron --•————- 

Lanthanum ———

Lithium —— ———

Magnesium —— —

Manganes e — ——

Mercury —— — — - 

Molybdenum — —

Nickel —— —— — 

Niobium — —— —

Phosphorus — —

Potassium —— — 

Scandium —— ——

Selenium -———

Sodium —— — ——

St ro nt i urn ———

Sulfur (total)

Titanium —— ——

Uranium —— ——

Vand 1 urn —— —— - 

Ytterbium ————

Ytterium —— ——

Zinc ———————

Zirconium — — —

Ratio

30:30

30:30

30:30

30:30 

30:30

30:30

30:30

30:30 

30:30 

30:30

20:30

30:30

30:30 

20:30 

30:30

22:30

30:30

30:30 

30:30 

29:30

30:30 

24:30

30:30

30:30 

29:30

30:30

30:30

30:30

30:30

30:30

29:30 

30:30 

25:30

30:30

30:30

30:30

Total 

Iog10 

variance

0.0088

.0708

.0286

.0441 

.0432

.1224

.0282

.0363 

.0634 

.0254

.0686

.0204

.0519 

.1454 

.0243

.0388

.0271

.0193 

.0570 

.0466

.0273 

.1162

.0140

.0455 

.0337

.0335

.0307

.0180

.0948

.0334

.1081 

.0569 

.0381

.0640

.0307

.1402

Percentage of total

Barbells, 

> 10 km 

distance

<j

*67

11

29

<1

47

<!

36

*69 

34 

24

29

14

31 

13

2

27

4 

48

4

13

<1

*65

<1

*66 

40

48

16

*41

5-10 km 

distance

28

3

*39

11 

*70

16

*71

8 

6 

36

6

*21

4 

12 

30

8

*47

19 

15 

14

33

<1

*52 

11

35

<1

24

7

35

*36 

5 

5

15

14

0

1-5 km 

distance

<j

3

<1

7

<1

4 

10

<j

<1

2 

7 

10

18

9

10 

40

24 

23

41

*27

<!

*50

<1

<1

3

*0

1

<1

<J

variance between

0.1-1 km 

distance

15

5

5

3 

33

8

15 

15

<!

<1

5 

13

<1

4 

16

4

8

6

*48

2

13

11

*33

7

<1

15

0

<0. 1 km 

distance

22

7

23

*41 

29

*17

11 

*38

24

13

9

2

*14

*25 

*54 

*21

*30 

16

14

9

*12

20

13

<1

<1

*45 

11 

*37

20

*34

32

Summary statistics

Analyses

35

16

23

16 

23

22

5

76 

17 

16

69

30

11 

45 

36

51

7

12 

8 

8

8 

63

10

6 

26

1

15

49

17

29

8 

12 

18

16

20

27

Geometric 

mean (ppm)

7.4

1,800

.15

25 

5.0

.092

3,500

1.3 

.27 

2.9

2.19

6

730 

1.2 

2.34

730

93

.24 

2.34

.90 

2.67

510

3,100 

2.26

.081

61

22

1,200

72

2.081 

1.2 

2.092

.84

12

8.1

Geometric 

deviation

1.24

1.85

1.48

1.62 

1.61

2.24

1.47

1.55 

1.79 

1.44

2 1.71

1.39

1.69 

2 2.40 

1.43

21.37

1.46

1.38 

1.73 

2 1.53

1.46 

2 2.02

1.30

1.63 

2 1.46

1.52

1.50

1.36

1.87

1.47

2 1.75 

1.73 

2 1.48

1.79

1.50

2.37

Observed Expected 95 percent 

range (ppm) range (baseline, ppm) 1

5

490

.050

9.2 

3.2

.037

2,100

.69 

.072 

1.6

<.12

3.0

220 

<.46 

.61

<.20

430

54 

.070

.46 

<.24

320

1,700 

•C.13

.040

37

10

280

37

<.035 

.47 

<.050

.22

5

1.7

11

- 4,700

.25

57 

16

.45

- 7,900

2.4 

.55 

7.3

.42

10

- 1,600 

4.4 

2.2

.60

- 1,800

200 

.70 

.82

1.7 

1.9

— 990

-12,000 

.57

.25

- 150

43

- 2,800

160

.27 

3.1 

.24

2.2

26

36

4.8 -

530

.070-

9.7 - 

2.0 -

.019-

1,600

.086- 

1.4 -

__

3.1 -

260 

.20 - 

.60 -

350

49 

.082- 

.13 -

.43 -

300

1,200 

.11 -

.036-

28

12

340

33

.018- 

0.40 - 

.038-

.27 -

5.4 -

1.4 -

11

6,200

.32

64 

13

.45

7,400

.84 

5.9

—

11

2,100 

6.2 

2.4

1,500

170 

.70 

.90

1.9

860

8,100 

.60

.18

130

40

4,200

160

.36 

3.6 

.22

2.6

26

46

Because of a significant regional variance component (>10 km), the summary statistics for aluminum, iron, sulfur, vanadium, and zirconium are biased if 

applied to specific areas of the soil association; they are unbiased if applied count/wide. Because of excessive analytical error (>50 percent), baselinas for 

chromium, europium, lithium, and niobium were not calculated.

The technique of Cohen (1959) was used to calculate the mean and deviation because one or more concentration values were outside of the limits of 

determination of the analytical method used.

ability for an element, a baseline (expected 95 percent 

range) was not calculated. A baseline based on excessive analytical error measures predominantly imprecision and not the natural variability in the data.

Tables 4 and 5 show that 14 percent of the elements 

in galleta, and none in snakeweed, have a significant (p 

less than 0.05) regional-variance component. This 

variation means that the concentrations of aluminum, 

iron, sulfur, vandium, and zirconium, in galleta differ 

significantly when sampled at sites more than 10 km 

apart. Further, greater than 65 percent of the total

variability for all of these elements, except zirconium, 

was measured at this distance increment. Of these 

elements, only the total zirconium amounts in A- and 

C-horizon soils also showed a significant regional 

component (Severson and Gough, 1981); however, in 

the soils less than 33 percent of the total variability in 

the zirconium data was explained. Although the summary statistics, including the 95 percent expected 

range, are given for these five elements, it should be 

realized that, because of the significant regional variance component, these data are biased if compared to
BIOGEOCHEMICAL VARIABILITY OF FLAP TS, SAN JUAN BASIN, NEW MEXICO 15

TABLE 5. — Variation in and summary statistics for the element concentn .tions in dry material of broom snakeweed growing in the Sheppard,

Shiprock and Doak soil association San Juan County (Study 2)

[Except as noted, the geometric deviation was calculated from the total variance; *, compone it of variance was tested as significant at the 0.05 probability level for the specified 

geographic distance increment; ratio, the proportion of the number of analyses having values ,bove the lower limit of determination to the total number of analyses. >, greater than; <, 

less than; ppm, parts per million]

Analysis of variance

Element 

or ash

Ash ————————

Al umi num ————

Ar s eni c —————

Barium ————— 

Boron ———————

Cadmi urn —————

Calcium —————

Chromium ————

Cobalt ——————

Copper ——————

Eur op ium ————

Fluorine ————

Ir on ——————— 

Lanthanum ———

Lead ——————— 

Lithium —————

Magnesium ———

Manganes e ———

Mercury —————

Molybdenum ———

Nickel —————— 

Niobium —————

Phosphorus ———

Potassium ———

Scandium ————

Selenium ————

Sodium ——————

Strontium ———

Sulfur (total)

Titanium ————

Uranium —————

Vanadium ————— 

Ytterbium ———

Yttrium ————

Zinc ————————

Zircon! urn ———

Ratio

27:27

27:27

27:27

27:27 

26:27

27:27

27:27

27:27

27:27

27:27

21:27 

27:27 

27:27 

25:27 

27:27

27:27

27:27

27:27

27:27

27:27

27:27 

22:27

27:27

27:27

27:27

27:27

27:27

27:27

27:27

27:27

27:27

27:27 

20:27

27:27

27:27

27:27

Total 

Iog10 

variance

0.0126

.0435

.0536

.0360 

.0210

.0309

.0387

.0391

.0851

.0266

.0529 

.0160 

.0360 

.0884 

.0363

.0370

.0221

.0173

.0175

.0400

.0382 

.0965

.0177

.0429

.0195

.0358

.1022

.0429

.0506

.0378

.0336

.0386 

.0448

.0412

.0221

.0972

Percentage of total

Barbells, 

> 10 km 

distance

11

<1

<1

£

<!

15

<1

<1

<1

24

5 

27

21

9

<1

11

27

30

38

<!

36

7

<1

<1

9

30

<J

<1

4

<J

5-10 km 

distance

29

27

17

21 

27

28

<1

<1

13

4

13 

3 

17

4 

28

*64

26

<1

<!

2

*50

*31

<!

9

<1

<1

2

*54

<j

26 

53

20

4

43

1-5 km 

distance

17

32

31

23

26

<1

*56

40

<1

1 

19 

30 

54

<1

*33

10

<J

*83 

8

2

<1

58

0

<1

<1

3

12

16

13 

7

41

<1

9

variance between

0.1-1 km 

distance

16

*26

28

65 

2

8

*71

<1

32

*34

2 

*32 

14 

9

33

5

<1

26

*61

6

<1

11

8

14

*78

*79

*68

<!

*29

*46 

*29

*27

*68

23

<0. 1 km 

distance

*24

5

*16

<!

*28

*11

12

<1

<1

25 

*44 

*15 

12 

*48

34

7

17

22

16

6

*16

*18

10

*33

*13

*17

*19

*16

4

8 

5

<1

*21

<J

Geometric 

Ar ilyses mean (ppm)

4

11

7

14 

48

10

3

32

15

62

61 

8 

6 

14 

12

5

4

15

42

13

5 

63

2

1

24

8

1

3

7

8

20

7 

6

11

4

24

7.0

1,600

.18

50 

2 2 1

.23

12,000

1.4

.27

9.9

2.22 

9.4 

640 

2 1.4 

1.6

.81

1,300

70

.19

.35

.69 

2.52

610

7,800

.26

.25

100

56

1,000

92

.098

1.2 

2.074

.74

18

5.3

Geometric 

deviation

1.29

1.62

1.70

1.55 

2 1.34

1.50

1.57

1.58

1.96

1.46

21.55 

1.34 

1.55 

2 1.77 

1.55

1.56

1.41

1.35

1.36

1.58

1.57 

2l.74

1.36

1.61

1.38

1.55

2.09

1.61

1.66

1.52

1.52

1.57 

2 1.42

1.60

1.41

2.05

Summary statistics

Observed 

range (ppm)

3.8

710

.050

25 

11

.084

6,100

.67

.084

6.6

<.084 

4.0 

280 

<.55 

.64

.42

760

42

.10

.14

.38

340

3,800

.14

.10

41

23

400

38

.049

.59 

<.037

.35

12

2.2

9.5

- 3,300

.45

85 

- >42

.45

-23,000

2.8

.67

15

.51 

14 

- 1,200 

3.8 

2.9

2.2

- 2,200

120

.30

.67

2.7 

1.6

— 920

-14,000

.38

.45

- 590

97

- 2,400

160

.19

2.4 

.14

1.4

35

14

Expected 95 percent 

range (baseline, ppm)

4.2 -

620 - 4

.064-

21 

11

.10 -

5,000 -29

.57 —

.072-

—

5.3 - 

270 - 1 

.37 - 

.68 -

.34 -

660 - 2

39

.10 -

.14 -

.28 -

330 - 1

3,100 -20

.14 -

.10 -

24

22

360 - 2

40

.043-

.84 - 

.028-

.29 -

9.2 -

1.3 -

11

,100

.51

120 

41

.51

,000

3.4

1.0

~

17 

,500 

5.3 

3.8

1.9

,500

130

.35

.86

1.7

,100

,000

.49

.59

420

140

,800

210

.22

1.7 

.19

1.8

35

22

Because of excessive analytical error (>50 percent), baselines for copper, eur plum, and niobium were not calculated.

2The technique of Cohen (1959) was used to calculate the mean and devlatio because one or more concentration values were outside of the limits of 

determination of the analytical method used.

a newly collected sample from a specific area of Study 

2. They are unbiased, however, if the baseline is used to 

characterize typical concentrations from the entire 

Study 2 area. Although none of the elements in snakeweed possessed a significant regional component, 

variability at the 5-10-km increment was significant 

for potassium, magnesium, phosphorus, and titanium. 

Most of the variability in the element data was associated with the local component (less than 0.1 km). 

Thirty-six percent of the elements in galleta and 44 

percent in snakeweed showed significant local variance. This significant variance means that samples of

gj lleta and snakeweed more than 10 km apart vary in 

th eir content of these elements little more than plants 

sampled as close together as 0.1 km. For most elem ants, therefore, the uniformity in the content of these 

tv o plant species indicates that only a few samples are 

m eded to characterize a rather large area. The gross 

a> ailability of these elements in the Sheppard, Shipre ck, and Doak soil series is assumed, therefore, to be 

si nilar throughout the Study 2 area.

The AoV described above was designed to assess the 

vi riability in the element data of the plants at six 

distance-related increments. An assessment of only
16 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

the difference between barbell clusters, as calculated 

by a multiple-mean comparison test (Natrella, 1966), 

is given in table 6. Many elements in galleta showed 

significant differences in the element means between 

barbells. Fourteen of the elements (aluminum, arsenic, 

barium, cobalt, fluorine, iron, lead, manganese, scandium, sulfur, vanadium, ytterbium, yttrium, and zirconium) were highest in their concentrations at barbells 1 and 2 and lowest at barbell 3 (table 6). Other 

elements (nickel, niobium, and strontium) and ash 

showed differences between barbells 1 and 3 but not 

between barbells 2 and 3. Again, the southeastern 

most barbell (barbell 3) was lowest in the average 

concentration of these three elements. For most elements in snakeweed, no difference between the barbell means was found, and, therefore, no trends (as for 

galleta) are apparent.

STUDY 3, SAN JUAN MINE

Our study at the San Juan mine had the following 

objectives: (1) to compare the element concentrations 

in fourwing saltbush growing at a mine rehabilitation 

site to basin-wide control samples; (2) to assess the 

partitioning of the distance-related variability in the 

element concentration of fourwing saltbush and alkali 

sacaton at a mine rehabilitation site; and (3) to examine soil-plant element uptake relationships at both 

altered and native sites.

PLANT-ELEMENT CONCENTRATION COMPARISONS

The geometric mean and observed ranges in the 

concentration of 35 elements in the dry material of 

basin-wide fourwing saltbush controls and San Juan 

mine saltbush samples are listed in tables 2 and 7, 

respectively. These data show that concentrations of 

aluminum, arsenic, boron, cobalt, copper, fluorine, 

iron, lead, manganese, sodium, and uranium in samples of fourwing saltbush growing over spoil at the 

mine generally exceed the concentrations in control 

samples growing in native soils. The mine samples are 

commonly two to five times higher in these elements 

than the control samples. Of particular interest are the 

concentrations of sodium in saltbush: the mine samples have concentrations that are 100 times higher 

than the control samples. The relations between mine 

samples and control samples for these 11 elements are 

depicted in figure 9. In this figure, element concentrations increase from left to right (along a logarithmic 

scale) and from the bottom of the figure to the top 

(from arsenic to aluminum). The GM for the controls is 

based on 10 samples, whereas the values for the six 

mine samples are individually plotted (dots). GeometTABLE 6.— Multiple-mean comparison of barbell units of 'the average 

element concentrations in galleta and broom snakeweed (Study 2)

[GM, geometric mean concentration of elements (and ash yield) in dry plant material; 

values proceeded by alpha superscript are not significantly different at the 0.05 probability level; all measurements are in parts per million unless otherwise noted; n, number 

of samples]

Element 

or ash

Ash1

Altai nun ————

Bariua —————

£lci^ ____

Chromiun~~~~~

Europium

Fluorine —— —

Iron _______

Lanthanum —— —

Lit hlun —— ——

Magnesium ———

Manganese ———

Mercury —————

Molybdenum ———

Hiobiui —— ——

Phosphorus ——

Fotasslin ———

Scandium — ——

Selenlun ——— —

Sodium ———— -~

Strontium —— —

Sulfur (total)

Titanium -— ——

Uranium —— ——

Vandlun -— ——

Ytterbium ———

ittriu. —— ——

Zl rconl _• —— —

GM, 

barball 1 

(n-10)

Bo i O..L

"2,600

a .17

•32

>>4.8

a.10

a3,300

•1.3

a .34

"3.0

a .20

"7.0

•980

b1.3

"1.4

a.42

b640

a!20

a .29

".33

•l.l

".91

C430

b2,500

a .33

a.068

"67

•25

•1.600

"65

a .10

"1.7

a .ll

•l.l

"14

"12

Galleta

GM, 

barbell 2 

(n-10)

ab7.4

"2,200

•.18

"29

b4.3

a.093

"3,400

"1.3

•.34

"2.8

a .20

•6.5

"930

"2.1

"1.2

b.34

»630

•110

b.18

a .39

ab.90

ab.59

b510

b2,700

a .30

a.080

b50

ab21

•1.800

"67

a .066

"1.6

a .ll

•l.l

bll

"12

GM, 

barball 3 

(n-10)

bfc a O.t}

b950

-.11 

•16

"6.2

*«083

a3,600

"1.4

b.17

"2.7

a .19

b4.7

b430

c.56

b.92

c .21

•960

b72

a .27

a.30

b.75

b.58

•600

"4,300

b .17

«.10

•69

b19

b590

•85

•.080

b.72

b .063

b.50

abn

»3.8

GM, 

barbell 1 

(n-8)

»6.4

•1,700

•.21

•58

•11 

ab.24

b9,500

•1.5

•.33 

•10

ab.12

•11

•710

•1.7

•1.6

•.92

bl,200

b58

a .20

ab.36

a .62 

».36

b500

b6,400

a .24

b.18

a ioo

"50

•1,300

b79

b.087

•1.4

•.074

b.77

•16

•5.2

Broom snakeweed

GM, GM, 

barball 2 barbell 3 

(n-10) (n-9)

b6.7 « '

•1,500

•.17 

ab49

"22

b.19

ab!2,000

•1.3

ab.30

"9.5

•.26

•10

•640

•1.6

•2.0

ab.85

b l,300

•74

a .18

•.42

•.78 

b.49

b570

b6,500

a .27

a.28

•90

•58

•1,100

b80

•.13

•1.2

•.075

•.84

•19 

•5.3

O* M.

1,600

.16 

44

23

.27 

13,000

1.4

.21 

10

.067

7.5

580

.95

1.1

.69

1,700

76

.20

.28

.68 

.75

770

12,000

.27

.29

110

59

800

120

.080

1.1

.064

.62

5.4

Values are In percent.

ric mean values for arsenic, boron, lead, and uranium 

were calculated using the technique of Cohen (1959) 

because of the occurrence of at least one value outside 

of the limit of analytical determination (table 2). The 

very narrow range of values for arsenic (GD 1.19) and 

the occurence of four censored values (table 2) caused 

the seemingly unusual positioning of the arsenic GM 

(fig. 9).

In addition to the 11 elements above, concentrations 

of barium, cadmium, chromium, molybdenum, nickel, 

zinc, and a few others were slightly higher in mine 

samples than in control samples based on a comparison of their GM values only. However, a comparison of 

their observed ranges shows both mine and control 

samples to be similar. The fact that the GM values for 

these six elements in the mine samples are higher than 

the control samples should be noted; however, the following discussion concentrates only on those 11 ele-
BIOGEOCHEMICAL VARIABILITY OF PLAI* TS, SAN JUAN BASIN, NEW MEXICO 17

TABLE 7. — Variation in and summary statistics for element concentrate ns in dry material offourwing saltbush growing over spoil at the

San Juan mine (Itudy 3)

[Except as noted, the geometric deviation was calculated from the total variance; *, componei t of variance was tested as significant at the 0.05 probability level for the specified 

geographic distane increment; ratio, proportion of the number of analyses having values abo\ \ the lower limit of determination to the total number of analyses; leaders (t «), no data 

available; ppm, parts per million; <, less than]

Analysis of variance

Percentage of total vari ince 

between

Element 

or ash

Aluminum ————

Ar senic ------

Barium — — ——

Boron -— —— -—

Cadmium —— ——— 

Calc ium — — -—

Chromi urn —— ——

Cobalt ——————

(JOppGlT

Europium -—— —

Fluorine ————

Iron —— — — -—

Lanthanum — ——

Lead —— —— ——

Lithium —————

Magnesium — ——

Manganese —— -

Mercury —— ——

Molybdenum ——

Nickel — — ——

Niobium — — ——

Phosphorus ———

Potassium — ——

Scandium ——— —

Selenium —— —

Sodium -—— ——

Strontium ———

Sulfur (total)

Titanium --——— 

Uranium -—— ——

Vanadium —— ———

Ytterbium —— —

Yttrium —— ——

Zinc —— ——— -

Zirconium -----

Total log1Q

Ratio . variance

6:6 

6:6

6:6

6:6

4:6

6:6 

6:6 

6:6

6:6 

6:6 

3:6

6:6

6:6

2:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6

4:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6 

6:6 

6:6

0:6

6:6

6:6

6:6

0.0013 

.0175

.0080

.0373

.0213

.1469 

.0121 

.0047

.0278 

.0041

.0177

.0108

—

.0158

.0284

.0029

.0410

.0396

.0389

.0442

.2039

.0108

.0090

.0070

.1032

.5355

.0199

.0046

.0315 

.0107 

.0107

.0232

.0543

.0428

25-100 m 5-25 m <5 m di stance Geometric 

p] us 

distance distance analytic! 1 splits mean (ppm)

<1 60 

<1 51

4 <1

<1 50

27 *72

6 <1 

*43 <1 

56 <1

60 16 

12 <1

<1 7

<1 <1

—

<1 <1

10 <1

<1 64

<1 92

<1 <1

<1 <1

24 <1

57 *42

16 <1

<1 50

<1 74

*90 2

17 <1

73 <1

<1 <1

<1 23 

38 <1 

<1 41

<1 50

<1 *95

<1 12

;;

<6

!0

1

<4

: 7

:4

18

! 3

9

9

1 0

>6

8

19

'9

'6

;i

14

iO

!6

8

53

.7

)9

'7 

>2 

>9

50

5

38

13 

1,200

.24

26

*57

.17 

10,000 

21

.47 

9.7

20

780

L l.l

1.2

1.7

7,200

160

.13

.73

1.9

h.o

840

26,000

.43

.22

7,400

48

4,500

43 

.11 

1.9

.81

56

5.1

Summary

Geometric 

deviation

1.09 

1.36

1.23

1.56

1 1.27

2.42 

1.29 

1.17

1.47 

1.16 

X 2.27

1.36

1.27

h.51

1.34

1.47

1.13

1.59

1.58

1.57

1.62

*2.62

1.27

1.24

1.21

2.10

5.39

1.38

1.17

1.50 

1.27 

1.27

1.42

1.71

1.61

statistics

Observed 

range (ppm)

12 

880

.20 -

17

40

.052 - 

9,100 

1.8 -

.28 - 

8.4 - 

<.24 -

15

620

<1.2 -

.096 -

1.1

610

90

.080 -

.53 -

1.1

<.52 -

650

21,000

.34 -

.10 -

540

35

4,000

25 

.072 - 

1.5 -

.60 -

27

3.1

15 

1,700

.30

42

>65

.26 

14,000 

25

.65 

12 

.74

34

1,100

1.8

1.7

2.5

8,400

240

20

1.3

3.4

3.0

1,200

34,000

.51

.45

20,000

71

5,200

64 

.13 

2.7

1.3

86

8.6

The technique of Cohen (1959) was used to calculate 

concentration values outside of the limits of determination.

the mean and deviation because there were one or more

ments (fig. 9) that showed the greatest differences.

Only selenium was consistently higher in saltbush 

samples from the basin when compared to samples

f *om the mine. A discussion of this trend follows in the 

s action on "Element toxicity and deficiency considerat ons." Trends for strontium and the major essential
18

Aluminum

1

100 

Iron

I

10

Sorlinm

1

10

Manganese

10

Boron 

Fluorine

Copper

|

1 0

Lead

I

0 1

Cobalt

Uranium

Arsenic

1

001

GEOCHEMICAL SURVEY OF '

i i i i i i i i | i i i . i i i i |

1000 10,000

1 ——————— A ————— 1 • :. •

, -ti.il, , i i .... , |

100 1000

// 1 1 1 1 1 1 1 1 1 1 // 1 1 1 1 1 1 1 1

100 10000

1 —————— A—— ——— ——¥. . .

' ' ' ' ' ' ' | ' 1 1 I I I .. | !

100 1000

1 ——— A —— • — T— »;>

1 —— A —— 1 ..« •

1 •'•'•' | ' ' ' i i - . i |

10 100

*-± —— 1 ... .. .

' '•'''' 1 ' ' 1 . i i 1 i 1

1 0 10

< ——— A ———— I • • ....

^ — A ——— 4 ••••

« —— • t t •

i i iiiiiii i i i i i i i i i

01 10

CONCENTRATION, IN PARTS PER MILLION

FIGURE 9. — Concentrations of elements (dry-weight basis) in fourwing saltbush at the San Juan mine (dots) that generally exceed 

values obtained in control samples from the San Juan Basin. Bars 

represent the observed concentration range in basin-wide samples, and triangles are the geometric mean of the ranges (10 

samples). Arrows indicate that either the lower or upper end of 

the range is indeterminate because at least one value exceeded the 

lower or upper limit of determination for the analytical methods 

used. >, greater than.

elements calcium, potassium, and phosphorus, are 

similar but less pronounced than those for selenium.

Alkali sacaton, a perennial bunchgrass, was sampled 

only at the mine, so we are unable to compare its 

summary statistics in table 8 with control samples. 

Provisional comparisons can be made, however, between sacaton collected at the mine and galleta grass 

collected throughout the basin (table 2). We recognize 

that these two grasses are genetically distinct; however, a comparison of the two is reasonable because 

their ash yields are similar and little difference was 

noted in their overall element concentrations. Only the 

concentrations of sodium show large differences between these two species — the mine sacaton samples 

are 20 times higher in sodium than the basin-wide 

galleta samples. Other elements, such as sulfur, uranium, and lead, showed slightly elevated levels in mine 

samples; however, concentrations of manganese, phosphorus, and selenium were slightly less in mine 

samples.

Comparisons of element concentrations between 

shrubs and grasses, both on and off altered sites, may 

be helpful in assessing gross soil-element availability 

differences. Interspecific inherent differences also

govern element uptake and must be considered when 

assessing the following observations. Mine-sampled 

sacaton has similar element content to basin-wide 

saltbush samples but is very much below the concentrations of elements in mine saltbush. Concentrations 

of mercury, lead, and uranium were the exceptions; 

these elements showed slightly higher concentrations 

in mine sacaton than in basin-wide saltbush or mine 

saltbush. These three elements (plus perhaps molybdenum) were the only elements that were elevated in 

their concentrations in sacaton tissue over concentrations observed for saltbush. Basin-wide galleta samples showed a similar tendency to accumulate mercury and molybdenum in particular, and lead and 

uranium to a lesser extent. This tendency may be a 

unique characteristic of these two grass species and 

merits additional study. Further discussions of the 

selective uptake of certain elements by the species 

sampled are included in the following section.

SOIL-PLANT ELEMENT RELATIONS

A detailed discussion of the extractable and total 

concentrations of more than 30 elements in native soils 

and mine soils from northwestern New Mexico is 

given in Severson and Gough (1981). The purpose of 

this discussion is to evaluate element concentration 

levels in soils and mine soils as they may affect plantelement levels.

Figure 9 shows the concentration of 11 elements in 

saltbush that had elevated levels in San Juan mine 

samples when compared to basin-wide samples. Assuming that the basin-wide samples are a good measure of background levels of these elements in saltbush, then the higher concentrations observed in the 

mine samples reflect a greater degree of availability of 

these elements in the replaced topsoil or spoil or both. 

Figures 10,11, and 12 show the GM values for the total 

concentration of 10 of these 11 elements (concentrations of fluorine were below the detection limit of 400 

ppm) in basin-wide A and C horizons of soil and in San 

Juan mine replaced topsoil and regraded spoil. These 

figures also give the DTPA-extractable concentrations (ppm in soil) of cobalt, copper, iron, manganese 

and lead, the water saturation-extractable amounts 

(meq/L of extract) of sodium, and the hot waterextractable levels (ppm in soil) of boron.

As shown in figure 10, in general, the total concentration of copper, iron, manganese, sodium, aluminum, and uranium were higher in the mine samples 

than in the basin-wide samples. Extractable levels of 

copper, iron, manganese, and sodium showed a similar 

trend (we do not have extractable data for aluminum 

or uranium). Figure 11 gives trends for cobalt and
BIOGEOCHEMICAL VARIABILITY OF PLA 4TS, SAN JUAN BASIN, NEW MEXICO 19

TABLE 8.— Variation in and summary statistics for element concentrate ons in dry material of alkali sacaton growing over spoil at the San

Juan mine (S udy 3)

[Except as noted, the geometric deviation was calculated from the total variance; *, componi nt of variance was tested as significant at the 0.05 probability level for the specified 

geographic distance increment; ratio, proportion of the number of analyses having values ab( re the lower limit of determination to the total number of analyses; leaders (e t), no data 

available; ppm, parts per million; < , less than]

Element

or ash

Arsenic -------

Barium1 ——— —

Boron ——— —— —

Cadmium — —— -

Calcium --—— —

Chromi um — — — —

Cobalt —— —— -

Copper — — — —

Europium -----

Fluorine — — —

Lanthanum —— —

Lead ——— — — -

Lithium —— ——

Magnesium -----

Manganese — ——

Mercury -------

Molybdenum ——

Nickel ——— ——— 

Niobium — ——— - 

Phosphorus ———

Potassium —— —

Scandium ——— —

Selenium — — —

Sodium ——— ———

Strontium —— —

Sulfur (total)

Titanium — — —

Uranium ~———

Vanadium —— — —

Ytterbium ———

Yttrium —— ——

Zinc —— — ———

Zirconium ———

Ratio

6:6 

6:6

6:6

5:5

6:6

6:6

6:6

6:6

6:6

6:6

3:6

6:6 

6:6

4:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6 

6:6 

6:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6

6:6

3:6

6:6 

6:6

6:6

Total log1Q

variance

0.0037

.0814

.0116

.0131

.0909

.0028

.0273

.0210

.0067

.0050 

.0311

.2187

.0108

.0551

. nnai uuoJ

.0325

.0135

.0123

.0154 

.0891 

.0033

.0158

.0148

.0027

.1119

.0090

.0061

.1713

.0076

.0278

_

.0261 

.0131

.1576

Analysi

Perc

25-100 m

distance

6

xi

<1

9

9

<1

< l

<!

<1

23

5

<1

<l

Zo

50

<1

19

29 

52

<!

<1

14

40

<!

41

<1

<1

<1

__

,-

5

<L

s of varia

entage of 

bet

5-25 m

distance

66

61

33

<1

21

*97

<*

<!

<1

—

49

<1

19

<l

Ni

<1

82

4

75 

17

44

*97

<1

<1

51

<1

70

73

31

_

48

45

nee

total var: ance 

ween

<5 m d stance

p! us 

analytic; 1 splits

: 9

i 7

1 0

0

3

1 9

1 9

1 9

7

1

1 4

.1

1 9

2

• 9

7

7

15

1 

.1

6

3

6

.0

9

>8

iO

17

8

._

>2

15

.5

Geometric

mean (ppm)

5.1

500

.12

14 

9.1

.048

4,100

.71

.23

2.4

7 Q• O

310

2.72

1.4

.29

1,000

50

.20

.70

.71 

.65 

340

2,400

.19

.096

1,500

21

1,500

16

.13

.94

2.046

.47 

14

1.9

Summary st

Geometric

deviation

1.15

1.93

1.28

1.24

1.30

2.00

1.13

1.46

1.40

1.21

22.30

1 1 ft • lo 

1.50

2 2.30

1.27

1.72

1.23

1.51

1.31

1.29

1 1 ft 1* Jo 

1.99 

1.14

1.34

1.32

1.13

2.21

1.24

1.20

2.59

1.22

1.47

2 1.32

1.45 

1.30

2.49

:atistics

Obsc

range

4.1

250

.10

11

7.8

.022

3,600

.47

.16

2.0

<.082

6 

210

<.38

1.0

.16

ft ozuOft

31

.15

.48

.49 

.26 

260

1,600

.14

.08

910

17

1,200

5.3

.10

.62

<.038

.30

11

.54

jrved

(ppm)

5.7

- 1,000

.15

17

12

.15

- 4,600

.91

.34

2.8

.33 

9

510

2.3

1.8

.46

- 1,200

74

.25

.90

1.1 

1.3 

390

- 3,500

.24

.10

- 4,500

27

- 1,800

44

.16

1.4

.068

Aft

17

5.3

Because of an erroneous concentration value, these statist cs are based on five samples, not six.

o

The technique of Cohen (1959) was used to calculate :he mean and deviation because there were one or more 

concentration values outside of the limits of determination.

lead. These two elements differ somewhat from the 

elements shown in figure 10 because cobalt and lead

\ ave similar total levels between native soils and mine 

s )ils, but the extractable levels in mine soils (particu-
o •

^P § 3

9 ep 2 % 3 ft) 3 ^ 02 90

* I * 3 1 -S 1 S-

,-. » H to 

IliTa

$ as ?

ft) 0> fD

DTPA-EXTRACTABLE CONCENTRATION TOTAL 

P H-. sr 02 . '

C* ft o

S 3 S

%3 fD

of 3-| ff*

2. 3

2.3

s

02 cr .

H-.

3 STa 3 p

3 P S ft) 3

P ET. H- 3 O3 <*

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CT 0

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B. P »^ 02

K-. ff,

fD 3 

02 i

2. 2 mt 5-'

02 fD

cr p

° <"' 

o ft>

3 02

P 2.

OLF

concentrations i

element concern

ITT M

"^ I3

S. 3 n~. 3 § 5' 

s ft

replaced topsoil 

differences wer 

these elements i

mine spoil.

Simple correl; 

relations existed

of ~ •

c+ O

^ »

ft) P

3 « p

cr 3 «*•

P O <n- O2 c-f- *f

B' &<*>

native C-horizoi difference was nelements inba

02 O ^ 

<—" £+ O2

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|s"s

fi, UJ HJ

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5: a »'

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fD ^

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c+- O

3" ft

fD O<

fD I O

oq

H- 3 M

P 5 ^

l:ss Iq O^ P. 3 

02 p O

2. 3 & 

P g^ 

S-^P

02 P . 02

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p a to -^

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0, 3 SO SO 0-333

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WATER-SATURATION EXTRACTABLE CONCENTRATION

________o_________o_________o 

I I |

lil — -Q c'

£f 3

.—• M_

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TOTAL CONC

TOTAL CONCENTRATION

10_____CO •&>

10 

O> O

Ic' 3
0.5

CC 

HQ.4

LU

o

§0.3

LU

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DC

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£

BIOGEOCHEMICAL VARIABILITY OF PLA> TS,

16

14

12

cc

I 10

c

z 

c

C

2.0

Cobalt (ppm)

1.0

0

Lead (ppm)

EXPLANATION 

Basin-wide samples 

A horizon

C horizon

San Juan mine samples 

Topsoil

Spoil

FIGURE 11. — Differences between the average values for basinwide native A- and C-horizon soils (47 samples) and replaced topsoil and spoil from the San Juan mine (12 samples) for extractable 

and total concentrations of cobalt and lead (from Severson and 

Gough, 1981). <, less than.

SAN JUAN BASIN, NEW MEXICO 

Boron (ppm)

6

21

Arsenic (ppm)

EXPLANATION

Basin-wide samples

A horizon 

C horizon

San Juan mine samples 

Topsoil

Spoil

I IGURE 12. — Differences between the average values for basinwide native A- and C-horizon soils (47 samples) and replaced topsoil and spoil from the San Juan mine (12 samples) for extractable 

and total concentrations of boron, and total concentrations of 

arsenic (from Severson and Gough, 1981).

i mall sample number of each material (n = 6), precludes the usefulness of prediction equations. Poor 

:nine soil-plant element correlations occur, even 

hough, compared with basin-wide soils, generally
22 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

elevated extractable- and total-soil levels are reflected 

in elevated levels in mine-sampled plant material 

(table 2). Plant material analyses are, therefore, preferred to conventional soil analyses as indicators of 

element availability. This availability will vary with 

time, however, and the values we report for August 

1977 will change as explained by Cornwell and Stone 

(1969, p. 118):

Availability [in minesoils] is controlled not by storage and release 

from a relatively stable reserve, or an organic matter cycle, as in 

natural soils, but by rate of solution from fresh mineral surfaces or 

freshly-exposed subsoil particles. Hence, element availability cannot be predicted from tried and tested parameters such as pH, 

because relationships between the two are unknown for spoil bank 

materials.

A linear regression analysis between 10 soil and plant 

samples from the basin was possible, and such soilplant relations under native conditions should be more 

stable with time than are relations under reclamation 

conditions. Prediction equations for native systems, 

therefore, are more reliable. As examples, the strong 

positive soil-plant availability relations for cobalt and 

copper follow:

Logio basin saltbush cobalt =-0.05 + 0.44 logio 

basin A-horizon DTPA-extractable cobalt (r2 = 0.67).

Logio basin saltbush copper = 3.1 + 1.8 logio 

basin A-horizon DTPA-extractable copper (r2 = 0.54).

Generally higher extractable levels in spoils (and 

not replaced topsoil) are assumed to be the source of 

the elevated levels of these elements in mine-sampled 

plants. An extreme example is sodium, which shows 

an increase of two orders of magnitude over basin wide 

C horizons of soil (fig. 10) that is also reflected by a 

similar increase of two orders of magnitude in sodium 

in mine-sampled saltbush (fig. 9). For a deep-rooted 

species like saltbush (Dayton, 1931, reported fourwing 

saltbush roots at a depth of 6 m below ground level), 

topsoiling to a depth of about 20 cm apparently does 

little to ameliorate the plant-uptake of elevated levels 

of some metals in spoil.

GEOGRAPHICAL DISTANCE-RELATED 

ELEMENT VARIABILITY

Tables 7 and 8 give estimates of the three components of distance-related variability in the element concentration of mine-sampled fourwing saltbush and 

alkali sacaton. These estimates are based on six samples and are therefore provisional. This variation is

partitioned into components representing distances of 

25-100 m, 5-25 m, and 0-5 m (local). The local component includes variation due to sample processing and 

analytical procedures (analytical error). Sixty-seven 

percent of the elements in saltbush (table 7) and 62 

percent in sacaton (table 8) show that the local component accounts for 50 percent or more of the total variability. Excessive censoring of europium, lanthanum, 

and ytterbium concentrations in saltbush; europium 

and ytterbium in sacaton; and an error in one value for 

barium in sacaton, resulted in no AoV calculations for 

these elements. Several other elements in both saltbush and sacaton had values either above or below the 

detection limit for the analytical method used; however, no more than one-third of the values were substituted prior to the AoV calculation.

Even though the analytical-error component cannot 

be separated from the local component, it would 

appear that plants spaced relatively close together 

vary greatly in their element compositions. Thus, 

samples of saltbush and sacaton as far apart as 100 m 

will probably vary in their element composition little 

more than plants sampled only a few meters apart. 

This observation is undoubtedly a reflection of the 

heterogeneous composition and element availability of 

the spoil material from place to place in the rehabilitated area (Severson and Gough, 1981).

None of the elements in sacaton show a significant (p 

< 0.05) component of variability associated with a distance of 5-100 m, and only calcium and selenium are 

significant in saltbush for this distance. Therefore, 

calcium and selenium levels in saltbush differ over 

broad areas within the mine, whereas saltbush sampled close together varies only a little. Total calcium in 

replaced topsoil at the mine has significant variation 

at 25-100 m (Severson and Gough, 1981); and spoil 

material showed a large, but nonsignificant variation 

at 5-25 m. There appears, therefore, to be a correspondence between mine-soil calcium differences and saltbush calcium. Selenium occurs in concentrations too 

small to be commonly detected in soils by the methods 

we used, so saltbush-mine soil selenium comparisons 

are not possible. Several additional elements (boron, 

manganese, niobium, and zinc in saltbush; and calcium, europium, and scandium in sacaton) show significant variation at 5-25 m, and, therefore, plants 

separated by this distance can be expected to differ 

significantly in their composition of these elements. 

Further, sacaton has large but nonsignificant variation at 5-25 m for ash, aluminum, mercury, nickel, 

titanium, uranium, and ytterbium, whereas saltbush 

shows similar trends for ash, aluminum, magnesium, 

and scandium.
BIOGEOCHEMICAL VARIABILITY OF PLAN TS, SAN JUAN BASIN, NEW MEXICO 23

ELEMENT TOXICITY AND DEFICIENCY CONSIDERATIONS

Excess boron in alkaline and saline soils can adversely affect plant growth. Our boron data for the grass 

(sacaton) and for the shrub (saltbush) at the San Juan 

mine do not appear excessive when compared to similar material collected elsewhere in the western United States (U.S. Geological Survey, unpublished data). 

The total and hot-water extractable levels of boron in 

mine soils (GM for replaced topsoil, 0.6 ppm; spoil, 1.8 

ppm) also indicate that boron toxicity problems are 

probably minimal — at least in the area we sampled. 

A few extractable values exceeded 2 ppm (toxicity 

threshold for some citrus species), and one value 

exceeded 11 ppm in spoil, which is high but probably 

not detrimental to nonagricultural species (Gough, 

Shacklette, and Case, 1979).

Boron levels in plant tissue are also diagnostic of 

potential plant toxicity problems. Boron concentrations in fourwing saltbush sampled at the mine are 

elevated (nearly double) when compared to basin-wide 

samples (tables 2 and 7). These values, however, do not 

exceed the range of values reported as suggestive of 

boron toxicity (Gough, Shacklette, and Case, 1979). 

Total boron in basin-wide soils is much greater than 

total boron in mine soils, yet the extractable levels of 

boron in the basin are far below those found at the 

mine (fig. 12). These levels suggest that in basin-wide 

soils boron is in a form (reservoir) not available to 

plants — this is certainly reflected by plant levels of 

boron (table 2). Mine spoil, however, is much higher in 

extractable boron than is mine topsoil, indicating that 

more boron in spoil should be available for plant 

uptake than in topsoil.

Mine soils, although highly variable, were predominantly alkaline, having a mean pH of 8.0 for topsoil 

and 7.6 for spoil. The observed pH ranges for topsoil 

and spoil, however, were 6.7-9.3 and 5.9-9.3, respectively. The occurrence of occasional low-pH soils at the 

mine may explain why mine-sampled plants were 

generally low in molybdenum and selenium when 

compared with basin-wide plant materials whose 

accompanying soils were never below a pH of 7.1 

(tables 2, 7, and 8).

Erdman and others (1978) found that coppermolybdenum ratios (dry-weight basis) in sweetclover 

from rehabilitated spoil sites at eight mines in the 

northern Great Plains were low enough to warrant 

concern for the health of grazing ruminants. They 

pointed out that various metabolic imbalance diseases, 

generally termed "molybdenosis," are caused by either 

low copper in forage coupled with slightly elevated 

molybdenum or normal copper levels with very much

hi, fher molybdenum. They noted that copper-molybde num ratios below about 5:1 are thought to be critica ; therefore, they viewed with concern the ratios at 

th >se mines that ranged from about 0.4:1 to 5:1. Our 

co )per-molybdenum ratios for alkali sacaton and 

foi irwing saltbush at the San Juan mine varied greatly — 

ra;ios for saltbush were commonly more than 14:1 

(d le to relatively high copper levels), whereas those of 

sa :aton ranged from 2.2:1 to 5.8:1. Because of these 

ge lerally acceptable ratios and also because of unifo] mly low molybdenum concentrations (usually less 

th in 1.0 ppm) in the plants sampled at the mine and 

th -oughout the basin, there appears to be little need 

fo] concern for potential molybdenosis in animals that 

m ght range over these rehabilitated areas. It should 

be noted that our mine and basin samples did not 

in< :lude any legumes, which are known to be generally 

hi; 'her in molybdenum than other forbs or grasses.

Figure 10 illustrates the large amount of extractabl; sodium in these soils. We reported in Chapter C 

(S^verson and Gough, 1981) that the mine soils, in 

ge neral, may be classified as saline sodic (U.S. Salinity 

LJ .boratory Staff, 1954) and possess the characteristic s listed in table 9. Sodium-dominated alkaline soils 

ar e reported by Sandoval and Gould (1978) to have the 

fo lowing properties which are unfavorable to plant 

growth: (1) potential loss of phosphorus through the 

fo 'iiiation of highly soluble sodium phosphate compc unds, (2) low availability of water because of either 

osnotic potential limitations, or decreased soil perm ^ability properties, or both, (3) formation of undesi 'eable sodium humates, and (4) inhibition of calci im, magnesium, and potassium uptake.

D ossible deficiencies of elements essential to plant 

gr >wth at rehabilitation sites were assessed both by the 

ex ;ractable levels in mine soils and by the levels in 

pi; tnt tissue. In Severson and Gough (1981) we reported

J BLE 9. — Saline-sodic characteristics of San Juan mine soils 

(Study 3)

Soil 

Parameter

Geometric Geometric 

Material mean deviation Range

Ha

So

Ex

Sp

er saturation

stractable sodium — ——

nllllequivalents

er liter)

ium adsorption

atio ———————————— 

hangeable sodium

ercentage (percent) ——

cific conductance — ——

nillimhos per

entimeter)

Topsoil —

Spoil —— -

Topsoil —

Spoil — — 

Topsoil —

Spoil ——

Topsoil —

Spoil — —

21

260

7.6

51 

4.8

32

2.8

9.6

3.15

1.69

2.26

1.56 

2.48

1.68

2.37

1.70

6.5-120

97 -500

2.7- 25

20 - 70 

1.5- 18

9.4- 49

1.3- 11

4.0- 17
24 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

that extractable zinc levels in mine soils were low and 

potentially limiting to the growth of certain plants. 

Sacaton and saltbush, however, appear to be assimilating enough zinc, although the saltbush levels are four 

times greater than the levels in sacaton (tables 7 and 8). 

Potassium levels in sacaton appear unusually low for 

grasses in general (GM 0.24 percent) and may be 

depressed because of high levels of mine-soil 

sodium. Our X-ray fluorescence method for the determination of total phosphorus in soils registered 

concentrations for all of our samples below the detection limit of 0.5 percent. It is well known that low 

available phosphorus levels in reclamation soils can be 

limiting to plant growth. The phosphorus levels in 

both sacaton and saltbush appear somewhat depressed; 

however, their healthy appearance indicates that, for 

these species, available phosphorus is adequate. As 

cattle pasturage, the phosphorus levels of both species 

and the copper and zinc levels in sacaton are inadequate, whereas levels of cobalt (Hodgson and others, 

1962), calcium, magnesium, iron, and manganese 

appear acceptable (Munshower and Neuman, 1978; 

Erdman and Ebens, 1979). Because the sacaton samples were washed and the saltbush samples were 

judged not to be excessively contaminated, these data 

represent material perhaps atypical of common forage. Grazing animals can greatly supplement their 

intake of trace metals by consuming vegetation with a 

coating of dust.

CONCLUSIONS

1. Samples of native galleta grass, broom snakeweed, and fourwing saltbush, from that part of 

the San Juan Basin most likely to be affected by 

energy development (Study 1), show large variability in their concentrations of 35 elements. 

The summary statistics for these elements can 

be used to judge the degree of change that future 

land-surface alterations may have on these 

plants. Plant-element composition, therefore, 

may be viewed as one measure of changes in the 

element availability in soils.

2. In Study 1, the concentrations of manganese, 

molybdenum, nickel, and uranium (and possibly 

iron and selenium) in galleta show regional 

trends. Concentrations of manganese, nickel, 

iron, and selenium are highest in the southcentral part of the Study 1 area (selenium also 

shows high values just west and south of Farmington). The molybdenum and uranium data are 

unique, because two populations of values were

defined. Concentrations of molybdenum in galleta were highest in the western edge of the study 

area, whereas uranium was highest in galleta 

samples collected west and southwest of Farmington.

3. Trends in the element composition of snakeweed 

and fourwing saltbush across the basin were not 

observed. For saltbush, however, the presence 

of such trends could not be measured because of 

too few samples. Of the species collected, however, saltbush exhibits the strongest relations 

between soil-plant element concentrations. For 

example, linear regressions for cobalt and copper between DTPA-extractable soil levels and 

plant levels explained 67 percent and 54 percent, respectively, of the total variability in the 

data.

4. Differences in the concentration of elements between Study 1 plant species are generally less 

than a factor of two except for the following: ash 

yield of saltbush is two times that of the other 

plants; boron in snakeweed and saltbush is four 

times greater than in galleta; iron in galleta is 

two times the levels found in saltbush; lead is 

much higher in galleta and snakeweed when 

compared to saltbush; the major essential elements calcium, magnesium, potassium, phosphorus, and sulfur are generally highest in saltbush; and selenium in saltbush is four times 

greater than the levels in galleta or snakeweed.

5. For plants growing in the Sheppard, Shiprock, 

and Doak soil series (Study 2), the GM, GD, 

observed range, and 95-percent expected range 

(baseline) for the concentration of 31 elements 

(and ash yield) in galleta and 32 elements (and 

ash yield) in snakeweed are given (tables 4 and 

5). Concentrations of aluminum, iron, sulfur, 

vanadium, and zirconium in galleta, however, 

show a significant regional (more than 10 km) 

variance component, indicating that the baseline estimates are biased if compared to a newly 

collected sample from a specific area of Study 2. 

They are unbiased, however, if the baseline is 

used to estimate the most typical concentrations 

from the entire Study 2 area.

6. Most of the variability in the element-concentration data from Study 2 was at the distance 

increment of less than 0.1 km. This means that 

samples of galleta and snakeweed further apart 

than 10 km vary in their element composition 

little more than plants sampled as close together 

as 0.1 km. For most elements, therefore, the
BIOGEOCHEMICAL VARIABILITY OF PLAN TS, SAN JUAN BASIN, NEW MEXICO 25

analysis of only a very few samples of these two 

species can effectively characterize their element composition for a rather large area.

7. In Study 2, the southeasternmost cluster of galleta 

samples (barbell 3) is generally lowest in the 

concentration of nearly half of the elements analyzed, however snake weed does not show a similar trend.

8. The concentrations of 35 elements (and ash yield) 

in alkali sacaton and fourwing saltbush collected on a spoil rehabilitation plot at the San 

Juan mine (Study 3, tables 7 and 8) are compared to samples of similar material from native 

(control) sites from throughout the San Juan 

Basin. Concentrations of aluminum, arsenic, 

boron, cobalt, copper, fluorine, iron, lead, manganese, sodium, and uranium in samples of saltbush growing over spoil generally exceed the 

levels of these elements in control samples. 

These element levels are elevated in plant samples from spoil sites by factors of from two to five 

times. Sodium concentrations in saltbush, however, are 100 times higher on spoil sites; and on 

the same sites the extractable-sodium levels in 

spoil material are also 100 times higher than 

C-horizon controls.

9. Simple correlation analysis shows that few significant positive correlations exist between either 

extractable- or total-element concentrations in 

mine soils and plant-element concentrations. 

Poor correlations occur even though generally 

elevated levels of elements are found in both the 

mine soils (extractable and total forms) and the 

mine-sampled plants. The generally higher 

extractable levels in spoils (and not replaced topsoil) are assumed to be the source of the elevated 

levels of these elements in mine-sampled plants.

10. Mine-sampled plants, spaced relatively close together (5 m or less), vary greatly in their element compositions. This variation reflects the 

heterogeneous composition and element availability of the spoil material.

11. As pasturage for cattle, mine-sampled fourwing 

saltbush is low in phosphorus, and sacaton is low 

in copper and zinc. Levels of cobalt, calcium, 

magnesium, iron, and manganese appear adequate in both plant species. No potential element-toxicity problems for grazing animals were 

noted. Except for some high boron and sodium 

levels, the mine soils do not contain element levels that are judged to be potentially toxic to 

native plant growth and development.

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26 GEOCHEMICAL SURVEY OF THE WESTERN ENERGY REGIONS

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IS GOVERNMENT PRINTING OFFICE 1981——777-O34/47