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United States
Department of

Agriculture
Forest Service

Intermountain
Research Station

Research Paper
INT-390

April 1988

Soil Characteristics
as an Aid to
Identifying Forest
Habitat Types in
Northern Idaho.

U Js 0

E72
—

Kenneth E. Neiman, Jr. a

THE AUTHOR

KENNETH E. NEIMAN, JR., is a private forest ecology/soils
consultant living in Moscow, ID. From 1981 to 1986, he
was forest ecologist assigned to the research work unit on
silviculture of cedar, hemlock, grand fir and Douglas-fir of
the Northern Rocky Mountains, Intermountain Research
Station, Moscow, ID. Dr. Neiman received his B.S. degree
in 1975 in range management and his M.S. degree in 1977
in forest and range ecology—both degrees from Washing-
ton State University. He received a Ph.D. degree in forest
ecology in 1986 from the University of Idaho. This paper
represents a portion of the research presented in his
dissertation.

RESEARCH SUMMARY

Scientists have long hypothesized that soils and plant
communities have predictable relationships. High correla-
tion between soil properties and shrub-steppe plant asso-
ciations has been repeatedly documented, but studies in
forested vegetation have produced conflicting results. The
objectives of this study were to investigate: spatial patterns
of numerically derived taxonomic soil units; relationships
between soil taxonomic units and plant associations; and
identifying soil characteristics for aid in forest habitat type
identification.

Vegetation, soil, and site information were collected on 89
sites within six similar habitat types of the Abies grandis,
Thuja plicata, and Tsuga heterophylla series. Univariate
and multivariate statistical analyses were used to evaluate
naturally occurring patterns within the soil data and between
soil and vegetation data. Four ordination techniques were
used to explore potential soil pattern delineation. Factor
analysis and descriptive discriminant analysis techniques
were employed to identify physical soil property descriptors
for use in habitat type discriminant function formulas.

Numerical patterns were not discernible among the
physical soil characteristics. Analysis of relationships
between forest habitat types and soil taxonomic units—
Order, Suborder, Great Group, and Family—proved
fruitless. Four soil characteristics were identified as useful
for classifying habitat type when used in conjunction with
site and vegetation data. Formulas developed from
discriminant functions are given for use in the field as an aid
to forest habitat type classification in northern Idaho.

The use of habitat types for refinement of silvicultural
prescriptions and site productivity assessment in northern
Idaho has proven to be highly valuable to forest resource
managers. This study indicates that further delineation of
these units, based on soil variation, will allow for greater
accuracy in predicting site capabilities and response to
disturbance.

Soil Characteristics as an Aid to
Identifying Forest Habitat Types
in Northern Idaho

Kenneth E. Neiman, Jr.

INTRODUCTION

Habitat types (after Daubenmire 1968) and other
vegetation-based land classification systems (Cooper and
others 1987; Daubenmire and Daubenmire 1968; Hall
1973; Hironaka and others 1983; Mueggler and Stewart
1980; Pfister and others 1977; Steele and others 1981,
1983; Tisdale 1979) have been adopted for use throughout
the Northern Rocky Mountains by the U.S. Department of
Agriculture, Forest Service, and other Federal and State
agencies. These systems rely on knowledge of the existing
floristics for identification of “climax” or long-term stable
plant associations. On forested lands that have not been
severely disturbed, habitat types can be identified with
relative ease by use of species presence lists. But as land
is disrupted by forest management, habitat types will
have to be identified from a secondary successional] plant
community, often having little floristic similarity to its
climax community. Even highly trained plant ecologists
find this to be a speculative and frustrating task. Land
managers and scientists need to classify seral communi-
ties and also to develop a means for extrapolating seral
community types to their respective habitat types with
the aid of both biotic and abiotic factors.

In studies of abiotic site factors, Jenny (1941, 1980)
theorized that soil development is a function of climate,
parent material, relief, and potential organisms interact-
ing over time. Major (1951) felt that species composition
of vegetation is a similar function of the same five factors.
Although soil and vegetation both appear to respond to
the same “functional factors,” this relationship cannot be
extended to indicate that soil and vegetation are corre-
lated on a one-to-one basis. Although we find sites with
similar vegetational composition, often these do not have
similar site characteristics, parent material, or age
(Barnes and others 1982; Daubenmire 1968; McCune and
Allen 1985; Pfister and Arno 1980). Vegetation responds
to both long-term and short-term environmental changes
(Daubenmire 1956), but is particularly responsive to ex-
tremes of temperature and moisture. Climatic pulses
tend to have a minor effect on soil formation processes.
Thus, different soils often develop beneath similar plant
communities and, conversely, different plant communities
occur on what outwardly appear to be similar soils.

In most physical systems, both internal and external
sets of independent factors determine the development of
individual characteristics. Nowhere is this more observ-

able than in the wide variety of soil horizonations.
Whether viewed regionally or locally no two cross sections
of soil are exactly alike. Yet, in an attempt to understand
this variability, taxonomic systems are devised that iden-
tify individuals as members of classification units. Soil
taxonomy (USDA SCS 1975), a soil classification system,
is based on differentiating characteristics assumed to be
the result of independent factors.

Jenny (1941, 1980) described five elements critical to all
soil development: climate, in the sense of regional macro-
climate; parent material, the basement rock or deposi-
tional material from which the soil originates; relief
(topography), the slope, aspect, elevation, landform, and
related ground water conditions; organisms, the micro-
and macro-organisms of plant and animal species poten-
tially available for site occupancy; and time, the zero point
being calculated from the initiation of soil formation or
since major disturbance to existing conditions.

Jenny (1958, 1980) further described plants as being
both dependent and independent variables. The species
that dominate the vegetational community will exert their
own particular influence on both plant community and
soil-forming processes. Thus, with all factors remaining
constant except time and natural succession, soil develop-
ment continues as a reaction to both independent and
dependent biotic components.

Many taxonomies have been developed for both plant
communities and soils, but little direct analysis of their
interrelationships has been attempted. In the Northern
Rocky Mountains, only one climax community classifica-
tion (Tisdale and Bramble-Brodahl 1983) and one succes-
sional community classification (Hann 1982) have aggres-
sively attempted to correlate specific plant communities
with specific soil and site characteristics.

In two of the major plant community classifications
developed for the Inland Northwest (Daubenmire 1970;
Daubenmire and Daubenmire 1968) extensive soil profile
data were collected in hopes of defining a soil-vegetation
relationship. But all such attempts failed due to multiple
soil series occurring in one habitat type. Further confu-
sion arose when soil families and Great Groups also did
not correlate with plant communities. Daubenmire (1970)
recognized the importance of soil factors to vegetation and
strongly emphasized “those soil properties suspected of
playing important roles in vegetation differentiation are
not among the characteristics emphasized in soil classifi-
cation.” Soil moisture and temperature regimes, aeration,

and nutrients are the important attributes for vegetation
(Daubenmire 1970; Loucks 1962). None of these are
adequately assessed by current soil taxonomic systems.

McCune and Allen (1985) were unable to statistically
relate site characteristics to climax tree species along the
eastern front of the Bitterroot Range in western Montana.
They attributed only 10 percent of the compositional vari-
ation to measured site factors, assigning the rest of the
variation mostly to historical factors.

Hann (1982) described three site types for both a for-
ested and nonforested habitat type in western Montana.
Although all soils classified to two closely associated fami-
lies, Hann stated that considerable variation was found
between sites. He qualitatively describes a number of
soil-parent material-environmental conditions which, in
his study area, relate very well to differing successional
communities and specific habitat types.

In classifying sagebrush-grass habitat types of southern
Idaho, Hironaka and others (1983) conducted a more
intensive but similar qualitative analysis of the
vegetation-soil relationship. Where soil-series-level
classifications were available, correlation between the soil
series or series-phase and habitat type was discussed.
Statistical analysis of the physical and chemical data
collected during this study would have greatly increased
the knowledge of individual and combined soil character-
istics relative to the vegetation being supported. Even
without this further analysis, this study is the most inten-
sive of regional plant communities and soil relationships
thus far published for the Western United States.

In a study of the major plant communities of the
Guadalupe Mountains of Texas and New Mexico, Bunting
(1978) conducted an extensive analysis of topoedaphic
variables as predictors of potential natural vegetation
groups. In addition to physical site and soil descriptions,
samples were analyzed for organic matter, pH, NO,, P,O,,
K,O, Mg**, Na’, CaO, total soluble salts, and carbonate
reaction. Discriminant function classification of stands
achieved 90-95 percent accuracy by using a combination
of topographic and edaphic variables.

Tisdale and Bramble-Brodahl (1983) conducted a statis-
tically based, intensive study of vegetation communities
and soil along the Salmon and Snake Rivers. On their
study area, much reduced in geographic scale compared to
either the Hironaka and others (1983) or Bunting (1978)
studies, they concluded the currently available vegetation
and soil classification systems are not compatible, possi-
bly due to a relative difference in scale. Soil units are
divided much more finely than vegetational units. A
second part of the Tisdale and Bramble-Brodahl study
analyzed 16 individual site and soil factors as independ-
ent variables for modeling vegetation-site relationships.
Discriminant function classification accuracy ranged from
85 to 100 percent. Of the leading six factors, the most
important (elevation and radiation index) were site loca-
tion and orientation dependent. The other four factors
were soil related. They concluded that a satisfactory set
of soil-site variables could be developed to identify the
habitat type of a site, even though only seral vegetation
might be present.

In Major’s (1951) factorial approach to plant ecology,
the same five functional factors that Jenny applied to soil

formation were used as independent formative factors ina
vegetation equation. Major concluded “.. .there are no
universal correlations between vegetation and soil;. . .soil
is not determined by vegetation, vegetation is not deter-
mined by soil; vegetation and soil develop concomitantly.”
I hasten to submit at this point that even though no uni-
versal relationships appear to exist between soil and vege-
tation, it is exactly this concomitant development ina
localized area that should provide quantifiable character-
istics by which we can understand the plant community
and soil-forming processes.

The objectives of this study were: to investigate numeri-
cal taxonomic techniques for analysis of patterns of physi-
cal soil characteristics; to investigate the relationship
between known habitat types and soil units created by
numerical taxonomy; and to develop the ability to predict
habitat type using physical soil characteristics. Due to an
acknowledged incompatibility of classification systems,
this study, unlike those of Daubenmire and Hironaka and
others, did not dwell on attempts to correlate habitat
types and soil family or series units. With knowledge of
the correlations between climax vegetation, soil, and site
characteristics within a specific geographic region, we
should be able to more accurately classify any given site
within that region to habitat type and phase. This will
also improve the ability to identify highly disturbed seral
vegetation stages to habitat type and phase and more
accurately position them within their successional devel-
opment pathway.

THE STUDY AREA

The study area comprised northern Idaho from the
Salmon River to the Canadian border (fig. 1). Sampling
was done on five National Forests (Kaniksu,

Figure 1—Study area comprised Idaho
panhandle north of the Salmon River.

Coeur d’Alene, St. Joe, Clearwater, and Nez Perce), and
on forested lands of the Idaho Department of Lands and
private properties.

Setting

The physical settings of the region vary from low-lying
riverine valleys, 300 m above sea level, to glacial trenches,
550 m above sea level, to six major mountain ranges
(Selkirk, Purcell, Cabinet, Coeur d’Alene, Clearwater,
and Bitterroot Mountains) having elevations as high as
2,745 m. Sampling was mainly restricted to a mideleva-
tional zone in this region, ranging from 550 to 1,400 m
above sea level.

The macroclimatic regime of northern Idaho is an in-
land expression of the Pacific Coast maritime climate
(Ross and Savage 1967). Estimates for precipitation at
sample locations range from 500 to 1,270 mm (Pacific
Northwest River Basin Commission 1969); the actual
values are dependent on elevation, north-south and east-
west location, and position relative to orographically influ-
enced precipitation patterns. Generally, precipitation
occurs between October and May. The June through
September period averages less than 25 mm rainfall per
month. The average monthly ambient temperatures for
these sites are equally variable. Mean summer tempera-
tures range from 29 to 36 °C and mean winter tempera-
tures range from —2 to -10 °C, with maximum extremes
that range from 41 to -50 °C (USDC NOAA 1985). Al-
though the aboveground climatic conditions are extremely
variable, the presence of complete snow cover during
winter months creates a moderate soil environment in
which soil temperature regimes (USDA SCS 1975) are
frigid or cryic and soil moisture regimes are generally udic
or ustic, with some drier sites having a xeric regime.

Geology

The study area includes two geological provinces. The
Columbia Intermontane Province (Thornbury 1965), from
the Seven Devils Mountains northward to Moscow, with
interfingering as far north as Coeur d’Alene, is character-
ized by variable thicknesses of wind-deposited silt (loess)
that overlies mid- to late-Tertiary Columbia River Plateau
basalts, which, in turn, overlie intrusions of early Tertiary
Idaho Batholith granite or Precambrian metasediments.

The Northern Rocky Mountains Province covers the
remainder of the study area from the southeast and
south-central Nez Perce National Forest to the Canadian
border. The Clearwater and Coeur d’Alene Mountain
ranges are an undifferentiated mass of Precambrian Belt
Supergroup metasediments and Idaho Batholith granodi-
orites and quartz monzonites. The eastern boundary of
the study area is formed by the Bitterroot Range, also
quite variable in composition of granite, gneiss, and
metasediments. North of Pend Oreille Lake, the Selkirk
Mountains and the Cabinet Mountains are both composed
of Belt Supergroup metasediments. Tertiary and Quater-
nary gravel and glacial till deposits occur sporadically
throughout the region. Major deposition of till from Pleis-
tocene Epoch continental glaciation occurs at all

elevations north of Sandpoint (Buol and others 1980; Ross
and Savage 1967). The geologic data collected for habitat
type classification in northern Idaho (Cooper and others
1987) identify over two dozen different parent materials.
The region has been subjected to periodic, violent erup-
tions of volcanos and subsequent deposition of ejecta over
wide areas of the Northern Rocky Mountains. Of the
three most recent eruptions—Glacier Peak, Mount
Mazama, and Mount St. Helens—the most significant was
the creation of Crater Lake with the climactic eruption of
Mount Mazama about 6,700 years ago. Ash from this
event is an important material we now find in both rela-
tively pure and mixed upper soil horizons, as deep as
1 meter, in northern Idaho (Nimlos and Zuuring 1982).

Vegetation

In this study, habitat type is the taxonomic unit used to
decribe plant communities (Daubenmire 1968). Habitat
type is defined as follows: All the area that now supports,
or within recent time has supported, and is still capable of
supporting one plant association. A habitat type may
encompass quite variable physical characteristics of
topography, climate, and soils, yet the effective environ-
ment for plant growth and reproduction remains rela-
tively constant. The diagnostic climax plant community
(association) acts as an integrator of climate, relief, and
soil through factor compensation, allowing for identifica-
tion of equivalent environments by means of simple floris-
tic lists of diagnostic species.

In the Northern Rocky Mountains, contiguous stands of
mesic maritime forests are unique to northern Idaho
(Cooper and others 1987; Daubenmire and Daubenmire
1968). These stands are characterized by the climax
dominance of the coastal species Tsuga heterophylla (Raf.)
Sarg. and Thuja plicata Donn. ex D. Don. This interpre-
tation of Pacific maritime climatic influence is supported
by numerous studies of coastal disjunct species found spo-
radically throughout northern Idaho (Johnson 1968;
Johnson and Steele 1978; Steele 1971). The six habitat
types chosen for this study represent the modal environ-
mental conditions for the three overstory species (T.
heterophylla, T. plicata, and Abies grandis [Dougl. ex D.
Don] (Lindl.) most directly associated with this maritime

climatic anomaly.

METHODS
Sampling Procedures

Vegetation Data—A set of 89 sample plots was se-
lected from those sampled by Cooper and others (1987) as
the data base for this study. Because similar studies have
shown that a large amount of variation can be expected in
the data (Base and Fosberg 1971; Monserud and others
1986; Sondheim and Klinka 1983), sample selection was
restricted to six similar habitat types: Abies grandis /
Clintonia uniflora habitat type-Clintonia uniflora phase
(ABGR/CLUN-CLUN); Abies grandis /Asarum caudatum
habitat type-Asarum caudatum phase (ABGR/ASCA-
ASCA); Thuja plicata/Clintonia uniflora habitat type-
Clintonia uniflora phase (THPL/CLUN-CLUN); Thuja

plicata/Asarum caudatum habitat type-Asarum cauda-
tum phase (THPL/ASCA-ASCA); Tsuga heterophylla /
Clintonia uniflora habitat type-Clintonia uniflora phase
(TSHE/CLUN-CLUN); and Tsuga heterophylla /Asarum
caudatum habitat type-Asarum caudatum phase (TSHE/
ASCA-ASCA). Association tables with site data and com-
plete species list with canopy coverage class per species
for this study’s sample set can be found in Neiman (1986).
Site selection technique and rationale for field procedures
employed is detailed in Pfister and Arno (1980) and
Cooper and others (1987). Hitchcock and Cronquist
(1973) was the authority used for all plant nomenclature.

Soil Data—One soil pit was dug per plot at an undis-
turbed point representative of each stand. Minimum data
collected were complete horizonation description (UDSA
SCS 1981) and assessment of local parent materials. The
set of samples utilized for this study contained 18 sepa-
rately identified parent materials (table 1). Depth of pits
was generally to the first or second C horizon. Time and
cost constraints did not allow for excavation to bedrock, or
for classification on site to soil family (USDA SCS 1975).
Approximately a 1-liter sample of each horizon was col-
lected and returned for laboratory analysis. This analysis
consisted of: a verification of tactile textural classification
for each horizon; assessment of moist and dry colors un-
der ideal conditions; sieving of samples to determine per-
centage gravel content by weight; and measurement of
pH, using a 1:1 ratio soil:water paste. Because the focus
of this study was on field-identifiable characteristics of
both vegetation and soil, no nutrient analyses were
performed.

Table 1—Parent materials associated with sub-
set of soil-vegetation samples selected
for analysis

Rock origin Parent material

Sandstone
Siltstone
Shale

Argillite
Quarizite
Phyllite
Schist

Mica schist
Gneiss
Biotite gneiss

Sedimentary

Metamorphic

Igneous Basalt
Quartz monzonite
Granite
Biotite granite

Alluvium, mixed
Glacial till, mixed
Volcanic ash
Sedimentary, mixed
Loess

Miscellaneous

Analytical Procedures

Vegetation Data—Analysis of the vegetation data was
performed during the original classification study (Cooper
and others 1987) using accepted vegetation ordination
techniques. But all plots were reassessed as to their origi-
nal classification to habitat type and phase.

Soil Data—The hypothesis tested was that soil taxo-
nomic classifications (USDA SCS 1975) have no ecological
meaning when applied to forest soil-forest vegetation
relationships. A subset of 50 soils formed from coarse-
textured parent materials (for example, glacial drift, gran-
ite, gneiss, and sandstone) was classified to family taxo-
nomic level by three soil scientists currently active in
classification and mapping of soils within the study area
(appendix A). These soil taxonomic units were then used
to analyze soil-vegetation relationships.

The numerical pattern analysis concentrated on physi-
cal characteristics generally identifiable in the field (per
instructions in Fosberg and Falen 1983) by non-soil scien-
tist personnel. Individual soil characteristics were quan-
tified for computer analysis and the data entered in an
association table format. The initial data set consisted of
the following 27 variables for each soil horizon in the
vertical sequum:

. Sequential horizon number — numbered as 1, 2, 3. .
. Horizon genetic designation - USDA SCS (1981)
. Depth — to base of horizon in centimeters

. Boundary — Soil Survey Staff (1981)

. Dry color — Hue — Munsell (1975)

. Dry color — Value — Munsell (1975)

. Dry color — Chroma — Munsell (1975)

. Moist color — Hue — Munsell (1975)

. Moist color — Value — Munsell (1975)

10. Moist color — Chroma — Munsell (1975)

11. Structural Grade - USDA SCS (1981)

12. Structural Size - USDA SCS (1981)

13. Structural Shape — USDA SCS (1981)

14. Texture — Gravel — presence/absence coding

15. Texture —-% Clay — percentage from textural

OMmAIMHHA kh wWONW-H

triangle

16. Texture — % Silt — percentage from textural
triangle

17. Texture —- % Sand — percentage from textural
triangle

18. Available Water Capacity (AWC) — calculated asa
function of textural water holding capacity (USDA SCS
1972), horizon depth, presence of volcanic ash, and per-
centage of coarse fragments per horizon

19. Root abundance — Size fine (USDA SCS 1981)

20. Root abundance — Size medium (USDA SCS 1981)

21. Root abundance — Size coarse (USDA SCS 1981)

22. Coarse fragments — Percent gravel by weight

23. Coarse fragments — Percent cobble by volumetric
estimate

24. Coarse fragments — Percent stone by volumetric
estimate

25. pH — 1:1 soil:water paste

26. Parent material 1 — coding for parent material

27. Parent material 2 — coding for parent material.

Five additional pedon summarization or site-specific
variables were included in the analysis of soil horizon
data: Total depth of organic litter layers; total depth of
sequum to C horizon; total effective depth, calculated as
the summation of each horizon depth times [(100 — per-
cent coarse fragment)/100] down to but not including the
C horizon; and total available water capacity, a summa-
tion of all horizon AWC’s. Soil temperature, moisture
regime, or chemical composition data, such as base satu-
ration or cation exchange capacity, were not available for
analysis. A complete set of these data and definitions for
variables are presented in Neiman (1986).

Data Matrix Design—Since root systems are not gen-
erally affected by the minor differences that are signifi-
cant to soil horizon classification, horizon data was ana-
lyzed in a simple sequential order, based on the depth
rather than genetic horizon (that is, first, second, third
horizon vs. Al, A2, AB, B2,...). This design was also
dictated by the similarity-dissimilarity index analysis and
ordination techniques available, wherein the presence or
absence of data for a group of variables is weighted more
heavily than are the individual quantitative values. Con-
sider, for example, two pedons identical in all respects
except for the presence of a 1-cm-deep A horizon in one of
the sequa. Based on the presence-absence relationships
in the first set of A horizon variables, ordination tech-
niques would place these two pedons in highly dissimilar
positions, whereas the presence of such a shallow A hori-
zon should be subordinate to similarities for variables in
the rest of the horizons.

Because categorical names are simply a summarization
of horizon characteristics (such as color, texture, . . .), the
quantitative data for these characteristics should contain
equivalent if not more definitive information. A major
problem arises when sequential horizonation rather than
genetic horizonation is used for analysis. The problem
occurs when one soil description begins with an A horizon
and another sample begins with a B horizon. By not us-
ing categorical names in the analysis, the ability to differ-
entiate A from Bis lost. Forest soils of northern Idaho
often do not develop an A horizon, yet when present, it
was considered to be potentially significant in analysis of
soil-vegetation relationships. Therefore, the first set of 27
horizon characteristics was allotted to only A horizon
data, allowing for simplified analysis of presence-absence
or quantitative data within only A horizons. For samples
having more than one A horizon, a weighted-by-thickness
average for all characteristics was used as the single set
of A horizon data. The second and subsequent sequential
horizon data sets record all other horizonation, and thus
are restricted to AB, E, B, C, and R type illuvial and par-
ent material horizons.

Data Analysis—Analysis was divided into three sepa-
rate processes: The first investigated noise and redun-
dancy of variables in the data set of 27 characteristics per
horizon; the second attempted to delineate naturally oc-
curring patterns of soil physical characteristics and assess
their relationship to the vegetation types that they sup-
port; and the third developed discriminant functions
based on soils data that are predictive for habitat type

classification. Due to a disparity in both size and units of
measure, all variables were standardized to a mean of 1
and a standard deviation of 0.1 (SAS 1982b). All data,
raw and standardized, were analyzed for normal, skewed,
or bimodal distribution (SAS 1982a) across the entire data
set and within sets stratified by habitat type.

Noise was considered as variation in one characteristic
being not coordinated with variation in another (Gauch
1982). Noise analysis was restricted to use of means and
range data, with only those variables which were constant
across the data (and therefore contain no useful informa-
tion) being removed from further analysis. Correlation
analysis of all possible pairs (SAS 1982a) and principal
components analysis (Gauch 1977) were used to evaluate
redundancy within and relationships between variables
across the entire data set and for data stratified by either
habitat types or parent material groups. The objective of
these analyses was to create a reduced data set of as few
independent variables as possible without sacrificing
meaningful information.

Pattern analysis was conducted using a series of ordina-
tion techniques: polar ordination (Bray and Curtis 1957);
principal components analysis (Gauch 1977); two-way
indicator species analysis (Hill 1979b); and detrended
correspondence analysis (Hill 1979a). All of these tech-
niques are described as dimensionality reduction tech-
niques, but each approaches the problem from a slightly
different perspective. All four techniques allow for ordi-
nation of both variables and samples in the same analy-
sis, which makes them useful for exploring variable re-
duction within samples, pattern analysis between
samples, and delineation of variables related to patterns
of samples.

Vegetation-soil relationships were analyzed using a
subset of samples stratified by parent material and fur-
ther stratified by habitat type. Techniques used to iden-
tify significant discriminators were: factor analysis (SAS
1982b); stepwise discriminant analysis (Dixon 1981); and
canonical discriminant analysis (SAS 1982b). Using the
set of significant variables identified by these programs,
classification models based on discriminant functions
were developed using discriminant analysis (SAS 1982b).

RESULTS AND DISCUSSION

Data Reduction

Criteria for retaining a variable in the data were as
follows: continuous or a class of continuous values; not
related to short-term vegetational changes or person-
caused disturbance; suited to accurate assessment in the
field; requires minimal subjective interpretation; and not
influenced by other characteristics. Based on these crite-
ria, a subset of 11 variables per horizon was selected for
use in all further analyses. These were: depth; moist color
value; moist color chroma; structural size and shape;
percentages of clay, silt, gravel, cobble, and stone; and pH.
All variables selected are quantified in terms of continu-
ous or classes of continuous units, except for structural
shape, which was quantified into categories whose in-
creasing values denote increasing development through
illuviation of fine soil material. Univariate analysis indi-

cated a reasonable normality of distribution for all
variables.

Initial ordinations were performed using data for all
horizons and all 89 pedons. These ordinations produced
groupings, based on the presence or absence of data for a
single horizon, within a larger sequence of horizons. The
number of pedons having data for a fifth and sixth hori-
zon was too few to allow meaningful analysis with those
horizons included in the data set. Analysis was then
reduced to using the physical characteristics of the first
four horizons only. Ordination groups created from this
reduced data set still contained very dissimilar soils ex-
cept for the presence or absence of a thin A horizon or the
presence or absence of a fourth horizon. The fourth hori-
zon, when present, contained genetic horizon data that
described highly dissimilar B, C, or R type characteristics.
Although stratification of the data by parent material was
considered to have future utility, further ordination
analysis, based on inclusion of the fourth horizon data,
was deemed meaningless.

Ordinations were next performed using data from the
upper three horizons and only those samples having an A
horizon present. A second set of ordinations was then
conducted on this same set of samples using only data
from the second and third horizons. Comparison of re-
sults of these ordinations indicated that very little infor-
mation was lost due to removal of the A horizon charac-
teristics. All further analyses use only data from the
second and third horizons. Because the data consist of
the same 11 variables found in two consecutive horizons,
a numerical suffix was added to the name of each of the
22 variables to identify the horizon of origin. Even
though an A horizon (that is, the first horizon) did not
occur in all pedons analyzed, for consistency the suffixes
used were 2 and 3.

Widely differing parent materials produce significantly
different textural and structural qualities, coarse frag-
ment contents, and pH values, but often do not create
differences in color or depth. Data were stratified into
coarse-textured vs. fine-textured parent material groups
in an attempt to eliminate these confounding factors.
Basalt was grouped separately due to its basic properties,
as opposed to the acidic nature of the other parent materi-
als. Three groups were created:

Coarse-textured Fine-textured Basalt
n=55 n=31 n=3

Alluvium — coarse Alluvium — fine Basalt

Glacial drift Argillite

Gneiss Loess

Granite Mica schist

Mixed sedimentary Phyllite

Quartzite Schist — fine

Quartz monzonite Siltite

Sandstone Siltstone

Schist — coarse

In all cases, voleanic ash, where present, is an overlying
amendment to the parent materials.

Pattern Analysis

If a soil-survey-oriented taxonomy can be developed
based on a combination of quantifiable and categorical

horizon variables, then numerical taxonomic analysis of
these variables should assign the same samples to clus-
ters of closely equivalent taxonomic units. One problem
created by the monothetic design of the soil taxonomy
(USDA SCS 1975) is the emphasis placed on single vari-
ables in the delineation of taxonomic units. Two soil se-
qua similar in all respects except color of the epipedon can
vary taxonomically in Order, Suborder, and/or Great
Group. The emphasis in this study was not to mimic the
currently accepted soil taxonomy, but rather to investi-
gate the classification of polypedons based on multivariate
statistical analysis of physical attributes. Because of this
approach, the data from individual horizons were not
combined into a control section format as used in soil
taxonomy (USDA SCS 1975), nor was emphasis in the
form of weighting placed on any single variable or set of
variables.

As soils are extremely variable and multivariate in
character, ordination was selected as the means to sum-
marize and reduce dimensionality of the data (Gauch
1982). Using the four ordination techniques and the 11
variables for each of two horizons as outlined above, no
identifiable relationships were discerned between numeri-
cally generated soil groupings, soil taxonomic units (using
all hierarchical units from Order to Family), and habitat
types within the full data set. Further stratification of
the data set to reduce internal variation appeared neces-
sary. The coarse-textured parent material group of 55
samples was selected for all further analyses.

Analyses of this reduced data set by three of the ordina-
tion techniques ranked samples in similar positions
within their respective ordinations (Neiman 1986). Even
though the rankings of each technique concurred in a
general way, a large amount of variation occurred among
the soils. Low eigenvalues of the principal component
analysis indicated that only 19 percent of the total vari-
ation was explained by the first axis, 62 percent by the
first five axes, and 86 percent by the first 10 axes. The so-
called “cloud” of sample points in multidimensional space
in this case truly lived up to its name. This large amount
of unexplained variation in the data indicated that either
the selected variables were not suitable for numerical
grouping or that identifying soil groups numerically at
this level of stratification has no statistical or ecological
interpretive power. Yet, the ability to develop consistent
rankings of samples by the various analytical techniques
indicated a potential to define soil groups. The problem in
doing so appears to be the small data set and high vari-
ation inherent in soils. Variation could be further reduced
by stratifying the coarse-textured parent material group
to create a subset containing samples from only granite,
quartz monzonite, quartzite, and gneiss. This was not
performed due to sample size restrictions.

Soil-Vegetation Relationships

The second objective was to investigate relationships
between soil characteristics and forest habitat types. A
lack of correlation between the two taxonomic units can
be seen in appendix A. If the work of Jenny (1941, 1958)
and Major (1951) is correct, then some relatively discrete
relationship between the functional factors for soil and

vegetation properties should exist. Because a soil series
or series-phase classification was not available for most of
the study area, and because the samples had not been
chemically analyzed, physical soil characteristics were
used to analyze soil-habitat type relationships.

Data were reduced by removing redundant variables.
An “inverse” ordination analysis, sometimes called Q-
technique (Williams and Lambert 1961), sorts sample-
pairs into similarity groups rather than species-pairs.
The four “inverse” ordinations of soil characteristics re-
sulted in a high concurrence of rankings of variables
(table 2). The assignment of statistical significance to
these rankings is meaningless, as the assumptions of
linear relationships and independence of terms cannot be
met. But almost identical rankings of variables at the
extremes of all four ordination techniques identified the
same primary group of variables. Structural ped size, ped
shape, and coarse fragment content contain variation that
appears to be related to internal structure of the data.
These relationships were supported by correlation coeffi-
cients greater than 0.70 between structural and coarse
fragment groups within horizons.

Factor analysis, an eigenvector analysis similar to prin-
cipal component analysis, describes covariance relation-
ships between two or more variables. If structural ped
size and shape, or any other group of variables, are sig-
nificant covariates, then a single variable is sufficient for
analysis. But if a set of variables are not related, then all
variables should be retained. Significant covariate rela-
tionships were found for seven groups in the first six fac-
tors of a varimax rotated factor analysis (SAS 1982b). In
Factor 1, the silt and clay content of horizons 2 and 3

Table 2—Comparison of first axis ordination selection of coarse-
textured parent material soil characteristics by polar
ordination (PO), centered principal components analysis
(PCA), two-way species indicator analysis (TWINSPAN),
and detrended correspondence analysis (DCA). Data set
consisted of 22 variables and n= 55. Variable suffix
indicates associated horizon number

Axis PO PCA TWINSPAN DCA

1 Size2 Size2 Shape2 Size2

2 Shape2 Shape2 Size2 Shape2

3 Shape3 Size3 Shape3 Shape3

4 Size3 Shape3 Size3 Size3

5 Depth3 Depth3 Chroma3 Depth3

6 Clay2 Clay2 Chroma2 pH3

if pH3 Depth2 Value3 Depth2

8 Silt2 Silt2 Depth3 Clay2

9 Depth2 pH3 pH2 Clay3
10 pH2 Silt3 Clay2 pH2

11 Silt3 Clay3 Value2 Silt3
12 Clay3 pH2 Depth2 Silt2

13 Value3 Value2 pH3 Value2
14 Value2 Value3 Clay3 Value3

15 %Stone2 Chroma3 Silt3 Chroma3
16 Chroma3 %Cobble3 Silt2 %Cobble3
Uz Chroma2 %Gravel3 %Stone3 Chroma2
18 %Cobble3 Chroma2 %Stone2 %Gravel3
19 %Stone3 %Cobble2 %Cobble2 %Stone2
20 %Cobble2 %Stone2 %Cobble3 %Cobble2
21 %Gravel3 %Stone3 %Gravel3 %Stone3
22 %Gravel2 %Gravel2 %Gravel2 %Gravel2

were highly related to each other. In Factor 2, structural
size and ped shape in horizon 2 and percentage of gravel
and cobble content, also in horizon 2, were related, but the
two pairs of variables are inversely related to each other.
This supports the positioning at the extremes of spatial
structure developed by ordination (table 2). The only
variables not exhibiting good covariate relationships were
chroma and pH of the second horizon and chroma, per-
centage gravel, percentage cobble, and pH of the third
horizon.

Stepwise discriminant analysis (Dixon 1981) computes
classification functions for subsets of quantitative vari-
ables by means of F values from an analysis of covariance.
Table 3 lists the stratification combinations and selected
variables for which F values were significant at the 0.90
level or greater. Through this analysis, 14 variables were
identified as containing useful information for discrimi-
nating between various stratifications of the data. These
variables were:

Chroma2 Clay2 Size3 Shape3 %Cobble3
Size2 %Cobble2 Depth3 Silt3 pH3
Shape2 pH2 Value3 %Gravel3

Canonical discriminant analysis of the coarse-textured
parent material samples stratified into six habitat types
resulted in the first three canonical components having
F values significant at the 90 percent probability level or
greater. All 22 variables had positive or negative correla-
tion values greater than 0.5 within the first three canoni-
cal components. This is not surprising because factor
analysis showed all variables, but five, were members of
highly related covariate groups. By selecting the two
largest positive and negative values within each of the
three canonical components, six pairs of soil variables
were identified as being good discriminators for habitat

types.

Positive canonical coefficient pairs:
Value3—Chroma2 %Gravel2 —- %Gravel3
pH3 - Shape3

Negative canonical coefficient pairs:
Depth3 — %Cobble3 Clay2 — Silt2
Shape2 — Size2

Calculations similar to those of stepwise discriminant
analysis were produced by canonical discriminant analy-
sis for each of the 11 other data stratifications. Due to re-
dundancy of results, these analyses are not presented.
Based on the results of principal component analysis,
factor analysis, and stepwise and canonical discriminant
analysis, the following four variables were chosen for use
in developing discriminant functions: Size2, Size3,
%Cobble2, and %Cobble3.

Discriminant functions are the most valuable when
analyzing homogeneous groups in which clusters of
samples overlap (Sneath and Sokal 1973). This appears
to be the situation among habitat types and soils. Statis-
tical significance can only be ascribed to discriminant
functions if the variables are multivariate normal, the
variance-covariance matrices are similar, prior probabili-
ties are identifiable, and the relationships between vari-
ables are linear (Greig-Smith 1983; Pielou 1977; Williams

Table 3—Variables selected, significant F value, and degrees of freedom (numerator and
denominator) produced by stepwise discriminant analysis on coarse-textured

parent material data

Degrees of freedom
Stratification F Value
of data Variable Sig. >0.90 Numerator Denominator
Six habitat types Size3 6.522 5 49
%Gravel3 3.277 5 48
pH3 2.902 5 47
Size2 3.157 5 46
%Cobble2 2.575 5 45
Overstory series pH2 5.421 2 52
ABGR-THPL-TSHE
Two overstory series pH2 9.008 1 41
ABGR - TSHE Value3 4.447 1 40
Silt3 6.218 1 39
Understory unions Size3 16.187 1 53
CLUN - ASCA Chroma2 7.083 1 52
%Cobble3 4.589 1 51
ABGR/CLUN - %Gravel3 5.108 1 16
ABGR/ASCA %Cobble3 6.765 1 15
Chroma2 7.411 1 14
Shape3 4.973 1 13
TSHE/CLUN - Size3 27.547 1 22
TSHE/ASCA %Cobble3 8.557 1 21
ABGR/CLUN - Size3 13.315 3 39
ABGR/ASCA - %Gravel3 5.534 3 38
TSHE/CLUN - Value3 4.341 3 3
TSHE/ASCA pH3 3.401 3 36
Depth3 3.841 3 35
ABGR/CLUN - pH2 6.429 1 12
TSHE/CLUN Clay2 10.740 1 13
Size3 15.882 1 12
Shape3 11.155 1 11
ABGR/ASCA - Size2 15.228 1 25
TSHE/ASCA %Gravel3 6.665 1 24
Shape2 6.951 1 23
THPLUCLUN - Size3 5.787 3 32
THPL/ASCA - %Cobble2 5.693 3 31
TSHE/CLUN -

TSHE/ASCA

1983). All four of these assumptions were violated to
some extent in these analyses, leaving exploratory gener-
alizations about both the data structure and discriminant
functions as the result, rather than statistically signifi-
cant conclusions.

Using four soil characteristics as variables, the proba-
bility of correct classification is equal to or greater than
57 percent for the Abies grandis and Tsuga heterophylla
series habitat types, with 33 percent or less accuracy for
Thuja plicata habitat types (table 4). The probability of
simply guessing the correct habitat type is 16.7 percent.
Considering the small sample size and the large amount
of unexplained variation indicated by principal component
analysis, this degree of classification accuracy is quite

good. Although it is somewhat circular to test results with
data used to develop the classification scheme, it does act
as an acceptable initial test of classification accuracy.

In an attempt to increase the sample size per group and
reduce apparent variation, the data set was stratified by
overstory climax species (that is, Abies grandis, Thuja
plicata, Tsuga heterophylla). Table 5 presents the classifi-
cation results of discriminant analysis for the three series
groups using the same four variables as above. The
probability of properly assigning a sample to the A.
grandis orT. heterophylla series using the discriminant
functions developed is roughly twice the probability of
guessing (33.3 percent), whereas for T. plicata it is one-
half. Possible reasons for the poor accuracy in T. plicata

Table 4—Results of classifying six habitat types by four soil characteristics (Size2, Size3, %Cobble2, %Cobble3) using discrim-
inant analysis. Probability of guessing correct classification group is 16.7 percent

Predicted group membership

Habitat ea aaa a eee ee
type Sample ABGR/CLUN ABGR/ASCA THPL/CLUN' THPL/ASCA TSHE/CLUN- TSHE/ASCA
-Phase size -CLUN -ASCA -CLUN -ASCA -CLUN -ASCA
ore rn ere ee re -e------- Percent - ------------------------------
ABGR/CLUN 7 57.1 0 (0) 0 28.6 14.6
-CLUN
ABGR/ASCA 12 16.7 66.7 0 8.3 0 8.3
-ASCA
THPL/CLUN 6 0 16.7 16.7 0) 33.3 33.3
-CLUN
THPL/ASCA 6 33.3 0 0 3.3 16.7 16.7
-ASCA
TSHE/CLUN 9 11.1 Valou 0 (0) 77.8 0
-CLUN
TSHE/ASCA 15 0 13.3 0 0 0 86.7

Table 5—Results of classifying three overstory series by four soil
characteristics using discriminant analysis

Predicted group membership

Sample Group ——s
series size ABGR THPL TSHE
---------- Percent - ---------
ABGR 19 63.2 5.3 SiS
THPL 12 Bors) 16.7 50.0
TSHE 24 Kk ic} 0 66.7

Table 6—Results of classifying two understory unions by four soil
characteristics using discriminant analysis

Predicted group membership

Sample Group
union size CLUN ASCA
-------- Percent - -- -----
CLUN 22 WES 22.7
ASCA 33 18.2 81.8

classification may be that a different set of variables is re-
quired as discriminators for this climax tree species, or
there simply is too much noise (for example, small data
set) in this midground portion of what appears to be a
relatively narrow environmental continuum. This prob-
lem also occurred in the stepwise discriminant analysis
(table 3), wherein no significant variables could be found

for habitat type groupings of T. plicata by itself or when
combined with samples from the A. grandis series.

A much greater accuracy of classification is achieved by
stratifying the data based on two understory unions of
Clintonia uniflora (Schult.) Kunth. and Asarum caudatum
Lindl. Table 6 presents the results of this discriminant
classification showing approximately 77 percent and 82
percent proper classification, respectively. Stratification of
the data into subsets of a single overstory species and two
different understory unions should further increase clas-
sification accuracy.

The analysis conducted with only 55 samples may have
produced results that reflect a simple random structure in
the data set. If so, statisticians refer to this model as “over-
fitting the data” and not a true response to the system
being modeled. Therefore, stratification of these data be-
yond the present level precludes further meaningful analy-
sis.

Tables 7, 8, and 9 present the discriminant score formu-
las produced for classification of unknown samples into one
of six habitat types, one of three overstory climax series, or
one of two understory unions. Appendix B defines values
for field quantification of structural ped size and percent-
age of cobbles.

Using four soil characteristics, the formulas calculate a
discriminant score for each vegetation unit within a strati-
fication group. The formula that produces the highest
discriminating score (DS) has the highest probability of
being classified correctly. As an example, one of the origi-
nal sample plots, assigned by vegetation analysis to the
ABGR/CLUN-CLUN habitat type, has the following values

for the four discriminating soil characteristics:

Size2 4 %Cobble2 10
Size3 4 %Cobble3 = 20

Table 7—Discriminant score formulas for six habitat types and phases and four soil char-

acteristics

Habitat type

-phase Formula

ABGR/CLUN DS = (17.3 Size2 + 15.0 Size3 + 4.5 Cobble2 — 0.01 Cobble3 + 227.9)
-CLUN

ABGRYASCA DS = (18.4 Size2 + 12.6 Size3 + 4.4 Cobble2 + 0.01 Cobble3 + 231.6)
-ASCA

THPL/CLUN DS = (16.9 Size2 + 13.1 Size3 + 4.3 Cobble2 + 0.04 Cobble3 + 233.7)
-CLUN

THPL/ASCA DS = (17.3 Size2 + 13.9 Size3 + 4.6 Cobble2 — 0.01 Cobble3 + 230.3)
-ASCA

TSHE/CLUN DS = (18.0 Size2 + 14.0 Size3 + 4.5 Cobble2 — 0.06 Cobble3 + 229.8)
-CLUN

TSHE/ASCA DS = (15.9 Size2 + 12.5 Size3 + 4.1 Cobble2 + 0.12 Cobble3 + 236.6)
-ASCA

Table 8—Discriminant score formulas for three overstory series and four soil charac-

teristics
Overstory
series Formula
ABGR DS = (13.7 Size2 + 7.4 Size3 + 2.9 Cobble2 + 0.56 Cobble3 + 179.3)
THPL DS = (13.0 Size2 + 7.5 Size3 + 2.9 Cobble2 + 0.56 Cobble3 + 180.4)

TSHE DS = (12.7 Size2 + 7.3 Size3 + 2.8 Cobble2 + 0.57 Cobble3 + 182.3)

Table 9—Discriminant score formulas for the modal phase of two understory unions

and four soil characteristics

Understory
union Formula
CLUN DS = (10.6 Size2 + 9.2 Size3 + 2.6 Cobble2 + 0.52 Cobble3 + 166.6)
ASCA DS = (10.5 Size2 + 8.2 Size3 + 2.5 Cobble2 + 0.57 Cobble3 + 168.7)

Using the six formulas in table 7, the discriminant scores
(DS) calculated for each of the six habitat types are:

ABGR/CLUN-CLUN DS = 401.9
ABGR/ASCA-ASCA DS = 399.8
THPL/CLUN-CLUN DS = 397.5
THPL/ASCA-ASCA DS = 400.9
TSHE/CLUN-CLUN DS = 401.6
TSHE/ASCA-ASCA DS = 393.6

The highest discriminant score, calculated by the ABGR/
CLUN-CLUN formula is 401.9, indicating this is the best

choice for classification based on four soil characteristics.
Table 4 shows a 57 percent probability that this is a cor-
rect classification. A rank order of scores can be used to
identify other potential habitat types for consideration as
classified units. In the example, the second best habitat
type choice would be TSHE/CLUN-CLUN. With highly
similar sites, classification errors can occur due to round-
ing of significant numbers in the formula. In all cases
where discriminating scores are within three-tenths
equivalent values (such as 401.9 vs. 401.6), further sup-
porting evidence from investigation of onsite or adjacent
vegetation is required for accurate classification.

Ecological Interpretations

Even though soil-vegetation relationships were identi-
fied, the ecological interpretations are extremely hypo-
thetical. The habitat types used to define the study envi-
ronment are positioned along a continuous moisture-
temperature gradient. Tsuga heterophylla can maintain
viable populations only in the most moderate moisture
and temperature regimes found in northern Idaho. Sites
adjacent to T. heterophylla, but either too dry, too wet, too
hot, or too cold for it to successfully reproduce are gener-
ally dominated by Thuja plicata. The harshest environ-
ments within this continuum, sites too hot and dry or too
cold for T. plicata, are dominated by Abies grandis. The
two understory unions likewise respond to environmental
gradients, which generally can be described as warm-
moist sites supporting both climax Asarum caudatum and
Clintonia uniflora, while the colder and/or drier sites
support only C. uniflora. Within the theorized functions
for soil (Jenny 1941) and vegetation properties (Major
1951), these environmental relations are incorporated in
the climate, relief, and parent material factors. Ifa
change in vegetation is related to changing environmental
factors, then a concurrent, but not necessarily convergent,
shift in soil properties should occur.

Within the data used for this study no statistically or
ecologically significant correlation could be found between
habitat types and taxonomic soil units. Reasons for this
failure are probably related to: the restricted amount of
available data and its nonconformity to statistical con-
straints; the relatively narrow environmental gradient
encompassed by the habitat types studied; and the broad
geographic region included within the data base.

Interpretation of ecological relationships between habi-
tat types and soil characteristics appears to be related
directly to and confounded by climatic conditions that
control soil genesis and species composition of the plant
community. The cooler and wetter climatic regimes af-
fecting northern Idaho are so recent (Mehringer 1985)
that most of the vegetation-soil ecosystems are stillina
state of flux. Primary successional development of plant
communities and soil horizonation are proceeding at dif-
ferent rates. Duchaufour (1982) refers to short-cycle and
long-cycle patterns of soil formation, with the dominant
functional factors being vegetation and climate, respec-
tively. The vegetation of northern Idaho has responded
rapidly to the climatic change, whereas the soils are im-
mature relative to the current conditions of climate and
vegetation. This could account for the high variance val-
ues for soil characteristics when viewed from the perspec-
tive of a narrow vegetational continuum. I hypothesize
that the habitat types used in this study are relatively
stable in composition given the current climate, but the
soils associated with these habitat types have not yet
stabilized.

CONCLUSIONS

For the geographic area studied, there appear to be no
universal soil variables or sets of variables that can be
used to predict the climax plant communities. The rela-

11

tionships between vegetation and soils are multifactorial
and dynamic; the effect upon plant growth or reproduc-
tion of any one soil variable changes quantitatively and/or
qualitatively with every variation in the complex of envi-
ronmental factors. Yet, identifiable relationships do exist
between a stratified set of soils and vegetation. This
study was able to identify soil characteristics usable for
differentiating pairs or groups of habitat types occurring
on specific groupings of parent materials in northern
Idaho. The concepts explored herein should be widely
useful. But they should be applied only to northern Idaho
ecosystems; only to the typal phase of the six habitat
types discussed; and only to soils developed from the
group of coarse-textured parent materials previously
defined.

The importance of these findings for forest managers is
twofold. First, with a large sample size and sufficient
insight, a unique set of soils can be correlated with indi-
vidual habitat types. Within a habitat type each set of
functional soil-forming factors will develop a soil specific
to that set of environmental conditions. Second, and
probably more important, a silvicultural prescription may
not produce a uniform vegetational response when ap-
plied to a specific habitat type or habitat type-phase occu-
pying more than one type of soil. The use of universal
guidelines for prescribed silvicultural treatments, site
preparation, selection of regeneration species, stocking
levels, and many other management activities has often
resulted in failure. Many of these failures were the result
of an inappropriate prescription chosen because of insuffi-
cient knowledge about these highly complex ecosystems.
Effective management requires an individualistic pre-
scription for each stand based on knowledge of its unique
features, particularly its soils.

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13

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APPENDIX A: SOILS CLASSIFIED TO FAMILY LEVEL
BASED ON PHYSICAL DATA. INCLUDES HABITAT
TYPES ASSOCIATED WITH FAMILY AND PLOT NUM-
BER OF SAMPLE CLASSIFIED TO THAT FAMILY

Great Group

Eutroboralf

Glossoboralf

Udifluvent
Udipsamment
Udorthent

Cryandept

Cryocrept

Cryumbrept

Dystrochrept

Dystrochrept

Subgroup

Typic

Eutric

Typic
Typic
Typic

Entic

Andic
Dystric

Typic

Entic

Andic

Typic

Typic

Umbric

Family

fine, mixed, frigid

fine-loamy, mixed,
frigid

fine-loamy, mixed

loamy, skeletal, mixed

sandy, mixed, frigid
sandy, mixed, frigid
sandy, mixed, frigid

medial over sandy or
sandy skeletal

coarse-loamy, mixed
sandy, mixed

loamy, skeletal, mixed

sandy, skeletal, mixed

fine loamy, mixed,
frigid

loamy, skeletal, mixed,

frigid

fine-loamy over sandy
or sandy-skeletal,
mixed, frigid

loamy over sandy or
sandy-skeletal, mixed,
frigid

loamy, skeletal, mixed,
frigid

coarse loamy, mixed,
frigid

sandy, skeletal, mixed,
frigid

sandy, skeletal, mixed,
frigid

Habitat
type

TSHE/CLUN

THPL/ASCA

ABGR/CLUN
TSHE/CLUN
TSHE/ASCA
TSHE/ASCA
TSHE/ASCA
TSHE/ASCA
TSHE/ASCA
THPL/ASCA

THPL/ASCA

ABGR/CLUN
ABGR/ASCA

ABGR/CLUN
ABGR/CLUN

ABGR/CLUN
ABGR/CLUN
THPL/CLUN

TSHE/CLUN
TSHE/ASCA

TSHE/ASCA

THPL/CLUN

THPL/ASCA

ABGR/ASCA
THPL/CLUN

TSHE/CLUN
ABGR/ASCA

ABGR/CLUN
ABGR/ASCA

14

Plot
No.

92141

92130

93110
93131
93136
94009
94038
92161
92158
40559

38503

38314
38308

38305
38555

38522
40740
94025
92139
92113

93156

40560

92118

40553
40548
92150
38566
38541
38706

(con.)

we pe eae ep La Se

APPENDIX A. (Con.)

Great Group Subgroup Family

Eutrochrept Typic sandy, mixed, frigid

Haplumbrept Andic loamy, skeletal, mixed,
frigid

Vitrandept Typic loamy, skeletal, mixed,
frigid

medial over loamy,
mixed, frigid

medial over loamy-
skeletal, mixed, frigid

Umbric loamy-skeletal,
mixed, frigid

medial over loamy-
skeletal, mixed, frigid

Habitat
type

ABGR/ASCA
ABGR/ASCA

THPL/ASCA

TSHE/CLUN
TSHE/CLUN

ABGR/ASCA
ABGR/ASCA
ABGR/ASCA
THPL/CLUN
THPL/CLUN
THPL/ASAC
TSHE/CLUN
TSHE/ASCA
TSHE/ASCA
TSHE/ASCA
TSHE/ASCA
TSHE/ASCA

ABGR/ASCA

ABGR/ASCA

15

Plot
No.

38707

40552

94011

92102
92134

93116
94043
94047
93154
94060
94029
93106
93111
93115
93125
93126
93129

93160

94026

APPENDIX B: DEFINITIONS AND PHYSICAL VALUES FOR FIELD
QUANTIFICATION OF ZCOBBLES AND STRUCTURAL PED SIZE
(FROM FOSBERG AND FALEN 1983)

Cobbles — Rock fragments of rounded, subrounded angular or irregular shape. Size range of 7.6 to 25 cm
(3 to 10 in) diameter.

%Cobbles — Visual estimate of percent of soil volume occupied by rock fragments of cobble size class.

Structural Ped Size — all ped shapes should be measured by the size classes for angular and sub-
angular blocky structure.

Size Diameter Size Diameter

1 <5 mm 4 20 to 50 mm

2 5 to 10mm
3 10 to 20 mm

16

Neiman, Kenneth E., Jr. 1988. Soil characteristics as an aid to identifying forest habitat
types in Northern Idaho. Res. Pap. INT-390. Ogden, UT: U.S. Department of Agriculture,
Forest Service, Intermountain Research Station. 16 p.

Vegetation and soil physical characteristics were analyzed to identify numerical patterns
within the soils data, relationships between soils and habitat types, and soil characteristics
related to specific habitat types. Ordination and discriminant analysis techniques were used
to identify four soil characteristics useful in identifying soils variation between six highly
similar habitat types in northern Idaho. Improved classification techniques will allow for
greater accuracy in predicting site capabilities and response of vegetation to disturbance.

KEYWORDS: soil-vegetation relationships, numerical soil taxonomy, multivariate soil-
vegetation analysis

INTERMOUNTAIN RESEARCH STATION

The Intermountain Research Station provides scientific knowledge
and technology to improve management, protection, and use of the
forests and rangelands of the Intermountain West. Research is
designed to meet the needs of National Forest managers, Federal
and State agencies, industry, academic institutions, public and
private organizations, and individuals. Results of research are made
available through publications, symposia, workshops, training
sessions, and personal contacts.

The Intermountain Research Station territory includes Montana,
Idaho, Utah, Nevada, and western Wyoming. Eighty-five percent of
the lands in the Station area, about 231 million acres, are classified as
forest or rangeland. They include grasslands, deserts, shrublands,
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minerals and fossil fuels for energy and industrial development, water
for domestic and industrial consumption, forage for livestock and
wildlife, and recreation opportunities for millions of visitors.

Several Station units conduct research in additional western
States, or have missions that are national or international in scope.

Station laboratories are located in:

Boise, Idaho

Bozeman, Montana (in cooperation with Montana State University)

Logan, Utah (in cooperation with Utah State University)

Missoula, Montana (in cooperation with the University of Montana)

Moscow, Idaho (in cooperation with the University of Idaho)

Ogden, Utah

Provo, Utah (in cooperation with Brigham Young University)

Reno, Nevada (in cooperation with the University of Nevada)

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