Prediction of available forage production of big sagebrush
by William Henry Creamer IV
A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in
Range Science
Montana State University
© Copyright by William Henry Creamer IV (1991)
Abstract:
The objective of this study was to develop a simple, reliable method to estimate the contribution of
sagebrush taxa to range production. Regression equations were developed for mountain big sagebrush
(Artemisia tridentata vaseyana), Wyoming big sagebrush (A. t. wyomingensis), and basin big
sagebrush (A. t. tridentata) in high and low use form classes. Linear measurements including major and
minor axis, height, and crown depth, along with average cover, and combinations of these
measurements were used to predict total annual winter forage production. Colinearities among some
variables required the development of 3 groups of variables (based on major or minor axis or elliptical
area) to be used in the model building. A natural logarithm transformation of the dependent variable,
forage, was necessary to stabilize nonconstant variance. Reliability of the relationships was based on
the adjusted R2 statistic. Adjusted R2 values were similar for each subspecies and form class
combination among variable groups. The variable group based on the measure of the maximum
diameter (major axis) was selected as the most efficient variable group based on ease of measurement
of the variables and high adjusted R2 values. Adjusted R2 values ranged from 0.78 to 0.90 for 3 to 4
variable models in this variable group. When the variable average seedhead weight was added to this
variable group, adjusted R2 values improved. Values ranged from 0.87 to 0.93 for 4 to 5 variable
models. Two variable models accounted for nearly as much variation in each case. The variables major
axis and average cover were often the most meaningful. Major axis alone accounted for > 69% of the
variation in low use taxa but < 59% in high use taxa. Average cover alone accounted for > 61% of the
variation in all taxa form class combinations investigated and in the case of high use basin big
sagebrush it accounted for 81% of the variation in production. PREDICTION OF AVAILABLE FORAGE PRODUCTION OF BIG SAGEBRUSH
by
William Henry Creamer IV
A thesis submitted in partial fulfillment
of the requirements for the degree
of
Master of Science
in
Range Science
MONTANA STATE UNIVERSITY
Bozeman, Montana
March 1991
ii
APPROVAL
of a thesis submitted by
William Henry Creamer IV
This thesis has been read by each member of the thesis committee
and has been found to be satisfactory regarding
content, English
usage, format, citations, bibliographic style, and consistency, and is
ready for submission to the College of Graduate Studies.
/
Date
H/JmcJj.mi
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Approved for the Major Department
Head/Major/Department
Approved for the College of Graduate Studies
f) / f f / _
Date
Graduate Dean
iii
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Permission
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iv
To my wife, Patty, for her steadfast support.
TABLE OF CONTENTS
Page
INTRODUCTION . . . .....................'............. '.........
I
3
LITERATURE REVIEW................................................
Preface ....................................................
Dimension Analysis for Trees.............. •........... .
.
Dimension Analysis for Shrubs ..............................
Canopy Cover............................................ . •
Taxonomy of Big Sagebrush ..................................
Value to W i l d l i f e .................................... - •
Form Classes..............................................
3
4
4
6
7
8
9
STUDY A R E A .......................................
Location......................................
Topography..........................
C l i m a t e .........................................
S o i l s ......................................................
Vegetation................................................
Wildlife....................................................
METHODS AND MATERIALS............................................
H
H
13
13
15
15
17
19
Data Collection............................................
19
Shrub Measurements.........................
21
Other Variables..........................................
23
Data A n a l y s i s ............................................
24
RESULTS AND DISCUSSION........................................ '
Comparison of 3 Groups of Variables .......... . . . . . .
Best Variable G r o u p ......................................
Addition of the Variable "Average Seedhead Weight (AS)" . .
Low Use Mountain Big Sagebrush (ATVL)......................
High Use Mountain Big Sagebrush (ATVH)....................
Low Use Wyoming Big Sagebrush (ATWL) . : ....................
High Use Wyoming Big Sagebrush (ATWH)......................
Low Use Basin Big Sagebrush (ATTL) . . ......................
High Use Basin Big Sagebrush (ATTH)........................
27
27
30
31
31
33
35
37
38
40
SUMMARY AND CONCLUSIONS............ • ..........................
41
REFERENCES CITED .
47
vi
TABLE OF CONTENTS-Continued
Page
APPENDICES....................................................
Appendix A
Appendix B
Appendix C
- Simple Statistics for Each Site.............
- Scatter Diagrams for Each Taxon.............
- Plant Species on the Study Area.............
55
56
63
70
■,
vii
LIST OF TABLES.
Table
Page
1.
Description and location of individual stands ................
11
2.
Groups of independent variables ..............................
26
3.
Highest adjusted R2 equations for each subspecies and
form class combination in each of 3 variable g r o u p s ..........28
4.
Highest adjusted R2 equations for each subspecies and
form class combination with average seedhead weight (AS)
added to each group as an independent variable................29
5..
Group I models for low use mountain big sagebrush (ATVL) . . .
32
6.
Group I models for high use mountain big sagebrush (ATVH) . . .
34
7.
Group I models for low'use Wyoming big sagebrush (ATWL)
....
35
8.
Group I models for high use Wyoming big sagebrush (ATWH) . . . .
37
9.
Group I models for low use basin big sagebrush (ATTL)
........
.39
10.
Group I models for high use basin big sagebrush (ATTH)........... 40
11.
Simple statistics for low use mountain big sagebrush (ATVL) . .
12.
Simple statistics for high use mountain big sagebrush (ATVL). . "58
13.
Simple statistics for low use Wyoming big sagebrush (ATWL)
. .
59
14.
Simple statistics for high use Wyoming big sagebrush (ATWL) . .
60
15.
Simple statistics for low use basin big sagebrush (ATTL) . . . .
61
16.
Simple statistics for high use basin big sagebrush (ATTL) . . .
62
17.
Plant species identified on the Gardiner study area ..........
71
57
viii
LIST OF FIGURES
Figure
Page
1.
Map of the Gardiner area showing topographic features
with elevation in meters...................................... 12
2.
Combinations of subspecies and form classes used in this
s t u d y ..................................................... . •
20
Scatter diagrams for low use mountain big sagebrush (ATVL)
showing linear relationships among variables as indicated
by colinearity analysis.........
'64
3.
4.
Scatter diagrams for high use mountain big sagebrush (ATVH)
showing linear relationships among variables as indicated
by colinearity analysis...........
.65
5.
Scatter diagrams for low use Wyoming big sagebrush (ATWL)
showing linear relationships among variables as indicated
by colinearity analysis............................ ■........ 66
6.
Scatter diagrams for high use Wyoming Big sagebrush (ATWH)
showing linear relationships among variables as indicated
by colinearity analysis ......................................
67
Scatter diagrams for low use basin big sagebrush (ATTL)
showing linear relationships among variables as indicated
by colinearity analysis ......................................
68
Scatter diagrams for high use basin big sagebrush (ATTH)
showing linear relationships among variables as indicated
by colinearity analysis.......... ....................... • •
69
7.
8.
ABSTRACT
The objective of this study was to develop a simple, reliable
method to estimate the contribution
of sagebrush taxa to range
production. Regression equations were developed for mountain big
sagebrush (Artemisia tridentata vaseyana), Wyoming big sagebrush (A.
t. wyomingensis), and basin big sagebrush (A. t. tridentata) in high
and low use form classes. Linear measurements including major and
minor axis, height, and crown depth, along with average cover, and
combinations of these measurements were used to predict total annual
winter forage production.Colinearities among some variables required
the development of 3 groups of variables (based on major or minor axis
or elliptical area) to be used in the model building.
A natural
logarithm transformation of the dependent variable, forage, was
necessary to stabilize nonconstant variance.
Reliability of the
relationships was based on the adjusted R2 statistic.
Adjusted
R2 values
were similar
for each subspecies
and form class
combination among variable groups. The variable group based on the
measure of the maximum diameter (major axis) was selected as the most
efficient variable
group based on ease of measurement
of the
variables
and high adjusted
R2 values. Adjusted
R2
values
ranged from 0.78 to 0.90 for 3 to 4 variable models in this variable
group. When the variable average seedhead weight was added to this
variable group,
adjusted R2 values improved. Values ranged from
0.87 to 0.93 for 4 to 5 variable models. Two variable models accounted
for nearly as much variation in each case. The variables major axis
and average cover were often the most meaningful. Major axis alone
accounted for > 69% of the variation in low use taxa but < 59% in high
use taxa. Average cover alone accounted for > 61% of the variation in
all taxa form class combinations investigated and in the case of high
use basin big sagebrush it accounted for 81% of the variation in
production.
I
INTRODUCTION
Big
sagebrush (Artemisia tridentata
Nutt.) is the most widespread
and abundant shrub on rangelands of the western United States (McArthur
>
et al. 1979) . Sagebrush dominated vegetation comprises over 58 million
hectares in the 11 western states (Beetle 1960). It occurs from southern
British
Columbia,
Alberta
northern
Arizona
mountain
chain formed by
coastal
the
and
Saskatchewan
and northern Baja,
Mexico. Its western
the Cascade, Sierra,
ranges, and the
Rocky Mountains in
to central .New
Mexico,
limit is the
and southern California
northern Baja mountains.
the southern half of its
Its eastern limit is
range and the western
Dakotas in the northern portion (Harvey 1981) .
Big
s,agebrush provides cover and forage for a variety of rangeland
wildlife species. Elk (Cervus elaphus nelsoni) and mule deer (Odocoileus
hemionus)
1984).
browse sagebrush
The distribution
heavily during the
fall and winter (McNeal
of pronghorn antelope
(Antildcarpaamericana)
and
sage grouse (Centrocercus urophasianus) are closely.associated with
the
distribution
1984).
of big
It .provides
songbird
sagebrush
excellent
cover for
nesting, and small mammals
important
to
other
(Sundstrom
ecosystem
et al. 1973,
elk calving,
Roberson
waterfowl
and
(Ewaschuk 1972). Sagebrush is also
functions
such as
snow accumulation,
nutrient cycling, wind chill reduction and shading.
A procedure to predict the forage produced by the sagebrush complex
is
important to
determine carrying
capacity
of the range,
to detect
trends in forage production and to measure plant response to management.
The
high
forage
values of
sagebrush
make
it important
for
range
2
managers
to accurately estimate forage production. Methods that involve
clipping
or
harvesting
are costly,
time consuming,
and
destructive
(Uresk et al. 1977) . Non-destructive procedures based on easily measured
plant
for
dimensions to estimate production and biomass have been developed
a variety of other plants (Tufts
1919, Pechanec and Pickford 1937,
Weaver 1977, Andrew et al 1979).
The
Beetle
ssp.
big sagebrush complex
consists of 4 subspecies
(Beetle 1960,
and Young 1965). Three subspecies, Mountain big sagebrush (A. t.
vaseyana
wyomingensis
(Rydb.)
Beetle
Beetle),
and
Wyoming
Young),and
big
Basin
sagebrush
(A.
big sagebrush
t. ssp.
(A. t. ssp.
trident at a) were of interest for this study. Differences in growth form,
distribution,
and
forage
qualities are well defined among these subspecies (Beetle 1960,
Winward
1970,
et
ecology,
phenology,
animal
preference,
Depuit et al. 1973, Kelsey et al. 1976, Morris et al. 1976, Welch
al. 1981,
Wambolt
et
Harvey 1981,
al
.1987).
Personius
et al 1987,
While taxonomic
regression
equations
used
to
subspecies
may be unique. Previous
Striby
differences
predict
forage
et al 1987,
may be
production
subtle,
for
each
historical use, as it affects shrub
morphology, may also affect prediction equations (Hughes et al. 1987) .
The
equations
objectives
of this
study
using easily measured plant
were
I) to
develop
regression
dimensions to accurately predict
forage production for mountain, Wyoming, and basin big sagebrush, and 2)
to
determine the effects,
if any, of historical
form classes, on regression analyses.
use, based on present
3
LITERATURE REVIEW
Preface
Rapid and reliable techniques for estimating plant production and
biomass
and
are needed.in range, wildlife, and ecosystem studies (Harniss
Murray 1976, Ganskopp
estimate
are
production or biomass from
important to
response
1982,
and Miller 1986). Methods
resource managers
to defoliation,
Rittenhouse
nutrient
and
and
estimates
easily obtained plant dimensions
to determine carrying
seeding success
(Murray
Sneva 1977) . Detailed
cycling, and competition
that accurately
capacity,
and Jacobson
succession
in plant communities
studies,
also require
of plant biomass and production (Ganskopp and Miller 1986) .
Procedures
that involve harvesting plants or parts of plants are time
consuming, destructive and costly (Uresk et al.1977). Using the weight
estimate
technique
considerable
technique
(Pechanec
and
Pickford
1937)
requires
a
amount of training and clipping to verify estimates. The
also results
in mental fatigue after
several hours of use
(Cabral and West 1986) .
A
rapid, accurate
clearly
desirable.
relationship
harvest
analysis.
One
technique. that requires
approach
little
is to establish
training is
a mathematical
between I or more easily obtained plant measurements and
data. This procedure
Uresk et al.
is called double
(1977) estimated that
sampling or dimension
clipping big sagebrush
phytomass was 120 times more expensive than dimension analysis.
4
Dimension Analysis for Trees
Foresters
use
plant
measurements
for
estimates
of
biomass
including board feet, cubic feet, and cords. Tufts (1919) found a high
correlation between trunk circumference and weight of the top of fruit
trees.
Since
Baskerville
1982)
live
then
many
others
(Kittredge
1965, Grier and Waring
1944, Attiwill
1962,
1974, Brown 1978, Weaver and Lund
have used various combinations of trunk diameter, total height,
crown length, ratios
of live crown length
to total height, and
crown widths to estimate tree biomass. These researchers showed strong
relationships from these simple measurements in most cases.
Dimension Analysis for Shrubs
In 1958, Evans and Jones addressed the importance of a method for
determining
forage
production
in which
clipping or mowing
necessary.
Since that time, much attention
predicting
production
variety
and
biomass
has been directed towards
for a variety
of variables. In most cases,
was not
of shrubs
with a
very strong relationships, with
high r and R2 values have been reported.
Measures
successfully
of
crown and shrub
areas and
volumes have
been used
to predict different plant fractions (Uresk et al. 1977,
Rittenhouse and Sneva 1977, Dean et al. 1981, Weaver 1977) . Murray and
Jacobson
(1982) found that
relationships
that
to
the observer
different plant shapes
weight varied between
should decide
used to calculate
plant species.
the most
appropriate
They suggest
shape in the
5
field,
and obtain the necessary measurements to calculate the area or
volume that best approximates that shape.
Lyon
(1968),
(Amelanchier
heights
predicted
size-mass
Montana
from simple
In
area,
aboveground
also
1981, Harvey
age,
and
production for Wyoming
and that
for twelve ecologically diverse
used similar
circumference,
masses were
of their sizes
and Sneva used elliptical
of aerial biomass
for Wyoming big
measurements
shrub
and included
volumes
to estimate
and mountain big sagebrushes, and
plains silversage (A. cana),
cudweed
found that shrub
measurements
production
for serviceberry
relationships based on crown
Also in 1977, Rittenhouse
area to predict
sagebrush.
production
Weaver (.1977),
relationships were similar
shrubs.
basal
twig
alnifolia) from volumetric
and diameters.
easily
crown
estimated
fringed sagewort (A. frigida), and
sagewort (A. ludoviciana) . Thus, a large variety of variables
have been used in dimension analysis of shrubs. The most useful appear
to
be variables that express
Log-log
species
(Bently et al.,
Kothmann
problem
(1979)
and quadratic
1979) . Under
with the
crown size (Murray
models have
been useful
1970, Rittenhouse and
or overestimation
log-log models (Tausch
and Jacobsen 1982).
for a number of
Sneva 1976, Bryant and
of biomass
appears to be a
1989). Bryant
and Kothmann
noted that the equation which should be used to predict weight
from crown volume depends not only on plant species, but also sampling
date.
Bonham
differences
individual
(1989)
among
concludes
years,
that
seasons,
site-year equations
because
the
and locations
should be used
magnitude
is so
of
great that
until sufficient data
are available for evaluation of site-regional equations.
6
Canopy Cover
Hanley
(1978) stated that canopy
and
useful parameter in
the
"best
single
(Lindsay
ecological
size of
dominance
range analysis. Cover
measure
community"
1956,
of a
plant
or
productivity,
enough
It is an
of plants on precipitation
soil temperature. Compared with
precise
importance
cover
for
in a
index of the
serves as a criterion
and
canopy
has been presented as
species'
Daubenmire 1959) .
the plant. It
and the influence
Evaluations
cover is a frequently measured
of relative
interception
other parameters such as biomass
is relatively
research
easily
purposes
measured.
do not
require
excessive field time.
A variety of methods have been devised for measuring plant canopy
cover,
but advantages and disadvantages vary with types of vegetation
sampled
and degrees
of confidence
and precision required.
intercept
method (Canfield
1941) is a frequently
measuring
canopy
shrubs in the
al. (1960)
compared results
cover of
used technique for
Great Basin.
of line interception,
The line
Kinsinger et
variable plot, and
loop methods for shrub cover in Nevada and found the line interception
method
to
be the most
line-interception
equivalent
accurate.
method
and
Hanley
the
0.1
(1978)
reported
m2 quadrat
that the
method
are
in accuracy of measuring canopy coverage of big sagebrush,
but that the line-interception method is more advantageous when a high
degree
cover
of precision and confidence
data as
mentioned by
is required.
Taylor (1986)
One disadvantage of
is that in
some species,
canopy cover may vary considerably among seasons and years.
7
The
value of canopy coverage measurements for predicting browse
mass was recognized by Weaver (1986) . He postulates that if browse can
be
accurately
wildlife
predicted
manager
to
from canopy
estimate
coverages,
quantities
it will
of available
allow a
browse
by
analysis of an aerial photograph of the site.
Taxonomy of Big Sagebrush
The
by
name Artemisia tridentata
Nuttall
(1841)
from a collection
expedition
in 1804. In
tridentata
and classified
1965,
first given
made by the
1916, Rydberg recognized
it as a complex of
to this species
Lewis
and Clark
variations among A.
5 separate species. In
Beetle and Young's classification, though similar to Rydberg's,
assigned
collections
separation
A.
was
to
of 3 subspecies:
t. wyomingensis,
subspecies
rather
than species.
Their
A. t. vaseyana (mountain big sagebrush),
(Wyoming big
sagebrush), and A.
t. tridentata,
(basin big sagebrush), has been widely accepted.
Since
phenology,
1965
research
has
verified
growth form, palatability,
differences
in ecology,
and chemical composition among
these three subspecies (Beetle 1960, Winward and Tisdale 1969, Winward
1970, Ward 1971, Morris et al. 1976, Wallmo et al 1977, McArthur 1979,
Beetle
and Johnson
accurately
1982, Blaisdell
categorize
sagebrush
et al
1982,). The
need to more
taxa for
the purpose
of improving
resource management was summarized by A. A. Beetle who said in 1977,
"It is no longer fashionable
to refer in a general way to
sagebrush... This wide variety in ecological adaptation, distribution,
and significance reflects important differences in site potential and
consequently in management technique. Knowledge of the ecological
8
characteristics .of the species,- and in many instances the subspecies
of sagebrush will be an important tool to the range manager."
Value to Wildlife
Sagebrush
animals.
taxa
Julander
provide
and
Low
cover
(1976)
and
found
forage
for
mule deer
big
to be
game
closely
associated with sagebrush grasslands. They found mule deer were scarce
in
pre-pioneer
and
pioneer
times,
but
deer
numbers
increased
dramatically between 1925-1950. The reasons for this increase include:
predator
control, limited harvests, and a change in range composition
from grass to shrubs.
Antelope
distribution
distribution
is
closely
of Wyoming big sagebrush
associated
with
the
(Sundstrom et al. 1973). These
animals are obligates to sagebrush and have been known to travel large
distances
due
to find sagebrush browse when local sources are unavailable
to snow cover.
found
that
Bighorn sheep
sagebrush
taxa can
studies in Montana
comprise
30-40%
and Idaho have
of
winter
diets
(Schallenberger 1966).
The
most
closely
associated
All
of.the requirements
are
found in the sagebrush
game
bird
for breeding, nesting,
is the
sagegrouse.
rearing, and feeding
dominated ecosystem
(Wallestad and Pyrah
a crude protein level
near 11% throughout
1974, Remington 1985) .
Big
the
sagebrush maintains
winter (Welch and McArthur 1979).
levels
of about
4% for
grasses.
This compares to crude protein
Thus, big sagebrush
valuable winter forage for browsing ungulates.
represents
a
9
McNeal
(1984)
utilization
et
as
"...surprising
(in McNeal 1984) found
5% of
postulates
reflect
a
amount
of
sagebrush
at elk feeding sites" on the Gardiner winter range. Greer
al. (1970)
much
noted
the diet
that
of Yellowstone
big sagebrush
special need
that big sagebrush comprised as
use
by elk on
for nutrients
long
migration. Elk also
fall
and winter
Park
elk.
McNeal
the winter
not provided by
(1984)
range may
grass after the
use big sagebrush grasslands heavily in the
for thermal
and hiding
cover (Mackie
1980) . These
areas provide safe calving grounds for the elk during the spring.
Form Classes
Cook
and Stoddard
(1960) showed
that repeated
herbage removal
could induce a hedged appearance in big sagebrush. McNeal (1984) found
that
subjectively
assigned form classes
in big sagebrush
reflected
actual
browsing use. Kelsey (1985)
observed no visible morphological
change
in mountain big sagebrush following 2 years of clipping 50% of
annual
growth. Several years of continuous
use are probably required
to visibly alter plant morphology (Personius 1985).
Weaver
(1977) concluded that if
browse biomass estimates, based
on lightly browsed shrubs were applied to plants with reduced (through
browsing)
diameters, actual
potential
browse
Hughes,
browse would be overestimated.
and above ground
Varner, and
Blankenship (1987)
greatly modified plant form will
separate
biomass would be
found that
However,
underestimated.
treatments which
probably require regression equations
from those for undisturbed
vegetation. Equations to predict
10
browse
needed.
for
both
heavily
and
lightly
used
shrubs
are therefore
11
STUDY AREA DESCRIPTION
Location
The
Gallatin
the
study
area
is located
near" Gardiner
National Forest of southwestern
Yellowstone River valley at 1694
Montana.
(Figure
I)
in the
Gardiner lies in
m (5505 ft) surrounded by peaks
reaching 3353 m (11,000 ft).
Stands
high
(Table I) of each
subspecies of big
or low use form classes were
Creek
to the
east, U.S. Highway
sagebrush in either
located in an area bounded by Bear
89 to the
south, and
Little Trail
Creek to the west. Stand #5 was located in Yankee Jim Canyon, about 22
km
northwest of Gardiner.
The Absaroka-Beartooth
Wilderness forms a
less distinct northern boundary.
Table I. Description and location of individual stands.
Stand
ssp
Elev.
Form
class- (m)
Habitat
type
I
A.t.va
low
2195
A.t.va/Feid
2
A.t.va
high
2049
A.t.va/Agsp
3
A.t.wy
low
1646
A.t.wy/Agsp
4
A.t.wy
high
1854
A.t.wy/Agsp
5
A.t.tr
low
1707
A.t.tr/Agsp
6
A.t.tr
high
1951
A.t.tr/Agsp
soil
group
Legal
desc..
SE corner, SWl/4
sec. 7, T9S, R9E
NE
corner, NE1/4
sandy
sec.13, T9S, R9E
SW corner, NE1/4
sandy loam
sec.16, T9S, R9E
sandy loam . SW corner, SWl/4
sec.10, T9S, R8E
NW
corner, NWl/4
silt loam
sec. 3, T8S., R7E
NE corner, SE1/4
silt
sec.18, T9S, R9E
silt loam
Common names of scientific name abbreviations are: A.t.va - mountain
big sagebrush; A.t.wy - Wyoming big sagebrush; A.t.tr - basin big
sagebrush; Feid - Idaho fescue; Agsp - Bluebunch wheatgrass.
12
Road
MONTANA
SI r e a m
1.6 K m
A BSAROKA -BEARTOOTH
WILDERNESS
PARKER
POINT
JARDINE
(2408
(2000 m)
m)
Little
Trail
Creek/
Cree
TRAVERTINE
FLATS
(1903 m )
DECKARD
FLATS
(1908
GARDINER /
(1 6 9 4 ^ ) /
YNP
Yellowstone
River
Figure I. Map of the Gardiner study area.
m)
13
Topography
The
topography of the study area
is characterized by relatively
flat
or rolling benchlands.rising
rain
shadow produced by these mountains
of
to high steep-sided mountains. The
makes the benches and slopes
the Gardiner valley an important wintering area for mule deer, and
elk.
Bison, bighorn sheep, and antelope also use some portions of the
area as well. These benches are dissected by deeply entrenched streams
(McNeaI
1984) .
Slopes of 50-60 % rise from the Yellowstone River and
Bear Creek to the 1-2 km wide basalt bench which extends from the Park
line
the
northwest to Little Trail Creek.
Absaroka Mountains
elevation
Morainal
increases
Slopes then rise abruptly into
from Deckard
1100
m within
and Travertine
5 km of the
Flats. Overall,
Yellowstone
River.
topography provides a more gradual ascent into the mountains
in the Eagle Creek drainage (McNeal 1984).
Much
greatly
slopes
of the study area has a south
enhances its value
and west facing aspect which
as a winter range.
are found along stream channels
North and east facing
and in the mountains. Concave
and convex shaped slopes of 2 - 70 % rise are present (McNeal 1984) .
Climate .
Thornthwaite's
humid
(1948) classification
with a summer water
shows the Gardiner area as
deficiency. However,
convectional showers
14
are common and provide some growing season moisture. Winter storms are
more
widespread and can
be severe. Snow may be
1-2 m deep in nearby
mountains yet absent in Gardiner (McNeal 1984).
Fames'
climate
created
isohyets
by
the
rain
shadow in
closely following land contours
elevation.
along
(1975) precipitation map of the area illustrates the dry
Annual average
the Yellowstone
benches
and
up to
the Gardiner
from 30.5 cm (12 in)
to about 40' cm (16 in)
76.1 cm (30
in) in
with
and greatly increasing with
precipitation ranges
River,
valley
on the basalt
the surrounding
mountains.
Roughly half of this moisture occurs as snow.
The U.S. Weather Bureau station at Mammoth is located about 300 m
higher
a
and 8 km upstream (south) from Gardiner. This station provides
good representation
Weather
data
of climatic
collected
precipitation
of
over
conditions
100
41.2 cm (16.25 in).
years
for the study
show
annual
February is the
area.
average
driest month,
averaging 2.7cm (1.05 in) and June is the wettest month, averaging 4.9
cm
(1.92
January
in). The
is
the
mean
annual
coldest
month
temperature
averaging
is
4.1°
-7.4°
C (39.9°
C (18.7°
F)
F) .
and
July is the warmest month averaging 17.3° C (63.1° F) .
Typically,
the
growing
season
is mid-April
to mid-September
although a killing frost can occur during any month. Fall regrowth can
be substantial if conditions are favorable (McNeaI 1984) .
15
Soils
Soils
deposition
in the area are
the result of glacial
scouring, morainal
and outwash sediments. The parent materials
are a mixture
of granites and limestones derived from glacial action in areas to the
south
and east. The soil climate is characterized by cold winters and
dry summers.
Glaciation has resulted in variable soil depths. Shallow soils of
only
a few
several
area
cm occur in
covered
scoured areas
m occur in depositional areas.
has a sandy
Course
glacially
loam texture
granite erratics
and a high
which
soils of
The glacial till in the study
fragment size ranges from gravels
with
and deep
course fragment
content.
to boulders. The surface is
probably
came from
the Black
Canyon of the Yellowstone (Pierce 1979 in McNeal 1984) .
Mollisols
loamy-skeletal
(Gallatin
Inceptisols
are the
most common soils.
Aridic Haploborolls
N. F., U. S.
can
Soil families
to fine-loamy
Pachic Argiborolls
Forest Service soils survey
occur near
bedrock outcrops
range from
in McNeal 1984).
and Alfisols
occur in
forested areas.
Vegetation
Study
54%
area vegetation is predominantly sagebrush-grassland. Over
of the area is open sagebrush-grass range with another 14% having
16
sagebrush
27%
dominated understory and
of the
area is continuous
a scattered tree overstory. About
forest
which
begins at 2300
m. The
remaining 5% is clearcut (McNeal 1984).
Three
nova
subspecies of big sagebrush and black sagebrush (Artemisia
A. Nels.) occur sympatrically
sagebrush
the
is closely associated
overstory
in
Pleistocene-aged
sandy till
limestone
throughout
the study area. Black
with calcareous
covering
rock
formed
soils and dominates
travertine
(a decorative,
from hot calcareous 'spring
water). Mountain big sagebrush is the most frequent and dominant shrub
in the area and is the only sagebrush taxon found above 2100 m (McNeal
1984) . Wyoming
big
resulting
glacial
silts.
lower
from
Basin
sagebrush
is
found on
outwash and
big sagebrush
deep sandy
more recently
grows downslope
portions of steep slopes, along
loam
placed
of basalt
soils
alluvial
outcrops, on
irrigation ditches or in other
areas where soil moisture is artificially increased.
Bluebunch
Idaho
fescue
understory
related
facing
(Agropyron
idahoensis
respective
spicatum (Pursh)
Elmer) are
species on the study area.
Scribn.) and
the 2 most
important
Dominance by either species is
tolerances
for aridity (McNeal
1984).
wheatgrass dominates lower elevation sites with steep south
exposures, sandy soil, and
factors.
not
(Festuca
to their
Bluebunch
north
wheatgrass
Idaho fescue is
facing slopes, and
other sites with moisture limiting
a prominent grass at
higher elevations, on
on deep silty soil sites
limiting. Other locally
important grasses
where moisture is
are prairie junegrass
17
(Koeleria
Trin.
pyramidata
(Lam.) Beauv.),
& Rupr.), and Indian ricegrass
needleandthread
(Stipa comata
(Oryzopsis hymenoides (R. & S .)
Ricker).
Wildlife
The
Large
study area is part of the northern Yellowstone winter range.
herds of elk often
months.
Most
move into the study
of the elk summer
area during the winter
on the Yellowstone
Plateau
while a
smaller number summer in the mountainous Absaroka-Beartooth Wilderness
(McNeal
1984) . Small summer herds
exposed
hillsides along the Yellowstone
fall
snows
deepen
begin to congregate on benches and
in the mountains.
River and its tributaries as
This assemblage
of
elk is a
portion of the northern Yellowstone elk herd. Although estimates vary,
there
may have been
as many as 25,000
animals during
the winter of
1987-1988 in the entire northern elk herd.
Elk
begin arriving in the Gardiner area as early as mid-November
(McNeaI ' 1984) . Many
continue
their
migration
to
the
area
as
conditions in the mountains and on the Yellowstone Plateau worsen. Elk
migration
in large numbers beyond
the Park boundary may occur I year
in 2 or 2 years in 3 (Houston 1978) . The number of elk using the study
area
year
may vary from a few hundred to several thousand depending on the
(McNeaI
Gardiner
1984) . Some may
travel as
area (Craighead et al. 1972).. The
far as 113 km
to reach the
winter range north of.the
Park is indispensable in severe winters to offset the lack of suitable
wintering
areas within
the Park.
Most of the
elk have usually left
the study area by May and returned to popular summering areas.
18
McNeal
occupants
and
of
or more mule deer are seasonal
the Gardiner winter range
Parks Dept, surveys
Most
the
(1984) estimates that 400
while Montana Fish, Wildlife
estimate over 600 in
of the deer appear
the winter of 1989-90.
to spend summers in the
nearby mountains of
Absaroka-Beartooth Wilderness,in Yellowstone Park or on the study
area
itself. They
usually begin to
arrive on the study
area around
October I and may remain through June.
Other
winter
along
big
game animals
range. Small bands
infrequently
appear
on
of bighorn sheep (Ovis
the
Gardiner
canadensis) winter
the bluffs of the Yellowstone River and Bear Creek near Deckard
Flats. Moose (Alces alces) are occasional visitors to the area. During
deep
snow
Yellowstone
winters,
a few bison
(Bison
bison)
migrating
down the
River valley may cross the Park boundary onto the Deckard
Flats
area and remain for short periods
1984,
the bison have migrated further
of time (McNeal 1984) . Since
down the Yellowstone River. In
the winter of 1988 bison traveled as far as Yankee Jim Canyon, some 14
miles north of Gardiner.
Additional large animal species sometimes present in the area are
Grizzly bears (Ursus arctos) and black bears (U. americanus) which are
present
in
herbivores.
the
fall
and
spring
Coyote (Canuslatrans)r
in association
with
the
large
bobcat (Lynxrufus) and mountain
lion (Fells concolor) also may be present in the area.
19
METHODS M D MATERIALS
Data Collection
Stands
located
of
in both high
Subspecies
were
classification
in
Wyoming, and
and low use
initially
basin big
sagebrushes
form classes during
identified
June of 1989.
taxonomically
of Beetle (1960). Identifications
were
following
the
were later verified
the lab using the ultraviolet light technique developed by Stevens
and
McArthur
appearance
of
mountain,
result
Continuous
branched
crowns. This
very distinctive
High
in more
plants
determined
produces
growth
by
the overall
form
shorter,
gives the plant a dense,
appearance. The crowns
slopes,
was
(Figure
more
hedged,
use stands
were
located on
remote areas (Fig.
were usually
interference
are more open, growthy,
club-like
situated
(i.e. roads)
near areas
Stands
where human
inhibited elk and mule
more
and relatively
south or west
2: B, D, F).
2).
intricately
Lightly browsed plants exhibit longer leaders and a
unbranched.
facing
of low use
occupation or
deer use, and in I
where deep snows prevented winter access (Fig. 2: A, C, E). The
following
(ATWH),
6
stands
were located:
low use Wyoming
sagebrush
basin
in a
browsing over time
appearance.
case,
Form class
of each shrub. Browsing by elk and mule deer over a number
years
bushy
(1974) .
(ATVH),
big sagebrush
(Figure 2).
high
use Wyoming
big sagebrush (ATWL),
low use
mountain big
(ATTH), and low
big
sagebrush
high use mountain big
sagebrush (ATVL),
use basin big
high use
sagebrush (ATTL)
20
E
F
Figure 2. Combinations of subspecies and form classes used in the
study. (A) low use basin big sagebrush (ATTL), (B) high use basin big
sagebrush (ATTH), (C) low use mountain big sagebrush (ATVL), (D) high
use mountain big sagebrush (ATVH), (E) low use Wyoming big sagebrush
(ATWL), (F) high use Wyoming big sagebrush (ATWH).
21
Sampling
September,
the
started
in
late
1989. This allowed
ephemeral
leaves
which
July, 1989,
and continued
for the nearly
are not
through
complete abscission of
considered
available
winter
browse. Only the current crop of perennial leaves persists over winter
(Miller and Shultz 1987).
A stratified random sampling design was used in order to obtain a
representative
Randomly
sample
selected
of the
medium or large
medium
and
sampling
of
sampled,
different,
equaled
sized
shrubs
design prevented the sampling
at each site.
(relatively)
for the site. The final
large shrubs
proportion
sized shrubs
plants were determined
small,
the
different
to be either
proportion of small,
an occular
for
each
estimate
site.
of
This
of all one size class. Thirty
shrubs were sampled on each site.
Shrub Measurements
The
the
overall height (HT) of each
sagebrush plant was measured to
nearest cm from its base to the highest non-reproductive foliage.
This
study was
therefore
heights
interested
in forage available
the maximum plant height
for mountain and
to deer
and elk and
was set at 140 cm. (55 in). Plant
Wyoming big sagebrush
were well below this
height (Appendix A ) . However, some basin big sagebrush individuals can
reach
heights of over
2m.,
so only plants under
135 cm. in height
were sampled.
Two
Sneva
measurements
1977) .
of
crown width
The major axis
were taken
(MJ) was considered
(Rittenhouse
to be
and
the maximum
22
horizontal distance across the plant crown from living plant tissue to
living
plant tissue.
perpendicular
to
The minor axis (MN) was the maximum crown width
the major axis, ■and was also measured
from living
tissue to living tissue. Only photosynthetic plant tissue was used for
the
beginning,
and
photosynthetic
are
included
the
end
points
of
openings in the canopy
in these
these
distances.
Non
-
were considered continuous and
measurements.
All measurements
were
to the
nearest I cm.
Line
intercept canopy
collected
cover measurements
as actively growing
leaves,
current
measurements
additional
to
for
were
plant material. This
twigs,
made along
axes. These
and
includes perennial
reproductive
the major
stalks.
and minor axes
axes were perpendicular
to the intersection of the
Cover
and along 2
to each other and 45
major and minor axes. Total cover
for each axis was
measured. These
4 cover totals
summed and divided by 4 to yield the variable average cover (AC)
each plant.
sampled.
of
growth
the nearest cm
were
1941) were
along 4 crown axes for each . shrub sampled. Intercepts were
defined
degrees
(Canfield
The crown
depth
(CD) was measured
This is the vertical distance
for
each shrub
in cm of the foliated portion
the crown (Dean et al. 1981) . Several measurements
were collected
and averaged for each plant. Only vegetative leaders were measured.
The
counted
After
reproductive stalks
for each
plant,
drying, seedheads
seedhead
weight (AS)
and
or seedheads were
oven
dried
were weighed to the
clipped at the base,
for 48
at 60
nearest 0.1 g.
was calculated ■ as the weight
divided by the number of the seedheads.
hours
C.
Average
of the seedheads
23
After
foliage
was removed from
ephemeral
easily
leaf
these measurements were collected
bud scars.
current
the plant. This includes .perennial leaves,
leaves (if, any), and current
discernible
in the field, the green
on the basis of
Browsing
twig growth. Young twigs were
color, texture
ungulates
may remove
twig growth, but for the purpose
of the bark, and
more than
just the
of this study, only current
twig growth is considered available annual winter forage.
After
was
oven drying
for at least
weighed to the nearest
48 hours at
0.1 g. This variable,
60° C. the foliage
forage (F), became
the dependent variable for the regression analysis.
Basal
stem
numbers and
diameters
sagebrush
(Dean et. al. 19-81), and
variables
in
therefore
measured for
encountered
collection
not
regression
had
diameters
for big
used successfully for independent
analysis.
Basal
stem
each plant, but most of
several
measured
trunks
diameters
were
the sagebrush plants
of irregular
shape.
This
made
of this variable very awkward and time consuming. This was
an easily
results,
have been
measured
variable
' for future
nor was it considered free
were not included
use
of this
studies
from human bias. Thus basal stem
in the variables
used in the regression
analysis.
Other Variables
The
other
previously described field measurements
variables
combinations
of
to
be used
the
field
in the
regression
measurements
were used to derive
analysis.
yielded
variables
Various
that
24
represent
elliptical
crown
area,
crown volume,
circular crown areas based on 2 different
Elliptical
canopy
pi(MJ/2)(MN/2),
where
respectively.
elliptical
as
area
MJ
and
MN are
by
the
formula
the major
and
minor
(1970),
axes,
Shrub volume was defined
is the plant height and
canopy. Peek
E =
as CV = E (CD), where E is the
area and CD is the crown depth.
of the
and
crown radii.
determined
Crown volume was defined
SV = E(HT), where HT
area
was
shrub volume,
and Harvey
E is the elliptical
(1981) refer
to this
variable as crown volume.
The
crowns of heavily
circular
crown
Jacobson's
(1982)
appropriate
circular
for
plant
were
considered.
suggestion
that
This
the
shape in the field.
more rounded, so
follows
observer
Murray
determine
Two variables
and
the
that represent
area of the canopy were investigated. The circular area (Cl)
the major
where
areas
browsed plants appeared
axis was determined
MJ is the major axis. And the
axis was determined
by the formula
by
the formula,
Cl = pi(MJ/2)2,
circular area (C2) for the minor
C2 = pi (MN/2)2,
where MN
is the
minor axis.
Data Analysis
The intention of this study was to I) develop reliable regression
equations
to predict
sagebrushes
contrast
class.
annual available
forage from each of the 3 big
in both high or low use form class, and 2) to compare and
easily computed
The models
regression
for each of the
models for each
taxon and form
6 combinations of
subspecies and
25
form
class were developed separately.
Several models were considered
and compared for each subspecies and form class combination.
Models were evaluated
adjusted
of
R2
statistic
determination
and
the sample
from
basis of adjusted
is calculated
(R2)
for
size.
by adjusting
the number
This
R2 values.
the
of parameters
adjustment
prevents
The
coefficient
in
the model
the R2
statistic
becoming artificially inflated as the number of variables in the
model
increases
generally
(Neter
similar
Scatter
and
on the
to
al.
1985).
R2 values,
diagrams were
dependent variables
variable
et
to
variable
(F)" and
(F)"
relationship
was added to
nonconstant
was log transformed
variance
exhibited
are
smaller.
pair of independent
tendencies. The
mountain big sagebrush (ATVL) was
(natural
in
with the
the model
No other curvilinear relationships were found.
"forage
R2 values
somewhat
to identify any curvilinear
have a curvilinear
"forage
though
constructed for each
"height (HT)" for low use
determined
Adjusted
dependent
as HT2 for ATVL.
The dependent variable
logarithm)
the initial
to stabilize
plots of ,predicted
values
versus residual error terms. Nonconstant variance is a direct
result
of the sampling method. The
variance
stabilizing
log transformation is
transformation
and
widely
a powerful
used (Murray
and
Jacobson 1977, Rittenhouse and Sneva 1977, Dean et al. 1981).
Colinearity
identified
analysis
for each taxon and form
colinearities among some
class combination
of the variables. The variables,
"major axis (MJ)", and "minor axis (MN)", were determined to be nearly
colinear
(E)".
with each other and also- with the variable "elliptical area
This may be
caused by the fact
that the crowns
of the plants
26
sampled
were
all similarly
variables
is nearly
variables
in
Variables
were
"major
any
identical so
I model
separated
axis (MJ)",
shaped.
Information
inclusion of more
is inappropriate
into 3
minor axis
derived
(MN)" and
than I of these
(Neter
groups to split
for the 3
et al.
up the
"elliptical
1985) .
variables
area (E)" and
thereby avoid colinearities (Table 2). Each of 3 groups of independent
variables
was analyzed
in the model building
procedure. Models were
evaluated and compared on the basis of the adjusted R2 statistic.
Table 2. Groups of independent variables. Each variable group includes
only "major axis (MJ)" or "minor axis
(MN)" or "elliptical area (E)"
in addition to the other variables measured.______________________■
Group I
MJ
Group 2
MN
Group 3
E
HT
HT2
CD
AC
AC
CV
C2
SV
Variable abbreviations: MJ-major axis, MN-minor axis, E-elliptical
HT-height, HT2 (for A t v l only), CD-crown depth, AC-average
area,
cover, Cl-circular areal, C2-cir area2, CV-crown volume, SV-shrub vol.
HT
HT2
CD
AC
Cl
HT
HT2
CD
Models are of the form: In(F) = bQ+ X1+ X2+ X3+ X4+ X5+ X6+ X7+ e
where: F = Available winter forage
bo= y-intercept
X°= major (MJ) or minor axis (MN) or elliptical area (E)
X2= height (HT)
X3= HT2 for low use mountain big sagebrush (ATVL) only
X4= crown depth (CD)
X = average cover (AC)
X6= circular areal (Cl) or cir area2 (C2) or crown volume (CV)
X7= shrub volume (SV)
e = residual error
all X terms have an associated constant = b
27
RESULTS M D DISCUSSION
This
highest
chapter is divided into 2 parts.
adjusted
combination
R2 models
for
each
In the first section, the
subspecies
and form
class
are presented for groups of variables that were chosen to
avoid colinearity. The second section will discuss the best models for
each
form class-taxon
themselves
combination
to easy field
with models
applications. My choice
in
the model and the ease of measuring those variables may not be the
adjusted
R2 model
R2 model since
(Tables 3 and
R2 values,
of the best models
the basis
adjusted
adjusted
that may lend
on
highest
of high
along
the number
the difference
of variables
between the highest
4) and a simpler
(fewer variables)
model may be very small, often <1%.
Comparison of 3 Groups of Variables
Colinearities
investigation
of
building process.
among
the
variables MJ,
3 separate variable
While it
MN, and E
groups (Table
is not possible
mandated the
2) in the model
to compare
adjusted
R2
values with a test statistic, Table 3 indicates that differences among
the 3 variable
groups
in adjusted
R2 values
are small.
That
is,
each variable group predicts annual winter forage production for all 3
subspecies and form classes of big sagebrush with essentially the same
precision.
This is also the case when
the variable "average seedhead
weight (AS)" is included in the model (Table 4).
28
Table 3. Highest adjusted R2’equations for each subspecies and form
_________ class combination in each of the 3 variable groups (Table 2).
Taxon
and form
Root
Adj R2 R2 MSE
Group I Models
class
ATVL
ATVH
In (F) = .647+.034(MJ)+ .031(AC)-.0002 (Cl)
In (F) = .311+.037(MJ)+ .047 (AC)-.0003(Cl)+ .017(CD)
.88
.90
.89
.91
.35
.24
ATWL
ATWH
In (F) = .322+.048(MJ)+ .017(AC)-.0003(Cl)
In (F) = .535+.008(MJ)+ .026 (AC)+ .029 (HT)+ .025(CD)
.88
.84
.90
.24
.26
ATTL
ATTH
In (F)=2.18+.013(MJ)+.019(AC)-.010(HT)+ .035(CD) . .78
.88
In (F)=1.89+.011(MJ) +.037 (AC) +.005 (HT) -.00005 (Cl)
.82
.86
.89
.35
.21
Group 2 Models
In (F)=1.059+.054 (MN)+ .025(AC)-.0004 (C2)
In (F) = .794+.041 (MN)+ .047 (AC)-.0005 (C2)+ .009 (HT)
+.033(CD)
.89
.90
.34
.89
.91
.25
ATWL
ATWH
In (F) = .729+.043(MN)+ .031(AC)-.0003 (C2)
In (F)=-.008+.055 (MN)+ .030 (AC)-.0007 (C2)+ .024(HT)
.81
.84
.83
.87
.30
.26
ATTL
ATTH
In (F)=2.53+.009 (MN)+.020 (AC)
In (F)=1.93+.021 (MN)+ .027(AC)-.0002 (C2)+ .006 (HT)
-.OlO(CD)
.73
.75
.40
.89
.91
.20
.81
.90
.83
.44
.92
.23
.83
.87
.28
.85
.88
.25
.78
.82
.36
.88
.89
.21
ATVL
ATVH
Group 3 Models
ATVL
ATVH
ATWL
ATWH
In (F)=1.34+.0005 (E)+ .029 (AC) + .0002 (HT2)
-.000006(SV)
In (F) =1.67-.0003 (E)+ .065 (AC)-.008 (HT)+ .00001 (CV)
In (F) = .124+.0008 (E)+ .018(AC)- . 0 2 (HT)-.000007 (SV)
- 0 0 0 0 0 2 (CV)+ . 0 6 8 (CD)
In (F)=-.246+.0007 (E)+ .031(AC)+ .043(HT)-.000009 (SV)
-.00004(CV)+.082(CD)
ATTL
In (F)=1.41+.0003 (E)+.020 (AC)-.005 (HT)-.00001 (CV)
+ . 0 8 2 (CD)
ATTH
In (F)=2.39+.030 (AC)+.007 (HT)-.016(CD)
Equation abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm)., HT-Height
(cm), AC-Average cover (cm), CD-Grown depth (cm),
Cl-Circular areal(cm2),
C2-Circular ■area2 (cm2), SV-Shrub
vol.
(cm3), E Elliptical
area (cm2), CV - Crown
volume (cm3),
Abbreviations for taxon and form class: ATVL-Iow use mountain big
sagebrush, ATVH - high use mountain big sagebrush, ATWL - low use
Wyoming big sagebrush, ATWH - high use Wyoming big sagebrush, ATTL low use basin big sagebrush, ATTH-high use basin big sagebrush. Root
MSE=MSEv2.
29
Table 4. Highest adjusted R2 equations for each subspecies and form
class combination when average seedhead weight (AS) is added
to each group as an independent variable.
Taxon
Root
and form
MSE
R2
Adj
R2
class
Group I Models
ATVL
ATVH
ATWL
ATWH
ATTL
ATTH
In (F) = .65+.380 (AS)+.029 (AC)+ .038 (MJ)-.0002 (Cl)
In (F) = .31+.047 (AC)+.037(MJ)+ .017(CD)-.0003 (Cl)
In (F) =.51+3.72(AS)+ .018(AC)+ .044 (MJ) -. 026 (CD)
-.0002(Cl)
In (F) = .68+.86 (AS)+ .026 (AC)+.009 (MJ)+ .043(HT)
In (F)=1.95+1.00 (AS)+.023(AC)+ .008 (MJ)
In (F)=1.89+.027 (AC)+ .011(MJ)-.005 (HT)
- .00005(C1)
.90
• .90
.91
.91
.33
.24
.93
.87
.89
.94
.89
.90
.18
.24
.25
.88
.89
.21
.93
.94
.27
.89
.91
.25
.88
.90
.24
.86
.86
.88
.88
.25
.28
.89
.91
.20
.86
.88
.38
.90
.92
.23
.87
.90
.24
.87
.89
.24
.89
.88
.91
.89
.25
.21
Group 2 Models
ATVL
ATVH
ATWL
ATWH
ATTL
ATTH
In (F) =1.45+.633(AS)+ .055 (MN)+ .017(AC)-.0003 (C2)
-.031(CD)
In (F) = .794+.041 (MN)+.047 (AC)-.0005 (CA2)+ .009 (HT)
+.033(CD)
In (F) = .856+4.69 (AS)+ .047 (MN)+ .026 (AC)-.0004 (C2)
-.037(CD)
In (F) = .203+.644(AS)+ .043 (MN)+ .030 (AC)-.0005 (C2)
+.022(HT)
In (F)=1.78+1.06 (AS)+006 (MN) +.022 (AC) + .006 (HT)
In (F)=1.93+.022(MN)+ .027 (AC)-.0002 (C2) + .006 (HT)
-.OlO(CD)
Group 3 Models
ATVL
ATVH
ATWL
ATWH
ATTL
ATTH
In (F)=1.30+.623(AS)+.0006 (E)+.021(AC) + .0002 (HT2)
-.000007(SV)
In (F) =1.73-.307 (AS)-.0003 (E)+ .065 (AC)-.008 (HT)
+.OOOOl(CV)
In (F) = .097+3.84 (AS)+ .0006 (E)+ .018(AC)-.018(HT)
-.000005(SV)-.000009(CV)
In (F) = .474+.772 (AS)+ .00-04 (E)+.772 (AC)+ .038 (HT)
-.000007(SV)
In(F) =1.60+1.02 (AS)+ .0002 (E)+.020 (AC)+ .010 (HT)
-.OOOOOl(SV)
In (F)=2.39+.030(AC)+.007 (HT)-.016(CD)
Equation abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm), AS-Average seedhead weight (g), HT-Height (cm), AC-Average cover
(cm), CD-Crown depth(cm), Cl-Circular areal(cm2, C2-Circular area2
(cm2), SV-Shrub
volume
(cm3), E-Elliptical
area
(cm2),
CVCrown volume
(cm3). Abbreviations for taxon and form class: ATVLIow use mountain big sagebrush, ATVH-high use mountain big sagebrush,
ATWL - low use Wyoming big sagebrush, ATWH - high use Wyoming big
sagebrush, ATTL - low use basin big sagebrush, ATTH - high use basin
big sagebrush. Root MSE=MSE^2.
30
Best Variable Group
Colinearity analysis indicated that the 3 variables MJf MN, and E
all
have
strong
investigation
relationships.
subspecies
linear
relationships
demonstrated
Scatter
and form
the
plots
with
each other.
strengths
(Appendix
class revealed
B)
of
of
Further
these
linear
MJ vs MN
for each
a linear relationship
for these
variables in each case. This evidence indicates that MJ is essentially
equal to MN for each taxon and form class combination. Mathematically,
E approaches the value of MJ times MN as the constants are absorbed in
the bn term in
equal
show
to
MN2.
Scatter
groups
that
each describe
measuring
E
vs
then E
MJ2
and
is
MN2
information and the 3 variable
the same relationships.
The logical
"best" variable group
the variables used in the
employing the most easily
criterion
then is the ease of
model. Group I has the advantage
measured variables.
The models in this
are based on a measurement of the major axis only. Groups 2 and
both require
major
of
= MN,
as expected. In the model building
area all describe the same
should determine the
group
plots
If MJ
for this data set, the variables major axis, minor axis, and
elliptical
3
MJ2 =
equation.
a strong linear relationship
process,
of
the regression
an additional
measurement
of the minor
axis as the
axis must be determined in order to locate the minor axis. This
additional measurement increases the amount of field time required and
does
Thus,
not seem
to increase
I conclude
that
the accuracy
Group
I is the
of the models
best variable
appreciably.
group
as the
variables are easier to collect and the resulting models are reliable.
31
Addition of the Variable "Average Seedhead Weight (AS)"
The addition of the variable, "average seedhead weight (AS)"
(Table 4)
values
for
somewhat
ATTL
to each
variable
group
does
most taxon-form
class
combinations.
time consuming to collect,
there
is improvement
in
increase
the adjusted
This
R2
variable
is
but, in the cases of ATVL, ATWL,
the
adjusted
R2 values,
and
a
decrease in the root MSE in each variable group. For the high use form
classes,
ATVH and
ATTH, there
is no
for ATWH
increase
in the
R2
values
and the increases
group.
This can be explained in terms of low inflorescence production
in
heavily used plants.
to
the model is most meaningful
are very small for
adjusted
Consequently,
each variable
the addition of this variable
for predicting
forage production in
low use subspecies.
Low Use Mountain Big Sagebrush (ATVL)
Annual
best
All
winter forage production can be reliably predicted by the
model (Table 5) for this subspecies
variables
variable
are significant
model has
at P < .01
easily measured
and form class combination.
for the best
parameters, and
model. This 3
a high adjusted
R2 value.
Dean
mountain
0.85.
precise
et al. (1981) developed a
big
model for predicting biomass of
sagebrush. Their model included 4 variables with R2 =
The best model for
as Dean
ATVL is in close agreement
(1981) included
the variable
and may be more
crown denseness
(%),
32
which
is
an ocular
consistency.
is
estimate ■ and
required
calibration
to
insure
Instead, the variable average cover (AC) is utilized. AC
preferable
as it is a practical,
rapid
and statistically
sound
technique, and is less subjective (Canfield 1941).
Table 5. Group I models for low use mountain big sagebrush (ATVL).
ROOT
MSE
R2
ADJ R2
Best model
In (F) = .647+.034 (MJ)+ .031(AC)-.0002(Cl)
.88
.89
.35
.83
.84
.42
.81
.79
.78
.83
.80
.80
' .44
.46
.47
.78
.70
.79
.71
.47
.56
.91. ;
.33
3 Variable model
In (F)=0.621+.037 (MJ)+ .046 (CD)-.00023 (Cl)
2 Variable models
In (F)=1.23+.038 (MJ)-.0001 (Cl)
In (F)=1.39+.019(MJ)+ .041(CD)
In (F)=1.96+.017(MJ)+.009 (AC)
I Variable models
In (F)=1.96+.021 (MJ)
In (F)=2.26+.037 (AC)
Model with AS included
In (F) = .65+.38 (AS)+ .038 (MJ)+.029 (AC)-.0002 (Cl) .90
Equation abbreviations; F-Forage (g), MJ-Major axis (cm), HT-Height
(cm), AC-Average cover (cm), CD-Crown depth(cm), Cl-Circular areal
(cm2), AS-Average seedhead weight (g) . Root MSE=MSE1/2.
The next highest
is
adjusted
R2 model
similar to the best model except
also has
3
variables
and
that the variable AC is replaced
by CD. There is a decrease in the adjusted R2 value of 0.05.
Three
ATVL. MJ
different 2 variable models predict forage fairly well for
is
included
in
each
with
either
Cl,
CD,
or AC.
The
33
differences
in
negligible.
presented
The
adjusted
easiest
includes
MJ
R2 values
2 variable
and
and
model
Cl. This
root
MSE
to use
model
terms
of the
requires
are
3 models
only
I field
measurement as Cl is calculated from the measurement of the major axis
and has an adjusted R2 = 0.81.
Two
These
single variables that predict forage for ATVL are MJ and AC.
2 models represent
high
reliability
very easily applied
with
adjusted
R2 =
models with reasonably
0.78
and
R2
=
0.70,
respectively. It is interesting to note that AC alone accounts for 70%
of the variability in predicting forage production for ATVL..
The
addition of the variable average
is significant
.88
to .90
variable
is
at P<.05,
for this
does
improve the
subspecies
and form
does require more field time
of this variable is useful
adjusted
class
R2 value
from
combination.
This
to collect, but the collection
straightforward and can be entrusted
addition
seedhead weight (AS),which
to less skilled workers. The
when a model to describe as much
variation as possible is required.
High Use Mountain Big Sagebrush (ATVH)
The highest
variables.
This
which
adjusted R2 model for
But the variable
ATVH in Table
CD is not significant
results in a model with the variables
are the same
variable
models
Adjusted R2 values
variables used
for
ATVH
are high
and root
4
and was excluded.
MJ, AC, and Cl, (Table 6)
in the best model
include
3 includes
the variables
MSE terms are
for ATVL.
MJ, AC,
Two
and Cl.
acceptable.
In
34
order to calculate Cl, the major axis must be measured. In the case of
ATVH, the
0.85
model
while
the 2
adjusted R2 =
variables
which combines
variable
0.69. So in
MJ, and
AC with
model
an adjusted
that combines
this case,
AC in the field
Cl has
MJ
and Cl
it is necessary
and calculate Cl
R2 =
has an
to collect
2
to use the best
model. A 2 variable model for ATVH does not improve efficiency.
Table 6. Group I models for high use mountain big sagebrush (ATVH)
ADJ R2
Best model
In (F)=0.489+.037 (MJ)+ .050 (AC)-.0003 (Cl)
ROOT
MSE
R2
.24
.90
.91
.85
.80
.69
.86
.29
.82
.71
.33
.42
.78
.46
.79
.48
.36
.56
2 Variable models
In (F)=1.36+.066 (AC)-. 0001 (Cl)
In (F)=1.73-.010(MJ)+.063 (AC)
In (F)=0.30+.081 (MJ)-.00005 (Cl)
I Variable models
In (F)=1.75+.046 (AC)
In(F)=2.36+.018(MJ)
Equation abbreviations; F-Forage (g), MJ-Major axis (cm), AC Average
cover(cm), CD-Crown depth(cm), Cl-Circular areal(cm2), Root MSE=
MSEv2.
Average
winter
ATVH.
forage
But MJ
mountain big
variable
cover (AC) alone
production,
only accounts
sagebrush.
is again a strong
accounting for
for 46%
In fact,
predictor of annual
78% of the
of the variation
low adjusted
variation for
for
R2 values
high use
for
this
are the case in each of the high use taxa studied (Tables 6,
8, and 10).■
35
The
addition
comparison
of AS
of Tables 3
did not
improve
the
and 4 reveals that AS
model
for ATVH. A
did not enter into the
model for ATVH.
Low Use Wyoming Big Sagebrush (ATWL)
Group I variables
this
the
R2
for
both
the
produce
taxon and form class
the highest
adjusted
combination (Table
R2 models
for
3). All 3 variables in
best model (Table 7) are significant at the PC.05 level. Adjusted
value is
high and
the
root MSE
is fairly
ATWL is similar to the best models
form classes. Indeed, the same
prediction
collection
equation.
This
low. The
best model
for mountain big sagebrush in
variables, MJ, AC, and Cl are in
simplifies
matters
in that
the
of relatively simple measurements will produce models that
work well for ATVL, ATVH, and ATWL.
Table 7. Group I models for low use Wyoming big sagebrush (ATWL).
.88
R2
.90
ROOT
MSE
.24
In (F)=1. 4 4 + . 0 1 6 (MJ)+ . 0 1 7 (AC)
.85
.84
.86
.86
.27
.27
I Variable models
In (F)=1.43+.027 (MJ)
In (F)=I.7 3 + . 0 3 8 (AC)
.81
.78
.82
.78
.30
.32
Model with AS included
In (F) = .41+2.76(AS)+.041. (MJ) + .015(AC)-.0002 (Cl)
.92
.93
.18 •
Best model
In (F) = .322+.048 (MJ)+ .017(AC)-.0003 (Cl)
2 Variable models
I n (F) = .270+.060 (MJ) -.0003 (Cl)
ADJ R2
Equation abbreviations; F-Forage (g), MJ-Major axis (cm), HT-Height
(cm), AC-Average cover (cm), CD-Crown depth(cm), Cl-Circular areal
(cm2), AS-Average seedhead weight (g). Root MSE=MSEv2.
36
This
3 variable
"best, model"
shows some
improvement
over the
model
of Dean et al. (1981) for Wyoming big sagebrush. The 4 variable
model
they reported
=0.86.
For
included
this study,
crown denseness
a 3 variable
model
and resulted
has
R2= O .90,
in an R2
and when
AS is added to the best model in Table 9, the R2=O.93.
The 2 variable models are very close in terms of reliability, and
the
measure of MJ with
very
functional
a subsequent calculation
model. The
additional measure
for Cl results in a
of AC
does not seem
necessary as no improvement in R2 is noted for a 2 variable model.
One
variable models explain
much of the variation
for ATWL. MJ
alone accounts for 81%, and AC accounts for 78%. Rittenhouse and Sneva
(1977)
accounted for 88% of the photosynthetic biomass of Wyoming big
sagebrush
with
techniques
the
variable,
log (MJ).
and the transformations
Even
though
the sampling
are different, these results seem
to be in close agreement.
The
the
addition of the variable
prediction
significant
of
AS results in
forage production
some improvement in
for ATWL. All variables
are
at P<.05. This equation is very similar to the prediction
equation for ATVL with AS included, probably because of similar growth
forms
for
variables
are
quite
these
2 subspecies
in
low use
are used in each prediction
different
so it
form classes.
The
same
equation, but the coefficients
is necessary
to
identify
subspecies in order to select the appropriate equation.
plants
to
37
High Use Wyoming Big Sagebrush (ATWH)
The highest adjusted
R2 model
for ATWH (Table
variable
CD which is not significant
excluded
from
the
R2,
or root MSB.
MJ,
AC, and
variable
any
of
model,
there
The best model
HT. These
the
at P<.05. When this variable is
is no
reduction
for ATWH (Table
3 variables
3) includes
are
in adjusted
R2,
8) has 3 variables,
significant
at PC.01.
The
HT is an important variable for ATWH, but did not enter into
the best
combinations
models
for the
investigated.
previous
3■ taxa and
This variable is included
form
class
in both of the
highest adjusted R2 models with 2 variables along with AC and M J .
Table 8. Group I models for high use Wyoming big sagebrush (ATWHj .
In (F) = .669+.008 (MJ)+ .029 (AC)+ .028 (HT)
.84
R2
CO
O')
Best model
ADJ R2
ROOT
MSE
.26
ln(F)=.716+.034(HT)+.036(AC)
In (F) = .940+.035 (HT)+ .014(MJ)
.82
.83
.28
.73
.75
.34
.61
.61
.53
.62
.62
.55
.41
.41
.45
.87
CO
2 Variable models
.24
I Variable models
In (F)=I.18+.051(HT)
In (F)=1.41+.054(AC)
In (F)=1.64+.024 (MJ)
Model with AS included
In (F) = .68+.86 (AS)+ .00.9(MJ) + .026 (AC)+.043(HT)
Equation abbreviations; F-Forage (g), MJ-Major axis (cm), HT-Height
(cm), AC-Average cover (cm), CD-Crown depth(cm), Cl-Circular areal
(cm2), AS-Average seedhead weight (g). Root MSE=MSEv2.
38
The differences between the best model, with 3 variables, and the
2
variable model
production
for
that includes HT
high use Wyoming
and AC are very
big sagebrush
small. So forage
can be
reliably and
easily determined by a 2 variable model that includes AC and HT.
Height alone accounts for 62% of the variation in forage for ATWH
as
does AC. The variable
class
as
compared
MJ accounts
to 82% for
for 55% for the
the low
use form
high use form
class of
the same
subspecies.
The addition of
to
AS improves
the model from
adjusted R2 =0.89. This improvement
sampling
time may not justify the
adjusted R2 =
is small,
0.84
so the increased
measure of this variable for ATWH.
Combining AS with other single variables does not improve the adjusted
R2 values from the values for 2 variable models presented in Table 8.
Low Use Basin Big Sagebrush •(ATTL)
This
adjusted
taxon and
R2 in all
form class
3 variable
difference
in overall
sagebrush.
Group
combination
groups.
growth form
I variables
This
resulted in
the lowest
is probably
due to
from the other subspecies
provided
the
highest
a
of big
adjusted
R2
values of the 3 variable groups investigated (Table 3).
Neither
class
CD in the
combination
significant
the
HT or
3)
is significant
for this taxa
at P<.05
and form
(both
are
at PC.10). If these variables are dropped from the model,
2 variable model
model.
(Table
Group I model
in Table 9 is
the result and
becomes the best
39
This
2
essentially
variable
equal
model
to the
has
highest
adjusted
R2= O .77
adjusted
which
R2 model.
The
is
2
variables in this model also appear as the best single variable models
for ATTL. The variable AC accounts for 69% of the variation by itself,
and MJ alone accounts for 65%.
Table 9. Group I models for low' use basin big sagebrush (ATTL) .■
,
Best model
ADJ R2
In (F)=2.37+.008 (MJ)+ .020 (AC)
R2
ROOT
MSE
.77
.78
.37
.69
.65
.70
.66
.42
.45
.89
.90
.25
I Variable models
In(F)=2.54+.032(AC)
In (F)=2.72+.016(MJ)
Model with AS included
In (F)=I.95+1.00 (AS) + .008 (MJ)+/023(AC)
Equation abbreviations; F-Forage (g), MJ-Major axis (cm), HT-Height
(cm), AC-Average cover (cm), CD-Crown depth(cm), Cl-Circular areal
(cm2), AS-Average seedhead weight (g) . Root MSE=MSEiy2.
The
addition of AS to the 2 variable model results in a dramatic
improvement
increase
in
both in
field
R2 values
and
for collection
of
adjusted
time
justified in the case of ATTL.
root MSE
AS data
values.
therefore
The
is
40
High Use Basin Big Sagebrush (ATTH)
The
highest
including
should
adjusted
R2 model
MJ, AC, HT, and Cl. The
There
are no
changes
ATTH
has
4 variables
variable Cl is not significant and
be omitted. This results in
Table ,12.
for
the 3 variable model presented in
in adjusted
R2, R2,
or root
MSE
for this 3 variable model and all variables are significant at PC.05.
or
The
2 variable models in Table 12
MJ.
The adjusted
R2, R2,
and
both include AC and either HT
root MSE
values
are
identical
for these 2 models, All variables are significant at PC. 01.
Average
cover
adjusted R2=O.84.
alone results
The
variable
in a very
MJ
alone
reliable model
accounts for
with an
59% of
the
variation for ATTH.
Table 10. Group I models for high use basin big sagebrush (ATTH).
Best model
In (F) =2.18+, 004 (MJ)+ .027 (AC)+ .004 (HT)
ADJ R2
R2
ROOT
MSE
.88
.89 .
.21
.86
.86
.87
.87
.22
.22
.84
.59
.84
.60
.24
.38
2 Variable models
In (F)=2.24+.032 (AC) + .005 (HT)
In (F)=2.41+.029(AC) + .005 (MJ)
I Variable models
In (F)=2.52+.035 (AC)
In (F)=2.77+.016(MJ)
Equation abbreviations; F-Forage (g), MJ-Major axis (cm), HT-Height
(cm), AC-Average cover (cm), CD-Crown depth (cm), Cl-Circular areal
(cm2), Root MSE=MSEiz2.
41
SUMMARY AND CONCLUSIONS
Difficulties
subspecific
in
level
identifying
are
well
the
known
big
sagebrushes
(Winward
to
and Tisdale
the
1977) .
Nevertheless, differentiation of big sagebrush subspecies is important
in
analyzing
identifying
1987),
site
potential,
site
animal preferences
condition
(Welch et al.
and predicting treatment response.
(Dean et
al. 1981),
1981, Personius et al.
Even though the prediction
equations
for low use Wyoming big
sagebrush and low use mountain big
sagebrush
use the same variables,
the y-intercept and the associated
coefficients
the
in each equation are
subspecific level
separate
regression
quite different. Identification to
is necessary
equations in
to predict forage
production as
this study have proven
to greatly
increase precision.
Heavy
growth
while
previous use can
form (Patton
range
treatment
greatly
site
be considered a
and Hall 1966).
did not affect
(shredding)
did.
They
from
consistent
those of
with this
that affects
Hughes et al. (1987)
found that
prediction equations,
mechanical
concluded
modify plant form will probably
different
treatment
undisturbed
that
treatments- which
require regression equations
vegetation.
premise as separate
Our findings
prediction
are
equations were
developed for each form class with a consequent increase in precision.
The
forage
relationship studied is between
production
and the
independent
big sagebrush annual winter
variables
(big sagebrush
parameters). It is expressed with a natural logarithmic transformation
of
the dependent
variable
in the
regression
equation
for
the 6
42
combinations
of
transformation
subspecies
and
form
classes
studied.
This
was necessary to stabilize nonconstant variance of the
error term as exhibited in the initial scatter plots of residual error
vs
predicted values. The
the
sampling
strict
may
strategy
nonconstant variance
employed:
is a direct result of
stratified
random sampling technique can
random sampling.
If a
be applied, this transformation
be avoided as the error terms would also be randomly distributed.
But a stratified sample is often desirable when there are risks that a
random
sample
interest
with
may not
satisfactorily
because of time
determination
sagebrush
of
represent
the population
or financial constraints.
annual
winter
forage
of
This is the case
production
for
big
taxa as sampling time is limited to the rather short period
between ephemeral leaf drop and winter.
Other
researchers
have
developed
log-log
equations
for
predicting
various components of big sagebrush (Rittenhouse and Sneva
1977,
Deanet
and
determined
specified
biomass
the
al. 1981,
that systematic
nonlinear
model
Hughes
et al,
1987).
bias from log-log
is
an important
Tausch
(1989)
transformations with a
factor
to consider
in
estimation. In this study, linear regression was justified by
fact that
nonlinear relationships
were
not indicated
in the
scatter plots of dependent versus independent variables.
Colinearity
(MJ)",
"minor
legitimately
different
analysis
axis (MN)",
used
indicated
and "elliptical
in the same
final equations
that the variables
equation.
area
(E)" could
This resulted
than those reported
"major axis
not be
in somewhat
by other researchers.
Rittenhouse and Sneva (1977) combined these variables in some of their
43
higher
and
Ffmodels. Dean et
minimum diameters
reported.
These
al. (1981) also
together in
studies
do
not
used measures
of maximum
the best fit equations
mention
colinearity
that they
tests.
If
colinearities among maximum diameter, minimum diameter, and elliptical
areas
were not indicated for their
data, then it would seem that the
overall shape of big sagebrush is more variable from site to site than
previously
determine
thought.
A
test for colinearity
is imperative
then to
the appropriateness of including different variables in the
same regression equation.
Three
based
on the
measure
crown
All
R2
variable groups were considered in this study. Group I was
measure of
of the
the major
minor axis
axis, group
and group 3 was
area, and included various measures
2 was based
based on the
on the
elliptical
of shrub and crown volume.
3 variable groups yield very similar results in terms of adjusted
values
consuming
(Tables
3’ and
4). Group
I
variables
were
less
time
to collect in the field and were used to determine the best
models.
The resulting
0.78
for
for low use
high
average
models have
adjusted
basin big sagebrush
use mountain
(Table 9)
big sagebrush
cover (AC), crown depth (CD),
R2 values
that range
from
to adjusted R2=O.90
(Table 6).
Major axis
(MJ),
height (HT) and circular areal
(Cl), were the most useful shrub characteristics in forage prediction.
These
the
R2
4),
are well defined and easily measured variables. The addition of
variable
values
"average seedhead
of some
subspecies
weight (AS)"
and form
but not all. This variable is
class
improved
the adjusted
combinations
(Table
more time consuming to collect but
44
may
be justified
classes
where
"Average
values
in predicting
sizeable
seedhead
enough
increases
in
(AS)"
may not
weight
to
forage production
justify
for 'low use form
adjusted
R2 values
improve
the increased
field
occur.
the adjusted
time
R2
for
forage
relationship
when
prediction of high use plants.
Uresk
et
predicting
the
fractions
(1977)
al.
of
(in
big
and
the
relationships
weight
(AS)"
forage
production
stalks
(R2=O.52).
Jacobson
for big sagebrush
poorest
of flowering
sagebrush
volume
included
found
phytomass
Murray
satisfactory
(1977)
plants.
alone accounts
Mack
1982)
between
were
with other independent
(1971)
to
and
unable
In
the low
use form
seedhead
variation in
classes.
variables, "average
develop
and canopy
this study, "average
10% of the
other
Murray
to
inflorescence weight
for less than
in each of
compared
But when
seedhead weight
(AS)" becomes a useful measurement for predicting annual winter forage
production. Including this variable increases precision by 2 % for low
use
mountain
sagebrush
big sagebrush
(Table 7),and
(Table 5), 4
% for low use
12 % for low use basin
Wyoming big
big sagebrush (Table
9) .
In
most cases, combining
"major
axis (MJ)" and
"average cover
(AC)" resulted in reliable and accurate prediction equations. Adjusted
R2
9),
values
range from
to 0.86 for
Wyoming
0.77 for
low use basin
high use basin
big sagebrush
big sagebrush
(Table
(Table
10). High use
big sagebrush was the exception. For this subspecies and form
45
class
combination,
R2 value (0.82)
the 2 variable
includes height
model
with the highest
(HT) and average
adjusted
cover (AC)
(Table
8) .
Rittenhouse and Sneva (1977) noted good results in estimating big
sagebrush production
with
the
of crown width
longest measure
study
also found
"forage
(F)"and
I variable.
a strong
the
for Wyoming big
relationship
"major
axis
low use
of big sagebrush.
Adjusted
R2 values
ranged
adjusted
for
=0.48 for
values
big sagebrush
(Table
high use mountain
for
prediction
value of
big
equations
7) to a low
sagebrush
was
big
of forage for other subspecies
a high
variable
Wyoming
predictor
from
this
R2=O.81) between
(Table
Wyoming
However,
for
for
sagebrush. This
(adjusted
(MJ)"
R2=O.88
sagebrush
use
7).
They reported
not a reliable
R2= O .81
value of
(Table 6).
with this
single
low
adjusted R2
Adjusted
variable
R2
were
consistently low for plants in the high use form classes (Tables 6, 8,
10) .
Average cover (AC) was also considered in a single variable model
and
found to
variable
R2
is well
values
(Table
be reliable for
8)
range
all subspecies
defined, consistent
from
to adjusted
for
R2=O.84
classes.This
and easy to collect.
high
use Wyoming
for high
big sagebrush
big
between
shrub cover and browse mass would allow a wildlife manager to
suggests
to
photographs
to
estimate
that a strong
sagebrush
10).
aerial
(1986) speculated
use basin
Adjusted
(Table
use
Weaver
0.61
and form
available
relationship
browse.
Our
study
that line intercept canopy cover (Canfield 1941) may be used
estimate
annual
winter
forage
production
for
big sagebrush
46
subspecies
develop
from
aerial
photographs.
Further
study is required
to
a technique to apply these findings ■. This may involve reading
intercepts directly from a photograph.
It
is important
to remember that
useful approximations.
may
be difficult if not
based
data
on variables
predict
are merely
The true relationships and important variables
impossible to identify.
that can be measured
examined in this study
variables
regression models
Models must then be
easily and
accurately. The
have shown that several
easily measured
can be used in different regression equations to accurately
annual
winter
forage
production
for 3
sagebrush in both low and high use form classes.
subspecies
of big
47
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48
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APPENDICES
56
APPENDIX A
SIMPLE STATISTICS FOR EACH SITE
57
Table 11. Simple statistics for low use mountain big sagebrush (ATVL).
Variable
MJ
MN
HT
AC
CD
HT2
E
CV
SV
Cl
C2
AS
F
Ln(F)
Minimum
Maximum
Range
160.020
182.880
• 121.920
132.08.0
55.880
86.360
97.790
112.395
14.817
26.670
6529.019
7458.050
929 .03 0 '
18788.757
18971.172
182.415
505961.167
503181.161
2780.006
1608697.045
1614257.058
5560.013
25857.343
410.434
26267.777
13620.329
13701.402
81.073
1.816
0.124
1.940 •
358.090
364.090
6.000
4.106
5.897
1.792
22.860
10.160
30.480
14.605
11.853
Variable
Variance
Std Dev
MJ
MN
HT
AC
CD
HT2
E
CV
SV
Cl
C2
AS
F
Ln(F)
1810.185
950.107
42.546
30.824
17.097
23.273
3.416
2057.429
4625.759
106894.923
387439.661
6719.053
3259.448
0.373
78.006
1.013
292.295
541.626
11.672
4233013.843
21397644.711
11426524665
150109490664
45145666.906
10623998.372
0.139
6084.985
1.025
Mean
86.868
54.568
62.907
42.037
17.685
4239.884
4658.806
91567.065
344278.590
7300.996
3059.971
0.375
71.197
3.799
Std Error
7.768
5.628
3.121
4.249
0.624
375.633
844.544
19516.254
70736.481
1226.726
595.091
0.068
14.242
0.185
Variable abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm), HT-Height
(cm), AC-Average cover (cm), CD-Grown depth (cm),
Cl-Circular areal(cm2),
C2-Circular
area2 (cm2), SV-Shrub
vol.
(cm3),
AS-Average
seedhead weight(g),
E-Elliptical
area (cm2),
CV-Crown volume (cm3). N=30.
58
Table 12. Simple statistics for high use mountain big sagebrush (ATVH).
Variable
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
Variable
Minimum
21.590
12.700
25.400
15.875
9.525
215.351
2826.125
7110.896
366.097
126.677
0.000
8.250
2.110
Variance
MJ
803.714
MN
421.238
HT
120.877
AC
209.513
CD
9.568
E
5358858.849
CV
1151734906.400
SV 14880940062.000
12598287.242
Cl
3293926.227
C2
0.023
AS
981.887
F
Ln(F)
0.569
Maximum
135.890
104.140
66.040
68.898
20.743
11114.655
128178.459
451699.561
14503.269
8517.773
0.660
123.280
4.814
Std Dev
28.350
20.524
10.994
14.475
3.093
2314.921
33937.220
121987.459
3549.407
1814.918
0.152
31.335
0.754
Range
114.300
91.440
40.640
53.023
11.218
10899.303
125352.334
444588.665
14137.172
8391.095
0.660
115.030
2.704
Mean
65.024
41.995
42.460
38.989
14.217
2481.648
36267.057
115423.196
3930.961
1704.906
0.193
44.748
3.550
Std Error
5.176
3.747
2.007
2.643
0.565
422.645
6196.060
22271.761
648.030
331.357
0.028
5.721
0.138
Variable abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm) f HT-Height
(cm), AC-Average cover (cm), CD-Crown depth (cm) ,
Cl-Circular areal(cm2),
C2-Circular
area2 (cm2 ) , SV-Shrub
vol.
(cm3),
AS-Average
seedhead weight(g),
E-Elliptical
area (cm2),
CV-Crown volume (cm3) . N=30.
59
Table 13. Simple statistics for low use Wyoming big sagebrush (ATWL).
Variable
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
Variable
Minimum
35.560
25.400
20.320
18.415
9.652
851.271
8803.354
18533.376
993.149
506.709
0.000
6.000
1.792
Variance
MJ
543.545
421.534
MN
HT
180.350
260.953
AC
13.194
CD
5141431.812
E
CV
1511613175.300
SV 20864962072.000
8185633.829
Cl
C2
3838200.390
0.003
AS
465.973
F
0.472
Ln(F)
Maximum
127.000
101.600
73.660
79.692
24.130
10134.173
147294.579
617779.203
12667.717
8107.339
0.231
84.600
4.438
Std Dev
23.314
20.531
13.429
16.154
3.632
'
2267.473
38879.470
144447.091
2861.055
1959.133
0.051
21.586
0.687
Range
91.440
76.200
53.340
61.277
14.478
9282.903
138491.225
599245.827
11674.568
7600.630
0.231
78.600
2.646
Mean
72.856
54.102
50.927
43.487
15.560
3410.234
54774.570
187751.144
4581.533
2618.924
0.085
35.685
3.372
Std Error
4.257
3.748
2.452
2.949
0.663
413.982
7098.388
26372.310
522.355
357.687
0.009
3.941
0.125
Variable abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm), HT-Height
(cm), AC-Average cover (cm), CD-Grown depth (cm), •
Cl-Circular- areal(cm2),
C2-Circular
area2 (cm2), SV-Shrub
vol.
(cm3),
AS-Average
seedhead weight(g),
E-Elliptical
area (cm2),
CV-Crown volume (cm3). N=30.
60
Table 14. Simple statistics for high use Wyoming big sagebrush (ATWH).
Variable
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
Variable
Minimum
22.860
15.240
15.240
13.335
3.810
334.428
1853.338
5096.678
410.434
182.415
0.000
3.550
1.267
Variance
423.017
MJ
MN
188.179
103.441
HT
92.855
AC
4.566
CD
1477730.225
E
99284818.814
CV
SV
3712485688.400
3781880.513
Cl
805581.344
C2
0.018
AS
210.873
F ■
0.430
Ln(F)
Maximum
93.980
66.040
55.880
56.515
15.240
4611.049
44910.342
210817.153
6936.842
3425.351 .
0.600
60.540
4.103
Std Dev
20.567
13.718
10.171
9.636
2.137
1215.619
9964.177
60930.171
1944.706
897.542
0.133
14.521
0.656
Range
71.120
50.800
40.640
43.180
11.430
4276.621
43057.004
205720.475
6526.408
3242.935
0.600
56.990
2.836
Mean
59.605
38.777
36.745
30.586
7.382
1978.866
14786.432
80164.788
3111.529
1323.861
0.129
25.335
3.050
Std Error
3.755
2.505
. 1.857
1.759
0.390
221.941
1819.201
.11124.276
355.053
163.868
0.024
2.651
0.120
Variable abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis •
(cm), HT-Height
(cm), AC-Average cover (cm), CD-Grown depth (cm),
Cl-Circular areal (cm2),
C2-Circular
area2 (cm2),; SV-Shrub
vol.
(cm3),
AS-Average
seedhead weight (g).,
E-Elliptical . -area (cm2),
CV-Crown volume (cm3). N=30.
61
Table 15. Simple statistics for low use basin big sagebrush (ATTL).
Variable
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
Variable
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
Minimum
Maximum
205.740
27.940
25.400
149.860
139.700
43.180
95.885
23.495
35.560
11.853
24215.607
557.380
599699.509
6606.805
3013874.454
24067.648
613.117
33245.155
17638.529
506.709
1.180
0.000 .
290.460
14.100
5.671
2.646
Variance
1485.613
878.612
514.563
396.016
20.095
26130377.940
15661259659
427007895455
50500695.728
15349985.738
0.072
5459.109
0.577
Std Dev
38.544
29.641
22.684
19.900
4.483
5111.788
125144.955
653458.411
7106.384
3917.906
0.269
73.886
0.760
Range
177.800
124.460
96.520
72.390
23.707
23658.228
593092.703
2989806.806
32632.038
17131.820
1.180
276.360
3.025
Mean
102.574
70.019
• 93.895
56.780
22.217
6391.708
146200.804
672828.693
9391.381
4517.646
0.320
101.563
4.359 .
Std Error
7.037
5.412
4.142
3.633
0.818
933.281
22848.238
119304.637
1297.442
715.308
0.049
13.490
0.139
Variable abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm), HT-Height
(cm), AC-Average cover (cm), CD-Crown depth (cm),
Cl-Circular areal (cm2),
C2-Circular
area2 (cm2), SV-Shrub
vol.
(cm3),
AS-Average
seedhead weight(g),
E-Elliptical
area (cm2),
CV-Crown volume (cm3). N=30.
62
Table 16. Simple statistics for high use basin big sagebrush (ATTH).
Variable
Minimum
Maximum
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
25.400
20.320
.45:720
19.050
405.367
10982.741
26770.432
506.709
324.294
0.000
19.180
2.954
144.780
96.520
129.540
84.455
27.093
9964.426
245865.091
1151588.699
16462.964
7316.873
0.890
191.820
5.257
Variable
Variance
Std Dev
860.836
514.941
468.462
239.161
34.654
7514557.734
2551531520.8
87862201339
15903121.177
4441533.149
0.035
1723.393
0.350
29.340
MJ
MN
HT
AC
CD
E
CV
SV
Cl
C2
AS
F
Ln(F)
7.239
22.692
21.644
15.465
5.887
2741.269
50512.687
296415.589
3987.872
2107.495
0.188
41.514
0.591
Range
Mean
119.380
76.200
83.820
65.405
19.854
9559.059
84.159
53.763
234882.350
1124818.267
15956.256
6992.580
0.890
172.640
2.303
79.968
44.429
16.294
3985.348
61383.257
343939.026
6216.302
2661.149
0.147
69.943
4.082
Std Error
5.357
4.143
3.952
2.823
1.075
500.485
9222.313
54117.835
728.082 '
384.774
0.034
7.579
0.108
Variable abbreviations; F-Forage(g), MJ-Major axis(cm), MN-Minor axis
(cm), HT-Height
(cm), AC-Average cover (cm), CD-Grown depth (cm),
Cl-Circular areal (cm2),
C2-Circular
area2 (cm2), SV-Shrub
vol.
(cm3),
AS-Average
seedhead weight(g),
E-Elliptical
area (cm2),
CV-Crown volume (cm3). N=30.
APPENDIX B
SCATTER DIAGRAMS FOR EACH TAXON
64
E vs MN2
MJ vs MN
200
20000 +
+
A
A A
A
MJ
A
AB
BBA AA
B B
AAA A
B
100 +
A
D A
A •
10000 +
A
ACA A
I'
A
I BAA A
I BH
0 +DA
-[_---------- 1
-0
10000
I
0 +
— +
150
-+-
0
50
100
MN2
MN
E vs (MJ X MN)
E vs MJ2
20000
20000 +
B
E
10000 +
I
A
AACA
I
A
I
CB
I CBBBA
0 +BC
—I
---------- 1
---------- h
0
20000
40000
MJ2
---- b
20000
+
I
I
A A
10000 +
A
I
EA
I
A
I DA
I CEB
0 +CB
— I- - - - - - 1- - - - - - 1- - - - - - 1—
0
10000
20000 30000
MJ x MN
Variable abbreviations: MJ-Major axis (cm), MN-Minor axis (cm), E:Elliptical area (cm2). For data points: A=I, B=2, C=3, etc. N=30.
Figure 3. Scatter diagrams for low use mountain big sagebrush (ATVL)
, showing linear relationships among variables as indicated
by colinearity analysis.
65
E vs MN2
MJ vs MN
E
MJ
E
I
150 +
15000 +
100 +
A
A A
I
AA A
I
ABB B A
50 +
BA A A
I A CBA
I A
0 +
- + --— rl100
0
SO
10000 +
I
I
A
A
5000 + A A
I B AB
I GFAB
0 +BB—I
------1
------- 1
------ 1—
— +
150
0
5000
10000 15000
MN
MN2
E vs MJ2
E vs (MJ x MN)
I
E
I
15000 +
15000 +
10000 +
10000 +
I
I
A
A
5000 +
A
A
I
AAAA A
I ADDDBA
0 + CA
—I
---------- 1
---------- h
0
10000
20000
MJ2
5000 +
AA
I
ACA
I IDC
0 +AC
— (------ 1------- 1
------ h-
0
5000
10000 15000
(MJ x MN)
Variable abbreviations: MJ-Major axis (cm), MN-Minor axis (cm), E Elliptical area (cm2), For data points: A=I, B=2, C=3, etc. N=30.
Figure 4. Scatter diagrams for high use mountain big sagebrush (ATVH)
showing linear relationships among variables as indicated
by colinearity analysis.
66
E vs MN2
MJ VS MN
I
MJ
A
10000 +
150 +■
I
E
A
I
100 +
I •
I
50' +
I
I
A
A
A
BA
AA AD
B CA
ADAB
B
AA
B
I
0 +
+ ‘----- +---
I
I
-+------ -+
150
100
I
■ A
A
5000 +
I
CA
I ACB
I CC
I EB
0 +
- - - - j•i—
+------ +10000 15000
5000
,MN2
MN
E vs (MJ x MN)
E vs MJ2
A
10000 +
10000 +
A
E
I
I
A A
B A .
5000 +
I
A
BB
I . AD A
I
C AAA
I AEA
0 +
—I
---------- 1---------- h
0
10000
20000
MJ2
I
BA
5000 +
A
I
CA
I
BD '
I
BD
I G
'
0 +
— I- - - - - 1- - - - - - h - - - - - H —
0
5000
10000 15000
(MJ x MN)
Variable abbreviations: MJ-Major axis (cm), MN-Minor axis (cm), E Elliptical area (cm2), For data points: A=I, B=2, C=3, etc. N=30.
Figure 5. Scatter diagrams for low use Wyoming big sagebrush (ATWL)
showing linear relationships among variables as indicated
by colinearity analysis.
67
E vs MN2
MJ vs MN
MJ
I
100 +
6000 +
I
I
I
I
A
A A
A AAA
I
B ''A
I
C A A
2000 +
BCAA
I
I BD B
0 +
-+----- -+—
-+—
0
2000
4000
4000 +
LO
CM
A
A B
75 +
AA
I
A A
I
A AA
50 +
C
C
I
A
A
I
+ ■
A A
-+— ---- +-----O
25
50
75
/MN2
MN
E vs (MJ x MN)
E vs MJ2
E
E
I
6000
I
6000 +
6000 +
I
I
I
A
A
)
AA
A
I
AA A
I .
A A A A A
2000 + ■
I
BAB A A
I ABD A
0 +
J---- +0
5000
10000
4000 +
MJ2
4000 +
A A ,
I
BB
I
AAA
2000 +
AD
I
C D
I AACC
• 0 +
—I
------1
------- H------ 1
—
0
2000
4000
6000
(MJ x MN)
Variable abbreviations: MJ-Major axis (cm), MN-Minor axis (cm), E Elliptical area (cm2), For data points: A=I, B=2, C=3, etc. N=3.0.
Figure 6. Scatter diagrams for high use Wyoming big sagebrush (ATWH)
showing linear relationships among variables as indicated
by colinearity analysis.
68
E vs MN2
MJ vs MN
200
+
I
MJ
E
I
I
30000 +
I
I
A
A AAAA
A
A
A AA BA
C CB
BAA
I
100 +
I
20000
I
A
+
-+------ +----- +------- + ■
0
50
100
150
I
A AAB
10000 +
AA
I A BAA ■
I GFC
0 +A
-H------ 1*—----- 1
------ 1
—
0
10000 20000 30000
MN2
MN
E vs (MJ x MN)
E vs MJ2
E
A
+
'
I
30000 ,+ •
30000 +
20000
20000
+
+
I
I
I
ABA A
10000 +
AA
I
BAB
I CGE A
0 +A
— I- - - - - - 1- - - - - - 1- - - - - - 1—
0
20000
MJ2
40000 60000
I
BBA
10000 +
AA
I
CAA
I AJE ■
0 +A
-H-------- .— I:------ -----h
0
20000
40000
(MJ x MN)
Variable abbreviations: MJ-Major axis (cm), MN-Minor axis (cm), E Elliptical area (cm2), For data points: A=I, B=2, C=3, etc. N=30.
Figure 7. Scatter diagrams for low use basin big sagebrush (ATTL)
showing linear relationships among variables as indicated
by colinearity analysis.
69
E vs MN2
MJ vs MN
10000 +
150 +
AA
MJ
100 +
A
A
AAA
50 +
A
AA A
A CAAA B
A AAA
A
A
A
BA B
BCAA
AAA '
CAA
+ A
-— +—
-+
5000
0
5000 +
-- +
100
— +50
0
A
A
0 +
-+ -
E vs (MJ x MN)
E vs MJ2
I
5000 +
0 +A
0
---- +
10000
MN2
MN
A
10000 +
A
A
AB
10000 +
B
BA
AA
A A A
A
5000 +
I
ACA
I
AF
I
AB
I AD
0 + A
A
CA A
ADAA
AAA
-- 1
------ 1
------ 1
—
10000 20000 30000
MJ2
- + -------- + —
0
5000
-- 1
------ 1—
10000 15000
(MJ x MN)
Variable abbreviations: MJ-Major axis (cm), MN-Minor axis (cm), E Elliptical area (cm2), For data points: A=I, B=2, C=3, etc. N=30.
Figure 8. Scatter diagrams for high use basin big sagebrush (ATTH)
showing linear relationships among variables as indicated
by colinearity analysis.
APPENDIX C
PLANT SPECIES ON THE STUDY AREA
71
Table 17. Plant species identified on the Gardiner study area1.
Graminoids
Agropyron cristatum *
A. smithii *
A. spicatum *
A. subsecundum *
A. trachycaulum *
Agrdstis exarata *
A. stolonifera *
Bouteloua gracilis *
Bromus anomalus
B . inermis *
B . japonicus *
B . marginatus *
B . tectorum *
Calamagrostis canadensis
C. rubescens *
Carex festivella
C. filifolia *
C. geyeri *
Danthonia intermedia *
Distichlis stricta *
Elymus cinereus *
Festuca idahoensis *
Hordeum jubatum *
Juncus balticus *'
Koeleria pyramidata *
Loliuih perenne
Melica spectabilis *
Oryzopsis hymenoides *
Phleum pratense *
Poa ampIa
P . cusickii
P . fendleriana
P . junctifolia
P . pratensis *
P . sandbergii *
Sitanion hystrix *
Stipa columbiana *
S . comata'*
Trisetum spicatum *
Forbs, Ferns, Mosses, Vines, and Cactus ■
Achillia millifolium *
Actaea rubra
Agroseris glauca *
Allium brevistylum
A. textile *
Antennaria dimorpha
A. rosea *
A. umbrinella
Arabis holboellii
.Arenaria congesta *
Arnica cordifolia *
Artemisia dracunculus
Aster canescens *
A. conspicuus
A. scopulorum
Astragalus cibarius
A. gilviflorus
A. miser
A. purshii
Balsamorhiza sagittata *
Campanula uniflora
Castelleja angustifolia'
Cerastium arvense *
Cirsium arvense *
C. foliosum
Clematis columbiana
C. hirsutissima
Collinsia parviflora
Camandra pallida
Crepis acuminata
Delphinium bicolor *
D . occidentals *
Dodecatheon conjugans
Draba paysonii
Epilobium angustifolium
Equisetum arvense *
Erigeron compositus
E . corymbosus
E . glabellus
E . gracilis
'E . ochroleucus
E. pumilus
72
Table 17. (Continued).
Eriogonum heracleoides *
E . ovalifolium
E . umbellatum
Erysimum asperum
Fragaria yesca
F . virginiana
Frasera speciosa
Frittilaria atropurpurea
F . pudica
Geranium richardsonii *
G. viscosissium *
Geum triflorum *
Grindelia squarrosa *
Haplopappus acaulis
Helanthella uniflora
Heracleum lanatum
Heterotheca villosa *
Hieracium cynoglossoides
Lathyrus bijugatus
Lesquerella alpina
Lewisia rediviva
Linaria dalmatica *
Linium lewisii
Lithospermin incisum
L . ruderale
Lomatium macrocarpum
L. triternatum
Lupinus sericeus *
Medicago sativa *
Meliotus officinalis
Mentha arvensis *
Mentzelia laevicaulis
Mertensia ciliata
Monotropa hypopithys
Myosotis alpestris
Oenothera oaespitosa
Opuntia polycantha *
Oxytropis sericea *
Paronchia sessiliflora
Penstemon cyaneus
Phacelia sericea
Phlox oaespitosa *
P . hoodii *
Plantago patagonica *
Polygonum bistoriodes
Potentillia glandulosa
P . gracillis
Peteridium aquilinum
Sedum stenopetalum *
Selagimella densa *
Senecio canus
S . serra
Sisymbrium altissium
Smilacina racemosa
S . stellata
Solidago canadensis *
Sphaeralcea coccinea *
Taraxacon officinale *
Thalictrum occidentale
Townsendia parryi
Tragopogon dubius
Trifolium haydenii
Viola adunca
V. purpurea
Zigadenus paniculatus *
73
Table 17. (Continued).
Shrubs, Half-Shrubs, and Trees
Abies lasiocarpa *
Acer glabrum *
Alnus tenuifolia *
Amelanchier alnifolia *
Arctostaphylos uva-ursi *
Artemisia frigida *
A. nova *
A.
tridentatasubsp. tridentata *
A.
tridentatasubsp. vaseyana *
A.
tridentatasubsp. wyomingensis *
Berberis repens *
Betula occidentalis *
Ceanothus velutinus *
Ceratoides lanata *
Chrysothamnus nauseosus *
C. viscidiflorus *
Cornus stolonifera *
Grayia spinosa *
Juniperus horizontalis *
J. scopulorum *
Leptodactylon pungens *
Physocarpus malvaceus *
Picea englmannii *
Pinus albicus
1 Plants identified by McNeal (1984).
* Plants observed in 1989.
I
P. contorta *
P . flexilis *
Populus angustifolia *
P . tremulpides *
Prunus virginiana *
Pseudotsuga menziesii *
Rhus trilobata *
Ribes cereum *
R. setosum *
R. viscossissimum *
Rosa woodsii *
Rubus idaeus
R. parviflorus
Salix spp. *
Sambucus melanocarpa *
Sarcobatus vermiculatus*
Shepherdia canadensis
Symphoricarpos aIbus *
S . occidentalis *
Tetradymia canescens *
Vaccinium membranaceum
V. scoparium
Xanthocephalum sarothrae