(— ~\ (— ~
i,
. .
fa J
Pecos Wilderness .
Summer Habitat % Pecos Wilderness
Winter Habitat
\ y

awa
/

( ~

°

a #
F
Santa Fe Bald:
Santa Fe Baldy . y
fs Winter Habitat
Summer Habitat
\ J /
"a >) ‘a ' ~)
ge
Wheeler Peak
Wheeler Peak Winter Habitat «|,
Ne Summer Habitat Ne

Figure 6. Potential summer and winter habitat for Rocky Mountain bighorn sheep in three alpine study areas in New
Mexico, Escape terrain patches are black; open slopes 20-59% are hatched. Study areas are not shown to scale.

A



Appendix F

“seBBJOAOd UseMyoq JULIA Plog UT ale SpUBUIIOD S[H “SeUTTINO pjoq UT ere AWIQeyINs JUeLMS
Sunjeynoyeo ur pasn sese1srod ‘Jenqey deoys uoreasjs-Moy Jus SuLmseour ioy sdoys oq} JO WesEIP MOLT “wT xipuaddy

ot Z'E pasaNG,
suommyy Areuntrg,

suai Cuppy

ALMODSLNOD MVHAEL
FAVOST INRRIAD}

maw

(ueaBorg xepuy Ayn3ya0D)

soqpyeg
urewoy adeosy

N cennane) HVA pensayedd

WHGREL POL

jouuajiog

AR Boyan) Hiv,

sjorduyy (ROL

A

LOasaa.LNi
syoedury jo vary

Tn aNOZ |

SADAING PIPL] somos
“‘puog oBury 00g

SKoams plort ‘somos
sjordwyy umunpy



mtn.CETCLP - min.CET after cells have been
clumped into patches.

mtn.CETLOC - locations of centers of escape ter-
rain patches in min.CETCLP.

Low-Elevation Habitat Only

mtn.CWATPRIM - Primary water sources not af-
fected by human impacts. Created by intersect-
ing mtn.PWATPRIM with min.CTH.
mtn.CETBUF - mtn.CET with a 200 m (220 yd)
buffer around escape terrain.

mitn.CWATPRIMB - min.CWATPRIM buffered to
3.2 km (2 mi).

mtn.CWATAVAIL - mtn.CWATPRIMB inter-
sected with mtn.CTH to determine how much to-
tal habitat is <3.2. km (2 mi) from piimary water
sources.

Alpine Habitat Onl

mtn.WINCTH - Total Habitat available during
winter that is not affected by human impacts.
Created by intersecting min.WR with min.CTH.
min.WINCET - Escape Terrain available during
winter that is not affected by human impacts. Cre-
ated by intersecting min.WR with min.CET.
min.WINCETCLP - min. WINCET after cells of
escape terrain have been clumped into patches.
mtn.WINCETLOC - locations of the centers of
escape terrain patches in mtn. WINCETCLP.



Table 1. GIS Measurements for Analyzing Alpine Bighorn Sheep Habitat

A.

Potential Suitability (All habitat that could
be used by bighorn sheep)

1. Potential Total Habitat (summer): AU
patches <5 km (3 mi) apart that have canopy
cover <25% and slopes >20%.

2. Potential Escape Terrain (summer): All
patches that have canopy cover <25% and
slopes >60%.

3. Potential Escape Terrain Contiguity
(summer):. Measure of size and proximity
of escape terrain patches.

4. Potential Total Habitat (winter): Snow-
free patches with canopy cover <25% and
slopes >60%.

5. Potential Escape Terrain (winter):
Snow-free patches with canopy cover <25%
and slopes >60%.

6. Potential Escape Terrain Contiguity
(winter): Measure of size and proximity of
snow-free patches of escape terrain.

B.

Current Suitability (Habitat not affected
by impacts)

1. Current Total Habitat (summer): Po-
tential total Habitat (summer) - impacted
areas.

2. Current Escape Terrain (summer): Po-
tential escape terrain (summer) - impacted
areas.

3. Current Escape Terrain Contiguity (sum-
mer): Measure of size and proximity of
patches of escape terrain not affected by
impacts.

4. Current Winter Habitat: Potential total
habitat - impacted areas.

5. Current Winter Escape Terrain: Poten-
tial escape terrain - impacted areas.

6. Current Escape Terrain Contiguity:
Measure of size and proximity of snow-free
patches of escape terrain not affected by
impacts.

Table 2. GIS Measurements for Analyzing Low-Elevation Bighorn Sheep Habitat

A.

Potential Suitability (All habitat that could
be used by bighorn sheep)

1. Potential Total Habitat: All patches <5
km (3 mi) apart with canopy cover <25%
and slopes >20%.

2. Potential Escape Terrain: All patches
of total habitat with slopes >60%.

3. Potential Escape Terrain Contiguity:
Measure of size and proximity of escape
terrain patches.

4. Potential Water Availability: Potential
Total Habitat <3.2 km (2 mi) from peren-
nial water sources that are <200 m (220 yds)
from escape terrain.

A

B.

Current Suitability (Habitat not affected
by impacts)

1. Current Total Habitat: Potential Total
Habitat - impacted areas.

2. Current Escape Terrain: Potential Es-
cape Terrain - impacted areas.

3. Current Escape Terrain Contiguity:
Measure of size and proximity of escape
terrain patches not affected by human im-
pacts.

4. Current Water Availability: Current to-
tal habitat <3.2 km (2 mi) from perennial
water sources that are <200 m (220 yds)
from escape terrain and not within impacted
areas.



Table 4. Carrying capacity calculations for five alpine Rocky Mountain bighorn sheep study areas in New Mexico. The
Habitat Suitability Score (0.825) and midpoint value (250) for carrying capacity of the Pecos Wilderness population are

the values for the occupied range in the calculations.

HSS,_ HSS;
where: CC, Oly
HSS, = the Habitat Suitability Score of the occupied range
CC, = the carrying capacity (number of animals) of the occupied range
HSS; = the Habitat Suitability Scire of study areai
CC, = the carrying capacity (number of animals) calculated for study area i
Study Areas Calculations Carrying Capacity
Wheeler Peak (250 x 0.87) /0.825 264 bighom
Pecos Wilderness (250 x 0.825)/0.825 250 bighorn
Latir Wilderness (250 x 0.47) /0.825. 142 bighorn
Culebra Range (250 x 0.225)/0,825 , 68-bighorn
Santa Fe Baldy (250 x 0.135)/0.825 40 bighorn

Low-Elevation Habitat

Thirteen desert bighom and six low-elevation
Rocky Mountain bighorn study areas in New
Mexico were evaluated with this methodology.
Habitat components of five study areas (hereaf-
ter referred to as historic ranges) that supported.
indigenous desert bighorn populations at least
until 1900 (Dunn 1994) were compared to com-
ponents of eight other study areas that didnot his-
torically support desert bighorn (hereafter referred
to as non-historic ranges).

Historic ranges contained an average of 204
km? (79 mi?) (range = 72-421 km2) of potential
total habitat and 20 km? (7.7 mi?) (range = 5.4-33
km2) of escape terrain, whereas non-historic
ranges contained an average of 66 km? (25.5 mi)
(range = 42-127 km?) of potential total habitat
and 5 km? (2 mi?) (range = 2-7.3 km2) of escape
terrain (Figure 8). Escape terrain in historic ranges
was more contiguous (X = 61.4; range = 43-80)
than in non-historic ranges (X = 38.8; range 14-
95). Water also was more available in historic

ranges than non-historic ranges. Twenty-six of
33 springs <200 m (220 yds) from escape terrain
were in historic ranges. Six non-historic ranges
had no water sources <200 m (220 yds) from es-
cape terrain. On average, 30.4% (range = 0-63%)
of total habitat in historic ranges was <3.2 km
(2 mi) from perennial water, whereas only 5.5%
(range = 0-16%) of total habitat in non-historic
tanges was <3.2 km (2 mi) from perennial water.

Impacts that affected desert bighorn study ar-
eas included poor range conditions, military ac-
tivities, mines, recteation sites, primitive roads,
trails, and fences (Figure 9): For all 13 study
areas, an average of 32.4% (range = 0-38%) of
total habitat and 20.2% (range = 0-28%) of es-
cape terrain were impacted. Contiguity did not
change for 6 of the 13 study areas. Water was
available to 20% less total habitat because of
impacts. Impacts did not affect water availabil-
ity in two study areas, but in two other study
areas all perennial water <200 m (220 yds) from
escape terrain were affected by human impacts.

rr



Appendix G
BASIC Program for Calculating Contiguity Indices

This program will calculate an escape terrain contiguity index from a file (see line 200) of northing

(“Y”) and easting (““X”) UTM coordinates (both in meters) and sizes (also in meters) of escape terrain
patches. ,

10 ‘ Program CONTIGUITY

20 ‘ By: Bruce T. Milne

30 ‘ University of New Mexico

40 ‘ Albuquerque, NM 87131

50 ‘ 4 Feb 93

60 ‘ (505)277-5356 bmilne@ golondrinas.unm.edu
70°

80 ‘ Modifications by: Gregory A. Baird

90 ‘ New Mexico Dept. of Game and Fish

100 ‘ Santa Fe, NM 87504

110 ‘ 20 May 93

120 ‘ (505)827-9927

130‘

140 DIM XY(110,3),T(110),D(110,2),N(108,2),G(108,2),S(108)
150 ‘ d(a,b) d(a,1) = neighbor patch id number

160 ‘ d(a,2) = distance to neighbor from target
170‘ g0 x,y graphing coordinates of patches
180‘

190 FILES”*.dat
200 INPUT”Enter name of data file to read “,Z$
210 OPEN Z$ FOR INPUT AS#1
220 ‘
230 INPUT#1,X,Y,MASS
- 240 C=C+1 ‘count patches
250 XY(C,1)=X ‘put x coordinate in array
260 XY(C,2)=Y ‘put y coordinate in array
270 XY(C,3)=MASS ‘put mass in array
280 IFEOF(1)=0 GOTO 230 ‘test for end of file

290 *

300 CLS:SCREEN 2

310°

320 PRINT” There are “;C;” patches.”
330 PRINT

340 PRINT “Enter the radius distance to be used “
350 INPUT “in forming the index (in meters): “,RADDIST



Acknowledgements

The New Mexico State Office of the Bureau of Land Management (BLM) furnished training in and use
of their GIS; B. T. Milne of the University of New Mexico wrote the program for calculating contiguity
indices; J. A. Bailey and M. Weisenberger reviewed the original manuscript; and R. Boyd, K. Rohling,
and H. Weiss of BLM’s National Applied Resource Sciences Center were instrumental in the final
production of this technical note. This study was a contribution of Federal Aid in Wildlife Restoration
Project W-127-R. Partial funding also was provided by a BLM Challenge Grant.



SLOPE - A neighborhood analysis command in
which a new coverage of percent slope is created
from a Digital Elevation Model. Within a 3x3 cell
window, elevation of the central, or target cell, is
compared to the elevation of and distance to sur-
rounding cells. The maximum slope determined
in the window is assigned to the target cell. Op-
tions: MATRIX specifies window size (in num-
ber of cells per side). MAXIMUM specifies that
the maximum slope calculated is to be assigned
to the target cell, MASK 0 specifies if any cell
in the window has an elevation of 0, the target
cell will be assigned a slope of 0.

Command Line: SLOPE (old coverage)
MAXIMUM MATRIX (#) MASK 0 FOR (new
coverage)

Example: SLOPE min.BASE MAXIMUM
MATRIX 3 MASK 0 FOR mtn.SLOPE

ZONE (buffer) - A raster distance analysis com-
mand in which a buffer or zone of user-specified
distance is created around any geographic data
identified .in the coverage.

Command Line: ZONE (old coverage) TO
(distance in meters) FOR (new coverage).

Example: ZONE min.WATPRIM TO
3200 FOR min. WATZONE



During winter, only 18% (range = 10-25%)
of potential total habitat and 16% (range = 0-
24%) of potential escape terrain occurred on
snow-free slopes (Figure 6). Escape terrain con-
tiguity averaged 54.4 (range = 20.5-88.2 ) in
summer, but only 15.3 (range = 0-30.3) in win-
ter, indicating that winter habitat was much more
fragmented.

Summer habitat was impacted by campsites
and trails in all areas (Figure 7) except the Culebra
Range, which was closed to public access . On
average, 22% of total habitat (range = 0-33.8%)
and 14.3% (range = 0-26.7%) of escape terrain

were impacted. Little decrease in escape terrain
contiguity occurred except on Wheeler Peak
(Figure 5). Escape terrain contiguity in the Pecos
Wildemess study area actually increased because
some small scattered patches were eliminated by
impacts. Winter habitat was not affected by im-
pacts.

Wheeler Peak and Pecos Wilderness had the
highest Habitat Suitability Scores because they
contained the most total habitat and escape ter-
rain (Table 3). Wheeler Peak ranked higher than
the Pecos Wildemess because it had more escape
terrain and higher escape terrain contiguity

Table 3. Calculations used to determine potential and current suitability for five alpine Rocky Mountain bighorn sheep

study areas in New Mexico.

STH. SET. SETC WTH. WET. WETC
(< ty, 1)4 i A FI i
gga STBaay” SETeax "BETO us” “ WTEaax ‘VET saz" ‘ VETCaa:
where:
SS = suitability score (potential or current)
STH = amount (km?) of total habitat during summer
SET = amount (km?) of escape terrain during summer

SETC = escape terrain contiguity during summer

WTH = amount (km?) of total habitat during winter

WET = amount (km?) of escape terrain during winter
WETC= escape terrain contiguity during winter.

i = value of the component for the study area

max = maximum value of that component for all study areas.

POTENTIAL SUITABILITY
Study Areas Calculations Scores
Wheeler Peak (52.5/64.4)+(14.3/14,3)-+(76.8/88.2)+(9.5/16,4)+(3.2/3.2)-+(30,3/30.3) 0.9
Pecos Wilderness (CALA (11 8114. 3)4(66 2088 2416 46 44.203 204(18.3/90.3) 0.81
Latir Wilderness (18/64:4)+(3.7/14.3)+(1 38. 2/88.204¢ 3.3/16.4)-+(0.9/3.2)+(24.4/30.3) 0.50
Culebra Range (203464. 4)247 4/14 396420, 7188. 20045 6116 40410 204(0090.2) 0.21
‘ 16.4)+(.22/3.2)+(3.5/30.3) 0.15

Santa Fe Baldy 14.8/64.4)+(1.5/14.3)+(20.5/88.2
: 6

FA



Discussion

Prior to the advent of GIS, bighorn habitat was
evaluated using one of two methods: (1) the con-
ceptual method in which habitat was considered
suitable if it visually appeared to contain the
needed components (see Cooperrider 1986:766),
or (2) a numerical scoring system (Hansen 1980,
Armentrout and Brigham 1986). Hansen’s (1980)
method has been the most commonly used for
numerical scoring. With this method, each square
mile of a study area was numerically scored for
quality (based on written descriptions) of each of
seven habitat parameters. Based on the total score,
each section was categorized as excellent, good,
fair, or poor habitat. Thus, a qualitative rating
was obtained, not a true measure of habitat.

Two problems can reduce the accuracy of habi-
tat analyses when numerical scoring systems are
used, First, sample units large enough to easily
rate an entire mountain range may be too coarse-
grained. Few mountainous areas contain homo-
geneous habitat throughout an entire square mile,
the sample unit used by Hansen (1980:321). This
problem can be rectified by having the entire sec-
tion represented by the dominant habitat type (i..,
if 400 acres are steep and 240 acres ate rolling
hills, the section is rated as steep) or by selecting
a compromise rating (i.e., moderately steep).
Either strategy could result in substantial error
over an entire study area.

Second, written descriptions upon which
scores are based can be subjective and will com-
promise repeatability. Ratings of the same habi-
tat type may vary as the attentiveness or the per-
ceptions of evaluators vary. For example, areas
rated as containing high human use by one evalu-
ator may be rated as having medium human use
by another. Subjectivity may be reduced and re-
peatability increased with more exact written de-
scriptions, but not as well as when habitat is actu-
ally measured.

Use of GIS and a landscape approach is an
effective and efficient means of evaluating and

comparing many areas of bighorn habitat be-
cause it provides (1) consistent measurements
across all study areas, so bias towards any one
study area is reduced, (2) measurement of the
amount and patchiness of bighorn habitat, (3) a
final score and ranking derived directly from the
values of the habitat components, (4) evaluation
of large areas with much less effort than if they
were entirely analyzed in the field, (5) both po-
tential and current suitability, so benefits of miti-
gation can be more accurately analyzed, and (6)
a graphic display of the amount and distribution
of bighorn habitat. High scores of historic al-
pine bighorn ranges and differences found be-
tween historic and non-historic desert bighorn
tanges in New Mexico are evidence that this
method is effective for determining where big-
horn can persist.

However, carrying capacities derived from
the formula in this method are crude estimates
and their use is best limited to separating areas
that can support >100 bighorn from those that
cannot. A multitude of factors, such as popula-
tion structure, food habits, and patterns of habi-
tat use, affect how many animals a range can
support (Boyd et al. 1986:524). Precise estimates
of carrying capacity are difficult, if not impos-
sible, to achieve (Strickland et al., 1994:451).

Measurement of impacts provided a quanti-
tative database from which mitigation alterna-
tives could be analyzed, but it was the most sub-
jective part of this methodology. Written de-
scriptions were used for identifying range con-

’ dition and road type, buffers were placed around

some impacts, and temporal variation of impacts
was not measured. Errors potentially introduced
by written descriptions for range condition and
road type should be minimal because of the con-
servative criteria used. Also, poor range condi-
tions generally represented a small percentage
of total impacts because of infrequent use of
steep slopes by cattle (Dodd and Brady 1986).

wr



Pecos Wilderness
Potential Summer Habitat

S /
(~ g 2 ( gg )

Pecos Wilderness
Current Summer Habitat

Santa Fe Baldy Santa Fe Baldy
q Potential Summer Habitat ) e Current Summer Habitat

a = > (—

/

Wheeler Peak on Wheeler Peak
\_ Potent Summer Habitat J N Current Summer Habitat

Figure 7. The effect of impacts on summer habitat in three Rocky Mountain bighorn sheep study areas in New Mexico.
Potential habitat includes the total area that could be inhabited by bighorn. Current habitat includes only those areas not
affected by impacts. Escape terrain patches are black; open slopes 20-59% are hatched. Study areas are not shown to scale.

FA



: Appendix B
Coverage Names Used in GIS Analysis of Bighorn Habitat.

Data Base Preparation

mtn.DEMS - DEM that has been projected to UIM
projection.

min.OPEN - areas with <25% canopy cover.
(Note: mtn. OPEN has a value of 1 for all cells
that are within areas where canopy cover is
<25%.)

min.BASE - mtn.DEM intersected with
min.OPEN.

min. SLOPE - initial slope coverage generated
from mtm.BASE using neighborhood analysis.

‘Potential Suitability

min.PTH - Potential Total Habitat. Slopes >20%
extracted from min.SLOPE.

min.PET - Potential Escape Terrain. Slopes >60%
extracted from min.PTH.

mtn.PETCLP - min.PET after cells have been
clumped into patches.

mtn.PETLOC - locations of center of escape ter-
rain patches in mtn.PETCLP.

Low Elevation Habitat Only

mtin.PERWAT - All perennial water sources.
mtn.PETBUEF - mtn.PET with a 200 m (220 yd)
buffer around escape terrain.

mtn.PWATPRIM - Primary water sources, i.e.
perennial water <200m (220 yds) from escape
terrain. Created by intersecting min. PERWAT
with mtn.PETBUF.

mtn. PWATPRIMB - mtn.PWATPRIM buffered to
3.2km 2 mi).

mtn. PWATAVAIL - mtn. WATPRIMB intersected
with min.PTH to determine how much total habi-
tat is <3.2 km (2 mi) from primary water sources.

Alpine Habitat Only

mtn.SFS - snow-free slopes available as alpine
winter range.

mtn, WINPTH - Potential Total Habitat available
during winter. Created by intersecting min.SFS
with min.PTH. .
mtn.WINPET - Potential Escape Terrain available
during winter. Created by intersecting min.SFS
with min.PET.

mtn. WINPETCLP - mtn. WINPET after cells have
been clumped into patches.

mtn, WINPETLOC - locations of centers of es-
cape terrain patches in min. WINPETLOC.

Current Suitability

mtn. IMPACTS - all impacts with their appropri-
ate buffers merged into one coverage. Data may
be stored separately in the following coverages:

mtn.PRC - coverage of areas in poor range
condition

min.MINES - mines with 500 m buffers.
mtn.RD - coverage of improved and primi-
tive roads with 200 and 500 m buffers,
respectively.

min.REC - recreation sites (picnic and
campgrounds, scenic viewpoints, Visitor
centers) with 500 m buffers.

mtn.TRL - biking trails with 200 m buffers.
mtn.MILZONE - area affected by military
operations.

min.CTH - Current Total Habitat. Created by
subtracting min. IMPACTS from min.PTH.
mtn.CET - Current Escape Terrain. Created by
subtracting min.IMPACTS from mtn.PET.

9 min - location to insert study area acronym, i.e., WP.PTH = Wheeler Peak Potential Total Habitat



max = maximum value of that component
for all study areas.

For low-elevation study areas, potential (us-
ing all habitat) and current (using only unimpacted
habitat) suitability scores were calculated with the
formula:

TH, EM y,, BTC, \ Way
5S= ( TE hax d( Ear ( ETC hax M+( Waray )
4
where:
SS = suitability score (potential or current)
TH = amount (km2) of total habitat
ET = amount (kmz2) of escape terrain
ETC = escape terrain contiguity
WA = water availability (km2)
i = value for the study area
max = maximum value of that component for
all study areas.

The rank of each study area was based on the
Habitat Suitability Score, the average of its po-
tential and current suitability scores. Study areas

<15km (9 mi) from domestic sheep were included
in the rankings but were not considered for trans-
plants until the domestic sheep were permanently
removed.

Calculating Carrying Capacity

Carrying capacity, the number of animals that
arange can support on a sustained basis (Boyd et '
al. 1986:524), was calculated using the ratio of
the Habitat Suitability Score to the Population Size
of an occupied range containing a population at
carrying capacity:

HSS, HSS,
CC, CC;

where:

HSS, = the Habitat Suitability Score of the
occupied range
= the carrying capacity (aumber of
animals) of the occupied range
HSS; = the Habitat Suitability Score of study
area i
CC; = the carrying capacity (number of
animals) calculated for study area i

CC

Results

Alpine Habitat

In New Mexico, alpine habitat occurs only in
small, widely-scattered patches above 3500 m
(11,500 ft) elevation. Because of a relatively mild
climate, forests grow to the tops of most moun-
tains, so alpine habitat is much more limited than
in the more northerly latitudes (Arno and
Hammerly 1984:16). Five alpine study areas were
evaluated with this methodology.

Most bighorn habitat was found in the Pecos
and Wheeler Peak Wilderness Areas, where big-

horn persisted until the late 1800s and where the
only alpine populations in New Mexico currently
reside. Bighorn were transplanted to the Pecos
Wilderness in 1964 and to Wheeler Peak in 1993.
The Pecos Wilderness has approximately 320 big-
horn; carrying capacity was estimated to be 175-
330 (Smith and Johnson 1979:102). Wheeler Peak
has 60 bighorn and the population is increasing.
These two areas contain 65% of the total habitat
and 77% of the escape terrain of the five alpine
areas (Figures 5 and 6).

FA



San Francisco River

if

® — bighorn habitat
“ “n:

I >60% slope

Figure 1. The importance of open habitat. If bighorn only required steep slopes for security, the forested slopes of the
Mogollon Mountains, of which Sheridan Mountain is a part, would be excellent bighorn habitat. In 1964, rams from the
‘Turkey Creek population of southwestern New Mexico traveled 30 km (18.5 mi) north to Sheridan Mountain. Eighteen
more bighorn subsequently were transplanted to Sheridan Mountain. Within a year, the entire population moved to habitat
along the San Francisco River (outlined area) which had less steep terrain, but was covered with open desert grass and shrub
vegetation types. Currently, the San Francisco River population contains approximately 100 animals.

related to sizes of escape terrain patches and in-
versely related to distances between escape ter-
rain patches (Berger 1991, Bleich 1993:87).

Seasonal Ranges

During winter, habitat use by alpine popula-
tions is restricted by deep snows. Many alpine
populations migrate to low elevation winter ranges
(Geist 1971:69, Festa-Bianchet 1986), but in New
Mexico, bighom remain on windswept, snow-free
slopes within alpine habitat (Smith and Johnson
1979:40).

Unlike alpine populations, low-elevation big-
horn populations generally do not have distinct
seasonal ranges (McCarty and Bailey 1994:11).

However, low-elevation populations may restrict
their ranges to areas near water during hot, dry
weather when water requirements are high. Dur-
ing this period, ewes with lambs generally remain
<3.2 km (2 mi) from water sources that are in
open habitat and close to escape terrain (Leslie
1978, Leslie and Douglas 1979:46, Sandoval
1979:192, Bleich 1993:49).

Population Size

The survival of a population is never guaranteed
no matter how large it is (Thomas 1990). The
minimum size for a population to be considered
viable and self-sustaining for more than a few
decades generally is about 100, although several

FA



Strickland, M. D., H. J. Harju, K. R.
McCaffery, H. W. Miller, L. M. Smith,
and R. J. Stoll. 1994. Harvest management.
Pages 445-473 in T. A. Bookhout, ed.
Research and management techniques
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Wildlife Society, Bethesda, MD. 740pp.

The Technical Staff. 1990. Guidelines for man-
agement of domestic sheep in the vicinity
of desert bighorn habitat. Desert Bighorn
Counc. Trans. 34:33-35.

Thomas, C.D. 1990. What do real population dy-
namics tell us about minimum viable
population sizes? Cons. Biol. 4:324-327.

Tilton, M. E., and E, E. Willard. 1982. Win-
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Todd, J. W. 1975. Foods of Rocky Moun-
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U. S. Geological Survey. 1987. Digital eleva-
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VanDyke, W.A., A. Sands, J. Yoakum, A. Polenz,
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37 pp.

Webster, D. 1988. Introduction to geographic
information systems and MOSS. TGS
Technology, Inc. Bur. of Land Manage.
Operations. Lakewood, CO. 245pp.

Wehausen, J.D. 1980. Sierra Nevada bighorn
sheep: history and population ecology.
Ph.D. Thesis, Univ. Michigan, Ann Arbor.
240pp.

. 1983. White Mountain bighom sheep: an
analysis of current knowledge and manage-
ment alternatives. Inyo Natl.Forest Admin.
Rept. Contract No. 53-9JC9-0-32, Bishop,
CA. 93pp.

. 1996. Rapid extinction of mountain sheep
populations revisited. J. Wildl. Manage. In
review.

Weisenberger, M.E., P.R. Krausman, M.C.
Wallace, D.W. De Young, and O.E.
Maughan. 1996. Effects of simulated jet air-
craft noise on heart rate and behavior of
desert ungulates. J. Wildl. Manage.
60:52-61.

Welsh, G. W. 1971. What’s happening to our
sheep? Desert Bighorn Counc. Trans.
15:63-73.

Whitfield, M. B., and B. L. Keller. 1984. Big-
horn sheep of the Teton range, Wyoming:
ecology of aremnant population. N. Wild
Sheep and Goat Counc. Trans. 4:120-136.

Witham, J. H., and E. L. Smith. 1979, Desert
bighorn movements in a southwestern Ari-
zona mountain complex. Desert Bighorn
Counc. Trans. 23:20-24.

Zill, D.G. 1992. Calculus. 3rd ed. PWS-
Kent Publ. Co. 1051pp.



Dodd, N. L., and W. W. Brady. 1986. Cattle
grazing influences on vegetation of a sym-
patric desert bighorn range in Arizona.
Desert Bighorn Counc. Trans. 30:8-13.

Dunn, W. C. 1993. Evaluation of Rocky Moun-
tain bighorn sheep habitat in New
Mexico. Final Rept., New Mexico Dept.
Game and Fish, Fed. Aid Wildl. Res. Proj.
W-127-R9. 60pp.

1994. Evaluation of desert bighorn |

sheep habitat in New Mexico. Arevision
of the Final Rept., New Mexico Dept.
Game and Fish, Fed. Aid Wildl. Res. Proj.
W-127-R-7. 43pp.

Elenowitz, A. S. 1983. Habitat use and population
dynamics of transplanted desert bighorn sheep
in the Peloncillo Mountains, New Mexico.
M. S. Thesis, New Mexico State Univ., Las
Cruces. 158pp.

Festa-Bianchet, M. 1986. Site fidelity and seasonal
range use by bighorn rams. Can. J. Zool.
64:2126-2132.

Foreyt, W.J., and D.A. Jessup. 1982. Fatal pneu-
monia of bighorn sheep following associa-
tion with domestic sheep. J. Wildl. Dis.
18:163-168.

Forman, R. T. T., and M. Godron. 1981. Patches
and structural components for a landscape
ecology. BioScience 31:733-740.

Franklin, I. R. 1980. Evolutionary change in small
populations. Pages 135-150 in M. E. Soule
and B. A. Wilcox, eds. Conservation biology:
an evolutionary-ecological perspective. Sinaur
Assoc., Inc., Sundorland, Mass. 395pp.

Geist, V. 1971. Mountain sheep: a study in behav-
ior and evolution. Univ. Chicago Press, Chi-
cago. 383pp.

Gilpin, M. E. 1987. Spatial structure and popula-
tion vulnerability. Pages 125-139 in M. E.
Soule, ed, -Viable populations for conserva-
tion. Cambridge Univ. Press, Cambridge:
189pp.

Gionfriddo, J. P., and P.R. Krausman. 1986. Sum-
mer habitat use by mountain sheep. J. Wildl.
Manage. 50:331-336.

Goodson, N. J. 1982. Effects of domestic sheep
grazing on bighorn sheep populations: a re-
view. North. Goat and Sheep Counc. Trans.
3:287-313.

. 1994, Persistence and population size in
mountain sheep: why different interpre-
tations? Cons. Biol. 8:617-618.

Hamilton, K., S.A. Holl, and C. L. Douglas. 1982.
An evaluation of the effects of recreational
activity on bighorn sheep in the San Gabriel
Mountains, California. Desert Bighorn
Counc. Trans. 26:50-55.

Hanley, T. A. 1982. The nutritional basis for food

selection by ungulates. J. Range Manage.
35:146-151.

Hansen, C. G. 1980. Habitat evaluation. Pages
320-335 in G. Monson and L. Sumner, eds.
The desert bighorn: its life history, ecol-
ogy and management. Univ. Arizona Press,
Tucson. 370pp.

Hass, C. 1990. Reproductive dynamics of big-
hom sheep in arid and alpine environments.
Rept. to New Mexico Dept. Game and Fish,
Santa Fe, NM. 17pp.

1991. Use of artificial salt stations to
reduce bighorn sheep-human interactions in
the Pecos Wilderness. Rept. to New Mexico
Dept. Game and Fish, Santa Fe, NM. 26pp.

FA



Appendix A - Glossary of MOSS and MAPS Commands Used in GIS
Analysis of Bighorn Habitat

Appendix B - Coverage Names Used in GIS Analysis of Bighorn
Habitat

Appendix C - Flow diagram of the steps for measuring potential
alpine bighorn sheep habitat

Appendix D - Flow diagram of the steps for measuring current
alpine bighorn sheep habitat ..........sssscsseesssesessssseesresseessnesnsnensecsassaessuseeensensersassses 33

Appendix E - Flow diagram. of the steps for measuring potential
low-elevation bighorn sheep habitat ........cssssssssssessncesceermesssnesstesseeesenensnensessneeneeneee 35

Appendix F - Flow diagram of the steps for measuring current
low-elevation bighorn sheep habitat ..........s:cssecescssesssresmesensenreessesnessssanennesnennennecnees 37

Appendix G - BASIC Program for Calculating Contiguity Indices ........ssssccsececsssseeeeecsteseeeenes 39



Table of Contents

Page
ADSUIACE....esssesssessssessssenscsssesneessecasecstsssesssessssssssssorsessssesusasssecsussusesscenensosssensesessesssssasesuegsuesscaneceseaeenesers iii
Acknowledgements .........sscssecssissssssssssessssssessneenessveesnecassssseccanssssstesscsnsssusssossessnssessessessuesaesaseseerecenseaes v
List Of Figures .....c..sssssssessssssssesescseesessssssssssnsessessssssvansssccnasscsneassevesccusseesssncssencsnssesncanssecasensaeeaceueatents ix
List of Tables

Introduction .

Habitat Requirement .........ccsccsssssessssssssseosssesssssscesecsssscesesatsassessesseceesassecsssseeneeseseeseessesseeecseeneeneneeee
‘Vegetative Cover and Topography.
Seasonal Ranges .
Population Size .

Range Conditions
Human Disturbance

PwWWWNHNrE

Domestic Sheep
4
Field Surveys . 4
GIS Analysis ... 4
OVETVIEW ...esessseseessere 4
Study Area Boundaries . 5
Digital Elevation Model Preparation . 7)
Potential Suitability 5
Current Suitability ..... 9
Ranking Study Areas . 9
Calculating Carrying Capacity 10

vii



Total Habitat (Winter)
Total Habitat (Summer) 70
7
60
60
50
50 “
E
5 40 | Eto
30 = 30
20 7 20 =
‘0 , Fa} -
10
° os * oo
we Pw LW cr SFB 0 We Pw lw cA SFB
Study Areas
Study Areas

Ne / Ne J
> ( >)
Escape Terrain (Summer) Escape Terrain (Winter)

“\2s

; a=

we PW LW CREB
Study Areas . WP Pw Lw CR SFB
Study Areas
\ ) L y
(— \ (—
Escape Terrain Contiguity (Summer) Escape Terrain Contiguity (Winter)
100 100

#0 80
80 z 80
70 Z 70
6 Co
50 50

40
40
so| =
% 20 Lal
20 mu 6m 10
* ° mm

we PW iw OR SFB
we PW LW OG SFB

L Study Areas Study Areas

” \ J

Figure 5. Values of six habitat components used to measure potential and current suitability in alpine Rocky Mountain
bighorn sheep study areas in New Mexico. Potential values are shown as black boxes; current values, if different, are
shown as white boxes. WP = Wheeler Peak, PW = Pecos Wilderness, LW = Latir Wilderness, CR = Culebra Range,

SFB = Santa Fe Baldy.


Introduction

Bighorn sheep were extirpated from much
of the West during the late 1800s. Since the
1930s, transplants have been used to return
bighorn to their former ranges (Bleich et al.
1990), although some transplanted populations
have become extinct or have not grown much
larger than the number transplanted (Smith et
al. 1988). One reason for unsuccessful trans-
plants may be inadequate habitat assessment,
especially the failure to identify and mitigate
impacts prior to release of bighorn.

Using Geographic Information Systems (GIS)
and a landscape approach in evaluating habitat
can increase the potential for successful trans-
plants. Sophisticated analyses of large sets of
spatial data are possible with GIS. With a land-
scape approach, habitat is viewed from a large-
scale perspective as an assemblage of patches in
which the spatial pattern of patches is an impor-
tant facet (Forman and Godron 1981). This is a

logical approach to evaluation of bighorn habitat
because (1) bighorn habitat is naturally patchy due
to the affinity of bighorn for terrain that is both
open and mountainous; (2) fragmentation (i.e.,
increased patchiness) often is the most severe
consequence of human disturbance; and (3) the
proximity and distribution of neighboring bighorn
ranges may be critical factors in determining ge-
netic and demographic support for small bighorn
populations (Bailey 1992). -

In this technical note, a method originally de-
veloped and used in New Mexico is described that
incorporates GIS and a landscape approach in
evaluation of Rocky Mountain (Ovis canadensis
candensis) and desert (O. c. mexicana) bighorn
habitat . Two objectives are accomplished using
this method: (1) habitat and impacts are mea-
sured, not simply rated; and (2) study areas are
objectively ranked.

Habitat Requirements

Vegetative Cover and Topography

Bighorn sheep rely on keen vision to detect
predators and rapid mobility on steep terrain to
escape from them. Thus, open, steep terrain is the
defining component of bighorn habitat McQuivey
1978, Risenhoover et al. 1988) (Figure 1). Because
of the need for open habitat, bighorn distribution
is limited mostly to areas above (i.e., alpine Rocky
Mountain bighorm habitat) or below (i.e., low-el-
evation Rocky Mountain or desert bighorn habi-
tat, hereafter referred to as low-elevation habitat)
forests and woodlands .1 Tilton and Willard (1982)
and Holland Bleich (1983:61) found vegetation
with canopy cover <25-30% open enough to be

regularly used by bighorn. Slopes >60% gener-
ally are considered steep enough to be classified
as escape terrain (Hansen 1980, Holl 1982,
Armentrout and Brigham 1988, McCarty and
Bailey 1994:1). Slopes <60% serve as foraging
areas and as corridors between patches of escape
terrain (Berger 1991, Bleich 1993). Escape ter-
rain is especially important for ewe-lamb groups
because of the high vulnerability of lambs to pre-
dation (Sandoval 1979:118, Holl and Bleich
1983:61, Gionfriddo and Krausman 1986, Berger
1991, Bleich 1993:57). Based on the affinity of
ewe-lamb groups to escape terrain, it is reason- ©
able to assume that the value of escape terrain in
providing protection from predators is positively

1 Some desert bighorn (0. c. nelsoni) populations in northern Nevada also occur above timberline.

rr



400 Total Habitat
350 35 Escape Terrain
300 30
250 25
20 3”
150 15
100 10
50 5
0 0
‘otential arrent a ‘
Historic Non-Historic Historic Non-Historic
Escape Terrain Contiguit ota
P auly 160 Water Availability
140
120
100
© 380
2

60
40
20

Historic Non-Historie Historic Non-Historic

Figure 8. Values of four habitat components used to measure potential and current suitability in five historic and eight non-
historic desert bighorn sheep ranges in New Mexico. Mean values are shown as bars; J-beams equal 1 standard deviation.

FA



EVALUATING
BIGHORN HABITAT:
A LANDSCAPE
APPROACH

Technical Note 395

U.S. DEPARTMENT OF THE INTERIOR
Bureau of Land Management

September 1996

Note: Pages Vi, are lalauni<

Boba, BU, AG, 86



and deVos 1992). Bighorn movements
can be impeded by highway right-of-
way fences and guardrails (Witham and
Smith 1979). Primitive roads provide
access into otherwise undisturbed areas,
which may result in habitat abandonment
and increased illegal harvest.

« Fences. Mortality from entanglement in
fences has been documented (Welsh 1971,
Elenowitz 1983).

* Aircraft. Bighorn are not visibly disturbed
by single-engine airplanes flying >100 m
(110 yds) above ground (Krausman and
Hervert 1983, Miller and Smith 1985) and
desert bighorn apparently habituate to re-
peated jet overflights on military reserva-

tions (Weisenberger et al. 1996). How-
ever, helicopters flying <450 m (500 yds)
above ewes have been linked to increased
heart rates (MacArthur et al. 1982) and
decreased foraging efficiency (Stockwell
et al. 1991).

Domestic Sheep

Domestic sheep carry diseases such as
bronchopneumonia and scabies that can cause sig-
nificant mortality in bighorn populations (Foreyt
and Jessup 1982, Goodson 1982, Jessup 1985,
Sandoval 1988). Therefore, the Technical Staff
of the Desert Bighorn Council (1990) recom-
mends > 13,5 km (8 mi) separation between big-
horn and domestic sheep unless topographic fea-
tures or physical barriers prevent interaction.

Methods

Field Surveys

Field surveys of each study area were con-
ducted to (1) examine perennial water sources in
low-elevation habitats, (2) map range conditions
and human impacts, and (3) ground-truth GIS
data. Water sources were evaluated for livestock
use, persistence (ephemeral or perennial), and
distance to escape terrain?. Sources that were
ephemeral, surrounded by fences higher than
1.1 m (3.6 ft) or lower than 0.5 m (1.5 ft)
(Helvie 1971), or in dense vegetation were not
included in the analysis. Poor range conditions
were visually identified by soil erosion, recent
gully formation, scarce ground litter, grasses with
few seedheads, browse plants with abundant dead
dance of Aristida spp., Gutierrezia spp., or Opun-

tia spp. (Oosting, 1956:396-97, Stoddart et al.,
1975:267-271). Human impacts evaluated in-
cluded mines, roads, hiking trails, recreation sites,
fences, and military impacts (i.e., areas affected
by bombing, shelling, or ground operations).

GIS Analysis
Overview

The BLM’s automated digitizing system
(ADS), vector-based Map Overlay and Statisti-
cal System (MOSS), and raster-based Map Analy-
sis Program System (MAPS) were used to create
and analyze spatial databases (hereafter referred
to as coverages) with a variety of operations such
as buffering of geographic features, measurement

2 Local residents and !and management agency personnel provided important information in determining livestock use and

A

persistence of water.



hundred is recommended (Soule 1980, Soule and
Simberloff 1986, Berger 1990, Thomas 1990).
Populations with fewer than 100 animals are sus-
ceptible to extinction from catastrophic events
such as disease outbreaks (Thomas 1990) and
may not have enough genetic diversity for long-
term persistence (Franklin 1980:147). Some
bighorn populations smaller than 100 animals
have survived for more than 50 years, but most
of these (1) were at low levels but had enough
habitat to increase to more than 100 bighorn
(Krausman et al. 1993, Goodson 1994), (2) had
been augmented with additional animals
(Goodson 1994), or (3) were part of an inter-
breeding group of populations known as a
metapopulation (Lande and Barrowclough 1987,
Wehausen 1996). The potential for interbreed-
ing among neighboring populations is positively
telated to population size and proximity to neigh-
boring populations (Gilpin 1987). Intermoun-
tain movements of 15 km (9 mi) by ewes and
25-50 km (15-30 mi) by rams have been well
documented for Rocky Mountain (Festa-
Bianchet 1986, Dunn 1993:29) and desert big-
horn (Elenowitz 1983, Cochran and Smith 1983,
Ough and deVos 1984, Schwartz et al. 1986,
Ramey 1993:118). Thus, populations within 25
km (15 mi) of each other would have a high prob-
ability of forming a metapopulation unless physi-
cal barriers prevented interchange of individuals.

Impacts
Range Conditions

Bighorn are foraging generalists and their diets
vary seasonally and throughout their geographic
range (Todd 1975, Johnson and Smith 1980,
Cooperider and Hansen 1982, Whitfield and Keller
1984, Miller and Gaud 1989). However, like all
ruminants, bighorn do best with highly nutritious
forage (Hanley 1982) and therefore can be adversely
affected by poor range conditions where the quality,
quantity, and diversity of forage are low (Stoddart
et al. 1975:267-271, Dodd and Brady 1986).

Human Disturbance

Reaction of bighorn to human disturbance
varies greatly and may be affected by the type
and frequency of disturbance, season of occur-
rence, amount of habitat affected, position of the
disturbance to the sheep, proximity of the sheep
to escape terrain, and degree of habituation
(Berger 1978, Wehausen 1980:192, 1983:74,
Miller and Smith 1985). Impacts that can dis-
turb sheep include:

* Mines and Construction Sites. Noise and
vehicle traffic may cause abandonment of
habitat-(Leslie and Douglas 1980,
Campbell and Remington 1981, DeForge
and Scott 1982).

¢ Recreation Use. Bighorn may react to
hikers or cross-country skiers that ap-
proach within 200 m (220 yds), especially
if escape terrain is unavailable (Wehausen
1980:194, 1983:75, Holl and Bleich
1983:81, Miller and Smith 1985, Stanger
et al. 1986). Harmful interactions, such
as bighorn being chased by dogs-and
physically restrained by recreationists,
have been observed in the Pecos Wilder-
ness of northern New Mexico (Hass 1990,
1991). Trails and campsites within big-
horn habitat increase the potential for
harmful interactions.

¢ Housing Developments. Noise, vehicle
traffic, and harassment from dogs and
humans may cause bighorn to abandon
habitat near housing developments
(DeForge and Scott 1982, MacArthur et
al. 1982).

* Roads. Vehicle traffic on two-lane high-
ways generally does not disturb sheep
(MacArthur et al. 1982, Miller and
Smith 1985), -but significant mortality
from vehicles can occur (Cunningham

A



810 IF DG,2)>Dd+1,2) THEN
HOLD(1)=D(+1,1):HOLD(2)=D(+1,2):D+1,1)=Dd,1):Dd+1,2)=D(.,2)
:Dd,1)=HOLD(1):D(,2)=HOLD(2):FLAG=1

820 NEXTI

830 IF FLAG=1 THEN GOTO 790

840 ‘

850 RETURN

_>


Ramey, R. R. 1993. Evolution genetics and sys-
tematics of North American mountain sheep:
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Risenhoover, K. L., and J. A. Bailey. 1985. For-
aging ecology of mountain sheep: impli-
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> , and L. A. Wakelyn. 1988. As-
sessing the Rocky Mountain bighom sheep
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16:346-352.

Sandoval, A. V. 1979. Preferred habitat of desert
bighorn sheep in the San Andres Mountains,
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Univ., Ft. Collins, CO. 314pp.

. 1988. Bighorn sheep die-off following
association with domestic sheep: case his-
tory. Desert Bighorn Counc. Trans.
32:36-38.

Schwartz, O., V. C. Bleich, and S. A. Holl. 1986.
Genetics and the conservation of mountain

sheep, Ovis canadensis nelsoni. Biol. Cons.
37:179-190.

Scott, J. M., F. Davis, B. Csuti, R. Noss, B.
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Smith, D. R., and B. K. Johnson. 1979. Bighorn
sheep distribution, habitat evaluation, and
food habits, Pecos Wilderness, New
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National Forests, New Mexico. 185pp.

Smith, T. S., J. T. Flinders, and D. W. Olsen. 1988.
Status and distribution of Rocky Mountain
bighorn sheep in Utah. Bienn. Symp. N.
Wild Sheep and Goat Counc. 6:5-12.

, ,and D. S. Winn. 1991. A habitat
evaluation procedure for Rocky Mountain
bighorn sheep in the intermountain west.
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36:4-9.

Soule, M.E. 1980. Thresholds for survival: main-
taining fitness and evolutionary potential. °
Pages 151-169 in M.E. Soule and B.A.
Wilcox, eds. Conservation Biology: an
evolutionary-ecological perspective.
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and D. Simberloff. 1986. What do genet-
ics and ecology tell us about the design of
nature reserves? Biol. Cons. 35:19-40.

Stanger, M. C., J. Cresto, G. W. Workman, and
T. D. Bunch. 1986. Desert bighorn
sheep-riverboat interactions in Cataract
Canyon, Utah. Desert Bighorn Counc.
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Stockwell, C. A., G. C. Bateman, and J. Berger.
1991. Conflicts in national parks: a case
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56:317-328.

Stoddart, L. A.,A. D. Smith, and T. W. Box. 1975.
Range management. 3rd ed. McGraw-Hill
book Co., New York. 532pp-

FA


Helvie, J. B. 1971. Bighorns and fences. Desert
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Holl, S. A. 1982. Evaluation of bighorn sheep
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34:14-22.

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Jessup, D. A. 1985. Diseases of domestic live-
stock which threaten bighorn sheep popu-
lations. Desert Bighorn Counc. Trans.
29:29-33.

Johnson, B. K., and D. L. Smith. 1980. Food
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sheep in alpine and subalpine communities.
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389. 64pp.

Krausman, P.R., and J.J. Hervert. 1983. Moun-

tain sheep responses to aerial surveys.
Wildl. Soc. Bull. 11:372-375

Krausman, P.R., R.C. Etchberger, and R.M. Lee.
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Lande, R., and G. Barrowclough. 1987. Effec-
tive population size, genetic variation, and
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87-123 in M. E. Soule, ed. Viable popula-
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Leslie, D. M., Jr. 1978. Differential utilization of
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River Mountains, Nevada. Desert Bighorn
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FA


List of Figures

Page

Figure 1. The importance of open habitat cevsesassesueestseetantvensannpneessuuasesenneesen sesseseeneaeeceseaeeees 2
Figure 2. Estimating canopy cover from aerial photographs ...........secscessssessseseseesesesssscaeareeeees 7
Figure 3. Using neighborhood analysis to calculate slope from

Digital Elevation Models ..........:csssessesstssessesssssessssssessssseesesesstsnesssnessseesnseuseesesnesneneaeeee 7
Figure 4. Measuring escape terrain contiguity 0... eee Seasavsososssssaeseassvsnsssucsusnensesesseasseseoes 8
Figure 5. Values of six habitat components used to measure potential

and current suitability in alpine Rocky Mountain bighom sheep

study areas in NeW MeXiCO ...sssesssesssssessscsseesssesssessssesseessteseesuceseasestesuesnieniesneeenesneeaeese i
Figure 6. Potential summer and winter habitat for Rocky Mountain bighorn

sheep in three alpine study areas in New Mexico ...
Figure 7. Potential and current summer habitat for Rocky Mountain bighorn

sheep in three alpine study areas in New MEXICO ..........ssssessessssseseessssetssseasteescsereesees 15
Figure 8. Values of four habitat components used to measure potential and

current suitability in five historic and eight non-historic desert

bighorn sheep ranges in New MEXICO .....cecssecsssesssesssessssesesseesstacesesneenessesnsensesntesseenes 17
Figure 9. The effects of impacts on three desert bighorn sheep study

areas in NeW MeXico .....cscssessssseesseeseescssessaeesseesssessseecseseeescsseensousecessesnsentesuesseesaensess 18

List of Tables
Page

Table 1. GIS measurements for analyzing alpine bighorn sheep habitat ..
Table 2. GIS measurements for analyzing low-elevation bighorn sheep habitat 0.0... 6
Table 3. Calculations used to determine potential and current suitability for five

alpine Rocky Mountain bighorn sheep study areas in New MeXico .........0.cse0 13-14
Table 4. Carrying capacity calculations for five alpine Rocky Mountain

bighorn sheep study areas in New Mexico...

ix


Table 3. Continued

CURRENT SUITABILITY
Study Areas Calculations Scores
Wheeler Peak (39.2/48.0)-+(11.8/11.8)+(56/83.5)+(9.5/16.4)+(3.2/3.2)+(30,3/303) 0.84
Pecos Wilderness (49/68) 10.5/11.8)4(72.2/83.5)+(16.4/16.4)-+(2.2/3.2)+(18.3/30.3) 0.84
Latir Wilderness ( auger 3.1/11.8)+(83.5/83.5)+(3.3/16.4)+(.9/3.2)-+(24,4/30.3) 0.47
Culebra Range (203120. 402071835 )+(5.6/16.4)+ (0/3.2)+(0/30.3) 0.24
0.12

Santa Fe Baldy (9.8/48)+(1.1/11.8)-+(20.5/83.5)+(1.5/16.4)+ (0.22/3.2)+(3.5/30.3)
6

HABITAT SUITABILITY
(average of potential and current suitability scores)
Study Areas Calculations Scores
Wheeler Peak (0.90 + 0.84)/2 0:870
Pecos Wilderness (0.81 + 0.84)/2 0.825
Latir Wilderness (0.50 + 0.47)/2 0.485
Culebra Range (0.21 + 0.24)/2 0.225
Santa Fe Baldy (0.15 + 0.12)/2 0.135

during both summer and winter. High escape ter-
rain contiguity contributed greatly to the Habitat
Suitability Score of Latir Wilderness. The Culebra
Range contained more total habitat than Latir
Wilderness, but ranked lower because of a lack of
escape terrain. Santa Fe Baldy was lacking in all
habitat components.

The midpoint value (250) of Smith and
Johnson’s (1979:102) carrying capacity estimates
for Pecos Wilderness were used to calculate car-
rying capacity (Table 4). Wheeler Peak, Pecos

. Wilderness and Latir Wilderness had adequate

habitat to support self-sustaining bighorn popu-
lations (>100 animals), but the Culebra Range and
Santa Fe Baldy did not.

A



Appendix D

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Suyefnoyeo uy pasn seBeesor yeyqey deays wroysrq ourdys suaxMo Suymseeur Joy sdoys ou JO wrederp Moy “Gq xipueddy

(eourwases)
ALINOLINOD NIVEUTL
Wav OST INTUAAD

(uresdoxg xopuy Aunayyu0D)

suo BOOT soyored
Yowg ureuey, adeosy, upexoy, adeosyy

ors) NIVARELE
IVIST INDUUAD

1s) NIEWUTAL
SAVOSS INEMHOD

Goria) LIST
TWLOL.INREIAD,

G2enqQns) IVIL Gonyqns) HI

Gomi ) IVLEVH Suruns) LKLIGVH
TWLOL TWHNGLOd, TWIOM, THEINELO’,



Appendix E

Sugejnoyed wi pesn sesereaoZ

*soSB10A09 UseMmjoq JULI pjog Ul are spueUIMOD STH ‘seuTpPNO pjoq ut ase AyIqeyins Tenue}od
‘eIeY doays usoys1q UONeAeTe-MoT [eNUS}od SuumseauE Joy sdays oy) JO WIeISeIP MOLT“

ipuaddy

ALTHAVIUVAV
MOLVA

(QoosraT) HIV

diy Z'€ pasayynq
sisieyy Areunig
(aung) ANOZ

(mua aduasg uggz>)
ssaqeyy Are

A

Goes) ELVIN

WOOT Perey

soqwAA [eyNUAsEg

A

999q) ANOZ

(evoamg pjats ssamog)
JeTw AA [eMMAINg,

ALEADLINOD
NIVUUALL dEVOSA

aN

(ureationg xopay <yynisuo)

€
Yyored wren, odeosy, urenoy, adeosy

FLVDOT “a>

NIVUAEL FAVS

(eozeradois)
LPLIGV WLOL

aBvIDA0D sdojg

(soya apy eamog)
SST> 1n0p Adour



Different-sized buffers around impacts rep-
resented a form of data weighting. Weighting
has been used to discriminate among the impor-
tance of components in habitat analyses
(Cooperrider 1986:766), but often it is subjec-
tive and not necessarily a true reflection of the
importance of each component in determining
habitat quality. Buffers were necessary in this
method to provide an adequate spatial measure
of point and linear impacts.

Temporal variation of impacts (e.g., number
of hikers and time of day trails were used) was
not measured because data on amount and period
of disturbance were not available for most study
areas. These data could be important in accurately
measuring the effects of impacts on bighorn. For
example, Hamilton et al. (1982) determined that
bighorn could be more adversely affected by ahigh
frequency of recreation use than by a high num-
ber of recreationists.

Several measures of habitat used in other stud-
ies were not included in this method. Horizontal
visibility was not measured using visual estima-
tion of vegetative obstruction (Risenhoover and
Bailey 1985, Armentrout and Brigham 1986).
Using canopy cover as a surrogate measure of
horizontal visibility is a much more rapid means
of analyzing large areas and has been shown to be
effective in identifying open vegetation by Tilton
and Willard (1982) and Holl and Bleich (1983:61).
However, field verification of the results is im-
portant, especially in moderately dense shrub types
where horizontal visibility may be greatly reduced
by the amount of foliage at the eye level of big-
horn (3 ft).

Aspect and elevation were not used as surro-
gate measurés of open vegetation (M. Gudorf,
Natl. Park Serv., Denver, CO, pers. commun.),
thermal cover (Gionfriddo and Krausman 1986),
or limits of usable habitat (Smith et al. 1991).
Southerly aspects and low elevations generally are
xeric and support more open vegetation but not
all vegetation in these areas is necessarily open
enough for bighorn. Thermal cover has not been
shown to be a major factor in determining more

than temporal distribution of bighorn (McCarty
and Bailey 1994:10). Elevations <2211 m (7250
ft) were used by Smith et al. (1991) to define the
boundary of usable habitat, but the relationship
was unique to their study area.

Escape terrain may have been overestimated in
this method because vertical slopes (i.e., slickrock
(Smith and Flinders 1992)) were not eliminated and
rockiness was not measured (Hansen 1980:325,
Amnentrout and Brigham 1986, Holl 1982). Verti-
cal slopes (>80°) comprised <2% of the total es-
cape terrain in the study areas because of the rela-
tively course grain (100x100 m) of 1:250,000 DEMs.
Rockiness was not used because most slopes >60%
contain at least some rocky areas and the amount of
rockiness needed for an area to provide adequate
escape terrain has not been quantified.

Escape terrain patches >2 ha (5 ac) were not de-
fined as lambing areas (Armentrout and Brigham
1986, Smith et al. 1991) because this measure has
not been empirically demonstrated as a minimum
requirement for lambing (Van Dyke et al. 1983:6)
and some desert bighorn populations do not consis-
tently lamb in specific areas (Leslie and Douglas
1979:40). Open, steep terrain is.the important habi-
tat component for lambing, so measure of the amount
and contiguity of escape terrain should be an ad-
equate indicator of the sufficiency of each study area
for lambing.

Forage diversity and abundance were not mea-
sured because bighorn can survive on wide variet-
ies (Todd 1975, Cooperrider and Hanson 1982) and
low densities of forage (Leslie and Douglas
1979:10), so the contribution of forage data in de-
termining adequacy of habitat may not be worth the
amount of effort needed to collect it.

Finally, the potential for movement between
neighboring bighorn ranges was not measured be-
cause of the difficulty in quantifying the effect of
barriers on intermouintain movements. However, the
contiguity index (Appendix G) could be used as a
measure of the potential for establishing a
metapopulation. Distance between ranges and
amount of escape terrain within each range would
be variables.

FA



Abstract

This technical note describes a method that incorporates a landscape approach with the use of Geo-
graphic Information Systems (GIS) to measure habitat and impacts for Rocky Mountain and desert
bighorn sheep and to rank potential transplant sites. A landscape approach, in which habitat is
viewed from a large-scale perspective as an assemblage of patches, is used because: (1) bighorn
habitat is naturally patchy due to the affinity of bighorn for terrain that is both open and mountain-
ous; (2) fragmentation (i.e., increased patchiness) often is the most severe consequence of human
disturbance; and (3) the proximity and distribution of neighboring bighorn ranges may be critical
factors in determining genetic and demographic support for small bighorn populations. Potential
suitability (the inherent capability to support bighorn sheep) and current suitability (the effect of
impacts) is determined for each study area. Habitat components measured in alpine habitat include
total habitat, escape terrain, and escape terrain contiguity in both summer and winter ranges. Habitat
components measured in low-elevation habitat include total habitat, escape terrain, escape terrain
contiguity, and water availability.



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September 1996

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|. TITLE AND SUBTITLE
Evaluating Bighorn Habitat: A Landscape Approach

5. FUNDING NUMBERS

. AUTHOR(S)
William C. Dunn

x

PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES)
U.S. Department of the Interior
Bureau of Land Management
National Applied Resource Sciences Center
P.O. Box 25047
Denver, CO 80225-0047

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BLM/RS/ST-96/005+6600

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11. SUPPLEMENTARY NOTES

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12b. DISTRIBUTION CODE

13. ABSTRACT (Maximum 200 words)

This technical note describes a method that incorporates a landscape approach with the use of
Geographic Information Systems (GIS) to measure habitat and impacts for Rocky Mountain and
desert bighorn sheep and to rank potential transplant sites. Potential suitability (the inherent
capability to support bighorn sheep) and current suitability (the effect of impacts) is determined

for each study area.

14. SUBJECT TERMS

| 15. NUMBER OF PAGES
* Landscape Approach 54

* Rocky Mountain Bighorn Sheep » Habitat
¢ Desert Bighorn Sheep *GIS
* Escape Terrain + Patches

+ Escape Terrain Contiguity

16. PRICE CODE

17. SECURITY CLASSIFICATION
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UL

NSN 7540-01-280-5500

# U.S. GOVERNMENT PRINTING OFFICE:

1996~774-016/40508

‘Standard Form 298 (Rev. 2-89)
Prescribed by ANS! Std, 23918
208-102


Canopy Cover
Template

Figure 2. Estimating canopy cover from aerial photographs. Canopy cover was estimated from 1:24,000 aerial photo-
graphs by using a 1.5-4x stereoscope and a canopy cover template (U.S. Forest Service North Central Forest and Range
Experiment Station, St. Paul, MN). Areas with <25% canopy cover were outlined on 7.5’ topographic maps and digitized to

create a coverage of open vegetation. The canopy of a pinyon-juniper woodland and the canopy cover template are shown
above.

Roving Window Roving Window

Figure 3. Using neighborhood analysis to calculate slope from Digital Elevation Models (DEM). Within a 3x3 cell window,
elevation of the central (or target) cell was compared by GIS to the elevation of and distance to surrounding cells. The maximum
slope determined in the window was assigned by GIS to the target cell (50%) and stored in an initial slope coverage.

FA



free slopes identified in the photographs were
outlined on 7.5' maps and digitized to create a
GIS coverage. This coverage was intersected with
the coverages of total habitat and escape terrain
to identify total habitat and escape terrain avail-
able during winter. Escape terrain contiguity was
measured from the resulting winter escape terrain
coverage.

For low-elevation habitat, water availability
was measured as the amount of total habitat <3.2
km (2 mi) from perennial water sources that were
within 200 m (220 yds) of escape terrain’. A cov-
erage of perennial water sources was intersected
with a coverage of escape terrain patches that had
been buffered to 200 m (220 yds) to create a cov-
erage of perennial water sources near escape ter-
rain. ‘A 3.2 km (2 mi) buffer was then created
around these water sources and this coverage was.
intersected with the coverage of total habitat to
measure water availability. :

Current Suitability

Current suitability was measured with the
same components used to measure potential
suitability, but only habitat not affected by im-
pacts was used. A coverage of impacts was
created that included: (1) range in poor condi-
tion;. (2) military impacts (i.e., areas where
bombing, shelling, or ground operations oc-
curred); (3) 200 m (220 yd) buffers around hik-
ing trails (based on flight distance of bighorn)
and improved roads; and (4) 500 m (550 yd)
buffers around primitive roads (the probable
maxitniim distance from which bighorn would.
be shot by poachers), recreation sites, and hous-
ing developments’. Current total habitat and
escape terrain were determined by eliminating
impacted areas from potential total habitat and
potential escape terrain, respectively. Current
escape terrain contiguity was measured from the

coverage of current escape terrain. Current wa-
ter availability was measured as the amount of
current total habitat <3.2 km (2 mi) from peren-
nial water sources that were <200 m (220 yd)
from escape terrain and not in areas affected by
impacts.

Ranking Study Areas

To rank study areas, data were normalized for
each habitat component by dividing the compo-
nent value of the study area by the maximum value
of that habitat component found among all study
areas. Thus, component values ranged from 0 to 1.
Atleast one range where the bighorn population size
was known and at carrying capacity was included
in the analysis to serve as a standard of comparison.

For alpine areas, potential (using all habitat)
and current suitability (using only unimpacted
habitat) scores were calculated with the formula:

SET,

STH; SETC, ¥TE, WET, VETC,
ln Rr i oi)

where:

SS = suitability score (potential or
current)

STH = amount (km2) of total habitat
during summer

SET = ‘amount (km2) of escape terrain
during summer

SETC = escape terrain contiguity during
summer

WTH = amount (km2) of total habitat
during winter

WET = amount (km2) of escape terrain
during winter

WETC = escape terrain contiguity during
winter

i = value of the component for the study
area

6 Occasionally, perennial water sources >200 m (220 yds) from escape terrain were included in measurement of water
availability because they were in terrain judged to be rugged enough to be regularly used by ewe-lamb groups.

7 Improved roads included paved and gravel roads that were regularly traveled. Primitive roads included unmaintained dirt

Fr

roads and jeep trails.


Any mention of trade names, commercial products, or specific company names in this guide does not constitute an
official endorsement or recommendation by the Federal Government. Bureau personnel or persons acting on be-
half of the Bureau are not liable for any damages (including consequential) that may occur from the use of any
information contained in this handbook. Since the technology and procedures are constantly developing and changing,
the information and specifications contained in this handbook may change accordingly.



Appendix C

“saBvs9s09 waaMyaq JUL plog UT ere spURLITIOD SID “seuTPNO prog UF ase Aypqeyms jepUe;od
Suyepores ui pasn saderoaor) ‘yeyqey dosys asoySrq ourdye yeruejod Suumsvout Joy sdays ay) Fo weiderp Mop] “D xipueddy

(sani) TLIADLENOD
NIVUWAL AdVOSA

* (aumuns) ZLINDIENOD
(wesBoug xopuy 4mn3gu09) NIVEMEL FEVOST

momoor
(pred urewie |, adeosy, urea, adeosy, (umatlosg xapuy Aym3q900D)

ALYDOT saased

SUOHBOO]
Apqwg wrexray, adeoss € uywrtay, advosg]

(aqui)
‘NIVERRL TAVOST

LOVaLXa

(ape)
LVIIQvH T¥LOL

Qaaso) HVA <

Berea; adorg,

adO1s

Gaess2u) HLA

(ssn :omog)
Waid poralorg

(smond ty :arnes)
%Sz>Ioa0g Adour,

Gowua pista :25mog)
sadoys 2043-moug



/

yy

a3 as
EE * . 23
Sm ‘ § 38
a3 ry gs
ag PY ge
CR i 35
Se ETS)
E
bs =
, /
~ >)
a3 aa
£3 Be)
eq EL:
ah
Qs
3 5°
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> >)
a3 23
a3 a3
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28 BE
6s 6°

\

NN

total area that could be inhabited by bighorn. Current habitat includes only those areas not affected by impacts. Escape

Figure 9. The effect of impacts on three desert bighorn sheep study areas in New Mexico. Potential habitat includes the
terrain patches are black; open slopes 20-59% are hatched. Study areas are not shown to scale.



EVALUATING BIGHORN HABITAT:
A LANDSCAPE APPROACH

Technical Note 395

wr

By William C. Dunn

Department of Game and Fish
State of New Mexico

Produced By:

U.S. Department of the Interior
Bureau of Land Management
National Applied Resource Sciences Center
Information and Communications Group

September 1996

BLM/RS/ST-96/005+6600



Appendix A

Glossary of MOSS and MAPS Commands
Used in GIS Analysis of Bighorn Habitat

CLUMP - A command to combine cells that
have a common value (i.e., >60% slope) and are
within a specified distance from one another.
Options: AT (distance) specifies the maximum
distance (measured in grid spaces between cell
centers) separating cells that are to be combined
together. The value was 2 (i.e. 200 m (220 yd)
real distance) for this analysis. DIAGONALLY
specifies that the otherwise circular neighborhood
defined by AT (distance) is to be extended diago-
nally to encompass a square.

Command Line: CLUMP (old coverage)
AT (distance) DIAGONALLY FOR (new
coverage)

Example: CLUMP min.PET AT 2 DIAGO-
NALLY FOR min.PETCLP

EXTRACT - Acommand to create a new cover-
age only of cells with certain values (i.e., >60%
slopes).

Command Line: EXTRACT (old cover-
age) FOR (new coverage) , ASSIGNING (new
value) TO (old value) ,

Example: EXTRACT mtn.PTH for
min.PET , ASSIGNING 1 TO 60 through 1000

MATH (intersect) - A command to create anew
coverage by multiplying the values of one cover-
age with those of another coverage on a cell-by-
cell basis. Note: EXTRACT is then used to cre-
ate a discrete coverages.

Command Line: MATH (old coverage) *
(old coverage) FOR (new coverage)

Example: MATH min.OPEN * min.DEM
FOR min.BASE

MATH (subtract)- A command to create a new
coverage by subtracting the values of one cover-
age from those of another coverage on a cell-by-
cell basis. Note: EXTRACT is used to create a
discrete coverage.

Command Line: MATH (old coverage) -
(old coverage) FOR (new coverage)

Example: MATH min.PTH- mtn. HUMIMP
FOR mtn.CTH

LOCATION - A data description command in
which the coordinate of any point on a map is
determined.

Command Line: LOCATE (cross hair
input)

PROJECT - A command to change the map pro-
jection of an existing coverage or Digital Eleva-
tion Model.

Command Line: PROJECT mtn.DEM

What is the projection for the input
map? UTM

What is the projection-for the out
put map? UTM

What is the ellipsoid for the output
map? Clarke 1866

Longitude of any point with the
UTM zone? -106

Latitude of any point within the
UTM zone? 34

Cell Width of output map? 100
Cell Height of output map? 100

8 Continuous coverages are comprised of essentially uninterrupted data sets. Discrete coverages are comprised of data
grouped into distinct categories (i.e., slopes 220%, slopes 260%).



360 ‘

370 FOR K=1TOC

380 ‘

390 ‘ get distances from target to all other sites

400 ‘

410 FORI=1 TOC

420 Dd,1)=0:D(,2)=0

430 Dd,)=1

440 Dd,2)=((XY(K,1)-XY(L,1))*2 + (XY(K,2)-XY(L,2))42)*.5
450 PSET(G(L,1),150-G(,,2))

460 NEXTI

470 *

480 ‘Sort the distances to find neighbors

490 GOSUB 770

500 ‘

510 ‘ distances have been sorted. Now, use the RADDIST to find
520 ‘ neighbors within that radius.

530 ‘

540 DISTX=0:DISTY=0:AREAXY=0

550 NUSED=0 ‘counter of legal neighbors used so far
560 CC=2 ‘pointer to closest patch in distance array
570 ‘

580 DISTX=XY(D(CC,1),1)

590. DISTY=XY(D(CC,1),2)

600 AREAXY=XY(D(CC,1),3)

610 R2=(KY(K,1)-DISTX)*2 + (XY(K,2)-DISTY)*2
620 DIST=SQR(R2)

630 ‘

640 IF DIST > RADDIST GOTO 720

650‘ Force between target and center of recipient.

660 FORCE=((XY(K,3)) * AREAXY)/R2

670 CONTIND = CONTIND+FORCE

680 NUSED=NUSED+1
690 CC=CC+1

700 GOTO 570

710‘

720 NEXT K

730 CONTIND = CONTIND/C

740 PRINT” Average force = “,CONTIND

750 PRINT” for “;C;” patches with radius of “;RADDIST;” meters.”
760 END

770 * sort

780 ‘

790 FLAG=0

800 FOR I=1 TOC-1

FA


Literature Cited

Armentrout, D. J., and W. R. Brigham. 1988.
Habitat suitability rating system for desert
bighorn sheep in the Basin and Range Prov-
ince. U. S. Dep. Interior, Bur. Land Man-
age. Tech. Note No. 384. 18pp.

Amo, S. F., and R. P. Hammerly. 1984. Timber-
line; mountain and arctic forest frontiers.
Douglas and McIntyre Ltd., Vancouver,
B.C. 304pp.

Bailey, J. A. 1992. Managing bighorn habitat
from a landscape perspective. Bienn.
Symp. North. Wild Sheep and Goat Counc.
8:49-57.

Berger, J. 1978. Group size, foraging, and
antipredator ploys: an analysis .of bighorn
sheep decisions. Behav. Ecol. Sociobiol.
4:91-99.

. 1990. Persistence of different-sized popu-
lations: an empirical assessment of rapid
extinctions in bighorn sheep. Cons. Biol.
4:91-98.

. 1991. Pregnancy incentives, predation
constraints and habitat shifts:experimental
and field evidence for wild bighorn sheep.
Anim. Behav. 41:61-77.

Bleich, V. C., J. D. Wehausen, and S. A. Holl.
1990. Desert-dwelling mountain sheep:
conservation implications of a naturally
fragmented distribution. Cons. Biol.
4:383-390.

. 1993. Sexual segregation in desert-dwell-
ing mountain sheep. Ph.D. Dissertation.
Univ. Alaska. Fairbanks, AK. 126pp.

Boyd, R. J., A. ¥. Cooperrider, P. C. Lent, and J.
A. Bailey. 1986. Ungulates. Pages 519-
564 in A. Y. Cooperrider, R. J. Boyd, and
H. R. Stuart, eds. Inventory and monitor-
ing of wildlife habitat. U.S. Dep. Int, Bur.
Land Manage. Service Center, Denver, CO.
858pp.

Campbell, B. H., and R. Remington. 1981. In-
fluence of construction activities on wa-
ter-use patterns of desert bighorn sheep.
Wildl. Soc. Bull. 9:63-65.

Cochran, M. H., and E. L. Smith. 1983. Inter-
mountain movements by a desert bighorn
ram in western Arizona. Desert Bighorn
Coune. Trans. 27:1-2.

Cooperrider, A. Y., and R. M. Hansen 1982.
Forage selection by bighorn sheep ewes
and lambs in south-central Colorado.
Northern Wild Sheep and Goat Counc.
Trans. 3:262-277.

Cooperrider, A: Y. 1986. Habitat evaluation sys-
tems. Pages 757-776 in A. Y. Cooperrider,
R. J. Boyd, and H. R. Stuart, eds. Inven-
tory and monitoring of wildlife habitat. U.
S. Dep. Int. Bur. Land Manage. Service
Center, Denver, CO. 858pp.

Cunningham, S. D., and J.C. deVos. 1992. Mor-
tality of mountain sheep in the Black Can-
yon area of northwest Arizona. Desert Big-
horn Counc. Trans. 36:27-29.

DePorge, J.R., and J. E. Scott. 1982. Ecologi-
cal investigations into high lamb mortal-
ity of desert bighorn sheep in the Santa
Rosa Mountains, California. Desert Big-
horn Counc. Trans. 26:65-76.

FA


of slope, and merging or intersecting several cov-
erages. Proficiency with eight GIS commands
was needed to accomplish this method (Appen-
dices A and B). Biologists not familiar with GIS
should refer to Webster (1988), Keating (1993),
or Scott et al. (1993) for introductory informa-
tion and should consult with GIS specialists for
assistance in using the method described in this
technical note.

Study areas were categorized as alpine (i.c., above
treeline) or low-elevation habitat (Le., below forested
areas). Potential suitability (the inherent capability
of an area to support bighorn) and current suitabil-
ity (the effect of impacts) were measured for each
study area (Appendices C, D, E, and F), All habitat
was included in the measure of potential suitability,
whereas only habitat not affected by poorrange con-
ditions or human disturbances was included in the
measute of current suitability. Six habitat compo-
nents were used to evaluate alpine habitat; total habi-
tat, escape terrain, and escape terrain contiguity
were measured for both summer and winter
ranges (Table 1). Four habitat components were
measured to evaluate low-elevation habitat: to-
tal habitat, escape terrain, escape terrain conti-
guity, and water availability (Table 2).

Study Area Boundaries

Only study areas >15 km? (6 mi?) were ana-
lyzed. Study area boundaries were defined as all
patches of habitat with <25% canopy cover and
>20% slope3 that were separated by <5 km (3 mi)
of flat terrain (<20% slope) or dense vegetation
(25% canopy cover). Tree and shrub canopy
cover was estimated from 1:24,000 aerial photo-
graphs by using a 1,5-4x stereoscope and a canopy
cover template (U.S. Forest Service North Cen-

tral Forest and Range Experiment Station, St.
Paul, MN) (Figure 2). Areas with <25% canopy
cover were outlined on 7.5' topographic maps and
digitized to create a coverage of open vegetation.

Digital Elevation Model Preparation

Digital Elevation Models (DEM) comprised
of 100x100 m cells (1:250,000 scale) were used
for topographic analysis (U.S. Geological Survey
1987)4. The portion of the DEM that encom-
passed each study area was extracted and con-
verted to Universal Transverse Mercator (UTM)
projection. Slope was measured from the DEM
with neighborhood analysis (Webster 1988:139)
(Figure 3). In this operation, elevation of the cen-
tral, or target, cell within a 3x3 cell window was
compared by GIS to the elevation of and distance
to the surrounding cells in the window. The maxi-
mum slope measured was assigned by GIS to the
target cell and stored in an initial slope coverage
that contained percent slope for each cell of the
study area,

Potential Suitability

Acoverage of slopes >20% was extracted from
the initial slope coverage. The coverage of slopes
>20% was intersected with the coverage of open
vegetation to identify total habitats. Cells with
slopes >60% were extracted from the total habi-
tat coverage to create a coverage of escape ter-
rain. A coverage of escape terrain patches was
created by combining (clumping) cells of escape
terrain that were <200 m (220 yds) apart. Loca-
tion (UTM coordinates) of the center of each es-
cape terrain patch was determined using a cursor.

3 Chosen because the base of most desert ranges analyzed occurred where slopes were 20%.

4 Higher resolution DEMs are also available for many areas: (1) two arc-second (60x60 m cells) DEMs with vertical
accuracy of +25 m, and (2) 7.5' DEMs (30x30 m cells) with vertical accuracy of +15 m (Keating, Jr. 1993),

fA

5 Called suitable habitat in Dunn (1993, 1994).



radius = 2 km (1.2 mi)

Figure 4. Measuring escape terrain contiguity. Each patch of escape terrain within the study area became a “focal” patch
and was measured against each neighboring escape terrain patch that was within 2 km (1.2 mi). For each one-to-one
comparison, the areas of the focal and neighboring patch were multiplied and the product was then divided by the distance
between the patches squared, The sum of all values calculated in these one-to-one comparisons was the contiguity value for
the focal patch. The contiguity index for the study area was the mean of the values calculated for all escape terrain patches

within the study area.

Contiguity of escape terrain was measured
using a modification of the formula for calculat-
ing gravitation attraction between two objects (Zill
1992:357) (Figure 4):

. 1 & € (ACAD
ol Capton 2% a,

where:

CI = contiguity index

n = total number of escape terrain patches
within the study area

c = number of escape terrain patches <2 km
(1.2 mi) of the focal escape terrain patch

Aj = area (km?) of the focal escape
terrain patch

Aj = area (km?) of the neighboring escape
terrain patch

dij = distance (km) between the focal and
neighboring escape terrain patch

To measure contiguity in a study area, each
patch of escape terrain became a “focal” patch
and was compared to each neighboring escape
terrain patch that was within 2 km (1.2 mi). For
these one-to-one comparisons, the areas of the
focal and neighboring patch were multiplied and
the product was divided by the distance between
the patches squared. The sum of all values calcu-
lated in the one-to-one comparisons was the con-
tiguity value for the focal patch. The contiguity
index for the study area was the mean of the val-
ues calculated for all escape terrain patches within
the study area. Appendix G contains a computer
program for calculating the escape terrain conti-
guity index of each study area using UTM coor-
dinates and sizes of escape terrain patches.

Winter ranges of alpine study areas in New
Mexico were identified from photographs taken
during a mid-February, fixed-wing flight that oc-
curred when snowpack was 120% of normal, so
winter range estimates were conservative. Snow-

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