observations of mule deer habitat use, movements, …...observation of mule deer habitat use,...

Observations of Mule Deer Habitat Use,

Movements, and Survival in Chelan County, Washington

Final

ROCKY REACH HYDROELECTRIC PROJECT FERC Project No. 2145

December 5, 2003

Prepared by: Washington Department of Fish and Wildlife

Olympia, Washington

Prepared for: Public Utility District No. 1 of Chelan County

Wenatchee, Washington

Observation of Mule Deer Habitat Use, Movements, and Survival in Chelan County

Final Rocky Reach Project No. 2145 December 5, 2003 Page i SS/4768

TABLE OF CONTENTS

CHAPTER 1: INTRODUCTION 1

CHAPTER 2: THE STUDY ENVIRONMENT 5 2.1 Introduction ........................................................................................................................ 5 2.2 Geophysical Characteristics ............................................................................................... 5 2.3 Climate ............................................................................................................................... 5 2.4 Vegetation .......................................................................................................................... 5

CHAPTER 3: DEER CAPTURE AND MONITORING 8 3.1 Introduction ........................................................................................................................ 8 3.2 Methods.............................................................................................................................. 8 3.3 Results ................................................................................................................................ 9 3.4 Discussion ........................................................................................................................ 10

CHAPTER 4: MULE DEER MOVEMENT AND HOME RANGES 15 4.1 Introduction ...................................................................................................................... 15 4.2 Methods............................................................................................................................ 15 4.3 Results .............................................................................................................................. 15 4.4 Discussion ........................................................................................................................ 16

CHAPTER 5: SURVIVAL PATTERNS AND PREGNANCY RATES 21 5.1 Introduction ...................................................................................................................... 21 5.2 Methods............................................................................................................................ 21 5.3 Results .............................................................................................................................. 21 5.4 Management Implications ................................................................................................ 22

CHAPTER 6: DISEASE, PARASITE, AND TRACE ELEMENT TESTING 23 6.1 Introduction ...................................................................................................................... 23 6.2 Disease Exposure ............................................................................................................. 24 6.3 Results .............................................................................................................................. 24 6.4 Discussion ........................................................................................................................ 25 6.5 Conclusions ...................................................................................................................... 26

CHAPTER 7: WINTER HABITAT USE 28 7.1 Introduction ...................................................................................................................... 28 7.2 Methods............................................................................................................................ 28 7.3 Results .............................................................................................................................. 31 7.4 Discussion ........................................................................................................................ 32

CHAPTER 8: WINTER HABITAT MODELING 53 8.1 Introduction ...................................................................................................................... 53 8.2 Methods............................................................................................................................ 53


Rocky Reach Project No. 2145 Final SS/4768 Page December 5, 2003 ii

8.3 Results .............................................................................................................................. 54 8.4 Discussion ........................................................................................................................ 55 8.5 Management Implications ................................................................................................ 56

APPENDIX A: LITERATURE REVIEW 67

APPENDIX B: BITTERBRUSH RESTORATION CONSIDERATIONS 74

LIST OF TABLES Table 3-1: A list of mule deer captured and fitted with radio-collars by date, animal

identification, and location in the eastern Washington between 2000-2002. ..............11 Table 7-1: Variable classes preferred by mule deer in Chelan County, WA during the winters

of 2001 and 2002. .........................................................................................................34 Table 7-2: Variable classes avoided by mule deer in Chelan County, WA during the winters

of 2001 and 2002. ........................................................................................................34 Table 7-3: Variable classes where mule deer use was equivalent to availability in Chelan

County, WA during the winters of 2001 and 2002......................................................35 Table 8-1: Description of data removal to assess the influence of biases on the mule..................56 Table 8-2: Resource selection function values determined from logistic regression

coefficients...................................................................................................................56 Table 8-3: Logistic regression coefficients to assist in determining model influences due

to biases........................................................................................................................57

LIST OF FIGURES Figure 1: Map showing the study area of Chelan County for the Cooperative Mule Deer

Project, 2000-2002........................................................................................................... 7 Figure 2: Locations of radio marked mule deer in Chelan County, WA between 2000-2002. .... 14 Figure 3: Map showing the distribution of summer and winter seasonal ranges of radio-marked

female mule deer in Chelan Co., Washington ............................................................... 20 Figure 4: Map showing locations of radio-marked deer with defined the winter use area in

Chelan Co., Washington ................................................................................................ 37 Figure 5: A map showing locations of radio-marked deer which defined the winter use area in

Chelan Co., Washington ................................................................................................ 38 Figure 6: A map showing aspect classes of mule deer winter ranges in Chelan Co.,

Washington .................................................................................................................... 39 Figure 7: A map showing aspect classes of winter ranges in Chelan Co., Washington ............... 40 Figure 8: A map showing slope classes of mule deer winter range in Chelan Co., Washington . 41 Figure 9: A map showing slope classes of mule deer winter range in Chelan Co.,

Washington .................................................................................................................... 42


Final Rocky Reach Project No. 2145 December 5, 2003 Page iii SS/4768

Figure 10: A map showing elevation classes of mule deer in winter range in Chelan Co., Washington ................................................................................................................. 43

Figure 11: A map showing elevation classes of mule deer in winter range in Chelan Co., Washington ................................................................................................................. 44

Figure 12: A map showing forest vegetation classes within Chelan Co., Washington ................ 45 Figure 13: A map showing forest vegetation classes within Chelan Co., Washington ................ 46 Figure 14: A map showing bitterbrush distribution with mule deer winter range in Chelan Co.,

Washington ................................................................................................................. 47 Figure 15: A map showing bitterbrush distribution with mule deer winter range in Chelan Co.,

Washington ................................................................................................................. 48 Figure 16: A map showing herbaceous productivity classes on mule deer winter range in

Chelan Co., Washington ............................................................................................. 49 Figure 17: A map showing herbaceous productivity classes on mule deer winter range in Chelan

Co., Washington ........................................................................................................... 50 Figure 18: A map showing the boundaries of the Tyee and Dinkleman fires in Chelan Co.,

Washington................................................................................................................... 51 Figure 19: A map showing the boundaries of the Tyee and Dinkleman fires in Chelan Co.,

Washington ................................................................................................................. 52 Figure 20: A map showing resource selection function values of mule deer winter range

grouped in 10% intervals covering mule deer in Chelan Co., Washington................ 59 Figure 21: A map showing resource selection function values of mule deer winter range

grouped in 10% intervals covering mule deer in Chelan Co., Washington................ 60 Figure 22: A map showing the lowest (0-40%) value range of resource selection function for

mule deer winter range in Chelan Co., Washington ................................................... 61 Figure 23: A map showing the lowest (0-40%) value range of resource selection function for

mule deer winter range in Chelan Co., Washington ................................................... 62 Figure 24: A map showing the mid (40-70%) value range of resource selection function for

mule deer winter range in Chelan Co., Washington ................................................... 63 Figure 25: A map showing the mid (40-70%) value range of resource selection function for mule

deer winter range in Chelan Co., Washington ............................................................ 64 Figure 26: A map showing the upper (70-100%) value range resource selection function for

mule deer winter range in Chelan Co. Washington ................................................... 65 Figure 27: A map showing the upper (70-100%) value range of resource selection function for

mule deer winter range in Chelan Co. Washington .................................................... 66


Final Rocky Reach Project No. 2145 December 5, 2003 Page 1 SS/4768

CHAPTER 1: INTRODUCTION W. L. Myers, W. Moore, and A. Gibson

Rocky Mountain mule deer (Odocoileus hemionus hemionus) are an important resource of the east slope Cascades and Chelan County. In recent years, mule deer have declined and have not rebounded in spite of relatively mild winter weather and snow conditions; the population in Chelan County has been depressed since the winter of 1996 (Musser1998). The winter of 1996 – 1997 consisted of early snowfall and extreme snow depth, which caused a major population reduction. Historically, mule deer populations associated with the Cascade Mountains have been density dependent, allowing for rapid population growth after severe “die-offs” (Ziegler, 1978). Aerial survey data (unpublished WDFW file data) indicates that mule deer throughout Chelan County primarily use lands within or adjacent to the Swakane, Entiat, and Chelan Butte Wildlife Areas for winter range. In 1994, catastrophic fires within these wildlife areas removed significant amounts of tree and shrub species. Musser (1998) hypothesized that Chelan County mule deer populations remained suppressed due to a lack of quality forage on their winter range as a result of habitat alterations caused by fire. The Chelan County Public Utility District (P.U.D) provided funds to Washington Department of Fish and Wildlife (WDFW) for purchase of these wildlife areas as mitigation for the dams on the Columbia River and to ensure continued protection of these ranges for mule deer and other wildlife species. This project was designed to provide baseline information concerning the most effective and efficient use of funds to enhance mule deer habitats.

Mule deer require adequate food, water, and cover to survive the winter season. Unfortunately, bitterbrush (Purshia tridentata), the preferred winter forage species by mule deer when present (Burrell 1977), was dramatically reduced during the 1988 and 1994 fires. The loss of this important winter forage species very likely had severe impacts to deer numbers since the quality of digestible winter forage affects survival (Hobbs 1989). The logical step for enhancing mule deer winter ranges in Chelan County would start with restoring bitterbrush stands to a level that could help the mule deer population recover from a combination of severe winters and wildfires. Restoring bitterbrush requires determining sites for enhancing existing bitterbrush stands or creating new stands. Determining optimal forage locations (e.g., areas preferred by mule deer) include identifying adequate abiotic and biotic factors for bitterbrush or other forage production that contain the least amount of snow cover (Mattise and Fritz 1994). Snow depth covering the winter range can affect mule deer survival either by directly reducing forage availability or restricting deer movements (Hobbs 1989). Snow depth varies throughout mountainous winter range in Chelan County. Determining areas with consistent mule deer use will focus restoration of bitterbrush stands to areas important for mule deer. Habitat use of winter ranges by mule deer may be influenced by cover, aspect, snow depth, and forage availability. Given these considerations, our goal was to provide deer managers in Chelan County with information on winter habitat use by mule deer so that winter use areas can be enhanced. More specifically, our study objectives were:


Rocky Reach Project No. 2145 Final SS/4768 Page December 5, 2003 2

1. Identify and describe winter use areas in Chelan County. 2. Capture up to 40 adult female mule deer during late winter or early spring and instrument

them with radio collars. 3. Determine seasonal home range sizes of radio marked mule deer. 4. Determine seasonal movement patterns of radio marked mule deer. 5. Describe survival rates and mortality patterns of radio marked female mule deer wintering in

eastern Chelan County. 6. Assess herd health parameters. 7. Measure and describe landscape attributes associated with mule deer use patterns during the

winter season. 8. Develop a mule deer resource selection function (RSF) for winter ranges in Chelan County. This report describes the methods used, and the results of fieldwork and analyses, completed to accomplish the goals listed above. Our report has been formatted into chapters addressing specific aspects of the study. Chapter Two provides a general over-view of the study area based upon literature review. Chapter Three addresses goals one and two by describing capture and monitoring efforts. Chapter Four describes deer home ranges and movement patterns (Goals 3 and 4). Goal 5, to describe survival and mortality patterns, is discussed in Chapter Five. Goal 6 is addressed in Chapter Six. Winter habitat use measurements and discussions (Goal 6) occur in Chapter Seven. The winter RSF for mule deer is discussed in Chapter Eight. A number of people assisted in a variety of ways and we are grateful for their efforts. This project would not have been possible without the guidance of John Musser and Paul Fielder. We are indebted to our chapter co-authors Bill Gaines, Jim Agee, Ken Raedeke, and Ann Gibson. Shawn Bushnell assisted with GIS analysis and mapping products. Many state and PUD agency staff and volunteers assisted in capture operations; these include Lou Bender, Briggs Hall, Tom McCall, Beau Patterson, Rick Petersen, Bob Perleberg, Todd West, Tracy Manning, Jim Kujala, Dale Williams, Carol Zitterkoff, Fred Zitterkoff, Ken Hayes, Chuck Sauter, and Tom Moore. A special thanks goes to our helicopter pilot, Jess Hagerman, gunner Rocky Spencer, and telemetry pilots Dave Parker, Wayne Emmel, and John May. Funding for this project was provided by Chelan County Public Utility District, USDA Wenatchee-Okanogan National Forests, and the Washington Department of Fish and Wildlife.



REFERENCES AND LITERATURE CITED

Bartmann, R.M., G.C. White, and L.H. Carpenter. 1992 Compensatory Mortality in a Colorado Mule Deer Population. Wildl. Monogr. 39pp.

Burrell, G. C. 1977.Bitterbrush (Purshia tridentata) in the Winter Ecology of the Entiat Mule

Deer Herd. M.S. University of Washington, Seattle, WA. Carter, M.R. 1993. Soil Sampling and methods of Analysis. Canadian Society of Soil Science,

Lewis Pub. Fowler, W.B.; Dealy, J.E. 1987.Behavior of mule deer on the Keating Winter Range. Res. Pap.

PNW-RP-373.: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; Portland, OR 25p.

Franklin, J.F.; Dyrness, C.T. 1988.Natural Vegetation of Oregon and Washington. Oregon State

University Press, Corvallis, OR Fu, P; Rich, P.M. 1999. Design and Implementation of the Solar Analyst: an ArcView Extension

for Modeling Solar Radiation at Landscape Scale. <http://www.hemisoft.com/doc/esri99>. Hobbs, T. N. 1989. Linking Energy Balance to Survival in Mule Deer: Development and Test of

a Simulation Model. Wildl. Monog. No. 101 Apr. Hubbard, Nord, L.L. Brown. Bitterbrush Reseeding - a Tool for the Game Range Manager. Misc.

Pap. No. 39. Berkeley, CA: Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Expeiment Station; 13p. 1959.

Mackie, R. J., D. F. Pac, K. L. Hamlin, and G. L. Dusek. 1998.Ecology and management of mule

deer and white-tailed deer in Montana. Montana Fish, Wildlife and Parks. Federal Aid Project W-120-R.

Mattise, S. N.; Fritz, C. 1994.Bitterbrush Rehabilitation, Squaw Butte Fire Complex. Tech. Bull.

No. 94-8 Aug. Idaho B.L.M. Musser, Washington Department of Fish and Wildlife. 1998. Game status and trend report.

Wildl. Manage. Prog., Dept. Fish and Wildl., Olympia. 228pp. Myers, W.L. 1999. Population Regulation and Habitat Ecology of Mule Deer in North-Central

and Northeast Washington. Washington. Report. Wildl. Manage. Prog., Dept. Fish and Wildl., Olympia: Washington.

Parker, K.L. 1983. Ecological Energetics of Mule Deer and Elk: Locomotion and

Thermoregulation. Dissertation, Washington State University.



Rich, P.M.; Fu, P. 2000. Enlightenment for Mapping Systems: Solar Radiation Modeling Looks

to the Sun for Answers. Resource Magazine Vol. 7(2):7-8. Weather Underground: Wenatchee Washington Forecast. <http://www. wunderground.com>. White, S.M.; Welch, B.L. 1981. Paired Comparison: A Method For Ranking Mule Deer

Preference For Various Browse Species. Logan, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 4p.

Zeigler, D. L. 1978. The Okanogan Mule Deer. Biological Bulletin No. 15, Wash. Dept. of

Game, Olympia. 106pp.



CHAPTER 2: THE STUDY ENVIRONMENT W. L. Myers and W. R. Moore

2.1 Introduction Field studies were conducted in the foothills and mountains of the east slope of the Cascade Mountains adjacent to the Columbia River in north central Washington (Figure 1). The study area was within the boundaries of Chelan County and included all of six Game Management Units (GMU): Clark (244), Chiwawa (245), Slide Ridge (246), Entiat (247), Alpine (249), and Swakane (250).

2.2 Geophysical Characteristics Chelan County comprises the east central portion of the Northern Cascades physiographic province (Franklin and Dyrness 1973) which covers much of north-central Washington. The jagged mountains of the North Cascades contain numerous glaciers and a parent material of Mesozoic crystalline and metamorphic rocks. The Cascade Mountains are bisected by glaciated and river formed valleys running west to east to the Columbia River. Elevations range from 300m (1000 ft.) along the Columbia River to nearly 3,300m (10,000 ft.) at the highest peaks. Soils of the North Cascades Province have been influenced by volcanic ash with some soils containing a thick volcanic ash mantle. Soils of the east slope of the North Cascades are dry during the summer and have a xeric moisture regime. The major soil types found in this portion of the east Cascades are, starting with most common, haploxerolls, xerochrepts, and haploxeralfs (Franklin and Dyrness 1973).

2.3 Climate Climate of the region is characterized by hot, dry summers and cool winters. The Cascade Mountains produce a major climatological influence; the Cascades create a rain-shadow which tempers maritime influences and results in a semi-arid continental type climate at lower elevations along the Columbia River. Most precipitation falls during winter in the form of snow. Thunderstorms are common during late summer and early fall when cool maritime air masses mix with hot, dry surface air; these storms historically produced periodic fires which have greatly influenced vegetation composition.

2.4 Vegetation Vegetation found in the Chelan County study area is highly variable, including shrub- steppe vegetation, shrub communities, and forest communities with dense over-story cover. Shrub-steppe communities are commonly found at lower and intermediate elevations and on the exposed, south-facing slopes. Common associations include big sage (Artemsia tridentata)-crested wheatgrass (Agropyron spicatum), A. tripartata- Idaho fescue (Festuca idahoensis).

Lower and intermediate elevations are predominately forested (Lillybridge, et al. 1995) and dominated by ponderosa pine (Pinus ponderosa) forests. Moving higher in elevation, the grand fir (A. grandis)-Douglas fir (Pseudotsuga menziesii) forest type is present along with lodgepole pine (Pinus contorta). Quaking aspen (Populas sp.) occur near moist areas at mid elevations.



Ponderosa pine, Douglas fir, and grand fir are found in both open and dense stands at intermediate and higher elevations. White fir, grand fir, Pacific silver fir, subalpine fir (A. lasiocarpa), Engelman spruce (P. engelmanni), and lodgepole pine are found on cool, moist sites at higher elevations. Alpine meadow and barren rocky areas are found at the highest elevations. The vegetation and vegetative communities in the region have been the subject of intensive study. Detailed descriptions of vegetation communities and vegetative associations can be found in Daubenmire and Daubenmire (1968), Daubenmire (1970), and Franklin and Dyrness (1973), and Lillybridge et al. (1995).

LITERATURE CITED

Daubenmire, R. and J.B. Daubenmire. 1968. Forest vegetation of eastern Washington and

northern Idaho. Wash.Exp.Stat.Tech.Bull.No. 60. 104pp. 1970. Steppe vegetation ofWashington.Wash.Exp.Stat.Tech.Bull.No.62. 131pp. Franklin, J.F. and C.T. Dyrness. 1973. Natural vegetation of Oregon and Washington. Oregon

State Univ. Press, Corvallis, OR. 452pp. Lillybridge, T.R., B.L. Kovalchik, C.K. Williams, and B.G. Smith. 1995. Field guide for forested

plant associations of the Wenatchee National Forest. USDA Forest Service, PNW-GTR-359.



Figure 1: Map showing the study area of Chelan County for the Cooperative Mule Deer Project, 2000-2002.



CHAPTER 3: DEER CAPTURE AND MONITORING W. L. Myers and W. R. Moore

3.1 Introduction In order obtain information which would allow us to make estimates of winter habitat use, seasonal movements, and migration patterns, we needed the ability to observe or locate mule deer from a distance at any time of the day during the course of field studies. Radio-telemetry was the most logical choice because it met these criteria (White and Garrott 1990). We needed to capture a total of 40 deer and mark them with radios across a relatively large study area. Our options for capturing deer were limited because the study area is remote and rugged. To achieve our goal of capturing a relatively large number of deer for instrumentation with radio-telemetry equipment over a broad study area, we needed the speed, range, and accessibility provided by aerial capture. Using a helicopter and some means of physical restraint were obvious choices of capture.

3.2 Methods Mule Deer Capture All deer were captured using helicopter netgunning (Firchow et al. 1986) each winter over three years, 2000-2002, between December and March. We used a Bell 206 Jet Ranger with a crew of three including the pilot, gunner, and mugger. Two capture operations were performed under contract with Heli-Skills, Salt Lake City, Utah. Captured deer were fitted with very high frequency (VHF) radio-collars containing a six-hour mortality switch (Advanced Telemetry Systems, Isanti, MN). In addition, five deer captured in 2002 were instrumented with radio collars containing global positioning system (GPS) and remote up-loading technology (Televilt International, Lindesberg, Sweden; Simplex GPS collars imported by Telemetry Solutions, Concord, CA) Blood and fecal samples were collected from captured deer. Ages of deer captured in 2000 and 2001 were estimated by tooth eruption and wear (Quimby and Gaab 1957); residual canines (I-4; extracted and submitted from deer captured during 2002 for cementum annuali analysis to determine age. Deer were vaccinated with a general combiotic and selenium-vitamin E compound (Mu Se) to mitigate stress from capture and handling. We evaluated body condition using two techniques, a subjective body condition scoring (BCS) technique and measured depths of subcutaneous fat stores using ultrasonography (Cook, et.al 2001). We estimated pregnancy and fetal rates using ultrasound technology. Telemetry Radio-collared deer were relocated at least twice monthly during the winter season between 0600 and 1800 hours. Aerial relocations were performed using a two-element yagi antenna attached to each strut of a Cessna 182, 336, or M-7 Maule aircraft. Aerial tracking (Gilmer et al. 1981, Mech 1983) occurred at 100-450 m (330-1500 ft.) above the ground at speeds of 130-180 km/hr (80-110 mph). A Telonics two-way switch box (Telonics, Inc., Mesa, AZ) allowed for isolation of each antenna when required. A CS R-1000 (Communications Specialists, Inc., Orange, CA)



or ATS 4000 receiver/scanner completed the receiving system. Ground telemetry locations were measured through visual observation or triangulation by one or more observers (White and Garrott 1990). Coordinates of aerial and ground relocations were determined by GPS receivers either mounted in the aircraft or in hand. Universal transmercator (UTM) coordinates (Snyder 1987) were identified for each location point. Mean location error (White and Garrott 1990) was calculated by comparing coordinates from radio telemetry flights to ground locations; ground locations were determined using stationary transmitters randomly placed by field personnel or by recovery of radios transmitting mortality signals. GPS radio-collars were programmed using Simplex Project Manager software (Televilt International, Lindesberg, Sweden) to obtain a fix every six hours seven days per week with a 2 hour off-set within a three day cycle (e.g., on day one, fix acquisition begins at 0000 hrs Pacific Standard time [PST], day two at 0200 hrs PST, and day three at 0400 hrs PST). VHF signals beaconed Tuesday through Saturday between 0821 and 1712 hrs PST daily. The GPS receiver was scheduled to transmit location data for three consecutive days (Tuesday through Thursday) every forth week. Transmitted location data were up-loaded remotely during aerial telemetry flights or with the collar in hand using a Televilt RX-900 receiver. Aerial telemetry error in location data from VHF radio collars was estimated by random placement of collars at locations unknown to aircraft crewmembers; distances between actual ground locations and estimated aerial locations provided a measurement of error. Error rates and fix rates under varying canopy and aspect for GPS radio collars were previously estimated (WDFW file data).

3.3 Results Deer Capture Field studies in Chelan County began during late March 1990 with efforts to capture deer. We attempted to capture deer across a wide geographic area that included four distinct, spatially separated winter use areas (Figure 1). Within two days, we captured 22 deer and instrumented 20 adult females with radio collars (one adult female died as a result of capture activities and one adult male was captured by mistake and subsequently released). Twenty-five deer were captured the last week of February 2001 (10 in Swakane Canyon, 10 west of the town of Entiat, and 5 near Navarre Coulee). The total number of radio-collared mule deer within the Chelan County study was 41 (Table 3-1) at the end of the 2001. During late January 2002, 27 deer wearing radio collars were recaptured; five of those deer were fitted with GPS radio collars. Similarly, we recaptured 21 radio-marked mule deer the last week of March 2002; one of those deer suffered a broken leg and was euthanized.



Telemetry Monitoring Does wearing VHF radio collars were located a total of 1,342 times (Figure 2), 450 summer season locations and 892 winter season locations. Mean location error for aerial telemetry was 497 m in rugged terrain based upon 25 randomly placed transmitters. We collected 540 locations from GPS collars. The error associated with GPS collars ranged from 87 meters for 2D fixes to 43 meters for 3D fixes.

3.4 Discussion Deer Capture Our capture exercises in Chelan County provide another example of netgun captures from helicopter being a relatively safe and expedient method to capture deer across a large geographic area in a short amount of time. The number of capture-associated mortalities was low (2) given the number of deer netted and handled (89). Telemetry Monitoring Most of our location points were collected from fix-winged aircraft which limited our ability to relocate radio marked deer to daylight hours during weather conditions which were suitable for flying. However, only half of deer locations during the winter months were obtained by conventional (aircraft and ground tracking) means. These locations were observed during winter daylight hours, usually between 0900 and 1600 hours PST. Such location data excludes over two-thirds of the 24-hour period and potentially could bias any results based upon these locations. However, use of GPS collars allowed us to record deer locations through the 24-hour day, reducing any potential bias. The major of locations registered by the GPS collars occurred during nocturnal periods (between 1700 and 0630 hours during winter months). Fixes from GPS collars accounted for half of the locations reported during field studies. Our experiences with GPS radio collars and their ability to collect multiple fixes day or night, good weather or poor, suggests that GPS collars provide location and habitat use information of higher quality and reliability. The location error was considerably less with GPS collars compared to VHF collars. We did, however, experience some problems with the GPS collars; remote downloading was not always reliable and some VHF beaconing was weak. Although we were not always able to remotely up-load relocation data, those data were stored and retrieved when the collar was in hand. In spite of these difficulties, we encourage the use of GPS collars in habitat studies whenever budgets allow their purchase.



Table 3-1: A list of mule deer captured and fitted with radio-collars by date, animal

identification, and location in the eastern Washington between 2000-2002.

Capture Date

Animal

Identification

Capture Location

Sex

Estimated

Age

3/24/00 A470 Narvarre F AD 3/24/00 A480 Narvarre F AD 3/24/00 A492 Narvarre F AD 3/24/00 A510 Chelan Butte F AD 3/24/00 A540 Chelan Butte F AD 3/24/00 A602 Chelan Butte F AD 3/24/00 A621 Chelan Butte F AD 3/24/00 A641 Chelan Butte F AD 3/24/00 A661 Chelan Butte F AD 3/25/00 A561 Narvarre W F 3+ 3/25/00 A582 Narvarre W F AD 3/25/00 A605 Narvarre W F AD 3/25/00 A680 Narvarre W F AD 3/25/00 A700 Narvarre W F 5+ 3/25/00 A719 Swakane F AD 3/25/00 A760 Swakane F 5+ 3/25/00 A792 Swakane F AD 3/25/00 A801 Swakane F AD 3/25/00 A805 Swakane F 5+ 3/25/00 A822 Swakane F AD 3/25/00 A841 Narvarre W F AD 3/25/00 A862 Swakane F 3+ 2/27/01 A319 Swakane F AD 2/27/01 B160 Swakane F 3+ 2/27/01 B261 Swakane F 4+ 2/27/01 B430 Swakane F AD 2/27/01 B532 Swakane F 2 2/27/01 C662 Swakane F AD 2/27/01 C941 Swakane F AD 2/27/01 D871 Swakane F YRLG 2/28/01 A131 Entiat F AD 2/28/01 A221 Navarre F AD 2/28/01 A282 Navarre F AD 2/28/01 A383 Entiat F AD 2/28/01 A420 Navarre F AD 2/28/01 A520 Entiat F YRLG 2/28/01 B022 Entiat F AD 2/28/01 B082 Dick Ranch F AD 2/28/01 B122 Entiat F AD 2/28/01 B14 Navarre F AD 2/28/01 B461 Navarre F AD



Capture

Date

Animal

Identification

Capture Location

Sex

Estimated

Age 2/28/01 B662 Entiat F AD 2/28/01 C562 Entiat F AD 2/28/01 C962 Entiat F AD 2/28/01 D843 Dick Ranch F AD 2/28/01 D920 Navarre F AD 1/22/02 B662 Swakane F AD 1/22/02 D319 Swakane F AD 1/22/02 C261 Swakane F AD 1/22/02 Ag201 Swakane F AD 1/23/02 D383 Entiat F AD 1/23/02 Ag071 Entiat F AD 1/23/02 Ag180 Entiat F AD 1/23/02 Ag270 Navarre F AD 1/23/02 Ag121 Navarre F AD 1/23/02 D661 Navarre F AD 1/23/02 D602 Navarre F AD 1/23/02 D42 Navarre F AD 1/23/02 D582 Navarre F AD 1/23/02 D492 Navarre F AD 1/23/02 D48 Navarre F AD 1/23/02 D841 Navarre F AD 1/23/02 D561 Navarre F AD 1/23/02 B965 Navarre F AD 1/24/02 A843 Entiat F AD 1/24/02 D822 Entiat F AD 1/24/02 D131 Entiat F AD 1/24/02 C082 Entiat F AD 1/24/02 C662 Entiat F AD 1/24/02 D52 Entiat F AD 1/24/02 C022 Entiat F AD 1/24/02 C122 Entiat F AD 1/24/02 B562 Entiat F AD



LITERATURE CITED

Cook, R.C., J.G. Cook, D. L. Murry, P. Zager, B.K. Johnson, and M.W. Gratson. 2001.

Nutritional condition models for elk: which are the most sensitive, accurate, and precise? J. Wildl. Manage. 65:988-997.

Firchow, K.M., M.R. Vaugh, and W.R. Mytton. 1986. Evaluation of the hand-held net gun for

capturing pronghorns. J. Wildl. Manage. 50(2): 320-322. Gilmer, D.S., L.M. Cowardin, R.L. Duval, L.M. Mechlin, C.W.Shaiffer, and V.B. Kuechle.

1981. Procedures for the use of aircraft in wildlife biotelemetry studies. Resour. Rep. 140. U.S. Fish and Wildl. Serv., Jamestown, ND. 19 pp.

Mech, L.D. 1983. Handbook of animal radio-tracking. Univ. of Minn. Press,

Minneapolis.107 pp. Snyder, J.P. 1987. Map projections used by the U.S. Geological Survey, Bull. 1532. U.S.

Geological Survey, Washington, D.C. 313 pp. White G.C. and R.A. Garrott. 1990. Analysis of wildlife radio-tracking data. Academic Press,

Inc. San Diego, CA. 383 pp.



Figure 2: Locations of radio marked mule deer in Chelan County, WA between 2000-2002.



CHAPTER 4: MULE DEER MOVEMENT AND HOME RANGES W. L. Myers, W. Moore, and A. Gibson

4.1 Introduction Mule deer move within their home ranges to meet basic needs of forage, water, and security. Comparisons of home range size may reflect some measure of differing levels in habitat quality and availability. Knowledge of seasonal movement patterns and mule deer fidelity to geographic localities over time is important to the mule deer manager when measuring population trends, assessing population and harvest goals, and evaluating harvest strategies. Our goals were to measure seasonal home range sizes and document seasonal movement patterns.

4.2 Methods Deer Movements Migratory animals were distinguished from non-migratory individuals by developing winter and summer home ranges and comparing: 1) distances between centers of activity between seasons and 2) spatial distribution of seasonal locations within seasonal home ranges. Animals were grouped into two classes reflecting degrees of migration: 1) migratory: >5000m between centers of seasonal use areas and/or no overlap of seasonal ranges, or 2) resident: usually <1500 m between seasonal activity centers and considerable (80%) overlap of at least one seasonal range within the other. Airline distances between seasonal home ranges were measured in ArcView 3.2. Home Range Analysis Home range and core area sizes were estimated by calculating the 90% and 50% utilization distributions (u.d.) of an adaptive Kernal home range within the Animal Movement Extension of ArcView 3.2 (ESRI, Redlands, CA). Outliers which we identified were eliminated and home range areas recalculated (Ackerman et al. 1990). We also estimated core use area size defined as the 50% u.d. using the adaptive Kernal home range. Because home range sizes can be highly correlated with number of locations, animals with relatively few locations were excluded from home range analysis. Seasonal home ranges were calculated for summer and winter seasons only; the summer season was defined as the period between May 15th and September 30th each year while winter season calculations were limited to the period between December 15th and March 31st.

4.3 Results Deer Movements Most radio marked mule deer (89%, n=33) showed migratory movement patterns, moving some distance between summer and winter use areas. Migratory deer moved a mean of 48.1 km (29.9 mi) to reach seasonal ranges. The longest distance we observed deer moving between summer and winter ranges was 57.7 km (36.5 mi) and the shortest distance was 18.2 km (11.3 mi). Only four marked deer (11%) were classified as resident, having over-lapping summer and winter use areas. The distribution of summer and winter seasonal ranges of radio marked deer keyed by winter herd groupings (Swakane, Entiat, and Navarre Coulee) is shown in Figure 3.



Seasonal Home Range Analysis Summer home ranges were calculated for 37 female mule deer which were relocated a total of 450 times. Mean size of summer Kernal home range sizes at 90% u.d. was 1212.99 hectares (SD=833.9, 2997.26 ac); the range was 178.4-4486.15 hectares (440.82-11085.12 ac). Mean summer core area measured at 50% u.d. was 283.1 hectares, (SD=230, 699.4 ac); the range was 42.3-1270.9 hectares (104.6-3140.4 ac). The mean elevation of summer home ranges was 1201.34 m (3904.36 ft); elevations ranged from 585.23 m (1902.00 ft) to 1753.40 m (5698.55ft.). Mean home range size at the 50% u.d. during the winter season was 724.62 hectares (1790.5 ac), with a range of 34.671 to 3762.816 hectares (85.670 to 9297.792 ac). Core areas within winter home ranges (50% u.d.) ranged from 6.634 to 668.812 hectares (16.392 to 1652.135 ac) with a mean of 180.045 hectares (444.886 ac). Mean elevations of winter use areas ranged from 405 m (1316 ft.) to 991 m (3222 ft.); the mean elevation was 600 m (1952 ft).

4.4 Discussion Most mule deer wintering above the Columbia River in eastern Chelan County are migratory, moving westerly or northwest in late spring to summer ranges at higher elevations and returning to lower elevation winter use areas in late fall or early winter. Some studies have suggested that mule deer migrate during the fall as a result of snowfall (Russell 1932, Dixon 1934, Leopold et al. 1951, Richens 1967, Gilbert et al. 1970). More recent studies suggest that seasonal movements are the result of seasonal changes in energy requirements of deer and quality and quantity of available forage (Garrott et al. 1987, Myers, et al. 1989). Our study doesn’t lend any support to either hypothesis, but it seems intuitive that higher elevation summer ranges contain lush, succulent forage compared to lower elevation ranges that are used during the summer. There has been local speculation that resident (non-migratory) deer numbers within the study area had declined due to unknown factors (P. Fielder, personal communication) resulting in a higher percentage of migratory deer compared to resident deer in the past. Without baseline information for comparison, it is difficult to determine if changes in numbers or percentages of migratory deer have occurred. However, our observations showed 89% of the deer were migratory which is similar to observations made in western Okanogan County where 95% of radio marked deer were migratory (Myers, et al. 1989); this would suggest that perhaps if changes have occurred, they were slight or affected both migratory and resident deer numbers equally. It is interesting to note that mean aerial distances of migration in Chelan County (48.1 km, range 18.2 – 57.7 km) were similar to those observed in western Okanogan County (51.8, range 19.3 – 83.5 km). Unfortunately, we were unable to closely document movement corridors during spring and fall migrations due to a lack of location points (which was the result of few aerial relocation surveys due to poor flying weather). Mule deer have shown strong fidelities to traditional movement corridors (Myers et al. 1989); disruption of migration caused by disturbance or other factors could have lasting impacts to migratory deer herds. We observed mean summer home ranges (1213 ha) to be considerably larger than winter home ranges (724 ha); similar observations were reported by Myers et al. (1989) for another migratory



deer herd in the North Cascades. Usually winter home ranges are larger than summer home ranges due to increased energy requirements; energy costs are increased during winter (Parker et al. 1984) as snow accumulation creates barriers to deer movement (Kersall 1969, Wallmo and Gill 1971) and reduces forage availability (Loveless 1967). Similarly, forage quality is reduced due to plant senescence during the winter (Short et al. 1966, Wallmo et al. 1977). Conversely, forage is more readily available during summer (Loveless 1967) so deer are not required to search for forage to meet their energy requirements. Our observations would suggest that summering deer need to move large distances to meet those requirements.



LITERATURE CITED

Ackerman, B.B., F.A. Leban, M.D. Samuel, and E.O. Garton. 1990. User's Manual for Program

HOME RANGE. Second Edition. Technical Report 15, Forestry, Wildlife, and Range Experiment Station, University of Idaho, Moscow. 80 pp.

Dixon, J.S. 1934. A study of the life history and food habitats of mule deer in California. Calif.

Fish and Game 20:181-182. Dixon, K.R. and J.A. Chapman. 1980. Harmonic means of animal activity areas. Ecology

61:1040-1044. Garrott, R.A., G.C. White, R.M. Bartmann, L.H. Carpenter, and A.W. Alldredge. 1987.

Movements of female mule deer in northwest Colorado. J. Wildl. Manage. 51:634-643. Gilbert, P.F., O.C. Wallmo and R.B. Gill. 1970. Effect of snow depth on mule deer in Middle

Park, Colorado. J. Wildl. Manage. 34:15-23. Kersall, J.P. 1969. Structural adaptations of moose and deer to snow. J. Mammal. 50:302-310. Leopold, A.S., T. Riney, R. McCain, and L. Tevis. 1951. The Jawbone deer herd. Calif. Fish and

Game Bull. No.4. 139pp. Loveless, C.M. 1967. Ecological characteristics of a mule deer range. Colo. Div. Game, Fish,

and Parks Tech. Bull.20. 124pp. Myers, W.L., R. Naney, and K.R. Dixon. 1989. Seasonal movements and home ranges of female

mule deer in western Okanogan County. PR Prog. Report W-95-R. Wash Dept. of Wildlife, Olympia.

Parker, K.L, C.T. Robbins, and T.A. Hanley. 1984. Energy expenditures for locomotion by mule

deer and elk. J.Wildl. Manage.48:474-488. Richens, V.B. 1967. Characteristics of mule deer herds and their range in northern Utah. J.

Wildl. Manage. 31:651-666. Russell, C.P. 1932. Seasonal migration of mule deer. Ecol. Monogr. 2:1-46. Samuel, M.D., D.J. Pierce, E.O. Garton, L.J. Nelson, and K.R. Dixon. 1983. User's program

manual of program HOME RANGE. Univ.Idaho For.Wildl.and Range Exp. Stat. Tech. Rep. No.15. 64 pp.

___________, D.J. Pierce, and E.O. Garton. 1985. Identifying areas of concentrated use within

the home range. J. Animal Ecology 54:711-719.



Short, H.L., D.R. Deitz, and E.E. Remmenga. 1966. Selected nutrients in mule deer browse

plants. Ecol. 47:222-229. Wallmo, O.C. and R.B. Gill. 1971. Snow, winter distribution, and population dynamics of mule

deer in the central Rocky Mountains. IN: A.O. Hagen, ed. 1971. Proc. snow and ice in relation to wildlife and recreation symposium. Iowa State Univ., Ames.

Wallmo, O.C., L.H. Carpenter, W.L. Regelin, R.G. Gill, and D.L. Baker. 1977. Evaluation of

deer habitat on a nutritional basis. J. Range Manage. 30:122-127. White, G.C, and R.A. Garrot. 1990. Home range estimation. pp 145-180. In G. C. White and R.

A. Garrot. Analysis of Wildlife Radio-Tracking Data. Academic Press, Inc., San Diego.



Figure 3: Map showing the distribution of summer and winter seasonal ranges of radio-

marked female mule deer in Chelan Co., Washington



CHAPTER 5: SURVIVAL PATTERNS AND PREGNANCY RATES W. L. Myers, W. Moore, and A. Gibson

5.1 Introduction Mule deer biologists and managers require basic knowledge of key population parameters to properly manage a mule deer herd and model potential management strategies. These key parameters include population trends, productivity (pregnancy rates), adult survival rates, and mortality sources (Caughley 1977, White and Bartmann 1998). These factors are important ingredients for population projection models. Our goals were to expand upon our knowledge of these key parameters by estimating survival rates and mortality sources of adult female mule deer.

5.2 Methods Survival Rates and Mortality Sources Dead mule deer were located as soon as possible after a mortality signal was received (usually within 7 days). Known fates of radio-marked mule deer and examination of remains and kill sites provided the measure of mortality sources (Wade and Browns 1982). Cause-specific mortalities were grouped into four major categories: 1) Hunting related (includes legal state-licensed and tribal harvests and wounding losses); 2) Poaching; 3) Predation; and 4) Other (includes accidents, road kills, disease, or undetermined cause of death). Radio-marked mule deer which died within 30 days of capture were not included in the survival analysis to eliminate any potential bias related to stress and unknown injury caused by capture. Similarly, radios which failed, were lost, or shed were censored at the time broadcast reception ceased or the radio left the animal. Timing of deaths was compared to determine appropriate temporal groupings for calculating unbiased survival estimates. Daily and annual rates of survival were estimated using the Mayfield (1975) estimator with calculations performed using QuattroPro (Corel Corporation, Ottawa, Canada) speadsheets. Pregnancy Rates We determined pregnancy rates by physically examining adult does captured during March or April (the does were well into the third trimester of pregnancy when fetal development should be large) using a portable sonogram. We collected blood samples from the first group of deer we examined with the sonogram and had the blood samples analyzed for fetal blood protein (fbp) levels (a test for pregnancy). We compared both results of pregnancy tests (sonogram and fbp levels) as a measure of accuracy of the sonogram results.

5.3 Results Mortality Patterns and Survival Rates We investigated the deaths of 14 radio-marked adult mule deer. However, 3 deaths occurred within 30 days of capture and were subsequently censored from analysis. Of the remaining 11 deaths, predation was responsible for 36% (n = 4) of all deaths. We determined poaching to be responsible for 18% (n = 2) deaths, and 45% (n = 5) of the deaths were classified as Other.



Our estimate of daily survival was 0.9995 based upon 21,300 radio days. We observed a mean annual survival rate of 0.8282 for adult female mule deer over three years of monitoring. Pregnancy Rates We examined 60 does for pregnancy status over three years. We observed a mean pregnancy rate of 0.85 (n = 51) in adult does and a mean fetal rate of 1.44 per doe.

5.4 Management Implications Using our estimates of survival, pregnancy, and fetal rates (assuming fetal rates in March or April equal parturition rates), we estimated over-winter doe:fawn ratios and fawn survival rates into the yearling age class to maintain current population levels. At least 34 fawns per 100 does at a fawn survival rate of 0.33 are required to keep adult doe population levels stable.

LITERATURE CITED Caughley, G. 1977. Analysis of vertebrate populations. John Wiley & Sons. New York. 234 p. Mayfield, H. 1975. Suggestions for calculating nest success. Wilson Bulletin 87:456-466. Wade, D. A. and J. E. Brown. 1982. Procedures for evaluating predation on livestock and

wildlife. Texas A&M Univ. Agr. Exp. Stat. Tech. Rep. B-1429. 42 pp. _________ and R. M. Bartmann. 1998. Mule deer management--what should be monitored?

Pages 104-118 in J. C. deVos Jr., editor. Proceedings of the 1997 deer/elk workshop--Arizona. Arizona Game and Fish Department, Phoenix, USA



CHAPTER 6: DISEASE, PARASITE, AND TRACE ELEMENT TESTING W. L. Myers

6.1 Introduction Although starvation and diseases such as meningeal worm infection, tuberculosis, and brucellosis can significantly affect the management of mule deer, few studies of free-ranging mule deer herds have assessed the effects of diseases, parasites, and trace elements. In general, most free-ranging mule deer herds appear healthy, but during times of physiological or nutritional stress, mortality associated with pre-existing conditions may be important. There are other reasons to monitor the health of free-ranging mule deer. Some bacterial diseases which mule deer might be exposed to have potential health and economic impacts to humans. Brucellosis and leptospirosis are infectious diseases that can be transmitted to humans and domestic livestock. Both brucellosis and leptospirosis have caused abortion and infertility in domestic cattle (Kistner 1982, Thorne 1982); consequently, these diseases are of great concern to the domestic livestock industry. Anaplasmosis is another bacterial disease of cattle and wild ruminants which costs the cattle industry millions of dollars, causing weight loss, abortions, and reduced milk production (Thorne 1982). Limited viral disease testing of mule deer has been for those respiratory diseases which affect domestic livestock. These viruses include bovine virus diarrhea (BVD) and infectious bovine rhinotracheitis (IBR) (Kistner 1982), parainfluenza type 3 (PI3), and respiratory syncytial virus (RSV). The extent of these diseases in mule deer is not well documented. Greater attention has been placed upon documenting the presence of blue tongue (BT), and the closely related epizootic hemorrhagic (EHD) disease in mule deer, diseases which have had major economic impacts in the livestock industry. Although mule deer are apparently susceptible to BT-EHD, the role of mule deer in the epidemiology of these diseases is not well understood (Kistner 1982). Meeting nutritional requirements is basic to the physical well-being of mule deer. Although the scope of our project to date does include measuring available forage at the nutritional level, documenting macro and trace element levels from blood samples provides an assessment of essential mineral up-take. There are 23 elements important in body functions; mineral levels can be potentially harmful if present in deficient or excess quantities. Knowledge of adequate element values in free-ranging mule deer is limited, making any additional information important. Mule deer are affected by a number of internal and external parasites. Excessive parasite loads can result in overt disease or declining physical condition. High parasite infestations may result from mule deer crowding when habitat conditions are poor, or when mule deer densities are extremely high. For these reasons and because existing knowledge is limited, health parameters in mule deer should be collected when available. We collected blood and fecal samples from captured mule



deer in Chelan County and other areas in eastern Washington to assess the impact of disease exposure, trace element values, and parasites. Methods

6.2 Disease Exposure Serum samples collected from mule deer captured during the spring of 2000 and 2001 were evaluated by the Washington Animal Disease Diagnostic Laboratory (WADDL) in Pullman, Washington for antibodies to the various serovors of Leptospira interrogans by the microscopic agglutination test (MAT). Since MAT is sero specific, representative isolates of eight serovars were used in the test including L. i. bratislava, autumnalis, canicola, grippotyphosa, icterohaemorrhagiae, pomona, hardjo, and szwajizak. Latex agglutination tests were performed to measure antibody titers of Brucella abortus. A serologic survey for Anaplasma sp. was conducted on samples collected in 1991. Virus neutralization tests were conducted for presence of antibodies to bovine virus diarrhea (BVD), infectious rhinotracheitus (IBR), parainfluenza type 3 (PI3), respiratory syncytial virus (RSV) and blue tongue (BT). Trace Element Analysis Serum samples were also analyzed for trace element concentrations. Selenium (Se) concentrations were measured from samples collected in 2000 and 2001. Copper (Cu), Zinc (Zn), Sodium (Na), Potassium (K), Phosphorus (P), Calcium (Ca), Magnesium (Mg), and Iron (Fe) concentrations were measured in 2000 samples. Fecal Parasite Analysis Fecal samples were evaluated for parasite eggs, larvae, and oocysts by WADDL. Feces were subjected to the fecal flotation technique using sugar solution (specific gravity=1.27) to detect nematode eggs and oocysts. A standard Baermann apparatus consisting of a glass funnel and cheesecloth filter (Beane and Hobbs 1983) was used to detect lungworm larvae in the feces.

6.3 Results Disease Exposure All samples (N=21) were negative for EHD, Leptospira canicolca, L. grippo, L. hardjo, L. ictero, and L. pomona. Two samples (9.5%) showed slight reactions to L. bratislava. Positive reactions were noted for BRS (62%), IBR (43%), BVD (76%), and parainfluenza (62%). All samples were negative for Brucella abortus. Trace Element Analysis Selenium concentrations ranged from 0.10-0.59 (N=20) with a mean of 0.38 (SE=0.35). Mean concentration of zinc was 0.81 (N=19, SE=0.05) and ranged of 0.61-1.6. Potassium concentrations from sera samples ranged from 61-115 (N=19) with a mean of 80.6. Iron levels ranged from 1.6 to 5.8 (N=19) with a mean of 2.48. Mean magnesium concentration was 34.11 (N=19) with a range of 26 to 89. Copper levels ranged from 0.5 to 0.85 with a mean of 0.69 (N=19, SE=0.03). Calcium concentrations ranged from 55-115 with a mean of 97.21 (N=19, SE=2.86).



Fecal Parasite Analysis No coccidia, capillaria, or moniezi were observed in fecal samples (N=22). Parelaphostrongylus sp. was found in 77% (N=22) and nematodirus was observed in one (5%) of the samples.

6.4 Discussion Disease exposure Brucellosis is a disease of major concern to the cattle industry; major conflicts over bison from Yellowstone National Park (YNP), some of which have been seropositive for B. abortus (Thorne et al. 1992), moving north out of YNP into Montana illustrate the extent of the industry’s concern. Fortunately, our results from mule deer from Chelan County and eastern Washington indicated no brucellosis exposure. Leptospirosis is another disease of significant interest to the livestock industry; leptospirosis in cattle has been associated with abortion, stillbirth, birth of weak calves, and infertility (Amatredjo and Campbell 1975, Bey and Johnson 1986). No samples were seropositive for leptospirosis. Little is known about the prevalence of these viruses in free-ranging mule deer. Exposure to these viruses may have resulted from contact with cattle. Based on our observations, no immediate threat to mule deer is likely from these viruses is indicated at this time; however, any long-term effects on herd health are unknown. Trace Element Analysis Selenium values in Chelan County mule deer appear within relatively normal ranges. However, normal Se values for free-ranging mule deer are not well documented. Se deficiency in domestic ruminants has been associated with white muscle disease and a pre-disposition to capture myopathy in domesticated red deer (Herbert et al. 1971). Questions surrounding deficient Se values and mule deer reproduction, growth rates, and recruitment have been raised. The significance of the relative values of the other trace elements (Zn, Na, K, P, Ca, Mg, and Fe) in mule deer is unknown. Zinc is involved in the synthesis of DNA and protein; deficiency of zinc may cause poor growth, anorexia, poor wound healing, or reproductive failure (Ullrey and Allen 1986). Sodium assists in cellular uptake of K ions, amino acids, and glucose; deficiency may cause salt craving, anorexia, and unthriftiness (Ullrey and Allen 1986). Potassium is involved in acid-base balance, osmotic pressure and cell membrane potential; K deficiency has been associated with damage to the myocardiu, skeletal muscle, and kidneys (Ullrey and Allen 1986). Phosphorus is found predominately in skeleton and teeth, but in soft phosphorus tissues assists in energy transformation, cell divisions, and reproduction; rickets in juveniles, anorexia, and irregular estrus have been associated with P deficiencies (Ullrey and Allen 1986). Calcium is found mostly in skeleton, but is important for neuromuscular activity and blood clotting; deficiency may cause rickets in juveniles and osteomalacia in adults (Ullrey and Allen 1986). Magnesium assists enzmes involved in muscular contraction, nerve conduction, and synthesis of protein, fat, carbohydrate, and nucleic acids (Ullrey and Allen 1986). While hemoglobin has



most of an organism’s iron, Fe assists in oxygen and electron transport; Fe deficiency has been associated with listlessness (Ullrey and Allen 1986). The element values we observed represent baseline information for free-ranging mule deer in North America. Fecal Parasite Analysis Very few parasites were found in fecal samples from Chelan mule deer. However, the most prevalent was Parelaphostrongulus; the impacts of this lungworm are unknown but of concern to deer managers.

6.5 Conclusions We were unable to determine any immediate threats to Chelan County mule deer from disease, parasite loads, or trace element deficiencies or poisoning. The level of disease exposure likely suggests some contact with domestic cattle. There is little probability of mule deer serving as reservoirs for disease that might threaten the local livestock industry. Analysis of trace elements showed Se and other trace element values to within normal ranges for deer but deficient when compared to domestic livestock values. The impacts of Se and Cu deficiencies on free-ranging mule deer are unknown although the importance of Se and Cu in cellular protection are well understood; the debate on if and how Se and Cu affects ungulate reproduction and recruitment will continue. The incidence of lungworm, Parelaphostrongulus sp., is a cause of concern; however, there is no evidence to indicate that lungworm infections in mule deer are a cause of mortality. Even though we were unable to measure any direct effects to mule deer at this time from disease, parasites, or trace element levels, any of these conditions could significantly impact herd health individually or collectively given declining habitat conditions. Certainly, these data add to the knowledge base of mule deer disease exposure, parasite infestations, and trace element values.



LITERATURE REVIEW Amatredjo, A. and R.S.F. Campell. 1975. Bovine leptospirosis. Vet. Bull. 43:875-891. Beane, B.R. and N.T. Hobbs. 1983. The Baermann technique for estimating Protostrongylus

infection in bighorn sheep: Effect of laboratory procedures. J. Wildl. Dis. 19:7-9. Bey, R.F. and R.C. Johnson. 1986. Current status of leptospiral vaccines. Prog. Vet. Microbiol.

Immun. 2:175-197. Herbert, D.M. 1971. White muscle disease in the mountain goat. J. Wildl. Manage. 35:752-756. Kistner, T.P. 1982. Diseases and parasites. Pp181-217. In J.W. Thomas and D.E. Toweill, eds.

Elk of North American: Ecology and management. Stackpole Books, Harrisburg, PA Thorne, E.T. 1982. Letospirosis. Pp46-52. In E.T. Thorne, N. Kingston, W.R. Jolley, and R.C.

Bergstrom. Diseases of wildlife in Wyoming. Wyoming Game and Fish Department, Cheyenne, Wyoming

__________, N. Kingston, W.R. Jolley, and R.C. Bergstrom. Diseases of wildlife in Wyoming.

Wyoming Game and Fish Department, Cheyenne, Wyoming Ullrey, D.E. and M.E. Allen. 1986. Principles of zoo mammal nutrition. pp516-532. In M.E.

Fowler, ed. Zoo and wild animal medicine. W.B. Saunders Co., Philadelphia, PA. 1127pp.



CHAPTER 7: WINTER HABITAT USE W. R. Moore, W. L. Myers, W. Gaines, J. Agee, K. Raedeke and S. Bushnell

7.1 Introduction The ability to understand and describe how mule deer use winter habitats within Chelan County is necessary for deer managers to maintain, restore, or enhance the necessary habitat components required by mule deer to survive. Winter ranges across the west vary in composition and the components selected by mule deer are not always consistent from one winter range to the next. Studies of winter habitat use by mule deer have found a variety of factors which appear to affect or contribute to use patterns during the winter season (Gilbert et al. 1970, Bloom 1978, Thomas, et al. 1979, Walmo 1981, Haerstad 1985, Dawson, et al. 1990, Schoen and Kirchhoff 1990, Armleder, et al. 1994, Mackie, et al. 1998). These variables range from mule deer use of snow-intercept cover to specific elevation bands (Armleder et al. 1994). This chapter documents our investigations of winter habitat use by radio-collared mule deer within of Chelan County, Washington. To understand mule deer winter habitat use, it is necessary to identify a general winter use area, map specific habitat variables within the winter use area, assess the availability of these habitat variables, and estimate mule deer use of habitat features. Evaluating the topographic, vegetative and anthropogenic variables within the winter use area will generate specific classes to be analyzed.

7.2 Methods Our approach was to perform a univariate analysis of each habitat variable associated with mule deer use to gain a measure of mule deer preferences. Those variables shown to have positive significance will be applied to a multivariate analysis (See Chapter 8). GIS Analysis We created data layers and analyzed habitat use patterns using the geographic information system (GIS) software ArcGIS 8.1 (ESRI, Inc., Redlands, CA) with the Spatial Analysis Extension. All GIS analyses were preformed in Central Washington University's Geography and Resource Management Lab. Winter Use Area A total of 890 relocation points collected from 37 radio marked does (34 VHF and 3 GPS radio collars) were used in evaluating winter use (Figure 4 and Figure 5). Of these location points, 271 points from aerial surveys during winter 2001 and 2002 were used to calculate a home range at a 95% adaptive kernal algorithm (Hooge and Eichenlaub 2000). This home range defined the area of use during winter. We omitted location points collected from ground surveys or GPS collars to eliminate any potential bias resulting from limited accessibility associated with winter ground surveys or including a large portion of GPS derived location points (539 of 890) from only three deer.



Habitat Variables Habitat variables included topographic variables (aspect, slope, and elevation), distance to roads, and vegetative variables (distance to bitterbrush, herbaceous productivity, and presence or absence of cover). Within these variables, classes were created based on the characteristics of the winter use area and the accuracy of the telemetry relocations. Telemetry error was accounted for by increasing minimum mapping unit size to be equal or greater than the mean error plus one standard deviation. This increased the probability of relocations to actually fall within the assigned class. Applying the Spatial Analyst Extension to a 30 m Digital Elevation Model (DEM) within ArcGIS 8.1 software allowed us to create the multiple classes needed for analysis. Topographic Variables The aspect, slope and elevation layers were created from the DEM using the Spatial Analyst Extension to ArcGIS. Five aspect classes (Figure 6 and Figure 7) we used included flat, north, east, south, and west. However, the flat classification was removed from the data set since no mule deer relocations fell within the flat class and this created low cell probabilities in the statistical analysis. We used five slope classes (Figure 8 and Figure 9): Class 1:0-8 degrees, Class 2:9-17 degrees, Class 3:18-26 degrees, Class 4:27-35 degrees, and Class 5:>35 degrees. Two elevation classes were used (Figure 10 and 11). The resulting classes were less than or equal to 750 meters and greater than 750 meters. The highest elevation in the winter use area was 1400 meter and the lowest elevation was 200 meters. Anthropogenic Variables Two road coverages were used in the analysis. The first was a 1:24,000 coverage obtained from the Wenatchee/Okanogan National Forest. The second road 1:100,000 coverage obtained from Washington Department of Fish and Wildlife. Following analysis, we determined road status on the winter range was not quantifiable due to an inability to determine which roads were open to vehicular traffic. Roads blocked by snow were not functional during the winter and yet snow depths varied throughout the season allowing some roads to be open intermittently. Our attempts to accommodate for these variables, plus snowmobile use, were inadequate. Consequently, we omitted distance to open roads as a predictor variable. Vegetative Variables Vegetation classes and the map (Figure 12 and Figure 13) used were the result of an analysis of the Grizzly Bear Level II data obtained from Wenatchee/Okanogan National Forest. These data were originally created from two images taken in July and August of 1986 by the Landsat MSS in four bands (green, red, and two infrared) at a resolution of 57m x 57 m. It was geo-referenced to the Universal Transverse Mercator (UTM) map projection, zone 10 (Gaines et al. 1994). The image classification created 56 vegetation classes, which were reclassified in 1996 to account for major disturbances caused by fires in 1988 and 1994. An accuracy assessment of these data were performed within our study by classifying 57 vegetation transects data taken in the summer of 2001. These data were interpreted into classes defined by the original and updated Grizzly Bear Level II data. This resulted in an accuracy of 72%, although the definition of non-vegetated was expanded to include areas dominated by cheatgrass (Bromus tectorum) to identify these areas. When expansion of the non-vegetated class was excluded, the accuracy was 79%.



The classes that contained bitterbrush were mapped (Figure 14 and Figure 15); an analysis of distance to bitterbrush was assessed using the following distance classes: 0 – 400 meters, 400.1 – 800 meters, 800.1 – 1200 meters and 1200.1 + meters. The width of these classes was scaled to account for the telemetry error. Distance was used since the bitterbrush habitats were small in area; the result was very few data points falling within the actual area of the bitterbrush stands. If deer were using these stands for foraging, their distribution patterns would be associated around or near the areas of bitterbrush. All herbaceous and shrub classes defined in the Grizzly Bear Level II data set were used to create the herbaceous productivity classes. Forested classes were excluded from productivity analysis. One-meter square of dry weight herbaceous clippings (see vegetation methodology) was collected at sampling sites to measure productivity within a stand or class. Productivity classes were created for combinations of elevations and aspects. Our analysis of herbaceous productivity (Figure 16 and Figure 17) used three classes, 1) high productivity = herbaceous areas <=750 meters, 2) low productivity = herbaceous areas >750 meters, 3) non-herbaceous areas. The analysis was performed using ArcGIS 8.1 on data points that were inclusive within each class. Cover was selected from the Grizzly Bear Level II and updated data set classes that contained forested or riparian stands. Shrub stands were not included as cover in this analysis. Two classes used were within cover and outside cover. A second group of cover classes was created to analyze distance to cover. The distance intervals were 0 – 400 meters, 400.1 – 800 meters, 800.1 – 1200 meters and 1200.1 + meters. Data Analysis Logistic regression may be used to build the most parsimonious model containing only significant variables (Hosmer and Lemeshow 1989, Mladenoff et al. 1995) or to evaluate the contribution of specific variables to describe how animals use their habitats (Mace et al. 1999, Gaines 2002). We used univariate logistic regression to explore the contribution of specific variables to describe winter habitat use by mule deer and to sort through variables to include in our multi-variate model (see Chapter Six). All location points within the winter use area (GPS Telemetry = 539, Aerial Telemetry = 271, Ground Telemetry = 80) were pooled and analyzed with logistic regression against random points to determine significant differences between habitat use and availability (Alldredge et al. 1998, Erickson et al.1998, Mace et al.1998). Availability was determined by generating 890 random points within the defined winter use area (Figure 4). Mule deer use was positively associated when coefficients for a variable were positive and significant. Conversely, deer use was considered to be negatively associated with a habitat variable when the coefficient was negative and significant. When a variable did not illustrate significance, use levels were assumed to be equivalent to availability. Variables were considered significant at p<0.05. All distance measurements between random and telemetry points were estimated using ArcInfo Workstation command Latticespot.



Since herbaceous productivity data were not normally distributed within elevation classes (< or > 750 meters), we used a Mann Whitney U test in statistical analyses. Similarly, our analysis of aspect was completed independently within the two elevation classes using a Kruskal Wallis test because aspect data were not normally distributed.

7.3 Results Topographic Features

Aspect Radio marked mule deer were positively associated with western aspects (Table 7-1) and conversely negatively associated with northern aspects (Table 7-2). Mule deer use was equivalent to availability in eastern and southern aspects (Table 7-3).

Slope Mule deer habitat use was positively associated with slopes with an angle larger than 17 degree (Table 7-1), while slope angles between 8 and 17 degrees were used in proportion to availability (Table 7-3). Relatively flat slopes of 0 to 8 degrees were negatively associated with use by mule deer (Table 7-2).

Elevation

Elevations less than or equal to 750 meter were positively associated with use by mule deer (Table 7-1). Conversely, elevations greater than 750 meters were negatively correlated to mule deer use (Table 7-3). Cover Mule deer use was positively associated to areas without cover (Table 7-1,Table 7-2This was also supported by the analysis of distance to cover. When analyzing distance to cover, mule deer habitat use was negatively associated with the class of 0 – 400 meters as well. All other classes greater than 400 meters illustrated mule deer use to be equivalent to habitat availability (Table 7-3). Distance to Bitterbrush The classes less than 1200 meters from bitterbrush all were negatively associated with use by mule deer during the winters of 2001 and 2002 (Table 7-2). The remaining class of 1200 + meters resulted in a significant positive (Table 7-1). Therefore mule deer use in habitats surrounding bitterbrush stands is less than what is available within the winter use area. Herbaceous Productivity The Mann Whitney U test illustrated a significant difference in productivity between elevation classes. The class <750 meters had greater productivity than >750 meters (n=114, p=0.001, tied ranks<25%). Our analysis showed mule deer use to be positively associated to both herbaceous productivity classes (Table 7-1). Conversely, mule deer use was negatively associated with non-herbaceous areas during the winters of 2001 and 2002 (Table 7-2).



7.4 Discussion The goal of the univariate analyses was to provide information on which variables to include in our multivariate winter habitat use model. Each univariate variable does not explain a large portion of the variability in mule habitat use preferences, but the univariate analysis does provide details of the relationship between use and availability for each class within a variable. These details are seen in p, ß and r² values which may be useful to a manager with more specific questions about habitat components that are not included within the model (see Table 7-1, Table 7-2 and Table 7-3). These values should be evaluated with consideration of high asymptotic correlation within most of the univariate variable classes, which is a violation of the assumptions of logistic regression. These high asymptotic correlation values were reduced within the multivariate model to better meet these assumptions (See Chapter 8). Topographic Features The strength of the relationship for mule deer use was highest in western slopes of the winter use area (see ß value Table 7-1). This may be a result of a combination of factors. One-meter squared clippings taken during our vegetation analysis revealed the highest mean of grams of herbaceous biomass per square meter on western aspects (n=114, mean: west aspects = 94.6, north aspects = 73.7, east aspects = 92.5, south aspects = 60.5). The data were not significantly different (p>0.05) when analyzed using a Kruskal-Wallis test. Herbaceous material may be one factor which influences aspect selection, but more investigation is necessary to determine if there is an actual significant difference in herbaceous productivity due to aspect within the study area. Interestingly, eastern aspects were avoided. These areas make-up 30.5% of the available habitat compared with only 22.2% of the winter use area in western aspects. Yet, herbaceous productivity values obtained from both east and west slopes were vary similar. It is possible that solar insolation may be affecting aspect use. Solar insolation probably differs between these two aspects with west facing slopes being exposed to solar radiation for greater time periods during winter months. The result would be that western aspects are more appealing than east facing slopes because of less or snow-free areas, earlier plant development, and/or greater opportunity for thermal regulation. Mule deer use of southern aspects was proportional to availability indicating some use of these areas. This is potentially due to the insolative qualities of southern aspects receiving the greatest intensity of solar insolation which in-turn may reduce the snow load on this aspect. Hobbs (1989) determined that snow load was one factor that increased the deer mortality during winter. Conversely, northern aspects were avoided for similar reasons (snow loads being greatest on these aspects). Only slope class 3 (17-35 degrees) showed a positive correlation with deer use; all other classes were used in proportion to availability except for class 1 which had a negative response. Rugged, relatively steep slopes like Class 3 potentially provide mule deer with escape cover from predators and harassment. These slopes, when orientated with a southern or southwestern exposure, would receive greater levels of direct solar radiation from a winter sun, resulting in potentially less snow cover and exposed vegetation. For wintering mule deer, this would mean less energy costs in movements and more readily available forage.



Elevations of less than or equal to 750 meters were preferred (Table 7-1). This is likely the result of several factors. The first being related to trends observed during the vegetation investigation portion of this project. Herbaceous areas were discovered to be more productive in elevations less than or equal to 750 meters. Bitterbrush was found to illustrate larger crown areas and produce a greater number of leaders. The mass of these leaders was also greater in elevations less than or equal to 750 meters. This information is considered only a trend since the number of herbaceous plots and bitterbrush plants sampled were not adequate to capture the variance in high and low herbaceous areas or bitterbrush stands. Another explanation for the significant amount of use at low elevations may be a result of lower snow pack, which reduces the energy demands of locomotion and increases potential for available forage. Cover has been considered important to mule deer in winter for two reasons: first, for providing snow-intercept cover, and second, as thermal cover. When looking at cover use for mule deer in Chelan County during the winters of 2001 and 2002, cover was not highly used. This may be due to these winters producing less than average snow pack and higher than average temperatures. This would reduce the necessity of snow-intercept cover and thermal cover. It may also potentially be attributed to the reduced value of the existing cover due to the fires of 1994. Distance to Bitterbrush Studies have shown bitterbrush to be selected over other available forage when present due to the high nutrient content present in the leaders of the plant (Burrell 1977,White 1981,Williams & Lillybridge 1987, and Zlatnik 1999). However, mule deer use was not positively associated with bitterbrush during our study.. This may be attributed to the fact that a large portion of these stands are of young seral stage as a result of much of the area being disturbed by the Tyee and Dinkleman fires (Figures 18 and 19). The current condition of these stands may range from regenerating at a normal rate to no regeneration. Because of varying rates of regeneration or lack of any development due to site disturbance, the real effect appears to be a response similar to that of no bitterbrush. It is possible that because of high levels of disturbance or other unknown factors, these bitterbrush stands provide relatively little available forage across the landscape; deer may not be using them due to a paucity of stands (e.g., lack of availability) and have shifted to more readily available herbaceous forage. Another potential reason could be the large amount of herbaceous material available due to more exposed areas caused by warmer than average temperatures during the winter of 2001 and 2002, drawing mule deer to areas with the greatest herbaceous productivity. Herbaceous Productivity The most descriptive univariate variable resulted from the analysis of herbaceous productivity. This analysis explained 24.9% of the variation in mule deer distribution across the winter use area. High use of areas containing increased herbaceous productivity could be attributed in part to the relatively mild winters of 2001 and 2002 in Chelan County; temperatures were unseasonably warm and precipitation levels low (NOAA Weather data online). During winter ground telemetry surveys, we observed grasses such as blue grass (Poa spp.) growing abundantly during the months of February and March. Blue grass is a perennial and one species known to be selected by mule deer during winter on the Entiat Wildlife Area (Burrel 1981). With little to no snow cover, small perennial grasses are exposed to allow foraging; conversely, these areas may not receive as much use when snow conditions are greater than normal.



Non-herbaceous areas consisted of non-vegetated areas (bare soil, rock, etc.), forested areas where cover such as ponderosa pine (Pinus ponderosa) douglas fir (Pseudotsuga menziesii) occurred, and riparian areas. Mule deer in Chelan County avoided these areas during the winters of 2001 and 2002. Table 7-1: Variable classes preferred by mule deer in Chelan County, WA during the

winters of 2001 and 2002. Variable Class ß Value Variables p

Value % Use % Available

Aspect West 2.882 0.000 29.6 22.2 17° - 25.9° 0.726 37.4 28.0 Slope 26° - 34.9° 0.652

0.000 33.5 27.0

Elevation <= 750 meters

1.874 0.000 92.1 64.3

Cover Outside Cover

3.298 0.000 96.4 73.0

Dist. to Cover

1200+ meters

2.1325 0. 000 16.4 6.5

Dist. to Putr

1200+ meters

8.694 0.000 24.9 2.6

Prod. High 2.634 83.6 41.5 Herbaceous Prod. Low 0.741

0.000 10.1 25.4

Table 7-2: Variable classes avoided by mule deer in Chelan County, WA during the winters

of 2001 and 2002. Variable Class ß Value Variables p

Value % use % Available

North -1.311 6.3 17.7 Aspect East -0.664

0.000 20.9 30.5

Slope 0° - 8° -0.923 0.000 3.0 11.8 Elevation > 750 meters -0.874 0.000 7.9 35.7

Cover Inside Cover -2.298 0.000 27.1 3.6 Dist. to Cover

0 – 400 meters -1.567 0.000 37.5 71.5

0 – 400 meters -2.495 56.6 71.1 400.1–800

meters -2.65 13.3 19.4

Dist. to Putr

800.1-1200 meters

-2.549

0.000

5.2 6.9

Herbaceous Non-herbaceous

-2.104 0.000 6.3 33.1



Table 7-3: Variable classes where mule deer use was equivalent to availability in Chelan County, WA during the winters of 2001 and 2002.

Variable Class % use % Available Aspect South 29.6 43.2

8.1° - 17° 19.9 23.7 Slope 35° + 6.2 9.6

400.1-800 meters 28.4 15.4 Dist. to Cover 800.1-1200 meters 17.6 6.6

LITERATURE CITED Armleder, H.M., M.J. Waterhouse, D.G. Keisker, and R.J. Dawson. 1994. Winter habitat use by

mule deer in the central interior of British Columbia. Canadian Journal of Zoology 72:1721-1725.

Burrell, G. C. 1977. Bitterbrush (Purshia tridentata) in the Winter Ecology of the Entiat Mule

Deer Herd. M.S. University of Washington, Seattle. Dawson, R.J>, H.M. Armleder, and M.J. Waterhouse. 1990. Preferences of mule deer for

Douglas-fir foliage from different sized trees. J. Wildl. Manage. 54:378-382. Gaines, W.L., R.H. Naney, P.H. Morrison, J.R. Eby, G.F Wooten, and J. A. Almack. 1994. Use

of landsat multispectral scanner imagery and geographic information systems to map vegetation in the North Cascades Grizzly Bear Ecosystem. International Conference on Bear Research and Management 9(1):533-547.

Gaines,. W.L. 2002. Relationships among black bears, roads and habitat in the North Cascades of

Washington. Ph.D. Dissertation , University of Washington, Seattle. Gilbert, P.F., O.C. Walmo, and R.B. Gill. 1970. Effect of snow depth on mule deer in Middle

Park, Colorado. J. Wildl. Manage. 34:15-23. Harestad, A.S. 1985. Habitat use by black-tailed deer on northern Vancouver Island. J. Wildl.

Manage. 49:946-950. Hooge P. N. and B. Eichenlaub. 2000. Animal movement extension to Arcview. ver. 2.0. Alaska

Science Center - Biological Science Office, U.S. Geological Survey, Anchorage, AK, USA

Hobbs, T. N. 1989. Linking Energy Balance to Survival in Mule Deer: Development and Test of

a Simulation Model. Wildl. Monog. No. 101 Hosmer, D.W., and S. Lemeshow. 1989. Applied logistic regression. Wiley and Sons, New

York.



Mace, R.D., J.S. Waller, T.L. Manley, K. Ake, and W.T. Wittinger. 1999. Landscape evaluation

of grizzly bear habitat in western Montana. Conservation Biology 13(2):367-377. Mackie, R.J., D.F. Pac, K.L. Hamlin, and G.L. Dusek. 1998. Ecology and management of mule


Mladenoff, D.J., T.A. Sickley, R.G. Haight, and A.P. Wydeven. 1995. A regional landscape

analysis and prediction of favorable gray wolf habitat in the Northern Great Lakes Region. Conservation Biology 9(2):279-294.

Thomas, J.W. editor. 1979. Wildlife habitats in managed forests: the Blue Mountains of Oregon

and Washington. U.S. Forest Service Agricultural Handbook 553. Washington, D.C., USA.

Walmo, O.C. 1981. Mule and black-tailed deer of North America. Wildlife Management

Institute, Washington, D.C. White, S.M.and B.L. Welch 1981. Paired Comparison: A Method For Ranking Mule Deer

Preference For Various Browse Species. Logan, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 4p.

Williams, C.K. and T.R. Lillybridge. 1987. Major indicator shrubs and herbs on National Forest

of eastern Washington. USDA Forest Service. Pacific Northwest Region. Portland, OR R6-TM-TP-304-87

Zlatnik, E. 1999 (revised from Bradley, A. 1987). Purshia tridentata. In: Fire Effects

Information System 1996 (Online). Available: www.fs.fed.us/database/feis/. GIS Data Depot. http://www.gisdatadepot.com



Figure 4: Map showing locations of radio-marked deer with defined the winter use area in

Chelan Co., Washington



Figure 5: A map showing locations of radio-marked deer which defined the winter use area

in Chelan Co., Washington



Figure 6: A map showing aspect classes of mule deer winter ranges in Chelan Co.,

Washington



Figure 7: A map showing aspect classes of winter ranges in Chelan Co., Washington



Figure 8: A map showing slope classes of mule deer winter range in Chelan Co.,

Washington



Figure 9: A map showing slope classes of mule deer winter range in Chelan Co.,

Washington



Figure 10: A map showing elevation classes of mule deer in winter range in Chelan Co.,

Washington



Figure 11: A map showing elevation classes of mule deer in winter range in Chelan Co.,

Washington



Figure 12: A map showing forest vegetation classes within Chelan Co., Washington



Figure 13: A map showing forest vegetation classes within Chelan Co., Washington



Figure 14: A map showing bitterbrush distribution with mule deer winter range in Chelan

Co., Washington



Figure 15: A map showing bitterbrush distribution with mule deer winter range in Chelan

Co., Washington



Figure 16: A map showing herbaceous productivity classes on mule deer winter range in




Figure 17: A map showing herbaceous productivity classes on mule deer winter range in




Figure 18: A map showing the boundaries of the Tyee and Dinkleman fires in Chelan Co.,

Washington



Figure 19: A map showing the boundaries of the Tyee and Dinkleman fires in Chelan Co.,

Washington



CHAPTER 8: WINTER HABITAT MODELING W. R. Moore, W. L. Myers, W. Gaines, J. Agee, K. Raedeke and S. Bushnell

8.1 Introduction The technology available in wildlife science has been changing at a dramatic rate over the last decade; these improvements have provided useful tools to wildlife managers and scientists. Wildlife relocation data sets that once were composed of weekly telemetry flights are being replaced with GPS data sets composed of hundreds of relocations per animal during a given season with an accuracy that was not possible with conventional very high frequency radio collars; the result has been exceptional level of information for evaluating habitat use. The ability to analyze attribute variables at a landscape scale with GIS has provided the access to data that wildlife managers had difficulty dealing with before. Statistical analysis that was once limited to the chi-square, goodness-of-fit test has evolved into the ability to easily analyze multiple variables. Multiple variable analyses such as logistic regression and principle component analysis have the ability to determine if a combination of variables explains the variance of habitat use illustrated by a particular wildlife population. In this chapter, the results from the univariate analysis discussed in Chapter Seven will be applied to create a multivariate model of mule deer habitat use in Chelan County, Washington. The objective for the multivariate model is to combine variables that will best explain the variance in female mule deer habitat selection, while including variables that are important from a manager’s perspective. We will explore a combination of topographic, anthropogenic and vegetative variables that will predict the probability of mule deer use within the defined winter use area. Model results will be applied to all winter use areas between Wenatchee and Chelan. In turn, a map will be created which graphically estimates the potential of each site for present or future use by mule deer. Such information will be invaluable to deer managers when making management decisions for this area. It will allow managers to more effectively assess how and where habitat improvements and protection will be most effective, as well as to provide guidelines for future management activities.

8.2 Methods Statistical Analysis We conducted multivariate logistic regression with SYSTAT 10.0 software (SPSS, Incorporated, Chicago, IL) for our analyses. Variable classes were checked to assure all cell value were valid and all asymptotic correlation values were less than 0.55. ArcGIS 8.1 with Spatial Analysis Extension (ESRI, Redlands, CA) was used in the creation of habitat classes and analysis to evaluate the relationship between telemetry and random points. All GIS layers were developed and analytical methods were preformed at Central Washington University’s Geography and Resource Management Lab. We used computer facilities at the USDA Forest Service’s Science Research Laboratory in Wenatchee, Washington for additional statistical analysis.



Variable Selection Significant differences were determined during the univariate analysis within topographic, anthropogenic and vegetative variables (see Chapter Five). Given these differences, variables were selected by three criteria; 1) identifying variables with logistic regression coefficients that have the potential to contribute to the explanation of mule deer resource selection relative to random availability, 2) selecting variables that are consistent and may be supported by the current literature, 3) selecting variables that may be important to managers. Assessing Biases Potential biases were identified and evaluated to determine their effect on the overall model. Potential biases were the result of the methodologies used in capture site selection and data collection, both of which were the result of time and funding limitations. The winter use area, as determined by the application of a 95% adaptive kernel, was split into southern and northern portions; this occurred due to the method of capture, which limited the selection of animals to be sampled. The introduction of GPS collars, which had been programmed to collect location fixes at six-hour intervals, resulted in a large portion of the overall location data set being comprised of locations from three individual animals wearing GPS collars during the winter of 2002. GPS collars collected and stored location data even during nighttime hours and poor weather; compared with daylight only locations from VHF collars, locations from GPS collars may illustrate significant differences when compared to the VHF relocation portion of the data set. Nighttime hours were determined by averaging nighttime hours from January 1st to April 1, which was defined as 18:00 to 06:59 hours. Resource Selection Function We used logistic regression, seasonal telemetry locations, and an equal number of random coordinates to model the probability of occurrence of female mule deer as a function of map variables (Manly et al. 1993, Mace et al. 1996, Mace et al. 1999). We calculated seasonal resource selection functions (RSF) for used (telemetry) and available (random) resources using equation 8.7 from Manly et al. (1993:127). The RSF values represented the relative probability of a mule deer doe using a habitat given a unique set of variable conditions. Manly et al. (1993) referred to this approach as a design II, in which use of resource units by individual animals was evaluated relative to the availability of those units in the entire study area.

8.3 Results Differences between the southern and northern portions of the winter use area were analyzed and no differences were detected (Mann-Whitney U, p ≥ 0.156). Potential GPS biases were analyzed to determine if significant differences in the model occurred when potentially biased portions of the data set were removed (Table 8-1). The winter logistic model was significant (-2LL = 111.076, χ² = 426.196, df = 6, p = 0.000, r² = .286), and all variables made significant contributions. The model correctly classified 79.7% of used sites and 63.3% of random sites. All classes were included in calculations since model results were significant (p = 0.000). Resulting RSF values were linear and, subsequently scaled from 0 to100 by solving a first order equation using the minimum and maximum RSF values. The resulting equation (y = 39.41686687 x + 26.2768013) was then applied to all intermediary



values. This was preformed to assist in future applications of the model when applied with other variables under varying circumstances. The sampled female mule deer were most strongly associated with low elevation western aspects (β = 2.565) that contain herbaceous plant associations (β = 1.804). Other important variables contributing to the model were non-herbaceous areas (β = -1.535) and elevations ≤750 meters (β = 0.860). The combinations of these variables and their associated classes (Figure 20) formed twenty-four RSF values scaled between 0 – 100 (Table 8-2). These probability levels illustrate where mule deer will most likely distribute themselves across the winter range given the variable included in the model. We created maps (Figure 22, Figure 23, Figure 24, Figure 25, Figure 26, and Figure 27) which illustrate these sites by probability ranges.

8.4 Discussion Day and Night Use Bias Assessment The analysis of the GPS data indicates that there is no difference between day and night habitat use. The analysis of these data sets resulted in consistent logistic regression coefficients, which indicates the model’s variables are equally significant during periods of daytime or nighttime use for the three GPS collared animals (see Table 8.3). Some slight differences were noted in slope use between night and daylight hours among the GPS marked deer. The β values for western slopes decrease slightly and increase in the southern slope class during daytime. This observation suggests that female mule deer use of southern slopes is potentially preferred during periods of daylight. This observation is supported by the larger All Day data set (Table 8-3). Similar patterns use of areas within low herbaceous productivity (less than available) during nighttime compared favorably with use levels during daylight hours as illustrated by the GPS Night data set, the entire GPS data set, GPS Day data set, and the All Day data set. GPS and Model Use Assessment An analysis using GPS data sets resulted in r² values greater than the original model (Table 6.3). This may be attributed to increased accuracy of the GPS telemetry data as compared to VHF radio data. VHF telemetry error approached ten times the error that occurs with a three-dimensional GPS relocation and three times the error of two-dimensional GPS relocation. This increased accuracy reduces the random sampling noise due to misclassification of relocation points even though classes were increased in size to incorporate maximum telemetry error. But classes such as aspect could not be broadened enough to accommodate for the radio data error. Consequently, random sampling noise could affect the ability of the model to explain the variation in mule deer habitat selection. The model has a greater capacity to explain more variation of the GPS collared animals. Additional research would assist in model validation and evaluation of variation caused by differences in telemetry error between GPS and VHF collar data. It is also possible that the increased r² values may be attributed to a reduction in variability of female mule deer use due to the small sample size of the GPS collared animals. The model is potentially being influenced by the GPS data set in the elevation classes. The β values of the GPS data set illustrate a strong preference of use to the elevation class of ≤750 meters. When a data set from any one of the GPS collared animals is removed, preference is reduced, suggesting most GPS relocations fell within the ≤750elevation class, as is the case.



However, since most deer were found to occupy elevations below 750 meters, model outputs may be enhanced by these large, precise data sets. The logistic regression coefficients remain relatively constant throughout the remaining variable classes. This indicates remaining variable classes are not dramatically influenced by the biases associated with day and night use or the GPS portion of the data set.

8.5 Management Implications Model results ranking mule deer winter range probabilities were grouped into twenty-four probability levels (RSF Values) based on the combination of aspect, elevation and herbaceous productivity (Table 8-2). These probability levels illustrate where mule deer will most likely distribute themselves across the winter range given the variable included in the model. The probability levels have been rescaled from 0 – 100. Managers will be able to combine these data with other data of similar scale and evaluate management options based upon multiple criteria. Since these levels of probability can be interpreted as an indicator of mule deer habitat quality, the manager can select a given level of habitat quality for a desired habitat enhancement project, identify lands for purchase which would benefit mule deer, make better informed decisions on how a given project might adversely affect wintering mule deer, or determine where a new project could be completed with the least amount of harm to mule deer winter habitat. Table 8-1: Description of data removal to assess the influence of biases on the mule deer

winter use model. Portion of the data set

Removed Remaining Portion of the Data set Analyzed

Use / Relocation n =

Available/ Random

n = All Night Relocation All VHF & GPS Day 576 576 All Night & All VHF GPS Day ** 225 220

All VHF & All GPS Day GPS Night** 314 307 All VHF GPS ** 539 531

GPS 148.121 All other GPS & VHF 634 634 GPS 148.270 All other GPS & VHF 844 844 GPS 148.071 All other GPS & VHF 653 644

• Use and Availability sample size may be slightly different due to the removal of the class flat from the aspect variable.

** All GPS relocations occurred in the northern portion of the winter use area therefore availability / random data was pulled only from the northern portion of the winter use area for these analyses. Table 8-2: Resource selection function values determined from logistic regression

coefficients. Aspect (N, E, S, W) Herbaceous

Productivity Elevation RSF Value

East Non >750 m 0.00 North Non >750 m 1.50 South Non >750 m 9.09



Aspect (N, E, S, W) Herbaceous Productivity

Elevation RSF Value

East Non ≤750 m 19.81 North Non ≤750 m 21.32 East Low >750 m 27.37

North Low >750 m 28.88 South Non ≤750 m 28.91 West Non >750 m 30.65 South Low >750 m 36.46 East Low ≤750 m 47.19

North Low ≤750 m 48.69 East High >750 m 49.54 West Non ≤750 m 50.46 North High >750 m 51.04 South Low ≤750 m 56.28 West Low >750 m 58.02 South High >750 m 58.63 East High ≤750 m 69.35

North High ≤750 m 70.86 West Low ≤750 m 77.83 South High ≤750 m 78.44 West High >750 m 80.19 West High ≤750 m 100.00

Table 8-3: Logistic regression coefficients to assist in determining model influences due to

biases. Group Model GPS GPS

Day GPS Night

All Day -GPS 8.121

-GPS 8.270

-GPS 8.071

r² = 0.286 0.457 0.412 0.507 0.221 0.239 0.291 0.245 Variables β Value

N -0.961 -1.136 -0.754 -1.466 -0.844 -0.652 -1.177 -0.861 E -0.681 -1.181 -0.706 -1.527 -0.224 -0.087 -0.706 -0.853 S 0.077 -0.187 0.208 -0.348 0.133 0.448 0.042 0.016 W 2.565 3.504 2.252 4.341 1.935 1.291 2.841 2.70

Herb High

1.804 2.221 2.114 2.178 1.797 1.79 1.774 1.604

Herb Low

0.731 0.207 0.784 -1.074 1.027 1.17 0.694 0.855

Non-Herb

-1.535 -1.428 -1.898 -0.104 -1.824 -1.96 -1.468 -1.459

≤750 m 0.86 3.165 3.241 2.967 0.457 0.702 0.825 0.791 >750 m 0.14 -2.165 -2.241 -1.967 0.543 0.298 0.175 0.209



LITERATURE CITED

Manly, B., L. McDonald, and D. Thomas. 1993. Resource Selection by Animals, Statistical

design and analysis for field studies. Chapman Hall, Great Britain Mace, R.D., J.S. Waller, T.L. Manley, K. Ake, and W.T. Wittinger. 1999. Landscape evaluation

of grizzly bear habitat in western Montana. Conservation Biology 13(2): 367-377.



Figure 20: A map showing resource selection function values of mule deer winter range

grouped in 10% intervals covering mule deer in Chelan Co., Washington



Figure 21: A map showing resource selection function values of mule deer winter range

grouped in 10% intervals covering mule deer in Chelan Co., Washington



Figure 22: A map showing the lowest (0-40%) value range of resource selection function for

mule deer winter range in Chelan Co., Washington



Figure 23: A map showing the lowest (0-40%) value range of resource selection

function for mule deer winter range in Chelan Co., Washington



Figure 24: A map showing the mid (40-70%) value range of resource selection function for




Figure 25: A map showing the mid (40-70%) value range of resource selection function for




Figure 26: A map showing the upper (70-100%) value range resource selection function for

mule deer winter range in Chelan Co. Washington



Figure 27: A map showing the upper (70-100%) value range of resource selection function

for mule deer winter range in Chelan Co. Washington



APPENDIX A: LITERATURE REVIEW

Mule Deer Winter Range Use Understanding how mule deer select their wintering habitat is dependent on numerous variables, and a wide variety of studies have been preformed to identify mule deer winter range use across an array of landscapes. These studies have illustrated numerous possibilities. Bartmann (1992) suggests that mule deer congregate into habitats that best support their needs during winter. Therefore one must identify the preferred components of the mule deer winter habitat. Vegetation did not correlate with mule deer winter use selection (Mackie 1998, Fowler 1987). According to Fowler (1987) the primary indicator for wintering mule deer in the Keating wildlife area of Oregon’s Powder River is elevation above the valley bottom, along with slope and aspect. This is contradictory to the finding in a study of mule deer habitat use in the rangelands along the Columbia River where xeric, mid-seral, bitterbrush brush (Purshia tridentata) communities with high amount of cover were preferred, and consistently selected were the xeric bitterbrush/needle-and-thread (Stipa comata) plant association over other bitterbrush associations (Griffith 1988).

Chelan County winter range consists of a high relief mountainous area, which produces a diverse array of topography. The behavior of mule deer selecting thermal habitat emphasizes energy conservation (Mackie 1998). Thermal habitats are attributed to aspect, slope and elevation and have the potential to be analyzed by insolation modeling (Fu and Rich 1999). Microhabitats on Chelan County winter range may be due to insolation resulting from the diverse topography of the region, creating highly valuable wintering areas intermixed with unusable or transitional habitats. Unused areas may lack resources necessary to sustain deer or consist of deep snow. In the case of winter range, heavy snow load predominates unused areas (Mackie 1998).

On a yearly basis snow load is not a predictable factor in Chelan County, but it affects the quantity of available winter range for mule deer as well as the availability of forage and may be the most influential factor affecting the winter energetic of deer (Hobbs 1989). Winter mortality is often a result of the combination of energy expenditure and decreased forage availability and quality (Parker and Robbins 1984). Temperature will affect mule deer energy expenditures when animals are exposed to temperatures less than -20ºC (Parker and Robbins 1983). Mule deer density shows an inverse relationship to snow depth (Fowler 1987, Zeigler 1978). Snow will cause deer to expend more energy during movement, and reduces the amount of available forage (Parker 1983). The most important factor for snow is depth (Parker et al, 1984) Snow depth will increase with the removal of conifer canopy snow intercept cover that occurs with logging or fire (Harestad and Bunnell 1981). The relative cost of locomotion through 50 cm of snow will be 498% in the open as compared to 5 cm of snow under a canopy cover of 70% will have a relative cost of 10% (Parker et al 1984). This may affect the mule deer of Chelan County due to the fires of 1994 and 1988 removing a vast amount of canopy cover across the Chelan County winter range. The affects of snow may be compounded with Sell (1997) finding the movement and home range size increases in burned areas over unburned areas. The removal of quality forage during the 1994 fire may cause wintering mule deer to range further in search of adequate winter



forage creating increased movement. Movement during winter is also suggested to increase due to human disturbance, therefore causing more energy expenditure (Parker et al, 1984). This movement occurs during a period of physiological stress and increases winter mortality (Hobbs 1989).

Forage Potential

Historic mule deer populations in Chelan County have been substantially higher indicating the quantity and quality of forage have been adequate in the past (Musser, 1999). Nutrient content and production of forage has been correlated with soil moisture and canopy cover along with site characteristic and successional stage (Urich 1973). The intense fires of 1988 and 1994 dramatically disturbed the historic vegetative forage communities within Chelan County potentially reducing the quality and quantity of winter forage available, therefore potentially lowering the carrying capacity of this winter range. Mule deer have a unique energy and foraging situation. Mule deer have small rumen capacity and overall small body size as compared to other ungulates; therefore the energy need per body weight is high (Hanley 1982, Robbins 1993). This restricts mule deer from foraging on plants with low nutrient value and that contain high amounts of cellulose (Hanley 1982). The strategy to resolve this situation is to select lignified forage material that is high in digestable cell solubles and low in cellulose (Sell 1997). The Entiat mule deer herd as some other mule deer herds predominately selects bitterbrush during winter over other forage species when present (Burrell 1977, Carson & Peek 1987). Carson (1985) found a preference for bitterbrush during winter as well in Washington’s Rufus Woods Lake area. Eriogonum niveum composes approximately twenty percent of the Chelan County mule deer winter diet leaving bitterbrush to fulfill seventy percent of their selected forage (Burrell, 1977). Although mule deer on other winter ranges have been reported to utilize bitterbrush heavily during the fall and transition to big sagebrush (Artemisia tridentata) during the winter (Welch et al. 1982). Alternative forage species within Chelan County include: Amelanchier alnifolia, Balsamorhiza saggittata, Berberis repens, Lupinus spp. Bromus tectorum, and Poa secunda (Burrell, 1977). Mule deer have also been observed foraging on Ceanothus velutinus. In the Blue Mountains of Oregon where bitterbrush is not abundant mule deer continued to utilize shrub in their winter diet at a rate of 25%, and grasses were shown to be utilized at a rate of 33%, while forbs were not significant during winter (Skovlin and Vavra 1979). The grasses that made the most significant contribution to mule deer on the winter range were Idaho fescue (Festuca idahoensis) and Sandberg Bluegrass (Poa sandbergii) (Skovlin and Vavra 1979). Antelope bitterbrush has been reported to supply greater amounts of carotene, crude protein and phosphorus during the winter, but less energy when compared to dormant grasses (Welch et al. 1982). Mule deer in Chelan County select these alternative species at a rate of less than five percent when bitterbrush is present (Burrell 1977). When compared to other possible forage restoration species bitterbrush is highly palatable and contains ample digestible nitrogen (White 1981) Bitterbrush has been reported to be essential forage when snow depth inhibits other forage species from being available. Mule deer in other locations use comparable species that include Sweetbrier rose hips Rosa eglanteria, and Curlleaf mahogany Cercocarpus ledifolius (White, 1981), although these are not found in abundance within the study area. Bitterbrush stand density, serial state and abundance of cover have shown to influence mule deer



habitat selection as well (Griffith 1988). The preferred selection by mule deer included bitterbrush stands of mid-serial state (Purshia tridentata / Stipa comata) with high density and available cover, and this preference continued until the availability of these areas reached between 80 and 95% (Griffith, 1988). Mid-seral bitterbrush communities showed to consist of the greatest perennial grass cover as compared to low-seral bitterbrush communities (Griffith 1988). In areas of shrub-dominate rangeland later seral communities are considered to be beneficial to mule deer (Griffith and Peek 1989).

Intense fires have the ability to destroy bitterbrush and their seeds within the soil making

succession to a climax stand a long if not impossible process (Mattise and Fritz 1994). The 1994 Tyee and 1988 Dinkleman fires spread across the majority of Chelan Counties mule deer winter range. Bitterbrush stands are not resistant to fire, Franklin and Dyrness (1988) and Daubenmire (1970) discuss that burning of bitterbrush stands will completely eradicate the plants involved. Bedunah, et al. (1999) stated a dramatic impact to bitterbrush occurs when subjected to fires of even low to moderate burn intensities. Therefore the stands involved in the Tyee and Dinkleman fires are in some state of succession and may or may not currently contain bitterbrush. Fire reduces the amount of bitterbrush on the winter range forcing higher mule deer usage on the remaining plants during years of high snow depth when bitterbrush is the only available forage. Increased foraging on the remaining plants, which occurred during the severe winter of 1996 may reduce the overall quality of the remaining bitterbrush plants in future years (Bishop et al 2001). The ability to reestablish bitterbrush in areas where it is vacant will reduce forage pressure. Bitterbrush restoration sites have been successful when certain factors are considered. Excessive rainfall during germination can be detrimental to survivability, although timing of planting can compensate (Mattise 1994, Hubbard 1959). Well drained course textured soils with a pH of 6.0 to 7.0, and a depth of five feet is ideal (Hubbard 1959). Commonly these are the Typic Haploxerolls and Vitrandepths (Franklin 1988).

Use and Availability Analysis

Researcher and managers have attempted to determine what components of a species habitat are essential to the population and what components are less essential. The ability to model areas selected by mule deer will assist wildlife manager in determining where habitat restoration will be most beneficial to a given species. Johnson (1980) created a method of comparing the quantity of components of the habitat that is accessible for the organism “available” to the quantity of the components utilized by the organism “usage”. This along with statistical advances discussed in Manly et al. (1993) allows for significant differences to be determine for habitat components being “preferred” indicating the organism is utilizing that component greater than accessible in the habitat or “avoided” indicating the organism is utilizing that component less than it is accessible within the habitat. Component usage have a hierarchy system to defining a geographical range (first-order selection), defining a home range of individuals (second-order selection) and ultimately defining usage of various components of the home range (third-order selection) (Johnson 1980). This will assists managers to determine spatial variation in a population density by evaluating the components of the selected habitats (Fagen 1988).

These methods were applied by Erickson, McDonald and Skinner’s (1998) research on

moose. In this research aerial sighting of moose were documented using GPS, data was classified



into one of twenty-two vegetative classes and buffered to be defined as used habitat units, while every 30m pixel was classified and buffered to be defined as available habitat units. Buffering was included to capture human and equipment error in the exact location of the sighted moose groups and to acknowledge moose use more than a 30m x 30m area when selecting use areas from the landscape (Erickson et al, 1998). The attempts to analyze all pixels for availability was found to be cumbersome, therefore systematically sub-sampled pixels with their associated buffers were analyzed and these were defined as available units, and when comparing the entire availability to the sub-sample found that the two to be identical (Erickson et al, 1998). The analysis of this data was done using logistic regression and resource selection function as described in Manly et al (1993) to map probability of use with GIS (Erickson et al, 1998). The research preformed by Erickson, McDonald and Skinner (1993) illustrated the use of logistic regression and resource selection function for the analysis one GIS covariate and Erickson goes on to state:

“We see no reason why the estimation techniques and general methodology we presented here for the estimation of selection functions would change when more GIS variables are considered.”

This technique has been preformed on other terrestrial organisms. Mace (et al 1999)

performed multivariate resource selection research on grizzly bears in Western Montana. In this research radio-telemetry data was evaluated within the GIS layers of roads / trails, human point disturbance, pseudo-habitat and elevation (Mace et al. 1999). This data was analyzed using logistic regression to determine potential and defined habitat effectiveness (Mace et al. 1999). Logistic regression coefficients allow researcher to understand the contribution of each habitat variable and when applied with resource selection function as describe in Manly et al. (1993) can be easily mapped within GIS as probability of use (Mace et al, 1999). Stability of the model was tested using a process of removal of an individual to allowing assessment of model and individuals (Mace et al. 1993).

The use of logistic regression is fairly new as compared to the chi-square technique that has dominated the literature for some time. This technique is becoming more successful with the advances in spatial modeling software and due to the easy of converting logistic regression results into resource selection functions to map the probability of use of any given cell within GIS. Use of multivariate statistical methods along with real habitat data has the potential to reduce validation problems allowing more objective results to be produced (Pereira and Itami 1991). Relocation data can be class into habitat characteristics within mule deer winter range resulting in nominal data to be analyzed, and logistic regression is a suitable modeling technique (Pereira and Itami 1991). The methods of modeling any species, including mule deer, habitat assumes that the presence of the species within given habitat variables can be taken as a measure of that habitats suitability (Pereira and Itami 1991).



References and Literature Cited

Bartmann, R.M., G.C. White, and L.H. Carpenter. 1992. Compensatory Mortality in a Colorado Mule Deer Population. Wildl. Monogr. 39pp.

Bedunah, D.J., M.G. Harrington, and D.M. Ayers. 1999. Effects of ecosystem-based

management treatments: Antelope bitterbrush and Scoulers’s willow response, shelterwood unit. Eighty-eight years of change in a managed ponderosa pine forest. Gen. Tech. Rep. RMRS-GTR-23. USDA Forest Service, Rocky Mountain Research Station. Ogden, UT. 55p.

Bishop, Chad J., Edward O. Garton, & James W. Unsworth. 2001. Bitterbrush and Cheatgrass

Quality on 3 Southwest Idaho Winter Ranges. Journal of Range Management 54(5): 595-602

Burrell, G. C. 1977. Bitterbrush (Purshia tridentata) in the Winter Ecology of the Entiat Mule

Deer Herd. M.S. University of Washington, Seattle, WA. Carson, R. G. 1985. Mule deer habitat selection and movement patterns in north-central

Washington. M.S. University of Idaho, Moscow, ID Carter, M.R. 1993. Soil Sampling and methods of Analysis Candian Society of Soil Science, Lewis Pub. Daubenmire, R. 1970. Steppe Vegetation of Washington. Washington State University

Extention. Pullman, WA Erickson, W.P., T. L. McDonald, and R. Skinner. 1998. Habitat selection using GIS data: A case

study. Journal of Agricultural, Biological, and Environmental Statistics. 3(3):296-310 Fagen, R. 1988. Population effects of habitat change: a quantitative assessment. J. Wildl.

Manage. 52:41-46

Fowler, W.B.; Dealy, J.E. 1987.Behavior of mule deer on the Keating Winter Range. Res. Pap. PNW-RP-373.: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station; Portland, OR 25p. Franklin, J.F.; Dyrness, C.T. 1988. Natural Vegetation of Oregon and Washington. Oregon State University Press, Corvallis, OR Fu, P; Rich, P.M. 1999. Design and Implementation of the Solar Analyst: an ArcView extention

for Modeling Solar Radiation at Landscape Scale. <http://www.hemisoft.com/doc/esri99>.



Griffith, D. B. 1988. Mule Deer Habitat Selection in Columbia River Rangelands of Northcentral Washington. Ph.D Dissertation. University of Idaho, Moscow, ID

Griffith, B., and J. M. Peek. 1989. Mule deer use of seral stage and habitat type in bitterbrush

communities. J. Wildl. Manage. 53:636-642 Hanley, T. A. 1982. The nutritional basis for food selection by ungulates. J. Range. Manage.

35:146-151

Harestad, A.S. and F.L. Bunnell. 1981. Prediction of snow-water equivalents in coniferous forests. Can J. For. Res. 11:854-857

Hobbs, T. N. 1989. Linking Energy Balance to Survival in Mule Deer: Development and Test of

a Simulation Model. Wildl. Monogr. No. 101 Apr. 39pp. Hubbard, Nord, L.L. Brown. Bitterbrush Reseeding - a Tool for the Game Range Manager.

Misc. Pap. No. 39. Berkeley, CA: Department of Agriculture, Forest Service, Pacific Southwest Forest and Range Expeiment Station; 13p. 1959. Johnson, D.H. 1980. The comparison of usage and availability measurement for evaluating

resource preferences. Ecology 61(1):65-71 Mace, R.D., J.S. Waller, T.L. Manley, K. Ake and W.T. Wittinger. 1999. Landscape evaluation

of grizzly bear habitat in western Montana. Conservation Biology 13(2):367-377 Mackie, R. J., D. F. Pac, K. L. Hamlin, and G. L. Dusek. 1998.Ecology and management of mule


Manly, B.F.J., L.L. McDonald, and D.L. Thomas. 1993. Resource Selection By Animals:

Statistical Design and Analysis of Field Studies. London: Chapman and Hall Mattise, S. N.; Fritz, C. 1994.Bitterbrush Rehabilitation, Squaw Butte Fire Complex Tech. Bull. No. 94-8 Aug. Idaho B.L.M. Musser, Washington Department of Fish and Wildlife. 1998 Game Status and Trend Report.

Wildl. Manage. Prog., Dept. Fish and Wildl., Olympia. 228pp. Myers, W.L. 1999. Population Regulation and Habitat Ecology of Mule Deer in North-Central

and Northeast Washington. Washington. Report. Wildl. Manage. Prog., Dept. Fish and Wildl., Olympia: Washington.

Parker, K.L. 1983. Ecological Energetics of Mule Deer and Elk: Locomotion and

Thermoregulation. Dissertation, Washington State University. Pullman, Washington



Parker, K.L., C.T. Robbins. 1983. Thermoregulation in mule deer and elk. Can. J. Zool. 62:1409-1422

Parker, K.L., C.T. Robbins, and T.A. Hanley. 1984. Energy expenditures for locomotion by mule

deer and elk. J. Wildl. Manage. 48(2):474-488 Pereira, J.M.C., R.M. Itami. 1991. GIS-Based habitat modeling using logistic multiple

regression: A study of the Mt. Graham red squirrel. Photogrammeteric Engineering & Remote Sensing. 57(11):1475-1486

Rich, P.M.; Fu, P. 2000. Enlightenment for Mapping Systems: Solar Radiation Modeling Looks

to the Sun for Answers*. Resource Magazine Vol. 7(2):7-8. Robbins, C. T. 1993. Wildlife feeding and nutrition. Academic Press, New York,N. Y. 352 pp. Sell, S. 1997. Mule deer habitat use on the Boise National Forest. M.S. University of Idaho, Moscow, ID Skovlin, J., M. Vavra. 1979. Winter diets of elk and deer in the Blue Mountains, Oregon. U. S.

Department of Agriculture, Forest Service, Pacific Northwest Forest and Range Experiment Station. Research Paper PNW-260

Urich, D. L. 1973. Nutrient levels in deer and moose forages in northern Minnesota as related to

site characteristics. M. S. thesis, Univ. Minn., St Paul. 138 pp. Weather Underground: Wenatchee Washington Forecast <http://www. wunderground.com>. Welch, B.L., S.B. Monsen, N.L. Shaw. 1982. Nutritive value of antelope and desert bitterbrush,

stansbury cliffrose, and apache-plume. pp 173-185 In Proceeding – research and management of bitterbrush and cliffose in Western North America. U.S. Dept. of Agriculture. Forest Service. Intermountain Forest and Range Experiment Station Ogden, UT 84401 General Technical report INT-152. August 1983.

White, S.M.; Welch, B.L. 1981. Paired Comparison: A Method For Ranking Mule Deer

Preference For Various Browse Species. . Logan, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station; 4p.

Zeigler, D. L. 1978. The Okanogan Mule Deer. Biological Bulletin No. 15, Wash. Dept. of

Game, Olympia. 106pp.



APPENDIX B: BITTERBRUSH RESTORATION CONSIDERATIONS W. R. Moore and W. L. Myers

We have discussed landscape features associated with winter use patterns of mule deer (Chapter Five) and used those results in a logistic regression analysis (Chapter Six) to model the occurrence of deer as a function of map variables. Model predictions provide area mule deer and habitat managers with the ability to evaluate sites for habitat treatments or restoration based upon benefits for mule deer. Now that we have ranked probabilities of site use by deer, the next logical step in our progression towards gaining greater understanding of winter habitat use by mule deer is to address potential habitat improvement. Historic mule deer populations in Chelan County have been substantially higher suggesting the quantity and quality of forage were likely at higher levels in the past than today (Musser, 1999). Although our analysis showed a negative correlation between deer use and bitterbrush stand distribution, bitterbrush is well documented to be a preferred winter food source (Burrell, 1977, Carson 1985, Carson and Peek, 1987). Because of the seemingly important role bitterbrush plays in maintaining numbers of Chelan mule deer and the paucity of bitterbrush stands after the fires of 1988 and 1994, bitterbrush restoration would be an important step to enhancing winter ranges and increasing carrying capacities. Such is the focus of this chapter. The 1994 Tyee and 1988 Dinkleman fires spread across the majority of Chelan Counties mule deer winter range and likely had a devastating effect upon existing bitterbrush since bitterbrush stands are not resistant to fire (Franklin and Dyrness 1988, Daubenmire 1970, Bedunah, et al. 1999). Because of the disturbance from the Tyee and Dinkleman fires, bitterbrush stands within the burned areas (which is extensive) are in some state of succession and may or may not currently contain bitterbrush. The result is mule deer usage on the remaining plants has probably been excessive especially during years of high snow depth when bitterbrush is the only available forage. Increased foraging on the remaining plants, which occurred during the severe winter of 1996, may have reduced the overall quality of remaining bitterbrush plants (Bishop et al 2001). The ability to reestablish bitterbrush in areas where it is vacant will reduce forage pressure on the remaining stands during severe winters and potentially increase quality throughout the winter range. The Entiat mule deer herd predominately selects bitterbrush during winter over other forage species when present (Burrell 1977). The height of bitterbrush plants allows it to remain available to mule deer when other forage is unavailable due to snow. Alternative forage species within Chelan County include: Amelanchier alnifolia, Balsamorhiza saggittata, Berberis repens, Lupinus spp. Bromus tectorum, and Poa secunda (Burrell 1977). Mule deer selected these species at a rate of less than five percent when bitterbrush is present (Burrell 1977). Eriogonum spp. composes approximately twenty percent of the mule deer winter diet leaving bitterbrush to fulfill seventy percent of their selected forage (Burrell 1977). When compared to other possible forage restoration species, bitterbrush is highly palatable and contains ample digestible nitrogen (White, 1981). Mule deer in other locations use comparable species that include Sweetbrier rose



hips Rosa eglanteria, and Curlleaf mahogany Cercocarpus ledifolius (White 1981), although these are not found in abundance within the study area (Burrell 1977).

Replanting of Bitterbrush

The restoration of bitterbrush is often costly with varying success, ranging from complete failure to modest establishment of new stands. Under natural conditions bitterbrush re-establishes burned areas from seed caches collected by mice and voles from neighboring bitterbrush stands (Franklin and Dyrness 1988, Clements 2002). However, natural re-establishment may not occur in areas subjected to large intense fires because the seed caches, along with the planting source (small mammals), have been destroyed. In such cases, managers have attempted to re-establish or augment bitterbrush stands using a variety of techniques. Replanting bitterbrush with seedling is documented by Clements and Young (2000) on a mule deer winter range in California. Plants were two-year-old stock that were grown in one-quart containers and transplanted into the study area of 5,500 feet elevation in mid April. In this study, bitterbrush was planted inside and outside of a big game exclosure. Planting treatments included tillage, herbicide application of 0.25 lb/ac of sethoxydim to control selective grasses, and the inoculation of the seedlings with soil from existing bitterbrush roots to introduce Frankia. (Frankia is the symbiotic microorganism that assists in the nitrogen fixing capabilities of bitterbrush). These plantings were tracked for two years; the results may be seen in Table B.1 Tillage and herbicide applications were performed to reduce competition with other plants. The inoculation of the seedling with Frankia may be useful since burned stands with no live bitterbrush may not contain this necessary microorganism (Clements 2002). Clements and Young (2002) evaluated seeding methodology to determine which method was most successful. The sites evaluated were on high elevation winter range with the average precipitation of approximately 12 inches. The cased-hole punch seeder, developed by Dr. Terrence Booth (Clements and Young 2002) is basically a plastic cylinder that will hold approximately three seeds and is pressed into the ground. The case was designed to prevent foraging by rodents on the seeds (Clements & Young 2002). This method was successful in producing shrubs, and its use is suggested when planting on rocky steep slopes where mechanized equipment is not appropriate. Preparation of the seed has been shown to be important in successful germination and growth. Cleaned seed performed better than uncleaned seeds, but it should be noted that the cleaned seeds grew at a very slow rate; growth did not exceed one foot in height after three years (Clement & Young 2002). Often seeds have been treated in 1 - 3% thiourea to break seed dormancy and increase seed vigor, but thiourea has been identified as a carcinogen and often created varying results (Booth 1999). An alternative to thiourea treatment was to expose seeds to a temperature of 2º C for 28 days within a humidity chamber; germination rates were similar to thiourea treated seeds (Booth 1999). Planting shrubs along with grasses has been demonstrated to increase seedling survival (Maestre et al, 1999). Crested wheatgrass (Agropyron cristatum) in combination with bitterbrush seed was



suggested to assist in reducing the competition for moisture from cheatgrass (Bromus tectorum) (Clements and Young 2002). Other studies suggest that bluebunch wheatgrass (Psedoroegneria spicata) will compete less with bitterbrush than crested wheatgrass or cheatgrass allowing for better seedling survival (Hall et al. 1999). In areas where cheatgrass infestation is high, this may be the preferred option. Bitterbrush restoration is an expensive management treatment that may result in limited success. Alternatives are limited to enhancing herbaceous species or letting natural processes proceed without artificial manipulation. We encourage restoration efforts be applied to historic bitterbrush stands which have a high probability of use by mule deer.

Table B-1: Results of Clements and Young (2000) Antelope Bitterbrush Seedling Transplant

Survival Study. Treatment Seedling Survival

Inside Exclosure Outside Exclosure % Survival

Control 6 0 Tillage 25 15

Herbicide 25 8 Inoculation 27 15

Table B-2: Results of Clements and Young (2002) Restoring Antelope Bitterbrush, Management

Guidelines for Overcoming the Challenges of Establishing Antelope Bitterbrush after a Wildfire.

Case Study #1 Method Shrubs / AcreCased-hole Punch Seeder 8,930Hand Seeding 508No-Till Drill 7,840Rangeland Drill 2,505Disc, Fallow, Drill 160Case Study #2 Method Shrubs / AcreRangeland Drill, Cleaned Seed 2,275Rangeland Drill, Uncleaned Seed 766Drilled Bitterbrush, Crested Wheatgrass 1,452 / 17,967Drilled Bitterbrush, Crested Wheatgrass, Foage Kochia 1,556 / 15,145 / 0



Literature Cited Bedunah, D.J., M.G. Harrington, and D.M. Ayers. 1999. Effects of ecosystem-based

management treatments: Antelope bitterbrush and Scoulers’s willow response, shelterwood unit. Eighty-eight years of change in a managed ponderosa pine forest. Gen. Tech. Rep. RMRS-GTR-23. USDA Forest Service, Rocky Mountain Research Station. Ogden, UT. 55p.

Bishop, Chad J., Edward O. Garton, & James W. Unsworth. 2001. Bitterbrush and Cheatgrass

Quality on 3 Southwest Idaho Winter Ranges. Journal of Range Management 54(5): 595-602

Booth, D.T. 1999. Imbibition temperatures affect bitterbrush seed dormancy and seedling vigor.

Journal of Arid Environments 43(1): 91-101 Clements, Charlie D., James A. Young. 2000. Antelope Bitterbrush Seedling Transplant

Survival. Rangelands 22(1): 15-17 ______________. 2002. Restoring Antelope Bitterbrush, Management guidelines for

overcoming the challenges of establishing antelope bitterbrush after a wildfire. Rangelands 24(4): 3-6

Daubenmire, R. 1970. Steppe Vegetation of Washington. Washington State University

Extension. Pullman, WA Franklin, Jerry F. & C.T. Dyrness. 1988. Natural Vegetation of Oregon and Washington. OSU Press, Corvallis OR Hall, Derek B,; Anderson, Val Jo; Monsen, Stephen B. 1999. Competitive effects of bluebunch

wheatgrass, crested wheatgrass, and cheatgrass on antelope bitterbrush seedling survival. Res. Pap. RMRS_RP-16, Ogden, UT: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. 7p.

Maestre, Fernado T.; Bautista, S.; Cortina, J.; Bellot, J. 1999. Potential for using facilitation by

grasses to establish shrubs on a semiarid degraded steppe. Ecological Applications, 11(6) 2001: 1641-1655.

observations of mule deer habitat use, movements, …...observation of mule deer habitat use,...

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