HomeMy WebLinkAboutAPA4125Alaska DePartment of Fish and Game
Division of Wildlife Conservation
Federal Aid in Wildlife Restoration
Research Progress RePort
1 Julv 1992 -30 June 1994
Lower Susitna ValleY Moose PoPulation
IdentitY and Movement Study
Ronald D. Modafferi
Grant W-24-2
Studv 1.38
December 1994
Alaska Department of Fish and Game
Division of Wildlife Conservation
December 1994
Lower Susitna Valley Moose Population
Identity and Movement Study
Ronald D. Modafferi
Federal Aid in Wildlife Restoration
Research Progress Report
1 July 1992–30 June 1994
Grant W-24-2
Study 1.38
This is a progress report on continuing research. Information may be refined at a later date.
If using information from this report, please credit author(s) and the Alaska Department of Fish and Game.
STATE OF ALASKA
Tony Knowles, Governor
DEPARTMENT OF FISH AND GAME
Carl L. Rosier, Commissioner
DIVISION OF WU..DLIFE CONSERVATION
Wayne L. Regelin, Acting Director
Persons intending to cite this material should receive permission from
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Because most reports deal with preliminary results of continuing
studies, conclusions are tentative and should be identified as such.
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P.O. Box 25526
Juneau, AK 99802
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State: Alaska
Project No.: W-24-1
W-24-2
Job No.:
Period Covered:
RESEARCH PROGRESS REPORT
Project Title: Big Game Investigations
Study Title: Lower Susitna Valley Moose
Population Identity and
Movement Study
1 July 1992-30 June 1994
SUMMARY
Alaska Department of Fish and Game (ADF&G) staff aerial surveyed, marked, and
radiotracked moose in the lower Susitna River valley in Southcentral Alaska from October
1985 to February 1991. Survey and radiotracking data gathered (April 1980 to May 1986)
during previous moose studies in lowland riparian areas of the lower Susitna River valley
were incorporated into the database. Site specific information on herd density, sex/age
composition, and distribution of moose were compiled from moose censuses and surveys in
the study area. Data on moose killed by collisions with trains in Alaska were collected,
analyzed in relation to snowpack depth, and published. Archived data on in utero fetus counts
of moose in Southcentral Alaska were gathered, analyzed, and published. Data collected on
survival of radiocollared moose were analyzed and written for publication. We analyzed data
on birthing chronology, twinning rates and calf/cow ratios of radiocollared female moose.
This report mainly contains data, findings, and discussions related to analysis of point-
location data collected in monitoring radiocollared moose.
To select equipment, methods and procedures for analyzing point-location data collected in
radiotelemetry studies, a radiotelemetry data analysis group was formed among Alaska
Department of Fish and Game staff. The working group included biologists, biometricians
and a data analyst/programmer. The group identified computer hardware and software for
conducting analyses of radiotelemetry point-location data. The group acknowledged that
point-location data collected in lower Susitna Valley moose studies would be used to refine
this process.
Equipment and software identified for use in analyzing point-location data were: a 486-based
computer with windows environment; CALHOME, a home range analysis software; Lotus
1-2-3, a spreadsheet program; ArcView, a geographic information system software; and
FoxPro, a relational database management system. The adaptive kernel method in
CALHOME was selected for home range analyses.
ADF&G staff outlined procedures for isolating and manipulating subsets of moose point-
location data from the database file through home range analyses. Adaptive kernel home
range analyses identified relationships between home range size, number of utilization
distributions and bandwidth. Home range size varied from 9 mi 2 to 205 km2
• Plots of home
ranges showed l (unimodal) to 8 (multimodal) discrete utilization distribution polygons.
Bandwidth had large effects on home range size and polygon number. In many cases,
bandwidth selected by minimizing the least squares cross validation value appeared to
provide a better estimate of home range than the optimum bandwidth selected by the
program. These analyses clarified the need to identify an objective method for selecting
bandwidth in adaptive kernel home range analysis. ADF&G staff discussed recommendations
for future activities.
II
CONTENTS
SUMMARY ..................................................................................................................................... i
BACKGROUND ............................................................................................................................ 2
OBJECTIVES ................................................................................................................................. 4
Primary ....................................................................................................................................... 4
Peripheral ................................................................................................................................... 4
STUDY AREA ............................................................................................................................... 5
METHODS ..................................................................................................................................... 6
Moose Distribution Surveys ....................................................................................................... 6
Capture, Radiocollaring, and Monitoring of Moose .................................................................. 7
Survival ...................................................................................................................................... 8
Censoring ................................................................................................................................... 8
Snow Conditions ........................................................................................................................ 9
Management and Analysis of Moose Point-location Data ......................................................... 9
Movements, Seasonal Range, and Home Range Analysis ....................................................... 10
Radiotelemetry Point-location data Analysis Working Group ................................................ 10
RESULTS AND DISCUSSION ................................................................................................... 10
Surveys of Moose in Postrut and Winter Areas ....................................................................... 10
Capture, Radiocollaring, and Monitoring Moose .................................................................... 11
Talkeetna Mountains ........................................................................................................... 11
Alexander Creek .. . . .. . . . . . .. . . . . .. . .. .. . . . . . . . . . . . . . . . . . .. .. . .. . . . . .. . .. . .. .. . .. .. . . . .. .. . . . . . . . .. . . . . .. . .. . .. . . .. . . . . . . . .. . . . .. 11
Y entna and Skwentna Rivers .............................................................................................. 11
Lower Susitna River ............................................................................................................ 11
Analysis of Radiotelemetry Point-location data ....................................................................... 11
Calendar Year and Seasonal Periods ................................................................................... 11
Radiotelemetry Data Analysis Working Group .................................................................. 12
Computer System and Software .......................................................................................... 12
Administrative and Analytical Procedures .......................................................................... 12
Year Effects on Point-location data .................................................................................... 13
Relationship Between Bandwidth, Home Range Size,
and Number of Utilization Distributions ................................................................................. 13
Spatial Relationship Between Seasonal Ranges ................................................................. 15
Publications .............................................................................................................................. 15
Completed ........................................................................................................................... 15
In Draft Form ...................................................................................................................... 15
In Preparation ...................................................................................................................... 15
Future Activities ....................................................................................................................... 15
ACKNOWLEDGMENTS ............................................................................................................ 16
LITERATURE CITED ................................................................................................................. 16
FIGURES ...................................................................................................................................... 21
TABLE .......................................................................................................................................... 33
APPENDIX A. Julian day number and calendar dates for prominent events in life history and
management of moose . . . .. .. ... . .. .. ... ... ............ ... . .... ..... .. .. .. .. . .. . . . .. .. .. .. . ..... ... . . ... .. 35
APPENDIX B. Sample of results of trial and error process used to select bandwidth for use in
CALHOME adaptive kernel home range analysis of moose point location
data ................................................................................................................... 43
APPENDIX C. Plots of CALHOME adaptive kernel home range analyses of moose point-
location data ..................................................................................................... 46
APPENDIX D. Summary of results of trial and error process used in selecting bandwidth for
use in CALHOME adaptive kernel home range analysis of moose point-
location data..................................................................................................... 98
APPENDIX E. Sample of database file showing format used to identify Julian day of point
locations in numbered polygons of utilization distributions of moose home
ranges .............................................................................................................. 100
APPENDIX F. Plots of moose home ranges with multiple utilization distribution
polygons...................... ...... .... ..... . .. ............... ... . ............ .......... ..... .. .. . ................ I 02
APPENDIX G. Title page of publication entitled "In utero pregnancy rate, twinning rate and
fetus production for age-groups of cow moose in Southcentral Alaska"........ 158
APPENDIX H. Draft of Manuscript Entitled "Survival of Radiocollared Adult Moose in
Lower Susitna Valley, Southcentral Alaska ..................................................... 159
BACKGROUND
Before statehood in 1959, the Susitna River Valley was ranked as the most productive moose
(Alces alces) habitat in the territory (Chatelain 1951 ). Today, the innate potential of this area
as habitat for moose is unsurpassed throughout the state.
The lower Susitna Valley is the focal point of more development than any other nonurban
region in the state. Proposed and progressing projects involving grain and crop agriculture,
dairy and grazing livestock, commercial forestry and logging, personal-use cutting of
firewood, mineral and coal mining, land disposals, wildlife ranges and refuges, human
recreation, human settlement, urban expansion, development of the highway and railway
systems, and increased railroad traffic in the region may greatly detract from the area's
potential to support moose.
Although development and associated activities may reduce the moose population in the
Susitna Valley, resource users have demanded increased allocations to satisfy consumptive
and nonconsumptive uses. This conflict created a tremendous need by local, state, and federal
2
land and resource management agencies for timely and accurate knowledge about moose
populations in Subunits 13E, 14A, 14B, 16A, and 16B. These informational needs will
intensify in response to ( 1) increased pressures to develop additional lands, (2) increased
numbers of users and types of resource use, and (3) more complex systems for allocating
resources to potential users.
The Division of Wildlife Conservation lacks necessary information about moose populations
in the lower Susitna Valley to accurately assess the effects from these increasing resource
demands. The division is unable to dispute or condone specific demands, or provide
recommendations to regulate and minimize negative effects on moose populations or habitat.
The division must be knowledgeable about moose population behavior to mitigate negative
effects to moose populations or their habitat.
The division is the source of much information on moose populations for decisions on land
use and resource allocation in the lower Susitna Valley. To be more effective in this capacity,
the division should consolidate available data and expand that database with studies on
movements and identity of moose populations.
Data from these studies will improve the division's ability to recognize, evaluate, and/or
mitigate activities affecting moose populations and their habitat.
Habitat and environmental conditions vary greatly in the lower Susitna Valley. Large
environmental differences may lead to area specific differences in moose population
behavior. Therefore, a series of interrelated moose movements and population identity studies
should be conducted at different locations in the lower Susitna Valley. Studies should be
initiated where there are immediate conflicts in resource use. After evaluating conflicts in
resource use in the lower Susitna Valley, we knew our studies should begin in the western
foothills of the Talkeetna Mountains in Subunits 14A and 14B. Some of the densest postrut
aggregations of moose in the region and, perhaps, the state are on Bald Mountain Ridge and
Willow Mountain in the western foothills of Talkeetna Mountains. Subunits 14A and 14B
provide recreation and resources to over half of Alaska's human population. This area is the
focus of many development activities and conflicts in resource use. These Subunits have
unique problems involving moose and transportation systems. Environmental information
required for the recent Susitna River hydroelectric project emphasized the inadequacy of
basic knowledge about moose populations in the area. Data from environmental assessment
studies for the hydroelectric project pointed out inaccurate assumptions about moose
populations in the lower Susitna Valley.
Historical information available on moose populations in the Susitna Valley is limited to (1)
harvest statistics (ADF&G files), (2) inconsistently conducted sex-age composition surveys
(ADF&G files); (3) inconsistently collected data for train-and vehicle-killed moose
(ADF&G files), (4) a population movement study based on resightings of "visually collared"
moose (ADF&G files), (5) studies on railroad mortality and productivity of the railbelt sub-
population (Rausch 1958, 1959), (6) a radiotelemetry population identity study in the Dutch
and Peters Hills (Didrickson and Taylor 1978), (7) an incomplete study of moose-snowfall
3
relationships in the Susitna Valley (ADF&G files), (8) a study of extensive moose mortality
in a severe winter ( 1970-71 ), for which there is no final report, and (9) a pilot study to
develop a rapid-assessment technique to identify and characterize moose winter range (Albert
and Shea 1986).
Recent studies designed to assess effects of a proposed hydroelectric project on moose have
provided much data on moose populations in areas adjacent to the Susitna River downstream
from Devil Canyon (Arneson 1981; Modafferi 1982, 1983, 1984, 1988b ). These studies
suggest that moose sex-age composition counts conducted in alpine habitat postrut
concentration areas of Subunits 14A and 14B were biased, including samples from unhunted
moose populations and excluding samples from segments of hunted moose populations.
Moose killed by hunters and trains in winter in Subunit 14B were fall residents of Subunit
16A. Fall resident moose of Subunit 16A migrated to winter areas in Subunit 14A. Moose
vulnerable in fall hunts in Subunit 16A were included in Subunit 14A and 14B population
composition and trend surveys. Moose that calved in Subunit 16A were fa11 residents of
Subunit 14B. These data indicate that assumptions about movements and identities of moose
populations in Subunits 14A and 14B (i.e., western foothills of the Talkeetna Mountains)
may be incorrect or too simplistic.
Previous progress reports on lower Susitna VaHey moose population identity and movement
studies have been published (Modafferi 1987, 1988a, 1990, 1992).
OBJECTIVES
Primary
• To more precisely delineate moose annual movement patterns and location, timing, and
duration of use of seasonal habitats
• To use movement patterns to identify and delineate major moose populations in the lower
Susitna Valley
• To assess effects of seasonal timing on results of annual fall sex-age composition and
population trend moose surveys
• To relate findings to moose population management in lower Susitna Valley
Peripheral
• To identify areas and habitats that are important for maintaining the integrity of moose
populations in the lower Susitna Valley
• To locate moose winter areas and calving areas in the lower Susitna Valley
4
• To identify moose populations that sustain hunting mortality and "accidental" mortality in
highway and railroad rights-of-way
• To determine moose natality rates and timing of calving
• To determine survival rates and timing calf and adult mortality
STUDY AREA
The study was conducted in a 25,000 km2 area in th~ lower Susitna Valley in Southcentral
Alaska (Fig. I). The area is bordered on the north and west by the Alaska Mountain Range,
on the east by the Talkeetna Mountains, and on the south by Cook Inlet. It encompasses all
watersheds of the Susitna River downstream from Devil Canyon and includes all or portions
of Subunits l4A, l4B, l6A, I6B, and l3E (Fig. 2).
Monthly mean temperatures range from 16 C in July to -13 C in January; maximum and
minimum temperatures of 25 and -35 C are not uncommon. Total annual precipitation varies
from about 40 em in the southern portion to over 86 em in the northern and western portions.
Maximum winter snow depth varies from less than 20 em in the southern portion to over 200
em in the northern and western portions. Climatic conditions generally become more
inclement away from the maritime influence of Cook Inlet. Elevations within the area range
from sea level to rugged mountain peaks well above 1500 m.
Vegetation in the area is diverse and varied, depending on elevation. Vegetation types include
wet coastal tundra and marsh, open low-growing spruce forest, closed spruce hardwood
forest, treeless bog, shrubby thicket and alpine tundra (Viereck and Little 1972). Dominant
habitat and canopy types in the area are characterized as: (I) floodplain -dominated by
willow (Salix spp.) and poplars (Populus spp.), (2) lowland -dominated by a mixture of wet
bogs and closed or open, mixed paper birch (Betula papyrifera)lwhite spruce (Picea
glauca)laspen (Populus tremuloides) forests, (3) mid elevation -dominated by mixed or pure
stands of aspen/paper birch/white spruce, (4) higher elevation -dominated by alder (Alnus
spp.), willow, and birch shrub thickets or grasslands ( Calamagrostis spp.), and (5) alpine
tundra -dominated by sedge ( Carex spp. ), ericaceous shrubs, prostrate willows, and dwarf
herbs. Vegetation, climate and geography of the area were described in detail by Viereck and
Little ( 1972) and Modafferi ( 1991 ). Moose surveys in postrut areas were conducted above
timberline in alpine tundra habitats, roughly between elevations from 600 to I ,200 m. Moose
surveys in winter areas were conducted in lowland floodplain habitats between elevations of
30 to 300m.
Moose populations in this region increased during 1980-84 and 1985-87 and decreased in
1984-85 and 1987-91 (Griese 1993a, I993b). Moose populations in the area were probably at
or very near carrying capacity before winter 1984-85. Moose were hunted during subunit-
specific open seasons. In most subunits, male moose were hunted every year during a
September season. In some areas, limited numbers of permits were issued for the harvest of
5
antlerless and/or cow moose during the September season and/or a December through
February season. Accidental collisions of moose with trains and highway vehicles were
noteworthy sources of mortality in the region, particularly in deep-snow winters (Rausch
1958, Modafferi 1991). Moose predators in the area included wolves (Canis lupus) and
brown (Ursus arctos) and black bears (V. americanus).
Information on predator densities in the area was largely circumstantial, but densities were
relatively low in relation to many other areas in Alaska. Wolf density estimates ranged from
about 1-2 wolves/100 km 2 in Subunits 14A, 14B and southern 16A to about 2-7 wolves/1000
km 2 in Subunits 13E, 16B and northern 16A (Ballard 1992a, Masteller 1994). In general,
wolf populations probably increased during 1980-91. Brown bear density estimates ranged
from 7-25 bears/1000 km2 in Subunits 14A and 14B to about 12-35 bears/1000 km 2 in
Subunits 13E, 16A, and 16B (Miller 1987, Grauvogel 1990, Griese 1993a). Brown bear
populations were probably increasing during the study. Black bear density estimates ranged
from about 35-104 bears/1000 km2 in Subunits 14A and 14B (Grauvogell990) to about 90-
193 bears/1000 km 2 in Subunits 13E, 16A, and 16B (Miller 1987, Griese 1993b). Black bear
hunting over bait and increasing brown bear populations may have caused a decrease in black
bear populations during the study.
METHODS
Moose Distribution Surveys
ADF&G staff conducted moose sex-age composition surveys during late autumn through
winter to gather information on moose distribution and utilization of postrut areas. Surveys
were conducted from October though April, 1986-91 in 7 discrete areas above timberline in
the western foothills of the Talkeetna Mountains in Subunits 14A and 14B. Surveys were
initiated in autumn after snowcover was adequate to observe moose. ADF&G staff surveyed
about every 1-2 weeks, unless weather and snow conditions impeded aerial surveying and
counting of moose. Surveys were terminated in spring when snow cover was inadequate to
observe moose. Search effort during surveys was to count all moose. Search intensity varied
with moose density but was usually about 1 minute/km2 . Surveys were conducted in PA-12
Super Cub aircraft. Moose were classified in categories of calf, nonantlered adult, antlered
yearlings (moose with antlers measuring <76 em; Gasaway 1975, Gasaway et al. 1987), and
antlered adults (moose with antlers ?. 76 em). In previous investigations in the study area,
similar survey procedures were used to conduct moose sex-age composition surveys in
lowland riparian winter areas during October through April (Modafferi 1988b). Surveys in
winter areas were conducted in the Susitna River floodplain between Devil Canyon and Cook
Inlet in Subunits 16A, 16B and 13E in 1981-85 and in the Alexander Creek, Moose Creek,
Deshka River and Yentna River floodplains in Subunits 16A and 16B in 1984-85. Data from
these surveys were analyzed to compile information on moose distribution in winter and
moose utilization of lowland winter areas.
6
Capture, Radiocollaring, and Monitoring Moose
Moose were captured for radiocollaring by darting either from a helicopter or approached on
foot or snowmachine. Moose were immobilized with etorphine hydrochloride (M99,
Lemmon Co., Sellersville, Pa.) with or without xylaxine hydrochloride (Rompun, Haver-
Lockhart, Shawnee, Kans.) or carfentanil citrate (Wildnil, Wildl. Lab., Fort Collins, Colo.).
M99 and Wildnil were antagonized with diprenorphine (M50-50, Lemmon Company,
Sellersville, Pa.), naloxone hydrochloride (Dupont Pharmaceuticals, Garden City, N.J.) or
naltrexone hydrochloride (Dupont Pharmaceuticals, Garden City, N.J.). Immobilized moose
were ear tagged and fitted with a visual-numbered canvas collar (Franzmann et al. 1974) and
radiotransmitter with or without a mortality option (Telonics, Mesa, Ariz.). Moose were
captured during December and January in postrut areas in Subunits 14A and 14B and during
January through April in low land winter areas in Subunits 16B. Capture procedures took
place in the western foothills of the Talkeetna Mountains between the south fork of Montana
Creek and the Little Susitna River in 1985-89, in the Alexander Creek floodplain in 1987,
and in the floodplains of the Yentna and the Skwentna Rivers between Lake Creek and Old
Skwentna in 1988-89 (Fig. 3 ).
Capture procedures in postrut areas began after 18 November 1985, when aerial surveys
indicated peak numbers of moose were present in those areas (Modafferi 1987). Capture
procedures in winter areas commenced after 1 January after numbers of moose in postrut
areas decreased and numbers of moose observed in winter areas increased. Radiocollars were
allocated within winter areas and within and between postrut areas in relation to distribution
of moose.
Age of captured moose was estimated mainly by incisor tooth wear. However, in the lower
Susitna River study, a first incisor tooth was removed from captured moose for cementum
aging (Sergeant and Pimlott 1959). Captured moose were > 18 months of age and few moose
were <30 months. All were considered adults.
Radiocollared moose were radiotracked 1-5 times each month for visual observation using a
telemetry-equipped Cessna-152, -180, -185 or a Piper PA-18 Super Cub fixed-wing aircraft
and standard aerial radiotracking procedures (Ballard et al. 1991 ). Not all radiocollared
moose were located on each survey, but radiofixes on >60 moose during a single 1-day
survey were common. I searched intensively at each site to confirm precise locations and to
verify the animal was alive. Moose were monitored from capture to death or date of censor. I
attributed death of moose at capture locations or within 4 days after collaring to capture
stress.
During 1980-85, 75 moose were captured, radiocollared and monitored as part of a moose
movement study in the lower Susitna River valley (Arneson 1981; Modafferi 1982, 1983,
1984, 1988b ). My study area encompassed radio fix point locations of these moose. Moose
point location and descriptive data collected in the former study were incorporated into my
database. Radiocollared moose that survived to January 1986 were monitored in my study.
7
Survival
Radiocollared moose were judged dead by direct observation, by transmitter pulse rate if the
transmitter contained the mortality/movement option, or radiofix location if radiofix locations
on consecutive surveys were identical. When a moose was judged to be dead, an intensive
aerial search was conducted to locate the radiocollar, parts of a moose carcass, and/or a
disturbed site suggesting the animal was dead. Locations were revisited and aerially searched
until sufficient evidence confirmed or refuted that the moose was dead. Locations were
visited on foot to verify death.
Date and cause of death of radiocollared moose that died from legal hunter-harvest, illegal
harvest, defense of life or property, and collisions with vehicles or trains were provided by
hunters, ADF&G, the Alaska Department of Public Safety, and the Alaska Railroad
Corporation.
Deaths of moose judged to be dead during radiotracking aerial surveys were categorized into
1 of 4 groups based on circumstances and/or evidence at the site of death: ( 1) illegal harvest,
(2) accident, (3) winter kill, or ( 4) other. Illegal harvest was assigned mainly to moose
radiotracked to a residential housing development during the hunting season. The accident
group included deaths resulting from injuries and drowning. Intact moose carcasses on the
snow with no evidence suggesting predation or accident were considered winter-killed. The
remaining group, other, included deaths caused by predation and wounding injuries. Several
moose deaths assigned to the other group were bulls that died in late September during or
shortly after the beginning of hunting season. Death of bulls in this calendar period likely
resulted from wounds inflected by hunters (Gasaway 1983, Fryxell et al. 1988) and/or from
wounds incurred during rut-related fights with other bull moose (Koore 1959). The category
other also included cows that died in the period mid May through July. Death of cows in this
calendar period likely resulted from complications with birthing (Markgren 1969) and/or
confrontations with bears (Ballard 1992 b).
Precise date of death was known for train kills, hunter harvest, illegal harvest, and kills in
defense of life or property. For deaths in which the date was unknown, the mid-point date
between the last two surveys was used. This interval was ,$15 days in 30% of the deaths, ,$35
days in 65% of the deaths, but ~5 days in 6 deaths.
Censoring
Moose were censored from the database if: ( 1) the transmitter was lost or failed, (2) an
animal emigrated from the study area, or (3) when the study was terminated. Lost or failed
transmitters were censored on the midpoint between the dates of the last 2 radiofixes. Hunter
harvested moose were censored on the reported date of kill.
Censoring of hunter harvested moose could affect estimates of survival if moose mortality in
the winter after hunting season was compensatory with hunter harvest of moose. I used
regression analysis to examine for evidence of a compensatory relationship between hunter
8
harvest in autumn and mortality the following winter. Hunter bull harvest was regressed on
bull deaths assigned to all sources, winter kill, and other. Analyses encompassed calendar
years 1980-90.
Snow Conditions
Snowpack depth measurements were used to appraise snow conditions. These measurements
were from Alaska Climatological Data Reports, U.S. Department of Commerce, NOAA,
National Environmental Satellite, Data and Information Service, National Climate Data
Center, Asheville, North Carolina for October through April during 1980-91. Snow
conditions were characterized using maximum snowpack depth and the duration of deep
snowpack from October through April. Measurements at Wasilla, Willow, Talkeetna, and
Skwentna weather stations were used to reflect general snow conditions in the study area. In a
few instances, snow measurement data were unavailable for a particular month at a weather
station. In these cases, data from the next nearest weather station were used to proportionalize
maximum snow depth for the month in question.
Management and Analysis of Moose Point-Location Data
Radiofix locations (audio-visual or audio) were plotted on 1 :63,360-scale USGS topographic
maps during radiotracking surveys. Radiofix point locations were later transferred to
translucent overlays of maps for computer digitizing. Digitized point-location data files were
joined to descriptive data files, forming unified data files. Unified data files from Lower
Susitna River, Talkeetna Mountains, Alexander Creek and Yentna/Skwentna rivers studies
were combined, forming a master file containing all data collected in studies of radiocollared
moose in lower Susitna River valley.
Data records of all monitored female radiocollared moose were used to study chronology of
calf birthing, chronology of breeding, twinning rates and calf/cow ratios in lower Susitna
River valley moose populations. Data fields containing information on survey date and
number of calves associated with radiomarked females were segregated from the database,
perused and "cleansed" of errors.
Data records from all radiocollared moose were used to study survival of moose in lower
Susitna River valley moose populations. Data fields containing information on capture date,
dates of point location, and date the moose was determined to be dead or censored were
segregated from the database, perused and "cleansed" of errors.
Data records from all radiocollared moose were used to study movements, seasonal range,
and home range in lower Susitna River valley moose populations. Data fields containing
information on number, date, x and y coordinates, number of point locations were segregated
from the database, perused and "cleansed" of errors.
To relate point locations, movements and home range of radiocollared moose to management
and biology of moose, prominent events in management and life history of moose were
9
identified and delimited with calendar dates and Julian days (Table I). Management and life
history events identified were calving, summer range, rut, postrut range, winter range, moose
surveys, fall hunts, and winter hunts. Point-location data were analyzed in relation to these
events and calendar date periods.
Movements, Seasonal Range, and Home Range Analyses
Environmental requirements of moose change during the calendar year. For example, moose
use different habitat in winter than during calving or postrut. If the habitat is patchy, moose
must move seasonally to access different habitats. Spatial relationship of seasonal habitat
patches determines the size and conformation of moose home ranges. Short distances
between patches of seasonal habitat lead to small home ranges and, possibly, unimodal
utilization distributions. Large distances between habitat patches lead to large home ranges,
and possibly, multimodal utilization distributions. To accurately describe moose home range,
one must include an assessment of the distance between utilization distributions (seasonal
ranges or habitats) along with area measurements. In many species, multimodal utilization
distributions include 2 nonoverlapping polygons, representing a summer and a winter range.
However, data in this study indicated that moose home range may have included more than 2
nonoverlapping utilization distributions and that longest annual movement may not be to a
winter range. In this study, ADF&G staff will investigate methods of evaluating spatial
relationship between seasonal ranges. Eventually, descriptions of moose home range will
include a measure of spatial relationship between seasonal ranges.
Radiotelemetry Point-Location Data Analysis Group
Select participants in a working group to organize and develop methods and procedures for
analyzing point-location data collected in wildlife radiotelemetry studies. Biologists,
biometricians and programmer/analysts will be represented in the group. The group will
review, select, and recommend methodologies and computer hardware and software to
analyze point-location data the ADF&G collects in radiotelemetry studies of movements and
home ranges of wildlife. Point-location data collected in lower Susitna River valley moose
movement studies will be used in developing this process.
RESULTS AND DISCUSSION
Surveys of Moose in Postrut and Winter Areas
Moose count data collected on aerial surveys in postrut areas (Modafferi 1990) and winter
areas (Modafferi 1988) were used to explain the relationship between snowpack depth,
moose movements, and the train moose-kill in Subunits 13E, 14A and 14B in lower Susitna
River Valley (Modafferi 1992). We did not analyze moose sex/age composition data.
10
Capture, Radiocollaring, and Monitoring Moose
Talkeetna Mountains:
Forty-four moose were captured and radiocollared in 7 discrete postrut areas in Subunits l4A
and l4B (Fig. 3, area A-G) from 23 December 1985 to 4 February 1986. On December 1987
and 1988, 8 moose were captured and radiocollared in these areas. In January 1987, 7 moose
were captured and radiocollared in lowland forest habitat (Fig. 4, Area H) located between
Little Willow Creek and the Kashwitna River in Subunit l4B. In February 1989, 5 moose
were captured and radiocollared at timber sale sites between Willow Creek and Iron Creek
(Fig. 4, Area H). In February and March 1988, 6 moose were captured and radiocollared at
personal-use firewood cutting sites near Coal Creek (Fig. 4, Area I). In April 1990, 7 moose
were captured and radiocollared at 6 sites along the Parks Highway between the Little Susitna
River in Subunit l4A and Sheep Creek in Subunit l4B (Fig. I).
Alexander Creek:
In March 1987, 20 moose were captured and radiocollared in Alexander Creek floodplain
(Fig. 4, Area J).
Yentna and Skwentna Rivers:
In February 1988 and 1989, 30 moose were captured and radiocollared in floodplains of the
Skwentna and Yentna rivers between Old Skwentna and McDougall (Fig. 4, Area K).
Lower Susitna River:
At the time I began this study, radiotransmitters on 32 moose captured and radiocollared
during previous studies in the lower Susitna River floodplain were operational and
transmitting audible radio signals. These moose were monitored in my study. Radio-
transmitters on some of these individuals exhibited either weak, infrequent, or no signals. I
presumed these transmitters were weakening and expiring from battery failure.
Analysis of Radiotelemetry Point-location Data
Calendar Year and Seasonal Periods:
The calendar year, 7 May to 6 May the following year, was used to study annual home range
of moose. A Julian calendar of I to 365 days was used to describe the 7 May to 6 May
calendar year. To study seasonal home ranges, the Julian calendar year was subdivided into
periods related to life history and management of moose (Table 1 and Appendix A).
II
Radiotelemetry Data Analysis Working Group:
The telemetry data analysis working group was selected. The group included biologists D. A.
Anderson and C. C. Schwartz; biometricians E. F. Becker and J. VerHoef;
analyst/programmer B. Strauch; and the principal investigator R. D. Modafferi. The group
convened in Anchorage September 1993 to discuss and outline a study plan.
Computer System and Software:
The working group selected a computer system, printer, and basic software appropriate to
conduct movement and home range analyses on radiotelemetry point-location data. The
computer system selected was a 486-66 with 8 Kb of RAM and a Windows operating system.
The printer selected was an 8 page per minute laser printer. The 4 softwares selected were
Lotus 1-2-3, a spreadsheet program (Lotus Development Corporation 1993); FoxPro, a
relational database management system (Microsoft FoxPro, 1993); ArcView, a geographic
information system (ArcView 1992) and CALHOME, a home range analysis program (Kie et
al. l994a, 1994b ). Fox Pro was used to select, segregate, and manipulate data used in
analyses. ArcView facilitated interactive manipulation of data fields within the point-location
database, overlaying views of point locations with geographic feature databases and printing
hardcopies of data. CALHOME ran home range analyses on X, Y coordinates of point-
location data. This program is menu driven, enabling the user to select between 4 home range
methods: adaptive kernel (Worton 1989), harmonic mean (Dixon and Chapman 1980),
bivariate normal (Jennrich and Turner 1969), and minimum convex polygon (Mohr 1947).
CALHOME provides hard copy and screen-display graphic representations of location points
and home range polygons. This program also creates an output file, listing distances between
successive point locations entered in home range analyses.
Administrative and Analytical Procedures:
Administrative procedures were initiated to purchase a 486-66 computer with a Windows
operating system, laser jet printer, and 3 software to conduct analyses of the point-location
database file. A copy of CALHOME (beta version) was requested and received (c/o John G.
Kie, U.S. Forest Service, Pacific Southwest Research Station, 2081 East Sierra Avenue,
Fresno, CA 93710). Computer hardware and the software ArcView and FoxPro were
delivered to the Palmer office 28 February 1994. The investigator was briefed on the
Windows-based computer system, printer, and ArcView and FoxPro software. The
investigator acquainted himself with the computer system, manipulating data with FoxPro
and conducting interactive analyses with Arc View on subsets of the point-location data.
The telemetry data analysis working group convened in Anchorage on 11 August 1994 for an
overview and discussion on using the updated version of the CALHOME home range
analysis program (version 1 ). The group concluded that the adaptive kernel method (AK) of
home range analysis was the most appropriate home range method for analysis of moose
point-location data. The AK method, which produces an unbiased density estimate, is least
influenced by effects of grid size and placement, and it provides realistic interpretations of
12
unimodal and multimodal utilization distributions. Goodness of fit was evaluated with a least
squares cross validation score (LSCV) (Fig. 4 and Fig. 5). In adaptive kernel analyses, the
CALHOME program allows the user to select bandwidth, grid cell size and utilization
distribution point percentage contours (i.e., % of points included in estimated home range).
Trial analyses of data verified compatibility of software and hardware, familiarized staff with
AK home range analysis, and simplified data manipulations and procedures for AK analyses.
Preliminary home range analyses indicated grid size and bandwidth were important
components affecting shape and fit of the utilization distribution contours and estimates of
home range. Grid density affects how well a plotted contour approximates the surface of
interest. Denser grids provided smoother contours. The group decided to use the densest grid,
50x 50, in all adaptive kernel home range analyses. The investigator used the 98% utilization
distribution contours in all AK analyses. Experience in monitoring moose and preliminary
data analyses indicated very few point locations were clearly extraneous to other point
locations.
Point-location data from a sample of moose were used to study relationship between
bandwidth size and the moose point-location data. These analyses were used to determine if a
single bandwidth could be used in all AK moose home range analyses. To examine this,
different size bandwidths were used in analyzing point-location data of each moose
(Appendix B). Bandwidths yielding the lowest LSCV, a measure of goodness of fit, were
selected as the "ideal" bandwidth for use in home range analyses (Appendix C). We
conducted final adaptive kernel analyses using the "ideal bandwidth." We produced hard
copy plots of final analysis home range polygons for the subset of moose (Appendix D).
Relationship between bandwidth, home range size, and number of utilization distributions:
Bandwidth selected by the trial and error process in Appendix B ranged from 400 m to 2600
m (Fig. 6). The bandwidths most frequently selected were between 1200 m and 1400 m.
Bandwidth from 700 m to 1700 m provided minimum LSCVs in about 80% of the moose.
The number of utilization distributions or polygons (i.e., polygon = >2 point locations
encircled) identified in a moose home range varied from 1 to 8 (Fig. 7). Area of utilization
distributions ranged from 9.2 mi 2 to 204.5 mi 2 (Fig. 8); 75% ranged from 20 mi 2 to 70 mi 2 .
There was an inverse relationship between number of utilization distribution polygons
(centroids) and bandwidth (Fig. 9). There was a positive relationship between home range
size and bandwidth (Fig. I 0) and between home range size and centroids (Fig. II).
Year Effects on Point-Location Data:
Radiotelemetry Point-location Data Analysis Working Group was concerned about year
effects on estimates of home range. If year effects were not present, home range analyses
conducted on point-location data collapsed over years. To explore for year effects in point-
location data, point-location data from a sample of moose that exhibited multimodal
utilization distributions (polygons) were examined for evidence of year-to-year philopatry in
winter to a single utilization distribution polygon.
13
I presumed that polygons were likely to include year effects. However, I was aware that
winter-season x year effects could be present in a unimodal utilization distribution. To force
appearance of multiple polygons, home range analyses were performed with shorter
bandwidths than those that provided the LSCVs. However, when examining the short
bandwidths, I avoided selecting those that fragmented utilization distribution into numerous
1-and 2-point polygons.
The process of selecting a bandwidth to force appearance of multimodal utilization
distributions was a very subjective procedure. When I was pleased with the representation of
multiple polygons in an AK home range analysis plots of the home range analyses were
produced (Appendix E) and the examination for year effects continued as follows: I) each
polygon was labeled with a number; polygons encompassing <3 points were considered as
transient ranges or outlying points (outliers) and were not identified as a seasonal range; 2)
outliers were classified into groups based on spatial relationship with respect to adjacent
polygons and were labeled with a number; 3) data on x,y coordinates, Julian day, and Julian
year of point locations (radiofix observations) were copied to a FoxPro database file; 4)
database files were translated in LOTUS to . wk 1 files; 5) . wk I files were sorted by the x or y
coordinate ("X-COORD,Y-COORD") to identify each location point on a CALHOME home
range hard copy output; 6) each location point was assigned the number of the polygon or
outlier group; 7) location point polygon numbers were entered into a field ("CENTROID") in
the lotus file; 8) lotus files were sorted by Julian year ("CYEAR") 7 May through 6 May the
following year (Appendix F); 9) Graphs of the point-location data, Julian day ("CJDA Y") x
polygon number, were created for each moose; and 1 0) graphs were examined visually for
evidence of overlap in utilization distributions during a common calendar period (i.e., were
several utilization distribution polygons represented during the same Julian day?). If the
overlap of polygons was a year effect, data from selected years were deleted from graphs to
determine if that eliminated the overlap.
There was a very shallow snowpack in lower Susitna River valley in Julian year 1985-86
(Fig. 4). Therefore, in exploring for year effects, I especially examined the hypothesis that
moose used different areas (polygons) in the winter of 1985-86 than in winter in other years.
The data examined supported this assertion. Many moose, particularly those radiocollared in
Unit l4B, used different areas (polygons) in winter 1985-86 than in other years. The data
indicated that in 1985-86 many moose stayed in postrut areas or moved only short distances
from postrut areas to winter areas. Data gathered on moose surveys in postrut areas supported
this movement pattern( Modafferi 1991 ). The data also indicate that moose did not use the
same winter area in years other than 1985-86. These analyses also indicated that some moose
used different areas during calving. However, this observation may be misleading because the
length of time cow moose utilize calving sites probably depends on neonate survival. Cow
moose that lose a calf shortly after calving may move immediately to a summer area, but
cows with calves may remain in the calving area longer.
14
Spatial Relationship Between Season Ranges:
Data analyzed from a cow moose that moved a great distance between utilization
distributions indicated the longest movements were between Julian day 340 and Julian day 60
the following year (Fig. 13). Timing of long distance movements correlates with movements
from a winter range (late winter) to a calving range (spring). Other long distance movements
occurred between Julian day 130 and Julian day 180. Timing of these movements correlates
with movements from a rut range to a postrut range. Of particular significance is that the
moose did not move great distances between postrut range and winter range.
Publications
Completed:
A manuscript titled "In Utero Pregnancy Rates, Litter Size and Productivity For Social
Classes of Cow Moose in South-Central Alaska" was prepared and submitted for publication
in the journal Alces. The manuscript was accepted and published in Alces 28:223-234.
In Draft Form:
A draft of the manuscript Survival of radiocollared adult moose in lower Susitna River
valley, Southcentral Alaska is in Appendix H.
In Preparation:
• Birthing chronology, breeding chronology, twinning rate and calf/cow ratios in
radiocollared cow moose in lower Susitna River valley in Southcentral Alaska:
Characteristics and Relationship with Weather
• Movements, Seasonal Range, and Home Range of Radiocollared Moose in Lower Susitna
Valley in Southcentral Alaska
Future Activities
Home range analyses conducted indicate that it is not possible to select a single bandwidth,
based on minimum LSCV, for use in analysis of moose point-location data. These analyses
indicated bandwidth must be examined in a trial and error process to select bandwidth based
on a minimum LSCV value. J. Kie (pers. commun.) indicated that in analyzing deer home
range data, the bandwidth selected was as the one which produced the lower LSCV value
between the following 2 CALHOME analyses: I) the program estimated optimum bandwidth
and 2) 0.8 times, the program estimated optimum bandwidth value.
Confer with members of telemetry data analysis working group to: 1) establish a
standardized method of selecting bandwidth for CALHOME AK home range analyses, 2)
outline procedures that adjust for year effects in home range analyses, 3) determine methods
15
outline procedures that adjust for year effects in home range analyses, 3) determine methods
of describing spatial relationship between seasonal home ranges in a home range, and 4)
determine a method of incorporating information on spatial relationships of seasonal home
ranges in home range analyses. Use these methodologies to analyze point-location data
collected in lower Susitna Valley moose studies for information on population identity and
moose movements.
ACKNOWLEDGMENTS
I especially thank staff of the Alaska Department of Fish and Game (ADF&G) for helping
with this study. D. C. McAllister assisted in many aspects of the study. I acknowledge many
ADF&G colleagues for assistance in moose capture and radiotracking procedures. P. A.
Arneson provided data on moose captured, radiocollared, and monitored during 1980 in
Subunits 16A and 13E. J. B. Faro contributed information on moose captured, radiocollared,
and monitored in Subunit 16B during 1987-88. My supervisors, K. B. Schneider, D. A.
Anderson, and C. C. Schwartz, provided guidance, peer review assistance, and administrative
support. I thank J. C. Didrickson, C. A. Grauvogel, H. J. Griese, and M. W. Masteller for
their support. I thank light aircraft pilots C. A. Allen, Charlie Allen Flight Service; M. Houte,
L. Rogers, C. R. and V. L. Lofstedt, Kenai Air Alaska; W. A. Woods, Woods Air Service;
and W. D. Wiederkehr, Wiederkehr Air Inc. for skill, dedication, and enthusiasm on aerial
radiotracking surveys. E. B. Becker and Jay VerHoef provided statistical advice and clarified
analytical concepts. B. Strauch managed and processed the point-location data file and
performed many GIS analyses. S. R. Peterson and other staff at ADF&G, Juneau, provided
advice and comments on reports and manuscripts.
LITERATURE CITED
ArcView. 1992. Arc View User's Guide. 2nd ed. Environmental Systems Research Institute,
Inc., Redlands, Calif.
Albert, S. W., and L. C. Shea. 1986. Moose winter habitat in the lower Susitna Valley,
Alaska: Pilot project on habitat suitability assessment. Alaska Dept. Fish and Game.
Tech.Rep. No. 86-6. Juneau. 105pp.
Arneson, P. 1981. Big game studies. Vol. II. Moose. Ann. Prog. Rep. Susitna Hydroelectric
Proj. Alaska Dep. Fish and Game. Juneau. 64pp.
Ballard, W. B. 1992a. Modelled impacts of wolf and bear predation on moose calf survival.
Alces 28:79-88.
Ballard, W. B. 1992b. Bear predation on moose: a review of recent North American studies
and their management implications. Alces. Suppl. 1: 162-176.
16
__ , J. S. Whitman, and D. J. Reed. 1991. Population dynamics of moose in south-central
Alaska. Wild!. Monogr. 114. 49pp.
Chatelain, E. F. 1951. Winter range problems of moose in the Susitna Valley. Proc. Alaska
Sci.; Conf. 2:343-347.
Didrickson, J. C., and K. P. Taylor. 1978. Lower Susitna Valley moose population identity
study. Alaska Dept. of Fish and Game. Fed. Wildl. Rest. Proj. Final Rept., W -17-8
and 9. Job 1.16R. Juneau. 20pp.
Dixon, K. R., and J. A. Chapman. 1980. Harmonic mean measure of animal activity areas.
Ecology 61:1040-1044.
Franzmann., A. W., P. D. Arneson, R. E. LeResche, and J. L. Davis. 1974. Developing and
testing new techniques for moose management. Alaska Dep. Fish and Game. Fed. aid
Wildl. Restor. final Rep., Proj. W-17-2, W-17-3, w-17-4, W-17-5, and W-17-6. 54pp.
Fryxell, J. M. , W. G .. Mercer, and R. B. Gellately. 1988. Population dynamics of
Newfoundland moose using cohort analysis. J. Wild!. Manage. 52:14-21.
Gasaway, W. C. 1975. Moose antlers: How fast do they grow? Alaska Dept. of Fish and
Game, Brochure. Juneau. 6pp.
__ , R. 0. Stephenson, J. L. Davis, and 0. E. Burris. 1983. Interrelationships of wolves,
prey and man in Interior Alaska. Wild!. Monogr. 84. 50pp.
__ , W. C. D. J. Preston, D. J. Reed, and D. J. Roby. 1987. Comparative antler
morphology and size of North American moose. Swedish Wildl. Res. (Suppl.) 1:311-
325.
Grauvogel, C. A. 1990. Unit 14 brown bear survey-inventory progress report. Pages 84-94 in
S. 0. Morgan, ed. Annual report of survey-inventory activities. Part V. brown/grizzly
bears. Vol. XX. Alaska Dep. Fish and Game. Fed. Aid in Wildl. Rest. Prog. Rep.
Proj. w-23-2. Study 4.0. Juneau.
Griese, H. J. l993a. Unit 14A moose survey-inventory progress report. Pages 113-125 inS.
A. Abbott, ed. Management Report of Survey-Inventory Activities. Moose. Alaska
Dep. Fish and Fame Fed. Aid in Wildl. Rest. Prog. Rep. Proj. W-23-3 and W-23-4.
Study 1.0. Juneau. 422pp.
___ . 1993b. Unit 14B moose survey-inventory progress report. Pages 126-135 in S. A.
Abbott, ed. Management Report of Survey-Inventory Activities. Moose. Alaska Dep.
Fish and Game Fed. Aid in Wildl. Rest. Prog. Rep. Proj. W-23-3 and W-23-4. Study
1.0. Juneau. 422pp.
17
Jennrich, R. I., and f. B. Turner. 1969. Measurement of non circular home range. J.
Theoretical Biology 22:227-237.
Kie, J. G., J. A. Baldwin, and C. J. Evans. 1994a. CALHOME-Data preparation utilities.
Electronic User's Manual. 8pp.
__ , J. A. Baldwin, and C. J. Evans. 1994b. CALHOME Home range analysis program
Electronic User's Manual. 19pp.
Knorre, E. P. 1959. Ekologiya locya. Trudy Pechora-llych. gos. Zapov. 7:5-167. in Lent, P. C.
1971. A review of rutting behavior in moose. Nat. Can. (Que.) 101:307-323.
Lotus Development Corporation. 1993. Lotus User's Guide. Lotus 1-2-3 for DOS, Release
2.4. 55 Cambridge Parkway, Cambridge, Mass. 435pp.
Markgren, G. 1969. Reproduction of moose in Sweden. Viltrevy 6:127-299.
Masteller. M. 1994. Unit 16 wolf survey-inventory progress report. 1-16 inS. A. Abbott, ed.
Management Report of Survey-Inventory Activities. Brown Bear. Alaska Dep. fish
and game. Fed. Aid in Wildl. Rest. Prog. Rep. Proj. W-23-4 and W-23-5. Study 4.0.
Juneau. In prep.
Microsoft FoxPro. 1993. Getting Started Microsoft FoxPro for MS-DOS. Microsoft Corp.,
Redmond, W A.
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bear and brown bear. Alaska Dep. Fish and Game. Anchorage. 276pp.
Modafferi, R. D. 1982. Big game studies. Vol II. Moose-Downstream. Final Phase I Rep.
Susitna Hydroelectric Proj. Alaska Dep. Fish Game. Juneau. 114pp.
_____ . 1983. Big game studies. Vol. II. Moose-Downstream. Prog. Rep. Phase II.
Susitna Hydroelectric Proj. Alaska Dep. Fish and Game. Juneau. 114pp.
_____ . 1984. Big game studies. Vol. II. Moose-Downstream. Prog. Rep. Phase II.
Susitna Hydroelectric Proj. Alaska Dep. Fish and Game. 116pp.
_____ . 1987. Lower Susitna Valley moose population identity and movement study.
Alaska Dep. Fish and Game. Fed. Aid Wildl. Rest. Prog. Rep. Proj. W-22-5. Job
1.38R. Juneau. 17pp.
_____ . 1988a. Lower Susitna Valley moose population identity and movement study.
Alaska Dep. Fish and Game. Fed. Aid Wildl. Rest. Prog. Rep. Proj. W-22-5 and W-
22-6. Job IB 1.38. Juneau. 60pp.
18
_____ . 1988b. Big game studies. Vol. I. Moose-Downstream. Final Rep. Susitna
Hydroelectric Proj. Alaska Dep. Fish and Game. 2llpp.
_____ . 1990. Lower Susitna Valley moose population identity and movement study.
Alaska Dep. Fish and Game. Fed. Aid Wildl. Rest. Prog. Rep. Proj. W-23-2. Job IB
1.38. Juneau. 46pp.
_____ . 1991. Train moose-kill in Alaska: Characteristics and relationship with
snowpack depth and moose distribution in lower Susitna Valley. Alces 27:193-207.
_____ . 1992. Lower Susitna Valley moose population identity and movement study.
Alaska Dep. Fish and Game. Fed. Aid Wildl. Rest. Prog. Rep. Proj. W-23-2. Job IB
1.38. Juneau. 39pp.
Mohr, C. 0. 1947. Table of equivalent population of North American mammals. Amer.
Midland Nat. 37:223-249.
Pimlott, D. H. 1959. Reproduction and productivity of Newfoundland moose. J. Wildl.
Manage. 23:381-401.
Rausch, R. A. 1958. The problem of railroad-moose conflicts in the Susitna Valley. Alaska
Dep. of Fish and Game. Fed. Aid Wildl. Rest. Final Rep. Proj. W-3-R. Job 1-4.
Juneau. 116pp.
_____ . 1959. Some aspects of population dynamics of the railbelt moose populations,
Alaska. M.S. Thesis. Univ. Alaska, Fairbanks. 8lpp.
Sergeant, D. E., and D. H. Pimlott. 1959. Age determination in moose from sectioned incisor
teeth. J. Wild/. Manage. 23:315-321.
Viereck, L. A., and E. L. Little, Jr. 1972. Alaska trees and shrubs. U.S. Dept. Agric. Forest
Serv. Handbook No. 410. 265pp.
Worton, B. J. 1989. Kernel methods for estimating the utilization distribution in home-range
studies. Ecology. 70: 164-168.
19
PREPARED BY:
Ronald D. Modafferi
Wildlife Biologist
SUBMITTED BY:
Charles C. Schwartz
Research Coordinator even R. Peterson,
Division of Wildlife Conservation
20
, ...
o· .......
P'lgure t. ..ap •howlno locaUon of the etudy area In Alaella with na111oe lleted fo;
riYora, lakee and othor JHOIIIInent landecape featurea.
21
Fig. 2. Location of Game Management Subunits (13E, 14A, 14B, 16A and 16B)
and state and national parks in the study area.
22
NORTH
Sc:ale ,:SHiCCO
·c
Km.
... !i ===--==-c:::=
c 0 c ~
ouat~
oj
fttman
ilia
I n 1 e t
Pig. 3. Locations of Talkeetna Mountains alpine habitat moose postrut
areaa (A-G), Kasllwitna Corridor Forest (E), Coal Creek t~e= cut area
(I), Alexander Creek (J) and the Lake Creek/Skwentna a:ea (K) where
moose were captured and radio-ma=ked. A = Bald Moun~air., B = Moss
Mountain, C • Willow Mountain, D = Witna Mountain, E = Brownie Mountain,
F c wolverine Mountain, and G = Sunshine Mountai~.
23
A
c
.~.. . :.· ...
·.·
...
· .. ' ...
. ·
B
0
·.' ...
Fig. 4. A unimodal utilization distribution estimated !rom 98Pt minimum convex polygon
(A), 98Vt bivariate normal (B), 98Pt harmonic mean (C), and 98Pt adaptive kernel (O-F)
home range methods in the CALHOME home range analysis program (Kie et al. 1994).
Bandwidth~ l,J6J m (program default, optimum) in D, 900 in E, and 700 in F. Home range
estimates (ha) • 4,499 (A), 6,266 (B), 5,880 (C), 5,165 (D), 4,411 (E), and 4,1J5 (F).
Axis scaling not the same in all Figs.
24
A B
r.y;;.
"'i?·· .; .. ;:
I I I I I I I t II j I I I l j I I I I l ' I
GD . . . . .
Fiq. 5. A bimodal utilization distribution estimated from 98Pt minimum convex polyqon
(A), 98Vt bivariate normal (B), 98Pt harmonic mean (C), and 98Pt adaptive kernel (D-F)
home ranqe methods in the CALHOME home ranqe analyses proqram (Kie et al. 1994).
Bandwidth ~ 6,405 m (proqram default, optimum) in D, 900 in E, and 700 in F. Home ranqe
estimates (ha) • 28,690 (A). 65,790 (8), 15,160 (C), 2J,570 (D), 12,050 (E), and 9,19J
(F). Axis scalinq not the same in all Figs.
25
"""' "' l[)
II z
"-"'
w
U1
0
0
~
u_
0
a:: w
Ill
~
:J z
7 r---------------------------------------------------------
5
4 I 3
2
BAND WIDTH 'NITH MINIMAL LSCV
Fig. 6. Frequency distribution of bandwidths (m) selected for
use in CALHOME adaptive kernel home range analyses performed on
moose point location data. Bandwidth selection was based on
minimizing the LSCV value.
26
w
U1
0
0
::!:
0 z
FREQUENCY DISTRIBUTION -NO. POLYGONS
N = 54 MOOSE
18 t
16
14
12
10
8
2 4 6 8
NO. UTILIZATION DISTRIBUTIONS
Fig. 7. Frequency distribution of the number of utilization
distribution polygons delieneated in CALHOME adaptive kernel home
range analyses performed on moose point location data.
Utilization distribtuion polygon = a polygon encompassing >2
point locations.
27
""' n
L0
II z
w
Vl
0
0
:::E
lo.
0
a:: w
(])
:::E
:J z
11
10
9
a
7
6
5
4
3
2
0
0
RANGE SIZE (SO Ml)
Fig~ a. Frequency distribution of estimates of home range size
(mi ) obtained in CALHOME adaptive kernel home range analyses
performed on moose point location data.
28
,...._
U1
"""" Q._
N
1\
'--"
U1
0
0 a::
"""" z
w u
LJ..
0
0:::
w
(IJ
2
::> z
9
8 1-0
7 -0
6 -
5 1-0 0 0
4 -0 r:IIJ 0 0
3 1-C: 0 OD 0 0 DO 0 0
2 1-c 0 0 DO om O]J 0 0
1 1-0 rnrniJ DOD DO 0
0
0 I 2 I 4
3
(Thousands)
BAND WIDTH (THOUSANDS)
Fig. 9. Relationship between bandwidth (m) and number of
utilization distribution polygons (centroids). Utilization
distribution polygon = a polygon encompassing >2 point locations.
Data obtained from CALHOME adaptive kernel analyses performed on
moose point location data. Bandwidth selection was based on
minimizing the LSCV value.
29
'""" I VJ
1-"0
0 c -a 3: (/]
0 :J z~
<I:!-
(!)'-"
4
0
.3 !--
0
oD
0~ 0
2 !--
0 cP
0 0
0 0 0 0
c? 0
DllJh cfl o0 ~CIJD liJ DO 0 1 -Dc8 0
0
0 0 CD 0
0 0
0 I I 0 100 200
50 150 250
RANGE SIZE (SO Ml)
Fig. 10. Relationship between bandwidth (m) and moose home range
size (mi 2 ). Data obtained from CALHOME adaptive kernel analyses
performed on moose point location data. Bandwidth selection was
based on minimizing the LSCV value.
30
"" (f1
1-a..
N
1\
(f1
0
0 a::
1-z w u
u._
0
0: w
ID
~
::> z
9
8 I-0
7 I-0
5 -
5 I-cc 0 0
4 1-[j ::rriJ DO
3 I-0 0 [] 0[11 0 c 0
2 1-OJ 0 0[][[] rn 0 0 0
1 I-rn::r: CCD!liiJ DO 0 0
0 I I 0 20 40 60
10 30 50
(Thousands)
RANGE SIZE (HA)
Fig. 11. Relationship between range size and number of
utilization distribution polygons (centroids) delieneated in
CALHOME adaptive kernel horne range analyses performed on moose
point location data. Utilization distribtuion polygon = a
polygon encompassing >2 point locations.
31
(l::
a_
<{
I
1-u
0
SUSITNA \fA.LLEY SNO\NPACK DEPTH 19 79-93
(79 1979-30, 30""1980-31, EIC)
JOO ~-------------------------------------------
100
79 31 83
80 82 84 86
YEAR
~WILLOW
~ T..G.LKEETN . .G.
88 90 92
Fig. 12. Maximum snowpack depth (em) measured during October
through April in Willow and Talkeetna in lower Susitna River
valley, south-cent~al Alaska, 1979-93.
32
Table I. Coarse and fine grain calendar and Julian date periods delineating important events in management and life history of
moose in lower Susitna River valley in Southcentral Alaska.
Grain Event Period Calendar date 1 Julian date 2 No. days
Coarse
Life history
Calving 7 May to 15 Jun 1 to 40 40
Summer 13 Jul to 15 Aug 56 to 101 46
Rut 7 Sep to 10 Oct 124 to 157 34
Post rut 11 oct to 1 Dec 158 to 209 52
Winter 15 Nov to 30 Apr 193 to 360 178
Management 3
Fall hunt 20 Aug to 30 Sep 106 to 147 42
Winter hunt 1 Jan to 28 Feb 240-298 59
Survey 7 Nov to 21 Dec 185 to 229 45
Table 1. Continued.
Grain Event Period Calendar date 1
Fine
Life history
Calving 16 May to 31 May
summer 13 Jul to 15 Aug
Rut 15 Sep to 5 Oct
Postrut 14 Oct to 1 Nov
Winter 10 Jan to 1 Mar
1 Calendar year = 7 May to 6 May the following year.
2 Julian day 1 = 7 May.
3 Periods and dates for management the same in fine grain.
Julian date2 No. days
10 to 25 16
56 to 101 46
132 to 152 21
161 to 179 19
249 to 300 52
APPENDIX A. JULIAN DAY NUMBER (CJDAY NO.) AND CALENDAR DATES FOR PROMINENT EVENTS IN Ll FE
HISTORY (SEASONS) AND MANAGEMENT (PERIODS) OF MOOSE
FILE: D:CJDAY.WK1 29 SEPTEMBER 1994 I./HERE: FINE CENTROID=CENTROID AND
COARSE CENTROID=MAXRANGE
FINE COARSE
CJDAY CALENDAR SEASON CENTROID CENTROID PERIOD NO. DAYS
DATE DATES DATES DATES DATES IN SEASON
07-May-83 CALVING 1
2 08-May-83 CALVING 2
3 09-May-83 CALVING 3
4 10-May-83 CALVING C-4 4
5 11-May-83 CALVING c 5
6 12-May-83 CALVING c 6
7 13-May-83 CALVING c 7
8 14-May-83 CALVING c 8
9 15-May-83 CALVING c 9
10 16-May-83 CALVING C-10 c 10
11 17-May-83 CALVING c c 11
12 18-May-83 CALVING c c 12
13 19-May-83 CALVING c c 13
14 20-May-83 CALVING c c 14
15 21-May-83 CALVING c c 15
16 22-May-83 CALVING c c 16
17 23-May-83 CALVING C-40 DAYS C 17 SEASON=REPLACE SEASON IJITH 'CALVING' FOR
18 24-May-83 CALVING c c 18 CJDAY <=40; N=758; DAYS = 40
19 25-May-83 CALVING c c 19 FINE CENTROID=REPLACE CENTROID IJITH 'C' FOR
20 26-May-83 CALVING c c 20 CJDAY >=10 AND CJDAY<=309; N=25; DAYS =16
21 27-May-83 CALVING c c 21 COARSE CENTROID=REPLACE MAXRANGE IJITH 'C' FOR
22 28-May-83 CALVING c c 22 CJDAY >=4 AND CJDAY<=25; N=956; DAYS = 22
23 29-May-83 CALVING c c 23
24 30-May-83 CALVING c c 24 TOTAL SEASON ='TRANS'; N=673
25 31-May-83 CALVING C-25 C-25 25 TOTAL CENTROID='T'; N=6267
26 01-Jun-83 CALVING 26 TOTAL MAXRANGE='T'; N=4799
27 02-Jun-83 CALVING 27
28 03-Jun-83 CALVING 28 PERIOD = NO = 7929
29 04-Jun-83 CALVING 29 SURV N= 934
30 05-Jun-83 CALVING 30 FHUNT N= 997
31 06-Jun-83 CALVING 31 I./HUNT N=1n8
32 07-Jun-83 CALVING 32
33 08-Jun-83 CALVING 33
34 09-Jun-83 CALVING 34
35 10-Jun-83 CALVING 35
36 11-Jun-83 CALVING 36
37 12-Jun-83 CALVING 37
38 13-Jun-83 CALVING 38
39 14-Jun-83 CALVING 39
40 15-Jun-83 CALVING 40
35
41 16-Jun-83 TRANS 1
42 17-Jun-83 TRANS 2
43 18-Jun-83 TRANS 3
44 19-Jun-83 TRANS 4
45 20-Jun-83 TRANS 5
46 21-Jun-83 TRANS 6
47 22-Jun-83 TRANS 7
48 23-Jun-83 TRANS 8
49 24-Jun-83 TRANS 9
so 25-Jun-83 TRANS 10
51 26-Jun-83 TRANS ,,
52 27-Jun-83 TRANS 12
53 28-Jun-83 TRANS 13
54 29-Jun-83 TRANS 14
55 30-Jun-83 TRANS 15
56 01-Jul-83 SUMMER 1
57 02-Jul-83 SUMMER 2
58 03-Jul-83 SUMMER 3
59 04-Jul-83 SUMMER 4
60 05-Jul-83 SUMMER 5
61 06-Jul-83 SUMMER 6
62 07-Jul-83 SliMMER 7
63 08-Jul-83 SUMMER 8
64 09-Jul-83 SUMMER 9
65 10-Jul-83 SUMMER 10
66 11 -Jul-83 SUMMER ,,
67 12-Jul-83 SUMMER 12
68 13-Jul-83 SUMMER S-68 S-68 13
69 14-Jul-83 SUMMER s s 14
70 15-Jul-83 SUMMER s s 15
71 16-Jul-83 SUMMER s s 16
72 17-Jul-83 SUMMER s s 17
73 18-Jul-83 SUMMER s s 18
74 19-Jul-83 SUMMER s s 19
75 20-Jul-83 SUMMER s s 20
76 21 -Jul-83 SUMMER s s 21
n 22-Jul-83 SUMMER s s 22
78 23-Jul-83 SUMMER s s 23 SEASON=REPALCE SEASON ~ITH 'SUMMER' FOR CJDAY
79 24-Jul-83 SUMMER s s 24 CJDAY <=101; N=937; DAYS = 46
80 25-Jul-83 SUMMER s s 25 FINE CENTROID=REPLACE CENTROID ~ITH 'S' FOR
81 26-Jul-83 SUMMER s s 26 CJDAY >=68 AND CJDAY <=101; N=696; DAYS = 34
82 27-Jul-83 SUMMER s s 27 COARSE CENTROID=REPLACE MAXRANGE ~ITH 'S' FOR
83 28-Jul-83 SUMMER s s 28 CJDAY >=68 AND CJDAY <=101; N=696; DAYS = 34
84 29-Jul-83 SUMMER s s 29
85 30-Jul-83 SUMMER S-46 DAYS S 30
86 31-Jul-83 SUMMER s s 31
87 01-Aug-83 SUMMER s s 32
88 02-Aug-83 SUMMER s s 33
89 03-Aug-83 SUMMER s s 34
90 04-Aug-83 SUMMER s s 35
36
l
l 91 05·Aug·83 SUMMER s s 36
92 06·Aug·83 SUMMER s s 37
r
93 07-Aug-83 SUMMER s s 38
94 08·Aug·83 SUMMER s s 39
95 09-Aug-83 SUMMER s s 40
96 10·Aug·83 SUMMER s s 41
I 97 11·Aug·83 SUMMER s s 42
98 12-Aug-83 SUMMER s s 43
99 13-Aug-83 SUMMER s s 44
I 100 14·Aug·83 SUMMER s s 45
101 15-Aug-83 SUMMER S-101 S-101 46
102 16-Aug-83 TRANS
103 17-Aug-83 TRANS 2
t 104 18·Aug·83 TRANS 3
105 19-Aug-83 TRANS 4
106 20-Aug-83 TRANS FH-106 5
I 107 21-Aug-83 TRANS FH 6
108 22-Aug-83 TRANS FH 7
109 23-Aug-83 TRANS FH 8
I 110 24-Aug-83 TRANS FH 9
111 25-Aug-83 TRANS FH 10
112 26-Aug-83 TRANS FH 11
113 27-Aug-83 TRANS FH 12
I 114 28-Aug-83 TRANS FH 13
115 29-Aug-83 TRANS FH 14
116 30-Aug-83 TRANS FH 15
r 117 31-Aug-83 TRANS FH 16
118 01-Sep-83 TRANS FH 17
119 02-Sep-83 TRANS FH 18
I 120 03-Sep-83 TRANS FH 19
121 04-Sep-83 TRANS FH 20
122 05-Sep-83 TRANS FH 21
123 06-Sep-83 TRANS FH 22
I 124 07-Sep-83 RUT FH
125 08-Sep-83 RUT FH 2
126 09-Sep-83 RUT FH 3
I' 127 10-Sep-83 RUT FH 4 PERIOO=REPLACE PERIOD ~ITH 'FH' FOR CJDAY >=1
128 11-Sep-83 RUT FH 5 CJDAY <=147; N=997; DAYS = 42
129 12-Sep-83 RUT FH 6
130 13-Sep-83 RUT FH 7 I 131 14-Sep-83 RUT FH 8
132 15-Sep-83 RUT R-132 R-132 FH 9
133 16-Sep-83 RUT R R FH 10
I 134 17-Sep-83 RUT R R FH 11
135 18-Sep-83 RUT R R FH 12
136 19-Sep-83 RUT R R FH 13
I 137 20-Sep-83 RUT R R FH 14
138 21-Sep-83 RUT R R FH 15
139 22-Sep-83 RUT R R FH 16
140 23-Sep-83 RUT R R FH 17
t
I
I 37
141 24-Sep-83 RUT R R FH 18 SEASON=REPLACE SEASON WITH 'RUT' FOR CJDAY >=
142 25-Sep-83 RUT R-34 DAYS R FH 19 CJDAY <=157; N=796; DAYS = 34
143 26-Sep-83 RUT R R FH 20 FINE CENTROID=REPLACE CENTROID WITH 'R' FOR C
144 27-Sep-83 RUT R R FH 21 CJDAY <=152; N=628; DAYS = 21
145 28-Sep-83 RUT R R FH 22 COARSE CENTROID=REPLACE MAXRANGE WITH 'R' FOR
146 29-Sep-83 RUT R R FH 23 CJDAY <=152; Nz628; DAYS = 21
147 30-Sep-83 RUT R R FH-147 24
148 01-0ct-83 RUT R R 25
149 02-0ct-83 RUT R R 26
150 03-0ct-83 RUT R R 27
151 04-0ct-83 RUT R R 28
152 05-0ct-83 RUT R-152 R-152 29
153 06-0ct-83 RUT 30
154 07-0ct-83 RUT 31
155 08-0ct-83 RUT 32
156 09-0ct-83 RUT 33
157 10-0ct-83 RUT 34
158 11-0ct-83 POST RUT P-158 35
159 12-0ct-83 POST RUT p 36
160 13-0ct-83 POST RUT p 37
161 14-0ct-83 POST RUT P-161 p 38
162 15-0ct-83 POST RUT p p 1
163 16-0ct-83 POST RUT p p 2
164 17-0ct-83 POST RUT p p 3
165 18-0ct-83 POST RUT p p 4
166 19-0ct-83 POST RUT p p 5
167 20-oct-83 POST RUT p p 1
168 21-0ct-83 POST RUT p p 2
169 22-0ct-83 POST RUT p p 3
170 23-0ct-83 POST RUT p p 4
171 24-0ct-83 POST RUT p p 5
172 25-oct-83 POST RUT p p 6
173 26-0ct-83 POST RUT p p 7
174 27-oct-83 POST RUT p p 8
175 28-0ct-83 POST RUT p p 9
176 29-0ct-83 POST RUT p p 10
177 30-0ct-83 POST RUT p p 11
178 31-0ct-83 POST RUT p p 12
179 01-Nov-83 POST RUT p p 13 SEASON=REPLACE SEASON WITH 'POSTRUT' FOR D
180 02-Nov-83 POST RUT p p 14 CJDAY >=198 AND CJDAY <=198; N=730; DAYS = 41
181 03-Nov-83 POST RUT p p 15 FINE CENTROID=REPLACE CENTROID WITH 'P' FOR
182 04-Nov-83 POST RUT P-182 p 16 CJDAY >=167 AND CJDAY<=185; N=314; DAYS = 19
183 05-Nov-83 POST RUT p 17 COARSE CENTROID=REPLACE MAXRANGE WITH 'P' FOR
184 06-Nov-83 POST RUT p 18 CJDAY >=158 AND CJDAY<=185; N=405; DAYS = 28
185 07-Nov-83 POST RUT P-185 SURV-185 19
186 08-Nov-83 POST RUT SURV 20
187 09-Nov-83 POST RUT SURV 21
188 10-Nov-83 POST RUT SURV 22
189 11-Nov-83 POST RUT SURV 23
190 12-Nov-83 POST RUT SURV 24
38
191 13-Nov-83 POST RUT SURV 25
192 14-Nov-83 POST RUT SURV 26
193 15-Nov-83 POST RUT SURV 27
194 16-Nov-83 POST RUT SURV 28
195 17-Nov-83 POST RUT SURV 29
196 18-Nov-83 POST RUT SURV 30
197 19-Nov-83 POST RUT SURV 31
198 20-Nov-83 POST RUT SURV 32
199 21-Nov-83 WINTER SURV 1
200 22-Nov-83 WINTER SURV 2
201 23-Nov-83 WINTER SURV 3
202 24-Nov-83 WINTER SURV 4
203 25-Nov-83 WINTER SURV 5
204 26-Nov-83 WINTER SURV 1
205 27-Nov-83 WINTER SURV 2
206 28-Nov-83 WINTER SURV 3
207 29-Nov-83 WINTER SURV 4 PERIOD=REPLACE PERIOD WITH 'SURV' FOR CJDAY >
208 30-Nov-83 WINTER SURV 5 CJDAY >= 185 AND CJDAY <=229; N=934
209 01-Dec-83 WINTER SURV 6
210 02-Dec-83 WINTER SURV 7
211 03-Dec-83 WINTER SURV 8
212 04-Dec-83 WINTER SURV 9
213 05-Dec-83 WINTER SURV 10
214 06-Dec-83 WINTER SURV 11
215 07-Dec-83 WINTER SURV 12
216 08-Dec-83 WINTER SURV 13
217 09-Dec-83 WINTER SURV 14
218 10-Dec-83 WINTER SURV 15
219 11-Dec-83 WINTER SURV 16
220 12-Dec-83 WINTER SURV 17
221 13-Dec-83 WINTER SURV 18
222 14-Dec-83 WINTER SURV 19
223 15-Dec-83 WINTER SURV 20
224 16-Dec-83 WINTER SURV 21
225 17-Dec-83 WINTER SURV 22
226 18-Dec-83 WINTER SURV 23
227 19-Dec-83 WINTER SURV 24
228 20-Dec-83 WINTER SURV 25
229 21-Dec-83 WINTER SURV-229 26
230 22-Dec-83 WINTER 27
231 23-0ec-83 WINTER 28
232 24-Dec-83 WINTER 29
233 25-Dec-83 WINTER 30
234 26-Dec-83 WINTER 31
235 27-Dec-83 WINTER 32
236 28-Dec-83 WINTER 33
237 29-Dec-83 WINTER 34
238 30-Dec-83 WINTER 35
239 31-Dec-83 WINTER 36
240 01-Jan-84 WINTER WH-240 37
39
241 02-Jan-84 WINTER
242 03-Jan-84 WINTER
243 04-Jan-84 WINTER
244 05-Jan-84 WINTER
245 06-Jan-84 WINTER
246 07-Jan-84 WINTER
247 08-Jan-84 WINTER
248 09-Jan-84 WINTER
249 10-Jan-84 WINTER
250 11-Jan-84 WINTER
251 12-Jan-84 WINTER
252 13-Jan-84 WINTER
253 14-Jan-84 WINTER
254 15-Jan-84 WINTER
255 16-Jan-84 WINTER
256 17-Jan-84 WINTER
257 18-Jan-84 WINTER
258 19-Jan-84 WINTER
259 20-Jan-84 WINTER
260 21-Jan-84 WINTER
261 22-Jan-84 WINTER
262 23-Jan-84 WINTER
263 24-Jan-84 WINTER
264 25-Jan-84 WINTER
265 26-Jan-84 WINTER
266 27-Jan-84 WINTER
267 28-Jan-84 WINTER
268 29-Jan-84 WINTER
269 30-Jan-84 WINTER
270 31-Jan-84 WINTER
271 01-Feb-84 WINTER
272 02-Feb-84 WINTER
273 03·Feb·84 WINTER
274 04-Feb-84 WINTER
275 05-Feb-84 WINTER
276 06-Feb-84 WINTER
277 07·Feb·84 WINTER
278 08·Feb·84 WINTER
279 09-Feb-84 WINTER
280 10-Feb-84 WINTER
281 11-Feb-84 WINTER
282 12-Feb-84 WINTER
283 13-Feb-84 WINTER
284 14-Feb-84 WINTER
285 15·Feb·84 WINTER
286 16-Feb-84 WINTER
287 17-Feb-84 WINTER
288 18-Feb-84 WINTER
289 19-Feb-84 WINTER
290 20-Feb-84 WINTER
W-279
w
w
w
w
w
w
w
w
w
w
w
W-258
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
w
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
WH
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59 PERIOD=REPLACE PERIOD WITH 'WH' FOR
60 CJOAY >=240 AND CJDAY <=298; N=1728
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86 SEASON=REPLACE SEASON WITH 'WINTER' FOR
87 CJDAY >=199 AND CJDAY <=360; N=4703; DAYS =
40
291 21-Feb-84 II INTER II II IIH 88 FINE CENTROID=REPLACE CENTROID IIITH 'II' FOR
292 22-Feb-84 II INTER II II IIH 89 CJDAY >=297 AND CJDAY<=309; N=973; DAYS = 31
293 23-Feb-84 IIINTER II II IIH 90 COARSE CENTROID=REPLACE MAXRANGE IIITH 'II' FOR
294 24-Feb-84 II INTER II II IIH 91 CJDAY >=258 AND CJDAY<=330; N=2376; DAYS = 52
295 25-Feb-84 II INTER II II IIH 92
296 26-Feb-84 II INTER II II IIH 93
297 27-Feb-84 II INTER II II IIH 94
298 28-Feb-84 II INTER II II IIH-298 95
299 29-Feb-84 II INTER II II 96
300 01-Mar-84 II INTER II II 97
301 02-Mar-84 II INTER II II 98
302 03-Mar-84 !liNTER II II 99
303 04-Mar-84 II INTER II II 100
304 05-Mar-84 II INTER II II 101
305 06-Mar-84 II INTER II II 102
306 07-Mar-84 II INTER II II 103
307 08-Mar-84 II INTER II II 104
308 09-Mar-84 IIINTER II II 105
309 10-Mar-84 IIINTER 11-309 II 106
310 11-Mar-84 !liNTER II 107
311 12-Mar-84 II INTER II 108
312 13-Mar-84 II INTER II 109
313 14-Mar-84 II INTER II 110
314 15-Mar-84 II INTER II 111
315 16-Mar-84 II INTER II 112
316 17-Mar-84 II INTER II 113
317 18-Mar-84 II INTER II 114
318 19-Mar-84 II INTER II 115
319 20-Mar-84 II INTER II 116
320 21-Mar-84 II INTER II
321 22-Mar-84 II INTER II 2
322 23-Mar-84 II INTER II 3
323 24·Mar·84 II INTER II 4
324 25-Mar-84 II INTER II 5
325 26-Mar-84 II INTER II 6
326 27-Mar-84 II INTER II 7
327 28-Mar-84 II INTER w 8
328 29-Mar-84 WINTER w 9
329 30-Mar-84 WINTER w 10
330 31-Mar-84 WINTER W-330 11
( 331 01-Apr-84 WINTER 12
332 02-Apr-84 WINTER 13
333 03-Apr-84 WINTER 14
f
334 04-Apr-84 WINTER 15
335 05-Apr-84 WINTER 16
336 06-Apr-84 WINTER 17
337 07-Apr-84 WINTER 18 I 338 08-Apr-84 WINTER 19
339 09-Apr-84 WINTER 20
340 10-Apr-84 WINTER 21
f
I
41
341 11-Apr-84 WINTER 22
342 12-Apr-84 WINTER 23
343 13-Apr-84 WINTER 24
344 14-Apr-84 WINTER 25
345 15-Apr-84 WINTER 26
346 16-Apr-84 WINTER 27
347 17-Apr-84 WINTER 28
348 18-Apr-84 WINTER 29
349 19-Apr-84 WINTER 30
350 20-Apr-84 WINTER 31
351 21-Apr-84 WINTER 32
352 22-Apr-84 WINTER 33
353 23-Apr-84 WINTER 34
354 24-Apr-84 WINTER 35
355 25-Apr-84 WINTER 36
356 26-Apr-84 WINTER 37
357 27-Apr-84 WINTER 38
358 28-Apr-84 WINTER 39
359 29-Apr-84 WINTER 40
360 30-Apr-84 WINTER 41
361 01-May-84 TRANS 42
362 02-May-84 TRANS 43
363 03-May-84 TRANS 44
364 04-May-84 TRANS 45
365 05-May-84 TRANS 46
366 06-May-84 TRANS 47
42
APPENDIX B. Sample of results of trial and error process used to select bandwidth for use in CALHOME adaptive
kernel home range analysis of moose point location data.
D:\CALHOMB\BANDSUMM.WK1
RBSULTS OF TRIAL PROCESS USBD TO IDENTIFY AND SBLBCT BAND WIDTHS ASSOCIATED WITH MINIMIZBD LSCV VALUBS
MOOSB ID BAND
WIDTH
153140
153140
153140
153140
153140
153140
153140
153140
153140
153140
153140
153140
153252
153252
153252
153252
153252
153252
153252
153252
153252
1532 52
153252
153252
153252
153252
153640
153640
153640
153640
153640
153640
153640
153640
153640
1S3640
1S3640
1900
1800
1700
1600
1500
1400
1300
1200
1100
1850
1850
1850
2700
2500
2400
2300
2200
2100
2000
1900
1800
2650
2550
2600
2600
2600
1200
1300
1400
1500
1600
1700
1800
1900
2000
2100
2200
98
98
98
98
98
98
98
98
98
98
97
95
98
98
98
98
98
98
98
98
98
98
98
98
97
95
98
98
98
98
98
98
98
98
98
98
98
CELL
SIZB
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
NO.
LSCV SCORE RANGE SIZB POLYS/ MINIMUM
(HA) (SQ MI) >2 PTS LVSC
-0.49690 B+10 21340 82.39
-0.49719 E+10 20570 79.42
-0.49625 B+10 19820 76.52
-0.49116 B+10 19110 73.78
-0.48593 E+10 17900 69.11
-0.48031 B+10 16580 64.01
-0.47199 E+10 16170 62.43
-0.45479 B+10 15800 61.00
-0.42128 B+10 14330 55.32
-0.49747 E+10 20970 80.96 2 ......
-0.49747 E+10 15120 58.37 ......
-0.49747 E+10 11040 42.62 ......
-0.42913 B+11 51830 200.1
-0.42809 B+11 52320 202.0
-0.42399 B+11 50660 195.5
-0.41629 B+11 49220 190.0
-0.40470 B+11 47890 184.9
-0.393 B+11 46500 179.5
-0.38398 B+11 44000 169.8
-0.37222 E+11 41950 161.9
-0.37 E+11 39200 151.3
-0.42993 B+11 52130 201.2
-0.42929 B+11 53250 205.5
-0.43002 E+11 52960 204.4 2 ......
-0.43002 B+11 39630 153.0 ......
-0.43002 E+11 28010 108.1 ......
-0.20303 E+11 18610 71.85
-0.27025 E+11 17410 67.22
-0.3192 E+11 18360 70.88
-0.3528 E+11 19190 74.09
-0.3797 E+11 20110 77.64
-0.41152 E+11 21S30 83.12
-0.43814 B+11 23480 90.6S
-0.46145 E+11 2S430 98.18
-0.47625 E+11 27340 105.5
-0.48703 E+11 29220 112.8
-0.49305 E+11 31110 120.1
43
153640
153640
153640
153640
153761
153761
153761
153761
153761
153761
153761
153721
153721
153721
153721
153721
153721
153721
153721
153730
153730
153730
153730
153730
153730
153839
153839
153839
153839
153839
153839
153839
153070
153070
153070
153070
153070
153070
153070
153070
153070
153070
153070
2300
2250
2250
2250
1200
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1150
1050
1050
1050
1600
1500
1300
1350
1450
1400
1400
1400
1300
1200
1100
1250
1250
1250
1300
1250
1150
1100
1200
1200
1200
1100
1200
1300
1400
1450
1500
1500
1450
1450
1450
1550
98
98
97
95
98
98
98
98
98
97
95
98
98
98
98
98
98
97
95
98
98
98
98
97
95
98
98
98
98
98
97
95
98
98
98
98
98
98
98
98
97
95
98
-50
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-50
-50
-50
-50
-50
-50
-50
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-50
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-50
-50
-50
-50
-so
-so
-50
-so
-50
-50
-so
-50
-49
-49
-49
-49
-47
-0.4928 E+11 32930 127.1
-0.49347 E+11 31990 123.5
-0.49347 E+11 31900 123.1
-0.49347 E+11 31320 120.9
-0.75093 E+11 17640 68.10
-0.77642 E+11 16420 63.39
-0.73415 E+11 15810 61.04
-0.76455 E+11 17190 66.37
-0.7809 E+11 16110 62.20
-0.7809 E+11 15360 59.30
-0.7809 E+11 14360 55.44
-0.8146 E+09
-0.82618 E+09
-0.81991 E+09
-0.83218 E+09
-0.83373 E+09
-0.83616 E+09
-0.83616 E+09
-0.83616 E+09
-0.26721 E+10
-0.26824 E+10
-0.25915 E+10
-0.26908 E+10
-0.26908 E+10
-0.26908 E+10
-0.46993 E+10
-0.47416 E+10
-0.47137 E+10
-0.46373 E+10
-0.46562 E+10
-0.46562 E+10
-0.46562 E+10
-0.99186 E+10
-0.1057 E+10
-0.11476 E+10
-0.12479 E+10
-0.13166 E+10
11470 44.28
10920 42.16
9697 37.44
9948 38.40
10240 39.53
9681 37.37
7805 30.13
5897 22.76
5129 19.80
5000 19.30
5489 21.19
5234 20.20
4764 18.39
4752 18.34
4621 17.84
4316 16.66
4126 15.93
4496 17.35
4264 16.46
3974 15.34
5788 22.34
6793 26.22
7959 30.72
9049 34.93
9761 37.68
-0.13518 E+10 NO POLY @ -50
-0.13427 E+10 10560 40.77
-0.13166 E+10 9761 37.68
-0.13166 E+10 9752 37.65
-0.13166 E+10 9722 37.53
-0.1363 E+10 11540 44.55
3
4
1
3
3
44
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
153070
153070
153070
153070
153070
153070
153620
153620
153620
153620
153620
153620
153620
153582
153582
153582
153582
153582
153582
153582
153582
153582
153582
1500
1600
1700
1500
1400
1100
1400
1350
1250
1200
1300
1300
1300
1300
1200
1100
1000
950
850
800
900
900
900
98
98
98
98
98
98
98
98
98
98
98
97
95
98
98
98
98
98
98
98
98
97
95
-47 -0.13215
-46 -0.14013
-46 -0.14593
-46 -0.13215
-46 -0.12394
-46 -0.10981
-so -0.1188
-50 -0.11917
-50 -0.1187
-50 0.11823
-50 -0.11941
-50 -0.11941
-so -0.11941
-50 -0.66838
-so -0.68723
-so -0.6923
-so -0.70311
-50 -0.70544
-50 -0.70327
-50 -0.70534
-50 -0.71179
-50 -0.71179
-so -0.71179
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+10
B+11
B+11
B+11
B+11
B+11
B+11
B+11
B+11
8+11
B+11
10590 40.88
12370 47.76
14070 54.32
10590 40.88
9134 35.26
5935 22.91
12800 49.42
12180 47.02
10650 41.11
9943 38.38
11440 44.16
11320 43.70
10740 41.46
20560 79.38
18900 72.97
17030 65.75
16240 62.70
16200 62.54
14020 54.13
13080 50.50
15320 59.15
13630 52.62
12170 46.98
3 ......
1 ......
......
......
3 ......
......
......
45
6995999
6999999
6985999
6989999
6975999
6979999
6965999
6969999
361999
APPENDIX C. Plots of CALHOHE adaptive kernel home range analyses of moose point-location data.
. ;. .. ., ~· . . ".~v . ;;~--... .l ...,i-1•: • • ,1/V:i~ ...•• .... ~,. . .
• • •
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Data£ile: 159299.DAT
Output File: 159299.0UT
Display Units: Meters
Adaptive Kernel
98PX 15999.99 ha ------
D o£ data points: 163
XMin: 362258.6
XMax: 378975.9
YMi n: .6961935.
YMax: 6994639.
Grid Size:
Avg. Dist:
Bandwidth:
989.8 M
2352.2 M
1899.9 M
LSCU score: -.43466E+11
6859999
6849999
6839999
6829999
681.1iU)99
6899999
261.999 281.999
Data~ile: 1.52936.DAT
Output File: 1.52936.0UT
Display Units: Mete~s
Adaptive J(e~nel
98PX 1.1.329. 99 ha ------
8 o~ data points: 1.95
XMin: 265489.8
XMax: 31.9592.6
VMin: 6892651..
VMax: 68241.83.
G~id Size:
Avg. Dist:
Bandwidth:
1.353.3 M
5628.8 M
759.9 M
LSCU sco~e: -.1.1.1.98E+1.2
6848999
6846999
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DataCile: 15294527.DAT
Output File: 152945.0UT
Display Units: Mete~s
Adaptive Me~nel
98P:X 9985 . 999 ha ------
M oC data points:
XMin: 359596.3
XMax: 374489.6
YMin: 6839853 .
YMax: 6846299.
79
G~id Size: 463.3 M
Avg. Dist: 3867.2 M
Bandwidth: 1359.9 M
LSCU sco~e: -.88652E+99
6894999
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DataCile: 152975.DAT
Output File: 152975.0UT
Display Units: Mete~s
Adaptive )(e~nel
98PX 6429.999 ha ------
8 oC data points: 44
XMin: 338254.7
XMax: 364984.8
VMin: 6865937.
VMax: 6878759.
G~id Size: 774.9 M
Avg. Dist: 5553.5 M
Bandwidth: 1299.9 M
LSCU score: -.36489E+19
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DataCile: J.52976.DAT
Output File: J.52976.0UT
Display Units: Meters
Adaptive Herne 1
98PX 1.4559.99 ha -----------
8 oC data points: 95
XMin: 396347.4
XMax: 34571.8.4
YMin: 6839958.
YMax: 6877926.
Grid Size: J.J.8J..J. M
Avg. Dist: 6734.9 M
Bandwidth: 1.959.9 M
LSCU score: -.23922E+l.l.
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DataCile: l.52l.4539.DAT
Output File: l.52l.45.0UT
Display Units: Meters
Adaptive Hernel
98PX 3554.999 ha
a oC data points: 95
XMin: 394932.3
XMax: 31.5937.7
VMin: 679731.9.
VMax: 6892986.
Grid Size: 393.1. M
Avg. Dist: 1.523.6 M
Bandwidth: 859.9 M
LSCU score: -.76593E+99
6855999
6845999
6835999
6825999
68J.5999
6895999
399999
0
329999 349999
DataCile: J.52J.56.DAT
Output File: J.52J.56.0UT
Display Units: Meters
Adaptive Hernel
98PX 28639.99 ha
8 oC data points: J.3J.
XMin: 3J.2993.9
XMax: 359J.8J..5
YMin: 68J.9949.
YMax: 685682J..
Grid Size: J.493.4 M
Avg. Dist: 565J..3 M
Bandwidth: 1659.9 M
LSCU score: -.6647J.E+J.J.
6837999
6832999
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31.9999 329999 339999 349990
DataCile: J.52J.75.DAI
Output File: J.52J.75C.OU
Display Units: Mete~s
Adaptive J<e~nel
98P:x J.4J.69. 99 ha ------
8 oC data points: 1.22
XMin: 3J.J.9J.J..3
XMax:
YMin:
324396.8
6825356.
YMax: 6852397.
G~id Size: 8J.J..2 M
Avg. Dist: 4568.7 M
Bandwidth: 1.299.9 M
LSCU sco~e: -.J.9J.62E+J.J.
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DataCile: ~52~9~.DAT
Output File: ~52~9~.0UT
Display Units: Mete~s
Adaptive He~nel
95PX 8~~4.999 ha
D oC data points:
XMin: 35~~27.2
XMax:
YMin:
379777.9
6839978.
YMax: 6859732.
G~id Size: 6~9.6 M
Aug. Dist:
Bandwi d tl1:
4892.6 M
~~99.9 M
LSCU sco~e: -.26995E+~9
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Dataf'ile: ~522UiJ5. DAT
Output File: ~522~95.0U
Display Units: Mete:rs
Adaptive Ke:rnel
98PX 99~9.999 ha
tt of' data points: 49
XMin: 338876.9
XMax: 36996~.9
YMin: 683~~52.
YMax: 68724~9.
G:rid Size: ~237.7 M
Aug. Dist: 8428.9 M
Bandwidth: ~299.9 M
LSCU sco:re: -.79279E+~~
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Data£ile: 152339.DAT
Output File: 152339.0UT
Display Units: Mete~s
Adaptive Ke~nel
98P:x 14469. 99 ha ------
B o£ data points:
XMin: 337843.1
XMax: 359948.3
VMin: 6866262.
VMax: 6885241.
79
G~id Size: 796.8 M
Avg. Dist: 4785.7 M
Bandwidth: 1599.9 M
LSCU sco~e: -.22612E+19
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Datafile: ~52369.DAT
Output File: ~52369.0UT
Display Units: Mete:rs
Adaptive Ke:rnel
98PX 4532.999 ha
ft of data points: ~49
XMin: 349237.9
XMax: 349624.9
YMin: 6948964 .
YMax: 6955273.
G:rid Size: 28~.6 M
Avg. Dist: ~922.3 M
Bandwidth: ~399.9 M
LSCU sco:re: -.48642E+99 r-
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DataCile: ~52759.DAT
Output File: ~52759.0UT
Display Units: Mete~s
Adaptive Ke~nel
98PX ~6429.99 ha
U oC data points:
XMin: 34~32~.2
XMax:
YMin:
YMax:
36~59~.9
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G~id Size:
Avg. Dist:
Bandwidth:
698.9 M
382~.2 M
2299.9 M
LSCU sco~e: -.22665E+~9
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6889999
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DataCile: 15281918.DAT
Output File: 152819.0UT
Display Units: Meters
Adaptiue )(ernel
98PX 25989. 99 ha ------
8 oC data points: 21
XMin: 264171.9
XMax: 289223.3
YMin: 6833839.
YMax: 6882679.
Grid Size: 1465.2 M
Aug. Dist: 19922.9 M
Bandwidth: 3899.9 M
LSCU score: -.19949E+11
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Datarile: ~528~9?9.DAT
Output File: ~528~9?9.0
Display Units: Mete~s
Adaptive He~nel
98PX ~5569.99 ha ------
B or data points: 59
XMin: 35~989.~
XMax: 3745~7.~
YMin: 6824977.
YMax: 6864446.
G:rid Size: ~~84.9 M
Avg. Dist: 598~.6 M
Bandwidth: 2299.9 M
LSCU sco~e: -.483~3E+~~
6999999
6899999
6889999
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6859999
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0
342999 362999
Datafile: 152959.DAT
Output File: 152959.0UT
Display Units: Meters
Adaptive Kernel
98PX 19929.99 ha -----------
U o£ data points:
XMin: 327993.8
XMax:
YMin:
368647.4
6853595.
YMax: 6878968.
68
Grid Size: 1219.6 M
Avg, Dist: 5432.4 M
Bandwidth: 1899.9 M
LSCU score: -.39578E+11
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Datatile: J.52989.DAT
Output File: J.52989.0UT
Display Units: Mete~s
Adaptive ](e~nel
98P~ 51.96.999 ha
B of data points:
XMin: 3449~6.9
XMax:
YMin:
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YMax: 6863552 .
59
G~id Size: 4~9.7 M
Avg. Dist: 2588.6 M
Bandwidth: J.259.9 M
LSCU sco~e: -.29538E+~9
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Data£ile: 1.53921..DAT
Output File: 1.53921..0UT
Display Units: Mete:rs
Adaptive Ke:rnel
98P:X 1.3399.99 ha
II o£ data points: 43
XMin: 398356.9
XMax: 31.9549.5
YMin: 6898997.
YMax: 681.9492.
G:rid Size: 388.4 M
Avg. Dist: 3469.9 M
Bandwi d tl1: 2399.9 M
LSCU sco:re: -.21.1.48E+99
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DataCile: J.5393J..DAT
Output File: J.5393J..OUT
Display Units: Mete~s
Adaptive J<e~nel
98PX 49979.99 ha
ft oC data points: 57
)(Min: 39881.7.5
XMax: 378J.J.5.9
YMin: 6854871..
YMax: 6878242.
G~id Size: 2978.9 M
Aug. Dist: 9625.1. M
Bandwidth: 2599.9 M
LSCU sco~e: -.94986E+J.J.
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Datafile: ~53979.DAT
Output File: ~53979.0UT
Display Units: Meters
Adaptive :Kernel
98P:x 976~. 999 ha ------
• of data points: 55
XMin: 346~38.2
XMax: 364596.4
YMin: 6862758.
YMax: 6876958.
Grid Size: 55~.9 M
Avg. Dist: 5~9~.9 M
Bandwidth: ~459.9 M
LSCU score: -.~3~66E+~9
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Datafile: 1.53981..DAT
Output File: 1.53981..0UT
Display Units: Mete~s
Adaptive :Ke~nel
98P:x 54?9. 999 J1a ------
8 or data points: 56
XMin: 3369?9.3
XMax: 354598.6
YMin: 686?56?.
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G~id Size: 5?4.8 M
Aug. Dist: 3568.? M
Bandwidth: 1.999.9 M
LSCU sco~e: -.25926E+1.9
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Data£ile: J.53J.99.DAT
Output File: J.53J.99.0UT
Display Units: Mete~s
Adaptive J<e~ne 1
98PX 39639.99 ha
ft o£ data points: 94
XMin: 31.2238.2
XMax: 339881..8
YMin: 6852549.
YMax: 6995631..
G~id Size:
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1.592.7 M
4335.6 M
Bandwidth: 2459.9 M
LSCU sco~e: -.J.3955E+J.2
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Datafile: 153192.DAT
Output File: 153192.0UT
Display Units: Mete:rs
Adaptive J(e:rnel
98Px 8169.999 ha -----------
8 or data points: 79
XMin: 339954.4
XMax: 361996.1
YMin: 6885846.
YMax: 6896397.
G:rid Size: 631.5 M
Aug. Dist: 4199.3 M
Bandwidth: 1299.9 M
LSCU sco:re: -.46671E+19
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Data£ile: 153119.DAT
Output File: 153119.0UT
Display Units: Mete~s
Adaptive Ke~nel
98PX 29529.99 ha -----------
0 o£ data points: 192
)(Min: 328929.2
XMax:
YMin:
YMax:
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G~id Size: 711.2 M
3992.5 M
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Avg. Dist:
Bandwidth:
LSCU sco~e: -.25636E+19
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DataCile: 153122.DAT
Output File: 153122.0UT
Display Units: Mete:rs
Adaptive J<e:rnel
98PX 17289.99 ha
• oC data points: 35
XMin: 262291.2
XMax: 397743.5
YMin: 6894434.
YMax: 6831388.
G:rid Size: 1363.5 M
Avg. Dis t: 5582.9 M
Bandwidth: 2259.9 M
LSCU sco:re: -.59943E+11 0 r-
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Datafile: 153123.DAT
Output File: 15123B.OUT
Display Units: Mete~s
Adaptive Ke~ne 1
98PX 15339.99 ha
M of data points: 92
XMin: 325194.5
XMax:
YMin:
YMax:
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68?9891.
6899916.
G~id Size: ??9.? M
4119.4 M
1259.9 M
Avg. Dist:
Bandwidth:
LSCU sco~e: -.33348E+19
·I
I
689Hit99
6886999
688~999
6876999
6871.999
6866999
31.8999
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DataCile: ~53~49.DAT
Output File: ~53~49.0UT
Display Units: Mete~s
Adaptive J<e~nel
98PX 29979.99 ha
8 oC data points: ~59
XMin: 32~494.5
XMax:
YMin:
34~426.7
6879926.
YMax: 6892~21..
G~id Size: 662.8 M
Avg. Dist: 3635.4 M
Bandwidth: ~859.9 M
LSCU sco~e: -.49747£+~9
6887999
6882999
6877999
6872999
6867999
6862999
6857999
328999 338999 348999
Datafile: 153179.DAT
Output File: 153179.0UT
Display Units: Mete~s
Adaptive Ke~nel
98PX 19129.99 ha
M of data points: 171
XMin: 329799.1
XMax: 338363.8
VMin: 6859641.
VMax: 6884595.
C~id Size: 745.9 M
Avg. Dist: 3976.4 M
Bandwidth: 759.9 M
LSCU sco~e: -.11447E+11
69~8999
69~3999
6998999
6993999
6898999
6893999
6888999
6883999
6878999
329999 339999 349999 359999 369999
DataCile: ~53239.DAT
Output File: ~53239.0UT
Display Units: Meters
Adaptive J<ernel
98PX ~4339.99 ha -----------
B oC data points: ~36
XMin: 323728.4
XMax: 359739.6
VMin: 6879779.
VMax: 68962~7.
Grid Size: ~989.9 M
Avg. Dist: 4~~2.9 M
Bandwidth: 999.9 M
LSCU score: -.25467E+~~
-.:t r-
6943999
6938999
6933999
6928999
6923999
69~8999
69~3999
6998999
6993999
6898999
6893999
295999 395999 3~5999 325999 335999
Datafile: ~53~39.DAT
Output File: ~53~39B.OU
Display Units: Mete~s
Adaptive He~nel
98PX ~9~29.99 ha
B of data points: 63
XMin: 298~66.6
XMax: 34~34~.9
YMin: 6896599.
YMax: 6927943.
G~id Size: ~295.2 M
Avg. Dist: 5985.4 M
Bandwidth: ~~99.9 M
LSCU sco~e: -.42834E+~~
6822999
681.7999
681.2999
6897999
6892999
6797999
288999
D
298999 398999
Data£ile: J.532J.l..DAT
Output File: l.532l.l.B.OU
Display Units: Mete~s
Adaptive Ke~nel
98PX 7676.999 ha
ft o£ data points: l.2l.
XMin: 299557.4
XMax: 31.4572.4
YMin: 6799244.
681.8963.
G~id Size:
Avg, Dist:
Bandwidth:
729.4 M
498J.. 2 M
799,9 M
LSCU sco~e: -.7J.932E+l.9
6993999
6898999
6893999
6888999
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6878999
6873999
6868999
6863999
6858999
343999 353999 363999 373999 383999
Datarile: ~532~5.DAT
Output File: ~532~5.0UT
Display Units: Meters
Adaptive Kernel
98PX ~56~9. 99 ha ------
8 or data points: 79
XMin: 3462~6.7
XMax: 386384.3
VMin: 6869376.
VMax: 6883592.
Grid Size: ~295.9 M
Avg. Dist: 5~78.4 M
Bandwidth: ~699.9 M
LSCU score: -.53523E+~~ r-r-
691.9999
6999999
6899999
6889999
68'?9999
6869999
6859999
6849999
6839999
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DataCile: 1.53229.DAT
Output File: 1.52229.0UT
Display Units: Meters
Adaptive Kernel
98PX 29989.99 ha -----------
8 oC data points: 1.9'?
XMin: 31.3486.1.
XMax: 3'?261.8.2
YMin: 6841.629.
YMax: 69991.63.
Grid Size: 1.'?'?3.9 M
Avg. Dist: 8884.8 M
Bandwidth: 959.9 M
LSCU score: .'?591.4E+1.2
69.17999
69.12999
6997999
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6897999
6892999
6887999
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329999 339999 349999 359999 369999
Data£ile: .153239.DAT
Output File: .153239.0UT
Display Units: Mete~s
Adaptive He~nel
98PX .15259.99 ha
ft o£ data points: .136
XMin: 323728.4
XMax: 359739.6
YMin: 6879779.
YMax: 68962.17.
G~id Size: .1989.9 M
Auog. Dist: 4.1.12.9 M
Bandwidth: 959.9 M
LSCU sco~e: -.2558.1E+.1.1
0\ r--
6936999
6935999
6934999
6933999
6932999
6931.999
6939999
6929999
6928999
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Data£ile: 1.53249.DAT
Output File: 1.53249Z.OU
Display Units: Mete~s
Adaptive He~nel
98PX 3395.999 ha
U o£ data points: 1.1.1.
XMin: 336554.8
XMax:
YMin:
344931..3
6927453.
YMax: 6933295 .
G~id Size: 251..2 M
Avg. Dist: 1.599.2 M
Bandwi d tll: 1.959.9 M
LSCU sco~e: -.25836E+99 0
00
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Data£ile: 153249.DAT
Output File: 153249.0UT
Display Units: Mete~s
Adaptive J<e~nel
98PX 3443.999 ha
8 oC data points: 111
XMin: 336554.8
XMax:
YMin:
YMax:
344931.3
6927453.
6933295.
C~id Size:
Avg. Dist:
Bandwidth:
251.2 M
1599.2 M
1399.9 M
LSCU sco~e: -.26153E+99
00
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6899999
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6884999
6879999
6874999
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DataCile: 153242.DAT
Output File: 153242.0UT
Display Units: Mete~s
Adaptive )(e~nel
98PX 17989.99 ha -----------
H oC data points: 54
XMin: 331149.1
XMax: 365953.3
YMin: 6866595.
YMax: 6879732.
C~id Size: 1917.4 M
Aug. Dist: 5877.7 M
Bandwidth: 2399.9 M
LSCU sco~e: -.16265E+11 N
00
6977999
6975999
6973999
6971.999
6969999
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DataCile: l.52243.DAT
Output File: l.52243.0UT
Display Units: Mete~s
Adaptive He~nel
98P:X 4229.999 ha
8 oC data points: 1.97
XMin: 352474.1.
YMin:
365293.8
6962428.
YMax: 6968628.
G~id Size: 384.5 M
Aug. Dist: 2344.6 M
Bandwidth: 799.9 M
LSCU sco~e: -.16877E+19
6886999
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6866999
6856999
6846999
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DataCile: 153252.DAT
Output File: 153252.0UT
Display Units: Mete~s
Adaptive Ke~nel
98PX 52969.99 ha
D oC data points: 169
XMin: 398981.1
339819.9
6842589.
YMax: 6882893 .
G~id Size: 1299.1 M
Avg. Dist: 5789.4 M
Bandwidth: 2699.9 M
LSCU sco~e: -.43992E+11
69J.J.999
6996999
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6896999
6891.999
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DataCile: J.53269.DAT
Output File: J.53269.0UT
Display Units: Mete~s
Adaptive He~ne 1
98PX 1.61.49.99 ha
M oC data points: 294
)(Min: 334399.4
)(Max: 35751.4.9
YMin: 6869249.
YMax: 6998395.
G~id Size:
Avg. Dist:
Bandwi d tll:
LSCU sco~e:
J.J.7J..6 M
4755.3 M
799.9 M
.J.4898E+J.2
VI
00
69~:.1999
6888999
6883999
3~2999 322999 332999 342999
DataCile: ~53263.DAT
Output File: ~53263.0UT
Display Units: Mete~s
Adaptive He~nel
98PX 26949.99 ha
B oC data points: ~96
XMin: 395822.3
XMax:
YMin:
YMax:
345236.9
6879945.
6994889.
G~id Size: ~~82.4 M
6762.~ M
~699.9 M
Avg. Dist:
Bandwidth:
LSCU sco~e: -.~877~E+~~
6999999
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6899999
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396999 31.6999 326999 336999
Data£ile: J.5329J..DAT
Output File: J.5329J..OUT
Display Units: Mete~s
Adaptive Ke~nel
98PX J.J.349.99 ha
8 o£ data points: J.59
XMin: 399391..3
XMax: 339249.3
YMin: 6867949.
YMax: 688J.J.99.
G~id Size: 895.7 M
Avg. Dist: 4273.9 M
Bandwidth: 499.9 M
LSCU sco~e: -.36539E+J.J. r-oo
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DataCile: 153399.DAT
Output File: 153399T.OU
Display Units: Meters
Adaptive J<ernel
98PX 11969.99 ha
B oC data points: 172
XMin:
XMax:
YMin:
335849.9
346975.9
6939268 .
YMax: 6951283 •
Grid Size: 459.5 M
Avg, Dist: 2986.4 M
Bandwidth:
LSCU score:
1299.9 M
-.59597E+99
00
00
6934999
6929999
6924999
69~9999
69~4999
6999999
3~9999
0
• • • : .. .. ... . . .. . . .. . .
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DataCile: ~533~~.DAT
Output File: ~533~~.0UT
Display Units: Mete~s
Adaptive He~nel
98PX ~2359.99 ha
ft oC data points: 78
XMin: 322672.6
XMax: 342957.4
YMin: 69~~9~3.
YMax: 6929655 .
G~id Size: 58~.5 M
Aug. Dist:
Bandwidth:
3639.~ M
~359.9 M
LSCU sco~e: -.26~73E+~9
6994999
6899999
6894999
6889999
6884999
6879999
6874999
6869999
6864999
6859999
6854999
395999 3~5999
D
• t
• 0
325999 335999 345999
Data£ile: ~53582.DAT
Output File: ~53582.0UT
Display Units: Meters
Adaptive J<ernel
98Px ~5329.99 ha -----------
M o£ data points: ~74
XMin: 397822.~
XMax: 336372.8
YMin: 6858799.
YMax: 69996~8.
Grid Size: ~254.8 M
Aug. Dist: 5472.5 M
Bandwidth: 999.9 M
LSCU score: -.7~~79E+~~
6899999
6889999
6879999
6869999
6859999
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6839999
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349999 369999 389999 499999
DataCile: ~53649.DAT
Output File: ~53649.0UT
Display Units: Mete~s
Adaptive He~nel
98PX 3~999.99 ha
N oC data points: 69
)(Min: 352284.4
)(Max: 369768.3
YMin: 682394~.
YMax: 6876743.
G~id Size:
Avg. Dis t :
Bandwidth:
~584.9 M
7856.7 M
2259.9 M
LSCU sco~e: -.49347E+~1
6939999
6929999
69~9999
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6879999
6869999
339999 359999 379999 399999
Dataf"ile: ~5376~.DAT
Output File: ~5376~.0UT
Display Units: Mete~s
Adaptive Ke~nel
98P:x ~6~~9.99 ha
II of" data points: 68
)(Min: 3362~3.4
)(Max: 39~725.2
YMin: 6869399.
YMax: 68854~9.
G~id Size: ~665.3 M
Avg. Dist: 9768.~ M
Bandwidth: ~959.9 M
LSCU sco~e: -.78999E+~~ N
0"1
6887999
6885999
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Data£ile: ~5372~.DAT
Output File: ~5372~.0UT
Display Units: Mete~s
Adaptive J<e~nel
98PX ~9249.99 ha ------
• o£ data points: 72
XMin: 362234.4
XMax: 374728.3
YMin: 687~629.
YMax: 6885546 .
G~id Size:
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2966.7 M
Bandwidth: ~499.9 M
LSCU sco~e: -.836~6E+99
6894999
6892999
6899999
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6886999
6884999
6882999
6889999
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DataCile: 153739.DAT
Output File: 153739.0UT
Display Units: Mete~s
Adaptive Ke~nel
98PX 5489.999 ha ------.. oC data points: 72
XMin: 342458.6
XMax: 359443.6
YMin: 6876944.
YMax: 6889515.
G~id Size: 599.5 M
Aug. Dist: 3972.4 M
Bandwidth: 1259.9 M
LSCU sco:re: -.26998E+19
"<:t
0\
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Data£ile: 1.5381.3.DAT
Output File: 1.5381.3L.OU
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Adaptive Kernel
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XMin: 35681.6.7
XMax:
YMin:
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695891.5 .
YMax: 6967999 .
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Avg. Dist: 1.788.9 M
Bandwidth: 1.1.99.9 M
LSCU score: -.38974E+99
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DataCile: ~53839.DAT
Output File: ~53839.0UT
Display Units: Mete:rs
Adaptive Ke:rnel
98PX 4293.999 ha
tl oC data points: 35
)(Min: 297593.3
)(Max: 392825.8
YMin: 683986~.
YMax: 6849646.
G:rid Size: 293.5 M
Avg. Dist: 2~89.5 M
Bandwidth: ~799.9 M
LSCU sco:re: -.5~479E+99 \0
0\
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Data£ile: 153839.DAT
Output File: 153839.0UT
Display Units: Mete~s
Adaptive )(e~nel
98PY. 4496.999 ha -----------
8 o£ data points: 42
XMin: 282969.6
XMax: 393553.8
YMin: 6839957.
YMa.x: 6849592.
G~id Size: 617.5 M
Avg. Dist: 4299.6 M
Bandwidth: 1299.9 M
LSCU sco~e: -.47562E+19
APPENDIX 0. Sl111118ry of results of trial and error process used in selecting bandwidth for use in CALHOME adaptive
kernel home range analysis of moose point•location data.
0:\CALHOME\BANDSUMM.~l
MOOSS ID BAND
WIDTH
153240
1S3300
1S33ll
153102
153813
153021
150200
152750
1S204S
lS:2243
1S3620
153100
153721
1S3240
1S2980
lSJllO
153830
153140
1S217S
1S3123
153291
153122
5281079
1S21S6
1S32S2
1S2243
1S2 950
1S3:242
5281018
15214S
1S3730
153640
tsJ:no
1S214S
153215
153 S82
153031
1S3070
153263
1S3070
H.Jl70
153211
1300
1200
1350
1200
llSO
2300
1800
2200
1350
700
1300
2450
1400
1300
12SO
1600
1700
1850
1200
l2SO
400
2250
2200
1650
2600
1050
1800
2300
3850
850
l2SO
2250
950
450
1600
900
2500
1450
1600
1700
750
700
P\
98
98
98
97
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
98
97
98
98
98
98
98
98
98
911
98
98
98
98
98
98
CBLL
SIZE
-so
-so
-so
-so
-so
-44
-so
-so
-so
-so
-so
-so
-50
-so
-so
-so
-so
-so
-so
-so
-so
-50
-50
-so
-so
-so
-so
-so
-so
-so
-so
-so
-so
600
-so
-50
-so
-u
·50
-46
_-so_
-so
NO.
LSCV SCORB RANGB SIZB POL'iS/ MINIMUM
(HA) (SQ Mil >2 PTS LVSC
·0.261S3 S+09 3443 13.29
-0.S9S97 8+09 11060 42.70
-0.26173 8+09 123SO 47.68
-0.46671 8+10 8022 30.97
-0.38974 8+09 6170 23.82
-0.21148 S+09 13300 Sl.3S
·0.43466 8+11 15000 S7.91
·0.2266S S+lO 16190 62.50
-0.886S2 8+09 908S 35.07
-0.16877 8+10 4220 16.29
·0.11941 8+10 11440 44.16
-0.13055 8+12 39630 153.0
-0.83616 8+09 10240 39.53
-0.26153 S+09
-0.20538 8+10
3379 lJ .04
5106 19.71
-0.25636 8+10 20520 79.22
-0.51479 8+09 4293 16.57
-0.49747 B+lO 20970 80.96
-0.10162 8+11 14160 54.67
-0.33348 8+10 15330 59.18
-0.36539 8+11 11340 43.78
-0.59943 8+11 17280 66.71
·0.48313 8+11 15560 60.07
-0.66471 i+ll 28630 110.5
·0.43002 8+11 S2960 204.4
-0.13272 8+10 4630 17.87
·0.30S78 8+11 19920 76.91
·O.l626S B+ll 16560 63.93
-0.19971 B+ll 26550 102.5
-0.76503 S+09 35S4 13.72
-0.26908 i+lO S489 21.19
-0.49347 B+ll 31990 l23.S
-0.2S581 B+ll 15250 S8.88
-O.S0037 S+09 2395 9.247
-0.53523 B+ll 1S610 60.27
-0.71179 E+ll lS320 S9.1S
-0.94986 8+11 40970 158.1
-0.13166 8+10 9761 37.68
-0.18771 8+11 26040 lOO.S
-0.14593 B+lO 14070 54.32
-0~ llc441 !!,i-lL 10120-J9. 0-7
·0. 71032 B+lO 7676 29.63
1
1
0 ....
1 0 •••
l .......
l 0 '*'*'*
l 0 '*'*'*
l .......
1 0 '*'*'*
1 0 •••
l 0 .....
2 0 '*'*'*
2 0 '*'*'*
2 0 '*'*'*
2 0 ......
2 0 ........
2 0 .....
2 0
l 0 .......
2 0 ***
2 0 •••
3 0 ......
3 0 .,.. ...
0 ., ...
3 0 ......
3 0 .......
3 0 '**'*
3 0 -~-
'l -o
4 0 **'*
98
1S3130 1100 98 -so -0.42834 B+11 10120 39.07 4 0 """
1S3761 10SO 98 -so -0.7809 B+11 16110 62.20 4 0 """
1S2210S 1200 98 -so -0.70279 B+11 9019 34.82 4 0 """
1S2191 1100 98 -so -0.2690S B+10 8319 32.11 4 0 """
1S2330 1SOO 98 -4S -0.22612 B+10 14460 SS.83 4 0 """
1S3081 1000 98 -46 -0.2S026 B+10 S479 21.1S 4 0 """
1S3260 700 98 -so -0.14898 B+12 16140 62.31 4 0 """
1S3230 900 98 -so -0.2S467 B+11 14330 SS.32 s 0 """
1S207S 1200 98 -so -0.3648 B+10 6420 24.78 s 0 """
1S2036 7SO 98 -so -0.11198 B+12 11320 43.70 s 0 """
1S2076 1000 98 -so -0.232S2 B+11 13450 51.93 7 0 """
1S3220 950 98 -so -0.75914 B+12 29080 112.2 8 0 """
99
APPENDIX E. Sample of database file used in indentifying Julian day of point locations in numbered
utilization distribution polygons of moose home ranges.
D: \CALHOME\5214539 COL 39 MOOSE NO. 152145 COL NO. 39
X_COORD Y_COORD OBS CENTROID DATE SEASON PERIOD CYEAR CJDAY CENTROID SY
314312.700000 6797319.000000
310713.300000 6800037.000000
311474.800000 6800416.000000
311061.900000 6801417.000000
313798.000000 6800214.000000
313720.600000 6800317.000000
313643.100000 6800788.000000
313490.500000 6800723.000000
313474.400000 6800123.000000
0
1
2
3
4
5
6
7
8
311955.200000 6799226.000000 9
311927.900000 6799481.000000 10
314196.700000 6798226.000000 11
311798.400000 6800713.000000 12
311949.100000 6800968.000000 13
312230.900000 6800695.000000 14
312529.400000 6799867.000000 15
311353.400000 6800361.000000 16
311060.400000 6799993.000000 17
315037.700000 6798593.000000 18
311036.900000 6799299.000000 19
311276.300000 6799191.000000 20
310207.800000 6800615.000000 21
311112.800000 6801323.000000 22
310493.100000 6800876.000000 23
310009.100000 6801366.000000 24
312028.300000 6798054.000000 25
309890.800000 6800510.000000 26
311427.700000 6800078.000000 27
313708.900000 6800206.000000 28
313655.300000 6800075.000000 29
313442.600000 6800160.000000 30
313500.100000 6800730.000000 31
313338.600000 6800429.000000 32
312655.700000 6798189.000000 33
312527.600000 6799279.000000 34
311718.600000 6799369.000000 35
312651.500000 6799311.000000 36
311667.300000 6800683.000000 37
312381.600000 6797895.000000 38
313693.400000 6799129.000000 39
311542.000000 6800037.000000 40
313829.100000 6801536.000000 41
313516.100000 6801816.000000 42
310801.800000 6800052.000000 43
310418.500000 6800652.000000 44
309899.400000 6802606.000000 45
6 26-Feb-82
4 03-Mar-82 W
5 24-Mar-82
4 05-Apr-82
5 16-Apr-82
5 26-Apr-82
5 10-May-82 c
5 17-May-82 c
5 26-May-82 c
5 08-Jun-82 c
5 17-Jun-82
6 29-Jun-82
09-Jul-82 S
5 27-Jul-82 S
5 06-Aug-82 S
5 16-Aug-82
31-Aug-82 H
4 23-Sep-82 H
6 05-0ct.-82 R
4 20-0ct.-82 P
4 10-Nov-82 V
4 13 -Dec-82 V
4 04 -Jan-83
4 21-Jan-83 w
4 04-Feb-83
5 16-Feb-83
4 04 -Mar-83 W
5 18-Mar-83 W
5 01-Apr-83
5 20-Apr-83
5 04-May-83
5 11-May-83 c
5 17-May-83 C
5 24-May-83 C
s 31-May-83 c
5 07-Jun-83 C
s 14-Jun-83 c
5 21-Jun-83
5 28-Jun-83
6 02 -Aug-83 S
5 27-Aug-83 H
5 06-Sep-83 H
5 19-Sep-83 H
4 03-0ct-83 R
4 21-0ct-83 P
3 09-Nov-83 v
WINT
CALF
CALF
SUMM
RUT
RUT
PRUT
WINT
CALF
CALF
CALF
SUMM
RUT
RUT
PRUT
100
8182
8182
8182
8182
8182
8182
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8283
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
296
301
322
334
345
355
4
11
20
33
42
54
64
82
92
102
117
140
152
167
188
221
243
260
274
286
302
316
330
349
363
11
18
25
32
39
46
53
88
113
123
136
150
168
187
6 2
4 2
5 2
4 2
5 2
5 2
5 3
5 3
5 3
5 3
5 3
6 3
5 3
5 3
5 3
5 3
5 3
4 3
6 3
4 3
4 3
4 3
4 3
4 3
4 3
5 3
4 3
3
5 3
3
5 3
5 4
5 4
5 4
5 4
5 4
5 4
5 4
5 4
6 4
5 4
5 4
5 4
4 4
4 4
3 4
309976.900000 6801137.000000 46
310214.300000 6801166.000000 47
309827.900000 6801156.000000 48
310108.600000 6800883.000000 49
307843.600000 6800934.000000 50
310038.400000 6800416.000000 51
310291.100000 6800813.000000 52
307562.100000 6802187.000000 53
313790.600000 6800757.000000 54
313796.000000 6800421.000000 55
313643.100000 6800280.000000 56
313415.500000 6801343.000000 57
312300.800000 6798837.000000 58
312436.000000 6799498.000000 59
312054.800000 6799604.000000 60
312507.500000 6800188.000000 61
311814.800000 6801275.000000 62
312309.300000 6799482.000000 63
312103.400000 6800076.000000 64
311079.200000 6799891.000000 65
310997.700000 6799159.000000 66
311109.300000 6799653.000000 67
310342.600000 6801074.000000 68
310744.100000 6800177.000000 69
310804.100000 6799234.000000 70
310574.400000 6801036.000000 71
306726.500000 6801078.000000 72
306545.900000 6802059.000000 73
307953.100000 6802044.000000 74
308061.300000 6801910.000000 75
307847.400000 6801259.000000 76
304932.300000 6801265.000000 77
307764.400000 6802577.000000 78
308014.500000 6802145.000000 79
306644.000000 6802534.000000 80
307644.300000 6802714.000000 81
313664.800000 6800897.000000 82
313679.800000 6801179.000000 83
313616.800000 6801063.000000 84
313769.600000 6800642.000000 85
313664.600000 6800719.000000 86
313481.300000 6800739.000000 87
311960.800000 6799474.000000 88
311258.500000 6801467.000000 89
307509.600000 6802986.000000 90
306976.600000 6801294.000000 91
307692.700000 6802839.000000 92
306275.100000 6801070.000000 93
305579.000000 6800625.000000 94
4 25-Nov-83 V
4 15-Dec-83 V
4 28-Dec-83
4 11-Jan-84
2 02-Feb-84
4 16-Feb-84
4 02 -Mar-84 W
2 14-Mar-84 W
5 27-Mar-84
5 10-Apr-84
5 24-Apr-84
5 15-May-84 c
5 21-May-84 c
5 29-May-84 C
5 04-Jun-84 c
5 18-Jun-84
5 11-Jul-84 s
5 30-Jul-84 s
5 10-Aug-84 s
4 05-Sep-84 H
4 26-Sep-84 H
4 17-0ct-84
4 06-Nov-84 P
4 19-Nov-84 V
4 06-Dec-84 V
4 20-Dec-84 V
2 09-Jan-85
2 21-Jan-85 w
2 06-Feb-85
2 18-Feb-85
2 07-Mar-85 W
1 19-Mar-85 W
2 02 -Apr-85
2 15-Apr-85
2 25-Apr-85
2 02-May-85
5 10-May-85 C
5 20-May-85 C
5 05-Jun-85 c
5 12-Jun-85 c
5 19-Jun-85
5 17-Mar-86 W
5 02 -Jun-86 c
4 26-Sep-86 H
2 21-Dec-86 V
2 17-Jan-87
2 05-Feb-87
2 12-Mar-87 W
2 23-Mar-87
WINT
CALF
CALF
CALF
SUMM
RUT
PRUT
WINT
CALF
CALF
CALF
RUT
101
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8384
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8485
8586
8586
8586
8586
8586
8586
8687
8687
8687
8687
8687
8687
8687
203
223
236
250
272
286
301
313
326
340
354
9
15
23
29
43
66
85
96
122
143
164
184
197
214
228
248
260
276
288
305
317
331
344
354
361
4
14
30
37
44
315
27
143
229
256
275
310
321
4 4
4 4
4 4
4 4
2 4
4 4
4 4
2 4
5 4
5 4
5 4
5 5
5 5
5 5
5 5
5 5
5 5
5 5
5 5
4 5
4 5
4 5
4 5
4 5
4 5
4 5
2 5
2 5
2 5
2 5
2 5
1 5
2 5
2 5
2 5
2 5
5 6
5 6
5 6
5 6
5 6
5 6
5 7
4 7
2 7
2 7
2 7
2 7
2 7
6847999
6845999
6843999
684~999
6839999
6837999
6835999
6833999
683~999
6829999
358999
APPENDIX F. Plots of moose home ranges with multiple utilization distribution polygons.
362999 366999 379999 374999
· Data~ile: ~52945.DAT
Output File: ~52945.0UT
Display Units: Mete~s
Adaptive Ke~nel
98PX 392~. 999 ha ------
D o~ data points: 79
XMin: 359596.3
XMax: 374489.6
YMin: 6839853.
6846299.
G~id Size:
Aug. Dist:
Bandwidth:
699.9 M
345~.2 M
659.9 M
LSCU sco~e: -.~4464E+99 N
0
0:: w
CD
2
:::> z
0
6
0::
f-z w u
_J
<( z
0:: w ::.:
10 152045-1985-90 (0:\CALHOME\ 152045)
OCCURRENCE IN CENTROID X JULIAN DAY
10 r--r----------------------------------------------1
8 f-
'l ~
5 ...._
c.; -
4 -IDIJIJ
3 r-
2 f-
l r--p 0 DO
0
0
0
aoo
0
DO llliDDD
0
0
OJ
CIIl !ITO OillO a:r:mJ CillO CD 0
I 200
100
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-90
103
0 0
[D oano o
0 []IJ
0
0
0 0
I ' 400
300
0:: w
IIl
2
~ z
0
0
0::
f-z w u
_J
<( z
0:: w
:>::
ID 1 52045-1985-86 (D:\CALHOME\ 152045)
OCCURRENCE IN CENTROID X JULIAN DAY
10 .--.----------------------------------------------~
a r-
6 :-
~ 1-0 0
0 0
2 f-0
DO 0
o~+--------~~------,--------.-~------.-~
0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-86
104
10 152045-1989-90 (D:\CALHOME\ 152045)
OCCURRENCE IN CENTROID X JULIAN DAY
10 .--.--------------------------------------------~
8 r-0
u: 0 0 0 w
Ill
2
::> 6 r z
0
6 0 0
u:
I-z w 4 r 0
u
...J
<t z
0: w
~ 2 r
0 0 0 0 0 0
0 I I 0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1989-90
105
6898999
6896999
6894999
6892999
6899999
6798999
6796999
394999 398999
• ••••• • • • •• •• •• • .. * ••
•
• •• •
•
• • • •
•
•
•• • •
•
312999
• • •
•
.i,
• • ,:-
5
Data£ile: 15214539.DAT
Output File: 5214539.0U
Display Units: Meters
Adaptive Herne 1
98PY. 2395.999 ha ----------
8 o£ data points: 95
XMin: 394932.3
XMax: 315937.7
YMin: 6797319.
YMax: 6892986.
Grid Size: 699.9 M
Avg. Dist: 1523.6 M
Bandwidth:
LSCU score:
459.9 M
.59937E+99
ID 15214539 85-91 (D:CALHOME 15214539)
~~
(1:
w
CD
~
::l z
0
6
(1: •
~ ~\..u..t.4
w u
...J
~ z
0:: w
~
~~~
OCCURRENCE IN CENTROIDS X JULIAN DAY
7 r--r------------------------------------------~
6 r
5 :-
4 f-
3 ~
2 r-
1 -
0
0 0 0 0
pmJDDIDD llll amiD ClD 0 o amo CIJIIJO
0
0 aiD liD [[)CD ClliJD:D 0 0 0 0 0
0
0 ODD llD 0 IJl1lD 0 0 00
0
I 200 I
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1981-91
107
400
-!0 1521 4539 1982--"-83 (D:CALHOME 1 521 4539)
OCCURRENCE IN CENTROIDS X JULIAN DAY
7 ~~------------------------------------------~
0 0
a:: 5 -DJO 00 0 000 0 0 DO 00
w
(!]
2
~ z 1 0 0 0 0 0 0 0 0
0
0 a::
"""" z w u
_.J
<(.
2 -z a:: w
~
1 -
0 I 0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1982-83
108
6848999
6846999
6844009
1.{)
6842999 'l-
G>
6849999
6838999
6836999
6834999
6832999
6839999
6828999
3
08
DataCile: 152166.DAT
Output 'File: 152166.0UT
Display Units: Mete~s
Adaptive )(e:rnel
98PY. 4237.000 ha -----------* oC data points: 79
)(Min: 356128.6
)(Max: 373133.1
YMin: 6829252.
YMax: 6844356.
G~id Size: 799.9 M
Avg. Dist: 3761.1 M
Bandwidth: 799.9 M
LSCU sco~e: -.26889E+19
ll': w ro
::E
:J z
0
6
ll':
f-z w
(.)
_J
<( z
ll': w
::.::
ID 1 521 66-1 986~91 (D:\ CALHOME\ 1 521 66)
OCCURRENCE IN CENTROIDS X JULIAN DAY
14 .--.--------------------------------------------~
12 1-
0
10 1-0 0 OCDC!ID CDO DO
0 0 0
8 r-0
0 0 0
6 1-0 00
OJlDJ 0 0 0 0 0
4 1-
2 ~ 0
:IO CDC!ID DOD cr:IJID CJJO 0 0 lli!JO
0 I I 0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1986-91
flO
10 152166-1985-86 (0:\CALHOME\ 152166)
OCCURRENCE IN CENTROIDS X JULIAN DAY
14
12 1-
0: 10 1-
w
m
:::::
:::> z 8 1-
0
6
0:
f-6 1-z w u
_j
<( z 4 -0
0: w 0 0 ~
2 1-0 0
0 0 0
0 I I 0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-86
lll
a::
w ro
2
::> z
0
6
0:: ..... z
w u
__)
<: z a::
w
::.::
10152166-1985-91 (D:\CALHOME\152166)
OCCURRENCE IN CENTROIDS X JULIAN DAY
14 r--r--------------------------------------------~
12 1-
10 -
8 1-
5 -
[]]I[JJ 0
4 1-
2 1-
:m
0
0
0
0 0 om am CIJOOO
0 0 0
0
0 0 0
0 DO
0 0 0 0
0
0 0
[!JilJD 000 r:JlJilJ mo 0 0
I 200
100
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-91
112
0
0
0
0 I[] [I][JO
\
300
400
6852999 Dataf'ile: ~52~9~.DAT
Output File: ~52~9~.0UT
<i'l-Display Units: Mete:rs
Adaptive J<e:rnel
98P:x 6298.999 ha .,.
~ U of' data points: 7~ 6847999 ~ ~ ~ XMin: 35~~27.2
XMax: 379777.9
YMin: 6839978.
2. YMax: 6859732.
6842999 +L( G:rid Size: 799.9 M
Avg. Dist: 5637.8 M
Bandwidth: 999.9 M
LSCU score: -.25763E+~9 ~
6837999
1::. ~\~
?.=~
6832999 '?> -:: s~ "1~'" ~t.Lt-\
s-::.. w~~ 'Rt;;-84
~?'-\ "''-.A Co'::-\)5>·v.,...~ tr..-S1 6827999
349999 359999 369999 B1 -SC(
B8-89
'"'-'to
qo-l\l
ID 152191-
7
6 f-
a:: 5 f-w
(!]
::;
::J z 4 f-
D
6
a::
I-3 -z w u
..J
<{ 2 -z
a:: w
::.::
1 -
0
0
1985-86 (D:\CALHOME\ 152191)
OCCURRENCE IN CENTROIDS X JULIAN DAY
DDJ DOD 0
I 200
100
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-86
114
I
300
0
400
ID 152191-1985-91 (0:\CALHOME\152191)
OCCURRENCE IN CENTROIDS X JULIAN DAY
7 .--.----------------------------------------------,
5 -0 0 om omom:mmo o o
0::: 5 1-oom 0 0 0 0
w
(!)
~
::> z 4 1-0 0
0
0
0:::
f-3 r-0 []1] IIliJ 01] [][]0 0 DO z w u
_/
<{
2 r-z 0 0 DO
0::: w
:>::
1 r-p [][][] [][IJO 0 ODD
0 I I 0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-91
115
6872999
6867999
6862999
6857999
6852999
6847999
6842999
6837999
6832999
6827999
336999 346999 356999 366999 376999
DataCile: 152219.DAT
Output File: 152219.0UT
Display Units: Mete~s
Adaptive J<e~nel
98PX 7859.999 lla ------
D oC data Points: 49
XMin: 338876.9
XMax: 369961.9
YMin: 6831152.
YMax: 6872419.
C~id Size: 1237.7 M
Avg. Dist: 9284.7 M
Bandwidth: 899.9 M
LSCU sco~e: -.89632E+11 ~
ID 152210-1986-89 (D:\CALHOME\152210)
OCCURRENCE IN CENTROID X JULIAN DAY
10 ,--,--------------------------------------------~
8 '--[!JIJ Cilil n::n:::IJ[IIJ 0 0
a:: 0 w
CD
:::.?;
::J 6 r-0 z
0
0 ITIJ CD 0 0 rn 0
a:: ,_
w z 4 f-0
u
....J
<i 0 0 0 DO []]O z a:: w
>:: 2 1-0
DD 0
0 I I 0 200 400
100 .300
JULIAN DAY (DAY 1 = 7 MAY)
0 1986-89
117
ID 152210-1985-86 (D:\CALHOME\ 15221 0)
OCCURRENCE IN CENTROID X JULIAN DAY
10 ,--,--------------------------------------------~
8 f-
0:: w
CD
2 :;, 6 f-DO 0 z
0
6 DO
0::
f-w 0 z 4 f-
u
_J
<{ 0 z
0:: w
~ 2 ~
0
0 I I 0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-86
118
6868999
6863999
6858999
6853999
6848999
6843999
6838999
6833999
356999 366999
'1.. '2..
GQ ' ~
4 0 0
Oc>Gs
376999 386999
b
0
DataCile: 152869.DAT
Output File: 152869.0UT
Display Units: Mete~s
Adaptive He~nel
98PX 7499.999 ha -----------
8 oC data points: 71
XMin: 359377.4
XMax: 399989.4
YMin: 6834357.
YMax: 6846689.
C~id Size:
Avg. Dist:
Bandwidth:
LSCU sco~e:
921.3 M
5122.1 M
659.9 M
.13645E+19
a:: w m
2
~ z
0
0 a::
1-z w u
_J
<t: z a:: w
~
10-152860-1985-86 (D:\CALHOME\ 152860
OCCURRENCE IN CNETROIDS X JULIAN DAY
7 .--.--------------------------------------------~
6 '-
5 .....
4 -
3 -
2 -
1 1-
0
0
0
CIJ DOD DO
I 200
100
JULIAN DAY (DAY 1 = 7 MAY)
0 1985-86
120
I
300
400
a::
w
CD
:::E
:J z
0
0 a::
1-z
w u
...J
<( z a::
w
::.::
ID-152860-1986-91 (D:\CALHOME\ 152860)
OCCURRENCE IN CENTROIDS X JULIAN DAY
7 ,--,----------------------------------------------~
6 -0 0 0
5 1-0 0 0
4 f-0 0 0
3 ,... 0 om OJDD::OO 0
2 -0 0 0
1 -fJ [JJ[JIJJ) 0 CIIJ ITID DIDO CIIliiJ [I]]JO 0 0 IIDJD
0~+--------~,------~-------~,------~~
0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
0 1986-91
121
6865999
6869999
6855999
6859999
6845999
6849999
6835999
6839999
6825999
6829999
34~999 35~999 36~999
(o (0 • •
37~999 38~999
DataCile: ~52969.DAT
Output File: ~52969.0UT
Display Units: Meters
Adaptive Mernel
98PX 9244.999 ha ------
D oC data points: 54
XMin: 34449~.3
XMax: 369997.6
YMin: 6823832.
YMax: 686373~.
Grid Size: ~~96.9 M
Avg. Dist: 6984.9 M
Bandwidth: 999.9 M
LSCU score: -.73~8~E+~9
0:: w
IIl
:::!:
:J z
0
6
0::
f-z w u
...J
<( z
0:: w
~
10 1 52960-1985-90 (D:\CALHOME\ 1 52960)
OCCURRENCE IN CENTROID X JULIAN DAY
7 ,--,----------------------------------------------,
6 r-ormrnoo
5 1-0
4 1-ODD DO 0
3 1-[[] ITID 0 DO CDIIJ 0 []] 0 rn 0 0 0 0 0
2 1-0 DO 0 0 0
1t-[]llJ 0 0 rn CIDO
0~+--------~~------~------~~--------~~
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100 300
JULIAN DAY (DAY 1 = 7 MAY)
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123
ID 152960-
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a: 5 ,__
w
CD :::;
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6 a:
f-3 1-z w u
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<{
2 z 1-
a: w ;,::
11-0
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I
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0
400
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a:: 5 r--
w
(IJ
~
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f-3 r--z w
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11-
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1985-86 (D:\CALHOME\ 152960)
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100
JULIAN DAY (DAY 1 = 7 MAY)
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I
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6993999
6898999
6893999
6888999
6883999
6878999
6873999
6868999
6863999
6858999
343999
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353999
~
3 ~
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363999 373999 383999
Dataf'ile: 1.5321.5.DAT
Output File: 1.5321.5.0UT
Display Units: Mete:rs
Adaptive He:rnel
98P:I.: 881.2.999 ha
II of' data points: 79
XMin: 34621.6.7
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6
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f-w z u
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<( z
0:: w
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3 f-0
2 r-p IIlDIIID em 0 0 DIDO a::IIIl 0 []J 0 0 [llJ [llJ 0 IJ!JO
1f-0 0 0
D~+--------~~------~------~~--------~
0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
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127
0:: w
lD :::;;
::l z
0
6
0::
1-w z u
_.J
<( z
0:: w
::(
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4 f-
3 !-
2 !-CD 0 DO 0 0
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OL-+--------~~------~-------~~------~~
0 200 400
100 300
JULIAN DAY (DAY 1 = 7 MAY)
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128
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6999999
6899999
6889999
6879999
6869999
6859999
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6839999
399999 329999 349999 369999
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98Px 35269.99 ha
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Bandwidth: ~299.9 M
LSCU sco~e: .68989E+~2
Ou.S\ ~ f1\.\v..\.. S ~ 'tJ
C!>u-.9r ~ ~ '2. -=-8
10 153220
6
5 1-0
0: w 4 rn -
2
:l z
0
6 3 -
0:
1-z w u
...J 2 -
<( z
0: w
::.::
r-
0
0
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0
CENTROIDS 1 ,2,3,4,5,6,7 AND 8
0
0
0 DO
0 0 0
200
100
JULIAN DAY (DAY 1 = 7 MAY)
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0
DO 0
400
300
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CD
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0
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I-z w
()
...J
<{ z e:: w
~
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9
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r-0
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0
CENTROIDS 1,2,3,4,5,6,7 AND 8
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[JIJ
0 0
CilJ
[]J) DO
200
100
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131
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[[![J
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400
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ID 153220
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w u
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Adaptive J<e~nel
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D o£ data points: 54
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Bandwidth:
5877.7 M
799.9 M
LSCU sco~e: -.39362E+19
0:: w
CD
:::E
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0
6
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l!
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<t z
0:: w ::.:
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OCCURRENCE IN CENTROID X JULIAN DAY
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6 f-0 DO
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100 300
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6883999
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6873999
6868999
6863999
6858999
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6848999
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DataCile: 1.53252.DAT
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Adaptive Ke~nel
98PX 1.5489.99 ha -----------
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XMin: 398981..1.
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LSCU sco~e: -.42727E+1.1.
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CD
:::;:
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0
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_J
<l:: z a:: w
:::.:
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OCCURRENCE IN CENTROIDS X JULIAN DAY
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4 -0 Dr:rnm::D Iiiii !I! II I II IIi' IIIII !I. I DID
3 r-0 0 0 0
2 t-0 !I] DD O[]J
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100 300
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6897999
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Adaptive J<e~nel
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Cl
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OCCURRENCE IN CENTROIDS X JULIAN DAY
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2 1-0
1f-f:D(]][] 0
0
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100
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6999999
6895999
6899999
6885999
6889999
6875999
68?9999
6865999
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Adaptive Kernel
98PX 9?65. 999 l1a ------
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Grid Size: 895.? M
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IIl
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:J z
0
6
0::
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..J
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5 .--.----------------------------------------------~
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2 1-0 0
11-~IIlDIIIIIICIT[I!JIIIm:n:m:DJOIDUJ[OlD ll Ill! II I !! C I I[] 00 0
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100 300
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0 1980-89
140
6922999
69~7999
6912999
6997999
6992999
6897999
6892999
6887999
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339999 ~ 349999 359999 369999 379999
DataCile: ~53579.DAT
Output File: ~53579.0UT
Display Units: Meters
Adaptive Kernel
98PX 7673.999 ha -----------
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XMin: 342833.5
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6894355.
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3 I-
2 r-
11-0
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100 300
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0 1985-86
142
ID 153570-1986-90 (D:\CALHOME\ 153570)
OCCURRENCE IN CENTROID X JULIAN DAY
8
7 -III D CD m 0 DO !JliiiD DDDI 00 0 0 DO 0
6 -0 0 0
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CD
2 5 r-0 rn DO 0
:::> z
0
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a:
>--z w u 3 r-0 []O
...J
<i z a: w 2 r-0 0 0
y
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100 300
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143
6994999
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6894999
6889999
6884999
6879999
6874999
6869999
6864999
6859999
6854999
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3J.5999 325999
~~~
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335999 345999
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Output File: 153582.0UT
Display Units: Mete~s
Adaptive J<e~nel
98PX 12619.99 ha -----------
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XMin: 397822.J.
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LSCU sco~e: -.67236E+J.J. ~
~
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ID
2
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0
6
0::
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...J
<l: z c:: w
~
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7 -
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4 1--p 0
3 I-mm 0
2 '-
1 -
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100 300
JULIAN DAY (DAY 1 = 7 MAY)
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145
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6870000
6860000
6850090
6840000
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6820000
6810000
340000 360000 380000 400000
DataCile: 153640.DAT
Output File: 153640.0UT
Display Units: Mete~s
Adaptive He~nel
98PX 17419.99 ha
B oC data points: 60
XMin: 352284.4
XMax: 369768.3
VMi n·: 6823941.
VMax: 6876743.
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LSCU sco~e: -.27025E+11
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IIl
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6
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_I
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OCCURRENCE IN CENTROIDS X JULIAN DAY
7 r--r----------------------------------------------~
6 f-0 0 DO
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4 -0
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2 i-DO 0 0 0
1 f-0 DO DOD cc::nn OCD 0 o ooooooorn
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100 300
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0 1985-91
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~
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0
6 n::
f-z w u
...J
<{ z n:: w :.::
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3 t--
2 t--
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100 300
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6886999
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6889999
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6876999
6874999
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I
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4
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DataCile: 153721.DAT
Output File: 153721.0UT
Display Units: Mete~s
Adaptive )(e~nel
98Px 3396. 999 ha ------
ft oC data points: 72
XMin: 362234.4
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OCCURRENCE IN CETROID X JULIAN DAY
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6 r-
5 1-
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100 300
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150
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2
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0
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e::: w :.::
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5 -D D 0
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2 -[!][] rn IIIJ ITID OJJIO []I] 0 0 0 D DO 0 D 0 0 liD
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0 L--o~---------,r---------2~oo----------r-,--------4~o-o~
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Adaptive Kernel
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3 1-J 0 0 DO 0
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100 300
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154
69391iUiJ9
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691.9999
6999999
6899999
6889999
6879999
6869999
339999 359999 379999 399999
Data£ile: 1.53?61..DAT
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Adaptive He~nel
98PX 1.5379.99 ha
M o£ data points: 68
XMin: 33621.3.4
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Avg. Dist: 9768.1. M
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CD
2
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OCCURRENCE IN CENTROIDS X JULIAN DAY
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157
APPENDIX G. Title page of manuscript entitled "In utero pregnancy rate, twinning rate, and fetus production
for age-groups of cow moose in south-central Alaska.
IN UTERO PREGNANCY RATE, TWINNING RATE AND FETUS
PRODUCTION FOR AGE-GROUPS OF COW MOOSE IN SOUTH-
CENTRAL ALASKA
Ronald D. Modafferi
Alaska Department of Fish and Game, 1800 Glenn Highway, Suite 4, Palmer, AK 99f15
ABSTRACT: The relationship of reproductive parameters (i.e., pregnancy rate, twinning rate and fetus
production) to 5 age-groups (calf= C, yearling= Y, teen= T, prime= P and senior= S) of cow moose
(Alces alces) were investigated. Age-class and in utero t~tus counts from 895 cow moose killed in 14
area-specific antlerless/cow-moose hunts (year/area (Y/A) samples) during November-February, 1964
to 1974, in south-central Alaska were analyzed. Measures of central tendency and dispersion were used
to characterize the reproductive parameters in each age-group classification. There was evidence of age-
group effects on pregnancy rate (P = 0.0000). None of the C moose examined carried a fetus(es). Age-
groups ordered by pregnancy rate were Y < T < S < P. The difference in pregnancy rate between P and
S age-groups was not statistically significant (P = 0.1019). Y/A effects on pregnancy status were
insignificant (P = 0.8414). There was evidence of age-group effects (P = 0.0001) andY/A effects (P =
0.0001) on occurrence of twinning. None of the Y age-group moose examined carried twin fetuses.
Age-groups ordered by twinning rate were T < S < P. The difference in twinning rate between T and
Page-groups was statistically significant (P = 0.05). Age-groups ordered by fetus production (fetuses/
100 cows) were Y < T < S < P. Based on the reproductive parameters studied, cow moose attain their
maximum productivity after 3-years-of-age. Findings emphasize the importance of considering cow
moose reproductive maturity in measuring productivity, interpreting information on productivity,
modeling moose population dynamics and implementing selective harvests of cow moose.
Simulation models are becoming impor-
tant tools in everyday management of moose
(Page 1987). Population models highlight
parameters that are basic and important in
understanding moose population dynamics
(Karns 1987). Productivity parameters are
important, basic components in models of
moose population dynamics and in manage-
ment of moose populations (Simlcin 1974,
ALCES VOL. 28 (1992) pp. 223-234
cow moose in south-central Alaska, (2) ex-
plore relationships between productivity pa-
rameters and age-class based age-grou;>s and
(3) provide moose managers, who are most
familiar with net productivity in fall in the
form of ratios of calves to adult cows, with
baseline information on moose gross produc-
tivity.
Verme 1974, Moen and Ausenda 1987). STUDY AREA
Quantitative information on some moose pro-Moose bunts took place in south-central
ductivity parameters is scarce (Karns 1987, Alaska (Fig. 1 ). The area included Alaska
Crichton 1988). Refinements in knowledge Game Management Unit (GMU) 7 and Game
about parameters of moose productivity will ManagementSubunits(GMS) 14A, 14B, 14C,
improve the quality of moose population 15A, 15B and 15C. Management Units 7,
models and lead to better moose management 15A, 15B and 15C were located on the Kenai
decisions. In this study, I was not particularly Peninsula (Kenai). GMSs 14A and 14B were
concerned with Y/A effects on cow moose located in the Matanuska and Susitna River
productivity. Rather, thepurposeofmystudy valleys (Mat-Su). The Ft. Richardson hunt
was to: (1) consolidate and analyze archived area (Ft. Rich) was located in GMS 14C near
information on productivity parameters for Anchorage. The Kenai, ~at-Su and Ft. Rich
223
158
Appendix H. Draft of manuscript entitled "Survival of radiocollared adult moose in Lower
Susitna valley, Southcentral Alaska."
21 July 1994 .
Ronald D. Modafferi
Alaska Department of Fish and Game
1800 Glenn Highway Suite 4
Palmer, AK 99645
907-745-6890
RH: Adult Moose Survival · Modafferi
SURVIVAL OF RADIO-COLLARED ADULT MOOSE IN LOWER SUSITNA VALLEY,
SOUTHCENTRAL ALASKA
Ronald D. Modafferi, Alaska Department of Fish and Game, 1800
Glenn Highway, Suite 4, Palmer, AK 99645
Abstract: Estimates of natural survival for adult moose (Alces
alces) are presented by sex, season, and year for 204 (66 males)
radio-collared adult moose monitored with aircraft in lower
Susitna Valley southcentral Alaska during 15 May 1980 through 25
February 1991. Deaths were attributed to capture problems,
accidents, defense of life or property, hunter harvest, illegal
harvest, train kill, winter kill and other. Hunter harvest and
captured related deaths were censored from survival calculations;
moose were also censored for loss of signal contact and shedding
of collars. Survival varied by sex, season, and calendar year.
summer survival rates were high for both cows (0.98) and bulls
{0.99). Survival in autumn was lower for bulls (0.89) than for
cows {0.98). Survival in winter and annual survival were highly
variable and affected largely by winter-kill deaths attributed to
snow accumulations. Survival in winter differed widely among
years for both bulls {P < 0.001) and cows {P < 0.001). Annual
survival differed among years for both bulls {P < 0.001) and cows
{P < = 0.001). Sightability varied by sex and season and was
attributed to habitat structure, moose behavior, physiology, and
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MODAFFERI
nutritive condition. Lower survival of cows (0.98) versus bulls
(0.99) during summer was attributed to bear predation on
parturient cows and cows with neonates. Lower survival of bulls
(0.89) versus cows (0.98) in autumn was ascribed to bullet
wounding, illegal harvest, and fatal encounters with bears. Low
and highly variable levels of survival in bulls (0.33 to 1.00)
and cows (0.70 to 1.00) in winter and large differences in
survival of bulls (0.29 to 1.00) and cows (0.65 to 0.99) among
years were attributed mainly to snow accumulations. Moose
managers must be cognizant of snow conditions through winter to
accurately predict size and composition of pre-hunt populations
and annual survival. Moose management programs must be
sufficiently responsive to modify autumn harvest quotas in
accordance with survival data obtained during the previous winter
after standard post-hunt population surveys are conducted.
~. WILDL. MANAGE. 00(0):000-000
Key words: adult moose, Alces alces, mortality, southcentral
Alaska, survival
Survival characteristics may provide important insight into
factors affecting population change. Ultimately, survival data
can be used to model population dynamics thereby strengthening
management decisions. As indicated by Van Ballenberghe (1983),
the literature contains few estimates of adult moose survival
rates. Karns (1987) indicates that estimates of survival rate
for adult moose (Mercer and Manuel 1974; Peterson 1977; Hauge and
Keith 1981, Mytton and Keith 1981, Gasaway et a1. 1983) was less
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MODAFFERI
common than for calf moose. Recent literature on survival of
moose includes Boar (1988), Bangs et al. (1989) and Larsen et al.
(1989). Accidents, disease, habitat, hunting, parasites, pests,
poaching, and weather are factors that influence moose survival
(Crichton 1987, Lankester 1987).
The purpose of this study was to provide information on the
non-hunting aspects of survival of radio-collared adult moose.
I especially thank staff of the Alaska Department of Fish
and Game (ADF&G) for helping with various aspects of this study.
E. B. Becker provided statistical advice, performed statistical
analyses, and clarified analytical concepts. D. C. McAllister,
assisted in many aspects of the study. I acknowledge many ADF&G
colleagues for assistance in moose capture and radio-tracking
procedures. P. A. Arneson provided data on radio-collared moose
captured and monitored during April through December 1980. J. B.
Faro contributed information on moose captured and monitored in
Alaska Game Management Subunit (Subunit) 16B during 1987-88. My
supervisors, K. B. Schneider, D. A. Anderson, and C. c. Schwartz
provided guidance, assistance and administrative support
throughout this study. C. C. Schwartz also reviewed the
manuscript and renewed my enthusiasm for completing this paper.
S. R. Peterson reviewed a draft of this manuscript and provided
many helpful comments. I thank area staff J. C. Didrickson, C. A.
Grauvogel, H. J. Griese, and M. w. Masteller for supporting this
study. I thank light-aircraft pilots c. A. Allen, Charlie Allen
Flight Service, M. Houte, L. Rogers, c. R. and V. L. Lofstedt,
Kenai Air Alaska, W. A. Woods, Woods Air Service, and W. D.
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Wiederkehr, Wiederkehr Air Inc. for skill, dedication, and
enthusiasm on aerial radio-tracking surveys. This study was
funded in part by Federal Aid in Wildlife Restoration.
STUDY AREA
Capture, radio-collaring, and radio-tracking of moose took
place in a 25,000 km 2 area in the lower Susitna Valley in
southcentral Alaska (Fig. 1). The area included portions of Game
Management Subunits (Subunits) 13E, 14A, 14B, 16A, and 16B.
Climate and geography of the area was described by Viereck and
Little (1972) and Modafferi (1991). Snow accumulation varied
greatly by year.
In general, moose populations in the region increased during
1980-84 and 1985-87 and decreased in 1984-85 and 1987-91. Moose
populations in the area were probably at or very near carrying
capacity before winter 1984-85. Moose were hunted during
subunit-specific open seasons. In most subunits, male moose were
hunted every year during a September season. In some areas,
limited numbers of permits were issued for the harvest of
antlerless and/or cow moose during the September season and/or a
December through February season. Accidental collisions of moose
with trains and highway vehicles were noteworthy sources of
mortality in the region, particularly, in deep-snow winters
(Rausch 1958, Modafferi 1991). Moose predators in the area
included wolves (Canis lupus) and brown (Ursus arctos) and black
bears (Q. americanus).
Information on predator densities in the area was largely
circumstantial. Wolf density estimates ranged from about 1-2
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MODAFFERI
wolves/1000 km 2 in Subunits 14A, 14B, and southern 16A to about
2-7 wolves/1000 km 2 in Subunits 13E, 16B, and northern 16A
(Ballard 1992a, Masteller 1994). In general, wolf populations
probably increased during 1980-91. Brown bear density estimates
ranged from 7-25 bears/1000 km 2 in Subunits 14A and 14B to about
12-35 bears/1000 km 2 in Subunits 13E, 16A, and 16B (Miller 1987,
Grauvogel 1990, Griese 1993a). Brown bear populations were
likely increasing during the study. Black bear density estimates
ranged from about 35-104 bears/1000 km 2 in Subunits 14A and 14B
(Grauvogel 1990) to about 90-193 bears/1000 km 2 in Subunits 13E,
16A, and 16B (Miller 1987, Griese 1993b). Black bear hunting
over bait and increasing brown bear populations probably caused a
decrease in black bear populations during the study.
METHODS
Capture, Radio-collarinq and Monitorinq of Moose
Moose were captured for radio-collaring mainly from a
helicopter by darting. Moose were also approached on foot or
snowmachine for darting. Moose were immobilized with etorphine
hydrochloride (M99, Lemmon Co., Sellersville, Pa.) with or
without xylaxine hydrochloride (Rompun, Haver-Lockhart, Shawnee,
Kans.) or carfentanil citrate (Wildnil, Wildl. Lab., Fort
Collins, Colo.). M99 and Wildnil were antagonized with
diprenorphine (M50-50, Lemmon Company, Sellersville, Pa.),
naloxone hydrochloride (Dupont Pharmaceuticals, Garden City,
N.J.) or naltrexone hydrochloride (Dupont Pharmaceuticals, Garden
City, N.J.). Immobilized moose were ear tagged and fitted with a
visual-numbered canvas collar (Franzmann et al. 1974) and radio-
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MODAFFERI
transmitter with or without a mortality option (Telonics, Mesa,
Ariz.). Moose were captured during December through January in
alpine postrut concentration areas in Subunits 14A and 14B and
during January-April in lowland winter concentration areas in
Subunits 13E, 16A, and 16B. Moose were primarily captured in 4
areas: lower Susitna River floodplain between Portage Creek and
Cook Inlet in 1980-85; western foothills of the Talkeetna
Mountains between South Fork of Montana Creek and the Little
Susitna River in 1985-89; Alexander Creek floodplain in 1987, and
floodplains of the Yentna and the Skwentna Rivers between Lake
Creek and Old Skwentna in 1988-89.
Age of captured moose was estimated mainly by incisor tooth
wear. However, early in the study a first incisor tooth was
removed from captured moose for cementum aging (Sergeant and
Pimlott 1959). Captured moose were >18 months of age and few
moose were <30 months. All were considered adults.
Radio-collared moose were visually located 1-5 times each
month for visual observation using Cessna-152, -180, and -185 or
a Piper Super Cub (PA-18) aircraft and standard aerial radio-
tracking procedures. Not all radio-collared moose were located
on each survey but it was common to obtain radio-fixes on >60
moose during a single 1-day survey. I searched intensively at
each site to confirm precise locations and to confirm that the
animal was alive. Moose were monitored from capture to death or
date of censor. Death of moose at capture locations or within 4
days after collaring was attributed to capture stress and
excluded from survival analyses.
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MODAFFERI
survival
Radio-collared moose were judged dead by direct observation,
by transmitter pulse rate if the transmitter contained the
mortality/movement option, or radio-fix location if radio-fix
locations on consecutive surveys were identical. When a moose
was judged to be dead, an intensive aerial search was conducted
to locate the radio-collar, parts of a moose carcass, and/or a
disturbed site suggesting the animal was dead. Locations were
revisited and aerially searched until sufficient evidence
confirmed or refuted that the moose was dead. Lastly, locations
were visited on foot to verify death.
Additionally, hunters, the ADF&G, the Alaska Department of
Public Safety, Division of Fish and Wildlife Protection, and the
Alaska Railroad Corporation provided the date and cause of death
of radio-collared moose that died from legal hunter-harvest,
illegal harvest, defense of life or property, and collisions with
vehicles or trains.
In many instances, the exact cause of death was unknown, but
circumstances and/or evidence at the site allowed me to
categorize these into 1 of 4 groups: (1) illegal harvest, (2)
accident, (3) winter kill, or (4) other. Illegal harvest was
assigned mainly to moose radio-tracked to a residential housing
development during the hunting season. The accident group
included deaths resulting from injuries and drowning. Intact
moose carcasses on the snow with no evidence suggesting predation
or accident were considered winter killed. Winter kill included
deaths from starvation and/or inclement weather. The remaining
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MODAFFERI
group, other, included deaths caused by predation and wounding
injuries. Several moose deaths assigned to the other group were
bulls that died in late-September during or shortly after start
of hunting season. The category other also included cows that
died in the period mid-May through July. Death of cows in this
calendar period likely resulted from complications with birthing
(Markgren 1969:195-196) and/or confrontations with bears (Ballard
1992b:l63).
Precise date of death was known for train kills, hunter
harvest, illegal harvest, and kills in defense of life or
property. For deaths in which the date was unknown, the mid-
point date between the last two surveys was used. This interval
was ~15 days in 30\ of the deaths, ~35 days in 65\ of the deaths,
but ~45 days in 6 deaths.
Censoring
Moose were censored from the database if: (1) the
transmitter was lost or failed, (2) an animal emigrated from the
study area, or (3) when the study was terminated. Lost or failed
transmitters were censored on the midpoint between the dates of
the last 2 radio-fixes. Hunter harvested moose were censored on
the reported date of kill.
Censoring of hunter harvested moose could impact estimates
of survival if moose mortality in the winter following hunting
season was compensatory with hunter harvest of moose. I used
regression analysis to examine for evidence of a compensatory
relationship between hunter harvest in autumn and mortality the
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MODAFFERI
following winter. Hunter bull harvest was regressed on bull
deaths assigned to all sources, winter kill, and other.
Analyses encompassed calendar years 1980-90.
survival Rate Estimation
Survival estimates were computed separately for males and
females with staggered entry Kaplan-Meier procedure (Kaplan and
Meier 1958, Pollock et al. 1989). I tested for differences among
years and lumped data with similar survival functions. A
modified SAS (SAS Inst. Inc. 1985) statistical procedure which
accommodated left (Hasbrouck et al. 1992) as well as right
censored data was used to compute a K-sample test for equality.
I accepted the null hypothesis that all annual Kaplan-Meier
survival curves (Lee 1980) were equal and rejected it if at least
1 curve was monotonically larger or smaller than the rest.
Following a rejection of the null hypothesis, the annual survival
curve responsible for the largest contribution to the x2
statistic was identified by year and compared for statistical
similarity versus the survival curve representing the rest of the
years. The process was repeated until a non-significant x2
statistic was obtained. Identical rational and hypothesis
testing procedures were used to examine survival by season for
differences in survival curves among years.
I calculated annual survival rates based on a calendar year
starting on 16 May. Seasonal survival rates were calculated for
summer (16 May through 31 August), autumn (1 Sep through 31
December), and winter (1 January through 15 May) during 1980-91.
Because winter-related mortality of moose can span into early May
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MODAFFERI
in a long and/or late winter, the 16 May through 15 May calendar
year aligned winter mortality with the appropriate winter and
year.
Snow Conditions
Snowpack depth measurements were used to appraise snow
conditions. These measurements were obtained from Alaska
Climatological Data Reports, U.S. Department of Commerce, NOAA,
National Environmental Satellite, Data and Information Service,
National Climate Data Center, Asheville, North Carolina for
October throught April during 1980-91. Snow conditions were
characterized using maximum snowpack depth and the duration of
deep snowpack from October through April. Duration of deep snow
was the number of months that snow depth was ~110 em, the
approximate chest of adult Alaskan moose. Measurements at
Wasilla, Willow, Talkeetna, and Skwentna weather stations were
used to reflect general snow conditions in the study area. In a
few instances, snow measurement data were unavailable for a
particular month at a weather station. In these cases, data from
the next nearest weather station were used to proportionalize
maximum snow depth for the month in question.
RESULTS
Capture, Radio-collaring, Monitoring, Moose Sightability, and
Population Sampling
During 1980-91, 204 moose (138 F) were captured, radio-
collared, and monitored in lower Susitna Valley in southcentral
Alaska. Number of moose monitored and at risk varied by calendar
year; these ranged from 6 to 25 in males and from 29 to 89 in
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MODAFFERI
females (Fig. 2). Moose were radio-tracked 9,754 times during
363 aerial surveys. Annual monitoring effort varied in relation
to the number of radio-collared moose at risk (Fig. 2). Surveys
ranged from 16 in 1985-86 to 43 in 1989-90. Fifteen moose (10 F)
died from problems with capture and were omitted from survival
analyses. Eighty-six of 128 females were monitored over 3 years
and 25 females were monitored over 6 years (Fig. 3A). Sixteen of
61 males were monitored over 3 years while no males were
monitored longer than 6 years (Fig. 3B).
Sightability of moose varied by season and sex (Fig. 4).
Monitoring effort was high during parturition (May-June), hunting
season (September), and winter (January-April). Sightability was
high from October through March when deciduous vegetation was
leafless, snow cover was present, and moose were in shrub
dominated open-canopy habitats. This situation coincided with
aggregation of moose in postrut and winter concentration areas.
However, sightability decreased from April through June when snow
was patchy, leaf-out occurred, and moose dispersed from winter
concentration areas. Moose sightability was particularly low in
August when moose were in forest habitats. Sightability of cow
moose was higher in late-May vs. early-May , June or July. While
bulls easier to see in July vs. late-May, June or August. Bull
moose were seen more frequently than cows in both August and
September.
Moose monitored for survival varied widely (Hair 1980).
Home range size of moose monitored >9 years ranged from 70 km 2 to
1200 km 2 . I monitored both migratory and non-migratory
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MODAFFERI
individuals (LeResche 1974, Sweanor and Sandegren 1988) that
moved <10 km and >35 km, respectively, between winter and summer
ranges. Annual home ranges at low (46-91 m) or high (914-2560 m)
elevation or annual ranges included seasonal ranges at both low
and high elevations (91-1920 m). Monitored moose lived year-
round in remote areas or in areas in close proximity to roadways,
railways and humans, or year-round ranges overlapped both types
of landscape. Home ranges could be adjacent to marine tidal
flats or up to 100 km inland. Winter ranges of individuals were
at both low (<100 m) and high (>1800 m) elevations. Postrutting
areas were in alpine shrub and lowland mixed forest habitat.
Winter areas were on lowland floodplains and in high elevation
watersheds.
censorship, Mortality, and causes of Mortality
Twenty-three (15 F) of 189 collared moose (128 F) were
censored before the end of the study because they had either shed
their collar or I lost contact.
alive at the end of the study.
Fifty-three moose (47 F) were
Twenty-nine (SF) of 113 moose (66
F) monitored until death, were killed by hunters. Consequently,
survival was based on 84 (61 F) deaths.
In total, 10 collared moose (5 F, 5 M) were censored for
shed collars of which 8 were censored <3 months after capture. I
expected some bulls to shed collars. Collars were fitted loosely
on bulls to accommodate rut related increases in neck size.
Additionally, moose slipped collars in spring and early summer
when neck hair was shedding and animals lowered their heads to
feed on field layer vegetation. Of 7 moose deaths (5 F, 2 M)
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MODAFFERI
assigned to illegal harvest, radio fix sites of 3 were in
residential housing developments. Another illegal harvest
involved a radio-collared cow observed in winter with a calf near
an occupied remote cabin. On the subsequent survey, the radio-
fix was at the cabin and a lone calf was nearby. That radio-
signal was not heard on subsequent surveys. Another illegal
kill, a male moose, was radio-tracked and observed near a well
travelled highway. On the next survey the radio-fix was in the
Talkeetna landfill.
Ten moose deaths (9 F, 1 M) were ascribed to accidents. The
accident classification included moose that had fallen or
slipped, drowned and/or died from exposure. Accidents included 1
in an ice jam during spring break-up, 1 in a log jam during
spring high water, 2 in flowing water at the base of 70-100 m
high steep rocky cliffs, 2 with only a head and neck protruding
through iced-covered streams, 2 in open water leads of ice-cover
rivers, and 1 with spayed hind legs on glare-ice of a frozen
river. The 1 defense of life or property death was a bull shot
by a rural homeowner because it aggressively precluded access to
an out-building.
I did not detect a relationship between hunter harvest of
bull moose in autumn and moose deaths in the following winter
assigned to all other sources (Ql = 0.087, R2 = 0.006), winter
kill (Ql = 0.059 R2 = 0.003), or other (Ql = 0.024, R2 = 0.001).
snowpack Depth
Maximum snowpack ranged from 10 to 276 em among 4 snow
stations and 6 months during October through April, 1980-90 (Fig.
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MODAFFERI
SA). Snowpack depth was greatest at Skwentna in 9 of 10 years
and lowest at Wasilla in all years. Among years and snow
stations calendar year maximum snowpack ranged from 46 to 276 em.
In 1985-86, maximum snowpack was not >46 em and in 1980-83, it
was not >89 em. During 1980-91, snowpack at Wasilla was not >69
em. In 1984-85 and 1989-90, maximum snowpack was >110 em at 3 or
4 snow stations but in 1989-90, snowpack depth was >225 em at
those 3 stations (Fig SB). In 1989-90, maximum snowpack was >110
em in 4 or 5 months at 3 snow stations. Winter in both 1984-85
and 1989-90 was considered severe with deep persistent snow for
most of the winter. October and November maximum snowpack at
Talkeetna and Skwentna were more than SO% greater in 1989-90 than
in 1984-85 (Fig. 6). Talkeetna and Skwentna maximum snowpacks
were greater in January 1990 than in October-April in 1984-85.
Talkeenta and Skwentna maximum snowpacks during January-April was
greater month by month in 1989-90 than in 1984-85. Early
accumulation of deep snow and long duration of deep snow in 1989-
90, provide evidence that winter conditions for moose, were more
severe in 1989-90 than in 1984-85.
Annual and seasonal survival
Annual survival curves for cow moose differed among calendar
years 1980-91 ( x2 = 57.9642, 10 df, E = o.oooo) with 1989-90
significantly lower (0.65) (X 2 = 49.3981, 1 df, E = 0.0000) than
the other years (Fig. 7A and Table 3). Survival did not differ
among the remaining 10 years 1980-89 and 1990-91 (0.92) (X 2 =
14.198, 9 df, E = 0.115), despite the relatively low overall
survival in 1984-85 (0.815). Apparently, above average survival
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MODAFFERI
through autumn moderated the effects of below average survival in
winter on the cumulative survival distribution in 1984-85.
Cow survival was not different among years within summer (X 2
= 8.335, 9 df, £ = 0.501) and autumn (X 2 = 7.192, 9 df, £ =
0.617)) seasons, so yearly data were combined within summer and
autumn seasons to generate a single survival curve for summer
(0.98) and fall (0.98) and the calendar period, summer through
autumn. Winter survival of cows was significantly different
among years 1980-90, with 1989-90 lower (0.70) x2 = 67.482, 10
df, £ = 0.0000) than the rest. After removing 1989-90 winter
data from the analysis, a difference was detected in winter
survival curves among the remaining 9 years (1980-89 and 1990-91)
(X 2 = 22.159, 8 df, £ = 0.005); survival in 1984-85 was lower
(0.82) (X 2 = 5.258, 1 df, £ = 0.022). After removing both 1984-
85 and 1989-90 data no difference among the remaining 8 years was
detected (X 2 = 2.509, 7 df, £ = 0.926) so data were pooled over
years to generate a single survival estimate (0.97) for a winter
with normal accumulations of snow. Cow survival estimates from
1984-85 and 1989-90 were not different (X 2 = 2.509, 7 df, £ =
0.926), thus data from these years were pooled to generate a
single survival estimate (0.74) for cows during a deep-snow
winter.
Annual survival curves of bulls differed among calendar
years 1980-91 ( x2 = 26.34, 10 df, £ = 0.003) with 1989-90
significantly lower (0.29) (X 2 = 17.379, 1 df, £ < 0.001) than
the other years (Fig. 7B and Table 3). Survival of bulls did not
differ among the remaining years, 1980-89 and 1990-91 (0.84) (X 2
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MODAFFERI
= 14.543, 9 df, £ = 0.104). Bull survival was lower in 1984-85
(0.69) than the average (0.83) but small sample sizes decreased
power of the test.
Bull survival was not different among years within the
summer (0.99) (X 2 = 5.985, 10 df, £ = 0.817) and autumn (0.90)
(X 2 = 8.117, 10 df, £ = 0.617) season, so yearly data were
combined within summer and autumn seasons to generate a single
survival curve for summer (0.99) and autumn (0.90) and the
calendar period, summer through autumn. Survival of bulls was
lower (95\ Cis) in autumn than in summer. Winter survival of
bulls was significantly different ( x2 = 31.717, 9 df, £ < 0.001)
among years (1980-90) with 1989-90 lower (0.33) (X 2 = 23.94, 1
df, £ < 0.001) than the rest. After removing the 1989-90 winter
data from the analysis, I failed to detect a difference among the
remaining 8 years (X 2 = 13.411, 8 df, £ = 0.098) so data were
pooled over the years 1980-90 to generate a single survival
estimate for bulls (0.94) in a winter with normal accumulations
of snow. Data from winter 1989-90 provided a survival estimate
(0.33) for bulls in a deep-snow winter.
Timinq and cause of Mortality
During 16 May 1980 through 25 February 1991, 112 radio-
collared moose (66 F) died (Tables 1 and 2). Twenty-nine (5 F)
of the 112 deaths were hunter kills and were excluded from this
analysis. Frequency of cow moose deaths peaked in winter in
March (26\) (Fig. SA); number of deaths was higher in winter
(65\) than in spring (16%) and/or autumn (18%) (Fig. 9A). No cow
deaths occurred in November. Frequency of bull moose deaths was
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MODAFFERI
low in May through August (4%), whereas 28% of the cow moose died
during May through August. However, during September and
October, frequency of deaths was clearly higher in bulls (39%)
than in cows (12%) (Fig. 9A). The frequency of deaths for all
moose was highest in winter (62%) with 38% in February through
March and 26% in March. In years with deep-snow, 83% of the
moose that died, did so in winter; 56% in February through March,
and 44 in March (Fig. 8B). However, in normal snow years, 60% of
the bulls died in autumn; 53% in September and October (Fig. 8C).
In 1980-91, the frequency of moose deaths was highest in March
with 23% of bulls and 26% of cows dying. In bulls, there was
little difference between the frequency of death in autumn (46%)
and winter (50%) (Fig. 9A), but in cows, there was a small
difference between deaths in summer (16%) and autumn (18.3) and a
large difference between those values and frequency of death in
winter (66%) (Fig. 9A). In deep-snow years, 85% of the cow
deaths and 75% of the bull deaths occurred in winter (Fig. 9B).
In 1984-85 the year deep snow accumulated late in winter (January
through March), frequency of deaths was higher in cows than in
bulls, whereas in 1989-90 the year deep snow accmulated early in
winter during October through January, frequency of deaths was
higher in bulls than in cows and bulls died earlier (0% after
April in the winter than cows (24% after April). In normal snow
accumulation years, frequency of deaths in cows was highest in
winter (43%) and lowest in autumn (25%), whereas in males,
frequency of deaths was highest in autumn (60%) and lowest in
summer (0%) (Fig. 9C).
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MODAFFERI
Cause of death was known for 42 (15 F) of 113 (66 F) moose
monitored until death (Tables 1 and 2): 10 (8 F) were ascribed to
train, 28 (4 F) to hunter harvest, 1 (lF) to defense of life or
property, and 2 (1 F) to illegal harvest. Cause of death for 71
(51 F) moose was not verified. Of these 71, 35 (26 F) were
assigned to winter kill, 10 (9 F) to accidents, 5 (4 F) to
illegal harvest, and 21 (12 F) to other. Hunter harvest was
omitted from succeeding analyses. Frequency of deaths and cause
of death varied in relation to snow accumulation and by sex of
moose (Fig. 10). In 1980-91, frequency of winter kill in moose
was 42%; 43% in females and 39% in males. In 1984-85 and 1989-
90, the years with deep accumulaitons of snow in winter,
frequency of winter kill was 66%; 63% in females and 75% in
males. In 1980-84, 1985-89 and 1990-91, the years with normal
accumulations of snow in winter, frequency of winter kill was
19%; 18% in females and 20% in males. In males, in years 1980-
84, 1985-89, and 1990-91 combined and in 1980-91, the cause of
death with highest frequency was other (47% and 39%,
respectively). In 1980-91, percent of moose deaths attributed to
accidents was >3 times greater in cows (15%) than in bulls (4%)
whereas, in the same years, the number of deaths ascribed to
other was >2 higher in males (39%) than in females (20%). In
1980-84, 1985-89, and 1990-91, the years with normal
accumulations of snow, frequency of moose deaths was similar
among accidents (19%), illegal harvest (16%), and winter kill
(19%).
DISCUSSION
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MODAFFERI
One goal of this study was to provide moose managers with
guideline data on survival to use in models of moose population
dynamics. To refine survival data for this use, hunter harvest,
a highly variable anthropogenic component of mortality was
censored from derivation of survival estimates. However,
censoring of the reported hunter harvest probably did not
completely cleanse survivorship data of effects of hunters and
hunting; death data likely remained confounded by death of moose
from hunter inflicted bullet wounds (Lykke and Cowan 1968,
Gasaway et al. 1983, Fryxell et al. 1988), unreported hunter
harvest (Rausch et al. 1974:710), and illegal harvest (Crichton
1987, Bangs et al. 1989). I found no evidence of compensatory
relationship between hunter harvest of bulls in autumn and all
deaths, winter-kill deaths, or unknown cause deaths of bulls the
following winter.
Annual survival of adult moose in lower Susitna Valley in
southcentral Alaska was greatly influenced by highly variable
snow conditions during winter. Survival rate for both bulls and
cows was highest and varied the least during spring and summer.
Survival rate for cows in autumn was not different from summer.
Survival rate for bulls was lower in autumn than in spring and
lower than that for cows in autumn. These findings re-emphasize
the fundamental importance of snow conditions and winter-related
mortality in the ecology and population dynamics of moose
suggested previously for Alaska (Bishop and Rausch 1974, Coady
1974, Gasaway 1983:29, Ballard et al. 1991:34-35, Modafferi 1991)
and elsewhere (Peterson and Allen 1974, Saether 1985, Mech et al.
177
MODAFFERI
1987, Anderson et al. 1991, Child et al. 1991). Snow conditions,
which influence movements (Sweanor et al. 1992), distribution
(Modafferi 1991) and mobility (Kelsall and Prescott 1971)
predictably affect survival of moose. Moose survive poorly in
early, long, deep-snow winters (Bishop and Rausch 1974, Peterson
and Allen 1974, Ballard et al. 1991). Moose survival during the
late May-early May calendar year, which included the deep-snow
winter of 1989-90 (females 0.65, males 0.29) was lower than
survival rates in many other moose studies (Albright and Keith
1981, Mytton and Keith 1981, Messier and Crete 1985, Boar 1988,
Fryxell et al. 1988, Bangs et al. 1989). However, in lower
Susitna Valley moose, survival was relatively high (cows 0.92 and
bulls 0.84) in 7 of 9 years with snow accumulations that were
relatively deep (81-157 em) compared to snow accumulations that
resulted in high mortality or limited distribution of moose in
other populations (Nasimovich 1955, Kelsall and Telfer 1974,
Peterson and Allen 1974, Wilhelmson and Sylven 1979). Clearly,
the accumulation of snow that elicites a "severe" winter with
respect to moose varies within a wide range of depths depending
on location over the global distribution of moose.
My data showing differences in timing and magnitude of
deaths of cows and bulls within and between the 2 deep-snow
winters, 1984-85 and 1989-90, provide evidence of sex-based
differences in winter survival of moose. Sex differences in
survival in winter may be related to differences in seasonal
dynamics of the nutritive condition and physiology of cow and
bull moose. Nutritive condition of bull moose is at a low point
178
MODAFFERI
after the autumn rut in early winter (Regelin et al. 1985,
Schwartz et al. 1987). Males must recoup their nutritive losses
in early winter to survive through winter. Whereas, nutritional
demands on cows are especially high during the final stages of
pregnancy in late winter (Schwartz et al. 1984, Schwartz et al.
1987). Females must sustain growth and maintenance of
reproductive tissues and a fetus(es) as well as maintain their
nutritive condition to survive through winter and support
lactation immediately following winter. Therefore, I believe
that in 1984-85, the accumulation of deep snow in late winter was
more harsh on cows than on bulls, but, in 1989-90, the
accumulation of deep snow in early winter was more stressful on
bulls than on cows. These findings suggest that the temporal
patterns of snow accumulation are important factors effecting
survival and the population dynamics of moose. Clearly, my data
point-out that 1) moose are subject to major die-offs in winter
after standard autumn post-hunt population surveys are conducted
(Gasaway et al. 1986); 2) managers must be cognizant of snow
conditions throughout winter to accurately assess the survival
and population status of moose; and 3) management programs must
have the flexibility to respond to late winter die-offs of moose
when setting autumn harvest levels.
In other moose studies, high survival of adults was
associated with low rates of predation (Ballard and Larsen 1987).
However, predation was not identified as a prominent factor
affecting survival of adult moose in lower Susitna Valley.
Wolves were present in the study area, but there was no evidence
179
MODAFFERI
of wolf predation on collared moose. During 11 years, I observed
evidence of wolf predation on moose only one time. In this
instance, there was evidence that 2 moose were killed by wolves
in the extreme northern part of the study area. Other predators,
including black bears, brown bears, and coyotes were observed in
encounters with cow moose with neonate(s). Nevertheless, I never
observed bears or coyotes killing or consuming moose. However,
my data show that from late-May through August survival of cows
was lower than bulls and that survival of cows from late-May
through June was lower than in July though August. These data
suggest that cow moose were vulnerable to sex x time specific
mortality associated with parturition and/or neonates. In other
moose studies, mortality of adult cows during this calendar
period was attributed to predation by brown bears (Boertje et al.
1988, Larsen et al. 1989). Bear predation was the most probable
cause of cow moose deaths during this calendar period in my
study.
The sex-biased mortality of bull moose in autumn, during
hunting season and through the rut, was not unexpected. In other
studies, death of moose, primarily males, during autumn was
attributed to hunter inflicted bullet wounds (Gasaway et al.
1983, Fryxell et al. 1988), wounds from rut-related fights with
other moose (Bubenik 1987:351-352, Gasaway 1992:28), and illegal
harvest (Mytton and Keith 1981, Bangs et al. 1989, McDonald
1991). Although these are the most probable causes of bull moose
mortality in autumn, wounds or predation from brown bears
(Boertje et al. 1988, Larsen et al. 1989) that respond to
180
MODAFFERI
vocalizations of rutting bulls, were also potential sources of
bull moose mortality.
Many investigators point out that accidents are a source of
moose mortality (Bangs et al. 1989, Larsen et al. 1989, Cederlund
and Sand 1991, Child et al. 1991, Lavsund and Sandegren 1991,).
However, I am aware of only 1 study (Danilov 1987:519-520) that
indicated accidents were a prominent source. In my study, in
years with normal accumulations of snow and excluding moose
deaths from collisions with trains or vehicles, 27% of the cow
moose mortality could be attributed to accidents. During 1980-
91, with train-kills omitted, 17% the cow moose deaths were from
accidents. Accidents and train-kills combined accounted for 28%
of the cow moose deaths. Collision with trains is a notable
source of moose mortality in the lower Susitna Valley (Modafferi
1991), as in other jurisdictions (Child 1983, Andersen et al.
1991), because a railway crosses major summer-winter migration
routes and winter ranges and in deep-snow years large numbers of
migrating moose aggregate in winter areas along the railway
corridor.
In many jurisdictions moose-vehicle collisions were an
important source of mortality (Child et al. 1991, Lavsund and
Sandegren 1991, McDonald 1991, Oosenburg et al. 1991). However,
in my study, although a high-volume roadway was located roughly
parallel to the railway, no collared moose were killed in
collisions with vehicles. This incongruity between railway (N =
10) and roadway moose kill rates may be attributed to age of the
moose I studied and to age-related learning processes. In
181
MODAFFERI
another moose study in Alaska, road-kill calf ratios were >3
times higher than the overall population calf ratios in the area
(Del Frate and Spraker 1991). Moose I studied were adults; at
the time of collaring no moose were <18 months. Perhaps, moose
learn to avoid collisions with vehicles through non-lethal
collisions with vehicles, whereas, few moose learn to avoid
collisions with trains because most moose-train collisions are
lethal.
Previous research indicates that moose select habitat based
on many factors including availability of cover, overstory and/or
forage (Peek et al. 1976, Pierce and Peek 1984). My radio-
tracked moose corroborate data from other studies that show
overstory and cover characteristics of habitats utilized by moose
differ by sex and time of year. Both sexes of moose were seen
most frequently in winter when moose utilize shrub dominated open
habitats in alpine or lowland landscapes. Cows were observed
more frequently than bulls in early-May and June when parturient
cows utilize wet, open black spruce bog climax communities
(LeResche et al. 1974:157, Bailey and Bangs 1980). Cow moose
were seen less frequently in June than in May. Decreased
observability of cows during early-summer through August may be
attributed to movement from the relatively open habitats used
during parturition to denser forest habitats that cows with
neonates utilize for concealment and isolation from other moose
(Miquelle et al. 1992), predators (Stringham 1974, Stephens and
Peterson 1984) and/or access to higher quality forage in shaded
forests (Hjeljord 1992). Higher observability of bulls in July
182
MODAFFERI
than in late-May-September and higher observability of bulls than
cows in July and August may be related to antler growth.
Whereas, cows with neonates select relative dense habitats in
late summer, bulls may avoid dense forests because their antlers
are growing (Bubenik and Bubenik 1987, Verme 1988). Damage to
growing antlers would be painful and malformed antlers would
affect the status of a male. Higher observability of bulls in
July than in August may be related to several factors including
nutritional requirements and forage quality in sunny versus
shaded growth sites (Hjeljord 1992). The higher observability of
both cows and bulls in October and November than in summer
correlates with movement of moose into open shrub dominated
alpine habitat after the rut. This movement is likely influenced
by forage quality (Thompson et al. 1981, Modafferi 1991). The
decrease in moose sightability during November through December
correlates with the movement of moose from open shrub dominated
postrut concentration areas through forest habitats to shrub
dominated winter areas (Modafferi 1991).
MANAGEMENT IMPLICATIONS
Accumulation of snow was a prominent and highly variable
source of mortality affecting annual survival of moose in lower
Susitna Valley in southcentral Alaska. My data indicate survival
of 33% and 74% for bulls and cows, respectively, in winter (1
January though 15 May) after traditional autumn post-hunt
population surveys are conducted. Clearly, annual management
decisions and harvest policies should not be solidified before
information on winter survival is available and analyzed.
183
MODAFFERI
Ideally, moose management programs should have provisions for
reacting to winter die-offs of moose and reevaluating modifying
management decisions and harvest policies formulated in late
autumn.
If winter die-offs of moose periodically perturb
manipulatively managed populations (Caughley and Sinclair
1994:2), should harvest policies and goals be commensurate with K
carrying capacity in a normal snow winter or a deep-snow winter?
To knowledgeably evaluate alternative forms of moose harvest
policy, managers must utilize simulation models (Erickson and
Sylven 1979, Sylven et al. 1987) to obtain baseline informatio on
population dynamics and public processes to solicite input on
allocation from social, political, and economic interests.
My data suggest that illegal harvest, unreported harvest,
and other mortality related to hunting caused significant
mortality in bulls in autumn. Moose managers should be aware of
these less perceptible forms of mortality that are additive
byproducts of hunter harvest.
Finally, my data indicate that in the absence of high
densities of predators, snow accumulation in winter is a
recurrent perturbing factor with large effects on size and
composition of moose populations in lower Susint Valley. If
predator densities increased to higher levels, winter weather
perturbations could cause moose populations to be at lower
density equilibria maintained by predators (i.e., a predator pit)
(Van Ballenberghe 1987).
184
MODAFFERI
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List of Fiqure Titles
Fig. 1. Radio-collared moose movement study area in lower
Susitna Valley southcentral Alaska, 1979-90.
Fig. 2. Radio-collared moose monitored for survival in lower
Susitna Valley, southcentral Alaska 1980-90.
Fig. 3. Number of years radio-collared moose were monitored from
date of capture to date of censoring or date of death, lower
Susitna valley in southcentral Alaska, 1980-91.
Fig. 4. Monitoring effort (A) and sightability (B) of moose
radio-tracked with aircraft in lower Susitna Valley in
southcentral Alaska 1980-91.
Fig. 5. Maximum snowpack depth (A) and duration and extent of
deep snow (B) measured at Wasilla, Willow, Talkeetna, and
Skwentna in lower Susitna Valley in southcentral Alaska, during
October through April, 1980-90.
Fig. 6. Snow accumulation in the deep-snow winters of 1984-85
and 1989-90 at Talkeetna (TK) and Skwentna (SK) in lower Susitna
Valley in southcentral Alaska.
Fig. 7. Survival of radio-collared moose monitored with aircraft
in lower Susitna Valley in southcentral Alaska, 15 May 1980
through 25 February 1991.
195
MODAFFERI
Fig. 8. Mortality of radio-collared adult male and female moose
monitored with aircraft in lower Susitna Valley in southcentral
Alaska, 1980-91.
Fig. 9. Mortality of radio-collared adult moose monitored with
aircraft during 3 seasons in lower Susitna Valley in southcentral
Alaska, 1980-91.
Fig. 10. Cause of death of radio-collared adult moose monitored
with aircraft in lower Susitna Valley, southcentral Alaska, 1980-
91. WK = winter kill, TK = train kill, AC = accidents, IH =
illegal harvest, DP = defense of life or property, and OT =
other.
196
LEGEND
-*-= Study area
13E = Game Management Subunit
~=Railroad
• • • =Roadway
• = Weather stations
WA =Wasilla
WI =Willow
TA =Talkeetna
SK = Skwentna
/
/
/
.... = Focal locations
AC = Alexander Creek
SA = Susitna River
TM = Talkeetna Mountains
YS = Yentna and Skwentna Rivers
197
~~~. i
100
0 80 w a:
~
z 60 0
~
w
C/)
0 40 0
~
d z 20
0
IE] FEMALES
ISJ MALES
80-81 81-82 82-83 83-84 84-85 85-86 86-87 87-88 88-89 89-90 90-91
CALENDAR YEAR
(16 MAY-15 MAY)
198
40
30
w
(/)
0
020
::2
10
0
40
30
w
(/)
0
020
~
10
0
(A) FEMALES
D CENSORED
ITlJ DEATHS
Q-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 1 Q-11
(B) MALES
0 CENSORED
[J DEATHS
0-1 1-2 2-3 3-4 4-5 5-6 6-7 7-8 8-9 9-10 1 Q-11
NO. YEARS MONITORED
199
1,000
800
(./) w
X u.. 600
6
0 < a: 400
0 z
200
0
100
(./) 80 w
X u::
6
0 60
~
I-< z 40
w w
(./)
';!. 20
0
MONITORING EFFORT
FEMALES •
MALES -,._
.... ...-~ . . --~ ' .. .... .... .,. ' "' .. _ 4'"' ... _ ~ _ .. ' •
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR EMY
FEMALES • MALES -.. _
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR EMY
MONTH
200
300-(A) SNOWPACK DEPTH
~ ~ 250 • (2] WASILLA
:I: D WILLOW
f-a.. [ill TALKEETNA
~ 200-~
150
• IQj SKWE~ CHE:T HEIGHT Of ADULT MOOSE
~ 100------------------------------~~ ----------------------
~ :: H ~ ::: ~ 50 -r " I r I " ",,_',,_·_:,,_'.-,_~ v c:1 E ~::: v:::: v v-v
0
80-81 81-82 82-83 83-84 84-85 85-86 86-87 87-88 88-89 89-90
6 -(B) DURATION AND EXTENT OF DEEP SNOW
tLj WASILLA ~ 5-(.) D WILLOW
0
~
A 4 •
0
CJ)
~ 3-
CJ)
:I:
f-z 0 2.
~
0 z 1 •
l]]l TALKEETNA
~SKWENTNA
0 I I I 1 1 I I
80-81 81-82 82-83 83-84 84-85 85-86 86-87 87-88 88-89 89-90
WINTER SEASON (OCT-APR)
201
300
0
OCT
TK 1984-85
•••••
SK 1984-85
•••••
TK 1989-90 -·-SK 1989-90
-·-
, • , \
, \
, , , ,
}\
\
\
\ \
, . ,
, -~ ... _J
' \ ' \ ' \ , ~ ... . '•·.. " '-~ ,' , ~
. . . ....... , . . . ·. ·... ·~ · ..
• • •• , ... ~ ... ·. ·. ·.... .
, ~I##••• • , ., ..
J ....
A '· _, --, .. r.·· .· --"" ... . . . .
NOV DEC JAN
MONTH
FEB
202
MAR APR
0.9
_J
<( > 0.8
> a:
:::>
(J)
0.7
0.6
0.5
0.8
_J
<( > 0.6
> a:
:::>
(J)
0.4
0.2
0
(A) FEMALES
.... ..,.-.-:::() .... -0·· ... 0 •. ···&.::: 8::.... . . ···E>·
o .. ···Q. ••• ·o..... a .... o ·····Q
+95%CL
····0····
AU LMY -DEC & NORMAL WINTERS • -95%CL
····~····
+95%CL
•••• El• •••
SEVERE WINTERS
I
-95% CL
•••• El· •••
·~
"f]
"•fi!
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR EMY
(8) MALES
"•1:)
......... ·0. .••• "0• •••• 0 .•.•• &...: .. 0 ••••• 0..
-o. ~: .. -o ••... 0 .... ·0
+95%CL
• • • ·E>· •••
AU LMY-DEC & NORMAL WINTERS •
-95%CL
• • • ·El· •••
+95%CL
•••• El· •••
SEVERE WINTERS •
-95%CL
•••• El· •••
·. · . ··~:O·····Q-····0
·.
• [J •
• .c
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR EMY
CALENDAR PERIOD
203
F\G-.. '1
30 (A) 1960-91 (ALL YEARS)
25 . f~ ~:~s
~ 20.
~ a: 15.
0
::::!:
rf. 10.
5.
0
50
40•
10.
0
n m • •
17
v
v
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR E'-IY
(B) 1984-85 AND 1989-90 (DEEP SNOW YEARS)
~ .. :n rn. ~::1 m. Fl
I I I I I
vii
v::
v:j:
v::: v
,, n. v: ltl
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR E'-IY
30 • (C) 1960-84, 1985-89, AND 1990-91 (NORMAL SNOW YEARS)
25-l~ ;~s I
~ 20•
:::i
~ a: 15.
0
::::!:
~ 10.
5•
v
v
v
v
v ··:.
:·::
.·
~ I I 1 fl ! I
0 L-~~~~.~~.~~~~~.~~~~~~~~._~.
LMY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR EMY
CAlENDAR PERIOD
204
70
60
50
~
~40
a::
0 ::::E30
?f.
20
10
0
0
(A) 1980-91 (All YEARS)
~~=sl
18 MAY·31 AUG 1 SEP-31 DEC 1 JAN·15MAY
(SUMMER) (AUTUMN) (WINTER)
(B) 1984-85 AND 1989-90 (DEEP SNOW YEARS)
18MAY·31 AUG
(SUMMER)
1 SEP-31 DEC
(AUTUMN)
1 JAN·15MAY
(WINTER)
(C) 1980-84, 1985-89, AND 1990-91 (NORMAL SNOW YEARS)
~ ~
16 MA.Y-31 AUG
(SUMMER)
1 SEP-31 DEC
(AUTUMN)
1 JAN·15MAY
(WINTER)
205
F\6. to.
WI( TK AC IH DP OT
(B) 1984-85 AND 1 ~ (DEEP SNOW YEARS)
80 ~~=sl
60
20
0
WI( TK AC IH DP OT
50 (C) 1980-84. 1~ 1990-91 (NORMAL SNOW YEARS)
~ 40
10
WI( TK AC IH OP OT
CAUSE OF MORTAUTY
206
MODAFFERI
Table 1. Fate of 138 adult female radio-collared moose in lower susitna Valley, in
southcentral Alaska, 1979-91.
Yentna
Susitna Talkeetna Alexander Skwentna
Fate River Mountains Creek Rivers All
Captured 54 50 17 17 138
Death at capture 3 5 2 0 10 r--
0
Monitored 51 45 15 17 128 N
Censored 13 0 1 1 15
Lost signal contact 9 0 1 0 10
Shed collar 4 0 0 1 5
Deaths 34 19 6 7 66
Cause of death verified 10 3 1 1 15
Train kill 7 1 0 0 8
Hunter harvest 2 1 1 1 5
Illegal harvest 0 1 0 0 1
MODAFFERI
Table 1. Continued.
Yentna
Susitna Talkeetna Alexander Skwentna
Fate River Mountains Creek · Rivers All
Defense of life or property 1 0 0 0 1
Cause of death unverifieda 24 16 5 6 51 00
0
killb N
Winter 14 5 3 4 26
Accident/injury 6 3 0 0 9
Illegal harvest 1 3 0 0 4
Otherc 3 5 2 2 12
Survivors 4 26 8 9 47
MODAFFERI
Table 1. Continued.
Yentna
susitna Talkeetna Alexander Skwentna
Fate River Mountains Creek Rivers All
a Cause of death determined by circumstances and evidence at site of radio-fix and/or
radio-collar.
b Winter kill = no evidence of predation, accident or illegal kill and substrate covered
with snow.
c other = circumstantial evidence insufficient to assign to a classification.
0"1
0
N
MODAFFERI
Table 2. Fate of 66 adult male radio-collared moose in lower Susitna Valley, in
southcentral Alaska, 1979-91.
Yentna
Susitna Talkeetna Alexander Skwentna
Fate River Mountains Creek Rivers All
Captured 23 27 3 13 66
Death at capture 2 2 0 1 5 0
N
Monitored 21 25 3 12 61
Censored 4 2 0 2 8
Lost signal contact 1 2 0 0 3
Shed collar 3 0 0 2 5
Deaths 17 19 2 9 47
Cause of death verified 12 11 1 3 27
Train kill 2 0 0 0 2
Hunter harvest 10 10 1 3 24
Illegal harvest 0 1 0 0 1
MODAFFERI
Table 2. Continued.
Fate
Defense of life or property
Cause of death unverifieda
Winter killb
Accident/injury
Illegal harvest
Otherc
survivors
Susitna
River
0
5
2
0
1
2
0
Talkeetna
Mountains
0
8
2
1
0
5
4
Alexander
Creek
0
1
0
0
0
1
1
Yentna
Skwentna
Rivers
0
6
5
0
0
1
1
All
0
20
9
('.I
1
1
9
6
MODAFFERI
Table 2. Continued.
Yentna
Susitna Talkeetna Alexander Skwentna
Fate River Mountains Creek Rivers All
a Cause of death determined by circumstances and evidence at site of radio-fix and/or
radio-collar.
b Winter kill = no evidence of predation, accident or illegal kill and substrate covered
with snow.
c Other = circumstantial evidence insufficient to assign to a classification.
MODAFFERI
Table 3. Kaplan-Meier survival estimates for radio-collared adult female and male moose
monitored with aircraft in lower Susitna Valley, southcentral Alaska 1980-91.
Year
Sex (16 May-15 May) Season a
Female 1980-89, 1990-91 Annual
1989-90 Annual
1980-91 Summer
1980-91 Autumn
1980-84, 1985-89 Winter-N
1984-85, 1989-90 Winter-s
Male 1980-89, 1990-91 Annual
1989-90 Annual
1980-91 Summer
1980-91 Autumn
1980-89 Winter-N
1989-90 Winter-S
No.
monitoredb
534
82
489
486
421
111
154
20
117
128
117
13
Survival
estimatec
0.9174
0.6499
0.9795
0.9765
0.9671
0.7394
0.8412
0.2865
0.9914
0.8879
0.9366
0.3333
95% CI
0.8928-0.9420
0.5483-0.7516
0.9668-0.9921
0.9628-0.9902
0.9501-0.9841
0.6605-0.8183
0.7782-0.9041
0.0928-0.4801
0 0 9745-1.0083
0.8235-0.9522
0.8922-0.9811
0.1156-0.5511
(")
N
MODAFFERI
Table 3. Continued.
Year
Sex (16 May-15 May) Season a
No.
monitoredb
Survival
estimatec 95% CI
a Annual = 16 May-15 May; Summer = 16 May-31 Aug; Autumn = 1 Sep-31 Dec; and winter = 1
Jan-15 May. N = winter(s) with normal accumulations of snow. S = winter(s) with deep
accumulations of snow.
b Includes individuals added in staggered entry but does not include moose that died
from problems with capture.
c Pollock et al., 1989.
Alaska's Game Management Units
OF
10
II . • • .. , ,·
The Alaska Department of Fish and Game administers all programs and activities free from discrimination
based on race, color, national origin, age, sex, religion, marital status, pregnancy, parenthood, or disability.
The department administers all programs and activities in compliance with Title VI of the Civil Rights Act
of 1964, Section 504 of the Rehabilitation Act of 1973, Title II of the Americans with Disabilities Act of
1990, the Age Discrimination Act of 1975, and Title IX of the Education Amendments of 1972.
If you believe you have been discriminated against in any program, activity, or facility, or if you desire
further information please write to ADF&G, P.O. Box 25526, Juneau, AK 99802-5526; U.S. Fish and
Wildlife Service, 4040 N. Fairfax Drive, Suite 300 Webb, Arlington, VA 22203 or O.E.O., U.S.
Department of the Interior, Washington DC 20240.
For information on alternative formats for this and other department publications, please contact the
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