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Page 1
MOVEMENT AND MORTALITY OF WHITE-TAILED DEER
IN SOUTHWEST MINNESOTA
BY
TODD J. BRINKMAN
A thesis submitted in partial fulfillment of the requirements for the
Master of Science
Wildlife Science
South Dakota State University
2003
Page 2
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MOVEMENT AND MORTALITY OF WHITE-TAILED DEER
IN SOUTHWEST MINNESOTA
This thesis is approved as a creditable and independent investigation by a
candidate for the Master of Science degree and is acceptable for meeting the thesis
requirements for this degree. Acceptance of this thesis does not imply that conclusions
reached by the candidate are necessarily the conclusions of the major department.
___________________________________
Dr. Jonathan A. Jenks
Date
Thesis Advisor
___________________________________
Dr. Charles G. Scalet
Date
Head, Department of Wildlife
and Fisheries Sciences
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ACKNOWLEDGEMENTS
I would like to thank my major co-advisors Dr. Jonathan A. Jenks and Dr.
Christopher S. DePerno for their constant support, guidance, and encouragement. I thank
you guys for granting me the freedom to think on my own, and providing me with
direction when things got complicated. Jon (The Old Gray Panther) and Chris, I can’t
think of a time you guys weren’t there for me. I respect you both a great deal on a
personal and professional level.
I thank Dr. Lester Flake for serving as a department representative on my
advisory committee and for his helpful comments and suggestions. Thanks to Dr.
Deanna Gilkerson for serving as my graduate faculty representative.
In no way, what so ever, was this study accomplished by the work of few. To you
all, I owe a great debt of gratitude. I thank R. Barrett, D. Carpenter, L. Cornicelli, D.
Drake, J. Erb, B. Haroldson, K. Haroldson, L. Holler, R. Janni, R. Kimmel, D.
Kitzberger, T. Klinkner, C. Kopplin, R. Kuecker, J. Longieliere, B. Osborn, D. Shultz, J.
Smith, T. Symens, D. Thompson, R. Wersal, and T. Zimmerman. So many people
contributed their time and effort; I sincerely apologize if anyone was missed.
Special thanks goes to all the landowners in the Lake Benton, Walnut Grove, and
Redwood Falls area who granted permission to access their land during deer capture
operations. In addition, thank you Pete Bauman and The Nature Conservancy crew for
your assistance.
I was very fortunate to have had the opportunity to work with three intelligent,
hard-working, and reliable technicians; Jaret Sievers, Chris Swanson, and Abbie Vander
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iv
Lugt. Simply put, the objectives of this study would not have been met without your
assistance.
My fellow graduate students, I thank all those that volunteered their time during
deer capture. I especially appreciate the intriguing conversations, stress alleviating
evenings, constant laughs, and high quality hunting and fishing expeditions we shared.
Good times!
Lastly, and most importantly, I thank my family. Mom, Shawn, and Mike, words
cannot explain. To my late father, whatever accomplishments that may come my way, no
matter how humble, they are because of you.
Funding for this study was provided by Bend of the River Chapter of Minnesota
Deer Hunters Association, Bluffland Whitetails Association, Cottonwood County Game
& Fish League, Des Moines Valley Chapter of Minnesota Deer Hunters Association,
Minnesota Bowhunters, Inc., Minnesota Deer Hunters Association, Minnesota
Department of Natural Resources, Minnesota State Archery Association I, NorthCountry
Bowhunters Chapter of Safari Club International, Rum River Chapter of Minnesota Deer
Hunters Association, South Dakota State University, South Metro Chapter of Minnesota
Deer Hunters Association, Whitetail Institute of North America.
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ABSTRACT
MOVEMENT AND MORTALITY OF WHITE-TAILED DEER
IN SOUTHWEST MINNESOTA
Todd J. Brinkman
May 2003
Knowledge of survival rates, causes of mortality, and information related to
movements are essential in understanding the population dynamics of white-tailed deer
(Odocoileus virginianus). In addition, proper deer management requires an
understanding of fawn mortality from birth to recruitment. No direct information is
available on population dynamics of deer in intensively cultivated areas in southwest
Minnesota. Primary objectives were to determine seasonal survival rates, seasonal
movement, and cause-specific mortality (e.g., hunting, vehicle collision, predation,
disease) of white-tailed deer in southwest Minnesota. Secondary objective was to
estimate seasonal home ranges. During 2001-02, radio telemetry was used to monitor the
movement and mortality of 61 adult (>1 year at capture), 16 fawn (∼8 months at capture),
and 39 neonate (<1 month at capture) white-tailed deer. From January 2001- August
2002, 6,867 deer locations were collected with a mean 95% error ellipse of 3.83 ha. Deer
had two seasonal home ranges, winter and summer. Mean home range size was 5.18 km
2
(n = 37, SE = 0.78) during winter and 2.27 km
2
(n = 93, SE = 0.18) during summer. Deer
occupied summer range for approximately 7 months, arriving from winter range during
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mid-April and departing to winter range during late November. Mean distance migrated
between seasonal ranges was 10.1 km (n = 95, SE = 0.70). Temperature and snow depth
were the primary factors influencing seasonal migration in southwest Minnesota.
Throughout the study, 14 female deer (10 adults, 4 fawns) died and the overall adult
survival rate was 0.75 (n = 77, SE = 0.05). In southwest Minnesota, survival of adult
female white-tailed deer was primarily dependant on human factors (i.e., hunting, vehicle
collisions). Natural causes of mortality such as predation and disease (14.2%) were
minor relative to human related causes (71.5%) for adult female deer. A total of six
neonate mortalities (predation = 4, disease = 1, vehicle collision = 1) occurred during the
study. Pooled summer neonate survival rate was 0.84 (n = 39, SE = 0.06). Adult female
and neonate white-tailed deer populations had high survival and minimal vulnerability to
death by natural causes in intensively cultivated areas. These data may be extrapolated to
white-tailed deer herds in other highly fragmented regions with intensive cultivation,
limited permanent cover, high hunter density, high road density, low predator density,
and large fluctuations in seasonal climate. These factors were significant influences on
movement and mortality of deer in southwest Minnesota. Data from this study will be
used to improve Minnesota’s farmland deer population model and assist wildlife
managers with decisions concerning white-tailed deer management. A landscape-level
approach is necessary to understand long-term trends and factors influencing deer
densities across farmland Minnesota.
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TABLE OF CONTENTS
Page
ACKNOWLEDGEMENTS............................................................................................... iii
ABSTRACT........................................................................................................................ v
LIST OF TABLES............................................................................................................. ix
LIST OF FIGURES........................................................................................................... xi
LIST OF APPENDICES.................................................................................................. xiv
CHAPTER 1 – INTRODUCTION..................................................................................... 1
BACKGROUND.................................................................................................... 2
JUSTIFICATION................................................................................................... 5
OBJECTIVES......................................................................................................... 7
CHAPTER 2 - STUDY AREA AND SITE SELECTION IN SOUTHWEST
MINNESOTA............................................................................................. 8
STUDY AREA....................................................................................................... 9
CHAPTER 3 - SURVIVAL OF FEMALE WHITE-TAILED DEER IN
SOUTHWEST MINNESOTA................................................................. 14
INTRODUCTION................................................................................................ 15
METHODS........................................................................................................... 18
RESULTS............................................................................................................. 20
DISCUSSION....................................................................................................... 26
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CHAPTER 4 - SURVIVAL OF WHITE-TAILED DEER NEONATES IN
SOUTHWEST MINNESOTA................................................................. 31
INTRODUCTION................................................................................................ 32
METHODS........................................................................................................... 32
RESULTS............................................................................................................. 35
DISCUSSION....................................................................................................... 38
CHAPTER 5 - MOVEMENT OF FEMALE WHITE-TAILED DEER IN
SOUTHWEST MINNESOTA.................................................................. 42
INTRODUCTION................................................................................................ 43
METHODS........................................................................................................... 44
RESULTS............................................................................................................. 46
DISCUSSION....................................................................................................... 49
CHAPTER 6 - MANAGEMENT IMPLICATIONS........................................................ 60
LITERATURE CITED..................................................................................................... 69
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LIST OF TABLES
Table
Page
1.
Percentages of major land use/cover types of deer permit areas in
southwest Minnesota (Minnesota Department of Natural Resources
2000)..................................................................................................................... 88
2.
Capture data by study site for female white-tailed deer in southwest
Minnesota, January 2001...................................................................................... 89
3.
Capture data by study site for female white-tailed deer in southwest
Minnesota, January 2002...................................................................................... 90
4.
Cause-specific, seasonal mortality for radiocollared female white-tailed
deer in southwest Minnesota, 2001-02................................................................. 91
5.
Annual survival rates by study site for radiocollared female white-tailed
deer in southwest Minnesota, 2001-02................................................................. 92
6.
Overall survival rates by study site for radiocollared female white-tailed
deer in southwest Minnesota, 2001-02................................................................. 93
7.
Survival rates by season for radiocollared female white-tailed deer
in southwest Minnesota, 2001-02......................................................................... 94
8.
Annual and overall survival rates by age for radiocollared female
white-tailed deer in southwest Minnesota, 2001-02............................................. 95
9.
Capture data for radiocollared white-tailed deer neonates in southwest
Minnesota, spring 2001-02................................................................................... 96
10.
Cause-specific, monthly mortality for radiocollared white-tailed deer
neonates in southwest Minnesota, summer 2001-02............................................ 97
11.
Monthly survival rates for radiocollared white-tailed deer neonates in
southwest Minnesota, 2001-02............................................................................. 98
12.
Monthly survival rates by sex for radiocollared white-tailed deer neonates in
southwest Minnesota, 2001-02............................................................................. 99
13.
Mean seasonal migration distance by study site for radiocollared
white-tailed deer in southwest Minnesota, 2001-02........................................... 100
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x
14.
Seasonal home range size by study site for radiocollared female
white-tailed deer in southwest Minnesota, 2001-02........................................... 101
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LIST OF FIGURES
Figure
Page
1.
Pre-settlement vegetation zones of Minnesota (Rosendahl and Butters 1928)... 102
2.
Farmland, Forest, and Metro Zones of Minnesota (DePerno et al. 1999).......... 103
3.
Southwest Minnesota white-tailed deer permit areas (PAs), 2000.................... 104
4.
Study area and white-tailed deer capture locations in southwest
Minnesota, 2001-02............................................................................................ 105
5.
Cluster analysis hierarchical tree diagram for deer permit areas in
southwest Minnesota. The average distance between clusters [x axis]
is defined as “the average of all the dissimilarities between all possible
pairs of points such that one of each pair is in each cluster” (Johnson 1998).... 106
6.
Principal components analysis clusters of scores for deer permit areas in
southwest Minnesota, 2000................................................................................. 107
7.
Permit areas selected for white-tailed deer capture sites in southwest
Minnesota, 2001-02............................................................................................ 108
8.
White-tailed deer neonate study area and capture locations in southwest
Minnesota, 2001-02............................................................................................ 109
9.
Cause-specific mortality for radiocollared female white-tailed deer in
southwest Minnesota, 2001-02 (Deer that died from train collision
was included in vehicle mortalities)................................................................... 110
10.
Suspected felid (i.e., bobcat, cougar) killed 2.5-year old female
white-tailed deer in southwest Minnesota, October 2001................................... 111
11.
Monthly deer winter severity index (DWSI) for individual study sites in
southwest Minnesota, 2000-01 (One point accumulated for each day with
an ambient temperature ≤-7 C°, and an additional point accumulated for
each day with snow depths ≥35.0 cm; National Climatic Data Center 2002,
Climatology Working Group 2003).................................................................... 112
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12.
Monthly deer winter severity index (DWSI) for individual study sites in
southwest Minnesota, 2001-02 (One point accumulated for each day with
an ambient temperature ≤-7 C°, and an additional point accumulated for
each day with snow depths ≥35.0 cm; National Climatic Data Center 2002,
Climatology Working Group 2003).................................................................... 113
13.
Annual deer winter severity index (DWSI) for individual study sites in
southwest Minnesota, 2000-02 (One point accumulated for each day
between November and March with an ambient temperature ≤-7 C°, and
an additional point accumulated for each day with snow depths ≥35.0 cm;
National Climatic Data Center 2002, Climatology Working Group 2003)........ 114
14.
To determine the age for white-tailed deer neonates, the distance from
growth ring to hairline was measured (mm) on front hoof
(Haugen and Speak 1958)................................................................................... 115
15.
Cause-specific mortality for radiocollared white-tailed deer neonates in
southwest Minnesota, summer 2001-02............................................................. 116
16.
Dispersal distance and direction for radiocollared female white-tailed deer in
southwest Minnesota, 2001................................................................................. 117
17.
Migrations for radiocollared female white-tailed deer at Lake Benton
study site in southwest Minnesota, 2001-02 (Refer to Figure 4 for location
of study site in southwest Minnesota)................................................................. 118
18.
Migrations for radiocollared female white-tailed deer at Walnut Grove
study site in southwest Minnesota, 2001-02 (Refer to Figure 4 for location
of study site in southwest Minnesota)................................................................. 119
19.
Migrations for radiocollared female white-tailed deer at Redwood Falls
study site in southwest Minnesota, 2001-02 (Refer to Figure 4 for location
of study site in southwest Minnesota)................................................................. 120
20.
Fall migration events by study site for radiocollared female white-tailed
deer in southwest Minnesota, 2001. The Y-axis is shared by all three
variables (i.e., temperature [C°], snow depth [cm], migrating [%]).
A migration event represents the percentage of total individuals at each
study site with known departure dates from summer range............................... 121
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21.
Spring migration events by study site for radiocollared female white-tailed
deer in southwest Minnesota, 2001. The Y-axis is shared by all three
variables (i.e., temperature [C°], snow depth [cm], migrating [%]).
A migration event represents the percentage of total individuals at each
study site with known departure dates from winter range.................................. 122
22.
Spring migration events by study site for radiocollared female white-tailed
deer in southwest Minnesota, 2002. The Y-axis is shared by all three
variables (i.e., temperature [C°], snow depth [cm], migrating [%]).
A migration event represents the percentage of total individuals at each
study site with known departure dates from winter range.................................. 123
23.
Southwest Minnesota white-tailed deer permit areas and major roads, 2000.… 124
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LIST OF APPENDICES
Appendix
Page
A.
Capture data for radiocollared female white-tailed deer in southwest
Minnesota, January 2001……………………………………………..……….. 125
B.
Capture data for radiocollared female white-tailed deer in southwest
Minnesota, January 2002……………………………………………..……….. 127
C.
Mortality for radiocollared female white-tailed deer in southwest
Minnesota, 2001-02…………………………………………………………… 128
D.
Capture data for radiocollared white-tailed deer neonates in southwest
Minnesota, spring 2001....................................................................................... 129
E.
Capture data for radiocollared white-tailed deer neonates in southwest
Minnesota, spring 2002....................................................................................... 130
F.
Mortality for radiocollered white-tailed deer neonates in southwest
Minnesota, 2001-02............................................................................................ 131
G.
Movement for individual radiocollared female white-tailed deer in
southwest Minnesota, 2001................................................................................. 132
H.
Movement for individual radiocollared female white-tailed deer in
southwest Minnesota, 2002................................................................................. 134
Page 15
CHAPTER 1
INTRODUCTION
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2
BACKGROUND
Prior to settlement, Minnesota consisted of three primary vegetation zones; Forest
(coniferous forest), Transition (deciduous forest/grassland mix), and Prairie (tall grass
prairie; Rosendahl and Butters 1928; Fig. 1). Pre-settlement, white-tailed deer
(Odocoileus virgninianus) were concentrated in the Transition Zone and wooded river
valleys in the southwest portion of the Prairie Zone (Erickson et al. 1961). As primitive
forests of the north were logged, thick secondary growth consisting of shrubs and small
trees provided favorable deer habitat. By 1920, deer distribution increased in the Forest
Zone (Erickson et al. 1961), while Transition and Prairie Zone deer numbers declined.
During the late 1800s, land clearing, intensified farming, market hunting, and
unregulated harvest for subsistence extirpated deer in Transition and Prairie Zones. As a
result, in 1923, deer hunting was banned in southern Minnesota (Erickson et al. 1961;
Berner, A. H., unpublished data, Minnesota Department of Natural Resources). With
legal protection, deer began to repopulate the southern and western parts of Minnesota.
In the 1940’s, hunting seasons were periodically opened in what is now known as the
Farmland Zone (previously Prairie and Transition Zones). Eventually, with the
expansion of the twin cities area (Minneapolis/St. Paul), Minnesota was separated into
three wildlife zones; Forest, Farmland, and Metro (DePerno et al. 1999, Fig. 2). The
Minnesota legislature and the Department of Natural Resources (DNR) adopted a deer
management policy in 1974 that included the following set of deer management
objectives: manage the deer population by maintaining the breeding population at the
highest level the habitat and landowners will tolerate, allow maximum recreational
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opportunities tolerated by the deer population while minimizing landowner/hunter
conflicts, have standardized, consistent season frameworks. With these objectives in
place, by the late 1970's deer hunting occurred throughout Minnesota, and approximately
44% of annual harvest occurred in the Farmland Zone (Berner, A. H., unpublished data,
Minnesota Department of Natural Resources). Currently, 500,000 hunters pursue
white-tailed deer each year in Minnesota, harvest roughly 200,000 deer (Minnesota
Department of Natural Resources 2002), and approximately 60% of the harvest occurs in
the Farmland Zone (DePerno et al. 1999).
Deer populations in Minnesota are managed within 125 permit areas (PAs)
through the allocation of hunting permits for the firearms deer season. Each PA has
population goals based on carrying capacity and landowner tolerance (Lenarz and
McAninch 1994). For large-scale management purposes, PAs located within the
Farmland and Forest Zones are managed separately through the use of population
models. The farmland deer population model and wildlife manager recommendations are
used to estimate the number of anterless permits required to maintain the deer population
within a goal range for each PA within the Farmland Zone.
Output from the deer model is based on animal density, which is determined by
using four age/sex groups; adult males, adult females, fawn males, and fawn females.
Initial population size, population structure (i.e., age/sex ratio), harvest data, summer and
winter survival rates, reproduction data (i.e., pregnancy rate, fetuses per doe, fetus sex
ratio), registration rate, illegal kill, and crippling loss are incorporated into the model.
Reproduction data determines the number of individuals added, and hunting and
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non-hunting mortality determines number of deer removed. Hunting losses are calculated
each year from deer registered by hunters, which is mandatory in Minnesota. Thus far,
non-hunting mortality rates incorporated into the farmland model have been educated
guesses based on information collected from the literature.
Because of the difficulty of monitoring animals that travel long distances,
managers typically ignore dispersal, or assume that immigration and emigration are equal
(Johnson 1994, Rosenberry et al. 1999). Similarly, Minnesota’s farmland deer model
also makes the assumption that emigration/immigration does not occur between PAs
(DePerno et al. 1999). Although wildlife managers and research biologists in farmland
Minnesota know this assumption to be false, empirical data to determine amount of
movement that may be occurring across PA boundaries does not exist. Hence, educated
guesses based on the literature must be used if the effects of dispersal across PA
boundaries are to be incorporated into the model. Because movements (i.e., seasonal
migration, home range patterns, dispersal) of white-tailed deer vary greatly over their
geographic range (Marchinton and Hirth 1984, Demarais et al. 2000), educated guesses
based on data collected elsewhere are not a reliable option. Region-specific, sound,
empirical information is needed to effectively manage Minnesota’s white-tailed deer
populations.
Just as the rate of deer dispersal across PA boundaries is unknown, information is
absent on whether deer migrate seasonally across Minnesota PA boundaries. Previous
studies in the Northern Forest and Midwest Agricultural regions have estimated seasonal
migration distances from 6-38 km (Verme 1973, Hoskinson and Mech 1976, Nixon et al.
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5
2001, Sabine et al. 2002). Potentially in southwest Minnesota, a deer herd's summer
range may be in a different PA than their winter home range. In this hypothetical
situation, management strategies in one PA would influence adjacent PAs. To anticipate
these effects, information on movements (e.g., migration timing, distance, direction) must
be available.
JUSTIFICATION
Few wildlife species in North America are a more valuable public resource than
white-tailed deer (Conover 1997). In Minnesota, big game hunting expenditures by
residents exceeded $250 million in 1996 (United States Department of Interior, Fish and
Wildlife Service 1998). The vast majority of those expenditures came from deer hunters,
whom outnumber other big game hunters 36 to 1. Furthermore, there are intangible
values associated with deer that are difficult to quantify, including the sense of well-being
that people feel from knowing that deer are thriving in nature (Krutilla 1967).
Conversely, deer likely cause more economic damage than any other wildlife species in
North America (Fagerstone and Clay 1997). For example, in the 10 largest corn (Zea
mays) producing states, deer damage exceeded $30 million in 1993 (Wywialowski 1996).
Also, deer are rated the most problematic wildlife species by farm bureaus, state
agencies, and extension agents (Conover and Decker 1991). Additional problems
associated with high deer populations include increased disease transmission and vehicle
collisions. For instance, the Minnesota Department of Natural Resources (MNDNR)
estimated that 15,000 deer are killed by vehicles annually. Moreover, high deer numbers
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may negatively impact habitat for other wildlife species, such as songbirds (DeCalesta
1997, McShea and Rappole 2000).
The complexity of social and economic aspects of white-tailed deer management
creates a dilemma for resource agencies. Wildlife managers in Minnesota strive to
maintain deer populations at levels that meet hunter expectations, while minimizing
conflicts with landowners. Identifying and maintaining this balance is difficult without
reliable empirical information specific to deer in Minnesota.
Knowledge of survival rates, cause-specific mortality, and information related to
movements are particularly important in understanding population dynamics of deer
(Halls 1984, Nixon et al. 1991, DePerno et al. 2000). In addition, proper deer
management requires data on neonate mortality from birth to recruitment (Huegel et al.
1985a). When managing a harvestable population, region-specific data are necessary to
avoid overexploitation (Nelson and Mech 1986a, Van Deelen et al. 1997), and to develop
management strategies that will be accepted by divergent groups interested in the species
(Nixon et al. 2001).
As research has been compiled on white-tailed deer in the northern part of their
range, it has become apparent that survival rates fluctuate regionally and seasonally with
sex, age, and deer density (DelGiudice et al. 2002). Numerous studies have been
conducted on white-tailed deer in forested (Kohn and Mooty 1971, Nelson and Mech
1986a, Mooty et al. 1987, Fuller 1990, DelGiudice 1990, 1998, DelGiudice and
Mangipane 1998, Filipiak 1998, DelGiudice et al. 2002) and urban (Doerr et al. 2001,
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Grund et al. 2002) habitats in Minnesota. However, no direct information is available on
population dynamics of deer in intensively cultivated areas in southwest Minnesota.
OBJECTIVES
To improve the accuracy and precision of Minnesota’s farmland deer population
model and assist wildlife managers with decisions concerning white-tailed deer
management, the objectives of this study were to determine the movements and mortality
of white-tailed deer in southwest Minnesota. Primary objectives were to determine
seasonal survival rates, seasonal movement, and cause-specific mortality (e.g., hunting,
vehicle collision, predation, disease). Secondary objective was to estimate seasonal home
ranges. More specifically, for female deer captured as adults (>1 year) and fawns (8
months), objectives were to calculate seasonal, annual, and overall (i.e., 20 month study)
survival rates, and to determine seasonal movements (i.e., migration, dispersal) and home
ranges. For male and female deer captured as neonates (<1 month), objectives were to
calculate monthly and summer (June-August) survival rates. Cause-specific mortality
was determined for adults, fawns, and neonates. As a pilot study, we attempted to
capture and radiocollar coyotes (Canis latrans) with the objective of estimating predator
density and determining predator movement within the neonate study area.
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CHAPTER 2
STUDY AREA AND SITE SELECTION
IN SOUTHWEST MINNESOTA
Page 23
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STUDY AREA
This study was conducted in a 34,627 km
2
area of southwest Minnesota (43º 29’
N to 45º 16’ N – 093º 38’ W to 096º 27’ W) containing 20 counties and 24 deer PAs (Fig.
3). Southwest Minnesota is composed of a highly fragmented landscape dominated by
cultivated land (85.6%, Table 1). For this study, cultivated land was defined as “areas
under intensive cropping or rotation, including fallow fields and fields seeded for forage
or cover crops that exhibit linear or other patterns associated with current tillage”
(Minnesota Department of Natural Resources 2000). According to the Minnesota
Agricultural Statistics Service (2002), corn and soybeans (Glycine max) consist of 96.0%
of the harvested cropland in the 20 county region of southwest Minnesota (Fig. 4), with
the other major harvested crops being hay (3.0%; e.g., alfalfa [Medicago sativa]), wheat
(Triticum aestivum; 0.7%), and oats (Avena sativa; 0.3%).
Grassland (6.5%), forest (3.0%), permanent bodies of water (1.6%), and wetlands
(0.8%) are the other major land use/cover types (Table 1, Minnesota Department of
Natural Resources 2000). In nature preserves, isolated pockets, and poor agricultural
sites (e.g., steep slopes, poorly drained sites, infertile soils), native tallgrass prairie exists,
commonly consisting of big bluestem (Andropogon geradii), little bluestem
(Schizachyrium scoparium), Indiangrass (Sorghastrum nutans), switchgrass (Panicum
virgatum), tall dropseed (Sporobolus asper), and sideoats grama (Bouteloua
curtipendula) (Johnson and Larson 1999). In forested areas, dominant overstory
vegetation includes eastern cottonwood (Populus deltoides), green ash (Fraxinus
pennsylvanica), basswood (Tilia americana), and bur oak (Quercus macrocarpa)
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(Minnesota Association of Soil and Water Conservation Districts Forestry Committee
1986).
Southwest Minnesota is characterized by a flat to rolling topography with
elevation ranging from 229 to 608 m above sea level (Albert 1995). The region has a
sub-humid continental climate, with great differences between winter and summer
temperature. At Marshall, Minnesota, which lies roughly in the center of the study area,
average temperatures (1971-2000) equal –9.8 C° in January and 23.1 C° in July, and
average annual precipitation and snowfall is 65.4 cm and 105.2 cm, respectively
(Midwest Regional Climate Center 2002).
In southwest Minnesota, the white-tailed deer is the only free-ranging cervid.
Coyotes, bobcats (Lynx rufus), and dogs (Canis familiaris) are the primary predators in
this region. Sightings of wolf (Canis lupus) and mountain lion (Puma concolor) have
been reported in the region, but occurrences are rare.
To select individual sites for deer capture within the study area, ArcView (ESRI,
Redlands, CA) was used to calculate the percentage of cultivated land, grassland, and
forest cover in each PA in southwest Minnesota. The MNDNR Minnesota Land
Use/Cover data set (Minnesota Department of Natural Resources 2000) was used and
cluster analysis was performed to classify permit areas into distinct groups based on land
cover (Johnson 1998). A permit area was chosen from each cluster to appropriately
represent major habitat types present throughout the southwest study. The objective of
study site selection was to maximize the variation of the habitats throughout the
southwest Minnesota region. Also, logistics such as travel time between sites were
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considered when selecting study sites. A fixed-wing aircraft was used to locate sufficient
winter deer concentrations to meet study sample goals within each study site. The
white-tailed deer neonate survival study was conducted at the closest study site to South
Dakota State University (SDSU), Brookings, South Dakota. Analyses were performed
using SAS (1999) and SYSTAT (Wilkinson 1990).
Using cluster analysis, a hierarchical cluster tree was constructed to identify how
PAs are connected and the order in which they are assigned to clusters (Fig. 5). The
average distance between clusters (x axis) was defined as “the average of all the
dissimilarities between all possible pairs of points such that one of each pair is in each
cluster” (Johnson 1998). We determined the cluster tree had three major branches
containing one large and two small clusters (Fig. 5). Next, a principal components
analysis was performed to plot scores and “fine tune” the clustering process. Three
distinct clusters were identified (Fig. 6). Taking into consideration logistics (e.g., travel
time between sites) and assignment on the cluster tree, permit areas 435, 450, and 451
were chosen from clusters to capture the greatest habitat variance across southwest
Minnesota (Fig. 7). Permit area 435 contained the second highest percentage of forest
land cover, PA 450 contained the second highest percentage of cultivated land cover, and
PA 451 contained the second highest percentage of grassland/shrub in southwest
Minnesota (Table 1). Sufficient deer winter concentrations were located near the cities of
Redwood Falls (PA 435), Walnut Grove (PA 450), and Lake Benton (PA 451) Minnesota
(Fig. 4). Because of the minimal distance (∼45 km) between the Lake Benton study site
and SDSU, this site was chosen to conduct the neonate survival study (Fig. 8).
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12
Permit areas contained distinct differences in cultivated, forest, and grassland
cover (Table 1). Permit area 435 was selected because of the high percentage of forest
cover (7.3%, Table 1). This was due to the location of the Minnesota River Valley, the
major river system in southwest Minnesota, which runs directly through PA 435.
Overstory vegetation within the river valley was similar to that located elsewhere in
southwest Minnesota, but was more concentrated and contained dense patches of willow
(Salix sp.) in the river valley bottom (Albert 1995). Just above the river valley, the land
cover was dominated by cultivated land (82.3%, Table 1). Deer capture in this area
occurred in the Minnesota River Valley near the city of Redwood Falls (Redwood Falls
study site, Fig. 4).
PA 450 was selected because of the high percentage of cultivated land and
intensive corn and soybean agriculture. PA 450 was almost entirely cultivated land
(93.4%) with small areas of grassland (2.4%) and forest cover (1.8%, Table 1). Deer
capture occurred near the city of Walnut Grove (Walnut Grove study site, Fig. 4).
PA 451 was chosen because of the higher than average, relative to the rest of the
study area, percentage of grassland (14.6%) and relatively low percentage of cultivated
land (81.1%, Table 1). Deer capture occurred near the city of Lake Benton (Lake Benton
study site; Fig. 4).
Because of the Lake Benton study site’s proximity to SDSU, this site was chosen
to conduct the white-tailed deer neonate survival study (Fig. 8). During deer parturition
(late May, early June) in Minnesota, crops (e.g., corn, soybeans) are beginning to emerge
during early June (Minnesota Agricultural Statistics Service 2002). Less than 20% of the
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13
land provides suitable cover for fawning, and habitat available for fawning was
composed of small patches of grassland and tree groves. Tree groves were primarily
shelterbelts and abandoned farmyards with ground vegetation consisting mainly of
smooth brome (Bromus inermis). Shelterbelts consisted of spruce (Picea sp.), cedar
(Juniperus spp.), Douglas fir (Pseudotsuga menziesii) and silver maple (Acer
saccharinum) (Minnesota Association of Soil and Water Conservation Districts Forestry
Committee 1986).
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14
CHAPTER 3
SURVIVAL OF FEMALE WHITE-TAILED DEER IN SOUTHWEST
MINNESOTA
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15
INTRODUCTION
Knowledge of survival and cause-specific mortality is crucial to understanding
white-tailed deer population dynamics. Numerous radiotelemetry studies have
demonstrated that mortality differs regionally and seasonally with sex, age, and density of
deer (Gavin et al. 1984, Dusek et al. 1992, Whitlaw et al. 1998, DePerno et al. 2000,
DelGiudice et. al. 2002). Also, influence of human-related factors (e.g., legal harvest,
poaching, vehicle collisions), weather conditions (e.g., winter severity), and predators on
deer populations may vary (Nelson and Mech 1986b, Fuller 1990). With numerous
fluctuating variables impacting deer dynamics, spatial and temporal-specific mortality
estimates are essential for proper white-tailed deer management. Increased use of
regional population models (Fuller 1990) designed to predict temporal changes in deer
populations has stressed the importance of sound empirical data (Grund 2001). Without
such data, overexploitation of hunted populations is possible (Hoskinson and Mech 1976,
Nelson and Mech 1981, 1986a, Fuller 1989, Delgiudice 1998).
Survival and cause-specific mortality of white-tailed deer has been well
documented in forested areas of Minnesota (Hoskinson and Mech 1976, Nelson and
Mech 1984, Fuller 1990, DelGiudice et al. 2002), but minimal information has been
collected in agricultural areas of Minnesota. The only documented study on adult
white-tailed deer mortality in farmland Minnesota was reported by Simon (1986). No
direct information exists on survival and cause-specific mortality in intensively cultivated
areas (>80% cultivated land cover) of Minnesota.
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Deer in southwest Minnesota occupy a much different environment compared to
other areas of Minnesota. Deer in highly fragmented, intensively cultivated areas of the
Midwest have developed unique behaviors to adapt to the landscape (Sparrowe and
Springer 1970, Gladfelter 1984, Nixon et al. 2001). Farmland deer have, with minimal
trouble, incorporated the annual growth and harvest of corn into habitat use, which
provides temporary unlimited diurnal cover (Nixon et al. 1991). In nutrition-rich
agricultural landscapes, food availability is often not a limiting resource, whereas forest
cover may be limiting (Dusek et al. 1989). Farmland movements, such as seasonal
dispersal, developed in response to agricultural landscapes, with many deer moving great
distances to seek out habitat with suitable forest cover. Also, deer of the Agricultural
Midwest Region experience less severe winter weather conditions than those of the
northern forests (Gladfelter 1984, Blouch 1984). Compared to northern Minnesota,
southwest Minnesota has a more intensive road network, lower deer density, less
permanent cover, and less severe winter weather conditions (Grund 2001, DelGiudice et
al. 2002). Furthermore, unlike southwest Minnesota, northern Minnesota has an
established wolf population. Numerous studies have determined that wolves can
significantly influence deer survival, especially during the winter (Hoskinson and Mech
1976, Nelson and Mech 1981, 1986b, Fuller 1989, Delgiudice 1998). Because of these
differences, northern Minnesota survival and cause-specific mortality information for
deer cannot be extrapolated to southwest Minnesota. To improve the accuracy and
precision of Minnesota’s farmland deer population model and assist wildlife managers
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17
with decisions concerning white-tailed deer management, the objectives of this study
were to determine survival and cause-specific mortality of deer in southwest Minnesota.
A variety of techniques have been used to capture white-tailed deer including
Stephenson box traps (Rongstad and McCabe 1984), Clover traps (Clover 1954),
drive-netting (Beasom et al. 1980), cannon (rocket) nets (Hawkins et al. 1968), drop nets
(Ramsey 1968), dart guns (Kilpatrick et al. 1997), and helicopter net-guns (Barrett et al.
1982). Numerous studies have compared and evaluated these capture methods (Hawkins
et al. 1967, White and Bartmann 1994, Beringer et al 1996, DelGiudice et al. 2001,
Haulton et al. 2001). Each technique has advantages and shortfalls. Therefore, the
capture method chosen should be sit and study specific.
The goal of the southwest Minnesota adult white-tailed deer capture was to
radiocollar 20 animals in each of three study sites (i.e., total sample size = 60
individuals). Ideally, all animals were to be radiocollared at approximately the same time
so that the starting period for survival and movement analyses was equal among study
sites. Due to logistics (e.g., set-up time, travel time between sites), ground capture
methods (e.g., Clover traps, Stephanson box traps, cannon nets) was not a viable option if
deer were to be captured simultaneously across study sites. Therefore, to meet study
needs, capture by use of net-guns deployed from helicopters was most appropriate for this
study.
Advantages of helicopter net-guns include quick and accurate deployment which
results in short capture and processing times (Firchow et al. 1986, Kock et al. 1987).
During a mule deer (Odocoileus hemionus) fawn study, White and Bartmann (1994)
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18
reported that net-gunning required 98% less person-days than drop netting. Along with
shorter chase-time, there is selectivity potential with net-gunning (Krausman et al. 1985).
Because we were radiocollaring only female deer, selectivity was particularly important
for this study.
Helicopter net-gunning has been identified as an efficient means of capture
without sacrificing the welfare of the animal. Kock et al. (1987) noted that use of
net-guns resulted in the lowest percentage of capture stress, lowest risk of capture
myopothy, and lowest risk of overall mortality compared to three other capture methods
used in a bighorn sheep (Ovis canadensis) study. Furthermore, in a mule deer fawn
study, net-gunning was reported to be a safer capture method than drop nets (White and
Bartmann 1994). In addition, helicopter net-gunning can be conducted without chemical
immobilization, thus avoiding the negative effects of drugs (Amstrup and Segerstrom
1981).
METHODS
Female white-tailed deer were netted from a helicopter at winter deer
concentrations near the cities of Lake Benton, Walnut Grove, and Redwood Falls (Fig.
4). Upon capture, a crewmember exited the helicopter and restrained, hobbled, and
blindfolded the deer to minimize stress. Deer were transported to a processing site where
blood samples were collected by venipuncture of the jugular vein for disease evaluation
and physical condition of deer was assessed. Rectal temperature was continuously
monitored as an indicator of stress. If temperature exceeded 40 C°, snow or bags of ice
were packed along the underside of deer to stabilize or reduce body temperature. If the
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19
temperature did not stabilize or decline, deer were released. Captured deer were aged as
fawn (8 months) or adult (>1 year), measured (chest and neck circumference [cm]),
ear-tagged, and administered an intramuscular injection of a broad-spectrum antibiotic.
Radiocollars (Advanced Telemetry System, Isanti, Minnesota) equipped with activity and
mortality sensors were placed around the neck of deer, and were set to switch to mortality
mode after the transmitter had remained still for 8 hours. After processing, hobbles and
blindfolds were removed and deer were released. Total handling time and distance from
the capture location to the processing site were recorded for each deer. All methods used
in this research were approved by the Institutional Animal Care and Use Committee at
SDSU (Approval number 00-A038).
Individual, radiocollared adult deer were monitored for mortality 2-3 times per
week using a vehicle mounted “null-peak” antenna system (Brinkman et al. 2002). Cause
of death was determined from field necropsy and ancillary evidence at the deer location
(White et al. 1987). If cause of death could not be determined in the field, carcasses were
transported to the SDSU Animal Disease Research Diagnostic Laboratory (ADRDL) for
further investigation. To verify age of each deer, lower incisors of adults were collected
post-mortem. Capture-related mortalities were censored from survival analysis. We
assumed mortality occurring <26 days post-capture was related to capture and handling
of deer (Beringer et al. 1996). To coincide with the Minnesota farmland deer population
model (Ch. 1), seasonal survival rates were separated into three time periods; pre-hunt
(1 May - 31 August), hunting (1 September - 31 December), and post-hunt
(1 January - 31 April). Hunting season was further divided into two categories; hunting
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20
and hunting-all. Hunting included only legal harvest mortalities in the survival rate, and
hunting-all included all mortalities (e.g., vehicle, predation) occurring during that time
period.
Survival rates of white-tailed deer were calculated using the Kaplan-Meier
procedure (Kaplan and Meier 1958) modified for a staggered entry design (Pollock et al
1989). Annual and overall (20-month) survival rates were calculated by age (adult,
fawn), season, and study site, and compared using Program CONTRAST (Hines and
Sauer 1989). Statistical analyses were performed using SYSTAT (Wilkinson 1990).
Alpha was set at P ≤ 0.05, and a Bonferroni correction factor was used to maintain the
experiment-wide error rate when multiple Chi-squared and t-tests were performed (Neu
et al. 1974).
RESULTS
During 22-24 January 2001, 58 female deer (44 adult, 14 fawn) were captured and
fitted with radiocollars (Table 2, Appendix A). To replace animals that died during the
first year, an additional 19 female deer (17 adult, 2 fawn) were captured and radiocollared
on 26 January 2002 (Table 3, Appendix B). A total of 28 deer was captured at Lake
Benton, 30 at Walnut Grove, and 19 at Redwood Falls study sites (Fig. 4).
Two capture related injuries occurred during helicopter net-gunning operations.
During 2001, an adult female broke the left front metacarpal bone. This injury was
discovered after release. After capture, the movements of this deer were consistent with
other radiocollared deer at this site. This animal died from a vehicle collision
approximately 6 weeks post-capture. Influence of the capture related injury on mortality
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21
was unknown, therefore, the individual was censored from the study. In 2002, an adult
female sustained a ruptured vertebrae and shattered pelvis (determined via necropsy)
during helicopter operations. This animal was euthanized at the processing site.
Total time spent handling each deer averaged 8.2 minutes (8.6 minutes in 2001
[Table 2, Appendix A], 6.8 minutes in 2002 [Table 3, Appendix B]), and varied between
4.0 - 15.0 minutes. Reduced handling time during 2002 capture operations was attributed
to a more experienced processing crew. Distance between capture location and
processing site averaged 1.7 km (1.6 km in 2001, 2.0 km in 2002), and ranged between
0.0 – 4.5 km. Rectal temperature ranged from 38.9 to 42.2 C° with a mean of 40.6 C° (n
= 77, SE = 0.08; 40.6 C° in 2001, 40.8 C° in 2002). Rectal temperatures were similar
between years (df = 1, t = -1.05, P = 0.2663), but differed (df = 1, t = -3.33, P = 0.0030)
between adults and fawns. Adult and fawn neck circumference averaged 43.7 and 35.2
cm, respectively, and ranged from 29.0 to 52.0 cm. Average chest circumference was
106.6 cm for adults and 86.9 cm for fawns and ranged from 122.0 to 77.0 cm.
Chest-girth measurements were used to predict live weight of captured deer. Equations
provided by Weckerly et al. (1987) were Ŷ = -15.97 + 0.08X for adult females and
Ŷ = -19.12 + 0.07X for fawns during the winter season. These equations indicated that
average live weight at capture for adults was 69.33 kg and 41.69 kg for fawns. These live
weight predictions indicate that deer were in excellent condition in southwest Minnesota
when compared to other populations (Kie et al. 1983, Verme and Ullrey 1984).
Blood samples were collected from 64 deer and screened for Epizootic
Hemorrhagic Disease (EHD), Bovine Tuberculosis (Micobacterium bovis), Bovine Viral
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Diarrhea (Pestivirus spp.), Infectious Bovine Rhinotracheitis, Anthrax (Bacillus
anthracis), Leptospirosis
(
Leptospira interrogans), Bovine Brucellosis (Brucella spp.),
Anaplasmosis (Anaplasma spp.), Toxoplasmosis (Toxoplasma gondii
)
, Lyme disease, and
Johne’s disease (Micobacterium paratuberculosis).
Seventeen deer died during the 20-month (January 2001-August 2002) time
period, and 14 were included in survival analyses (Table 4, Appendix C). Hunting was
the greatest cause of mortality, with six (42.9%) deer killed by firearms hunters (Fig. 9;
Table 7). In addition, 3 deer were killed by vehicle collisions (21.4%), one by train
collision (7.1%), one by predator (7.1%), one by disease (7.1%), and two mortalities were
from unknown (14.3%) causes. Median age of deer at death was 2.0 (n = 12;
range = 8.0). Of the eight mortalities from non-hunting causes included in survival
analysis, 50.0% occurred at Redwood Falls, 37.5% at Walnut Grove, and 12.5% at Lake
Benton (Table 4, Appendix C).
A 2.5-year old deer died on 16 October 2001. The carcass was almost entirely
cached under a fallen tree and covered with ground debris (e.g., leaves, grass, twigs; Fig.
10). A cache such as this one is typical behavior of bobcats and cougars, which will
often cover a partially eaten deer carcass, and return to feed later (Connolly 1981, Mech
1984, Rezendes 1992). The deer’s nose and two hoofs were the only visibly exposed
parts of the animal. Abrasions penetrating the skin and causing extensive hair loss were
present on the lower back and left hind quarters of the deer with claw marks penetrating
into the flesh (Fig. 10). During attack, bobcats or cougars will often jump on a deer’s
back, grasping the shoulders or neck with front claws (Mech 1984). The claw marks
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present on the deer’s rump may have been caused from the cat “raking” the deer with rear
claws. The right front and rear legs were fed on to the bone. No other parts of the animal
were consumed. Puncture marks were present on the throat with bruising and
hemorrhaging under the skin, which is typical of a cat kill. Large prey, such as a deer,
are killed with rapid bites to the throat, neck, or base of skull (Sunquist and Sunquist
2002).
The deer that died from disease was a 9-month-old female located dead on 19
February 2001 at the Walnut Grove study site. The fawn was lying in the fetal position
on a snow-packed trail on property in which the landowner provided supplemental feed
(i.e., corn) for deer during winter. Although temperatures were below 0 C°, the carcass
of the fawn had not begun to freeze, and the joints were flexible. It was estimated the
animal died within 12 hours. Externally, the doe fawn showed no signs of significant
trauma. The carcass was transported back to SDSU and submitted to ADRDL the
following day. Ancillary tests reported positive results for the presence of Clostridium
perfringens type A in three sections of small intestine that were submitted to
bacteriology. Clostridium perfringens induced enteritis was the suspected cause of death.
However, severe autolysis of most organ systems, including in particular the
gastrointestinal (GI) tract, prevented histologic evaluation necessary to confirm this
diagnosis. Based on the gross findings of significant intestinal hemorrhage and the
presence of abundant C. perfringens organisms from multiple sections of GI tract, the
hemorrhage and enteritis are believed to be secondary to this anaerobic pathogen. There
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24
were no other significant histologic lesions observed in the tissues and organ systems
examined.
Two mortalities of unknown cause occurred at the Redwood Falls study site
during April 2001-02. In April 2001, significant autumn precipitation, heavy winter
snowfall, less than ideal snowmelt scenarios, and record-breaking precipitation led to
major flooding on the Minnesota River (MN DNR Division of Waters 2003). On 17
April 2001, a mortality signal was received from an adult female deer. Flooding
prevented access to the estimated location of the deer. By 15 May, river levels receded to
a level where the radiocollar could be retrieved. The deer was not present at the location
of the radiocollar and no deer remains were located in the area. Several live signals were
received from 17 April to 15 May 2001. This may be due to scavengers or fluctuating
water levels moving the radiocollar, and thus, triggering a live signal. On 11 April 2002,
a mortality signal was tracked to a floating log jam in the Minnesota River. The
radiocollar was under water at an unknown depth and not retrievable due to river current
and poor water visibility. No deer remains were located. Drowning may have potentially
killed these deer. Two mortalities caused by drowning were reported by Nelson and
Mech (1986a) in a study conducted in northeastern Minnesota and DePerno et al. (2000)
reported a deer drowning in the Black Hills, South Dakota. However, because the deer
carcass or remains of the deer were not present, confirmation of cause-specific mortality
was not possible.
Eight deer were censored from survival analysis. In addition to the adult female
that sustained an injury during capture, two fawns (∼8 months) were censored from
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25
survival analysis because death may have been capture related (Appendix C). A predator
killed fawn died 13 February 2001 at the Redwood Falls study site. Because the
mortality occurred <26 days post-capture (Beringer et al. 1996), this deer was censored.
During 2002 capture operations, there were complications with processing of a fawn at
Redwood Falls. During release, the fawn repeatedly kicked at the radiocollar with her
left hind leg, catching her hoof underneath the collar. After the third occurrence, the
radiocollar was tightened and the deer was released without further problems. This fawn
was discovered dead 4 days later. The carcass of the fawn was fed on and drag marks
were present. During capture, this animal experienced a long handling time (12 min)
compared to the average (8.6 min). Maximum rectal temperature during handling was
42.1 C°. Considering rectal temperature, handling time, and additional stress experienced
by this deer, capture myopathy may have contributed to death and this deer was censored
from survival analyses. An additional five deer were censored at varying times
throughout the study because of failed radiocollars.
During 2001, annual survival rate of all radiocollared deer was 0.76 (n = 58,
SE = 0.06; Table 5). Overall (Jan. 2001–Aug. 2002) survival was 0.75 (n = 77,
SE = 0.05; Table 6). Annual survival across study sites was similar (df = 2, χ
2
= 3.362,
P = 0.186; Table 5). Overall survival was 0.89 (n = 28, SE = 0.06) at Lake Benton, 0.73
(n = 19, SE = 0.11) at Walnut Grove, and 0.64 (n = 30, SE = 0.09) at Redwood Falls
(Table 6). In 2001, survival differed between seasons (df = 3, χ
2
= 25.6914, P < 0.001).
Pre-hunt, hunting, hunting-all, and post-hunt season survival were 1.0, 0.88, 0.80, and
0.95, respectively, in 2001 (Table 7). In 2002, seasonal survival rates during pre-hunt
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26
and post-hunt were 1.0 and 0.98, respectively. Overall adult survival was similar (df = 1,
χ
2
= 0.475, P = 0.491) to fawn survival (Table 8).
Eleven deer that died during this study were observed through at least one
migratory period. Of these deer, 91% exhibited migratory behavior; traveling between
distinct winter and summer ranges. Survival for non-migratory individuals (0.89, n = 9,
SE = 0.10) was similar (df = 1, χ
2
= 1.06, P = 0.304) to migratory individuals (0.77,
n = 44, SE = 0.06). Mean migration distance (10.7 km, n = 10, SE = 2.5) of deer that
died was similar (df = 1, χ
2
= 0.05, P = 0.8172) to mean migration distance of all
radiocollared deer (10.1 km, n = 95, SE = 0.7).
DISCUSSION
Helicopter net-gunning was an efficient and safe method for capturing adult
white-tailed deer. Seventy-eight female deer were captured (77 radiocollared) in 3.5 days
(3.12 deer per hour) with one (1.3%) capture mortality, one (1.3%) capture related injury
that may have influenced mortality, and two (2.6%) mortalities that may be due to
capture myopathy. Capture-related mortality percentages were moderate to low
compared to other ungulate capture operations using net-guns deployed from helicopters
(12.0%, Barrett et al. 1982; 10%, Firchow et al. 1986; 2%, Kock et al. 1987; 0%, White
and Bartmann 1994; 2%, DelGiudice et al. 2001).
According to the temperature boundaries discussed by Kreeger (1986), deer
temperatures can be classified as low (<37.2 C°), normal (37.2 C° -39.4 C°), or high
(>39.4 C°, DelGiudice et al. 2001). Using these guidelines, no captured deer were
classified in the low range, 3.9% were classified in the normal range, and 96.1% would
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be classified in the high range. According to Kreeger (1986), cell damage begins at
≥40.0 C° and survival without adverse residual effects is unlikely if animals experience
temperatures of 42.2 C°. Furthermore, Beringer et al. (1996) suggested that deer with
higher temperatures during handling were at greater risk of capture myopothy. However,
DelGiudice et al. (2001) determined no relation between rectal temperature and capture
related mortality. Many deer captured during this study had rectal temperatures above
40.0 C° (Appendices A, B). Nevertheless, deer did not seem to be adversely affected by
these high temperatures.
Although not statistically different (df = 1, t = -1.57, P > 0.130), mean transport
distance was 0.4 km greater during 2002 than 2001 capture. Seasonal weather conditions
may have contributed to this difference. Deer winter severity index (DWSI) was
calculated in each study site by accumulating 1 point for each day with an ambient
temperature ≤-7° C, and an additional point accumulated for each day with snow depths
≥35.0 cm (DWSI discussed in detail in Ch. 5). Combined DWSI value for December
2000 and January 2001 (61.4) was nearly three times greater than the DWSI for
December 2001 and January 2002 (Figs. 11, 12, 13). Increased concentrations of deer in
wintering yards in response to severe weather has been well documented among
white-tailed deer in the northern part of their range (Blouch 1984). Therefore, a more
widely distributed deer population in response to mild winter weather conditions during
months prior to 2002 capture may have forced the helicopter crew to search a greater area
to meet the sample size requirements.
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Annual survival rate of adult female white-tailed deer (76%, Table 5) in
southwest Minnesota was similar to survival rates reported elsewhere for female
white-tailed deer (65%-80%, Gavin et al. 1984, Fuller 1990, Nixon et al. 1991, Whitlaw
et al. 1998, DePerno et al. 2000). Furthermore, the annual mortality rate for female deer
(22%) in southwest Minnesota was similar to that reported (average = 26%,
range = 39.7%) for white-tailed deer in north-central Minnesota (DelGiudice 2000).
In southwest Minnesota, survival of adult female white-tailed deer was dependant
on human factors (i.e., hunting, vehicle collisions). Natural causes of mortality such as
predation and disease (14.2%) were minor relative to human related causes (71.5%, Fig.
9). Hunting was the greatest cause of mortality (43%) among females and was consistent
with other northern white-tailed deer studies. In southern New Brunswick, most adult
females died from hunting with a pooled annual mortality rate of 0.13 (Whitlaw et al.
1998). Fuller (1990) reported a hunting-related female mortality rate of 0.19 in
northcentral Minnesota, with other causes of mortality being minor relative to hunting.
Dusek et al. (1992) noted that 74% of female deaths were attributed to hunting, and only
8% were due to natural causes. In a mixed agricultural/forest landscape of southeast
Minnesota, 86.4% of mortalities were hunter-related (Simon 1986). In heavily cultivated
areas, such as southwest Minnesota, vulnerability to mortality by human related causes
was likely due to the highly fragmented landscape with limited forest cover (Nixon et al.
1991), high hunter density (Hansen et al. 1997), and a well-established road network.
Majority of mortalities were concentrated during the hunting time period
(Sept.-Dec.), and no deer died during the pre-hunt period (May – Aug.; Tables 4, 7).
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Other studies have reported highest survival rates for female white-tailed deer during the
summer months (0.90 – 1.0; Dusek et al. 1989, Fuller 1990, Nixon et al. 1991,Van
Deelen et al. 1997, Whitlaw et al. 1998, DePerno et al. 2000). High summer survival
(100%) in southwest Minnesota indicated that these results support Nixon et al. (1991)
suggestion that high summer survival was likely due to condensed home ranges,
unlimited food and cover (e.g., corn fields), and minimal human activities affecting
survival.
DelGiudice (2002) noted that a severe winter (i.e., 1995-96) had “excessive”
impacts on deer herds in northern Minnesota. Furthermore, Grund (2001) reported that
survival rates were related to winter severity indices in central Minnesota. Because deer
were monitored for one mild winter season and part of a moderate winter, and few deer
mortalities occurred during this study, the direct influence of severe winter weather
conditions on farmland deer survival was undetermined. DePerno et al. (2000) suggested
low spring survival of female white-tailed deer in the Black Hills of South Dakota was
attributed to poor quantity and quality of forage on winter range, and limited escape
cover. In southwest Minnesota, high spring and summer survival may indicate that deer
are maintaining a high nutritional plane through winter, and weather conditions had a
minimal impact on survival.
All study sites were dominated by cultivated land, but differed in percentages of
forest and grassland cover (Table 1, Ch. 2). Hansen et al. (1997) suggested that in
landscapes under intensive row-crop agriculture, deer occupying areas with larger blocks
(>1 km
2
) of permanent cover are less vulnerable to harvest. Furthermore, Nixon et al.
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30
(1991) noted that females living in larger forests had lower mortality rates. This was not
apparent in southwest Minnesota. Lake Benton and Redwood Falls had similar
percentages (14 – 15%) of permanent cover (i.e., forest + grassland/shrub; Table 1), but
Redwood Falls had more harvest mortalities (n = 3) than Lake Benton (n = 1). Two deer
were harvested at Walnut Grove, which had the least permanent cover (4%). Also, most
non-hunting mortalities occurred at Redwood Falls (n = 4), followed by Walnut Grove (n
= 3), and Lake Benton (n = 1; Appendix C). However, due to the small number of
mortalities (n = 14), effects of land use/cover variability between study sites on survival
were speculative at best. Because of high survival across sites, it was concluded that
minor fluctuations in percentage of permanent cover (±6%) had negligible influences on
adult female white-tailed deer mortality in fragmented landscapes with >80% cultivation.
However, additional research would increase sample sizes to levels where influences of
land cover characteristics on survival in southwest Minnesota could be identified.
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31
CHAPTER 4
SURVIVAL OF WHITE-TAILED DEER NEONATES
IN SOUTHWEST MINNESOTA
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INTRODUCTION
Determining causes of mortality and survival rates of white-tailed deer neonates
(<1 month) is important for effective deer management and population modeling (Schulz
1982, Huegel et al. 1985a, Ballard et al. 1999). Many factors can contribute to
vulnerability of white-tailed deer neonates to mortality including date of parturition
(Whittaker and Lindzey 1999), maternal age (Ozoga and Verme 1986), dam-neonate
behavior (White et al. 1972, Ozoga et al. 1982) habitat quality (Nelson and Woolf 1987),
and predator density (Beasom 1974). Common causes of neonate mortality are predation,
disease, and emaciation (Schulz 1982). Of these causes, several studies have shown
predation to be the primary cause (Cook et al. 1971, Hamlin et al. 1984, Messier et al.
1986, Benzon 1998).
Although it may not be possible to eliminate or reduce the major factors affecting
neonate survival, knowledge of these factors is necessary to advance understanding of
deer herd dynamics and to improve predictive management strategies. Therefore, the
objectives were to determine survival and cause-specific mortality rates of white-tailed
deer neonates in an intensively cultivated region of southwest Minnesota. As a pilot
study, we attempted to capture and radiocollar coyotes with the objective of estimating
predator density and determining predator movement in the neonate study area.
METHODS
Use of net-guns deployed from helicopters and other capture methods were not
appropriate for the capture of white-tailed deer neonates. Because of the neonate’s
passive behavior, cryptic coloration, inactivity, and fragility during the first two weeks of
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life, capture by hand was necessary (Downing and McGinnes 1969, Nelson and Woolf
1987). To obtain accurate survival and cause-specific mortality information on neonates
from birth, the goal of the study was to capture and radiocollar neonates as soon as
possible after parturition without disturbing the dam-neonate bond (White et al. 1972).
Neonate white-tailed deer were located using ground and vehicle searches during
daylight and night hours in Lincoln and Pipestone counties, Minnesota (Fig. 8). Searches
were conducted in areas where females exhibited postpartum behavioral changes (Huegel
et al. 1985b). Ground searches were conducted by arranging crewmembers in an evenly
spaced linear format and walking areas with high quality fawning habitat. Furthermore,
in areas with a well-established road network, vehicle searches as described by Downing
and McGinnes (1969) were conducted. After a neonate was observed, a quick and noisy
approach was used to cause the female to flush if present, and the neonate to elicit the
“drop” response (Nelson and Woolf 1987).
Captured neonates were sexed, aged, and weighed. Age of neonate was
determined by measuring the distance from the hairline (outline of hair just above the
hoof) to the ridged growth line on the abaxial wall of a front hoof (Haugen and Speake
1958, Fig. 14). Sams et al. (1996) examined eight morphometric measures and noted that
hoof growth provided the most reliable and accurate aging model and was least affected
by gender and maternal nutrition. Neonates were placed in a 4.8 mm-mesh drawstring
sac and weighed to the nearest ounce using a digital hanging scale (Extech Instruments,
Melrose, Massachusetts).
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Neonates were fitted with expandable breakaway radiocollars (Telonics Inc.,
Mesa, Arizona) equipped with mortality sensors that activated after the collar had
remained still for 4 hours. To minimize foreign scent, radiocollars were stored two
weeks prior to capture in plastic bags filled with vegetation commonly found in fawning
habitat. In addition, to reduce the chance of human scent transferred to handled neonates,
gloves were worn by all personnel participating in neonate capture procedures. Capture
location was recorded using a Global Positioning System (GPS) and total processing time
was recorded.
Status of collared neonates was determined daily until approximately 9 weeks
post-capture. After 9 weeks, neonates were monitored 2-3 times per week. A period of 9
weeks was selected because the first two months of life have been reported to be the
“critical period” in which neonates are most vulnerable to mortality (Cook et al. 1971,
Nelson and Woolf 1987). A vehicle-mounted radiotelemetry antenna system (Brinkman
et al. 2001), and hand-held Yagi antennas were used for daily monitoring. Cause of death
was determined from field necropsy and ancillary evidence at the kill site (White et al.
1987). If cause of death could not be determined in the field, carcasses were transported
to the SDSU ADRDL for further investigation.
Survival rates were calculated using the Kaplan-Meier procedure (Kaplan and
Meier 1958) modified for a staggered entry design (Pollock et al 1989). Survival rates
were calculated monthly from June through August. Neonates were censored from
analysis when mortality was capture-related or collars fell off neonates. Survival rates
were compared between years, sex, and months using Program CONTRAST (Hines and
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Sauer 1989); alpha was set at P ≤ 0.05. A Bonferroni correction factor was used to
maintain alpha when multiple Chi-squared and t-tests were performed.
To capture coyotes, breakaway neck snares (Phillips et al. 1990) and padded
leg-hold traps (Olsen et al. 1986) were randomly placed along trails from April 2001
through June 2002 in Lincoln and Pipestone counties, Minnesota (Fig. 8). Snares were
equipped with a “stop” to prevent killing coyotes and avoid capturing non-target species
(e.g., skunk [Mephitis mephitis], raccoon [Procyon lotor]). Traps were checked 1-2 times
daily. In addition to our efforts, a professional trapper was contracted to assist with
coyote capture using similar methods. Captured coyotes were sexed, aged (pup or adult),
inspected for ectoparasites and general physical condition, and fitted with a radiocollar
(Advanced Telemetry System, Isanti, Minnesota).
RESULTS
A total of 39 (21 in 2001, 18 in 2002 [Table 9]) white-tailed deer neonates was
captured and radiocollared in Lincoln and Pipestone counties, Minnesota (Fig. 8; Table 9,
Appendix D, E). Neonates (17 male, 22 female) were captured between 22 May and 11
June. Eight neonates (20.5%) required a chase before capture, and several were able to
elude capture. Of 31 (79.5%) neonates that remained still when approached, 15 (48.4%)
were completely passive during handling. Four females (18.2%) and four males (23.5%)
required a chase, and 68.2% of females and 58.8% of males struggled during capture.
Average age at capture was 4.8 days (n = 34, SE = 0.6), and mean handling time was 3.4
minutes (n = 39, SE = 0.3; Table 9). Mean date of birth was 28 May (29 May in 2001, 27
May in 2002) based on estimated age at capture.
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A total of 19 neonates was captured using daylight-ground searches, and 20 using
vehicle searches (8 daylight, 12 night). A total of 107 search-hours (39 in 2001, 68 in
2002) and 469 man-hours (275 in 2001, 194 in 2002) were required to capture 39
neonates. An average of 2.7 search-hours (1.9 in 2001, 3.8 in 2002) and 12.0 man-hours
(13.1 in 2001, 10.8 in 2002) were required per neonate captured. An average of 3.0
vehicle-search-hours (6.0 man-hours), and 2.4 ground-search-hours (19.3 man-hours)
were required per neonate captured.
A total of eight mortalities occurred during 2001-02 (Appendix F, Table 10).
Four neonates (66.7%) were killed by predators (Fig. 15). One neonate died from
collision with a vehicle (16.7%), and another from disease (16.7%). According to SDSU
ADRDL, the neonate died from enteritis. Supporting evidence strongly suggests that
coccidia (Eimeria spp.) and coronavirus (Coronaviridae) were the disease causing
organisms.
Two neonate mortalities (1 female in 2001, 1 male in 2002) may have been
capture-related. Necropsies conducted at SDSU indicated that both neonates died of
starvation. Both struggled during handling and were located dead within 3 days of
capture <50 m from capture locations with no evidence of physical harm. It was
suspected that these neonates were abandoned by females. Schulz (1982) suggested that
if a female were to abandon her fawn due to human contact, the fawn would die 24-72
hours later because of high metabolic demands of the growing neonate. Until a neonate
reaches 2 weeks of age, it is completely dependent on the dam’s milk. At approximately
2-3 weeks of age, the neonate’s rumen takes on adult proportions, and the animal is able
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to begin consuming vegetation (Gauthier and Barrette 1985). Because it was
undetermined if the neonates were abandoned or died naturally, and it was less than one
week post capture, they were censored from the study. During 2001, an additional three
neonates were censored during 2001 because radiocollars fell off or broke-away from
neonates.
During 2001, neonate survival rate after 1-month post-capture was 1.0 (n = 21)
and 0.95 (n = 18) after 3 months of monitoring (Table 11). In 2002, survival rate was
0.78 (n = 18) after 1-month post-capture and 0.72 (n = 13) after 3 months of monitoring.
Pooled survival rate was 0.84 (n = 39) for June-August 2001-02 (Table 11). Although
comparisons of survival rates between months was similar (df = 2, χ
2
= 1.972, P = 0.373),
most mortalities (n = 4) occurred during the first month, with an estimated June survival
of 89.8%. July and August survival was 96.8% and 96.9%, respectively. Survival was
similar (df = 1, χ
2
= 0.302, P = 0.583) between females (0.81) compared to males (0.88;
Table 12).
A total of 1,350 trap nights (1 trap set for 24 hours) was employed to trap coyotes
during this study. Trapping efforts in 2001 (1000 trap nights) went unsuccessful with one
coyote being trapped in 2002 (350 trap nights). Based on personal communications with
local and state trappers, low coyote density may be due to an outbreak of sarcoptic mange
(Sarcoptes scabiei) that occurred in and around the study site during the late 1990s.
The coyote that was trapped and radiocollared was a male juvenile in good health.
The juvenile was completely passive during handling and remained still after released
from the trap. The coyote died one week later; death was capture related. The foot
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caught in the padded trap was chewed on extensively and became infected. However, at
capture the foot was swollen, but did not appear to have any major damage (e.g.,
fractures, cuts).
DISCUSSION
Identifying the species responsible for the predator kills was difficult due to the
lack of evidence at the neonate kill site. In three out of the four predator kills, only hair
and blood were located near the location of the radiocollar. Bite marks were present on
all four radiocollars. Scraps of deer hide and digestive tract accompanied one radiocollar,
but the carcass was absence. We identified two of the predator moralities as coyote kills.
This decision was based on sign (e.g., tracks, scat) near where the collar was located.
Furthermore, the only coyote trapped and radiocollared during the study was captured in
the same section of land where the two suspected coyote kills occurred. We were unable
to identify the predators responsible for the other two kills.
All predator mortalities occurred >10 days postpartum. This is likely attributed to
increased activity of the neonate, particularly in the absence of the dam. Neonates <2
weeks old were well protected by relatively dense ground cover, cryptic coloration, and
inactivity. As the neonate ages, it becomes more observable and susceptible to predation
(Nelson and Woolf 1987). Benzon (1998) suggested that higher mortality among male
neonates in the Black Hills of South Dakota was due to behavior. Males were more
likely to run when approached by capturers, whereas females remained passive. Thus,
Benzon's (1998) hypothesis was that when a predator was near, a young male neonate
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would run and be caught instead of remaining still, as females did, and allowing the
predator to pass. In southwest Minnesota, the oldest fawn killed by predation was
approximately 8 weeks of age (Appendix F). Nelson and Woolf (1987) reported that
neonates >8 weeks old were generally too swift to be caught by canids.
Pooled (2001-02) white-tailed deer neonate mortality (16%) in southwest
Minnesota was lower than reported elsewhere in the Midwest Agricultural Region; 21%
mortality in south-central Iowa (Huegel et al. 1985a), 30% mortality on a wildlife refuge
in southern Illinois (Nelson and Woolf 1987), and 33% in central Missouri (Bryan 1980).
Furthermore, heavy neonate losses have been reported in Texas (72%, Cook et al. 1971),
Black Hills of South Dakota (40%, Benzon 1998), Colorado (66%, Whittaker and
Lindzey 1999) and New Brunswick (53%, Ballard et al. 1999). Similar to this study,
Schulz (1982) reported a 15% neonate loss preceding hunting season on a deer refuge in
southeast Minnesota.
Grund (2001) noted that neonate survival may be related to winter severity, with
survival decreasing with increasing winter severity. Although neither winter during this
study was severe relative to the last 30 years (1971-2000; Midwest Regional Climate
Center 2002), neonate survival did not decrease with an increased DWSI (Fig. 13; DWSI
discussed in detail in Ch. 5). Neonate survival was higher in 2001 than in 2002, and
DWSI in 2000-01 was approximately twice that of the DWSI in 2001-02 (Fig. 13). In
fact, 2001 summer neonate survival (95%) in southwest Minnesota was one of the highest
survival rates reported for free-ranging white-tailed deer. During the first 30 days
postpartum, the time period when most neonate mortalities have been reported (Cook et
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al. 1971, Schultz 1982, Huegel et al. 1985a, Ballard et al. 1999), 2001 survival was
100%. Hansen et al. (1997) reported 100% survival between birth and 4 months for 17
marked neonates on an agricultural area in east central Illinois, and McGinnes and
Downing (1969) reported 92% neonate survival, for a confined deer herd in Virginia.
Several studies have shown predation to be the primary cause of mortality for
neonates (Cook et al. 1971, Hamlin et al. 1984, Messier et al. 1986, Nelson and Woolf
1987, Benzon 1998, Whittaker and Lindzey 1999), and overall losses are the highest
when a predator, such as the coyote, is present (White et al. 1972, Ballard et al. 1999).
Furthermore, local fluctuations in neonate survival rates have been attributed to changes
in predator density (Beasom 1974, Stout 1982). Using our trapping efforts as an indicator
of predator numbers, high neonate survival (84%) was likely associated with low
predator density in the study area.
High neonate survival also may be associated with nutritional condition of
females in southwest Minnesota. Using chest girth as an index (Ch. 3), adult does
captured at the Lake Benton study site were in excellent condition. In intensive
agricultural areas, does are maintained on a high nutritional plane because of access to a
nearly unlimited and nutritious diet (Gladfelter 1984, Nixon et al. 1991). Even during
winter months, deer can maintain a high quality diet on abundant waste grains left in crop
fields after harvest (Warner et al. 1989). In a captive deer study in Michigan, Verme
(1963) reported that mean birth weight of neonates of female deer maintained on a highly
nutritious diet was 1.6 kg (86%) greater than young born to malnourished does.
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Furthermore, newborn neonates that died during Verme’s (1963) study were an average
of 0.9 kg (31%) lighter at birth than those that survived.
In southwest Minnesota >80% of the land is cultivated (Ch. 2), and crops (e.g.,
corn, soybeans, oats, wheat) begin to emerge during early June (Minnesota Agricultural
Statistics Service 2002). Hence, <20% of the land provides adequate cover for fawning,
which occurs in late May and early June. Habitat available for fawning is composed of
small patches of grassland and tree groves. Tree groves in the Lake Benton study site
were primarily shelterbelts and abandoned farmyards with dense ground vegetation
consisting mainly of smooth brome. Huegel et al. (1985a) and Benzon (1998) reported
that regional differences in neonate survival may be largely influenced by vegetation
structure at neonate bed sites. Vegetative cover is particularly important during the
neonate’s first month of life when it largely relies on cryptic coloration and inactive
behavior to avoid being observed by predators (Nelson and Woolf 1987). With predators
relying primarily on visual cues (Wells and Lehner 1978), increased ground cover would
decrease a neonate’s risk of predation. Although the amount of suitable fawning habitat
is limited in southwest Minnesota, what is available is high quality and may be a
contributing factor to the high survival rates observed.
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CHAPTER 5
MOVEMENT OF FEMALE WHITE-TAILED DEER IN
SOUTHWEST MINNESOTA
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INTRODUCTION
In the northern part of their range, white-tailed deer are considered a migratory
species (Marchinton and Hirth 1984, Demarais et al. 2000). Research has indicated that
the onset of cold temperatures and snow depth exert the greatest influence on seasonal
movement from summer to winter home range (Verme 1968, Ozoga and Gysel 1972,
Verme 1973, Blouch 1984, Nelson 1995). During mild winters with below average
snowfall, deer may occupy the same range year round or only briefly visit a winter range
(Drolet 1976, Blouch 1984, Nelson 1995). White-tailed deer exhibit high site fidelity,
and have been reported to move through suitable habitat en route to previous seasonal
range (Tierson et al. 1985). Fawns and yearlings may disperse each year, moving from
their original home range and establishing a permanent range elsewhere (Nixon et al.
1991, Nelson 1993). Amount of dispersal occurring between neighboring deer
populations determines emigration and immigration rates, and may represent a significant
exchange of individuals across areas (Rosenberry et al. 1999), which is important to
management of PAs.
Movement of white-tailed deer has been well documented in the Forest Zone of
Minnesota (Rongstad and Tester 1969, Kohn and Mooty 1971, Hoskinson and Mech
1976, Moen 1976, Mooty et al. 1987, Nelson 1993, 1995, DelGiudice 2000). However,
literature is scarce for the Farmland Zone of Minnesota. Schulz (1982) determined
newborn fawn home ranges in a Wildlife Management Area in southeast Minnesota, and
Simon (1986) determined annual movements of fawn (≥6 months old) and adult (>1 year
old) deer in the same area. No direct information related to movement of white-tailed
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deer exists in intensive agricultural areas of the Farmland Zone. Therefore, the objectives
of this study were to determine seasonal movement (i.e., migration, dispersal) and home
ranges of white-tailed deer in southwest Minnesota.
METHODS
Female white-tailed deer were captured and radiocollared at winter deer
concentrations near the cities of Lake Benton, Walnut Grove, and Redwood Falls (Fig. 4;
see Ch. 3 for capture and handling methods). Individual, radiocollared fawn (∼8 months
at capture) and adult (>1 year at capture) white-tailed deer were monitored for mortality
2-3 times per week and located by ground triangulation twice per week. Azimuths (3-5)
were estimated from established telemetry stations using a vehicle mounted “null-peak”
antenna system (Brinkman et al. 2002) connected to an electronic compass (C100
Compass Engine, KVH Industries, Inc., Middletown, RI; Cox et al. 2002). If deer could
not be located from the ground, a fixed-wing aircraft was used. Locations of visually
observed and undisturbed individuals were assigned Universal Transverse Mercator
(UTM) coordinates. To calculate deer locations, azimuths were entered into the
computer program Locate II (Nams 2001), and plotted on USGS 3-meter Digital
Orthophoto Quadrangles using the software program ArcView (ESRI, Redlands, CA).
Fixed kernel method with least-squares cross-validation to determine smoothing
parameter was used to calculate home ranges (Seaman et al. 1999). Kernel estimators are
nonparametric, and are not based on an assumption the data conform to specified
distribution parameters (Seaman et al. 1999).
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Seasonal migration was calculated by measuring the distance between center
points of seasonal home ranges. If overlap existed between seasonal home ranges,
migration did not occurr (Nicholson et al. 1997). Seasonal movement was considered
dispersal if deer moved from original home ranges and established permanent home
ranges elsewhere (Marchinton and Hirth 1984). Deer were considered obligate migrators
(Sabine et al. 2002) if they migrated annually to winter range and remained there until
spring before returning to summer range. Deer were considered conditional migrators
(Nelson 1995) if they failed to migrate to a previous winter range, only briefly (<1
month) visited winter range, or made several migrations between seasonal ranges during
a single winter. Deer were considered residents (VerCauteren and Hygnstrom 1998) if
they remained non-migratory a minimum of three consecutive migratory periods. Only
individual deer that were monitored through three consecutive migratory periods were
assigned a migration strategy (i.e., obligate, conditional, permanent residents). Seasonal
movement from winter to summer range was classified as spring migration, and
movement from summer to winter range was classified as fall migration.
Statistical analyses were performed using SYSTAT (Wilkinson 1990), and
differences in movements between or among groups of deer were compared with t-tests
or Chi-squared tests. Alpha was set at P ≤ 0.05, and a Bonferroni correction factor was
used to maintain the experiment-wide error rate when multiple Chi-squared and t-tests
were performed (Neu et al. 1974).
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RESULTS
Seventy-seven deer (61 adult, 16 fawn) were captured and radiocollared during
January 2001 (n = 58; Table 2) and 2002 (n = 19; Table 3) at three study sites in
southwest Minnesota (Fig. 4). Thirty-one deer were captured at the Lake Benton study
site, 19 at Walnut Grove, and 27 at Redwood Falls. Deer were monitored from January
2001-August 2002. A total of 6,867 deer locations was collected with a mean 95% error
ellipse of 3.8 ha. A total of 149 seasonal movements was documented during three
migratory periods; spring 2001, fall 2001, and spring 2002 (Appendix G, H).
Thirty-nine, three, and 26 individual deer were monitored through 3, 2, and 1 migratory
period(s), respectively. A total of 130 individual home ranges were calculated during 4
seasonal range periods; winter 2000-01, summer 2001, winter 2001-02, and summer 2002
(Appendix G, H). One, 27, 11, and 23 home ranges were calculated for individual deer
during 4, 3, 2, and 1 seasonal range period(s), respectively.
SPRING MOVEMENT 2001
During spring 2001, 40 deer (75.5%) migrated a mean distance of 8.8 km
(SE = 1.1; range = 29.2 km; Table 13). Nine individuals (17.0%) did not migrate and
used at least part of their winter range as summer range. Four deer (7.5%) dispersed (2
fawn, 2 adult) and established permanent ranges elsewhere (Fig. 16). Mean dispersal
distance was 71.3 km (SE = 45.1) and varied from 16 to 205 km. Of the 44 deer that
migrated or dispersed from winter ranges, median departure date was 8 April and varied
from 10 March to 25 May.
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Mean migration distance in Lake Benton (Fig. 17), Walnut Grove (Fig. 18), and
Redwood Falls (Fig. 19) study sites was 8.5 km (SE = 1.2; n = 16), 7.8 km (SE = 2.2;
n = 14), and 11.6 km (SE = 2.4; n = 12), respectively (Table 13). Lake Benton had the
highest percentage (88.9%) of migrating deer in spring 2001, followed by Walnut Grove
(87.5%), and Redwood Falls (70.6%). Two dispersals occurred at Redwood Falls, one at
Walnut Grove, and one at Lake Benton (Fig. 15), with deer leaving original home range
and establishing new home ranges elsewhere.
FALL MOVEMENT 2001
During fall 2001, 23 (56.1%) deer migrated a mean distance of 11.2 km
(SE = 1.7; range = 28.8 km; Table 13). Of the 40 deer that migrated during spring 2001,
32 were monitored during fall 2001. Twenty-one (65.6%) of these deer migrated a mean
distance of 10.6 km (SE = 1.5) to their previous winter range. Of the 4 dispersals that
occurred during spring 2001, two were monitored during fall 2001, and migrated a mean
distance of 17.9 km (SE = 12.5) to a new winter home range. Eleven (34.4%) did not
migrate, and these remained on summer range through winter 2001-2. Median departure
date from summer home range for migratory individuals was 28 November (n = 21) and
ranged from 31 October to 22 December. Timing of migration of 2 individuals was
unidentifiable. In spring 2001, all 9 non-migratory individuals in spring 2001 remained
non-migratory during fall migration 2001.
Mean fall 2001 migration distances in Lake Benton, Walnut Grove, and Redwood
Falls study sites were 9.3 km (SE = 1.0; n = 10), 13.6 km (SE = 4.0; n = 8), and 11.2 km
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(SE = 4.0; n = 5), respectively (Table 13). Lake Benton had the highest percentage of fall
migraters (62.5%), followed by Walnut Grove (58.3%), and Redwood Falls (45.5%).
SPRING MOVMENT 2002
During spring 2002, 32 deer (58.2%) migrated a mean distance of 11.4 km
(SE = 1.2; Table 13). Because deer captured during winter 2001-02 were not monitored
during 2002 fall migration, spring 2002 dispersal was unknown. Of the 23 deer that did
not migrate, 15 were individuals that did not migrate from summer ranges during fall
2001, four were deer captured during winter 2001-02, and four were deer that had
migrated during previous seasons. Nearly all individuals (85.7%) who did not migrate
the previous spring (2001), and were monitored during spring 2002 (n = 7), remained
non-migratory. Median date of winter range departure spring 2002 was 18 April (n = 27)
and varied from 31 March – 30 May.
Mean spring 2002 migration distances in Lake Benton, Walnut Grove, and
Redwood Falls study sites were 9.4 km (SE = 1.2; n = 18), 13.8 km (SE = 4.8; n = 5), and
11.2 km (SE = 2.5; n = 8), respectively (Table 13). Lake Benton had the highest (df = 2,
χ
2
= 9.527, P = 0.009) spring migration (82.6%), followed by Walnut Grove (41.7%),
and Redwood Falls (40.0%).
HOME RANGE
Seasonal home ranges of individual deer were calculated using a minimum of 25
and a mean of 37.3 (SE = 0.8, n = 130) locations. Deer locations were estimated during
the last portion of winter 2000-01, and two individuals were located frequently enough to
calculate home ranges. Therefore, winter 2000-01 was pooled with winter 2001-02 in
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analyses. Summer 2001 home ranges did not differ (df = 1, t = 1.553; P = 0.124) from
summer 2002, and also were pooled in analyses.
Mean 95% and 50% winter home range use areas were 5.2 km
2
(range = 18.4
km
2
; n = 37), and 0.8 km
2
(range = 3.3 km
2
; n = 37), respectively (Table 14), and did not
differ among study sites (df = 2, χ
2
= 1.995, P = 0.369). Deer at Lake Benton had a mean
95% winter home range use area of 6.9 km
2
(SE = 2.1; n = 11), followed by Walnut
Grove (× = 5.7 km
2
; SE = 1.0; n = 12), and Redwood Falls (× = 3.4 km
2
; SE = 0.7;
n = 14; Table 14).
During summer 2001 and 2002, mean 95% and 50% home range use area was 2.3
km
2
(range = 12.4 km
2
; n = 93) and 0.3 km
2
(range = 1.6 km
2
; n = 93), respectively (Table
14), and did not differ between sites (df = 2, χ
2
= 5.246, P = 0.073). Deer at Lake Benton
had 95% summer home range use area mean of 2.6 km
2
(SE = 0.4; n = 36), followed by
2.2 km
2
(SE = 0.3; n = 26) at Walnut Grove, and 1.8 km
2
(SE = 0.2; n = 31) at Redwood
Falls (Table 14).
DISCUSSION
SEASONAL MIGRATION
Mean migration (10.1 km; Table 5) distance in southwest Minnesota was slightly
lower than reported in northern white-tailed deer studies (23.2 km, Sparrowe and
Springer 1970; 13.8 km, Verme 1973; 20.7 km, Hoskinson and Mech 1976; 11.0 km,
Simon 1986; 13.0 km, Nixon et al. 1991; 15.7 km, Griffin et al. 1994; 6.8-20.2, Sabine et
al. 2002). Mixed seasonal migration strategies have been well documented among
white-tailed deer populations (Rongstad and Tester 1969, Sparrowe and Springer 1970,
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Drolet 1976, Blouch 1984, Nelson 1995, VerCauteren and Hygnstrom 1998, Sabine et al.
2002). Results from this study support these earlier findings. In southwest Minnesota,
female deer exhibited a mixture of migration behavior strategies consisting of obligate
migrators, conditional migrators, and permanent residents. Of 39 deer that were
continuously monitored through three migration periods (i.e., spring 2001, fall 2001,
spring 2002), 15 (38.5%) were obligate migrators, 18 (46.2%) conditional migrators, 5
(12.8%) permanent residents, and one migration strategy could not be determined due to
insufficient data.
Among northern white-tailed deer, fluctuations in temperature and snow depth
exert the strongest effects on seasonal movement (Verme 1968, Ozoga and Gysel 1972,
Verme 1973, Blouch 1984, Beier and McCullough 1990, Nelson 1995). In addition,
Nicholson et al. (1997) reported that mule deer (Odocoileus hemionus) can maintain a
mixed migration strategy in areas with extremely variable precipitation and snow cover.
To determine if effects of temperature and snow depth apply to deer populations in
southwest Minnesota, a DWSI was derived from the literature and analyzed with
movement data from this study.
The effective critical temperature for an average size adult female deer has been
calculated at -7 Cº (DelGiudice 2000). At or below this temperature threshold, heat
losses may exceed energy expendature for standard metabolism and activity, and
additional heat is generated to maintain homeothermy (McDonald et al 1973). Also,
Tierson et al. (1985) and Nelson (1995) reported that temperatures of <-7 Cº can initiate
fall migration. Kelsall (1969) noted that deer are considerably restricted in movement
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when snow exceeds depths of 40 cm (about 20 cm less than deer chest height). Also,
such depths have been reported to initiate seasonal migration to winter range. Drolet
(1976) estimated the threshold for beginning migration to be 30.4 cm, and Nelson and
Mech (1981) reported 35-40 cm in northern Minnesota. Sabine et al. (2002) reported that
peak migration coincided with accumulation of 40 cm of snow in all 4 years of a New
Brunswick study. Using this information, DWSI for this study was calculated in each
study site by accumulating 1 point for each day mean ambient temperature was ≤-7º C,
and an additional point for each day snow depth was ≥35 cm during the months
November-March (National Climatic Data Center 2002, Climatology Working Group
2003). October and April were not included in the DWSI because no days were reported
with temperatures below -7 Cº, and the snow depth never was ≥35 cm. The DWSI
developed for this study was designed to be white-tailed deer specific.
Average DWSI during winter 2000-01 (138.7; Fig. 11) was greater than winter
2001-02 (50.7; Fig. 12). Drolet (1976), Blouch (1984), and Nelson (1995) reported that
during mild winters with below average snowfall, deer may occupy the same range year
round or become conditional migrators. Lower DWSI in all study sites during winter
2001-02, relative to DWSI during 2000-01 (Fig. 13), may explain why 34.4% of
individuals did not return to winter range during fall 2001, and exhibited conditional
migration. There were conditional migrating deer that returned to winter range in
2001-02, but only for a brief period, and others made several trips between summer and
winter range. For instance, adult female deer 611 (D611) at Lake Benton, made four
migrations of approximately 12 km between summer and winter range during winter
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2001-02. Deer 611 departed summer range on 28 November 2001, returned on 20
February, departed again on 27 March for winter range, and returned to summer range
and remained there a few days later. During spring 2002, adult D689 departed from
winter range on 2 May, traveling 22.4 km to summer range. Deer 689 was located on 7
and 9 May on summer range. On 10 May, D689 traveled 22.4 km back to winter range,
where she remained until 31 May. During the first week of June, D689 made her final
trip back to summer range where she remained until fall. Explanations as to why a
variation in the prevalence of conditional migration existed among deer in the same area
were speculative. However, Nelson (1995) suggested that differences in hunting
mortality on summer range, and lower population size and density may influence
migration among deer in adjacent wintering areas. Furthermore, Sabine et al. (2002)
suggested that distribution of the behavior among individual deer was influenced by
migration distance.
Temperature and snow depth influenced seasonal migration in southwest
Minnesota. Fall migration by most deer in southwest Minnesota coincided with
accumulation of snow and decreasing temperatures. During fall 2001, a winter storm
occurring 26-28 November initiated fall migration for 52% of 21 deer with known
summer range departure dates (Fig. 20). Migration was in response to snow depths of 36
cm at the Lake Benton and Walnut Grove study sites, and 16 cm at Redwood Falls.
Mean ambient daily temperatures during this migration were -4 C° to -6 C°, and were the
lowest recorded so far that fall.
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Crop harvest had minimal impacts on fall migration. According to the Minnesota
Agricultural Statistics Service (2002), the 1996-2000 average harvest completion date for
90% of the corn and soybeans was 3 November and 16 October, respectively, whereas
median date of fall migration was not until 28 November (n = 21) and ranged from 31
October – 22 December. In addition, the planting of crops did not have an effect on
spring migration. Median date of winter range departure was 8 April in 2001 (n = 44)
and 18 April in 2002 (n = 27), whereas the 1996-2000 average planting completion date
for 90% of the corn and soybeans was 30 May and 4 June, respectively (Minnesota
Agricultural Statistics Service 2002).
Similar to fall migration in southwest Minnesota, temperature and snow depth
influenced spring migration. However, movement was less abrupt compared to fall
migration (Figs. 21, 22). During winter 2000-2001, Lake Benton and Walnut Grove had
higher (df = 2, χ
2
= 100.04, P < 0.0001) DWSIs than Redwood Falls (Fig. 11). Thus,
spring migration influences were analyzed separately. Between 28-31 March 2001, nine
(29%) deer at Lake Benton and Walnut Grove departed winter range (Fig. 21). On
March 27, prior to migration, temperatures increased and stayed above -9 C° and snow
depths declined below 30 cm for the first time that month. The second spring migration
at Lake Benton and Walnut Grove began between 4-7 April, when 8 deer (26%)
responded to a week of temperatures ≥0 C°. In addition, snow depths were 28 cm on 31
March and had melted by 6 April. Factors influencing 2001 spring migration at
Redwood Falls were less apparent (Fig. 21). Departure from winter range occurred
between 10 March - 18 May with two individuals migrating in response to similar
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temperatures and snow depths. Compared to Walnut Grove and Lake Benton, Redwood
Falls experienced milder and more gradual changes in weather conditions with few days
where temperatures and snow depths reached migration thresholds.
Winter 2001-02 was much milder across all study sites compared to winter
2000-01 (Fig. 13). In 2002, the first group of deer to begin migration departed between
2-7 April, when 10 (37%) responded to a 10-15 C° rise in mean ambient temperature
across all study sites; temperatures increased from –6 C° on 3 April to 4 C° – 9 C° on 7
April (Figure 22). Snow depths were minimal (5 cm) at this time and likely played less
of a role in initiating migration. The second group to migrate simultaneously departed
between 1-3 May. No sudden shift in temperature coincided with this migration, nor was
there any snow accumulation at this time. A minor snow fall (2-5 cm) occurred on 28
April, but had melted the following day.
Late season migrators are likely less influenced by low ambient temperatures and
snow depths. These deer remain on winter range well into spring thaw, after snow has
melted and temperatures have risen and remained above 0 Cº. Migration among these
animals was initiated by other variables. Potential late season spring migration stimuli
include plant phenology (Nixon et al. 1991) and pre-parturition movement (Ozoga et al.
1982, Simon 1986). Hypothetically, because late season migrators were less sensitive to
winter severity, these deer were more likely to be obligate rather than conditional
migrators. Data from this study added some support to this hypothesis in that median
spring 2001 departure date for obligate migrators (9 April, n = 15) was five days after
median departure date (4 April, n = 16) of conditional migrators in spring 2001. During
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spring 2002, median departure date of obligate migrators (24 April, n = 10) was nearly
two weeks after median departure date (12 April, n = 6) for conditional migrators.
Because deer were not monitored daily, number of days between departure and
arrival on ranges was unknown. However, there was minimal meandering between
seasonal ranges, and the majority of monitored females completed migrations, regardless
of distance, in <1 week. Nixon et al. (1991) noted rapid migration in Illinois with deer
settling on summer ranges within 10-12 days of initial movement. In northeast
Minnesota, migrations from winter yards were completed in <2 weeks (Nelson and Mech
1981).
DISPERSAL
White-tailed deer within the Midwest Agricultural Region are unique because
annual female dispersal is common (Gladfelter 1984). Fifty percent of female fawns, and
21% of yearling females dispersed each spring in Illinois (Nixon et al. 1991). Similarly,
Nelson (1993) noted 20% yearling female dispersal in northeastern Minnesota. During
2001, 17% of fawns, and 5% of adults exhibited spring dispersal in southwest Minnesota.
Whether these adults were yearlings was unknown. Dispersal distance varied
significantly (n = 4, range = 189 km). Furthermore, dispersing deer had strikingly
different winter range departure behavior than migrating individuals. All dispersers
migrated to pre-dispersal home ranges before dispersing to new permanent ranges. For
example, adult deer D591 departed from the Redwood Falls winter range on 12 April.
We were unable to receive a signal until 3 June, when D591 was located 22.0 km
southwest of its previous location, and remained on this temporary range until 19 June
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when it dispersed. On 2 December 2001, D591 was located near the town of Oldham,
South Dakota, an approximate straight-line distance of 205 km from previous winter
range (Fig. 16), which is the longest female dispersal distance reported for white-tailed
deer. Kernohan et al. (1994) reported a dispersal distance of 213 km for a yearling male
in northeastern South Dakota, and Sparrow and Springer (1970) reported a 161-km
dispersal in eastern South Dakota. Nelson (1993) reported a 168 km dispersal in
northeastern Minnesota. Dispersing adult D372 at Lake Benton exhibited behavior
similar to that of D591. Departing from winter range on 19 April, D372 was not
relocated until 9 May, when it was located 6 km from its previous location. Deer 372
remained on this temporary range until 20 May, when it moved back to previous winter
range for 5 days and then dispersed. Deer 372 was located on 24 July approximately 18.7
km south of winter range where she established a new permanent summer range. Also,
dispersing fawns D782 and D862 established temporary ranges that were occupied for
roughly a month before dispersing on 28 June and 3 June, respectively. All dispersers
migrated to a new winter range during fall 2001.
Social pressures have been identified as the primary stimuli for dispersal
(Marchinton and Hirth 1984). Near parturition, the dam often runs off her previous
fawns, encouraging them to disperse (Downing and McGinnes 1969). We observed this
behavior during night hours under spotlight, while conducting vehicle searches for
white-tailed deer neonates in southwest Minnesota, spring 2002. Pregnant dams would
“flail” when approached by year-old fawns, or would run fawns off by chasing and using
a “foreleg kick”. The dam’s aggressive behavior towards the year-old fawns may explain
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the existence of the temporary range used by dispersing fawns D782 and D862. Because
mean date of migration has been identified to occur among female deer before
parturition, a window of time exists between the dam’s arrival at her summer range and
the time just before parturition when her previous fawns are forced to depart. In addition,
dispersal behavior among fawns is almost unknown before 11 months of age (Nixon and
Etter 2001). Hence, a 10-11 month-old fawn follows her mother to summer range, and
remains for roughly a month (i.e., pre-dispersal range) until forced to depart prior to
parturition. In intensive agricultural areas with limited available cover in the spring,
fawns often travel long distances before finding suitable habitat not occupied by other
females (Demarais et al. 2000; Nixon et al. 2001).
Explanations as to why adult females D372 and D591 dispersed are more
complicated. Exact ages of D372 and D591 were unknown. However, it is possible that
one or both were yearlings. If this were the case, it was possible that this was their initial
pregnancy and they dispersed to seek solitude to fawn. Ozoga et al. (1982) suggested
that “complete isolation is essential for proper mother-infant bond formation.” In
nutritient-rich landscapes (e.g., southwest Minnesota), competition among females for
parturition sites may be more important than food competition, and white-tailed deer will
forcefully defend fawning grounds (Nixon et al. 1991, 2001). Matriarch females defend
the same fawning area annually (Ozoga et al. 1982). Because of the difficulty of
establishing fawning grounds in highly fragmented, competitive agricultural
environments with limited cover, D591 and D372 may have been forced to move a great
distance before finding suitable habitat. Another explanation why D591 and D372
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dispersed was that they may have been unsuccessful mothers. Ozoga et al. (1982) noted
that unsuccessful mothers fail to exhibit any prolonged isolation or aggressive behavior.
Without fawns, they lack the innate behavior to defend summer ranges. Barren females
often revert to the social position of a fawn (Ozoga and Verme 1986, Nixon et al. 1991).
Therefore, to avoid confrontation with territorial does rearing fawns, D591 and D372
may have dispersed.
HOME RANGE
In northern regions, snow depth, deer density, and low temperatures have the
greatest influence on daily activity of white-tailed deer (Verme 1973, Tierson et al. 1985,
Beier and McCullough 1990). In response to severe weather conditions, deer will
minimize movement to conserve energy (Moen 1976, Parker et al. 1984). Hence, it is
predicted that white-tailed deer will have smaller ranges in winter than in summer. In
New York, female deer summer and winter home ranges averaged 2.21 km
2
and 1.32
km
2
, respectively (Teirson et al. 1985). In northeastern Minnesota, Nelson and Mech
(1981) noted mean summer and winter ranges of 0.83 km
2
and 0.44 km
2
, respectively,
and Mooty et al. (1987) reported mean summer and winter ranges of 0.69 km
2
and 0.43
km
2
, respectively. In southwest Minnesota, mean winter home range (5.18 km
2
,n = 37)
was more than double mean summer home range (2.27 km
2
, n = 93; Table 14). In
southwest Minnesota and other intensively cultivated areas, condensed summer home
ranges were likely due to unlimited cover, and nutritious food supplies throughout the
landscape provided by farming activities. Furthermore, Nixon et al. (1991) and Ozoga et
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al. (1982) noted a reduction in daily movement by females during the pre and
post-fawning period.
Home ranges of deer are extremely variable (Nicholson et al. 1997). Previous
reports of home ranges of northern white-tailed deer include estimates of 1.61-4.80 km
2
(Rongstad and Tester 1969); 2.50 km
2
(Sparrowe and Springer 1970); 1.67-4.71 km
2
(Kohn and Mooty 1971); 0.48-4.10 km
2
(Hoskinson and Mech 1976); 0.48-4.10 km
2
(Hoskinson and Mech 1976); 0.28-1.40 km
2
(Simon 1986);
1.89-3.77 km
2
(Griffin et al. 1994); 1.70 km
2
(VerCauteren and Hygnstrom 1998); and
4.37 km
2
(Kernohan et al. 2002). In general, home range estimates should be interpreted
with caution. Home range can vary with age and sex of the individual, habitat, and
season (Demarais et al. 2000), and are likewise affected by human activities (e.g.,
agricultural activities). VerCauteren and Hygnstrom (1998) reported that deer in
Nebraska shifted their range 174 m toward cornfields when corn development reached
tasseling-silking stage, and home range shifted again after harvest 157 m, with mean size
becoming 32% larger.
There were an insufficient number of locations to determine the approximate
effects of crop harvest on seasonal home range of southwest Minnesota deer. However,
impacts seemed minimal, and during crop harvest, radiocollared deer moved to nearby
(<1 km) forest or grassland habitat they occupied on their summer range prior to crops
reaching concealment height.
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CHAPTER 6
MANAGEMENT IMPLICATIONS
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To improve the “realism” of the simulated output of the farmland model, accurate
empirical data are required (Grund 2001). Thus far, researchers in southwest Minnesota
have used educated guesses, rather than empirical data specific to the region to evaluate
the biological parameters that are entered into the model. No direct information was
available on movement and mortality of white-tailed deer in intensively cultivated areas
of farmland Minnesota. This study documented that adult female and neonate
white-tailed deer populations have high survival and minimal vulnerability to death by
natural causes in intensively cultivated areas (Ch. 3, 4). Human-related mortalities (i.e.,
hunting, vehicle collision) are the primary factors impacting deer in southwest
Minnesota. Nutritious and abundant food supplies provided by farming activities set
carrying capacity well beyond current deer population levels in agricultural areas
(Hansen et al. 1997). Hence, keeping deer populations at levels tolerable to landowners,
while providing maximum hunter opportunities are primary management objectives.
Based on this research, annual reduction of deer numbers to meet population goals in
southwest Minnesota was almost entirely dependant on hunter harvest.
Intensive farming practices amplify the effectiveness of using hunting as the
primary tool for regulating deer numbers in southwest Minnesota. Crop harvest generally
occurs before Minnesota’s firearm hunting season. Therefore, deer in this highly
fragmented agricultural landscape occupy small remaining patches of permanent cover
leaving them vulnerable to hunters. Because of the scarce forest cover in southwest
Minnesota (Ch. 2), hunters can effectively reduce deer numbers annually to achieve
management population goals. Due to the extreme dependence on hunters to control deer
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numbers, special attention should be given to deer harvest data collection (e.g., number
harvested, age, sex) to minimize uncertainties (Grund 2001).
Under some circumstances, deer herds may not be efficiently managed through
the allocation of hunting permits. Nixon et al. (2001) suggested that thousands of private
landowners with varying opinions on hunting have created a mosaic of refuge and
non-refuge patches in central and northern Illinois. Hunting pressure is generally low on
many private properties (Hansen et al. 1997), and land acquired by conservation
organizations (e.g., The Nature Conservancy) and/or land set aside as a state/national
park may not allow hunting. Deer herds unregulated by hunting can cause high levels of
depredation, angering surrounding landowners and creating a dilemma for wildlife
managers. In this particular case, allocation of special depredation permits may be
necessary. Hansen et al. (1997) suggested that refuges in agricultural landscapes with
more than 5% permanent cover may hinder deer management efforts. Southwest
Minnesota has approximately 10% in permanent cover (Table 1), thus, special
consideration should be given to PAs that contain refuges. For instance, landowners
adjacent to refuges may be particularly vulnerable to depredation if deer herds are not
adequately managed annually through harvest.
Although allocation of hunting permits and harvest regulates populations,
managers need to consider effects of winter severity. DelGiudice (2002) noted that a
severe winter (i.e., 1995-96) had “excessive” impacts on deer herds in northern
Minnesota. Furthermore, Grund (2001) reported that survival rates were related to winter
severity indices in central Minnesota. Because this study was conducted during a
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moderate winter (2000-01) and mild winter (2001-02) with few deer mortalities (Ch. 3),
the direct influence of severe winter weather conditions on farmland deer survival was
undetermined. Additional monitoring during a "harsh" winter is necessary to identify
relationships between severe winters and mortality, and to evaluate variation between
years.
Northern deer in agricultural regions have adapted to highly fragmented
environments by dispersing and exhibiting seasonal migration. The farmland deer model
does not incorporate deer movement information. In fact, the farmland deer population
model assumes that emigration/immigration does not occur between PAs (DePerno et al.
1999). Although wildlife managers and research biologists know this assumption is
incorrect, empirical data to determine amount of movement occurring across PA
boundaries was lacking. Similarly, information on the frequency of seasonal migration
routes crossing PA boundaries was unknown. In this study, radiocollared deer dispersed
across and migrated between PAs. If dispersal and migration between PAs was equal
was undetermined.
Although the influence of winter weather on survival of deer in southwest
Minnesota was undetermined, a clear relationship was identified between movement and
winter severity. Migration was influenced by snow depth and mean ambient temperature.
For this study, mean distance migrated was 10.1 km (n = 95, SE = 0.7; Table 13) and
many deer had summer home ranges in PAs different from winter ranges (Figs. 17-19).
Therefore, migratory individuals harvested in one PA may potentially impact deer
numbers in another PA. In this particular case, population estimates during the summer
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may not adequately represent deer numbers during winter. For example, during 2001, the
majority of fall migration occurred in late November following a severe weather event
(Ch. 5, Fig. 20). In 2001, the firearms hunting season occurred during the first two
weekends of November. Therefore, during the 2001 deer season (when deer populations
were actively managed), the majority of migrating deer were occupying summer range.
If migration occurred before the hunting season, PA deer numbers and densities
potentially could have changed. For example, suppose temperature and snow depth
reached thresholds that initiate migration prior to the hunting season, deer would then
occupy winter ranges. In this hypothetical situation, PAs containing large wintering
yards could potentially have greater deer numbers, whereas PAs serving primarily as
summer ranges would have lower deer numbers. The influence on deer harvest is
speculative. However, the logical explanation for this scenario is that hunters in permit
areas with higher deer densities would have higher success at harvesting deer. On the
other hand, if wintering yards were located on refuge areas or private land with minimal
or no hunting, then harvest rates and hunter opportunities would be reduced. Because
southwest Minnesota deer migrate between PAs, and severe weather influenced
migration (Ch. 5), it is recommended that researchers and wildlife managers consider
timing of winter arrival and severity of winter weather when evaluating harvest data.
Continued monitoring of the relationship between migration and weather variables (i.e.,
snow depth, temperature) may help support thresholds (i.e., -7 C°, snow depth = 35 cm)
used to calculate the DWSI for farmland Minnesota. In addition, because timing of
arrival of severe weather may initiate migration of deer across PA boundaries, population
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surveys are recommended during summer and winter months to obtain accurate and
precise estimates of deer numbers in each PA. Also, seasonal population estimates may
provide insight into immigration/emigration and dispersal rates.
Although, there was evidence of deer movements across PA borders, the majority
of radiocollared female deer did not migrate across major highways, which supports the
use of major roads as deer PA boundaries (Fig. 23). For instance, only 4% of migrating
deer at Redwood Falls (n = 25) traveled across a major highway (Fig. 19), and less than
50% crossed highway systems at Lake Benton (Fig. 17) and Walnut Grove (Fig. 18). At
all three sites, major highways were located <3 km from capture location.
During 2001, 17% of female fawns (∼8 months at capture) and 5% of female
adults (>1 year at capture) dispersed a mean distance of 71.33 km (SE = 45.07); dispersal
and ranged from 16 to 205 km (Fig. 16). According to the farmland deer population
model, the estimated number of deer in southwest Minnesota (24 permit areas, Fig. 3)
during spring 2001 was 32,366 fawns (10-11 months) and 21,284 adults (Erb et al.,
Minnesota Department of Natural Resources, unpublished data). If the assumption was
made that 17% of fawns and 5% of adults uniformly dispersed across the study area,
6,566 deer could have potentially immigrated into and/or emigrated out of southwest
Minnesota PAs in 2001. This value may actually be much greater, because only females
were monitored and several studies have reported higher dispersal rates among males
(Nelson and Mech 1984, Nixon et al. 1991, Nelson 1993, Rosenberry et al. 1999).
Because dispersal was determined for only one spring migratory period, these estimates
should be interpreted with caution, and additional research is necessary.
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With dispersal potentially causing a significant exchange of individuals across
PAs, incorporating predicted emigration and immigration into the deer model would
improve management strategies. However, monitoring animals traveling over long
distances and measuring movements from unknown locations is difficult (Rosenberry et
al. 1999). For instance, in this study we were unable to determine the two longest
dispersal distances (Fig. 16) using radiotelemetry alone. Because deer move between
ranges with little meandering, we were unable to relocate D591 and D782 shortly after
they dispersed. Attempts at locating these individuals from a fixed-wing aircraft were
unsuccessful. Fortunately, a MN DNR Conservation Officer conducting a road survey
observed and reported D782 at its new location, and D591 was reported by an archery
hunter in South Dakota. Use of Global Positioning System (GPS) collars may remedy
this problem in the future, but budgetary restraints limit their use. Therefore, it is
recommended that deer monitoring be intensified during dispersal periods to minimize
lost signals due to long distance movements.
After dispersal is determined, the challenge remains as to whether immigration
equals emigration in southwest Minnesota PAs. Nixon et al. (1991) suggested that large
numbers of previous years fawns alive in the pre-fawning population may be the major
factor promoting high dispersal in Illinois. Using this hypothesis, Minnesota PAs with
high percentages of 9-month-old fawns relative to adjacent PAs would experience greater
emigration. Also, harvest data may provide cues to whether immigration or emigration is
occurring. In a region where deer have access to a nearly unlimited and nutritient rich
food supply (e.g., southwest Minnesota), social pressures have been identified as a reason
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for dispersal. Social pressure increases with deer density (Marchinton and Hirth 1984),
and this relationship may be amplified in intensively cultivated areas with limited cover.
Theoretically, PAs with high annual deer harvests would have fewer emigrations because
habitat vacancies would be created and deer would be required to disperse shorter
distances to find suitable habitat to establish new summer home ranges. In contrast, a PA
with high harvest may experience greater levels of immigration, especially if adjacent
PAs have high deer densities and low harvest rates. Furthermore, PAs with high rates of
female deer harvest may have lower dispersal rates because of an increased percentage of
orphaned fawns. Holzenbein and Marchinton (1992) noted that orphaned fawns were
76% less likely to disperse than those with does present that forced them to leave their
natal range. Future research evaluating major factors (e.g., deer density, habitat changes)
influencing dispersal timing and distance is recommended.
This study was designed to determine movement and mortality of white-tailed
deer in a 24 PA area of southwest Minnesota. To meet this goal, study sites were
carefully chosen to accurately represent the major land use/cover types throughout the
southwest region (Ch. 2). Movement and mortality differences between study sites have
been discussed (Ch. 3, 5). Also, information from this study may be applicable for other
areas of Minnesota. These data may be extrapolated to white-tailed deer herds in other
highly fragmented regions with intensive cultivation, limited permanent cover, high
hunter density, high road density, low predator density, and large fluctuations in seasonal
climate. These factors likely had the most significant influence on movement and
mortality in southwest Minnesota. Nevertheless, to determine landscape level thresholds
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68
in southwest Minnesota, long-term data is crucial. High survival over a relatively short
time period (20-month period) produced a small sample of mortalities (especially
non-hunting) to evaluate and compare across PAs. Furthermore, additional seasonal
monitoring is necessary to determine trends across years with varying winter severity.
Although study sites were chosen to encompass maximum land cover variability, the
southwest study area was relatively uniform (Table 1), which made it difficult to identify
unique ecological components affecting white-tailed deer in southwest Minnesota. A
landscape-level approach is necessary to understand long-term trends and effects of
varying deer densities across farmland Minnesota.
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Table 1. Percentages of major land use/cover types of deer permit areas in southwest
Minnesota (Minnesota Department of Natural Resources 2000).
Permit Area
Cultivated
Grassland/shrub
Forest
Water
Wetland
424
86.93
4.73
2.22
1.76
1.36
425
92.53
2.74
1.61
0.14
0.40
426
85.00
4.68
2.98
3.49
1.56
427
87.80
4.42
2.59
1.62
1.22
431
75.50
8.45
2.75
5.51
3.36
433
67.74
15.72
4.73
4.75
2.88
435
a
82.30
6.72
7.26
0.98
0.41
440
85.85
3.54
6.77
0.76
0.34
442
92.37
1.91
3.05
0.73
0.49
443
81.01
4.27
9.17
1.28
0.27
446
79.88
13.08
2.84
0.56
0.95
447
90.93
3.69
2.22
0.36
0.62
448
94.29
3.92
0.52
0.65
0.17
449
84.29
8.81
2.82
0.91
0.56
450
a
93.41
2.41
1.76
0.54
0.23
451
a
81.05
14.61
1.48
0.53
0.21
452
84.86
11.46
1.14
0.22
0.04
453
88.24
6.39
1.15
1.21
0.39
454
85.58
7.41
2.42
1.93
0.50
455
82.99
9.94
1.54
3.03
1.17
456
85.92
6.05
2.28
2.64
0.91
457
88.12
4.98
2.79
1.16
0.43
458
88.07
4.00
2.44
2.65
0.38
459
89.67
3.10
3.44
0.93
0.75
Average
85.60
6.54
3.00
1.60
0.82
a
Permit area selected as white-tailed deer study site
Page 103
Table 2. Capture data by study site for female white-tailed deer in southwest Minnesota, January 2001.
Lake Benton
Walnut Grove
Redwood Falls
All Sites
Age
Adult (>1 year)
Fawn (∼8 months)
Adult
Fawn
Total
Adult
Fawn
Total
Adult
Fawn
Total
Adult
Fawn
Total
Number of
deer captured
15
5
20
14
5
19
15
4
19
44
14
58
Mean (SE)
handling time (Minutes)
8.9
(0.4)
11.2
(1.3)
9.5
(0.5)
9.2
(0.5)
6.2
(0.9)
8.4
(0.6)
8.1
(0.6)
7.8
(1.1)
8.1
(0.5)
8.8
(0.3)
8.4
(0.8)
8.6
(0.3)
Mean (SE)
distance
a
(km)
1.4
(0.2)
0.7
(0.2)
1.2
(0.2)
1.4
(0.2)
1.8
(0.3)
1.5
(0.1)
2.2
(0.2)
1.6
(0.1)
2.1
(0.2)
1.7
(0.1)
1.4
(0.2)
1.6
(0.1)
Mean (SE)
rectal temperature (C°)
40.6
(0.2)
40.8
(0.2)
40.6
(0.1)
40.6
(0.2)
41.3
(0.4)
40.8
(0.2)
40.1
(0.2)
41.0
(0.3)
40.3
(0.2)
41.4
(0.1)
41.0
(0.2)
40.6
(0.1)
Mean (SE) neck
circumference (cm)
43.1
(0.9)
34.8
(1.5)
41.1
(1.1)
43.2
(0.8)
35.0
(1.6)
41.1
(1.1)
42.8
(0.6)
35.0
(1.1)
41.2
(0.9)
43.1
(0.4)
34.9
(0.8)
41.1
(0.6)
Mean (SE) chest
circumference (cm)
109.0
(1.2)
86.8
(2.2)
103.5
(2.4)
106.8
(1.9)
85.4
(1.5)
101.2
(2.7)
108.9
(2.1)
89.3
(2.5)
104.8
(2.5)
108.3
(1.0)
87.0
(1.2)
103.1
(1.5)
a
Distance (km) between capture site and processing site.
89
Page 104
Table 3. Capture data by study site for female white-tailed deer in southwest Minnesota, January 2002.
Lake
Benton
Redwood
Falls
All
Sites
Age
Adult (>1 year)
Fawn (∼8months)
Adult
b
Adult
Fawn
Total
Adult
Fawn
Total
Number of deer
captured
8
9
2
11
17
2
19
Mean (SE) handling
time (minutes)
6.9
(0.5)
6.1
(0.6)
9.5
(2.5)
6.7
(0.7)
6.4
(0.4)
9.5
(2.5)
6.8
(0.5)
Mean (SE)
distance
a
(km)
1.5
(0.2)
2.6
(0.5)
1.5
(0.1)
2.4
(0.4)
2.1
(0.3)
1.5
(0.1)
2.0
(0.3)
Mean (SE) rectal
temperature (C°)
40.7
(0.2)
40.6
(0.2)
42.1
(0.0)
40.8
(0.2)
40.6
(0.1)
42.1
(0.0)
40.8
(0.1)
Mean (SE) neck
circumference (cm)
45.8
(1.6)
45.2
(1.1)
37.0
(2.0)
43.7
(1.3)
45.5
(0.9)
37.0
(2.0)
44.6
(1.0)
Mean (SE) chest
circumference (cm)
102.4
(0.9)
102.3
(1.7)
86.0
(9.0)
99.4
(2.7)
102.4
(1.0)
86.0
(9.0)
100.6
(1.6)
a
Distance (km) between capture site and processing site.
b
All deer captured were adults.
90
Page 105
91
Table 4. Cause-specific, seasonal mortality for radiocollared female white-tailed deer in
southwest Minnesota, 2001-02.
Cause of
mortality
Pre-hunt
b
Hunting
b
Post-hunt
b
Totals
Harvest
0
6
0
6
Vehicle-
collision
a
0
3
1
4
Predation
0
1
0
1
Disease
0
0
1
1
Unknown
0
0
2
2
Totals
0
10
4
14
a
A deer that died from a train collision was included with vehicle-collision
category.
b
Seasons = Post-hunt (Jan. 1 - April 31), Pre-hunt ( May 1 – Aug. 31),
Hunting (Sept. 1 - Dec. 31), Hunting-all (Sept. 1 - Dec. 31).
Page 106
92
Table 5. Annual survival rates by study site for radiocollared female white-tailed deer in
southwest Minnesota, 2001-02.
Lake Benton
Walnut Grove Redwood Falls
All sites
Number at-risk
20
19
19
58
Number of
deaths
2
5
6
13
Number
censored
2
1
2
5
Survival rate
0.8889
0.7270
0.6667
0.7616
Confidence
interval (95%)
±0.1452
±0.2065
±0.2178
±0.1138
Variance
0.0055
0.0111
0.0123
0.0034
Page 107
93
Table 6. Overall survival rates by study site for radiocollared female white-tailed deer in
southwest Minnesota, 2001-02.
Lake Benton
Walnut Grove Redwood Falls
All sites
Number at-risk
28
19
30
77
Number of
deaths
2
5
7
14
Number
censored
3
2
3
8
Survival rate
0.8889
0.7270
0.6364
0.7487
Confidence
interval (95%)
±0.1211
±0.2150
±0.1682
±0.0992
Variance
0.0038
0.0120
0.0074
0.0026
Page 108
94
Table 7. Survival rates by season for radiocollared female white-tailed deer in southwest
Minnesota, 2001-02.
2001
2002
Season
a
Post-hunt
b
Pre-hunt
Hunting Hunting-all
c
Post-hunt
Pre-hunt
Number
at-risk
58
53
51
51
60
56
Number of
deaths
3
0
6
10
1
0
Number
censored
2
2
0
0
3
1
Survival rate
0.9483
1.0000
0.8824
0.8039
0.9833
1.0000
Confidence
interval (95%)
±0.0555
±0.0000
±0.0830
±0.0977
±0.0321
±0.0000
Variance
0.0008
0.0000
0.0020
0.0025
0.0003
0.0000
a
Seasons = Post-hunt (Jan. 1 - April 31), Pre-hunt ( May 1 – Aug. 31),
Hunting (Sept. 1 - Dec. 31), Hunting-all (Sept. 1 - Dec. 31).
b
Because deer were captured during the late January, only the end of this month
was included in analysis.
c
Non-hunting (e.g., vehicle, predator) mortalites included during the hunting-all
season time period.
Page 109
95
Table 8. Annual and overall survival rates by age for radiocollared female white-tailed
deer in southwest Minnesota, 2001-02.
Fawn
(∼8 months)
Adult
(>1 year)
Time period
Annual
Overall
Annual
Overall
Number at-risk
14
16
a
44
61
b
Number of deaths
4
4
9
10
Number censored
2
3
2
5
Survival rate
0.6753
0.6753
0.7871
0.7713
Confidence
interval (95%)
±0.2514
±0.2514
±0.1220
±0.1066
Variance
0.0165
0.0165
0.0039
0.0030
a
Two fawns were added during 2002 capture.
b
Seventeen adults were added during 2002 capture.
Page 110
Table 9. Capture data for radiocollared white-tailed deer neonates in southwest Minnesota, spring 2001-02.
2001
2002
Pooled 2001-02
Sex
Male
Female
All
Male
Female
All
Male
Female
All
Number of
neonates
captured
9
12
21
8
10
18
17
22
39
Mean (n, SE)
handling time
(minutes)
2.8
(9, 0.5)
2.4
(12, 0.1)
2.6
(21, 0.2)
4.0
(8, 0.7)
4.7
(10, 07)
4.4
(18, 0.4)
3.4
(17, 0.4)
3.5
(22, 0.4)
3.4
(39, 0.3)
Mean (n, SE)
age at capture
(± 3 days)
5.0
(7, 1.6)
2.9
(11, 0.7)
3.7
(18, 0.8)
5.0
(7, 1.0)
6.7
(9, 1.2)
5.9
(16, .08)
5.0
(14, 0.9)
4.6
(20, 0.8)
4.8
(34, 0.6)
96
Page 111
97
Table 10. Cause-specific, monthly mortality for radiocollared white-tailed deer neonates
in southwest Minnesota, summer 2001-02.
Cause of
mortality
June
July
August
Totals
Predation
3
0
1
4
Disease
1
0
0
1
Vehicle collision
0
1
0
1
Starvation
a
2
0
0
2
Totals
5
1
1
8
a
Unable to determine if neonate mortality was capture-related and was censored
from survival analysis.
Page 112
Table 11. Monthly survival rates for radiocollared white-tailed deer neonates in southwest Minnesota, 2001-02.
2001
2002
Pooled 2001-02
Month
June
July
August
June
July
August
June
July
August
Number
at-risk
21
19
18
18
13
13
39
32
31
Number of
deaths
0
1
0
4
0
1
4
1
1
Number
censored
2
0
2
1
0
0
3
0
2
Survival
rate
1.0000
0.9470
0.9470
0.7778
0.7778
0.7179
0.8974
0.8694
0.8413
Confidence
interval (95%)
±0.0000 ±0.0977
±0.1004
±0.1694
±0.1993
±0.2073
±0.0902
±0.1089
±0.1180
Variance
0.0000
0.0025
0.0026
0.0075
0.0103
0.0112
0.0021
0.0031
0.0036
98
Page 113
99
Table 12. Monthly survival rates by sex of radiocollared white-tailed deer neonates in
southwest Minnesota, 2001-02.
Female
Male
Month
June
July
August
June
July
August
Number
at-risk
22
17
16
17
15
15
Number of
deaths
3
1
0
1
0
1
Number
censored
2
0
0
1
0
2
Survival
rate
0.8636
0.8128
0.8128
0.9412
0.9412
0.8784
Confidence
interval
(95%)
±0.1332
±0.1671
±0.1723
±0.1085
±0.1155
±0.1550
Variance
0.0046
0.0073
0.0077
0.0031
0.0035
0.0063
Page 114
100
Table 13. Mean seasonal migration distance by study site for radiocollared
white-tailed deer in southwest Minnesota, 2001-02.
Lake Benton Walnut Grove
Redwood Falls
All Deer
2001 Spring
Migration
a
(km),
(n, SE)
8.5
(16, 1.2)
7.8
(14, 2.2)
11.6
(12, 2.2)
8.8
(40, 1.1)
2001 Winter
Migration
a
(km),
(n, SE)
9.3
(10, 1.0)
13.6
(8, 4.0)
11.2
(5, 4.0)
11.2
(23, 1.7)
2002 Spring
Migration
a
(km),
(n, SE)
9.4
(18, 1.2)
13.8
(5, 4.8)
11.2
(8, 2.5)
10.8
(32, 1.2)
Pooled Migration
a
(km), (n, SE)
9.1
(44, 0.7)
10.7
(27, 1.9)
11.4
(25, 1.6)
10.1
(95, 0.7)
a
Distance of deer movement between winter and summer home ranges.
Page 115
101
Table 14. Seasonal home range size by study site for radiocollared female
white-tailed deer in southwest Minnesota, 2001-02.
Lake Benton
Walnut Grove
Redwood Falls
All Sites
Winter 50%
(km
2
), (n, SE)
1.02
(11, 0.33)
0.96
(12, 0.20)
0.54
(14, 0.10)
0.82
(37, 0.13)
Winter 95%
(km
2
), (n, SE)
6.91
(11, 2.13)
5.66
(12, 1.03)
3.42
(14, 0.70)
5.18
(37, 0.78)
Summer 50%
(km
2
), (n, SE)
0.40
(36, 0.06)
0.33
(26, 0.05)
0.28
(31, 0.04)
0.34
(93, 0.03)
Summer 95%
(km
2
), (n, SE)
2.65
(36, 0.36)
2.25
(26, 0.29)
1.84
(31, 0.21)
2.27
(93, 0.18)
Page 116
102
Figure 1. Pre-settlement vegetation zones of Minnesota (Rosendahl and Butters 1928).
Page 117
103
FOREST ZONE
FARMLAND ZONE
METRO
Figure 2. Farmland, Forest, and Metro Zones of Minnesota (DePerno et al. 1999).
Page 118
104
Figure 3. Southwest Minnesota white-tailed deer permit areas (PAs), 2000.
Page 119
105
Figure 4. Study area and white-tailed deer capture locations in southwest Minnesota,
2001-02.
Page 120
106
Figure 5. Hierarchical cluster tree diagram for deer permit areas in southwest Minnesota.
The average distance between clusters (x axis) is defined as “the average of all the
dissimilarities between all possible pairs of points such that one of each pair is in each
cluster” (Johnson 1998).
Page 121
107
Figure 6. Principal components analysis clusters of scores for deer permit areas in
southwest Minnesota, 2000.
Page 122
108
Figure 7. Permit areas selected for white-tailed deer capture sites in southwest
Minnesota, 2001-02.
Page 123
109
Figure 8. White-tailed deer neonate study area and capture locations in southwest
Minnesota, 2001-02.
Page 124
110
42.9
28.6
14.3
7.1 7.1
0
10
20
30
40
50
60
Hu
nt
ing
Vehicl
e
Unknown
Disease
Predat
i
on
% Mortality
n = 1
n = 1
n = 2
n = 4
n = 6
Figure 9. Cause-specific mortality of radiocollared female white-tailed deer in southwest
Minnesota, 2001-02 (Deer that died from train collision was included in vehicle
mortalities).
Page 125
111
Figure 10. Suspected felid (i.e., bobcat, cougar) killed 2.5-year old female white-tailed
deer in southwest Minnesota, 16 October 2001.
Page 126
112
2000-01
0
10
20
30
40
50
60
No
v.
De
c.
J
an
.
F
eb
.
M
ar
ch
Deer Winter Severity Index
Lake Benton
Redwood Falls
Walnut Grove
Average
Figure 11. Monthly deer winter severity index (DWSI) for individual study sites in
southwest Minnesota, 2000-01. (One point accumulated for each day with an ambient
temperature ≤-7 C°, and an additional point accumulated for each day with snow depths
≥35.0 cm; National Climatic Data Center 2002, Climatology Working Group 2003).
Page 127
113
2001-02
0
10
20
30
40
50
60
Nov.
De
c
.
J
a
n
.
F
e
b
.
M
arc
h
Deer Winter Severity Index
Lake Benton
Redwood Falls
Walnut Grove
Average
Figure 12. Monthly deer winter severity index (DWSI) for individual study sites in
southwest Minnesota, 2001-02. (One point accumulated for each day with an ambient
temperature ≤-7 C°, and an additional point accumulated for each day with snow depths
≥35.0 cm; National Climatic Data Center 2002, Climatology Working Group 2003).
Page 128
114
0
20
40
60
80
100
120
140
160
180
200
2000-01
2001-02
Deer Winter Severity Index
Lake Benton
Redwood Falls
Walnut Grove
Average
Figure 13. Annual deer winter severity index (DWSI) for individual study sites in
southwest Minnesota, 2000-02. (One point accumulated for each day between November
and March with an ambient temperature ≤-7 C°, and an additional point accumulated for
each day with snow depths ≥35.0 cm; National Climatic Data Center 2002, Climatology
Working Group 2003)).
Page 129
115
Figure 14. To determine the age of white-tailed deer neonates, the distance from growth
ring to hairline was measured (mm) on front hoof (Haugen and Speak 1958).
Page 130
116
66.7
16.7
16.7
0
20
40
60
80
100
P
r
ed
ati
o
n
Disease
V
e
h
icl
e
Mortality %
n = 1
n = 1
n = 4
Figure 15. Cause-specific mortality for radiocollared white-tailed deer neonates in
southwest Minnesota, summer 2001-02.
Page 131
117
Figure 16. Dispersal distance and direction for radiocollared female white-tailed deer in
southwest Minnesota, 2001.
Page 132
118
Figure 17. Migrations for radiocollared female white-tailed deer at Lake Benton study
site in southwest Minnesota, 2001-02 (Refer to Figure 4 for location of study sites in
southwest Minnesota).
Page 133
119
Figure 18. Migrations for radiocollared female white-tailed deer at Walnut Grove study
site in southwest Minnesota, 2001-02 (Refer to Figure 4 for location of study sites in
southwest Minnesota).
Page 134
120
Figure 19. Migrations for radiocollared female white-tailed deer at Redwood Falls study
site in southwest Minnesota, 2001-02 (Refer to Figure 4 for location of study sites in
southwest Minnesota).
Page 135
121
LAKE BENTON
2001 FALL MIGRATION
n = 10
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Nov
16-Nov
1-Dec
16-Dec
31-Dec
% Migrated
Temperature
Snow Depth
WALNUT GROVE
2001 FALL MIGRATION
n = 6
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Nov
16-Nov
1-Dec
16-Dec
31-Dec
% Migrated
Temperature
Snow Depth
REDWOOD FALLS
2001 FALL MIGRATION
n = 5
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Nov
16-Nov
1-Dec
16-Dec
31-Dec
% Migrated
Temperature
Snow Depth
Figure 20. Fall migration events by study site for radiocollared female white-tailed deer in
southwest Minnesota, 2001. The Y-axis is shared by all three variables (i.e., temperature [C°],
snow depth [cm], migrating [%]). A migration event represents the cumulative percentage of
migating individuals at each study site with known departure dates from summer range.
Page 136
122
LAKE BENTON
SPRING MIGRATION
2001, n = 17
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Mar
16-Mar
31-Mar
15-Apr
30-Apr
15-May
30-May
% Migrated
Temperature
Snow Depth
WALNUT GROVE
SPRING MIGRATION
2001, n = 10
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Mar
16-Mar
31-Mar
15-Apr
30-Apr
15-May
30-May
% Migrated
Temperature
Snow Depth
REDWOOD FALLS
SPRING MIGRATION
2001, n = 13
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Mar
16-Mar
31-Mar
15-Apr
30-Apr
15-May
30-May
% Migrated
Temperature
Snow Depth
Figure 21. Spring migration events by study site for radiocollared female white-tailed deer in
southwest Minnesota, 2001. The Y-axis is shared by all three variables (i.e., temperature [C°],
snow depth [cm], migrating [%]). A migration event represents the cumulative percentage of
migrating individuals at each study site with known departure dates from winter range.
Page 137
123
LAKE BENTON
SPRING MIGRATION
2002, n = 13
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Mar
16-Mar
31-Mar
15-Apr
30-Apr
15-May
30-May
% Migrated
Temperature
Snow Depth
WALNUT GROVE
SPRING MIGRATION
2002, n = 6
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Mar
16-Mar
31-Mar
15-Apr
30-Apr
15-May
30-May
% Migrated
Temperature
Snow Depth
REDWOOD FALLS
SPRING MIGRATION
2002, n = 8
-20
-10
0
10
20
30
40
50
60
70
80
90
100
1-Mar
16-Mar
31-Mar
15-Apr
30-Apr
15-May
30-May
% Migrated
Temperature
Snow Depth
Figure 22. Spring migration events by study site for radiocollared female white-tailed deer in
southwest Minnesota, 2002. The Y-axis is shared by all three variables (i.e., temperature [C°],
snow depth [cm], migrating [%]). A migration event represents the cumulative percentage of
migrating individuals at each study site with known departure dates from winter range.
Page 138
124
Figure 23. Southwest Minnesota deer permit areas and major roads, 2000.
Page 139
125
Appendix A. Capture data for radiocollared female white-tailed deer in southwest
Minnesota, January 2001.
Capture
Date
Study
Site
Age at
capture
(fawn,
adult)
Collar
Frequency
Processing
Time
Rectal
Temp.
C°
Neck
Girth
(cm)
Chest
Girth
(cm)
Left
ear
tag #
Right
ear
tag #
Transport
distance
(km)
1/24/01
Redwood
Falls
A
232
6.00
39.6
41
121
1273
1274
3.57
1/24/01
Redwood
Falls
A
410
6.00
40.4
45
113
1275
1225
3.36
1/24/01
Redwood
Falls
A
352
6.00
39.5
42
108
1223
1224
1.48
1/24/01
Redwood
Falls
A
470
7.00
39.5
42
110
1220
1221
1.85
1/24/01
Redwood
Falls
A
171
6.00
40.3
46
109
1219
1222
2.17
1/24/01
Redwood
Falls
F
811
6.00
41.4
33
86
1217
1216
1.48
1/24/01
Redwood
Falls
F
782
7.00
41.1
34
93
1215
1214
2.06
1/24/01
Redwood
Falls
A
289
9.00
39.2
45
113
1165
1166
3.27
1/24/01
Redwood
Falls
A
111
7.00
40.2
43
122
1167
1168
2.54
1/24/01
Redwood
Falls
F
901
7.00
41.5
35
95
1169
1170
1.50
1/24/01
Redwood
Falls
A
689
9.00
41.1
44
111
1171
1172
2.66
1/24/01
Redwood
Falls
A
631
11.00
39.0
41
94
1173
1174
0.87
1/24/01
Redwood
Falls
A
722
6.00
39.5
39
101
1213
1212
1.98
1/23/01
Redwood
Falls
F
841
11.00
40.0
38
83
1265
1264
1.50
1/23/01
Redwood
Falls
A
662
11.00
40.3
38
116
1269
1266
0.00
1/23/01
Redwood
Falls
A
531
7.00
40.6
46
106
1163
1164
2.54
1/23/01
Redwood
Falls
A
054
14.00
41.1
45
111
1162
1161
1.77
1/23/01
Redwood
Falls
A
591
10.00
40.4
42
98
no
data
1270
2.24
1/23/01
Redwood
Falls
A
750
7.00
41.1
43
101
1271
1272
2.64
1/23/01
Walnut
Grove
A
741
10.00
40.8
41
105
1158
1157
1.34
1/23/01
Walnut
Grove
A
622
7.00
40.3
47
114
1159
1160
1.26
1/23/01
Walnut
Grove
A
331
7.00
41.4
47
107
1155
1156
1.42
1/23/01
Walnut
Grove
A
210
10.00
40.1
43
119
1259
1258
1.48
1/23/01
Walnut
Grove
F
803
7.00
40.8
29
81
1261
1260
1.43
1/23/01
Walnut
Grove
F
032
8.00
41.7
36
83
1263
1262
1.66
1/23/01
Walnut
Grove
A
653
12.00
38.9
41
102
1149
1150
1.77
1/23/01
Walnut
Grove
A
091
10.00
41.8
46
106
1153
1154
1.37
1/23/01
Walnut
Grove
F
890
4.00
41.6
36
89
1247
1248
0.95
Page 140
126
1/23/01
Walnut
Grove
A
6.00
40.3
44
119
1250
1249
0.43
1/23/01
Walnut
Grove
A
148
11.00
40.5
40
114
1142
1141
1.40
1/23/01
Walnut
Grove
A
571
9.00
39.9
46
106
1246
1251
0.93
1/23/01
Walnut
Grove
F
862
4.00
42.2
36
88
1253
1252
2.33
1/23/01
Walnut
Grove
A
390
10.00
40.7
45
103
1255
1254
2.41
1/23/01
Walnut
Grove
A
710
12.00
41.3
39
94
1147
1148
0.66
1/23/01
Walnut
Grove
A
449
9.00
40.2
45
104
1245
1244
2.06
1/23/01
Walnut
Grove
A
271
10.00
40.5
41
102
1143
1144
0.64
1/23/01
Walnut
Grove
A
680
6.00
42.2
40
100
1145
1146
1.77
1/23/01
Walnut
Grove
F
832
8.00
40.1
38
86
1256
1257
2.56
1/22/01
Lake
Benton
A
731
11.00
40.0
48
105
1025
1026
1.43
1/22/01
Lake
Benton
A
193
10.00
41.3
46
104
1121
1122
0.72
1/22/01
Lake
Benton
F
761
11.00
40.4
40
95
1123
1124
1.09
1/22/01
Lake
Benton
A
131
8.00
39.9
41
111
1127
1128
2.95
1/22/01
Lake
Benton
A
372
9.00
40.6
42
113
1129
1130
1.05
1/22/01
Lake
Benton
A
672
12.00
41.1
49
108
1131
1132
1.24
1/22/01
Lake
Benton
A
072
9.00
41.8
38
101
1235
1234
2.11
1/22/01
Lake
Benton
A
011
6.00
39.9
41
107
1136
1135
1.50
1/22/01
Lake
Benton
F
853
13.00
40.4
35
87
1238
1239
0.23
1/22/01
Lake
Benton
A
551
10.00
40.9
40
112
1241
1240
1.00
1/22/01
Lake
Benton
F
881
15.00
41.0
31
83
1243
1242
0.80
1/22/01
Lake
Benton
F
790
9.00
41.4
35
86
1133
1134
1.24
1/22/01
Lake
Benton
F
821
8.00
40.6
33
83
1137
1138
0.23
1/22/01
Lake
Benton
A
309
11.00
39.9
40
114
1139
1140
1.19
1/22/01
Lake
Benton
A
250
9.00
40.5
39
112
1233
1232
0.35
1/22/01
Lake
Benton
A
491
9.00
40.5
42
108
1025
1026
1.09
1/22/01
Lake
Benton
A
611
7.00
41.0
45
101
1237
1236
1.42
1/22/01
Lake
Benton
A
643
7.00
40.0
46
115
1226
1227
1.46
1/22/01
Lake
Benton
A
430
8.00
40.9
44
109
1229
1228
2.08
1/22/01
Lake
Benton
A
702
8.00
40.3
46
115
1231
1230
0.89
510
Page 141
127
Appendix B. Capture data for radiocollared female white-tailed deer in southwest
Minnesota, January 2002.
Capture
Date
Study
Site
Age at
capture
(fawn,
adult)
Collar
Frequency
Processing
Time
Rectal
Temp.
C°
Neck
Girth
(cm)
Chest
Girth
(cm)
Left
ear
tag #
Right
ear
tag #
Transport
distance
(km)
1/26/02
Lake
Benton
A
032B
7.00
40.5
52
107
1301
1302
1.28
1/26/02
Lake
Benton
A
352B
9.00
40.6
48
100
1337
1336
2.04
1/26/02
Lake
Benton
A
551B
9.00
39.9
44
104
1335
1334
1.18
1/26/02
Lake
Benton
A
771B
5.00
40.8
45
101
1303
1304
1.67
1/26/02
Lake
Benton
A
803B
6.00
40.2
41
99
1333
1332
2.11
1/26/02
Lake
Benton
A
832B
5.00
41.5
42
101
1331
1330
0.85
1/26/02
Lake
Benton
A
868B
7.00
40.8
52
103
1327
1326
1.70
1/26/02
Lake
Benton
A
901B
7.00
41.1
42
104
1329
1328
1.06
1/26/02
Redwood
Falls
A
149B
4.00
39.8
50
101
1306
1305
2.46
1/26/02
Redwood
Falls
A
171B
5.00
40.1
41
93
1310
1309
2.41
1/26/02
Redwood
Falls
F
193B
12.00
42.1
35
77
1346
1347
1.60
1/26/02
Redwood
Falls
A
391B
10.00
40.2
47
109
1308
1307
2.07
1/26/02
Redwood
Falls
A
512B
5.00
40.7
47
103
1312
1311
0.56
1/26/02
Redwood
Falls
A
680B
6.00
41.4
47
102
1314
1313
4.21
1/26/02
Redwood
Falls
F
702B
7.00
42.1
39
95
1344
1345
1.46
1/26/02
Redwood
Falls
A
770B
6.00
40.7
47
110
1318
1317
3.89
1/26/02
Redwood
Falls
A
792B
6.00
40.6
42
101
1338
1339
2.38
1/26/02
Redwood
Falls
A
862B
8.00
40.8
45
103
1350
1349
4.49
1/26/02
Redwood
Falls
A
871B
5.00
40.9
41
99
1342
1343
0.83
a
B represents a deer captured during January 2002.
Page 142
128
Appendix C. Mortality for radiocollared female white-tailed deer in southwest
Minnesota, 2001-02.
.
Capture
location
Age at Capture
a
Date of
Capture
Cause of
Death
Age at
Death (years)
Date of
Death
Redwood Falls
Fawn
1/24/01
Predation
b
0.5
2/13/01
Walnut Grove
Fawn
1/23/01
Bacterial
infection
0.5
2/19/01
Lake Benton
Adult
1/22/01
Vehicle
b
Unknown
3/6/01
Walnut Grove
Adult
1/24/01
Train
4.5
3/13/01
Redwood Falls
Adult
1/24/01
Unknown
Unknown
4/17/01
Lake Benton
Adult
1/22/01
Predation
2.5
10/16/01
Walnut Grove
Fawn
1/23/01
Hunting
1.5
11/3/01
Lake Benton
Adult
1/22/01
Hunting
Unknown
11/3/01
Redwood Falls
Adult
1/24/01
Hunting
Unknown
11/3/01
Redwood Falls
Adult
1/24/01
Hunting
8.5
11/3/01
Walnut Grove
Fawn
1/23/01
Hunting
1.5
11/4/01
Redwood Falls
Adult
1/24/01
Hunting
6.5
11/4/01
Redwood Falls
Adult
1/24/01
Vehicle
4.5
11/21/01
Redwood Falls
Adult
1/24/01
Vehicle
3.5
12/3/01
Walnut Grove
Fawn
1/23/01
Vehicle
1.5
12/4/01
Redwood Falls
Fawn
1/26/02
Capture
related
b
0.5
1/30/02
Redwood Falls
Adult
1/26/02
Unknown
Unknown
4/11/02
a
Fawns were ∼8 months old at capture and adults were >1 year old at capture.
b
Deer mortality may have been capture related and was censored from study.
Page 143
129
Appendix D. Capture data for white-tailed deer neonates in southwest Minnesota, spring
2001.
Radiocollar frequency
Date of Capture
Sex
Handling
Time (min.)
Estimated
Age (± 3 days)
760
5/22/2001
F
2
1
750
5/25/2001
M
5
1
820
5/25/2001
F
3
1
960
5/29/2001
F
2
3
880
5/29/2001
F
3
1
800
6/01/2001
F
3
2
940
6/01/2001
M
3
1
810
6/01/2001
F
3
2
770
6/01/2001
M
3
5
780
6/05/2001
F
2
2
870
6/05/2001
M
2
4
920
6/06/2001
F
2
2
890
6/07/2001
F
3
6
850
6/07/2001
M
3
8
910
6/07/2001
F
2
9
760
6/07/2001
M
2
13
900
6/07/2001
F
2
3
790
6/07/2001
M
5
5
930
6/09/2001
M
1
Unknown
860
6/09/2001
M
1
3
840
6/11/2001
F
2
Unknown
Page 144
130
Appendix E. Capture data for white-tailed deer neonates in southwest Minnesota, spring
2002.
Radiocollar
frequency
Date of Capture
Sex
Handling
Time (min.)
Estimated
Age (± 3 days)
270
5/23/02
M
3
2
050
5/25/02
F
5
8
180
5/25/02
M
1
4
070
5/28/02
M
4
7
130
5/29/02
M
6
3
799
5/30/02
M
4
8
768
6/1/02
M
6
3
160
6/1/02
F
1
Unknown
859
6/1/02
F
5
6
930
6/3/02
F
7
1
749
6/4/02
F
3
6
779
6/5/02
F
6
4
910
6/6/02
F
4
13
110
6/6/02
M
2
Unknown
889
6/7/02
M
6
8
940
6/7/02
F
4
10
210
6/7/02
F
6
7
789
6/8/02
F
6
5
Page 145
131
Appendix F. Mortality for radiocollered white-tailed deer neonates in southwest
Minnesota, summer 2001-02.
Sex
Year
Capture Date
Cause of mortality Mortality date
Female
2001
5/22/01
Starvation
a
5/25/01
Female
2001
6/1/01
Vehicle collision
7/29/01
Female
2002
5/25/02
Predation
6/2/02
Male
2002
6/1/02
Starvation
a
6/4/02
Male
2002
5/28/02
Disease
6/12/02
Female
2002
6/5/02
Predation
6/15/02
Female
2002
6/7/02
Predation
6/23/02
Male
2002
6/6/02
Predation
8/4/02
a
Fawn will be censored from study.
Page 146
132
Appendix G. Movement for individual radiocollared female white-tailed deer in
southwest Minnesota, 2001.
Deer
ID
Study
Site
Winter
50%
Home
Range
(ha)
Winter
95%
Home
Range
(ha)
Summer
50%
Home
Range
(ha)
Summer
95%
Home
Range
(ha)
Spring
Movement
(km)
Spring
Dispersal
(km)
Spring
Migration
(km)
Fall
Movement
(km)
Fall
Migration
(km)
111
RF
22.4
293.3
16.3
16.3
14.5
14.5
054
RF
37.7
88.9
0.0
0.0
171
RF
20.6
168.1
10.7
10.7
232
RF
43.9
212.5
1.6
1.6
1.6
1.6
289
RF
9.1
41.8
16.8
116.1
22.2
22.2
352
RF
12.3
64.0
13.8
13.8
410
RF
25.8
174.0
4.6
4.6
3.0
3.0
470
RF
38.0
297.0
22.6
22.6
531
RF
33.3
170.5
0.0
0.0
591
RF
205.0
205.0
631
RF
1.7
1.7
662
RF
22.1
164.3
0.0
0.0
689
RF
21.4
116.4
23.3
23.3
23.3
23.3
722
RF
30.9
163.4
0.0
0.0
750
RF
99.8
541.6
4.6
4.6
0.0
782
RF
46.0.
46.0
811
RF
31.5
151.5
0.0
0.0
841
RF
26.3
155.6
3.1
3.1
13.4
13.4
901
RF
032
WG
091
WG
106.8
593.6
15.1
15.1
13.7
13.7
148
WG
4.0
4.0
210
WG
14.6
187.7
3.2
3.2
0.0
271
WG
89.0
423.8
32.1
232.0
30.8
30.8
29.9
29.9
331
WG
43.4
400.1
4.5
4.5
0
390
WG
5.4
5.4
4.4
4.4
449
WG
68.2
268.3
2.8
2.8
0.0
510
WG
59.0
630.1
0.0
0.0
571
WG
18.4
136.0
3.7
3.7
0.0
622
WG
18.0
208.4
8.2
8.2
6.2
6.2
653
WG
20.3
115.9
17.5
17.5
17.5
17.5
680
WG
710
WG
22.4
180.9
5.1
5.1
5.1
5.1
741
WG
12.1
77.8
3.1
3.1
0.0
803
WG
4.6
4.6
832
WG
14.2
134.7
0.0
862
WG
48.7
185.0
15.7
15.7
30.4
30.4
890
WG
60.1
371.4
1.7
1.7
1.9
1.9
881
LB
12.5
81.7
0.0
0.0
853
LB
61.0
494.1
13.6
13.6
13.3
13.3
821
LB
58.8
314.6
4.7
4.7
0.0
790
LB
.
761
LB
22.3
182.9
7.1
7.1
7.1
7.1
731
LB
23.3
193.4
7.0
7.0
7.2
7.2
702
LB
672
LB
14.7
112.1
2.6
2.6
0.0
643
LB
21.0
227.5
4.6
4.6
4.6
4.6
611
LB
40.6
211.3
10.1
10.1
11.0
11.0
551
LB
40.8
285.3
2.4
2.4
Page 147
133
491
LB
28.2
182.6
13.4
13.4
13.4
13.4
430
LB
16.1
116.7
3.8
3.8
0.0
372
LB
18.7
18.7
5.4
5.4
309
LB
16.2
163.2
0.0
0.0
250
LB
54.9
382.5
10.3
10.3
8.0
8.0
193
LB
33.1
468.9
11.4
11.4
131
LB
10.5
97.8
11.2
11.2
11.4
11.4
072
LB
202.7
1277.7
11.9
11.9
12.0
12.0
011
LB
46.1
309.1
2.3
2.3
0.0
Blank cell represents “no data”.
Page 148
134
Appendix H. Movement for individual radiocollared female white-tailed deer in
southwest Minnesota, 2002.
Study
Site
Deer
ID
Winter
b
50%
Home Range
(ha)
Winter
b
95% Home
Range (ha)
Summer 50%
Home Range
(ha)
Summer 95%
Home Range
(ha)
Spring
Movement
(km)
Spring
Migration
(km)
RF
111
20.7
85.3
28.2
267.2
14.4
14.4
RF
054
40.6
323.7
13.3
74.6
0.0
RF
149B
a
41.5
242.8
19.5
124.3
0.0
RF
171B
a
74.0
208.2
RF
391B
a
16.8
100.5
18.2
18.2
RF
410
14.4
92.5
3.3
3.3
RF
512B
a
60.6
350.5
0.0
RF
531
17.3
167.1
9.5
87.6
0.0
RF
591
57.2
347.8
3.9
3.9
RF
662
71.6
576.0
34.4
228.3
0.0
RF
680B
a
56.8
412.0
0.0
RF
689
104.4
596.0
15.1
99.4
22.4
22.4
RF
702B
a
11.9
11.9
RF
722
25.8
268.6
12.9
119.6
0.0
RF
750
151.3
1053.0
22.5
180.3
0.0
RF
770B
a
19.5
197.3
0.0
RF
782
18.2
136.4
0.0
RF
811
61.6
387.8
13.1
71.5
0.0
RF
841
12.8
126.7
0.0
RF
862B
a
79.6
496.0
3.5
3.5
RF
871B
a
16.4
157.2
12.2
12.2
WG
091
207.6
1007.0
34.8
280.2
13.2
13.2
WG
210
76.2
524.5
11.4
57.3
0.0
WG
271
91.6
902.3
17.2
107.5
29.9
29.9
WG
331
76.0
552.2
17.0
182.2
0.0
WG
390
60.7
346.7
0.0
WG
449
103.8
552.6
16.7
96.0
0.0
WG
510
WG
571
17.9
158.0
3.9
42.9
0.0
WG
622
217.4
1014.3
58.8
318.4
6.4
6.4
WG
653
13.3
72.5
21.8
223.0
17.4
17.4
WG
710
151.0
1016.8
30.8
219.3
0.0
WG
741
14.7
72.1
12.7
52.5
0.0
WG
890
96.9
498.0
27.9
193.8
2.0
2.0
LB
901B
a
19.3
19.3
LB
881
23.8
191.5
9.7
59.4
0.0
LB
868B
a
96.7
574.1
19.4
19.4
LB
853
336.7
1803.7
19.7
150.1
13.3
13.3
LB
832B
a
4.3
4.3
LB
821
211.7
1866.7
0.0
LB
803B
a
79.0
327.0
6.8
6.8
LB
761
15.6
209.5
43.3
187.8
7.2
7.2
LB
731
71.6
456.6
33.8
255.2
7.6
7.6
LB
672
73.0
526.6
25.4
208.8
0.0
LB
643
12.8
133.3
38.4
204.5
4.5
4.5
Page 149
135
LB
611
15.5
88.8
19.2
248.6
11.4
11.4
LB
551B
a
58.9
242.7
3.2
3.2
LB
491
18.5
128.9
13.3
13.3
LB
430
19.2
187.4
3.0
3.0
LB
372
100.9
475.1
77.6
262.7
5.8
5.8
LB
352B
a
15.6
15.6
LB
309
69.5
457.6
5.8
5.8
LB
250
231.7
1629.6
19.6
156.0
8.2
8.2
LB
131
4.6
47.1
11.5
11.5
LB
072
47.6
345.9
11.9
11.9
LB
032B
a
34.1
267.6
16.7
16.7
LB
011
28.3
216.5
21.9
125.4
0.0
a
B represents a deer captured during January 2002.
b
Home range calculated using locations gathered during winter season 2001-
2002.
Blank cell represents “no data”.