Age-specific survival and space use of white-tailed deer in southern Michigan.



ABSTRACT

Assessments of age-specific space use and demographics are important for managing popular game species, such as the white-tailed deer (Odocoileus virginianus). Habitat management relies on knowledge of space use, and abundance estimation models (e.g., sex-age-kill) require estimates of age-specific data. Because these population characteristics often vary across landscapes, management is often based on specific land-unit areas. We captured and radiomarked 76 deer and aged them as fawn (<1 yr old; both sexes), yearling ([greater then or equal to] 1-< 2 yrs old; females) or adult ([greater then or equal to] 2 yrs old; females) to estimate survival, assess cause-specific mortality, and describe space use in southern Michigan. Annual survival varied by age class (fawn = 0.51, yearling = 0.94, adult = 0.56). Primary sources of mortality were canid predation, vehicle collisions (fawns), and hunter-harvest (fawns, adults). Age-specific space use varied seasonally (agricultural growing and non-growing seasons), with home-range sizes larger during fall for each age class. Yearlings generally had larger home ranges (growing season: [bar.x] = 201.8 ha [+ or -] 91.1 SE; non-growing season: x = 156.9 ha [+ or -] 28.2 SE) than fawns (60.2 ha [+ or -] HA; 116.3 ha [+ or -] 20.6) or adults (77.5 ha [+ or -] 9.6; 140.4 ha [+ or -] 23.4). Compared with other studies in Michigan, we observed several differences in survival and space use, suggesting that managers should consider landscape characteristics when setting objectives and implementing management programs.

INTRODUCTION

Regulated harvest and habitat management for game species are two primary management methods used by state wildlife agencies. Consequently, accurate descriptions of population characteristics such as estimates of age-specific demographics and space use are imperative during the development of harvest and habitat management plans for game species. For example, survival of cervids can be influenced by weather patterns (e.g., DelGiudice et al. 2006), predator-prey dynamics (e.g., Labisky and Boulay 1998), and hunter-harvest (e.g., Bender et al. 2000). Survival and other age-specific demographic estimates are also important parameters for managers using simulation modeling to predict species abundance.

Spatial and temporal changes in cover may affect species abundance and age and sex structure, and land-use or ownership changes may impact a state agency's ability to manage deer populations. Because home-range size varies by sex and age of the individual, as well as habitat and season (Demarais et al. 2000), specific information about space use may help guide management objectives and decisions. Increasing white-tailed deer (Odocoileus virginianus) and human populations coupled with land-use changes (e.g., urbanization) further add to the complexity of deer management across much of the United States (Demarais et al. 2000). Because population demographics and structure vary greatly across landscapes, management agencies often develop species-specific management units. Although management units are often based on non-ecological components, such as roads or county boundaries, these features are more apparent to hunters and other stakeholder groups.

Management of high deer populations through regulated hunting is probably the most cost-effective strategy (Demarais et al. 2000). Harvest objectives are often implemented at the management-unit level and may be age- and sex-specific for deer. For example, harvest objectives for antlerless deer (i.e., males < 1 yr old and females) are often adapted to achieve the agency's population goals, such as when the deer population is determined to be higher than desired. Thus, knowledge of area-specific survival and space use of female deer, under these circumstances is important as female deer are often the sex class of primary interest for managers desiring relatively large reductions in population densities through hunter-harvest (Carpenter 2000).

Our objectives were to describe age-specific survival, cause-specific mortality, and space use of male and female fawns and female yearling and adult white-tailed deer in south central Michigan, and to compare our findings with past research on deer in Michigan. We focused primarily on female white-tailed deer for our analyses, as south central Michigan currently has a relatively high deer density (~27/[km.sub.2] during fall 2005; Michigan Department of Natural Resources [MDNR] 2005), and southern Michigan deer numbers generally exceed the goals of the MDNR (Clute 2006).

STUDY AREA

Our study was conducted in eastern Jackson, western Washtenaw, and southwestern Livingston counties in south central Lower Michigan (Figure 1; Hiller et al. 2008). The study area (82,636 ha) included publicly owned lands, including the MDNR Waterloo (8,410 ha; 10.2% of study area) and Pinckney (4,276 ha; 5.2%) recreation areas, and privately owned lands. Although the study area was primarily rural (98% of total land area), the human population increased 16% and housing units increased 22% between 1990 and 2000 (U.S. Census Bureau 2003). Land-use activities in much of southern Michigan and other areas of the Midwest are expected to show increases similar to those seen in the study area (Madill and Rustem 2001).

[FIGURE 1 OMITTED]

The physiographic regions of this area are Hillsdale-Lapeer Hilly Upland, South Central Rolling Plain, and Southeastern Rolling Plain with alfisols as the major soil order (Sommers 1977). Elevation of the study area is approximately 180 to 300 m with limited relief (Sommers 1977). Annual precipitation was about 81 cm during 1971-2000 (based on conditions in Chelsea, Michigan; Midwestern Regional Climate Center 2008a). During 1971-2000, average annual snowfall was 99.3 cm and mean monthly temperatures ranged from -5.4 [degrees]C (Jan) to 21.8 [degrees]C (Jul) in Jackson County (Midwestern Regional Climate Center 2008b, 2008c). Total annual snowfall during our study was highly variable (90.9 cm in 2004, 149.9 cm in 2005, and 40.0 cm in 2006; conditions in Chelsea, Michigan; Midwestern Regional Climate Center 2008d).

Much of the study area is well suited for agriculture, with a 150-day growing season that generally occurs from 10 May to 7 October (Sommers 1977). In 2002, agriculture in Jackson and Washtenaw counties (total area = 366,483 ha) included corn (37,840 ha), soybeans (34,200 ha), hay (17,200 ha), winter wheat (7,970 ha), and oats (930 ha; Michigan Department of Agriculture 2002).

We generalized land-use, land-cover data (Michigan Center for Geographic Information 2001) using Arc View GIS v3.2 software (ESRI, Redlands, California, USA) and Spatial Analyst extension to define 13 cover types within the study area (Figure 1); agriculture (non-vegetated farmland, row crops, forage crops; 52.3% of study area); conifer (pines [Pinus spp.], other upland conifers; 1.5%); herbaceous openland (herbaceous vegetation with <25% woody cover; 2.9%); lowland deciduous forest (> 60% deciduous tree cover; 8.0%); lowland shrub (with >60% non-water cover; 9.9%); mixed wetland (floating aquatic vegetation, emergent wetland, mixed non-forest wetland; 3.1%); northern hardwood (>60% canopy cover of maple, beech, ash [Fraxinus spp.], cherry [Primus spp.], birch [Betula spp.]; 2.3%); oak association (> 60% canopy cover of oak; 1.6%); upland deciduous forest (> 60% canopy cover of a diverse mixture of upland deciduous trees; 11.6%); upland shrub (> 25% woody cover; <0.1%); urban (low and high intensity, roads, parks, golf courses; 2.8%); water (surface, flowing; 3.9%); and other (aspen [Populus spp.] association, orchards, bare ground; 0.1%). Patch size of cover types ranged from < 1 ha to > 11,000 ha (e.g., an agricultural matrix) and averaged 29.2 ha. Consequently, generalization of spatial data may have excluded certain fine-scale landscape characteristics (e.g., hedgerows, roads) in some instances.

METHODS

Capturing Deer

Winter. We trapped deer during winter Jan-Mar) 2004-2006, using single-door collapsible live traps (Clover 1954) baited with kernel corn. We checked traps twice per day to minimize stress and injury to deer. We restrained captured deer manually blindfolded them to reduce stress, and affixed metal ear tags bearing a unique identification number (Style 681; National Band and Tag, Newport, Kentucky, USA). Deer ages were estimated as fawn (< 1 year old), yearling ([greater than or equal to] l - & [less than or equal to]2 years old), or adult ([less than or equal to] 2 years old) based on morphometric differences (e.g., shape and size of head, body size) and dental characteristics (Severinghaus 1949). Ages of necropsied individuals later confirmed field observations for deer captured during winter.

Female deer were fitted with mortality-sensing radio-collars, either with VHF (Model 500; Telonics, Inc., Mesa, Arizona, USA) or VHF-GPS (Model G2000; Advanced Telemetry Systems, Isanti, Minnesota, USA) capabilities. Radio-transmitters had a unique frequency within the 150 MHz range, a mass of 270 g (VHF) or 1,100 g (VHF-GPS), and an expected minimum battery life of 36 months (VHF) or 12 months (VHF-GPS).

[ILLUSTRATION OMITTED]

Spring. We captured neonatal fawns from mid-May to mid-June 2004-2006. We expected capture success to peak around 1 June and instances of fawns flushing to increase greatly after mid-June (Pusateri Burroughs et al. 2006), thus reducing our ability for successful capture; fawns >2 weeks old generally start flushing when approached by humans (Carroll and Brown 1977). We captured neonates either by hand or in a fish-landing net (0.5 m diameter net, 2 m long extendable handle). A group of 2-6 people systematically searched potential fawning areas for neonates (Lund 1975; Ballard et al. 1999). Occasionally, isolated adult females provided behavioral signs of a nearby fawn (Downing and McGinnes 1969).

After capture, we weighed, sexed, ear-tagged (Style 681; National Band and Tag, Newport, Kentucky, USA) and radiomarked neonates. Ages of neonates were estimated using hoof-growth measurements (Haugen and Speake 1958). The mortality-sensing radio-transmitters (Model M4210; Advanced Telemetry Systems, Isanti, Minnesota, USA) had an expandable collar to allow for growth and were designed to drop off after 9-12 months (see Diefenbach et al. 2003), although we had transmitters active for longer periods ([less than or equal to] 18 mo). Transmitters had a unique frequency within the 151 MHz range, a mass of 60 g, and an expected minimum battery life of 12 months. The precise-event transmitter option provided a time-of-mortality estimate within 30 min for a maximum of ~ 5 days post-mortality (i.e., 5 days of no transmitter movement).

Neonates were classified as fawns at the time of capture. All radiomarked deer were reclassified to the next age class (i.e., fawn to yearling, yearling to adult) on 1 June of the year following capture. Michigan State University's All-University Committee on Animal Use and Care approved our capturing and handling procedures (Application No. 01/04-006-00).

Survival and Cause-specific Mortality

All deer were monitored 2-5 times/week to estimate survival and assess cause-specific mortality, but spring-captured fawns were monitored daily for the first 30 days following capture to potentially increase the accuracy of our assessments. We used the Mayfield method (Mayfield 1961, 1975), modified by Bunck and Pollock (1993) for censored individuals, to estimate survival. Daily survival was estimated for three time periods for fawns (0-6 months old [Hiller et al. 2008], 6-12 months old, and annual), and annually for yearlings and adults. To estimate survival, we excluded individuals that died [less than or equal to]7 days post-capture. Individuals were censored if we believed they were alive at the time of transmitter recovery, at the conclusion of field data collection, or when they moved into the next age class.

We assessed cause-specific mortality of recovered carcasses based on field observations and necropsies performed by a wildlife pathologist (MDNR Wildlife Disease Laboratory, East Lansing, Michigan, USA). We classified mortalities of individuals into five categories: canid (coyote [Cam's latrans], red fox [Vulpes vulpes], gray fox [Urocyon cinereoargenteus], or domestic dog [C. lupus familiaris]) predation; trauma or malnutrition (e.g., abandonment); vehicle collision; hunter-harvest; and unknown, If we did not have enough evidence to ascribe cause with reasonable certainty (e.g., total consumption of the carcass) we considered the cause of mortality to be unknown.

Space Use

We estimated Locations of deer 2-5 times/week using triangulation from telemetry signals (White and Garrott 1990) or from visual observations of known individuals. To increase the accuracy of space-use assessment, we located deer in a systematic manner during varying time schedules on a diel basis (Beyer and Haufler 1992), with [greater than or equal to] 1 nocturnal location/deer/week, except during capture periods. Bearings were estimated using a 3-element folding Yagi antenna (Advanced Telemetry Systems, Incorporated, Isanti, Minnesota, USA), portable radio receiver (Model R-1000, Communications Specialists, Incorporated, Orange, California, USA), and mirror-sighting compass. A global positioning system (GPS) handheld unit (Model GPS IV; GARMIN International, Incorporated, Olathe, Kansas, USA) was used to approximate the locations from which signals were received.

We used LOCATE III (Pacer, Truro, Nova Scotia, Canada) to estimate locations of deer using our triangulated data based on the maximum likelihood estimator (Length 1981a, 1981b), as recommended by White and Garrott (1990) and Nams and Boutin (1991). We also used LOCATE III to estimate bearing standard deviations and error ellipses for location estimates. We based our telemetry-error assessments on the relationship between mean error-ellipse size and mean landscape-patch size to determine if we had appropriate sample sizes (see Nams 1989).

We used the fixed-kernel home range estimator (Kernohan et al. 2001) and least-squares cross-validation to determine the smoothing parameter (Worton 1995; Seaman et al. 1999). Our description of space use included only individuals with > 30 locations/season (Seaman et al. 1999). Space-use data were pooled by age class and season based on agricultural crop production (i.e., the study area's growing season [10 May-7 Oct; 150 d] and non-growing season [8 Oct-9 May; 215 d]). We assumed that all location estimates that were classified in the cover type water were inaccurate and relocated each to the nearest alternative cover type. Only location data from VHF signals were used to avoid potential differences associated with location precision between VHF- and GPS-derived location estimates (i.e., we used only VHF-derived data from VHF-GPS transmitters; Hiller 2007).

We used Animal Movement extension in Arc View GIS v3.2 software (ESRI, Redlands, California, USA) to estimate space use. For statistical analyses, we used SYSTAT vll (Systat Software, Inc., San Jose, California, USA) and ProStat v4.02 (Poly Software International, Inc., Pearl River, New York, USA). Unless noted otherwise, we report 95% confidence limits (CLs) in our analyses because they provide an estimate of effect size and a measure of uncertainty (Johnson 1999).

RESULTS

Capture and Estimation of Locations

We captured and radiomarked 42 female deer during winter 2004-2006 and 34 neonates during spring 2004-2006. We pooled spring-captured animals by sex (56% male) and assumed behavioral differences were minimal between the sexes (Ozoga and Verme 1986) during the growing season, after which males were excluded from analyses. We pooled data by age class and season but could not consider year effects due to small sample sizes.

Survival and Cause-Specific Mortality

Two neonates died <7 days post-capture (1 canid predation, 1 capture stress) and 4 winter-captured deer died from capture-related causes (1 fawn, 1 yearling, and 1 adult from myopathy; 1 yearling from collar trauma; Table 1), resulting in 6 deer being excluded from survival analyses. Survival was lower for fawns 0-6 months old (0.67 for 6-month survival; 95% CL = 0.51-0.84; n = 32; Hiller et al. 2008) than for fawns 6-12 months old (0.90; 95% CL = 0.78-1.00-n = 23); annual fawn survival was estimated to be 0.51 (95% CL = 0.37-0.66; n = 48). Annual survival estimates for yearlings and adults were 0.94 (95% CL = 0.85-1.00; n = 28) and 0.56 (95% CL = 0.38-0.75; n = 28), respectively.
TABLE 1. Rue assessment of radiomarked female white-tailed deer
(Odocoileus virginianus) by age class (fawn--< 1 yr, yearling = [greater
than or equal to] I yr-[less than] 2 yr, adult = [greater than or equal
to] 2 yr), south central Michigan, USA, 2004-2006.

 Age class (a)

Fate (b) Fawn (n = 35) Yearling (n = 13) Adult (n = 28)

Mortality
 Hunter-harvest 4 0 11

 Vehicle collision 4 0 1

 Trauma or 1 1 1
 malnutrition

 Canid predation (c) 5 0 0

 Unknown 1 1 0

 Total 15 2 13
Censored

 Collar slipped or 6 5 6
 removed (d)

 Lost signal 3 1 3

 Alive at end of 9 3 5
 study

 Capture myopathy/ 2 2 1
 collar trauma
 (mortality)

 Total 20 11 15

(a) At time of fate assessment.

(b) Fates of individuals that died within the 7--day acclimation
period are presented as mortalities.

(c) Included coyote (Canis latrans), red fox (Vulpes vulpes), gray fox
(Urocyon cincreoargenteus), and domestic dog (C. lupus familiaris).

(d) Removed collars included break-away radio-transmitters designed to
drop off after 9-12 months and remotely removed GPS collars after 1 yr
of use.


The primary sources of mortality for fawns were canid predation (33% of mortalities), vehicle collisions, and hunter-harvest (27% each; Table 1). Fawns were the only age class depredated by canids. Of the 12 yearlings radiomarked, only 2 died, with the causes assessed as malnutrition related to trauma and unknown. Eleven of 13 (85%) adults that died were harvested by hunters. Mortalities related to capture or radio-collar trauma was about 7% of the total number of deer captured (Table 1).

Space Use

We pooled all location estimates (8,714) from all deer (n = 66 with 30 locations) to estimate overall telemetry error. The relationship between mean error-ellipse size (10.2 ha) and mean landscape-patch size (29.2 ha) on the study area provided an estimated minimum number of location estimates/deer (i.e., 2X that of our desired minimum of 30, or 60 locations/deer) to effectively describe space use (see Nams 1989, fig. 3). Based on this relationship, we considered our sample size ([bar.[chi]] = 66 locations/season/deer) appropriate for our analyses.

Seasonal home-range differences existed for fawns, yearlings, and adults (Table 2). Within the fawn and adult age classes, the growing season mean home-range size was approximately half that of the non-growing season mean home-range size (Table 2). Yearlings had similar mean home-range sizes for the growing (201.8 ha) and non-growing (156.88 ha) seasons, but variation was large (SE = 91.1 and 28.2, respectively). Ninety-five percent CLs of seasonal home ranges overlapped within each age class, but only slightly for adults. The mean amount (ha) of upland deciduous forest cover within kernel home ranges quadrupled for fawns from the growing to the non-growing season (Table 2). Yearlings selected 2 cover types in different proportions (Table 2): agricultural areas were selected at twice the proportion during the growing season (0.26) than during the non-growing season (0.12), while upland deciduous forests were selected in a higher proportion during the non-growing season (0.33 versus 0.19). Other cover types were selected in similar proportions within each age class.
TABLE 2. Space use (95% fixed-kernel home ranges) and cover composition
of female fawn (< 1 year old), yearling ([greater than or equal to]
1-< 2 yr old), and adult (> 2 yr old) white-tailed deer (Odocoileus
virginianus) during the agricultural growing (10 May-7 Oct; n = 28, 12,
and 23 respectively) and non-growing (8 Oct-9 May; n - 21, 15, and 25
respectively) seasons, south central Michigan, USA, 2004-2006
(CL = confidence limits).

 Kernel home range size (ha)

 Fawns
Season 95% CL
Cover type [bar.x] Lower Upper

Growing season 60.2 31.2 89.3
 Agriculture 19.2 8.2 30.2
 Conifer 13.2 5.7 20.6
 Herbaceous openland 0.2 0.0 0.4
 Lowland deciduous forest 9.3 3.1 15.5
 Lowland shrub 4.6 1.9 7.3
 Mixed wetland 4.4 0.0 9.5
 Northern hardwood 2.3 1.3 3.3
 Oak association 0.7 0.0 1.6
 Upland deciduous forest 4.5 2.5 6.5
 Upland shrub 0.0
 Urban 0.2 0.0 0.4
 Water 1.5 0.0 3.3
 Other 0.0
Non-growing season 116.3 73.3 159.2
 Agriculture 21.6 12.7 30.4
 Conifer 15.9 6.5 25.3
 Herbaceous openland 5.0 0.8 9.2
 Lowland deciduous forest 16.5 7.3 25.7
 Lowland shrub 18.5 3.9 33.1
 Mixed wetland 7.8 0.0 19.3
 Northern hardwood 4.2 2.8 5.7
 Oak association 3.0 1.3 4.7
 Upland deciduous forest 22.6 13.4 31.7
 Upland shrub 0.0
 Urban 0.1 0.0 0.3
 Water 0.8 0.0 1.9
 Other 0.0 0.0 0.1

 Kernel home range size (ha)

 Yearlings
Season 95% CL

Cover type [bar.X] Lower Upper

Growing season 201.8 1.3 402.4
 Agriculture 51.7 0.0 113.6
 Conifer 19.2 0.0 40.6
 Herbaceous openland 2.9 0.5 5.2
 Lowland deciduous forest 23.8 0.0 52.9
 Lowland shrub 36.3 3.3 69.2
 Mixed wetland 10.2 0.0 20.5
 Northern hardwood 11.2 0.0 26.0
 Oak association 3.7 0.0 10.3
 Upland deciduous forest 37.6 1.1 74.1
 Upland shrub
 Urban 0.0
 Water 5.1 4.0 13.8
 Other 0.1 0.0 0.3
Non-growing season 156.9 96.4 217.4
 Agriculture 18.3 5.1 31.6
 Conifer 13.6 1.5 25.7
 Herbaceous openland 4.3 0.7 7.8
 Lowland deciduous forest 25.9 9.0 42.8
 Lowland shrub 23.3 9.8 36.8
 Mixed wetland 3.5 0.0 7.1
 Northern hardwood 8.4 5.0 11.8
 Oak association 4.0 0.9 7.1
 Upland deciduous forest 52.2 29.1 75.3
 Upland shrub 0.0
 Urban 0.1 0.0 0.3
 Water 3.2 0.0 6.6
 Other 0.1 0.0 0.3

 Kernel home range size (ha)

 Adults
Season 95% CL

Cover type [bar.X] Lower Upper

Growing season 77.5 57.5 97.6
 Agriculture 11.2 4.6 17.9
 Conifer 8.2 2.3 14.1
 Herbaceous openland 0.3 0.0 0.6
 Lowland deciduous forest 4.7 1.5 7.9
 Lowland shrub 17.5 9.5 25.5
 Mixed wetland 2.7 0.0 6.9
 Northern hardwood 2.9 1.5 4.4
 Oak association 1.4 0.3 2.5
 Upland deciduous forest 27.1 15.3 39.0
 Upland shrub 0.0
 Urban 0.1 0.0 0.3
 Water 1.3 0.0 2.6
 Other 0.0
Non-growing season 140.4 91.6 189.2
 Agriculture 27.8 13.8 41.8
 Conifer 10.8 4.4 17.3
 Herbaceous openland 1.3 0.0 2.6
 Lowland deciduous forest 8.5 4.1 12.8
 Lowland shrub 33.5 17.9 49.1
 Mixed wetland 6.1 0.1 12.0
 Northern hardwood 3.9 2.0 5.7
 Oak association 2.3 1.2 3.3
 Upland deciduous forest 43.9 25.5 62.3
 Upland shrub 0.0
 Urban 0.1 0.0 0.3
 Water 2.4 0.6 4.2
 Other 0.0


Fawn home ranges during the growing season were smaller than both yearling and adult non-growing season estimates (Table 2). The yearling non-growing season estimate was only slightly larger than the adult growing-season estimate based on 95% CLs, indicating a minor transition in home-range size through time. Many differences in the amounts (ha) of each cover type in home ranges existed across age classes. Based on CLs, fawn home ranges during either season had compositional differences in herbaceous openlands, lowland shrubs, northern hardwoods, and upland deciduous forests with the composition of > 1 seasonal home range of other age classes (i.e., fawns occasionally used a different amount of cover in comparison to yearlings and adults, regardless of season). Yearlings during the non-growing season used more northern hardwoods (8.4 ha) and lowland deciduous forests (25.9 ha) than adults (2.9 and 4.7 ha, respectively) during the growing season.

Mean home-range sizes oscillated based on age and season, with home-range size greatest for yearlings during the growing season (Figure 2). Regardless of age, home ranges of deer generally increased during fall and peaked soon after hunters expended the most effort for harvest (i.e., following regular firearms season: ~ 30 Nov) in Lower Michigan. We assumed home-range size by season was consistent for deer > 2 years old (i.e., adult age class).

[FIGURE 2 OMITTED]

DISCUSSION

Numerous studies have investigated demographics and space use of free-ranging deer in Michigan. Research in the Upper Peninsula has included seasonal migrations and mortality of deer (Van Deelen et al. 1997; Van Deelen et al. 1998) as well as habitat use and browsing effects (Mackey 1996). In the northern Lower Peninsula, Sitar (1996) and Sitar et al. (1998) examined seasonal movements, habitat use, and population characteristics. In the Upper and northern Lower Peninsula, deer frequently migrate between summer and winter home ranges for sufficient winter thermal cover (Verme 1973; Van Deelen et al. 1998). Migratory behavior will affect space-use assessments and often estimates of demographics; differences in vegetation composition, agricultural growing season, and landscape characteristics (e.g., patch size and shape) may also affect population characteristics. Because of these variables, comparisons among regions often show interesting patterns. Our results can also he compared with the only other study within the southern Michigan region, an examination of the population characteristics and landscape-use patterns of deer on private lands in southwestern Lower Michigan (Pusateri 2003; Pusateri Burroughs et al. 2006).

Survival and Cause-Specific Mortality

Fawns. In a 2-year southwestern Lower Peninsula Michigan study, Pusateri Burroughs et al. (2006) estimated annual fawn survival on privately owned lands at 0.75 (95% CL = 0.59-0.91; Kaplan-Meier method), higher than our estimate of 0.51 (95% CL = 0.37-0.66). Mortality due to hunting and vehicle collisions (29% each) was similar to what we observed (27% each), despite higher densities of humans ([bar.x] = 1.39/ha versus [bar.x] = 1.00/ha; U.S. Census Bureau 2006) and deer (~ 27/[km.sup.2], MDNR 2005; -19/[km.sup.2], Pusateri 2003) on our study area, as well as differences in land-ownership and land-use patterns.

Although Pusateri Burroughs et al. (2006) estimated the mean home-range size of 27-week-old fawns (~ May-Dec), their estimate of 62.7 ha was similar to our growing season (~ May-Oct) estimate of 60.2 ha. Cover composition within kernel home ranges varied between studies, as our estimates contained more conifers (22% versus 10%) and less agriculture (32% versus 46%) and deciduous forests (23% versus 40%). Although not directly comparable because of differing time intervals, their mean annual home-range size estimate (75.4 ha) was only 65% of our mean non-growing season home-range estimate (116.3 ha) for fawns. Differences in cover availability likely contributed to some cover composition differences between study areas.

Yearlings. Survival estimates of yearling deer within the Lower Peninsula of Michigan are limited. Pooled by sex, annual survival of yearlings for a 2-year study (Sitar 1996; northern Lower Michigan) ranged from 0.29 (95% CL = 0.10-0.48) to 0.36 (95% CL = 0.18-0.54), which were both much lower than our pooled annual estimate (95% CL = 0.85-1.00). Sitar (1996) stated that yearling survival may have been somewhat overestimated due to relatively mild winter conditions during her study, which suggested chat long-term differences between our estimates may be even more pronounced. Long-term (1971-2000) winter conditions (e.g., mean monthly temperatures, mean annual snowfall) on her study area (Jan = -7.9 [degrees]C, annual snowfall = 142.2 cm; Alpena County; Midwestern Regional Climate Center 2008e, 2008f) were generally more severe than on our study area (Jan = -5.4 [degrees]C, annual snowfall = 99.3 cm; Jackson County; Midwestern Regional Climate Center 2008b, 2008c). Age- and sex-specific survival estimates of migratory deer in Michigan's Upper Peninsula showed that yearling females had a relatively high annual survival rate (0.89) in comparison to other age-sex classes (Van Deelen 1995); he reported that non-hunting mortality did not differ between sexes, but annual hunting mortality was much higher for males.

Adults. Deer >6 months old in southwestern Lower Michigan had annual survival estimates (Kaplan-Meier method) that ranged from 0.40 (95% CL [approximately equal to] 0.20-0.60) to 0.77 (95% CL [approximately equal to] 0.61-0.93; data pooled by sex; Pusateri 2003). Using the Mayfield method, Sitar (1996) estimated annual survival of adult deer in the northern Lower Peninsula of Michigan to be 0.53 (95% CL = 0.37-0.69) and 0.71 (95% CL = 0.50-0.92). Our survival estimate for adult females (0.56; 95% CL = 0.38-0.75) is consistent with these estimates.

In southwestern Lower Michigan, cause-specific mortality for female deer >6 months old (n = 48; 18 died) included hunter-harvest (61% of mortalities), vehicle collisions (28%), and trauma-related injuries (11%; Pusateri 2003). In northern Lower Michigan, the most prominent known mortality sources for male and female deer included hunter-harvest (37% of mortalities), natural causes (e.g., predation, drowning; 24%), illegal harvest (12%), and vehicle collisions (10%; Sitar 1996). The only significant cause of mortality on our study area for adult female deer was hunter-harvest (85% of mortalities). Based on radiomarked individuals, adults were also the age class of females most harvested by hunters, as only 4 fawns and no yearlings were harvested during our 3-year study.

Space Use

On our study area, home-range sizes of adult females during the non-growing season (140.4 ha) were approximately twice the size of home ranges during the growing season (77.5 ha). Adult female deer restrict movements near and following parturition (Marchinton and Hirth 1984), which may explain this difference in space use. Increases in time spent foraging due to increased energetic demands and potentially limited food resources during winter may also cause increased movements and space use. Interestingly, the mean proportion of each cover type within home ranges differed little by season for adults, suggesting that although space use increased, cover selection (i.e., based on selection indices) remained relatively constant.

Our non-growing-season adult home-range estimate (140.4 ha) was similar to the annual estimates of deer >6 months old (157-7 ha; non-dispersers; n = 53; 91% females) in southwestern Lower Michigan (Pusateri 2003). However, cover composition within home ranges differed somewhat. Home ranges in southwestern Lower Michigan consisted of a higher percentage of agricultural areas (39% versus 20%) and deciduous forests (47% versus 37% [upland and lowland combined]), but less conifer cover (<4% versus 8%) compared to our results. Lowland-shrub cover composed a relatively large percentage of our adult home ranges (~23% during both seasons), but this cover type was not defined by Pusateri (2003). Comparisons of cover use to other studies in Michigan would be less meaningful, as they were conducted in northern latitudes where deer often exhibit migratory behavior and vegetation becomes increasingly different.

Although the confidence limits of age-specific home-range sizes often overlapped somewhat, trends in home-range size on our study area were consistent with broad descriptions of space use by deer. As reported by Marchinton and Hirth (1984), young fawns have small home ranges, but as they get older their ranges begin to approximate those of their dams. Yearlings and young adults may move over larger areas than do older adults, at least in localities where migratory behavior is common (Marchinton and Hirth 1984), insinuating a trend similar to deer on our study area. Yearling females had relatively large home-range sizes (Figure 2), as expected (Marchinton and Hirth 1984). Although it may be counterintuitive that these space-use patterns would he coupled with the highest survival (0.94) among age classes, sources of age-specific mortality may provide an explanation. The primary sources of mortality for fawns and adults were vehicle collisions and hunter-harvest, respectively. Deer may be more susceptible to vehicle collisions until some type of avoidance behavior has been established, which may occur during the juvenile stage. Further, adults may be the age class of antlerless deer selected by or more available to hunters, as suggested by our data (Table 1).

We expected differences in survival and cause-specific mortality among age classes, but the magnitude of these differences was unknown, as were the relative differences when making comparisons with other studies of deer ecology in Michigan. Our information should help inform deer managers in Michigan and other areas of the Midwest experiencing increasing urbanization, as the population characteristics of white-tailed deer in an increasingly urbanizing landscape were heretofore relatively unknown.

MANAGEMENT IMPLICATIONS

Knowledge of population characteristics (e.g., demographics, space use) of white-tailed deer is directly applicable to management strategies. For example, area- and age-specific survival estimates can be incorporated into population estimation models (e.g., sex-age-kill model; Creed et al. 1984), which often help guide harvest management objectives. Age-specific space-use assessments are useful for both harvest (e.g., knowledge of deer-habitat relationships during the hunting season) and habitat (e.g., potential response of deer to changing landscapes, such as urbanization) management.

ACKNOWLEDGMENTS

We thank E. Arrow, B. Dodge, B. Gunderson, D. Haan, R. Havens, A. Leach, L. McNew, A. Nussbaum, M. Rubley, and various volunteers for assistance with field data collection. D. Etter, K. Sitar, and two anonymous reviewers provided comments that improved our manuscript. We also thank various landowners on the study area for access to their property. T. Cooley, MDNR, provided assessment or confirmation of cause-specific mortalities through necropsies of deer. Logistical support and input on project initiation were provided by B. Rudolph, S. Dubay, S. Hanna, R. Clute, E. Flegler, V Tisch, and F. Davis of the MDNR and S. Winterstein of Michigan State University. Financial contributors included the Michigan Agricultural Experiment Station, Michigan State University, the Michigan Department of Natural Resources through the Federal Aid in Restoration Act under Pittman-Robertson project W-147-R, Safari Club International, and Whitetails Unlimited.

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TIM L. HILLER (1) AND HENRY CAMPA III

Michigan State University

(1.) Current address: School of Natural Resources, University of Nebraska-Lincoln, 3310 Holdrege St., 404 Hardin Hall, Lincoln, NE 68583-0974, USA; email: thiller2@unl.edu

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