PLACE IN RETURN Box to remove this checkout from your record. To AVOID FINES return on or before date due. MAY BE RECALLED with earlier due date if requested. DATE DUE DATE DUE DATE DUE or MAY 1 6092005 AHiw 3 U 5% ‘fr MA! 9219““ JAN 2 2 20‘3 b70313 6/01 cJClRC/DateDuopes-sz WHITE-TAILED DEER POPULATION CHARACTERISTICS AND LANDSCAPE USE PATTERNS IN SOUTHWESTERN LOWER MICHIGAN. By Jordan S. Pusateri A THESIS Submitted to Michigan State University in partial fiilfillment of the requirements for the degree of MASTER OF SCIENCE Department of Fisheries and Wildlife 2003 ABSTRACT WHITE-TAILED DEER POPULATION CHARACTERISTICS AND LANDSCAPE USE PATTERNS IN SOUTHWESTERN LOWER MICHIGAN. By Jordan S. Pusateri Previous studies on white-tailed deer (Odocoileus virginianus) movement patterns and population dynamics in Michigan were conducted in the northern part of the state while similar data are limited for southern Michigan. The goal of this study was to gather scientific data on the ecology of deer in southwestern Lower Michigan; specifically, to quantify how landscape characteristics may influence deer movement patterns and survival. The study area was characterized as an agro-forest ecosystem dominated by 54% row crops and 29% upland deciduous forests. Fifiy—nine deer (42 females, 17 males) were captured and radio-collared during the winters of 2001 and 2002. During the spring/summer capture season, 75 fawns (40 females, 35 males) were captured and radio- collared. Seventeen of 75 fawns died from varying causes during the study, and 26 of 53 winter captured deer, used for analysis, died. Annual fawn and winter captured deer survival probabilities ranged from 0.75 - 0.76 and 0.40 — 0.77, respectively. Typical fawn (< 7 months old) and winter captured deer annual home ranges were 63 ha and 158 ha, respectively. Ten winter captured deer dispersed from their capture sites and 1 deer exhibited migratory behavior associated with hunting pressure. Study results can aid in developing, refining, and validating deer population models as well as devising and balancing white-tailed deer population management decisions based on the ecology of deer and the diversity of cover types and uses in southwest Lower Michigan. ACKNOWLEDGEMENTS I greatly appreciate the financial support of the agencies and organizations involved in this research. Primary funding was provided by the Michigan Agricultural Experiment Station, the Michigan Department of Natural Resources Wildlife Division, Michigan State University, and Pierce Cedar Creek Institute. Additional finding was provided by the Safari Club International - Michigan Involvement Committee, the Safari Club International - Novi Chapter, and Buckmasters American Deer Foundation. I am especially grateful to my major advisor, Dr. Rique Campa, for giving me the opportunity to work on this fantastic project and for all of his guidance, patience, advice, and enthusiasm. I also want to thank Dr. Scott Winterstein, Dr. Bill Moritz, Dr. Jim Sikarskie, and Brent Rudolph for serving on my graduate committee and providing valuable insights and direction throughout my research and preparation of this manuscript. A special thanks goes to Dr. Kelly Millenbah for her ArcView expertise and encouragement, and Dr. Dan Hayes for his technical support while organizing my data and help in writing SAS programs to analyze my data. I greatly appreciate Dr. Bill Taylor’s confidence, encouragement, mentoring, and support. I thank Steve Beyer, Mark Bishop, Mike Bailey and Tom Cooley, personnel of the Michigan Department of Natural Resources — Wildlife Division, who helped me with a variety of different tasks throughout my project. iii The cooperation and support of many landowners and individuals in the study area made this research possible. I am grateful for their support, enthusiasm, and patience in waiting for the results. Dr. Mark Garner taught me the secrets of how to successfully set-up a Clover trap and capture and radio-collar white-tailed deer; he also provided technical support during my many ArcView tribulations. I also thank my fellow graduate students, Ali Felix, Tammy Giroux, Sarah Panken, Nikki Lamp, and Drew Kramer for their technical support, encouragement, laughs, and friendship. I am forever indebted to my extraordinary field assistants and interns - Bill Barthen, Amy Belson, Jamie Chronert, Leslie Frattaroli, Jen Gutscher, Sara Kolesar, Mark Monroe, Scott Nienhuis, Joe Prenkert, Craig Pullins, and Tara Vanwyck —- they were invaluable to me and I greatly appreciate all of their enthusiasm, efforts, ideas, hard work, and friendship. My deepest gratitude goes to my parents, Tim and Jeri, and my brothers, Phil and Kyle, for their love, encouragement, confidence, support, and patience. I would also like to thank Bryan Burroughs for his assistance in the field and lab and his knowledge, ideas, support, friendship, and love. iv TABLE OF CONTENTS LIST OF TABLES .................................................................................. vii LIST OF FIGURES ................................................................................... x INTRODUCTION .................................................................................... 1 OBJECTIVES ......................................................................................... 3 STUDY AREA ....................................................................................... 4 METHODS ............................................................................................ 8 Capture and handling — winter .............................................................. 8 Capture and handling — spring/summer ................................................. 10 Radio telemetry ............................................................................. 14 Population parameters ..................................................................... 17 Survival Cause-specific mortality Sex ratio and age structure Home range and movements ............................................................. 19 Vegetation composition and structure ................................................... 2] WINTER CAPTURE RESULTS ................................................................. 24 Winter captures .............................................................................. 24 Survival ...................................................................................... 27 Cause-specific mortality .................................................................. 32 Home range and movements ............................................................. 36 Non-dispersers ..................................................................... 36 Dispersers .......................................................................... 43 SPRWG/SUMMER CAPTURE RESULTS .................................................... 53 Spring/ summer captures .................................................................... 53 Survival ...................................................................................... 55 Cause-specific mortality .................................................................. 55 Home range and movements ............................................................... 64 Annual home range analysis ...................................................... 64 Twenty-seven week analysis ..................................................... 67 VEGETATION RESULTS ........................................................................ 83 Vegetation composition and structure ................................................... 83 WINTER CAPTURE DISCUSSION ............................................................ 88 Survival ..................................................................................... 88 Cause-specific mortality .................................................................. 89 Home range and movements .............................................................. 92 SPRING/SUMMER CAPTURE DISCUSSION ................................................ 98 Survival ...................................................................................... 98 Cause-specific mortality ................................................................. 100 Home range and movements ............................................................. 107 VEGETATION DISCUSSION .................................................................. 110 Vegetation composition and structure .................................................. 110 MANAGEMENT IMPLICATIONS ............................................................ 112 LITERATURE CITED ........................................................................... 1 15 APPENDICES ...................................................................................... 125 vi Table 1. Table 2. Table 3. Table 4. Table 5. Table 6. Table 7. Table 8. Table 9. Table 10. Table 11. Table 12. Table 13. LIST OF TABLES Age and sex of white-tailed deer radio-collared in southwestern Lower Michigan in 2001 and 2002. . 25 Causes of winter captured white-tailed deer mortalities in southwestern Lower Michigan, 2001-2003. . . 33 Fates of winter captured white-tailed deer in southwestern Lower Michigan, 2001-2003. . . . 34 Timing of winter captured white-tailed deer mortalities in southwestern Lower Michigan, 2001-2003. . . 35 Landscape composition, by dominant cover type within home ranges, of winter captured white-tailed deer in southwestern Lower Michigan, 2001- 2002. . . 39 Mean percent of home range composed of each cover type for winter captured white-tailed deer (n=10) that dispersed from their capture site, 2001-2002. . . . 48 White-tailed deer fawn survival probabilities by number of days, 2001. . . . . . . 56 White-tailed deer fawn survival probabilities by number of days, 2002. . . . . . . 56 Causes of radio-collared white-tailed deer fawn mortalities in southwestern Lower Michigan, 2001-2003. . 61 Fates of spring/summer captured white-tailed deer in southwestern Lower Michigan, 2001-2003. . . 63 Mean percentages of cover types that compose home ranges of the 2001 and 2002 fawns. . . . 72 Home range composition (total ha) for white-tailed deer fawns that died during the tracking period (May to December), 2001and 2002. . . . 80 Home range composition (mean percent) for white- tailed deer fawns that died during the tracking period (May to December), 2001 and 2002. . . 80 vii Table 14. Table 15. Table 16. Appendix Table 1. Appendix Table 2. Appendix Table 3. Appendix Table 4. Appendix Table 5. Appendix Table 6. Appendix Table 7. Appendix Table 8. Summary of home range attributes for 2001 and 2002 radio-collared white-tailed deer fawns in southwestern Lower Michigan. . . . . . 82 Characteristics of vegetation types frequently used by radio-collared deer. . . . . . 84 Percent canopy cover and ground cover by vegetation type within forest stands frequently used by radio-collared deer. . . . . . 86 Frequency (radio-collar number), sex, age, capture location, date of capture, fate and date of radio-collared white-tailed deer in southwestern Lower Michigan, 2001-2003. . 126 Landscape composition (ha), by cover type within home ranges, of winter captured white-tailed deer (n=53) in southwestern Lower Michigan, 2001-2002. . . 129 Landscape composition (mean percent), by cover type within home ranges, of winter captured white-tailed deer (n=53) in southwestern Lower Michigan, 2001-2002. . . . . . . 131 Landscape composition (ha), by cover type within pre dispersal home ranges, of winter captured white-tailed deer (n=10) in southwestern Lower Michigan, 2001-2002. . . . . . . 133 Landscape composition (mean percent), by cover type within pre dispersal home ranges, of winter captured white- tailed deer (n=10) in southwestern Lower Michigan, 2001- 2002. . . . . . . . 134 Landscape composition (ha), by cover type within dispersal home ranges, of winter captured white-tailed deer (n=10) in southwestern Lower Michigan, 2001-2002. . 135 Landscape composition (mean percent), by cover type within dispersal home ranges, of winter captured white- tailed (n=10) in southwestern Lower Michigan, 2001-2002. . . . . . . 136 Frequency (radio-collar number), sex, age, weight, capture viii Appendix Table 9. Appendix Table 10. Appendix Table 11. Appendix Table 12. Appendix Table 13. Appendix Table 14. location, date of capture, date of last radio location, fate and date of radio-collared white-tailed deer fawns in southwestern Lower Michigan, 2001-2003. . . 137 All spring/summer captured white-tailed deer fawns censored as a result of a dropped collar, 2001-2003. . . . . . . 140 Landscape composition (ha), by cover type within home ranges, of spring/summer captured white-tailed fawns (n=24) that survived to 30 April 2002. . . 141 Landscape composition (mean percent), by cover type within home ranges, of spring/summer captured white- tailed fawns (n=24) that survived to 30 April 2002. . . . . . 142 Landscape composition (ha), by cover type within home ranges, of spring/summer captured white-tailed fawns (n=68) in southwestern Lower Michigan, 2001-2002. . . . . . . 143 Landscape composition (mean percent), by cover type within home ranges, of spring/summer captured white- tailed fawns (n=68) in southwestern Lower Michigan, 2001-2002. . . . . . . 146 Home range attribute comparison for all radio-collared fawns (n=68) during the May to December tracking period in southwestern Lower Michigan, 2001 and 2002. . 149 ix Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9. Figure 10. LIST OF FIGURES Study area for southwestern Lower Michigan white-tailed deer study (January 2001 — May 2003). . . 5 Winter capture sites of white-tailed deer in southwestern Lower Michigan, 2001 — 2002. . . . 26 Survival probability curve for white-tailed deer radio- collared during winter (January - March) 2001 in southwestern Lower Michigan. . . . 28 Survival probability curve for white-tailed deer radio- collared during winter (January — March) 2002 in southwestern Lower Michigan. . . . 29 Survival probability curves for white-tailed deer radio- collared in 2001 and 2002 in southwestern Lower Michigan. Survival of white-tailed deer collared in 2001 was statistically different (P = 0.0004) than the survival of deer collared in 2002. . . . . 31 Annual home range size (ha) frequency distribution for 2001 and 2002 winter captured white-tailed deer (n=53) southwestern Lower Michigan, 2001-2002. . . 38 Winter captured white-tailed deer home range frequency distribution for the mean percent of all home ranges (n=53) composed of agricultural land. . . . 41 Winter captured white-tailed deer home range frequency distribution for the mean percent of all home ranges (n=53) composed of deciduous forests. . . . 42 Relationship between the home range size (ha) of winter captured white-tailed deer and the percentage of agricultural land composing each home range (n=48) in southwestern Lower Michigan, 2001-2002. . . 44 Relationship between the home range size (ha) of winter captured white-tailed deer and the percentage of deciduous forests composing each home range (n=48) in southwestern Lower Michigan, 2001-2002. . . . . 45 Figure 11. Figure 12. Figure 13. Figure 14. Figure 15. Figure 16. Figure 17. Figure 18. Figure 19. Figure 20. Figure 21. Figure 22. Winter captured white-tailed deer dispersal direction from capture sites in southwestern Lower Michigan, 2001- 2002. . . . . . . . 46 Landscape composition (mean percent), by dominant cover type within pre dispersal home ranges, of winter captured white-tailed deer (n=10) in southwestern Lower Michigan, 2001-2002. . . . . . . 49 Landscape composition (mean percent), by dominant cover type within dispersal home ranges, of winter captured white-tailed deer (n=10) in southwestern Lower Michigan, 2001-2002. . . . . . . 49 Movement patterns of deer #260 in southwestern Lower Michigan, 2001-2003. . . . . 51 Percentages of each cover type composing each home range for deer #260 in southwestern Lower Michigan, 2001-2002. . . . . . . 52 White-tailed deer fawn spring/summer capture sites in southwestern Lower Michigan, 2001-2002. . . 54 Survival probability curve for 2001 radio-collared white- tailed deer fawns (n=35) in southwestern Lower Michigan. . . . . . . 57 Survival probability curve for 2002 radio-collared white- tailed deer fawns (n=40) in southwestern Lower Michigan. . . . . . . 58 Fawn survival probability curves for 2001 and 2002 fawns (n=75) in southwestern Lower Michigan. . . 59 Distribution for 2001 white-tailed deer fawns’ (n=24) annual home range size in southwestern Lower Michigan. . . . . . . 66 Home range frequency distribution for 2001 and 2002 white-tailed deer fawns (n=68) from approximately 27 May to 5 December in southwestern Lower Michigan. . 68 Home range frequency distributions by sex for 2001 and 2002 white-tailed deer fawns (n=68) from approximately 27 May to 5 December in southwestern Lower xi Figure 23. Figure 24. Figure 25. Figure 26. Figure 27. Figure 28. Figure 29. Appendix Figure 1. Michigan. . . . . . . 70 Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of agriculture. . . . . . . 73 Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of deciduous forests. . . . . . 73 Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of woody wetlands. . . . . . . 74 Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of evergreen forest. . . . . . 74 Relationship between the home range size (ha) of radio- collared white-tailed deer fawns and the percentage of agriculture land composing each fawn home range (n=68), 2001- 2002. . . . . . . 76 Relationship between the home range size (ha) of radio- collared white-tailed deer fawns and the percentage of deciduous forest composing each fawn’s home range (n=68), 2001-2002. . . . . . 77 Frequency distribution comparing white-tailed deer fawns that survived (n=56) and those fawns that died (n=12), 2001-2003. . . . . 79 Winter trap sites of white-tailed deer in southwestern Lower Michigan, 2001 - 2002. . . . 151 xii INTRODUCTION Management of white-tailed deer in Michigan has been controversial since as far back as the 18703 (Bartlett 1938). White-tailed deer have not always been as abundant across the southern Lower Peninsula of Michigan (southern Lower Michigan), as they are today. As a result of agriculture and the timber industry in the late 1800s, white-tailed deer nearly vanished from southern Lower Michigan (Jenkins and Bartlett 1959). Due to the diminishing deer population in this region, the Michigan Conservation Commission outlawed deer hunting across the southern agriculture counties in 1926 (Davenport 1948). To help revitalize the southern Michigan deer population, the Michigan Conservation Department released 21 deer in Allegan County in 1932 (Davenport 1948). Within 9 years, the deer population exploded causing excessive damage to agricultural crops. The Allegan County deer herd began spreading to adjacent counties causing serious crop damage (Davenport 1948). The Michigan Conservation Department was authorized to control the deer herd in Allegan County at a level consistent with the landscape through a county-wide hunting season (Davenport 1948). In 1948, hunting seasons for antlered deer were re-instated across southern Lower Michigan following the expiration of the Michigan Conservation Commission’s order of a closed hunting season (Davenport 1948). Based on historic estimates, the white-tailed deer population hit an initial peak in 1952 (Jenkins and Bartlett 1959). Thus, the Conservation Commission of the Department of Natural Resources was authorized to set a deer hunting season on bucks, does, and fawns in areas of deer crop damage or areas exhibiting excessive deer browsing within the Lower Peninsula (Jenkins and Bartlett 1959). White-tailed deer population dynamics are influenced by numerous factors including hunting pressure and the quality, quantity, and distribution of habitat. These factors can have direct effects on movement patterns, home range, and population demographics. While a wealth of information has been learned about white-tailed deer in northern Michigan over the last several decades (e. g., Ozoga and Verme 1970, Verme and Ozoga 1971, Van Deelen 1995, Sitar 1996, Campa et a1. 1997, Garner 2001), there is a lack of information on white-tailed deer landscape use patterns and population demographics in the southwestern region of the Lower Peninsula of Michigan. Information gathered from this study can aid in developing, refining and validating deer population models. Wildlife managers could also use information on deer movement patterns and home range characteristics to make management decisions to help prevent the spread of wildlife diseases. More specifically, information from this study can be used for developing and balancing white-tailed deer population management decisions based on the ecology of deer and the diversity of cover types and uses in southwestern Lower Michigan. Data from this research should be especially important for aiding management decisions and communicating management priorities to the public. OBJECTIVES Specific objectives of this study were to: 1. Quantify seasonal movement and habitat use patterns of white-tailed deer in southwestern Lower Michigan. 2. Quantify how land use patterns affect movement patterns of white-tailed deer in southwestern Lower Michigan. 3. Estimate survival rates, sex ratio, age distribution, and cause-specific mortality factors for deer (27 months of age) in the southwest Lower Peninsula of Michigan. 4. Estimate the survival rates, sex ratio, age distribution, and cause-specific mortality factors for deer fawns (< 7 months of age) in southwestern Lower Michigan. 5. Quantify the vegetation attributes and deer browsing intensities in forest vegetation types frequently used by deer. STUDY AREA This research was primarily conducted within Barry and Kalamazoo counties and moved to adjacent counties as deer dispersed across southwestern Lower Michigan (Figure 1). Land in the study area is within the Thomapple and Kalamazoo River watersheds. The soil in the study area is very deep and well drained and characterized as outwash plains. Barry and Kalamazoo counties were selected as the study area because of the diverse soil types that support many habitat types (e. g., dry and wet deciduous forest, oak (Quercus spp.) savanna, prairie) that may provide essential habitat components for white-tailed deer. Three soil series comprise the area including Fox, Boyer, and Oshtemo Series. Fox soil series forms in thin loess and in loamy alluvium. Native vegetation for this soil series is hardwood forest comprised of northern red oak (Quercus rubra), white oak (Quercus alba), sugar maple (Acer saccharum), black cherry (Prunus serotina), and white ash (F raxinus americana). Currently, the majority of the area within the Fox soil series is cropland including com, soybeans, small grains, and hay (Soil Survey Division, Natural Resources Conservation Service, United States Department of Agriculture [USDA] 1996). Boyer soil series soils forms in sandy and in loamy glacial drift. Native vegetation types for this soil are deciduous forests comprised of oak, hickory (Carya spp.), and maple (Acer spp.). These are rare vegetation types because soils are primarily cultivated for corn, small grains, soybeans, field beans and alfalfa hay (Soil Survey Division, Natural Resources Conservation Service, USDA 1996). Stratified loamy and sandy deposits formed the Oshtemo soil series. Native vegetation for this series includes oak, hickory, and sugar maple hardwood forests. Cultivation is Figure 1. Study area for southwestern Lower Michigan white-tailed deer study (January 2001 —- May 2003). also extensive in this soil series including small grains, soybeans, corn, and hay (Soil Survey Division, Natural Resources Conservation Service, USDA 1996). The soil maintains a'range of texture, natural drainage, and slope. The terrain is comprised of several glacial-formed lakes that lie amongst rolling hills (Michigan Department of Agriculture, Climatology Program 1980). The continental climates of Barry and Kalamazoo counties are influenced by the prevailing westerly winds producing a mild lake effect. The lake effect is limited to increased cloudiness during the late fall and early winter (Mich. Dept. Agriculture, Climatology Program 1980). In winter, the average temperature is — 4.11°C, and the average summer temperature is 20°C (Thoen 1990). Annual rainfall for this region of southwestern Lower Michigan is 0.818 m and 60% of the rain occurs in April through September. The average seasonal snowfall accumulates nearly 1.32 m (Thoen 1990). On average, 75 days per season have > 2.54 cm of snow on the ground; this is approximate and varies greatly from year to year (Thoen 1990). Average elevation for the study area is approximately 240 m above sea level (Thoen 1990). Barry County is approximately 149,337 ha in size; 54% of the land is used for agriculture (i.e., 28% row crops and 26% pasture/hay), 32% of the land is deciduous forest, and < 1% is residential development (United States Geological Survey (USGS) 1999). Kalamazoo County is approximately 150,200 ha; 55% is in agriculture (i.e., 42% row crop and 13% pasture/hay), 26% of the land is deciduous forest, and 7% is residential/urban development (USGS 1999). Primary vegetation types found across the study area include oak ridges, pine-oak (Pinus spp.) woodlands, central hardwoods, northern hardwoods (dominated by oak, hickory, maple, black cherry), and shrub/sedge dominated wetlands. The primary agricultural crops grown in the study area are com, hay, and soybeans (Thoen 1990). The study area also encompasses many land use categories (e. g., agriculture, deciduous forest, and residential development) that may influence deer movements, habitat use patterns and/or population dynamics. This study was conducted on a variety of private lands across Barry and Kalamazoo counties consisting of either large blocks of land owned by a single landowner/institution (i.e., Lux Arbor Reserve, Kellogg Biological Station and Pierce Cedar Creek Institute) or landscapes fragmented into smaller blocks owned by multiple landowners. METHODS Capture and handling - winter From January through March 2001 and 2002, white-tailed deer were captured in the study area using single-gate, collapsible modified Clover traps (Clover 1954, 1956, McCullough 1975) baited primarily with shelled corn. The traps were modified from the original Clover trap design so the traps could be collapsed on deer for more efficient processing (McCullough 1975). During the winter 2001 field season, 26 trap sites were baited for 2 to 4 days before setting the traps to increase the likelihood of capture (Sitar 1996) (Appendix Figure 1). Baiting prior to setting the traps in 2001 did not seem to affect capture success. Therefore, during the 2002 winter field season, 22 trap sites were not baited before setting the traps (Appendix Figure 1). Once traps were set they were checked in the morning and evening. An exception was made during the second winter capture season when some traps were left unchecked for a maximum of 24 hours. These were trap sites that captured few deer, and we speculated that increased human disturbance to the area by checking the traps may have deterred deer from entering the traps. Traps were not set when the temperature (or wind chill) was forecasted to be below -17.8 °C to decrease stress and the possibility of trap mortalities. During periods of freezing rain, the traps were not set because traps would freeze prohibiting the trap’s door to fall. On the rare occasions when traps were not set, they were tied open and baited with corn to allow deer to freely enter the traps. When deer were captured, they were manually restrained and blindfolded. During winter 2001, all captured deer were ear-tagged (National Band and Tag Company; Newport, Kentucky) and a subset was fitted with radio-collars (MOD-500 [HC]); Telonics Inc.; Mesa, Arizona) equipped with a 4-hour, time-delayed motion sensor to detect mortalities. The radio-collars weighed 270 g and were approximately <1% of the deer’s total body weight. During winter 2002, all deer captured were ear-tagged and fitted with the same type of collars used in 2001. Any fawns captured during the winter were fitted with the same radio-collars with an added strip of foam glued to the inside of the collar to allow room for growth. Processing time and noise were minimized during handling of deer to minimize capture stress (Beringer et a1. 1996). Handling procedures followed the guidelines specified by Beringer et a1. (1996). All deer were sexed and aged. Age was estimated according to Severinghaus’ (1949) tooth wear and replacement and classified as fawn (< 12 months) or adult. Hair samples were also extracted for potential future genetic analysis. Any superficial cuts or abrasions that occurred before or during capture were treated with a topical antibacterial (Furazolidone Aerosol Powder) prior to release based on suggestions by Dr. Steve Schmitt, DVM, Michigan Department of Natural Resources. Handling procedures for each deer averaged 10 minutes (median = 10 minutes). The winter captured deer were given color-coded ear-tags (Y-tex; Cody, Wyoming) with unique numbers; at each capture location I used different color ear-tags. During the 2001 winter capture season, deer radio-collared at Pierce Cedar Creek Institute (PCCI) were given blue ear-tags starting with number 1 and ending with ear-tag number 12; deer radio-collared at Kellogg Biological Station (KBS) were given orange ear-tags starting with number 1 and ending with ear-tag number 12; and deer radio- collared at Prairieville Township were given purple ear-tags starting with number 1 and ending with ear-tag number 15. During the 2002 winter capture season, deer radio- collared at Pierce Cedar Creek Institute (PCCI) were given blue ear-tags starting with number 13 and ending with ear-tag number 20; deer radio-collared at Kellogg Biological Station (KBS) and Ross Township were given orange ear-tags starting with number 13 and ending with ear-tag number 15; deer radio—collared at Barry Township were given one orange and purple ear-tag starting with number 43 and ending with ear-tag number 50; Lux Arbor Reserve radio-collared deer were given green ear-tags starting with 1 and ending with number 2; and Richland Township radio-collared deer were given yellow ear-tags starting with number 1 and ending with ear-tag number 9. To ensure proper animal handling protocol, I attended the Michigan State University (MSU) Laboratory Animal Resources (ULAR) seminar on animal use and care on 11 October 2000. The seminar presented MSU’s guidelines for investigators to follow when using animals in research or teaching. All capture and handling methods were reviewed and accepted by the MSU’s All—University Committee on Animal Use and Care (Permit No. 01/01-001-00). The capturing of deer for this project was approved by the Department of Natural Resources - Wildlife Division under Scientific Collector’s permit No. SC 816. Capture and handling — spring/summer Numerous techniques were used for capturing and radio-collaring fawns including: (1) doe behavioral observations (Downing and McGinnes 1969), (2) grid searching (Ballard et a1. 1998, 1999), and (3) cooperative landowner sightings (Porath 1980). Typically, fawns 52 weeks of age could be located within grass or hay fields and forest edges or primarily upland vegetation types (Porath 1980). When a fawn was 10 found, the field crew (2-6 people) would intensively search the surrounding area within 200 m to locate any siblings inhabiting the same area (Ozoga et a1. 1982). If twins were found, both individuals were radio-collared and ear-tagged. The “twins” may not be related and could just be bedding near each other. Radio-collaring twins may be of special interest if they were different sexes, tracking each individual could provide quantifiable data on each fawn’s dispersal from the family unit and home range use within a family unit. Doe behavioral observations (Downing and McGinnes 1969) during early May were used to identify does nearing parturition. Prior to fawning, does will isolate themselves and drive away previous year fawns to secure a secluded area to give birth (Marchinton and Hirth 1984). Decreased sociability (White et a1. 1972, Hirth 1977, Ozoga et a1. 1982), decreased home ranges and/or movements (Hawkins and Klimstra 1970, Sparrowe and Springer 1970, Bartush and Lewis 1978), and increased aggressive actions of pregnant females toward conspecifics (Bartush and Lewis 1978) are indicative of prepartum behavior. Continued antisocial and sedentary tendencies are considered clues that the young of the year survived (Ozoga et a1. 1982). A visual location of does would be optimal because following parturition does are reluctant to leave the vicinity when 1- to 10-day old fawns are present (Huegel et a1. 1985). During early May, potential fawning locations were searched for clues that would indicate that the fawning season had begun. To be efficient when capturing fawns, it is important to ignore groups of deer and concentrate on the behavior of single does (Downing and McGinnes 1969). If a fawn was suspected to be in the vicinity, the field crew would intensively search the area. Downing and McGinnes (1969) suggested being 11 persistent; if a doe is suspected of having a fawn nearby, recheck the area every half-hour until a fawn is sighted. A doe in the presence of a very young fawn will remain nearby and look nervously in the direction of the fawn several times per minute (Downing and McGinnes 1969). Does may also signal a “drop” to the fawn by making 1 or more very high bounds leaving the area (Downing and McGinnes 1969). The intent during fawn searching was to locate a solitary doe, observe her behaviors to determine if a fawn may be in the immediate area, and then intensively search the area. Instinctively, fawns s 5 days old remain motionless when approached; whereas, fawns Z 5 days old flush when approached and tried to outrun the field crew. When attempting to capture older fawns, the field crew would make a loud, fast approach causing the fawn to drop and remain motionless. Grid searching (Ballard et a1. 1998) was the most effective method to capture fawns when potential fawning habitat consisted of dense vegetation making it difficult to observe behavioral clues of solitary does. Grid searching was accomplished when the field crew intensely searched a vegetation type in an organized grid. During grid searching, I would first choose a site where fawns were likely to occur; the field crew would then space themselves 3 m apart and walk in unison across a site. The field crew used handheld walkie-talkies to enhance communications. Landowners in the study area were extremely helpful in locating fawns or fawn bedding sites. Over 20 landowners cooperated in the fawn capture portion of this study. They were asked to identify bedding locations of fawns or to identify gaunt does. Once a landowner ascertained an estimated location of a fawn bed site, they were asked to 12 immediately contact study personnel. The field crew would then locate the fawn’s bed site according to the directions given by the landowner. To quantify fawn survival (from birth to 7 months of age) and cause-specific mortality factors in southwestern Lower Michigan, fawns were fitted with a 4-hour, time- delayed motion sensitive breakaway radio-collar (M2110; Advanced Telemetry Systems, Inc.; Isanti, Minnesota) during May and June 2001 and 2002. Fawn collars were constructed of a double layer of neoprene impregnated cotton duck belting with the antenna exiting from between the 2 layers at the top of the collar (Advanced Telemetry Systems, Inc. 2001). Two folds were sewn into the collar using 2 types of thread of different durability to facilitate a delayed breakdown. As the threads deteriorated, the collar would gradually expand on the growing fawns. After a significant amount of wear and deterioration from the weather, the radio-collars would drop off after 9-12 months. The fawn collars weighed 60 g and had a pulse rate of 55 ppm (Advanced Telemetry Systems, Inc. 2001). The fawn radio-collars were also equipped with the precise event transmitter (PET) option to convey the amount of time since movement was last detected following the 4-hour mortality signal. The PET option conveyed the timing of the mortality through a 10-second binary pulse code emitted during the mortality signal; the pulse code was then decoded to determine time of death (to the closest half hour) (Advanced Telemetry Systems, Inc. 2001). Following capture, fawns were restrained and blindfolded. All captured fawns were sexed, aged (Haugen and Speake 1958), weighed (Pesola macro-scale), ear-tagged (National Band and Tag Company; Newport, Kentucky) and fitted with a breakaway radio-collar. Ages of the fawns were estimated using new hoof grth calculations 13 (Haugen and Speake 195 8), behavior (Haugen and Speake 1958, Downing and McGinnes 1969, White et a1. 1972), and condition of umbilicus (Haugen and Speake 195 8). All members of the field crew wore latex gloves when handling fawns and handling procedures for each fawn averaged 5 minutes (median = 5 minutes). All fawn capture and handling methods were reviewed and accepted by the MSU’s All-University Committee on Animal Use and Care (Permit No. 01/01-001-00). The capturing of fawns for this project was approved by the Department of Natural Resources — Wildlife Division under Scientific Collector’s permit No. SC 816. Radio telemetry All radio-collared deer were located a minimum of twice a week fi'om the time they were captured until they died, were censored (Pollock et a1. 1989), or the study’s end. Censoring occurred when the radio-collar stopped transmitting a signal and the deer could no longer be located (White and Garrott 1990). Locations for radio-collared deer were taken on a rotating basis in 8-hour time blocks (0800 to 1559, 1600 to 2359 and 2400 to 0759) to quantify diurnal and nocturnal movement patterns. Radio-collared fawns were located daily for survival during the first 30 days following capture. After the first 30 days, fawns were located a minimum of twice a week until they died, were censored, or the study’s end. We located as many deer as possible in a day and then finished the remaining deer in the following days and then repeated the pattern. We conducted locations according to capture area and grouped adjacent deer for each set of locations. The Universal Transverse Mercator (UTM) system was used as the coordinate system for recording deer locations. 14 Telemetry locations were taken using hand-held 3-element Yagi antennas (Advanced Telemetry Systems, Inc., Isanti, Minnesota) and portable hand-held receivers (Communications Specialists, Inc., Orange, California; Telonics Inc., Mesa, Arizona; Advanced Telemetry Systems, Inc., Isanti, Minnesota). An omni-directional antenna (Telonics Inc., Mesa, Arizona) magnetically attached to the top of a vehicle was used to search for missing animals and to check for survival. Global Positioning Systems (GPS) (Garmin International, Inc., Olathe, Kansas) were used to accurately determine each base location within 15-20 m (Samuel and Fuller 1996). When a visual location of a collared deer was obtained, we walked to the site and recorded locations with the GPS unit. These visual locations were included as locations in the home range analysis. Throughout any white-tailed deer telemetry study, radio signals may be unexplainably lost or the collar signal may fail (Sitar et a1. 1998). When this situation occurred, the deer were censored from the study and all of the locations up until censorship were included in the final evaluation. Any fawns radio-collared in spring 2001, still transmitting a signal, were censored on 1 May 2002. The gathering of telemetry locations, for the purpose of home range and movement analyses, ended on 5 December 2002. Survival data continued to be gathered for all deer transmitting a radio signal fi'om 6 December 2002 through 30 April 2003 (study’s end). Radio telemetry is a prevalent and important technique used to assess different ecological parameters associated with wildlife management issues. Sources of sampling error can significantly bias results obtained through telemetry locations (White and Garrott 1990). Several steps were taken to estimate, reduce, and account for the telemetry error. 15 When using radio telemetry, the most accurate estimation of an animal’s location is based on 3 bearings instead of 2 (Mech 1983). According to White and Garrott (1990), erroneous bearings are more detectable when using 3 bearings. We attempted to take locations from known map locations approximately 90 degrees from each telemetry station to obtain the most accurate location (Mech 1983). The amount of declination from the magnetic (compass) bearing was accounted for (Mech 1983). Some data were censored to eliminate poor-quality bearings and/or location estimates (White and Garrott 1990). In most cases, 4 bearings were taken for each location. When the bearing of a particular location did not seem probable, the bearing was eliminated to reduce its direct impacts on the quality of the data. Telemetry base stations’ locations were obtained using a handheld GPS unit. According to White and Garrott (1990:54), “the area of the error polygon is a measure of the precision of the point estimate derived from the intersection of two bearings.” Prior to the field season, radio-collars were placed in known positions and locations were taken to determine the degree of error. All members of the field crew participated in the location estimate testing, and an azimuth standard deviation was estimated to reduce sources of telemetry error. The field crew became familiar with the functions of the telemetry equipment prior to each field season. LOCATE II (Pacer, Truro, Nova Scotia, Canada) software uses the location data obtained throughout the study and processes the bearings into location estimates. Telemetry error was estimated using the maximum likelihood estimator algorithm in LOCATE H software (N ams 1989). Lenth (1981) developed the maximum likelihood estimator of the values x1 and y1 that maximizes the probability of observing the 16 recorded bearings (White and Garrott 1990). The amount of telemetry error was determined for each study personnel by averaging the error associated with each test location estimate. The standard deviation (bearing error) in LOCATE II was set at 8.0 for all locations taken throughout the study. Locations with 95% confidence area greater than 53.41 ha were determined to be unreliable and were eliminated from firture data analysis (Sitar 1996). This conservative error estimate was chosen as the threshold because an error area of 53.41 ha was approaching the size of a fawn’s home range. Population parameter - Survival Radio telemetry provides information on the timing of mortality, permitting the researcher to directly estimate survival. For good precision, Pollock et al. (1989) recommends that a minimum of 40-50 animals should be tagged at all times. A deer must survive through the acclimation period (< 7 days) to be considered in the study (Pollock et al. 1989). SAS (SAS Institute, Inc., Cary, North Carolina) software was used for the survival analyses in this study. The survivor function estimates were produced by PROC LIFETEST (Allison 1995). PROC LIFET EST generated survival estimates using the Kaplan-Meier estimator and required that all animals have a common date of entry into the study (to) (Winterstein et al. 2001). The total number of days a deer was observed (censor date/mortality date minus date of radio-collaring) was used as the input for this program. For the purposes of this study, I considered all deer “at risk” from a common starting time, regardless of the date they were collared. All winter captured deer were considered “at risk” on 1 January and all spring captured deer were “at risk” on 27 May. 17 Log-rank 1; test was used to compare survival curves to deterrrrine if a significant difference existed between the survival probabilities. The log-rank test was chosen because it was more likely to detect differences in survival curves that occurred later in the study (Winterstein et al. 2001). Comparisons were also made to determine if a difference existed in the survival probabilities between years and sexes of radio-collared fawns. Population parameter - Cause-specific mortality Causes of mortality were determined whenever possible during the study. The transmitter pulse signal increased (i.e., doubled) to indicate that a deer had been motionless for the specified time according to the transmitter’s sensor (i.e., adult collar 4- hour sensor; fawn collar 4-hour sensor). The collar was located and field observations of the site and carcass (if present) were recorded and photographed. Once field observations were recorded, the carcass was transported to the Michigan Department of Natural Resources — Wildlife Division Pathology Lab for complete necropsy and collection of tissue for bovine tuberculosis testing. Population parameter - Sex ratio and age structure Sex ratio and age structure were determined for deer captured within the study area. The age of each winter captured deer was classified as fawn (<12 months) or adult (2 12 months) based on tooth wear and the size of the deer. Sex was determined by identifying the presence or absence of antlers or buttons. I classified the age of spring captured deer according to their overall behavior at time of capture, condition of the 18 umbilicus, and amount of new hoof growth (Haugen and Speake 195 8, Downing and McGinnes 1969, White et al. 1972). The sex of spring captured deer was determined by observing the genitalia. Home range and movements To collect and analyze deer movement, home range, and landscape use data, ArcView 3.2 (Environmental Systems Research Institute, Inc. (ESRI), Redlands, California) was used to establish land cover and land use maps for the study area. The land cover data layer used in my analysis came from the 1992 National Land Cover Data which was developed from 30 m Landsat thermatic mapper data (United States Geological Survey (U SGS) 1999). The land cover data was then generalized using the Spatial Analyst Extension by merging clusters of less than 5 pixels to make the land cover layer more homogeneous and easier to interpret. Land cover layers were used in conjunction with the location layers from the telemetry data to examine patterns of deer mortality, survival rates, and home range sizes in relation to landscape composition. Home range has been defined as “the extent of area with a defined probability of occurrence of an animal during a specified time period” (Kemohan et al. 2001:126). To be consistent with northern Michigan deer studies (e. g., Van Deelen 1995, Sitar 1996, and Garner 2001), deer were considered migratory if winter and spring-summer home ranges did not overlap and the ranges were >1 km apart (Sitar et al. 1998). I used the fixed kernel home range estimator to determine the primary centers of activity within the white-tailed deer home range. The fixed kernel was appropriate because it provided a more precise and accurate estimate of the home range’s outer contours than did the 19 minimum convex polygon method (Kemohan et a1. 2001). The fixed kernel was also chosen because it has a lower bias and better surface fit than the adaptive kernel (Kemohan et al. 2001). I was primarily concerned with gross movement patterns and the delineation of the deer’s utilization distribution (UD). The kernel range estimate can be characterized as “the minimum area that includes a fixed percentage of the estimated UD volume” (Kemohan et al. 2001:141). See Table 5.1, page 134, in Kemohan et al. (2001) for further information on the evaluation of home range estimators. To obtain accurate home range estimates for computing seasonal and annual home ranges, 230 locations per season were used as a goal for the home range analysis (Kemohan et a1. 2001). To construct the home ranges, the Animal Movement Extension (Hooge and Eichenlaub 1997) within ArcView 3.2 was used. The least square cross validation (LSCV) smoothing factor within the Animal Movement Extension was used because the amount of smoothing and bandwidth controls the width of the individual kernels, and it has been recognized as a crucial component in kernel density estimation (Seaman et al. 1999, Kemohan et al. 2001). Home ranges were constructed using the 95% probability contour. Location estimates were output from the LOCATE 11 program and were converted into point coverages and reproj ected using Michigan GeoRef Coordinate System. The location data revealed 2 key components (1) timing of dispersal and movements of white-tailed deer, and (2) identification of cover types (e. g., agriculture, deciduous forest) that may act as refugia. Besides the timing of movements to a potential refuge, location data were used to quantify home range sizes, number of patches, and 20 whether deer used cover types in a predictive pattern (i.e., in relation to the arrangement of available cover types). Water bodies were excluded from the home ranges because they were considered uninhabitable. Any extraneous estimated locations that occurred in the center of water bodies were excluded from the analyses. Radio locations that were within the water body, but near the bank were moved to the closest land cover type for ease of home range analysis. The measure of habitat diversity across a landscape is useful when describing landscape characteristics. Interspersion is a habitat characteristic that describes the intermixing of habitat components (e. g., cover types) (Giles 1978). The measure of interspersion was one method used to evaluate the landscape in terms of its potential to support wildlife. An interspersion index was calculated following the methods described by Baxter and Wolfe (1972). The number of landowners’ property encompassed within a fawn home range was also determined during analyses. To be included in the analysis, a landowner had to own > 8.10 ha. This figure was chosen because it represented the approximate size of the smallest fawn home range. Vegetation composition and structure Vegetation sampling was conducted in forest cover types that were frequently used by telemetered deer. The 2001 vegetation sampling occurred in 6 stands frequently used by radio-collared deer. Three plots (30 m x 5 m) were randomly distributed across each stand to quantify the compositional (e. g., total stem density) and structural 21 vegetation attributes (e. g., percent canopy cover) of frequently used stands. Along the 30 m length of each plot, I systematically sampled canopy cover at 6 points (every 5 m starting at 0 m) using a moosehom densitometer (Geographic Resource Solutions [GRS], Arcata, Califomia). Canopy cover was estimated by looking into the moosehom densitometer to determine what percentage of the sky was blocked by trees. The percent of canopy cover was categorized within conifer stands and deciduous stands. Canopy cover was classified into categories of 0%, 25%, 50%, 75%, and 100% at sampling points along the transect. Woody stem density was determined for trees species with a diameter breast height (dbh) < 10.16 cm and 2 10.16 cm dbh in each plot. Two dbh classifications were used to describe the successional stage of frequently used forest stands. For each stand, stem density was calculated as Di = ni/A, where Di is the density for species i, ni is total number of individuals counted for species i, and A is the total area sampled (Brower et al. 1990) During 2002, the vegetation sampling technique was modified to better characterize the vegetation structure and composition of stands frequently used by deer. Four different forest types fiequently used by radio-collared deer were chosen for evaluation. I used 20 m x 5 m plots to quantify the compositional (e. g., total stem density) and structural vegetation attributes (e. g., percent canopy cover, and percent ground cover) of frequently used forest vegetation types. Forest canopy cover was quantified along the 20 m length of each plot; I systematically sampled canopy cover at 3 points (every 10 m starting at O m) using a moosehom densitometer (Geographic Resource Solutions [GRS], Arcata, California). As in 2001, canopy cover was classified 22 into categories of 0%, 25%, 50%, 75%, and 100% at sampling points along the transect. Ground cover was quantified using a modified Daubenmire frame (0.5 m x 0.5 m) and classified into categories of 0%, 25%, 50%, 75%, and 100% ground cover. The stem density of tree species with a dbh < 10.16 cm and 2 10.16 cm dbh, were determined using the same technique used during 2001. As in 2001, a description of height strata associated with the forest stand was also characterized. Deer browsing intensities were quantified in 3 forest stands frequently used by radio-collared deer. Three belt transects, l m-wide, were systematically chosen within each stand. The first 100 current annual grth twigs encountered were examined and the number of browsed twigs was tallied out of those 100 twigs. Only those twigs <2 m high were included in the analysis because vegetation >2 m high is generally unavailable for white-tailed deer (Gysel and Lyon 1980, Campa et al. 1996). This analysis gives an indication of the deer browsing intensities within forested stands frequently used by collared deer. 23 WINTER CAPTURE RESULTS Winter captures A total of 59 deer were captured and radio-collared from January to April during 2001 and 2002 (Appendix Table 1). In 2001, we radio-collared 29 deer (3 males, 26 females) across 4 different capture sites within the study area (Table l and Figure 2). The primary focus of the 2001 winter capture season was to radio-collar adult females. We intended to closely monitor radio-collared does at the time of parturition to increase our effectiveness of finding fawns for radio-collaring. In actuality, fawn searching efforts were extremely successful without closely monitoring the radio-collared does (see Spring/Summer Capture Results). Twelve deer were only ear-tagged (3 females, 9 males) and released during 2001 due to abnormal behavior of trying to kickoff radio- collars. Four deer were recaptured during the 2001 winter capture season; 1 deer was recaptured 4 times, another deer was recaptured 2 times, and 2 deer were each recaptured once. One deer died as a result of trapping efforts when it broke its neck during processing and was not included in the study. In 2002, we radio-collared 30 deer (14 males, 16 females) across 5 capture sites within the study area (Table l and Figure 2). During the 2002 winter trapping season, we radio-collared all deer regardless of sex. None of the deer radio-collared during 2002 had to have their collar removed by study personnel. Five deer collared during 2002 were recaptured; 1 deer was recaptured 4 times and 4 deer were recaptured once. One assumption taken into account when radio-collaring an animal is that the capturing and marking did not influence its future survival (Winterstein et al. 2001). To decrease the impact of radio-collating, radio-collars are suggested to weigh <5% of the 24 Table 1. Age and sex of white-tailed deer radio-collared in southwestern Lower Michigan in 2001 and 2002. Table entries represent number of radio-collared deer and numbers in parentheses are the number of deer only ear-tagged. Females Males Years Adults Fawns Adults F awns Totals 2001 18(1) 8(2) 0 3 (9) 29 (12) 2002 9 7 O 14 30 Totals 27 (1) 15(2) 0 17 (9) 59 (12) 25 1 Cam >90 E 3 :0 G a 3 O 0 ESQN G . 0.":me NCCN HOON . 50:2 .5 . _ owwo at m fl HODU 32. “not: w w “mm o o How «3 N 5550 ecu—WEa—avm at... V xx» 0 26 animal’s total body weight. The adult collars in this study weighed 270 g, well below the suggested weight proportion. Previous research indicates that radio-collars can have an impact on an animal’s survival and ultimately affect survival analyses (Withey et al. 2001). To offset this problem, an acclimation period was developed; a deer had to survive 2 7 days from the day of capture or recapture to be included in this study. Over the 2 winter capture seasons, 6 deer died during the acclimation period (3 deer collared in 2001, 3 from 2002). Three deer died from capture related stress, 2 mortalities were attributed to coyote predation and the other deer was mired in mud. All six of these deer were not included in the analyses. Survival Survival probabilities were determined on an annual basis; deer collared in 2001 that survived to the second year of tracking were included in a second year of survival probability estimates. Survival probabilities for deer collared in 2001 were similar between years of tracking. Annual survival estimates for deer collared in 2001 were 0.767 (SE i 0.833) from January 2001 to December 2001 and 0.750 (SE 3: 0.097) from January 2002 to December 2002 (Figure 3). A combined survival of 0.577 (SE at 0.097) was determined for all deer collared in 2001 through the duration of the study (2.5 years). The annual survival probability for deer collared in 2002 was 0.404 (SE i 0.098) (Figure 4). The survival probabilities remained constant January 2003 to April 2003 for both years because no additional mortalities were encountered. Among the deer collared in 2001, the survival probabilities were not significantly different between years of tracking (Log Rank x2 g 0.023; df=l; P=0.88). Survival 27 .5332 833 8883558 5 Son 3822 I F3233 “253 wage 33:86:5— uoow 933-333 new 023 >553th _m>§m .m Rama ass 2:: 8a: 832 85. 882 88m 8:: 8.3: 852 89: 882 88m 83 8:2 as: 85. . _ . . _ r _ t t . . r _ _ o llll|-|-}lllll I.-l-l,| l l-l;l..l-|ll llllllllilllilllullilllrlllllllllll-llllilllll l l. l-l r -lllll-llfi m C l r 1 r 1-- ; ll ll llllll .ll 1 l l l l -, llllilll: l r l - l i ll 11. N o I}: -1 I r i --i , 1-11111} ill I I i 113 l; - r I 1 1 l- i ll ill. 3 llllrillllllr -il ..... 1111111-- I ll} is l l l l l l l l l l l V? O \O o’ filtuqeqcud [mt/ms r '5 O r °9 O .1 ad 28 dawEouz .630..— EoumoBSSm E Noon 2032 I 553 ~35? wage 33:00-23." hoot 625-833 .8“ 256 ban—Boa 339m .w 2:me 385 as: 8 s2 8 a: 8 as 8 .62 8 3m 8 E 8 a: 8 a: 8 5: lllll l will ill! lllll l l l ll 3 l l l. lll llll.ll iiillllllllllillllllllillllllll l lull l l l l lillllllll lll ,llll ll l l .l ill llllllrlll .l N O l lllllr l l trill r l r l l llllil l llilll r i ll .1 all r |+ m o ‘12 o O muqvqwd IBAIWS I ‘o. O ". o °°. o l l l l l l l l l l l l l l l l l l l l l l | i l l l I l i l I l l 29 probabilities were highly significantly different between deer radio-collared in 2002 (n=27) and the second year of tracking of deer collared in 2001 (n=20) (Log Rank )8 5 12.724; df=1; P=0.0004) (Figure 5.). Pro-hunt (1 January — 30 September) survival probabilities were also calculated for both years of the study. Pre-hunt survival for year 1 for the 2001 radio-collared deer (n=26) was 1.00. Year 2 pre-hunt survival probability for the 2001 collared deer was 0.90 (SE d: 0.067). Pre-hunt survival for deer collared in 2002 was 0.768 (SE :1: 0.083). Pre-hunt survival was not different between years 1 and 2 for deer collared in 2001 (Log Rank x2 5 1.018; df=1; P=0.313). Similarly, pre—hunt survival was not significantly different between year 2 for the 2001 deer and the deer radio-collared in 2002 (Log Rank x2 5 2.28; df-—-1; P=0.131). Post-hunt survival was determined fi'om the start of deer hunting (1 October) to the conclusion of the deer hunting season (1 January). Post-hunt survival rates for year 1 of the 2001 collared deer was 0.767 (SE 3: 0.083) and 0.833 (SE i 0.088) for year 2 of tracking. The post hunt survival probability for deer collared in 2002 was 0.526 (SE :1: 0.115). There was neither a significant difference in post-hunt survival between years 1 and 2 of the 2001 deer (Log Rank )(2 3 0.5632; dfil; P=0.453), nor between deer collared in 2002 and year 1 for the 2001 deer (Log Rank X2 5 2.914; dfil; P=0.088). There was a significant difference in post—hunt survival between year 2 of deer collared in 2001 and deer collared in 2002 (Log Rank x2 5 11.029; df=l; P=0.001). Comparisons between sexes of the 2001 deer were not made because of the small sample size of males (n=l) compared to females (n=26). Annual survival probabilities were estimated for males (n=12) and females (n=15) of the deer collared in 2002. Males and females collared in 2002 had an annual survival probability of 0.364 (SE i 0.145) 30 doom 3 38:8 “one we 3323 05 55 938.0 n B Bogota bfioumufim mm? SON E 35:8 but B_§-BEB mo 335m .emmEBZ .833 acacia—Sm E Noam use Sow. E 33:86? “one eo_3..2£3 Sm mega 325305 fizgm .m oSwE £69595. oun— >oZ 80 new w=< 3.. E... .32 E< 52 new :3. l _ F _ r _ h _ F _ _ 8.9 l .lll:l.l lilllllllllllllllllllllll Irllll -l -lil;l l-llllll+ 2 o ll ll l .l,lrl ll 1 llllll-l, .ll omd 88+ one S Mao .. N > Somlll 93 m. 28>- 88+ m. Illrlr I r l l Ill .0 llllllllllll llllllll omom m. a. ll llllll l lllllllll l ll ll lllllllsfoodm.” (M llllll [lllll l llllllllllllllllll :lll llofio ll l III -1 llll ll lllllllllyowo 1l I l llr cod 31 and 0.509 (SE 3: 0.133), respectively. There was no significant difference in annual survival between sexes of deer collared in 2002 (Log Rank X2 5 0.003; df=1; P=0.957). Pre-hunt survival (1 January — 30 September) for females and males was 0.6545 (SE 3: 0.133) and 0.909 (SE 1: 0.087), respectively. Post-hunting season survival probability for females was 0.667 (SE d: 0.157) and 0.400 (SE :1: 0.155) for males. No significant difference existed between sexes in pre-hunt survival (Log Rank x; 5 2.44; df=l; P=0.118) or post-hunt survival (Log Rank 1; 5 1.241; df=1; P=0.265). Cause-specific mortality Over the 28-month study period (January 2001 to May 2003), 26 mortalities were identified for the 53 radio-collared deer (49%) (Table 2). Seventeen of the 26 mortalities (65%) were hunting related, 11 females and 6 males. All deer were harvested legally during the hunting seasons. Five males and 2 females were harvested during the archery season (1 October - 14 November & 1 December - 1 January). Six females and 1 male were harvested during the firearm season (15 November - 30 November). Three females were taken during the archery season/late firearm season (23 December - 1 January). Vehicle collisions accounted for 7 of 26 total mortalities (27%), 5 females and 2 males. Two mortalities resulted from trauma related injuries (8%) both were females. Two deer were censored from the study when their radio signals were lost and 25 deer were alive at study’s end (Table 3). Seven mortalities occurred during the winter/spring season, 5 vehicle collisions and 2 trauma related (Table 4). Vehicle collision accounted for 1 mortality during the summer season. As mentioned above, hunters harvested 17 deer during the fall/winter 32 am..— 30353 5635 0.323» I mooBOmom 3.3.82 we “Schumann newanz an 555.6“? t3 038 muomaefinuflon a $03 om 30H sew L 3883‘. ”mtiofiom ”A0533 9 26 bnmnokc «858,—. $3 L 5:53:32 3835. $8884. $3 an 983:8 o~oEo> :8 : wees: am“: but 3sz 280.85 33:86:08 mo Hons—=2 .mooméoom .335va .833 5883538 5 832388 Home moo—3-823 @2398 .3553 .«o 8325 .N 2an 33 Room :09“ omv 0:0 $830 05 .«o 0:5 05 00 03—0 0000 mo 0098520 9.3 Name—05$ .8635 0.52;? I 000580m 3832 m0 0:0:PHQ0Q fiwEB—Z .0 55500.? 3 0008 Beggar—0009 . $2: mm 130% x: a £032 o\ov N 8.8250 c\°m L ”magma? ”mumcout0m x05n00 9 26 bnaaoé 08:0; o\°N L scum—0:502 ”08:05. ”$000“: $2 k. 0:20:30 0_030> $2 2 0&5; 303 0000 008:8 808$ .23.. 0950 .«o 89:32 dear—com dmwflomz 0033 800003538 5 0000 00:00-83? Bang 00053 m0 003* .m 030,—. 34 =mm SEGA—m 3020: 00:53 5cm 8980009 cm mots. has: 88 03888 a 08:2 5.5: 88 3883 mm 8230 202$ 88 028080 8 50:3 §§m 88 508080 a 00030: 00055 Noom 00980009 ~ 085: 5:5: 88 38202 on 085: 5.5m 88 505052 on :05: 035m 58 38032 cm 5020: 000:5: SON 00:802. Z mm “mug: hows—m ~OON Hun—8952 a— 00020: “025$ Sow 038032 C 56500 0_0E0> Noom >02 am 0832 .253 88 038052 2 8&8 2°20> 88 ~32 MN 505: 00:53 Noom 009:0; Z v 08:85 Noon ~55 on 085: has: .88 03200 8 82:8 20.5; 88 532 R 085; 8.5m 88 000900 _~ «53¢ 88 532 2 “at“: 025m 88 .3900 on 5358 202; 88 532 M: 08:2 025: 88 02330 2 8&8 202$ 88 :3 m 8358 202$ 88. 3:5. 2 03m 0:5 03m 0:5 000m 0009 Smash _ - 3900 d 308380 8 - 22 a a“: z - @350 N0 05058053 88-88 .8022: 333 Eugene a 80:30.08 5% 33-323 02308 has, 0o mafia .v 0:3 35 deer seasons and l deer was killed by a vehicle collision (Table 4). One ear-tagged male, tagged in February 2001, lived long enough to be harvested during the 2001 hunting season. The hunter contacted study personnel and reported the ear-tag number of the deer that he had harvested. This male was not included in the mortality and survival analyses. Home range and movements Home ranges of winter-captured deer were constructed on an annual basis or from the date of capture — 31 December. Home range analyses were conducted for deer that demonstrated 2 types of movement patterns - non-dispersal and dispersal. Fifty-three deer (48 females, 5 males) home ranges were classified as non-dispersers. Of the 53 annual home ranges used in the analysis, 24 home ranges were from 2001 (23 female, 1 male) and 29 home ranges were from 2002 (25 female, 4 male) deer. Sixteen of the 25 female deer had home ranges used in analysis from 2001 and 2002. Ten deer (2 females, 8 males) were classified as dispersers. Ten deer (l deer collared in 2001, 9 deer collared in 2002) dispersed from their capture sites and subsequently established new home ranges. One female deer was classified as migratory and will be discussed separately. Home range and movements — Non-dispensers Mean annual home range size for both years of winter captured deer (n=53) was 157.73 ha (SE :1: 18.34) with a range of 49.84 ha — 739.7 ha (median = 117.9 ha). Mean home range for deer tracked in 2001 (n=25) was 146.46 ha (range = 49.84 — 635.71 ha; median = 119.21 ha) and 167.79 ha (range = 50.06 -— 739.72 ha; median = 115.54 ha) for 36 deer tracked in 2002. Sixteen deer were tracked for 2 consecutive years (2001 and 2002); and the mean home range size for 2001 was 124.54 ha (range = 49.84 — 188.5 ha; median = 127.6 ha) and 120.61 ha (range = 58.06 -— 227.5 ha; median = 111.85 ha) for 2002. Home range size distributions were generated for all winter captured deer (n=53) used in the home range analysis to determine if home ranges could be grouped into similar size classes (Figure 6). Forty-four of 48 female deer (92%) had a home range between 50 ha and 200 ha. Thirteen females had a home range between 100 ha and 125 ha. The largest female home range (265 ha) was similar in size to the smallest male home range (275 ha). Male (n=5) home range size ranged from 274 ha to 740 ha. The mean number of patches in each home range for 2001 and 2002 deer was 20 patches (SE :h 1.84) with a range of 4 — 68 (median = 16). Deer tracked during 2001 had a mean of 19 patches (SE :t 2.33) (range = 4 — 66; median = 16 patches). Deer tracked during 2002 had a mean of 22 patches (SE :1: 2.80) (range = 7 — 68; median = 16 patches). Mean number of patches per home range was quantified for deer collared in 2001 over the 2 years of monitoring. The mean number of patches for year 1 was 18 patches (range = 10 — 30; median = 19 patches) and 17 patches (range = 9 — 28; median = 16 patches) for year 2. Cover types within each home range were characterized as residential, deciduous forest, evergreen forest, agriculture (composed of pasture/hay and row crops), woody wetlands, and emergent herbaceous wetlands. Winter captured deer typically used 4 cover types (deciduous forest, agricultural land, woody wetland, evergreen forest), but use ranged from 3-6 cover types (Table 5). Only 7 deer of 53 had some portion of their home range composed of residential development averaging 0.75 ha, approximately <1 % 37 .Nooméoom .qawanz .533 500003528 5 Ammuav 0000 023.82? 0025.30 .6053 NOON 28 Sam com sauna—Emmy 5:093 A05 0N6 030.. 080: .0552 6 0.5me A05 006 ownfi 050m own can 63 ccw own com omN cow c2 2: an o _ . FML _ p _ _ _ _ . to \\\\\ I oo +11“ Lilli 1991330 .13qu -—--- l l i F ii VD V . l 1 l J | £2 0353 I 202 S N— e.— 38 OEN SN" KO" NNAm OOO ON 2% 3.3 N 80> OwN 8.2 3% mwdv SO OWN mN.w Oman _ 30> SON afim 2.2 Om.m fl O~.NN moé mN.: Nm.N~ no.3 NOON Ed 5.3 NN.O~ modm mO.N Nmé 5.: whee SON mmN no.3 mm.w 3.3 Nm.N mm.w Sum Ede NOON 0% SON mm $5 822 mm 9.5 $02 mm @5 502 mm ea 532 a; 098:0? 383 065— 0530tw< £008.“ 50w0>m $00.5.“ 332009 .88 -88 .5032: 533 assuage,“ E 000“. “00—50033 “00.50900 .8053 we £0qu 098: E53 25 00.60 .5588 3 doafioafig 0930053 .m 033. 39 of the total home range. Deciduous forests comprised, on average, 69.15 ha of the total home range (47%) of winter-captured deer. Evergreen forest was included in the home ranges of 37 deer with an average area of 8.33 ha composing 3.6% of the total home range. Some portion of all home ranges was composed of agriculture with an average area of 65.47 ha and composing 39% of the total home range. Fifty-two deer had woody wetlands within their home range, accounting for an average area of 16.05 ha (11%) of the home range. Emergent herbaceous wetlands were found in only 23 of 53 home ranges and had an average area of 3.31 ha, comprising only 2% of the total home range. A summary of all radio-collared deer (n=53) and the percentages of cover types that compose their home range is in Appendix Table 2 and Appendix Table 3. The proportion of agricultural or deciduous cover types in home ranges of collared deer was plotted to evaluate the most frequently used cover type. F orty-two of 53 deer had a total home range composed of 10% to 60% agricultural lands (Figure 7). Two deer had <10% of their home range composed of agriculture; 9 deer had 260% of their home range composed of agriculture. None of the deer had home ranges with 280% agricultural lands. Five deer had home ranges composed of <20% deciduous forest and 6 deer had home ranges composed of 270% deciduous forest. The majority of deer (42 of 53 deer) had home ranges composed of 20% to 70% deciduous forests (Figure 8). The size of each deer’s home range (ha) was correlated with the percentage of a specific cover type (e. g., agriculture, deciduous forest) within each home range. This correlation was graphically represented in a scatterplot to assess trends in the home range data. For this analysis, only female deer were used because of the small sample size of males (n=5) and the largest female home range was similar in size to the smallest male 40 .052 gfiotwa mo 303800 Ammncv m0w§ 0:8: :0 90 E083 508 05 8O sou—5.506 >0=0nc0b 0mg 080: 0000 00—3003? @2393 00:55 .N. osmfi 93— 30330.:w0 O0 @8388 0w§0 080: :38 mo 0wfia0000m o\oOO o\oow $2. $8 o\oOm .39. RON $ON Ax5— o\oO 199p JO JoqtunN N— 41 .3028 03022000 mo 0008800 Ammucv m0wcfi 080: :0 mo E0808“ 508 05 .5.“ nousnEmmO >0=0=~00¢ 0mg 080: 000v 025-0033 @0338 00053 .O 0.5mmm 302$ 05033000 mo 8009800 0w§0 080: 38 mo 0932080.“ $8 $8 o\oO~. £08 o\oOm .Il . gov °\oOm .XON o\°O_ o\oO 1 \0 Jeep JO .19qu v— 42 home range. According to the agriculture scatterplot (Figure 9), there was a weak positive relationship (R2 = 0.126; y = 0.1457x + 21.144) between home range size and percentage of agricultural lands within home ranges. This relationship (weakly) implies that as home range size increases so does the percentage of agricultural lands within the home range. In contrast, the scatterplot displaying home range size (ha) in comparison to the percentage of deciduous forest implied a weak negative relationship (R2 = 0.086; y = -0.1273x + 63.092) (Figure 10); thus, the percentage of deciduous forest composing each home range decreased as the home range size increased. Home range and movements — Dispersers Ten deer (3 females, 7 males) dispersed from their capture sites and subsequently established new home ranges. The average dispersal distance for both years was 9.55 km (SE :1: 1.56) with a range of 3.1 1 km — 18.38 km (median = 8.8 km). All deer did not disperse in the same direction, but most (8 of 10 deer) dispersed in a southwesterly direction (Figure 11). Of the 10 deer that dispersed, 7 dispersed during the spring (April, May, June) and 3 during October. Mean date for spring dispersal was 15 May (n=7) and 12 October for fall dispersal (n=3). Deer that dispersed spent an average of 150 days (SE i 23.9, median = 117) at their initial, pre-dispersal, home range and 159 days (SE i 46.5, median = 165) at their post-dispersal home range. One female deer (#260) was excluded from the group of deer that dispersed because her movements were more similar to a migration than dispersal, her movements will be discussed on subsequent pages. Two home ranges (pre-dispersal, post-dispersal) were generated for all deer that dispersed. Seaman et a1. (1999) and Kemohan et al. (2001) recommend using >30 43 .NOONAOON £03532 0030..— 5000035500 E vaunv 0mg 080: £000 wfimoafioo Ea— ?SH—aotwm O0 $300003 05 9% 00¢ O0_§-0HEB 603500 08:15.? A05 005 0mg 080: 06 8053 Oranges—0M .O 0.53m A05 00% 0mg 080m OOM OmN 8N o2 on: On O p _ 4. w ” Axe 0 $2 O o \ $0». I . - cl llili‘ rill- $9. \ . ~.. . O . , :1! it .5! ill It 11.1!1 11-1-11- $8 . $8 32.0 n m. o o 3_._N+finz.ou» 0 ‘1 I‘ll l l.l-1| l.' .l...|-l|lllll||||| Ill lll- llal. llll- ill:[:llllr O\OON| it o\oOw l l l I l l . 1 l I ; . | - = l 4 t l '0 t I l t l l. l p l l I I l l o l o l l l l l l 1 para] mmnnoufie JO posoduroo 98m auroq J0 moored lllli illillllllll l I vlilll [Is llrillli ll 1r. ll lilll. $8 c\ocO_ doom-Sow .fiwEomz 833 8883538 3 vaufi owafi 080: some magma—8 mama-5.“ mac—63% mo owfiaooba 05 28 bow “go—5-823 3593 82:3 mo 35 uni owcfi 080: 05 5253 maggots—um .2 oSmE com 95 36 0mg 080$ onN 8N o2 o2 om o w w - L- _- .xb . 1 -.ilL11J- -1.111|1|-1- $2 .d . . w . O 111 1111111 111- 11.1-11.1 1 1-1 . 1-1.1 :8 m . . . m. 0 0 I... V 11. . o\ocm U. . . o Sag-Na . w Nogmo + XMBN7 . . . 9 - 11-1- . . 11-.--.-1|-.1--111111- :8 1 . . . w . . . we can” . 11-1-1 - 1 - 1 1 -1! + 11111 111-1.3% w m 1 1- 1 1 - 1 - - - -. .11 - 1 1-1-..- - - 1111111 .11 1 -1---..\..8 .w 0 O O W O. O. O - - 1 -11 - 11 - - - - - 1-1-1. 11.1.11- ---_1--- - 111-11111:.3 m... . . . w 11 1 1 1 1 1 1, 1 -1 1- 1 1 1 - .11 1-1-1 111-1. - -1 1.- 1-111-111111xcow m. o m 111 111-11 111 -1- 111 11 -1--1 . 111-1.11:8 . o\oco_ 45 - .5 E . _ 2&5 com 58 damn—22 BBQ.— 5283508 E v.25 23000 82m canoe-Ev 30.8%? 0020 00:8 BE? 00.50.30 0 .3 H .N - . . . . 5:500 8:086 $2085 IV OcNafi—N—Nv— ESE-WU 86 0.5800 . 50:00 than 46 locations per season for home ranges analysis. I recognize this suggestion, but an exception was made for the home ranges of deer that dispersed. The number of locations used to generate home ranges varied (range 15 — 93 locations); I generated 5 of 20 home ranges using <30 locations. Home ranges of deer that disperse provides a coarse description of home range composition and size, and gives a general description of the dispersal ecology of deer in southwestern Lower Michigan. Due to the variation in the number of locations comprising each home range, I feel it would be inadequate to perform statistical comparisons. The mean size of the pre-dispersal home ranges was 139.29 ha (SE 3: 14.77) with a range of 63.46 — 194.50 ha (median=139.40 ha). Mean post-dispersal home range size was 315.29 ha (SE i 70.99) with a range of 69.80 — 685.60 ha (median=287.3 ha). Pre- dispersal and post-dispersal home ranges had similar percentages of emergent herbaceous wetland, woody wetlands, and evergreen forest composing the home ranges. Emergent herbaceous wetlands composed <2%, woody wetlands made up <10%, and evergreen forests composed <3% of the pre-dispersal and post-dispersal home ranges. Differences between pre-dispersal and post-dispersal home range composition were evident by comparing the percentages of agricultural land to deciduous forests (Appendix Tables 4- 7). Pre-dispersal home ranges had a greater percentage of deciduous forests (mean = 52%; SE i 5.07) than agricultural land (mean = 38%; SE :t 5.40) (Figure 12 and Table 6). In contrast, post-dispersal home ranges had a greater percentage of agricultural land (mean = 52%; SE :t 3.80) than deciduous forests (mean = 34%; SE i 2.84) (Figure 13 and Table 6). 47 omafi nae: game.“ 880 983 .ncdvmv 288% >285:me a 0950 080: mo; hmd mod wad cod wfio oodm ow.m Simm $2 mud de om.mm wwd wadm ~afionm€Lmom own—a 0:8: om; Nod mm; mod 3..— VMé om.wm 9mm smofim wfim mod awam owAm wow *vam Emponmmcopm 5:52 mm :32 5:82 mm E32 €602 mm :82 5:52 mm ENo2 5:52 mm :82 35:03 988:5: €533 @025 35: fins—35¢. £88.“ Guam—gm $880 anon—Boom “5908mm .Nooméom .86 05%". :2: Bob 380%? 35 87.5 5% “023-223 32598 .8053 80 25 .560 :80 mo 389:8 bug 080: we 80an "302 .o 2an 48 80% I150.070 I150.150 '° 70? g 0 0150.170 I150.310 60V 3 E: o I150.330 1. 3150.370 w 50°/ 3 S o I150.43o “’ 5 9150.450 40°/ .5 3 o I 150.470 ‘8 § 30% I150.54o go‘s 20% E a: 10% 0% Deciduous forests Agricultural lands Woody wetlands Covertype Figure 12. Landscape composition (mean percent), by dominant cover type within pre- dispersal home ranges, of winter captured white-tailed deer (n=10) in southwestern Lower Michigan, 2001-2002. 80% I 150.070 70% 150.150 1:1 150.170 60% 150.310 I 150.330 5 150.370 50% I 150.430 a! 150.450 E; 40% I 150.470 150.540 30% 20% 10% Percentage of home range composed of each cover 0% Deciduous forest Agricultural lands Woody wetlands Cover type Figure 13. Landscape composition (mean percent), by dominant cover type within post- dispersal home ranges, of winter captured white-tailed deer (n=10) in southwestern Lower Michigan, 2001-2002. 49 One female deer (#260) exhibited unusual movements that were categorized as migratory (Figure 14). The migratory patterns she exhibited did not coincide with seasonal migrations, rather migrations possibly related to hunting pressure. She was captured and radio-collared on 6 February 2001 and began dispersal on 26 April 2001. The deer traveled 9.75 km northwest and established a new home range. This deer remained at that post-dispersal home range until 15 November 2001; at that time, she moved back to the general location of her pre-dispersal home range. She stayed at the pre-dispersal home range for 70 days (17 November 2001 to 26 January 2002) and then returned to the same post-dispersal home range on 29 January 2002. Deer #260 spent 289 days (29 January 2002 to 14 November 2002) at the post-dispersal home range and dispersed once again on 14 November 2002. Radio contact was lost following her last dispersal. We speculated that she died during the 2002 deer hunting season. Nevertheless, we heard her radio signal in April 2003, and she was located halfway between her pre-dispersal and post-dispersal home range. Deer #260 exhibited variability in home range composition between pre-dispersal and post-dispersal home ranges (Figure 15). The pre-dispersal home range was predominantly agricultural lands (63%) and her post-dispersal home range was predominantly deciduous forests (68%). 50 .038 3 5.6.6 8: 2a moms—u 080: a .moom-8o~ ASE—~22 .533 5283558 5 cog .82. .«o 3.533 288962 .3 gamma .3550 2.52.3.5— av» _ V3» /. A88 88052 E 1 Noon 522 as ‘ .owfifi 0:8: Eur—0&6 8 Band . _ A88 .939: 8 1 SS. h2.552 a 3 _~ .omafl 08o: .nflonmmu ca 8 E33— A A SOON con—8052 m" I Sam =0“? omv L .038 0:8: BER—m5 , ....... ..... 28~ E3. 8 1 88 Saga 3 aomfic 080: 30256 2m 912 .5: o 2 51 .mooonoN .5365 633 588353.... a 8% 5% Ho.“ omqfi 080: :80 wfimomEOo 25 :38 :80 mo mowafioouom .m fl oBmE 9303 30083.5: 8mg go: Rmbamfiéom I ) ‘7‘”; “I. I. “A p ' JIM": )z' “Iiitéfiff’rx If; ',._ ‘nw, ‘wa as 850 82MB>m 8.589 Egg L§ - $2 - $o~ - $Om - $om 1 WQEEEE- :8 m. $8 $9. 12mm {[09910 pasoduloo 98u121 awoq qoea Jo maxed $8 52 SPRING/SUMMER CAPTURE RESULTS Spring/summer captures Seventy-six fawns were captured and radio-collared during 2001 and 2002 (Appendix Table 8). In 2001, 36 fawns were radio-collared (17 females, 19 males) across 4 different primary capture sites (Figure 16). In 2002, 40 fawns were radio- collared (24 females, 16 males) across 8 capture sites (Figure 16). Thirty-five of the fawn captures, during 2001, occurred from 17 May through June 6. After the first fawn mortality of the study on 10 June 2001, the radio-collar from the dead fawn was used to collar the thirty-sixth fawn on 28 June 2001. Finding the thirty-sixth fawn to radio—collar was a low a priority for this study, thus explaining the 3-week lull in radio—collating efforts. During 2002, fawn captures occurred from 12 May to 10 June. Estimated age at capture for both years ranged from <1-15 days and averaged 4 days (median 3.5). Fawn ages and weights (mean = 4.8 kg) were similar between years (Appendix Table 8). Approximately 7.5 person-hours were expended per fawn capture during 2001 (n=36) and 10 hours expended per fawn capture during 2002 (n=40). Most fawns were bedded at the time of capture. F awns approximately 7-days-old and older generally flushed during capture attempts. Sixty-five fawns were successfully captured without flushing and 11 fawns were captured afler flushing. All fawns captured appeared to be in good physical condition and health at the time of collaring. One fawn was excluded from the analysis because it was radio—collared within a 1 m high fenced enclosure (< 0.20 ha) and, therefore, may have had limited opportunities to venture outside the enclosure. Radio-collar retention for both years ranged from 40 to 448 days and averaged 53 .Nooméoom .fiwEomE 533 5883538 E 8%. 2:58 uogmhctam Esau .82. 93:36:55 .2 259m ”SE .5550 852523— Noom . SON 4 " an. 1' 0 av xx we 54 approximately 250 days (median = 245). The battery life of the fawn radio-collars was approximately 427 days (Advanced Telemetry Systems, Inc. 2001). According to the manufacturer (Advanced Telemetry Systems, Inc., Isanti, Minnesota, personal communication with H. Campa), the radio-collars were expected to deteriorate and “drop off” between 266 and 365 days. Survival Survival probabilities of 2001 and 2002 radio-collared fawns to 30 days post capture were 0.971 (SE i 0.0282) and 0.925 (SE 1 0.042), respectively (Table 7 and 8). Fawn survival probabilities at 180 days were 0.791 (SE i 0.071) for 2001 fawns and 0.846 (SE :1: 0.058) for 2002 fawns. From January 2002 to May 2002, fawns radio- collared during 2001 reached a stable survival probability of 0.759 (SE :1: 0.075) (Figure 17). On 30 April 2003, the annual survival probability for fawns radio-collared in 2002 was 0.748 (SE :1: 0.074) (Figure 18). Fawns radio-collared in 2001 and 2002 had a high survival probability (> 0.750) across the tracking period (May to April) (Figure 19). There was not a statistically significant difference between the survival probabilities of the 2 years (Log Rank )8 _<_ 0.000; df=1; P=0.99). Results also indicated that there was no difference between survival rates between sexes (Log Rank { 5 1.85; df=l; P=0.174). Cause-specific mortality Numerous fawn research studies (e. g., Carroll and Brown 1977, Verme 1977, Dood 1978, Huegel et al. 1985, Nelson and Woolf 1987, Long et a1. 1998, Vreeland 2002) have indicated that fawns are most vulnerable to predation and death by natural 55 Table 7. White-tailed deer fawn survival probabilities in southwestern Lower Michigan by number of days, 2001. Day Survival Probability 95% Confidence Interval 1 1.000 7* 0.971 0.916 - 1.000 68* 0.943 0.866 - 1.000 125* 0.914 0.822 - 1.000 141* 0.885 0.779 - 0.991 143* 0.854 0.736 - 0.973 169* 0.823 0.694 - 0.952 178* 0.791 0.653 - 0.929 225* 0.759 0.613 - 0.905 345 0.759 * Day mortality occurred Table 8. White-tailed deer fawn survival probabilities in southwestern Lower Michigan by number of days, 2002. Day Survival Probability 95% Confidence Interval 1 1.000 15* 0.975 0.927 - 1.000 24* 0.950 0.822 - 1.000 26* 0.925 0.843 - 1.000 33* 0.900 0.807 - 0.993 134* 0.873 0.768 - 0.977 168* 0.845 0.731 - 0.959 244* 0.814 0.689 - 0.939 254* 0.782 0.646 - 0.917 311* 0.748 0.603 - 0.893 345 0.748 * Day mortality occurred 56 dummy—£2 533 5883538 E Ammnav wgfl Sou 8:23-223 33:00-063 Bow .5.“ 955 525303 _ngm .5 onE 3:25 DEF >22 E< 32 com 5:. BO >02 80 new wa< Ba 5:. 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M lilill‘llllll lllilll lllllillll llllllluvdm. at 3.2.0 .3an 95:5: l 1 ‘11 l l l 1 m6 m _ .3390 m... 9o mm 56 w.o ad '— 57 .5322 633 628358... a 34.15 See 58 35-323 838.58 88 é 0&8 3:388 335m .M: 28E 388 25 .82 a< 32 sum 53. 8Q >02 80 mom w=< _2. =3 1 .11 _d 1 1 1111L T1 1.11111111 11.! 1 @230 520m ”5.55 ,. 11 111111-1111 1 — Ens—3n "1 o V: o 1 1 1 1 1 1 1 1 1 1 1 1 1 1 ‘ 1 1 1 1 1 1 1 1 1 1 1 g Amrqeqord [mums T11 1-1 1 1 1111 1 1111 AER—O new-"ow ”525—5 ~ eon—39¢ 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 o C 1 1 1 1 1 1 1 1 1 1 1 1 1 h C 11 al— ad 58 .88 é 059.88 322% 3:5 05 55 $310 21% :ooo.o.v. .0. seem mod Esme 8: as 88 é $532.. 3333 3058 BE. .5m232 0030..— EBmoBfisom 5 Am N15 gum Noam 0:0 SON 00% 83:0 523305 3333 Esau .3 Bummm Aeaoazee .32 a< .32 new 0a.. 009 >0Z ~00 new w=< 3.. =3. _ _ p p p p _ _ b p _ o 1 NOON .._______________.__._._.__ 1111111 111 ~o L 88 I 1 1 11 11111.2 11111111 md S 11.11111111111111111111111111111111111:..M A. m; 1111111 111 111111111111 1 1111111111111mdd m m. 1111 l 1111 11 11111111 1111111 1 11111111 1111111 0o m ,M 1 11111 111111111111111111111-lno 111111 1 1nd 1 1 1 1 1 1 1 1 — rI p-q 59 causes during the first 60 days of life. During this study, only 5 fawn mortalities of 75 fawns radio-collared (6.7%) occurred during the first 60 days. Dehydration, pneumonia, abandonment/malnutrition, vehicle collision, and predation by an unknown canid account for the 5 mortalities prior to 60 days. Of those 5 fawn mortalities, 4 occurred within their first 30 days of life (excluding the predation mortality). One fawn died prior to 60 days of the 2001 fawns (n=35), and 4 fawns died prior to 60 days of the 2002 fawns (n=40). From 60-days-old to the start of deer hunting seasons (1 October) 2 additional fawn mortalities occurred. One fawn died when its pelvic girdle became entangled in a fence, and the other fawn died from a vehicle collision. Prior to hunting season, 7 mortalities occurred out of 75 fawns radio-collared (9.3%). During the deer hunting season (1 October — 1 January), an additional 7 fawns died. Five fawns were legally harvested, 1 fawn died from a vehicle collision, and 1 fawn drowned. Of the fawns that died during hunting season, 3 fawns were collared in 2001 (n=35) and 2 fawns were collared in 2002 (n=40). From the end of the hunting season (2 January) to the conclusion of the study (30 April), an additional 3 mortalities occurred. Two fawns were involved in vehicle collisions and 1 fawn died from a bacterial infection. All 3 of the fawns that died following hunting season were radio- collared in 2002. Seventeen fawn mortalities occurred over two tracking periods, May 2001 - April 2002 and May 2002 - April 2003 (Table 9). Eight mortalities occurred during the 2001- 2002 tracking period and 9 mortalities occurred during the 2002-2003 tracking period. Hunting was one of the primary sources of fawn mortality, accounting for 5 of the 17 6O Table 9. Causes of radio-collared white-tailed deer fawn mortalities in southwestern Lower Michigan, 2001-2003. Number of Cause c313; Percent fawns Legal hunting 5 29% Vehicle collisions 5 29% Dehydration 1‘I 6% Enteritis, Bacterial (Probable) 1‘ 6% Unknown canid 1 6% Pneumonia; dehydration 1a 6% Pelvic girdle entangled in a fence 1 6% Drowning; pneumonia; stress; shock 1a 6% Abandoned; malnutrition 1a 6% Total 17 100% '1 Determinations made by veterinarian at Michigan Department of Natural Resources - Wildlife Bureau Pathology Lab 61 fawn mortalities (29.4%). Four male fawns and 1 female fawn died during the deer hunting seasons, 1 October to 1 January. Vehicle collisions accounted for 5 of the 17 fawn mortalities (29.4%). Four of 17 fawns (23.5%) died of natural causes (excluding predation) including dehydration, pneumonia, bacterial infection and starvation/abandonment. Bizarre accidents (i.e., mired/drowning and pelvic girdle entangled in a fence) accounted for 2 of the 17 fawn mortalities (11.7%). One of 17 fawn mortalities was attributed to canid predation. During a mortality investigation, no carcass was found — only a radio-collar with tooth marks. Canid attributed mortality was assumed because the collar was found in an area known to have high densities of coyotes. In summary, radio-collared fawns were tracked for approximately 345 days (May 15 to April 30), and 17 of 75 radio-collared fawns (23%) died, 21 fawns dropped their radio-collar (28%), 1 fawn was recollared with an adult radio-collar during the winter trapping season (1 %), and 37 fawns (including the recollared fawn) were known to be alive at the study’s end (49%) (Table 10). Of the 21 fawns that experienced collar drop- off, 11 were collared in 2001 and 10 were radio-collared in 2002 (Table 10 and Appendix Table 9). Fifteen of 35 fawns radio-collared in 2001 were alive at the end of tracking on 30 April 2002. Of the 40 fawns radio-collared in 2002, 21 fawns were alive at the study’s end (30 April 2003) and were censored (Appendix Table 10). As a side note, the fate of 4 censored fawns, radio-collared in 2001, lived long enough to be harvested during the 2002 deer hunting season. Hunters contacted study personnel and reported ear-tag numbers of the deer (1.5 years old) that they harvested. The 4 male deer killed during their second hunting season (2002) were not included in mortality or survival analyses. 62 A88 E3 on - 88 $6 Bea wise as .8 c8 05 a 0% 83a .6 598:2 . A88 =a< om - 88 36 33 3323 2:8 95 as a 2% BE... .8 sneaz ._ n3 awe—05mm .8339 8:23? .. $0.583“ 35.52 we E08599 fiwflomz “a 55:58.» .3 038 32358.8qu a $2: ow {coo— mm 30,—. 3mm LN $3 pg 022 $8 2 ex: m : 3:8 Base go o o\cm L 5:52:58 60:85.? £6 0 XE L :85 685 8895525 ”magnum o\eo o Rum _ 35¢ a E vflmqfiao 22E 32$ 9%. L o\co o gushing 8808525 gm 2 so 0 2:8 Ease: 3». L go o 3338.: steam stream .xcm L go o 5:83:an fox m .xb N 286500 2023» § N :5 m wares _swfi 838 make.“ Begum UHMHo “coupon vwmflwwmo me 86:52 me 568:2 Noam Son .moom-_oom .Smeomz .833 5882338 E Bow Leo—5-823 @0530 .5ng5..% mo 33m .2 2an 63 Home range and movements Fawn home ranges were constructed for 2 analysis periods. Within these analysis periods, 3 landscape use patterns were examined: home range size, number of patches within each home range, and composition of each home range. One set of home ranges was generated for all fawn radio locations from the date of capture (approximately 27 May) to censorship, mortality, or 5 December - whichever came first. December fifih was chosen because the collection of home range location data ceased on that date for the 2002 radio-collared fawns. A second set of home ranges was constructed for the 2001 fawns from the date of capture to censorship, mortality, or 30 April 2002 - whichever came first. In order to be included in this annual home range analysis, the 2001 fawns had to acquire >100 radio locations. One hundred radio locations was chosen as the minimum number of locations because approximately 35 locations comprise each season, thus indicating that the fawn had survived 3 seasons, based on recommendations by Kemohan et al. (2001). Home range and movements - Annual home range analysis Of the 35 fawns radio-collared during 2001, 24 fawns (12 males, 12 females) survived long enough to construct annual home ranges. The number of locations used to construct home ranges ranged from 111 to 192, with an average of 133 locations per fawn. The mean home range size for fawns used in the analysis was 75.36 ha (SE d: 4.47) with a range of 38.38 ha to 118.5 ha (median = 70.19 ha). Home range frequency distributions were generated for fawns (n=24) used in the annual home range analysis to 64 examine the home range sizes exhibited by fawns (Figure 20). Nineteen of 24 fawns (79%) had annual home ranges between 50 ha to 100 ha. Two fawns had home ranges <50 ha and 3 fawns had home ranges 3100 ha. For the purposes of this project, a patch was defined as a stand of any cover type that was surrounded by stands of different cover types. Patch size was used as an indication of the amount of fragmentation within a home range. The mean number of patches in each home range was 15.04 (SE i 0.698) with a range of 7 to 22 patches (median = 15) per home range. Cover types within each annual home range were characterized as residential, deciduous forest (i.e., oak-hickory), evergreen forest (i.e., Pine [Pinus spp.]), agriculture (composed of pasture, hay, and row crops), woody wetland, and emergent herbaceous wetland. Fawns used on average 4 cover types, but use ranged from 1- 6 cover types. Only 4 of the 24 fawns had some portion of their home ranges composed of residential development averaging 1.0 ha, approximately 1% of the total home range. A1124 fawns used in the annual home range analysis had some portion of their home range composed of deciduous forest averaging 27.74 ha (median = 26.8 ha) composing approximately 37% of the total home range. Evergreen forest was only found in 16 fawns’ home ranges with an average area of 8.07 ha composing 11% of the total home range. Some portion of all of the fawns’ home range was composed of agriculture with an average area of 34.28 ha (median = 31.7 ha) and composing 45% of the total home range. Twenty-three fawns had woody wetlands within their home range, accounting for an average area of 7.01 ha or approximately 10% of the total home range. Emergent herbaceous wetlands were found in only 13 of 24 fawns’ home ranges and had an average area of 2.07 ha, 65 u—‘ N _i O l 00 l Number of fawns 0 25 50 75 100 125 Home range size (ha) Figure 20. Distribution of 2001 white-tailed deer fawns’ (n=24) annual home range size in southwestern Lower Michigan. 66 comprising <3% of the total home range. A summary of all 24 fawns and the percentage of cover types that composed their annual home range is in Appendix Table 11 and Appendix Table 12. Home range and movements - T wenty-seven week analysis Radio locations taken during the first 27 weeks (to 5 December), were used in the home range analysis for 68 (34 fawns from 2001, 34 fawns from 2002) of 75 fawns. The 27-week time span was chosen because it was when the most fawn mortalities occurred. Six fawns died prior to gathering an adequate data set and an additional fawn was not used in the analysis due to ArcView shapefile availability. The number of locations used to construct home ranges ranged from 42 to 104 locations, with an average of 75 locations per fawn. Of the 68 fawns (31 male, 37 female) used in the analysis, 34 of the fawns were collared in 2001 and 34 were collared in 2002. The mean size of home ranges for all fawns used in this analysis was 62.65 ha (SE :t 3.76) with a range of 15.27 ha - 173.26 ha (median = 56.51 ha). Fawns radio- collared in 2001 (n=34) and 2002 (n=34) had a mean home range of 50.44 ha (SE :1: 3.89, median = 46.72 ha) and 74.85 ha (SE i 5.76, median = 69.18 ha), respectively. The mean size of home ranges for fawns collared in 2001 was significantly different (P < 0.001) than fawns collared in 2002. The distribution of home range sizes used by fawns demonstrated that 57 of 68 fawns had a home range _>_25 ha and <100 ha (Figure 21). Only 8 fawns had home ranges _>_100 ha and 3 fawns had home ranges < 25 ha. The distribution for fawns’ home range by sex (37 female, 31 male) indicated that 27 male fawns and 30 female fawns had 67 Away "in owg oEem m: o2 mm— 8— ms. gamma—0:2 633 5883538 5 BnEoQoD m 9 .32 AN bouaamxeaan Bob 80.1.5 33$ .83 625-333 Noom 28 Bow he.“ actuating 55:5 owed." 080: Am 0.3mm Om mm c i V) g“ O v—r sums} JO .roqumN 8 mm on 68 home ranges 225 ha and < 100 ha (Figure 22). Of those fawns with home ranges 3100 ha, 5 were female and 3 were male. Both of the distributions indicate that the majority of fawns have a home range 225 ha and <100 ha independent of sex. The mean number of patches in each home range for 2001 and 2002 fawns was 12 (SE 3: 0.44) with a range of 2 - 18 patches (median = 12). Fawns radio-collared in 2001 and 2002 had a mean number of patches of 12 (SE i: 0.55, median = 12) and 12 (SE 3: 0.69, median = 11), respectively. The mean number of patches between years was obviously the same. Cover types within each home range were characterized as residential, deciduous forest, evergreen forest, agriculture (composed of pasture, hay, and row crop, woody wetland, and emergent herbaceous wetland. Fawns used on average 4 cover types, but use ranged from 1- 6 cover types. Only 5 of the 68 fawns had some portion of their home ranges composed of residential development averaging 0.36 ha, < 1% of the total home range. Sixty-seven of 68 fawns had some portion of their home range composed of deciduous forest averaging 23.16 ha (median = 21.66 ha) composing approximately 40% of the total home range. Evergreen forest was only found in 34 fawns’ home ranges with an average area of 5.71 ha composing 10% of the total home range. Some portion of all of the fawns’ home range was composed of agriculture land with an average area of 31.15 ha (median = 24.64 ha) and composing 46% of the total home range. Fifty-seven fawns had woody wetlands within their home range, accounting for an average area of 6.31 ha or approximately 10% of the total home range. Emergent herbaceous wetlands were found in only 23 of 68 fawns’ home ranges and had an average area of 1.49 ha, comprising only 3.15% of the total home range. A summary of all 68 deer and the 69 2 Female IMale 10 .______-_ 1 1 1 1 1 1 1 8N 1 1 1 1 1 1 1 1 1 1 ., 00 \o V” N O - 14 12 same; JO .quumN 70 175 150 125 100 75 50 Home range size (ha) Figure 22. Home range frequency distributions by sex for 2001 and 2002 white-tailed deer fawns (n=68) fiom approximately 27 May to 5 December in southwestern Lower Michigan. percentages of cover types that composed their home range is in Table 11 (Appendix Table 13 and Appendix Table 14). The proportion of agriculture or deciduous cover types in home ranges of radio- collared deer was plotted to evaluate the most frequently used type of landscape. Thirty- eight of 68 fawns had a total home range composed of 40% to 80% agriculture (Figure 23). Twelve fawns had a total home range composed of 280% agriculture and 18 fawns have a home range composed of <40% agriculture. Five fawns had a home range composed of < 20% deciduous forest and 10 fawns had home ranges composed of 270% deciduous forest. The majority of fawns (53 of 68) had a total home range composed of 20% to 70% deciduous forest cover type (Figure 24). Two additional distributions were also constructed to examine the percentage of the total home ranges that were composed of woody wetland and evergreen forests (Figure 25, Figure 26). Thirty-four of 68 fawns had approximately 10 to 20% of their total home ranges composed of woody wetland. Eleven fawns had no woody wetlands within their home ranges, and 22 fawns have 20% to 50% of their home ranges made up of woody wetlands. One fawn had woody wetlands composing approximately 60% of the total home range. F ifiy—six fawns of 68 had <20% of their home ranges composed of evergreen forest, and 12 fawns had 20 to 50% of their total home ranges composed of evergreen forests. Scatterplots were constructed to examine the relationship between the percentage of a specific cover type (e. g., agriculture, deciduous) composing each home range compared to the total home range size (ha). This relationship was graphically represented 71 $015 two 2d mm; N2: 3d omdv cm..— bo.o_ 5N and». 2.0 mud Noomdloom cans Ed No; we; 3% mm.m no.3 cud $.Q “hm mmfiy 56 mod Noom cane mud cud SN 3.: 56 NE? o: Sum v~.m 3.6m 2.0 h: 88 mm 502 mm :82 um :32 mm 502 mm :82 mm :82 80> menace? 388983 35:03 mena— mpmouom £88m 33528“ EoWBEm 38>» Rafi—354m 3293mm 35:2qu age.“ Noon 28 Sea 05 .«o momcfi 080: 309:3 35 womb .850 we mowfieoobm 522 .2 053. 72 l4 12 .2- -— , a * ~ 1 -1 w— w¢w —---V—~~~~---~7~—~ Number of fawns 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean percent of agricultural lands within fawn home ranges Figure 23. Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of agricultural lands. 14 Number of fawns T 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean percent of deciduous forests within fawn home ranges Figure 24. Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of deciduous forests. 73 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean percent of woody wetlands within fawn home ranges Figure 25. F awn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of woody wetlands. 1 1 1 1 ‘4 Number of fawns u—o C I l 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Mean percent of evergreen forest within fawn home ranges Figure 26. Fawn home range frequency distribution for the mean percentage of all home ranges (n=68) composed of evergreen forests. 74 to evaluate whether one variable (e. g., percent of forest land) could predict the value of the other variable (e. g., home range size). According to the agriculture scatterplot (Figure 27), there appears to be a very weak positive linear relationship (R2 =0.08; y=0.23x + 31.6). The coefficient of determination (R2) is approaching zero indicating that the two variables are not closely related; thus, a change in the percent of agriculture composing a home range does not correspond to any predictable change in home range size. The only exception may be for deer with home ranges >100 ha; a weak positive relationship may occur indicating that as a fawn’s home range gets larger, a greater percentage of the home range is composed of agriculture. A scatterplot was also constructed to compare the percentage of deciduous forests composing fawn’s home ranges to the total home range size (ha) (Figure 28). There appeared to be very weak negative linear relationship (R2 =0.07; y = -0. 1697x + 49.63) between percent of deciduous forest compared to home range size. As fawns’ home ranges exceeded 100 ha, there appeared to be a weak negative relationship, converse to the agriculture scatterplot’s weak positive relationship. The relationship implied in home ranges >100 ha is misleading, because only 5 data points follow this tendency. Given this fact, no conclusions should be drawn as to whether the percentage of the home range composed of agriculture increases as the percentage of deciduous forest decreases. Of the 68 radio-collared fawns that were used in the May to December analysis, 12 fawns (7 fawns collared in 2001, 5 fawns collared in 2002) died over the course of the tracking period. Those 12 fawns formed a subset of the total fawns radio-tracked. Comparisons were made to determine if differences existed between those fawns that died and those that survived that might affect their vulnerability. 75 100% v o 90% ~ —» ~ —— — — _+_-_rzr,*_,_#___#__ 9 _EIIWW “A a 5’5” . 'm 80% —;————-——fi— ,.__,____ A.___-__f-_ _ _- 8. o ’ . g o “c o =0.233x+31.6 g g) R1=0.0869 H 60% _______*_ '—~—— — — — #2—2 .2__..,_fl 4’53 a '3‘ .0 '8 500/0 d #— A k 7 7 7 7 * .3 5350 a: ngm~- __-___._ ____, $0.: 8 g 30% -.__ _ 2 9._E9_._. __._ i__~___ fi+Wfi -2 o 9 . ‘3. . ’ o 5 20%~LA~~—-A-- .i t gmfifl2wmmz -_z_ o 2 . 1005‘“——‘# *4-o-#—O~——+ fifii7“__.#__.___ 9’. . (P/O T I I I l 1 1 l 0 20 60 80 100 120 140 160 180 200 Homerangesize (ha) Figure 27. Relationship between the home range size (ha) of radio-collared white-tailed deer fawns and the percentage of agriculture land composing each fawn home range (n=68), 2001 - 2002. 76 10096 20% -———~—~- 90% -~———i we A i v i ~~-—~ ~-—~———— e~~~vfiw~~vv+w~r no .5 §. 80% _zszww.” ~~4-~~—-— -— *w a ’0 ° 8 70% -_ -- 2* 2-- _ __L_-mm_._m. 2-2 r__ __ 23 a) o an r... O «9 560%-—————3— ° _Mmfifikfi- g a: o ‘. «>0 2 § ’ ’ 0 "o '0 £150% 4 u— U) . . . 3;“ 0’ 10 ‘. 33 E o o “940/0 . . ifs-g o 2 030% 4—-———o——— D U '6 a. 5 D 2 1096— w‘.— _4 y = -0.1697x + 49.63 1?.2 = 0.0692 0% a: 120 Home range size (ha) 140 160 180 200 Figure 28. Relationship between the home range size (ha) of radio-collared white-tailed deer fawns and the percentage of deciduous forest composing each fawn’s home range (n=68), 2001- 2002. 77 The mean home range size of all fawns in this mortality subset (n=12) was 69.32 ha (SE :22 10.15) with a range of 28.41 ha to 140.90 ha (median = 62.59 ha). Fawns that died during 2001 (n=7) and 2002 (n=5) had a home range of 49.07 ha (SE at 6.62, median = 44.95 ha) and 97.66 ha (SE i 15.61, median = 89.48 ha), respectively. A frequency distribution was constructed to compare the total home range size of the fawns that survived during the May to December tracking period (n=56) to those fawns that died (n=12) during that same time period (Figure 29). According to the frequency distribution, the fawns that died had similar home range sizes to fawns that survived. Both frequency distributions could be described as a half normal distribution, and both distributions have similar trends. It does not appear that home range size has an affect on survival. The mean number of patches used by fawns in the mortality subset (n=12) was 12 (SE :1: 1.13) with a range of 2 - 17 patches (median = 12). The mean number of patches in each home range for the 2001 and 2002 fawn mortality subsets was 11 (SE :1: 1.69, median = 11) and 12 (SE :1: 1.44, median = 12), respectively. Home ranges of the fawns that died (n=12) during the first 30 weeks (May to December) were analyzed to determine the specific cover types composing each home range (Table 12 and 13). Only 1 fawn of 12 had some portion of its home ranges composed of residential development - comprising < 1% of the total home range. Eleven of 12 fawns had some portion of their home range composed of deciduous forest averaging 23.14 ha (SE : 4.66 ha) composing approximately 33% of the total home range. Evergreen forest was only found in 7 fawns’ home ranges with an average area of 10.98 ha composing 14% of the total home range. Some portion of all of the fawns’ 78 22 20 I Alive Dead H 18 - l6 . 14 __ 1 1 1 1 H O l Number of fawns ac 100 125 150 “W 7 Home range size (ha) Figure 29. Frequency distribution comparing white-tailed deer fawns that survived (n=56) and those fawns that died (n=12), 2001-2003. 79 Table 12. Home range composition (total ha) for white-tailed deer fawns that died during the tracking period (May to December), 2001and 2002. b'rr'figér'rf Deciduous Evergreen Agricultural Woody herbaceous Frequency Residential forests forests lands wetlands wetlands All 151.1429‘ 0.00 8.78 4.75 46.21 0.00 0.00 59.74 151.163' 0.00 46.39 1.50 14.08 10.19 0.65 72.79 151.223' 0.00 7.05 0.00 27.10 9.46 0.00 43.61 151.2432' 0.00 35.01 0.86 28.17 1.07 0.33 65.44 151.662' 0.00 6.76 0.00 16.53 5.28 0.00 28.57 151.404' 0.07 21.71 3.94 15.34 1.07 2.83 44.95 151.482' 0.00 0.00 0.00 28.41 0.00 0.00 28.41 151.624b 0.00 26.05 0.00 22.48 5.54 0.00 54.07 151.402b 0.00 4.21 0.00 128.80 4.84 3.05 140.90 151.824” 0.00 23.72 34.71 21.11 0.00 0.00 79.54 152.344b 0.00 27.85 29.59 32.04 0.00 0.00 89.48 152.823b 0.00 47.05 1.51 46.67 29.04 0.00 124.30 2001 Mean 0.07 20.95 2.76 25.12 5.41 1.27 49.07 2002 Mean 0.00 25.78 21.94 50.22 13.14 3.05 97.66 2001 and 2002 Mean 0.07 23.14 10.98 35.58 8.31 1.72 69.32 ' White-tailed deer fawns radioocollared in 2002. b White-tailed deer fawns radio-collared in 2002. Table 13. Home range composition (mean percent) for white-tailed deer fawns that died during the traclgg period (May to December), 2001 and 2002. Emergent Deciduous Evergreen Agricultural Woody herbaceous Frequency Residential forests forests lands wetlands wetlands 151.1429‘ 0.00 14.70 7.95 77.35 0.00 0.00 151.163' 0.00 63.72 2.06 19.34 13.99 0.89 151.223' 0.00 16.17 0.00 62.13 21.70 0.00 151.2432' 0.00 53.49 1.31 43.05 1.64 0.51 151.662' 0.00 23.65 0.00 57.87 18.48 0.00 151.404' 0.16 48.29 8.76 34.11 2.38 6.30 151.482' 0.00 0.00 0.00 100.00 0.00 0.00 151.624b 0.00 48.18 0.00 41.57 10.25 0.00 151.402b 0.00 2.99 0.00 91.42 3.43 2.16 151.824b 0.00 29.82 43.64 26.54 0.00 0.00 152.344” 0.00 31.13 33.07 35.80 0.00 0.00 152.823b 0.00 37.86 1.21 37.56 23.37 0.00 2001 Mean 0.16 36.67 5.02 56.26 11.64 2.57 2002 Mean 0.00 30.00 25.97 46.58 12.35 2.16 2001 and 2002 Mean 0.16 33.64 14.00 52.23 11.91 2.47 ' White-tailed deer fawns radio-collared in 2002. ° White-tailed deer fawns radio-collared in 2002. 80 home range was composed of agriculture with an average area of 35.58 ha (SE 3; 9.02 ha) and composing 52% of the total home range. Eight fawns of 12 had woody wetlands within their home range, accounting for an average area of 8.31 ha or approximately 12% of the total home range. Emergent herbaceous wetlands were found in 4 of 12 fawns’ home ranges and had an average area of 1.72 ha, comprising <3% of the total home range. The total number of landowners’ property comprising each fawn’s home range and the amount of interspersion with a home range were determined for all 68 fawns. Interspersion is a habitat characteristic that describes the intermixing of habitat components (e.g., cover types). Fawns used landscapes composed of property owned by 3.5 landowners with a range of 1-12 landowners (median = 3) (Table 14). An interspersion index for all radio-collared fawns (n=68) was 7.2 with a range of 2-13 (median = 3) (Appendix Table 15). A comparison of interspersion index values and total number of landowners was made to determine if differences existed between fawns that survived to December (n=56) and those that died (n=12). The average number of landowners that comprised the fawns’ home ranges that died was 4.0 (range = 1-11, median = 3.5) and an average interspersion index of 7.3 (range of 2-1 1, median = 7) (Appendix Table 15). Of the fawns that survived, an average of 3.4 landowners’ property composed each home range (range of 1-12, median = 3) and had an average interspersion index of 7.2 (range of 2-13, median = 6.5) (Appendix Table 15). Obviously, no differences existed between those fawns that died during May to December and those that survived in regard to interspersion indices or total number of landowner holdings within each home range. 81 515 Ed 92 33 8.4 2.2 8% m2 : £83 a 555 Be 83am 685 and w: 8o :8 8.4 mm. 5 mac 9 £83 a a 2% 83E 815 mg 5. 8o Rm 8: 8.8 33 S 83¢ 3. mm 502 mm 502 mm 5...: mm 50: 8?: comm—09835 Eogou§_ mo Hons—=2 A56 86 awash 080m monogamwo 898:2 dawanz 833 €882,558 E 83am 8% 8%?823 88:00-23 meow v5 SON com 8:538 own—2 0:85 we bugm .3 2an 82 VEGETATION RESULTS Vegetation composition and structure Vegetation sampling was conducted in 10 stands frequently used by radio- collared deer in southwestern Lower Michigan. Red oak, sugar maple, black walnut (Juglans nigro), black cherry, white oak (Quercus alba), and swamp cottonwood (Populus heterophylla) were the primary forest vegetation types surveyed. We surveyed 3 stands of red oak, 2 stand of sugar maple, 2 stands of black walnut, and 1 black cherry stand, 1 white oak stand, and 1 swamp cottonwood stand. The mean diameter at breast height (dbh) was recorded for 6 of the 10 stands (Table 15). Mean dbh for 2 red oak vegetation types was 14.90 cm (SE i 2.5). Two sugar maple stands had a mean dbh of 17.69 cm (SE i 2.55). One black walnut and 1 white oak stand had a mean dbh of 12.57 cm (SE i 0) and 8.53 cm (SE :1: 0), respectively. Stem density of overstory trees (e. g., any tree with dbh >10.16 cm) was also recorded within each stand (Table 15). The stem density varied from 466.67 stems/ha (SE : 0) in the swamp cottonwood vegetation type to 88.89 stems/ha (SE :t 2.5) in the white oak vegetation type. Sugar maple, red oak, black walnut, and black cherry vegetation types had an overstory stem density ranging from 156 stems/ha to 207 stems/ha. Total woody stem density incorporated both understory (dbh <10.16cm) and overstory trees (Table 15). One stand characterized by the white oak vegetation type had the greatest total woody stem density of 21 1 1.11 stems/ha (SE :1: 0). The black cherry vegetation type had the smallest total woody stem density of 566.67 stems/ha (SE i 0). 83 28m _ no woman bacon a 83“ N 8 823 E85 . :8 nooom 38» 8.. >55 ans—m annfiom nm< 833 gm among an”. . o _ v8.5.8: 880% beacon: :0 3 «E E6 Mona 2338.5 8.8: :. : . on . a . . a . . . 238.33 38% _ N no 0 S 8— S 82 on he no com" mo 3. a Em mm? 8on .383 30,—. AaEmEBmV 3% So $83 3.3 $62 1.2 a 362 2.: a $.02 31. a 3.53 dev beans .3 bacon. 8on ma .3 a: sane mom a 3.: .onN « 8.: one £6 :82 ens” : ens” : €82 : 3.5.... a 3:5... 3 35% s woaBaouoU gm Mao 83>» P26 mom—m 335$ Mom—m 3&2 Smsm “:5 com bow 33:86me 3 com: 3:8:ch womb coufiowg we 3558820 .2 035. 84 The swamp cottonwood, black walnut, sugar maple, and red oak vegetation types had a total woody stem density between 1366 stems/ha to 1733 stems/ha. Dominant understory species was also recorded on a stand-by-stand basis (Table 15). The 3 stands of red oak vegetation type had black cherry, ironwood (Osttya virginiana), and beech (F agus grandifolia) as the dominant understory species per stand. Black cherry and American elm (Ulmus americana) were the dominant understory species of the sugar maple vegetation type. American elm, white ash, hombeam (Caminus carolint'ana), and black cherry were the dominant understory species for black walnut, black cherry, white oak, and swamp cottonwood vegetation types, respectively. Percent overstory canopy cover and percent ground cover (e. g., vegetation <05 m) by vegetation type were recorded for each forest stand frequently used by telemetered deer (Table 16). The sugar maple vegetation type had the highest percent of deciduous canopy cover of 80.55% (SE 3: 5.56). Percent deciduous canopy cover ranged from 58% to 75% for the remaining vegetation types. Percent conifer canopy cover was recorded for 2 vegetation types, black cherry had 5.56% (SE 1 5.56) conifer canopy cover and swamp cottonwood had 8.33% (SE :t 8.33) conifer canopy cover. Percent ground cover was recorded for 4 stands frequently used by telemetered deer (Table 16). The black cherry stand had 100% ground cover and the swamp cottonwood stand had 92% (SE i 4.81) ground cover. Black walnut and red oak had 64% (SE i 7.35) and 14% (SE i 2.78) ground cover, respectively. Deer browsing intensities were quantified in 3 forest vegetation types frequently used by telemetered deer. The sugar maple vegetation type had a mean browsing intensity of 37% (SE i 6.67). Browsing intensities for black cherry and swamp 85 25m _ no woman 883m . :3 um .8 a: oi u: a 3 a: “mum a E .28 23on ESE mm.w a mm.w ohm a 86 £800 wwm a 2.8 «.52 H 3% $6 a mm 36 a 2.8 end a 3.8 3.2 a 3.9 msozflooa c960 3058 “coupon 65% q 855 c 653 : Assam a 3.53 3 Exam c 838:8 95% fie 323 926 385 3:33 :85 2%: away. fio Ba coop 38:86:68 an com: bus—Scot 356 628 553, cab 538%?» an 830 23on use “260 >858 #6ch .3 2an 86 cottonwood vegetation types were 23% (SE is 11.85) and 29% (SE i 10.02), respectively. These estimates are given as a rough indication of browse intensities in forest vegetation types frequently used by telemetered deer. 87 WINTER CAPTURE DISCUSSION Survival Annual survival probabilities for deer radio-collared in 2001 were relatively high and similar between years of tracking (0.760 for 2001, 0.750 for 2002). Deer collared in 2002 had an annual survival probability of 0.404, significantly different than the annual survival probability of deer collared in 2001. Brinkman et al. (2001) reported a high annual survival probability (0.78) for female deer across a southwest Minnesota landscape dominated by agricultural lands. In central and northern Illinois, annual survival probabilities for female deer ranged fiom 0.56 to 0.92 and 0.35 to 0.76 for male deer (Nixon et al. 2001). In the northwestern Lower Peninsula of Michigan, Sitar (1996) documented similar survival probabilities between migratory and non-migratory deer of 0.50 and 0.81, respectively. There were relatively few mortalities between January and September and survival was the highest during the summer months (Table 4). Low human disturbance during this time may have increased the overall survival of deer (Nixon et al. 1991). The high annual survival of deer collared in 2001 was similar to the survival of deer living on refuges (e. g., Nixon et al. 1991, Hansen et al. 1997). The deer radio-collared during 2002 had a lower annual survival probability; several factors may have contributed to this lower survival. A portion of the 2002 capture sites were in a more residential and populated area than were the capture sites of 2001. Increased traffic volume may have contributed to the higher mortality in this area. In contrast, many of the 2001 capture sites were on large tracts of land with limited road access. 88 During 2002, a trap was placed on a deer run that was an obvious corridor between patches. The trap was placed in a small red pine (Pinus resinosa) stand surrounded by grassland and agricultural fields. All of the deer trapped from this location immediately established home ranges outside the capture site. The deer that were collared at this trap were displaying some type of movement at the time of capture; whereas, the majority of deer collared during 2001 established home ranges that encompassed the capture site. Cause-specific mortality The primary cause of mortality for winter captured deer in southwestern Lower Michigan was legal harvest (17 of 26 mortalities; 65%). Eleven females and 6 males were harvested across 2 years of hunting (8 deer collared in 2001, 9 deer collared in 2002). Twenty-six radio-collared deer (25 females, 1 male) were alive at the beginning of the 2001 deer hunting season (1 October), and 37 radio-collared deer (27 females, 10 males) were alive at the beginning of the 2002 deer hunting season. These figures could indicate a bias toward male deer during the hunting season because 55% of the males entering the hunting season were harvested and only 21% of the females. Van Deelen et al. (1997) recorded that 25 radio-collared deer (of 58 mortalities) were legally harvested in Delta County of Michigan’s Upper Peninsula between 1992- 1994. Hunting was also the greatest source of mortality (27 of 52 mortalities) for Sitar et al.’s (1998) study in the northeastern Lower Peninsula of Michigan during 1994 and 1995. McCullough (1979) reported that virtually all adult deer mortalities were attributed to hunter harvest within The George Reserve enclosure in southcentral Michigan. Within 89 intensively farmed regions of Illinois, Nixon et al.’s study (2001) documented that 313 of 455 white-tailed deer mortalities (68%) were harvest related. The number of mortalities associated with hunting could have been biased for several reasons. Prior to the 2001 hunting season, a rumor had been started within the study area that hunters were supposed to target and kill radio-collared deer. In order to dispel the rumor, fliers were posted throughout the study area explaining that it was legal to harvest collared deer and that the deer were part of an ongoing white-tailed deer study. Some landowners considered the radio-collared deer a novelty and enjoyed observing the radio-collared deer on their property. Those landowners may have been less likely to shoot a radio-collared deer if they encountered one during the hunting season. In other cases, individuals regarded a radio-collared deer as a “trophy” and intentionally sought out collared deer to harvest. Deer-vehicle collisions was the second leading cause of winter captured deer mortalities. Seven of 26 mortalities (27%) were associated with deer-vehicle collisions. The majority of deer-vehicle collisions (71%) occurred during winter/spring (2 January — 31 May). The remaining deer-vehicle collisions occurred on 3 July and 26 December. Etter et al. (2002) reported that deer-vehicle collisions were the leading cause of deer mortality in the suburban forest preserves of western Chicago, Illinois. Deer-vehicle collisions were the second leading cause of mortality (15%) in Nixon et al.’s (1991) study in Illinois. In contrast, deer-vehicle collisions made up the smallest percentages of deer mortalities in several northern deer studies (e. g., 5% Nelson and Mech 1986, 0% Van Deelen et a1. 1997, 6% Whitlaw et al. 1998, 9% Sitar et al. 1998). 90 Allen and McCullough (1976) documented that the largest number of deer-vehicle accidents in southwestern Lower Michigan occurred in the fall and early winter corresponding with the rut or the opening of deer hunting season. A less pronounced spring peak in deer-vehicle accidents occurred in May, coinciding with the breakup of family groups at the onset of parturition (Allen and McCullough 1976). I did not document an increase in deer-vehicle accidents during the fall possibly because of the disproportionate number of male deer radio-collared (n=l3). According to the Michigan Traffic Crash Facts (2001), 44% of the 2001 deer-vehicle collisions occurred between October —- December, and only 26% of the deer-vehicle collisions occurred during January to May. More deer-vehicle collisions occur on two-lane paved roads (74%) in Barry and Kalamazoo counties than on divided highways (Michigan Traffic Crash Facts 2001). Deer-vehicle accidents have dramatically increased across the study area fiom 7500 deer-vehicle collisions/year statewide in 1966 to approximately 67000 deer-vehicle collisions/year statewide in 2001. Predation is considered a leading cause of nonhunting mortality for deer in the northern portion of their Michigan range (Van Deelen et al. 1997). Nelson and Mech (1986) reported high predation by wolves in Minnesota. Coyote predation was also a leading cause of mortality in northern New Brunswick (Whitlaw et al. 1998). In contrast, none of the winter captured deer included in this analysis were killed by starvation or predation. Coyotes are the only documented natural predators in southwestern Lower Michigan. 91 Home range and movements The white-tailed deer’s home range must be large enough to enhance the everyday survival through adequate food supply and reproductive opportunities, yet small enough for the deer to become familiar to its surroundings (Marchinton and Hirth 1984). Annual home range sizes for deer (27 months) in this study (158 ha) correspond to those reported by Progluske and Baskett (1958) in Missouri (162 ha), Gladfelter (1978) in Iowa (177 ha), Larson et al. (1978) in Wisconsin (178 ha), Vercauteren and Hygnstrom (1998) in Nebraska (170 ha), Kammerrneyer and Marchinton (1976) in Georgia (127 ha). Home ranges in this study, however, were smaller than home ranges reported in other Michigan studies, Sitar (1996) in northeastern Lower Michigan (424 ha in 1994, 356 ha in 1995), Van Deelen (1998) in Michigan’s Upper Peninsula (smallest seasonal home range was 730 ha during winter 1992), Garner (2001) in northeastern Lower Michigan (mean winter range of 387 ha, mean summer range of 284 ha), and Muzo (2003) in northeastern Lower Michigan (mean seasonal range of 510 ha). Home ranges and movement patterns of deer in southwestern Lower Michigan were more similar to deer residing in agriculturally dominated landscapes of the Midwest than of the northern forested regions of the Great Lakes. Climate can have varying effects on home range size; deer residing in northern climates tend to have larger and less stable home ranges than in the south (Marchinton and Hirth 1984). The smaller home range sizes of deer in southwestern Lower Michigan may indicate that many life requisites are easily obtainable within a small area. The fragmented habitat provides deer with the opportunity to reside in dense forest stands for cover and to move to open fields to obtain forage (Bowers 1997). The resident (non-migratory) deer in this study appeared 92 to be content in their small home range and would undoubtedly remain there until the habitat conditions deteriorated or other disturbances affect their survival (Sparrowe and Springer 1970). Sparrowe and Springer (1970) also conclude that where seasonal weather extremes are not pronounced deer tend to exhibit non-migratory behavior. The resident male deer (n=5) in this study tended to have larger home ranges than the females (Figure 6) possibly due to the hunting pressure and reproductive pressure (rut) (Marchinton and Hirth 1984). Many studies quantifying movement patterns of white-tailed deer occur within the northern regions of their range (Sitar 1996, Van Deelen 1998, Garner 2001, Muzo 2003). Deer movement and migration patterns in southern Michigan had not been quantified until this study. The majority of deer in southwestern Lower Michigan are resident, or non-migratory, and they do not appear to demonstrate seasonal migratory patterns. Sparrowe and Springer (1970) concluded that deer may be resident, or non-migratory, if seasonal weather extremes are minimal and/or food is readily available, similar to the condition of southwestern Lower Michigan. The agro—forested region of southwestern Lower Michigan has an average summer temperature of 20°C and an average winter temperature of — 4°C. The average seasonal snowfall is approximately 1.30 m (Thoen 1990) Dispersal can be defined as a one-way movement from a previously established home range (Kemohan et al. 1994). Ten deer (3 females, 7 males) in this study dispersed fiom their capture site and subsequently established new home ranges. All deer were fawns or yearlings at time of dispersal. Mean dispersal distance reported in this study, 9.6 km (range 3.1 - 18.4 km), is similar to Grund (1998) in Minnesota (<10km), 93 Rosenberry et al. (1999) in Maryland (<12 km) and Etter et al. (2002) in Illinois (7.6 km). Kammermeyer and Marchinton (1976) reported smaller dispersal distances of 4.3 km for deer in Georgia and Nixon et al. (1991) reported much larger dispersal distance of 49 km for females and 42 km for males in Illinois. Other males could have potentially exhibited dispersal behavior, but they died prior to establishing a new home range. Most of the dispersal movements terminated on landscapes with more agricultural lands than deciduous forests, opposite of pre dispersal home ranges that were primarily deciduous forest with a smaller percentage of agricultural lands. This dispersal to home ranges dominated by agricultural lands may be an indication of greater forage availability and higher quality forage. Carlock et al. (1993) hypothesized that dispersal from forests to agricultural lands could be in response to low mast production. High population densities could also stimulate dispersal to a habitat of higher quality. Deer densities in the study area were approximately 19 deer/km2 (S. Beyer unpublished data). Kammermeyer and Marchinton (1976) documented contradictory results concerning male dispersal; male deer in Georgia originated in agricultural lands and terminated in wooded areas open to hunting. Two of the longest dispersals took place approximately 25 May, when 2 female fawns traveled >16 km to establish new home ranges. Nixon et al. (1991) documented the breeding histories of 9 doe fawns and recognized that barren yearlings oflen revert to the social status of a fawn (Ozoga and Verme 1986); Nixon et al. (1991) concluded that dispersal exhibited by female fawns may be a result of unsuccessful breeding (barren or lost fawn). Three male deer dispersed during October and their dispersal was probably stimulated by hunting pressure and/or mating activity (Wiles and Weeks 1992). 94 Wilson (1975) and Nelson and Mech (1984) hypothesized that deer may disperse to avoid an inbreeding depression. Dispersal from natal home range in white-tailed deer is often exhibited by yearling males; many males begin forming their new home range during their first breeding season ( 2:17 months old) or in the spring prior to fawning season (Nelson and Mech 1984). Males seem to benefit from dispersal more than females because of the dominance interactions that may prepare them for reproductive competition (Nelson and Mech 1984). Migration is defined as the roundtrip movement between two distinct ranges (Root et al. 1990) and can be characterized as either periodic or regular. White-tailed deer in the northern portions of their range may annually migrate between spring-summer and winter ranges (Sparrowe and Springer 1970, Broadfoot et al. 1996, Nelson 1998, Van Deelen et a1. 1998, Root et al. 1990). The movements from summer to winter range vary by region and are influenced by social behavior and land use patterns (Van Deelen et al. 1998), climate or presence of snow (Nelson 1998), land cover, and hunting (Sparrowe and Springer 1970, Root et al. 1990). The winters are more severe in northern Michigan than in southern Michigan; therefore, seasonal migration was a common behavior exhibited by white-tailed deer in northern Michigan (Van Deelen 1995, Sitar 1996, Garner 2001, Muzo 2003). White- tailed deer tend to move to areas with less intense winter conditions to conserve energy. A large number of deer yard up, or congregate, in the ideal shelter sites where the snow is less of a threat to survival (Ozoga and Gysel 1972). The disadvantage to yarding is that the high densities of deer that congregate in these areas deplete the forage and increase the likelihood of malnutrition and starvation (Moen 1976). As snow accumulates and 95 temperature decreases, deer have a tendency to reduce range size (Moen 1976). Broadfoot et al. (1996) indicated that deer densities within a typical winter range are 10 times greater than summer range densities. Deer migrations are often in response to a negative change in habitat characteristics that may ultimately affect survival and reproductive success (Nelson 1998). This is evident by deer #260’s dispersal near the opening of firearm deer hunting season. Unlike migratory deer in other studies that migrate seasonally in response to weather conditions, deer #260 migrated in response to what appeared to be hunting pressure (Figure 14). She moved from a pre (firearm) hunting season home range dominated by deciduous forest to a home range dominated by agricultural lands. The location she dispersed to during hunting season, also the location where she was radio- collared, is a biological research station owned by Michigan State University where hunting is limited. Hunting takes place on the property (1626 ha) with an average of 47 (median = 54) deer harvested each year between 1994 and 2001 (Johnson unpublished data 2002). There are some areas where hunting is prohibited due to ongoing vegetation research. In Missouri, Root et al. (1988) observed similar behaviors in their study when a doe moved home ranges on the second day of firearm season to a range mostly within the boundaries of a refiige. Kammermeyer and Marchinton (1976) also observed a similar phenomenon in Georgia where non-resident deer moved into a refuge during hunting season. The deer remained in the refiige near agricultural openings where the forage was of higher quality until February. Kammermeyer and Marchinton (1976) characterized this movement as seasonal short-range migration. Deer #260 possibly developed this 96 migration tradition to escape hunting harassment and take advantage of higher quality forage (Kammermeyer and Marchinton 1976). Nelson (1998:431) concluded “for most deer, the movements experienced and learned as fawns while following their mothers predetermined migratoriness in adulthood and perpetuated the migratory behavior and patterns of mothers.” The social connections rather than the environmental stimulus determined migratory behavior fawns adopted as adults (Nelson 1998). Nelson (1998) also pointed out that returning migratory dispersers showed a learned migration pattern instead of a physical need to return to a particular site. Migratory deer travel between ranges on well-developed trails and migration generally follows drainages (Sparrowe and Springer 1970). 97 SPRING/SUMMER CAPTURE DISCUSSION Prior to this research, no direct information was available on the survival and cause-specific mortality factors of Michigan’s white-tailed deer fawns in a non-captive setting. One focus of this study was to quantify survival to 6 months of age at which they are recruited into the huntable population. Another focus was to quantify the causes and rates of mortality for fawns on an annual basis in southwestern Lower Michigan. Understanding the survival rates of fawns is critical for modeling the population dynamics of deer in southwest Lower Michigan. The results fiom this study will provide baseline fawn data for comparisons by other fawn studies in Michigan and nationwide and will improve biologists predictive capabilities of white-tailed deer population dynamics. Survival The survival probabilities reported for southwestern Lower Michigan fawns are higher than most published studies. Survival probability estimates for fawns in southwest Lower Michigan at approximately 30 days post capture were extremely high, 0.971 in 2001 and 0.925 in 2002. Other studies report fawn survival probabilities during the first 30 days as 0.069 (Cook et a1. 1971), 0.448 (Garner et al. 1976), 0.863 (Huegel et al. 1985), >0.900 (Wickharn et a1. 1993), 1.000 (Brinkrnan et al. 2001). Survival rates to approximately 180 days are reported in the majority of published literature, thus the estimates are easily comparable across studies. Survival probabilities for this study at 180 days were 0.82 for 2001 fawns and 0.85 for 2002 fawns. A pre-hunt survival probability of 0.85 was reported for Schulz et al. (1983) within a Minnesota deer 98 refuge. Huegel et al.’s (1985) fawn study took place on Iowa’s “farmland deer habitat” composed of agricultural lands with intermixed oak-hickory woodlots, similar landscape to southwest Lower Michigan. Huegel et a1. (1985) recorded a 0.73 survival probability to 180 days. Nelson and Woolf (1987) worked on a landscape primarily composed of deciduous forests (46%) and agricultural fields (28%) in southern Illinois, and recorded a pre-hunt survival probability ranging from 0.62 to 0.79. Wickham et al. (1993) reported a pre-hunt survival probability of 0.91 across a 1330 ha farm in Maryland primarily composed of 50% deciduous forest and 33% agricultural fields. Brinkman et al. (2001) reported a pre-hunt survival probability of 0.83 for fawns in the agricultural dominated landscape of southwest Minnesota. A shared quality among these studies is the landscape composition comprised primarily of agriculture and interspersed woodlots. Fawn survival probabilities for 2001 and 2002 fawns in southwest Lower Michigan at the conclusion of the deer hunting seasons (approximately 230 days or 33 weeks) were 0.76 and 0.85, respectively (Figure 19). Similar fawn survival probabilities were recorded in a Maryland study that took place on a cash-grain farm and wildlife demonstration area called Remington Farms, where 50% of the landscape is forested and 33% of the 1330 ha farm is used for agricultural land (Wickham et al.1993). Wickham et al. (1993) was the only study from the list above to report post hunting season survival probabilities of 0.71 for 1991 and 0.81 for 1992. Annual survival probabilities for fawns radio-collared in southwestern Lower Michigan during 2001 and 2002 were 0.76 and 0.7 5, respectively. None of the studies fiom the list above report annual fawn survival probabilities. 99 Cause-specific mortality Wildlife managers devise white-tailed deer harvest strategies based on their understanding of deer population dynamics; fawn mortality is one critical aspect of these dynamics. Deer population models are largely based on estimates of deer recruitment, to 6 months, into the huntable population. McCullough (1984:217) defined recruitment rate as “the number of recruits per individual in the population producing the recruits.” Often these recruitment estimates are gathered from fawnzdoe ratios observed during summer road surveys (Panken 2002), fall bio data (sex-age-kill estimates) gathered at deer check stations (Mattson-Hansen 1998), and estimating gross production by quantifying the number of fetuses in road kill deer (McCullough 1979). Although these estimates may provide indices of production and mortality, they do not provide information on causes and magnitude of mortality as well as the temporal distribution of mortality (Porath 1980) Quantifying temporal aspects of cause-specific mortality factors is essential for understanding the ecology of white-tailed deer in southwestern Lower Michigan; wide variations in mortality have been reported for the species throughout its North American range (Porath 1980). East and Midwestern agricultural landscapes with interspersed forest cover types (i.e., similar to landscapes in southwestern Lower Michigan) report lower fawn mortality estimates (e. g., 15%, Schulz et al. 1983; 27%, Huegel et al. 1985; 30%, Nelson and Woolf 1987; 24%, Decker et al. 1992; 9%, Wickham et a1. 1993; 19%, Brinkman et al. 2001) than semiarid regions of the West and Southwest (72%, Cook et al. 1971; 64%, Logan 1972; 88%, Garner et a1. 1976). F awns in southwestern Lower Michigan had one of the lowest pre-hunt fawn mortality estimates (9.3%) among other 100 studies with similar telemetry techniques. Differences in predator densities and/or species, deer densities, harvest quotas, and habitat conditions may help explain variations in mortality among studies (Decker et al. 1992). Fawn mortality studies (e.g., Cook et a1. 1971, Garner et al. 1976, Huegel et a1. 1985, Nelson and Woolf 1987) often classify mortalities as predation-excluded or predation-involved mortalities — this classification does not apply to this research. During this study, only 1 of 17 mortalities (6%) was caused by a predator. Rather, mortalities in this study were classified as directly/indirectly influenced by human activities (e. g., hunter harvest, vehicle accidents, and fence entanglement) or natural mortality (e. g., pneumonia, bacteria, dehydration, drowning, abandonment/malnutrition, predation). Mortality — Directly or indirectly related to human activities Direct or indirect human activities accounted for 65% of the total fawn mortalities (11 of 17 mortalities), the majority occurred during deer hunting seasons (Table 9). Many published studies focus on fawn survival to recruitment into the huntable population (approximately 180 days or 26 weeks) (e. g., Bryan 1980, Schulz et al. 1983, Huegel el al. 1985, Nelson and Woolf 1987, Decker et al. 1992) few studies track fawns through the hunting season or annually (e. g., Logan 1972, Steigers and Flinders 1980 (mule deer), Wickham et al. 1993). Studies that reported high fawn mortality during the first 3 months were forced to stop gathering survival data because very few, if any, fawns were alive. In contrast, very few mortalities occurred over the duration of this study; therefore, it was critical for us to continue monitoring fawn survival in order to gather 101 enough information to accurately characterize the causes of mortality across the study area. Another major source of fawn mortality directly or indirectly related to human activities was vehicle collisions. Vehicle collisions accounted for 5 of 17 fawn mortalities, 29% of the total fawn mortalities. Mortality as a result of vehicle collision occurred throughout the study and appeared to be independent of age. Fawns succumbed to death as a result of vehicle collisions at 4 weeks, 18 weeks, 20 weeks, 36 weeks, and 44 weeks of age. During 2001, 66993 deer-vehicle collisions were reported in Michigan; 1354 deer-vehicle collisions were reported in Barry County and 1411 deer-vehicle collisions were reported in Kalamazoo County (Michigan Traffic Crash Facts 2001). The majority of deer-vehicle collisions occur between 6:00 pm and midnight. Forty-four percent (29441) of the 2001 deer-vehicle collisions occurred between October — December (Michigan Traffic Crash Facts 2001). Of the deer-vehicle collisions that took place in Barry and Kalamazoo counties during 2001, the majority of the accidents (74%) took place on local streets (Michigan Traffic Crash F acts 2001). Human-caused accidents were considered a priori by study personnel to be a primary source of mortality for fawns <2 weeks old. Newborn fawns are more susceptible to mortality resulting from agricultural equipment because they are less likely to “flush” if disturbed (Nelson 1984). Porath (1980) stated that fawn mortality could be significant in areas where hay crops are an important agricultural operation. Timing of agricultural practices, mowing and discing, in southwestern Lower Michigan varies by year. Several farms in the study area reported their first and second cuttings of hay occurred on 27 May and 5 July during 2001 and 9 June and 5 July during 2002 (personal. 102 comm. J. Bronson). Mean date of peak fawning across both years occurred on 23 May. During some years, fawns that were born following the first cutting may be agile enough by the second cutting to escape impalement by farming equipment. In other years, peak fawning may coincide with first cutting. No radio-collared fawns were lost to agricultural practices during this study, but several reports were made to study personnel concerning the occurrence of mowing/discing related fawn mortalities. Porath (1980) reported that 20 of 29 fawns were killed by hay mowing machinery in Missouri. Underlying biases may have existed concerning the mortality of radio-collared fawns as a result of farming equipment. In some instances, landowners/farmers would delay mowing or discing if they were aware of fawns (radio-collared or not) in a field. If a landowner/ farmer discovered a fawn while mowing, they would occasionally stop mowing and contact study personnel. One additional fawn mortality was indirectly related to human activities. The fawn mortality occurred when a fawn, attempting to jump through a fence, became entangled and died. The fawn was approximately 75 days old. Other farmers/landowners reported similar incidences of fence related mortalities (mortalities of non radio-collared fawns) to study personnel. Mortality - Natural Natural mortalities accounted for 35% of the total fawn mortalities (6 of 17 mortalities). Excluding predation, mortalities unrelated to human activities comprised 29.4% of the total mortalities (5 of 17). The majority of these mortalities (abandonment/malnutrition, pneumonia, and dehydration) occurred during the first 30 103 days of life. One of these mortalities was believed to have occurred as a result of capture/handling efforts. The 1 fawn that died from abandonment/malnutrition lost weight from 5.4 kg to 4.7 kg at time of death (7 days post collating). The remaining natural mortalities (excluding predation) occurred at 141 days (drowning) and 244 days (bacterial infection). One fawn died from canid predation (6% of total fawn mortality). Predation was assumed because the radio-collar was found in a location known to have an abundance of coyotes and the condition of the radio-collar. Additionally, many farmers reported seeing coyotes near the location the radio-collar was found. In many cases, farmers observed coyotes hunting for prey/carrion in newly mowed agricultural fields. Within 0.80 km of where the radio-collar was found, two winter captured deer were also killed by coyotes during the 2001 winter. Cook et al. (1971), Garner et al. (1976), and Yancy (1991) stated that coyotes tend to prey on fawns 530 days old. The canid-attributed mortality in this study occmred when the fawn was approximately 33 days old. In contrast to this study, coyote predation has been described as the leading cause of mortality in several published studies accounting for 69% of the total fawn losses in Oklahoma (Garner et a1. 1976), 69% in Illinois (Nelson and Woolf 1987), 54% in Iowa (Huegel et al. 1985), and 57% in Texas (Cook et al. 1971); whereas, Bryan (1980) and Schulz et al. (1983) reported little to no coyote mortality for their studies in Missouri and Minnesota, respectively. Perhaps, there were other factors that were leading causes of mortality (e. g., tick infestation, agricultural machinery). Some fawn mortalities may be associated to nutritional stress on pregnant females and those fawns may have died prior to becoming available to predators (Porath 1980). 104 No current research is being conducted on southwestern Lower Michigan coyotes; therefore, it is difficult to estimate coyote population densities. On numerous occasions, landowners communicated the occurrence of coyotes to study personnel. Coyotes were often seen and heard by landowners throughout the study area. No other documented fawn predators exist in southwestern Lower Michigan, aside from humans. The low mortality experienced by fawns in southwest Lower Michigan could be explained by numerous factors. The quality of the habitat (e. g., structure and density of vegetation) surrounding a fawn’s bedsite could impact survival (Huegel et al. 1985, Ozoga and Verme 1986). Fawn bedsites located in dense patches of vegetation could provide concealment from predators. Bedsites surrounded by vegetation with structural diversity could also provide protection from rain and/or wind. During this study, fawn bedsites were located primarily within brushy edges of forest patches and along fencerows of agricultural fields. Brushy edges and fencerows appear to provide adequate protection from weather conditions and concealment fiom predators. Bedsite selection varies by fawn. For example, in this study 1 fawn was found under woody cover along a fencerow and another fawn was found on the green of a golf course with no cover. Huegel et al. (1986) examined fawn bedsite selection in southcentral Iowa and concluded fawns bedded in both shrubby pastures and woodlands. F awns tend to choose bedsites with more woody cover for increased concealment (Huegel et al. 1986). Lund (1975) primarily searched for fawns in waist-high grass and clover fields of New Jersey. The low fawn mortality in southwest Lower Michigan could also be explained by the parental care of an older female. According to previous research (Ozoga and Verme 1986, Nixon and Etter 1995), the fawns of older, more experienced does had better 105 survival than primiparous does. More experienced does are also less likely to abandon fawns within the first month of life. Prime fawning habitat is often acquired by older does and less experienced does are left to search out less suitable fawning habitat. Ozoga and Verme (1986) suggest that experienced does may choose open fields as fawning habitat to watch over fawns and provide necessary distraction in the presence of a potential predator. We observed several does “watching over” large fields during our fawn search efforts. Upon our intrusion into the field, does would try to distract our search efforts by exhibiting 1+ high bounds while leaving the field. Doe behaviors often acted as clues to whether fawns were in the vicinity of the field. Over the course of the study, we may have unintentionally radio-collared fawns of older does producing a more conservative estimate of mortality. Ozoga and Clute (1988) recognized that fawns within an enclosure are most vulnerable to mortality during the first 48 hours of life. The first 48 hours are critical for imprinting and the establishment of the mother-fawn bond. F awns born to malnourished does are also more likely to succumb to death within the first 48 hours (Ozoga and Clute 1988). Mean age of radio-collared fawns during my study was 4.22 days (SE i 0.31) (ranged <1 to 15 days, median = 3.5 days), which was very similar to the mean fawn age (4.8 days [SE i 042]) reported by Ozoga and Clute (1988). We could have potentially biased the data by capturing and radio-collaring the “healthiest” fawns — fawns surviving beyond the 48-hour period of increased vulnerability. According to Carroll and Brown (1977), natality and mortality govern wildlife populations. Problems begin to develop when wildlife populations are unequally balanced toward either natality or mortality. In southwestern Lower Michigan, the deer 106 population appears to be skewed in favor of high natality and low mortality. In many cases when populations are experiencing high fecundity, natural mortality is not sufficient to regulate the population (Huegel et a1. 1985). It is critical for managers to be aware of this disproportion toward high fawn survival and adjust the management regulations accordingly. Home range and movements Burt (19432351) defined home range as “the area traversed by the individual in its normal activities of food gathering, mating, and caring for young.” A deer’s home range must be large enough to enhance the everyday survival, yet small enough for the deer to become familiar to its surroundings (Marchinton and Hirth 1984). Fawns are relatively inactive during the first 4 weeks of life; much of their time is spent nursing and bedding which coincides with their limited physical capabilities (or lack of quickness and agility to elude predators) (White et al. 1972, Schwede et al. 1992). A fawn’s home range generally increases in size as it gets older (Garner and Morrison 1977, Bartush 1978, Ozoga et al. 1982, Dalton 1985, Epstein et al. 1985) and movement patterns and home range size mimic that of the dams (Marchinton and Hirth 1984). By 4 to 6 weeks of age, fawns begin consuming vegetation to supplement their daily nursing sessions and begin accompanying does on foraging trips (Carroll and Brown 1977, Epstein et al 1985). Previous studies primarin focused their analyses on the home ranges of adult white-tailed deer and relatively few studies quantified fawn home ranges. During this study, I recorded a mean home range of 62.65 ha (range 15-173 ha, median = 57 ha) for fawns (n=68) to approximately 29 weeks post capture. A mean annual home range was 107 also recorded as 75.36 ha (range 38-119, median =70 ha) for fawns (n=24). Dalton (1985) concluded that fawns at 6 months of age have a mean home range of 50.2 ha using the modified minimum area technique (Harvey and Barbour 1965) and a mean home range of 103.8 ha using the minimum area method (Mohr 1947). Given the home range technique chosen, it is possible to have a 2—fold difference in mean home range size. The 29-week mean home range size recorded during my study more closely coincides with Dalton’s (1985) mean home range estimate of 50.2 ha. Schwede et al. (1992) recorded a mean fawn home range of 7.9 ha at approximately 6 months of age across a study area comprised predominantly of deciduous forests (85%). Home range composition for the fawns in this study was proportional to the composition of the landscape across the study area. Fawns were using similar proportions of agriculture and deciduous forests as were available across the landscape. Across the study area, 29% of the land is characterized as deciduous forests and 54% is agricultural land; whereas annual fawn home ranges were composed of 37% deciduous forest and 45% agricultural land. This relationship was different than what Nelson (1984) found in Illinois, in which wooded habitat was used in proportion to its occurrence on the study area, but agricultural fields were avoided. Yancy’s (1991) Illinois study concluded that fawns chose timber tracts and avoided agricultural fields and early- successional areas. Bryan (1980) and Dalton (1985) also observed minimal use of agricultural fields by fawns in Missouri. My study focused on a analyzing the landscape composition and structure of home ranges at 29 weeks and no other study was found that observed similar landscape composition at 29 weeks. 108 Several different factors could have an affect on the size of a fawn’s home range. Quality of the habitat can influence the home range size through the distance a fawn must travel to obtain quality forage and/or cover. A large home range could indicate that long distance travel is required to obtain foraging areas and/or cover due to a relatively open habitat (Garner and Morrison 1977, Steigers and Flinders 1980, Nelson 1984, Yancy 1991). The 3 largest fawn home ranges, in my study, had the greatest total area (ha) associated with agriculture. Predator densities can also have an affect on a fawn’s home range size. Bartush and Lewis (1978) attributed larger fawn home ranges to the density of predators and predator-related fawn mortalities in the area. Once a doe experienced a fawn mortality, she would often relocate surviving fawn(s) to another location outside the home range to increase the survival of the remaining fawn(s) (Bartush and Lewis 1978). In some landscapes, fawns may also move more frequently to escape the harassment of biting bugs (mosquitoes and black flies) (Epstein et al. 1985). I had difficulty making comparisons or drawing conclusions between my home range results and the home range results from other published studies because of differences in data collection and analysis. Radio tracking duration varied among studies from birth to 8 weeks (Carroll and Brown 1977, Steigers and Flinders 1980), 10 weeks (Geduldig 1981), 12 weeks (Gamer and Morrison 197 7, Riley and Dood 1984, Epstein et al. 1985), and 29 weeks (Schwede et al. 1992). Fawns in this study were tracked for survival for apprOximately 345 days; I was unable to find another fawn study that provided the breath of tracking to make accurate comparisons. Home range calculation procedures (e.g., minimum area method, modified minimum area method, fixed kernel) 109 varied or were not reported across studies making it difficult to compare my results. In many cases, the sample sizes reported in the published literature were too small to make meaningful comparisons. I concur with Riley and Dood’s (1984:1307) statement that concludes “differences in movements and home—range size between areas probably are related to the dissimilar habits and habitats of species and populations occupying different geographical regions and reflect how each species or population exploits its own unique environment.” VEGETATION DISCUSSION Vegetation composition and structure Browsing by white-tailed deer has the potential to severely impact a variety of plant species throughout a forest stand. Browsing can affect the quality and quantity of a forage species (Strole and Anderson 1992). Under differing browsing intensities there may be a shift in tree species. Potential shifts in the composition of the understory may ultimately influence the canopy composition (Strole and Anderson 1992). Deer browsing can also impact forest succession and regeneration. A forest’s structure and composition begins to change as a result of intensive browsing which can have an adverse impact local wildlife populations (Campa et al. 1993). Browsing can change the forest composition toward browse tolerant species and species of low use by deer (Strole and Anderson 1992). Forest species of low use to deer in fall and winter include sugar maple and white ash; whereas, white oak and shagbark hickory are preferred browse of deer (Strole and Anderson 1992). Consumption of acorns by deer could also reduce seedling production. Anderson and Katz (1993) 110 concluded that the loss of oaks had a profound affect on the availability of oak mast as a food source. In the heavily browsed central hardwoods stands in the eastern United States, there appears to be a shift fiom shade-intolerant oaks to sugar maple dominated stands (Strole 1988, Anderson and Katz 1993), similar to the stands surveyed in southwestern Lower Michigan. The replacement of oaks by sugar maples could be attributed to deer browsing, ecological disturbances that promote the growth of maple trees (e.g., fire suppression), or the combination of both factors (Anderson and Katz 1993). Many factors affect the use of a stand by deer including the amount of cover and forage it provides, the forest’s juxtaposition to agricultural fields, and the amount of human disturbance. Because there are few documented natural predators in southwestern Lower Michigan, hunting appears to be the only means to control the deer population. For this reason, wildlife managers require an understanding of the role of deer in a forest community, and how deer browsing intensities may influence the structure and composition of a forest. lll MANAGEMENT IMPLICATIONS Solid management decisions are formulated through the proper application of sound science. Wildlife managers depend on their accurate ability to predict deer population size to help develop white-tailed deer harvest regulations. Estimates of natality, mortality, immigration, emigration, and density are critical for understanding the dynamics of a white-tailed deer population. Wildlife management problems are very complex and require an understanding of all of these population parameters to aid in management decision making. Prior to this research, empirical data for some of these parameters were largely unavailable for wild white-tailed deer in southwestern Lower Michigan. The information gained fiom this study could help refine or validate deer population models for southwestern Lower Michigan. The goal of this study was to gather scientific data on the ecology of white-tailed deer in southwestern Lower Michigan; specifically, to formulate information on deer landscape use patterns and population characteristics across the study area. Given the results of this study, it appears that the behavior and ecology of deer in southwestern Lower Michigan are different than that of deer in the northern portions of the state. These differences should be taken into account when devising deer management objectives. Deer in southwestern Lower Michigan have similar dynamics as deer in some agricultural regions of the Midwest. Deer in southwestern Lower Michigan are well adapted to the structure, composition and landownership patterns in this agro-forested landscape. In this area, deer home ranges were primarily composed of agricultural lands and deciduous forests. The abundance of forage available to deer residing in fragmented agro-forested 112 landscapes exacerbates the already large deer population. Where agricultural crops are not as abundant, deer have the potential to reduce the diversity of forest plants through heavy browsing. Although there are relatively high densities of deer in southwestern Lower Michigan (19 deer/kmz) (person. com. S. Beyer), they appear to be healthy and there is little evidence of stress. Deer population goals must be compatible with the landscape. These goals may be different between a forested and agricultural landscape and reflect hunter demand balanced by human tolerance and landscape capacity. Deer mortality rates from this study reflect the importance of hunters playing a role in management; therefore, managers depend on hunting as a population management tool to control deer population densities. Landowners have varying ranges in tolerance and attitude when it comes to deer abundance. Managers realize that there is a fine line between too many deer from a crop damage perspective and not enough deer from a hunting perspective. Why does southwestern Lower Michigan experience high deer survival? There are several explanations for high deer survival, but the combinations of these reasons can make deer densities problematic from a management perspective. First, the fragmented landscape is composed of agricultural lands and deciduous forests providing cover and forage for deer within relatively close proximity. Also, fawn survival was extremely high throughout the study area with the primary mortality sources for deer being hunting and deer-vehicle collisions. All of the radio-collared deer resided on private lands where hunting was primarily limited to family and friends. Some hunters may have perceived that hunting access was difficult to obtain because the private land was heavily posted. Some landowners within this region discouraged the shooting of female deer on their 113 property and focused on harvesting males. Hunting pressure may not have been evenly distributed because of landowner attitudes in regard to hunting. Also, deer may have been less vulnerable to hunting in some areas due to landscape characteristics (e.g., swamps, difficult terrain) and “refuges” caused by a lack of hunting pressure. These factors may also contribute to the relatively small home ranges of deer in southwestern Lower Michigan. Hunting appears to be the only effective means to reduce the deer population; therefore, landowners and managers must work together to establish population levels and associated harvest levels in accordance with the landscape and landowner tolerance. To achieve these results, managers could devise hunting regulations and incentives to promote the harvesting of antlerless deer. 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Proceedings from the Indiana Academy of Science 101(3-4):309-317. Wilson, E. O. 1975. Sociobiology. Belknap Press, Harvard Univ. Press, Cambridge, Massachusetts, 697pp. Winterstein S. R., K. H. Pollock, and CM. Bunck. 2001. Analysis of survival data from radiotelemetry studies. Pages 351-380 in J. J. Millspaugh, and J. M. Marzluff, eds. Radio tracking and animal populations. Academic Press. San Diego, California, USA. Withey J. C., T. D. Bloxton, and J. M. Marzluff. 2001. Effects of tagging and location error in wildlife radiotelemetry studies. Pages 43-47 in J. J. Millspaugh, and J. M. Marzluff, eds. Radio Tracking and Animal Populations. Academic Press. San Diego, California, USA. Yancy, DC. 1991. Habitat use and mortality of white-tailed deer fawns in the Mississippi River bottomlands of southern Illinois. Thesis, Southern Illinois University, Carbondale, Illinois, USA. 124 APPENDICES 125 moon 82. 02 o2o2no> 222cm 82 oo 32 d 22.2. .2268 .2226 22:23 “2 2262.3 2 .33 22522. on 02?. 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Om 26.2 .222. .Om.08 .3226 262.2 .622. .N.08 .3 226 262.2 .222. .2 2.08 .3226 262.2 .622. .N.08 .3226 26.2 .622. .O.08 .3 232 26.2 .2N..2. .ON .08 .3 232 222.2 .622. .N.08 .3 226 26.2 .2N..2. .Om .08 .3 232 26.2 .222. .Om.08 .3226 262.2 .622. .N.08 .3 2.26 26.2 .222. .Om.08 .3 236 26.2 .622. .O.08 .3 232 262.2 .222. .2 2.08 .3226 26.2 .2N..2. .Om .08 .3 232 26.2 .222. .Om.08 .3226 26.2 .2N..2. .Om .08 .3 2292 22:20.4~ E8 2 E8 2 22:25. E8 2 22:2}V 22025. E8 2 E8 2 E82 22:20.8~ E8 2 E8 2 E8 2 E8 .2 E82 2222222222222222 Ohm.Om2 OOm.Om 2 OOm.Om 2 OmmOm 2 OVmOm 2 Omm.Om2 O2 WOW 2 O6V.Om 2 OmWOm 2 Othm 2 OOVOm 2 Omv.Om2 Ovam 2 Omv.Om2 ONv.Om 2 O2v.Om 2 28:80 .2 2.3. 0.88.2.8 128 Appendix Table 2. Landscape composition (ha), by cover type within home ranges, of winter captured white-tailed deer 9:53) in southwestern Lower Michigan, 2001-2002. Emergent Deciduous Evergreen Agricultural Woody herbaceous Frequency Residential forests forests lands wetlands wetlands All 2001 150.0100 0.00 38.89 0.00 33.51 5.50 0.00 77.89 150.0200 0.00 28.62 0.96 25.82 25.63 0.00 81.03 150.0300 0.00 84.02 0.00 31.00 17.30 3.64 136.00 150.0400 0.30 56.52 5.34 53.04 1.07 2.93 1 19.20 150.0500 0.00 1 10.30 9.42 24.1 1 19.98 1.26 165.10 150.0600 0.00 72.49 1.51 15.73 15.16 0.00 104.90 150.0900 0.17 105.00 1.51 45.43 28.93 0.00 181.00 150.1000 0.00 26.23 0.00 29.35 0.04 0.00 55.63 150.1200 0.00 44.88 1.73 137.70 1.84 2.32 188.50 150.1300 0.00 41.35 2.90 30.79 31.54 0.53 107.10 150.1400 0.00 26.55 0.52 94.13 20.96 0.00 142.20 150.1600 0.00 44.25 1.51 189.40 29.84 0.00 265.00 150.1800 0.00 50.08 1.51 13.14 3.77 0.00 68.50 150.1900 0.00 100.20 0.00 54.04 13.27 2.81 170.40 150.2000 0.00 80.03 1.51 72.52 32.29 0.00 186.30 150.2100 0.00 85.13 2.88 34.90 30.36 4.74 158.00 150.2200 0.00 1 13.50 1.51 33.48 20.69 0.00 169.20 150.2310 0.00 290.10 46.23 200.30 92.47 6.58 635.70 150.2410 0.00 10.44 0.00 38.14 21.42 0.00 70.01 150.2500 0.00 29.89 2.90 30.86 28.75 0.53 92.94 150.2700 0.00 23.95 0.00 67.00 22.96 0.00 1 13.90 150.2800 0.97 64.67 6.58 176.10 18.64 7.97 274.90 150.2900 0.00 6.79 0.00 74.72 13.20 0.00 94.71 150.3200 3.02 90.27 1.26 22.55 3.40 0.00 120.50 150.3400 0.00 45.12 12.51 48.76 0.70 0.00 107. 10 150.3500 0.00 29.79 0.03 19.97 0.04 0.00 49.84 150.3800 0.00 52.04 1.68 106.90 4.53 3.03 168.10 150.3900 0.00 61.81 1.73 96.01 2.11 2.32 164.00 150.4100 0.00 361.70 23.30 263.20 87.05 4.50 739.70 150.4400 0.00 156.80 66.87 255.90 6.93 1 1.45 498.00 150.4600 0.16 58.63 0.00 5.70 0.03 0.00 64.53 150.4800 0.00 157.00 55.72 163.60 22.34 0.00 398.70 150.4900 0.00 39.55 1 1.12 45.45 2.18 0.00 98.31 150.5100 0.00 53.18 2.71 17.67 6.01 0.00 79.57 150.5300 0.00 54.63 7.16 47.69 8.38 0.00 1 17.90 150.5700 0.82 59.51 0.42 2.43 0.00 0.00 63.18 2002 150.0202 0.00 47.91 1.09 38.09 26.15 0.00 113.20 150.0302 0.00 71.12 0.00 31.71 13.19 5.11 121.10 150.0402 0.00 50.04 5.50 46.28 1.07 3.55 106.40 150.0502 0.00 38.27 0.00 14.69 4.89 0.22 58.06 150.0602 0.00 73.1 1 1.51 15.31 6.82 0.00 96.74 150.0902 0.00 72.68 1.51 10.18 15.21 0.00 99.58 150.1002 0.00 32.50 0.00 89.68 0.39 0.00 122.60 150.1202 0.00 48.53 1.73 150.50 1.84 1.93 204.60 129 Appendix Table 2. (cont'd) 150.1302 0.00 39.55 13.04 49.19 0.58 0.00 102.40 150.1402 0.00 31.97 0.00 76.58 20.73 0.00 129.30 150.1902 0.00 70.51 0.00 27.53 8.19 4.30 1 10.50 150.2102 0.00 79.86 0.00 32.12 11.58 3.45 127.00 150.2412 0.00 13.13 0.00 28.69 24.12 0.00 65.94 150.2502 0.00 84.27 2.90 52.62 33.20 0.53 173.50 150.2702 0.00 25.19 0.00 40.42 25.32 0.00 90.93 150.3502 0.00 56.00 2.83 22.86 1.07 0.05 82.81 150.3902 0.54 76.33 5.17 142.30 0.90 2.32 227.50 Mean 2001 0.16 64.78 4.92 58.03 19.17 2.77 101.69 Mean 2002 1.34 73.05 11.23 72.10 13.16 3.72 276.35 Total mean 0.75 69.15 8.33 65.47 16.05 3.31 157.74 130 Appendix Table 3. Landscape composition (mean percent), by cover type within home ranges, of winter captured white-tailed deer (n=53) in southwestern Lower Michigan, 2001 -2002. Emergent Deciduous Evergreen Agricultural Woody herbaceous Fregency Residential forests forests lands wetlands wetlands All 2001 150.0100 0.00 49.92 0.00 43.02 7.06 0.00 100 150.0200 0.00 35.32 1.18 31.87 31.63 0.00 100 150.0300 0.00 61.80 0.00 22.80 12.72 2.68 100 150.0400 0.26 47.41 4.48 44.50 0.90 2.46 100 150.0500 0.00 66.82 5.71 14.61 12.10 0.76 100 150.0600 0.00 69.12 1.43 15.00 14.45 0.00 100 150.0900 0.10 57.99 0.83 25.09 15.98 0.00 100 150.1000 0.00 47.16 0.00 52.76 0.08 0.00 100 150.1200 0.00 23.81 0.92 73.07 0.98 1.23 100 150.1300 0.00 38.60 2.71 28.74 29.45 0.50 100 150.1400 0.00 18.68 0.37 66.21 14.74 0.00 100 150.1600 0.00 16.70 0.57 71.47 11.26 0.00 100 150.1800 0.00 73.11 2.20 19.18 5.51 0.00 100 150.1900 0.00 58.84 0.00 31.72 7.79 1.65 100 150.2000 0.00 42.95 0.81 38.92 17.33 0.00 100 150.2100 0.00 53.88 1.82 22.09 19.21 3.00 100 150.2200 0.00 67.09 0.89 19.79 12.23 0.00 100 150.2310 0.00 45.63 7.27 31.51 14.55 1.04 100 150.2410 0.00 14.91 0.00 54.49 30.60 0.00 100 150.2500 0.00 32.16 3.12 33.21 30.93 0.57 100 150.2700 0.00 21.03 0.00 58.82 20.16 0.00 100 150.2800 0.35 23.52 2.39 64.05 6.78 2.90 100 150.2900 0.00 7.16 0.00 78.90 13.94 0.00 100 150.3200 2.50 74.92 1.05 18.71 2.82 0.00 100 150.3400 0.00 42.13 1 1.68 45.53 0.65 0.00 100 150.3500 0.00 59.78 0.06 40.08 0.08 0.00 100 150.3800 0.00 30.95 1.00 63.56 2.69 1.80 100 150.3900 0.00 37.69 1.06 58.55 1.29 1.41 100 150.4100 0.00 48.90 3.15 35.58 11.77 0.61 100 150.4400 0.00 31.50 13.43 51.38 1.39 2.30 100 150.4600 0.25 90.86 0.00 8.83 0.05 0.00 100 150.4800 0.00 39.39 13.97 41.03 5.60 0.00 100 150.4900 0.00 40.23 1 1.32 46.23 2.22 0.00 100 150.5100 0.00 66.83 3.41 22.21 7.55 0.00 100 150.5300 0.00 46.35 6.07 40.47 7.1 1 0.00 100 150.5700 1.30 94.20 0.67 3.84 0.00 0.00 100 2002 150.0202 0.00 42.31 0.96 33.64 23.09 0.00 100 150.0302 0.00 58.71 0.00 26.18 10.89 4.22 100 150.0402 0.00 47.01 5.16 43.48 1.01 3.33 100 150.0502 0.00 65.91 0.00 25.29 8.42 0.37 100 150.0602 0.00 75.57 1.56 15.82 7.05 0.00 100 150.0902 0.00 72.99 1.51 10.22 15.27 0.00 100 150.1002 0.00 26.52 0.00 73.17 0.32 0.00 100 131 Appendix Table 3. 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Home range attribute comparison for all radio—collared fawns (n=68) during the May to December tracking period in southwestern Lower Michigan, 2001 and 2002. 149 Home range Total number Total number Interspersion Frequency Year . . Size (ha) of patches of landowners index 151.103 2001 33.88 7 1 3 151.123 2001 28.34 7 3 2 151.1429' 2001 59.74 12 3 6 151.143 2002 37.78 10 l 5 151.163 2002 63.65 10 2 8 151.163' 2001 72.80 17 5 10 151.182 2002 51.93 8 5 5 151.184 2001 27.57 11 3 13 151.2036 2001 79.07 16 2 14 151.204 2002 74.69 18 3 4 151.223 2002 44.85 6 4 7 151.223' 2001 43.61 12 5 11 151.2432' 2001 65.44 10 1 6 151.244 2002 58.21 10 2 5 151.2636 2001 26.68 7 3 9 151.2865 2001 57.64 13 3 5 151.3011 2001 64.03 10 4 5 151.3228 2001 32.03 13 2 6 151.3435 2001 81.85 13 1 9 151.3832 2001 55.37 14 4 5 151.402' 2002 140.94 12 11 5 151.404' 2001 44.95 1 1 1 6 151.4232 2001 48.49 8 5 6 151.442 2002 73.85 8 2 8 151.4634 2001 68.74 13 1 9 151.482 2002 86.21 10 5 6 151.482' 2001 28.41 2 3 2 151.504 2001 66.50 11 5 4 151.5236 2001 15.27 13 1 5 151.5426 2001 49.92 15 1 8 151.5632 2001 39.02 15 5 9 151.5829 2001 41.92 14 1 6 151.6027 2001 76.01 14 5 9 151.624' 2002 54.07 8 5 9 151.625 2001 29.80 9 3 5 151.6433 2001 101.90 12 3 5 151.662 2002 49.69 15 3 9 151.662' 2001 28.57 11 4 7 151.6836 2001 49.18 13 l 8 151.7033 2001 35.81 12 4 6 Appendix Table 14. (cont'd) 151.7226 2001 21.89 7 1 4 151.743 2001 36.95 13 4 9 151.7646 2001 112.23 17 4 9 151.7822 2001 40.10 14 4 7 151.8036 2001 51.42 12 l 6 151.824' 2002 79.54 11 1 9 151.835 2002 43.34 11 3 3 151.844 2002 79.06 11 7 10 151.854 2002 71.53 15 7 10 151.864 2002 26.35 6 l 3 151.893 2002 173.26 18 12 7 151.903 2002 82.09 9 4 9 151.914 2002 52.60 13 4 5 152.133 2002 58.84 8 2 5 152.163 2002 24.58 6 1 4 152.191 2002 95.71 18 6 12 152.223 2002 54.80 12 2 6 152.253 2002 108.29 18 6 9 152.283 2002 39.54 15 3 6 152.344ll 2002 89.48 15 2 10 152.402 2002 88.39 16 5 13 152.463 2002 66.84 8 2 7 152.494 2002 131.10 16 8 13 152.554 2002 65.02 7 2 4 152.644 2002 76.95 18 5 13 152.764 2002 54.54 8 2 8 152.794 2002 122.80 16 7 12 152.823' 2002 124.26 16 7 7 Mean 62.65 11.82 3.51 7.21 SE 3.76 0.44 0.28 0.35 ' Fawns that died during the tracking period. 150 Barry County . 2001 Trap sites I 2002 Trap sites Kalamazoo County Appendix Figure 1. Winter trap sites of white-tailed deer in southwestern Lower Michigan, 2001-2002. 151 IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII III III” I III IIIIII IIII IIIIIIII