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The ecology of movements made by Columbian black-tailed deer McNay, R. Scott 1995

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THE ECOLOGY OF MOVEMENTS MADE BY COLUMBIAN BLACK-TAILED DEER by R. Scott McNay B.Sc.F., University of New Brunswick, 1981 M.Sc., University of British Columbia, 1985  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Department of Forestry)  We accept this thesis as conforming to the required standard  THE UNIVERSITY OF BRITISH COLUMBIA March 1995 ©  Robert Scott McNay, 1995  In presenting this thesis in  partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission.  (Signature)  Department of  Fow -J  The University of British Columbia Vancouver, Canada  Date  DE.6 (2/88)  95.ôi. I  11  ABSTRACT  I used movements of 74 radio-collared black-tailed deer to investigate whether an hierarchically-structured decision process constrains habitat choices.  Constraints on habitat can lead to rejection of the ideal-free  distribution hypothesis for black-tailed deer where their habitats are prone to large and rapid disturbances.  I recorded 11,150 deer locations at 2 time  scales (2-hourly and weekly) at 4 study areas on Vancouver Island, British Columbia from 1982-1991.  I assessed the temporal and spatial independence  of these observations before examining the distance, frequency, timing, and direction of movements. Deer that migrated every year (n  12) occupied natal ranges at high  =  elevations and used alternate ranges for >6 mo/yr.  Migration route  directions varied and were used in the absence of snow.  Alternate ranges  were established at mid-slope elevations on southern aspects and always enclosed some old forest, the only forest type preferred more in winter than summer.  Areas of intense use on alternate ranges included old forests.  Deer that migrated less consistently (n  =  16) occupied natal ranges at mid-  to low-slope positions and used alternate ranges for <4 mo/yr.  All but 2 of  these deer migrated in response to snow accumulation or ablation and along routes consistent with valley direction.  They established alternate ranges  at similar topographic positions as other migratory deer but did not necessarily have access to old forests.  Non-migratory deer (n  =  44)  occupied natal ranges at mid- to low-elevations and used whatever forests were available.  Areas of intense use tended not to include old forest.  I  was unable to trap migratory deer in young forests and the survival rate I estimated for resident deer indicated a declining population.  111  Decisions concerning home- and seasonal-range establishment constrained more spatially specific decisions about habitat use.  Further,  these decisions were themselves likely to be limited by rigid tactics such as philopatry and site fidelity. These conclusions were tested by logging old forest winter habitat in 2 separate study areas. initial responses.  Fidelity, rather than habitat choice, dominated the  Deer accepted remaining habitats rather than finding old  forest winter habitat elsewhere.  Lack of freedom in choosing habitats has  implications for habitat management, for deer response to habitat change, and for factors that affect population dynamics.  iv TABLE OF CONTENTS Abstract Table of Contents  .  List of Tables  vii  List of Figures Acknowledgements Foreword Chapter 1  GENERAL OVERVIEW Background Thesis Structure Literature Cited  Chapter 2  INDEPENDENCE OF OBSERVATIONS IN MOVEMENTS Methods Deer Location Samples Analytical Techniques Results Independence of Location Observations Effect of Migrations on Independence of Locations Independence of Distance Between Consecutive Locations Discussion Management Implications Literature Cited  Chapter 3  1 1 4 5  SPATIAL AND TEMPORAL SCALES OF RESOURCE USE: MOVEMENTS Study Areas Methods Deer Location Samples Factors Used to Identify Spatial and Temporal Scales Analytical Procedures Results Deer Location Samples Dispersal: Life-time Scale Migration: Seasonal Scale Local Movement: Daily Scale Serial Movement: Hourly Scale Discussion Movements as Scalar Classes of Activity Movements as Hierarchically Structured Decisions Decision Information Transfer and Hierarchical Function Implications of Constraint on Movement Decisions Literature Cited .  .  .  .  14 14 18 18 21 23 26 30 32 32  .  •  •  •  .  •  .  •  .  57 58 66 67  V  Chapter 4  SPATIAL AND TEMPORAL SCALES OF RESOURCE USE: HABITAT PREFERENCE Study Areas Methods Deer Location Samples and Habitat Use Definitions and Habitat Features Analytical Procedures Results Sample Characteristics Home Range Preferences: Life-time Scale Habitat Preferences: Seasonal Scale Activity Nuclei Preferences: Daily Scale Discussion Hierarchy and Constraint of Habitat Choices Interpretation of Habitat Preference Implications for Research and Management Literature Cited  .  .  .  75 78 80 80 81 83 86 86 89 95 110 113 113 116 120 121  Chapter 5  RESPONSE TO LOGGING OF WINTER HABITAT Study Areas Methods Experimental Design Animal Capture and Monitoring Statistical Analysis Results Discussion Management Implications Literature Cited  127 128 129 129 130 131 132 136 137 139  Chapter 6  MORTALITY CAUSES AND SURVIVAL ESTIMATES Study Areas Methods Mortality Causes Mortality and Survival Rate Estimation Results Estimates of Cause-specific Mortality Factors Affecting Mortality and Survival Discussion Causes of Mortality Factors Affecting Survival Management Implications Literature Cited  143 144 146 146 147 148 148 151 156 156 157 158 160  Chapter 7  MANAGEMENT IMPLICATIONS OF CONSTRAINTS ON MOVEMENTS TACTICS Study Areas Methods Deer Location Samples Definitions and Analytical Procedures Results and Discussion Movements Habitat Use Response to Removal of Winter Habitat Survival Estimates Management Implications  164 165 166 166 167 170 170 176 179 181 182  vi Literature Cited Chapter 8  GENERAL CONCLUSIONS Independence of Observations in Movements Spatial and Temporal Scales in Movements Spatial and Temporal Scales in Habitat Use Response to Winter Habitat Logging Mortality Causes and Survival Estimates Literature Cited  185 189 189 190 191 192 193 194  vii LIST OF TABLES Table 2.1  Percentage of total, or seasonal, black-tailed deer home ranges where the hypothesis of independence of location observations was not rejected. Values in parentheses are the number of total, or seasonal, home ranges tested from data collected during 19821991 on Vancouver Island, British Columbia  15  Physical features of 4 study areas located on Vancouver Island, British Columbia  31  Straight-line distance between the estimated natal site and the site occupied in their last natal season for radio-collared, black-tailed deer caught as subadults (<2-yr-old) at 4 study sites on Vancouver Island, British Columbia, 1982-1991  43  Movement characteristics for 3 behaviour groups of radio-collared, black-tailed deer on Vancouver Island, British Columbia, 1982-1991. Moves are straight-line distances between successive locations (using 2-hour data for serial moves and weekly data for local moves) or between arithmetic mean range centres (for migrations), range deviation is the mean straight-line distance between arithmetic mean centres of successively used ranges of the same type, nuclei dispersion is the mean squared distance between nuclei chosen from utilization distributions for individual deer ranges, frequency of nuclei changes is the mean daily frequency of moving between nuclei, and distance of nuclei changes is serial distance moved to change nuclei  46  .  Table 3.1 Table 3.2  Table 3.3  Table 4.1  Table 4.2  Table 4.3  Table 4.4  .  .  .  Total sample sizes for deer and habitat samples recorded at 4 study areas located on Vancouver Island, British Columbia, 1982-1991  .  79  Multivariate analysis of variance results for tests of study area, deer behaviour (migration tactics and range types for selection of home ranges and migration tactics for selection of seasonal habitats), and the interaction of those main )b estimates for 1 effects, on habitata preference (a radio-collared, black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 1982-1991  91  Habitat preference estimates for 28 migratory and 44 resident, radio-collared, black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 1982-1991  93  Habitat preference estimates for 26 migratory and 35 resident, radio-collared, black-tailed deer at 3  viii  Table 4.5  Table 4.6  Table 4.7  Table 4.8  Table 4.9  Table 5.1  Table 6.1  Table 6.2  Table 6.3  study areas on Vancouver Island, British Columbia, 1982-1991  96  Habitat preference estimates for 26 migratory and 35 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver Island, British Columbia, 1982-1991  98  Habitat preference estimates for 28 migratory and 44 resident, radio-collared, black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 1982-1991  101  Habitat preference estimates for 26 migratory and 44 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver Island, British Columbia, 1982- 1991  104  Habitat preference estimates for 26 migratory and 44 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver Island, British Columbia, 1982- 1991  107  Maximum likelihood analysis of variance tables for frequency of habitat types forming the primary component of activity nuclei established by individual deer. Nuclei were determined from deer locations estimated weekly at 4 study areas on Vancouver Island, British Columbia, 1982-1991, and were pooled into 2 groups based on deer behaviour (migratory or resident)  111  Effect of forest logging on range sizes, use of old forests, and range fidelity of radio-collared, black-tailed deer where logging occurred inside (treatment) or outside (control) their pre disturbance home ranges. Deer were from 2 study areas on Vancouver Island, British Columbia, 19881991  135  Monthly cause-specific mortality (%) for radiocollared, adult female, black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 19821991  150  Mortality (%) by month of year for 3 leading causes of mortality on radio-collared, adult female, black tailed deer on Vancouver Island, British Columbia, 1982-1991  152  Akaike’s information criterion (AIC) and likelihood ratio tests (LR x ) between competing models of monthly fate (cause-specific mortality or survival) of radio-collared, adult female, black-tailed deer  ix on Vancouver Island, British Columbia, 1982-1991. Hierarchical models of 4 categorical variables (A study area, B seasonal movement of deer, E monthly mode of elevations used by deer, and M month of the year) are listed from the most general (all factors A’B.E’M) to the most reduced model (no factors N)  153  Monthly survival (%) for 2 known, and 1 unknown, seasonal movement types of radio-collared, adult female, black-tailed deer at 2 broad elevations on Vancouver Island, British Columbia, 1982-1991. Bracketed values are SE estimates  155  The sex, age classes, and number of relocations made for a sample of radio-collared, black-tailed deer at 4 study sites on Vancouver Island, British Columbia, 1982-1991. Superscripted values are the number of deer that lived long enough (>10 mo) to classify into seasonal movement types  171  -  -  -  -  -  -  Table 6.4  Table 7.1  x LIST OF FIGURES Figure 2.1  Figure 2.2  Figure 2.3  Figure 3.1  Figure 3.2  Figure 3.3  Figure 3.4  Figure 3.5  A chronological plot of Schoener’s (1981) Ratio calculations (top) and distance between consecutive locations (bottom) from location observations collected weekly between 1982 and 1991 on a radiocollared, black-tailed deer (#NRC13402) at Nanaimo River on Vancouver Island, British Columbia  16  A chronological plot of Schoener’s (1981) Ratio calculations (top) and distance between consecutive locations (bottom) from location observations collected weekly between 1982 and 1991 on a radiocollared, black-tailed deer (#N1M12901) at Nimpkish River on Vancouver Island, British Columbia  17  Scatter plot of distance (m) and time (days) between consecutive observations of individual, radiocollared, black-tailed deer location estimates observed from 1982 to 1991 on Vancouver Island, British Columbia. Inset shows detail of the relationship at lower axis positions. Location estimates were derived by maximum likelihood estimation and distance was the straight-line distance  19  Cumulative frequency distribution (%) for the distance between successively sampled locations (n 8,000) of radio-collared, black-tailed deer on Vancouver Island, British Columbia, 1982-1991  40  =  Number and timing of outlier moves (moves >1.2 km that were not dispersals or migrations) made by radio-collared black-tailed deer on Vancouver Island, British Columbia, 1982-1991  41  The average time-per-trip spent away from the natal range for migratory, radio-collared, black-tailed deer on Vancouver Island, British Columbia, 19821991. Individuals are ranked in their order of duration  45  Number of migratory moves made during each month of the year by 2 behaviour groups of radio-collared, black-tailed deer departing natal ranges (top) and returning to natal ranges (bottom) on Vancouver Island, British Columbia, 1982-1991  48  Directions of movement from natal ranges to alternate ranges for obligate migratory (solid arrows), and facultative migratory (dashed arrows), radio-collared, black-tailed deer at 4 study sites on Vancouver Island, British Columbia, 1982-1991  50  xi Figure 3.6  A representation of activity nuclei as determined from the harmonic mean utilization distribution for 1 radio-collared black-tailed deer (#15001) on its alternate range, Nanaimo River, British Columbia, November 1983 March 1984  52  Frequency of radio-collared, black-tailed deer, seasonal ranges having 1 or more activity nuclei  53  Mean distance between successive, 2-hour, relocations of radio-collared black-tailed deer by the hour of day as deer were departing nuclei (top), or within nuclei (bottom), at 4 study sites on Vancouver Island, British Columbia, 1982-1991  55  Forest seral age class abundance (total within study area), availability (total within home ranges), and use (locations from radio-collared black-tailed deer) at 4 study areas on Vancouver Island, British Columbia, 1982-1991  87  Elevation class abundance (total within study area), availability (total within home ranges), and use (locations from radio-collared black-tailed deer) at 3 study areas on Vancouver Island, British Columbia, 1982-1991  88  Aspect class abundance (total within study area), availability (total within home ranges), and use (locations from radio-collared black-tailed deer) at 3 study areas on Vancouver Island, British Columbia, 1982-1991  90  Observed frequencies and predicted proportional frequencies (maximum likelihood estimates) of forest habitats (clear, young, or old) forming the primary component of activity nuclei for individual migratory or resident black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 19821991  112  -  Figure 3.7 Figure 3.8  Figure 4.1  Figure 4.2  Figure 4.3  Figure 4.4  Figure 5.1  Habitat abundance (% of total) at 2 study areas on Vancouver Island, British Columbia, 1988-1991 .  Figure 6.1  Figure 7.1  .  .  Monthly mortality by cause recorded for radiocollared, adult, female black-tailed deer on Vancouver Island, British Columbia, 1982-1991. Values above histograms indicate the total sample size in number of deer-months Percent of total locations for radio-collared, black-tailed deer of 3 movement types (obligate migratory, facultative migratory, or resident) found at different elevation bands (m asi) on Vancouver  .  133  149  xii Island, British Columbia, 1982-1991 Figure 7.2  Figure 7.3  Figure 7.4  .  174  Habitats chosen by radio-collared, black-tailed deer at 4 study sites on Vancouver Island, British Columbia, 1982-1991. Habitats are: open, 0- to 5yr-old forests; non-merchantable (NMF) rock, water, subalpine, and alpine; young, 6- to 45-yr-old forests; or old, >250-yr-old forests  177  Habitats used by radio-collared, black-tailed deer of 3 movement types (obligate or facultative migratory, or resident) at natal ranges (top) and at alternate, winter ranges (bottom) on Vancouver Island, British Columbia, 1982-1991. Habitats were non-merchantable (NMF) rock, water, sub- alpine, or alpine or forests of ages: open, 0- to 5-yr-old; young, 6- to 45-yr-old; or old, >250-yr-old  178  The response of radio-collared black-tailed deer to removal of their old forest, winter habitat at 2 study areas on Vancouver Island, British Columbia, 1988-1991  180  xiii ACKNOWLEDGMENTS My involvement with the Vancouver Island Deer Project began in 1981 while I was a student at UBC. This association evolved into full-time employment with the BC Forest Service and, specifically, to this work which eventually took me on a return path to UBC. This last role as student at UBC and CSU and as project leader for the Forest Service was most rewarding. I owe many people thanks for helping me to obtain this role and then for filling it with a unique and rich learning experience. Fred Bunnell, Rick Ellis, and Brian Nyberg (my supervisors) demonstrated unprecedented patience as I reached for opportunities they helped put before me. Contrasting his always vociferous approach to office life, Fred had a quiet and unpretentious way of guiding my direction with this study. I think his complementary tacts were particularly well-chosen for my needs as a student. Also, Fred’s help in structuring and reviewing individual chapters is unmatched; my thanks for that assistance. Although my communication with Lee Gass occurred only infrequently, he always left me with much to think about. Lee’s questions helped me to sharpen my focus on many issues which ultimately clarified much of my writing. A. F. Nemec and 2 anonymous reviewers made helpful suggestions for improving chapter 2. R. M. Bartmann, K. R. Brunt, D. W. Janz, K. Mitchell, A. F. Nemec, J. B. Nyberg, R. E. Page, and 1 anonymous reviewer made helpful suggestions for improving chapter 6. Financial and logistical support came from the Integrated Wildlife Intensive Forestry Research program (1981-1991), a cooperative endeavour of BC Environment, BC Forest Service and the University of BC. Support in latter years also came from the South Moresby Forest Replacement Account (1989-1993), from the BC Forest Service, and from 3 companies of the BC Forest Industry: Canadian Forest Products, Englewood Logging Division (thanks to Al McLeod); Fletcher Challenge Canada, now TimberWest (thanks to Bob Willington); and MacMillian Bloedel Ltd. (thanks to Ron MacLaughlin). Over the years there have been many people who, through insightful discussions, have contributed to my thesis. Leading the way were my colleagues Don Doyle, Tom Hobbs, Doug Janz, Jeff Morgan, and Rick Page. Two books had a notable impact on my thinking; one I stole from Knut Atkinson (The Ecology of Animal Movements) and another that formed the basis for a discussion group headed by John Wiens at CSU (Perspectives in Ecological Theory). Thanks to Knut and John for making me aware of these publications. Also, the work reported here has been part of a larger project involving many employees. As equal members of a research team, we collectively gathered data so, in that respect, this product represents their efforts as well as mine. This is especially so in the case of the quorum who, with me, formed the deer crew in latter years: Myke Chutter, Don Doyle, Jeff Morgan, and Joan Voller. The eleventh hour saw the typical yet notoriously unpredictable spot fires and I owe thanks to Marvin Eng, Line Giguère, Les Peterson, and Joan Voller for helping me attend to those problems. While I am genuinely grateful to all those who assisted me, I want most to thank my wife and friend Line Giguère, her brother Yves, and her sister Nicole. Whenever times were tough and I seemingly had no where to turn, the Giguères were always there; not just waiting to help, but eager as well. Enthusiastic at any hour of the day or night and in any weather, their determination and pride of workmanship has left me with a deep respect for their talent in working with wildlife. Their efforts are entangled in every line herein and have contributed greatly to my completion of this product. -  xiv FOREWORD I chose to write each chapter of this thesis for publication in scientific journals and the available citations are listed below. As lead author, I led publication development from the inception of objectives and hypotheses through construction of analytical techniques, and interpretation of results, to writing of the final report. Co-authors, in their roles, contributed assistance in developing ideas, performing instructed analyses, and editing of early drafts of manuscripts. This description of effort is not intended to belittle the significant contributions made by co-authors but only to clarify our respective roles for the purpose of evaluating my efforts. Chapter 2 McNay, R. S., J. A. Morgan, and F. L. Bunnell. 1994. Characterizing independence of observations in movements of Columbian black-tailed deer. J. Wildi. Manage. 58:422-429. -  Chapter 3 McNay, R. S., and F. L. Bunnell. 1995. Spatial and temporal scales of resource use: Evidence of a functional hierarchy in Columbian black-tailed deer movements. Ecology 76: (in preparation). -  Chapter 4 McNay, R. S., and F. L. Bunnell. 1995. Spatial and temporal scales of resource use: Indication of hierarchical effects on habitat use by Columbian black-tailed deer. Ecology 76: (in preparation). -  Chapter 5 McNay, R. S. 1995. Columbian black-tailed deer response to logging of their winter habitat: fidelity, range sizes, and habitat use. J. Wildi. Manage. 59: (Submitted). -  Chapter 6 McNay, R. S., and J. M. Voller. 1994. Mortality causes and survival estimates for adult female, Columbian black-tailed deer. J. Wildi. Manage. 59:138-146. -  Chapter 7 McNay, R. S., and F. L. Bunnell. 1995. Behaviourial limits to movement: the effect on habitat choices for Columbian black-tailed deer. Trans. Congr. Tnt. Union Game Biol. 21(2):295-303. -  1 CHAPTER 1  -  GENERAL OVERVIEW  BACKGROUND This thesis is an extension of a major research initiative undertaken by the government of British Columbia (BC) to resolve what was once regarded as the wildlife issue of highest provincial priority.  Black-tailed deer  (Odocoileus hemionus columbianus) is a highly sought-after big-game species but some deer populations in coastal BC have been reported to decline recently from a combination of habitat loss (Harestad et al. 1982) and predation (Jones and Mason 1983).  Consequently, large areas of old forests  were delineated as winter range for deer and deferred from further logging. Similar events took place in southeast Alaska (Schoen et al. 1981). However, the deferrals in BC met with strong opposition from the forest industry (Bunnell 1985).  Implications of the deferrals were, among others,  constraints on wood supply and increased road inventories and costs (Addison 1978).  Government needed to resolve this apparent conflict over use of old  forests and research was initiated to determine: if old forests are essential for deer, when young forests begin to be suitable habitat, and how deer integrate habitat dispersion (Addison 1978). Attention toward old forests as winter habitat for black-tailed deer resulted from a series of mechanistic studies on various habitat components such as:  the amounts (Harestad 1979, Vales 1986) and quality (Rochelle  1980) of rooted forage, the abundance of arboreal lichens (Stevenson 1978), the ability of forest canopies to intercept snow (Kirchhoff and Schoen 1987, McNay et al. 1988), the rates of shrub burial by snow (Jones 1975, Harestad 1979), the depths to which deer sink in snow, and the energetic costs of moving in snow (Bunnell et al. 1990a,b).  The knowledge resulting from these  studies led to predictions about habitat values which I accepted in the  2 early stages of my research for the BC government; old forests were best for deer and use of young forests would be for only brief periods and limited to areas of low snowfall (Bunnell 1985, Hanley et al. 1989). Specifically, I used these habitat values in association with optimization theory (Schoener 1987) to study habitat use by radio-collared, black-tailed deer.  Optimization theory leads to the prediction, for  example, that individual radio-collared deer should choose habitats that maximize their chance for survival and/or reproduction.  Furthermore,  Fretwell (1972) extended optimization theory to propose that animals would fill habitats in direct relation to their value provided they had perfect knowledge about habitats and were free to choose the best.  This theoretical  construct is referred to as the “ideal-free” distribution of animals (Fretwell and Lucas 1970).  On the basis of the ideal-free distribution, I  expected to observe most deer in the best habitats and to observe individual, radio-collared deer using habitats in direct association with their apparent value.  After an initial period of research, however, I  considered some deer regularly made choices considered suboptimal under ideal-free distribution theory.  Suboptimal choices would have been expected  if the area was heavily populated by deer and optimal habitats were filled (Pulliam 1989) but, as stated above, deer populations were considered to be declining.  I then proposed black-tailed deer may not be distributed in an  ideal-free manner and this became the major hypothesis or thesis of study. One reason I considered the ideal-free hypothesis not to hold was because, in the event of rapid environmental change (e.g., disturbances from wildfire or logging), individual deer are unlikely to be aware of all post disturbance habitat choices (i.e., learning lags behind the pace of these environmental changes).  Choice of habitat, in such cases, may be  3 constrained by factors other than those typically related to survival and/or reproduction (Schoener 1987).  Whether or not deer use habitats in the  ideal -free manner is not trivial.  For example, Fagen (1988) assumed the  ideal-free distribution in demonstrating the effect of logging on deer populations in Alaska.  Hobbs and Hanley (1990) provided strong rationale in  opposition to Fagen’s assumption and, consequently, in his conclusions about habitat values based on habitat use/availability statistics.  If the ideal-  free distribution is constrained, animals may choose habitats for reasons other than their value in which case use/availability observations may serve only to undermine knowledge gained from more mechanistic studies.  Empirical  observations and tests concerning the ideal-free distribution of large ungulates are lacking and I considered it necessary to resolve this before I could make sound recommendations for the future management of habitat for black-tailed deer. I used estimated locations and movements to indicate specific habitat choices by individual deer.  I considered these choices in context of a  hierarchical resource acquisition process (Johnson 1980, Senft et al. 1987, Levin 1992) which I proposed would expose a major constraint on the idealfree distribution of black-tailed deer.  I evaluated potential effects of  this hierarchical process on habitat choices by investigating the nature of decision making by individual deer and how this decision making interacted with specific physiographic characteristics (elevation, aspects, and climate) of 4 study areas on Vancouver Island.  I also designed and  evaluated the effects of 2 large logging manipulations to test my propositions about the effects of hierarchical decision making on the ideal free distribution (i.e., that habitat choices can be constrained by factors other than habitat value).  Finally, I used estimates of survival rates and  4 mortality causes to make inferences regarding the implications of such constraints on deer populations.  THESIS STRUCTURE This thesis consists of a series of papers, 6 after this general introduction.  In the first paper (chapter 2), I evaluate the statistical  property of independence in the basic data which were deer locations in spatial and temporal dimensions.  Recently, Swihart and Slade (1985) warned  of potential bias and improper power-of-test calculations due to the collection of redundant or dependent animal location data.  A contrasting  view, however, is that few observations may reveal decidedly less information than many observations (Reynolds and Laundre 1990).  My  evaluation of independence was critical because deer locations were fundamental in testing all hypotheses about movement decisions, habitat choices, and response to habitat changes. The second paper (chapter 3) is a report of my investigation to determine the hierarchy of movements made by deer without regard to the habitats being used or the environmental parameters that may have caused the moves.  I considered that simple patterns in movements (distance, frequency,  timing, and direction of travel) would reveal a hierarchy of decisions (i.e., subclasses of decisions nested within superciasses) which, in itself, implies that superclass decisions may constrain subclass decisions (Dawkins 1976, Senft et al. 1987, Levin 1992).  The issue of constraint is brought  into deeper ecological significance in the third paper (chapter 4) where I use the hierarchy established in chapter 3 as a framework for organizing the investigation of habitat preferences. In the fourth paper (chapter 5), I assess the response of individual  5 deer to logging of their winter habitat.  This investigation specifically  addresses the assumption of the ideal-free distribution by imposing a prominent change in habitat on individual deer.  I considered that a  consistency in habitat use (before and after disturbance) would support the ideal-free hypothesis.  Alternatively, fidelity to winter range sites, in  the event of this extreme disturbance, would support the notion that hierarchical decision making constrains the ideal-free distribution of deer. In the fifth paper (chapter 6), I investigate survival rates and mortality causes especially as they relate to conclusions about movement and habitat use decisions considered in previous chapters and the sixth paper (chapter 7) contains a general discussion of results and a summary of conclusions specifically reported in the context of applied management. Chapter 8 is a more general summary of conclusions.  LITERATURE CITED Addison, R. B. 1978. Research needs for integrated management of timber, deer, and elk in the Vancouver Forest district Report on Phase 1. Special Studies Div., British Columbia Minist. of For., Victoria. 25pp. --  Bunnell, F. L. 1985. or cooperation.  Forestry and black-tailed deer: conflicts, crises, For. Chron. 61:180-184.  F. W. Hovey, R. S. McNay, and K. L. Parker. 1990a. Forest cover, snow conditions, and black-tailed deer sinking depths. Can. J. Zool. 68: 2403-2408. K. L. Parker, R. S. McNay, and F. W. 1-lovey. 1990b. Sinking depths of black-tailed deer in snow, and their indices. Can. J. Zoo]. 68:917-922. Dawkins, R. 1976. Hierarchical organisation: a candidate principle for ethology. Pages 7-53 in P. P. G. Bateson and R. A. Hinde, eds. Growing points in ethology. Cambridge Univ. Press, Cambridge, England. Fagen, R. 1988. Population effects of habitat change: a quantitative assessment. J. Wildi. Manage. 52:41-46.  6 Fretwell, S. D. 1972. Populations in a seasonal environment. Univ. Press, Princeton, N.J. 217pp.  Princeton  and H. L. Lucas, Jr. 1970. On territorial behaviour and other factors influencing habitat distribution in birds. I. Theoretical development. Acta Biotheoretica 19:16-36. Hanley, T. A., C. T. Robbins, and D. E. Spalinger. 1989. Forest habitats and the nutritional ecology of Sitka black-tailed deer: a research synthesis with implications for forest management. USDA For. Serv. Gen. Tech. Rep. PNW-230. Portland, Oreg. 52pp. Harestad, A. S. 1979. Seasonal movements of black-tailed deer on northern Vancouver Island. Ph.D. Thesis, Univ. of British Columbia, Vancouver. 98pp. J. A. Rochelle, and F. L. Bunnell. 1982. Old-growth forests and black-tailed deer on Vancouver Island. Trans. North Am. Wildl. Nat. Resour. Conf. 47:343-352. Hobbs, N. T., and T. A. Hanley. 1990. Habitat evaluation: do use/ availability data reflect carrying capacity? J. Wildi. Manage. 54:515-522. Johnson, D. H. 1980. The comparison of usage and availability measurements for evaluating resource preference. Ecology 61:65-71. Jones, G. W. 1975. Aspects of the winter ecology of black-tailed deer (Odocoileus hemionus columbianus Richardson) on northern Vancouver Island. M.S. Thesis, Univ. of British Columbia, Vancouver. 78pp. and B. Mason. 1983. Relationships among wolves, hunting, and population trends of black-tailed deer in the Nimpkish alley on Vancouver Island. British Columbia Minist. of Environ. Wildl. Rep. R 7. Victoria. 26pp. Kirchhoff, M. D., and J. W. Schoen. 1987. Forest cover and snow: implications for deer habitat in southeast Alaska. J. Wildl. Manage. 51:28-33. Levin, R. V. 1992. 73: 1943-1967.  The problem of pattern and scale in ecology.  Ecology  McNay, R. S., L. D. Peterson, and J. B. Nyberg. 1988. The influence of forest stand characteristics on snow interception in the coastal forests of British Columbia. Can. J. For. Res. 18:566-573. Pulliam, H. R. 1989. Individual behaviour and the procurement of essential resources. Pages 25-38 in J. Roughgarden, R. May, and S. Levin, eds. Perspectives in ecological theory. Princeton Univ. Press, Princeton, N.J. Reynolds, T. D., and J. W. Laundre. 1990. Time intervals for estimating pronghorn and coyote home ranges and daily movements. J. Wildi.  7 Manage. 54:316-322. Rochelle, J. A. 1980. The role of mature conifer forests in the winter nutrition of black-tailed deer. Ph.D. Thesis, Univ. of British Columbia, Vancouver. 295pp. Schoen, J. W., 0. C. Walimo, and M. D. Kirchhoff. 1981. Wildlife forest relationships: is a reevaluation of old growth necessary? Trans. North Am. Wildl. Nat. Resourc. Conf. 46:531-545. Schoener, T. W. 1987. A brief history of optimal foraging ecology. Pages 5-68 in A. C. Kainil, J. R. Krebs, and H. R. Pulliam, eds. Foraging behaviour. Plenum Press, N.Y. Senft, R. L., M. B. Coughenour, D. W. Bailey, L. R. Rittenhouse, 0. E. Sala, and D. M. Swift. 1987. Large herbivore foraging and ecological hierarchies. Bioscience 37:789-799. Stevenson, S. K. 1978. Distribution and abundance of arboreal lichens and their use as forage by black-tailed deer. M.S. Thesis, Univ. of British Columbia, Vancouver. 148pp. Swihart, R. K., and N. A. Slade. 1985. observations in animal movements.  Testing for independence of Ecology 66:1176-1184.  Vales, D. J. 1986. Functional relationships between salal understory and forest overstory. M.Sc. Thesis, Univ. of British Columbia, Vancouver. 164pp.  8 CHAPTER 2  -  INDEPENDENCE OF OBSERVATIONS IN MOVEMENTS’  The problem of temporal dependence of animal locations has been treated by Slade and Swihart (1983), Swihart and Slade (1985a,b; 1986), and Swihart et al. 1988.  Temporal independence of observations is important to  home-range size estimations because most parametric estimators require animal locations to be independent random samples (Ackerman et al. 1990). Home ranges will be consistently underestimated (biased) if based on dependent location observations (Dunn and Gipson 1977, Schoener 1981, Slade and Swihart 1983).  Swihart and Slade (1985b) documented a strong inverse  relationship between estimates of home-range size and the degree of dependence between location observations.  Further, because dependent data  contain redundant information, less information is available in dependent datasets compared with independent datasets of an equal size (Swihart and Slade 1985b).  Consequently, dependent data are likely to produce biased  estimates even for nonstatistical measures. The central issue, however, is independence of observations in inferential statistics.  Data are independent when the current observation  (e.g., position at the current point in time t) is not a function of the last observation (e.g., position at some time interval k previous to the current time t).  Alternatively, the variance between consecutive  observations is proportional to the overall variance (von Neumann 1941). Consequently, if observations are independent, each observation contributes similarly to the overall estimate of population parameters. While tests for independence of observations are known for data with 1 dimension (see Box and Jenkins 1976), they are relatively unknown for data 1  Published as: McNay, R. S., J. A. Morgan, and F. L. Bunnell. 1994. Characterizing independence of observations in movements of Columbian black tailed deer. J. Wildi. Manage. 58:422-429.  9 with more than 1 dimension such as animal location data (Schoener 1981) that usually are expressed as x and y spatial coordinates.  A second important  distinction of location data is that ordering of the sampled dataset is through a third dimension, time.  Location data can, but do not necessarily  have to, represent a rate of travel.  Additionally, location data can be  presented in 1 dimension, an example being the distance between consecutive locations (Fitch 1958).  Even though distance between consecutive locations  represents only 1 dimension it still is intimately connected to time and is a rate variable.  Reynolds and Laundre (1990) found, however, that increases  in the time interval between observations leads to poorer information about the true distance travelled during the interval. Swihart and Slade (1985a) examined Schoener’s Ratio statistic (1981) as a potentially useful measurement of independence when observations involve 2 spatial dimensions.  Also, Schoener (1981) suggested the ratio may  help determine the number of samples necessary for parametric estimation of home-range size.  Subsequent to testing Schoener’s Ratio, Swihart and Slade  (1985a) suggested further uses of the statistic to (1) determine the time interval necessary to obtain independent sample observations, (2) identify shifts or patterns in the use of space, and (3) make comparisons of the rate at which different animals use space. Since 1985, however, there has been little use of Schoener’s Ratio in the manner intended by Swihart and Slade.  Hoizenbein and Marchinton (1992)  used Schoener’s Ratio to assess independence of observations of white-tailed deer (0. virginianus) locations but presented no documentation of results. They assumed 4 hr, or greater, between observations to be sufficient for a deer to move to any point in its home range.  Other researchers demonstrated  loss of biological information when using only those animal locations that  10 were judged to be independent by Schoener’s Ratio (Reynolds and Laundre Finally, Kremsater and Bunnell (1992), while recognizing the  1990).  importance of independence of observations, developed alternative techniques (to Schoener’s Ratio) to address specific questions about deer use of landscape mosaics.  Kremsater and Bunnell (1992) also argued against testing  for independence when location data are to be used for assessing conditional probabilities of decision making. The lack of use of Schoener’s Ratio despite compelling arguments of Swihart and Slade (1985a) prompted us to evaluate independence in our observations of black-tailed deer locations.  Our primary interest was the  application of Schoener’s Ratio when observations included movements of migratory deer.  We also wanted to compare assessments of independence  between the 2 related measures of animal movements: (1) animal locations in space and (2) distance moved between consecutive locations.  Our specific  objectives were to: (1) evaluate independence in observations of blacktailed deer locations using Schoener’s Ratio;  (2) assess the influence of  migrations on Schoener’s Ratio; and (3) evaluate independence in observations of distance between consecutive locations.  METHODS Deer Location Samples We obtained location estimates for a sample of radio-collared deer monitored for another study (McNay and Doyle 1990) at 4 sites on Vancouver Island, British Columbia.  We used triangulation (White and Garrott 1990) to  locate deer with no less than 3 bearings recorded at separate and permanent stations marked at 100-rn intervals along roads.  Bearings for an individual  deer location were usually collected in <10 mm  at sites that were line-of-  11 sight with, and close to (<400 m), the transmitter being monitored. We located deer from January 1982 to June 1984 on an ad hoc schedule that generally resulted in each deer being located once per week.  After  June 1984 until project completion at June 1991, sampling was standardized so that, during a calendar month, each deer was located at least once per week and once within each quarter of a calendar day.  At specific times  (usually once per month) we established sessions of comparatively more intensive monitoring; sampling was increased to once every 2 hr for predetermined periods (usually from 3 to 5 days). We estimated final deer locations by 2 different techniques.  During  initial years of study, we plotted triangulation data and determined the location as the centroid of the polygon that resulted from overlapping bearings (Hupp and Ratti 1983).  In 1984 and subsequent years, bearing  information was retained and analyzed with the maximum likelihood estimator presented by Lenth (1981).  We modified a SAS program (SAS Inst. Inc. 1985)  by White and Garrott (1990:64) to accept bearings from any 3- to 5-sampling stations of known Universal Transverse Mercator grid coordinates. resultant  ’ 2 x  Using the  goodness-of-fit test for all bearings contributing to each  location and the location’s 95% error ellipse size, we made a final judgement on the quality of individual locations a posteriori.  If the  probability of observing poorer goodness-of-fit than that calculated was <0.10, the location estimate was considered poor.  We made exceptions when  the bearing set was collected for a location close to the observer’s location (antenna-to-animal distance <100 m).  In cases of poor bearing fit,  proximity was identified by error ellipses <1 ha.  We plotted all locations  (Borland Tnt. Inc. 1992) as a check against field data to identify any recording or coding errors.  12 Analytical Techniques We iteratively sampled data to construct 5 individual datasets (Swihart and Slade 1985a).  The first 2 datasets were constructed using data  collected during intensive monitoring sessions while the last 3 datasets included data collected on a weekly basis.  First, we used all data  collected during intensive monitoring sessions.  Second, we omitted any  intensive monitoring data for which the time interval between samples was <4 hr.  Third, we omitted data if the time interval between samples was <1 day.  Fourth, we disallowed time intervals <17 days and, fifth time intervals <38 days.  In the latter 3 datasets we wanted to obtain average sampling  intervals of approximately 1, 3, and 6 wk respectively.  Henceforth, we will  refer to these datasets as 2-hour, 4-hour, weekly, 3-week, and 6-week. We calculated Schoener’s Ratio (Schoener 1981) for each deer-session (2- and 4-hour datasets) and for each deer (weekly, 3-, and 6-week datasets).  We calculated the critical value of Schoener’s Ratio using  methods of Swihart and Slade (1985a,b) to test the null hypothesis that deer locations were independent.  We chose a  =  0.25 (Swihart and Slade 1986)  unless stated otherwise. We used 2 methods to examine the influence of migrations using the weekly, 3-, and 6-week datasets.  First, to identify the effect that each  location had on the overall statistic, we recalculated Schoener’s Ratio each time a new location was added to the dataset.  Second, we omitted migratory  movements and calculated Schoener’s Ratio for each spatially exclusive, seasonal home range.  We determined migratory movements by visual inspection  of chronological location plots for each deer.  Inspection of each movement  allowed us to identify those composing regular trips with predictable return moves (Sinclair 1984).  13 To examine independence of distances between consecutive locations, we measured the straight-line distance from the last location to the current location.  We first looked for indications that data reflected a rate of  travel by plotting distance between consecutive locations against time between consecutive locations and by testing for linear trends using correlation analysis (SAS Inst. Inc. 1985).  Secondly, we used the mean  square successive difference test, alternatively known as the V statistic (von Neumann et al. 1941), to evaluate independence in observations.  RESULTS We monitored 44 resident and 28 migratory deer for 253 deer-years and 12,572 locations.  We sampled 42 of those deer during 24 intensive  monitoring sessions for a total of 133 deer-sessions. sampled during each intensive monitoring session. accounted for 4,039 of the locations.  Not all deer were  Intensive monitoring  Complete bearing information was  available for 9,234 of the locations and with those data we calculated goodness-of-fit and error ellipse sizes for each location estimate.  Two  percent of the locations were generated from bearings with poor goodness-offit  2 (x  P  0.10) and large error ellipses (>1.0 ha).  had a 95% error ellipse of <1 ha  (  =  0.98 ha, SD  Generally, locations  6.5; n  =  12,103).  The average time interval between samples was 2.0 hr (SD 3,905) in the 2-hour dataset, and 5.4 hr (SD dataset. 10.3; n  2.2; n  =  =  1,613) in the 4-hour  Time intervals between less intensive samples were 8.1 days (SD =  8,464) in the weekly dataset, 24.6 days (SD  the 3-week dataset, and 46.4 days (SD dataset.  =  1.6; n  =  =  16.9; n  =  =  14.2; n  =  =  2,865) in  1,506) in the 6-week  14 Independence of Location Observations The hypothesis of independence was rejected (P  <  0.25) for most deer  location datasets regardless of the time interval between locations (Table 2.1), especially for deer that migrated.  With a 1-week time interval  between locations, none of the datasets for migratory deer were independent, but 10 and 18% of the datasets were independent at the 4-hour and 6-week intervals, respectively.  The highest percentage of independent datasets  (41% or 18 of 44 deer) came from resident deer with 6 wk between location samples.  Effect of Migrations on Independence of Locations Migratory movements affected Schoener’s Ratio.  The first occurrence  of a migration in each dataset caused Schoener’s Ratio to drop indicating lack of independence.  Subsequent migrations, however, were largely  undetectable (Fig. 2.1).  Migrations were not the only movements that led to  rejection of independence although they were the most conspicuous.  Datasets  for resident deer (or for migratory deer within a seasonal range) also became dependent if the deer moved to a unique place at the periphery of its range.  Alternatively, in cases where no migrations or outlier locations  were recorded, results of Schoener’s Ratio tests often oscillated between independence and dependence in an unpredictable pattern (Fig. 2.2).  Only 5  of 345 tests revealed datasets that were judged independent throughout the data collection period. When we removed migratory movements and recalculated Schoener’s Ratio from spatially distinct seasonal ranges, the independence statistic improved (Table 2.1).  In more than half of the seasonal range datasets (26 seasonal  ranges of 48) we did not reject (P  >  0.25) independence of location  behaviour  type  Migratory Migratory  Total  Seasonal  Migratory Migratory  Total  Seasonal  42 (62)  45 (71)  6 (62)  7 (71)  2-hour  82 (61)  61 (70)  10 (61)  4 (70)  4-hour  34 (56)  25 (28)  7 (44)  21 (56)  0 (28)  9 (44)  Weekly  50 (48)  57 (28)  43 (44)  54 (48)  7 (28)  27 (44)  3-week  % independent ranges in time interval  56 (41)  68 (28)  54 (44)  49 (41)  18 (28)  41 (44)  6-week  Two tests were used: (1) Schoener’s Ratio (Schoener 1981) tests independence of observations in 2dimensional space, and (2) von Neumann’s V (von Neumann et al. 1941) tests independence of observations of distances between consecutive locations.  a  Resident  Total  von Neumann’s V Test  Resident  Total  Schoener’s Ratio Test  Deer  Range  Table 2.1. Percentage of total, or seasonal, black-tailed deer ranges where the hypothesis of independencea of location observations was not rejected. Values in parentheses are the number of total, or seasonal, home ranges tested from data collected during 1982 to 1991 on Vancouver Island, British Columbia.  16  3.5 Calculated value 3 0  Critical value  2.5  A)  Cu  U)  2  I  a)  0  1.5  .  0  (1)  1 0.5 I  I  I  I  I  I  I  4,000 denotes migration  -  12,000  1  .210,000  II 1) I II 0  8,000  g  :1 I II II ‘I ‘I 0  6,000  • 4,000 C)  ,‘  I  i\ I  , II  II II I’  01 0 0  II I I I ,I ,I II I) II I •I II 0 I II I  II II I  (  •  •  ‘ I  — .L.1  B)  ,e  I  I I  ‘I II I II II II 0  2,00:  ,  ‘  h  — ..  —  r  i  —  MAMJJASONDJFMAMJJA Month Figure 2.1 A chronological plot of Schoener’s (1981) Ratio calculations (top) and distance between consecutive locations (bottom) from location observations collected weekly between 1982 and 1991 on a radio-collared, black-tailed deer (#NRC13402) at Nanainio River on Vancouver Island, British Columbia.  17  2.8 Calculated value  2.6 0 Cu  Critical value  2.4  A)  2.2 2  0  1.8  0  Cl)  1.6  I I I I  1.4 1.2  —I  I —S  1,600 1,400  —I  1,200  —i  BI  .1  1,000  —  I I  800  • I  600  —  II  1 jI  •i •t  Ill  400 —  200  II II II it ii I I II  —  —  •  Sp • III lgI II • It t • I I... S I  St gI 4gI  • • J S  — I  I  I S  i I’ I  a •I •i t SI 51 •I  •l  I% 4 I . I I • I  titi lbI g ii ,I I,%II ISi  ii I I II I % I  • I  • I,  %j  I I  I— I  I  I  I  I  I  AMJJASONDJFMAMJJAS Month Figure 2.2 A chronological plot of Schoener’s (1981) Ratio calculations (top) and distance between consecutive locations (bottom) from location observations collected weekly between 1982 and 1991 on a radio-collared, black-tailed deer (#N1M12901) at Nimpkish River on Vancouver Island, British Columbia.  18 observations when the time interval between observations was 3 wk (Table 2.1).  Independence of Distance Between Consecutive Locations Distance between consecutive locations was poorly associated with time between those locations (r  0.26; n  =  =  12,437; P  =  0.0001).  At most time  intervals, except the shortest (intensive monitoring), deer travelled a wide range of distances from 0 to 14 km (Fig. 2.3). Von Neumann’s V indicated more consistent independence among observations of distances than was achieved among spatial locations (Table 2.1).  We did not reject (P  >  0.25) independence in 82% of the intensive  monitoring sessions recorded for migratory deer using 4 hr between locations (50 of 61 deer-sessions).  The weekly dataset showed the poorest percentage  of independent datasets (7 to 34%).  There was no improvement in going from  a 3-week time interval to a 6-week time interval nor did eliminating migrations improve independence of data collected on migratory deer (Table 2.1).  DISCUSSION Most of our datasets on black-tailed deer were composed of statistically dependent observations (Table 2.1).  If our objectives were to  measure the amount of space used by deer, or the average distance travelled by deer, we would conclude that our estimates would likely be biased low (Schoener 1981, Swihart and Slade 1985b).  The bias would be the result of  the sample containing redundant observations. Migrations led to dependence in datasets for migratory black-tailed deer because they indicated relatively infrequent moves to different sites.  19  15,000 0  14,000 •  12,000  13,000  10,000  12,000  8,000  0  6,000  0  0 0 0  8  8 00  -  .2 .10,000  0  08  4,000  0  2,000  8  9,000 O°2  4°6-’  8,000  g  7,000  C  I  •.  ::: :  3,000 2,000  .  ——_.•_-‘  1 000 0  .  •*  •  .  — .  !_‘II..i..I.r:.I.II  0  28  .  .  56  I  84  II•  I  112  I  I  140  I  I  i  168  i 1  1 i  196  i  Ii I  224  252  Time between consecutive locations (days) Figure 2.3 Scatter plot of distance (m) and time (days) between consecutive observations of individual, radio-collared, black-tailed deer location estimates observed from 1982 to 1991 on Vancouver Island, British Columbia. Inset shows detail of the relationship at lower axis positions. Location estimates were derived by maximum likelihood estimation (Lenth 1981) and distance was the straight-line distance.  20 Such movements expanded the overall variance in one, or both, spatial coordinates and hence, daily use of sites within a seasonal range became comparatively redundant once a migration was made.  In this respect  Schoener’s Ratio performed well as a measure of a significant, first-time change in the use of space.  Because the statistic is calculated from  average deviations, however, subsequent changes in use of space went undetected (Fig. 2.1). Although our observations of distance between consecutive locations had marginally less dependence, we found similar results to those in our investigation of how deer use 2-dimensional space.  Again, in at least half  the cases, black-tailed deer infrequently made larger than normal moves, most of which were migrations.  Those large moves effectively caused the  more common moves (generally <250 m) to become comparatively redundant. The conclusion of dependence in both of the above cases, however, is based on a little mentioned, yet important, assumption of the 2 analytical techniques.  Both tests require normal data distributions because they are  calculated from average deviation of the samples (von Neumann 1941, Schoener 1981).  Application of the tests in circumstances of skewed data  distributions could lead to an apparent lack of independence even when independence is achieved.  Swihart and Slade (1985a,b) were careful to  ensure their constructed datasets came from a normal distribution.  When  they dealt with data collected from real observations, Swihart et al. (1988) dropped, from their calculations, any dataset in which the animal shifted its activity centre.  Swihart and Slade (1985b) alluded to this effect of  migrations by indicating that temporal rhythmicity in movements may reduce likelihood of independence.  We concur, noting that migrations, or other  temporal rhythmicity, would lead to non-normality and hence an apparent lack  21 of independence. Normality of location data is influenced by temporal use of space (or distance travelled).  If velocity was always constant, then distance  measurements at specific time intervals would tend to be normally distributed.  Movements, however, are the essence of the behaviour of mobile  animals and animals may choose to move at a variety of rates from running to no movement at all.  It is unlikely that movements could ever be expected to  be normally distributed.  Lack of normality in our data was most  conspicuously caused by migrations that could occur within the time interval between most location sampling (Fig. 2.3).  In fact, with the exception of  location sampling during intensive monitoring, deer had time to travel anywhere in their home ranges (i.e., our distance measurements were not likely to be indicative of any specific rate of travel).  We concluded that  because variation in behaviour contributed to the lack of a normal distribution in location observations, it impaired our ability to find an appropriate sampling interval for black-tailed deer and led to an apparent dependency in the data.  Had all our data been more indicative of a rate of  travel (e.g., the 2- and 4-hour datasets; Fig. 2.3, inset) the arbitrary culling of data (see Methods) should have produced better tests of independence and likely less dependence between samples.  MANAGEMENT IMPLICATIONS We concluded that testing our location data for independence employed techniques that were not robust to skewed data distributions that can be caused by migrations between seasonal ranges.  To avoid apparent dependence  of observations, we would have had to disregard about 90% of our data to end up with an average of 8 locations/deer-year.  Doing so would have resulted  22 in samples sizes below that required for many analyses and would have eliminated information about the dynamic manner in which black-tailed deer use space. In an operational sense, the primary concern about independence should focus on whether an animal has had time to move to any location within its home range before the next observation is taken (Lair 1987).  That was the  interpretation adopted by Holzenbein and Marchinton (1992) when they chose a sampling interval of 4 hr for their observations of white-tailed deer. White and Qarrott (1990:148) expanded on that principle by suggesting the real issue was to properly sample the time interval over which an estimate is to apply.  A systematic sample over specific time periods eliminates the  effects of bias due to redundant data (White and Garrott 1990:148) but still inflates n causing variance to be underestimated.  Biased variance, however,  would be of little concern in home range estimates because it is never calculated for a single home range. Furthermore, choosing appropriate time intervals to sample animal locations appears to be more a problem of study objectives than of statistical independence, provided samples are obtained systematically. Lair (1987) observed that minimum time intervals to statistical independence can be long enough to preclude investigations of home-range characteristics. While samples taken close together in time may be redundant statistically, they reveal comparatively better behaviourial information on use of space than samples taken farther apart in time (Lair 1987, Reynolds and Laundre 1990).  In this study, black-tailed deer were observed to travel even the  largest distances in less time than our sampling intervals in all but the intensive monitoring (Fig. 2.3).  For that reason we concluded that our  weekly observations were likely to have biological independence (Lair 1987)  23 even though they may be declared statistically dependent by the tests we used.  We regarded the datasets to have apparent dependence rather than  actual dependence because the data violated the assumption of normality required for the independence tests. We recommend that investigators strive to achieve biological independence in systematic observations of animal movements rather than passing the criteria of statistical tests that assume normality.  Data  distributions of animal movements will rarely follow a normal distribution because movements reflect behaviourial decisions.  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On finding the source of a signal.  Technometrics  McNay, R. S., and D. D. Doyle. 1990. The Integrated Wildlife-Intensive Forestry Research (IWIFR) program deer project. Northwest Environ. J. 6:389-390. Reynolds, T. D., and J. W. Laundre. 1990. Time intervals for estimating pronghorn and coyote home ranges and daily movements. J. Wildl. Manage. 54:316-322. Rossi, R. E., D. J. Mulla, A. G. Journel, and E. H. Franz. 1992. Geostatistical tools for modelling and interpreting ecological spatial dependence. Ecol. Monogr. 62:277-314. SAS Inst. Inc. 1985. SAS user’s guide: basics, version 5 edition. Institute Inc., Cary, N.C. 584pp. Schoener, T. W. 1981. An empirically based estimate of home range. Pop. Biol. 20:281-325.  SAS Theor.  Sinclair, A. R. E. 1984. The function of distance movements in vertebrates. Pages 240-258 in I. R. Swingland and P. J. Greenwood, eds. The ecology of animal movement. Claredon Press, Oxford, England. Slade, N. A., and R. K. Swihart. 1983. Home range indices for the hispid cotton rat (Sigmodon hispidus) in northeastern Kansas. J. Mammal. 64:580-590. Swihart, R. K., and N. A. Slade. 1985a. Testing for independence of observations in animal movements. Ecology 66:1176-1184. and 1985b. Influence of sampling interval on estimates of home-range size. J. Wildl. Manage. 49:1019-1025. .  and 1986. The importance of statistical power when testing for independence in animal movements. Ecology 67:255-258. .  25 and B. J. Bergstrom. 1988. Relating body size to the rate of home range use in mammals. Ecology 69:393-399. Tukey, J. 1977. Exploratory data analysis. Mass. 688pp.  Addison-Wesley,  Reading,  von Neumann, J. 1941. Distribution of the ratio of the mean square successive difference to the variance. Ann. Math. Stat. 12:367-395. R. H. Kent, H. R. Bellinson, and B. I. Hart. 1941. successive difference. Ann. Math. Stat. 12:153-162.  The mean square  White, G. C., and R. A. Garrott. 1990. Analysis of wildlife radio-tracking data. Academic Press, San Diego, Calif. 383pp.  26 CHAPTER 3  -  SPATIAL AND TEMPORAL SCALES OF RESOURCE USE:  We consider aspects of the general question:  MOVEMENTS  are patterns of resource  use products of an hierarchically-structured decision process?  We used  movements as a device for questioning because no point in space provides all necessary resources (Greenwood and Swingland 1984) and resources themselves are dynamic, often through temporally specific events (Van Home et al. 1988).  Movements are not only the mechanism for resource use but reflect  both specific decisions and broad tactics. Generally, studies of optimization in ecology have sought to understand evolution and processes of natural selection (Maynard-Smith 1978, Mayr 1983, Stevens and Krebs 1986, Pulliam 1989).  More specifically,  organisms use resources, allocate energy gained to specific life-functions, and accumulate a surplus of energy to use in procreation.  Organisms that  accomplish that best (optimally) are regarded the most fit by evidence of their survival and reproductive output (Schoener 1971).  Studies on optimal  use of resources are, however, not without criticism (Gould and Lewontin 1979, Zach and Smith 1981, Bunnell and Gillinghani 1985, Ollason 1987) and many lack precise quantitative support (Pyke 1984).  Also, some applications  of the theory have been problematic (Westoby 1974, Owen-Smith and Novellie 1981) especially when considering optimal choice of forage patches rather than specific diet choices (Schoener 1987). We concur with Schoener (1987) that extending optimal diet formulations to patch selection, or to non-laboratory conditions (Zach and Smith 1981), is not straight forward.  The concept of optimality, however,  remains an appealing basis for evaluating resource selection.  Recently,  interest in optimality theory has been restored by qualifying classical foraging models (e.g., MacArthur and Pianka 1966, Charnov 1976) within the  27 framework of constraints external to diet, thereby extending theory across several scales of ecological resolution (McNamara and Houston 1980, Real and Caraco 1986, Mangel and Clark 1986, McNamara and Houston 1986, Houston et a!. 1988, Huston et a!. 1988, Rosenzweig 1991).  Foraging decisions can be  set within the context of different ecological goals, the relative importance of which may vary among different ecological scales (Dawkins 1976, Pierce and Ollason 1987, Senft et a!. 1987, Orians and Wittenberger 1991).  Placing observations of foraging decisions within the context of  other, perhaps more general, scale-determined decisions helps refine conclusions about optimality of resource use (e.g., Orians and Wittenberger 1991). Scale-determined decisions may invoke a nested, functional hierarchy with its attendant criteria of containment and constraint of scalar activities (Dawkins 1976, Allen et al. 1984).  Activities are scalar if  measurements of the phenomenon can be isolated into distinct classes along some dimension, with isolation often achieved by activity rate changes (Wiens 1976, Senft et a!. 1987, O’Neill et al. 1989, Pulliam 1989). Movement for many terrestrial vertebrates is scalar because it can be isolated into several velocity-based classes: walking, or running.  resting (stationary),  Furthermore, these scalar classes of movement are  hierarchical if we consider the classes as immobility or mobility (with walking and running as subclasses of mobility).  Describing movements in  this way, we invoke a nested hierarchy because subclasses (walking and running) are contained within superciasses (mobility) (Dawkins 1976). Constraint in hierarchies is more generally referred to as information transfer among scales (Gass 1985, Levin 1992) and is identified differently depending on 2 opposing perspectives; 1 from the bottom of the hierarchy  28 looking up, the other from the top looking down.  Upward transfer of  information occurs when subclass activities define initiating conditions for superclass activities and downward transfer of information occurs when superclasses constrain the range of activities within subclasses (Dawkins 1976, Senft et al. 1987, O’Neill et al. 1988, Powell 1989, Levin 1992). A shift in perspective from simple rate of movement to factors instigating movement (e.g., resource use) can potentially provide an example of a functional hierarchy.  Senft et a!. (1987) provided compelling  arguments for considering resource use by large ungulates to have hierarchical function in time and space.  Such consideration implies a  different meaning ecologically than if resource use was hierarchial only by classification or was simply a scalar activity with no hierarchy.  In the  example of Senft et a!. (1987: Table 1), diets provide the basis upon which feeding areas are chosen, which in turn provide the basis for home range establishment.  Alternatively, home ranges constrain the availability of  feeding areas, which in turn constrain diets.  Implicit in this example is  that foraging (resource use) goals have varying degrees of importance among scales and that information is transferred to help form scale-dependent decisions about resource use.  Key aspects of each goal are:  for what  duration and to which specific locations does any particular decision commit the individual (Orians and Wittenberger 1991) and how quickly are animals able to adapt their decisions to new resource conditions (Gass 1985)? Levin (1992) proposed that, to understand natural systems efficiently, the examination of scales should be focused on “how information is transferred from one [class of a scalar activity] to another”.  Gass (1985)  considered such communication links to be key in understanding the ability of organisms to function efficiently.  Dawkins (1976) viewed transfer of  29 information among classes to be the basic characteristic of hierarchical function.  Allen et al. (1984) proposed that “the inter[class] relations in  hierarchical organization of complex systems, in part, explains why such systems usually total more than the sum of their parts”. We examined movements made by black-tailed deer assuming their movements indicated decisions about resource use.  Resources sought are  typically considered to include: 1) nutritious forage (Gates 1968, Rochelle 1980), cover for hiding from predators (Kufeld et al. 1988), cover from extremes in thermal environments (Parker 1988), cover from deep snow during winter (Bunnell and Jones 1984, Bunnell et al. 1990a,b), and access to mates and other family members (Hirth 1977, Bunnell and Harestad 1983, Hamlin and Mackie 1989).  We questioned whether resource use by deer followed the  hierarchical structure conceptualized by Senft et al. (1987) for large ungulates and, if so, what implications would hierarchical function have on optimization of resource use? Early studies on black-tailed deer were by direct observation and concerned local movement patterns in the context of foraging habits and diet choices (e.g., Cowan 1945).  Studies since the advent of radio-telemetry  were usually based on indirect observation and were primarily concerned with long movements and relative use of habitat patches (Sanderson 1966). McCullough (1985) classified movements of large terrestrial mammals as dispersal, nomadism, migration, and local movements but discussed only the first 3 classes independently of each other.  Our interest was in how  classes of movement were related or linked (Allen et al. 1984, Levin 1992) and the role of these linkages in the optimal use of resources. Specifically, we wanted to:  1) clarify the empirical justification for  scalar classes of movements described by others, 2) assess whether or not  30 the classes are hierarchical, and 3) identify if, and perhaps how, information is transferred among classes thereby assessing the existence of a functioning hierarchical decision process.  Clarifying the decision  process for movements is relevant to understanding resource choices in general (e.g., Orians and Wittenberger 1991), management of landscapes (O’Neill et al. 1988), and preservation of biodiversity (Franklin 1993).  STUDY AREAS We studied movements made by black-tailed deer at 4 locations on Vancouver Island, British Columbia (Table 3.1).  The Chemainus, Nanaimo, and  Nimpkish rivers are characterized by open, relatively flat-bottomed valleys (U-shaped) while Caycuse River ranges less in elevation but has steeper slopes and least area at lower elevations (V-shaped).  All study areas were  logged extensively by clearcutting resulting in habitats ranging from recently deforested (0- to 5-yr-old) to old (>250-yr-old) forests. Arrangement of habitats was typical of coastal logging with initial harvests coming from the bottom and downstream end of valleys and with subsequent harvests coming from the mid-slopes, from the headwaters, and most recently from higher elevations.  Our study areas were in a late stage of harvest  with most of the valley bottom in young (6- to 45-yr-old) forests and the mid-slopes deforested or in remnant patches of old forests. Climate in the Coastal Western Hemlock zone (Meidinger and Pojar 1991) on Vancouver Island is temperate and wet.  No month has a mean temperature  below 0 C and the mean temperature of the warmest month is 17 C.  In each  year there are usually 291 frost free days, an average of 820 mm of snow, and a mean precipitation of 2140 mm (Meidinger and Pojar 1991).  West Longitude  48°48’-124°30’ 48°56’-124°05’ 49°02’-124°12’ 50°08’-126°30’  Area  Caycuse River  Chemainus River  Nanaimo River  Nimpkish River 41  145  33  111  ) 2 (km  Area  43:32:12:12  29:50:16:04  06:82:10:02  200-1821  300-1541  300-1541  200-1249  (m asi)  Ratioa  44:36:20:00  Elevation  Forest Cover  315°  50°  125°  2700  Directionb  Drainage  Forest cover ratio is the percentage of study area in old (>250-yr-old), young (6- to 45-yr-old), recently deforested (0- to 5-yr-old), or subalpine habitats. b North is 0° and 360°.  a  North Latitude  Physical features of 4 study areas located on Vancouver Island, British Columbia.  Study  Table 3.1.  32 METHODS Deer Location Samples We presented details about catching, collaring, and monitoring deer in McNay et al. (1994).  We aged deer as fawns (0- to 1-yr-old), yearlings (1-  to 2-yr-old), or adults (>2-yr-old) at time of collaring by inspection of tooth wear and replacement (Robinette et al. 1957) and by body size and facial appearance.  About 25% of the ages were assessed and confirmed later  based on cementum annuli analysis (Thomas and Bandy 1973).  Briefly, we  monitored radio-collared deer at each study area by standard triangulation techniques (White and Garrott 1990).  Initially, we sampled deer locations  weekly on an ad hoc schedule but, in 1984, we standardized sampling so that, during a calendar month, each deer was monitored at least once-per-week and once within each quarter of a calendar day.  We also monitored deer more  intensively, once every 2 hr (2-hour), for 3- to 5-day periods during specific weather events at Nanaimo and Nimpkish rivers or once monthly at Caycuse River.  In rare situations when deer moved rapidly, triangulation  was impossible and data were not recorded.  When deer moved slowly (<150 m)  during sampling, we accepted potential errors up to 50 m on locations (Schmutz and White 1990).  Although we used 2 techniques to estimate deer  locations (see McNay et al. 1994), most (74%) locations were maximum likelihood estimates (Lenth 1981).  Factors Used to Identify Spatial and Temporal Scales We assessed straight-line distances and directions between successive relocations for spatial and temporal patterns after confirming that sample interval (weekly data) had little effect on estimated distance travelled (McNay et al. 1994).  We examined 4 attributes of movements in space and  33 time:  magnitude (distance), frequency, timing, and direction.  Patterns  were identified when movements could be described as periodic and repetitive. Local movements were designated as those in which distance between locations was not greater than the 95th percentile of all distances, excluding 2-hour samples.  The 2-hour samples were designated as serial  movements because they were considered to be serially dependent (McNay et al. 1994).  We grouped movements above the 95th percentile of all weekly  distances into 3 categories.  Dispersals were moves away from, and with no  return to, the original range (Howard 1960, Bunnell and Harestad 1983). Migrations (adapted from Sinclair 1984) were moves that: (1) were followed by 2 or more local moves before a return to the original location (i.e., use of a spatially distinct range for 2 wk), or (2) were immediately followed by a return to the original range but were repeated at other times (i.e., predictable returns between spatially distinct ranges).  Outliers were moves  above the 95th percentile that could not be defined as dispersal or migration. Seasons were termed natal (May and June), summer (July and August), hunting and rut (September through November), winter (December through February), and spring (March and April).  Annual periods began during one  natal season (June 15) and ended the next natal season (June 14). We assumed fawns followed their mothers for the first year of life (Harestad and Bunnell 1981, Nelson and Mech 1981) and dispersal could occur in any subsequent natal season (Bunnell and Harestad 1983, Hamlin and Mackie 1989, Nelson and Mech 1992).  Our definition of the natal season was based  on empirical observations of others (Cowan 1956, Golley 1957, Thomas 1970, Salwasser and Holl 1979, Livezey 1991).  We assumed that locations during  34 that season indicated the natal area for non-dispersing deer (Masters and Sage 1985, McCullough 1985, Hamlin and Mackie 1989) and would be where subsequent offspring were produced. remained on their natal ranges.  Non-dispersing, non-migratory deer  We termed that area the natal range while  any different ranges used by migratory deer, usually during other seasons, were alternate ranges. We segregated areas occupied by individual deer into separate ranges if they were spatially exclusive and if deer made migrations between the them.  We used the presence or absence of migrations to classify deer as  migratory or nonmigratory (resident).  We classified migratory deer as  obligate migratory deer if migrations occurred consistently among years and as facultative migratory deer if migrations occurred more irregularly. We termed isolated patches of concentrated use as activity nuclei (after Don and Rennolls 1983).  Each nucleus (from 1-4 in each range) was a  spatial clustering of deer locations (i.e., many locations in close proximity).  The centres of nuclei, determined for statistical convenience,  were the highest point(s) chosen from a graphical representation of the distribution of deer locations in space; the z dimension represented the degree of spatial clustering.  Dixon and Chapman (1980) called this  graphical representation the utilization distribution which formally is the first inverse areal moment of the harmonic mean (Samuel and Garton 1987):  H.= J  1 (3.1) x=1  calculated for each grid point  ix  1 is the j on an arbitrary plotting grid. H  35 harmonic mean value at grid point and  j, p is the number of animal locations,  is the distance between location x and grid point  j.  A potential  nucleus was accepted if it had >4 locations associated with it and was topographically isolated from other potential nuclei.  Additional locations  were assigned to a nucleus if <250 m from its centre.  Activity nuclei used  during night hours (18:00 hr to 09:00 hr) were referred to as night-time nuclei; those used at other times were day-time nuclei.  Analytical Procedures We assessed dispersal distances for deer, collared as fawns or yearlings, that survived at least their next natal season.  Dispersal  distance was calculated as the straight-line distance from the natal area to the position held during the last July (July being the first month after the natal period) of the deer’s life or during July 1991 (project end). Dispersers and deer that could not be classified as resident or migratory were omitted from analyses of migratory, local, and serial movements.  These omissions included: (1) 2 deer that dispersed from their  natal areas as yearlings; (2) 2 adults that made a single, outlier move just before death; and (3) 34 adults and 16 juveniles for which we had limited information due to brief data collection intervals  (  =  102 d, SE  =  12 d).  We calculated migration distances and directions based on straight lines between arithmetic mean centres of successively observed ranges.  We  measured fidelity to ranges (alternatively, deviation in range use) as the straight-line distance between arithmetic mean centres of successive use of the same range or between centres of successive annual ranges for resident deer.  Departure and arrival dates from one range to another were assumed to  be half-way between the date first observed on the new range (end of  36 migrations) and the date last observed on the former range (start of migrations). We assumed that within-range dispersion of locations could be represented by local movements measured as straight lines between successive location samples.  We estimated range areas (total area and the composite  area of segregated ranges) using the SAS program of White and Garrott (1990: 343) for the minimum convex polygon (Mohr 1947) excluding 2-hour samples. We weighted harmonic mean calculations by time spent at each location (Samuel and Garton 1987), where time spent was the duration between last and current locations.  Because the harmonic mean has been criticized (Worton  1989) as overly sensitive to choice of grid scale, we used 1 grid scale for all deer.  We investigated temporal patterns in use of nuclei with both  local and serial moves.  The former moves were used to assess repetitive use  of nuclei because these moves were temporally unrelated while the latter, temporally related, moves were used to assess duration of use of nuclei. Additional quantification of ranges was provided by the number and dispersion of nuclei within them.  Dispersion (r ) was estimated as the sum 2  of the mean squared distances between each individual nucleus and the arithmetic mean of all nuclei (after Swihart and Slade 1985):  1 r2=l  where nuclei are numbered I  =  _)2÷’ 2 (X 1 -Y) 1 (Y ;  (3.2)  1, 2, 3,..., n and X and Y are the spatial  coordinates representing nuclei centres. Samples of 2-hour data for migratory deer on natal ranges were sparse so we limited analyses of serial movement to winter months only.  We used a  37 trigonometric model to describe cycles of serial movements and tested for amplitude and phase differences in mean distance moved among groups of deer. We also tested for temporal effects through winter using calendar month as a factor.  Cycle lengths were visually estimated using PROC TIMEPLOT (SAS  Inst. Inc. 1985). We assessed data distributions using techniques in PROC UNIVARIATE (SAS Inst. Inc. 1985).  We used Bartlett’s test to assess homogeneity of  variance after checking normality of data distributions (Zar 1984) and assessed distribution of directional or temporal data with Rayleigh’s z-test (Batschelet 1981).  We assessed independence of observations in deer  movements (Swihart and Slade 1985) in a related study (McNay et al. 1994) and noted that movement data were unlikely to be independent.  Therefore, we  made the assumption that the distance between consecutive weekly samples was representative of local moves because they were collected systematically and because deer could have travelled anywhere in their home range during the time interval between most samples (McNay et al. 1994).  The 2-hour samples,  by comparison were intentionally collected as time-dependent samples (Reynolds and Laundre 1990, Kremsater and Bunnell 1992). We tested for differences in population parameters between behaviourial groups of migratory deer by either a two-sample t-test, when population distributions were normal, or by the normal approximation to the Mann-Whitney U-test when population distributions were not normal (Zar 1984).  The Kruskall-Wallis test (Zar 1984) was used when unequal variances  were encountered and when population comparisons included resident deer as well as both behaviour groups of migratory deer.  Because resident deer did  not use alternate ranges, we first compared all migratory classes (resident, facultative, and obligate) on natal ranges.  Then we compared migratory deer  38 (facultative or obligate) on both ranges (natal or alternate).  If data were  normal and homoscedastic (or could be transformed so) an F-test was used (SAS Inst. Inc. 1985), otherwise we used the Kruskall-Wallis test.  We used  Watson’s U -test (Zar 1984) to compare directional or temporal data. 2  RESULTS Deer Location Samples We caught and collared 30 juvenile deer but 13 (10 fawns and 3 yearlings) died before their next natal period leaving a total of 17 deer for analyses of dispersal.  For all migration and local movement analyses,  we treated 72 non-dispersing, collared deer (69 9 and 3 cc) monitored for a total of 253 deer-years and 8,533 weekly locations.  We described patterns  in serial movements using only 2-hour data collected during winter on 23 non-dispersing deer in 11 separate monitoring sessions for a total of 73 deer-sessions and 2,526 locations. Most collared deer were at Nanaimo River (n  =  40) including 32 adult  females, 7 yearling females, and 1 adult male that was released to the wild after being raised in captivity for another study (Bunnell et al. 1990a,b). We captured the remaining 32 deer, mostly adult females, at Caycuse River (n =  13), Nimpkish River (n  =  11), and Chemainus River (n  =  8).  Two of the  collared deer at Caycuse River were male fawns; we collared 1 female fawn each at Caycuse, Chemainus, and Nimpkish rivers. Initially (1982-1988), we sampled deer only at Nanaimo River.  We  collected most data during winter months and from day-break through to midnight.  Most (98%) triangulation produced locations estimated with a 95%  error ellipse (White and Garrott 1990: 72) of less than 1 ha and an acceptable goodness-of-fit for the bearings used (McNay et al. 1994).  39 Radio-collars functioned (i.e., produced an identifiable signal on a live deer) for an average 3.4 yr (SE  =  0.2; n  72).  =  At Nanaimo River 1  deer provided 9.0 yr of data and 4 others each provided 7.0 yr of data. Although the average number of weekly locations per deer was 173 (SE =  14; n  =  72), 18 deer were represented by less than 70 samples and 13 deer had more  than 150 samples. (SE  01; n  =  Average time intervals between weekly samples was 8.0 d  8,461) with a mode of 7.0 d.  =  The average time interval  between 2-hour (serial) samples was 2.0 hr (SE  =  0.03; n  2,453).  =  Analysis of straight-line distance between successive weekly locations showed that relatively few (n km (Fig. 3.1).  =  461 or 5%) of the total sample exceeded 1.2  Because that distance represented only 9% of the maximum  distance observed, it also represented a sharp deflection in the cumulative frequency distribution (Fig. 3.1).  We used that distance to distinguish  local movements (<1.2 km) from other movements.  Twenty-eight of the 72 deer  made a total of 256 moves that satisfied our definition of migration and hence were called migratory deer. moves as outliers (n  =  We classified the remaining non-local  185) or as fragments of migrations (n  =  20).  Outliers represented 2.1% of locations and reflected moves to traverse occupied ranges or moves off the range to locations used only once km, SD  =  0.6; n  =  185).  (  =  1.7  Those moves occurred most frequently in June and  November (Fig. 3.2) and were made at least once by all deer.  We could not  detect any effect on distance of outlier moves due solely to migratory class (F P  =  =  0.80; df  =  1,37; P  =  0.3743) or to class of range (F  =  0.32; df  =  1,37;  0.5733) being used at the time the movements occurred.  Dispersal:  Life-time Scale  Of 17 deer that could have dispersed (Table 3.2) only 2 (12%) did so.  40  120  100  U) 0  80 Cu C.)  0 Cu  0 ‘I  60 a)  C.) I  0. a) CU  E C-) 20  0  0  2  4  6  8  10  12  14  Distance between successive locations (km)  Figure 3.1 Cumulative frequency distribution (%) for the distance between successively sampled locations (n = 8,000) of radio-collared, black-tailed deer on Vancouver Island, British Columbia, 1982-1991.  41  35  30  25 Co 0 0  E 20 0 0 I,  0 I..  0 .0  15  E  z 10  5  0 J  F  MAM  JASON J Month  D  Figure 3.2 Number and timing of outlier moves (moves >1.2 km that were not dispersals or migrations) made by radio-collared black-tailed deer on Vancouver Island, British Columbia, 1982-1991.  42 None of the 7 fawns dispersed as yearlings; 2 died as yearlings and none of the remaining 5 dispersed as 2-year-olds.  Two of the 10 deer trapped as  yearlings dispersed in their second natal season and 3 others died in their second year without dispersing.  The remaining 5 yearlings lived into their  adult years but never dispersed.  The average distance between the estimated  natal site, for non-dispersing deer, and the site occupied in their last natal season (Table 3.2) was 0.7 km (SE= 0.1; n  =  15).  The 2 deer (#13592 and #15504 in Table 3.2) that dispersed were collared during the winter of 1988-89 and dispersed from the collaring site during the following natal period.  They were last recorded close to their  capture site on June 23 and were found together on July 04, >7 km to the east where they resided until September.  During the months of September and  October, both deer travelled a further 10 km east stopping for brief stays at each of 2 major rivers along the way.  On October 3 they reached their  maximum distance from the original site of collaring (about 25 km), stayed 2 wk, then began to return west. vehicle on October 13, 1989.  One disperser was hit and killed by a The surviving disperser finally settled at the  same site she used during the previous summer, still >7 km from the site of collaring.  She stayed there until the end of the study, and no further  signs of dispersal or migration were recorded through the following 1.5 yr of monitoring.  Migration:  Seasonal Scale  A total of 28 radio-collared deer migrated at least once: 2 of 8 at Chemainus River, 2 of 11 at Nimpkish River, 7 of 13 at Caycuse River, and 17 of 40 at Nanaimo River.  All 3 males migrated and, of the 25 migratory  females, 1 was a fawn who migrated along a similar route as her radio-  43 Table 3.2. Straight-line distance between the estimated natal site and the site occupied in their last natal season for radio-collared, black-tailed deer caught as subadults (<2-yr-old) at 4 study sites on Vancouver Island, British Columbia, 1982-1991.  Deer  Migrator  Sex  number  Age at  Age at  Distance  capture  death  moved  (yr)  (yr)  (km)  0.75  2.17  1.3  15301  Yes  17912  Yes  9  0.67  2.17  0.1  17991  No  9  1.50  2.42  0.5  18412  Unknown  d  1.83  2.42  0.5  19311  Unknown  0.75  1.17  0.6  19312  Yes  0.75  2.25  1.9  15101  No  9  0.75  3.17  0.2  13592  Unknown  9  1.75  2.33  8.1  15411  No  9  0.67  2.17  0.2  15504  No  9  1.58  3.17  7.9  9101  Yes  9  1.58  4.42  1.1  15001  Yes  9  1.58  6.75  1.0  15401  No  9  1.75  7.83  1.0  16102  Yes  9  1.83  4.75  0.1  16601  Yes  9  1.92  6.33  0.7  17701  No  9  1.92  6.67  0.5  17803  Unknown  9  0.75  1.58  0.4  44 collared mother.  Associated with the migrations were 12 movements composing  6 visits (<4 wk stays subsequently followed by longer stays), possibly to assess conditions at other ranges, and occasional moves between closely associated ranges along a single migration route (n 202 full migrations (n  =  42) leaving a total of  =  94 moves away from natal ranges and n  =  108 return  moves to natal ranges). The use and pattern of, or lack of, migrations revealed a difference among deer which led us to classify 3 behaviour types.  Resident deer  clearly chose different tactics than migratory deer (i.e., their tactics were devoid of large movements).  Among migratory deer, one group migrated  annually and spent long periods away from their natal range (obligate migratory) while a second group migrated less regularly and were away from their natal ranges for comparatively short and variable durations (facultative migratory).  Time spent away from the natal range by the 2  groups (Fig. 3.3), which were first discriminated by Duncan’s and LSD (df 59; p  <  0.05), differed (F  =  7.58; df  =  27,59; P  =  0.0001).  =  Facultative  migratory deer left their natal range for <80 d per trip while obligate migratory deer stayed away >148 d per trip.  On an annual basis, the average  time spent on alternate ranges was also different (t  =  61.14; df  =  65; P  <  0.0001); >6 mo for obligate migratory deer and <4 mo for facultative migratory deer (Table 3.3).  All 7 migratory deer at Caycuse River, 1 of the  2 migratory deer at each of the Chemainus and Nimpkish river sites, and 7 of the 17 migratory deer at Nanaimo River were facultative migratory deer. Magnitude. Table 3.3.  -  We summarized distances between ranges for migratory deer in On average, migration distances were different between the  behaviour types (U  =  8095; n =95,119; P  <  0.05).  More than half the  obligate group travelled >5 km while only 2 of 16 facultative deer migrated  45  300  I >‘  250  C’) Cu  ‘•i  a)  0) C Cu L.  .  200  mean+SE mean mean-SE  1•11  -.  Cu CU  C  E  0 .4-  150  I  CU  Cu C  a) a. Cl) a) E  100  0) CU I-  C) ‘I  50  0  I  Individual radio-collared deer  Figure 3.3 The average time-per-trip spent away from the natal range for migratory, radio-collared, black-tailed deer on Vancouver Island, British Columbia, 1982-1991. Individuals are ranked in their order of duration.  0.3  Range deviation: natal (km)  2.5  alternate (km ) 2  0.1 0.1  inside nuctei (km)  0.1  0.3  4.6  0.4  outside nuclei (km)  Serial moves: total (km)  distance (km)  Nuclei use: frequency (f/d)  alternate (km)  0.4  1.0  natal (km ) 2  Nuclei dispersion: natal (km)  11.0  0.4  0.4  Range area: total (km ) 2  alternate range (km)  Local moves: natal range (km)  0.5  5.6  Migration distance (km)  a’ternate (km)  209  Migration duration (d/yr)  91  29  120  99  8  6  10  12  12  12  779  544  35  30  95  34  0.1  0.2  0.1  0.2  2.4  0.2  0.1  4.9  0.8  7.2  0.2  0.3  0.9  0.3  3.7  51  0.1  0.1  0.1  0.3  2.1  0.6  0.4  2.0  1.7  7.4  0.3  0.3  0.6  0.4  3.0  66  348  281  629  162  27  5  11  16  16  16  314  1439  32  33  119  33  n  n  SD  Facultative  Obligate  0.1  0.2  0.2  0.2  1.3  0.5  0.2  2.7  1.8  6.2  0.3  0.2  1.1  0.3  2.0  58  SD  0.1  0.2  0.1  0.3  2.6  0.4  1.9  1.9  0.4  0.2  Deer Behaviour  807  281  1088  340  46  35  44  44  4924  106  n  Resident  0.1  0.2  0.1  0.2  1.6  0.1  1.8  1.8  0.2  0.1  SD  0.1  0.2  0.1  0.3  2.6  0.4  0.4  2.2  1.7  4.6  0.4  0.3  0.6  0.2  4.2  1246  591  1837  601  81  15  52  28  72  72  1093  6907  67  169  214  n  Pooled  0.1  0.2  0.1  0.2  1.7  0.3  0.2  3.7  1.7  5.6  0.2  0.2  1.0  0.2  3.2  SD  0)  Table 3.3. Movement characteristics for 3 behaviour groups of radio-collared, black-tailed deer on Vancouver Island, British Columbia, 1982-1991. Moves are straight-Line distances between successive locations (using 2-hour data for serial moves and weekly data for Local moves) or between arithmetic mean range centres (for migrations), range deviation is the mean straight-line distance between arithmetic mean centres of successively used ranges of the same type, nuclei dispersion is the mean squared distance (after Swihart and Slade 1985) between nuclei chosen from utilization distributions (Dixon and Chapman 1980) for individual deer ranges, frequency of nuclei changes is the mean daily frequency of moving between nuclei, and distance of nuclei changes is serial distance moved to change nuclei.  47 that far. Frequency.  -  Deer used individual ranges repetitively and displayed strong  fidelity to specific range locations.  The deviation in centres of ranges  was <0.5 km (Table 3.3) regardless of migratory behaviour (F 1,126; P  =  0.1166) or range type (F  =  0.81; df  1,126; P  =  2.5; df  =  =  0.3695).  =  Resident deer also showed strong affinity for their range locations, the centre of which varied <0.2 km on an annual basis  (  =  0.2, SE  =  0.1; n  =  106). Timing.  -  Departures from, and arrivals at, natal ranges were concentrated  around November and May, respectively (Fig. 3.4).  Obligate migratory deer  exhibited the most concentrated timing of moves overall (z P  <  =  35.43; n  0.05) when they returned to their natal ranges (May 26).  45;  =  By comparison,  facultative migratory deer returned to their natal ranges earlier (February 21) but the timing was not different than random (z 0.05).  10.03; n  =  =  63; P  >  On average, obligate migratory deer left their natal ranges on  October 20 (z  =  24.33; n  =  departed on December 10 (z  38; P =  0.05) while facultative migratory deer  <  20.19; n  56; P  =  <  0.05).  The migration dates  were significantly different between the 2 groups for both returns to (U 2 2.19; n  =  63,45; P  <  0.05), and departures from (U 2  1.89; n  =  =  56,38; P  =  <  0.05), natal ranges. We rarely were able to follow individuals as they migrated. estimate of the elapsed time for each trip, an average of 5.0 d (SD  Our =  6; n  204), estimates maximum duration because it is similar to the average time interval between location samples for all weekly data. Direction.  -  We found a significant pattern to the direction of migration  for facultative migratory deer (z for obligate deer (z  =  0.29; n  =  =  5.02; n  95; p  >  =  119; P  0.05).  <  0.05) but no pattern  Most facultative deer  =  48  30 Co 0  0  E ‘-20 0 >1  Cu  .2’l 5 E ‘I 010 0  E5  z 0  J  F  MAM  J  JASON  D  35 B) Facultative Obligate  >25 0  20 C,  0  1:1ZI J  F  MAM  . IIIIIIIIIIIII JASON J Month  . .:i D  Figure 3.4 Number of migratory moves made during each month of the year by 2 behaviour groups of radio-collared, black-tailed deer departing natal ranges (top) and returning to natal ranges (bottom) on Vancouver Island, British Columbia, 1982-1991.  49 migrated along their main river valleys (Fig. 3.5).  Seven of 12 obligate  migratory deer made notable deviations to valley directions by travelling across 1 or 2 valleys to an alternate range; only 2 of 16 facultative migratory deer made similar deviations.  Local Movement: Magnitude. =  0.2; n  -  Daily Scale  Most (95%) weekly locations were <1.2 km apart  0.4 km, SD  =  8000) and we never recorded any combination of local moves that  =  resulted in a switch from one range to another.  Distances between  successive locations on natal ranges did not differ (F P  (  0.65; df  =  =  1,6904;  0.4198) between obligate and resident deer (Table 3.3) but, for  =  facultative deer, that distance was significantly less (F 1,6904; P  =  0.0014).  =  10.16; df  =  Similar results occurred on alternate ranges where  consecutive locations for obligate deer were even more dispersed than those of facultative deer (F  =  27.44; df  =  3,3072; P  0.0001).  Observations of  movements (2-hour data) between activity nuclei (see Frequency below) resembled dispersion of weekly observations (Table 3.3). migratory deer made smaller movements on average (F  =  Again, facultative  14.06; df  =  1,598; P  0.0002). Another index of local movement is home range size (Fitch 1958). Total, or life-time, home range size for the 1 surviving disperser was 0.4 sq km at the site of collaring (January-June, 1989), 21.3 sq km during dispersal (July-November, 1989), and 0.3 sq km for the period following dispersal (December 1989  -  June 1991).  By comparison, the average total  home ranges for non-dispersing deer ranged from 1.9 to 11.0 sq km. Migratory deer had total home ranges that were 3 times (Ffacultative df  =  1,69; P < 0.0001) to 5 times (Fobllgate  =  60.46; df  =  =  36.68;  1,69; P < 0.0001)  =  —  0  1.0 K I I ornetres  2.0  Figure 3.5 Directions of movement from natal ranges to alternate ranges for obligate migratory (solid arrows), and facultative migratory (dashed arrows), radio-collared, black-tailed deer at 4 study sites on Vancouver Island, British Columbia, 1982-1991.  cuse Ric.en  1.0  51 larger than those used by resident deer (Table 3.3) yet smaller than that used by the disperser. Generally, size of individual seasonal ranges averaged 1.0 to 2.5 sq km (Table 3.3).  Although we could not detect differences in seasonal range  sizes between the 2 groups of migratory deer (F  =  0.70; df  3,51; P  =  =  0.5543), estimates of natal ranges were smaller for obligate migratory deer than for resident deer (F  =  4.94; df  =  1,69; p  =  0.0296).  Still another index of local movement is distance between activity nuclei (Fig. 3.6).  Dispersion of nuclei (Table 3.3) was not different among  behaviour types (F =  0.80; df  Freciuencv.  =  -  =  1,37; p  0.58; df =  =  2,49; p  =  0.5563) or between range types (F  0.3743), averaging 0.4 km (SE  =  0.2; n  =  67).  We monitored deer occupying separate ranges (i.e., separated in  space, such as natal or alternate ranges, and separated in time, such as a deer using a natal range after having used an alternate range) on 336 different events.  One hundred of these events were represented by <10  locations and in these cases of low sample size, and in some other cases of extremely dispersed locations (n activity nuclei.  =  16), we were unable to detect specific  The remaining sample (n  =  220) represented 87 of the  possible 100 individual deer x range combinations and 71 of the 72 nondispersing deer.  Most deer x range combinations had at least 2 nuclei (Fig.  3.7) and 67 showed repeated use of nuclei with successive periods of use. Although 2 nuclei (each being 39.2 ha by our definition) represents only 16% of the average range size (Table 3.3), they typically enclosed 61% of the locations.  Only 5 deer x range combinations had unique nuclei and 20 had no  replication of range use upon which to evaluate repetitive use of nuclei. When repetitively used, nuclei were displaced by 0.1 km (SE on average.  =  0.1; n  =  153)  —  -  CD  c-TI  c c —  -  CD  —  C  Utilization distribution  o  CD —  c-s.  - -  —  c-s. CD -“D  )  c-s. —a. CD  (DN(i’ 5JCD c-s. —  c-s. 0 CD  z  )  c-s.  , 0_ 0 —  CD c-s. 0 CD -I,  D)  rD ) C-) c-s. c-s. — — -“  —  o  r1 —‘<  C,)  —3 CD CD , -.  -  Q_  ,  CD  —  -5,  —  o C-.)  o  0  -a.. C LO )  co  — %_  -1CD  o  -  o  —I  CD  —J  CD D -  -.  o co  1W CD C)  C,  co o w  -  —I  ‘0) -1 D CD CD Q c-s. CD  C) CD  0) -  —I  0) — C_)  3  —  CD c- CD •  0) —  s. -  —I  CD3  CDCD CD -  53  100  80  Cl) C)  a)  60  C) C)  •a II  0 I..  C) .0  E  40  z  20  0 1  4 3 5 Number of nuclei in range  2  6  Figure 3.7 Frequency of radio-collared, black-tailed deer, seasonal ranges having 1 or more activity nuclei.  54 Timing.  -  Observations of movements in 2-hour intervals during winter  revealed that deer changed activity nuclei 2-3 times daily (Table 3.3). Obligate migratory deer changed nuclei twice as often as other deer (F 10.14; df  1,78, P =.0021).  =  =  We could not detect seasonal periodicity in  distance between locations once the effect due to behaviour type was removed (F  =  0.15; df  =  4,7985, P =.9640).  Similarly, we could not detect  periodicity in the day-time or night-time use of nuclei (only 12 of 48 comparisons were significant  2 [x  <  0.05]; the remaining 39 deer x range  comparisons had insufficient samples to complete the  2 x  analysis).  Observations of movements (2-hour data) between activity nuclei occurred most frequently at crepuscular hours (Fig. 3.8 top).  Sample size was  insufficient to evaluate periodicity in seasonal use of nuclei. Direction.  -  Directions for local movements based on weekly data did not  differ from random (P  >  0.05) for any deer on either range.  Serial Movement: Hourly Scale During winter, serial movements were dependent and cyclic in time and could be represented in the following trigonometric form:  D  =  148.6  —  37.2sin(1  *  HR)  +  76.9OBL  +  56.4DEC;  (3.3)  where D is mean distance between consecutive locations, HR is hour of day, OBL is a dummy variable for obligate migratory deer, and DEC is a dummy variable for the month December (r 2 0.0001; n  =  411).  =  0.12;  =  11.36; F  =  18.26; P  <  Largest movements typically occurred at 08:00 and 20:00  hr with least movement at 12:00 and 24:00 hr. All deer moved more in December than either January or February (P  <  55  500 A) C.)  z  400  0)  t  300  0  a) a) C.)  200  Cu CO •0  100  •0  4-’  Cu a)  0 500  a)  400  C.)  .  300  4-’  C.) Cu Cl) •0  A  02  468  10121416182022  L F  B)  200  4-’  Cu G)  100  0  02468  10121416182022 Hour of day  Figure 3.8 Mean distance between successive, 2-hour, relocations of radiocollared black-tailed deer by the hour of day as deer were departing nuclei (top), or within nuclei (bottom), at 4 study sites on Vancouver Island, British Columbia, 1982-1991.  56 0.001) and obligate migratory deer generally moved further than either facultative migratory or resident deer (P  <  0.001).  Distance of serial  movements, excluding those made as deer departed specific nuclei, were similar for all deer (Duncan’s; P  >  0.05, Table 3.3) but distance travelled  outside nuclei was twice that travelled inside nuclei (F 1,1822; P  <  0.0001).  =  66.25; df  =  Movements inside nuclei during crepuscular hours  generally were the longest (Fig. 3.8 bottom).  DISCUSSION Movements as Scalar Classes of Activity We found that movements could be isolated into distinct classes along several dimensions and hence were scalar (Levin 1992).  Movements were  clearly scalar in their frequency of occurrence; dispersal once in a life time, migration usually twice each year, local movements several times a day, and finally, serial movements more-or-less continuously in defined periods of activity. other dimensions.  Movements also differed, but less distinctly, along  Migrations and dispersals tended to be longer and more  directional than local or serial movements.  Timing differed as well with  dispersal occurring during the natal season, migrations occurring in earlyand late-winter, local movements occurring mostly at crepuscular hours, and serial movements throughout a calendar day. Our quantification of these scalar classes of movements supports the common use of terms described elsewhere.  Other workers also have reported  deer dispersal as a single event (once in a life-time) covering long distances and associated with the natal season (Harestad and Bunnell 1981, Schoen and Kirchhoff 1985, Hamlin and Mackie 1989, Nelson and Mech 1992). Migrations were similar in distance to those reported for black-tailed deer  57 in other studies (Harestad 1979, Schoen and Kirchhoff 1985, Livezey 1991) but were remarkably less than those reported for mule deer (0. h. hemionus) migrations (17-110 km in Garrott et al. 1987, 29 km in Kufeld et al. 1989, 27-51 km in Loft et al. 1989, 4-66 km in Brown 1992).  The difference could  be due to the topographically insular nature of study areas throughout the range of black-tailed deer.  Coastal watersheds in Washington, British  Columbia, and southeast Alaska tend to be steeper sloped and narrower than interior watersheds inhabited by mule deer, thereby creating potential constraints on migration while proximity of seasonal habitats is increased through topographic relief.  We found no other reports specifying patterns  of serial movements by deer but our observations of local movements and home range sizes were similar to those reported elsewhere (Harestad 1979, Schoen and Kirchhoff 1985, Livezey 1991).  Movements as Hierarchically Structured Decisions Movements revealed that resource use was not only a scalar activity but appeared to be based on decisions that were nested hierarchically. Local movements occurred as regular periodic activities and were always contained temporally and spatially within decisions made at higher classes of movement.  This physical aspect of movement was described as a nested  hierarchy by Gautestad and Mysterud (1993).  They used an hierarchical  movement process as the basis for area utilization assessments to offer “a more solid platform for parameter estimates and statistical tests than the traditional [home range] protocols”.  Their premise was that, by  distinguishing between physical aspects (i.e., nested subclasses within superclasses) and biological aspects of animal movements, local and temporal variation in movements could be more clearly correlated with environmental  58 parameters (Gautestad and Mysterud 1993).  For black-tailed deer, specific  decisions about seasonal ranges were contained within the more general decision about home-range settlement (the latter being a decision between 2 choices, dispersal or philopatry).  Similarly, when we could identify  specific nuclei, local moves between them were always on the same seasonal range and thus contained within the most recent decision to migrate. Further, serial movements showed a cyclic pattern of smaller movements within activity nuclei and thus were contained within decisions of local movements between nuclei.  Decision Information Transfer and Hierarchical Function Because movements by deer are scalar, and because the decisions they represent show containment, we concluded the resource use problem is a nested hierarchy.  Furthermore, we consider this particular hierarchy to be  more than a simple classification framework because it embodies a functional (information transfer) aspect.  We contend the pattern (magnitude,  direction, frequency, and timing) of movement decisions reveal that superclass decisions: (1) were based on a synthesis of subclass decisions and (2) once made, constrained the breadth of choices for those subclass decisions.  These are standard, physical implications of typical nested  hierarchical function (Dawkins 1976). Beyond the physical implications of the hierarchy, we also noted specific tactics for decisions about resource use at several scales.  These  tactics generally implied that black-tailed deer use a conservative approach to dealing with resource changes; that conservatism tends to reinforce constraints within the hierarchy of decisions about resource acquisition. We present these tactics at each scale of the hierarchy below.  59 Dispersal:  Life-time Scale.  -  The decision to disperse or to be philopatric  is an irrevocable commitment, made once, to a long-term tactic and, therefore, to a range of more specific tactics for subsequent resource use (Dawkins 1976, O’Neill et al. 1989, Levin 1992).  This decision at the life  time scale is one of choosing home ranges and, among deer in our study, appeared to constrain, for example, tactics for seasonal range selection. The lowest proportion of migratory deer (highest proportion of resident deer) came from Nimpkish River where our study was located close to valley bottom.  Kufeld et al. (1989) found that most deer in the Rocky Mountain  foothills of north-central Colorado were resident; deer at higher elevations in Colorado were reported to migrate seasonally (Garrott et al. 1987). Obligate migratory deer in our study came mostly from Nanaimo River, a relatively mountainous area (Table 3.1).  Harestad (1979) reported mostly  migratory deer at Davie River, located close to Nimpkish River but more mountainous.  These observations illustrate that the decision to migrate is  not only contained within the decision about where to live (dispersal) but may also be constrained by it. In comparison to that top-down perspective, synthesis of resource use decisions accumulated on both alternate and natal ranges could guide the decision of philopatry or dispersal.  Choice of home ranges, regardless of  the impetus (e.g., social interactions, population density, habitat quality, genetics), ultimately must be based on some form of information regarding composite resource conditions.  Because so much information needs to be  assessed, Orians and Heerwagen (1990) considered this process to be unwieldy and proposed that animals use environmental cues to act as indicators of overall suitability.  The long distance travelled by the 2 dispersers we  observed (Table 3.2; see also 1-tarestad and Bunnell 1981) and the circuitous  60 route taken suggests considerable exploration and testing to determine a final site to settle.  These activities would be necessary only if knowledge  about finer scales of resource use at new sites was incomplete yet important. The effort, incomplete knowledge, and risk involved in dispersal apparently contribute in a proximal way to home range selection among deer and taken together represent important mechanisms for developing evolutionary responses (Orians and Heerwagen 1990).  While the structure of  the hierarchy implies a transfer of information between levels concerning the decisions of dispersal and migration, empirical observation reveals specific tactics that also guide the final choices.  Loft et al. (1989)  suspected a high level of philopatry in a population of mule deer; a conclusion commonly reported from other studies of social interactions among deer (Hirth 1977, Nelson and Mech 1981, Hamlin and Mackie 1989).  We  observed only 2 dispersals and although we can not be certain, we believe most of our sample deer remained in matriarchical groups that overlapped, or at least were adjacent to, their true natal areas.  It seems that staying  close to family is a strong component of the tactics for settling home ranges. Philopatry implies an adaptive advantage in that young deer rely on maternal expectations of future resources (i.e., the maternal home range is well suited to the resource conditions).  Because the decision is made  infrequently, the advantage may not be realized if resource conditions as they are evaluated at the home range scale are, or become, unstable (Allen and Starr 1982, Gass 1985, Senft et al. 1987).  Forest growth and renewal  is, for the most part, relatively slow in coastal forests (Franklin and Spies 1984, Bunnell 1995) and although wildfire may effect large areas,  61 frequency of fires in coastal British Columbia tends to be high only in short periods isolated by 100 yr intervals (Schmidt 1970). Migration:  Seasonal Scale.  -  The cyclic nature and consistent timing of  migrations made by obligate migratory deer in our study resembled the patterns described elsewhere (Harestad 1979, Garrott et a!. 1987, Loft et a!. 1989, Brown 1992).  The facultative behaviour we observed is commonly  reported in studies of some birds (e.g., Terrill and Ohmart 1984) but has not been acknowledged as a distinct behaviour in populations of deer even though it has been described previously (Fairman 1966, Harestad 1979, Garrott et al. 1987, Hamlin and Mackie 1989, Brown 1992).  Obligate  migratory deer migrated further, followed less predictable directions, and used alternate ranges longer and more consistently than facultative migratory deer (Fig. 3.5 and Table 3.3).  The choice of migration tactics  (obligate, facultative, or no migration) commit individual deer to 1 tactic, at least for a season, resulting in a limited range of tactics for more specific decisions about habitat use.  This decision at the seasonal scale  involves selection of seasonal habitats.  Similar to the way that migration  tactics apparently depend on the choice of home ranges, we found that local movements (especially area of use and frequency of local moves; Table 3.3) apparently depend on specific migration behaviour.  Because we observed  consistent differences in local moves among classes of migratory behaviour, we concluded the choice of migration tactics constrains the breadth of tactics for local movements and subsequent resource use decisions. Previous attempts to understand migration patterns have led to explanations based primarily on changes in weather (McCullough 1964, Harestad 1979) and/or changes in condition and abundance of forage (Klein 1965, Garrott et al. 1987).  Such changes in resource condition could be  62 recognized by animals only through synthesis of an accumulation of finerscale resource use decisions.  For example, facultative migratory deer moved  to alternate ranges along valleys (only as far away as valley bottoms) and stayed for relatively short periods.  We considered this pattern to indicate  a tactic guided by a synthesis of environmental conditions.  It seems  doubtful, for example, that migrations could be as inconsistent temporally if the tactic was imposed by higher scale (home range) decisions. We found the highest proportion of facultative migratory deer came from Caycuse River, a coastal watershed with a high degree of maritime influence, little high elevation alpine habitat, steep slopes, and little low elevation flat topography.  This movement pattern for facultative  migratory deer, combined with that of obligate migratory deer (summarized above) and the lack of migration by resident deer, suggests that local climate and geography at natal ranges strongly influences the need to move to alternate ranges in winter.  In more mountainous areas than Caycuse for  example, deer would be more likely to migrate each year, as obligate migratory deer do by definition.  The timing of migrations made by obligate  migratory deer indicate avoidance of severe winter conditions that are likely to occur on high-elevation natal ranges during winter months.  At  mid-elevations, or where winter weather is moderated by coastal climate, deer may choose not to migrate except in times of severe weather pattern of facultative migration.  --  the  Finally, at lowest elevations (e.g., our  Nimpkish River site or valley bottoms at other sites) deer remain resident with no migration. Our study was not designed to address the adaptive significance of these tactics for selecting seasonal ranges.  For example, we are unable to  conclude if these are only partially migratory populations resulting from a  63 mixed strategy where groups remain reproductively isolated or from a conditional strategy where individuals choose a gradation of tactics depending on their individual circumstances (Swingland 1984). Deer displayed a tactic of fidelity to alternate ranges (Table 3.3, Garrott et al. 1987) indicating an element of tradition in the choice of actual sites.  Loft et a!. (1989) took such fidelity a step further by  suggesting that the selection of winter areas was based largely upon family members following each other.  Other studies of spatial and behaviourial  organization provide similar suggestions (Hirth 1977, Hamlin and Mackie 1989).  If choice of alternate ranges in winter was guided by a matriarch  then offspring should conform to the migration tendencies of their mothers. Sweanor and Sandegren (1988) reported similarity of behaviour among relatives in a population of partially migratory moose (Alces alces).  We  found no data to test the hypothesis for black-tailed deer; parenthood was unconfirmed, but female fawns appeared to follow their mothers during migration in this study.  This tactic must act in conjunction with the more  general tactic of maintaining suitable seasonal ranges where suitability is based upon resource conditions for a variety of needs but primarily seasonal forage quality and availability. Choices of seasonal ranges appeared to be strongly habitual, an action argued to be more common in static or predictable environments (Gass 1985). Again, as in philopatry and for the same reasons, we view fidelity to alternate ranges to be an adaptive advantage in old, coastal forests (especially when the choice is usually that of the matriarch). Local Movement:  Daily Scale.  -  Local movements represent decisions to move  between activity nuclei within which more refined decisions about resource use (serial moves) will be made.  At the daily scale, local moves indicate  64 habitat selection and specific decisions about resource use should be made within that choice of habitats.  Because habitats range widely in their  ability to meet the needs of deer we assume that habitat choices reflect a focus on 1 or several, but not all, needs (e.g., Miller 1970).  Local moves,  therefore, constrain the types of decisions about actual resources that may be acquired within the specific site chosen.  At this daily scale, specific  goals in resource use becomes more differentiated than at higher scales of resource use. Morgan (1994) and others (Klein 1965, Miller 1970) discussed intra seasonal adjustments in the locations of activity centres in relation to phenological changes in forage implying that deer move to new locations, expanding their seasonal ranges, as forage changes.  Morgan (1994) also  noted use of special activity sites located on the periphery or outside seasonal ranges and attributed movements to those locations for the purposes of parturition. movements.  His conclusion was based largely on the timing of the  Others have similarly considered such movements to unusual  locations as temporary escape from predators (Kufeld et al. 1988, Hoizenbein and Schwede 1989).  Our data (Fig. 3.2) also illustrate movements to  isolated sites, presumably to seek specific resources. We also noted resident and facultative migratory deer used natal areas twice the size of the areas used by obligates.  Because we assume few, if  any, barriers to movement during summer months, we concluded that the natal ranges of obligate migratory deer have the highest density of all resources sought by deer (Miller 1970).  Contrary to use of space on natal ranges,  obligate migratory deer used 25% more space on alternate ranges than either facultative migratory or resident deer.  On average they also had more  nuclei and changed between nuclei twice as often.  In winter, we assumed  65 barriers to movement would exist when interception of snow by forest canopies (Kirchhoff and Schoen 1987, McNay et al. 1988) was insufficient to arrest development of deep snowpacks.  Because snow buries forage we assumed  that mobility to locate remaining forage and windthrown arboreal forage would be an asset in forage rich communities (Harestad et al. 1982, Bunnell 1985) and a detriment where little or no forage was available (Parker et a!. 1984).  By evidence of their greater mobility, we concluded obligate  migratory deer likely had access to comparatively better habitats than other deer during periods with snow and local moves were primarily instigated by foraging decisions. Black-tailed deer have adopted local movement tactics that include diurnal timing and a tenacity for specific activity sites.  Similar to  philopatry and fidelity, these choices again represent habit although with a marked variation (Figs. 8 and 9).  Some of this variation has been  attributed to daily weather patterns (Miller 1970), to reproductive status (Hoizenbein and Schwede 1989), and to the occurrence of predators (Kufeld et al. 1988).  Presumably, because decisions on local movements and habitat  selection are made more frequently than those concerning home ranges or seasonal ranges, the ability to adapt to new resource conditions is greater than is apparent at those higher levels of resource use (Staddon 1983, Gass 1985, Senft et al. 1987). Summary.  -  Field studies that discuss movements in context of the complete  process of resource use are rare.  Orians and Wittenberger (1991) discussed  2 different spatial and temporal scales of habitat selection and concluded that goals about resource use were different at different scales.  We  describe 4 scales (temporal and spatial) of movement that form a functional hierarchy of resource use decisions.  Considered from the top of the  66 hierarchy, we observe constraints that occurred both as a function of the hierarchy and as a function of specific tactics.  Viewed from the bottom of  the hierarchy, we interpret a synthesis of resource use decisions that could form proximate instigation for higher scale decisions.  Decisions at the  hourly scale were frequent, flexible, and likely intended to achieve goals that were perhaps overlapping but not simultaneous.  For example, foraging  can be at the expense of thermal suitability, can increase exposure to predators, or can sacrifice conspecific connections (Stephens and Krebs 1986).  Goals at the daily scale are likely more general, somewhat flexible,  and must integrate the future value of resources over an entire season. Goals at the seasonal scale appear focused toward even more general conditions of resources, specifically those that change seasonally (e.g., snow conditions); facultative migratory behaviour strongly implies a focus on avoidance of extended temporal and spatially unfavourable environmental conditions.  Finally, at the life-time scale, choice of home range must  incorporate a synthesis of expectations for life-long resource conditions.  Implications of Constraint on Movement Decisions Philopatry, a constraint on species’ range expansion, on colonization, and on gene flow, also constrains the choice of migration tactics. to migration tactics constrains the range of habitat choices.  Fidelity  Tenacity for  specific sites constrains the range of resources and resource procurement tactics.  Together, this collection of tactics unifies different scales of  habitat selection.  But these tactics are, at the top of the hierarchy,  based on strong family bonds which limit mobility of individual black-tailed deer and thus amplify constraints on learning about altered resource conditions beyond those resulting from the hierarchical nature of resource  67 use.  Family groups, or demes, could be thought of as metapopulations (e.g.,  Harrison et al. 1988).  Hence, response to large-scale habitat alteration  becomes a function of metapopulation dynamics rather than the usual assumption of individual learning and subsequent redistribution based on habitat preferences (Pulliam and Danielson 1991).  Supporting evidence for  this effect can be found by considering the conclusions reached by others on such issues as dispersal and migration (Hamlin and Mackie 1989), genetics (Manlove et al. 1976), and general social behaviour (Hirth 1977). Matrilineal groups may imply competitive benefits because continued association with a matriarch and her resource choices ensures offspring will adopt and practise tactics that have worked in the past. tactic are beneficial when habitats change slowly.  We expect these  By far the most  widespread, rapid, and consistent alteration of deer home ranges is caused by logging and since the late 1960’s, this activity has been common throughout Vancouver Island.  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Kamil and T. D. Sargent, eds. Foraging behaviour. Garland STPM Press, N.Y. Zar, J. H. 1984. Biostatistical analysis. Cliffs, N.J. 718pp.  Prentice-Hall, Englewood  75 CHAPTER 4  -  SPATIAL AND TEMPORAL SCALES OF RESOURCE USE:  HABITAT USE  After seminal studies by Cowan (1945) and Einarsen (1946), forests exceeding 250 yr-of-age were considered relatively poor habitat for blacktailed deer.  Abundant forage and elevated deer use found in comparatively  young forests appeared to support this consideration (Brown 1961, Gates 1968, Smith 1968).  However, research conducted through more severe winters  (Willms 1971, Smith 1973, Jones 1975, Bloom 1978, Harestad 1979, Rose 1982, Schoen et al. 1985) demonstrated the importance of older forests.  Old  forests provide shelter from snow (Kirchhoff and Schoen 1987, McNay et al. 1988) which reduces costs of locomotion (Parker et al. 1984, Bunnell et al. 1990a,b) and rates of food burial (Jones 1975, Harestad 1979).  Some old  forests also provide abundant arboreal forage seldom found elsewhere (Stevenson 1978). We may assume from the notions above that, if winter habitats were chosen optimally, black-tailed deer would collectively spend most time in old forests (Schamberger and O’Neil 1986), especially in years of heavy snowfall.  Optimality, in this context, is largely a theoretical construct  of individual behaviour (reviewed by Schoener 1987) but can be integrated over populations of deer within another theory, the ideal-free distribution of animals (Fretwell and Lucas 1969, Fretwell 1972).  Ideal-free refers to  the main assumption in this model of habitat choice, that animals are free to choose habitats conferring greatest individual fitness. Little has been done, however, to test the notions of optimal habitat choices or ideal-free distributions in populations of black-tailed deer. Recent studies of habitat use (Jones 1975, Harestad 1985, Schoen and Kirchhoff 1990), for example, offer conclusions limited to the quality of old forests because data came from areas with only recent, little, or no  76 forest harvesting.  Although Yeo and Peek (1992) studied a wider range of  forest ages, and thus habitat choices, they were limited to low elevations and to non-migratory deer.  These limitations are not trivial because, at  minimum, animals must be sampled randomly, and all habitats must be available, before habitat use data can be used for inference about habitat quality or the optimality of habitat choices. Although it is tempting to consider habitat use as an index of habitat quality, we note that Hobbs and Hanley (1990) challenged Fagen’s (1988) direct association between habitat preference and habitat quality.  With  simulation modelling, they demonstrated that such an association assumes (1) the ideal-free distribution and (2) a long-term stable relationship between deer populations and the quality/quantity of their habitats.  Fagen (1988)  assumed only the former criterion while Hanley and Hobbs (1990) claimed the latter is rarely satisfied in nature. closely related.  We consider these criteria to be  If, for example, deer respond slowly to rapid changes in  habitat, not only would instability exist in the population/habitat association but the ideal-free distribution would be unlikely to hold. Habitat selection by individual deer would indicate past, rather than present, habitat conditions (Van Home 1983, Hobbs and Hanley 1990) making inference about optimal habitat choices unreliable.  Long lags in response  to change would make these inferences unjustifiable (Hobbs and Hanley 1990). We considered decisions that guide movements made by black-tailed deer to be constrained in 2 major ways, making their response to environmental change difficult (chapter 3).  First, because the decision process appears  to be hierarchical, superclass decisions could limit the breadth of choices for subclass decisions therefore producing a scalar constraining effect throughout the decision-making framework (Dawkins 1976, Allen et al. 1984,  77 Senft et al. 1987, Levin 1992).  Second, at each scale in the hierarchy, we  found evidence of specific tactics (i.e., philopatry and site fidelity) that reinforced this scalar constraint on movement decisions (Gass 1985, Fahrig and Paloheimo 1988).  These constraining effects led to our supposition that  black-tailed deer are unlikely to be distributed among habitats in an idealfree manner and hence individual habitat preferences should range widely as indicators of optimality.  This variance of habitat preference should be  explained in part by study area and/or by behaviourial effects which would otherwise not be revealed from data pooled over these factors (Thomas and Taylor 1990).  Here we have 2 broad objectives: (1) to examine habitat  preferences in context of the movement hierarchy to determine if there are general scalar constraints on habitat preferences and (2) to evaluate if knowledge of those scalar constraints confers greater insight or better interpretation of (i) an ideal-free distribution of black-tailed deer, (ii) apparent individual preferences for habitats, and (iii) habitat management initiatives for coastal British Columbia. These objectives are particularly important because, in British Columbia and southeast Alaska, old forests are ardently sought by the forest industry and by those that manage black-tailed deer (Schoen et al. 1981, Bunnell 1985).  Although management options have been proposed to resolve  this conflict in British Columbia (Bunnell 1985, Nyberg et al. 1986), the specific balance between the management options remains unclear and untested.  Our objectives will help clarify the relative quality of young  and old forests as deer habitat as a first step toward specifying the application of habitat management options.  78 STUDY AREAS We studied habitat selection by black-tailed deer at 4 locations on Vancouver Island, British Columbia from February 1982 through June 1991 (Table 4.1).  Specific physiographic parameters of study areas are provided  in chapter 3 (Table 3.1).  Nanaimo and Chemainus rivers are in neighbouring  valleys situated 43 km northeast of Caycuse River and 202 km southeast of Nimpkish River.  The Chemainus, Nanaimo, and Nimpkish rivers are  characterized by open, relatively flat-bottomed valleys (U-shaped) while Caycuse River ranges less in elevation but has steeper slopes and the least flat area at lower elevations (V-shaped). Extensive logging occurred throughout all study areas resulting in a range of habitats from recently clear-cut sites to old forests.  The spatial  arrangement of habitats was characteristic of historic, coastal logging patterns.  Initial harvests came from the bottom and downstream end of  valleys with subsequent harvests coming from the mid-slopes, and last from the headwaters and higher elevations.  Our study areas were all in a late  stage of harvest leaving most of the valley bottom in young, 6- to 45-yr-old forests and the mid-slopes deforested (0- to 5-yr-old clear-cuts) or in remnant patches of old forests. The dominant “zonal ecosystems” (Meidinger and Pojar 1991) on Vancouver Island are the Coastal Western Hemlock (CWH) zone at lower elevations and the Mountain Hemlock (MH) zone at higher elevations.  Western  hemlock (Tsuga heterophylla) is the most common tree species in the CWH, especially in old forests or in young forests at high elevations or cool aspects.  Western red cedar (Thuja plicata) and Douglas fir (Pseudotsuga  menziesii) are widespread and young forests are dominated by Douglas fir. Western hemlock is also the dominant tree species in the MH zone but  79 Table 4.1. Total sample sizes for deer and habitat samples recorded at 4 study areas located on Vancouver Island, British Columbia, 1982-1991.  Study Areas Samples  Caycuse  Chemainus  Nanaimo  Nimpkish  13  8  40  11  deer locations  1,582  784  5,261  860  deer home ranges (ha)  1,877  1,117  11,906  1,552  111  33  145  41  89-91  89-91  82-91  89-91  deer  study areas (km ) 2 years studied  80 amabilis fir (Abies ainabilis) and yellow-cedar (Chamaecyparis nootkatensis) are common.  Climate in both zones is temperate and wet.  In the CWH zone,  there is no month with a mean temperature <0 C and the mean temperature of the warmest month is 17 C (Meidinger and Pojar 1991). 291 frost free days and 82 cm of snow each year.  On average there are  The driest month averages  65 mm of precipitation and the mean annual precipitation is 2,140 mm.  METHODS Deer Location Samples and Habitat Use Methods for capturing deer, attaching radio-collars, and monitoring deer locations are described in detail in chapter 3.  Weekly samples for  collared-deer locations began in February 1982 at Nanaimo River and in February, or March, 1989 at other study areas and continued until death of the deer or June 1991.  In 1984, sampling was standardized so that, during a  calendar month, each deer was located at least once-per-week and once within each quarter of a calendar day. Triangulation data (White and Garrott 1990) were collected from permanent sampling stations at 100-rn intervals along forest roads.  In the  field, if at least 3 bearings intersected at 1 general site then bearing information was recorded along with signal frequency, time of day, and date. Because each study area had an extensive network of roads, we were able to collect data quickly and in close proximity to collared deer.  As a result,  error polygons (Lenth 1981) were usually <1.0 ha (McNay et al. 1994).  Home  ranges were estimated as the 95% minimum convex polygon (White and Garrott 1990:343). Habitat use and availability was estimated by querying forest cover and topographic maps, stored on Geographic Information Systems (Terrasoft;  81 Digital Resources, Nanaimo, B.C. and PAMAP; PAMAP Technologies Corp., Victoria, B.C.), at specific Universal Transverse Mercator grid co-ordinates representing polygons (bounded by study area corners or by home range vertices) and points (individual deer locations). Snow depth was recorded daily at airports near each study area and were supplied to us by the Atmospheric Environment Service (Environment Canada, Vancouver, B.C.).  Definitions and Habitat Features We defined natal ranges as areas occupied during the natal period, which for deer in coastal British Columbia is usually late-May through June (Cowan 1956, Thomas 1970).  We determined that annual migrations occurred  just prior to the natal period (chapter 3) so we assumed that the natal range included, or was adjacent to, the birth-site for non-dispersing deer (Masters and Sage 1985, McCullough 1985, Hamlin and Mackie 1989) and would be where subsequent offspring were produced each year.  Spatially separate  ranges occupied at other times were identified by migrations made between those areas and the natal range, and were termed alternate ranges. Migrations differed from dispersal or nomadic movements in that, whenever deer migrated, they would make return migrations to the original location (Sinclair 1984).  Dispersal involved no such predictable return to the  original location (chapter 3).  Deer behaviour types were determined on the  basis of seasonal movements where migratory deer made migrations and resident deer did not.  We defined activity nuclei (chapter 3) after Don and  Rennolls (1983) as isolated patches of relatively concentrated and repetitive use within individual home ranges.  Activity nuclei had 200-m  radii, the centres of which were the highest point(s) chosen from a  82 graphical representation of the harmonic utilization distribution (Dixon and Chapman 1980, chapter 3). Habitat types were characterised by 3 different vectors: (1) forests were old if >250-yr-old, young if 6- to 45-yr-old, or open if either 0- to 5-yr-old, non-commercial forest (subalpine or alpine), or non-forest (rock or water); (2) elevations asl were <400 m, 401-600 m, 601-800 m, or >800 m; and (3) aspects were north if 316° to 45°, east if 46° to 135°, south if 136° to 270°, west if 271° to 315°, or flat.  Chosen in this way, habitat  polygons typically exceeded 60 ha (e.g., statistics for the forest vector at Nanaimo River were:  =  65.5; n  =  199, SE  =  0.08) or >60 times the size of  most error polygons surrounding estimated locations for deer.  Summer was  designated May through October and winter designated November through April. Annual periods were from May 1 through April 30 of the following calendar year. Terms used to describe habitat selection vary and have been used indiscriminately (Thomas and Taylor 1990). recommended interpretations.  We followed Johnson’s (1980)  Specifically, abundance was the quantity of  habitats in the environment (study area), availability was the habitats’ accessibility to deer (that within individual home ranges), use was the quantity actually visited within a time period (seasonal locations for individual deer), selection was the process of choosing to visit habitats, and preference reflected the likelihood of choosing a particular habitat if all habitats were offered equally.  Our study was based on multiple spatial  scales of habitat use: (1) preference for home ranges within broad study areas, (2) preference for seasonal habitats within home ranges, and (3) use of “activity nuclei” (after Don and Rennolls 1983) within seasonal habitats by migratory and by resident deer both within and among study areas.  83 Preference for home ranges was assessed on the basis of the quantity of study area habitats while preference for seasonal habitats was related to accessibility of habitats to individual deer within their home ranges. Although study area boundaries were subjectively delineated, we considered our study areas large enough to negate the effects of habitat pattern on estimates of abundance (Porter and Church 1987, Verbyla and Chang 1994).  To corroborate this, we estimated relative diversity (Shannon 1948)  for the forest vector at Nanaimo River beginning with many 1-ha study areas then increasing study area sizes gradually to 11,000 ha, close to actual study area size (14,500 ha).  Habitat diversity for such a study area was  78% of the potential diversity obtainable if all forest types had been equally abundant.  The index began to oscillate and plateau, however, at a  hypothetical study area size of 1,600 ha.  We concluded that our study areas  were large enough to capture most of the forest type diversity at Nanaimo River and assumed this to be the case for other habitat vectors there and at other study areas.  Analytical Procedures We assessed data distributions using PROC UNIVARIATE (SAS Inst. Inc. 1985) and assessed independence of observations in deer movements (Swihart and Slade 1985) in a related study (McNay et al. 1994).  Based on those  analyses we assumed that locations taken once-per-week were independent, systematic samples of use of space.  Chi-square tests (Sokal and Rohlf 1981)  were used to assess homogeneity of habitat types among study areas. We attempted to counter unequal availability of habitat types (Johnson 1980, Thomas and Taylor 1990) using Manley’s measure of preference, a, (Manley 1974, Chesson 1978, 1983), which we interpreted as the proportion of  84 habitat use that would be of type i if all habitats were equally abundant or available.  The calculation is:  Ii  n.1  1=1  (4.1)  1 is the number of times habitat type I is selected, n where r 1 is the relative abundance or availability of habitat I, and m is the number of habitats considered.  Furthermore, we kept m low so at least some of each  type would be available to all deer (forests m aspects m  =  3, elevations m  =  =  4, and  5), and to limit the potential for experimentwise error (Thomas  and Taylor 1990).  Since a values must sum to 1.0 (Chesson 1983), random use  of seral age habitats occurred if  =  2 a  =  3 a  of aspect and elevation habitats occurred if a respectively.  0.33.  =  =  Similarly, random use  0.20 and 0.25,  We evaluated variation in habitat selection tactics among  individual animals (Thomas and Taylor 1990) by testing homogeneity of preferences within groups of deer (Manley 1974):  S  ()2  ’ 2 x 3VAR(c ) 1  ,  j1,..  (4.5) .  where s is the number of deer within the group and other symbols are as  85 above. We tested for potential effects of study area and a deer behaviour type x range type variable (migratory natal ranges, migratory alternate ranges, or resident ranges), and the interaction of those effects, on preference for home ranges within study areas using multivariate analysis of variance (MANOVA, SAS Inst. Inc. 1985).  Separate MANOVAs were used for each  habitat vector because our sample sizes were insufficient to consider the interactions of habitat vectors.  MANOVAs for elevation and aspect vectors  did not include data for Nimpkish River because they were unavailable.  We  used the same approach to test for similar effects (study area and deer behaviour as either migratory or resident) on preferences for seasonal habitats within individual home ranges.  Because habitat vectors were  linearly dependent (Manley’s a values sum to 1.0) we obtained overall F estimates (Wilks’ A) by omitting 1 a value producing a new vector length of in  -1.  Parameters for the omitted a were determined in a subsequent  univariate analysis of variance (ANOVA, SAS Inst. Inc. 1985). were significant (P  <  When effects  0.05), differences between adjacent means (e.g., among  habitats within a single vector) were compared using Duncan’s multiple range t-tests with P 1980).  =  0.10.  All means are least-squares estimates (Searle et al.  Paired-sample tests (Zar 1984) were used to test for significant  differences in habitat preferences between range types for migratory deer and between seasons for all deer. We tested for the effect of year (Schooley 1994), and it’s interaction with deer behaviour type, on seasonal habitat preferences at Nanaimo River where we had the longest data set (9 yr; Table 4.1).  Within a single  vector, individual dependent variables with significant annual variation were correlated with total annual snowfall (Nanaimo airport), proportion of  86 annual deer samples that were migratory deer, total number of deer sampled annually, and year of study using Spearman’s Rank correlation (SAS Inst. Inc. 1985). Finally, the potential effects of study area and deer behaviour type on use of activity nuclei was assessed using logistic regression (CATMOD, SAS Inst. Inc. 1985).  We obtained maximum likelihood estimates for the  probability of specific habitats being the primary (i.e., the highest percentage) component of individual nuclei.  RESULTS Sample Characteristics We collared most deer (56%) and collected most samples of deer locations (62%) at Nanaimo River where our study lasted longest (Table 4.1). Our sample was dominated by resident deer (n  =  44 or 61%), especially at  Chemainus and Nimpkish rivers where only 2 deer at each site were migratory (chapter 3). Abundance of habitats varied significantly among study areas for forests 6; P  <  2 (x  =  1016.75; df  =  6; P  0.001), and for aspects  <  2 (x  0.001), for elevations =  1863.26; df  =  8; P  <  2 (x  =  2994.44; df  0.001).  =  Although  young forests dominated each study area (Fig. 4.1), notable deviations occurred primarily at Chemainus River where more young, and less old, forest existed than would be expected under the assumption of homogeneity.  Other  deviations from homogeneity were the lack of open forest and the abundance of old forest at Nimpkish River and the abundance of open forest at Caycuse River (Fig. 4.1).  Caycuse River had less area >800 m and more area <400 m  than other study areas (Fig. 4.2).  Also, little area existed <400 m at  Chemainus River and, at Nanaimo River, more area existed >800 m than would  87  Caycuse River  Chemainus River  80 $todyo,,e  Home reogos Loretion,  60 50 40 30 20  0’  10  0  0  0  a)  Open  Young  Nanaimo River  C.)  80  I Nimpkish River  70  0  60 50 40 30 20  icili Old  Open  Young  Seral age classes  Figure 4.1 Forest seral age class abundance (total within study area), availability (total within home ranges), and use (locations from radiocollared, black-tailed deer) at 4 study areas on Vancouver Island, British Columbia, 1982-1991.  88  50  Caycuse River  50  Chemainus River  $hdy.rea  40  40  30  30  20  20  10  10  0 Cu 0  <400 401-600  0  50  0 C.) 0  40  0  0 >800  <400 601 -800 401 -600 >800  Nanaimo River  30 20 10 0  [1  <400 601 -800 401 -600 >800 Elevation classes (m asi)  Figure 4.2 Elevation class abundance (total within study area), availability (total within home ranges), and use (locations from radiocollared, black-tailed deer) at 3 study areas on Vancouver Island, British Columbia, 1982-1991.  89 be expected under the assumption of homogeneity.  The most notable  deviations from homogeneity of aspects was in Caycuse River where there was the least flat habitat and the most habitat with a western aspect (Fig. 4.3).  Home Range Preferences: Life-time Scale Homogeneity of Preferences. habitats.  -  Generally, deer preferred a wide range of  We rejected homogeneity of preferences in all tests concerning  forest types (n  =  3 habitats x 3 range types x 4 study areas  =  36 tests).  In only 3 of 36 elevation tests (4 habitats x 3 range types x 3 study areas), and in only 8 of 45 aspect tests (5 habitats x 3 range types x 3 study areas), were deer considered to have a similarity of preferences (P  >  0.05). Analysis of Main Effects.  -  Deer preferences for forests varied strongly  with study area and marginally with migration tactics and the type of range being used (Table 4.2).  Migration tactics and the type of range strongly  effected deer preferences for elevations while study area, and the interaction of study area and range type, had little effect (Table 4.2). Finally, deer preferences for aspects did not vary significantly with any of the main effects that we assessed (Table 4.2). Deer generally preferred young forests over old and preferred open forests least (pooled home range types: Table 4.3).  This lack of preference  for open forests was most evident at Nimpkish River (a where abundance of that type was low (Fig. 4.1).  =  0.02; Table 4.3)  Migratory deer deviated  notably from these general trends in their preference for old forests on alternate ranges (pooled study areas: a trend across all study areas.  =  0.47; Table 4.3), a consistent  Resident deer, by comparison, generally did  90  Caycuse River  70  Chemainus River  70  $h.dyoma  60  60  Home ranges Lonoee  Cu 0  50  50  40  40  30  30  20  20  10  10  0  Flat  East West North South  0  Flat  East West North South  .4-  0  Nanaimo River  70  a)  C.) I  60  0.  50 40 30 20  Flat  West South  EUL  North  Aspect classes Figure 4.3 Aspect class abundance (total within study area), availability (total within home ranges), and use (locations from radio-collared, blacktailed deer) at 3 study areas on Vancouver Island, British Columbia, 19821991.  2.31 1.03  Range type  Interaction  1.76 1.38  Behaviour  Interaction  2.45 6.49 1.61  Study area  Behaviour  Interaction  Seasonal Habitats: Winter  3.29  Study area  Seasonal Habitats: Surmier  3.43  Study area  Home Range  6,390  2,195  6,390  6,334  2,167  6,334  12,172  4,172  6,172  df(n,d)  and variables F  Serat age  Selection level  0.143  0.002  0.024  0.221  0.175  0.004  0.426  0.060  0.003  P  4.43  8.88  10.85  3.72  4.35  1.81  1.33  2.80  1.14  F  6,340  3,170  6,340  6,302  3,151  6,302  12,196  6,148  6,148  df(n,d)  Elevation  Habitat vectors  <0.001  <0.001  <0.001  0.001  0.006  0.097  0.202  0.013  0.340  E  3.14  1.47  3.39  1.94  0.80  3.02  0.78  1.34  1.42  F  8,334  4,167  8,334  8,300  4,150  8,300  16,224  8,146  8,146  df(n,d)  Aspect  0.002  0.214  0.001  0.055  0.527  0.003  0.286  0.227  0.192  P  Table 4.2. Multivariate analysis of variance results for tests of study area, deer behaviour (migration tactics and range types for selection of home ranges and migration tactics for selection of seasonal habitats), and the interaction of those main effects, on habitats preference (aj)b estimates for radio-collared, black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 1982-1991.  I.  cc  3.64 1.57  Behaviour  Interaction 6,286  2,143  6,286  0.157  0.029  0.141  P  2.07  2.17  0.60  F  6,254  3,127  6,254  df(n,d)  Elevation  Habitat vectors  0.057  0.095  0.727  P  1.49  0.59  1.02  F  8,250  4,125  8,250  df(n,d)  Aspect  0.160  0.672  0.418  P  r’3  Habitat vectors formed the dependent variables for 3 separate multivariate analysis of variances where habitats vectors were: (1) forest seral age (open, young, or old); (2) elevation (<400 m, 401 to 600 in, 601 to 800 m, or >800 in); and (3) aspect (316° to 45°, 46° to 135°, 136°to 270°, 271’ to 315°, or flat). b Preference estimates for each habitat were calculated for individual deer according to Chesson (1983) first coirparing home range habitats with study area habitats (home range selection level) and second, comparing habitats at deer locations with home range habitats (seasonal habitat selection level). Independent variables assessed in the multivariate analysis of variance were study area (Caycuse, Chemainus, Nanaimo, and Ninkish Rivers) and a deer behaviour x range type variable (migratory deer natal ranges, migratory deer alternate ranges, or resident deer natal ranges) at the home range level or study area and deer behaviour (migratory or resident deer) at the seasonal habitat level.  1.62  Study area  Seasonal Habitats: Paired sample difference  df(n,d)  and variables° F  Seral age  Continued.  Selection level  Table 4.2.  2 6 9  M-N  R-N  Pooled  17 17 23 57  M-A  M-N  R-N  Pooled  Nanaimo River  1  1.1-A  Chemainus River  20  6  R-N  Pooled  7  M-N  n  estimates  7  -  (a,)”  M-A  Caycuse River  home ranges  Study area and  Table 4.3. Habitats preference British Coluitia, 1982-1991.  0.34  0.39  0.35  0.27  0.28  0.31  0.13  0.41  0.25  0.32  0.32  0.12  a,  Open  0.03  0.04  0.05  0.05  0.08  0.08  0.14  0.19  0.04  0.08  0.O7AB  0.07  SE  0.42  0.41  0.45  0.42  0.39  0.52  0.52  0.13  0.43  0.41  0.58  029  a,  Young  0.04  0.06  0.07  0.07  0.12  0.11  0.20  0.28  0.06  0.11  0.11A  0.11  SE  Habitat vectors  0.24  0.20  0.20  0.31  0.33  0.17  0.35  0.46  0.32  0.27  0.10  0.59  a,  Old  0.03  0.05B  0.06B  0.06  0.11  0.10  0.18  0.26  0.06  0.10  0.1OB  0.1OA  SE  for 28 migratory and 44 resident, radio-cot lared, black-tailed deer at 4 study areas on Vancouver Island,  Continued.  2 9 13  M-N  R-N  Pooled  28 44  M-N  R-N 0.26  0.20  0.20  0.02  0.03  0.02  0.00  a,  Open  0.03  0.05  0.06  O.07x  O.06B  0.14  O.14B  SE  0.48  0.63  0.33  0.68  0.58  0.97  0.49  Young  0.05  0.08  0.09  0.10  0.09  O.20A  0.20  SE  Habitat vectors  0.26  0.17  0.47  0.30  0.39  0.01  0.51  1 a  Old  0.04  0.07  0.08w  0.09  0.09  0.18  0.18  SE  cO  Habitats were: open if rock, water, subalpine, alpine, or forested but <6 yr old; young if 6- to 45-yr-old; and old if >250-yr-old. Mean preference estimates for individual deer within study areas and behaviour groups (n) were calculated according to Chesson (1983), comparing home range habitats with study area habitats, and sum to 1 within a habitat vector (row). With 3 habitats, a = 0.33 indicates random use while greater a’s indicate preference and lesser a’s indicate lack of preference. Different letters within a row (upper case), or within a column for pooled estimates (lower case), indicate differences in means (Duncan’s-adjusted t-tests; E < 0.10). Home ranges, N for natal areas and A for alternate areas, were for resident deer, R, and migratory deer, M.  27  M-A  Pooled Study Areas  2  n  M-A  Nimpkish River  home ranges  Study area and  TabLe 4.3.  95 not prefer old forests except at Nimpkish River where the percent abundance of that particular forest type was greatest (Fig. 4.1).  Deer generally did  not prefer elevations <400 m or >800 m (pooled study areas: Table 4.4).  On  natal ranges, however, migratory deer contradicted that trend by strongly preferring elevations >600 m.  Although only significant in particular cases  (Table 4.5), preference for southern aspects tended to dominated all home ranges as did a general lack of preference for western aspects. Paired Difference in Preferences for Home Ranges by Migratory Deer.  -  Migratory deer preferred habitats differently when occupying alternate ranges than when occupying natal ranges. forest did not change (a  =  -0.09, T  for old forests increased (a  -1.59; n  =  0.25, T  =  While their preferences for open  =  3.29; n  preference for young forests decreased (a 0.014) on alternate ranges (Table 4.3).  =  27; P =  =  0.023) and elevations >800 m less (a  0.001) than on natal ranges (Table 4.4).  =  =  =  -0.16, T  24; P  0.003) and  =  -2.63; n  =  0.18, T  =  -0.27, T  =  =  -3.72; n  =  27; P  =  2.43; n  =  24; P  =  =  24;  Their preference for southern  aspects (Table 4.5) on alternate ranges also increased (a  n  0.123), preference  Also while on alternate ranges,  migratory deer preferred elevations <400 m more (a P  27; p  -0.16, T  =  =  =  0.24, T  =  2.22;  0.036) while their preference for northern aspects decreased (a  =  =  -2.12; n  Habitat Preferences:  =  24; P  =  0.045).  Seasonal Scale  Homogeneity of Preferences.  -  We had the opportunity to assess homogeneity  of preferences for forests in 208 tests (habitats x years x movement types x study areas) but only 78 of these had >5 deer/group. rejected in 57 of these 78 tests (P  <  0.05).  Homogeneity was  The cases in which deer did  have similar preferences were resident deer preferences for old forests (12  6  R-N  2 6 10  M-N  R-N  Pooled  17 17 23 55  M-A  N-N  R-N  Pooled  Nanaimo River  2  M-A  Chemainus River  20  7  M-N  Pooled  7  n  M-A  Caycuse River  home rangesc  Study areas and  -  0.17  0.16  0.11  0.23  0.09  0.19  0.02  0.06  0.30  0.55  0.00  035  <400 m  0.04  0.06C  0.07  0.O7AB  0.11  0.12  0.21  0.21  0.07  0.12A  0.11  0.11  SE  0.36  0.45  0.28  0.35  0.31  0.35  0.43  0.15  0.25  0.33  0.06  0.35  1 a  0.03  0.05A  0.06  0.06A  0.09  0.10  0.18  0.18  0.06  0.1OAB  0.10  0.10  SE  401 -600 m  1 a  0.31  0.31  0.30  0.32  0.30  0.22  0.26  0.43  0.27  0.11  0.44  0.03  0.05B  0.05  0.05A  0.08  0.09  0.15  0.15  0.05  0.O9BC  0.08A  0.08  SE  601 -800 m  0.26  Habitat vectors  0.16  0.08  0.31  0.10  0.30  0.24  0.29  0.36  0.18  0.01  0.50  0.04  a,  >800 m  0.03  0.04C  0.05  0.05B  0.08  0.09  0.15  0.15  0.05  0.09C  0.08A  0.08  SE  Table 4.4. Habitat preference (a1)” estimates for 26 migratory and 35 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver Island, British Columbia, 1982-1991.  cc  Continued.  26 35  M-N  R-N 0.30  0.04  0.21  a,  <400 m  0.06  0.08x  0.08  SE  0.38  0.25  0.28  a,  0.05  0.07  0.07  SE  401 -600 m a,  0.21  0.34  0.04  0.06  0.06  SE  601 -800 m  0.34  Habitat vectors  0.11  0.37  0.17  a,  >800 m  0.04  0.06w  0.06  SE  Habitats were elevations in m above sea level (asl) as indicated. Mean preference estimates for individual deer within study areas and behaviour groups (n) were calculated according to Chesson (1983), comparing home range habitats with study area habitats, and sun to 1 within a habitat vector (row). With 4 habitats, a = 0.25 indicates random use while greater a’s indicate preference and lesser a’s indicate lack of preference. Different letters within a row (upper case), or within a coluir for pooled values (lower case) indicate differences in means (Duncan’s-adjusted t-tests; P < 0.10). Home ranges, N for natal areas and A for alternate areas, were for resident deer, R, and migratory deer, M.  26  n  M-A  Pooled Study Areas  home ranges  Study areas and  Table 4.4.  6  R-N  2 6  10  M-N  R-N  Pooled  17  17  23  55  H-A  M-N  R-N  Pooled  Nanaimo River  2  M-A  Chemainus River  20  7  H-N  Pooled  7  n  M-A  Caycuse River  home ranges’  Study areas and  -  0.17  0.10  0.27  0.13  0.20  0.00  0.36  024  0.17  0.27  0.21  0.03  North  0.15  0.O4BC  0.O5AB  0.O5BC  0.15  0.08  0.15  0.15  0.05  0.08  008  0.08  SE  0.15  0.15  0.18  0.13  0.09  0.23  0.05  0.01  0.18  0.12  0.30  0.11  1 a  East  0.02  0.04B  0.O4BC  0.O4BC  0.06  0.07  0.12  0.12  0.04  0.07  0.07  0.07  SE  0.50  0.55  0.36  0.58  0.48  0.56  0.41  0.46  0.40  0.50  0.21  0.48  1 a  South  0.04  0.07A  0.08A  0.08A  0.12  0.13A  0.23  0.23  0.07  0.13A  0.12  0.12A  SE  Habitat vectors  0.04  0.03  0.09  0.01  0.02  0.01  0.01  0.04  0.14  0.04  0.21  0.17  a,  West  0.02  0.03C  0.03C  0.03C  0.05  0.05  0.09  0.09  0.03  0.05  0.05  0.05  SE  0.14  0.17  0.10  0.15  0.21  0.20  0.17  0.25  0.11  0.07  0.07  0.21  1 a  Flat  03  05B  06C  06B  09  10  18  18  06  10  09  09  SE  Table 4.5. Habitata preference (a) ’ estimates for 26 migratory and 35 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver IsLand, t British CoLumbia, 1982-1991.  co cx  Continued.  26  35  M-N  R-N 0.13  0.28  0.13  a,  North  0.04  0.06  0.06  SE  0.17  0.18  0.08  a,  East  0.04  0.05  0.05  SE  0.54  0.33  0.51  a,  South  0.07  0.09  0.09  SE  Habitat vectors  0.02  0.10  0.08  a,  West  0.03  0.04  0.04  SE  0.14  0.11  0.20  a,  Flat  05  07  07  SE  c)  Habitats were aspects: north (3160.450), east (4601350), south (1360.2700), west (271°-315°), or flat. Mean preference estimates for individual deer within study areas and behaviour groups (n) were calculated according to Chesson (1983), comparing home range habitats with study area habitats, and sum to 1 within a habitat vector (row). With 5 habitats, a = 0.20 indicates random use while greater a’s indicate preference and lesser a’s indicate lack of preference. Different letters within a row (upper case), or within a coluiri for pooled estimates (lower case), indicate differences in means (Duncan’s-adjusted t-tests; P < 0.10). Home ranges, N for natal areas and A for alternate areas, were for resident deer, R, and migratory deer, M.  26  n  M-A  Pooled Study Areas  home ranges  Study areas and  Table 4.5.  100 of 20 tests) and migratory deer preferences for open forests (4 of 6 tests). We had enough deer to assess homogeneity of preferences for elevations in 92 of 224 cases (habitats x years x movement types x study areas).  Only  13 of these 92 tests failed to reject the hypothesis of homogeneity (P  >  0.05); these were mostly resident deer preferences for elevations >800 m (8 of 16 tests). Similarly, we had enough deer to assess homogeneity of preferences for aspects in 115 of 280 cases (habitats x years x movement types x study In 40 of these 115 tests, we failed to reject the hypothesis of  areas).  homogeneity (P  >  0.05) and only 3 of those 40 were associated with aspects  other than north, west, or flat.  Resident deer preference for north, west,  or flat aspects composed most (29 of 40) cases of homogeneous preference. Summer.  -  Deer did not vary their summer-time preferences for habitat across  year-of-study for forests (F elevations (F =  28, 366; P  =  =  0.82; df 0.900).  =  =  1.07; df  21, 293; P  =  =  16, 206; P  =  0.390), for  0.696), or for aspects (F  =  0.67; df  Their preferences for forests and aspects did vary  significantly with study area while their preferences for elevations varied significantly with migration tactics and with the interaction of those tactics and study area (Table 4.2). Generally, deer preferred young forests and avoided old forests in summer (Table 4.6).  The effect of study area was largely due to a lack of  preference for open forest by deer at Nirnpkish River, where little open forest was available (Fig. 4.1).  As a result, deer at Nimpkish showed a  comparatively strong preference for young forest (Table 4.6).  Deer  generally used all elevations in a more balanced fashion within their summer ranges than was indicated by their preferences for elevations at the home ranges level (compare pooled study areas: Tables 4.4 and 4.7).  Migration  24 13 8 21  Pooled-S  14-U  R-W  Pooled-U  4 11 15 6 12 18  M-S  R-S  Pooled-S  M-W  R-W  Pooled-U  Chemainus River  10  R-S  estimates  14  -  (a,)b  M-S  Caycuse River  seasonal ranges  Study area and  Table 4.6. Habitats preference British Colitia, 1982-1991. for  0.31  0.24  0.37  0.28  0.42  0.14  0.40  0.50  0.30  0.47  0.46  0.47  1 a  Open  0.06  0.07  0.09  0.08  0.08  0.14  0.05  0.08  0.06  0.06  0.09  0.07  SE  0.45  0.67  0.23  0.51  0.51  0.51  0.39  0.41  0.36  0.47  0.44  0.49  a,  Young  0.07  0.08A  0.12  0.09  0.09  0.15  0.06  0.10  0.08  0.06  0.09  0.08  SE  Habitat vectors  0.24  0.09  0.40  0.21  0.07  0.35  0.21  0.09  0.34  0.06  0.10  0.04  a,  Old  0.07  0.08  0.11  0.06  0.06B  0.10  0.06  0.1OB  0.08  0.04  0.06B  0.05B  SE  28 migratory and 44 resident, radio-collared, black-tailed deer at 4 study areas on Vancouver Island,  cD  I—i  Continued.  68 121 57 83 140  R-S  Pooled-S  M-W  R-U  Pooled-U  3 13 16 5 20 25  N-S  R-S  Pooled-S  M-W  R-W  Pooled-U  Nimpkish River  53  n  M-S  Nanaimo River  seasonal ranges’  Study area and  Table 4.6.  0.12  0.17  0.07  0.09  0.19  0.00  0.27  0.32  0.22  0.37  0.39  0.35  a,  Open  0.Oóx  0.05  0.108  0.09x  0.08  0.16  0.02  0.028  0.03C  0.03  0.03  0.04B  SE  0.61  0.65  0.57  0.82  0.69  0.95  0.47  0.48  0.46  0.48  0.46  0.50  a,  Young  0.07  0.06A  0.13A  0.09w  O.08A  O.17A  0.02  O.03A  0.04A  0.03  0.04  0.04A  SE  Habitat vectors  0.27  0.18  0.36  0.09  0.12  0.05  0.26  0.20  0.32  0.15  0.15  0.15  a,  Old  0.07  0.06  0.12A8  0.06  0.05  0.11  0.02  O.03C  O.04B  0.02  0.03B  0.03C  SE  I  Continued.  81 123  M-W  R-W 0.31  0.24  0.37  0.24  1 a  Open  0.03  0.04  0.04  0.06  SE  0.55  0.41  0.52  0.61  1 a  Young  0.04w  0.05  0.04  0.06  SE  Habitat vectors  0.14  0.35  0.11  0.15  a,  Old  0.04  0.05w  0.03  0.04  SE  Habitats were: open if rock, water, subalpine, alpine, or forested but <6-yr-old; young if 6- to 45-yr-old; and old if >250-yr-old. Mean preference estimates for individual deer within study areas, behaviour groups, and seasons (n) were calculated according to Chesson (1983), comparing habitats at estimated deer locations with home range habitats, and stin to 1 within a habitat vector (row). With 3 habitats, a = 0.33 indicates random use while greater a’s indicate preference and lesser a’s indicate lack of preference. Different letters within a row (upper case), or within a column for pooled seasonal values (lower case), indicate differences in means (Duncan’s-adjusted t-tests; E < 0.10). Seasonal ranges were Sumer (S), May through October, and Winter (W), November through April, for resident deer, R, and migratory deer, ii.  102  R-S  b  74  n  M-S  Pooled Study Areas  seasonal ranges’  Study area and  Table 4.6.  10 24 13 8 21  R-S  Pooled-S  M-W  R-W  Pooted-W  4 11 15 6 12 18  M-S  R-S  Pooled-S  M-W  R-W  Pooled-W  Chemainus River  14  n  M-S  Caycuse River  seasonal ranges  Study areas and  -  0.03  0.07  0.00  0.07  0.14  0.00  0.38  0.49  0.27  0.27  0.53  0.00  <400 m  0.07  0.08  0.12C  0.08  0.09  0.14  0.07w  0.1OA  0.08  0.06  0.09A  0.08  SE  0.36  0.53  0.18  0.28  0.24  0.32  0.33  0.34  0.32  0.22  0.32  0.13  0.07  0.08A  0.11  0.09  0.09  0.15  0.06  0.1OAB  0.08  0.06  0.1OAB  0.08  SE  401 -600 m 1 a  0.27  0.25  0.29  0.25  0.20  0.30  0.24  0.17  0.31  0.34  0.15  0.06  0.07  0.10  0.08  0.08  0.14  0.06  0.O9BC  0.07  0.06  0.O9BC  0.07A  SE  601 -800 m  0.54  Habitat vectors  0.34  0.15  0.53  0.40  0.42  0.38  0.05  0.00  0.10  0.17  0.00  0.33  a  >800 m  0.04w  0.04  0.06A  0.09  0.09  0.15  0.03  0.05C  0.04  0.06  0.09C  0.08B  SE  Table 4.7. Habitat’ preference (a)” estimates for 26 migratory and 35 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver Island, British Columbia, 1982-1991.  I—’  cD  Continued.  68 120 57 82 139  R-S  Pooled-S  M-W  R-W  Pooled-W  89 76 102  R-S  M-W  R-W 0.23  0.14  0.27  0.05  0.16  0.15  0.17  0.14  0.15  0.13  a,  <400 m  0.05  0.05  0.04w  0.06  0.02  0.03C  0.04C  0.03  0.03  0.04B  SE  0.44  0.33  0.30  0.24  0.47  0.44  0.49  0.31  0.34  0.28  a,  0.04  0.05  0.05  0.06  0.02  0.03A  0.04A  0.03  0.04A  0.04  SE  401 -600 m 1 a  0.26  0.29  0.23  0.38  0.31  0.36  0.26  0.32  0.34  0.04  0.04  0.04  0.05  0.02  0.03B  0.03B  0.03  0.03A  0.04  SE  601 -800 m  0.30  Habitat vectors  0.07  0.24  0.20  0.33  0.07  0.05  0.08  0.23  0.17  0.29  a,  >800 m  0.02  0.02w  0.04  0.06  0.01  0.02D  0.020  0.03  0.04  0.04  SE  C),  I-,  Habitats were elevations in m above sea leveL (asl) as indicated. Mean preference estimates for individuaL deer within study areas, behaviour groups, and seasons (n) were calculated according to Chesson (1983), comparing habitats at estimated deer locations with home range habitats, and sum to 1 within a habitat vector (row). With 4 habitats, a = 0.25 indicates random use while greater a’s indicate preference and lesser a’s indicate lack of preference. Different letters within a row (upper case), or within a column for pooled seasonal estimates (lower case), indicate differences in means (Duncan’s-adjusted t-tests; < 0.10). Seasonal ranges were Sumer (5), May through October, and Winter (W), November through ApriL, for resident deer, R, and migratory deer, M.  70  M-S  Pooled Study Areas  52  n  M-S  Nanaimo River  seasonal ranges  Study areas and  Table 4.7.  106 tactics affected preferences for elevation during summer because migratory deer generally exhibited a strong lack of preference for elevations <400 m while preferring elevations >600 m (pooled study areas: Table 4.7).  By  comparison, residents were less responsive to elevation except at Nanaimo where they preferred elevations 401-600 m and 601-800 m (Table 4.7). Relative to deer preferences for aspects in entire home ranges (pooled study areas: Table 4.5), preferences for aspects within summer ranges were comparatively more balanced between northern, eastern, and southern aspects with deer again showing lack of preference for western aspects and flat areas (pooled study areas:  Table 4.8).  Study area affected summer-time  preferences for aspects largely because deer at Chemainus River had a strong preference for eastern aspects which were otherwise used randomly (Table 4.8). Winter.  Deer varied their winter habitat preferences with year-of-study  -  for all habitat features: forests (F elevations (F 36, 429; P  =  =  1.86; df  =  2.58; df  =  27, 342; P  =  =  18, 238; P  =  0.007), and aspects (F  0.001), =  1.62; df  =  Study area also affected deer preferences for all  0.014).  habitat features (Table 4.2) and preferences for forests and elevations also varied significantly with migration tactics (Table 4.2).  The interaction  between study area and migration tactics was significant in preferences for elevations and aspects but not for forests (Table 4.2). The annual variation was specifically significant in preferences for open forests (F 9,139; P m (F  =  =  =  4.36; df  =  9,139; P  0.007), for 601-800 m (F  2.99; df  3.06; df  =  =  9,138; P  9,136; P  =  =  0.003).  =  <  0.001) and old (F  2.07; df  =  9,138; P  =  2.68; df  =  0.037) and >800  =  0.003) elevations, and for north aspects (F  =  We could not explain these variations on the  basis of total annual snowfall (all P  >  0.354) or on the proportion of deer  24 13 8  Pooled-S  M-W  R-W  4  11  15 6 12  18  N-S  R-S  Pooled-S  N-U  R-W  Pooled-U  Chemainus River  21  10  R-S  Pooled-U  14  n  M-S  Caycuse River  seasonal rangesc  Study areas and  -  0.02  0.00  0.05  0.15  0.07  0.23  0.19  0.20  0.18  0.29  0.41  0.18  1 a  North  O.Ol,y  0.05  0.07  0.08  0.08C  0.13  0.04w  0.06B  0.O5BC  0.05  0.08A  0.07  SE  0.24  0.30  0.18  0.51  0.57  0.45  0.10  0.10  0.11  0.17  0.08  0.26  a,  East  0.06  0.07A  0.10  0.07w  0.07A  0.12  0.05  0.O9BC  0.O7CD  0.05  0.08  0.07  SE  0.54  0.42  0.66  0.21  0.22  0.20  0.56  0.70  0.41  0.33  0.44  0.21  a,  South  0.09  0.1OA  0.14A  0.10  0.1OB  0.17  0.08  0.12A  0.1OA  0.07  0.1OA  0.09  SE  Habitat vectors  0.00  0.00  0.00  0.00  0.00  0.00  0.15  0.00  0.29  0.18  0.07  0.28  1 a  West  0.04  0.05  0.07  0.06  0.O7BC  0.11  0.04w  0.06C  0.O5AB  0.05  0.07  0.06  SE  0.20  0.28  0.11  0.13  0.14  0.13  0.00  0.00  0.00  0.03  0.00  0.07  1 a  Flat  05w  06A  08  07  07B  12  04  07C  060  05  08  06  SE  Table 4.8. Habitats preference (a,)’ estimates for 26 migratory and 35 resident, radio-collared, black-tailed deer at 3 study areas on Vancouver Island, British Coluiia, 1982-1991.  Continued.  68  120  55  82  137  R-S  Pooled-S  M-W  R-W  Pooled-W  89  74  102  R-S  M-W  R-W 0.10  0.12  0.22  0.22  0.10  0.09  0.12  0.23  0.18  0.27  a,  North  0.03  0.03  0.04  0.05  0.Olx  0.02  0.02  0.02  0.03B  0.04  SE  0.18  0.16  0.28  0.31  0.18  0.16  0.19  0.21  0.19  0.23  a,  East  0.04  0.04  0.04  0.05  0.02  0.038  0.03  0.02  0.03B  0.03  SE  0.58  0.54  0.37  0.24  0.58  0.62  0.55  0.36  0.43  0.29  1 a  South  0.05  0.06  0.05  0.06  0.03  0.04A  0.05A  0.03  0.04A  0.05  SE  Habitat vectors  0.02  0.10  0.05  0.12  0.05  0.07  0.03  0.09  0.10  0.08  1 a  West  0.03  0.03  0.03  0.04  0.01  0.02  0.02C  0.02  0.03  0.03B  SE  0.12  0.08  0.08  0.11  0.09  0.06  0.12  0.11  0.09  0.13  a,  Flat  03  03  04  05  02  02  03  02  03  038  SE  Habitats were aspects: north (316’-45’), east (46°-135’), south (136-270°), west (271’-315), or flat. Mean preference estimates for individual deer within study areas, behaviour groups, and seasons (n) were calculated according to Chesson (1983), comparing habitats at estimated deer locations with home range habitats, and sum to 1 within a habitat vector (row). With 5 habitats, a = 0.20 indicates random use while greater as indicate preference and lesser a’s indicate lack of preference. Different Letters within a row (upper case), or within a column for pooled seasonal estimates (lower case), indicate differences in means (Duncan’s-adjusted t-tests; E < 0.10). Seasonal ranges were Sumer (S), May through October, and Winter (W), November through April, for resident deer, R, and migratory deer, M.  70  N-S  Pooled Study Areas  52  n  M-S  Nanaimo River  seasonal ranges  Study areas and  Table 4.8.  109 sampled annually that were migratory (all P  >  0.125).  Average preference  for old forest at Nanaimo River varied with year-of-study (Spearman’s Rank R =  0.96; n  Rank R R  =  =  10; P  <  0.001) as did preference for open forest (Spearman’s  -0.90; n  =  10; P  =  -0.90; n  10; p  =  correlated (P’s  <  <  <  0.001) and 601-800 m elevations (Spearman’s Rank  0.001).  The latter 2 were themselves strongly  0.002) with preferences for old forest indicating probable  covari ance. Generally, deer preferred young forests during winter (pooled study areas: Table 4.6), although migratory deer also preferred old forests. Study area was important in preference for forest types again primarily because of deer lacked preference for open forests at Nimpkish River (Table 4.6).  In winter, migratory deer maintained their preference for elevations  >800 m while resident deer avoided those areas (pooled study areas: Table 4.7).  Deer at Chemainus, however, generally preferred higher elevations  more than deer at other study areas (Table 4.7) thereby contributing to a significant study area effect.  Also contributing to the study area effect,  deer at Caycuse River preferred elevations <400 m more than deer at other study areas.  An interaction effect (study area x migration tactics) likely  occurred because migratory deer at Nanaimo River preferred the 401-600 m elevation more than resident deer.  Deer at Chemainus River preferred flat  habitats and lacked preference for northern aspects more than elsewhere (Table 4.8).  Deer at Caycuse preferred western and northern aspects more  than elsewhere.  An interaction between study area and migration tactics  occurred for preference of aspects during winter largely because migratory deer at Caycuse maintained seasonal preference for western aspects (Table 4.8). Paired Differences in Preferences for Seasonal Habitats.  -  Paired sample  110 tests of difference between seasonal habitat preferences indicated that deer significantly increased their preferences for open forest (a 3.17; n  =  152; P  0.10, T  =  -4.68; n  =  =  0.07, T  0.002), and decreased preference for old forest (a 152; P  0.001), in summer.  <  =  =  -  These differences varied  significantly with migration tactics (Table 4.2) primarily because the average decrease in preference for old forest was 0.05 (SE for resident deer and 0.17 (SE  0.04; n  =  =  0.02; n  =  90)  =  62) for migratory deer.  Differences in preferences for other habitats occurred more similarly for all deer because we found no effects due to study area or to migration tactics (Table 4.2). -0.15, T  =  -5.98; n  habitats (a  Deer decreased preferences for 401-600 m habitats (a =  0.17, T  =  135; P  <  7.26; n  =  0.001) and increased preferences for >800 m =  152; P  <  0.001), during summer.  summer, deer increased preferences for eastern (a P (a  0.020), northern (a  < =  0.03, T  =  2.08; n  for southern aspects (cx  =  0.12, T 134; P  =  =  =  <  5.45; n  =  =  =  134; P  0.06, T <  =  During  2.36; n  =  134;  0.001), and western  0.039) aspects and decreased preferences  -0.22, T  -7.10; n  =  =  134; P  <  0.001).  Activity Nuclei Preferences: Daily Scale Generally, the frequency of activity nuclei that were characterized by specific habitat features varied (P  =  0.337) with study area and migration  tactics for forests but not for elevations or aspects (Table 4.9).  Except  at Chemainus and Nimpkish Rivers, where sample sizes were relatively low, activity nuclei used by migratory deer were consistently more likely to be in old forests and less likely to be in open forests than they were for resident deer (Fig. 4.4).  Other characteristics of activity nuclei were  more similar between the behaviour types and among study areas except for  111 Table 4.9. Maximum likelihood analysis of variance tables for frequency of habitats features forming the primary component of activity nucleib established by individual deer. Nuclei were determined from deer locations estimated weekly at 4 study areas on Vancouver Island, British Columbia, 1982-1991, and were pooled into 2 groups based on deer behaviour (migratory or resident).  Habitat vector and variance source  df  2 X  Intercept  2  44.18  <0.001  Study area  6  17.91  0.007  Behaviour  2  10.24  0.006  Likelihood Ratio  6  6.82  0.337  Intercept  3  12.17  0.007  Study area  9  32.30  <0.001  Behaviour  3  24.06  <0.001  Likelihood Ratio  9  37.20  <0.001  Intercept  3  69.99  <0.001  Study area  6  6.02  0.420  Behaviour  3  11.57  0.009  Likelihood Ratio  6  14.98  0.020  Forest Habitat  Elevation Habitat  Aspect Habitat  Habitats for the forest vector were: open if rock, water, subalpine, alpine, or forested but <6- yr—old; young if 6- to 45-yr-old; and old if >250-yr-old. Habitats for the elevation vector were in m asi: <400, 401—600, 601-800, or ‘800. Habitats for the aspect vector were: north (316—45), east (46— 135’), south (136’—270’), or west (271’—315’) and flat. Activity nuclei had 200 m radii, the centres of which were established by peaks in the harmonic utilization distribution for home ranges (Dixon and Chapman 1980, chapter 3).  112  Caycuse River  1  12 MiSrto,y  1  Chemainus River  obn,d  15  SE  •.  prdidd  UO  12ovod Rido4  SE  11  06  06 6  . .1  0.4  i  o:IIjo:j1 Open Young  0  0.4  1  Open Young  Old  Nanaimo River  1  Old  Nimpkish River 13  0  t  o 0. o  0.8 48  0.8  51  06 04  06 04  18  02  02  Open Young  Old  —  Open Young  I  Old  Seral age classes Figure 4.4 Observed frequencies and predicted proportional frequencies (maximum likelihood estimates) of forest habitats (clear, young, or old) forming the primary component of activity nuclei (chapter 3) for individual migratory, or resident, black-tailed deer at 4 study areas on Vancouver Island, British Columbia, 1982-1991.  113 the use of open forests by deer at Caycuse River (Fig. 4.4).  DISCUSSION We drew 4 conclusions from patterns in variance of habitat preferences.  First, home range establishment appears to resolve general  habitat needs relative to overall habitat abundance.  Second, habitat  selection at subclass levels of use (e.g., within home ranges) is constrained by superclass-level decisions (e.g., home range establishment). Third, because of this constraint, its reinforcement through specific behaviourial tactics (chapter 3), and the rapid pace of recent habitat alterations, deer cannot be considered to have an ideal-free distribution (sensu Fretwell 1972); mean preference for habitat is unlikely to equate to habitat value (Hobbs and Hanley 1990).  Fourth, consistent changes in  seasonal habitat preferences indicated old forests, at mid-elevations, on southern aspects as relatively optimal winter habitat for individual deer.  Hierarchy and Constraint of Habitat Choices Except for the lack of open forest at Nimpkish River, other unique features of study areas had little effect on what habitats deer included within their home ranges (Table 4.2).  Alternatively, home range  compositions varied more consistently with migration tactics and range type and, as a result, deer preferences indicated at least 2 different tactics for establishing home ranges.  Migratory deer occupied natal ranges at  higher elevations than did resident deer (Table 4.4).  Conversely, alternate  ranges of migratory deer and natal ranges of resident deer were similar in elevation and aspect but differed in forest characteristics; migratory deer had a strong preference for old forests (Table 4.3).  We note from other  114 studies that preference for aspects was usually less variable within, than among, different regions (Kucera and McCarthy 1988, Garrott et al. 1987, Schoen and Kirchhoff 1990) revealing a tendency toward warm, dry aspects as regulated by regional climates.  The general trend for elevational migration  is more common (Harestad 1979, Garrott et al. 1987, Schoen and Kirchhoff 1990) and resident deer usually live at relatively low elevations (Kufeld et al. 1989, Schoen and Kirchhoff 1990, Brown 1992, Yeo and Peek 1992).  A  divergence in preference for forest types among groups of deer is not commonly reported, although in southeast Alaska, Schoen and Kirchhoff (1990) demonstrated a disproportionately greater use of older forests than its abundance by a sample of mostly migratory deer and Yeo and Peek (1992), also in southeast Alaska, documented a preference for clear-cuts and young forests by resident deer.  The latter study only considered preference for  seasonal habitats within home ranges. At the level of seasonal habitat selection, these effects due to the tactics of migration or residency were weak to nonsignificant and effects due to study area were common (Table 4.2).  We interpreted this to mean that  although tactics for selecting home ranges within study areas were evident, more specific use of habitats was constrained by availability (i.e., the resources chosen when establishing home ranges).  Deer did not prefer old  forests within home ranges in either season (Table 4.6).  Deer preferred  elevations similarly within home ranges as within study areas although this similarity became obscured by interactions between study area and migration tactics, particularly at mid-elevations (Table 4.7).  The basic tactic of  preference for southern aspects persisted at both the study area and home range levels of analysis.  Schoen and Kirchhoff (1990) did not report  comparisons of use within study areas and Harestad (1985) provided only  115 pooled estimates of seasonal use so we could not compare our results to those studies.  Our results depart from those of leo and Peak (1992) only by  our observations of migratory deer increasing their preference for old forests in winter.  Although we are not certain, it appeared that Yeo and  Peek (1992) had few if any migratory deer in their sample. Where 21% of the activity nuclei used by migratory deer were composed of old forests, the same forest type occurred in only 8% of the activity nuclei used by resident deer (Fig. 4.4).  Considering the combination of  strong site fidelity exhibited by most deer (Schoen and Kirchhoff 1985, Garrott et a!. 1987, Brown 1992), which we interpreted as philopatry (chapter 3), the regulation of seasonal movements by conditions at natal ranges, and the usual forest harvesting pattern in mountainous terrain (lower elevations first), it is not surprising that residents lack preference for activity nuclei in old forests; most resident deer in our study had little to none of that forest type available (Figs. 4.1 and 4.4) and so had little chance to display such preference. We concur with Garrott et al. (1987) who proposed that seasonal movements of mule deer are driven by seasonal changes in energy needs and the quality and quantity of available forage.  This proposal is consistent  with hierarchical habitat selection in that superclass decisions are based on an integration of subclass activities (Dawkins 1974, Senft et a!. 1987, O’Neill et a!. 1988, Levin 1992).  The integration of day-by-day resource  use by deer born at high elevations, for example, ultimately leads to the need for selecting winter habitat elsewhere since topographic and climatic conditions limit forage quantity and quality in winter. elevations do not need to migrate.  Deer born at low  Similarly, the integration of seasonal  conditions determines the need, or lack thereof, to disperse and to  116 establish entirely new home ranges.  Although the seasonal nutrition  hypothesis is a straight-forward basis for migration decisions, it is less straight-forward what forms the basis for dispersal (Robinette 1966, Hawkins and Klimstra 1970, Kammermeyer and Marchinton 1976, Bunnell and Harestad 1983, Masters and Sage 1985).  For ecological hierarchies, the bottom-up  synthetic perspective involves functional mechanisms basic to habitat selection (e.g., forage acquisition).  We consider the top-down,  constraining perspective to be important, however, because in combination with tactics such as philopatry and site fidelity (chapter 3), it limits habitat availability for individual deer which then constrains the establishment of activity nuclei to a specific subset of habitats.  The  clearest example of this constraint in our study came from the case of resident deer, most having relatively little or no access to old forests in their home ranges (Table 4.3), consequently lacking preference for old forests (Table 4.6), and choosing to establish activity nuclei mostly in other forest types (Fig. 4.4).  Interpretation of Habitat Preference Ideal-Free Distributions and Black-tailed Deer.  -  We believe there is  sufficient evidence to indicate that habitat selection tactics, especially those for winter habitat, are not in equilibrium with dynamics of habitat changes as those dynamics have taken place on Vancouver Island.  First,  logging has changed the basic spatial pattern of deer habitat by reducing the total amount of old forests at low elevations and by isolating the remaining old forests into widely separated patches. expanses of open and/or young forests are created.  Simultaneously, broad Second, this change  happens much faster than the more natural rate of change in an undeveloped  117 forest (Franklin and Spies 1984).  Third, constraints on habitat choices  force adaptation to changed habitats to be based more on generational dynamics than solely by individual learning.  O’Neill et a!. (1988)  suggested that resources clumped in space, limited in abundance, or providing a specialized need (all of which apply to old forests as winter habitat for deer) demand large scales of resource use. operate at relatively small scales by definition.  Resident deer  So, for example, not only  does habitat change occur more rapidly than changes in behaviourial tactics, resident for example are usually unaware of the condition of habitats that change (e.g., year by year knowledge about the location of remnant old forest patches). Philopatry and site fidelity for alternate ranges (Garrott et a!. 1987, Brown 1992, chapter 3) further suggest that even migratory deer may not be free to choose habitats because social relations may be stronger than habitat needs in terms of home range establishment.  Philopatry, for  example, means that establishment of new home ranges has as much or more to do with the mother’s historical choice than with an assessment, made by young deer, of current habitat quality.  Consequently, the freedom of  movement assumed by Fagen (1988) for Sitka black-tailed deer (0. h. sitkensis) is unlikely to hold for Columbian black-tailed deer on Vancouver Island. Finally, although we have reason to believe there are strong seasonal patterns in the manner of habitat selection by deer, we also have reason to believe that a longer-term pattern exists as well.  During severe winters  (as opposed to severe winter weather), the requirement for old forests has been known to be extreme not only on Vancouver Island (Smith 1973) but in southeast Alaska as well (Schoen and Kirchhoff 1990).  By comparison to the  118 winters of 1968-69 and 1971-72, winters during this study were relatively mild hence conferring more freedom for deer to use young and open forests. Individual Preferences for Habitats.  -  Individuals used habitats with a  diversity of tactics which ultimately led us to conclude a general lack of homogeneity in habitat preferences.  This could have resulted as a  consequence of the lack of freedom in establishing home ranges (discussed above).  For example, if home ranges were settled on the basis of the  matriarch’s historical choice and habitats were subjected to persistent, rapid changes from logging, then availability of habitats, and tactics for using habitats, would range widely for individual deer. Cases where homogeneity was not rejected were mostly those where preferences were low for particular habitats (e.g., migratory deer preferences for open forests, resident deer preferences for old forests, resident deer preferences for high elevations, and resident deer preferences for north, west, and flat aspects).  We interpreted this to imply that our  conclusions about lack of preference are likely more robust and general than conclusions associated with preferred habitats. Relative Habitat Qualities of Young and Old Forests.  -  In moving from summer  habitats to winter habitats, deer generally preferred open forests less and old forests more, they preferred elevations >800 m elevations less and elevations at 400 to 600 m more, and they preferred north and east aspects less and southern aspects more.  Because we were unable to assess the  possible interaction among the main habitat vectors, one could argue the preference for old forests, for example, is only a correlate with a more proximate need for deer to choose habitat at lower elevations during winter. Logging patterns in coastal forests, however, tend to produce a marked lack of old forests at low elevations so we concluded that the preference for old  119 forests was likely a strong determinant of habitat selection. Lack of behaviour (migration tactics) or study area effects on differences in seasonal preference for the topographic vectors led us to conclude these preferences (Tables 4.7 and 4.8) as general seasonal tactics. The attributes of old forests includes a long list of mechanistic benefits to deer: more abundant forage (Harestad 1979, 1985), higher quality forage (Van Home et a!. 1988, Happe et a!. 1990), an alternative and/or additional (to rooted) forage supply (Stevenson 1978, Rochelle 1980, Dawson et a!. 1990), and reduced snow depths (Kirchhoff and Schoen 1987, Bunnell et a!. 1990a) hence reduced cost of locomotion (Parker et al. 1984). benefits primarily accrue in winter months.  But these  Our data (Table 4.6), and those  of others (Harestad 1985, Schoen and Kirchhoff 1990, Yeo and Peek 1992), show a strong avoidance of old forests in summer.  In an evolutionary sense,  these data indicate migration is instigated as much to avoid old forests in summer as to preferentially select them in winter.  If migrations were  driven solely by topographic factors then we should have seen some migratory deer preferring a matrix of young and clear forests in winter; we found none.  Further, in periods of mild weather (sometimes lasting a number of  years), some otherwise migratory deer failed to migrate (chapter 3) and as a result, preferential use of old forests in winter was marginal (Table 4.6) even though it was highly preferred as a component of alternate ranges. While, the benefits of old forests to individual deer confirm it’s high quality in winter, we concluded it was relatively avoided in all but snowy weather.  Hence, old forests represent a special habitat in coastal  climates that seemingly cannot be replaced by young forests but is needed only when winter weather is severe; otherwise, a matrix of clear and young forests appears preferential and may negate the need to migrate if they  120  occur at mid- to low-elevations.  On a mechanistic basis (primarily forage  abundance and quality), however, we can forecast the quality of young forests in coastal British Columbia to decline with age considerably as these forests age beyond 30- to 40-yr-old (Cowan 1945, Gates 1968, Alaback 1982).  IMPLICATIONS FOR RESEARCH AND MANAGEMENT We concluded a lack of association between habitat preference and habitat value and yet we also assessed the value of old and young forests based on the same preference estimates.  The opportunity to confront these 2  objectives with the same data resulted from maintaining data in a segregated (rather than pooled) condition, from exploiting the significant variance contributed by effects such as behaviour and study area (Thomas and Taylor 1990), and by structuring analysis on a hierarchical framework corresponding to levels of habitat acquisition decisions (Johnson 1980, Senft et al. 1987).  We believe these are important concepts in analysis of most  use/availability data. The relative value of old and young forests should not be considered strictly discrete (Hobbs and Hanley 1990).  Although the importance of old  forests cannot be denied, young forests in coastal British Columbia likely provide better winter habitat in most places during most years.  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Cliffs, N.J. 718pp.  Prentice-Hall, Englewood  127 CHAPTER 5  -  RESPONSE TO LOGGING OF WINTER HABITAT  Recent studies of black-tailed deer have indicated that old forests (>250-yr-old) provide important habitat during winter (Jones 1974, Bloom 1978, Harestad 1979, Rose 1981, Schoen and Kirchhoff 1985).  This importance  was interpreted from a preference for old forests by deer, particularly those born in regions of high snowfall (chapter 4).  Old forests provide an  abundant source of arboreal forage seldom found in younger forests (Stevenson 1978) and provide shelter from snow (Kirchhoff and Schoen 1987, McNay et al. 1988) which permits reduced costs of locomotion (Parker et al. 1984, Bunnell et al. 1990a,b) and lower rates of food burial (Jones 1975, Harestad 1979).  For these reasons, old forests, especially those at low  elevations, are generally considered to be the highest valued winter habitat for black-tailed deer (Walimo and Schoen 1980, Bunnell and Jones 1984, chapter 4).  Furthermore, black-tailed deer are philopatric and develop  strong fidelity to specific sites (Schoen and Kirchhoff 1985, Weckerly 1993, chapter 3).  Therefore, loss of old forests (e.g., wildfire or logging) on  winter ranges could lead to conflict within affected deer between a behaviourial tendency for fidelity to their winter-range site and preference for old forests. The response of these deer to loss of winter habitat through logging is poorly understood.  Affected deer could: (1) remain in their existing  winter range where they are either in, or surrounded by, new habitat conditions; (2) remain in their existing winter range but shift habitat use away from the affected site; or (3) move to a new winter range which may be composed of at least some old forest.  I consider these potential responses  to vary from one dominated by site fidelity (response 1) to another dominated by habitat preference (response 3).  The intermediate response 2  128 could be considered site fidelity with a range size change.  My objective  was to assess which of these responses would be used by individual deer after controlled logging of old forests within their winter range. Knowledge of actual responses to logging may help explain why deer use a wide variety of habitats during winter (Yeo and Peek 1992, chapter 4) and may also help illuminate the population-level consequences of individual responses to habitat change.  This knowledge is also basic to planning  silvicultural prescriptions for restoration of winter habitat in young forests (e.g., Nyberg et al. 1986).  The specific tests were: (1) that site  fidelity did not differ between comparisons in which 1 year involved disturbance (i.e., logging) and comparisons without disturbance, or between deer subjected to disturbance and those not subjected to disturbance; (2) that annual variation in winter range sizes did not differ for deer subjected to disturbance compared to those not subjected to disturbance; and (3) that use of old forests in winter did not differ among years for those deer subjected to disturbance as a result of the disturbance.  STUDY AREAS I studied responses of black-tailed deer to logging in Chemainus (48°56’N, 124°05’W) and Nimpkish (50°08’N, 126°30’W) river valleys on Vancouver Island, British Columbia from 1988 to 1991.  Each study area was  1,600 ha in size and, in each, I identified an individual stand of old forest that was slated for logging 1-2 yr after the initiation of study. The stands met criteria for typical winter habitat (e.g., Bunnell 1985, Nyberg et a!. 1986, Bunnell 1990) and were isolated from similar stands. Hence, adjacent habitats were young, Douglas-fir forests (6- to 45-yr-old), recently logged areas (0- to 5-yr-old), or remnant areas of old forest  129 unsuitable for winter habitat because of topographic position (e.g., unsuitably high in elevation and/or on northern aspect).  No habitat existed  between the ages of 46- and 250-yr-old. A proposed, or recent, logging area is locally referred to as a cutblock and I use that term.  The Chemainus River cut-block was 60 ha with an  average tree volume of about 1,000 3 in / ha and was scheduled for logging during the summer of 1989. an aspect of 2100.  Elevation in the cut-block ranged from 800-1,000 m with Tree species composition was dominated by Douglas-fir  and western hemlock with a minor component of western redcedar.  Yellow-  cedar occurred at higher elevations. The Nimpkish River cut-block was situated about 202 km northwest of the Chemainus River site.  It was 47 ha with an average tree volume of about  700 m /ha and was scheduled for logging during the fall of 1990. 3 in the cut-block ranged from 300-650 m with an aspect of 160°.  Elevation Tree species  composition was similar to the Chemainus River site.  METHODS Experimental Design Study design was based on 3 criteria beyond the fact that candidate stands for logging had to be typical of deer winter habitat.  First, I  wanted to force a distinct and dramatic habitat change on individual deer. The treatment deer (i.e., those with logging within their home ranges) would have to make a clear choice between fidelity to their winter range site or continuity of their habitat choices.  I defined fidelity as overlap in  consecutive winter ranges and defined continuity of habitat choice as insignificant  2 (x  >  0.05) differences in the use of old forests during  consecutive winters (November through April).  To make results for this  130 comparison mutually exclusive, I aimed to have the candidate stands logged completely and to retain other stands of similar characteristics accessible (2-4 km away), but not directly adjacent to, each logged stand. Second, I expected a strong source of annual variation in observations of fidelity and habitat use. termed control deer.  Therefore, I monitored a second group of deer  Control deer, for comparisons of fidelity and winter  range size, were monitored close to each group of treatment deer (<2 km away).  Control deer, for habitat-use comparisons, were deer that used old  forests during the same years but in areas remote from any current logging activity (although logging had occurred in these areas in the past); we chose 2 deer from data collected at Nanaimo River which is a neighbouring valley to Chemainus River (chapter 4). Third, and again because I expected strong annual variation, I arranged the study so that the 2 forest stands were logged in successive years.  As a result, I had 2 pre-disturbance years at Nimpkish River and 2  post-disturbance years at Caycuse.  Comparisons of fidelity and use of old  forests between these years were termed non-disturbance comparisons while other comparisons were termed disturbance comparisons.  Animal Capture and Monitoring During the winters of 1988-1989 and 1989-1990 at Nimpkish River, and 1988-1989 at Chemainus River, traps (Clover 1956) were placed within the stands of old forest identified for logging and in nearby stands.  Once  trapped, female deer were restrained manually and collared with radio transmitters (Telonics Inc., Mesa, Ariz., USA and Lotek Inc., Newmarket, Ontario, Canada). Collared deer were located once each week with triangulation data  131 obtained at permanent stations marked at 100-rn intervals along roads throughout each study area.  Deer locations were estimated using the maximum  likelihood estimator described by Lenth (1981) and provided in a SAS program (SAS Inst. Inc.  1985) by White and Garrott (1990:64).  I determined that  error polygons (Lenth 1981) for estimated deer locations were usually <1.0 ha (McNay et al. 1994). I expressed winter ranges as the 95% minium convex polygon (Mohr 1947) based on locations of deer collected during winter.  I excluded migrations  and locations of migratory deer if they were still on summer ranges during this time (chapter 3).  I assessed independence of location observations in  a related study (McNay et al. 1994) and considered locations sampled onceper-week were biologically independent, systematic observations of use of space.  Range sizes, therefore, were an adequate index for comparisons among  deer through predetermined time intervals.  Individual deer were the  experimental units and I assumed they were independent of each other. Habitat use was determined by overlaying deer locations as query points to forest cover maps in a Geographic Information System (Terrasoft; Digital Resources, Nanaimo, B.C. and PAMAP; PAMAP Technologies Corp., Victoria, B.C.).  Habitats were:  open if 0- to 5-yr-old; young if 6- to 45-yr-old;  old-low if >250-yr-old and below, or at the same elevation as, the logged stand; or old-high if >250-yr-old and above the elevation of the logged stand.  Statistical Analysis For statistical convenience I constructed an index of fidelity based on a  2 x  test of independence between consecutive winter ranges (White and  Garrott 1990:136).  Positioned on the arithmetic centre of one range, I used  132 concentric circles with radii iteratively reduced by 100 m, starting at a radius of 1,500 m, to find the first test where deer locations in the next range were considered to be independently distributed (P in the previous range.  <  0.05) from those  This radius was termed the maximum radius for  independence (MRI) and small MRI values indicated strong fidelity.  Habitat  use was the percent of location observations in old-low forests where data were transformed using an arcsine transformation for percentage data (Zar 1984).  I used a repeated measures, analysis of variance based on year (for  range size and habitat use) or based on disturbance (for fidelity) to test for the potential effect of logging (control versus treatment deer) nested within study areas (Chemainus or Nimpkish).  All reported means are least-  squares estimates (Searle et al. 1980).  RESULTS Logging was most successfully carried out at Nimpkish River because no old-low forest was left adjacent to the cut-block.  About 180 ha of old-high  habitat remained adjacent to the block on its upper boundary but this was poor quality winter habitat.  Old-low habitat was available in a 15 ha stand  about 0.3 km below the bottom boundary of the cut-block and was separated from it by a stand of young forest. completed as planned. other half in 1990.  At Chemainus River, the logging was not  Half the cut-block was harvested in 1989 and the A 30 ha block of old-low habitat remained adjacent to  the cut-block because a gully impeded further logging.  There was another  57-ha stand of old-low forest about 1.3 km away from the cut-block.  In each  study area, half of the total old-low habitat was logged (Fig. 5.1). Twenty female deer were monitored in the 2 study areas for 2 or 3 winters between 1988-1989 and 1990-1991 for a total of 52 deer-winters.  Six  133  80 Chemainus River 70 Nimpkish River Logged habitat 60 s.0 0’ Cu  d)  F° 30  20  10  0  I  I  ]  Open  Young  Old-low Old-high Habitat types  Water  Figure 5.1 Habitat abundance (% of total) at 2 study areas on Vancouver Island, British Columbia, 1988-1991.  134 deer in each study area (3 treatment deer and 3 control deer) were monitored for all 3 years (Table 5.1). average (n  52, SD  =  8).  =  Each deer was monitored 16 times per winter on  Two other deer that had access to old forests at  Nanaimo River were monitored during the same study period. Fidelity to winter ranges was relatively stronger at Nimpkish River where the maximum radius for independence (MRI) between pre- and postdisturbance comparisons was 0.3 km compared to 0.5 km at Chemainus River (Table 5.1).  Fidelity measurements for deer with 3 years of data (i.e.,  both non-disturbance and disturbance comparisons) was significantly stronger  in non-disturbance comparisons (F  5.44; df  =  1; P  =  =  0.048) but this was  not associated with differences between treatment and control deer (F 0.23; df=3; P  =  =  0.871).  Home range sizes did not vary consistently through time for deer that were followed for 3 years at Chemainus River (F or at Nimpkish River (F  =  1.30; df  evidence of a disturbance effect (F  2; p  =  =  =  =  2.11; df  0.325).  =  2; P  0.184)  =  Also, there was no  0.66 and 0.63; df  =  2; P  =  0.463 and  0.470 at Chemainus and Nimpkish rivers, respectively) even though home range sizes did increase (F  =  6.90; df  =  1; P  =  0.018) when all deer were  considered for a comparison of 1-yr-pre- to 1-yr-post-disturbance (Table 5.1). df  =  In this latter comparison, there was no treatment effect (F 3; p  =  0.98;  =  0.425).  Use of old-low habitat by treatment deer that were monitored for 3 years did not differ (F  =  3.15 and 3.96; df  =  1; P  =  0.174 and 0.141 at  Chemainus and Ninipkish rivers, respectively), on average, from the control deer.  In both study areas, however, there was a temporal effect (F  and 23.55; df  =  2; P  =  =  13.21  0.006 and 0.001 at Chemainus and Nimpkish rivers,  respectively) caused by treatment deer using less old-low habitat after  54.2 (13.8)  47.2 ( 8.8)  47.9 (22.2)  1990-91  1-yr-pre  1-yr-post  55.8 (41.3)  24.1 (22.2)  10.4 C 7.9)  48.6 (19.8)  1989-90  1990-91  1-yr-pre  1-yr-post 81.1 (22.2)  18.8 C 8.8)  57.3 (22.2)  77.4 (41.3)  24.7 ( 8.3)  4  60.3 (16.8)  34.2 ( 6.7)  34.4 (13.8)  37.8 (15.1)  31.7 ( 7.4)  7  Treatment  30.7 C 8.1)  38.3 C 6.1)  42.3 (10.3)  38.9 C 8.9)  67.4 ( 5.2)  2  30.7 C 8.1)  38.3 C 6.1)  42.3 (10.3)  38.9 C 8.9)  67.4 ( 5.2)  2  Control  -  32.0 ( 7.0)  47.6 ( 5.3)  11.7 C 6.2)  37.7 C 7.3)  51.7 C 8.4)  4  5.7 C 5.3)  42.0 C 4.0)  9.2 ( 8.4)  42.3 C 7.3)  38.5 ( 4.2)  7  Treatment  Old forest (% of total use)  0.56 C 0.19)  0.66 C 0.16)  0.83 ( 0.29)  5  0.30 ( 0.21)  0.40 C 0.29)  0.23 C 0.16)  4  Control  0.52 C 0.21)  0.20 C 0.16)  0.53 C 0.29)  4  0.34 C 0.16)  0.33 ( 0.29)  0.17 ( 0.16)  7  Treatment  Fidelity indexd  0,  Logging occurred on a pre-selected stand of oLd-forest meeting typical winter habitat characteristics (Nyberg et al. 1986) in 1989 at Chemainus River and in 1990 at Nimpkish River. Range size was a 95% minimum convex polygon using Locations from November April. Locations of migratory deer on sumer ranges were omitted. % old forest values are arcsine transformations of the percent of total locations collected during one winter season (November through April) that were found in old forests at elevations equal to or below the logged stand. An index of fidelity was expressed as a home range radius which, when used to represent the pre-disturbance home range, was judged independent of the post-disturbance home range by a x 2 test of independence (P = 0.05). Sample sizes provided are for 1-yr-pre-disturbance versus 1-yr-post-disturbance comparisons. Samples for 3-yr annual statistics came from 3 control and 3 treatment deer in each study area. (‘3  12.0 C 8.3)  1988-89  n’  5  51.2 (15.1)  1989-90  Chemainus River  20.2 ( 7.4)  4  Control  Range size (ha)b  1988-89  e  Nimpkish River  Year  Study Area  TabLe 5.1. Effect of forest logging on range sizes, habitat use, and site fidelity of radio-collared, black-tailed deer where logging occurred inside (treatment) or outside (control) pre-disturbance home ranges. Deer were from 2 study areas on Vancouver Island, British Cott.mtia, 1988-1991.  136 logging compared to control deer (Table 5.1).  This temporal effect was only  evident in the second year after disturbance for treatment deer at Chemainus River and in the post-disturbance year for treatment deer at Nimpkish River.  DISCUSSION My conclusion from this study is that fidelity, rather than habitat choice, dominates initial (i.e., 1-2 yr post-disturbance) responses by deer to large, striking changes in habitat condition (i.e., 45-60 ha clear-cut logging of old forests).  Relative to the other potential responses I  measured, fidelity remained the most consistent between treatment and control deer through the 3 years of this study (Table 5.1).  Use of old-low  habitats, although steady in non-disturbance years, was reduced considerably after disturbance even when this same type was available within 0.5 km of the cut-block at Nimpkish River.  I noted, however, that when old-low  habitat was left adjacent to the cut-block at Chemainus River, use of that type continued.  After further logging at Chemainus River, however, use of  old-low habitat decreased similar to the response at Nimpkish River.  Winter  home range sizes were variable but did not vary in association with the disturbance. Other studies have shown similar results demonstrating strong fidelity to home sites during extreme disturbances in habitat (Hood and Inglis 1974; Hershey and Leege 1982; Edge et al. 1985; Gasaway et al. 1989).  Edge et al.  (1985) found that logging caused elk (Cervus elaphus nelsoni) to shift habitat use within the home range with no significant outward shift in home range size or site.  Fidelity is likely advantageous in stable environments  because a mother’s site-specific knowledge about food, cover, and potential hazards can be easily demonstrated to offspring (Edwards 1976, Hamlin and  137 Mackie 1989:233-238).  Alternatively, fidelity may indicate a simple lag in  decision-making while individuals obtain enough knowledge to evaluate trade offs provoked by rapid changes in habitat (Gass 1985).  Lags themselves may  be advantageous because quick selections of new home ranges, if deer were prone to such decisions, could decrease survival due to unknown hazards in unfamiliar areas (O’Bryan and McCullough 1985).  At very least, such forced,  quick decisions could decrease foraging efficiency (Provenza and Balph 1987), although persistence of such detrimental effects may not last long (Gillingham and Bunnell 1989).  However, fidelity to sites that have been  disturbed undoubtedly presents deer with new challenges in learning to adjust to the new habitat condition, at least in the short-term.  MANAGEMENT IMPLICATIONS Fidelity to specific sites has important management implications. First, Fagen’s (1988) assumption about freedom of movement between habitats (i.e., the ideal-free distribution of animals) was invalidated for our sample of deer.  If old-low forests were the best winter habitat for black-  tailed deer, my sample of deer failed to alter winter range sites to accommodate the reduction in range quality caused by logging.  Rather,  treatment deer simply increased the proportional use of other habitats within their winter ranges.  Even if this were simply a delay in decision  making, I am led to question the meaning of habitat use observations sampled in areas of extensive habitat disturbance.  Are habitats chosen as a matter  of preference, as a matter of habit, or as a default from fidelity to sites? I consider the data here to indicate that site-fidelity may play a large role in determining habitat preferences in post-disturbance situations. Consequently, this may help explain the wide variance in reports of habitat  138 preferences by black-tailed deer (Yeo and Peek 1992, chapter 4). In cases of continuous and contiguous logging, some deer are inevitably faced with no old forests to use.  In recent studies involving  such cases, none of the study deer trapped in (chapters 3 and 4), or immobilized in or near (Yeo and Peek 1992), young forests were migratory. Provided that site fidelity constrains habitat choice as my results appear to indicate, this absence of migratory deer in young forests could only occur if: (1) resident deer were superior competitors in the remaining young forests, or (2) the offspring of migratory deer in these situations develop non-migratory tactics.  Alternatively, if the site fidelity I observed was  only a lag in decision making then migratory deer may eventually seek out old forests elsewhere (i.e.; move to new winter range sites).  Although data  here are insufficient to address these hypotheses, I noted that deer at Chemainus River shifted home ranges to use old-low habitat when that type was adjacent to the cut-block.  Until further data are available, it would  seem that some deer (perhaps only migratory deer) may be able to make appropriate habitat adjustments when winter ranges are only partially logged.  If this were true, I would expect deer living in extensively  modified areas to be mostly resident, as our observations suggest (Yeo and Peek 1992, chapter 4).  In turn, this would mean migratory deer are forced  into increasingly smaller areas of old forests.  Where logging was carried  out preferentially in areas of prime winter range (Schoen and Kirchhoff 1985), the density problem intensifies because an increasing density of deer would occur in habitats of decreasing value.  Based simply on carrying  capacity theory, we would expect migratory deer populations to decline (Hobbs and Hanley 1990). Another implication of site fidelity constraints on habitat choices is  139 that management of young forests specifically to improve their suitability as winter habitat for deer (e.g., Nyberg et a!. 1986) could simply benefit deer in the immediate vicinity, at least initially.  This is not to say that  such improvements are fruitless, however, because in highly philopatric species, benefits would accrue to offspring and subsequent generations.  The  important point is that habitat improvements may be most beneficial long after the initial management efforts.  Such a lag in results from habitat  improvement attempts have been documented for moose in Alaska (Gasaway et al. 1989) and we consider our results to indicate the same would happen when managing forests for black-tailed deer.  LITERATURE CITED Bloom, A. M. 1978. Sitka black-tailed deer winter range in the Kadashan Bay area, southeast Alaska. J. Wildl. Manage. 42:108-112. Bunnell, F. L. 1985. Forestry and black-tailed deer: conflicts, crises, or cooperation. For. Chron. 61:180-184. 1990. Black-tailed deer ecology and forest management. Pages 31-63 in J. B. Nyberg and D. W. Janz, eds. Deer and elk habitats in coastal forests of southern British Columbia: a handbook for forest and wildlife managers. British Columbia Minist. of For. Special Rep. Ser. 5. Victoria. and G. W. Jones. 1984. Black-tailed deer and old-growth forests: a synthesis. Pages 411-420 in W. R. Meehan, T. R. Merrell, Jr., and T. A. Hanley, eds. Fish and wildlife relationships in old-growth forests. Proc. Symp. April 1982 in Juneau, Alaska. Am. Inst. Fish. Res. Biol., Morehead City, N.C. F. W. Hovey, R. S. McNay, and K. L. Parker. 1990a. Forest cover, snow conditions, and black-tailed deer sinking depths. Can. J. Zool. 68:2403-2408. K. L. Parker, R. S. McNay, and F. W. Hovey. 1990b. Sinking depths of black-tailed deer in snow, and their indices. Can. J. Zool. 68:917-922. Clover, M. R. 201.  1956.  Single-gate deer trap.  Edge, W. D., C. L. Marcum, and S. L. Olson.  Calif. Fish and Game. 42:1991985.  Effects of logging  140 activities on home-range fidelity of elk. 744.  J. Wildi. Manage. 49:741-  Edwards, J. 1976. Learning to eat by following the mother in moose calves. Am. Midl. Nat. 96:229-232. Fagen, R. 1988. Population effects of habitat change: a quantitative assessment. J. Wildl. Manage. 52:41-46. Gasaway, W. C., S. D. Dubois, R. D. Boertje, D. J. Reed, and D. T. Simpson. 1989. Response of radio-collared moose to a large burn in central Alaska. Can. J. Zool. 67:325-329. Gass, C. L. 1985. Behaviourial foundations of adaptation. Pages 63-107 in P. P. G. Bateson and P. H. Klopfer, eds. Perspectives in ethology. Vol. 6. Plenum Publishing Corp., N.Y. Gillingham, M. P., and F. L. Bunnell. 1989. Effects of learning on food selection and searching behaviour of deer. Can. J. Zool. 67:24-32. Hamlin, K. L., and R. J. Mackie. 1989. Mule deer in the Missouri River Breaks, Montana: a study of population dynamics in a fluctuating environment. Montana Dept. of Fish and Wildl., Missoula. 4Olpp. Harestad, A. S. 1979. Seasonal movements of black-tailed deer on northern Vancouver Island. Ph.D. Thesis, Univ. of British Columbia, Vancouver. 98pp. Hershey, T. J., and T. A. Leege. 1982. Elk movements and habitat use on a managed forest in north-central Idaho. Idaho Dep. Fish Game Wildl. Bull. 10. 24pp. Hobbs, N. T., and T. A. Hanley. 1990. Habitat evaluation: do use/availability data reflect carrying capacity? J. Wildl. Manage. 54:515-522. Hood, R. E., and J. M. Inglis. 1974. Behaviourial responses of whitetailed deer to intensive ranching operations. J. Wildl. Manage. 47:664-672. Jones, G. 1974. Influence of forest development on black-tailed deer winter range on Vancouver Island. Pages 139-148 in H. C. Black, ed. Wildlife and forest management in the Pacific Northwest. Oregon St. Univ., Corvallis. 1975. Aspects of the winter ecology of black-tailed deer (Odocoileus hemionus columbianus Richardson) on Vancouver Island. M.Sc. Thesis, Univ. of British Columbia, Vancouver. 78pp. Kirchhoff, M. D., and J. W. Schoen. 1987. Forest cover and snow: implications for deer habitat in southeast Alaska. J. Wildl. Manage. 51: 28-33. Lenth, R. V.  1981.  On finding the source of a signal. Technometrics  141 23:149-154. McNay, R. S., J. A. Morgan, and F. L. Bunnell. 1994. Characterizing independence of observations in movements of Columbian black-tailed deer. J. Wild]. Manage. 58:422-429. L. Peterson, and J. B. Nyberg. 1988. The influence of forest stand characteristics on snow interception in the coastal forests of British Columbia. Can. J. For. Res. 18:566-573. Mohr, C. 0. 1947. Tables of equivalent populations of North American small mammals. Am. Midi. Nat. 37:223-249. Nyberg, J. B., F. L. Bunnell, D. W. Janz, and R. M. Ellis. 1986. Managing young forests as black-tailed deer winter ranges. British Columbia Minist. For. Land Manage. Rep. 37. Victoria. 49pp. O’Bryan, M. K., and D. R. McCullough. 1985. Survival of black-tailed deer following relocation in California. J. Wild]. Manage. 49:115-119. Parker, K. L., C. T. Robbins, and T. A. Hanley. 1984. Energy expenditures for locomotion by mule deer and elk. J. Wild]. Manage. 48:474-488. Provenza, F. D., and D. F. Baiph. 1987. Diet learning by domestic ruminants: theory, evidence and practical implications. App]. Anim. Beh. Sd. 18:211-232. Rose, C. L. 1981. Deer response to forest succession on Annette Island, southeast Alaska. M.S. Thesis, Univ. of Alaska, Fairbanks. 56pp. SAS Inst. Inc. 1985. SAS user’s guide: basics, version 5 edition. Institute Inc., Cary, N.C. 584pp.  SAS  Schoen, J. W., and M. D. Kirchhoff. 1985. Seasonal distribution and homerange patterns of sitka black-tailed deer on Admiralty Island, southeast Alaska. J. Wildi. Manage. 49:96-103. Searle, S. R., F. M. Speed, and G. A. Milliken. 1980. Population marginal means in the linear mode]: an alternative to least square means. Am. Stat. 34:216-221. Stevenson, S. K. 1978. Distribution and abundance of arboreal lichens and their use as forage by black-tailed deer. M.S. Thesis, Univ. of British Columbia, Vancouver. l48pp. Walimo, 0. C. and J. W. Schoen. 1980. Response of deer to secondary forest succession in southeast Alaska. For. Sd. 26:448-462. Weckerly, F. W. 1993. Intersexual resource partitioning in black-tailed deer: a test of the body size hypothesis. J. Wildi. Manage. 57:475494. White, G., and R.A. Garrott. 1990. Analysis of Wildlife radio-tracking data. Academic Press Inc., San Diego, Calif. 383pp.  142 Yeo, J. J., and J. M. Peek. 1992. Habitat selection by female Sitka black tailed deer in logged forests of southeastern Alaska. J. Wildl. Manage. 56:253-261. Zar, J. H. 1984. Biostatistical analysis. Cliffs, N.J. 718pp.  Prentice-Hall, Englewood  143 CHAPTER 6  -  MORTALITY CAUSES AND SURVIVAL ESTIMATES 2  Interactions among wolves (Canis lupus), black-tailed deer, and deer hunters have been assumed to dominate the predator-ungulate system on Vancouver Island (Janz and Hatter 1986).  Declines of deer, resulting from  predation (50-70% from 1976-82) and hunter harvests have been contrary to management objectives.  Because predators were presumed the major cause of  the declines (Jones and Mason 1983), the argument for retention of winter habitat (Bunnell 1985) was difficult to make to those wishing to use the land for other purposes (Janz and Hatter 1986).  Managers were forced to  reduce deer mortality before addressing habitat concerns.  Consequently,  attention has been focused on population modelling (Hatter and Janz 1994) to foster management initiatives that help restore deer populations.  Also, bag  limits for deer hunters on Vancouver Island have been reduced and, in some areas, limited control of wolves has been provisionally instigated (Janz and Hatter 1986). Population management requires information on factors that cause population changes, principally survival and reproductive rates (Caughley 1976).  Although reproductive rates for black-tailed deer are documented  (Taber 1953, Cowan 1956, Golley 1957, Thomas 1970, Thomas and Cowan 1975, Thomas 1983), survival rates are not.  Survival is usually measured  indirectly in surveys of recruitment and gross population changes (Harestad and Jones 1981, Janz 1989; see Hatter 1988 for an exception), and mortality studies have focused on single, rather than all, mortality causes (Klein and Olson 1960, Smith 1968, Hebert et al. 1982, Jones and Mason 1983, van Ballenberghe and Hanley 1984). 2  Published as: McNay, R. S., and J. M. Voller. 1995. Mortality causes and survival estimates for adult female, Columbian black-tailed deer. J. Wildl. Manage. 59:138-146.  144 Typically, winter weather has been advanced as a factor influencing survival, having direct and indirect effects.  Snow covers forage and  impedes locomotion (Harestad et al. 1982), creating a direct effect on individual energy balances and, hence, survival.  Shifts in use of habitat  resulting from accumulations of snow can modify survival rates indirectly due to consequent shifts in predator efficiency and/or prey vulnerability (Mech 1977, Messier and Barrette 1985, Nelson and Mech 1991).  Without  specific information on the range of mortality causes, on mortality and survival rates, and on environmental and behaviourial factors affecting survival, changes in population management will be ad hoc. We investigated survival rates and mortality causes for adult, female black-tailed deer at 4 study areas on Vancouver Island.  Mortality data came  from deer that were radio-collared for another study (McNay and Doyle 1990) and were used to (1) document causes of mortality, (2) estimate average monthly cause-specific mortality (N), (3) estimate average monthly survival  (.),  and (4) assess the relative effect of 5 variables on 11 and  ,  (study  area, seasonal movement types of deer, average elevation used by deer (monthly), month of year, and mean monthly snowdepth).  STUDY AREAS The study took place at 4 locations on Vancouver Island: Nanaimo (49°02’N, 124°12’W), Chemainus (48°56’N, 124°05’W), Nimpkish (50°08’N, 126°30’W), and Caycuse (48°48’N, 124°30’W) river valleys.  Nanaimo and  Chemainus rivers are neighbouring valleys 43 km northeast of Caycuse River and 202 km southeast of Nimpkish River.  Study areas were 200-300 km 2 with  valley bottoms located at 200 m above sea level (asi). 1,249 m asl at Caycuse to 1,821 ni asl at Nimpkish.  Peaks ranged from  The Chemainus, Nanaimo,  145 and Nimpkish rivers were relatively open, flat-bottomed valleys (U-shaped), while Caycuse River had steeper slopes and least flat area at low elevations (a V-shaped valley). All study areas were extensively logged by clearcutting resulting in habitats ranging from recently deforested (0- to 5-yr-old) to old (>250-yrold) forests.  Arrangement of habitats was typical of coastal logging with  valley bottoms in young (6- to 45-yr-old) forests, most mid-slopes deforested, and higher elevations and headwaters in old forests.  Generally,  Chemainus River had the least old (about 6%) and most young (about 82%) forests.  Caycuse and Nimpkish sites had similar amounts of old (40%) and  young (30%) forests. young forest.  Nanaimo River had about 30% old forest and about 50%  Each study site was 10 to 20% deforested.  Wolves, cougars (Felis concolor), and humans preyed on adult deer on Vancouver Island (Jones and Mason 1983, Janz 1989).  Some wolves were  removed from all study areas as part of ongoing predator management, although the greatest effort occurred at Nanaimo (35 removed since 1982) and Nimpkish (44 wolves removed from 1987 to 1991) rivers.  Local trappers  sporadically removed wolves at Caycuse (1 wolf removed in 1985 and 2 in 1986).  We removed 1 cougar from Nanaimo River in 1988.  All areas were open  to buck deer and cougar hunting during their respective seasons.  In  addition, there was a limited entry hunt for antlerless deer at Nanainio River during weekends each November. Vancouver Island is temperate and wet; in the dominate ecological zone, no month has a mean temperature <0 C, and mean temperature of the warmest month is 17 C.  Over most of the island there is usually 291 frost  free days, an average 820 mm of snow, and a mean precipitation of 2,140 mm each year (Meidinger and Pojar 1991).  146 METHODS We fitted deer with radio transmitters containing mortality sensors from 1982-1988 at Nanaimo River, in 1989-1990 at Nimpkish and Caycuse rivers, and in 1989 at Chemainus River.  In 1988-1990, we captured most deer  during winter in Clover traps (Clover 1956) and manually restrained them while fitting radio collars.  During 1982-1987, and when trapping was  unsuccessful, we immobilized deer with powdered anectine delivered in darts shot from a 32-gauge CAP-CHUR gun.  We monitored radio-collared deer 1  time/week for 4 yr after capture or until death, provided we could maintain radio contact.  Deer dying 12 days after capture (n  =  3) were excluded from  analyses because we could not positively rule out capture myopathy (Harthoon 1977). We established age classes of deer by tooth wear and replacement at the time of collaring (Robinette et a!. 1957).  This technique was  sufficient to classify deer as fawns (<1-yr-old), yearlings (between 1- and 2-yr-old), or adults (>2-yr-old).  We assigned all subadults common birth  dates of 15 June, to determine when they reached adult status.  Mortality Causes Upon monitoring mortality signals, usually 1-4 days after death, we sought deer and determined likely mortality cause.  Death was by 1 of 6  causes: predation by wolves or cougars, human related (legal hunting and poaching), malnutrition, accident, or unknown.  We identified predation,  legal hunting, and poaching by state of the corpse (Roy and Dorrance 1976). We classified fresh kills with neck or head injuries, clean incisions at the gut, and partially buried remains as predation by cougars.  Kills with  considerable mid- and hind-section injury and comparatively more scattered,  147 unburied remains were classified as predation by wolves.  When there was  only a small amount of carcass remaining and no definite evidence of other mortality causes, we considered wolf predation as the likely cause of death. We determined nutritional status from examination of bone marrow (Cheatum 1949).  Red, gelatinous bone marrow suggested malnutrition was the  predisposing cause of mortality.  Mortality and Survival Rate Estimation We used logistic regression (SAS Inst. Inc. 1985) to compute maximum likelihood estimates for monthly II and effects of 5 variables.  ,,  while simultaneously assessing the  Four of the independent variables were categorical:  (1) study area had 4 levels (Caycuse, Chemainus, Nanaimo, and Nimpkish), (2) seasonal movement types of deer had 3 levels (migratory, resident, or unknown), (3) average elevations used within each month had 3 levels (<600 m asl, >600 m asl, and unknown), and (4) month had 12 levels corresponding to each month of the year.  The fifth variable was mean monthly snow depth  measured daily at the closest airport to each study area (Atmos. Environ. Serv., Environ. Can., Vancouver, BC).  (n  =  We censored deer with failed radios  3), or that lost collars prematurely (n  =  1), from analyses in the  month during which they could no longer be monitored.  We conducted tests  among individual survival estimates, or between estimates of 11, using the CONTRAST statement in PROC CATMOD (SAS Inst. Inc. 1985). Our philosophy for choosing the model that most closely mimicked the observed data followed White and Garrott (1990:222).  We posed different  hierarchical subsets of the main factors as models.  In the most general  model, all factors contributed to II and  &,  levels of the dependent variable.  We compared this model (the null hypothesis) with simpler nested models, or  148 subsets, of the main factors (alternate hypotheses) using likelihood-ratio tests (White and Bartmann 1983).  We continued iterative testing of nested  models until the null hypothesis was rejected, thereby revealing which alternate model was most economical and consistent with the observed rates. Choice of the best model was corroborated on the basis of the Akaike Information Criterion (AIC; Chatfield 1992:197), a statistic used to assess parsimony of model construction.  RESULTS We captured and monitored 95 adult, female deer through 1982-91. During the same period, we caught 12 female yearlings and 12 female fawns, of which 9 yearlings and 1 fawn aged into the adult cohort and were added to our sample.  Although we caught deer in each month, most captures (71%)  occurred from January through March reflecting our greater effort and increased capture success during these months.  Collectively, 105 deer  yielded 2,182 deer-months of data.  Estimates of Cause-specific Mortality We recorded 54 mortalities and 4 collar failures, most of which (64%) occurred either from April through June or during November (Fig. 6.1). Average monthly mortality due to predators was 1.5%, with predation by cougars being the most frequent (Table 6.1). greater (P  =  (Table 6.1).  Predation by cougars was  0.012) in Caycuse and Chemainus than in Nanaimo or Nimpkish Predation by wolves was similar to that by cougars, however,  we found no difference in predation by wolves among study areas (P  >  0.614).  Human-related mortality was 0.4% monthly, or half the mortality caused by either cougars or wolves.  149  14  12  10  a) a)  8  II  0 I  .0  E  z  6  4  2  0 J  FMAM  J J Month  ASOND  Figure 6.1. Monthly mortality by cause recorded for radio-collared, adult, female black-tailed deer on Vancouver Island, British Columbia, 1982-1991. Values above histograms indicate the total sample size in number of deermonths.  1  3  1  1  1  Wolf  Human  Unknown  Malnutrition  Accident 0.4  0.4  0.4  1.3  0.4  2.1  11  0.4  0.4  0.4  0.7  0.4  A 9 • 0  SE  0  0  2  1  2  5  n  0.8  0.4  0.8  2.0  N  0.6  0.4  0.3  A 5 • 0  SE  Chemainus  1  2  3  5  10  6  n  0.1  0.1  0.2  0.3  0.7  0.4  N  0.1  0.1  0.1  0.2  0.2  B 02  SE  Nanaimo  1  0  0  0  2  2  n  0.4  0.8  0.8  N  0.4  0.5  B 05  SE  Nimpkish  3  3  6  9  15  18  n  0.1  0.1  0.3  0.4  0.7  0.8  N  Total  0.1  0.1  0.1  0.1  2 O.  C 2 • 0  SE  0,  I-.  Maximum likelihood estimates of monthly mortality (%) were computed using logistic regression (CATMOD; SAS Inst. Inc. 1985). Estimates with the same or no letter among study areas, or among causes for totals, are not different (P > 0.10). b No. of deer killed by the specific mortality cause.  a  5  Cougar  b  Caycuse  Study areas  Table 6.1. Monthly cause-specific mortality (%)a for radio-collared, adult, female black-tailed deer in 4 study areas on Vancouver Island, British Columbia, 1982-1991.  151 Generally, monthly change in N was most evident in the leading causes of mortality (Table 6.2).  Peaks in N occurred in February and from April  through July due to wolves, from March through May due to cougars, and in November due to humans.  Factors Affecting Mortality and Survival The simplest, acceptable model that appeared to explain 11 and  .  used  the seasonal movement of deer as the independent variable (Table 6.3). model was slightly more parsimonious (AIC movement and elevation (AIC Average monthly annually.  .  =  =  624.02) than the model using  640.72).  was estimated to be 97.5% (SE  Survival was lower (P  =  overall  (.  0.3), or 74%  =  0.024) for resident deer  0.4%, or 77% annually) than for migratory deer annually).  That  (.  =  (.  99.2%, SE  =  =  97.8%, SE  =  0.3%, or 90%  Deer of unknown seasonal movement type had poorest survival =  71.2%, SE  =  5.6%) but were insufficient in number to reverse or  nullify the effect of seasonal movement.  Had all unknown deer been  migratory, behaviourial differences still would have been important (P  =  0.055). Some migratory deer, however, survived at rates more comparable with resident deer once elevation was considered. at low elevations was not different (P  =  Although  0.991) from  .  .  for migratory deer  for resident deer at  high elevations (Table 6.4), the opposite comparison (migratory deer at high elevations versus resident deer at low elevations) was significant (P  =  0.015).  0.142)  Survival did not differ between elevations for resident (P  or for migratory (P  =  =  0.184) deer.  In August, September, and December through March, deer dropped below 99%, or 89% annually (Table 6.4).  .  rarely  Survival for resident deer  152 Table 6.2. Mortality (%)a by month of year for 3 leading causes of mortality for radio-collared, adult, female black-tailed deer on Vancouver Island, British Columbia, 1982-1991.  Deer Month  Mortality cause  months  Cougar b  N  Wolf  SE  n  II  Human SE  n  11  SE  Jan  160  1  0.6  0.5  1  0.6  0.5  0  Feb  175  1  0.6  0.4  2  1.1  0.7  0  Mar  192  3  1.6  0.8  1  0.5  0.5  1  0.5  0.4  Apr  221  5  2.3  1.0  2  0.9  0.5  2  0.9  0.5  May  211  4  1.9  0.9  3  1.4  0.8  0  Jun  208  1  0.5  0.5  2  1.0  0.7  1  0.5  0.5  Jul  170  1  0.6  0.4  2  1.2  0.7  0  Aug  171  0  0  0  Sep  169  0  0  1  0.6  0.4  Oct  170  0  1  Nov  171  1  0.6  0.6  0  2.3  1.2  Dec  164  1  0.6  0.6  1  a  0.6  0.6  0 4  0.6  0.6  0  Maximum likelihood estimates of monthly mortality (%) were computed using logistic regression (CATMOD; SAS Inst. Inc. 1985). b No. of deer killed by the specific mortality cause.  153 Table 6.3. Akaike’s Information Criterion (AIC) and likelihood-ratio tests (LR x)° between competing models of monthly fate (cause-specific mortality or survival) of radio-collared, aduLt, female blacktailed deer on Vancouver Island, British Columbia, 1982-1991. Hierarchical models of 4 categorical variables (A = study area, B = seasonal movement types of deer, E = monthly elevations used by deer, and N = month of the yr) are listed from the most general (all factors = A•B•E•M) to the most reduced model (no factors = N).  General  AIC  A•B•E•M  720.19  A.BE  A•B•M  B•E•M  A•E•M  A•B  A•E  B•E  A•M  659.52  704.17  700.92  712.67  642.91  651.06  640.72  753.45  Reduced  LR  2 x  df  E  A•B•E  71.33  66  0.3051  A•B•M  7.98  12  0.7867  B•EM  16.73  18  0.5417  A•E•M  16.48  12  0.1702  A•B  7.39  12  0.8308  A•E  15.54  12  0.2132  BE  17.20  18  0.5094  AB  70.74  66  0.3225  A•M  73.28  12  0.0000  B•M  16.56  18  0.5535  B•E  71.80  66  0.2917  B•M  7.81  12  0.7998  E•M  15.18  12  0.2317  A•E  70.39  66  0.3330  A•M  64.78  12  0.0000  E•M  15.43  18  0.6323  A  82.12  12  0.0000  B  17.11  18  0.5156  A  73.97  12  0.0000  E  16.17  18  0.5807  B  7.30  12  0.8372  E  14.51  12  0.2693  A  79.58  66  0.1217  N  22.18  18  0.2241  154 Table 6.3.  Continued.  General  AIC  Reduced  B.M  684.73  E•M  692.10  LR  df  P  B  71.29  66  0.3063  M  78.90  12  0.0000  E  71.13  66  0.3110  M  71.53  18  0.0000  A  701.03  N  24.39  18  0.1427  B  624.02  N  89.40  12  0.0000  E  631.23  N  82.19  12  0.0000  M  739.63  N  81.79  66  0.0910  AIC indexes model parsimony (Chatfield 1992) and is calculated as (-2 X ln[max. likelihood] + 2 [no. of independent parameters]). Likelihood—ratio tests were (—2 X ln[max. likelihood reduced model]) (—2 X ln[max. likelihood general model]).  x  -  96.6  99.0  74  87  72  73  60  59  60  72  83  73  Mar  Apr  May  Jun  Jul  Aug  Sep  Oct  Nov  Dec 1.1  2.4  1.2  0.7  1.1  1.8  1.3  1.6  1.4  1.0  1.3  1.2  SE  25  22  30  41  45  43  48  50  36  35  37  35  n  99.2  99.5  97.8  99.9  99.9  98.8  97.9  97.2  96.0  99.1  99.0  99.2  0.7  0.4  2.4  0.0  0.0  0.9  1.7  1.7  2.8  0.6  0.8  0.7  SE  Migratory  37  38  19  21  23  22  25  33  37  37  44  39  n  99.3  93.8  99.7  99.1  99.4  98.4  98.7  98.5  97.7  98.7  99.1  99.3  Resident  0.6  3.7  0.4  1.0  0.8  1.1  0.9  0.9  1.2  0.8  0.7  0.6  SE  >600 m asl  27  26  47  45  42  42  55  46  43  34  25  22  n  99.6  99.7  99.9  99.9  99.9  99.5  99.6  99.1  99.2  99.5  99.5  99.6  £  0.4  0.3  0.2  0.0  0.0  0.4  0.3  0.6  0.6  0.4  0.4  0.4  SE  Migratory  Maximum likelihood estimates of monthly survival (%) were computed using logistic regression (CATMOD; SAS Inst. Inc. 1985). No. of deer killed by the specific mortality cause.  98.5  93.5  98.5  99.3  97.4  96.4  96.6  98.2  97.9  65  Feb  98.4  62  Jan  b  Resident  <600 m asl  Monthly mode of elevations used by deer  2  2  2  2  2  3  7  10  18  12  4  2  n  83.8  51.3  84.2  89.0  92.8  74.1  74.5  66.9  59.2  77.5  81.2  83.0  £  Unknown  Unknown  11.2  16.9  13.4  11.9  9.7  12.4  10.7  10.5  9.5  9.1  10.6  11.3  SE  (Ti c-fl  Table 6.4. MonthLy survival (%)‘ for 2 known and 1 unknown seasonal movement types of radio-collared, adult, female black-tailed deer at 2 broad elevations in m above sea level (asl) on Vancouver Island, British Coluthia, 1982-1991.  156 at low elevations from April through July, by comparison, was rarely >97% (73% annually).  By comparison,  &  for migratory deer at low elevations was  <97% only during April (Table 6.4). Except in November, survival in most winter months remained high (Table 6.4) and, consequently, we could demonstrate only a weak relationship between  .  and mean monthly snow depth (P  during the study was 11 cm (n  =  106, SD  =  =  0.098).  Mean monthly snow depth  18 cm) and ranged 0-66 cm.  DISCUSSION Causes of Mortality No estimates of cause-specific mortality rates have been published for black-tailed deer.  Our estimate of predation by wolves (0.7% mon.) was half  the 17% annual mortality reported by Nelson and Mech (1986) for predation on adult, female white-tailed deer by wolves but greater than that reported for predation on adult, female mule deer by coyotes (Canis latrans) (Hamlin and Mackie 1989).  The rate in our study is likely, in part, a reflection of  ongoing removal of wolves (Janz 1989).  Still, wolves and cougars were  primary causes of mortality for our radio-collared deer (Table 6.1) and, together, created a mortality rate similar to that reported by Nelson and Mech (1986) for wolves alone.  Klein and Olson (1960) reported starvation as  the most frequent cause of mortality for Sitka black-tailed deer but most mortality reported in their study came from areas having more severe winter weather than we had in our study.  In the absence of severe winter weather  and relatively heavy predation by wolves, cougars, and humans (78% of all deaths) it is not surprising that malnutrition was rarely observed. Cougars established activity centres, especially from March through May, and killed deer in isolated stands of old forests, most of which were  157 reserved as winter habitat for deer (see Janz 1989).  Predation by cougars  has been considered unimportant (Janz and Hatter 1986), but our data indicate they can have strong local effects that are intense in late winter months.  Predation by cougars may have increased during the mid- to late-  1980’s concomitant with removal of some wolves if predation was compensatory.  However, Hamlin and Mackie (1989) considered compensatory  mortality unlikely in the adult, female segment of a mule deer population (average annual mortality was only 6.2%).  Alternatively, we may simply  notice predation by cougars more now because kills occur in winter ranges that, through time and continued forest harvesting, have become more isolated in space. Wolves, by comparison, generally appeared to be less site specific and less seasonal in their kills even though most kills occurred during winter and spring. summer.  A notable lack of adult mortality by any cause existed in late  Both predators likely concentrate on fawns rather than adults as  prey during summer months (Scott and Shackleton 1980, Hatter 1988).  Factors Affecting Survival Estimates of survival, excluding deer of unknown movement type, ranged from 73% annually for resident deer when they were at low elevations to 95% annually for migratory deer when they were at high elevations. rates for adult, female deer from other studies  Survival  are similar to our  findings: 78% for white-tailed deer in Montana (Dusek et al. 1989), 79% for white-tailed deer in Minnesota (Nelson and Mech 1986), 57-97.8% for mule deer in Montana (Hamlin and Mackie 1989), and 76-100% for mule deer in Colorado (White and Bartmann 1983, White et al. 1987, Bartmann et al. 1992). We found that survival related more closely to seasonal movement of  158 deer, or to elevations used by deer, than to study area, month, or average monthly snow depth.  Migratory deer exhibited highest  S,  which is especially  relevant considering timing and primary causes of mortality.  Predators, the  most dominant mortality agent, concentrated on adult, female deer from February through July when differences between survival of migratory and resident deer were greatest.  Harestad (1979) found that most migratory deer  departed winter ranges during March, coincident with the onset of high predation rates.  After March, migratory deer were at higher elevations, in  steeper terrain, and in habitat with fewer roads compared with the habitats of resident deer (Harestad 1979, Schoen and Kirchhoff 1985, McNay unpubi. data).  We concluded that most migratory deer, either coincidentally or  purposefully, reduced risk of mortality due to predators by leaving low elevation winter ranges as soon as they could in spring. Our findings contrast with those of Nelson and Mech (1991) for whitetailed deer in Minnesota.  Their data showed that migratory deer suffered  greater mortality due to high risk during fall migrations.  Migration  distances were 16 km in Minnesota but were 8 km for black-tailed deer on Vancouver Island (Harestad 1979) or southeast Alaska (Schoen and Kirchhoff 1985).  Perhaps more important, migration is likely a more predictable event  in continental regions as a result of regular winter weather.  In coastal  climates with more ephemeral winter weather and insular valleys, migration is shorter and less predictable (Harestad 1979, Schoen and Kirchhoff 1985). In the former scenario, wolves may be more able to exploit the vulnerability of deer during migration.  MANAGEMENT IMPLICATIONS Annual survival rates for resident deer at low elevations (73%) were  159 unlikely adequate to sustain their populations.  We constructed a simple  Leslie matrix (Leslie 1945) to assess recruitment necessary to stabilize such a population.  We used age-specific productivity rates reported by  Thomas (1983), adult survival rates from this study, and held yearling survival constant at 60% while varying fawn recruitment within reported limits from Janz (1989).  At the upper level of reported recruitment (about  25%), all but the low elevation resident population increased.  To stabilize  that population with 27% adult mortality, it was necessary to have about 30% recruitment from the fawn population; a level rarely observed on Vancouver Island (Janz 1989).  Black-tailed deer are not as fecund as other  conspecifics (Thomas 1983) and are therefore more sensitive to adult mortality (van Ballenberghe and Hanley 1984).  Furthermore, recruitment into  the adult population is low in the presence of predation by wolves (Jones and Mason 1983, Hatter 1988, Janz 1989).  Although we could not estimate  subadult survival in this study, we judged it low.  Of the 24 female, radio  collared, subadults only 1 fawn and 9 yearlings lived to become adults.  We  concluded that risk of mortality to adults at low elevations likely outweighed potential benefits in habitat quality (Gates 1968) derived from the early seral forests that follow forest-harvesting operations.  Road  construction undoubtedly provides easy access throughout lower valley elevations for wolves, cougars, and humans and, hence, direct access to deer. Forest harvesting also creates an isolation of old-forest winter ranges and may directly influence predation by concentrating prey and focusing predators’ attention to specific sites, especially during late winter when deer are most vulnerable (Nelson and Mech 1986).  Isolation of  winter habitat seems particularly important because no migratory deer were  160 caught in young forests during winter.  Although migratory deer had high  annual survival (95%), we remain suspicious about the vitality of that portion of the population.  Their absence in young forests could imply 1 or  a combination, of several processes: (1) migratory deer concentrate increasingly in diminishing areas of old forest with eventual reduction of their survival due to deteriorating range condition, (2) mortality of migratory deer in subadult age classes is high, or (3) subadults abandon migratory tactics in favour of resident habitat selection patterns.  The  implication of losing migratory deer could be a reduction in population resiliency because the remaining resident deer are subject to comparatively higher mortality. In contrast to the weakening of rationale for retention of old-forest winter habitat for deer because of declining deer populations (Janz and Hatter 1986), we consider our results to indicate that a retention of older, intact forests is basic to rebuilding deer populations.  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Manage. 21:134-153. Roy, L. D., and M. J. Dorrance. 1976. Methods of investigating predation of domestic livestock. Alberta Agric., Edmonton. 54pp. SAS Institute Inc. 1985. SAS user’s guide: statistics. Version 5. Inst. Inc., Cary, N.C. 956pp.  SAS  163 Schoen, J. W., and N. D. Kirchhoff. 1985. Seasonal distribution and homerange patterns of Sitka black-tailed deer on Admiralty Island, southeast Alaska. J. Wild]. Manage. 49:96-103. Scott, B. N., and D. N. Shackleton. 1980. Food habits of two Vancouver Island wolf packs: a preliminary study. Can. J. Zool. 58:1203-1207. Smith, I. D. 1968. The effects of hunting and seral succession upon Vancouver Island black-tailed deer. M.S. Thesis, Univ. British Columbia, Vancouver. l4Opp. Taber, R. D. 1953. Studies of black-tailed deer reproduction on three chaparral cover types. Calif. Fish and Game 39:177-186. Thomas, D. C. 1970. The ovary, reproduction, and productivity of female Columbian black-tailed deer. Ph.D. Thesis, Univ. British Columbia, Vancouver. 2llpp. 1983. Age-specific fertility of female Columbian black-tailed deer. J. Wildl. Manage. 47:501-506. and I. M. Cowan. 1975. The pattern of reproduction in female Columbian black-tailed deer, Odocoileus hemionus columbianus. J. Reprod. Fert. 44:261-272. van Ballenberghe, V., and T. A. Hanley. 1984. Predation on deer in relation to old-growth forest management in southeastern Alaska. Pages 291-296 in W. R. Meehan, T. R. Merrell, Jr., and T. A. Hanley, eds. Fish and wildlife relationships in old-growth forests. Am. Inst. Fish. Res. Biol., Morehead City, N.C. White, G. C., and R. M. Bartmann. 1983. Estimation of survival rates from band recoveries of mule deer in Colorado. J. Wild]. Manage. 47:506511. and R. A. Garrott. 1990. Analysis of Wildlife radio-tracking data. Academic Press, San Diego, Calif. 383pp. R. M. Bartmann, L. H. Carpenter, and A. W. Alldredge. 1987. Survival of mule deer in northwest Colorado. J. Wildl. Manage. 51:852-859.  164 CHAPTER 7  -  MANAGEMENT IMPLICATIONS OF CONSTRAINTS ON MOVEMENT TACTICS 3  Prior to 1970, research on black-tailed deer was restricted largely to areas of low snowfall.  Findings suggested that deer populations responded  positively to forage increases generated by timber harvesting and forestry was assumed beneficial to deer (Bunnell 1985).  Initial research in areas of  higher snowfall documented contrary results; old forests were found to be beneficial to black-tailed deer (Walimo and Schoen 1980, Bunnell and Jones 1984, Bunnell 1985).  Potential conflict between forest harvesting and  maintenance of black-tailed deer populations was exposed.  That conflict  helped inspire 2 large research efforts: the Integrated Wildlife  -  Intensive  Forestry Research program (IWIFR) and Managed Stands for Deer Winter Range (MSDWR).  Both were designed to guide the type and distribution of forestry  practices that would maintain deer populations.  Black-tailed deer, however,  are relatively plastic and, among their apparent adaptations, show different movement patterns that initial analyses suggested were related to broad habitat features (Bunnell 1990).  For management to be effective these  patterns had to be documented well enough that responses to habitat alterations could be predicted under different conditions.  This paper  integrates results of several studies to document how behaviourial features of black-tailed deer limit their use of habitats and their responses to rapid alterations of habitat.  It documents general movement patterns of  black-tailed deer on Vancouver Island, relates these movements to broad environmental features including local climate and topography and to large scale habitat alteration, and notes how they interact with major causes of mortality.  Published as: McNay, R. S., and F. L. Bunnell. 1994. Behaviourial limits to movement: the effect on habitat choices for Columbian black-tailed deer. Trans. Congr. Tnt. Union Game Biol. 21(2):295-303.  165 STUDY AREAS We studied movements, habitat use, and survival of black-tailed deer at 4 locations on Vancouver Island, British Columbia, 1982-1991.  We also  studied individual responses to logging winter habitat at 2 of these study areas, 1989-1991.  The most northerly study site was located at the Nimpkish  River valley (50°08’N, 126°30’W).  The Nimpkish River flows primarily  northwest (315°) giving the general study site a southern aspect and the main valley ranges in elevation from about 200 m to 1,821 m asl.  The  Nanaimo River (49°02’N, 124°12’W) and the Chemainus River (48°56’N, 124°05’W) are neighbouring valleys on southeastern Vancouver Island.  Both  watersheds have a considerable southern exposure; the Nanaimo River flows in a direction of 125°.  500  while the Chemainus River flows more to the south at  These watersheds range in elevation from about 300 m to 1,540 m asl.  The last and most maritime study site was located at the Caycuse River (48°48’N, 124°30’W) on southwestern Vancouver Island.  The Caycuse River  flows due west but has many sub-drainages flowing south or north.  This  valley has the least area of flat valley bottom and ranges least in elevation from about 300 m to 1,249 m asl. The dominant ecosystem on Vancouver Island is the Coastal Western Hemlock (CWH) zone while the less prevalent Mountain Hemlock (MH) zone exists at high elevations (Meidinger and Pojar 1991).  While these  ecosystems were represented at each study site, the Caycuse River valley had only minor amounts of MH.  Extensive harvest of trees had occurred at each  study site but the resulting mosaic of forest seral-age classes differed among sites.  Generally, the Chenialnus River site had the least amount of  area remaining in old (>250-yr-old) forests (6%) and the most area in young (6- to 45-yr-old) forests (82%).  The Caycuse and Nimpkish River sites were  166 similar in that each had about 40% of their area in old forest and 30% in young forest.  The Nanairno River site was represented by about 30% old  forest and 50% young forest.  Each study site had from 10 to 20% of their  area in deforested (0- to 5-yr-old) clearcuts. Climate on Vancouver Island is characterized by temperate, wet weather.  In the CWH zone, no month has a mean temperature <0 C and the mean  temperature of the warmest month is 17 C.  Usually there are 291 frost free  days, an average of 820 mm of snow, and a mean precipitation of 2,140 mm each year (Meidinger and Pojar 1991).  METHODS Deer Location Samples Deer were captured using Clover traps (Clover 1956), or by immobilization using succinylcholine, and collared with radio transmitters (McNay and Voller 1995).  Collaring took place at Nanaimo River from 1982 to  1988, at Chemainus River in 1989, and at both the Caycuse and the Nimpkish Rivers in 1989 and 1990. Radio-collared deer were located by triangulation (White and Garrott 1990) using >2 bearings, each recorded at separate and permanent recording stations marked at 100-rn intervals along roads. collected in <10 mm  Bearings were usually  at sites that were line-of-sight with, and close to  (<400 m), the radio-transmitter being monitored. taken once-per-week on each collared deer.  Samples were generally  In 1984 the sampling schedule  was standardized so that, during a calendar month, each deer was located at least once-per-week and once within each quarter of a calendar day.  Deer  locations were estimated using the maximum likelihood estimator (Lenth 1981) presented in a SAS program (SAS Inst. Inc. 1985) by White and Garrott.  167 (1990:64).  Definitions and Analytical Procedures Movements and Habitat Use.  -  To document general movement patterns and  habitat use, we examined consecutive locations of individual, radio-collared deer to find the magnitude (distance), frequency, timing, direction, and habitat choices resulting from movements.  Natal ranges were defined as  those areas occupied during the natal period, which for deer in coastal British Columbia is anywhere from late-May throughout June (Cowan 1956, Thomas 1970).  We assumed that the natal range was close to the real  location of birth (Masters and Sage 1985, McCullough 1985, Hanilin and Mackie 1989) and would be where offspring were produced.  Spatially separate ranges  occupied at other times were identified by migrations made to go between them and the natal range and were termed alternate ranges.  Migrations and  dispersal differed in that migrations had predictable return migrations to the original position (Sinclair 1984) while dispersals did not (Howard 1960, Bunnell and Harestad 1983).  Migratory deer were termed obligate migratory  deer if they exhibited consistent annual migrations and facultative migratory deer if migrations occurred irregularly.  Non-migratory deer were  termed residents. Forest habitats were classified as: old (>250-yr-old), young (6- to 45-yr-old), open (unforested, 0- to 5-yr-old forests), or non-merchantable (rock, water, subalpine, and alpine). 250-yr-old.  No forests existed between 46- to  Elevations above sea level (asl) were considered in 200-rn bands  (201-400 m, 401-600 m, etc.) except in the study of deer survival where elevations were considered in 2 strata; above and below 600 m. All distances and directions were measured as a straight line, or  168 bearing, from the last recorded location to the current location.  Departure  and arrival dates from one range to another, or from one location to another, were taken to be the half-way point between the date last observed to the date of the current observation. We assessed independence of location data in a related study (McNay et al. 1994) and assessed the distributional properties of circular data (e.g., dates and directions) using Raleigh’s z test (Batschelet 1981).  The  proportion of migrations initiated with or without snow was tested for movement type (obligate or facultative) effects using Fisher’s exact test (Sokal and Rohif 1981).  Fidelity to natal ranges was tested for movement  type effects using the straight-line distance between arithmetic centres of consecutively used natal ranges in an F-test (Sokal and Rohlf 1981). Comparisons of habitat use between movement and range types were made with  2 x  tests (Sokal and Rohif 1981). Winter Range Removal.  -  We assessed responses by both control and treatment  deer to clear-cut logging of winter habitat.  For each deer, we compared  pre- and post-disturbance: (1) fidelity to winter ranges, (2) winter range sizes, and (3) use of old forests.  Candidate stands of old forest that met  characteristics of typical winter habitat (e.g., Bunnell 1985, Nyberg et al. 1986) were selected for harvest at the Chemainus and Nimpkish river sites (for detail see chapter 5).  These stands were isolated from stands of  similar characteristics and were to be entirely clear-cut logged.  Traps  were placed within the selected stands of old forest to trap treatment deer and at sites within nearby stands to trap control deer. habitat use comparisons were monitored at Nanaimo River.  Control deer for Analysis treated  only data for the winter season (November through April) prior to, and directly following, logging (i.e., 1 yr pre- and post- disturbance).  We  169 omitted migrations and data for migratory deer if they were on their natal ranges during winter months.  We defined fidelity as overlap in pre- and  post-disturbance winter ranges and defined continuity of habitat choice as insignificant winter.  2 I (x  P  >  0.05) differences in the use of old forests during  Home ranges were 95% minimum area polygons using data from winter.  For statistical convenience we constructed an index of fidelity based on a test of independence between consecutive winter ranges (White and Garrott 1990:136).  Positioned on the pre-disturbance range centre, we used  concentric circles with radii iteratively reduced by 100 m, starting at a radius of 1,500 m, to find the first test where deer locations in the postdisturbance range were considered to be independently distributed 0.05).  2 I (x  P  <  This radius was termed the maximum radius for independence (MRI) and  small MRI values indicated strong fidelity.  Habitat use was the percent of  location observations in old forests situated below, or at the same elevations as, the cut-block.  Old forests above the cut-block did not meet  criteria typical of deer winter habitat (Nyberg et al. 1986) and were considered a poor alternative to the logged habitat.  Habitat use data were  transformed using an arcsine transformation for percentage data (Zar 1984). We used a repeated measures (pre- and post-disturbance), analysis of variance to test for the potential effect of logging (control versus treatment deer) nested within study areas (Chemainus or Nimpkish).  All  reported means are least-squares estimates (Searle et al. 1980). Survival Estimation.  -  Deer were aged at the time of collaring and followed  until death at which time the cause of mortality was determined (for details see McNay and Voller 1995).  We used logistic regression (SAS Inst. Inc.  1985) to compute maximum likelihood estimates for monthly mortality (H) and survival  ()  of adult, female deer, while simultaneously assessing the  170 effects of 5 variables.  Four of the independent variables were categorical:  (1) study area had 4 levels (Caycuse, Chemainus, Nanaimo, and Nimpkish), (2) seasonal movement types of deer had 3 levels (migratory, resident, or unknown), (3) average elevations used (on a monthly basis) had 3 levels (<600 m asl, >600  in  asl, and unknown), and (4) month had 12 levels  corresponding to each month of the year.  The fifth variable was mean  monthly snow depth measured daily at the closest airport to each study area (Atmos. Environ. Serv., Environ. Can., Vancouver, BC).  We conducted tests  among individual survival estimates, or between estimates of N, using the CONTRAST statement in PROC CATMOD (SAS Inst. Inc. 1985).  RESULTS AND DISCUSSION Analysis treated 126 deer collared at 4 study areas (119 females and 7 males) monitored for a total 76,693 deer-days.  The majority of the deer  were collared as adults at Nanaimo River (Table 7.1).  One deer that made a  single abnormal move just before death and 51 deer for which we had <10 mo of data could not be classified by seasonal movement type and were omitted from analysis of movements and habitat use (Table 7.1).  We collected a  total of 8,624 weekly locations on the remaining 74 deer (Table 7.1).  Only  20 adult, female deer were alive long enough to be considered in the response to removal of winter habitat and all adult females were treated in the analysis of monthly survival rates (n  =  105).  Movements The only recorded dispersal movements were made by 2 female deer collared as juveniles at Nimpkish River.  Both deer left the collaring site  together late in June and spent much of the summer at a site 7 km to the  44(32)  1(1)  0  Nanainio  Nimpkish  12(10)  0  0  0  1  d  9  1  1  2(2)  Yearling  0  0 1(1)  5  4(1)  2(1)  5(2)  0  9  d  Fawn  0  1(1)  0  6(2)  d  Total  15(13)  57(39)  20(8)  27(11)  9  5307  951  784  1582  Locationsb  Number of  b  Locations are for superscripted deer only.  Age classes, determined at time of collaring, were: fawn (<1-yr-old), yearling (between 1- and 2-yrold), or adult (>2-yr-old).  a  15  0  Chemainus  24(10)  0  Caycuse  9  d  Study Site  Adult  Table 7.1. The sex, age classesa, and number of relocations made for a sample of radio-collared, black-tailed deer at 4 study sites on Vancouver Island, British Columbia. Superscripted values are the number of deer that lived long enough (>10 mo) to classify into seasonal movement types.  I-.  172 During the fall they began moving again and travelled another 25 km  east.  east before 1 of the 2 was killed in a collision with a vehicle.  The  remaining disperser continued travelling only to return and settle at the same location it had spent the previous summer. dispersal or migration was recorded.  No further indication of  Dispersal distances were longer that  most migratory moves (e.g., McNay and Doyle 1987). Migratory moves (n  202) occurred at all study sites with movements  =  to alternate ranges occurring at October 20 (z  =  24.33; n  =  38; p  <  0.05)  for obligate migratory deer and December 10 (z  =  20.19; n  =  56; P  <  0.05)  for facultative migratory deer.  Return migrations to the natal ranges  generally occurred before the birth of fawns for both types of migratory deer; about May 26 (z  =  35.43; n  and about February 21 (z migratory deer.  =  10.03; n  =  45; p =  0.05) for obligate migratory deer  <  63; P  >  0.05) for facultative  Obligate deer travelled to alternate ranges every year,  leaving before snowfalls began (only 2 of 38 moves were in snow) and returning after snow at low elevations had melted (0 of 45 moves were in snow).  Facultative migratory deer, by comparison, usually departed for  alternate ranges only after snowpacks had already accumulated on natal areas (42 of 56 moves were in snow) and returned before snow ablation had completed (16 of 63 moves were in snow).  The proportion of time that moves  were made in snow were significantly different (Fisher’s P leaving, and P groups.  =  0.016; n  =  0.001; n  <  =  94  108 returning to, the natal range) between the 2  If no snow fell, facultative migratory deer usually did not leave  their natal range.  Two exceptions occurred; 1 deer at both Nanaimo and  Chemainus left the natal range before winter (October) each year for short (<3 wk) intervals.  Obligate deer spent >6 mo  (  =  209 d; n  =  34, SE  =  8.77)  away from the natal range each year while facultative deer spent <3 mo away  173  (  =  66 d; n  =  33, SE  =  10.05).  Obligate migratory deer showed no  consistent directional tendencies in their movements; facultative migratory deer usually followed the direction of the valley (chapter 3). Facultative deer appeared reluctant to leave the natal area and did so only if snowfall modified their habitat.  These deer had established their  natal areas at mid- to high-elevations (Fig. 7.1) where snowpacks were ephemeral.  Obligate migratory deer established their natal ranges at higher  elevations (Fig. 71) and appeared to move in anticipation of inclement winter weather.  That observation is consistent with the proportion of  behaviour types observed at each study site.  For example, the Caycuse area  had the most maritime climate, the lowest maximum elevation, and the least area of flat valley bottom; it had no obligate migratory deer, only 1 resident deer, and 7 facultative migratory deer.  Nanaimo River, by  comparison, was the most variable study site with both low elevation habitats that were free of snow in some years through to mountainous subalpine habitats that received at least some snow every year.  Our sample  of deer there consisted of 23 resident deer, 7 facultative migratory deer, and 10 obligate migratory deer.  Studies from other locations support the  notion that tactics for seasonal movements depend on local topography (Kufeld et al. 1989, Brown 1992) and can be initiated by specific weather related phenomena (Mccullough 1964, Loft et al. 1989), snow depth (Richens 1967, Gilbert et a!. 1970), or the condition of seasonal forage (Garrott et al. 1987).  We concluded that local topography and climate at the natal  range provide a continuum in the need to use alternate ranges.  Where  inclement winter weather can be expected in most years, deer migrate annually to alternate ranges.  At lower elevations, where snowpacks are  ephemeral, deer are afforded the opportunity to leave the natal area only  174  70 Facultative  •  Obligate  Resident  60  0 0.  ;50  40 0 C  o o o  30  20 0  l:IH 2-4  4-6  i.  6-8  Elevation bands (m asl  8-10  *  10-12  100)  Figure 7.1 Percent of total locations for radio-collared, black-tailed deer of 3 movement types (obligate migratory, facultative migratory, or resident) found at different elevation bands (m asl) on Vancouver Island, British Columbia, 1982-1991.  175 when necessary.  Finally, at lowest elevations deer are resident because  inclement winter weather occurs infrequently and no topographic opportunity exists to provide any escape from inclement winter weather when it does occur. Similar to other studies (Linsdale and Tomich 1953, Bunnell and Harestad 1983, Garrott et al. 1987, Hamlin and Mackie 1989), dispersal was observed infrequently.  We therefore concluded that establishment of a natal  range to be dependent on, or at least associated with, the location of the maternal natal range.  That has been observed to be the case in at least 2  other studies (Hirth 1977, Hamlin and Mackie 1989) and we believe it to be the case in our study.  If that is the case, and provided that the need to  migrate is dependent on local topography and climate (above), then offspring should adopt similar migration tactics as their mothers and ranges should be used with a high degree of fidelity. Fidelity to the natal area was virtually unvarying among periods of use  (  =  0.3 km; n  =  66, SE  seasonal movement type (P (Garrott et a!. 1987).  >  =  0.3) and we could not detect any effect due to  0.05).  Other studies report similar results  Early mortality precluded unequivocal documentation  of fawns’ migration tactics relative to their mothers but, based on observations of surviving fawns, we believe fawns mostly adopted their mother’s tactics for selecting seasonal ranges.  Although we could find no  confirmation for deer, Sweanor and Sandegren (1988) reported an exact correlation between movements of offspring and movements of mothers in a partially migratory population of moose. We conclude that the need to select an alternate range depended on the local topographic and climatic conditions of the maternal natal range. Dispersal could act to reset natal range establishment but that would only  176 occur infrequently.  Habitat Use Deer used young forests 65-75% of the time at all sites except at Caycuse River where deer used this type about 45% of the time; the balance of locations at Caycuse River occurred in open habitats (Fig. 7.2). open habitats elsewhere ranged from 7% to 15%.  Use of  Use of old forests ranged  from 7% at Caycuse to 14% at Nimpkish. Habitat use differed  2 (x  =  410.8; df  =  3; p  <  0.001) between natal and  alternate ranges primarily in a switch of seral age classes (Fig. 7.3). Natal ranges were characterized by less use of old forests (9% versus 29%) and greater use of open habitats (21% versus 12%).  This difference was  clearer to us when we considered the frequency of deer using these habitats on each range type.  All 12 obligate migratory deer, and 10 of 16  facultative migratory deer, used old forests while on alternate ranges (Fig. 7.3).  Resident deer, by comparison, did not have as much access to old  forests and only 25 of 44 resident deer used it.  Of 6 facultative deer that  did not use old forests, 2 migrated outside the winter season (October) in the absence of snow, apparently for reasons other than to find winter habitat.  The remaining 4 facultative deer never had access to old forests  because none was available along their restricted path of movement, down in elevation and out the main valley.  Furthermore, of the 39 deer that were  trapped, all those trapped in young forests were resident deer (n  =  11) but  less than half of those trapped in old forests were resident. We conclude that old forests were the preferred habitat when inclement winter weather occurred.  Summer habitats were composed primarily of young  forests and open habitat combinations.  Resident deer, being lower in  0  20  40  60  80  Clear  Young Habitat types  Old  NMF  Figure 7.2 Habitats chosen by radio-collared, black-tailed deer at 4 study sites on Vancouver Island, British Columbia, 1982-1991. Habitats are: open, 0- to 5-yr-old forests; non-merchantable (NMF) rock, water, subalpine, and alpine; young, 6- to 45-yr-old forests; or old, >250-yr-old forests.  a  C) C) C)  C  0  0  Cu  0  Cu C.)  4.d  Cl) C 0  Cl)  •0  >1  Cu C) 1 Cu  100  —4  0  40  —.  Open  ::;.;  g•:•::•:::<.:::::•::•:::::•:•:::•::•::•:•:•:•::::::•  +  I  A T I  T  Young Habitat types  Old  ti  ::.  4  •  t  I  +  +  -SE Mean +SE Facultative Obligate Resident  NMF  I  •  A  +  Figure 7.3 Habitats used by radio-collared, black-tailed deer of 3 movement types (obligate or facultative migratory, or resident) at natal ranges (top) and at alternate, winter ranges (bottom) on Vancouver Island, British Columbia, 1982-1991. Habitats were non-merchantable (NMF) rock, water, sub alpine, or alpine or forests of ages: open, 0- to 5-yr-old; young, 6- to 45-yr-old; or old, >250-yrold.  IS C  .2  C  2O-4  Cu  :40-  60-  8  cc  179 elevation than other deer and consequently in a zone of greater forest harvest, used primarily young habitats year-round and only where remnant stands of old forests occurred did they use that habitat type.  Those  results are similar to those of Yeo and Peek (1992) in south-east Alaska. We conclude again that the mothers’ selection of natal range limits subsequent breadth of habitat availability on alternate ranges during inclement winter weather.  Response to Removal of Winter Habitat The most complete test of deer response to removal of their winter habitat occurred at Nimpkish River because the stand of old forest (47 ha) was completely logged as scheduled in the fall of 1990.  A stand of old  forest remained untouched by logging about 300 m below the bottom of the cut-block at Nimpkish River.  At Chenialnus River, the stand of old forest  (90 ha) was partially logged (30 ha each) during the spring of 1989 leaving 60 ha of old forest adjacent to the cut-block.  We found deer to retain  strong fidelity to their winter ranges at both study areas (Fig. 7.4) with no indication that logging altered this attraction to sites (F 3; p  =  (F  6.90; df  =  0.639).  =  0.59; df  =  Winter ranges were larger after logging than before logging =  1; P  =  0.018) but there was again no evidence of an effect  due to logging (F  =  deer (Fig. 7.4).  Finally, use of old forests was lowest during post-logging  monitoring (F  =  0.98; df  29.05; df  =  =  3; p  I.; P  =  =  0.425) between treatment and control  0.002) but only the treatment at Nimpkish  River had an effect on use of old forests (F  =  5.91; df  =  2; P  =  0.018).  Our observations of deer response to logging are preliminary in that they come from only one post-logging year.  Nevertheless, results show that  fidelity to site is a strong factor involved in habitat use following  -  120  0........................  ...............  i i  9  0 ....:.:.:....I:.:.:.:...:...:.:.::::I::.  ............................................  ...:  ..:....-.....-.•.•....:......  .C  • Chemainus River • Nimpkish River A Nanaimo River  T  i  •  I  0  I  o I  •  :::-:;•::::::::  Treatment deer  Post-disturbance  a  Control deer  Pre-disturbance  Figure 7.4 The response of radio-collared black-tailed deer to removal of their old forest, winter habitat at 2 study areas on Vancouver Island, British Columbia, 1988-1991.  0  c0  XG)  c2O  560  Ø Q 0 CA 0 C.  ¶-.  ocu  Ot  100  Upper se • Mean I Lower se  I-.  co  181 logging.  Even though old forest was still available closeby at Nimpkish  River, deer never left their winter ranges to access that type and as a result would have adjusted their habitat use in proportion to the amount of old forest lost through logging.  Deer at Chemainus River, however, still  had old forest left adjacent to the cut-block and appeared to adjust their winter range sizes (but only marginally) to use that habitat type.  Similar  results were obtained by Gasaway et al. (1989) when they studied response by moose to habitat improvements; only moose having previous experience in, or adjacent to, the manipulated area increased their use of that area after manipulation.  Others found no tendency for deer (Hood and Inglis 1974) or  elk (Hershey and Ledge 1980, Edge et a!. 1985) to move away from habitat disturbances that were considered to be detrimental.  Survival Estimates Of the 12 female yearlings and 12 female fawns that we collared, 9 yearlings and 1 fawn aged into the adult cohort and were added to the sample of 95 deer collared as adult females (Table 7.1).  We recorded mortalities  from about half of that sample (54 deaths and 4 collar failures of 105 collared deer) most of which occurred between February and June.  Predation  was the most frequent cause of death with an average monthly mortality of 1.5% (McNay and Voller 1995).  Human-related mortality occurred at 0.4%  monthly and most frequently during the hunting season in November. The simplest, acceptable model that explained survival used the seasonal movement of deer as the independent variable (McNay and Voller 1995).  A slightly better, but less efficient, model used both seasonal  movement type and elevation. or 74% annually.  Average monthly survival was 97.5% (SE  Survival was lower (P  =  =  0.3),  0.024) for resident deer (97.8%,  182 SE  =  0.4%) than for migratory deer (99.2%, SE  =  0.3%).  Although survival  for migratory deer at low elevations was not different (P  =  0.991) from that  for resident deer at high elevations (McNay and Voller 1995; Table 4), the opposite comparison (migratory deer at high elevations versus resident deer at low elevations) was significant (P  =  0.015).  In August, September, and  December through March, deer survival rarely dropped below 99%, or 89% annually.  Survival for resident deer at low elevations from April through  July, by comparison, was rarely >97% (73% annually).  By comparison,  survival for migratory deer at low elevations was <97% only during April (Table 6.4). We concluded that obligate migratory deer could have been coincidentally or purposefully in transit to, or from, their natal ranges during the periods of highest mortality which tended to be due to predators and concentrated at lowest elevations (McNay and Voller 1995).  That  conclusion contrasts the results found by Nelson and Mech (1986) for whitetailed deer in Minnesota where migratory deer had lower annual survival compared to residents.  The overall annual survival rates compared  favourably with those reported for white-tailed deer in Montana (Dusek et al. 1989) and mule deer in Colorado (White and Bartmann 1983).  MANAGEMENT IMPLICATIONS We draw 3 major management implications from these findings.  The  first concerns the interaction of decisions about resource use by black tailed deer and their loyalty to specific movement tactics (chapter 4). Because decisions about resource use create constraints that tend to cascade down through an hierarchy of decisions (e.g., home range choices constrain seasonal range selections), and because deer show a consistency of movement  183 patterns (e.g., philopatry, fidelity to seasonal ranges), their habitat choices and movements will not be easily changed or new tactics developed. This stands as a major constraint on habitat selection whenever habitats are altered in any rapid and extensive manner (e.g., wildfire or logging).  The  constraint, in turn, presents a lack of freedom in choosing habitats; freedom-of-choice being the fundamental assumption in the ideal -free distribution hypothesis of habitat selection (sensu Fretwell 1972).  That  is, deer will not behave as though omniscient with full knowledge of their surroundings, selecting and filling the best habitat first before occupying less favourable habitat.  Responses to changes in their habitat will lag in  time by some unknown amount, possibly generations.  We had some indication  of this in the experiment that involved removal of winter habitat.  That  delay has serious implications to researchers and managers attempting to interpret patterns of deer habitat use.  It is unclear when and what to  measure as a response to changes in habitat, such as those induced by forestry practices.  Interpretation of use/availability measures is  obscured, evaluation of habitat is obscured (Hobbs and Hanley 1990), and researcher credibility is potentially undermined.  For example, costly  efforts have been undertaken to create black-tailed deer winter range in managed stands (e.g., Bunnell 1985, Nyberg et al. 1986).  If deer respond to  those efforts with no more alacrity than they responded to winter range removal, efforts to create winter range may be successful without an obvious response by deer.  Conversely, deer may remain in areas where some  management action has created unfavourable habitat. The second major implication concerns old forests.  Black-tailed deer  use young forests and appear to do reasonably well there (e.g., Fig. 7.3). Our findings here, though, corroborate those of other researchers (see  184 reviews of Bunnell and Jones 1984, Bunnell 1985, 1990) that deer using old forests do best. young forests.  When snow is on the ground only resident deer are found in Facultative and obligate migratory deer more often attain  better habitat in snowy winters and also show higher annual rates of survival.  It is difficult to predict the consequences as more old forest is  harvested from lower elevations; much of it is already gone.  Our  observations suggest that as old forest winter habitat is removed there will be fewer and fewer migratory deer until only 1 movement tactic, the least successful over long periods, is retained.  We therefore expect resilience  of deer populations to decline as the amount of old forest winter habitat declines.  Deer currently present in young forests during winter may reflect  only that portion of the initial population that is non-migratory. Tactics for maintaining wildlife in forested ecosystems involve both stand prescriptions and watershed or landscape level planning (Bunnell and Kremsater 1993).  The seasonal movement types reported here have clear  implications to landscape level planning.  For example, it is apparent that  facultative migratory deer move down and out of valleys.  Similarly,  obligate migratory deer select winter ranges on south-facing, old forests at mid- to high elevations (e.g., Fig. 7.1).  Reserves of old forests to  provide winter ranges, and management of young forests to imitate winter range in managed stands (e.g., Nyberg et a!. 1986), should be few but large on southern aspects at mid-elevations.  That approach should accommodate  both facultative and obligate migratory deer (who will eventually find the areas) and reduce the effects of predators, such as wolves and cougars, which likely concentrate their efforts in small areas.  Because resident  deer move little and occur mostly at low elevations, reserves and silvicultural treatments at those elevations should be spread over many,  185 smaller areas.  Combined, this distribution of management actions across the  landscape will maintain all 3 seasonal movement types and resilience within deer populations.  LITERATURE CITED Batschelet, E. 37lpp.  1981.  Circular statistics in biology.  Academic Press, N.Y.  Brown, C. G. 1992. Movement and migration patterns of mule deer in southeastern Idaho. J. Wild]. Manage. 56:246-253. Bunnell, F. L. 1985. Forestry and black-tailed deer: conflicts, crises, or cooperation. For. Chron. 61:180-184 1990. Black-tailed deer ecology and forest management. Pages 31-63 in J. B. Nyberg and D. W. Janz, eds. Deer and elk habitats in coastal forests of southern British Columbia: a handbook for forest and wildlife managers. British Columbia Minist. For. Special Rep. Ser. 5. Victoria. and A. S. Harestad. 1983. Dispersal and dispersion of black-tailed deer: models and observations. J. Mamrn. 64:201-209. and G. W. Jones. 1984. Black-tailed der and old-growth forests a synthesis. Pages 411-420 in W. R. Meehan, T. R. Merrell, Jr., and T. A. Hanley, eds. Proc. symposium on fish and wildlife relations in old-growth forests. Am. Inst. Fish. Res. -  and L. L. Kremsater. 1993. Tactics for maintaining biodiversity in forested ecosystems (this proceedings). Clover, M.  1956.  Single-gate deer trap.  Calif. Fish and Game 42:199-201.  Cowan, I. McT. 1956. Life and times of the coast black-tailed deer. Pages 523-617 in W. P. Taylor, ed. The deer of North America. Stackpole Co., Harrisburg, Penn. Dusek, G. L., R. J. Mackie, J. D. Herriges, Jr., and B. B. Compton. 1989. Popul ati on ecology of white-tailed deer along the lower Yellowstone River, USA. Wildl. Monogr. 104. 68pp. Edge, W. D., C. L. Marcum, and S. L. Olson. 1985. Effects of logging activities on home-range fidelity of elk. J. Wild]. Manage. 49:741744. Fretwell, S. D. 1972. Populations in a seasonal environment. Univ. Press, Princeton, N.J. 217pp.  Princeton  Garrott, R. A., G. C. White, R. M. Bartmann, L. H. Carpenter, and A. W.  186 Alidredge. 1987. Movements of female mule deer in Northwest Colorado. J. Wildl. Manage. 51:634-643. Gasaway, W. C., S. D. Dubois, R. D. Boertje, D.J Reed, and D. T. Simpson. 1989. Response of radio-collared moose to a large burn in central Alaska. Can. J. Zool. 67:325-329. .  Gilbert, P. F., 0. C. Walimo, and R. B. Gill. 1970. Effect of snow depth on mule deer in Middle Park, Colorado. J. Wildl. Manage. 34:15-23. Hanilin, K. L. and R. J. Mackie. 1989. Mule deer in the Missouri River Breaks, Montana: A study of population dynamics in a fluctuating environment. Montana Dept. of Fish and Wildl., Missoula. 4Olpp. Hershey, T. J. and T. A. Leege. 1982. Elk movements and habitat use on a managed forest in north-central Idaho. Idaho Dep. Fish Game Wildl. Bull. 10. 24pp. Hirth, D. H. 1977. Social behaviour of white-tailed deer in relation to habitat. Wildi. Monogr. 53. 57pp. Hobbs, N. T., and T. A. Hanley. 1990. Habitat evaluation: do use/availability data reflect carrying capacity? J. Wildl. Manage. 54:515-522. Hood, R. E. and J. M. Inglis. 1974. Behavioral responses of white-tailed deer to intensive ranching operations. J.Wildl. Manage. 47:664-672. Howard, W. E. 1960. Innate and environmental dispersal of individual vertebrates. Am. Midl. Nat. 63:152-161. Kufeld, R. C., D. C. Bowden, and D. L. Schrupp. 1989. Distribution and movements of female mule deer in the rocky mountain foothills. J. Wildi. Manage. 53:871-877. Lenth, R. V. 1981. 23:149-154.  On finding the source of a signal.  Linsdale, J. M. and P. Q. Tomich. California Press, Berkeley.  Technometrics  1953. A herd of mule deer. 567pp.  Loft, E. R., R. C. Bertram, and D. L. Bowman. mule deer in the central Sierra Nevada. 19.  Univ.  1989. Migration patterns of Calif. Fish and Game 75:11-  Masters, R. D. and R. W. Sage, Jr. 1985. White-tailed deer fawn/dam interactions and fawn home range establishment. N.Y. Fish and Game J. 32:93-94. McCullough, D. R. 1964. black-tailed deer. •  Relationship of weather to migratory movements of Ecology 45:249-256.  1985. Long range movements of large terrestrial mammals. Pages 444-465. in M. A. Rankin, ed. Migration: Mechanisms and adaptive  187 significance.  Contrib. Marine Sci., Suppi. Vol. 27.  McNay, R. S. and D. D. Doyle. 1987. Winter habitat selection by blacktailed deer on Vancouver Island: a job completion report. British Columbia Minist. Environ. Parks and Minist. For. IWIFR-34, Victoria. 9Opp. and J. M. Voller. 1995. Survival and cause-specific mortality of Columbian black-tailed deer on Vancouver Island. J. Wild]. Manage. 59:138-146. J. A. Morgan, and F. L. Bunnel]. 1994. Characterizing independence of observations in movements of Columbian black-tailed deer. J. Wildl. Manage. 58:422-429. Meidinger, D. and J. Pojar. 1991. Ecosystems of British Columbia. British Columbia Minist. For. Special Rep. Ser. 6. Victoria. Nelson, M. E. and L. D. Mech. northeastern Minnesota.  330pp.  1986. Mortality of white-tailed deer in J. Wildl. Manage. 50:691-698.  Nyberg, J. B., F. L. Bunnell, D. W. Janz, and R. M. Ellis. 1986. Managing young forests as black-tailed deer winter ranges. British Columbia Minist. For. Land Manage. Rep. 37. Victoria. 49pp. Richens, V. B. 1967. Characteristics of mule deer herds and their range in northeastern Utah. J. Wildi. Manage. 31:651-666. SAS Inst. Inc. 1985. SAS user’s guide: basics, version 5 edition. Institute Incorporated, Cary, N.C. 584pp.  SAS  Searle, S. R., F. M. Speed, and G. A. Milliken. 1980. Population marginal means in the linear model: an alternative to least square means. Am. Stat. 34:216-221. Sinclair, A. R. E. 1984. The function of distance movements in vertebrates. Pages 240-258 in I. R. Swingland and P. J. Greenwood, eds. The ecology of animal movement. Claredon Press, Oxford, England. Sokal, R. R. and F. J. Rohlf. N.Y. 859pp.  1981.  Biometry.  W. H. Freeman and Company,  Sweanor, P. Y. and F. Sandegren. 1988. Migratory behaviour of related moose. Holarctic Ecol. 11:190-193. Thomas, D. C. 1970. The ovary, reproduction, and productivity of female Columbian black-tailed deer. Ph.D. Thesis, Univ. of British Columbia, Vancouver. 2llpp. Wallmo, 0. C. and J. W. Schoen. 1980. Response of deer to secondary forest succession in southeast Alaska. For. Sci. 26:448-462. White, G. C. and R. M. Bartmann.  1983.  Estimation of survival rates from  188 band recoveries of mule deer in Colorado. 511.  J. Wild].  Manage. 47:506-  and R. A. Garrott. 1990. Analysis of wildlife radio-tracking data. Academic Press Inc., San Diego, Calif. 383pp. Yeo, J. J. and J. M. Peek. 1992. Habitat selection by female Sitka black tailed deer in logged forests of southeastern Alaska. J. Wildl. Manage. 56:253-261. Zar, J. H. 1984. Biostatistical analysis. Cliffs, N.J. 718pp.  Prentice-Hall, Englewood  189 CHAPTER 8  -  GENERAL CONCLUSIONS  After preliminary field work observing habitat choices made by deer at Nanaimo River, I began to question the strictly economic properties of the optimal foraging theory (Schoener 1971) that formed a basic assumption in our study design.  Initially, I questioned the notion of optimality  generally because it appeared vague to me but, eventually, I couldn’t ignore its implicit and appealing association with the theory of natural selection. Then I considered that animals likely have goals beyond those implied in formulations of optimality established by Schoener (1971).  Indeed, in  recent years the whole aspect of animal behaviour has entered the minds of researchers more strongly and has led to an emergence of new kinds of research on optimality beyond an accounting of costs and benefits of foraging actions (e.g., Schoener 1987, Gass and Roberts 1992). this new research is addressing the question of  -  Primarily,  when do animals optimize  and draws into consideration the fact that, in the past, the effects of temporal and spatial scales on resource use problems has been neglected. This is specifically where I directed the objectives for the IWIFR Deer Project and what my conclusions are meant to address.  INDEPENDENCE OF OBSERVATIONS IN MOVEMENTS Because most hypotheses or investigations would be based on deer location data (chapter 1), it was necessary to assess the statistical independence of the data I collected.  This need was particularly strong  because of recent emphasis on the potential dangers in working with temporally dependent data (Swihart and Slade 1985).  I found most data were  temporally related and, further, a systematic elimination of data to create longer time intervals between samples did not correct the problem.  I  -  190 determined that the lack of independence was only an apparent problem because the distributional properties of my data invalidated the published test statistic.  The particular distributional properties I observed arose  largely from migrations and other occasional, long movements. Once I considered this distributional phenomenon of the data, I realized that movements result from behaviourial-related decisions made by deer and therefore are unlikely to ever be judged independent on that basis; each new movement will always be related in some way to previous movements. Rather than eliminate data in an attempt to achieve independence, I proposed that researchers should attempt to gather as much data as possible (more information) but to ensure these data are collected systematically in time.  SPATIAL AND TEMPORAL SCALES IN MOVEMENTS The importance of spatial and temporal scales in resource use problems was presented by Senft et a!. (1987).  But I didn’t see the clear link to  optimality until considering some thoughts by Schoener (1987) and Levin (1992).  Finally, and I admit this was only recently, these thoughts were  solidified by Gass and Roberts (1992) with their consideration of interactions between temporal scales and optimization.  Although not  eloquent perhaps, I approached this problem of scales by making the explicit recognition that resource use is based on an hierarchical decision-making framework (Chapters 3 and 4).  High elevation natal ranges cannot provide  suitable habitat conditions during winter months so, eventually, the many decisions deer must make about how to use a resource that diminishes through summer months, are synthesized into higher level decisions about migration. Gass and Roberts (1992) referred to this hierarchical phenomenon as an upward cascading of fine-scaled actions.  But the higher level decision to  191 migrate then constrains habitat use decisions within the general site chosen for winter range.  I found, for black-tailed deer, this constraint is  emphasized by a general tactic for fidelity to specific sites and likely by the fact that those sites are chosen by a matriarch.  In fact, affinity for  sites was a general rule expressed as philopatry at the level of home range, fidelity at the level of seasonal ranges, and a tenacity for specific sites (we referred to these as nuclei) within habitats at the level of habitat selection.  Together, the hierarchical nature of decision-making and the  behaviourial tenacity I observed, lead to individuals with a generally static approach to habitat use.  I expected this static nature of decision  making (habit) could form the basis for lags in response to rapid alteration of habitats (Chapter 5).  Therefore, optimality of resource use, although  correctly measured at fine-scale choices, must be considered and interpreted at, or least set in context of, all scales of resource use.  SPATIAL AND TEMPORAL SCALES IN HABITAT USE I consider this static approach to resource use also partially explains the variety of habitat preferences observed in chapter 4.  If  individuals maintained a single tactic for resource use while environments fluctuated (e.g., winters variably moderated by maritime weather patterns and/or rapid and extensive logging) I would record and interpret variable habitat preferences.  Even so, I observed consistent trends (paired sample  comparisons between summer and winter) among these preferences to indicate the superiority of old forests as winter habitat.  Other trends would  indicate the superiority of young forests and even open habitats whenever winter weather was not severe.  I also noted that migratory deer preferred  old forests more than resident deer; an observation strengthened by the fact  192 that I trapped no migratory deer in young forests (Chapter 7).  Conclusions  about optimality of habitat choices is out-of-reach, however, unless these choices are set within the context of both site- and time-specific events as well as the larger issues of ultimate survival and productivity differences that may exist between the 2 seasonal movement groups.  RESPONSE TO WINTER HABITAT LOGGING As in many large projects that take place in uncontrolled environments, problems of strict logistics seriously weakened my ability to draw firm conclusions on several initiatives; probably the most serious being the question concerning lags in response to logging (Chapter 5).  This  flaw is fundamental to my central thesis about constraints to the ideal-free distribution hypothesis.  A short-lived study species (due mostly to  predator efficiency), a limited technology (4-yr radio collars), and planned manipulations that were large and politically unstable led to small sample sizes and barely suitable manipulations.  Still, there is qualified support  for the notion that individual deer change their habitat use decisions little, even when subjected to large and abrupt changes in habitat structure.  I expected some deer to make dispersal-type decisions when their  winter ranges were essentially destroyed.  This did not occur.  The notion  that deer respond to logging by making subtle shifts in habitat use within their original home range is a notion firmly held in the minds of those that have experience observing black-tailed deer.  Until we have new information  to reject this notion, I must conclude that deer commit themselves early in life to specific tactics for seasonal range use and altering this commitment is difficult at best.  The implications this loyalty has is that subsequent  resource use decisions become increasingly more a function of habitat change  193 than of habitat quality.  Adaptation is limited to learning about new  resource conditions within the established home range rather than about other (may include better) habitats outside the home range.  Indirectly  then, this loyalty to specific tactics, implies a constraint to the idealfree distribution of deer.  MORTALITY CAUSES AND SURVIVAL ESTIMATES I could not avoid the logistical problems associated with observing productivity of individual deer and so, although I did observe adult female survival, this led to another major weakness in making conclusions about the implications of constrained resource use tactics.  However, I conclude from  the survival data that migratory deer survived better than resident deer. This differential in survival was primarily caused by residents being at high risk to predation from wolves and cougars in late-winter and spring, and to hunting from man during fall.  The higher risk was a consequence of  resident deer making up the greater proportion of the population living, in late-winter, at low elevations where access was perhaps enhanced by logging roads. This conclusion about differential survival, and its interaction with those from other chapters, led to 3 major management implications (Chapter 7).  First, because I expect that response by deer to any habitat  manipulations will lag in time, it is unclear what and when to measure as a response.  Hence, I bring into question previous interpretations of  use/availability measures and habitat evaluations.  Second, I am concerned  that, as whole valleys are changed from old forests to young forests, fewer and fewer migratory deer will exist.  With only resident deer remaining,  resilience of deer populations will likely decline.  Third, and on a more  194 positive note, the behaviour of deer populations would appear to be relatively predictable to the habitat manager based only on variables associated with topography, local climate, and the spatial locations and general ages of forest stands.  This knowledge could facilitate landscape  level planning (Chapter 7) to aid the maintenance of both migratory and resident deer within the same population thereby retaining a degree of population resilience against both predators and severe winter weather.  LITERATURE CITED Gass, C. L., and W. M. Roberts. 1992. The problem of temporal scale in optimization: Three contrasting views of hummingbird visits to flowers. Am. Nat. 140:829-853. Levin, R. V. 1992. 73:1943-1967.  The problem of pattern and scale in ecology.  Schoener, T. W. 1971. 2:369-404.  Theory of feeding strategies.  Ecology  Ann. Rev. Ecol. Syst.  1987. A brief history of optimal foraging ecology. Pages 5-68 in A. C. Kamil, J. R. Krebs, and H. R. Pulliam, eds. Foraging behaviour. Plenum Press, N.Y. Senft, R. L., M. B. Coughenour, D. W. Bailey, L. R. Rittenhouse, 0. E. Sala, and D. M. Swift. 1987. Large herbivore foraging and ecological hierarchies. BioScience 37:789-799. Swihart, R. K., and N. A. Slade. 1985. observations in animal movements.  Testing for independence of Ecology 66:1176-1184.  

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