Size Dependent Resource Use of a Hybrid Wolf (C. lycaon  C. lupus)
Population in Northeast Ontario
A thesis submitted to the Committee on Graduate Studies
in partial fulfillment of the requirements
for the degree of
Master of Science
in the faculty of Arts and Science
TRENT UNIVERSITY
Peterborough, Ontario, Canada
© Copyright by Josh Holloway, 2009
Environmental and Life Sciences Graduate Program
November, 2009
i
Abstract
Morphology, genetics, and behavior have important implications for animal
population management and conservation. We assessed morphology as a driving factor
on the predatory behavior of wolf packs from Ontario. We first related the genetic
ancestry of several packs to their body weight. We then analyzed the influence of
morphology on behavior by comparing body weight to metrics of predation and habitat
use. Genetic ancestry was not related to body weight but revealed that our study
population was part of a gray wolf  eastern wolf (Canis lupus  lycaon) hybrid
population. Predation and habitat use were related to body size, but increased body size
only marginally increased predatory abilities. Although body size may limit moose
predation by small wolves, gene flow and group foraging may work to maintain relatively
small eastern wolf-like phenotypes in a large prey system. We conclude that efforts to
conserve a desired wolf phenotype should consider aspects of prey populations.
Keywords: hybridization, eastern wolf, gray wolf, morphology, predation, microsatellite,
resource utilization
ii
Acknowledgments
First and foremost I must recognize my family for the love and support they have
provided me over the many years I have pursed my chosen profession. For better or
worse, they have been unwavering in providing me the most stable and reliable feature of
my life. Without them I would not be me.
Any field project of this magnitude could not be accomplished without the
dedication of field technicians willing to endure the rigors of strenuous work in remote
locations. Numerous people participated in various aspects of the field work for which I
greatly appreciate. The following people went above and beyond reasonable
expectations: Oliver Barker, Kristine Terwissen, Dan Andres, and Kevin Downing. I
want to thank Lynn Landriault for providing friendship and advice from day one and for
always being trustworthy enough to hold the fort in my absence. I thank Lyle Walton for
his friendship and dedication to the project. While I played with the wolves, Lyle was
working hard to replace everything I broke and to keep me well equipped.
Several pilots provided many safe hours of telemetry flights. Because they did
their job well, I am here to tell about it. Mike Coin of OMNR worked particularly hard to
get us in the air when many government bureaucrats thought otherwise.
I am not a geneticist, nor do I pretend to be one. Because of many helpful
discussions with Linda Rutledge and Tyler Wheeldon this did not matter. Tyler also
analyzed my genetic data and contributed far more than he received. I also thank Jen Dart
for taking on the frustrating task of cataloging and conducting the laboratory portion of
the genetic analysis.
iii
I have made many friends during this experience that were always willing to talk
about wolves in the office or over a beer. For this I must thank Karen Loveless, Erin
Reese, Chris Sharp, Tina Fridgen, and Eric Howe. Stacey Lowe spent endless hours
discussing wildlife, statistics, and life in general. Her persistence was admirable and
friendship invaluable. Collin Garroway and Kevin Middel provided helpful programming
assistance.
I would like to thank my advisors Brent Patterson and Dennis Murray for
providing me the opportunity to do this work and for their support. I especially thank my
committee member Bruce Pond for his careful scrutiny of my work, for finding problems
we all missed and for his willingness to help solve them.
Perhaps the best and most unexpected outcome of this experience has been the
introduction of 2 people into my life. I could not have asked for anyone more devoted to
wolves, Horwood, and to enjoying life as I found in them. Lindsay contributed greatly to
the field work through the worst of conditions and proved to be a good friend. For that I
thank her. Ashley did that and more. Although it was rarely easy, she was there to the end
and was a more positive and meaningful part of my experience and my life than she will
ever know. Thank you.
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TABLE OF CONTENTS
ABSTRACT............................................................................................................................ II
LIST OF FIGURES ...........................................................................................................VIII
CHAPTER 1: GENERAL INTRODUCTION..................................................................... 1
Overview ..................................................................................................................................................... 1
Adaptations to Predation in Canis.................................................................................................... 1
Canidae in Northeastern North America ....................................................................................... 4
Objective of Thesis .................................................................................................................................. 6
CHAPTER 2: THE GENETIC BASIS OF BODY SIZE IN HYBRID WOLVES
(CANIS SP.) IN NORTHEASTERN ONTARIO ................................................................. 9
Introduction ............................................................................................................................................... 9
Study Area................................................................................................................................................ 10
Methods ..................................................................................................................................................... 11
Capture and Data ......................................................................................................... 11
Data Analysis................................................................................................................ 12
Results ........................................................................................................................................................ 14
Discussion ................................................................................................................................................. 15
CHAPTER 3: BODY SIZE AND ITS EFFECT ON RESOURCE USE IN A
HYBRID WOLF POPULATION........................................................................................ 20
Introduction ............................................................................................................................................. 20
Study Area................................................................................................................................................ 23
Methods ..................................................................................................................................................... 24
Capture and Radiotelemetry ........................................................................................ 24
Morphology and Kill Rates .......................................................................................... 24
Landscape Attributes.................................................................................................... 25
Resource Utilization Models........................................................................................ 27
Morphological, Predation, and Habitat Use Correlates............................................. 29
Results ........................................................................................................................................................ 31
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Predation ...................................................................................................................... 31
Habitat Use................................................................................................................... 32
Discussion ................................................................................................................................................. 32
Foraging Behavior....................................................................................................... 33
Management Implications ........................................................................................... 35
CHAPTER 4: GENERAL DISCUSSION .......................................................................... 48
Overview ................................................................................................................................................... 48
General Conclusions ............................................................................................................................ 50
LITERATURE CITED ........................................................................................................ 52
APPENDIX A. ....................................................................................................................... 62
APPENDIX B ........................................................................................................................ 63
APPENDIX C ........................................................................................................................ 66
APPENDIX D ........................................................................................................................ 69
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LIST OF TABLES
Table 3.1. Definitions of land cover and landscape attributes used to describe habitat use
and a predictive beaver hunting layer for 11 wolf packs in northeastern Ontario. Veg
describes vegetative land cover, metrics describe landscape metrics, and prey describes
landscape attributes related to the abundance and hunting of prey……………………..37
Table 3.2. Contrast weight matrix for the contrast weighted edge density layer of land
cover types in northwestern Ontario. Weights reflect a quantity of edge (m/ha) that is
sensitive to the interface of mature and young stands of cover that provide high
juxtaposition of cover and food beneficial for moose………………………………...…38
Table 3.3. Akaike weights (ωi) for resource utilization covariates in each pack. Shown is
the average weight across all packs and whether it is within 10% of the top ranked
weight…………………………………………………………………………………….39
Table 3.4. Beta-coefficient estimates for resource utilization covariates in each pack….40
Table 3.5. Spearman’s rank correlation results for average pack body size (average body
weight/pack) versus resource utilization coefficients for 10 wolf pack wintersa within the
boreal forest of northeast Ontario………………………………………………………..41
Table 3.6. The proportion of cover types and average measures of landscape metrics
measured in each of 10 wolf territories defined by the 100% contour of the fixed kernel
density estimator. Values given with +/- 1 standard error……………………………….42
vii
LIST OF FIGURES
Figure 2.1. Distribution of Canid genetic samples used in STRUCTURE analysis from
Ontario, Canada………………………………………………………………………….17
Figure 2.2. Plot of individual proportional memberships of Canis in Ontario, Canada to
populations inferred by STRUCTURE under F-model (K=3)…………………………..18
Figure 2.3. Distribution of Canid genetic samples used in STRUCTURE analysis from
Ontario, Canada. Factorial correspondence analysis of microsatellite loci for Canis
sample groups from northwest Ontario (NWON), northeast Ontario (NEON), the present
study area (TIM), Algonquin Provincial Park (APP), and the Frontenac Axis (FRAX)...19
Figure 3.1. The relationship between the squared ratio of consumption of killed moose
biomass to the metabolic requirements relative to the average pack body size of 11 wolf
packs in northeastern Ontario. Consumption was estimated from food sources found
during winter GPS cluster searches and metabolic requirements were estimated from the
mass dependent metabolic rate of adult wolves in each pack……………………………44
Figure 3.2. Relative weights (kg), chest girth (cm), shoulder height (cm), and ground
clearance (cm) of putatively pure Algonquin-type eastern wolves (ALG; male n = 43;
female n = 45) and gray wolf  eastern wolf hybrids from northeastern Ontario (NE ON;
male n = 17, female n = 26). Data presented as mean +/- 1 SE………………………..45
Figure 3.3 Coefficients of variation for male and female wolf weights from several North
American populations.……………………………………………………...……………46
viii
Chapter 1: General Introduction
Overview
Although some species are habitat specialists and have restricted geographic
ranges, others occur in a variety of habitats and are found across a broad geographic
range. When distributions are broad, animals may experience different environmental
conditions and selective regimes in different habitats. These experiences manifest
themselves through behavioral and morphological adaptations which may become
significant factors in determining patterns of resource use (Turingan et al. 1995).
Unfortunately, most studies ignore such factors when examining the availability and
distribution of resources to describe patterns of selection. While this approach is
sufficient to make generalized predictions about a population (Manly et al. 2002) it may
fail to capture features of fine-scale selection that are important to managing isolated and
hybridized populations. Because wildlife managers often wish to predict an organism’s
response to changing environmental conditions, examining how intra-specific adaptations
relate to local conditions will provide more precise predictions.
Adaptations to Predation in Canis
Morphological adaptations to predation have evolved largely in response to the
environmental features used to find, pursue and capture prey (Husseman et al. 2002,
Hopcraft et al. 2005), and the biomechanical requirements associated with killing and
handling prey (Andersson and Werdelin 2003, VanValkenburgh 2007). Of these
adaptations, body size has emerged as a particularly important trait that has been selected
for with relative consistency across divergent taxa. Fossil records show that the general
trend for animal body size is to increase with time (Cope’s rule; Alroy 1998), and in
North American Canids this general pattern seems to have occurred several times during
1
the past 50 million years (Finarelli 2007, VanValkenburgh et al. 2004). Morphological
characteristics associated with body size can constrain a predator’s ability to pursue,
attack, and subdue large prey (Gittleman 1985). Moreover, intense competition among
carnivores may lead to body size selection as larger individuals tend to dominate and kill
smaller competitors (Palomeres and Caro 1999). It is not surprising, then, that the
tendency for larger body size is particularly advantageous for carnivores as it can lead to
the acquisition of more profitable prey and greater diet diversification (Gittleman 1985,
Radloff and Du Toit 2004).
The influence of body size on predator ability to prey on large animals has been
documented through comparisons across taxa (Carbone et al. 1999, Cohen et al. 1993,
Gittleman 1985, Owen-Smith and Mills 2008, Radloff and Du Toit 2004, Vezina 1985)
and between genders (Erlinge 1979, Sand et al. 2006, Brecko et al. 2008). The extent to
which intra-specific predatory ability varies with body size was only recently
demonstrated using a gray wolf population in Yellowstone National Park (MacNulty et
al. 2009). Larger wolves were more successful at preying on elk (Cervus elaphus)
(MacNulty et al. 2009). This lends evidence to suggest that size selection can be strong in
Canis populations and is supported by observations of size co-variation in North
American Canis populations and their primary prey species (Kolenosky and Standfield
1975, Schmitz and Kolenosky 1985, Thurber and Peterson 1991, Nowak 1995).
Although ecological theory predicts selection for large-bodied predators where large
prey predominate (Van Valkenburgh et al. 2004, Van Valkenburgh 2007), this simple
relationship may be confounded by cooperative and learned hunting behaviors of social
carnivores like wolves (Mech 1970). Specifically, the abilities of large experienced pack
2
members may subsidize smaller, less capable pack members (Stander 1992). Cooperative
hunting in carnivores is attributed, in part, to the increased use of large prey that may
otherwise be unavailable to an individual (Mech 1970, Packer and Ruttan 1988). Wolves
may overcome constraints of small body size and forage more efficiently by hunting in
packs, which enable the capture of larger, more profitable prey (Mech 1970, Bekoff et al.
1984). Likewise, the size of a pack may increase with increasing prey size (Murie 1944,
Mech 1970) up to a theoretical optimum, where food intake/wolf is maximized and
hunting costs are minimized (Nudds 1978). Although reasonable, such a presumed linear
relationship is not always observed. For example, for wolves preying primarily on large
ungulates (moose [Alces alces], caribou [Rangifer tarandus] and bison [Bison bison]),
pack sizes are known to be highly variable and not dissimilar from those using smaller
prey (Mech and Peterson 2003). Other factors such as prey abundance, human
exploitation, pack relatedness (Mech and Peterson 2003), and individual hunting abilities
(MacNulty et al. 2009) may account for the wide variation in prey size and hunting
success.
The relation between pack size and hunting efficiency is complicated by observations
of single wolves killing large prey (Thurber and Peterson 1993) and because pack
members do not always contribute equally to the act of killing (Mech and Boitani 2003).
Furthermore, cooperative hunting leading to increased foraging efficiency does not hold
true for wolves given that food intake per wolf decreases significantly beyond pack sizes
of 1 or 2 individuals (Mech and Boitani 2003, Thurber and Peterson 1993). Social
behavior in wolves can then be construed as being beneficial for territory and den defense
and communal pup rearing (Mech 1970, Packer et al. 1990), and thus is not a necessity
3
for killing large prey (Mech and Boitani 2003). Although social hunting may not always
be necessary for the persistence of wolves as it pertains to hunting success, it is likely an
important attribute that benefits small or inept pack members (Packer and Ruttan 1988,
Stander 1992).
Canidae in Northeastern North America
The taxonomic classification of Canis species in northeastern North America has
had a tumultuous history because of uncertainty regarding ancestral origin (Wilson et al.
2000, Nowak 2003, Kyle et al. 2006), morphological distinction (Kolenosky and
Standfield 1975, Nowak 1995), hybridization (Roy et al. 1994, Wheeldon 2009, Wilson
et al. 2009) and the mechanisms by which these factors interact with landscape features
(Geffen et al. 2004, Kyle et al. 2006) and prey populations (Forbes and Theberge 1995,
Kolenosky and Standfield 1975). It is not surprising, then, that a broad area across central
Ontario has become recognized for its extensive hybridization due to the convergence of
several Canis types, prey species, and eco-regions. As researchers move toward a
consensus regarding genetic origin of some groups of wolves (Wilson et al. 2000, 2009)
many of the ecological factors that led to and/or perpetuated this diversity remain
unknown. Studying these factors provides valuable knowledge for predicting the impact
of changing ecological landscapes and its relevance to Canis conservation.
Contemporary evaluation of Canis species in northeastern North America has
revealed that several genetically distinct “races” have converged to form a hybrid zone
represented by a continuum of genetic groups and morphological characteristics (Sears et
al. 2003, Nowak 1995, Wheeldon 2009, Wilson et al. 2000, 2009). The putatively pure
populations for which this wolf hybrid zone is derived are represented by gray wolves
4
(Canis lupus), eastern wolves (C. lycaon), and eastern coyotes (C. lycaon  latrans)
(Wheeldon 2009). Historical adaptations to environmental and prey conditions, as well as
reproductive isolation, led to the initial divergence of each species from one another
(Nowak 2003).
The eastern wolf and western coyote (C. latrans) are North American-evolved
species that diverged from a common ancestor 150,000-300,000 year ago (Wilson et al.
2000). The size differentiation between the 2 species likely represents adaptation of the
eastern wolf to preying on deer (Odocoileus sp.) in forested habitats and the coyote to
smaller prey in arid regions (Wilson et al 2000). Changing landscape conditions and
human persecution following European colonization of northeastern North America lead
to the range redistribution of eastern wolves and coyotes, resulting in hybridization
between the 2 species (Kyle et al. 2006). Where extensive hybridization has occurred, a
hybrid swarm seems to have emerged which has resulted in the eastern coyote which
constitutes a hybrid of eastern wolves and coyotes (Wilson et al. 2009). These animals
seem to be phenotypicically intermediate to their parental populations (Kolenosky and
Standfield 1975).
Although many sub-specific forms of the gray wolf have been identified in North
America, it is generally accepted that their progenitors evolved in Eurasia and migrated to
North America during periods of glaciation (Lehman et al. 1991, Nowak 1995, Wilson et
al. 2000). Larger than North American evolved Canis species, gray wolves often prey on
larger ungulate species than eastern wolves and coyotes (see Mech and Peterson 2003).
Although hybridization occurs with eastern wolves where their ranges overlap, direct
hybridization between gray wolves and coyotes generally does not occur (Pilgrim et al.
5
1998, Wheeldon 2009, Wilson et al. 2009). Because eastern wolves readily hybridize
with both gray wolves and coyotes, they act as a vector for gene flow between the two
“parental” species (Wheeldon 2009, Wilson et al. 2009). Where this has occurred, a
hybrid zone has been formed and has given rise to a diversity of phenotypic and
genotypic forms of Canis that may be differentially adapted to exploiting local resources
(Kolenosky and Standfield 1975, Sears et al. 2003, Wilson et al. 2000). Consequently, the
diversification of morphological forms of wolves currently observed in northeastern
North America may be attributed to adaptations to extrinsic factors and hybridization
between specific and sub-specific forms of Canis species (Nowak 2003).
Objective of Thesis
The eastern wolf was given federal designation as a species of special concern
under the Species at Risk Act in Canada in 2001 (COSEWIC 2006). Similar provincial
designation was granted under the Species at Risk in Ontario Act in 2004 (OMNR 2005).
While it is formally recognized that hybridization occurs and may be a threat to eastern
wolves (OMNR 2005), the ecological mechanisms that facilitate hybridization are poorly
understood. Given the apparent interrelatedness of genetics, morphology, and prey use,
understanding aspects of prey and habitat features as they relate to eastern wolves is
necessary to predict the impacts of changing ecological landscapes and for setting
conservation guidelines. The limited prey availability and presence of Canis hybrids in
northeastern Ontario provides a unique opportunity to examine these relationships, and to
predict the genetic adaptations to local environmental conditions that may be important to
continued persistence of the eastern wolf genome. Accordingly, the goals of this thesis
are to provide information about the genetic and morphological nature of hybrid wolves
6
in the boreal forest of northeastern Ontario, and to determine how these features interact
with the environment to help guide conservation strategies. In addition, we seek to shed
light on poorly-understood aspects of the basic ecology of Canis hybridization.
Initial field observations of wolves in our study area suggested wide variation in
both morphology and foraging success among packs. The reported genotypes throughout
Ontario suggested that these variations could be a result of mixed ancestry due to
hybridization between C. lupus and C. lycaon (Kyle et al. 2006). Because of the limited
prey diversity in our study area, we expected a bi-modal foraging strategy such that
wolves would select habitat to maximize hunting success on either moose or beaver
(Castor canadensis) (Forbes and Theberge 1996). We therefore hypothesized that i)
levels of C. lupus genetic representation in a pack of C. lycaon individuals will be related
to overall body size of its members, ii) body size will limit a pack’s ability to kill moose,
whereby larger bodied packs will forage on moose at greater rates than small bodied
packs, and iii) habitat use by wolf packs will reflect their foraging patterns; more
specifically we expect packs will use habitats that increase moose vulnerability in
proportion to the amount of moose in their diet.
The hypothesis that genetic ancestry is related to morphology and resource use in
wolves may be applicable to other hybridizing species and could serve as a guide for
conservation strategies that aim to predict range distribution and geographic expansion or
loss of unique genotypes. Furthermore, informed predictions regarding the consequences
that resource manipulations may have on hybrid populations can serve to help
management plans that aim to prevent or promote hybridization. Overall, this study
7
should improve our understanding of the ecological factors underlying Canis ecology in
northeastern North America.
8
Chapter 2: The Genetic Basis of Body Size in Hybrid Wolves (Canis sp.) in
Northeastern Ontario
Introduction
Contemporary evaluation of Canis species in northeastern North America has
revealed that several genetically distinct “races” have converged to form a hybrid zone
represented by a continuum of genetic groups and morphological characteristics (Kyle et
al. 2006). The putatively pure populations from which this wolf hybrid zone is derived
are represented by gray wolves (Canis lupus), eastern wolves (C. lycaon), and coyotes
(C. latrans) (Wheeldon 2009). Because the eastern wolf readily hybridizes with both gray
wolves and coyotes, it acts as a conduit for gene flow between the two latter species
(Wheeldon 2009, Wilson et al. 2009). Historical adaptations to environmental and prey
conditions, as well as reproductive isolation, led to the initial divergence of each species
from one another (Nowak 2003). Consequently, the diversification of morphological
forms of wolves currently observed in northeastern North America may be attributed to
adaptations to extrinsic factors and hybridization between specific and sub-specific forms
of Canis species (Nowak 2003).
The size differentiation between gray and eastern wolves likely represents
adaptation of the eastern wolf to preying on deer (Odocoileus sp.) and gray wolves on
larger prey (e.g. moose; Nowak 1995, Wilson et al 2000). Morphological characteristics
associated with body size can constrain a predator’s ability to pursue, attack, and subdue
large prey (Gittleman 1985, MacNulty et al. 2009). It is surprising, then, that across their
presumed current core range eastern wolves can prey extensively upon moose, although
their kill rates are lower than in areas occupied by gray wolves (Voigt et al. 1976,
9
Messier and Crete 1985, Forbes and Theberge 1995). Ecological theory predicts selection
for large-bodied predators where large prey predominate (Van Valkenburgh 2007, Van
Valkenburgh et al. 2004). However, the effects of size selection may be masked by gene
flow from populations of small-bodied eastern wolves. Where this occurs, we would
predict greater morphological diversity in the Canis population than would be predicted
by prey size alone.
Herein we evaluate the biological impacts of hybridization by quantifying the
relationship between morphological and genetic variation in a Canis population in
northeastern North America. We sought to test the hypothesis that wolf body size will be
a function of the average proportional C. lupus and C. lycaon genetic contribution to pack
genomes. Specifically, we predicted that i) body size varies considerably among wolves
in the hybrid zone. Furthermore we speculated that ii) overall body size is positively
correlated with the proportional C. lupus genomic contribution to each wolf.
Study Area
We examined the genetic basis of morphological variation in a wolf population in
an area of ~10,000 km2, centered around Horwood Lake (N48° 06’, W82° 19’, 295m
altitude) near Timmins, Ontario. The area was predominantly industrial forest with an
extensive network of secondary and tertiary roads throughout. Forest cover was
dominated by white spruce (Picea glauca), trembling aspen (Populus tremuloides), white
birch (Betula papyrifera) and jack pine (Pinus banksiana) in well-drained upland sites,
and balsam fir (Abies balsamea), black spruce (Picea mariana), and eastern white cedar
(Thuja occidentalis) in lowland sites. Topography ranged from flat to rolling hills, and
lakes, rivers, and wetlands were abundant throughout.
10
Moose were the primary ungulate prey of wolves and occurred throughout the
study area, although moose densities were relatively low (~0.13 moose/km2, Bisset and
McLaren 1999). Beaver were common throughout the study area whereas white-tailed
deer were rare (P. Davis, Ontario Ministry of Natural Resources, personal
communication).
Methods
Capture and Data
We captured and radiocollared 58 wolves using steel foot-hold traps (No. 7 ezgrip, Livestock Protection Co., Alpine, TX., U.S.A.) between July and November 2005
and 2006, and aerial net gunning from rotary-winged aircraft (Bighorn Helicopters,
Calgary, AB) during January 2006. Trapped wolves were chemically immobilized with
an intramuscular injection of Telazol® (Fort Dodge Laboratories, Inc., Fort Dodge, IA)Xylazine mixture whereas net-gunned wolves were physically restrained without
chemical immobilization. We equipped wolves with VHF or downloadable and/or Argosequipped GPS collars (4400M; Lotek Engineering, Newmarket, ON, Canada).
Morphological measurements (body weight, shoulder height, chest girth, and ground
clearance; Appendix A), and blood and hair samples for genetic analysis were collected
from each animal.
Number of wolves per pack was determined from visual sightings during
telemetry flights, aerial track surveys, and from track counts during frequent backtracking
in winter. Estimates were supplemented by analysis of genetic samples collected at the
time of capture and during frequent snow tracking within known pack territories, to
11
identify unique individuals and to determine genetic ancestry of individuals in each pack
(see below).
We opportunistically collected scat, urine, and blood spots encountered along
roads and travel routes, along with bed hair and estrous blood spots found at resting and
kill sites discovered during winter tracking. Samples were collected and stored following
standard methodology (Scandura 2005, McKelvey et al. 2006). Samples were triaged
prior to lab analysis to exclude those that may have been contaminated, contained DNA
from >1 wolf, or when pack membership was uncertain.
Data Analysis
DNA was extracted using a DNeasy Blood and Tissue Kit (Qiagen Inc.,
Mississauga, ON). Amplification of eight microsatellite loci was attempted for each
sample (Wilson et al. 2000) and microsatellite alleles were scored in Genemarker (v1.7,
SoftGenetics LLC, State College, PA) and unique genotypes were obtained for 96
samples at 6 to 8 loci. Approximately 39% of the samples (n = 37) were from low
template DNA sources (i.e. scat or hair) and low-amplifying homozygous allele scores
were confirmed where possible to reduce potential scoring errors arising from allelic
dropout. Genotyping error was assumed to be low overall since most samples were from
high template DNA sources (i.e. blood and tissue) and suspect allele scores were checked
by repeat amplification.
Additional samples (Wheeldon 2009, n = 251) genotyped at the same eight loci
were included in the genetic analyses to place our results into proper context: we included
C. lupus-lycaon hybrids from northwestern Ontario (n = 81) and northeastern Ontario (n
= 64; Appendix I); primarily C. lycaon from Algonquin Provincial Park (n = 54) (L.
12
Rutledge, unpublished data); eastern coyotes (C. latrans lycaon) from the Frontenac Axis
( n = 52; Figure 2.1). The microsatellite genotype data from all 347 canids was analyzed
using the program STRUCTURE (v2.2, Pritchard et al. 2000), using previously described
methods (Wheeldon 2009). Based on quantitative criteria (LnP[D] from Pritchard et al.
2000; delta K from Evanno et al. 2005) the number of populations K was determined to
be three. In addition, a non-model based Factorial Correspondence Analysis (FCA) was
used on microsatellite data using GENETIX (v4.05, Belkhir et al. 2004). Two factorial
components FC-1 and FC-2, which account for 7.32% and 3.68% of the total inertia
respectively, were plotted to visualize relative clustering of the study region animals
relative to those from the surrounding sampling locations.
We assumed body weight to be a valid representation of body size (MacNaulty et
al. 2009). We therefore used body weight measurements from 44 adult wolves captured
during the study to index body size (MacNaulty et al. 2009; Appendix B). Our primary
objective was to identify genetic differences among wolves and examine how these relate
to overall body size. Accordingly, we used linear least squares regressions on dependent
variables represented by the body weight for each wolf versus the proportional C. lupus
admixture assignment from the STRUCTURE analysis of each wolf and on the Factorial
Correspondence scores corresponding to C. Lupus admixture (FC-1) from the FCA
analysis of microsatellite data (Zar 1996).
13
Results
Six mtDNA haplotypes were observed in the study region: 4 were C. lycaon in
origin (n = 31) and 2 were C. lupus (n = 64). Haplotypes C22 (n = 46) and C23 (n = 18)
are of C. lupus origin and haplotypes C1 (n = 10) and C13 (n = 4) are of C. lycaon origin
(Wilson et al. 2000; Wheeldon and White 2009). Haplotypes C9 (n = 7) and C14 (n = 10)
are of C. latrans origin, although they occur throughout the range of lycaon/latrans
hybridization and may be derived from C. lycaon in the study region.
Based on pack size estimates obtained throughout the observation period and the
number of unique genetically-identified wolves in each pack, we believe that 100% of
wolves in 9 packs and 86% of wolves in 2 packs were sampled genetically. These
combined methods yielded a median early winter pack size of 5 wolves (range 2-8) for
the 11 GPS-monitored packs.
The analysis of microsatellite genotypes in STRUCTURE revealed three genetic
clusters corresponding to eastern coyotes or C. lupus-lycaon hybrids (Figure 2.2). Most
wolves in the study area were genetically similar and had high ancestry assignment to the
group of C. lupus-lycaon hybrids from northern Ontario, with the exception of one
animal with high ancestry assignment to the eastern coyote group and two animals with
high ancestry assignment to the eastern wolf cluster (Figure 2.2). The results of the FCA
(Figure 2.3) were congruent with findings from STRUCTURE, revealing three groups
(Figure 2.2).
On average, we measured 1.8 (range 1-4) wolves/pack (n = 43) and recorded body
weights for 51% of the wolves in each pack (Appendix B). Twenty-seven (63%) wolves
that we weighed occupied packs with winter size estimates. We were unable to relate
14
body size to the genetic assignment of C. lupus because all but 3 animals were highly
assigned to the C. lupus-lycaon hybrid group (see above, Figure 2.2). Specifically, we
found no relationship between body size and the proportional assignment of each pack to
the C. lupus-lycaon group (R2 = 0.02, p = 0.44) or the FC-1 scores (R2 = 0.02, p = 0.17).
Discussion
Our results indicate that body size variation among wolves in our study could not
be explained by our genetic characterization of the population. The apparent size
difference between C. lupus and C. lycaon led us to predict a positive relationship
between body size and proportional C. lupus admixture. However, we found our study
area to be occupied predominantly by C. lycaon-lupus hybrids that had poor assignment
to groups representing putatively pure C. lupus or C. lycaon populations. Although the
apparent size difference between C. lupus and C. lycaon suggests hybridization may
contribute to the size variations we observed, our results do not support this contention.
Although we acknowledge the limitations of using only 8 microsatellite loci to
detect genetic co-variation with quantitative traits, a sub-sample of wolves analyzed at 12
loci (n = 65) had similar ancestral assignment to those measured at 8 loci (J. Holloway,
unpublished data) and  8 markers have been found sufficient to identify hybridization at
coarse resolutions (Boeklen and Howard 1997). Of greater significance is that our genetic
assignment was based on markers assumed to be selectively neutral (Jarne and Lagoda
1996), and these tend to be poor indicators of selection (McKay and Latta 2002).
Despite no detectible correlation between morphology and the genetic markers we
assessed, we speculate that the hybrid nature of the genetic composition reflects a greater
diversity of morphological traits than would occur in pure C. lupus or C. lycaon
15
populations. Introgression from parental populations may introduce new alleles that can
increase genetic and phenotypic diversity in a hybrid swarm faster than would be
expected by mutation (Arnold 1992), such that individuals in a hybrid swarm become
introgressed to various degrees and may not reflect the morphology of their parental
populations (Allendorf et al. 2001).
Given the hybrid nature of our population, genetic divergence between parental
populations due to local adaptation or genetic drift is the most parsimonious explanation
for the morphological variation we observed. Although we predicted selection for large
body size in our system (Van Valkenburgh 2007, Van Valkenburgh et al. 2004), we did
not test metrics of fitness to demonstrate body size has affects fitness. Constrained
foraging behaviors should presumably lead to poor body condition and/or risky foraging
behaviors that translate into poor fitness. If body size is an indication of fitness in our
system, then this should correspond to low survival and/or fecundity rates. A closer
examination of these aspects may reveal the degree to which prey populations actually
influence genetic variation and the practicality of managing prey populations to promote
a desired phenotype.
16
Figure 2.1. Distribution of Canid genetic samples used in STRUCTURE analysis from
Ontario, Canada.
17
Frontenac
Axis
Algonquin
Park
Northeast Ontario
(Present Study)
Northeast
Ontario
Northwest Ontario
Figure 2.2. Plot of individual proportional memberships of Canis in Ontario, Canada to populations inferred
by STRUCTURE under F-model (K=3).
18
1.5
NWON
(red)
NEON
(blue)
1
This Study
(black)
FC2 (3.44%)
0.5
ALG
(green)
FRAX
(yellow)
0
-1
-0.5
0
0.5
1
1.5
2
-0.5
-1
-1.5
FC1 (7.26%)
Figure 2.3. Factorial correspondence analysis of microsatellite loci for Canis sample groups from
northwest Ontario (NWON), northeast Ontario (NEON), this study, Algonquin Provincial Park
(ALG), and the Frontenac Axis (FRAX).
19
Chapter 3: Body Size and its Effect on Resource Use in a Hybrid Wolf Population
Introduction
Morphological characteristics associated with body size can constrain a predator’s
ability to pursue, attack, and subdue large prey (Gittleman 1985, MacNulty et al. 2009).
Moreover, intense competition among carnivores may lead to selection for large-body
size as larger individuals tend to dominate and kill smaller competitors (Palomeres and
Caro 1999). It is not surprising then that large body size is particularly advantageous for
carnivores as it can lead to the acquisition of more profitable prey and greater diet
diversification (Gittleman 1985, Radloff and Du Toit 2004). If such an advantage
increases fitness, then it may contribute to the general trend for animal body size to
increase with time (Cope’s rule; Alroy 1998). Because this trend seems to have occurred
several times in North American Canids during the past 50 million years
(VanValkenburgh et al. 2004, Finarelli 2007), prey body size may be a significant
selective force, increasing body size in carnivores (Gittleman 1985, VanValkenburgh et
al. 2004).
Although ecological theory predicts selection for large bodied predators where
large prey predominate (Van Valkenburgh et al. 2004, Van Valkenburgh 2007), this
relationship may be confounded by cooperative and learned hunting behaviors of social
carnivores like wolves (Canis sp.; Mech 1970). Specifically, the abilities of large or
experienced pack members may facilitate food acquisition by smaller, less capable pack
members (Stander 1992). However, this advantage may be limited under 2 conditions: i)
if prey diversity is limited and the predator:prey body size ratio is large small, such as in
wolf-moose systems common to the northern boreal forests of North America, or ii) if
20
variation in body size is high and unequal among packs, with some being comprised
primarily of relatively small individuals. In such cases, packs composed of the smallest
wolves may be the most food-stressed, or at least exhibit lower foraging rates,
particularly as the availability of young and disadvantaged prey decreases (Vucetich and
Peterson 2004). Packs with larger-bodied members may maintain a more consistent food
intake rate regardless of fluctuations in prey abundance because they are presumably
more capable of subduing prime-aged and healthy members of the prey population
(MacNulty et al. 2009). Accordingly, behavioral differences related to body size of pack
members should be apparent, particularly in areas where pack sizes tend to be small.
Eastern wolves are small and morphologically distinct from their western and
northern counterparts (Nowak 2003); this trait likely reflects species adaptation to
preying upon white-tailed deer (Odocoileus virginianus), their primary prey (Schmitz and
Kolenosky 1985). Surprisingly, across their current core range eastern wolves can prey
extensively upon moose, although their kill rates are lower than in areas occupied by grey
wolves (Voigt et al. 1976, Messier and Crete 1985, Forbes and Theberge 1995). Recent
studies suggest that introgression of genetic material from gray wolves into the eastern
wolf population has occurred (Wilson et al. 2000, Grewal et al. 2004), leading to the
logical inference that eastern wolf body size may have increased subsequent to this
hybridization, thereby leading to increased ability to prey upon moose (MacNulty et al.
2009, Radloff and Du Toit 2004). Not surprisingly then, where deer give way to moose at
the northern extent of their distribution in northeastern North America, a hybrid zone
seems to exist between eastern and gray wolves (Kolenosky and Standfield 1975, Wilson
et al. 2000, Wheeldon 2009). The distribution of wolf genotypes in the eastern Great
21
Lakes region and field observations support this idea; with some animals being more
representative of relatively small bodied eastern wolves, and some resembling the larger
bodied gray wolves. This size variation affords a unique opportunity to examine the
influence of body size on ecological aspects of predatory behavior. Apart from predatorprey size correlations made across taxa (Carbone et al. 1999, Cohen et al. 1993,
Gittleman 1985, Owen-Smith and Mills 2008, Radloff and Du Toit 2004, Vezina 1985)
and between genders (Erlinge 1979, Sand et al. 2006, Brecko et al. 2008), the extent to
which variation in individual body size influences predatory behavior has received little
attention.
Here we use morphometric analysis and bioenergetic and habitat models, in
conjunction with data on prey selection to evaluate the hypothesis that large body size
facilitates the use of i) large prey and ii) encourages use of habitats that facilitate
acquisition of large prey. Foraging rates have been attributed to metabolic demands
(Williams et al. 2004), but may also be attributed to size-related variation in predation
efficiency. To determine the relative importance of each factor, we compared the
relationship of prey intake of individual wolf packs with their estimated caloric demands
and average body size. We then evaluated the habitat packs used to hunt with respect to
body size and examined the results in the context of landscape features known to
facilitate moose predation. We predicted that predation rates on moose would be greater
for large-bodied wolf packs. Furthermore, we predicted large-bodied packs would use
habitat features that optimized moose predation in greater proportion than small-bodied
packs which presumably foraged more on alternative prey. We tested these predictions
using wolf packs in an area containing a hybrid swarm of gray wolf  eastern wolf
22
hybrids, where prey availability was more limited than in other areas in core wolf range
(Bisset and McLaren 1999, Mech and Peterson 2003), the primary prey size was large,
and average body size varied greatly among packs. Thus, the challenges to small-bodied
animals could be substantial.
Study Area
Our study area was ~10,000 km2, centered on Horwood Lake (N48° 06’, W82°
19’, 295m altitude) near Timmins, Ontario. The study area boundary was defined by the
distribution of 15 wolf pack territories that were monitored via telemetry. The area was
predominantly industrial forest with an extensive network of secondary and tertiary roads
throughout. Forest cover was dominated by white spruce (Picea glauca), trembling aspen
(Populus tremuloides), white birch (Betula papyrifera) and jack pine (Pinus banksiana)
in well-drained upland sites, and balsam fir (Abies balsamea), black spruce (Picea
mariana), and eastern white cedar (Thuja occidentalis) in lowland sites. Topography
ranged from flat to rolling hills, and lakes, rivers, and wetlands were abundant
throughout.
Moose were the primary ungulate prey of wolves and occurred throughout the
study area, although their densities were relatively low (~0.13 moose/km2, Bisset and
McLaren 1999). Beaver were common throughout the study area whereas white-tailed
deer were rare (P. Davis, Ontario Ministry of Natural Resources, personal
communication).
23
Methods
Capture and Radiotelemetry
Wolves were captured and radiocollared as described in Chapter 2. GPS collars
were programmed to record wolf locations every 1.5 hrs from November 1 - April 15
each year, and every 6 hours during the remainder of the year. Throughout the study we
sought to maintain on air one GPS and at least 2 VHF collars in each of 10-12 packs.
Because wolves tend to travel in packs especially during winter (Mech 1970), and given
the typically small pack sizes in our study area (median = 5, range 2-8; Chapter 2), we
assumed location data from individuals were representative of broad-scale movement
patterns in the pack. During September 2005-June 2007, we retrieved via fixed-wing
aircraft, wolf GPS locations from some collars, whereas those from Argos-equipped
collars were downloaded automatically every 48 hrs. Overall, GPS collars collected
19,208 locations from 11 wolves (or pack-units) (range = 510-2766, SE = 60) over an
average of 130 monitoring days (range 50-165, SE = 4). We did not consider habitatinduced GPS bias and error because they were minimal in similar habitat (A. Maxie and
M. Obbard, unpublished data) and because of our coarse assessment of habitat types.
Pack sizes were estimated from previously described methods (Chapter 2).
Morphology and Kill Rates
Wolf body weight was used as an index of body size (see Chapter 2) and related
to kill rates and patterns of resource use. Winter location clusters for GPS-collared
wolves were searched to determine prey killing and scavenging patterns. Potential kill
sites were identified as sites where wolves spent ≥7.5 consecutive hours within a 200m
radius. Webb et al. (2008) identified 90-100% of all large prey using similar criteria. In
24
addition, we qualitatively assessed movement patterns between wolf locations to identify
additional clusters that wolves frequently visited but failed to meet our kill site criteria (n
= 61). Clusters were visited between 0.5-8 months after occurrence and evidence of
predation were identified and any prey remains were recovered. Moose remains often
were evident ≥ 2 years after the time of predation, but wolves often consumed entire
beaver carcasses in one sitting so evidence of beaver predation was scant regardless of
the elapsed time between the predation event and our site visit (J. Holloway personal
observation). Foraging rates were calculated for each pack as the number of moose
kills/day/wolf and as a consumption rate calculated as killed biomass/day/wolf (Hayes et
al. 2000, Hebblewhite et al. 2003), where the number of monitoring days was 165
(November 1 - April 15) or until monitoring ceased (i.e. wolf mortality or collar
malfunction).
Biomass was calculated from published average prey mass for moose (bulls =
483kg, cows = 440kg, and yearlings, calves and unknown = 250kg; Quinn and Aho
1989), black bear (Ursus americanus; sub-adults/adults = 65 kg; M. Obbard unpublished
data), and deer (yearlings/adults = 88kg; Kolenosky 1972). When the skull could not be
located, yearling and calf moose remains were often indistinguishable but assumed to be
yearlings/calves. We assumed consumption rates of 0.75 for killed moose and 0.90 for
killed deer and bears (Messier and Crete 1985, Hebblewhite et al. 2003).
Landscape Attributes
We selected for our investigation landscape features known to influence moose
habitat selection and predator-prey dynamics (Thompson and Vukelich 1981, Peek 1998,
Kunkel and Pletscher 2000, Dussault et al. 2005, 2006, Hebblewhite et al. 2005, Bergman
25
et al. 2006). These landscape features included forest cover type by age (mixed-hardwood
 10 years old, mixed-hardwood 11-49 years old, mixed-hardwood ≥ 50 years old,
conifer < 30 years old, conifer > 30 years old, water), distance to roads and water, moose
density, contrast-weighted edge density, and a beaver hunting habitat probability layer
(see Table 3.1 for definitions of each landscape feature). The beaver hunting probability
layer was generated from a resource selection probability function (RSPF; Manly et al.
2002) constructed using landscape features measured at known beaver hunting/killing
sites identified from GPS-clusters (see Appendix C for complete details). The size and
distribution of these features were measured in ArcGIS 9.2 (ESRI, Redlands, CA, USA)
at a 30m resolution.
We used Ontario Forest Resource Inventory maps (FRI; OMNR 2005) to classify
forest cover types in the study area. Land cover was updated based on forest harvesting
and silvicultural activities to reflect accurate stand ages (~100% accuracy), and cover
type classification was validated at >74% accuracy (Chen and Vasiliauskas, unpublished
data). Relative moose density was estimated following McKenney et al. (1998) using
aerial moose survey data from 2000 to 2005 (R. Rempel, unpublished data). Distance to
roads and water were calculated from the FRI and Natural Resources and Values
Information System (OMNR 2002). Contrast weights for the contrast weighted edge
density layer (Table 3.1) were assigned hierarchically (Table 3.2), giving highest weights
to edge types determined most valuable to moose (Dussault et al. 2005). The layer was
then calculated in FragStats using a moving analysis window of 400m because moose
rarely venture more than 200m from cover in this region (OMNR 1988; see McGarigal
and Marks 1995 for calculation formula). All layers were measured at a 30m2 resolution.
26
Landscape attributes were screened for collinearity using tolerance scores
(Menard 2002) which led to the exclusion of distance to water and water from the final
wolf habitat models due to correlation with the beaver hunting probability layer.
Categorical habitat variables were entered into models using dummy variable codes.
Resource Utilization Models
We used resource utilization functions to relate landscape attributes to the
utilization distribution (UD) of each pack (Marzluff et al. 2004). Utilization distributions
were estimated with the fixed kernel density estimator implemented in Home Range
Tools (Rodgers et al. 2007). We used the plug-in approach for bandwidth selection
(Wand and Jones 1995), and calculated kernels to the 100% isopleth to reduce
subjectivity in defining resource availability (Marzluff et al. 2004).
We built UD grids from GPS locations collected between November 1 and
December 31, in 2005 and 2006 (7,637 locations, range = 221-936, SE = 60; average
monitoring days = 58, range = 36-61, SE = 2) and a fix acquisition rate of 78% (range 3796%). One collar (T17, Kamiskotia pack) had low fix acquisition success because of
battery failure, but fix success was not considered habitat-biased. All packs were
consistently monitored during this time, and the short time period under consideration
reduced potential bias associated with shifting habitat preferences over time. Telemetry
locations associated with large food sources (ungulate kills, landfills, and trapper bait
sites) were filtered from the data set so that locations associated with food handling time
would not be reflected in the UDs. Filtered GPS locations were defined as any 2
chronologically consecutive points within a 1 km buffer of a known food source,
beginning when the pack first visited the food source. We censored an average of 32% of
27
the original dataset for each pack (range 8-46%, SE = 3%). The remaining sample size
was sufficient to meet minimum requirements for kernel density estimation (mean = 468
locations/pack, range 176-635; Seaman et al. 1999).
We intersected individual UD grids with the landscape attribute layers within
ArcGIS and then used the gam function from the mgcv package in program R to conduct
the resource utilization analysis (Wood 2006). The height of the UD at each location
(grid cell) is the response variable regressed against the corresponding resource attributes
for each 30m  30m pixel in a pack’s home range (Marzluff et al. 2004). The gam
function accounts for the spatial auto-correlation induced by kernel smoothing by using
spatial location (x and y coordinates) as a predictor variable and a non-parametric
smoothing function to describe the effect of location (Preisler et al. 1997, Wood 2006).
The un-standardized regression coefficients are necessary to predict population level
resource utilization across species distribution (Marzluff et al. 2004). Because scales of
measurement may differ among resources, coefficients must be standardized to allow
comparisons of the relative influence of resources on space use. Accordingly, we used the
standardized RUF coefficients following Marzluff et al. (2004) to compare relative
importance of habitat attributes among packs.
Because our objective was to compare habitat use among packs, we used a
constrained model selection approach to select a constant set of independent covariates to
compare across models. We first built a candidate set of hypothesized models (Appendix
D) for each pack and ranked each model using AIC weights (Burnham and Anderson
2002). After first rescaling AIC values we averaged AIC weights for each covariate and
28
ranked each in order of importance. From this ranked set we chose covariates within 0.10
of the highest ranked weight to use in our RUF models (Burnham and Anderson 2002).
Morphological, Predation, and Habitat Use Correlates
We predicted that foraging rates would vary with pack morphology because body
size should positively correlate with killing and consumption rates. All variables met
assumptions of normality (all Shapiro-Wilk tests p > 0.15; Zar 1996) and parametric and
non-parametric tests provided qualitatively similar results (J. Holloway unpublished
data). Nonetheless, visual inspections of distributions and the small sample size made
assumptions of normality suspect. We therefore chose to use non-parametric statistical
tests. Specifically, we used Kendall’s partial rank correlation coefficient (Txy*z; Siegel
and Castellan 1988) to examine the relative influence of average pack body size and pack
size on moose killing and biomass consumption rates. Although we hypothesized that
predation rates should be a function of pack body size, pack size is also known to
influence predation rates. Kendall’s partial rank correlation tested the relationship
between the dependent variable (average pack body size) with one independent variable
(body size or pack size) while controlling for the effect of another covariate (Siegel and
Castellan 1988).
Because foraging rates are also associated with metabolic demands (Williams et
al. 2004), we estimated the average daily food requirements (kg of biomass) for each
wolf/pack based on the average body mass of measured animals in each pack (appendix
I). The basal metabolic rate (BMR) for a fasting and resting animal is a function of body
mass W and measured in kJ/day by the equation:
BMR = 91.8W 0.813 * 4.18
29
(McNabb1988). The required kg of biomass/day is given by:
(BMR * 5) / 7700
which assumes 5 times the BMR for active wolves (Peterson and Ciucci 2003) and the
average energy content of prey to be 7700 kJ/kg (Glowacinski and Profus 1997).
To discern whether foraging rates were a function of size-related predation
abilities or of caloric requirements, we compared the relationship between consumption
rates and estimated average daily food requirements to the average pack body size.
Because we hypothesized small-bodied wolves would be inefficient moose predators, the
ratio of consumed moose biomass to average daily food requirements was expected to
approach 1 as the average pack body size increased. Accordingly, we entered the
consumed moose biomass:average daily food requirement ratio as the independent
variable in a least squares regression against pack body size. We conducted analyses at
the pack level (i.e. average pack body size) rather than individually because i) predation
on large animals such as moose is often done cooperatively by adult pack members
(Mech 1970), ii) wolf capture was generally biased towards mature adults, but iii) the
largest bodied pack member was not always captured.
Attributes in the RUF were selected based on their assumed importance to moose
habitat selection and wolf predation behavior. Consequently, coefficients can be viewed
as independent measures of habitat use as it relates to wolf foraging behavior. To
examine the influence of body size on habitat use, we used Spearman’s rank correlation
coefficient (rs) (Zar 1999) to compare the standardized RUF coefficient estimates to the
average pack body size. Although we hypothesized body size influences habitat use
within territories, habitat availability may also influence habitat selection. Therefore, we
30
explored the influence of resources on habitat use by comparing availability of landscape
attributes in each pack’s territory to the standardized RUF coefficient of each attribute
also using Spearman’s rank correlation coefficient (Zar 1996). For both tests we applied
to the Bonferroni adjustment for multiple correlations.
Results
Predation
We field-searched 628 GPS location clusters and identified remains of 68 moose,
3 black bears (Ursus americanus), and 1 deer that were either killed (81%) or scavenged
(19%) by wolves. We noted frequent use of landfills and trapper baits by some packs,
although we could not measure biomass consumption at these sites (J. Holloway,
unpublished data). We estimated that wolf packs killed 0.002-0.015 moose/wolf/day
(mean = 0.008) and consumed 0.3-5.1 kg of killed biomass/wolf/day (mean = 2.5 kg).
Moose consumption (Txz*y = 0.53, p = 0.03) and killing rates (Txz*y = 0.45, p = 0.06)
increased with body size while controlling for pack size. Pack size while controlling for
body size was not related to consumption (Txz*y = -0.06, p = 0.81) or killing rates (Txz*y =
0.20, p = 0.42).
We explored the influence of mass dependent energy requirements on foraging
rates by examining the relationship between pack body size with the consumed moose
biomass and the estimated biomass requirements for each pack to. The ratio of consumed
moose biomass:required biomass, was only marginally significant with pack body size
(average body weight/pack; p = 0.010, Figure 3.1), indicating that moose predation is
only marginally important to meeting the caloric requirements as pack body size
increases.
31
Habitat Use
One wolf pack (Oswald 06) spent ≥ 32% of its time at scavenging sites and
followed direct routes along roads to revisit them ~ every 7-10 days. This pack only
killed 1 moose and concentrated most of its beaver hunting activities in close proximity
to scavenging sites. We censored this pack from subsequent analyses. For the remaining
10 packs, all 9 covariates were retained in the top ranking RUF models (Table 3.3,
Appendix D). We therefore used the global model to derive RUF coefficients (Table 3.4)
to compare habitat use among packs (Appendix D).
Overall, larger pack body size was positively associated with high density of high
contrast edge (rs = 0.75, p = 0.01; Table 3.5). Examination of resource availability in each
pack’s home range (Table 3.6) revealed that availability of high contrast edge (the
average density [m/ha] of contrast-weighted edge/home range) had a negative association
with the use of high density high contrast edge (rs = -0.76, p = 0.01), indicating opposing
directions of utilization between scales. In other words, the observed trend suggests that
as pack body size increases, use of high contrast edge within the home range also
increases but the amount of high contrast edge within each home range decreases.
Discussion
By examining the ecological effect of body size, we not only were able to show
that wolf packs exhibiting eastern wolf phenotypes exhibit lower moose predation rates,
but also that prey body size might constrain the extent and direction of hybridization
among Canis species. The positive effect of body size on foraging rates was partially
consistent with our predictions. Moose consumption increased with body size, but only
marginally greater than would be predicted by metabolism alone. Although ecological
32
theory suggests large-bodied wolves should be more effective predators (e.g. Gittleman
1985, MacNaulty et al. 2009), our data suggests wolves are consuming moose at a rate
consistent with metabolic needs regardless of size. However our results also extend the
morphological relationship of the wolf-prey system to patterns of habitat use. Large
wolves made greater use of high contrast edge which may increase moose vulnerability
within their territories. The same wolves, however, appeared to tradeoff access to
vulnerable moose by occupying territories with low human access. Although habitat use
by small wolves did not relate to landscape features specifically linked to their foraging
behavior the gradient of habitat use across body size generally reflects increasing use of
moose resources.
Foraging Behavior
Per capita consumption rates of predators are dictated by energy demands and
quality of prey. It is seems reasonable to assume then that wolves kill moose at a rate
sufficient to meet metabolic and reproductive requirements. Our results were consistent
with this notion and suggest predatory abilities across our study population are only
marginally correlated with body size. Wolves readily use small prey (Peterson and Ciucci
2003), and all packs preyed on beaver despite our finding no relationship between body
size and time spent in high quality beaver hunting habitat. Because beaver are an
important alternative prey (Shelton and Peterson 1983) and were widely distributed
throughout our study area, wolves may frequently hunt beaver regardless of body size or
territory location. Nonetheless, energetic costs of predation are not offset by the caloric
value of small prey for predators weighing ≥ 21kg (Carbone et al. 1999). Consequently,
most extant carnivores weighing above this threshold forage on prey as large as or larger
33
than themselves (Carbone et al. 1999). Overall, the inability to meet energy requirements
from moose predation may lead to alternative foraging strategies, such as scavenging at
anthropogenic sites and in neighboring territories, which are inherently risky and may
have negative fitness effects (Messier 1985, Forbes and Theberge 1995).
Messier (1987) suggested that where moose are the primary large prey, wolves
become nutritionally stressed below densities of 0.2 moose/km2. Moose densities in our
study area were low relative to other areas where wolf predation has been studied (0.13
moose/km2; Bisset and McLaren 1999, Eberhardt et al. 2003). Consequently, the
relationship we observed between body size and predation may also reflect growth
suppression from nutritional stress. However, we are cautious in this interpretation as we
found greater sex-specific size differences than Messier (1987; Table 3.7) and putatively
pure eastern wolves are known to readily use moose at higher moose densities (Loveless
2009). We therefore suggest that genetic ancestry likely made a significant contribution
to the size variations we observed (Sutter et al. 2007).
Habitat Use
Our comparison of habitat use among packs resulted in 2 striking patterns: coarse
scale avoidance of features associated with humans and fine scale use of features
associated with moose vulnerability. Overall, RUF analysis indicates that within their
territories, large wolves use areas with high contrast edge but occupied territories with
low densities of high contrast edge. We interpret this opposing direction of use between
scales as wolves choosing territories with minimal human access which typically occur in
younger cuts where edge-contrast is highest. Hunting efficiency is then maximized within
their territory by using areas where edge contrast is high and moose are more abundant
34
and/or vulnerable (Bergman et al. 2006, Kunkel and Pletscher 2000). Rettie and Messier
(2000) suggested this pattern of use is characteristic of animals selecting habitats at
coarse scales to avoid factors that most limit individual fitness, with less important
limiting factors influencing selection at finer scales.
Human-caused wolf mortality was significant in our study area, particularly in
winter (J. Holloway, unpublished data). Although mortality risk is presumed to increase
with road density (Mech et al. 1988, Mladenoff et al. 1995), we found no correlation
between the average distance from roads in each home range and habitat use within the
home range. Extensive road networks existed throughout all of the pack territories we
examined but many were largely overgrown and inaccessible to humans. Human access
is probably greatest where edge contrast is high due to the construction and maintenance
of roads associated with recent logging activities and the source of most of the edge
within our study area. Therefore, high contrast edge likely reflects human access and
increased mortality risk in our data set and was a feature wolves avoided at coarse scales.
Management Implications
Eastern wolves are listed as a species of special concern in Canada (COSEWIC
2006) and Ontario (OMNR 2005) and pose unique challenges to conservationists. Their
ability to hybridize with gray wolves and coyotes (Wheeldon 2009, Wilson et al. 2009)
makes distribution and density estimations problematic and even complicates the clear
definition of just what (and where) an eastern wolf is (e.g. Allendorf et al. 2001). Given
their small body size, the size, vulnerability, and density of prey may be significant
factors in determining geographic limits of eastern wolf phenotypes.
35
Although morphological variation was not clearly linked to the neutral markers
we assessed (see Chapter 2), the size differentiation between the putatively pure
Algonquin eastern wolf and our study animals is clear (Figure 3.2, Patterson et al.
unpublished data). We found variable foraging behavior was closely associated with
body size, suggesting the distribution of smaller wolf phenotypes may be influenced by
the availability of small (i.e. deer; Forbes and Theberge 1995, Wilson et al. 2000) and/or
vulnerable large prey (Vucetich and Peterson 2002). Nonetheless, “small” wolves were
present in our study population and persist north of continuous deer range. This may
reflect subsidization through cooperative hunting in packs with some large members
and/or high rates of immigration from small bodied populations. Conversely, smallbodied wolves may be marginally capable of meeting caloric requirements through
moose predation as any size related constraint on predatory abilities may be small.
Regardless, the range of weight variation observed in our study is within ranges observed
in other North American wolf populations (Table 3.7, Figure 3.3). Even if the size
variation we observed results from hybridization, wolves in general may be sufficiently
adapted to meeting caloric requirements across a wide range of body sizes.
Despite apparent mechanisms that allow small-bodied phenotypes to persist
where moose predominate, they may be at a competitive disadvantage to larger-bodied
individuals (MacNaulty et al. 2009). Whether habitat and prey populations can be
manipulated to propagate a desired phenotype remains to be tested. Mild winters or
forestry manipulations that create favorable conditions for deer may promote smaller
eastern wolf phenotypes, but the degree to which larger phenotypes may be excluded by
increased human-related mortality resulting from increased road densities/access remains
36
unknown. Likewise, management actions that increase moose densities may promote
larger gray wolf phenotypes, but likely not exclusively where human-related mortality is
high.
37
Table 3.1. Definitions of land cover and landscape attributes used to describe habitat
use and a predictive beaver hunting layer for 11 wolf packs in northeastern Ontario.
Veg describes vegetative land cover, metrics describe landscape metrics, and prey
describes landscape attributes related to the abundance and hunting of prey.
Term
Description
A) Veg
Mixed-hardwood  10 yrs
old
Hardwood dominated stands and mixed hardwood/softwood
stands between 0-20 years old.
Mixed-hardwood 11-49
yrs old
Hardwood dominated stands and mixed hardwood/softwood
stands between 21-50 years old.
Mixed-hardwood ≥ 50 yrs
old
Hardwood dominated stands and mixed hardwood/softwood
stands >50 years old.
Conifer  30 yrs old
Upland and lowland conifer stands ≤30 old.
Conifer > 30 yrs old
Upland and lowland conifer stands >30 years old.
Water
Water (streams, rivers, ponds, lakes).
B) Metrics
Distance to water
Distance to Road
Contrast weighted edge
density
C) Prey
Beaver hunting
Moose Density
Distance from nearest water source (streams, rivers, ponds,
lakes).
Distance to the nearest road.
The sum of each edge (interface between cover types)
segment, multiplied by a contrast weight, and divided by the
landscape area (400m2 window). Values are scaled to m/ha.
Resource selection probability function predicting habitats
where wolves hunt beavers. The layer is estimated with
logistic regression comparing a sample of beaver hunting and
killing locations throughout the study area to 1000 random
locations, and then relating them to land cover and landscape
attributes. See Appendix C for full description.
Relative moose density derived from a spatially explicit model
(McKenney et al. 1998).
38
Table 3.2. Contrast weight matrix for the contrast weighted edge density layer of land cover types in northwestern
Ontario. Weights reflect a quantity of edge (m/ha) that is sensitive to the interface of mature and young stands of cover
that provide high juxtaposition of cover and food beneficial for moose.
MixedMixedhardwood <
Mixed-hardwood hardwood ≥ 50 Conifer < 30 Conifer > 30
10 yrs old
11-49 yrs old
yrs old
yrs old
yrs old
Water
Mixed0
hardwood < 10
yrs old
Mixed0.25
0
hardwood 1149 yrs old
Mixed0.5
0.25
0
hardwood ≥ 50
yrs old
Conifer < 30
0.5
0.1
0.75
0
yrs old
Conifer > 30
yrs old
1
0.25
0.1
0.1
0
Water
0
0.1
0.25
0.01
0.25
0
39
Table 3.3. Akaike weights (ωi) for resource utilization covariates in each pack. Shown is the average weight across all packs and
whether it is within 10% of the top ranked weight.
Oswald 05
Coppell
IVP
Kenogaming
Kamiskotia
Delahey
Biggs
Wakami
Mattagami
Mariana
Average ωi
≥ωi-10%
Beaver
hunting
0.25
0.25
0.25
0.2
0.18
0.25
0.25
0.21
0.19
0.25
0.23
Y
Moose
density
0.25
0.25
0.25
0.2
0.18
0.25
0.25
0.21
0.19
0.25
0.23
Y
Distance
to road
0.25
0.25
0.25
0.14
0.15
0.25
0.25
0.19
0.16
0.25
0.21
Y
Mixedhardwood
<10 yrs old
0.25
0.25
0.25
0.2
0.17
0.25
0.25
0.14
0.15
0.25
0.22
Y
Mixedhardwood
11-49 yrs
old
0.25
0.25
0.25
0.2
0.17
0.25
0.25
0.14
0.15
0.25
0.22
Y
Mixedhardwood
≥ 50 yrs
old
0.25
0.25
0.25
0.2
0.17
0.25
0.25
0.14
0.15
0.25
0.22
Y
Conifer
Conifer < > 30 yrs
30 yrs old old
0.25
0.25
0.25
0.25
0.25
0.25
0.2
0.2
0.17
0.17
0.25
0.25
0.25
0.25
0.14
0.14
0.15
0.15
0.25
0.25
0.22
0.22
Y
Y
Contrast
weighted
edge
density
0.25
0.25
0.25
0.14
0.15
0.25
0.25
0.19
0.16
0.25
0.21
Y
40
Table 3.4. Beta-coefficient estimates for resource utilization covariates in each pack.
Pack
Biggs Lk
Coppell Lk
Delahey
IVP
Kamiskotia
Kenogaming
Mariana
Mattagami
Oswald 05
Wakami
Beaver Moose
hunting density
0.16
0.08
-6.73
1.55
2.05
0.54
0.37
0.60
-2.97
-0.01
-0.08
-0.11
-0.20
-0.45
-0.02
-0.84
1.05
4.58
1.15
-0.53
Distance
to road
-0.22
0.61
-2.04
-0.05
0.31
0.30
3.91
1.35
-13.20
-0.46
Mixedhardwood
10 yrs old
-0.10
6.78
2.62
0.31
1.03
-4.23
4.78
-0.12
-1.68
-0.91
Mixedhardwood
11-49 yrs
old
1.07
2.54
-3.42
-1.74
0.25
-8.80
-5.20
0.63
-0.09
-1.12
Mixedhardwood
≥ 50 yrs
old
0.16
5.71
0.00
-0.30
0.08
0.06
0.51
0.71
-0.69
-1.14
Conifer <
30 yrs old
0.16
17.43
0.10
-0.08
-0.28
-0.25
-0.58
1.40
2.93
0.76
Conifer >
30 yrs old
0.10
12.28
0.20
-0.45
0.02
-0.06
-0.15
0.45
-1.86
0.96
Contrast
weighted
edge
density
-0.80
0.16
-0.83
-0.28
0.18
-0.10
-0.72
-0.66
-5.18
-0.07
41
Table 3.5. Spearman’s rank correlation results for average pack
body size (average body weight/pack) versus resource
utilization coefficients for 10 wolf pack wintersa within the
boreal forest of northeast Ontario.
Attribute
rs
p
-0.18
0.63
Mixed-hardwood  10yrs old
Mixed-hardwood 11-49yrs
old
Mixed-hardwood ≥ 50yrs old
-0.20
0.58
-0.29
0.42
Conifer  30 yrs old
-0.24
0.51
Confer > 30yrs old
0.30
0.39
Distance to roads
Contrast weighted edge
density
Moose density
-0.05
0.89
0.75
0.01
-0.21
0.56
Beaver hunting
-0.06
0.87
a
Oswald 06 was censored from the analysis. See text for
details.
42
Table 3.6. The proportion of cover types and average measures of landscape metrics
measured in each of 10 wolf territories defined by the 100% contour of the fixed kernel
density estimator. Values given with +/- 1 standard error.
Conifer > 30 yrs old
Minimum
Maximum
Mean
1 SE
0.13
0.07
0.51
0.29
0.29
0.16
0.04
0.02
Confer  30yrs old
Mixed-hardwood  10yrs
old
0.02
0.22
0.10
0.02
Mixed-hardwood 11-49yrs
old
<0.001
0.24
0.06
0.02
Mixed-hardwood ≥ 50yrs
old
0.14
0.59
0.36
0.04
-8.0
-5.1
-6.9
0.3
Beaver huntinga
Distance to roads (m)
379.5
978.1
719.7
62.9
Contrast weighted edge
density (m/ha)
16.1
23.0
19.2
0.6
b
Moose density
3.3
5.7
4.5
0.3
a
The average value of the predictive beaver hunting RSF described in Appendix I,
measured at each 30 X 30m pixel.
b
Relative moose density measured at each 30 X 30m pixel
43
Table 3.7. The primary available prey, average weights, and standard deviations (SD) from several North American wolf populations.
Source
Location
Male (SD)
n
Female (SD)
n
Primary Available Prey
Messier 1987
Quebec
27.4 (5.4) 65*
24.2 (5.2)
Moose, Deer
Patterson et al. unpubl.
Algonquin
29.3 (4.2)
43
24.4 (3.5)
44
Moose, Deer
Gogen et al. 2004
NC Minnesota
32.7 (4.4)
9
30.8 (3.4)
13
Moose, Deer
Forschner 2000
NW Ontario
36.5 (10.3) 11
26.9 (4.8)
11
Moose, Deer
Mech 2006
Minnesota
36.8 (3)
30
28.1 (2.3)
25
Deer
Fritts and Mech 1981
NW Minnesota
37.1 (4.9)
8
30 (2.9)
13
Moose, Deer
This Study
NE Ontario (This Study)
37.8 (5.4)
16
27.6 (3.5)
28
Moose
Walton, unpubl.
NWT
42.3 (5.4)
26
35.6(3.7)
29
Caribou
Peterson et al. 1984**
Kenai Peninsula, Alaska
43.9 (18.3) 21
34.2 (15.5)
33
Moose, Caribou
Ballard et al. 1997b
SC Alaska
44.6 (5.2)
50
39.7 (5.3)
28
Moose
Ballard et al. 1997a
NW Alaska
46.8 (5)
31
42.2 (5.6)
24
Caribou
Stuart 2007
Wisconsin
38 (9.4)
146
27.7 (8.9)
146
Deer
* Combined male and female
** Standard deviation may be inflated because of significant seasonal weight differences and the inclusion of older pups in sample.
44
Ratio of Consumed Biomass:Estimated
Required Biomass
1.2
1
y = 0.04x - 0.66
2
R = 0.28
p = 0.10
0.8
0.6
0.4
0.2
0
24.00
29.00
34.00
39.00
Body Size (Average Weight/Pack)
Figure 3.1. The relationship between the squared ratio of consumption of killed moose
biomass to the metabolic requirements relative to the average pack body size of 11 wolf
packs in northeastern Ontario. Consumption was estimated from food sources found
during winter GPS cluster searches and metabolic requirements were estimated from the
mass dependent metabolic rate of adult wolves in each pack.
45
Figure 3.2. Relative weights (kg), chest girth (cm), shoulder height (cm), and ground
clearance (cm) of putatively pure Algonquin-type eastern wolves (ALG; male n = 43;
female n = 45) and gray wolf  eastern wolf hybrids from northeastern Ontario (NE ON;
male n = 16, female n = 28). Data presented as mean +/- 1SE.
46
Figure 3.3 Coefficents of variation for male and female wolf weights from several North
American populations (Ballard et al. 1997a, Ballard et al. 1997b, Forschner 2000, Fritts
and Mech 1981, Gogen et al. 2004, Mech 2006, Messier 1987, Patterson et al.
unpublished data., Peterson et al. 1984, Walton, unpublished data, Stuart, 2007).
47
Chapter 4: General Discussion
Overview
We studied a Canis population in northeastern Ontario to examine the genetic
basis of body size, to explore the influence of body size on moose predation rates, and to
relate this to the habitat use of individual packs. In Chapter two we used microsatellite
loci to genetically characterize wolf packs with high morphological variability. We found
no relationship between the genetic markers we used and morphological variation among
packs. The lack of genetic concordance with body size is likely due to the neutral markers
we used which tend to be poor indicators of selection. However, we found our population
was predominately composed of C. lupus-lycaon hybrids. We further concluded that
hybridization was a possible cause of the morphological variation we observed despite
our inability to quantify a genetic relationship.
In Chapter three, we used morphometric analysis with bioenergetic and habitat
models, in conjunction with data on prey selection, to explore the influence of body size
on foraging behavior and resource use. We found a strong relationship between foraging
rates and body size that was marginally related to metabolic demands. Next, we
developed resource use models for each pack to explore the relationship between body
size and the use of landscape features associated with moose predation. We found largebodied packs made greater use of resources that increase moose vulnerability at fine
scales but had lower use of features associated with human access at coarse scales. We
concluded that body size significantly influences the foraging behavior of wolf packs in
our study area as they forage on large bodied prey while minimizing human-related
mortality. We suggest the smaller eastern wolf phenotype could be limited by the
48
distribution of small and vulnerable prey and management to promote a desired
phenotype should consider prey populations and human-related mortality.
Our results imply that adaptation to prey size might be a significant factor in the
divergence of C. lycaon and C. lupus. Initially, the influence of body size on foraging
ecology was not surprising as it corresponded to a priori predictions of predator:prey size
and metabolic relationships (Gittleman 1985). We expected, however, that selection for
large size would have resulted in greater homogeny of body sizes in our population. The
most parsimonious reason for the apparent lack of size selection in our population is that
wolves are not overly constrained in their predatory abilities across the size variations we
observed. However, wolves are also highly mobile and capable of dispersing long
distances (Mech 1987). Immigration from surrounding, and morphologically diverse,
populations likely occurs and may outpace any selective effects for large body size
detectable at the population level. We are cautious, however, in putting undue emphasis
on this interpretation. Any selective advantage afforded by large body size should have a
positive affect on fitness. Despite any competitive disadvantage small body size might
confer in our study, we could not relate this to direct measures of fitness. Future research
should consider this relationship empirically before firm conclusions are drawn about the
evolutionary affects of body size in this hybrid population. Furthermore, we acknowledge
the low inter-pack size variation we observed in our study and suggest the predator-prey
size relationship we sought to test may be more pronounced over larger gradients of body
size than we observed. Likewise, an analysis at an individual level within a pack might be
more appropriate if exceptionally large individuals in a pack account for higher predation
success. The interpretation of our results relies on the notion that morphological
49
constraints occur within our population. However, there is a general paucity of examples
that demonstrate size-related predation constraints on an individual basis (but see
MacNulty et al. 2009), and suggests predatory abilities vary over larger gradients of body
size than typically occur within a taxa or population. If predators have a wide range of
available prey sizes, the prey:predator size ratio is small, and/or prey densities are high,
then size constraints on the predator may be relaxed as many options exist to meet caloric
requirements. Under these conditions, we believe selection for large body size could be a
significant feature to the evolution of Canis phenotypes.
General Conclusions
As issues of taxonomic and molecular classification are slowly being resolved for
Canis species in northeastern North America (Kyle et al. 2006), it is becoming
increasingly clear that the intermixing of specific and sub-specific genotypes throughout
this region is a conservation concern (e.g. Leonard and Wayne 2008). Though it can be
argued that hybridization is a natural process of adaptation and wolves should be allowed
to adapt to current landscapes (Kyle et al. 2006), current guidelines under the Endangered
Species Act in the United States (http://www.fws.gov/le/pdffiles/ESA.doc) and the
Committee on the Status of Endangered Wildlife in Canada (COSWIC 2006) do not
support such policies (e.g. Murray and Waits 2007). Overall, managers must consider that
the entire gene pool for eastern wolves is contained in a continuous metapopulation of
interbreeding conspecifics (gray wolves, eastern wolves, and coyotes). If a desired
genotype/phenotype is to be maintained, the mechanisms that control genomic responses
to environmental conditions must ultimately be understood.
50
This research contributes to our understanding of Canis hybridization by
suggesting eastern wolf-like phenotypes (i.e. small body size) may forage less efficiently
where moose are the predominant prey species. Although low prey densities in our study
area were ideal for testing our hypothesis, they limit the inferences we can make
regarding the potential efficiency of eastern wolves as predators of moose at higher
moose densities. Accordingly, we recommend replication of our study across a cline of
prey densities and diversity.
Furthermore, we have shown that behavioral adaptations to landscape attributes
are reflective of phenotypic variations, and we suggest that selective pressures in this
Canis hybrid zone go beyond characteristics of prey populations. Management for a
desired wolf phenotype should consider aspects of prey populations and landscape
characteristics that facilitate wolf predation. Specifically, management to promote
moose-only prey systems and limit human access would likely favor gray wolf-like
phenotypes. It is unclear however, if eastern wolf-like phenotypes would persist
exclusive of gray wolf-types in deer dominated systems. Anecdotal evidence suggests
human-caused mortality may influence this latter relationship and warrants further
research.
51
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Appendix A.
62
Appendix B
Table B1. Pack name, wolf ID, and morphological measurements from 44 captured adult
wolves.
Pack
Biggs LK
Biggs LK
Black Creek
Black Creek
Black Creek
Black Creek
Coppell Lk
Coppell Lk
Coppell Lk
08
Deep LK
Deep LK
Delahey
Delahey
Fortune Lk
Fortune Lk
Fortune Lk
IVP
IVP
IVP
IVP
IVP 06
IVP 07
Kamiskotia
Kamiskotia
Kenogaming
Kenogaming
Kenogaming
06
Kenogaming
07
Kenogaming
07
Mariana
Mattagami
Mattagami
Mattagami
Sex
F
M
F
F
F
M
M
M
Weight
(kg)
30
31.5
27.5
29
30
46
35
30
Chest
Girth
(cm)
69
71
64.5
66
65.5
77
69
65.5
Shoulder
Height
(cm)
73
76
66
71
74.5
78
75.5
77
Ground
Clearance
(cm)
39
42
40.5
22.5
43.5
45
42
48
T49
T52
T53
T10
T27
T43
T13
T19
T14
T15
T26
T09
T50
T51
T12
T17
T16
T11
F
F
M
F
M
F
F
F
F
F
F
M
M
F
F
M
F
M
31
22.5
36.5
30
34.5
32
25
31
34
27
26
39
43
28
25
48
31
45
69.8
61
72.5
70
68
71
61
71
76
82
61
75
72
64
62
86
76
83.5
62
67
80
71
77
83
69
72
60
66
72
76
68
63
72.5
76
75
82.5
37
34.5
43.5
38
43.5
46
44
40
30
32
44.5
40.5
38.5
36.5
38
42
37
44
T38
F
25
68
74
43
T46
M
35
74.5
80.5
41
T58
T35
T23
T24
T25
M
F
F
F
F
35
29
27
26
25
70
70
63
65
63
64
75
74
75
73
36
47
43
45.5
43
Wolf
ID
T31
T28
T21
T22
T47
T48
T06
T08
63
Pack
Mattagami
North Pack
Oates
Oswald 05
Oswald 05
Oswald 05
Oswald 06
Oswald 06
Star LK
Star LK
Wakami
Wolf
ID
T30
T34
T03
T02
T05
T04
T05
T36
Sex
M
F
F
F
F
M
F
M
T20
F
T55
F
T29
M
Average
Female (SD)
Average Male
(SD)
Weight
(kg)
40
20
29
20.2
23
32
29
36
Chest
Girth
(cm)
79.5
58.5
66
60.5
59.5
66.5
60
73
Shoulder
Height
(cm)
85.5
69.5
74
65
66
74.5
66.5
72
Ground
Clearance
(cm)
50
41.5
39
34
38
42.5
38
40
29.5
30
38
27.5
(3.5)
37.8
(5.4)
67
66
76
66.5
(5.7)
73.7
(5.8)
71
69
82.5
38.5
40.5
45.5
70.5 (5)
39.1 (5.4)
76.6 (5.4)
42.8 (3.4)
64
Table B2. Average weight and standard deviation for 11 wolf packs in northeast Ontario,
Canada.
Biggs LK
Coppell Lk
Delahey
IVP
Kamiskotia
Kenogaming
Mariana
Mattagami
Oswald 05
Oswald 06
Wakami
Average
Weight
(kg)
30.8
32.5
32.3
31.5
36.5
38
29
30.5
25.1
29.5
38
SD
1.1
3.5
3.2
6.1
16.3
9.9
0
7
6.2
4.9
0
65
Appendix C
Resource Selection Function for the Predictive Beaver Hunting Layer
Because we wanted to examine how wolves use the landscape for different
foraging behaviors, we used a resource selection function (RSF) to identify resources
used by wolves while hunting for beaver. The RSF was estimated using logistic
regression to compare 90 locations where wolves hunted (68) or killed (22) beaver during
winter months (November 1 to April 15, 2005 to 2008) to 1000 random locations drawn
from within the combined territories of 11 packs defined by their 100% utilization
distributions. Beaver hunting/killing sites were identified by clusters of GPS locations
where wolves spent ≥7.5 accumulative hrs within a 100m radius in areas with obvious
signs of beaver presence. Sites were searched during snow-free periods to locate evidence
of a kill (e.g. bone and hair fragments) or to determine if the site was used for beaver
hunting. Sites were classified as beaver hunting when GPS clusters were centered in close
proximity (<20m) to fresh beaver activity (e.g. fresh cut trees) in a likely ambush position
(e.g. along beaver trails). The location where the majority of prey remains were found for
kill locations, or the center of the GPS cluster for hunting locations, was recorded with a
handheld GPS at each site.
We used the forest cover types from the FRI (table 3.1) to describe resource
attributes at the 90 used and 1000 random locations. Only a sampling of potential beaver
hunting sites was searched, and was likely biased by their accessibility from roads,
therefore, the distance to road variable was excluded from the analysis. We constructed
an a priori set of candidate models and included a null model to evaluate the hypothesis
that wolves select beaver hunting habitat based on cover type and proximity to water. The
66
top model was selected using AICc weights, ωi, (Burnham and Anderson 1998), and
evaluated with Spearman’s rank correlation with k-folds partitions (Boyce et al. 2002).
The top model (m4) was Y = 0.11 + (0.07* XMix-hardwood 10 yrs old) + (0.04 * X Mixedharrdwood11-49yrs old)
+ (0.04 * XConifer  30yrs old) + (0.06 * XWater + (-0.0002 *X Distance from water)
and had good fit (all likelihood ratio-test χ2 P-values < 0.05, Nagerleke’s R2 = 0.42) and
strong support from the Spearman rank correlation with k-fold cross-validation (rs = 0.82
± 0.05 SE; k-partitions = 5). In general, wolves chose to hunt beaver in or near water in
areas with young and middle aged (0-50 yrs) hardwood and mixed-hardwood stands and
young conifer (≤30 yrs) stands (Table C1).
67
Table C1. A priori candidate models to predict habitats in the study area that wolf
packs use to hunt beaver. For each model we give the model name, description, number
of parameters (K), logliklihood (LL), Nagerleke's R2 (R2), the change in AICc (Δi)
relative to the lowest scoring model, and the Akaike weight (ωi).
Model
Model Description
K
LL
R2
Δi
ωi
Mixed hardwood  10yrs old + Mixed
hardwood 10-30yrs old + Conifer  30yrs
a
m4
old + Water + Distance to water
5
-181.9 0.42 0 0.60
Mixed hardwood  10yrs old + Mixed
hardwood 10-30yrs old + Mixed
hardwood > 30yrs old + Conifer > 30yrs
old + Conifer  30yrs old + Distance to
m2
water
6
-181.8 0.43 2 0.22
Mixed hardwood  10yrs old + Mixed
hardwood 10-30yrs old + Mixed
hardwood > 30yrs old + Conifer > 30yrs
old + Conifer  30yrs old + Water +
m1
Distance to water
7
-181.8 0.43 3 0.13
Mixed hardwood  10yrs old + Mixed
hardwood 10-30yrs old + Water +
m10 Distance to water
4
-185.5 0.41 6 0.03
Mixed hardwood  10yrs old + Water +
m9
Distance to water
3
-187.2 0.41 7 0.02
Mixed hardwood  10yrs old + Mixed
hardwood 10-30yrs old + Conifer  30yrs
m3
old + Distance from water
4
-187
0.41 9 <0.01
a
-Top model selected based on AICc wieghts.
See table 3.1 in for definitions of model parameters.
68
Appendix D
Pack name, model description (model), model AIC values, model AIC rescaling factor (Model AIC/1000000), change in
model AIC (ΔModel AIC), covariate name, and average covariate AIC weight (Covariate ωi) for a priori candidate
models describing habitat use of 10 wolf packs in northeastern Ontario.
Pack
Oswald 06
Modela
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
AIC/
AIC
100000 ΔAIC
25624530 256
291
25491030 255
290
25664650 257
291
25459563 255
289
25383580 254
289
25538460 255
290
-3478639
-35
0
Model ωi
6.36E-64
1.24E-63
5.20E-64
1.45E-63
2.12E-63
9.78E-64
1.00E+00
Coppell
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
25624530
25491030
25664650
25459564
25383580
25538460
-10057510
3.29E-78
6.42E-78
2.69E-78
7.51E-78
1.10E-77
5.06E-78
1.00E+00
256
255
257
255
254
255
-101
357
355
357
355
354
356
0
Covariateb
Beaver hunting
Confer > 30yrs old
Conifer £ 30 yrs old
Contrast weighted edge density
Distance to roads
Moose density
Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Beaver hunting
Confer > 30yrs old
Conifer £ 30 yrs old
Contrast weighted edge density
Distance to roads
Moose density
Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Covariate
ωi
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
69
Pack
IVP
Modela
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
AIC
25624560
25491030
25664650
25538460
25383580
25538460
-20495710
AIC/
100000
256.25
254.91
256.65
255.38
253.84
255.38
-205
ΔAIC
461.2
459.9
461.6
460.3
458.8
460.3
0
Coppell
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
25624530
25491030
25664650
25459564
25383580
25538460
-10057510
256
255
257
255
254
255
-101
357
355
357
355
354
356
0
Model ωi
Covariateb
7.10E-101 Beaver hunting
1.38E-100 Confer > 30yrs old
5.81E-101 Conifer £ 30 yrs old
1.09E-100 Contrast weighted edge density
2.37E-100 Distance to roads
1.09E-100 Moose density
1.00E+00 Mixed-hardwood £ 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
3.29E-78 Beaver hunting
6.42E-78 Confer > 30yrs old
2.69E-78 Conifer £ 30 yrs old
7.51E-78 Contrast weighted edge density
1.10E-77 Distance to roads
5.06E-78 Moose density
1.00E+00 Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Covariate
ωi
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
70
Pack
Modela
Kamiskotia Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
Delahey
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
AIC/
AIC
100000 ΔAIC Model ωi
Covariateb
-8337858
-83
2
1.00E-01 Beaver hunting
-8362813
-84
2
1.14E-01 Confer > 30yrs old
-8126820
-81
4
3.49E-02 Conifer < 30 yrs old
-8464957
-85
1
1.89E-01 Contrast weighted edge density
-8438228
-84
1
1.66E-01 Distance to roads
-8408397
-84
1
1.43E-01 Moose density
-8522735
-85
0
2.53E-01 Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
25624530 256
2
1.67E-76 Beaver hunting
25491030 255
1
3.25E-76 Confer > 30yrs old
25664650 257
3
1.36E-76 Conifer < 30 yrs old
25459563 255
1
3.81E-76 Contrast weighted edge density
25383580 254
0
5.56E-76 Distance to roads
25538460 255
2
2.57E-76 Moose density
-9272441
-93
-347 1.00E+00 Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Covariate
ωi
0.18
0.17
0.17
0.15
0.15
0.18
0.17
0.17
0.17
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
71
Pack
Biggs
Modela
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
AIC/
AIC
100000 ΔAIC
25624530 256
364
25491030 255
363
25664650 257
365
25459563 255
363
25383580 254
362
25538460 255
363
-10808860 -108
0
Model ωi
7.69E-80
1.50E-79
6.29E-80
1.75E-79
2.57E-79
1.18E-79
1.00E+00
Wakami
Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
-9428393
-9657376
-9495814
-9695797
-9853191
-9647525
-9882976
3.29E-02
1.03E-01
4.61E-02
1.25E-01
2.75E-01
9.83E-02
3.19E-01
-94
-97
-95
-97
-99
-96
-99
5
2
4
2
0
2
0
Covariateb
Beaver hunting
Confer > 30yrs old
Conifer < 30 yrs old
Contrast weighted edge density
Distance to roads
Moose density
Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Beaver hunting
Confer > 30yrs old
Conifer < 30 yrs old
Contrast weighted edge density
Distance to roads
Moose density
Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Covariate
ωi
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.21
0.14
0.14
0.18
0.18
0.21
0.14
0.14
0.14
72
AIC/
AIC
100000 ΔAIC Model ωi
Covariateb
25602370 256
3
7.06E-02 Beaver hunting
25491030 255
2
1.23E-01 Confer > 30yrs old
25664650 257
3
5.17E-02 Conifer < 30 yrs old
25434570 254
1
1.63E-01 Contrast weighted edge density
25383580 254
1
2.11E-01 Distance to roads
25514410 255
2
1.10E-01 Moose density
25333150 253
0
2.71E-01 Mixed-hardwood £ 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
Veg
25624530 256
379 6.44E-83 Beaver hunting
Mariana
Prey
25491030 255
377 1.26E-82 Confer > 30yrs old
Metrics
25664650 257
379 5.27E-83 Conifer < 30 yrs old
Prey+Veg
2549563
26
148 8.24E-33 Contrast weighted edge density
Prey+Metrics
25383580 254
376 2.15E-82 Distance to roads
Veg+Metrics
25538460 255
378 9.91E-83 Moose density
Prey+Veg+Metrics -12225810 -122
0
1.00E+00 Mixed-hardwood < 10yrs old
Mixed-hardwood 11-49yrs old
Mixed-hardwood ≥ 50yrs old
a Description of model covariates is given in Table 3.1.
Pack
Modela
Mattagami Veg
Prey
Metrics
Prey+Veg
Prey+Metrics
Veg+Metrics
Prey+Veg+Metrics
Covariate
ωi
0.19
0.15
0.15
0.16
0.16
0.19
0.15
0.15
0.15
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
73