KHussey ThesisDraft6 FINAL

7/30/2019 KHussey ThesisDraft6 FINAL

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SPACE USE PATTERNS OF MOOSE (ALCES ALCES) IN RELATION TO

FOREST COVER IN SOUTHEASTERN ONTARIO, CANADA

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 Karen Hussey, 2009

Environmental and Life Sciences Graduate Program

January 2010


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Space use patterns of moose (Alces alces) in relation to

forest cover in southeastern Ontario, Canada.

ABSTRACT

I investigated habitat use relative to distance to cover of moose (Alces alces) in

Algonquin Provincial Park, Ontario, Canada to validate assumptions in the Ontario

Ministry of Natural Resources (OMNR) habitat suitability model for moose. I compared

distances to cover of 21 GPS-collared female moose to random points within seasonal

home ranges to determine selection and calculate several distance-to-cover parameters.

At the population level moose showed no selection for proximity to cover in the summer

and marginal selection in the winter. When considering four habitat types, moose

generally showed no selection for proximity to cover in either season while in any of the

habitat groups. Considerable variation in selection for cover existed within and among

individuals, with many individuals selecting proximity to cover in one year and avoiding

or showing no selection in other years. I identified several areas where the existing

habitat suitability model could be improved. I recommend further testing of an adapted

model that 1) redefines stand age according to the most recent harvest date instead of the

age of the oldest trees, 2) adopts a broader list of cover types defined as cover in the

growing season, and 3) increases the distance moose are assumed to travel through open

areas in the dormant season from 200 to 300 meters.

Keywords:Alces alces, Algonquin Provincial Park, cover, distance, habitat, habitat

suitability model, home range, moose, Ontario, ungulate


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ACKNOWLEDGMENTS

I am indebted to many who have provided me with assistance and support

throughout the project. First of all I would like to thank my supervisors Brent Patterson of

the Ontario Ministry of Natural Resources (OMNR) and Dennis Murray of Trent

University for the great opportunity to be a part of the moose project and for their

financial and academic contributions. Id also like to thank my third committee member,

Bruce Pond, of the OMNR for his thoughtful input and supportive demeanor. Funders of

the project included the Natural Sciences and Engineering Research Council of Canada

(NSERC), Ontario Federation of Anglers and Hunters (OFAH), Canada Foundation for

Innovation (CFI), OMNR Wildlife Research & Development Section, and Ontario Parks

(Algonquin Provincial Park). Id especially like to thank the Algonquin Forestry

Authority (AFA) for providing the critical funding allowing me to finish my degree.

I am deeply indebted to several office extras who generously shared their time

and expertise during numerous and impromptu occasions: Raul Ponce-Hernandez, Joe

Nocera, Jeff Bowman, and especially Kevin Middel and Colin Garroway, for without

their technical help, I would still be processing my sea of data. Phil Elkie of OMNR

provided assistance with technical aspects of the model and Joe Yaraskavitch, Keith

Fletcher, and Gord Cumming of AFA answered all the forestry-related questions I could

throw at them. Id especially like to thank Linda Cardwell who is a pillar of support for

all of us graduate students and tirelessly holds the Environmental and Life Sciences

Department together.

There are many who have assisted with moose captures and/or field work: Andy

Silver, Stacey Lowe, Ken Mills, John Benson, Kevin Downing, Kiira Siitari, Josh Sayers,


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Tom Habib, Mike Ward, and especially Karen Loveless (aka Kwolf) who ran the field

component until I arrived and was a wonderful field mentor and friend from the

beginning.

Lastly, Id like to thank my friends and family for all their support and especially

my husband, Travis Hussey, who sacrificed so much to join me on this journey. I cant

imagine completing this endeavor without all your love, support, patience, and tasty

dinners. Thank you, Travis, from the bottom of my heart.


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TABLE OF CONTENTS

Page

ABSTRACT...iiACKNOWLEDGEMENTS... iiiTABLE OF CONTENTS...vLIST OF TABLES..... viLIST OF FIGURES....... viii

CHAPTER 1: INTRODUCTION AND LITERATURE REVIEW. 1CHAPTER 2: METHODS.... 5

2.1 Study area.52.2 Field methods....... 7

2.3 Habitat data and validation...... 82.4 Analyses... 11

2.4.1 Selection of cover..... 11

Population level selection . .... 11Home range level selection.... 14Year effect..15

2.4.2 Distance parameters...... 152.4.3 Model validation... 18

Model description.. 18Validation method 1...20

Validation method 2. ..21

Validation method 3...22CHAPTER 3: RESULTS...... 22

3.1 Selection of cover year effect... 233.2 Selection of cover home range level.... 24

3.3 Population level seasonal results (selection & distance parameters).... 263.4 Population level habitat results (selection & distance parameters).. 273.5 Model validation.. 38

3.5.1 Validation method 1......383.5.2 Validation method 2..... 41

3.5.3 Validation method 3..... 45CHAPTER 4: DISCUSSION 46

4.1 Selection of cover............ 464.1.1 Prediction 1... 464.1.2 Prediction 2... 484.1.3 Prediction 3... 49

4.2 Model validation.......... 494.3 Model recommendations. 544.4 Implications of variability in habitat studies. ...... 55

LITERATURE CITED.. 59APPENDIX A: delineation of habitat groups, seral stages, and cover values...........69APPENDIX B: selection ratio normality plots.......... 72


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LIST OF TABLES

Page

Table 2.1: The Ontario Ministry of Natural Resources habitat suitability models

description of cover categories (Naylor et al. 1999) and mycorresponding habitat groups.. 20

Table 2.2: Distance-to-cover assumptions in the Ontario Ministry of NaturalResources habitat suitability model for moose and expected 95

percentile distances from moose locations to distance categories ........ 20

Table 3.1: Mean temperature and snow depths ( SD) for the analysis periodstaken from Algonquin Park East Gate weather station (EnvironmentCanada 2008). Historic normals are averaged from 1971-2000. Year

effect analyses were performed using growing season and midwinterseason. Asterisks indicate snow depths known to influence moosemovement........24

Table 3.2a: Results of habitat group level mature conifer cover selectionANOVAs. Asterisks indicate marginal significance. ........ 29

Table 3.2b: Results of habitat group level selection of the lesser cover categoriesassociated with presapling and sapling in the dormant season.Asterisks indicate marginal significance. Sapling plus refers to thecombination of any sapling or mature forest. ........ 29

Table 3.3a: Moose median observed and expected distances to cover plus 95%confidence intervals (in meters) by season and habitat group for allhome ranges, those where cover was selected, and those where coverwas avoided. N refers to the number of home ranges in each group.%S = % home ranges showing selection, %NS = % showing noselection, and %A = % showing avoidance. . 30

Table 3.3b: Moose observed and expected ninety-five percentile distances plus

95% confidence intervals (in meters) to cover by season and habitatfor all home ranges, those where cover was selected, and those wherecover was avoided. N refers to the number of home ranges in each

group. %S = % home ranges showing selection, %NS = % showingno selection, and %A = % showing avoidance....................................... 31


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Table 3.4a: Moose observed and expected median distances plus 95% confidenceintervals (in meters) to lesser cover categories associated withpresapling and sapling in the dormant season for all home ranges,

those where cover was selected, and those where cover was avoided.N refers to the number of home ranges in each group. Sapling plusrefers to the combination of any sapling or mature forest. . 32

Table 3.4b: Moose observed and expected ninety-five percentile distances plus95% confidence intervals (in meters) to lesser cover categoriesassociated with presapling and sapling in the dormant season for allhome ranges, those where cover was selected, and those where coverwas avoided. N refers to the number of home ranges in each group.Sapling plus refers to the combination of any sapling or matureforest....... 32

Table 3.5: Breakdown of distance assumption violations for the dormant season.The mature conifer assumption refers to points greater than 1600

meters from cover. Mature forest assumption refers to points greaterthan 400 meters from all mature habitat groups (hardwood, mixed,and cover). Sapling plus assumption refers to points greater than200 meters from saplings and all mature habitat groups combined. Nrefers to the number of animals associated with each result. * The sum

of distance assumption violations 1 - 3 may exceed 100% in a givenhabitat group because some points violated more than one

assumption.. 45

APPENDIX A

Table 1: Forest Resource Inventory Landscape Guide Forest Units (LGFU)

included in the three forest types used in this study... 69

Table 2: Delineation ofseral stages for my study groups in relation to theForest Resource Inventory Landscape Guide Forest Units (LGFUs)that make up each study group. LGFU descriptions are located in

Appendix A Table 1 70

Table 3: Delineation ofcover values for my mature study groups in relation tothe Forest Resource Inventory Landscape Guide Forest Units(LGFUs) that make up each study group. LGFU descriptions arelocated in Appendix A, Table 1. Cover values apply to forest standsclassified in the FRI as immature, mature, or old... 71


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LIST OF FIGURES

PageFigure 2.1: Study area- Algonquin Provincial Park in southeastern Ontario,

Canada ..... 7

Figure 2.2a: Observed and expected distributions of distance-to-cover fromhardwood locations for Moose7 in the dormant season of 2006-2007.Preference zone edge can be estimated as the general area where theexpected values begin to outnumber the observed values...... 17

Figure 2.2b: Linear regression for Moose7 in the dormant season of 2006-2007when in hardwood stands. The preference zone is the area within 394

meters of cover. A Y value of 0.301 is equal to the selection ratio ofone .. 18

Figure 3.1a: Variation of selection behavior at the home range level for matureconifer cover. S = selection, A = avoidance, and NS = no selection. Nrefers to the number of home ranges in each category. 25

Figure 3.1b: Variation of selection behaviour at the home range level for the lesser

cover categories associated with presapling and sapling forest in thedormant season (mature forest and sapling plus). S = selection, A

= avoidance, and NS = no selection. N refers to the number of home

ranges in each category. ..... 26

Figure 3.2a: Moose observed (o) and expected (e) average median distances tocover ( 95% CI) of home ranges where selection of cover occurred

for each season and habitat group. %S is the percent of all homeranges where cover was selected and n is the number of home ranges

where cover was selected 33

Figure 3.2b: Moose observed (o) and expected (e) average 95 percentile distances

to cover ( 95% CI) of home ranges where selection of coveroccurred for each season and habitat group. %S is the percent of all

home ranges where cover was selected and n is the number of homeranges where cover was selected ... 34

Figure 3.2c: Moose average preference zones for cover ( 95% CI) for each seasonand habitat group. %S is the percent of all home ranges where coverwas selected and n is the number of home ranges where cover wasselected... .... 35


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Figure 3.3a: Moose observed (o) and expected (e) average median distances (

95% CI) to lesser cover categories associated with presapling andsapling forest in the dormant season for home ranges where selection

of cover occurred. Sapling plus refers to the combination of anysapling or mature forest. %S is the percent of all home ranges wherecover was selected and n is the number of home ranges where cover

was selected.... 36

Figure 3.3b: Moose observed (o) and expected (e) average 95 percentile distances

( 95% CI) to lesser cover categories associated with presapling andsapling forest in the dormant season for home ranges where selectionof cover occurred. Sapling plus refers to the combination of anysapling or mature forest. %S is the percent of all home ranges where

cover was selected and n is the number of home ranges where cover

was selected .... 37

Figure 3.3c: Moose average preference zones ( 95% CI) in relation to lessercover categories associated with presapling and sapling in the dormantseason. Sapling plus refers to the combination of any sapling ormature forest. %S is the percent of all home ranges where cover was

selected and n is the number of home ranges where cover was selected.... 38

Figure 3.4a: Map of the growing season range calculated in OMNRs moosehabitat suitability model and the seasonal moose locations falling in

and outside of the available habitat in the southwestern portion ofAlgonquin Provincial Park, Ontario, Canada. Distance assumptionviolations occurred in 49% of moose locations.......... 40

Figure 3.4b: Map of the dormant season range calculated in OMNRs moosehabitat suitability model and the seasonal moose locations falling inand outside of the available habitat in the southwestern portion ofAlgonquin Provincial Park, Ontario, Canada. Distance assumptionviolations occurred in 0.1% of moose locations. 41

Figure 3.5a: Map of the growing season range created from my adapted model and

the seasonal moose locations falling in and outside of the availablehabitat in the southwestern portion of Algonquin Provincial Park,Ontario, Canada. Distance assumption violations occurred in 3% ofmoose locations... 42


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Figure 3.5b: Map of the dormant season range created from my adapted model andthe seasonal moose locations falling in and outside of the availablehabitat in the southwestern portion of Algonquin Provincial Park,

Ontario, Canada. Distance assumption violations occurred in 12% ofmoose locations... 43

APPENDIX B

Figure 1: Distance to cover selection ratio normality plots for moose inAlgonquin Park by year-season for year affect analysis. 72

Figure 2: Distance to cover selection ratio normality plots for moose inAlgonquin Park by season.. 73

Figure 3: Distance to cover selection ratio normality plots for moose inAlgonquin Park by habitat group-season 74

Figure 4: Distance to cover selection ratio normality plots for moose inAlgonquin Park for lesser cover categories 75


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CHAPTER 1: INTRODUCTION & LITERATURE REVIEW

Because cover provides protection from harsh environmental conditions and

concealment from predators, it is a critical habitat component for species of many taxa,

including insects (Sih 1990), fish (Gilliam and Fraser 1987), amphibians (Holomuzki

1986) , birds (Radford et al. 2005), and mammals (Holmes 1984, Kotler and Blaustein

1995). How ever, these protective areas are often deficient in other high quality resources

(typically food) needed to adequately sustain individuals and/or facilitate reproduction

(Lima and Dill 1990, Mysterud and stbye 1995, Moody et al. 1996, Dussault et al 2004,

2005, Sinclair et al. 2006, p. 69). Consequently individuals must make trade-offs to

maximize fitness and often this involves deciding how far to stray from cover to acquire

resources.

For moose and other ungulates, cover can be important in all seasons for reasons

including protection from predation, and relief from deep snow, heat, or extreme cold and

wind (Peek 1997, pp. 368-372). Moose are susceptible to predation from wolves, bears

and humans (Van Ballenberghe and Ballard 1994). To hide from predators, ungulates

need lateral cover which is comprised of dense low and mid-level vegetation that breaks

up the shape of their bodies making them harder to detect by coursing predators

(Timmermann and McNicol 1988, Mysterud and Ostbye 1999, Altendorf et al. 2001,

White and Berger 2001). Concealment cover is presumed to be especially critical for

moose calves because of their high susceptibility to predation, with various studies

reporting neonate predation-induced mortality to be between 30 and 70% (Franzmann et

al. 1980, Ballard et al. 1981, Larsen et al. 1989, Osborne et al. 1991, Gasaway et al. 1992,

Garner 1994).


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Deep snow is energetically costly for any ungulate species and although moose

are well-adapted for this condition, their movements can still be limited by snow depth

and condition, especially for calves. As snow depth increases, movement becomes more

costly and eventually moose need to shift to habitats dominated by mature conifers with

less snow accumulation (Coady 1974, Timmermann and McNicol 1988, Courtois et al.

2002). Depths of 70 cm impede moose movement and at 90-100 cm moose are confined

to areas with a dense coniferous canopy (Coady 1974). Peterson and Allen (1974) found

that more calves on Isle Royale were killed when snow depths exceeded 76 cm and

Loveless (2009) found that wolves in Algonquin Park consumed the most moose biomass

when snow depth was highest. In addition to lower snow depths, dense coniferous

canopies provide softer snow often making travel easier for moose. In a mild winter, Peek

(1971) found that moose moved to denser canopies at a snow depth of only 30 cm

because snow hardness under open canopies made movement more costly.

Thermal stress is presumed to be a common condition for moose though nearly

always due to heat, not cold (Schwartz and Renecker 1997, p. 468). Moose have many

physiological adaptations for low temperatures, including their insulative pelage and

large size which helps by conserving heat and reducing energy needs in the winter. At the

northern edge of their distribution, moose may be constrained not by cold temperatures

but by the absence of forest (Kelsall and Telfer 1974). Lower critical temperatures have

not been well-tested, although adult moose have been reported to show no visual sign of

distress at -40C (Schwartz and Renecker 1997, p. 469). Evidence suggests cold stress

can occur more commonly in calves (at -30C, Renecker et al. 1978) and in late winter as

a result of tick-induced hair loss (Blyth and Hudson 1987, Glines and Samuel 1989).


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However, the southern range periphery is thought to be determined largely by

temperature as moose are reported to be heat-stressed in the winter at 5C, and in the

summer at 14C, with a panting threshold at 20C (Renecker and Hudson 1986). To avoid

heat stress moose may seek mature stands with coniferous trees to reduce exposure to

solar radiation (Schwab and Pitt 1991, Dussault et al. 2004, but see Lowe 2009).

Because forest stands providing high quality cover for moose are usually

dominated by closed coniferous canopies, they offer food resources at a lower quantity

and quality than other forested habitat. Moose generally prefer hardwood browse over

conifer species (Crte 1989, Conrad 2000) possibly because conifers have elevated levels

of secondary compounds, such as tannins, that can affect palatability and nutrient

absorption (Robbins et al. 1987, Bryant et al. 1991). Additionally, the closed canopy of

cover habitat allows less sunlight to penetrate and less vegetation is able to grow in the

midstory, diminishing browse quantity. Therefore moose need to leave cover in order to

better meet their nutritional requirements.

The concept that moose movements are constrained by the abundance and

juxtaposition of cover has been well-documented (Coady 1974, Crte 1977, Telfer 1978,

Welsh et al.1980, Hamilton et al. 1980, Thompson and Vukelich 1981, Eastman and

Retcey 1987, Peek et al. 1987, Courtois and Beaumont 2002, Dussault et al. 2006). In

Ontario, Hamilton et al. (1980) found declining trends in winter browse use with

increasing distance to cover. Although they did not find an upper distance limit from

cover to feeding locations , 95% of browse use was recorded within 80 m of forested

cover. Thompson and Vukelich (1981) found that although exceptional distances of 400

meters were found in early winter, average distance to forested cover was 27 meters.


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Habitat suitability models for moose commonly include a component related to

availability or proximity to cover (Allen et al. 1987, Puttock et al. 1996, Naylor et al.

1999, Dussault et al. 2006), yet these components are often assumed without empirical

validation, especially in the geographic region in which it is intended to be applied. This

is the case for moose habitat models in the temperate forests of the Great Lakes Region.

Two models were created in 1987 by the United States Fish and Wildlife Service

(USFWS) (Allen et al. 1987). The models operate at different scales with Model I

estimating carrying capacity at the fine scale of 600 ha (the assumed size of a moose

home range) and Model II estimating carrying capacity at a coa rser scale of

approximately 9000 ha (an area presumed large enough to support a population) (Allen et

al. 1987). In 1999 the Ontario Ministry of Natural Resources (OMNR) created a new

model based upon components of Model I to aid in forest planning in the Great Lakes

St. Lawrence Forest (Naylor et al. 1999). The distance to cover as sumptions in this model

as well as Model I upon which it was based have never been empirically validated,

although a few partial validations of Model II have occurred (Allen et al.1991, Naylor et

al. 1992, Puttock et al. 1996, Rempel et al. 1997, Koitzsch 2002). However these

validations were based on winter aerial survey data or in the la tter case, harvest data, so

have limited interpretation value. Harvest data are spatially coarse and subject to biases

(Koitzsch 2002) and aerial surveys provide only a snapshot of moose behaviour because

they reflect only the winter season and the particular conditions on the day of flight: snow

depth, snow hardness, temperature, etc. In contrast, GPS collars provide concise,

frequent, and consistent location data for all seasons, daily periods, and weather

conditions and therefore are a better tool for model validation.


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The purpose of this study was to determine if moose select areas closer to cover

and if the selection depends upon season or habitat. I also was interested in determining

specific distance parameters: are moose constrained to certain distances from cover , how

close do they prefer to stay to cover, and do these values differ by season or habitat type

the moose is in? The second goal of my study was to validate the distance to cover

assumptions used in OMNRs habitat suitability model for moose. I expected to find that:

1) overall, moose would select areas close to cover, 2) cover in the dormant season would

be more important than in the growing season, and 3) moose would be closer to cover

when they were in early successional stands than when in mature forest, especially during

the dormant season.

CHAPTER 2: METHODS

2.1 Study Area

This study took place in a 2545 km2 portion on the western side of Algonquin

Provincial Park located in southeastern Ontario, Canada in the Great Lakes- St. Lawrence

forest region (45 North, 78 West, Figure 2.1). The 7600 km2 park is comprised of

shade -tolerant upland hardwoods on poorly drained glacial till, and is intersperse d with

lakes, wetlands, and mixed and conifer stands in the lower areas (Crins et al. 2008).

Elevation ranges from 150 - 590 meters above sea level (Friends of Algonquin Park,

2005) and dominant tree species include sugar maple (Acer saccharum), American beech

(Fagus grandifolia ), eastern hemlock (Tsuga canadensis), yellow birch (Betula

alleghaniensis), and red maple (Acer rubrum) (Crins et al. 2008, Appendix A, Table 1).

Timber harvesting occurs in 78% of the park (56% with water and non-harvestable areas


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within the harvesting zones excluded) and is comprised largely of selective and

shelterwood cuts with clearcuts consisting of < 5% (Cumming 2009). Highway 60, a

major 2-lane route, bisects the southern portion of the park. Recreational human use

(camping, boating, fishing, hiking) is focused on the major lakes and the few side roads

within the highway corridor. Interior (logging) roads are not open to public vehicles,

though backcountry access is available via canoe routes.

Although Aboriginals harvest moose on the east side of the park, my study area in

the western side was not subject to moose harvest. During the study period, moose

population estimates for Wildlife Management Unit 51, the area comprising the majority

of the park and the entire study area , increased from 2100 in 2006 to 3100 in 2009,

producing a density of 0.29/km2 and 0.43/km2 , respectively (Steinberg and Francis 2006,

Steinberg, 2009).


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Figure 2.1. Study area- Algonquin Provincial Park in southeastern Ontario,

Canada.

2.2 Field Methods

A total of 21 adult female moose (mean age : 4 2 SD years, age range: 1-7) were

equipped with Lotek 3300L store-on-board GPS collars (Lotek Wireless, Newmarket,

ON, Canada) in January 2006 and February 2007. Animals were captured via helicopter

by net-gunning in 2006 (Bighorn Helicopters Inc., Cranbrook, BC, Canada), and by

darting in 2007 (Heli-horizon Inc., Quebec City, QC, Canada). The anaesthetic applied in

2007 included a mixture of carfentanil (Wildlife Pharmaceuticals Inc., Ft. Collins,

Colorado, USA) at approximately 0.0070 mg/kg combined with xylazine hydrochloride

at approximately 0.2 mg/kg. This drug combination was reversed with naltrexone at


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approximately 0.7 mg/kg. Radio collars recorded fixes at a two-hour interval and were

deployed for approximately two years. Survival was monitored usually once or twice per

month by fixed-wing aircraft and mortalities were investigated from the ground to

determine cause of death. Collars were removed in March 2008 by Heli-horizon Inc.

using the same drugging method described above. Field methods were approved by the

Trent University and Ontario Ministry of Natural Resources Animal Care Committees

under the guidelines of the Canadian Council on Animal Care.

GPS collar positional accuracy was believed to be similar to results of an

accuracy study done with the same collar model in the same study area (Maxie 2009).

Approximately 1150 fixes were collected in open, hardwood, conifer, and mixed conifer-

hardwood habitats with an average 3D fix error of 14 2 m SD, 2D fix error of 34 4 m

SD, and 3D fixes comprising 83.5% of all locations.

2.3 Habitat Data and Validation

Habitat analysis was conducted using a Forest Resource Inventory (FRI) map of

the study area. The FRI is a digital database created from visual interpretation of 1:15,840

aerial photos, calibrated by ground data, and updated with fire and harvest events every

five years. It provides stand-level detail including species composition, age, height, and

stocking (OMNR 2008) with a minimum stand size of 4 ha in the study area. The current

FRI for the study area was interpreted from photos taken in 1989. Recent timber harvest

spatial data including harvest type and date was provided by the Algonquin Forestry

Authority (Huntsville, Ontario, Canada) and used to determine forest age.


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In 2007, we evaluated species composition accuracy of the FRI in the study area

by comparison with plot-based field estimates of species composition (Maxie et al. in

press). In our study area, 25 standard forest units and 8 non-productive forest units were

derived from the FRI (Elkie et al. 2007). We collapsed similar units into eight study

groups, including six forest types, wetlands and water. Poor agreement between the FRI

and field data led us to further collapse forest types to four. These four forest types

(hemlock, conifer, hardwood, and mixed conifer-hardwood) yielded an overall accuracy

level of 77%. According to our ground data, hemlock stands were misclassified as

hardwood 50% of the time (Maxie et al. in press).

Because of poor map accuracy, I was unable to include the full spectrum of

standard forest units in my analyses and model validation, and although hemlock may be

an important source of cover for moose (Forbes and Theberge 1993, Naylor et al, 1999), I

merged it into the hardwood group because of the frequency of misclassification as

hardwood. The resulting habitat groups in the analysis consist of three forest types

(conifer, hardwood, and mixed) and three seral groups (presapling, sapling, and mature)

yielding an accuracy of 91%. Appendix A, Table 1 illustrates how FRI-based standard

forest units were combined into the three forest types.

Seral stage refers to periods of forest succession and is defined according to age

and FRI-based forest unit. Because some types of forest mature faster than others, the age

at which a forest advances to the next stage depends on the specific forest type. Appendix

A, Table 2 illustrates how seral stages were calculated for the habitat groups in relation to

the FRI forest units that make up each study group. I designed the seral stage age cut-

offs to reflect those of the majority of FRI forest units within each study group. However,


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I strayed from the FRI in the way in which I calculated age. The FRI, and consequently

OMNRs moose model, defines age based on the oldest trees but I defined age according

to the most recent harvest. Although clear-cutting was not used in this study area, 30-35%

of the total volume is typically removed in each cut (AFA 2005). Thus, factors important

to moose such as browse availability and canopy closure more closely reflect younger

seral stages than older seral stages. Field observations during our habitat accuracy

exercise supported this belief. Consequently, I have slightly adapted the seral stage

definitions found in Holloway et al. (2004).

I defined the presapling seral stage as the youngest categor y of forest where

vegetation includes herbaceous plants, shrubs, and tree seedlings


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mixed (11%), and mature conifer (5%). Non-forested areas comprised 22% consisting

almos t entirely of water and wetland, and were excluded from analysis.

Though some recent moose studies measured a single level of cover (Dussault et

al. 2006, Lowe 2009), I considered a gradient of cover in my analyses. I will refer to

mature conifer as cover because it represents the highest quality cover of my habitat

groups in both seasons. I additionally considered two lesser cover categories in the

dormant season, mature forest, and sapling plus, with the latter term referring to any

forest at sapling stage or older. These lesser cover categories are explained more

thoroughly in the model validation section of analysis methods.

2.4 Analyses

2.4.1 Selection of Cover

Population Level Selection

Selection of proximity to cover, which I will refer to as selection of cover, was

assessed using a type III study design in which use and availability are measured

separately for each individual instead of as a group (Thomas and Taylor 1990, Manly et

al. 2002). A Euclidean distance approach was used with distance of forested animal

locations from cover as use and distance from random forested locations within home

ranges from cover as available (Conner and Plowman 2001). Spatial analyses were

performed using ArcInfo (Environmental Systems Research Institute Inc. 1999, 2006)

and home ranges were created in Home Range Tools for ArcGIS (Rodgers et al. 2007).

Seasonal home ranges were created using 99% fixed kernels and a referenced

bandwidth. A fixed kernel density estimator was used instead of an adaptive kernel model


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because as Kenward and Hodder (1996) suggested, the adaptive model widened the

kernels in the outlying regions of the home range, effectively over-expanding the

utilization distribution and leading to an overly liberal area of availability for my

analysis. The referenced bandwidth smoothing parameter (Worton 1995) was chosen

because it smoothed all home ranges in a consistent way so that they were comparable. In

contrast, the least squares cross-validation method (Bowman 1984, Rudemo 1982) failed

to calculate a smoothing parameter for some home ranges and defaulted to a smaller

smoothing parameter that resulted in highly under-smoothed utilization distributions

compared to the rest. Biased cross-validation (Scott and Terrell 1987), the plug-in

approach (Wand and Jones 1995) and Brownian bridge method (Horne et al. 2007) each

produced overly conservative (under-smoothed) home ranges for my purpose, effectively

excluding areas that were likely available to non-territorial and highly mobile species

such as moose.

Separate home ranges were estimated for each individual during each season for

each year (Rettie and Messier 2000). Seasons were based on browse availability and

defined according to the OMNRs moose habitat suitability model (HSM) with a growing

season from May 16th to September 30th and a dormant season from October 1st to May

15th (Naylor et al. 1999). Selection was determined for cover (mature conifer) in both

seasons and for the lesser cover categories (mature forest and sapling plus) in the dormant

season.

To represent availability, random locations were generated within each home

range using Hawths Analysis Tools (Beyer 2004). Locations were stratified in a 1:1 ratio

by habitat group according to the animals proportional use in each group. For example,


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if in a given home range, an animal had 400 locations in mature hardwood, then 400

random locations were created in mature hardwood within that home range. The distance

from each random location to the nearest element of each of three levels of cover was

determined by creating Euclidean distance rasters (10-meter cell size) in ArcGIS Spatial

Analyst Tools and extracting distance values at the random points. These va lues were

averaged for each seasonal home range and for each habitat group within each home

range, producing the expected values under the null hypothesis of no selection. The same

procedure was applied using the moose locations to calculate the average observed

distances to cover. Selection ratios of observed mean distances to expected mean

distances were calculated for each home range and for each habitat group within each

home range. These ratios were used to determine selection of cover, with ratios

significantly less than one indicating selection and significantly greater than one

indicating avoidance (Conner and Plowman 2001, Conner et al. 2003).

Selection of cover was determined for each season and for each habitat group

within season using selection ratios as the dependent variable in ANOVAs (Statistica 7.0,

StatsSoft Inc 2001). The expected value of the null hypothesis was converted from one to

zero by subtracting a constant of one from the ratio data and consequently an ANOVA

intercept significantly different than zero would lead to a rejection of the null hypothesis

(Conner and Plowman 2001). Individual animal was used as a blocking variable to assess

variation in individuals and account for multiple measures of the same animal because

seasonal selection ratios were calculated separately for 2-3 consecutive years

(Chamberlain and Leopold 2000). Individual seasonal home ranges were removed from

habitat-level analysis if they had fewer than 20 locations in that habitat. This helped


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avoid spurious selection ratios resulting from insufficient sample sizes and ensure that

adequate numbers of locations existed for bootstrapping at the home range level

(described below) (Stine 1985, Boos and Brownie 1989, Zhang et al. 1991).

Home Range Level Selection

Because I was interested not only in population level behaviour but also in

variation within the population, I tested selection of the three types of cover on a home

range basis, both for the entire home range and for each habitat group within the home

range. To determine selection for each home range , I bootstrapped the means of both the

observed and expected distances (Gillingham and Parker 2008) 1000 times to create 95%

confidence intervals using the statistical package R (R Development Core Team 2006).

Each of the 1000 replications used 95% of the sample size and was sampled with

replacement. Bootstrapping was chosen over the parametric standard error of the mean

because the distance data were not normally distributed and some home ranges had small

sample sizes (approaching as low as n = 20). Selection of cover was inferred to have

occurred in a home range if the confidence intervals around the means of observed and

expected distances did not overlap and the observed mean was lower than the expected

mean. Because approximately 450 comparisons were made, experiment-wise error likely

resulted in approximately 5% or 23 false positives, underestimating the outcome of no

selection by 5% and over-estimating the outcomes of selection and avoidance by

2.5% each. I considered this when interpreting results.


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Year Effect

Environmental conditions affecting habitat use such as temperature and snow

depth can vary annually so I tested for a year effect for both dormant and growing

seasons using ANOVA. Because radio-collars were deployed in the middle of the first

dormant season and removed in the middle of the third dormant season, the three years

represented different periods and werent comparable. Accordingly, to test for a year

effect in the dormant season I created separate home ranges and random points to

calculate selection ratios based only on the overlapping mid-winter period, February 2nd -

March 6th. The dependent variable in the ANOVAs was selection ratio and individual

animal was again used as the blocking factor.

2.4.2 Distance Parameters

After selection was assessed, three distance parameters were calculated on a home

range basis and summarized at the population level: median distance, 95-percentile

distance, and for home ranges where selection occurred, the preference zone was

determined. Distance parameters were calculated with all habitats combined as well as

with each habitat separate. Ninety-five percentile distances were calculated to indicate

how far animals strayed from cover barring temporary excursions and anomalous

movements. Preference zone is described below.

The preference zone was defined as the area within which moose preferred to be

from cover. Visually the edge of this zone is the point in the distance distribution where

the number of expected locations exceeds the number observed locations (See Figure


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2.2a for example). To calculate the preference zone edge, observed and expected

distance-to-cover values for each home range were binned into 100-meter sections. In

each distance bin, selection ratios were formed using the count of observed locations over

expected locations with ratios above one indicating selection for the given distance

category and ratios below one indicating avoidance. These selection ratios and the

midpoint of their associated distance bins were used in a linear regression as the

dependent and independent variables, respectively. However, because the relationship

between selection ratio and distance followed a negative exponential distribution, I log-

transformed the selection ratios after adding a constant of one. Because selection ratios

created from a very small number of locations may be overly influential outliers, I

weighted the regression by the total number of observed and expected locations in each

distance category. I then solved all the linear regression equations for a selection ratio of

one (y value of 0.301) to find the edge of the preference zone for each home range where

selection for cover occurred (See Figure 2.2b for example).

Determining the preference zone is a new approach and may be more useful than

traditional measures (such as means) for management and habitat modeling because it

describes actual animal behaviour. Unlike a simple mean distance, the preference zone

incorporates both use and expected distributions and is a more valid measure of resource

selection.


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Observed Mean = 357Expected Mean = 435

ObservedExpected

1 0 0 2 00 300 400 5 0 0 6 00 7 00 8 00 9 00 1 00 0 1 10 0 12 00 1 30 0 1 40 0

Distance to cover (meters)

0

20

40

60

80

100

120

140

160

180

200

220

240

Numberofobservations

Figure 2.2a. Observed and expected distributions of distance-to-cover from hardwood

locations for Moose7 in the dormant season of 2006-2007. Preference zone edge can be

estimated as the general area where the expected values begin to outnumber the observed

values.


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Figure 2.2b. Linear regression for Moose7 in the dormant season of 2006-2007 when in

hardwood stands. The preference zone is the area within 394 meters of cover. A Y value

of 0.301 is equal to the selection ratio of one.

2.4.3 Model Validation

Model Description

The second goal of the study was to validate the distance to cover parameters in

OMNRs habitat suitability model (HSM) (Naylor et al. 1999). In the growing season the

HSM delineates two types of forest stands: those which provide thermal cover and those

which do not, and it assumes that moose are confined to the area within 1500 meters of

thermal cover. In the dormant season, the model delineates four types of forest providing


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various degrees of cover: late winter cover, early winter cover, lateral cover, and no

cover. It assumes that moose are always constrained to areas within 1600 meters of late

winter cover. Additionally, when moose are in a stand providing only lateral cover, they

are assumed to also be constrained to areas within 400 meters of early or late winter

cover. Three assumptions exist when moose are in a stand that provides no cover. In

addition to the assumptions above, they are assumed to also be constrained to areas

within 200 meters ofany level of cover (lateral, early winter, or late winter). HSM

descriptions of these levels of cover and my corresponding habitat groups are given in

Table 2.1 and a summary of the distance assumptions is given in Table 2.2. I assigned my

habitat groups to the models cover categories in the way that most closely reflected the

majority of FRI forest units within each group (See Appendix A, Table 3). I will refer to

the 1600-meter assumption as mature conifer, the 400-meter assumption as mature

forest, and the 200-meter assumption as sapling plus. (These terms refer respectively

to distance 1, distance 2, and distance 3 in the HSM documentation.)

The HSM uses the distance assumptions to create grids indicating the range of

available forested habitat for moose in each season. The full model carrying capacity map

is a product of three sub-model carrying capacity maps: dormant season, growing season,

and aquatic feeding. The dormant and gr owing season maps are a product of their season-

specific range and forage grids. The range grids I am validating in this study directly

affect the model output because areas that fall outside of the available range result in a

carrying capacity of zero in the sub-model and subsequently in the final model. I assessed

the validity of the distance to cover parameters using three methods.


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Season Cover Category Description Habitat group

Dormant No cover Height < 3 m Presapling

Lateral cover Height between 3 and 6 m Sapling

Early winter cover Height > 6 m with some conifer cover Mature hardwood and mixed

Late winter cover Height > 10 m and conifer or conifer-dominated mixedwood

Cover (mature conifer)

Growing Thermal cover Height = 10 m and lowland conifer,lowland mixed, or lowland hardwood

Cover (mature conifer)

Table 2.1. The Ontario Ministry of Natural Resources habitat suitability models

description of cover categories (Naylor et al. 1999) and my corresponding habitat groups.

Season Habitat moose is inMature

conifer

Mature

forest

Sapling

plusDormant Presapling 1600 m 400 m 200 m

Sapling 1600 m 400 m --Mature hardwood or mixed 1600 m -- --

Growing Non-thermal cover forest 1500 m -- --

Table 2.2. Distance-to-cover assumptions in the Ontario Ministry of Natural Resources

habitat suitability model for moose and expected 95 percentile distances from moose

locations to distance categories.

Validation Method 1

The first validation method simply involved running the HSM and plotting the

moose locations over the seasonal range outputs from the model in ArcGIS. Each range

output is a binary surface of hexagonal parcels with each hectare parcel indicating that it

is either available habitat (parcel value of 1) or unavailable (parcel value of 0). I

calculated the percent of all forested moose locations that fell within the forested area


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deemed unavailable. This helped to determine if the models distance parameters were

overly conservative (i.e., if moose were willing to travel further from cover than

assumed). To understand which specific assumptions may be overly conservative, I

further calculated the percent of moose locations that fell within the assumed unavailable

area for each habitat group. Inferential statistics could not be used here to compare

habitat groups because of the lack of variance but the percentage measures were still able

to indicate effect size.

Validation Method 2

Beyond this first simple approach, I was limited in my ability to validate the HSM

directly as it is based upon the full spectrum of FRI forest units and low map accuracy

forced me to use combined habitat groups. However, I was able to evaluate how well my

habitat groups performed in the model. As a second validation method, I substituted the

HSMs levels of cover with my corresponding habitat groups (Table 2.1) to determine the

available and unavailable areas of my study area. As in the first method, I calculated

the percent of all forested moose locations that fell within the unavailable area and

further calculated the percent of locations in each habitat group that fell within that area.

I was also able to quantify which of the three distance assumptions were violated for each

location using the distance values extracted from the Euclidean distance rasters (see

Analyses: Population Level Selection). Again, inferential statistics could not be used to

compare habitat groups because of the lack of variance but the percentage measures were

still able to indicate effect size.


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Validation Method 3

The two approaches described above were only able to detect instances where the

model may be too conservative. To determine if any of the models distance parameters

are overly liberal (i.e., if moose arent willing to travel as far from cover as assumed), I

compared the distance assumptions to the 95 percentile of the distribution of distances

that moose were located from cover. Unlike the first two validation methods that used

pooled moose locations, this comparison operated at the individual seasonal home range

level. The validation for the growing season was simple since there is only one distance

assumption (1500 m from cover). Because the dormant season has multiple assumptions

depending on the habitat type the animal is in, I calculated 95 percentile distances to three

groups: my cover group (mature conifer), the three mature forest groups combined

(mature forest), and the sapling and three mature forest groups combined (sapling

plus). The 95 percentiles were compared to the HSM distance assumptions (Table 2.2).

CHAPTER 3: RESULTS

A total of 94 home ranges (21 individual animals) comprised of 58 dormant

season and 36 growing season ranges were calculated for the analyses. Growing season

analyses included 19 individuals because one moose died and one collars data were

censored from analyses for the growing seasons due to poor fix success. Seasonal home

range sizes were quite variable, averaging 50 km2 ( 44 SD) in the 7.5-month dormant

season and 38 km2 ( 23 SD) in the 4.5-month growing season. Overall GPS collar fix


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success was 91% but many of the missed fixes originated from the one collar that

malfunctioned during the summers. With this collar removed, fix success averaged 94%

12% SD.

3.1 Selection of Cover - Year Effect

Environmental conditions were similar in the growing seasons but differed in the

dormant seasons with the second dormant season having less snow depth than the first

and third (Table 3.1). Home ranges were larger during the second dormant season but this

increased use of space did not affect distance to cover. Selection of cover was not

significantly affected by year in either the growing or dormant season (F1,16 = 2.83, p =

0.11 and F2,34 = 1.484, p = 0.24, respectively). All ANOVA assumptions were met.

Normality plots are depicted in Appendix B: Figure 1.


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Season Dates

Mean DailyMax. Temp

(C)

Mean DailyMin. Temp

(C)Mean

Temp (C)Mean SnowDepth (cm)

Growing 2006 May 16 - Sept 30 22 ( 5.6) 8 ( 5.0) 15 ( 4.8) --Growing 2007 23 ( 4.8) 7 ( 4.7) 15 ( 4.2) --

Historic Normal 21 9 15 --

Dormant 2005-2006 Oct 1 - May 15 4 ( 9.3) -8 ( 10.1) -2 ( 9.2) 32 ( 27.9)

Dormant 2006-2007 4 ( 9.2) -8 ( 10.4) -2 ( 9.3) 12 (11.7)

Dormant 2007-2008 4 ( 9.6) -10 ( 10.5) -3 ( 9.6) 34 ( 12.2)

Historic Normal 4 -7 -2 28

Midwinter 2006 Feb 2 - Mar 6 -5 ( 4.7) -19 ( 9.0) -12 ( 6.2) 72 ( 7.4)

Midwinter 2007 -7 ( 5.4) -21 ( 7.2) -14 ( 5.6) 27 ( 5.1)

Midwinter 2008 -4 ( 5.1) -19 ( 9.3) -11 ( 6.8) 35 ( 4.5)

Historic Normal -4 -16 -10 65

Table 3.1. Mean temperature and snow depths ( SD) for the analysis periods taken from

Algonquin Park East Gate weather station (Environment Canada 2008). Historic normals

are averaged from 1971-2000. Year effect analyses were performed using growing season

and midwinter season. Asterisks indicate snow depths known to influence moose

movement.

3.2 Selection of Cover - Home Range Level

At the home range level, bootstrapping revealed great variability in selection of

mature conifer cover in each habitat group of each season (Figure 3.1). In the growing

season the number of home ranges where cover was selected was greater than or equal to

the number of home ranges where cover was avoided for each habitat group; in the

dormant season the number of home ranges indicating selection always exceeded the

number of home ranges indicating avoidance (Figure 3.1a). Selection of the lesser cover

categories in the dormant season (mature forest and sapling plus) revealed less

variation. The number of home ranges where cover was selected always outnumbered

those where cover was avoided but in each case, the most common outcome, no


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selection, comprised 50% of home ranges (Figure 3.1b). Because approximately 450

comparisons were made, experiment-wise error likely resulted in 5% or 23 false

positives, potentially underestimating the outcome of no selection by 5% and over-

estimating the outcomes of selection and avoidance by 2.5% each. However, this

adjustment for experiment-wise error does not mask the trends in variance of selection.

0%

10%

20%

30%

40%

50%

60%

70%

Combined

habitats

Mature

Hardwood

Mature

Mixed

Presapling

Sapling

Combined

habitats

Mature

Hardwood

Mature

Mixed

Presapling

Sapling

n = 36 n = 36 n = 31 n = 15 n = 22 n = 58 n = 54 n = 49 n = 26 n = 40

Growing Season Dormant Season

% S

% NS

% A

Figure 3.1a. Variation of selection behaviour at the home range level for mature conifer

cover. S = selection, A = avoidance, and NS = no selection. N refers to the number of

home ranges in each category.


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0%

10%

20%

30%

40%

50%

60%

Sapling plus Mature forest Mature forest

Presapling Presapling Sapling

n = 26 n = 26 n = 40

% S

% NS

% A

Figure 3.1b. Variation of selection behaviour at the home range level for the lesser cover

categories associated with presapling and sapling forest in the dormant season (mature

forest and sapling plus). S = selection, A = avoidance, and NS = no selection. N refers

to the number of home ranges in each category.

3.3 Population Level Seasonal Results

In the growing season the intercept of the ANOVA indicated no selection for

proximity of cover, meaning that at the population level, moose tended to be the same

distance from cover within their home ranges as expected by chance (F1,17 = 0.908, p =

0.354, selection ratio = 1.00 0.23 SD) (Table 3.2a). However, moose ID was a

significant source of variation (F18,17 = 7.339, p < 0.01) and bootstrapping revealed that in

only 19% of home ranges did moose show no selection with moose in 42% of home

ranges showing selection, and 39% showing avoidance. The average median distance

animals were from cover was 470 meters and 95% of the locations were within 1144


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meters of cover. Those figures for just the moose that selected cover (n = 15 of 36) were

321 and 991 meters, respectively (Table 3.3). The average preference zone edge was 621

meters.

In the dormant season the intercept of the ANOVA indicated a marginally

significant trend in selection of cover at the population level (F1,37 = 3.176, p = 0.083,

0.93 0.21 SD) (Table 3.2a), with moose ID also being marginally significant (F20,37 =

1.715, p = 0.076). The average median distance animals were from cover was 427 meters

and 95% of the locations were within 1135 meters. When just considering those moose

that selected cover (n = 36 of 58), those figures were 304 and 1014 meters, respectively

(Table 3.3). The average preference zone edge was 532 meters. All ANOVA assumptions

were met. Normality plots associated with these ANOVAs are shown in Appendix B,

Figure 2.

3.4 Population Level- Habitat Results

During the growing season, moose as a whole did not significantly select or avoid

proximity to cover when they were in any of the habitat groups, although there was a

marginally significant avoidance when moose were in saplings(F1,10 = 3.747, p = 0.082,1.407 1.04 SD) (Table 3.2a). Distance parameters for all home ranges, those selecting

cover, and those avoiding cover are presented in Table 3.3. Figures 3.2a-c illustrate

distance parameters for home ranges showing selection for cover. Depending upon the

habitat group, selection of cover occurred in 32 to 46% of growing season home ranges.

In the dormant season, moose as a whole did not significantly select or avoid

proximity to cover when they were in any of the habitat groups, although there was


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marginally significant selection in mature hardwoods (F1,34 = 3.919, p = 0.056, 0.937

0.29 SD) (Table 3.2a). The preference zone edge was significantly lower in mature

hardwood than in presapling stands (Figure 3.2c). The proportion of dormant season

home ranges in which selection of cover occurred ranged from 38 to 48% depending

upon the habitat group. For every habitat group in this season, proportionately more

individuals selected cover and distance parameters were consistently lower, indicating

that cover is more important in the dormant season than the growing season. All ANOVA

assumptions were met. Normality plots at the habitat group level for both seasons are

shown in Appendix B, Figure 3.

In the dormant season I also assessed selection and distance parameters of the two

lesser categories of cover, mature forest and sapling plus. When in young forest,

moose as a whole did not select areas closer to any lesser type of cover (Table 3.2b).

Distance parameters for all home ranges, those selecting cover, and those avoiding cover

are presented in Table 3.4a-b. Figures 3.3a-c illustrate distance parameters for home

ranges that showed selection for cover. All ANOVA assumptions were met. Normality

plots are shown in Appendix B, Figure 4.


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A.

Season Habitat Group ANOVA resultsSelection

Ratio SD

Growing Combined F1,17 = 0.908, p = 0.354 1.003 0.234

Hardwood F1,16 = 0.153, p = 0.700 0.989 0.325

Mixed F1,14 = 0.122, p = 0.732 1.010 0.321

Presapling F1,7 = 1.305, p = 0.291 0.999 0.280

Sapling* F1,10 = 3.747, p = 0.082 1.407 1.042

Dormant Combined* F1,37 = 3.176, p = 0.083 0.934 0.212

Hardwood* F1,34 = 3.919, p = 0.056 0.937 0.288

Mixed F1,30 = 2.002, p = 0.167 0.901 0.287

Presapling F1,16 = 0.219, p = 0.646 1.065 0.456

Sapling F1,23 = 1.814, p = 0.191 1.031 0.486

B.

Habitat Cover category ANOVA resultsSelection

Ratio SD

Presapling Sapling plus F1,16 = 0.007, p = 0.936 0.955 0.107Presapling Mature forest F

1,16= 0.484, p = 0.497 1.018 0.093

Sapling Mature forest F1,24 = 2.645, p = 0.117 0.935 0.105

Table 3.2. Results of habitat group level mature conifer cover selection ANOVAs (A)

and selection of the lesser cover categories associated with presapling and sapling in the

dormant season. Asterisks indicate marginal significance. Sapling plus refers to the

combination of any sapling or mature forest (B).


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Season Habitat Group n

%

S

%

NS

%

A

All home ranges Home ranges selecting cover Home ranges avoiding cover

Observed Expected Observed Expected Observed Expected

Median CI Median CI Median CI Median CI Median CI Median CI

GrowingSeason

Combined

habitats 36 42 19 39 470 82 446 63 321 43 408 56 545 94 411 68

MatureHardwood 36 46 29 26 407 66 393 4 0 246 54 362 60 596 79 380 68

Mature Mixed 31 35 39 26 448 82 395 47 277 65 395 90 638 191 354 103

Presapling 15 40 20 40 589 160 597 148 561 260 700 153 558 125 443 117

Sapling 22 32 36 32 531 168 497 165 323 95 456 96 695 217 458 239

DormantSeason

Combinedhabitats 58 62 14 24 427 81 448 72 304 50 396 54 749 235 601 248

MatureHardwood 54 48 31 20 335 56 343 29 217 32 327 41 584 164 351 40

Mature Mixed 49 39 41 20 350 56 376 29 212 40 347 44 581 150 444 76

Presapling 26 38 31 31 565 99 546 83 436 104 648 151 822 174 480 121

Sapling 40 40 30 30 503 136 461 118 242 99 374 131 832 314 523 319

Table 3.3a. Moose median observed and expected distances to cover plus 95% confidence intervals (in meters) by season and habitat

group for all home ranges, those where cover was selected, and those where cover was avoided. N refers to the number of home

ranges in each group. %S = % home ranges showing selection, %NS = % showing no selection, and %A = % showing avoidance.


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All home rangesHome ranges

selecting coverHome ranges

avoiding cover

Observed Expected Observed Expected Observed Expected

Season Habitat Group n%S

%NS

%A 95% CI 95% CI 95% CI 95% CI 95% CI 95% CI


Mature Hardwood 36 46 29 26 1000 120 1079 98 856 179 1067 151 1188 213 1060 196

Mature Mixed 31 35 39 26 932 118 1044 122 759 86 1127 143 1034 246 995 235

Presapling 15 40 20 40 1135 196 1328 298 1132 284 1535 385 970 172 926 222

GrowingSeason

Sapling 22 32 36 32 1171 291 945 269 943 379 1243 206 1364 421 1013 454


Mature Hardwood 54 48 31 20 943 81 1054 72 760 78 988 97 1223 118 1107 146

Mature Mixed 49 39 41 20 911 94 1061 88 785 152 1048 140 1197 175 1111 213

Presapling 26 38 31 31 1062 134 1223 154 1059 263 1394 264 1136 179 1050 177

DormantSeason

Sapling 40 40 30 30 1132 216 1219 224 837 314 1134 367 1383 438 1309 461

Table 3.3b. Moose observed and expected ninety-five percentile distances plus 95% confidence intervals (in meters) to cover by

season and habitat for all home ranges, those where cover was selected, and those where cover was avoided. N refers to the number of

home ranges in each group. %S = % home ranges showing selection, %NS = % showing no selection, and %A = % showing

avoidance.


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Observed Expected Observed Expected Observed ExpectedHabitatGroup

CoverCategory n

%S

%NS

%A Median CI Median CI Median CI Median CI Median CI Median CI

Presapling Sapling plus 26 35 50 15 115 16 123 16 119 29 157 20 131 63 93 49

Presapling Mature forest 26 35 50 15 189 37 183 32 194 54 224 48 274 148 178 118

Sapling Mature forest 40 30 50 20 147 38 149 37 131 60 194 83 262 73 173 64

Table 3.4a. Moose observed and expected median distances plus 95% confidence intervals (in meters) to lesser cover categories

associated with presapling and sapling in the dormant season for all home ranges, those where cover was selected, and those where

cover was avoided. N refers to the number of home ranges in each group. Sapling plus refers to the combination of any sapling or

mature forest.


Observed Expected Observed Expected Obser ved ExpectedHabitat

Group

Cover

Category n%

S

%

NS

%

A 95% CI 95% CI 95% CI 95% CI 95% CI 95% CI

Presapling Sapling plus 26 35 50 15 310 33 361 45 312 42 456 62 341 161 293 163

Presapling Mature forest 26 35 50 15 432 56 461 61 435 87 521 87 495 212 513 254

Sapling Matu re forest 40 30 50 20 396 88 412 85 409 158 541 179 654 189 541 136

Table 3.4b. Moose observed and expected ninety-five percentile distances plus 95% confidence intervals (in meters) to lesser cover

categories associated with presapling and sapling in the dormant season for all home ranges, those where cover was selected, and

those where cover was avoided. N refers to the number of home ranges in each group. Sapling plus refers to the combination of any

sapling or mature forest.


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Figure 3.2a. Moose observed (o) and expected (e) average median distances to cover (

95% CI) of home ranges where selection of cover occurred for each season and habitat

group. %S is the percent of all home ranges where cover was selected and n is the

number of home ranges where cover was selected.


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Figure 3.2b. Moose observed (o) and expected (e) average 95 percentile distances to

cover ( 95% CI) of home ranges where selection of cover occurred for each season and

habitat group. %S is the percent of all home ranges where cover was selected and n is the



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0

200

400

600

800

1000

1200

1400

1600

1800

Combinedhabitats

Hardwood Mixed Presapling Sapling Combinedhabitats

Hardwood Mixed Presapling Sapling

% S = 42 % S = 46 % S = 35 % S = 40 % S = 32 % S = 62 % S = 48 % S = 39 % S = 38 % S = 40

n = 15 n = 15 n = 11 n = 6 n = 7 n =36 n = 26 n = 19 n = 10 n = 16

Growing Season Dormant Season

Meters

Figure 3.2c. Moose average preference zones for cover ( 95% CI) for each season and

habitat group. %S is the percent of all home ranges where cover was selected and n is the



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Figure 3.3a. Moose observed (o) and expected (e) average median distances ( 95% CI)

to lesser cover categories associated with presapling and sapling forest in the dormant

season for home ranges where selection of cover occurred. Sapling plus refers to the

combination of any sapling or mature forest. %S is the percent of all home ranges where

cover was selected and n is the number of home ranges where cover was selected.


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Figure 3.3b. Moose observed (o) and expected (e) average 95 percentile distances ( 95%

CI) to lesser cover categories associated with presapling and sapling forest in the dormant

season for home ranges where selection of cover occurred. Sapling plus refers to the

combination of any sapling or mature forest. %S is the percent of all home ranges where

cover was selected and n is the number of home ranges where cover was selected.


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0

50

100

150

200

250

300

350

Sapling plus Mature forest Mature forest

Presapling Presapling Sapling

% S = 35 % S = 35 % S = 30

n = 9 n = 9 n = 12

Meters

Figure 3.3c. Moose preference zone edge ( 95% CI) in relation to lesser cover

categories associa ted with presapling and sapling in the dormant season. Sapling plus

refers to the combination of any sapling or mature forest. %S is the percent of all home

ranges where cover was selected and n is the number of home ranges where cover was

selected.

3.5 Model Validation

3.5.1 Validation Method 1

For the first validation approach I simply plotted the moose locations on top of the

habitat availability range grids from the HSM. The growing season range grid designated

40% of the study area, mainly in the greater Highway 60 corridor, as unavailable habitat.

The large unavailable area is a result of the limited forest types that the model assumes

to provide thermal cover. Specifically, thermal cover as defined by the HSM model


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composed less than 2% of the study area and 49% of all forested moose locations fell in

the unavailable area, including one animals entire home range (Figure 3.4a).

To find out if violations occurred more often in any particular habitat group, I

compared the proportion of points that violated the assumptions to points that did not for

each habitat group. If moose were equally likely to violate the HSM assumptions in any

given habitat, then I would expect approximately the same proportion of locations (49%

5%) within each habitat group to fall within area deemed unavailable. This was the case

for all habitat groups except for the sapling group where 63% of the locations were in

violation of the distance assumptions , a magnitude of 14 percentage points more than the

expected 49%. These violations occurred in locations of all 12 of the moose that used the

habitat group. In all other habitat groups, violation proportions were within 5 percentage

points of the expected 49% and therefore less likely to be of particular interest.

The dormant season range grid designated only 1% of the study area as

unavailable and virtually no forested moose locations (0.1%) occurred within this limited

area (Figure 3.4b).


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Figure 3.4a. Map of the growing season range calculated in OMNRs moose habitat

suitability model and the seasonal moose locations falling in and outside of the

available habitat in the southwestern portion of Algonquin Provincial Park, Ontario,

Canada. Distance assumption violations occurred in 49% of moose locations.


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Figure 3.4b. Map of the dormant season range calculated in OMNRs moose habitat

suitability model and the seasonal moose locations falling in and outside of the

available habitat in the southwestern portion of Algonquin Provincial Park, Ontario,

Canada. Distance assumption violations occurred in 0.1% of moose locations.


The second method of validation was independent of the HSM; I substituted my

habitat groups into the models groups that represent various degrees of cover: no cover,

lateral cover, early winter cover, and late winter cover (Table 2.1). My adapted grids

designated 8% of the study area as unavailable in the growing season and 10% as

unavailable in the dormant season (Figures 3.5a -b).


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Figure 3.5a. Map of the growing season range created from my adapted model and the

seasonal moose locations falling in and outside of the available habitat in the

southwestern portion of Algonquin Provincial Park, Ontario, Canada. Distance

assumption violations occurred in 3% of moose locations.


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Figure 3.5b. Map of the dormant season range created from my adapted model and the

seasonal moose locations falling in and outside of the available habitat in the

southwestern portion of Algonquin Provincial Park, Ontario, Canada. Distance

assumption violations occurred in 12% of moose locations.

Distance assumption violations in the growing season were minimal with only 3%

of forested moose locations falling within the area assumed to be unavailable. To find out

if these violations occurred more often in any particular habitat group, I compared the

proportion of points that violated the assumptions to points that did not for each habitat

group and expected the same proportion of locations (3%) within each habitat group to

fall within the area deemed unavailable. Instead, 19% of sapling locations were in


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violation of the distance assumption, though these violations only occurred among three

of 12 animals. Violations occurring in the other habitat groups were all minor at 1%, 1%,

and 3% for presapling, mature hardwood, and mature mixed, respectively and therefore

not of particular interest.

In the dormant season distance assumption violations occurred in 12% of forested

moose locations. Again, if violations occurred equally in the habitat groups, I would

expect the same proportion of locations (12%) within each habitat group to fall within the

area deemed unavailable. In contrast 41 and 31% of the locations in presapling and

sapling, respectively, were in violation and locations in mature hardwood and mature

mixed forest were only in violation 0.3 and 0.5%, respectively. The vast majority of

violations that occurred in young forest were violations of the distance assumptions of

lesser cover: i.e., the animals were further than 200 meters from all types of forest

providing cover (sapling plus) or further than 400 meters from all types of mature

forest (Table 3.5).


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Assumption Violations*

Habitat group n% of pointsin violation n

Matureconifer n

Matureforest n

Saplingplus n

Presapling 10 41% 10 0.03% 2 47% 10 77% 10Sapling 16 31% 9 48% 4 74% 8 -- --Mature hardwood 20 0% 0 -- - -- -- -- --

Mature mixed 19 1% 5 100% 5 -- -- -- --

Table 3.5. Breakdown of distance assumption violations for the dormant season. The

mature conifer assumption refers to points greater than 1600 meters from cover.

Mature forest assumption refers to points greater than 400 meters from all mature

habitat groups (hardwood, mixed, and cover). Sapling plus assumption refers to points

greater than 200 meters from saplings and all mature habitat groups combined. N refers

to the number of animals associated with each result. * The sum of distance assumption

violations 1 - 3 may exceed 100% in a given habitat group because some points violated

more than one assumption.


The third validation method compared the distance assumptions to the 95

percentile distances of moose locations from cover (see Tables 3.3b and 3.4b). In all

instances, moose at the population level did not significantly select areas closer to cover,

meaning the 95 percentile distances merely reflect what was available within the home

ranges. Therefore, these distances dont necessarily reflect the true limits for the moose

population as a whole, but they at least represent a minimum threshold of the distance

moose are willing to travel from cover. All of the 95 percentile distances were below the

model assumptions except for presapling stands in the dormant season, where 95% of the


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moose locations were within 310 (33) meters from any type of cover (sapling plus)

instead of the HSM-assumed 200 meters.

CHAPTER 4: DISCUSSION

4.1 Selection of Cover

4.1.1 Prediction 1: Overall, moose will select areas close to cover

Overall, I found that cover selection by moose in Algonquin was not as strong as I

had hypothesized but I did note a high degree of variation in selection ratios among home

ranges. If distance to cover is not an important factor for moose, then I would expect

individuals in the majority of home ranges to show no selection. Instead the population-

level result of no selection was caused by more of a bimodal distribution of selection

behaviour, with the number of home ranges where cover was selected cancelled out by

home ranges where cover was avoided. This was most evident in the selection of mature

conifer cover (Figure 3.1a). The variance in selection behaviour was not due to a year

effect nor was it fully explained by an animal effect, as several animals switched their

selection behaviour during the study. In the dormant season, 65% of individuals switched

selection behaviour during the three years and 24% switched behaviour during the two

growing season years. I suspect that some of this variation could be explained by calf

presence, with encumbered females staying closer to cover while unencumbered moose

are able to forage more freely away from cover. The belief that calf presence influences

cow movements with respect to cover is fairly well documented in the literature. In

southwestern Montana, bulls and cows without calves made greater use of open areas

than cows with calves (Peek 1962). In northeastern Ontario, cows with calves made


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much more use of areas abundant with cover and forage than open areas (Thompson and

Vukelich 1981). In Quebec, Dussault et al. (2005) found cows with calves to have a

higher preference for 10-year old mixed and deciduous stands and mature conifer stands,

habitats that provide the most concealment cover, indicating a focus on protection from

predators. However, to my knowledge, no study has fully examined habitat selection in

relation to specific distance to cover and the needs of calves. These distance parameters

would be valuable for modeling and management purposes.

Factors causing moose to choose areas far from cover are less apparent but it may

be a behavioural response to those selecting cover. Moose, like most ungulates are

polygynous and tend to sexually segregate during many parts of the year (Bleich et al.

1997, Bowyer 1984, Bowyer et al. 1996, 2001, Kie and Bowyer 1999, Miller and

Litvaitis 1992, Miquelle et al. 1992). In a winter habitat management study, Bowyer et al.

(2001) crushed areas of old-growth willow to provide more accessible forage and found

the crushed areas to be beneficial only to males, as females with calves were deterred by

the lack of cover. The authors consequently recommended that the sexes be treated as

separate species in terms of habitat management and emphasized the importance of

predation risk in management intended to aid females. Kie and Bowyer (1999) had the

same recommendation for white-tailed deer. My study revealed variation in selection of

cover even within females. However, this same mechanism may have been at work as

Dussault et al. (2005) found habitat use of unencumbered females to more closely

resemble that of males than that of females with calves.

This spatial segregation may be a behavioural response of two potential processes.

Moose may be employing a density-dependent foraging strategy, in which unencumbered


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individuals choose to forage in lower quality feeding areas (areas far from cover) to avoid

intraspecific competition with the encumbered cows. A second factor affecting this

spatial segregation could be a predator avoidance strategy, in which individuals avoid

aggregating to reduce chances of detection by predators, a common strategy among

ungulates of forested habitats where concealment options exist (Kie 1999).

4.1.2 Prediction 2: Cover will be more important in the dormant season than in the

growing season

I hypothesized that moose would show greater select ion for cover in the dormant

season than the growing season and found evidence supporting this. In the dormant

season the percent of home ranges where cover was selected was higher than in the

growing season. In addition all median and 95-percentile distances were lower in the

dormant season than in the growing season. These results are not surprising because the

difference between cover and non-cover is more distinct when dec iduous foliage is

absent. In the dormant season, deciduous trees provide considerably less concealment

cover and thermal protection than they do in the growing season. Additionally, movement

in the dormant season through stands dominated by deciduous species becomes more

energetically costly as snow accumulates. These phenomena are compounded as winter

progresses and ungulates are weakened by a less nutritious diet (Schwartz et al. 1987,

1988, Renecker and Hudson 1989, Schwartz 1992), making proximity to cover in the

dormant season even more important relative to the growing season.


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4.1.3 Prediction 3: Moose will be closer to cover when in early successional stands than

when in mature stands, especially during the dormant season.

At the habitat level in the dormant season, I expected that distance to cover would

be least important to moose when they were in mature stands , especially mature mixed

stands, since mature forest most closely resembles cover. Also a large body of literature

supports the belief that browsing in open areas is focused at edges with stands providing

cover (Neu et al. 1974, Bangs et al. 1985, Allen et al. 1991, Dussault et al. 2006).

However, my data showed more support for the opposite effect, with selection ratios and

distance parameters tending to be lower in mature forest than in young forest. Perhaps

young forest provided such a high quality feeding area in my study that they were

important to moose regardless of distance to protective cover. This apparent non-

selection for proximity to cover in young forest could also be an artifact of the spatial

patterns in my study area. Young stands (i.e. timber harvests) were typically greater than

500 m from cover, possibly too far to influence space use within those habitats.

Repeatable studies in other locations would help clarify these patterns and mechanisms.

4.2 Model Validation

All three of my validation approaches indicated that the model could be

improved, with the first approach illustrating this most clearly. In Algonquin the biggest

apparent weakness in the full HSM is the growing season thermal cover assumptions that

designated a large area, mostly in the greater Highway 60 corridor, as unavailable to

moose. This unavailable designation gets carried through the growing season sub-model

and further to the full model, resulting in a predicted carrying capacity in that area of zero


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moose/km2. The nearly 50% of moose locations, including all locations of an individual,

that fell in this unavailable area provides strong evidence that the model is flawed.

Indeed, a kriged map based on OMNRs winter aerial surveys predicted the area to have a

density between 0.4 and 0.5 moose/km2 (Loveless 2009) and the two other sub-models,

dormant season carrying capacity and aquatic feeding carrying capacity, predicted this

area to be capable of supporting between 0.6 and 0.8 moose/km2. The estimates of these

two sub-models better agree with aerial survey data than the growing season sub-model,

especially considering that estimations of carrying capacity (in the HSM) will be

somewhat higher than estimates of density (from aerial surveys). It is apparent in our

study area that the parameters related to thermal cover are too conservative, biasing low

the estimate of available growing season area, growing season carrying capacity, and

finally total carrying capacity.

There are a few possible reasons why the thermal cover assumptions in the

growing season are flawed. One possibility is that moose are willing to feed much further

from thermal cover than the 1500 m presently assumed. Although this could be true, the

HSMs unavailable area is occupied by entire home ranges of a relatively high density of

moose that have no access to thermal cover as defined by the model. Hence it is more

likely that either 1) moose dont need thermal cover at all (Lowe 2009) or 2) that they are

using other habitats as thermal cover. It is unlikely that moose dont need thermal cover

because during the study period, ambient summer temperature exceeded the presumed

critical temperature for moose 65% of the time and the panting threshold 31% of the time

(Lowe 2009). The most plausible answer is that the forest types that comprise thermal

cover in the model are too restrictive. The model assumes that thermal cover is provided


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only by four types of forest: cedar, lowland conifer, lowland

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