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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
Combinedhabitats 36 42 19 39 1144 154 1225 149 991 143 1152 113 1149 190 1106 136
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
Combinedhabitats 58 62 14 24 1135 117 1247 116 1014 130 1203 141 1496 272 1451 276
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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All home ranges Home ranges selecting cover Home ranges avoiding cover
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.
All home ranges Home ranges selecting cover Home ranges avoiding cover
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
number of home ranges where cover was selected.
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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
number of home ranges where cover was selected.
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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.
3.5.2 Validation Method 2
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.
3.5.3 Validation Method 3
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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