University of Alberta

The Influence of Landscape Structure on the Distribution of the Nonh American Red Squirrel. Tnmicrscir~nrs hrrdsoriic~ts. in a Heterogeneous Boreal Mosaic

Jason Thomas Fisher 0

A thesis submitted to the Friculty of Graduate Studies and Reseiirch in partiai fulfilment

of the requirements for the degree of Master of Science

in Environmental Biology and Ecology

Department of Biological Sciences

Edmonton, Alberta

Fall, 1999

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ABSTRACT

This study was conducted to determine whether naturally derived landscape

structure could predict animal distribution. The presence / absence of the red squirrel, a

conifer speciÿlist. was censused in three areas in the mixed-wood boreal forest of

northem Alberta: a Reference area, a Bumed area, and a Managed Area. Landscape

structure assessed at multiple spatial scales was a significant prediçtor of squirrel

presence. The significant variables changed with spatial scale and betwren the Bumed.

Reference. and ~Managed areas. Landscape structure was still significant when local

vegetation was accounted for: it significantly predicted squirrel presence at sites where

conifer trees were abundant but did not where conifer was scarce. The results show that

relationships between landscape structure and animai abundance can be dependent on

scale of quantification. yeru. local vegetation, and area. Extrapolation of landscape effects

from one area to another may not br possible without an understanding of the underlying

mechanisrns.

ACKNO WLEDGEMENTS

My supervisor, Stan Boutin. was the catalyst that pemitted this reaction to occur.

1 thank him for teaching me that not everything is as it seems. and that interpretation is a

pemanently open door. Susan Hannon provided the squirrel data that saved me from a

calamitous field season in 1997, and her expertise in conducting landscape-level research

in the tangled domain of the boreal forest. John Addicott and Fiona Schmieeelow

provided üdvice and reviews. Cindy McCallum and Tim Martin were instrumental in

completing the GIS work. Stan Boutin. Susan Hannon, Richard Moses. Jens Roland, Phi1

Taylor, and Marc-André Villard were responsible for sampling design and project

construction. The Department of Biological Sciences at the U of A provided financial and

logistical support. and more irnportantly, a fertile resrtirch environment. KD. hot dogs.

and beer were provided by a Natural Sciences and Engineering Research Council PGSA.

a Province of Alberta Graduate Scholarship. and a Biological Sciences Teaching

Assistantship. The research was funded by the Sustainable Forest Management Network

under the Landscape Structure and Biodiversity Project, and by the Canadian

Circurnpolar Institutr. Crissy Corkum, Jrff Hoyt. the 1 1 th tloor crrw. and the Stün Clan

generously contnbuted ideas. Ainsley Sykes helped in more ways than 1 can count.

Juanita Constible, my "girl in the field, made Owl River n sweet place to be. Stephen

Peterson and Mandrew Hamilton were great help there too. Mike Kroetsch was my left

arm and sometimes my right, and 1 thank him for being the best research piurner a guy

could hope for. Special thanks go to the 'Sanity 5-oh': Matt Wheat!ey. Carrie Mrlanson.

Shem Fownes, and Chris Schmidt provided sage wisdom. tnie friendship. and crazy good

times. Most of al1 I thank Tom, Jean, and Tobi JO Fisher for steering me through the

labyrinth, and Vashti Thornpson for ail her love. This one's for you!

TABLE OF CONTENTS Page

Chapter One . The Rationale for Natural Landscape Research .............................................................................................. 1 . 0 Introduction 1

1.1 Methods 1 . 1 . 1 S tudy area ................................................................................. 8 1.1.2 Sampiing design ........................................................................ 8

........................................................................................ 1.2 Literature Cited 1 1

Chapter Two . Using Foraging Range to Demarcate a Functionai Landscape 2.0 htroduction .............................................................................................. 16 2.1 Methods

2.1.1 Squirrel locations ................................................................... 19 2.1.2 Home range analysis ................................................................. 20

2.2 Results ..................................................... 2.2.1 Squirrel foraging range sizes 21

73 2.2.2 Squirrel use of stand types ..................................................... -- 2.3 Discussion

2.3.1 Assessment of accuracy and the 100% MCP ............................ 24 2.3.2 Spatial scale of landscape anaiysis ............................................ 25

.................................................... 2.3.3 Cornparison to other systerns 26 2.3.4 Territories and time frame ....................................................... 27 2.3.5 Matrix use by squirrels .............................................................. 28

2.4 Conclusions ............... ... .... .., ..................................................................... 29 2.5 Literature Cited ....................................................................................... 30

Chapter Three . Squirrel Responsr to Landscape Srructure at Mut t i plct Spatial Sctiles . .............................................................................................. 3.0 introduction 34

3.1 Methods 3.1 . 1 Squirrel presence ....................................................................... 38 3.1.2 Landscape classification ........................................................... 38 3.1.3 Areal composition ..................................................................... 42 . . ............................................................. 3.1.4 Landscape composition 42 3.1.5 Landscape configuration: Heterogeneity .................................. 43 3.1.6 Statistical analysis ..................................................................... 43

3.2 Results ......................................................... 3.2.1 The upland/Iowland grain 46

3.2.2 Forest cover: Composition of the study areas ........................... 46 3.2.3 Goodness of fit of the logistic mode1 ........................................ 50 3.2.4 Landscape structure and squirrel presence ............................ -50

3.3 Discussion .................................................... 3.3.1 The explanatory spatial scale 56

3.3.2 Explanatory variables differ between areas and scales ............. 58 ........................................................ 3.3.3 Differences between yeas 61 . . ........................................................ 3 .3.4 Management implications 6 2

............................................................................................. 3.4 Conclusions 63 3.5 Literature Cited ................................................................................. 64

Chapter Four . The Influence of Local Vegetation on Squirrel Response to Landscape Structure .

4.0 Introduction ............................................................................................ 69 4.1 Methods

4.1.1 Vegetation sampling ............................................................. 72 4.1.2 Building a forced logistic model ........................................... 73 4.1.3 Splitting the squirrel dataset ................................................. 73

4.2 Results 4.2.1 The forced mode1 .................................................................... 74 4.2.2 The split squirrel dataset ......................................................... 76

4.3 Discussion 43.1 The relative importance of habitat beyond the focal patch ...... 79 4.3.2 Two different responses to landscapr stnicture ....................... 82

............................................................................................. 4.4 Cor.clusions 84 ....................................................................................... 4.5 Literature Cited 85

Chapter Five . The Protean Nature of Landscape Effects . ............................................................................................ 5.0 Conclusions 58

............................................................. ................... 5.1 Literature Cited .. 93

............................................... Appendix 1 : The fit of the logistic regression modrl 95

Appendix 2: Output of logistic regression models (landscape only) ....................... 96

Appendix 3: Output of logistic regression models that include local vegetation ..... 97

Appendix 4: Output of logistic regression models using conifer-abundant si tes ...... 100

LIST OF TABLES Page

. . Table 2.1: Red squirrel foraging range statistics ...................................................... 73

Table 2.2: Relative sizes of squirrel foraging ranges (ha) with differing ................................................................................... conifer content 23

Table 3.1 : Reclassification protocol applied to Alberta Vegetation Index forest inventory maps to produce the landscape composition variables used in analysis ................................................................... 40

Table 3.2: FRAGSTATS landscape configuration variables chosen for use in analysis ................................................................................... 45

UST OF FIGURES Page

............................... Figure 1.1 : Location and design of the sampling system 10

............................. Figure 3.1. Landscape composition of the Managed Grid 47

................................ Figure 3.2. Landscape composition of the Burned Grid 48

Figure 3.3 : Landscape composition of the Reference Grid ............................ 49

Figure 3.4. 3.5. 3.6 : The deviance explained by multiple logistic regrrssion models in which squirrel presence was regressed against landscape structural variables .

Figure 3 . 4 ~ Managed Mega Grid 1996 .......................................... 52 Figure 3.4b. Managed Mega Grid 1997 ............................................. 52 Figure 3 . 5 ~ Managed Meso Gnd 1996 ............................................. 53

....................................... Figure 3.5b. Managed Meso Grid 1997 5 3 ..................................................... Figure 3 . 6 ~ Reference Mega Grid 54 ..................................................... Figure 3.6b. Reference Meso Grid 54

......................................................... Figure 3 . 7 ~ Bumed Mega Grid 55

......................................................... Figure 3.7b. Burned Meso Grid 55

Figure 4.1 : The relative effects of local vegetation and landscape structure .

............................................. Figure 4.1 a: Managed Mega Grid 1997 75 ........................................... . Figure 4 I b: Reference Mega Grid 1997 75

Figure 4.2: The influence of landscape structure at conifer-abundant sites .

............................................. Figure 1 . 2 ~ Managed Mega Grid 1997 78 ........................................... Figure 4.2b. Reference Mega Grid 1997 78

Chapter One --

The Rationale for Natural Landscape Research

1.0 INTRODUCTION

The patch has been the nucleus of ecological study. A patch - a relatively discrete.

homogeneous unit of simiiar intemal habitat and microclimatic attributes (Kotliar and

Wiens, 1990) - h a characteristics that influence population dynamics or individual

behaviour, the net result of which are to impede or facilitate its occupation by an animal

species. Typically patch characteristics are referred to by the nebulous epithet "habitat"

(Southwood. 1977). As a dictate of rigorous scientific design. habitat is controlled for and

then ignored in most ecological experiments. Those that deal with the explicit influence

of habitat in rnaintaining animal populations have focused on patches with one or two

disparate intrinsic characteristics and related these to organisms' survival, fecundity, or

behaviour (see Wiens. 1976 for review). However. it is intuitively obvious that a patch

must be influenced by its surroundings: i t follows that population processes w i thin that

patch must also be irnpacted by these surroundings. The milieu in which a patch exists

has been referred to a s a landscape.

If the definition of habitat is vague then that of the landscape can only be

described as amorphous. Urban et al. (1987) define a landscape as a "mosaic of

heterogeneous land f'onns. vegetation types. and land uses". Turner ( 1989) designates a

landscape as any spatially heterogeneous area typicaily several hectares to several square

kilometres in size. Dunning et al. ( 1992) describe i t as the rnosaic of patches in which the

focal patch of intrrest is ensconced. They acknowledge that the Iandscape is species-

speci fic; that is. responses to landscapr rlemrnts occur somew hcre between an

organisms' home range and its regional distribution. This organism-centric landscüpe is

sirnilar to the "ecoiogical neighbourhood" of Addicott et al. (1987). the size of which is

bounded by the spatial and temporal extent of a particular process. With the intent of

simplicity. I will define a landscape as rhr üren siirroiinding a focal piitçh. within 11

circuliir boundary of a givsn radius. The appropriüts niagnitudr u f thut radius is a marrer

for debate. and will be discussed in Chapter Two.

A Iandscape can be quantified in terms of composition - the relative amounts of

different patch types within its boundaries - and configuration or physiognomy - the

physical placement of those patch types in spx r . relative to one ünother (Dunning et al..

IW?). Toy lor et ;il. t 1993) hiive proposrd ;i third mriisiirr. connsctivity. ro descri be

functionül linkagrs within a Iandscüpe ( . s e ülso Baudry and klcrriam. 1988: Mrrriam.

1988. 199 1). Connectivity is species-sprcific. and often process-specific. and is difiicult

to detïnr without extensive empirical testing (cg . . Pither and Taylor. 1998: Tirbout and

Anderson. 1997) or using models of movement probabilities (Henein and Merriam. 1990:

Henein et al.. 1 %W. Composition und configuration remain the two most objective

rneasures of landscape heterogeneity: togrther the two drscribe what is rrferred to herein

as landscape structure.

Landscape structure has been shown to be a signitïcant predictor of organisms'

abundance and distribution. Most studies have bern conducted in agricultural or other

anthropogenically fragmented Iandscapes. The distance to nrürest habitat patch. the

presence of a suitüble linear comdor (Merriam and Lanoue, 1990: Bennett et al., 1994;

Andreassen et al., 1996: Schmiegelow et al.. 1997), composition of the matrix (Aberg et

al.. 1995; Donovan et al., 1997: Fitzgibbon. 1997). can al1 contribute to spatial

distribution of organisms. The mechanisrns behind this influence are numerous. Isolation

of habitat patches frorn others (by barriers or simple distance) çan prevent recoionisation

after local extinction (Kozakiewicz. 1993: S toufîèr and Bierregaard, 1995: Fitzgibbon,

1997: Wiens et al., 1997; Henein and Merriam 1998). The presence of linear corridors

rnay ameliorate this effect (Henderson et al.. 1985: Henein and Merriam, 1990: Bennett et

al.. 1994: Schmiegelow et ai.. l997 i . Lineiir corridors miiy iilso facilitate movement into

sink habitat patchrs where death riire rxçeeds birth rate. cind popultirion p w t h is zero

(Pulliam. 1988: Pulliam and Danielson. 199 1 : Danielson. 199 1. 1992). These procrssrs

in tluence distri bution by impeding or î'acilitating niovement by individucils into patchrs:

in other words; thry act on t hc: procrss of dispersal. Genr rall y landscape configuration

influences thrse procrssrs. and dors so tir i ü r p spatial scales.

Landscape composition may intlucnce an orgctnism's disrribution through its

differential response to the sizr, shape. or arringement of different habitat patches.

Intuitively, ri patch's s i x will affect its use by an individual or population. This can lead

to ephemeral extinction in small patches. as in the metapopulation mode1 suggested by

Levins ( 1970). Coupled with patch isolation. this u n rrsult in prolonged local extinction

(Fahrig and Merriam. 1994). Patch shape can irnpede or f~cilitate rnoveinent of

individuals in and out of the patch (Tamahazi. 1996: but see H q e r et al.. 1993). Shape

also influences the perimeter-area ratio of the patch and hence the extent of edge effects

(Stamps et al., 1987). deleteriously impacting "interior" spccirs and encouraging the

introduction of other plant or animal species (Forman, 1997).

The arrangement of patclies influences distribution through landscape

complernentation, landscape supplemrntation. iind neighbourhood rffects (Dunning et al..

1992). Complementation occurs whrn an organism uses several patch types for non-

substitutcible rcsources: rupplcment<ttion occurs uhcn the orynism iiscs othcr ptitch typcs

to supplement resources that are scarce in the focal patch. In either case. the juxtaposition

of different patch types affects the übundance of orpnisms that clin survive in the

immediate xea. Neighbourhood clfeçts - the proxirnity of patchrs and pemeability of

intrrpatch boundaries - mediate these processes. Final l y. what Dunning et al. ( 1997)

tenned "indirect Ilindscüpe rlfeçts" - the responsr to Itindsctipe srructurr by prediitors.

prey. cornpetitors. rnutualists, rrc. - will dso influence an orglinism's presrnce or absence

within a plitch. However. by considering landscape composition. one ücknowledgss that

the effects of landscüpe structure cire contextual. 3 crucial point that many studies have

M e d to consider.

The fundamental driver behind londscapr structure is hrterogenci ty. He tsrogenrity

is perceived differently by different species, or even iigelsrx classes within a species

(Kotliar and Wiens. 1990). Heterogeneity became a focus of interest in rcology under the

pseudonym of fragmentation. Like "landscapc". fragmentation hris mliny descriptions. but

is broadiy considered as an "unnaturd detaching or separation of expansive tracts [of

habitat] into spatialiy separated fragments" (Harris and Silva-Lopez. 1992). Agriculture

and forestry are two principal causes of fragmentation. Fragmentation can be intrusive,

with srnall pockets of disturbance in an undisturbrd matrix. or rnveloping. with patches

of natural habitat lrft in a matrix of anthropogenic patches (Harris and Silva-Lopez.

1997). The effects of fragmentation on animal populations are often considered to be

synonymous with habitat loss. although Andren ( 1994: 1996) suggests true fragmentation

effects occur when contiguity of nürurai landscape remnants is lost and patches become

isolated: the detrimental etTrcts on cinimal populiitions :ire preitrr than the rffects of

habitat loss done. It should bs nored in the preceding quotation thiit fragmentation is

synonymous w ith unnarural. or anthropogenis. ilisturbance. As of the timr of this writing

few. if any. studies have addresstrd the effects of natural or non-iinthropogenic

hrterogeneity on animai distribution: this will br üddressed in Chnpter Threr.

Despite the growing nwueness of the importance of Ilindsçapr rt'fects thüt has

emerged in the last two decadrs, few could deny that habitat plays a vital role in

determining the presrncehbsencr or abundancc of lin organism. The plant (or animal)

species composition within a pütch comprises the food base for an or,oanism: floristic

structure provides nesting sites and shelter from predators (Southwood. 1977). By this

token the ecological processes occurring within ri given pütch will be dictated by the

habitat within it: an organisrn may forage in one patch type. den in another. disperse

through yet another. As Iündscape effects are manifrsted by their influence on ecological

processes (Wiens et al.. 1993). it follows that iandscape cffects on animals within pütches

may v;uy with different local-scale vegetrition. The relative effects of local habitat and

landscape structure will be exarnined in Chapter Four.

Studies of the effects of landscape structure have used a wide range of taxa as

study subjects, including birds (Wegner and Mcrriam. 1979: Donovan et al.. 1997:

Stouffer and Bierregaard. 1997: Schmiegelow et al.. 1997); mamrnals (Kozakiewicz,

1993; Szacki et al.. 19931, and invertebrates (Middlrton and Merriam, 1983: Taylor and

Merriam. 1996; Jonsen and Fahrig. 1997; Roland and Taylor. 1997: Wirns et al.. 1997).

In most cases the study orgrintsms tire ones that rire known to rely un pritches that

comprise only n small part of the landscape; patches thar are set into n less suitable (or

completely unsuitable) matrix. Hence. the Iandscape is heterogeneous from the

organism's perspective. and the researcher may use known habitat prekrences to

extrapolate the nature of that heterogeneity (Andren et al.. 1997). In frügmented

landscüpes it is enough to choosr a specitis that is affiliated wirh remnant native habitats

and avoids novrl patch types. To g a g e the rffects of landscape structure in a naturally

heterogrneous area. 1 chose to study the North Americün red squirrel. Tmrii~lsci~tn<s

hlsor t i c u ~

T. Ii~rdsuriiais is a conifer speciülist. The sttrds found in the conrs of conifer trees

such as Abies. Piceci. and Piniis spp. are its prirnüry food source (Smith. 1968: Kemp and

Krith, 1970: Rusch and Reeder. 1975; Rirge. 199 1 ). Although red squirrels also feed on

rnushrooms. bemes. and nuts (Gurnell. 1983; Yahner. 1987). thry rely on conifer cones

for ovenvinter survival (Rusch and Reeder. 1978). Squirrels collect cones in the late

summed early Cal1 and cache them in middens (Hurly and Robertson. 1990: Dempsey and

Keppie. 1993). Large middens contain the decomposed brricts of previous yrars' cones

and provide ri food-hoarding location as wwrll as shelter for the squirrel. There may be

several middens on a single squirrel temtory.

Territories are relatively permanent entities (Rusch and Reeder. 1978 ) that are

heavily defended (Stuart-Smith and Boutin. 1994). The territorial d l . or rattle, denotes

the squirrel's home turf and wams potential intmdrrs of that area's occupation (Lair.

f 990). The size of these territories varies with location and habitat type (Smith. 1968;

Rusch and Reeder. 1978). There rire no differenciis in territory s i x between males and

fernales and no overlüp evists (Smith. 1965). Plales iire permitred ont0 a lemale's territory

only during a one-day oestrous. which is typically once a year (Lüir. 1985). Brerding

occurs in carly March to mid June and litters are born btttwcen April and Ju ly (Larsen.

1993: Becker sr al.. 1998). Young typically rnust tïnd thrir own territorirs. dthough some

evidence of bequrathal or shüring cxists (Pricr and Boutin. 1993). Squirrels without a

permanent territory and thus a reliüblr source of ovenvintering food have a low

probabili ty of survival ( Rusch and Reeder. 1978).

From thesr studies 1 post~ilüte thüt red squirrels prrcrive mixrd-wood boreiil

landscapes that contain ü mix of conifer and other stiind types as hrtrrogenrous; that such

a landscape is a rnosaic of suitable patchrs embrddrd in a lrss suitable rnatrix. Basrd on

this premise. the following chapters will riddress three questions:

1 ) What is the spatial scale at which wt: expect landscape structure to predict squirrel

presence?

2) Does landscape structure derived From natural hrterogeneity predict squirrel presence?

3) Does local vrgetation influence the relationship betwern landscape structure and

squirrel presence'?

Conservation biology carries with it an undeniable urgency that has spawned a

generation of fraspentation studies. The resiilts of thrsr studies have generared some

intriguing theoretical ecological questions about or_eanisms' response to the patterns of

spatial heterogeneity in which they are immersed. This thesis will attempt to address

some of these fundamental questions against the bückdrop of the mixed-wood borriil

forest of northern Alberta.

1.1 METHODS

1.1.1 Study Area

This study was conductrd nonh of Lac La Biche. Alberta. on the AlbertaPacific

Forest industries i AlPac) Forest Management A r a (FMAL This areii Iays within rhe

mixed-wood boreal forest. and is covered miiinly by b l ~ k spnicr b o p i Picro rrrciriror~i)

in hygric areris: aspen (Popirlrrs trr~~riiloitlrs) and bnlsiirn poplür (Popiiliis bolswiiferd

mixed sparsely with white spruce ( Picm g l t l r u ~ ) in rnesic areas: and jack pine (Piniis

banksicmcl) in xrric areas (Strong and Leggar. 1992). The rarly 1990's herüldrd the cutting

of aspen and some conifer stands iii small cutblocks from some townships.

1.12 Sampling Design

Threr areas had bren selectrd by the Sustainable Forestry Management Interstand

Dynamics Project. b s e d on general Iandscapr composition: a Managed. Burned. and

Reference Area. The Managed Area had experienced somr timber harvesting (ca. 9% of

area). It encompassed townships T69 R 13 W4 and T70 R 13 W4: central coordinate was

55"5' W 1 12'15' N (Figure 1. L ) . The Burned Area was situareci on townships T73 RI 1

WJ. T73 R 12 W4, T74 R 1 1 W4. and T74 R 12 W4, with centrai coordinate 55'30' W

I 12'OYN. The third area was not bumed and had experienced very slight (ca. 1%) timber

harvesting activity; we terrned this the Reference Area. It was located on T70 R 13 W4.

T7 t R I 3 W4. T70 R1J W4. and T7 1 RL4 W4. with central coordinate of 55" W t 13"309

N (Figure 1.1 ). Within the iManüged and Referencr Areris ii sysremütic sompling grid was

installed. with points 1 km apart in a ssven by n inr pattern (Figure 1 . 1 ). These were

ccilled megs grids. Only upland sites were sampled. so points hlling within a bllick spruce

bog or other wetland were dropped and others added to the edges of rhr grid ro

compensate. Thrrr were a maximum of 64 siimplins points in eüch of rhé Miin@ and

Ret'srçncr landscaprs. Dur to Liccrss problcrns o systcmatic design was impossible for the

Burned landscapr. so a stratified design was used thiit placed points at Iwst 1 km apart,

with points in rach upland patch type. Twenty-eight sampling points were estÿblished

there.

Within each of these mcga grids. meso grids wrre instdlrd i F i p r r I . I 1. The

position of the mes0 grid wüs chosen to cncompass a representati v r slirnplr of al1

landscape types. Points in mrso gnds were 250 m üpart in the samr pattern as mega grids,

for a total of 64 points in each grid. Criteria for establishing points were the same as for

mega grids. Two meso gnds were establishrd on rach of the Managed and Reference

grids: only one was cstablishrd on the Bumrd grid. Landscnpr structure. squirrctl

presence. and local vegetûtion were tissrssed within these grid systrrns. and will be

discussed in the subsequent chapters.

Figure 1.1: Location and design of the snmpling system. The Reference mega and meso grids are shown. Circles indicate mrgû grid points. and squares represent mrso ;rid points. Distance br twr rn m z y y i d points is I km: betwrrn mrso srid points. 150 m.

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Andreassen. H.P.. S. Halle. and R.A. Irns. 1996. Optirniil width of movemrnt corridors for root voles: not too nanow and not too widc.. Journal of Applied Ecolopy 33: 63-70.

Andren. H. 1994. Elfects of habitat fragmentation on birds and rnamrnals in landscapes with different proportions of suitablr habitat: a review. Oikos 71: 355-366.

Andren. H. 1996. Population responses to habita: fragmentation: statistical power and the random sample hypothesis. Oi kos 76: 235-242.

Andren. H. A. Deliii. and A. Seiler. 1997. Population rrsponsr to Iiindsccipr changes depends on spccialization to different landsctipe clernents. Oikos SO( 1 ): 193- 196.

Baudry. J.. and G. Merriam. 1 Y 88. Connectivity and connectrdness: Functional vcrsus structural patterns in landscapes. In: Schreiber. K.-F. (rd.). Connectivity in landscape rcology. Proc.?nd Intrmationül Association for Landscüpe Ecology. Munstrrsche Geogr. Arbeiten 29. pp 23-28.

Becker. C.D.. S. Boutin. and K.W. Larsen. 1998. Constraints on first reproduction in North Amerîcan red squirrels. Oikos 81: 5 1-92.

Bennett. A., K. Henein. and G. Merririm. 1994. Corridor use and the elements of corridor quiility - chipmunks and fencerows in a farmliind mosaic. Biological Conservation 68: 155- 165.

Danielson. 199 1. Communities in a landscape: The influence of habitat heterogeneity on the interactions between sprcies. The Amencan Naturalist 138(5): 1 105-1 120.

Danielson. B.J. 1992. Habitat selection. interspecific interactions and landsclipe composition. Evolutionüry Ecology 6: 399-4 1 1.

Dempsey, J.A.. and D.M. Krppie. 1993. Foraging patterns of eastern red squirrels. Journal of Mammalogy 74(J): 1007- 10 13.

Donovan, T.iM., P.W. Jones, E.iM. Annand. and F.R. Thompson m. 1997. Variation in local-scale edge effects: mechanisms and Iandscape context. Ecology 78(7): 7064-3075.

Dunning, J.B.. B.J. Danielson. and H.R. Pulliüm. 1991. Ecological processes that affect populations in complex landscapes. Oikos 65: 169- 175.

Fahrig, L., and G. Merriam. 1994. Conservation of friigrnrnted populations. Conservation Biology 8( 1 ): 50-59.

Fitzgibbon, C.D. 1997. Smnll mammals in hm woodlots: rhr rffects of habitat. isolation and surrounding land-use patterns. Journal of Applied Ecology 34: 530-539.

Gumell. J. 1983. Squirrel nurnbers and the abundance of tree sreds. Mammal Review 13(2/3/4): 133- 1.18.

Harper. S.J.. E.K. B o l l i n p . and G.W. Barrett. i993. Efkcts of patch shape on populations dynamics of meadow voles (Micruru prtitisylvci>iicrrs). Journal of ~Mammalogy 74(4): 1045- 1055.

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Henderson. M.T.. G. Merriam. and J. Wqner. 1982. Patchy rnvironmrnts and specirs survival: chipmunks in an cigricultural mosaic. Biological conservation 3 1: 95- 105.

Henein. K., and G . Merriam. 1990. The elements of connrctivity whrre corridor qudity is variable. Landscape Ecology 4: 1 57- 1 70.

Hurly, T.A.. and R.J. Robertson. 1990. Variation in the food hoardinz behaviour of red squirrels. Behaviourd Ecology and Sociobiology 16: 9 1 -97.

Kemp. GA.. and L.B. Kctith. 1970. Dynamics and regulation of red squirrel (Tmzicisciwris hudsonicus) populations. Ecology 510): 763-779.

Kotliar. N.B.. and J.A. Wiens. 1990. Multiple scalrs of patchiness and patch structure: a hierarchical framework for the study of heterogeneity. Oikos 59: 33-260.

Kozakiewicz, M. 1993. Habitat isolation and ecological barriers - the cffect on small marnmal populations and communities. Acta Theriologica 38( 1): 1-30.

Lair, H. 1985. Mating seasons and fertiiity of red squirrels in southern Quebec. Canadian Journal of Zoology 63: 3323-2377.

Lair, 1990. The calls of the red squirrel: a contextual analysis of function. Behaviour 115: 254-282.

Larsen. K.W. 1993. Female reproductive success in the North American red squirrel, T~~nriiisciun~s htrdsonicris. Pt1.D. Thrsis. University of Alberta. Edmonton.

Levins. R. 1970. Extinction. Pp. 77-20? In Lccturcs on mathematics in the life scicnccs. Vol. 2. M. Grrstenhaber. ed. American Mathematical Society. Providence. Ri.

Merriam, G. 1988. Landscape ccology: the ecology of heterogeneous systems. Pp. 43-50 In Landscape Ecolov and Management. M. Moss. ed. Polyscience Publications Inc.. Montreal.

merr ria m. G. 199 1. Corridors and connectivity: animal populations in heterogeneous rnvironmenrs. pp. 133- 142 Nature Conservation 2: The Rolr of Corridors. Edited by D.A. Süunders and R.J. Hobbs. Surrey Beatty and Sons Pty Ltd. Chipping Australia.

merr ria m. G.. and A. Lanour. 1990. Corridor use by small mümmals: field for three experimental types of Perurnyc~is leucopus. Landsçape Ecology 131.

Middleton. J.. and G. Merriam. 1983. Distribution of woodland species in woods. Journal of Applicd Ecology 20: 625-64.

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Price, K., and S. Boutin. 1993. Territorial bequeathal by red squirrel mothers. Behavioural Ecology 4: 144- 1 50.

Pulliam. H.R. 1988. Sources. sinks. and population regulation. The Arnerican Naturalist 132(5): 652-66 1.

PuIlirim. H.R.. and B.J. Danielson. 199 1 . Sources. sinks, and habitat selection: a landscape perspective on population dynamics. The American Naturalist 137: S5-S66.

Riege, D.A. 1991. Habitat specialization and social factors in distribution of red and grey squirrels. Journal of Marnmalogy 72( 1): 152- 162.

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Taylor, P.D.. and G. Memam. L996. Habitat fragmentation and parasitisrn of a forest damseltly. Landscape Ecology 1 l ( 3 ) : 18 1 - 189.

Tiebout, H.M.. and R.A. Anderson. 1997. A cornplirison of corridors and intrinsic connectivity to promote dispersal in transient successional lündscapes. Conservation Biology l l ( 3 ) : 620-627.

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wood and iidjoining farmland habitats. Journal of Applied Ecology 16: 349-357.

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Chapter Two

Using Foraging Range to Demarcate a Functional Landscape

The cardinal consideration of any rcological study must be the identification of

the spatial scale most appropriate to the pattern or process under investigation (Wiens.

1989). In landscape ecology. this translates into definin; the patch and the landscape by

some critrria pertinent to the organism under consideration. and describin; a landscape

relevant to the mechanisms by which londscapr structure might intliirncr distribution

(Wiens et al.. 1993). Rècognizing rhiit pütchcs and landscüpes are not fixrd entities.

Kotliar and Wisns (, 1990) proposed a theoretic;il mode1 by which patchiness is arranged

in a hierarchy. A given "patch" might have internai heterogeneity (e.g.. in floral species

composition or physiognomy) but from the organisms' perspective during a given

iictivity, this variance is üverüged out so that it apprars horno,vrnrous. Pütches üggrepatr

to f o m larger functional units. which Addicotr et al. ( 1987) dubbed "rcological

nrighbourhoods". Ecologicül neighbourhoods are drfined by an ecological process. an

appropriate time scale, and an organism's activity. Therefore. what constitutes an

rcological neighbourhood or functional patch aggregate is not only specirs-specific. but

will Vary within a sprcirs depending on the procrss beins çonsidrred.

An organism's response to its surroundings may actually occur at several different

spatial scales (Morris. 1987, 1992; Urban et ai., 1987). The smüllest spatial scd r at which

a particular organism responds to patchiness is its "grain". while the largest scale that

influences an organism is its "extent" (Kotliar and Wirns. 1990). Grain and rxtrnt are

also process-specitic. as suggested by ecological nrighbourhoods. Howevrr the grain of

study is often a matter ot'convenience for researchers. a laiinching point limited by the

resolution of the data. It is assumed that below this grain. al1 things are equal. The extent,

as suggsstrd by the ecological neighbourhood (Addicott et ai.. 19873. will vüry depending

on the process undrr considrrtitioii: or alternütively. the dominant ccologiccil process thar

is effecting the rrsponse to hrterogeneity. To combine terminology. the grain c m b r

considered the patch. and the extent is the landscape.

Defining landscape rxtent (1 prion is a dangerous business i f done without proper

consideration of the relevant spatial and time scüles: this is ünalogous to conducting a

population scology study without first dclinrlitinp the population. Following the example

of ecologicd neighbourhoods. 1 definr the landscapr extent büsrd on the spatial scale at

which ecological processes occur to rfkct a response to landscape structure. This

demarcation is based on the prernise that there are two main mcchanisms influencing

patch occupancy by an organism: I ) it must get there, which is a dispersal phenornenon,

and 2 ) it must survive there. which is relateci to foraging. among other things. Obviously

there are many othrr fiictors involved. but I will limit my aryrnent to thesr two for the

sake of simplicity.

If an organism's distribution is dispersal-limited, aspects of the landscape beyond

the patch should predict its presence. Landscape configuration. in terms of patch isolation

(Fahrig and Merriam 1994). connrctivity (Taylor et al.. L993: Pithcr and Taylor, i 998).

and boundary permeability (Wiens et al., 1985) could play a role. So could composition.

in that a proximal expanse of suitable habitat may act as a source for dispersers (Pulliam

and Danielson. 199 1 ; Danielson, 199 1. 1992). In this scenario. landscape structure and a

species' rnovement patterns intrrüct to determine population distribution i Wirns sr ai..

1993). .Alternativrly. i f an organism is forage-limitçd. one would cxpect ti responsr to

landscape structure at smüller. foraging sçales. Lanclsciipr complrmzntation or

supplementation may facilitate occupation by an individual (Dunning et al., 1992). Patch

size. shape and composition may constrain occupation (Hiirper et al.. 1993: Tamahazi.

1996). tn this crise local-sçüle hrterogeneity would drive distribution and Iarger-scüle

phenornena would br Iess important.

Red squirrels are not behaviourally restricted to conifer and c m move through

deciduous habitats (Rusch and Reeder. 1978: Yahner. 1957). Given that the red squirrel is

dependent on conifer for overwinter survival (Gumefl 1987). and the areas in which L

conducted this study are conifer-depüupcirw (sa. 20-30'2 of the areal. 1 postulatecl thüt

red squirrels are forage-. not dispersal-limited. I trstrd the predictions thüt i rrd squirrels

are able to rnove through the matrix and thus are not dispersal-limited and 2) the response

to landscape structure exists at small scales. the ecoIogica1 neighbourhood of the foraging

range. The aim of this chapter is to test the first prediction. and to provide an estimate of

the foraging range of the red squirrel in the mixrd-wood boreal ecosystern. as a preludr to

the multiscalar landscape analysis in Chaptrr Thrrr thnt tests the second prediçrion. The

current chapter will also iissess squirrels' use of die matrix (non-conifer) patches, a

fundamental consideration when ascenaining whether larger-scale isolation could play a

role in squirrel distribution.

The territory size of the red squirrel has been well studied in a variety of high-

quality habitats. including white spruce in the Yukon (Price et al.. 1986). lodgepole pine

in Colorado (Gumell, 1984). and jack pine in Alberta (Larsen. 1993). Rusch and Reeder

( 1978) examinrd territory size in conifer stands in the southern mixed-wood boreal forest

of Alberta. However tlizre is no documentation of foraging range size (whish include

important extraterritorial movemcnts (,Don. 1983)) in the marginal squirrel habitat of the

rnixed-wood boreal of north-central Alberta. where deciduous stands f o m the matrix and

conifer is limiting.

To obtliin an rstimate of the foraging scale at which response to landscape

structure wiis expected. 1 uscd radio trlenietry to iissess the size and content of the

summer foraging range of red squirrrls in the study area. This information will be used in

subsequent çhapters as a benchmark for assessing the appropriate extent of Iündsclipe

analysis. a pivotal first step in cvaluating the effects of landscapc structure.

2.1 METHODS

2.1.2 Squirrel locations

To track squii-rels within jack pine stands, grid points from within the Reference

area were identified at which squirrel territorial calls had been recordrd. and which had

suitable access. Ten of these were selected randomly. For squirrels within

spruce/deciduous/cut stands, the same protocol was followed on the illanaged grid. Due

to low capture success on these latter ten sites three extra sites wrre added. identified on

19

the ground as containing spruce (adjacent to a deciduous stand or cutblock) and having a

resident squirrel present.

Tomahawk live traps (Tomahawk Livetrap Co.. Tomahawk. Wisconsin) were

baited with ca. 5 g of peanut butter and located at middens. runways. and piles of strippcd

cones at each site: 4-6 traps were used per site. Seven squirrels were trapped in the

Reference area. and nine were trapped in the mrinüged area, for a total of 16 individuals

( 1 1 females. 5 males. a11 adults). Squirrels were handled in canvas and mesh bag. The

mass and sex of cüch squirrel were recordrd. Squirrels were fitted with radio transmitter

collars (PD-ZC: mass = 3.8 g; Holohil Systrms. Woodlawn. Ontario) thrn released. Froin

Junr - August (inclusive) 1997. radio telrmrtry (TR-2 receiver with RA- 14 antenna:

Telonics Canada. Winnipeg. Manitoba) was used to locatr these individuals twice per

day, moming and evening. for approximately two wreks. to yield 17-75 locations each.

Trlemetry was usrd to visually loçatr the individual. ïts position wlis marked. and the

angle and distance from a referencr point with known Universal Transverse Mercator

(UTM) coordinates were established.

2.1.2 Home range analysis

Angles and distances from a known central location (obtained using the Global

Positioning System) were translated into UTM coordinates and entered into the ArdInfo

geographic information system (GIS) (Esri Lnc.. Colifornia). The outtrmost points of each

range were connected using a 100% minimum convex polygon to create a foraging range

for each individual. Although exploration. breeding, or other activities may have been

occurring dunng these movements, the polygons were temed "foraging ranges" as

foraging is a daily cictivity known to have consistently occurred during the tracking

period. The use of this term separates this range from home ranges, which typically

rncompass larger time-frames (q. v. ).

Ranges were overliiid on digital Alberta Vegetation Index ( AVI) forest inventory

coverages reclnssified to describe age and stand type (see Appendix 1 Ior reclassification

protocols). The size of the range. and landscape composition and configuration within

each. was quantified using FRAGSTATS (McGarigal and Marks. 1994). Tests for

differences between rnean range sizr by sen and by conifer content were perfomed using

the non-parümetric Rank Sum test (Ambrosr and Ambrosr. 1995) for low sample sizes

and non-normal data.

2.2 RESULTS

2.2.1 Sqzcirrel foragirtg range sizes

Thrre was some variability in squirrel forüging range size. with the Irirgrst bring 8.12 ha

in size and the srnaIIest 0.1 ha in size (Table 2.1 ). There w u no difference between male

(n=j) and female (n=I 1 ) foraging range sizes (Rank Sum test. p0.05). The percent of

conifer stands in the home range was defined by the landscape mnps and included pure

black spruce, white spruce. larch. and jack pine. as well mixed-deciduous stands in which

conifer constituted the majority of trees within the stand. There was a difference in size

between ranges with ~ 9 0 % conifer stands within them. (n=8) and >90% conifer stands

(n=8), as defined by landscape maps (Rank Sum test. pc0.05; Table 2.2).

>90% conifer within their boundaries were smaller than those containing

Ranges with

other elements,

which included pure- and mixed-deciduous stands. and clearcuts.

2.2.2 Squirrel use of stand types

Squirrels were radio-iocatcd in black spmce. white sprucc. jack pinr . cispen /

balsam poplar, and clearcuts. For those squirrels whose range included jack pine (n=7),

an average of 86% +/- 14 (standard deviation) of the locations were within the jack pine,

with the remainder falling in deciduous or deciduous/jack pine mix. Of the squirrels

whose ranges includrd spruce. distribution of radio locations was quite variable. Two of

the ranges did not incliidr ciny conifer stands. lis definrd by the AVI miips. Ground-

rruthing revealed the prescnce of a k w large cone-bexing white spruce trees within the

deciduous parch. One range, the smallest. was entirely within old white spruce. Of those

squirrels who used spruce nrxt to deciduous patches (n=3). 37.33% +/- 32.5 locations fell

within spruce: 62.67% +/- 32.5 fell within deciduous. Of those squirrels whose ranges

bordrred a clearcut (n=3). 70% +/- 22.9 frll within sprucr: 23.33% +/- 16.07 frli within

the cut. These results suggest that squirrels are not confined to conifer habitats and do

make use of the matrix.

Table 2.1 : Red squirrel foraging r ang statistics.

(n= 16) Foraging range size (ha) % conifer within range

mean

std dev

median

minimum O. 1 O

Table 1.2: Relative sizes of squirrel foraging ranges (lia) with difkring conifer content.

~ 9 0 % conifer 990% conifer

mean area (ha)

std dev

maximum

minimum

2.3 DISCUSSION

2.3.1 Assessment of accuracy and the 100% minimum convex polygon

As squirrels were visually located. there is no error polygon iissociated with rach

observation. of the kind that should be prrsentrd with triangulation data (Harris et al..

1990). For each squirrel, the relationship brtween sample size (number of locations

obtained) and the amount of m a measured is not linear but does not display a perfect

asymptote. This indicates rhar more locations per squirrel rnay have been required to

obtain a more complete foraging range size. This wns compensated for by usin2 a

relatively consistent nurnbcr of points per individual (,cil. 17-25) cind employing a non-

statistical home range estimator.

Statisticiil and non-statistical home-range estimators clin be used to analyse radio

location data (Harris et ai.. 1990). Statistical methods iisually focus on the seneration of

probability ellipses (Dixon and Chaprnan. 1980). Mrthods such as thrse attempt to

describe 'utilization distributions'. or the distribution of an animal's position in space

(Harris et cil.. 1990). The harmonic mean method. for cxample. is a statistical rstimator

that uses areal moments to calculate the most intensely used area. or 'centre of activity' as

indicated by density of radio locations (Dixon and Chiipman. 1980). Parametric methods

such as these are susceptible to problems with iiutocorrelation of location data and sample

size.

Non-panmetric statistical estirnators do not assume adherence to ii theoretical

distribution. Techniques include fixed and adaptive kemel methods (Worton. 1989)

which delineate areas of intense usage. using a non-parametric smoothing function.

However, these methods are quite sensitive to both skewness of location data and to the

type of smoothing parameters used. Harris et al. ( 1990) suggest that these methods are

more reliable for exarnininy relative range (habitat) use than total size.

The simplest non-statisrical home range estimator is the minimum convex

polygon (MCP) (Don, 1953). With the MCP ii researcher c m use either 1008 of the

points. as this study does. or restrict the nurnber in such (i w3y that outliers are omitted to

provide a more consrrvativr estimate of size. Whilr considered a more robust éstimator

than statistical methods when number of fixes is smltll (Harris et al., 1990), it is still

strongly ciffected by outliers and can include portions of habitat arw beyond the main areLi

of activity or chat aren't visitrd by the individual. The MICP wiis decmcd suitable for this

srudy as i t was not my intention to delineate core areas of habitat or determine the most

parsimonious riingr or territory sizr: the goal of this study was to determine al1

movrments by individuals. inçluding ail extra-territorial forays and movements off cure

areas. AI1 of the points were usrd ( 100% MCP) rather thün rmploying ri restricted

polygon, as longer movements within the forliging time frame wrre considerrd important

indicators of the scde of squirrels' areal use.

2.3.2 Spatial scnle of Zan dscape analysis

A mean foraging range sizr of 1.7 hri corresponds to a circular landscape extent

with a 73.6 m radius. The minimum and maximum-sized ranges correspond to 17.8 m

and 160.8 m radii. respectively. Given the variation in foraging range s i x and the

possibility that ranges are possibly underestimated. it is prudent to say that if squirrels rue

responding to landscape structure at the foraging scülr. then landscape structural variables

will becorne significant predictors of squirrel presence rit the 50 m or 100 m scales of

analysis. with less predictive power at the 250 m scale of andysis. If r i stronger response

occurs beyond these radii (250 m. 500 rn or 1 O00 ml thrn othrr large-sctilc processes.

processes, such as dispersai (natal or otherwise: Rusch and Reeder, 1978: Boutin et al.,

1 OF)?: Larsen and Boutin. 1994) may be responsible.

2.3.3 Coiriparisuri fo utlr er sys tem

Don ( 1983) in his review of tree squirrel home range sizes from difkrent studies

and geographicül arcas reportrd lin average size of approximatrly I ha. In white sprucr

forests in Kluanr. YT. squirrel territory size was 0.2-0.5 ha (Price et al.. 1986). In

subalpine lodgepolr pine in Colorado, Gume11 ( 1984) found an average homr range size

0.56 ha with a much less variable range (0.18 - 0.80 ha: n=9) than found herein. He also

noted extraterritorial forays in rxçess of 150 m. though did not quüntify thrir total cxtent.

in jack pinr stands in Alberta. Larsen ( 1993) meitsured ;i rneiin territory size of 0.65 ha.

Movements as h r as 500 m off territories were also reported for adults (Larsen and

Boutin. 1994). in the southern rnixed-wood b o r d of Alberta. Rusch and Reeder ( 1978)

reponed mean rerrirory sizr of 0.24 ha +/- 0.03 (SE: n=6) in white spruce: 0.35 ha +/-

0.02 (n=l I ) in mixed spruce: and 0.66 ha +/- O. 17 (n=7) in jack pinr. M u n movernents

ofca. 64 rn in spruce. 1 10 m in jack pine. and 185 m in rispen were nlso obsrrved (Rusch

and Rerder. 1978). Variability in territory or homr range size can be attributed to

differences in food availability (Smith. 1968), with larger ranses found in mixed

coniferlaspen habitats (Rusch and Reeder. 1978). However, ranges rneasured herein are

larger and much more variable than those previously reported. This should be noted with

caution. however. as problems with compxability arise that must br considered by those

who seek to use published home ranges to qulintify landscape rxtent. Utilization

distributions. kemels. or even territories are nor accurate sauges of the spatial scale of

local ( foraging) processes. nor of the influences of landscape structure upon them. In

these cases use of the matrix by the study animal may br overlookrd.

2.3. J Territories and time frame

Squirrels defend traditional territories al1 year (Smith. 1968: Kemp and Krith.

1970: Rusch and Reeder. 1978). Despite the cost of territoriülity (Stuart-Smith and

Boutin. 1991) defence is zealous. as maintaining a trrritory is critical foi overwinter

survival (Rusch and Reeder. 1978; Gurnell, 1987; Lrirsttn and Boutin. 1994).

Gurnrll ( 1984) distinguishes between the trrritory and the home range. rhe former

being ncstrd within the latter. As u much frequented and defended area. a territory more

çlosely approximatrs a high-density utilization distribution than a minimum convex

polygon. which includes al1 locations (Harris et al.. 1990). The ranges reponed here are

akin to the "domain" of Don ( 1983). which includrs long-distance foriiys as well ris

intensely utilised areas. Thesr lire not often reporteci in the literature. making home-range

cornpiirison difficult. As an additional note, the timr-frame in which my ranges and

published territory sizes were measured, differ. Harris et al. ( 1990) emphasise that the

timr period during which tracking occurs cm greatly influence the outcomr of the

observed home-range size. In this study locations were obtained only over a period of 2-3

weeks in June. July. and hugust. Thrrefore these ranges do iiot describe ii complete

(annual) home range size. nor demarcate temtory boundaries; they do create an index of

the spatial scale of summer !and use by these small marnmals. and determine the extent of

movements between conifer and rn~1tri.u habitats.

2.3.5 Matrixuse by squirrels

Frorn the data I can conclude that squirrels do make use of the deciduous 1

clearcut matrix. and are not conthed to conifer habitats. Fisher and Merriam (in press)

found that red squirrels in Ontario made use of an agricultural matrix when forriging. sven

whrn conifer habitats wrre within close proximity. In .Albsrt;t. Kcmp and Kcith ( 1970)

suggested that decidiious habitats containeci lightly deknded seasonal territories that

maintained juvenile populations until spmce territories brcame availüble. In the sarne

area. Rusch and Rreder ( 1975) reponed movements betwern spnice, jack pine. and aspen

habitats that ranged betwern 1 . 1 and 5.3 km. coinciding with spring shuftk and FdI

dispersal. Yahner ( 1987) t'ound red squirrcls mude use of m q i n u l deciduous habitats in

Pennsylvania where çonikr is limiting. The miitrix indisputobly is used by red squirrels

for movement and feeding.

The data suggest that squirrels may be using landscapr supplementation (Dunning

r t al.. 1992) to provide a substitutable resourcr. rspecilill y in mixrd sprucr/drciduous/cut

habitats. Berries, mushrooms. m i h;urslnutb u n be founci i n LItxidu~us st;inds m d

clearcuts (J. Fisher. unpubl. data). Alternative food sources such as these may br

important components of a squirrel's diet (see Gumell. 1983 for review). Since home

ranges are srnaller within contiguous conifer habitat than heterogeneous areas. i t is

possible that where conifer is limiting their srrds are used by squirrels mainly for

ovenvinter survival, while summrr forage is obiainrd frorn ridjoining patch types.

Temtorial rattles were often heard within both conikr and deciduous habitats, although

were never noted in clearcuts, despite the extensive amount of tirne spent there. I would

tentatively suggest that while deciduous habitat may be included within the boundaries of

defended territories. clearcut rnovements represent extra-territorial forays for

opportunistic foraging rather than ü defended resource. However. much more resèarch is

needed to test such a hypothesis. The data do reveal rhat North Amcrican red squirrels

enter the matnx quite readily. and are not behaviourally restricted from using the matrix.

This suggests that dispersal or other movements are lrss likely ro b r impedrd by iück of

connrctivity (Taylor et al.. 1993) unlikr Eurasian rrd squirrels (Verboom and van

Apeldoorn 1990: Andren and Delin. 1994: van Apeldoorn et al.. 1994: Celada et ai..

1993: Wauters et al.. 1994) and other habitat specialists in heterogenrous landscapes

(Wegner and Memam 1979: Henderson et al.. 1955: Merriüm et al.. 1989: Fitzgibbon.

1993. 1997: but see Szacki et al.. 1993: Wegner and Merriam. 1990).

2. J CONCL USIONS

In the mixed-wood boreal forest of northeast-central Alberta. 100% MCP's

obtained for seventeen squirrels show that these organisms have highly variable foraging

range sizes that exceed territorial esrimiires from other systerns and studirs. The mean

rcological neighbourhood of foraging corresponds ro an area 50 m to 100 m in radius.

Squirrels travel through several piitch types. suggesting that larger-scdr effects sternrning

from lack of connectivity may be less relevant than local-scale phenornena.

2.5 LITERATURE CITED

Addicott, J.F., J.M. Aho. M.F. Antoiin, D.K. Padillli. J.S. Richardson. and D A . Soluk. 1987. Ecolo_gical nrighbourhoods: scaling environmental patterns. Oi kos 49: 340-346.

Ambrose, H.W. DI, and K.P. Ambrose. 1995. A Handbook of Biological Investigation. 5th Edition. Hunter Textbooks Inc., NC.

Andren. K., and A. Delin. 1994. Habitat selection in the Eurasian red squirrel. Scilinis vrilgcuis. in relation to forest fr~gmentation. Oikos 70: 43-18.

van Apeldoorn. R., C. Crlada. and W. Niruwsnhuizrn. 1991. Distribution and dynÿmics of the red squirrel (Sciunis ~dyciris L. ) in n Iandscapti with fragmsnted habitat. Landscüpe Ecology 9(3): 227-235.

Boutin, S., Z. Tooze and K. Price. 1992. Post-breeding dispersa! by female red squirrels (T~nnirisci~inis Izudsonic~is): the effect of local vacüncies. Behavioural Ecology 4(2): 15 1 -

Crlüda, C.. G. Bogliani. A. Giiriboldi. anci A. Mnrxci. 1993. Occupmcy of isolüted woodlots by the red squirrel Scirirrrs vrr1,~ciris L. in Itcily. Biological Conserviition 69: 177- 153.

Danielson. 199 1. Communities in a landscapc: The influence of habitat hererogeneity on the interactions betwern spccies. The Arnrricün Naturalist 138(5): 1 105- 1 120.

Danielson. B.J. 1992. Habitat selection, interspeci fic interactions and landscape composition. Evolutionary Ecology 6: 399-4 1 1.

Dixon. KR.. and J.A. Chapman. 1980. Hürmonic niriin mrasure of animal activity arrcis. Ecology 61(5): 1040- 1 O U .

Don, B A C . Horne range characteristics and correlates in trer squirrels. Mammd Rrview 13 (3/3/4): 123- 132.

Dunning, J.B.. B.J. Danielson, and H.R. Pulliam. 1992. Ecological processes that affect populations in complex landscapes. Oikos 65: 169- 175.

Fahng, L.. and G. Memam. 1994. Conservation of frûgrnented populations. Conservation Biology 8( 1 ): 50-59.

Fitzgibbon, C.D. 1993. The distribution of grey squirrel dreys in f ~ m i woodiand: the intluence of wood area, isolation and management. Journal of Applied Ecology 30: 736-

Fitzgibbon. C.D. 1997. Small mammals in farm woodlots: the effects of habitat. isolation and surrounding land-use patterns. Journal of Applied Ecology 34: 530-539.

Gurnell. J. 1983. Squirrel numbers and the abundonce of trer seeds. Mammal Review 13(3/3/4): 1 33- 148.

Gurnell, J. 1984. Home rangel territoriality. caching behaviour and food supply of the red squirrel (Tmtiasci~inis h~rdsoniciis~keuionri) in a su bal pine lodgepole forest. Animal Behaviour 32: 1 1 19-1 13 t .

Gurnell. J. 1987. The Narural Historv of Squirrels. Chrisropher Helm. London.

H q e r . S.J.. E.K. Bollinger. and G.W. Btirrett. 1993. Effects of patch shape on populations dy mnics of meadow voles ( îVIi~wri is pr~i~iqlvlliticrrs 1. Journ;il of Mammalogy 74.1): 1045- 10%.

Harris. S.. W.J. Cresswell. P.G. Forde. W.J. Trewhellü. T. Woollard. and S. Wray. 1990. Home-range analysis using radio-tracking data - a revie w of problrms and techniques particularly as applied to the study of mammiils. iMammnl Rrvicw 20(1/3): 97- 173.

Kemp. G A . and L.B. Keith. 1970. Dynlimics and regulation of red squirrcl (T~~inicucirinis hriclso~liais) populations. Ecolo_ey j l ( 5 ) : 763-779.

Kotliar. N.B.. and J.A. Wiens. 1990. iMultiple sclilrs of patchiness and parch structure: a hierarchiclil frümework for the study of hrtrrogeneity. Oikos 59: 153-260.

Larsen, K.W. 1993. Female reproductive success in the North American red squirrel, Trimiuscirtnrs hurlsonicris. Ph.D. Thesis. University of Alberta. Edmonton.

Larsen. K.W., and S. Boutin. 1994. Movements. survival. and settlcment of red squirrel (Tmiictscirirru hirdsonicus) offspring. Ecology 75( 1 ): 11 4-223.

Merriam, G., iM. Kozakiewicz. E. Tsuchiya. and K. Haw ley. 1989. Barriers as boundarirs for metapopulations and demes of Perumycrrs lriicup~is in fmn landscapes. Landscape Ecology 2(4): 227-235.

McGarigal, K.. and B.J. Marks. 1994. FRAGSTATS - Spatial Pattern Analysis Program for Quantifying Landscape Structure. Version 2.0. Oregon State University. Corvallis, OR.

Moms, D.W. 1987. Ecological scale and habitat use. Ecology 68(2):362-369.

Morris, D. W. 1992. Scales and costs of habitat selection in heterogeneous landscapes. Evolutionary Ecology 6: 4 12-432.

Pither. J.. and P.D. Taylor. 1998. An experimental assessrnent of landscape connectivity. Oikos 83: 166- 174.

Price. K.. K. Broughton. S. Boutin. and A.R.E. Sinclair. 1956. Territory size and ownership in red squirrels: responsé to removals. Canadian Journal of Zoology 64: 1144- 1147.

Pulliam, H.R., and B.J. Danielson. 199 1 . Sources. sinks, and habitat selection: a landscape perspective on population dynarnics. The American Naturalis t 137: S5-S66.

Rusch. D.. and W. Reeder. 1978. Population ecology o f Alberta red squirrels. Ecology 59(3): 400-420.

Smith. C.C. 1968. The adiiptive nature of s o d orpnizatiun in the Grnus of thrcit: (sici squirrels Tmrticisciunrs. Ecological iLlonogrriphs 38( 1 ): 3 1 -63

Southwood. T.R.E. 1977. Habitat. the trrnplrt for ecologiçal strategirs? Journal of Animal Ecology 46: 332-365.

Stuart-Smith, K., and S. Boutin. 1994. Costs of escalated territorial del'ence in red squirrels. Canadian Journal of Zoology 72: 1 162- 1 167.

Szacki. I.. 1. Babinska-Wrrka. anci A. Liro. 1903. The influence of landscape spatial structure on srncill rniirnmal rnovemrnts. Act~i Thrriologicci 38( 2 1: 1 13- 123.

Tarnahazi. T. 1996. Effects of patch shape on the number of organisrns. Landscripr Ecology 1 l (5 ) : 299-306.

Taylor. P.D., L. Fahrig, K. Henein., and G. Merriam. 1993. Connectivity is a vital element of landscape structure. Oikos 68: 57 1-573.

Urban. D.L.. R.V. O'Neill. and H.H. Shugart. Jr. 1987. Landscape ecology: a himuchiciil perspective cün help scientists understand spatial patterns. Bioscisnce 37(2): 1 19- 127.

Verboom. B.. and R. van Apeldoorn. 1990. Effects of habitat fragmentation on the red squirrel. Scilints vulgcrris L. Landscape Ecology 4(1/3): 17 1 - 176.

Wwters, L., P. Casale, and A. Dhondt. 1994. S p x e use and dispersa1 of red squirrels in fragmented habitats. Oikos 69: 140- 146.

Wegner. J.F.. and G. Merriam. 1979. Movernents by birds and srnail mammÿls between a wood and adjoining farmland habitats. Journal of Applied Ecology 16: 349-357.

Wegner, J., and G. Merriam. 1990. Use of spatial tlements in a familand mosaic by a woodlmd rodent. Biological Conservation 54: 263-267.

Wiens, J.A. 1989. Spatial scltling in ecology. Functional Ecology 3: 385-3137.

Wiens. J.X.. C S . Crawford. and J.R. Gosz. 1985. Boundary dynamics: a conceptual framework for studying landscaps ecosystrms. Oikos 45: 42 1-427.

Wiens. J.A., N.C. Stznsech. B. Van Horne. and R A . tms. 1993. Ecologiccil mrchüiiisms and landscape ecology. Oikos 66: 369-330.

Worton, B.J. 1989. Kzrnel rnethods for cstimiiting the utiliziition distribution in home- range studies. Ecology 70( 1 ): 164- 168.

Yahner. R.H. 1987. Feeding site use by red sqiiirrels. Tmiictscirinls Irirdso~iicus. in ri marginal habitat in Pennsylvania. Canadian Field Naturalist 101(4): 586-589.

Chapter Three

Squirrel Response to Landscape Structure at Multiple Spatial Scales

3.0 INTRODUCTION

The relationship betwern landscape structure and animal abundance hiis been

frequently demonstrated in agicultural and other anthropognically rnodified landscapes.

In these systems fragmentation is extensive: few small patches of native habitat remain:

and organisms typicrilly prrceive considerable contrasr betwren the native and nov4

habitat patches. Insects (Jonsen and Fahri;, 1997: Pither and Taylor. 199s ). songbirds

(Wegner and Merriüm. 1979). [etriionids (Aberg et al.. 1993). and small mrtrnmals

(Wegner and Merriam. 1979: Hrnderson et 21.. 1985: Kozakiewicz. 1993: Bennett et al..

1994: Fitzgibbon. 1997: but see Wegner and Merriam 1990) have shown a response to

fragmented landscape structure. The possible mrchanisms dri ving th is response Vary. but

in general an afinity for some aspect of the landscripe - be i t proximity of remnünt

patches (Aberg et al.. 1993, corridors ( Wegner and Merriam. 1979: Hcndrrson et al..

1985; Memam, 1988, 199 1 ; Merriam and tanoue, 1990: Fitzgibbon. 1997). edge

(Donovan et al., 1997). diversity (Tapper and Bames. 1986; Jonsen and Fahrig, 1997). or

novel patches (Fisher and Merriam. in press) - coupled with a negative response to some

other aspect. results in a net increasr or decrease in abundiince in cornparison with a

native system.

Although pervasive in modem landscapes, anthropogenic habitat fragmentation is

not the sole fom of heterogeneity. Naturai processes also cause variation in landscape

pattern (see Wiens, 1976; Urban et al.. 1987 for reviews). In the Canüdian mixed-wood

boreal Forest. for example. relarively srnall topographic differences can cause marked

differences in the vegetation growing on a site. Fire. disease. pest outbreak. and

windthrow al1 contribute to the creation of a diverse mosaic of plant and animal life

(Hansson. 1997). While fragmentation bas becn the golden calf of landscape ecolog for

more than a drcade. very few studies hiive sxplored the possibility that landscapr

structure might inthence animal populations in nriturally lietero, aeneous tireas.

Natural landscapes are generally more cornplrx than anthropogenic ones. Incking

linear pattern and possessin~ higher fractül dimensions i Turner. 1 989). Native patch types

are generally contiguous except when rare in the Iündscape. Edges betwern native patches

(inherent edges) are generally more graduai than their anthropogenic coiinterparts

(inducrd rdges: Vollrr, 1998), and are generally characterised by gradua1 ecotones rather

than sharp edges (Hansson. 1994). Any existing :inthropogcniç frqrnrntation is intrusive

rather than enveloping (Harris and Silva-Loprz. 1992). An exümple of such a landscape is

the boreal mixed-wood forest of northern Alberta. In this region tirnber hürvest for pulp

has comrnenced within the Iast decade. creating small pockrts of anthropogenic early-

successional patches within the naturally heterogeneous matrix.

Like anthropogenic landscapes. perception of the degree of contrast betwern

different patch types in natural landscapes is species-specific (Wiens. 1976: Kotliar and

Wiens. 1990). By logical extension the intluence of landscape structure on animal

distribution is also species-specific, and depends on the oganisrn's specialization to

different pûtch types (Andren et al.. 1997). A behaviourülly tlexible (Wegner and

Merriam. 1990; Henein et al.. 1998: Pither and Taylor. 1998) or 'mosaic species'(sensn

Bright. 1993) would view the landscape as more homogeneous, or heterogeneously

undivided'(Addic0tt et al.. 1987) and would be able to use several patch types in the

mosaic. Alternatively. a behaviourally inflexible or sprcialist sprcies relies upon one or

two spscific pritch types. rendering othcr patdi types in the Iandscapz marginal or èven

unusable habitat (Bright. 1093: Andren et al.. 1997). For the latter species. if a required

patch type was sparsely distributrd within a mritrix of less suitable habitat. a naturally

heterogeneous Iandscape could pardlel an anthropogenicall y fragmentrd one. 1 postulated

that this was the case for North Amsrican r d squirrels.

In the rnixed-wood boreül Forest of northrrn Alberta where this study w;is

conducted. conifer is limitrd and occurs mainly in patchrs within the cispen matrix. This

suggesrs that red squirrels. as conifrr speciiilists (Smith. 1968: Kemp and Keith. 1970:

Rusch and Reeder. 1978: Riege. 199 1). would perceive i t as heterogeneous and would be

susceptible to the effects of Iündscape structure. I crnsusrd r a i squirrrls within threr

study areas. each with a different source of hrterogenrity: a Managed areri. a Burned areü.

and a Reference area. I tested the prediction that red squirrel presence or absence in these

areas was related to the composition and configuration of the landscape. measured at a

number of spatial scales.

The appropriate spatial s a l e at which ro quantify a landscape has becn n matter of

some debate. An organism's response to heterogeneity can occur at srveral spatia: scales

(Wiens, 1976, 1989; Morris, 1987). If habitat selection (either behavioural or functional)

is driving the response to landscape structure. thrn the integration of selection at several

nested hierarchies of patchiness will produce non-random distribution patterns at a given

scale (Kotliar and Wiens, 1990). For example. herbivores rnay respond to clurnps of food

plants within a stand. and stands within a landscape (Srnft et al.. 1987). Thiis. response to

landsciipe structure rnay occur at different grains, and different extents. and the relative

importance of these will diffcr. When exploring rhc possible influence of hndsciipe

structure on organisms'distribution. it is critical to investigtitc that relationship in the

context of an ecological process and the spatial s r i i l r tir which i t occurs.

In the absence of data tc suggest which eçologictil process might be the most

important in effecring the response to Iandscape heterogeneity. a prudent approach would

be to examine the Iandscape at several different spatial scales (Wicns. 1989). In this

chapter. I have quantifird the landscüpe üt fivr different cxtents and iit two different

grains. The five landscape rxtents wsre chnrncterised by circu lrir boundxies of di fferrnt

radii. The smallest radii (50 rn and 100 m) correspond to the scalr nt which red squirrel

foraging occurs (Chapter One): the largest radii purportedly correspond to the spatial

scalc ai which longer-range movements. such as thosr undertaken during dispersal. occur.

Regression analyses were performed to look for relritionships between red squirrel

presence and landscape s tructure as quanti tird iit thrsr di fferent spatial scülcs. The grain

of landscape analysis that yields significant predictors of squirrel presence should reveal

the level of patchiness that squirrels respond to. whereas the spatial extent at which

landscape structure becomes a significant predictor of squirrel presence should indicare

the ecological process driving red squirrels' response to natural heterogeneity in this

mixed-wood boreal sys tern.

3.1 METHODS

3.1.1 Squirrel presence

Presence 1 absence data for red squirrels were obtained through cal1 surveys.

Points were visited 4 times over the surnrners of 1996 (managrd grïd only) and 1997 (al1

grids except one Managed meso grid). The occurrence of red squirrel territorial calls over

a ten-minure period were noted tit rach visit: audible calls are assumed to originate from

squirrels within 100 m of the point. Squirrels were termed "presrnt" at a station where at

least one cal1 was henrd and "absent" where thry were not. within a given year. to yield n

binüry response variable.

3.1.2 Landscape classification

The study landscapes had bern mapprd using Alberta Vcgrtütion Index ( N I )

protocols and digitized using i\rc/Info grogrtiphic information systems i GIS ). Thrss

digital coverages. provided by Alberta-Pacific Forest Industries Ltd.. describe canopy

closure, dominant and subordinate stand types. and yeu of origin, for discrete stands

temed polygons. Polygons in these covr rages were reclüssi fied to describe the landscape

in two grains (sensu Kotliar and Wiens, 1990): FORCOV (forest cover) and UPLOW

(upland 1 lowland). The UPLOW reclassification described each polygon ris simply water.

lowland (wetlands and bogs) or upland (mesic to xenc areas with no standing water). The

FORCOV reclrissification (Table 3.1) was based on dominant stand types to yield ten

forest cover classes on the Managed and Reference grids. On the Bumed grid the

38

dominant tree species within the polygon was combined with burn information to

categorize the polygon as bumrd lowland (bumed SBLT or MARSH). drsignatrd

BULOW. bumed deciduous (BDEC). or bumed pine (BPINE).

The landscape structure around each grid point was quantified using

FRAGSTATS (McGarigal and Marks. 1994) at fivr spatial scales. To examine the effects

of scals. landscape boundaries of 50 m. 100 m. 150 ni. 500 m. and 1000-m radii were

created around rach mega grid point. Liindscapr structure wiis also and ysèd on the meso

urids at 50 m. 100 m. and 250 m. Landscaps configuration and compositionül variables at b

cach scalr. around rach point. wrre clilçulated for the FORCOV - reclassified coverages

and the UPLOW - reclassified covrrages.

Table 3.1: Reclassification protocol applied to Alberta Vegetation Index forest inventory maps to produce the landscape composition variables used in analysis. Variables printed in bold were transformed and included in regression analyses.

---

AVI Forest Cover Type Relative Amount Variable

CUT

DEC

anthropogrniç vrgetlitrd anthropogenic non-vrgetated

cut

trembling cispen balsam poplx birch

non-forest type

trernbling rispen birch balsam poplar

and jack pine lodgepole pine

trembling rispen birch balsam poplar

and white sprucr black spnice 1 arc h balsam Cir

pure oi mixed

Table 3.1 (cont.)

XVI Forest Cover Type Relative Amount Variable

SBLT

jack pine lodgepole pine

or jack pine lodgepole pinc

and l arc h black spruce white spruce lodgepole pine balsam f'ir jack pine

black spruce or larch or

black spruce l ;ire h

and jack pine lodgepole pine black spruce white spruce balsam tir lrirçh

white spruce baisam fir

or LV hite spruce balsam fir

and l arc h black spruce lodgepole pine jack pine balsam fir

pure

20-30%

pure

pure

3.1.3 Areal composition

In Arclinfo ii buffer was creüted that extrnded approximately 1 km out from the

outermost points on the klanaged. Reference. and Burned Areii mega ;ind rnsso grids. The

coverages were clipped. and these new digital covrragrs were imported into ArcView

GIS (Esri Inc., California. USA). In ArcView the total areas in square metres ofeach

FORCOV class type were calculated. to describe the overall composition of each study

rireri.

3.1.4 Lairdscape compositiorl

The diffrrent FORCOV cllissifications generated by our recliissification procedure

are listed in Table 3.1. However. many of thesr occurred in small amounts within each

radius landscape. leading to a Poisson distribution rather than a normal one. Variables

were selccted that did not have a large preponderancr of zero vtilues in the dataset: those

thlrt were included are printed in bold font in Tuble 3.1.

Thrsr variables were still not normülly distributrd ÿt smaller sçales (Kolmogorov-

Smirnov test for normality; p>0.05: SPSS Inc.. 1996) and so were transfomed usin; the

formula:

arcsin (square roottp) }

where p is the proportion of a @en habitat type within the landscape lit riny givrn scale

(Zar, 1996). All composition variables measured at al1 scales were transfomird using this

method. This resul ted in variables not significantly diFferent from a normal distribution

(Kolmogorov-Smimov test for normality; pc0.05; SPSS 1996), or variables that more

closely approximated normality.

3.1.5 Londscape configuration: Heterogeneity

Although FRAGSTATS output includes many configuration variables. only a few

were selected for analysis. based on limitations of our vector dataset and type of

information that the variables provide (Table 3 . 2 ) . l'hese indices are highly correlüted.

and provide much the same information. To condense thrrn inro ii single mrüsure of

Iündscape configuration. cdge densi ty (ED), largsst paich indss (LPI). mean parch size

(MPS), mean shape index (MSI). patch density (PD). patch richness density (PRD). and

Simon's rvenness index (SEI) were entered into a principle cornpoticnts analysis (PCA)

iising factor andysis by principlr componrnts in SPSS (SPSS Inc.. 1996). The PCA was

run for èach sclilr. within züch grid. within rach rireü. This analysis incorporated

information from each configuration variable into ii single componcnt thüt 1 termed

heterogeneity (HET). Essentially. as HET increiises. so does cdge density, mean shape

index. patch density. patch richness. and evenness: largest patch index and mean patch

size decrease. This HET variable was used to represent Ilindscüpr configuration in funher

analyses.

3.1.6 Stati.stical analysis

To look for differences in the explanatory power of Iandscape structure at

different scales. logistic regression analyses (Hosmer and Lrmeshow. 1989) were used to

search for relationships between squirrel presencdabsence and the landscape variables on

the Managed. Bumed. and Rrferewe mesa grids. A multiple logistic regression mode1

(SPSS inc.. 1996) regressed squirrel presence agüinst the composition variables listed i n

bold in Table 3.1 and HET, measured rit each scale. Also included were the interaction

terms between HET and those transformed composition variables that made up the Iargest

portions of the Iandscape. 1 transformed variables for a bsttèr mode1 f i t cilthough normiil

distribution of variables is not ÿs n r c r s s q for lo_oistiç regression us it is for linrtir

regression (Menard, 1995). A forward conditional selection procedure with a significance

for inclusion criterion of p=0.05 was used (SPSS Inc., 1996) as this method reveals those

prediçtor variables rhüc explüin the rnost variation in the rzsponsr variable (Hosmer and

Lemeshow. 1989: Menard. 1995).

Regressions were ccirried out for variables nitasureci Lit eüch s d e hcpariitely. For

the 1000 m-scale ünülysis adjacent points were dropped systematically to prevent overlap

and maintain independence between landscapes.

To augment the number of lirelis invrstigüied. rrgressions were also performed on

data from the !Mcinaged. Burned. and Rrfsrenci: meso srida. Adjxrnt points were

dropped systrrnatically ür the 250-m anaiysis on the meso grids to muintain st~itistical

independence. As the scale range was limited on these grids. and the number of data

points inciuded in analysis differed from the mega grids. this analysis was conducted only

to determine if the landsclipe variables predicting squirrel presence were the sarne as on

the mega grid systenis. not for sciiliir compürison.

Table 3.2: ERAGSTATS landscape configuration variables chosen for use in analysis. The relationship between each variable to the PCA component HET (heterogeneity) is shown. An increase in HET within a landscape indicates more edge, no large contiguous patches, srnall mean patch size, cornplex-shaped patchrs. a high density of ptitches and different patch types. and high landscape diversity.

Variable name Description Variable acronym

Con tribut ion to HET

ED

LPI

MPS

MSI

PD

PRD

S IEI

edge density

Iargest piitch index

mean pütch size

rnean shape index

patch density

patch richnrss density

SirnonS Evenness Index

A rneiisure of total edge + brtween al1 patch types in the Iandscape, per unit area.

The percçntage of total - landscape area cornprisrd by the liirgest patch.

The average patch size of al1 pütch types in the landscape.

Compares the shape of a patch + to a circular standard; averriges xross dl patchss in the landscape.

The numbcr of patches of al1 Iündscape types. per unit area.

The number of patch types present. per unî t riren.

The observed piitch divrrsity. dividrd by total possible diversity. while controlling for patch richness.

Multiple stepwise logistic regression milyses wrre repcitrd for vciriablrs

measured at each scalr, tit each Area. and within each year, separately. These analyses

were expected to reveal the important predictors of squirrel presence rit eiich scale. The

spatial scale at which the regression rnodel has the greatest deviance rxplained

(predictive power), as measured by the Nagelkerkr R' (Na_prlkerke' 199 1 ) should indicate

the spatial scale at which squirrels respond to Iündscape structure.

3.2 RESLILTS

3.2.1 The upland / lowland grain

The percent of upland in the study ürea did not predict squirrel presence at itny

scale in any area; nor did FRAGSTATS configuration variables derived h m these

reclassified landscapes. The remainder of the analyses focused on the forest cover

(FORCOV) reclassi fication.

3.2.2 Forest cover: Composition of the stiidy areas

Figure 3.1 depicts the composition of the ~Mantiged Area. DEC dominates the

landscape. covering 43% of the area. SBLT constitutes the next lurgest ürea iit 1 1%. PJ

(7%) and SW (2%) comprise the remaining conifer element. iMDS and MDP cover only

1% and 2% of the landscapr. respectively. Only 9% of the landscüpe lias bcen harvested

for timber (CUT): another 2 4 hlis been clrüred for roads. pipelines. anci agriculture ( A-

OPEN).

Figure 3.2 portrays the composition of the Burned Area. Ii is dorninated by

BULOW. which covers 34% of the areü. Unburned SBLT covers 10%: burned m d

unbumed pine cover 1 1 % luid 2%. respectively. Unbumed DEC comprises 1 2 8 and

bumed DEC another 7%. Salvage logging hüs occurred on 2% of the landscüpe.

Like the Managed Area. the Reference Area is dominated by DEC (40%) (Figure 3.3).

SBLT (21%) and PJ ( 18%) müke up the majority of conifer. MDP (3%) and MDS ( 1%) is

lirnited. as is SW (2%). Two percent of the area hüs been cur and another 1 % cleared for

roads and pipelines.

Figure 3.1 : Landscape composition of the Managed Grid.

Figure 3.2: Landscape composition of the Bumed Grid.

Figure 3.3: hdscape Composition of the Reference Grid.

?

'EN

3.2.3 Goodness of fit of the logistic model

The -2 log likrlihood (-ILL) cornputed in logistic regression approximates a X2

distribution and allows one to test if the model is significantly different from a logistic

function (Menard, 1995). Appendix 1 lists the -2LL's of each of the models and the p-

value associated with it (Zar, 1996). At every scale on the mega grids. the modrls created

do nor deviate significantly from a lo;istic funcrian. They do. howcvçr. drviate sli_ohtly

on the Maniigcd meso grids at 50 m and 100 m ( 1996). and on the Reierence rneso grids

at 50 m. In thest: cases the -2LL values of the models are very close to the ' X 2 values

associatrd with cc = 0.05. Therefore the logistic function is the best one to describe the

reliitionship betwern the Iiindscape variables and squirrel presence / absence.

3.2.4 Landscape striictiire and sqziirrel presertce

The predictive powzr of the regression models US niecisured by the Nagclkerke

R') changed with spatial scale. but did not do so consistently ücross grids. Different

landscape variables significantly predicted squirrel presence in each grid. and at each

scale within a grid. The attained statistical significance. the percentage of correctly

classified cases. and the number of cases frorn al1 modrls are listrd in Appendix 2.

On the Mnnaged Area rnega grid in 1996. the prrdictivr power of landscüpe

structure peaked at 100 m but was roughly similar iicross al1 scales except 1000 m. where

no variables predicted squirrel presence (Figure 3.4a). Heterogeneity and jack pine were

significant at 50 m: deciduous and black spruce. or interactions. were significant at larger

scales. On the Maniiged mes0 grid in 1996 deviançe rxplained peaked iit 150 m. with

deciduous and heterogeneity as significant predictors at different scdes (Figure 3%).

50

In 1997 on the Managed Area mega grid, R' decreased from 50 m to 250 m. then

increased from 250 m to 1000 rn (Figure 3.lb). At 50 m to 750 m. conifer composition

variables were significant. At 500 m and 1000 m. intrrction terms brtween çonifer

composition and hrterogeneity were ülso significant: interestingly. there is a nrgative

relationship betwcrn hsrsrogeneous black spruce and Irirch, and squirrel presence iit the

500-m scrile, unlike the positive rellitionships obsenrrd at other scslrs. On the Manliged

mes0 grid in 1997 only HET was significant. rxplaining a low rimorint of deviance (R'

~ 0 . 2 ) rit 50 rn and 100 m (Figure 3.5b). No vxiables were si@ïcrint at 750 m.

On the Rekrence Area mega srid. predictive power was greatrst at the 250-m

scalr (Figure 3.6a). Drciduoits wiis a significant ncgativi: prediçtor of squirrel presence

dong with marsh. at 50 m. Conifer composition variables were significant iit the 100 m

and 250 m scales. Deciduous was the only significant (negative) variable at 500 rn:

nothing wiis significant at 1000 m. On the Refsrenct: meso grid. deciduous nrgiitivrly

predicted squirrel presence at 5Om. whilr white spnicr and jack pine predictsd squirrel

prrscnce at 100 m. with greatrr deviance rxplüincd (Figure 3.6b). Nothing was significant

rit the 250-m scale.

On the Bumed Area mega grid. predictive power was highest at 50 m. with no

significant modeis at 250 m and 500 m (Figure 3 . 7 ~ ~ ) . Minsd deciduous-pine and the

heterogeneity-deciduous interaction were significant nt the 50-m scÿlr: the latter

negatively predicted squirrel presencr. Deciduous was a positive predictor at 100 m.

unlike the other Areas. On the Bumed meso grid predictive power is also greater ai 100

rn, but different variables are significant at each scale (Figure 3.7b).

Figure 3.4: The drviance explained by multiple logistic reyession models in which squirrel presence wns regressed against landsclipe structural variables. The significant variables (pc0.05 ) included using a forward selection procedure are listrd on top of the deviance bars in the order in which they rntered the model.

Managed Mega Grid 1996

HET APJ

ADEC*HET XSBLT -ADEC

ASBLT -ADEC ASBLT

Ltindsciipe Radius

Managed Mega Grid 1997

Lrindscapc: Radius

Figure 3.5: The deviance explained by multiple logistic regession models in which squirrel presznce was regressed ilgüinst landscape stnicturlil variables. The significant variables (p<0.05) included using LI forwtird selecrion procedure are listed on top of the deviance bus in the order in which they entered the model.

Managed Meso Grid 1996

50 100 25 O

Liindscrioti radius

Managed Meso Grid 1997

Ltindscape Radius

Figure 3.6: The deviance rxplained by multiple logistic regression models in which squirrel presence was regressed against landscape structural variables. The significant variables (pc0.05) included using a forward selection procedure are listed on top of the deviance bars in the order in which they entered the model.

Reference Mega Grid 1997

ASBLT APJ

APJ -AMDS

- . . . .

50 100 250 500 1000 Llindscape Radius

Reference Meso Grici 1997

1.0 ;

50 1 O0 250

Landscape Radius

Figure 3.7: The deviance explaincd by multiple logistic regession rnodels in which squirrel presence was regressrd agains t landscape structural variables. The significiint variables (pc0.05) included usin_o a forwcird selection procedure are Listcd on top of the deviance bars in the order in which they entered the model.

Burned Mega Grid 1997

AMDP ADEC

Burned Meso Grid 1997

XDEC -ABDEC .AhlIDP

XBuLOW*HET ABPINE*HET

Lrindscape Radius

3.3 DISCUSSION

3.3.1 The explanatory spatial scde

The reclassification of forest cover types into itpland or lowland proved too coarse

a grain to predict squirrel presence. The data suggest that squirrels are respondins to

patchiness that occurs at a finer resolution. Although this conclusion is intuitively

obvious from the known habitat preferences of red squirrels (Smith. 1968: Kemp and

Keith. 1970; Rusch and Reeder. 1978). this exsrcise serves to demonstrate the importance

of detrrmining the appropriate grain of anal ysis bcforr searc hing for urganisms' respnse

to landscape structure.

At the forest cover (FORCOV) grain. no one Iandscüpr cxtent consistently

predicted squirrel prescncr on any of the mega grids. On the Manngrd Area. more of the

deviünce was explainrd nt the 50 m scülr than the 100 rn or 250 m scales. as

hypothrsisrd. Howevrr the difkrence betwren scalés wcis marginal: drviançr explainrd

actually peaked at the 500 m and 1000 m scalrs. On the Burnrd mega grid. deviancr

rxplained peaked at 50 m. with nothing significant at scales beyond 100 m. On the

Rrference mega grid the 250 m scale brst predicted squirrel presence. although there

were significant rnodels üt al1 scüles. Clearly, a single spatial scalr at which landscapr

structure affects squirrels' distribution is not disccrniblr using this iipproach.

Response to patchiness at multiple landscapr rxtents (hereafter synon ymous with

spatial scale) has been observed in a host of othrr cross-scalar studies csee Wiens. 1989

for review). h a study very similar to this one. Monkkonen et al. ( L997) found that the

probability of patch occupation by flying squirrels in Finland was related to landscape

structure quantified at severai different landscape radii. including the presurned home

range radius of an individual and the 'local range' radius of srveral individuals. but not at

the population scair. Tree frog occupation of ponds in the Nethrrlands was found to be

related to landscape elernents at several concentric landscape extents. as well as local

factors (Vos and Stumpel. 1995). Rukkr and Midtgüiird ( l91)S) found that response of

fungivorous beetlès to spatial structure rneciliured iit local (babidiocarp) and largér (Forest

island) scales but not intermediate ones. Colony performance of Forest tent caterpillars in

Alberta (Rothman and Roland. 1998). as well lis rites of parasitoid infection (Roland and

Taylor. 1997) were dso predicted by fragrnrn tcd forest structure at srveral crncentric

spatial scales.

The scales of forüging range and dispersal might be the two rnost likely to

intluence patterns of animal distribution (Morris. 1992). but no evidence of a strong

response to eithcr was found in this study. Kotliar and Wiens ( 1990) suggçst that since

hrterogcneity occurs over a range of scales of patchiness. thrn responsr to that piitchiness

also has an integrated multidimensional property. For example. red squirrels rnay be

responding to iiggregations of conrs within ii trer. trees within stand. stands within a

stand aggregate. and aggregates within a landscape. The intluence of several different

patch levels in the hirrarchy interact to effcct the responsr obsrrved at any givrn spatial

scale (Urban et al.. 1987: Wirns et al.. 19931. Therefore the 'ecological neighbourhood'

(Addicott et al.. 1987) may be delimited by the spatial and temporal scale associated with

one particular ecological process. but it is likely that several processes operating

concurrently ultimately influence the probability of patch occupation by red squirrels. The

relative importance of each process might be context-specific. My data suggest that the

scale at which response is mont prominent (hishsst deviance explained) varies with areal

composition. This is aiso true of the landscapc stmctural variables thrmselves.

3.3.2 Explanatory variables differ betwern areas and scales

Naturaily derived heterogeneiry, as quantifird by landscape composition and

configuration. did predict red squirrel presence in a11 three study areas. The important

variables selected by the regression procedure changed with landscape radius.

Heterogeneity (HETI was often inçluded as (i positive signifiçant pixiiçtor of squirrel

presence on the Managcd and Reference grids ( 1997). Hetrrosencity ülonr wiis

signi ficant on the Managed meso grid: positive intertictions wi th conifer composition

were tound on the Mannged megii grid and both Burned grids. This interaction suggests

that whrn the relative amount of conirer W U low. hrtrrogeneity wiis important in

predicting squirrel presence. Small piitches of çonifer with I q e r arnounts of edgé habitat.

juxtaposed with othrr marginal habitat types (deciduous). hüd highrr probübility of

occupation. Whrre the relative iimount of conifer in the lnndscapr increased. the

importance of heterogeneity decreased; probability of occupation was higher in

contiguous conifer tracts than in t'ragmented (naturally or otherwise) patches. A

contradiction occurred at the 500 rn scale on the Managed mrga g id ( 19971. whrre

heterogeneous conifer negatively predicted squirrel presence once conti_euous coni fer

entered the model. in contrast. heterogeneity did not predict squirrel presence at any scale

on the Reference mega or meso grids. This area is characterised by luge contiguous tracts

of conifer (Figure 3.3), which may explain why heterogeneity failed to predict (positively

or negatively) squirrel presence.

The relative importance of contiguous versus patchy conifcrous merits further

investigation. A source-sink dynamic rnay be operating (Pullirim. 1988: Danielson, 1992).

wherein srnall sink patches of conifer set into a deciduous matrix can support individuals

that 'overtlow' from larger contiguous source patchcs. but whrre mortality rxcrrds

natality. This is suggested by the habitat-spccifis dernogriiphic data of Kcmp and Keith

(1970) and Rusch and Rrrder ( 1978). Such ovrrtlow systems can be important in

inaintaining a population of territorial animals where juveniles may have to wait until a

temtory suitable for reproduction becornes avriilable ( i .r . . ideal pre-rmptive distribution:

Pulliarn and Danielson. 199 1 ). This supposition is supported by the nrgative intluence of

heterogeneity at larger scales. which wouid be exprcted to effectively 'draw' individuals

awiiy from a site rathrr than contribute to occup;ition probability. h more involved

investigation is required to test this hypothesis however: this study providrs information

on patch occupancy. but habitat- and landscape-specific demographic rates are needed to

provide more insight into the mechanisms behind the distribution patterns obsrrvrd.

In terms of landsciipe composition. the amount of conifer (black spruce. white

spruce. jack pine. and mixed conifedasprn) was positivrly. and the amount of deciduous

negatively. related to squirrel presence on the Managed and Rrferencr Grids. This

occurred at the local scale and at larger scales. The relative importance of e x h patch type

in the mode1 changed with scaie and with grid. The importance of conifer in general was

obviously expected. as squirrels rely on conifer for ovenvinter survivd (Smith. 1968:

Kemp and Keith, 1970: Rusch and Reeder. 1978: Riege. 199 1 ). It is interesting to note

that the presence of conifer beyond the patch - at radii of 500 m and 1000 m - also

predicted squirrel presence, often with variables dissimilu from those that at smaller

scales. This suggests that there is a cross-sçalar association btltwern differen t patch types

within the mosaic. Deprnding on local frequency. somr pcitch types ( c g . black spruce)

may be important for squirrel presence iocally, in conjunction with other patch types (e.y.

jack pine) within the largtr landscape. X sirnilar situation was discovered by Monkkonen

et al. ( 1997); thry found that nlthough open habitats nrgatively predictrd tlying squirrel

presence üt larger scales. ni the scdr of individual mowmrnt the prescncr of open

habitats positive1 y predic ted squirrel prrsrncr. Local-scdr Iieterogenei t y hvoured

squirrel presence through the juxtaposition of pÿtch types and the presence of rdge.

although at larger scales open habitats sirnply represented non-preferred habitat. A cross-

scliliir patch-type interaction. or a shift in the direction of ü rrlationship with sçalr. has

rarely brrn considered and htis sipnificünt implication5 for rculogisth iind thoe \crkin_r ro

manage wildlife habitat.

Also of vital importance is the iden that the çontrxt in which the landsclipe is

situated is a critical component of the overall rffect of landscape structure. On the Bumed

grid. for rxamplr. the amount of deciduous cover positive1 y predic ted scpirrel presence

(mega and meso grids) contrary to results frorn the Mtinaged and Referencr cireas and

known red squirrel habitat preferences. In the Burned area whrre little mast remains. a

few mature conifer trees located in stands classified by AVI maps as unbumed deciduous

are possibly important habitat for the population. Context-dependency was noted by

Andren ( 1994, 1996, 1999) who suggested that in areas where large tracts of contiguous

suitable habitat are present. landscape composition would be the driving influence of

animal distribution. In areas where suitable habitat is rare, then configuration is more

important. Investigation of the importance of Iandscape context in mediating response to

landscape structure has been addressed in few studies and mainly as a periphçral issue

@.y., Aberg et al.. 1995: Delattre et al.. 1996: Donovnn et al.. 1997: Hansson. 1998). The

problem becomes especially cornplcx when one considers that the response ro landscapr

structure diffus not only across space. but iicross time iis well.

3.3.3 Di/fereences betwecn years

The differences between regression models drawn from 1996 and 1997 data rire

marked, urspite the Fact that from one yrar to the next. only the amount harvestsd (and

conversrly the amount of deciduous habitat) changed slightly. Noncthelrss the relative

predictive power of tach scale changed between years: in 1 996 landscape structure at

1000 m radius failed entirely to predict squirrel presrncr. but i n 1997 most of the

deviance was explainrd at this scale. The interaction brtween heterogenrity and

deciduous was important at the small sçalrs in 1996: in 1997 the hrtrrogeneity -jack pinr

interaction was significant üt large scalcs. These results indicüte a large amount of

temporal variability in squ irrels' responsr to landscape structure in this system.

Patchy distributions of organisms are often speculiited to be the product of tïtness

differentials among patch types: ti tacit assumption of this is thüt there is no temporal

relaxation of selection pressure to occupy prime habitat (Wiens. 1976). This assurnption

is often violated in heterogeneous habitats throiighout the year. Population dynamics are

often adapted to the degree of temporal variübility in a system, especially in systems

where natural disturbance is frequent (Wiens. 1976) such 3s the boreal forest (Hansson.

1992). Red squirrels may br rspeciiilly prons to temporal tluctuations in habitat quality,

as the cone crop produced by conifers (white spruce and black spruce. but not necrssarily

jack pine) varies dramatically over time. and red squirrel survival, immigration. and

hencc density are known to fluctuate in response to cone crops (Wheatley. 1999). Habitat-

dependent variability in demography and density wcluld have a signifisant effect on a

source-sink dynamic if i t were operating in this systrm. and would iiccounr for the

striking change in the relative cffects of landscapr structure ovsr rime. This temporal

variability should be alÿrming to most landscape ecologists, as many studies are

conducted within the space of only one year. The next genrration of landscape ecology

studies should focus on the causes of this temporal variation. (is this may shed light on the

al!-to-oftrn ignored mechrinisms brhind orgcinisrns' responsc to Iiindscapr structure.

3.3.4 Manageme~t implications

This study was rnandated undrr the Sustainable Forestry Management Network of

Centres of Excellence to determine the importance of landscapr structure in detemining

animal übundance in the hopes of creating management plans thüt would minimise the

impact of tirnber huvcsting on wildlik. The percent of the Iündscitpr ciit (CCT) was not

a significant predictor of squirrel presrnce in the Manliged A r a This may bc because

only 9% of the landscape was harvested (Figure 3.1). The radio tracking data (Chapter

One) show that squirrels are found in the cutblocks and are not behaviourally restricted

from travelling or foraging in them. Extrapolation of these results to landscapes with

more extensive modification is not possible: evrn extrapolation brtwrrn similar naturall y

heterogeneous areas is untenable. Given the high degree of variability in the patterns

detected it is impossible to create a universal harvest management plan. For example, one

may conjecture thot where black spruce is scarce in the landscapr removal of the aspen

habitat may exliggerate effects of isolation (sre Andren 1994. although there rnay be

little impact in areas where spmce patches are contiguous. A better understanding of the

rnechanisms driving distribution. rrither than pattern-seeking, is required before sound

management recornrnendations can be made. These müy have to be made on a case-by-

case basis: as it stands no single variable or spatial scale hüs been shown to bs

consistently important. The only sound conclusion is rhat Iandscapr structure dors

intluence the distribution of red sqiiirrels in this area and that planning should occur on

the landscape Irvrl. as red squirrels are influenced by large-scalr landscape structure.

3.4 CONCLUSIONS

Nütural (non-anthropogrnic) landscape composition and configuration did predict

squirrel prrscncr. txplriining up to 70% of the drviance in the sq~iirrel dataset. indicüting

that response to Iündscape structure is not an artefact of anthropogenic fragmentation. but

is a naturally occumng phenornenon. The scale at which this response occurred varied

with landscape context. with different scales dominating on the Burned. Managed, and

Reference grids. It also vaned between yecirs on the Managed grid. There is no evidence

to suggest that response to landscape structure is associatrd mainiy with local-scülc

processes such as foragin; or alternatively with Iÿrger-scde processes such as dispersal. It

is possible that a variety of processes interact to generate the observed distribution pattern

of red squirrels.

The variables that predictrd squirrel presence varisd with spatial scale and

Iandscape context. They also changed between years within the iManaged grid. In general.

increased heterogenei ty and conifer was positive1 y associated w i th squirrel presence,

while contiguous deciduous forest predicted sqiiirrel absence. The shift in relative

importance of Ilindscrtpr vtirirtblrs ma' br î function of the fluctu:itions of resource use

by squirrels in response to habitat qmlity (çonr crop). A shift in ernphüsis from pattem-

seeking to hypothesis testing is required to rstablish the mechanisms causing the multi-

faceted response to heterogeneity observed in this study.

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Chapter Four

The Influence of Local Vegetation on Squirrel Response tu Landscape Structure

%O INTRODUCTION

Context sets the stage for any ccological process. The srarch for general laws or a

unifying theory in rcology has only substantiüted the importance of variability in defining

system function (Allen and Hoekstra. 1992: Lawton. 1999). In an (ironic) attempt to

organise and classi fy ecological inconsistêncies. hierarchy throry wüs implemented.

Implicit in hierarchy throry is the notion thüt ';cale. both spatial and temporal. is variable.

and chat patch characteristics change dcpending on the way in which they are virwed

(Allen and Starr. 1982: Allen and Wyleto. 1983). Traditionaily patches cire considered

internally homogeneous, as they have bcen in this study. Grain. the lowest level of

resolution. has been set as the forest srand type as definrd by Alberta Vegetlition Index

(AVI) data. Howevrr. CI priuri üssumptions of grain ignore the friçt that intemal

heterogeneity within a patch does exist (Kotliar and Wiens. 1990). Whiie apparently

interna11 y homogenous at some scale of anal ysis. reduc in; the scale (increasing

resolution) of analysis of the patch reveals interna1 heterogeneity (Wiens. 1989). This

heterogeneity may exhibit rither randorn or clurnped dispersion, low or high contrast.

Elements with high contrüst and aggregtion form patches wirhin the contrxt of the larger

patch. This hierarchy of patchiness progresses as one decreases scale. Alternatively. the

areas we have defined as patches can aggregate to f o m patches on a larger scale (Wiens,

1989; Kotliar and Wiens, 1990).

69

Becnuse processes at any scale ore iiggregarrs of thosc occurring at several lower

levels in the hierarchy (Pickett et al., 1989: Senft et al.. 19871. a correlation betwern some

independent variables measured at a pre-defined grain, and the response variable, cannot

always be detected even if a relationship does exist. In the scnse that response to

landscape structure rnay change as the observer changes txtrnt (Cliiiptrr Three). response

to structure rnay also change as one changes grain. Siniply sratcd. the intsrnal

composition of a patch may alter the dstectability of landscape effects oprrating on

organisms within it. In this chüpter 1 endeiivour to iinswer the qliestion: Does the apparent

relationship between landscape structure and red squirrel presrnce change when the

effects of local vegetation within the patch hiive been xcounted for'?

As previously reported. the Norrh Amsrican red sqiiirrel is a conifer spscialisr.

relying on Ahirs. Pirirts. and Pice~i seeds to ovrrwinter (Smith. 1965: Kemp and Keith.

1970: Rusch and Rrcdrr. 1975: Riege. 199 1 ) . It also tëeds on mushroorns. berries. nuts.

and other ephemeral foods opponunistically (Giirnrll. 1983; Yahnrr. 1987). Therefore.

one may expect that in the summer nionths (iMny-August). sqiiirrels may be hund in

association with a varirty of vegetation types. They probably do nor rstüblish territories in

non-conifer habitats (Smith. 1968: Kemp and Kcith. 1970). but do use thern to feed.

luvenilrs disperse through them in search of an cmpty territory. Squirrels' use of the

landscape, and hrnce the effects of structure. may Vary depending on the context in which

a squirrel is found.

The Alberta Vegetation Index (AVI) data from which the landscape structure data

originate are derived from air photo interpretation. Forested stands are classified by

species, age of origin, and canopy closure. Although gound-tmthed for consistency. fine-

scale d e t d is missed or averaged nway in such a coarse-seale analysis. In this study (and

many, perhaps al], others) a patch as drfined by the caveiit of internal homogeneity is not

always a patch in the truest sense. Interna1 heterogeneity - local vegetation - may guide

processes that confound (or exaggeratr) our ability to detect the effects of landscape

structure.

Other researchrrs investigating landscape effects have included in thrir ünalysis

variables descnbing local vegetation. ml thrse variables are often the onrs that exp!ain

= some the most devinnce in the response variables. with landscape qtructure explainin,

iiddi tional drvirince rifter the îict (Verboom and van Apeldoorn. 1 Y !JO: Fi tzgi bbon. 1993:

van Apeldoorn et al.. 1994; Vos and Stumpel. 1995: McGarigal and iMcComb. 1995: but

see Celada et al.. 1994. whrre vegetation w3s not significant). To determine if red

squirrels in the mixrd-wood boreal show ü similar relative rcsponse to vegrtation and

landscape structure. the regrrssion models of Chapter Three have brrn modifird to

includr internal patch characteristics: that is. ü variable dsscribing the number of conifer

trees within plots at ü site. I predict that 1 ) the number of conifer trees at a site will be a

significant predictor of squirrel presence and that 2) landscape structure will be a

significant predictor of presence after trer number has bren üccountrd for. Given the

disparity of the responses to Iandscape structure displayed in Chapter Three. I also predict

that 3) the relative influence of vegetation and landscape structure will differ between the

Mrinaged. Bumed. and Reference mega grids.

Most landscape studies of this nature have chosen study sites priori based on

known habitat preferences of the animal and thus have a low range of variation in their

patch characteristic (vegetation) datasets ( e-g. Monkonnen et al., 1997: Fisher and

Memam, in press). No studies were uncovered that have addressed the possibility that

response to landscape may xtually differ deprnding on the local habitat: thar is.

organisms in patches with one set of vegetational charticteristics may have a different

relationship with Iiindscape structure than rhose in patches with a different set of

chriracteristics. In terrns of hierarchy theory, srlection occurring at lower hierarchical

lrvels may alter the effects of selrction observed at higher levels.

To address this question. 1 dividrd the red squirrel data into two subsrts: sites with

conifer trees present. and sites üt which çonifrr trers wrre scarcr. L then reprtitcd the

landscape ancilysis as previousl y describrd on èach su bset sepürately. to determine i f the

relationship between landscape structure and squirrel presence changed with local

vegetation. Disparity between the two analyses would suggest that diffrrent mechrinisms

operatr within eüch subset of patch types. Pattern-serking nt different cxtents has

rrvealed varying influence of Iandscape structure lit eüçh: by rlir same roken pattern-

seeking at different grains may begin to reveal potential mechanisms thar genrrate the

obsrrved patterns of animal distribution.

4.1 METHODS

4. I . 1 Vegetation Sampling

The vegetntion ai rach prid point was assayed in the summers of 1996 and 1997.

Three IO-m by 20-m plots were surveyed at each site: one at the point and two at

72

randomly selected bisected cardinal directions. The Iatrrr two were placrd 65 in out from

the point and it was therefore assumed that vegetütion survryed would be representative

of an 85-rn radius around each point. The species and number of conifer trees (Piceri

glmicci, Picen marinno, Pinus bnnksinntr, Abics bnlsmnea. and Lnrix lriricincz), was

recorded and averaged across plots ro obtain ü single abundance value per species per

point. Thess abundance values were surnrned ticross species to yirld the variable

SUMCON. The squirrel presence / absence dataset was collrcted as described in Chapter

Three. To üscenain the relative importance of Iandscape structure and local (within-

patch) vegetation. two approaçhes were employed.

41.2 Building a forced logistic rnodel

Ln the first approach. a multiple logistic regression model wns built in blocks

(SPSS Inc.. 19961. In the first block. squirrcl prrsencr was regressrd ~igainst SUMCON

using a standard 'enter' model (SPSS Inc.. 1996). In the second block. aII of the landscape

structural variables used in Chapter 3 were included in a multiple 'forward conditional'

logistic regression procedure with an inclusion criterion of p = 0.05. This model thus

tested for a relationship between squirrel presence and SUMCON: if sigificmt. i t

retainrd this model with its explainrd deviançe and searched for Iiindscüpe variables that

might explain further drviancr in the 1997 sqiiirrrl datüset. This analysis was repeated for

the Managed, Reference. and Burned Mega grids iit e x h scale.

4.1.3 Splitting the squirrel dataset

Each site was designated 'conifer-abundant' or 'conifer-poor based on the average

number of trees per point. Those sites wirh 1 or lrss conifer trers prr point were

considered 'conifer-poor'. Those with more than 2 were denoted 'conifer-abundant'. The

squirrel dataset was then divided into subsets based on each site's conifer designation.

This division is somewhat arbitrary but was designrd to split the daraset into roughly

equal parts. Given our vrgetation sarnpling design. zero conifer fo'ound within plots iit ü

sire does not necessarily imply that çonifer are coinplrtely absent frorn that site. so

splitting the set using zero conifer per plot would also b r somewhat arbitrary.

Data from the Mmaged and Reference areas were used. as the Bumrd area had a

low number of squirrel presencrs thiit did no[ lend itself to dütasrt division. Only

variables rneasured at the four lowesr scüles were used. due to low siirnplr size at the

su bsarnplcd IOOU-m scals. Analysing each su bset srparatel y. squirrel presrncr wüs

regressrd iigainst landscape structural variables using a multiple 'forward conditional'

logistic regression analysis (SPSS Inc.. 1996). using the same variables and inclusion

criteria outlinrd in Chapter Three. This analysis wns enprctrd ro show Jiffrrrncc

response to landscapr structure by red squirrcls living in conifer-poor and conifer-

ribundanr habitats.

4.2 RESULTS

42.2 The forced mode1

The SUMCON variable. representing the average number of conifer trres per

point summed across al1 conifer species. significiintly predicted red squirrel presence on

the Managed mrga gnd (n= 58) (Figure 4. la; see also Appendix 3). The R' of these

regressions was 0.295.

Figure 4.1: The relative effects of local ve_oetation and landscape stnicture. The deviance explained in squirrel presence by the number of conifers ar a site is presented in black bars. Further deviance explained by landscape structure at each extent is illustrated in grey bars. The significant variables in the final model. and direction of the relationship. are listed on top of the deviance bars.

50 100 150 500 I O 0 0

Llindscape Radius

The 1000-m radius, which had a lower n ( 16) because adjacent points were dropped. had

a higher R' (0.699). After local conifer was accounted for. landscape structure explained

iùrther deviance at al1 scules rxcept the 1000-m radius. The amount of bluck spmcr /

larch explained ca. 10% of the deviance at 50 r n and 100 m. There wris a negative

relationship with the amount of cut area at 750 m and 500 m. The most variation

explained by landsciipr occurred at 500 m. with block spruce positively and cutblocks

negatively predicting squirrel presence. SUMCON fiiiléd to predict squirrel presence on

the meso grids.

On the Rrference mrga grid. SUMCON also significantly predicted squirrel

presence at nll scales (Figure 4.1 b). The devilince rxplained for the 50 - 500 m scales ( R ~

=0.303: n = 63) was sirnilar to that for the Managed grid üt these scales. but waï less

thün the Miinaged üt the 1000 m scale (R' = 0.394: n = 18). Interesringly. once local

conifer trres were accountrd for. furthrr drviiincr w u cxplained by landscüpç: struct~irr

only at the 250-m scalr. Blück sprucr / Iarch positivrly. and mixrd deciduous spruce

negativel y, predicted squirrel presence.

On the Bumed are3 the relationship between SUMCON and squirrel presence was

not significant at the p = 0.05 level. Funher analysis of the block of Iiindscapr variables

was therefore not pursued.

42.2 The split squirrel dataset

Within the Managed Area, landscape structure hiled to predict squirrel presence

at the sires designated conifer-poor. At the conifer-abundant sites the deviance explained

by landscape structure again vuied with scale. peaking at 100 m and again at 500 rn at cri.

708 explained (Figure 4.2a; see also Appendix 4). Black spnice positively predicted

presence at the smaller scales. CUT was again a negative predictor at 500 m. but

interestingly so was white spruce. At al1 scales oniy composition was significant:

configuration or interactions fai lrd to enter the rnodel.

On the Reference area. landscape structure was not significant iit the conifer-poor

sites except at 250 rn (-ASBLT*HET: R' = 260: n = 26). At the conifer-abundant sites

landscape structure predicted squirrel presence at the 50 and 100 m scales but not at

larger scales (Figure 4.2b). R' at each su i e was low (O. 1 J6 to 0.271). peaking at the 50 m

scale. MARSH nrgativrly predicted presencr at 50 and 100 m. Hrterogrneous black

spruce nrgatively predictrd presençr Iir the 250-m sccile. Therr were no positive

significanr predictors of squirrel presrncr on the Reference gnd.

Figure 4.2: The influence of landscape structure at conifer-abundant sites. The deviance explained in squirrel presence by landscape structure tit each extent is illustrated in grey bars. The significrint variables in the final model. and direction of the relationship. are listed on top of the devilince bars.

Managed Mega Grid 1997 (coni fer-abundan t si tes)

XSBLT -hhfXRSH

r 1

x 0.0 4 1 .AS BLT

Lrindscripe Radius

4.3 DISCUSSION

4.3.1 The relative importance uf habitat beyond the focal patch

As one might rxpect from known habitat preferences of red squirrels. the number

of conifers at a site positively prrdicted squirrel presrnce on the Münüged and Reference

mega grids with a relatively high amount deviance rxplained. but did not on the Burned

grid. This emphasises the importance of context when sreking relritionships between

animal abundance and habitat use. As well, including local vegetation in the mode1

changed the patterns sren in Chapter Three (compare Figures 4. Ia and 3.4b: 4.1 b and

3.6a).

In the Burnrd areü. mnny live conifers that were recorded were jack pine sredlings

re-tstablishing themselves from serotinous cones after the forest fire in 1989 and were not

y r t mature mut-beciring trers capable of supplying cones to squirrels. Ln the othrr üreiis.

mature mast-braring conifers were cornmon and therefore strongly associatrd with

squirrel presence. As landscape was a significant predictor on the Burncd grid (Figure

3.7), i t appelirs that the proximity to stands skipped by t'ire (i.a.. unburned deciduous! was

more important to site occupation than the local presencr of conifer. On the Evlanaged

grid. once local vegetation was accounted for the proximity of black spnicr in the

immediate and larger landscapes. and the absence of cutblocks in the larger landscapes.

were significant predictors. On the Reference grid vegetation was of sole importance at

al1 scales except at 250 m. where the absence of heterogeneous black spruce patches was

important. The unique patterns observed between the three g i d s show that the relative

effects of vegetation and landscape are context-dependent. similar to the strictly

landscape effects iri Chapter Three. Because no replication of Areas was possible 1 cannot

conclude what caused the disparity in response between the threr Areas. but tentatively

attribute i t to differences in areal composition. The relative importance of dominant stand

types (spruce. pine. and iispen) for squirrels is possibly different in each area.

On the Managed Grid. the importance of black spruce at the smaller radii. which

are witliin the summer forüging range of the red squirrel (Chapter One). suggcst that

landscape supplementation is ocçurring (Dunning et al.. 1992). With high squirrel

densities the relative amount of conifer within each territory in bliick spnice patchrs may

be limited. If so, squirrels in thrse netirby stands miiy have travelled io the focal sires to

obtain substitutable resources. as suggested by Iÿrger foraging ranges in conifer-limited

areas (Chapter One). On the Reference grid where jiick pine is more dominant and stands

are larger and more contiguous, supplementation may not have brrn neçessary.

The importance of black spnicr at Iiirger radii on the Managed Grid suggests a

source-sink population dynamic (Pulliam. 1988; Pulliüm iind Danielson. 199 1 ) as in

Chapter Thrrr. For a patch to be colonised i t is important to have conifer present (for

overwinter survivül). but proximity to large amounts of black spruce is also consequential

as a source for dispersers. The source-sink hypothrsis. unlikr the supplrmentation

hypothesis. implies that the individuals recorded at a site are permanent residcnts and not

ephemeral occupants. A study that tracks the movement, mortality. and fecundity of

individuals at di fferent sites is required to test these two hypotheses.

The negative intluence of cutblocks on squirrel distribution in the Managed Area

is interesting, as only 9% of the area has been harvested for timber (Fiy 3.1 ). As red

;quine1 individuals do use cutblocks (Chapter One). and behaviour and survival are

apparently are not greatly affected by anthropogenic edges (Elizabeth Anderson. unpubl.

data). this raises somr interesting ecological questions. Are squirrels attractrd away from

conifer trees to summer forage at cutblocks, a scrnario predicted by landscape

supplementation (Dunning et al., 1 EU)'? Or are more indirect processes (Dunning et al.,

1992) operating, such as increased pressure from prcdators artractcd CO thcsc open high-

divrrsity areas (Andren er al.. 1955: Hansson. 1989. 1994) 7 If this is tme. why are the

çt'frcts seen only at the largrr scales. rather than rhe smaller ones? A risorous test for

these mechanisms would illuminate the large void in our knowledge of spatial dynarnics

in naturally heterogeneous systems. While rxtensively pontificiitrd (Levin. 1970: Pulliam.

1988). there is little empirical evidence for the existence of supplrrnentütion. source-sink

dynümics. or related processes. Whütrver the mechanism. it çan br concluded thiit in this

heterogeneous area landscape structure does intlurnct: the probübil i ty of patch occupation

by red squirrels. over and above the influences of local vegetcition. and that this

relationship is context-dependent.

Similar results have been shown for other specirs in othrr systems. Vos and

Stumpel ( 1995) found tree frog occupation of ponds was predicted by distance to the

neiirest occupird pond once the chemical conditions with the pond were accountrd for.

Verboom and van Apeldoorn ( 1990) and van Apeldoorn et al. ( 1994) showed that the

proximity to large "source" stands. and hedgerow connections to them. predicted

occupation of woodlots by Eurasian rrd squirrels once conifrr within the patch had

entered the model. and that this relationship changed over time. The latter authors

proposed that this relationship was due to a source-sink model: that small woodlots were

occupied by excess individuals from larger stands in years when those stands reached

maximum capacity. Fitzgibbon ( 1993) found similar results for grey squirrels. In contrast

Celada et al. ( 1994) found that local vegetiition characteristics wrre not significant; only

patch xea predicted squirrel presence in one study area. while proximity to source ueas

predicted presence in another study rirea. The. dso invokcd the source-sink model to

rxplain these distribution patterns, although conceded that landscape supplemrntation

müy also be occurring among severül small patchcs.

My results support the classic tenets of Ilindscape rcology - that landsciipe

rlements beyond the local patch tire significant intluences on the distribution of

organisrns. While many authors have indirect1 y acknow lcdged the i mportancr of interna1

patch heterogeneity in mediating the rrsponse to Iündscape stnicture. the nature of thrse

effects has bren largely ignored in Favour of the search for broad-sçalr patterns. This

drreliction may lrad to misleading results. Combining sites of differing quality causes

one to overlook the possibility that individuals living within different patch types may

respond to landscape structure differently. This is a fundamental assumption of sourco-

sink dynamics and relütrd rnechanisms such as idral pre-emptive distribution but is nrver

explicitly addressrd.

4.3.2 Two differmt responses to landscape structure

The relationship between landscape structure and squirrel presence was markedly

different between the conifer-poor and conifer-abundant sites. There wris a single scale on

the Reference grid where landscape was a significant predictor in the conikr-poor subset.

In the conifer-abundant sites on the Managed Gnd, the deviance explained by landscape

was generally higher than the same regression performed on the entire set of grid points

(Figure 3.4b). although this was not true of the Rrferencr grid (Figure 3.6a). Stiitistically.

this disparity in response indicates that combining the two site types into a single analysis

increases random error m o n g experimen ta1 uni ts. reducin; the sensitivity of the

statistical test (Hurlben. 1984). rlitreby obscuring tlis detestion of existing patterns.

Ecologically. the striking contrasr between the two conifer designations indicares that

there are (at least) two different processes at work ciffecting sqiiirrel presrncr in this

system. On those sites where one would assume the probübility of squirrel survival is

hi& - the conifrr-übundant sites - black spruce posi tivrl y predicted squirrel presence.

Deciduous. white spnice. cutblocks (Manageci Area). marsh. and hrtrrogeeneous black

spmce (Rrference Areal negatively predicted it. This suggests thüt piitch occupation at

these sites areas is affected by proximity to other patch types in the Ilindscape. possibly as

sources of dispersing individuals or sources of cornpetitors and predators at the larger

scales. and juxtaposed patch types ai smiiller scüles (Dunning et al.. lW2). In contrast. at

the conifer-poor sites where one would exprct survivd to be low. landscüpe structure is

apparently not important. There are a several possible txplünations for this disparity.

This survey was conducted in the late spring and early sumrnrr of 1997. a time

when young squirrels aie dispersing to find their own territories (Kemp and Keith. 1970).

It is possible that the individuals survryed in the conifer-poor sites were summer

tnnsients without a defended winter food supply (conifer cones) searching for a territory.

Although Larsen and Boutin ( 1995) found that adult squirrels don't redistnbute to better

vacant temtories within jack pine. data from Klenner ( 1 990) and Whr;itley < 1999 i

suggsts that this might occur in habitats where territory quülity (food iivailnbility) is

more highly contrasted. The lack of landscape effects found here indicates that the

location of these individuals is not influenced by proximity to preferred conifer habitat as

one might expect. Factors not measured here may contribute to an sphemrrd distribution.

or it rnay in fact be random. Although othcr studics ( 2 . r. Smith. 196s: Kcrnp and Kcith.

1970) have established the existence of individuals without tsrritorics (rhüt rnay ücquirr

one later on) no studies have exiimined the processes driving their distribution in a

heterogenrous mosoic. If these are individuals withour temtories waiting to acquire one.

and are as important to population dynamics as Pulliam 1 1988) and Pulliam and

Danielson ( 199 I ) suggest. rhen this segment of the popiilotion and the effects of hribitric

and Iündscripe stnicture upon its distribution mcrit furthrr investigation.

Nonrthrless I c m safely conclude thüt thrre is an interaction brtwern local

vegetiition and landscape structure and their influence on red squirrel distribution. The

data strongly suggest that the response to landscape structure is local habitat-dependent.

The processes operating on individuiils iit one site may bc: quite differenr froni thosr

oprriiting at another. resulting in diffrrent probübilities of occupation and hcncr disparate

patterns of abundance.

4.4 CONCLUSIONS

Local habitat accounted for some deviance in squirrel presence on the Mnnaged

and Reference areas but not the Burned area. Landscape stnicture accounted for further

deviance on the Managed Grid at several scales but only one on the Reference Grid.

Different variables were important at each. When sites were separated into conifer-poor

and conifer-rich categories different responses to structure were observed at cach. The

results are important in that 1 ) Iündscape structural éffects are nor restrictcd to

anthropogenically fragmented landscapes; 7) context-deprndency indicates Iündscape

studirs limitsd to one area cannot be txtrapolatrd to another: 3) combining sites of

differing qudity in these studies may obscure detection of existing patterns: and 4) the

interaction between local vegetation and landscape structure supports the idea that

different mrchanisms rffect the response to landscaps structiire in diifcrent piitch types.

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Wheatley. M.T. 1999. Does Density Retlect Habitat Quality for Red Squinels During a Spruce Cone Failure? M.Sc. Thesis. University of Alberta. Edmonton. AB.

Wiens, LA. 1989. Spatial scaling in ecology. Functional Ecology 3: 385-397.

Yahner. R.H. 1987. Feeding site use by red squirreis, Tcirninsciiirus Izrrdsonic~ts. in a marginal habitat in Pennsy lvania. Canadian Field Naturilist 101(4): 586-589.

Chapter 5

The Protean Nature of Landscape Effects

5. O CONCL US1 O I S

The rarliest Imdscapc ecology resrarch rrvealed thtit the observeci influences of

structure on animal distribution were species-dcpendent (see for exarnple Wegner and

Merriam, 1979; Middleton and klerriam. 1983). This specificity is often attributed to

behavioural differencrs. degree of specialization. or varying levels of adaptive tlexibility

(Bright. 1993: Andrrn. 1994: Andren et al.. 1997: Henrin et al.. 1998). As well.

differences in body s i x influence how heteroyeity i i pcrceived. with Iürger vagilr

organisms perceiving more a fine-grain mosaic than smaller less vagile ones. More

technically. the response to heterogeneity differs depending on how many hierarchical

lrvels of heterogeneity between the organism's grain and rxtent are percrived (Kotliar

and Wiens. 1990) and the spatial and tirne scale of the procrss thiit is rffecting thlit

responsr to hcteropeity for an or_oanisrn i hddicott et al.. 1987).

Landscape structural effects have been demonstratrd almost exclusively in

anthropogenicall y fragmen ted landscapes. This thesis has revealed that landscape

structural effects are not lirnited to thesr areas but also exist in naturally heterogeneous

landscapes with minimal anthropogenic impact. More imponantly. it also reveals thiit the

reliitionship between Iandscape structure and red squirrel distribution is dependent on a

number of factors beyond specîes-speci fici ty.

Tirne-dependent: The relationship between squirrel presence and landscape structure

changed brtween the sunimers of 1996 and 1997 on the Manoged area. As a prescriptive

conclusion, landscape ecology studies should be conducted over a number of years in

order to compensate for temporal variability in the system. This may be especially

important in 'rphemeral' Iandscaprs such as agricultural environments where matrix

pntch types (crops) change bctween ycm or cven selisons (Szacki et 21.. 1993). or in

boreal forests where fluctuations in pütch chiiractrristics may profoundly influence

dynamics within thein (Hansson. 1993). The obvious theoretical conclusion is that the

mechanism driving the response to heterogenrity rnay change between years. In the case

of red squirrels. the arnount of forage and hence quülity of a pütch (ii stand of some tree

species) rnay change with time (Wheatlry. 1999). thus changing the relative importance

of Inndscape composition and configuration and hrncr squirrel distribution patterns.

Detrcting temporal variability may be as important as spatial variability in çonstructing

plausible hypothesis regrirding distribution patterns.

Scale-dependent: The response of red squirrel presence to landscape structure occurred at

a number of different spatial extents. including small scales corresponding to the sumrner

foraging range (Chapter One) and to larger scalrs. It also çhanged drpcndin_r on the scale

of squirrel sampling, i.r.. betwern the mega grids and the meso grids within the same area

(Chapter Three). This suggests that more than one mechanism was responsible for the

observed pattern in distribution. The response to heterogeneity at small scales. squirrels'

use of several different patch types, and the existence of larger ranges where conifer is

limiting, al1 suggest that landscape supplementation (Dunning et al., 1992) rnay be

occurring. The response to structure at larger scales indicates that processes

encompassing larger areas, such as dispersal, are involved. The positive relationship with

large homogeneous black spruce tracts. and negative association with marginal deciduous

stands and cutblocks. suggest that a source-sink dynamic (Pulliarn. 1988: Pulliarn and

Danielson. 199 1 ) exists that ( in pan) causes the obsrrved pattern of distribution. I can

conclude that the vliriliblrs thiit predict squirrel distribution change depending on the

scale of landscape quantification. This conclusion is not really innovative (see Wiens.

1989). However what is provocative is that the relative importance of e x h scale changes

between areas or contexts.

Context-dependent: The deviance explaineci at rüch sciile was different brtwern the

Burned. Managed. and Referencr areas. The landscapr variables that significantly

predicted squirrel presencr lilso varird brtwcrn the three cireas. These inconsistencies

strongly suggest that different rnechanisrns drive the responsr to heterogeneity in different

rireas. Smaller-scale mrchanisms such üs çornplementation mny be more important in

somr areas w hile Iarger-scde procrssrs such as source-si nk dynümics müy dominatr in

others. The net result of a particular rnechanism may also differ: in burnrd areas tracts of

unburned deciduous may be important as 'sources' whereas on the Managed and Burned

areas these were most likely sinks. The rnechanisms effecting the response to

heterogeneity can only be hypothesised üt this tirne. but the Finding that landscape effects

differ by area is a criticai one. Few studies have analysed several different areas

independently, and combining points in different areas into a single andysis could yield

spurious results (Hurlbert. 1984). To extrapolate specific iandscape effects from one area

to another would be erroneous.

While this pattern-seeking approach has revealed that there are different responses

in the different areas. why this is so is completely open ro debate. The areas have different

compositions. and hence different ranges of vüriability in the independent datnset. so

statistically it is expected that dissimilar variables would become important within each.

Statistical reasons aside, the ecological importance of patch types rnay change between

areas. For example heterogeneity (HET) rnay be beneficial for squirrels in one nrea. at one

scale, but detrimental in another area or at another scale. To claim that deciduous is a

vital landscapr rlzrnrnt for squirrels basrd a study conducrêd on the Burnrd m a . evcn

with a fair sample size (30-50 points). would be spurious if ripplicd to the Managed or

Referencr areas. Instead of yielding müny answers. this thesis instrad poses a crucial

question: Under what circumstances are certain landscape structures important elements

of animal occupation'? I can conclude rhat these effects are context-specific. but the nature

of that specificiry remüins the salirnt ecolo_pical issue.

Local habitai-dependent: The relative influence of liindscape structure is dso local-

habitat-dependent. The number of conifer trers üt a site is an important predictor of

squirrel presence in the non-burned areas. but landscüpe structure still explains more

deviÿnce once this has been accounted for. How it does so differs between landscape

extents and areas. More interestingly. the importance of landscape strucrure shifts from

being insignifiant in conifer-poor patches. to hijhly significant in conifer-abundant

patches. Pooling these patch types into a single analysis obscures the detection of

processes dnving distribution. While conifer-abundmt sites may be subject to source-sink

dynamics (Pulliam. 1988: Pulliam and Danielson. 199 11, neighbourhood or indirect

effects (Dunning et al.. 1992). a metapopulation mode1 (Levins. 1970) or any other

number of processes that are heavily influrnced by Iiindscapz structure. conitèr-poor sites

appear unaffected by any of these. These results brg the sarnr question proposrd in the

last paragraph, but framed in a slightly different context: Under whüt circumstances are

csnain landscape structures important clzmrnts of animal occupation within a specific

habitat typeb?

Thüt landscape structure p l a y a rolr in inHurncing animal distribution is

incontrovertible. Xlthough landscape ecology is firmly entrenchrd in conservation-

oriented studies of anthropogenic systems (ser for enample Forman. 1997) we now know

that the intluence of structure is a naturally oçcurring phenornenon in hrterogeneous

areas. We dso know that the nature of these effects are not easily extrcipolated from one

year. habitat type. or area. to the next. The setirch for processes to explain thrse

inconsistencies will require a mariage of landscape ecology and population ecology - a

dernographic study that explicitly considers landscape structure lis ii treatment effect. As

scientists we ciin then p i n insights into the diversity of processes that shape the observed

patterns. As managers we can then make predictions about the effects of landscape

alteration (cg. timber harvest) on animal populations. If nothing rlse. we will be one strp

closer to understanding the protean nature of ecological systrms. and the role of

stochasticity in shaping those patterns we thought to be concrete.

5.1 LITERA TURE CITED

Addicott, J.F., J.M. Aho, M.F. Antolin, D.K. Padillri, J.S. Richardson, and D.A. Soluk. 1987. Ecological neighbourhoods: scaling environmental patterns. Oikos 19: 340-346.

Andren. H. 1994. Effects of habitat fragmentation on birds and rnrimmals in landscapes with different proportions of suitable habitat: a revirw. Oikos 71: 355-366.

Andren, H, A. Delin. and .A. Srilrr. 1997. Population response to Iündsciipe changes depends on specialization to different landscape elrrnents. Oikos 80( 1 ): 193- 196.

Bright, P. W. 1993. Habitat fragmentation - problems and predictions t'or British mammals. Mammal Review 23(3/4): 10 1 - 1 1 1.

Dunning. J.B.. B.J. Danielson. and H.R. Pulliiim. 1992. Ecologicül processes that affect populations in complrx Iandscapes. Oikos 65: 169- 175.

Fonan. R.T.T. 1997. Land Mosaics: The crçolorv of Irindscapes and rerions. Cambridge University Press, UK.

Hansson. L. 1992. Landscape ecology of boreal forests. Trends in Ecology and Evolution 7(9): 299-232.

Henein, K.. J. Wrgner. and G. Merriam. 1998. Population effects of Iandscape mode1 manipulation on two behaviourally diffsrent woodland small mümmals. Oikos 81: 168- 186.

Kotliar. N.B.. and J.A. Wiens. 1990. Multiple scales of patchiness and pritch structure: a hierarchical framrwork for the study of hrterogrnriry. Oikos 59: 153-260.

Lrvins. R. 1970. Extinction. Pp. 77-207 In Lectures on mathematics in the life sciences. Vol. 2. M. Grrstenhaber. ed. American Mtithrmaticül Society. Providence. RI.

Pulliam, H.R. 1988. Sources, sinks. and population regulation. The .4merican Naturalist L32(5): 652-66 1 .

Puliiarn, KR., and B.J. Danielson. 199 1. Sources, sinks, and habitat selection: a landscape perspective on population dynamics. The American Naturalist 137: S5-S66.

Szacki, J.. J . Babinskli-Werka, and A. Liro. 1993. The intluence of landscape spatial structure on srnall mamrnal movrments. Acta Thenologica 38(2): 1 13- 123.

Wegner, J.F.. and G. Merriam. 1979. Movements by birds and srnaIl rnamrnrils between a wood and adjoining farrnland habitats. Journal of Applied Ecology 16: 349-357.

Whcatley. M.T. 1999. Doss Density Retlect Hübitar Quality for Red Squirrels During a Spmce Conç Failure'? M.Sç. Thesis. University of Al berta. Edmonton. m.

Wiens. J.A. 1989. Spatial scaling in ecology. Functional Ecology 3: 385-397.

Appendix 1: The fit of the logistic regession model. -2 log Iikelihood values of the significant multiple logistic regression models obtained at each spatial scale. grid. and area are tested for significant deviation from a X2 distribution given the number of samples in each regression ( n ) and the number of parameters in the regression model (pr). The degrees of Freédom tquals n-pr. The u = 0.05.

Area Grid Scale n - pr -2LL diffirent frorn X' Managed ( 1996) mega

yes y es no

Managed ( 1997) rnqü

Burned

Re ference mega

meso 50 100

yes no

Appendix 7: Output of logistic regession modèls. Squirrel presençe was regresscd against landscape composition itnd configuration variables: the significant variables sclected by the forwnrd selection procedure. the p value. and fit of the final mode1 are listed for each landscapr scale at each grid.

scale significant p(mode1) N Naglkerke overall variables R ' %correct

50 APJ 0.0001 b4 O.-? 13 67. 19 HET

100 ASBLT O. 0000 64 0.4 12 73.44 ADECPHET

250 -ADEC 0.000 1 64 0.355 7 1.88 ASBLT

500 -ADEC 0.0000 64 0.362 70.3 1 ASBLT

b) Manageci Mrga Grill 1997

scale significant p( mode]) N Nagel kerke %correct variables R~

50 AMDP 0.000 1 58 0.400 74.14 AlMDS APJ ASBLT

APJ ASBLT

APJ ASBLT

APJ ASBLT -ASBLT*HET

C ) Milnaged Meso Grid 1996

scale significant p(mode1) N Nagelkerke %correct variables R:

-ADEC 0.0002 127 O. 17 1 67.72 HET

HET 0.000 1 -AMARSH

250 HET 0.00 15 34 0.342 73.53

d) Managed Meso Grid 1997

SC& signitïcanr p( mudel Y N q e l ksrke %correct variables R:

50 HET 0.004 1 64

1 O0 HET 0.0073 64

350 none I S

e) Referencc Mega Grid 1997

Nage1 kerkr %correct RI

scrile significtint p(modrli Y variables

-ADEC 0.0006 MARSH

MJ ASBLT

ASBLT - M D S APJ

ti) Reference Meso Grid 1997

Scale significant p(modr1) N Nagel kerke %correct variables R'

APJ ASW

g) Bumed iMega Gnd 1997

scale significant p(modc1) N variables

Nagelkerke %correct R '

-ADEC:'HET 0.00 19 AMDP

ADEC AMDP

h) Burned Meso Grid 1997

scale significmt ptrnodel) N variables

-ABDEC 0.0000 57 AMARSH ABuLOW*HET

ADEC AMDP ABPINE*HET

Appendix 3: Output of logistic regression models that includr local vegetation. Squirrel presence wiis regressrd against nrimbcr of conikr trees at a sire: then Iandscape composition and configuration variables wsrr iil!owed to enter the mode]. The significant landscape variables srlrctsd by the forword srlection procedure. the p value, and fit of the final mode1 are lisred for each Iündscüpe scalr at rach grid.

a) Managed Mega Grid 1997

scde signi fïcant p(mode1) N Nagel kerke overall variables R' %correct

50 CON-TREES 0.000 1 58 0.374 70.69 ASBLT

100 CON-TREES 0.0000 5s 0.390 70.69 ASBLT

350 CON-TREES 0.000 1 58 0.375 72.4 1 -ACUT

500 CON-TREES 0.0000 58 0.459 77.59 -ACUT ASBLT

1 O00 CON-TREES 0.0007 16 0.699 87.50

b) Reference Mrga Grid 1997

scale significant p(mode1) N Nagel kerke %correct variables R'

50 CON-TREES 0.000 1 63 0.303 65.08

1 O0 CON-TREES 0.000 1 63 0.303 65.05

250 CON-TREES 0.0000 63 0.474 73 .O3 - M D S ASBLT

500 CON-TREES 0.000 1 63 0.303 65.08

1000 CON-TREES 0.0 L 23 18 0.394 77.78

Appendix 4: Output of logistic regression rnodels using conifer-abundant sites. Squirrel presence was regressed against landscape composition and configuration variables. The significant landscape variables selected by the fonvard selection procedure, the p value. and fit of the final mode1 are listed for each landscape scale at sach grid.

a) iManaged Mega Grid 1997 (Conikr-abundant)

scale significünt p(model ) N Nagelkerke %correct variables R'

---

50 ASBLT 0.00 14 25 0.484 72.00

1 O0 ASBLT O. 0002 25 0.719 34.00 -NMARSH

5 0 0 -AS W O. 0006 25 0.730 92.00 -ACUT AMDS

b) Rrference Mega Grid 1997 (Conifer-abundlint)

scale significanr pi modd ) N variables

scale significlint p(mode 1) N variables

Nagel kerke %correct R'