Allen Press
Test of a Habitat Suitability Index for Black Bears in the Southern Appalachians
Author(s): Michael S. Mitchell, John W. Zimmerman and Roger A. Powell
Source: Wildlife Society Bulletin, Vol. 30, No. 3 (Autumn, 2002), pp. 794-808
Published by: Wiley on behalf of the Wildlife Society
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794
HABITATSUITABILITY INDEX FOR BLACKBEARS
Test of a habitat suitabilityindex for
black bears in the southern
Appalachians
and RogerA. Powell
MichaelS. Mitchell,
JohnW Zimmerman,
index(HSI)modelforblackbears(Ursusamericanus)livAbstractWe presenta habitatsuitability
thentestthat
wasdeveloped
a priori
from
theliterature,
inginthesouthern
Appalachians
inthePisgah
datacollected
BearSanctuary,
North
Cared usinglocation
andhomerange
andinitially
tested
habitat
and
using
olina,overa 12-year
period.TheHSIwasdeveloped
Weincreased
number
ofhabitat
sambeardatacollected
over2 yearsinthesanctuary.
inareasaffected
usedmorerecent
bytimber
harvest,
plingsites,included
datacollected
of
tocreatea moreaccurate
Information
depiction
System
(GIS)technology
Geographic
ofinput
on HSIvalues,andduplitheHSIforthesanctuary,
evaluated
effects
variability
thattheHSIpredicted
habitat
selectestsusingmoredata.Wefound
catedtheoriginal
bears
levelsandthedistribution
ofcollared
tionbybearson population
andindividual
relationship
between
withHSIvalues.Wefounda stronger
werepositively
correlated
HSI. Weevaluated
ourmodelwith
habitat
selection
bybearsanda second-generation
and
andKernohan
HSImodelreliability
criteria
(1999)forevaluating
suggested
byRoloff
concluded
thatourmodelwasreliable
androbust.Themodel's
strength
is thatitwas
therelationship
between
as an a priori
critical
hypothesis
directly
modeling
developed
theHSIspaandfitness
withindependent
data.Wepresent
resources
ofbearsandtested
contribution
ofhabitat
tothefitness
as a continuous
fitness
surface
wherepotential
tially
ofa bearisdepicted
ateachpointinspace.
habitat
blackbear,fitness
habitat
suitability
surface,
selection,
KeywordsAppalachian
Mountains,
index,NorthCarolina,Ursusamericanus
Habitatand habitatqualityare termsthatoften
are poorlydefinedand therefore
are reducedto jargon (Hall et al. 1997, Mitchelland Powell 2002).
Ambiguitiesnotwithstanding,
these concepts are
commonlyemployedby managersand researchers
workingwithwild animalpopulations.A common
techniqueof defininghabitatand habitatqualityin
such cases is thehabitatsuitability
index(HSI; United StatesFish andWildlifeService [USFWS]1981).
HSI models have received considerablecriticism,
largelybecause theyare rarely"validated"(i.e., tested withindependentdata;Brooks1997,Roloffand
Kernohan1999,but see criticismby Garshelis2000
and Hilbornand Mangel 1997 on use of the term
"validate").TestsofHSI modelsare rarein theliterature (Lancia et al. 1982,Thomasma et al. 1991,
Brooks1997,Roloffand Kernohan1999). HSI models also commonlysufferfromeffectsof arbitrary
classification
schemesin whichhabitatsuitability
is
definedwithouta theoreticalor empiricalrelationship to animalfitness(Mitchelland Powell 2002).
In lightofcriticismthatwe and othershave leveled
at HSI models,we presentan evaluationof an HSI
forblack bears (Ursus americanus) livingin the
southern Appalachian mountains (Zimmerman
1992,Mitchell1997,Powell et al. 1997).
AddressforMichael S. Mitchell:UnitedStatesGeological Survey,Alabama CooperativeFishand WildlifeResearchUnit,School
of Forestryand Wildlife Sciences, 108 M. White Smith Hall, Auburn University,Auburn, AL 36849, USA; e-mail:
mikemitchell@auburn.edu.AddressforJohnW. Zimmerman:MountSt.Clare College, 400 NorthBluffBlvd.,Clinton,IA 52732,
USA. AddressforRogerA. Powell: Departmentof Zoology,NorthCarolina StateUniversity,
Raleigh,NC 27695-761 7, USA.
WildlifeSocietyBulletin2002, 30(3):794-808
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Peer refereed
indexforblack bears * Mitchellet al.
Habitatsuitability
The HSI model forsouthernAppalachianblack
bears was developed by Zimmerman(1992) and
pertinentdetailswere presentedby Powell et al.
(1997). The HSI was developed a prioriby reviewand modelingthevalue of imporing the literature
tant,potentiallylimitingresources to bears (i.e.,
Live RequisiteVariables for food [LRVF],escape
cover [LRVE],and denningresources[LRVDI).The
modeled relationshipfor each componentof the
HSI explicitlydepicteda hypothesizedcontribution
of a criticalresourceto bear fitness(sensu Fisher
795
HSI values correctlypredictedhabitatuse forthe
population(r2=0.21,P<0.05) butpoorlypredicted
individualselection. The HSI value assigned to a
patch also was correlatedpositivelywiththe number of home rangesthatwould includethatpatch,
suggestingthat areas with abundant patches of
highHSI could supportmorebearsthanareaswithout. We emphasizethatthese analysesconstituted
a truetest(sensu Platt1964) oftheHSI because the
model was evaluatedwith data not used to generate it. Eventhoughsome model componentswere
1930, Stearns 1992). Unlike many HSI models, the
one presentedby Powell et al. (1997) was spatially
PISGAHBEAR SANCTUARY
meaningthatspatialorientationand coninformed,
figurationsof key habitat components (e.g., the
and juxtapositionof food and escape
interspersion
resources,spatial extent of available habitat,dis3930
arbitrary
tance to roads) were important.Further,
of habitatsuitability
classification
based on classes
(e.g., forestcover type) was minimal,with most
componentsemphasizingspecificresourcesimportantto bears ratherthan assumingan association 3925
between vegetation classificationschemes and
(Mitchelland Powell 2002).
resourcedistributions
Because mostof the componentswere distributed
continuouslyin space independentof each other,
the combinationof componentsformingthe HSI
could only be portrayedas a continuoussurface
ratherthana collectionofdistinctpolygons(Figure
2a). This portrayalhas intuitiveappeal because in
realitymanycriticalresourcesforanimalsare dis- 3915
S~t
tributedcontinuouslyovera landscape and are not
isolatedto patches.
conveniently
Afterthe HSI's development,Powell et al. (1997)
tested its abilityto predictbehaviorand distribu-,
tionofcollaredblackbearslivingin thePisgahBear 3910
Sanctuaryin westernNorthCarolina. To portray
the HSI forthe sanctuary,
theysampled HSI components at 59 evenly distributed,systematically
located sites in 1983-1984, and values for each 3905
componentwere interpolatedbetweensites. Com345
355
340
350
biningall componentsresultedin an HSI map of
the sanctuarywith HSI values potentiallyranging
from0 (poor suitability)
to 1 (high suitability;
FigNorth Carolina
ure 2a). Home rangedata from19 bears (9 males
and 10 females)in 1983-1985 were used to test
how HSI values predicted 1) habitatselection of
the bear population(i.e., across the 19 bears sampled and presumedto representall bears livingin
Pisgah),2) habitatselectionofindividualbears,and
and itsUniNorthCarolina,
Figure1. PisgahBearSanctuary,
3) numberof bear home rangesthatwould incor- versalTransverse
Dotsonthemap
Mercator
(UTM)coordinates.
ofC. Powell.
andridges.Courtesy
porate a given habitatpatch (Powell et al. 1997). indicatemajormountains
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796
WildlifeSocietyBulletin 2002, 30(3):794-808
data on finalHSI values to
better understand relationships between measured habitat characteristicsand theHSI. Powell et
al.'s (1997) analyseswere
based on the home ranges
of 19 bears,a smallsubset
of the data we now possess for the Pisgah Bear
Sanctuary,
where research
has been underwaysince
improve1981. Additionally,
ments in GIS capabilities
and data availabilitysince
Powell et al. (1997) was
published,combinedwith
Figure2. Habitatsuitabilityindex (HSI) values forblack bears in the Pisgah Bear Sanctuary, additional collection of
modeled
North
2a depictstheHSIas originally
Carolina,1994. Figure
PisgahNationalForest,
ofdatabetweensys- habitat data in the field,
(1992) and Powellet al. (1997),usingonlyinterpolation
byZimmerman
2h depictstheHSIas modeledbyMitchell
locatedsampling
(1997) enabled us to substantially
points.Figure
tematically
at systematical-improve accuracy and
coverdata;datacollectedinfieldsampling
roads,andforest
usinglandform,
and data
and interpolation;
modeling
lylocatedsampling
pointsand mappedusinglandform
detail in HSI maps over
forest.
selectedstandsofregenerating
collectedfrom
those used by Powell et
al. (1997). A largerdata
in the set for bears and improvedHSI maps enabled a
estimatedor at times modeled arbitrarily
the assumed rela- more rigoroustest of the HSI than performedby
absence of reliableinformation,
tionshipswere tested(Powell et al. 1997). Had they Powell et al. (1997). Our second objective thereit was unlikely(though forewas to replicatethe analysesof Powell et al.
been modeled incorrectly,
not impossible) they would have predictedbear (1997) by 1) testingrelationshipsbetween HSI and
behaviorand thenumberofbearsincludinga given HSI2 and habitatselection at populationand indipatchin theirhome range.Based on resultsoftheir vidual scales,and 2) testingrelationshipsbetween
analyses,Powell et al. (1997) also evaluateda varia- HSI and HSI2 values assigned to a patch and the
tion of the HSI model,HSI2,which containedonly numberof bear home rangesincludingthatpatch.
thefoodand denningcomponentsoftheHSI. They Because we evaluatedHSI2 usingdata independent
foundthatHSI2 betterpredictedhabitatselection ofthoseused to develop it,our analysesare thefirst
truetestsof HSI2.
by bears on a populationscale (r2=0.73, P<0.05).
modeldevelBecause HSI2was a second-generation
oped in response to analysesof the HSI, its relaStudyarea
tionshipto bear behaviorwas correlativeand thereThe Pisgah Bear Sanctuary (35017'N, 82047'W;
forehypotheticaland untested.
In thisstudywe undertooka morecompleteand Figure1) was the largest(235 kM2)of 28 bear sancrigorousevaluationof the HSI thanthatperformed tuariesestablishedin NorthCarolinain 1971 and
by Powell et al. (1997), followingrecommendations was contained completelywithinPisgah National
ofRoloffand Kernohan(1999). Powell et al. (1997) Forest.The mountainousterrainrangedin elevation
of the HSI from650-1,800 m and was dominatedbyBigPisgah
did not formally
evaluatethe sensitivity
to observed variationin measuredhabitatcharac- Ridge, which bisected the sanctuaryand along
teristicsused to calculateitscomponents;thus,the which ranthe Blue RidgeParkway.The regionwas
withannualrainpotential for modeled relationships that were considereda temperaterainforest,
influential
to fallapproaching250 cm/yr(Powell et al. 1997).
inconsequentialor disproportionately
Majorforesttypesin the sanctuarywere eastern
bias analysesof the HSI was unknown(Roloffand
was hemlock(Tsuga canadensis),cove hardwoods(LiriKernohan1999). Our firstobjectivetherefore
Magnolia spp., Betula spp.),
to evaluate the effectsof variabilitywithininput odendron tulipifera,
(rid(QT)NW
(b)
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Habitat
suitability
indexforblackbears* Mitchell
etal. 797
itly(e.g.,distance,area). We mapped 7 ofthe20 HSI
componentsforwhich no GIS data existed (Fy1,
Fsp2,Fsu1,Ff2,E2,D2, and D4;Table 1) usingregression of fielddata on landformcharacteristics
(e.g.,
elevation,slope,aspect,exposure,net curvatureof
slope; Fels 1994, Mitchell 1997) or interpolation
(Mitchell1997). We hand-digitized
and mappedthe
2 remainingcomponents(FY2a,Fy2d). For componentsthatchanged over time (e.g., due to timber
harvests,road construction,changes in anthropogenic food sources, forestaging),we created
maps foreveryyear1981-1994;we createda single
map forcomponentsthatdid not changewithtime
(e.g., slope). We produced finalHSI maps forthe
Methods
sanctuaryforeach yearby combiningcomponent
EstimatingHSI values and modelanalysis maps. We set the grain(cell size) of finalimages
During1993-1994 we collecteddata to calculate used fortestsat 250 x 250-m"cells"to approximate
the 6 ground-survey
componentsof the HSI (Table median errorforour telemetrylocationsof bears
1) at 63 new samplingsites located systematically(260-mradius;Zimmermanand Powell 1995).
To determinethe relativecontributionof each
(intersectionsof odd-numbered1-km Universal
TransverseMercator [UTM] gridlines)across the HSI componentto the finalmodel,we conducted
sanctuary.We combinedthesedata withthose col- sensitivityand elasticityanalyses (Caswell 1978,
lected by Powellet al. (1997) at 59 similarly
located Stearns1992). For each HSI component,we calcusites(except at even-numbered
UTM intersections) lateda setofHSI estimatesacrosstherangeofinput
in 1983-1984 fora totalof 122 sites. Otherthan values observed for that component,with values
forestaging,changes in bear habitatduringour for all other components held constant. As an
to variationin thatcompostudywere due primarilyto timberharvestand index of HSI sensitivity
road building. Because these timbermanagement nent (i.e., absolute effectsof inputvalues of that
practiceswere ongoingthroughoutour studyand componenton finalHSI estimation),
we used
in
HSI
resultingchanges
were potentiallyimporn
tant,we incorporatedtheireffectsin HSI compoZ (HSI, -HSI)
nentmaps foreach yearto ensurethe best accuralocated sampling
cy possible. No systematically
n
sitesfellwithina harvestedstand;therefore,
effects
of timberharveston the HSI were unknown. To As an indexofHSI elasticity
to variationin thatcomestimateground-survey
componentsin harvested ponent(i.e., proportionaleffectsof inputvalues of
we used
stands,we collected data in 48 of 133 harvested thatcomponenton finalHSI estimation),
standsin the sanctuary.Harvestedstandsfellinto3
n
broadclasses accordingto age and regenerating
forI - HSli /HSI)
E
est type:stands<10 yrold (n= 15),pine stands>10
E=i=1
yrold (n= 16), and hardwoodstands>10 yrold (n
n
= 17). We averagedobservationswithineach class
to estimatevalues forground-survey
components For both indices,HSIi=the finalHSI value calculatof the HSI forall harvestedstandswithinthe sanc- ed usingobservationi ofthecomponentacrossthe
tuary.We mapped all other HSI componentsfor observed range i...n of that component. Neither
harvestedstandsusing the same approaches used index reflectedthe weight a given component
forsystematically
located sites.
receivedin the HSI; rather,
S and E reflectedhow
We used GeographicInformation
in the componentcould affectfinalHSI
System(GIS) variability
software(IDRISI,ClarkUniversityWorcester,
Mass.) values,given the weightingassigned to it in the
to map 11 of the 20 HSI componentsthatcould be model. Therefore,
equal values of S and E among
derived from GIS data and digitized databases componentssuggesteda balanced model,whereas
(Table 1) and to measurespatialphenomenaexplic- unequal values indicatedthat variationin model
oak-hickory (Quercus spp., Carya spp.), pine
(Pinus spp.), and pine-hardwoodmix. Littlepriand mature
maryforestremainedin the sanctuary,
standsaveraged85?25 (SD) yearsin age in 1994.
Forest
The UnitedStatesDepartmentofAgriculture
Service(USDAFS) activelymanagedfortimberproduction,and as of 1994,timberhad been harvested
(generallyby clearcutting)
froma totalof 133 sites
averaging7.6?3.8 ha in size. Harvestedstandsaveraged 15?7 yearsin age. Most standsregenerated
naturallyafterinitialsite preparationwith herbicides and were unmanagedthereafter.
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798
WildlifeSocietyBulletin2002, 30(3):794-808
index(HSI) forblackbearsin thesouthern
and functions
fora habitatsuitability
Table 1. Components,
samplingmethods,
Zimmerman
1981-1994. Summarized
from
Appalachians,
(1992)and Powelletal. (1997).
SubModel Indexindexa Habitatfeature
modeled
HSI
Survey
Function
methodology
HSI= [(LRVF+ LRVE+ LRVD)/ 3] X ILRV
LRVF= Fy/7 +(Fsp/7 + 2Fsu/7 + 4Ff/7) x If,
Liferequisitevariablefor
LRVFb
< 1.0;
forFy/7+(Fsp/7+2Fsu/7+4Ff/7)xlf
foodresources
Fy
LRVF= 1.0,
forFy/7+ (Fsp/7+2Fsu /7 +4Ff/7)x If> 1.0
Fy= Fy1+ Fy2,forFy1+ Fy2 < 1 0
Fy= 1.0,forFy1+ Fy2> 1.0
Year-round
foods
Fy1b Abundance
ofcolonial
insects,
FY2
Source(s)
Groundsurvey Fyl = 0.00082x+0.1, forx <1,100;
Fy1= 1.0 forx > 1100, where:
x = number
offallenlogs/ha
Zimmerman
(1992)
FY2= (Fy2ax FY2bx FY2d/ 3
Anthropogenic
foods
Zimmerman
Fy2a QualityofanthropogenicAerialFy2a= [(A+R)/21S,where:A = food
foodsource
ground
survey available(high=1.0,
medium=0.6,
(1992)
low=0.1),R = riskofreprisal
(high=1,
of
medium=0.5,
low=0.1),S= number
seasonsavailabletobears(0 to 3)
dividedby3
= 1.0,forx < 1.5;
of
Costs
to
GIS
Beeman(1975),
traveling
Fy2b
Fy2b
foodsource
Garshelis
etal.
anthropogenic
Fy2b= -0.667x+ 2,
for1.5 < x < 3.0;
(1983)
Fy2b= 0, forx > 3.0,
where:x = distance(km)
to anthropogenic
foodsource
McCollum(1973),
Fy2c Accesstoescapecover Topographic Fy2c= 1.0 forx < 25;
>400 ha from
US FishandWildlife
map
Fy2c= -0.0017x+ 1.0425,
for25< x < 200;
food
Service(1982),
anthropogenic
and
source
Fy2c= -0.0015x + 0.6,
Rogers
for200 < x < 400; Fy2c= 0,
Allen(1987)
forx > 400, where:x = distance(m)
Fsp
Spring
foods
ofvegetationGIS
Fsp1 Productivity
associatedwithmoist
habitats
andavailability
of waterafterdenning
ofspring
Fsp2 Productivity
vegetation
Fsu
Summer
foods
ofberry
Fsu1 Productivity
speciesc
ofsquaw
Fsu2 Productivity
root(Conopholis
indexedby
americana),
ofredoak
prevalence
inoverstory
(Continued)
betweenanthropogenic
food
sourceandescapecover
Fsp=(2 Fsp1+ FSP2)/ 3
Fsp= 1.0,forx < 0.64;
Fsp= 1.167x+ 1.75,
for0.64 < x < 1.5;
Fsp = 0, forx > 1.5,
Beemanand
Pelton(1980),
Carlocketal.
(1983),
where:x = distance(km)to
Rogersand
water
perennial
Allen(1987)
Groundsurvey Fsp2= 0.08x,forx < 12.5;
US FishandWildlife
Service(1982)
Fsp2= 1.0,forx > 12.5,where:
x = percent
coverofSmilaxspp.
Fsu= Fsu1+ Fsu2,forFsu1+ Fsu2< 1.0;
Fsu = 1.0, forFsu1 + Fsu2 > 1.0
Groundsurvey Fsu1= (0.027+ 0.005n)x,
for(0.027+ 0.005n)x< 1.0;
Fsu1= 1.0,for(0.027+ 0.005n)x
ofberry
2 1.0,where:n = number
x = percent
cover
generapresent,
inberry
plants
0.0 to0.1,See Powelletal. (1997),
CISCd
USDAForest
Service(1982)
a Variables
combinedintoa singleindex.
Rogers
and
Allen(1987)
Bairdand
Riopel(1986),
Zimmerman
(1992)
b LRV= LifeRequisite
Variable.
c Includesblueberries
(Vaccinium
spp.),huckleberries
(Gaylusaccia
spp.)andblackberries
(Ruhusspp.).
d Digitized
Continuous
Information
ofStandCondition
(CISC),UnitedStatesDepartment
ofAgriculture
Forest
Service.
e Diameter
breastheight.
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Habitatsuitability
etal.
indexforblackbears* Mitchell
799
Table 1 (continued). Components,samplingmethods,and functionsfora habitatsuitabilityindex (HSI) forblack bears in the
southernAppalachians,1981-1994. SummarizedfromZimmerman(1992) and Powell et al. (1997).
SubModel Index indexa
Ff]b
Age of stand
Ff2
Productivity
of grapes
(Vitisspp.)
Ff3
Effect
of roads on access
to hardmast
If
LRVE
E1
E2
E3
E4
LRVD
D1
Survey
methodology
Habitatfeaturemodeled
Function
Fflb = 0, forx < 20;
Fflb = 0.025x - 0.5, for20 < x < 60;
Ffib = 1.0, for60 < x < 100;
Ff1b =-0.004x+ 1.4, for100 <x< 125;
Fflb= 0.9, forx > 125,
where:x = age (years) of stand
Groundsurvey Ff2= 0.005x, forx < 200;
Ff2= 1.0, forx > 200, where:x =
numberof grapevines/ ha
CISC
Source(s)
Goodrumet al.
(1971),
Brody(1984)
Collins (1983),
Eileret al. (1989),
Zimmerman(1992)
0.0 to 0.1, See Powell et al. (1997),
Quigley (1982),
Villarubia(1982),
USDA ForestService(1982), where:
x = distance (km)to nearestroad,
Collins(1983)
road type= temporary,
improveddirt,or paved
Interspersion
of food
Beeman (1975),
GIS
If= 1.0, forx < 5;
resources
Eubanks(1976),
If= -0.07x + 1.35, for5 < x < 19;
If= 0, forx > 19,
Garshelisand
where:x = distance(km)
Pelton(1981)
Liferequisitevariablefor
LRVE= (E1 +0.5E2 + 0.25E3) x E4,
escape resources
for(E1 +0.5E2 + 0.25E3) x E4 < 1.0;
LRVE =1.0,
for(E1 +0.5E2 + 0.25E3) x E4 > 1 0
Accessibilityvia roads
GIS
USDA Forest
El = 0 forx < 4;
Service(1982)
El = 1.11 [log1O(x x 100)1-2.89,
for4 < x < 32;
E1 = 1.0, forx > 32, where:
x = area (ha) of conterminous
forestnotbisectedby roads
Densityof understory
Groundsurvey E2 = 0, forx < 20;
Zimmerman
E2 = -0.007x + (2.38 x 10-)x2 + 0.06,
(1992)
for20 < x < 80;
E2 = 1.0, forx > 80, where:x =
percent closure of understory
Steepnessofterrain
GIS
E3 = 0, forx < 15;
Zimmerman
E3 = 0.0333x - 0.5, for15 < x < 45;
(1992)
E3 = 1.0, forx > 45, where:x = slope
(degrees)of terrain
Distance fromroads
GIS
Collins (1983)
E4 = 0, forx = 0;
E4 = 0.156x+0.195x2 =0.25, for0<x< 1.6;
E4 = 1.0, forx >1.6, where:x =
distance (km)to nearestroad
Liferequisitevariable
LRVD= [(D1 +D2)/2](D3 +D4)05,
fordenningresources
for([(D1 +D2) /2](D3 + D4)}0.5 < 1.0;
LRVD= 1.0,
for(l(D1 +D2)/221(D3+ D4)}05 > 1.0
D1 = 0, forx < 2;
Accessibilityvia roads
GIS
Beeman (1975),
Eubanks(1976),
DI = (9.8 x 10-2)x - 0.20,
for2 < x < 12.25;
Garshelisand
D=
1.0, forx > 12.25, where:
Pelton(1981),
x = area (ha) of conterminous
Warburton(1984),
forestnotbisectedby roads
Zimmerman(1992)
GIS
(Continued)
a Variablescombined intoa singleindex.
b LRV= LifeRequisiteVariable.
c Includesblueberries(Vacciniumspp.), huckleberries
(Gay/usacciaspp.) and blackberries(Rubusspp.).
d DigitizedContinuousInformation
of StandCondition(CISC), UnitedStatesDepartmentofAgriculture
ForestService.
e
Diameterbreastheight.
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800
WildlifeSocietyBulletin2002,30(3):794-808
Table 1 (continued). Components,samplingmethods,and functionsfora habitatsuitabilityindex (HSI) forblack bears in the
southernAppalachians,1981-1994. SummarizedfromZimmerman(1992) and Powell et al. (1997).
SubModel Index indexa
ILRV
Survey
methodology
Habitatfeaturemodeled
Function
Source(s)
D2
Availability
of dense
Aerial
standsof rhododendron photographs
(Rhododendronsp). or
mountainlaurel(Kalmia
latifolia)forgrounddens
D2 = 0.0333x, forx < 30;
Zimmerman
(1992)
D3
Availability
of cave and
rockdens
D3 = tan(x),forx < 45;
D3 = 1.0, forx > 45, where:x = slope
D4
Availability
of tree
cavitydens
(degrees)of terrain
Groundsurvey D4 = 0.564(log1ox)- 0.352, forx < 250;
D4 = 1.0, forx > 250, where:x = number
of trees>90 cm DBHe / ha
Zimmerman
(1992)
of all
Interspersion
GIS
resources
GIS
D2 - 1.0, forx > 30, where:x
= area (ha) in rhododendronor
mountainlaurel
ILRV= 1.0, forx < 5;
ILRV= -0.07x + 1.35, for5 < x < 19;
ILRV= 0, forx > 19, where:x = distance(km)
a Variablescombined intoa singleindex.
b LRV= LifeRequisiteVariable.
c Includesblueberries(Vacciniumspp.), huckleberries
(Gaylusacciaspp.) and blackberries(Rubusspp.).
d DigitizedContinuousInformation
ForestService.
of StandCondition(CISC), UnitedStatesDepartmentofAgriculture
e Diameterbreastheight.
outputwas due primarily
to a subsetofmodelcomponents (i.e., strongimbalance in S or E among
components indicated that the model could be
reducedto a subsetof componentswithoutchanging model predictionssubstantially).
Whereassensitivity
and elasticity
analysescould identify
relative
importanceof model componentsgiveninputdata
used to generatethemodel,theycould notindicate
anythingabout biologicalrelevancyof the components,whichmustultimately
be testedwithdataon
habitatuse or demographycollectedfromanimals.
Newmarket,
Ont.,Canada;3M andWildlink,
both of
St.Paul,Minn.).We capturedand handledall bears
in compliance with requirementsof the InstitutionalAnimalCare and Use CommitteesforNorth
Carolina State University(IACUC# 96-011) and
AuburnUniversity
(IACUC #0208-R-2410).Forour
analyses,we consideredbears to be adult at 3.5
yearsold; we classifiedfemalesknownto produce
cubs at age 3 as adultat age 2.5.
FromAprilor Mayeach yearuntilbears denned
(late Novemberto mid-December),we estimated
locationsusing telemetryreceivers(Telonics Inc.,
Trappingof bears, telemetry,
and home
Mesa,Ariz.)and truck-mounted
or hand-heldantenrange estimation
nas. We estimatedlocationsby triangulating
comWe capturedbearsfromMaythroughmid-August pass bearingstakenfroma minimumof 3 separate
of 1981-1994 (except 1991 and 1992) usingmodi- locationswithin15 minutes(Zimmermanand PowfiedAldrichfootsnares(Johnsonand Pelton 1980) ell 1995). When practicable,we located each bear
or barreltraps. Everyeffort
was made each yearto every2 hoursfor8 consecutivehours. We repeatcaptureall bears in the centralportionofthe study ed samplingevery 32 hours to standardizebias
area,althoughtrappingeffortvariedamongyears. fromautocorrelation
within8-hrsamplingperiods
We immobilizedcapturedbears using a combina- and to eliminatebias betweenperiods(Swihartand
tion of Ketaset,Rompun,and carbocaine (approx. Slade 1985,Powell 1987).
200 mg ketamine hydrodrochloride
+ 100 mg
Each observercollectingtelemetry
data also regxylazinehydrochloride/cc;
Cook 1984) or Telazol ularlyestimatedlocationsof "test"collarsto docuadministered
witha jabstickor blowgun.We fitted ment telemetryerror (Zimmermanand Powell
immobilized bears with ear tags, then sexed, 1995;M.S.Mitchell,
unpublisheddata). Zimmerman
weighed,measured,and drew blood samples. We and Powell (1995) evaluatedtelemetryerrorusing
extracteda firstpremolarto estimateage. We fitted test collar data and foundthatmedian errorwas
selected bears withmotion-sensitive
radiotransmit-261 m (n = 371), 95% of estimateswere <766 m
ter collars (Telonics,Inc., Mesa, Ariz.;Lotek,Inc., fromthe truelocation,angleerrorwas significantly
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et al. 801
indexforblackbears* Mitchell
Habitatsuitability
leptokurtoticaround 0, and error did not differ
amongobservers(P>0.05).
We estimatedhome rangesfromlocationsusing
a fixed-kernelestimatorwith bandwidth determined by cross validation(programKERNELHR;
Seaman et al. 1998). We used a gridsize of 250 m
for kernel estimationto match resolutionof our
telemetry
and habitatmaps. A minimumof20 locationswas requiredforhome rangeestimates(Noel
1993, Seaman and Powell 1996), and home ranges
were defined as the area containing 95% of the esti-
matedutilitydistribution.
HSI and habitat selectionby bears
With more data we repeated Powell et al.'s
(1997) testsoftheabilityofHSI and HSI2to predict
bear selection of habitat (second-orderhabitat
selectionJohnson1980) at populationand individual scales, and to predict how many bear home
rangeswould include patches based on theirHSI
values. Foreach bear each year,we used the kernel
densityassignedto each cell ofits95% kernelhome
range to index the value of thatcell to thatbear
(Powell 2000). For maps of HSI and HSI2 foreach
year,we rounded all HSI and HSI2 values to the
nearest0.05 and calculatedpercentavailabilityof
cells foreach of 20 HSI classes withinthe sanctuary. Combininghome range and habitatdata for
each year,we used Ivlev's electivityindex (Ivlev
1961,Powellet al. 1997) to calculatea habitatselection index,P, forspace use by each bear based on
classes of HSI and HSI2:
0
use of Class HSI1 - % availabilityof Class HSI1
classes a bear could encounterin the southern
thatthisdefAppalachians.We concludedtherefore
initionof availabilityminimizedthe likelihoodof
bias in our analysesofhabitatselection(McClean et
al. 1998).
For all habitatselectionanalyses,we used individualbears as the experimentalunits.We used linear regressionto evaluate the abilityof HSI and
HSI2 to predictvalues of P at two levels of resolution:the bear populationand individualbears. To
discern habitatselection at the population scale,
we averagedvaluesofP foreach HSI and HSI2class
over all bears withineach yearpriorto regression
analysis(Proc GLM,SASInstitute1990). To discern
habitat selection on an individual scale, we
regressedvaluesofP forindividualsagainstHSI and
HS12 classes (Proc GLM, SAS Institute 1990).
Because bear behaviorcan varywithsex and maturity(i.e., juvenile or adult),we included these as
explanatoryvariablesin the analysisof individual
habitatselection. We blocked observationsin the
finalanalysisby sex or maturity
ifeitherexplained
a significant(Type III sums of squares P<0.05)
amount of variabilityin the data. To determine
whether HSI and HS12 predictedthe numberof
bear home ranges that included a given habitat
patch,we regressednumberofhome rangesincluding each cell againstHSI classes assignedto cells
(Proc GLM,SASInstitute1990).
Results
EstimatingHSI values and modelanalysis
We created HSI and HSI2 maps forPisgah Bear
Sanctuaryfor each year between and including
1981 to 1994 (e.g.,Figure2b). Our maps captured
P standardizedthe use of habitatclasses by their considerablymore detail than those preparedfor
so thatselectiveuse byanimalscould be the firstevaluationof the HSI (e.g.,Figure2a; Powavailability
discerned.ValuesforP rangedfrom-1 (avoidance) ell et al. 1997). The increasein detailwas due to our
to 1 (strongselection). Anyindex of habitatselec- abilityto map standsharvestedfortimberand to
tion is sensitive to how habitat availabilityis map HSI componentsexplicitlyusingGIS data not
defined,and no objectivebiologicallybased means availablewhen the HSI was firsttested(e.g.,digital
of definingavailabilityexist. Because we were elevationmodelsand digitizedoverstory
data),thus
interestedin the bear populationlivingwithinPis- requiringPowell et al. (1997) to interpolateall 20
gah Bear Sanctuary,
we used all habitattypescon- componentsbetweenisolatedsamplingpoints.
tained withinthe sanctuaryto defineavailability. Sensitivity
and elasticityanalysessuggestedthat
Nothing precluded bears from using all habitat no componentor set of componentsexertedinorclasses withinthe sanctuary.The sanctuarywas dinate influenceover the HSI, althoughcompolargeenough to compriseall HSI classes,and inter- nents did varyin proportionaleffects.Sensitivity
spersion of HSI classes throughoutthe sanctuary and elasticity
were highestfornumberoffallenlogs
was high. Further,
the distribution
of HSI classes (Fy1),anthropogenicfood source (Fy2a),distance
was representativeof the full range of habitat between anthropogenicfood source and escape
P
=
% use ofClass HSI. + % availability
ofClassHSIi
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802
WildlifeSocietyBulletin 2002, 30(3):794-808
cover (Fy2), distanceto nearestroad (E4),and area 1997),we foundthattherelationship
betweenHSI2
coveredin rhododendron(Rhododendronspp.) or and habitatselectionby bears was much stronger
mountainlaurel(Kalmia latifolia;D2). Variationin on a populationscale thanforHSI (r2=0.90,g 1159
all other components had approximatelyequal = 1476.53, P= 0.0001, Figure3b). Unlike original
effectson finalHSI calculations(Table 2).
analyses,we found both HSI (r2=0.14, F1, 1617=
269.18,P=0.000 ,Figure3c) and HSI2 (r2=0.62,F1
HSI and habitat selectionby bears
1430= 2327.18, P = 0.0001, Figure 3d) predicted
We used 127 annualhome ranges(38 belonging habitatselectionby individualbears,althoughnot
to adult males, 32 to juvenile males, 55 to adult as strongly
as at the populationscale. The number
anygivencell correfemales,2 to juvenilefemales;mean locationsper of home rangesincorporating
annual home range=121.5+72.28 [SD]) observed lated positivelywith both HSI (F1 179=15.75,P=
for 81 collared bears (mean number of annual 0.0001) and HlSI2(F1,159=13.41,P=0.0003).
home rangesper bear= 1.56?0.95) in thesanctuary
from1981-1994 to analyzerelationshipsbetween
Discussion
habitatuse and HSI and HSI2. HSI explainednearly
halfthe variability
in habitatselection(P), forthe
Roloffand Kernohan(1999) set out 7 criteriafor
bear population (r2 = 0.45, F1 181= 145.67, P =
assessingreliability
of habitatmodels:evaluationof
0.0001,Figure3a). Neithersex,maturity,
normulti- model components,assessment of variabilityin
ple home rangesfromindividualbears affectedthe input data,use of valid comparativetests,use of
relationshipbetween habitatuse and habitatsuit- appropriatespatial scale fortesting,evaluationof
abilityindices (P1>0.05), and blocking was not modelsacross entirerangeof habitatquality,
use of
required. Similarto originalanalyses(Powell et al. a validpopulationindex fortesting,
and use of animal data collected over
durationto proTable 2. Sensitivity
and elasticityof a habitatsuitability
indexforblack bears in the Southern sufficient
Appalachians calculated fromdata collected in the Pisgah Bear Sanctuary,NorthCarolina, vide robust tests. We
1983-1 994.
addressed each to the
extentpracticablein our
Sensitivity
Elasticity
evaluationof the HSI for
Component Habitatcharacteristic
sampled
Mean
SD
Mean
SD
thePisgahBearSanctuary.
Fy1
Numberof fallenlogs/ha
-0.044
0.064
-0.087
0.013
Roloff and Kernohan
Anthropogenic
food source
-0.048
0.000
-0.096
0.000
Fy2a
(1999)
recommended
Distance to anthropogenicfood source
0.008
0.005
0.015
0.010
FY2b
evaluating each of 4
Distance betweenanthropogenicfood
Fy2c
modeling components:
source and escape cover
-0.048
0.000
-0.096
0.001
assumptions,input variDistance
to
perennial
water
0.004
0.010
0.006
0.018
Fsp,
Fsp2
ables, relationships bePercentcover of Smilaxspp.
0.004
0.005
0.007
0.009
Percentcover in berryspecies
0.016
0.016
0.029
0.030
tweeninputvariablesand
FsuI
Presenceof oak species
0.016
0.016
Fsu2
0.029
0.028
output,and accuracy of
Ffia
Forestcover type
0.004
0.000
0.007
0.000
output. Thomasmaet al.
Age of stand
0.004
0.000
0.007
0.000
FfIb
(1991) stated that 3
Ff2
Numberof grapevines/ha
0.004
0.000
0.007
0.000
underlying assumptions
Ff3
Distance to nearestroad
0.004
0.000
0.007
0.000
applied to mosttestingof
Area of conterminousforestnot
El
HSI
models: 1) the study
bisectedby roads
0.004
0.049
0.007
0.089
site
must be within the
Percentclosure of understory
0.002
0.042
0.004
0.077
E2
E3
Slope ofterrain
current
range of the ani0.011
0.014
0.021
0.026
E4
Distance to nearestroad
-0.084
0.082
-0.154
0.149
mal forwhich the model
Area of conterminousforestnot
D,
was developed,2) individbisectedby roads
0.018
0.043
0.032
0.078
ual animals had unobArea in rhododendronor mountain
D2
structed access to the
laurel
0.047
0.023
0.086
0.043
totalarea,and 3) the popSlope ofterrain
0.006
0.037
0.011
D3
0.068
ulation of animals was
Numberof trees>90 cm DBHa/ha
0.006
0.037
D4
0.011
0.068
unharvested.In our test,
a Diameterbreastheight.
assumption1 was clearly
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et al.
indexforblackbears* Mitchell
Habitatsuitability
803
Because mostHSI components were modeled
0o60
041directly from empirical
0.2 0
studies,the only assump2
.9
-0O2.-02
we made about the
tion
~~~~
-04
~~~~~~~~~-0.8 ~~~~~
-0.6
validityof the
biological
-1.
~>-0.8
-1.2
components was that
-1
1
0.8
0.2
0.2
OA
1
0
0.6
0
0.4
0.8
0.6
studieson whichthecomHS12
HSI
ponentswere based were
r2 0.14
not spurious. We did not
08
0.8
LI1
0.6
Tr
directlytest this assumption
beyond the critical
-0.8 1 -- j-0.8
.
-0.8
evaluationofthosestudies
-1~~~~~~~~~~~~~~~~~~~~~~~~~
duringHSI development.
0.4
0.
0.8
1
0.2
0
0.2
04
0.8
~~ ~~~~
0.8
61
~~~~~~~~02
Beyond replicating the
studieson which the HSI
was based in our study
indices,HSI and HSI2
suitability
use and 2 habitat
betweenhabitat
3. Relationships
Figure
Carolina.Eachfig- area, which would be
North
PisgahNationalForest,
forblackbearsinthePisgahBearSanctuary,
it is not clear
calculatedusingdata unrealistic,
/ [use+ availability])
uredepictshabitatselection([use- availability]
2a to us how thisassumption
from1981to 1994. Figures
from127 homerangesofblackbearslivingintheSanctuary
classesforbearsineachyear)selection could
(averageuseofhabitat
and2b depictpopulation-level
be more rigorously
selection
2c and2d depictindividual-level
Figures
forclassesofHSI and HSI2,respectively.
We assessed
evaluated.
bear)forHSI and HSI2,respectively.
foreach individual
classesestimated
(useofhabitat
the relativeeffectsof individual components on
and elasticityanalyjustified.Violationof assumption2 was unlikely model outputwith sensitivity
thatHSI outputwas sensitive
because PisgahNationalForestrepresentedone of ses and demonstrated
the largestblocks of contiguousbear habitatin to variationin all model components,althoughnot
North Carolina,presentingfew obstacles to bear disproportionatelyto any single component.
a pro- Stronglydisproportionaleffectsamong compoaccess to habitat.Althoughwe were studying
tectedpopulation,severalof the 81 collaredbears nentswould indicatethe need fordiscardingthose
we trackedwere knownto be poached withinthe with littleeffecton model calculations. Considerbetweeninputvariablesand
sanctuary(n=6) or legallykilledby huntersoutside ing onlyrelationships
the sanctuary(n = 6) while we were trackingthem. model output,our resultssuggestedthatmostcomviolat- ponentswere importantto model outputand did
speaking,assumption3 was therefore
Strictly
moreparsimonious
can
relaxed
for
the not indicatethata significantly
this
be
assumption
ed; however,
make
predictionssimilar
the
HSI
would
version
of
al.
tested
an
Thomasma
et
(1991)
HSI we evaluated.
HSI.
we
were able to thorthe
Finally,
to
complete
not
effects
of
human
trappers
HSI thatdid
include
of
model
predictions
assess
accuracy
As
an
such,
oughly
on fisher(Martespennanti) habitat.
on habitat
data
tests
independent
using
through
evaluationof theirHSI on a harvestedpopulation
home
and
distribution.
range
would be biased if trappinginfluencedhabitat use
Roloffand Kernohan(1999) identified2 sources
choices of fishers. The thirdassumptioncould
of
errorin inputdata thatshouldbe assessed:samto meanthat
thereforebe morebroadlyinterpreted
conditions modeled by an HSI must accurately plingerrorin assigningvaluesto mapped unitsand
reflectexistingconditionsforthe populationused mappingerrorin depictingmapped units. We did
in part
to testthe HSI. Because severalcomponentsof our not assess eithersource of erroranalytically,
for
an HSI
of
so
the
doing
impracticality
HSI explicitlymodeled effects of exposure to because of
required
The
ground-truthing
on bear habitat(e.g., Fy2a, as complex as ours.
human-causedmortality
Fy2c,Ff3,LRVE,D1), it was a reasonablemodel fora to verifyassignedvalues and mapped boundaries
populationwhereindividualsare occasionallykilled would have been dauntingfor 20 independently
by people. We conclude thatour test satisfiesthe modeled HSI components mapped on a 235
intentof assumption3, and anyviolationin a strict km2landscape. Nonetheless,the potentialforerror to bias our observationsor to contributeto
sense is unlikelyto bias our findings.
3a
3b
1 1_I
0.8 A
0.6
0.4
02
=
0.45
o.
0
-
-0
4)
iT
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804
WildlifeSocietyBulletin2002, 30(3):794-808
in P is unknown. We can finergrainwould risk spuriousfindingsresulting
unexplainedvariability
speculate,however,on the strengthof thispoten- fromtelemetryerror. Therefore,habitatinformatial. Onlytheforestoverstorydata containedin the tiondepictedat grainsfinerthan250 x 250 m was
USDAFS ContinuousInformationof Stand Condi- essentiallyirrelevantto our analyses. Grain at
tion (CISC) database consistedof vegetationclass- which habitatwas mapped also correspondedto
es, and because only4 HSI componentswere gen- the biologicalresolutionof questionsbeing asked.
eratedusingCISC,we were comfortable
relyingon We were notseekingto predictfine-scalebehaviors
standardsand mappingprecision of bears correspondingto a fine-grained
ground-truthing
depiction
set by the USDAFS. Similarly,
we were comfortable of habitat (e.g., foragingin a particularberry
with standardsset by the UnitedStatesGeological patch). By addressinghabitat selection within
SurveyforDigitalElevationModels and topograph- annualhome ranges,however,we targeteda levelof
ical maps fromwhich 9 HSI components were resolutionin bear behaviorthatwe deemed approderived. Errormighthave been more of a factor, priateto the confidencewe had in our telemetry
however,forthe 7 componentsforwhich we had locations. Our analysiswas unlikelyto be strongly
no GIS data and mapped using field data (with affectedby a coarse-grained
depictionof habitat.
potentialsamplingerror)throughlandformmodelFinally,
because we modeled the HSI as a contining or interpolation(with potentialfor mapping uous surfaceofpixels,notas polygonsrepresenting
error).This presenteda potentialconcernbecause habitatclasses,developinga measureof errorsuch
the HSI, though not stronglyinfluencedby any as a confidenceinterval(Bender et al. 1996) on a
component,was relativelysensitiveto 2 compo- pixel-by-pixel
basis,particularly
fora modelas comnentsmapped usinglandformmodelingand inter- plex as our HSI,became problematic.Althoughitis
polation(Fy1and D2,Table2). We cannotbe certain conceptuallyappealing,we do notknow oftoolsto
how samplingor mapping errorassociated with accomplishthis.
these componentsaffectedthe HSI, althoughas 2
Our largesample size of animals(n =127) using
of 20 largelyindependentcomponents,we expect habitatacrossnearlythe fullrangeof the HSI easily
that theireffectswere proportionallysmall. We satisfiedRoloffand Kernohan's(1999) criterionfor
believe the large numberof mainlyindependent validityof comparativetests (Johnson1981), and
componentsthatwere combined to generatethe the 12 years over which our data were collected
HSI likelymitigatedeffectsof samplingerrorin any satisfiedtheirdurationcriterion.Because our samone component.
ple size was largeand containeda reasonablecrossFor all HSI components,we expect that the section of sex and maturity
classes (only juvenile
potentialeffectsof mappingerrorwere in part a femaleswere underrepresented),
we have confifunctionof extentand grainof the maps we gener- dence thatour findingson habitatselectionwere
ated. Because extentof the landscape we mapped representative
for all bears livingin Pisgah. Our
was largerelativeto the scale at which bears used confidencein how HSI could predictthe number
habitat(235 km2 compared to an average home of home rangesincorporating
a patch based on its
rangesize of43.3?27.9 km2;Powellet al. 1997) and HSI value,however,is more qualified.These findbecause of the large numberof home rangeswe ings were based on the untestedassumptionthat
assessed,the likelihoodof isolatedmappingerrors our average annual sample size (approx. 10
resultingin consistentbias across habitatclasses bears/yr)was sufficientto ensure no bias from
and bears sampledwas probablysmall.The aggre- uncollared bears whose home ranges were
gationof spatialdata we used to convertHSI com- uncountedin patches we analyzed. Withoutestiponentmaps generatedat a finegrain(30 x 30 m) mates of bear densitythat we could compare to
to finalcoarse-grained
maps of HSI used foranaly- numberof collaredbears each year,we could not
ses (250 x 250 m) would have reduced effectsof be certainthisassumptionwas unviolated;our consamplingerrorthroughaveragingbut also would clusions on how HSI predictednumberof home
have increasedmappingerrorforspatialinforma- ranges that included a given patch are therefore
tiondistributed
on a grainfinerthan250 x 250 m. tentative.
We chose the coarsergrainforour analyses,howBecause we evaluatedthe HSI at both the popuever,to matchthe grainof habitatmappingwith lation(an aggregationof home ranges)and individour confidencelevel in bear locations based on ual levels(a largenumberofhome rangeswithvaritelemetryerror. Assessinghabitatselection at a able home rangesizes),we addressedapplicability
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Habitatsuitability
indexforblackbears* Mitchell
etal.
805
on multiplespatial scales appropriateto the bio- gah bears. These findingswere consistentwith
logical resolutionof our questions (e.g., second- other work on southern Appalachian bears
order habitatselection,Johnson 1980), satisfying (Garshelisand Pelton1981). Bearsin Pisgahdo not
we believe the
Roloffand Kernohan's(1999) criterionof appro- appear to be territorial;
therefore,
forevalua- habitatselection patternswe observed were reapriatespatialscaling.Theirrequirement
tion of an HSI across its entirerangeof values was sonable reflectionsof fitness-based
foragingdecinearlysatisfiedsince HSI valuesforareas ofthe Pis- sions made withoutstronginfluencefromsocial
gah Bear Sanctuaryused by bears rangedfrom0.1 interactions.
Our tests supportedPowell et al.'s (1997) conto 0.85.
Roloffand Kernohan(1999) listedreproductive clusionthattheHSI capturedhabitatcharacteristics
as appropri- important
to blackbearsin thesouthernAppalachirate,fecundity,
survival,and mortality
ate surrogatesforfitnessin evaluatingan HSI. Real- ans. Therefore, the HSI could be used by
to collectfor researchersand managersto make meaningful
istically,
datasuch as thoseare difficult
prea largecarnivoresuch as bears (and formanyother dictionsabout thebehaviorofbearsand how bears
animalsas well), even in a long-term
studysuch as mightbe distributedon a landscape. No habitat
ours. Fitness,however,can also be inferredindi- model, however, can capture the relationship
rectlyfrombehaviorof animalsusingthelogic that between an animal and habitatperfectlybecause
naturalselectionfavorsanimalsthatselect habitat social behavior,reproduction,and other activities
characteristics
whichenhance theirfitness.Strong also affectuse of space, so room existsforimprovtheoreticalfoundationsforthisapproachare based ing the HSI. The numberof variablesin the HSI is
in foragingecology (optimality)and in empirical high,raisingquestionsaboutparsimonyand ease of
fromthe perspectiveof
researchshowingthatnaturalselectionhas molded use by managers. Strictly
foragingdecisions,patch selection,and time of how inputvariablesshape HSI calculations,future
patchoccupationto maximizefitness(or indicesof improvementsof the HSI could focus on those
fitness;Pykeet al. 1977,Stephensand Krebs 1986). componentswe observedto have the greatestsenIn fact,thisfoundationunderliesall studiesof habi- sitivitiesand elasticities,althoughthis would not
tat selection. Althoughthis approach can suffer necessarily improve biological meaning of the
fromsignificantdrawbacksbecause key assump- model. Removingthe life requisitevariable for
tionsmustbe made about bothbehaviorof animals escape (LRVE) from the model to create HSI2
and what animalsselect (Garshelis2000, Mitchell improvedour abilityto predictbear behavior,howand Powell 2002), it maybe the onlyviable option ever,suggestingthat a more parsimoniousmodel
for studiesunable to collect data on more direct could have morebiologicalmeaning,at leastin the
surrogatesforfitness.Cautionis also warrantedif Pisgah Bear Sanctuary.We do not know whether
competitionor social antagonismamongstudyani- the greaterpredictivepower forHS12was because
mals can bias the findingsbecause all animalsdo LRVE inaccurately modeled escape resources,
not have equal access to all resources. Both traits whether discarding LRVE eliminated possible
are common among carnivores,with intrasexual redundancy(area of conterminousforestand slope
territoriality
prevalentamong solitarycarnivores of terrainare in LRVD and LRVE;Table 1), or
(Powell 1979). In an analysisof home rangeover- whetherescape resourcessimplywere notlimiting
lap, however,Powell (1987) documentedbroadly to the relativelyprotectedpopulationof bears in
overlappinghome rangesamongadultfemalebears thePisgahBear Sanctuary.Discerningthesefactors
in Pisgah,withno exclusiveuse of anypartof their could be the subjectforfuturework. Further,
interhome ranges. In a comparisonof Pisgah bears to relationshipsamong variables,establishingmeanbears livingin Minnesota(and knownto be territo- ingfulconfidencelimitson an HSI expressedas a
rial;Rogers1977,1987),Powellet al.(1997) used an continuoussurface,and relationshipsbetween the
energetic model of territoriality
(Carpenter and HSI and moredirectmeasuresoffitnessneed to be
MacMillen 1976) to predictthat Minnesotabears explored. Nonetheless,the primaryvalue of the
should be territorial
whereas southernAppalachi- HSI is thatit representsan a priorimodel of the
ans bears should not. Powell et al. (1997) tested ecology of black bears that was evaluated and
thispredictionby comparinghome rangeoverlap shown to relatestrongly
to observablecharacteris(Lloyd 1967) betweenPisgahand Minnesotabears, ticsofa bearpopulation.Furtherrefinement
ofthe
findingthatoverlapwas significantly
higherforPis- HSI, also performedin a hypothetico-deductive
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All use subject to JSTOR Terms and Conditions
806
WilFtfeSocietyBulletin-2002, 30(3):794-808
approach,would thereforerefineour understand- Foundation, Earthwatch-The Center for Field
ing of bears.
Research,FederalAidinWildlifeRestoration
Project
Beyond the abilityto make robustpredictions W-57 administratedthroughthe North Carolina
about bear habitatuse and its heuristicvalue for WildlifeResourcesCommission,GrandValleyState
McNairsScholarsProgram,
International
achievingmore insights,perhaps the most novel University
G.
attributeof thisHSI is thatit is expressedas a con- AssociationforBear Researchand Management,
tinuous surface. Such a depiction makes sense and D. King,McIntireStennisfunds,the National
when one considers that the diversehabitatele- GeographicSociety,the NationalParkService,the
mentsa bear requiresare distributed
in space gen- NationalRifleAssociation,the NorthCarolinaAgrierallyindependentlyof one another. Althougha culturalResearchService,NorthCarolinaStateUniPortClydeand StinsonCanningCompanies,
commonpracticein HSI developmentis to assign versity,
values to vegetationclasses thoughtto contain 3M Co., the UnitedStatesDepartmentof Agriculessentialresources,no logical or biologicallinkage ture Forest Service,WildlandsResearch Institute,
exists between any vegetationclass and variables Wil-Burt
Corp.,andWildlink,
Inc. We thank2 anonysuch as percent berrycover,distance to anthro- mous reviewersfor constructivecommentsthat
pogenic food sources,or numberof downed logs. helped improvethismanuscript.
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MichaelS. Mitchell
(photo)is assistant
unit leader (wildlife)fortheAlabamaCooperative
Fish
and WildlifeResearchUnit,United
States Geological
Survey, and an
assistantprofessor
in the School of
and WildForestry
lifeSciencesatAuburn
University,
Alabama.Mikereceivedhis
B.S. inbiologyfrom
JamesMadisonUniversity,
and hisM.S. in
wildlife
biologyandPh.D.inzoologyfrom
North
CarolinaState
University.
Hisinterests
includeappliedlandscapeecology,
the
ecologyand management
oflargecarnivores,
fireecology,and
forest
wildlife
management.
is associate
JohnW.Zimmerman
professor
and ChairoftheScienceDivisionat MountSt.Clare
College,Clinton,Iowa. He receivedhis B.S. and M.S. in
wildlife
ecologyfrom
OklahomaStateUniversity,
andhisPh.D.
in zoologyfromNorthCarolinaStateUniversity.
His interests
includeecologyandenvironmental
studies.RogerA.Powellis
in the Department
a professor
of Zoologyat NorthCarolina
StateUniversity.
studies
He receivedhisB.A.inenvironmental
fromCarleton
Collegeand hisPh.D. in biologyfromtheUniversity
of Chicago. His interests
includebehavioral
ecology,
oflimiting
on ecolevolutionary
ecology,andeffects
resources
and morphology
ofanimals,
ogy,behavior,
population
biology,
especiallymammals.
r
Associate editor: Whittaker
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