j. RaptorRes.32(2):82-89
¸ 1998 The Raptor ResearchFoundation, Inc.
ESTIMATING CORE RANGES: A COMPARISON OF TECHNIQUES
USING THE COMMON
KATHY H.
HODDER
BUZZARD
AND ROBERT
(BUTEO BUTEO)
E. KENWAP, I•
Instituteof Terrestrial
Ecology,
Furzebrook
Research
Station,Wareham,
Dorset,BH20 5AS, U.K.
SEAN S. WALI•S
Biotrack, 52 FurzebrookRoad, Wareham, Dorset, BH20 5AX, U.K.
RALPH T. CLARKE
Instituteof Terrestrial
Ecology,
Furzebrook
Research
Station,Wareham,
Dorset,BH20 5AS, U.K.
ABSTRACT.--The
need to describethe relative intensitywith which an animal usesdifferent parts of its
home range hasbeen recognizedfor at leasthalf a century.Suchdescriptionsare particularlyimportant
for wide-rangingraptors with home rangescovering a variety of habitats.In studiesof many taxa, the
descriptionof internal range structure is addressedby describinga core range of most intensiveuse.
However,there is still no broadlyaccepteddefinition of a core or method of objectivelyestimatingcore
ranges.Here, we proposethat a core range can be usefullydefined by the exclusionof excursiveactivity
with the assumptionthat behavior differs between core and excursiveactivities.Two methods of excluding excursiveactivity are presented for winter ranges of the Common Buzzard (Buteobuteo)in
lowland U.K. The first involvessubjectiveexclusionof outlying locations,using the outermostdiscontinuity in the utilizationdistribution(UD). IncrementalClusterPolygonsare usedto producethe UD
becausethismethod providesthe closestspatialrelationshipto the animallocationsand the mostclearly
defined discontinuities.
The potentialfor error or bias in this subjectivemethod may often be unacceptable,particularlyfor home rangeswhich do not have well-definedcore areas.The secondmethod
is a new applicationof incrementalclusteranalysisthat objectivelyexcludesexcursivelocations.The
objectiveand subjectiveapproachesare compared,and implicationsof core range definition in habitat
and socialityanalysisof raptors are explored in the context of publishedanalyseson raptors and other
taxa.
KEYWORDS: homerange;,
corerange;,
radiotelemetry;
incremental
cluster
analysis;
Buteo buteo.
Estimaci6nde rangosc.... ,ale,: Una comparaci6nde ttcnicasutilizandoa Buteobuteo
RESUMEN.--La
necesidadde describirla intensidadrelativaen que un animalutilizalasdiferentespartes
de su rango de hogar ha sido reconocidapor lo menosdesdehace medio siglo.Estasdescripcionesson
particularmenteimportantespara las avesde presaque tiene rangosamplioscon una gran variedadde
habitats.En los estudiosde muchostaxones,la descripci0nde la estructuradel rango interno es abordada mediante la descripci0n del rango central como el mas utilizado. No obstante,no existe afin una
definici0n aceptadadel centro o de un mttodo para estimar en forma objetivalos rangos centrales.
Aqui proponemos que un rango central puede ser definido en forma fitil mediante la exclusitn de las
actividadesde incursitn. Dos mttodos para excluir la actividad de incursitn son presentadospara los
rangosde invierno de Buteobuteoen el Reino Unido.
[Traduccitn de Ctsar Mfirquez]
Radiotelemetry has been used to study raptors
for nearly three decades,providing data for many
aspectsof ecological research (Kenward 1985a).
However,improvement in the collection and analysisof data has been slowerthan the technicaldevelopmentsin radio-tracking (Lance and Watson
1980, Harris et al. 1990, Kenward 1992). The de82
scription of the intensitywith which animals use
different partsof their home rangespresentsa fundamental problem (Hayne 1949). Animals generally live in a spatiallyheterogeneousenvironment
in terms of food availability,nest and roost sites,
density of competitors,and other factors. Therefore, they tend to have one or more core areasof
JUNE 1998
CO• RANGEESTIMATES
FORCOMMONBUZZarDS
intensive use in their home ranges (Kaufman
1962) and it is likely that their behavior will differ
in the core and outer areas of the home range.
Many raptors are wide-ranging and their outer
home-range boundaries may enclose habitat
known to be avoided (e.g., Stahlecker and Smith
1993). Raptors may also make excursions from
their usual range. Common buzzards(Buteobuteo),
for instance,often make brief movementsof up to
20 km during their first year (Walls and Kenward
1994), before returning to ranges typically less
than 1.1 km in diameter
(Walls and Kenward
1995). Therefore, an analysismethod for core
ranges should exclude excursive locations (Burt
1943) and areas within an outer home-range
boundary that are avoided (White and Garrott
1990). A number of methodshavebeen proposed
for estimatingrange cores,but none are widelyaccepted.Therefore, comparisonsbetweenstudiesis
generallynot possible.
The processof finding a core rangecan be split
into three stages:(1) the descriptionof the internal range structure,givingnominal cores;(2) the
choice of a percentageinclusionof radio locations
that selectsa biologicallymeaningfulcore for each
individual;and (3) setting a standardcore range
(in terms of a standardpercentageof locations)
for a sampleof animalsto allow statisticalcomparison within the sample. Here, we present a comparison of techniques used to describe the core
rangesof raptors.
83
version of RANGES V (Kenward and Hodder
1996).
DESCRIPTION OF RANGE STRUCTURE
Several different
methods
have been used for de-
scribinginternal range structure.The efficacyof
these methods can be judged by their conformation to the locations,includingtheir abilityto conform to multinuclearcores.A further major requirement is efficiency.To give time for data collection on a sufficient number of animals, most
projects require an analysismethod that can estimate the range structurefrom a minimum number
of locationsper animal. The grid cell approach
(Siniff and Tester1965,Ables1969,Voigt and Tinline 1980, Samuel et al. 1985, Samuel and Green
1988) conforms to location distribution. However,
this method may require more than 150 locations
to calculatea stablehome range that does not increase in size as more locations are added (Doncaster and Macdonald 1991). The tessellation tech-
nique proposed by Tray et al. (1992a) also requires a large number of locations.Contouring
methods (Dixon and Chapman 1980, Worton
1989) stabilize with fewer than 50 locations. How-
ever, their accuracyof fit to the locations is influ-
encedby dependencyon an arbitrarygrid and the
use of parametric estimation functions (Spencer
and Barrett 1984, Kenward 1992, Wray et al.
1992b). Ellipses(Jennrich and Turner 1969) give
stability with even fewer locations but conform
poorly to the locationsand can only provide one
nucleus.Polygon-basedtechniqueshave stableoutCOLLECTION OF DATA
er edgeswith 30 locations (Kenward 1982, Parish
and Kruuk 1982,Kenward1987);however,peeled
Common Buzzards in southern Dorset, U.K.
were instrumentedwith radio-tagsat the nestjust convexpolygons(Kenward1985b,1987) providea
before fledging. Each radio-tagweighed30 g and poor fit to multinuclear or curved ranges (White
wasmounted asa backpackwith 6-mm-wideTeflon and Garrott 1990). These problemsmay be avoided with Incremental Cluster Polygons(ICP) (Kenribbon (Biotrack, 52 Furzebrook Rd., Wareham,
ward
1987). ICP analysisis based on forming
U.K.). These tags had a life of up to 4 yr and a
maximum range of 40 km from the ground and groupsof locationsand separatingoutliers.Convex
polygons drawn around the clusters provide a
80 km from the air. To avoid disturbance
of the
study animals, buzzard locationswere determined range outline that is not influenced by a grid or
by triangulation from roadsides.Error associated the position of outlyinglocations(Kenward 1987).
Also, the outlinesproduced by elimination of outwith the locations was estimated
at about 100 m.
lying locations stabilize at less than 50 locations
Standard30 locationhome ranges(three locations
(Kenward1992). Therefore, we adoptedICP asthe
per d for 10 d [Kenward1987]) were recordedfor
method for estimatinginternal range structurein
122 buzzards from 1990-96.
These included
buzthis paper.
zardsaged between 1 and 4 yr. Data were collected
in the nonbreedingseasonafter the main dispersal SELECTION OF A BIOLOGICALLY MEANINGFUL CORE SIZE
period, when the buzzardshad settledin relatively
The outline methods discussedcan provide
stableranges.Data were analyzedusinga modified nominal core areasat any percentageinclusionof
84
HODDV.R V.T•a•.
VOL. 32, No. 2
locations.The secondproblem is to selecta core
that has biological significance.In the literature,
ical foraging (Kenward 1977, Walls and Kenward
the selection
corded, it is likely that the nearestneighbor (NN)
of core areas of most intense
use has
1995). Therefore, if sufficient locations were re-
been largely subjective(Kenward 1985b, Harris et
distances
al. 1990, Wauters and Dhont 1992), or even arbi-
quencydistributionsrepresentingcore and excursiveactivity.In thesehypotheticaldistributions,the
trary (Mohr and Stumpf 1966, Anderson 1982,
Wray et al. 1992b, Hohmann 1994). For instance,
mean
between
distance
locations
between
NN
would
form
locations
two
fre-
in the core
the core rangehasbeendefinedasa 50% contour would be expected to be smaller than the mean of
(Heikkila et al. 1996) or a 95% ICP core (Hulbert
et al. 1996) but there is no biologicalbasisfor this.
The subjectiveapproach commonly usesthe utilization distribution (UD) which is the polygon or
contour area plotted againstthe percentageinclusion of locations (Van Winkle 1975). Identification
of a discontinuityin this plot indicates the point
where outlyingfixesare excluded (Kenward1985b,
1987,Harris et al. 1990). ICP isparticularlysuitable
for this method becauseit producesa steppedUD,
unlike contour methods for which the plot tends
to be smooth (Kenward 1987).
An objectivemethod for estimatinga core range
has been proposedby Samuel et al. (1985). However, to achievegood conformity to the locations,
their method depends on a large sample size of
locations.In this paper, we analyzeICP rangesfor
buzzardsto compare the subjectivemethod (using
the UD) with a new objectivemethod for excluding outlying (excursive)locations.
NN
distances
whether there is a core at 95% or 100% inclusion
to q, where q = EXP(-0.5 (NN/hi)2). This is the
excursive.
where
In most
at least one of the locations
animal
location
is
data sets excur-
sionsare relatively rare. Therefore, the frequency
distribution of NN distanceswould be expectedto
be negativelyskewedwith the positivetail indicating excursiveactivity.
The frequency distribution of NN distanceswas
examined in a subsampleof 10 buzzard ranges.For
each of the ranges,the NN distancewascalculated
for all the locations (N = 30). To compensatefor
differences in the frequency distributionsof NN
distancesbetween ranges,the distanceswere stan-
dardizedby dividingby hi for rangelwhere hi =
%/(«{•r•xi
+ •2yi}
) and•xi andO'y
i arethestandard
deviationsof the location coordinatesfor rangelin
the x and y directions (Worton 1989). As expected,
the pooled frequency distributionof the standardized distancesfor all 10 rangeswashighlynegatively skewed,with many shortdistancesand fewerlonger distances(Fig. 2).
A transformation was sought that would highSELECTION OF PERCENTAGE LOCATIONS TO BE INCLUDED
light the excursivelocationsasoutliers.There were
IN CORE RANGES
only a small number of NN distances in each
Subjective Exclusion of Excursive Locations. range; therefore, it wasnot possibleto use testsof
When locations
recorded
for an animal
include
normality to seek an optimal transformation. Inrelatively long distanceexcursions(Fig. la), the stead, we sought the transformation that minioutermost discontinuityon the slope of a utiliza- mized the coefficient of variation (SD/x-) for the
tion distribution can be very clearly defined (Fig. frequency distribution of NN distances.The effect
lb). In such cases,it is easy to visuallyselect the of a number of logarithmic,reciprocal,and expowas examined
on each of
point on the UD curve that indicates the size of nential transformations
the Excursion-Excluded Core (EEC). However, the 10 ranges.The transformationwhich mostfreother individualsmay have lesswell-defined cores quently minimized the coefficient of variation was
(Fig. lc and ld). In this case,it is difficult to decide generally the negative exponential transformation
of locations (Fig. ld). EEC rangeswere subjectively Gaussian kernel function of Worton (1989). This
estimated
from the UD curve for the ICP winter
transformation was therefore applied to all 122
buzzard ranges which were used to estimate cores
rangesof each of 122 buzzardswe studied.
Objective Exclusionof ExcursiveLocations.The with the subjective(UD) method. In order to sepseparationof excursionsfrom core rangesis based arate the excursivelocationsin each range, minion the assumption that behavior differs between mum excursion distances (MED) were estimated.
excursiveand core activity.For example, buzzards These were the lower P = 0.05 (MED 0.05) and P
and Northern Goshawks(Accipitergentilis)tend to = 0.01 (MED 0.01) percentilesof the normal disNN distances.
fly for much of the time during excursionsbut tribution fitted to the transformed
make shorter,lessfrequent flightsduring more typ- Clusterswere then formed incrementallyuntil the
JUNE 1998
CO• RANGEESTmATESFORCOMMONBUZZARDS
85
lkm
95%
core?
100
100
90%
r
100
80
60
40
20
80
60
40
20
Percent inclusion of fixes
Percent inclusion of fixes
Figure 1.
100
(a) Range areasand fixes of a juvenile male Common Buzzard (Buteobuteo)(JM906).Hatched line indi-
catesthe 100% Minimum ConvexPolygon,solidline showsthe 90% IncrementalClusterPolygons.(b) Utilization
distributionfor incrementalclusteranalysis(ICP) of the winter range of male buzzard (JM906). There is a clearly
defined discontinuityin the curve at 90% inclusionof fixes. (c) Rangeareasand fixesof a juvenile male Common
Buzzard(JM939). Hatched line indicatesthe 100% Minimum ConvexPolygon,solidline showsthe 90% Incremental
ClusterPolygons.(d) Utilizationdistributionfor incrementalclusteranalysis(ICP) of the winter range ofjuvemle
male buzzard (JM939). There is no clearlydefined discontinuityin the curve.
NN distance
of the next fix to be added would
have
exceededthe MED. Any locationsbeyondthis level
were
treated
as excursive.
The
area of the convex
polygon around the fixes in each cluster was
summed to estimatethe core range area.
Core percentagesand areas obtained with the
two probability levels of the MED method were
compared with those estimated by the subjective
(UD) method. Resultsobtained by the UD and the
MED
methods
were
similar
when
MED
= 0.05 and
buzzard JM939 had a 96% core at MED -- 0.05,
similar to the tentativecore assignedwith the UD
method (Fig. ld) but had a 100% core at MED =
0.01. BuzzardJM906 had a core at 90% inclusion
of locationswith the UD method (Fig. lb) and the
MED method (0.05 and 0.01). Core areas of the
122 buzzard ranges estimatedby the UD method
correlated well with core areas estimated using
MED --- 0.05 (r = 0.82, N = 122), and MED -- 0.01
(r = 0.89, N-- 122). However, the distribution of
often larger when usingMED -- 0.01. For instance, the points around the 1:1 line showed that with
86
HoI•I•EI• ET •.
VOL. 32, No. 2
258 -
40_
800700-
30_
600-
20_
500
400-
10_
300
200-
0 •
I
I
I
I
I
I
I
I
I
0
10
20
30
40
50
60
70
80
tOO0-
tOO
2•0
300
Core areasestimatedusingutilization
distributioncurves(ha)
Figure 2. Frequency distribution of nearest neighbor
(NN) interfix distancespooled from locations(N = 296)
from 10 winter ranges of Common Buzzards. NN distancesfor each range are dividedby a smoothingfactor
(h). Note that the y-scaleis truncated.
MED = 0.05 all except one range were closeto the
line, whereaswith MED = 0.01 the majority of core
areaswere larger than thoseestimatedsubjectively
(Fig. 3a and 3b). Therefore, MED = 0.05 wasconsidered appropriate for this sample of animals.
This comparison showed that the MED method
can give resultswhich correlate well with the UD
method, with the advantagethat it is automated
and doesnot involvesubjectiveassessment
for each
i
0
Distanceof eachfix fromits nearestneighbor
(NN) / hi (standardizing
factor)
• '•
5004
animal.
200 ß
SETTING A STANDARD CORE RANGE SIZE FOR A SAMPLE OF
ANIMALS
If a core is required with a standard percentage
of locationsfor a sampleof animals,it is advisable
to select a percentage at which cores have been
estimatedfor most of the ranges.We recommend
settinga standardwhere 95% of the rangeshave
been cored, becausethis permits one range in 20
to have many excursivelocationswithout a disproportionate reduction in the samplecore size. The
resulting standard core percentage will only be
larger than the core rangesof 5% of the sample.
Therefore, few standard core ranges will include
excursivelocations.This is important becauseinclusion of excursivelocationsgreatlyincreasesthe
core range area, and thus the variance of areas in
the sample.A larger proportion of the cores may
be larger than the standardpercentage.However,
thishas a smalleffect on samplevariance,because
o
0
,
tOO
ßß
,
200
300
Coreareasestimated
usingutilization
distributioncurves(ha)
Fiõure 3. Comparisonof core areasof 122 winter ranões of Common Buzzards obtained using subjective(UD)
and objective (MED) methods. (a) wi•h MED P = 0.05
and (b) with MED P = 0.01. The dotted line is the 1:1
line.
the removal of peripheral locations from these
cores reduces the area much
of excursive
less than the removal,
locations.
This processwas applied to the sample of 122
buzzard ranges. The number of ranges that had
been cored increasedas a function of the percent-
JUNE 1998
Cove RANGEESTIMATES
FORCOMMONBUZZAV.
I)S
87
DISCUSSION
In analysesof radio-trackingdata, the choice of
method for estimatinghome range size and structure dependson the goal of the research (Voigt
100
95
and Tinline 1980, Kenward and Walls 1994). Incre-
•
50
100
95
90
85
Percentage
of fixesincludedin the
ExcursionExcludedCore (EEC)
80
mental ClusterPolygonanalysisis particularlyuseful for identifyingfrequentlyused areas,aswell as
producing range structure statistics. Subjective
(UD) and objective (MED) methods can be used
to select the core ICP by excluding excursivelocations. However, the subjective choice of core
from the UD maybe difficult,particularlyfor ranges that do not have a clearly defined core, and this
has the potential to introduce error or even bias.
For example, interpretation of the utilization distribution could be influencedby prior knowledge
of a typical percentage inclusion for cores in the
sample of animals. In contrast the MED method
provides a means to objectivelyplot a boundary
that delimits the usual area of the study animal.
The transformationapplied to the frequencydistribution of NN distancesand the choiceof probability level of the MED need to be testedwith other data sets.In the future, improvement in core
range delineation might be achieved by plotting
restrictededge polygons(Stickel 1954, Harvey and
Barbour 1965, Voigt and Tinline 1980, Wolton
1985) rather than convex polygonsaround the
clusters.
Further
work
is also desirable
to test the
efficacyof this approach for data other than the
standard30riocationrange, especiallydata including variable numbers of locationsor collected during the breeding season.
I
I
I
I
I
100
95
90
85
80
We suggestthat excursion-excludedcore ranges
will provide important insightsin behavioral ecolPercentage
of fixesincludedin the
ExcursionExcludedCore(EEC)
ogy,especiallyin studiesof socialityand habitat seFigure 4. The core range size (percentage inclusion of lection. In analysesof habitat use, a core range eslocations) at which excursiveactivityis excluded by (a)
timator allowsdata to be viewed at three spatial
subjectiveappraisalof the Incremental ClusterPolygon levels: overall availability, familiar area, and the
utilization distribution for buzzard home ranges (N =
usual area sensuBurt (1943). The studyarea (usu122) and (b) by the Minimum ExcursionDistance(0.05) ally arbitrarily defined) can be used for overall
for buzzard home ranges (N = 122).
availability.A range outline including all the locations such as the Minimum Convex Polygon
(MCP) (Mohr 1947) or a probabilisticcontour
age of core locations, for the objective (MED =
0.05) and subjective(UD) coring methods(Fig. 4a (e.g., Worton 1989) can delineatethe familiar area
and 4b). In each case, cores had been estimated of the animal. Finally, a biologically meaningful
for 95% of the buzzardrangeswhen 15% of the core can revealthe usual area. The importanceof
locationswere removed (i.e., the excursion exclud- including internal range structure in studies of
ed core contained 85% of the locations). With habitat use has been demonstratedin analysesusboth methods, all the ranges reached cores that ing ICP cores.For example, in a study of Tawny
included at least 80% of the locations.
Owls (Strix aluco)in woodland patches,Redpath
88
HOBBER ET AL.
VOL. 32, NO. 2
C.P. AND D.W. MACDONALD.1991. Drifting
(1995) showedthat the owlshad much larger MCP DONCASTER,
territoriality in the red fox Vulpesvulpes.
J. Anim.Ecol.
ranges where woodland was fragmented in com60:423-439.
parison to owlsin continuouswoodland. However,
HARRIS,S., WJ. CRESSWELL,
P.G. FORDE,WJ. TREWHELI•k,
ICP cores (multinuclear polygons),were of a similar size in the different
classes of woodland.
T. WOOLLARD
ANDS. WRAY.1990. Home-range analysis
using radio-tracking data--a review of problems and
techniques particularly as applied to the study of
The estimation of a core range may also be extremely important in studiesof behavioralintermammals. Mammal Rev. 20:97-123.
actions.Least overlap with conspecificshas been HARVEY,MJ. AND R.W. BAR•OUR.1965. Home range of
used to define core range boundaries(Auffenberg
Microtusochrogaster
as determined by a modified minimum area method. J. Mammal. 46:398-402.
1978, Christian et al. 1986). However,a core range
that can showif individualsregularlyuse the same RAYNE,D.W. 1949. Calculation of size of home range. J.
Mammal. 30:1-18.
areas, is more ecologicallyinformative. For inHEIKKILA, R., K. NYGREN, S. HARKONEN AND A. MYKKANEN.
stance,when resourcesare very concentrated,in1996. Characteristicsof habitatsusedby female moose
dividualsmay haveoverlappinghome ranges,such
in the managed forest area. Acta Theriologica
41:321as Northern Goshawkshunting near pheasantre326.
leasepens (Kenward and Walls 1994). Conspecifics HOHMANN,U. 1994. Status specific habitat use in the
that have overlapping outer ranges may show
Common Buzzard Buteobuteo.Pages359-366 in B.-U.
avoidance in their core (Kenward 1985b, Samuel
et al. 1985, Harris et al. 1990). Intraspecific differ-
encesin spaceuse may also be maskedby outer
home range boundariesbut revealed by the cores
(Harris et al. 1990).
Meyburg and R.D. Chancellor [EDS.], Raptor conservation today.Proceedingsof the IV world conference
on birds of prey and owls, Berlin, Germany. Pica
Press, East Sussex, U.K.
HULBERT,I.A.R., G.R. IASON,D.A. ELSTONAND P.A. RACEY.
1996. Home-range sizesin a stratified upland landICP coresselectedby the MED for the buzzard
scapeof two lagomorphswith different feeding stratgive a biologically useful estimate of the core
egies.J. Appl.Ecol.33:1479-1488.
range. We anticipate that the method presented JENNRICH,R.I. ANDF.B. TURNER.1969. Measurement of a
here will prove useful for raptors and for other
noncircularhome range.J. Theor.Biol.22:227-237.
taxa.
ACKNOWLEDGMENTS
KAUFMAN,
J.H. 1962. Ecologyand socialbehaviourof the
coati Nasua nirica on Barro Colorado island, Panama.
Univ. Calif. Publ. Zool.60:95-222.
We are grateful to the landownerson our studyarea KENWARD,
R.E. 1977. Predation on releasedpheasants
for allowing us access.We also thank Amber Budden,
Phasianuscolchicus
by goshawksAccipiter
gentilisin cenDavid Hall, Adam Kelly, Maarit Pahkala, and Mark Wiltral Sweden. Swed. Game Res. 10:79-112.
liams for their help in locating and climbing to nests.
1982. Techniquesfor monitoring the behaviour
Biotrack provided a landrover for radio-tracking.Nigel --.
of grey squirrelsby radio. Pages 175-196 in C.L.
Webb kindly commented on a draft of the manuscript
Cheeseman and R.B. Mitson [EDS.], Telemetric studand the suggestionsmade by three referees were very
ies of vertebrates. Academic Press, London, U.K.
helpful.
1985a. Raptor radio-tracking and telemetry.
LITERATURE CITED
Pages 409-420 in I. Newton and R.D. Chancellor
[EDS.], Conservation studies of raptors. ICBP, CamA•LES,E.D. 1969. Home range studiesof red foxes Vulpes
bridge, U.K.
vulpes.
J. Mammal.50:108-120.
1985b. Ranging behaviour and population dyANDERSON,
DJ. 1982. The home range: a new nonparanamicsin grey squirrels.Pages319-330 in R.M. Sibly
metric estimation technique. Ecology
63:103-112.
and R.H. Smith [EDS.], Behavioural ecology:ecologiAUFFENBERC,
W. 1978. Socialand feeding behaviorin Varcal consequences
of adaptivebehaviour.BlackwellScianuskomodoensis.
Pages77-491 in N. Greenbergand
entific, Oxford, U.K.
P.D. MacLean [EDS.], Behavior and neurology of lizards. DHEW, New York, NY U.S.A.
1987. Wildlife radio-tagging equipment, field
techniques and data analysis.Academic Press,LonBURT,W.H. 1943. Territoriality and home range as apdon, U.K.
plied to mammals.J. Mammal.24:346-352.
CHRISTIAN, K., W.P. PORTER AND C. TRACEY. 1986. Core
1992. Quantity versusquality:programmedcollection and analysisof radio-trackingdata. Pages231areaswithin the home ranges of land iguanasCono244 in I.G. Priede and S.M. Swift [EDS.], Wildlife telophus
palidus.J. Hevpetol.
20:272-276.
lemetry: remote monitoring and tracking of animals.
DIXON, K.R. ANDJ.A. CHAPMAN.1980. Harmonic mean
Ellis Horwood, London, U.K.
measure of animal activitymeasures.Ecology
61:10401044.
--AND
K.H. HODDER.1996. Ranges V: an analysis
Jt•NE 1998
COpsE
RANGEESTIMATES
FORCOMMONBt•ZZAm)S
systemfor biological location data. Natural Environment Research Council, U.K.
89
STICKEL,L.F. 1954. A comparison of certain methods of
measuringranges of small mammals.J. Mammal. 35.
1-15.
--A•D
S.S.WALLS.1994. The systematicstudyof radio-taggedraptors: 1. survival,home-rangeand habi- VANWINKLE,W. 1975. Comparisonof severalprobablhstat-use. Pages 303-316 in B.-U. Meyburg and R.D.
tic home range models.J. Wildl. Manage.39:118-123
Chancellor [EDs.], Raptor conservationtoday. Pro- VOIGT, D.R. ANDR.R. Tlr•Llr•E.1980. Strategiesfor analysingradio-trackingdata. Pages387-404 in CJ. Amceedingsof the IV world conferenceon birds of prey
laner and D.W. Macdonald [EDs.], A handbook on
and owls, Berlin, Germany. Pica Press,East Sussex,
U.K.
biotelemetryand radio-tracking.PergamonPress,Ox-
LANCE, A.N. AND A. WATSON. 1980. A comment
on the
ford, U.K.
use of radio-tracking in ecological research. Pages WALLS,S.S. AND R.E. KENWARD.1994. The systematic
studyof radio-taggedraptors:II. socialityand dispers355-359 in CJ. Amlaner and D.W. Macdonald [EDS.],
al. Pages317-324 in B.-U. Meyburgand R.D. ChanA handbook on biotelemetry and radio-tracking.Percellor [EDs.],Raptor conservationtoday.Proceedings
gamon Press,Oxford, U.K.
of the IV world conferenceon birds of prey and owls,
MOHR, C.O. 1947. Table of equivalent populationsof
North American
small mammals. Am. Midl. Nat. 37:
Berlin, Germany.Pica Press,East Sussex,U.K.
223-249.
-AND --.
1995. Movements of radio-tagged
Common BuzzardsButeobuteoin their first year. Ibzs
--AND
W.A. STUMPF.1966. Comparisonof methods
137:177-182.
for calculatingareasof animal activity.J. Wildl. ManWAUTERS,
L. ANDA.A. DHONT. 1992. Spacingbehaviour
age.30:293-304.
of red squirrels, Sciurusvulgari•. variation between
PARISH, T. AND H. KRUUK. 1982. The uses of radio-tracking combined with other techniques in studies of
badger ecology in Scotland. Pages 291-299 in C.L.
Cheeseman and R.B. Mitson [EDS.], Telemetric studies of vertebrates. Academic Press, London, U.K.
habitats and the sexes. Anim. Behav. 43:297-311.
WHITE, G.C. ANDR.A. GAPmOTr.1990. A•alysis ofwildhfe
radio tracking data. AcademicPress,San Diego, CA
U.S.A.
REDPATH,S.M. 1995. Habitat fragmentation and the in- WORTOr•,B.J. 1989. Kernel methods for estimating the
utilization distributionin home range studies.Ecology
dividual:TawnyOwls Strixalucoin woodlandpatches.
70:164-168.
J. Anim. Ecol.64:652-661.
WOLTON,
R.J. 1985. The ranging and nesting behaviour
SAMUEL,M.D., DJ. PIERCEANDE.O. GARTON.1985. Idenof wood mice Apodemus
sylvatica(Rodentia: Muridae),
tifying areas of concentrated use within the home
as revealedby radio-tracking.J. Zool.206:203-224.
range.J. Anim. Ecol.54:711-719.
WRA¾,S., W.J. CRESSWELL
ANDD. ROGERS.1992a. Dirichlet
--AND
R.E. GREEN.1988. A revisedtest procedure
tesselations:a new nonparametric approach to home
for identifying coreswithin the home range.J. Anim.
range analysis.Pages247-255 in I.G. Priede and S.M.
Ecol. 57:1067-1068.
Swift [EI•s.], Wildlife telemetry: remote monitoring
SINIFF,D.B. ANDJ.R. TESTER.1965. Aspects of animal
and tracking of animals.Ellis Horwood, London, U.K.
movementand home range data obtainedby telem, WJ. CREssWEI•, P.C.L. WHITE AND S. H•a•s.
etry. Trans.N. Am. Wildl. Nat. Res.Conf.30:379-392.
1992b.What, if anythingis a core area?An analysisof
SPENCER,W.D. AND R.H. BARm•TT. 1984. An evaluation of
the problemsof describinginternal range configurathe harmonic mean measure for determining carnitions. Pages256-271 in I.G. Priede and S.M. Swift
vore activitymeasures.Acta Zool.Fenn. 171:255-259.
lEDS.I, Wildlife telemetry: remote monitoring and
ST•a-•LE½:I•R,
D.W. ANDT.G. SMITH.1993. A comparison
tracking of animals.Ellis Horwood, London, U.K.
of home range estimatesfor a Bald Eaglewintering
in New Mexico.J. RaptorRes.27(1 ):42-45.
Received1 July 1997; accepted12 February 1998