application of satellite collars to the study of home range and activity of the amur tiger (panthera...

ISSN 1062�3590, Biology Bulletin, 2011, Vol. 38, No. 8, pp. 834–847. © Pleiades Publishing, Inc., 2011.Original Russian Text © V.V. Rozhnov, J.A. Hernandez�Blanco, V.S. Lukarevskiy, S.V. Naidenko, P.A. Sorokin, M.N. Litvinov, A.K. Kotlyar, D.S. Pavlov, 2011, publishedin Zoologicheskii Zhurnal, 2011, No. 5, pp. 580–594.

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INTRODUCTION

Remote tracking methods have been successfullyused in studies on spatial ecology and physiology andin monitoring animal populations since the secondhalf of the 20th century (Mech, 1967; Pavlov and Pod�dubnyi, 1993; Mech and Barber, 2002). They make itpossible to obtain more accurate data on species ecol�ogy and more optimally monitor the use of space bythe animals studied because they provide timely andreliable information on an animal’s location. In forestbiotopes with rough terrain, where direct visual obser�vation of animals are impossible, remote trackingmethods are optimal for studying the animals' move�ments and dispersal. In addition, their use permitsdecreasing the human disturbance level (Creel, Creel,and Monfort, 1997).

By remote tracking is meant obtaining informationon an animal via radio signals emitted by a deviceattached to its body. There are three types of remotelytracking animals by means of different types of trans�mitters: (1) traditional VHF radio tracking; (2) satel�lite tracking; (3) tracking with the use of the Global;Positioning System (GPS).

Traditional VHF radio tracking provides informa�tion on an animal’s location within 10 km if it istracked from the ground and 40 km if it is tracked fromthe air. This method has the advantages of accuratedata, long time of service of the transmitters, and theirlow cost; but it is labor�consuming and weather�sensi�tive and usually requires the use of ground vehiclesand/or aircrafts. As a rule, its use is only justifiable for

tracking animals within their home ranges and is inef�fective for monitoring movements in dispersing spe�cies or those migrating over long distances (hundredsof kilometers).

Satellite tracking requires somewhat more power�ful transmitters (0.125 W and more). The signal emit�ted by a transmitter is used by the Argos positioningsystem (France–European Union–United States)consisting of six space satellite devices to locate a radiobeacon on the basis of the Doppler effect. The dataobtained from the satellites are processed in an infor�mation center, from which they are send to aresearcher’s computer. This method allows an animal tobe located at a relatively low accuracy (between 250 mand 1.5 km). Because of a high energy consumption,the time of service of such radio beacons variesbetween several months and two to three years. Theyare especially suitable for the cases when animalsmigrate over long distances within a short interval oftime (Fancy et al., 1988).

In GPS tracking, a special receiver receives a signalfrom a “constellation of satellites” (triangulation),and a built�in computer calculates and stores its owncoordinates according to a programmed scheme. Thismethod allows an object studied to be located moreaccurately (within 3 m). The disadvantages of this sys�tem are that it is complicated and consumes muchenergy, which affects its weight and time of service(Zimmerman et al., 2001). There are four methods forretrieving data from the built�in microcomputer: (1)the GPS data are stored in the built�in computer andare available to the researcher only after the device is

Application of Satellite Collars to the Study of Home Range and Activity of the Amur Tiger (Panthera tigris altaica)

V. V. Rozhnova, J. A. Hernandez�Blancoa, V. S. Lukarevskiya, S. V. Naidenkoa, P. A. Sorokina, M. N. Litvinovb, A. K. Kotlyarb, and D. S. Pavlova

a Severtsov Institute of Ecology and Evolution, Russian Academy of Sciences, Moscow, 119071 Russia; e�mail: [email protected]

b Ussuriisky State Nature Reserve, Far East Branch, Russian Academy of Sciences, Ussuriisk, 692530 RussiaReceived January 11, 2011

Abstract—The possibility of application of satellite collars to the study of home range and activity of Amurtigers has been analyzed. The possibility of obtaining information about the size and structure of a homerange, discerning the home range core areas, seasonal changes in the use of space by tigers, and collectingdetailed data on animals' activity and characteristics of the use of different elements of landscape and terrainhas been demonstrated. In contrast to VHF transmitters, satellite collars allow tigers to be tracked even in thecases of very long travels.

Keywords: satellite collars, Argos, Amur tiger, home range, daily activity.

DOI: 10.1134/S1062359011080073

BIOLOGY BULLETIN Vol. 38 No. 8 2011

APPLICATION OF SATELLITE COLLARS 835

taken off the animal; this system is light and has a longservice time; (2) the GPS data are remotely retrievedfrom the computer by means of a portable receiverfrom a distance of no more than 3 km; these devicescombine a GPS receiver, a VHF transmitter, and ashort�wave transceiver; (3) the GPS data stored in thebuilt�in computer are transmitted, at certain intervals,in the form of text messages via the cellular phone net�work provided the given area is covered by it; (4) theGPS data stored in the built�in computer are transmit�ted to the researcher’s computer via the Argos posi�tioning and data collection system (France–EuropeanUnion–United States) or via satellite telecommuni�cation systems, such as Thuraya (United Arab Emir�ates) and Iridium (United States). This method ischaracterized by the highly accurate location typicalof the GPS equipment, as well as quick supply of datato the user and global coverage provided that low�orbitsatellite systems (Argos, Iridium, etc.) are used fordata transmission.

Despite the considerable experience accumulatedin using the methods for remotely tracking animalsand their undoubted advantages over other methods ofthe study of dispersal and migration, they are still notwidely used in Russia.

The specificity of the biology of the Amur tiger(Panthera tigris altaica), the subspecies of the tiger thatinhabits forests of the Russian Far East, its seclusiveway of life, large home ranges, and sensitivity tohuman disturbance complicate the collection of dataon these animals and makes many traditional methodsineffective (Matyushkin, 2000; Hayward et al., 2002).The use of radio�tracking has made it possible toobtain data on some characteristics of the tiger repro�ductive biology (Kerley et al., 2003), movements ofradiocollared individuals (Goodrich et al., 2005), theeffect of human disturbance on the survival of adulttigers and their progeny (Kerley et al., 2002), and theresults of translocation of problem animals (Goodrichand Miquelle, 2005). We have begun to study the use ofspace by the Amur tiger by means of a set of methods(Rozhnov et al., 2009a) that include the use of collarswith GPS�Argos satellite radio beacons, as well asindividual identification of tigers using photo trapsand molecular genetic analysis of the feces left bytigers (Rozhnov et al., 2009b). The set of differentmethods allows us to accurately characterize the use ofspace by the animals.

The purpose of this study, which is part of the “Pro�gram of Studying the Amur Tiger in the Russian FarEast” in the framework of the Permanent Expeditionof the Russian Academy of Sciences for the Study ofAnimals Listed in the Red Data Book of the RussianFederation and Other Highly Important Animals ofthe Russian Fauna, was to determine the structure of atiger’s home range and the parameters of its dailyactivity with the use of GPS�Argos satellite radio bea�cons in the southwest Primorye krai (Russia), includ�ing the location of the home range boundaries, analy�

sis of the characteristics of the use of its different parts,selectivity with respect to different elements of thelandscape and terrain, and estimation of the distancesof daily movements and the budget of activity.

MATERIALS AND METHODS

The study was performed in the Komarov Ussuri�isky State Nature Reserve of the Far East Branch(FEB) of the Russian Academy of Sciences (RAS)(Primorye krai, Russia) and its vicinities. Tigers werecaptured using Aldrich foot snares (Goodrich et al.,2001; Frank, Simpson, and Woodroffe, 2003) andthen immobilized with drugs.

In this study, we only analyze the data receivedfrom a Telonics radio beacon (United States) withwhich an adult female tiger was radiocollared onNovember 1, 2008 (at the moment of capture, therewere three cubs aged about four months together withthis tiger). These data were used to adjust the protocolof data processing and collect data, which proved to beunique notwithstanding the fact that they concernedonly a single animal. In total, four collars equippedwith GPS�Argos radio beacons were put on Amurtigers in 2008–2009. In addition to this female tiger,Sirtrack (New Zealand) collars with GPS�Argos radiobeacons were put on three other tigers (an adult femaleand two young males) on September 16, 2009; Octo�ber 20, 2009; and October 26, 2009.

A satellite collar equipped with a Telonics radiobeacon (United States) consists of two electronicmodules built in a reinforced collar of synthetic rub�berized cord. Two aerials in the form small cables3 mm in diameter protrude from the electronic mod�ules, one transmitting a VHF signal and the other, asignal to the Argos satellites.

Thed GPS module was programmed to operate intwo alternating cycles with periods of 58 days and1 day, respectively. In the first cycle, the GPS receiverwas switched on to determine the coordinates for nomore than 180 s (on average, the device fulfilled thetask within 48 s) three times a day (UTC 4 a.m.,12 noon, and 8 p.m.), the daily movement distancewas calculated every day as the sum of lengths ofstraight segments sequentially connecting the threedaily location points. In the second cycle, the GPSreceiver began operating at 12 midnight and thendetermined the coordinates every 20 min for 24 h; thedaily movement distance was determined once everytwo months as the sum of straight segments sequen�tially connecting the 72 daily location points, whichmore accurately reflected the trajectory of the ani�mal’s daily movement.

The collar also contained an uGPSi�20 sensor ofthe animal’s activity (Telonics) with a built�in acceler�ometer, which estimated the current accelerations ofthe animal in three planes every second. If an acceler�ation in at least one plane was detected, the second atwhich the measurement was made was considered

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“active.” Then, the number of “active” seconds duringeach 30�s period was counted and stored to be subse�quently transmitted to the satellite. From December 21,2008 to March 6, 2009, the sensor did not workbecause of low air temperatures.

The transmission of information on the locationcoordinates and data on the animal’s activity from thesensor to Argos satellites was programmed in such away that it was send to the satellites every 200 s in theperiod from 8:15 p.m. to 0:15 a.m. The ephemeredes(spatiotemporal coordinates) of the Argos satelliteswere renewed before the satellite collar was activated.

The satellite collar with the Telonics radio beacon(United States) was put off the female tiger when it wasrecaptured 354 days after the collar was activated. Thecollar itself was apparently undamaged, but the aerialresponsible for the data transmission to Argos satellites

was broken (probably, bitten off by the cubs); there�fore, the communication with the collar during thestudy period was only possible via the VHF signal. TheGPS module and activity sensor functioned normallythroughout the period of the collar operation; thecoordinates of locations and parameters of daily activ�ity were stored in the memory of the modules and weretransmitted to a computer after the collar was put off.We obtained a total of 1222 GPS locations, including395 locations for six daily movements of the tiger, dur�ing 354 days of operation of the collar (from November1, 2008 to October 20, 2009). The success of locationsfor daily movements was 91.4%. The success of loca�tions in the mode of determining the coordinates threetimes a day was 79.2%. Data on the tiger’s activity for16 992 half�hour intervals were obtained from theactivity sensor.

Ussuriisky State

(FEB, RAS)

The female tigerhome range

The female tigerhome range Locations, n = 284Nature Reserve

(100% MCP) (95% MCP)

Fig. 1. The annual home range of the female tiger (from November 2008 to October 2009) determined by the MCP method.

km



The boundaries of the home range of the radiocol�lared female tiger were determined by the minimumconvex polygon (MCP) method (Hayne, 1949) on thebasis of 100 and 95% of locations (in the latter case,long�distance movements beyond the home rangewere automatically excluded), as well as by the methodof fixed contour referred to as the kernel method(Worton, 1989; Seaman and Powell, 1996). According

to the kernel method, the tiger’s home range wasdescribed by polygons approximating the two�dimen�sional distribution of locations in a plane, whichallowed the probability density to be calculated for thisdistribution with its statistical nature taken intoaccount. The method makes it possible to specify dif�ferent probabilities of finding the animal from 95 to5% at 5% intervals, with the number of the polygons

Locations,

Ussuriisky State

(FEB, RAS)Nature Reserve

Straight lines through consecutive locations

Kernel, h = 0.019869, n = 524

The area encompassing 50% of random locationsThe area encompassing 55% of random locations (the core area)The area encompassing 75% of random locationsThe area encompassing 95% of random locations

n = 284

Fig. 2. The annual home range and the core area of the female tiger (from November 2008 to October 2009) determined by thekernel method.

km

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drawn chosen by the researcher; here, we present the95, 75, and 50% polygons.

The core area was calculated by the kernel method.The classical notion of the core area as the areaencompassing 50% of locations is not always correct;therefore, we defined the core area as the area wherethe difference between the expected and actual areasencompassing a specified proportion of locations wasthe largest (Powell, 2000). In the given case, the core

area of the annual home range contained 55% of loca�tions.

In order to detect the possible changes in the use ofspace by the tiger during the year, we sequentiallysuperposed of the 95% polygons obtained by the ker�nel method for each month, as well as the contours ofthe core areas plotted for each month. The 95% kernelcontours and core area contours were compared sepa�rately. If the areas of the polygons for two consecutive

N

h = 0.017545n = 58

0 6km

November 2008 N

h = 0.011536n = 76

0 6km

December 2008 N

h = 0.022197n = 80

0 6km

January 2009

N

h = 0.011749n = 65

0 6km

February 2009 N

h = 0.28662n = 80

0 6km

March 2009 N

h = 0.022612n = 72 0 6

km

April 2009

N

h = 0.019142n = 80

0 6km

May 2009 N

h = 0.033877n = 69

0 6km

June 2009 N

h = 0.026218n = 71

0 6km

July 2009

N

h = 0.027517n = 58

0 6km

August 2009 N

h = 0.027626n = 72

0 6km

September 2009 N

h = 0.034371n = 43

0 6km

October 2009

Ядерная зона95%

Straight lines throughconsecutive locations

Locations, n = 284

Ussuriisky StateNature Reserve (FEB, RAS)

h Kernel coefficient of smoothing

Kernel analysis

Fig. 3. The parts of the home range used by the female tiger in different months.



months coincided by at least 50%, we considered theused space unchanged during the given period(Garshelis and Pelton, 1981).

When analyzing the annual activity, we distin�guished the snowy (November–March) and snowless(April–October) periods. For analysis of the dailyactivity, we distinguished the daytime, nighttime, andtwilight (1 h before and 1 h after the sunset and sun�rise).

In order to reveal the selectivity with respect tolandscape and terrain elements, we used layers of pro�cessed LandSat satellite photographs where the land�scape and terrain categories were designated andsuperposed the recorded locations onto them. Theselectivity index for landscape elements was taken tobe equal to the percentage of locations falling within agiven landscape element divided by the proportion ofthe area of this element. Similarly, to calculate the ter�rain selectivity index, we divided the percentage oflocations falling within a given terrain element by theproportion of the area of this element.

The data on the home range spatial structure andthe animal’s movements were treated using the Map�Info 9 software and the Animal Movements V.2 mod�ule for ArcView 3.2a (Hooge and Eichenlaub, 2000).Microsoft Access 2003 databases were used to preparethe data for subsequent treatment. The Statistica 7.0,GràðhÐàd Prism 5, and Microsoft Excel 2003 soft�ware was used for statistical treatment.

RESULTS

Home range. Analysis of the data obtained usingthe satellite collar showed that the home range area ofthe adult female tiger with three cubs calculated by theMCP method was 869.8 or 686.7 km2 as estimated on thebasis of 100 or 95% of locations, respectively (Fig. 1).According to the analysis by the kernel method, thehome range of this tiger was 811.2 km2 as estimated by thearea encompassing 95% of random locations (hereafter,the 95% kernel) (Fig. 2). The core area (encompassing55% of random locations) was 151.4 km2.

800

700

600

500

400

300

200

100

0

November 2008

December 2008

January 2009

Febriary 2009

April 2009

May 2009

March 2009

June 2009

July 2009

August 2009

September 2009

October 2009

95% kernelCore area

Are

a, k

m2

Fig. 4. The areas used in different months.

Table 1. Parameters of the 95% kernel and core area contours based on the locations of the adult female tiger in December 2008and 2009

Month 95% ker�nel, km2

Percentage of the 95% kernel area in the reserve

Core area, km2

Percentage of the core area in the reserve h kernel N

December 2008 89.61 95 21.19 100 0.0115 76

December 2009 460.44 31 123.50 26 0.0333 22

h kernel, the smoothing coefficient; N, the number of locations per month.

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The area of the home range used by the tiger duringone month varied during the period from November2008 to August 2009 (Fig. 3). The overlap area of thecontours of the probability of random detection of95% of locations for an individual month varied from6 to 42%. The area within the home range usedmonthly was not considerably changed from August toOctober 2009 (the overlap was 68–80%). The corearea substantially varied with month (the overlap arearanging from 0 to 46%). The area of the used spacetended to increase in the period from winter to autumn(Fig. 4), with the maximum values recorded in Juneand October. This was accompanied by a considerablysmaller variation of the core area.

The comparison of the contours of the 95% kernelsand core areas in December 2008 and December 2009(Fig. 5) did not show repetitions in the use of space bythe tiger: there were neither overlaps nor quantitativecoincidences of the areas (Table 1).

In order to compare the sizes of the tiger’s homerange and the protected area, we calculated the pro�portion of the home range that was within the bound�aries of the Ussuriisky State Nature Reserve. Accord�ing to the MCP calculation, 39.57% of the tiger’shome range (344.2 km2) as estimated for 100% of loca�tions or 48.07% (330.1 km2) as estimated for 95% 0flocations was in the reserve. According to the 95% ker�nel calculation, the hone range area overlapped withthe Ussuriisky State Nature Reserve area by 43.15%(350 km2). The overlap between the reserve and thecore areas was 76.02% (115.1 km2). We found consid�erable seasonal differences in the use of the area of thereserve by the tiger (Fig. 6): the tiger preferred to staywithin the reserve in winter and only periodically vis�ited the reserve in the warm season. In summer, mostof the core area was outside the reserve.

Daily movement distance. The satellite collar with aGPS transmitter was programmed to provide the dataon the tiger’s daily movements with different accura�

Kernel analysis

Core area95%

Straight lines throughconsecutive locationsLocations, n = 284


h Kernel coefficient

N

h = 0.017545n = 58

0 6km

December 2008 N

h = 0.03332

n = 22

0 6km

December 2009

of smoothing

Fig. 5 The parts of the home range used by the female tiger in December 2008 and December 2009.

Table 2. Characteristics of six daily movements of the female tiger in different seasons

No. Date n Daily movement distance, km Mean velocity, m/s SD Maximum

velocity, m/s

1 18 Nov 2008 71 1.60 0.05 0.07 0.60

2 16 Jan 2009 65 8.56 0.23 0.54 4.01

3 16 Mar 2009 66 1.43 0.14 0.63 5.22

4 14 May 2009 61 11.58 0.16 0.20 1.28

5 12 Jul 2009 64 17.33 0.22 0.29 1.42

6 9 Sep 2009 68 14.55 0.18 0.26 1.13

n, the number of locations per day.



cies: daily as the distance between three sequentiallyconnected points and every other month as a completedaily movement distance with 61–71 locations within24 h.

We obtained data on six complete daily movementsof the tiger located in different parts of its home range(Fig. 7, Table 2). The mean daily movement distancewas 3.8 km (N = 3) in the snowy period and 14.48 km(N = 3) in the snowless period.

The analysis of the daily movement distance calcu�lated as the distance between three sequentially con�nected locations did not show significant differencesbetween months throughout the year (Kruskal–Wallistest, p > 0.05); nor did pairwise comparison of allmonths show significant differences between them(Dunn’s multiple comparison test, p > 0.05). The dailymovement distance averaged over the period of opera�tion of the satellite collar was 4.704 km; the maximumdistance during this period was 22.07 km.

While the mean daily movement distance was rela�tively short, the tiger periodically traveled over longdistances (more than 10 km; sometimes as far as20 km; N = 45) within a short time (8 h). It made such“sprints,” on average, every 9.4 days (N = 15) duringthe snowy period and every 7.8 days (N = 30) duringthe snowless period. We did not find a differencebetween the two periods in either the frequency of the“sprints” (Fisher’s test, p > 0.05) or their mean length12.95 and 13.49 km, respectively, in the snowy andsnowless periods; the Mann–Whitney U test, U = 195,p > 0.05).

Daily and annual activities. During the period whenthe activity sensor operated (272 days), the female

tiger was active for 11.33% of time (N = 23 500 800).None of the 30�min intervals of activity measurement(N = 13056) contained more than 1184 active secondsout of the maximum possible number of 1800. Thismeans that the tiger made pauses in the activity peri�ods at least once every 20 min. During about half(49.27%) of all 30�min intervals, the tiger was activeno longer than 3.3 min; only in 0.4% of these intervalsdid its activity lasted for 16 to 20 min. The longestinactive period (less than 10 active seconds within30 min) lasted for 9 h.

As can be seen in Table 3 showing the monthlyparameters of the activity budget, the proportion ofactive time only slightly varied in different months.

However, the numbers of active seconds per hour inMarch, when the tiger was the least active, and June,when it was the most active, significantly differed fromeach other (Student’s t test, t = 2.93727, p < 0.05)(Fig. 8).

We found a significant difference between thetiger’s daytime activities in the snowy (N = 585) andsnowless (N = 2398) periods of the year (Dunn’s mul�tiple comparison test, p = 0.015). These periods alsodiffered in the tiger’s activity during the twilight(Dunn’s multiple comparison test, p = 0.0053; N =278 for the snowy period and N = 806 for the snowlessperiod) and the nighttime (Dunn’s multiple compari�son test, p = 0.0008; N = 798 for the snowy period andN = 1644 for the snowless period).

The analysis of daily activity throughout the periodof operation of the sensor showed that the tiger was themost active in the twilight, less active in the daytime,and the least active in the nighttime (Fig. 9a). The

120

100

80

60

40

20

0

November

December

January

February April May

MarchJune

JulyAugust

September

October

95% kernelCore area

Per

cen

tage

of a

rea

wit

hin

th

e re

serv

e

Fig. 6. The proportions of the space used within the nature reserve in different months.

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tiger’s activity pattern was similar during the snowlessperiod (Fig. 9c), but we did not find a significant dif�ference between its activities in the daytime and twi�light during the snowy period (Fig. 9b).

A more detailed study of the daily activity pattern(at 1�h intervals) (Fig. 10) showed that the activity wasthe highest in the evening twilight (except for Decem�ber, when the activity peaked in the daytime). Thedaily activity pattern changed during the year. Onedaily peak of activity was observed between Novemberand March. In April, there were two daily activitypeaks (first in the evening twilight and then in themorning twilight). From May to July, there was againonly one daily activity peak. The period from Augustto October was characterized by two daily activitypeaks, the second peak being observed in the early

daytime in August and the morning twilight in Sep�tember and October.

The use of different landscape and terrain ele�ments. The numbers of the tiger’s locations in differ�ent elements of terrain (slopes, plateaus, upperreaches of rivers, river valleys, and watersheds) weredifferent in the snowy and snowless periods (Pearson’sχ2 test, p < 0.001). During the snowy period, the tigerpreferred slopes, whereas it more frequently occurredon plateaus, in the upper reaches of rivers, and in rivervalleys during the snowless period (Fig. 11a). Thewatershed selectivity indices for the snowy and snow�less periods did not differ significantly from each other(Fisher’s test with Bonferroni’s correction).

The numbers of the tiger’s locations in differentlandscape elements (dark coniferous, broad�leaved,and deciduous forests; shrubberies; roads and bare

5

3

2

6

1

4

The tiger’s daily movement


Fig. 7. The map of (1–6) complete daily movements of the female tiger (locations 61–71) in its home rasnge.

km



soil; sparse and grassy vegetations; and places withoutvegetation) during the snowy and snowless periodsalso differed from each other (Pearson’s χ2 test, p <0.001). During the snowy period, the tiger was mainlyfound in dark coniferous forests, more rarely in broad�leaved forests, and still more rarely in deciduous for�ests and shrubberies. During the snowless period, theanimal preferred deciduous forests and shrubberies(Fig. 11b). The high selectivity index for roads and soilwithout the vegetation cover is related to the smallareas of these elements.

DISCUSSION

The use of GPS technologies in remotely monitor�ing the behavioral ecology of large carnivores with vasthome ranges that live in places with rough terrain hasa number of advantages over traditional methods ofremote tracking (VHF radio tracking). The dataobtained using this method permit detailed analysis ofnot only the size of an animal’s home range, but alsoits internal structure, which is apparently very impor�tant for studying the ecology of large carnivores(Adams and Davies, 1967; Hernandez�Blanco,Poyarkov, and Krutova, 2005).

The transmission of the data obtained using sensorsbuilt in a collar via the Argos satellite system allows thematerial to be quickly analyzed and information onthe current locations of the radiocollared individualsto be obtained irrespective of the distances of theirmovements, which is extremely important in studieson endangered species.

According to our data, the home range of a femaleAmur tiger in the southern Primorye krai has proved tobe almost two times larger than those of female tigersin the northern Sikhote�Alin and is comparable withthe home ranges of male tigers in that region (Goo�drich et al., 2005). This is important because the homerange sizes are used as the basis for calculation of the

potential numbers of Amur tigers in the Russian FarEast. These differences may be related to the compo�sition and state of the food reserves in these regions(Miquelle et al., 1999).

Note the methodical characteristics of the methodsused for calculating a female tiger’s home range size inour study. The area of the tiger’s home range calculatedby the MCP method for 100% of locations (869.8 km2)has proved to be very close to that calculated by the95% kernel method (811.2 km2).

For the analysis of the activity and daily movementdistances of the female tiger, we have obtained twotypes of data on its daily movements: daily but lessaccurate data (the distance along straight linesbetween three sequential locations during a day) anddata that were obtained once every other month butare more accurate (the distance along straight linesbetween 72 sequential locations during a day). Thedata on the daily movement distances in the snowlessperiod are especially valuable.

The tiger’s daily movement distance calculated bysequentially connecting three locations during a day inthe snowy period is 4.8 km, which is 1 km longer thanthe mean daily movement distance calculated on thebasis of 72 locations during a day. Apparently, in thecase of a small number of accurately measured dailymovement distances, there is a high probability that, atthe moment when this measurement is made, the ani�mal stays at the same place, e.g., near a prey, for a longtime. In our opinion, the mean daily movement dis�tance of the tiger measured in the snowless period(14.48 km) is closer to the actual situation. Thus, ourdata show that tracking the daily movement once intwo months is insufficient; for obtaining representativedata, the sample size should be increased, which canbe ensured by programming the GPS satellite radiobeacon module.

We noticed an important and interesting feature ofthe pattern of the tiger’s daily movements: the animal

Table 3. Time budget of the female tiger between November 2008 and October 2009

Period Percentage of active time Number of days of operation of the sensor N

November 2008 10.59 23 1987200

December 2008 10.39 21 1814400

March 2009 8.96 26 2246400

April 2009 11.21 30 2592000

May 2009 10.71 31 2678400

June 2009 13.61 30 2592000

July 2009 12.07 31 2678400

August 2009 10.25 31 2678400

September 2009 11.71 30 2592000

October 2009 13.53 19 1641600

N, number of locations during the period.

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periodically (slightly more frequently than once aweek) traveled over long distances within a short time(8 h). This characteristic of the tiger’s daily move�ments has a practical implication: during winter cen�suses, trails of the same size category located far fromone another are usually recorded as those left by differ�ent individuals (Miquelle et al., 2006). Our data indi�cate that the notion of what is a “long distance” for atiger should be revised since even a female not only cantravel, but quite often actually travels, over long dis�tances (in some cases, 20 km for 8 h).

Our data suggest an extremely economical activitybudget of the tiger: in contrast to animals of some spe�cies (e.g., wolves) characterized by alternation of longperiods of activity and rest (Theuerkauf et al., 2003),this tiger was never active more than 20 min in a row.Most of its activity occurred in the evening twilight; inthe daytime, it was less active; and in the nighttime,the least active. In winter, as well as in summer, thetiger’s activity was shifted towards the daytime and hadonly one daily peak. Two peaks of daily activity wereobserved only in spring and autumn. This may havebeen related to almost equal durations of the daytimeand nighttime and moderate air temperatures.

In winter, the tiger moved along slopes, where thesnow cover was thinner than in valleys (especially onsouthern slopes). Our data confirm the resultsobtained by winter tracking in other parts of the Amurtiger species range (Matyushkin, 1977). In thisrespect, dark coniferous forests, where the probabilityto encounter a boar in this season is higher (Litvinov,2007) and the snow cover is thicker, are preferable;broad�leaved forest are less preferable. In the snowlessperiod, the tiger was more frequently found in theupper reaches of rivers, river valleys, and ridges, pre�ferring shrubberies and deciduous forests.

We found an important characteristic feature of theuse of space by the female tiger: the core area (encom�passing 55% random locations) consisted of several(five) separate fragments not forming a continuousarea. The area encompassing 75% of random locationsunited all these fragments.

The analysis of the use of space during a yearshowed that the part of the home range where thefemale tiger lived for a month changed during the firsthalf of our study (from November to early June). Thisindicates that the tiger used the strategy of seasonal useof different zones in its home range rather than thestrategy of systematically moving over it as, e.g., wolvesdo (Mel’nik et al., 2007). At the end of the studyperiod (June–October), the tiger exhibited more con�stancy in the space use: it was located in the same part

14000

12000

10000

8000

6000

0

November

DecemberApril May

MarchJune

JulyAugust

September

October

Num

ber

of a

ctiv

e se

con

ds p

er h

our

4000

2000

p < 0.05

Fig. 8. The tiger’s activity levels in different months duringthe year.

700600500400300200100

0 Daytime

p < 0.001

Num

ber

of a

ctiv

e se

con

ds p

er h

our

Twilight Nighttime

Entire period of operation of the sensor

p < 0.001 p < 0.001

n = 2983

n = 1084

n = 2442

(a)

600

500

400

300

200

100

0 Daytime

p < 0.001

Twilight Nighttime

Snowy period

n.s. p < 0.001

n = 585 n = 278

n = 798

(b)

700600500400300200100

0 Daytime

p < 0.001

Twilight Nighttime

Snowless period

p < 0.001 p < 0.001

n = 2983

n = 806

n = 1644

(c)

Fig. 9. The durations of daily activity (a) throughout theperiod of operation of the sensor, (b) in the snowy period,and (c) in the snowless period.



Nov

embe

r

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

Number of active seconds per hour

1200 40

0 0

800

*

*

*

4.00

6.

00

8.00

n =

23

Mar

ch

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

4.00

6.

00

8.00

n =

26

May

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

4.00

6.

00

8.00

n =

31

July

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

4.00

6.

00

8.00

n =

31

Sep

tem

ber

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

**

*

4.00

6.

00

8.00

n =

30

*

*

*

*

Dec

embe

r

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

*

4.00

6.

00

8.00

n =

21

Apr

il

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

4.00

6.

00

8.00

n =

30

Jun

e

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

4.00

6.

00

8.00

n =

30

Aug

ust

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

*

4.00

6.

00

8.00

n =

31

Oct

ober

t

10.0

0 12

.00

16.0

0 18

.00

14.0

020

.00

22.0

00.

002.

00

1200 40

0 0

800

**

4.00

6.

00

8.00

n =

19

*

**

*

*

*

*

*

**

Day

tim

eT

wil

igh

tN

igh

ttim

e

Loc

al t

ime

(UT

C +

10)

* –

Seq

uen

tial

com

pari

son

of t

he

indi

cate

d va

lues

(t

tes

t, p

< 0

.05)

Fig

. 10.

Th

e da

ily

acti

viti

es in

dif

fere

nt

mon

ths

(tim

e is

indi

cate

d ac

cord

ing

to t

he

24�h

sys

tem

).

846


ROZHNOV et al.

of the home range for three months. However, the corezones calculated for every month of the year did notcoincide anywhere. It is difficult to reveal the factorsthat affected the tiger’s behavior during the studyperiod, because the tiger was often accompanied bythree cubs from November to early June (as evidencedby the results of tracking and photographs taken withphoto traps). Apparently, these factors were the pres�ence of the offspring and whether conditions. Appar�ently, the tiger’s activity became the highest when thecubs began a relatively independent life; this period isalso characterized by the largest area used by the adulttiger. However, the tiger moved over long distancesboth in the presence and in the absence of the cubs. Itcannot be excluded that the increased activity of thefemale tiger was related to avoidance of contacts withgrown�up cubs.

Protected wildlife areas are important for conserv�ing the Amur tiger population. Therefore, it was veryinteresting to estimate how much the tiger used thearea belonging to the Ussuriisky State Nature Reserve.The data on the tiger’s home range size obtained bydifferent methods indicate that its area is almost twiceas large as that of the nature reserve. The female tigerprefers to stay in the nature reserve in winter and early

spring and in autumn but often moves beyond itsboundaries in late spring and summer. This may berelated to a high level of human disturbance in theareas adjacent to the nature reserve, where huntinggrounds are located, during the hunting season. Thesedata should be taken into account when discussing theextension of the reserve’s protected zone or regulationof nature use in adjacent areas.

ACKNOWLEDGMENTS

We are grateful to the staff of Komarov UssuriiskyState Nature Reserve (FEB, RAS) for the assistancewith data collection, M.D. Chistopolova andE.M. Litvinova for the help with treatment of theresults, Yu.G. Puzachenko for kindly providing thelayers of the geographical information system of thevegetation cover and terrain, and A.L. Sal’man for thetechnical support of the study.

This study was performed in the framework of theProgram for the Study of the Amur Tiger in the RussianFar East and supported by the Konstantinovskii Inter�national Charity Foundation, OAO Transneft’ OilTransportation Stock Company, OAO Tekhsnabek�sport, and the Russian National Geographic Society.

60

50

40

30

20

10

0

Wat

ersh

eds

Ele

vate

d pl

atea

usan

d up

per

reac

hes

of

rive

rs

Dee

p va

lley

sof

larg

e ri

vers

Slo

pes

* p < 0.0125; Fisher’s test Snowy period Snowless period

Sel

ecti

vity

inde

x

(a)

45

40

30

20

15

10

0

No

vege

tati

on

Dar

k co

nif

erou

s fo

rest

s

Sel

ecti

vity

inde

x

(b)

35

25

5

Spa

rse

orgr

assy

vege

tati

on

Sh

rubb

erie

s

Roa

ds a

nd

bare

soi

l

Dec

iduo

us fo

rest

s

Bro

ad�l

eave

d fo

rest

s

Wat

er b

odie

s

*

*

**

*

*

*

Fig. 11. The selectivity indices for different (a) terrain and (b) landscape elements.



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application of satellite collars to the study of home range and activity of the amur tiger (panthera...

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