Population estimates of Endangered Mongoliansaiga Saiga tatarica mongolica: implications foreffective monitoring and population recovery

J u l i e K . Y o u n g , K i m M . M u r r a y , S a m a n t h a S t r i n d b e r g

B a y a r b a a t a r B u u v e i b a a t a r and J o e l B e r g e r

Abstract The global population of saiga Saiga tatarica,categorized as Critically Endangered on the IUCN Red List,declined by . 95% at the end of the 20th century, resulting inseveral conservation initiatives to protect the species. Pre-viously used methods to monitor population trends wereinadequate to properly assess numbers of saiga. We reportfindings from the first survey for Mongolian saiga S. tataricamongolica to utilize statistically rigorous methodology, usingline transect distance sampling in 2006 and 2007 to obtainpopulation estimates in and around the Sharga Nature Re-serve, the southern part of the species current range. Weestimate a density of 0.54 and 0.78 saiga km-2 in 2006 and 2007,respectively. Our best models suggest that 4,938 (95% confi-dence interval, CI 5 2,762–8,828) saiga occupied the 4,524-km2 study area in 2006 and 7,221 (95% CI 5 4,380–11,903) the4,678-km2 study area in 2007. Although these estimates, withtheir large confidence intervals, preclude an assessment of theimpacts of conservation initiatives on population trends, theysuggest that the Mongolian saiga population is larger thanprevious reports based on minimum counts, and adequate tosupport in situ population recovery. Modifications to thesurvey protocol hold promise for improving the precision offuture estimates. Distance sampling may be a useful, scientif-ically defensible method for monitoring saiga populationtrends and assessing the effectiveness of conservation effortsto stabilize and recover populations.

Keywords Antelope, distance sampling, endangered species,line transects, Mongolia, population estimate, Saiga tatarica

Introduction

Estimates of population size are vital for understandingspecies ecology, providing information on fluctuations

in size and enabling monitoring of population trends. Forthreatened species population estimates are crucial fordeveloping conservation strategies and assessing their ef-fectiveness but obtaining estimates of species that occur atlow numbers and inhabit large geographical areas is logis-tically difficult. Consequently, information on populationsize is often lacking for such species.

Saiga Saiga tatarica is a nomadic, sexually dimorphicspecies that was formerly widespread across the CentralAsian steppe (Bekenov et al., 1998; Schaller, 1998). S.tatarica tatarica occurs in Kazakhstan, Russia, Uzbekistanand Turkmenistan, and Mongolian saiga S. tataricamongolica, categorized as Endangered on the IUCN RedList (Mallon, 2008), is now only found in Mongolia. TheMongolian subspecies is ecologically, phenotypically andbehaviourally distinct (Bannikov et al., 1961; Kholodovaet al., 2001). Because of overharvesting, saiga suffereda global population decline of c. 95% in , 15 years(Milner-Gulland et al., 2001). This severe decline led tothe signing of a Memorandum of Understanding (MOU)among range states, under the Convention of MigratorySpecies (CMS, 2006), which established the need to adopta standardized monitoring protocol to regularly assesspopulation numbers. The MOU emphasized the needto evaluate the impact of natural and human-inducedthreats on saiga populations. Although the Mongoliansaiga is not officially included in the MOU, the declineand resulting CMS raised awareness of the paucity ofinformation on the population status and distribution ofthis subspecies (Lushchekina et al., 1999). Varied but con-sistent counts suggest that , 5,000 Mongolian saiga remainin the wild (Clark & Javzansuren, 2006; Chimeddorj et al.,2009).

Methods used previously for estimating population sizesof Mongolian saiga provided only a measure of relativeabundance, with no corresponding measure of uncertainty,precluding statistical comparisons (Chimeddorj et al.,2009). An absolute estimate of abundance, and its associ-ated estimate of variance, is required to properly assess andmonitor population size. Methods should be accurate,repeatable and statistically rigorous. We therefore used

JULIE K. YOUNG* (Corresponding author), KIM M. MURRAYy and JOEL

BERGERz Wildlife Conservation Society, Northern Rockies Field Office,

University of Montana, Missoula, Montana, USA. E-mail young@iws.org

SAMANTHA STRINDBERG Wildlife Conservation Society, Bronx, New York,USA.

BAYARBAATAR BUUVEIBAATAR Institute of Biology, Mongolian Academy ofSciences, Ulaanbaatar, Mongolia.

*Current address: Institute for Wildlife Studies, PO Box 1104, Arcata,California, USA.y

Current address: Snow Leopard Trust, Seattle, Washington, USA.z

Also at: Division of Biological Science, University of Montana, Missoula,Montana, USA.

Received 8 April 2009. Revision requested 29 April 2009.Accepted 11 June 2009.

ª 2010 Fauna & Flora International, Oryx, 0(0), 1–8 doi:10.1017/S0030605309990858

distance sampling (Buckland et al., 2001) because it isa well-developed methodology that has been successfullyapplied to many ungulate species (Biswas & Sankar, 2002;Koenen et al., 2002; Seddon et al., 2003; Whittaker et al.,2003; Focardi et al., 2005). Distance sampling methods areflexible and efficient for sampling sparse populationsdistributed over large regions (Olson et al., 2005), factorsimportant for adoption of a technique to estimate popula-tion size throughout saiga range states.

In line-transect distance sampling observers traversea series of transects and record perpendicular distance todetected groups. The group is used as the unit of observa-tion when there is dependence among individuals in speciesthat aggregate, and group size is recorded. Radial distanceand angle are measured to calculate the perpendiculardistance between the transect line and centre of the group.The probability of detecting a group is modelled asa function of the observed perpendicular distances andthen combined with the estimated group encounter rateand estimated expected group size to calculate the densityof individuals in the study area; abundance is also calcu-lated if the total area of the study region is known(Buckland et al., 2001). Estimating the probability ofdetecting a group corrects for the number of animalsundetected and provides absolute density and abundanceestimates. Estimates are considered valid if four assump-tions are met: (1) all individuals or groups on the transectline are detected with certainty, (2) animals are detected attheir original location, (3) measurements are exact, and (4)transects are randomly placed with respect to animal orgroup distribution (Buckland et al., 2001).

We report population estimates of Mongolian saigabased on 2 years of distance sampling surveys, and discussimplications for in situ recovery of Mongolian saiga. Wediscuss the challenges in meeting the first two assumptionsfor estimating population density of saiga and suggestmodifications to survey methodology that could improvethe precision of estimates, making the technique suitable forrange-wide adoption as a standardized monitoring protocol.

Study area

The study was conducted in and around the Sharga NatureReserve (Gobi-Altai Aimag) in western Mongolia (Fig. 1).Clark & Javzansuren (2006) suggest that c. 90% of theMongolian saiga population occurs within or near theReserve. Our study area included c. 35% of the currentrange of Mongolian saiga (L. Amgalan, pers. comm.). Meantotal annual precipitation is c. 50 mm year-1, with much of itfrom winter snow. Annual temperatures are -30 to +30�C.The area is semi-desert and the predominant vegetationincludes Allium, Stipa, Anabasis and Salsola. The onlyother ungulate to occupy the area is goitered gazelle Gazellasubgutturosa. Altitudes are 1,300–2,100 m.

Methods

We established 24 15-km transects (Fig. 1), placed system-atically to provide complete coverage of the study area.Although the starting point for the first transect was notselected randomly it was chosen without prior informationregarding habitat, terrain, or other ecological character-istics. Cardinal directions for travel were selected so thattransects traversed the numerous ravines that extendedalong the slopes of the valley because saiga often used theseravines as cover from wind and sun when resting duringthe day. Transects were spaced $ 5 km apart to avoiddouble counting, which could occur if saiga were displacedby observers from one transect to the next. Distancesampling was conducted in , 2 weeks in September 2006

and 2007, with 1–5 transects completed each day. Mostobservers were different between the two surveys. Sometransects could not be completed in their entirety becauseof difficult terrain and mechanical failures of the vehicles.Therefore, the length of transects varied slightly betweenyears and two of the transects were not used in 2006. Thisresulted in differences in sampling effort and size of thestudy area between years (Table 1).

Transects were driven during daylight hours usinga global positioning system for orientation. When saigawere detected group size, radial distance (r) and sightingangle (h) were recorded, using a compass, binoculars,spotting scope and rangefinder. Saiga often began to runafter we detected them and, in these cases, we useda landscape feature at the point of detection to measurer and h. From these data we calculated perpendiculardistance as x 5 r sin (h). Density of saiga groups withinthe area surveyed (Dg) was then estimated as:

Dg5nfð0Þ2L

; ð1Þ

where L denotes the aggregate length of the transects, n isthe number of saiga groups observed and f(0) is theprobability density function of observed perpendiculardistances evaluated at x 5 0 (Buckland et al., 2001). Thus,density estimates are obtained from estimates of f(0) andencounter rate (n/L). f(0) is equal to 1/l, where l is theeffective strip half-width, corresponding to the perpendic-ular distance from the transect line within which thenumber of undetected groups is equal to the number ofgroups detected beyond it. Multiplying double the effectivestrip half-width by the aggregate length of the transectsyields the effective area surveyed. Saiga density (D) isobtained by multiplying the estimated group density bythe estimated expected group size EðsÞ. The density ofindividuals is multiplied by the surface area of the studyarea or survey stratum to obtain the corresponding abun-dance estimate (N).

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Data were analysed using Distance v. 5 (Thomas et al., inpress). The variance of the encounter rate was estimatedempirically using the replicate transect lines as samples,and maximum likelihood methods were used to estimatethe variance of the effective strip width. There may bea tendency for smaller saiga groups to be missed more oftenthan larger groups at further distances from the transectline, which can lead to size bias if the average group size �S isused (Buckland et al., 2001). To test for bias in the estimateof group size we applied a statistical hypothesis test at the15% a-level to the regression of natural logarithm of groupsize against the probability of detection at distance x fromthe line, using Distance. If the regression is statisticallysignificant, the expected group size [E(s)] is used, otherwiseaverage group size ð�SÞ is used to estimate populationdensity and size.

We first conducted exploratory analyses to examineoptions for truncation and grouping intervals to improvemodel fit for the detection function. Following Bucklandet al. (2001) a variety of key functions and adjustment termcombinations were considered to model the detectionfunction (i.e. uniform + cosine or simple polynomial, half-

normal + cosine or simple polynomial, hazard rate + cosineor hermite polynomial). Goodness-of-fit tests were used toidentify violations of assumptions. Akaike’s informationcriterion (Akaike, 1973) was used in model selection, withparticular attention paid to model fit at distances near zerobecause this is important for robust estimation (Bucklandet al., 2001).

Results

We observed 241 saiga in 93 groups in 2006 and 421 saiga in121 groups in 2007 (Table 1). The survey area (Table 1) wassmaller in 2006 because we were unable to complete twotransects in that year (Fig. 1). Group sizes were 1–14 and1–35 in 2006 and 2007, respectively (Table 2). For both yearsthe size bias regression was significant (P , 0.01), and thusthe expected group size (Table 2) was used to estimatedensity. Encounter rate (n/L) was considerably lower in2006 than in 2007 but with a similar percentage coefficientof variation (Table 3).

Saigas were typically far from observers when firstdetected (577.7 – SE 28.7 m) but were detected as close as80 m. Detection and the effective strip width were esti-mated separately for each year (Table 4) because we hadsufficient data per year and suspected that detectabilityvaried between the two years because of difference in theamount of rainfall and resulting greenness of the vegeta-tion: 2006 was dry and the vegetation was brown during thesampling periods, whereas 2007 was wet and green, result-ing in a greater contrast between saiga coat colour andvegetation. In addition, although all observers were trained,the use of some different observers between years may haveconfounded comparisons.

FIG. 1 The study area in and aroundSharga Nature Reserve. Black linesillustrate the 24 transects used for distancesampling of Mongolia saiga Saiga tataricamongolica in September 2006 and 2007.The two transects completed only in 2007are circled. The rectangle on the insetindicates the location of the main figure inwestern Mongolia.

TABLE 1 Details of size of the study area (Fig. 1), number oftransects, total effort (L), and number of groups and individualsof Mongolian saiga Saiga tatarica mongolica observed in 2006

and 2007.

YearArea(km2)

No. oftransects L (km)

No.of groups

No. ofindividuals

2006 4,524 22 318.10 93 2412007 4,678 24 357.1 117 421

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We inspected detailed histograms of detection frequen-cies by distance from the transect line to identify problemswith the data (Figs 2a,c). In 2006 the histograms show toomany observations close to zero and potential rounding atconvenient values, which may be because of inaccurateangle measurements, especially at smaller angles (Fig. 2a).This tends to result in an effective strip width that isnarrower than it should be, leading to a potential over-estimate of abundance. In contrast, in 2007, the dataindicate too many animals in the intervals slightly furtherfrom the line (Fig. 2c), suggesting animal movement awayfrom the observers before the distance and angle measure-ments could be obtained. This tends to lead to an effectivestrip width that is wider than it should be and a potentialunderestimate of density and abundance.

To improve model fit the data were right truncated at1,000 m for 2006 and 900 m for 2007. In addition, to dealwith the problem of animal movement and imprecision inthe perpendicular distances resulting from inaccurate mea-surements, the data were grouped into equal-sized intervals(seven in 2006 and six in 2007). Considering Akaike’sinformation criterion values and model fit close to zero forboth years, a hazard rate model with no adjustment termswas selected to estimate detection probability (Figs 2b,d).

Estimates of saiga density were 0.54 and 0.78 km-2 in2006 and 2007, respectively (Table 5) but were notstatistically different between years (z 5 0.98, P 5 0.33;Buckland et al., 2001). Estimates of total abundance were4,938 and 7,221 in 2006 and 2007, respectively (Table 5).

Discussion

Our population estimates for Mongolian saiga in the ShargaNature Reserve and surrounding area are considerably larger

than previous estimates, based on minimum counts, of theentire Mongolian saiga population (Lushchekina et al.,1999; Amgalan et al., 2008). Our results suggest that, withadequate protection, sufficient numbers exist to facilitate insitu population recovery. To date, the international con-servation strategy for saiga has often been based around anapparent need to establish a captive herd and breedingprogramme. Captive breeding of Mongolian saiga wasidentified as a priority within the CMS Medium TermWork Programme (CMS, 2006). However, this need wasidentified based on minimum counts that estimated only c.1,800 saiga remained in the wild (Amgalan et al., 2008).

Our estimates were conducted in the autumn so thatsaiga would be widely distributed in small groups, tofacilitate distance sampling, and at this time the observablepopulation was probably at its highest for the year. Saiga,especially calves, may suffer high levels of mortality duringperiodically harsh Mongolian winters, known as dzuds(Bekenov et al., 1998). Previous counts were conducted inJanuary (Amgalan et al., 2008), and differences between ourresults and Amgalan et al. (2008) may reflect overwintermortality. Yet, no dzuds were reported during the samplingyears for either study. Rather, it is likely that previoussurvey techniques resulted in underestimates of saigapopulations. Underestimates of population size based onminimum counts probably resulted from the failure toestimate and correct for detection probability, as well as theexclusion of potentially suitable habitat for saiga from thesurvey area (e.g. minimum counts excluded canyons, toincrease efficiency; Amgalan et al., 2008). Similarly, linetransect estimates of ecologically equivalent North Americanpronghorn Antilocapra americana that did not account fordetectability underestimated population abundance (Pojar &Guenzel, 1999).

Although our estimates are substantially larger thanminimum counts (Lushchekina et al., 1999; Amgalan et al.,2008), it is unclear whether the Mongolian saiga populationis increasing. Saiga move according to vegetation, water andclimactic conditions. Because these conditions may varybetween years it is possible that the higher density in 2007

was a result of saiga movement into our study area ratherthan to an actual increase in the population. In addition,two estimates of population size are insufficient to evaluatelong-term trends.

TABLE 2 Estimate of mean �s and expected group sizes ½EðSÞ� ofMongolian saiga in 2006 and 2007, with the 95% confidenceinterval (95% CI) and percentage coefficient of variation (%CV)for the latter.

Year �s EðSÞ 95% CI %CV

2006 2.47 1.90 2.018–3.028 10.242007 3.20 2.62 2.552–4.012 11.45

TABLE 3 The number of observed groups (n) after righttruncation and the estimate of encounter rate (n/L) per km forMongolian saiga in 2006 and 2007, with the 95% confidenceinterval (95% CI) and percentage coefficient of variation (%CV)for the latter.

Year n n/L (km-1) 95% CI %CV

2006 89 0.280 0.180–0.436 21.552007 110 0.308 0.196–0.485 22.18

TABLE 4 Estimate of fð0Þ and effective strip width ðlÞ for theMongolian saiga surveys in 2006 and 2007, with the 95%confidence intervals (95% CI) and percentage coefficient ofvariation (%CV) for both fð0Þ and l

Year fð0Þðm�1Þ 95% CI l ðmÞ 95% CI %CV

2006 0.0020 0.0014–0.0029 495.90 342.68–717.62 18.762007 0.0019 0.0017–0.0023 514.55 442.11–598.85 7.67

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Population size is an important predictor of populationpersistence (Berger, 1990; Shaffer et al., 2000; Reed et al.,2003), although human threats can have profound andunpredictable impacts even on large populations (Ceballos& Ehrlich, 2002; Altizer et al., 2003). For saiga, over-harvesting and poaching resulted in their rapid anddramatic population decline (Milner-Gulland et al., 2001).Yet, as with pronghorn in North America (Byers & Moodie,1990; Byers, 1997), saiga demonstrate great recovery poten-tial because females exhibit high fecundity and regular rates

of twinning (Kuhl et al., 2007). In addition, c. 90% of femalesaiga may reproduce within their first year (Fadeev &Sludskii, 1982). Our population estimates, together withsaiga reproductive characteristics, suggest that Mongoliansaiga numbers are sufficient to withstand natural fluctua-tions caused by dzuds. In addition, with adequate pro-tection from poaching, and habitat management to ensurethe availability of forage during winter (Berger et al., 2008),saiga numbers appear adequate to allow the populationto remain at their historical population size (. 5,000;Lushchekina et al., 1999).

Improving the precision of population estimates is a pre-requisite for evaluating the effectiveness of conservationmeasures, as wide confidence intervals complicate detectionof any trends. Such evaluations also depend on themagnitude of fluctuations in population size due to factorssuch as disease and weather relative to the impact ofconservation actions such as anti-poaching measures.Because of constraints that limited survey effort, our

FIG. 2 Distance sampling data for Mongolian saiga with 5% of the larger observations truncated for 2006 (a) and 2007 (c). In 2006 thereis heaping at zero and possible rounding at other distances. In 2007 there is potential movement before the distance measurements wereobtained. (b) and (d) illustrate, for 2006 and 2007 respectively, the detection function for the hazard rate model, with no adjustmentterms, fitted to the perpendicular distances of observations of saiga groups. Data were grouped for final analysis using seven and sixequal-spaced intervals in 2006 and 2007, respectively (with truncation at 1,000 m in 2006 and 900 m in 2007).

TABLE 5 Estimates of density ðDÞ and abundance ðNÞ ofMongolian saiga in 2006 and 2007, with their 95% confidenceintervals (95% CI) and the percentage coefficient of variation(%CV) for both ðDÞ and ðNÞ

Year DðKm�1Þ 95% CI N 95% CI %CV

2006 0.5366 0.300–0.959 4,938 2,762–8,828 29.732007 0.7847 0.476–1.294 7,221 4,380–11,903 25.05

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population estimates had high variances, making it impos-sible to determine whether conservation efforts could haveresulted in differences in population size between the2 years. Similarly, large confidence intervals preventeddetection of changes in population size of a threatenedwallaby Onychogalea fraenata (Fisher et al., 2000). Becauseit is notoriously difficult to detect changes in populationabundance of small and widely distributed populations, animportant next step in developing a standardized monitor-ing protocol is to identify methods that improve precision,thereby increasing our ability to detect changes that resultfrom a reduction in threats.

We have identified three modifications to our techni-ques that could reduce variance in distance samplingsurveys. Firstly, accuracy and precision in data collectioncould be increased. Because saiga are chased with vehiclesby poachers, and by people wanting to see saiga, theytypically flee from vehicles as far as 1 km distant. Todetermine preflight saiga locations we had to spot saigaand measure the radial distance (r) from long distances.While our rangefinders were of high quality our compassesonly measured to the nearest 2�. For example, one third ofour sighting distances were $ 644 m. If we observeda group of saiga at 644 m and 60�, but the animal wasactually at 644 m and 64�, then we would calculate thegroup’s perpendicular distance 21 m too far from its actualdistance to the transect line. We therefore recommend theuse of compasses that provide more accurate sightingangles.

Secondly, to further reduce problems associated with thefleeing behaviour of saiga alternative modes of transporta-tion could be tested. Eliminating the use of vehicles maydecrease distances to which observers can approach andobtain original locations. We observed saiga near livestock,especially camels and horses, grazing near human settle-ments and gers (Mongolian nomadic home). If saiga aretolerant of livestock, distance sampling by camel orhorseback could improve ability to detect saiga at theiroriginal locations and thus obtain more accurate measure-ments. However, this may not be appropriate for a smallteam conducting range-wide surveys because of the sub-stantial time and associated logistics required. Additionalteams and training would be needed to complete sucha range-wide survey, and associated increase in costs couldbe prohibitive. This technique may, however, be useful forrugged areas where travel by vehicle impedes the ability todetect saiga. We identified a small and rugged area thatincludes c. 1,056 km2 of Mongolian saiga range in whicha pilot survey could evaluate the feasibility of using horsesor camels. Another option is aerial surveys, which wouldfacilitate distance sampling of the entire Mongolian saigarange (c. 6,900 km2). Aerial surveys have been conductedfor distance sampling of large mammals in southernMongolia (Reading et al., 1999) but, to our knowledge,

there are currently no planes or helicopters in Mongoliathat could fly at the slow speeds required and there is nonational plan to facilitate the use of planes from other rangestates. More information is needed to determine if thelogistical costs and potential disadvantages (imperfect de-tection on the line because of the type or speed of theaircraft) of an aerial survey would outweigh any benefits.Although Norton-Griffiths & McConville (2007) evaluatedsurvey techniques for saiga in Kazakhstan the differences inbiology, habitat and population sizes between the two saigasubspecies may preclude useful comparisons of the suit-ability of techniques to the two areas.

Thirdly, we conducted a power analysis using Trends v.3.0 (Gerrodette, 1993), setting the %CV at 10%, a valuerecently determined as acceptable for population estimatesof Mongolian saiga by the Mongolian Academy of Sciences(L. Amgalan, pers. comm.). We calculated the samplingeffort needed to produce estimates with the precisionrequired for monitoring of population trends (Bucklandet al., 2001). We based our parameter values for encounterrate and variance in group size on the results of thisstudy and determined that sampling should include atleast 1,500 km of effort, assuming an encounter rate ofc. 0.3 saiga km-1. If the encounter rate were to increase to0.5 saiga km-1, then c. 900 km of effort would be sufficient.This required level of effort is not definitive, however. Oursampling efforts were 318.1 and 357.1 km in 2006 and 2007,respectively, giving a %CV of 30 and 25%, respectively, andour survey design was based on the best informationavailable at the time. Only after using a modified designwill it become evident if our recommended changes aresufficient or if further modifications are still required.

Although variance needs to be reduced and the fieldprotocol for distance sampling improved, our resultsdemonstrate that distance sampling can be an effectivetechnique for monitoring saiga populations in Mongolia.Distance sampling has the potential to produce unbiasedabsolute values of abundance. It is a practical method that isnon-invasive and comparatively inexpensive for large geo-graphical areas (Fisher et al., 2000). Successful applicationto Mongolian saiga suggests that it could be used todetermine population size and assess impacts of naturaland human-induced threats on saiga populations through-out the range states. The methodology’s flexibility allowsfor the differences in landscape and species characteristicsacross the range states. For example, the area that saigaoccupy in some range states, such as Kazakhstan andUzbekistan, are vast compared to the range of Mongoliansaiga. The mode of transport (i.e. aerial vs terrestrial) can beadapted to take account of this difference. The populationand group size of Mongolian saiga are small compared tosaiga in Kazakhstan and Russia and distance sampling canbe applied to a range of population sizes. However, groupsizes in the hundreds and thousands are common in

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Kazakhstan, which would make distance sampling harderto apply. Pilot studies are necessary in other range statesto compare distance sampling to alternative samplingmethods. We suggest that signatories of the MOU (CMS,2006) consider adopting distance sampling as the stan-dardized monitoring protocol for regular survey of saigapopulations.

Acknowledgements

We are indebted to L. Amgalan, B. Lkhagvasuren, A. Fineand P. Zahler for assistance with this project. We thankstaff at the Institute of Biology of the Mongolian Academyof Sciences and at the Wildlife Conservation Society,Mongolia Country Program Office for their logisticalsupport. D. Chin-Unen and many others assisted withfieldwork and at the camp. H. Weaver and B. Hudgenscommented on earlier drafts of this article. The project wasfunded by a grant from the National Geographic Society.

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Biographical sketches

J U L I E K. Y O U N G is a spatial and behavioural ecologist. She hasworked on conservation-based research projects in Asia, Africa, andSouth and North America. K I M M. M U R R A Y is a population ecologistand has studied caribou, moose, pronghorn and saiga antelope, andcoyotes, wolves and snow leopards. S A M A N T H A S T R I N D B E R G isa biostatistician focused on wildlife population assessment at WildlifeConservation Society field sites. She has a particular interest inthe design of distance sampling surveys and helped developthe automated survey design component of the software Distance.B A Y A R B A A T A R B U U V E I B A A T A R was the recipient of a ZoologicalSociety of London EDGE grant for his work on Mongolian saiga andspends many field hours researching Mongolia’s threatened species.J O E L B E R G E R ’s research focuses on the conservation of species andintact ecosystems.

J. K. Young et al.8

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