R E S E A R C H A R T I C L E High connectivity among argali sheep from Afghanistan and adjacent countries: Inferences from neutral and candidate gene microsatellites G. Luikart • S. J. Amish • J. Winnie • A. Beja-Pereira • R. Godinho • F. W. Allendorf • R. B. Harris Received: 1 July 2010 / Accepted: 28 January 2011 / Published online: 6 April 2011 � Springer Science+Business Media B.V. 2011 Abstract We quantified population connectivity and genetic variation in the Marco Polo subspecies of argali mountain sheep (Ovis ammon polii) by genotyping 9 neutral and 8 candidate gene microsatellite loci in 172 individuals noninvasively sampled across five study areas in Afghani- stan, China, and Tajikistan. Heterozygosity and allelic richness were generally high (mean H = 0.67, mean A = 6.1), but were significantly lower in the China study area (H = 0.61, P \ 0.001; A = 4.9, P \ 0.01). One mar- ker in an immune system gene (TCRG4) showed an excess of rare alleles compared to neutral expectations. Another immune system gene (GLYCAM-1) showed excessive dif- ferentiation (high FST) between study areas. Estimates of genetic differentiation were similar (FST = 0.035 vs. 0.033) with and without the two loci deviating from neutrality, suggesting that selection is not a primary driver of overall molecular variation, and that candidate gene loci can be used for connectivity monitoring, as long as selection tests are conducted to avoid biased gene flow estimates. Adequate protection of argali and maintenance of inter-population connectivity will require monitoring and international cooperation because argali exhibit high gene flow across international borders. Keywords Bottlenecks � Habitat fragmentation � Gene flow � Ovis ammon � Pamir Mountains � Natural selection � Adaptation � Infectious disease � Noninvasive genetic monitoring � Mountain ungulate Introduction Genetic assessments and monitoring are increasingly cru- cial for delineating population boundaries and movement corridors, and possibly for understanding adaptation to environmental change in extreme environments (Shackl- eton 1997; Schwartz et al. 2007). Availability of candidate adaptive gene markers (e.g. Kohn et al. 2006) along with neutral loci could make feasible the assessment of both adaptive and neutral connectivity, i.e. gene flow and adaptation to changing environments (Black et al. 2001). Argali (Ovis ammon) are an ecologically and economi- cally important species, but are increasingly threatened throughout their range. The Marco Polo subspecies of argali (Ovis ammon polii) is among the largest wild sheep and is perhaps the most charismatic wild animal in the Pamir Mountains of Tajikistan, China, Kyrgyzstan, and Afghani- stan (Fedosenko and Blank 2005). The Marco Polo sub- species is categorized as Near Threatened on the IUCN Red List (IUCN 2009). Argali in the Pamir Mountains are important because of their role as a flagship species for the entire ecosystem (Schaller and Kang 2008). However, populations are susceptible to human pressures includ- ing poaching, displacement, competition, and disease G. Luikart (&) Fish and Wildlife Genomics Group, Flathead Lake Biological Station, University of Montana, Polson, MT 59860, USA e-mail: gordon.luikart@mso.umt.edu G. Luikart � S. J. Amish � F. W. Allendorf Fish and Wildlife Genomics Group, Division of Biological Sciences, University of Montana, Missoula, MT 59812, USA G. Luikart � A. Beja-Pereira � R. Godinho CIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, Universidade do Porto, Campus Agrário de Vairão, 4485-661 Vairão, Portugal J. Winnie � R. B. Harris Department of Ecosystem and Conservation Science, University of Montana, Missoula, MT 59812, USA 123 Conserv Genet (2011) 12:921–931 DOI 10.1007/s10592-011-0195-z transmission from livestock, as well as possible habitat fragmentation (Shackleton 1997). Due to their innate hab- itat preferences, argali are generally distributed patchily, with areas of inappropriate habitat separating populations. Unlike the related mountain sheep (e.g., O. canadensis) of North America, argali are generally believed to be willing to traverse long distances, possibly across seemingly inhospitable terrain. Thus the degree to which populations are truly fragmented, either demographically or genetically, is often an open question. Unfortunately, argali are among the most difficult of wild ungulates to study due to their wary nature, choice of remote and precipitous habitats, and low population den- sity. Argali, unlike North American wild sheep, are cur- sorial and will move long-distances to escape predators or disturbance. Little is known about argali movements or migrations because they are difficult to capture, tag or collar, and track. In part because they are intolerant of human disturbance, argali typically live only where human access is difficult or infrequent. Marco Polo argali in the Wakhan Corridor of Afghani- stan are found only in a small section of the Big Pamir Mountains, in the eastern Little Pamirs, and in the Wakhjir Valley; thus, possible isolation among populations is a legitimate concern (Fig. 1). In contrast, it is likely that populations in Tajikistan are more contiguous in nature (Weinberg et al. 1997). The exact status of Marco Polo argali populations in China is uncertain, although they are known to exist in relatively high numbers in most of the Taxkorgan Nature Reserve in Kashi Prefecture, Xinjiang (Schaller et al. 1987; Gong et al. 2007; Schaller and Kang 2008). Noninvasive and remote genetic sampling (Taberlet et al. 1999; Beja-Pereira et al. 2009) facilitates research on elusive species such as argali. Molecular genetic markers and metapopulation models allow assessment of migration rates over the recent past (dozens to hundreds of genera- tions, assuming migration-drift equilibrium), and also current migration rates by identifying actual immigrants, e.g. using individual-based assignment tests (Cornuet et al. 1999; Paetkau et al. 2004) or by quantifying immigrant ancestry (Wilson and Rannala 2003). Molecular markers also allow detection of recent population size reductions or bottlenecks (e.g., Cornuet and Luikart 1996). Detecting loci under selection is important because selection can bias estimates of population genetic parame- ters, e.g. FST (Luikart et al. 2003). Detecting selection signatures also can help infer if a population has experi- enced a recent selection or stress event such as environ- mental change or disease die-off, which could help infer the cause of population declines. For example, Simões et al. (2008) studied the genetic response to selection and detec- ted both a reduced effective population size (increased drift at multiple microsatellite loci) and directional selection (FST-outlier effects at a single microsatellite locus) during the adaptation to a new environment in populations of Drosophila. The authors suggest that selection at a single locus was associated with adaptive challenges that increased mortality, contributing to genome-wide drift and reduced effective population size. Selection can be detected as extremely high (or low) genetic differentiation (FST) between populations at a sin- gle locus compared to neutral loci. Researchers have developed ‘‘FST outlier’’ tests (Beaumont and Nichols 1996; Antão et al. 2008) and shown that they have rea- sonable power (Beaumont and Balding 2004) to detect directional selection between populations. Selection can also be detected using neutrality tests within populations (Watterson 1978). For example, Paterson (1998) detected even allele frequencies at an MHC locus (Major Histo- compatibility Complex includes more than 100 genes) in a population of Soay sheep (Ovis aries). Microsatellites in genes affected by selection will undergo genetic hitch- hiking and show the selection signature of the gene under Fig. 1 Map of approximate distribution of the Marco Polo argali subspecies, O. a. polli (modified from Schaller and Kang 2008) showing national boundaries and the five study areas in the Pamir Mountains. The large grey area is only a coarse range depiction in that argali populations are not distributed continuously throughout the entire shaded area. Black filled circles show approximate locations of our Murghab (M) study area in southern Tajikistan, the Taxkorgan (T) study area in Xinjiang, China, three study areas in Afghanistan: the Big Pamir (BP), Little Pamir (LP), Wakhjir (Waghjir) Valley (W) 922 Conserv Genet (2011) 12:921–931 123 selection. This approach has been implemented in large- scale genome scans of thousands of loci to identify genes or genome regions under selection (Payseur et al. 2002; Vasemägi et al. 2005). We genotyped neutral and candidate adaptive gene microsatellites from fecal DNA sampled from five study areas within three countries with resident Marco Polo argali populations (Afghanistan, China, and Tajikistan) to assess the genetic and demographic status of argali across the region. Our specific objectives were to (1) quantify connectivity of argali among countries and among the three remaining areas with argali in Afghanistan, (2) test for reduced variation and bottleneck signatures within study areas, and (3) test for signatures of selection at immune system genes that might result from adaptive differentia- tion or stress events such as disease die-offs. Methods Study area Although geographers have not agreed on precise bound- aries, the Pamir Mountains are generally viewed as con- stituting the eastern portion of Tajikistan, the northern half of the Wakhan Corridor in Afghanistan, and the southwest corner of Xinjiang, China. This arid (annual precipitation typically *130 mm/year) and high elevation (highest peaks [7,000 m) range is typified by broad valleys and relatively gentle slopes (‘‘pamir’’ refers to broad, grassy plateau-like topography). We sampled from argali in five different locations within the Pamir Mountains; three within Wakhan District, Badakhshan Province, Islamic Republic of Afghanistan, one within Gorno-Bakakhshan, Tajikistan, and one within Taxkorgan County, Kashi Prefecture, Xinjiang Autono- mous Region, People’s Republic of China. We also col- lected samples from the Karichinai Valley in Khunjerab National Park, Pakistan, but because none yielded DNA these are not discussed further. We termed our five study areas (Fig. 1) the Big Pamir Mountains, the Little Pamir Mountains, and the Wakhjir Valley (all within Afghani- stan), the Murghab study area (in southern Gorno-Ba- dakhshan Province, Tajikistan), and the Taxkorgan study area (in Xinjiang, China). All five study areas were typified by rolling hills and rugged mountains at elevations of 3,900–5,300 m, vegetated by arid, steppe vegetation com- munities of grasses and low-lying shrubs. The Big Pamir Mountains extend from approximately 378N to 378200N latitude and 72�450E to 73�300E longi- tude, and are bordered on the south by the Wakhan River and on the north by the Panj (Amu Darya) River, which also forms the border between Afghanistan and Tajikistan. Within the Big Pamirs, we sampled only from approxi- mately 1000 km 2 centered on 37�N latitude and 73�E longitude. The Little Pamir Mountains, located approxi- mately 150 km east of the Big Pamir Mountains, are cen- tered on 37�230N latitude and 74�200E longitude. The Wakhjir Valley, located approximately 37�030N latitude and 74�300E longitude, forms a small spur of Afghanistan that separates Xinjiang, China from Gilgit-Baltistan of Pakistan. Our Murghab study area in south-eastern Taji- kistan extended along an east–west line of approximately 45 km located about 10–30 km north of the Afghan border. Our Taxkorgan study area was located within the Taxk- organ Nature Reserve in Taxkorgan County, Kashi Pre- fecture in Xinjiang, and was centered on approximately 37�230N latitude and 75�200E longitude. Geographic distance between study areas ranged from a minimum of *15 km between Little Pamirs and Murghab to a maximum of 212 km between Taxkorgan and the Big Pamirs (Fig. 1). Distance within Afghanistan ranged from 38 km between the Wakhjir Valley and the Little Pamirs to 164 km between the Big Pamirs and the Little Pamirs. Sampling All field work was conducted either on foot, horseback, or ‘‘yak-back’’. Because argali move frequently through dif- ficult terrain and our own movements were circumscribed by the valley systems separated by steep ridges, we made no attempt to impose a standardized geographic sampling regime. Instead, we attempted to survey for argali by walking to high vantage points to search for animals during early morning and late afternoon time periods. Whenever we encountered fecal pellets we were certain had been freshly deposited by argali, we collected three fecal pellets from each pellet group (i.e. pellet pile). We only collected pellets adjacent to each other within the group, reducing to inconsequential the probability of [1 individual argali being represented within each individual sample. We avoided collecting from pellet groups that were scattered over more than approximately a 0.1 m 2 area, or that appeared to have been deposited while the animal was moving. We took GPS locations for each sample (unless samples were within a few paces of an existing GPS fix, in which case we recorded the same location), and noted the date, time, and name of the collector. Fecal pellets were stored in sterile 30 cm centrifuge tubes with securely fitting screw-tops to which internal ‘‘sporks’’ were attached (which allowed individual han- dling of each sample without risk of contamination; Evergreen Scientific, Los Angeles, CA, USA). We placed three fecal pellets into approximately six parts of 95% ethyl alcohol (ETOH) for each part fecal material, and stored them at room temperature for 1–4 months before Conserv Genet (2011) 12:921–931 923 123 extraction. We collected and extracted DNA from one pellet from each of 240 pellet groups. DNA extraction, genotyping, and sexing Genetic analyses were conducted in two laboratories. Ini- tial work was undertaken at CTM/CIBIO (Centro de Tes- tagem Molecular), Portugal, where fecal samples were extracted and eight microsatellite markers were co-ampli- fied in three multiplex PCR reactions as described in Harris et al. (2010). All individual fecal samples were initially genotyped twice to quantify the quality of the nuclear DNA for producing genotype data. Samples with reliable amplifications (electropherogram peak height [ 50 units and identical genotypes from the two replicate genotypings at 8 loci) in this first step were selected to continue the genotyping process. The samples with reliable amplifica- tions were independently re-genotyped three to six times total for each of the 8 loci. The remaining genetic analyses were conducted at the University of Montana Conservation Genetics Laboratory (MCGL), Missoula, Montana, USA. Six microsatellite loci (MAF36, FCB304, FCB266, ADC, MAF33, KRT2) were genotyped in both labs on a large subset of samples as a data quality check. Eleven additional loci were genotyped the MGCL for a total of 17 loci. Yet another locus (MAF226) was genotyped at MCGL in all five populations but was excluded due to allelic dropout and a strong deviation from Hardy–Weinberg proportions (mean FIS = 0.43). All loci genotyped at MCGL were re-geno- typed three to six times, as in the Portugal laboratory. Individuals with less than 14 (of 17) loci with a consensus genotype (from at least three successful genotypings per locus) were excluded from all analyses. Among the 17 microsatellites, 9 were putatively neutral loci, and 8 were located in candidate (functional) genes, including 7 located in introns of genes (KRT2, MHC2 (i.e., OLADRBps), TCRG4, IFNG, MMP9, GLYCAM-1, LIF), and one located a few hundred base pairs upstream from the candidate gene (ADCYAP-1). All candidate genes have some immune system function, except for KRT2 which codes for keratin, a molecule in horn and hair. All loci are described in Luikart et al. 2008a, b. Multiplex and a single-locus PCRs were optimized and 10ul reactions were performed on MJR PTC200 thermocy- clers using touch-down profiles (Table 1). Each reaction contained: 2.5 ll of template DNA, 4.5 ll of QIA multiplex mix (Qiagen), and either 1 ll of 109 primer mix, or 1 ll of 2 pM forward and reverse primers. Two different touch- down profiles with 35 cycles were used, one with an initial annealing temperature of 63�C stepping down to 58�C, and another starting at 58�C and stepping down to 53�C. Fluo- rescently labeled DNA fragments were visualized on an ABI 3130xl automated capillary sequencer (Applied Biosystems) in the Murdock DNA Sequencing Facility at the University of Montana. Allele sizes were determined using the ABI GS600LIZ ladder (Applied Biosystems). Chromatograms were viewed and analyzed using GeneMapper software v3.7 (Applied Biosystems). Consensus (i.e. most probable genotypes) genotypes were identified as in previous work (Luikart et al. 2008a; Harris et al. 2010). Consensus genotypes for microsatellite loci were based on three to six independent sample runs. Rules for determining genotypes were as follows: for a sample to be heterozygous at a locus, both alleles had to be observed twice; for a sample to be homozygous the single allele had to be observed in three independent (replicate) genotypings. In addition, ten percent of samples were randomly chosen, re-extracted, and repeat genotyped to monitor for errors. No genotype differences or errors were detected. Sex was determined in the MCGL laboratory by PCR amplification of the amelogenin gene as in Pidancier et al. (2006). Two PCR products (*315 and 359 bp) were obtained for males but only the longer product for females. Due to the large size of the fragments at the amelogenin locus, consensus genotypes were determined as follows: heterozygotes (males) were only accepted only if a male Table 1 Characteristics of the 17 loci genotyped Locus Name N A SE He SE FIS MAF36 34.2 6.0 0.71 0.74 0.01 -0.03 MAF48 33.2 7.6 0.24 0.81 0.01 0.02 MAF209 33.6 4.6 0.51 0.67 0.02 0.09 FCB304 33.8 6.0 0.84 0.69 0.05 -0.01 FCB266 34.4 6.2 0.86 0.76 0.03 0.05 HH62 33.8 7.8 0.73 0.72 0.02 0.10 MAF33 34.4 5.6 0.51 0.53 0.04 0.16 MAF65 33.6 8.6 0.81 0.81 0.02 0.07 ILST30 33.4 2.8 0.37 0.48 0.02 0.15 ADCYAP-1 34.4 6.8 0.58 0.79 0.01 -0.03 KRT2 34.2 9.8 0.20 0.84 0.02 -0.01 MHC2 29.2 6.6 0.68 0.76 0.04 0.06 TCRG4 32.2 5.4 0.93 0.53 0.09 0.07 IFNG 34.2 1.2 0.20 0.01 0.01 -0.02 MMP9 32.8 6.4 0.51 0.74 0.02 0.07 GLYCAM-1 30.0 9.2 1.16 0.79 0.04 -0.04 LIF 29.8 3.8 0.20 0.65 0.01 0.00 The top nine loci are presumed to be selectively neutral and are not near coding genes N is the mean number of individuals genotyped among the five study areas, for each locus. A is the mean allelic richness among the five study areas 924 Conserv Genet (2011) 12:921–931 123 band was only observed twice in a heterozygous genotype or if the male band was observed three or more times; homozygotes where only the female band was observed less than three times (e.g. of three independent PCRs) were classified as of unknown gender. Sex was determined for 163 of the 172 samples. Data analysis The probability that two unrelated individuals (or two random siblings) would have identical genotypes (PID) was computed using DROPOUT (McKelvey and Schwartz 2005) and API-CALC (Ayres and Overall 2004). Principal correspondent analysis (PCA) and multilocus genotype matching were conducted in GENALEX (Peakall and Smouse 2006) to identify outliers due to potential genotyping errors or non-argali samples, and to identify identical genotypes. Loci contributing significantly more unique individuals than expected were found with DROPOUT (McKelvey and Schwartz 2005). We estimated expected heterozygosity, tested for gametic (linkage) dis- equilibrium, and assessed departures from Hardy–Wein- berg proportions using exact tests and a Markov chain as implemented in GENEPOP 3.4 (Raymond and Rousset 1995). Allelic richness estimates were corrected for sample sizes using rarefaction (Kalinowski 2005). We quantified genetic differentiation among study areas using exact tests for allele frequency differences and using GENEPOP 3.4. We tested for reduced allelic richness and reduced het- erozygosity (e.g. in study areas with low variation) using Wilcoxon’s signed-ranks test. This is a nonparametric test for paired comparisons that is appropriate and powerful when homologous loci are examined in related populations. We tested for genetic signatures of recent population bot- tlenecks using heterozygosity excess (i.e., deficit of rare alleles) across multiple neutral loci (Cornuet and Luikart 1996, Luikart and Cornuet 1998). We used two mutation models (the stepwise model SMM; and two-phase model TPM with 80% SMM and 20% multi-step mutations with variance of 12) to cover the range of likely mutations models for microsatellite loci (Piry et al. 1999). Connectivity among populations was assessed using indices of genetic differentiation (FST) as well as the number of migrants (Nm) estimated from both equilibrium models and assignment test approaches that do not assume equilibrium. Equilibrium migration rate models included the private alleles method (in GENEPOP 3.4), and the FST method assuming an island model of migration. Two nonequilibrium methods included a Bayesian assignment test approach (BAYESASS; Wilson and Rannala 2003) and an individual-based the assignment test of Rannala and Mountain (1997) coded in GENECLASS 2.0 (Piry et al. 2004). We tested for locus-specific signatures of selection in two ways. First we tested for evenness of allele frequencies at an individual locus within populations using BOTTLE- NECK, which gives a probability for each locus being at mutation-drift equilibrium (Cornuet and Luikart 1996). We also plotted the probability values of each locus to help assess genome-wide patterns caused by demographic events (e.g. bottlenecks) that affect all loci similarly. Selection on individual loci can cause an excess of deficit of heterozygosity (i.e. rare alleles) compared to mutation- drift equilibrium expectations. Second, we tested each locus for an excessively high or low FST compared to the mean observed FST by using FST- outlier tests (Beaumont and Balding 2004, implemented in Antão et al. 2008). An excessively high FST at a locus compared to thousands of simulated neutral loci indicates possible divergent selection; an excessively low FST sug- gests possible balancing selection. Results We identified 172 individuals from the five study areas. Among the 17 loci, eleven pairs of loci deviated signifi- cantly (P \ 0.01) from gametic disequilibrium. However among our 1,360 tests (272 pairs of loci in each of 5 populations), approximately 13.6 deviations were expected by chance alone (a = 0.01). No pair of loci deviated strongly from gametic disequilibrium in more than one population. The PID for identifying sibling pairs was esti- mated to be less than one in one hundred thousand (10 -5 ) in all study areas for the 17 loci, making power high for resolving between random individuals and sibs (Table 2). Genetic variation Heterozygosity ranged from a low of 0.61 in Taxkorgan (China) to a high of 0.69 in Murghab (Table 2). Allelic Table 2 Genetic variation in each of the five study areas Study area N He (SE) A FIS PIDsibs Big Pamir 63 0.68 (0.05) 6.40 0.010 8.6E-07 Taxkorgan 38 0.61 (0.05) 4.88 -0.027 6.1E-06 Little Pamir 29 0.65 (0.06) 6.65 0.050* 2.1E-06 Murghab (Tajik.) 24 0.69 (0.05) 7.31 0.028 8.2E-07 Wakhjir 18 0.69 (0.05) 5.94 0.017 1.0E-06 Mean 34.4 0.67 6.12 0.040 2.2E-6 N is the number of individuals sampled. He is mean expected heter- ozygosity. A is allelic richness (mean number of alleles per locus corrected for sample size N). PIDsibs is the probability of identity among sibs (Waits et al. 2001) * P \ 0.05 Conserv Genet (2011) 12:921–931 925 123 richness ranged from 4.8 in Taxkorgan to 6.7 in Murghab. Taxkorgan had significantly lower heterozygosity and allelic richness than each other study areas (P \ 0.05; Wilcoxon signed-ranks test). Little Pamir and Wakhjir had the second lowest heterozygosity and allelic richness, respectively. Genetic sex identification yielded a sex ratio of 44% males (72 males to 90 females) over all study areas. Within study areas, sex ratios ranged from 80% males in Wakhjir, 63% males in Taxkorgan, to 44% males in Big Pamir, 36% males in Murghab, to only 4% (one male) in Little Pamir. Genetic structure and connectivity Mean FST for the 17 loci was 0.035 among the five study areas. Mean FST for the nine putatively neutral loci (0.033) was similar to FST for the candidate loci (0.04) (Table 3), so most results below are reported for all 17 loci, unless stated otherwise. Pairwise mean FST’s ranged from 0.008 (between Murghab and Big Pamir) up to 0.055 (between Taxkorgan and Wakhjir). Taxkorgan had the highest pair- wise FST’s ranging from a low of 0.033 (with Murghab) to 0.055 (with Wakhjir). Murghab had the lowest FST’s ranging from only 0.008 with Big Pamir, to the 0.033 with Taxkorgan. We obtained estimates of 5.1 and 6.6 migrants per generation using the private alleles method and FST-based method (assuming an island model), respectively. The mean frequency of private alleles p(1) was 0.026. The Bayesian approach (BAYESASS) for estimating the cur- rent number of migrants did not yield informative results because there was not enough information in the data given the relatively low FST (Faubet et al. 2007), despite our fairly large number of loci with high heterozygosity. Six highly probable immigrant individuals (P [ 0.99) were identified in four of the five populations using the individual-based assignment test of Rannala and Mountain (1997). The probable migrants included the following: two into Murghab (females from Big Pamir), one in Little Pa- mir (a male from Wakhjir), one in Big Pamir (a male from Murghab), and two in Wakhjir (a male from Big Pamir and a male from Little Pamir). The estimated probability of each of the six putative immigrants actually being an immigrant ranged from 99.90% for the immigrant in Little Pamir to 99.95% for the immigrant into Big Pamir. When we lowered the stringency criterion for identification of a migrant (from P [ 0.99 to P [ 0.90), five additional migrants were identified, including two into Murghab, two in Wakhjir, and one in Big Pamir. No immigrants were identified in the China study area of Taxkorgan. In fact, only one individual of 38 from Taxkorgan could potentially be an immigrant, but the probability of that individual being a resident from Taxk- organ was still 11% (Fig. 2). When we lowered the crite- rion of certainty for the identification of an immigrant (from P [ 0.99 to P [ 0.90), Taxkorgan, unlike all other study areas, still showed no evidence of immigrants (e.g., Fig. 2). Selection tests and FST The TCRG4 gene microsatellite had a significant excess (P \ 0.01) of rare alleles (i.e., uneven allele frequency distribution compared to neutral expectations) in both the Murghab and the Little Pamir study areas. None of the nine neutral loci or the other seven candidate gene loci deviated from expected allele frequencies under mutation-drift equilibrium (Fig. 3). Two candidate gene microsatellite loci had an FST value significantly different from neutral expectations. GLY- CAM-1 had a significantly higher FST (FST = 0.068; P = 0.02) and ADCYAP-1 had a significantly lower FST (FST = 0.002, P = 0.03) than expected under neutrality. Neither FST deviation was significant at the 0.01 level. No neutral loci gave evidence of selection or deviated from mutation-drift equilibrium (Fig. 4). Estimates of genetic differentiation were similar with and without the three outlier loci (GLYCAM-1, ADCYAP-1, and TCRG4): the mean FST decreased slightly from FST = 0.035 for all 17 loci to FST = 0.033 for the 14 loci with no selec- tion signature. Pairwise FST, computed after removing out- liers, changed most for the Taxkorgan area in China. For example, FST declined from 0.051 to 0.033 when removing the three outlier microsatellites in candidate genes. Discussion Our study of neutral and candidate adaptive genes in argali populations suggests relatively high variation within, and low differentiation among populations compared to other mountain sheep (e.g., Gutierrez-Espeleta et al. 2000; Worley et al. 2006; Epps et al. 2005; Hogg et al. 2006; Luikart et al. 2008a). This is similar to results obtained in Table 3 FST between all pairs of sampling areas Big Pamir Taxkorgan Little Pamir Murghab Wakhjir Big Pamir – 0.075 0.038 0.007 0.020 Taxkorgan 0.030 – 0.060 0.058 0.055 Little Pamir 0.040 0.048 – 0.029 0.016 Murghab 0.009 0.024 0.038 – 0.009 Wakhjir 0.016 0.051 0.044 0.011 – The 9 putatively neutral loci are included below the diagonal. The eight candidate gene loci are included above the diagonal 926 Conserv Genet (2011) 12:921–931 123 Mongolia using mtDNA of argali populations previously assumed to represent different subspecies (Tserenbataa et al. 2004). Genetic variation and bottlenecks Heterozygosity and allelic richness were high compared to many of the same loci genotyped in other wild sheep, in which mean heterozygosity is approximately H = 0.60 or A Taxkorgan 0 10 20 30 40 50 60 70 80 90 100 Individual number P ro b a b il it y o f o ri g in i n T a x k o rg a n B Big Pamir 0 10 20 30 40 50 60 70 80 90 100 Individual number P ro b a b il it y o f o ri g in i n B ig P a m ir C Murghab 0 10 20 30 40 50 60 70 80 90 100 Individual number P ro b a b il it y o f o ri g in i n M u rg h a b Probability threshold for being a resident P< 0.1, P<0.05, P<0.01 MFMMMF FF FM M M 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 1 3 5 7 9 11 13 15 17 19 21 23 Fig. 2 Assignment test estimates of the probability of local origin of each individual in the study area from which it was sampled. In a, the individual (#38) least likely to originate from Taxkorgan (China) still had an estimated 11% probability of originating in Taxkorgan. In the Big Pamir b, one individual (#63) had a probability of only 0.04% of originating locally, and was therefore considered an immigrant. In c the Murghab (Tajikistan) study area, two individuals (#23 and #24) had a very low probability of local origins (\0.3%). The letters M and F designate male and female individuals with a reasonably low probability of being local residents Fig. 3 FST-outlier test results showing: a the GLYCAM-1 locus with excessively high FST among a all five study areas, b between Taxkorgan (China) versus Big Pamir. Dots represent loci. White area with most dots represents the expected area for neutral loci (99% confidence area) A -4 -2 0 2 Locus H e te ro zy g o s it y e x c e s s B -4 -2 0 2 0 5 10 15 0 5 10 15 Locus H e te ro zy g o s it y e x c e s s TCRG4 TCRG4 Fig. 4 One locus (TCRG4, see arrow) had a significant deviation from mutation-drift equilibrium, i.e., a deficit of heterozygosity (also called an excess of rare alleles), in two populations: a Little Pamir, and b Murghab (Tajikistan). Loci (dots) at mutation-drift equilibrium will have zero heterozygosity excess (y-axis). Loci are in the same order as listed in Table 1. Vertical dashed line separates the 9 neutral (1–9) and 8 candidate adaptive gene loci (10–17) Conserv Genet (2011) 12:921–931 927 123 lower (Ozut 2001; Epps et al. 2005; Hogg et al. 2006; Luikart et al. 2008a, b), and even lower in other wild ungulate species (e.g., Gebremedhin et al. 2009 and papers cited therein). This suggests that Marco Polo argali popu- lations have relatively high effective population sizes and that our study areas are not yet isolated or inbred, as has been feared (Shackleton 1997; Harris et al. 2010). Nonetheless, the significantly lower heterozygosity and allelic richness in Taxkorgan (China) compared to our other study areas suggests this population is smaller (Schaller et al. 1987, 2008), and perhaps relatively more isolated than the other populations. The Taxkorgan popu- lation is near the southeastern edge of the range of argali and there is a long fence (350 km) near the Tajik-Chinese border (Schaller et al. 2008) that could potentially reduce connectivity of the Chinese argali with other populations in Murghab and the Afghan Pamirs; however it is uncertain if the fence is a barrier because, for example, it is not con- tinuous (e.g. open on some high slopes) and argali might jump over it in some locations. The absence of strong bottleneck signatures, even in Taxkorgan, and the reasonably high allelic richness suggest no evidence of recent or severe reductions in population size. Power for detecting severe reductions is reasonably high when using seventeen highly variable microsatellite loci and 38 individuals (Cornuet and Luikart 1996; Luikart and Cornuet 1998), as we have from Taxkorgan. Thus if the Taxkorgan population has become genetically bottle- necked or increasingly isolated, which seems likely, the signal might not detectable if the isolation was recent (e.g. \2–4 argali generations ago). Bottleneck signatures can take several generations to become detectable if the bot- tleneck effective size (Ne) remains fairly large (e.g. [50; Fig. 3 in Cornuet and Luikart 1996). Genetic bottleneck signatures also might be obscured by recent immigration. Differentiation and connectivity The genetic differentiation (FST) in argali is similar to or lower than other mountain sheep sampled at similar spatial scales in North America. For example, in desert bighorn sheep (O. canadensis nelsoni) from Arizona, FST’s ranged from 0.04 to 0.20 (Gutierrez-Espeleta et al. 2000). Fur- thermore, over a geographic distance of only 5 km, FST ranged from 0.046 to 0.113 in desert bighorn sheep pop- ulations without and with a barrier (e.g. road), respectively (Epps et al. 2005). Worly et al. (2006) found that genetic differentiation in thinhorn sheep populations (Ovis dalli) from western Canada was similar to that reported in desert bighorn sheep (Gutierrez-Espeleta et al. 2000). Rocky Mountain bighorn sheep (O. canadensis canadensis) also show FST of *0.11 at this spatial scale (e.g. 40 km across Glacier National Park, Luikart et al. 2008a). The lower differentiation among argali is consistent with their more cursorial nature compared to North American sheep. Argali tend to migrate and run for long distances following threat rather short sprints into steep escape ter- rain as bighorn sheep do. Argali are built for running, having longer legs than North American sheep. They also will move across large valleys, a behaviour which is less common in North American sheep. The highest differentiation (FST) for Taxkorgan among our study areas is consistent with the significantly reduced genetic variation there (compared with other study areas), and suggests increased isolation or a smaller population size. We recommend additional studies of Taxkorgan and other argali on the Chinese side of international borders, along with monitoring of genetic variation to ensure early detection of population declines, isolation, or recruitment problems, which could potentially be developing. The lowest genetic differentiation in Murghab (FST’s 0.008–0.033) is consistent with it having the highest genetic variation and being centrally located in the heart of the distribution range of Marco Polo argali. To estimate gene flow, mean FST values can give only very rough estimates of average number of migrants per generation, and only if populations are near mutation-drift equilibrium (Whitlock and McCauley 1999). Because most natural populations are seldom near equilibrium, and vio- late other assumptions, our estimates of *5 or 6 migrants per generation must be interpreted with great caution; the actual number of migrants could be far higher, for example. Current (contemporary) gene flow can be detected from the identification of actual migrants by using individual- based assignment tests (Paetkau et al. 2004). For example, Taxkorgan had no detectable immigrants (out of 38 indi- viduals sampled) suggesting relatively low connectivity. The identification of putative immigrants in all other populations suggests they currently are not isolated. The threshold of 99% certainty for identification of a migrant could be viewed as overly stringent. With our sample size of 172 individuals (and a \ 0.01), we expect 1.7 migrants to be identified by chance alone (as false positives); whereas we identified 6 migrants (P [ 0.99). Because we detected 6 probable migrants, it seems likely that several true migrants exist and that most populations, except per- haps Taxkorgan in China, have current migration rates greater than zero. The use of 95% certainty (a \ 0.05) for each individual assignment resulted in identification of 11 probable migrants when *8 were expected by chance alone. Selection and adaptation We detected evidence for selection only at candidate gene loci, not at neutral loci, suggesting candidate gene 928 Conserv Genet (2011) 12:921–931 123 approaches can potentially identify loci under selection when using noninvasive sampling in wild sheep. The GLYCAM-1 microsatellite showed higher FST than neutral expectations (Fig. 2). This could potentially result from selection at this locus or at other genes nearby such as IFNg which is less than 20 centimorgans away from GLYCAM-1 in domestic sheep, and which has been associated with parasite load in sheep (Coltman et al. 2001) and other ungulates (Ezenwa et al. 2010). GLYCAM-1 function involves mediating the trafficking of blood-born lympho- cytes into secondary lymph nodes, and also is expressed in the mammary gland of lactating mammals (Hou et al. 2000; Rasmussen et al. 2002). Further studies and collec- tion of parasite data are needed to assess if GLYCAM-1 genotypes are associated with disease resistance in sheep. The lower FST than neutral expectations at ADCYAP-1 could reveal balancing selection for even and similar allele frequencies in multiple study areas (e.g., Paterson 1998). The ADCYAP-1 gene (adenylate cyclase-activating poly- peptide) is involved in regulating production of interleukin 6 that activates the production of T-helper cell 2 (Th2) cytokines involved in defense against helminths and other extracellular parasites (Mosmann and Sad 1996). ADC- YAP-1 was recently found to be associated with nematode parasite infection in domestic sheep (Crawford et al. 2006), and heterozygotes had lower parasite loads in wild bighorn sheep (Luikart et al. 2008b). If argali suffer significant mortalities from disease it is possible that parasites and disease in the Pamirs have lead to selection at ADCYAP-1. Future research is needed to assess potential effects of disease in argali and on ADCYAP-1. Removal of the two FST outlier loci caused little change in mean FST among the five study areas (from 0.035 to 0.30, without ADCYAP-1 and GLYCAM-1). Similarly, removal of the locus (TCRG4) with a heterozygosity- excess had little effect on our multilocus FST estimates among study areas. Interestingly, removal of GLYCAM-1 decreased FST between Taxkorgan (China) and other study areas. For example, FST changed from *0.05 to 0.04 when we removed GLYCAM-1 when comparing Taxkorgan with Big Pamir or Taxkorgan to Wakhjir (Fig. 2b). Removal of GLYCAM-1 did not substantially reduce FST between other study areas, suggesting this GLYCAM-1 gene contributes substantially to the relatively high multi-locus FST observed between Taxkorgan and other study areas. The MHC locus also had the second highest FST between Taxkorgan and other study areas. These observations raise the speculative hypothesis that some disease-related selection differential exists between Taxkorgan and other study areas. How could selection tests and genotyping of both neu- tral and candidate adaptive loci help advance conservation genetics studies? Many candidate adaptive loci will behave as neutral loci, and thus can be used to assess genetic variation, differentiation (FST), and demography (Nm and change in Ne). However, if a locus reliably shows a response to selection, it could be used to monitor or detect adaptive challenges (e.g. disease die-offs or environmental change) or to identify adaptively-differentiated populations that have exceptionally high FST only at candidate genes associated with selection gradients (e.g. disease or tem- perature). Future developments in genomics will allow noninvasive analyses of hundreds of neutral and candidate adaptive genes, which will not only help detect population declines but perhaps infer their cause; For example, if disease candidate genes show high FST then a disease- related die-off could be inferred as the cause of a popula- tion bottleneck (Simões et al. 2008). Conclusions Our study illustrates the potential usefulness of genotyping both neutral and candidate adaptive loci, which can allow inferences about both demographic status (migration and bottlenecks) and selection events such a disease epizootics and environmental change. Our study suggests that candi- date gene loci can be used for connectivity monitoring as long as ‘‘outlier tests’’ are conducted to avoid using non- neutral loci when estimating parameters (e.g. FST) that can be biased by natural selection. Future noninvasive studies will include 100s of loci (e.g., SNPs) in candidate genes thanks to advances in genotyping technologies for par- tially-degraded DNA, such as RT-QPCR assays, which we are developing for use in Fluidigm SNP-chip dynamic arrays (Allendorf et al. 2010). Argali populations appear to have high genetic variation and connectivity in the Pamirs within Wakhan District of Afghanistan, and Murghab (Tajikistan), but potentially are becoming isolated in Taxkorgan, China. We recommend additional studies, including genetic and demographic monitoring of connectivity, along with disease status, to help maintain connectivity and ensure persistence of argali populations. The establishment of international coordina- tion involving Afghanistan, China, Tajikistan (as well as Pakistan, where a few argali remain), would help monitor connectivity and facilitate conservation of argali, their habitat, and other species in the region (Schaller 2007). Acknowledgments Our study was part of the Afghanistan Biodi- versity Conservation Program of the Wildlife Conservation Society (WCS), supported by the United States Agency for International Development (USAID). For field assistance we thank B. Habib, Z. Moheb, Sabir, and A. Khairzad. We also thank S. Kondratov, K.J. Zhang, S. Ostrowski, D. Bedunah, S. Nikzad, Z. Ejlasi, I. Farahmand, Q. Sahar, K. Sediqi, K. Sidiqi, G. Sediq, A. Ahamad, R. King, and L. Conserv Genet (2011) 12:921–931 929 123 Yook. We thank A. Dehgan, P. Zahler, P. Smallwood, P. Bowles, and A. Simms for advice and administrative assistance. AB-P was sup- ported by SFRH/BPD/17822/2004 RG by SFRH/BPD/36021/2007, and this work was supported by POCI/CVT/567558/2004 all from Fundacao para a Ciencia e Tecnologia (FCT), Portugal. G.L. and F.W.A. were supported in part by a grant from U.S. National Science Foundation (Grant DEB 074218). 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Mol Ecol 15:623–637 Conserv Genet (2011) 12:921–931 931 123 High connectivity among argali sheep from Afghanistan and adjacent countries: Inferences from neutral and candidate gene microsatellites Abstract Introduction Methods Study area Sampling DNA extraction, genotyping, and sexing Data analysis Results Genetic variation Genetic structure and connectivity Selection tests and FST Discussion Genetic variation and bottlenecks Differentiation and connectivity Selection and adaptation Conclusions Acknowledgments References << /ASCII85EncodePages false /AllowTransparency false /AutoPositionEPSFiles true /AutoRotatePages /None /Binding /Left /CalGrayProfile (Gray Gamma 2.2) /CalRGBProfile (sRGB IEC61966-2.1) /CalCMYKProfile (ISO Coated v2 300% \050ECI\051) /sRGBProfile (sRGB IEC61966-2.1) /CannotEmbedFontPolicy /Error /CompatibilityLevel 1.3 /CompressObjects /Off /CompressPages true /ConvertImagesToIndexed true /PassThroughJPEGImages true /CreateJobTicket false /DefaultRenderingIntent /Perceptual /DetectBlends true /DetectCurves 0.1000 /ColorConversionStrategy /sRGB /DoThumbnails true /EmbedAllFonts true /EmbedOpenType false /ParseICCProfilesInComments true /EmbedJobOptions true /DSCReportingLevel 0 /EmitDSCWarnings false /EndPage -1 /ImageMemory 1048576 /LockDistillerParams true /MaxSubsetPct 100 /Optimize true /OPM 1 /ParseDSCComments true /ParseDSCCommentsForDocInfo true /PreserveCopyPage true /PreserveDICMYKValues true /PreserveEPSInfo true /PreserveFlatness true /PreserveHalftoneInfo false /PreserveOPIComments false /PreserveOverprintSettings true /StartPage 1 /SubsetFonts false /TransferFunctionInfo /Apply /UCRandBGInfo /Preserve /UsePrologue false /ColorSettingsFile () /AlwaysEmbed [ true ] /NeverEmbed [ true ] /AntiAliasColorImages false /CropColorImages true /ColorImageMinResolution 149 /ColorImageMinResolutionPolicy /Warning /DownsampleColorImages true /ColorImageDownsampleType /Bicubic /ColorImageResolution 150 /ColorImageDepth -1 /ColorImageMinDownsampleDepth 1 /ColorImageDownsampleThreshold 1.50000 /EncodeColorImages true /ColorImageFilter /DCTEncode /AutoFilterColorImages true /ColorImageAutoFilterStrategy /JPEG /ColorACSImageDict << /QFactor 0.40 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /ColorImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000ColorACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000ColorImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasGrayImages false /CropGrayImages true /GrayImageMinResolution 149 /GrayImageMinResolutionPolicy /Warning /DownsampleGrayImages true /GrayImageDownsampleType /Bicubic /GrayImageResolution 150 /GrayImageDepth -1 /GrayImageMinDownsampleDepth 2 /GrayImageDownsampleThreshold 1.50000 /EncodeGrayImages true /GrayImageFilter /DCTEncode /AutoFilterGrayImages true /GrayImageAutoFilterStrategy /JPEG /GrayACSImageDict << /QFactor 0.40 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /GrayImageDict << /QFactor 0.15 /HSamples [1 1 1 1] /VSamples [1 1 1 1] >> /JPEG2000GrayACSImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /JPEG2000GrayImageDict << /TileWidth 256 /TileHeight 256 /Quality 30 >> /AntiAliasMonoImages false /CropMonoImages true /MonoImageMinResolution 599 /MonoImageMinResolutionPolicy /Warning /DownsampleMonoImages true /MonoImageDownsampleType /Bicubic /MonoImageResolution 600 /MonoImageDepth -1 /MonoImageDownsampleThreshold 1.50000 /EncodeMonoImages true /MonoImageFilter /CCITTFaxEncode /MonoImageDict << /K -1 >> /AllowPSXObjects false /CheckCompliance [ /None ] /PDFX1aCheck false /PDFX3Check false /PDFXCompliantPDFOnly false /PDFXNoTrimBoxError true /PDFXTrimBoxToMediaBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXSetBleedBoxToMediaBox true /PDFXBleedBoxToTrimBoxOffset [ 0.00000 0.00000 0.00000 0.00000 ] /PDFXOutputIntentProfile (None) /PDFXOutputConditionIdentifier () /PDFXOutputCondition () /PDFXRegistryName () /PDFXTrapped /False /CreateJDFFile false /Description << /ARA /BGR /CHS /CHT /CZE /DAN /ESP /ETI /FRA /GRE /HEB /HRV (Za stvaranje Adobe PDF dokumenata najpogodnijih za visokokvalitetni ispis prije tiskanja koristite ove postavke. 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