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Rheinische Friedrich-Wilhelms-Universität Bonn Landwirtschaftliche Fakultät & Technische Universität München Fachgebiet für Wildbiologie und Wildtiermanagement Diplomarbeit Effects of habitat fragmentation and isolation on the genetic variability of khulan (Equus hemionus) in Mongolia vorgelegt von Stephanie Pietsch Februar 2007 Prüfer: PD Dr. Ralph Kühn Fachgebiet für Wildbiologie und Wildtiermanagement Technische Universität München Prof. Dr. K. Schellander Institut für Tierzucht und Tierhaltung Universität Bonn Betreuer: Dipl.-Biol. Bernhard Gum & PD Dr. Ralph Kühn Fachgebiet für Wildbiologie und Wildtiermanagement Abstract Abstract Habitat fragmentation and isolation caused by anthropogenic activities are the major factors for wildlife population and species extinction worldwide. Thus, assessing the species-specific effects of habitat fragmentation on population genetic structure is important to evaluate the viability of wildlife populations in fragmented landscapes. In this study, the Mongolian khulan (E. hemionus) was exemplarily used to investigate the effects of human disturbance on the genetic variability as well as the level of gene flow and population differentiation. Eighty samples were collected in the three main distribution areas (Great Gobi A, Gobi B and Small Gobi) of Equus hemionus in southern Mongolia. DNA was extracted from different biological material (bones, tissue and faeces) and cross-amplified at eleven equid microsatellite loci. Reliable noninvasive genetic monitoring of khulan populations was enabled using (i) a PCRRFLP analysis for species identification and (ii) a quantitative real-time PCR assay for the evaluation of DNA quality and quantity. The levels of intra- and interpopulation genetic diversity were investigated with different population genetic methods like F-statistics, AMOVA, demographic analysis and Bayesian population assignment. In order to detect potential spatial population boundaries, we used the ´isolation by distance` model and the Monmonier`s maximum difference algorithm. The statistical analysis revealed a detectable population differentiation of khulans in the study area presumably due to human disturbance in terms of poaching and increased livestock competition. So far the population fragmentation does not affect the genetic diversity within the khulan populations. These findings support the hypothesis that anthropogenic barriers act as a moderator of gene flow because of a high resistance to khulan movements, and hence their cumulative effect has led to the differentiation of two genetic units. I List of Abbreviations List of Abbreviations A average number of alleles per locus AMOVA hierarchical analysis of molecular variance AP private alleles AR mean allelic richness per population BLAST Basic Local Alignment Search Tool bp base pair BSA bovine serum albumin °C degrees Celsius Ct cycle threshold cyt b cytochrome b DNA deoxyribonucleic acid dNTPs deoxynucleotide triphosphate EtOH ethanol Exon expressed region Fig. figure FST fixation index GA Gobi A GAPDH glyceraldehyde-3-phosphate dehydrogenase GB Gobi B GPS Global Positioning System GS Small Gobi h hour He expected heterozygosity Ho observed heterozygosity HWE Hardy-Weinberg equilibrium IAM infinite allele model IBD isolation by distance model k number of alleles K number of subpopulations km kilometre LD linkage disequilibrium LINE long interspersed elements log logarithm II List of Abbreviations M mole MCA melting curve analysis MCMC Markov chain Monte Carlo simulation mg milligram MgCl2 Magnesium chloride min minute ml millilitre mM millimole mRNA messenger ribonucleic acid mtDNA mitochondrial deoxyribonucleic acid N number NaOH sodium hydroxide Ne effective population size NGS noninvasive genetic sampling NTC non template control PCR polymerase chain reaction pg picogram PHW Hardy-Weinberg probability test q average proportion of membership qPCR quantitative PCR r range of allele size RFLP restriction fragment length polymorphism s second SINE short interspersed elements SMM stepwise mutation model SPA strictly protected area TPM two-phase model u units V volt µ mutation rate µl microlitre µm micrometre µM micromole III Table of Contents Table of Contents Abstract................................................................................................. I List of Abbreviations ..........................................................................II List of Figures ................................................................................... VI List of Tables ....................................................................................VII 1 Introduction....................................................................................1 2 Materials and Methods .................................................................4 2.1 2.2 2.3 2.4 Study site ................................................................................................. 4 Study species ........................................................................................... 5 Sampling and sample preservation ........................................................ 5 Laboratory procedures............................................................................ 5 2.4.1 2.4.2 2.4.3 2.4.4 2.4.5 2.4.6 2.5 Statistical analysis................................................................................. 14 2.5.1 2.5.2 2.5.3 2.5.4 2.5.5 3 Intrapopulation genetic diversity.................................................................. 14 Interpopulation genetic diversity .................................................................. 14 Demographic analysis .................................................................................... 14 Bayesian population assignment ................................................................... 15 Spatial analysis ............................................................................................... 15 Results ...........................................................................................17 3.1 3.2 3.3 3.4 Species identification ............................................................................ 17 Quantitative PCR .................................................................................. 18 Microsatellite amplification and reliable genotyping ......................... 21 Statistical analysis................................................................................. 23 3.4.1 3.4.2 3.4.3 3.4.4 3.4.5 4 DNA extraction ................................................................................................. 5 Species identification........................................................................................ 7 Quantitative PCR ............................................................................................. 9 Microsatellite amplification........................................................................... 10 Contamination control ................................................................................... 13 Genotyping criteria and analysis .................................................................. 13 Intrapopulation genetic diversity.................................................................. 23 Interpopulation genetic diversity .................................................................. 24 Demographic analysis .................................................................................... 25 Bayesian population assignment ................................................................... 26 Spatial analysis ............................................................................................... 29 Discussion .....................................................................................30 4.1 4.2 4.3 Species identification ............................................................................ 30 Quantitative PCR and reliable genotyping.......................................... 32 Statistical analysis................................................................................. 33 4.3.1 4.3.2 4.3.3 4.3.4 4.3.5 Intrapopulation genetic diversity.................................................................. 33 Demographic analysis .................................................................................... 34 Interpopulation genetic diversity .................................................................. 34 Bayesian population assignment ................................................................... 35 Spatial analysis ............................................................................................... 35 5 References.....................................................................................37 APPENDIX I ......................................................................................46 IV Table of Contents APPENDIX II.....................................................................................47 APPENDIX III ...................................................................................48 APPENDIX IV ...................................................................................52 APPENDIX V.....................................................................................53 APPENDIX VI ...................................................................................56 APPENDIX VII..................................................................................61 6 Acknowledgments........................................................................66 V List of Figures List of Figures Fig. 1: Map of the three study areas ....................................................................................... 4 Fig. 2: Sequence alignment of a 335bp fragment of the mitochondrial cyt b gene ............ 9 Fig. 3: RFLP banding patterns of an amplified 335 bp fragment of the cyt b gene obtained from two different Mongolian equid species................................................ 18 Fig. 4: Plot of the known initial DNA concentration........................................................... 18 Fig. 5: Melting curve analysis of the standard dilution series from equid GAPDH gene amplified via qPCR. ....................................................................................................... 19 Fig. 6: Distribution of DNA concentration .......................................................................... 20 Fig. 7: Categorization of DNA concentrations calculated via qPCR................................. 20 Fig. 8: Genotyping electropherogram patterns of the 6-fold DNA dilutions series ......... 22 Fig. 9: AMOVA analysis conducted for three populations in one group.......................... 24 Fig. 10: Bayesian clustering results of STRUCTURE ........................................................ 26 Fig. 11: Results of the assignment test (GENECLASS)...................................................... 27 Fig. 12: The means (averaged over posterior probabilities) of migration rates between khulan subpopulations as detected by BAYESASS. ................................................... 28 Fig. 13: Mantel test on isolation by distance between khulan populations....................... 29 Fig. 14: Detection of genetic boundaries .............................................................................. 29 VI List of Tables List of Tables Table 1: Characterization of the analysed equine microsatellite loci in Mongolian khulan (E. hemionus). ................................................................................................................. 12 Table 2: DNA concentrations were calculated from the slope and Y-intercept of the trend line from the standard curve............................................................................... 19 Table 3: Microsatellite diversity indices of khulan (Equus hemionus) populations. ....... 23 Table 4: Matrix of pairwise of FST values (Weir and Cockerham 1984) between the khulan populations in Mongolia. .................................................................................. 24 Table 5: AMOVA analysis performed for three group-combinations .............................. 25 Table 6: Bayesian clustering results of STRUCTURE for the assignment of individuals to each subpopulation .................................................................................................... 27 VII Introduction 1 Introduction Habitat fragmentation and isolation caused by anthropogenic activities are the major factors for population, metapopulation and species extinction worldwide (Wilcox and Murphy 1985). Habitat loss in particular can limit gene flow between populations (Hedrick 1995). This reduced connectivity is suspected to increase inbreeding (Saccheri et al. 1998) and accelerate the loss of genetic diversity because of genetic drift (Frankel and Soulé 1981). In addition these processes limit the evolutionary potential to adapt to environmental changes (Lande 1998, Fraser and Bernatchez 2001). Consequently, in conservation biology and wildlife management, the species-specific effects of habitat destruction on population genetic structure and viability are the most important contemporary conservation issue (Lacy 1987, Wiens 1996). The Asiatic wild ass (khulan: Equus hemionus), once distributed across Central Asia, continuously declined in both numbers and range during the 19th century (Reading et al. 2001). In the IUCN Equid Action Plan the status of Equus hemionus is classified as “insufficiently known” and the species is listed as vulnerable (IUCN 1996; Feh et al. 2002). Currently, the Gobi Desert region in southern Mongolia holds an estimated 20,000 khulan with distribution restricted to the five Strictly Protected Areas (SPAs) Great Gobi A, Great Gobi B, Gobi Gurvan Saikhan, Small Gobi A and Small Gobi B (Mongolian Ministry of Nature and Environment 2003). The largest free-ranging khulan populations have received full protection by law in Mongolia since 1953 (Reading et al. 2001). Deterministic factors for recent regional declines include poaching for meat (Duncan 1992), increased inter-specific competition with livestock for resources (Zhirnov and Ilyinsky 1986) and habitat barriers like fenced borders and railroads (Kaczensky and Walzer 2003). Unfortunately, logistic and technical difficulties of comprehensive traditional field monitoring (e.g. via radiotelemetry) impede the investigation of wildlife population dynamic and genetic consequences of habitat fragmentation across large spatial and temporal scales (Tishendorf and Fahrig 2000, Kaczensky and Walzer 2003). However, using highly variable genetic markers like microsatellites (Goldstein and Schlötterer 1999) along with noninvasive sampling methods (Taberlet and Bouvet 1992, Morin and Woodruff 1996), researchers can monitor free-ranging endangered wildlife populations without capture or direct handling of individual animals. 1 Introduction Various materials, either (i) left behind from the living animal, (ii) derived from skeletal remains of carcasses or (iii) obtained from museum specimens provide valuable sources of DNA, e.g. faeces (Flagstad et al. 1999), urine (Valiere and Taberlet 2000, Hausknecht et al. in prep.), hair (Taberlet and Bouvet 1992), sloughed skin (Valsecchi et al. 1998), bones (Hardy et al. 1994), teeth (Wandeler et al. 2003) and horn (Worley et al. 2004). Several studies illustrated the great potential of noninvasive genetic sampling (NGS) and its application to a wide array of wildlife research topics such as the detection of rare species (Valiere et al. 2003), population size and habitat use (Kohn et al. 1999), individual identification and sexing (Murphy et al. 2003), genetic diversity and gene flow (Fernando et al. 2000) and social structure (Garnier et al. 2001). Despite its great potential, recent controversies have demonstrated potential pitfalls and limitations of NGS. Genotyping errors such as allelic dropout (Navidi et al. 1992, Taberlet et al. 1996, 1999) and false alleles (Goossens et al. 1998; Bradley and Vigilant 2002) are reported to reduce the reliability and accuracy of microsatellite genotyping data. Genotyping errors can lead to misinterpretation of genotypes (Taberlet et al. 1999) and thus bias the inferred biological results (Creel et al. 1999). Consequently, wildlife scientists suggest strategies for reliable microsatellite genotyping based on systematic tracking of the causes and consequences of genotyping errors (Taberlet and Luikart 1999, Bonin et al. 2004, Broquet and Petit 2004, Pompanon et al. 2005). Stringent guidelines have been established, either to reduce genotyping errors, e.g. the multiple tube approach (Taberlet and Luikart 1999) and prescreening of template DNA via qPCR (Morin et al. 2001), or to avoid misinterpretation of genotypes, e.g. the pairwise mismatching method (Paetkau 2003), the maximum likelihood approach (Miller et al. 2002) and statistical computer simulations (Taberlet et al. 1996, Valiere et al. 2002). The implementation of a reliable and feasible NGS method contributes to verify the working hypothesis of this study: Habitat fragmentation and potential population isolation have already led to a detectable genetic differentiation among khulan populations in southern Mongolia. This hypothesis is based on the results of a radio-telemetry study on free-ranging khulans in southwest Mongolia conducted by Kaczensky et al. (2003). They observed reduced movement patterns of khulans between the SPAs Gobi A and B. Khulan populations in the Gobi B are supposed to be isolated. In order to particularly interpret population genetic structure across different geographical scales, a new scientific field, landscape genetics, provides a powerful tool (Manel et al. 2003, 2 Introduction Scribner et al. 2005). The basic principle of landscape genetics is to delineate spatial population boundaries like clines (Sokal 1998), metapopulations to gene flow (Hanski 1998), isolation by distance (Cassens et al. 2000), genetic barriers to gene flow (Piertney et al. 1998) and random patterns (Piglucci and Barbujani 1991). Several studies revealed clear associations between habitat fragmentation and population genetic structure of particularly highly mobile, long-lived and large-bodied mammal species such as coyote and bobcat (Riley et al. 2006), roe deer (Coulon et al. 2006), bighorn sheep (Epps et al. 2005) and grizzly bears (Proctor et al. 2002). However, a comprehensive population genetic study has not yet been applied to free-ranging khulans in Mongolia. The overall objective of this study was to assess the genetic variability as well as the level of gene flow and population differentiation of khulan populations within the framework of landscape genetics (Manel et al. 2003) in the three main distribution areas (Great Gobi A, Gobi B and Small Gobi) of Equus hemionus in southern Mongolia. The secondary goals of the study were: • to establish a panel of 10-12 equine microsatellites, that successfully cross-amplify polymorphic loci in khulan, • to investigate the utility of different sample material from khulan such as faeces, bones and tissue from skeletal remains of carcasses, or preserved museum samples as potential source of DNA for routine noninvasive genetic monitoring of khulan, • to develop a simple and rapid khulan species determination using speciesdiscriminating PCR-RFLP of the mitochondrial cytochrome b gene to prescreen problematic biological specimens, if the species identity is in question, • to design a reliable protocol for microsatellite genotyping based on the preselection of samples via qPCR (Morin et al. 2001), • to identify spatial genetic patterns within and among khulan populations, and • to test for correlations of genetic discontinuities with landscape and environmental variables (Manel et al. 2003, Scribner et al. 2005). The successful application of geo-referenced individual multilocus genotypes can provide a basis for large-scale monitoring of khulan population responses to anthropogenic habitat fragmentation and sustainable management strategies. 3 Materials and Methods 2 Materials and Methods 2.1 Study site The study area in Southern Mongolia included the three SPAs: Great Gobi A, Great Gobi B and Small Gobi (Fig. 1). The size of each study site is as follows: Gobi A - 9,000 km2, Gobi B - 44,000 km2 and Small Gobi – 18,000 km2. Together they encompass 71,000 km² of the potential E. hemionus distribution range in southern Mongolia. From west to east, the three study areas are separated by the following geographic distances: GB-GA ~ 500 km, GA-GS ~ 1200 km. N= 19 N= 18 N= 43 Fig. 1: Map of the three study areas (SPAs: Great Gobi A, Great Gobi B, Small Gobi) with the corresponding sample size and the distribution range of E. hemionus in Mongolia (Kaczensky et al. 2003) The climate of the Gobi region is strongly continental and arid, characterized by extreme temperatures of -35°C in winter and +45°C in summer. Precipitation averages 100mm/year with 70 days of snow cover (Zhirnov and Ilyinsky 1986). The study area is predisposed to large environmental fluctuations and catastrophic events that can cause large fluctuations in wildlife and livestock population numbers (Reading et al. 2001). Habitat conditions range from true desert areas with almost no anthropogenic use in the Gobi A (Von Wehrden et al. in prep.), through desert-steppe areas with moderate livestock grazing pressure in the Gobi B (Kaczensky et al. 2003, Zhirnov and Ilyinsky 1986), to desert-steppe areas that are heavily overstocked and impacted by mining activities in parts of the Small Gobi (Kaczensky et al. 2006). 4 Materials and Methods 2.2 Study species E. hemionus belongs to the family of Equidae. The Gobi khulan is a grazer specialized on monocotyledons. They prefer desert and mountain steppes and oases as year-round habitat. E. hemionus form stable, non-territorial families and all-male groups. The life expectancy of a free-ranging khulan is less than 12-14 years with the highest mortality rates between age classes 4-6 years. This corresponds to the age where both sexes are at the beginning of their reproductive period (Feh et al. 2001). Khulans have large home range sizes from 10,747 km² to 43,105 km², as indicated by radio-telemetry data in the southeast Gobi (Kaczensky et al. 2006). 2.3 Sampling and sample preservation A total of 80 khulan samples were collected by P. Kaczensky and members of the International Takhi Group (ITG) in southern Mongolia during 2002 and 2005. The number of individuals sampled in each SPA was as follows: 19 in Gobi B, 18 in Gobi A and 43 in Small Gobi. Different kinds of biological specimens from free-ranging khulans served as source for noninvasive genetic studies. Faecal samples (N=15) were collected from defecation sites. Bones (N= 14) and tissue (N=51) were obtained from skeletal remains of carcasses in the field. GPS data from each sample location were recorded. Two different sample preservation methods were applied. Fresh faecal pellets were stored in 90% ethanol. Old faecal samples, tissue and bones were preserved dry, packed in plastic bags and stored at -20°C prior to DNA extraction. 2.4 Laboratory procedures 2.4.1 DNA extraction Commercially available DNA extraction kits (QIAGEN and MACHEREY-NAGEL) were used with some modifications to prepare highly pure genomic DNA from different kinds of biological specimens, such as (i) deep-frozen faecal pellets, (ii) 90% ethanol preserved faecal pellets (iii) bones and (iv) dried tissue and tendon samples. 5 Materials and Methods (i) Frozen faecal pellets Genomic DNA was extracted from faeces using the QIAamp® Stool Mini Kit (QIAGEN GmbH, Germany) according to the manufacturer’s instructions but with the following modifications: 400 mg were scraped with a razor blade from the outermost layer of each frozen faecal pellet and incubated at room temperature in ASL buffer for 10 minutes (Wehausen et al. 2004). Each sample was centrifuged at full speed to pellet faecal particles. 1.4 ml of the supernatant was equally portioned on two 2 ml tubes: 350 µl 5 M NaCl and half an InhibitEx tablet were added. Following the supplier’s instructions, ten samples and two negative controls were processed simultaneously. Both processed solutions of each faecal sample were united and loaded on a QIAmp spin column for DNA purification. After washing, each sample column was centrifuged at full speed for 1 minute and additionally dried at room temperature for 10 minutes to volatilize ethanol residues. DNA from frozen faecal pellets was recovered in 100 µl of elution buffer, aliquoted and stored at -20 °C. (ii) 90% ethanol preserved faecal pellets Ethanol preserved faecal samples were processed like the frozen faecal pellets but with an extra initial step for DNA extraction. After centrifugation at full speed the ethanol supernatant was discarded and 400 mg of the remaining faecal pellet was dried at 50°C to remove ethanol residues. DNA was finally recovered in 100 µl of BE buffer. (iii) Bones A layer of 1 mm was removed from the surface of the bone samples by grinding with a drilling machine. Thus, contamination from previous handling was reduced. A drill machine was used with low revolution speed to harvest 5 g of fine bone powder from different locations of each bone sample. Genomic DNA from powdered bones was isolated with a NucleoSpin Tissue Kit (MACHEREY-NAGEL GmbH & Co. KG, Germany) by using, with some modifications, the tissue-isolation protocol provided by the manufacturer. 1 g of the powdered bone samples was processed with the double volume of each kit reagent. The prelysis step was elongated. The bone samples were incubated at 56°C in a shaking incubator for 10 hours. Before loading the spin columns, all insoluble particles were separated by centrifugation at full speed. The clear supernatant was transferred to a new tube and processed according the protocol. Finally, DNA was eluted with 65 µl preheated buffer BE and stored at –20 °C. 6 Materials and Methods (iv) Dried tissue and tendon samples DNA was isolated with NucleoSpin Tissue Kit (MACHEREY-NAGEL GmbH & Co. KG, Germany) using the standard tissue-isolation protocol. DNA was recovered in 85 µl of BE buffer. 2.4.2 Species identification A polymerase chain reaction (PCR) coupled with a restriction fragment length polymorphism (RFLP) analysis of the mitochondrial cytochrome b (cyt b) gene was used for discriminatory determination of Mongolian khulan (Equus hemionus). (i) DNA amplification The equid-specific primer panel (Forward (CytB 1L): 5`-CTAATTAAAATCATCAATC-3` and Reverse (CytB 2H): 5`-AAAAGTAGGATGATTCCAAT-3`) described by Orlando et al. (2003) targets a 335-bp-long DNA fragment of the cyt b gene from perissodactylas´s mtDNA. Amplifications were carried out in a total volume of 25 µl, containing 2 µl template DNA, 0.2 µM of each primer (CytB 2H/ CytB 1L), 0.2 mM dNTPs, 1x-PCR buffer (10x BD buffer: pH 9.4-9.5 800mM Tris-HCl, 200 mM (NH4)2SO4; Solis BioDyne Inc., Estland), 3 mM MgCl2 (Solis BioDyne Inc., Estland), 0.1 µg bovine serum albumin (BSA, Fermentas Inc.) and 1 U ® of Taq-Polymerase (FIREPol , Solis BioDyne Inc., Estland). The PCR profile on an Eppendorf PCR Mastergradient thermal cycler were as follows: 94°C for 3 min for denaturation, 35 cycles of amplification (94°C for 30 s, 50°C for 30 s, 72°C for 30 s) and final extension at 72°C for 10 minutes. PCR products were examined by electrophoresis through a 1.8% ethidium bromide stained agarose gel. Template DNA of the Mongolian horse (Equus caballus) and the Mongolian khulan (Equus hemionus), extracted from different biological specimens such as (i) bones, (ii) tissue, (iii) ETOH preserved faecal pellets and (iv) deep-frozen faecal pellets were analysed. (ii) Sequence analysis and identification of restriction sites The amplified 335 bp long PCR products from Equus caballus and Equus hemionus were purified (QIAquick Gel Extraction Kit) and sequenced (Sequiserve GmbH Germany). Both sequences and the primer pair (CytB 1L, CytB 2H) were then subjected to an internetaccessible BLAST search (http://www.ncbi.nlm.nih.gov/BLAST/). Sequence information of the cyt b gene from two additional Mongolian equids (i.e. donkey (Equus asinus) GenBank Accession No. X97337 and Przewalski horse (Equus przewalskii) GenBank Accession No. DQ223534) were verified. These sequences were obtained from the National Centre of 7 Materials and Methods Biotechnology Information (NCBI) database (http://www.ncbi.nlm.nih.gov/entrez/query. fcgi?db= Nucleotide). All equid mitochondrial cyt b sequences were aligned using MEGA3 (Kumar et al. 2004) (Fig. 2). Species-specific restriction sites of the 335 bp equid cyt b sequences were identified with the program NEBcutter Version 2.0 (Vincze et al. 2003). The restriction enzymes AatII (target sequence: GACGT↓C) and PagI (target sequence: T↓CATGA) were selected by the following criteria: Both REs produce easily distinguishable differences in RFLP banding profiles. AatII restriction site is discriminatory for Equus hemionus (+) versus the other Mongolian equids (-). PagI cuts all tested equid sequences and thus failure of restriction, e.g. through inhibitors, can be excluded. AatII plus PagI are compatible for double digestion. Restriction fragment patterns were expected to produce the following RFLP pattern: Mongolian khulan (Equus hemionus) (59/131/145 bp), Mongolian horse (Equus caballus) (59/276 bp), Donkey (Equus asinus) (59/276 bp) and, Przewalski horse (Equus przewalskii) (59/276 bp). (iii) Endonuclease digestion and RFLP analysis To test whether RFLP analysis is really diagnostic for species identification, three samples from Equus caballus and twelve Equus hemionus from different geographical regions in Mongolia (Small Gobi, Gobi A, Gobi B) were analysed by RFLP. Restriction enzyme incubation with AatII plus PagI were performed in 15µl double digestion volumes according to the manufacturer`s instruction (Fermentas). 10 µl of the PCR product was digested with 1 U PagI (Fermentas), 1 U AatII (Fermentas), 1x restriction buffer green (Fermentas) and 0.1 µg BSA for 1½ h at 37°C. The digested PCR products were separated on a 1.8% ethidiumbromide stained agarose gel and visualized by ultraviolet irradiation. For size reference, a pUC19 DNA/ MspI (HpaII) marker (Fermentas) was used. 8 Materials and Methods Fig. 2: Sequence alignment of a 335bp fragment of the mitochondrial cyt b gene from E. hemionus (Equ_he), E.caballus (Equ_cab_mo), E. asinus (Equ_as), E. przewalskii (Equ_pr) and the equid specific primer pair (CytB_1L, CytB_2H). Recognition sites of the two restriction enzymes are highlighted with coloured frames, i.e. PagI T↓CATGA (blue) and AatII GACGT↓C (red). 2.4.3 Quantitative PCR Quantitative PCR enables the evaluation of the quality and quantity of the DNA template. Quantification of genomic DNA in each sample was performed with real-time PCR and SYBR Green fluorescence detection on a Light Cycler (Roche Diagnostics GmbH) (Wittwer et al. 1997). Optimized, specific PCR primers (F: 5´GGTCGGAGTAAACGGATTTG 3´ and R: 5´AATGAAGGGGTCATTGATGG 3´) were designed with a primer design software program PRIMER3 (Whitehead Institute for Biomedical Research, Cambridge, MA, USA; http://frodo.wi.mit.edu/cgibin/primer3/primer3_www.cgi) by using the GAPDH gene from Equus caballus (Accession no. AF097178). The 100 bp target sequence was placed in the exon of the GAPDH gene (glyceraldehyde-3-phosphate dehydrogenase), which has 9 Materials and Methods previously been used as housekeeping gene for equine mRNA quantitative PCR (qPCR) studies (Leuttenegger et al. 1999). In order to evaluate the success of cross-species amplification, the GAPDH PCR product was sequenced (Sequiserve GmbH Germany) and pairwise sequence alignment between Equus caballus and Equus hemionus was performed using GeneDoc software. Quantitative PCRs were performed in 10 µl reaction volume containing 1µl DNA, 0.2 µM of each primer, 0.1 µg BSA (Fermentas), 1x LightCycler Fast Start DNA MasterPLUS (Roche Diagnostics GmbH, Germany: MgCl2, dNTPs, Hot Start Taq and SYBR Green). The following cycle conditions were used: initial denaturation step at 95°C for 10 min, followed by 45 cycles at 95°C for 15 s, annealing at 58°C for 10 s, 82°C for 3 s and elongation at 72°C for 20 s. The amplification-associated fluorescence was detected at each cycle during PCR. Analysis was performed using Lightcyler software and quantities were checked independently using a standard curve and calculations in Microsoft Excel. The standard curve was created from a standard DNA dilution series of khulan tissue quantified first by absorbance (A260) in a spectrophotometer and then by qPCR assay. DNA amounts of standard dilutions were (in 1 µl): 10 ng, 5 ng, 1 ng, 750 pg, 250 pg, 100 pg, 75 pg, 25 pg, 10 pg, 7.5 pg. When the known initial DNA concentrations (expressed in log, X axis) are plotted against the corresponding cycle threshold (Ct, Y axis) obtained by qPCR, the result is a line representing the linear correlation between the two parameters. All samples including a no-template control and three positive controls of known DNA amounts were amplified by qPCR. DNA concentrations for each sample were calculated from the slope and Y-intercept of the trendline from the standard curve: DNA amount = 10 ((Ct sample –Yint STD)/slope STD) The slope of the standard curve can be used to determine the exponential amplification and efficiency of the PCR reaction by the following equation: Efficiency = [10(-1/slope)] – 1 The optimal real-time PCR efficiency of 90-100% is indicated by a slope of -3.3 (Pfaffl 2001). Melting curve analysis was performed to identify sequence-specific PCR products and exclude primer dimers and non-specific amplicons. The shape of a melting curve is a function of GC/AT ratio, length and sequence (Ririe 1997). 2.4.4 Microsatellite amplification Eleven equine microsatellites were selected from an initial set of fifteen loci (Bailey et al. 2000, Swinburne et al. 2000, Chowdhary et al. 2003, Guerin et al. 2003, Krüger et al. 2005): COR70, SGCV28, ASB23, ASB2, COR58, LEX68, COR18, UM11, COR007, LEX74, 10 Materials and Methods COR71. These were tested for successful cross-species amplification and high polymorphism in E. hemionus. To avoid linkage, principally microsatellite loci from different chromosomes of Equus caballus were chosen for genotyping (Genome map of the horse: www.thearkdb.org). PCR conditions were optimized for Equus hemionus using the following protocol: Amplifications were carried out in a total volume of 10 µl, containing 2-4 µl template DNA, 0.3 µM of each primer (CytB 2H/ CytB 1L), 0.2 mM dNTPs, 1x-PCR buffer (10x BD buffer: pH 9.4-9.5 800mM Tris-HCl, 200 mM (NH4)2SO4; Solis BioDyne Inc., Estland), x mM MgCl2 (Solis BioDyne Inc., Estland) (see Table 1), 0.1 µg bovine serum albumin (BSA, ® Fermentas Inc.) and 1 U of Taq-Polymerase (FIREPol , Solis BioDyne Inc., Estland). The amplification conditions on a BIOMETRA UNO II thermocycler were: initial denaturation at 95°C for 3 min, 40 cycles of 30 s at 95°C, annealing at x°C (see Table 1) for 30 s, 30s at 72°C, final elongation at 72°C for 3 min. The forward primers were fluorescently end-labelled with Tamra, Hex or 6-Fam for genotyping with an ABI 377 DNA sequencer. Six loci (ASB23, ASB2, COR70, SGCV28, COR58 and LEX68) were exemplarily tested with a template DNA standard dilution series (500, 200, 100, 50, 10 and 1 pg/µl per reaction) to examine the marker-specific minimum DNA amount necessary for reliable genotyping. PCRs were performed with adjusted levels of standardized DNA concentration (500, 120 and 40 pg/µl per PCR reaction). One quarter of each PCR product (including a negative control) was run on a 1.8% ethidiumbromide stained agarose gel to check amplification success and to estimate dilutions for multiplexing on the DNA sequencer. 11 Multiplexsystem I 12 Multiplexsystem II Multiplexsystem III Tamra Hex 6 Fam F: AAGAGTGCTCCCGTGTG R: GACAATGCAGAACTGGGTAA F: CTTGGGCTACAACAGGGAATA R: CTGCTATTTCAAACACTTGGA LEX 74 COR 71 Hex F: TGAAAGTAGAAAGGGATGTGG R: TCTCAGAGCAGAAGTCCCTG UM 11 F: GTGTTGGATGAAGCGAATGA R: GACTTGCCTGGCTTTGAGTC 6 Fam F: AGTCTGGCAATATTGAGGATGT R: AGCAGCTACCCTTTGAATACTG COR 18 COR 007 6 Fam F: AAATCCCGAGCTAAAATGTA R: TAGGAAGATAGGATCACAAGG LEX 68 Tamra F: CCTTCCGTAGTTTAAGCTTCTG R: CACAACTGAGTTCTCTGATAGG ASB 2 6 Fam 6 Fam F: GCAAGGATGAAGAGGGCAGC R: CTGGTGGGTTAGATGAGAAGTC ASB 23 F: GGGAAGGACGATGAGTGAC R: CACCAGGCTAAGTAGCCAAG Hex F: CTGTGGCAGCTGTCATCTTGG R: CCCAATTCCAGCCCAGCTTGC SGCV 28 COR 58 6 Fam F: CATCTGTTCCGTGGCATTA R: TTCAGGTGTGGGTTTTGAATC di di di di di di di di di di di Fluorescent Repeat dye type COR 70 Genotyping Multiplex Locus name Primer sequences (5´- 3´) systems 26 24 17 20 25 11 12 15 3 7 6 174-212 151-177 157-185 144-172 241-283 136-160 190-224 162-188 133-173 151-171 257-291 58 58 58 60 60 52 60 58 60 60 60 3 3 3 3 3 3 3 3 1.5 1.5 3 Ruth et al. 1999 Achmann et al. 2001 Lear et al. 1998 Godard et al. 1997 Tallmadge et al. 1999 Reference AF142608 AF212260 AF083450 AF195130 AF083461 Tallmadge et al. 1999 Bailey et al. 2000 Hopman et al. 1999 Meyer et al. 1997 Hopman et al. 1999 ECA001605 Coogle et al. 1999 AF108375 X93516 X93537 U90604 AF142607 Optimized Chromosome MgCl2 GenBank annealing Allele conc. Accession location in size (bp) temperature (mM) no. Equus caballus (°C) Table 1: Characterization of the analysed equine microsatellite loci in Mongolian khulan (E. hemionus). Locus and groupings used in multiplex genotyping gels, primer sequences, fluorescent dye, repeat type, chromosome location in domestic horse (E. caballus), allele size (bp), optimized annealing temperature (°C), MgCl2 in mM, GenBank accession number and reference. Materials and Methods Materials and Methods 2.4.5 Contamination control Working with noninvasive genetic sampling is similar to ancient DNA studies (e.g. Stoneking 1995). Therefore the same guidelines to avoid contamination by PCR products or concentrated genomic DNA should be followed (Taberlet et al. 1999). In this study, the following steps were taken to avoid contamination: (i) Pre- and post-PCR experiments were conducted at separate locations with dedicated instruments, reagents and filter pipette tips. (ii) Preparation of all samples was performed with glove changes between samples. All instruments (drill bit, razor blade) and surfaces were decontaminated during extraction process with 90% ETOH or DNA AWAY (Molecular Bio Products). (iii) The PCR bench and pipettes were regularly decontaminated by UV light. (iv) Blank extractions and no-template PCR controls were included during all steps of the experiment. 2.4.6 Genotyping criteria and analysis Genotypes were scored on an ABI Prism 377 DNA sequencer (Applied Biosystems) with ROX 79-362 (DeWoody et al. 2004) as an internal size standard and analyzed using the program Genescan (Applied Biosystems) and Genotyper 2.0 (Applied Biosystems) DNA fragment analysis software. An automated system of multiplexing, through co-electrophoresis of multiple markers in each lane of the gel, was established. The ability to multiplex is dependant on the differences in the relative sizes of fragments and the number of the fluorescent primer dye labels compatible with the gel electrophoresis system (see Table 1). Using multiplex gels for genotyping post PCR products saves time and reduces costs. The gels were pre-run at 3 KV for 1 hour to overcome electrophoresis artefacts (Fernando et al. 2001). All samples were electrophoresed using an additional reference sample with a known genotype and an internal size standard (ROX 362) to ensure consistent scoring of genotypes across all gels. 2.4 µl of the mixed PCR product received 1.4 µl formamide (Sigma), 0.3 µl ROX 362 and was heated to 95°C for 3 minutes, immediately cooled to < 0°C and 4.1 µl was loaded in each lane of the 6% polyacrylamide gel. Rigid criteria for scoring and accepting consensus genotypes were applied to minimize potential genotyping errors (Schlötterer and Tautz 1992): (i) False peaks, which resulted from leakages of PCR products in the neighbouring lanes, were rejected. 13 Materials and Methods (ii) Lanes were loaded alternately with 2 minutes of short electrophoresis run in between. (iii) Scoring of alleles was based on the existence of characteristic microsatellite stutter bands. (iv) Individual peaks were scored according to the peak amplitude threshold ≥ 50. 2.5 Statistical analysis 2.5.1 Intrapopulation genetic diversity The following microsatellite diversity indices were used: Number of private alleles (AP), average number of alleles per locus (A), mean allelic richness per population (AR), expected and observed heterozygosity (He, Ho). All measures were calculated with GDA version 1.1 (Lewis and Zaykin 2001). GENEPOP on the Web version 3.4 (Raymond and Rousset 1995; www.wbiomed.curtin.edu.au/genepo/) was used to measure deviations from Hardy-Weinberg equilibrium (HWE) and linkage disequilibrium (LD) across all pairs of loci. All probability tests were based on Markov chain Monte Carlo simulation (MCMC) with 100,000 iterations and 1000 burn-in steps (Guo and Thompson 1992, Raymond and Rousset 1995). Sequential Bonferroni adjustments were used to correct for the effect of multiple tests (Rice 1989). 2.5.2 Interpopulation genetic diversity The degree of genetic divergence among putative subpopulations was estimated as pairwise multilocus FST (Weir and Cockerham 1984) using GENEPOP on the Web. A hierarchical analysis of molecular variance (AMOVA) implemented in ARLEQUIN version 3.1 (Excoffier et al. 2005) was performed to partition the total variance into covariance components at three different hierarchical levels: (i) intra-individual differences, (ii) inter-individual differences and (iii) inter-population differences. 2.5.3 Demographic analysis Two different methods for the detection of a recent reduction of the effective population size through bottlenecks were carried out: (i) the ´heterozygosity excess` test (Cornuet and Luikart 1996) implemented in the program BOTTLENECK version 1.2.02 (Piry et al. 1999) and, (ii) the ´M ratio` analysis (Garza and Williamson 2001). • (i) The BOTTLENECK program was used to perform a Wilcoxon sign-rank test. The significance of heterozygote excess is tested under three different mutation models twophase model (TPM), infinite allele model (IAM) and stepwise mutation model (SMM) with 5% multistep changes and variance of 12, following the recommendations of Piry et 14 Materials and Methods al. (1999). Heterozygosity excess indicates a population size reduction, because allelic diversity is reduced faster than gene diversity (Nei et al. 1975, Cornuet and Luikart 1996). • (ii) With the ´M` ratio analysis (Garza and Williamson 2001), the mean ratio of the number of alleles (k) to the range of allele size (r) (M = k/r) was calculated for each population. M is expected to decrease after a population size reduction because the range in allele size at a locus (r) decreases more slowly than the number of alleles (k) under genetic drift. The parameters were set as follows: assumed effective population size Ne=50, mutation rate µ= 10-4 (Weber and Wong 1993). 2.5.4 Bayesian population assignment Three different Bayesian assignment tests were applied in order to (i) infer the population structure, (ii) identify the population of origin of individuals, and (iii) estimate recent migration rates. • (i) A model-based Bayesian clustering method implemented in the program STRUCTURE 2.1 (Pritchard et al. 2000) was used to calculate the number of subpopulations, assuming no prior information on population origin of individuals. For the estimation of the number of subpopulations (K), five independent runs of K= 1-4 were carried out with a burn-in period of 50,000 steps and a chain length of 500,000. The most probable number of populations was taken using the log-likelihood of K. Individuals were then assigned to each subpopulation, based on the highest percentage of membership (q). • (ii) The program GENECLASS version 2 (Piry et al. 2004) was applied to estimate the likelihood of an individual`s multilocus genoytype to be correctly assigned to its source population (Cornuet et al. 1999, using the ´as it is`option). • (iii) BAYESASS+ version 1.3 (Wilson and Rannala 2002) was used to estimate rates of recent migration among populations. 2.5.5 Spatial analysis The khulan populations were analysed within the framework of landscape genetics (Manel et al. 2003). The basic principle of landscape genetics is to detect potential spatial population boundaries like (i) isolation by distance (Slatkin 1993) and (ii) genetic barriers to gene flow (Monmonier 1973). These parameters were calculated using two different software programs: • (i) A Mantel`s test (with 1000 permutations) between the genetic differentiation [FST / (1FST)] (Rousset 1997) and the log-transformed geographical distance tests for the presence of an isolation by distance model (IBD) (Slatkin 1993). Geographic distances between 15 Materials and Methods pairs of sampling sites were calculated based on the coordinates of the approximate centre of the sampling area. IBD tests were performed using Isolation By Distance Web Service version 3.02 software (Jensen et al. 2005; www.ibdws.sdsu.edu/). • (ii) BARRIER version 2.2 (Manni et al. 2004) was used to identify genetic boundaries from allele frequency spatial distributions. This program uses the Monmonier maximum difference algorithm (Monmonier 1973) on a Delaunay triangulation approach (Brassel and Reif 1979) in order to identify zones of abrupt genetic change between different groups of populations. The robustness of the genetic boundaries was assessed by bootstrap (100), implemented in the BARRIER software. Geographic locations of each sampling area were expressed in mean latitude/longitude coordinates and genetic distances were estimated using pairwise FST-matrices (Weir and Cockerham 1984). 16 Results 3 Results 3.1 Species identification A simple and rapid PCR-RFLP analysis of the cytochrome b was established to pre-screen different kinds of problematic samples for reliable identification of the Mongolian khulan (Equus hemionus). A 335 bp fragment of the cytb gene from two Mongolian equid species was successfully amplified from (i) bones, (ii) tissue, (iii) ETOH preserved faecal pellets and (iv) deep-frozen faecal pellets. To confirm species identity within the Equidae family, the equid sequences (Equ_he, Equ_ca) and the equid specific primer pair (CytB 1L, CytB 2H) were subjected to a BLAST search. Comparison of reference versus database nucleotide sequences resulted in highest correspondence (95-100%) with the database sequence of E. caballus. Multiple sequence alignment of the cyt b sequences from the four Mongolian equid species revealed interspecies polymorphisms. Pairwise alignment of E. hemionus with E. caballus displayed fifteen point mutations within the 335 bp sequence analysed. Applicable for analytical purposes is the transition at the position 192 bp (C↔T), enabling the discrimination between E. caballus and E. hemionus by RFLP analysis. As expected, the double digestion treatment with the six-cutter restriction enzymes Pag I and Aat II resulted in different, easily distinguishable banding patterns for the Mongolian horse (Equus caballus) (59/276 bp) and the Mongolian khulan (Equus hemionus) (59/131/145 bp) (Fig 3). The 131 and 145 bp fragments of E. hemionus are very close in length and therefore cannot be identified as two bands. A narrow band pattern with the original fragment size (335bp) is still present after double digestion due to partial digestion. The intraspecific banding patterns for the twelve reference samples of E. hemionus were consistent throughout the geographical range sampled (see Fig 3, Lane 6-17). 17 Results Fig. 3: RFLP banding patterns of an amplified 335 bp fragment of the cyt b gene obtained from two different Mongolian equid species after double digestion with restriction enzymes AatII and PagI. Lane 1: PCR product Equus caballus, undigested (335 bp); Lanes 2-4: E. caballus, digested (59/276 bp); Lane 5: PCR product Equus hemionus, undigested (335 bp); Lanes 6-17: E. hemionus from different geographical regions in Mongolia derived from different biological specimens (Lanes: 6-8: tissue, Lanes: 9-11: bones, Lanes: 12-14: frozen faecal pellets, Lanes 15-17: ETOH stored faecal pellets), digested (59/131/145 bp); M: pUC19 MspI size marker. 3.2 Quantitative PCR DNA quantification was performed with real-time PCR and SYBR Green fluorescence detection on a Light Cycler (Roche Diagnostics GmbH) (Wittwer et al. 1997). First, the standard curve from the dilution series was created. The resulting regression line has a slope of -3.543, Y-intercept of 37.399 and a correlation coefficient of R²= 0.9956 (Fig 4). 36.0 34.0 y = -3.543x + 37.399 R2 = 0.9956 average Ct 32.0 30.0 28.0 26.0 24.0 22.0 20.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 log DNA concentration (pg/µl) Fig. 4: Plot of the known initial DNA concentration (expressed in log, X-axis) and the corresponding cycle threshold (Ct, Y-axis) obtained by qPCR using primer GAPDH. DNA concentrations calculated according to the equation of the trend line from the standard curve are presented in Table 2. 18 Results Table 2: DNA concentrations were calculated from the slope and Y-intercept of the trend line from the standard curve. DNA amount pg/µl 10000 5000 1000 750 250 100 75 25 10 7.5 Ct values Log pg/µl 4.0 3.7 3.0 2.9 2.4 2.0 1.9 1.4 1.0 0.9 23.0 24.4 26.8 27.1 29.2 30.0 30.9 32.8 34.0 33.9 Calculated DNA amount pg/µl 11366 4606 1000 786 210 119 71 19 9 6 The real-time PCR efficiency of the standard curve was 1.915 (93%). Melting curve analysis (MCA) was performed for all samples to identify sequence-specific PCR products and primer dimers (Ririe 1997). Sequence-specific PCR products (100bp) from the standard curve of the equid GAPDH gene had a melting point peak at 85°C. Primer dimers and non-specific amplicons having lower melting temperatures (80°C) were excluded by additional fluorescence detection at 82°C (Fig 5). Melting point peak 85 °C Primer dimers Fig. 5: Melting curve analysis of the standard dilution series from equid GAPDH gene amplified via qPCR. Distribution of DNA concentration in extracts (N=80) from E. hemionus is shown in Fig. 6. More than 77% of all extracts exceeded 1000 pg/µl (N=62). Samples with ≤ 100 pg/µl were rejected for microsatellite DNA amplification. 19 Results 20 18 16 No. of extracts 14 12 10 8 6 4 2 0 100 - 500 500 - 1000 1000 - 5000 5000 - 10000 10000 - 50000 > 50000 DNA categories (pg/µl) Fig. 6: Distribution of DNA concentration ( ≥100 pg/µl) in 80 extracts from E. hemionus. Extracts obtained from the different sample material show varying concentrations of DNA calculated via qPCR (Fig. 7). Tissue samples constitute more than 70% of the DNA categories ≥ 1000 pg/µl. The DNA concentration of 500-1000pg/µl was calculated for extracts obtained from bones (60%) and frozen faecal pellets (40%). The DNA category of 100-500 pg/µl consists of extracts derived from ethanol-preserved faecal pellets (40%) and frozen faecal pellets (55%). 100% Biological sample material in % 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 100 - 500 500 - 1000 1000 - 5000 5000 - 10000 10000 - 50000 > 50000 DNA categories (pg/µl) Tissue Bones Frozen faecal pellets Ethanol preserved faecal pellets Fig. 7: Categorization of DNA concentrations calculated via qPCR for extracts obtained from different biological materials like tissue, bones, frozen faecal pellets and ethanol-preserved faecal pellets. 20 Results 3.3 Microsatellite amplification and reliable genotyping Six loci (ASB23, ASB2, COR70, SGCV28, COR58 and LEX68) were exemplarily tested with a template DNA standard dilution series (500, 200, 100, 50, 10 and 1 pg/µl per reaction) to examine the marker-specific minimum DNA amount necessary for reliable genotyping. All PCR products from the DNA dilution series were diluted 1:15 for comparative scoring on the DNA sequencer. A correlation between genotyping errors or failed amplification with low DNA concentrations in the PCR reaction was detected (Fig. 8). The correct single-locus genotype derived from extracts containing high amounts of nuclear DNA (500 pg/µl per PCR reaction) is presented in each first line of Fig. 8. Loci ASB23-6Fam and COR58-6Fam show erroneous genotypes with allelic dropout at DNA concentrations of ≤ 50 pg/µl per reaction. Loci COR70-6Fam, SGCV28-Hex and LEX68-6Fam show missing data at critical DNA concentrations of ≤ 10pg/µl per reaction. Correct genotypes for the complete six-fold DNA dilution series were scored for locus ASB2-tamra. Results showed that a minimum DNA amount of 40 pg/µl per reaction was required to reliably determine genotypes for the panel of six loci presented here. Thus, PCRs from 80 extracts of varying DNA amounts (≥100 pg/µl) were performed with adjusted levels of standardized DNA concentration (500, 120 and 40 pg/µl per PCR reaction). 21 Results Allelic dropout Allelic dropout No size data No size data No size data Fig. 8: Genotyping electropherogram patterns of the 6-fold DNA dilutions series amplified with the microsatellite loci ASB2-tamra, ASB23-6Fam, COR58-6Fam, COR70-6Fam, LEX68-6Fam and SGCV28-Hex. Genotyping errors or missing data correlated with low DNA concentrations in the PCR reaction are highlighted. The horizontal scale represents the number of bp. The vertical scale on the right expresses the number of fluorescence units. The Y-axis on the left shows the DNA concentrations in pg/µl per PCR reaction. 22 Results 3.4 Statistical analysis 3.4.1 Intrapopulation genetic diversity An average number of 9.4 alleles have been observed for the eleven microsatellite loci applied in this study (see Table 3). The average number of alleles per locus was 13.27. Allelic variation, expressed by the average number of alleles per locus (A) and allelic richness (AR), varied between populations. Maximum values were found in the Gobi B population (A= 9.55; AR= 9). The lowest observed values for allelic diversity (A = 8.27; AR =8.1) were found in the Gobi A population. Expected (HE) and observed (HO) heterozygosity were HE≥ 0.82 and HO≥ 0.70 for each population. The number of individuals per population was positively correlated with the mean number of alleles per locus (A), but not with allelic richness (AR), expected (HE) and observed (HO) heterozygosity. Thus, the genetic parameters are not biased by differences in sample size (N). A total of 14 private alleles (AP) were detected (Gobi B= 8; Gobi A= 4 and Small Gobi= 2). Table 3: Microsatellite diversity indices of khulan (Equus hemionus) populations in Mongolia. Sample size (N), average number of alleles per locus (A), mean allelic richness per population (AR), number of private alleles (AP), expected (HE) and observed (HO) heterozygosity, results of Hardy-Weinberg probability test for deviation from expected Hardy-Weinberg proportions (PHW), test of heterozygosity excess (HE) using Wilcoxon sign-rank test based on infinite allele model (IAM), two-phased model (TPM) and stepwise mutation model (SMM) and M ratio (M) analysis. Population N A AR AP HE HO PHW HE(IAM/TPM/SMM) ´M`ratio GB 19 9.55 9 8 0.84 0.77 n.s. y/n/n 0.727 GA 18 8.27 8.1 4 0.83 0.70 n.s. y/n/n 0.713 GS 43 10.36 8.2 2 0.82 0.70 n.s. y/n/n 0.765 26.67 9.39 8.43 4.67 0.83 0.72 average n.s.: not significant (p≥ 0.05) Linkage disequilibrium The test for genotypic disequilibrium for each pair of the eleven microsatellite loci over all populations gave one significant value (P < 0.05) for 55 comparisons (3 significant values are expected by chance at the 5% level). After undertaking the Bonferroni correction for multiple tests, none of the combinations remained significant at the experimental level (P< 0.0009). When each population was tested separately, a linkage equilibrium between all pairs of loci was generally observed with only few exceptions: two significant values for the Gobi B population, one for the Gobi A population and two for the Small Gobi population. Different 23 Results loci were involved in these cases. Generally, this test implies that the genotypes of the loci used in this study segregated independently. Hardy-Weinberg equilibrium After the Bonferroni correction, the probability test using the Markov chain method based on the ´exact HW test` of Haldane (1954) for each locus in each population showed only three significant deviations: population Gobi A at locus COR 18 and population Small Gobi at loci COR007 and LEX74. These deviations are not systematic, occurring at different loci and two different populations. 3.4.2 Interpopulation genetic diversity The highest FST values were observed between the populations Gobi B and Small Gobi. The lowest FST values were observed between Gobi A and Small Gobi. The overall level of FST values was low (see Table 4). Wright (1978) identified the problem of interpreting FST values as an absolute value based on highly polymorphic loci and proposed that a FST < 0.05 still could indicate a considerable population differentiation. Table 4: Matrix of pairwise of FST values (Weir and Cockerham 1984) between the khulan populations in Mongolia. Population GB GA GB 0,000 GA 0.0088 0.000 GS 0.0191 0.0068 GS 0.000 The AMOVA analysis of hierarchical gene diversity revealed that 88% of the variance is explained by individual variation, 10.9% by variation among individuals within populations and 1.1% by variation among populations (see Fig 9). AMOVA "One group- all populations" 1.1% 10.9% Among populations Among individuals within populations within individuals 88.0% Fig. 9: AMOVA analysis conducted for three populations in one group. 24 Results Subdivision of the three populations into two groups (K=2) defined by the model-based Bayesian clustering method in STRUCTURE 2.1 (Pritchard et al. 2000) produced slightly different results from AMOVA. Three possible two-group combinations were formed and hierarchical gene diversity was compared among them: (i) Gobi A and Small Gobi versus Gobi B (ii) Gobi B and Small Gobi versus Gobi A (iii) Gobi B and Gobi A versus Small Gobi The changes of the covariance components between the three combinations are presented in Table 5. The results of the combination (i) differ from (ii) and (iii), especially regarding the variation among groups (1.18) which is three times higher than among populations within groups (0.43) (see Table 5). Table 5: AMOVA analysis performed for three group-combinations: (i) Gobi A and Small Gobi versus Gobi B, (ii) Gobi B and Small Gobi versus Gobi A, (iii) Gobi B and Gobi A versus Small Gobi. Percentage of variation Percentage of variation Percentage of variation Gobi A and Small Gobi Gobi B and Small Gobi Gobi A and Gobi B versus versus versus Source of variation Gobi B Gobi A Small Gobi Among groups 1.18 -1.48 0.51 Among populations 0.43 2.01 0.72 within groups Among individuals 10.82 10.94 10.86 within populations Within individuals 87.57 88.53 87.9 3.4.3 Demographic analysis The Wilcoxon sign rank test (p< 0.05) did not detect a significant excess of heterozygosity in the studied populations according to the two-phase model (TPM) and the stepwise mutation model (SMM) (see Table 3). The TPM is generally expected to best reflect the mutational process of microsatellite loci (recommended by Piry et al. 1999). Based on the infinite allele model (IAM), all populations exhibited a significant heterozygosity excess after the Bonferroni correction. Therefore, there is some indication of a severe reduction of effective population sizes of all khulan populations. But this cannot be demonstrated using the BOTTLENECK program (Cornuet and.Luikart 1996). 25 Results Similarly, the M ratio analysis revealed that none of the populations has gone through a recent reduction in population size. All M values were above the critical threshold value of 0.68 (Garza and William 2001) (see Table 3). 3.4.4 Bayesian population assignment (i) Detecting cluster number in STRUCTURE The model choice criterion implemented in STRUCTURE to detect the true number of subpopulations (K) is an estimate of the posterior probability of the data for a given K, Pr (X|Y) (Pritchard et al. 2000). The true number of populations (K) is identified by using the maximum value of the mean likelihood [mean Ln(K)] returned by structure. The Bayesian clustering model clearly indicated the presence of substructure in this sample of khulans. The probability of data [Ln Pr(X|Y)] was maximum for K= 2 (see Fig. 10). This shows that the three putative populations (Gobi A, B and Small Gobi) cluster into two subpopulations. -3450 Mean Ln (K) -3500 -3550 -3600 -3650 -3700 1 2 3 4 K Fig. 10: Bayesian clustering results of STRUCTURE for the detection of the true number of subpopulations (K): Plot of the estimated Mean Ln(K) over 5 runs versus the number of populations (K). Samples were placed into the respective subpopulation based upon the highest percentage of membership (q). The proportion of membership of each pre-defined population (3 putative populations: Gobi A, Gobi B, Small Gobi) in each of the K= 2 clusters is shown in Table 6. Results indicate that the samples from Gobi B and Small Gobi both cluster separately (70%). Samples from Gobi A are equally divided into both clusters. 26 Results Table 6: Bayesian clustering results of STRUCTURE for the assignment of individuals to each subpopulation: K represents the number of subpopulations, with values in bold indicating the most likely values for K. For K= 2, the average proportion of membership (q) and sample size per subpopulation cluster (in parentheses) are presented. Subpopulation clusters K Mean Ln(K) 1 -3593 2 -3532 3 -3567 4 -3678 1 2 0.692 (19) Gobi B 0.546 (18) Gobi A 0.71 (43) Small Gobi (ii) Identification of the population of origin of individuals in GENECLASS The assignment of the individual`s multilocus genotype based on the Bayesian method (option ´as it is`) implemented in GENECLASS version 2 (Cornuet at al. 1999) revealed that possible source populations (Gobi B and Small Gobi) had high probability values for the correct assignments to their origin (85% and 82%, respectively) (see I and III in Fig 11). Probability of assigned individuals belonging to khulan population Gobi B 6% 9% Gobi B Gobi A Small Gobi 85% IA Probability of assigned individuals belonging to khulan population Gobi A Probability of assigned individuals belonging to khulan population Small Gobi 18% 5% 30% Gobi B Gobi A Small Gobi Gobi B Gobi A Small Gobi 82% 52% B II 13% C III Fig. 11: Results of the assignment test (GENECLASS, Cornuet et al. 1999): Circular charts represent each individual`s mean probability of belonging to its source population and to another reference population. 27 Results The individual`s assignment of population Gobi A was much lower (52%). Some 48% of the Gobi A population exhibited genotypes of the possible source populations, Gobi B and Small Gobi. This result corresponds to the geographic location of the Gobi A population in the middle of Gobi B and Small Gobi. In contrast, only a small percentage (6%) of the Gobi B population exhibited genotypes from the neighbouring Gobi A population. Some 9 % of the Gobi B population had genotypes from the population Small Gobi - the population with the highest geographical distance (~1200km) to Gobi B. (iii) Bayesian inference of recent migration rates with BAYESASS+ Current migration rates among populations were estimated using the program BAYESASS+ version 1.3 (Wilson and Rannala 2002). Migration occurred mainly either between Gobi A and Small Gobi (29%) (see II Fig 12) and between Gobi B and Small Gobi (26%) (see I Fig. 12). The migration pattern is predominantly unidirectional. Gene flow is directed from the eastern Small Gobi population into the western Gobi A and Gobi B populations. There is nearly no migration into Small Gobi (2%) (see III Fig 12). Migration rates into khulan subpopulation Gobi B 4% 26% Gobi B Small Gobi Gobi A 70% IA Migration rates into khulan subpopulation Gobi A Migration rates into khulan subpopulation Small Gobi 1% 2% 1% 29% Gobi B Small Gobi Gobi A Gobi B Small Gobi Gobi A 69% 98% II B III C Fig. 12: The means (averaged over posterior probabilities) of migration rates between khulan subpopulations as detected by BAYESASS. 28 Results 3.4.5 Spatial analysis (i) ´Isolation by distance` model An “isolation by distance” model, defined as the significant positive correlation between genetic and geographical distances, was not observed for the three khulan populations in the Mantel analysis (r²= 0.0115; p> 0.05) (Fig 13). Fig. 13: Mantel test on isolation by distance between khulan populations. Pairwise genetic distances [FST/(1FST)] (Rousset 1997) (Y-axis) are plotted against log-transformed geographic distances (geographical centre of each sampling area) (X-axis). (ii) Genetic boundaries Monmonier`s maximum difference algorithm identified two barriers (I and II in Fig. 14) showing a constant decrease from higher to lower genetic distances. The main genetic boundary (I) separates the population of Gobi B from Gobi A with a high bootstrap value (89%). The second barrier (II) splits the Gobi A population from Small Gobi (11%). Gobi B A I (89%) B II (11%) Gobi A Small Gobi Fig. 14: Detection of genetic boundaries (red lines) to gene flow between the three khulan populations in Mongolia using the Monmonier algorithm (Monmonier 1973). Green dots indicate the geographical centre of the populations, and blue lines show the connections of localities based on the Delauney triangulation (Brassel and Reif 1979). The bootstrap values are shown in brackets next to the corresponding barrier. 29 Discussion 4 Discussion 4.1 Species identification Rapid and reliable species identification from problematic biological sources (e.g.: bones, meat, feces, blood, hair) is used in forensic science, food science and in ecological studies. Three different techniques suitable for species identification have been developed so far: PCR amplification of short and long interspersed elements (SINEs, LINEs) for species-specific detection and quantification of livestock animal DNA (Walker et al. 2003). PCR-RFLP analysis of the mitochondrial cytB (Meyer et al. 1999). Sequencing of the mitochondrial cytb gene and a subsequent basic local alignment search tool (BLAST) search (Brodmann et al. 2001). Kocher et al. (1989) showed that the highly conserved regions of the mitochondrial cytb gene are suitable for species-level identification in vertebrates and thus is the most widely applied target gene for phylogenetic studies. Mitochondrial DNA is especially appropriate when dealing with degraded DNA extracted from field-collected samples. Advantageous in this record are the following mtDNA characteristics: each diploid cell contains a high number of copies, mtDNA is free of heterocygosity because of maternal inheritance, mtDNA is variable enough to allow differentiation between closely related species. In this study the relatively short length of PCR product (335 bp) and the specificity of the equid primers contribute to the amplification success of the mitochondrial cytb. The RFLP analysis is performed according to the double digest method of the manufacturer (Fermentas). The RFLP setup was designed to match two criteria: (i) to produce speciesspecific, easy distinguishable banding patterns and (ii) to verify failure of restriction, e.g. through inhibitory compounds. Degraded DNA from various biological samples such as tissue, bones, faecal pellets proved to be a valuable source for the identification of the study species E. hemionus. The sampling strategy of this population genetic study on E.hemionus exclusively relies on non-invasive collected genetic samples. Thus, the established RFLP analysis can be used to pre-screen samples, when the species identity is in question. 30 Discussion Indeed, PCR-RFLP analysis has been demonstrated to be an essential tool for reliable khulan species determination, especially for the sampling strategy applied in this study. The following facts support the application of RFLP analysis for this study: Different Mongolian equid species co-occur in same habitat. Identification of the species of origin from poached carcasses, meat and blood is impossible if morphological evidence from hair, skin, tail or bone is not available. All Mongolian equids produce optically similar pellet material. DNA from all Mongolian equids is supposed to successfully amplify the same microsatellite loci like E.hemionus. This is due to the fact, that the microsatellite markers were originally developed in E. caballus and cross-amplified in E. hemionus. Some studies revealed shortcomings of RFLP analysis of cytb for species identification. Ambiguous RFLP banding patterns may be the result. Two determining factors were identified: (i) the coamplification of nuclear cytb-pseudogenes (Burgener and Hübner 1998) or (ii) intraspecies polymorphisms which occur in a restriction site by chance thus affecting successful restriction (Wolf et al. 1999). The RFLP method was optimised for this study to circumvent these shortcomings. First, primers were selected to specifically amplify the mitochondrial cytb, thereby avoiding possible co-amplification of nuclear pseudo-cytb genes. Second, the primer pair exclusively targets DNA of species from the family Equidae. Thus non-target DNA is expected not to amplify. Third, potential intraspecies sequence polymorphisms within the species E. hemionus were examined in more detail by subjecting twelve individuals from different geographical regions in Mongolia to RFLP analysis. Intraspecific banding patterns were consistent throughout the geographical range sampled. This result confirms that either no intraspecies polymorphisms within the 335 bp fragment of the the cytb gene occur in E.hemionus, or that possible mutations do not affect the restriction site of the species discriminatory restriction enzyme Aat II. The unambiguous identification of E. hemionus via RFLP analysis might be hampered by undetected intraspecies DNA sequence polymorphism. To fully explore the level of intraspecific polymorphism, more individuals of E. hemionus should be sequenced and screened for intraspecies mutations. If intraspecies polymorphisms affect the enzyme restriction site by chance, an alternative battery of restriction enzymes has to be selected. 31 Discussion 4.2 Quantitative PCR and reliable genotyping Quantitative PCR and SYBR Green fluorescence detection applied in this study enabled the evaluation of the quality and quantity of the DNA template. Other studies that have applied qPCR to noninvasively collected samples like faeces from chimpanzee and mountain gorilla (Nsubuga et al. 2004) and feathers from capercaillie (Segelbacher 2002) pointed out three important facts: (i) Genotyping errors can lead to misinterpretation of genotypes (Taberlet et al. 1999) and thus bias the inferred biological results (Creel et al. 1999). (ii) Genotyping errors are negatively correlated with the amplifiable template DNA amount (Morin et al. 2001). (iii) There is an unpredictable variation of DNA quality and quantity among extracts (Morin et al. 2001). Thus, many studies investigated the influence of multiple variables on the DNA yield. Determining factors are: • Species–specific variability (Taberlet and Luikart 1999). • Differences in diet, digestive systems and living conditions of the study species (Murphy et al. 2003; Nsubuga et al. 2004). • Differences in the setup of sample collection (Roeder et al. 2004), preservation (Frantzen et al. 1998), DNA extraction (Flagstad et al. 1999) and microsatellite amplification (Waits and Paetkau 2005). • Locus-specific variability based on size (Buchan et al. 2005) and repeat type (Bradley et al. 2000). • Differences in fluorescence detection systems of qPCR: DNA binding agents (SYBR Green: Wittwer et al. 1997) versus hydrolysis probes (5´exonuclease assay: Livak et al. 1995). Based on these facts, it was considered too time-consuming and cost-intensive to fully explore the true factors affecting the DNA quality and quantity in a comprehensive khulan population genetic study. Thus, the preselection of samples via qPCR (Morin et al. 2001) proves to be a powerful tool contributing to a beneficial cost-value ratio of genetic analyses by minimizing genotyping errors. For this study an optimized real-time PCR setup based on SYBR Green fluorescence detection was established. 32 Discussion First, we designed primers amplifying a short 100bp fragment located in the exon of the GAPDH gene from E. caballus. Thus, we improved species-specific amplification of potentially degraded genomic DNA fragments retrieved from non-invasive samples (Leutenegger 1999). A standard curve with an optimal real-time PCR efficiency of 93% (Pfaffl 2001) was created from a 10-fold DNA standard dilution series. DNA concentrations for each sample were calculated from the equation of the standard trendline. Additionally, melting curve analysis (MCA) was performed and sequence-specific PCR products were identified for all samples (Ririe 1997). Furthermore, six loci were exemplarily tested with a template DNA standard dilution series to analyse the locus-specific minimum DNA amount necessary for reliable genotyping. Consequently, all extracts with a minimum DNA amount of ≥ 100pg/µl proved to be a valuable source of template DNA for reliable genotyping the eleven equid microsatellites. 4.3 Statistical analysis The results of our study provided good support for the hypothesis stated in the introduction as follows: Habitat fragmentation and potential population isolation have already led to a detectable genetic differentiation among khulan populations in southern Mongolia. Thus, we can draw the following two conclusions: (i) Human disturbance of khulan habitat due to poaching and increased livestock competition for forage and water represent a barrier to gene flow between local khulan populations leading to a population differentiation. (ii) The observed fragmentation of populations does not yet affect the genetic diversity within khulan populations. 4.3.1 Intrapopulation genetic diversity Our results indicate that all Mongolian khulan populations have a relatively high level of overall microsatellite diversity with an average of 9.39 alleles per locus and a mean HE = 0.83 across all populations. The amount of heterozygosity examined in this study is consistent with previous studies on other equids using the corresponding microsatellite panel (Krüger et al. 2005). We found no evidence for reduced genetic diversity within the analysed khulan populations as reported for other highly mobile animal species suffering from habitat 33 Discussion fragmentation [Ovis canadensis nelsoni (Epps et al. 2005) and Puma concolor (McRae et al. 2005)]. 4.3.2 Demographic analysis One reason may be that isolation due to human disturbance did not last long enough to cause a reduced genetic diversity within fragmented populations. Thus, demographic analysis was performed with the two most promising methods for detecting recent bottlenecks – the ´M`ratio analysis (Garza and William 2001) and the ´heterozygosity excess`, under a two-phase mutation model (Cornuet and Luikart 1996). The combination of both software programs was used to identify different possible genetic scenarios (Williamson-Natesan 2005): • ´M ratio` was the method most likely to correctly identify a bottleneck when a bottleneck lasted several generations, the population had made a demographic recovery, and mutation rates were high or pre-bottleneck population sizes were large. • The ´heterozygosity excess` test was most likely to correctly identify a bottleneck when a bottleneck was more recent and less severe and when mutation rates were low or pre-bottleneck population sizes were small. Both analyses revealed a lack of severe isolation effects (see Table 3). 4.3.3 Interpopulation genetic diversity Our analyses indicate clearly that khulan in Mongolia are not a panmictic population. A population differentiation with at least two genetic units (K=2; STRUCTURE see Fig. 10) were inferred, exhibiting a very low level of differentiation (pairwise FST< 0.05) (see Table 4). In this context recommendations of Wright (1978) are important, as he proposed that a FST< 0.05 could indicate a considerable population differentiation. Indeed, the analysis of genetic variation (AMOVA) between the three khulan populations revealed an apparent fragmented population structure. Even though 88% of the genetic variance is explained by within individual variation, the genetic variance explained by variation between population Gobi B and the other population unit (Gobi A and Small Gobi) is three times higher than the one given by variation among populations Gobi A and Small Gobi (see (i) Table 5). 34 Discussion The population Gobi B clusters separately from Gobi A and Small Gobi. However microsatellite data of HE = 0.84, AR=9 and AP= 8 in the population Gobi B suggest that this site is not genetically isolated (Table 3). Especially the high genetic diversity of the segregated population Gobi B leads to the assumption of considerable gene flow between Gobi B and neighbouring (un-analysed) or further distant khulan populations in the backcountry. 4.3.4 Bayesian population assignment The three Bayesian population assignment tests applied in this study revealed the identification of (i) population substructure with STRUCTURE (Pritchard et al. 2000), (ii) the population of origin of individuals with GENECLASS (Piry and Cornuet 1999) and (iii) recent migration rates with BAYESASS (Wilson and Rannala 2002). The underlying population substructure was K=2, with population Gobi B clustering separately as confirmed by Bayesian clustering in STRUCTURE (see Fig. 10 and Table 6). GENECLASS results indicated that the populations Gobi B and Small Gobi showed highest probability values (> 80%) for the correct assignment to their origin (I and III Fig.11). In contrast, population Gobi A showed a high admixture of multilocus genotypes (48%) and therefore a high gene flow between populations. We can assume that populations Gobi B and Small Gobi mainly constitute the two population clusters. BAYESASS revealed medium levels of gene flow from population Small Gobi into populations Gobi A (29%) and Gobi B (26%) (see I and II in Fig. 12). The Bayesian approach applied in the program BAYESASS might be sensitive to varying population sample sizes used in this study (Gobi B=19, Gobi A=18, Small Gobi 43). Consequently, the high genetic diversity of the segregated population Gobi B can be explained by considerable gene flow between Small Gobi and Gobi B during former times. This overall trend is indicated by the results of both statistical programs, GENECLASS and BAYESASS. 4.3.5 Spatial analysis Tools from landscape genetics were applied to test for correlations of genetic discontinuities with landscape and environmental variables (Manel et al. 2003, Scribner et al. 2005). The isolation by distance model (Slatkin 1993) revealed the absence of a correlation between geographical and genetic distance at the local scale (Mantel test, Fig. 13). The lack of 35 Discussion isolation by distance could be explained by the high dispersal rates of khulan, a conclusion confirmed by the movements from radio-tagged khulan (Kaczensky 2006). Consequently, it suggests that other factors potentially caused the population differentiation. The Monmonier`s maximum difference algorithm identified two barriers to gene flow (I and II in Fig. 14). The main genetic boundary separates the populations Gobi B from Gobi A. The spatial genetic structure is shaped by effects of anthropogenic habitat fragmentation. Human disturbance is no absolute and impermeable barrier to gene flow as indicated by migration rates between Gobi B and Small Gobi. Anthropogenic barriers act as a moderator of gene flow because of a high resistance to khulan movements, and hence their cumulative effect has led to the differentiation of two genetic units. Comparative studies on other animal species [Ovis canadensis nelsoni (Epps et al. 2005) and Gulo gulo (Cegelski et al. 2003)] showed that anthropogenic barriers constitute a severe threat to the persistence of naturally fragmented populations. We can assume that this case is also true for khulan in Southern Mongolia. 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Wehausen J D, Ramey RR, Epps CW (2004) Experiments in DNA Extraction and PCR Amplification from Bighorn Sheep Feces: the Importance of DNA Extraction Method. Journal of Heredity, 95, 503–509. Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution, 38, 1358-1370. Wiens JA (1996) Wildlife in patchy environments: metapopulations, mosaics, and management. In: Metapopulations and Wildlife Conservation Management (ed. McCullough D). pp.53-84. Island Press, Washington, D.C.. Wilcox A, Murphy D (1985) Conservation Strategy: The Effects of Fragmentation on Extinction. The American Naturalist, 125, 879-887. 44 References Williamson-Natesan EG (2005) Comparison of methods for detecting bottlenecks from microsatellite loci. Conservation Genetics, 6, 551-562. Wilson GA, Rannala B (2003) Bayesian Inference of Recent Migration Rates Using Multilocus Genotypes. Genetics, 163, 1177–1191. Wittwer CT, Ririe KM, Andrew RV et al. (1997) The LightCycler: a microvolume multisample fluorimeter with rapid temperature control. Biotechniques, 22, 176-181. Wolf C, Rentsch J, Hübner P (1999) PCR-RFLP Analysis of Mitochondrial DNA: A Reliable Method for Species Identification. Journal of Agricultural and Food Chemistry, 47, 1350 -1355. Worley K, Strobeck C, Arthur S et al. (2004) Population genetic structure of North American thinhorn sheep (Ovis dalli). Molecular Ecology, 13, 2545–2556. Wright S (1965) The Interpretation of Population Structure by F-Statistics with Special Regard to Systems of Mating. Evolution, 19, 395-420. Zhirnov L, Ilinski V (1986) The Great Gobi National Park, a refuge for rare animals of the Central Asian Deserts. UNEP, Moscow. 45 Appendix I APPENDIX I Table A1: Results from the ´M´ratio analysis of the three populations GB, GA, GS. GA_50 4*Ne*(mutation rate)= 0.2 %larger mutations = 0.1 mean step size = 3.5 Locus 0: # alleles = 14 range = 16 ratio = 0.875 sample size = 36 Locus 1: # alleles = 7 range = 11 ratio = 0.636364 sample size = 34 Locus 2: # alleles = 12 range = 14 ratio = 0.857143 sample size = 30 Locus 3: # alleles = 8 range = 9 ratio = 0.888889 sample size = 32 Locus 4: # alleles = 9 range = 20 ratio = 0.45 sample size = 36 Locus 5: # alleles = 7 range = 10 ratio = 0.7 sample size = 32 Locus 6: # alleles = 6 range = 10 ratio = 0.6 sample size = 36 Locus 7: # alleles = 6 range = 11 ratio = 0.545455 sample size = 36 Locus 8: # alleles = 5 range = 5 ratio = 1 sample size = 36 Locus 9: # alleles = 10 range = 14 ratio = 0.714286 sample size = 34 Locus 10: # alleles = 7 range = 12 ratio = 0.583333 sample size = 36 Average M = 0.713679 Simulating 10000 replicates, please wait... 0.05% of the time you expect a smaller ratio at equilibrium GB_50 4*Ne*(mutation rate)= 0.2 %larger mutations = 0.1 mean step size = 3.5 Locus 0: # alleles = 14 range = 21 ratio = 0.666667 sample size = 38 Locus 1: # alleles = 9 range = 12 ratio = 0.75 sample size = 36 Locus 2: # alleles = 11 range = 13 ratio = 0.846154 sample size = 38 Locus 3: # alleles = 9 range = 13 ratio = 0.692308 sample size = 36 Locus 4: # alleles = 7 range = 12 ratio = 0.583333 sample size = 38 Locus 5: # alleles = 11 range = 18 ratio = 0.611111 sample size = 38 Locus 6: # alleles = 7 range = 8 ratio = 0.875 sample size = 38 Locus 7: # alleles = 9 range = 11 ratio = 0.818182 sample size = 38 Locus 8: # alleles = 8 range = 11 ratio = 0.727273 sample size = 38 Locus 9: # alleles = 11 range = 14 ratio = 0.785714 sample size = 38 Locus 10: # alleles = 9 range = 14 ratio = 0.642857 sample size = 38 Average M = 0.727145 Simulating 10000 replicates, please wait... 0.14% of the time you expect a smaller ratio at equilibrium GS_50 4*Ne*(mutation rate)= 0.2 %larger mutations = 0.1 mean step size = 3.5 Locus 0: # alleles = 15 range = 17 ratio = 0.882353 sample size = 86 Locus 1: # alleles = 8 range = 12 ratio = 0.666667 sample size = 84 Locus 2: # alleles = 11 range = 13 ratio = 0.846154 sample size = 86 Locus 3: # alleles = 8 range = 9 ratio = 0.888889 sample size = 86 Locus 4: # alleles = 12 range = 22 ratio = 0.545455 sample size = 86 Locus 5: # alleles = 11 range = 12 ratio = 0.916667 sample size = 84 Locus 6: # alleles = 8 range = 12 ratio = 0.666667 sample size = 84 Locus 7: # alleles = 6 range = 7 ratio = 0.857143 sample size = 82 Locus 8: # alleles = 11 range = 15 ratio = 0.733333 sample size = 72 Locus 9: # alleles = 14 range = 20 ratio = 0.7 sample size = 86 Locus 10: # alleles = 10 range = 14 ratio = 0.714286 sample size = 86 Average M = 0.765237 Simulating 10000 replicates, please wait... 0.36% of the time you expect a smaller ratio at equilibrium 46 Appendix II APPENDIX II Table A2: Results from the WILCOXON TEST in BOTTLENECK. Estimation based on 1000 replications. Population : GB WILCOXON TEST Assumptions: all loci fit I.A.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.99976 Probability (one tail for H excess): 0.00049 Probability (two tails for H excess and deficiency): 0.00098 Assumptions: all loci fit T.P.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.96631 Probability (one tail for H excess): 0.04150 Probability (two tails for H excess or deficiency): 0.08301 Assumptions: all loci fit S.M.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.23242 Probability (one tail for H excess): 0.79346 Probability (two tails for H excess or deficiency): 0.46484 Population : GA WILCOXON TEST Assumptions: all loci fit I.A.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.99976 Probability (one tail for H excess): 0.00049 Probability (two tails for H excess and deficiency): 0.00098 Assumptions: all loci fit T.P.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.98950 Probability (one tail for H excess): 0.02686 Probability (two tails for H excess or deficiency): 0.05371 Assumptions: all loci fit S.M.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.87988 Probability (one tail for H excess): 0.13916 Probability (two tails for H excess or deficiency): 0.27832 Population : GS WILCOXON TEST Assumptions: all loci fit I.A.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.99951 Probability (one tail for H excess): 0.00073 Probability (two tails for H excess and deficiency): 0.00146 Assumptions: all loci fit T.P.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.83984 Probability (one tail for H excess): 0.18262 Probability (two tails for H excess or deficiency): 0.36523 Assumptions: all loci fit S.M.M., mutation-drift equilibrium. Probability (one tail for H deficiency): 0.01050 Probability (one tail for H excess): 0.99194 Probability (two tails for H excess or deficiency): 0.02100 47 Appendix III APPENDIX III Table A3: Results for the ´Genotypic disequilibrium´ analysis in GenePop web version 3.4. Number of population detected: 3 Numer of loci detected: 11 Markov chain parameters Dememorization: 1000 Batches: 100 Iterations per batch: 1000 Pop Locus#1 Locus#2 P-Value ------- ------- ------------------GB ASB23 ASB2 0.03074 GB ASB23 COR70 0.05293 GB ASB2 COR70 0.19139 GB ASB23 SGCV28 0.05590 GB ASB2 SGCV28 0.43341 GB COR70 SGCV28 0.14873 GB ASB23 COR18 1.00000 GB ASB2 COR18 0.27597 GB COR70 COR18 0.42795 GB SGCV28 COR18 1.00000 GB ASB23 COR58 0.12365 GB ASB2 COR58 0.12703 GB COR70 COR58 0.13238 GB SGCV28 COR58 0.16804 GB COR18 COR58 1.00000 GB ASB23 LEX68 0.11333 GB ASB2 LEX68 0.69892 GB COR70 LEX68 0.47099 GB SGCV28 LEX68 0.62997 GB COR18 LEX68 1.00000 GB COR58 LEX68 0.32488 GB ASB23 UM11 1.00000 GB ASB2 UM11 0.28149 GB COR70 UM11 1.00000 GB SGCV28 UM11 1.00000 GB COR18 UM11 1.00000 GB COR58 UM11 1.00000 GB LEX68 UM11 1.00000 GB ASB23 COR007 0.09729 GB ASB2 COR007 0.32054 GB COR70 COR007 0.45784 GB SGCV28 COR007 0.62046 GB COR18 COR007 0.95533 GB COR58 COR007 0.39585 GB LEX68 COR007 0.06070 GB UM11 COR007 0.63709 GB ASB23 COR71 0.00582 GB ASB2 COR71 0.07238 GB COR70 COR71 0.13895 GB SGCV28 COR71 0.10185 GB COR18 COR71 1.00000 GB COR58 COR71 0.02683 GB LEX68 COR71 0.19789 GB UM11 COR71 1.00000 GB COR007 COR71 0.21082 S.E. 0.01404 0.02096 0.03197 0.02138 0.04202 0.03061 0.00000 0.02649 0.03295 0.00000 0.03112 0.02910 0.03133 0.03236 0.00000 0.02447 0.02829 0.03505 0.03511 0.00000 0.03674 0.00000 0.03558 0.00000 0.00000 0.00000 0.00000 0.00000 0.02522 0.03130 0.03742 0.03809 0.00792 0.04245 0.01467 0.03624 0.00582 0.02453 0.03264 0.02839 0.00000 0.01373 0.02929 0.00000 0.03626 48 Appendix III GB GB GB GB GB GB GB GB GB GB GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA GA ASB23 LEX74 1.00000 0.00000 ASB2 LEX74 1.00000 0.00000 COR70 LEX74 1.00000 0.00000 SGCV28 LEX74 1.00000 0.00000 COR18 LEX74 1.00000 0.00000 COR58 LEX74 1.00000 0.00000 LEX68 LEX74 0.34816 0.03480 UM11 LEX74 1.00000 0.00000 COR007 LEX74 1.00000 0.00000 COR71 LEX74 0.08356 0.02603 ASB23 ASB2 1.00000 0.00000 ASB23 COR70 1.00000 0.00000 ASB2 COR70 1.00000 0.00000 ASB23 SGCV28 no information ASB2 SGCV28 1.00000 0.00000 COR70 SGCV28 1.00000 0.00000 ASB23 COR18 1.00000 0.00000 ASB2 COR18 0.71680 0.02259 COR70 COR18 1.00000 0.00000 SGCV28 COR18 0.31400 0.03127 ASB23 COR58 no information ASB2 COR58 0.28334 0.03170 COR70 COR58 1.00000 0.00000 SGCV28 COR58 1.00000 0.00000 COR18 COR58 0.07295 0.01363 ASB23 LEX68 1.00000 0.00000 ASB2 LEX68 0.61089 0.02900 COR70 LEX68 1.00000 0.00000 SGCV28 LEX68 0.27840 0.03306 COR18 LEX68 0.11566 0.01945 COR58 LEX68 0.37283 0.03165 ASB23 UM11 1.00000 0.00000 ASB2 UM11 1.00000 0.00000 COR70 UM11 1.00000 0.00000 SGCV28 UM11 0.39083 0.02976 COR18 UM11 1.00000 0.00000 COR58 UM11 1.00000 0.00000 LEX68 UM11 0.72579 0.02309 ASB23 COR007 0.10012 0.02074 ASB2 COR007 1.00000 0.00000 COR70 COR007 0.12210 0.01904 SGCV28 COR007 1.00000 0.00000 COR18 COR007 0.16644 0.01663 COR58 COR007 0.44822 0.02285 LEX68 COR007 0.78851 0.02120 UM11 COR007 0.51612 0.02459 ASB23 COR71 1.00000 0.00000 ASB2 COR71 0.40543 0.03429 COR70 COR71 0.04410 0.01721 SGCV28 COR71 1.00000 0.00000 COR18 COR71 0.47354 0.03209 COR58 COR71 1.00000 0.00000 LEX68 COR71 1.00000 0.00000 UM11 COR71 0.54349 0.03398 COR007 COR71 0.13681 0.02173 ASB23 LEX74 1.00000 0.00000 ASB2 LEX74 1.00000 0.00000 COR70 LEX74 1.00000 0.00000 SGCV28 LEX74 1.00000 0.00000 COR18 LEX74 0.14173 0.02298 COR58 LEX74 1.00000 0.00000 LEX68 LEX74 0.33174 0.03260 49 Appendix III GA GA GA GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS GS UM11 COR007 COR71 ASB23 ASB23 ASB2 ASB23 ASB2 COR70 ASB23 ASB2 COR70 SGCV28 ASB23 ASB2 COR70 SGCV28 COR18 ASB23 ASB2 COR70 SGCV28 COR18 COR58 ASB23 ASB2 COR70 SGCV28 COR18 COR58 LEX68 ASB23 ASB2 COR70 SGCV28 COR18 COR58 LEX68 UM11 ASB23 ASB2 COR70 SGCV28 COR18 COR58 LEX68 UM11 COR007 ASB23 ASB2 COR70 SGCV28 COR18 COR58 LEX68 UM11 COR007 COR71 LEX74 LEX74 LEX74 ASB2 COR70 COR70 SGCV28 SGCV28 SGCV28 COR18 COR18 COR18 COR18 COR58 COR58 COR58 COR58 COR58 LEX68 LEX68 LEX68 LEX68 LEX68 LEX68 UM11 UM11 UM11 UM11 UM11 UM11 UM11 COR007 COR007 COR007 COR007 COR007 COR007 COR007 COR007 COR71 COR71 COR71 COR71 COR71 COR71 COR71 COR71 COR71 LEX74 LEX74 LEX74 LEX74 LEX74 LEX74 LEX74 LEX74 LEX74 LEX74 1.00000 0.54136 0.30704 1.00000 1.00000 0.78736 1.00000 0.04736 0.28816 1.00000 0.05726 0.73117 0.92550 1.00000 0.45283 0.42648 0.18124 0.49024 0.64211 0.49135 0.83729 0.71654 0.29763 0.08677 0.53530 0.96051 0.93292 0.46681 0.97747 1.00000 0.32391 1.00000 0.67114 0.77407 0.76317 0.15182 0.48980 0.29921 0.90565 1.00000 0.59121 0.00983 0.15625 0.93249 0.23850 0.91168 0.88234 0.84562 0.26997 0.09533 0.05223 0.18917 0.22038 0.04983 0.27091 0.74333 0.28664 0.73379 0.00000 0.02913 0.03911 0.00000 0.00000 0.03359 0.00000 0.01571 0.04021 0.00000 0.01620 0.04103 0.01878 0.00000 0.04326 0.04809 0.03521 0.04584 0.04298 0.03639 0.03255 0.03040 0.03758 0.02175 0.04493 0.01100 0.02133 0.03623 0.01040 0.00000 0.03288 0.00000 0.03641 0.03542 0.03151 0.03001 0.04500 0.03519 0.02009 0.00000 0.04256 0.00699 0.03299 0.02269 0.04136 0.02523 0.02575 0.03248 0.04300 0.02243 0.02145 0.03228 0.03489 0.01780 0.03980 0.03674 0.04120 0.04038 P-value for each locus pair across all populations (Fisher's method) -------------------------------------------------------- 50 Appendix III Locus pair Chi2 df ------------------------- ---ASB23 & ASB2 6.964 ASB23 & COR70 5.878 ASB2 & COR70 3.785 ASB23 & SGCV28 5.768 ASB2 & SGCV28 7.772 COR70 & SGCV28 6.300 ASB23 & COR18 0.000 ASB2 & COR18 8.961 COR70 & COR18 2.324 SGCV28 & COR18 2.472 ASB23 & COR58 4.181 ASB2 & COR58 8.233 COR70 & COR58 5.749 SGCV28 & COR58 6.983 COR18 & COR58 6.662 ASB23 & LEX68 5.241 ASB2 & LEX68 3.123 COR70 & LEX68 1.861 SGCV28 & LEX68 4.148 COR18 & LEX68 6.738 COR58 & LEX68 9.111 ASB23 & UM11 1.250 ASB2 & UM11 2.616 COR70 & UM11 0.139 SGCV28 & UM11 3.403 COR18 & UM11 0.046 COR58 & UM11 0.000 LEX68 & UM11 2.896 ASB23 & COR007 9.263 ASB2 & COR007 3.073 COR70 & COR007 6.280 SGCV28 & COR007 1.495 COR18 & COR007 7.448 COR58 & COR007 4.886 LEX68 & COR007 8.492 UM11 & COR007 2.423 ASB23 & COR71 10.293 ASB2 & COR71 8.108 COR70 & COR71 19.435 SGCV28 & COR71 8.281 COR18 & COR71 1.635 COR58 & COR71 10.103 LEX68 & COR71 3.425 UM11 & COR71 1.470 COR007 & COR71 7.427 ASB23 & LEX74 2.619 ASB2 & LEX74 4.701 COR70 & LEX74 5.904 SGCV28 & LEX74 3.330 COR18 & LEX74 6.932 COR58 & LEX74 5.998 LEX68 & LEX74 6.929 UM11 & LEX74 0.593 COR007 & LEX74 3.726 COR71 & LEX74 7.945 P-value ---------6 0.324 6 0.437 6 0.706 4 0.217 6 0.255 6 0.390 6 1.000 6 0.176 6 0.888 6 0.872 4 0.382 6 0.222 6 0.452 6 0.322 6 0.353 6 0.513 6 0.793 6 0.932 6 0.657 6 0.346 6 0.167 6 0.974 6 0.855 6 1.000 6 0.757 6 1.000 6 1.000 6 0.822 6 0.159 6 0.800 6 0.393 6 0.960 6 0.281 6 0.559 6 0.204 6 0.877 6 0.113 6 0.230 6 0.003 6 0.218 6 0.950 6 0.120 6 0.754 6 0.961 6 0.283 6 0.855 6 0.583 6 0.434 6 0.766 6 0.327 6 0.423 6 0.327 6 0.997 6 0.714 6 0.242 Normal ending. 51 Appendix V APPENDIX IV Table A4: Results for the Pairwise FST for population pairs implemented in Genepop web version of 3.4. (FST is estimated as in Weir and Cockerham 1984) Number of samples detected: 3 Number of loci detected: 11 Code for pop names: ---- ------------1 GB 2 GA 3 GS ---------------------Estimates for each locus: ------------------------ASB23: -------------------pop 1 2 2 0.0231 3 0.0528 0.0048 LEX68: -------------------pop 1 2 2 0.0170 3 0.0123 0.0149 ASB2: -------------------pop 1 2 2 0.0441 3 0.0000 0.0190 UM11: -------------------pop 1 2 2 0.0006 3 -0.0154 0.0201 COR70: -------------------pop 1 2 2 0.0057 3 0.0434 0.0456 COR007: -------------------pop 1 2 2 -0.0265 3 -0.0035 -0.0068 SGCV28: -------------------pop 1 2 2 0.0070 3 0.0183 -0.0052 COR71: -------------------pop 1 2 2 0.0116 3 0.0160 -0.0085 COR18: -------------------pop 1 2 2 -0.0160 3 0.0138 -0.0010 LEX74: -------------------pop 1 2 2 -0.0009 3 0.0524 -0.0038 COR58: -------------------pop 1 2 2 0.0203 3 0.0068 -0.0065 Estimates for all loci: ------------------------pop 1 2 2 0.0088 3 0.0191 0.0068 Normal ending 52 Appendix V APPENDIX V Table V: Results from the AMOVA analysis with different group combinations in Arlequin 3.1. No. of Groups = 1 StructureName = "New Edited Structure" NbGroups = 1 IndividualLevel = 0 DistMatLabel = "" Group={"GA""GB""GS")- one group= all populations Distance method: No. of different alleles (FST) Reference: Weir, B.S. and Cockerham, C.C. 1984. Excoffier, L., Smouse, P., and Quattro, J. 1992. Weir, B. S., 1996. ---------------------------------------------------------------------Source of Sum of Variance Percentage variation d.f. squares components of variation ---------------------------------------------------------------------Among populations 2 14.419 0.04999 Va 1.14 Among individuals within populations 77 369.131 0.47508 Vb 10.87 Within individuals 80 307.500 3.84375 Vc 87.98 ---------------------------------------------------------------------Total 159 691.050 4.36882 ---------------------------------------------------------------------- No. of Groups = 2 StructureName = "New Edited Structure" NbGroups = 2 IndividualLevel = 0 DistMatLabel = "" Group={"GA""GS") Group="GB" ---------------------------------------------------------------------Source of Sum of Variance Percentage variation d.f. squares components of variation ---------------------------------------------------------------------Among groups 1 8.659 0.05161 Va 1.18 Among populations within groups 1 5.760 0.01904 Vb 0.43 Among individuals within 53 Appendix V populations 77 369.131 0.47508 Vc 10.82 Within individuals 80 307.500 3.84375 Vd 87.57 ---------------------------------------------------------------------Total 159 691.050 4.38948 ---------------------------------------------------------------------No. of Groups = 2 StructureName = "New Edited Structure" NbGroups = 2 IndividualLevel = 0 DistMatLabel = "" Group="GA" Group={"GB""GS" } ---------------------------------------------------------------------Source of Sum of Variance Percentage variation d.f. squares components of variation ---------------------------------------------------------------------Among groups 1 5.037 -0.06420 Va -1.48 Among populations within groups 1 9.383 0.08706 Vb 2.01 Among individuals within populations 77 369.131 0.47508 Vc 10.94 Within individuals 80 307.500 3.84375 Vd 88.53 ---------------------------------------------------------------------Total 159 691.050 4.34168 ---------------------------------------------------------------------No. of Groups = 2 StructureName = "New Edited Structure" NbGroups = 2 IndividualLevel = 0 DistMatLabel = "" Group={"GS"} Group={"GB""GA"} ---------------------------------------------------------------------Source of Sum of Variance Percentage variation d.f. squares components of variation ---------------------------------------------------------------------Among groups 1 8.462 0.02250 Va 0.51 Among populations within groups 1 Among individuals within populations 77 5.957 369.131 0.03147 Vb 0.47508 Vc 0.72 10.86 54 Appendix V Within individuals 80 307.500 3.84375 Vd 87.90 ---------------------------------------------------------------------Total 159 691.050 4.37280 ---------------------------------------------------------------------- 55 Appendix VI APPENDIX VI Table A6: Results of HE and HO per locus and per population calculated with GDA version 1.1. Data matrix has 3 populations, 11 loci, and 80 individuals Descriptive statistics for locus: ASB23 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 14 14 2 GA 18 1 14 14 3 GS 43 1 15 15 ---------- ---------- ---------- ---------- ---------Mean 26.666667 1.000000 14.333333 14.333333 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.921764 0.894737 0.030111 2 GA 0.920635 1.000000 -0.088968 3 GS 0.899590 0.906977 -0.008310 ---------- ---------- ---------- ---------Mean 0.913996 0.933905 -0.022195 Descriptive statistics for locus: ASB2 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 18 1 9 9 2 GA 17 1 7 7 3 GS 42 1 8 8 ---------- ---------- ---------- ---------- ---------Mean 25.666667 1.000000 8.000000 8.000000 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.831746 0.777778 0.066667 2 GA 0.795009 0.882353 -0.113689 3 GS 0.775674 0.571429 0.265672 ---------- ---------- ---------- ---------Mean 0.800810 0.743853 0.073945 Descriptive statistics for locus: COR70 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 11 11 2 GA 15 1 12 12 3 GS 43 1 11 11 ---------- ---------- ---------- ---------- ---------Mean 25.666667 1.000000 11.333333 11.333333 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.883357 0.842105 0.047934 2 GA 0.912644 0.866667 0.052083 3 GS 0.837483 0.790698 0.056492 ---------- ---------- ---------- ---------- 56 Appendix VI Mean 0.877828 0.833157 0.052136 Descriptive statistics for locus: SGCV28 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 18 1 9 9 2 GA 16 1 8 8 3 GS 43 1 8 8 ---------- ---------- ---------- ---------- ---------Mean 25.666667 1.000000 8.333333 8.333333 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.880952 0.722222 0.184502 2 GA 0.846774 0.687500 0.193154 3 GS 0.818605 0.744186 0.091892 ---------- ---------- ---------- ---------Mean 0.848777 0.717969 0.156905 Descriptive statistics for locus: COR18 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 7 7 2 GA 18 1 9 9 3 GS 43 1 12 12 ---------- ---------- ---------- ---------- ---------Mean 26.666667 1.000000 9.333333 9.333333 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.729730 0.578947 0.211155 2 GA 0.776190 0.388889 0.506224 3 GS 0.793981 0.697674 0.122563 ---------- ---------- ---------- ---------Mean 0.766634 0.555170 0.279157 Descriptive statistics for locus: COR58 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 11 11 2 GA 16 1 7 7 3 GS 42 1 11 11 ---------- ---------- ---------- ---------- ---------Mean 25.666667 1.000000 9.666667 9.666667 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.899004 0.894737 0.004878 2 GA 0.826613 0.500000 0.402985 3 GS 0.868904 0.714286 0.179727 ---------- ---------- ---------- ---------Mean 0.864840 0.703008 0.190324 Descriptive statistics for locus: LEX68 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 7 7 2 GA 18 1 6 6 57 Appendix VI 3 GS 42 1 8 8 ---------- ---------- ---------- ---------- ---------Mean 26.333333 1.000000 7.000000 7.000000 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.833570 0.894737 -0.075571 2 GA 0.814286 0.833333 -0.024096 3 GS 0.766208 0.738095 0.037121 ---------- ---------- ---------- ---------Mean 0.804688 0.822055 -0.021822 Descriptive statistics for locus: UM11 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 9 9 2 GA 18 1 6 6 3 GS 41 1 6 6 ---------- ---------- ---------- ---------- ---------Mean 26.000000 1.000000 7.000000 7.000000 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.823613 0.684211 0.173145 2 GA 0.750794 0.666667 0.114967 3 GS 0.785306 0.731707 0.069046 ---------- ---------- ---------- ---------Mean 0.786571 0.694195 0.119562 Descriptive statistics for locus: COR007 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 8 8 2 GA 18 1 5 5 3 GS 36 1 11 11 ---------- ---------- ---------- ---------- ---------Mean 24.333333 1.000000 8.000000 8.000000 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.742532 0.684211 0.080550 2 GA 0.717460 0.500000 0.309255 3 GS 0.756260 0.638889 0.157068 ---------- ---------- ---------- ---------Mean 0.738751 0.607700 0.180638 Descriptive statistics for locus: COR71 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 11 11 2 GA 17 1 10 10 3 GS 43 1 14 14 ---------- ---------- ---------- ---------- ---------Mean 26.333333 1.000000 11.666667 11.666667 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.871977 0.789474 0.096990 2 GA 0.887701 0.764706 0.142268 58 Appendix VI 3 GS 0.872230 0.674419 0.228870 ---------- ---------- ---------- ---------Mean 0.877303 0.742866 0.156734 Descriptive statistics for locus: LEX74 Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 19 1 9 9 2 GA 18 1 7 7 3 GS 43 1 10 10 ---------- ---------- ---------- ---------- ---------Mean 26.666667 1.000000 8.666667 8.666667 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.859175 0.684211 0.208122 2 GA 0.826984 0.611111 0.266667 3 GS 0.798085 0.511628 0.361658 ---------- ---------- ---------- ---------Mean 0.828081 0.602317 0.277737 Descriptive statistics (by population): Population n P A Ap ---------- ---------- ---------- ---------- ---------1 GB 18.818182 1.000000 9.545455 9.545455 2 GA 17.181818 1.000000 8.272727 8.272727 3 GS 41.909091 1.000000 10.363636 10.363636 ---------- ---------- ---------- ---------- ---------Mean 25.969697 1.000000 9.393939 9.393939 Population He Ho f ---------- ---------- ---------- ---------1 GB 0.843402 0.767943 0.091741 2 GA 0.825008 0.700111 0.155374 3 GS 0.815666 0.701817 0.141043 ---------- ---------- ---------- ---------Mean 0.828025 0.723290 0.129150 Descriptive statistics (by locus): Locus n P A Ap ---------- ---------- ---------- ---------- ---------ASB23 80.000000 1.000000 20.000000 20.000000 ASB2 77.000000 1.000000 11.000000 11.000000 COR70 77.000000 1.000000 16.000000 16.000000 SGCV28 77.000000 1.000000 10.000000 10.000000 COR18 80.000000 1.000000 16.000000 16.000000 COR58 77.000000 1.000000 14.000000 14.000000 LEX68 79.000000 1.000000 10.000000 10.000000 UM11 78.000000 1.000000 10.000000 10.000000 COR007 73.000000 1.000000 13.000000 13.000000 COR71 79.000000 1.000000 15.000000 15.000000 LEX74 80.000000 1.000000 11.000000 11.000000 ---------- ---------- ---------- ---------- ---------All 77.909091 1.000000 13.272727 13.272727 Locus He Ho f ---------- ---------- ---------- ---------ASB23 0.925708 0.925000 0.000769 ASB2 0.801884 0.688312 0.142431 59 Appendix VI COR70 0.883966 0.818182 0.074872 SGCV28 0.844071 0.727273 0.139159 COR18 0.778459 0.600000 0.230363 COR58 0.871912 0.714286 0.181756 LEX68 0.800048 0.797468 0.003245 UM11 0.788586 0.705128 0.106446 COR007 0.740671 0.616438 0.168698 COR71 0.880593 0.721519 0.181592 LEX74 0.832940 0.575000 0.311025 ---------- ---------- ---------- ---------All 0.831713 0.717146 0.138519 60 Appendix VII APPENDIX VII Table 7: Results of the Hardy-Weinberg test per locus aand population calculated in Genepop web version of 3.4. Number of populations detected: 3 Number of loci detected: 11 Estimation of exact P-values by the Markov chain method --------------------------------------------Markov chain parameters for all tests Dememorization : 5000 Batches : 500 Iterations per batch : 5000 Results by locus ==================================================== Locus: ASB23 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.7108 0.0066 +0.030 +0.054 GA 0.9856 0.0013 -0.089 -0.050 GS 0.8437 0.0045 -0.008 -0.012 All (Fisher's method) : chi2 : 1.051538 Df : 6 Prob: 0.983583 Locus: ASB2 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.0900 0.0028 +0.067 +0.044 GA 0.4963 0.0032 -0.114 -0.057 GS 0.0316 0.0013 +0.266 +0.215 All (Fisher's method) : chi2 : 13.124575 Df : 6 Prob: 0.041100 Locus: COR70 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.1560 0.0042 +0.048 +0.095 GA 0.5197 0.0069 +0.052 +0.067 GS 0.0807 0.0031 +0.056 +0.089 All (Fisher's method) : 61 Appendix VII chi2 : 10.058878 Df : 6 Prob: 0.122194 Locus: SGCV28 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.0126 0.0008 +0.185 +0.260 GA 0.1438 0.0026 +0.193 +0.309 GS 0.0512 0.0013 +0.092 +0.219 All (Fisher's method) : chi2 : 18.564795 Df : 6 Prob: 0.004965 Locus: COR18 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.0172 0.0008 +0.211 +0.141 GA 0.0000 0.0000 +0.506 +0.566 GS 0.0046 0.0008 +0.123 +0.067 All (Fisher's method) : chi2 : Infinity Df : 6 Prob: High. sign. Locus: COR58 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.3398 0.0053 +0.005 +0.016 GA 0.0177 0.0008 +0.403 +0.382 GS 0.0653 0.0022 +0.180 +0.184 All (Fisher's method) : chi2 : 15.679942 Df : 6 Prob: 0.015579 Locus: LEX68 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.0067 0.0004 -0.076 -0.001 GA 0.2185 0.0018 -0.024 +0.140 GS 0.1085 0.0025 +0.037 +0.271 All (Fisher's method) : chi2 : 17.480860 Df : 6 62 Appendix VII Prob: 0.007669 Locus: UM11 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.3105 0.0043 +0.173 +0.066 GA 0.0747 0.0013 +0.115 +0.244 GS 0.5773 0.0021 +0.069 +0.014 All (Fisher's method) : chi2 : 8.625926 Df : 6 Prob: 0.195734 Locus: COR007 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.0936 0.0026 +0.081 -0.000 GA 0.0447 0.0007 +0.309 +0.293 GS 0.0009 0.0003 +0.157 +0.311 All (Fisher's method) : chi2 : 24.958443 Df : 6 Prob: 0.000348 Locus: COR71 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.7207 0.0050 +0.097 +0.031 GA 0.0100 0.0008 +0.142 +0.085 GS 0.0059 0.0008 +0.229 +0.121 All (Fisher's method) : chi2 : 20.122300 Df : 6 Prob: 0.002634 Locus: LEX74 ------------------------------------------------------------------Fis: -----------------------POP P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----GB 0.2158 0.0036 +0.208 +0.128 GA 0.1119 0.0018 +0.267 +0.213 GS 0.0000 0.0000 +0.362 +0.208 All (Fisher's method) : chi2 : Infinity Df : 6 Prob: High. sign. 63 Appendix VII Results by population ==================================================== Pop: GB -----------------------------------------------------------Fis: ------------------LOCUS P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----ASB23 0.7108 0.0066 +0.030 +0.054 ASB2 0.0900 0.0028 +0.067 +0.044 COR70 0.1560 0.0042 +0.048 +0.095 SGCV28 0.0126 0.0008 +0.185 +0.260 COR18 0.0172 0.0008 +0.211 +0.141 COR58 0.3398 0.0053 +0.005 +0.016 LEX68 0.0067 0.0004 -0.076 -0.001 UM11 0.3105 0.0043 +0.173 +0.066 COR007 0.0936 0.0026 +0.081 -0.000 COR71 0.7207 0.0050 +0.097 +0.031 LEX74 0.2158 0.0036 +0.208 +0.128 All (Fisher's method) : chi2 : 49.0403 Df : 22 Prob: 0.0008 Pop: GA -----------------------------------------------------------Fis: ------------------LOCUS P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----ASB23 0.9856 0.0013 -0.089 -0.050 ASB2 0.4963 0.0032 -0.114 -0.057 COR70 0.5197 0.0069 +0.052 +0.067 SGCV28 0.1438 0.0026 +0.193 +0.309 COR18 0.0000 0.0000 +0.506 +0.566 COR58 0.0177 0.0008 +0.403 +0.382 LEX68 0.2185 0.0018 -0.024 +0.140 UM11 0.0747 0.0013 +0.115 +0.244 COR007 0.0447 0.0007 +0.309 +0.293 COR71 0.0100 0.0008 +0.142 +0.085 LEX74 0.1119 0.0018 +0.267 +0.213 All (Fisher's method) : chi2 : Infinity Df : 22 Prob: High. sign. Pop: GS -----------------------------------------------------------Fis: ------------------LOCUS P-val S.E W&C R&H Matr ------------------------ ------ ------ ------ ------ -----ASB23 0.8437 0.0045 -0.008 -0.012 ASB2 0.0316 0.0013 +0.266 +0.215 COR70 0.0807 0.0031 +0.056 +0.089 SGCV28 0.0512 0.0013 +0.092 +0.219 COR18 0.0046 0.0008 +0.123 +0.067 COR58 0.0653 0.0022 +0.180 +0.184 - 64 Appendix VII LEX68 UM11 COR007 COR71 LEX74 0.1085 0.0025 +0.037 +0.271 0.5773 0.0021 +0.069 +0.014 0.0009 0.0003 +0.157 +0.311 0.0059 0.0008 +0.229 +0.121 0.0000 0.0000 +0.362 +0.208 - All (Fisher's method) : chi2 : Infinity Df : 22 Prob: High. sign. 65 Acknowledgements 6 Acknowledgments This work was conducted at the Technische Universität München, funded by the working group ´Molecular Ecology and Conservation Genetics`. I would like to thank Petra Kaczensky from the ITG (International takhi group) for providing samples from the Mongolian khulan. I would also like to thank Ralph Kühn and Bernhard Gum for supervision. Many thanks to my friends Eike Müller, Kristina Salzer and Helmut Bayerl for inspiring discussions and for their support. I highly appreciated their friendly company during field trips in the Alps, thus we compensated the everyday life in the lab. Furthermore, I thank my mother for her encouragement and her financial support. 66 Eidesstattliche Erklärung Eidesstattliche Erklärung Ich erkläre hiermit wahrheitsgemäß, dass ich - die eingereichte Arbeit selbständig und ohne unerlaubte Hilfsmittel angefertigt habe, - nur die im Literaturverzeichnis aufgeführten Hilfsmittel benutzt und fremdes Gedankengut als solches kenntlich gemacht habe, - alle Personen und Institutionen, die mich bei der Vorbereitung und Anfertigung der Abhandlung unterstützt haben, genannt habe und - die Arbeit noch keiner anderen Stelle zur Prüfung vorgelegt habe Ort, Datum Unterschrift (Vor- und Zuname) 67