SUPPORTING INFORMATIONS1 Title: Genetic landscape with sharp discontinuities shaped by complex demographic history in moose (Alcesalces) Authors: Lovisa Wennerström, Nils Ryman, Jean-Luc Tison, Anna Hasslow, Love Dalén, and Linda Laikre Affiliation of first and senior authors (LW, NR, LL): Division of Population Genetics, Department of Zoology, Stockholm University, SE-106 91 Stockholm, Sweden E-mail adresses: lovisa.wennerstrom@zoologi.su.se, nils.ryman@popgen.su.se, linda.laikre@popgen.su.se Journal: Journal of Mammalogy Samples and allozyme data incl. statistical power and summary statistics We used material from a frozen tissue bank collection kept at the Division of Population Genetics, Department of Zoology, Stockholm University, that includes material from over 20,000 moose sampled all over Sweden during the late 1970s to early 1980s hunting seasons. From this tissue bank, and from genotypic databases linked to it, a dataset was chosen, and for subsamples new genetic data were generated as follows. First, we combined collections from different provinces representing samples from a single year (1980), for which geographic coordinates and genotypic data at 3 polymorphic allozyme loci were available for each individual (Pmi, Mdh-2, and Pgi-1, each segregating for 2 alleles; Ryman et al. 1980). This dataset comprised 20,358 moose that had not been analyzed jointly before, although parts of the data have been reported previously (Ryman et al. 1977, 1980; Reuterwall 1980, 1981; Chesseret al. 1982; Charlier et al. 2008). The samples cover most parts of rural Sweden (except for the most northern mountainous regions; Fig. S1). The very large number of individuals results in good statistical power for detecting allele frequency heterogeneity despite the analyses being limited to only 3 allozyme loci. We divided Sweden into "squares" of 50x50 km, and moose of each square were regarded as a "subpopulation" resulting in over 100 squares comprising on average 160 moose per square. We used POWSIM (Ryman and Palm 2006; Ryman et al. 2006) to simulate probabilities of obtaining statistical significance (chi-square P<0.05) when sampling 100 moose from each of a series of such squares with different true degrees of divergence (FST) at 3 bi-allelic loci with similar allele frequencies as in our allozymes. Each simulation was 1,000 runs. Sampling 100 moose from each of 25 squares provides >96 percent probability of detecting a true FST of 0.005. With 100 squares sampled this probability is almost unity, and the probability of detecting an FST=0.001 is ~70 percent. 1 Table S1.1.Summary statistics for the 3 di-allelic allozyme loci for the full dataset used in the present study. The measures were obtained after grouping the material – 20,358 individual moose samples (cf. Fig. S1) - into 50x50 km squares and using squares with n≥50 moose, resulting in a total of 19,283 moose. The allele frequency range refers to the variant (less common) allele at each locus.HT is expected heterozygosityfor the total population (all 19,238 individuals included in the analysis). We used FSTAT v. 2 (Goudet 2001) and CHIFISH (Ryman 2006) for estimation of F-statistics and significance testing. *P<0.05; ***P<0.001 Locus Allele frequency range HT FST FIS FIT Mdh-2 0.000 - 0.017 0.002 0.005*** -0.005 -0.001 Pgi-1 0.000 - 0.102 0.029 0.031*** -0.002 0.029*** Pmi 0.014 - 0.305 0.252 0.045*** 0.015* 0.059*** 0.094 0.044*** 0.013* 0.057*** Total Fig. S1. Distribution of 20,358 moose collected during the 1980 hunt and genotyped at 3 allozyme loci; each mark represents an individual moose. Areas void of samples include regions where the moose is rare or non-existing, including the northern mountainous regions, the city of Stockholm, large lakes (e.g. Lakes Vänern, and Vättern) the Island of Gotland, and the farmland of southernmost Sweden. 2 Table S1.2. Number of moose analyzed for 3 allozyme (AZ) loci (20,358 individuals) and 12 microsatellite (MS) loci (1207 individuals). All individuals genotyped for allozymes have data for all 3 allozyme loci. Among the microsatellites, missing data is present. All individuals have a unique geographic coordinate. County codes and names as of 1980, map over geographic location of counties to the right. After 1980 the counties O, P, and R have been merged into a single county. Age County Code County Name AB AC BD C D E F G H K LM N O P R S T U W X Y Z Stockholm Västerbotten Norrbotten Uppsala Södermanland Östergötland Jönköping Kronoberg Kalmar Blekinge Skåne Halland Gbg och Bohus Älvsborg Skaraborg Värmland Örebro Västmanland Dalarna Gävleborg Västernorrland Jämtland Total Number of individuals AZ MS Adults AZ MS Sex Calves AZ MS Unkn AZ MS AZ F MS AZ MS Unkn AZ MS 14 56 23 17 19 19 24 38 17 8 8 11 9 21 17 46 15 10 18 21 18 19 128 837 752 314 231 421 569 459 216 90 147 183 192 655 312 1409 578 387 935 1387 846 1051 15 108 39 24 12 35 27 52 14 10 20 10 24 42 32 46 33 18 31 47 39 38 11 57 42 19 10 16 31 46 19 5 11 12 18 34 17 84 31 18 72 78 40 75 1 4 2 1 1 448 12099 716 746 43 210 1338 1094 544 429 758 1011 843 387 156 214 330 318 1100 506 2525 943 670 1560 2385 1322 1715 30 168 64 42 32 54 52 94 32 18 29 21 35 66 50 97 48 30 51 74 58 62 138 950 848 348 251 475 668 494 234 92 121 210 207 767 322 1764 629 442 1116 1484 982 1198 20 123 46 30 20 35 33 63 17 11 15 13 23 44 31 59 42 17 39 42 46 36 61 289 158 170 153 255 315 296 138 57 85 106 92 273 158 646 269 184 360 810 262 395 8 34 10 9 11 17 17 24 15 7 12 6 10 18 17 34 5 12 9 31 6 22 11 99 88 26 25 28 28 53 15 7 8 14 19 60 26 115 45 44 84 91 78 122 2 11 8 3 1 2 2 7 2 2 2 4 2 4 1 1 3 1 6 4 71 444 300 211 188 321 411 338 152 61 56 135 108 411 177 1032 334 265 553 920 436 589 20358 1207 13740 805 5532 334 1086 68 7513 3 M 1 4 1 1 2 3 1 5 2 2 6 1 5 Table S1.3.Expected heterozygosity (He) and summary F statistics for 3 allozyme and 12 microsatellite loci for 1,207 Swedish moose from the two major subpopulations detected in this study - the northern and the southern one, including the transition zone. We identified the approximate border between these 2 main clusters by drawing an arbitrary line in the center of the transition zone (Supporting information 5), and refer individuals north of this line to the northern subpopulation and individuals south of it to the southern one.Map of the geographic location of samples and subpopulations to the right. *P < 0.05, ** P< 0.01, *** P< 0.001. No. of alleles No. of individuals Freq. of most common allele Allelic richness He Mdh-2 2 1201 0.999 1.255 0.001 Pgi-1 2 1206 0.979 2.000 Pmi 2 1203 0.862 2.000 All allozymes 6 1207 0.947 BM757 11 1076 BM848 6 CSSM39 Locus RST FST FIT FIS - 0.001 0.001 0.040 - 0.042 *** 0.071 0.239 - 0.016 *** 0.115 ** 0.101 ** 1.752 0.093 - 0.018 *** 0.110 * 0.094 * 0.272 9.569 0.815 0.002 0.064 *** 0.095 *** 0.034 1055 0.380 5.496 0.721 0.027 0.226 *** 0.285 *** 0.077 5 1071 0.589 5.272 0.504 0.009 0.129 *** 0.148 * 0.023 CSSM003 6 1178 0.661 4.211 0.551 0.004 0.027 *** 0.090 * 0.064 * IDGA8 8 1089 0.285 7.995 0.810 0.048 0.089 *** 0.161 *** 0.079 *** IDGVA29 11 1028 0.428 9.406 0.746 0.211 0.010 *** 0.134 * 0.039 * MAF46 11 1200 0.484 8.453 0.700 0.034 0.046 *** 0.090 *** 0.046 * McM130 4 1186 0.624 3.258 0.539 0.001 0.016 *** 0.090 *** 0.075 ** McM58 11 1195 0.318 9.385 0.767 0.035 0.209 *** 0.226 *** 0.022 * McM64 7 1073 0.362 5.774 0.740 0.066 0.056 *** 0.069 * 0.014 OarCP21 2 1074 0.688 2.000 0.430 0.004 0.004 * 0.043 Allozymes - -0.001 - 0.030 Microsatellites *** 0.039 RT30 9 1202 0.397 7.699 0.720 0.024 0.104 *** 0.160 *** 0.062 * All microsatellites 91 1207 0.457 6.543 0.670 0.070 0.096 *** 0.139 *** 0.048 *** All loci 97 1207 0.555 5.585 0.555 - 0.093 *** 0.138 *** 0.050 *** 4 Literature cited (not provided in the paper) RYMAN, N. and S. PALM. 2006. POWSIM: a computer program for assessing statistical power when testing for genetic differentiation. Molecular Ecology Notes 3:600-602. RYMAN, N., S. PALM, C. ANDRE, G: R. CARVALHO, T. G. DAHLGREN, P. E. JORDE, L. LAIKREET AL. 2006. Power for detecting genetic divergence: differences between statistical methods and marker loci. Molecular Ecology 15:2031-2045. 5