1
1
2
3
4
Supplementary Table 1. Chi-square (2) goodness-of-fit tests for the deviation from Hardy
Weinberg Equilibrium (HWE) from 16 microsatellite loci, across 328 samples, for three
management units of bighorn sheep (Ovis canadensis), and three individual herds. Significance
(asterisk) was adjusted for multiple tests by Dunn- Šidák correction (N=48; p< 0.001).
Locus
BM203
BMC1009
CELP15
MAF33
MAF36
MAF48
MAF65
MAF209
OarAE16
OarAE129
OarFCB11
OarFCB19
OarFCB30
OarHH47
OarHH62
TGLA387
5
All
N=328
CBS
N=68
434.756* 99.420*
109.141*
8.798
606.252* 68.094*
77.952*
4.214
331.642* 72.091*
453.126*
2.971
442.628* 110.058*
203.362*
4.022
378.491* 59.347*
949.222*
0.527
72.637* 18.233
210.127* 10.415
76.343*
8.424
638.624* 123.659*
466.184* 18.013
193.101* 12.471
DBS
N=219
RMBS
N=41
322.407*
64.792*
0.001
2.025
275.784*
148.049*
80.239*
67.180*
266.530*
532.864*
28.363*
51.288*
11.503
414.288*
364.018*
205.160*
45.029*
1.506
25.662*
5.650
51.021*
24.021
16.597
5.609
8.768
4.852
11.601
7.865
0.207
5.518
14.404
24.966*
Black
Mts.
N=12
8.333
0.023
0.717
1.333
0.001
0.023
n/a
5.392
7.320
0.023
0.245
2.587
1.499
0.099
4.670
2.449
Gabb’s
Valley
N=40
128.471*
6.415
31.289*
4.194
43.727
57.778*
38.950*
33.662
40.571
42.735
12.328
126.408*
9.184
13.890
75.061
53.056*
Ruby
Mts.
N=37
27.256
0.783
n/a
8.762
45.897*
41.677*
33.349
14.318
10.273
87.988*
1.656
6.113
2.871
25.741
15.439
73.397*
2
6
7
8
9
10
11
12
Supplementary Table 2. Management Unit PCA (see Fig 5A) loadings from 600 (200 per unit CBS, DBS, RMBS) pseudo-presence samples of bighorn sheep (Ovis canadensis) within
NBSMAs (see Fig. 1 and Fig 5B). Initially 26 variables were explored, but 13 variables were
removed for strong correlation coefficients (r=|0.70|). The remaining variables were used in PCA
and the first four components passed the Kaiser threshold criterion and together account for
>75% of variation. Below the biplot (Gabriel, 1971) and loadings following Varimax rotation
that were used for interpretation.
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
Variable
bio1
bio2
bio3
bio8
bio9
bio12
bio15
bio18
elev
LC14
LC24
ndvi_sd_ca
slope_degr
Comp.1
0.38449715
0.05182381
-0.03322271
0.22542144
0.20890510
-0.39915258
0.26659423
-0.40686580
-0.42796637
0.11444747
-0.11342299
-0.32984479
-0.20016364
Comp.2
0.12193380
-0.60448566
-0.53576179
-0.13837081
0.28393261
0.20290719
0.38811605
0.12514705
-0.04351716
0.01624897
-0.03414622
0.12815501
0.07556262
Comp.3
0.24729678
-0.13035163
-0.38436326
0.58368888
-0.41067820
-0.14732015
-0.28726426
0.07049304
-0.03957669
-0.18137175
0.26570767
0.07237932
0.21277626
Comp.4
-0.021516417
0.088448877
0.042942506
-0.053789086
0.004594864
-0.009639216
0.144354652
-0.078019959
-0.056831296
0.685883630
0.631123303
0.100616190
0.278981573
Comp.5
0.15621459
0.11408028
0.22529747
-0.15720608
0.29469143
-0.09363809
0.12289414
-0.11158650
-0.12360343
-0.56196760
0.22738387
0.12742867
0.60408399
Eigen Value 5.09977948
SD
2.2582691
Prop. Var. 0.3922907
2.19092858
1.4801786
0.1685330
1.43459346
1.1977452
0.1103533
1.04636733
1.02292098
0.08048979
0.91497630
0.95654394
0.07038279
Loadings:
bio1
bio2
bio3
bio8
bio9
bio12
bio15
bio18
elev
LC14
LC24
ndvi_sd_ca
slope_degr
Comp.1 Comp.2 Comp.3 Comp.4
0.400 0.250
0.130 -0.588 0.173
-0.658
0.362 0.202 0.489
-0.525 -0.108
-0.450
-0.117
0.138 0.170 -0.524
-0.401 0.136 0.112
-0.416
0.111
0.114 -0.174 -0.325 0.606
0.125 0.683
-0.316 0.117
0.156
-0.140 0.125 0.123 0.344
3
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
Supplementary Table 3. Nevada Bighorn Sheep Management Areas (NBHSMA, N=600) and
random background samples (N=600) PCA results for 13 niche variables (see Supplementary
Figure 2). Four components were sufficient to account for >70% of the variation. Loadings were
those Eigenvectors that were most important after Varimax rotation, depicted in the biplot
(below), and used for interpretation.
bio1
bio2
bio3
bio8
bio9
bio12
bio15
bio18
elev
LC14
LC24
ndvi_sd_ca
slope_degr
PC1
-0.44184262
-0.02801914
0.14778156
-0.30415596
-0.19654533
0.31636551
-0.12882823
0.39538181
0.46152278
-0.13177481
0.01912755
0.30463554
0.23337214
Eigen Value 4.10703445
SD
2.0265820
Prop. Var. 0.3159257
PC2
0.132027469
-0.424185785
0.001567514
-0.108434130
0.287663410
0.388295582
0.567595688
-0.114387087
-0.104504400
-0.334920448
0.266160559
0.139152274
0.093830207
PC3
-0.09268205
0.49889469
0.69121131
-0.26610613
0.25037132
0.05992710
0.13618124
-0.26857204
-0.05623956
-0.09534908
-0.11204582
-0.06457505
-0.08942433
PC4
0.049972670
-0.188391258
-0.156106572
-0.294595569
0.519649186
-0.171646467
-0.068762601
0.043373856
0.001480063
0.082139578
-0.647185216
0.081951526
0.335312428
PC5
-0.071809722
0.016406455
-0.026867232
-0.300122144
0.118915457
-0.114084055
0.055778084
-0.185382899
0.004404174
0.698142984
0.499081244
0.101271960
0.303947118
2.32599031
1.5251198
0.1789223
1.59344509
1.2623173
0.1225727
1.11876107
1.05771502
0.08605854
0.88892460
0.94282798
0.06837882
Loadings:
bio1
bio2
bio3
bio8
bio9
bio12
bio15
bio18
elev
LC14
LC24
ndvi_sd_ca
slope_degr
PC1
PC2
-0.435
-0.240
0.219
-0.209 -0.149
-0.309 0.113
0.198 0.494
-0.284 0.524
0.455
0.475
-0.379
0.402
0.268 0.171
0.213
PC3
PC4
-0.184
0.631
0.683
-0.161 -0.412
0.588
-0.179
-0.583
0.116
-0.175 0.326
4
58
59
60
61
Supplementary Table 4. Translocated bighorn sheep (Ovis canadensis) and source herds
(British Columbia and Colorado) PCA eigenvectors for 13 niche variables (see Fig. 6). Two
components were sufficient to account for >80% variation. Below are the biplot and loadings
following Varimax rotation and used for interpretation.
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
Variable
bio1
bio2
bio3
bio8
bio9
bio12
bio15
bio18
elev
LC14
LC24
ndvi_sd_ca
slope_degr
Comp.1
0.3371871
-0.3334024
0.3206005
0.3190291
-0.1302214
-0.3140763
0.2130467
0.1852279
-0.3081962
0.3251032
-0.1387812
0.3242564
-0.2240631
Comp.2
-0.04903508
-0.10683417
-0.16466409
-0.07449225
-0.59437432
0.20217148
0.17301062
0.54270180
-0.01425693
0.03435445
0.46936764
-0.07818380
0.08428769
Comp.3
-0.02261678
-0.10561384
0.20988483
0.26423151
-0.11799994
-0.04216249
-0.72902767
-0.13394187
-0.22107642
-0.25408390
0.42388142
0.06506532
-0.10953754
Comp.4
-0.13934742
0.11557372
-0.15780334
-0.01332354
0.03857559
-0.07964365
0.27929035
-0.14327333
-0.16788623
-0.12028639
0.21065505
-0.25139149
-0.82798323
Comp.5
0.041418657
0.019892601
0.035869101
-0.034535808
0.257452520
-0.286612557
0.372496005
-0.322125487
-0.238136923
0.065447345
0.614293986
-0.006500954
0.407517965
Eigen Value 8.393889133 2.015665682 0.814878555 0.669203091 0.443194740
SD
2.8972209
1.4197414
0.90270624 0.81804834 0.6657287
Prop. Var. 0.6456838
0.1550512
0.06268297 0.05147716 0.0340919
Loadings:
bio1
bio2
bio3
bio8
bio9
bio12
bio15
bio18
elev
LC14
LC24
ndvi_sd_ca
slope_degr
PC1
PC2
PC3
0.293
0.176
-0.310 -0.179
0.409
0.411
-0.619
-0.338 0.135
-0.181
0.755
0.532 0.252
-0.361 -0.106
0.160
0.380
0.510 -0.397
0.328
-0.262
5
81
82
Supplementary Table 5. Multiplex PCR design with fluorescently labeled microsatellite primers used for genotyping bighorn sheep
(N=328) in Nevada with locus-specific number of observed alleles (k) and range, plus observed (Ho) and expected (He) heterozygosity.
Multiplex - Ta
A - 59°
B - 48°
C - 59°
D - 63°
83
84
Locus
Label
k
Range
Ho
He
OarFCB304
VIC
7
128-142
0.588
0.697
OarFCB193
PET
8
105-121
0.652
0.760
OarHH62
6-FAM
15
98-128
0.784
0.854
MAF33
NED
6
117-131
0.609
0.677
BMC1009
PET
7
278-290
0.364
0.430
TGLA387
PET
7
133-145
0.563
0.796
MAF209
NED
7
107-121
0.511
0.686
OarFCB266
6-FAM
N/A
OarCP20
VIC
N/A
MAF36
6-FAM
9
86-102
0.591
0.804
OarHH47
VIC
9
120-140
0.396
0.766
MAF48
PET
9
120-136
0.488
0.641
CELJP15
NED
5
159-169
0.123
0.307
OarAE129
6-FAM
15
163-191
0.543
0.852
MAF65
VIC
11
110-136
0.558
0.758
OarFCB11
NED
5
119-127
0.409
0.616
BM203
6-FAM
16
215-249
0.676
0.886
OarAE16
PET
11
84-104
0.591
0.800
Forward/Reverse 5’-3’
CCCTAGGAGCTTTCAATAAAGAATCGG
CGCTGCTGTCAACTGGGTCAGGG
TTCATCTCAGACTGGGATTCAGAAAGGC
GCTTGGAAATAACCCTCCTGCATCCC
TAATGAGTCAAACACTACTGAGAGAC
AATATATAAAGAGAAAAGCTGGGGTGCC
GATCTTTGTTTCAATCTATTCCAATTTC
GATCATCTGAGTGTGAGTATATACAG
GCACCAGCAGAGAGGACATT
ACCGGCTATTGTCCATCTTG
CAAAGTCTTAGAATAAACTGGATGG
GTCCCTTTGTTTACTTTGATAAAAC
TCATGCACTTAAGTATGTAGGATGCTG
GATCACAAAAAGTTGGATACAACCGTGG
GGCTTTTCCACTACGAAATGTATCCTCAC
GCTTGGAAATAACCCTCCTGCATCCC
GATCCCCTGGAGGAGGAAACGG
GGCATTTCATGGCTTTAGCAGG
CATATACCTGGGAGGAATGCATTACG
TTGCAAAAGTTGGACACAATTGAGC
TTTATTGACAAACTCTCTTCCTAACTCCACC
GTAGTTATTTAAAAAAATATCATACCTCTTAAGG
GGAAACCAAAGCCACTTTTCAGATGC
AGACGTGACTGAGCAACTAAGTACG
GGAAATACCTTATCTTTCATTCTTGACTGTGG
CCTTCTTTCTCATTGCTAACTTATATTAAATATCC
AATCCAGTGTGTGAAAGACTAATCCAG
GTAGATCAAGATATAGAATATTTTTCAACACC
AAAGGCCAGAGTATGCAATTAGGAG
CCACTCCTCCTGAGAATATAACATG
GGCCTGAACTCACAAGTTGATATATCTATCAC
GCAAGCAGGTTCTTTACCACTAGCACC
GGGTGTGACATTTTGTTCCC
CTGCTCGCCACTAGTCCTTC
CTTTTTAATGGCTCGGTAATATTCCTC
CATCAGAGGAATGGGTGAAGACGTGG
Reference
Buchanan & Crawford, 1993
Buchanan & Crawford, 1993
Ede et al., 1994
Buchanan & Crawford, 1992
Bishop et al., 1994
Georges & Massey, 1992
Buchanan & Crawford, 1992
Buchanan & Crawford, 1993
Ede et al., 1995
Swarbrick et al., 1991
Henry et al., 1993
Buchanan et al., 1991
unp. see Marshal et al., 1998
Penty et al., 1993
Buchanan et al., 1992
Buchanan & Crawford, 1993
Bishop et al., 1994
Penty et al., 1993
(Buchanan et al., 1991; Swarbrick et al., 1991; Buchanan & Crawford, 1992; Buchanan et al., 1992; Georges & Massey, 1992; Buchanan &
Crawford, 1993; Henry et al., 1993; Penty et al., 1993; Bishop et al., 1994; Ede et al., 1994; Marshall et al., 1998)
6
Supplementary Figure 1. Comparison of PCoA scatter plots of putative identifications (based
on management units) and STRUCTURE assignments for K=2 and K=3 of bighorn sheep (Ovis
canadensis) in the Great Basin and northern Mojave Deserts. In the lower two panels (K=2,
K=3), squares show individuals that failed the assignment test for potentially admixed (open
grey) and high statistical confidence hybrids (black). Blue=CBS, Green=DBS, Orange=RMBS.
7
Supplementary Figure 2. Test for spatial autocorrelation. PCA of Nevada Bighorn Sheep
Management Areas (NBSMAs; see Fig. 1, N=600, black symbols) versus randomized
background samples (N=600, grey symbols). Two axes account for 50% of the variation. Strong
loadings on PC1 included mean annual temperature (+) and elevation (-), and PC2 were
seasonality of precipitation (+) and mean diurnal temperature range (-).
8
SUPPLEMENTRY METHODS
Sampling
Tissues
Fresh tissues were the primary source of genetic material and consisted of samples
obtained from hunter-harvest return (muscle) and the NDOW disease-monitoring program
(blood). We obtained samples from 55 herds representing the three management units
maintained in Nevada. We use the term “management units” to refer to traditionally recognized
subspecies (i.e., CBS, DBS, and RMBS) that are commonly used by natural-resource agencies.
To augment some poorly sampled locations, we acquired fresh fecal pellets from individuallyobserved bighorn sheep. We extracted genomic DNA using DNeasy® Blood & Tissue Kit and
modified protocols (Wehausen et al., 2004) for QIAamp® Stool Kit (Qiagen Valencia, CA,
USA).
Genetic data
We generated multi-locus genotypes for 18 microsatellite loci previously used in bighorn
sheep using multiplex (QIAGEN MULTIPLEX PCR KIT, Qiagen) polymerase chain reactions
(PCR) with fluorescently labeled primers (Supplementary Table 5). We performed a minimum of
two replicate PCRs per sample and per locus to detect genotyping errors that may result from
degraded DNA (Taberlet et al., 1999). Consensus genotypes were those observed in both runs,
but if we observed an allele only once, we conducted an additional set of PCRs. In all reactions,
we included two negative controls, which did not include template DNA, to test for
contamination of PCR reagents, and six positive control samples (known genotypes) to
standardize analyses. We combined fluorescently labeled amplicons with ABI GENESCANTM 500
LIZ allelic ladder (APPLIED BIOSYSTEMS, Foster City, California) and analyzed them on an ABI
3730 DNA Analyzer at the Nevada Genomics Center (NGC). We scored individual peaks with
GENEMARKER® v.1.85 (SOFTGENETICS LLC; State College, Pennsylvania) software and verified
all genotypes manually. Two loci failed to consistently amplify (OarFCB266 and OarCP20) so
were removed from further analyses resulting in 16 loci for characterizing genetic variation.
To provide an independent sequence-based perspective of differentiation among genetic
units, we sequenced a segment (1181bp) of the mitochondrially encoded NADH dehydrogenase
5 (ND5) gene for 110 samples representing the geographic breadth and taxonomic units of sheep
in the study area. The ND5 gene was amplified by double-stranded PCR using two novel primers
designed for this study: ND5-L2 (5°-AGTAGTTATCCATTCGGTCTTAGG-3°) and ND5-R3A
(5°-GGTCTTTGGAGTAGAATCCGGTGAG-3°). PCRs consisted of an initial denaturation at
94 °C for 5 min, followed by 35 cycles of 30s at 94 °C (denaturation), 30s at 50 °C (annealing),
and 60s at 72 °C (extension) concluding with a final extension of 5 min at 72 °C. PCRs consisted
of 30 L total volume, containing 3.0 L of MgCl2-free buffer, 3.0 L of MgCl2 solution, 3.0 L
of dNTPs (2.0 m), 0.6 L of each primer (10 m), 0.2 L Taq polymerase, 1.0 L of template
(c. 50-100 ng double-stranded DNA), and 18.6 L of sterile water. Negative controls, were used
9
as checks for contamination of PCR reagents. We then purified PCR products using a 20% dilute
aliquot of EXOSAP-IT (Affymetrix, Santa Clara California) and sequenced products in both
directions using PCR primers plus an internal primer designed specifically for this study: ND5F2 (5°-CCAACACAGCAGCCTTACAAGC-3°). Sequencing reactions were conducted with
ABI BIGDYE TERMINATOR CYCLE SEQUENCING KIT 3.1, and we assessed purified products on an
ABI 3730 DNA Analyzer in the NGC. We used GENEIOUS v.6.1.4 (Biomatters, San Francisco,
California) to inspect products and aligned sequences using MUSCLE (Edgar, 2004). To verify
authentic mtDNA, we translated the protein-coding regions of nucleotides into amino acid
sequences, and deposited sequences in GenBank.
Genetic diversity
We assessed the microsatellite dataset quality using the program MICRO-CHECKER (Van
Oosterhout et al., 2004), which can detect genotyping errors, allelic dropout, and null alleles. We
estimated indices of genetic diversity including mean number of alleles per locus (NA) and mean
observed (HO), expected (HE), and unbiased (uHE) heterozygosity using GENALEX v.6.5
(Peakall & Smouse, 2006, 2012). We tested for Hardy-Weinberg equilibrium (HWE) deviation
using the chi-squared (2) goodness-of-fit test within the genetic units (identified by Bayesian
clustering analyses, see below), and at a fine-scale for three well-sampled herds (Black Rock
N=12, Muddy N=45, and Ruby N=36). The nominal level of statistical significance (=0.05)
was adjusted to 0.001068 given the large number of comparisons (N=48) using the Dunn-Šidák
correction factor (1-(1-)1/n) (Dunn, 1961; Šidák, 1967) considered an appropriate assessment for
genetic data (Weir, 1996).
Niche data
To assess niche variation among translocated bighorn sheep, we used a novel approach
considering there are few historical locations (Hall, 1946; Hall, 1981; Shackleton, 1985) with
high quality information suitable for characterizing the scenopoetic niche (Peterson et al., 2011).
Our goals were to 1) characterize ecotypic differences among management units, 2) place those
differences within the context of spatial autocorrelation, and 3) compare bioclimatic conditions
of translocated areas to those of source ranges (British Columbia and Colorado). Consequently,
we characterized the distribution of bighorn sheep in Nevada using a pseudo-presence technique.
Pseudo-presence identifies points randomly from within the range of the focal taxon with
random points generated using Geographical Information Systems (GIS). Datasets from pseudopresence records can be prone to over-prediction because of broad extent-of-occurrence, where
samples may represent both suitable and unsuitable areas, thereby exaggerating the occupied
conditions (Graham & Hijmans, 2006; Hurlbert & Jetz, 2007; Jetz et al., 2008). The concern of
exaggerated distributions may be less of an issue with large-bodied and mobile mammals
(Richmond et al., 2010). Because precise, individual location data are not available from
throughout our study region, our analysis is based on the areas within the state of Nevada
10
allocated to bighorn sheep management (Nevada Bighorn Sheep Management Areas; NBSMAs).
As such, our analysis does not reflect actual usage of these areas, but rather what is potentially
available to translocated animals.
We generated three sets of points using ARCGIS 10 (ESRI, 2011). First, we generated
randomized localities from within the NBSMAs (obtained June 2013 from Western Association
of Fish and Wildlife Agencies Wild Sheep Working Group - WAFWAWSWG; Fig. 1) with 5 km
spacing and 200 samples per management unit (CBS, DBS, RMBS; N=600). We also generated
5000 random points across the study area (Nevada) to assess correlation among bioclimatic
variables and identify spatial autocorrelation (McCormack et al., 2010), and then down-sampled
to 600 points to equate sample variance, and then compared environmental variation between
NBSMAs versus background. Finally, to compare characteristics of the NBSMAs with those
available in source ranges, we generated 200 random points from source management areas in
Colorado and British Columbia (obtained from WAFWAWSWG).
Temperature, precipitation, elevation, and slope are considered important for the
persistence of bighorn sheep (Epps et al., 2004; Epps et al., 2007) and likely important in
Grinnellian niche space (Soberón, 2007) and consequently the scenopoetic niche (Peterson et al.,
2011). Niche variables were extracted for all randomly generated points. Niche-based variables
included 19 bioclimatic and elevation (Hijmans et al., 2005) from the WorldClim database (2.5
minute, http//www.worldclim.org). We used a 10 m digital elevation model (DEM) from ESRI
to characterize the degree slope using spline interpolation. Because species likely respond to a
complex set of environmental (abiotic) conditions and biotic associations (McCormack et al.,
2010; Moritz & Agudo, 2013) we incorporated five biotic vegetation-based variables including
two land cover classifications, percent tree-cover, plus Normalized Difference Vegetation Index
(NDVI) and NDVI standard deviation (www.landcover.org). NDVI and NDVI-SD that we
standardized across 2010-2012, and corresponded to the period of genetic sampling.
Literature Cited
Bishop, M.D., Kappes, S.M., Keele, J.W., Stone, R.T., Sunden, S., Hawkins, G.A., Toldo, S.S., Fries, R.,
Grosz, M.D. & Yoo, J. (1994) A genetic linkage map for cattle. Genetics, 136, 619-639.
Buchanan, F. & Crawford, A. (1992) Ovine dinucleotide repeat polymorphism at the MAF33 locus.
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