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Supplementary Material
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Table S1: Inferred population genetic structure using data sets consisting of juvenile red deer and
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adults of different sexes. Inferred K=1: no sub-structure inferred, K=2: two genetic clusters inferred.
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The individual assignment results are presented in Fig. S7.
Algorithm
STRUCTURE
Settings
Not considering sampling locations
STRUCTURE
Using sampling locations as priors
GENELAND
Admixture model
BAPS
spatial
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1
Data set
Adult males
Adult females
juveniles
Adult males
Adult females
juveniles
Adult males
Adult females
juveniles
Adult males
Adult females
juveniles
Inferred K
1
1
2
2
2
2
2
1
2
1
1
2
2
3
4
5
50
1
no. of genetic clusters
2
3
4
5
no. of genetic clusters
-13500
-12500
-13000
log-likelihood
-12000
(d)
-12500
(c)
log-likelihood
DeltaK
40
30
20
10
0
DeltaK
log-likelihood
150
100
50
1
-38000 -37000 -36000 -35000
(a)
(b)
-36400 -36000 -35600 -35200
log-likelihood
(a)
1
2
3
4
5
1
no. of genetic clusters
2
3
4
5
no. of genetic clusters
8
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Figure S1: Inference of genetic clusters in the study region using the STRUCTURE algorithm for the
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red deer (a & b) and wild boar (c & d) dataset both not using (a & c) and using (b & d) sampling
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location as a prior. STRUCTURE was run using the admixture and correlated allele frequencies
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models.
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Figure S2: Modal assignment of red deer to the two clusters (grey and black dots) inferred using
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programme GENELAND. Admixture values were averaged across the ten best-supported runs. Blue =
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motorway; Dark Green = other roads; Light green= forests; solid black = political borders.
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Fig. S3: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 450 individuals each, using a standard
STRUCTURE analysis without sampling locations as priors. The log-likelihood values (left) inferred the presence of two populations for each subsample (average log-likelihood value for K=2> average log-likelihood value for K=1). Log-likelihood values and bar plots (right) with the same number
correspond to each other. STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S4: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 300 individuals each, using a standard
STRUCTURE analysis without sampling locations as priors. Log-likelihood values (right) and bar plots (right) with the same number correspond to each
other. The STRUCTURE runs where the log-likelihood values inferred the presence of two populations are highlighted with an asterisk (average loglikelihood value for K=2> average log-likelihood value for K=1). STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S5: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 200 individuals each, using a standard
STRUCTURE analysis without sampling locations as priors. Log-likelihood values (right) and bar plots (right) with the same number correspond to each
other. The one STRUCTURE run where the log-likelihood values inferred the presence of two populations are highlighted with an asterisk (average loglikelihood value for K=2> average log-likelihood value for K=1). STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S6: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 100 individuals each, using a standard
STRUCTURE analysis without sampling locations as priors. Log-likelihood values (right) and bar plots (right) with the same number correspond to each
other. The log-likelihood values do not provide statistical support for the presence of population genetics structure (average log-likelihood value for
K=2 < average log-likelihood value for K=1). STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S7: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 450 individuals each, using a STRUCTURE
analysis with sampling locations as priors. The log-likelihood values (left) inferred the presence of two populations for each sub-sample (average loglikelihood value for K=2> average log-likelihood value for K=1). Log-likelihood values and bar plots (right) with the same number correspond to each
other. STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S8: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 300 individuals each, using a STRUCTURE
analysis with sampling locations as priors. The log-likelihood values (left) inferred the presence of two populations for each sub-sample (average loglikelihood value for K=2> average log-likelihood value for K=1). Log-likelihood values and bar plots (right) with the same number correspond to each
other. STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S9: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 200 individuals each, using a STRUCTURE
analysis with sampling locations as priors. The log-likelihood values (left) inferred the presence of two populations for each sub-sample (average loglikelihood value for K=2> average log-likelihood value for K=1). Log-likelihood values and bar plots (right) with the same number correspond to each
other. STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S10: Analysis of the genetic composition of the 10 sub-samples of the red deer dataset consisting of 100 individuals each, using a STRUCTURE
analysis with sampling locations as priors. The STRUCTURE runs where the log-likelihood values (left) inferred the presence of two populations are
highlighted with an asterisk (average log-likelihood value for K=2> average log-likelihood value for K=1). Log-likelihood values and bar plots (right)
with the same number correspond to each other. STRUCTURE was run with the admixture and correlated allele frequency models.
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Fig. S11: Analysis of the genetic composition of sub-samples of the red deer dataset. Bar plots are
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shown for the GENELAND analyses that inferred the presence of two clusters in datasets consisting of
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(a) 200, (b) 300 and (c) 450 individuals.
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Fig. S12: Analysis of the genetic composition of sub-samples of the red deer dataset. Bar plots with
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modal assignments of individuals to populations are shown for the BAPS analyses that – taking
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account of the spatial coordinates – inferred the presence of two clusters in datasets consisting of (a)
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300 and (b) 450 individuals.
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Fig. S13: Analysis of the genetic composition of sub-samples of the red deer dataset consisting of
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juveniles only and of adults of different sexes. Bar plots are shown only for analyses that inferred the
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presence of two clusters in the datasets.(a) STRUCTURE analysis without sampling locations as priors;
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(b)STRUCTURE analyses with sampling locations as priors for (b) adult males, (c) adult females and (d)
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juveniles; Geneland analyses for (e) adult males, (f) juveniles; spatial BAPS analysis for (g) juveniles.
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STRUCTURE was run with the admixture and correlated allele frequency models.
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