Appendix S1 – Extended methods
Laboratory procedures
The PCRs were performed with a GeneAmp PCR System 9700 (Applied Biosystems) in a 10
µL reaction volume containing Qiagen Multiplex PCR Master Mix (1x), 0.2 µM of each
primer, approx. 20 ng genomic DNA and RNase-free water. PCR conditions were as follows:
15 min denaturation at 95C; followed by 30 cycles of 94C for 30 s, 57C for 90 s, and 72C
for 60 s; and 30 min final extension at 60C. The thermal profile for DBY4 differed in
annealing temperature, which was 48C. Amplified products of the ZFX gene in males were
separated on 2% low-melting agarose gel. The appropriate bands were cut from the agarose
gel and then dissolved in 100 µl distilled water. Then 2 µl of the product was used in the next
PCR with the same protocol and primers. All amplified products were purified using shrimp
alkaline phosphatase (SAP) and exonuclease I, E. coli (Fermentas) in the enzymatic reaction
during 15 min at 37C followed by inactivation of enzymes for 15 min at 85C. A cycle
sequencing reaction was performed using BDT v3.1 chemistry according to the
manufacturer’s protocol (Life Technologies) and the forward primers for CR mtDNA, ZFX
gene and Y chromosome-specific fragments. The cyt b gene was sequenced in both directions.
Cycle sequencing reactions included 25 cycles of 95C (20 s), 50C (15 s) and 60C (1 min).
Unincorporated dideoxynucleotides were eliminated using the ExTerminator Kit (A&A
Biotechnology). The sequencing products were run on an ABI 3130 automated capillary
sequencer (Applied Biosystems).
AMOVA and SAMOVA analyses
Genetic diversity analyses AMOVA and SAMOVA were conducted using only the samples
from Poland. Analysis of molecular variance (AMOVA) was used to assess the partitioning of
genetic differences among populations and species-specific CR mtDNA lineages (Excoffier et
al. 1992). Indices Φ, which are molecular equivalents of Wright’s (1965) F-statistics, were
computed using ARLEQUIN. The statistical significance of variance components at different
hierarchical levels (between species-specific mtDNA lineages, between populations within
species-specific mtDNA lineages, and within populations) and Φ indices was evaluated by
randomization. Additionally, we did spatial analysis of molecular variance (SAMOVA;
Dupanloup et al. 2002) to identify genetically distinct populations based on CR mtDNA
haplotype data for comparison to their geographic location. SAMOVA employs a simulated
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annealing procedure and uses haplotype sequence and frequency along with the geographic
location of populations to define population groups (K) that exhibit close genetic
relationships. The analyses were conducted for K = 2–10 with 10 000 simulated annealing
steps, each starting from 500 sets of initial conditions, separately for 377 individuals
possessing mtDNA of C. capreolus (from 21 populations) and 75 individuals possessing
mtDNA of C. pygargus (grouped in 12 populations).
Species distribution modelling
Roe deer species distribution modelling was conducted in order to estimate the probability of
their co-occurrence in Central Europe in the past (simultaneously or consecutively).
Maximum entropy method using MAXENT 3.3.3k software (Phillips et al. 2006; Elith et al.
2011) was used to build the models of distribution at the present conditions, last glacial
maximum (LGM, c. 21 kyr BP) and last inter-glacial (LIG, c. 120–140 kyr BP). Occurrence
data of roe deer were downloaded from IUCN Redlist website (Lovari et al. 2008; Gonzalez
& Tsysulina 2008) and transformed to point data with 30 sec spatial resolution in ArcGIS
software. Before uploading to model, we have excluded a part of Scandinavian Peninsula
from the range of European roe deer because it was inhabited in second half of twentieth
century (Danilkin & Hewison 1996). Ten bioclimatic variables for each studied period were
downloaded from WorldClim website (Hijmans et al. 2005) with 2.5 min spatial resolution:
annual mean temperature, temperature seasonality, maximum temperature of the warmest
month, minimum temperature of the coldest month, temperature annual range, annual
precipitation, precipitation of the wettest and driest months, precipitation of the wettest and
coldest quarters. Variables from LIG were downloaded with 30 sec spatial resolution and
downscaled to 2.5 min in ArcGIS software.
Occurrence data were randomly split into training data (75%) and test data (25%). Ten
replicates for each modelling procedure were performed. Present distribution model was
projected to LIG (Otto-Bliesner et al. 2006) and to LGM under two atmospheric circulation
models constructed in a course of Paleoclimate Modelling Intercomparison Project Phase II
(Braconnot et al. 2007): Community Climate System Model (CCSM; Collins et al. 2006) and
Model for Interdisciplinary Research on Climate (MIROC; Hasumi & Emori 2004).
References
2
Braconnot P, Otto-Bliesner B, Harrison S et al. (2007) Results of PMIP2 coupled simulations
of the Mid-Holocene and Last Glacial Maximum – Part 1: experiments and large-scale
features. Climate of the Past, 3, 261–277.
Collins WD, Bitz CM Blackon ML et al. (2006) The Community Climate System Model
version 3 (CCSM3). Jounal of Climate, 19, 2122–2143.
Danilkin AA, Hewison AJM (1996) Behavioural ecology of Siberian and European roe deer.
Chapman and Hall, London.
Dupanloup I, Schneider S, Excoffier L (2002) A simulated annealing approach to define the
genetic structure of populations. Molecular Ecology, 11, 2571–2581.
Elith J, Phillips SJ, Hastie T, Dudík M, Chee YE, Yates CJ (2011) A statistical explanation of
MaxEnt for ecologists. Diversity and Distributions, 17, 43-57.
Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from
metric distances among DNA haplotypes: application to human mitochondrial DNA
restriction data. Genetics, 131, 479–491.
Gonzalez T, Tsysulina K (2008) Capreolus pygargus. In: IUCN 2012. IUCN Red List of
Threatened Species. Version 2012.2. <www.iucnredlist.org>.
Hasumi H, Emori S (2004) K-1 coupled model (MIROC) description. K-1 Technical report 1.
Tokyo, Japan: Center for Climate System Research, University of Tokyo.
Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A (2005) Very high resolution
interpolated climate surfaces for global land areas. International Journal of Climatology, 25,
1965-1978.
Lovari S, Herrero J, Conroy J et al. (2008) Capreolus capreolus. In: IUCN 2012. IUCN Red
List of Threatened Species. Version 2012.2. <www.iucnredlist.org>.
Otto-Bliesner BL, Marshall SJ, Overpeck JT, Miller GH, Hu A (2006) Simulating Arctic
climate warmth and icefield retreat in the last interglaciation. Science, 311, 1751–1753.
Phillips SJ, Anderson RP, Schapire RE (2006) Maximum entropy modeling of species
geographic distributions. Ecological Modelling, 190, 231–259.
Wright S (1965) The interpretation of population structure by F-statistics with special regard
to systems of mating. Evolution, 19, 395–420.
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