Reconstructing the history of a fragmented and heavily exploited red deer population using ancient and contemporary DNA
© Rosvold et al.; licensee BioMed Central Ltd. 2012
Received: 31 May 2012
Accepted: 21 September 2012
Published: 26 September 2012
Red deer (Cervus elaphus) have been an important human resource for millennia, experiencing intensive human influence through habitat alterations, hunting and translocation of animals. In this study we investigate a time series of ancient and contemporary DNA from Norwegian red deer spanning about 7,000 years. Our main aim was to investigate how increasing agricultural land use, hunting pressure and possibly human mediated translocation of animals have affected the genetic diversity on a long-term scale.
We obtained mtDNA (D-loop) sequences from 73 ancient specimens. These show higher genetic diversity in ancient compared to extant samples, with the highest diversity preceding the onset of agricultural intensification in the Early Iron Age. Using standard diversity indices, Bayesian skyline plot and approximate Bayesian computation, we detected a population reduction which was more prolonged than, but not as severe as, historic documents indicate. There are signs of substantial changes in haplotype frequencies primarily due to loss of haplotypes through genetic drift. There is no indication of human mediated translocations into the Norwegian population. All the Norwegian sequences show a western European origin, from which the Norwegian lineage diverged approximately 15,000 years ago.
Our results provide direct insight into the effects of increasing habitat fragmentation and human hunting pressure on genetic diversity and structure of red deer populations. They also shed light on the northward post-glacial colonisation process of red deer in Europe and suggest increased precision in inferring past demographic events when including both ancient and contemporary DNA.
Humans have a major impact on the evolution and survival of other life forms . Our activities may modify the genetic diversity and structure of other species through e.g. heavy harvesting, habitat fragmentation and translocation of populations [2, 3], often reducing their fitness and future adaptive potential [4, 5]. Red deer (Cervus elaphus) are one of the most common and widespread of European ungulates today . However, due to heavy hunting and habitat alterations, many populations were severely reduced in numbers in previous centuries. Several populations were driven to extinction or confined to scattered and isolated refugia from which they have later expanded . Even today the widespread distribution is often patchy and fragmented, a trend that is increasing in some areas as a result of habitat loss and overhunting . Red deer have also been subjected to poorly documented human mediated translocations of animals, often over large distances, leading to mixing with or substitution of the indigenous population . Such translocations could potentially disrupt long-term local adaptations of populations .
Norwegian red deer represents the end point of the northward post-glacial colonisation of the western European red deer lineage  and relatively low genetic diversity has been found in contemporary populations [10, 11]. Traditionally it was described as a separate subspecies (C. e. atlanticus), but this is not supported in more recent analyses based on skull morphology  or molecular phylogenetics [13, 14]. Norwegian deer are, nevertheless, differentiated both morphologically and genetically from Swedish and Danish populations and seem more closely related to Scottish red deer [10, 15]. This differentiation among Scandinavian red deer has been explained either by post-glacial separation and adaptations to different environments, or alternatively, that they originate from different source populations [15, 16]. Explanations for the latter hypothesis suggest two waves of post-glacial immigration to northern Europe, with the first wave dominating the Norwegian population, and a later human mediated translocation of animals possibly by Vikings [10, 15, 17]. There are numerous findings of prehistoric red deer bones in Norway and, according to older documents e.g. , they were widespread and numerous in the 16th century AD before rapidly declining in numbers, allegedly as an effect of increased hunting pressure and wolf predation. A similar decline was reported in Sweden  and in the 19th century the Scandinavian red deer had been reduced to a few isolated locations, estimated to consist of only a few hundred animals in total . Following strict hunting regulations, an almost exponential growth took place during the last century and the current estimated Norwegian census size is well above 100,000 individuals, mainly distributed along the west coast of the Scandes mountains .
In this study we analyse contemporary and ancient genetic diversity in Norwegian red deer in a time-series of mitochondrial DNA (mtDNA) sequences spanning the last 7,000 years. Our aim is to estimate the timing, pattern and magnitude of the historic population decline using different methods to detect past demographic changes. In addition, we explore the relationship to other European populations and the possibility of human translocation of red deer into Norway. We analyse contemporary DNA together with ancient DNA (aDNA) using Bayesian methods and investigate whether including aDNA significantly changes the outcome of our analyses. Our results provide direct insights into the effects of increasing agricultural land use and human hunting pressure on genetic diversity and structure on a long timescale, and shed light on the northward post-glacial colonization process of red deer in Europe.
Sampling and dating
Archaeological sites from which samples were taken for aDNA analysis
c. Age (BP)
Sogn & Fjordane
DNA extraction, amplification and sequencing
(Ce116F) 5Â´-CCACCAACCACACAACAAAA-3Â´ with (Ce343R) 5Â´-GATCTAGGGGACGGGATACG-3Â´;
(CeCRF251) 5Â´-TGCCCCATGCATATAAGCATG-3Â´ with (CeCRR519) 5Â´-TAGGTGAGATGGCCCTGAAAAAAG-3Â´.
Amplifications were performed in 25 μl reaction mixtures containing 4–8 μl DNA extract, 0.625 U of PfuTurbo Hotstart DNA Polymerase (Stratagene), 2.5 μl 10x Pfu buffer, 12.5 pmol of each primer, 2.5 μg bovine serum albumin (Sigma) and 200 μM of each dNTP. The PCR profile was 2 min denaturation at 95°C followed by 46 cycles of 30 s denaturation at 95°C, 30 s annealing at 72°C, 60 s of extension at 72°C, and a final extension step of 10 min at 72°C. Amplified PCR products were cleaned using ExoSAP-IT (USB). Sequencing of both strands was performed using BigDye terminator cycle sequencing kit v.1.1 on an ABI 3100 genetic analyser. Sequences were inspected and aligned by eye with aid of MEGA v.4 .
Several precautions were taken to ensure amplification of authentic DNA from the ancient samples . All equipment and working surfaces were cleaned using sodium hypochlorite, ethanol or UV-light. Drilling and extraction were done in designated labs physically separated from post-PCR laboratories, and where no previous work on red deer had been done before. Lab coats and breathing masks were used, gloves were changed frequently and drill bits were changed for each sample. Samples were mechanically cleaned and the outer surface was removed before drilling out the powder. Blank extraction and PCR controls were used in each reaction and only DNA sequences which could be replicated from at least two independent amplifications of each primer pair were used in the subsequent analyses. In addition, the primer pairs were overlapping for 84 bp of the target sequence yielding four replicates or more of the most variable region, thereby ensuring that nuclear copies (NUMTS) could be avoided . We cloned and sequenced a sample of each new haplotype found, using 10 clones from each sample. Cloning was performed according to the Topo TA Cloning protocols (Invitrogen) with PCR-products extracted from gel using the MinElute Gel Extraction Kit protocol (Invitrogen).
Analysing genetic diversity and population structure
A previous analysis using microsatellites found a strong genetic differentiation of the present Norwegian populations north and south of the Sognefjord area . We therefore pooled our contemporary data into three geographic regions along a north–south transect: the northern part of the distribution, including Møre & Romsdal and Trøndelag counties (N); the Sognefjord area (W); and the southwest, including Rogaland and Hordaland counties (SW). We tested for population structure between the three areas by calculating F-statistics using only haplotype frequencies (FST)  and ΦST which also accounts for genetic distance between haplotypes . Significance values were obtained after 10,000 permutations using the software Arlequin v. 3.1 .
Estimates may become biased when analysing populations with a pronounced population structure [37–49], and to better meet assumptions of panmixia we excluded “site 1” from the ancient samples and the N region from the contemporary data set focusing only on “western Norway” on subsequent analyses of genetic diversity through time. The data was divided into four time periods which were treated as separate “populations” in the analyses: c. 7,000-3,500 yr BP (mid-Holocene); c. 2,500-2,000 yr BP (representing the period before any suspected bottleneck and translocations); c. 1,500-500 yr BP (a period including the human Migration Period, the Viking Age and the Middle Ages); and the present. We calculated standard genetic diversity indices, θk, Tajima’s D  and Fu’s Fs on both the present and ancient data sets using the software Arlequin.
Bayesian skyline plot
A Bayesian Skyline Plot (BSP)  implemented in BEAST v.1.6.1  was used to explore past demographic changes in the red deer population. This approach was also used to obtain a direct estimate of substitution rate by using the age of individual sequences as calibrating information . Sequence ages were rounded to the nearest 500 yr BP as most samples were not directly dated but were obtained from well dated stratigraphic layers with a certain age range (Additional file 1: Table S3). Substitution model was selected using the Aikake information criterion in jModelTest v. 0.1.1 [46, 47], suggesting that the HKY model had the best fit to the data set . Both a strict and a relaxed molecular clock were applied; the results were similar and a strict clock was used for the final runs. We summarized the lineage coalescent events into ten groups to smooth the estimates over time. MCMC was run for 100 mill iterations, sampling every 10,000th step and with a 10% burn-in. To estimate model parameters we ran two independent runs which were combined and inspected using TRACER v.1.5 . To evaluate support for the BSP model we also did a run using a constant size coalescent tree prior instead of the BSP, comparing the different runs using Bayes factors .
Approximate Bayesian computation analysis
In two initial runs we compared the effects of using larger effective population size of females than of males (with a ratio of 0.1), compared to a scenario where they were equal, using similar parameter settings and prior ranges as described below. The results did not differ between the two approaches (results not shown), and we therefore did a full scale analysis assuming an equal sex ratio. We modelled two populations: Norway and Europe, with current effective population sizes NNOR and NEUR respectively. To represent the European diversity, one haplotype from 15 different western European populations were randomly picked, 13 from Skog et al. , one from Nielsen et al.  and one from McDevitt et al. . Such sampling should yield properties approximating those of a random mating population [53, 54]. We used the same time periods as described above to represent a serial sampling of the population at four time points (t0-t3), with t0 representing the contemporary sample and t3 the oldest of the ancient samples (Figure 2). In order to convert time estimates to years instead of generations, we assumed a generation time of four years in female red deer . In scenario 1, the Norwegian population went through a population bottleneck starting at tBS and ending at tBE years back in time, where the effective population size of the Norwegian population is NB. At time t1, which we allow to occur either within or before the bottleneck period (i.e. we do not a priori determine the relative size of tBS and t1), the Norwegian population is sampled, and prior range for t1 is set to between 500 and 1,700 yr BP. The sampling at year t2 and year t3 is assumed to happen before tBS, i.e., in a period with effective size NNOR, with prior ranges 2,000 – 2,500 yr BP for t2 and 3,500 – 7,000 yr BP for t3. In the period between tD – x and tD the Norwegian population experienced a founder event as a result of the colonisation of Norway, with tD marking the time of divergence from the European population. In scenario 2 we assumed a similar history as in scenario 1, except that there was an immigration and admixture event from the European population before tBS and either before or after the sampling at t2. The second immigrated population underwent a bottleneck for a total of x years and has an effective size NNOR at the time of admixture. A fraction r of the original Norwegian population and a fraction 1 – r of the second immigration contribute to the admixed present day population.
We assumed no migration between populations after divergence, implying that the divergence time estimates are to be treated as minimum estimates. Prior ranges of the effective population size were set to 1,000 – 100,000 for NNOR and NB, and 1,000 – 1,000,000 for NEUR. Based on the results from the Bayesian skyline plot (see below) we assumed that a putative bottleneck started between 500 – 2,500 yr BP and ended 1 – 500 years ago. The postglacial divergence of the Norwegian and European populations was set to occur between 9,000 and 20,000 yr BP. In scenario 2 the second divergence of the Norwegian population from the European was set to occur between 3,500 and 20,000 yr BP, with an original bottleneck lasting between 1 and 500 years. The admixture between the first and the second Norwegian populations occurred between 500 and 2,500 yr BP, before or after the sampling at t2.
Mutation rates were treated as nuisance parameters, and we assumed a Kimura 2-parameter model  with prior mutation rate estimate range obtained from the BEAST analyses (see below). Summary statistics were computed for each sample in each simulation, i.e. number of haplotypes, Tajima’s D, number of private segregating sites and average number of pairwise differences for each pair of populations sampled together. Each scenario was tested using 1 mill simulations, i.e., 2 mill simulations in total for the testing of scenarios 1 and 2.
The scenarios were compared using two approaches; one by directly comparing the summary statistics with the observed diversity in the data set and counting the frequency of the various scenarios among the most similar simulated datasets [56, 57], and one by doing a logistic regression of each scenario probability for the most similar simulated data sets on the deviations between simulated and observed summary statistics [58, 59]. In the direct comparisons approach the 500 simulated data closest to the observed values were used, while in the regression approach we used 1% of the simulated data closest to the observed data set. Confidence in scenario choice was evaluated by choosing scenario 1 as the true scenario and then simulating 500 data sets using this scenario and parameter values drawn randomly from the prior distributions, and then doing the same for scenario 2. The proportion of times the most likely scenario did not have the highest posterior probability when it was the true scenario was used as an estimate of type I error. The number of times the most likely scenario had the highest probability when it was not the true scenario was used as an estimate of type II error. Parameters were estimated for the most likely scenario using the 1% simulations for a given scenario most similar to the observed data set for the summary statistics employed. In order to evaluate the performance of the estimation procedure, we generated pseudo-observed data sets with known parameter values drawn from the posterior distribution given the most likely scenario. The mean relative bias (MRB), , was estimated, where e i is the i’th estimate of the pseudo-generated true value v, and averaged over the n = 500 data sets.
In order to study the impact of including aDNA samples, we chose the scenario with the highest estimated posterior probability (scenario 1, see below), and re-ran the ABC analyses using only extant DNA samples. In these simulations we assumed a similar history as in scenario 1 above, except that we had no aDNA samples, i.e. t1 – t3 were not included.
Relationships between haplotypes
Relationships between haplotypes were investigated both on a national (Norwegian) and western European level using three different phylogenetic network methods: median-joining in Network v.126.96.36.199 , statistical parsimony in TCS v.1.21  and NeighborNet in SplitsTree4 v.4.11.3 .
Amplification success and authentication of ancient samples
None of the negative controls contained red deer DNA, but some samples showed contamination from cattle (Bos taurus) when using primer pair II, a common problem in aDNA studies . All clones produced identical sequences, except four clones in sample A1348 (NO9) which showed a G-A transition, typical of post-mortem DNA decay . This was only expressed in the clones and was distinct from the segregating site that distinguished this haplotype. Most of the variable sites were also within the overlapping part of the two primer pairs and the independent replicates were always identical. We therefore have good reason to believe that all resulting sequences are authentic.
Present population structure and diversity
Present mtDNA genetic structure and diversity of red deer for three geographic regions in Norway
Genetic diversity indices
Genetic diversity through time in western Norway
Genetic diversity through time in western Norway (W & SW areas)
c. 500–1500 yr BP
c. 2000–2500 yr BP
c. 3500–7000 yr BP
Bayesian skyline analysis
ABC historical parameter values
7160 (13 700)
32 000 (63 700)
1 030 000 (1 000 000)
684 000 (639 000)
329 000 (325 000)
3 200 000 (2 530 000)
22 700 (34 100)
85 700 (92 500)
15 280 (15 000)
19 160 (18 520)
19 560 (19 560)
The results from the simulations only using extant samples of DNA, and assuming scenario 1, are shown in parentheses in Table 4. The precision of the estimates are markedly reduced for some of the parameters causing MRB estimates to increase up to 6.8 times for various parameters when excluding aDNA samples compared to estimates including aDNA (average increase in MRB equal to 3.14 across all comparable parameters). However, the parameter estimates have partly overlapping credible intervals and in general the estimates are very similar, except that credible intervals on average is 20% larger in analyses excluding aDNA, i.e., including aDNA is seemingly increasing the precision of estimates.
Placement in the western European lineage
Red deer colonised western Norway some time before 8,000 yr BP and skeletal remains indicate that they were well established by the start of the period covered by this analysis (Rosvold et al. unpublished results). While significant Fu’s Fs indicate a population expansion during the early part of our study period (Table 3), the BSP (Figure 4) suggests a relatively stable female effective population size until about 2,000 yr BP. However, as the samples for this early period are widely dispersed in time, the posterior density interval of the BSP is large and the estimate is thus insecure. The Early Iron Age sample (c. 2,500 – 2,000 yr BP) has the highest diversity indices of all periods considered (Table 3), indicating that the population size may have been particularly high during this time.
Historical texts tell of a widespread and large population in Norway until around 500 yr BP, after which there was a dramatic decrease in numbers, allegedly caused by high harvesting rates and increased numbers of predators [18, 68]. However, as indicated by the diversity indices, the BSP and the ABC analyses, this decrease appears to have been more prolonged, probably starting before medieval times. The estimated effective population sizes should be interpreted with caution [69, 70], but the relative changes give a reliable picture of the magnitude of the demographic bottleneck. Accordingly, and supported by an earlier study on contemporary microsatellite DNA , the bottleneck seems not to have been as dramatic as the historic texts may suggest. The mtDNA diversity in the present population is, however, relatively low (Table 2) and especially the nucleotide diversity is low compared to other European populations . The lowest diversity estimates are found in the northernmost region (N), as expected from a peripheral population loosing diversity during postglacial colonisation . The current high population density of red deer in Norway is generally believed to be a recent phenomenon , but our results might indicate that at least the effective female population size, as measured through genetic diversity, was even higher in the past.
Following the spread of agriculture along the coast of western Norway the once dense coastal forests were transformed into the present day heathlands [73, 74]. A process which was greatly accelerated from around 2,000 yr BP through intensified agricultural activities , iron, coal, and salt production, and later by mining and timber export . These changes in the landscape probably facilitated and exacerbated the effects of heavy hunting by increasing habitat fragmentation and possibly reducing migration between areas, thereby isolating populations. The loss of the previously most abundant haplotype (NO4) may indicate extensive genetic drift within these fragmented populations, reducing genetic diversity on a local scale. However, the number of isolated populations were relatively large (at least six) and evenly spread along large parts of its former distribution . Thus, the overall genetic diversity may have been better maintained by the wide geographic spread of the populations than if they had been reduced to a single but larger population . Population fragmentation and isolation is expected to cause increased genetic differentiation , and indeed, there is a high degree of genetic structuring among the present Norwegian female red deer (Figure 1 and Table 2). This indicates that few females have migrated between the areas since the population size reduction, supporting findings that fjords may act as significant dispersal barriers [34, 78].
The ten Norwegian haplotypes observed in the ancient samples are closely related to the rest of the western European clade. The star-like structuring (Figure 3) coupled with low nucleotide diversity (Table 3) is indicative of a population expansion from an ancestral haplotype  which in this case seems to be NO4. This close relationship, coupled with the observation that all present-day haplotypes except NO3 have been found in samples dating to 2,000 yr BP or older, is an indication of no human translocation of female red deer into Norway during historic times. This conclusion is also supported by ABC analyses, where a scenario of only one post-glacial colonisation of Norway gets highest support. NO3 is first found at low frequency in late medieval samples and has so far only been found in Norway (Figure 5). It is possible that NO3 originated in Norway and became frequent in the Sognefjord area (W, Figure 1) as an effect of genetic drift and subsequent population increase during recent times.
An estimated divergence time of the Norwegian population of around 15,000 yr BP (9,960 – 19,560), as indicated by the ABC analysis (Table 4), coincides with the start of the northward colonisation of Europe after the Last Glacial Maximum . The haplotype network for the western European red deer (Figure 5) confirms previous findings of a close relationship within the western European clade [13, 14], with little or no apparent geographic structure and several cross-links indicating uncertain relationships. Out of the ten haplotypes found in the ancient Norwegian dataset five are shared with other countries. Of these, the two central haplotypes NO1 and NO4 are widespread, being present in Scotland and the border forests between Germany and the Czech Republic [14, 23, 25], with NO4 also found in Spain , and NO1 being one of the most common types found in the Scottish highlands today . None of the extant Norwegian haplotypes are shared with other Scandinavian countries; although the ancient NO6 is found in Denmark today . The Swedish population seems to have experienced a more severe bottleneck than the Norwegian as only one haplotype, closely related to NO1, is found among indigenous animals [11, 14]. This low diversity makes it hard to postulate the relationship to Swedish animals, but present Scandinavian diversity indicates that some haplotypes never reached Norway and that a large part of those passing through Denmark during the post-glacial colonization (i.e. the Norwegian types) were later lost. Sampling aDNA from both Sweden and Denmark could shed more light on if this was caused by genetic bottlenecks or if they were replaced by later immigrants that never reached Norway.
Two star shaped patterns are apparent among the western European samples, separated by an A-G transition. One of these centres on NO1, which has been described before , while the other centres on the closely related NO4 and is made more apparent by our ancient samples. This could be an indication of two subgroups within the western European haplogroup, possibly reflecting different refugial areas in France and Iberia . Most of the European populations have undergone severe population reductions during recent centuries and several translocations which could have distorted any phylogeographic patterns within the haplogroup [6, 71]. A large-scale sampling of aDNA from other European populations would thus provide valuable insights into the phylogeography of European red deer.
Our results indicate that the current genetic diversity of Norwegian red deer can be explained by one post-glacial immigration event followed by a gradual loss of diversity and increasing population sub-division due to heavy exploitation and habitat fragmentation during the last two millennia. They also show that only females from the western European red deer lineage colonised Scandinavia, supporting previous findings that the eastern lineage had a more limited dispersal into Europe [14, 80]. The population decline seems to have been more prolonged in time than what is reflected in historic documents, indicating that even early human land use practices had an effect on red deer.
We are very grateful to Hallvard Haanes and Anna Skog for providing additional info on the mtDNA diversity of the Norwegian red deer, and to Hallvard, John Linnell, Frank E. Zachos and an anonymous reviewer for valuable comments on the manuscript. We would also like to thank Liv Midthjell and Gro Bjørnstad for lab assistance, and Tore Fredriksen for help with providing samples. Funds were provided by the Directorate for Nature Management.
- Frankham R, Ballou JD, Briscoe DA: Introduction to conservation genetics. 2002, Cambridge: Cambridge University PressView ArticleGoogle Scholar
- Keyghobadi N: The genetic implications of habitat fragmentation for animals. Can J Zool. 2007, 85: 1049-1064.View ArticleGoogle Scholar
- Allendorf FW, England PR, Luikart G, Ritchie PA, Ryman N: Genetic effects of harvest on wild animal populations. Trends Ecol Evol. 2008, 23: 327-337.PubMedView ArticleGoogle Scholar
- Reed DH, Frankham R: Correlation between fitness and genetic diversity. Conserv Biol. 2003, 17: 230-237.View ArticleGoogle Scholar
- Garner A, Rachlow JL, Hicks JF: Patterns of genetic diversity and its loss in mammalian populations. Conserv Biol. 2004, 19: 1215-1221.View ArticleGoogle Scholar
- Linnell JDC, Zachos FE: Status and distribution patterns of European ungulates: genetics, population history and conservation. Ungulate management in Europe: problems and practices. Edited by: Putman R, Apollonio M, Andersen R. 2011, Cambridge: Cambridge University Press, 12-53.View ArticleGoogle Scholar
- Burbaité L, Csányi S: Red deer population and harvest changes in Europe. Acta Zool Lit. 2010, 20: 179-188.View ArticleGoogle Scholar
- Rhymer JM, Simberloff D: Extinction by hybridization and introgression. Annu Rev Ecol Syst. 1996, 27: 83-109.View ArticleGoogle Scholar
- Sommer RS, Zachos FE, Street M, Jöris O, Skog A, Benecke N: Late Quaternary distribution dynamics and phylogeography of the red deer (Cervus elaphus) in Europe. Quatern Sci Rev. 2008, 27: 714-733.View ArticleGoogle Scholar
- Gyllensten U, Ryman N, Reuterwall C, Dratch P: Genetic differentiation in four European subspecies of red deer (Cervus elaphus L.). Heredity. 1983, 51: 561-580.View ArticleGoogle Scholar
- Haanes H, Røed KH, Perez-Espona S, Rosef O: Low genetic variation support bottlenecks in Scandinavian red deer. Eur J Wildl Res. 2011, 57: 1137-1150.View ArticleGoogle Scholar
- Lowe VPW, Gardiner AS: A re-examination of the subspecies of red deer (Cervus elaphus) with particular reference to the stocks in Britain. J Zool. 1974, 174: 185-201.View ArticleGoogle Scholar
- Ludt CJ, Schroeder W, Rottmann O, Kuehn R: Mitochondrial DNA phylogeography of red deer (Cervus elaphus). Mol Phylogenet Evol. 2004, 31: 1064-1083.PubMedView ArticleGoogle Scholar
- Skog A, Zachos FE, Rueness EK, Feulner PGD, Mysterud A, Langvatn R, Lorenzini R, Hmwe SS, Lehoczky I, Hartl GB, et al: Phylogeography of red deer (Cervus elaphus) in Europe. J Biogeogr. 2009, 36: 66-77.View ArticleGoogle Scholar
- Ahlén I: Studies on the red deer, Cervus elaphus L., in Scandinavia. II. Taxonomy and osteology of prehistoric and recent populations. Viltrevy. 1965, 3: 89-176.Google Scholar
- Ekman S: Djurvärldens utbredningshistoria på Skandinaviska halvön. 1922, Stockholm: Albert Bonniers FörlagGoogle Scholar
- Langvatn R: Hjortens erobring av Norge. Brennpunkt Natur 98/99. Edited by: Brox KH. 1998, Trondheim: Tapir, 49-71.Google Scholar
- Claussøn Friis P: Norriges oc omliggende øers sandfærdige bescriffuelse. 1632, Kjøbenhavn: Melkior MartzanGoogle Scholar
- Ahlén I: Studies on the red deer, Cervus elaphus L., in Scandinavia. I. History of distribution. Viltrevy. 1965, 3: 1-88.Google Scholar
- Collett R: Hjorten i Norge (Cervus elaphus, atlanticus), nogle biologiske meddelelser. Bergens Museums Aarbog. 1909, 6: 1-31.Google Scholar
- Stuiver M, Reimer PJ: Extended C-14 data base and revised CALIB 3.0 C-14 age calibration program. Radiocarbon. 1993, 35: 215-230.Google Scholar
- Reimer PJ, Baillie MGL, Bard E, Bayliss A, Beck JW, Bertrand CJH, Blackwell PG, Buck CE, Burr GS, Cutler KB, et al: IntCal04 terrestrial radiocarbon age calibration, 0–26 cal kyr BP. Radiocarbon. 2004, 46: 1029-1058.Google Scholar
- Fickel J, Bubliy OA, Stache A, Noventa T, Jirsa A, Heurich M: Crossing the border? Structure of the red deer (Cervus elaphus) population from the Bavarian–Bohemian forest ecosystem. Mamm Biol. 2012, 77: 211-220.Google Scholar
- McDevitt AD, Edwards CJ, O'Toole P, O'Sullivan P, O'Reilly C, Carden RF: Genetic structure of, and hybridisation between, red (Cervus elaphus) and sika (Cervus nippon) deer in Ireland. Mamm Biol. 2009, 74: 263-273.Google Scholar
- Pérez-Espona S, Pérez-Barberia FJ, Goodall-Copestake WP, Jiggins CD, Gordon IJ: Genetic diversity and population structure of Scottish Highland red deer (Cervus elaphus) populations: a mitochondrial survey. Heredity. 2009, 102: 199-210.PubMedView ArticleGoogle Scholar
- Hmwe SS, Zachos FE, Sale JB, Rose HR, Hartl GB: Genetic variability and differentiation in red deer (Cervus elaphus) from Scotland and England. J Zool. 2006, 270: 479-487.View ArticleGoogle Scholar
- Nielsen EK, Olesen CR, Pertoldi C, Gravlund P, Barker JSF, Mucci N, Randi E, Loeschcke V: Genetic structure of the Danish red deer (Cervus elaphus). Biol J Linn Soc. 2008, 95: 688-701.View ArticleGoogle Scholar
- Randi E, Mucci N, Claro-Hergueta F, Bonnet A, Douzery EJP: A mitochondrial DNA control region phylogeny of the Cervinae: speciation in Cervus and implications for conservation. Anim Conserv. 2001, 4: 1-11.View ArticleGoogle Scholar
- Haanes H, Røed KH, Mysterud A, Langvatn L, Rosef O: Consequences for genetic diversity and population performance of introducing continental red deer into the northern distribution range. Conserv Genet. 2010, 11: 1653-1665.View ArticleGoogle Scholar
- Tamura K, Dudley J, Nei M, Kumar S: MEGA4: Molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol. 2007, 24: 1596-1599.PubMedView ArticleGoogle Scholar
- Cooper A, Poinar HN: Ancient DNA: do it right or not at all. Science. 2000, 289: 1139-PubMedView ArticleGoogle Scholar
- den Tex R-J, Maldonado JE, Thorington R, Leonard JA: Nuclear copies of mitochondrial genes: another problem for ancient DNA. Genetica. 2010, 138: 979-984.PubMedView ArticleGoogle Scholar
- Haanes H, Røed KH, Flagstad Ø, Rosef O: Genetic structure in an expanding cervid population after population reduction. Conserv Genet. 2010, 11: 11-20.View ArticleGoogle Scholar
- Wright S: The genetical structure of populations. Ann Eugenics. 1951, 15: 323-354.View ArticleGoogle Scholar
- Excoffier L, Smouse PE, Quattro JM: Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992, 131: 479-491.PubMedPubMed CentralGoogle Scholar
- Excoffier L, Laval G, Schneider S: Arlequin (version 3.0): An integrated software package for population genetics data analysis. Evol Bioinform. 2005, 1: 47-50.Google Scholar
- Navascués M, Emerson BC: Elevated substitution rate estimates from ancient DNA: model violation and bias of Bayesian methods. Mol Ecol. 2009, 18: 4390-4397.PubMedView ArticleGoogle Scholar
- Ho SYW, Shapiro B: Skyline-plot methods for estimating demographic history from nucleotide sequences. Mol Ecol Resour. 2011, 11: 423-434.PubMedView ArticleGoogle Scholar
- Pannell JR: Coalescence in a metapopulation with recurrent local extinction and recolonization. Evolution. 2003, 57: 949-961.PubMedView ArticleGoogle Scholar
- Ewens WJ: The sampling theory of selectively neutral alleles. Theor Popul Biol. 1972, 3: 87-112.PubMedView ArticleGoogle Scholar
- Tajima F: Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics. 1989, 123: 585-595.PubMedPubMed CentralGoogle Scholar
- Fu YX: Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection. Genetics. 1997, 147: 915-925.PubMedPubMed CentralGoogle Scholar
- Drummond AJ, Rambaut A, Shapiro B, Pybus OG: Bayesian coalescent inference of past population dynamics from molecular sequences. Mol Biol Evol. 2005, 22: 1185-1192.PubMedView ArticleGoogle Scholar
- Drummond A, Rambaut A: BEAST: Bayesian evolutionary analysis by sampling trees. BMC Evol Biol. 2007, 7: 214-PubMedPubMed CentralView ArticleGoogle Scholar
- Drummond AJ, Nicholls GK, Rodrigo AG, Solomon W: Estimating mutation parameters, population history and genealogy simultaneously from temporally spaced sequence data. Genetics. 2002, 161: 1307-1320.PubMedPubMed CentralGoogle Scholar
- Guindon S, Gascuel O: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol. 2003, 52: 696-704.PubMedView ArticleGoogle Scholar
- Posada D: jModelTest: phylogenetic model averaging. Mol Biol Evol. 2008, 25: 1253-1256.PubMedView ArticleGoogle Scholar
- Hasegawa M, Kishino H, Yano T-a: Dating of the human-ape splitting by a molecular clock of mitochondrial DNA. J Mol Evol. 1985, 22: 160-174.PubMedView ArticleGoogle Scholar
- Tracer v1.4. http://beast.bio.ed.ac.uk/Tracer,
- Suchard MA, Weiss RE, Sinsheimer JS: Bayesian selection of continuous-time Markov Chain evolutionary models. Mol Biol Evol. 2001, 18: 1001-1013.PubMedView ArticleGoogle Scholar
- Beaumont MA, Zhang W, Balding DJ: Approximate Bayesian computation in population genetics. Genetics. 2002, 162: 2025-2035.PubMedPubMed CentralGoogle Scholar
- Cornuet J-M, Santos F, Beaumont MA, Robert CP, Marin J-M, Balding DJ, Guillemaud T, Estoup A: Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation. Bioinformatics. 2008, 24: 2713-2719.PubMedPubMed CentralView ArticleGoogle Scholar
- Städler T, Haubold B, Merino C, Stephan W, Pfaffelhuber P: The impact of sampling schemes on the site frequency spectrum in nonequilibrium subdivided populations. Genetics. 2009, 182: 205-216.PubMedPubMed CentralView ArticleGoogle Scholar
- Stenøien HK, Shaw AJ, Shaw B, Hassel K, Gunnarsson U: North American origin and recent European establishments of the amphi-Atlantic peat moss Sphagnum angermanicum. Evolution. 2011, 65: 1181-1194.PubMedView ArticleGoogle Scholar
- Kimura M: A simple method for estimating evolutionary rates of base substitutions through comparative studies of nucleotide sequences. J Mol Evol. 1980, 16: 111-120.PubMedView ArticleGoogle Scholar
- Miller N, Estoup A, Toepfer S, Bourguet D, Lapchin L, Derridj S, Kim KS, Reynaud P, Furlan L, Guillemaud T: Multiple transatlantic introductions of the western corn rootworm. Science. 2005, 310: 992-PubMedView ArticleGoogle Scholar
- Pascual M, Chapuis MP, Mestres F, BalanyÀ J, Huey RB, Gilchrist GW, Serra L, Estoup A: Introduction history of Drosophila subobscura in the New World: a microsatellite-based survey using ABC methods. Mol Ecol. 2007, 16: 3069-3083.PubMedView ArticleGoogle Scholar
- Fagundes NJR, Ray N, Beaumont M, Neuenschwander S, Salzano FM, Bonatto SL, Excoffier L: Statistical evaluation of alternative models of human evolution. Proc Natl Acad Sci USA. 2007, 104: 17614-17619.PubMedPubMed CentralView ArticleGoogle Scholar
- Beaumont MA: Joint determination of topology, divergence time, and immigration in population trees. Simulations, genetics and human prehistory. Edited by: Matsura S, Forster P, Renfrew C. 2008, Cambridge: McDonald Institute for Archaeological Research, 135-154.Google Scholar
- Bandelt HJ, Forster P, Röhl A: Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol. 1999, 16: 37-48.PubMedView ArticleGoogle Scholar
- Clement M, Posada D, Crandall KA: TCS: a computer program to estimate gene genealogies. Mol Ecol. 2000, 9: 1657-1659.PubMedView ArticleGoogle Scholar
- Huson DH, Bryant D: Application of phylogenetic networks in evolutionary studies. Mol Biol Evol. 2006, 23: 254-267.PubMedView ArticleGoogle Scholar
- Zhang Z, Schwartz S, Wagner L, Miller W: A greedy algorithm for aligning DNA sequences. J Comput Biol. 2000, 7: 203-214.PubMedView ArticleGoogle Scholar
- Leonard JA, Shanks O, Hofreiter M, Kreuz E, Hodges L, Ream W, Wayne RK, Fleischer RC: Animal DNA in PCR reagents plagues ancient DNA research. J Archaeol Sci. 2007, 34: 1361-1366.View ArticleGoogle Scholar
- Gilbert MTP, Binladen J, Miller W, Wiuf C, Willerslev E, Poinar H, Carlson JE, Leebens-Mack JH, Schuster SC: Recharacterization of ancient DNA miscoding lesions: insights in the era of sequencing-by-synthesis. Nucleic Acids Res. 2007, 10: 1-10.Google Scholar
- Baker K: Population genetic history of the British roe deer (Capreolus capreolus) and its implications for diversity and fitness. PhD thesis. 2011, School of Biological and Biomedical Sciences: Durham UniversityGoogle Scholar
- Ho SYW, Lanfear R, Phillips MJ, Barnes I, Thomas JA, Kolokotronis S-O, Shapiro B: Bayesian estimation of substitution rates from ancient DNA sequences with low information content. Syst Biol. 2011, 60: 366-375.PubMedView ArticleGoogle Scholar
- Pontoppidan E: Norges naturlige historie: 1752–1753. 1753, København: Rosenkilde og Bagger, Facsimile edition (1977)Google Scholar
- Bazin E, Glémin S, Galtier N: Population size does not influence mitochondrial genetic diversity in animals. Science. 2006, 312: 570-572.PubMedView ArticleGoogle Scholar
- Nabholz B, Glémin S, Galtier N: The erratic mitochondrial clock: variations of mutation rate, not population size, affect mtDNA diversity across birds and mammals. BMC Evol Biol. 2009, 9: 54-PubMedPubMed CentralView ArticleGoogle Scholar
- Zachos FE, Hartl GB: Phylogeography, population genetics and conservation of the European red deer Cervus elaphus. Mamm Rev. 2011, 41: 138-150.View ArticleGoogle Scholar
- Eckert CG, Samis KE, Lougheed SC: Genetic variation across species' geographical ranges: the central-marginal hypothesis and beyond. Mol Ecol. 2008, 17: 1170-1188.PubMedView ArticleGoogle Scholar
- Kaland PE: The origin and management of Norwegian coastal heaths as reflected by pollen analysis. Anthropogenic indicators in pollen diagrams. Edited by: Fasteland A. 1986, Rotterdam: A.A.Balkema, 19-36.Google Scholar
- Høgestøl M, Prøsch-Danielsen L: Impulses from agro-pastoralism in the 4th and 3rd millennia BC on the south-western coastal rim of Norway. Environ Archaeol. 2006, 11: 19-34.View ArticleGoogle Scholar
- Myhre B: Agriculture, landscape and society ca. 4000 BC - AD 800. Norwegian agricultural history. Edited by: Gjerdåker B, Lunden K, Myhre B, Øye I. 2004, Trondheim: Tapir Academic Press, 12-77.Google Scholar
- Rolstad J, Framstad E, Gundersen V, Storaunet KO: Naturskog i Norge. Definisjoner, økologi og bruk i norsk skog- og miljøforvaltning. Aktuelt fra skogforskningen. 2001, 1: 1-53.Google Scholar
- Lacy RC: Loss of genetic diversity from managed populations: interacting effects of drift, mutation, immigration, selection, and population subdivision. Conserv Biol. 1987, 1: 143-158.View ArticleGoogle Scholar
- Pérez-Espona S, Pérez-Barberia FJ, McLeod JE, Jiggins CD, Gordon IJ, Pemberton JM: Landscape features affect gene flow of Scottish Highland red deer (Cervus elaphus). Mol Ecol. 2008, 17: 981-996.PubMedView ArticleGoogle Scholar
- Richards M, Macaulay V, Hickey E, Vega E, Sykes B, Guida V, Rengo C, Sellitto D, Cruciani F, Kivisild T, et al: Tracing European founder lineages in the near eastern mtDNA pool. Am J Hum Genet. 2000, 67: 1251-1276.PubMedPubMed CentralView ArticleGoogle Scholar
- Niedzialkowska M, Jedrzejewska B, Honnen A-C, Otto T, Sidorovich VE, Perzanowski K, Skog A, Hartl GB, Borowik T, Bunevich AN, et al: Molecular biogeography of red deer Cervus elaphus from eastern Europe: insights from mitochondrial DNA sequences. Acta Theriol. 2011, 56: 1-12.PubMedPubMed CentralView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.