Comparative phylogeography of two related plant species with overlapping ranges in Europe, and the potential effects of climate change on their intraspecific genetic diversity

  • Gemma E Beatty1 and

    Affiliated with

    • Jim Provan1Email author

      Affiliated with

      BMC Evolutionary Biology201111:29

      DOI: 10.1186/1471-2148-11-29

      Received: 4 September 2010

      Accepted: 27 January 2011

      Published: 27 January 2011

      Abstract

      Background

      The aim of the present study was to use a combined phylogeographic and species distribution modelling approach to compare the glacial histories of two plant species with overlapping distributions, Orthilia secunda (one-sided wintergreen) and Monotropa hypopitys (yellow bird's nest). Phylogeographic analysis was carried out to determine the distribution of genetic variation across the range of each species and to test whether both correspond to the "classic" model of high diversity in the south, with decreasing diversity at higher latitudes, or whether the cold-adapted O. secunda might retain more genetic variation in northern populations. In addition, projected species distributions based on a future climate scenario were modelled to assess how changes in the species ranges might impact on total intraspecific diversity in both cases.

      Results

      Palaeodistribution modelling and phylogeographic analysis using multiple genetic markers (chloroplast trn S- trn G region, nuclear ITS and microsatellites for O. secunda; chloroplast rps 2, nuclear ITS and microsatellites for M. hypopitys) indicated that both species persisted throughout the Last Glacial Maximum in southern refugia. For both species, the majority of the genetic diversity was concentrated in these southerly populations, whereas those in recolonized areas generally exhibited lower levels of diversity, particularly in M. hypopitys. Species distribution modelling based on projected future climate indicated substantial changes in the ranges of both species, with a loss of southern and central populations, and a potential northward expansion for the temperate M. hypopitys.

      Conclusions

      Both Orthilia secunda and Monotropa hypopitys appear to have persisted through the LGM in Europe in southern refugia. The boreal O. secunda, however, has retained a larger proportion of its genetic diversity in more northerly populations outside these refugial areas than the temperate M. hypopitys. Given that future species distribution modelling suggests northern range shifts and loss of suitable habitat in the southern parts of the species' current distributions, extinction of genetically diverse rear edge populations could have a significant effect in the rangewide intraspecific diversity of both species, but particularly in M. hypopitys.

      Background

      Paleoclimatic evidence indicates that the Earth's temperature has been continually changing over time [13]. The glacial and interglacial cycles that characterised the Quaternary period (ca. 2.6 MYA - present) have had a significant effect on the distributions of species, particularly in the northern latitudes [4, 5]. Temperate species were generally confined to low-latitude refugia throughout glacial periods and recolonized from these areas as the climate warmed during interglacials [6, 7]. For plant species, however, whose spread is primarily via dispersal of seeds, the capacity to track changes in suitable habitat is limited, particularly for animal-dispersed species [8].

      Understanding the past movements of species may help us understand how present and future climate change might affect species' ranges [9, 10]. Within the last decade, it has become evident that anthropogenically induced climate change is causing shifts in the distribution ranges of many species [1114]. As projections of carbon emissions suggest that this period of global warming will not end soon, these range shifts are likely to continue, but where species lack the migratory capacity to track changes in climate and available habitat, population extinctions may become increasingly frequent, particularly at species' low-latitude range edges [1417]. Range-edge populations have generally been perceived as being genetically depauperate [18, 19], although it has recently been suggested that some rear-edge populations may serve as reservoirs of unique genetic variation [20]. The processes of persistence in southern refugia during glacial maxima followed by northward recolonization have led to a pattern of "southern richness versus northern purity" [2123], where the majority of genetic variation is found in populations that currently occupy previous refugial areas, with a northward decrease in genetic diversity due to progressive founder effects during the recolonization process (but see [2427]). Consequently, if rear-edge populations are at particular risk of extinction due to the effects of climate change, their loss may have a disproportionally detrimental impact on overall levels of within-species genetic diversity, and such genetic erosion might compromise the long-term evolutionary potential of impacted species [28]. Assuming that species will shift their ranges north in response to global warming, genetically diverse southern edge populations of temperate species may be at the greatest risk of extinction, whereas cold-adapted species that might have persisted in more northerly refugia [2427] could conceivably retain a larger proportion of their genetic diversity since this variation may not be concentrated in low latitude populations.

      The aim of the present study was to use a combined phylogeographic and species distribution modelling approach to compare the glacial histories of two plant species, Orthilia secunda (one-sided wintergreen) and Monotropa hypopitys (yellow bird's nest). Both species belong to the Monotropoideae and have largely overlapping ranges in Europe (Figures 1A and 1B), as well as being found in North America, where they both exhibit disjunct east/west distributions. O. secunda is generally found in boreal forests, whereas M. hypopitys is usually associated with more temperate tree species and thus a comparison of the two should provide insights into the relative effects of climate change on a temperate species vs. a boreal species. Phylogeographic analysis was carried out to determine the distribution of genetic variation across the range of each species and to test whether both correspond to the "classic" model of high diversity in the south, with decreasing diversity at higher latitudes, or whether the cold-adapted O. secunda might retain more genetic variation in northern populations. In addition, projected species distributions based on a future climate scenario were modelled to assess how changes in the species ranges might impact on total intraspecific diversity in both cases.
      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig1_HTML.jpg
      Figure 1

      Distributions ofO. secundaandM. hypopitys, and modelled LGM, current and future distributions. (A) Distribution of O. secunda (Source: Naturhistoriska riksmuseet) (B) Distribution of M. hypopitys (Source: Naturhistoriska riksmuseet) (C) Modelled LGM (ca. 18 KYA) distribution of O. secunda (D) Modelled LGM (ca. 18 KYA) distribution of M. hypopitys (E) Modelled current distribution of O. secunda (F) Modelled current distribution of M. hypopitys (G) Modelled future (2100) distribution of O. secunda (D) Modelled future (2100) distribution of M. hypopitys.

      Methods

      Sampling and DNA extraction

      Samples of Orthilia secunda and Monotropa hypopitys were obtained from 35 and 19 locations respectively throughout Europe (Tables 1 and 2). DNA was extracted using the Qiagen DNeasy kit. For O. secunda, 206 individuals were sequenced for the chloroplast trn S- trn G intergenic spacer, 154 individuals were sequenced for the nuclear internal transcribed spacer (ITS) region, and 218 individuals genotyped for five nuclear microsatellite loci. For M. hypopitys, 100 individuals were sequenced for part of the chloroplast rps 2 gene, 100 individuals were sequenced for the nuclear ITS region, and 111 individuals were genotyped for eight nuclear microsatellite loci.
      Table 1

      Orthilia secunda populations analysed in this study

      Country

      Location

      Code

      Lat

      Long

      N cp

      N ITS

      N micro

      Collector

      Austria

      Radmer an der Stube

      ATRS

      47.5556

      14.7861

      5

      2

      5

      Apollonie Mayr

       

      Steiermark

      ATS1

      47.4967

      14.3522

      7

      5

      8

      Peter Schönswetter

       

      Steiermark

      ATS2

      47.4389

      14.9233

      8

      7

      8

      Peter Schönswetter

      Czech Republic

      Kosatky

      CZKO

      50.3178

      14.6719

      7

      6

      7

      Petr Kotlik

      Estonia

      Jõgevamaa

      EEJO

      58.6338

      26.9453

      7

      2

      8

      Teene Talve

       

      Nigula Nature Reserve

      EENN

      58.0194

      24.6825

      7

      5

      8

      M. Reintal

       

      Põlvamaa

      EEPO

      58.0956

      27.0302

      8

      4

      8

      T. Oja

      France

      Cervieres

      FRCE

      44.8667

      6.7225

      7

      5

      8

      Rolland Douzet

       

      Sauvas

      FRSA

      44.6004

      5.9037

      5

      4

      7

      Arne Saatkamp

       

      Station Alpine Joseph Fourier

      FRJF

      45.0360

      6.4002

      6

      5

      8

      Rolland Douzet

      Ireland

      Correl Glen

      IECG

      54.4372

      -7.8744

      4

      4

      4

      Gemma Beatty

       

      Cranny Burn

      IECB

      54.9114

      -6.0409

      4

      4

      4

      Gemma Beatty

      Italy

      Valle D'Aosta

      ITVA

      45.7125

      7.1639

      6

      5

      6

      Nationaal Herbarium Nederland

      Montenegro

      Durmitor Mountains

      MEDM

      43.1611

      19.2028

      8

      7

      8

      Anna & Michal Ronikier

       

      Komovi Massif

      MEKM

      42.6947

      19.6672

      5

      4

      5

      Anna & Michal Ronikier

      Norway

      Buskerud

      NOBU

      60.1208

      10.3833

      8

      6

      8

      Andreas Tribsch

       

      Oslo

      NOOS

      59.9939

      10.7064

      8

      6

      8

      Andreas Tribsch

       

      Selvikstaken

      NOSE

      58.8625

      6.0750

      4

      4

      4

      Andreas Tribsch

       

      Troms Fylke

      NOTF

      68.9500

      19.7500

      4

      4

      5

      W. Paul

      Poland

      Bialystok

      PLBI

      53.1167

      23.1167

      8

      4

      8

      Ada Wroblewska

       

      Kielce

      PLKI

      50.8400

      20.5800

      5

      5

      5

      W. Paul

       

      Pomorze Zachodnie

      PLPZ

      54.0047

      19.9983

      7

      6

      8

      Joanna Julia & Lech Galosz

      Scotland

      Glen Glass

      SCGG

      57.6816

      -4.4226

      4

      4

      4

      Peter McEvoy

       

      Glen Mhor

      SCGM

      56.8844

      -3.6315

      4

      4

      4

      Peter McEvoy

      Slovakia

      Muranska Planina

      SKMP

      48.7825

      19.9600

      8

      5

      8

      Anna & Michal Ronikier

       

      Nizke Tatry

      SKNT

      48.9983

      19.5875

      8

      5

      8

      Anna & Michal Ronikier

       

      Slovensky Raj

      SKSR

      48.9305

      20.2897

      2

      2

      2

      Anna & Michal Ronikier

       

      Zapadne Tatry

      SKZT

      49.1453

      19.7850

      7

      6

      7

      Anna & Michal Ronikier

      Slovenia

      Kaminske Alpe

      SIKA

      46.3922

      14.6000

      8

      6

      8

      Peter Schönswetter

      Sweden

      Flurkmark

      SEFL

      64.1273

      20.1322

      8

      7

      8

      Stefan Ericsson

       

      Lomselenas

      SELO

      65.1441

      17.3139

      8

      6

      8

      Stefan Ericsson

       

      Ranas

      SERA

      59.8128

      18.2883

      5

      1

      8

      Arne Anderberg

      Switzerland

      Chasseron

      CHCH

      46.8287

      6.5508

      6

      6

      6

      Philippe Druart

       

      Valais

      CHVA

      46.0000

      7.6833

      5

      5

      5

      Nationaal Herbarium Nederland

           

      206

      154

      218

       

      Species distribution modelling

      Ecological niche modelling (ENM) was carried out to determine suitable climate envelopes for O. secunda and M. hypopitys in Europe for the LGM (ca. 18KYA), and the year 2100 under a future climate scenario using the maximum entropy approach implemented in the MAXENT software package (V3.2.1; [29]). Species occurrence data were downloaded from the Global Biodiversity Information Facility data portal (http://​www.​gbif.​org), totalling 14,221 and 8,829 occurrences for O. secunda and M. hypopitys respectively. A principal component analysis (PCA) was carried out on the 19 BIOCLIM variables in the WorldClim data set [30] to remove correlated variables, since these can lead to overfitting of the model. After removing variables that exhibited a strong correlation (Spearman's rank correlation >0.5; [31]), three variables (P1 [Annual Mean Temperature], P4 [Temperature Seasonality] and P14 [Precipitation of Driest Period]) were used to generate ENMs at 2.5 minute resolution using MAXENT with the default parameters for convergence threshold (10-5) and number of iterations (500), and projected onto reconstructed LGM data (Community Climate System Model [CCSM]; Palaeoclimate Modelling Intercomparison Project Phase II: http://​pmip2.​lsce.​ipsl.​fr) to identify potential refugial areas. The current climate envelope was also projected onto climate grids corresponding to the same three bioclimatic variables in the year 2100 under the National Centre for Atmospheric Research general circulation model (CCM3 model) that simulates double CO2 emissions [32]. Duplicate records from the same locality were removed to reduce the effects of spatial autocorrelation. Presence thresholds were determined using the sensitivity-specificity sum maximisation approach [33] and the performance of the models were tested using 25% of the occurrence data points to determine the area under the receiver operating characteristic (ROC) curve (AUC).

      Molecular genetic analyses - O. secunda

      206 individuals were sequenced for the chloroplast trn S- trn G intergenic spacer. A product was amplified using the O. secunda -specific primers and reaction conditions described in [34]. 5 μl PCR product were resolved on 1.5% agarose gels and visualised by ethidium bromide staining, and the remaining 15 μl sequenced in both directions using the BigDye sequencing kit (V3.1; Applied Biosystems) and run on an AB 3730XL DNA analyser.

      154 individuals were sequenced for a section of the nuclear ITS region. Primers were designed from GenBank sequence accession number AF133747: OS-ITS-F 5'-TGTTTGTACACTTGGGGAAGC-3' and OS-ITS-R 5'-TCGCGGTCAATGTACCGTAG-3'. PCR and sequencing were carried out as described in [34], except that an annealing temperature of 55°C was used for the PCR.

      218 individuals were genotyped for five O. secunda microsatellite loci previously described in [35]. Forward primers were modified by the addition of a 19 bp M13 tail (5'-CACGACGTTGTAAAACGAC-3') and reverse primers were modified by the addition of a 7 bp tail (5'-GTGTCTT-3'). PCR was carried out in a total volume of 10 μl containing 100 ng genomic DNA, 10 pmol of dye-labelled M13 primer (6-FAM or HEX), 1 pmol of tailed forward primer, 10 pmol reverse primer, 1× PCR reaction buffer, 200 μM each dNTP, 2.5 mM MgCl2 and 0.25 U GoTaq Flexi DNA polymerase (Promega). PCR was carried out on a MWG Primus thermal cycler using the conditions described in [35] and genotyping was carried out on an AB3730xl capillary genotyping system. Allele sizes were scored in GENEMAPPER V4.1 using ROX-500 size standards and were checked by comparison with previously sized control samples.

      Molecular genetic analyses - M. hypopitys

      100 individuals were sequenced for a section of the chloroplast rps 2 gene. Primers were designed from GenBank sequence accession number AF351956 (Bidartondo and Bruns 2001): MH-rps2-F 5'-TTCGCCGATTTAGTATCACG-3' and MH-rps2-R 5'-GGGATTCCCAAAGTAATACATTCTA-3'. PCR and sequencing were carried out as described in [34].

      100 individuals were sequenced for a section of the nuclear ITS region. Primers were designed from GenBank sequence accession number AF384126[36]: MH-ITS-F 5'-GGTTGGCCTACCCTTTATTTT-3' and MH-ITS-R 5'-GAAGTAATCCAATCATAACACTGACA-3'. PCR and sequencing were carried out as described in [34], except that an annealing temperature of 55°C was used.

      111 individuals were genotyped for five M. hypopitys microsatellite loci previously described in [37] - Mono02, Mono15, Mono20, Mono21 and Mono22. Three additional loci developed using the ISSR-cloning technique outlined in [38] were also used (Table 2). Forward primers were modified by the addition of a 19 bp M13 tail (5'-CACGACGTTGTAAAACGAC-3') and reverse primers were modified by the addition of a 7 bp tail (5'-GTGTCTT-3'). PCR was carried out in a total volume of 10 μl containing 100 ng genomic DNA, 10 pmol of dye-labelled M13 primer (6-FAM or HEX), 1 pmol of tailed forward primer, 10 pmol reverse primer, 1× PCR reaction buffer, 200 μM each dNTP, 2.5 mM MgCl2 and 0.25 U GoTaq Flexi DNA polymerase (Promega). PCR was carried out on a MWG Primus thermal cycler using the conditions described in [39] and genotyping was carried out on an AB3730xl capillary genotyping system. Allele sizes were scored in GENEMAPPER V4.1 (Applied Biosystems) using ROX-500 size standards and were checked by comparison with previously sized control samples.
      Table 2

      Monotropa hypopitys populations analysed in this study

      Country

      Location

      Code

      Lat

      Long

      N cp

      N ITS

      N micro

      Collector

      Austria

      Karnten

      ATKA

      46.5228

      13.9539

      2

      2

      2

      Peter Schönswetter

      Czech Republic

      Polom

      CZPO

      49.7892

      15.7595

      1

      1

      1

      Jakub Tiesetel

      England

      Peasmarsh

      ENPE

      50.9667

      -0.6667

      6

      6

      8

      Jonathan Simmons

      Estonia

      Jõgevamaa

      EEJO

      58.6338

      26.9453

      6

      6

      8

      Teene Talve

       

      Põlvamaa

      EEPO

      58.0956

      27.0302

      7

      8

      8

      T. Ota

      Ireland

      Ely Lodge

      IEEL

      54.4567

      -7.9002

      8

      7

      8

      Gemma Beatty

       

      Straidkilly

      IEST

      54.9914

      -6.0409

      8

      7

      8

      Gemma Beatty

      Poland

      Czarne Lake

      PLCL

      53.4667

      20.6000

      8

      7

      8

      Ada Wroblewska

       

      Lake Golun

      PLLG

      54.0047

      17.9983

      8

      8

      8

      Ada Wroblewska

       

      Knyszyn

      PLKN

      53.3333

      22.9167

      8

      8

      8

      Joanna Julia & Lech Galosz

      Romania

      Retezat Mountains

      RORM

      45.3097

      22.9678

      8

      8

      8

      Anna & Michal Ronikier

        

      ROVG

      46.2070

      25.5400

      4

      4

      6

      Anna Maria Csergo

      Slovakia

      Muranska Planina

      SKMP

      48.7825

      19.9600

      2

      2

      2

      Anna & Michal Ronikier

       

      Nizke Tatry

      SKNT

      48.9983

      19.5875

      4

      4

      6

      Anna & Michal Ronikier

      Slovenia

      Dolenjska

      SIDO

      45.9236

      15.0958

      2

      3

      3

      Peter Schönswetter

       

      Soca Valley

      SISV

      46.3450

      13.6800

      8

      8

      8

      Peter Schönswetter

      Sweden

      Ranas

      SERA

      59.8128

      18.2883

      3

      4

      4

      Arne Anderberg

      Switzerland

      Chasseron

      CHCH

      46.8287

      6.5508

      5

      5

      5

      Philippe Druart

           

      100

      100

      111

       

      Data analysis

      Alignments were constructed using BIOEDIT (V7.0.9.0) [40] for the O. secunda chloroplast trn S- trn G intergenic spacer and nuclear ITS, and for the M. hypopitys chloroplast rps 2 and nuclear ITS. Length variation at any mononucleotide repeat regions was removed, since the bidirectional mutation model operating at such regions can give rise to homoplasy [41]. The alignments were used to construct statistical parsimony networks using the TCS software package (V1.2.1) [42]. Where reticulations were present in the networks, these were broken following the rules described in [43].

      Tests for linkage disequilibrium between pairs of microsatellite loci in each population were carried out in the program FSTAT [44]. Levels of genetic diversity were calculated for populations with a sample size of N ≥ 4. Gene diversity (H) based on haplotype frequencies for the O. secunda chloroplast trn S- trn G region and nuclear ITS, and the M. hypopitys chloroplast rps 2 and nuclear ITS, and observed and expected heterozygosity (H O and H E ) based on nuclear microsatellite allele frequencies were calculated using the ARLEQUIN software package (V3.01) [45]. Population structuring based on the microsatellite data was determined using the STRUCTURE software package (V 2.2) [46]. Five independent runs were carried out for all values of K, the number of clusters, between 2 and 20. The program was run each time using 50,000 burn-in iterations followed by 500,000 Markov Chain Monte Carlo iterations, and the most likely value of K was determined using the ΔK statistic [47].

      Results

      Species distribution modelling

      For all models, the area under the receiver operating curve (AUC) statistic was consistently higher than 0.95, indicating good performance.

      Distribution modelling for O. secunda and M. hypopitys at the LGM indicated extensive areas of suitable habitat for both species in southern Europe (Figures 1C and 1D). For O. secunda, two of the French populations (FRSA and FRCE), one of the Swiss populations (CHVA) and the two populations from Montenegro lay within the suitable climate envelope indicated by the ENM. None of the M. hypopitys populations studied lay within the suitable climate envelope indicated by the ENM.

      The future distribution model indicated an extensive loss of suitable habitat for O. secunda relative to the modelled current climate envelope (Figure 1E), particularly in northern central Europe (Figure 1G). The majority of the suitable remaining habitat in southern Europe would be largely restricted to the mountainous regions of the Pyrenees, the Alps, the Carpathians and the Dinaric Alps. For M. hypopitys, the model indicated a general northward shift in the species' distribution, with a loss of suitable habitat in southeastern Europe but an increase in northern Europe, particularly in Scandinavia (Figures 1F and 1H).

      O. secunda phylogeography

      Removal of length polymorphism at three mononucleotide repeat regions from the chloroplast trn S- trn G alignment resulted in an overall alignment length of 495 bp and seven distinct haplotypes (Table 3; Figure 2; GenBank sequence accession numbers HQ864688-HQ864694). Three of these (Haplotypes 5, 6 and 7) were unique to a single individual. The three most common haplotypes exhibited a general north-south split, with the Haplotype 2 (yellow) found predominantly in southern populations whilst northern populations contained primarily the two blue haplotypes (Haplotypes 1 and 3). Two populations contained all three of these haplotypes: the FRCE population (France) and the SKMP population (Slovakia). The fourth non-unique haplotype, Haplotype 4 (green), was found in a single individual in both the ATST1 (Austria) and the SELO (Sweden) populations.
      Table 3

      Diversity statistics for O. secunda populations

      Country

      Code

      H E

      cpDNA haplotype

      ITS haplotype

         

      1

      2

      3

      4

      5

      6

      7

      1

      2

      3

      4

      5

      Austria

      ATRS

      0.729

      -

      5

      -

      -

      -

      -

      -

      5

      -

      -

      -

      -

       

      ATS1

      0.529

      -

      6

      -

      1

      -

      -

      -

      7

      -

      -

      -

      -

       

      ATS2

      0.629

      -

      5

      2

      -

      1

      -

      -

      8

      -

      -

      -

      -

      Czech Republic

      CZKO

      0.736

      7

      -

      -

      -

      -

      -

      -

      4

      2

      -

      -

      -

      Estonia

      EEJO

      0.768

      1

      -

      6

      -

      -

      -

      -

      1

      -

      -

      -

      -

       

      EENN

      0.752

      -

      1

      6

      -

      -

      -

      -

      -

      -

      5

      -

      -

       

      EEPO

      0.797

      -

      7

      1

      -

      -

      -

      -

      1

      -

      -

      -

      -

      France

      FRCE

      0.737

      4

      2

      1

      -

      -

      -

      -

      3

      1

      -

      -

      1

       

      FRSA

      0.811

      5

      -

      -

      -

      -

      -

      -

      2

      -

      -

      2

      -

       

      FRJF

      0.765

      6

      -

      -

      -

      -

      -

      -

      5

      -

      -

      -

      -

      Ireland

      IECG

      0.400

      -

      4

      -

      -

      -

      -

      -

      4

      -

      -

      -

      -

       

      IECB

      0.643

      -

      4

      -

      -

      -

      -

      -

      4

      -

      -

      -

      -

      Italy

      ITVA

      0.637

      6

      -

      -

      -

      -

      -

      -

      5

      -

      -

      -

      -

      Montenegro

      MEDM

      0.757

      8

      -

      -

      -

      -

      -

      -

      7

      -

      -

      -

      -

       

      MEKM

      0.807

      4

      -

      1

      -

      -

      -

      -

      4

      -

      -

      -

      -

      Norway

      NOBU

      0.727

      3

      4

      -

      -

      -

      1

      -

      6

      -

      -

      -

      -

       

      NOOS

      0.839

      -

      4

      4

      -

      -

      -

      -

      5

      -

      -

      1

      -

       

      NOSE

      0.839

      -

      -

      4

      -

      -

      -

      -

      4

      -

      -

      -

      -

       

      NOTF

      0.409

      -

      3

      1

      -

      -

      -

      -

      4

      -

      -

      -

      -

      Poland

      PLBI

      0.493

      8

      -

      -

      -

      -

      -

      -

      4

      -

      -

      -

      -

       

      PLKI

      0.582

      5

      -

      -

      -

      -

      -

      -

      5

      -

      -

      -

      -

       

      PLPZ

      0.770

      7

      -

      -

      -

      -

      -

      -

      6

      -

      -

      -

      -

      Scotland

      SCGG

      0.429

      -

      -

      4

      -

      -

      -

      -

      4

      -

      -

      -

      -

       

      SCGM

      0.529

      -

      4

      -

      -

      -

      -

      -

      4

      -

      -

      -

      -

      Slovakia

      SKMP

      0.807

      4

      2

      2

      -

      -

      -

      -

      4

      1

      -

      -

      -

       

      SKNT

      0.772

      7

      -

      -

      -

      -

      -

      1

      5

      -

      -

      -

      -

       

      SKSR

      NC

      2

      -

      -

      -

      -

      -

      -

      2

      -

      -

      -

      -

       

      SKZT

      0.763

      7

      -

      -

      -

      -

      -

      -

      6

      -

      -

      -

      -

      Slovenia

      SIKA

      0.755

      8

      -

      -

      -

      -

      -

      -

      4

      2

      -

      -

      -

      Sweden

      SEFL

      0.517

      -

      4

      3

      1

      -

      -

      -

      7

      -

      -

      -

      -

       

      SELO

      0.435

      -

      1

      7

      -

      -

      -

      -

      6

      -

      -

      -

      -

       

      SERA

      0.735

      -

      3

      2

      -

      -

      -

      -

      1

      -

      -

      -

      -

      Switzerland

      CHCH

      0.673

      6

      -

      -

      -

      -

      -

      -

      6

      -

      -

      -

      -

       

      CHVA

      0.755

      5

      -

      -

      -

      -

      -

      -

      5

      -

      -

      -

      -

      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig2_HTML.jpg
      Figure 2

      Geographical distribution ofO. secundachloroplasttrnS-trnG haplotypes. Pie chart sizes are approximately proportional to sample size, with the smallest circles representing N = 1 and the largest representing N = 8. Inset shows the phylogenetic relationships between the seven haplotypes. Small black circles represent unique haplotypes i.e. those found in a single individual. The population of origin of each unique haplotype is indicated.

      The 475 bp nuclear ITS alignment contained five distinct haplotypes (Table 3; Figure 3; GenBank sequence accession numbers HQ864695-HQ864699). The most common haplotype, Haplotype 1 (red), was found in all populations with the exception of the EENN population (Estonia). Only six populations exhibited any within-population variation (FRCE, FRSA [both France], SIKA [Slovenia], SKMP [Slovakia], CZKO [Czech Republic] and NOOS [Norway]) and only the FRCE population contained more than two haplotypes. The EENN population was fixed for Haplotype 3 (blue), which was not found elsewhere.
      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig3_HTML.jpg
      Figure 3

      Geographical distribution ofO. secundanuclear ITS haplotypes. Pie chart sizes are approximately proportional to sample size, with the smallest circles representing N = 1 and the largest representing N = 8. Inset shows the phylogenetic relationships between the five haplotypes.

      No significant linkage disequilibrium was detected between pairs of microsatellite loci after sequential Bonferroni correction. Between 16 and 30 alleles were detected at the five loci studied (mean = 20.20) and levels of expected heterozygosity (H E ) calculated for populations with a sample size of N ≥ 4 ranged from 0.400 (IECG [Ireland]) to 0.839 (NOOS and NOSE [both Norway]), with a mean value of 0.677 (Table 3; Figure 4). The STRUCTURE analysis of the microsatellite data indicated that the most likely number of genetic clusters was K = 2 (Figure 5).
      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig4_HTML.jpg
      Figure 4

      Expected heterozygosity (H E ) inO. secundapopulations based on five nuclear microsatellite loci. Circle sizes are indicative of level of H E (see inset).

      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig5_HTML.jpg
      Figure 5

      Assignment ofO. secundapopulations toK= 2 clusters based onSTRUCTUREanalysis of the nuclear microsatellite data.

      M. hypopitys phylogeography

      The 320 bp chloroplast rps 2 alignment contained seven distinct haplotypes (Table 4; Figure 6; GenBank sequence accession numbers HQ864700-HQ864706). The two most common haplotypes, Haplotypes 1 and 2 (depicted in blue and yellow), exhibited a largely east-west split. Only four populations exhibited any within-population variation (ATKA [Austria], SIDO [Slovenia], RORM and ROVG [both Romania]) and of these, only the RORM population contained more than two haplotypes.
      Table 4

      Diversity statistics for M. hypopitys populations

      Country

      Code

      H E

      cpDNA haplotype

      ITS haplotype

         

      1

      2

      3

      4

      5

      6

      7

      1

      2

      3

      Austria

      ATKA

      NC

      1

      -

      1

      -

      -

      -

      -

      -

      2

      -

      Czech Republic

      CZPO

      NC

      1

      -

      -

      -

      -

      -

      -

      1

      -

      -

      England

      ENPE

      0.624

      -

      6

      -

      -

      -

      -

      -

      6

      -

      -

      Estonia

      EEJO

      0.690

      -

      6

      -

      -

      -

      -

      -

      -

      6

      -

       

      EEPO

      0.573

      7

      -

      -

      -

      -

      -

      -

      -

      8

      -

      Ireland

      IEEL

      0.500

      8

      -

      -

      -

      -

      -

      -

      7

      -

      -

       

      IEST

      0.370

      8

      -

      -

      -

      -

      -

      -

      7

      -

      -

      Poland

      PLCL

      0.516

      -

      -

      8

      -

      -

      -

      -

      7

      -

      -

       

      PLLG

      0.716

      -

      8

      -

      -

      -

      -

      -

      -

      8

      -

       

      PLKN

      0.740

      -

      8

      -

      -

      -

      -

      -

      -

      8

      -

      Romania

      RORM

      0.731

      -

      4

      -

      1

      1

      1

      1

      -

      8

      -

       

      ROVG

      0.710

      3

      -

      -

      1

      -

      -

      -

      3

      1

      -

      Slovakia

      SKMP

      NC

      2

      -

      -

      -

      -

      -

      -

      2

      -

      -

       

      SKNT

      0.682

      4

      -

      -

      -

      -

      -

      -

      3

      1

      -

      Slovenia

      SIDO

      NC

      -

      -

      1

      1

      -

      -

      -

      1

      1

      1

       

      SISV

      0.530

      8

      -

      -

      -

      -

      -

      -

      8

      -

      -

      Sweden

      SERA

      0.674

      -

      3

      -

      -

      -

      -

      -

      4

      -

      -

      Switzerland

      CHCH

      0.750

      5

      -

      -

      -

      -

      -

      -

      4

      -

      1

      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig6_HTML.jpg
      Figure 6

      Geographical distribution ofM. hypopityschloroplastrps2 haplotypes. Pie chart sizes are approximately proportional to sample size, with the smallest circles representing N = 1 and the largest representing N = 8. Inset shows the phylogenetic relationships between the eight haplotypes. Open diamonds represent missing haplotypes.

      The 287 bp nuclear ITS alignment contained three distinct haplotypes (Table 4; Figure 7; GenBank sequence accession numbers HQ864707-HQ865709). The distribution of these haplotypes was broadly congruent with that of the chloroplast rps 2 haplotypes. Only the CHCH (Switzerland), SIDO (Slovenia), SKNT (Slovakia) and ROVG (Romania) populations exhibited any within-population variation, with all three haplotypes being found in the SIDO population.
      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig7_HTML.jpg
      Figure 7

      Geographical distribution ofM. hypopitysnuclear ITS haplotypes. Pie chart sizes are approximately proportional to sample size, with the smallest circles representing N = 1 and the largest representing N = 8. Inset shows the phylogenetic relationships between the three haplotypes. Open diamonds represent missing haplotypes.

      No significant linkage disequilibrium was detected between pairs of microsatellite loci after sequential Bonferroni correction. Between 10 and 22 alleles were detected at the eight loci studied (mean = 15.125) and levels of expected heterozygosity (H E ) calculated for populations with a sample size of N ≥ 4 ranged from 0.370 (IEST [Ireland]) to 0.750 (CHCH [Switzerland]), with a mean value of 0.629 (Table 4; Figure 8). The STRUCTURE analysis of the microsatellite data indicated that the most likely number of genetic clusters was K = 2 (Figure 9).
      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig8_HTML.jpg
      Figure 8

      Expected heterozygosity (H E ) inM. hypopityspopulations based on five nuclear microsatellite loci. Circle sizes are indicative of level of H E (see inset).

      http://static-content.springer.com/image/art%3A10.1186%2F1471-2148-11-29/MediaObjects/12862_2010_1636_Fig9_HTML.jpg
      Figure 9

      Assignment ofM. hypopityspopulations toK= 2 clusters based onSTRUCTUREanalysis of the nuclear microsatellite data.

      Discussion

      It is now apparent that phylogeographic inferences based on a single, non-recombining marker can be misleading [48, 49]. Consequently, phylogeographic studies are increasingly using multiple genetic markers and/or palaeodistribution modelling to draw more reliable inferences on population history. The results of the paleodistribution modelling and the patterns of genetic variation revealed by the phylogeographic analyses suggest that both Orthilia secunda and Monotropa hypopitys persisted throughout the LGM in Europe in southern refugia. Although both species generally exhibited a "southern richness vs. northern purity" distribution of genetic variation [21], this was more pronounced in the temperate M. hypopitys, where the only populations that displayed any within-population genetic variation for both the chloroplast rps 2 and nuclear ITS regions were located closest to the modelled refugial areas. Northern populations of O. secunda were more diverse, but the signatures of refugial areas i.e. high diversity coupled with unique haplotypes [27] were restricted to southern populations.

      Based on the weight of evidence across modelling and the different markers used, our findings indicate a possible refugial area for O. secunda in Europe located in the vicinity of the French Alps. A second area of high diversity and endemic haplotypes included the Austrian Alps and Slovakia, but these populations lie outside the suitable climate envelope indicated by the palaeodistribution model. Nevertheless, although the precise locations of putative refugia are difficult to identify accurately, it is clear that the majority of genetic diversity is contained in southern populations. The occurrence of a fixed endemic ITS haplotype in one of the Estonian populations (EENN) more likely represents a relatively recent mutation that has become fixed through genetic drift, rather than indicating an extreme northern refugium. For M. hypopitys, the modelling and genetic data both indicated a likely refugial area in southeastern Europe. The identification of two genetic clusters with a broadly northern/eastern vs. southern/western geographical distribution for both species based on microsatellite data could indicate isolation in separate refugia followed by differential recolonization after the retreat of the ice [24].

      Many studies have used modelling approaches to determine the effects of present and future climate change on the distribution ranges of plant species (e.g. [5052]). We can extend this approach to investigate the potential effects of such distribution changes on intraspecific genetic diversity. The future modelled distributions of both O. secunda and M. hypopitys indicate substantial changes in the ranges of both species. For M. hypopitys in particular, these changes could have a profound impact on the genetic diversity of the species in Europe. Previous studies have suggested that range contraction during previous phases of climate change was characterized by population extinction, rather than migration [6, 53]. Although the future model indicates a range expansion at the northern edge, it also suggests extensive loss of suitable habitat in southeastern Europe. Given that this area represents the centre of genetic diversity for the species, extinction of these populations would lead to massive loss of genetic diversity since more northerly populations are genetically depauperate relative to populations in the southeast. A northern expansion of the species' range would not counter this, because the leading edge colonization would be from these low-diversity northern populations. Northern populations of O. secunda, however, tended to be more genetically diverse than those of M. hypopitys. Consequently, the loss of southern and central European O. secunda populations indicated by the species distribution model would not have the same overall effect on total intraspecific genetic diversity across the continent. Nevertheless, although the populations from the species' centres of diversity in the French and Austrian Alps would still lie within the future modelled climate envelope, this would most likely be as a result of altitudinal migration, since the mountain ranges of southern and eastern Europe represent the only climatically suitable areas in the region. Whilst altitudinal migration offers some short-term potential for countering the effects of climate change [5457], its scope is ultimately limited [58]. The situation in Europe is somewhat different from that in North America, where the occurrence of northern refugia for both species means that a lower proportion of the total genetic diversity in the continent is concentrated in southern populations [[34], Beatty & Provan, unpublished results] and thus the impact of loss of rear-edge populations might not be as extreme. It should also be borne in mind that models of future (and, indeed, past) climate are not guaranteed to be 100% accurate, and that many other factors such as changes in species tolerances through adaptation and species-species interactions will also determine species current and future ranges. Nevertheless, at least in Europe, the adverse encroachment of human activity on the boreal and temperate woodlands that form the natural habitat for these species, coupled with the fact that climate is changing faster now than at any time in the past, means that the impacts on the gene pools and subsequent adaptive potential of these, and possibly many other species, are likely to be potentially serious.

      Conclusions

      Both Orthilia secunda and Monotropa hypopitys appear to have persisted through the LGM in Europe in southern refugia. The boreal O. secunda, however, has retained a larger proportion of its genetic diversity in more northerly populations outside these refugial areas than the temperate M. hypopitys. Given that future species distribution modelling suggests northern range shifts and loss of suitable habitat in the southern parts of the species' current distributions, extinction of genetically diverse rear edge populations could have a significant effect in the rangewide intraspecific diversity of both species, but particularly in M. hypopitys.

      Declarations

      Acknowledgements

      We are extremely grateful to everybody who provided samples for this project (listed in Tables 1a and 1b). Jan Wieringa (Nationaal Herbarium Nederland) provided valuable herbarium specimens. Gemma Beatty's PhD research is funded by the Department of Agriculture and Rural Development, Northern Ireland.

      Authors’ Affiliations

      (1)
      School of Biological Sciences, Queen’s University Belfast

      References

      1. Emiliani C: Quaternary paleotemperatures and the duration of high temperature intervals. Science 1972, 178: 398–401.PubMedView Article
      2. Winograd IJ, Szabo BJ, Coplen TB, Riggs AC: A 250 000-year climatic record from Great Basin vein calcite: implications for Milankovitch theory. Science 1988, 242: 1275–1280.PubMedView Article
      3. Jansen E, Sjoholm J: Reconstruction of glaciations over the past 6 Myr from ice-borne deposits in the Norwegian Sea. Nature 1991, 349: 600–603.View Article
      4. FAUNMAP Working Group: Spatial response of mammals to Late Quaternary environmental fluctuations. Science 1996, 272: 1601–1606.View Article
      5. Hewitt GM: Ice ages: their impact on species distributions and evolution. In Evolution on Planet Earth. Edited by: Rothschild LJ, Lister AM. Academic Press, London; 339–361.
      6. Bennett KD, Tzedakis PC, Willis KJ: Quaternary refugia of north European trees. J Biogeogr 1991, 18: 103–115.View Article
      7. Bennett KD, Provan J: What do we mean by 'refugia'? Quaternary Sci Rev 2008, 27: 2449–2455.View Article
      8. Comes HP, Kadereit JW: The effects of Quaternary climatic changes on plant distribution and evolution. Trends Ecol Evol 1998, 8: 432–438.
      9. Hu FS, Hampe A, Petit RJ: Paleoecology meets genetics: deciphering past vegetational dynamics. Front Ecol Environ 2009, 7: 371–379.View Article
      10. Harrison SP, Sanchez Goñi MF: Global patterns of vegetation response to millennial-scale variability and rapid climate change during the last glacial period. Quaternary Sci Rev 2010.
      11. Walther GR, Post E, Convey P, Menzel A, Parmesan C, Beebee TJC, Fromentin JM, Hoegh-Gulberg O, Bairlein F: Ecological responses to recent climate change. Nature 2002, 416: 389–395.PubMedView Article
      12. Parmesan C, Yohe G: A globally coherent fingerprint of climate change impacts across natural systems. Nature 2003, 421: 37–42.PubMedView Article
      13. Root TL, Price JT, Hall KR, Schneider SH, Rosenzweig C, Pounds JA: Fingerprints of global warming on wild animals and plants. Nature 2003, 421: 57–60.PubMedView Article
      14. Parmesan C: Ecological and evolutionary response to recent climate change. Ann Rev Ecol Evol Syst 2006, 37: 637–669.View Article
      15. Thomas CD, Cameron A, Green RE, Bakkenes M, Beaumont LJ, Collingham YC, Erasmus BFN, de Siqueira MF, Grainger A, Hannah L, Hughes L, Huntley B, van Jaarsveld AS, Midgley GF, Miles L, Ortega-Huerta MA, Peterson AT, Phillips OL, Williams SE: Extinction risk from climate change. Nature 2004, 427: 145–148.PubMedView Article
      16. Foden W, Midgely GF, Hughes GO, Bond WJ, Thuiller W, Hoffman MT, Kaleme P, Underhill LG, Rebelo AG, Hannah L: A changing climate is eroding the geographical range of the Namib Desert tree Aloe through population declines and dispersal lags. Diversity Distrib 2007, 13: 645–653.View Article
      17. Gaston KJ: The Structure and Dynamics of Geographic Ranges. Oxford: Oxford University Press; 2003.
      18. Vucetich JA, Waite TA: Spatial patterns of demography and genetic processes across the species' range: null hypotheses for landscape conservation genetics. Conserv Genet 2003, 4: 639–645.View Article
      19. 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 Article
      20. Hampe A, Petit RJ: Conserving biodiversity under climate change: the rear edge matters. Ecol Lett 2005, 8: 461–467.PubMedView Article
      21. Hewitt GM: The genetic legacy of the Quaternary ice ages. Nature 2000, 405: 907–913.PubMedView Article
      22. Taberlet P, Fumagalli L, Wust-Saucy AG, Cossons JF: Comparative phylogeography and post-glacial recolonization routes in Europe. Mol Ecol 1998, 7: 453–464.PubMedView Article
      23. Hewitt GM: Post-glacial recolonisation of European biota. Biol J Linnean Soc 1999, 68: 87–112.View Article
      24. Petit RJ, Auinagalde I, de Beaulieu J-L, Bittkau C, Brewer S, Cheddadi R, Ennos R, Fineschi S, Grivet D, Lascoux M, Mohanty A, Muller-Starck GM, Demesure-Musch B, Palme A, Martin JP, Rendell S, Vendramin GG: Glacial refugia: hotspots but not melting pots of genetic diversity. Science 2003, 300: 1563–1565.PubMedView Article
      25. Stewart JR, Lister AM: Cryptic northern refugia and the origins of the modern biota. Trends Ecol Evol 2001, 16: 608–613.View Article
      26. Provan J, Bennett KD: Phylogeographic insights into cryptic glacial refugia. Trends Ecol Evol 2008, 23: 564–571.PubMedView Article
      27. Stewart JR, Lister AM, Barnes I, Dalén L: Refugia revisited: individualistic responses of species in space and time. Proc Roy Soc B 2010, 277: 661–671.View Article
      28. Frankham R: Genetics and extinction. Biol Conserv 2005, 126: 131–140.View Article
      29. Phillips SJ, Anderson RP, Schapire RE: Maximum entropy modeling of species geographic distributions. Ecol Model 2006, 190: 231–259.View Article
      30. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A: Very high resolution interpolated climate surfaces for global land areas. J Climatol 2005, 25: 1965–1978.View Article
      31. Booth GD, Niccolucci MJ, Schuster EG: Identifying proxy sets in multiple linear regression: an aid to better coefficient interpretation. Research paper INT-470. United States Department of Agriculture Forest Service, Ogden, UT; 1994.
      32. Govindasamy B, Duffy PB, Coquard J: High resolution simulations of global climate, part 2: effects of increased greenhouse gases. Clim Dynamics 2003, 21: 391–404.View Article
      33. Cantor SB, Sun CC, Tortolero-Luna G, Richards-Kortum R, Follen M: A comparison of C/B ratios from studies using receiver operating characteristic curve analysis. J Clin Epidem 1999, 52: 885–892.View Article
      34. Beatty GE, Provan J: Refugial persistence and postglacial recolonization of North America by the cold-tolerant herbaceous plant Orthilia secunda . Mol Ecol 2010, 19: 5009–5021.PubMedView Article
      35. Beatty GE, McEvoy PM, Sweeney O, Provan J: Range-edge effects promote clonal growth in peripheral populations of the one-sided wintergreen ( Orthilia secunda ). Diversity Distrib 2008, 14: 546–555.View Article
      36. Bidartondo MI, Bruns TD: Extreme specificity in epiparasitic Monotropoideae (Ericacea): widespread phylogenetic and geographic structure. Mol Ecol 2001, 10: 2285–2295.PubMedView Article
      37. Klooster MR, Hoenle AW, Culley TM: Characterization of microsatellite loci in the myco-heterotrophic plant Monotropa hypopitys (Ericaceae) and amplification in related taxa. Mol Ecol Resources 2009, 9: 219–221.View Article
      38. Provan J, Wilson PJ: Development of microsatellites for the peat moss Sphagnum capillifolium using ISSR cloning. Mol Ecol Notes 2007, 7: 254–256.View Article
      39. Beatty GE, Provan J: High clonal diversity in threatened peripheral populations of the yellow bird's nest ( Hypopitys monotropa ; syn. Monotropa hypopitys ). Annals Bot 2011, in press.
      40. Hall TA: BIOEDIT : a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/NT. Nucleic Acids Symp Ser 1999, 41: 95–98.
      41. Provan J, Powell W, Hollingsworth PM: Chloroplast microsatellites: new tools for studies in plant ecology and systematics. Trends Ecol Evol 2001, 16: 142–147.PubMedView Article
      42. Clement M, Posada D, Crandall KA: TCS: a computer program to estimate gene genealogies. Mol Ecol 2000, 9: 1657–1659.PubMedView Article
      43. Pfenninger M, Posada D: Phylogeographic history of the land snail Candidula unifasciata (Helicellinae, Stylommatophora): fragmentation, corridor migration and secondary contact. Evolution 2002, 56: 1776–1788.PubMed
      44. Goudet J: FSTAT, version 2.9.3, A program to estimate and test gene diversities and fixation indices. [http://​www.​2.​unil.​ch/​popgen/​softwares/​fstat.​htm]
      45. Excoffier L, Laval LG, Schneider S: ARLEQUIN, Version 3.0: An integrated software package for population genetic data analysis. Evol Bioinf Online 2005, 1: 47–50.
      46. Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics 2000, 155: 945–959.PubMed
      47. Evanno G, Regnaud S, Goudet J: Detecting the number of clusters of individuals using the software STRUCTURE : a simulation study. Mol Ecol 2005, 14: 2611–2620.PubMedView Article
      48. Bermingham E, Moritz C: Comparative phylogeography: concepts and applications. Mol Ecol 1998, 7: 367–369.View Article
      49. Schaal BA, Hayworth DA, Olsen KM, Rauscher JT, Smith WA: Phylogeographic studies in plants: problems and prospects. Mol Ecol 1998, 7: 465–474.View Article
      50. Thuiller W, Lavorel S, Araújo MB, Sykes MT, Prentice IC: Climate change threats to plant diversity in Europe. Proc Natl Acad Sci USA 2005, 102: 8245–8250.PubMedView Article
      51. Hijmans RJ, Graham CH: The ability of climate envelope models to predict the effect of climate change on species distributions. Global Change Biol 2006, 12: 2272–2281.View Article
      52. McKenney DW, Pedlar JH, Lawrence K, Campbell K, Hutchinson MF: Potential impacts of climate change on the distribution of North American trees. Bioscience 2007, 57: 939–948.View Article
      53. Dalen L, Nystrom V, Valdiosera C, Germonpre M, Sablin M, Turner E, Angerbjorn A, Arsuaga JL, Gotherstrom A: Ancient DNA reveals lack of postglacial habitat tracking in the Arctic fox. Proc Natl Acad Sci USA 2007, 104: 6726–6729.PubMedView Article
      54. Hill JK, Thomas CD, Fox R, Telfer MG, Willis SG, Asher J, Huntley B: Responses of butterflies to twentieth century climate warming: implications for future ranges. Proc Roy Soc B 2002, 269: 2163–2171.View Article
      55. Daniels LD, Veblen TT: Spatiotemporal influences of climate on altitudinal treeline in northern Patagonia. Ecology 2004, 85: 1284–1296.View Article
      56. Parolo G, Rossi G: Upward migration of vascular plants following a climate warming trend in the Alps. Basic Appl Ecol 2008, 9: 100–107.View Article
      57. Lenoir J, Gegout JC, Marquet PA, de Ruffray P, Brisse H: A significant upward shift in plant species optimum elevation during the 20 th century. Science 2008, 320: 1768–1771.PubMedView Article
      58. Jump AS, Matyas C, Peñuelas J: The altitude-for-latitude disparity in the range retractions of woody species. Treends Ecol Evol 2009, 24: 694–701.View Article

      Copyright

      © Beatty and Provan; licensee BioMed Central Ltd. 2011

      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.

      Advertisement