Genome size and climate segregation suggest distinct colonization histories of an orchid species from Neotropical high-elevation rocky complexes

Genome size and climate segregation suggest distinct colonization histories of an orchid species... Abstract Knowledge about the geographical distribution of cytotypes is crucial to understand the role of polyploidy in diversification. High-elevation rocky complexes (HERCs) are heterogeneous formations found in elevated areas of eastern Brazil. They harbour one of the most endemic floras within the Neotropical region. Yet, we lack knowledge concerning the association of environmental variables and plant cytotypes in this region. Here, we investigate whether the frequency of Zygopetalum mackayi orchid cytotypes is related to climate conditions in the HERCs. We describe chromosome counts, genome size estimates and their association with climate variables for 432 individuals from 19 localities. We show, for the first time, a strong association between climate variation and cytotype variation in a species from the HERCs. We confirm two cytotypes for Z. mackayi (2n = 48 and 2n = 96), which are geographically structured, and describe an intermediate cytotype (2n = 72) restricted to a contact zone. We discuss the implications of our results for chromosome evolution in this species and provide hypotheses for the origin and maintenance of cytotypes. chromosome number, environmental predictor, hybrid zone, mixed cytotype population, Neotropics, Orchidaceae, polyploid, Brazil INTRODUCTION Polyploidy is a common process in the origin and divergence of plant species (Soltis et al., 2016; Spoelhof, Soltis & Soltis, 2017). An immediate effect of polyploidy is the emergence in the population of two or more cytotypes, i.e. individuals with different numbers of chromosomes in their somatic cells (Kolář et al., 2017). The study of the geographical distribution of cytotypes is a key requirement to understand the role of polyploidy in diversification (e.g. Hageroup, 1933; Stebbins, 1942; Sonnleitner et al., 2010). Cytogeographical patterns may unravel significant amounts of diversity by identifying multiple chromosomal races within a single taxonomic species and, therefore, contribute to the conservation of rare species and ecological restoration (Soltis et al., 2007). However, most of the studies on the geographical distribution of cytotypes are concentrated in temperate regions, especially in Europe and North America (revised by Ramsey & Ramsey, 2014). If we are to understand the role of polyploidy in diversification on a worldwide scale, we need more information on the geographical distribution of cytotypes in tropical regions (Ramsey & Ramsey, 2014), where the incidence of polyploidy is likely to be underestimated. The high-elevation rocky complexes (HERCs) are among the most diverse areas in the Neotropical region (Safford, 1999; Alves et al., 2014). In southern Brazil, HERCs are highly heterogeneous formations, where climate, geology (i.e. topography, soil depth, fertility and drainage) and the surrounding vegetation define distinct floristic compositions (Safford, 1999; Benites et al., 2007; Alves & Kolbek, 2010; Schaefer et al., 2016). This environmental heterogeneity may explain, in part, the high species richness and endemism reported for these areas (Alves & Kolbek, 2010; Echternacht et al., 2011). Besides environmental heterogeneity, polyploidization and hybridization are also considered important mechanisms promoting plant diversification in Brazilian HERCs (e.g. Giulietti, Pirani & Harley, 1997; Antonelli et al., 2010). Yet, the few available studies are restricted to the description of chromosome counts and cytotype variation within few populations (Benko-Iseppon & Wanderley, 2002; Mansanares, Forni-Martins & Semir, 2002; Viccini et al., 2005; Yamagishi-Costa & Forni-Martins, 2009). Despite the extraordinary diversity of HERCs, little is known about the role of polyploidy in plant diversification in these areas. Basic information on patterns of geographical distribution of chromosome variation is, therefore, urgent. The orchid Zygopetalum mackayi Hook. occurs almost exclusively in granitic and quartzite HERCs from the southern to north-eastern Brazil (Hoehne, 1953; Flora do Brasil, 2020). In this species, there are anecdotic reports of two cytotypes, one with 48 chromosomes and another with 96 chromosomes (Tanaka & Kamemoto, 1984; Brandham, 1999; Félix & Guerra, 2000). However, there is no information on the place of origin of these two cytotypes, and no detailed data on how their frequencies vary along the large distribution range of Z. mackayi. Considering that environmental conditions show great variation along the distribution range of the species, this orchid offers an ideal opportunity to explore the role of ecological processes on the diversification of genome size in the flora from Neotropical HERCs. In this study, we quantify the frequency of the two cytotypes of Z. mackayi in several populations along the distribution range of the species. Moreover, considering that the occurrence of polyploids in natural populations may be influenced (negatively or positively) by the climate (Ramsey & Ramsey, 2014), we investigate whether the population frequency of the two cytotypes is related to local climatic conditions. In the southernmost range of Z. mackayi, temperature is markedly seasonal and annual precipitation high (nearly 1500–3000 mm/year). In contrast, in the northernmost range of the species, temperature shows little seasonal variation and annual precipitation is lower (nearly 800–1500 mm/year). Thus, we expected that population variations in the frequency of the two cytotypes will be related to the environmental gradient imposed by climate variations. MATERIAL AND METHODS Sampling We collected leaf samples from adult plants across the entire geographical range of Z. mackayi from August 2014 to April 2016 (Table 1, Fig. 1). Sample size ranged from five to 59 individuals per population. Within the same locality, we collected specimens at least 10 m apart to avoid clonal plants attributable to sympod fragmentation and apomixis (Campacci et al., 2017). We used at least five specimens of each ploidy to determine chromosome numbers. Table 1. Means of genome sizes (2C) estimated by flow cytometry and presumed ploidy levels of Zygopetalum mackayi Hook., collected from 432 individuals of 19 populations Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  CV, coefficient of variation; 1 pg of DNA is equivalent to 978 Mpb. Vouchers are deposited at Universidade Estadual de Campinas (UEC). View Large Table 1. Means of genome sizes (2C) estimated by flow cytometry and presumed ploidy levels of Zygopetalum mackayi Hook., collected from 432 individuals of 19 populations Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  CV, coefficient of variation; 1 pg of DNA is equivalent to 978 Mpb. Vouchers are deposited at Universidade Estadual de Campinas (UEC). View Large Figure 1. View largeDownload slide Sampled populations of Zygopetalum mackayi Hook. from 19 localities, considering ploidy levels of all 432 sampled individuals. Population identifiers correspond to Table 1. The colour of the circles indicates ploidy level: blue, 2n = 48; red, 2n = 72; and yellow, 2n = 96. Figure 1. View largeDownload slide Sampled populations of Zygopetalum mackayi Hook. from 19 localities, considering ploidy levels of all 432 sampled individuals. Population identifiers correspond to Table 1. The colour of the circles indicates ploidy level: blue, 2n = 48; red, 2n = 72; and yellow, 2n = 96. Genome size estimation To estimate genome size, we macerated ~25 mg of leaf tissue with the same amount of the internal reference standard Vicia faba (2C = genome size 26.90 pg; following Doležel, Sgorbati & Lucretti, 1992). We macerated the leaves with 1 mL of cold LB01 buffer using a scalpel blade to release the nuclei into suspension (Doležel & Binarová, 1989). For nuclear staining, we added 25 µL of a 1 mg/mL solution of propidium iodide (PI; Sigma, USA) followed by 5 µL of RNase (100 µg/mL). We estimated genome sizes using a BD FACSCanto II (Becton, Dickinson and Company, USA) flow cytometer, with histograms generated in the software Cell Quest. We performed statistical analyses using the Flowing Software 2.5.1 (http://www.flowingsoftware.com). We considered three samples from each individual for genome size estimation. We carried out an ANOVA to describe variation in DNA content within and among ploidy levels. Chromosome counts We performed chromosome counts from root tips ~1.5 cm in length collected from plants grown in pots. We treated root tips with 0.002 M 8-hydroxyquinoline (Sigma, USA) for 24 h at 9 °C, fixed them in Carnoy solution (3:1 ethanol:glacial acetic acid) and stored them in a refrigerator for at least 24 h. We then washed the material in distilled water and digested it with an enzymatic solution of 4% cellulase Onozuka R10 (Yakult, Japan) and 1% pectinase (Sigma, USA) diluted in citrate buffer at 37 °C for 7 h. For slide preparation, we used the air-drying technique (Carvalho & Saraiva, 1993), staining them with 5% Giemsa solution (Merck, Germany). We analysed the slides under a light microscope (Olympus BX51, Japan) at ×1000, magnification considering ≥ 20 complete metaphases from each individual. Association between climate and frequency of cytotypes For each individual record, we extracted values for six high-resolution (30 arc-s) WorldClim environmental variables (Hijmans et al., 2005) using the software Q-GIS (QGIS Development Team, 2016). As temperature and precipitation affect the production of inflorescences and vegetative growth in Z. mackayi (Campacci et al., 2017), we considered annual mean temperature, temperature seasonality (standard deviation), maximum temperature of the warmest month, minimum temperature of the coldest month, annual precipitation and precipitation seasonality (coefficient of variation) as putative environmental conditions related to differential fitness among cytotypes. For each sampling locality, we calculated the mean values for each one of these variables. The raw data matrix covering abiotic variables and ploidy levels considered in this study is given in the Supporting information (Table S1). WorldClim variables may be correlated with one another and thus handling multicollinearity is strongly recommended before analysis (Wiens & Graham, 2005; Dormann et al., 2013). We calculated Pearson correlation coefficients between every pair of variables to assess possible high correlations (i.e. r ≥ 0.7; Dormann et al., 2013). As most variables were highly correlated, we implemented a principal component analysis (PCA) to summarize variation, minimize collinearity and maximize the percentage of variation of climatic variables. We examined associations between climate variables and frequency of cytotypes in the populations using a generalized linear model (GLM) with a binomial distribution and logit link function. We considered the fixed explanatory variable as the first component axis of the PCA and the response variable as the proportion of 2n = 96 cytotypes within each population. We performed the GLM using the package Stats in the software R version 3.1.3 (R Development Core Team, 2008). RESULTS Genome size and number of chromosomes We estimated the genome size of 432 individuals from 19 populations. Histograms indicate three discrete, non-overlapping groups of genome size with the following means (±SD): (group 1) 7.38 ± 0.081 pg; (group 2) 10.48 ± 0.077 pg; and (group 3) 14.07 ± 0.132 pg (Table 1; Fig. 2A–C). The average coefficient of variation for the G0/G1 peaks was 2.63% and thus considered satisfactory, because it was < 5% (Galbraith et al., 2001; Praça-Fontes et al., 2011). We did not observe any statistically significant difference in the DNA contents among individuals of each group (group 1, F = 0.893, N = 183; group 2, F = 1.113, N = 32; and group 3, F = 0.712, N = 217). Figure 2. View largeDownload slide Average amount of DNA and number of chromosomes observed for Zygopetalum mackayi Hook. A–C, representative histograms of flow cytometry of three different cytotypes: A, 2C = 7.38 pg; B, 2C = 10.48 pg; and C, 2C = 14.07 pg. D–F, mitotic chromosomes stained with 5% Giemsa solution. D, ZM172 (population IT), with 2n = 8x = 48. E, ZM125 (population SC), with 2n = 12x = 72. F, ZM004 (population SB), with 2n = 16x = 96 chromosomes. a and b represent G1 peaks of the sample and the reference standard, respectively. Scale bars represent 5 μm. Figure 2. View largeDownload slide Average amount of DNA and number of chromosomes observed for Zygopetalum mackayi Hook. A–C, representative histograms of flow cytometry of three different cytotypes: A, 2C = 7.38 pg; B, 2C = 10.48 pg; and C, 2C = 14.07 pg. D–F, mitotic chromosomes stained with 5% Giemsa solution. D, ZM172 (population IT), with 2n = 8x = 48. E, ZM125 (population SC), with 2n = 12x = 72. F, ZM004 (population SB), with 2n = 16x = 96 chromosomes. a and b represent G1 peaks of the sample and the reference standard, respectively. Scale bars represent 5 μm. We observed three or four peaks (Fig. 2A–C). Four peaks represent sequentially the following stages: G0/G1 and G2 of the sample (orchid), and the peaks G0/G1 and G2 of the internal standard Vicia faba (Fig. 2A, B). Histograms with only three peaks show, respectively, G0/G1 of the sample (orchid), G2 of the sample (orchid) overlapped with the peak G0/G1 of V. faba and, lastly, the peak G2 of V. faba (Fig. 2C). We also did not observe additional peaks of higher DNA content when comparing histograms of young and older leaves from the same individual. This result might suggest the absence of endoreduplication (i.e. replication of the nuclear genome in the absence of mitosis), which is an additional cause of polyploidy. However, as G2 peaks were generally much higher than G1 peaks within the same individual, mainly in cytotype 2n = 48, we cannot exclude the possibility of endoreduplication. Genome size was positively related to chromosome number: 2C = 7.38 pg corresponds to 2n = 48 (Fig. 2A, D); 2C = 10.48 pg corresponds to 2n = 72 (Fig. 2B, E), and 2C = 14.07 pg correspond to 2n = 96 chromosomes (Fig. 2C, F). Considering x = 6 as the basic chromosome number for this species (see Discussion section), the cytotypes correspond, respectively, to octaploid, dodecaploid and hexadecaploid. Climate correlation of cytotypes The geographical distributions of cytotypes from groups 1–3 followed a north-eastern to southern pattern. Mixed-ploidy populations comprising two or three cytotypes were mainly located in a narrow region of north-eastern of the state of Minas Gerais in a region of contact between 2n = 48 and 2n = 96 cytotypes (Fig. 1). The larger genome (group 3) was the most common, whereas individuals with intermediate genome sizes (group 2) were less frequent (Fig. 1). The PCA’s first principal component (PC1), composed of the variables mean annual precipitation and temperature seasonality, accounted for 84.42% of the variation in the geographical distribution of cytotypes (Figs 3, 4). Factor loadings of PC1 indicate a gradient from sites with high annual precipitation and marked temperature seasonality to sites with low annual precipitation and subtle temperature seasonality (Figs 3, 4). The probability of occurrence of the larger cytotype (2n = 96) decreases as water availability and seasonal temperature fluctuations decrease (P > 0.001; Table 2, Figs 3, 4). Figure 3. View largeDownload slide Graphical display of the generalized linear model for the proportion of 2n = 96 cytotpes of Zygopetalum mackayi Hook. in each population as a function of the first component axis of a principal component analysis composed of the variables mean annual precipitation and temperature seasonality. Open circles represent each of the 19 sampled locations. Figure 3. View largeDownload slide Graphical display of the generalized linear model for the proportion of 2n = 96 cytotpes of Zygopetalum mackayi Hook. in each population as a function of the first component axis of a principal component analysis composed of the variables mean annual precipitation and temperature seasonality. Open circles represent each of the 19 sampled locations. Figure 4. View largeDownload slide Temperature seasonality and annual precipitation values as a function of the first axis of the principal component analysis. Open circles represent each of the 19 sampled locations for Zygopetalum mackayi Hook. Figure 4. View largeDownload slide Temperature seasonality and annual precipitation values as a function of the first axis of the principal component analysis. Open circles represent each of the 19 sampled locations for Zygopetalum mackayi Hook. Table 2. Association of climate variables (annual water availability and seasonal temperature fluctuations) with genome size in Zygopetalum mackayi Hook., as analysed with a generalized linear model, considering a binomial distribution   Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13    Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13  The first component axis of a principal component analysis (PCA1) was the fixed explanatory variable, and the random response variable was the proportion of 2n = 96 cytotypes within each population. View Large Table 2. Association of climate variables (annual water availability and seasonal temperature fluctuations) with genome size in Zygopetalum mackayi Hook., as analysed with a generalized linear model, considering a binomial distribution   Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13    Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13  The first component axis of a principal component analysis (PCA1) was the fixed explanatory variable, and the random response variable was the proportion of 2n = 96 cytotypes within each population. View Large DISCUSSION In this study, we confirm earlier reports of two cytotypes for the orchid Z. mackayi (an octaploid, 2n = 8x = 48, and a hexadecaploid, 2n = 16x = 96) and describe a third, less frequent, cytotype of intermediate genome size (a dodecaploid, 2n = 12x = 72). Moreover, we describe great variation in the frequency of these three cytotypes along a large geographical range of this species. Finally, we show a strong association between climate and genome size in a typical species of HERCs from Brazil. In what follows, we discuss our results in the light of chromosome evolution and the role of polyploidy in lineage diversification. Chromosome evolution The chromosome numbers identified for Z. mackayi portray a polyploid series. The basic chromosome number (i.e. the number of different chromosomes that make up a single complete set) was estimated for orchids as x = 7 (Félix & Guerra, 2000, 2005). As this value is uncommon for orchids, we hypothesize the occurrence of successive cycles of polyploidization to originate exclusively polyploid genera (Félix & Guerra, 2000). We propose that x = 6 originated by descending dysploidy (i.e. variation in chromosome numbers by less than a whole set of chromosomes), later giving rise to the chromosome numbers observed for Z. mackayi: 2n = 8x = 48 (octaploid), 2n = 12x = 72 (dodecaploid) and 2n = 16x = 96 (hexadecaploid). Empirical studies show that dysploidy is a recurrent event in the chromosomal evolution of orchid species (e.g. Felix & Guerra, 2010; Moraes, Leitch, & Leitch, 2012). Our findings reinforce the idea that polyploidy and chromosomal dysploidy events are common mechanisms involved in the karyotype evolution of orchids (Moraes et al., 2012). Geographical patterns of cytotype variation The frequency of the three cytotypes of Z. mackayi is geographically structured and associated with climatic conditions. Cytotype 2n = 96 has a larger distribution range and is found mainly in areas with marked temperature seasonality and high precipitation levels. Conversely, cytotype 2n = 48 is found in areas with subtle temperature seasonality and low precipitation levels. There is a narrow contact zone among cytotypes, where individuals with the intermediate cytotype 2n = 72 are found (Fig. 1). The association of climate and cytotype occurrence is well established in the literature (revised by Ramsey & Ramsey, 2014). Polyploidy contributes to increased cell and organ size and may result in increased hydraulic conductivity through the enlargement of stomata and vessel diameter (Maherali, Walden & Husband, 2009). Its effect on physiological tolerance, however, is not consistently predictable. Drought tolerance in polyploids may be lower than in diploids because of a trade-off between water transport efficiency and safety against hydraulic failure as a result of cavitation (Hao et al., 2013). This may explain why cytotype 2n = 96 is found in areas of higher annual precipitation. Polyploidy is also positively correlated with the capacity for growth at low temperature, because mitosis is more rapidly inhibited than cell expansion as temperature decreases (Bretagnolle & Thompson, 1996). Therefore, differences in growth may also explain the climatic differentiation of Z. mackayi cytotypes and the larger range of cytotype 2n = 96. Mixed-cytotype populations Two alternative scenarios may explain the occurrence of mixed cytotype populations of Z. mackayi. Novel 2n = 96 cytotypes could arise repeatedly from autopolyploidization of 2n = 48 individuals within populations. In this case, we expect to find a high incidence of rare cytotypes among adults in most populations (Kolář et al., 2017). In the second scenario, populations of 2n = 48 and/or 96 cytotypes have been isolated and came into secondary contact after divergence. In this scenario, we expect to find pure populations of different cytotypes and a contact zone with mixed-ploidy populations and intermediate cytotypes (Kolář et al., 2017). Our findings support the second scenario because there is: (1) a clear geographical structure of cytotypes; (2) a well-defined contact zone between the range areas of pure cytotypes; and (3) restriction of intermediate cytotypes to the contact zone. However, the interpretation of ploidy‐driven shifts in ecological requirements of coexisting cytotypes must be considered carefully. Ecological segregation of cytotypes may not be a direct consequence of polyploidy, but a result of independent evolutionary histories of cytotypes (i.e. allopatry), which regained contact after geographical isolation (Mráz et al., 2012). Populations with multiple cytotypes are ideal to study the role of ploidy variation in adaptation and speciation (Kolář et al., 2017). Newly established cytotypes must compete for reproductive opportunities (minority cytotype exclusion; Levin, 1975). Gametes of newly formed polyploids may be lost when uniting with more abundant gametes of the more common cytotype. But there are several mechanisms that allow polyploid establishment in the mixed-cytotype populations (Kolář et al., 2017). Our results suggest that ecological differentiation of cytotypes in Z. mackayi might cause spatial segregation, leading to reproductive isolation between cytotypes. Common garden experiments and ecophysiological studies are underway to identify whether the cytotype distribution within and among populations of this species represent adaptations to different environments. Facultative apomixis, observed in 2n = 72 and 2n = 96 cytotypes (G. Costa, unpublished data), may also allow newly polyploid cytotypes to expand and thrive (Levin, 1975). Although Z. mackayi is pollinator dependent for fruit formation (Campacci et al., 2017), ongoing studies indicate that 2n = 72 and 2n = 96 cytotypes may lower inbreeding depression through the production of apomictic seeds (G. Costa, unpublished data). Finally, the occurrence of geographically close populations with very different cytotype frequencies (Fig. 1, populations PI and SA) suggests that demographic stochasticity may act randomly, increasing or fixing cytotypes (Levin, 1975; Kolář et al., 2017). In finite populations, such as those inhabiting HERCs, genetic drift is commonly a predominant force governing population size (Pinheiro et al., 2014). Conclusion To our knowledge, this is the first study to investigate geographical patterns of cytotype distribution in a representative species of a Neotropical HARC. Temperature seasonality and annual precipitation are good predictors of the frequency of different cytotypes in Z. mackayi. Further cytogeographical studies of additional HARC endemics are necessary to evaluate the effect of climate in the establishment and diversification of other polyploid plants within this habitat. Polyploidy is known to be an important process in the diversification of mountain lineages (Ramsey & Schemske, 1998), but knowledge about its relationship with plant diversity and endemism of Brazilian HERCs is still sparse. We foresee exciting times ahead for polyploid research in this geographical region. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher's web-site: Tabel S1. Population code, individual code, genome size, locality information and geographical coordinates of individuals of Zygopetalum mackayi considered in this study. Average values per population for each bioclimatic variable are indicated in the last six columns on the left. ACKNOWLEDGEMENTS This study is the result of the PhD thesis of S.S.L.G., developed in the Graduate Programme of Biological Sciences at Universidade Federal de Juiz de Fora, Brazil, with a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. We thank R. Vasconcelos for all the help with the fieldwork, the staff from Orquidário Colibri and Instituto de Botânica de São Paulo for logistical support in cultivating orchid specimens, D. Muniz for the assistance with statistical analyses, and E. Forni-Martins, G. Machado and two anonymous reviewers for comments on previous versions of the manuscript. Permission for collection and transportation of samples was given by Instituto Chico Mendes de Conservação da Biodiversidade (#16746). S.K. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, Brazil (grant number 2014/04426-5). L.F.V. was supported by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (grant number APQ-02096-14/PPM 00478-16). 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Genome size and climate segregation suggest distinct colonization histories of an orchid species from Neotropical high-elevation rocky complexes

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Abstract

Abstract Knowledge about the geographical distribution of cytotypes is crucial to understand the role of polyploidy in diversification. High-elevation rocky complexes (HERCs) are heterogeneous formations found in elevated areas of eastern Brazil. They harbour one of the most endemic floras within the Neotropical region. Yet, we lack knowledge concerning the association of environmental variables and plant cytotypes in this region. Here, we investigate whether the frequency of Zygopetalum mackayi orchid cytotypes is related to climate conditions in the HERCs. We describe chromosome counts, genome size estimates and their association with climate variables for 432 individuals from 19 localities. We show, for the first time, a strong association between climate variation and cytotype variation in a species from the HERCs. We confirm two cytotypes for Z. mackayi (2n = 48 and 2n = 96), which are geographically structured, and describe an intermediate cytotype (2n = 72) restricted to a contact zone. We discuss the implications of our results for chromosome evolution in this species and provide hypotheses for the origin and maintenance of cytotypes. chromosome number, environmental predictor, hybrid zone, mixed cytotype population, Neotropics, Orchidaceae, polyploid, Brazil INTRODUCTION Polyploidy is a common process in the origin and divergence of plant species (Soltis et al., 2016; Spoelhof, Soltis & Soltis, 2017). An immediate effect of polyploidy is the emergence in the population of two or more cytotypes, i.e. individuals with different numbers of chromosomes in their somatic cells (Kolář et al., 2017). The study of the geographical distribution of cytotypes is a key requirement to understand the role of polyploidy in diversification (e.g. Hageroup, 1933; Stebbins, 1942; Sonnleitner et al., 2010). Cytogeographical patterns may unravel significant amounts of diversity by identifying multiple chromosomal races within a single taxonomic species and, therefore, contribute to the conservation of rare species and ecological restoration (Soltis et al., 2007). However, most of the studies on the geographical distribution of cytotypes are concentrated in temperate regions, especially in Europe and North America (revised by Ramsey & Ramsey, 2014). If we are to understand the role of polyploidy in diversification on a worldwide scale, we need more information on the geographical distribution of cytotypes in tropical regions (Ramsey & Ramsey, 2014), where the incidence of polyploidy is likely to be underestimated. The high-elevation rocky complexes (HERCs) are among the most diverse areas in the Neotropical region (Safford, 1999; Alves et al., 2014). In southern Brazil, HERCs are highly heterogeneous formations, where climate, geology (i.e. topography, soil depth, fertility and drainage) and the surrounding vegetation define distinct floristic compositions (Safford, 1999; Benites et al., 2007; Alves & Kolbek, 2010; Schaefer et al., 2016). This environmental heterogeneity may explain, in part, the high species richness and endemism reported for these areas (Alves & Kolbek, 2010; Echternacht et al., 2011). Besides environmental heterogeneity, polyploidization and hybridization are also considered important mechanisms promoting plant diversification in Brazilian HERCs (e.g. Giulietti, Pirani & Harley, 1997; Antonelli et al., 2010). Yet, the few available studies are restricted to the description of chromosome counts and cytotype variation within few populations (Benko-Iseppon & Wanderley, 2002; Mansanares, Forni-Martins & Semir, 2002; Viccini et al., 2005; Yamagishi-Costa & Forni-Martins, 2009). Despite the extraordinary diversity of HERCs, little is known about the role of polyploidy in plant diversification in these areas. Basic information on patterns of geographical distribution of chromosome variation is, therefore, urgent. The orchid Zygopetalum mackayi Hook. occurs almost exclusively in granitic and quartzite HERCs from the southern to north-eastern Brazil (Hoehne, 1953; Flora do Brasil, 2020). In this species, there are anecdotic reports of two cytotypes, one with 48 chromosomes and another with 96 chromosomes (Tanaka & Kamemoto, 1984; Brandham, 1999; Félix & Guerra, 2000). However, there is no information on the place of origin of these two cytotypes, and no detailed data on how their frequencies vary along the large distribution range of Z. mackayi. Considering that environmental conditions show great variation along the distribution range of the species, this orchid offers an ideal opportunity to explore the role of ecological processes on the diversification of genome size in the flora from Neotropical HERCs. In this study, we quantify the frequency of the two cytotypes of Z. mackayi in several populations along the distribution range of the species. Moreover, considering that the occurrence of polyploids in natural populations may be influenced (negatively or positively) by the climate (Ramsey & Ramsey, 2014), we investigate whether the population frequency of the two cytotypes is related to local climatic conditions. In the southernmost range of Z. mackayi, temperature is markedly seasonal and annual precipitation high (nearly 1500–3000 mm/year). In contrast, in the northernmost range of the species, temperature shows little seasonal variation and annual precipitation is lower (nearly 800–1500 mm/year). Thus, we expected that population variations in the frequency of the two cytotypes will be related to the environmental gradient imposed by climate variations. MATERIAL AND METHODS Sampling We collected leaf samples from adult plants across the entire geographical range of Z. mackayi from August 2014 to April 2016 (Table 1, Fig. 1). Sample size ranged from five to 59 individuals per population. Within the same locality, we collected specimens at least 10 m apart to avoid clonal plants attributable to sympod fragmentation and apomixis (Campacci et al., 2017). We used at least five specimens of each ploidy to determine chromosome numbers. Table 1. Means of genome sizes (2C) estimated by flow cytometry and presumed ploidy levels of Zygopetalum mackayi Hook., collected from 432 individuals of 19 populations Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  CV, coefficient of variation; 1 pg of DNA is equivalent to 978 Mpb. Vouchers are deposited at Universidade Estadual de Campinas (UEC). View Large Table 1. Means of genome sizes (2C) estimated by flow cytometry and presumed ploidy levels of Zygopetalum mackayi Hook., collected from 432 individuals of 19 populations Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  Population code  Locality  Geographical coordinates  Cytotype  No individuals  Mean 2C value ± SD (pg DNA)  2C CV (%)  2C DNA content (Mpb)  CF  Cambará do Sul/RS  29°03.511′ 49°57.427′  2n = 96  11  14.15 ± 0.171  3.0  13838.700  IT  Santo Antônio do Itambé/MG  18°23.532′ 43°19.544′  2n = 48  14  7.38 ± 0.063  2.80  7216.662        2n = 72  6  10.50 ± 0.094  2.39  10268.022        2n = 96  28  14.10 ± 0.132  2.30  13787.844  MC  Garuva/SC  19°20.135′ 41°34.243′  2n = 96  6  13.98 ± 0.030  2.88  13672.440  PA  Ouro Preto/MG  20°22.188′ 43°30.330′  2n = 96  5  14.13 ± 0.052  2.94  13821.096  PB  Santa Maria Madalena/RJ  21°56.354′ 41°59.351′  2n = 96  7  14.18 ± 0.134  2.71  13864.128  PD  Itaipé/MG  17°29.243′ 41°37.042′  2n = 48  30  7.39 ± 0.076  2.57  7223.508  PF  Biritiba-Mirim/SP  23°39.29′ 46°02.05′  2n = 48  1  7.24 ± 0.000  2.00  7080.720        2n = 96  19  14.02 ± 0.099  2.53  13710.582  PG  Caraí/MG  17°22.138′ 41°44.211′  2n = 48  23  7.43 ± 0.065  2.53  7265.562  PI  Alvarenga/MG  19°15.519′ 41°35.266′  2n = 48  36  7.37 ± 0.070  2.52  7204.926        2n = 72  1  10.60 ± 0.000  2.76  10366.800        2n = 96  10  14.05 ± 0.126  2.51  13739.922  PM  Piquete/SP  22°29.788′ 45°07.348′  2n = 96  32  14.02 ± 0.099  2.77  13708.626  PP  Campina Grande do Sul/PR  25°14.26′ 48°49.41′  2n = 72  1  10.57 ± 0.000  2.94  10337.460        2n = 96  24  14.05 ± 0.134  2.98  13740.900  PY  Paty de Alferes/RJ  22°26.438′ 43°23.009′  2n = 96  13  14.12 ± 0.163  2.81  13810.338  SA  Conselheiro Pena/MG  19°20.184′ 41°34.234′  2n = 48  3  7.32 ± 0.110  2.54  7161.894        2n = 72  17  10.45 ± 0.064  2.41  10218.144        2n = 96  3  14.32 ± 0.047  2.54  14001.960  SB  São José do Barreiro/SP  22°42.052′ 44°37.483′  2n = 96  34  14.08 ± 0.128  2.6  13774.152  SC  Santana do Riacho/MG  21°56.354′ 41°59.351′  2n = 72  5  10.55 ± 0.028  2.6  10317.900        2n = 96  6  14.05 ± 0.139  2.6  13742.856  SD  Guiné/BA  12°45.284′ 41°30.309′  2n = 48  20  7.35 ± 0.079  2.70  7189.278  SF  Passa Quatro/MG    2n = 96  13  14.05 ± 0.127  2.62  13744.812  SP  Nova Belém/MG  18°31.461′ 41°09.044′  2n = 48  57  7.39 ± 0.090  2.63  7226.442        2n = 72  1  10.45 ± 0.000  2.03  10220.100        2n = 96  1  14.08 ± 0.000  2.87  13770.240  ST  São Tomé das Letras/MG  21°43.550′ 44°58.985′  2n = 96  5  13.98 ± 0.064  2.87  13674.396  CV, coefficient of variation; 1 pg of DNA is equivalent to 978 Mpb. Vouchers are deposited at Universidade Estadual de Campinas (UEC). View Large Figure 1. View largeDownload slide Sampled populations of Zygopetalum mackayi Hook. from 19 localities, considering ploidy levels of all 432 sampled individuals. Population identifiers correspond to Table 1. The colour of the circles indicates ploidy level: blue, 2n = 48; red, 2n = 72; and yellow, 2n = 96. Figure 1. View largeDownload slide Sampled populations of Zygopetalum mackayi Hook. from 19 localities, considering ploidy levels of all 432 sampled individuals. Population identifiers correspond to Table 1. The colour of the circles indicates ploidy level: blue, 2n = 48; red, 2n = 72; and yellow, 2n = 96. Genome size estimation To estimate genome size, we macerated ~25 mg of leaf tissue with the same amount of the internal reference standard Vicia faba (2C = genome size 26.90 pg; following Doležel, Sgorbati & Lucretti, 1992). We macerated the leaves with 1 mL of cold LB01 buffer using a scalpel blade to release the nuclei into suspension (Doležel & Binarová, 1989). For nuclear staining, we added 25 µL of a 1 mg/mL solution of propidium iodide (PI; Sigma, USA) followed by 5 µL of RNase (100 µg/mL). We estimated genome sizes using a BD FACSCanto II (Becton, Dickinson and Company, USA) flow cytometer, with histograms generated in the software Cell Quest. We performed statistical analyses using the Flowing Software 2.5.1 (http://www.flowingsoftware.com). We considered three samples from each individual for genome size estimation. We carried out an ANOVA to describe variation in DNA content within and among ploidy levels. Chromosome counts We performed chromosome counts from root tips ~1.5 cm in length collected from plants grown in pots. We treated root tips with 0.002 M 8-hydroxyquinoline (Sigma, USA) for 24 h at 9 °C, fixed them in Carnoy solution (3:1 ethanol:glacial acetic acid) and stored them in a refrigerator for at least 24 h. We then washed the material in distilled water and digested it with an enzymatic solution of 4% cellulase Onozuka R10 (Yakult, Japan) and 1% pectinase (Sigma, USA) diluted in citrate buffer at 37 °C for 7 h. For slide preparation, we used the air-drying technique (Carvalho & Saraiva, 1993), staining them with 5% Giemsa solution (Merck, Germany). We analysed the slides under a light microscope (Olympus BX51, Japan) at ×1000, magnification considering ≥ 20 complete metaphases from each individual. Association between climate and frequency of cytotypes For each individual record, we extracted values for six high-resolution (30 arc-s) WorldClim environmental variables (Hijmans et al., 2005) using the software Q-GIS (QGIS Development Team, 2016). As temperature and precipitation affect the production of inflorescences and vegetative growth in Z. mackayi (Campacci et al., 2017), we considered annual mean temperature, temperature seasonality (standard deviation), maximum temperature of the warmest month, minimum temperature of the coldest month, annual precipitation and precipitation seasonality (coefficient of variation) as putative environmental conditions related to differential fitness among cytotypes. For each sampling locality, we calculated the mean values for each one of these variables. The raw data matrix covering abiotic variables and ploidy levels considered in this study is given in the Supporting information (Table S1). WorldClim variables may be correlated with one another and thus handling multicollinearity is strongly recommended before analysis (Wiens & Graham, 2005; Dormann et al., 2013). We calculated Pearson correlation coefficients between every pair of variables to assess possible high correlations (i.e. r ≥ 0.7; Dormann et al., 2013). As most variables were highly correlated, we implemented a principal component analysis (PCA) to summarize variation, minimize collinearity and maximize the percentage of variation of climatic variables. We examined associations between climate variables and frequency of cytotypes in the populations using a generalized linear model (GLM) with a binomial distribution and logit link function. We considered the fixed explanatory variable as the first component axis of the PCA and the response variable as the proportion of 2n = 96 cytotypes within each population. We performed the GLM using the package Stats in the software R version 3.1.3 (R Development Core Team, 2008). RESULTS Genome size and number of chromosomes We estimated the genome size of 432 individuals from 19 populations. Histograms indicate three discrete, non-overlapping groups of genome size with the following means (±SD): (group 1) 7.38 ± 0.081 pg; (group 2) 10.48 ± 0.077 pg; and (group 3) 14.07 ± 0.132 pg (Table 1; Fig. 2A–C). The average coefficient of variation for the G0/G1 peaks was 2.63% and thus considered satisfactory, because it was < 5% (Galbraith et al., 2001; Praça-Fontes et al., 2011). We did not observe any statistically significant difference in the DNA contents among individuals of each group (group 1, F = 0.893, N = 183; group 2, F = 1.113, N = 32; and group 3, F = 0.712, N = 217). Figure 2. View largeDownload slide Average amount of DNA and number of chromosomes observed for Zygopetalum mackayi Hook. A–C, representative histograms of flow cytometry of three different cytotypes: A, 2C = 7.38 pg; B, 2C = 10.48 pg; and C, 2C = 14.07 pg. D–F, mitotic chromosomes stained with 5% Giemsa solution. D, ZM172 (population IT), with 2n = 8x = 48. E, ZM125 (population SC), with 2n = 12x = 72. F, ZM004 (population SB), with 2n = 16x = 96 chromosomes. a and b represent G1 peaks of the sample and the reference standard, respectively. Scale bars represent 5 μm. Figure 2. View largeDownload slide Average amount of DNA and number of chromosomes observed for Zygopetalum mackayi Hook. A–C, representative histograms of flow cytometry of three different cytotypes: A, 2C = 7.38 pg; B, 2C = 10.48 pg; and C, 2C = 14.07 pg. D–F, mitotic chromosomes stained with 5% Giemsa solution. D, ZM172 (population IT), with 2n = 8x = 48. E, ZM125 (population SC), with 2n = 12x = 72. F, ZM004 (population SB), with 2n = 16x = 96 chromosomes. a and b represent G1 peaks of the sample and the reference standard, respectively. Scale bars represent 5 μm. We observed three or four peaks (Fig. 2A–C). Four peaks represent sequentially the following stages: G0/G1 and G2 of the sample (orchid), and the peaks G0/G1 and G2 of the internal standard Vicia faba (Fig. 2A, B). Histograms with only three peaks show, respectively, G0/G1 of the sample (orchid), G2 of the sample (orchid) overlapped with the peak G0/G1 of V. faba and, lastly, the peak G2 of V. faba (Fig. 2C). We also did not observe additional peaks of higher DNA content when comparing histograms of young and older leaves from the same individual. This result might suggest the absence of endoreduplication (i.e. replication of the nuclear genome in the absence of mitosis), which is an additional cause of polyploidy. However, as G2 peaks were generally much higher than G1 peaks within the same individual, mainly in cytotype 2n = 48, we cannot exclude the possibility of endoreduplication. Genome size was positively related to chromosome number: 2C = 7.38 pg corresponds to 2n = 48 (Fig. 2A, D); 2C = 10.48 pg corresponds to 2n = 72 (Fig. 2B, E), and 2C = 14.07 pg correspond to 2n = 96 chromosomes (Fig. 2C, F). Considering x = 6 as the basic chromosome number for this species (see Discussion section), the cytotypes correspond, respectively, to octaploid, dodecaploid and hexadecaploid. Climate correlation of cytotypes The geographical distributions of cytotypes from groups 1–3 followed a north-eastern to southern pattern. Mixed-ploidy populations comprising two or three cytotypes were mainly located in a narrow region of north-eastern of the state of Minas Gerais in a region of contact between 2n = 48 and 2n = 96 cytotypes (Fig. 1). The larger genome (group 3) was the most common, whereas individuals with intermediate genome sizes (group 2) were less frequent (Fig. 1). The PCA’s first principal component (PC1), composed of the variables mean annual precipitation and temperature seasonality, accounted for 84.42% of the variation in the geographical distribution of cytotypes (Figs 3, 4). Factor loadings of PC1 indicate a gradient from sites with high annual precipitation and marked temperature seasonality to sites with low annual precipitation and subtle temperature seasonality (Figs 3, 4). The probability of occurrence of the larger cytotype (2n = 96) decreases as water availability and seasonal temperature fluctuations decrease (P > 0.001; Table 2, Figs 3, 4). Figure 3. View largeDownload slide Graphical display of the generalized linear model for the proportion of 2n = 96 cytotpes of Zygopetalum mackayi Hook. in each population as a function of the first component axis of a principal component analysis composed of the variables mean annual precipitation and temperature seasonality. Open circles represent each of the 19 sampled locations. Figure 3. View largeDownload slide Graphical display of the generalized linear model for the proportion of 2n = 96 cytotpes of Zygopetalum mackayi Hook. in each population as a function of the first component axis of a principal component analysis composed of the variables mean annual precipitation and temperature seasonality. Open circles represent each of the 19 sampled locations. Figure 4. View largeDownload slide Temperature seasonality and annual precipitation values as a function of the first axis of the principal component analysis. Open circles represent each of the 19 sampled locations for Zygopetalum mackayi Hook. Figure 4. View largeDownload slide Temperature seasonality and annual precipitation values as a function of the first axis of the principal component analysis. Open circles represent each of the 19 sampled locations for Zygopetalum mackayi Hook. Table 2. Association of climate variables (annual water availability and seasonal temperature fluctuations) with genome size in Zygopetalum mackayi Hook., as analysed with a generalized linear model, considering a binomial distribution   Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13    Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13  The first component axis of a principal component analysis (PCA1) was the fixed explanatory variable, and the random response variable was the proportion of 2n = 96 cytotypes within each population. View Large Table 2. Association of climate variables (annual water availability and seasonal temperature fluctuations) with genome size in Zygopetalum mackayi Hook., as analysed with a generalized linear model, considering a binomial distribution   Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13    Estimate  Standard error  z value  Pr(>|z|)  (Intercept)  3.317855669  0.550154524  6.030770489  1.63 × 10−9  PCA1  −6.596906389  0.908743684  −7.25936973  3.89 × 10−13  The first component axis of a principal component analysis (PCA1) was the fixed explanatory variable, and the random response variable was the proportion of 2n = 96 cytotypes within each population. View Large DISCUSSION In this study, we confirm earlier reports of two cytotypes for the orchid Z. mackayi (an octaploid, 2n = 8x = 48, and a hexadecaploid, 2n = 16x = 96) and describe a third, less frequent, cytotype of intermediate genome size (a dodecaploid, 2n = 12x = 72). Moreover, we describe great variation in the frequency of these three cytotypes along a large geographical range of this species. Finally, we show a strong association between climate and genome size in a typical species of HERCs from Brazil. In what follows, we discuss our results in the light of chromosome evolution and the role of polyploidy in lineage diversification. Chromosome evolution The chromosome numbers identified for Z. mackayi portray a polyploid series. The basic chromosome number (i.e. the number of different chromosomes that make up a single complete set) was estimated for orchids as x = 7 (Félix & Guerra, 2000, 2005). As this value is uncommon for orchids, we hypothesize the occurrence of successive cycles of polyploidization to originate exclusively polyploid genera (Félix & Guerra, 2000). We propose that x = 6 originated by descending dysploidy (i.e. variation in chromosome numbers by less than a whole set of chromosomes), later giving rise to the chromosome numbers observed for Z. mackayi: 2n = 8x = 48 (octaploid), 2n = 12x = 72 (dodecaploid) and 2n = 16x = 96 (hexadecaploid). Empirical studies show that dysploidy is a recurrent event in the chromosomal evolution of orchid species (e.g. Felix & Guerra, 2010; Moraes, Leitch, & Leitch, 2012). Our findings reinforce the idea that polyploidy and chromosomal dysploidy events are common mechanisms involved in the karyotype evolution of orchids (Moraes et al., 2012). Geographical patterns of cytotype variation The frequency of the three cytotypes of Z. mackayi is geographically structured and associated with climatic conditions. Cytotype 2n = 96 has a larger distribution range and is found mainly in areas with marked temperature seasonality and high precipitation levels. Conversely, cytotype 2n = 48 is found in areas with subtle temperature seasonality and low precipitation levels. There is a narrow contact zone among cytotypes, where individuals with the intermediate cytotype 2n = 72 are found (Fig. 1). The association of climate and cytotype occurrence is well established in the literature (revised by Ramsey & Ramsey, 2014). Polyploidy contributes to increased cell and organ size and may result in increased hydraulic conductivity through the enlargement of stomata and vessel diameter (Maherali, Walden & Husband, 2009). Its effect on physiological tolerance, however, is not consistently predictable. Drought tolerance in polyploids may be lower than in diploids because of a trade-off between water transport efficiency and safety against hydraulic failure as a result of cavitation (Hao et al., 2013). This may explain why cytotype 2n = 96 is found in areas of higher annual precipitation. Polyploidy is also positively correlated with the capacity for growth at low temperature, because mitosis is more rapidly inhibited than cell expansion as temperature decreases (Bretagnolle & Thompson, 1996). Therefore, differences in growth may also explain the climatic differentiation of Z. mackayi cytotypes and the larger range of cytotype 2n = 96. Mixed-cytotype populations Two alternative scenarios may explain the occurrence of mixed cytotype populations of Z. mackayi. Novel 2n = 96 cytotypes could arise repeatedly from autopolyploidization of 2n = 48 individuals within populations. In this case, we expect to find a high incidence of rare cytotypes among adults in most populations (Kolář et al., 2017). In the second scenario, populations of 2n = 48 and/or 96 cytotypes have been isolated and came into secondary contact after divergence. In this scenario, we expect to find pure populations of different cytotypes and a contact zone with mixed-ploidy populations and intermediate cytotypes (Kolář et al., 2017). Our findings support the second scenario because there is: (1) a clear geographical structure of cytotypes; (2) a well-defined contact zone between the range areas of pure cytotypes; and (3) restriction of intermediate cytotypes to the contact zone. However, the interpretation of ploidy‐driven shifts in ecological requirements of coexisting cytotypes must be considered carefully. Ecological segregation of cytotypes may not be a direct consequence of polyploidy, but a result of independent evolutionary histories of cytotypes (i.e. allopatry), which regained contact after geographical isolation (Mráz et al., 2012). Populations with multiple cytotypes are ideal to study the role of ploidy variation in adaptation and speciation (Kolář et al., 2017). Newly established cytotypes must compete for reproductive opportunities (minority cytotype exclusion; Levin, 1975). Gametes of newly formed polyploids may be lost when uniting with more abundant gametes of the more common cytotype. But there are several mechanisms that allow polyploid establishment in the mixed-cytotype populations (Kolář et al., 2017). Our results suggest that ecological differentiation of cytotypes in Z. mackayi might cause spatial segregation, leading to reproductive isolation between cytotypes. Common garden experiments and ecophysiological studies are underway to identify whether the cytotype distribution within and among populations of this species represent adaptations to different environments. Facultative apomixis, observed in 2n = 72 and 2n = 96 cytotypes (G. Costa, unpublished data), may also allow newly polyploid cytotypes to expand and thrive (Levin, 1975). Although Z. mackayi is pollinator dependent for fruit formation (Campacci et al., 2017), ongoing studies indicate that 2n = 72 and 2n = 96 cytotypes may lower inbreeding depression through the production of apomictic seeds (G. Costa, unpublished data). Finally, the occurrence of geographically close populations with very different cytotype frequencies (Fig. 1, populations PI and SA) suggests that demographic stochasticity may act randomly, increasing or fixing cytotypes (Levin, 1975; Kolář et al., 2017). In finite populations, such as those inhabiting HERCs, genetic drift is commonly a predominant force governing population size (Pinheiro et al., 2014). Conclusion To our knowledge, this is the first study to investigate geographical patterns of cytotype distribution in a representative species of a Neotropical HARC. Temperature seasonality and annual precipitation are good predictors of the frequency of different cytotypes in Z. mackayi. Further cytogeographical studies of additional HARC endemics are necessary to evaluate the effect of climate in the establishment and diversification of other polyploid plants within this habitat. Polyploidy is known to be an important process in the diversification of mountain lineages (Ramsey & Schemske, 1998), but knowledge about its relationship with plant diversity and endemism of Brazilian HERCs is still sparse. We foresee exciting times ahead for polyploid research in this geographical region. SUPPORTING INFORMATION Additional Supporting Information may be found in the online version of this article at the publisher's web-site: Tabel S1. Population code, individual code, genome size, locality information and geographical coordinates of individuals of Zygopetalum mackayi considered in this study. Average values per population for each bioclimatic variable are indicated in the last six columns on the left. ACKNOWLEDGEMENTS This study is the result of the PhD thesis of S.S.L.G., developed in the Graduate Programme of Biological Sciences at Universidade Federal de Juiz de Fora, Brazil, with a fellowship from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior. We thank R. Vasconcelos for all the help with the fieldwork, the staff from Orquidário Colibri and Instituto de Botânica de São Paulo for logistical support in cultivating orchid specimens, D. Muniz for the assistance with statistical analyses, and E. Forni-Martins, G. Machado and two anonymous reviewers for comments on previous versions of the manuscript. Permission for collection and transportation of samples was given by Instituto Chico Mendes de Conservação da Biodiversidade (#16746). S.K. was supported by Fundação de Amparo à Pesquisa do Estado de São Paulo, Brazil (grant number 2014/04426-5). L.F.V. was supported by Fundação de Amparo à Pesquisa do Estado de Minas Gerais (grant number APQ-02096-14/PPM 00478-16). 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