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Genome size variation is of fundamental biological importance and has been a longstanding puzzle in evolutionary biology (Bennett & Leitch, 2005, 2011). Genome size, measured as the haploid nuclear DNA content (C value), varies over 2400-fold across angiosperm lineages (Pellicer et al., 2010). Such wide variation has been hypothesized to be the result of several genetic mechanisms. Among these, polyploidization and the proliferation of transposable elements are considered the most prevalent processes contributing to genome size increase, while high rates of deletion and selection against transposable elements, unequal crossing over, and illegitimate recombination are believed to be the major factors leading to genome size decrease (Morgan, 2001; Wendel et al., 2002; Ma et al., 2004; Bennetzen et al., 2005). Several evolutionary models have been proposed to explain genome size variation, including neutral (Petrov, 2002), maladaptive (Lynch & Conery, 2003) and adaptive models (Gregory, 2002; Vinogradov, 2004). However, the relative importance of these models, their biological mechanisms and the evolutionary consequences of genome size diversity remain controversial.
In the past decade, model-based phylogenetic comparative methods have become a standard statistical approach to understanding trait evolution, including the mode and tempo of genome size evolution. For example, using this approach, a mode of punctuated genome size evolution was found in Orobanche (Weiss-Schneeweiss et al., 2006), Liliaceae (Leitch et al., 2007) and Allium subgenus Melanocrommyum (Gurushidze et al., 2012), while a gradual mode was found in Hieracium (Chrtek et al., 2009). The phylogenetic comparative approach has also allowed for tests of correlated evolution between genome size and other organismal traits, such as morphological, cytological, reproductive, physiological, and ecological traits (Beaulieu et al., 2007a; Knight & Beaulieu, 2008; Whitney et al., 2010; Herben et al., 2012). Although the observation of a significant correlation between genome size and other phenotypic characters in many previous studies has led to the hypothesis of adaptive evolution of genome size, the validity of this hypothesis remains to be established. In these studies, the biological link between genome size variation and the phenotypic traits was not clearly revealed and the causes of these correlations were not convincingly determined (Oliver et al., 2007). For example, the fact that specific leaf area (SLA) lies at the intersection of cell structure and leaf functions (Wright et al., 2004; Poorter et al., 2009) leads to the predication that SLA variation might be linked to the evolution of DNA content in the cells of a species. Recently, two meta-analyses suggested that 2C DNA content is positively correlated with SLA in angiosperms, but the relationship was negative in phylogenetic independent contrasts (PIC) analysis (Beaulieu et al., 2007b; Knight & Beaulieu, 2008). Furthermore, latitudinal range has normally been used as a proxy for a suite of environmental variables such as temperature and precipitation (De Frenne et al., 2013). Genome size variation of phylogenetically independent lineages along latitudinal gradients is often interpreted as an adaptive response to variation in a temperature- and/or precipitation-based selective regime; however, previous studies have often produced conflicting results (Knight & Ackerly, 2002).
Most comparisons of genome sizes have been meta-analyses of diverse phylogenetic clades representing deep divergences such as families (Kellogg, 1998), floras (Grime & Mowforth, 1982; Knight & Ackerly, 2002) or life forms (Bennett & Leitch, 1995), with limited sampling of species within the respective clades. Such analyses at higher taxonomic ranks have little power to disentangle the different evolutionary forces at work. Several recent studies indicate that ecological factors probably play a more important role in shaping genome size variation at lower taxonomic levels than at higher levels (Jakob et al., 2004; Eilam et al., 2007; Dušková et al., 2010). Hawkins et al. (2008) pointed out that phylogenetic scale is an important factor in comparative analyses of genome size variation, and suggested that analyses among closely related species within a single genus should provide greater interpretive power than analyses comparing more distant lineages at higher taxonomic ranks. Comparative analyses of closely related species and populations allow detection of ongoing evolutionary forces and mechanisms driving genome size evolution. Unfortunately, to date, studies addressing genome size variation among closely related species and its relationship to phenotypes and environmental factors are still scarce (but see Šmarda et al., 2007; Díez et al., 2013).
As one of the most important biodiversity characters, C values have been estimated for > 7500 angiosperm species (Bennett & Leitch, 2012), representing c. 2% of flowering plant species and > 50% of all angiosperm families (APG III, 2009). These data are biased towards particular regions such as Europe and North America. The generality of previous findings on plant genome size evolution may thus be limited, as plant genome sizes in many other regions of the world with much higher species richness and geographic endemism remain unknown (Bennett & Leitch, 2011). One of the most interesting regions is the area of Karst in southern and southwestern China, which boasts over 20 000 plant species and whose flora is ranked as the most endemic-rich subtropical flora in the world. However, limited information is available on genome sizes of Karst plants. In southern and southwestern China, the Karst landform is characterized by high edaphic and topographic heterogeneity and offers a multitude of ecological niches for plant diversification and speciation. The Karst environment is generally characterized by low soil water content, periodic water deficiency, and poor nutrient availability, which exert strong selective forces on plant evolution, resulting in remarkably high species richness and endemism in the region. For example, many calcicoles (species adapted to calcareous soil) have evolved in the flora. Because of their highly diverse and unique biota, Karst regions in Southeast Asia have long been regarded as ‘natural laboratories’ for ecological and evolutionary studies to understand natural selection and speciation (Clements et al., 2006). The assessment of genome size variation and its correlation with ecological and geographic traits should provide insights into the mode and mechanisms of genome size evolution, as well as its roles in the evolution and diversification of the Karst flora. In this study, we conducted such analyses on Primulina, a genus that is highly diversified in the Karst region of southern China.
Primulina, based on circumscription of recent molecular phylogenetic analyses, is one of the largest genera of the Old World Gesneriaceae (Wang et al., 2011; Weber et al., 2011). The newly revised Primulina is a monophyletic group comprising > 140 species of perennials that are widely distributed throughout the Karst regions of China and adjacent countries of Southeast Asia. Approximately 85% of the species (120 species) are endemic to southern and southwestern China. The genus occurs in a wide latitudinal range (18°N–31°N) and is adapted to remarkably diverse habitats and niches from steep cliffs and cave entrances to lowland sandstone. However, most species are ‘point endemics’ (Samways & Lockwood, 1998) found only in a single or microareal location. Nutrient constraints in calcareous soils, particularly for nitrogen (N) and phosphorus (P), nutrients that are essential for the synthesis of nucleic acids, might have selectively favored smaller genome sizes (Hessen et al., 2010).
The rich species diversity of the genus along with the high degree of microhabitat specialization makes Primulina an ideal system for studying evolutionary divergence, adaptation and speciation. In this study, we investigated genome size variation and its relationship to SLA and latitudinal distribution to gain insights into the mechanisms driving genome size evolution. We report a large data set of new chromosome counts for 56 Primulina species, and DNA content and SLA measurements for > 100 species. We examine genome size variation within and between species, test phylogenetic signal in genome size, SLA and latitude, and assess the fit of these variables or traits to different evolutionary models. We further evaluate the relationship of genome size to SLA and latitude in a phylogenetic framework.
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The chromosome numbers of the 61 Primulina species are presented in Table S1 and the somatic chromosomes are illustrated in Fig. S2. Of these species, chromosome numbers were determined for the first time in this study for 56 species, and all have 2n = 36. The chromosome number of P. longgangensis estimated in this study is 2n = 36 (Fig. S2), which is consistent with that reported by Liu et al. (2012), but different from the report of Christie et al. (2012), suggesting intraspecific variation of ploidy levels in the species. Our results confirmed that chromosome numbers in Primulina are uniform, as reported before (Christie et al., 2012; Liu et al., 2012). The flow cytometry analysis yielded high-resolution histograms with coefficient of variance (CV) < 5% for the majority of measurements (Table S1). Representative histograms are shown in Fig. S3. The 2C values assessed among Primulina species show a 2.27-fold difference between the lowest (Primulina huajiensis; 1.12 pg) and the highest (Primulina gueilinensis var. brachycarpa; 2.54 pg) (Table S1), with a mean value of 1.92 pg. Although substantial intraspecific variation in DNA content was detected for a few species (up to 41.23% for Primulina linearifolia), genome size is intraspecifically stable, with variation for most species < 10% (74 out of 101 species; Table S1).
Reconstructing ancestral genome size on the phylogeny indicated that the estimated ancestral genome size for the genus is 1.692 pg. Genome size was observed to both increase and decrease across the range of the genus, with a general increase northward (to high latitude) and decrease southward (to low latitude) (Fig. S4). SLA in Primulina species showed 25.05-fold variation, from 47.23 cm2 g−1 in Primulina ophiopogoides to 1183.27 cm2 g−1 in Primulina xiuningensis (mean SLA 282.14 cm2 g−1) (Table S1). Species with low SLA are always found in the southern part of the distribution of Primulina, while species with high SLA can been found throughout the genus' range.
For genome size, SLA and latitude, the likelihood scores for model B were not significantly greater than those for model A, indicating that the evolution of these traits does not show any general trend toward either increase or decrease. Estimates of Pagel's λ indicated that all three variables exhibited a significant amount of phylogenetic signal, with λ significantly different from 0 (Table 1). The holoploid genome size (2C DNA) was mostly influenced by the phylogenetic relationships (λ = 0.900), followed by latitude (λ = 0.819) and SLA (λ = 0.752). λ values of these variables also differed significantly from 1.0, implying that the traits' evolution did not entirely result from a pure drift process alone. The estimates of the parameter κ suggested that genome size and latitude evolved according to a gradual model of trait evolution with increased rates of evolution in shorter branches (0 < κ < 1; Table 1). By contrast, the evolution of SLA was consistent with a punctuated mode (κ = 0.245; not significantly different from 0; Table 1). Estimates of the δ parameter revealed a model of species-specific adaptation in the evolution of genome size (δ = 2.792), while the evolution of SLA and latitude had constant rates over time (Table 1; δ not significantly different from 1). Maximum likelihood tests of the continuous models showed that genome size and latitude best fit a lambda-based model, while SLA best fit the OU model. However, the OU model could not be distinguished from the lambda (P = 0.420) and kappa (P = 0.351) models for SLA (Table 2), and the lambda model could not be distinguished from the kappa model for latitude (P = 0.130).
Table 1. Likelihood ratio tests (LRTs) for the observed versus expected values of phylogenetic scaling parameters for different models of trait evolution of the Primulina genus
|Trait||Observed value||Log likelihood||P for LRT|
| λ estimated || 0.900 || 79.007 || |
|λ forced = 1||–||57.817||< 0.0001|
|λ forced = 0||–||48.167||< 0.0001|
| κ estimated || 0.513 || 70.345 || |
|κ forced = 1||–||57.817||0.0004|
|κ forced = 0||–||60.338||0.0015|
| δ estimated || 2.792 || 63.041 || |
|δ forced = 1||–||57.817||0.022|
| λ estimated || 0.752 || −67.345 || |
|λ forced = 1||–||−79.204||0.0005|
|λ forced = 0||–||−77.658||0.0013|
|κ estimated||0.245||−67.780|| |
|κ forced = 1||–||−79.204||0.0007|
|κ forced = 0|| – ||−68.825||0.3066|
|δ estimated||1.944||−77.885|| |
|δ forced = 1|| – ||−79.204||0.2507|
| λ estimated || 0.819 || 150.587 || |
|λ forced = 1||–||136.801||0.0002|
|λ forced = 0||–||120.833||< 0.0001|
| κ estimated || 0.306 || 149.443 || |
|κ forced = 1||–||136.801||0.0003|
|κ forced = 0||–||120.833||< 0.0001|
|δ estimated||1.647||137.510|| |
|δ forced = 1|| – || 136.801 || 0.3997 |
Table 2. Model selection statistics for the evolution of genome size, specific leaf area (SLA) and latitude of the Primulina genus
|Model||Parameters||Log likelihood|| k ||AICc|
| Lambda ||λ = 0.939|| 79.007 || 3 ||−151.780|
|Delta||δ = 2.792||63.041||3||−119.846|
|Kappa||κ = 0.512||70.345||3||−134.455|
|OU||α = 55.724||64.425||3||−122.614|
|Lambda||λ = 0.753||−67.670||3||141.575|
|Delta||δ = 1.788||−79.733||3||165.466|
|Kappa||κ = 0.245||−67.780||3||141.795|
| OU ||α = 98.302||−67.345|| 3 || 140.926 |
| Lambda ||λ = 0.819|| 150.587 || 3 ||−294.939|
|Delta||δ = 1.648||137.510||3||−268.785|
|Kappa||κ = 0.306||149.442||3||−292.885|
|OU||α = 62.927||143.811||3||−281.386|
Fig. 1 shows the phylogenetic tree of Primulina species with genome size and SLA trait information indicated for each species. The results of the trait correlation detected by different models in phylogenetic regression analyses are summarized in Table 3. Overall, results of PGLS were almost identical to those of OLS in both of the comparisons, although the PGLS yielded a weaker explanatory power (R2 = 0.050–0.068) than OLS (R2 = 0.137–0.271) (Table 3). A significant positive relationship between SLA and genome size was also detected by PIC regression (R2 = 0.128; P < 0.0001), but no relationship was found in the latitude and genome size comparisons (Table 3). In all analyses, the PGLS model had much higher likelihoods than the nonphylogenetic OLS (i.e. λ = 0) or PIC (i.e. λ = 1) (Table 3), and the LRTs showed that the PGLS model fitted the data better than both the OLS and PIC models (P < 0.0001). Significant positive correlations were detected for both of the comparisons by the PGLS model. These results are consistent with our linear quantile regression model (Fig. 2), in which both of the comparisons showed a significant positive relationship (P < 0.05) for all six quantiles considered (0.15, 0.30, 0.45, 0.60, 0.75 and 0.90).
Table 3. Summary of trait correlation as estimated by three types of linear regression models of the Primulina genus: ordinary least-squares regression (OLS; i.e. nonphylogenetic regression), phylogenetic independent contrasts (PIC) and phylogenetic generalized least-squares (PGLS)
|Model||SLA–genome size||Latitude–genome size|
|Lh|| b || R 2 || P ||Lh|| b || R 2 || P |
|OLS (λ = 0)||56.07||0.114||0.137|| < 0.0001 ||65.075||1.026||0.271|| < 0.0001 |
|PIC (λ = 1)||65.15||0.099||0.128|| < 0.0001 ||58.154||−0.166||0.006||0.519|
|PGLS (λ = MLE)||82.53||0.063||0.068|| 0.0007 ||80.575||0.350||0.050|| 0.005 |
Figure 1. Genome size (white) and specific leaf area (SLA) (gray) mapped on a phylogenetic tree of 104 species of Primulina. Didymocarpus hancei and Petrocodon dealbatus are outgroups. #, data not available.
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Figure 2. Scatter plot and quantile regression showing the relationships between (a) specific leaf area (SLA) and genome size and (b) latitude and genome size for the Primulina species (n = 101). The dashed gray lines correspond to the quantiles (0.15, 0.30, 0.45, 0.60, 0.75 and 0.90), the solid blue line shows the median fit to the data, and the red line is the least-squares estimate of the conditional mean function.
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The PGLS analyses further showed a significant correlation of genome size with most temperature-related variables, but the lack of a significant relationship of genome size with precipitation-related variables (Table S3). By contrast, under the PGLS model, SLA was significantly correlated with only two of the 19 climate variables analyzed, that is, mean diurnal range (mean of monthly (max temp – min temp)) (BIO2) and precipitation of warmest quarter (BIO18) (Table S3).