4. Discussion
R. aureum exhibited a higher level of genetic diversity at the species level (I = 0.584, H = 0.402) than other Ericaceae species similarly researched with AFLP markers, such as R. ledebourii, R. dauricum, R. sichotense (Tikhonova, Polezhaeva, & Pimenova, 2012); and the high level of genetic diversity were in accordance with studies on other arctic and alpine species (H. A. PERSSON, 2001). High genetic diversity was also observed of R. aureum by RAPD and ISSR markers at the species level(Liu et al., 2012). Plant species with wide altitudinal ranges encounter different environmental conditions across the elevation gradient, which may lead to genetic variation as well as phenotypic variation among populations (Anna-Barbara Utelli, 1995; Forsman, 2014; Nicotra et al., 2015; Ohsawa & Ide, 2008). R. aureum is a long-lived, perennial, evergreen, dwarf shrub which altitude range from 1000m to 2600m in alpine regions. Along elevational gradients of alpine area, large changes in environmental factors, such as temperature, precipitation (Figure C ), solar radiation, and wind, occur over short distances, resulting from obvious changes in the selection pressures of R. aureumindividuals. Heterogeneous habitats strengthen disruptive selection to increase variation and divergent selection pressures promote the evolution of traits adapted to their local environment, (Freeland, 2005). Divergent selection can promote genetic differentiation by reducing gene flow among sites with contrasting ecological conditions(Forester, Jones, & Joost, 2016). Results also showed that the genetic variability was even greater among populations (68.87%) but smaller within populations (31.13%), and there are high levels of differentiation among populations (ΦST= 0.689). Meanwhile, the high population differentiation could possibly accelerate local adaptation. Local adaptation and directional selection should have locus-specific effects of reducing genetic diversity within populations and increasing differentiation between populations (Magdy et al., 2016). Furthermore, long-lived perennial species with mixed breeding systems usually have relatively high genetic diversity (Nybom & Bartish, 2000). In the long-term evolutionary process, the high genetic variation held by R. aureummay have provided abundant genotypes for its adaptation to changing climatic conditions. There were some populations got the relatively lower genetic diversity than others. Population N7 inhabit on the low altitude in the coniferous forest which has a forest barrier from the others. Possible explanation for the low diversity found in the population is that small populations and habitat fragmentation are more susceptible pollen limitation, limited gene flow and genetic drift leading to loss of genetic diversity (Norman C. Ellstrand 1993; Vranckx, Jacquemyn, Muys, & Honnay, 2012).
Genetic divergence between populations is shaped by a combination of drift, migration, and selection, yielding patterns of isolation-by-distance (IBD) and isolation-by-environment (IBE)(Weber, Bradburd, Stuart, Stutz, & Bolnick, 2017). Some researches on population genetic structure discovered that IBD plays a more important role in intraspecific genetic differentiation than IBE(Mosca, González‐Martínez, & Neale, 2014), however, IBE was implied to have a stronger effect than IBD on genetic structure in other plant taxa(Gray et al., 2014). A stronger effect of IBE versus IBD was found for the genetic differentiation of R. aureum . A Mantel test, partial Mantel test and MMRR analysis all supported the effect of isolation by environmental distance. In the cluster analysis, the fact that some geographically close populations are separated by larger genetic divergence than expected also proved the IBD is not the major driver of population divergence of R. aureum . The prominence of IBE suggests factors related to the environment play a greater role in divergence of R. aureumpopulations than geographical isolation.R. aureum lives in diversified habitats across its distribution region, and ecological landscape heterogeneity may influence gene flow and connectivity among populations that are adapted to different environments. Possible mechanisms responsible for IBE are selection pressures from climate and relief factors.
In identifying outlier loci or adaptive loci, we sought to determine how selection may play a role in shaping genetic differentiation and adaptation along sharp environmental clines. All 42 outlier loci identified by both BayeScan and Dfdist was undergoing putative diversifying selection and balancing selection (Figure 3). Most of the outlier associated with environmental predictors across the alpine environmental gradient (Table 4), suggesting these regions of the genome seem to be diverging and that climate may play a role. Most outliers were associated with temperatures related predictors (especially BIO1 and BIO3), probably due to the steep gradient in temperatures along our sampled region. In addition, many outliers were associated with precipitation and relief related environmental predictors, suggesting that precipitation and relief may also be exerting spatially divergent pressure on genetic. As expected, temperature, precipitation were estimated as the major driving factors influencing allele frequencies at outlier loci, consistent with other studies examining drivers of adaptive genetic divergence in plants(Manel, Poncet, Legendre, Gugerli, & Holderegger, 2010a; Yoder et al., 2014). Temperatures and precipitation factors are very important for plant growth, development, survival, reproduction and defense(Poncet et al., 2010). However, there are little researches has found the relief related factors influence the adaptive genetic divergence(Manel et al., 2010a). In this study, we found many outlier loci were related to the relief factors, such as 5 outlier loci were related to topographic position index (tpi), 4 outlier loci were related to aspect (asp), 2 outlier loci were related to slope (slp) with high values of Radj2. The relief has complex indirect effects on the combination of snow distribution and slope specific interception of radiation, and has the direct influence of exposure on microclimate during the growing season(Körner, 2003).
We used MAXENT to predict the distribution ofR. aureum under LGM (Last Glacial Maximum), present and future climate conditions. MAXENT captured well a major portion of current distribution of R. aureum . With the climate changing from the LGM to future, R. aureum decreased its future distribution range under a climatic warming scenario, especially under the RCP (Representative Concentration Pathways) 85 scenario which higher level greenhouse gases are emitted than RCP 26 in the years to come. We found the suitable distribution range of R. aureum would be reduced to the high altitude tundra area but would lose the low altitude area in Changbai Mountain. This is consistent with previous studies on other alpine area. Ecosystems at high latitudes and altitudes are particularly sensitive to climate change. Climate change is causing many species to shift their geographical ranges as reviewed in many researches (Bellard, Bertelsmeier, Leadley, Thuiller, & Courchamp, 2012; Dawson, Jackson, House, Prentice, & Mace, 2011). The abundance and dominance of shrub species have increased in alpine and subarctic tundra ecosystems in recent decades (Brandt, Haynes, Kuemmerle, Waller, & Radeloff, 2013; Myers-Smith et al., 2011; Myers-Smith et al., 2015; Sturm, Racine, & Tape, 2001; Sturm et al., 2005; Tape, Sturm, & Racine, 2006) and climate warming has been considered the dominant factor driving these range expansions of shrubs (Brandt et al., 2013; Li et al., 2016; Naito & Cairns, 2011; Walker et al., 2006; Yu, Luedeling, & Xu, 2010). As an effect of global warming, upward shifting of plant species in high mountain systems was predicted for the near future(Pauli, Gottfried, & Grabherr, 1996). Climate-induced range shifts and population declines are expected to increase the prevalence of population bottlenecks and reduce genetic diversity within and among species. Long-lived species are particularly vulnerable to climate changes because they experience longer generation times, lower population turnover rates and slower rates of evolution(Staudinger et al., 2012).
5. Conclusions
In summary, by using AFLP markers, landscape genetic, and species distribution modeling analysis together, we are able to identify many environmental factors that have influenced on the genetic diversity and genetic structure, and we can predict the potential distribution area of R. aureum . Our analyses revealed high genetic variation and differentiation among populations and moderate levels of genetic diversity within populations of R. aureum . A significant correlation between genetic distance and environmental distance was identified, which suggested that environmental factors were the primary cause of the population differentiation. 42 outlier loci were identified in 36 populations of R. aureum alone the environmental gradient and most of the outlier loci are associated with environmental factors, suggesting that these loci are linked to genes that are involved in the adaptability of R. aureum to environment. The SDM indicates that climate change drastically reduces the potential distribution range ofR. aureum . An urgent area of future study is identification of genomic regions that are associated with environment factors by RAD-Seq(Hohenlohe, Catchen, & Cresko, 2012) and EST (expressed sequence tags). We should take measures to protect this species, such as translocate the populations or establish captive populations that would otherwise go extinct.
Author Contributions: Conceptualization, X.C; methodology, W.Z., Y.H., Y.Z. and L.L.; formal analysis, W.Z. and X.L.W.; data curation, W.Z. and J.N.L; writing—original draft preparation, W.Z.; writing—review and editing, X.C. All authors have read and agreed to the published version of the manuscript.
Funding: This work was supported by grants from the Ji Lin province Natural Science Foundation [20190201298JC].
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A
Table A. The primers used for AFLP analysis