Title:Evaluation of environmental factors effect on the genetic diversity, genetic structure and the potential distribution ofRhododendron aureum Georgi under changing climate
Authors:Wei Zhao1,2, Xiaolong Wang3, Lin Li3,Jiangnan Li1,2 Hang Yin4,5,Ying Zhao4,5 and Xia Chen1,2*
1 National & Local United Engineering Laboratory for Chinese Herbal Medicine Breeding and Cultivation, Jilin University;
2 School of Life Science, Jilin University;
3 Medical technology department, Qiqihar Medical University;
4 Jilin Provincial Joint Key Laboratory of Changbai Mountain Biocoenosis and Biodiversity;
5 Academy of Sciences of Changbai Mountain;
* Correspondence: cbs1981@163.com, chenxia@163.com;
Abstract:Understanding genetic variation and structure, adaptive genetic variation and its relationship with environmental factors is of great significance to understand how plants adapt to climate change and design effective conservation and management strategies. The objective of this study was to (I) investigate the genetic diversity and structure by AFLP markers in 36 populations of R. aureum from northeast China, (Ⅱ) reveal the relative contribution of geographical and environmental impacts on the distribution and genetic differentiation of R. aureum ; (Ⅲ) identify outlier loci under selection and evaluate the association between outlier loci and environmental factors and (Ⅳ) exactly calculate development trend of population of R. aureum ,as it is confronted with severe climate change and to provide information for designing effective conservation and management strategies. We found high genetic variation (I = 0.584) and differentiation among populations (ΦST = 0.711) and moderate levels of genetic diversity within populations of R. aureum . A significant relationship between genetic distance and environmental distance was identified, which suggested that the differentiation of different populations was the caused by environmental factors. Using BayeScan and Dfdist, 42 outlier loci identified and most of the outlier loci are associated with climate or relief factors, suggesting that these loci are linked to genes that are involved in the adaptability of R. aureum to environment. Species distribution models (SDM) showed that climate warming will cause a significant reduction of suitable area for R. aureum especially under the RCP 85 scenario. Our results help to understand the potential response ofR. auruem to climatic changes, and provide new perspectives for R. auruemresource management and conservation strategies.
Keywords: Environmental factors, Rhododendron aureumGeorgi, Genetic diversity, Genetic structure, Distribution, Climate change
1. Introduction
Genetic diversity is the basic requirement for species to long-term survive and adapt to environmental changes on an evolutionary time scale(E.E.K. Donald A. Falk, 2001; Frankham, 2005). Genetic structure is important as it can provide insights into the history of a population, and the current levels and distribution of genetic variation can influence the future success of populations(Erickson, Hamrick, and Kochert (2004). Under any combination of natural selection and random genetic drift, populations separated by geographic distance may diverge due to reduced gene flow and population connectivity (isolation by geographical distance, IBD)(Nosil & Rundle, 2012). Population divergence may still occur when reproductive isolation evolves between neighboring populations as a result of ecologically-based divergent selection in different environments (isolation by environment IBE)(I. J. Wang & Bradburd, 2014). Geographical processes may influence the population genetic structure at large spatial scales, while ecological processes may influence the population genetic structure at small spatial scales(Sacks, Brown, & Ernest, 2004).
Global climate change has become one of the major threats to biodiversity (M. B. Davis & Shaw, 2001; Camille Parmesan, 2006). Species may respond to global climate change by local adaptation(Margaret B. Davis, Shaw, & Etterson, 2008; C Parmesan, 2006), individual migration (Breshears, Huxman, Adams, Zou, & Davison, 2008; Lenoir, Gegout, Marquet, de Ruffray, & Brisse, 2008), range reduction(Thuiller, Lavorel, Araujo, Sykes, & Prentice, 2005) or a combination of these(Margaret B. Davis et al., 2008). Local adaptation has been found to be a conventional way of responding to climate change in various plant species. (Coop, Witonsky, Di Rienzo, & Pritchard, 2010; Gonzalez-Martinez, Krutovsky, & Neale, 2006; Hancock et al., 2011; Savolainen, Pyhäjärvi, & Knürr, 2007). Uncovering the genetic basis of local adaptations governed by natural selection is particularly important for understanding how plants adapt to their environment and respond to climate change. Reciprocal transplant experiments, quantitative trait locus (QTL) mapping and multiple-marker-based “neutrality” tests were used to investigate the local adaptations (Chartier, Pélozuelo, Buatois, Bessière, & Gibernau, 2013; Storz, 2005; Tanksley, 1993). However, because reciprocal transplant experiments and QTL mapping need to be based on phenotypic variation as a starting point, these approaches are generally restricted to a consideration of measurable traits that have already been implicated as candidates for different selection by independent lines of evidence, and they are unsuited to analyse adaptive genetic responses to climate change for the species which experience long juvenile phase in their life history(Savolainen et al., 2007; Storz, 2005). Genome scans have been an approach to identify marker loci that are linked to selectively relevant target loci (outlier loci) through “genetic hitchhiking”(Luikart, England, Tallmon, Jordan, & Taberlet, 2003), and are widely used to detect the local adaptation of species to environmental conditions(Magdy, Werner, McDaniel, Goffinet, & Ros, 2016; T. Wang, Wang, Xia, & Su, 2016; A. H. Yang, Wei, Fritsch, & Yao, 2016b).Dfdist and BayeScan are two most commonly used methods. Dfdist builds an expected neutral distribution ofFSTvalues under a classic symmetrical island model and loci potentially under positive selection can be identified if they exhibit unusually high FST deviations from neutral estimates(M. A. Beaumont & Balding, 2004; Mark A. Beaumont & Nichols, 1996); BayeScan evaluates population-specific FST values by considering different demographic histories and different amounts of genetic drift between populations(Foll & Gaggiotti, 2008). In this method,FST -based population genomic methods can be used to seek adaptive loci by scanning a lots of markers such as amplified fragment length polymorphism (AFLP) technique (Bensch & Akesson, 2005). The AFLP technique(Pieter Vos, 1995) has been commonly used to detect genetic diversity within and among populations, particularly in non-model organisms for which no prior genomic information is available. AFLP genome scans have been extensively employed to study plant populations, such as Liriodendron chinense (A. H. Yang, Wei, Fritsch, & Yao, 2016a), Gentiana nivali s(Bothwell et al., 2013),Arabidopsis halleri (Meyer, Vitalis, Saumitou-Laprade, & Castric, 2009), and Sphaeralcea ambigua (Shryock et al., 2015).
A major problem with genome scans is that they often detect false positives due to deviations from Hardy–Weinberg equilibrium and the assumption of the population structure model(L. Excoffier, Hofer, & Foll, 2009). Natural selection along environmental gradient or heterogeneity generates gradual changes (i.e. clinal variation) in allele frequencies at loci linked to selected genes(Manel, Poncet, Legendre, Gugerli, & Holderegger, 2010b). Consequently, outlier loci can potentially be detected by a closely association between allele frequencies and environmental parameters(Coop et al., 2010). The correlative approach need not consider the population structure and can be used to seek affirmation of outlier loci from the identification of candidate loci with genome scan methods(Joost et al., 2007; Nunes, Beaumont, Butlin, & Paulo, 2011; T. Wang et al., 2016; A. H. Yang et al., 2016b).
Natural population responses to global climate change by changing their geographical distribution, and species distribution models (SDM) have become increasingly popular tools for predicting the geographic ranges of species and have been important for predicting changes in distribution from past or future climatic events and for conservation(Hijmans & Graham, 2006; Kremen et al., 2008). Maxent, one of the most commonly used methods for inferring species distributions and environmental tolerances from occurrence data, allows users to fit models of arbitrary complexity(Warren & Seifert, 2011). Maxent calculates probability distributions based on incomplete information and does not require absence data, making it appropriate for modeling species distributions based on presence-only herbarium records(Merow, Smith, & Silander, 2013; Phillips SJ, 2006). During the past decades, many species’ distribution have been studied by the Maxent, such as predicting habitat suitability of alien invasive weeds(Wan, Wang, Tan, & Yu, 2017), predicting the potential distribution of threatened medicinal plants Fritillaria cirrhosa and Liliumnepalense(Rana, Rana, Ghimire, Shrestha, & Ranjitkar, 2017), hindcasting the distributions of neotropical savanna tree species during the Last Glacial Maximum and Last Inter-Glacial(Bueno et al., 2017).
Rhododendron aureum Georgi (syn. Rh. Chrysanthum Pall.), the target plant species in this study, is a perennial evergreen creeping shrub with a large number of branched stems inhabiting alpine regions of Korea, China, Japan, and the Kamchatka peninsula. This plant can grow up to 1 m in height, and blooms from June to July in Korea with pale yellow flowers. It has been shown to always occupy the snowmelt gradient and especially to dominate in early exposed places(Kudo, 1992). In China, it grows mainly in the alpine tundra and the Betula ermaniipopulation belts of Changbai Mountain, ranging from 1,000 to 2,506 m a.s.l.(Kudo, 1993). The R. aureum is one of the constructive and dominant species on the alpine tundra ecosystem, and it plays an important role in maintaining the ecological balance and preventing and controlling soil erosion.
Alpine environment is locally variable as small changes in altitude can lead to large changes in temperature, humidity, exposure, and other types of changes(Byars, Papst, & Hoffmann, 2007; Hovenden & Jkvander, 2004). With the global climate changing, in some alpine area, the increase in air temperature was more than twice as great as the increase in global mean air temperature during the 20th century(Bohm et al., 2001). Plant species are particularly vulnerable under the climate changing environment in alpine. Understanding the contemporary and historical ecological (climatic, geographical) factors shaping population genetic diversity is of great significance for studying molecular ecology, conservation biology and evolutionary biology (And & Hamrick, 1984; Holderegger, Buehler, Gugerli, & Manel, 2010).
In this study we adopted AFLP markers for characterizing the adaptive loci under selection using BayeScan and Dfdist, employed Multiple Linear Regression (MLR) to detect potential adaptive loci that are under selection from existing environmental factors, and using species distribution models (SDM) to predict potential distribution of R. aureum during the Last Glacial Maximum (LGM) and the future. The objective of this study was to (i) investigate the genetic variation and genetic structure of R. aureum ; (ii) reveal the relative contribution of geographical and environmental impacts on the distribution and genetic differentiation of R. aureum ; (iii) identify outlier loci under selection and assess the association between outlier loci and climate and (iv) exactly calculate development trend of population of R. aureum ,as it is confronted with severe climate change and to provide information for designing effective conservation and management strategies.
2. Materials and Methods