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