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