TAXONOMY CLASSIFICATION
Evolutionary ecology
1 |
INTRODUCTION
Genetic diversity is a prerequisite for evolutionary change in all
organisms; preservation of a species’ genetic diversity likely increases
its chances of surviving over evolutionary time when facing
environmental changes. Plant evolutionary biologists, foresters, and
conservation geneticists have long been interested in the genetic
differences among populations and the degree to which these may
contribute to local adaptation (see Table 1 for the definition of
population genetic terms cited in this mini review). This interest
traces back to the common garden experiments of Turesson et al. (1922)
and the reciprocal transplants of Clausen et al. (1941). For decades,
common garden and reciprocal transplant experiments have been
instrumental in advancing our understanding of how natural selection
shapes geographic phenotypic variation (reviewed in Flanagan et al.,
2018; Sork, 2018). As putatively neutral molecular genetic markers
(i.e., allozymes and DNA-based dominant and codominant loci) became
available, plant biologists were able to compare the levels of genetic
diversity and the degree of divergence seen at phenotypic traits with
those at single gene markers (Reed & Frankham, 2001; De Kort et al.,
2013; Leinonen et al., 2013; Marin et al., 2020).
Applications of the knowledge of traditional marker-based neutral
genetic variation (NGV hereafter) to the conservation and restoration of
plant species have been somewhat controversial due to the assumed
evolutionary neutrality of used markers and their limitations to be
informative about the adaptive potential (García-Dorado & Caballero,
2021; Teixeira & Huber, 2021). Although levels of NGV might not be
always predictive of adaptive genetic variation (AGV hereafter; Teixeira
& Huber, 2021), it is possible that NGV under the current conditions
may become AGV under changed environmental conditions. However, NGV,
largely corresponding to within-population genetic variation from
allozymes to nucleotide sequences as reflected in the percentage of
polymorphic loci (%P ), allelic richness (AR ), or gene
diversity (Hardy-Weinberg expected heterozygosity,H e), is regarded to be a poor “proxy” of levels
of AGV in quantitative traits (i.e., narrow- and broad-sense
heritabilities [h 2 andH 2]; Reed & Frankham, 2001; Depardieu et
al., 2020).
The same applies to the relationship between measures of
among-population genetic differentiation (e.g., Merilä & Crnokrak,
2001). The comparison between F ST ([Wright,
1951] or its analogs estimated from neutral genetic markers
[Merimans & Hedrick, 2010]; see Holsinger & Weir [2009] for
different definitions and interpretations of F ST)
and Q ST (F ST analog for
quantitative traits; Spitze, 1993; Depardieu et al., 2020), i.e.,Q ST–F ST comparisons or
relationships, was formalized with the adoption ofQ ST in the 1990s. Q STcreates an explicit quantitative prediction of the expectation for
quantitative traits under neutrality which, thus, solidified the
inference that quantitative traits typically show greater genetic
divergence among populations than expected under neutrality (Merilä &
Crnokrak, 2001; De Kort et al., 2013; Leinonen et al., 2013). Assuming
that the used genetic markers are neutral, this supports the view that
the divergence of quantitative traits among populations is predominantly
driven by natural selection. Although F ST is
generally a poor predictor of Q ST, many
researchers still follow or in part support the assumption that levels
of NGV would be indicative of those of AGV (e.g., Oostermeijer et al.,
1994; Hamrick & Godt, 1996; Ottewell et al., 2016; DeWoody et al.,
2021; García-Dorado & Caballero, 2021, but see Teixeira & Huber,
2021).
Although there is already an ongoing transition from conservation
genetics to conservation genomics (Allendorf et al., 2010; Sork, 2018),
genomic data for many rare plants are still scarce, and hence,
conservation managers and practitioners need to continuously utilize
information on NGV, if any, to support their decision making.
Comparative (i.e.,Q ST–F ST comparisons) and,
particularly, theoretical studies of NGV and AGV within and among
populations in a variety of organisms are very abundant in the
literature (e.g., Reed & Frankham, 2001, 2003; Hendry, 2002; McKay &
Latta, 2002; Leinonen et al., 2013 and references therein; Li et al.,
2019). So far, however, there have been few studies that have appliedQ ST–F ST comparisons to
conservation, even though several such applications are possible (Reed
& Frankham, 2003; but see McKay et al., 2001; Petit et al., 2001;
Gravuer et al., 2005; Rodríguez-Quilón et al., 2016).
As a different issue from the above, there have been increasing
recommendations in lowering the gap between conservation science and
practice (sometimes coined as “the conservation genetics gap”, “the
research-implementation gap”, or “the science-practice gap”) (Taylor
et al., 2017; Britt et al., 2018; Dubios et al., 2019; Fabian et al.,
2019; Holderegger et al., 2019). It is agreed that conservation
researchers should communicate with practitioners to integrate their
genetic findings into conservation implementation (Ottewell et al.,
2016; Chung et al., 2021). To achieve this, a generally and clearly
written narrative coveringQ ST–F ST in seed plants
might be needed to lower the threshold for plant conservation
practitioners to employ population genetics information in conservation
practice.
With this in mind, we first introduce the current knowledge about
within-population genetic variation and among-population differentiation
both in NGV and AGV in seed plants to highlight the distinction between
the approaches used for each system to identify NGV and AGV. Next, we
introduce the known general application ofQ ST–F ST comparisons to
plant biology. We also provide management suggestions as to how to
capture germplasms (e.g., seeds) covering most AGV and NGV based on the
analyses of molecular and quantitative trait data.
2 |
COMPARISON OF WITHIN-POPULATION GENETIC VARIATION: NEUTRAL MARKERS
VERSUS ADAPTIVE TRAITS
As neutral genetic markers reflect demographic processes (including past
demographic histories) within local populations, they are informative
for the management and conservation of genetic purposes. Small
populations are generally susceptible to the loss of NGV and less
adaptive to novel environments due to the loss of AGV through genetic
drift (Reed & Frankham, 2003). The degree of individuals’
heterozygosity (estimated as the number of loci for which each
individual is heterozygous) is often correlated with fitness
(Oostermeijer et al., 1994; Reed & Frankham, 2003). Even when there is
a real relationship between an individual’s heterozygosity and fitness,
this does not imply that there should be a relationship betweenH e and h 2 at the
population level. These are determined by somewhat different processes.
In a meta-analysis of 71 (60 out of these with allozymes) published
datasets, H e is only weakly correlated withh 2 or H 2: r =
0.217 (–0.88 to 0.90, SD ± 0.433), indicating that neutral marker-based
measures only explain 4% of the variation in quantitative traits (Reed
& Frankham, 2001). In addition, the correlation between allozymeH e and h 2 for 17 metric
characters in seven populations of the annual Phlox drummondii is
highly variable, ranging from r = –0.714 to r = 0.355
(recalculated from Schwaegerle et al., 1986). Likewise, the correlation
between microsatellite H e andH 2 of five phenotypic traits in seven
populations of the endangered herb Psilopeganum sinense ranges
from r = –0.707 to 0.261 (Ye et al., 2014). However, caution is
needed because, at a degree of freedom of five with seven populations,
the critical value of r for α = 0.05 is very high at r =
0.75, giving a very low power; when Bonferroni correction is applied
across the five phenotypic traits, this becomes even higher at r= 0.87. Similar results revealing a weak correlation between NGV and AGV
are available from other wild plant species as well: the rare perennial
herb Scabiosa canescens and its common congener S.
columbaria (allozymes, H e vs.H 2; Waldmann & Andersson, 1998); the annualClarkia dudleyana (allozymes, H e vs.CV G, coefficient of genetic variation of
quantitative traits; Podolsky, 2001), the annual Hordeum
spontaneum (allozymes, H e vs.H 2, Volis et al., 2005), and the selfing annualSenecio vulgaris (amplified fragment length polymorphisms
[AFLPs], H e vs. H 2;
Steinger et al., 2002).
These studies suggest that NGV has a limited ability to predict AGV
within populations. Reed & Frankham (2001) listed six factors that
could be responsible for the low correlation between NGV and AGV.
Namely, these are differential selection, non-additive genetic
variation, different mutation rates (µ ), low statistical power,
environmental effects on quantitative characters, and impact of
regulatory variation. In addition, various forms of natural selection
affecting the level of neutral polymorphism at linked sites may also
contribute to the lack of a relationship between NGV and AGV. The most
dramatic effect on neutral variation occurs when beneficial alleles at
loci contributing to AGV spread into a population, a process known as a
“selective sweep” (Nielsen, 2005; Stephan, 2019). Selective sweep
leads to a dramatic reduction of local H e andAR along the chromosome segment (Kreitman, 2001).H e and AR for non-neighboring or unlinked
neutral regions are likely not affected by such events (Nielsen, 2005)
because linkage disequilibrium between NGV and AGV decays gradually
under the influence of recombination.
It should be noted that, however, invoking selective sweep as a factor
that lowers the correlation between NGV and AGV could be problematic.
The sweeping of one beneficial allele means that the AGV in that gene
also disappears. Therefore, since AGV and NGV can be both high when a
selective sweep does not occur, but they are both reduced after a sweep,
a positive correlation between AGV and NGV can be still maintained.
Therefore, we need to ask whether there are other forms of natural
selection in which NGV is lowered without reducing AGV. One such
scenario, the hitchhiking effect of fluctuating selection, was provided
by Barton (2000): fluctuating environment causing the adaptive alleles
to oscillate between low and high frequencies, thus maintaining AGV
without fixation or loss, is expected to reduce the levels of the
surrounding NGV. The feasibility of such an evolutionary scenario is
receiving growing attention, as fitness is indeed found to fluctuate
rapidly and widely in natural populations (Bell, 2010; Messer et al.,
2016) and population genomic studies have revealed seasonal oscillations
of allele frequencies at a large number of sites (Bergman et al., 2014;
Machado et al., 2021).
Under balancing selection, different alleles affecting fitness are
maintained via heterozygote advantage, rare-allele advantage, or
temporally/spatially heterogeneous selection. By definition, such loci
harbor high levels of AGV (Aguilar et al., 2004; Charlesworth, 2006).
The level of NGV is also expected to be elevated at sites closely linked
to the loci of stable balanced polymorphism (Charlesworth, 2006).
However, only very closely neighboring neutral sites may experience such
an increase in polymorphism because meiotic recombination quickly erodes
linkage disequilibrium around the selected loci (Fijarczyk & Babik,
2015). This suggests that a high level of AGV can be maintained by
balancing selection without a proportional increase in NGV on the
genomic average. Therefore, balancing selection should also contribute
to the lack of a positive correlation between NGV and AGV.
To summarize, heterozygosity at adaptive and neutral loci is expected to
be impacted by different evolutionary factors, which may explain why
estimators of NGV are poor surrogates for AGV within plant populations.
3 |
COMPARISON OF AMONG-POPULATION DIFFERENTIATION: NEUTRAL MARKERS VERSUS
ADAPTIVE TRAITS
Since sessile plants are subject to spatially divergent selection,
elucidating the effects of local adaptation on population
differentiation has become more important in light of adaptation to
changing environments, including global climate change (Ehrich & Raven,
1969; Savolainen, 2011; Colautti et al., 2012). A commonly used way to
infer the impact of divergent selection on plant population
differentiation is by comparing Q ST (reflecting
differentiation caused by both neutral and selective forces) versusF ST estimates (reflecting differentiation due to
neutral processes including genetic drift) (Whitlock, 2008). The
neutrality expectation depends on the assumption that mutation rates
(µ ) are substantially lower than migration rates (m )
(Hendry, 2002). Neutral markers having high µ (e.g.,
microsatellites) are not recommended to be used inQ ST–F ST comparisons
(Hendry, 2002; Edelaar et al., 2011). However, Li et al. (2019)
suggested the use of microsatellites by discarding the most variable
loci (i.e., outliers).
The Q ST–F ST comparisons
(i.e., elucidation of the relative magnitudes ofQ ST and F ST) have already
provided valuable insights into responses of plant traits to
spatiotemporal environmental heterogeneity (Kremer et al., 1997; Merilä
& Crnokrak, 2001; McKay & Latta, 2002; Volis et al., 2005; Savolainen
et al., 2007; Leinonen et al., 2008, 2013). TheQ ST–F ST relationship can
have three different outcomes that have different interpretations
(Merilä & Crnokrak, 2001; Leinonen et al., 2008):Q ST > F ST,Q ST ≈ F ST, andQ ST < F ST.
First, if Q ST >F ST, the observed trait differentiation exceeds
neutral expectations and the fraction not explained by neutral processes
is likely to have been caused by disruptive (divergent) selection.
Second, if Q ST ≈ F ST,
trait differentiation is indistinguishable from the effects of drift,
and, thus, there is no evidence for selection (Lande, 1992). Finally, ifQ ST < F ST, trait
divergence among populations is less than expected due to genetic drift
alone; this pattern is suggestive of spatially uniform or stabilizing
selection (favoring average phenotypes) across populations.
Using several simple generalized linear models, Leinonen et al. (2008)
carried out a meta-analysis of 55 animal and plant studies that used the
same populations for both F ST andQ ST estimation. Their results confirmed the main
conclusions of Merilä & Crnokrak (2001), who found a low but
significant positive correlation between Q ST andF ST (Spearman rank correlation,r s = 0.39, P = 0.017; Leinonen et al.,
2008), and, on average, Q ST >F ST (P < 0.001). Leinonen et al.
(2008) suggested that genetic differentiation due to natural selection
and local adaptation is the “norm,” not the exception. The positive
correlation between the degree of adaptive phenotypic divergence and
differentiation at neutral loci is mainly caused by limited gene flow
and enhanced local genetic adaptation, known as “isolation by
adaptation” (Nosil et al., 2007). Leinonen et al. (2008) further found
that the study design (viz. , wild, broad sense, and narrow
sense), marker type (restriction fragment length polymorphisms, random
amplified polymorphic DNAs, microsatellites, allozymes, and AFLPs), and
trait type (morphological traits and life-history traits) rarely explain
any significant variance in the Q ST data.
Furthermore, Leinonen et al. (2008) pointed out two potential biases in
finding that 70% of Q ST values exceed the
associated F ST values. First, a sampling bias due
to the deliberate selection of populations from contrasting environments
to be investigated, as well as focus on populations previously known to
be phenotypically divergent. Second, a publication bias favoring studies
reporting Q ST >F ST outcomes, possibly because of difficulties
interpreting Q ST ≈ F ST andQ ST < F STpatterns. For example, Q ST <F ST could be due to canalization, which is a
process or tendency in which “species genetic backgrounds share the
same genetic constraints” (Lamy et al., 2012) and “a fundamental
feature of many developmental systems” (Hall et al., 2007). To
partially distinguish canalization and uniform selection, Lamy et al.
(2012) suggested “a bottom-up approach” that combines information fromQ ST–F ST comparisons and
phylogenetic reconstruction. For a given trait, ifQ ST < F ST and
phylogenetically closely related species occurring under different
environmental conditions exhibit trait conservatism, then canalization
could be inferred as an alternative to the classical uniform selection
hypothesis (cf. fig. 3 in Lamy et al. [2012]). Well-known examples
of canalization in plants are leaf shape in Arabidopsis thalianaand cavitation resistance found in all Pinus species (Hall et
al., 2007; Lamy et al., 2011). The R package “driftsel” (Ovaskainen et
al., 2011; Karhunen et al., 2013; 2014) can be used to differentiate
between stabilizing selection, diversifying selection, and random
genetic drift, allowing to circumvent a lot of the problems with the
traditional Q ST–F STcomparisons.
The study by De Kort et al. (2013) was the first meta-analysis ofQ ST–F ST comparisons (401
cases that included each Q ST value per trait for
each entry) exclusively focusing on plants. The authors compiled 51
entries representing 44 plant species from 18 families covering 17
entries for annuals, 19 for herbaceous perennials, and 15 for woody
species. De Kort et al. (2013) found that averageQ ST values were significantly larger than the
corresponding F ST values (0.345 versus 0.214,
Wilcoxon signed-rank test, P = 0.003: paired t -test,P = 0.000, recalculated from original data from De Kort et al.,
2013). The authors also found that the excess ofQ ST relative to F ST was
significantly negatively correlated with F ST(β = –0.484, P < 0.01). A weak but positive
overall relationship between pairwise Q ST andF ST values (r s = 0.278,P = 0.048; β = 0.464, P = 0.003, recalculated from
De Kort et al., 2013) suggests that F ST in
neutral markers could be to some degree predictive ofQ ST in quantitative traits. These correlations
are what one would expect because (i) Q STreflects both neutral forces and natural selection caused by
environmental differences and F ST only measures
neutral processes including genetic drift and gene flow, (ii)Q ST and F ST estimates are
based on the same (among-population) partition of total genetic
variation, differing only in the data used in estimation—quantitative
adaptive loci (the former) and neutral loci (the latter), and (iii)
divergent selection that causes Q ST could also
lead to the increase of F ST by restricting gene
flow (“isolation by adaptation”; Nosil et al., 2007). In addition, De
Kort et al. (2013) found a significant positive correlation between the
average inter-population distance and theirQ ST–F STdifference values (P < 0.05), suggesting that isolation
by distance plays an important role in adaptive evolution. The authors’
meta-analysis suggests that plant species are generally differentiated
by natural selection in various types of traits (viz ., fitness
[reproductive and physiological traits] and non-fitness
[biomass-related and phenological traits] both in early life and in
the adult stage). For example, the authors detected a largerQ ST–F ST difference values
for non-fitness traits than for fitness traits, confirming the
expectation that the former respond, in general, faster to directional
selection than the latter (Merilä & Sheldon, 1999; Leinonen et al.,
2008). Finally, De Kort et al. (2013) found slightly higherQ ST–F ST difference values
for annuals than perennials (0.143 versus 0.123), but the difference was
not significant. This may not support the prediction (De Kort et al.,
2013) that perennials can respond to selection slower than annuals.
In summary, these differences in F ST andQ ST are a product of the different evolutionary
forces such as drift, gene flow, and selection (Slatkin, 1973), which
are further complicated by potential biasing effects caused by
phenotypic plasticity, environmental maternal effects, non-additive
genetic interaction, pleiotropic effects, and, as mentioned above,
different µ in F ST andQ ST (for more details see De Kort et al., 2013).