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\begin{document}

\title{A review on \emph{Q}\textsubscript{ST}--\emph{F}\textsubscript{ST}
comparisons of seed plants: Insights for conservation}



\author[1]{Mi Yoon Chung}%
\author[2]{Juha Merilä}%
\author[3]{Yuseob Kim}%
\author[4]{Kangshan Mao}%
\author[5]{Jordi López-Pujol}%
\author[6]{Myong Gi  Chung}%
\affil[1]{Chungnam National University College of Biological Sciences and Biotechnology}%
\affil[2]{University of Helsinki}%
\affil[3]{Ewha Womans University}%
\affil[4]{Sichuan University}%
\affil[5]{Botanic Institute of Barcelona}%
\affil[6]{Gyeongsang National University}%


\vspace{-1em}



  
  \date{May 23, 2024}


\begingroup
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\selectlanguage{english}
\begin{abstract}
Increased access to genome-wide data provides new opportunities for
plant conservation. However, information on neutral genetic diversity in
a small number of marker loci can still be valuable because genomic data
are not available to most rare plant species. In the hope of bridging
the gap between conservation science and practice, we outline how
conservation practitioners can more efficiently employ population
genetic information in plant conservation. We first review the current
knowledge about the within-population genetic variation and
among-population differentiation in neutral genetic variation (NGV) and
adaptive genetic variation (AGV) in seed plants. We then introduce the
estimates of among-population genetic differentiation in quantitative
traits (\emph{Q}\textsubscript{ST}) and neutral markers
(\emph{F}\textsubscript{ST}) to plant biology and summarize conservation
applications derived from
\emph{Q}\textsubscript{ST}--\emph{F}\textsubscript{ST} comparisons,
particularly on how to capture most AGV and NGV on both in-situ and
ex-situ programs. Based on a review of published studies, we found that,
on average, two and four populations would be needed for woody
perennials (\emph{n} = 18) to capture 99\% of neutral and adaptive
genetic variation, respectively, whereas four populations would be
needed in case of herbaceous perennials (\emph{n} = 14). On average,
\emph{Q}\textsubscript{ST} is about 3.6, 1.5, and 1.1 times greater than
\emph{F}\textsubscript{ST} in woody plants, annuals, and herbaceous
perennials, respectively. We suggest using maximum
\emph{Q}\textsubscript{ST} rather than average
\emph{Q}\textsubscript{ST} among trait comparisons. Hence, conservation
and management policies or suggestions based solely on inference on
\emph{F}\textsubscript{ST} could be misleading, particularly in woody
species. We recommend conservation managers and practitioners consider
this when formulating further conservation and restoration plans for
plant species, and for woody species in particular.%
\end{abstract}%



\sloppy


\emph{REVIEW ARTICLE}

\textbf{A review
on\emph{Q}\textsubscript{ST}--\emph{F}\textsubscript{ST} comparisons of
seed plants: Insights for conservation}

\selectlanguage{ngerman}\textbf{Mi Yoon Chung\textsuperscript{1}, Juha
Merilä\textsuperscript{2,3}, Yuseob Kim\textsuperscript{4,5}, Kangshan
Mao\textsuperscript{6}, Jordi López-Pujol\textsuperscript{7}, and Myong
Gi Chung\textsuperscript{8}}

\textsuperscript{1}Department of Biological Sciences, Chungnam National
University, Daejeon 34134, Republic of Korea

\textsuperscript{2}Ecological Genetics Research Unit, Organismal and
Evolutionary Biology Research Program, Faculty of Biological and
Environmental Sciences, PO Box 65, FI-00014 University of Helsinki,
Finland

\textsuperscript{3}Area of Ecology \& Biodiversity, School of Biological
Sciences, The University of Hong Kong, Hong Kong SAR

\textsuperscript{4}Division of EcoScience, Ewha Womans University, Seoul
03760, Korea

\textsuperscript{5}Department of Life Science, Ewha Womans University,
Seoul 03760, Korea

\textsuperscript{6}Key Laboratory for Bio-resources and Eco-environment
of Ministry of Education, College of Life Science, State Key Laboratory
of Hydraulics and Mountain River Engineering, Sichuan University,
Chengdu 610065, P. R. China

\textsuperscript{7}Botanic Institute of Barcelona (IBB, CSIC-Ajuntament
de Barcelona), Barcelona ES 08038, Catalonia, Spain

\textsuperscript{8}Division of Life Science and RINS, Gyeongsang
National University, Jinju 52828, Republic of Korea

\textbf{Correspondence}

Myong Gi Chung, Division of Life Science and RINS, Gyeongsang National
University, Jinju 52828, Republic of Korea.

Email: \emph{mgchung@gnu.ac.kr}

\textbf{Funding information}

This work was supported by the Basic Science Research Program through
the National Research Foundation of Korea (NRF) funded by the Ministry
of Education (NRF-2020R1I1A3074635 to MYC), by the National Natural
Science Foundation of China (grant no. 31622015 to MK), and by the
Spanish Ministerio de Ciencia e Innovación (project PID2020-119163GB-I00
funded by MCIN/AEI/10.13039/501100011033 to JL-P).

\textbf{Abstract}

Increased access to genome-wide data provides new opportunities for
plant conservation. However, information on neutral genetic diversity in
a small number of marker loci can still be valuable because genomic data
are not available to most rare plant species. In the hope of bridging
the gap between conservation science and practice, we outline how
conservation practitioners can more efficiently employ population
genetic information in plant conservation. We first review the current
knowledge about the within-population genetic variation and
among-population differentiation in neutral genetic variation (NGV) and
adaptive genetic variation (AGV) in seed plants. We then introduce the
estimates of among-population genetic differentiation in quantitative
traits (\emph{Q} \textsubscript{ST}) and neutral markers (\emph{F}
\textsubscript{ST}) to plant biology and summarize conservation
applications derived from\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{ST} comparisons, particularly on how to capture most AGV
and NGV on both in-situ and ex-situ programs. Based on a review of
published studies, we found that, on average, two and four populations
would be needed for woody perennials (\emph{n} = 18) to capture 99\% of
neutral and adaptive genetic variation, respectively, whereas four
populations would be needed in case of herbaceous perennials (\emph{n} =
14). On average,\emph{Q} \textsubscript{ST} is about 3.6, 1.5, and 1.1
times greater than\emph{F} \textsubscript{ST} in woody plants, annuals,
and herbaceous perennials, respectively. We suggest using
maximum\emph{Q} \textsubscript{ST} rather than average\emph{Q}
\textsubscript{ST} among trait comparisons. Hence, conservation and
management policies or suggestions based solely on inference on\emph{F}
\textsubscript{ST} could be misleading, particularly in woody species.
We recommend conservation managers and practitioners consider this when
formulating further conservation and restoration plans for plant
species, particularly woody species.

\textbf{KEYWORDS}

Adaptive variation, conservation, genetic diversity, herbaceous plants,
neutral variation, woody species

\textbf{TAXONOMY CLASSIFICATION}

Evolutionary ecology

\textbf{1} \textbf{\textbar{} 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 (\emph{\%P} ), allelic richness (\emph{AR} ), or gene
diversity (Hardy-Weinberg expected heterozygosity,\emph{H}
\textsubscript{e}), is regarded to be a poor ``proxy'' of levels of AGV
in quantitative traits (i.e., narrow- and broad-sense heritabilities
{[}\emph{h} \textsuperscript{2} and\emph{H} \textsuperscript{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 \emph{F} \textsubscript{ST} ({[}Wright,
1951{]} or its analogs estimated from neutral genetic markers
{[}Merimans \& Hedrick, 2010{]}; see Holsinger \& Weir {[}2009{]} for
different definitions and interpretations of \emph{F}
\textsubscript{ST}) and \emph{Q} \textsubscript{ST} (\emph{F}
\textsubscript{ST} analog for quantitative traits; Spitze, 1993;
Depardieu et al., 2020), i.e.,\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{ST} comparisons or relationships, was formalized with the
adoption of\emph{Q} \textsubscript{ST} in the 1990s. \emph{Q}
\textsubscript{ST}creates 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 \emph{F}
\textsubscript{ST} is generally a poor predictor of \emph{Q}
\textsubscript{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.,\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{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 applied\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{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 covering\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{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 of\emph{Q}
\textsubscript{ST}--\emph{F} \textsubscript{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.

\textbf{2} \textbf{\textbar{} 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 between\emph{H}
\textsubscript{e} and \emph{h} \textsuperscript{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, \emph{H} \textsubscript{e} is only weakly correlated
with\emph{h} \textsuperscript{2} or \emph{H} \textsuperscript{2}:
\emph{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
allozyme\emph{H} \textsubscript{e} and \emph{h} \textsuperscript{2} for
17 metric characters in seven populations of the annual \emph{Phlox
drummondii} is highly variable, ranging from \emph{r} = --0.714 to
\emph{r} = 0.355 (recalculated from Schwaegerle et al., 1986). Likewise,
the correlation between microsatellite \emph{H} \textsubscript{e}
and\emph{H} \textsuperscript{2} of five phenotypic traits in seven
populations of the endangered herb \emph{Psilopeganum sinense} ranges
from \emph{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 \emph{r} for \selectlanguage{greek}α \selectlanguage{english}= 0.05 is very high at \emph{r} =
0.75, giving a very low power; when Bonferroni correction is applied
across the five phenotypic traits, this becomes even higher at \emph{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 \emph{Scabiosa canescens} and its common congener \emph{S.
columbaria} (allozymes, \emph{H} \textsubscript{e} vs.\emph{H}
\textsuperscript{2}; Waldmann \& Andersson, 1998); the
annual\emph{Clarkia dudleyana} (allozymes, \emph{H} \textsubscript{e}
vs.\emph{CV} \textsubscript{G}, coefficient of genetic variation of
quantitative traits; Podolsky, 2001), the annual \emph{Hordeum
spontaneum} (allozymes, \emph{H} \textsubscript{e} vs.\emph{H}
\textsuperscript{2}, Volis et al., 2005), and the selfing
annual\emph{Senecio vulgaris} (amplified fragment length polymorphisms
{[}AFLPs{]}, \emph{H} \textsubscript{e} vs. \emph{H}
\textsuperscript{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 (\selectlanguage{greek}\emph{µ} \selectlanguage{english}), 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 \emph{H} \textsubscript{e}
and\emph{AR} along the chromosome segment (Kreitman, 2001).\emph{H}
\textsubscript{e} and \emph{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.

\textbf{3} \textbf{\textbar{} 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 \emph{Q} \textsubscript{ST} (reflecting
differentiation caused by both neutral and selective forces)
versus\emph{F} \textsubscript{ST} estimates (reflecting differentiation
due to neutral processes including genetic drift) (Whitlock, 2008). The
neutrality expectation depends on the assumption that mutation rates
(\selectlanguage{greek}\emph{µ} \selectlanguage{english}) are substantially lower than migration rates (\emph{m} )
(Hendry, 2002). Neutral markers having high \selectlanguage{greek}\emph{µ} \selectlanguage{english}(e.g.,
microsatellites) are not recommended to be used in\emph{Q}
\textsubscript{ST}--\emph{F} \textsubscript{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 \emph{Q} \textsubscript{ST}--\emph{F} \textsubscript{ST} comparisons
(i.e., elucidation of the relative magnitudes of\emph{Q}
\textsubscript{ST} and \emph{F} \textsubscript{ST}) have already
provided valuable insights into responses of plant traits to
spatiotemporal environmental heterogeneity (Kremer et al., 1997; Meril\selectlanguage{ngerman}ä
\& Crnokrak, 2001; McKay \& Latta, 2002; Volis et al., 2005; Savolainen
et al., 2007; Leinonen et al., 2008, 2013). The\emph{Q}
\textsubscript{ST}--\emph{F} \textsubscript{ST} relationship can have
three different outcomes that have different interpretations (Merilä \&
Crnokrak, 2001; Leinonen et al., 2008):\emph{Q} \textsubscript{ST}
\textgreater{} \emph{F} \textsubscript{ST},\emph{Q} \textsubscript{ST} \selectlanguage{english}[?]
\emph{F} \textsubscript{ST}, and\emph{Q} \textsubscript{ST} \textless{}
\emph{F} \textsubscript{ST}. First, if \emph{Q} \textsubscript{ST}
\textgreater{}\emph{F} \textsubscript{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 \emph{Q} \textsubscript{ST}
\selectlanguage{english}[?] \emph{F} \textsubscript{ST}, trait differentiation is
indistinguishable from the effects of drift, and, thus, there is no
evidence for selection (Lande, 1992). Finally, if\emph{Q}
\textsubscript{ST} \textless{} \emph{F} \textsubscript{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 \emph{F} \textsubscript{ST} and\emph{Q}
\textsubscript{ST} estimation. Their results confirmed the main
conclusions of Merila \& Crnokrak (2001), who found a low but
significant positive correlation between \emph{Q} \textsubscript{ST}
and\emph{F} \textsubscript{ST} (Spearman rank correlation,\emph{r}
\textsubscript{s} = 0.39, \emph{P} = 0.017; Leinonen et al., 2008), and,
on average, \emph{Q} \textsubscript{ST} \textgreater{}\emph{F}
\textsubscript{ST} (\emph{P} \textless{} 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 (\emph{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 \emph{Q} \textsubscript{ST} data.
Furthermore, Leinonen et al. (2008) pointed out two potential biases in
finding that 70\% of \emph{Q} \textsubscript{ST} values exceed the
associated \emph{F} \textsubscript{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 \emph{Q} \textsubscript{ST}
\textgreater{}\emph{F} \textsubscript{ST} outcomes, possibly because of
difficulties interpreting \emph{Q} \textsubscript{ST} \selectlanguage{english}[?] \emph{F}
\textsubscript{ST} and\emph{Q} \textsubscript{ST} \textless{} \emph{F}
\textsubscript{ST}patterns. For example, \emph{Q} \textsubscript{ST}
\textless{}\emph{F} \textsubscript{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
from\emph{Q} \textsubscript{ST}--\emph{F} \textsubscript{ST} comparisons
and phylogenetic reconstruction. For a given trait, if\emph{Q}
\textsubscript{ST} \textless{} \emph{F} \textsubscript{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 \emph{Arabidopsis
thaliana}and cavitation resistance found in all \emph{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 \emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{ST}comparisons.

The study by De Kort et al. (2013) was the first meta-analysis
of\emph{Q} \textsubscript{ST}--\emph{F} \textsubscript{ST} comparisons
(401 cases that included each \emph{Q} \textsubscript{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 average\emph{Q}
\textsubscript{ST} values were significantly larger than the
corresponding \emph{F} \textsubscript{ST} values (0.345 versus 0.214,
Wilcoxon signed-rank test, \emph{P} = 0.003: paired \emph{t}
-test,\emph{P} = 0.000, recalculated from original data from De Kort et
al., 2013). The authors also found that the excess of\emph{Q}
\textsubscript{ST} relative to \emph{F} \textsubscript{ST} was
significantly negatively correlated with \emph{F}
\textsubscript{ST}(\selectlanguage{greek}\emph{β} \selectlanguage{english}= --0.484, \emph{P} \textless{} 0.01). A
weak but positive overall relationship between pairwise \emph{Q}
\textsubscript{ST} and\emph{F} \textsubscript{ST} values (\emph{r}
\textsubscript{s} = 0.278,\emph{P} = 0.048; \selectlanguage{greek}\emph{β} \selectlanguage{english}= 0.464, \emph{P} =
0.003, recalculated from De Kort et al., 2013) suggests that \emph{F}
\textsubscript{ST} in neutral markers could be to some degree predictive
of\emph{Q} \textsubscript{ST} in quantitative traits. These correlations
are what one would expect because (i) \emph{Q}
\textsubscript{ST}reflects both neutral forces and natural selection
caused by environmental differences and \emph{F} \textsubscript{ST} only
measures neutral processes including genetic drift and gene flow,
(ii)\emph{Q} \textsubscript{ST} and \emph{F} \textsubscript{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 \emph{Q}
\textsubscript{ST} could also lead to the increase of \emph{F}
\textsubscript{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 their\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{ST}difference values (\emph{P} \textless{} 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 (\emph{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 larger\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{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 (Meril\selectlanguage{ngerman}ä \&
Sheldon, 1999; Leinonen et al., 2008). Finally, De Kort et al. (2013)
found slightly higher\emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{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 \emph{F} \textsubscript{ST} and\emph{Q}
\textsubscript{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
\selectlanguage{greek}\emph{µ} \selectlanguage{english}in \emph{F} \textsubscript{ST} and\emph{Q} \textsubscript{ST}
(for more details see De Kort et al., 2013).

\textbf{4 \textbar{} APPLICATION
OF\emph{Q}\textsubscript{ST}--\emph{F}\textsubscript{ST} COMPARISONS TO
PLANT BIOLOGY}

Many \emph{Q} \textsubscript{ST}--\emph{F} \textsubscript{ST}
comparisons falling roughly into seven categories (\emph{viz} . local
adaptation, sexual selection, evolutionary stasis, human-induced
evolution, artificial selection, biological invasions, and management
and/or conservation) can be applied to plant biology, have been
conducted to infer ecological and evolutionary processes. Perhaps the
most commonly studied issue is to identify natural selection as a cause
of broad-scale clinal variation in morphological and life-history traits
(local adaptation, e.g., in \emph{Campanulastrum americanum}
{[}Prendeville et al., 2013{]}, in \emph{Helianthus maximiliani}
{[}Kawakami et al., 2011{]}, in two subspecies of \emph{Antirrhinum
majus} {[}Marin et al., 2020{]} or various tree species {[}Savolainen et
al., 2007{]}). As an example of sexual selection, Yu et al. (2011)
detected sex-specific selection as the cause of the evolution of sexual
dimorphism in\emph{Silene latifolia} . Using \emph{Pinus pinaster} as a
study species, Lamy et al. (2011) identified selective constraints
explaining phenotypic uniformity across species distributions
(evolutionary stasis, i.e., canalization or uniform selection).

Other examples are the demonstration of how human-induced habitat
changes can either cause or impair adaptation (human-induced evolution,
e.g., in \emph{Thlaspi caerulescens} {[}Jim\selectlanguage{ngerman}énez-Ambriz et al., 2007{]}
and \emph{Arabidopsis halleri} {[}Meyer et al., 2010{]}). In addition,
studies on how selective breeding shapes diversification and population
structuring of crop species (artificial selection) have been conducted
in \emph{Oryza sativa} (Sreejayan et al., 2011) and \emph{Zea
mays}(Pressoir \& Berthaud, 2004). By performing\emph{Q}
\textsubscript{ST}--\emph{F} \textsubscript{ST} comparisons between the
invasive species' native and invasive ranges (biological invasions),
several researchers provided information on the evolution of
invasiveness and the adaptive potential of invasive plant species such
as \emph{Hypericum canariense} (Dlugosch \& Parker, 2007),\emph{Ambrosia
artemisiifolia} (Chun et al., 2011), \emph{Lythrum salicaria} (Chun et
al., 2009), and \emph{Geranium carolinianum} (Shirk \& Hamrick, 2014).

For management purposes, Gravuer et al. (2005) identified units or
populations suitable for translocation in \emph{Liatris scariosa} .
Furthermore, some authors demonstrated that setting conservation
priorities should not be based only on neutral marker diversity and
that\emph{Q} \textsubscript{ST}--\emph{F} \textsubscript{ST} comparisons
can be used to identify populations that are suitable for translocation
in\emph{Arabis fecunda} (McKay et al., 2001) and \emph{Araucaria
araucana}(Bekessy et al., 2003). The last two issues, i.e., conservation
and management, will be the focus of the next section.

\textbf{5} \textbf{\textbar{} INSIGHTS INTO CONSERVATION AND RESTORATION
DERIVED FROM\emph{Q}\textsubscript{ST}--\emph{F}\textsubscript{ST}
COMPARISONS}

Because \emph{F} \textsubscript{ST} estimates are significantly lower in
trees than in most herbaceous perennials and annuals, Chung et al.
(2020) recommended that conservation genetic strategies be designed
differently for tree species versus other types of plant species. That
is, seeds of most tree species (which generally show low values
of\emph{F} \textsubscript{ST}) could be sourced from a few populations
distributed across the species' range, whereas seeds of rare herbaceous
species (often with high \emph{F} \textsubscript{ST} values) should be
taken from many populations to capture the highly localized genetic
diversity. Based on a small body of available data on seed plant species
(data from Lamy et al., 2012; De Kort et al., 2013; Leinonen et al.,
2013), on average, \emph{Q} \textsubscript{ST} is higher than\emph{F}
\textsubscript{ST} in common forest tree species, indicating that their
quantitative traits have been subject to diversifying selection and
local adaptation (Kremer et al., 1997; Savolainen et al., 2007). It has
been suggested that more populations would be needed to preserve enough
AGV for adaptively significant quantitative traits than for NGV,
particularly in trees (McKay et al., 2001; Hamrick et al., 2006; Chung
et al., 2020). More specifically, conservation practitioners may need
information about how to capture most AGV and NGV based on known levels
of NGV and AGV from population or conservation genetic studies.

Population(s) to be protected in situ or to be sampled for seed banking
purposes could be estimated using the formulae: PNGV = 1 --\emph{F}
\textsubscript{ST} (or\emph{G}
\textsubscript{ST})\emph{\textsuperscript{N}} for NGV, where PNGV =
proportion of NGV captured by sampling, \emph{N} = number of populations
(Ceska et al., 1997; Hamrick et al., 2006) and PAGV = 1 --\emph{Q}
\textsubscript{ST}\emph{\textsuperscript{N}} for AGV, where PAGV =
proportion of AGV captured by sampling. However, one should be aware
that if there are more than two alleles per locus for the neutral
markers, then \emph{Q} \textsubscript{ST} and \emph{G}
\textsubscript{ST}are on different scales, and the formulae PAGV = 1
--\emph{Q} \textsubscript{ST}\emph{\textsuperscript{N}} and PNGV = 1
--\emph{F} \textsubscript{ST}\emph{\textsuperscript{N}} cannot be
interpreted in the same way (J. D. Nason, pers. comm.). For
multi-allelic markers, it depends on \selectlanguage{greek}\emph{µ} \selectlanguage{english}whether this is
problematic. In addition, as \selectlanguage{russian}\emph{Ф} \textsubscript{ST} (a function of
the among-population variance component and the within-population
component, which is based on genetic distances among alleles for the
neutral markers) is conceptually similar to \emph{Q} \textsubscript{ST},
it is advisable to use \emph{Ф} \textsubscript{ST} rather than\emph{G}
\textsubscript{ST}, \emph{F} \textsubscript{ST}, or \selectlanguage{greek}\emph{θ}\selectlanguage{english}(Edelaar et
al., 2011). The calculations for 99\% capture of AGV and NGV can be key
to figuring out ideal sample sizes, especially when resources are
limited. Based on the average values of De Kort et al. (2013)
for\emph{F} \textsubscript{ST} and \emph{Q} \textsubscript{ST}
(annuals,\emph{n} = 19, 0.308 versus 0.451 {[}i.e., \emph{Q}
\textsubscript{ST} is about 1.5 times greater than \emph{F}
\textsubscript{ST}{]}; herbaceous perennials, \emph{n} = 14, 0.267
versus 0.299 {[}\emph{Q} \textsubscript{ST} is about 1.1 times
greater{]}; woody perennials, \emph{n} = 18, 0.074 versus 0.269
{[}\emph{Q} \textsubscript{ST} is about 3.6 times greater{]};
recalculated from De Kort et al., 2013), to capture 99\% of NGV and AGV
for woody perennials, just two and four populations would be needed
using the abovementioned formulae, respectively. On the other hand, on
average, four populations of herbaceous perennials would be needed to
secure 99\% of NGV and AGV, respectively, because the average difference
between\emph{Q} \textsubscript{ST} and \emph{F} \textsubscript{ST} is
small (0.032). For annuals, on average, four and six populations are
needed to secure 99\% of NGV and AGV, respectively.

We apply this approach to a real-life example: for the widespread
tree\emph{Populus balsamifera} , Keller et al. (2011) reported a
mean\selectlanguage{russian}\emph{Ф} \textsubscript{ST} value of 0.067 estimated from 310
nuclear SNP loci and a mean \emph{Q} \textsubscript{ST} value of 0.421
(range = 0.127--0.832) obtained from 13 ecophysiological and
phenological traits originating from 20 populations across North
America. To capture 99\% of NGV, two populations of this tree would be
needed using the above formula. When we apply the mean \emph{Q}
\textsubscript{ST} value to the formula, at least six populations would
be necessary to capture the same level of AGV. However, the value of
\emph{Q} \textsubscript{ST} depends on the trait under consideration:
for traits with a high\emph{Q} \textsubscript{ST}, more populations
should be sampled than for traits with a low \emph{Q}
\textsubscript{ST}. Given this, the prudent thing would be not to use
the average \emph{Q} \textsubscript{ST} but the maximum \emph{Q}
\textsubscript{ST} in these calculations. If this logic is applied to
\emph{P. balsamifera} , as for bud set\emph{Q} \textsubscript{ST} =
0.832, then up to 25 populations would need to be targeted to maintain
enough variation for the trait. This does not mean that NGV is not
essential; there is probably a reservoir of genetic variation in every
population that is neutral now but may be selectively important if
environmental conditions change. Furthermore, NGV can be very
informative about the populations' past demography which is often of
interest in conservation biology (Frankham, 2015; Allendorf, 2017;
DeWoody et al., 2021; Garc\selectlanguage{ngerman}ía-Dorado \& Caballero, 2021).

The application of the above formulae to plants with different life
forms, as well as the example of \emph{Populus balsamifera} , suggests
that conservation and management policies or actions based solely
on\emph{F} \textsubscript{ST} could potentially be misleading. Again,
these findings stress that guidelines and conservation genetic
strategies should be designed based on genetic information on both NGV
and AGV together for tree and herbaceous (whether perennial or annual)
species. In addition, managers or practitioners should design
restoration and conservation strategies by knowing that, on
average,\emph{Q} \textsubscript{ST} is about 3.6, 1.5, and 1.1 times
greater than\emph{F} \textsubscript{ST} in woody plants, annuals, and
herbaceous perennials, respectively.

To summarize, \emph{F} \textsubscript{ST} estimates appear to be more
closely related to AGV than within-population genetic diversity metrics
(e.g., \emph{H} \textsubscript{e},\emph{\%P} , or \emph{AR} ) in seed
plant species. Thus,\emph{F} \textsubscript{ST} should be considered as
a more predictable parameter for conservation and restoration purposes.
Together with the metrics of \emph{H} \textsubscript{e}, \emph{\%P} , or
\emph{AR} , the particular degree of \emph{F} \textsubscript{ST} (i.e.,
low, moderate, or high) is important for prioritizing populations for
collection and identifying appropriate sources for reintroductions
(Hamrick \& Godt, 1996; Ottewell et al., 2016; Chung et al., 2021).
Thus, the importance of the proper consideration of \emph{F}
\textsubscript{ST} information (and \emph{Q} \textsubscript{ST}, if
available) in conservation management cannot be overstated, particularly
when it comes to annuals and herbaceous perennials.

\textbf{6 \textbar{} CONCLUSIONS AND PERSPECTIVE}

Within-population genetic variation, both natural and restored, is
crucial for the response to short-term environmental stresses and
long-term evolutionary change. Although the levels of\emph{H}
\textsubscript{e} are often correlated with fitness (Oostermeijer et
al., 1994; Reed \& Frankham, 2003; Szulkin et al., 2010), \emph{H}
\textsubscript{e} of NGV is poorly correlated with heritability
(\emph{h} \textsuperscript{2} or\emph{H} \textsuperscript{2}) of
quantitative traits (AGV). As discussed above, the relationship of
\emph{H} \textsubscript{e} to\emph{h} \textsuperscript{2} or \emph{H}
\textsuperscript{2} is often very weak, while the relationship between
\emph{F} \textsubscript{ST} and\emph{Q} \textsubscript{ST} is
comparatively stronger; thus,\emph{F} \textsubscript{ST} could be
considered a proxy of\emph{Q} \textsubscript{ST}. However, whenever
logistically possible, common garden and/or transplant studies are
strongly recommended to quantify patterns of adaptive genetic variation
and differentiation (de Villemereuil et al., 2016; Sork, 2018; Capblancq
et al., 2020). The most comprehensive studies conducted so far are
generally those carried out with many commercially important tree
species (e.g., eucalypts, oaks, poplars, pines, and spruces), as plants
(including propagules) with well-adapted genotypes are used to replant
clear-cut areas (Depardieu et al., 2020). Exemplifying this, 14 out of
18 entries for woody perennials (seven genera) used in our analyses
belong to the eucalypt-oak-pine-spruce-poplar group (De Kort et al.,
2013). More studies on \emph{Q} \textsubscript{ST}--\emph{F}
\textsubscript{ST}comparisons are needed, particularly on rare woody
species and common herbaceous species, to avoid bias in the inferences,
as well as to balance entries among the different life forms. Although
the differences between herbaceous and woody plants regarding \emph{F}
\textsubscript{ST}were large in the data reviewed here, similar average
values for\emph{Q} \textsubscript{ST} were relatively common in the data
set used (De Kort et al., 2013). With a larger dataset, one could also
expect some generalizations to emerge concerning the\emph{Q}
\textsubscript{ST}--\emph{F} \textsubscript{ST} relationships regarding
life history characteristics and morphological/anatomical traits. Such
generalizations could aid conservation managers and practitioners in
using neutral \emph{F} \textsubscript{ST} estimates to predict
approximate \emph{Q} \textsubscript{ST} values and aid the conservation
and restoration of plant species. Multiple approaches, including
molecular markers (NGV), quantitative traits and/or quantitative trait
loci coding for traits and contemporary genome-wide association
approaches in the context of a common garden experiment, and
environmental variation (e.g., designation of climatic zonation) are
needed to gain comprehensive insights into conservation of herbs and
trees (de Villemereuil et al., 2016; Rodríguez-Quilón et al., 2016;
Sork, 2018).

\textbf{ACKNOWLEDGEMENTS}

The corresponding author (M.G.C.) thanks J. L. Hamrick, J. D. Nason, S.
R. Keller, and P. Meirmans who have kindly responded to questions
related to this mini review. The authors also thank three anonymous
reviewers for valuable comments on earlier versions of this paper when
submitted to another journal. This research was supported by the Basic
Science Research Program through the National Research Foundation of
Korea (NRF) funded by the Ministry of Education (NRF-2020R1I1A3074635 to
M.Y.C.). K.M. was supported by the National Natural Science Foundation
of China (grant no. 31622015), and J.L.-P. was also supported by the
Spanish Ministerio de Ciencia e Innovación (project PID2020-119163GB-I00
funded by MCIN/AEI/10.13039/501100011033).

\textbf{CONFLICT OF INTEREST}

All the authors state that there is no conflict of interest.

\textbf{DATA AVAILABILITY STATEMENT}

There was no new data created or analyzed for this manuscript.

\textbf{ORCID}

Mi Yoon Chung\emph{https://orcid.org/0000-0002-8756-5367}

Juha Merilä\emph{https://orcid.org/0000-0001-9614-0072}

Yuseob Kim\emph{https://orcid.org/0000-0002-7975-6147}

Kangshan Mao\emph{https://orcid.org/0000-0002-0071-1844}

Jordi López-Pujol\emph{https://orcid.org/0000-0002-2091-6222}

Myong Gi Chung\emph{https://orcid.org/0000-0002-1283-3574}

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\textbf{TABLE 1} Definitions of terms used in this mini review\selectlanguage{english}
\begin{longtable}[]{@{}ll@{}}
\toprule
\textbf{Term} & \textbf{Definition}\tabularnewline
Adaptation & A trait that increases the ability of a population or an
organism to survive in its environment.\tabularnewline
Allelic richness (\emph{AR}) & A measurement of the number of alleles
per locus with rarefaction adjusting for differences in sample
sizes.\tabularnewline
Balancing selection & A process in which more than one allele is
maintained at a locus at a frequency higher than expected by chance.
Balancing selection can come about due to overdominance (heterozygote
advantage) or frequency-dependent selection.\tabularnewline
Broad-sense heritability (\emph{H}\textsuperscript{2}) & The ratio of
total genetic variance to total phenotypic variance within a
population.\tabularnewline
Common garden experiment & A traditional experiment in which genotypes
from different populations (provenances) are grown under a common
environment to test the relative contribution of genetic and
environmental variation on a given phenotypic trait.\tabularnewline
Conservation genetics & A branch of (population) genetics aimed to
reduce the risk of population and species extinctions and to design
strategies for their preservation or restoration.\tabularnewline
Conservation genomics & The use of genome-scale data with the same aims
of conservation genetics, i.e., ensuring the viability of populations
and the biodiversity of living organisms.\tabularnewline
\emph{F}\textsubscript{ST} & The probability of identity by descent
(\emph{ibd}; describing the pair of homologous DNA sequences {[}for
simplicity, \emph{alleles}{]} carried by the gametes that produced it
from a recent ancestor) resulting from population subdivision
(independent of inbreeding within subdivisions);
\emph{F\textsubscript{ST}} measures the probability of \emph{ibd} of
alleles within subpopulations relative to the total
population.\tabularnewline
\emph{G}\textsubscript{ST} & The proportion of total genetic diversity
found among populations averaged over all polymorphic loci; it is
regarded as a multiallelic variant of Wright's
\emph{F}\textsubscript{ST} (1951).\tabularnewline
Gene diversity (\emph{H}\textsubscript{e}) & Hardy-Weinberg expected
heterozygosity both at monomorphic and polymorphic loci. The probability
that an individual will be heterozygous at a given locus, based on
allele frequencies at that locus.\tabularnewline
Gene flow & The movement of alleles from one population to another
population, which for plants is achieved by the transport of pollen and
seeds by wind, water, or animals.\tabularnewline
Genetic drift & A change in allele frequencies in a population over time
resulting from a random sampling of gametes (i.e., error) to produce
zygotes in the next generation and from chance variation in individuals'
survival and/or reproductive success. Thus, it results in nonadaptive
evolution.\tabularnewline
Genetic markers & Any type of neutral (see below) genetic information
(e.g., allozymes, amplified fragment length polymorphism, inter-simple
sequence repeats, microsatellites, DNA sequences {[}e.g., single
nucleotide polymorphisms, SNPs{]} that can be used to identify
differences between individuals, populations, and/or
species.\tabularnewline
Isolation by distance & A process by which geographically restricted
gene flow results in a genetic differentiation being an increasing
function of geographic distance.\tabularnewline
Linkage disequilibrium & A state in which genes are combined in a
dependent manner (i.e., linkage). It arises when genotypes at one locus
within a population are non-randomly distributed with respect to
genotypes at another locus.\tabularnewline
Local adaptation & A situation in which resident genotypes have a
relatively higher fitness in their local environments than in other
environments.\tabularnewline
Narrow-sense heritability (\emph{h}\textsuperscript{2}) & The ratio of
additive genetic variance to the phenotypic variance in a trait within a
population.\tabularnewline
Neutral & Molecular markers that do not affect fitness, i.e.,
individuals with different genotypes
\emph{A}\textsubscript{1}\emph{A}\textsubscript{1} vs.
\emph{A}\textsubscript{1}\emph{A}\textsubscript{2} have the same
fitness.\tabularnewline
Non-additive genetic variation & Results from interactions between an
allele at the same locus (dominance) or at different loci
(epistasis).\tabularnewline
Percentage of polymorphic loci (\emph{\%P}) & A measure used to quantify
genetic diversity.\tabularnewline
\emph{Q}\textsubscript{ST} & The proportion of total additive genetic
variance that is due to among-population differences in a quantitative
trait.\tabularnewline
\emph{Q}\textsubscript{ST}--\emph{F}\textsubscript{ST} comparison
experiment & The comparison of the degree of genetic differentiation in
quantitative traits (\emph{Q}\textsubscript{ST}) with that in neutral
molecular markers (\emph{F}\textsubscript{ST}). This comparison allows
the identification of a trait divergence caused by natural selection, as
opposed to genetic drift.\tabularnewline
Reciprocal transplant experiment & A traditional experimental approach
in which living organisms from two different environments are
reciprocally grown in their respective environments. If the phenotype of
the transplanted individuals does not converge towards that of
individuals in receiving population would be evidence for the strong
genetic basis of the focal trait. The opposite outcome would be evidence
for plasticity in determining the trait value.\tabularnewline
Translocation & The deliberate (human-mediated) transfer of plants
(entire plants, seeds, or propagules) from an ex-situ collection or a
natural population to a new location, usually in the
wild.\tabularnewline
\bottomrule
\end{longtable}

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