Introduction
Functional traits are morphological, physiological or phenological
features of organisms that influence the components of fitness, i.e.
survival and reproduction (Reich et al . 2003, Violle et
al . 2007, Adler et al . 2014). Intraspecific variation in
functional traits is widely documented and has important implications
for population dynamics (Hughes et al . 2008, Villellas & García
2017), evolutionary trajectories (Moran et el. 2016, Caruso et
al. 2020), community assembly (Violle et al . 2012, Des Rocheset al . 2018), and ecosystem functioning (Crutsinger et al .
2006, Breza et al . 2012). Disentangling the environmental drivers
of functional trait variation is thus of great ecological and
evolutionary interest (Liancourt et al. 2013, van de Pol et al 2016,
Bruelheide et al 2018), and can improve predictions of species responses
to global change (Benito Garzón et al . 2011, Violle et al .
2014, Moran et al . 2016).
The predominant approach to identify the drivers of functional trait
variation has relied upon assembling large databases of observedin situ trait variation (e.g., Enquist et al . 2016, Moranet al . 2016, Iversen et al . 2017, Kattge et al .
2020) and the association of these trait values with candidate
environmental drivers. However, interpreting trait-environment
relationships inferred from observational field datasets requires
understanding the processes underlying trait variation. Intraspecific
trait variation observed in situ among populations may arise from
genetic differentiation and/or phenotypic plasticity (Chevin et
al . 2010, Merilä & Hendry 2014). Across large environmental gradients,
genetic differentiation among populations can result from adaptation to
local conditions (but see the role of neutral and historical processes
in Keller et al . (2009), Santagelo et al . (2018)).
Genetically determined traits are thus expected to show correlations
with the source environment. However, genetic differentiation might be
obscured by phenotypic plasticity (which can also be adaptive; see
Matesanz et al . 2010, Palacio-López et al. 2015), reducing
the consistency of trait-environment relationships across environmental
contexts.
Combining experimental and in situ field data enables us to
assess the potential uses and misuses of observational trait datasets. A
common way to partition trait variation is through a common garden
experiment (Clausen et al. 1940, MacColl 2011, Franks et
al . 2014). Specifically, by growing offspring from multiple provenances
together in a set of controlled conditions, we can disentangle the
effects of source environments (leading to genetic differentiation) from
those of exposure environments (driving phenotypic plasticity). Notably,
by evaluating the different scenarios involving genetic and plastic
effects on traits, we can assess the utility of observational data for
predicting genetic differentiation (Fig. 1). For example, a predominance
of genetic over plastic effects decreases the relative importance of
genotype-by-environment interactions, and increases the predictability
of trait values from average environmental conditions of source
populations (Fig. 1a,f). In contrast, a high level of plasticity causes
traits to be more strongly determined by the exposure environment,
decreasing trait predictability from source environment (Fig. 1b,g,h).
Source and exposure environments can have similar or opposing effects on
traits (Fig. 1c-e), with opposing effects known as countergradient
variation (Conover & Schultz 1995, Conover et al . 2009).
Countergradient variation may lead to an apparent absence of trait
variation among populations in the field (Fig. 1d), or even to patterns
counter to those of genetic differentiation (Fig. 1e).
The role of genetic differentiation and phenotypic plasticity on
intraspecific variation differs among functional traits (Albert et
al . 2010a, Funk et al . 2017, Münzbergová et al . 2017).
Species may show evolutionary conservation of the traits most directly
related to fitness through genetic differentiation (Scheiner 1993,
Stearns & Kawecki 1994, Sih 2004), and instead display plasticity in
underlying morphological or physiological traits, to buffer
environmental perturbations (Sultan 1995, Richards et al . 2006).
This view is also supported by demographic studies finding that the most
influential processes on population growth rate show relatively low
variability (Pfister 1998, Burns et al . 2010, Hilde et al .
2020; but see McDonald et al . 2017). In plants, vegetative traits
often show higher plasticity than reproductive traits (Bradshaw 1965,
Schlichting & Levin 1984, Frazee & Marquis 1994). For example, both
biomass and reproductive investment per unit biomass determine
reproduction, but while biomass is expected to show high plasticity due
to its influence on several demographic parameters (Harper 1977),
reproductive investment per unit biomass may be more conserved. This
might be especially true for short-lived taxa, in which reproduction
usually has the highest influence on population growth (Silvertownet al . 1996, García et al . 2008, Shefferson and Roach
2012). Yet reproductive investment may appear to be strongly driven by
plasticity if evaluated at the whole plant level, due to the inclusion
of a more labile biomass-dependent component (Biere 1995, Weineret al . 2009). It is important therefore to partition reproductive
traits into biomass dependent and independent components, to better
understand the role of genetic differentiation and plasticity.
Despite the abundance of studies analysing trait-environment
relationships at local or regional scales (e.g., Oleksyn et al .
1998, Villellas & García 2013, Preite et al . 2015, Münzbergováet al . 2017), there is a critical gap in knowledge about the
drivers of intraspecific trait variation at global scales (MacColl
2011). Environmental effects may be difficult to detect if drivers are
assessed independently from each other, or if studies omit significant
parts of a species’ environmental niche (Matesanz et al . 2010,
Hulme & Barrett 2013, Shipley et al. 2016). Widespread plants
offer a unique opportunity to unravel the multiple drivers of trait
variation from local to global scales. While some studies have analysed
trait genetic differentiation and plasticity across species’ ranges
(e.g., Joshi et al . 2001, Maron et al . 2004, Alexanderet al . 2012), we lack global assessments of the responses of
different types of traits to multiple environmental drivers using the
combined power of experimental and observational data.
Here we analyse responses of vegetative vs. reproductive traits of the
short-lived herb Plantago lanceolata to a set of environmental
drivers, both in a common garden and in the field. By growing
individuals from multiple populations under several light and water
conditions, we tested 1) whether vegetative traits (plant biomass,
specific leaf area and root:shoot ratio) showed higher levels of
plasticity than reproductive traits (probability of flowering and
fecundity), and 2) whether reproductive traits showed more consistent
population genetic differentiation across exposure treatments than
vegetative traits, and higher consistency between genetic and plastic
responses. To account for the potential size-dependency of plant
reproductive investment, we examined reproductive traits by both
including and excluding plant biomass as a covariate in the analyses.
Finally, by comparing experimental results with trait-environment
relationships detected from a global-scale observational survey, we
evaluated 3) whether observational data provided a better prediction of
genetic differentiation for reproductive than vegetative traits.