Introduction
In the current context of biodiversity erosion, it is crucial to improve
our understanding of the mechanisms underlying plant community dynamics
for getting reliable predictions of the fate of plant species. Two
decades ago, functional approaches have emerged and have allowed to
rebuild community ecology and to produce general principles on assembly
mechanisms (Calow, 1987; McGill et al., 2006). By considering the
concept of ecological function, where species are no longer described by
their taxonomic identities alone but by their biological attributes
through the measurements of functional traits (Albert, Thuiller, Yoccoz,
Douzet, et al., 2010), functional approaches have been fruitfully
developed for addressing a range of key pending ecological questions.
Functional traits correspond to any morphological, physiological, and
phenological characteristics that impact individual performance, either
directly or indirectly, through individual growth, reproduction or
survival (Violle et al., 2007). More recent studies evidenced that large
trait variations can also occur in the field at inter- but also
intraspecific levels (Albert, Thuiller, Yoccoz, Soudant, et al., 2010;
Messier et al., 2010; Taudiere & Violle, 2016). These variations would
be the response to a range of biological processes operating at
different spatial and temporal scales, and ultimately leading to species
coexistence.
So far, organismal trait variation has usually been observed through the
lens of space, time, or phylogeny independently, and this has resulted
in a fragmented view of community assembly (but see Fyllas et al., 2020;
Liu & Ng, 2020; Messier et al., 2010 for variance partitioning across
spatial and phylogenetic scales). Temporal trait variation is, for
example, reported as part of plant seasonal strategies allowing them to
permanently adjust trait performance to better deal with external
stresses. Adjustment of physiological (photosynthetic capacitye.g. Grassi et al., 2005; Muraoka et al., 2010) or morphological
traits (leaf thickness e.g. Fullana-Pericas et al., 2017) is thus
frequently reported in plant species. Spatial scales, owing to the
diversity of environmental conditions they encompass, also shape overall
trait variations, which can arise from species specific responses to
various environmental filtering processes. Consistently, both
aboveground (Reich et al., 2004) and belowground (Zadworny et al., 2016)
traits have been found to spatially vary depending on environmental
temperatures (Reich et al., 2004). By subsequently decomposing trait
variations through phylogeny (McGill, 2008; Swenson et al., 2006) so
that inter- vs. intraspecific trait variations can be considered,
it is possible to determine whether species identity is a critical
factor in the filtering processes or whether individuals are rather
filtered out depending on their trait values. Overall, studies providing
information on the scale which carries the most important trait
variations are of particular interest, as the results should be
promising to understand many community patterns and processes (McGill,
2008; Taudiere & Violle, 2016; Violle et al., 2007, 2012), and thus to
assess the scale dependency issue in community ecology (e.g. ,
Cavender-Bares et al., 2009; Swenson et al., 2006).
Over the last ten years, studies reported the major role of
intraspecific trait variability (ITV) in ecological processes, and
showed that ITV can account for up to 30% to 40% of trait variance
within a plant community (Albert, Thuiller, Yoccoz, Soudant, et al.,
2010; Jung et al., 2010). ITV would allow a better resistance to filters
(Jung et al., 2010; Violle et al., 2012), in particular by adaptation
(genetic variability) and/or phenotypic plasticity (Albert et al., 2011;
Byars et al., 2007; Ghalambor et al., 2007). Most morphological traits
in plants are key for coping with different environmental filters (seee.g. Lavorel & Garnier, 2002). Responses to both biotic and
abiotic filters have indeed been extensively evidenced in traits
involved in growth or resource acquisition (in particular Specific Leaf
Area SLA, Leaf Dry Matter Content LDMC, height, and root traits)
(Bittebiere & Mony, 2015; Butterfield & Callaway, 2013; Colom &
Baucom, 2020; Cornelissen et al., 2003; Dostálek et al., 2020; Lavorel
& Garnier, 2002), as well as in clonal traits (internode length,
specific connection mass) (Bittebiere & Mony, 2015; Huber & Stuefer,
1997; Hutchings et al., 1997; Pilon & Santamaria, 2002; Rusch et al.,
2011). It remains however unclear how traits will respond to
simultaneously varying biotic and abiotic conditions, as their effects
are usually studied separately (see e.g. Dudley & Schmitt, 1996;
Greulich & Bornette, 1999; Riis et al., 2012; Suding et al., 2008). In
some cases, this knowledge gap may lead to misinterpretation of patterns
of traits variations, especially for studies conducted in situ(Cadotte & Tucker, 2017; Kraft et al., 2015). We thus argue that
partitioning intraspecific trait variations should help determining the
role of traits in species biotic and abiotic resistance to environmental
filters (Le Bagousse-Pinguet et al., 2017; R. Liu et al., 2018). Traits
would simultaneously respond to both biotic and abiotic conditions, but
with varying strengths depending on the trait under consideration.
Variations in traits ultimately influence individual performance
(i.e. production of vegetative biomass, and reproduction rates)
(Geber & Griffen, 2003) and thus the species fate in the plant
community (Ghalambor et al., 2007). However, direction and intensity of
the influence of the trait on organismal performance will depend on the
measured trait. For instance, in plant individuals, clonal traits mainly
drive spatial positioning and resource storage (Klimešová et al., 2021),
while aerial (e.g. height, SLA) and root traits (e.g.specific root length) determine efficiency of resources acquisition
(Garnier et al., 2004). We thus expect aerial and root traits to have
stronger importance on the intensity and direction of individual
performance in comparison with clonal traits. Nevertheless, it is also
very important to bear in mind that several traits co-vary and/or are
subjected to tradeoffs, within plant individuals to coordinate different
biological functions (Maire et al., 2013; Valladares et al., 2007;
Violle et al., 2007). Yet, literature studies usually neglect these
trait relationships that can result in indirect influence on individual
performance. The identification of the direct and indirect influence of
traits on organismal performance likely imposes hierarchies among plant
traits, and this can be revealed through Structural Equation Modelling
(Ackerly et al., 2000; Dwyer & Laughlin, 2017; Saiz et al., 2018; Vile
et al., 2006).
Recently, a growing number of theories and concepts in ecology have been
tested in the sub-Antarctic region, as they represent valuable sentinel
ecosystems and open air laboratories (Bergstrom & Chown, 1999). In
these regions, climate change, in particular warming, is especially
rapid (Walther et al., 2002), with strong impacts on aquatic ecosystems,
including ponds (Douce et al., in prep). Moreover, their plant
communities (macrophytes) are remarkably poor, resulting in simplified
interaction networks. In this study, we used macrophyte communities as
system models, and measured different categories of traits (aerial,
root, clonal, and of performances) on all pond-occurring species at the
French sub-Antarctic Iles Kerguelen. Trait variations were studied
across spatial, temporal and phylogeny scales, and covered multiple
habitat abiotic and biotic conditions. Consequences for plant individual
performance were also explored. The following hypotheses were
specifically tested:
- Partitioning of trait variance among spatial, temporal, and
phylogenetic scales should be quite promising as it would allow to
detect the mechanisms involved in plant community assembly.
- Traits are expected to simultaneously respond to both biotic and
abiotic conditions, but the direction and intensity of responses
should be trait-dependent.
- Traits related to resource acquisition (aerial, and root traits) would
affect individual performance directly, with stronger influence than
clonal traits. However, relationships between traits may modulate
these influences.