Introduction
Climate is changing faster now than it has in the last 2000 years (IPCC
2021), and ecological communities are being reshuffled as a consequence
(Davis & Shaw 2001; Parmesan & Yohe 2003). Predicting which types of
species are likely to suffer or benefit from changing climate and
interaction networks is needed for accurate estimates of future
community composition and ecosystem function.
How plant species respond to environmental change depends on their
eco-physiological properties (Beyschlag & Ryel 2007; Taiz & Zeiger
2010). Knowledge of species’ physiology can improve predictions of
mortality (Anderegg et al. 2016), species’ distributions
(Borchert 1994; Kearney & Porter 2009) and ecosystem-level processes
(Verheijen et al. 2013; Fyllas et al. 2014). Nevertheless,
as plant eco-physiological traits can be time-consuming to measure and
thus often absent from trait databases, they are rarely used to predict
changes in plant community composition. Instead, most studies predicting
climate change responses in plant communities rely on morphological
traits (e.g. leaf mass per area, height), which are assumed to
indirectly describe species physiology and resource acquisition (Lavorel
& Garnier 2002; Shipley et al. 2016).
Functional trait based-approaches have had varying success in
identifying “winning” or “losing” traits under environmental change.
For example, warming benefits alpine plants with thick leaves and high
leaf mass per area (Soudzilovskaia et al. 2013) and tall Arctic
species (Bjorkman et al. 2018). Nevertheless, many studies using
the same set of traits have shown them to be poor predictors of
population dynamics (Paine et al. 2015; Yang et al. 2018;
Swenson et al. 2020) or ecosystem properties (van der Plaset al. 2020). This has raised the question whether successful
trait-based predictions of environmental responses are the exception
rather than the rule (Yang et al. 2018). It remains unclear
whether failures of traits to predict population trends reflect the
limitations of the traits commonly measured, or limitations of the
trait-based approach more generally.
The focus on easily-measured morphological traits has three potential
problems. First, the narrow selection of traits is likely to miss
important functional dimensions (e.g. belowground or night-time
processes; Yang et al. 2018; Medeiros et al. 2019).
Second, traits are often measured under fixed environmental conditions
(Yang et al. 2018). Yet, trait plasticity can be an important
predictor of demographic change, either as an indicator of species’
ability to acclimate to new environmental conditions (Siefert et
al. 2015; Zhang et al. 2020), or species’ ability to rapidly
respond to a change in the environment (Volaire et al. 2020).
Third, the indirect nature of the commonly measured morphological traits
hides the mechanism underlying the relationship between a given trait
value and the response of interest (Rosado et al. 2013; Brodribb
2017). For example, one of the most frequently measured traits, leaf
mass per area, can be used to describe photosynthetic capacity,
herbivory tolerance, water economy, cold-hardiness and competitive
ability (Westoby 1998; Vendramini et al. 2002; Poorter et
al. 2009). Ultimately, not knowing the mechanism behind plant response
to the environment makes it challenging to predict ecosystem-scale
responses to environmental perturbations (Smith et al. 2009;
Griffin‐Nolan et al. 2018).
In addition to the shortcomings caused by the narrow selection of
traits, the role of biotic interactions in determining the “winning”
traits is rarely disentangled from the role of the abiotic environment.
This is true despite the fact that these two aspects of the environment
are changing in parallel (Suttle et al. 2007; Vandvik et
al. 2020) and are likely to favor different trait combinations (e.g.
stress tolerance vs competitive ability, De Bello et al. 2005;
Grime 2006). Thus, using traits as predictors of species’ response to
changing climate might be unsuccessful if the role of biotic
interactions in shaping the successful trait combination is not
considered.
We leveraged a transplant experiment along a 1000 m elevation gradient
in the Swiss Alps to explore how plant morphological and
eco-physiological traits predict species’ demographic responses to
climate warming and changing competitive interactions. We measured the
demographic responses and traits of alpine species transplanted to lower
elevations to simulate climate warming, to answer the following
questions:
- Do traits measured under current alpine conditions predict differences
in how species respond to warmer climate? Alternatively, is the
plastic response to warming more predictive of the demographic
response?
- Do eco-physiological traits perform better than morphological traits
as predictors of demographic responses to climate warming?
- Do the traits associated with greater performance under warming differ
from those associated with persistence with novel competitors?
We expected the following: (1) Given that resource-acquisitive traits
can be detrimental when resources are limiting, and advantageous when
not, (Fridley et al. 2016; Zhang et al. 2020), species
with resource-acquisitive traits should be most responsive to the
manipulations (either suffering e.g. due to water shortage or benefiting
e.g. from a longer growing season). (2) As the ability to acclimate can
be important for survival in changing environments (Ripley et al.2020; Zhang et al. 2020), species with greater trait plasticity
should perform better with warming. (3) Assuming physiological
measurements better indicate plant resource acquisition than do indirect
morphological traits (Rosado et al. 2013), eco-physiological
traits should be the better predictors of species’ demographic response
to the manipulations. (4) Finally, as theory predicts that plant
strategies for tolerating abiotic vs biotic stress trade-off with one
another (Grime 2006), different sets of traits should be beneficial in
dealing with warming vs. novel competitors. We found that traits related
to size and conservative water use when measured under current climate
were most predictive of species’ demographic response to the climate
manipulation. Our results show that both morphological and
eco-physiological traits can be used for predictions, but with different
interpretation of the underlying mechanism.
Materials and methods