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:
  1. 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?
  2. Do eco-physiological traits perform better than morphological traits as predictors of demographic responses to climate warming?
  3. 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