Revisiting the Holy Grail: using plant functional traits to understand ecological processes

Abstract

One of ecology's grand challenges is developing general rules to explain and predict highly complex systems. Understanding and predicting ecological processes from species' traits has been considered a 'Holy Grail' in ecology. Plant functional traits are increasingly being used to develop mechanistic models that can predict how ecological communities will respond to abiotic and biotic perturbations and how species will affect ecosystem function and services in a rapidly changing world; however, significant challenges remain. In this review, we highlight recent work and outstanding questions in three areas: (i) selecting relevant traits; (ii) describing intraspecific trait variation and incorporating this variation into models; and (iii) scaling trait data to community- and ecosystem-level processes. Over the past decade, there have been significant advances in the characterization of plant strategies based on traits and trait relationships, and the integration of traits into multivariate indices and models of community and ecosystem function. However, the utility of trait-based approaches in ecology will benefit from efforts that demonstrate how these traits and indices influence organismal, community, and ecosystem processes across vegetation types, which may be achieved through meta-analysis and enhancement of trait databases. Additionally, intraspecific trait variation and species interactions need to be incorporated into predictive models using tools such as Bayesian hierarchical modelling. Finally, existing models linking traits to community and ecosystem processes need to be empirically tested for their applicability to be realized.

Gap-filling a spatially explicit plant trait database: comparing imputation methods and different levels of environmental information

“…As expected (Gelman and Hill, 2007), Mean imputation severely altered trait distributions (Fig. S6) and introduced larger errors in selected trait correlations (Fig.…”

“…The move from a taxonomic perspective of biodiversity towards a focus on continuous axes of functional variation R. Poyatos et al: Gap-filling a spatially explicit plant trait database holds promise for greater generalisation, synthesis and predictive ability in ecology (Funk et al, 2016;Shipley et al, 2016). As a result, plant ecologists have increasingly embraced trait-based approaches because they may be specially suited to study plant strategies (Reich, 2014), community assembly and dynamics (McGill et al, 2006), or ecosystem functioning, particularly in the context of global environmental change (Reichstein et al, 2014).…”

“…Within these approaches, species mean or median imputation are probably the most widely used methods in ecology, but they ignore the variance of the imputed variables. Model-based imputation methods use other variables in the dataset to impute missing data, but they substantially alter the univariate trait distributions and the covariance structure of the dataset (Gelman and Hill, 2007). Approaches such as k nearest neighbour (kNN) or machine-learning methods (Stekhoven and Bühlmann, 2012) may be more appropriate to impute multivariate datasets, preserving their covariance structure (Eskelson et al, 2009;Penone et al, 2014).…”

On the challenges of using field spectroscopy to measure the impact of soil type on leaf traits

“…There is currently great interest in using plant traits to understand the influence of environmental filtering and species identity on the functioning of plant communities and to model community responses to the environmental change (MacGillivray et al, 1995;McGill et al, 2006;Green et al, 2008;Funk et al, 2016). Traits vary at multiple scales within individuals, within populations, between populations and between species (Albert et al, 2011), and analysis of this variation is key to evaluating the strength of various filtering processes on communities growing along environmental gradients Violle et al, 2012).…”

“…Traits vary at multiple scales within individuals, within populations, between populations and between species (Albert et al, 2011), and analysis of this variation is key to evaluating the strength of various filtering processes on communities growing along environmental gradients Violle et al, 2012). For example, intraspecific variation in traits may reflect differences in microclimate driven by competition, disturbance, environmental conditions and age (Funk et al, 2016), whereas interspecific and intersite variation may reflect both genetic variation and phenotypic plasticity in response to environment Sultan, 2001;Donohue et al, 2005). Despite subPublished by Copernicus Publications on behalf of the European Geosciences Union.…”