Towards a New Generation of Trait-Flexible Vegetation Models
Abstract
Plant trait variability, emerging from eco-evolutionary dynamics that range from alleles to macroecological scales, is one of the most elusive, but possibly most consequential, aspects of biodiversity. Plasticity, epigenetics, and genetic diversity are major determinants of how plants will respond to climate change, yet these processes are rarely represented in current vegetation models. Here, we provide an overview of the challenges associated with understanding the causes and consequences of plant trait variability, and review current developments to include plasticity and evolutionary mechanisms in vegetation models. We also present a roadmap of research priorities to develop a next generation of vegetation models with flexible traits. Including trait variability in vegetation models is necessary to better represent biosphere responses to global change.
What the paper shows and why it matters
(AI-generated)
Dynamic vegetation models are the main tools for projecting how vegetation responds to climate change. They usually describe each species, or each plant functional type (a group of species treated as one), by a single set of fixed trait values. This keeps the number of types manageable, and the models have been instrumental for understanding the global distribution of biomes. Plants, however, vary within species through plasticity (one genotype producing different traits in different environments), genetic differences and epigenetic inheritance. Rapid evolution also suggests that traits can change on ecological timescales. The paper takes on the assumption that a species or functional type is a functionally static unit, which leaves the models ill-equipped to explore what trait variability does for ecosystems and adds uncertainty to their predictions.
The review argues that dynamic vegetation models need a new generation of “trait-flexible” versions, in which trait variability emerges from plastic and evolutionary processes instead of being fixed or merely prescribed. Existing attempts fall into three approaches: models of plasticity, which describe how traits adjust to the environment but assume that all individuals of a species respond alike; models with prescribed trait distributions, which take trait values from data without explaining them; and eco-evolutionary models, in which traits arise through inheritance, mutation and selection. Models of European beech that combine physiology, demography and genetics serve as an example: in simulations, a few generations sufficed for budburst timing to differ genetically along a climatic gradient. The roadmap then names the steps needed, among them lagged plastic responses, closing the “demographic loop” of reproduction, growth and mortality (a constant seed rain, as in many models, is inadequate for simulating evolution because it makes reproduction independent of trait performance), spatial processes and dispersal, and the link from genotype to phenotype.
Such models would let researchers quantify how far trait variability buffers biodiversity and ecosystem functioning against climate change, and could help separate heritable from plastic variation. The paper warns that added processes risk making models more complex, harder to parameterise and more uncertain, suggests optimality-based components, which in principle need fewer inputs, and sees immediate progress as possible. Westerband et al. (2021) cite the review for the same caveat: that models with explicit trait variation are often computationally demanding, hard to parameterise and uncertain.
References
- Westerband et al. (2021). Intraspecific trait variation in plants: a renewed focus on its role in ecological processes. Annals of Botany 127, 397–410. doi:10.1093/aob/mcab011