The Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM): a diverse approach to representing terrestrial biogeography and biogeochemistry based on plant functional trade-offs

Abstract

Abstract. Terrestrial biosphere models typically abstract the immense diversity of vegetation forms and functioning into a relatively small set of predefined semi-empirical plant functional types (PFTs). There is growing evidence, however, from the field ecology community as well as from modelling studies that current PFT schemes may not adequately represent the observed variations in plant functional traits and their effect on ecosystem functioning. In this paper, we introduce the Jena Diversity-Dynamic Global Vegetation Model (JeDi-DGVM) as a new approach to terrestrial biosphere modelling with a richer representation of functional diversity than traditional modelling approaches based on a small number of fixed PFTs. JeDi-DGVM simulates the performance of a large number of randomly generated plant growth strategies, each defined by a set of 15 trait parameters which characterize various aspects of plant functioning including carbon allocation, ecophysiology and phenology. Each trait parameter is involved in one or more functional trade-offs. These trade-offs ultimately determine whether a strategy is able to survive under the climatic conditions in a given model grid cell and its performance relative to the other strategies. The biogeochemical fluxes and land surface properties of the individual strategies are aggregated to the grid-cell scale using a mass-based weighting scheme. We evaluate the simulated global biogeochemical patterns against a variety of field and satellite-based observations following a protocol established by the Carbon-Land Model Intercomparison Project. The land surface fluxes and vegetation structural properties are reasonably well simulated by JeDi-DGVM, and compare favourably with other state-of-the-art global vegetation models. We also evaluate the simulated patterns of functional diversity and the sensitivity of the JeDi-DGVM modelling approach to the number of sampled strategies. Altogether, the results demonstrate the parsimonious and flexible nature of a functional trade-off approach to global vegetation modelling, i.e. it can provide more types of testable outputs than standard PFT-based approaches and with fewer inputs. The approach implemented here in JeDi-DGVM sets the foundation for future applications that will explore the impacts of explicitly resolving diverse plant communities, allowing for a more flexible temporal and spatial representation of the structure and function of the terrestrial biosphere.

Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

“…The PFT concept also provides a practical solution to the problem that many of the plant traits required to parameterize a model at a species level are difficult to obtain (Ustin and Gamon, 2010). Second-generation DGVMs are currently addressing some of the limitations posed by the PFT concept as plant trait data become more widely available (Kattge et al, 2011), as model structure becomes more computationally efficient (Fisher et al, 2010), or as modeling concepts move toward adaptive trait rather than "fixed" values (Pavlick et al, 2013;Scheiter and Higgins, 2009). This paper describes the LC_CCI land cover classification and presents a conversion scheme that "cross-walks" the categorical UNLCCS land cover classes to their PFT fractional equivalent.…”

Progress in DGVMs: a comment on "Impacts of trait variation through observed trait–climate relationships on performance of an Earth system model: a conceptual analysis" by Verheijen et al. (2013)

“…This is an interpretation that Verheijen et al (2013) appear, in apparent contradiction to their statement we cite above, to share in their discussion, when they state that "some DGVMs also implement the concept of environmental filtering, like the JeDi-DGVM (Pavlick et al, 2013)". In the discussion, JeDi-DGVM (Pavlick et al, 2013) is further criticised because its traits are "not-measurable". While we cannot assess what is measurable, we would like to point out that invitingly measurable traits are not inherently more useful than traits that can be inferred using inverse statistical methods (see Hartig et al, 2012, for an overview of using inverse methods in the context of DGVMs).…”

“…While the aim of this piece is primarily to set the record straight, we additionally point out similarities and differences between the approach described by Verheijen et al (2013) and that described by Scheiter et al (2013). Verheijen et al (2013) motivate their study by stating in reference to the Jena Diversity-DGVM (JeDi-DGVM) (Pavlick et al, 2013) and aDGVM2 (Scheiter et al, 2013) that "none of the approaches so far tried to maximally include trait variation based on observational trait data and capture multiple sources of this variation by relating trait data to environmental variables". Although we appreciate that this statement was designed to illustrate the uniqueness of Verheijen et al (2013) and the statistical approach they adopt, it does have the side-effect of suggesting that these two papers ignored variations in traits and the relationships between traits and the environment.…”

“…Our impression from reading Pavlick et al (2013) is that the traits that JeDi-DGVM predicts at a site are, as is the case with aDGVM2, a function of how environmental attributes select for trait combinations. This is an interpretation that Verheijen et al (2013) appear, in apparent contradiction to their statement we cite above, to share in their discussion, when they state that "some DGVMs also implement the concept of environmental filtering, like the JeDi-DGVM (Pavlick et al, 2013)". In the discussion, JeDi-DGVM (Pavlick et al, 2013) is further criticised because its traits are "not-measurable".…”

“…In summary, the important difference is that Verheijen et al (2013) use a direct statistical method to parameterise plant functional diversity, whereas Scheiter et al (2013) and Pavlick et al (2013) define trade-offs between plant functional traits, which allow functional diversity to emerge as a by-product of model dynamics. The approach followed by Scheiter et al (2013) and Pavlick et al (2013) is reliant on the specification of tradeoffs between functional traits and how they together with modelled and forced environmental factors influence birth, death and growth rates in the models. While this may sound simple in principle, Scheiter et al (2013) outline some of the non-trivial challenges involved in developing such models to the stage where they can approach the reliability of existing DGVMs.…”