Modeling terrestrial carbon and water dynamics across climatic gradients: Does plant trait diversity matter?

Background/Question/Methods

Plant trait diversity in many process-based vegetation models is crudely represented using a discrete classification of a handful of 'plant types' (named Plant Functional Types; PFTs). The parameterization of PFTs reflects mean properties of observed plant traits over broad categories ignoring most of the inter- and intra-specific plant trait variability. Taking advantage of well-established plant trait cross-correlations described by the leaf economics spectrum as well as documented plant drought strategies, we generate an ensemble of hypothetical species with coordinated attributes, rather than using few PFTs. The behavior of these proxy species is tested using a mechanistic ecohydrological model (T&C) that translates plant traits into plant performance. Simulations are carried out for a range of climates representative of different elevations and wetness conditions in the European Alps. 

Results/Conclusions

Using this framework we investigate the sensitivity of ecosystem responses to plant-trait diversity and compare it with climate variability. Plant-trait diversity leads to highly divergent vegetation carbon dynamics (fluxes and pools) and to a lesser extent water fluxes (transpiration). Abiotic processes, such as soil water dynamics and evaporation, are only marginally affected. In addition, significant aggregation biases emerge in the simulated carbon and water dynamics when average plant trait values are employed. These findings highlight the need for revising the representation of biotic attributes within terrestrial ecosystem models, moving beyond the coarse and static PFT concept and the associated limitations. Alternative, probabilistic approaches should be adopted, which mimic the floristic complexity using multivariate distributions of coordinated whole-plant trait spectra. This will allow for a better representation of terrestrial ecosystem dynamics and resilience to climate variability.