Abstract
Current Earth System Models (ESMs) prescribe plants that are static in time and microbial traits that are static in both time and space, thus unrealistically limiting the range of ecosystem responses to environmental change. We hypothesize that ecosystem responses to a changing climate will be governed by changes in these traits and the costs associated with them. The shift in traits will occur through trait filtering; a fundamental process in ecological assembly of communities. In some cases this trait filtering process will lead to changes in biome boundaries, while in others it will lead to shifts in trait distributions within a single biome. Therefore, the next generation of ESM land models will need to evaluate plant and microbial traits and trade-offs relevant under rising atmospheric CO2, elevated temperatures, changes in precipitation intensity and frequency, and changes in disturbance regimes. The cohort-based approach of ALM-ED we are developing for ACME, which includes plant competition and coexistence, and our recent modeling work on soil carbon and nitrogen biogeochemistry, are ideal platforms in which to develop explicit trait-filtering processes.
Here we address two questions: (1) How can the representation of plant and microbial traits and trade-offs help address ACME mission goals? and (2) What are the most theoretically sound and numerically robust approaches to represent these traits?
In the current ACME ALM-ED framework we believe implementing new approaches to represent life-history strategies for trees in closed canopy systems is an important first step. We hypothesize that as trees reach their maximum size there is a preferential shift of allocating carbon towards seed generation, reproduction, or defense. These trade-offs are typically correlated with a plant’s successional status and once included in the model could improve the competitive exclusion bias and improve prediction of plant distributions. Future directions will be to assess the tradeoffs that govern the current distribution of plant traits, as well as how trait distributions are likely to change in response to changing climate; examples include include wood density and leaf economic trait trade-offs as well as those that govern the distributions of plants along multiple ecotones, including resistance to fire, drought, pests, cold, and other disturbance. With respect to representing microbial traits and trade-offs, we believe that extending our recent work applying thermodynamic constraints, Dynamic Energy Budget Theory, Synthesizing Unit concepts, and community composition dynamics can improve predictions of microbial biogeochemistry under changing temperature, moisture, and substrate input conditions. Integrating these types of traits and their associated trade-offs represent a substantial investment, but we believe the benefits for ACME would be substantial and that we are well situated to make rapid advances in this area.