Terrestrial ecosystems respond to changes in climate and the atmospheric environment, which they in turn help to regulate. But the lack of established theory for many key processes has hindered development of reliable models of land ecosystem processes. REALM set out to improve this situation by developing new theory and using the fast-growing body of relevant observations at scales from leaf-level trait and photosynthesis measurements to global satellite data sets.
REALM has developed a universal theory and a parameter-sparse model for gross primary production, GPP (total ecosystem photosynthesis) that outperforms previous models despite its far greater simplicity and transparency. The model reproduces both diurnal and seasonal cycles and temporal trends in GPP at flux-measurement sites using the same parameter values for all biomes. Further new theory explains the "leaf economics spectrum" and successfully predicts how leaf economics vary along environmental gradients. Seasonal patterns of green vegetation cover, as observed by satellites, can also be predicted by a universal optimality principle; as can geographic patterns in the maximum allocation of biomass to leaves.
This new body of theory has improved understanding of the interaction of carbon (C) and nitrogen (N) cycles. Many models assume that N supply (from soil and N deposition) to plants determines leaf-level N and thence, photosynthetic capacity. But experiments show that N addition mainly affects biomass allocation, with a positive effect of leaf N but little or none on photosynthesis. This insight has led to (ongoing) research on the effects of phosphorus (P) addition. A comprehensive theory of C-N-P cycle interactions is within sight.
This revised understanding of plant-nutrient interactions has practical consequences. For example, the decline in leaf N over recent decades does not (as was once claimed) signify increasing N limitation of primary production. Instead, it follows from optimal resource use by plants, responding to warming and rising atmospheric CO2.
Further insights have been gained by coupling the new GPP model to a minimal representation of how heterotrophic respiration (primarily CO2 release from soil due to decomposition of dead organic matter) varies with the soil environment. The observed growth in the amplitude of seasonal variations in high-latitude atmospheric CO2, and the seasonal geographic pattern of net ecosystem exchange of CO2 between the atmosphere and land, are predicted accurately.
Three reasons have underpinned the success of REALM in developing improved ecosystem model components for ESMs.
1) A first-principles approach based on eco-evolutionary optimality hypotheses has generated clear and testable governing equations for the most important plant and ecosystem processes.
2) Huge increases in the amount and variety of relevant observations have allowed these equations to be tested robustly. This approach is more effective than "end-of-pipe" benchmarking of multi-component models, which rarely (if ever) allows problems with specific process representations to be diagnosed and resolved.
3) The "legacy" models of plant and ecosystem processes are essentially physical, with the role of biology limited to providing (over-)generalized parameter values for "plant functional types" (PFTs). The REALM approach puts biology at the centre by harnessing the power of natural selection, so that key adaptive processes need no longer be treated as add-ons that make models even more complex. Instead, they are integral to the way that REALM models represent plant and ecosystem function.
