The problem of scale has been a critical impediment to incorporating important fine-scale processes into global ecosystem models. Our knowledge of fine-scale physiological and ecological processes comes from a variety of measurements, ranging from forest plot inventories to remote sensing, made at spatial resolutions considerably smaller than the large scale at which global ecosystem models are defined. In this paper, we describe a new individual-based, terrestrial biosphere model, which we label the ecosystem demography model (ED). We then introduce a general method for scaling stochastic individual-based models of ecosystem dynamics (gap models) such as ED to large scales. The method accounts for the fine-scale spatial heterogeneity within an ecosystem caused by stochastic disturbance events, operating at scales down to individual canopy-tree-sized gaps. By conditioning appropriately on the occurrence of these events, we derive a sizeand age-structured (SAS) approximation for the first moment of the stochastic ecosystem model. With this approximation, it is possible to make predictions about the large scales of interest from a description of the fine-scale physiological and population-dynamic processes without simulating the fate of every plant individually. We use the SAS approximation to implement our individual-based biosphere model over South America from 15Њ N to 15Њ S, showing that the SAS equations are accurate across a range of environmental conditions and resulting ecosystem types. We then compare the predictions of the biosphere model to regional data and to intensive data at specific sites. Analysis of the model at these sites illustrates the importance of fine-scale heterogeneity in governing large-scale ecosystem function, showing how population and community-level processes influence ecosystem composition and structure, patterns of aboveground carbon accumulation, and net ecosystem production.

[1] Insights into how terrestrial ecosystems affect the Earth's response to changes in climate and rising atmospheric CO 2 levels rely heavily on the predictions of terrestrial biosphere models (TBMs). These models contain detailed mechanistic representations of biological processes affecting terrestrial ecosystems; however, their ability to simultaneously predict field-based measurements of terrestrial vegetation dynamics and carbon fluxes has remained largely untested. In this study, we address this issue by developing a constrained implementation of a new structured TBM, the Ecosystem Demography model version 2 (ED2), which explicitly tracks the dynamics of fine-scale ecosystem structure and function. Carbon and water flux measurements from an eddy-flux tower are used in conjunction with forest inventory measurements of tree growth and mortality at Harvard Forest (42.5°N, 72.1°W) to estimate a number of important but weakly constrained model parameters. Evaluation against a decade of tower flux and forest dynamics measurements shows that the constrained ED2 model yields greatly improved predictions of annual net ecosystem productivity, carbon partitioning, and growth and mortality dynamics of both hardwood and conifer trees. The generality of the model formulation is then evaluated by comparing the model's predictions against measurements from two other eddy-flux towers and forest inventories of the northeastern United States and Quebec. Despite the markedly different composition throughout this region, the optimized model realistically predicts observed patterns of carbon fluxes and tree growth. These results demonstrate how TBMs parameterized with field-based measurements can provide quantitative insight into the underlying biological processes governing ecosystem composition, structure, and function at larger scales.

Numerous current efforts seek to improve the representation of ecosystem ecology and vegetation demographic processes within Earth System Models (ESMs). These developments are widely viewed as an important step in developing greater realism in predictions of future ecosystem states and fluxes. Increased realism, however, leads to increased model complexity, with new features raising a suite of ecological questions that require empirical constraints. Here, we review the developments that

For the period 1980-89, we estimate a carbon sink in the coterminous United States between 0.30 and 0.58 petagrams of carbon per year (petagrams of carbon = 10(15) grams of carbon). The net carbon flux from the atmosphere to the land was higher, 0.37 to 0.71 petagrams of carbon per year, because a net flux of 0.07 to 0.13 petagrams of carbon per year was exported by rivers and commerce and returned to the atmosphere elsewhere. These land-based estimates are larger than those from previous studies (0.08 to 0.35 petagrams of carbon per year) because of the inclusion of additional processes and revised estimates of some component fluxes. Although component estimates are uncertain, about one-half of the total is outside the forest sector. We also estimated the sink using atmospheric models and the atmospheric concentration of carbon dioxide (the tracer-transport inversion method). The range of results from the atmosphere-based inversions contains the land-based estimates. Atmosphere- and land-based estimates are thus consistent, within the large ranges of uncertainty for both methods. Atmosphere-based results for 1980-89 are similar to those for 1985-89 and 1990-94, indicating a relatively stable U.S. sink throughout the period.

Carbon accumulation in forests has been attributed to historical changes in land use and the enhancement of tree growth by CO2 fertilization, N deposition, and climate change. The relative contribution of land use and growth enhancement is estimated by using inventory data from five states spanning a latitudinal gradient in the eastern United States. Land use is the dominant factor governing the rate of carbon accumulation in these states, with growth enhancement contributing far less than previously reported. The estimated fraction of aboveground net ecosystem production due to growth enhancement is 2.0 +/- 4.4%, with the remainder due to land use.