1. Figure 1. Spatial distribution of pinyon-juniper and ponderosa pine forests is shown for the southwestern United States. Red dots indicate location of different research sites.
Figure 2. Annual temperature (red) and precipitation (blue) records for the Southwestern united states. Yellow shaded areas indicate the 1950s and 2000 periods of drought (Breshears et al 2005). A
Figure 3 A. Changes in vegetation cover between 1954 and 1963 in the study area, showing persistent ponderosa pine forest (365 ha), persistent pinyon–juniper woodland (1527 ha), and the ecotone shift zone (486 ha) where forest changed to woodland. B. Field observations of the persistence () and mortality () of ponderosa pines, determined from remains of dead trees as a function of elevation and topographic position. Across most topographic positions, mortality was greatest at lower elevations. From Allen and Breshears (1998).
Figure 4. Long-term fire frequency in the Western United States (Swetnam and Betancourt 1990).

2. Figure 5 Regional drought-induced vegetation changes
Figure 5 Regional drought-induced vegetation changes. (a) Change in Normalized Difference Vegetation Index (NDVI) for region encompassing P. edulis distribution within Arizona, New Mexico, Colorado, and Utah, based on deviation in relative to the pre-drought mean ( ) during the period late-May to June. (B) Aerial survey map of pinyon-juniper woodlands, delineating areas that experienced noticeable levels of tree mortality (including larger, older trees) conducted by the United States Forest Service (U.S. Forest Service, 2003) with four study areas throughout the region corroborate the NDVI and aerial survey maps and document stand-level estimates of mortality that range from 40% to 80% of non-seedling trees. (From Breshears et al., 2005).
=> Positive correlation, i.e. warmer & wetter, or cooler drier-cooler
=> Negative correlation, i.e warmer & drier, or cooler & wetter 
Figure 6 Correlation between predicted changes in temperature T) and changes in soil moisture () between present day and 2100 predicted by the NCAR-CSM1.4 coupled carbon-climate model. From Fung et al
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Figure 7: (a) ED2 Model structure and processes: (a) Each grid-cell is sub-divided into a series of patches that represents the proportion of canopy-gap sized areas within the grid cell that have a similar canopy structure. (b) ED2 computes the multi-layer canopy fluxes of water, energy and carbon, within each sub-grid scale patch within a grid cell. This calculation can also take into account the structure of neighboring areas. (c) Summary of the long-term vegetation dynamics within each canopy gap y. Plant structural and living tissues grow at rates gs and ga, respectively. Canopy mortality occurs at rate , and recruitment occurs at rate f. Recruits are then dispersed within and between gaps. Rates gs, ga,  and f vary as a function of the type x, size z and resource environment r of the plants. Disturbances occur at rate F calculated by the disturbance sub-model that incorporates canopy gap formation, fire and land-use change. Hydrological and decomposition sub-models track the accompanying dynamics of Water (W), Carbon (C) and Nitrogen (N) within each patch.

3. Howland Howland Harvard Forest initial observed initial observed
Figure 8. Preliminary model simulations showing (a) ED2 Predicted long-term vegetation dynamics showing aboveground biomass dynamics staring from a near-bare earth initial condition for a grid cell in New Mexico (35.5o S, 106.5)o Lines show changing aboveground biomass of the different plant types (black = total biomass, brown = drought-adapted evergreen tree, red = C4 herbs and grasses. The biomass for each tree type is the sum of the biomass across trees of different size (z) distributed in different sub-grid scale patches given by the solution of the structured ecosystem model (PDEs). (b) Fire-frequency regime associated with biomass dynamics shown in (a).
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Figure 9. (a) Results of model optimization in New England: ED2 model was simultaneously optimized against CO2 flux observations and forest inventory growth and mortality measurements for the period 1995 and 1996 (blue box in panel a). Fitted model was then run for 10 years at Harvard Forest and showed a marked improvement in its prediction of long-term Net Ecosystem Productivity (NEP) (panel a), and its predictions of tree basal area growth increments and mortality increments (not shown). (b)-(d) Predictions of the optimized model at Howland Forest. With no further adjustment of model parameters, the optimized model displays a substantial improvement in model’s long-term predictions of Net Ecosystem Productivity (NEP) and tree basal area growth increments at Howland.