The Moderate Resolution Spectroradiometer (MODIS) sensor has provided near real-time estimates of gross primary production (GPP) since March 2000. We compare four years (2000 to 2003) of satellite-based calculations of GPP with tower eddy CO 2 flux-based estimates across diverse land cover types and climate regimes. We examine the potential error contributions from meteorology, leaf area index (LAI)/fPAR, and land cover. The error between annual GPP computed from NASA's Data Assimilation Office's (DAO) and tower-based meteorology is 28%, indicating that NASA's DAO global meteorology plays an important role in the accuracy of the GPP algorithm. Approximately 62% of MOD15-based estimates of LAI were within the estimates based on field optical measurements, although remaining values

[1] In the southeastern United States (SE), the conversion of abandoned agricultural land to forests is the dominant feature of land-cover change. However, few attempts have been made to quantify the impact of such conversion on surface temperature. Here, this issue is explored experimentally and analytically in three adjacent ecosystems (a grass-covered old-field, OF, a planted pine forest, PP, and a hardwood forest, HW) representing a successional chronosequence in the SE. The results showed that changes in albedo alone can warm the surface by 0.9°C for the OF-to-PP conversion, and 0.7°C for the OF-to-HW conversion on annual time scales. However, changes in eco-physiological and aerodynamic attributes alone can cool the surface by 2.9 and 2.1°C, respectively. Both model and measurements consistently suggest a stronger over-all surface cooling for the OF-to-PP conversion, and the reason is attributed to leaf area variations and its impacts on boundary layer conductance. Citation: Juang, J.-Y., G. Katul, M. Siqueira, P. Stoy, and K. Novick (2007), Separating the effects of albedo from eco-physiological changes on surface temperature along a successional chronosequence in the southeastern United States,

We combined Eddy-covariance measurements with a linear perturbation analysis to isolate the relative contribution of physical and biological drivers on evapotranspiration (ET) in three ecosystems representing two end-members and an intermediate stage of a successional gradient in the southeastern US (SE). The study ecosystems, an abandoned agricultural field [old field (OF)], an early successional planted pine forest (PP), and a late-successional hardwood forest (HW), exhibited differential sensitivity to the wide range of climatic and hydrologic conditions encountered over the 4-year measurement period, which included mild and severe droughts and an ice storm. ET and modeled transpiration differed by as much as 190 and 270 mm yr À1 , respectively, between years for a given ecosystem. Soil water supply, rather than atmospheric demand, was the principal external driver of interannual ET differences. ET at OF was sensitive to climatic variability, and results showed that decreased leaf area index (L) under mild and severe drought conditions reduced growing season (GS) ET (ET GS ) by ca. 80 mm compared with a year with normal precipitation. Under wet conditions, higher intrinsic stomatal conductance (g s ) increased ET GS by 50 mm. ET at PP was generally larger than the other ecosystems and was highly sensitive to climate; a 50 mm decrease in ET GS due to the loss of L from an ice storm equaled the increase in ET from high precipitation during a wet year. In contrast, ET at HW was relatively insensitive to climatic variability. Results suggest that recent management trends toward increasing the land-cover area of PP-type ecosystems in the SE may increase the sensitivity of ET to climatic variability.

We introduce an analytical model, the Wald analytical long-distance dispersal (WALD) model, for estimating dispersal kernels of wind-dispersed seeds and their escape probability from the canopy. The model is based on simplifications to well-established three-dimensional Lagrangian stochastic approaches for turbulent scalar transport resulting in a two-parameter Wald (or inverse Gaussian) distribution. Unlike commonly used phenomenological models, WALD's parameters can be estimated from the key factors affecting wind dispersal--wind statistics, seed release height, and seed terminal velocity--determined independently of dispersal data. WALD's asymptotic power-law tail has an exponent of -3/2, a limiting value verified by a meta-analysis for a wide variety of measured dispersal kernels and larger than the exponent of the bivariate Student t-test (2Dt). We tested WALD using three dispersal data sets on forest trees, heathland shrubs, and grassland forbs and compared WALD's performance with that of other analytical mechanistic models (revised versions of the tilted Gaussian Plume model and the advection-diffusion equation), revealing fairest agreement between WALD predictions and measurements. Analytical mechanistic models, such as WALD, combine the advantages of simplicity and mechanistic understanding and are valuable tools for modeling large-scale, long-term plant population dynamics.