Paul C. Stoy

Fine‐root respiration in a loblolly pine (Pinus taeda L.) forest exposed to elevated CO2 and N fertilization

Forest ecosystems release large amounts of carbon to the atmosphere from fine-root respiration (R(r)), but the control of this flux and its temperature sensitivity (Q(10)) are poorly understood. We attempted to: (1) identify the factors limiting this flux using additions of glucose and an electron transport uncoupler (carbonyl cyanide m-chlorophenylhydrazone); and (2) improve yearly estimates of R(r) by directly measuring its Q(10)in situ using temperature-controlled cuvettes buried around intact, attached roots. The proximal limits of R(r) of loblolly pine (Pinus taeda L.) trees exposed to free-air CO(2) enrichment (FACE) and N fertilization were seasonally variable; enzyme capacity limited R(r) in the winter, and a combination of substrate supply and adenylate availability limited R(r) in summer months. The limiting factors of R(r) were not affected by elevated CO(2) or N fertilization. Elevated CO(2 )increased annual stand-level R(r) by 34% whereas the combination of elevated CO(2) and N fertilization reduced R(r) by 40%. Measurements of in situ R(r) with high temporal resolution detected diel patterns that were correlated with canopy photosynthesis with a lag of 1 d or less as measured by eddy covariance, indicating a dynamic link between canopy photosynthesis and root respiration. These results suggest that R(r) is coupled to daily canopy photosynthesis and increases with carbon allocation below ground.

Using information theory to determine optimum pixel size and shape for ecological studies: Aggregating land surface characteristics in arctic ecosystems

Quantifying vegetation structure and function is critical for modeling ecological processes, and an emerging challenge is to apply models at multiple spatial scales. Land surface heterogeneity is commonly characterized using rectangular pixels, whose length scale reflects that of remote sensing measurements or ecological models rather than the spatial scales at which vegetation structure and function varies. We investigated the ‘optimum’ pixel size and shape for averaging leaf area index (LAI) measurements in relatively large (85xa0m2 estimates on a 600xa0×xa0600-m2 grid) and small (0.04xa0m2 measurements on a 40xa0×xa040-m2 grid) patches of sub-Arctic tundra near Abisko, Sweden. We define the optimum spatial averaging operator as that which preserves the information content (IC) of measured LAI, as quantified by the normalized Shannon entropy (ES,n) and Kullback–Leibler divergence (DKL), with the minimum number of pixels. Based on our criterion, networks of Voronoi polygons created from triangulated irregular networks conditioned on hydrologic and topographic indices are often superior to rectangular shapes for averaging LAI at some, frequently larger, spatial scales. In order to demonstrate the importance of information preservation when upscaling, we apply a simple, validated ecosystem carbon flux model at the landscape level before and after spatial averaging of land surface characteristics. Aggregation errors are minimal due to the approximately linear relationship between flux and LAI, but large errors of approximately 45% accrue if the normalized difference vegetation index (NDVI) is averaged without preserving IC before conversion to LAI due to the nonlinear NDVI-LAI transfer function.

Upscaling as ecological information transfer: a simple framework with application to Arctic ecosystem carbon exchange

Transferring ecological information across scale often involves spatial aggregation, which alters information content and may bias estimates if the scaling process is nonlinear. Here, a potential solution, the preservation of the information content of fine-scale measurements, is highlighted using modeled net ecosystem exchange (NEE) of an Arctic tundra landscape as an example. The variance of aggregated normalized difference vegetation index (NDVI), measured from an airborne platform, decreased linearly with log(scale), resulting in a linear relationship between log(scale) and the scale-wise modeled NEE estimate. Preserving three units of information, the mean, variance and skewness of fine-scale NDVI observations, resulted in upscaled NEE estimates that deviated less than 4% from the fine-scale estimate. Preserving only the mean and variance resulted in nearly 23% NEE bias, and preserving only the mean resulted in larger error and a change in sign from CO2 sink to source. Compressing NDVI maps by 70–75% using wavelet thresholding with the Haar and Coiflet basis functions resulted in 13% NEE bias across the study domain. Applying unique scale-dependent transfer functions between NDVI and leaf area index (LAI) decreased, but did not remove, bias in modeled flux in a smaller expanse using handheld NDVI observations. Quantifying the parameters of statistical distributions to preserve ecological information reduces bias when upscaling and makes possible spatial data assimilation to further reduce errors in estimates of ecological processes across scale.

Processing arctic eddy-flux data using a simple carbon-exchange model embedded in the ensemble Kalman filter

Continuous time-series estimates of net ecosystem carbon exchange (NEE) are routinely made using eddy covariance techniques. Identifying and compensating for errors in the NEE time series can be automated using a signal processing filter like the ensemble Kalman filter (EnKF). The EnKF compares each measurement in the time series to a model prediction and updates the NEE estimate by weighting the measurement and model prediction relative to a specified measurement error estimate and an estimate of the model-prediction error that is continuously updated based on model predictions of earlier measurements in the time series. Because of the covariance among model variables, the EnKF can also update estimates of variables for which there is no direct measurement. The resulting estimates evolve through time, enabling the EnKF to be used to estimate dynamic variables like changes in leaf phenology. The evolving estimates can also serve as a means to test the embedded model and reconcile persistent deviations between observations and model predictions. We embedded a simple arctic NEE model into the EnKF and filtered data from an eddy covariance tower located in tussock tundra on the northern foothills of the Brooks Range in northern Alaska, USA. The model predicts NEE based only on leaf area, irradiance, and temperature and has been well corroborated for all the major vegetation types in the Low Arctic using chamber-based data. This is the first application of the model to eddy covariance data. We modified the EnKF by adding an adaptive noise estimator that provides a feedback between persistent model data deviations and the noise added to the ensemble of Monte Carlo simulations in the EnKF. We also ran the EnKF with both a specified leaf-area trajectory and with the EnKF sequentially recalibrating leaf-area estimates to compensate for persistent model-data deviations. When used together, adaptive noise estimation and sequential recalibration substantially improved filter performance, but it did not improve performance when used individually. The EnKF estimates of leaf area followed the expected springtime canopy phenology. However, there were also diel fluctuations in the leaf-area estimates; these are a clear indication of a model deficiency possibly related to vapor pressure effects on canopy conductance.

AmeriFlux US-Dk3 Duke Forest - loblolly pine

This is the AmeriFlux version of the carbon flux data for the site US-Dk3 Duke Forest - loblolly pine. Site Description - The site was established in 1983 following a clear cut and a burn. Pinus taeda L. (loblolly pine) seedlings were planted at 2.4m by 2.4m spacing and ecosystem development has not been managed after planting. Canopy height increased from 16m in 2001 to 18m in 2004. The canopy is comprised primarily of P. taeda with some emergent Liquidambar styraciflua L. and a diverse and growing understory with 26 different woody species of diameter breast height 42.5 cm. The flux tower lies upwind of the CO2-enriched components of the free atmosphere carbon enrichment (FACE) facility located in the same pine forest. EC instrumentation is at 20.2m on a 22m tower.

AmeriFlux US-Dk2 Duke Forest-hardwoods

This is the AmeriFlux version of the carbon flux data for the site US-Dk2 Duke Forest-hardwoods. Site Description - private land adjacent to the Duke Forest in November 2002

AmeriFlux US-Dk1 Duke Forest-open field

This is the AmeriFlux version of the carbon flux data for the site US-Dk1 Duke Forest-open field. Site Description - The Duke Forest grass field is approximately 480×305 m, dominated by the C3 grass Festuca arundinacea Shreb. (tall fescue) includes minor components of C3 herbs and the C4 grass Schizachyrium scoparium (Michx.) Nash, not considered here. The site was burned in 1979 and is mowed annually during the summer for hay according to local practices. Lai, C.T. and G.G. Katul, 2000, The dynamic role of root-water uptake in coupling potential to actual transpiration , Advances in Water Resources, 23, 427-439; Novick , K.A., P. C. Stoy, G. G. Katul, D. S. Ellsworth, M. B. S. Siqueira, J. Juang, R. Oren, 2004, Carbon dioxide and water vapor exchange in a warm temperate grassland, Oecologia, 138, 259-274; Stoy PC, Katul GG, Siqueira MBS, Juang J-Y, McCarthy HR, Oishi AC, Uebelherr JM, Kim H-S, Oren R (2006). Separating the effects of climate and vegetation on evapotranspiration along a successional chronosequence in the southeastern U.S. Global Change Biology 12:2115-2135