Equilibrium responses of global net primary production and carbon storage to doubled atmospheric carbon dioxide: sensitivity to changes in vegetation nitrogen concentration

We ran the terrestrial ecosystem model (TEM) for the globe at 0.5° resolution for atmospheric CO2 concentrations of 340 and 680 parts per million by volume (ppmv) to evaluate global and regional responses of net primary production (NPP) and carbon storage to elevated CO2 for their sensitivity to changes in vegetation nitrogen concentration. At 340 ppmv, TEM estimated global NPP of 49.0 1015 g (Pg) C yr−1 and global total carbon storage of 1701.8 Pg C; the estimate of total carbon storage does not include the carbon content of inert soil organic matter. For the reference simulation in which doubled atmospheric CO2 was accompanied with no change in vegetation nitrogen concentration, global NPP increased 4.1 Pg C yr−1 (8.3%), and global total carbon storage increased 114.2 Pg C. To examine sensitivity in the global responses of NPP and carbon storage to decreases in the nitrogen concentration of vegetation, we compared doubled CO2 responses of the reference TEM to simulations in which the vegetation nitrogen concentration was reduced without influencing decomposition dynamics (“lower N” simulations) and to simulations in which reductions in vegetation nitrogen concentration influence decomposition dynamics (“lower N+D” simulations). We conducted three lower N simulations and three lower N+D simulations in which we reduced the nitrogen concentration of vegetation by 7.5, 15.0, and 22.5%. In the lower N simulations, the response of global NPP to doubled atmospheric CO2 increased approximately 2 Pg C yr−1 for each incremental 7.5% reduction in vegetation nitrogen concentration, and vegetation carbon increased approximately an additional 40 Pg C, and soil carbon increased an additional 30 Pg C, for a total carbon storage increase of approximately 70 Pg C. In the lower N+D simulations, the responses of NPP and vegetation carbon storage were relatively insensitive to differences in the reduction of nitrogen concentration, but soil carbon storage showed a large change. The insensitivity of NPP in the N+D simulations occurred because potential enhancements in NPP associated with reduced vegetation nitrogen concentration were approximately offset by lower nitrogen availability associated with the decomposition dynamics of reduced litter nitrogen concentration. For each 7.5% reduction in vegetation nitrogen concentration, soil carbon increased approximately an additional 60 Pg C, while vegetation carbon storage increased by only approximately 5 Pg C. As the reduction in vegetation nitrogen concentration gets greater in the lower N+D simulations, more of the additional carbon storage tends to become concentrated in the north temperate-boreal region in comparison to the tropics. Other studies with TEM show that elevated CO2 more than offsets the effects of climate change to cause increased carbon storage. The results of this study indicate that carbon storage would be enhanced by the influence of changes in plant nitrogen concentration on carbon assimilation and decomposition rates. Thus changes in vegetation nitrogen concentration may have important implications for the ability of the terrestrial biosphere to mitigate increases in the atmospheric concentration of CO2 and climate changes associated with the increases

Modeling the probability distribution of positional errors incurred by residential address geocoding

BACKGROUND: The assignment of a point-level geocode to subjects' residences is an important data assimilation component of many geographic public health studies. Often, these assignments are made by a method known as automated geocoding, which attempts to match each subject's address to an address-ranged street segment georeferenced within a streetline database and then interpolate the position of the address along that segment. Unfortunately, this process results in positional errors. Our study sought to model the probability distribution of positional errors associated with automated geocoding and E911 geocoding. RESULTS: Positional errors were determined for 1423 rural addresses in Carroll County, Iowa as the vector difference between each 100%-matched automated geocode and its true location as determined by orthophoto and parcel information. Errors were also determined for 1449 60%-matched geocodes and 2354 E911 geocodes. Huge (> 15 km) outliers occurred among the 60%-matched geocoding errors; outliers occurred for the other two types of geocoding errors also but were much smaller. E911 geocoding was more accurate (median error length = 44 m) than 100%-matched automated geocoding (median error length = 168 m). The empirical distributions of positional errors associated with 100%-matched automated geocoding and E911 geocoding exhibited a distinctive Greek-cross shape and had many other interesting features that were not capable of being fitted adequately by a single bivariate normal or t distribution. However, mixtures of t distributions with two or three components fit the errors very well. CONCLUSION: Mixtures of bivariate t distributions with few components appear to be flexible enough to fit many positional error datasets associated with geocoding, yet parsimonious enough to be feasible for nascent applications of measurement-error methodology to spatial epidemiology

Loureirin B, an essential component of Sanguis Draxonis, inhibits Kv1.3 channel and suppresses cytokine release from Jurkat T cells

Sanguis draxonis (SD), also known as “Dragon’s Blood”, is a traditional herb medicine that has been used to treat a variety of complications with unknown mechanisms. Recent studies show that SD displays immunosuppressive activities and improves symptoms of type I diabetes in animal models. However, the mechanisms underlying SD’s immunosuppressive actions are not completely understood. The voltage-gated Kv1.3 channel plays a critical role in the pathogenesis of autoimmune diseases by regulating the functions of both T cells and B cells. Here we investigated the effect of SD and one of its active components loureirin B (LrB) on Kv1.3. Both SD and LrB inhibited Kv1.3-mediated currents, produced a membrane depolarization, and reduced Ca(2+) influx in Jurkat T cells. In addition, application of LrB inhibited phytohemagglutinin (PHA)-induced IL-2 release from activated Jurkat T cells. Furthermore, point mutations in the selective filter region significantly reduced the inhibitory effect of LrB on Kv1.3. The results of these experiments provide evidence that LrB is a channel blocker of Kv1.3 by interacting with amino acid residues in its selective filter region. Direct inhibition of Kv1.3 in T cells by SD and LrB might be the cellular and molecular basis of SD-mediated immunosuppression

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO\u3csub\u3e2\u3c/sub\u3e flux tower data

Abstract Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security

Modeling gross primary production of irrigated and rain-fed maize using MODIS imagery and CO\u3csub\u3e2\u3c/sub\u3e flux tower data

Abstract Information on gross primary production (GPP) of maize croplands is needed for assessing and monitoring maize crop conditions and the carbon cycle. A number of studies have used the eddy covariance technique to measure net ecosystem exchange (NEE) of CO2 between maize cropland fields and the atmosphere and partitioned NEE data to estimate seasonal dynamics and interannual variation of GPP in maize fields having various crop rotation systems and different water management practices. How to scale up in situ observations from flux tower sites to regional and global scales is a challenging task. In this study, the Vegetation Photosynthesis Model (VPM) and satellite images from the Moderate Resolution Imaging Spectroradiometer (MODIS) are used to estimate seasonal dynamics and interannual variation of GPP during 2001–2005 at five maize cropland sites located in Nebraska and Minnesota of the U.S.A. These sites have different crop rotation systems (continuously maize vs. maize and soybean rotated annually) and different water management practices (irrigation vs. rain-fed). The VPM is based on the concept of light absorption by chlorophyll and is driven by the Enhanced Vegetation Index (EVI) and the Land Surface Water Index (LSWI), photosynthetically active radiation (PAR), and air temperature. The seasonal dynamics of GPP predicted by the VPM agreed well with GPP estimates from eddy covariance flux tower data over the period of 2001–2005. These simulation results clearly demonstrate the potential of the VPM to scale-up GPP estimation of maize cropland, which is relevant to food, biofuel, and feedstock production, as well as food and energy security