Trends in North American net primary productivity derived from satellite observations, 1982-1998

Net primary productivity (NPP) in North America was computed for the years 1982–1998 using the Carnegie‐Ames‐Stanford approach (CASA) carbon cycle model. CASA was driven by a new, corrected satellite record of the normalized difference vegetation index at 8‐km spatial resolution. Regional trends in the 17‐year NPP record varied substantially across the continent. Croplands and grasslands of the Central Plains and eastern Canadian forests experienced summer increases in NPP. Peak NPP trends in Alaska and western Canada occurred in late spring or early summer, suggesting an earlier onset of the growing season in these regions. Forests and woodlands of the southeastern United States showed NPP increases in spring and fall, also suggesting an increase in the length of the growing season. An analysis of climate variables showed that summer precipitation increased in the Central Plains, indicating that climate changes probably play some role in increasing NPP in this region, though intensive management of agricultural ecosystems has also increased productivity. Similarly, increased summer precipitation possibly increased NPP in eastern Canada, but another possible explanation is forest recovery after insect damage. NPP in the southeastern United States increased in the absence of climate variation. Much of this region consists of aggressively managed forests, with young stand ages and intensive silviculture resulting in increased NPP. The high latitudes of western Canada and Alaska experienced spring warming that could have increased NPP in late spring or early summer

Satellite-derived increases in net primary productivity across North America, 1982-1998

We used a new 17‐year, high spatial resolution satellite record and a carbon cycle model to explore how changing net primary productivity (NPP) contributed to a proposed carbon (C) sink in North America. We found a small but significant increase in NPP, 0.03 Pg C yr^(−2) or 8% over 17 years, that could explain a substantial fraction of the C sink. The largest increases occurred in the central and southeastern United States, eastern Canada, and northwestern North America, and were consistent with NPP trends derived from forest inventories and crop yields. Interannual NPP variability was small, implying that the large interannual variability in the C sink found in previous studies were driven by changes in heterotrophic respiration

Satellite - derived increases in net primary productivity across North America, 1982–1998

We used a new 17-year, high spatial resolution satellite record and a carbon cycle model to explore how changing net primary productivity (NPP) contributed to a proposed carbon (C) sink in North America. We found a small but significant increase in NPP, 0.03 Pg C yr-2 or 8% over 17 years, that could explain a substantial fraction of the C sink. The largest increases occurred in the central and southeastern United States, eastern Canada, and northwestern North America, and were consistent with NPP trends derived from forest inventories and crop yields. Interannual NPP variability was small, implying that the large interannual variability in the C sink found in previous studies were driven by changes in heterotrophic respiration

Trends in North American net primary productivity derived from satellite observations, 1982-1998

Net primary productivity (NPP) in North America was computed for the years 1982–1998 using the Carnegie‐Ames‐Stanford approach (CASA) carbon cycle model. CASA was driven by a new, corrected satellite record of the normalized difference vegetation index at 8‐km spatial resolution. Regional trends in the 17‐year NPP record varied substantially across the continent. Croplands and grasslands of the Central Plains and eastern Canadian forests experienced summer increases in NPP. Peak NPP trends in Alaska and western Canada occurred in late spring or early summer, suggesting an earlier onset of the growing season in these regions. Forests and woodlands of the southeastern United States showed NPP increases in spring and fall, also suggesting an increase in the length of the growing season. An analysis of climate variables showed that summer precipitation increased in the Central Plains, indicating that climate changes probably play some role in increasing NPP in this region, though intensive management of agricultural ecosystems has also increased productivity. Similarly, increased summer precipitation possibly increased NPP in eastern Canada, but another possible explanation is forest recovery after insect damage. NPP in the southeastern United States increased in the absence of climate variation. Much of this region consists of aggressively managed forests, with young stand ages and intensive silviculture resulting in increased NPP. The high latitudes of western Canada and Alaska experienced spring warming that could have increased NPP in late spring or early summer

Satellite-derived increases in net primary productivity across North America, 1982-1998

We used a new 17‐year, high spatial resolution satellite record and a carbon cycle model to explore how changing net primary productivity (NPP) contributed to a proposed carbon (C) sink in North America. We found a small but significant increase in NPP, 0.03 Pg C yr^(−2) or 8% over 17 years, that could explain a substantial fraction of the C sink. The largest increases occurred in the central and southeastern United States, eastern Canada, and northwestern North America, and were consistent with NPP trends derived from forest inventories and crop yields. Interannual NPP variability was small, implying that the large interannual variability in the C sink found in previous studies were driven by changes in heterotrophic respiration

Impacts of disturbance on the terrestrial carbon budget of North America

Because it is an important regulator of terrestrial carbon cycling in North America, extensive research on natural and human disturbances has been carried out as part of the North American Carbon Program and the CarboNA project. A synthesis of various components of this research was carried out, and the results are presented in the papers contained in this special section. While the synthesis primarily focused on the impacts of fire, insects/disease, and harvesting on terrestrial carbon cycling in forests, several groups focused on impacts of disturbance on woody encroachment in western U. S. dry lands and on soil carbon present in northern high-latitude regions. Here, we present a summary of the results from these papers, along with the findings and recommendations from the disturbance synthesis. Citation: Kasischke, E. S., B. D. Amiro, N. N. Barger, N. H. F. French, S. J. Goetz, G. Grosse, M. E. Harmon, J. A. Hicke, S. Liu, and J. G. Masek (2013), Impacts of disturbance on the terrestrial carbon budget of North America, J. Geophys. Res. Biogeosci., 118, 303-316, doi:10.1002/jgrg.20027.Keywords: Permafrost thaw, United States, Mountain pine beetle, Boreal forest, Canada, Climate change, Dynamics, Fire severity, Ice storm damage, Forest gap model

Global satellite monitoring of climate-induced vegetation disturbances

Terrestrial disturbances are accelerating globally, but their full impact is not quantified because we lack an adequate monitoring system. Remote sensing offers a means to quantify the frequency and extent of disturbances globally. Here, we review the current application of remote sensing to this problem and offer a framework for more systematic analysis in the future. We recommend that any proposed monitoring system should not only detect disturbances, but also be able to: identify the proximate cause(s); integrate a range of spatial scales; and, ideally, incorporate process models to explain the observed patterns and predicted trends in the future. Significant remaining challenges are tied to the ecology of disturbances. To meet these challenges, more effort is required to incorporate ecological principles and understanding into the assessments of disturbance worldwide

Aptamer-based multiplexed proteomic technology for biomarker discovery

Interrogation of the human proteome in a highly multiplexed and efficient manner remains a coveted and challenging goal in biology. We present a new aptamer-based proteomic technology for biomarker discovery capable of simultaneously measuring thousands of proteins from small sample volumes (15 [mu]L of serum or plasma). Our current assay allows us to measure ~800 proteins with very low limits of detection (1 pM average), 7 logs of overall dynamic range, and 5% average coefficient of variation. This technology is enabled by a new generation of aptamers that contain chemically modified nucleotides, which greatly expand the physicochemical diversity of the large randomized nucleic acid libraries from which the aptamers are selected. Proteins in complex matrices such as plasma are measured with a process that transforms a signature of protein concentrations into a corresponding DNA aptamer concentration signature, which is then quantified with a DNA microarray. In essence, our assay takes advantage of the dual nature of aptamers as both folded binding entities with defined shapes and unique sequences recognizable by specific hybridization probes. To demonstrate the utility of our proteomics biomarker discovery technology, we applied it to a clinical study of chronic kidney disease (CKD). We identified two well known CKD biomarkers as well as an additional 58 potential CKD biomarkers. These results demonstrate the potential utility of our technology to discover unique protein signatures characteristic of various disease states. More generally, we describe a versatile and powerful tool that allows large-scale comparison of proteome profiles among discrete populations. This unbiased and highly multiplexed search engine will enable the discovery of novel biomarkers in a manner that is unencumbered by our incomplete knowledge of biology, thereby helping to advance the next generation of evidence-based medicine

Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery

Mountain pine beetle outbreaks have caused widespread tree mortality in North American forests in recent decades, yet few studies have documented impacts on carbon cycling. In particular, landscape scales intermediate between stands and regions have not been well studied. Remote sensing is an effective tool for quantifying impacts of insect outbreaks on forest ecosystems at landscape scales. In this study, we developed and evaluated methodologies for quantifying aboveground carbon (AGC) stocks affected by mountain pine beetle using field observations, lidar data, and multispectral imagery. We evaluated methods at two scales, the plot level and the tree level, to ascertain the capability of each for mapping AGC impacts of bark beetle infestation across a forested landscape. In 27 plots across our 5054-ha study area in central Idaho, we measured tree locations, health, diameter, height, and other relevant attributes. We used allometric equations to estimate AGC content of individual trees and, in turn, summed tree AGC estimates to the plot level. Tree-level and plot-level AGC were then predicted from lidar metrics using separate statistical models. At the tree level, cross-validated additive models explained 50–54% of the variation in tree AGC (root mean square error (RMSE) values of 26–42 kg AGC, or 32–48%). At the plot level, a cross-validated linear model explained 84% of the variation in plot AGC (RMSE of 9.2 Mg AGC/ha, or 12%). To map beetle-caused tree mortality, we classified high-resolution digital aerial photography into green, red, and gray tree classes with an overall accuracy of 87% (kappa=0.79) compared with our field observations. We then combined the multispectral classification with lidar-derived AGC estimates to quantify the amount of AGC within beetle-killed trees at the field plots. Errors in classification, apparent tree lean caused by off-nadir aerial imagery, and a bias between percent cover and percent AGC reduced accuracy when combining multispectral and lidar products. Plot-level models estimated total plot AGC more accurately than tree-level models summed for plots as determined by RMSE (9.2 versus 21Mg AGC/ha, respectively) and mean bias error (0.52 versus −6.7 Mg AGC/ha, respectively). When considering individual tree classes (green, red, gray) summed for plots, comparisons of plot-level and tree-level methods exhibited mixed results, with some accuracy measures higher for plot-level models. Despite a lack of clear improvement in tree-level models, we suggest that tree level models should be considered for assessing situations with high spatial variability such as beetle outbreaks, especially if apparent tree lean effects can be minimized such as through the use of satellite imagery. Our methods illustrate the utility of combining lidar and multispectral imagery and can guide decisions about spatial resolution of analysis for understanding and documenting impacts of these forest disturbances

Climate-driven tree mortality : insights from the piñon pine die-off in the United States

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