Change in net primary production and heterotrophic respiration: How much is necessary to sustain the terrestrial carbon sink?

In recent years, the chief approaches used to describe the terrestrial carbon sink have been either (1) inferential, based on changes in the carbon content of the atmosphere and other elements of the global carbon cycle, or (2) mechanistic, applying our knowledge of terrestrial ecology to ecosystem scale processes. In this study, the two approaches are integrated by determining the change in terrestrial properties necessary to match inferred change in terrestrial carbon storage. In addition, a useful mathematical framework is developed for understanding the important features of the terrestrial carbon sink. The Carnegie‐Ames‐Stanford Approach (CASA) biosphere model, a terrestrial carbon cycle model that uses a calibrated, semimechanistic net primary production model and a mechanistic plant and soil carbon turnover model, is employed to explore carbon turnover dynamics in terms of the specific features of terrestrial ecosystems that are most important for the potential development of a carbon sink and to determine the variation in net primary production (NPP) necessary to satisfy various carbon sink estimates. Given the existence of a stimulatory mechanism acting on terrestrial NPP, net ecosystem uptake is expected to be largest where NPP is high and the turnover of carbon through plants and the soil is slow. In addition, it was found that (1) long‐term, climate‐induced change in heterotrophic respiration is not as important in determining long‐term carbon exchange as is change in NPP and (2) the terrestrial carbon sink rate is determined not by the cumulative increase in production over some pre‐industrial baseline, but rather by the rate of increase in production over the industrial period

Interannual variation in global-scale net primary production: Testing model estimates

Testing estimates of year‐to‐year variation in global net primary production (NPP) poses some challenges. Large‐scale, multiyear records of production are not readily available for natural systems but are for agricultural systems. We use records of agricultural yields at selected sites to test NPP estimates produced by CASA, a global‐scale production model driven by both meteorological data and the satellite‐derived normalized difference vegetation index (NDVI). We also test estimates produced by the Miami model, which has underlain several analyses of biosphere response to interannual changes in climate. In addition, we test estimates against tree ring data for one boreal site for which data from both coniferous and deciduous species were available. The agricultural tests demonstrate that CASA can reasonably estimate interannual variation in production. The Miami model estimates variation more poorly. However, differences in NDVI‐processing algorithms substantially affect CASA's estimates of interannual variation. Of the four versions tested, the FASIR NDVI most closely reproduced yield data and showed the least correlation with changes in equatorial crossing time of the National Oceanic and Atmospheric Administration satellites. One issue raised is the source of the positive trends evident in CASA's NDVI‐based estimates of global NPP. The existence of these trends is consistent with potential stimulation of terrestrial production by factors such as CO2 enrichment, N fertilization, or temperature warming, but the magnitude of the global trends seen is significantly greater than suggested by constraints imposed by atmospheric fluxes

Change in net primary production and heterotrophic respiration: How much is necessary to sustain the terrestrial carbon sink?

In recent years, the chief approaches used to describe the terrestrial carbon sink have been either (1) inferential, based on changes in the carbon content of the atmosphere and other elements of the global carbon cycle, or (2) mechanistic, applying our knowledge of terrestrial ecology to ecosystem scale processes. In this study, the two approaches are integrated by determining the change in terrestrial properties necessary to match inferred change in terrestrial carbon storage. In addition, a useful mathematical framework is developed for understanding the important features of the terrestrial carbon sink. The Carnegie‐Ames‐Stanford Approach (CASA) biosphere model, a terrestrial carbon cycle model that uses a calibrated, semimechanistic net primary production model and a mechanistic plant and soil carbon turnover model, is employed to explore carbon turnover dynamics in terms of the specific features of terrestrial ecosystems that are most important for the potential development of a carbon sink and to determine the variation in net primary production (NPP) necessary to satisfy various carbon sink estimates. Given the existence of a stimulatory mechanism acting on terrestrial NPP, net ecosystem uptake is expected to be largest where NPP is high and the turnover of carbon through plants and the soil is slow. In addition, it was found that (1) long‐term, climate‐induced change in heterotrophic respiration is not as important in determining long‐term carbon exchange as is change in NPP and (2) the terrestrial carbon sink rate is determined not by the cumulative increase in production over some pre‐industrial baseline, but rather by the rate of increase in production over the industrial period

Bias-voltage dependence of the magneto-resistance in ballistic vacuum tunneling: Theory and application to planar Co(0001) junctions

Motivated by first-principles results for jellium and by surface-barrier shapes that are typically used in electron spectroscopies, the bias voltage in ballistic vacuum tunneling is treated in a heuristic manner. The presented approach leads in particular to a parameterization of the tunnel-barrier shape, while retaining a first-principles description of the electrodes. The proposed tunnel barriers are applied to Co(0001) planar tunnel junctions. Besides discussing main aspects of the present scheme, we focus in particular on the absence of the zero-bias anomaly in vacuum tunneling.Comment: 19 pages with 8 figure

Oral health indices predict individualised recall interval

Objectives: The individualised recall interval (IRI) is part of the oral health examination. This observational, register-based study aimed to explore how oral health indices DMFT (decayed, missing, filled teeth), DT (decayed teeth), CPI (Community Periodontal Index, maximum value of individual was used) and number of teeth are associated with IRI for adults. Methods: Oral health examination includes an assessment of all oral tissues, diagnosis, a treatment plan and assessment and a determination of the interval before the next assessment. It is called the IRI. This cross-sectional study population included 42,533 adults (age range 18-89 years), who had visited for an oral health examination during 2009, provided by the Helsinki City Social Services and Health Care. The recall interval was categorised into an ordinal scale (0-12, 13-24, 25-36 and 37-60 months) and was modelled using a proportional odds model. ORs less than one indicated a shorter recall interval. Results: Recall interval categories in the study population were 0-12 months (n = 4,569; 11%), 13-24 months (n = 23,732; 56%), 25-36 months (n = 12,049; 28%), and 37-60 months (n = 2,183; 5%). The results of statistical models clearly showed an association between the length of recall intervals and oral health indices. In all models, higher values of DMFT, DT and CPI indicated a shorter recall interval. The number of teeth were not so relevant. The association was not influenced when different combinations of other predictors (age, gender, socioeconomic status, chronic diseases) were included in the model. The severity of periodontitis predicted a short recall interval, for example, in the Model 1, CPI maximum value 4 was OR = 0.35 (95% confidence interval 0.31-0.40). Conclusions: The oral health indices showed a clear association with the length of the IRI. Poor oral health reduced IRI. The indices provide information about the amount of oral health prevention required and are useful to health organisations.Peer reviewe

Proteomic analysis at the sites of clinical infection with invasive Streptococcus pyogenes

Invasive Streptococcus pyogenes infections are rare, with often-unexplained severity. Prompt diagnosis is desirable, as deaths can occur rapidly following onset and there is an increased, but preventable, risk to contacts. Here, proteomic analyses of clinical samples from invasive human S. pyogenes infections were undertaken to determine if novel diagnostic targets could be detected, and to augment our understanding of disease pathogenesis. Fluid samples from 17 patients with confirmed invasive S. pyogenes infection (empyema, septic arthritis, necrotising fasciitis) were analysed by proteomics for streptococcal and human proteins; 16/17 samples had detectable S. pyogenes DNA. Nineteen unique S. pyogenes proteins were identified in just 6/17 samples, and 15 of these were found in a single pleural fluid sample including streptococcal inhibitor of complement, trigger factor, and phosphoglycerate kinase. In contrast, 469 human proteins were detected in patient fluids, 177 (38%) of which could be identified as neutrophil proteins, including alpha enolase and lactotransferrin which, together, were found in all 17 samples. Our data suggest that streptococcal proteins are difficult to detect in infected fluid samples. A vast array of human proteins associated with leukocyte activity are, however, present in samples that deserve further evaluation as potential biomarkers of infection