Codominant water control on global interannual variability and trends in land surface phenology and greenness

Identifying the relative importance of climatic and other environmental controls on the interannual variability and trends in global land surface phenology and greenness is challenging. Firstly, quantifications of land surface phenology and greenness dynamics are impaired by differences between satellite data sets and phenology detection methods. Secondly, dynamic global vegetation models (DGVMs) that can be used to diagnose controls still reveal structural limitations and contrasting sensitivities to environmental drivers. Thus, we assessed the performance of a new developed phenology module within the LPJmL (Lund–Potsdam–Jena managed Lands) DGVM with a comprehensive ensemble of three satellite data sets of vegetation greenness and ten phenology detection methods, thereby thoroughly accounting for observational uncertainties. The improved and tested model allows us quantifying the relative importance of environmental controls on interannual variability and trends of land surface phenology and greenness at regional and global scales. We found that start of growing season interannual variability and trends are in addition to cold temperature mainly controlled by incoming radiation and water availability in temperate and boreal forests. Warming‐induced prolongations of the growing season in high latitudes are dampened by a limited availability of light. For peak greenness, interannual variability and trends are dominantly controlled by water availability and land‐use and land‐cover change (LULCC) in all regions. Stronger greening trends in boreal forests of Siberia than in North America are associated with a stronger increase in water availability from melting permafrost soils. Our findings emphasize that in addition to cold temperatures, water availability is a codominant control for start of growing season and peak greenness trends at the global scale.     

Sodium-dependent myo-inositol transporter 1 is a cellular receptor for Mus cervicolor M813 murine leukemia virus

Retrovirus infection is initiated by binding of the surface (SU) portion of the viral envelope glycoprotein (Env) to specific receptors on cells. This binding triggers conformational changes in the transmembrane portion of Env, leading to membrane fusion and cell entry, and is thus a major determinant of retrovirus tissue and species tropism. The M813 murine leukemia virus (MuLV) is a highly fusogenic gammaretrovirus, isolated from Mus cervicolor, whose host range is limited to mouse cells. To delineate the molecular mechanisms of its restricted host range and its high fusogenic potential, we initiated studies to characterize the cell surface protein that mediates M813 infection. Screening of the T31 mouse-hamster radiation hybrid panel for M813 infectivity localized the receptor gene to the distal end of mouse chromosome 16. Expression of one of the likely candidate genes (slc5a3) within this region in human cells conferred susceptibility to both M813 infection and M813-induced fusogenicity. slc5a3 encodes sodium myo-inositol transporter 1 (SMIT1), thus adding another sodium-dependent transporter to the growing list of proteins used by MuLVs for cell entry. Characterization of SMIT1 orthologues in different species identified several amino acid variations within two extracellular loops that may restrict susceptibility to M813 infection.     

Uncertainty analysis of vegetation distribution in the northern high latitudes during the 21st century with a dynamic vegetation model

This study aims to assess how high-latitude vegetation may respond under various climate scenarios during the 21st century with a focus on analyzing model parameters induced uncertainty and how this uncertainty compares to the uncertainty induced by various climates. The analysis was based on a set of 10,000 Monte Carlo ensemble Lund-Potsdam-Jena (LPJ) simulations for the northern high latitudes (45(o)N and polewards) for the period 1900-2100. The LPJ Dynamic Global Vegetation Model (LPJ-DGVM) was run under contemporary and future climates from four Special Report Emission Scenarios (SRES), A1FI, A2, B1, and B2, based on the Hadley Centre General Circulation Model (GCM), and six climate scenarios, X901M, X902L, X903H, X904M, X905L, and X906H from the Integrated Global System Model (IGSM) at the Massachusetts Institute of Technology (MIT). In the current dynamic vegetation model, some parameters are more important than others in determining the vegetation distribution. Parameters that control plant carbon uptake and light-use efficiency have the predominant influence on the vegetation distribution of both woody and herbaceous plant functional types. The relative importance of different parameters varies temporally and spatially and is influenced by climate inputs. In addition to climate, these parameters play an important role in determining the vegetation distribution in the region. The parameter-based uncertainties contribute most to the total uncertainty. The current warming conditions lead to a complexity of vegetation responses in the region. Temperate trees will be more sensitive to climate variability, compared with boreal forest trees and C3 perennial grasses. This sensitivity would result in a unanimous northward greenness migration due to anomalous warming in the northern high latitudes. Temporally, boreal needleleaved evergreen plants are projected to decline considerably, and a large portion of C3 perennial grass is projected to disappear by the end of the 21st century. In contrast, the area of temperate trees would increase, especially under the most extreme A1FI scenario. As the warming continues, the northward greenness expansion in the Arctic region could continue.     

Tropical forests and the global carbon cycle: impacts of atmospheric carbon dioxide, climate change and rate of deforestation

The remaining carbon stocks in wet tropical forests are currently at risk because of anthropogenic deforestation, but also because of the possibility of release driven by climate change. To identify the relative roles of CO2 increase, changing temperature and rainfall, and deforestation in the future, and the magnitude of their impact on atmospheric CO2 concentrations, we have applied a dynamic global vegetation model, using multiple scenarios of tropical deforestation (extrapolated from two estimates of current rates) and multiple scenarios of changing climate (derived from four independent offline general circulation model simulations). Results show that deforestation will probably produce large losses of carbon, despite the uncertainty about the deforestation rates. Some climate models produce additional large fluxes due to increased drought stress caused by rising temperature and decreasing rainfall. One climate model, however, produces an additional carbon sink. Taken together, our estimates of additional carbon emissions during the twenty-first century, for all climate and deforestation scenarios, range from 101 to 367 Gt C, resulting in CO2 concentration increases above background values between 29 and 129 p.p.m. An evaluation of the method indicates that better estimates of tropical carbon sources and sinks require improved assessments of current and future deforestation, and more consistent precipitation scenarios from climate models. Notwithstanding the uncertainties, continued tropical deforestation will most certainly play a very large role in the build-up of future greenhouse gas concentrations.     

Evaluation of climate‐related carbon turnover processes in global vegetation models for boreal and temperate forests

Turnover concepts in state‐of‐the‐art global vegetation models (GVMs) account for various processes, but are often highly simplified and may not include an adequate representation of the dominant processes that shape vegetation carbon turnover rates in real forest ecosystems at a large spatial scale. Here, we evaluate vegetation carbon turnover processes in GVMs participating in the Inter‐Sectoral Impact Model Intercomparison Project (ISI‐MIP, including HYBRID4, JeDi, JULES, LPJml, ORCHIDEE, SDGVM, and VISIT) using estimates of vegetation carbon turnover rate (k) derived from a combination of remote sensing based products of biomass and net primary production (NPP). We find that current model limitations lead to considerable biases in the simulated biomass and in k (severe underestimations by all models except JeDi and VISIT compared to observation‐based average k), likely contributing to underestimation of positive feedbacks of the northern forest carbon balance to climate change caused by changes in forest mortality. A need for improved turnover concepts related to frost damage, drought, and insect outbreaks to better reproduce observation‐based spatial patterns in k is identified. As direct frost damage effects on mortality are usually not accounted for in these GVMs, simulated relationships between k and winter length in boreal forests are not consistent between different regions and strongly biased compared to the observation‐based relationships. Some models show a response of k to drought in temperate forests as a result of impacts of water availability on NPP, growth efficiency or carbon balance dependent mortality as well as soil or litter moisture effects on leaf turnover or fire. However, further direct drought effects such as carbon starvation (only in HYBRID4) or hydraulic failure are usually not taken into account by the investigated GVMs. While they are considered dominant large‐scale mortality agents, mortality mechanisms related to insects and pathogens are not explicitly treated in these models.     

Interferon alpha-armed nanoparticles trigger rapid and sustained STAT1-dependent anti-viral cellular responses

Interferon-α (IFNα) has enormous potential for anti-proliferative and anti-viral treatments. However, clinical success is still hampered due to its limited bioavailability and thus, lack of sustained modulation of disease-relevant protective programs. Consequently, we here examined whether IFNα immobilized on nanoscale ferromagnetic R-Chitosan carriers is capable of inducing rapid and sustained activation of STAT1 signaling. We report the spontaneous formation of a stable nanoparticle-IFNα protein corona, which was exploited to generate IFNα-loaded spheres, obviating the need to specifically couple the cytokine to the nanoparticles (NPs). Notably, comprehensive experimental approaches ensure that formation of the IFNα NP-corona does not affect the biological activity of the cytokine, as STAT1 signaling was efficiently activated. Employing human prostate cancer and melanoma cell models, we found that the intensity and duration of STAT1 phosphorylation as well as the downstream activation of pathobiologically relevant genes were dose and particle dependent. In comparison with free IFNα, IFNα-loaded spheres resulted in a more sustained biologically relevant STAT1 activation, demonstrated also by conferring innate cellular immunity against vesicular stomatitis virus (VSV) infection. For one, our study demonstrates the advantages of biodegradable IFNα-coated R-Chitosan NPs for controlled cytokine release, and thereby improved therapy. Second, we reveal that the permanent presence of IFNα and not just the initial STAT1 phosphorylation ensures sustained IFNα-dependent signaling.