Long-term effects of environmental change and species diversity on tree radial growth in a mixed European forest

Norway spruce (Picea abies), European beech (Fagus sylvatica), silver fir (Abies alba) and Scots pine (Pinus sylvestris) typically co-occur in European forests, but show contrasting response to climate and environmental change. Sustainable forest management therefore depends on species- and regional-specific information. Here, we use tree-ring width measurements of 334 beech, 280 fir, 144 spruce and 63 pine trees from 75 inventory plots in Slovakia to assess the predominant climatic factors that control radial stem growth of Europe’s economically most important forest species. All four species exhibit significant shifts in stem growth over the past 100 years. Ring width patterns were, however, not significantly affected by tree species diversity and site elevation. The resistance, resilience and recovery of all species to the extreme summer droughts between 1950 and 2003 suggest that spruce is the species most unsuitable for the predicted warmer and drier future. Silver fir may benefit from warmer conditions, although we cannot conclude that it will not suffer from predicted increased frequency of climate extremes. Forest management in this locality should aim to avoid significant loss of forest cover by replacing Norway spruce monocultures with mixed stands of silver fir and European beech

Identification of Years with Extreme Vegetation State in Central Europe Based on Remote Sensing and Meteorological Data

Background and Purpose: Determination of an extreme year from the aspect of the vegetation activity using only meteorological data might be ambiguous and not adequate. Furthermore, in some ecosystems, e.g. forests, the response is not instantly visible, but the effects of the meteorological anomaly can be seen in the following year. The aim of the present paper is to select and characterize typical and anomalous years using satellite-based remote sensing data and meteorological observations during the recent years of 2000-2014 for Central Europe, based on the response of the vegetation. Materials and Methods: In the present study vegetation characteristics were described using remotely sensed official products of the MODerate resolution Imaging Spectroradiometer (MODIS), namely NDVI, EVI, FPAR, LAI, GPP, and NPP, with 8-day temporal and 500 meter spatial resolution for the period of 2000-2014. The corresponding mean temperature and precipitation data (on the same grid) were derived from the Open Database for Climate Change Related Impact Studies in Central Europe (FORESEE) daily meteorological dataset. Land cover specific anomalies of the meteorological and vegetation characteristics were created and averaged on a country-scale, where the distinction between the main land cover types was based on the synergetic use of MODIS land cover and Coordination of Information on the Environment (CORINE) Land Cover 2012 datasets. Results: It has been demonstrated that the anomaly detection based solely on basic meteorological variables is ambiguous since the strength of the anomaly depends on the selected integration time period. In contrast, the effect-based approach exploiting the available, state-of-the-art remote sensing based vegetation indices is a promising tool for the characterization of the anomalous behaviour of the different land cover types. The selection of extreme years was performed in an explicit way using percentile analysis on pixel level. Conclusions: Plant status in terms of both positive and negative anomalies shows strong land cover dependency in Central Europe. This is most likely due to the differences in heat and drought resistance of the vegetation, and species composition. The selection of country-specific extreme years can serve as a basis for forthcoming research

Fine-scale variation in projected climate change presents opportunities for biodiversity conservation in Europe

Climate change is a major threat to global biodiversity, although projected changes show remarkable geographical and temporal variability. Understanding this variability allows for the identification of regions where the present-day conservation objectives may be at risk or where opportunities for biodiversity conservation emerge. We use a multi-model ensemble of regional climate models to identify areas with significantly high and low climate stability persistent throughout the 21st century in Europe. We then confront our predictions with the land coverage of three prominent biodiversity conservation initiatives at two scales. The continental-scale assessment shows that areas with the least stable future climate in Europe are likely to occur at low and high latitudes, with the Iberian Peninsula and the Boreal zones identified as prominent areas of low climatic stability. A follow-up regional scale investigation shows that robust climatic refugia exist even within the highly exposed southern and northern macro-regions. About 23-31 % of assessed biodiversity conservation sites in Europe coincide with areas of high future climate stability, we contend that these sites should be prioritised in the formulation of future conservation priorities as the stability of future climate is one of the key factors determining their conservation prospects. Although such focus on climate refugia cannot halt the ongoing biodiversity loss, along with measures such as resilience-based stewardship, it may improve the effectiveness of biodiversity conservation under climate change

Supporting environmental modelling with Taverna workflows, web services and desktop grid technology

Ecosystem functioning, climate change, and multiple interactions among biogeochemical cycles, climate system, site conditions and land use options are leading-edge topics in recent environmental modelling. Terrestrial ecosystem models are widely used to support carbon sequestration and ecosystem studies under various ecological circumstances. Our team uses the Biome-BGC model (Numerical Terradynamic Simulation Group, University of Montana), and develops an improved model version of it, called Biome-BGC MuSo. Both the original and the improved model estimate the ecosystem scale storage and fluxes of energy, carbon, nitrogen and water, controlled by various physical and biological processes on a daily time-scale. Web services were also developed and integrated with parallel processing desktop grid technology. Taverna workflow management system was used to build up and carry out elaborated workflows like seamless data flow to model simulation, Monte Carlo experiment, model sensitivity analysis, model-data fusion, estimation of ecosystem service indicators or extensive spatial modelling. Straightforward management of complex data analysis tasks, organized into appropriately documented, shared and reusable scientific workflows enables researchers to carry out detailed and scientifically challenging ‘in silico’ experiments and applications that could open new directions in ecosystem research and in a broader sense it supports progress in environmental modelling. The workflow approach built upon these web services allows even the most complicated computations to be initiated without the need of programming skills and deep understanding of model structure and initialization. The developments enable a wider array of scientists to perform ecosystem scale simulations, and to perform analyses not previously possible due to high complexity and computational demand

Terrestrial ecosystem process model Biome-BGCMuSo v4.0: summary of improvements and new modeling possibilities

The process-based biogeochemical model Biome- BGC was enhanced to improve its ability to simulate carbon, nitrogen, and water cycles of various terrestrial ecosystems under contrasting management activities. Biome-BGC version 4.1.1 was used as a base model. Improvements included addition of new modules such as the multilayer soil module, implementation of processes related to soil moisture and nitrogen balance, soil-moisture-related plant senescence, and phenological development. Vegetation management modules with annually varying options were also implemented to simulate management practices of grasslands (mowing, grazing), croplands (ploughing, fertilizer application, planting, harvesting), and forests (thinning). New carbon and nitrogen pools have been defined to simulate yield and soft stem development of herbaceous ecosystems. The model version containing all developments is referred to as Biome-BGCMuSo (Biome-BGC with multilayer soil module; in this paper, Biome-BGCMuSo v4.0 is documented). Case studies on a managed forest, cropland, and grassland are presented to demonstrate the effect of model developments on the simulation of plant growth as well as on carbon and water balance

Prevalence of other autoimmune diseases in polyglandular autoimmune syndromes type II and III

Polyglandular autoimmune syndromes (PAS) are complex, heterogeneous disorders in which various autoimmune diseases can occur, affecting both endocrine and non-endocrine organs. In this meta-analysis, the prevalence of associated autoimmune disorders was investigated in PAS II and III.A comprehensive search in MEDLINE and Embase databases identified 479 studies with the keywords of PAS II and PAS III. 18 records containing a total of 1312 patients fulfilled our inclusion criteria (original studies reporting at least 10 cases and containing the combination of other autoimmune disorders) and were selected for further analysis. A meta-analysis of prevalence was performed using the random-effects model with the calculation of 95% confidence intervals (CI). Results of each meta-analysis were displayed graphically using forest plots.Distinction between PAS II and PAS III was made in 842 cases, of which 177 and 665 were PAS II and III (21.1 vs 78.9%), respectively. The prevalence of Hashimoto's thyroiditis was significantly higher than that of Graves's disease (39% [95% CI 17-65%] vs. 4% [95% CI 0-10%], respectively; p = 0.001). In PAS II, Addison's disease (AD) coexisted with AITDs, T1DM or the combination of these conditions in 65, 18 and 10% of cases, respectively. In addition, one other endocrine and five non-endocrine organ-specific autoimmune disorders were reported. In PAS III, two other autoimmune endocrinopathies, six non-endocrine organ-specific, and four systemic autoimmune disorders were found in combination with AITDs.AITDs, T1DM and AD are the most common combinations in PAS, thus screening for these conditions seems to be reasonable