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The paper deals with the management problem how to decide on tree species suitability under changing environmental conditions. It presents an algorithm that classifies the output of a range shift model for major tree species in Europe into multiple classes that can be linked to qualities characterizing the ecological niche of the species. The classes: i) Core distribution area, ii) Extended distribution area, iii) Occasional occurrence area, and iv) No occurrence area are first theoretically developed and then statistically described. The classes are interpreted from an ecological point of view using criteria like population structure, competitive strength, site spectrum and vulnerability to biotic hazards. The functioning of the algorithm is demonstrated using the example of a generalized linear model that was fitted to a pan-European dataset of presence/absence of major tree species with downscaled climate data from a General Circulation Model (GCM). Applications of the algorithm to tree species suitability classification on a European and regional level are shown. The thresholds that are used by the algorithm are precision-based and include Cohen's Kappa. A validation of the algorithm using an independent dataset of the German National Forest Inventory shows good accordance of the statistically derived classes with ecological traits for Norway spruce, while the differentiation especially between core and extended distribution for European beech that is in the centre of its natural range in this area is less accurate. We hypothesize that for species in the core of their range regional factors like forest history superimpose climatic factors. Problems of uncertainty issued from potentially applying a multitude of modelling approaches and/or climate realizations within the range shift model are discussed and a way to deal with the uncertainty by revealing the underlying attitude towards risk of the decision maker is proposed. [Copyright &y& Elsevier]

Abstract: Energy efficiency in buildings is essential for economic, social, and environmental reasons. Under current European legislation, buildings have to be certified with an energy efficiency index. To provide a fair comparison of the energy performance in buildings we need a global and unified representation. In this paper we propose the Energy Efficiency Ontology (EEOnt) to provide a foundation upon which this requirement can be built. EEOnt extends the applicability of other ontological models of buildings incorporating energy efficiency data and knowledge that can be calculated with high spatial and temporal resolution in what we call an Energy Efficiency Landscape (EELB). These EELB''s provide significant information which allows a detailed diagnosis of the energy efficiency within the building. Consequently, EEOnt is capable of representing buildings and energy efficiency in a unified way. Furthermore, it supplies a tool for managing the energy efficiency in buildings and constructing national inventories. These two features link the global and particular needs of governments and users, thus becoming key to both improve the energy efficiency in buildings and to implement viable certification schemes. [Copyright &y& Elsevier]

Understanding the climate change processes requires application of special methodologies for revealing a ground surface temperature history (GSTH). It was proved by different authors that the GSTH may be determined on the basis of analysis of the temperature field observed in short boreholes. In this paper, the authors analyze four mathematical models describing the GSTH: (1) sudden change, (2) linear increase, (3) square root of time increase and (4) exponential increase. Fifteen borehole temperature profiles from Europe, Asia and North America were selected in three groups based on their geographical proximity. After careful analysis of temperature-depth profiles in these boreholes it was found out that two models (linear increase and square root of time increase) provide the best fit with field data. The calculated warming rates in the 20th century were compared with those obtained by a few parameters estimation (FPE) technique. [ABSTRACT FROM AUTHOR]

Background: West Nile virus (WNV) is transmitted by mosquitoes in both urban as well as in rural environments and can be pathogenic in birds, horses and humans. Extrinsic factors such as temperature and land use are determinants of WNV outbreaks in Europe, along with intrinsic factors of the vector and virus.Methods: With a multivariate model for WNV transmission we computed the probability of WNV infection in 2014, with July 2014 temperature anomalies. We applied the July temperature anomalies under the balanced A1B climate change scenario (mix of all energy sources, fossil and non-fossil) for 2025 and 2050 to model and project the risk of WNV infection in the future. Since asymptomatic infections are common in humans (which can result in the contamination of the donated blood) we estimated the predictive prevalence of WNV infections in the blood donor population.Results: External validation of the probability model with 2014 cases indicated good prediction, based on an Area Under Curve (AUC) of 0.871 (SD = 0.032), on the Receiver Operating Characteristic Curve (ROC). The climate change projections for 2025 reveal a higher probability of WNV infection particularly at the edges of the current transmission areas (for example in Eastern Croatia, Northeastern and Northwestern Turkey) and an even further expansion in 2050. The prevalence of infection in (blood donor) populations in the outbreak-affected districts is expected to expand in the future.Conclusions: Predictive modelling of environmental and climatic drivers of WNV can be a valuable tool for public health practice. It can help delineate districts at risk for future transmission. These areas can be subjected to integrated disease and vector surveillance, outreach to the public and health care providers, implementation of personal protective measures, screening of blood donors, and vector abatement activities. [ABSTRACT FROM AUTHOR]

Future anthropogenic climate change is likely to increase the air temperature ( T) across Europe and increase the frequency, duration and magnitude of severe heat stress events. Heat stress events are generally associated with clear-sky conditions and high T, which give rise to high radiant heat load, i.e. mean radiant temperature ( T). In urban environments, T is strongly influenced by urban geometry. The present study examines the effect of urban geometry on daytime heat stress in three European cities (Gothenburg in Sweden, Frankfurt in Germany and Porto in Portugal) under present and future climates, using T as an indicator of heat stress. It is found that severe heat stress occurs in all three cities. Similar maximum daytime T is found in open areas in all three cities despite of the latitudinal differences in average daytime T. In contrast, dense urban structures like narrow street canyons are able to mitigate heat stress in the summer, without causing substantial changes in T in the winter. Although the T averages are similar for the north-south and east-west street canyons in each city, the number of hours when T exceeds the threshold values of 55.5 and 59.4 °C-used as indicators of moderate and severe heat stress-in the north-south canyons is much higher than that in the east-west canyons. Using statistically downscaled data from a regional climate model, it is found that the study sites were generally warmer in the future scenario, especially Porto, which would further exacerbate heat stress in urban areas. However, a decrease in solar radiation in Gothenburg and Frankfurt reduces T in the spring, while the reduction in T is somewhat offset by increasing T in other seasons. It suggests that changes in the T under the future scenario are dominated by variations in T. Nonetheless, the intra-urban differences remain relatively stable in the future. These findings suggest that dense urban structure can reduce daytime heat stress since it reduces the number of hours of high T in the summer and does not cause substantial changes in average and minimum T in the winter. In dense urban settings, a more diverse urban thermal environment is also preferred to compensate for reduced solar access in the winter. The extent to which the urban geometry can be optimized for the future climate is also influenced by local urban characteristics. [ABSTRACT FROM AUTHOR]

Bioclimatic envelope models are often used to make projections of species’ potential responses to climate change. It can be hypothesized that species with different kinds of distributions in environmental niche and geographical space may respond differently to changes in climate. Here, we compare projections of shifts in species ranges with simple descriptors of species niche (position and breadth) and geographical (range size) distributions. Europe. The future distribution for 1200 European plant species were predicted by niche-based models using seven climate variables known to have an important role in limiting plant species distributions. Ecological niche properties were estimated using a multivariate analysis. Species range changes were then related to species niche properties using generalized linear models. Generally, percentage of remaining suitable habitat in the future increased linearly with niche position and breadth. Increases in potential suitable habitat were associated with greater range size, and had a hump-shaped relationship with niche position on temperature gradient. By relating species chorotypes to percentage of remaining or gained habitat, we highlighted biogeographical patterns of species sensitivity to climate change. These were clearly related to the degree of exposure according to regional patterns of projected climate change. This study highlights general patterns about the relationships between sensitivity of species to climate change and their ecological properties. There is a strong convergence between simple inferences based on ecological characteristics of species and projections by bioclimatic ‘envelope’ models, confirming macroecological assumptions about species sensitivity based on niche properties. These patterns appear to be most strongly driven by the exposure of species to climate change, with additional effects of species niche characteristics. We conclude that simple species niche properties are powerful indicators of species’ sensitivity to climate change. [ABSTRACT FROM AUTHOR]