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Understanding the genetic bases underlying climate adaptation is a key element to predict the potential of species to face climate warming. Although substantial climate variation is observed at a micro-geographic scale, most genomic maps of climate adaptation have been established at broader geographical scales. Here, by using a Pool-Seq approach combined with a Bayesian hierarchical model that control for confounding by population structure, we performed a genome-environment association (GEA) analysis to investigate the genetic basis of adaptation to six climate variables in 168 natural populations of Arabidopsis thaliana distributed in south-west of France. Climate variation among the 168 populations represented up to 24% of climate variation among 521 European locations where A. thaliana inhabits. We identified neat and strong peaks of association, with most of the associated SNPs being significantly enriched in likely functional variants and/or in the extreme tail of genetic differentiation among populations. Furthermore, genes involved in transcriptional mechanisms appear predominant in plant functions associated with local climate adaptation. Globally, our results suggest that climate adaptation is an important driver of genomic variation in A. thaliana at a small spatial scale and mainly involves genome-wide changes in fundamental mechanisms of gene regulation. The identification of climate-adaptive genetic loci at a micro-geographic scale also highlights the importance to include within-species genetic diversity in ecological niche models for projecting potential species distributional shifts over short geographic distances.

Abstract In many areas which call for solar radiation harvesting, from buildings to solar power plants, all manners of windows and protective glass envelopes are required. A high transmission of solar light through glass is therefore mandatory, mostly in the visible range for lighting purposes and also in the near infrared range for power generation. For this purpose, antireflective coatings (ARCs) are deposited on glass surfaces to avoid parasitic reflection and increase transmission of the incident solar light. Porous SiO 2 or compact MgF 2 have been identified in the literature as good antireflective materials for glass due to their low refractive index. The total solar power transmitted through coated glass is determined by the wavelength-dependent optical properties (spectral complex refractive index) of both the glass and the ARC deposited on it. For many optical applications other than solar, the glass industry takes into account the impact of the variation with wavelength of the glass refractive index, represented by the Abbe number, on its transmittance. For solar applications however, in most reported studies on ARCs for solar glass, the optimization of the ARC in terms of porosity and thickness is done by considering the refractive index of the glass as a single value independent of wavelength. This approximation can affect the accuracy and pertinence of the solar ARC optimization. In this work, we therefore propose to join these two approaches by including the glass refractive index variation with wavelength in the optimization of a typical solar ARC, for a large catalog of glasses available in the industry. To achieve this goal, we simulated and optimized with a stochastic algorithm the solar antireflective properties of a porous SiO 2 coating on 24 different glasses. We studied the impact on their solar transmittance of the glass refractive index variation with wavelength, the incident solar spectrum (Direct + Circumsolar or Global Tilt), the incidence angle and the glass solar absorptance. Glasses with low refractive indices and high Abbe numbers were found to be necessary to ensure high solar transmittance. Such properties are met in crown glass such as FK ® , PK ® or BK ® .

The functional trait framework provides a powerful corpus of integrated concepts and theories to assess how environmental factors influence ecosystem functioning through community assembly. While common in plant ecology, this approach is under-used in microbial ecology. After an introduction of this framework in the context of microbial ecology and enzymology, we propose an approach 1) to elucidate new links between soil microbial community composition and microbial traits; and 2) to disentangle mechanisms underlying “total” potential enzyme activity in soil (sum of 7 hydrolase potential activities). We address these objectives using a terrestrial grassland ecosystem model experiment with intact soil monoliths from three European countries (Switzerland, France and Portugal) and two management types (Conventional-intensive and Ecological-intensive), subjected to 4 rain regimes (Dry, Wet, Intermittent and Normal) under controlled conditions in a common climate chamber. We found tight associations between proxies of microbial ecoenzymatic community-weighted mean traits (enzymatic stoichiometry and biomass-specific activity) and community composition, bringing new information on resource acquisition strategy associated with fungi, Gram positive and Gram negative bacteria. We demonstrate that microbial biomass explained most of the total enzyme activity before altered rain regimes, whereas adjustments in biomass-specific activity (enzyme activity per unit of microbial biomass) explained most variation under altered rain regime scenarios. Furthermore, structural equation models revealed that the variation of community composition was the main driver of the variation in biomass-specific enzyme activity prior to rain perturbation, whereas physiological acclimation or evolutionary adaptation became an important driver only under altered rain regimes. This study presents a promising trait-based approach to investigate soil microbial community response to environmental changes and potential consequences for ecosystem functioning. We argue that the functional trait framework should be further implemented in microbial ecology to guide experimental and analytical design.

The investigation of degradation of seven distinct sets (with a number of individual cells of n $ 12) of state of the art organic photovoltaic devices prepared by leading research laboratories with a combination of imaging methods is reported. All devices have been shipped to and degraded at Risø DTU up to 1830 hours in accordance with established ISOS-3 protocols under defined illumination conditions. Imaging of device function at different stages of degradation was performed by laser-beam induced current (LBIC) scanning; luminescence imaging, specifically photoluminescence (PLI) and electroluminescence (ELI); as well as by lock-in thermography (LIT). Each of the imaging techniques exhibits its specific advantages with respect to sensing certain degradation features, which will be compared and discussed here in detail. As a consequence, a combination of several imaging techniques yields very conclusive information about the degradation processes controlling device function. The large variety of device architectures in turn enables valuable progress in the proper interpretation of imaging results—hence revealing the benefits of this large scale cooperation in making a step forward in the understanding of organic solar cell aging and its interpretation by state-of-the-art imaging methods.

Decarbonisation of energy systems requires deep structural change. The purpose of this research was to analyse the rates of change taking place in the energy systems of each Member State of the European Union (EU), and the EU in aggregate, in the light of the EU's climate change mitigation objectives. Trends on indicators such as sectoral activity levels and composition, energy intensity, and carbon intensity of energy were compared with decadal benchmarks derived from deep decarbonisation scenarios. The methodology applied provides a useful and informative approach to tracking decarbonisation of energy systems. The results show that while the EU has made significant progress in decarbonising its energy system. On a number of indicators assessed the results show that a significant acceleration from historical levels is required in order to reach the rates of change seen on the future benchmarks for deep decarbonisation. The methodology applied provides an example of how the research community and international organisations could complement the transparency mechanism developed by the Paris Agreement on climate change, to improve understanding of progress toward low-carbon energy systems.

Biotechnology can greatly improve the efficiency of forest tree breeding for the production of biomass, energy, and materials. However, EU regulations impede the market introduction of genetically modified (GM) trees so their socioeconomic and environmental benefits are not realized. European policy makers should concentrate on a science-based regulatory process.