Measurements of surface reflectance of permanently shadowed areas near Mercury's north pole reveal regions of anomalously dark and bright deposits at 1064-nanometer wavelength. These reflectance anomalies are concentrated on poleward-facing slopes and are spatially collocated with areas of high radar backscatter postulated to be the result of near-surface water ice. Correlation of observed reflectance with modeled temperatures indicates that the optically bright regions are consistent with surface water ice, whereas dark regions are consistent with a surface layer of complex organic material that likely overlies buried ice and provides thermal insulation. Impacts of comets or volatile-rich asteroids could have provided both dark and bright deposits.

The pressure-induced B3-B1 phase transition and some interesting thermodynamic properties for B3 structure of ideal stoichiometric technetium mononitride (TcN) have been studied systematically by first-principles calculations. It is found that TcN has a B3 ground-state phase at zero pressure and the transition pressure from B3 to B1 structure determined by the energy vs volume curves is about 35 GPa. Through the quasi-harmonic Debye model, the dependences of thermal expansion coefficient, constant volume heat capacity, and constant-pressure heat capacity of TcN with B3 phase on temperature up to 1600 K are successfully predicted at 0, 10, 20 and 35 GPa pressures, respectively. (C) 2016 Elsevier B.V. All rights reserved.

The phase transition, electronic band structure, and equation of state (EOS) of cubic TcN are investigated by first-principles pseudopotential method based on density-functional theory. The calculated enthalpies show that TcN has a transformation between zincblende and rocksalt phases and the pressure determined by the relative enthalpy is 32 GPa. The calculated band structure indicates the metallic feature and it might make cubic TcN a better candidate for hard materials. Particular attention is paid to the predictions of volume, bulk modulus and its pressure derivative which play a central role in the formulation of approximate EOSs using the quasi-harmonic Debye model. (C) 2016 Elsevier B.V. All rights reserved.

A study of the high-pressure elastic properties of new synthetic Ir2P in the anti-fluorite structure is conducted using ab initio calculations based on density functional theory. The elastic constants C-11, C-12 and C-44 for the cubic Ir2P are obtained by the stress-strain method and the elastic stability calculations under pressure indicate that it is stable at least 100 GPa. Additionally, the electronic density of states, the aggregate elastic moduli, that is bulk modulus, shear modulus, and Young's modulus along with the Debye temperature, Poisson's ratio, and elastic anisotropy factor are all successfully obtained. Moreover, the pressure dependence of the longitudinal and shear wave velocities in three different directions [100], [110], and [111] for Ir2P are also predicted for the first time. (C) 2016 Elsevier B.V. All rights reserved.

This paper illustrates the potential for seasonal prediction of wind and solar energy resources through a case study in the Yangtze River estuary. Sea surface temperature and geopotential height-based climate predictors, each with high correlation to ensuing seasonal wind speed and solar radiation at the Baoshan weather observing station, are identified and used to build statistical models to predict seasonal wind speed and solar radiation. Leave-one-out-cross-validation is applied to verify the predictive skill of the best performing candidate model for each season. We find that predictive skill is highest for both wind speed and solar radiation during winter, and lowest during summer. Specifically, we find the most skill when using climate information from the July-September season to predict wind speed or solar radiation during the subsequent November-January season. The ability to predict wind and solar energy availability in the upcoming season can help energy system planners and operators anticipate seasonal surpluses or shortfalls and take precautionary actions.

Estuaries at the global scale are significant but highly uncertain sources of atmospheric nitrous oxide (N2O), which is an intense greenhouse gas and ozone depletion agent. As the largest estuary in the United States, the Chesapeake Bay is suggested to be a spatially and temporally variable source and sink of N2O. However, limited observations of N2O cycling preclude us from estimating and predicting its net N2O flux. To improve our mechanistic understanding of the processes that control the N2O flux at the point of production, we applied multiple N-15 tracers (NH4+15$$ {}<^>{15}{\mathrm{NH}}_4<^>{+} $$, N-15-urea, NO2-15,$$ {}<^>{15}{\mathrm{NO}}_2<^>{-}, $$ and NO3-15$$ {}<^>{15}{\mathrm{NO}}_3<^>{-} $$) to separately track N2O production from nitrification and denitrification under in situ and manipulated O-2 concentrations in the Chesapeake Bay. Nitrification was the major N2O production pathway in oxic waters (up to 7.5 nmol N2O L-1 d(-1)). In contrast, denitrification dominated N2O production from hypoxic/anoxic waters (up to 20 nmol N2O L-1 d(-1)). N2O production from urea was observed for the first time in estuarine waters. The contribution from urea was small, but interestingly, showed a depth pattern distinct from other N2O precursors. Experimentally lowering the O-2 concentration substantially enhanced N2O production. Therefore, the expansion of hypoxic and anoxic zones in the Chesapeake Bay under climate change as suggested by some climate models may favor the production of N2O, potentially providing positive feedback on warming. Overall, our study provides mechanistic constraints on N2O dynamics that could benefit modeling studies to better estimate the N2O flux in the Chesapeake Bay and other coastal environments.

Estuaries emit a large but highly uncertain amount of Nitrous oxide (N2O) into the atmosphere. To better understand N2O cycling processes in estuaries, we provide the first direct observations of N2O consumption in the seasonally anoxic Chesapeake Bay, the largest estuary in the United States. N2O consumption rates in anoxic waters reached up to 3.3 nmol L-1 d(-1) but were generally undetectable in oxygenated waters. However, N2O consumption rates were substantially enhanced when the oxygen concentration was experimentally decreased in initially oxygenated samples, indicating the potential of N2O consumption in oxygenated environments, for example, surface waters. These potential N2O consumption rates followed Michaelis-Menten kinetics as a function of increasing N2O substrate concentration. N2O-consuming microbes that predominantly contained the clade II nitrous oxide reductase gene were detected throughout the water column. These new observations of environmental controls on N2O consumption will benefit the modeling of N2O cycling and help to constrain the estuarine N2O flux.

The Plant Ontology (PO; ext-link-type="uri" xlink:href="http://www.plantontology.org/" xmlns:xlink="http://www.w3.org/1999/xlink">http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary ('ontology') of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to > 110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.

The disjunct temperate rainforests of the Pacific Northwest of North America (PNW) are characterized by late-successional dominant tree species Thuja plicata (western redcedar) and Tsuga heterophylia (western hemlock). The demographic histories of these species, along with the PNW rainforest ecosystem in its entirety, have been heavily impacted by geological and climatic changes the PNW has experienced over the last 5 million years, including mountain orogeny and repeated Pleistocene glaciations. These environmental events have ultimately shaped the history of these species, with inland populations potentially being extirpated during the Pleistocene glaciations. Here, we collect genomic data for both species across their ranges to test multiple demographic models, each reflecting a different phylogeographical hypothesis on how the ecosystem-dominating species may have responded to dramatic climatic change. Our results indicate that inland and coastal populations in both species diverged similar to 2.5 million years ago in the early Pleistocene and experienced decreases in population size during glacial cycles, with subsequent population expansion. Importantly, we found evidence for gene flow between coastal and inland populations during the mid-Holocene. It is likely that intermittent migration in these species during this time has prevented allopatric speciation via genetic drift alone. In conclusion, our results from combining genomic data and demographic inference procedures establish that populations of the ecosystem dominants Thuja plicata and Tsuga hetero-phyla persisted in refugia located in both the coastal and inland regions of the PNW throughout the Pleistocene, with populations expanding and contracting in response to glacial cycles with occasional gene flow.

We sought to assess effects of fragmentation and quantify the contribution of ecological processes to community assembly by measuring species richness, phylogenetic, and phenotypic diversity of species found in local and regional plant communities. Specifically, our fragmented system is Craters of the Moon National Monument and Preserve, Idaho, USA. CRMO is characterized by vegetated islands, kipukas, that are isolated in a matrix of lava. We used floristic surveys of vascular plants in 19 kipukas to create a local species list to compare traditional dispersion metrics, mean pairwise distance, and mean nearest taxon distance (MPD and MNTD), to a regional species list with phenotypic and phylogenetic data. We combined phylogenetic and functional trait data in a novel machine-learning model selection approach, Community Assembly Model Inference (CAMI), to infer probability associated with different models of community assembly given the data. Finally, we used linear regression to explore whether the geography of kipukas explained estimated support for community assembly models. Using traditional metrics of MPD and MNTD neutral processes received the most support when comparing kipuka species to regional species. Individually no kipukas showed significant support for overdispersion. Rather, five kipukas showed significant support for phylogenetic clustering using MPD and two kipukas using MNTD. Using CAMI, we inferred neutral and filtering models structured the kipuka plant community for our trait of interest. Finally, we found as species richness in kipukas increases, model support for competition decreases and lower elevation kipukas show more support for habitat filtering models. While traditional phylogenetic community approaches suggest neutral assembly dynamics, recently developed approaches utilizing machine learning and model choice revealed joint influences of assembly processes to form the kipuka plant communities. Understanding ecological processes at play in naturally fragmented systems will aid in guiding our understanding of how fragmentation impacts future changes in landscapes.