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In Iceland, there is a large variation in daylight between summer and winter. The aim of the study was to identify how this large variation influences physical activity (PA) and sedentary behavior (SB). Free living PA was measured by a waist-worn accelerometer for one week during waking hours in 138 community-dwelling older adults (61.1% women, 80.3 ± 4.9 years) during summer and winter months. In general, SB occupied about 75% of the registered wear-time and was highly correlated with age (β = 0.36). Although the differences were small, more time was spent during the summer in all PA categories, except for the moderate-to-vigorous PA (MVPA), and SB was reduced. More lifestyle PA (LSPA) was accumulated in ≥5-min bouts during summer than winter, especially among highly active participants. This information could be important for policy makers and health professionals working with older adults. Accounting for seasonal difference is necessary in analyzing SB and PA data.

Oceanic crust is the largest potential habitat for life on Earth and may contain a significant fraction of Earth's total microbial biomass; yet, empirical analysis of reaction rates in basaltic crust is lacking. Here we report the first assessment of oxygen consumption in young (~8 Ma) and cool (<25 °C) basaltic crust, which we calculate from modelling dissolved oxygen and strontium pore water gradients in basal sediments collected during Integrated Ocean Drilling Program Expedition 336 to 'North Pond' on the western flank of the Mid-Atlantic Ridge. Dissolved oxygen is completely consumed within the upper to middle section of the sediment column, with an increase in concentration towards the sediment-basement interface, indicating an upward supply from oxic fluids circulating within the crust. A parametric reaction transport model of oxygen behaviour in upper basement suggests oxygen consumption rates of 1 nmol cm(-3)ROCK d(-1) or less in young and cool basaltic crust.

Abstract Climate change is altering disturbance regimes and recovery rates of forests globally. The future of these forests will depend on how climate change interacts with management activities. Forest managers are in critical need of strategies to manage the effects of climate change. We co‐designed forest management scenarios with forest managers and stakeholders in the Klamath ecoregion of Oregon and California, a seasonally dry forest in the Western US subject to fire disturbances. The resultant scenarios span a broad range of forest and fire management strategies. Using a mechanistic forest landscape model, we simulated the scenarios as they interacted with forest growth, succession, wildfire disturbances and climate change. We analysed the simulations to (a) understand how scenarios affected the fire regime and (b) estimate how each scenario altered potential forest composition. Within the simulation timeframe (85 years), the scenarios had a large influence on fire regimes, with fire rotation periods ranging from 60 years in a minimal management scenario to 180 years with an industrial forestry style management scenario. Regardless of management strategy, mega‐fires (>100,000 ha) are expected to increase in frequency, driven by stronger climate forcing and extreme fire weather. High elevation conifers declined across all climate and management scenarios, reflecting an imbalance between forest types, climate and disturbance. At lower elevations (<1,800 m), most scenarios maintained forest cover levels; however, the minimal intervention scenario triggered 5 × 105 ha of mixed conifer loss by the end of the century in favour of shrublands, whereas the maximal intervention scenario added an equivalent amount of mixed conifer. Policy implications. Forest management scenarios that expand beyond current policies—including privatization and aggressive climate adaptation—can strongly influence forest trajectories despite a climate‐enhanced fire regime. Forest management can alter forest trajectories by increasing the pace and scale of actions taken, such as fuel reduction treatments, or by limiting other actions, such as fire suppression.

AbstractAverage power losses due to wind turbine wakes are of the order of 10 to 20% of total power output in large offshore wind farms. Accurately quantifying power losses due to wakes is, therefore, an important part of overall wind farm economics. The focus of this research is to compare different types of models from computational fluid dynamics (CFD) to wind farm models in terms of how accurately they represent wake losses when compared with measurements from offshore wind farms. The ultimate objective is to improve modelling of flow for large wind farms in order to optimize wind farm layouts to reduce power losses due to wakes and loads.The research presented is part of the EC‐funded UpWind project, which aims to radically improve wind turbine and wind farm models in order to continue to improve the costs of wind energy. Reducing wake losses, or even reduce uncertainties in predicting power losses from wakes, contributes to the overall goal of reduced costs. Here, we assess the state of the art in wake and flow modelling for offshore wind farms, the focus so far has been cases at the Horns Rev wind farm, which indicate that wind farm models require modification to reduce under‐prediction of wake losses while CFD models typically over‐predict wake losses. Further investigation is underway to determine the causes of these discrepancies. Copyright © 2009 John Wiley & Sons, Ltd.

AbstractThe regional variability in tundra and boreal carbon dioxide (CO2) fluxes can be high, complicating efforts to quantify sink‐source patterns across the entire region. Statistical models are increasingly used to predict (i.e., upscale) CO2 fluxes across large spatial domains, but the reliability of different modeling techniques, each with different specifications and assumptions, has not been assessed in detail. Here, we compile eddy covariance and chamber measurements of annual and growing season CO2 fluxes of gross primary productivity (GPP), ecosystem respiration (ER), and net ecosystem exchange (NEE) during 1990–2015 from 148 terrestrial high‐latitude (i.e., tundra and boreal) sites to analyze the spatial patterns and drivers of CO2 fluxes and test the accuracy and uncertainty of different statistical models. CO2 fluxes were upscaled at relatively high spatial resolution (1 km2) across the high‐latitude region using five commonly used statistical models and their ensemble, that is, the median of all five models, using climatic, vegetation, and soil predictors. We found the performance of machine learning and ensemble predictions to outperform traditional regression methods. We also found the predictive performance of NEE‐focused models to be low, relative to models predicting GPP and ER. Our data compilation and ensemble predictions showed that CO2 sink strength was larger in the boreal biome (observed and predicted average annual NEE −46 and −29 g C m−2 yr−1, respectively) compared to tundra (average annual NEE +10 and −2 g C m−2 yr−1). This pattern was associated with large spatial variability, reflecting local heterogeneity in soil organic carbon stocks, climate, and vegetation productivity. The terrestrial ecosystem CO2 budget, estimated using the annual NEE ensemble prediction, suggests the high‐latitude region was on average an annual CO2 sink during 1990–2015, although uncertainty remains high.

Nonlinear power flow constraints render a variety of power system optimization problems computationally intractable. Emerging research shows, however, that the nonlinear AC power flow equations can be successfully modeled using Neural Networks (NNs). These NNs can be exactly transformed into Mixed Integer Linear Programs (MILPs) and embedded inside challenging optimization problems, thus replacing nonlinearities that are intractable for many applications with tractable piecewise linear approximations. Such approaches, though, suffer from an explosion of the number of binary variables needed to represent the NN. Accordingly, this paper develops a technique for training an "optimally compact" NN, i.e., one that can represent the power flow equations with a sufficiently high degree of accuracy while still maintaining a tractable number of binary variables. We show that the resulting NN model is more expressive than both the DC and linearized power flow approximations when embedded inside of a challenging optimization problem (i.e., the AC unit commitment problem). added acknowledgement, first two authors equally contributed, 8 pages, 3 figures, 1 table

AbstractBiological effects of climate change are expected to vary geographically, with a strong signature of latitude. For ectothermic animals, there is systematic latitudinal variation in the relationship between climate and thermal performance curves, which describe the relationship between temperature and an organism's fitness. Here, we ask whether these documented latitudinal patterns can be generalized to predict arthropod responses to warming across mid‐ and high temperate latitudes, for taxa whose thermal physiology has not been measured. To address this question, we used a novel natural experiment consisting of a series of urban warming gradients at different latitudes. Specifically, we sampled arthropods from a single common street tree species across temperature gradients in four US cities, located from 35.8 to 42.4° latitude. We captured 6746 arthropods in 34 families from 111 sites that varied in summer average temperature by 1.7–3.4 °C within each city. Arthropod responses to warming within each city were characterized as Poisson regression coefficients describing change in abundance per °C for each family. Family responses in the two midlatitude cities were heterogeneous, including significantly negative and positive effects, while those in high‐latitude cities varied no more than expected by chance within each city. We expected high‐latitude taxa to increase in abundance with warming, and they did so in one of the two high‐latitude cities; in the other, Queens (New York City), most taxa declined with warming, perhaps due to habitat loss that was correlated with warming in this city. With the exception of Queens, patterns of family responses to warming were consistent with predictions based on known latitudinal patterns in arthropod physiology relative to regional climate. Heterogeneous responses in midlatitudes may be ecologically disruptive if interacting taxa respond oppositely to warming.