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Climate change models are predicting increased frequency and severity of droughts in arid and semiarid environments, and these areas are responsible for much of the world's livestock production. Because cattle (Bos Taurus) grazing can impact the abundance, distribution, and ecological function of native plant and animal communities, it is important to understand how cattle might respond to increasingly arid conditions. Here, we evaluate changes in habitat selection by cattle across an 8-yr period as a function of rainfall and other environmental covariates. Using resource selection functions, we evaluated habitat selection based on 2 behaviors, stationary and mobile. Models revealed similarity in cattle habitat selection across years, with only modest changes in selection as a function of precipitation, despite marked seasonal and interannual differences in rainfall. Cattle preferred gentle slopes, forest edges, wet meadows, and areas near water as well as areas far from water on plateaus. Cattle avoided areas at intermediate distances from water, typically associated with steep slopes. As conditions became drier during the late season, cattle did not switch selection patterns but instead contracted their selection around water. Cattle also selected similar habitats whether they were mobile or stationary, possibly making microsite decisions therein. This consistent pattern of selection across years could be particularly problematic for riparian communities as climates become drier; however, it may also simplify cattle management, as range managers can focus vegetation monitoring efforts on riparian areas. Due to the uncertainty surrounding future climatic conditions, it is imperative that both range and wildlife managers develop long-term plans to continue managing these multiuse landscapes in an ecologically sustainable manner based on expected patterns of livestock grazing.

The developmental and behavioral effects of prenatal exposure to cocaine and/or ethanol were examined in rats. Pregnant rats received ethanol (E; 2 g/kg, b.i.d.) orally, cocaine (C; 6 mg/kg/day, IV), or both (C/E) on gestational days 8-20. Controls consisted of pair-fed (PF) and untreated (UNT) groups. Offspring were weighed and examined for developmental markers beginning postnatal day one (PD1). On PD21 pups were individually observed in an open-field following either an injection of cocaine (10 mg/kg, IP), an injection of saline, or no treatment. Drug-treated and PF dams ate less food and gained less weight than the UNT dams. C and E litters had slightly increased mortality rates. Pups from both the C and E groups appeared less sensitive to the locomotor stimulant effect of cocaine. Pups from the E group engaged in significantly less spontaneous stereotypic locomotion than UNT and PF pups, while male pups from the C group exhibited a decrease in spontaneous exploratory behavior. Thus, prenatal exposure to C or E altered spontaneous and/or cocaine-induced behavior in weanling-aged rats, while the C/E combination did not augment either effect.

Abstract In developed countries, buildings are involved in almost 50% of total energy use and 30% of global green-house gas emissions. Buildings' operational energy is highly dependent on various building physical, operational, and functional characteristics, as well as meteorological and temporal properties. Besides physics-based building energy modeling, machine learning techniques can provide faster and higher accuracy estimates, given buildings' historic energy consumption data. Looking beyond individual building levels, forecasting buildings’ energy performance helps city and community managers have a better understanding of their future energy needs, and plan for satisfying them more efficiently. Focusing on an urban-scale, this study systematically reviews 70 journal articles, published in the field of building energy performance forecasting between 2015 and 2018. The recent literature have been categorized according to five criteria: 1. Learning Method, 2. Building Type, 3. Energy Type, 4. Input Data, and 5. Time-scale. The scarcity of building energy performance forecasting studies in urban-scale versus individual level is considerable. There is no study incorporating building functionality in terms of space functionality share percentages, nor assessing the effects of climate change on urban buildings energy performance using machine learning approaches and future weather scenarios. There is no optimal criteria combination for achieving the most accurate machine learning-based forecast, as there is no universal measure able to provide such global comparison. Accuracy levels are highly correlated with the characteristics of forecasting problems. The goal is to provide a comprehensive status of machine learning applications in urban building energy performance forecasting, during 2015–2018.

Abstract The growth and development of maize (Zea mays L.) largely depends on its nutrient uptake through the root. Hence, studying its growth, response, and associated metabolic reprogramming to stress conditions is becoming an important research direction. A genome-scale metabolic model (GSM) for the maize root was developed to study its metabolic reprogramming under nitrogen stress conditions. The model was reconstructed based on the available information from KEGG, UniProt, and MaizeCyc. Transcriptomics data derived from the roots of hydroponically grown maize plants were used to incorporate regulatory constraints in the model and simulate nitrogen-non-limiting (N+) and nitrogen-deficient (N−) condition. Model-predicted flux-sum variability analysis achieved 70% accuracy compared with the experimental change of metabolite levels. In addition to predicting important metabolic reprogramming in central carbon, fatty acid, amino acid, and other secondary metabolism, maize root GSM predicted several metabolites (l-methionine, l-asparagine, l-lysine, cholesterol, and l-pipecolate) playing a regulatory role in the root biomass growth. Furthermore, this study revealed eight phosphatidylcholine and phosphatidylglycerol metabolites which, even though not coupled with biomass production, played a key role in the increased biomass production under N-deficient conditions. Overall, the omics-integrated GSM provides a promising tool to facilitate stress condition analysis for maize root and engineer better stress-tolerant maize genotypes.

• Premise of the study: Climate change is associated with phenological shifts in an increasing number of organisms worldwide. However, accurate estimates of these shifts are dependent on long‐term data sets that include phenological observations from before annual average temperatures began to rise.• Methods: We compared the first flowering times of native prairie plants between 2007 and 2010 with historical data recorded by O. A. Stevens from 1910 to 1961. By merging climate variable data from the same time period, it also was possible to correlate first flowering dates with associated climate variables.• Key results: Over the past 100 years, spring temperatures in the Red River Valley near Fargo, North Dakota, USA, have increased, and growing seasons have lengthened significantly. Seventy‐five percent of the 178 species observed by Stevens had flowering times that were sensitive to at least one variable related to temperature or precipitation. Over the past 4 yr, 5% to 17% of the species observed have significantly shifted their first flowering time either earlier or later relative to the previous century.• Conclusions: The results of this study indicate that as spring temperatures in the northern Great Plains have increased and the growing season has lengthened, some spring flowering species have advanced their first flowering time, some fall species have delayed their first flowering, and some species have not changed. Given the importance of flowering timing for reproductive success, the changing climate in the Great Plains is expected to have long‐term ecological and evolutionary consequences for native plant species.

AbstractThe adoption of a bioenergy crop is affected by various factors, including but not limited to the characteristics of farmers, farm economics, market forces, and physical environment. This study develops a spatially explicit agent‐based model for ascertaining the adoption rate of carinata (Brassica carinata) among the farmers in the Little River Experimental Watershed located in the southern state of Georgia in the United States. Each farmer's adoption behavior is modeled using the profitability difference between traditional crop rotations (with and without carinata at different contract prices), the adoption rate of neighboring farmers, and their land allocation decisions from managing a risky portfolio of enterprises. Carinata production in the winter season once every 3 years has no conflict with the most profitable and popular traditional row crop rotations, such as cotton‐cotton‐cotton and cotton‐cotton‐peanut, to a larger extent. The results show that 28% and 85% of farmers in the watershed will adopt carinata after 33 years at a contract price of $13/bushel (bu) under two different assumptions of low (2.5%) and high (5%) initial neighborhood adoption rates. The proportions of land allocated to carinata to the total farmland under field crops are 38% and 85% after 33 years under the same low and high neighborhood adoption rates, respectively. Our results suggest that fixing the appropriate contract price of carinata will bring additional profits to farmers without any significant foreseeable agronomic risks, thereby increasing the adoption rate of carinata at a regional level.

Abstract Kenya has made considerable policy efforts to expand its renewable energy portfolio to meet energy demand and mitigate greenhouse gas (GHG) emissions. Development of proper policies requires a robust framework for analyzing the benefits of renewable energy investments. Towards this end, this study applied a choice experiment analysis to determine how attributes (type of energy, ownership, impact on environment, distance and visibility, community job creation, and yearly renewable energy tax) impact the public willingness to pay for renewable energy development in Kenya. A nationwide survey of 1020 households was conducted in nine counties using conditional logit (MNL) and random parameter logit (RPL) frameworks. The results reveal that the Kenyan public places a high value on environmental impact, followed by type of renewable energy and community job creation, respectively. On the other hand, respondents do not place much emphasis on ownership or distance and visibility. Policy simulation suggests that while renewable energy adoption is highly valued by households, the total willingness to pay is not enough to cover the higher capital cost for the development of various renewable energy technologies.

Integrated assessment models (IAMs) are playing an increasingly important role in long-run sustainability analysis. At their core is a set of global economic and environmental accounts which capture a complete set of inter-industry and inter-regional relationships in the global economy in a consistent manner. While much attention is focused on the raw data and parameterization required to expand or add sectoral detail to IAMs, only rarely is there discussion of how different matrix balancing methods (i.e. translating disparate raw data sources into the consistent database) affect modeling results. This article offers an in-depth look into the database–modeling nexus in IAMs, focusing on the electric power sector which is both a major source of CO2 emissions and a critical vehicle for climate change mitigation. Comparisons of the prevailing matrix balancing algorithms show how the choice of database reconciliation methodology affects modeling results using policy-relevant simulations in the context of the electric power sector. The resulting insights can be applied to the disaggregation of other, technology rich sectors in the economy. We conclude that appropriate selection of database reconciliation methodologies can reduce the deviation between bottom-up and top-down modeling.

AbstractIn Laurentian Great Lakes coastal wetlands (GLCWs), dominant emergent invasive plants are expanding their ranges and compromising the unique habitat and ecosystem service values that these ecosystems provide. Herbiciding and burning to control invasive plants have not been effective in part because neither strategy addresses the most common root cause of invasion, nutrient enrichment. Mechanical harvesting is an alternative approach that removes tissue‐bound phosphorus and nitrogen and can increase wetland plant diversity and aquatic connectivity between wetland and lacustrine systems. In this study, we used data from three years of Great Lakes‐wide wetland plant surveys, published literature, and bioenergy analyses to quantify the overall areal extent of GLCWs, the extent and biomass of the three most dominant invasive plants, the pools of nitrogen and phosphorus contained within their biomass, and the potential for harvesting this biomass to remediate nutrient runoff and produce renewable energy. Of the approximately 212,000 ha of GLCWs, three invasive plants (invasive cattail, common reed, and reed canary grass) dominated 76,825 ha (36%). The coastal wetlands of Lake Ontario exhibited the highest proportion of invasive dominance (57%) of any of the Great Lakes, primarily from cattail. A single growing season's biomass of these invasive plants across all GLCWs was estimated at 659,545 metric tons: 163,228 metric tons of reed canary grass, 270,474 metric tons of common reed, and 225,843 metric tons of invasive cattail, and estimated to contain 10,805 and 1144 metric tons of nitrogen and phosphorus, respectively. A one‐time harvest and utilization for energy of this biomass would provide the gross equivalent of 1.8 million barrels of oil if combusted, or 0.9 million barrels of oil if converted to biogas in an anaerobic digester. We discuss the potential for mitigating non‐point source nutrient pollution with invasive wetland plant removal, and other potential uses for the harvested biomass, including compost and direct application to agricultural soils. Finally, we describe the research and adaptive management program we have built around this concept, and point to current limitations to the implementation of large‐scale invasive plant harvesting.