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Atmospheric methane (CH4) is a potent greenhouse gas, and its mole fraction has more than doubled since the preindustrial era. Fossil fuel extraction and use are among the largest anthropogenic sources of CH4 emissions, but the precise magnitude of these contributions is a subject of debate. Carbon-14 in CH4 (14CH4) can be used to distinguish between fossil (14C-free) CH4 emissions and contemporaneous biogenic sources; however, poorly constrained direct 14CH4 emissions from nuclear reactors have complicated this approach since the middle of the 20th century. Moreover, the partitioning of total fossil CH4 emissions (presently 172 to 195 teragrams CH4 per year) between anthropogenic and natural geological sources (such as seeps and mud volcanoes) is under debate; emission inventories suggest that the latter account for about 40 to 60 teragrams CH4 per year. Geological emissions were less than 15.4 teragrams CH4 per year at the end of the Pleistocene, about 11,600 years ago, but that period is an imperfect analogue for present-day emissions owing to the large terrestrial ice sheet cover, lower sea level and extensive permafrost. Here we use preindustrial-era ice core 14CH4 measurements to show that natural geological CH4 emissions to the atmosphere were about 1.6 teragrams CH4 per year, with a maximum of 5.4 teragrams CH4 per year (95 per cent confidence limit)—an order of magnitude lower than the currently used estimates. This result indicates that anthropogenic fossil CH4 emissions are underestimated by about 38 to 58 teragrams CH4 per year, or about 25 to 40 per cent of recent estimates. Our record highlights the human impact on the atmosphere and climate, provides a firm target for inventories of the global CH4 budget, and will help to inform strategies for targeted emission reductions.

Improving Deliberative Environmental Management Under Uncertainty examined similarities and differences between expert and public understanding of uncertainty. This collection directly compares expert and layperson interpretations and understandings of different expressions of uncertainty, in the context of evaluating the consequences of proposed environmental management actions that influence economic, social, or health concerns. Data were collected via a Web-based survey where respondents were asked a series of questions after they were given four hypothetical scenarios on the following topics: wind farms, vegetation management, superfund site, and salmon. Each scenario described an environmental proposal along with pros and cons then respondents selected a response option with costs and benefits of the proposal in mind. The first scenario focused on a plan to manage forest vegetation in the northeastern United States, using either conventional methods involving aerial spraying of herbicides or more expensive hand spraying methods intended to reduce adverse impacts on local moose populations. The second scenario focused on a proposal to build a new windfarm in a western state, which would lower electricity rates to local communities but could have negative effects on resident songbird populations. The third scenario focused on a plan to clean up hazardous waste at a large industrial Superfund site. The waste was estimated to have caused 200 children to develop serious respiratory illness from exposure to contaminated drinking water; building a decontamination facility would reduce the number of sick children but would be very expensive and would take time to build. The fourth scenario focused on a plan to reduce the declining population of Chinook Salmon. In order to reduce the Chinook Salmon declines in the Seshon River, an advisory committee must find a balance between the protection of salmon and the use of water to generate electricity, which is a cause in salmon reduction. Participants responded to hypothetical but realistic scenarios involving trade-offs between options presented and other objectives, and were asked a series of questions about their comprehension of the uncertainty information, their preferred choice among the alternatives, and the associated difficulty and amount of effort. Respondents were asked general questions which ranged from how they felt about a particular issue to how easy or difficult it was to answer the questions associated with each scenario. Demographic information includes gender, age and education level. Public Sample: Nationally representative, convienence sample of Decision Research web-panel participants located throughout the United States. Expert Sample: Web site organized by United States Fish and Wildlife Services (USFWS) for employees who have undertaken some previous training in resource management and decision-making. Please refer to Original P.I. Documentation in the ICPSR Codebook for further information on sampling. Response Rates: The response rate for the public is 95 percent. The response rate for the expert sample is 27 percent. Please refer to the Original P.I. Documentation in the ICPSR Codebook for further information on response rates. web-based surveySpecial collaborators for Improving Deliberative Environmental Management Under Uncertainty, 2009-2010, include Nathan Dieckmann and Ellen Peters. Please refer to the Original P.I. Documentation in the ICPSR Codebook for further information on study design. Datasets: DS1: Improving Deliberative Environmental Management Under Uncertainty, 2009-2010 Decision Research Web-panel participants located throughout the United States. Presence of Common Scales: Two 10-item Numeracy Scales This collection contains 113 variables. none

Analyses of ancient food webs reveal important paleoecological processes and responses to a range of perturbations throughout Earth’s history, such as climate change. These responses can inform our forecasts of future biotic responses to similar perturbations. However, previous analyses of ancient food webs rarely accounted for key differences between modern and ancient community data, particularly selective loss of soft-bodied taxa during fossilization. To consider how fossilization impacts inferences of ancient community structure we (1) analyzed node-level attributes to identify correlations between ecological roles and fossilization potential and (2) applied selective information loss procedures to food web data for extant systems. We found that selective loss of soft-bodied organisms has predictable effects on the trophic structure of “artificially fossilized” food webs, because these organisms occupy unique, consistent food web positions. Fossilized food webs misleadingly appear less stable (i.e., more prone to trophic cascades), with less predation and an overrepresentation of generalist consumers. We also found that ecological differences between soft- and hard-bodied taxa—indicated by distinct positions in modern food webs—are recorded in an Early Eocene web, but not in Cambrian webs. This suggests that ecological differences between the groups have existed for ≥ 48 million years. Our results indicate that accounting for soft-bodied taxa is vital for accurate depictions of ancient food webs. However, the consistency of information loss trends across the analyzed food webs means it is possible to predict how the selective loss of soft-bodied taxa affects food web metrics, which can permit better modeling of ancient communities. Repository Contents: Supplementary Information: Containing Supplementary Text, Figures, Tables, and Data descriptions. Supplementary Data 1: Food web data (adjacency matrices and metadata; see publication; see Related Works). Supplementary Data 2: Additional references consulted for preservation group assignments. Supplementary Data 3: Data and R scripts to recreate analyses from this study. S3_AllWebTaxonomy_updated_200903.csv: Taxonomy data for all food web nodes. S3_AnalysisOfTaxonomicRanks.csv: Lowest taxonomic rank for each node. S3_MainFigures_CaimanComparison.R: Compare the three food webs contained in (Roopnarine and Hertog 2013). S3_MainFigures_ComparisonFunctions.R: Functions for calculating metrics and generating trophic species webs. S3_MainFigures_FossilizationFunctions.R: Functions for artificially fossilizing networks. S3_MainFigures_Setup_200826.R: Setup to import food webs. S3_MainFigures_Code.R: Code to apply functions. S3_pbdb_data_200504.csv: Data from the Paleobiology Database, excluding Lagerstätten (see publication). S3_PresGrAssignments_updated_200902.csv: Preservation group assignments for all nodes. Fossil faunal lists were downloaded from the PBDB on 17th January 2020. Any data processing steps are shown in R Scripts and described in publication. Paleobiology Database is licensed under a CC BY 4.0 International License. https://creativecommons.org/licenses/by/4.0/. We analyzed food webs for four modern marine systems, one modern freshwater system, two ancient marine systems, and one ancient lake system from previous publications. All webs have similar, broad higher-rank taxonomic compositions and contain at least 85 nodes (the size of the smallest ancient network considered). For comparisons with ancient diversity, we downloaded fossil occurrences from the Paleobiology Database (PBDB) on 17th January 2020. 

Ambient conditions shape microbiome responses to both short- and long-duration environment changes through processes including physiological acclimation, compositional shifts, and evolution. Thus, we predict that microbial communities inhabiting locations with larger diel, episodic, and annual variability in temperature and pH should be less sensitive to shifts in these climate-change factors. To test this hypothesis, we compared responses of surface ocean microbes from more variable (nearshore) and more constant (offshore) sites to short-term factorial warming (+3 °C) and/or acidification (pH -0.3). In all cases, warming alone significantly altered microbial community composition, while acidification had a minor influence. Compared with nearshore microbes, warmed offshore microbiomes exhibited larger changes in community composition, phylotype abundances, respiration rates, and metatranscriptomes, suggesting increased sensitivity of microbes from the less-variable environment. Moreover, while warming increased respiration rates, offshore metatranscriptomes yielded evidence of thermal stress responses in protein synthesis, heat shock proteins, and regulation. Future oceans with warmer waters may enhance overall metabolic and biogeochemical rates, but they will host altered microbial communities, especially in relatively thermally stable regions of the oceans. In order to allow full comparability with other ocean acidification data sets, the R package seacarb (Gattuso et al, 2019) was used to compute a complete and consistent set of carbonate system variables, as described by Nisumaa et al. (2010). In this dataset the original values were archived in addition with the recalculated parameters (see related PI). The date of carbonate chemistry calculation by seacarb is 2020-10-20.

The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Lo��ve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.

AbstractBillions of years ago, the Earth's waters were dominated by cyanobacteria. These microbes amassed to such formidable numbers, they ushered in a new era—starting with the Great Oxidation Event—fuelled by oxygenic photosynthesis. Throughout the following eon, cyanobacteria ceded portions of their global aerobic power to new photoautotrophs with the rise of eukaryotes (i.e. algae and higher plants), which co‐existed with cyanobacteria in aquatic ecosystems. Yet while cyanobacteria's ecological success story is one of the most notorious within our planet's biogeochemical history, scientists to this day still seek to unlock the secrets of their triumph. Now, the Anthropocene has ushered in a new era fuelled by excessive nutrient inputs and greenhouse gas emissions, which are again reshaping the Earth's biomes. In response, we are experiencing an increase in global cyanobacterial bloom distribution, duration, and frequency, leading to unbalanced, and in many instances degraded, ecosystems. A critical component of the cyanobacterial resurgence is the freshwater‐marine continuum: which serves to transport blooms, and the toxins they produce, on the premise that “water flows downhill”. Here, we identify drivers contributing to the cyanobacterial comeback and discuss future implications in the context of environmental and human health along the aquatic continuum. This Minireview addresses the overlooked problem of the freshwater to marine continuum and the effects of nutrients and toxic cyanobacterial blooms moving along these waters. Marine and freshwater research have historically been conducted in isolation and independently of one another. Yet, this approach fails to account for the interchangeable transit of nutrients and biology through and between these freshwater and marine systems, a phenomenon that is becoming a major problem around the globe. This Minireview highlights what we know and the challenges that lie ahead.

As residential photovoltaic (PV) system installations continue to increase rapidly, utilities need to identify the locations of these new components to manage the unconventional two-way power flow and maintain sustainable management of distribution grids. But historical records are unreliable and constant re-assessment of active residential PV locations is resource intensive. To resolve these issues, we propose to model the solar detection problem in a machine learning set up based on labeled data, e.g., supervised learning. However, the challenge for most utilities is limited labels or labels on only one type of users. Therefore, we design new semi-supervised learning and one-class classification methods based on autoencoders, which greatly improve the nonlinear data representation of human behavior and solar behavior. The proposed methods have been tested and validated not only on synthetic data based on a publicly available dataset, but also on real-world data from utility partners. The numerical results show robust detection accuracy, laying down the foundation for managing distributed energy resources in distribution grids.

This work introduces statistical models for the energy harvested from the in-home hybrid power line-wireless channel in the frequency band from 0 to 100 MHz. Based on numerical analyses carried out over the data set obtained from a measurement campaign together with the use of the maximum likelihood value criterion and the adoption of five distinct power masks for power allocation, it is shown that the log-normal distribution yields the best model for the energies harvested from the free-of-noise received signal and from the additive noise in this setting. Additionally, the total harvested energy can be modeled as the sum of these two statistically independent random variables. Thus, it is shown that the energies harvested from this kind of hybrid channel is an easy-to-simulate phenomenon when carrying out research related to energy-efficient and self-sustainable networks.