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At least 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

AESD (Advanced Energy System Design) Records API [SWRs-17-50, 17-51, 17-52]

The Records API (application program interface) for Advanced Energy System Design (AESD) enables software that serves multidimensional record-oriented data to interoperate with software than uses such data. In the context of the Records API, multidimensional data records are simply tuples of real numbers, integers, and character strings, where each data value is tagged by a variable name, according to a pre-defined schema, and each record is assigned a unique integer identifier. Conceptually, these records are isomorphic to rows in a relational database, JSON objects, or key-value maps. Records servers might supply static datasets, sensor measurements that periodically update as new telemetry become available, or the results of simulations as the simulations generate new output. Records client software might display or analyze the data, but in the case of simulations the client request the creation of new ensembles for specified input parameters. It is also possible to chain records clients and servers together so that a client consuming data from a server might transform that data and serve it to additional clients. This minimalist API avoids imposing burdensome metadata, structural, or implementation requirements on developers by relying on open-source technologies that are readily available for common programming languages. In particular, the API has been designed to place the least possible burden on services that provide data. This document defines the message format for the Records API, a transport mechanism for communicating the data, and the semantics for interpreting it. The message format is specified as Google Protocol Buffers and the transport mechanism uses WebSockets. We discuss three major use cases for serving and consuming records data: (i) static data, (ii) dynamically augmented data, (iii) on-demand simulations, (iv) with filters, and (v) with bookmarks. Separate implementations of the API exist in C++, Haskell, JavaScript, Python, and R.

Bush, Brian↗
DOE OSTI2014

Open Smart Energy Gateway (OpenSEG)

The Open Smart Energy Gateway (OpenSEG) aims to provide near-real time smart meter data to consumers without the delays or latencies associated with it being transported to the utility data center and then back to the consumer's application. To do this, the gateway queries the local Smart Meter to which it is bound to get energy consumption information at pre-defined intervals (minimum interval is 4 seconds). OpenSEG then stores the resulting data internally for retrieval by an external application.

McParland, Charles↗
DOE OSTI2023

LowFive v1.0

LowFive is a new data transport layer based on the HDF5 data model, for in situ workflows. Executables using LowFive can communicate in situ (using in-memory data and MPI message passing), reading and writing traditional HDF5 files to physical storage, and combining the two modes. Minimal and often no source-code modification is needed for programs that already use HDF5. LowFive maintains deep copies or shallow references of datasets, configurable by the user. More than one task can produce (write) data, and more than one task can consume (read) data, accommodating fan-in and fan-out in the workflow task graph. LowFive supports data redistribution from n producer processes to m consumer processes.

Morozov, Dmitriy↗
DOE OSTI2024

PV Generation and Load Forecasting for Adjuntas PR Community Microgrids

Existing frameworks to forecast time-series photovoltaic (PV) output power and consumer load for microgrid operations and controls assume a near-continuous availability of real-time input features from the field assets such as PV inverters, energy meters, and weather station. These incoming data points are used to periodically retrain models and update forecast snapshots over a moving horizon window, be it one hour-ahead, one-day ahead, or one-week ahead. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. Hence, there is a need for programs that assume no availability of real-time microgrid asset data and still make reliable forecasts that can be used for decision-making. Such programs would be apt to function in extreme weather events such as hurricanes and would use lightweight recursive time-series models to independently forecast solar irradiance and ambient temperature, then compute PV power from those forecasts, as well as independently forecast consumer load. The codebase performs forecasting for the scenario of when the microgrid does not have a reliable access to forecasts or real-time observations of solar irradiance (I) and ambient temperature (AT) and load (Load) to be able to adequately forecast, in real-time, the PV power production or a business' load. In this case, using historical values of PV power and load, a univariate forecasting of generation and consumption are respectively made. The use-case in particular has two sub-scenarios: one, a normal 7-day ahead forecast where the unavailability of real-time data is assumed due to infrastructure issues such as loss of communication or sensor maintenance or service downtimes. Whereas a hurricane-caused unavailability of real-time data requires a second model trained specifically on historical hurricane days to be able to capture the extreme day behavior of generation in particular, and load if applicable. A gradient boosted regression tree comprises an ensemble of additive models that map between the input of historical values (be it irradiance, temperature, or load) and their corresponding output forecasts of a given horizon such that the individual learner predictions are summed up over the total number of such learners in the ensemble to produce an aggregate forecast. A weighting mechanism is applied to the training data in each iteration, where actual and forecast values are compared to penalize incorrect forecasts by increasing the weight and reducing it to reward correct forecasts. The code's benefits are that it: (a) accounts for a contingency where communication loss renders newly measured real-time data unavailable for model tuning and snapshot updates; (b) presents blind forecasting that recursively determines the next time-step value in a horizon using the forecast of the same attribute from a prior step; and (c) employs lightweight models that, once trained, can reliably generalize for different horizons, which make them suitable for enhancing the resilience of field microgrids prone to extreme events that encounter disruptions to data availability.

Sundararajan, Aditya [Oak Ridge National Laborator↗
DOE OSTI2018

Trip Energy Estimation Methodology and Model Based on Real-World Driving Data for Green-Routing Applications

A data-informed model to predict energy use for a proposed vehicle trip has been developed in this paper. The methodology leverages roughly one million miles of real-world driving data to generate the estimation model. Driving is categorized at the sub-trip level by average speed, road gradient, and road network geometry, then aggregated by category. An average energy consumption rate is determined for each category, creating an energy rate look-up table. Proposed vehicle trips are then categorized in the same manner, and estimated energy rates are appended from the look-up table. The methodology is robust and applicable to a wide range of driving data. The model has been trained on vehicle travel profiles from the Transportation Secure Data Center at the National Renewable Energy Laboratory and validated against on-road fuel consumption data from testing in Phoenix, Arizona. When compared against the detailed on-road conventional vehicle fuel consumption test data, the energy estimation model accurately predicted which route would consume less fuel over a dozen different tests. When compared against a larger set of real-world origin-destination pairs, it is estimated that implementing the present methodology should accurately select the route that consumes the least fuel 90% of the time. The model results can be used to inform control strategies in routing tools, such as change in departure time, alternate routing, and alternate destinations to reduce energy consumption. This work provides a highly extensible framework that allows the model to be tuned to a specific driver or vehicle type.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Children, Income, and the Impact of Home Delivery on Household Shopping Trips

Expanding e-commerce and delivery benefit consumers through increased flexibility and convenience. However, there is a potential impact on vehicle miles traveled (VMT) by delivery and personal vehicles, and the resulting energy consumption, air quality, and congestion. Delivery trips could replace personal vehicle trips, but, if not a replacement, could add to (or supplement) shopping-related VMT for a given household. Here, we examined the benefits of e-commerce to consumers and the impact on personal shopping trips, and how these differed across item types, household child status, and income. We found that high-income households and households with children cared relatively more about time saving from deliveries. We found that, on average, deliveries substituted for 12% of vehicle shopping trips, but supplemented personal shopping trips for 9% of purchase deliveries. Underlying these averages were two main types of households: those for whom all deliveries substitute for trips (between 55% and 70% of households) and those for whom all deliveries supplement trips (between 20% and 35% of households). There was significant heterogeneity across households with and without children and with high or low incomes with respect to the use of delivery. Although time savings were more likely to motivate higher-income households and households with children to use delivery, this did not translate through to these households substituting for more of their trips; deliveries of prepared meals for both these categories of household were relatively more likely (15% for households with children, and 12% for higher-income households) to supplement, and not substitute for, personal trips.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Resiliency in numerical algorithm design for extreme scale simulations

Here this work is based on the seminar titled ‘Resiliency in Numerical Algorithm Design for Extreme Scale Simulations’ held March 1–6, 2020, at Schloss Dagstuhl, that was attended by all the authors. Advanced supercomputing is characterized by very high computation speeds at the cost of involving an enormous amount of resources and costs. A typical large-scale computation running for 48 h on a system consuming 20 MW, as predicted for exascale systems, would consume a million kWh, corresponding to about 100k Euro in energy cost for executing 10 23 floating-point operations. It is clearly unacceptable to lose the whole computation if any of the several million parallel processes fails during the execution. Moreover, if a single operation suffers from a bit-flip error, should the whole computation be declared invalid? What about the notion of reproducibility itself: should this core paradigm of science be revised and refined for results that are obtained by large-scale simulation? Naive versions of conventional resilience techniques will not scale to the exascale regime: with a main memory footprint of tens of Petabytes, synchronously writing checkpoint data all the way to background storage at frequent intervals will create intolerable overheads in runtime and energy consumption. Forecasts show that the mean time between failures could be lower than the time to recover from such a checkpoint, so that large calculations at scale might not make any progress if robust alternatives are not investigated. More advanced resilience techniques must be devised. The key may lie in exploiting both advanced system features as well as specific application knowledge. Research will face two essential questions: (1) what are the reliability requirements for a particular computation and (2) how do we best design the algorithms and software to meet these requirements? While the analysis of use cases can help understand the particular reliability requirements, the construction of remedies is currently wide open. One avenue would be to refine and improve on system- or application-level checkpointing and rollback strategies in the case an error is detected. Developers might use fault notification interfaces and flexible runtime systems to respond to node failures in an application-dependent fashion. Novel numerical algorithms or more stochastic computational approaches may be required to meet accuracy requirements in the face of undetectable soft errors. These ideas constituted an essential topic of the seminar. The goal of this Dagstuhl Seminar was to bring together a diverse group of scientists with expertise in exascale computing to discuss novel ways to make applications resilient against detected and undetected faults. In particular, participants explored the role that algorithms and applications play in the holistic approach needed to tackle this challenge. This article gathers a broad range of perspectives on the role of algorithms, applications and systems in achieving resilience for extreme scale simulations. The ultimate goal is to spark novel ideas and encourage the development of concrete solutions for achieving such resilience holistically.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Verification of diesel spray ignition phenomenon in dual-fuel diesel-piloted premixed natural gas engine

Dual-fuel (DF) engines, in which premixed natural gas and air in an open-type combustion chamber is ignited by diesel-fuel pilot sprays, have been more popular for marine use than pre-chamber spark ignition (PCSI) engines because of their superior durability. However, control of ignition and combustion in DF engines is more difficult than in PCSI engines. In this context, here we focus on the ignition stability of n-heptane pilot-fuel jets injected into a compressed premixed charge of natural gas and air at low-load conditions. To aid understanding of the experimental data, chemical-kinetics simulations were carried out in a simplified engine-environment that provided insight into the chemical effects of methane (CH4) on pilot-fuel ignition. The simulations reveal that CH4 has an effect on both stages of n-heptane autoignition: the small, first-stage, cool-flame-type, low-temperature ignition (LTI) and the larger, second-stage, high-temperature ignition (HTI). As the ratio of pilot-fuel to CH4 entrained into the spray decreases, the initial oxidization of CH4 consumes the OH radicals produced by pilot-fuel decomposition during LTI, thereby inhibiting its progression to HTI. Using imaging diagnostics, the spatial and temporal progression of LTI and HTI in DF combustion are measured in a heavy-duty optical engine, and the imaging data are analyzed to understand the cause of severe fluctuations in ignition timing and combustion completeness at low-load conditions. Images of cool-flame and hydroxyl radical (OH*) chemiluminescence serve as indicators of LTI and HTI, respectively. The cycle-to-cycle and spatial variation in ignition extracted from the imaging data are used as key metrics of comparison. The imaging data indicate that the local concentration of the pilot-fuel and the richness of the surrounding natural-gas air mixture are important for LTI and HTI, but in different ways. In particular, higher injection pressures and shorter injection durations increase the mixing rate, leading to lower concentrations of pilot-fuel more quickly, which can inhibit HTI even as LTI remains relatively robust. Decreasing the injection pressure from 80 MPa to 40 MPa and increasing the injection duration from 500 µs to 760 µs maintained constant pilot-fuel mass, while promoting robust transition from LTI to HTI by effectively slowing the mixing rate. This allows enough residence time for the OH radicals, produced by the two-stage ignition chemistry of the pilot-fuel, to accelerate the transition from LTI to HTI before being consumed by CH4 oxidation. Thus from a practical perspective, for a premixed natural gas fuel–air equivalence-ratio, it is possible to improve the “stability” of the combustion process by solely manipulating the pilot-fuel injection parameters while maintaining constant mass of injected pilot-fuel. This allows for tailoring mixing trajectories to offset changes in fuel ignition chemistry, so as to promote a robust transition from LTI to HTI by changing the balance between the local concentration of the pilot-fuel and richness of the premixed natural gas and air. This could prove to be a valuable tool for combustion design to improve fuel efficiency or reduce noise or perhaps even reduce heat-transfer losses by locating early combustion away from in-cylinder walls.

42 ENGINEERING↗
DOE OSTI2015

Cilantro microbiome before and after nonselective pre-enrichment for Salmonella using 16S rRNA and metagenomic sequencing

Background: Salmonella enterica is a common cause of foodborne gastroenteritis in the United States and is associated with outbreaks in fresh produce such as cilantro. Salmonella culture-based detection methods are complex and time consuming, and improvements to increase detection sensitivity will benefit consumers. In this study, we used 16S rRNA sequencing to determine the microbiome of cilantro. We also investigated changes to the microbial community prior to and after a 24-hour nonselective pre-enrichment culture step commonly used by laboratory analysts to resuscitate microorganisms in foods suspected of contamination with pathogens. Cilantro samples were processed for Salmonella detection according to the method in the United States Food and Drug Administration Bacteriological Analytical Manual. Genomic DNA was extracted from culture supernatants prior to and after a 24-hour nonselective pre-enrichment step and 454 pyrosequencing was performed on 16S rRNA amplicon libraries. A database of Enterobacteriaceae 16S rRNA sequences was created, and used to screen the libraries for Salmonella, as some samples were known to be culture positive. Additionally, culture positive cilantro samples were examined for the presence of Salmonella using shotgun metagenomics on the Illumina MiSeq. Results: Time zero uncultured samples had an abundance of Proteobacteria while the 24-hour enriched samples were composed mostly of Gram-positive Firmicutes. Shotgun metagenomic sequencing of Salmonella culture positive cilantro samples revealed variable degrees of Salmonella contamination among the sequenced samples. Conclusions: Our cilantro study demonstrates the use of high-throughput sequencing to reveal the microbiome of cilantro, and how the microbiome changes during the culture-based protocols employed by food safety laboratories to detect foodborne pathogens. Finding that culturing the cilantro shifts the microbiome to a predominance of Firmicutes suggests that changing our culture-based methods will improve detection sensitivity for foodborne enteric pathogens

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Data for Complete and Efficient Conversion of Plant Cell Wall Hemicellulose into High-Value Bioproducts by Engineered Yeast

Plant cell wall hydrolysates contain not only sugars but also substantial amounts of acetate, a fermentation inhibitor that hinders bioconversion of lignocellulose. Despite the toxic and non-consumable nature of acetate during glucose metabolism, we demonstrate that acetate can be rapidly co-consumed with xylose by engineered Saccharomyces cerevisiae . The co-consumption leads to a metabolic re-configuration that boosts the synthesis of acetyl-CoA derived bioproducts, including triacetic acid lactone (TAL) and vitamin A, in engineered strains. Notably, by co-feeding xylose and acetate, an engineered strain produces 23.91 g/L TAL with a productivity of 0.29 g/L/h in bioreactor fermentation. This strain also completely converts a hemicellulose hydrolysate of switchgrass into 3.55 g/L TAL. These findings establish a versatile strategy that not only transforms an inhibitor into a valuable substrate but also expands the capacity of acetyl-CoA supply in S. cerevisiae for efficient bioconversion of cellulosic biomass.

Conversion↗
DOE OSTI2012

Life-Cycle Assessment of Pyrolysis Bio-Oil Production*

As part ofthe Consortium for Research on Renewable Industrial Materials' Phase I life-cycle assessments ofbiofuels, lifecycle inventory burdens from the production of bio-oil were developed and compared with measures for residual fuel oil. Bio-oil feedstock was produced using whole southern pine (Pinus taeda) trees, chipped, and converted into bio-oil by fast pyrolysis. Input parameters and mass and energy balances were derived with Aspen. Mass and energy balances were input to SimaPro to determine the environmental performance of bio-oil compared with residual fuel oil as a heating fuel. Equivalent functional units of 1 MJ were used for demonstrating environmental preference in impact categories, such as fossil fuel use and global warming potential. Results showed near carbon neutrality of the bio-oil. Substituting bio-oil for residual fuel oil, based on the relative carbon emissions of the two fuels, estimated a reduction in CO2 emissions by 0.075 kg CO2 per MJ of fuel combustion or a 70 percent reduction in emission over residual fuel oil. The bio-oil production life-cycle stage consumed 92 percent of the total cradle-to-grave energy requirements, while feedstock collection, preparation, and transportation consumed 4 percent each. This model provides a framework to better understand the major factors affecting greenhouse gas emissions related to bio-oil production and conversion to boiler fuel during fast pyrolysis.

Steele, Philip↗
DOE OSTI2015

Recent advances in SRS on hydrogen isotope separation using thermal cycling absorption process

TCAP (Thermal Cycling Absorption Process) is a gas chromatograph in principle using palladium in the column packing, but it is unique in the fact that the carrier gas, hydrogen, is being isotopically separated and the system is operated in a semi-continuous manner. TCAP units are used to purify tritium. The recent TCAP advances at Savannah River Site (SRS) include compressor-free concept for heating/cooling, push and pull separation using an active inverse column, and compact column design. The new developments allow significantly higher throughput and better reliability from 1/10 of the current production system's footprint while consuming 60% less energy. Various versions are derived in the meantime for external customers to be used in fusion energy projects.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Tritium accountancy in fusion systems

The US Department of Energy (DOE) has clearly defined requirements for nuclear material control and accountability (MCA) of tritium whereas the International Atomic Energy Agency (IAEA) does not since tritium is not a fissile material. MCA requirements are expected for tritium fusion machines and will be dictated by the host country or regulatory body where the machine is operated. Material Balance Areas (MBA) are defined to aid in the tracking and reporting of nuclear material movements and inventories. Material sub-accounts (MSA) are established along with key measurement points (KMP) to further subdivide a MBA to localize and minimize uncertainties in the inventory difference (ID) calculations for tritium accountancy. Fusion systems try to minimize tritium inventory which may require continuous movement of material through the MSA. The ability of making meaningful measurements of these material transfers is described in terms of establishing the MSA structure to perform and reconcile ID calculations. For fusion machines, changes to the traditional ID equation will be discussed which includes breeding, burn-up, and retention of tritium in the fusion device. The concept of 'net' tritium quantities consumed or lost in fusion devices is described in terms of inventory taking strategies and how it is used to track the accumulation of tritium in components or fusion machines. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2012

Ethanol Distribution, Dispensing, and Use: Analysis of a Portion of the Biomass-to-Biofuels Supply Chain Using System Dynamics

The Energy Independence and Security Act of 2007 targets use of 36 billion gallons of biofuels per year by 2022. Achieving this may require substantial changes to current transportation fuel systems for distribution, dispensing, and use in vehicles. The U.S. Department of Energy and the National Renewable Energy Laboratory designed a system dynamics approach to help focus government action by determining what supply chain changes would have the greatest potential to accelerate biofuels deployment. The National Renewable Energy Laboratory developed the Biomass Scenario Model, a system dynamics model which represents the primary system effects and dependencies in the biomass-to-biofuels supply chain. The model provides a framework for developing scenarios and conducting biofuels policy analysis. This paper focuses on the downstream portion of the supply chain-represented in the distribution logistics, dispensing station, and fuel utilization, and vehicle modules of the Biomass Scenario Model. This model initially focused on ethanol, but has since been expanded to include other biofuels. Some portions of this system are represented dynamically with major interactions and feedbacks, especially those related to a dispensing station owner's decision whether to offer ethanol fuel and a consumer's choice whether to purchase that fuel. Other portions of the system are modeled with little or no dynamics; the vehicle choices of consumers are represented as discrete scenarios. This paper explores conditions needed to sustain an ethanol fuel market and identifies implications of these findings for program and policy goals. A large, economically sustainable ethanol fuel market (or other biofuel market) requires low end-user fuel price relative to gasoline and sufficient producer payment, which are difficult to achieve simultaneously. Other requirements (different for ethanol vs. other biofuel markets) include the need for infrastructure for distribution and dispensing and widespread use of high ethanol blends in flexible-fuel vehicles.

02 PETROLEUM↗
DOE OSTI2012

Using Phage and Yeast Display to Select Hundreds of Monoclonal Antibodies: Application to Antigen 85, a Tuberculosis Biomarker

Background: Current diagnostic methods for tuberculosis (TB), a major global health challenge that kills nearly two million people annually, are time-consuming and inadequate. During infection a number of bacterial molecules that play a role in the infective process are released and have been proposed as biomarkers for early TB diagnosis. Antigen 85 (Ag85) is the most abundant secreted TB protein, and a potential target for this diagnostic approach. One of the bottlenecks in the direct detection of such bacterial targets is the availability of robust, sensitive, specific antibodies. Methods: Using Ag85 as a model, we describe a method to select antibodies against any potential target using a novel combination of phage and yeast display that exploits the advantage of each approach. Results: The efficiency of this approach was attested to by the 111 specific antibodies identified in initial screens. These were assessed for binding to the different Ag85 subunits, affinity, and activity in sandwich assays. Conclusions: The novelty of this approach lies in the possibility of screening the entire output of a phage antibody selection in a single experiment by yeast display. This can be considered analogous to carrying out a million ELISAs. The monoclonal antibodies (mAbs) identified in this way show high binding affinity and selectivity for the antigens and offer an advantage over traditional mAbs produced by relatively expensive and time consuming techniques. This approach has wide applicability, and the affinity of selected antibodies can be significantly improved, if required.

Antibodies↗
DOE OSTI2022

Cumulative effects of piscivorous colonial waterbirds on juvenile salmonids: A multi predator-prey species evaluation

We investigated the cumulative effects of predation by piscivorous colonial waterbirds on the survival of multiple salmonid ( Oncorhynchus spp.) populations listed under the U.S. Endangered Species Act (ESA) and determined what proportion of all sources of fish mortality (1 –survival) were due to birds in the Columbia River basin, USA. Anadromous juvenile salmonids (smolts) were exposed to predation by Caspian terns ( Hydroprogne caspia ), double-crested cormorants ( Nannopterum auritum ), California gulls ( Larus californicus ), and ring-billed gulls ( L . delawarensis ), birds known to consume both live and dead fish. Avian consumption and survival probabilities (proportion of available fish consumed or alive) were estimated for steelhead trout ( O . mykiss ), yearling Chinook salmon ( O . tshawytscha ), sub-yearling Chinook salmon, and sockeye salmon ( O . nerka ) during out-migration from the lower Snake River to the Pacific Ocean during an 11-year study period (2008–2018). Results indicated that probabilities of avian consumption varied greatly across salmonid populations, bird species, colony location, river reach, and year. Cumulative consumption probabilities (consumption by birds from all colonies combined) were consistently the highest for steelhead, with annual estimates ranging from 0.22 (95% credible interval = 0.20–0.26) to 0.51 (0.43–0.60) of available smolts. The cumulative effects of avian consumption were significantly lower for yearling and sub-yearling Chinook salmon, with consumption probabilities ranging annually from 0.04 (0.02–0.07) to 0.10 (0.07–0.15) and from 0.06 (0.3–0.09) to 0.15 (0.10–0.23), respectively. Avian consumption probabilities for sockeye salmon smolts was generally higher than for Chinook salmon smolts, but lower than for steelhead smolts, ranging annually from 0.08 (0.03–0.22) to 0.25 (0.14–0.44). Although annual consumption probabilities for birds from certain colonies were more than 0.20 of available smolts, probabilities from other colonies were less than 0.01 of available smolts, indicating that not all colonies of birds posed a substantial risk to smolt mortality. Consumption probabilities were lowest for small colonies and for colonies located a considerable distance from the Snake and Columbia rivers. Total mortality attributed to avian consumption was relatively small for Chinook salmon (less than 10%) but was the single greatest source of mortality for steelhead (greater than 50%) in all years evaluated. Results suggest that the potential benefits to salmonid populations of managing birds to reduce smolt mortality would vary widely depending on the salmonid population, the species of bird, and the size and location of the breeding colony.

Evans, Allen F.↗
DOE OSTI2023

Assessing springtime vertebrate prey of sympatric mesopredators in the southeastern United States using metabarcoding analysis

Coyotes (Canis latrans) colonized the eastern United States over the last century and formed a 3-species predator guild with bobcats (Lynx rufus) and gray foxes (Urocyon cinereoargenteus) across much of the southeastern United States. Diets among the three species vary along with respective impacts on game species such as white-tailed deer (Odocoileus virginianus) and wild turkeys (Meleagris gallopavo). To determine predation impacts on vertebrate prey and dietary overlap in consumption of prey items, we assessed diets of coyote, bobcat, and gray fox during spring, coinciding with white-tailed deer fawning and wild turkey nesting and brood rearing. We sampled across three sites along the Savannah River in South Carolina from mid-May through mid-June of 2020–2021. We collected 180 scat samples along 295.9 kilometers (71.1–122.4 km/site) of unpaved secondary roads and used DNA metabarcoding to determine vertebrate diet items. We identified predator species of scat using DNA metabarcoding and species-specific mtDNA fragment analysis (153 were coyote, 20 bobcat, and seven gray fox). Overall, we found evidence that two species, coyote and bobcat, consumed deer while all three consumed turkeys. Frequency of deer in the diet varied across sites for coyotes from 62–86% and wild turkey was present with a frequency of occurrence of 9% for coyotes, 5% for bobcats, and 14% for gray fox. Vertebrate diet specialization was evident across predator species with high frequency of deer in coyote diets, rabbits and small mammals in bobcat diets, and herpetofauna in gray fox diets. During deer fawning and wild turkey nesting and brood rearing, dietary overlap appears to be mediated by disparate selection of prey items, which reduced competition among coyotes, bobcats, and gray foxes. Use of DNA metabarcoding may augment our understanding of dietary preferences within this predator guild by providing increased resolution of diet composition among important game species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

The impact of climate change on Korea’s agricultural sector under the national self-sufficiency policy

Evolving environmental conditions due to climate change have brought about changes in agriculture, which is required for human life as both a source of food and income. International trade can act as a buffer against potential negative impacts of climate change on crop yields, but recent years have seen breakdowns in global trade, including export bans to improve domestic food security. For countries that rely heavily on imported food, governments may institute policies to protect their agricultural industry from changes in climate-induced crop yield changes and other countries’ potential trade restrictions. This study assesses the individual and combined effects of climate impacts and food self-sufficiency policies in Korea, which is highly dependent on imports. We use the Global Change Analysis Model (GCAM), a global integrated assessment model, to explore (1) the direct impact of climate change on Korea’s agricultural yields, (2) the full impacts of global climate change on agricultural production, including trade-induced changes due to yield changes in other regions, (3) the impacts of food self-sufficiency policy, and (4) the interactive impact of climate change and self-sufficiency policies. We find that, in Korea, the direct impact of climate change on agricultural yields would be overshadowed by the impact of global climate change due to changing trade patterns. Second, global climate change leads to a rise (rice and wheat) or a decline (soybeans) in Korean producer revenues, while simultaneously raising consumer expenditures on both staples and non-staples. Third, implementing self-sufficiency policies for wheat and soybeans in Korea boosts the nation’s producer revenues, in conjunction with the effects of climate change, at the cost of additional increases in consumer expenditures for both staples and non-staples.

Science & Technology - Other Topics↗