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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method

Abstract. Hydrologic modeling is an essential strategy for understanding and predicting natural flows, particularly where observations are lacking in either space or time or where complex terrain leads to a disconnect in the characteristic time and space scales of overland and groundwater flow. However, significant difficulties remain for the development of efficient and extensible modeling systems that operate robustly across complex regions. This paper introduces the Simulator for Hydrologic Unstructured Domains (SHUD), an integrated, multiprocess, multiscale, flexible-time-step model, in which hydrologic processes are fully coupled using the finite volume method. SHUD integrates overland flow, snow accumulation/melt, evapotranspiration, subsurface flow, groundwater flow, and river routing, thus allowing physical processes in general watersheds to be realistically captured. SHUD incorporates one-dimensional unsaturated flow, two-dimensional groundwater flow, and a fully connected river channel network with hillslopes supporting overland flow and baseflow. The paper introduces the design of SHUD, from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed in northern California. Ongoing applications of the SHUD model include hydrologic analyses of hillslope to regional scales (1 m2 to 106 km2), water resource and stormwater management, and interdisciplinary research for questions in limnology, agriculture, geochemistry, geomorphology, water quality, ecology, climate and land-use change. The strength of SHUD is its flexibility as a scientific and resource evaluation tool where modeling and simulation are required.

58 GEOSCIENCES↗
DOE OSTI2022

Ecosystem consequences of introducing plant growth promoting rhizobacteria to managed systems and potential legacy effects

The rapidly growing industry of crop biostimulants leverages the application of plant growth promoting rhizobacteria (PGPR) to promote plant growth and health. However, introducing nonnative rhizobacteria may impact other aspects of ecosystem functioning and have legacy effects; these potential consequences are largely unexplored. Nontarget consequences of PGPR may include changes in resident microbiomes, nutrient cycling, pollinator services, functioning of other herbivores, disease suppression, and organic matter persistence. Importantly, we lack knowledge of whether these ecosystem effects may manifest in adjacent ecosystems. The introduced PGPR can leave a functional legacy whether they persist in the community or not. Legacy effects include shifts in resident microbiomes and their temporal dynamics, horizontal transfer of genes from the PGPR to resident taxa, and changes in resident functional groups and interaction networks. Ecosystem functions may be affected by legacies PGPR leave following niche construction, such as when PGPR alter soil pH that in turn alters biogeochemical cycling rates. Here, we highlight new research directions to elucidate how introduced PGPR impact resident microbiomes and ecosystem functions and their capacity for legacy effects.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Predicting Short-Term Deformation in the Central Valley Using Machine Learning

Land subsidence caused by excessive groundwater pumping in Central Valley, California, is a major issue that has several negative impacts such as reduced aquifer storage and damaged infrastructures which, in turn, produce an economic loss due to the high reliance on crop production. This is why it is of utmost importance to routinely monitor and assess the surface deformation occurring. Two main goals that this paper attempts to accomplish are deformation characterization and deformation prediction. The first goal is realized through the use of Principal Component Analysis (PCA) applied to a series of Interferomtric Synthetic Aperture Radar (InSAR) images that produces eigenimages displaying the key characteristics of the subsidence. Water storage changes are also directly analyzed by the use of data from the Gravity Recovery and Climate Experiment (GRACE) twin satellites and the Global Land Data Assimilation System (GLDAS). The second goal is accomplished by building a Long Short-Term Memory (LSTM) model to predict short-term deformation after developing an InSAR time series using LiCSBAS, an open-source InSAR time series package. The model is applied to the city of Madera and produces better results than a baseline averaging model and a one dimensional convolutional neural network (CNN) based on a mean squared error metric showing the effectiveness of machine learning in deformation prediction as well as the potential for incorporation in hazard mitigation models. The model results can directly aid policy makers in determining the appropriate rate of groundwater withdrawal while maintaining the safety and well-being of the population as well as the aquifers’ integrity.

58 GEOSCIENCES↗
DOE OSTI2023

Host genetic variation drives the differentiation in the ecological role of the native Miscanthus root-associated microbiome

Microbiome recruitment is influenced by plant host, but how host plant impacts the assembly, functions, and interactions of perennial plant root microbiomes is poorly understood. Here we examined prokaryotic and fungal communities between rhizosphere soils and the root endophytic compartment in two native Miscanthus species (Miscanthus sinensis and Miscanthus floridulus) of Taiwan and further explored the roles of host plant on root-associated microbiomes. Our results suggest that host plant genetic variation, edaphic factors, and site had effects on the root endophytic and rhizosphere soil microbial community compositions in both Miscanthus sinensis and Miscanthus floridulus, with a greater effect of plant genetic variation observed for the root endophytic communities. Host plant genetic variation also exerted a stronger effect on core prokaryotic communities than on non-core prokaryotic communities in each microhabitat of two Miscanthus species. From rhizosphere soils to root endophytes, prokaryotic co-occurrence network stability increased, but fungal co-occurrence network stability decreased. Furthermore, we found root endophytic microbial communities in two Miscanthus species were more strongly driven by deterministic processes rather than stochastic processes. Root-enriched prokaryotic OTUs belong to Gammaproteobacteria, Alphaproteobacteria, Betaproteobacteria, Sphingobacteriia, and [Saprospirae] both in two Miscanthus species, while prokaryotic taxa enriched in the rhizosphere soil are widely distributed among different phyla. We provide empirical evidence that host genetic variation plays important roles in root-associated microbiome in Miscanthus. The results of this study have implications for future bioenergy crop management by providing baseline data to inform translational research to harness the plant microbiome to sustainably increase agriculture productivity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Chromosome-level genome assembly of Quercus variabilis provides insights into the molecular mechanism of cork thickness

Quercus variabilis is a deciduous woody species with high ecological and economic value and is a major source of cork in East Asia. Cork from thick softwood sheets have higher commercial value than those from thin sheets. It is extremely difficult to genetically improve Q. variabilis to produce high quality softwood due to the lack of genomic information. Here, we present a high-quality chromosomal genome assembly for Q. variabilis with length of 791,89 Mb and 54,606 predicted genes. Comparative analysis of protein sequences of Q. variabilis with 11 other species revealed that specific and expanded gene families were significantly enriched in the "fatty acid biosynthesis" pathway in Q. variabilis, which may contribute to the formation of its unique cork. Additionally, based on weighted correlation network analysis of time-course (i.e., five important developmental ages) gene expression data in thick-cork versus thin-cork genotypes of Q. variabilis, we identified one co-expression gene module associated with the thick-cork trait. Within this co-expression gene module, 10 hub genes were associated with suberin biosynthesis. Furthermore, we identified a total of 198 suberin biosynthesis-related new candidate genes that were up-regulated in trees with a thick cork layer relative to those with a thin cork layer. Also, we found that some genes related to cell expansion and cell division were highly expressed in trees with a thick cork layer. Collectively, our results revealed that two metabolic pathways (i.e., suberin biosynthesis, fatty acid biosynthesis), along with other genes involved in cell expansion, cell division, and transcriptional regulation, were associated with the thick-cork trait in Q. variabilis, providing insights into the molecular basis of cork development and knowledge for informing genetic improvement of cork thickness in Q. variabilis and closely related species.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Variation in forest root image annotation by experts, novices, and AI

Abstract Background The manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest. Results Less experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation ( p = 0.01). The CNN net root length change results were closer to manual ( p = 0.08) but there remained substantial variation. Conclusions Manual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Handy, Grace↗
DOE OSTI2023

The direct and indirect drivers shaping RNA viral communities in grassland soil

Recent studies have revealed diverse RNA viral communities in soils. Yet, how environmental factors influence soil RNA viruses remains largely unknown. Here, we recovered RNA viral communities from 24 metatranscriptomes sequenced from grassland soils managed under a range of environmental conditions including 1) water content: 100% and 25% water holding capacity, 2) plant presence: planted with tall wheatgrass (Thinopyrum ponticum) and bare soil, 3) cultivar type: Alkar and Jose, and 4) soil depth: 0-5 cm and 15-25 cm. The recovered RNA viral communities were novel with nearly one-third of the RNA viral contigs uniquely detected in the studied grassland. The classified RNA viral contigs are mostly known as eukaryotic RNA viruses (74.7%) belonging to Phyla Duplornaviricota, Kitrinoviricota, Lenarviricota, and Pisuviricota. Eukaryotic RNA viruses of Family Mitoviridae as well as their natural hosts, Fungi, are one of the most dominant taxa. Consistent with the results of nonmetric multidimensional scaling analysis, the four environmental conditions (water content, plant presence, cultivar, and soil depth) significantly influence the assemblages of soil RNA viral communities as suggested by the correlation analysis and the random forest algorithm. The modularity analysis of the factor network and the structural equation modeling further support the hierarchical associations among the four environmental factors and the community factors representing the co-existing eukaryotic, prokaryotic, and RNA viral communities. The soil water content, plant presence, and type of cultivar demonstrate a significant positive impact on eukaryotic RNA viral richness directly as well as indirectly on eukaryotic RNA viral abundance via influencing the co-existing eukaryotic members in this soil. Our data also provide statistical support for the negative influence of soil depth on soil eukaryotic richness and abundances resulting in its indirect impact on soil eukaryotic RNA viral communities. This study provides field-relevant information on how environmental and community factors collectively shape soil RNA communities and contribute to ecological understanding of RNA viral survival under various environmental conditions and virus-host interactions in soil.

Wu, Ruonan↗
DOE OSTI2023

Determining the biogeochemical transformations of organic matter composition in rivers using molecular signatures

Inland waters are hotspots for biogeochemical activity, but the environmental and biological factors that govern the transformation of organic matter (OM) flowing through them are still poorly constrained. Here we evaluate data from a crowdsourced sampling campaign led by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium to investigate broad continental-scale trends in OM composition compared to localized events that influence biogeochemical transformations. Samples from two different OM compartments, sediments and surface water, were collected from 97 streams throughout the Northern Hemisphere and analyzed to identify differences in biogeochemical processes involved in OM transformations. By using dimensional reduction techniques, we identified that putative biogeochemical transformations and microbial respiration rates vary across sediment and surface water along river continua independent of latitude (18°N–68°N). In contrast, we reveal small- and large-scale patterns in OM composition related to local (sediment vs. water column) and reach (stream order, latitude) characteristics. These patterns lay the foundation to modeling the linkage between ecological processes and biogeochemical signals. We further showed how spatial, physical, and biogeochemical factors influence the reactivity of the two OM pools in local reaches yet find emergent broad-scale patterns between OM concentrations and stream order. OM processing will likely change as hydrologic flow regimes shift and vertical mixing occurs on different spatial and temporal scales. As our planet continues to warm and the timing and magnitude of surface and subsurface flows shift, understanding changes in OM cycling across hydrologic systems is critical, given the unknown broad-scale responses and consequences for riverine OM.

rivers↗
DOE OSTI2023

Abstract for CRADA among NETL, Mälardalen University, University College Cork (acting through its Tyndall National Institute), and Micro Electricity Generation Association

GeoCoHorT aims to accelerate the transition to 4 th generation district heating and cooling (4GDHC) in Europe and globally, by assessing, optimizing, and demonstrating the integration of shallow geothermal heat extraction with other renewable sources and smart buildings. The target geo-source is ground water from River Shannon (Limerick, Ireland), which will provide both an efficiency and noise-reduction benefit to the micro-district and a method of combatting climate-change-warming of the Shannon Estuary. The heat from this low-temperature source will be recycled and supplied to a smart district located in Limerick, Ireland, through heat pumps powered by renewable electricity and by means of a suitable DH network design. The mandate is to find solutions that work for entire communities in a fair and sustainable manner, with the involvement of the communities themselves to highlight their needs. The project brings together four partners from Ireland, Sweden, and the U.S.A. with strong multi-disciplinary competencies. The Tyndall Institute at University of Cork (UCC) and Mälardalen University (MDU) will design and optimize the 4GDH micro-district system, and the Micro Electricity Generation Association (MEGA) will work with the community to build a sense of ownership of the Climate Challenge and ensure close community involvement. The National Energy Technology Laboratory of the U.S. Department of Energy (NETL) will design the prospective geo-fluid loop to optimize heat-extraction effectiveness with ecological safety and assess the environmental benefits of the proposed solutions. Industrial advisors (Eskilstuna Strängnäs Energi och Miljö (ESEM) and Mimer in Sweden, and Smart MPOWER in Ireland) will steer the technology development to meet the needs of public utilities and consumers. Current infrastructure in the smart district in Limerick (demonstration site of an EU Lighthouse project) will be exploited and modified with support of MEGA, UCC, and MPOWER to integrate micro-DH from low-temperature heat sources, such as the river heat, and to allow increasing penetration of prosumers in the grid. Additional case studies will be developed with the help of industrial advisors to extend the results to other regions (e.g., Sweden and USA) and provide valuable insights for the development of 4GDH worldwide.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Using the red chromatic coordinate to characterize the phenology of forest canopy photosynthesis

Vegetation phenology has received increasing attention in climate change research. Near-surface sensing using digital repeat photography has proven to be useful for ecosystem-scale monitoring of vegetation phenology. However, our understanding of the link between phenological metrics derived from digital repeat photography and the phenology of forest canopy photosynthesis is still incomplete, especially for evergreen plant species. Using 49 site-years of digital images from the PhenoCam Network from eight evergreen needleleaf forest (ENF) and six deciduous broadleaf forest (DBF) sites in North America, we explored the potential of the green chromatic (GCC) and red chromatic coordinates (RCC) in tracking forest canopy photosynthesis by comparing camera-derived start- and end-of-growing season (SOS and EOS, respectively) with corresponding estimates derived from eddy covariance-derived daily gross primary productivity (GPP). We found that for DBF sites, both GCC and RCC performed comparable in capturing SOS and EOS. However, similar to earlier studies, GCC had limited potential in capturing GPP-based SOS or EOS for ENF sites. In contrast, we found RCC was a better predictor of both GPP-based SOS and EOS for ENF sites. Environmental and ecological explanations were both provided that phenological transitions derived from RCC were highly correlated with spring and autumn meteorological conditions, as well as having overall higher correlations with phenology based on LAI, a critical variable for describing canopy development. Our results demonstrate that RCC is an underappreciated metric for tracking vegetation phenology, especially for ENF sites where GCC failed to provide reliable estimates for GPP-based SOS or EOS. Our results improve confidence in using digital repeat photography to characterize the phenology of canopy photosynthesis across forest types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Improving the particle dry deposition scheme in the CMAQ photochemical modeling system

Dry deposition of atmospheric aerosols in large-scale models is a critical, but highly uncertain, sink process with a strong dependence on particle size, meteorological conditions, and land surface properties. This study investigates the particle dry deposition scheme implemented in the standard Community Multiscale Air Quality (CMAQ) model v5.2.1, characterizes its underlying parameterized components with comparison to a similar scheme in a contemporary regional-scale model, and proposes two updated schemes that are then evaluated with available ambient particle deposition velocity (V d ) measurements. Both updated schemes reduce the surprisingly strong dependence of deposition velocity on the aerosol mode width, with one scheme further introducing a dependence on vegetation coverage that is broadly consistent with variability in observations between vegetated and non-vegetated surfaces. Compared to the base scheme, the updated scheme with vegetation dependence increases V d for submicron particles and decreases it for larger particles by an average of 37% and –66%, respectively. This scheme performs statistically better than the base scheme, reducing fractional biases by 56%–97% for vegetated land-use types and has roughly equivalent performance over water. The base and updated schemes are tested with three annual CMAQ (v5.2.1) simulations for the year 2011; predicted ambient aerosol concentrations are evaluated with routine monitoring network observations and predicted dry deposition fluxes are evaluated with data from the Clean Air Status and Trends Network (CASTNET). The updated scheme with vegetation dependence reduces negative fractional biases for PM 10 by 41% and positive fractional biases for PM 2.5 organic carbon by 15%. This scheme has been incorporated into the most recent publicly accessible versions of CMAQ (v5.3 and beyond) to replace the scheme used in previous versions of CMAQ (v4.5 through v5.2.1).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Achieving the End State for the Pahute Mesa Corrective Action Units at the Nevada National Security Site - 20355

Underground nuclear testing at the Nevada National Security Site (NNSS) ended in 1992. To address the impact of radionuclide contamination from underground nuclear testing on the groundwater resources of Nevada, the U.S. Department of Energy (DOE) Environmental Management (EM) Nevada Program created the Underground Test Area (UGTA) activity to characterize the radionuclides in groundwater, understand the nature and extent of radionuclide migration, and forecast the distribution of radionuclides in groundwater for 1,000 years. The UGTA activity is regulated by the Federal Facility Agreement and Consent Order (FFACO), an agreement negotiated between the Nevada Division of Environmental Protection (NDEP), DOE, and the U.S. Department of Defense (DoD). DOE is close to achieving the end state (closure in place with monitoring and institutional control) for three of the five UGTA corrective action units (CAUs) on the NNSS. A number of challenges remain to achieve the end state of the other two CAUs, both located on Pahute Mesa. Pahute Mesa was the location of 82 underground nuclear tests, less than 10% of the total number of tests on the NNSS. Yet, Pahute Mesa contains slightly more than 60% of the total radionuclides (in curies). Based on groundwater sampling in monitoring wells, two radionuclide plumes have migrated several kilometers (km) in groundwater from selected test cavities and have crossed the boundary of the NNSS (yet remain within the boundaries of Federally controlled land). The path to achieve the end state continues to evolve as more data are collected and a better understanding of radionuclide migration in groundwater is developed. DOE has invested in drilling and sampling more than 50 characterization and monitoring groundwater wells over the past 25 years. The data indicate that the primary radionuclide of concern is tritium as it is about 89% of the total radionuclide inventory (in curies). As well, only tritium has been measured in groundwater outside of cavities at concentrations exceeding the Safe Drinking Water Act (SDWA) standard. For one of the tritium plumes that has migrated across the NNSS boundary, the average rate of migration has been measured as about 45 meters (m) per year with the rate of migration at the leading edge of the plume of about 85 meters per year. At that rate of migration, the tritium plume will decay to safe levels and not reach the publicly accessible environment at concentrations above the SDWA standard. Other radionuclides, at concentrations below the SDWA standards, will be monitored to ensure they remain at safe levels. Consequently, the end state path forward for Pahute Mesa has evolved to take full advantage of the data from the monitoring network to constrain uncertainty in model forecasts and to reduce reliance on probabilistic simulations. The focus of the end state approach relies on developing a monitoring well network that is protective of human health by increasing confidence that no radionuclide plume in the groundwater will migrate undetected to the accessible environment, located about 22 km from the nearest up-gradient underground nuclear test. Using the measured data to remove uncertainty, the evaluation of radionuclide migration from Pahute Mesa is directed toward meeting the goals of the end state. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

An approach to urban waterway assessment using holistic values and reciprocity

Current aquatic ecosystem assessment methods and tools often focus on physical, chemical, and biological indicators of ecosystem health. This approach to ecosystem assessment is not always straightforward to execute in urban environments and ignores potential connectivity between social and environmental outcomes. During a workshop at the Symposium on Urbanization and Stream Ecology in Brisbane, Australia in 2023 (SUSE6), we developed an approach to urban aquatic ecosystem assessment that incorporates a holistic perspective. Specifically, our approach considers both environmental (biological, chemical, and physical integrity) and social (community connection, human safety, resource use) values of urban waterways. This approach is inclusive of Indigenous perspectives, such as the concept of reciprocity, whereby consideration of both the environment and society leads to a healthy ecosystem. Here, to highlight how this holistic assessment approach could be used, we present real-world examples that included assessing both environmental and societal values informed by reciprocity or balanced perspectives. This approach can be broadly applied and adapted to specific aquatic ecosystem conditions and projects, providing an inclusive, community-centered approach for assessing the health of waterways in urban environments.

Indigenous knowledge↗
DOE OSTI2021

Novel and Emerging Capabilities that Can Provide a Holistic Understanding of the Plant Root Microbiome

In recent years, the root microbiome (i.e., microorganisms growing inside, on, or in close proximity to plant roots) has been shown to play an important role in plant health and productivity. Despite its importance, the root microbiome is challenging to study because of its complexity, heterogeneity, and subterranean location. Fortunately, root microbiome research has seen a tremendous influx of novel technologies (e.g., imaging tools, robotics, and molecular analyses), experimental platforms (e.g., micro- and mesocosms), and data integration, modeling, and prediction tools in the past decade that have greatly increased our ability to dissect the complex network of interactions between above- and belowground environmental parameters, plants, bacteria, and fungi that dictate soil and broader ecosystem health. Herein, we discuss methods that are currently used in root microbiome research and that can be expanded to phytobiome research in general ranging from laboratory studies to mesocosm-scale studies and, finally, to field studies; evaluate their relevance to ecosystem studies; and discuss future root microbiome research directions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Riverine organic matter functional diversity increases with catchment size

A large amount of dissolved organic matter (DOM) is transported to the ocean from terrestrial inputs each year (~0.95 Pg C per year) and undergoes a series of abiotic and biotic reactions, causing a significant release of CO 2 . Combined, these reactions result in variable DOM characteristics (e.g., nominal oxidation state of carbon, double-bond equivalents, chemodiversity) which have demonstrated impacts on biogeochemistry and ecosystem function. Despite this importance, however, comparatively few studies focus on the drivers for DOM chemodiversity along a riverine continuum. Here, we characterized DOM within samples collected from a stream network in the Yakima River Basin using ultrahigh-resolution mass spectrometry (i.e., FTICR-MS). To link DOM chemistry to potential function, we identified putative biochemical transformations within each sample. We also used various molecular characteristics (e.g., thermodynamic favorability, degradability) to calculate a series of functional diversity metrics. We observed that the diversity of biochemical transformations increased with increasing upstream catchment area and landcover. This increase was also connected to expanding functional diversity of the molecular formula. This pattern suggests that as molecular formulas become more diverse in thermodynamics or degradability, there is increased opportunity for biochemical transformations, potentially creating a self-reinforcing cycle where transformations in turn increase diversity and diversity increase transformations. We also observed that these patterns are, in part, connected to landcover whereby the occurrence of many landcover types (e.g., agriculture, urban, forest, shrub) could expand DOM functional diversity. For example, we observed that a novel functional diversity metric measuring similarity to common freshwater molecular formulas (i.e., carboxyl-rich alicyclic molecules) was significantly related to urban coverage. These results show that DOM diversity does not decrease along stream networks, as predicted by a common conceptual model known as the River Continuum Concept, but rather are influenced by the thermodynamic and degradation potential of molecular formula within the DOM, as well as landcover patterns.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Crowdsourcing Global Perspectives in Ecology Using Social Media

Transparent, open, and reproducible research is still far from routine, and the full potential of open science has not yet been realized. Crowdsourcing–defined as the usage of a flexible open call to a heterogeneous group of individuals to recruit volunteers for a task –is an emerging scientific model that encourages larger and more outwardly transparent collaborations. While crowdsourcing, particularly through citizen- or community-based science, has been increasing over the last decade in ecological research, it remains infrequently used as a means of generating scientific knowledge in comparison to more traditional approaches. We explored a new implementation of crowdsourcing by using an open call on social media to assess its utility to address fundamental ecological questions. We specifically focused on pervasive challenges in predicting, mitigating, and understanding the consequences of disturbances. In this paper, we briefly review open science concepts and their benefits, and then focus on the new methods we used to generate a scientific publication. We share our approach, lessons learned, and potential pathways forward for expanding open science. Our model is based on the beliefs that social media can be a powerful tool for idea generation and that open collaborative writing processes can enhance scientific outcomes. We structured the project in five phases: (1) draft idea generation, (2) leadership team recruitment and project development, (3) open collaborator recruitment via social media, (4) iterative paper development, and (5) final editing, authorship assignment, and submission by the leadership team. We observed benefits including: facilitating connections between unusual networks of scientists, providing opportunities for early career and underrepresented groups of scientists, and rapid knowledge exchange that generated multidisciplinary ideas. We also identified areas for improvement, highlighting biases in the individuals that self-selected participation and acknowledging remaining barriers to contributing new or incompletely formed ideas into a public document. While shifting scientific paradigms to completely open science is a long-term process, our hope in publishing this work is to encourage others to build upon and improve our efforts in new and creative ways.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

An adaptive adversarial domain adaptation approach for corn yield prediction

Recently, statistical machine learning and deep learning methods have been widely explored for corn yield prediction. Though successful, machine learning models generated within a specific spatial domain often lose their validity when directly applied to new regions. To address this issue, we designed an unsupervised adaptive domain adversarial neural network (ADANN). Specifically, through domain adversarial training, the ADANN model reduced the impact of domain shift by projecting data from different domains into the same subspace. Also, the ADANN model was designed to be trained in an adaptive way, which guaranteed the model can learn the domain-invariant features and perform accurate yield prediction simultaneously. Informative variables including time-series vegetation indices and sequential weather observations were first collected from multiple data sources and aggregated to the county level. Then, we trained the ADANN model with the extracted features and corresponding reported county-level corn yield from the U.S. Department of Agriculture (USDA). Finally, the trained model was evaluated in four testing years 2016–2019. The U.S. corn belt was used as the study area and counties under study were grouped into two diverse ecological regions. Overall, the experimental results showed that the developed ADANN model had better performance than three other state-of-the-art machine learning models in both local experiments (train and test in the same region) and transfer experiments (train and test in different regions). As the first study using adversarial learning for crop yield prediction, this research demonstrates a novel solution for improving model transferability on crop yield prediction.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Machine learning of factors for improving oyster hatchery production

Oyster aquaculture and restoration in the Chesapeake Bay are vital, yet hatcheries frequently struggle with inconsistent larval growth and sudden mass mortality events. Unpredictable disruptions in larval production cause large economic losses, represent a perceived risk to growers, and impede industry expansion. To better understand associations between production yield and its potential predictors, we applied machine learning (random forest, and neural network) and statistical (generalized additive model) models to a comprehensive dataset of environmental, water quality, and operational parameters from a Maryland oyster hatchery, aiming to identify key yield predictors and develop a robust forecasting tool. We used recursive Boruta algorithm for variable selection, pinpointing critical predictors, and employed cross-validation to fine-tune model settings. Shapley value analysis offered crucial insights into model interpretations, highlighting week number, Normalized Difference Vegetation Index, salinity, turbidity, and fecundity as primary drivers of yield variability. For low-yield cases, salinity-related variables were particularly important. Our findings provide an early warning system for potential production downturns, empowering hatchery operators to make data-driven decisions for optimizing water conditions, feeding schedules, and broodstock management. By boosting predictability and efficiency, this research directly supports economic stability of the oyster industry and ecological health of the Chesapeake Bay.

Vishwakarma, Srishti [Oak Ridge National Laborator↗