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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

CG-Kit: Code Generation Toolkit for performant and maintainable variants of source code applied to Flash-X hydrodynamics simulations

CG-Kit is a new Code Generation tool-Kit that we have developed as a part of the solution for portability and maintainability for multiphysics computing applications. The development of CG-Kit is rooted in the urgent need created by the shifting landscape of high-performance computing platforms and the algorithmic complexities of a particular large-scale multiphysics application: Flash-X. To efficiently use computing resources on a heterogeneous node, an application must have a map of computation to resources and a mechanism to move the data and computation to the resources according to the map. Most existing performance portability solutions are focussed on abstracting the expression of computations so that a unified source code can be specialized to run on different resources. However, such an approach is insufficient for a code like Flash-X, which has a multitude of code components that can be assembled in various permutations and combinations to form different instances of applications. Similar challenges apply to any code that has composability, where a single specified way of apportioning work among devices may not be optimal. Additionally, use cases arise where the optimal control flow of computation may differ for different devices while the underlying numerics remain identical. This combination leads to unique challenges including handling an existing large code base in Fortran and/or C/C++, subdivision of code into a great variety of units supporting a wide range of physics and numerical methods, different parallelization techniques for distributed and shared memory systems and accelerator devices, and heterogeneity of computing platforms requiring coexisting variants of parallel algorithms. All of these challenges demand that scientific software developers apply existing knowledge about domain applications, algorithms, and computing platforms to determine custom abstractions and granularity for code generation. There is a critical lack of tools to tackle those problems. CG-Kit is designed to fill this gap by providing a user with the ability to express their desired control flow and computation-to-resource map in the form a pseudocode-like recipe. It consists of standalone tools that can be combined into highly specific and, we argue, highly effective portability and maintainability toolchains. Here we present the design of our new tools: parametrized source trees, control flow graphs, and recipes. The tools are implemented in Python. They are agnostic to the programming language of the source code targeted for code generation. In conclusion, we demonstrate the capabilities of the toolkit with two examples, first, multithreaded variants of the basic AXPY operation, and second, variants of parallel algorithms within a hydrodynamics solver, called Spark, from Flash-X that operates on block-structured adaptive meshes.

Algorithmic portability↗
DOE OSTI2025

Comprehensive techno-economic and life cycle greenhouse gases analysis of green ammonia production utilizing PV and wind energy: Jordan as a case study

Ammonia (NH 3 ) has emerged as a critical player in the global energy transition due to its potential as a low-carbon fuel. Conventional ammonia production methods, primarily through steam methane reforming, are significant contributors to global CO 2 emissions. This study investigates the potential of green ammonia production in Jordan by leveraging the country's abundant solar photovoltaic and wind energy resources. A comprehensive techno-economic and life cycle greenhouse gas analysis was conducted to compare green ammonia production with conventional grey and blue ammonia pathways. Here, the study utilized spatial mapping to assess renewable energy capacity factors across Jordan and optimized the integration of hybrid PV/wind systems with proton exchange membrane (PEM) electrolyzers. The results indicate that the levelized cost of green ammonia (LCOA) in Jordan varies between 900 USD/kg NH 3 and 2500 USD/kg NH 3 which is significantly higher than grey ammonia (similar to 360 USD/kg NH 3 ). Nevertheless, the carbon intensity of green ammonia production (between 0.1 kg CO 2eq /kg NH 3 and 0.5 kg CO 2eq /kg NH 3 ) is much lower than the grey ammonia (1.8 kg CO 2eq /kg NH 3 ). Finally, the results indicate that considering future reductions in capital costs and advancement in renewable energy systems and PEM electrolyzers as well as the ability to sell the co-product O 2 can reduce the LCOA by up to 92 % and make it competitive with grey ammonia.

Green Ammonia↗
DOE OSTI2025

Design and synthesis of biobased superhydrophobic biochar catalyst derived from Citrus sinensis for biodiesel production using inedible oil feedstocks

In an one-pot in situ trans/esterification of low-grade feedstocks, the simultaneous presence of triglycerides (TAGs) and free fatty acids (FFAs) presents a dual catalytic challenge. While TAGs undergo transesterification to form biodiesel and glycerol, FFAs present in the feedstocks are esterified simultaneously, producing water as a by-product. This water can deactivate acid catalysts by interacting with their active sites, reducing catalytic efficiency and reusability. To overcome this, we developed a hydrophobic sulfonic acid-functionalized biochar catalyst derived from Citrus sinensis (orange peel) via sulfonation with H 2 SO 4 and subsequent silylation using hexamethyldisilazane (HMDS). The optimized catalyst exhibited a high surface area (355.09 m 2 g −1 ), sulfur content (5.33 wt%), and strong hydrophobicity (water contact angle: 154°). Compared to non-hydrophobic analogs, it showed enhanced activity and reusability (up to 10 cycles). Using response surface methodology with central composite design, a biodiesel yield of 99.1 ± 0.4% was achieved under optimal conditions. Life cycle assessment was performed to evaluate the environmental impacts of biodiesel production utilising the synthesized catalyst, considering 1000 kg of biodiesel produced as 1 functional unit. In conclusion, the recorded results showed the cumulative abiotic depletion of fossil resources over the entire biodiesel production process as 87 243.423 MJ and global warming potential as 4103.494 kg CO 2 equivalent.

Biochar↗
DOE OSTI2024

Development of a Colorimetric Loop-Mediated Isothermal Amplification Assay for the Detection of Trypanosoma cruzi in Low-Resource Settings

Chagas disease is an inflammatory parasitic infection caused by Trypanosoma cruzi (T. cruzi). Early diagnosis is crucial in guiding treatment and slowing disease progression; however, current diagnostic methods have insufficient detection limits and often require skilled technicians. Molecular tests, especially isothermal nucleic acid assays, are advantageous due to their excellent sensitivity, specificity, speed, and simplicity. Here, we optimized a colorimetric loop-mediated isothermal amplification (LAMP) assay for T. cruzi. We can detect as few as 2 genomic copies/reaction using three different T. cruzi strains. We examined selectivity using other parasitic protozoans and successfully detected T. cruzi DNA extracted from parasites in human whole blood down to 1.2 parasite equivalents/reaction. We also performed a blinded study using canine blood samples and established a 100% sensitivity, specificity, and accuracy for the colorimetric LAMP assay. Finally, we used a heated 3D printer bed and an insulated thermos cup to demonstrate that the LAMP incubation step could be performed with accessible, low-cost materials. Altogether, we have developed a high-performing assay for T. cruzi with a simple colorimetric output that would be ideal for rapid, low-cost screening at the point of use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Offshore Wind Farm Turbine and Energy Storage Optimization

Abstract This paper evaluates the technical and economic feasibility of repurposing decommissioned offshore oil and gas platforms as electrical substations for offshore wind projects in the U.S. Gulf of America, a region characterized by relatively low and highly variable wind speeds, extensive legacy offshore infrastructure, and exposure to merchant electricity markets. A unified techno-economic framework is developed using the Repurposing Offshore Infrastructure for Continued Energy (ROICE) Economic Model (REM) to integrate Gulfspecific wind resource assessment, commercial wind turbine performance, offshore infrastructure cost modeling, and wholesale electricity market exposure. Gulf wind speed data are vertically extrapolated to turbine hub height and combined with manufacturer power curves to compute annual energy production and capacity factors across a broad portfolio of commercial turbines, enabling identification of turbine designs best suited for low-wind offshore environments. Hourly electricity price data from the Midcontinent Independent System Operator (MISO) day-ahead market are incorporated to characterize revenue potential, price volatility, and the temporal alignment between wind generation and market conditions. In addition, a conceptual framework for offshore battery energy storage system (BESS) integration is developed to support future investigation of market-responsive energy shifting at repurposed platforms. Results from the turbine evaluation demonstrate that machines with lower cut-in wind speeds and earlier ‘rated-power’ characteristics significantly outperform larger, industry-standard offshore turbines for the same net power under Gulf wind conditions, underscoring the need for region-specific technology selection. Market analysis further reveals substantial price variability and limited intrinsic alignment between wind production and high-price periods, motivating consideration of operational flexibility mechanisms. While storage optimization is not implemented in this study, the REM framework establishes a transparent and replicable foundation for co-evaluating turbine selection, infrastructure constraints, and market exposure, providing a practical pathway for assessing the potential role of repurposed offshore platforms in enabling economically viable offshore wind development in the Gulf of America.

02 PETROLEUM↗
DOE OSTI2024

4th Big Data for Nuclear Power Plants Workshop 2023

The Ohio State University and Idaho National Laboratory organized the 4 th Big Data for Nuclear Power Plants Workshop in November, 2023 in Columbus, Ohio. Workshop topics were chosen to understand the challenges and gaps that need to be addressed to maximize the impact of data on the nuclear industry, as well as the associated applications and risks. Discussions were focused around six specific application areas: Operation and Maintenance; Machine Learning in Nuclear Materials and Advanced Manufacturing; Cybersecurity; High-Performance Computing and Massive Computation; Big Data and Digital Twins; and Nuclear Non-Proliferation. The opportunities, challenges, and risks identified in the six focus areas explored in this workshop are diverse, but some common themes emerge, such as the importance of data integrity, quality, coverage, privacy, and traceability. Big data and AI/ML tools can be leveraged to reduce costs, optimize human tasking, and reduce human error across various application areas. In order for the nuclear industry to benefit from big data and advanced analytic capabilities, it is essential to address challenges and risks, such as data privacy, model reliability, and computational resource availability. Learning from other industries that have successfully implemented big data and AI/ML technologies, like the aerospace industry, can help the nuclear industry successfully integrate these technologies.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

CRiSPPy: An advanced hydropower scheduling tool for the Colorado River Storage Project

The Western Area Power Administration (WAPA) plays a vital role in delivering reliable and cost-effective hydroelectric power to millions of customers across the western United States. The Colorado River Storage Project (CRSP) carries out WAPA’s mission in Arizona, Utah, Colorado, New Mexico, Nevada, Wyoming and Texas. Achieving this mission requires effective management of the Colorado River system, and depends on the use of advanced analytical tools and modeling methodologies. For many years, CRSP has relied on the Generation and Transmission Maximization Superlite (GTMax SL) model for its mid-term and long-term hydroscheduling needs. However, the evolving energy market, power system operations, environmental rules, and hydrology conditions, coupled with advancements in computational capabilities, have necessitated the development of a more modern and robust solution. This report introduces the Colorado River Storage Project Python-based (CRiSPPy) model, a new, advanced hydropower scheduling tool developed to address CRSP ever-evolving challenges. CRiSPPy represents a significant leap forward in our ability to model and optimize the operation of the Colorado River system. It incorporates state-of-the-art optimization algorithms, enhanced data management capabilities, and an advanced graphical user interface, providing WAPA CRSP personnel with unprecedented insights and decision-making support. This document details the development, capabilities, and implementation of CRiSPPy. It is intended to serve as a comprehensive resource for WAPA staff, stakeholders, and anyone interested in the future of hydropower scheduling in the Colorado River Basin. We are confident that CRiSPPy will enhance WAPA's mission while adapting to the challenges of a dynamic and increasingly complex environment. The version of CRiSPPy described in this report is the version 2.3. New versions of CRiSPPy will be developed as the tool keeps evolving to address CRSP challenges.

13 HYDRO ENERGY↗
DOE OSTI2026

Compilation and utilization of a sorghum transcriptome compendium for gene regulatory network analysis and crop trait engineering

Sorghum bicolor (Sorghum) is a drought and heat tolerant C4 grass crop used to produce grain, forage, biofuels, and other bioproducts. Genetic improvement of sorghum hybrid crops is aided by a large and diverse germplasm, sorghum's diploid inbreeding genetics, and a relatively small genome that has facilitated genomic research. Over the past 20 years, the sorghum research community characterized the cytogenetic and recombinant landscapes of sorghum's 10 chromosomes, sequenced and annotated the sorghum genome, and used that information to identify genes/alleles that modulate flowering time, plant height, seed shattering, and other important traits. More recently, >1000 RNA-seq transcriptome profiles were collected from 15 sorghum genotypes to help understand the genetic basis of variation in growth and development of sorghum stems, tillers, roots, and leaves, and the regulation of biosynthetic pathways that produce epicuticular wax, dhurrin, and RFOs, compounds that contribute to sorghum's resilience. Transcriptome studies were designed to identify differentially expressed genes that are co-expressed during development or in response to a treatment to enable construction of gene regulatory networks. Co-expression and network analysis identified transcription factors and their cognate binding sites in target gene promoters and signaling pathways that modulate gene regulatory networks providing gene editing targets for further trait optimization. RNA-seq data from >20 experiments targeting sorghum organs, tissues, cell types, developmental stages, and responses to environmental conditions (i.e., diel, day-length, shading, water-deficit, temperature) has been compiled in a sorghum transcriptome compendium. The goal of this resource paper is to describe compendium content, accessibility, and a compendium data analysis pipeline and to illustrate the types of information that can be derived from the compendium with a focus on the elucidation of gene regulatory networks useful for guiding the improvement of sorghum traits through gene editing.

RNA-seq↗
DOE OSTI2024

Simplified Approximations of Direct Cumulus Entrainment and Detrainment

Abstract In recent years, direct calculations of simulated cumulus entrainment and detrainment have facilitated new physical insights into these highly elusive but critically important processes. However, these calculations require substantial computational resources that may limit their widespread usage. To facilitate such calculations, two simplified approximations of direct cumulus entrainment and detrainment are examined herein. The first approximation, termed the “semidirect” method, follows a standard bulk approach but makes more realistic assumptions about the sources of entrained and detrained air near the cloud edges. In contrast, the second approximation (the “projection” method) uses the governing equations of motion to project whether grid points near the cloud edge will entrain or detrain as the mean cloud ascends by one grid point. Verification exercises using large-eddy simulations reveal that both methods generally agree better with corresponding direct entrainment/detrainment estimates than the traditional bulk formulation, with the projection method outperforming the semidirect method. The two methods can be used in a synergistic fashion, with the semidirect method helping to optimize the projection method, to suit a wide range of applications. Because the latter incorporates the essential dynamics of entrainment and detrainment at the local scale, it can be used to gain physical insight into the causal mechanisms regulating these complex processes.

Meteorology & Atmospheric Sciences↗
DOE OSTI2025

CEC Quest: Long Duration Energy Storage Impact Analysis Tool

SAND2025-14389O CEC Quest is a Python tool with a user interface designed to analyze the greenhouse gas impacts of long-duration energy storage projects in California. The tool automates data collection from public sources and uses an Application Programming Interface (API) to enable users to download photovoltaic resource availability, marginal operating emissions rate, and utility rate data. It guides users in inputting parameters for a battery energy storage model and uploading site electrical load data, while also prompting for relevant analysis parameters like timestep and grid limits. CEC Quest performs monthly optimization of one year of data to assess impacts on the site’s electrical bill and the grid’s greenhouse gas emissions. Finally, it conducts a lifecycle analysis to evaluate changes over a defined quantification period, with results aggregated through automated report generation. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Rosewater, David [Sandia National Lab. (SNL-CA), L↗
DOE OSTI2024

Frequency Support From Electric Vehicles for Advancing Renewable Energy Integration

The integration of renewable energy resources (RERs) in the modern power grid is increasing rapidly because of aggressive decarbonization goals, lower costs, and increased government investment. However, higher penetrations of inverter-based generation can lead to frequency stability issues because of reduced system inertia. This paper develops a framework for quantifying the contribution of electric vehicles (EVs) toward providing frequency support to the grid and thus increasing the penetration limit of renewable energy resources (RERs). EVs are considered to provide both inertial response and primary frequency response support to the grid. A stochastic approach incorporating the uncertainties associated with the behavior of EVs is developed to derive the discharge limit of EV aggregators. A multi-machine system frequency response (MM-SFR) model is developed, which incorporates the dynamic virtual inertia and droop coefficients of EV aggregators derived from the EV control modules. Frequency security constraints are developed from this MM-SFR model, which, along with the converter voltage security and low voltage ride-through constraints, are integrated within a nonlinear optimization framework to determine the RER integration limit. Here, the efficacy of the proposed approach is validated using the RTS-GMLC test system.

25 ENERGY STORAGE↗
DOE OSTI2024

Evaluate Synergies of Using Hydrothermal Liquefaction and Anerobic Digestion Treatment Technologies for Wastewater Resource Recovery Facilities (CRADA 516 Final Report)

The research focuses on utilizing a new anaerobic digestion (AD) configuration to treat the aqueous by-product generated by hydrothermal liquefaction (HTL) of sewage sludge. This report found that for Anaerobic Digestion for HTL By-product, Anaerobic biofilms can degrade some HTL wastewater contaminants, but co-digestion is essential to address nutrient deficiencies and optimize performance. Without AD, toxicity of HTL aqueous streams may limit broader adoption in wastewater treatment plants (WWTPs). Great Lakes Water Authority (GLWA) used an innovative reactor design, involving a dynamic membrane anaerobic bioreactor to promote biofilm growth, improving contaminant degradation. The tree-like structure inside the reactor supports biofilm development with recirculation enhancing microbial activity. Overall, a 70% chemical oxygen demand (COD) removal was achieved, although nutrient supplementation is required for stability. The reactor achieved a diverse microbial community, including methanogens and bacteria capable of degrading phenols and aromatics.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2026

Toward memory-efficient melt pool monitoring: a classification framework using event-based imaging and sparse sensing technique

Vision sensors like CMOS and CCD cameras are often used for in-process monitoring of melt pools in laser-based additive and welding processes, but they require transferring large amounts of data and computational processing resources. Event-based neuromorphic imagery, on the other hand, detects only the change in pixel intensity, thus potentially reducing the data amount and latency. With an event imager, this study develops a framework for melt pool condition classification, including image construction, time scale selection, optimal pixel selection, and sparse classification, to achieve a highly memory-efficient scheme. These are based on sparse sensing techniques with singular value decomposition (SVD) and QR pivoting, the two fundamental matrix transformations for linear dimensionality reduction. The framework is then validated by classifying a controlled experiment by exciting various mode shapes of liquid gallium pools of varying depths (3, 6, and 8 mm). At 200 pixels, the classifier can reach overall accuracy of 75%, while at 2000 pixels (0.013% of the total possible pixels), the accuracy is nearly 90% (89.86%). At the same number of pixels, random selection can only achieve 46% and 67%, respectively. The memory savings of the sparsely sampled event data compared to a conventional imager is about 500 times. In addition to performance, implementation and limitations of the framework are also discussed.

42 ENGINEERING↗
DOE OSTI2026

District-Scale Analysis of Electricity Load and Strategies to Improve Energy Reliability Using Prototype District Models

Projected increases in electricity demand in the U.S. highlight the urgent need for effective load management to ensure grid reliability. As the building sector accounts for approximately 75% of electricity usage, enhancing energy efficiency and flexibility in this sector is crucial. Adopting district-level approaches offers significant advantages over traditional individual building analyses by enabling shared infrastructure and economies of scale. To navigate the data and computational challenges associated with modeling energy at the district level, prototype district models have been proposed as holistic, system-level solutions that capture complex interactions within typical configurations. This study presents these models as a reference tool for analyzing district-scale energy systems across various climate zones in the U.S. Developed with input from stakeholders, these models integrate varied building characteristics, inter-building connections, and energy system interactions. A case study utilizing the Urban Edge prototype district model, implemented on the URBANopt™ platform, evaluates multiple demand scenarios and the impact of distributed energy resources such as fuel-fired backup generators, photovoltaic systems, and batteries. Findings suggest that while new electric systems can significantly reduce annual energy use, they may also elevate peak electricity loads, with a notable 43% increase in heating-dominant climate zone 5B. The optimal backup power solutions vary based on location, influenced by factors such as utility rates and incentives. For example, PV and batteries perform well in high-cost regions like New York City, while diesel backup generators are more suitable for backup needs in climate zone 3A, such as Atlanta. Thus, this research highlights the importance of prototype district models for future district-scale energy planning.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Engineered enzymatic cascade converts diols to amino alcohols

Aliphatic amino alcohols such as 6-amino-1-hexanol are potential platform chemicals for a variety of advanced materials, but applications are currently limited by reagent costs. Aliphatic amino alcohols can currently be synthesized from biomass-derived diols at elevated temperatures and pressures using Ru-based catalysts that produce a mixture of amino-alcohol, diamine, and cyclic amine products. Replacing chemical amination with an enzymatic cascade would reduce resource needs and enable reactions under milder conditions. In this work, we characterized a two-enzyme cascade that selectively converts C4–C7 diols to the corresponding amino alcohols under aqueous conditions at room temperature and pressure. By engineering the rate-limiting enzyme and optimizing reaction conditions, we increased amino alcohol production nearly 30-fold, achieving a selectivity of 99%. The same enzyme cascade could also be used to convert amino alcohols into cyclic amines through reduction of the corresponding cyclic imine. This engineered cascade provides a green opportunity to sustainably synthesize asymmetric bifunctional platform chemicals.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Machine Learning-Assisted Distribution System Network Reconfiguration Problem

High penetration from volatile renewable energy resources in the grid and the varying nature of loads raise the need for frequent line switching to ensure the efficient operation of electrical distribution networks. Operators must ensure maximum load delivery, reduced losses, and the operation between voltage limits. However, computations to decide the optimal feeder configuration are often computationally expensive and intractable, making it unfavorable for real-time operations. This is mainly due to the existence of binary variables in the network reconfiguration optimization problem. To tackle this issue, we have devised an approach that leverages machine learning techniques to reshape distribution networks featuring multiple substations. This involves predicting the substation responsible for serving each part of the network. Hence, it leaves simple and more tractable Optimal Power Flow problems to be solved. This method can produce accurate results in a significantly faster time, as demonstrated using the IEEE 37-bus distribution feeder. Compared to the traditional optimization-based approaches, a feasible solution is achieved approximately ten times faster for all the tested scenarios.

deep neural networks↗
DOE OSTI2025

Hybrid renewable energy systems

In the pursuit of ecologically sustainable and resilient energy systems, increasingly more attention is being devoted to a diversity of energy generation and storage methods. As the landscape of generation technology gains nuance and complexity, a wide-ranging set of technical questions has emerged, touching on topics that range from control and optimization of hybrid systems to finance and economic viability to multi-fidelity modeling and scientific machine learning. In the context of this special issue, hybrid renewable energy systems are any systems that consider the combined dynamics of more than one form of generation, storage, or grid subsystem. Research endeavors have delved into improving the flexibility of energy systems by utilizing existing resources, introducing novel operational strategies, deploying enhanced renewable forecasts, and exploring emerging technologies. In conclusion, the interconnection among various sectors has garnered heightened attention, not only due to the provision of additional tradable energy products but also for furnishing flexible headroom to system operators.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The transition from resistance to acceptance: Managing a marine invasive species in a changing world

Abstract Marine invasive species can transform coastal ecosystems, yet mitigating their effects can be difficult, and even impractical. Often, marine invasive species are managed at poorly matched spatial scales, and at the same time, rates of spread and establishment are increasing under climate change and can outpace resources available for population suppression. These circumstances challenge traditional conservation goals of maintaining a historic environmental state, especially for a species like the European green crab ( Carcinus maenas ), a formidable invader with few examples of successful long‐term removal programs. A management paradigm where decision alternatives include resisting or accepting a new ecological trajectory may be needed. We apply mathematical concepts from decision theory to develop a quantitative framework for navigating management decisions in this new resist‐accept paradigm. We develop a model of European green crab growth, removal and colonization, and we find optimal levels of removal effort that minimize both ecological change and removal cost. We establish a benchmark of colonization pressure at which green crab density becomes decoupled from a decision maker's actions, such that population control can no longer shape the invasion trajectory. For informing the decision boundary between resistance and acceptance, our results highlight that a decision maker's understanding of how removal cost scales with removal effort is more important than understanding the density‐impact relationship. We show that assuming stationary system dynamics can result in sub‐optimal levels of species removal effort, highlighting the importance of developing anticipatory management strategies by accounting for non‐stationary dynamics. Policy implications . For marine invasive species that can disperse across long distances and recolonize rapidly after removal, the focus of conservation policy should shift away from understanding how to resist change to understanding when to stop resisting change. Navigating this decision problem involves trade‐offs among competing objectives, highlighting the need for structured approaches to elicit objective weights that reflect the values of the decision maker. For natural resource managers facing possible ecosystem transformation, this decision framework can enable proactive and strategic decisions made under uncertainty in a changing world.

Keller, Abigail G. [Department of Environment Scie↗