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At least 1,117 records · Page 62

Acceleration of the particle-in-cell code Osiris with graphics processing units

Fully relativistic particle-in-cell (PIC) simulations are crucial for advancing our knowledge of plasma physics. Modern supercomputers based on graphics processing units (GPUs) offer the potential to perform PIC simulations of unprecedented scale, but require robust and feature-rich codes that can fully leverage their computational resources. In this work, this demand is addressed by adding GPU acceleration to the PIC code Osiris. An overview of the algorithm, which features a CUDA extension to the underlying Fortran architecture, is given. Detailed performance benchmarks for thermal plasmas are presented, which demonstrate excellent weak scaling on NERSC's Perlmutter supercomputer and high levels of absolute performance. The robustness of the code to model a variety of physical systems is demonstrated via simulations of Weibel filamentation and laser-wakefield acceleration run with dynamic load balancing. Finally, measurements and analysis of energy consumption are provided that indicate that the GPU algorithm is up to ~14 times faster and ~7 times more energy efficient than the optimized CPU algorithm on a node-to-node basis. The described development addresses the PIC simulation community's computational demands both by contributing a robust and performant GPU-accelerated PIC code and by providing insight into efficient use of GPU hardware.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Development of an Energy-Efficient and High-Productivity Ammonia Recovery and Removal Process Using Resin-Wafer Electrodeionization

As efforts to develop various energy resources, ammonia energy is emerging as a promising carbon-free fuel alternative. Recovering high-concentration ammonia and ammonium from wastewater potentially offers significant environmental and economic benefits. However, research on recovery technologies for industries such as semiconductors remains limited. This study develops an efficient, energy-saving ammonia recovery technology using Resin Wafer Electrodeionization (RW-EDI), specifically for the semiconductor industry. RW-EDI shows promise for recovering ammonia from high-concentration wastewater. By optimizing parameters, such as voltage and initial concentration, a balance between productivity and energy consumption is achieved. Results indicate that ammonium and fluoride ion transport kinetics are similar, with minimal competition and selectivity values between 0.95 and 1.08. As the initial ammonia/ammonium concentration increases from 500 to 8000 ppm, reaction rate constants and the overall mass transfer coefficient decrease. Increasing the voltage can enhance mass transfer and eliminate barriers. Ion transport primarily occurs in the RW solid phase, accounting for 93.89% of the total current. Additionally, RW-EDI shows superior specific energy consumption within a concentration range of 500 ppm to 8000 ppm, outperforming technologies like electrodialysis by reducing specific energy consumption from 8−15 kWh/kg-NH 4 + to 1.2−2.5 kWh/kg-NH 4 + . This study highlights RW-EDI’s potential for ammonia recovery, providing valuable insights for future applications in wastewater treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Impacts of Distinct Flexibility Enhancements on the Value and Dynamics of Natural Gas Power Plant Operations

The rapid increase of renewable generation and its anticipated continued growth requires greater operational flexibility in modern power systems. Previous studies explored the addition of more flexible resources to improve system flexibility, but this may also be achieved at lower cost by enhancing existing power plants via flexibility retrofits. Potential flexibility upgrades to existing thermal plants include modifications that enable faster ramp rates, increase maximum load levels, decrease minimum load levels, and provide faster and lower cost startup operations. In this paper, we focus on improvements to existing natural gas combined cycle generators, and apply a unit commitment model to analyze the impact that each specific upgrade has on their operational dynamics and profitability from both the system perspective and from the asset owner perspective. We show that the value of each type of flexibility improvement varies depending on the capacity factor and usage pattern of the generator before the upgrade, and that the relative benefit of each type of flexibility varies. We demonstrate diminishing returns to increased flexibility when upgrading multiple generators simultaneously. Sensitivity tests for several shares of renewable generation, different generation mixes and different net load realizations confirm the generality of the results shown.

03 NATURAL GAS↗

Influence of Mo- and Ga-Supported HZSM-5 Co-Catalyst Configuration in Microwave-Assisted Methane and Ethane Dehydroaromatization

Microwave (MW)-assisted dehydroaromatization (DHA) using an Mo-supported HZSM-5 catalyst (Mo/HZSM-5) enhances the value of natural gas resources by converting stranded or underutilized natural gas into value-added chemicals in modular microwave reactor systems. This approach offers the potential to generate economic value. However, natural gas mixtures often contain multiple components, including ethane (C2H6) and propane (C3H8), which complicate the reaction pathways. Ga-supported HZSM-5 (Ga/HZSM-5) catalysts are generally inactive toward CH4 but exhibit higher activity toward C2H6 and C3H8. Therefore, investigating the combination of Mo/HZSM-5 and Ga/HZSM-5 in various catalyst bed configurations is essential. This study explores different co-catalyst bed configurations and CH4/C2H6 feed compositions to determine the most effective way for enhanced natural gas conversion and benzene production.

cocatalyst bed configuration↗

Subsurface H 2 Storage: A Williston Basin Commercial-Scale Resource Study

Poster for the 2024 NETL Resource Sustainability Project Review Meeting, Pittsburgh, Pennsylvania, April 2-4, 2024. This poster presents a commercial‑scale assessment of subsurface hydrogen storage potential in the North Dakota portion of the Williston Basin, evaluating saline formations, depleted oil and gas reservoirs, and salt formations. The study integrates laboratory characterization, reservoir simulation, and basinwide analysis to assess storage capacity, injectivity, recovery, and risks related to geochemical, microbial, and wellbore interactions. Results support the feasibility of large‑volume, secure hydrogen storage and provide a framework to guide future hydrogen commercialization and infrastructure development.

08 HYDROGEN↗

A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google’s Colaboratory

X-ray micro-computed tomography (X-ray μCT) has enabled the characterization of the properties and processes that take place in plants and soils at the micron scale. Despite the widespread use of this advanced technique, major limitations in both hardware and software limit the speed and accuracy of image processing and data analysis. Recent advances in machine learning, specifically the application of convolutional neural networks to image analysis, have enabled rapid and accurate segmentation of image data. Yet, challenges remain in applying convolutional neural networks to the analysis of environmentally and agriculturally relevant images. Specifically, there is a disconnect between the computer scientists and engineers, who build these AI/ML tools, and the potential end users in agricultural research, who may be unsure of how to apply these tools in their work. Additionally, the computing resources required for training and applying deep learning models are unique, more common to computer gaming systems or graphics design work, than to traditional computational systems. To navigate these challenges, we developed a modular workflow for applying convolutional neural networks to X-ray μCT images, using low-cost resources in Google’s Colaboratory web application. Here we present the results of the workflow, illustrating how parameters can be optimized to achieve best results using example scans from walnut leaves, almond flower buds, and a soil aggregate. We expect that this framework will accelerate the adoption and use of emerging deep learning techniques within the plant and soil sciences.

59 BASIC BIOLOGICAL SCIENCES↗

Interregional Renewable Energy Zones

Interregional Renewable Energy Zones (IREZs) are a component of the National Transmission Planning Study (NTPS), which analyzes strategies for building out transmission infrastructure to support decarbonizing the electricity sector by 2035. In particular, IREZs envision long-distance transmission corridors for moving large amounts of low-cost renewable energy. An IREZ is a transmission collection point that is easily accessible to a very high volume of low-cost developable wind and solar energy potential. This dataset includes 155 IREZs identified across the continental United States where each IREZ is characterized by its primary and secondary resource.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Technical and Economic Evaluation of the First Ever Polymer Flood Field Pilot to Enhance the Recovery of Heavy Oils on Alaska's North Slope via Machine Assisted History Matching

Polymer flooding has become globally established as a potential enhanced oil recovery method for heavy oils. To determine whether this technology may be useful in developing the substantial heavy oil resources on the Alaska North Slope, a polymer flood field pilot commenced at the Milne Point Unit in August 2018. This study seeks to evaluate the results of the field pilot on a technical and economic basis. A reservoir simulation model is constructed and calibrated to predict the oil recovery performance of the pilot through machine-assisted reservoir simulation techniques. To replicate the early water breakthrough observed during waterflooding, transmissibility contrasts are introduced into the simulation model, forcing viscous fingering effects. In the ensuing polymer flood, these transmissibility contrasts are reduced to replicate the restoration of injection conformance during polymer flooding. Transmissibility contrasts are later reinstated to replicate fracture overextension interpreted in one of the producing wells. The calibrated simulation models produced at each stage of the history matching process are used to forecast oil recovery. These forecasts are used as input for economic analysis, incremental to waterflooding expectations. The simulation forecasts indicate that polymer flooding significantly increases the heavy oil production for this field pilot compared to waterflooding alone, yielding attractive project economics. However, meaningful variations between simulation scenarios demonstrate that a simulation model is only valid for prediction if flow behavior in the reservoir remains consistent with that observed during the history matched period. Critically, this means that a simulation model calibrated for waterflooding may not fully capture the technical and economic benefits of an enhanced oil recovery process such as polymer flooding. Subsequently, the simulation model and economic model are used in conjunction to conduct a sensitivity analysis for polymer flood design parameters, from which recommendations are provided for both the continued operation of the current field pilot and future polymer flood designs. The results demonstrate that a higher polymer concentration can be injected due to the development of fractures in the reservoir. The throughput rate should remain high without exceeding operating constraints. A calculated point-forward polymer utilization parameter demonstrates the decreasing efficiency of the polymer flood at later times in the pattern life. Future projects will benefit from starting polymer injection earlier in the pattern life. A pattern with tighter horizontal well spacing will observe a greater incremental benefit from polymer flooding.

Keith, Cody↗

Exploring Multidimensional Spatial-Temporal Hydropower Operational Flexibilities by Modeling and Optimizing Water-Constrained Cascading Hydroelectric Systems

Because of unique characteristics such as clean and cost-competitive electricity as well as fast-ramping and storage abilities, the power industry continues to evolve its operation strategies for cascading hydroelectric (CHE) systems for providing enhanced values to the grid, especially under the deeper renewable resource integration. However, existing operation practices of CHEs predate the integration of renewables, which could prohibit the effective utilization of their inherent flexibilities in delivering maximum financial benefits and providing valuable grid services to the power system and electricity market operations. Indeed, modeling and optimizing these resource-limited while flexible CHE assets with uncertainties and imperfect information across multiple spatial-temporal dimensions present significant challenges. To facilitate CHE facility operators in effectively coordinating water usage and hydropower plant operations across multiple timescales, this project aims to fill the existing gaps by developing a suite of accurate water inflow (WI) forecast models as well as enhanced CHE modeling and optimization approaches with proper consideration of their unique characteristics, which would help explore their multidimensional spatial-temporal operational flexibility potentials. The developed approaches could better align reservoir operation strategies with variability and uncertainty of future water availability. They can also promote more effective utilization of multidimensional spatial-temporal hydropower operational flexibility potentials by designing long-term evacuation plans of reservoirs and short-term operation of CHEs, along with their coordination with other types of renewables. The project leverages various resources to facilitate the research and development activities, including actual characteristics data of CHE systems and a library of current and future cases of Portland General Electric (PGE). These realistic data enable the project team to study how to maximize the value of CHEs under current and future portfolios and evaluate opportunities to improve operation practices.

13 HYDRO ENERGY↗

Computational and experimental study of different brines in temperature swing solvent extraction desalination with amine solvents

Rapid global urbanization and high-salinity wastewater disposal from industrial activities have exerted significant pressure on water resources. Over the past few years, temperature swing solvent extraction (TSSE) has been identified as a promising technique to desalinate hypersaline brines. Despite its potential, the TSSE desalination literature has been mainly based on empirical insights, and the limited molecular simulation studies have primarily focused on NaCl brines. Herein, we use molecular dynamics (MD) simulations to study the TSSE desalination of four different brines, namely, KCl, KBr, NaCl, and NaBr using diisopropylamine as the solvent. Based on both bulk and interfacial brine-diisopropylamine MD results, here we investigate the qualitative and quantitative performance of the simulations by benchmarking these results against our experimental evaluations of these same systems. MD results provide satisfactory qualitative agreement with the experimental data of water solubilization in the organic phase and amine solubilization in the aqueous phase for the KBr, KCl, and NaBr brines. Also, the molecular mechanism of solvation of ionic species by water molecules over diisopropylamine suggested by the MD simulations is in agreement with our experimental data. However, larger qualitative and quantitative deviations were observed for the NaCl brines, and this is likely due to polarization and charge transfer effects, as quantified by our quantum chemical calculations.

42 ENGINEERING↗

Choosing the Best Modeling Platform for Radiological Risk Assessment Models - 20468

Radiological risk assessments, in the form of performance or safety assessments, are often required under regulations or guidance for remediation of contaminated land, decommissioning of contaminated buildings or structures, and radioactive waste disposal. These risk assessments are usually supported by fate and transport models that address decay and ingrowth of radionuclides, as well as their movement through engineered systems and the natural environment. These models are often projected thousands, or more, years into the future, largely because the radioactive species change through decay and ingrowth, and hence the magnitude of the radioactive effect changes with time. There are many computer codes that are available to address this type of modeling. They range from addressing specific pathways or processes such as infiltration of water, groundwater, surface water, air, biota, diffusion and advection of water and gases, to those that try to couple all processes together to evaluate the impact of fate and transport through the entire system to places in space and time to which access is assumed. These different types of codes are sometimes separated with the monikers process-level and systems-level codes, although it is often not clear that this separation does justice to the capabilities of the many codes that are available to evaluate fate and transport of radionuclides. The focus of this paper is the latter group of modeling codes. Several systems level modeling codes exist and are used. There are differences between these codes in terms of utility, flexibility, complexity and cost. The purpose of this paper is to compare a few of these codes in the context of work currently being performed by the International Atomic Energy Agency (IAEA) Modeling and Data for Radiological Impact Assessments (MODARIA) II Working Group 1 (WG1). The MODARIA II WG1's main focus is how stakeholder engaged decision analysis can, or should, be applied to radiological contamination problems so that better, longstanding, sustainable, solutions are reached. However, the WG1 also recognizes the potential impact of the modeling tools that are chosen to address radiological risk, which is often a primary objective of decision making for radiological problems. Other objectives might also be important, such as constraining costs, obtaining financing, minimizing impact on ecosystems, saving cultural resources, saving jobs, farmland, environmental justice, etc., in a full decision analysis for a given radiological contamination problem, but none of these other objectives have the same types of complex modeling needs as minimize radiological dose. Consequently, a further focus of WG1 is to evaluate the potential impacts on decision making of the choice of fate and transport, and risk assessment, modeling codes that are used to support decision making. The WG1 will produce a report at the end of 2020 that will focus on an approach to effective decision making and stakeholder engagement. The report will also consider the role that performance assessment modeling should play in the decision-making process, including the impact of the choice of modeling tools or computer codes on risk-informed decision making. Several sites around the World have been made available by Member States for these model comparisons, and several modeling tools have been considered. However, the focus of this paper is on two of the sites, one in Belgium and one in Ukraine, and on three of the tools: NORMALYSA (NORM And Legacy Site Assessment); GoldSim{sup C}, and AMBER{sup C}. The final report from this working group will also cover other modeling tools, including RESRAD, and PC-Cream{sup R}. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Potential of the Julia Programming Language for High Energy Physics Computing

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified.

97 MATHEMATICS AND COMPUTING↗

ICED: An Integrated CGRA Framework Enabling DFVS-Aware Acceleration

oarse-grained reconfigurable arrays (CGRAs) are a promising solution to enable energy-efficient acceleration of applications from different domains. By leveraging reconfiguration at the functional level, they can adapt to significantly different computational patterns. Existing CGRA mapping approaches extract instruction-level parallelism, exploit loop-pipelining opportunities, guarantee the data dependency, and target high throughput of a given loop. However, the recurrence data-dependency in the DFG and the mismatch between required and available computing/communication resources complicate the mapping, and might lead to significant unbalances in the utilization of the CGRA's tiles. This results in wasted power for tiles with low utilization. Applying dynamic voltage and frequency scaling (DVFS) can potentially solve this challenge and improve energy efficiency by adjusting voltage and frequency of different tiles independently. CGRAs have also been successful in accelerating data-dependent streaming applications. However, in these applications, the execution time of each kernel in the pipeline might dynamically vary depending on the characteristics of the input. This also leads to under-utilization of resources for the dynamically changing kernels that do not limit the application throughput. DVFS can also improve energy efficiency for these applications by dynamically changing the voltage and frequency levels of tiles that host non performance-constraining kernels. This paper proposes ICEDTEA -- an integrated DVFS-aware framework to map applications on CGRAs that support power islands. ICEDTEA proposes a CGRA architecture supporting DVFS islands at varying granularity (from a single tile to a group of tiles) and the related DVFS-aware compilation and mapping toolchain. ICEDTEA is the first work that introduces DVFS support for spatio-temporal CGRAs at power-island levels. The experimental evaluation shows that ICEDTEA improves average utilization by 2.3$\times$ and energy-efficiency by 1.32$\times$ over a conventional CGRA. With streaming applications, ICEDTEA improves energy efficiency by 1.12$\times$ over a state-of-the-art CGRA that introduces partial dynamic reconfiguration to adapt to variations in kernels' throughput.

Tan, Cheng↗

Modeling Assessment of Tidal Energy Extraction in the Western Passage

Numerical models have been widely used for the resource characterization and assessment of tidal instream energy. The accurate assessment of tidal stream energy resources at a feasibility or project-design scale requires detailed hydrodynamic model simulations or high-quality field measurements. This study applied a three-dimensional finite-volume community ocean model (FVCOM) to simulate the tidal hydrodynamics in the Passamaquoddy–Cobscook Bay archipelago, with a focus on the Western Passage, to assist tidal energy resource assessment. IEC Technical specifications were considered in the model configurations and simulations. The model was calibrated and validated with field measurements. Energy fluxes and power densities along selected cross sections were calculated to evaluate the feasibility of the tidal energy development at several hotspots that feature strong currents. When taking both the high current speed and water depth into account, the model results showed that the Western Passage has great potential for the deployment of tidal energy farms. The maximum extractable power in the Western Passage was estimated using the Garrett and Cummins method. Different criteria and methods recommended by the IEC for resource characterization were evaluated and discussed using a sensitivity analysis of energy extraction for a hypothetical tidal turbine farm in the Western Passage.

16 TIDAL AND WAVE POWER↗

Automating NISQ Application Design with Meta Quantum Circuits with Constraints (MQCC)

Near-term intermediate scale quantum (NISQ) computers are likely to have very restricted hardware resources, where precisely controllable qubits are expensive, error-prone, and scarce. Programmers of such computers must therefore balance trade-offs among a large number of (potentially heterogeneous) factors specific to the targeted application and quantum hardware. To assist them, we propose Meta Quantum Circuits with Constraints (MQCC), a meta-programming framework for quantum programs. Programmers express their application as a succinct collection of normal quantum circuits stitched together by a set of (manually or automatically) added meta-level choice variables, whose values are constrained according to a programmable set of quantitative optimization criteria. MQCC’s compiler generates the appropriate constraints and solves them via an SMT solver, producing an optimized, runnable program. We showcase a few MQCC’s applications for its generality including an automatic generation of efficient error syndrome extraction schemes for fault-tolerant quantum error correction with heterogeneous qubits and an approach to writing approximate quantum Fourier transformation and quantum phase estimation that smoothly trades off accuracy and resource use. We also illustrate that MQCC can easily encode prior one-off NISQ application designs-–multi-programming (MP), crosstalk mitigation (CM)—as well as a combination of their optimization goals (i.e., a combined MP-CM).

97 MATHEMATICS AND COMPUTING↗

Predicting Initial Trans-Membrane Pressure for Optimized Operations in UF Unit Using Random Forest

With the growing scarcity of freshwater, innovative process design mechanisms like Reverse Osmosis (RO) are increasingly gaining attention among water treatment utilities to address the rising demand. Ensuring reliable water production necessitates efficient resource utilization, minimizing downtime in (ultra-filtration) UF systems. Recent advancements in machine learning (ML) have enabled the development of accurate data-driven models for Model Predictive Control (MPC), often requiring minimal prior knowledge of underlying physical processes. In this study, we present predictive regression models based on Random Forest (RF) and Auto-Regressive (AR) approaches to forecast the initial Trans-Membrane Pressure (TMP) for each filtration cycle in data generated by Direct Potable Reuse (DPR) systems. The proposed RF-based model demonstrates superior performance compared to baseline methods, including historical mean, Last Observation Carried Forward (LOCF), and naïve AR models, across various forecasting horizons in terms of root mean square error (RMSE) metric. To evaluate how different classes of process variables contribute to TMP dynamics over time, we examine the feature importance of independent covariates across multiple forecast horizons. This analysis provides insight into the temporal relevance of operational and sensor-derived features, guiding control and monitoring strategies. Additionally, the impact of hyperparameter tuning on TMP prediction performance is studied for both direct and recursive RF modelling approaches across increasing forecast horizons. Accurate prediction of initial TMP is critical for optimizing RO operations, as it enables the development of robust modelling frameworks by accurately estimating membrane fouling trends, thereby enhancing process efficiency and long-term reliability. The demonstrated efficacy of the RF-based approach highlights its potential as a tool for real-time decision-making in water treatment systems, paving the way for advanced process optimization and sustainable water resource management.

Mukherjee, Subrata [ORNL] (ORCID:0000000309930338)↗

Potential impacts of climate change on wind and solar electricity generation in Texas

Wind and solar energy sources are climate and weather dependent, therefore susceptible to a changing climate. in this paper, we quantify the impacts of climate change on wind and solar electricity generation under high concentrations of greenhouse gases in Texas. We employ mid-twenty-first century climate projections and a high-resolution numerical weather prediction model to generate weather variables in the future and produce wind and solar generation time series. We find that mid-twenty-first century projections based on five global climate models agree on the multiyear average increases across Texas in direct normal irradiance, global horizontal irradiance, surface air temperature, and 100-m wind speed of up to 5%, 4%, 10%, and 1%, respectively. These changes lead to multiyear average relative changes across Texas of -0.6 to + 2.5% and of + 1.3 to + 3.5% in solar and wind capacity factors, respectively, with significant regional, seasonal, and diurnal differences. Areas with low solar resource show an increase in solar capacity factors but reductions in wind capacity factors. Areas with high solar resource show reductions in solar capacity factors. The spatial and temporal differences in our results highlight the importance of using high-resolution data sets to study the potential impacts of climate change on wind and solar power.

14 SOLAR ENERGY↗

The impact of plant‐derived fire management prescriptions on fire‐responsive bird species

Abstract In fire‐prone regions, the occurrence of some faunal species is contingent on the presence of resources that arise through post‐fire plant succession. Through planned burning, managers can alter resource availability and aim to provide the conditions required to promote biodiversity. Understanding how species occurrence changes at different spatial and temporal scales after fire is essential to achieve this goal. However, many fire prescriptions are guided primarily by the responses of fire‐sensitive plants when setting tolerable fire intervals. This approach assumes that maintaining floristic diversity will satisfy the requirements of fauna. We surveyed bird species in two semi‐arid vegetation types across an environmental gradient in south‐eastern Australia. We conducted four surveys at each of 253 sites across a 75‐year chronosequence of time since fire and used generalized additive mixed models to examine changes in the occurrence of birds in response to time since fire. Model predictions were compared to plant‐derived fire prescriptions currently guiding fire management in the region. Time since fire was a significant predictor for 18 of 28 species modeled, in at least one vegetation type, over a gradient of 1.3° of latitude. We detected considerable variation in the responses of some species, both between vegetation types and geographically within a vegetation type. Our evaluation of plant‐derived fire prescriptions suggests that the intervals considered acceptable for maintaining floristic diversity may not be sustainable for populations of birds requiring longer unburnt vegetation, with 6 of the 12 species assessed attaining a mean occurrence probability of 20.3% by the minimum tolerable fire interval, and 57.3% by the maximum tolerable fire interval, in their respective vegetation types. Our findings highlight the potential vulnerability of fire‐responsive bird species if fire prescriptions are applied in a manner that fails to account for the slow development of habitat resources needed by some species, and the variation detected within the responses of species. This highlights the need for species‐specific data collected at an appropriate spatial scale to inform management plans.

Makdissi, Rhys↗