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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Region and cloud regime dependence of parametric sensitivity in E3SM atmosphere model

Abstract The Department of Energy (DOE)’s Energy Exascale Earth System Model (E3SM), including its atmosphere model (EAM), has many relatively new features. In a previous study we conducted a systematic parametric sensitivity analysis for EAM based on short, perturbed parameter ensemble (PPE) simulations, mainly focusing on global mean climate features and metrics. While parameter values in global climate models are generally invariant in space and time, model response to parameters perturbation may vary by regions and climate regimes, which motivates the need to better understand the EAM model behaviors and physics at regional scale and process level. In this study, using the same set of PPE simulations and a similar sensitivity analysis framework, we identify parameters that cause largest sensitivities over different regions and compare model responses in fast atmospheric processes to the parameters across different cloud regimes for several important cloud-related fidelity metrics. We find that cloud forcing has opposite response to some parameters over mid-latitude vs. tropical land. We also analyze how the parametric sensitivity varies as stratocumulus transitions to shallow convection and to deep convection over ocean. Low cloud forcing and shortwave cloud forcing in the subtropical eastern Pacific are most sensitive to the parameters controlling the width of the probability density function (PDF) of the subgrid vertical velocity ( w’ ) ( gamma ) and the damping of the w’ skewness ( c8 ) near the coast but become more sensitive to the parameter affecting the damping of the w’ variance ( c1 ) further offshore. Detailed interpretation of the spatial dependence of parametric sensitivity is provided. We also investigate how the parametric sensitivity evolves with prediction duration. This study improves our process-level understanding of cloud physics and parameterization and provides insights for developing more advanced regime-aware parameterization schemes in global climate model.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Assessing microbial residues in soil as a potential carbon sink and moderator of carbon use efficiency

Abstract A longstanding assumption of glucose tracing experiments is that all glucose is microbially utilized during short incubations of ≤2 days to become microbial biomass or carbon dioxide. Carbon use efficiency (CUE) estimates have consequently ignored the formation of residues (non-living microbial products) although such materials could represent an important sink of glucose that is prone to stabilization as soil organic matter. We examined the dynamics of microbial residue formation from a short tracer experiment with frequent samplings over 72 h, and conducted a meta-analysis of previously published glucose tracing studies to assess the generality of these experimental results. Both our experiment and meta-analysis indicated 30–34% of amended glucose-C ( 13 C or 14 C) was in the form of residues within the first 6 h of substrate addition. We expand the conventional efficiency calculation to include residues in both the numerator and denominator of efficiency, thereby deriving a novel metric of the potential persistence of glucose-C in soil as living microbial biomass plus residues (‘carbon stabilization efficiency’). This new metric indicates nearly 40% of amended glucose-C persists in soil 180 days after amendment, the majority as non-biomass residues. Starting microbial biomass and clay content emerge as critical factors that positively promote such long term stabilization of labile C. Rapid residue production supports the conclusion that non-growth maintenance activity can illicit high demands for C in soil, perhaps equaling that directed towards growth, and that residues may have an underestimated role in the cycling and sequestration potential of C in soil.

Geyer, Kevin (ORCID:0000000252182745)↗
DOE OSTI2024

Efficient estimation of the modified Gromov–Hausdorff distance between unweighted graphs

Abstract Gromov–Hausdorff distances measure shape difference between the objects representable as compact metric spaces, e.g. point clouds, manifolds, or graphs. Computing any Gromov–Hausdorff distance is equivalent to solving an NP-hard optimization problem, deeming the notion impractical for applications. In this paper we propose a polynomial algorithm for estimating the so-called modified Gromov–Hausdorff (mGH) distance, a relaxation of the standard Gromov–Hausdorff (GH) distance with similar topological properties. We implement the algorithm for the case of compact metric spaces induced by unweighted graphs as part of Python library , and demonstrate its performance on real-world and synthetic networks. The algorithm finds the mGH distances exactly on most graphs with the scale-free property. We use the computed mGH distances to successfully detect outliers in real-world social and computer networks.

Oles, Vladyslav (ORCID:0000000188727463)↗
DOE OSTI2024

Assessing compounding climate-related stresses and development pathways on the power sector in the central U.S.

Future configurations of the power system in the central region of the USA are dependent on relative costs of alternative power generation technologies, energy and environmental policies, and multiple climate-induced stresses. Higher demand in the summer months combined with compounding supply shocks in several power generation technologies can potentially cause a “perfect storm” leading to failure of the power system. Potential future climate stress must be incorporated in investment decisions and energy system planning and operation. We assess how projected future climate impacts on the power system would affect alternative pathways for the electricity sector considering a broad range of generation technologies and changes in demand. We calculate a “potential supply gap” metric for each pathway, system component, and sub-region of the US Heartland due to climate-induced effects on electricity demand and power generation. Potential supply gaps range from 5% in the North Central region under mild changes in climate to 21% in the Lakes-Mid Atlantic region under more severe climate change. We find increases in electricity demand to be more important in determining the size of the potential supply gap than stresses on power generation, while larger shares of renewables in the power system contribute to lower supply gaps. Our results provide a first step toward considering systemic climate impacts that may require changes in managing the grid or on potential additional capacity/reserves that may be needed.

Gurgel, Angelo Costa↗
DOE OSTI2024

Applying queueing theory to evaluate wait-time-savings of triage algorithms

Abstract In the past decade, artificial intelligence (AI) algorithms have made promising impacts in many areas of healthcare. One application is AI-enabled prioritization software known as computer-aided triage and notification (CADt). This type of software as a medical device is intended to prioritize reviews of radiological images with time-sensitive findings, thus shortening the waiting time for patients with these findings. While many CADt devices have been deployed into clinical workflows and have been shown to improve patient treatment and clinical outcomes, quantitative methods to evaluate the wait-time-savings from their deployment are not yet available. In this paper, we apply queueing theory methods to evaluate the wait-time-savings of a CADt by calculating the average waiting time per patient image without and with a CADt device being deployed. We study two workflow models with one or multiple radiologists (servers) for a range of AI diagnostic performances, radiologist’s reading rates, and patient image (customer) arrival rates. To evaluate the time-saving performance of a CADt, we use the difference in the mean waiting time between the diseased patient images in the with-CADt scenario and that in the without-CADt scenario as our performance metric. As part of this effort, we have developed and also share a software tool to simulate the radiology workflow around medical image interpretation, to verify theoretical results, and to provide confidence intervals for the performance metric we defined. We show quantitatively that a CADt triage device is more effective in a busy, short-staffed reading setting, which is consistent with our clinical intuition and simulation results. Although this work is motivated by the need for evaluating CADt devices, the evaluation methodology presented in this paper can be applied to assess the time-saving performance of other types of algorithms that prioritize a subset of customers based on binary outputs.

Thompson, Yee Lam Elim (ORCID:0000000196537707)↗
DOE OSTI2023

Quantifying Pore Morphology in Spray-Formed Tantalum Using X-ray Micro-computed Tomography

In order to establish quantitative process–structure–property relationships in thermal spray coatings, a robust framework for defining (micro)structural characteristics is needed. Here, we present a quantitative characterization of the three-dimensional morphology of porosity in spray-formed tantalum samples based on high-resolution X-ray micro-computed tomography. Using synchrotron facilities, we acquired dozens of high-resolution scans, enabling a statistically meaningful comparison across multiple samples, different regions within samples, and spray processes. We quantify the spatial distribution, size, and topology of porous inclusions, with a significant focus on variability across samples and different spray processes (plasma and cold sprayed), as well as sensitivity to image segmentation and resolution. Based on a typical segmentation, we report porosities ranging from 0.9 to 1.7 pct for all samples tested, with significant sensitivity due to image segmentation resulting in estimates as low as 0.6 pct and as high as 4.8 pct. For the complex pore space morphology observed in these materials, we argue that a conventional analysis based on identifying individual pores is not well suited, and propose an alternative approach based on morphological metrics with a rich history in porous media literature, such as spatial correlations, local pore thickness, and scale-dependent sub-sampling. Spatial correlations indicate anisotropic splat structures, but only mildly anisotropic pores. Various measures of pore size show a wide distribution of sizes, ranging from sub-micron to 10-micron length scales. Scale-dependent variations in porosity suggest that representative volumes of several hundred microns are required for convergence of morphological metrics, with larger volumes for cold-spray materials. This work provides a robust quantitative basis for describing three-dimensional pore structure in thermal spray coatings.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Assessing the performance of solar radiation management geoengineering simulations

Offsetting the global warming caused by anthropogenic increases in atmospheric greenhouse gases by deliberate injection of aerosols into the stratosphere is the most studied of solar radiation management geoengineering schemes. The long-term success or failure of such schemes in achieving their stated goals is assessed by comparing simulated geoengineered temperature, precipitation and tropical cyclones metrics to equivalent fields in the simulated targeted climate simulations. Results using available data sets from three single model stabilized climate target experiments and three multimodel climate change reduction experiments are presented and compared against a measure of internal variability. While all but one experimental scheme is successful in achieving their targeted global mean annual surface temperature, their success at regional scales varies significantly and is often larger than the internal variability metric used here.

climate model evaluation↗
DOE OSTI2024

Expert and operator perspectives on barriers to energy efficiency in data centers

Abstract It was last estimated in 2016 that data centers (DCs) comprise approximately 2% of total US electricity consumption. However, this estimate is currently being updated to account for the massive increase in computing needs due to streaming, cryptocurrency, and artificial intelligence (AI). To prevent energy consumption that tracks with increasing computing needs, it is imperative we identify energy efficiency strategies and investments beyond the low-hanging fruit solutions. In a two-phased research approach, we ask: What non-technical barriers still impede energy efficiency (EE) practices and investments in the data center sector, and what can be done to overcome these barriers? In particular, we are focused on social and organizational barriers to EE. In Phase I, we performed a literature review and found that technical solutions are abundant in the literature, but fail to address the top-down cultural shifts that need to take place in order to adapt new energy efficiency strategies. In Phase II, reported here, we interviewed 16 data center operators/experts to ground-truth our literature findings. Our interview protocols focus on three aspects of DC decision-making: procurement practices, metrics and monitoring, and perceived barriers to energy efficiency. We find that vendors are the key drivers of procurement decisions, advanced efficiency metrics are facility-specific, and there is convergence in the design of advanced facilities due to the heat density of parallelized infrastructure. Our ultimate goals for our research are to design DC decarbonization policies that target organizational structure, empower individual staff, and foster a supportive external market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Algae-Based Beneficial Re-use of Carbon Emissions Using a Novel Photobioreactor: a Techno-Economic and Life Cycle Analysis

Despite the many advantages of microalgae, the feasibility of large-scale cultivation requires significant amounts of carbon dioxide (CO 2 ) to enable high growth rates. A synergistic union typically proposed for the supply of CO 2 is the coupling of algal cultivation with emissions from power plants. This study investigates the sustainability of a novel microalgae platform coupled with coal-based flue gas. The proposed system consists of a novel photobioreactor (PBR) for the production of biomass followed by a two-stage dewatering process. Here, a systems model, which quantifies the CO 2 and energy consumption of the proposed system, was developed and the minimum biomass selling price (MBSP) was determined by a techno-economic analysis (TEA). TEA results indicate that a facility with the capacity to capture 30% of the emissions from a 1 MW power plant requires a biomass production of 1280 metric tonnes per year, which when scaled to a nth of kind facility, can produce biomass at a MBSP of $2322 per tonne. The environmental impact of the proposed facility was determined by a life cycle assessment methodology and results indicate a carbon capture potential of 1.16 x 10 4 metric tons of CO 2 equivalent. In addition, an energy analysis indicates a desirable net energy ratio of 0.1, which is lower than conventional PBRs. Discussion focuses on the requirements to reduce biomass production cost, including research investment areas for increasing productivity while decreasing energy requirements.

09 BIOMASS FUELS↗
DOE OSTI2025

An Analysis of Input Parameters for Film-Based Flash X-Ray Radiography

Flash X-ray radiography (flash) is a commonly used diagnostic technique in dynamic experiments. An analysis of the effects of input parameters on resulting metrics of image quality can aid the experimentalist in configuring the X-ray input parameters to produce the highest quality radiograph for a given experiment. Here, a flash X-ray test bed with HS800 film and a LANEX Medium F intensifier screen was used with an L3 450 kVp pulser and Scandiflash X-ray tube for this study. Input parameters including charge voltage, source filtering, and film-pack assembly were investigated for their impact on contrast-to-noise ratio (CNR), contrast, and contrast transfer function (CTF). Using VIDAR’s NDT Pro industrial film digitizer, scanner parameters such as optical density range, pixel spacing, scan mode, and digital bit-depth were also examined for their impact on image quality metrics. The highest CNR values were found with two LANEX intensifiers and no filtering. Charge voltage had no direct impact on CNR values. LANEX screen count and filtering resulted both in direct effects on CNR and interaction effects with each other and CNR value. Uncertainty bounds for CNR comparisons and repeatability of CTF evaluations are also discussed. Finally, the film results are compared with a previous study using other detector types, specifically Carestream INDUSTREX Flex GP, Flex HR, Flex XL Blue, and HPX-DR 3543.

dynamic radiography↗
DOE OSTI2022

The Evolution of the Peridynamics Co-Authorship Network

We report peridynamics is a relatively new field in continuum mechanics that has developed over the past 20 years. This paper studies the evolution of collaborations in the field of peridynamics since its inception using social network analysis. For this purpose, we construct a network for each year from 2000 to 2019 describing co-authorship between scientists in peridynamics. In these networks, each node represents a scientist and each link connects two co-authoring scientists with a link weight representing the frequency and strength of their collaboration; each network as a whole can be thought of as a graph representation of the peridynamics community for the given year. By constructing a network for each year, we are able to analyze the evolution of the network in time and discuss the implications of this evolution for the peridynamics community. Our study demonstrates that the peridynamics community has been growing exponentially in size in recent years. Centrality metrics are also used to identify the most collaborative scientists in the community. Moreover, we compute link recommendations based on both elevating a scientist’s position in the network with respect to certain centrality metrics or closing structural holes in the network identified with persistent homology. We further extend the analysis to higher-order networks whose nodes represent groups of scientists in the community and whose links connect collaborating groups. In some sense, our work studies the past, present, and future of the peridynamics community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Water availability modulates maximum canopy heights of low-elevation Amazonian second-growth forests

Tropical second-growth forests of the Amazon sequester large amounts of carbon and are important carbon sinks, contributing substantially to climate change mitigation, biodiversity conservation, and providing crucial ecosystem services. Deforestation due to selective logging and shifting cultivation is expanding second-growth forest areas in tropical forest regions, which if well managed, regenerate rapidly over time. Maximum forest canopy height is an important metric of biomass and carbon accumulation in second-growth forests and is strongly influenced by water availability. The water limitation hypothesis explains the positive influence of water availability on maximum tree heights and has been examined and demonstrated at a small-scale using field data, and at a global scale, with limited accuracy, using remote sensing data in tropical ecosystems. However, this hypothesis concerning maximum canopy height has not been much studied at regional and national scales for tropical second-growth forests. In this study, we leveraged NASA GEDI spaceborne lidar data across the Brazilian Amazon and derived second-growth forest relative height metrics for delineating the influence of water availability, second-growth forest age, and topographic elevation on maximum canopy height. Water availability was found to significantly influence the maximum canopy height of second-growth forest trees, of age range from 30 to 35 years, at elevations less than 500 m and maximum precipitation thresholds of 1500 mm. Our results indicate that changing precipitation patterns or increased drought conditions under different climate change regimes could impact forest structure, plant communities, ecosystem functioning, and carbon sequestration capabilities of tropical second-growth forests in the Amazon.

Carbon sequestration↗
DOE OSTI2026

Quantifying atmospheric carbon removal at pulp and paper mills: a life cycle assessment across system boundaries

The pulp and paper industry is a promising yet underexplored platform for large-scale carbon dioxide removal (CDR) due to its use of biogenic feedstocks and production of concentrated CO 2 emissions from point sources. This study presents the first comprehensive life cycle assessment (LCA) of retrofitting an amine-based carbon capture and storage (CCS) system into a representative virgin kraft pulp and paper mill in the Southeastern U.S. We evaluate carbon removal across five system configurations, applying both static and dynamic LCA methods under multiple functional units: CO 2 captured, biomass input, and paper output. Results show that CCS retrofits can convert a conventional mill from a net emitter into a net carbon sink, with total removal efficiencies from 17% to 92% (metric tonnes of CO 2 removed per metric tonne of CO 2 available for removal under selected boundary conditions). When carbon removal is normalized to the quantity of biogenic CO 2 captured—a narrow, gate-to-gate system boundary that considers only CCS facility emissions—removal efficiencies reached as high as 92%. The use of such narrow boundaries aligns with precedents in traditional LCA methodology, where gate-to-gate assessments are commonly applied to isolate process-level performance and allocate emissions accordingly, providing a consistent basis for comparison across technologies. Under broader cradle-to-grave boundaries—which begin tracking carbon at the point of its physical removal from the atmosphere via photosynthesis in the forest, and extend to include upstream forest operations, mill-wide emissions, and downstream product decomposition—efficiencies declined, ranging from 17% to 46% under static assumptions and dropping to 12% when accounting for dynamic biogenic carbon fluxes over time. These results underscore how system boundary definitions influence reported outcomes, while also illustrating the complementary roles of narrow and broad perspectives for different decision-making contexts.

09 BIOMASS FUELS↗
DOE OSTI2023

Chapter 8 - Testing and Evaluation Methods for Protection Systems

In this chapter, we aim to present different approaches used formally and informally for the evaluation of protection devices and schemes. This chapter starts with presenting background on generalization of protection device components, followed by the resources necessary to evaluate the protection system. Further, this chapter presents the different approaches used in protection evaluation with their advantages and disadvantages. Finally, this chapter closes by presenting background on technology readiness level metrics. Technology readiness level metric is borrowed from the space industry for the purpose of comparing the maturity of the different protection technologies and using the appropriate evaluation methods from the list presented here.

power systems↗
DOE OSTI2021

Energy flexibility of residential buildings: A systematic review of characterization and quantification methods and applications

With building electric demand becoming increasingly dynamic, and a growing percentage of intermittent renewable power generation from solar photovoltaics and wind turbines, the power grid is facing increasing challenge to manage the real time balance between the supply and demand. With advancements in smart sensing and metering, smart appliances, electric vehicles, and energy storage technologies, demand side management of residential buildings can help the grid to improve stability by optimizing flexible loads. This paper reviews recent studies on residential building demand side management, with a focus on characterization and quantification of energy flexibility covering various types of flexible loads, metrics, methods, and applications. The reviewed studies showed four levels of applications: building level (45%), district or community level (29%), system level (19%), and building sector level (7%). Shifting loads is the dominant flexibility type in 60% of applications, followed by shedding (19%), generation (16%), and modulating (6%). Depending on the technology and application scope, flexible operations have a wide range of performance, with peak power reductions of 1%~65%, energy savings up to 60%, operational cost reduction of 1%~48%, and greenhouse gas emission reductions of up to29%. More than half (51%) of the studies employed control strategies to achieve flexibility; among those 72% used optimal controls, while 28% used rule-based controls. About 58% of the studies used mathematical formulation to quantify energy flexibility. Most studies were based on simulation, while less than 15% of the studies had measurements from experiments or field tests. The review reveals research opportunities to address significant gaps in the existing literature: (1) establishing a common definition and performance metrics for energy flexibility of buildings that are technology and application agnostic, (2) developing an ontology to standardize representation of flexibility resources for interoperability, (3) integrating occupant impacts into the quantification and optimization of energy flexibility, and (4) developing requirements and credits of energy flexibility in building energy codes and standards. Findings from the review can inform future research and development of energy flexible buildings which are essential to a reliable and resilient power grid.

Li, H↗
DOE OSTI2022

Evaluating the intrinsic resistance to balling of alloys: A High-throughput physics-informed and data-enabled approach

To date, the vast majority of work on metal additive manufacturing (AM) has been framed in terms of the need to tune processing conditions for a particular AM technology in order to print conventional alloys, oftentimes developed for fabrication methods other than AM. This approach overlooks the fact that historically, many engineering alloy system has been designed with a particular processing route in mind, e.g., ingot metallurgy, powder metallurgy, rapid quenching, etc. There are thus significant opportunities to design alloys specifically for AM. A key challenge is that alloy design requires performance metrics that can be optimized by exploring the alloy chemistry space. Here, we present a study in which we examine how intrinsic thermophysical properties can be used to estimate performance metrics related to the behavior of a solidifying metal droplet under AM-relevant conditions. By identifying these intrinsic properties, it is possible to directly incorporate ‘intrinsic printability’, specifically ‘intrinsic resistance to balling’, into AM-focused alloy design.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2020

Landscape composition and configuration have scale-dependent effects on agricultural pest suppression

Increasing landscape heterogeneity (composition and configuration) can enhance natural enemy populations and support pest suppression in agricultural landscapes. Using a network-based data mining approach, we examined independent gradients of landscape composition and configuration at six spatial scales that were associated with pest suppression services measured at 32 sites in Michigan and Wisconsin, USA. We compared the relative effects of landscape composition and configuration across scales with those of local crop type (corn or grassland). We found that multiple gradients of configurational heterogeneity were independent of composition and strongly associated with pest suppression, with different configuration metrics being predictive of pest suppression depending on the spatial scales and regions considered. Landscapes that were more configurationally heterogeneous at smaller spatial scales consistently supported higher pest suppression. In Michigan, pest suppression increased in landscapes with high edge contrast between annual crops and surrounding habitats and high edge density of grassland within 250-500 m radii. In Wisconsin, pest suppression increased with large core area of grassland and high field density within a 250 m radius. The main compositional effect we found was a positive relationship between grassland cover and pest suppression occurring at larger spatial scales (1000-1500 m) and occurring in Wisconsin but not in Michigan. Our findings demonstrate that effects of landscape composition and configuration on pest suppression differ across spatial scales and vary regionally. The network-based data mining techniques used here could be useful for disentangling intercorrelated landscape metrics in a variety of other contexts in landscape ecology.

54 ENVIRONMENTAL SCIENCES↗