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DOE OSTI2021

Data-Driven Cyber-Attack Detection for PV Farms via Time-Frequency Domain Features

The internetworking of grid-connected power electronics converters (PECs) in photovoltaic (PV) farms has inevitably expanded the cyber-attack surfaces. Here this paper presents a comprehensive study on cyber-attack detection and diagnosis for PEC-enabled PV farms via single waveform sensor to distinguish between normal conditions, open-circuit faults, short-circuit faults, and cyber-attacks. To our knowledge, this has not been attempted before. Firstly, we propose frequency-domain magnitude-based residuals to identify short-circuit faults and a time-domain mean current vector-based feature to distinguish open-circuit faults from other threats. These features can fully reflect the specific physical characteristics of PV farms during threat duration. Secondly, unlike micro phasor measurement units (µPMU) and raw electric waveform-based methods, the proposed innovative features can address novel cyber-attacks that are excluded from the training process. Thirdly, an online hardware-in-the-loop (HIL) testbed using the OPAL-RT real-time digital simulator has verified the effectiveness. The monitoring system runs in real-time while using HIL as an operational solar farm and a National Instruments (NI) data acquisition card as the electric waveform sensor at the point of coupling.

42 ENGINEERING↗

DOE OSTI2024

The Integration and Mapping of an Open-Source National Well Resource to Inform Geologic Carbon Storage Site Selection and Risk Prevention: The CO2-Locate Database

Geologic carbon storage (GCS) offers a way to capture and permanently store CO₂ from fossil fuel operations in underground geologic structures, aiding in the transition to a carbon-neutral energy economy. However, CO₂ injection sites can experience gas leakage through existing wells that penetrate storage reservoirs, making knowledge of well locations and characteristics crucial for permitting, infrastructure reusability, and risk assessment in GCS. Currently, public wellbore data from state, federal, and tribal entities are inconsistent and fragmented, with gaps and redundancies. To address this, the National Energy Technology Laboratory (NETL) developed CO2-Locate, an open-source, geospatial database and online application. CO2-Locate integrates over 50 data sources from federal, state, and tribal entities, creating a standardized national well database. Funded by the Bipartisan Infrastructure Law, the database is publicly available through the Energy Data eXchange (EDX) and viewable via the CO2-Locate web mapping application. This tool allows users to query, filter, and visualize well data to support GCS planning, permitting, and risk assessments. This presentation covers the methods used to create CO2-Locate, including data acquisition, processing, attribute mapping, and integration, much of which is automated for future updates. The web mapping application and its role in GCS site selection will also be discussed.

Tetteh, Daniel A.↗

DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

The Authenticatable Container Tracking System (ACTS) is an extensible secure container tag that can be used to optimize chain-of-custody monitoring for packaged nuclear materials as they are being stored, processed, and transported. Based on the ultra–low power TI MSP430 mixed-signal microcontroller, ACTS is an active device that uses an almost-universal core platform that can be appropriately configured with expansion modules to provide the application-specific data acquisition, data logging, container sealing, and communications functions needed for 21st century material accountancy, monitoring, and tracking applications. This core architecture enables appropriately designed modules to be easily interfaced to the basic system, providing an integration path for current and new technologies. ACTS contains a built-in set of sensors and supports an expansion bus for up to six additional communications, tracking, authentication, encryption, sealing, location, or sensing modules that may enhance the monitoring and tracking of designated containers for specific applications. To date, testing has used an IEEE 802.15.4-2011 ultra-wideband–compliant wireless transceiver module that has additional capabilities for indoor positioning and proximity location. Proximity-based location (i.e., where an ACTS tag knows where it is relative to other ACTS tags) will be explored as a continuity-of-knowledge mechanism to determine unauthorized movement of containers in storage arrays of many items. Partners for this system are Oak Ridge National Laboratory, Argonne National Laboratory, Savannah River National Laboratory, Pacific Northwest National Laboratory and commercialization partner the Aquila Group.

Younkin, Jim↗

DOE OSTI2018

Advancing the Authenticatable Container Tracking System (ACTS)

Younkin, James↗

DOE OSTI2016

A high performance cost-effective digital complex correlator for an X-band polarimetry survey

The detailed knowledge of the Milky Way radio emission is important to characterize galactic foregrounds masking extragalactic and cosmological signals. The update of the global sky models describing radio emissions over a very large spectral band requires high sensitivity experiments capable of observing large sky areas with long integration times. Here, we present the design of a new 10 GHz (X-band) polarimeter digital backend to map the polarization components of the galactic synchrotron radiation field of the Northern Hemisphere sky. The design follows the digital processing trends in radio astronomy and implements a large bandwidth (1 GHz) digital complex cross-correlator to extract the Stokes parameters of the incoming synchrotron radiation field. The hardware constraints cover the implemented VLSI hardware description language code and the preliminary results. The implementation is based on the simultaneous digitized acquisition of the Cartesian components of the two linear receiver polarization channels. The design strategy involves a double data rate acquisition of the ADC interleaved parallel bus, and field programmable gate array device programming at the register transfer mode. The digital core of the back-end is capable of processing 32 Gbps and is built around an Altera field programmable gate array clocked at 250 MHz, 1 GSps analog to digital converters and a clock generator. The control of the field programmable gate array internal signal delays and a convenient use of its phase locked loops provide the timing requirements to achieve the target bandwidths and sensitivity. This solution is convenient for radio astronomy experiments requiring large bandwidth, high functionality, high volume availability and low cost. Of particular interest, this correlator was developed for the Galactic Emission Mapping project and is suitable for large sky area polarization continuum surveys. The solutions may also be adapted to be used at signal processing subsystem levels for large projects like the square kilometer array testbeds.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2015

Standard Flow Liquid Chromatography for Shotgun Proteomics in Bioenergy Research

Over the past 10 years, the bioenergy field has realized significant achievements that have encouraged many follow on efforts centered on biosynthetic production of fuel-like compounds. Key to the success of these efforts has been transformational developments in feedstock characterization and metabolic engineering of biofuel-producing microbes. Lagging far behind these advancements are analytical methods to characterize and quantify systems of interest to the bioenergy field. In particular, the utilization of proteomics, while valuable for identifying novel enzymes and diagnosing problems associated with biofuelproducing microbes, is limited by a lack of robustness and limited throughput. Nano-flow liquid chromatography coupled to high-mass accuracy, high-resolution mass spectrometers has become the dominant approach for the analysis of complex proteomic samples, yet such assays still require dedicated experts for data acquisition, analysis, and instrument upkeep. The recent adoption of standard flow chromatography (ca. 0.5 mL/min) for targeted proteomics has highlighted the robust nature and increased throughput of this approach for sample analysis. Consequently, we assessed the applicability of standard flow liquid chromatography for shotgun proteomics using samples from Escherichia coli and Arabidopsis thaliana, organisms commonly used as model systems for lignocellulosic biofuels research. Employing 120 min gradients with standard flow chromatography, we were able to routinely identify nearly 800 proteins from E. coli samples; while for samples from Arabidopsis, over 1,000 proteins could be reliably identified. An examination of identified peptides indicated that the method was suitable for reproducible applications in shotgun proteomics. Standard flow liquid chromatography for shotgun proteomics provides a robust approach for the analysis of complex samples. To the best of our knowledge, this study represents the first attempt to validate the standard flow approach for shotgun proteomics.

09 BIOMASS FUELS↗

NASA NTRS1996

Computational Modeling of Magnetically Actuated Propellant Orientation

Unlike terrestrial applications where gravity positions liquid at the "bottom" of the tank, the location of liquid propellant in spacecraft tanks is uncertain unless specific actions are taken or special features are built into the tank. Some mission events require knowledge of liquid position prior to a particular action: liquid must be positioned over the tank outlet prior to starting the main engines and must be moved away from the tank vent before vapor can be released overboard to reduce pressure. It may also be desirable to positively position liquid to improve propulsion system performance: moving liquid away from the tank walls will dramatically decrease the rate of heat transfer to the propellant, suppressing the boil-off rate, thereby reducing overall mission propellant requirements. The process of moving propellant to a desired position is referred to as propellant orientation or reorientation. Propulsive reorientation relies on small auxiliary thrusters to accelerate the tank. The inertia of the liquid causes it to collect in the aft-end of the tank if the acceleration is forward. Liquid Acquisition Devices (LAD's) rely on surface tension to hold the liquid within special geometries, (i.e. vanes, wire-mesh channels, start-baskets), to positively position propellants. Both of these technologies add significant weight and complexity to the spacecraft and can be limiting systems for long duration missions. The subject of the present research is an alternate technique for positively positioning liquid within spacecraft propellant tanks: magnetic fields. LOX is paramagnetic (attracted toward a magnet) and LH2 is diamagnetic (repelled from a magnet). Order-of-magnitude analyses, performed in the 1960's to determine required magnet size, concluded that the magnets would be prohibitively massive and this option has remained dormant during the intervening years. Recent advances in high-temperature superconducting materials hold the promise of electromagnets with sufficient performance to support cryogenic propellant management tasks. In late 1992, NASA MSFC began a new investigation in this technology commencing with the design of the Magnetically-Actuated Propellant Orientation (MAPO) experiment. A mixture of ferrofluid and water is used to simulate the paramagnetic properties of LOX and the experiment is being flown on the KC-135 aircraft to provide a reduced gravity environment. The influence of a 0.4 Tesla ring magnet on flow into and out of a subscale Plexiglas tank is being recorded on video tape. The most efficient approach to evaluating the feasibility of MAPO is to compliment the experimental program with development of a computational tool to model the process of interest. The goal of the present research is to develop such a tool. Once confidence in its fidelity is established by comparison to data from the MAPO experiment, it can be used to assist in the design of future experiments and to study the parameter space of the process. Ultimately, it is hoped that the computational model can serve as a design tool for full-scale spacecraft applications.

Hochstein, John I.↗

DOE OSTI2025

MOOSE ProbML: Parallelizable Probabilistic Machine Learning and Uncertainty Quantification Capabilities

The Multiphysics Object Oriented Simulation Environment (MOOSE) is a widely used open- source finite element software for performing multiphysics multiscale simulations in a massively parallel fashion. Recently, the computational team at Idaho National Laboratory (INL) has implemented Probabilistic Machine Learning (ProbML) capabilities in MOOSE—in a parallelized fashion—and enable active learning with large-scale computational models for tasks such as surrogate model development, scale bridging, forward/inverse uncertainty quantification (UQ), Bayesian optimization, etc. This presentation summarizes these developments in MOOSE along with demonstrations on several real applications relevant to nuclear energy. At the fundamental level, samplers like Monte Carlo/Latin Hypercube, variance reduction, parallelized Markov Chain Monte Carlo (MCMC) support uncertainty propagation in both forward and inverse settings. These samplers can be integrated with the Gaussian processes (GP) suite in MOOSE, which offer several variants like scalar GPs, multi-output GPs, and deep GPs, to enable active learning. These GPs can be tuned using gradient-based optimization methods like Adam and its variants or gradient-free methods like the elliptical slice sampler (a variant of MCMC adept under Gaussian settings) for more complex covariance kernels or likelihoods whose gradient computations can be cumbersome. A variety of batch acquisition functions permit parallelized evaluation of the computational model and support different learning objectives with high efficiency like Bayesian inference, global surrogate development, optimization, etc. Furthermore, libtorch integration supports training, evaluation, and re-training of neural networks and other complex machine learning models in active learning settings. The impacts of these developments are shown on several real applications: (1) nuclear fuel inverse UQ and model inadequacy assessment using the Kennedy O’Hagan framework; (2) uncertainty aware surrogate modeling for additive manufacturing to predict field quantities; (3) nuclear reactor rare events analysis; and (4) complex fluid flow prediction using a global surrogate with quantified prediction uncertainty. Finally, the outlook of MOOSE ProbML is discussed for both outer-loop and inner-loop computations in the broad view to accelerate fuels and materials qualification, address gaps in knowledge and data, and assess new reactor/fuel systems.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2013

Electrical Resistance Tomographic Profile L2, Site 0, Barrow, AK

This archive contains the Electrical Resistivity Tomography (ERT) data inferred from an electrical survey conducted along a ~500 m long transect, as published in Figure 7 of Hubbard et al. (2013). The data were acquired within the Barrow Environmental Observatory (BEO) on the Alaskan Arctic Coastal Plain, approximately 4 miles from the Beaufort Sea near Utqiaġvik, Alaska. This dataset has been used to improve the quantification of subsurface properties and the understanding of co-variability between subsurface and surface characteristics. The reference list attached to this archive includes studies and other measurements associated with this dataset. This archive provides the inverted data as presented in Hubbard et al. (2013). More details on data acquisition, processing, and inversion are available in Dafflon et al. (2016). The raw data are provided in Dafflon et al. (2025). The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska, and 2) multiple areas in the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Integrated System Health Management: Pilot Operational Implementation in a Rocket Engine Test Stand

This paper describes a credible implementation of integrated system health management (ISHM) capability, as a pilot operational system. Important core elements that make possible fielding and evolution of ISHM capability have been validated in a rocket engine test stand, encompassing all phases of operation: stand-by, pre-test, test, and post-test. The core elements include an architecture (hardware/software) for ISHM, gateways for streaming real-time data from the data acquisition system into the ISHM system, automated configuration management employing transducer electronic data sheets (TEDS?s) adhering to the IEEE 1451.4 Standard for Smart Sensors and Actuators, broadcasting and capture of sensor measurements and health information adhering to the IEEE 1451.1 Standard for Smart Sensors and Actuators, user interfaces for management of redlines/bluelines, and establishment of a health assessment database system (HADS) and browser for extensive post-test analysis. The ISHM system was installed in the Test Control Room, where test operators were exposed to the capability. All functionalities of the pilot implementation were validated during testing and in post-test data streaming through the ISHM system. The implementation enabled significant improvements in awareness about the status of the test stand, and events and their causes/consequences. The architecture and software elements embody a systems engineering, knowledge-based approach; in conjunction with object-oriented environments. These qualities are permitting systematic augmentation of the capability and scaling to encompass other subsystems.

Figueroa, Fernando↗

NASA NTRS2011

In Situ Observations and Sampling of Volcanic Emissions with Unmanned Aircraft: A NASA/UCR Case Study at Turrialba Volcano, Costa Rica

Burgeoning new technology in the design and development of robotic aircraft-unmanned aerial vehicles (UAVs)-presents unprecedented opportunities for the volcanology community to observe, measure, and sample eruption plumes and drifting volcanic clouds in situ. While manned aircraft can sample dilute parts of such emissions, demonstrated hazards to air breathing, and most particularly turbine, engines preclude penetration of the zones of highest ash concentrations. Such areas within plumes are often of highest interest with respect to boundary conditions of applicable mass-loading retrieval models, as well as Lagrangian, Eulerian, and hybrid transport models used by hazard responders to predict plume trajectories, particularly in the context of airborne hazards. Before the 2010 Ejyafyallajokull eruption in Iceland, ICAO zero-ash-tolerance rules were typically followed, particularly for relatively uncrowded Pacific Rim airspace, and over North and South America, where often diversion of aircraft around ash plumes and clouds was practical. The 2010 eruption in Iceland radically changed the paradigm, in that critical airspace over continental Europe and the United Kingdom were summarily shut by local civil aviation authorities and EURO CONTROL. A strong desire emerged for better real-time knowledge of ash cloud characteristics, particularly ash concentrations, and especially for validation of orbital multispectral imaging. UAV platforms appear to provide a viable adjunct, if not a primary source, of such in situ data for volcanic plumes and drifting volcanic clouds from explosive eruptions, with prompt and comprehensive application to aviation safety and to the basic science of volcanology. Current work is underway in Costa Rica at Turrialba volcano by the authors, with the goal of developing and testing new small, economical UAV platforms, with miniaturized instrument payloads, within a volcanic plume. We are underway with bi-monthly deployments of tethered SO2-sondes and are in the planning stages for the deployment of the SIERRA UAV to our site in March 2013. We will be conducting in situ observations simultaneously with ASTER orbital multispectral TIR data acquisitions, in order to compare in situ measurements with estimates of SO2 mass loading and dispersion derived from ASTER data. Though small UAVs are now being considered for use in active volcanic areas for in situ sampling of emissions (e.g., efforts by our group, and by our colleagues at the INGV in Italy and the Applied Science University in Dusseldorf, Germany, and others in the United Kingdom and Iceland), and also for remote sensing, much more needs to be done in the way of instrument development, and in developing small UAVs for both low altitude (tropospheric) and high altitude (stratospheric) applications. In particular, the development of all weather and day/night operational flight capabilities in close proximity to hazardous topography is crucial to a truly responsive volcano in situ measurement system. Finally, it is imperative that national civil aviation authorities recognize the unique benefits of such platforms. It is important that authorities understand that severely restricting or not deploying such tools in airspace over restless volcanoes or within eruption plumes, ostensibly because of the perceived (small) risk that such unmanned aircraft pose to manned air operations, itself poses a bigger transcendental risk to proximal populations and particularly to the aviation community, itself.

Volcanic Ash Advisory Centers (VAACs)↗

DOE OSTI2021

Ecological stoichiometry as a foundation for omics-enabled biogeochemical models of soil organic matter decomposition

Coupled biogeochemical cycles drive ecosystem ecology by influencing individual-to-community scale behaviors; yet the development of process-based models that accurately capture these dynamics remains elusive. Soil organic matter (SOM) decomposition in particular is influenced by resource stoichiometry that dictates microbial nutrient acquisition (‘ecological stoichiometry’). Despite its basis in biogeochemical modeling, ecological stoichiometry is only implicitly considered in high-resolution microbial investigations and the metabolic models they inform. State-of-science SOM decomposition models in both fields have advanced largely separately, but they agree on a need to move beyond seminal pool-based models. This presents an opportunity and a challenge to maximize the strengths of various models across different scales and environmental contexts. To address this challenge, we contend that ecological stoichiometry provides a framework for merging biogeochemical and microbiological models, as both explicitly consider substrate chemistries that are the basis of ecological stoichiometry as applied to SOM decomposition. We highlight two gaps that limit our understanding of SOM decomposition: (1) understanding how individual microorganisms alter metabolic strategies in response to substrate stoichiometry and (2) translating this knowledge to the scale of biogeochemical models. We suggest iterative information exchange to refine the objectives of high-resolution investigations and to specify limited dynamics for representation in large-scale models, resulting in a new class of omics-enabled biogeochemical models. Assimilating theoretical and modelling frameworks from different scientific domains is the next frontier in SOM decomposition modelling; advancing technologies in the context of stoichiometric theory provides a consistent framework for interpreting molecular data, and further distilling this information into tractable SOM decomposition models.

Carbon cycling↗

NASA NTRS1997

Attitudinal Responses to Changes in Noise Exposure in Residential Communities

The purpose of this study is (1) to investigate the current body of knowledge encompassing two related topics: (a) to what extent can we reliably predict the change in people's attitudes in response to an abrupt change in noise exposure, and (b) after the change, is there a decay in the abrupt-change effect whereby people's attitudes slowly shift from their initial reaction to a steady-state value? and (2) to provide recommendations for any future work that may be needed. The literature search located 23 studies relating to one or both of the above topics. These prior studies shed considerable light on the current ability to predict initial reaction and decay effects. The literature makes one point very clear: Great care in both experimental design and data analysis is necessary to produce credible, convincing findings, both in the reanalysis of existing data and for planning future data acquisition and analysis studies. New airport studies must be designed to minimize nuisance variables and avoid past design features that may have introduced sufficient unexplained variance to mask sought after effects. Additionally, the study must be designed to tie in with previous investigations by incorporating similar survey questions and techniques.

Horonjeff, Richard D.↗

DOE OSTI2021

A library of AI-assisted FAIR water cycle and related disturbance datasets to enable model training, parameterization and validation

This whitepaper is responsive to focal area Data acquisition and assimilation enabled by machine learning, AI, and advanced methods. Here we describe how FAIR (Findable, Accessible, Reusable, Interoperable) datasets related to water cycle extremes are essential for successful implementation of ML in Earth System and other models. We also describe how AI can be used to acquire and integrate water cycle data related to extreme events to create a library of FAIR datasets for training and evaluating algorithms.

58 GEOSCIENCES↗

DOE OSTI2020

An Automated Disruption-Tolerant Key Management Framework for Critical Systems

Key management is critical to secure operation. Distributed control systems, such as Supervisory Control and Data Acquisition (SCADA) systems, have unique operational requirements that make conventional key management solutions less effective and burdensome. This paper presents a novel Kerberos-based framework for automated, disruption-tolerant key management for control system environments. Here, experimental tests and their results are presented to quantify the expected performance overhead of this approach. Additionally, Zeek sensor analytics are presented to aid in monitoring the health and security of the key management framework operation.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗

NASA NTRS2003

Academy Sharing Knowledge (ASK). The NASA Source for Project Management Magazine, Volume 11, March 2003

APPL is a research-based organization that serves NASA program and project managers, as well as project teams, at every level of development. In 1997, APPL was created from an earlier program to underscore the importance that NASA places on project management and project teams through a wide variety of products and services, including knowledge sharing, classroom and online courses, career development guidance, performance support, university partnerships, and advanced technology tools. ASK Magazine grew out of APPL's Knowledge Sharing Initiative. The stories that appear in ASK are written by the 'best of the best' project managers, primarily from NASA, but also from other government agencies and industry. Contributors to this issue include: Teresa Bailey, a librarian at the Jet Propulsion Laboratory, Roy Malone, Deputy Director in the Safety and Mission Assurance (S&MA) Office at the NASA Marshall Space Flight Center (MSFC), W. Scott Cameron, Capital Systems Manager for the Food and Beverage Global Business Unit of Procter and Gamble, Ray Morgan, recent retiree as Vice President of AeroVironment, Inc., Marty Davis, Program Manager of the Geostationary Operational Environmental Satellite (GOES) at the NASA Goddard Space Flight Center (GSFC) in Greenbelt, Maryland, Todd Post, editor of ASK Magazine, and works for EduTech Ltd. in Silver Spring, Maryland, Dr. Owen Gadeken, professor of Engineering Management at the Defense Acquisition University, Ken Schwer, currently the Project Manager of Solar Dynamics Observatory, Dr. Edward Hoffmwan, Director of the NASA Academy of Program and Project Leadership, Frank Snow, a member of the NASA Explorer Program at Goddard Space Flight Center since 1992, Dr. Alexander Laufer, Editor-in-Chief of ASK Magazine and a member of the Advisory Board of the NASA Academy of Program and Project Leadership, Judy Stokley, presently Air Force Program Executive Officer for Weapons in Washington, D.C. and Terry Little, Director of the Kinetic Energy Boost Office of the Missile Defense Agency.

Source record↗

DOE OSTI2019

Towards a fully automated algorithm driven platform for biosystems design

Large-scale data acquisition and analysis are often required in the successful implementation of the design, build, test, and learn (DBTL) cycle in biosystems design. However, it has long been hindered by experimental cost, variability, biases, and missed insights from traditional analysis methods. Here, we report the application of an integrated robotic system coupled with machine learning algorithms to fully automate the DBTL process for biosystems design. As proof of concept, we have demonstrated its capacity by optimizing the lycopene biosynthetic pathway. This fully-automated robotic platform, BioAutomata, evaluates less than 1% of possible variants while outperforming random screening by 77%. A paired predictive model and Bayesian algorithm select experiments which are performed by Illinois Biological Foundry for Advanced Biomanufacturing (iBioFAB). BioAutomata excels with black-box optimization problems, where experiments are expensive and noisy and the success of the experiment is not dependent on extensive prior knowledge of biological mechanisms.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2017

Climate, soil and plant functional types as drivers of global fine-root trait variation

Ecosystem functioning relies heavily on below-ground processes, which are largely regulated by plant fine-roots and their functional traits. However, our knowledge of fine-root trait distribution relies to date on local- and regional-scale studies with limited numbers of species, growth forms and environmental variation. We compiled a world-wide fine-root trait dataset, featuring 1115 species from contrasting climatic areas, phylogeny and growth forms to test a series of hypotheses pertaining to the influence of plant functional types, soil and climate variables, and the degree of manipulation of plant growing conditions on species fine-root trait variation. Most particularly, we tested the competing hypotheses that fine-root traits typical of faster return on investment would be most strongly associated with conditions of limiting versus favourable soil resource availability. We accounted for both data source and species phylogenetic relatedness. We demonstrate that: (i) Climate conditions promoting soil fertility relate negatively to fine-root traits favouring fast soil resource acquisition, with a particularly strong positive effect of temperature on fine-root diameter and negative effect on specific root length (SRL), and a negative effect of rainfall on root nitrogen concentration; (ii) Soil bulk density strongly influences species fine-root morphology, by favouring thicker, denser fine-roots; (iii) Fine-roots from herbaceous species are on average finer and have higher SRL than those of woody species, and N 2 -fixing capacity positively relates to root nitrogen; and (iv) Plants growing in pots have higher SRL than those grown in the field. Synthesis. This study reveals both the large variation in fine-root traits encountered globally and the relevance of several key plant functional types and soil and climate variables for explaining a substantial part of this variation. Climate, particularly temperature, and plant functional types were the two strongest predictors of fine-root trait variation. High trait variation occurred at local scales, suggesting that wide-ranging below-ground resource economics strategies are viable within most climatic areas and soil conditions.

54 ENVIRONMENTAL SCIENCES↗