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The NASA's new millennium program (NMP) focuses on testing high-risk, advanced technologies in space with low-cost flights. The objective of the NMP technology validation missions is to enable future science missions. The NMP missions are technology-driven, with the principal requirements coming from the needs of the advanced technologies that form the 'payload'.
Translating the ever-increasing wealth of information on microbiomes (environment, host, or built environment) to advance the understanding of system-level processes is proving to be an exceptional research challenge. One reason for this challenge is that relationships between characteristics of microbiomes and the system-level processes they influence are often evaluated in the absence of a robust conceptual framework and reported without elucidating the underlying causal mechanisms. The reliance on correlative approaches limits the potential to expand the inference of a single relationship to additional systems and advance the field. We propose that research focused on how microbiomes influence the systems they inhabit should work within a common framework and target known microbial processes that contribute to the system-level processes of interest. Here we identify three distinct categories of microbiome characteristics (microbial processes, microbial community properties, and microbial membership) and propose a framework to empirically link each of these categories to each other and the broader system level processes they affect. We posit that it is particularly important to distinguish microbial community properties that can be predicted from constituent taxa (community aggregated traits) from and those properties that are currently unable to be predicted from constituent taxa (emergent properties). Existing methods in microbial ecology can be applied to more explicitly elucidate properties within each of these categories and connect these three categories of microbial characteristics with each other. We view this proposed framework, gleaned from a breadth of research on environmental microbiomes and ecosystem processes, as a promising pathway with the potential to advance discovery and understanding across a broad range of microbiome science.
The rapidly emerging field of macrogenetics focuses on analysing publicly accessible genetic datasets from thousands of species to explore large-scale patterns and predictors of intraspecific genetic variation. Facilitated by advances in evolutionary biology, technology, data infrastructure, statistics and open science, macrogenetics addresses core evolutionary hypotheses (such as disentangling environmental and life-history effects on genetic variation) with a global focus. Yet, there are important, often overlooked, limitations to this approach and best practices need to be considered and adopted if macrogenetics is to continue its exciting trajectory and reach its full potential in fields such as biodiversity monitoring and conservation. Here, we review the history of this rapidly growing field, highlight knowledge gaps and future directions, and provide guidelines for further research.
Invasive alien species (IAS) are a rising threat to biodiversity, national security, and regional economies, with impacts in the hundreds of billions of U.S. dollars annually. Proactive or predictive approaches guided by scientific knowledge are essential to keeping pace with growing impacts of invasions under climate change. Although the rapid development of diverse technologies and approaches has produced tools with the potential to greatly accelerate invasion research and management, innovation has far outpaced implementation and coordination. Technological and methodological syntheses are urgently needed to close the growing implementation gap and facilitate interdisciplinary collaboration and synergy among evolving disciplines. A broad review is necessary to demonstrate the utility and relevance of work in diverse fields to generate actionable science for the ongoing invasion crisis. Here, we review such advances in relevant fields including remote sensing, epidemiology, big data analytics, environmental DNA (eDNA) sampling, genomics, and others, and present a generalized framework for distilling existing and emerging data into products for proactive IAS research and management. This integrated workflow provides a pathway for scientists and practitioners in diverse disciplines to contribute to applied invasion biology in a coordinated, synergistic, and scalable manner.
The mission of the U.S. Geological Survey's (USGS) National Mapping Division (NMD) is to meet the Nation's need for basic geopspatial data, ensuring access to and advancing the application of these data and other related earth science information for users worldwide.
The Wyoming Landscape Conservation Initiative (WLCI) focuses on conserving world-class wildlife resources while facilitating responsible energy development in southwestern Wyoming. To further advance the objectives of the WLCI long-term, science-based effort, a comprehensive inventory of energy resource and production data is being published in two parts. Energy maps, data, documentation and spatial data processing capabilities are available in geodatabase, published map file (pmf), ArcMap document (mxd), Adobe Acrobat PDF map, and other digital formats that can be downloaded at the USGS website.
The US Geological Survey (USGS) offers continuously improving technologies for mapping oceans and coasts providing unique opportunity for characterizing the marine environment and to expand the understanding of coastal and ocean processes, resources, and hazards. USGS, which has been designated as a leader for mapping the Exclusive Economic Zone, has made an advanced strategic plan, Facing Tomorrow's Challenges- US Geological Survey Science in the Decade 2007 to 2017. This plan focuses on innovative and transformational themes that serve key clients and customers, expand partnerships, and have long-term national impact. The plan includes several key science directions, including Understanding Ecosystems and Predicting Ecosystem Change, Energy and Minerals for America's Future, and A National Hazards, Risk, and Resilience Assessment Program. USGS has also collaborated with diverse partners to incorporate mapping and monitoring within interdisciplinary research programs, addressing the system-scale response of coastal and marine ecosystems.
The "faster, cheaper, better" Clementine spacecraft mission mapped the Moon from February 19 to May 3, 1994. Global coverage was acquired in 11 spectral bandpasses from 415 to 2792 nm and at resolutions of 80-330 m/pixel; a thermal-infrared camera sampled ???20% of the surface; a high-resolution camera sampled selected areas (especially the polar regions); and a lidar altimeter mapped the large-scale topography up to latitudes of ??75??. The spacecraft was in a polar, elliptical orbit, 400-450 km periselene altitude. Periselene latitude was -28.5?? for the first month of mapping, then moved to +28.5??. NASA is supporting the archiving, systematic processing, and analysis of the ???1.8 million lunar images and other datasets. A new global positional network has been constructed from 43,000 images and ???0.5 million match points; new digital maps will facilitate future lunar exploration. In-flight calibrations now enable photometry to a high level of precision for the uv-visible CCD camera. Early science results include: (1) global models of topography, gravity, and crustal thicknesses; (2) new information on the topography and structure of multiring impact basins; (3) evidence suggestive of water ice in large permanent shadows near the south pole; (4) global mapping of iron abundances; and (5) new constraints on the Phanerozoic cratering rate of the Earth. Many additional results are expected following completion of calibration and systematic processing efforts. ?? 1997 COSPAR. Published by Elsevier Science Ltd.
Correlations of hydraulic properties with easily measured physical properties are useful for purposes of site characterization in heterogeneous sites. Approximately 600 samples of volcanic rocks from Yucca Mountain, Nevada, representing lithologies with a large range of hydraulic properties, were analyzed to develop correlations of effective porosity with saturated hydraulic conductivity and moisture-retention curve-fit parameters that relate to lithologies of varying depositional history and alteration processes. Effective porosity, ??e, defined as the porosity calculated using drying at a relative humidity of -70 MPa, is used in a generalized Kozeny-Carman equation to predict saturated hydraulic conductivity, Ks = b??en, where b and n are constants. The entire dataset has an R2 of 0.36. When samples are grouped according to general lithology, correlations result in an R2 of 0.71 for the crystallized/vitric samples, 0.24 for samples with mineral alteration, and 0.34 for samples with microfractures, thus increasing the predictive capability over that of the total dataset. Published by Elsevier Science Ltd.
The number and diversity of phenological studies has increased rapidly in recent years. Innovative experiments, field studies, citizen science projects, and analyses of newly available historical data are contributing insights that advance our understanding of ecological and evolutionary responses to the environment, particularly climate change. However, many phenological data sets have peculiarities that are not immediately obvious and can lead to mistakes in analyses and interpretation of results. This paper aims to help researchers, especially those new to the field of phenology, understand challenges and practices that are crucial for effective studies. For example, researchers may fail to account for sampling biases in phenological data, struggle to choose or design a volunteer data collection strategy that adequately fits their project’s needs, or combine data sets in inappropriate ways. We describe ten best practices for designing studies of plant and animal phenology, evaluating data quality, and analyzing data. Practices include accounting for common biases in data, using effective citizen or community science methods, and employing appropriate data when investigating phenological mismatches. We present these best practices to help researchers entering the field take full advantage of the wealth of available data and approaches to advance our understanding of phenology and its implications for ecology.
An important statistical development of the last 30 years has been the advance in regression analysis provided by generalized linear models (GLMs) and generalized additive models (GAMs). Here we introduce a series of papers prepared within the framework of an international workshop entitled: Advances in GLMs/GAMs modeling: from species distribution to environmental management, held in Riederalp, Switzerland, 6-11 August 2001. We first discuss some general uses of statistical models in ecology, as well as provide a short review of several key examples of the use of GLMs and GAMs in ecological modeling efforts. We next present an overview of GLMs and GAMs, and discuss some of their related statistics used for predictor selection, model diagnostics, and evaluation. Included is a discussion of several new approaches applicable to GLMs and GAMs, such as ridge regression, an alternative to stepwise selection of predictors, and methods for the identification of interactions by a combined use of regression trees and several other approaches. We close with an overview of the papers and how we feel they advance our understanding of their application to ecological modeling. ?? 2002 Elsevier Science B.V. All rights reserved.
Advanced information technology infrastructure is increasingly being employed in the Earth sciences to provide researchers with efficient access to massive central databases and to integrate diversely formatted information from a variety of sources. These geoinformatics initiatives enable manipulation, modeling and visualization of data in a consistent way, and are helping to develop integrated Earth models at various scales, and from the near surface to the deep interior. This book uses a series of case studies to demonstrate computer and database use across the geosciences. Chapters are thematically grouped into sections that cover data collection and management; modeling and community computational codes; visualization and data representation; knowledge management and data integration; and web services and scientific workflows. Geoinformatics is a fascinating and accessible introduction to this emerging field for readers across the solid Earth sciences and an invaluable reference for researchers interested in initiating new cyberinfrastructure projects of their own.
In recent years, the availability of airborne imaging spectroscopy (hyperspectral) data has expanded dramatically. The high spatial and spectral resolution of these data uniquely enable spatially explicit ecological studies including species mapping, assessment of drought mortality and foliar trait distributions. However, we have barely begun to unlock the potential of these data to use direct mapping of vegetation characteristics to infer subsurface properties of the critical zone. To assess their utility for Earth systems research, imaging spectroscopy data acquisitions require integration with large, coincident ground‐based datasets collected by experts in ecology and environmental and Earth science. Without coordinated, well‐planned field campaigns, potential knowledge leveraged from advanced airborne data collections could be lost. Despite the growing importance of this field, documented methods to couple such a wide variety of disciplines remain sparse. We coordinated the first National Ecological Observatory Network Airborne Observation Platform (AOP) survey performed outside of their core sites, which took place in the Upper East River watershed, Colorado. Extensive planning for sample tracking and organization allowed field and flight teams to update the ground‐based sampling strategy daily. This enabled collection of an extensive set of physical samples to support a wide range of ecological, microbiological, biogeochemical and hydrological studies. We present a framework for integrating airborne and field campaigns to obtain high‐quality data for foliar trait prediction and document an archive of coincident physical samples collected to support a systems approach to ecological research in the critical zone. This detailed methodological account provides an example of how a multi‐disciplinary and multi‐institutional team can coordinate to maximize knowledge gained from an airborne survey, an approach that could be extended to other studies. The coordination of imaging spectroscopy surveys with appropriately timed and extensive field surveys, along with high‐quality processing of these data, presents a unique opportunity to reveal new insights into the structure and dynamics of the critical zone. To our knowledge, this level of co‐aligned sampling has never been undertaken in tandem with AOP surveys and subsequent studies utilizing this archive will shed considerable light on the breadth of applications for which imaging spectroscopy data can be leveraged.
Within the next 50 to 100 years, the support and maintenance of an extended human family of 8 to 11 billion people will be difficult at best. The authors of this Policy Forum describe changes that are required if we hope to meet the needs and aspirations of humans while improving the health of our planet's ecosystems. Problems as diverse as disease transmission and global climate change have benefited substantially from advances in ecology. Such advances have set the stage for emergence of a proactive ecological science in which social and political realities are acknowledged and attention is turned decisively toward the future. The ecological sciences must chart an understanding of how ecosystem services can persist given their extensive human use. Innovative research on the sciences of ecosystem services, ecological restoration, and ecological design must be massively accelerated and must be accompanied by more effective communication of ecological knowledge to society.
Computational sustainability aims to develop computational methods to help solve environmental, economic, and societal problems and thereby facilitate a path towards a sustainable future. Sustainability problems are unique in scale, impact, complexity, and richness, offering challenges but also opportunities for the advancement of the state of the art of computing and information science.
The U.S. Geological Survey (USGS) operates a low-enriched uranium-fueled, pool-type reactor located at the Federal Center in Denver, Colorado. The mission of the Geological Survey TRIGA® Reactor (GSTR) is to support USGS science by providing information on geologic, plant, and animal specimens to advance methods and techniques unique to nuclear reactors. The reactor facility is supported by programs across the USGS and is organizationally under the Associate Director for Energy and Minerals, and Environmental Health. The GSTR is the only facility in the United States capable of performing automated delayed neutron analyses for detecting fissile and fissionable isotopes. Samples from around the world are submitted to the USGS for analysis using the reactor facility. Qualitative and quantitative elemental analyses, spatial elemental analyses, and geochronology are performed. Few research reactor facilities in the United States are equipped to handle the large number of samples processed at the GSTR. Historically, more than 450,000 sample irradiations have been performed at the USGS facility. Providing impartial scientific information to resource managers, planners, and other interested parties throughout the world is an integral part of the research effort of the USGS.
This second edition is extensively revised throughout with expanded discussion of modeling fundamentals and coverage of advances in model calibration and uncertainty analysis that are revolutionizing the science of groundwater modeling. The text is intended for undergraduate and graduate level courses in applied groundwater modeling and as a comprehensive reference for environmental consultants and scientists/engineers in industry and governmental agencies.
The Synthesis of Everglades Restoration andEcosystem Services (SERES) Project was funded in 2010 by the U.S. Department of Interior (DOI) through the Critical Ecosystem Studies Initiative (CESI) and established to synthesize the ever-growing body of Everglades scientific information with the goal of addressing topics that have hampered restoration since the Comprehensive Everglades Restoration Plan (CERP) was passed in 2000. A distinguishing characteristic of this synthesis effort was that the target end-user was a management/ decision-maker audience. Specifically, the aim was to address the questions of the water managers and other decision leaders in a way that would illuminate and inform but not constrain or specify decisions. Since its inception, the SERES Project has been managed by the Everglades Foundation; however, a core group of scientifc experts from agencies, academic institutions, and the private sector have contributed to the project (see list on page 4). We began the project by interviewing key officials, including resource managers, decision-makers, and heads of agencies and environmental organizations. The objective of these interviews was to establish the Key Science Management Questions that needed to be addressed in order to advance restoration of the Everglades. The resulting questions led to the organization of project teams focused on Hydrology, Water Quality, Soils, Trophic Dynamics, and Landscape Pattern. In order to establish the technical basis for the project, we conducted in depth reviews of the recent scientifc literature, evaluation tools and models, and available data in each of these core areas. Finally, we developed a suite of restoration options that would aid us in addressing the Key Questions and evaluated their relative performance from hydrological, ecological, and economic perspectives. General fndings of the SERES Project are described in subsequent sections, and technical reviews and results of analyses supporting this document are available in reports on the project website.