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At least 199 records · Page 11Linked to original sources
USGS2023

A proposed methodology for conducting threats assessments within the Great Lakes Coregonines restoration framework

This document serves to fulfill the Coregonine Threats Assessment Science Team’s charge of providing a written recommendation for a methodology to conduct threats assessments for Great Lakes coregonines within the Coregonine Restoration Framework (CRF). Through a series of team meetings that included presentations by experts on five candidate threats assessment frameworks followed by structured deliberations, we came to consensus to recommend the threats assessment framework used by Fisheries and Oceans Canada under Canada’s Species at Risk Act, with three modifications: (1) a conceptual modeling step, (2) the use of a “point spreading” approach to incorporate uncertainty when scoring threats, and (3) the use of a modified Delphi or “estimate-talk-estimate” approach when scoring key elements in the assessment. We recommend that this approach be applied to the spatial units delineated by the CRF Resolve Taxonomy and Gap Analysis science teams. In brief, the assessment process includes providing background information on the spatial unit and threats under assessment, constructing a conceptual model linking threats to key processes and vital rates, and scoring or ranking threats across six elements: likelihood of occurrence, level of impact, strength of evidence, unit-level threat occurrence, unit-level threat frequency, and unit-level threat extent. We provide detailed instructions for completing each step of the assessment and generating associated results, with particular attention paid to our suggested modifications. The Coregonine Threats Assessment Science Team also conducted two test runs to assess the applicability and effectiveness of our recommended framework for Great Lakes coregonine populations and their threats. We conducted these test runs on two examples of Great Lakes coregonines that represented two extremes of data availability, as well as two different management contexts. We chose Kiyi (Coregonus kiyi) in Lake Ontario as an example of a data-poor, extirpated population, and we chose Cisco (Coregonus artedi) in Lake Superior as an example of a data-rich, extant population. We provide the results of these test runs in Appendices 1-2. We also describe the lessons we learned from these test runs throughout this document and highlighted them in the “Recommendations for avoiding challenges during application” section.

Report↗
USGS2020

Moving from decision to action in conservation science

Biodiversity loss is a major threat to the integrity of ecosystems and is projected to worsen, yet the path to successful conservation remains elusive. Decision support frameworks (DSFs) are increasingly applied by resource managers to navigate the complexity, uncertainty, and differing socio-ecological objectives inherent to conservation problems. Most published conservation research that uses DSFs focuses on analytical stages (e.g., identifying an optimal decision), making it difficult to assess and learn from previous examples in a conservation practice context. Here, we (1) evaluate the relationship between the application of decision science and the resulting conservation outcomes, and (2) identify and address existing barriers to the application of DSFs to conservation practice. To do this, we develop a framework for evaluating conservation initiatives using decision science that emphasizes setting attainable goals, building momentum, and obtaining partner buy-in. We apply this framework to a systematic review of amphibian conservation decision support projects, including a follow-up survey of the pertinent conservation practitioners, stakeholders, and scientists. We found that all projects identified optimal solutions to reach stated objectives, but positive conservation outcomes were limited when implementation challenges arose. Further, we identified multiple barriers (e.g., dynamic and hierarchical leadership, scale complexity, limited resource availability) that can inhibit the progression from decision identification to action implementation (i.e., ‘decision-implementation gap’), and to successful conservation outcomes. Based on these results, we provide potential actionable steps and avenues for future development of DSFs to facilitate the transition from decision to action and the realization of conservation successes.

Biological Conservation↗
USGS2017

The Wetland and Aquatic Research Center strategic science plan

Introduction The U.S. Geological Survey (USGS) Wetland and Aquatic Research Center (WARC) has two primary locations (Gainesville, Florida, and Lafayette, Louisiana) and field stations throughout the southeastern United States and Caribbean. WARC’s roots are in U.S. Fish and Wildlife Service (USFWS) and National Park Service research units that were brought into the USGS as the Biological Research Division in 1996. Founded in 2015, WARC was created from the merger of two long-standing USGS biology science Centers—the Southeast Ecological Science Center and the National Wetlands Research Center—to bring together expertise in biology, ecology, landscape science, geospatial applications, and decision support in order to address issues nationally and internationally. WARC scientists apply their expertise to a variety of wetland and aquatic research and monitoring issues that require coordinated, integrated efforts to better understand natural environments. By increasing basic understanding of the biology of important species and broader ecological and physiological processes, this research provides information to policymakers and aids managers in their stewardship of natural resources and in regulatory functions. This strategic science plan (SSP) was developed to guide WARC research during the next 5–10 years in support of Department of the Interior (DOI) partnering bureaus such as the USFWS, the National Park Service, and the Bureau of Ocean Energy Management, as well as other Federal, State, and local natural resource management agencies. The SSP demonstrates the alignment of the WARC goals with the USGS mission areas, associated programs, and other DOI initiatives. The SSP is necessary for workforce planning and, as such, will be used as a guide for future needs for personnel. The SSP also will be instrumental in developing internal funding priorities and in promoting WARC’s capabilities to both external cooperators and other groups within the USGS.

Open-File Report↗
USGS2001

A model for lignin alteration - Part I: A kinetic reaction-network model

A new quantitative model is presented which simulates the maturation of lignin-derived sedimentary organic matter under geologic conditions. In this model, compositionally specific reactants evolve to specific intermediate and mobile products through balanced, nth order processes, by way of a network of sequential and parallel reactions. The chemical kinetic approach is based primarily on published observed structural transformations of naturally matured, lignin-derived, sedimentary organic matter. Assuming that Upper Cretaceous Williams Fork coal in the Piceance Basin is primarily lignin-derived, the model is calibrated for the Multi-Well Experiment(MWX) Site in this basin. This kind of approach may be applied to other selectively preserved chemical components of sedimentary organic matter. ?? 2001 Elsevier Science Ltd. All rights reserved.

Organic Geochemistry↗
USGS2024

Science target prioritization framework for remote sensing

Behind the scenes of a remote sensing mission there are complex decision making and planning operations. Streamlining these operations, with a quantitative scientific value framework, aids efficient and optimized science data collection. While there have been previous efforts to quantify the science value for specific science scenarios, our work aims to develop a general framework which can be applied across different scenarios. We describe a pipeline of processes which combines model forecast and observation data, in computational forms, as dictated by the mission objectives set forth by subject matter experts. The framework is described with use cases involving the monitoring of nitrogen dioxide (NO2) concentrations over the Gulf of Mexico and methane concentrations over interior Alaska.

Conference Paper↗
USGS2009

The USGS and the Gulf of Mexico

The U.S. Geological Survey (USGS) is committed to mapping, monitoring, and conducting research in the Gulf of Mexico and adjacent watersheds. Through a network of science centers in the five Gulf States and across the Nation, the USGS applies its biologic, geologic, geographic, and hydrologic expertise to provide unbiased scientific findings to decisionmakers, particularly members and supporters of the Gulf of Mexico Alliance (Gulf Alliance). The overarching goal of USGS Gulf Coast activities is to provide the scientific information, knowledge, and tools required to facilitate management decisions that promote restoration, increase coastal resilience, and mitigate risks associated with both artificial and natural hazards.

Fact Sheet↗
USGS2024

The spatially adaptable filter for error reduction (SAFER) process: Remote sensing-based LANDFIRE disturbance mapping updates

LANDFIRE (LF) has been producing periodic spatially explicit vegetation change maps (i.e., LF disturbance products) across the entire United States since 1999 at a 30 m spatial resolution. These disturbance products include data products produced by various fire programs, field-mapped vegetation and fuel treatment activity (i.e., events) submissions from various agencies, and disturbances detected by the U.S. Geological Survey Earth Resources Observation and Science (EROS)-based Remote Sensing of Landscape Change (RSLC) process. The RSLC process applies a bi-temporal change detection algorithm to Landsat satellite-based seasonal composites to generate the interim disturbances that are subsequently reviewed by analysts to reduce omission and commission errors before ingestion them into LF’s disturbance products. The latency of the disturbance product is contingent on timely data availability and analyst review. This work describes the development and integration of the Spatially Adaptable Filter for Error Reduction (SAFER) process and other error and latency reduction improvements to the RSLC process. SAFER is a random forest-based supervised classifier and uses predictor variables that are derived from multiple years of pre- and post-disturbance Landsat band observations. Predictor variables include reflectance, indices, and spatial contextual information. Spatial contextual information that is unique to each contiguous disturbance region is parameterized as Z scores using differential observations of the disturbed regions with its undisturbed neighbors. The SAFER process was prototyped for inclusion in the RSLC process over five regions within the conterminous United States (CONUS) and regional model performance, evaluated using 2016 data. Results show that the inclusion of the SAFER process increased the accuracies of the interim disturbance detections and thus has potential to reduce the time needed for analyst review. LF does not track the time taken by each analyst for each tile, and hence, the relative effort saved was parameterized as the percentage of 30 m pixels that are correctly classified in the SAFER outputs to the total number of pixels that are incorrectly classified in the interim disturbance and are presented. The SAFER prototype outputs showed that the relative analysts’ effort saved could be over 95%. The regional model performance evaluation showed that SAFER’s performance depended on the nature of disturbances and availability of cloud-free images relative to the time of disturbances. The accuracy estimates for CONUS were inferred by comparing the 2017 SAFER outputs to the 2017 analyst-reviewed data. As expected, the SAFER outputs had higher accuracies compared to the interim disturbances, and CONUS-wide relative effort saved was over 92%. The regional variation in the accuracies and effort saved are discussed in relation to the vegetation and disturbance type in each region. SAFER is now operationally integrated into the RSLC process, and LANDFIRE is well poised for annual updates, contingent on the availability of data.

Fire↗
USGS2023

Learning from arid and urban aquatic ecosystems to inform more sustainable and resilient futures

The hydrology and aquatic ecology of arid environments has long been understudied relative to temperate regions. Yet spatially and temporally intermittent and ephemeral waters characterized by flashy hydrographs typify arid regions that comprise a substantial proportion of the Earth. Additionally, drought, intense storms, and human modification of landscapes increasingly affect many temperate regions, resulting in hydrologic regimes more similar to aridlands. Here we review the contributions of Dr. Nancy Grimm to aridland hydrology and ecology, and applications of these insights to urban ecosystems and resilience of social-ecological-technological systems. Grimm catalyzed study of nitrogen cycling in streams and characterized feedbacks between surface water-groundwater exchange, nitrogen transformations, and aquatic biota. In aridlands, outcomes of these interactions depend on short- and long-term variation in the hydrologic regime. Grimm and colleagues applied hydrological and biogeochemical insights gained from study of aridland streams to urban ecosystems, integrating engineering, social and behavioral sciences, and geography. These studies evolved from characterizing the spatial heterogeneity of urban systems (i.e., watersheds, novel aquatic systems) and its influence on nutrient dynamics to an approach that evaluated human decision-making as a driver of disturbance regimes and changes in ecosystem function. Finally, Grimm and colleagues have applied principles of urban ecology to look toward the future of cities, considering scenarios of sustainable and resilient futures. We identify cross-cutting themes and approaches that have motivated discoveries across Grimm’s multi-decadal career, including spatial and temporal heterogeneity, hydrologic connectivity and regime, disturbance, systems thinking, and resilience. Finally, we emphasize Grimm’s broad contributions to science via support of long-term research, dedication to mentoring, and extensive collaborations that facilitated transdisciplinary research.

Journal of Hydrology↗
USGS2003

Modeling paleoclimates

Paleoclimatic data and climate models play a complimentary role in understanding climate change. This chapter provides an overview of the process of climate-system modeling, presents the taxonomy of the models recently applied in the study of Quaternary climate change and variation, and discusses the development of climate modeling since the 1965 International Union for Quaternary Science (INQUA) volume and its companions are published. Models based on physical principles do have the potential to provide mechanistic explanations of past climatic variations, provided they are known to work, are applied in an appropriately designed experiment, and explicitly account for all of the components of the climate system that are involved in a particular climate change. Climate models can be classified according to the applications to which they are put, which include simulating the temporal evolution and spatial patterns of the climate system and the attendant responses of environmental subsystems. The objective of paleoclimate modeling is to quantify the behavior and variations of the components that describe the climate system.

Developments in Quaternary Sciences↗
USGS2013

U.S. Department of the Interior South Central Climate Science Center strategic science plan, 2013--18

The Department of the Interior (DOI) recognizes and embraces the unprecedented challenges of maintaining our Nation’s rich natural and cultural resources in the 21st century. The magnitude of these challenges demands that the conservation community work together to develop integrated adaptation and mitigation strategies that collectively address the impacts of climate change and other landscape-scale stressors. On September 14, 2009, DOI Secretary Ken Salazar signed Secretarial Order 3289 (amended February 22, 2010) entitled, “Addressing the Impacts of Climate Change on America’s Water, Land, and Other Natural and Cultural Resources.” The Order establishes the foundation for two partner-based conservation science entities to address these unprecedented challenges: Climate Science Centers (CSCs and Landscape Conservation Cooperatives (LCCs). CSCs and LCCs are the Department-wide approach for applying scientific tools to increase understanding of climate change and to coordinate an effective response to its impacts on tribes and the land, water, ocean, fish and wildlife, and cultural-heritage resources that DOI manages. Eight CSCs have been established and are managed through the U.S. Geological Survey (USGS) National Climate Change and Wildlife Science Center (NCCWSC); each CSC works in close collaboration with their neighboring CSCs, as well as those across the Nation, to ensure the best and most efficient science is produced. The South Central CSC was established in 2012 through a cooperative agreement with the University of Oklahoma, Texas Tech University, Louisiana State University, the Chickasaw Nation, the Choctaw Nation of Oklahoma, Oklahoma State University, and NOAA’s Geophysical Fluid Dynamics Lab; hereafter termed the ”Consortium” of the South Central CSC. The Consortium has a broad expertise in the physical, biological, natural, and social sciences to address impacts of climate change on land, water, fish and wildlife, ocean, coastal, and cultural resources. The South Central CSC will provide scientific information, tools, and techniques that managers and other parties interested in land, water, wildlife, and cultural resources can use to anticipate, monitor, and adapt to climate change, actively engaging LCCs and other partners in translating science into management decisions. This document is the first Strategic Science Plan for the South Central CSC (2013-18). Using the January 2011 DOI guidance as a model, this document (1) describes the role and interactions of the South Central CSC among partners and stakeholders including Federal, State, and non-governmental organizations throughout the region; (2) describes a concept of what the center will provide to its partners; (3) defines a context for climate impacts in the south central United States; and (4) establishes the science priorities the center will address through research. Science priorities are currently organized as immediate or future research needs; however, this document is intended to be reevaluated and modified as partner needs change and as scientific work progresses.

Open-File Report↗
USGS2014

A new analysis of Mars "Special Regions": findings of the Second MEPAG Special Regions Science Analysis Group (SR-SAG2)

A committee of the Mars Exploration Program Analysis Group (MEPAG) has reviewed and updated the description of Special Regions on Mars as places where terrestrial organisms might replicate (per the COSPAR Planetary Protection Policy). This review and update was conducted by an international team (SR-SAG2) drawn from both the biological science and Mars exploration communities, focused on understanding when and where Special Regions could occur. The study applied recently available data about martian environments and about terrestrial organisms, building on a previous analysis of Mars Special Regions (2006) undertaken by a similar team. Since then, a new body of highly relevant information has been generated from the Mars Reconnaissance Orbiter (launched in 2005) and Phoenix (2007) and data from Mars Express and the twin Mars Exploration Rovers (all 2003). Results have also been gleaned from the Mars Science Laboratory (launched in 2011). In addition to Mars data, there is a considerable body of new data regarding the known environmental limits to life on Earth—including the potential for terrestrial microbial life to survive and replicate under martian environmental conditions. The SR-SAG2 analysis has included an examination of new Mars models relevant to natural environmental variation in water activity and temperature; a review and reconsideration of the current parameters used to define Special Regions; and updated maps and descriptions of the martian environments recommended for treatment as "Uncertain" or "Special" as natural features or those potentially formed by the influence of future landed spacecraft. Significant changes in our knowledge of the capabilities of terrestrial organisms and the existence of possibly habitable martian environments have led to a new appreciation of where Mars Special Regions may be identified and protected. The SR-SAG also considered the impact of Special Regions on potential future human missions to Mars, both as locations of potential resources and as places that should not be inadvertently contaminated by human activity.

Astrobiology↗
USGS2007

Efficient implementation of the Metropolis-Hastings algorithm, with application to the Cormack-Jolly-Seber model

Judicious choice of candidate generating distributions improves efficiency of the Metropolis-Hastings algorithm. In Bayesian applications, it is sometimes possible to identify an approximation to the target posterior distribution; this approximate posterior distribution is a good choice for candidate generation. These observations are applied to analysis of the Cormack-Jolly-Seber model and its extensions. ?? Springer Science+Business Media, LLC 2007.

Conference Paper↗
USGS2026

Cascading consequences and interventions for hazards after wildfire in northern California

This report documents the process and outcomes of a workshop convened by the U.S. Geological Survey in December 2024 to identify cascading postfire hazards and other effects stemming from the 2021 Dixie Fire in northern California and to explore potential intervention strategies. The Postfire Hazards and Consequences Workshop brought together stakeholders with expertise across disciplines relevant to postfire management. The two-day workshop applied the chains of consequences methodology developed by the Department of the Interior Strategic Sciences Group. Workshop participants identified key issues emerging from postfire conditions and discussed actionable strategies to enhance resilience in fire-prone regions. Findings revealed a complex network of interacting consequences, many of which transcended disciplinary boundaries and affected multiple sectors of society, underscoring the systemic nature of wildfire recovery challenges and indicating that long-term planning approaches could be integrated to help reduce society’s risks from wildfire.

California↗
USGS1990

A quantitative micropaleontologic method for shallow marine peleoclimatology: Application to Pliocene deposits of the western North Atlantic Ocean

A transfer function was developed to estimate summer and winter paleotemperatures for arctic to tropical regions of the western North Atlantic Ocean using fossil ostracode assemblages. Q-mode factor analysis was run on ostracode assemblages from 100 modern bottom sediment samples from continental shelves of North America, Greenland and the Caribbean using 59 ostracode taxa. Seven factors accounting for 80% of the variance define assemblages that correspond to frigid, subfrigid, cold temperate, mild temperate, warm temperate, subtropical and tropical climatic zones. Multiple regression of the factor matrix against observed February and August bottom temperatures yielded an astracode transfer function with an accuracy of about ??2??C. The transfer function was used to reconstruct middle Pliocene (3.5-3.0 Ma) shallow marine climates of the western North Atlantic during the marine transgression that deposited the Yorktown Formation (Virginia and North Carolina), the Duplin Formation (South and North Carolina) and the Pinecrest beds (Florida). Middle Pliocene paleowater temperatures in Virginia averaged 19??C in August and 13.5??C in February, about 5??C to 8??C warmer than at comparable depths off Virginia today. August and February water temperatures in North Carolina were 23??C and 13.4??C, in South Carolina about 23??C and 13.5??C and in southern Florida about 24.6??C and 15.4??C. Marine climates north of 35??N were warmer than today; south of 35??N, they were about the same or slightly cooler. Thermal gradients along the coast were generally not as steep as they are today. The North Atlantic transfer function can be applied to other shallow marine Pliocene and Pleistocene deposits of eastern North America. ?? 1990 Elsevier Science Publishers B.V.

Marine Micropaleontology↗
USGS2025

Application of a data-limited stock assessment model to understand the historical population of cisco in Lake Erie

Historically, cisco (Coregonus artedi) supported one of the largest commercial fisheries in Lake Erie with over 25 million kilograms landed in a single year during the fisheries’ peak in 1912. Like many other coregonines in the Laurentian Great Lakes, the Lake Erie cisco population eventually collapsed and is now considered extirpated. Management interest in restoring coregonine abundance and diversity across the Great Lakes motivated the development of a multi-agency, science-based framework aimed at restoring or enhancing populations. As part of that framework, we applied a data-limited model (depletion-based stock reduction analysis) to a reconstructed catch series to estimate historical levels of abundance of cisco in Lake Erie. Estimated historical carrying capacity was 171 million kilograms (66.5 kg/ha) and estimated annual exploitation was as high as 70 %. Model projections suggested the population should have rebuilt following a fishery collapse in 1925; however, the fishery remained depressed and cisco disappeared from Lake Erie despite the end of targeted exploitation in 1965. Other factors, such as habitat degradation and invasive species, are believed to have played a part in impeding recovery of cisco. The results of this study could be used in setting potential restoration targets for Lake Erie cisco given current conditions. The likelihood of achieving recovery targets based on historical population size will be dependent on the suitability of current conditions for sustaining the population.

Lake Erie↗
USGS2001

Digital terrain modelling and industrial surface metrology - Converging crafts

Quantitative characterisation of surface form, increasingly from digital 3-D height data, is cross-disciplinary and can be applied at any scale. Thus, separation of industrial-surface metrology from its Earth-science counterpart, (digital) terrain modelling, is artificial. Their growing convergence presents an opportunity to develop in surface morphometry a unified approach to surface representation. This paper introduces terrain modelling and compares it with metrology, noting their differences and similarities. Examples of potential redundancy among parameters illustrate one of the many issues common to both disciplines. ?? 2001 Elsevier Science Ltd. All rights reserved.

International Journal of Machine Tools and Manufac↗
USGS2023

Seventy questions of importance to the conservation of the North Central grasslands of the United States in a changing climate

Successful conservation of ecosystems in a changing climate requires actionable research that directly supports the rethinking and revising of management approaches to address changing risks and opportunities. As an important first step toward actionable research, we reviewed and synthesized grassland management-related documents to identify broadly shared questions that, if answered, would help to support collective conservation of the grasslands in the northern Great Plains of the United States in a changing climate. A Management Priorities Working Group reviewed 183 grassland-relevant management documents and identified 70 questions. Feedback was iteratively provided by a Climate and Ecology Working Group, an Advisory Committee, and representatives from grassland management agencies and organizations. The identified questions generally fall under 15 topics: land conversion; restoration; disturbance regimes; woody encroachment; herbaceous invasives; grazing; water quality, quantity, and availability; animal species; private land; public understanding; legal and policy changes; economic incentives; coordination across management entities; accessibility of science and tools; and novel ways of thinking. These questions can inform a research agenda for researchers looking to conduct actionable science in the Great Plains grassland ecosystems. Both the approach and the questions presented here can also be adapted and applied in other regions and ecosystems.

Colorado, Kansas, Montana, Nebraska, North Dakota,↗
USGS2011

Using models for the optimization of hydrologic monitoring

Hydrologists are often asked what kind of monitoring network can most effectively support science-based water-resources management decisions. Currently (2011), hydrologic monitoring locations often are selected by addressing observation gaps in the existing network or non-science issues such as site access. A model might then be calibrated to available data and applied to a prediction of interest (regardless of how well-suited that model is for the prediction). However, modeling tools are available that can inform which locations and types of data provide the most 'bang for the buck' for a specified prediction. Put another way, the hydrologist can determine which observation data most reduce the model uncertainty around a specified prediction. An advantage of such an approach is the maximization of limited monitoring resources because it focuses on the difference in prediction uncertainty with or without additional collection of field data. Data worth can be calculated either through the addition of new data or subtraction of existing information by reducing monitoring efforts (Beven, 1993). The latter generally is not widely requested as there is explicit recognition that the worth calculated is fundamentally dependent on the prediction specified. If a water manager needs a new prediction, the benefits of reducing the scope of a monitoring effort, based on an old prediction, may be erased by the loss of information important for the new prediction. This fact sheet focuses on the worth or value of new data collection by quantifying the reduction in prediction uncertainty achieved be adding a monitoring observation. This calculation of worth can be performed for multiple potential locations (and types) of observations, which then can be ranked for their effectiveness for reducing uncertainty around the specified prediction. This is implemented using a Bayesian approach with the PREDUNC utility in the parameter estimation software suite PEST (Doherty, 2010). The techniques briefly described earlier are described in detail in a U.S. Geological Survey Scientific Investigations Report available on the Internet (Fienen and others, 2010; http://pubs.usgs.gov/sir/2010/5159/). This fact sheet presents a synopsis of the techniques as applied to a synthetic model based on a model constructed using properties from the Lake Michigan Basin (Hoard, 2010).

Fact Sheet↗