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At least 19 recordsLinked to original sources
USGS2020

Minnesota landowners’ trust in their department of natural resources, salient values similarity and wildlife value orientations

Due to extensive land conversion over the last century, much of the native prairie pothole ecosystem has been converted to agricultural or other human uses. The prairie pothole ecosystem is found in the northern plains of Iowa, Minnesota, South Dakota, North Dakota, and Montana. Because most of the land in this region is privately owned and used for agricultural production, most impacts to wildlife habitat are the result of decisions by individual landowners. Landowner trust in natural resource management agencies is important for agencies to effectively accomplish their mission. We measured the nature (competence and fairness) and level of trust that western Minnesota landowners have in the Minnesota Department of Natural Resources (MnDNR) and landowners’ wildlife value orientations (WVO). Landowners rated MnDNR slightly higher in competence than fairness; however, these two dimensions were strongly correlated. We developed a MnDNR trust scale (six items) and a three-cluster model dividing landowners along the MnDNR trust scale, which we named Negative (28%), Neutral (43%), and Positive (29%). We provide evidence supporting the salient values similarity (SVS) model that states people have trust in agencies holding similar values; landowners reporting greater importance for wildlife consideration when making land-use decisions also reported greater trust in the MnDNR. In addition, mutualist landowners had the highest trust in the MnDNR and utilitarian landowners the lowest level of trust, which is opposite of the trust relationship reported for the general public with state wildlife agencies. Based on the SVS model, our results suggest that mutualist landowners perceive greater congruence with MnDNR goals related to wildlife habitat compared to utilitarian landowners.

Minnesota↗
USGS2023

Structured decision making remains underused in ecological restoration despite opportunities

Ecological restoration is considered an essential activity as we attempt to repair anthropogenic degradation. Yet, resources are limited and it is important that efforts focus on activities that are effective and yield successful restoration. Structured decision making (SDM) is an organized framework that is designed to incorporate differing values across stakeholders and evaluate alternatives. The SDM framework typically consists of six steps: define the decision problem, define objectives and evaluation criteria, develop alternatives, estimate consequences, evaluate trade-offs, and decide, implement, and monitor. Here, we posit that SDM is well suited for ecological restoration, yet remains underused. Specifically, tools such as stakeholder surveys, conceptual modeling, and multi-criteria decision analysis are notably useful in ecological restoration and can be applied under the SDM framework to ensure robust and transparent decision making. We illustrate the application of SDM to ecological restoration with case studies that used SDM alongside ecosystem service assessments, for species-as-risk management, and to assess action desirability across large and diverse stakeholder groups. Finally, we demonstrate how SDM is equipped to handle many of the challenges associated with ecological restoration by identifying commonalities. We contend that increased use of SDM for ecological restoration by environmental managers has the potential to yield wise use of limited resources and more effective restoration outcomes.

Environment Systems and Decisions↗
USGS2016

Panarchy use in environmental science for risk and resilience planning

Environmental sciences have an important role in informing sustainable management of built environments by providing insights about the drivers and potentially negative impacts of global environmental change. Here, we discuss panarchy theory, a multi-scale hierarchical concept that accounts for the dynamism of complex socio-ecological systems, especially for those systems with strong cross-scale feedbacks. The idea of panarchy underlies much of system resilience, focusing on how systems respond to known and unknown threats. Panarchy theory can provide a framework for qualitative and quantitative research and application in the environmental sciences, which can in turn inform the ongoing efforts in socio-technical resilience thinking and adaptive and transformative approaches to management.

Environment Systems and Decisions↗
USGS2017

Features of resilience

The National Academy of Sciences (NAS) definition of resilience is used here to organize common concepts and synthesize a set of key features of resilience that can be used across diverse application domains. The features in common include critical functions (services), thresholds, cross-scale (both space and time) interactions, and memory and adaptive management. We propose a framework for linking these features to the planning, absorbing, recovering, and adapting phases identified in the NAS definition. The proposed delineation of resilience can be important in understanding and communicating resilience concepts.

Environment Systems and Decisions↗
USGS2016

Use of structured decision-making to explicitly incorporate environmental process understanding in management of coastal restoration projects: Case study on barrier islands of the northern Gulf of Mexico

Coastal ecosystem management typically relies on subjective interpretation of scientific understanding, with limited methods for explicitly incorporating process knowledge into decisions that must meet multiple, potentially competing stakeholder objectives. Conversely, the scientific community lacks methods for identifying which advancements in system understanding would have the highest value to decision-makers. A case in point is barrier island restoration, where decision-makers lack tools to objectively use system understanding to determine how to optimally use limited contingency funds when project construction in this dynamic environment does not proceed as expected. In this study, collaborative structured decision-making (SDM) was evaluated as an approach to incorporate process understanding into mid-construction decisions and to identify priority gaps in knowledge from a management perspective. The focus was a barrier island restoration project at Ship Island, Mississippi, where sand will be used to close an extensive breach that currently divides the island. SDM was used to estimate damage that may occur during construction, and guide repair decisions within the confines of limited availability of sand and funding to minimize adverse impacts to project objectives. Sand was identified as more limiting than funds, and unrepaired major breaching would negatively impact objectives. Repairing minor damage immediately was determined to be generally more cost effective (depending on the longshore extent) than risking more damage to a weakened project. Key gaps in process-understanding relative to project management were identified as the relationship of island width to breach formation; the amounts of sand lost during breaching, lowering, or narrowing of the berm; the potential for minor breaches to self-heal versus developing into a major breach; and the relationship between upstream nourishment and resiliency of the berm to storms. This application is a prototype for using structured decision-making in support of engineering projects in dynamic environments where mid-construction decisions may arise; highlights uncertainty about barrier island physical processes that limit the ability to make robust decisions; and demonstrates the potential for direct incorporation of process-based models in a formal adaptive management decision framework.

Mississippi↗
USGS2012

The U.S. Geological Survey Ecosystem Science Strategy, 2012-2022 - Advancing discovery and application through collaboration

Ecosystem science is critical to making informed decisions about natural resources that can sustain our Nation’s economic and environmental well-being. Resource managers and policy-makers are faced with countless decisions each year at local, state, tribal, territorial, and national levels on issues as diverse as renewable and non-renewable energy development, agriculture, forestry, water supply, and resource allocations at the urban-rural interface. The urgency for sound decision-making is increasing dramatically as the world is being transformed at an unprecedented pace and in uncertain directions. Environmental changes are associated with natural hazards, greenhouse gas emissions, and increasing demands for water, land, food, energy, mineral, and living resources. At risk is the Nation’s environmental capital, the goods and services provided by resilient ecosystems that are vital to the health and well-being of human societies. Ecosystem science—the study of systems of organisms interacting with their environment and the consequences of natural and human-induced change on these systems—is necessary to inform decision-makers as they develop policies to adapt to these changes. This Ecosystems Science Strategy is built on a framework that includes basic and applied science. It highlights the critical roles that USGS scientists and partners can play in building scientific understanding and providing timely information to decision-makers. The strategy underscores the connection between scientific discoveries and the application of new knowledge. The strategy integrates ecosystem science and decision-making, producing new scientific outcomes to assist resource managers and providing public benefits. The USGS is uniquely positioned to play an important role in ecosystem science. With its wide range of expertise, the agency can bring holistic, cross-scale, interdisciplinary capabilities to the design and conduct of monitoring, research, and modeling and to new technologies for data collection, management, and visualization. Collectively, these capabilities can be used to reveal ecological patterns and processes, explain how and why ecosystems change, and forecast change over different spatial and temporal scales. USGS science can provide managers with options and decision-support tools to use resources sustainably. The USGS has long-standing, collaborative relationships with the DOI and other partners in the natural sciences, in both conducting science and its application. The USGS engages these partners in cooperative investigations that otherwise would lack the necessary support or be too expensive for a single bureau to conduct. The heart of this strategy is a framework and vision for USGS ecosystems science that focuses on five long-term goals, which are seen as interconnected and reinforcing components: • Improve understanding of ecosystem structure, function, and processes. The focus for this goal is an understanding of how ecosystems work, including the dynamics of species, their populations, interactions, and genetics, and how they change across spatial and temporal scales. • Advance understanding of how drivers influence ecosystem change. The challenges here are explaining the drivers of ecosystem change, their spatio-temporal patterns, their uncertainties and interactions, and their influence on ecosystem processes and dynamics. • Improve understanding of the services that ecosystems provide to society. Here the emphasis is on the measurement of environmental capital and ecosystem services, and the identification of sources and patterns of change in space and time. • Develop tools, technologies, and capacities to inform decision-making about ecosystems. This includes developing new technologies and approaches for conducting applications-oriented ecosystem science. A principal challenge will be how to quantify uncertainty and incorporate it in decision analysis. • Apply science to enhance strategies for management, conservation, and restoration of ecosystems. These challenges include development of novel approaches to monitoring, assessment, and restoration of ecosystems; new methods to address species of concern and communities at risk; and innovations in decision analysis and support to address imminent ecosystem changes or those that are underway. Closely integrated with the five goals are four strategic approaches that provide the path forward for the USGS Ecosystems Mission Area. These approaches cross-cut all of the goals and are seen as essential to the implementation of this strategy: • Assess information needs for ecosystem science through enhanced partnerships. Work with the DOI and other agencies and institutions to identify, design, and implement priority decision-driven ecological research. • Promote the use of interdisciplinary ecosystem science. Design and conduct interdisciplinary process-oriented research in ecosystem science. • Enhance modeling and forecasting. Build models to forecast ecosystem change, assess future management scenarios, and reduce uncertainties through an adaptive learning process. • Support decision-making. Use quantitative approaches to assess the vulnerabilities of ecosystems, habitats, and species, and evaluate strategies for adaptation, restoration, and sustainable management. Following the strategic approaches are a set of proposed actions that represent a sampling of specific activities that align with this strategy and that address the Nation’s most pressing environmental needs. The strategy emphasizes coordination of activities across the USGS mission areas pursuant to these goals. Ecosystem science is inherently interdisciplinary and requires a broad perspective that incorporates the biological and physical sciences, climate science, information technology, and scientific capacity in mission areas across the Bureau. With its emphasis on coordination, this strategy can provide a critical underpinning for integrated science efforts with scientists from multiple mission areas of the USGS working together. Of course, the USGS will continue to conduct both discipline-specific and interdisciplinary investigations, and both will continue to be vital parts of the ecosystem science portfolio. Finally, the strategy stresses the importance of coordination with other Federal agencies and organizations in the natural resources community. The USGS collaborates with resource agencies in the DOI and other organizations throughout the world to meet societal needs for species and ecosystem management. Working with these agencies and organizations, the USGS will play a key role over the next decade in advancing the scientific foundation for sustaining the natural resources that diverse, productive, resilient ecosystems provide.

Open-File Report↗
USGS2015

Comparing models of Red Knot population dynamics

Predictive population modeling contributes to our basic scientific understanding of population dynamics, but can also inform management decisions by evaluating alternative actions in virtual environments. Quantitative models mathematically reflect scientific hypotheses about how a system functions. In Delaware Bay, mid-Atlantic Coast, USA, to more effectively manage horseshoe crab ( Limulus polyphemus ) harvests and protect Red Knot ( Calidris canutus rufa ) populations, models are used to compare harvest actions and predict the impacts on crab and knot populations. Management has been chiefly driven by the core hypothesis that horseshoe crab egg abundance governs the survival and reproduction of migrating Red Knots that stopover in the Bay during spring migration. However, recently, hypotheses proposing that knot dynamics are governed by cyclical lemming dynamics garnered some support in data analyses. In this paper, I present alternative models of Red Knot population dynamics to reflect alternative hypotheses. Using 2 models with different lemming population cycle lengths and 2 models with different horseshoe crab effects, I project the knot population into the future under environmental stochasticity and parametric uncertainty with each model. I then compare each model's predictions to 10 yr of population monitoring from Delaware Bay. Using Bayes' theorem and model weight updating, models can accrue weight or support for one or another hypothesis of population dynamics. With 4 models of Red Knot population dynamics and only 10 yr of data, no hypothesis clearly predicted population count data better than another. The collapsed lemming cycle model performed best, accruing ~35% of the model weight, followed closely by the horseshoe crab egg abundance model, which accrued ~30% of the weight. The models that predicted no decline or stable populations (i.e. the 4-yr lemming cycle model and the weak horseshoe crab effect model) were the most weakly supported.

The Condor↗
USGS2008

Restoration of Circum-Arctic Upper Jurassic source rock paleolatitude based on crude oil geochemistry

Tectonic geochemical paleolatitude (TGP) models were developed to predict the paleolatitude of petroleum source rock from the geochemical composition of crude oil. The results validate studies designed to reconstruct ancient source rock depositional environments using oil chemistry and tectonic reconstruction of paleogeography from coordinates of the present day collection site. TGP models can also be used to corroborate tectonic paleolatitude in cases where the predicted paleogeography conflicts with the depositional setting predicted by the oil chemistry, or to predict paleolatitude when the present day collection locality is far removed from the source rock, as might occur due to long distance subsurface migration or transport of tarballs by ocean currents. Biomarker and stable carbon isotope ratios were measured for 496 crude oil samples inferred to originate from Upper Jurassic source rock in West Siberia, the North Sea and offshore Labrador. First, a unique, multi-tiered chemometric (multivariate statistics) decision tree was used to classify these samples into seven oil families and infer the type of organic matter, lithology and depositional environment of each organofacies of source rock [Peters, K.E., Ramos, L.S., Zumberge, J.E., Valin, Z.C., Scotese, C.R., Gautier, D.L., 2007. Circum-Arctic petroleum systems identified using decision-tree chemometrics. American Association of Petroleum Geologists Bulletin 91, 877-913]. Second, present day geographic locations for each sample were used to restore the tectonic paleolatitude of the source rock during Late Jurassic time (???150 Ma). Third, partial least squares regression (PLSR) was used to construct linear TGP models that relate tectonic and geochemical paleolatitude, where the latter is based on 19 source-related biomarker and isotope ratios for each oil family. The TGP models were calibrated using 70% of the samples in each family and the remaining 30% of samples were used for model validation. Positive relationships exist between tectonic and geochemical paleolatitude for each family. Standard error of prediction for geochemical paleolatitude ranges from 0.9?? to 2.6?? of tectonic paleolatitude, which translates to a relative standard error of prediction in the range 1.5-4.8%. The results suggest that the observed effect of source rock paleolatitude on crude oil composition is caused by (i) stable carbon isotope fractionation during photosynthetic fixation of carbon and (ii) species diversity at different latitudes during Late Jurassic time. ?? 2008 Elsevier Ltd. All rights reserved.

Organic Geochemistry↗
USGS2013

Moving forward with imperfect information

This chapter summarized the scope of what is known and not known about climate in the Southwestern United States. There is now more evidence and more agreement among climate scientists about the physical climate and related impacts in the Southwest compared with that represented in the 2009 National Climate Assessment (Karl, Melillo, and Peterson 2009). However, there remain uncertainties about the climate system, the complexities within climate models, the related impacts to the biophysical environment, and the use of climate information on decision making. Uncertainty is introduced in each step of the climate planning-an-response process--in the scenarios used to drive the climate models, the information used to construct the models, and the interpretation and use of the model' data for planning and decision making (Figure 19.1). There are server key challenge, drawn from recommendations of the authors of this report, that contribute to these uncertainties in the Southwest: - There is a dearth of climate observations at high elevations and on the lands of Native nations. - There is limited understanding of the influence of climate change on natural variability (e.g. El Niño-Southern Oscillations, Pacific Decadal Oscillation), extreme events (droughts, floods), and the marine layer align coastal California. - Climate models, downscaling, and resulting projection of the physical climate are imperfect. Representing the influence of the diverse topography of the Southwest on regional climate is a particular challenge. - The impacts of climate change on key components of the natural ecosystems (including species and terrestrial ecosystems) are ill-defined. - The adaptive capacity of decision-making entities and legal systems to handle climate impacts is unclear. This creates a challenge for identifying vulnerabilities to climate in the Southwest. - Regulation, legislation, and political and social responses too climate all play important roles in our ability to adapt to climate impacts and mitigate greenhouse gas (GHG) emissions. - Climate change is one of multiple stresses affecting the physical, biological, social, and economic systems of the Southwest, with population growth (and its related resource consumption, pollution, and land-sue changes) being particularly important.

Book chapter↗
USGS2021

A new approach for representing agent-environment feedbacks: Coupled agent-based and state-and-transition simulation models

Context Agent-based models (ABMs) and state-and-transition simulation models (STSMs) have proven useful for understanding processes underlying social-ecological systems and evaluating practical questions about how systems might respond to different scenarios. ABMs can simulate a variety of agents (autonomous units, such as wildlife or people); agent characteristics, decision-making, adaptive behavior, and mobility; and agent-environment interactions. STSMs are flexible and intuitive stochastic landscape models that can track scenarios and integrate diverse data. Both can be run spatially and track metrics of management success. Objectives Due to the complementarity of these approaches, we sought to couple them through a dynamic linkage and demonstrate the relevance of this advancement for modeling landscape processes and patterns. Methods We developed analytical techniques and software tools to couple these modeling approaches using NetLogo, R, and the ST-Sim package for SyncroSim. We demonstrated the capabilities and value of this coupled approach through a proof-of-concept case study of bison-vegetation interactions in Badlands National Park. Results The coupled ABM-STSM: (1) streamlined handling of model inputs and outputs; (2) allowed representation of processes at multiple temporal scales; (3) minimized assumptions; and (4) generated spatial and temporal patterns that better reflected agent-environment interactions. Conclusions These developments constitute a new approach for representing agent-environment feedbacks; modelers can now use output from an ABM to dictate landscape changes within an STSM that in turn influence agents. This facilitates experimentation across domains (agent and environment) and creation of more realistic and management-relevant projections, and opens new opportunities for communicating models and linking to other methods.

Landscape Ecology↗
USGS2022

Science mission requirements for a globally ranging, riserless drilling vessel for U.S. Scientific Ocean Drilling

Through the collection and analysis of shallow and deep subseafloor sediments, rocks, fluids, and life, scientific ocean drilling has enriched our understanding of the complex Earth system. Among other achievements, scientific ocean drilling has documented the history of Earth’s climate, the waxing and waning of polar ice sheets, the past changes in ocean and atmospheric circulation, the existence and function of microbial life in the subseafloor, the compositional variations in Earth’s crust and underlying mantle, and the physical and chemical processes acting at subduction zones, including those associated with tsunamigenic earthquakes. Over the decades, more than 12,000 articles that depend on analyses of scientific ocean drilling samples and geophysical data have been published, many detailing breakthrough contributions to global knowledge about the Earth system. Approximately 45% of these publications were led by U.S.-affiliated authors (International Ocean Discovery Program Publication Services, 2021). Since the mid-1980s, the workhorse of this multidisciplinary, international research effort has been the riserless D/V JOIDES Resolution, operated by Texas A&M University with funding from the U.S. National Science Foundation (NSF). D/V JOIDES Resolution has conducted the vast majority of scientific ocean drilling expeditions and collected most of the scientific cores over that period, including 82% of the expeditions and 93% of the cores in the last decade alone, despite being one of three platforms that is operated within the International Ocean Discovery Program. However, D/V JOIDES Resolution is approaching the end of its useful life. With a strong commitment to continue scientific ocean drilling beyond the end of the current phase, the community developed a document outlining the research frontiers that should be pursued. Exploring Earth by Scientific Ocean Drilling: 2050 Science Framework (Koppers and Coggon, 2020) describes seven scientific strategic objectives that focus on understanding interconnections within the Earth system and five flagship initiatives that integrate these objectives into long-term research efforts that address issues facing society. Additional elements in the 2050 Science Framework, including STEM education, workforce development, technology development, and innovative applications of data analytics, will advance the goals of scientific ocean drilling. Addressing the 2050 Science Framework also requires building partnerships with allied U.S. and international science programs and strengthening existing ones. To implement a significant portion of the 2050 Science Framework, the U.S. scientific community seeks to lease or acquire a newly built, globally ranging, state-of-the art, riserless drilling vessel. The many and varied technical and human resources requirements for successful accomplishment of scientific and educational goals summarized in this document and described in detail in the 2050 Science Framework require broad community input and careful consideration. Following receipt of NSF’s formal Request for Assistance to the United States Science Support Program (USSSP), the U.S. scientific ocean drilling community conducted a one-year exercise to identify its national scientific needs and priorities in order to determine the Science Mission Requirements (SMRs) presented here. This community effort included: (1) a U.S. community-wide survey to identify the specific operational and technical capabilities critical to addressing science in the 2050 Science Framework; (2) a series of online workshops focusing on critical capabilities identified by the survey; and (3) a large in-person workshop to synthesize the results of the survey and the virtual workshops (Appendix 1). The approach was designed to reach as many participants as possible. Overall, 278 survey responses were received from U.S. community members, representing 104 unique institutions from 39 states and the District of Columbia, and 137 unique individuals participated in the workshops (Appendix 2). The results of this effort comprise two classes of SMRs: Foundational Science Mission Requirements and Primary Science Mission Requirements. Foundational SMRs define minimum criteria for a new riserless drilling vessel that can address significant portions of the 2050 Science Framework. Primary SMRs build upon the Foundational SMRs and will create more robust science opportunities and data collection capabilities, will increase progress in addressing the 2050 Science Framework objectives, and will provide more real-time ship-to-shore interaction to improve science productivity, engagement, and outreach. Modern safety and environmental standards, including meeting standards to access protected waters such as exclusive economic zones, extended continental shelves, or high latitudes, while being cognizant of the vessel’s environmental footprint. Safe and efficient operations in global locations and in water depths from 70 m to 6000+ m, with total drill string length of at least 7000 m. High-quality core and data collection from a range of key subseafloor environments. Advanced heave compensation, dynamic positioning, and drill pipe stability. Modern mud and cement/casing systems. Critical onboard measurements for safety, operational decision-making, documentation of ephemeral properties, mission-specific science, and long-term science goals that extend beyond a single expedition. Designated and appropriate space for sample and data preservation. Highly skilled onboard personnel, including technical staff for curation and core handling; support for safety, time-sensitive, and critical shipboard measurements; computer support; equipment and instrument repair; application support; and data assurance. Primary Science Mission Requirements include: Flexible shipboard space for laboratories and on deck to ensure safe, successful implementation of diverse science objectives and operations. Minimizing contamination of recovered samples. Over-the-side capabilities for science-supporting technology (e.g., remotely operated vehicles, water- column sampling, sediment-water interface sampling). Downhole logging and measurements. Expanded borehole observatory capabilities. Reliable and consistent ship-to-shore communications. NSF’s investment in a new globally ranging, riserless drilling vessel will have a powerful economic multiplier effect, including the infusion of additional science support funds in the United States for training and research, the development of new technologies and tools, and the associated scientific and technical workforce development. The skills and knowledge gained through scientific ocean drilling are translatable to careers in fields such as sustainable energy development (e.g., geothermal and offshore wind), carbon sequestration, data management and cyberinfrastructure, biotechnology, communications, science education, policy, hazard mitigation, and environmental management. The United States is a leader in a well-established and internationally collaborative scientific ocean drilling community. A modern, globally ranging, riserless drilling vessel will allow the United States to expand its leadership position, address broad scientific questions that current capabilities preclude, and cultivate equitable international, multidisciplinary collaborations that will ensure scientific ocean drilling’s future success.

Report↗
USGS2024

Reducing uncertainty with iterative model updating parses effects of competition and environment on salamander occupancy

Making timely management decisions is often hindered by uncertainty. Monitoring reduces two key types of uncertainty. First, it serves to reduce structural uncertainty of how the system works and provides support for expectations of how a system works. Second, it serves to reduce parametric uncertainty of the drivers of system dynamics. By combining monitoring data and quantitative models, we can reduce structural and parametric uncertainty. To demonstrate this, we focus on the Shenandoah salamander ( Plethodon shenandoah ), a United States Federally Endangered Species. Early work suggested that P. shenandoah extinction risk results from competition with a conspecific ( Plethodon cinereus ). However, more recent work has found equivocal support for this claim, instead suggesting that abiotic factors, such as moisture and temperature, drive P. shenandoah persistence. Using long-term monitoring data, we find that while competition may play a part in P. shenandoah extinction risk, measures of surface moisture are better predictors of occupancy dynamics. Further, we find decreased detection rates of P. shenandoah when P. cinereus is present, suggesting a conflation of detection probability with actual competition, which cautions against making inference from unadjusted observations of occurrence. Using multiple lines of inquiry allows for more robust understanding of system drivers in the face of high uncertainty, increasing opportunities to manage extinction risk.

Virginia↗
USGS2001

Continental shelf GIS for the Monterey Bay National Marine Sanctuary

A marine sanctuary is an environment where the interests of science and society meet. Sanctuary managers need access to the best scientific data available that describe the environment and environmental processes in sanctuaries. Seafloor mapping and sampling in the Monterey Bay National Marine Sanctuary have revealed new details about the geology, morphology, and active geologic processes of this region. Data from sidescan sonar, multibeam sonar bathymetry, physical samples, and instrument moorings, are consolidated with new and existing maps in a geographic information system (GIS). The GIS provides researchers and policymakers a view of the relationship among data sets to assist science studies and to help with economic and social policy-making decisions regarding this protected environment.

California↗
USGS2022

Landsat data ecosystem case study: Actor perceptions of the use and value of landsat

It is well-known that Earth observation (EO) data plays a critical role in scientific understanding about the global environment. There is also growing support for the use of EO data to provide context-specific insights, with significant implications for their use in decision support systems. Technological development over recent years, including cloud computing infrastructure, machine learning techniques, and rapid expansion of the velocity, volume, and variety of space-borne data sources, offer huge potential to provide solutions to the myriad environmental problems facing society and the planet. The USGS/NASA Landsat Program, the longest continuously gathered source of land surface data, has played a central role in our understanding of environmental change, particularly for its contribution of longitudinal products that offer greater context for present research and decision support activities. The challenge facing the Landsat and EO data community, however, now lies in moving beyond context-specific knowledge generation to translating such knowledge into tangible value for society. Drawing from an open data ecosystem framework and qualitative social science methods, we map the Landsat data ecosystem (LDE) and the relationships linking multiple actors responsible for processing, indexing, analyzing, synthesizing, and translating raw Landsat data into information that is useful, useable, and used by end users in particular social-environmental contexts. Both the role of Big Data and associated technologies are discussed as they relate to the ultimate use of Landsat-derived information products to guide decision-making, and key data ecosystem characteristics that shape the likelihood of these products’ use are highlighted.

Frontiers in Environmental Science↗
USGS2013

Integrated environmental modeling: a vision and roadmap for the future

Integrated environmental modeling (IEM) is inspired by modern environmental problems, decisions, and policies and enabled by transdisciplinary science and computer capabilities that allow the environment to be considered in a holistic way. The problems are characterized by the extent of the environmental system involved, dynamic and interdependent nature of stressors and their impacts, diversity of stakeholders, and integration of social, economic, and environmental considerations. IEM provides a science-based structure to develop and organize relevant knowledge and information and apply it to explain, explore, and predict the behavior of environmental systems in response to human and natural sources of stress. During the past several years a number of workshops were held that brought IEM practitioners together to share experiences and discuss future needs and directions. In this paper we organize and present the results of these discussions. IEM is presented as a landscape containing four interdependent elements: applications, science, technology, and community. The elements are described from the perspective of their role in the landscape, current practices, and challenges that must be addressed. Workshop participants envision a global scale IEM community that leverages modern technologies to streamline the movement of science-based knowledge from its sources in research, through its organization into databases and models, to its integration and application for problem solving purposes. Achieving this vision will require that the global community of IEM stakeholders transcend social, and organizational boundaries and pursue greater levels of collaboration. Among the highest priorities for community action are the development of standards for publishing IEM data and models in forms suitable for automated discovery, access, and integration; education of the next generation of environmental stakeholders, with a focus on transdisciplinary research, development, and decision making; and providing a web-based platform for community interactions (e.g., continuous virtual workshops).

Environmental Modelling and Software↗
USGS2016

Adaptive management for improving species conservation across the captive-wild spectrum

Conservation of endangered species increasingly envisages complex strategies that integrate captive and wild management actions. Management decisions in this context must be made in the face of uncertainty, often with limited capacity to collect information. Adaptive management (AM) combines management and monitoring, with the aim of updating knowledge and improving decision-making over time. We provide a guide for managers who may realize the potential of AM, but are unsure where to start. The urgent need for iterative management decisions, the existence of uncertainty, and the opportunity for learning offered by often highly-controlled captive environments create favorable conditions for AM. However, experiments and monitoring may be complicated by small sample sizes, and the ability to control the system, including stochasticity and observability, may be limited toward the wild end of the spectrum. We illustrate the key steps to implementing AM in threatened species management using four case studies, including the management of captive programs for cheetah (Acinonyx jubatus) and whooping cranes (Grus americana), of a translocation protocol for Arizona cliffroses Purshia subintegra and of ongoing supplementary feeding of reintroduced hihi (Notiomystis cincta) populations. For each case study, we explain (1) how to clarify whether the decision can be improved by learning (i.e. it is iterative and complicated by uncertainty) and what the management objectives are; (2) how to articulate uncertainty via alternative, testable hypotheses such as competing models or parameter distributions; (3) how to formally define how additional information can be collected and incorporated in future management decisions.

Biological Conservation↗
USGS2022

Local environment and individuals’ beliefs: The dynamics shaping public support for sustainability policy in an agricultural landscape

Agricultural landscapes are the bleeding-edge in the advancement of sustainability and climate change adaptation. Our study focuses on how individual support for sustainability policy is shaped in coupled natural and human systems. We present an agent-based model in which a cultural decision-rule quantifies the probability that a stakeholder decides to support an easement policy for a region in the Central Great Plains, USA. Our model defines a cultural threshold used to assess how culturally meaningful the policy is for each stakeholder. The individual cultural threshold is estimated using the value-belief-norm framework and is modified by perceived changes in the environment. Results demonstrated that few stakeholders support the policy in the average cultural setting (8.9%). However, enough stakeholders would support the policy under a lower cultural threshold (40.7%). Our results indicate that sustainability policies do not need to be cheap if they are culturally meaningful.

Kansas↗
USGS2006

Benthic habitat and geologic mapping of the outer continental shelf of north-central California

The Fanny Shoal area is located between North Farallon Island and Cordell Bank approximately 40 miles west of San Francisco, California. The area lies within the Gulf of the Farallones National Marine Sanctuary (GFNMS) which is located just a few miles from San Francisco. The waters within the GFNMS are part of a nationally significant marine ecosystem encompassing a diversity of highly productive marine habitats. Protection of the living and cultural resources at the sites are administered by the National Oceanic and Atmospheric Administration (NOAA). The U.S. Geological Survey (USGS) in cooperation with the Golden Gate National Recreation Area (GGNRA) and NOAA, including the GFNMS, and Monterey Bay National Marine Sanctuary (MBNMS), collected side-scanning sonar, and underwater video data over three cruises in July of 2003, and April of 2004. The data are consolidated into a geographic information system (GIS) to produce benthic habitat and geologic maps that provide researchers and those involved in decision making with crucial, georeferenced geologic information that will aid in preserving the area's environment.

California↗