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At least 37 records · Page 2Linked to original sources
USGS2010

Developing an Analytical Framework: Incorporating Ecosystem Services into Decision Making - Proceedings of a Workshop

The analytical framework for understanding ecosystem services in conservation, resource management, and development decisions is multidisciplinary, encompassing a combination of the natural and social sciences. This report summarizes a workshop on 'Developing an Analytical Framework: Incorporating Ecosystem Services into Decision Making,' which focused on the analytical process and on identifying research priorities for assessing ecosystem services, their production and use, their spatial and temporal characteristics, their relationship with natural systems, and their interdependencies. Attendees discussed research directions and solutions to key challenges in developing the analytical framework. The discussion was divided into two sessions: (1) the measurement framework: quantities and values, and (2) the spatial framework: mapping and spatial relationships. This workshop was the second of three preconference workshops associated with ACES 2008 (A Conference on Ecosystem Services): Using Science for Decision Making in Dynamic Systems. These three workshops were designed to explore the ACES 2008 theme on decision making and how the concept of ecosystem services can be more effectively incorporated into conservation, restoration, resource management, and development decisions. Preconference workshop 1, 'Developing a Vision: Incorporating Ecosystem Services into Decision Making,' was held on April 15, 2008, in Cambridge, MA. In preconference workshop 1, participants addressed what would have to happen to make ecosystem services be used more routinely and effectively in conservation, restoration, resource management, and development decisions, and they identified some key challenges in developing the analytical framework. Preconference workshop 3, 'Developing an Institutional Framework: Incorporating Ecosystem Services into Decision Making,' was held on October 30, 2008, in Albuquerque, NM; participants examined the relationship between the institutional framework and the use of ecosystem services in decision making.

Open-File Report↗
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↗
USGS2023

Integrating community science and agency-collected monitoring data to expand monitoring capacity at large spatial scales

Monitoring species to better understand their status, ecology, and management needs is a major expense for agencies tasked with biodiversity conservation. Community science data have the potential to improve monitoring for minimal cost, given appropriate analytical frameworks. We describe a framework for integrating data from the eBird community science platform with agency-collected monitoring data using a multistate occupancy model. Our model accounts for the structural differences across datasets and allows for estimation of both occupancy and breeding probabilities. The framework was applied to Common Loons ( Gavia immer ) in Washington State. A total of 766 sites had observation effort, of which 713 sites had only eBird effort, 26 sites had only Washington Department of Fish and Wildlife (WDFW) effort, and 27 sites had both. We predicted that the probability of occupancy was only 0.07 (95% Bayesian credible interval, BCI = 0.02–0.51) at the 2324 sites in our sampling frame, though the probability that Common Loons were breeding at occupied sites was 0.95 (95% BCI = 0.71–1.00). We found that probability of occupancy was positively related to waterbody size (probability of a positive effect = 0.88) and negatively related to an index of human influence (probability of a negative effect = 0.94). We found that probability of breeding at occupied sites was positively related to tree canopy cover (0.86), negatively related to elevation (0.99), and negatively related to barren, scrub/shrub, and herbaceous land cover (0.98). We found that state agency biologists were 16 times more likely to detect breeding Common Loons at a site than were eBird users (0.94, 95% BCI = 0.78–0.99 for agency biologists vs. 0.08, 95% BCI = 0.06–0.10 for eBird users). However, the amount of effort expended by eBird users meant that they confirmed Common Loons at 94 sites while agency biologists confirmed them at just 24 sites, although evidence of reproduction was only contributed by agency biologists. Our results provide a better understanding of the distribution of Common Loons in Washington, while further demonstrating that community science data can be a valuable complement to agency-collected data, if appropriate frameworks are developed to integrate these data sources.

Ecosphere↗
USGS2016

Detecting failure of climate predictions

The practical consequences of climate change challenge society to formulate responses that are more suited to achieving long-term objectives, even if those responses have to be made in the face of uncertainty 1 , 2 . Such a decision-analytic focus uses the products of climate science as probabilistic predictions about the effects of management policies 3 . Here we present methods to detect when climate predictions are failing to capture the system dynamics. For a single model, we measure goodness of fit based on the empirical distribution function, and define failure when the distribution of observed values significantly diverges from the modelled distribution. For a set of models, the same statistic can be used to provide relative weights for the individual models, and we define failure when there is no linear weighting of the ensemble models that produces a satisfactory match to the observations. Early detection of failure of a set of predictions is important for improving model predictions and the decisions based on them. We show that these methods would have detected a range shift in northern pintail 20 years before it was actually discovered, and are increasingly giving more weight to those climate models that forecast a September ice-free Arctic by 2055.

Nature Climate Change↗
USGS2022

Status and trends of North American bats: Summer occupancy analysis 2010-2019

• We developed an analytical pipeline supported by web-based infrastructure for integrating continental scale bat monitoring data (stationary acoustic, mobile acoustic, and capture records) to estimate summer (May 1–Aug 31) occupancy probabilities and changes in occupancy over time for 12 North American bat species. This serves as one of multiple lines of evidence that inform the status and trends of bat populations. • We analyzed data from a total of 12 bat species (Table 1), 11 of which have tested positive for Pseudogymnoascus destructans (Pd), a fungal pathogen that causes white-nose syndrome (WNS)—a disease that has led to significant rates of mortality for subterranean hibernating bat species in North America. A twelfth species was also selected because of high rates of mortality at wind energy facilities. Additional species were considered but not selected due to data limitations. • We estimated occupancy probabilities for 2010 through 2019 for three species ( Myotis lucifugus , MYLU; Myotis septentrionalis , MYSE; and P erimyotis subflavus , PESU). For an additional nine species, we estimated occupancy probabilities for 2016 through 2019 ( Myotis evotis , MYEV; Myotis grisescens , MYGR; Myotis leibii , MYLE; Myotis thysanodes , MYTH; Myotis volans , MYVO; Myotis yumanensis , MYYU; Eptesicus fuscus , EPFU; Lasionycteris noctivagans , LANO; and Lasiurus cinereus , LACI). • For each species, we provide range-wide occupancy probability predictions (e.g., predicted summer occupancy distribution maps) each year at a spatial resolution of 100 km2 and provide regional estimates of mean occupancy probability aggregated at larger spatial scales (state/province/territory, range-wide). • For each species, we also provide trends over time (average annual change rate and total change rate) in mean occupancy probabilities at multiple spatial scales (state/province/territory, range-wide) and when possible, over multiple timescales (short, medium, long). • Results suggest that over the short-term (2016-2019), two ( Myotis lucifugus and Perimyotis subflavus ) of 12 species have experienced declines in range-wide average occupancy probability with at least 95% certainty. Seven species showed either minor increases or decreases in range-wide average occupancy probability but with less than 95% certainty in both trend indicators. Results over the longer term (eight years and 10 years of sampling) suggest that three hibernating species known to be highly affected by white-nose syndrome ( Myotis lucifugus , Myotis septentrionalis , and Perimyotis subflavus ) have experienced marked declines in range-wide average occupancy probabilities, with severity varying by species and region. Finally, the results for three species ( Eptesicus fuscus , Lasiurus cinereus , Lasionycteris noctivagans ) were inconclusive due to 1) borderline convergence issues in the model fitting procedure which suggests potentially unreliable estimates, 2) failure to reliably distinguish between false positives and true positive detections for ambiguous detections, and 3) largely uninformative covariates for occupancy and detection. • For Myotis lucifugus , Myotis septentrionalis , and Perimyotis subflavus we found meaningful associations in space and time between declining winter populations (likely a result of WNS) and summer occupancy distributions. • The representativeness of sampling data for each species’ status and trend estimates (e.g., state/province/territory) were also evaluated based on the percent of grid cells sampled each year with a goal of understanding the reliability of regional estimates and improving future monitoring efforts. • This work represents the most comprehensive effort to date to model North American bat distributions across their continental ranges. Despite current limitations highlighted in the discussion, the analytical methods and resulting status and trends estimates provide the best available science on summer bat populations across North America and will continue to improve over time as monitoring data sets and analytical methods improve. • Moving forward, our occupancy analyses will continue to improve with submission of more 1) data from currently underrepresented areas (i.e., improved geographic representation), 2) manually-vetted acoustic recordings, 3) capture records, and 4) roost location and count data (summer and winter).

Report↗
USGS2018

The potential for citizen science to produce reliable and useful information in ecology

We examined features of citizen science that influence data quality, inferential power, and usefulness in ecology. As background context for our examination, we considered topics such as ecological sampling (probability based, purposive, opportunistic), linkage between sampling technique and statistical inference(designbased,modelbased),andscientificparadigms(confirmatory,exploratory).Wedistinguished several types of citizen science investigations, from intensive research with rigorous protocols targeting clearly articulated questions to mass-participation internet-based projects with opportunistic data collection lacking samplingdesign,andexaminedoverarchingobjectives,design,analysis,volunteertraining,andperformance. We identified key features that influence data quality: project objectives, design and analysis, and volunteer training and performance. Projects with good designs, trained volunteers, and professional oversight can meet statistical criteria to produce high-quality data with strong inferential power and therefore are well suited for ecological research objectives. Projects with opportunistic data collection, little or no sampling design, and minimal volunteer training are better suited for general objectives related to public education or data exploration because reliable statistical estimation can be difficult or impossible. In some cases, statistically robust analytical methods, external data, or both may increase the inferential power of certain opportunistically collected data. Ecological management, especially by government agencies, frequently requires data suitable for reliable inference. With standardized protocols, state-of-the-art analytical methods, and well-supervised programs, citizen science can make valuable contributions to conservation by increasing the scope of species monitoring efforts. Data quality can be improved by adhering to basic principles of data collection and analysis, designing studies to provide the data quality required, and including suitable statistical expertise, thereby strengthening the science aspect of citizen science and enhancing acceptance by the scientific community and decision makers.

Conservation Biology↗
USGS2023

Improving ecological data science with workflow management software

Pressing environmental research questions demand the integration of increasingly diverse and large-scale ecological datasets as well as complex analytical methods, which require specialized tools and resources. Computational training for ecological and evolutionary sciences has become more abundant and accessible over the past decade, but tool development has outpaced the availability of specialized training. Most training for scripted analyses focuses on individual analysis steps in one script rather than creating a scripted pipeline, where modular functions comprise an ecosystem of interdependent steps. Although current computational training creates an excellent starting place, linear styles of scripting can risk becoming labor- and time-intensive and less reproducible by often requiring manual execution. Pipelines, however, can be easily automated or tracked by software to increase efficiency and reduce potential errors. Ecology and evolution would benefit from techniques that reduce these risks by managing analytical pipelines in a modular, readily parallelizable format with clear documentation of dependencies. Workflow management software (WMS) can aid in the reproducibility, intelligibility and computational efficiency of complex pipelines. To date, WMS adoption in ecology and evolutionary research has been slow. We discuss the benefits and challenges of implementing WMS and illustrate its use through a case study with the targets r package to further highlight WMS benefits through workflow automation, dependency tracking and improved clarity for reviewers. Although WMS requires familiarity with function-oriented programming and careful planning for more advanced applications and pipeline sharing, investment in training will enable access to the benefits of WMS and impart transferable computing skills that can facilitate ecological and evolutionary data science at large scales.

Methods in Ecology and Evolution↗
USGS2018

Generating community-built tools for data sharing and analysis in environmental networks

Rapid data growth in many environmental sectors has necessitated tools to manage and analyze these data. The development of tools often lags behind the proliferation of data, however, which may slow exploratory opportunities and scientific progress. The Global Lake Ecological Observatory Network (GLEON) collaborative model supports an efficient and comprehensive data–analysis–insight life cycle, including implementations of data quality control checks, statistical calculations/derivations, models, and data visualizations. These tools are community-built and openly shared. We discuss the network structure that enables tool development and a culture of sharing, leading to optimized output from limited resources. Specifically, data sharing and a flat collaborative structure encourage the development of tools that enable scientific insights from these data. Here we provide a cross-section of scientific advances derived from global-scale analyses in GLEON. We document enhancements to science capabilities made possible by the development of analytical tools and highlight opportunities to expand this framework to benefit other environmental networks.

Inland Waters↗
USGS2013

Volcano crisis response at Yellowstone volcanic complex - after-action report for exercise held at Salt Lake City, Utah, November 15, 2011

A functional tabletop exercise was run on November 14-15, 2011 in Salt Lake City, Utah, to test crisis response capabilities, communication protocols, and decision-making by the staff of the multi-agency Yellowstone Volcano Observatory (YVO) as they reacted to a hypothetical exercise scenario of accelerating volcanic unrest at the Yellowstone caldera. The exercise simulated a rapid build-up of seismic activity, ground deformation, and hot-spring water-chemistry and temperature anomalies that culminated in a small- to moderate-size phreatomagmatic eruption within Yellowstone National Park. The YVO scientific team's responses to the unfolding events in the scenario and to simulated requests for information by stakeholders and the media were assessed by (a) the exercise organizers; (b) several non-YVO scientists, who observed and queried participants, and took notes throughout the exercise; and (c) the participants themselves, who kept logs of their actions during the exercise and later participated in a group debriefing session and filled out detailed questionnaires. These evaluations were tabulated, interpreted, and summarized for this report, and on the basis of this information, recommendations have been made. Overall, the YVO teams performed their jobs very well. The exercise revealed that YVO scientists were able to successfully provide critical hazards information, issue information statements, and appropriately raise alert levels during a fast-moving crisis. Based on the exercise, it is recommended that several measures be taken to increase YVO effectiveness during a crisis: 1. Improve role clarification within and between YVO science teams. 2. Improve communications tools and protocols for data-sharing and consensus-building among YVO scientists, who are geographically and administratively dispersed among various institutions across the United States. 3. Familiarize YVO staff with Incident Command System (ICS) procedures and protocols, and provide more in-depth training to appropriate staff members, as needed. 4. Train all science team members in the use of all analytical and computational tools available to them, in order to maximize effectiveness of teams in tracking and interpreting possible accelerating unrest at Yellowstone. Desirable pre-crisis preparations include: (a) updating a catalog of existing map and information products (and identifying additional products) that would be helpful during a crisis; (b) creating "to do" lists of early-crisis tasks for each scientific team; (c) coordinating radio frequencies among partner agencies; and (d) brief training on and promotion of the internal YVO Web log as a repository for scientific observations, data, photographs, and other material to be shared among YVO scientific teams during a crisis. This exercise was designed as an opportunity to practice response to a fast-developing volcano crisis and to test for organizational and procedural weaknesses that could emerge during a real crisis. This report is based upon the observations of the exercise organizers during the one-day exercise and upon written evaluations by the participants. It does not attempt to evaluate any other aspect of YVO or the scientific expertise of any of the highly competent YVO staff. Participants unanimously found the exercise to be helpful for improving their response capabilities, and it is our hope that the report will be a starting point for internal discussions that will make YVO even better-prepared for some future volcano crisis.

Utah↗
USGS2005

Method of analysis at the U.S. Geological Survey California Water Science Center, Sacramento Laboratory - determination of haloacetic acid formation potential, method validation, and quality-control practices

An analytical method for the determination of haloacetic acid formation potential of water samples has been developed by the U.S. Geological Survey California Water Science Center Sacramento Laboratory. The haloacetic acid formation potential is measured by dosing water samples with chlorine under specified conditions of pH, temperature, incubation time, darkness, and residual-free chlorine. The haloacetic acids formed are bromochloroacetic acid, bromodichloroacetic acid, dibromochloroacetic acid, dibromoacetic acid, dichloroacetic acid, monobromoacetic acid, monochloroacetic acid, tribromoacetic acid, and trichloroacetic acid. They are extracted, methylated, and then analyzed using a gas chromatograph equipped with an electron capture detector. Method validation experiments were performed to determine the method accuracy, precision, and detection limit for each of the compounds. Method detection limits for these nine haloacetic acids ranged from 0.11 to 0.45 microgram per liter. Quality-control practices include the use of blanks, quality-control samples, calibration verification standards, surrogate recovery, internal standard, matrix spikes, and duplicates.

Scientific Investigations Report↗
USGS2025

A comprehensive assessment of membrane bioreactor contaminant removal efficacy through analytical chemistry, fish exposures, and microbiome characterization

Treated municipal wastewater effluent is an important pathway for Contaminants of Emerging Concern (CEC) to enter aquatic ecosystems. As the aging wastewater infrastructure in many industrialized countries requires upgrades or replacement, assessing new treatment technologies in the context of CEC effects may provide additional support for science-based resource management. Here, we used three lines of evidence, analytical chemistry, fish exposure experiments, and fish and water microbiome analysis, to assess the effectiveness of membrane bioreactor treatment (MBR) to replace traditional activated sludge treatment. To do this, we sampled a municipal wastewater treatment plant with a split wastewater stream, a portion of which was treated with an MBR and another via an oxidation ditch (OXI). The OXI and MBR treatments substantially reduced most measured CECs compared to the primary effluent (PRI). Only pesticides and some pharmaceuticals were recalcitrant to both secondary treatment methods. While ammonia toxicity of PRI prevented its inclusion in fish exposure experiments, exposure of fish with waters from the OXI or MBR treated wastewater produced only subtle biological differences with no adverse apical outcomes. These findings were consistent with low chemically derived exposure: activity ratios for OXI and MBR. Microbiome analysis of fish and wastewater highlighted the significant reduction of microbial abundance and diversity in the MBR treatment compared to all other treatments. The comparable removal efficacy of CECs in MBR makes it an attractive alternative to traditional OXI, especially when MBR may eliminate the need for tertiary treatment for wastewater disinfection.

Environmental Toxicology and Chemistry↗
USGS2023

U.S. Geological Survey Rocky Mountain Region 2022 science exchange, showcasing interdisciplinary and state-of-the-art USGS science

Introduction The Rocky Mountains and the Colorado River Basin in the Western United States represent complex, interconnected systems that sustain a number of species, including tens of millions of humans. These systems face several challenges, including worsening drought, altered wildfire regimes, climate change, and the spread of invasive species. These factors can exacerbate one another, further contributing to habitat loss and affecting species of conservation concern. Characterizing and managing these challenges require interdisciplinary communities of scientists to develop information and decision-support tools that can inform holistic land and water management solutions. The U.S. Geological Survey (USGS) Rocky Mountain Region 2022 Science Exchange focused on the use of interdisciplinary and state-of-the-art science being conducted by USGS scientists in the region to address these complex problems. The USGS Rocky Mountain Regional Office organized its first Science Exchange in 2017 to share scientific information between leaders and early career scientists throughout the region. Science Exchanges held in 2018 and 2020 focused on drought science relevant to the region and the Earth Monitoring, Analyses, and Prediction (EarthMAP) concept, which is designed to facilitate interdisciplinary, timely, and actionable science related to drought in the Colorado River Basin and other areas. Based on the emerging need for more holistic approaches to address increasingly complex natural resource issues that affect society, the Region hosted a virtual fourth Science Exchange for three days in April 2022. This event focused on barriers and bridges to interdisciplinary science and highlighted studies from the Region to inspire collaboration across disciplines. Presentations described recent and ongoing research that applied collaborative and state-of-the-art methods to address problems in the fields of geology, hydrology, ecology, and natural hazards. Science collaboration and outreach to all levels of stakeholders are vital elements needed for providing timely and actionable data, interpretations, analytical tools, and products. These presentations led to active online chats and panel discussions and showcased interdisciplinary science and advanced methods that may inform and lead to more effective, holistic management decisions as the Western United States adapts to ongoing and future changes.

Fact Sheet↗
USGS2021

U.S. Geological Survey landscape science strategy 2020–2030

Across our Nation, multiple Federal, State, Tribal, and local governments are working with stakeholders and landowners to restore, conserve, and manage lands and resources to benefit fish, wildlife, and people. One of the largest Federal efforts is led by the U.S. Department of the Interior (DOI), with multiple DOI agencies working to conserve and manage public lands, resources, and cultural heritage for the benefit and enjoyment of current and future generations. As a science provider within the DOI, the U.S. Geological Survey (USGS) has an important role in developing actionable science products that can inform decision making on public lands and across all of our Nation’s landscapes. Our landscapes and resources are undergoing continual change from a complex and interacting suite of stressors that include traditional and nontraditional land uses, a changing climate, a dynamic economy, and a culturally diverse and ever-changing society. Landscape science seeks to understand how the physical, biological, and social components of ecosystems and landscapes interact with each other and are affected by these stressors across local to global scales. The USGS has developed this landscape science strategy to focus and strengthen the agency’s efforts to inform critical conservation, restoration, and management decisions for American landscapes. The strategy directly supports the overarching 21st-century science strategy of the USGS and expands our perspective and focus on partnerships, including with internal, external, traditional, and nontraditional partners. The vision for USGS landscape science is to integrate multiple disciplines and approaches to create and deliver relevant, timely, and scientifically sound products that enable our partners to make informed decisions about how to manage complex interacting natural and human systems across changing American landscapes. This strategy seeks to leverage the expertise, data, analytical capacity, and tools of the agency and to strengthen integration of science efforts across programs and scales. A cornerstone of the strategy is working closely with landowners and resource managers to identify the science that is needed to inform management actions, and then working collaboratively with those same partners to coproduce science and data products that are timely, practical, and useful for their decision making. Many individual efforts within the USGS already embrace landscape science ideas and practices. With the development of this strategy, we are seeking to build on these efforts and to focus agency attention and expertise on expanding, connecting, and institutionalizing core practices and approaches for landscape science that produces actionable science products and tools that decision makers can use to help conserve and manage American landscapes.

Circular↗
USGS2019

Integration of social and ecological sciences for natural resource decision making: Challenges and opportunities

The last 25 years have witnessed growing recognition that natural resource management decisions depend as much on understanding humans and their social interactions as on understanding the interactions between non-human organisms and their environment. Decision science provides a framework for integrating ecological and social factors into a decision, but challenges to integration remain. The decision-analytic framework elicits values and preferences to help articulate objectives, and then evaluates the outcomes of alternative management actions to achieve these objectives. Integrating social science into these steps can be hindered by failing to include social scientists as more than stakeholder-process facilitators, assuming that specific decision-analytic skills are commonplace for social scientists, misperceptions of social data as inherently qualitative, timescale mismatches for iterating through decision analysis and collecting relevant social data, difficulties in predicting human behavior, and failures of institutions to recognize the importance of this integration. We engage these challenges, and suggest solutions to them, helping move forward the integration of social and biological/ecological knowledge and considerations in decision-making.

Environmental Management↗
USGS2007

Striving for collaborative science and communication through the Consortium for Research and Education on Emerging Contaminants (CREEC)

Current analytical capabilities are allowing scientists to identify possible contaminants in the environment that were previously unmonitored or were present at concentrations too low for detection. New scientific evidence about the exposure pathways and potential impacts of some of these compounds on human or environmental health is regularly being published (Woodling et al., 2006; Drewes et al., 2005; Kinney et al., 2006; Gibs et al., 2007; Veldhoen et al., 2006). Recent news headlines have declared potential human health and ecological concerns regarding the occurrence of personal care products and pharmaceuticals in our environment. These are products that we regularly use (or create) in our homes, businesses, farms and industry, including plasticizers, flame retardants, detergents, pesticides and herbicides, antibacterial agents, steroids, antibiotics, and disinfection byproducts. These ‘emerging contaminants’ (ECs) are compounds that have recently been shown to occur widely in one or more environmental media, have been identified as being a potential public health or ecological risk, and yet adequate data are lacking to determine their actual risk (Younos, 2005; Soin and Smagghe, 2007; Hutchinson, 2007).

Water Resources Impact↗
USGS2013

Spatial Relation Predicates in Topographic Feature Semantics

Topographic data are designed and widely used for base maps of diverse applications, yet the power of these information sources largely relies on the interpretive skills of map readers and relational database expert users once the data are in map or geographic information system (GIS) form. Advances in geospatial semantic technology offer data model alternatives for explicating concepts and articulating complex data queries and statements. To understand and enrich the vocabulary of topographic feature properties for semantic technology, English language spatial relation predicates were analyzed in three standard topographic feature glossaries. The analytical approach drew from disciplinary concepts in geography, linguistics, and information science. Five major classes of spatial relation predicates were identified from the analysis; representations for most of these are not widely available. The classes are: part-whole (which are commonly modeled throughout semantic and linked-data networks), geometric, processes, human intention, and spatial prepositions. These are commonly found in the ‘real world’ and support the environmental science basis for digital topographical mapping. The spatial relation concepts are based on sets of relation terms presented in this chapter, though these lists are not prescriptive or exhaustive. The results of this study make explicit the concepts forming a broad set of spatial relation expressions, which in turn form the basis for expanding the range of possible queries for topographical data analysis and mapping.

Book chapter↗
USGS2025

Estimates of global surface water dynamics harnessing near real-time land cover observations and open science geospatial capabilities

Spatio-temporal changes to our world’s surface water resources are escalating. Translating how these changes impact communities and ecosystems requires time-varying data of Global Surface Water Extents (GSWE). Traditionally, GSWE mapping has been limited to static estimates, with recent efforts focusing on annual averages, frequency and occurrence of long-term variations. Building upon these foundational capabilities, we harnessed remotely sensed Sentinel-2 based near real-time Dynamic World (DW) land cover products to produce the first-of-its-kind 10 m resolution GSWE dataset representing 2015–2023. Our dataset estimated 2.5 million km 2 of permanent waters and 8 million km 2 of seasonal waters worldwide. Comparing our Sentinel-2 based data to contemporary Landsat-based GSWE, we observed that our data mapped less water within the >50% probability of occurrence range, suggesting a lower presence of open permanent water especially in high latitudes, deviating from what we previously learnt from Landsat data. Statistical analysis compared to well-established observational products and widely used GSWE datasets across some of the world’s most ecologically significant regions, including Pantanal in South America and Haor in South Asia, supports the overall physical realism of our data in predicting global open surface water dynamics. Our key contribution is a prototype Open Science operational framework that extracts routinely available DW products, runs geospatial analytics, and creates actionable water information for educators, researchers, and stakeholders at any scale of practical interest. We present examples of this operational capability through instant mapping of flood in Spain and drought in Lake Urmia, Central Asia, frequent monitoring of river extent changes at the Ganges–Brahmaputra confluence, and above all, interoperability with other existing GSWE applications.

Environmental Research Letters↗
USGS2013

Resilience thinking and a decision-analytic approach to conservation: strange bedfellows or essential partners?

There has been some tendency to view decision science and resilience theory as opposing approaches, or at least as contending perspectives, for natural resource management. Resilience proponents have been especially critical of optimization in decision science, at least for those cases where it is focused on the aggressive pursuit of efficiency. In general, optimization of resource systems is held to reduce spatial, temporal, or organizational heterogeneity that would otherwise limit efficiency, leading to homogenization of a system and making it less able to cope with unexpected changes or disturbances. For their part, decision analysts have been critical of resilience proponents for not providing much practical advice to decision makers. We believe a key source of tension between resilience thinking and application of decision science is the pursuit of efficiency in the latter (i.e., choosing the “best” management action or strategy option to maximize productivity of one or few resource components), vs. a desire in the former to keep options open (i.e., maintaining and enhancing diversity). It seems obvious, however, that with managed natural systems, there must be a principle by which to guide decision making, which at a minimumallows for a comparison of projected outcomes associated with decision alternatives. This is true even if the primary concern of decision making is the preservation of system resilience. We describe how a careful framing of conservation problems, especially in terms of management objectives and predictive models, can help reduce the purported tension between resiliencethinking and decision analysis. In particular, objective setting in conservation problems needs to be more attuned to the dynamics of ecological systems and to the possibility of deep uncertainties that underlie the risk of unintended, if not irreversible, outcomes. Resilience thinking also leads to the suggestion that model development should focus more on process rather than pattern, on multiple scales of influence, and on phenomena that can create alternative stability regimes. Although we acknowledge the inherent difficulties in modeling ecological processes, we stress that formulation of useful models need not depend on a thorough mechanistic understanding or precise parameterization, assuming that uncertainty is acknowledged and treated in a systematic manner.

Ecology and Society↗