SEARCH · Geology Reports
Results for “Environmental Science”
Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Citizen science can improve conservation science, natural resource management, and environmental protection
Citizen science has advanced science for hundreds of years, contributed to many peer-reviewed articles, and informed land management decisions and policies across the United States. Over the last 10 years, citizen science has grown immensely in the United States and many other countries. Here, we show how citizen science is a powerful tool for tackling many of the challenges faced in the field of conservation biology. We describe the two interwoven paths by which citizen science can improve conservation efforts, natural resource management, and environmental protection. The first path includes building scientific knowledge, while the other path involves informing policy and encouraging public action. We explore how citizen science is currently used and describe the investments needed to create a citizen science program. We find that: Citizen science already contributes substantially to many domains of science, including conservation, natural resource, and environmental science. Citizen science informs natural resource management, environmental protection, and policymaking and fosters public input and engagement. Many types of projects can benefit from citizen science, but one must be careful to match the needs for science and public involvement with the right type of citizen science project and the right method of public participation. Citizen science is a rigorous process of scientific discovery, indistinguishable from conventional science apart from the participation of volunteers. When properly designed, carried out, and evaluated, citizen science can provide sound science, efficiently generate high-quality data, and help solve problems.
Bayesian applications in environmental and ecological studies with R and Stan
Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.
Groundwater contamination downstream of a contaminant penetration site. II. Horizontal penetration of the contaminant plume
Part I of this study (Rubin, H.; Buddemeier, R.W. Groundwater Contamination Downstream of a Contaminant Penetration Site Part 1: Extension-Expansion of the Contaminant Plume. J. of Environmental Science and Health Part A (in press).) addressed cases, in which a comparatively thin contaminated region represented by boundary layers (BLs) developed within the freshwater aquifer close to contaminant penetration site. However, at some distance downstream from the penetration site, the top of the contaminant plume reaches the top or bottom of the aquifer. This is the location of the "attachment point," which comprises the entrance cross section of the domain evaluated by the present part of the study. It is shown that downstream from the entrance cross section, a set of two BLs develop in the aquifer, termed inner and outer BLs. It is assumed that the evaluated domain, in which the contaminant distribution gradually becomes uniform, can be divided into two sections, designated: (a) the restructuring section, and (b) the establishment section. In the restructuring section, the vertical concentration gradient leads to expansion of the inner BL at the expense of the outer BL, and there is almost no transfer of contaminant mass between the two layers. In the establishment section, each of the BLs occupies half of the aquifer thickness, and the vertical concentration gradient leads to transfer of contaminant mass from the inner to the outer BL. By use of BL approximations, changes of salinity distribution in the aquifer are calculated and evaluated. The establishment section ends at the uniformity point, downstream from which the contaminant concentration profile is practically uniform. The length of the restructuring section, as well as that of the establishment section, is approximately proportional to the aquifer thickness squared, and is inversely proportional to the transverse dispersivity. The study provides a convenient set of definitions and terminology that are helpful in visualizing the gradual development of uniform contaminant concentration distribution in an aquifer subject to contaminant plume penetration. The method developed in this study can be applied to a variety of problems associated with groundwater quality, such as initial evaluation of field data, design of field data collection, the identification of appropriate boundary conditions for numerical models, selection of appropriate numerical modeling approaches, interpretation and evaluation of field monitoring results, etc.
Tapping environmental history to recreate America's colonial hydrology
Throughout American history water resources have played integral roles in shaping patterns of human settlement and networks of biological and economic exchange. In turn, humans have altered hydrologic systems to meet their needs. A paucity of climate and water discharge data for the seventeenth and eighteenth centuries, however, has left America's preindustrial hydrology largely unstudied. As a result, there have been few detailed, quantifiable, regional assessments of hydrologic change between the time of first European settlement and the dawn of industrial expansion. As scientists labor to understand present-day hydrologic systems and make predictions about the future, the value of expanding the geographic (1, 2) and temporal scopes (3, 4) of their studies has become increasingly evident. Pollen and tree-ring analyses have helped shed light on past climate and land-use patterns. But other nonscientific sources and methods can be equally revealing and in some cases complement empirical studies (5). This paper argues that environmental science, particularly that concerned with the human dimensions of water resources, stands to profit from using historical literature and archival sources. By considering work in environmental history, forging closer working relationships between the geophysical and social sciences, and seriously entertaining narratives as a form of evidence, environmental scientists can not only look farther into the past and across broader geographic areas, but they can also more accurately describe the nuances and complexities that define the ways humans have changed the world around them. In this paper, we present the recommendations of a multidisciplinary summer institute that developed 1) a conceptual and methodological framework for conducting historical hydrology, and 2) suggestions for ways that historical information can be used to inform the hydrologic sciences. Our intent here is to encourage further work along these or similar lines. We believe that future efforts that build on our famework and draw and expand upon the sources referenced below will produce scholarship of great utility to both environmental and social sciences.
Centering voices of scientists from marginalized backgrounds to understand experiences in climate adaptation science and inform action
Identifying and building solutions to help people and ecosystems adapt to climate change requires participation of all people; however, Science, Technology, Engineering, and Mathematics (STEM) fields, including environmental sciences, continue to lack diversity. To address this issue, many institutions have increased programming to recruit and retain people from historically marginalized backgrounds in STEM fields. Institutions use surveys to evaluate the experiences of community members and identify areas for improvement; however, surveys often summarize and reflect majority perspectives and disregard voices of historically marginalized individuals. In June 2021, a survey of graduate students, postdocs, faculty, staff, and researchers affiliated with the Northeast Climate Adaptation Science Center (NE CASC) evaluated their experiences of diversity, equity, inclusion, and justice (DEIJ) using Likert-based and long-answer questions. We analyzed the results as a whole, but also focused on the responses of people who self-identified as members of a marginalized group (“marginalized respondents”) in climate adaptation science to center their voices. Marginalized respondents reported being motivated to enter climate adaptation science to improve society and the environment rather than for intellectual curiosity, which motivated one third of non-marginalized respondents. Once in science, marginalized respondents reported feeling less supported and comfortable at work and were more likely to have considered leaving science and academia in the last year. Long-answer responses of marginalized respondents indicated distrust in the ability of leadership and existing DEIJ initiatives to effectively tackle systemic issues and emphasized the importance of focusing on equity and inclusion before recruitment. Marginalized respondents identified additional funding to support existing DEIJ efforts and undergraduates as priorities. By allowing participants to self-identify as part of a marginalized group, we were able to highlight experiences and needs without risking exposure based on race, gender, disability status, or sexual orientation. This approach can be applied to other small organizations with limited demographic diversity.
College and university environmental programs as a policy problem (Part 2): Strategies for improvement
Environmental studies and environmental sciences programs in American and Canadian colleges and universities seek to ameliorate environmental problems through empirical enquiry and analytic judgment. In a companion article (Part 1) we describe the environmental program movement (EPM) and discuss factors that have hindered its performance. Here, we complete our analysis by proposing strategies for improvement. We recommend that environmental programs re-organize around three principles. First, adopt as an overriding goal the concept of human dignity—defined as freedom and social justice in healthy, sustainable environments. This clear higher-order goal captures the human and environmental aspirations of the EPM and would provide a more coherent direction for the efforts of diverse participants. Second, employ an explicit, genuinely interdisciplinary analytical framework that facilitates the use of multiple methods to investigate and address environmental and social problems in context. Third, develop educational programs and applied experiences that provide students with the technical knowledge, powers of observation, critical thinking skills and management acumen required for them to become effective professionals and leaders. Organizing around these three principles would build unity in the EPM while at the same time capitalizing on the strengths of the many disciplines and diverse local conditions involved.
Science during crisis: the application of social science during major environmental crises
Historical and contemporary experience suggests that science plays an increasingly critical role in governmental and institutional responses to major environmental crises. Recent examples include major western wildfires (2009), the Deepwater Horizon oil spill (2010), the Fukushima nuclear accident (2011), and Hurricane Sandy (2012). The application of science during such crises has several distinctive characteristics, as well as essential requirements if it is to be useful to decision makers. these include scope conditions that include coupled natural/human systems, clear statement of uncertainties and limitations, description of cascading consequences, accurate sense of place, estimates of magnitude of impacts, identification of beneficiaries and those adversely affected, clarity and conciseness, compelling visualization and presentation, capacity to speak "truth to power", and direct access to decision makers. In this chapter, we explore the role and significance of science – including all relevant disciplines and focusing attention on the social sciences – in responding to major environmental crises. We explore several important questions: How is science during crisis distinctive? What social science is most useful during crises? What distinctive characteristics are necessary for social science to make meaningful contributions to emergency response and recovery? How might the social sciences be integrated into the strategic science needed to respond to future crises? The authors, both members of the Department of the Interior's innovative Strategic Sciences Group, describe broad principles of engagement as well as specific examples drawn from history, contemporary efforts (such as during the Deepwater Horizon oil spill), and predictions of environmental crises still to be confronted.
Leveraging artificial intelligence and machine learning to advance Chesapeake Bay research and management: A review of status, challenges, and opportunities
The Chesapeake Bay and its watershed (hereafter “Chesapeake Bay region”) have been the focus of extensive restoration efforts for several decades. These restoration efforts are guided by the Chesapeake Bay Watershed Agreement (Chesapeake Executive Council 2014) which outlines 10 goals and 31 measurable outcomes. The Chesapeake Bay is globally recognized as a model for coastal restoration due to long-term investments in monitoring, modeling, implementation and research by the Chesapeake Bay Program (CBP) partnership. These monitoring network spans tidal and non-tidal regions and provides data across multiple scales. Artificial intelligence (AI), particularly machine-learning (ML) and deep learning (DL), has emerged as a powerful tool for analyzing large, complex datasets. These techniques have gained widespread adoption across various disciplines, including ecology, hydrology, and environmental science. In the Bay context, AI/ML is increasingly being used to explore drivers of environmental change, analyze system dynamics, and predict conditions in areas with limited monitoring. The CBP partnership, particularly its Scientific and Technical Advisory Committee (STAC), has increasingly recognized the growing role of AI/ML in watershed and estuarine management. Recent Chesapeake Community Research Symposium sessions and initiatives such as the Chesapeake Global Collaboratory highlight increasing regional momentum to apply big data and AI/ML for environmental solutions. Together, these developments underscore the timely need to explore how AI/ML can help advance Chesapeake Bay restoration and management. This STAC workshop, titled “Leveraging Artificial Intelligence and Machine learning to Advance Chesapeake Bay Research and Management: A review of status, challenges, and opportunities,” was held from February 24-25, 2025, in Edgewater, Maryland to bring together over 50 federal, state, and academic scientists and partners to synthesize the current state of AI/ML applications and identify research gaps in Chesapeake Bay research and management. The workshop focused on three main objectives: 1. Summarize recent AI/ML applications and lessons learned in both tidal and nontidal areas of the Chesapeake Bay region. 2. Identify challenges and gaps in applying AI/ML approaches to Chesapeake Bay data. Such challenges and gaps may include data limitations, harmonization issues, ineffective communication of AI/ML insights, and a lack of coordination among research and management institutions. 3. Develop recommendations and identify opportunities for leveraging AI/ML to address issues across the Chesapeake Bay region. Key areas of focus may include generating new information to support watershed management, delivering AI/MLgenerated insights to managers in a clear and actionable way, and fostering greater collaboration among stakeholders within the CBP Partnership. Workshop participants engaged in science presentations and breakout sessions to develop recommendations for advancing the integration of AI/ML techniques into research and management across the Chesapeake Bay region. By synthesizing current applications, identifying challenges, and exploring new opportunities, the workshop has provided valuable insights and recommendations for better leveraging AI/ML approaches to support the success of Bay restoration efforts. Together, these recommendations provide a roadmap for enhancing data-driven, science-based decision making aligned with the goals and outcomes of the Chesapeake Bay Watershed Agreement.
EAARL Coastal Topography-Pearl River Delta 2008: Bare Earth
These remotely sensed, geographically referenced elevation measurements of Lidar-derived bare earth (BE) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the University of New Orleans (UNO), Pontchartrain Institute for Environmental Sciences (PIES), New Orleans, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the Pearl River Delta in Louisiana and Mississippi, acquired March 9-11, 2008. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of Lidar data in an interactive or batch mode. Modules for presurvey flight line definition, flight path plotting, Lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or first surface topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.
EAARL Coastal Topography-Pearl River Delta 2008: First Surface
These remotely sensed, geographically referenced elevation measurements of Lidar-derived first surface (FS) topography were produced as a collaborative effort between the U.S. Geological Survey (USGS), Florida Integrated Science Center (FISC), St. Petersburg, FL; the University of New Orleans (UNO), Pontchartrain Institute for Environmental Sciences (PIES), New Orleans, LA; and the National Aeronautics and Space Administration (NASA), Wallops Flight Facility, VA. This project provides highly detailed and accurate datasets of a portion of the Pearl River Delta in Louisiana and Mississippi, acquired March 9-11, 2008. The datasets are made available for use as a management tool to research scientists and natural resource managers. An innovative airborne Lidar instrument originally developed at the NASA Wallops Flight Facility, and known as the Experimental Advanced Airborne Research Lidar (EAARL), was used during data acquisition. The EAARL system is a raster-scanning, waveform-resolving, green-wavelength (532-nanometer) Lidar designed to map near-shore bathymetry, topography, and vegetation structure simultaneously. The EAARL sensor suite includes the raster-scanning, water-penetrating full-waveform adaptive Lidar, a down-looking red-green-blue (RGB) digital camera, a high-resolution multi-spectral color infrared (CIR) camera, two precision dual-frequency kinematic carrier-phase GPS receivers, and an integrated miniature digital inertial measurement unit, which provide for submeter georeferencing of each laser sample. The nominal EAARL platform is a twin-engine Cessna 310 aircraft, but the instrument may be deployed on a range of light aircraft. A single pilot, a Lidar operator, and a data analyst constitute the crew for most survey operations. This sensor has the potential to make significant contributions in measuring sub-aerial and submarine coastal topography within cross-environmental surveys. Elevation measurements were collected over the survey area using the EAARL system, and the resulting data were then processed using the Airborne Lidar Processing System (ALPS), a custom-built processing system developed in a NASA-USGS collaboration. ALPS supports the exploration and processing of Lidar data in an interactive or batch mode. Modules for presurvey flight line definition, flight path plotting, Lidar raster and waveform investigation, and digital camera image playback have been developed. Processing algorithms have been developed to extract the range to the first and last significant return within each waveform. ALPS is used routinely to create maps that represent submerged or first surface topography. Specialized filtering algorithms have been implemented to determine the 'bare earth' under vegetation from a point cloud of last return elevations.
Spatial integration of biological and social objectives to identify priority landscapes for waterfowl habitat conservation
Waterfowl population management and habitat conservation compose one of the oldest and most successful adaptive management frameworks in the world. Since its inception, the North American Waterfowl Management Plan (NAWMP) has emphasized strategically targeted conservation investments in regions that most affect waterfowl population dynamics. By 2012, regional conservation had progressively become more science-based and strategic: many migratory bird partnerships had initiated or completed projects on mapping and modeling waterfowl distribution and abundances using geospatial techniques. However, when developing a map depicting and titled “Areas of Greatest Continental Significance to North American Ducks, Geese, and Swans” for the 2012 NAWMP Revision, waterfowl professionals articulated the need for improved decision frameworks and use of consistent datasets for refining large-scale spatial products depicting priority areas for waterfowl and people. This report describes a framework for developing a spatial value model to support the identification of North American geographies of importance to waterfowl during the breeding and non-breeding periods and to resource users who could potentially support (financially and (or) politically) waterfowl habitat conservation. Objectives used to identify priority geographies were determined through a collaborative process of the NAWMP Science Support Team, Priority Landscapes Committee (PLC), and other experts in the fields of waterfowl biology and ecology, environmental science, and human dimensions. ArcGIS Desktop was used as the platform for managing, analyzing, combining and displaying the spatial data as well as producing new data through spatial analysis functions. Thirty-eight spatial layers were developed, and several composite spatially explicit products (maps of North America) were produced based on PLC recommendations. The composite products have extensive similarities to the 2012 NAWMP map depicting areas of greatest continental significance to North American waterfowl. There are also some differences, especially in regions of the high Arctic and in Mexico. These differences between spatial value model maps and the 2012 NAWMP output likely arose from inclusion of social objectives, reduced dependence on expert opinion to generate abundance estimates, lack of population surveys in some regions and availability of expanded survey data in other regions, and use of model-based waterfowl population estimates for some unsurveyed areas. The structured decision-making framework application in this study is discussed, and the appropriate use of the products and their limitations are outlined. Additionally, options for future improvements are presented by identifying gaps in data collection, waterfowl-habitat association assumptions, and uncertainties related to social objectives. These spatial products are intended for use by national, regional, and province/state level wildlife professionals to aid their decisions in targeting waterfowl habitat conservation.
Investing in citizen science can improve natural resource management and environmental protection
Citizen science has made substantive contributions to science for hundreds of years. More recently, it has contributed to many articles in peer-reviewed scientific journals and has influenced natural resource management and environmental protection decisions and policies across the nation. Over the last 10 years, citizen science—participation by the public in a scientific project—has seen explosive growth in the United States, particularly in ecology, the environmental sciences, and related fields of inquiry. In this report, we explore the current use of citizen science in natural resource and environmental science and decision making in the United States and describe the investments organizations might make to benefit from citizen science.
The role of remote sensing observations and models in hydrology: The science of evapotranspiration
Over 15 years ago, Morton (1994) summarized the state of evapotranspiration (ET) research pessimistically: ‘There have been few significant advances in our knowledge of evaporation on an environmental scale over the past four decades, a state of affairs linked to the current sterility of hydrology and related environmental sciences. Furthermore, almost none of the advances have been used successfully in practice.’ He did not foresee the rapid progress in the ensuing years. These advances can be attributed largely to three convergent themes: 1) technical innovation; 2) synergy between disciplines; and 3) expressed need. The papers in this special issue address all of these three themes on remote sensing methods for ET estimation.
National Park Service Vegetation Mapping Inventory Program: Great Smoky Mountains National Park vegetation mapping project
The National Park Service (NPS) Vegetation Mapping Inventory (VMI) Program is an effort to classify, describe, and map existing vegetation communities in national park units throughout the United States. The NPS VMI Program is managed by the NPS Natural Resource Stewardship and Science Inventory and Monitoring Program and provides baseline vegetation information to natural resource managers, researchers, and ecologists. The U.S. Geological Survey Upper Midwest Environmental Sciences Center, NatureServe, and NPS Great Smoky Mountains National Park (GRSM, also referred to as the “Park”) have completed vegetation classification and mapping of GRSM, including the Foothills Parkway, for the NPS VMI Program. Mappers, ecologists, and botanists collaborated to affirm vegetation types of GRSM and to determine how best to map the vegetation types by using aerial imagery. A vegetation classification developed in 2003 by NatureServe and the NPS served as a foundation to further classify and map the vegetation types of the Park. Data from an additional 10 vegetation plots supported vegetation types either rare or not documented in the 2003 classification. Data from 203 verification sites were collected to test the field key to vegetation types and the application of vegetation types to a sample set of map polygons. Furthermore, data from 972 accuracy assessment (AA) sites were collected (of which 966 were used to test accuracy of the vegetation map layer). This GRSM vegetation mapping project identified 112 vegetation types consisting of 105 association types in the U.S. National Vegetation Classification (USNVC), 2 “park-special” types, 1 “map-special” type, and 4 cultural types in the USNVC. To map the vegetation and land cover of GRSM, 52 map classes were developed. Of these 52 map classes, 46 represent natural (including ruderal) vegetation types, most of which types are recognized in the USNVC. For the remaining 6 of the 52 map classes, 4 represent USNVC cultural types for agricultural and developed areas, and 2 represent non-USNVC types for nonvegetated open water and nonvegetated rock. Features were interpreted from viewing four-band digital aerial imagery using digital onscreen three-dimensional stereoscopic workflow systems in geographic information systems; digital aerial imagery was collected during September 23–October 30, 2015. The interpreted data were digitally and spatially referenced, thus making the spatial-database layers usable in a geographic information system. Polygon units were mapped to either a 0.5- or 0.25- hectare (ha) minimum mapping unit, depending on vegetation type. A geodatabase containing several feature-class layers and tables provides the locations and data of USNVC vegetation types (vegetation map layer), vegetation plots, verification sites, AA sites, project boundary extent, and aerial image centers and flight lines. Covering 210,875 ha, the feature-class layer and related tables for the vegetation map layer provide 34,084 polygons of detailed attribute data when special modifiers are not considered (average polygon size of 6.2 ha) and 36,589 polygons of detailed attribute data when special modifiers are considered (average polygon size of 5.8 ha). Each map polygon is assigned a map-class code and name and, when applicable, are linked to USNVC classification tables within the geodatabase. The vegetation map extent includes the administrative boundary for GRSM and the Foothills Parkway. A summary report, generated from the vegetation map layer, concludes that the 46 map classes representing natural (including ruderal) vegetation types apply to 99.2% of polygons (33,797 polygons; average size of 6.2 ha) and cover 98.6% of the Park (207,971.4 ha). Further broken down, map classes representing natural vegetation types indicate that the Park is 97.7% forest and woodland (205,882.5 ha), 0.6% shrubland (1,174.6 ha), and 0.4% herbaceous (914.3 ha). Map classes representing cultural vegetation types apply to 0.8% of polygons (259 polygons; average size of 4.9 ha) and cover 0.6% of the Park (1,277.4 ha). Map classes representing nonvegetation open and flowing water and unvegetated rock apply to 0.08% of polygons (28 polygons; average size of 58.1 ha) and cover 0.8% of the Park (1,625.9 ha). A thematic AA study was completed of map classes representing the natural (including ruderal) vegetation types of the Park. Initial AA results were discussed with NPS staff from the Park. Following input from NPS staff on how to handle map classes that fell below accuracy standards, adjustments were made to the vegetation map layer. Final results indicate an overall accuracy of 80.64% (kappa index of 79.96% for chance agreements) based on data from 966 of the 972 AA sites. Most individual map-class themes exceed the NPS VMI Program standard of 80% with a 90% confidence interval. The GRSM vegetation mapping project delivers many geospatial and vegetation data products, including an in-depth project report discussing methods and results, which includes map classification and map-class descriptions. This suite of products also includes descriptions and a field key to vegetation types; a database of vegetation plots, verification sites, and AA sites; digital images of field sites; field data sheets; digital aerial imagery; hardcopy and digital maps; a geodatabase of vegetation and land cover (map layer), field sites (vegetation plots, verification sites, and AA sites), aerial imagery index, project boundary, and metadata; and a contingency table listing AA results. Geospatial products are projected in the Universal Transverse Mercator, Zone 17 North, by using the North American Datum of 1983. Information on the NPS VMI Program and completed mapping projects are on the internet at https://www.nps.gov/im/vegetation-inventory.htm.
Navigating the field of decision analysis
Managers, policy makers, and decision makers with responsibility for environmental decisions have an extraordinarily difficult job. The systems they manage are complex (coupled human-natural systems), with many dimensions and complicated dynamics. Our knowledge of how those systems respond to management actions is often limited, so many of the decisions have to be made in the face of uncertainty. The field of decision analysis provides a comprehensive set of tools for structuring, analyzing, and making decisions. Arising initially as a way to understand and manage risk, the field has expanded in the last 80 years to cover such topics as multipleobjective trade-offs, time-dependent linked decisions, the value of information, and competition among multiple decision makers. At the same time, cognitive psychologists and behavioural economists have studied human decisions to understand when our innate processes work and when they fail. Formal decision analysis is increasingly being used in many sectors, including economic, industrial, manufacturing, agricultural, transportation, and medical sectors, by individuals, corporations, non-profits, and government agencies. Occasional application of decision analysis to environmental decisions began in the mid-1970s, but the environmental science and management world was largely unaware of decision analysis until the late 1990s and early 2000s. (A major aim of the ARC Centre of Excellence for Environmental Decisions – CEED – was to better connect decision analysis and environmental management). Today, the field of environmental decision analysis is coming of age with the maturation of a rich set of tools to help decision makers frame, analyze, and implement decisions. Sometimes the wealth of tools is daunting, but there are core principles that tie decision analysis together. To help the reader navigate this complexity, we summarize the field of decision analysis in four key messages.
Our evolving conceptual model of the coastal eutrophication problem
A primary focus of coastal science during the past 3 decades has been the question: How does anthropogenic nutrient enrichment cause change in the structure or function of nearshore coastal ecosystems? This theme of environmental science is recent, so our conceptual model of the coastal eutrophication problem continues to change rapidly. In this review, I suggest that the early (Phase I) conceptual model was strongly influenced by limnologists, who began intense study of lake eutrophication by the 1960s. The Phase I model emphasized changing nutrient input as a signal, and responses to that signal as increased phytoplankton biomass and primary production, decomposition of phytoplankton-derived organic matter, and enhanced depletion of oxygen from bottom waters. Coastal research in recent decades has identified key differences in the responses of lakes and coastal-estuarine ecosystems to nutrient enrichment. The contemporary (Phase II) conceptual model reflects those differences and includes explicit recognition of (1) system-specific attributes that act as a filter to modulate the responses to enrichment (leading to large differences among estuarine-coastal systems in their sensitivity to nutrient enrichment); and (2) a complex suite of direct and indirect responses including linked changes in: water transparency, distribution of vascular plants and biomass of macroalgae, sediment biogeochemistry and nutrient cycling, nutrient ratios and their regulation of phytoplankton community composition, frequency of toxic/harmful algal blooms, habitat quality for metazoans, reproduction/growth/survival of pelagic and benthic invertebrates, and subtle changes such as shifts in the seasonality of ecosystem functions. Each aspect of the Phase II model is illustrated here with examples from coastal ecosystems around the world. In the last section of this review I present one vision of the next (Phase III) stage in the evolution of our conceptual model, organized around 5 questions that will guide coastal science in the early 21st century: (1) How do system-specific attributes constrain or amplify the responses of coastal ecosystems to nutrient enrichment? (2) How does nutrient enrichment interact with other stressors (toxic contaminants, fishing harvest, aquaculture, nonindigenous species, habitat loss, climate change, hydrologic manipulations) to change coastal ecosystems? (3) How are responses to multiple stressors linked? (4) How does human-induced change in the coastal zone impact the Earth system as habitat for humanity and other species? (5) How can a deeper scientific understanding of the coastal eutrophication problem be applied to develop tools for building strategies at ecosystem restoration or rehabilitation?
Evaluating environmental change and behavioral decision-making for sustainability policy using an agent-based model: A case study for the Smoky Hill River Watershed, Kansas
Sustainability has been at the forefront of the environmental research agenda of the integrated anthroposphere, hydrosphere, and biosphere since the last century and will continue to be critically important for future environmental science. However, linking humans and the environment through effective policy remains a major challenge for sustainability research and practice. Here we address this gap using an agent-based model (ABM) for a coupled natural and human systems in the Smoky Hill River Watershed (SHRW), Kansas, USA. For this freshwater-dependent agricultural watershed with a highly variable flow regime influenced by human-induced land-use and climate change, we tested the support for an environmental policy designed to conserve and protect fish biodiversity in the SHRW. We develop a proof of concept interdisciplinary ABM that integrates field data on hydrology, ecology (fish richness), social-psychology (value-belief-norm) and economics, to simulate human agents' decisions to support environmental policy. The mechanism to link human behaviors to environmental changes is the social-psychological sequence identified by the value-belief-norm framework and is informed by hydrological and fish ecology models. Our results indicate that (1) cultural factors influence the decision to support the policy; (2) a mechanism modifying social-psychological factors can influence the decision-making process; (3) there is resistance to environmental policy in the SHRW, even under potentially extreme climate conditions; and (4) the best opportunities for policy acceptance were found immediately after extreme environmental events. The modeling approach presented herein explicitly links biophysical and social science has broad generality for sustainability problems.