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At least 235 records · Page 13Linked to original sources

Application of the FluEgg model to predict transport of Asian carp eggs in the Saint Joseph River (Great Lakes tributary)

The Fluvial Egg Drift Simulator (FluEgg) is a three-dimensional Lagrangian model that simulates the movement and development of Asian carp eggs until hatching based on the physical characteristics of the flow field and the physical and biological characteristics of the eggs. This tool provides information concerning egg development and spawning habitat suitability including: egg plume location, egg vertical and travel time distribution, and egg-hatching risk. A case study of the simulation of Asian carp eggs in the Lower Saint Joseph River, a tributary of Lake Michigan, is presented. The river hydrodynamic input for FluEgg was generated in two ways — using hydroacoustic data and using HEC-RAS model data. The HEC-RAS model hydrodynamic input data were used to simulate 52 scenarios covering a broad range of flows and water temperatures with the eggs at risk of hatching ranging from 0 to 93% depending on river conditions. FluEgg simulations depict the highest percentage of eggs at risk of hatching occurs at the lowest discharge and at peak water temperatures. Analysis of these scenarios illustrates how the interactive relation among river length, hydrodynamics, and water temperature influence egg transport and hatching risk. An improved version of FluEgg, which more realistically simulates dispersion and egg development, is presented. Also presented is a graphical user interface that facilitates the use of FluEgg and provides a set of post-processing analysis tools to support management decision-making regarding the prevention and control of Asian carp reproduction in rivers with or without Asian carp populations.

Michigan

Sediment connectivity: A framework for analyzing coastal sediment transport pathways

Connectivity provides a framework for analyzing coastal sediment transport pathways, building on conceptual advances in graph theory from other scientific disciplines. Connectivity schematizes sediment pathways as a directed graph (i.e., a set of nodes and links). This study presents a novel application of graph theory and connectivity metrics like modularity and centrality to coastal sediment dynamics, exemplified here using Ameland Inlet in the Netherlands. We divide the study site into geomorphic cells (i.e., nodes) and then quantify sediment transport between these cells (i.e., links) using a numerical model. The system of cells and fluxes between them is then schematized in a network described by an adjacency matrix. Network metrics like link density, asymmetry, and modularity quantify system‐wide connectivity. The degree, strength, and centrality of individual nodes identify key locations and pathways throughout the system. For instance, these metrics indicate that under strictly tidal forcing, sand originating near shore predominantly bypasses Ameland Inlet via the inlet channels, whereas sand on the deeper foreshore mainly bypasses the inlet via the outer delta shoals. Connectivity analysis can also inform practical management decisions about where to place sand nourishments, the fate of nourishment sand, or how to monitor locations vulnerable to perturbations. There are still open challenges associated with quantifying connectivity at varying space and time scales and the development of connectivity metrics specific to coastal systems. Nonetheless, connectivity provides a promising technique for predicting the response of our coasts to climate change and the human adaptations it provokes.

Ameland Inlet

Recovery of Delaware Bay horseshoe crabs following harvest reductions

Objective Horseshoe crabs Limulus polyphemus play a vital role in the Delaware Bay ecosystem. The migratory stopover of several shorebird species occurs during the horseshoe crab spawning season, and the eggs of horseshoe crabs provide an essential food source to fuel their northward migration to breeding areas. High commercial fishery use of horseshoe crabs as bait during the 1990s coincided with a decline in crabs and shorebirds, particularly the red knot Calidris canutus rufa , which has been listed as threatened under the U.S. Endangered Species Act since 2015. In response to the population decline of shorebirds, the Atlantic States Marine Fisheries Commission began reducing the harvest of horseshoe crabs in 2000 with a goal of rebuilding the population of horseshoe crabs and shorebirds that depend upon them. The objective of this analysis was to determine whether horseshoe crab harvest management in the Delaware Bay region has increased the abundance of the species in recent years. Methods We analyzed data from fisheries-independent trawl surveys of horseshoe crab relative abundance using a Bayesian hierarchical model to determine whether harvest management has resulted in the rebuilding of the horseshoe crab population to levels seen in 1990—a period before the overuse of horseshoe crabs and the decline in the population of red knots. Results Data from multiple surveys showed that the horseshoe crab population in Delaware Bay declined from the 1990s through approximately 2005, was relatively low and stable until 2010, and then increased through 2023, with a 0.38 probability of exceeding the 1990 level. Conclusions The results of this analysis support the effectiveness of management decisions related to horseshoe crabs in the Delaware Bay region. In response to harvest restrictions, the abundance of horseshoe crabs has neared levels observed in the early 1990s—a period prior to high commercial use and a decline in both horseshoe crabs and shorebirds that depend on them for food during annual migrations.

Delaware, Maryland, New Jersey

User engagement testing with a pilot decision support tool aimed to support species managers

Species status assessments (SSAs) are required for endangered species by the U.S. Fish and Wildlife Service and focus on the resiliency, redundancy, and representation of endangered species. SSAs must include climate information, because climate is a factor that will impact species in the future. To aid in the inclusion of climate information, a decision support system (DSS) entitled Climate Analysis and Visualization for the Assessment of Species Status (CAnVAS) was developed by the State Climate Office of North Carolina using a coproduction approach. In this study, users viewed a mock-up version of the CAnVAS interface displaying a sample layout of future projections for three key climate variables (average precipitation, average maximum temperature, and occurrence of maximum temperature) at a location of interest. This assessment of the pilot version of the CAnVAS DSS was the first step in refining CAnVAS for species-manager use. This research analyzed the differences in usability between two pilot versions of the CAnVAS DSS through eye tracking and subsequent interviews with novice users. The two pilot versions of CAnVAS differed in the way data were displayed on graphs and the color ramps used on regional maps. We found that graphically displaying temporal climate information through box-and-whisker plots and spatially through a sequential color ramp from white to purple were more effective than alternative displays at communicating climate information on endangered species. The results of this research will be used to further develop the CAnVAS DSS tool for future implementation.

Weather, Climate, and Society

Circum-Arctic petroleum systems identified using decision-tree chemometrics

Source- and age-related biomarker and isotopic data were measured for more than 1000 crude oil samples from wells and seeps collected above approximately 55??N latitude. A unique, multitiered chemometric (multivariate statistical) decision tree was created that allowed automated classification of 31 genetically distinct circumArctic oil families based on a training set of 622 oil samples. The method, which we call decision-tree chemometrics, uses principal components analysis and multiple tiers of K-nearest neighbor and SIMCA (soft independent modeling of class analogy) models to classify and assign confidence limits for newly acquired oil samples and source rock extracts. Geochemical data for each oil sample were also used to infer the age, lithology, organic matter input, depositional environment, and identity of its source rock. These results demonstrate the value of large petroleum databases where all samples were analyzed using the same procedures and instrumentation. Copyright ?? 2007. The American Association of Petroleum Geologists. All rights reserved.

American Association of Petroleum Geologists Bulle

Ecosystem performance monitoring of rangelands by integrating modeling and remote sensing

Monitoring rangeland ecosystem dynamics, production, and performance is valuable for researchers and land managers. However, ecosystem monitoring studies can be difficult to interpret and apply appropriately if management decisions and disturbances are inseparable from the ecosystem's climate signal. This study separates seasonal weather influences from influences caused by disturbances and management decisions, making interannual time-series analysis more consistent and interpretable. We compared the actual ecosystem performance (AEP) of five rangeland vegetation types in the Owyhee Uplands for 9 yr to their expected ecosystem performance (EEP). Integrated growing season Normalized Difference Vegetation Index data for each of the nine growing seasons served as a proxy for annual AEP. Regression-tree models used long-term site potential, seasonal weather, and land cover data sets to generate annual EEP, an estimate of ecosystem performance incorporating annual weather variations. The difference between AEP and EEP provided a performance measure for each pixel in the study area. Ecosystem performance anomalies occurred when the ecosystem performed significantly better or worse than the model predicted. About 14% of the Owyhee Uplands showed a trend of significant underperformance or overperformance ( P <0.10). Land managers can use results from weather-based rangeland ecosystem performance models to help support adaptive management strategies.

Oregon;Idaho

The computer program estimate trend (ESTREND), a system for the detection of trends in water-quality data

Computerized statistical and graphical procedures were developed for use in U.S. Geological Survey (USGS) investigations of trend in stream water-quality data. These procedures, identified as EStimate TREND (ESTREND), are described in this paper to assist USGS investigators involved in multiple-station studies of water-quality trends. Additional discussion focuses on certain statistical and operational decisions required in multiple-station analysis of trends. The statistical methods used in ESTREND overcome common statistical problems encountered by conventional statistical trend techniques in the analysis of water-quality data. The problems include data that are non-normal and seasonally varying and water-quality records with missing values, 'less-than' (censored) values, and outliers, all of which adversely affect the performance of conventional statistical techniques. Parametric and nonparametric statistical trend tests are used in ESTREND. A nonparametric method, the Seasonal Kendall test, is used for data that have few less-than values or data that have been censored at only one reporting limit. A parametric test for trend involving a maximum likelihood estimation method is used for data that have been censored at multiple reporting limits. The Seasonal Kendall test for uncensored data allows for the removal of flow variability in water-quality data which improves the performance of the statistical trend tests. Menu-driven procedures in ESTREND allow the user to easily retrieve water-quality data, analyze data for trend, and view tabular and graphical results of analyses.

Water-Resources Investigations Report

Spatial and seasonal responses of precipitation in the Ganges and Brahmaputra river basins to ENSO and Indian Ocean dipole modes: Implications for flooding and drought

We evaluated the spatial and seasonal responses of precipitation in the Ganges and Brahmaputra basins as modulated by the El Ni&ntilde;o Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) modes using Global Precipitation Climatology Centre (GPCC) full data reanalysis of monthly global land-surface precipitation data from 1901 to 2010 with a spatial resolution of 0.5&deg; &times; 0.5&deg;. The GPCC monthly total precipitation climatology targeting the period 1951&ndash;2000 was used to compute gridded monthly anomalies for the entire time period. The gridded monthly anomalies were averaged for the years influenced by combinations of climate modes. Occurrences of El Ni&ntilde;o alone significantly reduce (88% of the long-term average (LTA)) precipitation during the monsoon months in the western and southeastern Ganges Basin. In contrast, occurrences of La Ni&ntilde;a and co-occurrences of La Ni&ntilde;a and negative IOD events significantly enhance (110 and 109% of LTA in the Ganges and Brahmaputra Basin, respectively) precipitation across both basins. When El Ni&ntilde;o co-occurs with positive IOD events, the impacts of El Ni&ntilde;o on the basins' precipitation diminishes. When there is no active ENSO or IOD events (occurring in 41 out of 110 years), precipitation remains below average (95% of LTA) in the agriculturally intensive areas of Haryana, Uttar Pradesh, Rajasthan, Madhya Pradesh, and Western Nepal in the Ganges Basin, whereas precipitation remains average to above average (104% of LTA) across the Brahmaputra Basin. This pattern implies that a regular water deficit is likely, especially in the Ganges Basin, with implications for the agriculture sector due to its reliance on consistent rainfall for successful production. Historically, major droughts occurred during El Ni&ntilde;o and co-occurrences of El Ni&ntilde;o and positive IOD events, while major flooding occurred during La Ni&ntilde;a and co-occurrences of La Ni&ntilde;a and negative IOD events in the basins. This observational analysis will facilitate well-informed decision making in minimizing natural hazard risks and climate impacts on agriculture, and supports development of strategies ensuring optimized use of water resources in best management practice under a changing climate.

Brahmaputra Basin, Ganges Basin

Fish-habitat modeling for gap analysis to conserve the endangered Topeka shiner (Notropis topeka)

Decision support tools that predict fish distribution over broad spatial scales are needed to assist in planning watershed management and endangered species recovery. We developed a geographical information system model with multivariate logistic regression to rank valley segments for probable occurrence of the endangered Topeka shiner (Notropis topeka) using stream condition variables (stream size, groundwater potential, channel slope, streamflow, network position) and land-cover variables (percent pasture, percent trees) in streams characteristic of the North American Great Plains. The stream condition and land-cover models correctly classified 89% and 68% of outcomes (i.e., presence or absence), respectively. Field tests of maps of predicted species distribution resulted in more species occurrences than expected in valley segments classified as high potential for presence and less than expected in low-potential valley segments. Gaps between high-priority segments and protected land parcels were found in all basins. In 37 basins with Topeka shiners, protected land coverage was <1% in 17 basins, 1-5% in 10 basins, and 5-21.8% in 10 basins. Conservation activities in gaps are long-term conservation measures, but maps of predicted species distribution have many immediate applications.

Canadian Journal of Fisheries and Aquatic Sciences

Climate change refugia hotspots for priority species: A case study in East Africa

Natural resource managers and policymakers need actionable climate data to guide conservation decisions. Conserving climate change refugia, areas relatively buffered from contemporary climate change, is increasingly considered an effective strategy for adaptation. Despite tropical species facing heightened vulnerability to climate change, the tropics remain underserved in climate adaptation research. We coproduced with Tanzanian partners the first comprehensive assessment of climate change refugia across Tanzania through extensive consultation, in-person conversations, and field visits to priority ecosystems, ensuring our analysis addressed local conservation needs and decision-making contexts. We developed species distribution models for 33 terrestrial animal species using maximum entropy and boosted regression tree algorithms. We projected future suitable habitats for SSP126 and SSP585, for 2011–2040 and 2071–2100, using GFDL Earth System and the UK Earth System models. More than half under SSP126 and 79% of focal species under SSP585 lost their suitable habitat by 2100. Serengeti National Park, Northern Highlands Forest Reserve, and the Eastern Arc Mountains emerged as key climate change refugia, while other protected areas, including Kigosi and Ugalla River National Parks, had no climate change refugia. This assessment provides actionable insights for Tanzania's conservation prioritization while identifying critical research gaps in western and montane ecosystems.

Conservation Science and Practice

Supporting natural resource management—The role of economics at the Department of the Interior—A workshop report

The first U.S. Department of the Interior Economics Workshop was held April 5–7, 2017 in Washington, D.C., to identify, highlight, and better understand needs and opportunities for economic analysis to support the Department of the Interior’s mission. The Economics Workshop, jointly convened by the Department of the Interior Office of Policy Analysis and the U.S. Geological Survey Science and Decisions Center, provided an opportunity for Department of the Interior’s economists to share expertise and experiences and to build collaboration and communication channels across the Department of the Interior. Natural and cultural resource managers face complex questions and often have to balance competing stakeholder interests. Per the mission statement, the Department of the Interior “protects and manages the Nation’s natural resources and cultural heritage; provides scientific and other information about those resources; and honors its trust responsibilities or special commitments to American Indians, Alaska Natives, and affiliated island communities.” Economic analysis is relevant to issues integral to nearly all the land and water management decisions made by the Department of the Interior. More than 80 Department of the Interior economists gathered at the Economics Workshop to share their work, discuss common challenges, and identify approaches to advance the use and contribution of economics at the Department of the Interior.

Open-File Report

Holistic understanding of uncertainty for collaborative and proactive global change decision making

Global change is accelerating and pushing the planet's ecosystems beyond the range of historical observations, creating increasing uncertainty in future system conditions. Despite general agreement that proactive environmental action is warranted, environmental decision conversations often end by identifying additional data needed to reduce uncertainty before taking novel action. Given the inherent uncertainty in complex issues such as global change, quantitative data alone are likely insufficient to support proactive environmental action. Holistic understanding of uncertainty includes scientific quantification of uncertainty paired with emotional responses and transcendental grounding to help people work together toward proactive action in uncertain decision contexts. Holistic understanding arises from the four ways in which humans perceive the world, termed the Four Realms: Physical (e.g., how I observe), Mental (e.g., how I think), Emotional (e.g., how I feel), and Transcendental (e.g., how I connect to greater meaning or purpose). Environmental scientists and decision makers are generally trained in Physical and Mental Realm observation and analysis, but not in how to apply Emotional and Transcendental Realm understanding. Emotional and Transcendental processing occurs in scientists and decision makers whether it is acknowledged or not and contributes to different people interpreting the same information in different ways. Thus, when the role of Emotional and Transcendental Realms in an individual's interpretation process is not understood, it can derail conversations and perpetuate the status quo. Explicitly recognizing all Four Realms can bring people together across differences and inspire shared, novel decision making even in increasing uncertainty. To illustrate the benefits of holistic understanding, we share stories from our experiences in environmental decision contexts. Because accessing the Four Realms requires experiential and embodied techniques, while still relying on core scientific tenets of observation and analysis, we also present techniques for readers to learn to feel their own emotional understanding and connect to their own transcendental understanding. Holistic understanding can enhance data-driven decisions by recognizing that human responses to uncertainty inherently include interactions between emotions, thoughts, transcendental connections, and behavior. Ultimately, holistic understanding can help anchor data-driven decisions in intra- and interpersonal connections, inspiring action in the face of uncertainty.

Ecosphere

A decision framework to analyze tide-gate options for restoration of the Herring River Estuary, Massachusetts

The collective set of decisions involved with the restoration of degraded wetlands is often more complex than considering only ecological responses and outcomes. Restoration is commonly driven by a complex interaction of social, economic, and ecological factors representing the mandate of resource stewards and the values of stakeholders. The authors worked with the Herring River Restoration Committee (HRRC) to develop a decision framework to understand the implications of complex tradeoffs and to guide decision making for the restoration of the 1,100-acre Herring River estuary within Cape Cod National Seashore, which has been restricted from tidal influence for more than 100 years. The HRRC represents decision maker and stakeholder interests in the restoration process. For a 25-year planning horizon, decisions involve the rate at which newly constructed water-control structures allow tidal exchange, and the timing and location of implementing numerous secondary management options. Decisions affect multiple stakeholders, including residents of two adjacent towns who value the watershed for numerous benefits and whose economy relies on seasonal activities and aquaculture. System response to management decisions is characterized by a high degree of uncertainty and risk with positive and negative outcomes possible. Decision policies will affect biophysical (for example, sediment transport, discharge of fecal coliform bacteria) and ecological (for example, vegetation response, fish passage, effects on shellfish) processes, as well as socioeconomic interests (for example, effects on property, viewscapes, recreation). The framework provides a structured approach for evaluating tradeoffs among multiple objectives (ecological and social) while appropriately characterizing relevant uncertainties and accounting for levels of risk tolerances and the values of decision makers and stakeholders. Consequences of tide-gate management options are predicted using a range of methods from quantitative physical process models to elicited expert judgement. The decision framework is presented, and the software developed to implement the tradeoff analysis is introduced. The results from an initial prototype analysis using a software application developed for analyses of tradeoffs and of sensitivity of the decision to risk and uncertainty are presented. The next step is to use the decision-support application to analyze options using improved predictions.

Massachusetts

Groundwater quality, age, and susceptibility and vulnerability to nitrate contamination with linkages to land use and groundwater flow, Upper Black Squirrel Creek Basin, Colorado, 2013

The Upper Black Squirrel Creek Basin is located about 25 kilometers east of Colorado Springs, Colorado. The primary aquifer is a productive section of unconsolidated deposits that overlies bedrock units of the Denver Basin and is a critical resource for local water needs, including irrigation, domestic, and commercial use. The primary aquifer also serves an important regional role by the export of water to nearby communities in the Colorado Springs area. Changes in land use and development over the last decade, which includes substantial growth of subdivisions in the Upper Black Squirrel Creek Basin, have led to uncertainty regarding the potential effects to water quality throughout the basin. In response, the U.S. Geological Survey, in cooperation with Cherokee Metropolitan District, El Paso County, Meridian Service Metropolitan District, Mountain View Electric Association, Upper Black Squirrel Creek Groundwater Management District, Woodmen Hills Metropolitan District, Colorado State Land Board, and Colorado Water Conservation Board, and the stakeholders represented in the Groundwater Quality Study Committee of El Paso County conducted an assessment of groundwater quality and groundwater age with an emphasis on characterizing nitrate in the groundwater. Groundwater-quality samples were collected from 50 randomly selected wells between May and June 2013. The samples were analyzed for major ions, nutrients, dissolved gases, tritium ( 3 H), chlorofluorocarbons (CFC-11, CFC-12, and CFC-113), and fuel products (such as benzene, toluene, ethylbenzene, and xylenes). None of the groundwater samples exceeded the U.S. Environmental Protection Agency (EPA) National Primary Drinking Water Regulations for primary maximum contaminant levels (MCL) for major ions. Secondary maximum contaminant levels, which are not health concerns and affect mainly taste, color, or odor of the water, were observed in rare instances for pH (2 samples), chloride (1 sample), iron (3 samples), and manganese (8 samples). The secondary maximum contaminant level for total dissolved solids was also exceeded for two samples. Nitrate (nitrite plus nitrate as nitrogen in groundwater) was elevated above the estimated background concentration of natural recharge waters of 1 milligram per liter (mg/L) in 44 of the 50 wells sampled and showed a median concentration of 5.4 mg/L. Nitrate concentrations were above the MCL of 10 mg/L in 5 of the 50 wells sampled and above half of the EPA MCL (5 mg/L) in 27 of the 50 wells sampled, which included samples above the MCL. Dissolved-oxygen concentrations exceeded 0.5 mg/L in 95 percent of reported values (40 of 42 samples) and exceeded 2.0 mg/L in 90 percent of reported values (38 of 42 samples). The oxidized conditions observed in most areas indicate that nitrate from fertilizers and animal or human waste was geochemically stable and could persist in the groundwater for decades or perhaps longer. A historical analysis of median nitrate concentrations over nearly three decades showed an increase in nitrate of approximately 1 mg/L from 4.3 to 5.4 mg/L, although the increase was not determined to be significantly different using nonparametric statistical methods. Major-ion data indicate that groundwater representative of the primary aquifer was classified as calcium-sodium bicarbonate type water. Other water samples from wells located mainly along the periphery of the primary aquifer had cation-anion compositions consistent with distinct water sources, including groundwater contributions from the underlying bedrock aquifers. The areas with differentiable water sources were located mainly where alluvial deposits were thin and geologic contacts to the underlying bedrock aquifers were relatively shallow. Nitrate concentrations in the groundwater were evaluated for relations to land use. An agricultural region was defined using a sequence of land satellite imagery. Groundwater flow directions interpreted from median water-table elevations measured from 2000 to 2013 were used in conjunction with cropland locations to define the agricultural region boundaries by encompassing potential pathways of nitrate transport in the groundwater from nitrogen-based fertilizers. A statistically significant higher median nitrate concentration was observed for areas inside the agricultural region (6.7 mg/L) compared to areas outside the agricultural region (2.3 mg/L), although median concentrations in both areas were below the MCL (10 mg/L). Median nitrate concentration was also significantly greater in land parcels with septic use (4.9 mg/L) compared to nonseptic parcels (1.7 mg/L). In general, agriculture or septic use was identified as the primary source of nitrate, depending on location, while commercial, county, grazing, and residential land uses were generally secondary sources of nitrate. Apparent groundwater ages were estimated from chlorofluorocarbons (CFC-11, CFC-12, and CFC-113) and tritium ( 3 H) data using models that assumed piston flow and binary mixing (dilution of a young component with old, tracer-free water). The mean and median groundwater ages were about 30 years and the standard deviation was 6 years, indicating that most groundwater in the primary aquifer was &ldquo;young&rdquo; water that had recharged to the aquifer over the last few decades (post-1950s). The median fraction of young water was about 71 percent, and the standard deviation was 29 percent. The remaining water predated the 1950s, which may have originated from deeper geologic formations or may represent slow moving groundwater within the primary aquifer. Some of the oldest groundwater ages (older than 30 years) were observed in the upper reaches of the aquifer to the northwest where the primary aquifer is thin and intersects bedrock, supporting the hypothesis of geochemically distinct groundwater entering the primary aquifer from below. Groundwater that had reached the central part of the aquifer from upgradient areas of the basin was variable in age because of differences in flow paths and travel velocities. The groundwater age analysis showed that current (2013) land-use practices could affect water quality over decades to come, and that responses to remedial actions could be slow, especially for constituents, such as nitrate, that are stable under oxidized conditions. Fuel products (including acetone, benzene, diisopropyl ether, ethylbenzene, methyl acetate, methyl tertiary butyl ether (MTBE), methyl tert-pentyl ether, m- + p-xylene, o-xylene, tert-amyl alcohol, tert-butyl alcohol, tert-butyl ethyl ether, and toluene) were analyzed in groundwater from 49 of the 50 wells. Water from seven sites had detections for fuel compounds; all concentrations were below MCL. The results provided assurance of water quality and a valuable baseline to evaluate future trends of fuel constituents as the region is further developed. Probability maps were developed from logistic regression models to examine the likelihood that nitrate concentrations in groundwater exceeded specified levels. Susceptibility analysis examined relations between mid-level (5.0 mg/L) nitrate concentrations and climatic, hydrologic, and geologic variables; the significant variables were identified as depth to groundwater, soil organic matter, and soil water storage to 25-centimeter (cm) depth. The vulnerability assessments included natural factors driving susceptibility but also human factors related to land use and septic use. Vulnerability to low-level (2.5 mg/L) nitrate was related to depth to groundwater, septic zoning, and soil organic matter. The results highlighted that septic zoning affected low-level nitrate concentrations. Vulnerability to mid-level (5.0 mg/L) nitrate was examined using all 50 samples and also with two data outliers removed, which showed relatively high nitrate concentrations but also anomalous water chemistry or were located beyond the primary study area. Vulnerability to mid-level (5.0 mg/L) nitrate using all 50 samples was related to depth to groundwater, land use, septic use within a 500-meter (m) radius, soil water storage to a 25-cm depth, soil organic matter, and whether a location was within the agricultural region. The mid-level (5.0 mg/L) vulnerability model using 48 samples (two outliers removed) produced the best overall fit and was related to the same variables as when using all samples except septic use. The results for mid-level vulnerability provided additional support that septic use was associated with low levels of nitrate in the groundwater. Soil properties and land use were identified as the main drivers of moderate nitrate concentrations. Probabilities of exceeding low-level nitrate concentrations were high in most areas with the lowest probabilities usually to the northwest along thin geologic deposits in the upper part of the basin. The results of this investigation offer the foundational information needed for developing best management practices to mitigate nitrate contamination, basic concepts on water quality to aid public education, and information to guide regulatory measures if policy makers determine this is warranted. Science-based decision making will require continued monitoring and analysis of water quality in the future.

Colorado

Quantifying the substantive influence of public comment on United States federal environmental decisions under NEPA

A citizen’s right to comment on, and criticize, government decisions makes a difference. The U.S. National Environmental Policy Act of 1969 (NEPA) institutionalized public engagement in environmental review in the belief it would lead to better decisions and more sustainable outcomes. But, 50 years later, NEPA’s public comment process has been criticized as costly and slow, while doing little to change outcomes. Data science now makes it possible to track progress and evaluate the influence of public participation. We examined 108 environmental impact statement (EIS) processes spanning 22 years. Our analysis revealed that public comments resulted in substantive decision alterations in 62% of cases, with 64% showing modifications to alternatives, 42% showing modifications to mitigation plans and 11% leading to the selection of an entirely new preferred alternative. When federal agencies changed project alternatives (78 EISs), 88% of the time (69 of the 78 EISs) they credited public comments as the reason. In 45 of the 108 EISs, agencies modified mitigation plans and credited public comments as the reason 100% of the time. Agencies only occasionally selected a new preferred alternative (21 out of 104 EISs), but when they did, they credited public comments as the reason 100% of the time. As the United States and the 190+ states and countries that have adopted NEPA’s example consider how to address environmental change, it is important to assess the role of public participation in environmental decision making. Our data say public comments matter.

Environmental Research Letters

The influence of sea level rise on the regional interdependence of coastal infrastructure

Sea level rise (SLR) is placing both immediate and long‐term pressures on coastal communities to take protective actions. Projects in the United States, and in many locations throughout the world, generally involve local jurisdictions raising the elevation of shoreline protection elements, with limited or no analysis of the feedback between shoreline management decisions and the impacts to water levels regionally. Our study examines the impact of local shoreline development on regional flood risk and considers SLR scenarios up to 1.5 m using a large‐scale numerical model, as an example, for San Francisco Bay. Here we show that measures to prevent flooding along an embayment shoreline in one location or subregion may increase inundation elsewhere in the system. The network of interactions occurs not only within subbasins of the Bay but also across the greater geographic extent from one end of the Bay to the other, and local jurisdiction may have either reciprocal relationships with or asymmetric impacts on one other. Importantly, the nature of the interaction network is seen to evolve with SLR: interactions are purely subregional at current sea level but with higher sea level (e.g., 1 m of SLR), not only do the subregional interdependencies strengthen but also regional interdependences emerge.

California

Hierarchical distance-sampling models to estimate population size and habitat-specific abundance of an island endemic

Population size and habitat-specific abundance estimates are essential for conservation management. A major impediment to obtaining such estimates is that few statistical models are able to simultaneously account for both spatial variation in abundance and heterogeneity in detection probability, and still be amenable to large-scale applications. The hierarchical distance-sampling model of J. A. Royle, D. K. Dawson, and S. Bates provides a practical solution. Here, we extend this model to estimate habitat-specific abundance and rangewide population size of a bird species of management concern, the Island Scrub-Jay ( Aphelocoma insularis ), which occurs solely on Santa Cruz Island, California, USA. We surveyed 307 randomly selected, 300 m diameter, point locations throughout the 250-km 2 island during October 2008 and April 2009. Population size was estimated to be 2267 (95% CI 1613-3007) and 1705 (1212-2369) during the fall and spring respectively, considerably lower than a previously published but statistically problematic estimate of 12 500. This large discrepancy emphasizes the importance of proper survey design and analysis for obtaining reliable information for management decisions. Jays were most abundant in low-elevation chaparral habitat; the detection function depended primarily on the percent cover of chaparral and forest within count circles. Vegetation change on the island has been dramatic in recent decades, due to release from herbivory following the eradication of feral sheep ( Ovis aries ) from the majority of the island in the mid-1980s. We applied best-fit fall and spring models of habitat-specific jay abundance to a vegetation map from 1985, and estimated the population size of A. insularis was 1400-1500 at that time. The 20-30% increase in the jay population suggests that the species has benefited from the recovery of native vegetation since sheep removal. Nevertheless, this jay's tiny range and small population size make it vulnerable to natural disasters and to habitat alteration related to climate change. Our results demonstrate that hierarchical distance-sampling models hold promise for estimating population size and spatial density variation at large scales. Our statistical methods have been incorporated into the R package unmarked to facilitate their use by animal ecologists, and we provide annotated code in the Supplement.

California

Methodology for quantitative spatial sensitivity analysis of volcanic geodetic networks

Introduction This report introduces a methodology for assessing the state of the U.S. Geological Survey Volcano Observatories’ geodetic monitoring networks that measure how volcanoes deform or change shape. This new method uses a model-based approach that considers the uniqueness of the instrument environments at each volcano. This report focuses on simplified volcanic sources, is independent of the shape or size of the volcano, or the network geometry, and thus highlights the strengths and potential vulnerabilities of each volcano’s geodetic network in an actionable visual format. This analysis can help observatories to make informed decisions about whether volcanoes have an adequate level of geodetic monitoring and indicate where improvements are needed.

Lassen Peak, Mount Shasta