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At least 883 records · Page 49Linked to original sources
USGS2023

Linear regression model documentation for computing water-quality constituent concentrations or densities using continuous real-time water-quality data for the Kansas River above Topeka Weir at Topeka, Kansas, November 2018 through June 2021

The Kansas River and its associated alluvial aquifer provide drinking water to more than 950,000 people in northeastern Kansas. Water suppliers that rely on the Kansas River as a water-supply source use physical and chemical processes to treat and remove contaminants before public distribution. An early-notification system of changing water-quality conditions allows water suppliers to proactively make decisions that affect water treatment. The U.S. Geological Survey (USGS), in cooperation with the Kansas Water Office (funded in part through the Kansas Water Plan), the Kansas Department of Health and Environment, The Nature Conservancy, the City of Lawrence, the City of Manhattan, the City of Olathe, the City of Topeka, WaterOne, and Evergy, began collecting water-quality data at the Kansas River above Topeka Weir at Topeka, Kansas (USGS site 06888990, hereafter referred to as the “Topeka site”), during November 2018 to develop linear regression models that relate continuous in situ water-quality sensor measurements to discretely sampled water-quality constituent concentrations or densities. The addition of the Topeka site expanded an existing water-quality monitoring network, which included the upstream Kansas River at Wamego, Kans., and downstream Kansas River at De Soto, Kans., sites. Linear regression analysis was used to develop models that compute real-time concentrations or densities for total dissolved solids, major ions, hardness as calcium carbonate, nutrients (nitrogen and phosphorus species), chlorophyll a , total suspended solids, suspended sediment, and Escherichia coli at the Topeka site using data collected during November 2018 through June 2021. Water-quality constituent concentrations or densities computed from the models documented in this report are available at the USGS National Real-Time Water-Quality website (https://nrtwq.usgs.gov), are useful to the public for cultural and recreational purposes, and can be used to guide water-treatment processes, compare conditions with Federal and State water-quality criteria, and characterize changes in Kansas River water-quality conditions through time.

Kansas↗
USGS2023

Stress gradients structure spatial variability in coastal tidal marsh plant composition and diversity in a major Pacific coast estuary

Understanding the drivers of variability in plant diversity from local to landscape spatial scales is a challenge in ecological systems. Environmental gradients exist at several spatial scales and can be nested hierarchically, influencing patterns of plant diversity in complex ways. As plant community dynamics influence ecosystem function, understanding the drivers of plant community variability across space is paramount for predicting potential shifts in ecosystem function from global change. Determining the scales at which stress gradients influence vegetation composition is crucial to inform management and restoration of tidal marshes for specific functions. Here, we analyzed vegetation community composition in 51 tidal marshes from the San Francisco Bay Estuary, California, USA. We used model-based compositional analysis and rank abundance curves to quantify environmental (elevation/tidal frame position, distance to channel, and channel salinity) and species trait (species form, wetland indicator status, and native status) influences on plant community variability at the marsh site and estuary scales. While environmental impacts on plant diversity varied by species and their relationships to each other, overall impacts increased in strength from marsh to estuary scales. Relative species abundance was important in structuring these tidal marsh communities even with the limited species pools dominated by a few species. Rank abundance curves revealed different community structures by region with higher species evenness at plots higher in the tidal frame and adjacent to freshwater channels. By identifying interactions (species–species, species–environment, and environment–trait) at multiple scales (local, landscape), we begin to understand how variability measurements could be interpreted for conservation and land management decisions.

California↗
USGS2000

Genetic diversity and epidemiology of infectious hematopoietic necrosis virus in Alaska

Forty-two infectious hematopoietic necrosis virus (IHNV) isolates from Alaska were analyzed using the ribonuclease protection assay (RPA) and nucleotide sequencing. RPA analyses, utilizing 4 probes, N5, N3 (N gene), GF (G gene), and NV (NV gene), determined that the haplotypes of all 3 genes demonstrated a consistent spatial pattern. Virus isolates belonging to the most common haplotype groups were distributed throughout Alaska, whereas isolates in small haplotype groups were obtained from only 1 site (hatchery, lake, etc.). The temporal pattern of the GF haplotypes suggested a 'genetic acclimation' of the G gene, possibly due to positive selection on the glycoprotein. A pairwise comparison of the sequence data determined that the maximum nucleotide diversity of the isolates was 2.75% (10 mismatches) for the NV gene, and 1.99% (6 mismatches) for a 301 base pair region of the G gene, indicating that the genetic diversity of IHNV within Alaska is notably lower than in the more southern portions of the IHNV North American range. Phylogenetic analysis of representative Alaskan sequences and sequences of 12 previously characterized IHNV strains from Washington, Oregon, Idaho, California (USA) and British Columbia (Canada) distinguished the isolates into clusters that correlated with geographic origin and indicated that the Alaskan and British Columbia isolates may have a common viral ancestral lineage. Comparisons of multiple isolates from the same site provided epidemiological insights into viral transmission patterns and indicated that viral evolution, viral introduction, and genetic stasis were the mechanisms involved with IHN virus population dynamics in Alaska. The examples of genetic stasis and the overall low sequence heterogeneity of the Alaskan isolates suggested that they are evolutionarily constrained. This study establishes a baseline of genetic fingerprint patterns and sequence groups representing the genetic diversity of Alaskan IHNV isolates. This information could be used to determine the source of an IHN outbreak and to facilitate decisions in fisheries management of Alaskan salmonid stocks.

Diseases of Aquatic Organisms↗
USGS2023

Foundations of modeling resilience of tidal saline wetlands to sea-level rise along the U.S. Pacific Coast

Context Tidal saline wetlands (TSWs) are highly threatened from climate-change effects of sea-level rise. Studies of TSWs along the East Coast U.S. and elsewhere suggest significant likely losses over coming decades but needed are analytic tools gauged to Pacific Coast U.S. wetlands. Objectives We predict the impacts of sea-level rise (SLR) on the elevation capital (vertical) and migration potential (lateral) resilience of TSWs along the Pacific Coast U.S. over the period 2020 to 2150 under a 1.5-m SLR scenario, and identified TSWs at risk of most rapid loss of resilience. Here, we define vertical resilience as the amount of elevation capital and lateral resilience as the amount of TSW displacement area relative to existing area. Methods We used Bayesian network (BN) modeling to predict changes in resilience of TSWs as probabilities which can be useful in risk analysis and risk management. We developed the model using a database sample of 26 TSWs with 147 sediment core samples, among 16 estuary drainage areas along coastal California, Oregon, and Washington. Results We found that all TSW sites would lose at least 50% of their elevation capital resilience by 2060 to just before 2100, and 100% by 2070 to 2130, depending on the site. Under a 1.5-m sea-level rise scenario, nearly all sites in California will lose most or all of their lateral migration resilience. Resilience losses generally accelerated over time. In the BN model, elevation capital resilience is most sensitive to elevation capital at time t, mean tide level at time t, and change in sea level from time 0 to time t. Conclusions All TSW sites were projected with declines in resilience. Our model can further aid decision-making such as prioritizing sites for potential management adaptation strategies. We also identified variables most influencing resilience predictions and thus those potentially prioritized for monitoring or development of strategies to prevent loss regionally.

California, Oregon, Washington↗
USGS2019

Predicting surf zone injuries along the Delaware coast using a Bayesian network

Personnel at Beebe Healthcare in Lewes, Delaware, collected surf zone injury (SZI) data for eight summer seasons from 2010 through 2017. Data include, but are not limited to, time of injury, gender, age, and activity. More than 2000 SZI events, including 196 spinal injuries and 6 fatalities, occurred at the five most populated beaches along the 25 miles of Atlantic-fronting coast. SZI are predominantly wave related incidents associated with wading (50.1%), body surfing (18.4%), and body boarding (13.3%). The episodic nature of SZI indicate the importance of linking the environmental conditions and human behavior in the surf zone to predict days with high injury rates. Higher order statistics are necessary to effectively consider all associated factors related to SZI. Two Bayesian networks (BN) were constructed to model SZI and predict changes in injury rate (proportion of injuries to bathers) and injury likelihood (probability of at least one injury occurrence) on an hourly basis. The models incorporate environmental data collected by weather stations, wave gauges, and researcher personnel on the beach. The models include prior (e.g., historic) information to infer relationships between provided parameters. Sensitivity analysis determined the most influential parameters related to injury rates were significant wave height, foreshore slope, and water temperature. Exposure parameters (e.g., air temperature) influenced the number of people in the water, resulting in strong correlation between injury likelihood and the related meteorological conditions (variance reduction > 0.4%). Log likelihood ratio (LLR) scores indicate the network predicts SZI likelihood during any specified hour with more skill than prior predictions with the best performing model improving prediction 69.1% of the time (LLR = 69.1%). An alternative BN predicting injury rate performed worse with the prior probability model out predicting the injury rate network (positive LLR = 36.7%). Issues persist with predicting SZI that have an LLR ≪ -1 (< 5% of 2017 injuries) and occur in conditions different than when most other SZI occur. Better understanding of SZI will improve awareness techniques to both educate beachgoers and assist beach patrol decision making during high risk conditions.

Delaware↗
USGS2017

Multi-model comparison highlights consistency in predicted effect of warming on a semi-arid shrub

A number of modeling approaches have been developed to predict the impacts of climate change on species distributions, performance, and abundance. The stronger the agreement from models that represent different processes and are based on distinct and independent sources of information, the greater the confidence we can have in their predictions. Evaluating the level of confidence is particularly important when predictions are used to guide conservation or restoration decisions. We used a multi-model approach to predict climate change impacts on big sagebrush ( Artemisia tridentata ), the dominant plant species on roughly 43 million hectares in the western United States and a key resource for many endemic wildlife species. To evaluate the climate sensitivity of A. tridentata , we developed four predictive models, two based on empirically derived spatial and temporal relationships, and two that applied mechanistic approaches to simulate sagebrush recruitment and growth. This approach enabled us to produce an aggregate index of climate change vulnerability and uncertainty based on the level of agreement between models. Despite large differences in model structure, predictions of sagebrush response to climate change were largely consistent. Performance, as measured by change in cover, growth, or recruitment, was predicted to decrease at the warmest sites, but increase throughout the cooler portions of sagebrush's range. A sensitivity analysis indicated that sagebrush performance responds more strongly to changes in temperature than precipitation. Most of the uncertainty in model predictions reflected variation among the ecological models, raising questions about the reliability of forecasts based on a single modeling approach. Our results highlight the value of a multi-model approach in forecasting climate change impacts and uncertainties and should help land managers to maximize the value of conservation investments.

Global Change Biology↗
USGS2010

Climate change threatens polar bear populations: A stochastic demographic analysis

The polar bear (Ursus maritimus) depends on sea ice for feeding, breeding, and movement. Significant reductions in Arctic sea ice are forecast to continue because of climate warming. We evaluated the impacts of climate change on polar bears in the southern Beaufort Sea by means of a demographic analysis, combining deterministic, stochastic, environment-dependent matrix population models with forecasts of future sea ice conditions from IPCC general circulation models (GCMs). The matrix population models classified individuals by age and breeding status; mothers and dependent cubs were treated as units. Parameter estimates were obtained from a capture-recapture study conducted from 2001 to 2006. Candidate statistical models allowed vital rates to vary with time and as functions of a sea ice covariate. Model averaging was used to produce the vital rate estimates, and a parametric bootstrap procedure was used to quantify model selection and parameter estimation uncertainty. Deterministic models projected population growth in years with more extensive ice coverage (2001-2003) and population decline in years with less ice coverage (2004-2005). LTRE (life table response experiment) analysis showed that the reduction in ?? in years with low sea ice was due primarily to reduced adult female survival, and secondarily to reduced breeding. A stochastic model with two environmental states, good and poor sea ice conditions, projected a declining stochastic growth rate, log ??s, as the frequency of poor ice years increased. The observed frequency of poor ice years since 1979 would imply log ??s ' - 0.01, which agrees with available (albeit crude) observations of population size. The stochastic model was linked to a set of 10 GCMs compiled by the IPCC; the models were chosen for their ability to reproduce historical observations of sea ice and were forced with "business as usual" (A1B) greenhouse gas emissions. The resulting stochastic population projections showed drastic declines in the polar bear population by the end of the 21st century. These projections were instrumental in the decision to list the polar bear as a threatened species under the U.S. Endangered Species Act. ?? 2010 by the Ecological Society of America.

Ecology↗
USGS2019

Characterizing 12 years of wildland fire science at the U.S. Geological Survey: Wildland Fire Science Publications, 2006–17

Wildland fire characteristics, such as area burned, number of large fires, burn intensity, and fire season duration, have increased steadily over the past 30 years, resulting in substantial increases in the costs of suppressing fires and managing damages from wildland fire events (National Academies of Sciences, Engineering, and Medicine, 2017). Wildland fire management could benefit from sound decision making based on reliable scientific information. Fire scientists produce data, tools, and information to support fire and land management decision making. With ever-changing land use scenarios, environmental conditions, and emerging technological capabilities, new assessments and studies are continually needed. Established by Congress in 1879, the U.S. Geological Survey (USGS) is the primary science branch of the Department of the Interior (DOI), which manages more than 400 million acres of public lands in the United States. The USGS has more than 100 scientists across seven Mission Areas that help address the wildland fire science needs of DOI bureaus and their stakeholders. The diverse expertise of these scientists allows them to address complex interdisciplinary challenges. In this report, we identify and characterize scientific literature produced by USGS scientists during 2006–17 that addresses topics associated with wildland fire science. Our goals were to (1) make the most complete list possible of product citations readily available in an organized format, and (2) use bibliometric analysis approaches to highlight the productivity of USGS scientists and the impact of contributions that the Bureau has provided to the scientific, land management, and fire management communities.

Open-File Report↗
USGS2014

Placing prairie pothole wetlands along spatial and temporal continua to improve integration of wetland function in ecological investigations

We evaluated the efficacy of using chemical characteristics to rank wetland relation to surface and groundwater along a hydrologic continuum ranging from groundwater recharge to groundwater discharge. We used 27 years (1974–2002) of water chemistry data from 15 prairie pothole wetlands and known hydrologic connections of these wetlands to groundwater to evaluate spatial and temporal patterns in chemical characteristics that correspond to the unique ecosystem functions each wetland performed. Due to the mineral content and the low permeability rate of glacial till and soils, salinity of wetland waters increased along a continuum of wetland relation to groundwater recharge, flow-through or discharge. Mean inter-annual specific conductance (a proxy for salinity) increased along this continuum from wetlands that recharge groundwater being fresh to wetlands that receive groundwater discharge being the most saline, and wetlands that both recharge and discharge to groundwater (i.e., groundwater flow-through wetlands) being of intermediate salinity. The primary axis from a principal component analysis revealed that specific conductance (and major ions affecting conductance) explained 71% of the variation in wetland chemistry over the 27 years of this investigation. We found that long-term averages from this axis were useful to identify a wetland’s long-term relation to surface and groundwater. Yearly or seasonal measurements of specific conductance can be less definitive because of highly dynamic inter- and intra-annual climate cycles that affect water volumes and the interaction of groundwater and geologic materials, and thereby influence the chemical composition of wetland waters. The influence of wetland relation to surface and groundwater on water chemistry has application in many scientific disciplines and is especially needed to improve ecological understanding in wetland investigations. We suggest ways that monitoring in situ wetland conditions could be linked with evolving remote sensing technology to improve our ability to better inform decisions affecting wetland sustainability and provide periodic inventories of wetland ecosystem services to document temporal trends in wetland function and how they respond to contemporary land-use change.

North Dakota↗
USGS2011

Ecoregional-scale monitoring within conservation areas, in a rapidly changing climate

Long-term monitoring of ecological systems can prove invaluable for resource management and conservation. Such monitoring can: (1) detect instances of long-term trend (either improvement or deterioration) in monitored resources, thus providing an early-warning indication of system change to resource managers; (2) inform management decisions and help assess the effects of management actions, as well as anthropogenic and natural disturbances; and (3) provide the grist for supplemental research on mechanisms of system dynamics and cause-effect relationships (Fancy et al., 2009). Such monitoring additionally provides a snapshot of the status of monitored resources during each sampling cycle, and helps assess whether legal standards and regulations are being met. Until the last 1-2 decades, tracking and understanding changes in condition of natural resources across broad spatial extents have been infrequently attempted. Several factors, however, are facilitating the achievement of such broad-scale investigation and monitoring. These include increasing awareness of the importance of landscape context, greater prevalence of regional and global environmental stressors, and the rise of landscape-scale programs designed to manage and monitor biological systems. Such programs include the US Forest Service's Forest Inventory and Analysis (FIA) Program (Moser et al., 2008), Canada's National Forest Inventory, the 3Q Programme for monitoring agricultural landscapes of Norway (Dramstad et al., 2002), and the emerging (US) Landscape Conservation Cooperatives (USDOI Secretarial Order 3289, 2009; Anonymous, 2011). This Special Section explores the underlying design considerations, as well as many pragmatic aspects associated with program implementation and interpretation of results from broad-scale monitoring systems, particularly within the constraints of high-latitude contexts (e.g., low road density, short field season, dramatic fluctuations in temperature). Although Alaska is the focus of most papers in this Special Section, we posit that many of the issues that characterize the remote, relatively undisturbed ecosystems of high northern latitudes are widespread and thus applicable to natural-resource management and conservation across northern portions of the Holarctic ecozone and indeed anywhere broad-scale monitoring is contemplated.

Biological Conservation↗
USGS1993

Preservation of genetic variation in the Green Lake strain lake trout derived from remnant domestic and feral populations

The Green Lake, Wisconsin, strain of lake trout Salvelinus namaycush was discontinued as a hatchery brood stock in 1976 after Lake Michigan was stocked with the 1975 year‐class. In 1982, a decision was made to restore the Green Lake strain as a production brood stock. Five groups were produced by spawning marked fish that were survivors from the 1976 stocking of southern Lake Michigan. A sixth group was produced from a remnant of the Green Lake brood stock held at the Genoa (Wisconsin) National Fish Hatchery. Hatchery accidents reduced both the number of groups and the effective population number of each group. We studied a procedure for reconstructing a composite Green Lake brood stock from individuals of these six groups. Genetic variability was evaluated by allozyme electrophoresis in five of these groups. Twelve of 18 loci were polymorphic. Allelic frequencies were similar in all five groups; however, significant differences occurred in nine systems. Heterozygosity level (mean ± SE) was lowest in the domestic group (0.119 ± 0.043) and ranged from 0.139 ± 0.043 to 0.166 ± 0.052 in the feral groups. Cluster analysis of genetic distances grouped the four feral groups together, but separate from the domestic group. Progeny from fish captured on Black Can Reef, Lake Michigan, in 1986 and 1988 were the most similar. A modified diallel mating design was developed to produce a composite brood stock from remnant feral and domestic fish. Pooled families from 1991 and 1992 diallel matings will be reared to maturity, then reciprocal crosses of the two year‐classes will be made to form the new composite Green Lake brood stock.

Wisconsin↗
USGS2022

What makes wildfires destructive in California?

As human impacts from wildfires mount, there is a pressing need to understand why structures are lost in destructive fires. Despite growing research on factors contributing to structure loss, fewer studies have focused on why some fires are destructive and others are not. We characterized overall differences between fires that resulted in structure loss (“destructive fires”) and those that did not (“non-destructive wildfires”) across three California regions. Then, we performed statistical analyses on large fires only (≥100 ha) to distinguish the primary differences between large destructive large fires and large non-destructive fires. Overall, destructive fires were at least an order of magnitude larger than non-destructive fires, with the largest area burned varying by season in different regions. Fire severity was also significantly higher in destructive than non-destructive fires. The statistical analysis showed that, in the San Francisco Bay Area and the northern Sierra Nevada foothills, proximity to the Wildland Urban Interface (WUI) was by far the most important factor differentiating destructive and non-destructive wildfires, followed by different combinations of short-term weather, seasonal climate, topography, and vegetation productivity. In Southern California, wind velocity on the day of the fire ignition was the top factor, which is consistent with previous assumptions that wind-driven fires tend to be most destructive and most of the destruction occurs within the first 24 h. Additionally, Southern California’s high population density increases the odds that a human-caused wildfire may occur during a severe fire-weather event. The geographical differences among regions and the variation of factors explaining the differences between large destructive and large non-destructive fires reflects the complexity inherent in decision-making for reducing wildfire risk. Land use planning to reduce future exposure of housing development to fire and increased focus on wildfire ignition prevention emerge as two approaches with substantial potential.

California↗
USGS2016

Population dynamics of mallards breeding in eastern Washington

Variation in regional population trends for mallards breeding in the western United States indicates that additional research into factors that influence demographics could contribute to management and understanding the population demographics of mallards across North America. We estimated breeding incidence and adult female, nest, and brood survival in eastern Washington in 2006 and 2007 by monitoring female mallards with radio telemetry and tested how those parameters were influenced by study year (2006 vs. 2007), landscape type (agricultural vs. natural), and age (second year [SY] vs. after second year [ASY]). We also investigated the effects of female body condition and capture date on breeding incidence, and nest initiation date and hatch date on nest and brood survival, respectively. We included population parameters in a stage-based demographic model and conducted a perturbation analysis to identify which vital rates were most influential on population growth rate (λ). Adult female survival was best modeled with a constant weekly survival rate (0.994, SE = 0.003). Breeding incidence differed between years and was higher for birds in better body condition. Nest survival was higher for ASY females (0.276, SE = 0.118) than SY females (0.066, SE = 0.052), and higher on publicly managed lands (0.383, SE = 0.212) than agricultural (0.114, SE = 0.058) landscapes. Brood survival was best modeled with a constant rate for the 7-week monitoring period (0.50, SE = 0.155). The single variable having the greatest influence on λ was non-breeding season survival, but the combination of parameters from the breeding grounds explained a greater percent of the variance in λ. Mallard population growth rate was most sensitive to changes in non-breeding survival, nest success, brood survival, and breeding incidence. Future management decisions should focus on activities that improve these vital rates if managers want to increase the production of mallards in eastern Washington.

Washington↗
USGS2026

Modeling the temperature-mediated link between demography and biomass in ectotherms

Population models can forecast the effects of environmental change, disturbance, and management decisions on populations of interest. Most population models focus on abundance of individuals, but biomass may be a more relevant quantity at the community or ecosystem scale, especially when investigating interaction of multiple stressors and cross-ecosystem energy flows. We developed a stage-structured population model that integrates a temperature-mediated tradeoff between development rate and final body size of invertebrates, allowing projection of both population size and standing stock biomass in ecosystems that experience disturbances. We parameterized the model for four aquatic invertebrate life-history types spanning a range of lifespan and susceptibility to disturbance. We observed several emergent properties typical of invertebrate populations but not explicitly included in the model, including self-organization of cohort structure under seasonal environments, a tradeoff between lifespan duration and rate of post-disturbance population recovery, and chaotic population dynamics under high growth rates. Sensitivity analysis revealed that although elevated mean temperature increased population size, total population biomass remained unchanged or even decreased due to declining individual biomass, which has implications for ecosystem processes under a changing climate. The seasonal timing of disturbance showed a strong interaction with temperature regime, with summer versus winter pulse disturbances affecting biomass and abundance in characteristically different ways. Linking invertebrate abundance and biomass dynamics to abiotic drivers through this time-varying matrix population model reveals how life-history shapes aquatic invertebrate response to shifting seasons, temperatures, and disturbance, which is key to understanding the impacts of climate change on aquatic ecosystems.

Ecological Modelling↗
USGS2017

Contaminants of emerging concern in the Great Lakes Basin: A report on sediment, water, and fish tissue chemistry collected in 2010-2012

Despite being detected at low levels in surface waters and sediments across the United States, contaminants of emerging concern (CECs) in the Great Lakes Basin are not well characterized in terms of spatial and temporal occurrence. Additionally, although the detrimental effects of exposure to CECs on fish and wildlife have been documented for many CECs in laboratory studies, we do not adequately understand the implications of the presence of CECs in the environment. Based on limited studies using current environmentally relevant concentrations of chemicals, however, risks to fish and wildlife are evident. As a result, there is an increasing urgency to address data gaps that are vital to resource management decisions. The U.S. Fish and Wildlife Service, in collaboration with the U.S. Geological Survey, is leading a Great Lakes Basin-wide evaluation of CECs (CEC Project) with the objectives to (a) characterize the spatial and temporal distribution of CECs; (b) evaluate risks to fish and wildlife resources; and (c) develop tools to aid resource managers in detecting, averting, or minimizing the ecological consequences to fish and wildlife that are exposed to CECs. This report addresses objective (a) of the CEC Project, summarizing sediment and water chemistry data collected from 2010 to 2012 and fish liver tissue chemistry data collected in 2012; characterizes the sampling locations with respect to potential sources of CECs in the landscape; and provides an initial interpretation of the variation in CEC concentrations relative to the identified sources. Data collected during the first three years of our study, which included 12 sampling locations and analysis of 134 chemicals, indicate that contaminants were more frequently detected in sediment compared to water. Chemicals classified as alkyphenols, flavors/ fragrances, hormones, PAHs, and sterols had higher average detection frequencies in sediment compared to water, while the opposite was observed for pesticides, pharmaceuticals, and plasticizers/flame retardants. The St. Louis River and Maumee River sampling locations had the most CEC detections in water and sediment, relative to other sites, as well as the largest number of maximum detected concentrations across all sites in the Basin. No consistent temporal CEC occurrence patterns were observed at locations sampled multiple times each day. Most appearances and increases in chemical concentrations in sediments occurred at sites immediately downstream from wastewater treatment plants and at sites with predominantly developed land use. The location with the most observed appearances and increases was the St. Louis River. Perfluorinated compounds were commonly detected in fish liver tissues with detections in 100% of both benthic and pelagic species. The occurrence of these chemicals in liver tissue of benthic and pelagic species was generally similar. Abstract

Great Lakes Basin↗
USGS2009

Web-based decision support and visualization tools for water quality management in the Chesapeake Bay watershed

Federal, State, and local water quality managers charged with restoring the Chesapeake Bay ecosystem require tools to maximize the impact of their limited resources. To address this need, the U.S. Geological Survey (USGS) and the Environmental Protection Agency's Chesapeake Bay Program (CBP) are developing a suite of Web-based tools called the Chesapeake Online Assessment Support Toolkit (COAST). The goal of COAST is to help CBP partners identify geographic areas where restoration activities would have the greatest effect, select the appropriate management strategies, and improve coordination and prioritization among partners. As part of the COAST suite of tools focused on environmental restoration, a water quality management visualization component called the Nutrient Yields Mapper (NYM) tool is being developed by USGS. The NYM tool is a web application that uses watershed yield estimates from USGS SPAtially Referenced Regressions On Watershed (SPARROW) attributes model (Schwarz et al., 2006) [6] to allow water quality managers to identify important sources of nitrogen and phosphorous within the Chesapeake Bay watershed. The NYM tool utilizes new open source technologies that have become popular in geospatial web development, including components such as OpenLayers and GeoServer. This paper presents examples of water quality data analysis based on nutrient type, source, yield, and area of interest using the NYM tool for the Chesapeake Bay watershed. In addition, we describe examples of map-based techniques for identifying high and low nutrient yield areas; web map engines; and data visualization and data management techniques.

Conference Paper↗
USGS2011

Bayesian adaptive survey protocols for resource management

Transparency in resource management decisions requires a proper accounting of uncertainty at multiple stages of the decision‐making process. As information becomes available, periodic review and updating of resource management protocols reduces uncertainty and improves management decisions. One of the most basic steps to mitigating anthropogenic effects on populations is determining if a population of a species occurs in an area that will be affected by human activity. Species are rarely detected with certainty, however, and falsely declaring a species absent can cause improper conservation decisions or even extirpation of populations. We propose a method to design survey protocols for imperfectly detected species that accounts for multiple sources of uncertainty in the detection process, is capable of quantitatively incorporating expert opinion into the decision‐making process, allows periodic updates to the protocol, and permits resource managers to weigh the severity of consequences if the species is falsely declared absent. We developed our method using the giant gartersnake ( Thamnophis gigas ), a threatened species precinctive to the Central Valley of California, as a case study. Survey date was negatively related to the probability of detecting the giant gartersnake, and water temperature was positively related to the probability of detecting the giant gartersnake at a sampled location. Reporting sampling effort, timing and duration of surveys, and water temperatures would allow resource managers to evaluate the probability that the giant gartersnake occurs at sampled sites where it is not detected. This information would also allow periodic updates and quantitative evaluation of changes to the giant gartersnake survey protocol. Because it naturally allows multiple sources of information and is predicated upon the idea of updating information, Bayesian analysis is well‐suited to solving the problem of developing efficient sampling protocols for species of conservation concern.

California↗
USGS2017

The importance of parameterization when simulating the hydrologic response of vegetative land-cover change

Computer models of hydrologic systems are frequently used to investigate the hydrologic response of land-cover change. If the modeling results are used to inform resource-management decisions, then providing robust estimates of uncertainty in the simulated response is an important consideration. Here we examine the importance of parameterization, a necessarily subjective process, on uncertainty estimates of the simulated hydrologic response of land-cover change. Specifically, we applied the soil water assessment tool (SWAT) model to a 1.4 km 2 watershed in southern Texas to investigate the simulated hydrologic response of brush management (the mechanical removal of woody plants), a discrete land-cover change. The watershed was instrumented before and after brush-management activities were undertaken, and estimates of precipitation, streamflow, and evapotranspiration (ET) are available; these data were used to condition and verify the model. The role of parameterization in brush-management simulation was evaluated by constructing two models, one with 12 adjustable parameters (reduced parameterization) and one with 1305 adjustable parameters (full parameterization). Both models were subjected to global sensitivity analysis as well as Monte Carlo and generalized likelihood uncertainty estimation (GLUE) conditioning to identify important model inputs and to estimate uncertainty in several quantities of interest related to brush management. Many realizations from both parameterizations were identified as behavioral in that they reproduce daily mean streamflow acceptably well according to Nash–Sutcliffe model efficiency coefficient, percent bias, and coefficient of determination. However, the total volumetric ET difference resulting from simulated brush management remains highly uncertain after conditioning to daily mean streamflow, indicating that streamflow data alone are not sufficient to inform the model inputs that influence the simulated outcomes of brush management the most. Additionally, the reduced-parameterization model grossly underestimates uncertainty in the total volumetric ET difference compared to the full-parameterization model; total volumetric ET difference is a primary metric for evaluating the outcomes of brush management. The failure of the reduced-parameterization model to provide robust uncertainty estimates demonstrates the importance of parameterization when attempting to quantify uncertainty in land-cover change simulations.

Hydrology and Earth System Sciences↗