SEARCH · Geology Reports

Results for “Decision Analysis”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49Linked to original sources

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↗

USGS2020

Do empirical observations support commonly-held climate change range shift hypotheses? A systematic review protocol

Background Among the most widely anticipated climate-related impacts to biodiversity are geographic range shifts, whereby species shift their spatial distribution in response to changing climate conditions. In particular, a series of commonly articulated hypotheses have emerged: species are expected to shift their distributions to higher latitudes, greater elevations, and deeper depths in response to climate change, reflecting an underlying hypothesis that species will move to cooler locations to track spatial changes in the temperature of their current range. Yet, many species are not demonstrating range shifts consistent with these hypotheses. Resolving this discrepancy and providing effective explanations for the observed variability in species’ range shifts is urgently needed to help support a range of natural resource management decisions. Here, we propose a protocol to review the body of evidence for commonly-held climate change range shift hypotheses at the species level focusing on observed latitudinal, longitudinal, elevational, and depth shifts in response to temperature and precipitation changes. We aim to answer the question: what is the impact of anthropogenic climate change (specifically changes in temperature and precipitation) on species ranges? Methods In this review protocol, we propose to conduct a systematic search of literature from internet databases and search engines in English. Articles will be screened in a two-stage process (title/abstract and full text) to evaluate whether they meet a list of eligibility criteria (e.g., presents species-level data, compares >1 time period). Initial data coding and extraction will be completed by four reviewers and checked by a secondary reviewer from among our co-authors. We will perform a formal meta-analysis to document estimated effect size using the subset of available range-shift data expressed in distance per time (e.g., km/decade). We will also use multinomial logistic regression models to assess the probability that species are shifting in a direction that supports our hypotheses (i.e. towards higher latitudes, greater elevations, and deeper depths). We will account for study methodology as a potential source of variation.

Environmental Evidence↗

USGS2013

Refinement of regression models to estimate real-time concentrations of contaminants in the Menomonee River drainage basin, southeast Wisconsin, 2008-11

In 2008, the U.S. Geological Survey and the Milwaukee Metropolitan Sewerage District initiated a study to develop regression models to estimate real-time concentrations and loads of chloride, suspended solids, phosphorus, and bacteria in streams near Milwaukee, Wisconsin. To collect monitoring data for calibration of models, water-quality sensors and automated samplers were installed at six sites in the Menomonee River drainage basin. The sensors continuously measured four potential explanatory variables: water temperature, specific conductance, dissolved oxygen, and turbidity. Discrete water-quality samples were collected and analyzed for five response variables: chloride, total suspended solids, total phosphorus, Escherichia coli bacteria, and fecal coliform bacteria. Using the first year of data, regression models were developed to continuously estimate the response variables on the basis of the continuously measured explanatory variables. Those models were published in a previous report. In this report, those models are refined using 2 years of additional data, and the relative improvement in model predictability is discussed. In addition, a set of regression models is presented for a new site in the Menomonee River Basin, Underwood Creek at Wauwatosa. The refined models use the same explanatory variables as the original models. The chloride models all used specific conductance as the explanatory variable, except for the model for the Little Menomonee River near Freistadt, which used both specific conductance and turbidity. Total suspended solids and total phosphorus models used turbidity as the only explanatory variable, and bacteria models used water temperature and turbidity as explanatory variables. An analysis of covariance (ANCOVA), used to compare the coefficients in the original models to those in the refined models calibrated using all of the data, showed that only 3 of the 25 original models changed significantly. Root-mean-squared errors (RMSEs) calculated for both the original and refined models using the entire dataset showed a median improvement in RMSE of 2.1 percent, with a range of 0.0–13.9 percent. Therefore most of the original models did almost as well at estimating concentrations during the validation period (October 2009–September 2011) as the refined models, which were calibrated using those data. Application of these refined models can produce continuously estimated concentrations of chloride, total suspended solids, total phosphorus, E. coli bacteria, and fecal coliform bacteria that may assist managers in quantifying the effects of land-use changes and improvement projects, establish total maximum daily loads, and enable better informed decision making in the future.

Wisconsin↗

USGS2012

Habitat associations of fish species of greatest conservation need in wadeable Iowa streams

Fish and habitat data were collected from 84 wadeable stream reaches in the Mississippi River drainage of Iowa to predict the occurrences of seven fish species of greatest conservation need and to identify the relative importance of habitat variables measured at small (e.g., depth, velocity, and substrate) and large (e.g., stream order, elevation, and gradient) scales in terms of their influence on species occurrences. Multiple logistic regression analysis was used to predict fish species occurrences, starting with all possible combinations of variables (5 large-scale variables, 13 small-scale variables, and all 18 variables) but limiting the final models to a maximum of five variables. Akaike's information criterion was used to rank candidate models, weight model parameters, and calculate model-averaged predictions. On average, the correct classification rate (CCR = 80%) and Cohen's kappa (κ = 0.59) were greatest for multiple-scale models (i.e., those including both large-scale and small-scale variables), intermediate for small-scale models (CCR = 75%; κ = 0.49), and lowest for large-scale models (CCR = 73%; κ = 0.44). The occurrence of each species was associated with a unique combination of large-scale and small-scale variables. Our results support the necessity of understanding factors that constrain the distribution of fishes across spatial scales to ensure that management decisions and actions occur at the appropriate scale.

Iowa↗

USGS2024

U.S. Geological Survey data strategy 2023–33

The U.S. Geological Survey (USGS) has long recognized the strategic importance and value of well-managed data assets as an integral component of scientific integrity and foundational to the advancement of scientific research, decision making, and public safety. The USGS investment in the science lifecycle, including collection of unbiased data assets, interpretation, peer review, interpretive publications, and data release, ultimately contributes to the transparency and availability of science. Foundational Government directives and laws, such as the Foundations for Evidence-Based Policymaking Act of 2018 (Public Law 115–435, 132 Stat. 5529) as well as Executive Order 13642, “Making Open and Machine Readable the New Default for Government Information,” provide a framework for addressing strategic data management. The USGS Data Strategy builds on that framework by outlining high-level goals and objectives that serve as a long-term, decadal guide toward achieving a broad, data-focused vision. Benefits of the USGS Data Strategy are many. The USGS will contribute to open science by increasing efficiencies in the consistent management of valuable data assets; driving innovation that results in modernized capabilities to ensure data are analysis ready; increasing data skills across the Bureau to enhance workforce data literacy; broadening capacity to understand and address needs of stakeholders; and measuring progress in producing findable, accessible, interoperable, and reusable (FAIR) data products. The major goals and objectives of the USGS Data Strategy promote maximizing the utility of USGS data based on stakeholder needs, promoting data innovation, coordinating common data practices, modernizing our USGS enterprise data architecture, and enhancing our data-centric culture. The goals and objectives in the strategy align with other Bureau strategic plans, guidance, and directives from the Department of the Interior and the Federal Government. This strategy is a key component to strengthen the Bureau’s data ecosystem to ensure a relevant, long-term capacity that supports internal needs and achieves its scientific mission in the most efficient and effective manner.

Circular↗

USGS2023

The relative stability of planktic foraminifer thermal preferences over the past 3 million years

Stationarity of species’ ecological tolerances is a first-order assumption of paleoenvironmental reconstruction based upon analog methods. To test this and other assumptions used in quantitative analysis of foraminiferal faunas for paleoceanographic reconstruction, we analyzed paired alkenone unsaturation ratio ( U 37 K ′ )   "> U K ′ 37 ) 37′) sea surface temperature (SST) estimates and relative abundances of planktic foraminifera within Late Pliocene assemblages. We established Pliocene temperature preferences for nine species in the North Atlantic: Dentoglobigerina altispira, Globorotalia menardii, Globoconella puncticulata, Neogloboquadrina atlantica, Neogloboquadrina incompta, Neogloboquadrina pachyderma, Trilobatus sacculifer, Globigerinita glutinata, and Globigerina bulloides. We compared these to the temperature preferences of the same extant species, and in the three cases where the species are now extinct ( Dentoglobigerina altispira, Neogloboquadrina atlantica, and Globoconella puncticulata ), comparisons were made to either the descendant species or other modern species commonly used as analogs. In general, the taxa tested show similar temperature responses in both Late Pliocene and present-day (core-top) distributions. The data from these comparisons are mostly encouraging, supporting past paleoceanographic conclusions, and are otherwise valuable for testing previous taxonomic grouping decisions that are often necessary for interpreting the paleoenvironment based upon Pliocene foraminiferal assemblages.

Geosciences↗

USGS2020

Resource partitioning across a trophic gradient between a freshwater fish and an intraguild exotic

The introduction of exotic species has the potential to cause resource competition with native species and may lead to competitive exclusion when resources are limiting. On the other hand, information is lacking to predict under what alternate trophic conditions coexistence may occur. Comparing diets of native yellow perch Perca flavescens and nonindigenous white perch Morone americana , we examined variation in resource partitioning and body condition across a prominent longitudinal nutrient gradient in Lake Erie (north‐eastern United States, Canada). As measured with Analysis of Similarity and Schoener's index, diet similarity declined monotonically from west to east tracking declines in nutrients, productivity and relative abundance of both species. Additionally, diet similarity increased from spring through fall, following seasonal development of stratification and hypolimnetic hypoxia—phenomena which tend to increase spatial overlap between these species. Finally, relative weights of both species peaked in the Central Basin (relative weights > 0.85), which, on average, had intermediate values of prey diversity, ecosystem trophic status and water clarity. Our results highlight that native yellow perch coexist with invasive white perch under a wide range of trophic conditions. Of importance to fishery managers, mesotrophy in the Central Basin correlated with the highest body conditions and intermediate prey resource partitioning, although the effect size was small and variable. While competitive exclusion appears unlikely, the goal of reducing nutrient inputs in Lake Erie could affect not only the distributions of both species but also stakeholder decisions about where to fish.

Lake Erie↗

USGS2011

Development of a high-resolution binational vegetation map of the Santa Cruz River riparian corridor and surrounding watershed, southern Arizona and northern Sonora, Mexico

This report summarizes the development of a binational vegetation map developed for the Santa Cruz Watershed, which straddles the southern border of Arizona and the northern border of Sonora, Mexico. The map was created as an environmental input to the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM) that is being created by the U.S. Geological Survey for the watershed. The SCWEPM is a map-based multicriteria evaluation tool that allows stakeholders to explore tradeoffs between valued ecosystem services at multiple scales within a participatory decision-making process. Maps related to vegetation type and are needed for use in modeling wildlife habitat and other ecosystem services. Although detailed vegetation maps existed for the U.S. side of the border, there was a lack of consistent data for the Santa Cruz Watershed in Mexico. We produced a binational vegetation classification of the Santa Cruz River riparian habitat and watershed vegetation based on NatureServe Terrestrial Ecological Systems (TES) units using Classification And Regression Tree (CART) modeling. Environmental layers used as predictor data were derived from a seasonal set of Landsat Thematic Mapper (TM) images (spring, summer, and fall) and from a 30-meter digital-elevation-model (DEM) grid. Because both sources of environmental data are seamless across the international border, they are particularly suited to this binational modeling effort. Training data were compiled from existing field data for the riparian corridor and data collected by the NM-GAP (New Mexico Gap Analysis Project) team for the original Southwest Regional Gap Analysis Project (SWReGAP) modeling effort. Additional training data were collected from core areas of the SWReGAP classification itself, allowing the extrapolation of the SWReGAP mapping into the Mexican portion of the watershed without collecting additional training data.

Arizona↗

USGS2008

Trends and spatial distribution of annual and seasonal rainfall in Ethiopia

As a country whose economy is heavily dependent on low-productivity rainfed agriculture, rainfall trends are often cited as one of the more important factors in explaining various socio-economic problems such as food insecurity. Therefore, in order to help policymakers and developers make more informed decisions, this study investigated the temporal dynamics of rainfall and its spatial distribution within Ethiopia. Changes in rainfall were examined using data from 134 stations in 13 watersheds between 1960 and 2002. The variability and trends in seasonal and annual rainfall were analysed at the watershed scale with data (1) from all available years, and (2) excluding years that lacked observations from at least 25% of the gauges. Similar analyses were also performed at the gauge, regional, and national levels. By regressing annual watershed rainfall on time, results from the one-sample t -test show no significant changes in rainfall for any of the watersheds examined. However, in our regressions of seasonal rainfall averages against time, we found a significant decline in June to September rainfall (i.e. Kiremt) for the Baro-Akobo, Omo-Ghibe, Rift Valley, and Southern Blue Nile watersheds located in the southwestern and central parts of Ethiopia. While the gauge level analysis showed that certain gauge stations experienced recent changes in rainfall, these trends are not necessarily reflected at the watershed or regional levels.

International Journal of Climatology↗

USGS2011

Particle-size dependence on metal(loid) distributions in mine wastes: Implications for water contamination and human exposure

The mining and processing of metal-bearing ores has resulted in contamination issues where waste materials from abandoned mines remain in piles of untreated and unconsolidated material, posing the potential for waterborne and airborne transport of toxic elements. This study presents a systematic method of particle size separation, mass distribution, and bulk chemical analysis for mine tailings and adjacent background soil samples from the Rand historic mining district, California, in order to assess particle size distribution and related trends in metal(loid) concentration as a function of particle size. Mine tailings produced through stamp milling and leaching processes were found to have both a narrower and finer particle size distribution than background samples, with significant fractions of particles available in a size range (???250 ??m) that could be incidentally ingested. In both tailings and background samples, the majority of trace metal(loid)s display an inverse relationship between concentration and particle size, resulting in higher proportions of As, Cr, Cu, Pb and Zn in finer-sized fractions which are more susceptible to both water- and wind-borne transport as well as ingestion and/or inhalation. Established regulatory screening levels for such elements may, therefore, significantly underestimate potential exposure risk if relying solely on bulk sample concentrations to guide remediation decisions. Correlations in elemental concentration trends (such as between As and Fe) indicate relationships between elements that may be relevant to their chemical speciation. ?? 2011 Elsevier Ltd.

Applied Geochemistry↗

USGS2026

Seasonal trophic dynamics drive growth potential and predation risk for reintroduced Chinook salmon in Shasta Reservoir

Reservoir ecosystems can significantly affect anadromous salmon populations reintroduced upstream of impassable high-head dams. In this study, we examine how a novel reservoir food web created by an impoundment can limit juvenile growth or survival through the seasonal production or access to food, promote competition for available resources, and affect risk of the food supply or predation mortality, all of which can be strongly influenced by the thermal regime. Shasta Reservoir, the largest impoundment in California’s Central Valley Project, presents opportunities and risks for winter-run Chinook salmon ( Oncorhynchus tshawytscha ), a federally endangered population targeted for reintroduction into the McCloud River, which flows into the reservoir. We integrated field sampling, stable isotope analysis, hydroacoustic surveys, and bioenergetics modeling to characterize Shasta Reservoir’s food web and evaluate seasonal growth potential and predation risk for juvenile salmon. Zooplankton, dominated by Daphnia , provided favorable foraging conditions in spring but declined sharply by late summer, coinciding with high consumption demand from abundant threadfin shad ( Dorosoma petenense ). Bioenergetics simulations indicated that fry that would ordinarily enter the reservoir in autumn would face poor growth opportunities. They would also be exposed to elevated predation risk driven by warm temperatures and high metabolic demand, particularly from piscivorous salmonids and Sacramento pikeminnow ( Ptychocheilus grandis ). Limited information on predator abundances precludes the ability to quantify total predation demand. Alternatively, juveniles entering the reservoir the following spring would encounter greater prey availability and reduced predation pressure. These findings highlight the strong influence of seasonal thermal structure and food web dynamics on reservoir constraints and underscore the need to incorporate the dynamics of key habitats into reintroduction management and decisions. Our framework provides a quantitative, mechanistically-based approach for evaluating the role of reservoirs in salmon reintroductions above high-head dams.

California↗

USGS2016

Model analysis of check dam impacts on long-term sediment and water budgets in southeast Arizona, USA

The objective of this study was to evaluate the effect of check dam infrastructure on soil and water conservation at the catchment scale using the Soil and Water Assessment Tool (SWAT). This paired watershed study includes a watershed treated with over 2000 check dams and a Control watershed which has none, in the West Turkey Creek watershed, Southeast Arizona, USA. SWAT was calibrated for streamflow using discharge documented during the summer of 2013 at the Control site. Model results depict the necessity to eliminate lateral flow from SWAT models of aridland environments, the urgency to standardize geospatial soils data, and the care for which modelers must document altering parameters when presenting findings. Performance was assessed using the percent bias (PBIAS), with values of ±2.34%. The calibrated model was then used to examine the impacts of check dams at the Treated watershed. Approximately 630 tons of sediment is estimated to be stored behind check dams in the Treated watershed over the 3-year simulation, increasing water quality for fish habitat. A minimum precipitation event of 15 mm was necessary to instigate the detachment of soil, sediments, or rock from the study area, which occurred 2% of the time. The resulting watershed model is useful as a predictive framework and decision-support tool to consider long-term impacts of restoration and potential for future restoration.

Arizona↗