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At least 73 records · Page 4Linked to original sources
USGS2021

Improving the ability of a BACI design to detect impacts within a kelp‐forest community

Distinguishing between human impacts and natural variation in abundance remains difficult because most species exhibit complex patterns of variation in space and time. When ecological monitoring data are available, a before‐after‐control‐impact (BACI) analysis can control natural spatial and temporal variation to better identify an impact and estimate its magnitude. However, populations with limited distributions and confounding spatial‐temporal dynamics can violate core assumptions of BACI‐type designs. In this study, we assessed how such properties affect the potential to identify impacts. Specifically, we quantified the conditions under which BACI analyses correctly (or incorrectly) identified simulated anthropogenic impacts in a spatially and temporally replicated data set of fish, macroalgal, and invertebrate species found on nearshore subtidal reefs in southern California, USA. We found BACI failed to assess very localized impacts, and had low power but high precision when assessing region‐wide impacts. Power was highest for severe impacts of moderate spatial scale, and impacts were most easily detected in species with stable, widely distributed populations. Serial autocorrelation in the data greatly inflated false impact detection rates, and could be partly controlled for statistically, while spatial synchrony in dynamics had no consistent effect on power or false detection rates. Unfortunately, species that offer high power to detect real impacts were also more likely to detect impacts where none had occurred. However, considering power and false detection rates together can identify promising indicator species, and collectively analyzing data for similar species improved the net ability to assess impacts. These insights set expectations for the sizes and severities of impacts that BACI analyses can detect in real systems, point to the importance of serial autocorrelation (but not of spatial synchrony), and indicate how to choose the species, and groups of species, that can best identify impacts.

Ecological Applications↗
USGS2011

Dose-Response Calculator for ArcGIS

The Dose-Response Calculator for ArcGIS is a tool that extends the Environmental Systems Research Institute (ESRI) ArcGIS 10 Desktop application to aid with the visualization of relationships between two raster GIS datasets. A dose-response curve is a line graph commonly used in medical research to examine the effects of different dosage rates of a drug or chemical (for example, carcinogen) on an outcome of interest (for example, cell mutations) (Russell and others, 1982). Dose-response curves have recently been used in ecological studies to examine the influence of an explanatory dose variable (for example, percentage of habitat cover, distance to disturbance) on a predicted response (for example, survival, probability of occurrence, abundance) (Aldridge and others, 2008). These dose curves have been created by calculating the predicted response value from a statistical model at different levels of the explanatory dose variable while holding values of other explanatory variables constant. Curves (plots) developed using the Dose-Response Calculator overcome the need to hold variables constant by using values extracted from the predicted response surface of a spatially explicit statistical model fit in a GIS, which include the variation of all explanatory variables, to visualize the univariate response to the dose variable. Application of the Dose-Response Calculator can be extended beyond the assessment of statistical model predictions and may be used to visualize the relationship between any two raster GIS datasets (see example in tool instructions). This tool generates tabular data for use in further exploration of dose-response relationships and a graph of the dose-response curve.

Data Series↗
USGS2024

Spatio-temporal ecological models via physics-informed neural networks for studying chronic wasting disease

To mitigate the negative effects of emerging wildlife diseases in biodiversity and public health it is critical to accurately forecast pathogen dissemination while incorporating relevant spatio-temporal covariates. Forecasting spatio-temporal processes can often be improved by incorporating scientific knowledge about the dynamics of the process using physical models. Ecological diffusion equations are often used to model epidemiological processes of wildlife diseases where environmental factors play a role in disease spread. Physics-informed neural networks (PINN) are deep learning algorithms that constrain neural network predictions based on physical laws and therefore are powerful forecasting models useful even in cases of limited and imperfect training data. In this paper, we develop a novel ecological modeling tool using PINNs, which fits a feedforward neural network and simultaneously performs parameter identification in a partial differential equation (PDE) with varying coefficients. We demonstrate the applicability of our model by comparing it with the commonly used Bayesian stochastic partial differential equation method and traditional machine learning approaches, showing that our proposed model exhibits superior prediction and forecasting performance when modeling chronic wasting disease in deer in Wisconsin. Furthermore, our model provides the opportunity to obtain scientific insights into spatiotemporal covariates affecting spread and growth of diseases. This work contributes to future machine learning and statistical methodology development by studying spatio-temporal processes enhanced by prior physical knowledge.

Spatial Statistics↗
USGS2004

Estimation of Total Nitrogen and Phosphorus in New England Streams Using Spatially Referenced Regression Models

The U.S. Geological Survey (USGS), in cooperation with the U.S. Environmental Protection Agency (USEPA) and the New England Interstate Water Pollution Control Commission (NEIWPCC), has developed a water-quality model, called SPARROW (Spatially Referenced Regressions on Watershed Attributes), to assist in regional total maximum daily load (TMDL) and nutrient-criteria activities in New England. SPARROW is a spatially detailed, statistical model that uses regression equations to relate total nitrogen and phosphorus (nutrient) stream loads to nutrient sources and watershed characteristics. The statistical relations in these equations are then used to predict nutrient loads in unmonitored streams. The New England SPARROW models are built using a hydrologic network of 42,000 stream reaches and associated watersheds. Watershed boundaries are defined for each stream reach in the network through the use of a digital elevation model and existing digitized watershed divides. Nutrient source data is from permitted wastewater discharge data from USEPA's Permit Compliance System (PCS), various land-use sources, and atmospheric deposition. Physical watershed characteristics include drainage area, land use, streamflow, time-of-travel, stream density, percent wetlands, slope of the land surface, and soil permeability. The New England SPARROW models for total nitrogen and total phosphorus have R-squared values of 0.95 and 0.94, with mean square errors of 0.16 and 0.23, respectively. Variables that were statistically significant in the total nitrogen model include permitted municipal-wastewater discharges, atmospheric deposition, agricultural area, and developed land area. Total nitrogen stream-loss rates were significant only in streams with average annual flows less than or equal to 2.83 cubic meters per second. In streams larger than this, there is nondetectable in-stream loss of annual total nitrogen in New England. Variables that were statistically significant in the total phosphorus model include discharges for municipal wastewater-treatment facilities and pulp and paper facilities, developed land area, agricultural area, and forested area. For total phosphorus, loss rates were significant for reservoirs with surface areas of 10 square kilometers or less, and in streams with flows less than or equal to 2.83 cubic meters per second. Applications of SPARROW for evaluating nutrient loading in New England waters include estimates of the spatial distributions of total nitrogen and phosphorus yields, sources of the nutrients, and the potential for delivery of those yields to receiving waters. This information can be used to (1) predict ranges in nutrient levels in surface waters, (2) identify the environmental variables that are statistically significant predictors of nutrient levels in streams, (3) evaluate monitoring efforts for better determination of nutrient loads, and (4) evaluate management options for reducing nutrient loads to achieve water-quality goals.

Scientific Investigations Report↗
USGS2012

Mapping outdoor recreationists' perceived social values for ecosystem services at Hinchinbrook Island National Park, Australia

Coastal ecosystems are increasingly faced with human impacts. To better understand these changing conditions, biophysical and economic values of nature have been used to prioritize spatial planning efforts and ecosystem-based management of human activities. Less is known, however, about how to characterize and represent non-material values in decision-making. We collected on-site and mailback survey data ( n = 209), and analyzed these data using the Social Values for Ecosystem Services (SolVES) GIS application to incorporate measures of social value and natural resource conditions on Hinchinbrook Island National Park, Australia. Our objectives in this paper are to: 1) determine the spatial distribution and point density of social values for ecosystem services; 2) examine the relationship between social values and natural resource conditions; and 3) compare social value allocations between two subgroups of outdoor recreationists. Results suggest that high priority areas exist on Hinchinbrook's land and seascapes according to the multiple values assigned to places by outdoor recreationists engaged in consumptive (e.g., fishing) and non-consumptive (e.g., hiking) activities. We examine statistically significant spatial clustering across two subgroups of the survey population for three value types that reflect Recreation, Biological Diversity, and Aesthetic qualities. The relationship between the relative importance of social values for ecosystem services and spatially-defined ecological data is explored to guide management decision-making in the context of an island national park setting.

Hinchinbrook Island National Park↗
USGS2023

At what scales does a river meander? Scale-specific sinuosity (S3) metric for quantifying stream meander size distribution

Stream bend geometry is linked to terrain features, hydrologic and ecologic conditions, and anthropogenic forces. Knowledge of the distributions of geometric properties of streams advances understanding of changing landscape conditions and associated processes that operate over a range of spatial scales. Statistical decomposition of sinuosity in natural linear features has proven a longstanding challenge and a particular impediment to automated analysis. This paper demonstrates that sinuosity can vary with the magnitude of units at which it is measured (measurement scales). The paper derives a scale-specific sinuosity (S3) metric intended to measure stream bend geometry across a range of measurement scales. The metric is warranted for analysis and modeling at measurement scales equal or similar to the spatial process and landscape conditions under investigation. Derived from the Richardson (1961) plot, the S3 metric quantifies a frequency signature of planform bend sizes spanning a range of measurement units, enabling visual and quantitative analysis of bend geometry in linear stream features. Derivation of the metric makes evident a systematic relationship between stream bend size, sinuosity, and measurement scale, formalizing a relationship between sinuosity and fractal dimension. The paper shows the utility of the S3 metric in examining bend patterns for synthetic and real-world linear stream data.

Geomorphology↗
USGS2019

Advances in quantifying streamflow variability across continental scales: 2. Improved model regionalization and prediction uncertainties using hierarchical Bayesian methods

The precise estimation of process effects in hydrological models requires applying models to large scales with extensive spatial variability in controlling factors. Despite progress in large‐scale applications of hydrological models in conterminous United States (CONUS) river basins, spatial constraints in model parameters have prevented the interbasin sharing of data, complicating quantification of process effects and limiting the accuracy of model predictions and uncertainties. Hierarchical Bayesian methods enable data sharing between basins and the identification of the causes of model uncertainties, which can improve model accuracy and interpretability; however, computational inefficiencies have been an obstacle to their large‐scale application. We used a new generation of Bayesian methods to develop a hierarchical version of a previous hybrid (statistical‐mechanistic) SPAtially Referenced Regression On Watershed attributes model of long‐term mean annual streamflow in the CONUS. We identified hierarchical (regional) variations in model coefficients and uncertainties and evaluated their effects on model accuracy and interpretability across diverse environments in 16 major CONUS regions. Hierarchical coefficients significantly improved spatial accuracy of model predictions, with the largest improvements in humid eastern regions, where uncertainties were approximately one third of those in arid western regions. Half of the coefficients varied regionally, with the largest variations in coefficients associated with water losses in streams and reservoirs. Our unraveling of the causes of model uncertainties identified a small latent process component of runoff that varies inversely with river size in most CONUS regions. Our study advances the use of hierarchical Bayesian methods to improve the predictive capabilities of hydrological models.

Water Resources Research↗
USGS2020

Spatial and temporal patterns of low streamflow and precipitation changes in the Chesapeake Bay Watershed

Spatial and temporal patterns in low streamflows were investigated for 183 streamgages located in the Chesapeake Bay Watershed for the period 1939–2013. Metrics that represent different aspects of the frequency and magnitude of low streamflows were examined for trends: (1) the annual time series of seven‐day average minimum streamflow, (2) the scaled average deficit at or below the 2% mean daily streamflow value relative to a base period between 1939 and 1970, and (3) the annual number of days below the 2% threshold. Trends in these statistics showed spatial cohesion, with increasing low streamflow volume at streamgages located in the northern uplands of the Chesapeake Bay Watershed and decreasing low streamflow volume at streamgages in the southern part of the watershed. For a small subset of streamgages (12%), conflicting trend patterns were observed between the seven‐day average minimum streamflow and the below‐threshold time series and these appear to be related to upstream diversions or the influence of reservoir‐influenced streamflows in their contributing watersheds. Using multivariate classification techniques, mean annual precipitation and fraction of precipitation falling as snow appear to be broad controls of increasing and decreasing low‐flow trends. Further investigation of seasonal precipitation patterns shows summer rainfall patterns, driven by the Atlantic Multidecadal Oscillation, as the main driver of low streamflows in the Chesapeake Bay Watershed.

Delaware, Maryland, New York, Pennsylvania, Virgin↗
USGS1986

Principles of logic and the use of digital geographic information systems

Digital geographic information systems allow many different types of data to be spatially and statistically analyzed. Logical operations can be performed on individual or multiple data planes by algorithms that can be implemented in computer systems. Users and creators of the systems should fully understand these operations. This paper describes the relationships of layers and features in geographic data bases and the principles of logic that can be applied by geographic information systems and suggests that a thorough knowledge of the data that are entered into a geographic data base and of the logical operations will produce results that are most satisfactory to the user. Methods of spatial analysis are reduced to their primitive logical operations and explained to further such understanding.

Circular↗
USGS2019

Probability of streamflow permanence model (PROSPER): A spatially continuous model of annual streamflow permanence throughout the Pacific Northwest

The U.S. Geological Survey (USGS) has developed the PRObability of Streamflow PERmanence (PROSPER) model, a GIS raster-based empirical model that provides streamflow permanence probabilities (probabilistic predictions) of a stream channel having year-round flow for any unregulated and minimally-impaired stream channel in the Pacific Northwest region, U.S. The model provides annual predictions for 2004-2016 at a 30-m spatial resolution based on monthly or annually updated values of climatic conditions and static physiographic variables associated with the upstream basin. Predictions correspond to any pixel on the channel network consistent with the medium resolution National Hydrography Dataset channel network stream grid. Total annual precipitation and percent forest cover were consistently the most important predictor variables among global and most subregional models, which had error rates between 17 and 22%. Probabilities were converted to wet and dry streamflow permanence classes with an associated confidence. Wet and dry classifications were used to derive descriptors that characterize the statistical and spatial distribution of streamflow permanence in three focal basins. Predicted dry channel segments account for 52 to 92% of the stream network across the three focal basins; streamflow permanence decreased during climatically drier years. Predictions are publicly available through the USGS StreamStats platform. Results demonstrate the utility of the PROSPER model as a tool for identifying areas that may be resilient or sensitive to drought conditions, allowing for management efforts that target protecting critical reaches. Importantly, PROSPER’s successful predictive performance can be improved with new datasets of streamflow permanence underscoring the importance of field observations.

Idaho, Montana, Nevada, Oregon, Utah, Washington↗
USGS2020

Animal movement models with mechanistic selection functions

A suite of statistical methods are used to study animal movement. Most of these methods treat animal trajectory data in one of three ways: as discrete pro- cesses, as continuous processes, or as point processes. We brie y review each of these approaches and then focus in on the latter. In the context of point processes, so-called resource selection analyses are among the most common way to statis- tically treat animal trajectory data. However, most resource selection analyses provide inference based on approximations of point process models. The forms of these models have been limited to a few types of specications that provide infer- ence about relative resource use and, less commonly, probability of use. For more general spatio-temporal point process models, the most common type of analysis often proceeds with a data augmentation approach that is used to create a binary data set that can be analyzed with conditional logistic regression. We show that the conditional logistic regression likelihood can be generalized to accommodate a variety of alternative specications related to resource selection. We then provide an example of a case where a spatio-temporal point process model coincides with that implied by a mechanistic model for movement expressed as a partial dier- ential equation derived from rst principles of movement. We demonstrate that inference from this form of point process model is intuitive (and could be useful for management and conservation) by analyzing a set of telemetry data from a mountain lion in Colorado, USA, to understand the eects of spatially explicit environmental conditions on movement behavior of this species.

Spatial Statistics↗
USGS2014

Quality of groundwater in the Denver Basin aquifer system, Colorado, 2003-5

Groundwater resources from alluvial and bedrock aquifers of the Denver Basin are critical for municipal, domestic, and agricultural uses in Colorado along the eastern front of the Rocky Mountains. Rapid and widespread urban development, primarily along the western boundary of the Denver Basin, has approximately doubled the population since about 1970, and much of the population depends on groundwater for water supply. As part of the National Water-Quality Assessment Program, the U.S. Geological Survey conducted groundwater-quality studies during 2003–5 in the Denver Basin aquifer system to characterize water quality of shallow groundwater at the water table and of the bedrock aquifers, which are important drinking-water resources. For the Denver Basin, water-quality constituents of concern for human health or because they might otherwise limit use of water include total dissolved solids, fluoride, sulfate, nitrate, iron, manganese, selenium, radon, uranium, arsenic, pesticides, and volatile organic compounds. For the water-table studies, two monitoring-well networks were installed and sampled beneath agricultural (31 wells) and urban (29 wells) land uses at or just below the water table in either alluvial material or near-surface bedrock. For the bedrock-aquifer studies, domestic- and municipal-supply wells completed in the bedrock aquifers were sampled. The bedrock aquifers, stratigraphically from youngest (shallowest) to oldest (deepest), are the Dawson, Denver, Arapahoe, and Laramie-Fox Hills aquifers. The extensive dataset collected from wells completed in the bedrock aquifers (79 samples) provides the opportunity to evaluate factors and processes affecting water quality and to establish a baseline that can be used to characterize future changes in groundwater quality. Groundwater samples were analyzed for inorganic, organic, isotopic, and age-dating constituents and tracers. This report discusses spatial and statistical distributions of chemical constituents and evaluates natural and human-related processes that affect water quality. Findings are synthesized to assess the vulnerability of the Denver Basin aquifer system to groundwater contamination. The chemistry of groundwater samples collected from the water-table wells was generally different from that of samples collected from the bedrock-aquifer wells. Samples from the water-table wells tended to have higher concentrations of total dissolved solids and most major ions. Concentrations of several constituents with potential human-health concerns, including nitrate, selenium, uranium, and arsenic, decreased with depth and were highest in samples from the water-table wells. Exceedances of drinking-water standards and water-quality benchmarks were more frequently associated with shallow groundwater samples; concentrations of total dissolved solids and sulfate exceeded water-quality benchmarks for about half or more of samples from the water-table wells. The sediments and rocks of the Denver Basin are natural sources of the trace elements selenium, uranium, and arsenic, which affect their concentrations in groundwater. Detections of organic contaminants, which are typically indicative of human sources of contamination to groundwater, were more frequent in samples from the water-table wells. Pesticide compounds and volatile organic compounds were detected in 33 and 62 percent, respectively, of water-table well samples. Detected organic contaminant concentrations were much less than the associated drinking-water standards. Samples collected from the bedrock aquifers had lower concentrations of total dissolved solids than did samples collected from the water-table wells, although within the bedrock-aquifer samples, concentrations increased from the Dawson to Denver to Arapahoe to Laramie-Fox Hills aquifers. Concentrations of total dissolved solids and many constituents varied spatially and with depth in the bedrock aquifers, likely as a result of ion-exchange and oxidation-reduction reactions, which are important processes affecting water quality. Major-ion chemistry generally evolved from a calcium-bicarbonate to calcium-sulfate composition, with some sodium-bicarbonate and sodium-sulfate facies in the deeper bedrock aquifers, likely resulting from longer residence times and more extensive water-rock interaction. Oxidation-reduction conditions generally evolved from oxic at the water table to anoxic with increasing depth in the bedrock aquifers. Most samples from the bedrock aquifers were anoxic. Exceedances of drinking-water standards and water-quality benchmarks for the bedrock aquifers occurred in 1 percent or less of samples for nitrate, selenium, or arsenic; there were no exceedances for uranium. Exceedances for total dissolved solids, sulfate, manganese, and iron were generally between about 10 and 20 percent for the bedrock-aquifer samples. Radon concentrations, which were only measured in samples collected from two of the bedrock aquifers, exceeded the lower proposed drinking-water standard for more than 90 percent of samples but exceeded the higher alternative standard for less than 5 percent of samples. Pesticide compounds and volatile organic compounds were detected in 3 and 22 percent, respectively, of bedrock-aquifer samples, all at concentrations that were that were much less than drinking-water standards. Water-quality data were synthesized to evaluate factors that affect spatial and depth variability in water quality and to assess aquifer vulnerability to contaminants from geologic materials and those of human origin. The quality of shallow groundwater in the alluvial aquifer and shallow bedrock aquifer system has been adversely affected by development of agricultural and urban areas. Land use has altered the pattern and composition of recharge. Increased recharge from irrigation water has mobilized dissolved constituents and increased concentrations in the shallow groundwater. Concentrations of most constituents associated with poor or degraded water quality in shallow groundwater decreased with depth; many of these constituents are not geochemically conservative and are affected by geochemical reactions such as oxidation-reduction reactions. Groundwater age tracers provide additional insight into aquifer vulnerability and help determine if young groundwater of potentially poor quality has migrated to deeper parts of the bedrock aquifers used for drinking-water supply. Age-tracer results were used to group samples into categories of young, mixed, and old groundwater. Groundwater ages transitioned from mostly young in the water-table wells to mostly mixed in the shallowest bedrock aquifer, the Dawson aquifer, to mostly old in the deeper bedrock aquifers. Although the bedrock aquifers are mostly old groundwater of good water quality, several lines of evidence indicate that young, contaminant-bearing recharge has reached shallow to moderate depths in some areas of the bedrock aquifers. The Dawson aquifer is the most vulnerable of the bedrock aquifers to contamination, but results indicate that the older (deeper) bedrock aquifers are also vulnerable to groundwater contamination and that mixing with young recharge has occurred in some areas. Heavy pumping has caused water-level declines in the bedrock aquifers in some parts of the Denver Basin, which has the potential to enhance the transport of contaminants from overlying units. Results of this study are consistent with the existing conceptual understanding of aquifer processes and groundwater issues in the Denver Basin and add new insight into the vulnerability of the bedrock aquifers to groundwater contamination.

Colorado↗
USGS2021

Projected change in rangeland fractional component cover across the sagebrush biome under climate change through 2085

Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic influences are unknown. While many studies have projected the effects of climate change using several modeling approaches, none has evaluated the impacts to fractional component cover at a 30-m resolution across the full sagebrush ( Artemisia spp.) biome. We used fractional component cover data for rangeland functional groups and weather data from the 1985 to 2018 reference period in conjunction with soils and topography data to develop empirical models describing the spatiotemporal variation in component cover. To investigate the ramifications of future change across the western United States, we extended models based on historical relationships over the reference period to model landscape effects based on future weather conditions from two emission scenarios and three time periods (2020s, 2050s, and 2080s). We tested both generalized additive models (GAMs) and regression tree models, finding that the former led to superior spatial and statistical results. Our results indicate more xeric vegetation across most of the study area, with an increasing dominance of non-sagebrush shrubs, annual herbaceous cover, and bare ground over herbaceous and sagebrush cover in both the representative concentration pathway (RCP) 4.5 and 8.5 scenarios. In general, both scenarios yielded similar results, but RCP 8.5 tended to be more extreme, with greater change relative to the reference period. Results demonstrate that in cool sites some degree of warming to growing season maximum temperature or nongrowing season minimum temperature could be beneficial to sagebrush and shrub growth. However, warming nongrowing season maximum temperature was beneficial to shrub, but not to sagebrush growth. Our results inform rangeland managers of potential future vegetation composition, cover, and species distributions, which could improve prioritization of conservation and restoration efforts.

Arizona, California, Colorado, Idaho, Montana, Nev↗
USGS2017

Projected warming portends seasonal shifts of stream temperatures in the Crown of the Continent Ecosystem, USA and Canada

Climate warming is expected to increase stream temperatures in mountainous regions of western North America, yet the degree to which future climate change may influence seasonal patterns of stream temperature is uncertain. In this study, a spatially explicit statistical model framework was integrated with empirical stream temperature data (approximately four million bi-hourly recordings) and high-resolution climate and land surface data to estimate monthly stream temperatures and potential change under future climate scenarios in the Crown of the Continent Ecosystem, USA and Canada (72,000 km2). Moderate and extreme warming scenarios forecast increasing stream temperatures during spring, summer, and fall, with the largest increases predicted during summer (July, August, and September). Additionally, thermal regimes characteristic of current August temperatures, the warmest month of the year, may be exceeded during July and September, suggesting an earlier and extended duration of warm summer stream temperatures. Models estimate that the largest magnitude of temperature warming relative to current conditions may be observed during the shoulder months of winter (April and November). Summer stream temperature warming is likely to be most pronounced in glacial-fed streams where models predict the largest magnitude (> 50%) of change due to the loss of alpine glaciers. We provide the first broad-scale analysis of seasonal climate effects on spatiotemporal patterns of stream temperature in the Crown of the Continent Ecosystem for better understanding climate change impacts on freshwater habitats and guiding conservation and climate adaptation strategies.

Climatic Change↗
USGS2017

Pre-mining trace element and radiation exposure to biota from a breccia pipe uranium mine in the Grand Canyon (Arizona, USA) watershed

The risks to wildlife and humans from uranium (U) mining in the Grand Canyon watershed are largely unknown. In addition to U, other co-occurring ore constituents contribute to risks to biological receptors depending on their toxicological profiles. This study characterizes the pre-mining concentrations of total arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), mercury (Hg), nickel (Ni), selenium (Se), thallium (Tl), U, and zinc (Zn); radiation levels; and histopathology in biota (vegetation, invertebrates, amphibians, birds, and mammals) at the Canyon Mine. Gross alpha levels were below the reporting limit (4 pCi/g) in all samples, and gross beta levels were indicative of background in vegetation (<10–17 pCi/g) and rodents (<10–43.5 pCi/g). Concentrations of U, Tl, Pb, Ni, Cu, and As in vegetation downwind from the mine were likely the result of aeolian transport. Chemical concentrations in rodents and terrestrial invertebrates indicate that surface disturbance during mine construction has not resulted in statistically significant spatial differences in fauna concentrations adjacent to the mine. Chemical concentrations in egg contents and nestlings of non-aquatic birds were less than method quantification limits or did not exceed toxicity thresholds. Bioaccumulation of As, Pb, Se, Tl, and U was evident in Western spadefoot ( Spea multiplicata ) tadpoles from the mine containment pond; concentrations of As (28.9–31.4 μg/g) and Se (5.81–7.20 μg/g) exceeded toxicity values and were significantly greater than in tadpoles from a nearby water source. Continued evaluation of As and Se in biota inhabiting and forging in the mine containment pond is warranted as mining progresses.

Arizona↗
USGS2009

Mesoscale raised rim depressions (MRRDs) on Earth: A review of the characteristics, processes, and spatial distributions of analogs for Mars

Fields of mesoscale raised rim depressions (MRRDs) of various origins are found on Earth and Mars. Examples include rootless cones, mud volcanoes, collapsed pingos, rimmed kettle holes, and basaltic ring structures. Correct identification of MRRDs on Mars is valuable because different MRRD types have different geologic and/or climatic implications and are often associated with volcanism and/or water, which may provide locales for biotic or prebiotic activity. In order to facilitate correct identification of fields of MRRDs on Mars and their implications, this work provides a review of common terrestrial MRRD types that occur in fields. In this review, MRRDs by formation mechanism, including hydrovolcanic (phreatomagmatic cones, basaltic ring structures), sedimentological (mud volcanoes), and ice-related (pingos, volatile ice-block forms) mechanisms. For each broad mechanism, we present a comparative synopsis of (i) morphology and observations, (ii) physical formation processes, and (iii) published hypothesized locations on Mars. Because the morphology for MRRDs may be ambiguous, an additional tool is provided for distinguishing fields of MRRDs by origin on Mars, namely, spatial distribution analyses for MRRDs within fields on Earth. We find that MRRDs have both distinguishing and similar characteristics, and observation that applies both to their mesoscale morphology and to their spatial distribution statistics. Thus, this review provides tools for distinguishing between various MRRDs, while highlighting the utility of the multiple working hypotheses approach. ?? 2008 Elsevier Ltd.

Planetary and Space Science↗
USGS2015

Distribution of near-surface permafrost in Alaska: estimates of present and future conditions

High-latitude regions are experiencing rapid and extensive changes in ecosystem composition and function as the result of increases in average air temperature. Increasing air temperatures have led to widespread thawing and degradation of permafrost, which in turn has affected ecosystems, socioeconomics, and the carbon cycle of high latitudes. Here we overcome complex interactions among surface and subsurface conditions to map nearsurface permafrost through decision and regression tree approaches that statistically and spatially extend field observations using remotely sensed imagery, climatic data, and thematic maps of a wide range of surface and subsurface biophysical characteristics. The data fusion approach generated medium-resolution (30-m pixels) maps of near-surface (within 1 m) permafrost, active-layer thickness, and associated uncertainty estimates throughout mainland Alaska. Our calibrated models (overall test accuracy of ~85%) were used to quantify changes in permafrost distribution under varying future climate scenarios assuming no other changes in biophysical factors. Models indicate that near-surface permafrost underlies 38% of mainland Alaska and that near-surface permafrost will disappear on 16 to 24% of the landscape by the end of the 21st Century. Simulations suggest that near-surface permafrost degradation is more probable in central regions of Alaska than more northerly regions. Taken together, these results have obvious implications for potential remobilization of frozen soil carbon pools under warmer temperatures. Additionally, warmer and drier conditions may increase fire activity and severity, which may exacerbate rates of permafrost thaw and carbon remobilization relative to climate alone. The mapping of permafrost distribution across Alaska is important for land-use planning, environmental assessments, and a wide-array of geophysical studies.

Alaska↗
USGS2019

On the relationship between conditional (CAR) and simultaneous (SAR) autoregressive models

We clarify relationships between conditional (CAR) and simultaneous (SAR) autoregressive models. We review the literature on this topic and find that it is mostly incomplete. Our main result is that a SAR model can be written as a unique CAR model, and while a CAR model can be written as a SAR model, it is not unique. In fact, we show how any multivariate Gaussian distribution on a finite set of points with a positive-definite covariance matrix can be written as either a CAR or a SAR model. We illustrate how to obtain any number of SAR covariance matrices from a single CAR covariance matrix by using Givens rotation matrices on a simulated example. We also discuss sparseness in the original CAR construction, and for the resulting SAR weights matrix. For a real example, we use crime data in 49 neighborhoods from Columbus, Ohio, and show that a geostatistical model optimizes the likelihood much better than typical first-order CAR models. We then use the implied weights from the geostatistical model to estimate CAR model parameters that provides the best overall optimization.

Spatial Statistics↗