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Bringing modeling to the masses: A web based system to predict potential species distributions

Predicting current and potential species distributions and abundance is critical for managing invasive species, preserving threatened and endangered species, and conserving native species and habitats. Accurate predictive models are needed at local, regional, and national scales to guide field surveys, improve monitoring, and set priorities for conservation and restoration. Modeling capabilities, however, are often limited by access to software and environmental data required for predictions. To address these needs, we built a comprehensive web-based system that: (1) maintains a large database of field data; (2) provides access to field data and a wealth of environmental data; (3) accesses values in rasters representing environmental characteristics; (4) runs statistical spatial models; and (5) creates maps that predict the potential species distribution. The system is available online at www.niiss.org, and provides web-based tools for stakeholders to create potential species distribution models and maps under current and future climate scenarios.

Future Internet

Fine-scale habitat use of reintroduced black-footed ferrets on prairie dog colonies in New Mexico

Black-footed ferrets ( Mustela nigripes ) are among the most endangered animals in North America. Reintroductions of captive-born ferrets onto prairie dog ( Cynomys spp.) colonies are crucial to the conservation of the species. In September 2007, captive-born ferrets were released on a black-tailed prairie dog ( Cynomys ludovicianus ) colony at the Vermejo Park Ranch, New Mexico. Ferret kits experimentally released in areas of comparatively low and high prairie dog burrow densities were located via spotlight surveys. Some maturing ferret kits were subsequently translocated to areas of low and high burrow densities on nearby prairie dog colonies. For 2 months, fine-scale habitat use was quantified by mapping all burrow openings within a 30-m radius of each ferret location. Spatial statistics accounted for autocorrelation in the burrow densities in areas used by ferrets. It was hypothesized that ferrets would select areas of high burrow densities within colonies; however, burrow densities in areas used by ferrets were generally similar to the available burrow densities. Because ferrets used areas with burrow densities similar to densities available at the colony level and because of the potential energetic benefits for ferrets using areas with high burrow densities, releasing ferrets on colonies with high burrow densities might increase reintroduction success.

New Mexico

Spatial heterogeneity in statistical power to detect changes in lake area in Alaskan National Wildlife Refuges

Over the past 50 years, the number and size of high-latitude lakes have decreased throughout many regions; however, individual lake trends have been variable in direction and magnitude. This spatial heterogeneity in lake change makes statistical detection of temporal trends challenging, particularly in small analysis areas where weak trends are difficult to separate from inter- and intra-annual variability. Factors affecting trend detection include inherent variability, trend magnitude, and sample size. In this paper, we investigated how the statistical power to detect average linear trends in lake size of 0.5, 1.0 and 2.0 %/year was affected by the size of the analysis area and the number of years of monitoring in National Wildlife Refuges in Alaska. We estimated power for large (930–4,560 sq km) study areas within refuges and for 2.6, 12.9, and 25.9 sq km cells nested within study areas over temporal extents of 4–50 years. We found that: (1) trends in study areas could be detected within 5–15 years, (2) trends smaller than 2.0 %/year would take >50 years to detect in cells within study areas, and (3) there was substantial spatial variation in the time required to detect change among cells. Power was particularly low in the smallest cells which typically had the fewest lakes. Because small but ecologically meaningful trends may take decades to detect, early establishment of long-term monitoring will enhance power to detect change. Our results have broad applicability and our method is useful for any study involving change detection among variable spatial and temporal extents.

Alaska

A spatial cluster analysis of tractor overturns in Kentucky from 1960 to 2002

Background: Agricultural tractor overturns without rollover protective structures are the leading cause of farm fatalities in the United States. To our knowledge, no studies have incorporated the spatial scan statistic in identifying high-risk areas for tractor overturns. The aim of this study was to determine whether tractor overturns cluster in certain parts of Kentucky and identify factors associated with tractor overturns. Methods: A spatial statistical analysis using Kulldorff's spatial scan statistic was performed to identify county clusters at greatest risk for tractor overturns. A regression analysis was then performed to identify factors associated with tractor overturns. Results: The spatial analysis revealed a cluster of higher than expected tractor overturns in four counties in northern Kentucky (RR = 2.55) and 10 counties in eastern Kentucky (RR = 1.97). Higher rates of tractor overturns were associated with steeper average percent slope of pasture land by county (p = 0.0002) and a greater percent of total tractors with less than 40 horsepower by county (p<0.0001). Conclusions: This study reveals that geographic hotspots of tractor overturns exist in Kentucky and identifies factors associated with overturns. This study provides policymakers a guide to targeted county-level interventions (e.g., roll-over protective structures promotion interventions) with the intention of reducing tractor overturns in the highest risk counties in Kentucky.

Kentucky

Spatial and temporal statistical analysis of bycatch data: Patterns of sea turtle bycatch in the North Atlantic

Loggerhead (Caretta caretta) and leatherback (Dermochelys coriacea) sea turtle distributions and movements in offshore waters of the western North Atlantic are not well understood despite continued efforts to monitor, survey, and observe them. Loggerhead and leatherback sea turtles are listed as endangered by the World Conservation Union, and thus anthropogenic mortality of these species, including fishing, is of elevated interest. This study quantifies spatial and temporal patterns of sea turtle bycatch distributions to identify potential processes influencing their locations. A Ripley's K function analysis was employed on the NOAA Fisheries Atlantic Pelagic Longline Observer Program data to determine spatial, temporal, and spatio-temporal patterns of sea turtle bycatch distributions within the pattern of the pelagic fishery distribution. Results indicate that loggerhead and leatherback sea turtle catch distributions change seasonally, with patterns of spatial clustering appearing from July through October. The results from the space-time analysis indicate that sea turtle catch distributions are related on a relatively fine scale (30-200 km and 1-5 days). The use of spatial and temporal point pattern analysis, particularly K function analysis, is a novel way to examine bycatch data and can be used to inform fishing practices such that fishing could still occur while minimizing sea turtle bycatch. ?? 2008 NRC.

Canadian Journal of Fisheries and Aquatic Sciences

Spatial autoregressive models for statistical inference from ecological data

Ecological data often exhibit spatial pattern, which can be modeled as autocorrelation. Conditional autoregressive (CAR) and simultaneous autoregressive (SAR) models are network‐based models (also known as graphical models) specifically designed to model spatially autocorrelated data based on neighborhood relationships. We identify and discuss six different types of practical ecological inference using CAR and SAR models, including: (1) model selection, (2) spatial regression, (3) estimation of autocorrelation, (4) estimation of other connectivity parameters, (5) spatial prediction, and (6) spatial smoothing. We compare CAR and SAR models, showing their development and connection to partial correlations. Special cases, such as the intrinsic autoregressive model (IAR), are described. Conditional autoregressive and SAR models depend on weight matrices, whose practical development uses neighborhood definition and row‐standardization. Weight matrices can also include ecological covariates and connectivity structures, which we emphasize, but have been rarely used. Trends in harbor seals ( Phoca vitulina ) in southeastern Alaska from 463 polygons, some with missing data, are used to illustrate the six inference types. We develop a variety of weight matrices and CAR and SAR spatial regression models are fit using maximum likelihood and Bayesian methods. Profile likelihood graphs illustrate inference for covariance parameters. The same data set is used for both prediction and smoothing, and the relative merits of each are discussed. We show the nonstationary variances and correlations of a CAR model and demonstrate the effect of row‐standardization. We include several take‐home messages for CAR and SAR models, including (1) choosing between CAR and IAR models, (2) modeling ecological effects in the covariance matrix, (3) the appeal of spatial smoothing, and (4) how to handle isolated neighbors. We highlight several reasons why ecologists will want to make use of autoregressive models, both directly and in hierarchical models, and not only in explicit spatial settings, but also for more general connectivity models.

Ecological Monographs

A statistical estimator of the spatial distribution of the water-table altitude

An algorithm was designed to statistically estimate the areal distribution of water-table altitude. The altitude of the water table was bounded below by the minimum water-table surface and above by the land surface. Using lake elevations and stream stages, and interpolating between lakes and streams, the minimum water-table surface was generated. A multiple linear regression among the minimum water-table altitude, the difference between land-surface and minimum water-table altitudes, and the water-level measurements from surficial aquifer system wells resulted in a consistently high correlation for all groups of physiographic regions in Florida. A simple linear regression between land-surface and water-level measurements resulted in a root-mean-square residual of 4.23 m, with residuals ranging from -8.78 to 41.54 m. A simple linear regression between the minimum water table and the water-level measurements resulted in a root-mean-square residual of 1.45 m, with residuals ranging from -7.39 to 4.10 m. The application of the multiple linear regression presented herein resulted in a root-mean-square residual of 1.05 m, with residuals ranging from -5.24 to 5.63 m. Results from complete and partial F tests rejected the hypothesis of eliminating any of the regressors in the multiple linear regression presented in this study.

Ground Water

Status and understanding of groundwater quality in the Klamath Mountains study unit, 2010: California GAMA Priority Basin Project

Groundwater quality in the Klamath Mountains (KLAM) study unit was investigated as part of the Priority Basin Project of the California Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in Del Norte, Humboldt, Shasta, Siskiyou, Tehama, and Trinity Counties. The GAMA Priority Basin Project is being conducted by the California State Water Resources Control Board in collaboration with the U.S. Geological Survey (USGS) and the Lawrence Livermore National Laboratory. The GAMA Priority Basin Project was designed to provide a spatially unbiased, statistically robust assessment of the quality of untreated (raw) groundwater in the primary aquifer system. The assessment is based on water-quality data and explanatory factors for groundwater samples collected in 2010 by the USGS from 39 sites and on water-quality data from the California Department of Public Health (CDPH) water-quality database. The primary aquifer system was defined by the depth intervals of the wells listed in the CDPH water-quality database for the KLAM study unit. The quality of groundwater in the primary aquifer system may be different from that in the shallower or deeper water-bearing zones; shallow groundwater may be more vulnerable to surficial contamination. This study included two types of assessments: (1) a status assessment , which characterized the status of the current quality of the groundwater resource by using data from samples analyzed for volatile organic compounds, pesticides, and naturally occurring inorganic constituents, such as major ions and trace elements, and (2) an understanding assessment , which evaluated the natural and human factors potentially affecting the groundwater quality. The assessments were intended to characterize the quality of groundwater resources in the primary aquifer system of the KLAM study unit, not the quality of treated drinking water delivered to consumers by water purveyors. Relative-concentrations (sample concentrations divided by the health- or aesthetic-based benchmark concentrations) were used for evaluating groundwater quality for those constituents that have Federal or California regulatory or non-regulatory benchmarks for drinking-water quality. A relative-concentration greater than (>) 1.0 indicates a concentration greater than a benchmark, and a relative-concentration less than or equal to (≤) 1.0 indicates a concentration less than or equal to a benchmark. Relative-concentrations of organic constituents were classified as “high” (relative-concentration > 1.0), “moderate” (0.1 < relative-concentration ≤ 1.0), or “low” (relative-concentration ≤ 0.1). For inorganic constituents, the boundary between low and moderate relative-concentration was set at 0.5. Aquifer-scale proportion was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the percentage of the area of the primary aquifer system with a relative-concentration greater than 1.0 for a particular constituent or class of constituents; percentage is based on an areal rather than a volumetric basis. Moderate and low aquifer-scale proportions were defined as the percentages of the primary aquifer system with moderate and low relative-concentrations, respectively. The KLAM study unit includes more than 8,800 square miles (mi 2 ), but only those areas near the sampling sites, about 920 mi 2 , are included in the areal assessment of the study unit. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. To confirm this methodology, 90 percent confidence intervals were calculated for the grid-based high aquifer-scale proportions and were compared to the spatially weighted results, which were found to be within these confidence intervals in all cases. Grid-based results were selected for use in the status assessment unless, as was observed in a few cases, a grid-based result was zero and the spatially weighted result was not zero, in which case, the spatially weighted result was used. The status assessment showed that inorganic constituents with human-health benchmarks were detected at high relative-concentrations in 2.6 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The high aquifer-scale proportion for inorganic constituents mainly reflected the high aquifer-scale proportions of boron. Inorganic constituents with secondary maximum contaminant levels were detected at high relative-concentrations in 13 percent of the primary aquifer system and at moderate relative-concentrations in 10 percent of the system. The constituents present at high relative-concentrations included iron and manganese. Organic constituents with human-health benchmarks were not detected at high relative-concentrations, but were detected at moderate relative-concentrations in 1.9 percent of the primary aquifer system. The 1.9 percent reflected a spatially weighted moderate aquifer-scale proportion for the gasoline additive methyl tert-butyl ether. Of the 148 organic constituents analyzed, 14 constituents were detected. Only one organic constituent had a detection frequency of greater than 10 percent—the trihalomethane, chloroform. The second component of this study, the understanding assessment , identified the natural and human factors that may have affected the groundwater quality in the KLAM study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were aquifer lithology, land use, hydrologic conditions, depth, groundwater age, and geochemical conditions. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the KLAM study unit. Groundwater age distribution (modern, mixed, or pre-modern), redox class (oxic, mixed, or anoxic), and dissolved oxygen concentration were the explanatory factors that best explained occurrence patterns of the inorganic constituents. High concentrations of boron were found to be associated with groundwater classified as mixed or pre-modern with respect to groundwater age. Boron was also negatively correlated to dissolved oxygen and positively correlated to specific conductance. Iron and manganese concentrations were strongly associated with low dissolved oxygen concentrations, anoxic and mixed redox classifications, and pre-modern groundwater. Specific conductance concentrations were found to be related to pre-modern groundwater, low dissolved oxygen concentrations, and high pH. Chloroform was selected for additional evaluation in the understanding assessment because it was detected in more than 10 percent of wells sampled in the KLAM study unit. Septic tank density was the only explanatory factor that was found to relate to chloroform concentrations.

California

A methodology for the assessment of unconventional (continuous) resources with an application to the Greater Natural Buttes gas field, Utah

The Greater Natural Buttes tight natural gas field is an unconventional (continuous) accumulation in the Uinta Basin, Utah, that began production in the early 1950s from the Upper Cretaceous Mesaverde Group. Three years later, production was extended to the Eocene Wasatch Formation. With the exclusion of 1100 non-productive (“dry”) wells, we estimate that the final recovery from the 2500 producing wells existing in 2007 will be about 1.7 trillion standard cubic feet (TSCF) (48.2 billion cubic meters (BCM)). The use of estimated ultimate recovery (EUR) per well is common in assessments of unconventional resources, and it is one of the main sources of information to forecast undiscovered resources. Each calculated recovery value has an associated drainage area that generally varies from well to well and that can be mathematically subdivided into elemental subareas of constant size and shape called cells. Recovery per 5-acre cells at Greater Natural Buttes shows spatial correlation; hence, statistical approaches that ignore this correlation when inferring EUR values for untested cells do not take full advantage of all the information contained in the data. More critically, resulting models do not match the style of spatial EUR fluctuations observed in nature. This study takes a new approach by applying spatial statistics to model geographical variation of cell EUR taking into account spatial correlation and the influence of fractures. We applied sequential indicator simulation to model non-productive cells, while spatial mapping of cell EUR was obtained by applying sequential Gaussian simulation to provide multiple versions of reality (realizations) having equal chances of being the correct model. For each realization, summation of EUR in cells not drained by the existing wells allowed preparation of a stochastic prediction of undiscovered resources, which range between 2.6 and 3.4 TSCF (73.6 and 96.3 BCM) with a mean of 2.9 TSCF (82.1 BCM) for Greater Natural Buttes. A second approach illustrates the application of multiple-point simulation to assess a hypothetical frontier area for which there is no production information but which is regarded as being similar to Greater Natural Buttes.

Utah

Spatial stream network modeling of water temperature within the White River Basin, Mount Rainier National Park, Washington

Water temperature is a primary control on the occurrence and distribution of fish and other ectothermic aquatic species. In the Pacific Northwest, cold-water species such as Pacific salmon ( Oncorhynchus spp.) and bull trout ( Salvelinus confluentus ) have specific temperature requirements during different life stages that must be met to ensure the viability of their populations. Rivers draining Mount Rainier in western Washington, including the White River along its northern flank, support a number of cold-water fish populations, but the spatial distribution of water temperatures, particularly during late-summer baseflow during August and September, and the climatic, hydrologic, and physical processes regulating it are not well constrained. Spatial stream network (SSN) models, which are generalized linear models that incorporate streamwise spatial autocovariance structures, were fit to mean and 7-day average daily maximum water temperature for August and September for the White River Basin. The SSN models were calibrated using water temperature measurements collected in 2010 through 2020. The extent of the models included the White River and its tributaries upstream from its confluence with Silver Creek in Mount Rainier National Park, Washington. SSN models incorporated covariates hypothesized to represent the climatic, hydrologic, and physical processes that influence water temperature. SSN models were fit to the measured data and compared to generalized linear models that lacked spatial autocovariance structures. Statistically significant covariates within the best-fit models included the proportion of ice cover and forest cover within the basin, mean August air temperature, the proportion of consolidated geologic units, and snow-water equivalent. Statistical models that included spatial autocovariance structures had better predictive performance than those that did not. Additionally, models of mean August and September water temperature had better predictive performance than those of 7-day average daily maximum temperature in August and September. Predictions of the spatial distribution of water temperature were similar between August and September with a general warming in the downstream part of the mainstem White River compared to cooler water temperatures in the high-elevation headwater streams. The proportion of ice cover emerged as an inversely related significant covariate to both mean August and September water temperature because streams that receive glacial meltwater are colder than non-glaciated streams. Water temperatures of the upper White River increased downstream and are attributed to warming of water temperature from accumulated solar radiation and inflow of non-glaciated tributaries. Estimated water temperatures for the upper White River model are 3–4 degrees Celsius (°C) warmer for tributaries, but 1–2 °C cooler for the mainstem compared to the regional-scale model. Differences between the upper White River SSN model and the regional-scale NorWeST model are attributed to the fact that the upper White River SSN included water temperature observations specific to the upper White River, whereas water temperature observations from lower elevation streams and downstream from the Mount Rainer National Park boundary were used in the regional scale model.

Washington

Projected climate-induced habitat loss for salmonids in the John Day River network, Oregon, U.S.A.

Climate change will likely have profound effects on cold-water species of freshwater fishes. As temperatures rise, cold-water fish distributions may shift and contract in response. Predicting the effects of projected stream warming in stream networks is complicated by the generally poor correlation between water temperature and air temperature. Spatial dependencies in stream networks are complex because the geography of stream processes is governed by dimensions of flow direction and network structure. Therefore, forecasting climate-driven range shifts of stream biota has lagged behind similar terrestrial modeling efforts. We predicted climate-induced changes in summer thermal habitat for 3 cold-water fish species&mdash;juvenile Chinook salmon, rainbow trout, and bull trout (Oncorhynchus tshawytscha, O. mykiss, and Salvelinus confluentus, respectively)&mdash;in the John Day River basin, northwestern United States. We used a spatially explicit statistical model designed to predict water temperature in stream networks on the basis of flow and spatial connectivity. The spatial distribution of stream temperature extremes during summers from 1993 through 2009 was largely governed by solar radiation and interannual extremes of air temperature. For a moderate climate change scenario, estimated declines by 2100 in the volume of habitat for Chinook salmon, rainbow trout, and bull trout were 69&ndash;95%, 51&ndash;87%, and 86&ndash;100%, respectively. Although some restoration strategies may be able to offset these projected effects, such forecasts point to how and where restoration and management efforts might focus.

Oregon

Status of groundwater quality in the Santa Barbara Study Unit, 2011: California GAMA Priority Basin Project

Groundwater quality in the 48-square-mile Santa Barbara study unit was investigated in 2011 as part of the California State Water Resources Control Board’s Groundwater Ambient Monitoring and Assessment (GAMA) Program Priority Basin Project. The study unit is mostly in Santa Barbara County and is in the Transverse and Selected Peninsular Ranges hydrogeologic province. The GAMA Priority Basin Project is carried out by the U.S. Geological Survey in collaboration with the California State Water Resources Control Board and Lawrence Livermore National Laboratory. The GAMA Priority Basin Project was designed to provide a statistically unbiased, spatially distributed assessment of the quality of untreated groundwater in the primary aquifer system of California. The primary aquifer system is defined as that part of the aquifer corresponding to the perforation interval of wells listed in the California Department of Public Health database for the Santa Barbara study unit. This status assessment is intended to characterize the quality of groundwater resources in the primary aquifer system of the Santa Barbara study unit, not the treated drinking water delivered to consumers by water purveyors. The status assessment for the Santa Barbara study unit was based on water-quality and ancillary data collected in 2011 by the U.S. Geological Survey from 23 sites and on water-quality data from the California Department of Public Health database for January 24, 2008–January 23, 2011. The data used for the assessment included volatile organic compounds; pesticides; pharmaceutical compounds; two constituents of special interest, perchlorate and N -nitrosodimethylamine (NDMA); and naturally present inorganic constituents, such as major ions and trace elements. Relative-concentrations (sample concentration divided by the health- or aesthetic-based benchmark concentration) were used to evaluate groundwater quality for those constituents that have federal or California regulatory and non-regulatory benchmarks for drinking-water quality. For inorganic, organic, and special-interest constituents, a relative-concentration greater than 1.0 indicates a concentration greater than the benchmark and is classified as high. Inorganic constituents are classified as moderate if relative-concentrations are greater than 0.5 and less than or equal to 1.0 and are classified as low if relative-concentrations are less than or equal to 0.5. For organic and special-interest constituents, the boundary between moderate and low relative-concentrations was set at 0.1. Aquifer-scale proportion was used as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the areal percentage of the primary aquifer system with a relative-concentration greater than 1.0 for a particular constituent or class of constituents. Moderate and low aquifer-scale proportions were defined as the areal percentage of the primary aquifer system that had moderate and low relative-concentrations, respectively. Two statistical approaches—grid based and spatially weighted—were used to calculate aquifer-scale proportions for individual constituents and constituent classes. Grid-based and spatially weighted estimates were comparable in this the study (within 90-percent confidence intervals). Grid-based results were selected for use in the status assessment unless, as was observed in a few cases, a grid-based result was zero and the spatially weighted result was not zero, in which case, the spatially weighted result was used. Inorganic constituents that have human-health benchmarks were present at high relative-concentrations in 5.3 percent of the primary aquifer system and at moderate concentrations in 32 percent. High aquifer-scale proportions of inorganic constituents primarily were a result of high aquifer-scale proportions of boron (5.3 percent) and fluoride (5.3 percent). Inorganic constituents that have aesthetic-based benchmarks, referred to as secondary maximum contaminant levels, were present at high relative-concentrations in 58 percent of the primary aquifer system and at moderate concentrations in 37 percent. Iron, manganese, sulfate, and total dissolved solids were the inorganic constituents with secondary maximum contaminant levels present at high relative-concentrations. In contrast, organic and special-interest constituents that have health-based benchmarks were not detected at high relative-concentrations in the primary aquifer system. Of the 218 organic constituents analyzed, 10 were detected—9 that had human-health benchmarks. Organic constituents were present at moderate relative-concentrations in 11 percent of the primary aquifer system. The moderate aquifer-scale proportions were a result of moderate relative-concentrations of the volatile organic compounds methyl tert- butyl ether (MTBE, 11 percent) and 1,2-dichloroethane (5.6 percent). The volatile organic compounds 1,1,1-trichloroethane, 1,1-dichloroethane, bromodichloromethane, chloroform, MTBE, and perchloroethene (PCE); the pesticide simazine; and the special-interest constituent perchlorate were detected at more than 10 percent of the sites in the Santa Barbara study unit. Perchlorate was present at moderate relative-concentrations in 50 percent of the primary aquifer system. Pharmaceutical compounds and NDMA were not detected in the Santa Barbara study unit.

California

Status and understanding of groundwater quality in the South Coast Range-Coastal study unit, 2008: California GAMA Priority Basin Project

Groundwater quality in the South Coast Range–Coastal (SCRC) study unit was investigated from May through November 2008 as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The study unit is located in the Southern Coast Range hydrologic province and includes parts of Santa Barbara and San Luis Obispo Counties. The GAMA Priority Basin Project is conducted by the U.S. Geological Survey (USGS) in collaboration with the California State Water Resources Control Board and the Lawrence Livermore National Laboratory. The GAMA Priority Basin Project was designed to provide a statistically unbiased, spatially distributed assessment of untreated groundwater quality within the primary aquifer system. The primary aquifer system is defined as that part of the aquifer corresponding to the perforation interval of wells listed in the California Department of Public Health (CDPH) database for the SCRC study unit. The assessments for the SCRC study unit were based on water-quality and ancillary data collected in 2008 by the USGS from 55 wells on a spatially distributed grid, and water-quality data from the CDPH database. Two types of assessments were made: (1) status, assessment of the current quality of the groundwater resource, and (2) understanding, identification of the natural and human factors affecting groundwater quality. Water-quality and ancillary data were collected from an additional 15 wells for the understanding assessment. The assessments characterize untreated groundwater quality, not the quality of treated drinking water delivered to consumers by water purveyors. The first component of this study, the status assessment of groundwater quality, used data from samples analyzed for anthropogenic constituents such as volatile organic compounds (VOCs) and pesticides, as well as naturally occurring inorganic constituents such as major ions and trace elements. Although the status assessment applies to untreated groundwater, Federal and California regulatory and non-regulatory water-quality benchmarks that apply to drinking water are used to provide context for the results. Relative-concentrations (sample concentration divided by benchmark concentration) were used for evaluating groundwater. A relative-concentration greater than (>) 1.0 indicates a concentration greater than the benchmark and is classified as high. Inorganic constituents are classified as moderate if relative-concentrations are >0.5 and less than or equal to (≤) 1.0, or low if relative-concentrations are ≤0.5. For organic constituents, the boundary between moderate and low relative-concentrations was set at 0.1. Aquifer-scale proportion was used in the status assessment as the primary metric for evaluating regional-scale groundwater quality. High aquifer-scale proportion is defined as the areal percentage of the primary aquifer system with a high relative-concentration for a particular constituent or class of constituents. Moderate and low aquifer-scale proportions were defined as the areal percentage of the primary aquifer system with moderate and low relative-concentrations, respectively. Two statistical approaches—grid-based and spatially weighted—were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable for the study (within 90 percent confidence intervals). For inorganic constituents with human-health benchmarks, relative-concentrations were high for at least one constituent for 33 percent of the primary aquifer system in the SCRC study unit. Arsenic, molybdenum, and nitrate were the primary inorganic constituents with human-health benchmarks that were detected at high relative-concentrations. Inorganic constituents with aesthetic benchmarks, referred to as secondary maximum contaminant levels (SMCLs), had high relative-concentrations for 35 percent of the primary aquifer system. Iron, manganese, total dissolved solids (TDS), and sulfate were the inorganic constituents with SMCLs detected at high relative-concentrations. In contrast to inorganic constituents, organic constituents with human-health benchmarks were not detected at high relative-concentrations in the primary aquifer system in the SCRC study unit. Of the 205 organic constituents analyzed, 21 were detected—13 with human-health benchmarks. Perchloroethene (PCE) was the only VOC detected at moderate relative-concentrations. PCE, dichlorodifluoromethane (CFC-12), and chloroform were detected in more than 10 percent of the primary aquifer system. Of the two special-interest constituents, one was detected; perchlorate, which has a human-health benchmark, was detected at moderate relative-concentrations in 29 percent of the primary aquifer system and had a detection frequency of 60 percent in the SCRC study unit. The second component of this study, the understanding assessment, identified the natural and human factors that may have affected groundwater quality in the SCRC study unit by evaluating statistical correlations between water-quality constituents and potential explanatory factors. The potential explanatory factors evaluated were land use, septic tank density, well depth and depth to top-of-perforations, groundwater age, density and distance to the nearest formerly leaking underground fuel tank (LUFT), pH, and dissolved oxygen (DO) concentration. Results of the statistical evaluations were used to explain the occurrence and distribution of constituents in the study unit. DO was the primary explanatory factor influencing the concentrations of many inorganic constituents. Arsenic, iron, and manganese concentrations increased as DO concentrations decreased, consistent with patterns expected as a result of reductive dissolution of iron and (or) manganese oxides in aquifer sediments. Molybdenum concentrations increased in anoxic conditions and in oxic conditions with high pH, reflecting two mechanisms for the mobilization of molybdenum—reductive dissolution and pH-dependent desorption under oxic conditions from aquifer sediments. Nitrate concentrations decreased as DO concentrations decreased which would be consistent with degradation of nitrate under anoxic conditions (denitrification). It also is possible that nitrate concentrations decreased in relation to increasing depth and groundwater age and not as a result of denitrification. Groundwater age was another explanatory factor frequently correlated to several inorganic constituents. Iron and manganese concentrations were higher in pre-modern (water recharged before 1952) or mixed-age groundwater. This correlation is one indication that iron and manganese are from natural sources. Nitrate, TDS, and sulfate concentrations were higher in modern groundwater (water recharged since 1952) and may indicate that human activities increase concentrations of nitrate, TDS, and sulfate. Land use was a third explanatory factor frequently correlated with inorganic constituents. Nitrate, TDS, and sulfate concentrations were higher in agricultural land-use areas than in natural land-use areas, indicating that increased concentrations may be a result of agricultural practices. Organic constituents usually were detected at low relative-concentrations; therefore, statistical analyses of relations to explanatory factors usually were done for classes of constituents (for example, pesticides or solvents) as well as for selected constituents. The number of VOCs detected in a well was not correlated to any of the explanatory factors evaluated. The number of pesticide and solvent detections and PCE and CFC-12 concentrations were higher in modern groundwater than in pre-modern groundwater. PCE and CFC-12 also were positively correlated to the density of LUFTs. PCE was negatively correlated to natural land use. Chloroform concentrations were positively correlated to the density of septic systems. Perchlorate concentrations were greater in agricultural areas than in urban or natural areas. Correlation of perchlorate with DO may indicate that perchlorate biodegradation under anoxic conditions may occur. Anthropogenic sources have contributed perchlorate to groundwater in the SCRC study unit, although low levels of perchlorate may occur naturally.

California

Status and understanding of groundwater quality in the two southern San Joaquin Valley study units, 2005-2006 - California GAMA Priority Basin Project

Groundwater quality in the southern San Joaquin Valley was investigated from October 2005 through March 2006 as part of the Priority Basin Project of the Groundwater Ambient Monitoring and Assessment (GAMA) Program. The GAMA Priority Basin Project is conducted by the U.S. Geological Survey (USGS) in collaboration with the California State Water Resources Control Board and the Lawrence Livermore National Laboratory. There are two study units located in the southern San Joaquin Valley: the Southeast San Joaquin Valley (SESJ) study unit and the Kern County Subbasin (KERN) study unit. The GAMA Priority Basin Project in the SESJ and KERN study units was designed to provide a statistically unbiased, spatially distributed assessment of untreated groundwater quality within the primary aquifers. The status assessment is based on water-quality and ancillary data collected in 2005 and 2006 by the USGS from 130 wells on a spatially distributed grid, and water-quality data from the California Department of Public Health (CDPH) database. Data was collected from an additional 19 wells for the understanding assessment. The aquifer systems (hereinafter referred to as primary aquifers) were defined as that part of the aquifer corresponding to the perforation interval of wells listed in the CDPH database for the SESJ and KERN study units. The status assessment of groundwater quality used data from samples analyzed for anthropogenic constituents such as volatile organic compounds (VOCs) and pesticides, as well as naturally occurring inorganic constituents such as major ions and trace elements. The status assessment is intended to characterize the quality of untreated groundwater resources within the primary aquifers in the SESJ and KERN study units, not the quality of drinking water delivered to consumers. Although the status assessment applies to untreated groundwater, Federal and California regulatory and non-regulatory water-quality benchmarks that apply to drinking water are used to provide context for the results. Relative-concentrations (sample concentration divided by benchmark concentration) were used for evaluating groundwater. A relative-concentration greater than 1.0 indicates a concentration greater than the benchmark and is classified as high. The relative-concentration threshold for classifying inorganic constituents as moderate or low was 0.5; for organic constituents the threshold between moderate and low was 0.1. Aquifer-scale proportion was used as the primary metric for assessing the quality of untreated groundwater for the study units. High aquifer-scale proportion is defined as the areal percentage of the primary aquifers with a high relative-concentration for a particular constituent or class of constituents. Moderate and low aquifer-scale proportions were defined as the areal percentage of the primary aquifers with moderate and low relative-concentrations, respectively. Two statistical approaches&mdash;grid-based and spatially weighted&mdash;were used to evaluate aquifer-scale proportions for individual constituents and classes of constituents. Grid-based and spatially weighted estimates were comparable for the two study units in the southern San Joaquin Valley (within 90 percent confidence intervals). The status assessment showed that inorganic constituents were more prevalent than organic constituents and that relative-concentrations were higher for inorganic constituents than for organic constituents. For inorganic constituents with human-health benchmarks, the relative-concentration of at least one constituent in the SESJ study unit was high in 30 percent of the primary aquifers. In the KERN study unit, the relative-concentration of at least one constituent was high in 23 percent of the primary aquifers. In the SESJ and KERN study units, the inorganic constituents with human-health benchmarks detected at high relative-concentrations in more than 2 percent of the primary aquifers were arsenic, boron, vanadium, nitrate, uranium, and gross alpha radioactivity. Additional constituents with human-health benchmarks&mdash;antimony, radium, and fluoride&mdash;were detected at high relative-concentrations in the KERN study unit. For inorganic constituents with aesthetic benchmarks (secondary maximum contaminant levels, SMCLs), the relative-concentration of at least one constituent in the SESJ study unit was high in 6.6 percent of the primary aquifers. In the KERN study unit, the relative-concentration of at least one constituent was high in 22 percent of the primary aquifers. Inorganic constituents with aesthetic benchmarks detected at high relative-concentrations in the primary aquifers in the SESJ and KERN study units were iron and manganese. Additional constituents with aesthetic benchmarks&mdash;total dissolved solids (TDS), sulfate, and chloride&mdash;were detected at high relative-concentrations in the KERN study unit. In contrast, the status assessment for organic constituents with human-health benchmarks showed that relative-concentrations were high in 4.8 percent and 2.1 percent of the primary aquifers in the SESJ and KERN study units, respectively. The special-interest constituent, perchlorate, was detected at high relative-concentrations in 1.2 percent of the primary aquifers in the SESJ study unit. Twenty-eight of the 78 VOCs (not including fumigants) analyzed were detected. Of these 28 VOCs, benzene had high relative-concentrations in the SESJ study unit, and relative-concentrations for the other 27 VOCs were moderate and low. Five of the 10 fumigants were detected; 1,2-dibromo-3-chloropropane (DBCP) was the only fumigant with high relative-concentrations in the SESJ and KERN study units. Of the 136 pesticides and pesticide degradates analyzed, 33 were detected. Human-health benchmarks were established for eighteen of the detected pesticides. Dieldrin was detected at moderate relative-concentrations in the SESJ and KERN study units. All other pesticides detected with human-health benchmarks were present at low relative-concentrations. The detection frequencies for two of these pesticides&mdash;simazine and atrazine&mdash;were greater than or equal to 10 percent in the SESJ and KERN study units. The understanding assessment of groundwater quality included an analysis of correlations of selected water-quality constituents or classes of constituents with potential explanatory factors. The understanding assessment indicated that the concentrations of many trace elements and major ions were correlated to well depth, groundwater age, and/or geochemical conditions. Many trace elements were positively correlated with depth. Arsenic, boron, vanadium, fluoride, manganese, and iron concentrations increased with well depth or depth to top-of-perforations. The concentrations for these trace elements also were higher in older (pre-modern) groundwater. In contrast, uranium concentrations decreased with increasing depth and groundwater age. Most trace elements were correlated to geochemical conditions. Arsenic, antimony, boron, fluoride, manganese, and iron concentrations generally were higher wherever the pH of the groundwater was greater than 7.6. Concentrations for these constituents generally were higher at low concentrations of dissolved oxygen (DO). Uranium was the exception; uranium concentrations generally were lower at high pH and at high concentrations of DO. Nitrate concentrations generally were lower in deeper wells. Nitrate concentrations also were higher in groundwater with higher DO. Total dissolved solids, sulfate, and chloride concentrations were higher in the KERN study unit than in the SESJ study unit. Total dissolved solids were negatively correlated with pH in the KERN study unit. Total dissolved solids and sulfate were higher in areas with more agricultural land use. Chloride concentrations increased with depth to top-of-perforations in the KERN study unit. Organic constituents and constituents of special interest, like many inorganic constituents, were correlated with well depth, groundwater age, and DO. Unlike most trace elements, however, solvent and pesticide detections, and total trihalomethanes (THM), DBCP, and perchlorate concentrations decreased with increasing well depth. Volatile organic compound, solvent, and pesticide detections, and THM concentrations also were lower in older (pre-modern) groundwater than in modern-age groundwater. Solvent detections and total THM, DBCP, and perchlorate concentrations increased with increasing DO concentrations.

California

Mining Claim Activity on Federal Land in the United States

Several statistical compilations of mining claim activity on Federal land derived from the Bureau of Land Management's LR2000 database have previously been published by the U.S Geological Survey (USGS). The work in the 1990s did not include Arkansas or Florida. None of the previous reports included Alaska because it is stored in a separate database (Alaska Land Information System) and is in a different format. This report includes data for all states for which there are Federal mining claim records, beginning in 1976 and continuing to the present. The intent is to update the spatial and statistical data associated with this report on an annual basis, beginning with 2005 data. The statistics compiled from the databases are counts of the number of active mining claims in a section of land each year from 1976 to the present for all states within the United States. Claim statistics are subset by lode and placer types, as well as a dataset summarizing all claims including mill site and tunnel site claims. One table presents data by case type, case status, and number of claims in a section. This report includes a spatial database for each state in which mining claims were recorded, except North Dakota, which only has had two claims. A field is present that allows the statistical data to be joined to the spatial databases so that spatial displays and analysis can be done by using appropriate geographic information system (GIS) software. The data show how mining claim activity has changed in intensity, space, and time. Variations can be examined on a state, as well as a national level. The data are tied to a section of land, approximately 640 acres, which allows it to be used at regional, as well as local scale. The data only pertain to Federal land and mineral estate that was open to mining claim location at the time the claims were staked.

Data Series

Identifying when tagged fishes have been consumed by piscivorous predators: application of multivariate mixture models to movement parameters of telemetered fishes

Background Consumption of telemetered fishes by piscivores is problematic for telemetry studies because tag detections from the piscivore could introduce bias into the analysis of telemetry data. We illustrate the use of multivariate mixture models to estimate group membership (smolt or predator) of telemetered juvenile Chinook salmon ( Oncorhynchus tshawytscha ), juvenile steelhead trout ( O. mykiss ), striped bass ( Morone saxatilis ), smallmouth bass ( Micropterus dolomieu ) and spotted bass ( M. punctulatus ) in the Sacramento River, CA, USA. First, we estimated two types of track statistics from spatially explicit two-dimensional movement tracks of telemetered fishes: the L&eacute;vy exponent ( b ) and tortuosity ( &tau; ). Second, we hypothesized that the distribution of each track statistic would differ between predators and smolts. To estimate the distribution of track statistics for putative predators and smolts, we fitted a bivariate normal mixture model to the mixed distribution of track statistics. Lastly, we classified each track as a smolt or predator using parameter estimates from the mixture model to estimate the probability that each track was that of a predator or smolt. Results Tracks classified as predators exhibited movement that was tortuous and consistent with prey searching tactics, whereas tracks classified as smolts were characterized by directed, linear downstream movement. The estimated mean tortuosity was 0.565 (SD&thinsp;=&thinsp;0.07) for predators and 0.944 (SD&thinsp;=&thinsp;0.001) for smolts. The estimated mean L&eacute;vy exponent was 1.84 (SD&thinsp;=&thinsp;1.23) for predators and -0.304 (SD&thinsp;=&thinsp;1.46) for smolts. We correctly classified 90% of the Micropterus species and 72% of the striped bass as predators. For tagged smolts, 80% of Chinook salmon and 74% of steelhead trout were not classified as predators. Conclusions Mixture models proved valuable as a means to differentiate between salmonid smolts and predators that consumed salmonid smolts. However, successful application of this method requires that telemetered fishes and their predators exhibit measurable differences in movement behavior. Our approach is flexible, allows inclusion of multiple track statistics and improves upon rule-based manual classification methods.

California

Estimates of diffuse phosphorus sources in surface waters of the United States using a spatially referenced watershed model

The statistical watershed model SPARROW (SPAtially Referenced Regression On Watershed attributes) was used to estimate the sources and transport of total phosphorus (TP) in surface waters of the United States. We calibrated the model using stream measurements of TP from 336 watersheds of mixed land use and spatial data on topography, soils, stream hydrography, and land use (agriculture, forest, shrub/grass, urban). The model explained 87% of the spatial variability in log transformed stream TP flux (kg yr -1 ). Predictions of stream yield ( kg ha -1 yr -1 ) were typically within 45% of the observed values at the monitoring sites. The model identified appreciable effects of soils, streams, and reservoirs on TP transport, The estimated aquatic rates of phosphorus removal declined with increasing stream size and rates of water flushing in reservoirs (i.e. areal hydraulic loads). A phosphorus budget for the 2.9 million km2 Mississippi River Basin provides a detailed accounting of TP delivery to streams, the removal of TP in surface waters, and the stream export of TP from major interior watersheds for sources associated with each land-use type.

Water Science and Technology