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At least 523 records · Page 29Linked to original sources

USGS2005

A spatial model of potential jaguar habitat in Arizona

The jaguar (Panthera onca) is an endangered species that occasionally visits the southwestern United States from Mexico. The number of jaguar sightings per decade has declined over the last 100 years in Arizona, USA, raising conservation concerns for the species at a local and national level. In 1997, state, federal, and local governments with land-management responsibilities agreed to characterize and identify potential jaguar habitat in Arizona and New Mexico. Specifically, the objectives of our analysis were 2-fold: (1) characterize potential jaguar habitat in Arizona from historic sighting records and (2) create a statewide habitat suitability map. We used a Geographic Information System (GIS) to characterize potential jaguar habitat by overlaying historic jaguar sightings (25) on landscape and habitat features believed important (e.g., vegetation biomes and series, elevation, terrain ruggedness, proximity to perennial or intermittent water sources, human density). The amount of Arizona (%) identified as potential jaguar habitat ranged from 21% to 30% depending on the input variables. Most jaguar sightings were in scrub grasslands between 1,220 and 1,829-m elevation in southeastern Arizona, in intermediately to extremely rugged terrain, and within 10 km of a water source. Conservation efforts should focus on protecting the most suitable jaguar habitat in southeastern Arizona (i.e., Santa Cruz, Pima, Cochise, Pinal, Graham counties), travel corridors within and outside Arizona, and jaguar habitat in the Sierra Madres of Sonora, Mexico.

Journal of Wildlife Management↗

USGS2010

Summary and statistical analysis of precipitation and groundwater data for Brunswick County, North Carolina, Water Year 2008

Groundwater conditions in Brunswick County, North Carolina, have been monitored continuously since 2000 through the operation and maintenance of groundwater-level observation wells in the surficial, Castle Hayne, and Peedee aquifers of the North Atlantic Coastal Plain aquifer system. Groundwater-resource conditions for the Brunswick County area were evaluated by relating the normal range (25th to 75th percentile) monthly mean groundwater-level and precipitation data for water years 2001 to 2008 to median monthly mean groundwater levels and monthly sum of daily precipitation for water year 2008. Summaries of precipitation and groundwater conditions for the Brunswick County area and hydrographs and statistics of continuous groundwater levels collected during the 2008 water year are presented in this report. Groundwater levels varied by aquifer and geographic location within Brunswick County, but were influenced by drought conditions and groundwater withdrawals. Water levels were normal in two of the eight observation wells and below normal in the remaining six wells. Seasonal Kendall trend analysis performed on more than 9 years of monthly mean groundwater-level data collected in an observation well located within the Brunswick County well field indicated there is a strong downward trend, with water levels declining at a rate of about 2.2 feet per year.

North Carolina↗

USGS2008

Characteristics and Classification of Least Altered Streamflows in Massachusetts

Streamflow records from 85 streamflow-gaging stations at which streamflows were considered to be least altered were used to characterize natural streamflows within southern New England. Period-of-record streamflow data were used to determine annual hydrographs of median monthly flows. The shapes and magnitudes of annual hydrographs of median monthly flows, normalized by drainage area, differed among stations in different geographic areas of southern New England. These differences were gradational across southern New England and were attributed to differences in basin and climate characteristics. Period-of-record streamflow data were also used to analyze the statistical properties of daily streamflows at 61 stations across southern New England by using L-moment ratios. An L-moment ratio diagram of L-skewness and L-kurtosis showed a continuous gradation in these properties between stations and indicated differences between base-flow dominated and runoff-dominated rivers. Streamflow records from a concurrent period (1960-2004) for 61 stations were used in a multivariate statistical analysis to develop a hydrologic classification of rivers in southern New England. Missing records from 46 of these stations were extended by using a Maintenance of Variation Extension technique. The concurrent-period streamflows were used in the Indicators of Hydrologic Alteration and Hydrologic Index Tool programs to determine 224 hydrologic indices for the 61 stations. Principal-components analysis (PCA) was used to reduce the number of hydrologic indices to 20 that provided nonredundant information. The PCA also indicated that the major patterns of variability in the dataset are related to differences in flow variability and low-flow magnitude among the stations. Hierarchical cluster analysis was used to classify stations into groups with similar hydrologic properties. The cluster analysis classified rivers in southern New England into two broad groups: (1) base-flow dominated rivers, whose statistical properties indicated less flow variability and high magnitudes of low flow, and (2) runoff-dominated rivers, whose statistical properties indicated greater flow variability and lower magnitudes of low flow. A four-cluster classification further classified the runoff-dominated streams into three groups that varied in gradient, elevation, and differences in winter streamflow conditions: high-gradient runoff-dominated rivers, northern runoff-dominated rivers, and southern runoff-dominated rivers. A nine-cluster division indicated that basin size also becomes a distinguishing factor among basins at finer levels of classification. Smaller basins (less than 10 square miles) were classified into different groups than larger basins. A comparison of station classifications indicated that a classification based on multiple hydrologic indices that represent different aspects of the flow regime did not result in the same classification of stations as a classification based on a single type of statistic such as a monthly median. River basins identified by the cluster analysis as having similar hydrologic properties tended to have similar basin and climate characteristics and to be in close proximity to one another. Stations were not classified in the same cluster on the basis of geographic location alone; as a result, boundaries cannot be drawn between geographic regions with similar streamflow characteristics. Rivers with different basin and climate characteristics were classified in different clusters, even if they were in adjacent basins or upstream and downstream within the same basin.

Scientific Investigations Report↗

USGS2017

Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales

The distribution and abundance of pinyon ( Pinus monophylla ) and juniper ( Juniperus osteosperma, J. occidentalis ) trees (hereinafter, "pinyon-juniper") in sagebrush ( Artemisia spp.) ecosystems of the Great Basin in the Western United States has increased substantially since the late 1800s. Distributional expansion and infill of pinyon-juniper into sagebrush ecosystems threatens the ecological function and economic viability of these ecosystems within the Great Basin, and is now a major contemporary challenge facing land and wildlife managers. Particularly, pinyon-juniper encroachment into intact sagebrush ecosystems has been identified as a primary threat facing populations of greater sage-grouse ( Centrocercus urophasianus ; hereinafter, "sage-grouse"), which is a sagebrush obligate species. Even seemingly innocuous scatterings of isolated pinyon-juniper in an otherwise intact sagebrush landscape can negatively affect survival and reproduction of sage-grouse. Therefore, accurate and high-resolution maps of pinyon-juniper distribution and abundance (indexed by canopy cover) across broad geographic extents would help guide land management decisions that better target areas for pinyon-juniper removal projects (for example, fuel reduction, habitat improvement for sage-grouse, and other sagebrush species) and facilitate science that further quantifies ecological effects of pinyon-juniper encroachment on sage-grouse populations and sagebrush ecosystem processes . Hence, we mapped pinyon-juniper (referred to as conifers for actual mapping) at a 1 × 1-meter (m) high resolution across the entire range of previously mapped sage-grouse habitat in Nevada and northeastern California. We used digital orthophoto quad tiles from National Agriculture Imagery Program (2010, 2013) as base imagery, and then classified conifers using automated feature extraction methodology with the program Feature Analyst ™ . This method relies on machine learning algorithms that extract features from imagery based on their spectral and spatial signatures. We classified conifers in 6,230 tiles and then tested for errors of omission and commission using confusion matrices. Accuracy ranged from 79.1 to 96.8, with an overall accuracy of 84.3 percent across all mapped areas. An estimated accuracy coefficient (kappa) indicated substantial to nearly perfect agreement, which varied across mapped areas. For this mapping process across the entire mapping extent, four sets of products are available at https://doi.org/10.5066/F7348HVC , including (1) a shapefile representing accuracy results linked to mapping subunits; (2) binary rasters representing conifer presence or absence at a 1 × 1 m resolution; (3) a 30 × 30 m resolution raster representing percentages of conifer canopy cover within each cell from 0 to 100; and (4) 1 × 1 m resolution canopy cover classification rasters derived from a 50-m-radius moving window analysis. The latter two products can be reclassified in a geographic information system (GIS) into user-specified bins to meet different objectives, which include approximations for phases of encroachment. These products complement, and in some cases improve upon, existing conifer maps in the Western United States, and will help facilitate sage-grouse habitat management and sagebrush ecosystem restoration.

California, Nevada↗

USGS2024

Quantitative risk of earthquake disruption to global copper and rhenium supply

Earthquakes have the potential to substantially affect mining operations, potentially leading to supply chain disruptions and adversely affecting the global economy. This study explores the quantification of earthquake risk to copper and rhenium commodity supply by examining the spatial concentration of high earthquake hazard areas and the commodity-specific mining, smelting, and refining operations across the globe. Because many of the largest facilities are concentrated geographically near the highly seismic regions of South America, East Asia, and the Pacific, there is a potential for cascading effects on the entire supply chain. The analysis indicates that the expected annual disruption of global production is 0.3–1.1 percent for copper mines, 1.8–4.0 percent for smelters, and 1.5–3.3 percent for refineries. Expected annual disruption of global rhenium production capacity is 0.32–1.32 percent. The research highlights that the potential lost revenue from earthquake disruptions is from $315 million to $1.29 billion for copper mining, $1.92 billion to $4.33 billion for copper smelting, $2.06 billion to $4.52 billion for copper refining, and $337,000 to $1.40 million for rhenium production capacity.

Open-File Report↗

USGS1998

Probability of detecting atrazine/desethyl-atrazine and elevated concentrations of nitrate (NO2+NO3-N) in ground water in the Idaho part of the upper Snake River basin

Draft Federal regulations may require that each State develop a State Pesticide Management Plan for the herbicides atrazine, alachlor, cyanazine, metolachlor, and simazine. This study developed maps that the Idaho State Department of Agriculture might use to predict the probability of detecting atrazine and desethyl-atrazine (a breakdown product of atrazine) in ground water in the Idaho part of the upper Snake River Basin. These maps can be incorporated in the State Pesticide Management Plan and help provide a sound hydrogeologic basis for atrazine management in the study area. Maps showing the probability of detecting atrazine/desethyl-atrazine in ground water were developed as follows: (1) Ground-water monitoring data were overlaid with hydrogeologic and anthropogenic data using a geographic information system to produce a data set in which each well had corresponding data on atrazine use, depth to ground water, geology, land use, precipitation, soils, and well depth. These data then were downloaded to a statistical software package for analysis by logistic regression. (2) Individual (univariate) relations between atrazine/desethyl-atrazine in ground water and atrazine use, depth to ground water, geology, land use, precipitation, soils, and well depth data were evaluated to identify those independent variables significantly related to atrazine/ desethyl-atrazine detections. (3) Several preliminary multivariate models with various combinations of independent variables were constructed. (4) The multivariate models which best predicted the presence of atrazine/desethyl-atrazine in ground water were selected. (5) The multivariate models were entered into the geographic information system and the probability maps were constructed. Two models which best predicted the presence of atrazine/desethyl-atrazine in ground water were selected; one with and one without atrazine use. Correlations of the predicted probabilities of atrazine/desethyl-atrazine in ground water with the percent of actual detections were good; r-squared values were 0.91 and 0.96, respectively. Models were verified using a second set of groundwater quality data. Verification showed that wells with water containing atrazine/desethyl-atrazine had significantly higher probability ratings than wells with water containing no atrazine/desethylatrazine (p <0.002). Logistic regression also was used to develop a preliminary model to predict the probability of nitrite plus nitrate as nitrogen concentrations greater than background levels of 2 milligrams per liter. A direct comparison between the atrazine/ desethyl-atrazine and nitrite plus nitrate as nitrogen probability maps was possible because the same ground-water monitoring, hydrogeologic, and anthropogenic data were used to develop both maps. Land use, precipitation, soil hydrologic group, and well depth were significantly related with atrazine/desethyl-atrazine detections. Depth to water, land use, and soil drainage were signifi- cantly related with elevated nitrite plus nitrate as nitrogen concentrations. The differences between atrazine/desethyl-atrazine and nitrite plus nitrate as nitrogen relations were attributed to differences in chemical behavior of these compounds in the environment and possibly to differences in the extent of use and rates of their application.

Idaho↗

USGS2011

Using terrestrial light detection and ranging (lidar) technology for land-surface analysis in the Southwest

Emerging technologies provide scientists with methods to measure Earth processes in new ways. One of these technologies--ultra-high-resolution, ground-based light detection and ranging (lidar)--is being used by USGS Western Geographic Science Center scientists to characterize the role of wind and fire processes in shaping desert landscapes of the Southwest United States.

Fact Sheet↗

USGS2006

Analysis of sensitivity of simulated recharge to selected parameters for seven watersheds modeled using the precipitation-runoff modeling system

Recharge is a vital component of the ground-water budget and methods for estimating it range from extremely complex to relatively simple. The most commonly used techniques, however, are limited by the scale of application. One method that can be used to estimate ground-water recharge includes process-based models that compute distributed water budgets on a watershed scale. These models should be evaluated to determine which model parameters are the dominant controls in determining ground-water recharge. Seven existing watershed models from different humid regions of the United States were chosen to analyze the sensitivity of simulated recharge to model parameters. Parameter sensitivities were determined using a nonlinear regression computer program to generate a suite of diagnostic statistics. The statistics identify model parameters that have the greatest effect on simulated ground-water recharge and that compare and contrast the hydrologic system responses to those parameters. Simulated recharge in the Lost River and Big Creek watersheds in Washington State was sensitive to small changes in air temperature. The Hamden watershed model in west-central Minnesota was developed to investigate the relations that wetlands and other landscape features have with runoff processes. Excess soil moisture in the Hamden watershed simulation was preferentially routed to wetlands, instead of to the ground-water system, resulting in little sensitivity of any parameters to recharge. Simulated recharge in the North Fork Pheasant Branch watershed, Wisconsin, demonstrated the greatest sensitivity to parameters related to evapotranspiration. Three watersheds were simulated as part of the Model Parameter Estimation Experiment (MOPEX). Parameter sensitivities for the MOPEX watersheds, Amite River, Louisiana and Mississippi, English River, Iowa, and South Branch Potomac River, West Virginia, were similar and most sensitive to small changes in air temperature and a user-defined flow routing parameter. Although the primary objective of this study was to identify, by geographic region, the importance of the parameter value to the simulation of ground-water recharge, the secondary objectives proved valuable for future modeling efforts. The value of a rigorous sensitivity analysis can (1) make the calibration process more efficient, (2) guide additional data collection, (3) identify model limitations, and (4) explain simulated results.

Scientific Investigations Report↗

USGS2006

Modeling regional initiation of rainfall-induced shallow landslides in the eastern Umbria Region of central Italy

We model the rainfall-induced initiation of shallow landslides over a broad region using a deterministic approach, the Transient Rainfall Infiltration and Grid-based Slope-stability (TRIGRS) model that couples an infinite-slope stability analysis with a one-dimensional analytical solution for transient pore pressure response to rainfall infiltration. This model permits the evaluation of regional shallow landslide susceptibility in a Geographic Information System framework, and we use it to analyze susceptibility to shallow landslides in an area in the eastern Umbria Region of central Italy. As shown on a landslide inventory map produced by the Italian National Research Council, the area has been affected in the past by shallow landslides, many of which have transformed into debris flows. Input data for the TRIGRS model include time-varying rainfall, topographic slope, colluvial thickness, initial water table depth, and material strength and hydraulic properties. Because of a paucity of input data, we focus on parametric analyses to calibrate and test the model and show the effect of variation in material properties and initial water table conditions on the distribution of simulated instability in the study area in response to realistic rainfall. Comparing the results with the shallow landslide inventory map, we find more than 80% agreement between predicted shallow landslide susceptibility and the inventory, despite the paucity of input data.

Landslides↗

USGS1989

Genetic differentiation among lake trout strains stocked into Lake Ontario

The lake trout Salvelinus namaycush is the focus of an international effort by fishery management agencies to restore this once‐native species to Lake Ontario. Evaluation of reproductive success and comparisons among stocked lake trout strains require genetic markers. We used allozyme variation to make genetic comparisons among strains of lake trout stocked into Lake Ontario. Forty‐two proteins presumed to be encoded by 102 loci were resolved electrophoretically; 18 loci were polymorphic. Among 16 samples from five principal origins (Finger Lakes, Great Lakes basin, northern New York–Adirondack, Manitoba, and mixed origins–Lake Ontario), the average observed heterozygosity was 0.029, and the proportion of polymorphic loci was 0.125. Significant differences occurred among the 16 samples at all 18 possible locus comparisons. The average F st value was 0.14. Hierarchical analysis indicated that the variation among samples within an origin was greatest within the Great Lakes basin, which included the greatest number of samples and represented the largest geographic area. Most variation observed among samples, however, occurred among origins. The 1983 and 1984 hatchery year classes produced from the 1978 Seneca brood stock did not differ significantly. The 1981 Seneca brood stock more closely resembled the sample of wild lake trout from Seneca Lake than progeny from the 1978 brood stock. The closer affinity of the 1981 brood stock to wild Seneca lake trout may be due to the larger number of wild adults from Seneca Lake used to found these fish than was used to establish the 1978 brood stock. Siscowet (“fat”) and “lean” lake trout from Lake Superior were significantly different from each other; however, the level of variation between them was not greater than that among samples from other origins, and thus did not support recognition of siscowet lake trout as a distinct subspecies. The Jenny Lake strain possessed a genetic affinity to the siscowet sample from Lake Superior. Historical reports about the origin of this strain suggest that siscowet lake trout from northern Lake Michigan may have been used to found this strain. Therefore, the Jenny Lake strain may serve as a gene source for the establishment of the siscowet lake trout in Lake Ontario. The substantial differentiation among lake trout strains reported here supports the feasibility of using allozyme markers to identify the parental sources of naturally produced young.

Lake Ontario↗

USGS2008

Advanced Remote Sensing Research

'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).

Fact Sheet↗

USGS2007

Development of an interactive shoreline management tool for the lower Wood River Valley, Oregon, phase 1: Stage-volume and stage-area relations

This report presents the parcel and inundation area geographic information system (GIS) layers for various surface-water stages. It also presents data tables containing the water stage, inundation area, and water volume relations developed from analysis of detailed land surface elevation derived from Light Detection and Ranging (LiDAR) data recently collected for the Wood River Valley at the northern margin of Agency Lake in Klamath County, Oregon. Former shoreline wetlands that have been cut off from Upper Klamath and Agency Lakes by dikes might in the future be reconnected to Upper Klamath and Agency Lakes by breaching the dikes. Issues of interest associated with restoring wetlands in this way include the area that will be inundated, the volume of water that may be stored, the change in wetland habitat, and the variation in these characteristics as surface-water stage is changed. Products from this analysis can assist water managers in assessing the effect of breaching dikes and changing surface-water stage. The study area is in the approximate former northern margins of Upper Klamath and Agency Lakes in the Wood River Valley.

Oregon↗

USGS1983

The role of digital cartographic data in the geosciences

The increasing demand of the Nation's natural resource developers for the manipulation, analysis, and display of large quantities of earth-science data has necessitated the use of computers and the building of geoscience information systems. These systems require, in digital form, the spatial data on map products. The basic cartographic data shown on quadrangle maps provide a foundation for the addition of geological and geophysical data. If geoscience information systems are to realize their full potential, large amounts of digital cartographic base data must be available. A major goal of the U.S. Geological Survey is to create, maintain, manage, and distribute a national cartographic and geographic digital database. This unified database will contain numerous categories (hydrography, hypsography, land use, etc.) that, through the use of standardized data-element definitions and formats, can be used easily and flexibly to prepare cartographic products and perform geoscience analysis. ?? 1983.

Computers & Geosciences↗

USGS2012

National assessment of shoreline change: Historical shoreline change in the Hawaiian Islands

Sandy beaches of the United States are some of the most popular tourist and recreational destinations. Coastal property constitutes some of the most valuable real estate in the country. Beaches are an ephemeral environment between water and land with unique and fragile natural ecosystems that have evolved in equilibrium with the ever-changing winds, waves, and water levels. Beachfront lands are the site of intense residential and commercial development even though they are highly vulnerable to several natural hazards, including marine inundation, flooding and drainage problems, effects of storms, sea-level rise, and coastal erosion. Because the U.S. population continues to shift toward the coast where valuable coastal property is vulnerable to erosion, the U.S. Geological Survey (USGS) is conducting a national assessment of coastal change. One aspect of this effort, the National Assessment of Shoreline Change, uses shoreline position as a proxy for coastal change because shoreline position is one of the most commonly monitored indicators of environmental change (for example, Fletcher, 1992; Dolan and others, 1991; Douglas and others, 1998; Galgano and others, 1998). Additionally, the National Research Council (1990) recommended the use of historical shoreline analysis in the absence of a widely accepted model of shoreline change. A principal purpose of the USGS shoreline change research is to develop a common methodology so that shoreline change analyses for the continental U.S., portions of Hawaii, and Alaska can be updated periodically in a consistent and systematic manner. The primary objectives of this study were to (1) develop and implement improved methods of assessing and monitoring shoreline movement, and (2) improve current understanding of the processes controlling shoreline movement. Achieving these ongoing long-term objectives requires research that (1) examines the original sources of shoreline data (for example, maps, air photos, global positioning system (GPS), Light Detection and Ranging (lidar)); (2) evaluates the utility of different shoreline proxies (for example, geomorphic feature, water mark, tidal datum, elevation), including the errors associated with each; (3) investigates bias and potential errors associated with integrating different shoreline proxies from different sources; (4) develops standard, uniform methods of shoreline change analysis; (5) examines the effects of human activities on shoreline movement and rates of change; and (6) investigates alternative mathematical methods for calculating historical rates of change and uncertainties associated with them. This report summarizes historical shoreline changes on the three most densely populated islands of the eight main Hawaiian Islands: Kauai, Oahu, and Maui. The report emphasizes the hazard from “chronic” (decades to centuries) erosion at regional scales and strives to relate this hazard to the body of knowledge regarding coastal geology of Hawaii because of its potential impact on natural resources, the economy, and society. Results are organized by coastal regions (island side) and sub-regions (common littoral characteristics). This report of Hawaii coasts is part of a series of reports that include text summarizing methods, results, and implications of the results. In addition, geographic information system (GIS) data used in the analyses are made available for download (Romine and others, 2012). The rates of shoreline change and products presented in this report are not intended for site-specific analysis of shoreline movement, nor are they intended to replace any official source of shoreline change information identified by local or State government agencies, or other Federal entities that are used for regulatory purposes. Rates of shoreline change presented herein may differ from other published rates, and differences do not necessarily indicate that the other rates are inaccurate. Some discrepancies are to be expected, considering the many possible ways of determining shoreline positions and rates of change, and the inherent uncertainty in calculating these rates. Rates of shoreline change presented in this report represent shoreline movement under past conditions and are not intended for use in predicting future shoreline positions or future rates of shoreline change.

Hawai'i↗

USGS1998

Geospatial Technology Applications and Infrastructure in the Biological Resources Division

Executive Summary -- Automated spatial processing technology such as geographic information systems (GIS), telemetry, and satellite-based remote sensing are some of the more recent developments in the long history of geographic inquiry. For millennia, humankind has endeavored to map the Earth's surface and identify spatial relationships. But the precision with which we can locate geographic features has increased exponentially with satellite positioning systems. Remote sensing, GIS, thematic mapping, telemetry, and satellite positioning systems such as the Global Positioning System (GPS) are tools that greatly enhance the quality and rapidity of analysis of biological resources. These technologies allow researchers, planners, and managers to more quickly and accurately determine appropriate strategies and actions. Researchers and managers can view information from new and varying perspectives using GIS and remote sensing, and GPS receivers allow the researcher or manager to identify the exact location of interest. These geospatial technologies support the mission of the U.S. Geological Survey (USGS) Biological Resources Division (BRD) and the Strategic Science Plan (BRD 1996) by providing a cost-effective and efficient method for collection, analysis, and display of information. The BRD mission is 'to work with others to provide the scientific understanding and technologies needed to support the sound management and conservation of our Nation's biological resources.' A major responsibility of the BRD is to develop and employ advanced technologies needed to synthesize, analyze, and disseminate biological and ecological information. As the Strategic Science Plan (BRD 1996) states, 'fulfilling this mission depends on effectively balancing the immediate need for information to guide management of biological resources with the need for technical assistance and long-range, strategic information to understand and predict emerging patterns and trends in ecological systems.' Information sharing plays a key role in nearly everything BRD does. The Strategic Science Plan discusses the need to (1) develop tools and standards for information transfer, (2) disseminate information, and (3) facilitate effective use of information. This effort centers around the National Biological Information Infrastructure (NBII) and the National Spatial Data Infrastructure (NSDI), components of the National Information Infrastructure. The NBII and NSDI are distributed electronic networks of biological and geographical data and information, as well as tools to help users around the world easily find and retrieve the biological and geographical data and information they need. The BRD is responsible for developing scientifically and statistically reliable methods and protocols to assess the status and trends of the Nation's biological resources. Scientists also conduct important inventory and monitoring studies to maintain baseline information on these same resources. Research on those species for which the Department of the Interior (DOI) has trust responsibilities (including endangered species and migratory species) involves laboratory and field studies of individual animals and the environments in which they live. Researchboth tactical and strategicis conducted at the BRD's 17 science centers and 81 field stations, 54 Cooperative Fish and Wildlife Research Units in 40 states, and at 11 former Cooperative Park Study Units. Studies encompass fish, birds, mammals, and plants, as well as their ecosystems and the surrounding landscape. Biological Resources Division researchers use a variety of scientific tools in their endeavors to understand the causes of biological and ecological trends. Research results are used by managers to predict environmental changes and to help them take appropriate measures to manage resources effectively. The BRD Geospatial Technology Program facilitates the collection, analysis, and dissemination of data and informat

Information and Technology Report↗

USGS2014

Assessing potential effects of highway runoff on receiving-water quality at selected sites in Oregon with the Stochastic Empirical Loading and Dilution Model (SELDM)

In 2012, the U.S. Geological Survey and the Oregon Department of Transportation began a cooperative study to demonstrate use of the Stochastic Empirical Loading and Dilution Model (SELDM) for runoff-quality analyses in Oregon. SELDM can be used to estimate stormflows, constituent concentrations, and loads from the area upstream of a stormflow discharge site, from the site of interest and in the receiving waters downstream of the discharge. SELDM also can be used to assess the potential effectiveness of best management practices (BMP) for mitigating potential effects of runoff in receiving waters. Nominally, SELDM is a highway-runoff model, but it is well suited for analysis of runoff from other land uses as well. This report provides case studies and examples to demonstrate stochastic-runoff modeling concepts and to demonstrate application of the model. Basin characteristics from six Oregon highway study sites were used to demonstrate various applications of the model. The highway catchment and upstream basin drainage areas of these study sites ranged from 3.85 to 11.83 acres and from 0.16 to 6.56 square miles, respectively. The upstream basins of two sites are urbanized, and the remaining four sites are less than 5 percent impervious. SELDM facilitates analysis by providing precipitation, pre-storm streamflow, and other variables by region or from hydrologically similar sites. In Oregon, there can be large variations in precipitation and streamflow among nearby sites. Therefore, spatially interpolated geographic information system data layers containing storm-event precipitation and pre-storm streamflow statistics specific to Oregon were created for the study using Kriging techniques. Concentrations and loads of cadmium, chloride, chromium, copper, iron, lead, nickel, phosphorus, and zinc were simulated at the six Oregon highway study sites by using statistics from sites in other areas of the country. Water‑quality datasets measured at hydrologically similar basins in the vicinity of the study sites in Oregon were selected and compiled to estimate stormflow-quality statistics for the upstream basins. The quality of highway runoff and some upstream stormflow constituents were simulated by using statistical moments (average, standard deviation, and skew) of the logarithms of data. Some upstream stormflow constituents were simulated by using transport curves, which are relations between stormflow and constituent concentrations. Stochastic analyses were done by using SELDM to demonstrate use of the model and to illustrate the types of information that stochastic analyses may provide: 1. An analysis was done to demonstrate use of dilution factors as an initial reconnaissance tool for comparing relative risk among sites. 2. An analysis of hardness-dependent, water-quality criteria was done to illustrate the effects of variations in hardness and flow on the application and interpretation of such criteria. This analysis shows that hardness-dependent criteria can vary by an order of magnitude among storm events because hardness is diluted by stormflows. 3. An analysis of uncertainties in input and output values was done to demonstrate that properly selected robust datasets are needed to represent conditions at a site of interest. This analysis shows that the rate of water-quality exceedances that are measured or simulated may depend on sample size and the luck of the draw. 4. An analysis was done to demonstrate that SELDM and other Monte Carlo models may generate extreme values from input statistics, which may or may not be feasible based on physicochemical or hydrological limits. 5. An analysis of BMP modeling methods was done to demonstrate use of the model for estimating treatment requirements for meeting water-quality objectives. 6. An analysis of the use of grab sampling and nonstochastic upstream modeling methods was done to evaluate the potential effects on modeling outcomes. Additional analyses using surrogate water-quality datasets for the upstream basin and highway catchment were provided for six Oregon study sites to illustrate the risk-based information that SELDM will produce. These analyses show that the potential effects of highway runoff on receiving-water quality downstream of the outfall depends on the ratio of drainage areas (dilution), the quality of the receiving water upstream of the highway, and the concentration of the criteria of the constituent of interest. These analyses also show that the probability of exceeding a water-quality criterion may depend on the input statistics used, thus careful selection of representative values is important.

Oregon↗

USGS1989

Descriptive statistics and spatial distributions of geochemical variables associated with manganese oxide-rich phases in the northern Pacific

Tables, graphs, and maps are used to portray the frequency characteristics and spatial distribution of manganese oxide-rich phase geochemical data, to characterize the northern Pacific in terms of publicly available nodule geochemical data, and to develop data portrayal methods that will facilitate data analysis. Source data are a subset of the Scripps Institute of Oceanography's Sediment Data Bank. The study area is bounded by 0° N., 40° N., 120° E., and 100° W. and is arbitrarily subdivided into 14-20°x 20° geographic subregions. Frequency distributions of trace metals characterized in the original raw data are graphed as ogives, and salient parameters are tabulated. All variables are transformed to enrichment values relative to median concentration within their host subregions. Scatter plots of all pairs of original variables and their enrichment transforms are provided as an aid to the interpretation of correlations between variables. Gridded spatial distributions of all variables are portrayed as gray-scale maps. The use of tables and graphs to portray frequency statistics and gray-scale maps to portray spatial distributions is an effective way to prepare for and facilitate multivariate data analysis.

Bulletin↗

USGS2018

Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change

Unique responses to climate change can occur across intraspecific levels, resulting in individualistic adaptation or movement patterns among populations within a given species. Thus, the need to model potential responses among genetically distinct populations within a species is increasingly recognized. However, predictive models of future distributions are regularly fit at the species level, often because intraspecific variation is unknown or is identified only within limited sample locations. In this study, we considered the role of intraspecific variation to shape the geographic distribution of ponderosa pine ( Pinus ponderosa ), an ecologically and economically important tree species in North America. Morphological and genetic variation across the distribution of ponderosa pine suggest the need to model intraspecific populations: the two varieties (var. ponderosa and var. scopulorum ) and several haplotype groups within each variety have been shown to occupy unique climatic niches, suggesting populations have distinct evolutionary lineages adapted to different environmental conditions. We utilized a recently-available, geographically-widespread dataset of intraspecific variation (haplotypes) for ponderosa pine and a recently-devised lineage distance modeling approach to derive additional, likely intraspecific occurrence locations. We confirmed the relative uniqueness of each haplotype-climate relationship using a niche-overlap analysis, and developed ecological niche models (ENMs) to project the distribution for two varieties and eight haplotypes under future climate forecasts. Future projections of haplotype niche distributions generally revealed greater potential range loss than predicted for the varieties. This difference may reflect intraspecific responses of distinct evolutionary lineages. However, directional trends are generally consistent across intraspecific levels, and include a loss of distributional area and an upward shift in elevation. Our results demonstrate the utility in modeling intraspecific response to changing climate and they inform management and conservation strategies, by identifying haplotypes and geographic areas that may be most at risk, or most secure, under projected climate change.

Systematic Biology↗