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At least 217 records · Page 12Linked to original sources

USGS2003

Inferences about nested subsets structure when not all species are detected

Comparisons of species composition among ecological communities of different size have often provided evidence that the species in communities with lower species richness form nested subsets of the species in larger communities. In the vast majority of studies, the question of nested subsets has been addressed using information on presence-absence, where a '0' is interpreted as the absence of a given species from a given location. Most of the methodological discussion in earlier studies investigating nestedness concerns the approach to generation of model-based matrices. However, it is most likely that in many situations investigators cannot detect all the species present in the location sampled. The possibility that zeros in incidence matrices reflect nondetection rather than absence of species has not been considered in studies addressing nested subsets, even though the position of zeros in these matrices forms the basis of earlier inference methods. These sampling artifacts are likely to lead to erroneous conclusions about both variation over space in species richness and the degree of similarity of the various locations. Here we propose an approach to investigation of nestedness, based on statistical inference methods explicitly incorporating species detection probability, that take into account the probabilistic nature of the sampling process. We use presence-absence data collected under Pollock?s robust capture-recapture design, and resort to an estimator of species richness originally developed for closed populations to assess the proportion of species shared by different locations. We develop testable predictions corresponding to the null hypothesis of a nonnested pattern, and an alternative hypothesis of perfect nestedness. We also present an index for assessing the degree of nestedness of a system of ecological communities. We illustrate our approach using avian data from the North American Breeding Bird Survey collected in Florida Keys.

Florida↗

USGS2007

Drought-trigger ground-water levels in Chester County, Pennsylvania, for the period of record ending May 2006

This report presents the results of a study by the U.S. Geological Survey (USGS), in cooperation with the Chester County Water Resources Authority (CCWRA), to update the drought-trigger water levels for the Chester County observation-well network. The Chester County observation-well network was established in 1973 through a cooperative agreement between the CCWRA and the USGS to monitor local ground-water levels and trends and to determine drought conditions. In 1990 and again in 1997, drought-warning and drought-emergency water-level triggers were determined for the majority of wells in the existing Chester County observation-well network of 23 wells. Since 1997, the Chester County observation-well network expanded to 29 wells, some of the original wells were destroyed, and additional monthly water-level observations were made to allow for better statistical relations. Because of these changes, new statistics for water-level triggers were required. For this study, 19 of the 29 wells in the observation-well network were used to compute drought-trigger water levels. An additional 'drought-watch water-level trigger' category was developed to make the Chester County drought-trigger water-level categories consistent with those implemented by the Pennsylvania Department of Environmental Protection (PaDEP). The three drought-trigger water-level categories, as defined by PaDEP are 1) 'drought watch' when at the 75th-percentile level; 2) 'drought warning' when at the 90th-percentile level; and 3) 'drought emergency' when at the 95th-percentile level. A revised methodology, resulting from longer periods of record representing ground-water and climatic conditions and changes in local water use, has resulted in some observed differences in drought-trigger water levels. A comparison of current drought-trigger water levels to those calculated in 1997 shows the largest mean annual change in percentile values was in northeastern Chester County. In this northeastern region, the average 90th-percentile water levels changed by as much as 1.84 feet (17.5 percent) from those determined in 1997. Other observation wells in the county generally exhibited a smaller mean annual change in percentile values; a typical change was less than 0.5 foot. While most revisions in the drought-trigger water levels generally can be attributed to different methodology and (or) the additional years of data collected under existing climatic conditions, anthropogenic influences, such as the cessation of local pumping stresses, also were shown to cause changes in the drought-trigger water levels determined in this study.

Pennsylvania↗

USGS2025

One-hundred fundamental, open questions to integrate methodological approaches in lake ice research

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real-time in situ monitoring, remote sensing, and modeling. The recent progress in low-power in situ sensing and data telemetry allows continuous tracing of under-ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process-based or statistical data-driven), with the recent technological advancements and integration of machine learning and hybrid process-based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less-studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross-pollination of intensive and extensive modes of study to broaden knowledge of the winter period.

Water Resources Research↗

USGS2021

A novel framework to predict relative habitat selection in aquatic systems: Applying machine learning and resource selection functions to acoustic telemetry data from multiple shark species

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and management strategies.

Buck Island Reef National Monument, St Croix↗

USGS2003

Statistical sampling to characterize recent United States land-cover change

The U.S. Geological Survey, in conjunction with the U.S. Environmental Protection Agency, is conducting a study focused on developing methods for estimating changes in land-cover and landscape pattern for the conterminous United States from 1973 to 2000. Eleven land-cover and land-use classes are interpreted from Landsat imagery for five sampling dates. Because of the high cost and potential effect of classification error associated with developing change estimates from wall-to-wall land-cover maps, a probability sampling approach is employed. The basic sampling unit is a 20 x 20 km area, and land cover is obtained for each 60 x 60 m pixel within the sampling unit. The sampling design is stratified based on ecoregions, and land-cover change estimates are constructed for each stratum. The sampling design and analyses are documented, and estimates of change accompanied by standard errors are presented to demonstrate the methodology. Analyses of the completed strata suggest that the sampling unit should be reduced to a 10 x 10 km block, and poststratified estimation and regression estimation are viable options to improve precision of estimated change. ?? 2003 Elsevier Inc. All rights reserved.

Remote Sensing of Environment↗

USGS2012

A comparison among observations and earthquake simulator results for the allcal2 California fault model

In order to understand earthquake hazards we would ideally have a statistical description of earthquakes for tens of thousands of years. Unfortunately the ∼100‐year instrumental, several 100‐year historical, and few 1000‐year paleoseismological records are woefully inadequate to provide a statistically significant record. Physics‐based earthquake simulators can generate arbitrarily long histories of earthquakes; thus they can provide a statistically meaningful history of simulated earthquakes. The question is, how realistic are these simulated histories? This purpose of this paper is to begin to answer that question. We compare the results between different simulators and with information that is known from the limited instrumental, historic, and paleoseismological data. As expected, the results from all the simulators show that the observational record is too short to properly represent the system behavior; therefore, although tests of the simulators against the limited observations are necessary, they are not a sufficient test of the simulators’ realism. The simulators appear to pass this necessary test. In addition, the physics‐based simulators show similar behavior even though there are large differences in the methodology. This suggests that they represent realistic behavior. Different assumptions concerning the constitutive properties of the faults do result in enhanced capabilities of some simulators. However, it appears that the similar behavior of the different simulators may result from the fault‐system geometry, slip rates, and assumed strength drops, along with the shared physics of stress transfer. This paper describes the results of running four earthquake simulators that are described elsewhere in this issue of Seismological Research Letters. The simulators ALLCAL (Ward, 2012), VIRTCAL (Sachs et al., 2012), RSQSim (Richards‐Dinger and Dieterich, 2012), and ViscoSim (Pollitz, 2012) were run on our most recent all‐California fault model, allcal2. With the exception of ViscoSim, which ran for 10,000 years, all the simulators ran for 30,000 years. Presentations containing content similar to this paper can be found at http://scec.usc.edu/research/eqsims/.

Seismological Research Letters↗

USGS1992

Sampling design for spatially distributed hydrogeologic and environmental processes

A methodology for the design of sampling networks over space is proposed. The methodology is based on spatial random field representations of nonhomogeneous natural processes, and on optimal spatial estimation techniques. One of the most important results of random field theory for physical sciences is its rationalization of correlations in spatial variability of natural processes. This correlation is extremely important both for interpreting spatially distributed observations and for predictive performance. The extent of site sampling and the types of data to be collected will depend on the relationship of subsurface variability to predictive uncertainty. While hypothesis formulation and initial identification of spatial variability characteristics are based on scientific understanding (such as knowledge of the physics of the underlying phenomena, geological interpretations, intuition and experience), the support offered by field data is statistically modelled. This model is not limited by the geometric nature of sampling and covers a wide range in subsurface uncertainties. A factorization scheme of the sampling error variance is derived, which possesses certain atttactive properties allowing significant savings in computations. By means of this scheme, a practical sampling design procedure providing suitable indices of the sampling error variance is established. These indices can be used by way of multiobjective decision criteria to obtain the best sampling strategy. Neither the actual implementation of the in-situ sampling nor the solution of the large spatial estimation systems of equations are necessary. The required values of the accuracy parameters involved in the network design are derived using reference charts (readily available for various combinations of data configurations and spatial variability parameters) and certain simple yet accurate analytical formulas. Insight is gained by applying the proposed sampling procedure to realistic examples related to sampling problems in two dimensions.

Advances in Water Resources↗

USGS2025

Marginalizing time in habitat selection and species distribution models improves inference

Aim Recent methodological advances for studying how animals move and use space with telemetry data have focused on fine-scale, more mechanistic inference. However, in many cases, researchers and managers remain interested in larger scale questions regarding species distribution and habitat use across study areas, landscapes, or seasonal ranges. Point processes offer a unified framework for many methods applied in studies of species distribution and resource selection; however, challenges remain in terms of dealing with temporal autocorrelation common in many types of telemetry data collected from animal locations. Innovation Space–time point processes (STPPs) have a unique property, in that marginalising time offers a connection between individual animal movement and broader point processes, yet this property has seen little attention in both statistical and applied research. In this paper, we first present some of the details of this marginalisation property and methods for applying marginalised STPPs (mSTTPs) to autocorrelated telemetry data and then apply a mSTTP in a case study on the summer space use and habitat selection of female caribou ( Rangifer tarandus ) in Denali National Park and Preserve, Alaska. Main Conclusions The case study demonstrated that an mSTPP approach can improve inference over other commonly used methods in terms of its ability to account for temporal autocorrelation and offers greater precision in parameter estimates and improved predictions of space use. As this method fits conveniently into the existing point process frameworks, it offers a practical solution to dealing with temporal autocorrelation inherent to many types of telemetry data when research questions center around broader scale patterns of animal habitat selection and space use.

Alaska↗

USGS2010

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↗

USGS2009

Trends in streamflow characteristics of selected sites in the Elkhorn River, Salt Creek, and Lower Platte River Basins, Eastern Nebraska, 1928-2004, and evaluation of streamflows in relation to instream-flow criteria, 1953-2004

The Nebraska Department of Natural Resources approved instream-flow appropriations on the Platte River to maintain fish communities, whooping crane roost habitat, and wet meadows used by several wild bird species. In the lower Platte River region, the Nebraska Game and Parks Commission owns an appropriation filed to maintain streamflow for fish communities between the Platte River confluence with the Elkhorn River and the mouth of the Platte River. Because Elkhorn River flow is an integral part of the flow in the reach addressed by this appropriation, the Upper Elkhorn and Lower Elkhorn Natural Resources Districts are involved in overall management of anthropogenic effects on the availability of surface water for instream requirements. The Physical Habitat Simulation System (PHABSIM) and other estimation methodologies were used previously to determine instream requirements for Platte River biota, which led to the filing of five water appropriations applications with the Nebraska Department of Natural Resources in 1993 by the Nebraska Game and Parks Commission. One of these requested instream-flow appropriations of 3,700 cubic feet per second was for the reach from the Elkhorn River to the mouth of the Platte River. Four appropriations were granted with modifications in 1998, by the Nebraska Department of Natural Resources. Daily streamflow data for the periods of record were summarized for 17 streamflow-gaging stations in Nebraska to evaluate streamflow characteristics, including low-flow intervals for consecutive durations of 1, 3, 7, 14, 30, 60, and 183 days. Temporal trends in selected streamflow statistics were not adjusted for variability in precipitation. Results indicated significant positive temporal trends in annual flow for the period of record at eight streamflow-gaging stations - Platte River near Duncan (06774000), Platte River at North Bend (06796000), Elkhorn River at Neligh (06798500), Logan Creek near Uehling (06799500), Maple Creek near Nickerson (06800000), Elkhorn River at Waterloo (06800500), Salt Creek at Greenwood (06803555), and Platte River at Louisville (06805500). In general, sites in the Elkhorn River Basin upstream from Norfolk showed fewer significant trends than did sites downstream from Norfolk and sites in the Platte River and Salt Creek basins, where trends in low flows also were positive. Historical Platte River streamflow records for the streamflow-gaging station at Louisville, Nebraska, were used to determine the number of days per water year (Sept. 30 to Oct. 1) when flows failed to satisfy the minimum criteria of the instream-flow appropriation prior to its filing in 1993. Before 1993, the median number of days the criteria were not satisfied was about 120 days per water year. During 1993 through 2004, daily mean flows at Louisville, Nebraska, have failed to satisfy the criteria for 638 days total (median value equals 21.5 days per year). Most of these low-flow intervals occurred in summer through early fall. For water years 1953 through 2004, of the discrete intervals when flow was less that the criteria levels, 61 percent were 3 days or greater in duration, and 38 percent were 7 days or greater in duration. The median duration of intervals of flow less than the criteria levels was 4 consecutive days during 1953 through 2004.

Nebraska↗

USGS2026

Individual encounter data of six African carnivore species optimized for multi-species density estimation

The ability to estimate abundances of multiple wildlife species within an area is valuable for both conservation and ecological inquiry. Spatially explicit capture–recapture (SCR) methods are commonly used to obtain reliable population size estimates, particularly for low-density and individually identifiable carnivore species. However, estimating abundance within multi-species communities poses a methodological challenge as survey designs and analytical tools are primarily tailored for single target species. Here, we present a dataset of spatially referenced individual encounter histories of six carnivore species with varying space requirements (lion, Panthera leo ; leopard, Panthera pardus ; spotted hyena, Crocuta crocuta ; cheetah, Acinonyx jubatus ; serval, Leptailurus serval ; large-spotted genet, Genetta tigrina ). These data were collected in a South African game reserve using a camera trap array optimized for multi-species density estimation using SCR methods. This dataset will be a valuable resource for studying spatial processes among potentially interacting carnivores without the common pitfalls that come with by-catch data of non-target species, and will provide a much-needed case study for the further development of multi-species statistical method development.

Munywana Conservancy↗

USGS2015

Climate change impacts on lake thermal dynamics and ecosystem vulnerabilities

Using water column temperature records collected since 1968, we analyzed the impacts of climate change on thermal properties, stability intensity, length of stratification, and deep mixing dynamics of Lake Tahoe using a modified stability index (SI). This new SI is easier to produce and is a more informative measure of deep lake stability than commonly used stability indices. The annual average SI increased at 16.62 kg/m 2 /decade although the summer (May–October) average SI increased at a higher rate (25.42 kg/m 2 /decade) during the period 1968–2014. This resulted in the lengthening of the stratification season by approximately 24 d. We simulated the lake thermal structure over a future 100 yr period using a lake hydrodynamic model driven by statistically downscaled outputs of the Geophysical Fluid Dynamics Laboratory Model (GFDL) for two different green house gas emission scenarios (the A2 in which greenhouse-gas emissions increase rapidly throughout the 21 st Century, and the B1 in which emissions slow and then level off by the late 21 st Century). The results suggest a continuation and intensification of the already observed trends. The length of stratification duration and the annual average lake stability are projected to increase by 38 d and 12 d and 30.25 kg/m 2 /decade and 8.66 kg/m 2 /decade, respectively for GFDLA2 and GFDLB1, respectively during 2014–2098. The consequences of this change bear the hallmarks of climate change induced lake warming and possible exacerbation of existing water quality, quantity and ecosystem changes. The developed methodology could be extended and applied to other lakes as a tool to predict changes in stratification and mixing dynamics.

California, Nevada↗

USGS2021

Improved fire severity mapping in the North American boreal forest using a hybrid composite method

Fire severity is a key driver shaping the ecological structure and function of North American boreal ecosystems, a biome dominated by large, high-intensity wildfires. Satellite-derived burn severity maps have been an important tool in these remote landscapes for both fire and resource management. The conventional methodology to produce satellite-inferred fire severity maps generally involves comparing imagery from 1 year before and 1 year after a fire, yet environmental conditions unique to the boreal have limited the accuracy of resulting products. We introduce an alternative method – the ‘hybrid composite’ – based on deriving mean severity over time on a per-pixel basis within the cloud-computing environment of Google Earth Engine. It constructs the post-fire image from satellite data composited from all valid images (i.e., clear-sky and snow-free) acquired in the time period immediately after fire through the early growing season of the following year. We compare this approach to paired-scene and composite approaches where the post-fire time period is from the growing season 1 year after fire. Validation statistics based on field-derived data for 52 fires across Alaska and Canada indicate that the hybrid composite method outperforms the other approaches. This approach presents an efficient and cost-effective means to monitor and explore trends and patterns across broad spatial domains, and could be applied to fires in other regions, especially those with frequent cloud cover or rapid vegetation recovery.

Alaska↗

USGS2018

Estimating vegetation biomass and cover across large plots in shrub and grass dominated drylands using terrestrial lidar and machine learning

Terrestrial laser scanning (TLS) has been shown to enable an efficient, precise, and non-destructive inventory of vegetation structure at ranges up to hundreds of meters. We developed a method that leverages TLS collections with machine learning techniques to model and map canopy cover and biomass of several classes of short-stature vegetation across large plots. We collected high-definition TLS scans of 26 1-ha plots in desert grasslands and big sagebrush shrublands in southwest Idaho, USA. We used the Random Forests machine learning algorithm to develop decision tree models predicting the biomass and canopy cover of several vegetation classes from statistical descriptors of the aboveground heights of TLS points. Manual measurements of vegetation characteristics collected within each plot served as training and validation data. Models based on five or fewer TLS descriptors of vegetation heights were developed to predict the canopy cover fraction of shrubs (R 2 = 0.77, RMSE = 7%), annual grasses (R 2 = 0.70, RMSE = 21%), perennial grasses (R 2 = 0.36, RMSE = 12%), forbs (R 2 = 0.52, RMSE = 6%), bare earth or litter (R 2 = 0.49, RMSE = 19%), and the biomass of shrubs (R 2 = 0.71, RMSE = 175 g) and herbaceous vegetation (R 2 = 0.61, RMSE = 99 g) (all values reported are out-of-bag). Our models explained much of the variability between predictions and manual measurements, and yet we expect that future applications could produce even better results by reducing some of the methodological sources of error that we encountered. Our work demonstrates how TLS can be used efficiently to extend manual measurement of vegetation characteristics from small to large plots in grasslands and shrublands, with potential application to other similarly structured ecosystems. Our method shows that vegetation structural characteristics can be modeled without classifying and delineating individual plants, a challenging and time-consuming step common in previous methods applying TLS to vegetation inventory. Improving application of TLS to studies of shrub-steppe ecosystems will serve immediate management needs by enhancing vegetation inventories, environmental modeling studies, and the ability to train broader datasets collected from air and space.

Idaho↗

USGS2006

Apparent Consumption vs. Total Consumption--A Lead-Acid Battery Case Study

Introduction: This report compares estimates of U.S. apparent consumption of lead with estimates of total U.S. consumption of this mineral commodity from a materials flow perspective. The difference, attributed to the amount of lead contained in imported and exported products, was found to be significant for this sector. The study also assesses the effects of including mineral commodities incorporated in manufactured products on the interpretation of observed trends in minerals consumption and trade. Materials flow is a systems approach to understanding what happens to the materials we use from the time a material is extracted, through its processing and manufacturing, to its ultimate disposition. The U.S. Geological Survey (USGS) provides accurate and detailed mineral production and mineral commodity consumption statistics that are essential for government, nongovernment organizations, and the public to gain a better understanding of how and where materials are used and their effect on the environment and society. Published statistics on mineral apparent consumption are limited to estimates of consumption of raw material forms (ore, concentrate, and [or] refined metal). For this study, apparent consumption is defined as mine production + secondary refined production + imports (concentrates and refined metal) ? exports (concentrates and refined metal) + adjustments for government and industry stock changes. These estimates do not account for the amount of mineral commodities contained in manufactured products that are imported to the United States, nor do they deduct the amount of these mineral commodities contained in manufactured products that are exported from the United States. When imports or exports of manufactured products contribute significantly to the total use of a particular raw material, an estimate of consumption that does not consider the incorporated forms of these mineral commodities within imported or exported manufactured products can be either under- or overreported (depending on the net trade flow). Factors that influence consumption and trade patterns include variations in industry structure, labor or financial markets, legislation, and technology. As U.S. trade patterns of manufactured products change, omitting mineral commodities incorporated into these goods as part of U.S. mineral commodity consumption estimates may affect the interpretation of observed trends in minerals consumption and trade. Although it may be desirable to include minerals contained in manufactured products as part of consumption estimates, collection and estimation of these data are sometimes difficult. Consumption and trade data for every traded product may not be readily available. Compiling comprehensive consumption statistics for mineral commodities, which have many end uses, each including multiple products, may be time consuming. For these reasons, studies of all mineral commodities are not feasible. Mineral commodity selection for this study is based on data accessibility considerations and the relative importance of lead contained in imported and exported products when considered part of total U.S. lead consumption. Lead was selected for this initial evaluation of total mineral consumption because of the need to understand the consumption pattern of this potentially toxic metal and its compounds, the relative simplicity of this sector?s end-use structure, and the availability of trade data. This study draws upon the findings of an earlier lead consumption study (Biviano and others, 1999) conducted by the USGS for the period 1984 to 1993, but uses a different study methodology for an industry whose structure has changed from that considered in the earlier study. Figure 1 shows the quantity of material contributing to U.S. total consumption of lead metal from domestic and foreign industrial sectors in 2004, based upon trade data reported by the USGS and the U.S. International Trade Commission (USITC). For

Scientific Investigations Report↗

USGS2004

Quantitative expression profiling of immune response genes in rainbow trout following infectious haematopoietic necrosis virus (IHNV) infection or DNA vaccination

Infectious haematopoietic necrosis virus (IHNV) is a well-studied virus of salmonid fishes. A highly efficacious DNA vaccine has been developed against this virus and studies have demonstrated that this vaccine induces both an early and transient non-specific anti-viral phase as well as long-term specific protection. The mechanisms of the early anti-viral phase are not known, but previous studies noted changes in Mx gene expression, suggesting a role for type I interferon. This study used quantitative real-time reverse transcriptase PCR methodology to compare expression changes over time of a number of cytokine or cytokine-related genes in the spleen of rainbow trout following injection with poly I:C, live IHNV, the IHNV DNA vaccine or a control plasmid encoding the non-antigenic luciferase gene. The target genes included Mx-1, viral haemorrhagic septicaemia virus induced gene 8 (Vig-8), TNF-α1, TNF-α2, IL-1β1, IL-8, TGF-β1 and Hsp70. Poly I:C stimulation induced several genes but the strongest and significant response was observed in the Mx-1 and Vig-8 genes. The live IHN virus induced a significant response in all genes examined except TGF-β1. The control plasmid construct and the IHNV DNA vaccine marginally induced a number of genes, but the main difference between these two groups was a statistically significant induction of the Mx-1 and Vig-8 genes by the IHNV vaccine only. The gene expression profiles elicited by the live virus and the IHNV DNA vaccine differed in a number of aspects but this study confirms the clear role for a type I interferon-like response in early anti-viral defence.

Fish & Shellfish Immunology↗

USGS2010

Comment on RamaRao et al. [1995] and LaVenue et al. [1995]

A method for stochastic modeling of groundwater flow systems using a combination of pilot point parameterization and conditional simulation was presented by RamaRao et. al [1995] and LaVenue et. al [1995]. (We will collectively term these two papers RLMM and term the method developed in RLMM the CS method here.) RLMM (pp. 478-479) state that the CS method is intended to provide a frequency distribution of possible alternative spatial transmissivity T distributions that (1) are statistically similar to the observed T distribution, (2) are equally likely given the calibration data, and (3) closely reproduce the measured pressures. The frequency distribution of transmissivities is then used to form frequency distribution of derived functions, such as travel times, which are summarized in the form of uncertainty measures, such as (RLMM, p. 512) "confidence (or tolerance) intervals," on the actual values, which in the case of travels times, are values that could occur sometime in the future (RLMM, p.513). Cooley [2000] analyzes the RLMM method using linearization and bootstrap theory and concludes that that the method can yield accurate uncertainty estimates but only under some limited circumstances. In this comment we use Cooley's analysis to critique the method. We also identify and discuss some statements made my by RLMM about model calibration and their methodology that appear to be misleading. It is unusual to comment on a paper so long after publication. Subsequent work has expanded the method of RLMM, and the method was used advantageously in the testing documented by Zimmerman et. al [1998], so it is clear the method has significant strengths. We go back to the 1995 papers for this comment, however, because they display most clearly the methodological difficulties with which we are concerned.

Water Resources Research↗

USGS2020

Potential groundwater recharge rates for two subsurface-drained agricultural fields, southeastern Minnesota, 2016–18

Subsurface drainage is used to efficiently drain saturated soils to support productive agriculture in poorly drained terrains. Although subsurface drainage alters the water balance for agricultural fields, its effect on groundwater resources and groundwater recharge is poorly understood. In Minnesota, subsurface drainage has begun to increase in southeastern Minnesota, even though this part of the State is underlain by permeable karstic bedrock aquifers with only a thin layer of glacial sediments separating these aquifers from land surface. To gain a better understanding of groundwater recharge effects from subsurface drainage, the U.S. Geological Survey (USGS), in cooperation with the Legislative-Citizen Commission on Minnesota Resources, led a 2-year hydrologic study to investigate this connection for two agricultural fields in southeastern Minnesota with subsurface drainage. A total of three monitoring plots were used between the two agricultural fields: two monitoring plots that included an actively drained area with peripheral, undrained areas, and a third monitoring plot without any subsurface drainage. Multiple piezometer transects were set up across the three monitoring plots to characterize the unsaturated zone and shallow water-table flow using pressure transducers and soil moisture probes. From these piezometers, groundwater recharge rates were derived using two different methods: the RISE Water-Table Fluctuation (WTF) method and the DRAINMOD model. In addition to these two methods, the USGS Soil-Water-Balance (SWB) model was used to estimate potential recharge rates for three different monitoring plots. In addition to deriving groundwater recharge rates, the hydrologic budget was analyzed to interpret the water-table surface elevation and soil volumetric water content time series. At one of the two drained plots, the transects exhibited varying water-table surface elevation patterns. Frequent backflow from the adjacent ditch caused subsurface drainage flow to slow down or stop drainage through the main collector drain and cause pipe pressurization, so the closest transect appeared to be mostly controlled by the drain pressurization, whereas the farthest transect was more efficiently drained. Both of the drained monitoring plots had an elevation gradient parallel to the pattern tiles, sloping downward towards the collector drain that aggregated the parallel lines into a single drain. Because the transects were set at different gradients in the field, some of the water-table surface elevation differences were also attributed to lateral flow towards the lowest parts of the field. Three methods were used to derive potential groundwater recharge rates: the RISE WTF method, the USGS SWB model, and DRAINMOD-derived deep seepage rates. Potential groundwater recharge rates, using the RISE WTF method, across all piezometers were 1.55 and 1.94 inches per year, respectively, for water years 2017 and 2018. More differentiation of potential recharge rates between different piezometer types occurred for water year 2018. Although the difference was slightly more than 1 inch between the drained and nondrained piezometers for water year 2018, this difference was statistically significant based on a t-test with a p -value of 0.036 ( α =0.05). When looking at recharge based on distance from the drain, the subsurface drain did not affect potential recharge, although other factors such as variability in screen depths, well construction, and specific yield variability cannot be eliminated. The SWB model was also used to estimate potential recharge rates for water years 2017–18, with rates between 2.44 and 5.92 inches per year for the two drained sites, generally higher than the RISE WTF estimates. DRAINMOD-derived potential recharge rates were generally the highest of the three methods, with potential recharge rates varying from 2.07 to 9.49 inches per year. Overall, there was a lack of agreement between the three methods. These results were not remarkable, considering the fundamental differences in the methodology for each method. However, all methods did show a fundamental difference between piezometers within the drained area and piezometers outside the drained area, including the third undrained monitoring plot. The drained areas show a lower overall potential groundwater recharge compared to the nondrained areas for all three estimates. The difference for the 2018 recharge estimates was slightly higher than 1 inch for the RISE WTF method, the difference was almost double for the nine sites for the DRAINMOD model, and the difference between the drain and undrained plots was even more significant for the SWB model.

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