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At least 775 records · Page 43Linked to original sources
USGS2021

Exploring the regional dynamics of U.S. irrigated agriculture from 2002 to 2017

The United States has a geographically mature and stable land use and land cover system including land used as irrigated cropland; however, changes in irrigation land use frequently occur related to various drivers. We applied a consistent methodology at a 250 m spatial resolution across the lower 48 states to map and estimate irrigation dynamics for four map eras (2002, 2007, 2012, and 2017) and over four 5-year mapping intervals. The resulting geospatial maps (called the Moderate Resolution Imaging Spectroradiometer (MODIS) Irrigated Agriculture Dataset or MIrAD-US) involved inputs from county-level irrigated statistics from the U.S. Department of Agriculture, National Agricultural Statistics Service, agricultural land cover from the U.S. Geological Survey National Land Cover Database, and an annual peak vegetation index derived from expedited MODIS satellite imagery. This study investigated regional and periodic patterns in the amount of change in irrigated agriculture and linked gains and losses to proximal causes and consequences. While there was a 7% overall increase in irrigated area from 2002 to 2017, we found surprising variability by region and by 5-year map interval. Irrigation land use dynamics affect the environment, water use, and crop yields. Regionally, we found that the watersheds with the largest irrigation gains (based on percent of area) included the Missouri, Upper Mississippi, and Lower Mississippi watersheds. Conversely, the California and the Texas–Gulf watersheds experienced fairly consistent irrigation losses during these mapping intervals. Various drivers for irrigation dynamics included regional climate fluctuations and drought events, demand for certain crops, government land or water policies, and economic incentives like crop pricing and land values. The MIrAD-US (Version 4) was assessed for accuracy using a variety of existing regionally based reference data. Accuracy ranged between 70% and 95%, depending on the region.

Conterminous United States↗
USGS2012

Science summary in support of Manatee Protection Area (MPA) design in Puerto Rico

The Antillean manatee (Trichechus manatus manatus), a subspecies of the West Indian manatee, is listed as endangered by the US Department of Interior. In accordance with its listing, the U.S. Fish and Wildlife Service’s Caribbean Field Office (USFWS) is mandated to create one or more Manatee Protection Areas (MPAs) for Puerto Rico. Designation of these areas must comply with the legal definition of an MPA’s purpose: to prevent or reduce take of manatees (CFR 50: 44 FR 60964, Oct. 22, 1979). To meet this goal, we pursued two objectives: 1) identify areas which include the specific ecological attributes necessary to support manatee populations, and 2) identify areas where take can be reduced through approved MPA regulatory frameworks. We achieved these objectives through literature review, expert elicitation, and geospatial modeling. This report delivers to USFWS a set of nine potential MPA regions. These regions represent the spatial realization of experts’ hypotheses regarding manatee requirements and threats, and the potential to implement MPA strategies (e.g. watercraft access, speed regulations, signage and boater education). The nine regions are compared based on a number of factors, including their potential to reduce take, quality of the habitat encompassed, and total area. These maps and statistics serve as suitable starting points to select one or more MPA sites, but we recommend that the mapped attributes and threats (i.e., boating activity) of MPAs be ground-truthed to visually confirm the local presence of resources, threats, and manatees before any area is selected. Once established, the effectiveness of MPAs can be monitored and updated through processes of adaptive monitoring and management. Aerial surveys, radio tracking studies, and public surveys are all valuable tools to assess the success of an MPA. Establishing MPAs is a management action that, integrated within the species Recovery Plan, should enhance the conservation of manatees.

Cooperator Science Series↗
USGS2016

Characterization and relation of precipitation, streamflow, and water-quality data at the U.S. Army Garrison Fort Carson and Piñon Canyon Maneuver Site, Colorado, water years 2013–14

To evaluate the influence of military training activities on streamflow and water quality, the U.S. Geological Survey, in cooperation with the U.S. Department of the Army, began a hydrologic data collection network on the U.S. Army Garrison Fort Carson in 1978 and on the Piñon Canyon Maneuver Site in 1983. This report is a summary and characterization of the precipitation, streamflow, and water-quality data collected at 43 sites between October 1, 2012, and September 30, 2014 (water years 2013 and 2014). Variations in the frequency of daily precipitation, seasonal distribution, and seasonal and annual precipitation at 5 stations at the U.S. Army Garrison Fort Carson and 18 stations at or near the Piñon Canyon Maneuver Site were evaluated. Isohyetal diagrams indicated a general pattern of increase in total annual precipitation from east to west at the U.S. Army Garrison Fort Carson and the Piñon Canyon Maneuver Site. Between about 54 and 79 percent of daily precipitation was 0.1 inch or less in magnitude. Precipitation events were larger and more frequent between July and September. Daily streamflow data from 16 sites were used to evaluate temporal and spatial variations in streamflow for the water years 2013 and 2014. At all sites, median daily mean streamflow for the 2-year period ranged from 0.0 to 9.60 cubic feet per second. Daily mean streamflow hydrographs are included in this report. Five sites on the Piñon Canyon Maneuver Site were monitored for peak stage using crest-stage gages. At the Piñon Canyon Maneuver Site, five sites had a stage recorder and precipitation gage, providing a paired streamflow-precipitation dataset. There was a statistically significant correlation between precipitation and streamflow based on Spearman’s rho correlation (rho values ranged from 0.17 to 0.35). Suspended-sediment samples were collected in April through October for water years 2013–14 at one site at the U.S. Army Garrison Fort Carson and five sites at the Piñon Canyon Maneuver Site. Suspended-sediment-transport curves were used to illustrate the relation between streamflow and suspended-sediment concentration. All these sediment-transport curves showed a streamflow dependent suspended-sediment concentration relation except for the U.S. Geological Survey station Bent Canyon Creek at mouth near Timpas, CO. Water-quality data were collected and reported from seven sites on the U.S. Army Garrison Fort Carson and the Piñon Canyon Maneuver Site during water years 2013–14. Sample results exceeding an established water-quality standard were identified. Selected water-quality properties and constituents were stratified to compare spatial variation among selected characteristics using boxplots. Trilinear diagrams were used to classify water type based on ionic concentrations of water-quality samples collected during the study period. At the U.S. Army Garrison Fort Carson and the Piñon Canyon Maneuver Site, 27 samples were classified as very hard or brackish. Seven samples had a lower hardness character relative to the other samples. Four of those nine samples were collected at two U.S. Geological Survey stations (Turkey Creek near Fountain, CO, and Little Fountain Creek above Highway 115 at Fort Carson, CO), which have different geologic makeup. Three samples collected at the Piñon Canyon Maneuver Site had a markedly lower hardness likely because of dilution from an increase in streamflow.

Colorado↗
USGS2000

Methodology and application of combined watershed and ground-water models in Kansas

Increased irrigation in Kansas and other regions during the last several decades has caused serious water depletion, making the development of comprehensive strategies and tools to resolve such problems increasingly important. This paper makes the case for an intermediate complexity, quasi-distributed, comprehensive, large-watershed model, which falls between the fully distributed, physically based hydrological modeling system of the type of the SHE model and the lumped, conceptual rainfall-runoff modeling system of the type of the Stanford watershed model. This is achieved by integrating the quasi-distributed watershed model SWAT with the fully-distributed ground-water model MODFLOW. The advantage of this approach is the appreciably smaller input data requirements and the use of readily available data (compared to the fully distributed, physically based models), the statistical handling of watershed heterogeneities by employing the hydrologic-response-unit concept, and the significantly increased flexibility in handling stream-aquifer interactions, distributed well withdrawals, and multiple land uses. The mechanics of integrating the component watershed and ground-water models are outlined, and three real-world management applications of the integrated model from Kansas are briefly presented. Three different aspects of the integrated model are emphasized: (1) management applications of a Decision Support System for the integrated model (Rattlesnake Creek subbasin); (2) alternative conceptual models of spatial heterogeneity related to the presence or absence of an underlying aquifer with shallow or deep water table (Lower Republican River basin); and (3) the general nature of the integrated model linkage by employing a watershed simulator other than SWAT (Wet Walnut Creek basin). These applications demonstrate the practicality and versatility of this relatively simple and conceptually clear approach, making public acceptance of the integrated watershed modeling system much easier. This approach also enhances model calibration and thus the reliability of model results. (C) 2000 Elsevier Science B.V.Increased irrigation in Kansas and other regions during the last several decades has caused serious water depletion, making the development of comprehensive strategies and tools to resolve such problems increasingly important. This paper makes the case for an intermediate complexity, quasi-distributed, comprehensive, large-watershed model, which falls between the fully distributed, physically based hydrological modeling system of the type of the SHE model and the lumped, conceptual rainfall-runoff modeling system of the type of the Stanford watershed model. This is achieved by integrating the quasi-distributed watershed model SWAT with the fully-distributed ground-water model MODFLOW. The advantage of this approach is the appreciably smaller input data requirements and the use of readily available data (compared to the fully distributed, physically based models), the statistical handling of watershed heterogeneities by employing the hydrologic-response-unit concept, and the significantly increased flexibility in handling stream-aquifer interactions, distributed well withdrawals, and multiple land uses. The mechanics of integrating the component watershed and ground-water models are outlined, and three real-world management applications of the integrated model from Kansas are briefly presented. Three different aspects of the integrated model are emphasized: (1) management applications of a Decision Support System for the integrated model (Rattlesnake Creek subbasin); (2) alternative conceptual models of spatial heterogeneity related to the presence or absence of an underlying aquifer with shallow or deep water table (Lower Republican River basin); and (3) the general nature of the integrated model linkage by employing a watershed simulator other than SWAT (Wet Walnut Creek basin). These applications demonstrate the practicality and ve

Journal of Hydrology↗
USGS2003

Comparison of temperature, specific conductance, pH, and dissolved oxygen at selected basic fixed sites in south-central Texas, 1996-98

One component of the surface-water part of the U.S. Geological Survey National Water-Quality Assessment Program is the use of continuous water-quality monitors to help characterize the spatial and temporal distribution of general water quality in relation to hydrologic conditions. During 1996-98, six continuous water-quality monitors in the South-Central Texas study unit collected water temperature, specific conductance, pH, and dissolved oxygen data. The data were compared among the six sites using boxplots of monthly mean values, summary statistics of monthly values, and hydrographs of daily mean values.

Texas↗
USGS2005

Correlation analysis of a ground-water level monitoring network, Miami-Dade County, Florida

The U.S. Geological Survey cooperative ground-water monitoring program in Miami-Dade County, Florida, expanded from 4 to 98 continuously recording water-level monitoring wells during the 1939-2001 period. Network design was based on area specific assessments; however, no countywide statistical assessments of network coverage had been performed for the purpose of assessing network redundancy. To aid in the assessment of network redundancy, correlation analyses were performed using S-PLUS 2000 statistical analysis software for daily maximum water-level data from 98 monitoring wells for the November 1, 1973, to October 31, 2000 period. Because of the complexities of the hydrologic, water-supply, and water-management systems in Miami-Dade County and the changes that have occurred to these systems through time, spatial and temporal variations in the degree of correlation had to be considered. To assess temporal variation in correlation, water-level data from each well were subdivided by year and by wet and dry seasons. For each well, year, and season, correlation analyses were performed on the data from those wells that had available data. For selected wells, the resulting correlation coefficients from each year and season were plotted with respect to time. To assess spatial variation in correlation, the coefficients determined from the correlation analysis were averaged. These average wet- and dry-season correlation coefficients were plotted spatially using geographic information system software. Wells with water-level data that correlated with a coefficient of 0.95 or greater were almost always located in relatively close proximity to each other. Five areas were identified where the water-level data from wells within the area remained correlated with that of other wells in the area during the wet and dry seasons. These areas are located in or near the C-1 and C-102 basins (2 wells), in or near the C-6 and C-7 basins (2 wells), near the Florida Keys Aqueduct Authority Well Field (2 wells), near the Hialeah-Miami Springs Well Field (6 wells), and near the West Well Field (21 wells). Data from the remaining 65 wells (most of the wells in the network) generally were not correlated with those of other wells during both the wet and dry seasons with an average coefficient of 0.95 or greater for the comparison. Because many of the wells near the West Well Field and some near the Hialeah-Miami Springs Well Field had not been in operation for very long (most having been installed in 1994), the averaged correlation coefficients for these wells were often determined using only a few seasons of data. For the few instances where water-level data were found to be well correlated on average for a lengthy period of record, short-term declines in correlation were often identified. In general, it would be beneficial to compare data for longer periods of record than currently available.

Open-File Report↗
USGS2016

Daniel Goodman’s empirical approach to Bayesian statistics

Bayesian statistics, in contrast to classical statistics, uses probability to represent uncertainty about the state of knowledge. Bayesian statistics has often been associated with the idea that knowledge is subjective and that a probability distribution represents a personal degree of belief. Dr. Daniel Goodman considered this viewpoint problematic for issues of public policy. He sought to ground his Bayesian approach in data, and advocated the construction of a prior as an empirical histogram of “similar” cases. In this way, the posterior distribution that results from a Bayesian analysis combined comparable previous data with case-specific current data, using Bayes’ formula. Goodman championed such a data-based approach, but he acknowledged that it was difficult in practice. If based on a true representation of our knowledge and uncertainty, Goodman argued that risk assessment and decision-making could be an exact science, despite the uncertainties. In his view, Bayesian statistics is a critical component of this science because a Bayesian analysis produces the probabilities of future outcomes. Indeed, Goodman maintained that the Bayesian machinery, following the rules of conditional probability, offered the best legitimate inference from available data. We give an example of an informative prior in a recent study of Steller sea lion spatial use patterns in Alaska.

PeerJ↗
USGS2023

Mapping landslide susceptibility over large regions with limited data

Landslide susceptibility maps indicate the spatial distribution of landslide likelihood. Modeling susceptibility over large or diverse terrains remains a challenge due to the sparsity of landslide data (mapped extent of known landslides) and the variability in triggering conditions. Several different data sampling strategies of landslide locations used to train a susceptibility model are used to mitigate this challenge. However, to our knowledge, no study has systematically evaluated how different sampling strategies alter a model's predictor effects (i.e., how a predictor value influences the susceptibility output) critical to explaining differences in model outputs. Here, we introduce a statistical framework that examines the variation in predictor effects and the model accuracy (measured using receiver operator characteristics) to highlight why certain sampling strategies are more effective than others. Specifically, we apply our framework to an array of logistic regression models trained on landslide inventories collected at sub-regional scales over four terrains across the United States. Results show significant variations in predictor effects depending on the inventory used to train the models. The inconsistent predictor effects cause low accuracies when testing models on inventories outside the domain of the training data. Grouping test and training sets according to physiographic and ecological characteristics, which are thought to share similar triggering mechanisms, does not improve model accuracy. We also show that using limited landslide data distributed uniformly over the entire modeling domain is better than using dense but spatially isolated data to train a model for applications over large regions.

California, Kentucky, North Carolina, West Virgini↗
USGS2017

Using variance structure to quantify responses to perturbation in fish catches

We present a case study evaluation of gill-net catches of Walleye Sander vitreus to assess potential effects of large-scale changes in Oneida Lake, New York, including the disruption of trophic interactions by double-crested cormorants Phalacrocorax auritus and invasive dreissenid mussels. We used the empirical long-term gill-net time series and a negative binomial linear mixed model to partition the variability in catches into spatial and coherent temporal variance components, hypothesizing that variance partitioning can help quantify spatiotemporal variability and determine whether variance structure differs before and after large-scale perturbations. We found that the mean catch and the total variability of catches decreased following perturbation but that not all sampling locations responded in a consistent manner. There was also evidence of some spatial homogenization concurrent with a restructuring of the relative productivity of individual sites. Specifically, offshore sites generally became more productive following the estimated break point in the gill-net time series. These results provide support for the idea that variance structure is responsive to large-scale perturbations; therefore, variance components have potential utility as statistical indicators of response to a changing environment more broadly. The modeling approach described herein is flexible and would be transferable to other systems and metrics. For example, variance partitioning could be used to examine responses to alternative management regimes, to compare variability across physiographic regions, and to describe differences among climate zones. Understanding how individual variance components respond to perturbation may yield finer-scale insights into ecological shifts than focusing on patterns in the mean responses or total variability alone.

Transactions of the American Fisheries Society↗
USGS2018

Analysis of vegetation recovery surrounding a restored wetland using the normalized difference infrared index (NDII) and normalized difference vegetation index (NDVI)

Watershed restoration efforts seek to rejuvenate vegetation, biological diversity, and land productivity at Cienega San Bernardino, an important wetland in southeastern Arizona and northern Sonora, Mexico. Rock detention and earthen berm structures were built on the Cienega San Bernardino over the course of four decades, beginning in 1984 and continuing to the present. Previous research findings show that restoration supports and even increases vegetation health despite ongoing drought conditions in this arid watershed. However, the extent of restoration impacts is still unknown despite qualitative observations of improvement in surrounding vegetation amount and vigor. We analyzed spatial and temporal trends in vegetation greenness and soil moisture by applying the normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) to one dry summer season Landsat path/row from 1984 to 2016. The study area was divided into zones and spectral data for each zone was analyzed and compared with precipitation record using statistical measures including linear regression, Mann– Kendall test, and linear correlation. NDVI and NDII performed differently due to the presence of continued grazing and the effects of grazing on canopy cover; NDVI was better able to track changes in vegetation in areas without grazing while NDII was better at tracking changes in areas with continued grazing. Restoration impacts display higher greenness and vegetation water content levels, greater increases in greenness and water content through time, and a decoupling of vegetation greenness and water content from spring precipitation when compared to control sites in nearby tributary and upland areas. Our results confirm the potential of erosion control structures to affect areas up to 5 km downstream of restoration sites over time and to affect 1 km upstream of the sites.

Cuenca Los Ojos, San Bernardino National Wildlife ↗
USGS2013

Remote sensing of soil moisture using airborne hyperspectral data

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation ( R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

GIScience and Remote Sensing↗
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↗
USGS2011

Derived crop management data for the LandCarbon Project

The LandCarbon project is assessing potential carbon pools and greenhouse gas fluxes under various scenarios and land management regimes to provide information to support the formulation of policies governing climate change mitigation, adaptation and land management strategies. The project is unique in that spatially explicit maps of annual land cover and land-use change are created at the 250-meter pixel resolution. The project uses vast amounts of data as input to the models, including satellite, climate, land cover, soil, and land management data. Management data have been obtained from the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) and USDA Economic Research Service (ERS) that provides information regarding crop type, crop harvesting, manure, fertilizer, tillage, and cover crop (U.S. Department of Agriculture, 2011a, b, c). The LandCarbon team queried the USDA databases to pull historic crop-related management data relative to the needs of the project. The data obtained was in table form with the County or State Federal Information Processing Standard (FIPS) and the year as the primary and secondary keys. Future projections were generated for the A1B, A2, B1, and B2 Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) scenarios using the historic data values along with coefficients generated by the project. The PBL Netherlands Environmental Assessment Agency (PBL) Integrated Model to Assess the Global Environment (IMAGE) modeling framework (Integrated Model to Assess the Global Environment, 2006) was used to develop coefficients for each IPCC SRES scenario, which were applied to the historic management data to produce future land management practice projections. The LandCarbon project developed algorithms for deriving gridded data, using these tabular management data products as input. The derived gridded crop type, crop harvesting, manure, fertilizer, tillage, and cover crop products are used as input to the LandCarbon models to represent the historic and the future scenario management data. The overall algorithm to generate each of the gridded management products is based on the land cover and the derived crop type. For each year in the land cover dataset, the algorithm loops through each 250-meter pixel in the ecoregion. If the current pixel in the land cover dataset is an agriculture pixel, then the crop type is determined. Once the crop type is derived, then the crop harvest, manure, fertilizer, tillage, and cover crop values are derived independently for that crop type. The following is the overall algorithm used for the set of derived grids. The specific algorithm to generate each management dataset is discussed in the respective section for that dataset, along with special data handling and a description of the output product.

Open-File Report↗
USGS2014

Elk monitoring in Lewis and Clark National Historical Park: 2008-2012 synthesis report

Maintaining elk (Cervus elaphus roosevelti) herds that frequent Lewis and Clark National Historical Park (NHP) is central to the park’s purpose of preserving the historic, cultural, scenic, and natural resources associated with the winter encampment of the Lewis and Clark expedition. Elk were critically important to the Lewis and Clark expedition in providing food and hides that sustained the expedition during the winter of 1805-06 and supplied them for their return east during 1806. Today, elk remain a key component of interpreting the Lewis and Clark story to over 200,000 park visitors each year at the Fort Clatsop visitor center. In 2008, the US Geological Survey (USGS) began collaborating with Lewis and Clark NHP and the NPS North Coast and Cascades Network to develop a protocol for monitoring long-term changes in the magnitude and spatial patterns of elk use within and adjacent to Lewis and Clark NHP (Griffin et al. 2011). Specific objectives of the monitoring program were to measure trends in (1) relative use of the Fort Clatsop unit by elk during winter; (2) the proportion of areas where elk sign is present in the Fort Clatsop unit in winter; and (3) the frequency of elk sightings from roads in and around the Fort Clatsop unit. This report synthesizes the results of the first four years of monitoring elk distribution and use in Lewis and Clark NHP from 2008-2012. We also present data from FY2012 (Appendix 1), in lieu of an annual report for that year. We used fecal pellet group surveys as the cornerstone for monitoring trends in both relative use of the Fort Clatsop Unit by elk and the proportion of areas where elk sign was present at the end of winter. We estimated pellet group density based on data collected from a network of fecal pellet plots distributed systematically throughout the unit. We developed a double observer sampling scheme that enabled us to estimate detection biases and improve the accuracy of pellet group density estimates. We computed the estimated detection probability for any pellet group observed; this probability was a function of the pellet group size and stage of decay, as well as lighting and vegetation conditions, and the number of observers (one or two) searching for pellets in that subplot. We then used these estimated detection probabilities to adjust the raw counts of the detected pellet groups to account for groups that likely went undetected under similar pellet and environmental conditions (each observed pellet group was weighted by the inverse of its estimated detection probability). We also used results from the late winter fecal pellet surveys to quantify the proportion of areas where elk pellets occurred (PAO), which was based on the presence of fecal pellet groups and estimation of detection biases (i.e., accounting for pellet groups that likely went undetected by both observers). In this synthesis, we report temporal trends in both pellet group density and PAO from 2008-2012, based on weighted linear regression analyses as well as spatial variation of pellet group densities over time. We completed late winter fecal pellet surveys at 61-66 plots annually, depending on yearly variation in access. We cleared fecal pellets at survey points in late October / early November each year and returned in late February / early March to count pellet groups left by elk over the winter. The estimated probability that a pellet group was detected by any one observer during late winter was affected most by the pellet group size and was less affected by decay class and lighting conditions. Per-observer detection probabilities ranged from as low as ~10-15% for single pellets to ~85-90% for pellet groups with 50 pellets. Average pellet group density in the Fort Clatsop unit ranged annually from 0.58 (+/- 1.43 standard error [SE]) to 0.93 (+/- 2.25 SE) pellet groups per 3-m radius subplot. Pellet group density declined over time, at approximately 8.8% per year (+/- 2.5% SE), but that slope was not statistically distinguishable from zero (2- tailed P=0.16). Following correction for detection biases, the proportion of surveyed points used by elk (i.e., PAO) ranged from 0.44 (+/- 0.07 SE) to 0.53 (+/- 0.07 SE) during the 4 winters. The estimated proportion of areas where elk pellets occurred (PAO) declined at a rate of 2.6% per year (+/- 1.2% per year SE), but that trend also was not statistically distinguishable from zero (2- tailed P=0.17). Statistical significance of a measure’s trend depends on both the magnitude (i.e., slope) of the observed trend and the number of years the trend continues in the same increasing or decreasing direction. Through simulation modeling we determined how many additional years of surveys would be required to reveal a statistically significant trend, based on the same trends in pellet group density and PAO, and associated variation, observed from 2009-2012. Assuming the same trends persist in the future, simulations indicated that there is a 70% probability that a statistically significant trend would be detected after two more years of conducting pellet group surveys. Relative use by elk during winter, as indexed by elk pellet group density, was generally greatest in the southeast region of the Fort Clatsop unit in or near the large freshwater marsh at the mouth of Colewort Creek and adjacent upland areas. Pellet group density was also higher than average in the north-central forested area, not far from a privately-owned pasture north of the park boundary. This spatial pattern in pellet group densities across the Fort Clatsop unit was consistent across all four years, although specific pellet group densities varied from year to year. Pellet group density declined significantly over time at two points in the southeast of the Fort Clatsop unit, even though pellet group density at those points remained higher than the unit average. Pellet group density increased significantly over time at one point in the north-central region, and at one point in the south-central region of the unit, indicating a slight shift in the distribution of elk use within the Fort Clatsop Unit over the four years. As an index of visitors’ opportunities to see elk in and around the Fort Clatsop Unit, we conducted replicated roadside elk surveys 3-5 times monthly during February, April, June, August, October and December 2008-2012. During each morning of survey, we searched for elk along four routes that totaled 32 km. We examined bimonthly trends in the numbers of elk groups seen, the total number of elk seen, and the observed composition ratios for those six months of the year. The average number of elk groups seen per survey ranged from 0.75 (+/- 0.32 SE) during February to a peak of 1.95 (+/- 0.36 SE) during June. Despite this seasonal variation in numbers of elk groups seen, the average total number of elk seen per morning was less variable. The average ratios of antlered elk to antlerless adult elk (i.e., bulls:cows) and calves to antlerless adult elk (i.e. calves:cows) varied seasonally, with the highest of both average ratios observed in August. We detected no significant trends in the average number of elk groups and total numbers of elk seen per survey from 2008-2012. Similarly, ratios of calves and antlered elk per antlerless elk did not differ over time. Elk groups were frequently seen from January to August in the southeast region of the Fort Clatsop unit, in the vicinity of Colewort Creek. Outside of NPS lands, we observed elk most frequently in open areas near the Astoria regional airport, in the pastures and forests immediately north of the Fort Clatsop unit and, prior to the construction of a residential development, in a pasture northwest of the Fort Clatsop unit. Elk monitoring at Lewis and Clark NHP is still in its initial years and additional monitoring will be required to verify trends that appear to be emerging. For example, the initial monitoring suggested incipient declining trends in both pellet group density and proportion of plots with pellets present, as well as, potentially, a small shift in elk distribution away from a new trail that was recently constructed in the southeast portion of the Fort Clatsop unit. Continued monitoring will aid in determining whether this local change in distribution persists (or, alternatively, resulted from short-term random variation), and whether there will be any positive or negative effect in the northern portion of the unit where a new trail has been constructed. High variability in road counts prevented our ability to find any clear trend in numbers or composition of elk observed in and near Fort Clatsop, but changes in the patterns of observations of elk from roadways suggest that residential development outside the park has reduced the available habitat for elk in some of the areas surveyed, and may have affected spatial use patterns of elk adjacent to some areas of the park. In addition to monitoring future effects of land use changes outside the park, continued monitoring may also prove useful for assessing elk responses to natural succession in forests disturbed by windthrow in December 2007 and to NPS vegetation management activities such as variable density thinning in the forest, trail development, and restoration at Otter Point tidal area and Colewort Creek Slough.

Oregon;Washington↗
USGS2011

Understanding interaction effects of climate change and fire management on bird distributions through combined process and habitat models

Avian conservation efforts must account for changes in vegetation composition and structure associated with climate change. We modeled vegetation change and the probability of occurrence of birds to project changes in winter bird distributions associated with climate change and fire management in the northern Chihuahuan Desert (southwestern U.S.A.). We simulated vegetation change in a process-based model (Landscape and Fire Simulator) in which anticipated climate change was associated with doubling of current atmospheric carbon dioxide over the next 50 years. We estimated the relative probability of bird occurrence on the basis of statistical models derived from field observations of birds and data on vegetation type, topography, and roads. We selected 3 focal species, Scaled Quail ( Callipepla squamata ), Loggerhead Shrike ( Lanius ludovicianus ), and Rock Wren ( Salpinctes obsoletus ), that had a range of probabilities of occurrence for our study area. Our simulations projected increases in relative probability of bird occurrence in shrubland and decreases in grassland and Yucca spp. and ocotillo ( Fouquieria splendens ) vegetation. Generally, the relative probability of occurrence of all 3 species was highest in shrubland because leaf-area index values were lower in shrubland. This high probability of occurrence likely is related to the species' use of open vegetation for foraging. Fire suppression had little effect on projected vegetation composition because as climate changed there was less fuel and burned area. Our results show that if future water limits on plant type are considered, models that incorporate spatial data may suggest how and where different species of birds may respond to vegetation changes.

Chihuahuan Desert↗
USGS2011

Modelling the distribution of chickens, ducks, and geese in China

Global concerns over the emergence of zoonotic pandemics emphasize the need for high-resolution population distribution mapping and spatial modelling. Ongoing efforts to model disease risk in China have been hindered by a lack of available species level distribution maps for poultry. The goal of this study was to develop 1 km resolution population density models for China's chickens, ducks, and geese. We used an information theoretic approach to predict poultry densities based on statistical relationships between poultry census data and high-resolution agro-ecological predictor variables. Model predictions were validated by comparing goodness of fit measures (root mean square error and correlation coefficient) for observed and predicted values for 1/4 of the sample data which were not used for model training. Final output included mean and coefficient of variation maps for each species. We tested the quality of models produced using three predictor datasets and 4 regional stratification methods. For predictor variables, a combination of traditional predictors for livestock mapping and land use predictors produced the best goodness of fit scores. Comparison of regional stratifications indicated that for chickens and ducks, a stratification based on livestock production systems produced the best results; for geese, an agro-ecological stratification produced best results. However, for all species, each method of regional stratification produced significantly better goodness of fit scores than the global model. Here we provide descriptive methods, analytical comparisons, and model output for China's first high resolution, species level poultry distribution maps. Output will be made available to the scientific and public community for use in a wide range of applications from epidemiological studies to livestock policy and management initiatives.

Agriculture, Ecosystems and Environment↗
USGS2014

A sub-national scale geospatial analysis of diamond deposit lootability: the case of the Central African Republic

The Central African Republic (CAR), a country with rich diamond deposits and a tumultuous political history, experienced a government takeover by the Seleka rebel coalition in 2013. It is within this context that we developed and implemented a geospatial approach for assessing the lootability of high value-to-weight resource deposits, using the case of diamonds in CAR as an example. According to current definitions of lootability, or the vulnerability of deposits to exploitation, CAR's two major diamond deposits are similarly lootable. However, using this geospatial approach, we demonstrate that the deposits experience differing political geographic, spatial location, and cultural geographic contexts, rendering the eastern deposits more lootable than the western deposits. The patterns identified through this detailed analysis highlight the geographic complexities surrounding the issue of conflict resources and lootability, and speak to the importance of examining these topics at the sub-national scale, rather than relying on national-scale statistics.

The Extractive Industries and Society↗
USGS2012

Advances in the simulation and automated measurement of well-sorted granular material: 1. Simulation

1. In this, the first of a pair of papers which address the simulation and automated measurement of well-sorted natural granular material, a method is presented for simulation of two-phase (solid, void) assemblages of discrete non-cohesive particles. The purpose is to have a flexible, yet computationally and theoretically simple, suite of tools with well constrained and well known statistical properties, in order to simulate realistic granular material as a discrete element model with realistic size and shape distributions, for a variety of purposes. The stochastic modeling framework is based on three-dimensional tessellations with variable degrees of order in particle-packing arrangement. Examples of sediments with a variety of particle size distributions and spatial variability in grain size are presented. The relationship between particle shape and porosity conforms to published data. The immediate application is testing new algorithms for automated measurements of particle properties (mean and standard deviation of particle sizes, and apparent porosity) from images of natural sediment, as detailed in the second of this pair of papers. The model could also prove useful for simulating specific depositional structures found in natural sediments, the result of physical alterations to packing and grain fabric, using discrete particle flow models. While the principal focus here is on naturally occurring sediment and sedimentary rock, the methods presented might also be useful for simulations of similar granular or cellular material encountered in engineering, industrial and life sciences.

Journal of Geophysical Research F: Earth Surface↗