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At least 73 records · Page 4Linked to original sources
USGS2014

Strategies for preventing invasive plant outbreaks after prescribed fire in ponderosa pine forest

Land managers use prescribed fire to return a vital process to fire-adapted ecosystems, restore forest structure from a state altered by long-term fire suppression, and reduce wildfire intensity. However, fire often produces favorable conditions for invasive plant species, particularly if it is intense enough to reveal bare mineral soil and open previously closed canopies. Understanding the environmental or fire characteristics that explain post-fire invasive plant abundance would aid managers in efficiently finding and quickly responding to fire-caused infestations. To that end, we used an information-theoretic model-selection approach to assess the relative importance of abiotic environmental characteristics (topoedaphic position, distance from roads), pre-and post-fire biotic environmental characteristics (forest structure, understory vegetation, fuel load), and prescribed fire severity (measured in four different ways) in explaining invasive plant cover in ponderosa pine forest in South Dakota’s Black Hills. Environmental characteristics (distance from roads and post-fire forest structure) alone provided the most explanation of variation (26%) in post-fire cover of Verbascum thapsus (common mullein), but a combination of surface fire severity and environmental characteristics (pre-fire forest structure and distance from roads) explained 36–39% of the variation in post-fire cover of Cirsium arvense (Canada thistle) and all invasives together. For four species and all invasives together, their pre-fire cover explained more variation (26–82%) in post-fire cover than environmental and fire characteristics did, suggesting one strategy for reducing post-fire invasive outbreaks may be to find and control invasives before the fire. Finding them may be difficult, however, since pre-fire environmental characteristics explained only 20% of variation in pre-fire total invasive cover, and less for individual species. Thus, moderating fire intensity or targeting areas of high severity for post-fire invasive control may be the most efficient means for reducing the chances of post-fire invasive plant outbreaks when conducting prescribed fires in this region.

South Dakota, Wyoming↗
USGS2005

Ground-water vulnerability to nitrate contamination at multiple thresholds in the mid-Atlantic region using spatial probability models

The U.S. Geological Survey, in cooperation with the U.S. Environmental Protection Agency?s Regional Vulnerability Assessment Program, has developed a set of statistical tools to support regional-scale, ground-water quality and vulnerability assessments. The Regional Vulnerability Assessment Program?s goals are to develop and demonstrate approaches to comprehensive, regional-scale assessments that effectively inform managers and decision-makers as to the magnitude, extent, distribution, and uncertainty of current and anticipated environmental risks. The U.S. Geological Survey is developing and exploring the use of statistical probability models to characterize the relation between ground-water quality and geographic factors in the Mid-Atlantic Region. Available water-quality data obtained from U.S. Geological Survey National Water-Quality Assessment Program studies conducted in the Mid-Atlantic Region were used in association with geographic data (land cover, geology, soils, and others) to develop logistic-regression equations that use explanatory variables to predict the presence of a selected water-quality parameter exceeding a specified management concentration threshold. The resulting logistic-regression equations were transformed to determine the probability, P(X), of a water-quality parameter exceeding a specified management threshold. Additional statistical procedures modified by the U.S. Geological Survey were used to compare the observed values to model-predicted values at each sample point. In addition, procedures to evaluate the confidence of the model predictions and estimate the uncertainty of the probability value were developed and applied. The resulting logistic-regression models were applied to the Mid-Atlantic Region to predict the spatial probability of nitrate concentrations exceeding specified management thresholds. These thresholds are usually set or established by regulators or managers at National or local levels. At management thresholds of 1 milligram per liter and 3 milligrams per liter as nitrogen, the probability of nitrate concentrations exceeding these levels is greater than 50 percent (0.50) throughout much of the Mid-Atlantic Region. This includes extensive areas throughout central Maryland, southeastern Pennsylvania, northwestern Pennsylvania, and the Delmarva Peninsula. In addition, extensive areas in North Carolina and Virginia also have high probabilities of nitrate concentrations in ground water exceeding management thresholds of 1 milligram per liter and 3 milligrams per liter. The mapped areas showing a high predicted probability of nitrate concentrations in ground water exceeding 1 milligram per liter and 3 milligrams per liter correspond to areas that are mapped as cultivated land cover and/or overlying carbonate rocks. At a management threshold of 10 milligrams per liter (corresponding to the U.S. Environmental Protection Agency standard for nitrate in drinking water of 10 milligrams per liter), the predicted probability of nitrate concentrations in ground water exceeding this level is low for most of the Mid-Atlantic Region, except for the Delmarva Peninsula, southeastern Pennsylvania, and areas mapped as carbonate rocks in Virginia, Maryland, and Pennsylvania.

Scientific Investigations Report↗
USGS2022

Thirteen novel ideas and underutilized resources to support progress towards a range-wide American eel stock assessment

A robust assessment of the American eel ( Anguilla rostrata ) stock, required to guide conservation efforts, is challenged by the species’ vast range, high variability in demographic parameters and data inadequacies. Novel ideas and underutilised resources that may assist both analytic assessments and spatially oriented modelling include (1) species and environmental databases; (2) mining of data from scattered sources; (3) infilling of data gaps by spatial analysis; (4) age estimation from measurements of DNA methylation; evaluation of eel abundance by (5) larval, (6) glass-bottom boat, (7) net enclosure and (8) eDNA surveys; (9) accounting for dam-induced habitat increases in eel watercourse modelling; (10) spatially oriented modelling with and without temporal components; (11) geographically nested modelling of glass eel recruitment; (12) spawner per recruit modelling and (13) life cycle modelling to examine larval allocation effects. Eel biologists are too few to gather the required assessment data across all of the species’ range. Public posting of electrofishing and eDNA metabarcoding data sets and the use of machine learning techniques to comprehensively inventory small dams will help meet some data needs. These approaches address only a small proportion of the assessment challenges that face American eels. Worldwide collaboration amongst Anguilla scientists is a key enabler of progress towards stock assessment goals.

North America, South America↗
USGS2023

2023 Coastal master plan: ICM-wetlands – Submerged aquatic vegetation (SAV) updates

Submerged aquatic vegetation (SAV) provides critical structural habitat for valuable nekton and wildlife species across coastal ecosystems and can buffer the negative effects of land loss. Landscape change and restoration efforts across coastal Louisiana can impact the occurrence, coverage, and species assemblages of SAV, and changes to these foundational species can have cascading impacts across food webs. To support the 2023 Coastal Master Plan efforts, a unique SAV model was developed to assess coverage and occurrence of SAV across aquatic waterbodies in response to environmental variables evaluated. This effort created a spatial model describing the probability of presence of SAV across the study area in response to changing conditions over the modeled time period. To develop the initial coverage data layer, we used remotely sensed Normalized Difference Vegetation Index (NDVI) and modified Normalized Difference Water Index (mNDWI) data from 2015-2018 to identify areas containing variable vegetation and water spectral reflectance. Key environmental variables evaluated included total suspended sediments (TSS), salinity, and physical exposure. Seasonal estimates for TSS and salinity were used, as research indicates that seasonal environmental variability is a significant driver for SAV establishment. Seasonal salinity was derived from Coast-wide Reference Monitoring Station (CRMS) data, and seasonal TSS was estimated from hyperspectral imagery. Estimates of physical exposure have previously been provided by calculating fetch (the distance across water over which waves can propagate), but this proved to be too computationally intensive to be feasible, and we found distance to land to be a reasonable proxy for exposure. To represent geographic conditions and historical factors influences on SAV establishment and occurrence (e.g., variables too numerous and complex to model) we developed a basin variable that served as a proxy for complex historical, or prior, conditions, determined by the forested, fresh, intermediate, brackish, or saline (FFIBS) score. The final model included spring TSS, spring salinity, distance to land, and the basin prior. The model performed well for the area evaluated, correctly classifying SAV (as present or absent) 89% of the time (Kappa = 580). SAV probability of presence responded as expected to change in these environmental variables, with likelihood of occurrence decreasing in response to increasing spring TSS, spring salinity, and distance to land. However, the model was more accurate at predicting absence (true negative = 0.940) than predicting presence (true positive = 0.626), suggesting that the scale of the model may limit the ability to predict presence. Moreover, the simplicity of the model limited the accuracy in highly dynamic environments, for example near the outflow of diversions or areas of significant changes in salinity or TSS. Through incorporating underwater communities like SAV, this master plan provides a holistic view of coastal change and restoration. To create healthy ecological structure and function in wetland habitats, the submergent communities must be considered alongside the emergent habitats. As the benefits of SAV are increasingly recognized, both here in Louisiana and beyond, SAV restoration and the use of SAV communities in assessing and improving ecological condition are becoming more common.

Louisiana↗
USGS2012

Ensemble forecasting of potential habitat for three invasive fishes

Aquatic invasive species pose major ecological and economic threats to aquatic ecosystems worldwide via displacement, predation, or hybridization with native species and the alteration of aquatic habitats and hydrologic cycles. Modeling the habitat suitability of alien aquatic species through spatially explicit mapping is an increasingly important risk assessment tool. Habitat modeling also facilitates identification of key environmental variables influencing invasive species distributions. We compared four modeling methods to predict the potential continental United States distributions of northern snakehead Channa argus (Cantor, 1842), round goby Neogobius melanostomus (Pallas, 1814), and silver carp Hypophthalmichthys molitrix (Valenciennes, 1844) using maximum entropy (Maxent), the genetic algorithm for rule set production (GARP), DOMAIN, and support vector machines (SVM). We used inventory records from the USGS Nonindigenous Aquatic Species Database and a geographic information system of 20 climatic and environmental variables to generate individual and ensemble distribution maps for each species. The ensemble maps from our study performed as well as or better than all of the individual models except Maxent. The ensemble and Maxent models produced significantly higher accuracy individual maps than GARP, one-class SVMs, or DOMAIN. The key environmental predictor variables in the individual models were consistent with the tolerances of each species. Results from this study provide insights into which locations and environmental conditions may promote the future spread of invasive fish in the US.

Aquatic Invasions↗
USGS2013

Vegetation classification and mapping, Vicksburg National Military Park, Mississippi

Executive Summary The National Park Service (NPS) Gulf Coast Inventory and Monitoring Network, with the support of the National Park Service Vegetation Inventory completed vegetation classification and mapping for Vicksburg National Military Park (VICK), in Warren County, Mississippi, from 2004 to 2010. VICK is one of more than 250 NPS units to be covered by the Vegetation Inventory. Methods and procedures follow those of the NPS Vegetation Inventory as of August, 2012 ( http://science.nature.nps.gov/im/inventory/veg/index.cfm ). Ecologists collected floristic and environmental data from 45 vegetation field plots and classified and described 13 plant community types corresponding to US National Vegetation Classification (USNVC) associations from these data. This classification was used to map 712 hectares (1,698 acres) of Vicksburg National Military Park, in ten map classes corresponding to vegetation associations and two non-vegetated land cover classes, from a digital orthomosaic created from 1:24,000 color infrared aerial photographs and digital elevation model (DEM) data. Methods used to delineate stands varied according to vegetation type and were a combination of incorporating stand boundaries from an existing map, new “heads-up” interpretation and modeling of digital environmental spatial data. A thematic accuracy assessment collected vegetation data from 118 field observations stratified across the vegetation classes. The overall accuracy of the vegetation map was estimated to be 60.5% at the thematic level of USNVC association and 92.4% at the thematic level of USNVC group. Estimates of areas of vegetation types, as adjusted for mapping error, were calculated for all types mapped and/or observed during the thematic accuracy assessment. An important finding of this study is that the composition of the forests of VICK is substantially different from that described by earlier, more qualitative, accounts. Implications of these findings and possible applications of the data for resource monitoring and management are presented. This report summarizes the methods and general results of the Vegetation Inventory for Vicksburg National Military Park. Appendices include descriptions of vegetation types, a field key to the types, a list of plant species, and accuracy assessment contingency tables. This report is supplemented by the project data, which include: spatial database containing map polygon attributes. Geospatial products are in Universal Transverse Mercator (UTM) projection, Zone 15, using the North American Datum of 1983. all field data (plot, observation point, and accuracy assessment point) stored in a Microsoft Access database metadata for all digital products The data and reports produced by this investigation reside at: https://irma.nps.gov/App/ and/or at: http://biology.usgs.gov/npsveg/

Mississippi↗
USGS2012

Large-scale monitoring of shorebird populations using count data and N-mixture models: Black Oystercatcher ( Haematopus bachmani ) surveys by land and sea

Large-scale monitoring of bird populations is often based on count data collected across spatial scales that may include multiple physiographic regions and habitat types. Monitoring at large spatial scales may require multiple survey platforms (e.g., from boats and land when monitoring coastal species) and multiple survey methods. It becomes especially important to explicitly account for detection probability when analyzing count data that have been collected using multiple survey platforms or methods. We evaluated a new analytical framework, N -mixture models, to estimate actual abundance while accounting for multiple detection biases. During May 2006, we made repeated counts of Black Oystercatchers ( Haematopus bachmani ) from boats in the Puget Sound area of Washington ( n = 55 sites) and from land along the coast of Oregon ( n = 56 sites). We used a Bayesian analysis of N-mixture models to (1) assess detection probability as a function of environmental and survey covariates and (2) estimate total Black Oystercatcher abundance during the breeding season in the two regions. Probability of detecting individuals during boat-based surveys was 0.75 (95% credible interval: 0.42–0.91) and was not influenced by tidal stage. Detection probability from surveys conducted on foot was 0.68 (0.39–0.90); the latter was not influenced by fog, wind, or number of observers but was ~35% lower during rain. The estimated population size was 321 birds (262–511) in Washington and 311 (276–382) in Oregon. N-mixture models provide a flexible framework for modeling count data and covariates in large-scale bird monitoring programs designed to understand population change.

Oregon;Washington↗
USGS2022

Maximizing species distribution model performance when using historical occurrences and variables of varying persistency

Occurrence data used to build species distribution models often include historical records from locations in which the species no longer exists. When these records are paired with contemporary environmental values that no longer represent the conditions the species experienced, the model creates false associations that hurt predictive performance. The extent of mismatching increases with the number of historical occurrences and with inclusion of environmental variables that are prone to change over time. Indeed, the mismatch between occurrence data and contemporaneous environmental variables is a common dilemma when modeling rare or cryptic species, especially those of conservation concern that were once more abundant. Herein, we assess (1) the impact of historical occurrences on model performance across three sets of environmental variables of increasing persistency and (2) the performance of models built using selected-historical occurrences from locations that showed evidence of limited environmental change over time. Concepts are tested on federally listed flatwoods salamanders, reflecting real-world conservation management efforts. We predicted that, compared to other occurrence sets, (1) historical occurrences would perform best with environmental variables that were more persistent, (2) recent occurrences would perform best when the environmental variables were more impersistent, and that (3) our selected-historical occurrences would perform best with a combination of persistent and impersistent variables. Our results showed the expected inversion of model performance of recent and historical occurrences across environmental variables of increasing persistency when evaluated by correct predictions. However, the inversion was not seen in area under the curve performance, in which historical occurrences outperformed recent occurrence models across all variable sets. Selected-historical occurrences did not notably improve performance over all-historical occurrences in any metric or variable set. To maximize utility and performance, modelers could acknowledge potential trade-offs from inclusion of historical occurrences and consider number and age of recent and historical occurrences available, the persistency of environmental variables considered, and how their conservation goals are reflected in model design and evaluation, particularly with respect to sensitivity versus specificity. Our study lends support for inclusion of historical occurrences, with the potential exception of mostly impersistent variables when sensitivity is the highest priority.

Alabama, Florida, Georgia, South Carolina↗
USGS2011

Compilation of watershed models for tributaries to the Great Lakes, United States, as of 2010, and identification of watersheds for future modeling for the Great Lakes Restoration Initiative

As part of the Great Lakes Restoration Initiative (GLRI) during 2009–10, the U.S. Geological Survey (USGS) compiled a list of existing watershed models that had been created for tributaries within the United States that drain to the Great Lakes. Established Federal programs that are overseen by the National Oceanic and Atmospheric Administration (NOAA) and the U.S. Army Corps of Engineers (USACE) are responsible for most of the existing watershed models for specific tributaries. The NOAA Great Lakes Environmental Research Laboratory (GLERL) uses the Large Basin Runoff Model to provide data for the management of water levels in the Great Lakes by estimating United States and Canadian inflows to the Great Lakes from 121 large watersheds. GLERL also simulates streamflows in 34 U.S. watersheds by a grid-based model, the Distributed Large Basin Runoff Model. The NOAA National Weather Service uses the Sacramento Soil Moisture Accounting model to predict flows at river forecast sites. The USACE created or funded the creation of models for at least 30 tributaries to the Great Lakes to better understand sediment erosion, transport, and aggradation processes that affect Federal navigation channels and harbors. Many of the USACE hydrologic models have been coupled with hydrodynamic and sediment-transport models that simulate the processes in the stream and harbor near the mouth of the modeled tributary. Some models either have been applied or have the capability of being applied across the entire Great Lakes Basin; they are (1) the SPAtially Referenced Regressions On Watershed attributes (SPARROW) model, which was developed by the USGS; (2) the High Impact Targeting (HIT) and Digital Watershed models, which were developed by the Institute of Water Research at Michigan State University; (3) the Long-Term Hydrologic Impact Assessment (L–THIA) model, which was developed by researchers at Purdue University; and (4) the Water Erosion Prediction Project (WEPP) model, which was developed by the National Soil Erosion Research Laboratory of the U.S. Department of Agriculture. During 2010, the USGS used the Precipitation-Runoff Modeling System (PRMS) to create a hydrologic model for the Lake Michigan Basin to assess the probable effects of climate change on future groundwater and surface-water resources. The Water Availability Tool for Environmental Resources (WATER) model and the Analysis of Flows In Networks of CHannels (AFINCH) program also were used to support USGS GLRI projects that required estimates of streamflows throughout the Great Lakes Basin. This information on existing watershed models, along with an assessment of geologic, soils, and land-use data across the Great Lakes Basin and the identification of problems that exist in selected tributary watersheds that could be addressed by a watershed model, was used to identify three watersheds in the Great Lakes Basin for future modeling by the USGS. These watersheds are the Kalamazoo River Basin in Michigan, the Tonawanda Creek Basin in New York, and the Bad River Basin in Wisconsin. These candidate watersheds have hydrogeologic, land-type, and soil characteristics that make them distinct from each other, but that are representative of other tributary watersheds within the Great Lakes Basin. These similarities in the characteristics among nearby watersheds will enhance the usefulness of a model by improving the likelihood that parameter values from a previously modeled watershed could reliably be used in the creation of a model of another watershed in the same region. The software program Hydrological Simulation Program–Fortran (HSPF) was selected to simulate the hydrologic, sedimentary, and water-quality processes in these selected watersheds. HSPF is a versatile, process-based, continuous-simulation model that has been used extensively by the scientific community, has the ongoing technical support of the U.S. Environmental Protection Agency and USGS, and provides a means to evaluate the effects that land-use changes or management practices might have on the simulated processes.

Great Lakes Basin↗
USGS2025

Evaluation of daily stream temperature predictions (1979-2021) across the contiguous United States using a spatiotemporal aware machine learning algorithm

Stream temperature controls a variety of physical and biological processes that affect ecosystems, human health, and economic activities. We used 42 years (1979–2021) of data to predict daily summary statistics of stream temperature across >50,000 stream reaches in the contiguous United States using a recurrent graph convolution network. We comprehensively documented the performance – both across all reaches and by stream type (e.g., reservoir or groundwater influence) – as a baseline for future improvement. The model showed reach-level RMSE of <2 °C with 90 % prediction intervals that contain 90.7 % of observations. We also assessed how the model captured variability in ecologically relevant metrics (e.g., R 2 for annual 7-day maximum = 0.76; R 2 for days exceeding 25 °C = 0.75). This model does not outperform state-of-the-art machine learning efforts (e.g., RMSE ≤1.5 °C) due to a limited input set but does provide the most spatially complete modeling to date to support water availability assessments.

contiguous United States↗
USGS1988

Guidance for modeling causes and effects in environmental problem solving

Environmental problems are difficult to solve because their causes and effects are not easily understood. When attempts are made to analyze causes and effects, the principal challenge is organization of information into a framework that is logical, technically defensible, and easy to understand and communicate. When decisionmakers attempt to solve complex problems before an adequate cause and effect analysis is performed there are serious risks. These risks include: greater reliance on subjective reasoning, lessened chance for scoping an effective problem solving approach, impaired recognition of the need for supplemental information to attain understanding, increased chance for making unsound decisions, and lessened chance for gaining approval and financial support for a program/ Cause and effect relationships can be modeled. This type of modeling has been applied to various environmental problems, including cumulative impact assessment (Dames and Moore 1981; Meehan and Weber 1985; Williamson et al. 1987; Raley et al. 1988) and evaluation of effects of quarrying (Sheate 1986). This guidance for field users was written because of the current interest in documenting cause-effect logic as a part of ecological problem solving. Principal literature sources relating to the modeling approach are: Riggs and Inouye (1975a, b), Erickson (1981), and United States Office of Personnel Management (1986).

Report↗
USGS2004

Mute swans: Natural (?) environmental indicators

The rapid expansion of the Chesapeake Bay's population of feral mute swans (Cygnus olar), coupled with a dramatic Bay-wide decline in submerged aquatic vegetation (SAV), has fueled much of the current debate surrounding the need for a management plan to protect the aquatic food resources that are critical to many species native to the Bay. Crucial to this decision process is a sound understanding of the ecological ramifications of having the year-round presence of a large, nonnative, aquatic herbivore on the Bay. Ultimately, this will require a quantitative assessment of the ecological harm currently posed by mute swans before a biologically defensible management strategy can be developed. Unfortunately, very little new information specific to the Bay's mute swan population has been gathered since Reese first studied them in the late 1960s and 1970s. While the debate over what to do about the rapidly expanding mute swan population continues, there is much that can be gained from study of this beautiful intruder. Several recent studies of the feeding habits of mute swans have shown that mutes can provide a unique barometer, or indicator, of environmental conditions. Because of their reliance on SAV as a primary food source, monitoring the density of swans utilizing a particular area can give some indication of the status of the area's grass beds. This phenomenon was clearly demonstrated during the summer of 1999 when there was a dramatic decline in the number of swans observed around the Eastern Neck NWR, a traditional population stronghold. The shift in bird use was precipitated by a rapid, large-scale collapse of the area's aquatic grass beds, possibly the result of a prolonged drought. During the winter of 2000/2001, a similar ecological assessment was conducted by comparing body weights of swans collected from Tangier Sound, an area with relatively abundant grass beds, and swans from the waters adjacent to Eastern Neck Island. Swans weights tended to reflect the conditions of their surroundings, with the Tangier Sound birds being slightly heavier at the onset of the breeding season. Interestingly, the birds at Eastern Neck showed a 1 kg decline in weight after dispersal from their wintering locations and entered the breeding season with noticeably less subcutaneous fat than the birds sampled in Tangier Sound. The fact that mute swans are nonmigratory and feed exclusively on benthic food items makes them an ideal organ-ism to monitor the degree of contamination of sediments within the Bay. In 1995, we compared the accumulation of metals by mute swans and other waterfowl and related it to metal concentrations in the sediments from the areas where the birds were collected. This study led to the development of an exposure model that more accurately assesses the risk of exposure to environmental contaminants through incidental ingestion of sediments, as opposed to the traditional assessment of contaminant accumulation through the food chain. This sediment exposure pathway was subsequently shown to be the primary route of exposure of swans to metals in risk assessments conducted at two Superfund sites.

Book chapter↗
USGS2025

Meta-analysis of a megafish: Assessing patterns and predictors of Alligator Gar movement across multiple populations

Background Freshwater megafishes are among some of the most commercially and ecologically important aquatic organisms yet are disproportionately threatened with range and population reduction. Anthropogenic alterations of rivers influencing migrations are among the most significant causes for these declines. However, migratory fishes do not always respond similarly to movement barriers and thus it is necessary to develop models to predict movements of freshwater migratory fishes in the face of anthropogenic alteration. Predicting movement of freshwater fishes is often investigated using statistical packages. However, empirical studies assessing these packages have led to mixed results, questioning its applicability to all taxa. We argue that spatial, temporal, and environmental attributes are more influential for movement of a migratory megafish, the Alligator Gar ( Atractosteus spatula ), than the current parameters explored in a globally relevant fish movement model. Methods This study explored two independent mobile telemetry datasets investigating Alligator Gar movement on the Brazos and Trinity rivers. Environmental associations were investigated to predict Alligator Gar displacement and dispersal using generalized additive models, generalized linear models, and model selection. Leptokurtosis of Alligator Gar populations was also assessed. Predictability of the movement model was tested by comparing observed to model derived stationary and mobile components making up a leptokurtic movement distribution. Results Our study suggests that current and antecedent measures of discharge and water temperature are positively correlated with Alligator Gar displacement and dispersal. However, these patterns are only detectable when monthly relocation intervals are explored rather than seasonal scales. Leptokurtosis was observed in both Alligator Gar populations. However, movement was normally distributed (i.e., mesokurtic) under tracking events following high flood pulses. Additionally, predicted Alligator Gar movement was significantly farther under modeled values compared to observed values, in part because the species undergoes cyclical migrations for reproduction that are sensitive to water temperature and discharge. Discussion In conclusion, this study provides an alternative framework to assess the movement patterns of migratory fishes, which could be tested on additional freshwater fishes, and suggests that assessing spatial, environmental, and temporal processes simultaneously are necessary to capture the complexities of fish movement which currently are unavailable for the movement model we investigated.

Texas↗
USGS2022

Balancing model generality and specificity in management-focused habitat selection models for Gunnison sage-grouse

Identifying, protecting, and restoring habitats for declining wildlife populations is foundational to conservation and recovery planning for any species at risk of decline. Resource selection analysis is a key tool to assess habitat and prescribe management actions. Yet, it can be challenging to map suitable resource conditions across a wide range of ecological contexts and use the resulting models to identify effective and universal habitat improvement actions. We developed a management-centric modeling approach that sought to balance the need to evaluate the consistency of key habitat conditions and improvement actions across multiple, distinct populations, while allowing context-specific environmental variables and spatial scales to nuance selection responses that form the basis of location-specific management prescriptions. To demonstrate this approach, we developed a set of habitat selection models for Gunnison sage-grouse ( Centrocercus minimus ), a threatened species under the U.S. Endangered Species Act. Conservation, species recovery, and habitat management efforts are needed in six isolated satellite populations (San Miguel, Crawford, Piñon Mesa, Dove Creek, Cerro Summit-Cimarron-Sims, and Poncha Pass) where environmental conditions differ, and the already small number of birds are declining. We used multi-scale and seasonal resource selection analyses to quantify relationships between environmental conditions and sites used by animals. All models included key habitat variables often altered through management actions to assess their differential influences across models. We found important similarities and differences among satellites, indicating that, although some rules of thumb are generally well-grounded, the consideration of population-specific environmental differences could increase the efficiency of local habitat improvement actions. Sage-grouse also had diverse responses to resource conditions at different scales, indicating that regional spatial (e.g., landscape) and local patch scale can differentially influence expected habitat improvements associated with where such management actions are implemented. Although context variables such as topography cannot be manipulated, sage-grouse associations revealed information that could guide the siting of improvement actions. This approach to balancing management objectives associated with habitat assessment may benefit spatially-structured populations with different environmental contexts and species with complex habitat needs and associations.

Colorado↗
USGS2017

Continued geophysical logging near the GMH Electronics National Priorities List Superfund site near Roxboro, North Carolina

The U.S. Geological Survey South Atlantic Water Science Center collected borehole geophysical logs and images and continuous water-level data near the GMH Electronics National Priorities List Superfund site near Roxboro, North Carolina, during December 2012 through July 2015. Previous work by the U.S. Geological Survey South Atlantic Water Science Center at the site involved the collection of borehole geophysical log data in 15 wells, in addition to surface geologic mapping and passive diffusion bag sampling. In a continued effort to assist the U.S. Environmental Protection Agency in developing a conceptual groundwater model to assess current contaminant distribution and future migration of contaminants, more than 900 subsurface features (primarily fracture orientations) in 10 open borehole wells were delineated and continuous water-level data information from 14 monitoring wells within close proximity of the initially drilled boreholes was collected to observe any induced water-level fluctuations during drilling operations

North Carolina↗
USGS1996

Developing a temporal database of urban development for the Baltimore/Washington region

The U.S. Geological Survey (USGS), the University of Maryland Baltimore County (UMBC), and the U.S. Bureau of the Census are working together as a multiagency, multidisciplinary team in developing a temporal database that documents the growth of the Baltimore-Washington metropolitan region. This database consists of urban development, principal transportation, shoreline, and population density change. The urban development theme, considered a primary data layer in the study of urban land transformation resulting from human impact on the land, is the focus of this paper. The Baltimore-Washington Spatial Dynamics and Human Impacts Study builds on earlier research efforts that mapped urban land use change for the San Francisco Bay area (Acevedo and Bell, 1994; Bell and others, 1995; Kirtland and others, 1994). In developing a temporal database (Acevedo and others, in press), the team participants hope to provide data that can be used to study patterns of urban growth; assess ecological, environmental, and climatic impacts of urban change; and model and predict future urbanization patterns and impacts (Clarke and others, 1996). Both the San Francisco and Baltimore-Washington regions were selected because of the rapid urban growth and resulting impacts on their ecosystems. The Chesapeake Bay region in particular has undergone extensive environmental agitation due to the hydrologic problems that have arisen from the increase in impermeable surfaces and structures, that is buildings and pavement that physically cover the soil. Because of the inability of water to percolate into the ground, little purification occurs by filtration. Water runs over paved surfaces and quickly washes high levels of toxins directly into the water system. Toxins like gasoline, oil, and fertilizer have dramatically affected the local streams, rivers, and the bay. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. This paper describes the techniques used to map the extent of urban areas for Phase I and does not discuss Phase II in detail because the work is still in progress. In this study, urban development is defined as areas of intensive use, with much of the land covered by structures. The built-up areas are characterized by the existence of a systematic street pattern, and the relative concentration of buildings and associated intensive use areas, such as parking lots. Using this definition, urban development does not refer to political boundaries and may include incorporated or unincorporated areas as well as military reservations. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. To build the urban component of the temporal database, a multidisciplinary team was assembled and a phased approach initiated. Expanding on procedures developed for the San Francisco Regional Study (Bell and others, 1995), the team developed data definitions, a classification scheme, compilation criteria, mapping specifications, guidelines for source materials, and metadata specifications to support development of a logically consistent dataset. Extensive documentation procedures were established to ensure consistency in data collection, and for subsequent application to other regions. Phase II was the implementation of the regional mapping effort. ABSTRACT The U.S. Geological Survey, the University of Maryland Baltimore County, and the U.S. Bureau of the Census are developing a temporal database to study urban development in the Baltimore-Washington region. The primary data layer, the extent of urban or built-up areas, was compiled using a geographic information system and historical maps, remotely sensed data, digital land use data, and census information from a variety of sources. Urban land use change has been documented by the Baltimore-Washington Spatial Dynamics & Human Impact Study Team for the last 200 years. The methods, definitions, and collection criteria used to define urban or built-up areas were developed by a multi-disciplinary team that also ensures consistency in collection techniques and documentation methods for subsequent application in other regions. Animation techniques were used to visualize the database and to document the evolution of the region's urban landscape. The database is an important tool to urban and regional planners, ecologists, and global change researchers for measuring trends in urban sprawl, analyzing patterns of water pollution, understanding the impacts of development on ecosystems, and developing predictive modeling techniques to better forecast areas of urban growth. The study area for Phase I consisted of an approximate area of 15- by 15-minute segment centered around the city of Baltimore (fig. 1). Phase I was used as a prototype for the technique development and integration that the multiagency collaborative effort would require. The regional study, Phase II, encompassed a 2-degree square centered on Washington, D.C. With more than 7 million people spread across 39 counties, the Baltimore-Washington region is one the Nation's fastest growing metropolitan areas. The two cities are rapidly merging into one.

Maryland↗
USGS2016

Integrated modeling approach for fate and transport of submerged oil and oil-particle aggregates in a freshwater riverine environment

The Enbridge Line 6B pipeline release of diluted bitumen into the Kalamazoo River downstream of Marshall, Michigan, U.S.A., in July 2010 was one of the largest oil spills into freshwater in North American history. A portion of the oil interacted with river sediment and submerged requiring the development and implementation of new approaches for detection and recovery of oil mixed with river sediment. Hydrodynamic and sediment transport modeling became an integral part of containment and recovery operations for decision support about the potential fate and migration of submerged oil and oiled sediment. Three models were developed for the U.S. Environmental Protection Agency to cover a range of spatial scales of interest to onsite operations. Two-dimensional (2D) hydrodynamic and sediment transport models from the Environmental Fluid Dynamics Code and the sediment bed model SEDZLJ1 were used to simulate potential resuspension, migration, and deposition of submerged oil and oiled sediment along a 38-mile reach of the Kalamazoo River affected by the oil from Marshall to Kalamazoo. An algorithm was added to SEDZLJ to represent three additional particle size classes of oilparticle aggregates (OPAs) with a range of sizes, specific gravities, and settling velocities. Field and laboratory experiments and flume tests were done to support the numerical modeling of OPAs. A three-dimensional hydrodynamic model was developed to simulate hydrodynamics and OPA tracking through Morrow Lake, the most downstream impoundment. This model incorporated wind and dam operations into high and low flow, lake drawdown, and containment simulations. Finally, a 2D unstructured grid model, HydroSed2D, was used to simulate flows and sediment transport along 1- to 2-mile segments of the Kalamazoo River around islands and through side channels and backwater areas that are particularly prone to submerged oil deposition. Integrated models could be developed quickly due to the availability of information and services combined with spill response operations that included: bathymetry and topography data, fieldbased geomorphic mapping of submerged oil, and discharge measured at U.S. Geological Survey streamflow gauges. Modeling results were included in a multiple-lines-of-evidence approach that was used by the Federal On-Scene Coordinator and operations staff for decision-making related to assessment and recovery of submerged oil, as well as net environmental benefit analysis. Similar modeling approaches will likely be useful for future oil spills in riverine environments.

Michigan↗