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USGS2014

Beach science in the Great Lakes

Monitoring beach waters for human health has led to an increase and evolution of science in the Great Lakes, which includes microbiology, limnology, hydrology, meteorology, epidemiology, and metagenomics, among others. In recent years, concerns over the accuracy of water quality standards at protecting human health have led to a significant interest in understanding the risk associated with water contact in both freshwater and marine environments. Historically, surface waters have been monitored for fecal indicator bacteria (fecal coliforms, Escherichia coli , enterococci), but shortcomings of the analytical test (lengthy assay) have resulted in a re-focusing of scientific efforts to improve public health protection. Research has led to the discovery of widespread populations of fecal indicator bacteria present in natural habitats such as soils, beach sand, and stranded algae. Microbial source tracking has been used to identify the source of these bacteria and subsequently assess their impact on human health. As a result of many findings, attempts have been made to improve monitoring efficiency and efficacy with the use of empirical predictive models and molecular rapid tests. All along, beach managers have actively incorporated new findings into their monitoring programs. With the abundance of research conducted and information gained over the last 25 years, “Beach Science” has emerged, and the Great Lakes have been a focal point for much of the ground-breaking work. Here, we review the accumulated research on microbiological water quality of Great Lakes beaches and provide a historic context to the collaborative efforts that have advanced this emerging science.

Great Lakes↗

USGS2022

Bayesian applications in environmental and ecological studies with R and Stan

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

Book↗

USGS2013

Developing an outcome-based biodiversity metric in support of the field to market project: Final report

Our objective was to create a metric that would calculate the relative impact of common commercial agricultural practices on terrestrial vertebrate richness. We sought to define impacts in fields (including field borders) of the southeastern region’s commercial production of corn, wheat, soy, and cotton. The metric is intended to serve as an educational tool, allowing producers to see how operational decisions made at the field level impact overall vertebrate species richness and to explore decision impacts to targeted species groups (e.g. game, pest, or beneficial species). Agricultural landscapes are often mistakenly thought to be unsuitable habitat for most species. However, as demonstrated by results reported here, even large-scale, conventional agricultural producers are potentially important partners in biodiversity conservation. Many vertebrate species do inhabit agricultural landscapes, benefitting from the provision of water, food, or shelter within cultivated fields and their immediate borders (e.g., Holland et al. 2012). In the Southeastern US, of the 613 terrestrial vertebrate species modeled by the Southeast Gap Analysis Program (SEGAP) (http://www.basic.ncsu.edu/segap/index.html), 263 utilize row crop and associated agricultural land cover classes as potential habitat (Box 1). While some species may be sensitive to certain operational practices (e.g., tillage, pest management, or field border management practices), others are generally tolerant, and some may benefit either directly or indirectly. For example, field margins and ditches often serve as semi-natural habitats providing foraging resources and shelter for vertebrates and are shown to positively influence species richness and abundance (Billeter et al. 2007; Herzon & Helenius 2008; Marshall & Moonen 2002; Shore et al. 2005; Weibull et al. 2003; Wuczyńskia et al. 2011). Biodiversity responses are, therefore, complex, as an individual species’ responses to agricultural production practices depends on that animal’s resource specialization, mobility, and life history strategies (Jeanneret et al. 2003a, b; Jennings & Pocock 2009). The knowledge necessary to define the biodiversity contribution of agricultural lands is specialized, dispersed, and nuanced, and thus not readily accessible. Given access to clearly defined biodiversity tradeoffs between alternative agricultural practices, landowners, land managers and farm operators could collectively enhance the conservation and economic value of agricultural landscapes. Therefore, Field to Market: The Keystone Alliance for Sustainable Agriculture and The Nature Conservancy jointly funded a pilot project to develop a biodiversity metric to integrate into Field to Market’s existing sustainability calculator, The Fieldprint Calculator (http://www. fieldtomarket.org/). Field to Market: The Keystone Alliance for Sustainable Agriculture is an alliance among producers, agribusinesses, food companies, and conservation organizations seeking to create sustainable outcomes for agriculture. The Fieldprint Calculator supports the Keystone Alliance’s vision to achieve safe, accessible, and nutritious food, fiber and fuel in thriving ecosystems to meet the needs of 9 billion people in 2050. In support of this same vision, our project provides proof-of-concept for an outcome-based biodiversity metric for Field to Market to quantify biodiversity impacts of commercial row crop production on terrestrial vertebrate richness. Little research exists examining the impacts of alternative commercial agricultural practices on overall terrestrial biodiversity (McLaughlin & Mineau 1995). Instead, most studies compare organic versus conventional practices (e.g. Freemark & Kirk 2001; Wickramasinghe et al. 2004), and most studies focus on flora, avian, or invertebrate communities (Jeanneret et al. 2003a; Maes et al. 2008; Pollard & Relton 1970). Therefore, we used an expert-knowledge-based approach to develop a metric that predicts expected impacts to shelter and forage resources, individual species, and overall biodiversity (species richness). This approach is modeled after an ecosystems services concept (WRI 2005), except that we examine services (i.e., resources) provided to vertebrate wildlife rather than service provided to the human population. SEGAP predicts species that are potentially present in an area given landscape-scale habitat availability, configuration, and context (e.g., patch size, proximity to resources, connectivity, potential for disturbance). Based on the prediction of species that may be potentially present, the impacts of management decisions within fields and around their borders can be analyzed based on the impact of those practices to the availability of species’ resources. The final metric provides an index of a producer’s relative impact, but perhaps even more importantly, the underlying database allows producers to explore details such as which species are most impacted or how alternative decisions would impact their score.

Technical Bulletin↗

USGS2003

Quaternary vegetation and climate change in the western United States: Developments, perspectives, and prospects

This chapter explores the strengths and shortcomings of the major sources of data on Quaternary vegetation and climate change and discusses the use of models as a means to explore past and potential future environmental changes. The flora and major vegetation types of the western United States are present for several million years. Ongoing changes in atmospheric chemistry , climate, and human activities may lead to major vegetation changes over the coming decades to centuries. The combination of observations from the paleoenvironmental record, modern ecological studies, and modeling now permit assessments of the magnitude of potential future changes in the context of natural variability. They also provide opportunities for hypothesis testing and identification of the processes driving past changes in vegetation and climate. Understanding the dynamics of paleoenvironmental change can contribute to current conservation and natural resource management efforts and will help conservation and natural resource managers anticipate the potential rate, magnitude, and complexity of future vegetation change.

western United States↗

USGS2017

Local environmental context conditions the impact of Russian olive in a heterogeneous riparian ecosystem

Local abiotic and biotic conditions can alter the strength of exotic species impacts. To better understand the effects of exotic species on invaded ecosystems and to prioritize management efforts, it is important that exotic species impacts are put in local environmental context. We studied how differences in plant community composition, photosynthetically active radiation (PAR), and available soil N associated with Russian olive presence are conditioned by local environmental variation within a western U.S. riparian ecosystem. In four sites along the South Fork of the Republican River in Colorado, we established 200 pairs of plots (underneath and apart from Russian olive) to measure the effects of invasion across the ecosystem. We used a series of a priori mixed models to identify environmental variables that altered the effects of Russian olive. For all response variables, models that included the interaction of environmental characteristics, such as presence/absence of an existing cottonwood canopy, with the presence/absence of Russian olive canopy were stronger candidate models than those that just included Russian olive canopy presence as a factor. Compared with reference plots outside of Russian olive canopy, plots underneath Russian olive had higher relative exotic cover (exotic/total cover), lower perennial C4 grass cover, and higher perennial forb cover. These effects were reduced, however, in the presence of a cottonwood canopy. As expected, Russian olive was associated with reduced PAR and increased N, but these effects were reduced under cottonwood canopy. Our results demonstrate that local abiotic and biotic environmental factors condition the effects of Russian olive within a heterogeneous riparian ecosystem and suggest that management efforts should be focused in open areas where Russian olive impacts are strongest.

Colorado↗

USGS2024

Prioritizing river basins for nutrient studies

Increases in fluxes of nitrogen (N) and phosphorus (P) in the environment have led to negative impacts affecting drinking water, eutrophication, harmful algal blooms, climate change, and biodiversity loss. Because of the importance, scale, and complexity of these issues, it may be useful to consider methods for prioritizing nutrient research in representative drainage basins within a regional or national context. Two systematic, quantitative approaches were developed to (1) identify basins that geospatial data suggest are most impacted by nutrients and (2) identify basins that have the most variability in factors affecting nutrient sources and transport in order to prioritize basins for studies that seek to understand the key drivers of nutrient impacts. The “impact” approach relied on geospatial variables representing surface-water and groundwater nutrient concentrations, sources of N and P, and potential impacts on receptors (i.e., ecosystems and human health). The “variability” approach relied on geospatial variables representing surface-water nutrient concentrations, factors affecting sources and transport of nutrients, model accuracy, and potential receptor impacts. One hundred and sixty-three drainage basins throughout the contiguous United States were ranked nationally and within 18 hydrologic regions. Nationally, the top-ranked basins from the impact approach were concentrated in the Midwest, while those from the variability approach were dispersed across the nation. Regionally, the top-ranked basin selected by the two approaches differed in 15 of the 18 regions, with top-ranked basins selected by the variability approach having lower minimum concentrations and larger ranges in concentrations than top-ranked basins selected by the impact approach. The highest ranked basins identified using the variability approach may have advantages for exploring how landscape factors affect surface-water quality and how surface-water quality may affect ecosystems. In contrast, the impact approach prioritized basins in terms of human development and nutrient concentrations in both surface water and groundwater, thereby targeting areas where actions to reduce nutrient concentrations could have the largest effect on improving water availability and reducing ecosystem impacts.

Environmental Monitoring and Assessment↗

USGS2012

Wildlife contact analysis: Emerging methods, questions, and challenges

Recent technological advances, such as proximity loggers, allow researchers to collect complete interaction histories, day and night, among sampled individuals over several months to years. Social network analyses are an obvious approach to analyzing interaction data because of their flexibility for fitting many different social structures as well as the ability to assess both direct contacts and indirect associations via intermediaries. For many network properties, however, it is not clear whether estimates based upon a sample of the network are reflective of the entire network. In wildlife applications, networks may be poorly sampled and boundary effects will be common. We present an alternative approach that utilizes a hierarchical modeling framework to assess the individual, dyadic, and environmental factors contributing to variation in the interaction rates and allows us to estimate the underlying process variation in each. In a disease control context, this approach will allow managers to focus efforts on those types of individuals and environments that contribute the most toward super-spreading events. We account for the sampling distribution of proximity loggers and the non-independence of contacts among groups by only using contact data within a group during days when the group membership of proximity loggers was known. This allows us to separate the two mechanisms responsible for a pair not contacting one another: they were not in the same group or they were in the same group but did not come within the specified contact distance. We illustrate our approach with an example dataset of female elk from northwestern Wyoming and conclude with a number of important future research directions.

Behavioral Ecology and Sociobiology↗

USGS2013

Comparing approaches to spatially explicit ecosystem service modeling: a case study from the San Pedro River, Arizona

Although the number of ecosystem service modeling tools has grown in recent years, quantitative comparative studies of these tools have been lacking. In this study, we applied two leading open-source, spatially explicit ecosystem services modeling tools – Artificial Intelligence for Ecosystem Services (ARIES) and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) – to the San Pedro River watershed in southeast Arizona, USA, and northern Sonora, Mexico. We modeled locally important services that both modeling systems could address – carbon, water, and scenic viewsheds. We then applied managerially relevant scenarios for urban growth and mesquite management to quantify ecosystem service changes. InVEST and ARIES use different modeling approaches and ecosystem services metrics; for carbon, metrics were more similar and results were more easily comparable than for viewsheds or water. However, findings demonstrate similar gains and losses of ecosystem services and conclusions when comparing effects across our scenarios. Results were more closely aligned for landscape-scale urban-growth scenarios and more divergent for a site-scale mesquite-management scenario. Follow-up studies, including testing in different geographic contexts, can improve our understanding of the strengths and weaknesses of these and other ecosystem services modeling tools as they move closer to readiness for supporting day-to-day resource management.

Arizona;Sonora↗

USGS2017

Harvesting wildlife affected by climate change: a modelling and management approach for polar bears

The conservation of many wildlife species requires understanding the demographic effects of climate change, including interactions between climate change and harvest, which can provide cultural, nutritional or economic value to humans. We present a demographic model that is based on the polar bear Ursus maritimus life cycle and includes density-dependent relationships linking vital rates to environmental carrying capacity ( K ). Using this model, we develop a state-dependent management framework to calculate a harvest level that (i) maintains a population above its maximum net productivity level (MNPL; the population size that produces the greatest net increment in abundance) relative to a changing K , and (ii) has a limited negative effect on population persistence. Our density-dependent relationships suggest that MNPL for polar bears occurs at approximately 0·69 (95% CI = 0·63–0·74) of K . Population growth rate at MNPL was approximately 0·82 (95% CI = 0·79–0·84) of the maximum intrinsic growth rate, suggesting relatively strong compensation for human-caused mortality. Our findings indicate that it is possible to minimize the demographic risks of harvest under climate change, including the risk that harvest will accelerate population declines driven by loss of the polar bear's sea-ice habitat. This requires that (i) the harvest rate – which could be 0 in some situations – accounts for a population's intrinsic growth rate, (ii) the harvest rate accounts for the quality of population data (e.g. lower harvest when uncertainty is large), and (iii) the harvest level is obtained by multiplying the harvest rate by an updated estimate of population size. Environmental variability, the sex and age of removed animals and risk tolerance can also affect the harvest rate. Synthesis and applications . We present a coupled modelling and management approach for wildlife that accounts for climate change and can be used to balance trade-offs among multiple conservation goals. In our example application to polar bears experiencing sea-ice loss, the goals are to maintain population viability while providing continued opportunities for subsistence harvest. Our approach may be relevant to other species for which near-term management is focused on human factors that directly influence population dynamics within the broader context of climate-induced habitat degradation.

Journal of Applied Ecology↗

USGS2015

A collision risk model to predict avian fatalities at wind facilities: an example using golden eagles, Aquila chrysaetos

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle ( Aquila chrysaetos ) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year -1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year -1 ) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80 th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

PLoS ONE↗

USGS2008

Hydrochemical Regions of the Glacial Aquifer System, Northern United States, and Their Environmental and Water-Quality Characteristics

The glacial aquifer system in the United States is a large (953,000 square miles) regional aquifer system of heterogeneous composition. As described in this report, the glacial aquifer system includes all unconsolidated geologic material above bedrock that lies on or north of the line of maximum glacial advance within the United States. Examining ground-water quality on a regional scale indicates that variations in the concentrations of major and minor ions and some trace elements most likely are the result of natural variations in the geologic and physical environment. Study of the glacial aquifer system was designed around a regional framework based on the assumption that two primary characteristics of the aquifer system can affect water quality: intrinsic susceptibility (hydraulic properties) and vulnerability (geochemical properties). The hydrochemical regions described in this report were developed to identify and explain regional spatial variations in ground-water quality in the glacial aquifer system within the hypothetical framework context. Data analyzed for this study were collected from 1991 to 2003 at 1,716 wells open to the glacial aquifer system. Cluster analysis was used to group wells with similar ground-water concentrations of calcium, chloride, fluoride, magnesium, potassium, sodium, sulfate, and bicarbonate into five unique groups. Maximum Likelihood Classification was used to make the extrapolation from clustered groups of wells, defined by points, to areas of similar water quality (hydrochemical regions) defined in a geospatial model. Spatial data that represented average annual precipitation, average annual temperature, land use, land-surface slope, vertical soil permeability, average soil clay content, texture of surficial deposits, type of surficial deposit, and potential for ground-water recharge were used in the Maximum Likelihood Classification to classify the areas so the characteristics of the hydrochemical regions would resemble the characteristics of the clusters. The result of the Maximum Likelihood Classification is a map showing five hydrochemical regions of the glacial aquifer system. Statistical analysis of ion concentrations (calcium, chloride, fluoride, magnesium, sodium, potassium, sulfate, and bicarbonate) in samples collected from wells completed in the glacial aquifer system illustrates that variations in water quality can be explained, in part, by related environmental characteristics that control the movement of ground water through the aquifer system. A comparison of median concentrations of chemical constituents in ground water among the five hydrochemical regions indicates that ground water in the Midwestern Agricultural Region, the Urban-Influenced Region, and the Western Agriculture and Grassland Region has the highest concentrations of major and minor ions, whereas ground water in the Northern and Great Lakes Forested Region and the Mountain and Coastal Forested Region has the lowest concentrations of these ions. Median concentrations of barium, arsenic, lithium, boron, strontium, and nitrite plus nitrate as nitrogen also are significantly different among the hydrochemical regions.

Scientific Investigations Report↗

USGS1995

Assessment of intrinsic bioremediation of jet fuel contamination in a shallow aquifer, Beaufort, South Carolina

Field and laboratory studies show that microorganisms indigenous to the ground-water system underlying Tank Farm C, Marine Corps Air Station Beaufort, S.C., degrade petroleum hydrocarbons under aerobic and anaerobic conditions. Under aerobic conditions, sediments from the shallow aquifer underlying the site mineralized radiolabeled (14C) toluene to 14CO2 with first-order rate constants of about -0.29 per day. Sediments incubated under anaerobic conditions mineralized radiolabeled toluene more slowly, with first-order rate constants of -0.001 per day. Although anaerobic rates of biodegradation are low, they are significant in the hydrologic and geochemical context of the site. Because of low hydraulic conductivities (1.9-9.1 feet per day) and low hydraulic gradients (about 0.004 feet per feet), ground water flows slowly (approximately 20 feet per year) at this site. Furthermore, aquifer sediments contain organic-rich peat that has a high sorptive capacity. Under these conditions, hydrocarbon contaminants have moved no further than 10 feet downgradient of the jet fuel free product. Digital solute-transport simulations, using the range of model parameters measured at the site, show that dissolved contaminants will be completely degraded before they are discharged from the aquifer into adjacent surface-water bodies. These results show that natural attenuation processes are containing the migration of soluble hydrocarbons, and that intrinsic bioremediation is a potentially effective remedial strategy at this site.

South Carolina↗

USGS2012

Digital outcrop model of stratigraphy and breccias of the southern Franklin Mountains, El Paso, Texas

This chapter reviews and synthesizes the lithostratigraphy, biostratigraphy, chronostratigraphy, and breccia types of the southwestern part of the great American carbonate bank in the southern Franklin Mountains (SFM), El Paso, Texas. Primary stratigraphic units of focus are the Lower Ordovician El Paso and Upper Ordovician Montoya Groups. These groups preserve breccias formed by collapse of a paleocave system. Precambrian and Silurian units are discussed in the context of breccia clast composition and relative timing of breccia emplacement. Specific attention is paid to the juxtaposition of the top-Sauk second-order supersequence unconformity between the El Paso and Montoya Groups and its relationship to breccias above and below it. The unconformity represents a 10-m.y. exposure event that separates Upper and Lower Ordovician carbonates. The top-Sauk exposure has been previously documented as a significant karst horizon across much of North America. The breccias of the SFM were previously described as the result of collapsed paleocaves that formed during subaerial exposure related to the Sauk-Tippecanoe unconformity. A new approach in this work uses traditional field mapping combined with high-resolution ( 1-m [ 3.3-ft] point spacing) airborne light detection and ranging (LIDAR) data over 24 km 2 (9 mi 2 ) to map breccia and relevant stratal surfaces. Airborne LIDAR data were used to create a digital outcrop model of the SFM from which a detailed (1:2000 scale) geologic map was created. The geologic map includes formation, fault, and breccia contacts. The digital outcrop model was used to interpret three-dimensional spatial relationships of breccia bodies with respect to the current understanding of the tectonic and stratigraphic evolution of the SFM. The data presented here are used to discuss potential stratigraphic, temporal, and tectonic controls on the formation of caves within the study area that eventually collapsed to form the breccias currently exposed in outcrop.

Texas↗

USGS2008

Arctic lake physical processes and regimes with implications for winter water availability and management in the national petroleum reserve alaska

Lakes are dominant landforms in the National Petroleum Reserve Alaska (NPRA) as well as important social and ecological resources. Of recent importance is the management of these freshwater ecosystems because lakes deeper than maximum ice thickness provide an important and often sole source of liquid water for aquatic biota, villages, and industry during winter. To better understand seasonal and annual hydrodynamics in the context of lake morphometry, we analyzed lakes in two adjacent areas where winter water use is expected to increase in the near future because of industrial expansion. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus imagery acquired between 1985 and 2007 were analyzed and compared with climate data to understand interannual variability. Measured changes in lake area extent varied by 0.6% and were significantly correlated to total precipitation in the preceding 12 months (p < 0.05). Using this relation, the modeled lake area extent from 1985 to 2007 showed no long-term trends. In addition, high-resolution aerial photography, bathymetric surveys, water-level monitoring, and lake-ice thickness measurements and growth models were used to better understand seasonal hydrodynamics, surface area-to-volume relations, winter water availability, and more permanent changes related to geomorphic change. Together, these results describe how lakes vary seasonally and annually in two critical areas of the NPRA and provide simple models to help better predict variation in lake-water supply. Our findings suggest that both overestimation and underestimation of actual available winter water volume may occur regularly, and this understanding may help better inform management strategies as future resource use expands in the NPRA. ?? 2008 Springer Science+Business Media, LLC.

Environmental Management↗

USGS2016

Normalized burn ratios link fire severity with patterns of avian occurrence

Context Remotely sensed differenced normalized burn ratios (DNBR) provide an index of fire severity across the footprint of a fire. We asked whether this index was useful for explaining patterns of bird occurrence within fire adapted xeric pine-oak forests of the southern Appalachian Mountains. Objectives We evaluated the use of DNBR indices for linking ecosystem process with patterns of bird occurrence. We compared field-based and remotely sensed fire severity indices and used each to develop occupancy models for six bird species to identify patterns of bird occurrence following fire. Methods We identified and sampled 228 points within fires that recently burned within Great Smoky Mountains National Park. We performed avian point counts and field-assessed fire severity at each bird census point. We also used Landsat™ imagery acquired before and after each fire to quantify fire severity using DNBR. We used non-parametric methods to quantify agreement between fire severity indices, and evaluated single season occupancy models incorporating fire severity summarized at different spatial scales. Results Agreement between field-derived and remotely sensed measures of fire severity was influenced by vegetation type. Although occurrence models using field-derived indices of fire severity outperformed those using DNBR, summarizing DNBR at multiple spatial scales provided additional insights into patterns of occurrence associated with different sized patches of high severity fire. Conclusions DNBR is useful for linking the effects of fire severity to patterns of bird occurrence, and informing how high severity fire shapes patterns of bird species occurrence on the landscape.

Landscape Ecology↗

USGS2011

Hydrogeophysical methods for analyzing aquifer storage and recovery systems

Hydrogeophysical methods are presented that support the siting and monitoring of aquifer storage and recovery (ASR) systems. These methods are presented as numerical simulations in the context of a proposed ASR experiment in Kuwait, although the techniques are applicable to numerous ASR projects. Bulk geophysical properties are calculated directly from ASR flow and solute transport simulations using standard petrophysical relationships and are used to simulate the dynamic geophysical response to ASR. This strategy provides a quantitative framework for determining site‐specific geophysical methods and data acquisition geometries that can provide the most useful information about the ASR implementation. An axisymmetric, coupled fluid flow and solute transport model simulates injection, storage, and withdrawal of fresh water (salinity ∼500 ppm) into the Dammam aquifer, a tertiary carbonate formation with native salinity approximately 6000 ppm. Sensitivity of the flow simulations to the correlation length of aquifer heterogeneity, aquifer dispersivity, and hydraulic permeability of the confining layer are investigated. The geophysical response using electrical resistivity, time‐domain electromagnetic (TEM), and seismic methods is computed at regular intervals during the ASR simulation to investigate the sensitivity of these different techniques to changes in subsurface properties. For the electrical and electromagnetic methods, fluid electric conductivity is derived from the modeled salinity and is combined with an assumed porosity model to compute a bulk electrical resistivity structure. The seismic response is computed from the porosity model and changes in effective stress due to fluid pressure variations during injection/recovery, while changes in fluid properties are introduced through Gassmann fluid substitution.

Groundwater↗

USGS2014

Time-domain electromagnetic surveys at Fort Irwin, San Bernardino County, California, 2010–12

Between 2010 and 2012, a total of 79 time-domain electromagnetic (TEM) soundings were collected in 12 groundwater basins in the U.S. Army Fort Irwin National Training Center (NTC) study area to help improve the understanding of the hydrogeology of the NTC. The TEM data are discussed in this chapter in the context of geologic observations of the study area, the details of which are provided in the other chapters of this volume. Selection of locations for TEM soundings in unexplored basins was guided by gravity data that estimated depth to pre-Tertiary basement complex of crystalline rock and alluvial thickness. Some TEM data were collected near boreholes with geophysical logs. The TEM response at locations near boreholes was used to evaluate sounding data for areas without boreholes. TEM models also were used to guide site selection of subsequent boreholes drilled as part of this study. Following borehole completion, geophysical logs were used to ground-truth and reinterpret previously collected TEM data. This iterative process was used to site subsequent TEM soundings and borehole locations as the study progressed. Although each groundwater subbasin within the NTC boundaries was explored using the TEM method, collection of TEM data was focused in those basins identified as best suited for development of water resources. At the NTC, TEM estimates of some lithologic thicknesses and electrical properties in the unsaturated zone are in good accordance with borehole data; however, water-table elevations were not easily identifiable from TEM data.

California↗

USGS2020

A decision-support tool to prioritize candidate landscapes for lesser prairie-chicken conservation

Context Development of systematic methods for conservation planning has improved effectiveness and efficiency of implementing such plans. The lesser prairie-chicken ( Tympanuchus pallidicinctus ) is a grouse species of conservation concern native to the southwestern Great Plains of the United States. Recent lesser prairie-chicken conservation planning has involved identifying ecologically important areas but has not incorporated economic data into prioritization of areas to target for conservation management. Objectives We used the program Marxan to develop a decision-support tool for managers in Kansas to prioritize tracts for improving lesser prairie-chicken habitat quality and increasing habitat availability. We developed three different conservation scenarios and evaluated the tradeoffs among multiple planning objectives in these scenarios. Methods We incorporated population targets from an existing conservation plan and agricultural economic data to help select land with maximum ecological value and minimum economic productivity to prioritize for lesser prairie-chicken conservation. We compared potential conservation plans and incorporated a post hoc connectivity model to test potential for individuals to travel among habitat patches in these plans during dispersal events. Results We found that different conservation scenarios led to different solutions, though differences varied by ecoregion. Potential solutions for all scenarios contained habitat patches not currently included in existing conservation plans and had high connectivity potential. Conclusions These results provide context for spatial prioritization of lesser prairie-chicken habitat management in Kansas. Application of this approach to species of conservation interest could help managers incorporate socioeconomic factors into planning methods and identify important tracts for conservation currently overlooked by existing planning methods.

Kansas↗