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At least 487 records · Page 27Linked to original sources
USGS2013

The influence of global climate change on the scientific foundations and applications of Environmental Toxicology and Chemistry: Introduction to a SETAC international workshop

This is the first of seven papers resulting from a Society of Environmental Toxicology and Chemistry (SETAC) international workshop titled “The Influence of Global Climate Change on the Scientific Foundations and Applications of Environmental Toxicology and Chemistry.” The workshop involved 36 scientists from 11 countries and was designed to answer the following question: How will global climate change influence the environmental impacts of chemicals and other stressors and the way we assess and manage them in the environment? While more detail is found in the complete series of articles, some key consensus points are as follows: (1) human actions (including mitigation of and adaptation to impacts of global climate change [GCC]) may have as much influence on the fate and distribution of chemical contaminants as does GCC, and modeled predictions should be interpreted cautiously; (2) climate change can affect the toxicity of chemicals, but chemicals can also affect how organisms acclimate to climate change; (3) effects of GCC may be slow, variable, and difficult to detect, though some populations and communities of high vulnerability may exhibit responses sooner and more dramatically than others; (4) future approaches to human and ecological risk assessments will need to incorporate multiple stressors and cumulative risks considering the wide spectrum of potential impacts stemming from GCC; and (5) baseline/reference conditions for estimating resource injury and restoration/rehabilitation will continually shift due to GCC and represent significant challenges to practitioners.

Environmental Toxicology and Chemistry↗
USGS2026

Environment, taxonomy, and socioeconomics predict non-imperilment in freshwater fishes

Freshwater fishes are among the most threatened taxa, yet conservation assessments remain incomplete for many species. Freshwater fishes provide essential ecosystem services such as food security, recreational opportunities, and cultural significance. Despite heavy alterations to freshwater ecosystems, the reasons for species’ sensitivity and resistance to imperilment are unclear. To address this need, we develop a machine learning framework to predict global imperilment status for 10,631 freshwater fish species using a comprehensive set of environmental, socioeconomic, and intrinsic species-level predictors. Using updated IUCN Red List data, we train and validate Random Forest classifiers to distinguish imperiled (Vulnerable, Endangered, Critically Endangered) from non-imperiled species. We examine the relative influence of 52 variables derived from 12 global sources describing extrinsic environmental and socioeconomic factors and intrinsic species-specific characteristics. Our models achieve higher accuracy for non-imperiled species (90.1%) compared to imperiled species (81.8%), reflecting the greater heterogeneity of threats and conditions driving imperilment. Across models, key predictors include habitat variables, taxonomic order, hydrological characteristics, and disturbance indicators, underscoring the interplay between ecology, geography, and human pressures. This integrative, reproducible approach demonstrates the utility of machine learning for guiding proactive conservation and provides a scalable framework for global biodiversity risk assessment.

Nature Communications↗
USGS2011

Are there pre-Quaternary geological analogues for a future greenhouse warming?

Given the inherent uncertainties in predicting how climate and environments will respond to anthropogenic emissions of greenhouse gases, it would be beneficial to society if science could identify geological analogues to the human race’s current grand climate experiment . This has been a focus of the geological and palaeoclimate communities over the last 30 years, with many scientific papers claiming that intervals in Earth history can be used as an analogue for future climate change. Using a coupled ocean–atmosphere modelling approach, we test this assertion for the most probable pre-Quaternary candidates of the last 100 million years: the Mid- and Late Cretaceous, the Palaeocene–Eocene Thermal Maximum (PETM), the Early Eocene, as well as warm intervals within the Miocene and Pliocene epochs. These intervals fail as true direct analogues since they either represent equilibrium climate states to a long-term CO 2 forcing—whereas anthropogenic emissions of greenhouse gases provide a progressive (transient) forcing on climate—or the sensitivity of the climate system itself to CO 2 was different. While no close geological analogue exists, past warm intervals in Earth history provide a unique opportunity to investigate processes that operated during warm (high CO 2 ) climate states. Palaeoclimate and environmental reconstruction/modelling are facilitating the assessment and calculation of the response of global temperatures to increasing CO 2 concentrations in the longer term (multiple centuries); this is now referred to as the Earth System Sensitivity, which is critical in identifying CO 2 thresholds in the atmosphere that must not be crossed to avoid dangerous levels of climate change in the long term. Palaeoclimatology also provides a unique and independent way to evaluate the qualities of climate and Earth system models used to predict future climate.

Philosophical Transactions of the Royal Society A:↗
USGS2018

Assessment of potential risks from renewable energy development and other anthropogenic factors to wintering Golden Eagles in the western United States

Wind and other energy development are expanding rapidly and on an unprecedented scale within the range of the Golden Eagle ( Aquila chrysaetos ) while other anthropogenic-related changes, wildfires, invasive plants, drought, and climate change are altering or destroying native habitats occupied by Golden Eagles. However, the potential effects of these factors on North American Golden Eagle populations are largely unknown and the most recent evidence indicates that the population in the western United States is declining slightly. Impediments to evaluating the potential effects of energy development projects on wintering Golden Eagles include issues of scale and a paucity of available information about eagle winter use areas and ecology. We applied a predictive model of eagle winter distribution developed for Idaho and Montana, to Idaho, Utah, Nevada and eastern Oregon to help identify potential wintering areas and identify risks that occur in those areas. The model identifies ~40% of the four state study area as potentially suitable eagle winter habitat and provides a basis for spatial assessment of possible risk factors to eagles wintering there. We used eBird and Christmas Bird Count citizen science datasets for an independent evaluation of the accuracy of our predictive distribution model. The model was robust, accurately predicting the presence of wintering Golden Eagles significantly more often than expected. We used digital environmental datasets (layers) of potential risk factors, in conjunction with model predicted eagle distribution, to better understand and estimate the extent of risks to the wintering eagle population in the study area. These layers represent available data for some of the factors previously identified as risks in the landscape to wintering Golden Eagles. The majority of predicted eagle wintering areas occurred where there was little habitat fragmentation (<10%). All predicted winter areas contained at least one potential risk factor (e.g., potential for energy development); 39.4% of predicted winter areas contained at least two known risk factors. The greatest number of risks often occurred where the human footprint was highest and where eagles were less likely to occur during winter. Our results can be used to help prioritize field surveys for identifying important Golden Eagle winter areas in the western United States and determine potential locations where energy development is least likely to have negative effects on wintering eagles. Survey efforts can be allocated in consideration of management and conservation objectives based on predicted habitat suitability and risk factors. For example, surveys for areas of high suitability and low risk can identify places to focus management for conservation of eagle winter areas. Further, sites proposed for wind energy development could be reviewed initially based on model predicted eagle wintering areas and then surveyed to determine if permitting for development is appropriate.

Book chapter↗
USGS2022

Are we falling short on restoring oysters at a regional scale?

Across coastal areas of the northern Gulf of Mexico, the Deepwater Horizon oil spill resulted in significant ecological injury, and over 8 billion USD directed to restoration activities. Oyster restoration projects were implemented with regional goals of restoring oyster abundance, spawning stock, and population resilience. Measuring regional or large-scale ecosystem restoration outcomes challenges traditional project-specific monitoring and outcome reporting. We examine the outcomes of oyster restoration at the project-level and discuss potential pathways to measure progress toward region-level goals. An estimated 15 km 2 of oyster habitat was restored across 11 different estuaries with 62 individual reef footprints created, ranging in size from ~0.2 to 1.45 km 2 . Individual sites were distributed across the salinity gradient, and all reefs were subtidal. One-year post-restoration, mean total oyster density across all sites was 53.0 ± 60.7 ind m −2 of which 38.4 ± 42.2 ind m −2 were adult (>25 mm shell height) oysters. Recent data (2018/2019) available for all sites indicates reduced densities of total oysters (44.6 ± 70.9 ind m −2 ) and adult oysters (14.6 ± 21.6 ind m −2 ). These data provide insight into project specific outcomes, suggesting an overall enhancement in oyster abundance compared to pre-restoration, but fall short of informing outcomes at the regional-level that incorporate cumulative effects on adjacent and connected reef populations, or inform overall resiliency of the regional oyster resource. Developing regional outcome benchmarks that enable assessment of cumulative and synergistic impacts of individual projects may benefit from broader spatial and temporal monitoring requirements that can better inform development of regional tools or models. Such tools would enable cumulative effects analyses examining net resource change, resilience and assess impacts of restoration activities on regional resource status.

northern Gulf of Mexico↗
USGS2007

Phosphorus Concentrations in Stream-Water and Reference Samples - An Assessment of Laboratory Comparability

In 2003, a study was conducted to evaluate the accuracy and precision of 10 laboratories that analyze water-quality samples for phosphorus concentrations in the Catskill Mountain region of New York State. Many environmental studies in this region rely on data from these different laboratories for water-quality analyses, and the data may be used in watershed modeling and management decisions. Therefore, it is important to determine whether the data reported by these laboratories are of comparable accuracy and precision. Each laboratory was sent 12 samples for triplicate analysis for total phosphorus, total dissolved phosphorus, and soluble reactive phosphorus. Eight of these laboratories reported results that met comparability criteria for all samples; the remaining two laboratories met comparability criteria for only about half of the analyses. Neither the analytical method used nor the sample concentration ranges appeared to affect the comparability of results. The laboratories whose results were comparable gave consistently comparable results throughout the concentration range analyzed, and the differences among methods did not diminish comparability. All laboratories had high data precision as indicated by sample triplicate results. In addition, the laboratories consistently reported total phosphorus values greater than total dissolved phosphorus values, and total dissolved phosphorus values greater than soluble reactive phosphorus values, as would be expected. The results of this study emphasize the importance of regular laboratory participation in sample-exchange programs.

Open-File Report↗
USGS2015

Non-lethal assessment of freshwater mussel physiological response to changes in environmental factors

The development of effective nonlethal biomonitoring techniques is imperative for the preservation of imperiled freshwater mussel populations. Changes in hemolymph chemistry profiles and tissue glycogen are potential biomarkers for nonlethally monitoring stress in mussels. We sampled three species in the Flint River Basin over 2 years to evaluate how these hemolymph and tissue biomarkers responded to environmental changes. We used hierarchical linear models to evaluate the relationships between variation in the biomarkers and environmental factors and found that the responses of the hemolymph and tissue parameters were strongly related to stream discharge. Shifts in alanine aminotransferase and glycogen showed the largest relations with discharge at the time of sampling, while magnesium levels were most explained by the discharge for 5 days prior to sampling. Aspartate aminotransferase, bicarbonate, and calcium showed the strongest relations with mean discharge for 15 days prior to sampling. The modeling results indicated that biomarker responses varied substantially among individuals of different size, sex, and species and illustrated the value of hierarchical modeling techniques to account for the inherent complexity of aquatic ecosystems.

Canadian Journal of Fisheries and Aquatic Sciences↗
USGS2012

Quality of streams in Johnson County, Kansas, 2002--10

Stream quality in Johnson County, northeastern Kansas, was assessed on the basis of land use, hydrology, stream-water and streambed-sediment chemistry, riparian and in-stream habitat, and periphyton and macroinvertebrate community data collected from 22 sites during 2002 through 2010. Stream conditions at the end of the study period are evaluated and compared to previous years, stream biological communities and physical and chemical conditions are characterized, streams are described relative to Kansas Department of Health and Environment impairment categories and water-quality standards, and environmental factors that most strongly correlate with biological stream quality are evaluated. The information is useful for improving water-quality management programs, documenting changing conditions with time, and evaluating compliance with water-quality standards, total maximum daily loads (TMDLs), National Pollutant Discharge Elimination System (NPDES) permit conditions, and other established guidelines and goals. Constituent concentrations in water during base flow varied across the study area and 2010 conditions were not markedly different from those measured in 2003, 2004, and 2007. Generally the highest specific conductance and concentrations of dissolved solids and major ions in water occurred at urban sites except the upstream Cedar Creek site, which is rural and has a large area of commercial and industrial land less than 1 mile upstream on both sides of the creek. The highest base-flow nutrient concentrations in water occurred downstream from wastewater treatment facilities. Water chemistry data represent base-flow conditions only, and do not show the variability in concentrations that occurs during stormwater runoff. Constituent concentrations in streambed sediment also varied across the study area and some notable changes occurred from previously collected data. High organic carbon and nutrient concentrations at the rural Big Bull Creek site in 2003 decreased to at least one-fourth of those concentrations in 2007 and 2010 likely because of the reduction in upstream wastewater discharge contributions. The highest concentrations of trace metals in 2010 occurred at urban sites on Mill and Indian Creeks. Zinc was the only metal to exceed the probable effects concentration in 2010, which occurred at a site on Indian Creek. In 2007, chromium and nickel at the upstream urban Cedar Creek site exceeded the probable effects concentrations, and in 2003, no metals exceeded the probable effects concentrations. Of 72 organic compounds analyzed in streambed sediment, 26 were detected including pesticides, polycyclic aromatic hydrocarbons (PAHs), fuel products, fragrances, preservatives, plasticizers, manufacturing byproducts, flame retardants, and disinfectants. All 6 PAH compounds analyzed were detected, and the probable effects concentrations for 4 of the 6 PAH compounds analyzed were exceeded in 2010. Only five pesticide compounds were detected in streambed sediment, including carbazole and four pyrethroid compounds. Chronic toxicity guidelines for pyrethroid compounds were exceeded at five sites. Biological conditions reflected a gradient in urban land use, with the less disturbed streams located in rural areas of Johnson County. About 19 percent of sites in 2010 (four sites) were fully supporting of aquatic life on the basis of the four metrics used by Kansas Department of Health and Environment to categorize sites. This is a notable difference compared to previous years when no sites (in 2003 and 2004) or just one site (in 2007) was fully supporting of aquatic life. Multimetric macroinvertebrate scores improved at the Big Bull Creek site where wastewater discharges were reduced in 2007. Environmental variables that consistently were highly negatively correlated with biological conditions were percent impervious surface and percent urban land use. In addition, density of stormwater outfall points adjacent to streams was significantly negatively correlated with biological conditions. Specific conductance of water and sum of PAH concentrations in streambed sediment also were significantly negatively correlated with biological conditions. Total nitrogen in water and total phosphorus in streambed sediment were correlated with most of the invertebrate variables, which is a notable difference from previous analyses using smaller datasets, in which nutrient relations were weak or not detected. The most important habitat variables were sinuosity, length and continuity of natural buffers, riffle substrate embeddedness, and substrate cover diversity, each of which was correlated with all invertebrate metrics including a 10-metric combined score. Correlation analysis indicated that if riparian and in-stream habitat conditions improve then so might invertebrate communities and stream biological quality. Sixty-two percent of the variance in macroinvertebrate community metrics was explained by the single environmental factor, percent impervious surface. Invertebrate responses to urbanization in Johnson County indicated linearity rather than identifiable thresholds. Multiple linear regression models developed for each of the four macroinvertebrate metrics used to determine aquatic-life-support status indicated that percent impervious surface, as a measure of urban land use, explained 34 to 67 percent of the variability in biological communities. Results indicate that although multiple factors are correlated with stream quality degradation, general urbanization, as indicated by impervious surface area or urban land use, consistently is determined to be the fundamental factor causing change in stream quality. Effects of urbanization on Johnson County streams are similar to effects described in national studies that assess effects of urbanization on stream health. Individually important environmental factors such as specific conductance of water, PAHs in streambed sediment, and stream buffer conditions, are affected by urbanization and, collectively, all contribute to stream impairments. Policies and management practices that may be most important in protecting the health of streams in Johnson County are those minimizing the effects of impervious surface, protecting stream corridors, and decreasing the loads of sediment, nutrients, and toxic chemicals that directly enter streams through stormwater runoff and discharges.

Kansas↗
USGS2011

Hierarchical modeling of an invasive spread: The eurasian collared-dove streptopelia decaocto in the United States

Invasive species are regularly claimed as the second threat to biodiversity. To apply a relevant response to the potential consequences associated with invasions (e.g., emphasize management efforts to prevent new colonization or to eradicate the species in places where it has already settled), it is essential to understand invasion mechanisms and dynamics. Quantifying and understanding what influences rates of spatial spread is a key research area for invasion theory. In this paper, we develop a model to account for occupancy dynamics of an invasive species. Our model extends existing models to accommodate several elements of invasive processes; we chose the framework of hierarchical modeling to assess site occupancy status during an invasion. First, we explicitly accounted for spatial structure and how distance among sites and position relative to one another affect the invasion spread. In particular, we accounted for the possibility of directional propagation and provided a way of estimating the direction of this possible spread. Second, we considered the influence of local density on site occupancy. Third, we decided to split the colonization process into two subprocesses, initial colonization and recolonization, which may be ground-breaking because these subprocesses may exhibit different relationships with environmental variations (such as density variation) or colonization history (e.g., initial colonization might facilitate further colonization events). Finally, our model incorporates imperfection in detection, which might be a source of substantial bias in estimating population parameters. We focused on the case of the Eurasian Collared-Dove (Streptopelia decaocto) and its invasion of the United States since its introduction in the early 1980s, using data from the North American BBS (Breeding Bird Survey). The Eurasian Collared-Dove is one of the most successful invasive species, at least among terrestrial vertebrates. Our model provided estimation of the spread direction consistent with empirical observations. Site persistence probability exhibits a quadratic response to density. We also succeeded at detecting differences in the relationship between density and initial colonization vs. recolonization probabilities. We provide a map of sites that may be colonized in the future as an example of possible practical application of our work. ?? 2011 by the Ecological Society of America.

Ecological Applications↗
USGS2025

From sample to sonde to Sentinel-2: Insights from a multi-scale chlorophyll-a monitoring effort in the Hudson River, New York

Monitoring cyanobacteria and other nuisance phytoplankton in the Hudson River is of great interest given its societal and ecological importance. Satellite remote sensing provides a cost-effective method to monitor chlorophyll- a (chl-a), a common proxy for algal biomass; however, the dynamic nature of rivers complicates approaches traditionally applied to lakes and oceans. During 2021–2023, we collected discrete samples for laboratory measurement of chl-a and measured in situ chl-a fluorescence during a series of longitudinal boat surveys along a 220-km reach of the lower Hudson River. Surveys were timed to coincide with Sentinel-2 satellite overpasses. We first investigated relations between laboratory-measured chl-a concentration and field-measured chl-a fluorescence, observing a weak correlation ( r 2 = 0.25) that improved substantially after splitting data by day (mean r 2 = 0.53). Separately, to estimate chl-a fluorescence using satellite data, we developed a series of random forest models leveraging the rich fluorescence dataset collected. We tested three model types: individual day models, leave-one-out models trained on all days except a holdout test day, and a single pooled model trained on all days. Generally, individual day models exhibited lowest error (mean of mean absolute error [MAE] = 0.16 relative fluorescence units [RFU]), followed by the single pooled model (MAE = 0.22 RFU). Daily holdout models showed highest error (mean MAE = 0.40 RFU); this approach was intended to represent model performance on a day unseen in the training set, providing a more conservative estimate of performance than the more traditional pooled approach. Findings from both analyses emphasize the importance of considering temporal variability when modeling riverine systems.

New Jersey, New York↗
USGS2026

3D semantic mapping of surface geological features

Semantic mapping in 3D is fundamental to a wide range of geoscientific studies and applications, including geomorphology, hazard assessment, and environmental monitoring. However, automatically segmenting geological features from large-scale photogrammetric datasets remains a significant challenge. We present a methodology to address this gap. Using overlapping images collected over environments of interest, Structure-from-Motion (SfM) produces georeferenced point clouds and estimates camera poses. Existing large vision models, such as Segment Anything Model, segment objects in the images, generating pixel-segmentation associations. To produce pixel-point associations, we project the points back onto the camera image planes. As objects are independently segmented across multiple images with different perspectives, we develop a segmentation mosaicking algorithm to build probabilistic point-segmentation associations that combines the pixel-segmentation associations and pixel-point associations. Our methodology is validated using both synthetic data generated by Kubric and real-world UAV-SfM data. The implementation is designed to be compatible with existing SfM software, including Agisoft and OpenDroneMap, for photogrammetry mapping in geoscience studies. As a case study, we apply our method to the semantic mapping of precariously balanced rocks (PBRs), which provide upper-bound constraints on historical ground motion shaking intensity. To support object-level identification of PBRs, we additionally integrated Grounding DINO, enabling text-prompted segmentation of features of interest within UAV imagery. This case study demonstrates the effectiveness of our method in generating a 3D semantic map of PBRs, enabling spatial distribution of PBR fragility for earthquake hazard analysis.

Computers & Geosciences↗
USGS2023

A retrospective assessment of fuel break effectiveness for containing rangeland wildfires in the sagebrush biome

Escalated wildfire activity within the western U.S. has widespread societal impacts and long-term consequences for the imperiled sagebrush ( Artemisia spp.) biome. Shifts from historical fire regimes and the interplay between frequent disturbance and invasive annual grasses may initiate permanent state transitions as wildfire frequency outpaces sagebrush communities’ innate capacity to recover. Therefore, wildfire management is at the core of conservation plans for sagebrush ecosystems, especially critical habitat for species of conservation concern such as the greater sage-grouse ( Centrocercus urophasianus ; hereafter sage-grouse). Fuel breaks help facilitate wildfire suppression by modifying behavior through fuels modification and allowing safe access points for containment by firefighters. The Bureau of Land Management has proposed to roughly double the existing fuel break network in the western U.S., centered on the Great Basin. To our knowledge, no broad-scale examination of fuel break effectiveness or the environmental conditions under which fuel breaks are expected to be most effective has been conducted. We performed a retrospective assessment of probability of fuel break contributing to wildfire containment on recorded wildfire and fuel break interactions from 1985 to 2018 within the western U.S. We characterized environmental, fuels, and weather conditions within 500 m of wildfire contact, and within 5 km of the approaching wildfire. We used a binomial mixed model within a Bayesian framework to identify relationships between these variables and fuel break success. Fuel breaks were least successful in areas classified as having low resilience to disturbance and low resistance to invasion, in areas composed of primarily woody fuels, and when operating in high temperature and low precipitation conditions. Fuel breaks were most effective in areas where fine fuels dominated and in areas that were readily accessible. Maintenance history and fuel break type also contributed to the probability of containment. Overall results indicate a complex and sometimes paradoxical relationship between landscape characteristics that promote wildfire spread and those that impact fuel break effectiveness. Finally, we developed predictive maps of fuel break effectiveness by fuel break type to further elucidate these complex relationships and to inform urgently needed fuel break placement and maintenance priorities across the sagebrush biome.

Journal of Environmental Management↗
USGS2016

Estimating national water use associated with unconventional oil and gas development

The U.S. Geological Survey&rsquo;s (USGS) Water Availability and Use Science Program (WAUSP) goals are to provide a more accurate assessment of the status of the water resources of the United States and assist in the determination of the quantity and quality of water that is available for beneficial uses. These assessments would identify long-term trends or changes in water availability since the 1950s in the United States and help to develop the basis for an improved ability to forecast water avail- ability for future economic, energy-production, and environmental uses. The National Water Census ( http://water.usgs.gov/watercensus/ ), a research program of the WAUSP, supports studies to develop new water accounting tools and assess water availability at the regional and national scales. Studies supported by this program target focus areas with identified water availability concerns and topical science themes related to the use of water within a specific type of environmental setting. The topical study described in this fact sheet will focus on understanding the relation between production of unconventional oil and gas (UOG) for energy and the water needed to produce and sustain this type of energy development. This relation applies to the life-cycle of renewable and nonrenewable forms of UOG energy and includes extraction, production, refinement, delivery, and disposal of waste byproducts. Water-use data and models derived from this topical study will be applied to other similar oil and gas plays within the United States to help resource managers assess and account for water used or needed in these areas. Additionally, the results from this topical study will be used to further refine the methods used in compiling water-use data for selected categories (for example, mining, domestic self-supplied, public supply, and wastewater) in the USGS&rsquo;s 5-year national water-use estimates reports ( http://water.usgs.gov/watuse/ ).

Fact Sheet↗
USGS2023

Assessment of resource potential from mine tailings using geostatistical modeling for compositions: A methodology and application to Katherine Mine site, Arizona, USA

The mining industry, in most cases, targets a specific valuable commodity that is present in small quantities within large volumes of extracted material. After milling and processing, most of the extracted material and the effluents are stored as waste (tailings) in impoundments, such as dams or waste dumps, or are backfilled into underground mines. In time, tailing materials may become an issue of environmental and health concern due to the hazardous elements, ions, and oxides contained within the waste material. In addition, handling and storage of such waste in dams may pose the risk of dam failure with catastrophic consequences to nature and nearby communities. On the other hand, tailings may offer potential as secondary sources of critical elements (CEs), including rare earth elements (REEs), which may have been overlooked during primary production and processing. Therefore, treating mine tailings as a resource has economic and environmental benefits by reducing the waste from new and historical mine sites through remining. One of the critical steps for taking advantage of these benefits is to spatially quantify the resources and the pollutants, which require the application of adequate data analysis and modeling methods, often to compositional geochemical data. Utilizing adequate methods is especially important for correctly quantifying resource potential, as the quantities will often be at low concentrations. This work presents quantification of resource potential (Au, Ag, Cu, Zn, Pb) and elements of environmental concern (Hg and As) from the tailings of a historic mine site, Katherine Mine, AZ, USA. Data reported by the U.S. Bureau of Mines (USBM) after extensive field campaigns in the 1990s, including sampling from tailing impoundment and surrounding areas for geochemical characterization and geophysical surveys, were used. First, compositional data (CoDa) analysis was employed to explore associations of sampling locations, geochemical parts, and the clustering of samples. Next, sequential Gaussian simulation (SGSIM) was applied to samples that showed a genetic link to tailing material after isometric log-ratio transformation (ilr) and mix/max autocorrelation factor (MAF) transformation for spatial modeling and uncertainty evaluation. Geostatistical results revealed spatial variability of concentrations within the tailing area. Uncertainty evaluation based on realizations indicated that Cu (14.27–20.01 t), Zn (44.23–76.23 t), and Pb (22.56–38.28 t) are the most abundant elements within a 5 %–95 % interval, followed by Ag and Au (~5.3 and 0.18 t, at 50th percentile), respectively. Of the elements of health concern, As was found to be ~4.8 t (50th percentile) in the tailing area. The work also showed that ~0.51 t As, 0.005 t Hg, 0.020 t of Au, and 0.62 t of Ag were carried to Lake Mohave by an ephemeral stream called Katherine Wash, which transects the tailings.

Arizona↗
USGS2014

Effects of environmental amenities and locational disamenities on home values in the Santa Cruz watershed: a hedonic analysis using census data

For this study, we used the hedonic pricing method to measure the effects of natural amenities on home prices in the U.S-side of the Santa Cruz Watershed. We employed multivariate spatial regression techniques to estimate how difference factors affect median home values in 613 census block groups of the 2000 Census, accounting for spatial autocorrelation, spatial lags, and/or spatial heterogeneity in the data. Diagnostic tests suggest that failure to account for the hedonic model can be classified as (1) physical features of the housing stock, (2) neighborhood characteristics, and (3) environmental attributes. Census data was combined with GIS data for vegetation and land cover, land administration, measures of species richness and open space, and proximity to amenities and disamenities. Census block groups close to the US-Mexico border of airports/air bases were negative. Results suggest that policies to maintain biodiversity and open space provide economic benefits to homeowners, reflected in higher home values. Future research will quantify the marginal effects of regression explanatory variables on home values to assess their economic and policy significant. These marginal effects will be used as input indicators to discern potential economic impacts of various scenarios in the Santa Cruz Watershed Ecosystem Portfolio Model (SCWEPM). Future research will also expand this effort into the Mexican-portion of the watershed.

Arizona↗
USGS2026

Biological and environmental drivers of early life fawn survival in a declining pronghorn population

Pronghorn Antilocapra americana occupy only a portion of their historical range and in Oklahoma occur at the eastern edge of the species' contemporary distribution. Monitoring has suggested pronghorn populations in Oklahoma have declined in recent years. We captured and collared 125 adult females across two winters, monitored them for signs of parturition during each subsequent spring, and then captured and radio-collared 70 fawns ≤ 4 days old. We assessed cause-specific mortality, estimated proportional survival, and visualized survival of fawns through 60 days of life with Kaplan–Meier curves. Nearly 87% of fawn mortalities were attributed to predation, with > 77% of predations attributed to coyotes Canis latrans . Our results indicated that fawn survival was lowest during the first 15 days of life, with 33% of fawns surviving to 15 days and 12% surviving to 60 days. We used known-fate models to evaluate the influence of biological factors (i.e. sex, mass, birth timing), environmental factors (i.e. ambient temperature, precipitation, vegetation), and temporal variation on the probability of early life (i.e. the first 15 days) survival. For each adult female with a collared fawn, we used female space-use patterns for 30 days before and 15 days after parturition to collect environmental covariates. Early life probability of survival was lower for larger fawns, those born earlier in the parturition period (i.e. earlier in the year relative to the range of parturition days), and those with higher pre-parturition temperatures; daily probability of survival decreased with time-since-parturition within the first 15 days of life. Our results indicate poor fawn survival, highlight a potential limitation of population growth, and can inform population management by identifying factors influencing early life fawn survival.

Oklahoma↗
USGS2023

Modeling habitat suitability across different levels of invasive plant abundance

Predicting where invasive plants are likely to spread and become abundant is critical for informing invasive plant management. Species distribution models are a key tool for informing the geography of invasion risk, but most distribution models are limited by their use of presence data, including no information on invader population abundance. In this study, we ask how habitat suitability varies for different levels of abundance for three invasive plants: stiltgrass ( Microstegium vimineum ), sericea lespedeza ( Lespedeza cuneata ), and privet ( Ligustrum sinense ). For each species, we used an ensemble distribution modeling approach to compare suitability for invasion estimated from subsets of point location data: all presences vs. locations with percent cover ≥ 1%, ≥ 5%, ≥ 10%, ≥ 25%, and ≥ 50%. For all species, the total area predicted as suitable for abundant populations was 32%–68% less than the area predicted as suitable for presence. For stiltgrass and sericea lespedeza, the area suitable for invasion decreased when predicted from higher levels of abundance, whereas for privet, suitable area was similar across abundance levels. Stiltgrass and sericea lespedeza are therefore likely to become highly abundant in a smaller portion of their ranges, while privet could become highly abundant anywhere it can establish at low abundance. Different environmental predictors explained suitability for presence versus abundance, suggesting the environmental niche associated with presence differs from that associated with high population abundance. Analyses of more species and growth forms are still needed, but our results combined with previous studies consistently show that fitting distribution models to point locations with ≥ 5–10% cover refines range maps and can produce a more targeted assessment of invasion risk.

Biological Invasions↗
USGS2022

A statistical framework for modelling migration corridors

1. Management of animal populations requires spatially explicit knowledge of movement corridors, such as those used during seasonal migrations. GPS tracking data allows for mapping of corridors from directly observed movements, providing important insights, but tracking data is absent for many populations. 2. We developed a novel statistical corridor modeling approach that predicts movement corridors from cost-distance movement models fit directly to migration tracking data. Unlike existing predictive approaches, this does not require the ad-hoc transformation of habitat suitability surfaces into resistance surfaces. We tested the ability of the approach to recover parameters used to generate simulated movements. We then used GPS data from three migrating mule deer (Odocoileus hemionus) herds in Idaho and Wyoming to model corridors as a function of elevation, slope, aspect, percent shrub, date of peak green-up, snow-off date, and human footprint. We assessed the predictive ability of the fitted models using validation tracks from the same herd as well as from the other herds. 3. The approach reproduced parameters used to generate the simulated movements, predicted the corridors used by migratory populations, and described the direction, magnitude, and confidence levels of the effects of environmental variables on corridors. The effects environmental variables had on corridors differed depending on the herd. Within-herd validation indicated that fitted corridor models are more accurate at predicting migration corridors than null models, and cross-herd validation indicated that fitted models for some herds accurately predicted the observed migrations of other herds. 4. In addition to the practical benefit of mapping corridors for management, our statistical corridor modeling framework sets the stage for evaluating fundamental questions about the fitness tradeoffs, navigation, learning, fidelity, and movement constraints that influence migratory and other corridor-generating behavior. Models of predictive corridors can inform management and planning for the conservation of migrations across taxa, including the potential restoration of corridors. Our corridor modeling approach is also readily applied to non-migratory animal movements.

Idaho, Wyoming↗