SEARCH · Search USGS

Results for “Environmental Modeling & Assessment”

Search indexed USGS publications on groundwater, aquifers, geologic maps, mineral resources and earthquakes. Explore source records by subject and place.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 595 records · Page 33Linked to original sources

USGS2025

Retrospective stepwise prioritization of chemicals detected in Great Lakes tributaries (2008–2018)

Through the U.S. Great Lakes Restoration Initiative, a 10-year, multiagency chemical monitoring effort was undertaken across the Great Lakes. In this effort, 586 chemicals were monitored and 334 were detected in grab/composite water samples. To help inform potential future actions, a stepwise prioritization framework was used to identify compounds for which publicly accessible water quality guidelines or effects information suggested there was potential aquatic ecotoxicity. Because water quality guidelines were only available for some chemicals, this framework used apical toxicity data collated from publicly accessible databases (e.g., the ECOTOXicology Knowledgebase) and alternative data, including literature-derived non-apical effect concentrations, in vitro bioactivities from high-throughput screening, and modeled ecotoxicity. To account for the diverse levels of confidence in these data, chemicals were prioritized within specific action categories, which suggested potential management or experimental activities that may be considered based on the types of data available for each compound. Overall, 11 detected chemicals were identified as high priority in different action categories. This included four chemicals prioritized for environmental management or targeted risk assessment, three chemicals prioritized for effects-based monitoring, one chemical prioritized for apical effects assessment, and three chemicals targeted for non-apical effects evaluation. This framework also identified 164 low-priority chemicals, among which more than 50% were prioritized based on water quality guidelines or apical effect concentrations (thus could be considered low priority for future risk assessment or management activities). Results aim to help regulatory agencies, environmental managers, and other stakeholders focus available resources on carrying out monitoring, experimental, and risk assessments for the chemicals that display the greatest potential to adversely impact Great Lakes ecosystems.

Great Lakes↗

USGS2016

Detection limits of quantitative and digital PCR assays and their influence in presence-absence surveys of environmental DNA

A set of universal guidelines is needed to determine the limit of detection (LOD) in PCR-based analyses of low concentration DNA. In particular, environmental DNA (eDNA) studies require sensitive and reliable methods to detect rare and cryptic species through shed genetic material in environmental samples. Current strategies for assessing detection limits of eDNA are either too stringent or subjective, possibly resulting in biased estimates of species’ presence. Here, a conservative LOD analysis grounded in analytical chemistry is proposed to correct for overestimated DNA concentrations predominantly caused by the concentration plateau, a nonlinear relationship between expected and measured DNA concentrations. We have used statistical criteria to establish formal mathematical models for both quantitative and droplet digital PCR. To assess the method, a new Grass Carp ( Ctenopharyngodon idella ) TaqMan assay was developed and tested on both PCR platforms using eDNA in water samples. The LOD adjustment reduced Grass Carp occupancy and detection estimates while increasing uncertainty – indicating that caution needs to be applied to eDNA data without LOD correction. Compared to quantitative PCR, digital PCR had higher occurrence estimates due to increased sensitivity and dilution of inhibitors at low concentrations. Without accurate LOD correction, species occurrence and detection probabilities based on eDNA estimates are prone to a source of bias that cannot be reduced by an increase in sample size or PCR replicates. Other applications also could benefit from a standardized LOD such as GMO food analysis, and forensic and clinical diagnostics.

Molecular Ecology Resources↗

USGS1993

U.S. Geological Survey; North Carolina's water resources; a partnership with State, Federal and local agencies

For more than 80 years, the Federal-State Cooperative Program in North Carolina has been an effective partnership that provides timely water information for all levels of government. The cooperative program has raised awareness of State and local water problems and issues and has enhanced transfer and exchange of scientific information. The U.S. Geological Survey (USGS) conducts statewide water-resources investigations in North Carolina that include hydrologic data collection, applied research studies, and other interpretive studies. These programs are funded through cooperative agreements with the North Carolina Departments of Environment, Health, and Natural Resources; Human Resources; and Transportation, as well as more than a dozen city and county governmental agencies. The USGS also conducts special studies and data-collection programs for Federal agencies, including the Department of Defense, the U.S. Soil Conservation Service, the Tennessee Valley Authority, and the U.S. Environmental Protection Agency that contribute to North Carolina's water information data base. Highlights of selected programs are presented to show the scope of USGS activities in North Carolina and their usefulness in addressing water-resource problems. The reviewed programs include the statewide data-collection program, estuarine studies, the National Water-Quality Assessment program, military installation restoration program, and groundwater flow model-development program in the Coastal Plain and Piedmont provinces.

North Carolina↗

USGS2021

Ensemble species distribution model identifies survey opportunities for at-risk bearded beaksedge (Rhynchospora crinipes) in the southeastern United States

Locating additional occurrences of at-risk species can inform assessments of their status and conservation needs (including potential legal protections). The perennial bearded beaksedge ( Rhynchospora crinipes ) ranges from Mississippi to North Carolina, but known occurrences are limited. Because of the species' apparent rarity, a model to identify areas with suitable habitat conditions for the species will allow conservationists to effectively prioritize and allocate scarce surveying resources. We used known occurrence records, a suite of environmental datasets, and four species distribution modeling techniques (generalized additive, GAM; maximum entropy, MaxEnt; generalized boosted, GBM; and weighted ensemble) to generate maps to inform surveys for R. crinipes . The ensemble approach improved predictive performance (AUC-PR = 0.95) compared to other techniques (individual model AUC-PR ranged from 0.7 to 0.8). We also obtained quantitative insights on the species' habitat relationships, including the likelihood of R. crinipes 's presence near Atlantic white cedar ( Chamaecyparis thyoides ) habitat and floodplains, which is consistent with prior field observations. The ensemble model indicated that 3.6% of the study area could be suitable habitat, but only 0.38% had high suitability. Small stream riparian habitats and Atlantic swamp forests in Alabama, Florida, and Georgia had the highest proportion of suitable areas. Prioritizing surveys in areas with model-indicated high habitat suitability is expected to reveal additional R. crinipes occurrences. We suggest surveying efforts for other at-risk species may benefit from using an ensemble modeling approach to identify and prioritize survey areas and improve ecological knowledge of these species.

Alabama, Florida, Georgia, Mississippi, North Caro↗

USGS2022

Modeling the spatial and temporal dynamics of land-based polar bear denning in Alaska

Although polar bears ( Ursus maritimus ) of the Southern Beaufort Sea (SBS) subpopulation have commonly created maternal dens on sea ice in the past, maternal dens on land have become increasingly prevalent as sea ice declines. This trend creates conditions for increased human–bear interactions associated with local communities and industrial activity. Maternal denning is a vulnerable period in the polar bear life cycle, and den disturbance could lead to den abandonment, cub mortality, and negative population impacts. We used published long-term data to parameterize a Bayesian hierarchical model of annual land den abundance during 2000–2015, in 4 regions of northern Alaska, USA, with current or potential future oil and gas activity. We also estimated long-term (1982–2015) shifts in the spatial distribution of land dens within and among regions using kernel density estimation and assessed the influence of local and regional sea ice and snow conditions on den site selection using a random forest resource selection function. Our objectives were to quantify current den distribution and abundance, test for distributional shifts over time, and investigate if those shifts could be attributed to environmental variables related to den habitat. We estimated that between 2000 and 2015, the SBS contained a median 123 dens in a typical year, of which 68 occurred on land. The region between the Colville and Canning rivers, where most current oil and gas activity occurred, also contained the largest fraction of land dens. Overall, land dens were disproportionately concentrated on barrier islands and on land within 30 km of the coast. The probability of dens occurring on land varied from 1982–1999 to 2000–2015 in all regions, and the overall distribution of land dens shifted west between those periods. This regional-scale change in den distribution was predictable based on spatial and temporal heterogeneity in snow and sea ice conditions within 50 km of individual den locations. Land denning is likely to become increasingly common with continued sea ice loss, and our results and modeling framework could be used to design additional mitigation strategies for reducing the risk of incidental take due to den disturbance.

Alaska↗

USGS2026

A fresh perspective - Advancing fish immunotoxicology in a complex world

Understanding how environmental changes affect the health of organisms and ecosystems is complex, but recent interdisciplinary advances and the recognition of immune function as a dynamic mediator offer exciting progress. Environmental immunotoxicology in teleost fishes is evolving beyond cataloguing stressors towards a mechanistic, integrative framework that leverages omics, in vivo tracking and cross-disciplinary modelling. However, knowledge gaps in immune mechanisms, toxicokinetics and multi-stressor interactions remain. The present work highlights these gaps, advocating for immune function as both a mechanistic lens and an integrative health indicator. Such a framework can improve predictive risk assessments, management strategies and our understanding of contaminant effects on resilience, disease susceptibility and population viability. While challenges remain, the field is poised for significant growth through collaborative innovation and advancing technology.

FEBS Letters↗

USGS2023

Environmental, morphological, and molecular data reveal a new species of freshwater mussel, Strophitus howellsi, endemic to the Edwards Plateau in Texas

Freshwater mussels are considered the most imperiled group of organisms in North America and systematics research has played an integral role in the development and implementation of their conservation. Despite the importance of systematics in conservation planning, the evolutionary relationships between many mussel taxa remain poorly explored, clearly illustrated by Strophitus undulatus . This species is wide-ranging, occurring in streams across the United States and Canada with a disjunct population in the Colorado River drainage in central Texas. The widespread distribution of S. undulatus , as well as high intraspecific morphological variation, has led previous authors to doubt the taxon is representative of a single species. In this study, we set out to investigate species boundaries in S. undulatus by integrating environmental, molecular, and morphological datasets. Molecular and morphological data supported S. undulatus from the Colorado River as distinct, which was supplemented by a species distribution modeling approach, suggesting potential adaptation to Edwards Plateau streams has contributed to speciation. Given our findings, we formally describe a new species of freshwater mussel, Strophitus howellsi , endemic to streams along the Edwards Plateau in the Colorado River drainage. A conservation assessment of S. howellsi suggests the species is extremely rare within a highly restricted distribution and may warrant future recovery actions. Our findings build on a growing body of literature highlighting aquatic endemism along the Edwards Plateau and have significant conservation implications for freshwater mussels in Texas.

Conservation Genetics↗

USGS2023

Data integration reveals dynamic and systematic patterns of breeding habitat use by a threatened shorebird

Incorporating species distributions into conservation planning has traditionally involved long-term representations of habitat use where temporal variation is averaged to reveal habitats that are most suitable across time. Advances in remote sensing and analytical tools have allowed for the integration of dynamic processes into species distribution modeling. Our objective was to develop a spatiotemporal model of breeding habitat use for a federally threatened shorebird (piping plover, Charadrius melodus ). Piping plovers are an ideal candidate species for dynamic habitat models because they depend on habitat created and maintained by variable hydrological processes and disturbance. We integrated a 20-year (2000–2019) nesting dataset with volunteer-collected sightings (eBird) using point process modeling. Our analysis incorporated spatiotemporal autocorrelation, differential observation processes within data streams, and dynamic environmental covariates. We evaluated the transferability of this model in space and time and the contribution of the eBird dataset. eBird data provided more complete spatial coverage in our study system than nest monitoring data. Patterns of observed breeding density depended on both dynamic (e.g., surface water levels) and long-term (e.g., proximity to permanent wetland basins) environmental processes. Our study provides a framework for quantifying dynamic spatiotemporal patterns of breeding density. This assessment can be iteratively updated with additional data to improve conservation and management efforts, because reducing temporal variability to average patterns of use may cause a loss in precision for such actions.

Montana, North Dakota, South Dakota↗

USGS2017

Widespread occurrence and potential for biodegradation of bioactive contaminants in Congaree National Park, USA

Organic contaminants with designed molecular bioactivity, such as pesticides and pharmaceuticals, originate from human and agricultural sources, occur frequently in surface waters, and threaten the structure and function of aquatic and terrestrial ecosystems. Congaree National Park in South Carolina (USA) is a vulnerable park unit due to its location downstream of multiple urban and agricultural contaminant sources and its hydrologic setting, being composed almost entirely of floodplain and aquatic environments. Seventy-two water and sediment samples were collected from 16 sites in Congaree National Park during 2013 to 2015, and analyzed for 199 and 81 targeted organic contaminants, respectively. More than half of these water and sediment analytes were not detected or potentially had natural sources. Pharmaceutical contaminants were detected (49 total) frequently in water throughout Congaree National Park, with higher detection frequencies and concentrations at Congaree and Wateree River sites, downstream from major urban areas. Forty-seven organic wastewater indicator chemicals were detected in water, and 36 were detected in sediment, of which approximately half are distinctly anthropogenic. Endogenous sterols and hormones, which may originate from humans or wildlife, were detected in water and sediment samples throughout Congaree National Park, but synthetic hormones were detected only once, suggesting a comparatively low risk of adverse impacts. Assessment of the biodegradation potentials of 8 14 C-radiolabeled model contaminants indicated poor potentials for some contaminants, particularly under anaerobic sediments conditions.

South Carolina↗

USGS2023

Evidence of population-level impacts and resiliency for Gulf of Mexico shelf taxa following the Deepwater Horizon oil spill

The goal of this paper was to review the evidence of population-level impacts of the Deepwater Horizon Oil Spill (DWH) on Gulf of Mexico (GOM) continental shelf taxa, as well as evidence of resiliency following the DWH. There is considerable environmental and biological evidence that GOM shelf taxa were exposed to and suffered direct and indirect impacts of the DWH. Numerous assessments, from mesocosm studies to analysis of biopsied tissue or tissue samples from necropsied animals, revealed a constellation of physiological effects related to DWH impacts on GOM biota, some of which clearly or likely resulted in mortality. While the estimated concentrations of hydrocarbons in shelf waters and sediments were orders of magnitude lower than measured in inshore or deep GOM environments, the level of mortality observed or predicted was substantial for many shelf taxa. In some cases, such as for zooplankton, community shifts following the spill were ephemeral, likely reflecting high rates of population turnover and productivity. In other taxa, such as GOM reef fishes, impacts of the spill are confounded with other stressors, such as fishing mortality or the appearance and rapid population growth of invasive lionfish ( Pterois spp.). In yet others, such as cetaceans, modeling efforts to predict population-level effects of the DWH made conservative assumptions given the species’ protected status, which post-DWH population assessments either failed to detect or population increases were estimated. A persistent theme that emerged was the lack of precise population-level data or assessments prior to the DWH for many taxa, but even when data or assessments did exist, examining evidence of population resiliency was confounded by other stressors impacting GOM biota. Unless efforts are made to increase the resolution of the data or precision of population assessments, difficulties will likely remain in estimating the scale of population-level effects or resiliency in the case of future large-scale environmental catastrophes.

Gulf of Mexico↗

USGS2025

Genetic and environmental factors associated with survival of a rare songbird in a fragmented urban landscape

The coastal Cactus Wren ( Campylorhynchus brunneicapillus ) persists in small and fragmented populations throughout southern California that are subject to genetic drift and inbreeding. We combined individual banding and resighting data and genotyped individuals at 22 microsatellite loci to assess whether heterozygosity was associated with survival across three regional Cactus Wren populations on conserved lands in Orange and San Diego Counties between 2009 and 2020. Using Cormack-Jolly-Seber models (CJS) to analyze the 5-year capture histories of 528 individual wrens, we found that age class (hatch year or after hatch year) was the strongest predictor of survival. Individual heterozygosity and precipitation also had positive effects on survival, with survival up to 2 times higher in the most heterozygous individuals compared to the least and up to 1.5 times higher in high precipitation years versus drought years. Multi-locus heterozygosity was significantly correlated across loci, suggesting that inbreeding depression is likely driving the association between survival and heterozygosity. Study results support that genetic rescue efforts that reduce inbreeding have the potential to improve fitness and mitigate further loss of genetic variation in managed populations.

California↗

USGS2015

Development of an assessment tool for agricultural best management practice Implementation in the Great Lakes Restoration Initiative priority watersheds—Alger Creek, tributary to Saginaw River, Michigan

The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.

Michigan↗

USGS2015

Development of an assessment tool for agricultural best management practice implementation in the Great Lakes restoration initiative priority watersheds—Eagle Creek, tributary to Maumee River, Ohio

Introduction The Great Lakes face a number of serious challenges that cause damage to water quality, habitat, ecology, and coastal health. Excess nutrients from point and nonpoint sources have a history of causing harmful algal blooms (HABs); since the late 1990s, a resurgence of HABs have forced beach closures and resulted in water quality impairments across the Great Lakes. Studies increasingly point to phosphorus (P) runoff from agricultural lands as the cause of these HABs. In 2010, the Great Lakes Restoration Initiative (GLRI) was launched to revitalize the Great Lakes. The GLRI aims to address the challenges facing the Great Lakes and provide a framework for restoration and protection. As part of this effort, the Priority Watersheds Work Group (PWWG), cochaired by the U.S. Environmental Protection Agency (EPA) and the U.S. Department of Agriculture-Natural Resources Conservation Service (USDA–NRCS), is targeting Priority Watersheds (PWs) to reduce the amount of P reaching the Great Lakes. Within the PWs, USDA–NRCS identifies small-scale subbasins with high concentrations of agriculture for coordinated nutrient reduction efforts and enhanced monitoring and modeling. The USDA–NRCS supplies financial and/or technical assistance to producers to install or implement best management practices (BMPs) to lessen the negative effects of agriculture to water quality; additional funding is provided by the GLRI through USDA–NRCS to saturate the small-scale subbasins with BMPs. The watershed modeling component, introduced in this fact sheet, assesses the effectiveness of USDA–NRCS funded BMPs, and nutrient reductions because of GLRI or other funding programs are differentiated. Modeling scenarios consider BMPs that have already been applied and those planned to be implemented across the small-scale subbasins.

Ohio↗

USGS2015

Development of an assessment tool for agricultural best management practice implementation in the Great Lakes Restoration Initiative priority watersheds—Upper East River, tributary to Green Bay, Wisconsin

Wisconsin↗

USGS2026

Habitat-based predictions of bridle shiner ( Notropis bifrenatus ) in the northeastern U.S.

We sought to assess bridle shiner ( Notropis bifrenatus ) habitat associations at local and regional scales across southern Maine and New Hampshire. We used local habitat data at 95 Maine sites to predict occupancy with classification and regression trees (CART). We then used ensemble species distribution models (SDMs) to model the historical (1898–2008) and current (2009–2022) ranges of the species. We used the BIOMOD platform to model the association between 35 environmental variables and bridle shiner presence during both time periods and at fine (pseudo-HUC14) and coarse (HUC12) spatial scales. We then calculated the change in predicted occupied drainages to estimate the change in the species' distribution at both scales. Within a site, bridle shiners were associated with submerged aquatic vegetation, organic substrate, and watermilfoil ( Myriophyllum spp.). SDMs revealed an association with Appalachian (Hemlock-)Northern Hardwood Forest, sand substrate, and low-elevation terrain (at both spatial scales). Ensemble fine-scale SDMs suggest a substantial loss of historical bridle shiner habitat in both Maine (36% of drainages) and New Hampshire (16%), with comparable described losses (of 21% and 14%) at a coarse scale. Our local and regional models may be used to focus surveys on areas with high predicted habitat suitability or to inform habitat restoration efforts.

Maine, New Hampshire↗

USGS2015

Habitat influences distribution of chronic wasting disease in white-tailed deer

Chronic wasting disease (CWD) is a transmissible spongiform encephalopathy that was first detected in 1967 in a captive research facility in Colorado. In the northeastern United States, CWD was first confirmed in white-tailed deer ( Odocoileus virginianus ) in 2005. Because CWD is a new and emerging disease with a spatial distribution that had yet to be assessed in the Northeast, we examined demographic, environmental, and spatial effects to determine how each related to this spatial distribution. The objectives of our study were to identify environmental and spatial effects that best described the spatial distribution of CWD in free-ranging white-tailed deer and identify areas that support deer that are at risk for CWD infection in the Northeast. We used Bayesian hierarchical modeling that incorporated demographic covariates, such as sex and age, along with environmental covariates, which included elevation, slope, riparian corridor, percent clay, and 3 landscapes (i.e., developed, forested, open). The model with the most support contained landscape covariates and spatial effects that represented clustering of CWD in adjacent grid cells. Forested landscapes had the strongest relationship with the distribution of CWD, with increased risk of CWD occurring in areas that had lesser amounts of forest. Our results will assist resource managers in understanding the spatial distribution of CWD within the study area, and in surrounding areas where CWD has yet to be found. Efficiency of disease surveillance and containment efforts can be improved by allocating resources used for surveillance in areas with deer populations that are at greatest risk for infection.

Journal of Wildlife Management↗

USGS2018

Population vulnerability to tsunami hazards informed by previous and projected disasters: A case study of American Samoa

Population vulnerability from tsunamis is a function of the number and location of individuals in hazard zones and their ability to reach safety before wave arrival. Previous tsunami disasters can provide insight on likely evacuation behavior, but post-disaster assessments have not been used extensively in evacuation modeling. We demonstrate the utility of post-disaster assessments in pedestrian evacuation modeling for tsunami hazards and use the US territory of American Samoa as our case study. We model pedestrian travel times out of tsunami inundation zones recreated for the 2009 M w 8.1 Samoa earthquake, as well as for a probable maximum tsunami zone for future threats. Modeling assumptions are guided by fatality trends and observations of 2009 evacuation behavior, including insights on departure delays, environmental cues, transportation mode, and demographic characteristics. Differences in actual fatalities from the 2009 disaster and modeled population vulnerability suggest that a single set of estimated travel times to safety does not fully characterize evacuation potential of a dispersed, at-risk population. Efforts to prepare coastal communities in American Samoa for future tsunamis may be challenging given substantial differences in wave characteristics and evacuation potential of the probable maximum hazard compared to the 2009 event.

American Samoa↗

USGS2020

A modeling workflow that balances automation and human intervention to inform invasive plant management decisions at multiple spatial scales

Predictions of habitat suitability for invasive plant species can guide risk assessments at regional and national scales and inform early detection and rapid-response strategies at local scales. We present a general approach to invasive species modeling and mapping that meets objectives at multiple scales. Our methodology is designed to balance trade-offs between developing highly customized models for few species versus fitting non-specific and generic models for numerous species. We developed a national library of environmental variables known to physiologically limit plant distributions and relied on human input based on natural history knowledge to further narrow the variable set for each species before developing habitat suitability models. To ensure efficiency, we used largely automated modeling approaches and human input only at key junctures. We explore and present uncertainty by using two alternative sources of background samples, including five statistical algorithms, and constructing model ensembles. We demonstrate the use and efficiency of the Software for Assisted Habitat Modeling [SAHM 2.1.2], a package in VisTrails, which performs the majority of the modeling analyses. Our workflow includes solicitation of expert feedback on model outputs such as spatial prediction results and variable response curves, and iterative improvement based on new data availability and directed field validation of initial model results. We highlight the utility of the models for decision-making at regional and local scales with case studies of two plant species that invade natural areas: fountain grass ( Pennisetum setaceum ) and goutweed ( Aegopodium podagraria ). By balancing model automation with human intervention, we can efficiently provide land managers with mapped predicted distributions for multiple invasive species to inform decisions across spatial scales.

PLoS ONE↗