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Franklin AM, Weller DL, Durso LM, Bagley M, Davis BC, Frye JG, Grim CJ, Ibekwe AM, Jahne MA, Keely SP, Kraft AL, McConn BR, Mitchell RM, Ottesen AR, Sharma M, Strain EA, Tadesse DA, Tate H, Wells JE, Williams CF, Cook KL, Kabera C, McDermott PF, Garland JL. A one health approach for monitoring antimicrobial resistance: developing a national freshwater pilot effort. FRONTIERS IN WATER 2024; 6:10.3389/frwa.2024.1359109. [PMID: 38855419 PMCID: PMC11157689 DOI: 10.3389/frwa.2024.1359109] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2024]
Abstract
Antimicrobial resistance (AMR) is a world-wide public health threat that is projected to lead to 10 million annual deaths globally by 2050. The AMR public health issue has led to the development of action plans to combat AMR, including improved antimicrobial stewardship, development of new antimicrobials, and advanced monitoring. The National Antimicrobial Resistance Monitoring System (NARMS) led by the United States (U.S) Food and Drug Administration along with the U.S. Centers for Disease Control and U.S. Department of Agriculture has monitored antimicrobial resistant bacteria in retail meats, humans, and food animals since the mid 1990's. NARMS is currently exploring an integrated One Health monitoring model recognizing that human, animal, plant, and environmental systems are linked to public health. Since 2020, the U.S. Environmental Protection Agency has led an interagency NARMS environmental working group (EWG) to implement a surface water AMR monitoring program (SWAM) at watershed and national scales. The NARMS EWG divided the development of the environmental monitoring effort into five areas: (i) defining objectives and questions, (ii) designing study/sampling design, (iii) selecting AMR indicators, (iv) establishing analytical methods, and (v) developing data management/analytics/metadata plans. For each of these areas, the consensus among the scientific community and literature was reviewed and carefully considered prior to the development of this environmental monitoring program. The data produced from the SWAM effort will help develop robust surface water monitoring programs with the goal of assessing public health risks associated with AMR pathogens in surface water (e.g., recreational water exposures), provide a comprehensive picture of how resistant strains are related spatially and temporally within a watershed, and help assess how anthropogenic drivers and intervention strategies impact the transmission of AMR within human, animal, and environmental systems.
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Affiliation(s)
- Alison M. Franklin
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Daniel L. Weller
- U.S. Centers for Disease Control and Prevention, Atlanta, GA, United States
| | - Lisa M. Durso
- U.S. Department of Agriculture, Agricultural Research Service (USDA, ARS), Agroecosystem Management Research, Lincoln, NE, United States
| | - Mark Bagley
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Benjamin C. Davis
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Jonathan G. Frye
- USDA ARS, U.S. National Poultry Research Center, Poultry Microbiological Safety and Processing Research Unit, Athens, GA, United States
| | - Christopher J. Grim
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
| | - Abasiofiok M. Ibekwe
- USDA, ARS, Agricultural Water Efficiency and Salinity Research Unit, Riverside, CA, United States
| | - Michael A. Jahne
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Scott P. Keely
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
| | - Autumn L. Kraft
- Oak Ridge Institute for Science and Education, USDA, ARS, Beltsville, MD, United States
| | - Betty R. McConn
- Oak Ridge Institute for Science and Education, USDA, ARS, Beltsville, MD, United States
| | - Richard M. Mitchell
- Environmental Protection Agency, Office of Water, Washington, DC, United States
| | - Andrea R. Ottesen
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Manan Sharma
- USDA, ARS Environmental Microbial and Food Safety Laboratory, Beltsville, MD, United States
| | - Errol A. Strain
- Center for Food Safety and Applied Nutrition, U.S. Food and Drug Administration, College Park, MD, United States
| | - Daniel A. Tadesse
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Heather Tate
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Jim E. Wells
- USDA, ARS, U.S. Meat Animal Research Center, Meat Safety and Quality, Clay Center, NE, United States
| | - Clinton F. Williams
- USDA, ARS, US Arid-Land Agricultural Research Center, Maricopa, AZ, United States
| | - Kim L. Cook
- USDA, ARS Nutrition, Food Safety and Quality National Program Staff, Beltsville, MD, United States
| | - Claudine Kabera
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Patrick F. McDermott
- Center for Veterinary Medicine, National Antimicrobial Resistance Monitoring System (NARMS), U.S. Food and Drug Administration, Laurel, MD, United States
| | - Jay L. Garland
- United States (U.S.) Environmental Protection Agency, Office of Research and Development, Cincinnati, OH, United States
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Hsu TTD, Yu D, Wu M. Predicting Fecal Indicator Bacteria Using Spatial Stream Network Models in A Mixed-Land-Use Suburban Watershed in New Jersey, USA. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4743. [PMID: 36981647 PMCID: PMC10049084 DOI: 10.3390/ijerph20064743] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 06/18/2023]
Abstract
Good water quality safeguards public health and provides economic benefits through recreational opportunities for people in urban and suburban environments. However, expanding impervious areas and poorly managed sanitary infrastructures result in elevated concentrations of fecal indicator bacteria and waterborne pathogens in adjacent waterways and increased waterborne illness risk. Watershed characteristics, such as urban land, are often associated with impaired microbial water quality. Within the proximity of the New York-New Jersey-Pennsylvania metropolitan area, the Musconetcong River has been listed in the Clean Water Act's 303 (d) List of Water Quality-Limited Waters due to high concentrations of fecal indicator bacteria (FIB). In this study, we aimed to apply spatial stream network (SSN) models to associate key land use variables with E. coli as an FIB in the suburban mixed-land-use Musconetcong River watershed in the northwestern New Jersey. The SSN models explicitly account for spatial autocorrelation in stream networks and have been widely utilized to identify watershed attributes linked to deteriorated water quality indicators. Surface water samples were collected from the five mainstem and six tributary sites along the middle section of the Musconetcong River from May to October 2018. The log10 geometric means of E. coli concentrations for all sampling dates and during storm events were derived as response variables for the SSN modeling, respectively. A nonspatial model based on an ordinary least square regression and two spatial models based on Euclidean and stream distance were constructed to incorporate four upstream watershed attributes as explanatory variables, including urban, pasture, forest, and wetland. The results indicate that upstream urban land was positively and significantly associated with the log10 geometric mean concentrations of E. coli for all sampling cases and during storm events, respectively (p < 0.05). Prediction of E. coli concentrations by SSN models identified potential hot spots prone to water quality deterioration. The results emphasize that anthropogenic sources were the main threats to microbial water quality in the suburban Musconetcong River watershed. The SSN modeling approaches from this study can serve as a novel microbial water quality modeling framework for other watersheds to identify key land use stressors to guide future urban and suburban water quality restoration directions in the USA and beyond.
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Affiliation(s)
- Tsung-Ta David Hsu
- New Jersey Center for Water Science and Technology, Montclair State University, Montclair, NJ 07043, USA
| | - Danlin Yu
- Department of Earth and Environmental Studies, Montclair State University, Montclair, NJ 07043, USA
| | - Meiyin Wu
- New Jersey Center for Water Science and Technology, Montclair State University, Montclair, NJ 07043, USA
- Department of Biology, Montclair State University, Montclair, NJ 07043, USA
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McManus MG, D'Amico E, Smith EM, Polinsky R, Ackerman J, Tyler K. Variation in stream network relationships and geospatial predictions of watershed conductivity. FRESHWATER SCIENCE (PRINT) 2020; 39:1-18. [PMID: 33747635 PMCID: PMC7970528 DOI: 10.1086/710340] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Secondary salinization, the increase of anthropogenically-derived salts in freshwaters, threatens freshwater biota and ecosystems, drinking water supplies, and infrastructure. The various anthropogenic sources of salts and their locations in a watershed may result in secondary salinization of river and stream networks through multiple inputs. We developed a watershed predictive assessment to investigate the degree to which topology, land-cover, and land-use covariates affect stream specific conductivity (SC), a measure of salinity. We used spatial stream network models to predict SC throughout an Appalachian stream network in a watershed affected by surface coal mining. During high-discharge conditions, 8 to 44% of stream km in the watershed exceeded the SC benchmark of 300 μS/cm, which is meant to be protective of aquatic life in the Central Appalachian ecoregion. During low-discharge conditions, 96 to 100% of stream km exceeded the benchmark. The 2 different discharge conditions altered the spatial dependency of SC among the stream monitoring sites. During most low discharges, SC was a function of upstream-to-downstream network distances, or flow-connected distances, among the sites. Flow-connected distances are indicative of upstream dependencies affecting stream SC. During high discharge, SC was related to both flow-connected distances and flow-unconnected distances (i.e., distances between sites on different branches of the network). Flow-unconnected distances are indicative of processes on adjacent branches and their catchments affecting stream SC. With sites distributed from headwaters to the watershed outlet, the extent of impacts from secondary salinization could be better spatially predicted and assessed with spatial stream network models than with models assuming spatial independence. Importantly, the assessment also recognized the multi-scale spatial relationships that can occur between the landscape and stream network.
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Affiliation(s)
- Michael G McManus
- Center for Environmental Measurement and Modeling, Office of Research and Development, United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268 USA
| | - Ellen D'Amico
- Pegasus Technical Services c/o United States Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268 USA
| | - Elizabeth M Smith
- Water Division, United States Environmental Protection Agency, Region IV, 61 Forsyth Street Southwest, Atlanta, Georgia 30303 USA
| | - Robyn Polinsky
- Water Division, United States Environmental Protection Agency, Region IV, 61 Forsyth Street Southwest, Atlanta, Georgia 30303 USA
| | - Jerry Ackerman
- Laboratory Services and Applied Science Division, United States Environmental Protection Agency, Region IV, 980 College Station Road, Athens, Georgia 30605 USA
| | - Kip Tyler
- Water Division, United States Environmental Protection Agency, Region IV, 61 Forsyth Street Southwest, Atlanta, Georgia 30303 USA
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Kattwinkel M, Szöcs E, Peterson E, Schäfer RB. Preparing GIS data for analysis of stream monitoring data: The R package openSTARS. PLoS One 2020; 15:e0239237. [PMID: 32941523 PMCID: PMC7498020 DOI: 10.1371/journal.pone.0239237] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Accepted: 09/01/2020] [Indexed: 11/21/2022] Open
Abstract
Stream monitoring data provides insights into the biological, chemical and physical status of running waters. Additionally, it can be used to identify drivers of chemical or ecological water quality, to inform related management actions, and to forecast future conditions under land use and global change scenarios. Measurements from sites along the same stream may not be statistically independent, and the R package SSN provides a way to describe spatial autocorrelation when modelling relationships between measured variables and potential drivers. However, SSN requires the user to provide the stream network and sampling locations in a certain format. Likewise, other applications require catchment delineation and intersection of different spatial data. We developed the R package openSTARS that provides the functionality to derive stream networks from a digital elevation model, delineate stream catchments and intersect them with land use or other GIS data as potential predictors. Additionally, locations for model predictions can be generated automatically along the stream network. We present an example workflow of all data preparation steps. In a case study using data from water monitoring sites in Southern Germany, the resulting stream network and derived site characteristics matched those constructed using STARS, an ArcGIS custom toolbox. An advantage of openSTARS is that it relies on free and open-source GRASS GIS and R functions, unlike the original STARS toolbox which depends on proprietary ArcGIS. openSTARS also comes without a graphical user interface, to enhance reproducibility and reusability of the workflow, thereby harmonizing and simplifying the data pre-processing prior to statistical modelling. Overall, openSTARS facilitates the use of spatial regression and other applications on stream networks and contributes to reproducible science with applications in hydrology, environmental sciences and ecology.
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Affiliation(s)
- Mira Kattwinkel
- Institute for Environmental Sciences (iES), University of Koblenz-Landau, Landau, Germany
- * E-mail:
| | - Eduard Szöcs
- Institute for Environmental Sciences (iES), University of Koblenz-Landau, Landau, Germany
| | - Erin Peterson
- Institute for Future Environments, Queensland University of Technology, Brisbane, Australia
- Australian Research Council Centre of Excellence in Mathematical and Statistical Frontiers (ACEMS), Brisbane, Australia
| | - Ralf B. Schäfer
- Institute for Environmental Sciences (iES), University of Koblenz-Landau, Landau, Germany
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Pennino MJ, Leibowitz SG, Compton JE, Hill RA, Sabo RD. Patterns and predictions of drinking water nitrate violations across the conterminous United States. THE SCIENCE OF THE TOTAL ENVIRONMENT 2020; 722:137661. [PMID: 32192969 PMCID: PMC8204728 DOI: 10.1016/j.scitotenv.2020.137661] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/07/2020] [Revised: 02/28/2020] [Accepted: 02/29/2020] [Indexed: 05/12/2023]
Abstract
Excess nitrate in drinking water is a human health concern, especially for young children. Public drinking water systems in violation of the 10 mg nitrate-N/L maximum contaminant level (MCL) must be reported in EPA's Safe Drinking Water Information System (SDWIS). We used SDWIS data with random forest modeling to examine the drivers of nitrate violations across the conterminous U.S. and to predict where public water systems are at risk of exceeding the nitrate MCL. As explanatory variables, we used land cover, nitrogen inputs, soil/hydrogeology, and climate variables. While we looked at the role of nitrate treatment in separate analyses, we did not include treatment as a factor in the final models, due to incomplete information in SDWIS. For groundwater (GW) systems, a classification model correctly classified 79% of catchments in violation and a regression model explained 43% of the variation in nitrate concentrations above the MCL. The most important variables in the GW classification model were % cropland, agricultural drainage, irrigation-to-precipitation ratio, nitrogen surplus, and surplus precipitation. Regions predicted to have risk for nitrate violations in GW were the Central California Valley, parts of Washington, Idaho, the Great Plains, Piedmont of Pennsylvania and Coastal Plains of Delaware, and regions of Wisconsin, Iowa, and Minnesota. For surface water (SW) systems, a classification model correctly classified 90% of catchments and a regression model explained 52% of the variation in nitrate concentration. The variables most important for the SW classification model were largely hydroclimatic variables including surplus precipitation, irrigation-to-precipitation ratio, and % shrubland. Areas at greatest risk for SW nitrate violations were generally in the non-mountainous west and southwest. Identifying the areas with possible risk for future violations and potential drivers of nitrate violations across U.S. can inform decisions on how source water protection and other management options could best protect drinking water.
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Affiliation(s)
- Michael J Pennino
- U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA.
| | - Scott G Leibowitz
- U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA
| | - Jana E Compton
- U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA
| | - Ryan A Hill
- U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Pacific Ecological Systems Division, Corvallis, OR, USA
| | - Robert D Sabo
- U.S. EPA, Office of Research and Development, Center for Public Health and Environmental Assessment, Health & Environmental Effects Assessment Division, Washington, DC, USA
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Hu Y, Sampata AM, Ruiz-Mercado GJ, Zavala VM. Logistics Network Management of Livestock Waste for Spatiotemporal Control of Nutrient Pollution in Water Bodies. ACS SUSTAINABLE CHEMISTRY & ENGINEERING 2019; 7:18359-18374. [PMID: 32983653 PMCID: PMC7511004 DOI: 10.1021/acssuschemeng.9b03920] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Abstract
Nutrient pollution is a widespread water quality problem, which originates from excess nutrient runoff from agricultural land, improperly managed farming operations, and point sources such as wastewater treatment plants. Some nutrient pollution impacts include harmful algal blooms (HABs), hypoxia, and eutrophication. HABs are major environmental events that cause severe health threats and economic losses (e.g., tourism, real estate, commercial fishing). A dimension of the nutrient pollution problem that has not received much attention is that this interacts with organic waste management practices. As a result, it is important to connect the time and location of point and nonpoint nutrient source releases, nutrient soil content, spatial layout, and hydrology of agricultural lands with the transport of nutrients to water bodies and their impacts on aquatic ecosystems. In this work, we show how nutrient concentration in water bodies and other spatiotemporal factors are related to HAB development and how logistics management of livestock waste can be used to conduct space-time management of nutrient pollution. A case study for the Upper Yahara Watershed in the State of Wisconsin (U.S.) is employed to demonstrate the practicability of the modeling framework. Our framework reveals that logistics network management for waste and nutrients can reduce the incidence rates of HABs, but reducing it to nonharmful levels would require long-term efforts such as installing nutrient recovery technologies, coordinating manure storage and application, and deploying management incentive plans.
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Affiliation(s)
- Yicheng Hu
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States
| | - Apoorva M. Sampata
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States
| | - Gerardo J. Ruiz-Mercado
- Office of Research and Development, U.S. Environmental Protection Agency, 26 West Martin Luther King Drive, Cincinnati, Ohio 45268, United States
| | - Victor M. Zavala
- Department of Chemical and Biological Engineering, University of Wisconsin-Madison, 1415 Engineering Drive, Madison, Wisconsin 53706, United States
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Kaylor MJ, White SM, Saunders WC, Warren DR. Relating spatial patterns of stream metabolism to distributions of juveniles salmonids at the river network scale. Ecosphere 2019. [DOI: 10.1002/ecs2.2781] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023] Open
Affiliation(s)
- Matthew J. Kaylor
- Department of Fisheries and Wildlife Oregon State University Corvallis Oregon USA
| | - Seth M. White
- Columbia River Inter‐Tribal Fish Commission Portland Oregon USA
| | - W. Carl Saunders
- Department of Watershed Sciences Utah State University Logan Utah USA
| | - Dana R. Warren
- Department of Fisheries and Wildlife Oregon State University Corvallis Oregon USA
- Department of Forest Ecosystems and Society Oregon State University Corvallis Oregon USA
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Johnson B, Smith E, Ackerman JW, Dye S, Polinsky R, Somerville E, Decker C, Little D, Pond G, D'Amico E. Spatial Convergence in Major Dissolved Ion Concentrations and Implications of Headwater Mining for Downstream Water Quality. JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION 2019; 55:247-258. [PMID: 33354106 PMCID: PMC7751627] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Spatial patterns in major dissolved solute concentrations were examined to better understand impact of surface coal mining in headwaters on downstream water chemistry. Sixty sites were sampled seasonally from 2012 to 2014 in an eastern Kentucky watershed. Watershed areas (WA) ranged from 1.6 to 400.5 km2 and were mostly forested (58%-95%), but some drained as much as 31% surface mining. Measures of total dissolved solutes and most component ions were positively correlated with mining. Analytes showed strong convergent spatial patterns with high variability in headwaters (<15 km2 WA) that stabilized downstream (WA > 75 km2), indicating hydrologic mixing primarily controls downstream values. Mean headwater solute concentrations were a good predictor of downstream values, with % differences ranging from 0.55% (Na+) to 28.78% (Mg2+). In a mined scenario where all headwaters had impacts, downstream solute concentrations roughly doubled. Alternatively, if mining impacts to headwaters were minimized, downstream solute concentrations better approximated the 300 μS/cm conductivity criterion deemed protective of aquatic life. Temporal variability also had convergent spatial patterns and mined streams were less variable due to unnaturally stable hydrology. The highly conserved nature of dissolved solutes from mining activities and lack of viable treatment options suggest forested, unmined watersheds would provide dilution that would be protective of downstream aquatic life.
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Affiliation(s)
- Brent Johnson
- United States Environmental Protection Agency, Office of Research and Development, National Exposure Research Laboratory, Cincinnati, OH, USA
| | - Elizabeth Smith
- United States Environmental Protection Agency, Region 4, Water Protection Division, Atlanta, GA, USA
| | - Jerry W Ackerman
- United States Environmental Protection Agency, Region 4, Science and Ecosystem Support Division, Athens, GA, USA
| | - Sue Dye
- United States Environmental Protection Agency, Region 4, Science and Ecosystem Support Division, Athens, GA, USA
| | - Robyn Polinsky
- United States Environmental Protection Agency, Region 4, Water Protection Division, Atlanta, GA, USA
| | - Eric Somerville
- United States Environmental Protection Agency, Region 4, Water Protection Division, Atlanta, GA, USA
| | - Chris Decker
- United States Environmental Protection Agency, Region 4, Water Protection Division, Atlanta, GA, USA
| | - Derek Little
- United States Environmental Protection Agency, Region 4, Science and Ecosystem Support Division, Athens, GA, USA
| | - Gregory Pond
- United States Environmental Protection Agency, EPA Gulf of Mexico Program, Gulfport MS, USA
| | - Ellen D'Amico
- GIS Support specialist, Pegasus Technical Services Inc., Cincinnati, OH, USA
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Martin RW, Waits ER, Nietch CT. Empirically-based modeling and mapping to consider the co-occurrence of ecological receptors and stressors. THE SCIENCE OF THE TOTAL ENVIRONMENT 2018; 613-614:1228-1239. [PMID: 28958130 PMCID: PMC6092948 DOI: 10.1016/j.scitotenv.2017.08.301] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2017] [Revised: 08/25/2017] [Accepted: 08/30/2017] [Indexed: 05/22/2023]
Abstract
Part of the ecological risk assessment process involves examining the potential for environmental stressors and ecological receptors to co-occur across a landscape. In this study, we introduce a Bayesian joint modeling framework for use in evaluating and mapping the co-occurrence of stressors and receptors using empirical data, open-source statistical software, and Geographic Information Systems tools and data. To illustrate the approach, we apply the framework to bioassessment data on stream fishes and nutrients collected from a watershed in southwestern Ohio. The results highlighted the joint model's ability to parse and exploit statistical dependencies in order to provide empirical insight into the potential environmental and ecotoxicological interactions influencing co-occurrence. We also demonstrate how probabilistic predictions can be generated and mapped to visualize spatial patterns in co-occurrences. For practitioners, we believe that this data-driven approach to modeling and mapping co-occurrence can lead to more quantitatively transparent and robust assessments of ecological risk.
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Affiliation(s)
- Roy W Martin
- USEPA Office of Research and Development, Cincinnati, OH 45213, United States.
| | - Eric R Waits
- USEPA Office of Research and Development, Cincinnati, OH 45213, United States
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