Item talk:Q60681: Difference between revisions

{"@context": "https://schema.org", "@type": "CreativeWork", "additionalType": "USGS Numbered Series", "name": "Selenium in ecosystems within the mountaintop coal mining and valley-fill region of southern West Virginia-assessment and ecosystem-scale modeling", "identifier": [{"@type": "PropertyValue", "propertyID": "USGS Publications Warehouse IndexID", "value": "pp1803", "url": "https://pubs.usgs.gov/publication/pp1803"}, {"@type": "PropertyValue", "propertyID": "USGS Publications Warehouse Internal ID", "value": 70058790}, {"@type": "PropertyValue", "propertyID": "DOI", "value": "10.3133/pp1803", "url": "https://doi.org/10.3133/pp1803"}], "inLanguage": "en", "isPartOf": [{"@type": "CreativeWorkSeries", "name": "Professional Paper"}], "datePublished": "2013", "dateModified": "2013-12-23", "abstract": "Coal and associated waste rock are among environmental selenium (Se) sources that have the potential to affect reproduction in fish and aquatic birds. Ecosystems of southern West Virginia that are affected by drainage from mountaintop coal mines and valleys filled with waste rock in the Coal, Gauley, and Lower Guyandotte watersheds were assessed during 2010 and 2011. Sampling data from earlier studies in these watersheds (for example, Upper Mud River Reservoir) and other mining-affected watersheds also are included to assess additional hydrologic settings and food webs for comparison. Basin schematics give a comprehensive view of sampled species and Se concentration data specific to location and date. Food-web diagrams document the progression of Se trophic transfer across suspended particulate material, invertebrates, and fish for each site to serve as the basis for developing an ecosystem-scale model to predict Se exposure within the hydrologic conditions and food webs of southern West Virginia. This approach integrates a site-specific predator\u2019s dietary exposure pathway into modeling to ensure an adequate link to Se toxicity and, thus, to species vulnerability.\n\nSite-specific fish abundance and richness data in streams documented various species of chub, shiner, dace, darters, bass, minnow, sunfish, sucker, catfish, and central stoneroller (Campostoma anomalum), mottled sculpin (Cottus bairdii), and least brook lamprey (Lampetra aepyptera). However, Se assessment species for streams, and hence, model species for streams, were limited to creek chub (Semotilus atromaculatus) and central stoneroller. Both of these species of fish are generally considered to have a high tolerance for environmental stress based on traditional comparative fish community assessment, with creek chub being present at all sites. Aquatic insects (mayfly, caddisfly, stonefly, dobsonfly, chironomid) were the main invertebrates sampled in streams. Collection of suspended particulate material acted as an integrator of organic-rich, fine-grained biomass present in streams.\n\nThe base-case food web modeled for streams was suspended particulate material to aquatic insect to creek chub, with comparative modeling of a direct particulate-to-stoneroller food web. Model species for a reservoir setting were based on an earlier study of bluegill sunfish (Lepomis macrochirus), green sunfish (Lepomis cyanellus), and largemouth bass (Micropterus salmoides). Several reservoir food webs were considered based on a variety of invertebrates (insect, snail, clam). For stream and reservoir settings, predicted Se concentrations in exposure scenarios showed a high degree of correlation (r2 = 0.91 for invertebrates and 0.75 for fish) with field observations of Se concentrations when modeling was initiated from suspended-particulate-material Se concentrations and model transfer parameters defined previously in the literature were used. These strong correlations validate the derived site-specific model and establish sufficient confidence that the predictions from the developed model can be quantitatively applied to the ecosystems in southern West Virginia.\n\nAn application of modeling used a metric describing the partitioning of Se between particulate material and dissolved phases (Kd) to allow determination of a dissolved Se concentration that would be necessary to attain a site-specific Se fish body burden. The operationally defined Kd quantifies the complex process of transformation at the base of a food web on a site-specific basis. The magnitude of this metric is known to vary with such factors as Se speciation, particulate-material type, and hydrology. This application (1) ties dissolved Se concentrations to fish tissue concentrations; (2) allows consideration of different choices for intervening site-specific exposure steps that set Se bioaccumulation, partitioning, and bioavailability; and (3) generates implications for management decisions that define protection through different regulatory pathways and guidelines. The range of model outcomes accounts for critical sources of variability and establishes whether site and food-web characterization were adequate to represent the dynamics of the system with certainty. This is especially true in terms of particulate-material phases at the base of the food web and utilization of Kd in different hydrologic settings. For streams, a range of field-derived Kdds were applied to food-web exposure scenarios within a framework of locational and hydrologic variables (area of stream basin; stream gradient and discharge) that may affect the magnitude of Kd. Overlaying even a coarse temporal scale that acknowledges variability in stream dissolved Se and Se speciation, such as through seasonal derivation of Kd, can substantially narrow model uncertainty.\n\nModeling that constrains the place and time of greatest ecosystem Se sensitivity within a specified food web gives insight into Se risk and identifies controlling management alternatives within a watershed or stream basin. If there is a range of hydrologic settings, specificity is needed to establish a hierarchy of in-stream and off-stream habitats for a watershed approach that takes into account Se-enriched water moving through different Kd and food web environments. If there is a range of predator vulnerabilities (measured as a combination of food-web Se biodynamics and response in Se toxicity tests) within the site-specific community of fish species to be protected, then choice of fish species is critical to protection because it determines the food web and, hence, the magnitude of biotransfer through which Se is modeled. Whether creek chub is representative of the vulnerability to Se of all fish species encountered within the study-site ecosystems will require additional species-specific data and analysis. A range of site-specific scenarios illustrated here set model outcomes, but the final quantitative evaluation of alternatives and their implications will be those generated through choices and guidance formulated by state and other agencies in their decisionmaking processes.\n\nProposed additions and refinements to the ecosystem-scale site-specific approach developed here include consideration of:\n\nmeasurement of temporally matched pairs of dissolved and suspended-particulate-material Se concentrations across a broader range of stream sites to expand the stream Kd database and to test the representativeness of a suspended-particulate-material sample within a stream;\ncharacterization of different phases of particulate material across seasons to better define the base of the food web and connect to invertebrate feeding;\nrefinement of model assumptions concerning dietary preferences and composition for fish to develop additional trophic transfer factors (TTFs) (for example, calculation of TTFinvertebrate composite for mixed diets);\nexpansion of modeling of fish species and their food webs to include Se-vulnerable species;\ntemporal characterization of a predator\u2019s life cycle and habitat use as additional model layers to integrate with Se biodynamics in streams;\ninvestigation of the effect of stream gradient on Kd based on a finer scale than presented here in terms of such variables as residence time, watershed dilution, and physical habitat attributes (for example, amount of ponding versus run or riffle within a stream); and\nlinkage to discharge through use of stream gaging to record variability and enable model organization within water-year types and discharge seasons.\nInvestigating the presence and variability of prey and predator species in demographically open systems such as streams also is key to model outcomes given the overall environmental stressors (for example, general landscape change, food-web disruption, recolonization potential) imposed on the composition of biological communities in coal mining and valley-fill affected watersheds", "description": "vi, 86 p.", "publisher": {"@type": "Organization", "name": "U.S. Geological Survey"}, "author": [{"@type": "Person", "name": "Presser, Theresa S. tpresser@usgs.gov", "givenName": "Theresa S.", "familyName": "Presser", "email": "tpresser@usgs.gov", "identifier": {"@type": "PropertyValue", "propertyID": "ORCID", "value": "0000-0001-5643-0147", "url": "https://orcid.org/0000-0001-5643-0147"}, "affiliation": [{"@type": "Organization", "name": "National Research Program - Western Branch", "url": "https://www.usgs.gov/programs/national-geological-and-geophysical-data-preservation-program"}]}], "funder": [{"@type": "Organization", "name": "National Research Program - Western Branch", "url": "https://www.usgs.gov/programs/national-geological-and-geophysical-data-preservation-program"}], "spatialCoverage": [{"@type": "Place", "additionalType": "country", "name": "United States", "url": "https://geonames.org/6252001"}, {"@type": "Place", "additionalType": "state", "name": "West Virginia", "url": "https://geonames.org/4826850"}, {"@type": "Place", "geo": [{"@type": "GeoShape", "additionalProperty": {"@type": "PropertyValue", "name": "GeoJSON", "value": {"type": "FeatureCollection", "features": [{"type": "Feature", "properties": {}, "geometry": {"type": "Polygon", "coordinates": [[[-81.8207, 37.4749], [-81.8207, 38.634], [-80.1453, 38.634], [-80.1453, 37.4749], [-81.8207, 37.4749]]]}}]}}}, {"@type": "GeoCoordinates", "latitude": 38.05445, "longitude": -80.98299999999999}]}]}