The U.S. Geological Survey (USGS), in cooperation with the Grand River Dam Authority, completed a high-resolution multibeam bathymetric survey to compute a new area and capacity table for Grand Lake O’ the Cherokees in northeastern Oklahoma. Area and capacity tables identify the relation between the elevation of the water surface and the volume of water that can be impounded at each water-surface elevation. The area and capacity of Grand Lake O’ the Cherokees were computed from a triangular irregular network surface created in Global Mapper Version 21.0.1. The triangular irregular network surface was created from three datasets: (1) a multibeam mapping system bathymetric survey of Grand Lake O’ the Cherokees completed during April–July 2019, (2) a previous bathymetric survey of the Neosho, Spring, and Elk Rivers, and (3) a 2010 USGS lidar-derived digital elevation model. The digital elevation model data were used in areas with land-surface elevations greater than 744 feet above the North American Vertical Datum of 1988 where the multibeam sonar data could not be collected. The 2019 multibeam sonar data were the predominant data used to compute the new area and capacity table for Grand Lake O’ the Cherokees.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3467","collaboration":"Prepared in cooperation with the Grand River Dam Authority","usgsCitation":"Hunter, S.L., Trevisan, A.R., Villa, J., and Smith, K.A., 2020, Bathymetric map, surface area, and capacity of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2019: U.S. Geological Survey Scientific Investigations Map 3467, 2 sheets, https://doi.org/10.3133/sim3467.","productDescription":"2 Sheets: 36.00 x 42.00 inches; Data Release","onlineOnly":"Y","ipdsId":"IP-116457","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":381444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3467/coverthb.jpg"},{"id":381448,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3467/sim3467_sheet1.pdf","text":"Sheet 1","size":"5.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3467 Sheet 1"},{"id":381449,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3467/sim3467_sheet2.pdf","text":"Sheet 2","size":"26.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3467 Sheet 2"},{"id":381450,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KA2T3Z","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data release of bathymetric map, surface area, and capacity of Grand Lake O’ the Cherokees, northeastern Oklahoma, 2019"}],"country":"United States","state":"Oklahoma","otherGeospatial":"Grand Lake O’ the Cherokees","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -95.10589599609375,\n 36.436751611390264\n ],\n [\n -94.60601806640625,\n 36.436751611390264\n ],\n [\n -94.60601806640625,\n 36.8510544475565\n ],\n [\n -95.10589599609375,\n 36.8510544475565\n ],\n [\n -95.10589599609375,\n 36.436751611390264\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
The first reported invasion of Asian swamp eels (Monopterus albus, ASE) in the continental United States was in the state of Georgia in 1994. This population was first discovered within several ponds on a private nature centre, but the ponds drained via an outflow pipe into marsh habitats along the Chattahoochee River. Our objective was to delineate the current invasion extent of ASE in the Chattahoochee River, Georgia, by sampling juvenile ASE within an occupancy modelling framework. We sampled 111 and 100 sites in 2015 and 2016 respectively, on 10 occasions, each within a 2-km radius of the purported invasion point to estimate the spatial extent of their invasion in this system. Leaf-litter traps (LLTs) were effective at documenting an increase in the invasion extent of ASE, from within 100 m of the Chattahoochee Nature Center pond outflow to 1.6 km away. Documenting the extent of invasion of this population has proven elusive in the past, but the use of LLTs to target juvenile eels has documented a larger invasion extent than previously known in the study system. The results of this research can be used to develop effective control and management strategies, such as locating potential breeding areas for targeted removal sampling.
The health and well-being of plants and soil is crucial for all life on Earth. It is well-known that vegetation cover follows climatic zones, and plants respond to climatic drivers such as temperature and precipitation (Seddon et al., 2016; Kattge et al., 2020). It is also well-known that plant health depends on the properties and health of the soil (Ephrath et al., 2020), and that strong interactions among biota above and belowground dictate the functioning of both realms (Van der Putten et al., 2013). Yet, soils and the processes occurring belowground are often considered a “black box,” and are treated very simplistically in our efforts to understand, quantify, and model the future of the planet. Our understanding of the interactions between plants and soils is also far from complete and offers some of the most important research frontiers in community ecology, biogeochemistry, and global change science.
","language":"English","publisher":"Frontiers","doi":"10.3389/fpls.2020.621235","usgsCitation":"Sevanto, S., Grossiord, C., Klein, T., and Reed, S., 2020, Editorial: Plant-soil interactions under changing climate: Frontiers in Plant Science, v. 11, 621235, 2 p., https://doi.org/10.3389/fpls.2020.621235.","productDescription":"621235, 2 p.","ipdsId":"IP-124196","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":454637,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3389/fpls.2020.621235","text":"Publisher Index Page"},{"id":382085,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"11","noUsgsAuthors":false,"publicationDate":"2020-12-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Sevanto, Sanna","contributorId":150845,"corporation":false,"usgs":false,"family":"Sevanto","given":"Sanna","email":"","affiliations":[],"preferred":false,"id":807980,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Grossiord, Charlotte","contributorId":207749,"corporation":false,"usgs":false,"family":"Grossiord","given":"Charlotte","email":"","affiliations":[{"id":37625,"text":"Earth and Environmental Sciences Division, Los Alamos National Laboratory","active":true,"usgs":false}],"preferred":false,"id":807981,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Klein, Tamir","contributorId":181981,"corporation":false,"usgs":false,"family":"Klein","given":"Tamir","email":"","affiliations":[],"preferred":false,"id":807982,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Reed, Sasha C. 0000-0002-8597-8619","orcid":"https://orcid.org/0000-0002-8597-8619","contributorId":205372,"corporation":false,"usgs":true,"family":"Reed","given":"Sasha C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":807983,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70219575,"text":"70219575 - 2020 - Assessing contributions of cold-water refuges to reproductive migration corridor conditions for adult salmon and steelhead trout in the Columbia River, USA","interactions":[],"lastModifiedDate":"2021-04-14T12:03:12.831455","indexId":"70219575","displayToPublicDate":"2020-12-17T06:59:34","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5513,"text":"Journal of Ecohydraulics","active":true,"publicationSubtype":{"id":10}},"title":"Assessing contributions of cold-water refuges to reproductive migration corridor conditions for adult salmon and steelhead trout in the Columbia River, USA","docAbstract":"Diadromous fish populations face multiple challenges along their migratory routes. These challenges include suboptimal water quality, harvest, and barriers to longitudinal and lateral connectivity. Interactions among factors influencing migration success make it challenging to assess management options for improving migratory fish conditions along riverine migration corridors. We describe a spatially explicit simulation model that integrates complex individual behaviors of fall-run Chinook Salmon (Oncorhynchus tshawytscha) and summer-run steelhead trout (O. mykiss) during migration, responds to variable habitat conditions over a large extent of the Columbia River, and links migration corridor conditions to fish condition outcomes. The model is built around a mechanistic behavioral decision tree that drives individual interactions of fish within their simulated environments. By simulating several thermalscapes with alternative scenarios of thermal refuge availability, we examined how behavioral thermoregulation in cold-water refuges influenced migrating fish conditions. Outcomes of the migration corridor simulation model show that cold-water refuges can provide relief from exposure to high water temperatures, but do not substantially contribute to energy conservation by migrating adults. Simulated cooling of the Columbia River decreased reliance on cold-water refuges and there were slight reductions in migratory energy expenditure. This modeling of simulated thermalscapes provides a framework for assessing the contribution of cold-water refuges to the success of migrating fishes, but any final determination will depend on analyzing fish survival and health for their entire migration, water temperature management goals and species recovery targets.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/24705357.2020.1855086","usgsCitation":"Snyder, M.N., Schumaker, N.H., Dunham, J.B., Keefer, M., Leinenbach, P., Brookes, A., Palmer, J., Wu, J., Keenan, D.M., and Ebersole, J.L., 2020, Assessing contributions of cold-water refuges to reproductive migration corridor conditions for adult salmon and steelhead trout in the Columbia River, USA: Journal of Ecohydraulics, 14 p., https://doi.org/10.1080/24705357.2020.1855086.","productDescription":"14 p.","onlineOnly":"Y","ipdsId":"IP-122783","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":454644,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8059528","text":"External Repository"},{"id":385075,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Washington, Oregon, Idaho","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.65234374999999,\n 44.02442151965934\n ],\n [\n -114.78515624999999,\n 44.02442151965934\n ],\n [\n -114.78515624999999,\n 46.73986059969267\n ],\n [\n -118.65234374999999,\n 46.73986059969267\n ],\n [\n -118.65234374999999,\n 44.02442151965934\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2020-12-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Snyder, Marcia N. 0000-0003-2202-2668","orcid":"https://orcid.org/0000-0003-2202-2668","contributorId":217972,"corporation":false,"usgs":false,"family":"Snyder","given":"Marcia","email":"","middleInitial":"N.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814220,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schumaker, Nathan H.","contributorId":199151,"corporation":false,"usgs":false,"family":"Schumaker","given":"Nathan","email":"","middleInitial":"H.","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814221,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Dunham, Jason B. 0000-0002-6268-0633 jdunham@usgs.gov","orcid":"https://orcid.org/0000-0002-6268-0633","contributorId":147808,"corporation":false,"usgs":true,"family":"Dunham","given":"Jason","email":"jdunham@usgs.gov","middleInitial":"B.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":814222,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Keefer, Matthew","contributorId":217975,"corporation":false,"usgs":false,"family":"Keefer","given":"Matthew","affiliations":[{"id":36394,"text":"University of Idaho","active":true,"usgs":false}],"preferred":false,"id":814223,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Leinenbach, P.T.","contributorId":217976,"corporation":false,"usgs":false,"family":"Leinenbach","given":"P.T.","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814224,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brookes, Allen","contributorId":217977,"corporation":false,"usgs":false,"family":"Brookes","given":"Allen","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814225,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Palmer, John","contributorId":217980,"corporation":false,"usgs":false,"family":"Palmer","given":"John","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814226,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Wu, Jennifer","contributorId":217979,"corporation":false,"usgs":false,"family":"Wu","given":"Jennifer","email":"","affiliations":[{"id":13529,"text":"US Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814227,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Keenan, Druscilla M","contributorId":257427,"corporation":false,"usgs":false,"family":"Keenan","given":"Druscilla","email":"","middleInitial":"M","affiliations":[{"id":6914,"text":"U.S. Environmental Protection Agency","active":true,"usgs":false}],"preferred":false,"id":814228,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ebersole, Joseph L.","contributorId":146938,"corporation":false,"usgs":false,"family":"Ebersole","given":"Joseph","email":"","middleInitial":"L.","affiliations":[{"id":12657,"text":"EPA NEIC","active":true,"usgs":false}],"preferred":false,"id":814229,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70216871,"text":"sir20205091 - 2020 - Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15","interactions":[],"lastModifiedDate":"2021-04-08T21:42:55.915848","indexId":"sir20205091","displayToPublicDate":"2020-12-16T09:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5091","displayTitle":"Simulation of Groundwater Flow in the Regional Aquifer System on Long Island, New York, for Pumping and Recharge Conditions in 2005–15","title":"Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15","docAbstract":"A three-dimensional groundwater-flow model was developed for the aquifer system of Long Island, New York, to evaluate (1) responses of the hydrologic system to changes in natural and anthropogenic hydraulic stresses, (2) the subsurface distribution of groundwater age, and (3) the regional-scale distribution of groundwater travel times and the source of water to fresh surface waters and coastal receiving waters. The model also provides the groundwater flow components used to define model boundaries for possible inset models used for local-scale analyses.
The three-dimensional, groundwater flow model developed for this investigation uses the numerical code MODFLOW–NWT to represent steady-state conditions for average groundwater pumping and aquifer recharge for 2005–15. The particle-tracking algorithm MODPATH, which simulates advective transport in the aquifer, was used to estimate groundwater age, delineate the areas at the water table that contribute recharge to coastal and freshwater bodies, and estimate total travel times of water from the water table to discharge locations.
A three-dimensional, 1-meter (3.3-foot) topobathymetric model was used to determine land-surface altitudes for the island and seabed altitudes for the surrounding coastal waters. The mapped extents and surface altitudes of major geologic units were compiled and used to develop a three-dimensional hydrogeologic framework of the aquifer system, including aquifers and confining units. Lithologic data from deep boreholes and previous aquifer-test results were used to estimate the three-dimensional distribution of hydraulic conductivity in principal aquifers. Natural recharge from precipitation was estimated for 2005–15 using a modified Thornthwaite-Mather methodology as implemented in a soil-water balance model. Components of anthropogenic recharge—wastewater return flow, storm water inflow, and inflow from leaky infrastructure—also were estimated for 2005–15. Groundwater withdrawals for various sources, including public water supply, industrial, remediation, and agricultural, were compiled or estimated for the same period.
These data were incorporated into the model development to represent the aquifer system geometry, boundaries, and initial hydraulic properties of the regional aquifers and confining units within the Long Island aquifer system. Average hydraulic conditions—water levels and streamflows—for 2005–15 were estimated using existing data from the U.S. Geological Survey National Water Information System database. Model inputs were adjusted to best match average hydrologic conditions using inverse methods as implemented in the parameter-estimating software PEST. The calibrated model was used to simulate average hydrologic conditions in the aquifer system for 2005–15.
About 656 cubic feet per second of water was withdrawn on average annually for 2005–15 for water supply and an average of about 349 cubic feet per second of water recharged the aquifer annually from return flow and leaky infrastructure. Parts of New York City have drawdowns exceeding 25 feet, mostly because of urbanization and associated large decreases in recharge rates. Large areas in the western part of the island have drawdowns exceeding 10 feet, mostly from large groundwater withdrawals and sewering, which largely eliminates wastewater return flow. Water-table altitudes in eastern parts of the island increased by more than 2 feet in some areas as a result of wastewater return flow in unsewered areas and changes in land use. Changes in streamflows show a similar pattern as water-table altitudes. Streamflows generally decrease in western parts of the island where there are large drawdowns and increase in eastern parts of the island where water-table altitudes increase.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205091","collaboration":"Prepared in cooperation with the New York State Department of Environmental Conservation","usgsCitation":"Walter, D.A., Masterson, J.P., Finkelstein, J.S., Monti, J., Jr., Misut, P.E., and Fienen, M.N., 2020, Simulation of groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15: U.S. Geological Survey Scientific Investigations Report 2020–5091, 75 p., https://doi.org/10.3133/sir20205091.","productDescription":"Report: ix, 75 p.; 3 Data Releases","numberOfPages":"75","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112206","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":381521,"rank":7,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2020/5091/images/"},{"id":381195,"rank":5,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.pdf","text":"Report","size":"35 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5091"},{"id":381194,"rank":4,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5091/coverthb2.jpg"},{"id":381192,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P954DLLC","text":"USGS data release","linkHelpText":"Aquifer texture data describing the Long Island aquifer system"},{"id":381191,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9KWQSEJ","text":"USGS data release","linkHelpText":"MODFLOW–NWT and MODPATH6 used to simulate groundwater flow in the regional aquifer system on Long Island, New York, for pumping and recharge conditions in 2005–15"},{"id":381190,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90B6OTX","text":"USGS data release","linkHelpText":"Time domain electromagnetic surveys collected to estimate the extent of saltwater intrusion in Nassau and Queens Counties, New York, October-November 2017"},{"id":381520,"rank":6,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2020/5091/sir20205091.XML"}],"country":"United States","state":"New York","otherGeospatial":"Long Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.102783203125,\n 40.55554790286311\n ],\n [\n -73.7017822265625,\n 40.49709237269567\n ],\n [\n -72.8778076171875,\n 40.65147128144057\n ],\n [\n -72.037353515625,\n 40.91351257612758\n ],\n [\n -71.6143798828125,\n 41.16211393939692\n ],\n [\n -71.9384765625,\n 41.178653972331674\n ],\n [\n -72.191162109375,\n 41.236511201246216\n ],\n [\n -72.65808105468749,\n 41.11660732012896\n ],\n [\n -73.4490966796875,\n 40.967455873296714\n ],\n [\n -73.751220703125,\n 40.89275342420696\n ],\n [\n -74.0203857421875,\n 40.718119379753446\n ],\n [\n -74.102783203125,\n 40.55554790286311\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
Inlet-interrupted sandy coasts are dynamic and complex coastal systems with continuously evolving geomorphological behaviors under the influences of both climate change and human activities. These coastal systems are of great importance to society (e.g., providing habitats, navigation, and recreational activities) and are affected by both oceanic and terrestrial processes. Therefore, the evolution of these inlet-interrupted coasts is better assessed by considering the entirety of the Catchment-Estuary-Coastal (CEC) systems, under plausible future scenarios for climate change and increasing pressures due to population growth and human activities. Such a holistic assessment of the long-term evolution of CEC systems can be achieved via reduced-complexity modeling techniques, which are also ably quantifying the uncertainties associated with the projections due to their lower simulation times. Here, we develop a novel probabilistic modeling framework to quantify the input-driven uncertainties associated with the evolution of CEC systems over the 21st century. In this new approach, probabilistic assessment of the evolution of inlet-interrupted coasts is achieved by (1) probabilistically computing the exchange sediment volume between the inlet-estuary system and its adjacent coast, and (2) distributing the computed sediment volumes along the inlet-interrupted coast. The model is applied at three case study sites: Alsea estuary (United States), Dyfi estuary (United Kingdom), and Kalutara inlet (Sri Lanka). Model results indicate that there are significant uncertainties in projected volume exchange at all the CEC systems (min-max range of 2.0 million cubic meters in 2100 for RCP 8.5), and the uncertainties in these projected volumes illustrate the need for probabilistic modeling approaches to evaluate the long-term evolution of CEC systems. A comparison of 50th percentile probabilistic projections with deterministic estimates shows that the deterministic approach overestimates the sediment volume exchange in 2100 by 15–30% at Alsea and Kalutara estuary systems. Projections of coastline change obtained for the case study sites show that accounting for all key processes governing coastline change along inlet-interrupted coasts in computing coastline change results in projections that are between 20 and 134% greater than the projections that would be obtained if only the Bruun effect were taken into account, underlining the inaccuracies associated with using the Bruun rule at inlet-interrupted coasts.
Effective volcanic hazard management in regions where populations live in close proximity to persistent volcanic activity involves understanding the dynamic nature of hazards, and associated risk. Emphasis until now has been placed on identification and forecasting of the escalation phase of activity, in order to provide adequate warning of what might be to come. However, understanding eruption hiatus and post-eruption unrest hazards, or how to quantify residual hazard after the end of an eruption, is also important and often key to timely post-eruption recovery. Unfortunately, in many cases when the level of activity lessens, the hazards, although reduced, do not necessarily cease altogether. This is due to both the imprecise nature of determination of the “end” of an eruptive phase as well as to the possibility that post-eruption hazardous processes may continue to occur. An example of the latter is continued dome collapse hazard from lava domes which have ceased to grow, or sector collapse of parts of volcanic edifices, including lava dome complexes. We present a new probabilistic model for forecasting pyroclastic density currents (PDCs) from lava dome collapse that takes into account the heavy-tailed distribution of the lengths of eruptive phases, the periods of quiescence, and the forecast window of interest. In the hazard analysis, we also consider probabilistic scenario models describing the flow’s volume and initial direction. Further, with the use of statistical emulators, we combine these models with physics-based simulations of PDCs at Soufrière Hills Volcano to produce a series of probabilistic hazard maps for flow inundation over 5, 10, and 20 year periods. The development and application of this assessment approach is the first of its kind for the quantification of periods of diminished volcanic activity. As such, it offers evidence-based guidance for dome collapse hazards that can be used to inform decision-making around provisions of access and reoccupation in areas around volcanoes that are becoming less active over time.
About one-quarter of the water supply for the Village of Ruidoso, New Mexico, is from groundwater pumped from wells located along North Fork Eagle Creek in the National Forest System lands of the Lincoln National Forest near Alto, New Mexico. Because of concerns regarding the effects of groundwater pumping on surface-water hydrology in the North Fork Eagle Creek Basin and the effects of the 2012 Little Bear Fire, which resulted in substantial loss of vegetation in the basin, the U.S. Department of Agriculture Forest Service, Lincoln National Forest, has required monitoring of a portion of North Fork Eagle Creek for short-term geomorphic change as part of the permitting decision that allows for the continued pumping of the production wells. The objective of this study is to address the geomorphic monitoring requirements of the permitting decision by conducting annual geomorphic surveys of North Fork Eagle Creek along the stream reach between the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station (U.S. Geological Survey [USGS] site 08387550) and the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station (USGS site 08387600). The monitoring of short-term geomorphic change in the stream reach began in June 2017 with surveys of select cross sections and surveys of all woody debris accumulations and pools found in the channel. In June 2018, the monitoring of short-term geomorphic change continued with another geomorphic survey of the stream reach (with some modification to the monitoring methods).
The 2017 and 2018 surveys were conducted by the USGS, in cooperation with the Village of Ruidoso, and were the first two in a planned series of five annual geomorphic surveys. The results of the 2017 geomorphic survey were summarized and interpreted in a previous USGS Open-File Report, and the data were published in the companion data release of that report. In this report, the results of the 2018 geomorphic survey are summarized, interpreted, and compared to the results of the 2017 survey. The data from the 2018 geomorphic survey are published in the companion data release of this report.
The study reach surveyed in June 2018 is 1.89 miles long, beginning about 260 feet upstream from the North Fork Eagle Creek near Alto, New Mexico, streamflow-gaging station and ending at the Eagle Creek below South Fork near Alto, New Mexico, streamflow-gaging station. Large sections of the study reach are characterized by intermittent streamflow, and where streamflow is normally continuous (including at the upper and lower portions of the study reach, near the streamflow-gaging stations), the streamflow typically remains less than 2 cubic feet per second throughout the year except during seasonal high flows, which most often result from rainfall during the North American monsoon months of July, August, and September or from snowmelt runoff in March, April, and May. Between the 2017 and 2018 surveys, high-flow events resulting from both rainfall (during the North American monsoon season) and snowmelt runoff (during the winter) occurred in the study reach, and those high-flow events appeared to have caused some minor and localized geomorphic changes in the study reach, which were evaluated through comparison of the 2017 and 2018 survey results.
For the 2017 geomorphic survey of North Fork Eagle Creek, cross sections were established and surveyed at 14 locations along the study reach, and in 2018, those same 14 cross sections were resurveyed. Comparisons of the cross-section survey results indicated that minor observable geomorphic changes had occurred in 3 of the 14 cross sections. These minor observable geomorphic changes included aggradation or degradation of surface materials by about 1–2 feet in some parts of the affected cross sections.
To further assess geomorphic changes within the study reach, other features, including woody debris accumulations and pools, were surveyed in both 2017 and 2018. During the 2018 geomorphic survey, 112 distinct accumulations of woody debris and 71 pools were identified in the study reach. Charred wood or burn-marked wood was present in at least 17 of the identified woody debris accumulations (and was present in some of the woody debris accumulations identified during the 2017 survey), indicating that some of the woody debris in the channel may have been sourced from trees or forest litter that had burned during 2012 Little Bear Fire. Only 22 of the 112 woody debris accumulations identified during the 2018 survey were certain to have also been present during the 2017 survey (when 58 woody debris accumulations were identified), indicating that most of the woody debris accumulations surveyed in 2017 were likely transported during the high-flow events between the 2017 and 2018 surveys but also indicating that the flows during those events were not high enough to remove some of the more firmly anchored woody debris accumulations. Most woody debris accumulations identified in 2018 did not appear to have substantially influenced geomorphic change in the locations where they were found. However, the formation of 10 of the 71 pools identified in the study reach in 2018 appeared to have been influenced by the presence of woody debris, indicating that some woody debris accumulations may have driven local geomorphic changes. Notably, pool totals from the 2017 survey could not be accurately compared to the pool totals from the 2018 survey because of differences between the two surveys in the methods used to identify pools.
Because the study began 5 years after the 2012 Little Bear Fire, and because the period and geomorphic scope of the study have so far been limited, it cannot be said that the geomorphic changes observed between the 2017 and 2018 surveys are representative of a pattern of geomorphic change following the 2012 Little Bear Fire. Though, once geomorphic changes between the 2017 and 2018 surveys can be compared with results from geomorphic surveys planned for 2019, 2020, and 2021, it may be possible to develop an understanding of the patterns in geomorphic change following the 2012 Little Bear Fire.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201121","collaboration":"Prepared in cooperation with the Village of Ruidoso, New Mexico","usgsCitation":"Graziano, A.P., 2020, Geomorphic survey of North Fork Eagle Creek, New Mexico, 2018: U.S. Geological Survey Open-File Report 2020–1121, 37 p., https://doi.org/10.3133/ofr20201121.","productDescription":"Report: v, 37 p.; Data Release","numberOfPages":"47","onlineOnly":"Y","ipdsId":"IP-112737","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":381235,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1121/ofr20201121.pdf","text":"Report","size":"16.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1121"},{"id":381236,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94ZQHKU","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data supporting the 2018 geomorphic survey of North Fork Eagle Creek, New Mexico"},{"id":381234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1121/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"North Fork Eagle Creek","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.5621337890625,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 32.99023555965106\n ],\n [\n -104.7930908203125,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 33.770015152780125\n ],\n [\n -105.5621337890625,\n 32.99023555965106\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd. NE
Albuquerque, NM 87113
Management of stressors requires an understanding of how multiple stressors interact, how different species respond to those interactions and the underlying mechanisms driving observed patterns in species' responses. Salinization and rising temperatures are two pertinent stressors predicted to intensify in freshwater ecosystems, posing concern for how susceptible organisms achieve and maintain homeostasis (i.e. allostasis). Here, glucocorticoid hormones (e.g. cortisol), responsible for mobilizing energy (e.g. glucose) to relevant physiological processes for the duration of stressors, are liable to vary in response to the duration and severity of salinization and temperature rises. With field and laboratory studies, we evaluated how both salinity and temperature influence basal and stress-reactive cortisol and glucose levels in age 1+ mottled sculpin (Cottus bairdii), mountain sucker (Catostomus platyrhynchus) and Colorado River cutthroat trout (Oncorhynchus clarki pleuriticus). We found that temperature generally had the greatest effect on cortisol and glucose concentrations and the effect of salinity was often temperature dependent. We also found that when individuals were chronically exposed to higher salinities, baseline concentrations of cortisol and glucose usually declined as salinity increased. Reductions in baseline concentrations facilitated stronger stress reactivity for cortisol and glucose when exposed to additional stressors, which weakened as temperatures increased. Controlled temperatures near the species' thermal maxima became the overriding factor regulating fish physiology, resulting in inhibitory responses. With projected increases in freshwater salinization and temperatures, efforts to reduce the negative effects of increasing temperatures (i.e. increased refuge habitats and riparian cover) could moderate the inhibitory effects of temperature-dependent effects of salinization for freshwater fishes.
","language":"English","publisher":"Springer","doi":"10.1093/conphys/coaa107","usgsCitation":"Walker, R.H., Smith, G.D., Hudson, S.B., Susannah S. French, S.S., and Walters, A.W., 2020, Warmer temperatures interact with salinity to weaken physiological facilitation to stress in freshwater fishes: Conservation Physiology, v. 8, no. 1, coaa107, 18 p., https://doi.org/10.1093/conphys/coaa107.","productDescription":"coaa107, 18 p.","ipdsId":"IP-109141","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":454658,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/conphys/coaa107","text":"Publisher Index Page"},{"id":436697,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9IBV1RJ","text":"USGS data release","linkHelpText":"Salinity-temperature Interactions on Freshwater Fish Physiology (2015-2018)"},{"id":395556,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Upper Green River basin, Wyoming Range","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.73556518554688,\n 42.703632059618045\n ],\n [\n -109.8193359375,\n 42.67536823702857\n ],\n [\n -109.90859985351561,\n 42.62385465855651\n ],\n [\n -110.07064819335938,\n 42.53689200787317\n ],\n [\n -110.14068603515625,\n 42.48728928565912\n ],\n [\n -110.12763977050781,\n 42.407234661551875\n ],\n [\n -110.14892578125,\n 42.36158819524629\n ],\n [\n -110.2313232421875,\n 42.259016415705766\n ],\n [\n -110.20111083984375,\n 42.18579390537848\n ],\n [\n -110.20523071289061,\n 42.12674735753131\n ],\n [\n -110.14892578125,\n 41.98603585974727\n ],\n [\n -109.92095947265625,\n 41.90636538970964\n ],\n [\n -109.77539062499999,\n 41.72828028223453\n ],\n [\n -109.5391845703125,\n 41.45301999377133\n ],\n [\n -109.54193115234374,\n 41.3500103516271\n ],\n [\n -109.4073486328125,\n 41.29431726315258\n ],\n [\n -109.28375244140625,\n 41.413895564677304\n ],\n [\n -109.5611572265625,\n 41.84910468610387\n ],\n [\n -110.04180908203124,\n 42.338244963350846\n ],\n [\n -109.86328125,\n 42.559149812115876\n ],\n [\n -109.6490478515625,\n 42.68041629144619\n ],\n [\n -109.73556518554688,\n 42.703632059618045\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Walker, Richard H.","contributorId":274736,"corporation":false,"usgs":false,"family":"Walker","given":"Richard","email":"","middleInitial":"H.","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":833270,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Smith, Geoffrey D.","contributorId":274737,"corporation":false,"usgs":false,"family":"Smith","given":"Geoffrey","email":"","middleInitial":"D.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833271,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Hudson, Spencer B","contributorId":274740,"corporation":false,"usgs":false,"family":"Hudson","given":"Spencer","email":"","middleInitial":"B","affiliations":[{"id":40829,"text":"uwy","active":true,"usgs":false}],"preferred":false,"id":833272,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Susannah S. French, Susannah S.","contributorId":274743,"corporation":false,"usgs":false,"family":"Susannah S. French","given":"Susannah","email":"","middleInitial":"S.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":833273,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Walters, Annika W. 0000-0002-8638-6682 awalters@usgs.gov","orcid":"https://orcid.org/0000-0002-8638-6682","contributorId":4190,"corporation":false,"usgs":true,"family":"Walters","given":"Annika","email":"awalters@usgs.gov","middleInitial":"W.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833269,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70225719,"text":"70225719 - 2020 - Density dependence and adult survival drive the dynamics in two high elevation amphibian populations","interactions":[],"lastModifiedDate":"2021-11-04T14:37:39.384923","indexId":"70225719","displayToPublicDate":"2020-12-15T09:25:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1398,"text":"Diversity","active":true,"publicationSubtype":{"id":10}},"title":"Density dependence and adult survival drive the dynamics in two high elevation amphibian populations","docAbstract":"Amphibian conservation has progressed from the identification of declines to mitigation, but efforts are hampered by the lack of nuanced information about the effects of environmental characteristics and stressors on mechanistic processes of population regulation. Challenges include a paucity of long-term data and scant information about the relative roles of extrinsic (e.g., weather) and intrinsic (e.g., density dependence) factors. We used a Bayesian formulation of an open population capture-recapture model and >30 years of data to examine intrinsic and extrinsic factors regulating two adult boreal chorus frogs (Pseudacris maculata) populations. We modelled population growth rate and apparent survival directly, assessed their temporal variability, and derived estimates of recruitment. Populations were relatively stable (geometric mean population growth rate >1) and regulated by negative density dependence (i.e., higher population sizes reduced population growth rate). In the smaller population, density dependence also acted on adult survival. In the larger population, higher population growth was associated with warmer autumns. Survival estimates ranged from 0.30–0.87, per-capita recruitment was <1 in most years, and mean seniority probability was >0.50, suggesting adult survival is more important to population growth than recruitment. Our analysis indicates density dependence is a primary driver of population dynamics for P. maculata adults.
","language":"English","publisher":"MDPI","doi":"10.3390/d12120478","usgsCitation":"Kissel, A.M., Tenan, S., and Muths, E.L., 2020, Density dependence and adult survival drive the dynamics in two high elevation amphibian populations: Diversity, v. 12, no. 12, 478, 15 p., https://doi.org/10.3390/d12120478.","productDescription":"478, 15 p.","ipdsId":"IP-122660","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":454660,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/d12120478","text":"Publisher Index Page"},{"id":436698,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9229ZLM","text":"USGS data release","linkHelpText":"Chorus frog density and population growth, Cameron Pass, Colorado, 1986-2020"},{"id":391386,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"Lily Pond, Matthews Pond","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.86,\n 40.6\n ],\n [\n -105.82,\n 40.6\n ],\n [\n -105.82,\n 40.56\n ],\n [\n -105.86,\n 40.56\n ],\n [\n -105.86,\n 40.6\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"12","noUsgsAuthors":false,"publicationDate":"2020-12-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Kissel, Amanda M.","contributorId":211917,"corporation":false,"usgs":false,"family":"Kissel","given":"Amanda","email":"","middleInitial":"M.","affiliations":[{"id":36678,"text":"Simon Fraser University","active":true,"usgs":false}],"preferred":false,"id":826397,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Tenan, Simone","contributorId":177519,"corporation":false,"usgs":false,"family":"Tenan","given":"Simone","email":"","affiliations":[],"preferred":false,"id":826398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Muths, Erin L. 0000-0002-5498-3132 muthse@usgs.gov","orcid":"https://orcid.org/0000-0002-5498-3132","contributorId":1260,"corporation":false,"usgs":true,"family":"Muths","given":"Erin","email":"muthse@usgs.gov","middleInitial":"L.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":826396,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70216930,"text":"70216930 - 2020 - The roles of flood magnitude and duration in controlling channel width and complexity on the Green River in Canyonlands, Utah, USA","interactions":[],"lastModifiedDate":"2020-12-17T12:49:49.681317","indexId":"70216930","displayToPublicDate":"2020-12-15T06:55:14","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"The roles of flood magnitude and duration in controlling channel width and complexity on the Green River in Canyonlands, Utah, USA","docAbstract":"Predictions of river channel adjustment to changes in streamflow regime based on relations between mean channel characteristics and mean flood magnitude can be useful to evaluate average channel response. However, because these relations assume equilibrium sediment transport, their applicability to cases where streamflow and sediment transport are decoupled may be limited. These general relations also lack the specificity that is required to connect specific characteristics of the streamflow and sediment regime with the dynamics of channel morphological change that create channel complexity, which is often of ecological interest. We integrate historical records of channel change, observations of scour and fill during a snowmelt flood, measurements of sediment transport, and predictions from a two-dimensional streamflow model to describe how annual peak flow magnitude and peak-flow duration interact with the upstream sediment supply to control channel form for a 15-km study reach on the regulated Green River in Canyonlands National Park, Utah. Two major decadal-scale episodes of channel narrowing have occurred within the study area. For each of these episodes, the reduction in average channel width was consistent with the change predicted by hydraulic geometry relations as a function of average flood magnitude. However, channel narrowing occurred during periods of exceptionally low annual floods. The most recent episode of channel narrowing occurred between 1988 and 2009, during low-flow cycles when the 5-yr mean peak flow was less than 60% of the long-term (1959–2016) mean peak flow. These findings, together with findings from previous studies, demonstrate that decreases in peak-flow magnitude caused by streamflow regulation, climate change, or a combination of those factors have driven episodes of channel narrowing on the Green River. Observations of streamflow, sediment-transport, and morphologic change coupled with predictions from a two-dimensional streamflow model indicate that peak flow magnitudes of at least 75% of the long-term mean peak flow are required to transport bed-material sand in suspension in all regions of the multi-thread channel and that the ~2-month duration of the snowmelt flood played an important role in creating conditions necessary to maintain channel conveyance. These results indicate that detailed characterizations of channel response such as these are needed to predict how river channels will respond to changes in streamflow regime that affect annual peak flow magnitude and duration.
The U.S. Geological Survey (USGS) Precipitation Chemistry Quality Assurance project (PCQA) operated five distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2017–18. The National Trends Network programs included (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate variability attributed to automated precipitation samplers. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate consistently low levels of sample contamination, generally strong analytical laboratory performance, and low overall variability in concentration data imparted by field equipment. The NADP operations moved from its 40-year home at the Illinois State Water Survey to the Wisconsin State Laboratory of Hygiene in June 2018. The PCQA programs were modified and (or) temporarily curtailed during the transition in 2018. Bias and variability of sample analysis results were evaluated for the two Central Analytical Laboratories, and ongoing monitoring will be helpful to differentiate true environmental signals from the effects of changing laboratory conditions and performance. Results of quality assurance sample analyses are provided to document that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20205084","usgsCitation":"Wetherbee, G.A., and Martin, R., 2020, External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2017–18: U.S. Geological Survey Scientific Investigations Report 2020–5084, 31 p., https://doi.org/10.3133/sir20205084.","productDescription":"Report: vii, 31 p.; 2 Data Releases","onlineOnly":"Y","ipdsId":"IP-110354","costCenters":[{"id":37786,"text":"WMA - Observing Systems Division","active":true,"usgs":true}],"links":[{"id":381227,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94RC4GD","text":"USGS data release","linkHelpText":"Data for the U.S. Geological Survey Precipitation Chemistry Quality Assurance Project for the National Atmospheric Deposition Program, 1978–2017"},{"id":381224,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5084/coverthb.jpg"},{"id":381225,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5084/sir20205084.pdf","text":"Report","size":"1.54 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5084"},{"id":381226,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZKXD8N","text":"USGS data release","linkHelpText":"U.S. Geological Survey Precipitation Chemistry Quality Assurance Project Data 2017 – 2018"}],"contact":"Director, Observing Systems Division
U.S. Geological Survey
Buildings 2101, 2204 HIF
Hydrologic Instrumentation Facility
Stennis Space Center, MS 39529
The mission of the U.S. Geological Survey National Climate Adaptation Science Center is to provide actionable, management-relevant research on climate change effects on ecosystems and wildlife to U.S. Department of the Interior bureaus. Providing this kind of useful scientific information requires understanding how natural-resource managers make decisions and identifying research priorities that support those decision-making processes. Migratory bird management and conservation of migratory bird habitat are central components of the U.S. Department of the Interior’s mission. In particular, the U.S. Fish and Wildlife Service has an intensive, complex decision-making process for identifying high-priority parcels of land that will contribute to migratory bird conservation through permanent acquisition or easement. Climate change introduces several uncertainties into this decision-making process, and additional climate change research should help to support more informed decision making regarding habitat acquisition.
Not all climate change related uncertainties, however, will have a meaningful effect on acquisition decisions; therefore, understanding which uncertainties have the most potential to alter decision making is crucial. This document summarizes a multiyear effort to clarify the major sources of climate change uncertainty that affect migratory bird management and to articulate related research priorities. We worked with U.S. Fish and Wildlife Service staff to assess the primary ways in which climate change is likely to affect migratory birds and their habitats; to clarify uncertainties surrounding these effects; and to assess how uncertainties may affect habitat acquisition decisions. Using a modified structured decision-making approach, we assessed a set of hypotheses about how climate change will affect migratory birds and their habitats. Then, we used a qualitative value of information assessment to rank the most important topics for future research. The ranking process was built on an assessment of three primary characteristics: the magnitude of uncertainty, the topic’s relevance to habitat acquisition decision making, and the feasibility of reducing the uncertainty. Based on the results of this process, high-priority topics for future research include the following:
In addition to describing high-priority research needs, this document provides a summary of the methodology used to identify, assess, and rank uncertainties. This method was developed for a climate change related topic where a full quantitative value of information approach may not be feasible. The results and methodology described here may be useful for U.S. Geological Survey and other science-funding agencies interested in improving the applicability of their research to natural-resource management decision making.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1472","usgsCitation":"Rubenstein, M.A., Rushing, C.S., Lyons, J.E., and Runge, M.C., 2020, Research priorities for migratory birds under climate change—A qualitative value of information assessment: U.S. Geological Survey Circular 1472, 18 p., https://doi.org/10.3133/cir1472.","productDescription":"vi, 18 p.","numberOfPages":"18","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-118784","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":36940,"text":"National Climate Adaptation Science Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":381217,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1472/coverthb.jpg"},{"id":381218,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1472/cir1472.pdf","text":"Report","size":"1.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"CIR 1472"}],"contact":"National Climate Adaptation Science Center
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 516
Reston, VA 20192
Building trust in science and evidence-based decision-making depends heavily on the credibility of studies and their findings. Researchers employ many different study designs that vary in their risk of bias to evaluate the true effect of interventions or impacts. Here, we empirically quantify, on a large scale, the prevalence of different study designs and the magnitude of bias in their estimates. Randomised designs and controlled observational designs with pre-intervention sampling were used by just 23% of intervention studies in biodiversity conservation, and 36% of intervention studies in social science. We demonstrate, through pairwise within-study comparisons across 49 environmental datasets, that these types of designs usually give less biased estimates than simpler observational designs. We propose a model-based approach to combine study estimates that may suffer from different levels of study design bias, discuss the implications for evidence synthesis, and how to facilitate the use of more credible study designs.
Many at-risk species lack standardized surveys across their range or quantitative data capable of detecting demographic trends. As a result, extinction risk assessments often rely on ordinal categories of risk based on explicit criteria or expert elicitation. This study demonstrates a Bayesian approach to assessing extinction risk based on this common data structure, using three freshwater mussel species being considered for listing under the US Endangered Species Act. The probability that a population is classified under each risk category was modeled as a function of projected landscape change using ordered probit regression, assuming observed categories reflect a latent, continuous probability of persistence. All three species were more likely than not (mean probability >0.5) to be classified as extirpated or low condition throughout their range based on effects of urban development and hydrologic alteration. Spatial variation in estimates revealed strongholds and high-risk areas relevant to conservation decision making. Projected change in probabilities of each risk category based on multiple land-use and climate models was generally small relative to high baseline risk resulting from past landscape changes. Assessing extinction risk based on probabilities of ordinal condition as a function of landscape patterns may provide a flexible and robust approach for many at-risk taxa by adjusting species' demographic criteria to match relative risk categories, following standardized criteria, or using expert elicitation for data-deficient species. This approach provides decision makers with a useful measure of uncertainty around ordinal classifications and provides a framework for estimating future risk based on projections of anthropogenic stressors.
Across the globe, conflicting priorities exist in how land and resources are managed. In the American West, conflicts are common on public lands with historical mandates for multiple uses. We explored the impacts of multiple uses of land in a case study of Agassiz's Desert Tortoises (Gopherus agassizii), a federally threatened species, in the western Sonoran Desert. The tortoise has declined for many reasons, most of which relate to management of land and habitat. Frequently cited causes are livestock grazing, roads, vehicle-oriented recreation, predators, and disease. In spring of 2009, we conducted a survey to evaluate relationships between desert tortoises, vegetation associations, topography, predators, and anthropogenic uses. We sampled a 93-km2 area with 200 independent 1-ha plots. Density (± SE) of adult tortoises was low, 2.0 ± 1.0/km2, and the annualized death rate for adults during the 4 yr preceding the survey was high, 13.1%/yr. We observed tortoise sign, most of which was recent, on 22% of the 200 plots, primarily in the southwestern part of the study area. More tortoise sign occurred on plots with Brittlebush (Encelia spp.) vegetation at higher elevations. Most plots (91.0%) had ≥1 human-related impacts: feral burro scat (Equus asinus; 84.0%), recent vehicle tracks and trails (34.0%), trash (28.0%), burro trails and wallows (26.5%), and old vehicle tracks (24.0%). We used a multimodel approach to model presence of tortoise sign on the basis of 12 predictor variables, and calculated model-averaged predictions for the probability of tortoise presence. Importance values revealed two apparent top drivers: feral burros and vegetation association. This is the first study to identify a negative association between presence of desert tortoises and feral burros.
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
","language":"English","publisher":"The Royal Society Publishing","doi":"10.1098/rspb.2020.2219","usgsCitation":"Lasky, J.R., Hooten, M., and Adler, P., 2020, What processes must we understand to forecast regional-scale population dynamics?: Proceedings of the Royal Society B: Biological Sciences, v. 287, no. 1940, 20202219, 12 p., https://doi.org/10.1098/rspb.2020.2219.","productDescription":"20202219, 12 p.","ipdsId":"IP-122452","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":454688,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1098/rspb.2020.2219","text":"Publisher Index Page"},{"id":429878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"287","issue":"1940","noUsgsAuthors":false,"publicationDate":"2020-12-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Lasky, Jesse R.","contributorId":338090,"corporation":false,"usgs":false,"family":"Lasky","given":"Jesse","email":"","middleInitial":"R.","affiliations":[{"id":24698,"text":"PSU","active":true,"usgs":false}],"preferred":false,"id":902949,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hooten, Mevin 0000-0002-1614-723X mhooten@usgs.gov","orcid":"https://orcid.org/0000-0002-1614-723X","contributorId":2958,"corporation":false,"usgs":true,"family":"Hooten","given":"Mevin","email":"mhooten@usgs.gov","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":12963,"text":"Colorado Cooperative Fish and Wildlife Research Unit, Fort Collins, CO","active":true,"usgs":false}],"preferred":true,"id":902948,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Adler, Peter B.","contributorId":338091,"corporation":false,"usgs":false,"family":"Adler","given":"Peter B.","affiliations":[{"id":28050,"text":"USU","active":true,"usgs":false}],"preferred":false,"id":902950,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70217001,"text":"70217001 - 2020 - Non-analog increases to air, surface, and belowground temperature extreme events due to climate change","interactions":[],"lastModifiedDate":"2021-01-19T16:05:23.116846","indexId":"70217001","displayToPublicDate":"2020-12-09T06:41:01","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1246,"text":"Climate Change","onlineIssn":"1573-1480","printIssn":"0165-0009","active":true,"publicationSubtype":{"id":10}},"title":"Non-analog increases to air, surface, and belowground temperature extreme events due to climate change","docAbstract":"Air temperatures (Ta) are rising in a changing climate, increasing extreme temperature events. Examining how Ta increases are influencing extreme temperatures at the soil surface and belowground in the soil profile can refine our understanding of the ecological consequences of rising temperatures. In this paper, we validate surface and soil temperature (Ts: 0–100-cm depth) simulations in the SOILWAT2 model for 29 locations comprising 5 ecosystem types in the central and western USA. We determine the temperature characteristics of these locations from 1980 to 2015, and explore simulations of Ta and Ts change over 2030–2065 and 2065–2100 time periods using General Circulation Model (GCM) projections and the RCP 8.5 emissions scenario. We define temperature extremes using a nonstationary peak over threshold method, quantified from standard deviations above the mean (0-σ: an event
The northern long‐eared bat (Myotis septentrionalis) is one of the bat species most affected by white‐nose syndrome. Population declines attributed to white‐nose syndrome contributed to the species’ listing as federally threatened under the 1973 Endangered Species Act. Although one of the most abundant Myotine bats in eastern North America prior to white‐nose syndrome, little is known about northern long‐eared bats in the upper Midwest, USA. We assessed the habitat associations of the northern long‐eared bats on a regional scale using occupancy models that accounted for uncertainty in nightly detection to provide needed information on the distribution as white‐nose syndrome has recently arrived in this area. We monitored bat activity using zero‐crossing frequency‐division bat detectors for 10–15 nights at 20 detector sites at each of 3 sampling areas in Michigan, USA, and 6 sampling areas in Wisconsin, USA, stratified by mesic and xeric habitat types. We constructed northern long‐eared bat nightly detection histories for our occupancy analysis using maximum likelihood estimates from 2 commercially‐available automated identification programs: Kaleidoscope and Echoclass. We sampled for a total of 2,174 detector‐nights. Both Kaleidoscope and Echoclass identified northern long‐eared bat passes on 110 detector‐nights, whereas on 1,968 detector‐nights neither program identified a northern long‐eared bat call. Only one program or the other identified northern long‐eared bat calls on 206 detector‐nights, indicating an overall agreement rate of 35% on nights when calls were detected. We analyzed these data using an occupancy analysis accounting for the potential for false positives to assess the relationship between northern long‐eared bat presence and habitat characteristics. Our analyses indicated that the probability of a false positive at a site was low (0.015; 95% CI 0.009–0.021), and detection probability, but not occupancy, declined from 2015 to 2016 for sites in Wisconsin sampled in both years. Occupancy was positively associated with distance into the forest interior (distance from nearest road).
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wsb.1138","usgsCitation":"Hyzy, B.A., Russell, R., Silvis, A., Ford, W., Riddle, J.D., and Russell, K.R., 2020, Occupancy and detectability of northern long-eared bats in the Lake States Region: Wildlife Society Bulletin, v. 44, no. 4, p. 732-740, https://doi.org/10.1002/wsb.1138.","productDescription":"9 p.","startPage":"732","endPage":"740","ipdsId":"IP-095702","costCenters":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"links":[{"id":503728,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://zotero.org/groups/5435545/items/AGGCIIRI","text":"External Repository"},{"id":381445,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Michigan, Wisconsin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -90.52734374999999,\n 42.53689200787315\n ],\n [\n -87.802734375,\n 42.601619944327965\n ],\n [\n -87.6708984375,\n 44.574817404670306\n ],\n [\n -87.802734375,\n 45.042478050891546\n ],\n [\n -87.03369140625,\n 45.73685954736049\n ],\n [\n -85.4736328125,\n 46.07323062540835\n ],\n [\n -85.869140625,\n 46.649436163350245\n ],\n [\n -86.7041015625,\n 46.45299704748289\n ],\n [\n -88.00048828124999,\n 46.9502622421856\n ],\n [\n -88.9453125,\n 46.965259400349275\n ],\n [\n -90.37353515625,\n 46.63435070293566\n ],\n [\n -90.98876953125,\n 46.63435070293566\n ],\n [\n -90.76904296874999,\n 46.9052455464292\n ],\n [\n -91.97753906249999,\n 46.7248003746672\n ],\n [\n -92.28515625,\n 45.321254361171476\n ],\n [\n -91.0546875,\n 44.071800467511565\n ],\n [\n -90.52734374999999,\n 42.53689200787315\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"44","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-12-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Hyzy, Brenna A.","contributorId":171457,"corporation":false,"usgs":false,"family":"Hyzy","given":"Brenna","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":806603,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Russell, Robin E. 0000-0001-8726-7303","orcid":"https://orcid.org/0000-0001-8726-7303","contributorId":219536,"corporation":false,"usgs":true,"family":"Russell","given":"Robin E.","affiliations":[{"id":456,"text":"National Wildlife Health Center","active":true,"usgs":true}],"preferred":true,"id":806604,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Silvis, Alexander","contributorId":171585,"corporation":false,"usgs":false,"family":"Silvis","given":"Alexander","email":"","affiliations":[{"id":26923,"text":"Virginia Polytechnic Institute, Blacksburg, VA","active":true,"usgs":false}],"preferred":false,"id":806605,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ford, W. Mark 0000-0002-9611-594X wford@usgs.gov","orcid":"https://orcid.org/0000-0002-9611-594X","contributorId":172499,"corporation":false,"usgs":true,"family":"Ford","given":"W. Mark","email":"wford@usgs.gov","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":false,"id":806606,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Riddle, Jason D.","contributorId":146462,"corporation":false,"usgs":false,"family":"Riddle","given":"Jason","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":806607,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Russell, Kevin R.","contributorId":150351,"corporation":false,"usgs":false,"family":"Russell","given":"Kevin","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":806609,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70216134,"text":"sim3464 - 2020 - Geologic map of Jezero crater and the Nili Planum region, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:11:08.032517","indexId":"sim3464","displayToPublicDate":"2020-12-02T15:18:47","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3464","displayTitle":"Geologic Map of Jezero Crater and the Nili Planum Region, Mars","title":"Geologic map of Jezero crater and the Nili Planum region, Mars","docAbstract":"The cratered highlands located northwest of Isidis Planitia have been recognized as one of the best preserved Noachian landscapes currently exposed on Mars; the area hosts a record of diverse surface processes, diagenesis, and aqueous alteration. This region has consistently been considered a high priority for landed-mission exploration and includes the anticipated landing site of the Mars 2020 Perseverance rover within Jezero crater. Past mapping, focused on Jezero crater and the surrounding area, Nili Planum, has varied in spatial extent, map scale, and purpose, though no previous maps have provided a continuous, high-resolution geologic map at uniform scale connecting the two locations. This map represents the first, large-scale, continuous geologic map spanning both Jezero crater and Nili Planum that is based on high-resolution images.
The map area contains the majority of both Jezero crater and Nili Planum at a publication map scale of 1:75,000, which was chosen to encompass the Jezero and southern Nili Planum landing sites under consideration for the Mars 2020 mission at the time of project initiation. This map covers an area that is exactly 1° by 1° (~60 by 60 km), spanning lat 76.8° N. to long 77.8° E. and lat 17.7° to long 18.7° N. The primary base map used for this geologic map is composed of Mars Reconnaissance Orbiter’s Context Camera (CTX) images, compiled into a 6 meter per pixel (m/pixel) mosaic. A nighttime Thermal Emission Imaging System 100 m/pixel image mosaic, digital terrain models constructed from CTX images, High-Resolution Stereo Camera (HRSC) topographic data, and High Resolution Imaging Science Experiment (HiRise) images also aided in unit identification and the assessment of stratigraphic relations. We defined map units on the basis of various characteristics visible in the CTX data at map scale, such as their texture, tone, morphology, marginal characteristics, geographic location, and stratigraphic relations to other units. Some units occur solely within Jezero crater, while Nili Planum contains a sequence of units that are present across the broader northwest Isidis Planitia region. Other units occur in both Jezero crater and Nili Planum, including bedrock, aeolian, and crater units. This map publication provides a regional geologic framework that connects the geologic units across Jezero crater and Nili Planum and the history they imply, facilitates future local-scale observations by landed missions of the Jezero crater and Nili Planum region, and enables the extrapolation of units that have been defined primarily by mineralogic composition to areas where there is no existing orbital spectroscopic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3464","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Sun, V.Z., and Stack, K.M., 2020, Geologic map of Jezero crater and the Nili Planum region, Mars: U.S. Geological Survey Scientific Investigations Map 3464, pamphlet 14 p., 1 sheet, scale 1:75,000, https://doi.org/10.3133/sim3464.","productDescription":"Pamphlet: iv, 14 p.; 1 Map: 56.60 x 45.62 inches; Metadata; Database; Read Me","numberOfPages":"14","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-118085","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":436704,"rank":9,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3464 Geologic Map of Jezero Crater and the Nili Planum Region"},{"id":380236,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_pamphlet.pdf","text":"Pamphlet","size":"728 KB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464 Pamphlet"},{"id":380235,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3464/sim3464.pdf","text":"Map","size":"36.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3464"},{"id":380240,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_database.zip","size":"349.3 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3464 Database"},{"id":380239,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_readme.txt","size":"4 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Readme txt"},{"id":380238,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.xml","size":"21 KB","linkFileType":{"id":8,"text":"xml"},"description":"SIM 3464 Metadata xml"},{"id":380237,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3464/sim3464_metadata.txt","size":"21 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3464 Metadata txt"},{"id":380234,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3464/coverthb.jpg"},{"id":400813,"rank":8,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9CZYIO7","text":"Interactive map","linkHelpText":"- Geologic Map of Jezero Crater and the Nili Planum Region, Mars, 1:75,000. Sun and Stack (2020)"}],"contact":"Contact Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Water samples from 122 sites in the U.S. Geological Survey National Water Quality Network were collected in 2013–17 to document ambient water-quality conditions in surface water of the United States and to determine status and trends of loads and concentrations for nutrients, contaminants, and sediment to estuaries and streams. Quality-control (QC) samples collected in the field with environmental samples were combined with QC samples from laboratory processing to provide information and documentation about the quality of the environmental data.
Quality assurance for inorganic and organic compounds assessed in the National Water Quality Network includes collection of field blanks to determine contamination bias and field replicates to determine variability bias. No contamination bias was found for 6 of the 13 nutrient compounds analyzed, and some potential contamination bias for some years was found for the other 7 nutrient compounds. Contamination bias was not found for carbon compounds or ultraviolet-absorbance measurements and was not assessed for sediment. All major ions and trace elements except potassium and lithium showed moderate contamination bias for at least 1 water year; generally, this bias was not at environmentally relevant concentrations. All compounds in the nutrient, carbon, and sediment group and in the major ions and trace elements group had low variability both in detection frequency and in concentration. Exceptions to this low variability were total particulate inorganic carbon and sediment for 2015, both of which are particulate substances with intrinsically high sampling variability.
The risk of contamination bias for pesticides in National Water Quality Network samples was low, as indicated by very few detections in field blanks. Sixteen pesticide compounds showed potential contamination bias based on unexpected detections in third-party blind spikes (false-positive results for compounds that are not included in the spike mixture of a sample, where the identity as a QC sample is unknown to the analyst), and 47 different compounds (out of 225 pesticide compounds) showed potential contamination bias from laboratory blanks. However, when timing and relative magnitudes of detections in blank samples, environmental samples, and benchmark concentrations are considered, most of this potential contamination is not relevant to interpretation of published pesticide results. Overall variability in detection frequency for pesticides from field replicates was low or moderate. Also based on field replicates, 55 pesticides had overall high variability in concentrations for at least 1 water year, although these assessments likely overestimate high variability.
At least 1 QC issue was found for 87 pesticides; however, most of the QC issues had no or little effect on the interpretation of environmental results because the U.S. Geological Survey National Water Quality Laboratory addressed the QC issue before publishing the environmental results, environmental results were almost entirely nondetections, concentrations of environmental results were higher than potential contamination bias, or benchmark concentrations were orders of magnitude higher than all environmental results. Eight compounds affected by two QC issues had a benchmark less than 100 nanograms per liter and warranted careful consideration of timing and magnitude of QC results in relation to surface-water results before interpretive use.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205116","usgsCitation":"Medalie, L., and Bexfield, L.M., 2020, Quality of data from the U.S. Geological Survey National Water Quality Network for water years 2013–17: U.S. Geological Survey Scientific Investigations Report 2020–5116, 21 p., https://doi.org/10.3133/sir20205116.","productDescription":"Report: v, 21 p.; Data Releases; 9 Tables","numberOfPages":"21","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-115536","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":436706,"rank":17,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94F31R8","text":"USGS data release","linkHelpText":"Nutrient and pesticide data collected from the USGS National Water Quality Network and previous networks, 1963-2018"},{"id":380873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5116/coverthb2.jpg"},{"id":380874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116.pdf","text":"Report","size":"1.65 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020-5116"},{"id":380877,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90BFKA4","text":"USGS data release","linkHelpText":"Field, laboratory, and third-party data for assessment of the quality of pesticide results reported by the National Water Quality Laboratory for groundwater samples collected by the National Water-Quality Assessment Project, 2013–18"},{"id":380878,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96VY980","text":"USGS data release","linkHelpText":"Field blank and field replicate datasets for inorganic and organic compounds collected for the National Water Quality Network, water years 2013–17"},{"id":380879,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables01-09.xlsx","text":"Tables 1-9","size":"221 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Tables 1-9 as a Microsoft Excel file"},{"id":380880,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables01-09_csv.zip","text":"CSV Package","size":"44 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Tables 1-9 as nine CSV files and a Read Me file"},{"id":380881,"rank":7,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_tables_readme.txt","text":"CSV Read Me","size":"19.6 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Read Me file to accompany the CSV files"},{"id":380887,"rank":13,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table06.csv","text":"Table 6","size":"14.7 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380886,"rank":12,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table05.csv","text":"Table 5","size":"16.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380885,"rank":11,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table04.csv","text":"Table 4","size":"4.1 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380884,"rank":10,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table03.csv","text":"Table 3","size":"29.1 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380890,"rank":16,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table09.csv","text":"Table 9","size":"24.1 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380889,"rank":15,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table08.csv","text":"Table 8","size":"17.1 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380888,"rank":14,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table07.csv","text":"Table 7","size":"2.97 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380883,"rank":9,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table02.csv","text":"Table 2","size":"5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"},{"id":380882,"rank":8,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2020/5116/sir20205116_table01.csv","text":"Table 1","size":"33.5 KB","linkFileType":{"id":7,"text":"csv"},"linkHelpText":"- CSV file"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -130.67138671875,\n 54.686534234529695\n ],\n [\n -129.9462890625,\n 55.36662484928637\n ],\n [\n -130.1220703125,\n 56.145549500679074\n ],\n [\n -131.9677734375,\n 56.9449741808516\n ],\n [\n -135.3076171875,\n 59.833775202184206\n ],\n [\n -136.38427734375,\n 59.65664225341022\n ],\n [\n -136.6259765625,\n 59.23217626921806\n ],\n [\n -137.52685546875,\n 58.938673187948304\n ],\n [\n -137.65869140625,\n 59.33318942659219\n ],\n [\n -138.8232421875,\n 60.009970961180386\n ],\n [\n -139.21874999999997,\n 60.108670463036\n ],\n [\n -139.04296875,\n 60.403001945865476\n ],\n [\n -139.85595703125,\n 60.337823495982015\n ],\n [\n -140.99853515625,\n 60.337823495982015\n ],\n [\n -141.15234374999997,\n 69.71810669906763\n ],\n [\n -143.4375,\n 70.17020068549206\n ],\n [\n -145.1953125,\n 70.08056215839737\n ],\n [\n -149.765625,\n 70.58341752317065\n ],\n [\n -152.40234375,\n 70.61261423801925\n ],\n [\n -152.314453125,\n 70.95969716686398\n ],\n [\n -157.1484375,\n 71.35706654962706\n ],\n [\n -159.9609375,\n 70.8734913192635\n ],\n [\n -162.0703125,\n 70.31873847853124\n ],\n [\n -163.916015625,\n 69.06856318696033\n ],\n [\n -166.376953125,\n 68.942606818121\n ],\n [\n -166.376953125,\n 68.26938680456564\n ],\n [\n -163.30078125,\n 66.86108230224609\n ],\n [\n -161.982421875,\n 66.47820814385636\n ],\n [\n -163.564453125,\n 66.08936427047088\n ],\n [\n -163.564453125,\n 66.6181218846659\n ],\n [\n -165.76171875,\n 66.40795547978848\n ],\n [\n -168.0908203125,\n 65.69447579373418\n ],\n [\n -166.55273437499997,\n 65.14611484756372\n ],\n [\n -166.904296875,\n 65.05360170595502\n ],\n [\n -166.3330078125,\n 64.41592147626879\n ],\n [\n -162.861328125,\n 64.39693778132846\n ],\n [\n -160.927734375,\n 64.90491004905083\n ],\n [\n -161.0595703125,\n 64.47279382008166\n ],\n [\n -161.4990234375,\n 64.49172504435471\n ],\n [\n -160.8837890625,\n 63.87939001720202\n ],\n [\n -161.1474609375,\n 63.470144746565424\n ],\n [\n -162.6416015625,\n 63.64625919492172\n ],\n [\n -163.212890625,\n 63.05495931065107\n ],\n [\n -164.2236328125,\n 63.37183226679281\n ],\n [\n -166.1572265625,\n 61.75233128411639\n ],\n [\n -165.3662109375,\n 60.54377524118842\n ],\n [\n -167.431640625,\n 60.326947742998414\n ],\n [\n -167.255859375,\n 59.866883195210214\n ],\n [\n -165.8935546875,\n 59.7563950493563\n ],\n [\n -162.68554687499997,\n 59.734253447591364\n ],\n [\n -162.3779296875,\n 60.174306261926034\n ],\n [\n -161.806640625,\n 59.46740794183739\n ],\n [\n -162.0263671875,\n 59.108308258604964\n ],\n [\n -161.806640625,\n 58.768200159239576\n ],\n [\n -162.20214843749997,\n 58.65408464530598\n ],\n [\n -160.83984375,\n 58.44773280389084\n ],\n [\n -159.9609375,\n 58.6769376725869\n ],\n [\n -159.08203125,\n 58.309488840677645\n ],\n [\n -156.88476562499997,\n 58.92733441827545\n ],\n [\n -157.5,\n 58.516651799363785\n ],\n [\n -157.8076171875,\n 57.61010702068388\n ],\n [\n -161.54296875,\n 56.022948079627454\n ],\n [\n -168.6181640625,\n 53.4357192066942\n ],\n [\n -174.9462890625,\n 52.26815737376817\n ],\n [\n -178.2421875,\n 51.83577752045248\n ],\n [\n -173.1884765625,\n 51.590722643120145\n ],\n [\n -162.5537109375,\n 54.23955053156177\n ],\n [\n -155.302734375,\n 55.52863052257191\n ],\n [\n -151.4794921875,\n 57.51582286553883\n ],\n [\n -146.9970703125,\n 60.08676274626006\n ],\n [\n -145.546875,\n 60.21799073323445\n ],\n [\n -144.228515625,\n 59.689926220143356\n ],\n [\n -142.3828125,\n 59.93300042374631\n ],\n [\n -138.3837890625,\n 58.83649009392136\n ],\n [\n -135.6591796875,\n 56.31653672211301\n ],\n [\n -133.2421875,\n 54.521081495443596\n ],\n [\n -130.67138671875,\n 54.686534234529695\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -66.796875,\n 44.902577996288876\n ],\n [\n -67.67578124999999,\n 45.583289756006316\n ],\n [\n -67.939453125,\n 47.57652571374621\n ],\n [\n -69.2578125,\n 47.338822694822\n ],\n [\n -71.19140625,\n 45.27488643704891\n ],\n [\n -75.146484375,\n 44.96479793033101\n ],\n [\n -78.046875,\n 43.644025847699496\n ],\n [\n -79.1015625,\n 43.51668853502906\n ],\n [\n -79.1015625,\n 42.87596410238256\n ],\n [\n -82.68310546875,\n 41.65649719441145\n ],\n [\n -83.14453125,\n 42.049292638686836\n ],\n [\n -83.07861328125,\n 42.374778361114195\n ],\n [\n -82.529296875,\n 42.601619944327965\n ],\n [\n -82.24365234375,\n 43.6599240747891\n ],\n [\n -82.41943359375,\n 45.058001435398275\n ],\n [\n -83.60595703125,\n 45.85941212790755\n ],\n [\n -83.49609375,\n 46.027481852486645\n ],\n [\n -83.7158203125,\n 46.164614496897094\n ],\n [\n -83.95751953125,\n 46.07323062540835\n ],\n [\n -84.24316406249999,\n 46.558860303117164\n ],\n [\n -84.72656249999999,\n 46.558860303117164\n ],\n [\n -84.90234375,\n 46.92025531537451\n ],\n [\n -88.41796875,\n 48.3416461723746\n ],\n [\n -89.3408203125,\n 47.96050238891509\n ],\n [\n -90.76904296874999,\n 48.122101028190805\n ],\n [\n -90.87890625,\n 48.22467264956519\n ],\n [\n -91.51611328125,\n 48.10743118848039\n ],\n [\n -92.2412109375,\n 48.37084770238366\n ],\n [\n -92.39501953125,\n 48.23930899024907\n ],\n [\n -92.94433593749999,\n 48.61838518688487\n ],\n [\n -93.44970703125,\n 48.63290858589535\n ],\n [\n -94.7021484375,\n 48.748945343432936\n ],\n [\n -94.833984375,\n 49.23912083246698\n ],\n [\n -95.1416015625,\n 49.396675075193976\n ],\n [\n -95.20751953125,\n 49.009050809382046\n ],\n [\n -123.22265625000001,\n 48.99463598353405\n ],\n [\n -123.0908203125,\n 48.80686346108517\n ],\n [\n -123.24462890625,\n 48.66194284607006\n ],\n [\n -123.1787109375,\n 48.32703913063476\n ],\n [\n -124.78271484375,\n 48.472921272487824\n ],\n [\n -124.93652343749999,\n 48.16608541901253\n ],\n [\n -124.365234375,\n 46.58906908309182\n ],\n [\n -124.541015625,\n 44.15068115978094\n ],\n [\n -124.93652343749999,\n 42.69858589169842\n ],\n [\n -124.541015625,\n 41.22824901518529\n ],\n [\n -124.73876953125,\n 40.43022363450862\n ],\n [\n -124.03564453125,\n 39.35129035526705\n ],\n [\n -124.01367187499999,\n 38.8225909761771\n ],\n [\n -122.05810546875,\n 36.12012758978146\n ],\n [\n -120.95947265624999,\n 34.88593094075317\n ],\n [\n -120.80566406250001,\n 34.08906131584994\n ],\n [\n -118.21289062499999,\n 32.2313896627376\n ],\n [\n -117.22412109375,\n 32.54681317351514\n ],\n [\n -114.78515624999999,\n 32.713355353177555\n ],\n [\n -114.78515624999999,\n 32.491230287947594\n ],\n [\n -110.98388671874999,\n 31.3348710339506\n ],\n [\n -108.21533203125,\n 31.297327991404266\n ],\n [\n -108.2373046875,\n 31.765537409484374\n ],\n [\n -106.435546875,\n 31.765537409484374\n ],\n [\n -104.9853515625,\n 30.600093873550072\n ],\n [\n -104.47998046875,\n 29.592565403314087\n ],\n [\n -103.20556640625,\n 28.94086176940557\n ],\n [\n -102.65625,\n 29.76437737516313\n ],\n [\n -102.3486328125,\n 29.84064389983441\n ],\n [\n -101.49169921875,\n 29.7453016622136\n ],\n [\n -100.83251953125,\n 29.267232865200878\n ],\n [\n -100.30517578125,\n 28.246327971048842\n ],\n [\n -99.60205078124999,\n 27.586197857692664\n ],\n [\n -99.47021484375,\n 27.31321389856826\n ],\n [\n -99.228515625,\n 26.52956523826758\n ],\n [\n -98.2177734375,\n 26.05678288577881\n ],\n [\n -97.75634765625,\n 26.03704188651584\n ],\n [\n -97.44873046875,\n 25.839449402063185\n ],\n [\n -97.20703125,\n 25.93828707492375\n ],\n [\n -96.8994140625,\n 26.194876675795218\n ],\n [\n -96.78955078125,\n 27.858503954841247\n ],\n [\n -93.75732421875,\n 29.420460341013133\n ],\n [\n -90.2197265625,\n 28.998531814051795\n ],\n [\n -88.22021484375,\n 29.05616970274342\n ],\n [\n -87.91259765625,\n 30.14512718337613\n ],\n [\n -86.5283203125,\n 30.183121842195515\n ],\n [\n -85.2978515625,\n 29.49698759653577\n ],\n [\n -84.13330078125,\n 29.80251790576445\n ],\n [\n -82.81494140625,\n 28.555576049185973\n ],\n [\n -83.21044921875,\n 27.800209937418252\n ],\n [\n -82.77099609375,\n 26.941659545381516\n ],\n [\n -82.08984375,\n 25.878994400196202\n ],\n [\n -81.5625,\n 25.264568475331583\n ],\n [\n -82.28759765625,\n 24.467150664739002\n ],\n [\n -82.0458984375,\n 24.046463999666567\n ],\n [\n -80.6396484375,\n 24.56710835257599\n ],\n [\n -79.78271484375,\n 25.34402602913433\n ],\n [\n -79.60693359375,\n 27.27416111737468\n ],\n [\n -80.68359375,\n 30.713503990354965\n ],\n [\n -80.66162109375,\n 31.50362930577303\n ],\n [\n -76.81640625,\n 34.07086232376631\n ],\n [\n -75.16845703124999,\n 35.263561862152095\n ],\n [\n -75.498046875,\n 37.055177106660814\n ],\n [\n -73.58642578125,\n 39.90973623453719\n ],\n [\n -71.3671875,\n 40.84706035607122\n ],\n [\n -69.63134765625,\n 40.9964840143779\n ],\n [\n -70.0048828125,\n 42.342305278572816\n ],\n [\n -70.3564453125,\n 42.89206418807337\n ],\n [\n -67.2802734375,\n 44.37098696297173\n ],\n [\n -67.0166015625,\n 44.69989765840318\n ],\n [\n -66.796875,\n 44.902577996288876\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -67.2308349609375,\n 17.96305758238804\n ],\n [\n -67.2198486328125,\n 17.910795834978483\n ],\n [\n -66.5716552734375,\n 17.866361230891894\n ],\n [\n -66.16790771484375,\n 17.90556881196468\n ],\n [\n -65.85205078125,\n 17.973508079068797\n ],\n [\n -65.7861328125,\n 18.04142122189195\n ],\n [\n -65.50323486328125,\n 18.06231230454674\n ],\n [\n -65.2587890625,\n 18.114529138838503\n ],\n [\n -65.269775390625,\n 18.15629140283545\n ],\n [\n -65.4400634765625,\n 18.18238775108558\n ],\n [\n -65.51422119140625,\n 18.14324176648384\n ],\n [\n -65.5609130859375,\n 18.40665471391907\n ],\n [\n -65.64880371093749,\n 18.404048629104647\n ],\n [\n -65.77789306640625,\n 18.417078658661257\n ],\n [\n -65.9124755859375,\n 18.46918890441719\n ],\n [\n -66.24755859375,\n 18.510865709091377\n ],\n [\n -66.4837646484375,\n 18.503052080569763\n ],\n [\n -66.98638916015625,\n 18.51347017266187\n ],\n [\n -67.115478515625,\n 18.534304453676864\n ],\n [\n -67.181396484375,\n 18.48742375381096\n ],\n [\n -67.16217041015625,\n 18.432713391700858\n ],\n [\n -67.2637939453125,\n 18.375379094031825\n ],\n [\n -67.19238281249999,\n 18.2397859708389\n ],\n [\n -67.2308349609375,\n 17.96305758238804\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New England Water Science Center
U.S. Geological Survey
10 Bearfoot Road
Northborough, MA 01532
No abstract available.
","conferenceTitle":"5th Colloquium of the International Geoscience Programme (IGCP-638)","conferenceDate":"December 4-5, 2022","conferenceLocation":"Accra, Ghana","language":"English","publisher":"UNESCO (ICGP)","usgsCitation":"Taylor, C.D., Bradley, D., Finn, C.A., Zerrouki, A., Ayad, B., Belanteur, N.F., Bouchilaoune, N., Johnson, M., Meziane, G., Mihalasky, M.J., Mouchene, H., Oughou, S., Smith, S.M., Solano, F., and Zerrouk, S., 2020, Metallogenic implications of a new geodynamic model for the Eglab, Algeria, 5th Colloquium of the International Geoscience Programme (IGCP-638), Accra, Ghana, December 4-5, 2022, 3 p.","productDescription":"3 p.","ipdsId":"IP-111457","costCenters":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":490840,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Taylor, Cliff D. 0000-0001-6376-6298 ctaylor@usgs.gov","orcid":"https://orcid.org/0000-0001-6376-6298","contributorId":1283,"corporation":false,"usgs":true,"family":"Taylor","given":"Cliff","email":"ctaylor@usgs.gov","middleInitial":"D.","affiliations":[{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819862,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bradley, Dwight 0000-0001-9116-5289 bradleyorchard2@gmail.com","orcid":"https://orcid.org/0000-0001-9116-5289","contributorId":2358,"corporation":false,"usgs":true,"family":"Bradley","given":"Dwight","email":"bradleyorchard2@gmail.com","affiliations":[{"id":119,"text":"Alaska Science Center Geology Minerals","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819863,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Carol A. 0000-0002-6178-0405 cfinn@usgs.gov","orcid":"https://orcid.org/0000-0002-6178-0405","contributorId":1326,"corporation":false,"usgs":true,"family":"Finn","given":"Carol","email":"cfinn@usgs.gov","middleInitial":"A.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":819864,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Zerrouki, Arezki","contributorId":261344,"corporation":false,"usgs":false,"family":"Zerrouki","given":"Arezki","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819865,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Ayad, Brahim 0000-0003-2136-8384","orcid":"https://orcid.org/0000-0003-2136-8384","contributorId":261345,"corporation":false,"usgs":false,"family":"Ayad","given":"Brahim","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819866,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Belanteur, Nadjib F. 0000-0002-4149-3812","orcid":"https://orcid.org/0000-0002-4149-3812","contributorId":220949,"corporation":false,"usgs":false,"family":"Belanteur","given":"Nadjib","email":"","middleInitial":"F.","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819867,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bouchilaoune, Nabyl 0000-0002-2665-9845","orcid":"https://orcid.org/0000-0002-2665-9845","contributorId":261346,"corporation":false,"usgs":false,"family":"Bouchilaoune","given":"Nabyl","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819868,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnson, Michaela 0000-0001-6133-0247 mrjohns@usgs.gov","orcid":"https://orcid.org/0000-0001-6133-0247","contributorId":182462,"corporation":false,"usgs":true,"family":"Johnson","given":"Michaela","email":"mrjohns@usgs.gov","affiliations":[],"preferred":true,"id":819869,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Meziane, Ghania","contributorId":261347,"corporation":false,"usgs":false,"family":"Meziane","given":"Ghania","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819870,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mihalasky, Mark J. 0000-0002-0082-3029 mjm@usgs.gov","orcid":"https://orcid.org/0000-0002-0082-3029","contributorId":3692,"corporation":false,"usgs":true,"family":"Mihalasky","given":"Mark","email":"mjm@usgs.gov","middleInitial":"J.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":662,"text":"Western Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true}],"preferred":false,"id":819871,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mouchene, Hamza","contributorId":261348,"corporation":false,"usgs":false,"family":"Mouchene","given":"Hamza","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819872,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Oughou, Samir","contributorId":261349,"corporation":false,"usgs":false,"family":"Oughou","given":"Samir","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819873,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Smith, Steven M. 0000-0003-3591-5377 smsmith@usgs.gov","orcid":"https://orcid.org/0000-0003-3591-5377","contributorId":1460,"corporation":false,"usgs":true,"family":"Smith","given":"Steven","email":"smsmith@usgs.gov","middleInitial":"M.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819874,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Solano, Federico 0000-0002-0308-5850 fsolanoc@usgs.gov","orcid":"https://orcid.org/0000-0002-0308-5850","contributorId":4302,"corporation":false,"usgs":true,"family":"Solano","given":"Federico","email":"fsolanoc@usgs.gov","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":819875,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Zerrouk, Siham","contributorId":261350,"corporation":false,"usgs":false,"family":"Zerrouk","given":"Siham","affiliations":[{"id":40298,"text":"Algerian Geological Survey Agency","active":true,"usgs":false}],"preferred":false,"id":819876,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70228275,"text":"70228275 - 2020 - Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","interactions":[],"lastModifiedDate":"2022-02-08T16:10:19.163337","indexId":"70228275","displayToPublicDate":"2020-12-01T09:47:52","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","title":"Ecological risk assessment of environmental stress and bioactive chemicals to riverine fish populations: An individual-based model of smallmouth bass Micropterus dolomieu","docAbstract":"Ecological risk assessments play an important role in environmental management and decision-making. Although empirical measurements of the effects of habitat changes and chemical exposure are often made at molecular and individual levels, environmental decision-making often requires the quantification of management-relevant, population-level outcomes. In this study, we generalized a modeling framework to evaluate population-level ecological risk of environmental stress and bioactive chemicals. The modeling framework includes (1) a biological model module that incorporates complex and interacting biological and ecological processes, and environmental stochasticity, (2) an effect module that links the impacts of environmental changes and chemical exposure to individual characteristics, and (3) a population module that makes decisions on the choice of population-level properties to best capture the effects and thus to track in the model based on the target species and the research and management interest. This framework is a 3-module procedure that provides an alternative way for researchers to organize, present and communicate the risk assessment modeling studies. To demonstrate this framework, we used a socioeconomically important riverine fish species, smallmouth bass Micropterus dolomieu, as the model species. We developed an individual-based model as the biological model module. We evaluated the impacts of changing water temperature and flow regimes, and the impacts of exposure to estrogenic endocrine disrupting compounds (EEDC) on smallmouth bass populations in the Chesapeake Bay Watershed, USA. Warm summer water temperatures and year-round high flows had the most severe impacts on the smallmouth bass population. An increase in exposure level to EEDC, both year-round and in summer months, substantially reduced population size, spawner and recruit abundance, and the proportion of quality-length individuals. Acute exposure to EEDC was more detrimental to the population than chronic exposure. Acute exposure during spawning season had the most severe impacts. This modeling framework can be extended to other species, environmental factors and chemicals, and can be used to inform management and conservation decisions.