Even within a single population, individuals can display striking differences in behavior, with consequences for their survival and fitness. In reintroduced populations, managers often attempt to promote adaptive behaviors by controlling the early life experiences of individuals, but it remains largely unknown whether this early life training has lasting effects on behavior. We investigated the behavior of reintroduced whooping cranes (Grus americana) trained to migrate using two different methods to see whether their migration behavior remained different or converged over time. We found that the behavior of the two groups converged relatively rapidly, indicating that early life training may not produce lasting effects, especially in species that display lifelong learning and behavioral adaptation. In some cases, managers may consider continual behavioral interventions after release if desired behaviors are not present. Understanding the roles early life experience and animal cultures play in determining behavior is crucial for successful reintroduction programs.
","language":"English","publisher":"Society for Conservation Biology","doi":"10.1111/conl.12599","usgsCitation":"Teitelbaum, C., Converse, S.J., and Mueller, T., 2019, The importance of early life experience and animal cultures in reintroductions: Conservation Letters, v. 12, no. 1, e12599, 7 p., https://doi.org/10.1111/conl.12599.","productDescription":"e12599, 7 p.","ipdsId":"IP-096398","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":458841,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/conl.12599","text":"Publisher Index Page"},{"id":395370,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"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 -86.66015624999999,\n 41.64007838467894\n ],\n [\n -88.59374999999999,\n 40.97989806962013\n ],\n [\n -90.439453125,\n 39.095962936305476\n ],\n [\n -82.79296874999999,\n 29.38217507514529\n ],\n [\n -81.298828125,\n 29.305561325527698\n ],\n [\n -82.353515625,\n 33.43144133557529\n ],\n [\n -86.66015624999999,\n 41.64007838467894\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"1","noUsgsAuthors":false,"publicationDate":"2018-07-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Teitelbaum, Claire S.","contributorId":274277,"corporation":false,"usgs":false,"family":"Teitelbaum","given":"Claire S.","affiliations":[{"id":56593,"text":"Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":832871,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Converse, Sarah J. 0000-0002-3719-5441 sconverse@usgs.gov","orcid":"https://orcid.org/0000-0002-3719-5441","contributorId":173772,"corporation":false,"usgs":true,"family":"Converse","given":"Sarah","email":"sconverse@usgs.gov","middleInitial":"J.","affiliations":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":832870,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mueller, Thomas","contributorId":274278,"corporation":false,"usgs":false,"family":"Mueller","given":"Thomas","affiliations":[{"id":56593,"text":"Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":832872,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70212321,"text":"70212321 - 2019 - Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","interactions":[],"lastModifiedDate":"2020-08-14T14:34:26.531968","indexId":"70212321","displayToPublicDate":"2020-07-01T09:26:56","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1584,"text":"Estuaries and Coasts","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","title":"Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus)","docAbstract":"American horseshoe crabs (Limulus polyphemus) have varied migration patterns and harvesting pressure throughout their range, potentially leading to regional differences in population dynamics. Here, a multi-state mark–recapture model was used to estimate annual survival and exchange rates of adult horseshoe crabs across three geographic regions in Long Island, NY (South Shore, North Shore, and Jamaica Bay areas). Under the New York Horseshoe Crab Monitoring program, a total of 22,525 adult horseshoe crabs were tagged and 879 (3.9%) unique recaptures were observed from 2007 to 2016. Model-averaged annual survival in the North Shore population was higher at 68% (95% confidence interval (CI) 61.9–73.4) when compared to the South Shore (56.8%, 95% CI 51.1–62.2) and Jamaica Bay (54.5%, 95% CI 47.0–61.7) regions. Differences in survival between the North Shore and South Shore may reflect the greater harvest pressure directed along the South Shore. Contrary to expectations for a primarily closed region, Jamaica Bay survival was low, but not attributable to reported harvest related activities. Annual movement from the Jamaica Bay into the adjacent South Shore region was 19.8% (95% CI 13.1–28.9), but annual exchange rates ranging from 0.5 to 5.0% were observed between other regions. For example, movement from the South Shore and North Shore into Jamaica Bay was 3.5% (95% CI 2.3–5.9) and 0.5% (95% CI 0.0–1.0), respectively. There was strong support for sex-specific differences in survival, primarily driven by the low survival of females in Jamaica Bay (33.8%, 95% CI 21.1–50.5). Our findings reveal potential management implications, such as regional survival differences within a uniformly managed stock, and net emigration from a predominantly closed to open harvest region reducing the effectiveness of a protected area.
","language":"English","publisher":"Springer","doi":"10.1007/s12237-019-00595-1","usgsCitation":"Bopp, J.J., Sclafani, M., Smith, D.R., McKown, K., Sysak, R., and Cerrato, R., 2019, Geographic-specific capture-recapture models reveal contrasting migration and survival rates of adult horseshoe crabs (Limulus polyphemus): Estuaries and Coasts, v. 42, p. 1570-1585, https://doi.org/10.1007/s12237-019-00595-1.","productDescription":"16 p.","startPage":"1570","endPage":"1585","ipdsId":"IP-106088","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":377519,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Jersey, New York","otherGeospatial":"Jamaica Bay, Long Island, North Shore, South Shore","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.1302490234375,\n 40.06125658140474\n ],\n [\n -73.916015625,\n 40.027614437486655\n ],\n [\n -73.8775634765625,\n 40.49709237269567\n ],\n [\n -72.65808105468749,\n 40.66813955408042\n ],\n [\n -71.74072265625,\n 41.03793062246529\n ],\n [\n -71.9439697265625,\n 41.20345619205131\n ],\n [\n -72.333984375,\n 41.29431726315258\n ],\n [\n -72.894287109375,\n 41.261291493919884\n ],\n [\n -73.49853515625,\n 41.062786068733026\n ],\n [\n -73.8885498046875,\n 40.84290487729676\n ],\n [\n -74.0863037109375,\n 40.66397287638688\n ],\n [\n -74.080810546875,\n 40.576412521044425\n ],\n [\n -74.2730712890625,\n 40.49291502689579\n ],\n [\n -74.234619140625,\n 40.451127265872316\n ],\n [\n -74.0643310546875,\n 40.32141999593439\n ],\n [\n -74.1302490234375,\n 40.06125658140474\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","noUsgsAuthors":false,"publicationDate":"2019-07-01","publicationStatus":"PW","contributors":{"authors":[{"text":"Bopp, Justin J.","contributorId":238554,"corporation":false,"usgs":false,"family":"Bopp","given":"Justin","email":"","middleInitial":"J.","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":796384,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sclafani, Matthew","contributorId":238556,"corporation":false,"usgs":false,"family":"Sclafani","given":"Matthew","email":"","affiliations":[{"id":47742,"text":"Cornell Cooperative Extension","active":true,"usgs":false}],"preferred":false,"id":796385,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Smith, David R. 0000-0001-6074-9257 drsmith@usgs.gov","orcid":"https://orcid.org/0000-0001-6074-9257","contributorId":168442,"corporation":false,"usgs":true,"family":"Smith","given":"David","email":"drsmith@usgs.gov","middleInitial":"R.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":796386,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"McKown, Kim","contributorId":238557,"corporation":false,"usgs":false,"family":"McKown","given":"Kim","email":"","affiliations":[{"id":47744,"text":"New York Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":796387,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sysak, Rachel","contributorId":238558,"corporation":false,"usgs":false,"family":"Sysak","given":"Rachel","email":"","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":796388,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cerrato, Robert","contributorId":238559,"corporation":false,"usgs":false,"family":"Cerrato","given":"Robert","email":"","affiliations":[{"id":36488,"text":"Stony Brook University","active":true,"usgs":false}],"preferred":false,"id":796389,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70203838,"text":"70203838 - 2019 - Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture","interactions":[],"lastModifiedDate":"2020-05-29T19:13:50.212524","indexId":"70203838","displayToPublicDate":"2020-05-29T14:03:13","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5959,"text":"Wisconsin Geological and NaturalHistory Survey Bulletin","active":true,"publicationSubtype":{"id":2}},"seriesNumber":"B112","title":"Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture","docAbstract":"A groundwater flow model for western Chippewa County, Wisconsin, was developed by the Wisconsin Geological and Natural History Survey (WGNHS) and the U.S. Geological Survey (USGS) using the computer program MODFLOW. The model is the result of a five-year groundwater study commissioned by Chippewa County in 2012 to evaluate the effects of industrial sand mining and irrigated agriculture on the county’s water resources. The study incorporates existing data and newly acquired data from fieldwork conducted within the study area. The groundwater model may be useful for future investigations, such as evaluation of proposed high-capacity well sites, development of municipal wellhead protection plans, and studies that seek to further quantify surface water-groundwater relationships.
The model conceptualizes the hydrostratigraphy of western Chippewa County as six stacked layers. Each layer is distinct, beginning with unlithified glacial material at the surface, and alternating between sandstones (that act as aquifers) and shale units (that serve as aquitards). The model is bounded below by Precambrian crystalline bedrock and its perimeter was derived from a regional-scale groundwater flow model.
The MODFLOW model represented average conditions during 2011–2013 with “steady-state” assumptions, meaning that simulated water levels do not fluctuate seasonally or from year to year. Steady-state models simplify natural variability, making results of scenario simulations easier to interpret and compare while also maximizing effects of stressors because the simulated stress is always applied (not halted after a few months or years). Model calibration used the parameter estimation code (PEST), and calibration targets included heads (groundwater levels) and streamflows. Calibration focused on 2011–2013 because a large amount of head and streamflow data were available for that period.
The MODFLOW model explicitly simulates all sources and sinks of water, including groundwater/surface-water interaction with streamflow routing. Model input included estimates of aquifer hydraulic conductivity and a spatial groundwater recharge distribution developed using a GIS-based soil-water-balance (SWB) model applied to the model area. Groundwater withdrawals were simulated for 269 high-capacity wells across the entire model domain, which includes western Chippewa County and adjacent portions of Dunn, Barron, and Rusk Counties. Collectively, these wells withdrew about 1.14 million gallons per year between 2011 and 2013.
Once the model was calibrated, it was applied to two distinct scenarios of increased groundwater withdrawals: one evaluating hydrologic effects of more intensive industrial sand mining and the second evaluating the hydrologic effects of more intensive agricultural irrigation practices. Each scenario was developed with input from Chippewa County and a stakeholder group established expressly for this study. The scenarios were designed to represent reasonable future buildout conditions for both mining and irrigated agriculture. The mining scenario underscores the potential hydrologic effects related to changing land-use practices (i.e., hilltops and farmland becoming sand mines), while the irrigated agriculture scenario illustrates the potential hydrologic effects of intensifying existing land-use practices (i.e., installing new wells to irrigate farm fields).
While each scenario evaluated distinctly different conditions, modeling results demonstrated the potential of both scenarios to lower the water table and reduce baseflows in headwater streams within the modeled area. In the case of irrigated agriculture, hydrologic effects were associated directly with groundwater withdrawals. By assuming that irrigation did not decrease, this steady-state simulation represented a sustained future effect. By contrast, hydrologic effects of industrial sand mining were the result of both groundwater withdrawals at mines and land-use changes that effectively reduced recharge to groundwater over distinct phases of active mining. This scenario included a post-mining phase, during which groundwater withdrawals stopped and mined areas were reclaimed to undeveloped prairie grass cover. If reclamation to undeveloped prairie indeed occurs as simulated, long-term increases in the water table and stream baseflows are possible. In this sense, the scenario representing build out of irrigated agriculture led to long-term baseflow declines while the future buildout of industrial sand mining led to declines that dissipated following mine reclamation to undisturbed prairie.
Future investigations in similar hydrogeologic settings may find the following insights gleaned from this study useful:
❚❚ The characterization of hydrogeologic properties, delineation of hydrogeologic units, and calibration of groundwater flow models benefited from incorporation of accurate well construction reports, high-quality borehole geophysical logs, and streamflow gaging data.
❚❚ Infiltration testing performed in active mining areas provided evidence that reducing the degree and extent of compaction and enhancing areas designed to retain and infiltrate stormwater runoff could potentially reduce runoff and increase groundwater recharge.
❚❚ Similarly, reclaiming mined areas to prairie grasses would be expected to reduce runoff and increase groundwater recharge by reducing compaction and improving soil structure and vegetation that can slow runoff and enhance infiltration.
","language":"English","publisher":"Wisconsin Geological and Natural History Survey","usgsCitation":"Parsen, M., Juckem, P.F., Gotkowitz, M., and Fienen, M.N., 2019, Groundwater flow model for Western Chippewa County–Including analysis of water resources related to industrial sand mining and irrigated agriculture: Wisconsin Geological and NaturalHistory Survey Bulletin B112, 74 p.","productDescription":"74 p.","ipdsId":"IP-093476","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":375174,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":375173,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://wgnhs.wisc.edu/pubs/b112/"}],"country":"United States","state":"Wisconsin","county":"Chippewa County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -91.8511962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 44.859762688042736\n ],\n [\n -91.31011962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 45.55060191034006\n ],\n [\n -91.8511962890625,\n 44.859762688042736\n ]\n ]\n ]\n }\n }\n ]\n}","publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Parsen, Michael","contributorId":216283,"corporation":false,"usgs":false,"family":"Parsen","given":"Michael","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764401,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Juckem, Paul F. 0000-0002-3613-1761 pfjuckem@usgs.gov","orcid":"https://orcid.org/0000-0002-3613-1761","contributorId":1905,"corporation":false,"usgs":true,"family":"Juckem","given":"Paul","email":"pfjuckem@usgs.gov","middleInitial":"F.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gotkowitz, Madeline","contributorId":216284,"corporation":false,"usgs":false,"family":"Gotkowitz","given":"Madeline","affiliations":[{"id":39043,"text":"Wisconsin Geological and Natural History Survey","active":true,"usgs":false}],"preferred":false,"id":764402,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fienen, Michael N. 0000-0002-7756-4651 mnfienen@usgs.gov","orcid":"https://orcid.org/0000-0002-7756-4651","contributorId":171511,"corporation":false,"usgs":true,"family":"Fienen","given":"Michael","email":"mnfienen@usgs.gov","middleInitial":"N.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":764403,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70206903,"text":"sir20195136 - 2019 - Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York","interactions":[],"lastModifiedDate":"2020-05-14T11:35:30.264583","indexId":"sir20195136","displayToPublicDate":"2020-05-13T16:20:00","publicationYear":"2019","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":"2019-5136","displayTitle":"Geohydrology and Water Quality of the Unconsolidated Aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York","title":"Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York","docAbstract":"From 2013 to 2018, the U.S. Geological Survey, in cooperation with the Town of Enfield and the Tompkins County Planning Department, studied the unconsolidated aquifer in the Enfield Creek Valley in the town of Enfield, Tompkins County, New York. The valley will likely undergo future development as the population of Tompkins County increases and spreads out from the metropolitan areas. The Town of Enfield, Tompkins County, and the New York State Departments of Health and Environmental Conservation need geohydrologic information to help planners develop a more comprehensive approach to water-resource management in Tompkins County.
The Enfield Creek Valley is underlain by an unconfined aquifer that consists of saturated alluvium, alluvial-fan deposits, and ice-contact (kame) sand and gravel. A confined aquifer of discontinuous ice-contact sand and gravel overlies bedrock. Depth to bedrock in the valley ranges from about 50 feet below land surface from just north of the Enfield Creek divide in the northern part of the aquifer to the confluence of Fivemile Creek to at least 140 feet below land surface from Fivemile Creek to where the valley orientation changes from north-south to northwest-southeast. Depth to bedrock is much shallower from the valley orientation change to the southeastern part of the aquifer because Enfield Creek has carved through overlying sediments into bedrock as the creek drops 450 feet into the Cayuga Inlet Valley. A small buried valley running south to north was identified within the Fivemile Creek drainage along the western edge of the town. However, the valley fill consists of glacial till, and no sand-and-gravel aquifer is present.
The unconfined aquifers are recharged by direct infiltration of precipitation, surface runoff, and shallow subsurface flow from hillsides, and by seepage loss from streams overlying the aquifer. The confined aquifers are recharged mostly by precipitation that enters the adjacent valley walls, by groundwater flowing from bordering till or bedrock, and by flow from the bottom of the valley. Also, some recharge may be occurring where confining units are absent or from confining units with sediments of moderate permeability.
Groundwater discharges to Enfield Creek, its tributaries, and wetlands and is lost through evapotranspiration from the water table or is withdrawn from domestic, commercial, and agricultural wells. About 700 individual well owners depend on the unconsolidated aquifers for their water supply. An estimated 28,300,000 gallons per year are withdrawn.
Groundwater samples were collected from eight test wells drilled for this study, and six surface-water samples were collected from five locations on Enfield Creek. Of the eight wells sampled, two were finished in unconfined sand-and-gravel aquifers, two were finished in confined sand-and-gravel aquifers, and four were finished at or near the shale bedrock surface.
Water quality in the study area generally met State and Federal drinking-water standards. However, some samples exceeded maximum contaminant levels for barium (25 percent of samples) and secondary maximum containment levels for chloride (25 percent), dissolved solids (25 percent of samples), iron (70 percent of samples), and manganese (75 percent of samples). Groundwater from 75 percent of the wells sampled for methane had concentrations greater than the Office of Surface Mining Reclamation and Enforcement recommended action level of 10 to 28 milligrams per liter. The two deepest wells sampled, TM1075 and TM1077, had the highest specific conductance, chloride, and sodium concentrations of all wells sampled. The chloride/bromide ratios of these samples suggest the source may represent a mixture of saline formation waters with shallow dilute groundwater and may receive recharge contribution from two tributaries overlying bedrock to the west and southwest of the aquifer. In general, the highest yields are from wells completed within about 50 feet below land surface, which may tap either type of aquifer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195136","collaboration":"Prepared in cooperation with the Town of Enfield and the Tompkins County Planning Department","usgsCitation":"Fisher, B.N., Heisig, P.M., and Kappel, W.M., 2019, Geohydrology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York (ver. 1.1, May 2020): U.S. Geological Survey Scientific Investigations Report 2019–5136, 52 p., https://doi.org/10.3133/sir20195136.","productDescription":"Report: vi, 52 p.; 3 Data Releases","numberOfPages":"62","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103465","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":370147,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZFTDFY","text":"USGS data release","linkHelpText":"A horizontal-to-vertical spectral ratio soundings and depth-to-bedrock data for geohydrology and water quality investigation of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York, April 2013–August 2015"},{"id":370148,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P90FOTQV","text":"USGS data release","linkHelpText":"Records of selected wells for geohydrology and water quality investigation of the unconsolidated aquifers in the Enfield Creek Valley, town of Enfield, Tompkins County, New York, 2013–2018"},{"id":374790,"rank":5,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5136/coverthb2.jpg"},{"id":374788,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3U63J","text":"USGS data release","linkHelpText":"Geospatial datasets for the hydrogeology and water quality of the unconsolidated aquifers in the Enfield Creek Valley, Town of Enfield, Tompkins County, New York"},{"id":374789,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5136/versionhist.txt","text":"Version history","size":"1.35 KB","linkFileType":{"id":2,"text":"txt"}},{"id":374792,"rank":6,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5136/sir20195136.pdf","text":"Report","size":"5.95 MB","linkFileType":{"id":1,"text":"pdf"}}],"country":"United States","state":"New York","county":"Tompkins County","otherGeospatial":"Enfield Creek Valley, Town of Enfield","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.69281005859375,\n 42.47817430242155\n ],\n [\n -76.68731689453125,\n 42.39050147746088\n ],\n [\n -76.5692138671875,\n 42.39405131362432\n ],\n [\n -76.57230377197266,\n 42.48222557002593\n ],\n [\n -76.69281005859375,\n 42.47817430242155\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.1: May 13, 2020; Version 1.0 December 13, 2019","contact":"Director, New York Water Science Center
U.S. Geological Survey
425 Jordan Road
Troy, NY 12180–8349
Vector-borne diseases in the United States have recently increased as a result of the changing nature of vectors, hosts, reservoirs, pathogens, and the ecological and environmental conditions. Current information on vector habitats and how mosquito community composition varies across space and time is vital to successful vector-borne disease management. This study characterizes mosquito communities in urban areas of Oklahoma, United States, an ecologically diverse region in the southern Great Plains. Between May and September 2016, 11,996 female mosquitoes of 34 species were collected over 798 trap nights using three different trap types in six Oklahoma cities. The most abundant species trapped were Culex pipiens L. complex (32.4%) and Aedes albopictus (Skuse) (Diptera: Culicidae) (12.0%). Significant differences among mosquito communities were detected using analysis of similarities (ANOSIM) between the early (May–July) and late (August–September) season. Canonical correlation analysis (CCA) further highlighted the cities of Altus and Idabel as relatively unique mosquito communities, mostly due to the presence of Aedes aegypti (L.) and salt-marsh species and absence of Aedes triseriatus (Say) in Altus and an abundance of Ae. albopictus in Idabel. These data underscore the importance of assessing mosquito communities in urban environments found in multiple ecoregions of Oklahoma to allow customized vector management targeting the unique assemblage of species found in each city.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/jme/tjz039","usgsCitation":"Bradt, D., Wormington, J., Long, J.M., Hoback, W., and Noden, B., 2019, Differences in mosquito communities in six cities in Oklahoma: Journal of Medical Entomology, v. 56, no. 5, p. 1395-1403, https://doi.org/10.1093/jme/tjz039.","productDescription":"9 p.","startPage":"1395","endPage":"1403","ipdsId":"IP-100881","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":488509,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/jme/tjz039","text":"Publisher Index Page"},{"id":379491,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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The gap between such “snapshot” measures of diversity and actual diversity can be especially large in systems that undergo great temporal variation in environmental conditions. To adequately quantify diversity in these temporally varying ecosystems, individual measures of diversity collected throughout the range of environmental variation, i.e., temporal alpha-diversity measures, must be combined to obtain temporal gamma-diversity. Such a time-integrated gamma-diversity measure will be a much closer approximation of a site’s true alpha-diversity and provide a measure better comparable to spatial alpha-diversity measures of sites with lower temporal variation for which a single or a few “snapshot” measures may suffice. We used aquatic-macroinvertebrate community-composition data collected over a 24-year period from a complex of 16 prairie-pothole wetlands to explore the rate that taxa accumulate over time at sites with differing degrees of temporal variation. Our results show that the rate of taxa accumulation over time, i.e., the slope of the species–time relationship, is steeper for wetlands with ponds that frequently dry compared to those with more-permanent ponds. Additionally, we found that a logarithmic function better fit species accumulation data for seasonally ponded wetlands whereas a power function better fit accumulations for permanently and semi-permanently ponded wetlands. Thus, interpretations of ecological diversity measures, and conservation decisions that rely on these interpretations, can be biased if temporal variations in community composition are not adequately represented.
","language":"English","publisher":"Springer","doi":"10.1007/s10531-019-01756-1","usgsCitation":"Mushet, D.M., Solensky, M.J., and Erickson, S.F., 2019, Temporal gamma-diversity meets spatial alpha-diversity in dynamically varying ecosystems: Biodiversity and Conservation, v. 28, p. 1783-1797, https://doi.org/10.1007/s10531-019-01756-1.","productDescription":"15 p.","startPage":"1783","endPage":"1797","ipdsId":"IP-101040","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":458843,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10531-019-01756-1","text":"Publisher Index Page"},{"id":382738,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake Study Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -100.69725036621094,\n 47.848187594394815\n ],\n [\n -100.64849853515625,\n 47.848187594394815\n ],\n [\n -100.64849853515625,\n 47.884348247770006\n ],\n [\n -100.69725036621094,\n 47.884348247770006\n ],\n [\n -100.69725036621094,\n 47.848187594394815\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"28","noUsgsAuthors":false,"publicationDate":"2019-04-04","publicationStatus":"PW","contributors":{"authors":[{"text":"Mushet, David M. 0000-0002-5910-2744 dmushet@usgs.gov","orcid":"https://orcid.org/0000-0002-5910-2744","contributorId":1299,"corporation":false,"usgs":true,"family":"Mushet","given":"David","email":"dmushet@usgs.gov","middleInitial":"M.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":809215,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Solensky, Matthew J. 0000-0003-4376-7765 msolensky@usgs.gov","orcid":"https://orcid.org/0000-0003-4376-7765","contributorId":4784,"corporation":false,"usgs":true,"family":"Solensky","given":"Matthew","email":"msolensky@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":809216,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Erickson, Shay F. 0000-0002-5378-9821","orcid":"https://orcid.org/0000-0002-5378-9821","contributorId":248466,"corporation":false,"usgs":false,"family":"Erickson","given":"Shay","email":"","middleInitial":"F.","affiliations":[{"id":49922,"text":"USGS/NPWRC Student Contractor","active":true,"usgs":false}],"preferred":false,"id":809217,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204591,"text":"70204591 - 2019 - Bottom trawl assessment of Lake Ontario prey fishes","interactions":[],"lastModifiedDate":"2020-05-29T17:27:53.041846","indexId":"70204591","displayToPublicDate":"2020-03-31T12:25:14","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"12","title":"Bottom trawl assessment of Lake Ontario prey fishes","docAbstract":"Collaborative Lake Ontario bottom trawl surveys, led by the United States Geological Survey (USGS), provide science and management information for evaluating Fish Community Objectives including predator-prey balance and prey fish community diversity. In 2018, the New York State Department of Environmental Conservation (NYSDEC), Ontario Ministry of Natural Resources and Forestry (OMNR), and the (USGS) completed an April bottom trawl survey (n = 208 tows) and an October survey (n = 118 tows), at depths 6-228 m, and captured 384,651 fish from 31 species. Alewife were 80% of the total catch by number and round goby, deepwater sculpin, and rainbow smelt comprised 12, 4, and 3% of the catch, respectively. The adult alewife abundance index for U.S. waters decreased in 2018 relative to 2017, while the index in Canadian waters increased. While lake wide density increased, biomass indices for Age-2 alewife decreased. Alewife condition indices were below the 10-year average for both the April and October indices. The 2018 Age-1 alewife abundance index, which measures reproductive success the previous year, was the third lowest observed in U.S. waters over the past 22 years. The Canadian Age-1 index 2018 value was four-times larger than the U.S. value. Within-year differences between Canadian and U.S. alewife abundance indices, highlight the importance of assessing Lake Ontario fishes at a whole-lake scale. Abundance indices for rainbow smelt, threespine stickleback and emerald shiner were similar to 2017. New experimental trawl sites in embayment habitats generally captured more species, a higher proportion of native species, and higher densities relative to similar depth sites in the main lake and regions adjacent to embayments. Pelagic prey fish diversity continues to be low because a single species, alewife, dominates the catch. Deepwater sculpin and round goby were the most abundant demersal (bottom-oriented) prey fishes in 2018. Slimy sculpin and native nearshore demersal prey fishes, which were historically more abundant in trawl catches, are rare and restricted to specific habitats, since round goby proliferation. Despite declines in some species, demersal prey fish community diversity continues to increase as deepwater sculpin and round goby comprise more even portions of the community in contrast to when a single species, slimy sculpin, dominated the community. Five bloater were captured in the 2018 surveys which is the largest number captured in Lake Ontario since restoration stocking began in 2012.
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State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":767663,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Holden, Jeremy","contributorId":139654,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","affiliations":[{"id":12864,"text":"OMNRF","active":true,"usgs":false}],"preferred":false,"id":767665,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204147,"text":"70204147 - 2019 - 2017 Status of the Lake Ontario Lower Trophic Levels","interactions":[],"lastModifiedDate":"2023-05-09T14:18:16.489349","indexId":"70204147","displayToPublicDate":"2020-03-31T10:38:58","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":2,"text":"State or Local Government Series"},"seriesTitle":{"id":5114,"text":"NYSDEC Lake Ontario Annual Report ","active":true,"publicationSubtype":{"id":2}},"title":"2017 Status of the Lake Ontario Lower Trophic Levels","docAbstract":"Significant Findings for Year 2017:\n \n1)\tOffshore spring total phosphorus (TP) in 2017 was 4.4 µg/L; values remained stable since 2001. Offshore soluble reactive phosphorus (SRP) remained low (1.1 µg/L) in 2017; Apr/May – Oct mean values have been stable in nearshore and offshore habitats since 1998 (range, 0.4 – 3.3 µg/L). Apr/May – Oct mean TP concentrations were low at both nearshore and offshore locations (range, 3.7 – 9.0 µg/L). TP and SRP concentrations were significantly higher in nearshore compared to offshore habitats (7.9 µg/L vs 5.3 µg/L, TP; 1.7 µg/L vs 1.0 µg/L, SRP). \n2)\tChlorophyll-a and Secchi depth values are indicative of oligotrophic conditions in nearshore and offshore habitats. Offshore summer chlorophyll-a was stable 2000 – 2017. Nearshore chlorophyll-a increased 1995 - 2004 but then declined 2005 – 2015; values were above the long-term mean for 2016 and 2017. In 2017, epilimnetic chlorophyll-a averaged between 1.2 and 2.6 μg/L across sites, and offshore and nearshore Apr/May – Oct concentrations were not significantly different. Summer Secchi depth increased significantly in the offshore 2005 – 2017 and in the nearshore 1995 – 2004. There was no trend in either habitat 1995 – 2017. Apr/May – Oct Secchi depth ranged from 4.4 m to 12.5 m at individual sites and was not significantly different between offshore (8.9 m) and nearshore (5.7 m) locations.\n3)\tIn 2017, nearshore summer zooplankton biomass was at an all-time low (10.3 mg/m3). Apr/May – Oct epilimnetic zooplankton density was not different between the offshore and the nearshore, but zooplankton size and biomass were significantly higher in the offshore. (0.7 mm vs 0.52 mm and 14.2 mg/m3 vs 7.7 mg/m3). Daphnid, calanoid copepod, and cyclopoid biomass were all higher in the offshore. \n4)\tPeak (July) epilimnetic biomass of Cercopagis was 2.5 mg/m3 in the nearshore and 1.6 mg/m3 in the offshore. Peak (September/October) epilimnetic biomass of Bythotrephes was 0.9 mg/m3 in the nearshore and 0.5 mg/m3 in the offshore. Bythotrephes biomass has increased significantly in the nearshore, 1995 – 2017.\n5)\tSummer nearshore zooplankton density and biomass declined significantly 1995 – 2004 and then remained stable 2005 – 2017. The decline was due mainly to reductions cyclopoids. \n6)\tSummer epilimnetic offshore zooplankton density and biomass increased significantly 2005 – 2017, due mainly to increases in cyclopoids and daphnids. In 2017, offshore summer epilimnetic zooplankton biomass was 14 mg/m3—well below the mean from 2005 – 2016 (21 mg/m3).\n7)\tMost offshore zooplankton biomass was found in the metalimnion in July and September, and in the hypolimnion in October. Limnocalanus and cyclopoids dominated the metalimnion in July while daphnids and cyclopoids comprised most of the biomass in September. Daphnids dominated the October hypolimnion. Whole water column samples show a declining zooplankton biomass 2015 – 2017. Bythotrephes biomass in whole water column tows is the highest it has been, 2010 – 2017.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"NYSDEC Lake Ontario Annual Report 2019","largerWorkSubtype":{"id":2,"text":"State or Local Government Series"},"language":"English","publisher":"New York State Department of Environmental Conservation","usgsCitation":"Holeck, K.T., Rudstam, L.G., Hotaling, C., McCullough, R., Lemon, D., Pearsall, W., Lantry, J., Connerton, M., Legard, C., LaPan, S., Biesinger, Z., Lantry, B.F., and Weidel, B., 2019, 2017 Status of the Lake Ontario Lower Trophic Levels: NYSDEC Lake Ontario Annual Report , 30 p.","productDescription":"30 p.","startPage":"Addendum-1","endPage":"Addendum-30","ipdsId":"IP-100108","costCenters":[{"id":324,"text":"Great Lakes Science 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concept","interactions":[],"lastModifiedDate":"2021-08-17T13:03:52.918818","indexId":"70223178","displayToPublicDate":"2020-03-20T08:01:35","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Left out in the rain: Comparing productivity of two associated species exposes a leak in the umbrella species concept","docAbstract":"Multi-species approaches to wildlife management have become commonplace and purport to benefit entire biological communities. These strategies aim to manage different, often taxonomically distant species under a single regime based on shared habitat associations and/or co-occurrence in the landscape. We tested the efficacy of multi-species management in the context of creating and maintaining early-successional forest cover types using two species of migratory birds that breed in eastern North America and are each the focus of intensive, concurrent, and overlapping management. American woodcock (Scolopax minor) and golden-winged warblers (Vermivora chrysoptera) breed in similar diverse-forest landscapes. Each species purportedly benefits from management for the other species and both are often used as flagship species for the creation of young forest and the conservation of associated avian communities. However, the landscape-species relationships that drive reproductive success and population stability in these species have not been explicitly compared. Here, we use previously published spatially-explicit models of productivity (the number of juveniles raised to a biologically significant milestone) to identify the relationship(s) between productivity of American woodcock and golden-winged warblers across a shared landscape. We found productivity to be negatively associated between these species on the same landscape at all spatial scales we modelled (1 m2–100 ha). Our results suggest that, with regards to productivity, American woodcock and golden-winged warblers have opposing relationships with the composition of the landscapes in which they coexist and therefore should not be assumed to benefit similarly from any individual management action at any relevant spatial scale.
We estimated detection probabilities of bird carcasses along sandy beaches and in marsh edge habitats in the northern Gulf of Mexico to help inform models of bird mortality associated with the Deepwater Horizon oil spill. We also explored factors that may influence detection probability, such as carcass size, amount of scavenging, location on the beach, habitat type, and distance into the marsh. Detection probability for medium-sized carcasses (200–500 g) ranged from 0.82 (SE = 0.09) to 0.93 (SE = 0.04) along sandy beaches. Within sandy beaches, we found that intact/slightly scavenged carcasses were easier to detect than heavily scavenged ones and did not find strong effects of location on the beach on detection probability. We estimated detection rate for each combination of scavenging state, carcass size, and position along sandy beaches. In marsh edge habitats, detection ranged from 0.04 (SE = 0.04) to 0.86 (SE = 0.10), with detection rates rapidly increasing from small (< 200 g) to medium carcass sizes and leveling off between medium and extra-large (> 1000 g) carcasses regardless of vegetation type (Spartina or Phragmites). Carcasses of all sizes were generally harder to locate in Spartina-dominated marshes than in Phragmites-dominated ones. A subset of the data for which we could adequately assess the effect of distance into the marsh indicated that detection rates generally declined the farther a carcass was into marsh vegetation. Based on power analyses, our ability to identify predictors that influence detection rates would be higher with larger numbers of carcasses, greater numbers of search trials per carcass, or more balanced sampling distributions across predictor values.
","language":"English","publisher":"Springer Nature","doi":"10.1007/s10661-019-7924-z","usgsCitation":"Zimmerman, G.S., Varela, V., and Yee, J.L., 2019, Detection probabilities of bird carcasses along sandy beaches and marsh edges in the northern Gulf of Mexico: Environmental Monitoring and Assessment, v. 191, no. suppl 4, 816, 15 p., https://doi.org/10.1007/s10661-019-7924-z.","productDescription":"816, 15 p.","ipdsId":"IP-094446","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":458847,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s10661-019-7924-z","text":"Publisher Index Page"},{"id":375518,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.2352294921875,\n 29.544787796199465\n ],\n [\n -94.0814208984375,\n 29.640320395351402\n ],\n [\n -93.61450195312499,\n 29.72145191669099\n ],\n [\n -93.218994140625,\n 29.754839972510933\n ],\n [\n -92.5213623046875,\n 29.559123451577964\n ],\n [\n -92.2247314453125,\n 29.501768632523262\n ],\n [\n -92.08740234375,\n 29.559123451577964\n ],\n [\n -91.8841552734375,\n 29.482643134466617\n ],\n [\n -91.1810302734375,\n 29.204918463909035\n ],\n [\n -90.5438232421875,\n 29.16655229520015\n ],\n [\n -90.3076171875,\n 29.28160772298835\n ],\n [\n -90.2691650390625,\n 29.017748018496047\n ],\n [\n -89.9395751953125,\n 29.176145182559758\n ],\n [\n -89.70886230468749,\n 29.3295093083647\n ],\n [\n -89.132080078125,\n 28.93124697186731\n ],\n [\n -88.978271484375,\n 29.257648503615542\n ],\n [\n -89.1485595703125,\n 30.12612436422458\n ],\n [\n -89.1925048828125,\n 30.311245603935003\n ],\n [\n -88.65966796875,\n 30.306503259848835\n ],\n [\n -88.3245849609375,\n 30.372875188118016\n ],\n [\n -88.1927490234375,\n 30.306503259848835\n ],\n [\n -87.77526855468749,\n 30.14512718337613\n ],\n [\n -87.3797607421875,\n 30.292274851024256\n ],\n [\n -86.517333984375,\n 30.358656420788783\n ],\n [\n -85.8251953125,\n 30.183121842195515\n ],\n [\n -85.45166015624999,\n 29.935895213372444\n ],\n [\n -85.3143310546875,\n 30.050076521698735\n ],\n [\n -85.7977294921875,\n 30.37761431777479\n ],\n [\n -86.3031005859375,\n 30.519681272749402\n ],\n [\n -87.0172119140625,\n 30.62845887475364\n ],\n [\n -87.4566650390625,\n 30.519681272749402\n ],\n [\n -88.39050292968749,\n 30.50548389892728\n ],\n [\n -89.1925048828125,\n 30.48181700282728\n ],\n [\n -89.62646484375,\n 30.330212685432734\n ],\n [\n -90.362548828125,\n 29.859701442126756\n ],\n [\n -90.68115234375,\n 29.506549442788593\n ],\n [\n -91.2579345703125,\n 29.654642479663647\n ],\n [\n -91.7962646484375,\n 29.92637417863576\n ],\n [\n -92.30712890625,\n 29.78821690967894\n ],\n [\n -93.3673095703125,\n 29.88351825335318\n ],\n [\n -94.47143554687499,\n 29.716681287231072\n ],\n [\n -94.2352294921875,\n 29.544787796199465\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"191","issue":"suppl 4","noUsgsAuthors":false,"publicationDate":"2020-03-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Zimmerman, Guthrie S.","contributorId":42473,"corporation":false,"usgs":false,"family":"Zimmerman","given":"Guthrie","email":"","middleInitial":"S.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":790710,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Varela, Veronica","contributorId":225184,"corporation":false,"usgs":false,"family":"Varela","given":"Veronica","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":790711,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":790712,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70207596,"text":"sir20195149 - 2019 - An update of hydrologic conditions and distribution of selected constituents in water, Eastern Snake River Plain aquifer and perched groundwater zones, Idaho National Laboratory, Idaho, emphasis 2016–18","interactions":[],"lastModifiedDate":"2022-04-25T20:30:32.652286","indexId":"sir20195149","displayToPublicDate":"2020-02-18T10:32:38","publicationYear":"2019","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":"2019-5149","displayTitle":"An Update of Hydrologic Conditions and Distribution of Selected Constituents in Water, Eastern Snake River Plain Aquifer and Perched Groundwater Zones, Idaho National Laboratory, Idaho, Emphasis 2016–18","title":"An update of hydrologic conditions and distribution of selected constituents in water, Eastern Snake River Plain aquifer and perched groundwater zones, Idaho National Laboratory, Idaho, emphasis 2016–18","docAbstract":"Since 1952, wastewater discharged to infiltration ponds (also called percolation ponds) and disposal wells at the Idaho National Laboratory (INL) has affected water quality in the eastern Snake River Plain (ESRP) aquifer and perched groundwater zones underlying the INL. The U.S. Geological Survey (USGS), in cooperation with the U.S. Department of Energy, maintains groundwater-monitoring networks at the INL to determine hydrologic trends and to delineate the movement of radiochemical and chemical wastes in the aquifer and in perched groundwater zones. This report presents an analysis of water-level and water-quality data collected from the ESRP aquifer and perched groundwater wells in the USGS groundwater monitoring networks during 2016–18.
From March–May 2015 to March–May 2018, water levels in wells completed in the ESRP aquifer declined in the northern part of the INL and increased in the southwestern part. Water-level decreases ranged from 0.5 to 3.0 feet (ft) in the northern part of the INL and increases ranged from 0.5 to 3.0 ft in the southwestern part.
Detectable concentrations of radiochemical constituents in water samples from wells in the ESRP aquifer at the INL generally decreased or remained constant during 2016–18. Decreases in concentrations were attributed to radioactive decay, changes in waste-disposal methods, and dilution from recharge and underflow.
In 2018, concentrations of tritium in water samples collected from 46 of 111 aquifer wells were greater than the reporting level of three times the sample standard deviation and ranged from 260±50 to 5,100±190 picocuries per liter (pCi/L). Tritium concentrations in water from 10 wells completed in deep perched groundwater above the ESRP aquifer near the Advanced Test Reactor (ATR) Complex generally were greater than or equal to the reporting level during at least one sampling event during 2016–18, and concentrations ranged from 150 ±50 to 12,900 ±200 pCi/L.
Concentrations of strontium-90 in water from 17 of 60 ESRP aquifer wells sampled during April or October 2018 exceeded the reporting level, ranging from 2.2±0.7 to 363±19 pCi/L. Strontium-90 was not detected in the ESRP aquifer beneath the ATR Complex. During at least one sampling event during 2016–18, concentrations of strontium-90 in water from eight wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex equaled or exceeded the reporting levels, and concentrations ranged from 0.57±0.17 to 34.3±1.2 pCi/L.
During 2016–18, concentrations of cesium-137 were less than the reporting level in all but one ESRP aquifer well, and concentrations of plutonium-238, -239, and -240 (undivided), and americium-241 were less than the reporting level in water samples from all ESRP aquifer wells.
In April 2009, the dissolved chromium concentration in water from one ESRP aquifer well, USGS 65, south of the ATR Complex equaled the maximum contaminant level (MCL) of 100 micrograms per liter (μg/L). In April 2018, the concentration of chromium in water from that well had decreased to 76.0 μg/L, less than the MCL. Concentrations in water samples from 62 other ESRP aquifer wells sampled ranged from less than 0.6 to 21.6 μg/L. During 2016–18, dissolved chromium was detected in water from all wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex, and concentrations ranged from 4.2 to 98.8 μg/L.
In 2018, concentrations of sodium in water from most ESRP aquifer wells in the southern part of the INL were greater than the western tributary background concentration of 8.3 milligrams per liter (mg/L). After the new percolation ponds were put into service in 2002 southwest of the Idaho Nuclear Technology and Engineering Center (INTEC), concentrations of sodium in water samples from the Rifle Range well increased steadily until 2008, when concentrations generally began decreasing. The increases and decreases were attributed to disposal variability in the new percolation ponds. During 2016–18, dissolved sodium concentrations in water from 18 wells completed in deep perched groundwater above the ESRP aquifer at the ATR Complex ranged from 6.37 to 143 mg/L.
In 2018, concentrations of chloride in most water samples from ESRP aquifer wells south of the INTEC and at the Central Facilities Area exceeded the background concentrations. Chloride concentrations in water from wells south of the INTEC generally have decreased since 2002 when chloride disposal to the old percolation ponds was discontinued. After the new percolation ponds southwest of the INTEC were put into service in 2002, concentrations of chloride in water samples from one well rose steadily until 2008 then began decreasing. During 2016–18, dissolved chloride concentrations in deep perched groundwater above the ESRP aquifer from 18 wells at the ATR Complex ranged from 3.89 to 176 mg/L.
In 2018, sulfate concentrations in water samples from ESRP aquifer wells in the south-central part of the INL exceeded the background concentration of sulfate and ranged from 22 to 151 mg/L. The greater-than-background concentrations in water from these wells probably resulted from sulfate disposal at the ATR Complex infiltration ponds or the old INTEC percolation ponds. In 2018, sulfate concentrations in water samples from wells near the Radioactive Waste Management Complex (RWMC) mostly were greater than background concentrations and could have resulted from well construction techniques and (or) waste disposal at the RWMC or the ATR complex. The maximum dissolved sulfate concentration in shallow perched groundwater above the ESRP aquifer near the ATR Complex was 215 mg/L in well CWP 3 in April 2016. During 2018, dissolved sulfate concentrations in water from wells completed in deep perched groundwater above the ESRP aquifer near the cold-waste ponds at the ATR Complex ranged from 65.8 to 171 mg/L.
In 2018, concentrations of nitrate in water from most ESRP aquifer wells at and near the INTEC exceeded the western tributary background concentration of 0.655 mg/L. Concentrations of nitrate in wells southwest of the INTEC and farther away from the influence of disposal areas and the Big Lost River show a general decrease in nitrate concentration through time. Two wells south of the INTEC show increasing trends that could be the result of wastewater beneath the INTEC tank farm being mobilized to the aquifer.
During 2016–18, water samples from several ESRP aquifer wells were collected and analyzed for volatile organic compounds (VOCs). Sixteen VOCs were detected. At least 1 and as many as 7 VOCs were detected in water samples from 15 wells. The primary VOCs detected include carbon tetrachloride, trichloromethane, tetrachloroethene, 1,1,1-trichloroethane, and trichloroethene. In 2016–18, concentrations for all VOCs were less than their respective MCLs for drinking water, except carbon tetrachloride in water from two wells and trichloroethene in one well.
During 2016–18, variability and bias were evaluated from 37 replicate and 15 blank quality-assurance samples. Results from replicate analyses were investigated to evaluate sample variability. Constituents with acceptable reproducibility were major ions, trace elements, nutrients, and VOCs. All radiochemical constituents had acceptable reproducibility except for gross alpha- and beta-particle radioactivity. The gross alpha- and beta-particle radioactivity samples that did not meet reproducibility criteria had low concentrations. Bias from sample contamination was evaluated from equipment, field, and source-solution blanks. Cadmium had a concentration slightly greater than its reporting level in a source-solution blank, and chloride and ammonia had concentrations that were slightly greater than their respective reporting levels in field and equipment blanks. Subtracting concentrations of chloride and ammonia in field blanks from the concurrently collected equipment blank indicates that adjusted concentrations for chloride and ammonia in the equipment blanks were less than their respective reporting levels. Therefore, no sample bias was observed for any of the sample periods.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195149","collaboration":"DOE/ID-22251Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
Bottomhole temperature (BHT) measurements offer a useful way to characterize the subsurface thermal regime as long as they are corrected to represent in situ reservoir temperatures. BHT correction methods calibrated for the domestic onshore Gulf of Mexico basin were established in this study. These corrections are empirically derived and based on newly compiled databases of BHT wireline measurements and, to a lesser extent, drill stem test data. A unified BHT correction for the onshore Gulf Coast region, as well as 12 distinct BHT correction equations for each of the 12 physiographic provinces within the onshore Gulf Coast region, are provided. This study also characterizes the geothermal gradient across the onshore Gulf of Mexico basin, which ranges from 1.89 degrees Fahrenheit per 100 feet in the Sabine Uplift area to 1.39 degrees Fahrenheit per 100 feet in the Southern Louisiana Salt Basin. This report disseminates the slides presented at the 68th annual convention of the Gulf Coast Association of Geological Societies and the Gulf Coast Section of the Society of Economic Paleontologists and Mineralogists that was held September 30–October 2, 2018, in Shreveport, Louisiana.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/ofr20191143","usgsCitation":"Burke, L.A., Pearson, O.N., and Kinney, S.A., 2020, New method for correcting bottomhole temperatures acquired from wireline logging measurements and calibrated for the onshore Gulf of Mexico basin, U.S.A.: U.S. Geological Survey Open-File Report 2019–1143, 12 p., https://doi.org/10.3133/ofr20191143.","productDescription":"iii, 12 p.","onlineOnly":"Y","ipdsId":"IP-102769","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":399430,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109663.htm"},{"id":371939,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1143/ofr20191143.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1143"},{"id":371937,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1143/coverthb.jpg"}],"country":"United States","state":"Alabama, Arkansas, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.06640625,\n 30.675715404167743\n ],\n [\n -87.978515625,\n 31.50362930577303\n ],\n [\n -87.8466796875,\n 32.194208672875384\n ],\n [\n -91.23046875,\n 33.394759218577995\n ],\n [\n -95.64697265625,\n 33.52307880890422\n ],\n [\n -97.470703125,\n 33.30298618122413\n ],\n [\n -98.3056640625,\n 30.694611546632277\n ],\n [\n -98.10791015625,\n 29.611670115197377\n ],\n [\n -100.4150390625,\n 28.786918085420226\n ],\n [\n -99.6240234375,\n 27.566721430409707\n ],\n [\n -99.404296875,\n 27.15692045688088\n ],\n [\n -99.11865234374999,\n 26.43122806450644\n ],\n [\n -98.63525390624999,\n 26.27371402440643\n ],\n [\n -98.15185546874999,\n 26.05678288577881\n ],\n [\n -97.36083984375,\n 25.918526162075153\n ],\n [\n -97.0751953125,\n 25.918526162075153\n ],\n [\n -96.87744140625,\n 27.449790329784214\n ],\n [\n -95.03173828125,\n 28.76765910569123\n ],\n [\n -93.3837890625,\n 29.477861195816843\n ],\n [\n -91.99951171875,\n 29.19053283229458\n ],\n [\n -90.703125,\n 28.9600886880068\n ],\n [\n -89.62646484375,\n 28.459033019728043\n ],\n [\n -87.7587890625,\n 29.132970130878636\n ],\n [\n -88.06640625,\n 30.675715404167743\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Central Energy Resources Science Center
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Given the predicted imbalance between water supply and demand in the Southwest region of the United States, and the widespread problems with excessive nutrients and suspended sediment, there is a growing need to quantify current streamflow and water quality conditions throughout the region. Furthermore, current monitoring stations exist at a limited number of locations, and many streams lack streamflow and water quality information. SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed for hydrologic conditions representative of 2012 in order to understand how climate, land use, and other landscape characteristics control the yields of water, total nitrogen, total phosphorus, and suspended sediment across the Southwest region. The calibration data (mean annual streamflow and loads) for each of the four SPARROW models were based on continuous streamflow and discrete water-quality observations from throughout the region. Explanatory variables for the models consisted of regional datasets representing a range of potential sources of streamflow, nitrogen, phosphorous, and sediment, and processes that control the transport from land to water and attenuate loads within streams and waterbodies. Calibration and explanatory data were referenced to a surface water drainage network that allowed for routing and transport of water and loads through the region. The model results showed that wastewater discharge is the largest contributor to total nitrogen and total phosphorus yield from the Southwest region and forest land is the largest contributor to suspended-sediment yield, but that other sources such as atmospheric nitrogen deposition, agricultural runoff, and runoff from developed land are locally important across the region. The results from this study could complement research and inform water-quality management activities in the Southwest region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195106","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., Anning, D.W., and Miller, O.L., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment transport in streams of the southwestern United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5106, 66 p., https://doi.org/10.3133/sir20195106.","productDescription":"Report: viii, 66 p.; Data Release","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-105772","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":437233,"rank":10,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P94EKLPP","text":"USGS data release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 Base Year (ver. 2.0, October 2020)"},{"id":370350,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5106/coverthb.jpg"},{"id":371969,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5106/sir20195106.pdf","text":"Report","size":"21.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5106"},{"id":370352,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9GFLBG8","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southwestern United States, 2012 base year"},{"id":370377,"rank":4,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southwest-2012","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370703,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370704,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370705,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":376099,"rank":9,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2019/5106/VersionHist.txt"},{"id":370706,"rank":8,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"country":"United States","otherGeospatial":"Southwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.927734375,\n 32.39851580247402\n ],\n [\n -114.873046875,\n 32.76880048488168\n ],\n [\n -111.09374999999999,\n 31.203404950917395\n ],\n [\n -108.28125,\n 31.052933985705163\n ],\n [\n -108.19335937499999,\n 32.02670629333614\n ],\n [\n -105.8203125,\n 30.977609093348686\n ],\n [\n -103.447265625,\n 28.76765910569123\n ],\n [\n -102.83203125,\n 29.6880527498568\n ],\n [\n -101.689453125,\n 29.76437737516313\n ],\n [\n -100.1953125,\n 27.916766641249065\n ],\n [\n -98.349609375,\n 26.115985925333536\n ],\n [\n -97.20703125,\n 26.509904531413927\n ],\n [\n -96.767578125,\n 28.22697003891834\n ],\n [\n -93.515625,\n 29.53522956294847\n ],\n [\n -99.49218749999999,\n 33.87041555094183\n ],\n [\n -103.623046875,\n 38.8225909761771\n ],\n [\n -104.94140625,\n 40.245991504199026\n ],\n [\n -111.97265625,\n 42.94033923363181\n ],\n [\n -113.90625,\n 41.376808565702355\n ],\n [\n -116.806640625,\n 41.77131167976407\n ],\n [\n -118.740234375,\n 44.213709909702054\n ],\n [\n -120.498046875,\n 44.59046718130883\n ],\n [\n -120.84960937499999,\n 41.44272637767212\n ],\n [\n -118.65234374999999,\n 36.10237644873644\n ],\n [\n -115.927734375,\n 32.39851580247402\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: January 7, 2020; Version 1.1: June 2, 2020","contact":"NAWQA Science Team
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12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
In this report, SPAtially Referenced Regression On Watershed attributes (SPARROW) models developed to describe long-term (2000–14) mean-annual streamflow, total nitrogen (TN), total phosphorus (TP), and suspended-sediment (SS) transport in streams of the Midwestern part of the United States (the Mississippi River, Great Lakes, and Red River of the North Basins) are described. The nutrient and suspended-sediment models have a base year of 2012, which means they were developed based on source inputs and management practices similar to those existing during or near 2012 and average hydrological conditions detrended to 2012 (2000–14), whereas the streamflow model has base years of 2000–14, which means it was developed based on the average input precipitation minus actual evapotranspiration from 2000 to 2014. In developing the models, several updates and improvements were made to the data inputs and statistical approaches used to calibrate/develop the models from those used in the previous 2002 SPARROW models. The 2012 SPARROW models were constructed using a higher resolution stream network, which resulted in a mean catchment size of 2.7 square kilometers compared to 480 square kilometers in the 2002 models; more detailed and updated wastewater treatment plant contribution estimates; inputs from background phosphorus sources that were not included in the 2002 model; and more accurate loads for calibration that were computed using a modified Beale ratio-estimator technique whenever no trend in load was determined. Statistical approaches were added to compensate for the unequal effect of each monitoring site during the calibration process by adjusting for the fraction of the basin included in other upstream monitored sites (nested share) and thinning the calibration sites if a negative statistical correlation between nearby sites was determined.
Results from 2012 SPARROW models describe how much of each water, TN, TP, and SS source was delivered to the stream network, and the major landscape factors that affected their delivery. Atmospheric deposition and natural (background) sources of TN and TP, respectively, were the dominant sources in anthropogenically unaffected areas (especially in the Rocky Mountains and north-central areas of the Midwest), whereas fertilizers, manure, and fixation were dominant sources in agricultural areas, especially in the Corn Belt and near the Mississippi River. Urban sources of TN and TP were typically localized, but they were still important for some large areas, especially the Lake Erie Basin. All of the land-to-water delivery variables in the nutrient and sediment SPARROW models, such as runoff, soil erodibility, basin slope, and the amount of tile drains, are commonly included in process-driven models. In the SPARROW TN and TP models, best management practices (BMPs) reduced the delivery of these nutrients to streams.
Long-term mean-annual flows and nutrient and sediment loads were simulated in streams throughout the Midwest. The simulated flows from the SPARROW flow model were used in the SPARROW TN, TP, and SS models to help describe nutrient and sediment transport from the watershed and through the stream network. Outputs from the TN, TP, and SS models describe loads and yields of these constituents throughout the Midwest, and from major drainage basins throughout the Midwest. Highest TN, TP, and SS yields and delivered yields were from the Lake Erie, Ohio River, Upper Mississippi River, and Lower Mississippi River Basins, whereas lowest yields were spread over most other areas. Losses during downstream delivery resulted in part of the TN, TP, and SS that reach the stream network not reaching the downstream receiving bodies: 14, 15, and 28 percent of the TN, TP, and SS, respectively, are lost during delivery to the Great Lakes and 19, 23, and 52 percent of the TN, TP, and SS, respectively, are lost during delivery to the Gulf of Mexico. The largest losses of nutrients and sediments during transport were in the Missouri and Arkansas River Basins.
Information from these SPARROW models can help guide nutrient and sediment reduction strategies throughout the Midwest. Model results provide information on what may be the most appropriate general type of actions to reduce total loading by describing the relative importance of each source, and where to most efficiently place the efforts to reduce loading by describing the distribution of nutrient and sediment loading. By implementing management efforts addressing the major sources of the loads in areas contributing the highest loads, it may be possible to reduce nutrient loading throughout the Mississippi River Basin and thus reduce the size of the hypoxic zone in the Gulf of Mexico; reduce nutrient loading into lakes, and thus reduce the occurrence of harmful algal blooms; and reduce sediment losses, and thus improve the benthic habitat in streams and rivers throughout the Midwest.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195114","collaboration":"National Water Quality Program","usgsCitation":"Robertson, D.M., and Saad, D.A., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Midwestern United States: U.S. Geological Survey Scientific Investigations Report 2019–5114, 74 p. including 5 appendixes, https://doi.org/10.3133/sir20195114.","productDescription":"Report: ix, 74 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103244","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":370714,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"},{"id":370711,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370712,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":371971,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5114/sir20195114.pdf","text":"Report","size":"43.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5114"},{"id":370371,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-midwest-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370713,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":370369,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93QMXC9","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Midwestern United States, 2012 base year"},{"id":370914,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5114/coverthb3.jpg"}],"otherGeospatial":"Midwestern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.11328125,\n 44.213709909702054\n ],\n [\n -79.27734374999999,\n 43.389081939117496\n ],\n [\n -79.541015625,\n 42.61779143282346\n ],\n [\n -82.529296875,\n 41.44272637767212\n ],\n [\n -82.177734375,\n 43.068887774169625\n ],\n [\n -82.705078125,\n 45.02695045318546\n ],\n [\n -84.287109375,\n 46.6795944656402\n ],\n [\n -90,\n 47.931066347509784\n ],\n [\n -94.5703125,\n 49.210420445650286\n ],\n [\n -95.361328125,\n 49.26780455063753\n ],\n [\n -95.44921875,\n 48.922499263758255\n ],\n [\n -114.2578125,\n 49.095452162534826\n ],\n [\n -113.90625,\n 46.73986059969267\n ],\n [\n -112.236328125,\n 45.336701909968134\n ],\n [\n -110.12695312499999,\n 42.48830197960227\n ],\n [\n -106.69921875,\n 38.06539235133249\n ],\n [\n -101.42578124999999,\n 32.99023555965106\n ],\n [\n -97.20703125,\n 28.536274512989916\n ],\n [\n -92.900390625,\n 29.305561325527698\n ],\n [\n -89.912109375,\n 33.063924198120645\n ],\n [\n -79.541015625,\n 39.774769485295465\n ],\n [\n -76.11328125,\n 44.213709909702054\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Spatially Referenced Regression On Watershed attributes (SPARROW) models were applied to describe and estimate mean-annual streamflow and transport of total nitrogen (TN), total phosphorus (TP), and suspended sediment (SS) in streams and delivered to coastal waters of the southeastern United States on the basis of inputs and management practices centered near 2012, the base year of the model. Previously published TN and TP models for 2002 served as a starting point and reference for comparison. The datasets developed for the 2012 models not only represent updates of previous conditions but also incorporate new approaches for characterizing sources and transport processes that were not available for previous models.
Variability in streamflow across the southeastern United States was explained as a function of precipitation adjusted for evapotranspiration, spring discharge, and municipal and domestic wastewater discharges to streams. Results from the streamflow model were used as input to the water-quality SPARROW models, and areas with large streamflow prediction errors—urban areas and karst areas—were used to provide guidance on where additional data are needed to improve routing of flow.
Variability in TN transport in Southeast streams was explained by the following five sources in order of decreasing mass contribution to streams: atmospheric deposition, agricultural fertilizer, municipal wastewater, manure from livestock, and urban land. Variable rates of TN delivery from source to stream were attributed to variation among catchments in climate, soil texture, and vegetative cover, including the extent of cover crops in the watershed. Variability in TP transport in Southeast streams was explained by the following six sources in order of decreasing mass contribution to streams: parent-rock minerals, urban land, manure from livestock, municipal wastewater, agricultural fertilizer, and phosphate mining. Varying rates of TP delivery were attributed to variation in climate, soil erodibility, depth to water table, and the extent of conservation tillage practices in the watershed.
Variability in SS transport in Southeast streams was explained by variable sediment export rates for different combinations of land cover and geologic setting (for upland sources of sediment) and by gains in stream power caused by longitudinal changes in channel hydraulics (for channel sources of sediment). Sediment yields for the transitional land cover (shrub, scrub, herbaceous, and barren) varied widely depending on geologic setting and on agricultural land cover. Varying rates of SS delivery, like those for TP, were attributed to variation in climate, soil erodibility, and the extent of conservation tillage practices in the watershed, as well as to areal extent of canopy land cover in the 100-meter buffer along the channel. Relatively large uncertainty, compared to the other three models, for almost all the SS source coefficients indicates the need for caution when interpreting the results from the sediment model.
TN, TP, and SS inputs to streams from sources were balanced in the models with losses from physical processes in streams and reservoirs and with water withdrawals. The losses in streams and reservoirs along with withdrawals removed 35, 44, and 65 percent of the TN, TP, and SS load, respectively, that entered streams before reaching coastal waters.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195135","collaboration":"National Water Quality Program","usgsCitation":"Hoos, A.B., and Roland, V.L. II, 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in the Southeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5135, 91 p., https://doi.org/10.3133/sir20195135.","productDescription":"Report: xi, 87 p.; Data Release; HTML","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101532","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":370725,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":371973,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5135/sir20195135.pdf","text":"Report","size":"10.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5135"},{"id":370724,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370723,"rank":3,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370721,"rank":1,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9A682GW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Southeastern United States, 2012 base year"},{"id":370722,"rank":2,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-southeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370726,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195118","text":"SIR 2019–5118","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Northeastern United States"},{"id":371031,"rank":7,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5135/coverthb3.jpg"}],"otherGeospatial":"Southeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.2890625,\n 37.19533058280065\n ],\n [\n -81.34277343749999,\n 37.47485808497102\n ],\n [\n -83.9794921875,\n 34.95799531086792\n ],\n [\n -88.8134765625,\n 33.8339199536547\n ],\n [\n -90.1318359375,\n 33.43144133557529\n ],\n [\n -90.2197265625,\n 30.44867367928756\n ],\n [\n -88.154296875,\n 30.29701788337205\n ],\n [\n -85.69335937499999,\n 29.878755346037977\n ],\n [\n -84.72656249999999,\n 29.49698759653577\n ],\n [\n -84.111328125,\n 29.84064389983441\n ],\n [\n -82.96875,\n 27.955591004642553\n ],\n [\n -82.5732421875,\n 26.54922257769204\n ],\n [\n -80.9033203125,\n 25.20494115356912\n ],\n [\n -80.0244140625,\n 25.3241665257384\n ],\n [\n -79.8486328125,\n 27.527758206861886\n ],\n [\n -80.85937499999999,\n 30.031055426540206\n ],\n [\n -80.771484375,\n 31.615965936476076\n ],\n [\n -78.57421875,\n 33.50475906922609\n ],\n [\n -77.16796875,\n 33.97980872872457\n ],\n [\n -76.11328125,\n 34.95799531086792\n ],\n [\n -75.41015624999999,\n 35.96022296929667\n ],\n [\n -75.8056640625,\n 36.80928470205937\n ],\n [\n -76.2890625,\n 37.19533058280065\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
SPAtially Referenced Regression On Watershed attributes (SPARROW) models were developed to quantify and improve the understanding of the sources, fate, and transport of nitrogen, phosphorus, and suspended sediment in the northeastern United States. Excessive nutrients and suspended sediment from upland watersheds and tributary streams have contributed to ecological and economic degradation of northeastern surface waters. Recent efforts to reduce the flux of nutrients and suspended sediment in northeastern streams and to downstream estuaries have met with mixed results, and expected ecological improvements have been observed in some areas but not in others. Effective watershed management and restoration to improve surface-water quality are complicated by the multitude of nutrient sources in the Northeast and the multitude of natural and human landscape processes affecting the delivery of nutrients and suspended sediment from upland areas to and within surface waters. Individual models were constructed representing streamflow and the loads of total nitrogen, total phosphorus, and suspended sediment from watersheds draining to the Atlantic Ocean from southern Virginia through Maine.
Northeastern streams contribute 303,000 metric tons (t) of nitrogen, 25,300 t of phosphorus, and 14,700,000 t of suspended sediment, annually (on average), to waters along the Atlantic Coast of North America. Although atmospheric deposition and natural mineral erosion contribute to nitrogen and phosphorus loads, respectively, in northeastern streams, most of the contributions are attributable to urban or agricultural sources. Within the Northeast, average yields of nutrients are therefore generally greater from densely populated or intensively cultivated areas of the mid-Atlantic region, the Hudson, Mohawk, and Connecticut River valleys, and the coastal areas of southern New England than in predominantly forested areas such as northern New England. Average upland sediment yields are similarly greater from agricultural areas than from urban or forested areas and are therefore generally greatest in areas yielding the greatest nutrients. Landscape conditions that are significant to nitrogen delivery from uplands to streams likely reflect the importance of groundwater transport in carbonate settings and of denitrification for removing nitrogen from uplands. Nitrogen losses to streams in agricultural areas are apparently mitigated by the use of cover crops but are exacerbated by the use of conservation tillage or no-till practices. The transport of phosphorus and suspended sediment from uplands to streams is greater in areas of more erodible soils but mitigated in agricultural areas with greater use of conservation tillage or no-till practices. Loads of nutrients and suspended sediment are significantly reduced within the stream network in impounded reaches, and nitrogen load is also significantly reduced in small flowing reaches.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195118","collaboration":"National Water Quality Program","usgsCitation":"Ator, S.W., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Northeastern United States: U.S. Geological Survey Scientific Investigations Report 2019–5118, 57 p., https://doi.org/10.3133/sir20195118.","productDescription":"Report: ix, 57 p.; Data Release","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103253","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":371972,"rank":8,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5118/sir20195118.pdf","text":"Report","size":"31.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5118"},{"id":370715,"rank":4,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195106","text":"SIR 2019–5106","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southwestern United States"},{"id":370717,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195114","text":"SIR 2019–5114","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Midwestern United States"},{"id":370376,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://sparrow.wim.usgs.gov/sparrow-northeast-2012/","text":"Mapping application","linkHelpText":"– Online mapping tool to explore 2012 SPARROW Models"},{"id":370375,"rank":2,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9NKNVQO","text":"USGS data release","description":"USGS Data Release","linkHelpText":"SPARROW model inputs and simulated streamflow, nutrient and suspended-sediment loads in streams of the Northeastern United States, 2012 base year"},{"id":370917,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5118/coverthb4.jpg"},{"id":370716,"rank":5,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195112","text":"SIR 2019–5112","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Pacific Region of the United States"},{"id":370718,"rank":7,"type":{"id":7,"text":"Companion Files"},"url":"https://doi.org/10.3133/sir20195135","text":"SIR 2019–5135","linkHelpText":"– Spatially Referenced Models of Streamflow and Nitrogen, Phosphorus, and Suspended-Sediment Loads in Streams of the Southeastern United States"}],"otherGeospatial":"Northeastern United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.81640625,\n 37.43997405227057\n ],\n [\n -75.05859375,\n 37.37015718405753\n ],\n [\n -73.564453125,\n 40.38002840251183\n ],\n [\n -71.455078125,\n 40.64730356252251\n ],\n [\n -70.048828125,\n 42.032974332441405\n ],\n [\n -70.48828125,\n 43.32517767999296\n ],\n [\n -69.169921875,\n 43.77109381775651\n ],\n [\n -66.884765625,\n 44.77793589631623\n ],\n [\n -68.02734375,\n 47.21956811231547\n ],\n [\n -69.43359375,\n 47.338822694822\n ],\n [\n -70.13671875,\n 45.521743896993634\n ],\n [\n -74.970703125,\n 44.84029065139799\n ],\n [\n -77.080078125,\n 43.32517767999296\n ],\n [\n -79.8046875,\n 40.38002840251183\n ],\n [\n -82.001953125,\n 38.61687046392973\n ],\n [\n -76.81640625,\n 37.43997405227057\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"NAWQA Science Team
U.S. Geological Survey
12201 Sunrise Valley Drive, MS 413
Reston, VA 20192–0002
Although spatial information describing the supply and quality of surface water is critical for managing water resources for human uses and for ecological health, monitoring is expensive and cannot typically be done over large scales or in all streams or waterbodies. To address the need for such data, the U.S. Geological Survey developed SPAtially Referenced Regression On Watershed attributes (SPARROW) for the Pacific region of the U.S. for streamflow and three water-quality constituents–total nitrogen, total phosphorus, and suspended sediment, based on a decadal time frame centered on the year 2012. The domain for these models included the Columbia River basin, the Puget Sound, the coastal drainages of Washington, Oregon, and California, and the Central Valley of California. Landscape runoff (represented by the difference between precipitation and evapotranspiration) was the largest source of streamflow, wastewater discharge, and atmospheric deposition were the largest contributors to total nitrogen yield from the Pacific region, wastewater discharge was the largest contributor to total phosphorus yield, and forest land was the largest contributor to suspended-sediment yield. Watersheds with relatively high water yields also generally had relatively high yields of total nitrogen, total phosphorous, and suspended sediment–except where there were large contributions from developed land and wastewater discharge.
The data used in this study, including many that improved upon existing national data or were compiled specifically for the Pacific region, characterized the complex hydrologic and water-quality conditions in the region more completely than previous models. By using these new datasets, this investigation was able to account for the complex network of water diversions and transfers, quantify the contribution of nutrients from different sources of livestock manure, discern a signal from unpaved logging roads in the suspended-sediment yields from forested coastal watersheds, show how recent wildfire disturbance influences phosphorus and sediment delivery to streams, and how sediment delivery to streams is also sensitive to the intensity of cattle grazing. The results from this study could complement research and inform water-quality management activities in the Pacific region. Examples might include identifying potentially impaired waterbodies and guiding remediation efforts where impairment has been documented, explaining the spatial patterns in harmful algal blooms, and providing estimates of sediment and nutrient loadings to Pacific coast estuaries where such data are scarce or non-existent.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195112","collaboration":"National Water Quality Program","usgsCitation":"Wise, D.R., 2019, Spatially referenced models of streamflow and nitrogen, phosphorus, and suspended-sediment loads in streams of the Pacific region of the United States (ver. 1.1, June 2020): U.S. Geological Survey Scientific Investigations Report 2019-5112, 64 p., https://doi.org/10.3133/sir20195112.","productDescription":"Report: x, 64 p.; Data Release; Application Site; Companion Files; Version History; Read Me","numberOfPages":"78","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-103780","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":437240,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9V1RY3N","text":"USGS data release","linkHelpText":"Application of nutrients generated by non-cattle livestock to farmland within the Pacific drainages of the United States, 2012"},{"id":437239,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92EZQO3","text":"USGS data release","linkHelpText":"Distribution of grazing cattle within the Pacific drainages of the United State, 2012"},{"id":437238,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SCZMJB","text":"USGS data release","linkHelpText":"Application of manure nutrients generated at cattle animal feeding operations to farmland within the Pacific drainages of the United State, 2012"},{"id":437237,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9RCPKPC","text":"USGS data release","linkHelpText":"Application of manure nutrients generated by grazing cattle to grazing land within the Pacific drainages of the United States, 2012"},{"id":437236,"rank":11,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MYHLJ6","text":"USGS data 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Streams of the Southwestern United States"}],"country":"United States","otherGeospatial":"Pacific Region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.42187500000001,\n 33.7243396617476\n ],\n [\n -120.58593749999999,\n 38.54816542304656\n ],\n [\n -120.58593749999999,\n 42.16340342422401\n ],\n [\n -118.828125,\n 42.16340342422401\n ],\n [\n -117.24609374999999,\n 43.58039085560784\n ],\n [\n -113.5546875,\n 44.84029065139799\n ],\n [\n -115.83984375,\n 48.3416461723746\n ],\n [\n -119.53125,\n 49.38237278700955\n ],\n [\n -124.27734374999999,\n 48.922499263758255\n ],\n [\n -124.98046874999999,\n 47.39834920035926\n ],\n [\n -124.8046875,\n 43.70759350405294\n ],\n [\n -124.45312499999999,\n 38.41055825094609\n ],\n [\n -120.76171875,\n 33.87041555094183\n ],\n [\n -117.42187500000001,\n 33.7243396617476\n ]\n ]\n ]\n }\n }\n 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Bulletin 17C (B17C) recommends fitting the log-Pearson Type III (LP−III) distribution to a series of annual peak flows at a streamgage by using the method of moments. The third moment, the skewness coefficient (or skew), is important because the magnitudes of annual exceedance probability (AEP) flows estimated by using the LP−III distribution are affected by the skew; interest is focused on the right-hand tail of the distribution, which represents the larger annual peak flows that correspond to small AEPs. For streamgages having modest record lengths, the skew is sensitive to extreme events like large floods, which cause a sample to be highly asymmetrical or “skewed.” For this reason, B17C recommends using a weighted-average skew computed from the station skew for a given streamgage and a regional skew. This report generates an estimate of regional skew for a study area encompassing most of the Great Lakes Basin (hydrologic unit 04) and part of the Ohio River Basin (hydrologic unit 05). A total of 551 candidate streamgages that were unaffected by extensive regulation, diversion, urbanization, or channelization were considered for use in the skew analysis; after screening for redundancy and pseudo record length greater than 36 years, 368 streamgages were selected for use in the study. Flood frequencies for candidate streamgages were analyzed by employing the Expected Moments Algorithm, which extends the method of moments so that it can accommodate interval, censored, and historic/paleo flow data, as well as the Multiple Grubbs-Beck test to identify potentially influential low floods in the data series. Bayesian weighted least squares/Bayesian generalized least squares regression was used to develop a regional skew model for the study area that would incorporate possible variables (basin characteristics) to explain the variation in skew in the study area. Twelve basin characteristics were considered as possible explanatory variables; however, none produced a pseudo coefficient of determination greater than 5 percent; as a result, these characteristics did not help to explain the variation in skew in the study area. Therefore, a constant model having a regional skew coefficient of 0.086 and an average variance of prediction (AVPnew) (which corresponds to the mean square error [MSE]) of 0.13 at a new streamgage was selected. The AVPnew corresponds to an effective record length of 54 years, a marked improvement over the Bulletin 17B national skew map, whose reported MSE of 0.302 indicated a corresponding effective record length of only 17 years.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195105","usgsCitation":"Veilleux, A.G., and Wagner, D.M., 2019, Methods for estimating regional skewness of annual peak flows in parts of the Great Lakes and Ohio River Basins, based on data through water year 2013: U.S. Geological Survey Scientific Investigations Report 2019–5105, 26 p., https://doi.org/10.3133/sir20195105.","productDescription":"Report: vi, 25 p.; 5 Figures; Table; Data Release","numberOfPages":"36","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-101994","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":371689,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_table1.xlsx","text":"Table 1","size":"99.5 KB","linkFileType":{"id":3,"text":"xlsx"},"linkHelpText":"- Streamgages in parts of the Great Lakes and Ohio River Basins considered for use in regional skew analysis"},{"id":371684,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01a.pdf","text":"Figure 1A","size":"5.25 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing 4-digit hydrologic units"},{"id":371685,"rank":6,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig01b.pdf","text":"Figure 1B","size":"2.41 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map of study area in the Great Lakes and Ohio River Basins showing locations of streamgages used in skew analysis"},{"id":371682,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N7UAFJ","text":"USGS data release","linkHelpText":"Annual peak-flow data, PeakFQ specification files and PeakFQ output files for 368 selected streamflow gaging stations operated by the U.S. Geological Survey in the Great Lakes and Ohio River basins that were used to estimate regional skewness of annual peak flows"},{"id":371681,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105.pdf","text":"Report","size":"3.32 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5105"},{"id":371680,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5105/coverthb.jpg"},{"id":399546,"rank":10,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109629.htm"},{"id":371688,"rank":9,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig05.pdf","text":"Figure 5","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing residuals from constant model of skew for 368 streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371687,"rank":8,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig03.pdf","text":"Figure 3","size":"1.95 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing unbiased station skew of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"},{"id":371686,"rank":7,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2019/5105/sir20195105_fig02.pdf","text":"Figure 2","size":"1.94 MB","linkFileType":{"id":1,"text":"pdf"},"linkHelpText":"- Map showing the pseudo record lengths of streamgages in the Great Lakes and Ohio River Basins used in the regional skew analysis"}],"country":"United States, Canada","otherGeospatial":"Great Lakes and Ohio River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.3740234375,\n 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Water Mission Area
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
Bedrock geologic mapping of the Lahore, Va., 7.5-minute quadrangle was completed as part of a broader project, undertaken jointly between the U.S. Geological Survey, the Virginia Division of Geology and Mineral Resources, and other Federal and State agencies to better understand the causative mechanisms of the magnitude-5.8 (M5.8) earthquake that occurred near Mineral, Va., on August 23, 2011. This project involved detailed mapping of eight quadrangles in the epicentral region of the Mineral, Va., earthquake in order to improve our understanding of the geologic framework of the central Virginia seismic zone, which has a long record of historical and prehistoric seismicity.
The Lahore 7.5-minute quadrangle contains the contact between Ordovician to Silurian, dioritic and granodioritic rocks of the Lahore and Ellisville plutons and older metasedimentary and metavolcanic rocks. The Lahore quadrangle is northeast of the Ferncliff and Louisa, Va., quadrangles, where the Shores complex is intruded by the Ellisville pluton along the pluton’s southwestern margin. The new mapping in the Lahore quadrangle shows that the Shores complex continues northeast of the Ellisville pluton. A northeast-trending mafic- and ultramafic-bearing belt within the Shores complex is a fault-bounded accretionary zone (accretionary wedge) between rocks of the Chopawamsic Formation and Laurentian slope-and-rise deposits. In the Lahore quadrangle, this belt contains several mappable, northeast- to southwest-trending mafic bodies and also includes small exposures of gabbro and talc schist.
The Lahore quadrangle contains structures of both early- and late-Paleozoic age that correspond to the Taconic and Alleghanian orogenies. Taconic (Late Ordovician) S1 schistosity in layered rocks is typically fine-grained and parallel to compositional layering, when present. Alleghanian (Pennsylvanian) S2 schistosity is coarser and more micaceous than S1 and is locally accompanied by a lineation that is represented by mineral lineations, micro-crenulations, or mullion fabric, and represents the hinges of F2 folds. A foliation in the plutonic rocks is represented by an equilibrium assemblage of aligned mafic minerals and is early Paleozoic in age.
Metamorphic grade in the non-plutonic rocks of the Lahore quadrangle ranges from lower-greenschist to the northwest to upper-greenschist to the southeast, as represented by mineral assemblages in non-plutonic rocks. The biotite isograd may be, in part, lithologically controlled by the contact between the informally-named Hardware and Byrd Mill formations, and locally affected by contact metamorphism by the Lahore pluton (western part of map). The Taconic garnet isograd is defined by the sparse presence of small (less than 1 millimeter), euhedral garnet crystals. Both the biotite and garnet isograds continue along strike to the southwest into the Ferncliff and Louisa quadrangles, where the isograds have been identified as Ordovician age (Taconic orogeny) based on muscovite, biotite, and amphibole 40Ar/39Ar cooling ages.
Regionally, the most common trend and plunge of joints is northwest and subvertical, respectively, and orthogonal to the regional strike of foliation. Early Mesozoic extension may have reactivated the Harris Creek fault (southeast corner of map), a late Paleozoic (Alleghanian orogeny) transpressional fault that marks the contact between granodiorite of the Ellisville pluton and the Chopawamsic Formation.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191110","usgsCitation":"Burton, W.C., 2019, Preliminary bedrock geologic map of the Lahore 7.5-minute quadrangle, Orange, Spotsylvania, and Louisa Counties, Virginia: U.S. Geological Survey Open-File Report 2019–1110, 1 sheet, scale 1:24,000, https://doi.org/10.3133/ofr20191110.","productDescription":"1 Sheet: 37.00 x 34.00 inches; ReadMe; Database; Metadata","numberOfPages":"1","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-098254","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":399417,"rank":7,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109624.htm"},{"id":371645,"rank":5,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/of/2019/1110/LahoreVAOFR20191110_database.zip","text":"Geodatabase","linkFileType":{"id":6,"text":"zip"}},{"id":371644,"rank":6,"type":{"id":7,"text":"Companion Files"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110_openaccess.zip","text":"Open Access","linkFileType":{"id":6,"text":"zip"}},{"id":371643,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/of/2019/1110/LahoreVAOFR20191110Metadata.zip","text":"Metadata","linkFileType":{"id":6,"text":"zip"}},{"id":371642,"rank":3,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110_readme.txt","text":"Read Me","linkFileType":{"id":2,"text":"txt"}},{"id":371347,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/of/2019/1110/ofr20191110.pdf","text":"Geologic Map","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1110"},{"id":370151,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1110/coverthb.jpg"}],"scale":"24000","country":"United States","state":"Virginia","county":"Louisa County, Orange County, Spotsylvania County","otherGeospatial":"Lahore 7.5-minute quadrangle","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -78,\n 38.125\n ],\n [\n -77.875,\n 38.125\n ],\n [\n -77.875,\n 38.25\n ],\n [\n -78,\n 38.25\n ],\n [\n -78,\n 38.125\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Florence Bascom Geoscience Center
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926A National Center
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Reston, VA 20192
Published on an annual basis, this report is the earliest Government publication to furnish estimates covering nonfuel mineral industry data and is available at https://minerals.usgs.gov/minerals/pubs/mcs/. Data sheets contain information on the domestic industry structure, Government programs, tariffs, and 5-year salient statistics for more than 90 individual minerals and materials.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/70202434","usgsCitation":"U.S. Geological Survey, 2019, Mineral commodity summaries 2019: U.S. Geological Survey, 200 p., https://doi.org/10.3133/70202434.","productDescription":"204 p.","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-104826","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":361621,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/graphics/minerals-commodity-2019.jpg"},{"id":361618,"type":{"id":15,"text":"Index Page"},"url":"https://minerals.usgs.gov/minerals/pubs/mcs/"}],"contact":"Director, National Minerals Information Center
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12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
The U.S. Geological Survey National Crustal Model (NCM) is being developed to assist with earthquake hazard and risk assessment by supporting estimates of ground shaking in response to an earthquake. The period-dependent intensity and duration of shaking depend upon the three-dimensional seismic velocity, seismic attenuation, and density distribution of a region, which in turn is governed to a large degree by geology and how that geology behaves under varying temperatures and pressures.
A three-dimensional temperature model is presented here to support the estimation of physical parameters within the U.S. Geological Survey NCM. The crustal model is defined by a geological framework consisting of various lithologies with distinct mineral compositions. A temperature model is needed to calculate mineral density and bulk and shear modulus as a function of position within the crust. These properties control seismic velocity and impedance, which are needed to accurately estimate earthquake travel times and seismic amplitudes in earthquake hazard analyses. The temperature model is constrained by observations of surface temperature, temperature gradient, and conductivity, inferred Moho temperature and depth, and assumed conductivity at the base of the crust. The continental plate is assumed to have heat production that decreases exponentially with depth and thermal conductivity that exponentially changes from a surface value to 3.6 watts per meter-Kelvin at the Moho. The oceanic plate cools as a half-space with a geotherm dependent on plate age. Under these conditions, and the application of observed surface heat production, predicted Moho temperatures match Moho temperatures inferred from seismic P-wave velocities, on average. As has been noted in previous studies, high crustal temperatures are found in the western United States, particularly beneath areas of recent volcanism. In the central and eastern United States, elevated temperatures are found from southeast Texas, into the Mississippi Embayment, and up through Wisconsin. A USGS ScienceBase data release that supports this report is available and consists of grids covering the NCM across the conterminous United States, for example, surface temperature and temperature gradient, that are needed to produce temperature profiles.
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Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
To address the lack of information on the spatial and temporal variability of recharge to groundwater systems in Maine, a study was initiated in cooperation with the Maine Geological Survey to use the U.S. Geological Survey Soil-Water-Balance model to evaluate annual average potential recharge across the State over a 25-year period from 1991 to 2015. The Maine Soil-Water-Balance model was calibrated using annual observations of recharge, runoff, and evapotranspiration for 32 calibration watersheds in the State during 2001–12 (902 total observations). Observations of recharge, runoff, and evapotranspiration were developed for each watershed to reduce the possibility of nonunique combinations of model parameters during the calibration. The Maine Soil-Water-Balance model was run using an optional evapotranspiration calculation method that provides more control for calibration than the standard method. The model was calibrated using the Parameter ESTimation software suite.
The overall mean model error (average of all annual residuals for recharge, runoff, and precipitation) was 0.39 inch. The mean of the absolute value of the residuals, or the mean absolute error, was 2.32 inches. The root mean squared error for the calibrated model overall was 3.14 inches. Statistical tests indicated that the model residuals are normally distributed. To determine the potential uncertainty in the median annual potential recharge that results from uncertainty in the parameters as they relate to information contained in the observations, 300 alternate model realizations were run, and the standard deviation of the median potential recharge value at every pixel was calculated.
Simulated 25-year median potential recharge across the State is widely variable; this variability closely follows patterns of precipitation, with additional variability contributed by the patchwork nature of the combinations of land-use class and hydrologic soil group inputs, and distribution of available water capacity in the soil across the State. Overall, the 25-year median annual potential recharge across the State is 7.5 inches, ranging from a low of about 5 inches to over 30 inches. The statewide range in the 25-year minimum values is from just over 2 inches to just over 20 inches. The statewide range in the 25-year maximum potential recharge is between 15 and 48 inches per year.
The model areas with the highest simulated median potential recharge include areas underlain by type A soils (sandy and well drained), particularly those that also have land uses with low or little vegetation (blueberry barrens, developed, open space, scrub/shrub, and cropland, for example). The potential recharge values for these areas are similar to previously published values for comparable soil types.
The 25-year average potential recharge grids were compared to recharge evaluated through groundwater-flow models or other methods in four hydrogeologic settings at six study areas in the State. A key factor in the ability of the Soil-Water-Balance model to reproduce the earlier study results was whether the available water-capacity data were an appropriate match for the hydrologic soil groups. The Maine Soil-Water-Balance model does a good job in representing an accurate potential recharge under circumstances where the surficial mapped soils extend below the surface to the water-table aquifer and where the available water-capacity data are in an appropriate range for the hydrologic soil group. One hydrogeologic setting that was challenging for the model was where a silt and clay layer was below a shallow soil unit that did not have available water-capacity data that were appropriate for the hydrologic soil group. In these cases, typically the available water-capacity data were very low, not accounting for the impedance of water flow provided by the underlying soil. The model also does not simulate well areas where bedrock surfaces are above the water table but below the plant rooting zone.
The data products accompanying this report are intended to be used to provide first-cut estimates of recharge for geographic areas no smaller than the smallest watersheds used in the calibration of the model—or about 1.5 square miles. It is recommended that the grids are used to calculate an area-wide average potential recharge for any given area of study, and an uncertainty around the mean should be calculated from the standard deviation grid at the same time.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20195125","collaboration":"Prepared in cooperation with the Maine Geological Survey","usgsCitation":"Nielsen, M.G., and Westenbroek, S.M., 2019, Groundwater recharge estimates for Maine using a Soil-Water-Balance model—25-year average, range, and uncertainty, 1991 to 2015: U.S. Geological Survey Scientific Investigations Report 2019–5125, 58 p., https://doi.org/10.3133/sir20195125.","productDescription":"Report: vii, 56 p.; Tables; 2 Data Releases","numberOfPages":"68","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-106360","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true},{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":380621,"rank":7,"type":{"id":34,"text":"Image 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\"}}]}","contact":"Director, New England Water Science Center
U.S. Geological Survey
331 Commerce Way, Suite 2
Pembroke, NH 03275
This report is for U.S. Geological Survey (USGS)—and any other—hazard scientists who want to improve the understanding and use of their scientific information, particularly by non-experts. In order for people to use science, they need to understand it. The highly technical, specialized nature of scientific information makes that difficult, particularly when few scientists are trained to communicate with people outside their fields. These issues are of special importance to the USGS because it has many users who are not scientists and because it develops and applies hazard science to help protect the safety, security, and economic well-being of our Nation.
In 2010, the Science Application for Risk Reduction group at the USGS discovered the Center for Research on Environmental Decisions (CRED) guide, “The Psychology of Climate Change Communication.” Ever since, a growing number of USGS staff who need to communicate about hazards have used that guide and have asked CRED for a companion report dedicated to hazard communication to harness knowledge from more than 50 years of social science research.
In 2016, the USGS and CRED launched a collaboration to develop that companion report. Ultimately, a CRED hazard communication guide would be a Columbia University publication with a wide focus and would include many hazards that are outside the USGS purview. This report is a first step and concentrates strictly on hazard communication needs at the USGS.
To identify those needs and tailor this effort to USGS hazard communication priorities, this collaboration began with telephone interviews and an online survey of USGS staff. This report is the result; it summarizes social science research and experience in the areas of hazard communication that USGS participants deemed most important to include.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/cir1449","usgsCitation":"Milch, K.F., Perry, S.C., and Bruce, J.L., 2019, Communicating hazards—A social science review to meet U.S. Geological Survey needs: U.S. Geological Survey Circular 1449, 67 p., https://doi.org/10.3133/cir1449.","productDescription":"vi, 67 p.","onlineOnly":"Y","ipdsId":"IP-095565","costCenters":[{"id":508,"text":"Office of the AD Hazards","active":true,"usgs":true}],"links":[{"id":371014,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/circ/1449/cir1449.pdf","text":"Report","size":"25.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Circular 1449"},{"id":371013,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/circ/1449/coverthb.jpg"}],"contact":"Office of Associate Director, Natural Hazards
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Bi-State Distinct Population Segment (Bi-State DPS) of greater sage-grouse (Centrocercus urophasianus, hereinafter “sage-grouse”) represents a genetically distinct and geographically isolated population that straddles the border between Nevada and California. The primary threat to these sage-grouse populations is the expansion of single-leaf pinyon (Pinus monophylla) and Utah juniper (Juniperus osteosperma) into sagebrush ecosystems, which fragments and reduces population connectivity and survival. Other important threats include low water availability during brood-rearing, particularly during drought, and increased predation by common ravens (Corvus corax), a generalist predator often associated with anthropogenic resource subsidies. Although the Bi-State DPS occurs at high elevations relative to sage-grouse range-wide, changes in historical wildfire cycles and the conversion of native shrubs to invasive annual grasslands still threaten these populations. The Bi-State DPS has undergone multiple federal status assessments and associated litigation. For example, in October of 2013, the Bi-State DPS was proposed for listing as threatened under the Endangered Species Act of 1973 by the U.S. Fish and Wildlife Service (USFWS), then withdrawn in April 2015. The withdrawal decision was challenged, and in May 2018, a Federal district court ordered the withdrawal decision to be vacated, and USFWS was required to re-open the October 2013 listing evaluation.
In response, the U.S. Geological Survey (USGS), with State and Federal collaborators, embarked on a multipronged analysis to provide current and best available science regarding population status of sage-grouse within the Bi-State DPS. Using data from a long-term monitoring program, we carried out four analytical study objectives, and here, we provide preliminary results of these analyses. First, we used integrated population modeling (IPM) to predict annual population abundance and annual finite rate of population change for the Bi-State DPS, as a whole, and for each subpopulation between 1995 and 2018. Because sage-grouse exhibit population cycles (periodic increases and decreases in abundance across approximately 6- to 10-year wavelengths), we estimated trends across three nested temporal scales that represent one (11 years), two (18 years), and three (24 years) complete population cycles. These estimates of relatively long-term averaged population change account for temporal (that is, interannual) variation. Our model predicted population abundance for the Bi-State DPS during 2018 at 3,305 individuals (2,247–4,683), with the majority occupying Bodie Hills and Long Valley. The model also predicted cyclic dynamics in abundance through time with evidence of 24-year population growth and slight trends of decline over the past 18 years. Specifically, across the Bi-State DPS as a whole, we estimated annual average at 0.99, 0.99, and 1.02 over the one, two, and three population cycles, which equated to a 10.5 percent, 16.6 percent decrease, and 60.0 percent increase in abundance over the 11-, 18-, and 24-year cycles. Estimated abundance in 2018 had not reached numbers lower than those predicted during 1995. However, we found spatial variation in population trends across the three cycles. Bodie Hills subpopulation comprised the greatest (1,521) and exhibited average annual greater than 1.0 across all periods resulting in average annual increases of 7 percent. This relatively large subpopulation has grown 5 times larger than what was predicted in 1995 while experiencing cyclical dynamics within that period.
Conversely, other smaller subpopulations within the Bi-State DPS exhibited average annual equal to or less than 1.0 resulting in estimated 10-year risks of extirpation ranging from 2.0 to 76.1 percent. In general, evidence of decline among smaller subpopulations was greatest for the most recent period (2008–18) compared to a period that encompassed three full population cycles (24-year). This difference coincides with an intense period of drought that began in 2012.
For comparative purposes as part of this first objective, we conducted a similar analysis for populations of sage-grouse within Nevada and California but outside the Bi-State DPS. We developed a region-wide and distance-weighted IPM using lek count from Nevada Department of Wildlife (NDOW) and California Department of Fish and Wildlife (CDFW) databases and with telemetry data collected by USGS across 12 sage-grouse subpopulations. Our models predicted similar patterns in population cycling outside the Bi-State DPS but with much stronger evidence of long-term declines across 24 years. Specifically, median averaged across each year of the 11-, 18-, and 24-year periods resulted in average annual values of 0.94, 0.97, and 0.99, respectively. These values equate to 41.0 percent, 38.5 percent, and 21.3 percent declines over the corresponding periods.
Second, we used lek count data in a state-space modeling framework to compare trends in population abundance across different spatial scales (that is, leks versus Bi-State DPS). This hierarchical framework allowed us to disentangle declines associated with climate conditions as opposed to other local level factors that might signal the need for management intervention. Specifically, we identified 7 leks that were both declining and recently decoupled from larger spatial scale trends, typically governed by climatic conditions (referred to as soft or hard signals). The goal of this analysis was to provide an early warning system that might have implications for conservation actions at local scales.
Third, we developed phenological (spring, summer–fall, and winter) and reproductive life stage (nesting, early brood-rearing, and late-brood rearing) based resource selection functions using various environmental covariates. We report rankings of variable importance for each season and life stage, developed maps of habitat selection indices (HSI), binned categories representing low, moderate, and high classes of quality (where any category greater than or equal to low indicated selected habitat) for each phenological season and life stage, and produced composite maps by selected phenological and reproductive stage to estimate annual habitat.
Fourth, we used for each lek within the Bi-State DPS to carry out a spatial analysis that quantified substantial changes in the distribution of occupied habitat across long- (24-year) and short- (11-year) term periods. Owing to differences among available datasets, the long-term analysis primarily reflected spatial shifts among subpopulations comprising the majority of the Bi-State DPS (that is, Bodie Hills and Long Valley) while the short-term analysis also quantified changes among subpopulations along the periphery. Over long and short-term periods, the overall distribution of occupied habitat (as measured by 99 percent utilization distributions intersecting any quantified habitat) was reduced by 20,573 ha and 55,492 ha, respectively. Occupied core areas (as measured by 50 percent utilization distributions intersecting any quantified habitat) over long-term periods were solely located in Bodie Hills and Long Valley. Although nearly all subpopulations experienced contractions in occupied overall and core distribution, Bodie Hills experienced spatial expansion that occurred with concomitant spatial contraction at Long Valley over both periods. Subpopulations at the northern (Pine Nuts), central (Sagehen) and southern (White-Mountains) extents of the Bi-State DPS also experienced spatial contraction over the short-term period. These findings, coupled with those of population trends, indicate long-term patterns in redistribution of sage-grouse from Long Valley and peripheral subpopulations to Bodie Hills. That is, sage-grouse subpopulations at the periphery are declining while the largest population at the core is increasing, which could have meaningful impacts on overall metapopulation persistence. We provide evidence for loss of occupied habitat (reduced distribution) given local extirpation of subpopulations.
Fifth, we calculated percentages of selected phenological, life stage, and annual habitat that each subpopulation contributed to the Bi-State DPS. We then intersected these maps with a composite estimate of occupied habitat from the fourth objective and calculated percentages of selected habitat likely occupied by sage-grouse that each subpopulation contributed to the Bi-State DPS. These values provide evidence for loss of occupied habitat and subsequent reductions in spatial distribution given reductions in abundance and, in some cases, extirpation of leks within subpopulations.
Lastly, we carried out an initial in-depth analysis of selection for irrigated pastures and wet meadows during the brood-rearing stage for the Long Valley subpopulation. We chose this subpopulation because it represents a population core, representing 26.5 percent of total sage-grouse within the Bi-State DPS, and has exhibited long-term declines in abundance and distribution. This subpopulation is highly sensitive to precipitation and other factors that influence water availability. Models predicted higher use of the interior portions of irrigated pastures and wet meadows during late brood-rearing period, which represented a potentially risky use of habitat that was exacerbated during periods of low moisture (for example, drought, reduced water delivery, or both). Sage-grouse typically used edges of riparian areas and pastures, largely because the interior of these mesic areas consisted of considerably less overhead concealment cover (for example, shrubs) that likely resulted in a higher risk of mortality. We found that a lack of water delivery to pastures in the form of overwinter precipitation or diversion ditches increased the movements of sage-grouse to the interior of pastures. Although further investigation of water delivery impacts on chick survival are warrented, our initial findings regarding resource selection may explain recent declines in population growth at Long Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191149","collaboration":"Prepared in cooperation with the U. S. Fish and Wildlife Service, Bureau of Land Management, California Department of Fish and Wildlife, Nevada Department of Wildlife, and the U.S. Forest Service","usgsCitation":"Coates, P.S., Ricca, M.A., Prochazka, B.G., O’Neil, S,T., Severson, J.P., Mathews, S.R., Espinosa, S., Gardner, S., Lisius, S., and Delehanty, D.J., 2020, Population and habitat analyses for greater sage-grouse (Centrocercus urophasianus) in the bi-state distinct population segment—2018 update: U.S. Geological Survey Open-File Report 2019–1149, 122 p., https://doi.org/10.3133/ofr20191149.","productDescription":"x, 122 p.","onlineOnly":"Y","ipdsId":"IP-113768","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":371329,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149.pdf","text":"Report","size":"8.6 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149"},{"id":371330,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1149/ofr20191149_hirez.pdf","text":"Report — High resolution graphics","size":"82.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1149 Hi Rez"},{"id":371328,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1149/coverthb.jpg"}],"country":"United States","state":"California, Nevada","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -120.234375,\n 38.90813299596705\n ],\n [\n -119.92675781249999,\n 38.436379603\n ],\n [\n -119.72900390625001,\n 37.50972584293751\n ],\n [\n -118.85009765625,\n 36.90597988519294\n ],\n [\n -117.83935546874999,\n 36.12900165569652\n ],\n [\n -117.586669921875,\n 36.1733569352216\n ],\n [\n -117.31201171875001,\n 35.862343734896484\n ],\n [\n -116.806640625,\n 36.075742215627\n ],\n [\n -115.224609375,\n 35.79108281624994\n ],\n [\n -115.587158203125,\n 37.125286284966805\n ],\n [\n -117.23510742187501,\n 38.62545397209084\n ],\n [\n -119.5751953125,\n 39.16414104768742\n ],\n [\n -120.234375,\n 38.90813299596705\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819