This report summarizes a framework for monitoring hydrogeomorphic and vegetation responses to environmental flows in support of the Willamette Sustainable Rivers Program (SRP). The SRP is a partnership between The Nature Conservancy (TNC) and U.S. Army Corps of Engineers (USACE) to provide ecologically sustainable flows downstream of dams while still meeting human needs and congressionally authorized purposes. TNC, USACE, and U.S. Geological Survey (USGS) developed this framework specifically for the spawning reaches and lower, alluvial reaches of the Middle Fork Willamette, McKenzie, North Santiam, South Santiam, and main-stem Santiam Rivers. This monitoring framework links stakeholder-defined ecological goals and environmental flow recommendations with measurable objectives and monitoring activities to assess whether those objectives are achieved. Monitoring activities are described for distinct spatial scales (reaches, zones, and sites), which are coupled with appropriate measurement frequency (monthly to decadal or following specific flow conditions). Initial monitoring efforts could focus on developing baseline datasets for tracking future changes and developing robust relationships between flow and hydrogeomorphic and vegetation processes. These relationships would support stakeholders in developing refined environmental flow recommendations that could be efficiently evaluated in the future using continuous discharge records and strategic field-based monitoring.
Environmental flow recommendations were developed to achieve certain hydraulic targets (generally defined through water-surface elevation and inundation extent) to support critical habitats for native species at different times of the year. Additionally, flow recommendations were created to support geomorphic processes that create and sustain important riparian and aquatic habitats. The spatial extent, depth, timing, duration, and frequency of inundation extents can be monitored using a combination of water-level loggers, crest-stage gages, surveys, and mapping from aerial photographs or satellite images. Changes in channel morphology (such as increases in gravel bars, side channels or channel width) can be evaluated through repeat mapping of aerial photographs or lidar and carried, and repeat surveys of channel-bed elevations could document patterns of incision or aggradation. Changes in bed texture (such as fining or coarsening) could focus on spawning habitats for spring Chinook salmon (Oncorhynchus tshawytscha). Deposition of fine-grained sediment in floodplain channels could be evaluated with deposition pads, repeat surveys, or lidar.
Environmental flow recommendations also were developed to promote various stages of floodplain forest succession, with a focus on black cottonwood (Populus trichocarpa) because its life history is tightly coupled with floodplain hydrology and disturbance processes. Monitoring approaches for vegetation include strategies for tracking all phases of stand recruitment, establishment, and succession for black cottonwood. Potential recruitment sites can be identified by mapping unvegetated gravel bars from aerial photographs or lidar. Reach-scale patterns of stand recruitment and early succession can be monitored at the reach scale by mapping seral stages of floodplain vegetation from aerial photographs and lidar at the decadal scale. These monitoring approaches also could identify areas of stand recruitment or floodplain recycling. Site-scale monitoring of black cottonwood recruitment and establishment could focus on vegetation plots situated along floodplain transects within laterally dynamic monitoring zones to track seedling establishment or stem exclusion and early seral succession. Reach-scale landcover mapping from aerial photographs and lidar would complement site-scale observations and aid in characterizing overall status and condition of floodplain forests, which could be related to streamflows and hydrogeomorphic processes.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181157","collaboration":"Prepared in cooperation with The Nature Conservancy and the U.S. Army Corps of Engineers","usgsCitation":"Wallick, J.R., Bach, L.B., Keith, M.K., Olson, M., Mangano, J.F., and Jones, K.L., 2018, Monitoring framework for evaluating hydrogeomorphic and vegetation responses to environmental flows in the Middle Fork Willamette, McKenzie, and Santiam River Basins, Oregon: U.S. Geological Survey Open-File Report 2018–1157, 66 p.,\nhttps://doi.org/10.3133/ofr20181157.","productDescription":"vi, 66 p.","onlineOnly":"Y","ipdsId":"IP-090522 ","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359441,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1157/ofr20181157.pdf","text":"Report","size":"11.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1157"},{"id":359440,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1157/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Middle Fork Willamette, McKenzie, and Santiam River Basins","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.33,\n 43.8333\n ],\n [\n -122.1667,\n 43.8333\n ],\n [\n -122.1667,\n 45\n ],\n [\n -123.33,\n 45\n ],\n [\n -123.33,\n 43.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Hearing ability is well studied across teleost fishes in general, and vertebrates more broadly, but little is known about sound detection abilities of lampreys (Petromyzontiformes), a basal extant vertebrate group. The sea lamprey (Petromyzon marinus) is a destructive invader of the Laurentian Great Lakes, while numerous lamprey species (including the sea lamprey) are imperiled in their native ranges. In both management scenarios, behavioral manipulation tactics to control movement and distribution are desired. Therefore, we describe the hearing ability and behavioral responses of adult and juvenile sea lamprey to sound to reveal how hearing may have evolved in vertebrates and determine possible management applications. Based on auditory evoked potentials, sea lamprey detected tones of 50–300 Hz with equal sensitivity, but did not detect sounds above 300 Hz. In a laboratory bioassay, sea lamprey behaviorally responded to sound range of 50–200 Hz, with a general increase in swimming and a decrease in resting behaviours at both juvenile and adult stages relative to no-sound controls. To our knowledge, this is the first test of lamprey hearing, and the results support that sound may be a means to modify lamprey behaviour for management purposes.
","language":"English","publisher":"Canadian Science Publishing","doi":"10.1139/cjfas-2018-0359","usgsCitation":"Mickle, M., Miehls, S.M., Johnson, N., and Higgs, D.M., 2018, Hearing capabilities and behavioural response of sea lamprey (Petromyzon marinus) to low frequency sounds: Canadian Journal of Fisheries and Aquatic Sciences, v. 76, no. 9, 8 p., https://doi.org/10.1139/cjfas-2018-0359.","productDescription":"8 p.","ipdsId":"IP-101835","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":468251,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2018-0359","text":"External Repository"},{"id":379983,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"76","issue":"9","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Mickle, Megan","contributorId":244234,"corporation":false,"usgs":false,"family":"Mickle","given":"Megan","email":"","affiliations":[{"id":48871,"text":"University of Windsor","active":true,"usgs":false}],"preferred":false,"id":803546,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Miehls, Scott M. 0000-0002-5546-1854 smiehls@usgs.gov","orcid":"https://orcid.org/0000-0002-5546-1854","contributorId":5007,"corporation":false,"usgs":true,"family":"Miehls","given":"Scott","email":"smiehls@usgs.gov","middleInitial":"M.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":803547,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Johnson, Nicholas S. 0000-0002-7419-6013 njohnson@usgs.gov","orcid":"https://orcid.org/0000-0002-7419-6013","contributorId":150983,"corporation":false,"usgs":true,"family":"Johnson","given":"Nicholas S.","email":"njohnson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":803548,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Higgs, Dennis M.","contributorId":244235,"corporation":false,"usgs":false,"family":"Higgs","given":"Dennis","email":"","middleInitial":"M.","affiliations":[{"id":48871,"text":"University of Windsor","active":true,"usgs":false}],"preferred":false,"id":803549,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70200380,"text":"sir20185110 - 2018 - Deep aquifer recharge in the Columbia River Basalt Group, upper Umatilla River Basin, northeastern Oregon","interactions":[],"lastModifiedDate":"2018-11-14T16:07:08","indexId":"sir20185110","displayToPublicDate":"2018-11-14T09:49:45","publicationYear":"2018","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":"2018-5110","displayTitle":"Deep Aquifer Recharge in the Columbia River Basalt Group, Upper Umatilla River Basin, Northeastern Oregon","title":"Deep aquifer recharge in the Columbia River Basalt Group, upper Umatilla River Basin, northeastern Oregon","docAbstract":"Groundwater is an important component of the water resources of the upper Umatilla River Basin of northeastern Oregon. As such, understanding the capacity of the resource is vital. Past studies have estimated recharge in the study area. One recent study of the upper Umatilla River Basin indicated that about 80 percent of recharge entering the groundwater system is discharged to streams in the study area through shallow groundwater-flow paths, leaving about 20 percent of recharge to infiltrate deeper parts of the aquifer system. The purpose of this work is to quantify the spatial distribution and variability of deep aquifer recharge in the study area and to understand the reasons for a relatively low percentage of total recharge reaching the deeper parts of the groundwater-flow system.
The study area is divided into two distinct physiographic regions—the highly dissected Blue Mountains and the lowland plains. Underlying both regions of the study area are basalts of the Columbia River Basalt Group (CRBG), which is the principal aquifer in the study area. Deep incision by streams in the Blue Mountains disrupts the lateral continuity of the CRBG aquifer units, and infiltrating water is more readily diverted laterally and discharged to streams and springs. In the lowland plains, incision is less pronounced. The shallow CRBG units might be disrupted, but deeper aquifer units retain their lateral continuity and enable groundwater to infiltrate deeper and flow laterally farther downgradient before discharging.
Recharge to the deep basalt aquifers is estimated as the difference between total recharge and base flow. Total recharge is the portion of precipitation and applied irrigation water that infiltrates past the root zone to become groundwater recharge. Of this total recharge, a proportion discharges to springs and streams in the study area, and the remaining water infiltrates below the base level of streams and recharges the deep basalt aquifers and contributes to the regional groundwater flow system. The portion of total recharge that recharges the regional flow system is referred to as deep aquifer recharge.
Total recharge is the portion of precipitation and applied irrigation water that infiltrates past the root zone to become groundwater recharge. It is the sum of recharge from precipitation and recharge from infiltration of irrigation water. Recharge from precipitation was calculated using a regression method developed for the Columbia Plateau. Recharge from infiltrating irrigation water was obtained from a water balance model developed for the Columbia Plateau.
Base flow, the component of streamflow that represents groundwater discharge as opposed to runoff from the land surface, was estimated using the Base Flow Index Modified (BFI-Modified) method, an empirical hydrograph separation technique. Base flow was estimated in eight subbasins with streamgages within the study area. Five of the eight subbasins in which base flow was estimated had permitted water rights for irrigation that specified surface water as the primary source of water. Maximum surface-water withdrawal for irrigation was estimated for all subbasins in which water rights for irrigation occur.
The base-flow estimate from BFI-Modified is assumed to be the minimum amount of base flow. The sum of the BFIModified base-flow estimate and the maximum permitted surface-water withdrawal estimate for each subbasin is assumed to be the maximum amount of base flow at the streamgage. These minimum and maximum estimates of base flow were used to calculate minimum and maximum values of deep aquifer recharge in each subbasin analyzed within the study area. Subbasin estimates were scaled up to the Blue Mountains and lowland plains regions, and to the entire study area.
Mean annual total recharge for 1981–2010 in the subbasins, analyzed as part of this work, ranged from 6 inches (in.) in the Patawa and Wildhorse Creek subbasins in the lowland plains to as much as 20 in. in the Umatilla River above Meacham Creek subbasin. Mean annual total recharge totaled 4 in. in the lowland plains region and 14 in. in the Blue Mountains. Mean annual total recharge for the entire study area was 11 in.
Mean annual base flow ranged from 1 in. in the Patawa and Wildhorse Creek subbasins in the lowland plains to as much as 14 in. in the Umatilla River above Meacham Creek subbasin in the Blue Mountains.
Mean annual deep aquifer recharge ranged from 4 in. in the Patawa and Wildhorse Creek subbasins in the lowland plains to as much as 8 in. in the Isqu’ulktpe Creek subbasin in the Blue Mountains. Deep aquifer recharge was 3–4 in. in the lowland plains region and 6 in. in the Blue Mountains. Over the entire study area, mean annual deep aquifer recharge was 5 in.
Most groundwater recharge (both total and deep aquifer) in the study area occurred in the Blue Mountains, which highlights the importance of the Blue Mountains as the principal source of groundwater for the study area and for aquifers farther downgradient. Total recharge in the Blue Mountains region represents 86 percent of the mean annual total recharge in the study area in an area that encompasses 65 percent of the study area. However, only 43–44 percent of the mean annual total recharge remains in the system to recharge the deeper, regional aquifer system because the rest is discharged as base flow within the Blue Mountains region. Within the lowland plains region of the study area, an estimated 67–84 percent of the mean annual total recharge remains in the system to recharge the deep, regional aquifer system. Although total recharge in the study area represents only 14 percent of the total recharge across the study area, it contributes 20–24 percent of the water to the deep aquifer.
The difference in the percentage of deep groundwater recharge in the Blue Mountains and the lowland plains is attributed to differences in the degree of stream incision. Stream channels are more incised in the Blue Mountains region than they are in the lowland plains. The dissection of the landscape in the Blue Mountains disrupts the lateral continuity of the CRBG aquifer units and allows groundwater to discharge to springs and streams rather than infiltrate more deeply. In the lowland plains region, incision is much less pronounced and deeper CRBG units likely retain their lateral continuity, enabling groundwater to infiltrate more deeply than in the Blue Mountains.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185110","collaboration":"Prepared in cooperation with the Bureau of Reclamation","usgsCitation":"Pischel, E.M., Johnson, H.M., and Gingerich, S.B., 2018, Deep aquifer recharge in the Columbia River Basalt Group, upper Umatilla River Basin, northeastern Oregon: U.S. Geological Survey Scientific Investigations Report 2018–5110, 23 p., https://doi.org/10.3133/sir20185110.","productDescription":"Report: iv, 23 p.; Data release","onlineOnly":"Y","ipdsId":"IP-095179","costCenters":[{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true}],"links":[{"id":359396,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5110/sir20185110.pdf","text":"Report","size":"6.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5110"},{"id":359397,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9E548IN","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selected data from deep aquifer recharge in the Columbia River Basalt Group, Upper Umatilla River Basin, northeastern Oregon"},{"id":359395,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5110/coverthb.jpg"}],"country":"United States","state":"Oregon","otherGeospatial":"Upper Umatilla River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -119,\n 45.25\n ],\n [\n -118,\n 45.25\n ],\n [\n -118,\n 46\n ],\n [\n -119,\n 46\n ],\n [\n -119,\n 45.25\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oregon Water Science Center
U.S. Geological Survey
2130 SW 5th Avenue
Portland, Oregon 97201
Large carnivores are imperiled globally, and characteristics making them vulnerable to extinction (e.g., low densities and expansive ranges) also make it difficult to estimate demographic parameters needed for management. Here we develop an integrated population model to analyze capture-recapture, radiotelemetry, and count data for the Chukchi Sea subpopulation of polar bears (Ursus maritimus), 2008–2016. Our model addressed several challenges in capture-recapture studies for polar bears by including a multievent structure reflecting location and life history states, while accommodating state uncertainty. Female breeding probability was 0.83 (95% credible interval [CRI] = 0.71–0.90), with litter sizes of 2.18 (95% CRI = 1.71–2.82) for age-zero and 1.61 (95% CRI = 1.46–1.80) for age-one cubs. Total adult survival was 0.90 (95% CRI = 0.86–0.92) for females and 0.89 (95% CRI = 0.83–0.93) for males. Spring on-ice densities west of Alaska were 0.0030 bears/km2 (95% CRI = 0.0016–0.0060), similar to 1980s-era density estimates although methodological differences complicate comparison. Abundance of the Chukchi Sea subpopulation, derived by extrapolating density from the study area using a spatially-explicit habitat metric, was 2,937 bears (95% CRI = 1,552–5,944). Our findings are consistent with other lines of evidence suggesting the Chukchi Sea subpopulation has been productive in recent years, although it is uncertain how long this will continue given sea-ice loss due to climate change.
","language":"English","publisher":"Scientific Reports","doi":"10.1038/s41598-018-34824-7","usgsCitation":"Regehr, E.V., Hostetter, N.J., Wilson, R.H., Rode, K.D., St. Martin, M., and Converse, S.J., 2018, Integrated population modeling provides the first empirical estimates of vital rates and abundance for polar bears in the Chukchi Sea: Scientific Reports, v. 8, 16780, 12 p., https://doi.org/10.1038/s41598-018-34824-7.","productDescription":"16780, 12 p.","ipdsId":"IP-098279","costCenters":[{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"links":[{"id":468252,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-018-34824-7","text":"Publisher Index Page"},{"id":381215,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","noUsgsAuthors":false,"publicationDate":"2018-11-14","publicationStatus":"PW","contributors":{"authors":[{"text":"Regehr, Eric V. 0000-0003-4487-3105","orcid":"https://orcid.org/0000-0003-4487-3105","contributorId":66364,"corporation":false,"usgs":false,"family":"Regehr","given":"Eric","email":"","middleInitial":"V.","affiliations":[{"id":12428,"text":"U. S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":806679,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hostetter, Nathan J. 0000-0001-6075-2157 nhostetter@usgs.gov","orcid":"https://orcid.org/0000-0001-6075-2157","contributorId":198843,"corporation":false,"usgs":true,"family":"Hostetter","given":"Nathan","email":"nhostetter@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":806680,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Wilson, Ryan H. 0000-0001-7740-7771","orcid":"https://orcid.org/0000-0001-7740-7771","contributorId":130989,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan","email":"","middleInitial":"H.","affiliations":[{"id":6987,"text":"U.S. Fish and Wildlife Sevice","active":true,"usgs":false}],"preferred":false,"id":806681,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Rode, Karyn D. 0000-0002-3328-8202 krode@usgs.gov","orcid":"https://orcid.org/0000-0002-3328-8202","contributorId":5053,"corporation":false,"usgs":true,"family":"Rode","given":"Karyn","email":"krode@usgs.gov","middleInitial":"D.","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true}],"preferred":true,"id":806682,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"St. Martin, Michelle","contributorId":150114,"corporation":false,"usgs":false,"family":"St. Martin","given":"Michelle","email":"","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":806683,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":806684,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70204265,"text":"70204265 - 2018 - Uncertainty in United States coastal wetland greenhouse gas inventorying","interactions":[],"lastModifiedDate":"2019-07-17T12:13:48","indexId":"70204265","displayToPublicDate":"2018-11-12T14:40:08","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1562,"text":"Environmental Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Uncertainty in United States coastal wetland greenhouse gas inventorying","docAbstract":"Coastal wetlands store carbon dioxide (CO2) and emit CO2 and methane (CH4) making them an important part of greenhouse gas (GHG) inventorying. In the contiguous United States (CONUS), a coastal wetland inventory was recently calculated by combining maps of wetland type and change with soil, biomass, and CH4 flux data from a literature review. We assess uncertainty in this developing carbon monitoring system to quantify confidence in the inventory process itself and to prioritize future research. We provide a value-added analysis by defining types and scales of uncertainty for assumptions, burial and emissions datasets, and wetland maps, simulating 10 000 iterations of a simplified version of the inventory, and performing a sensitivity analysis. Coastal wetlands were likely a source of net-CO2-equivalent (CO2e) emissions from 2006–2011. Although stable estuarine wetlands were likely a CO2e sink, this effect was counteracted by catastrophic soil losses in the Gulf Coast, and CH4 emissions from tidal freshwater wetlands. The direction and magnitude of total CONUS CO2e flux were most sensitive to uncertainty in emissions and burial data, and assumptions about how to calculate the inventory. Critical data uncertainties included CH4 emissions for stable freshwater wetlands and carbon burial rates for all coastal wetlands. Critical assumptions included the average depth of soil affected by erosion events, the method used to convert CH4 fluxes to CO2e, and the fraction of carbon lost to the atmosphere following an erosion event. The inventory was relatively insensitive to mapping uncertainties. Future versions could be improved by collecting additional data, especially the depth affected by loss events, and by better mapping salinity and inundation gradients relevant to key GHG fluxes.
","language":"English","publisher":"IOP","doi":"10.1088/1748-9326/aae157","usgsCitation":"Holmquist, J., Windham-Myers, L., Bernal, B., Byrd, K.B., Crooks, S., Gonneea Eagle, M., Herold, N., Knox, S., Kroeger, K.D., John McCombs, J. Patrick Megonigal, Meng, L., James Morris, Ariana Sutton-Grier, Tiffany Troxler, and Donald Weller, 2018, Uncertainty in United States coastal wetland greenhouse gas inventorying: Environmental Research Letters, v. 13, no. 11, 115005, 16 p., https://doi.org/10.1088/1748-9326/aae157.","productDescription":"115005, 16 p.","ipdsId":"IP-101677","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true},{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":468253,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/aae157","text":"Publisher Index Page"},{"id":365628,"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 \"geometry\": {\n \"type\": \"MultiPolygon\",\n \"coordinates\": [\n [\n [\n [\n -94.81758,\n 49.38905\n ],\n [\n -94.64,\n 48.84\n ],\n [\n -94.32914,\n 48.67074\n ],\n [\n -93.63087,\n 48.60926\n ],\n [\n -92.61,\n 48.45\n ],\n [\n -91.64,\n 48.14\n ],\n [\n -90.83,\n 48.27\n ],\n [\n -89.6,\n 48.01\n ],\n [\n -89.27292,\n 48.01981\n ],\n [\n -88.37811,\n 48.30292\n ],\n [\n -87.43979,\n 47.94\n ],\n [\n -86.46199,\n 47.55334\n ],\n [\n -85.65236,\n 47.22022\n ],\n [\n -84.87608,\n 46.90008\n ],\n [\n -84.77924,\n 46.6371\n ],\n [\n -84.54375,\n 46.53868\n ],\n [\n -84.6049,\n 46.4396\n ],\n [\n -84.3367,\n 46.40877\n ],\n [\n -84.14212,\n 46.51223\n ],\n [\n -84.09185,\n 46.27542\n ],\n [\n -83.89077,\n 46.11693\n ],\n [\n -83.61613,\n 46.11693\n ],\n [\n -83.46955,\n 45.99469\n ],\n [\n -83.59285,\n 45.81689\n ],\n [\n -82.55092,\n 45.34752\n ],\n [\n -82.33776,\n 44.44\n ],\n [\n -82.13764,\n 43.57109\n ],\n [\n -82.43,\n 42.98\n ],\n [\n -82.9,\n 42.43\n ],\n [\n -83.12,\n 42.08\n ],\n [\n -83.142,\n 41.97568\n ],\n [\n -83.02981,\n 41.8328\n ],\n [\n -82.69009,\n 41.67511\n ],\n [\n -82.43928,\n 41.67511\n ],\n [\n -81.27775,\n 42.20903\n ],\n [\n -80.24745,\n 42.3662\n ],\n [\n -78.93936,\n 42.86361\n ],\n [\n -78.92,\n 42.965\n ],\n [\n -79.01,\n 43.27\n ],\n [\n -79.17167,\n 43.46634\n ],\n [\n -78.72028,\n 43.62509\n ],\n [\n -77.73789,\n 43.62906\n ],\n [\n -76.82003,\n 43.62878\n ],\n [\n -76.5,\n 44.01846\n ],\n [\n -76.375,\n 44.09631\n ],\n [\n -75.31821,\n 44.81645\n ],\n [\n -74.867,\n 45.00048\n ],\n [\n -73.34783,\n 45.00738\n ],\n [\n -71.50506,\n 45.0082\n ],\n [\n -71.405,\n 45.255\n ],\n [\n -71.08482,\n 45.30524\n ],\n [\n -70.66,\n 45.46\n ],\n [\n -70.305,\n 45.915\n ],\n [\n -69.99997,\n 46.69307\n ],\n [\n -69.23722,\n 47.44778\n ],\n [\n -68.905,\n 47.185\n ],\n [\n -68.23444,\n 47.35486\n ],\n [\n -67.79046,\n 47.06636\n ],\n [\n -67.79134,\n 45.70281\n ],\n [\n -67.13741,\n 45.13753\n ],\n [\n -66.96466,\n 44.8097\n ],\n [\n -68.03252,\n 44.3252\n ],\n [\n -69.06,\n 43.98\n ],\n [\n -70.11617,\n 43.68405\n ],\n [\n -70.64548,\n 43.09024\n ],\n [\n -70.81489,\n 42.8653\n ],\n [\n -70.825,\n 42.335\n ],\n [\n -70.495,\n 41.805\n ],\n [\n -70.08,\n 41.78\n ],\n [\n -70.185,\n 42.145\n ],\n [\n -69.88497,\n 41.92283\n ],\n [\n -69.96503,\n 41.63717\n ],\n [\n -70.64,\n 41.475\n ],\n [\n -71.12039,\n 41.49445\n ],\n [\n -71.86,\n 41.32\n ],\n [\n -72.295,\n 41.27\n ],\n [\n -72.87643,\n 41.22065\n ],\n [\n -73.71,\n 40.9311\n ],\n [\n -72.24126,\n 41.11948\n ],\n [\n -71.945,\n 40.93\n ],\n [\n -73.345,\n 40.63\n ],\n [\n -73.982,\n 40.628\n ],\n [\n -73.95232,\n 40.75075\n ],\n [\n -74.25671,\n 40.47351\n ],\n [\n -73.96244,\n 40.42763\n ],\n [\n -74.17838,\n 39.70926\n ],\n [\n -74.90604,\n 38.93954\n ],\n [\n -74.98041,\n 39.1964\n ],\n [\n -75.20002,\n 39.24845\n ],\n [\n -75.52805,\n 39.4985\n ],\n [\n -75.32,\n 38.96\n ],\n [\n -75.07183,\n 38.78203\n ],\n [\n -75.05673,\n 38.40412\n ],\n [\n -75.37747,\n 38.01551\n ],\n [\n -75.94023,\n 37.21689\n ],\n [\n -76.03127,\n 37.2566\n ],\n [\n -75.72205,\n 37.93705\n ],\n [\n -76.23287,\n 38.31921\n ],\n [\n -76.35,\n 39.15\n ],\n [\n -76.54272,\n 38.71762\n ],\n [\n -76.32933,\n 38.08326\n ],\n [\n -76.99,\n 38.23999\n ],\n [\n -76.30162,\n 37.91794\n ],\n [\n -76.25874,\n 36.9664\n ],\n [\n -75.9718,\n 36.89726\n ],\n [\n -75.86804,\n 36.55125\n ],\n [\n -75.72749,\n 35.55074\n ],\n [\n -76.36318,\n 34.80854\n ],\n [\n -77.39763,\n 34.51201\n ],\n [\n -78.05496,\n 33.92547\n ],\n [\n -78.55435,\n 33.86133\n ],\n [\n -79.06067,\n 33.49395\n ],\n [\n -79.20357,\n 33.15839\n ],\n [\n -80.30132,\n 32.50935\n ],\n [\n -80.86498,\n 32.0333\n ],\n [\n -81.33629,\n 31.44049\n ],\n [\n -81.49042,\n 30.72999\n ],\n [\n -81.31371,\n 30.03552\n ],\n [\n -80.98,\n 29.18\n ],\n [\n -80.53558,\n 28.47213\n ],\n [\n -80.53,\n 28.04\n ],\n [\n -80.05654,\n 26.88\n ],\n [\n -80.08801,\n 26.20576\n ],\n [\n -80.13156,\n 25.81677\n ],\n [\n -80.38103,\n 25.20616\n ],\n [\n -80.68,\n 25.08\n ],\n [\n -81.17213,\n 25.20126\n ],\n [\n -81.33,\n 25.64\n ],\n [\n -81.71,\n 25.87\n ],\n [\n -82.24,\n 26.73\n ],\n [\n -82.70515,\n 27.49504\n ],\n [\n -82.85526,\n 27.88624\n ],\n [\n -82.65,\n 28.55\n ],\n [\n -82.93,\n 29.1\n ],\n [\n -83.70959,\n 29.93656\n ],\n [\n -84.1,\n 30.09\n ],\n [\n -85.10882,\n 29.63615\n ],\n [\n -85.28784,\n 29.68612\n ],\n [\n -85.7731,\n 30.15261\n ],\n [\n -86.4,\n 30.4\n ],\n [\n -87.53036,\n 30.27433\n ],\n [\n -88.41782,\n 30.3849\n ],\n [\n -89.18049,\n 30.31598\n ],\n [\n -89.59383,\n 30.15999\n ],\n [\n -89.41373,\n 29.89419\n ],\n [\n -89.43,\n 29.48864\n ],\n [\n -89.21767,\n 29.29108\n ],\n [\n -89.40823,\n 29.15961\n ],\n [\n -89.77928,\n 29.30714\n ],\n [\n -90.15463,\n 29.11743\n ],\n [\n -90.88022,\n 29.14854\n ],\n [\n -91.62678,\n 29.677\n ],\n [\n -92.49906,\n 29.5523\n ],\n [\n -93.22637,\n 29.78375\n ],\n [\n -93.84842,\n 29.71363\n ],\n [\n -94.69,\n 29.48\n ],\n [\n -95.60026,\n 28.73863\n ],\n [\n -96.59404,\n 28.30748\n ],\n [\n -97.14,\n 27.83\n ],\n [\n -97.37,\n 27.38\n ],\n [\n -97.38,\n 26.69\n ],\n [\n -97.33,\n 26.21\n ],\n [\n -97.14,\n 25.87\n ],\n [\n -97.53,\n 25.84\n ],\n [\n -98.24,\n 26.06\n ],\n [\n -99.02,\n 26.37\n ],\n [\n -99.3,\n 26.84\n ],\n [\n -99.52,\n 27.54\n ],\n [\n -100.11,\n 28.11\n ],\n [\n -100.45584,\n 28.69612\n ],\n [\n -100.9576,\n 29.38071\n ],\n [\n -101.6624,\n 29.7793\n ],\n [\n -102.48,\n 29.76\n ],\n [\n -103.11,\n 28.97\n ],\n [\n -103.94,\n 29.27\n ],\n [\n -104.45697,\n 29.57196\n ],\n [\n -104.70575,\n 30.12173\n ],\n [\n -105.03737,\n 30.64402\n ],\n [\n -105.63159,\n 31.08383\n ],\n [\n -106.1429,\n 31.39995\n ],\n [\n -106.50759,\n 31.75452\n ],\n [\n -108.24,\n 31.75485\n ],\n [\n -108.24194,\n 31.34222\n ],\n [\n -109.035,\n 31.34194\n ],\n [\n -111.02361,\n 31.33472\n ],\n [\n -113.30498,\n 32.03914\n ],\n [\n -114.815,\n 32.52528\n ],\n [\n -114.72139,\n 32.72083\n ],\n [\n -115.99135,\n 32.61239\n ],\n [\n -117.12776,\n 32.53534\n ],\n [\n -117.29594,\n 33.04622\n ],\n [\n -117.944,\n 33.62124\n ],\n [\n -118.4106,\n 33.74091\n ],\n [\n -118.51989,\n 34.02778\n ],\n [\n -119.081,\n 34.078\n ],\n [\n -119.43884,\n 34.34848\n ],\n [\n -120.36778,\n 34.44711\n ],\n [\n -120.62286,\n 34.60855\n ],\n [\n -120.74433,\n 35.15686\n ],\n [\n -121.71457,\n 36.16153\n ],\n [\n -122.54747,\n 37.55176\n ],\n [\n -122.51201,\n 37.78339\n ],\n [\n -122.95319,\n 38.11371\n ],\n [\n -123.7272,\n 38.95166\n ],\n [\n -123.86517,\n 39.76699\n ],\n [\n -124.39807,\n 40.3132\n ],\n [\n -124.17886,\n 41.14202\n ],\n [\n -124.2137,\n 41.99964\n ],\n [\n -124.53284,\n 42.76599\n ],\n [\n -124.14214,\n 43.70838\n ],\n [\n -124.02053,\n 44.6159\n ],\n [\n -123.89893,\n 45.52341\n ],\n [\n -124.07963,\n 46.86475\n ],\n [\n -124.39567,\n 47.72017\n ],\n [\n -124.68721,\n 48.18443\n ],\n [\n -124.5661,\n 48.37971\n ],\n [\n -123.12,\n 48.04\n ],\n [\n -122.58736,\n 47.096\n ],\n [\n -122.34,\n 47.36\n ],\n [\n -122.5,\n 48.18\n ],\n [\n -122.84,\n 49\n ],\n [\n -120,\n 49\n ],\n [\n -117.03121,\n 49\n ],\n [\n -116.04818,\n 49\n ],\n [\n -113,\n 49\n ],\n [\n -110.05,\n 49\n ],\n [\n -107.05,\n 49\n ],\n [\n -104.04826,\n 48.99986\n ],\n [\n -100.65,\n 49\n ],\n [\n -97.22872,\n 49.0007\n ],\n [\n -95.15907,\n 49\n ],\n [\n -95.15609,\n 49.38425\n ],\n [\n -94.81758,\n 49.38905\n ]\n ]\n ]\n ]\n },\n \"properties\": {\n \"name\": \"United States\"\n }\n }\n ]\n}","volume":"13","issue":"11","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Holmquist, James","contributorId":217021,"corporation":false,"usgs":false,"family":"Holmquist","given":"James","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766255,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Windham-Myers, Lisamarie 0000-0003-0281-9581 lwindham-myers@usgs.gov","orcid":"https://orcid.org/0000-0003-0281-9581","contributorId":2449,"corporation":false,"usgs":true,"family":"Windham-Myers","given":"Lisamarie","email":"lwindham-myers@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":766254,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bernal, Blanca","contributorId":217022,"corporation":false,"usgs":false,"family":"Bernal","given":"Blanca","email":"","affiliations":[{"id":39564,"text":"Winrock International","active":true,"usgs":false}],"preferred":false,"id":766256,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Byrd, Kristin B. 0000-0002-5725-7486 kbyrd@usgs.gov","orcid":"https://orcid.org/0000-0002-5725-7486","contributorId":3814,"corporation":false,"usgs":true,"family":"Byrd","given":"Kristin","email":"kbyrd@usgs.gov","middleInitial":"B.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":766257,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Crooks, Steve","contributorId":217023,"corporation":false,"usgs":false,"family":"Crooks","given":"Steve","email":"","affiliations":[{"id":38182,"text":"Silvestrum Climate Associates","active":true,"usgs":false}],"preferred":false,"id":766258,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Gonneea Eagle, Meagan 0000-0001-5072-2755 mgonneea@usgs.gov","orcid":"https://orcid.org/0000-0001-5072-2755","contributorId":174590,"corporation":false,"usgs":true,"family":"Gonneea Eagle","given":"Meagan","email":"mgonneea@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":766259,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Herold, Nathan","contributorId":217024,"corporation":false,"usgs":false,"family":"Herold","given":"Nathan","email":"","affiliations":[{"id":39565,"text":"NOAA Office for Coastal Management","active":true,"usgs":false}],"preferred":false,"id":766260,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knox, Sara 0000-0003-2255-5835","orcid":"https://orcid.org/0000-0003-2255-5835","contributorId":216996,"corporation":false,"usgs":false,"family":"Knox","given":"Sara","email":"","affiliations":[{"id":6986,"text":"Stanford University","active":true,"usgs":false}],"preferred":false,"id":766261,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kroeger, Kevin D. 0000-0002-4272-2349 kkroeger@usgs.gov","orcid":"https://orcid.org/0000-0002-4272-2349","contributorId":1603,"corporation":false,"usgs":true,"family":"Kroeger","given":"Kevin","email":"kkroeger@usgs.gov","middleInitial":"D.","affiliations":[{"id":41100,"text":"Coastal and Marine Hazards and Resources Program","active":true,"usgs":true}],"preferred":true,"id":766262,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"John McCombs","contributorId":217025,"corporation":false,"usgs":false,"family":"John McCombs","affiliations":[{"id":39565,"text":"NOAA Office for Coastal Management","active":true,"usgs":false}],"preferred":false,"id":766263,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"J. Patrick Megonigal","contributorId":217026,"corporation":false,"usgs":false,"family":"J. Patrick Megonigal","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766264,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Meng, Lu","contributorId":217027,"corporation":false,"usgs":false,"family":"Meng","given":"Lu","email":"","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766265,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"James Morris","contributorId":176971,"corporation":false,"usgs":false,"family":"James Morris","affiliations":[],"preferred":false,"id":766266,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Ariana Sutton-Grier","contributorId":217028,"corporation":false,"usgs":false,"family":"Ariana Sutton-Grier","affiliations":[{"id":7041,"text":"The Nature Conservancy","active":true,"usgs":false}],"preferred":false,"id":766267,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Tiffany Troxler","contributorId":217029,"corporation":false,"usgs":false,"family":"Tiffany Troxler","affiliations":[{"id":7017,"text":"Florida International University","active":true,"usgs":false}],"preferred":false,"id":766268,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Donald Weller","contributorId":217030,"corporation":false,"usgs":false,"family":"Donald Weller","affiliations":[{"id":13510,"text":"Smithsonian Environmental Research Center","active":true,"usgs":false}],"preferred":false,"id":766269,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ,{"id":70216313,"text":"70216313 - 2018 - The role of a non-native tree in riparian vegetation expansion and channel narrowing along a dryland river","interactions":[],"lastModifiedDate":"2020-11-12T12:50:01.767733","indexId":"70216313","displayToPublicDate":"2018-11-11T12:47:56","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1447,"text":"Ecohydrology","active":true,"publicationSubtype":{"id":10}},"title":"The role of a non-native tree in riparian vegetation expansion and channel narrowing along a dryland river","docAbstract":"Along rivers, native and invasive species may establish and persist on active channel\nbedforms as part of channel narrowing. Using historical aerial photography and\ndendrochronology, we quantified spatial and temporal patterns of narrowing and\nvegetation expansion, including native Fremont cottonwood (Populus fremontii)\nand non‐native Russian olive (Elaeagnus angustifolia), along the largely unregulated\nEscalante River in south‐western United States. Russian olive establishment was\nexamined with respect to hydrologic and climate variables. Narrowing along the\nEscalante River was initiated during a mid‐20th century drought. Cottonwood rapidly\ncolonized higher, bar surfaces between the 1950s and 1981. Small numbers of\nRussian olive established in moist sites during this period as the channel narrowed\nby nearly 80%. After 1981, there was no obvious cottonwood establishment but\nlow channel bars and banks were rapidly colonized by Russian olive. Hydroclimate\npredictors were equivocal but exponential growth of this large‐seeded, shade‐tolerant\nspecies lagged its introduction by 30 years, apparently because of delayed reproductive\nmaturity, limited seed availability, and widespread availability of favourable establishment\nsites following initial channel narrowing. Sediment trapping, levee formation,\nand modification of channel form by dense, channel‐edge bands of Russian olive\nprogressively limited new establishment sites and by 2000, recruitment declined\nsharply. Our results have implications for management of non‐native tree invasions\nalong arid‐region rivers, including identification of low, moist, active channel bars\nwhere the establishment and physical impacts of Russian olive appear to be most\npronounced and where focused management efforts are likely to be most effective.","language":"English","publisher":"Wiley","doi":"10.1002/eco.1988","usgsCitation":"Scott, M., Reynolds, L.V., Shafroth, P., and Spencer, J.R., 2018, The role of a non-native tree in riparian vegetation expansion and channel narrowing along a dryland river: Ecohydrology, v. 11, no. 7, https://doi.org/10.1002/eco.1988.","productDescription":"e1988, 17 p.","startPage":"e1988","ipdsId":"IP-096763","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437685,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7KH0MMK","text":"USGS data release","linkHelpText":"Geomorphic, climate, streamflow and vegetation data sets to reconstruct channel and vegetation changes associated with the invasion of Russian olive along the Escalante River, Utah 1950-2015."},{"id":380423,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Utah","otherGeospatial":"Escalante River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -112.43408203124999,\n 37.00255267215955\n ],\n [\n -111.29150390625,\n 37.00255267215955\n ],\n [\n -111.29150390625,\n 38.03078569382294\n ],\n [\n -112.43408203124999,\n 38.03078569382294\n ],\n [\n -112.43408203124999,\n 37.00255267215955\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"11","issue":"7","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Scott, Michael L.","contributorId":244803,"corporation":false,"usgs":false,"family":"Scott","given":"Michael L.","affiliations":[{"id":6682,"text":"Utah State University","active":true,"usgs":false}],"preferred":false,"id":804638,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Reynolds, Lindsay V.","contributorId":141182,"corporation":false,"usgs":false,"family":"Reynolds","given":"Lindsay","email":"","middleInitial":"V.","affiliations":[{"id":6737,"text":"Colorado State University, Department of Ecosystem Science and Sustainability, and Natural Resource Ecology Laboratory","active":true,"usgs":false}],"preferred":false,"id":804639,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafroth, Patrick B. 0000-0002-6064-871X","orcid":"https://orcid.org/0000-0002-6064-871X","contributorId":225182,"corporation":false,"usgs":true,"family":"Shafroth","given":"Patrick B.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":804640,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Spencer, John R.","contributorId":167381,"corporation":false,"usgs":false,"family":"Spencer","given":"John","email":"","middleInitial":"R.","affiliations":[],"preferred":false,"id":804641,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70216312,"text":"70216312 - 2018 - Evaluation of chronic toxicity of sodium chloride or potassium chloride to a unionid mussel (Lampsilis siliquoidea) in water exposures using standard and refined toxicity testing methods","interactions":[],"lastModifiedDate":"2021-05-07T19:15:10.750685","indexId":"70216312","displayToPublicDate":"2018-11-11T12:05:37","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1571,"text":"Environmental Toxicology and Chemistry","active":true,"publicationSubtype":{"id":10}},"displayTitle":"Evaluation of chronic toxicity of sodium chloride or potassium chloride to a unionid mussel (Lampsilis siliquoidea) in water exposures using standard and refined toxicity testing methods","title":"Evaluation of chronic toxicity of sodium chloride or potassium chloride to a unionid mussel (Lampsilis siliquoidea) in water exposures using standard and refined toxicity testing methods","docAbstract":"Freshwater mussels are generally underrepresented in toxicity databases used to derive water quality criteria, especially for long‐term exposures. Multiple tests were conducted to determine the chronic toxicity of sodium chloride (NaCl) or potassium chloride (KCl) to a unionid mussel (fatmucket, Lampsilis siliquoidea). Initially, a 4‐wk NaCl test and a 4‐wk KCl test were conducted starting with 2‐mo‐old mussels in water exposures with and without a thin layer of sand substrate. A feeding study was conducted later to refine test conditions for longer‐term 12‐wk exposures, and 3 chronic NaCl tests were then conducted following the refined method to assess the influence of test duration (4–12 wk) and age of organisms (starting age ∼1 wk to 2 mo) on mussel sensitivity. Biomass (total dry wt of surviving mussels in a replicate) was generally a more sensitive endpoint compared to survival and growth (length and dry wt). In the 4‐wk NaCl or KCl test started with 2‐mo‐old juveniles, a 20% effect concentration (EC20) based on biomass (264 mg Cl/L from the NaCl test or 8.7 mg K/L from the KCl test) in the exposure with sand was 2‐fold lower than the EC20 in the exposure without sand. The longer‐term 12‐wk NaCl tests started with the 1‐wk‐old and 2‐mo‐old juveniles were successfully completed under refined test conditions based on the feeding study, and younger juveniles were more sensitive to NaCl than older juveniles. The NaCl toxicity did not substantially change with extended exposure periods from 4 to 12 wk, although the 4‐wk EC20s for biomass were slightly greater (up to 37%) than the 12‐wk EC20s in the 2 longer‐term exposures. Including the toxicity data from the present study into existing databases would rank fatmucket the most sensitive species to KCl and the second most sensitive species to NaCl for all freshwater organisms.
The extensive development of oil and natural-gas resources in south Texas during the past 10 years has led to questions regarding possible environmental effects of processes associated with oil and natural-gas production, in particular the process of hydraulic fracturing, on water and other natural resources. Part of the lower San Antonio River watershed intersects an area of oil and natural-gas production from the sedimentary rocks that compose the Eagle Ford Group.
The rapid expansion of infrastructure associated with oil and natural-gas production increases potential pathways for inorganic and organic contaminants to enter surface-water systems. The U.S. Geological Survey, in cooperation with the San Antonio River Authority, analyzed geospatial data from different years (2008 and 2015) to evaluate changes in land cover associated with oil and natural-gas production activities in the lower San Antonio River watershed. Impervious surface in this study is defined as land cover consisting of well pads, oil- and gas-related features, or roads. The areal coverage associated with impervious surface increased from 201 acres to 5,390 acres (net increase of 5,189 acres) between 2008 and 2015. The total percentage of the study area accounted for by impervious surface resulting from oil and natural-gas production activities increased from 0.034 percent to 0.912 percent, which is an increase of approximately 27-fold. Collectively, 0.878 percent of the study area was converted to new impervious surface between 2008 and 2015. If the area associated with new storage ponds (0.066 percent) is added to the estimate of total land-cover changes as a result of oil and natural-gas production, then 0.944 percent of the study area was altered.
During 2015–17, surface-water samples collected from 5 sites and streambed-sediment samples collected from 17 sites in the lower San Antonio River watershed were analyzed for a broad range of constituents that might be associated with oil and natural-gas production. All major elements, trace elements, semivolatile organic compounds (SVOCs), and volatile organic compounds (VOCs) measured in surface-water samples were detected at concentrations less than any of the U.S. Environmental Protection Agency’s water-quality standards. In general, the greatest SVOC and VOC concentrations were observed in samples collected from sites upstream from the area of active oil and natural-gas production and just downstream from urban areas. The lack of benzene, toluene, ethylbenzene, and all isomers of xylene (hereinafter referred to as BTEX) for most sites within the area of active oil and natural-gas production indicates that little, if any, local runoff associated with the area of active oil and natural-gas production has contaminated the surface water with BTEX compounds. Glycols, which are commonly used in hydraulic fracturing fluids as scale inhibitors, were detected in one surface-water sample from Ecleto Creek within the area of oil and natural-gas production; however, the presence of glycols does not necessarily indicate contamination from hydraulic fracturing fluid. The glycols detected also have other potential sources including the use of diethylene and ethylene glycols in antifreeze used in vehicles and the use of triethylene glycol in antibacterial air sanitizers.
The concentrations of select constituents in the streambed-sediment samples were compared to sediment quality guidelines (SQGs). The SQGs evaluate the potential toxicity of bed sediments to sediment-dwelling organisms. Two SQG concentration levels are used: (1) a lower level, called the threshold effect concentration (TEC), below which harmful effects to benthic biota are not expected, and (2) a higher level, the probable effect concentration (PEC), above which harmful effects are expected to occur frequently. The PEC for arsenic was exceeded in a sample collected from one site on Ecleto Creek. The origin of the elevated arsenic concentration is unknown; the contamination likely is not related to oil and natural-gas production because the site of the sample collection is located upstream from the area of active oil and natural-gas production. Streambed-sediment samples were analyzed for selected polycyclic aromatic hydrocarbons (PAHs) because PAHs can be used as indicators of petroleum hydrocarbons associated with produced waters. Each streambed-sediment sample was analyzed for two size fractions of PAHs: less than (<) 63 micrometers (μm) and < 2 millimeters (mm). Total PAH concentrations in all samples, regardless of size fraction, were less than the TEC for total PAHs of 1,610 micrograms per kilogram. Total PAH concentrations generally were greater in the <63-μm size-fraction samples than in the <2-mm size-fraction samples, indicating that PAHs could potentially sorb more readily to the exclusively silt- and clay-sized particles that compose <63-μm size-fraction samples than to the mixture of silt and clay and larger sized particles that compose the <2-mm size-fraction samples. Total PAH concentrations typically were greater in the samples collected from the sites upstream from the area of active oil and natural-gas production compared to those collected from sites within the area in both the <2-mm and <63-μm size-fraction samples. The smaller PAH concentrations measured in samples collected from within the area of active oil and natural-gas production in comparison to the upstream urbanized areas indicate relatively minor additional local contributions of PAHs of uncertain origin to the watershed.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185119","collaboration":"Prepared in cooperation with the San Antonio River Authority","usgsCitation":"Crow, C.L., Opsahl, S.P., Pedraza, D.E., Pease, E.C., and Kushnereit, R.K., 2018, Land-cover changes associated with oil and natural-gas production and concentrations of selected constituents in surface-water and streambed-sediment samples collected upstream from and within an area of oil and natural-gas production, south Texas, 2008–17: U.S. Geological Survey Scientific Investigations Report 2018–5119, 52 p., https://doi.org/10.3133/sir20185119.","productDescription":"Report: vii, 52 p.; Data Release","numberOfPages":"64","onlineOnly":"N","ipdsId":"IP-095610","costCenters":[{"id":583,"text":"Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":437689,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F74J0DDQ","text":"USGS data release","linkHelpText":"Land-Cover, Surface-water, and Streambed-sediment data Collected Upstream from and Within an Area of Oil and Natural-Gas Production, South Texas, 2008-17"},{"id":359271,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://dx.doi.org/10.5066/F74J0DDQ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Land-use, water-quality, and sediment-quality data from an area upstream from and within an area of oil and natural-gas production in the lower San Antonio River watershed, south Texas, 2008–17"},{"id":359269,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5119/coverthb.jpg"},{"id":359270,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5119/sir20185119.pdf","text":"Report","size":"4.51 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5119"}],"country":"United States","state":"Texas","otherGeospatial":"San Antonio River watershed","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.38531494140624,\n 28.459033019728043\n ],\n [\n -96.91314697265625,\n 28.459033019728043\n ],\n [\n -96.91314697265625,\n 29.652255607121884\n ],\n [\n -98.38531494140624,\n 29.652255607121884\n ],\n [\n -98.38531494140624,\n 28.459033019728043\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, Texas 78754–4501
Estimates of fish entrainment on the Sacramento River near the Fremont Weir are a critical component in determining the feasibility and design of a proposed notch in the weir to increase access to the Yolo Bypass, a seasonal floodplain of the Sacramento River. Detailed hydrodynamic and velocity measurements were made at a river bend near the Fremont Weir in the winter and spring of 2016 to examine backwater conditions and estimate the hydraulic entrainment zone, a zone where fish would be predicted to be entrained into the notch. Secondary circulation near the river bend was shown to shift the velocity and discharge distributions toward the outside of the bend. Variability in the stage-discharge relation was shown to be the biggest source of uncertainty in determining the location of the hydraulic entrainment zone. Outflow from the Sutter Bypass and high flow on the Feather River resulted in backwater conditions near the Fremont Weir about 25 percent of the time over the 27-year period from April 1990–April 2017. Velocity measurements used to estimate the critical streakline position (the outer edge of the hydraulic entrainment zone) were not made over a sufficient range of conditions to explicitly quantify the variability in the location of the critical streakline. The variability in the critical streakline position was therefore represented stochastically with a random effects model. The estimated position of the critical streakline and the random effects model are input parameters used in a simulation designed to estimate fish entrainment over a 15-year period. The estimates of the critical streakline and likely fish entrainment could be much improved with velocity measurements over a broader range of stage and discharge conditions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185115","collaboration":"Prepared in cooperation with the California Department of Water Resources and U.S. Bureau of Reclamation","usgsCitation":"Stumpner, P.R., Blake, A.R., and Burau, J.R., 2018, Hydrology and hydrodynamics on the Sacramento River near the Fremont Weir, California—Implications for juvenile salmon entrainment estimates: U.S. Geological Survey Scientific Investigations Report 2018–5115, 50 p., https://doi.org/10.3133/sir20185115. ","productDescription":"Report: viii, 50 p.","numberOfPages":"62","onlineOnly":"Y","ipdsId":"IP-092827","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":437691,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QZ296Z","text":"USGS data release","linkHelpText":"Velocity mapping using moving boat acoustic Doppler current profiler on the Sacramento River near the western end of the Fremont Weir in February and March 2016, and May 2017"},{"id":359279,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5115/sir20185115_.pdf","text":"Report","size":"5.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5115"},{"id":359284,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5115/coverthb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sacramento River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122,\n 38.5833\n ],\n [\n -121.5,\n 38.5833\n ],\n [\n -121.5,\n 39.0833\n ],\n [\n -122,\n 39.0833\n ],\n [\n -122,\n 38.5833\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Director,
Utah Water Science Center
U.S. Geological Survey
2329 West Orton Circle
Salt Lake City, UT 84119-2047
We integrated field measurements, hydroponic experiments, microscopy, and spectroscopy to investigate the effect of Ca(II) on dissolved U(VI) uptake by plants in 1 mM HCO3– solutions at circumneutral pH. The accumulation of U in plants (3.1–21.3 mg kg–1) from the stream bank of the Rio Paguate, Jackpile Mine, New Mexico served as a motivation for this study. Brassica junceawas the model plant used for the laboratory experiments conducted over a range of U (30–700 μg L–1) and Ca (0–240 mg L–1) concentrations. The initial U uptake followed pseudo-second-order kinetics. The initial U uptake rate (V0) ranged from 4.4 to 62 μg g–1 h–1 in experiments with no added Ca and from 0.73 to 2.07 μg g–1 h–1 in experiments with 12 mg L–1 Ca. No measurable U uptake over time was detected for experiments with 240 mg L–1 Ca. Ternary Ca–U–CO3complexes may affect the decrease in U bioavailability observed in this study. Elemental X-ray mapping using scanning transmission electron microscopy–energy-dispersive spectrometry detected U–P-bearing precipitates within root cell walls in water free of Ca. These results suggest that root interactions with Ca and carbonate in solution affect the bioavailability of U in plants. This study contributes relevant information to applications related to U transport and remediation of contaminated sites.
","language":"English","publisher":"ACS","doi":"10.1021/acs.est.8b02724","usgsCitation":"El Hayek, E., Torres, C., Rodriguez-Freire, L., Blake, J., De Vore, C.L., Brearley, A.J., Spilde, M.N., Cabaniss, S., Ali, A.S., and Cerrato, J.M., 2018, Effect of calcium on the bioavailability of dissolved uranium(VI) in plant roots under circumneutral pH: Environmental Science & Technology, v. 52, no. 22, p. 13089-13098, https://doi.org/10.1021/acs.est.8b02724.","productDescription":"10 p.","startPage":"13089","endPage":"13098","ipdsId":"IP-096227","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":460811,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/6341987","text":"External Repository"},{"id":360794,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"52","issue":"22","publishingServiceCenter":{"id":5,"text":"Lafayette PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-09","publicationStatus":"PW","contributors":{"authors":[{"text":"El Hayek, Eliane","contributorId":207797,"corporation":false,"usgs":false,"family":"El Hayek","given":"Eliane","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755189,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Torres, Chris","contributorId":211908,"corporation":false,"usgs":false,"family":"Torres","given":"Chris","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755190,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rodriguez-Freire, Lucia","contributorId":211909,"corporation":false,"usgs":false,"family":"Rodriguez-Freire","given":"Lucia","email":"","affiliations":[{"id":38351,"text":"New Jersey Institute of Technology","active":true,"usgs":false}],"preferred":false,"id":755191,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Blake, Johanna M. 0000-0003-4667-0096","orcid":"https://orcid.org/0000-0003-4667-0096","contributorId":211907,"corporation":false,"usgs":true,"family":"Blake","given":"Johanna M.","affiliations":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755188,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"De Vore, Cherie L.","contributorId":211910,"corporation":false,"usgs":false,"family":"De Vore","given":"Cherie","email":"","middleInitial":"L.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755192,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Brearley, Adrian J.","contributorId":211911,"corporation":false,"usgs":false,"family":"Brearley","given":"Adrian","email":"","middleInitial":"J.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755193,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Spilde, Michael N.","contributorId":211912,"corporation":false,"usgs":false,"family":"Spilde","given":"Michael","email":"","middleInitial":"N.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755194,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Cabaniss, Stephen","contributorId":211913,"corporation":false,"usgs":false,"family":"Cabaniss","given":"Stephen","email":"","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755195,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Ali, Abdul-Mehdi S.","contributorId":211914,"corporation":false,"usgs":false,"family":"Ali","given":"Abdul-Mehdi","email":"","middleInitial":"S.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755196,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Cerrato, Jose M.","contributorId":211915,"corporation":false,"usgs":false,"family":"Cerrato","given":"Jose","email":"","middleInitial":"M.","affiliations":[{"id":36307,"text":"University of New Mexico","active":true,"usgs":false}],"preferred":false,"id":755197,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70219471,"text":"70219471 - 2018 - Volcanic hail detected with GPS: The 2011 eruption of Grímsvötn Volcano, Iceland","interactions":[],"lastModifiedDate":"2021-04-08T12:23:23.066734","indexId":"70219471","displayToPublicDate":"2018-11-09T07:20:20","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1807,"text":"Geophysical Research Letters","active":true,"publicationSubtype":{"id":10}},"title":"Volcanic hail detected with GPS: The 2011 eruption of Grímsvötn Volcano, Iceland","docAbstract":"Volcanic plumes are challenging to detect and characterize rapidly, but insights into processes such as hail formation or ash aggregation are valuable to hazard forecasts during volcanic crises. Global Navigation Satellite System (GNSS, which includes GPS) signals traveling from satellites to ground receivers can be disturbed by volcanic plumes. To date, two effects aiding plume detection from GNSS observations have been described: (a) ash‐rich plumes scatter the signal, lowering the signal‐to‐noise ratio (SNR), and (b) some plumes refract and thus delay GNSS signals. Using GNSS data from the VEI 4 2011 Grímsvötn eruption, we show that tephra and water contents of plumes distinctly affect SNR and phase residuals. The signals suggest high‐altitude freezing of plume water into volcanic hail—corroborated by 1‐D modeling and volcanic hail deposits. Combining GNSS SNR and phase residual analyses is valuable for detecting processes that rapidly scrub fine ash out of the atmosphere.
Burning and grazing are natural processes in native prairies that also serve as important tools in grassland management to conserve plant diversity, to limit encroachment of woody and invasive plants, and to maintain or improve prairies. Native prairies managed by the U.S. Fish and Wildlife Service (FWS) in the Prairie Pothole Region of the northern Great Plains have been extensively invaded by nonnative, cool-season species of grasses. These invasions were believed to reflect a common management history of long-term rest and little or no defoliation by natural processes (burning and grazing). To address the challenges associated with these invasive species, the FWS embraced a collaborative approach in 2008, in partnership with U.S. Geological Survey, to restore native prairies on lands managed by FWS. This approach is known as the Native Prairie Adaptive Management (NPAM) initiative and was based on the application of an adaptive decision-support framework to assist managers in selecting management actions despite uncertainty and in maximizing learning from management outcomes. The primary objective of this approach was to increase the composition of native grasses and forbs on native, unbroken sod while minimizing costs. The alternative management actions that were used to meet this objective include grazing, burning, burning and grazing, and rest (no action).
A major challenge for FWS resource managers participating in the NPAM initiative was the recognition that other taxa, besides native grasses and forbs, may be affected by the alternative management practices, thus complicating the adaptive-management cycle and deepening the uncertainty. Specifically, many grassland birds are sensitive to changes in vegetation composition and structure, and thus management that alters vegetation also may affect bird populations. The primary objectives of this study were to assess the effects of alternative management actions on grassland birds on FWS-owned grasslands that are managed under the adaptive-management framework, and to assess the association of vegetation structure and composition as mechanisms for triggering grassland bird responses to management.
We surveyed breeding birds and sampled vegetation on 89 native prairie NPAM units managed by the FWS during 2011–13, including 55 units in 2011, 87 units in 2012, and 87 units in 2013. The NPAM units were in 19 FWS refuge complexes and wetland management districts, including 14 complexes in FWS Region 6 (North Dakota, South Dakota, and Montana) and 5 complexes in FWS Region 3 (Minnesota). Generalized linear mixed models were used to evaluate the effects of management actions on vegetation structure, vegetation composition, and densities of common bird species. Vegetation structure and composition varied among study units and years, and many of these differences were linked to specific management activities or to the recency of those activities. We recorded 110 bird species in the 89 adaptive-management units. Models of bird abundance reflected not only disturbance-derived changes in vegetation structure and species-specific vegetation preferences but also the influence of defoliation treatments. Vegetation composition was less important to grassland birds than vegetation structure; in particular, mean vertical obstruction (vegetation height-density), bare-ground cover, and litter depth positively or negatively influenced densities of some grassland bird species. The diversity of bird responses to management in this study underscores the complexity of natural grassland systems and the need for heterogeneity management in grasslands in this region.
Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Sheet 1: This image mosaic is based on observations acquired by the Mercury Dual Imaging System (MDIS; Hawkins and others, 2009), an instrument on the National Aeronautics and Space Agency (NASA) MErcury Surface, Space ENvironment, Geochemistry, and Ranging (MESSENGER) spacecraft (Solomon and others, 2007). The Mercator projection is used between latitudes ±57°, with a central meridian at 0° longitude and latitude equal to the nominal scale at 0°. The polar stereographic projection is used for the regions north of the +55° parallel and south of the –55° parallel, with a central meridian set for both at 0° and a latitude of true scale at +90° and –90°, respectively. All features greater than 100 km in diameter or length were included unless they were not visible at the printed map scale. Some selected well-known features less than 100 km in diameter or length were also included. For listed references, please open the full PDF.
Sheet 2: This map is based on data acquired by the Mercury Dual Imaging System (MDIS; Hawkins and others, 2009) and Mercury Laser Altimeter (MLA; Cavanaugh and others, 2007) instruments on the National Aeronautics and Space Agency (NASA) MErcury Surface, Space ENvironment, Geochemistry, and Ranging (MESSENGER) spacecraft (Solomon and others, 2007). The topographic shaded-relief maps were generated from the original MDIS- and MLA-based DEMs with a sun angle of 45° from horizontal and a sun azimuth of 270°, as measured clockwise from north, with no vertical exaggeration. The DEM values were then mapped to a global color look-up table, with each color representing a range of 1 km of elevation. The shaded-relief and color files were then merged and scaled to 1:20,000,000 for the Mercator portion and 1:12,157,366 for the two polar stereographic parts with a resolution of 300 pixels per inch. The two projections have a common scale at ±56° latitude. The Mercator projection is used between latitudes ±57°, with a central meridian at 0° longitude and latitude equal to the nominal scale at 0°. The polar stereographic projection is used for the regions north of the +55° parallel and south of the –55° parallel, with a central meridian set for both at 0° and a latitude of true scale at +90° and –90°, respectively. All features greater than 200 km in diameter or length were included unless they were not visible at the printed map scale. Some selected well-known features less than 200 km in diameter or length were also included. Sheet 2 is offered digitally as a layered PDF with two elevation color ramp options—the original printed version and a multicolored ramp developed by the MESSENGER team for their global products. For listed references, please open the full PDF.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3404","collaboration":"Prepared for the National Aeronautics and Space Administration","usgsCitation":"Hunter, M.A., Hare, T.M., Hayward, R.K., Chabot, N.L., Hash, C.D., Denevi, B.W., Ernst, C.M., Murchie, S.L., Blewett, D.T., Malaret, E.R., Solomon, S.C., Becker, K.J., Becker, T.L., Weller, L.A., Edmundson, K.L., Neuman, G.A., Mazarico, E., and Perry, M.E., 2018, Image mosaic and topographic maps of Mercury: U.S. Geological Survey Scientific Investigations Map 3404, scale 1:20,000,000 and 1:12,157,366, https://doi.org/10.3133/sim3404.","productDescription":"2 Sheets: 34.95 x 33.38 inches and 35.0 x 38.0 inches","ipdsId":"IP-089995","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":359290,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3404/sim3404_sheet1_.pdf","text":"Sheet 1","size":"8.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3404 Sheet 1","linkHelpText":" - Image Map of Mercury"},{"id":359291,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3404/sim3404_sheet2_.pdf","text":"Sheet 2","size":"15.7 MB layered","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3404 Sheet 2","linkHelpText":" - Topographic Map of Mercury"},{"id":359289,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3404/coverthb2.jpg"}],"otherGeospatial":"Mercury","contact":"Astrogeology Research Program staff
Astrogeology Science Center
U.S. Geological Survey
2255 N. Gemini Dr.
Flagstaff, AZ 86001
In this report, we constructed and parameterized the Stream Salmonid Simulator (S3) for the 64-kilometer “Restoration Reach” of the Trinity River, just downstream of Lewiston Dam in northern California. S3 is a deterministic life-stage-structured population model that tracks daily growth, movement, and survival of juvenile salmon. A key theme of the model is that river flow affects habitat availability and capacity, which in turn drives density-dependent population dynamics. To explicitly link population dynamics to habitat quality and quantity, the river environment is constructed as a one-dimensional series of linked habitat units, each of which has an associated daily timeseries of discharge, water temperature, and useable habitat area or carrying capacity. In turn, the physical characteristics of each habitat unit and the number of fish occupying each unit drive survival and growth within each habitat unit and movement of fish among habitat units.
The physical template of the Restoration Reach was formed by classifying the river into 356 meso-habitat units comprised of runs, riffles, and pools. For each habitat unit, we developed a timeseries of daily flow, water temperature, amount of available spawning habitat, and fry and parr carrying capacity. Capacity timeseries were constructed using state-of-the-art models of spatially explicit hydrodynamics and quantitative fish habitat relationships developed for the Trinity River. These variables were then used to drive population dynamics such as egg growth and survival and juvenile movement, growth, and survival.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20181174","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Perry, R.W., Jones, E.C., Plumb, J.M., Som, N.A., Hetrick, N.J., Hardy, T.B., Polos, J.C., Martin, A.C., Alvarez, J.S., and De Juilio, K.P., 2018, Application of the Stream Salmonid Simulator (S3) to the restoration reach of the Trinity River, California—Parameterization and calibration: U.S. Geological Survey Open-File Report 2018-1174, 64 p., https://doi.org/10.3133/ofr20181174.","productDescription":"vi, 65 p.","onlineOnly":"Y","ipdsId":"IP-092954","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":359376,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2018/1174/ofr20181174.pdf","text":"Report","size":"10.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2018-1174"},{"id":359375,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2018/1174/coverthb2.jpg"}],"country":"United States","state":"California","otherGeospatial":"Trinity River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.12927246093751,\n 40.635319920747456\n ],\n [\n -122.7674102783203,\n 40.635319920747456\n ],\n [\n -122.7674102783203,\n 40.77950154452172\n ],\n [\n -123.12927246093751,\n 40.77950154452172\n ],\n [\n -123.12927246093751,\n 40.635319920747456\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
U.S. Geological Survey
6505 NE 65th Street
Seattle, Washington 98115
In many simulations of historical daily streamflow distributional bias arising from the distributional properties of residuals has been noted. This bias often presents itself as an underestimation of high streamflow and an overestimation of low streamflow. Here, 1168 streamgages across the conterminous USA, having at least 14 complete water years of daily data between 1 October 1980 and 30 September 2013, are used to explore a method for rescaling simulated streamflow to correct the distributional bias. Based on an existing approach that separates the simulated streamflow into components of temporal structure and magnitude, the temporal structure is converted to simulated nonexceedance probabilities and the magnitudes are rescaled using an independently estimated flow duration curve (FDC) derived from regional regression. In this study, this method is applied to a pooled ordinary kriging simulation of daily streamflow coupled with FDCs estimated by regional regression on basin characteristics. The improvement in the representation of high and low streamflows is correlated with the accuracy and unbiasedness of the estimated FDC. The method is verified by using an idealized case; however, with the introduction of regionally regressed FDCs developed for this study, the method is only useful overall for the upper tails, which are more accurately and unbiasedly estimated than the lower tails. It remains for future work to determine how accurate the estimated FDCs need to be to be useful for bias correction without unduly reducing accuracy. In addition to its potential efficacy for distributional bias correction, this particular instance of the methodology also represents a generalization of nonlinear spatial interpolation of daily streamflow using FDCs. Rather than relying on single index stations, as is commonly done to reflect streamflow timing, this approach to simulation leverages geostatistical tools to allow a region of neighbors to reflect streamflow timing.
","language":"English","publisher":"European Geosciences Union","doi":"10.5194/hess-22-5741-2018","usgsCitation":"Farmer, W.H., Over, T.M., and Kiang, J.E., 2018, Bias correction of simulated historical daily streamflow at ungauged locations by using independently estimated flow duration curves: Hydrology and Earth System Sciences, v. 22, p. 5741-5758, https://doi.org/10.5194/hess-22-5741-2018.","productDescription":"18 p.","startPage":"5741","endPage":"5758","ipdsId":"IP-092889","costCenters":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"links":[{"id":468256,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/hess-22-5741-2018","text":"Publisher Index Page"},{"id":360785,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","volume":"22","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-08","publicationStatus":"PW","contributors":{"authors":[{"text":"Farmer, William H. 0000-0002-2865-2196 wfarmer@usgs.gov","orcid":"https://orcid.org/0000-0002-2865-2196","contributorId":4374,"corporation":false,"usgs":true,"family":"Farmer","given":"William","email":"wfarmer@usgs.gov","middleInitial":"H.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":502,"text":"Office of Surface Water","active":true,"usgs":true}],"preferred":true,"id":755284,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Over, Thomas M. 0000-0001-8280-4368 tmover@usgs.gov","orcid":"https://orcid.org/0000-0001-8280-4368","contributorId":1819,"corporation":false,"usgs":true,"family":"Over","given":"Thomas","email":"tmover@usgs.gov","middleInitial":"M.","affiliations":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"preferred":true,"id":755285,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kiang, Julie E. 0000-0003-0653-4225 jkiang@usgs.gov","orcid":"https://orcid.org/0000-0003-0653-4225","contributorId":2179,"corporation":false,"usgs":true,"family":"Kiang","given":"Julie","email":"jkiang@usgs.gov","middleInitial":"E.","affiliations":[{"id":502,"text":"Office of Surface Water","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":755286,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70200225,"text":"sir20185133 - 2018 - Hydrology-driven chemical loads transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17","interactions":[],"lastModifiedDate":"2018-11-14T15:57:29","indexId":"sir20185133","displayToPublicDate":"2018-11-08T11:19:18","publicationYear":"2018","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":"2018-5133","displayTitle":"Hydrology-Driven Chemical Loads Transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17","title":"Hydrology-driven chemical loads transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17","docAbstract":"The sediments in the Lower Duwamish Waterway Superfund site in Seattle, Washington, are contaminated with chemicals including metals such as arsenic, polychlorinated biphenyls (PCBs), carcinogenic polycyclic aromatic hydrocarbons (cPAHs), and dioxins/furans from decades of intense anthropogenic activities. The U.S. Geological Survey, in cooperation with the Washington State Department of Ecology, collected new data from 2013 to 2017 to estimate sediment and chemical loads transported by the Green/Duwamish River to the Lower Duwamish Waterway Superfund site (the final 8-kilometer reach of the river) in support of sediment remediation within the site. Chemical loads were calculated as the product of river suspended-sediment loads and suspended sediment-bound chemical concentrations measured at river kilometer 16.7.
Using four different approaches, annual suspended sediment-bound chemical load estimates transported by the river to the Lower Duwamish Waterway were in the range of 1,120–1,470 kilograms arsenic, 2,810–8,200 grams (g) toxic equivalent cPAHs, 205–407 milligrams toxic equivalent dioxins/furans, and 340–1,180 g PCBs. Storm events contributed a disproportionately large amount of the load of anthropogenic organic compounds such as cPAHs (54 percent), dioxins/furans (44 percent), and PCBs (52 percent) as compared to overall time (17 percent).
Chemical concentrations and load estimates often were underestimated using results from unfiltered water analysis only, especially in samples with high suspended-sediment concentrations and for hydrophobic organic chemicals such as cPAHs that prefer to sorb to particulates and are at low concentrations near or below the analytical limits of water methods. For metals and PCBs, the dissolved concentration was relatively low and consistent between sampling events, whereas the suspended sediment-bound chemical concentrations contributed most of the chemical concentration in the water column during periods of high river suspended-sediment concentrations. However, the dissolved fraction, on average, contributed more than one-third of the estimated total chemical load in the river system for arsenic and PCBs, even given the hydrophobic nature of the chemicals. These results suggest that the sum of the chemical concentrations measured on two separate fractions—the particulate fraction and the dissolved fraction—more fully represents the total chemical concentration as compared to analysis of an unfiltered water sample, especially in samples with high suspended-sediment concentrations.
Most of the suspended-sediment load (97 percent) and sediment-bound chemical load (92–94 percent) occurred during the wet winter half of the year from October 15 to April 14. However, the highest sediment-bound chemical concentrations often occurred during short intense storms or “first flush” autumn runoff events during the dry summer half of the year from April 15 to October 14. Because of the highly variable and dynamic river system characteristics (including precipitation, discharge, sediment concentration, and tidal fluctuations), it is critical to characterize the occurrence, frequency, concentrations, and loads during extreme conditions (for example, when the river is affected by storm-derived runoff) rather than time-averaged conditions. These short extreme events have a high potential for acute effects on ecological and human health, and may have a great influence on the effectiveness of the sediment remediation activities that are underway in the Lower Duwamish Waterway.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185133","collaboration":"Prepared in cooperation with the Washington State Department of Ecology","usgsCitation":"Conn, K.E., Black, R.W., Senter, C.A., Peterson, N.T., and Vanderpool-Kimura, A., 2018, Hydrology-driven chemical loads transported by the Green River to the Lower Duwamish Waterway near Seattle, Washington, 2013–17: U.S. Geological Survey Scientific Investigations Report 2018-5133, 37 p., https://doi.org/10.3133/sir20185133.","productDescription":"vii, 37 p.","onlineOnly":"Y","ipdsId":"IP-099196","costCenters":[{"id":622,"text":"Washington Water Science Center","active":true,"usgs":true}],"links":[{"id":359329,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5133/coverthb2.jpg"},{"id":359330,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5133/sir20185133.pdf","text":"Report","size":"6.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018-5133"}],"country":"United States","state":"Washington","city":"Seattle","otherGeospatial":"Green-Duwamish River, Lower Duwanish Waterway","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.39627838134766,\n 47.458272792347074\n ],\n [\n -122.22290039062499,\n 47.458272792347074\n ],\n [\n -122.22290039062499,\n 47.59875528481801\n ],\n [\n -122.39627838134766,\n 47.59875528481801\n ],\n [\n -122.39627838134766,\n 47.458272792347074\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Washington Water Science Center
U.S. Geological Survey
934 Broadway, Suite 300
Tacoma, Washington 98402
The rocks and landforms of the Fort Collins 30′ × 60′ 1:100,000-scale U.S. Geological Survey quadrangle reveals a particularly complete record of geologic history in the northern Front Range of Colorado. The Proterozoic basement rocks exposed in the core of the range preserve evidence of Paleoproterozoic marine sedimentation, volcanism, and regional soft-sediment deformation, followed by regional folding and gradational metamorphism. Mesoproterozoic time was marked by intrusion of the Berthoud Plutonic Suite into crust that was structurally neutral or moderately extending in an east-northeast direction.
Evidence of the late Paleozoic Anasazi uplift (Ancestral Rocky Mountains uplift) within the quadrangle is recorded by removal of Permian and older sediments and deposition of proximal Pennsylvanian and Permian strata unconformably onto the exhumed Proterozoic basement rocks. The Phanerozoic sediments indicate a steady progression of fluvial, eolian, and lacustrine environments throughout most of the Mesozoic Era which was a time of relatively slow sediment accumulation. Early Cretaceous time was marked by incursion of the Cretaceous Western Interior Seaway, a shallow-water marine embayment that persisted throughout the latter part of the Mesozoic Era. Sedimentation rates increased significantly in the latter part of this period during down-warping related to distant crustal loading by thrusting along the western continental margin.
With onset of the Laramide orogeny in latest Cretaceous time, mountain building resumed in this region. This deformation placed Proterozoic rock over Cretaceous and Paleocene strata along the western margin of the Front Range and Medicine Bow Mountains. Post-Laramide time was marked by a prolonged period of weathering, erosion, and planation of the basement-rock surface, extending perhaps into late Oligocene or early Miocene time.
Erosion on the eastern slope of the Front Range in late Paleogene to early Neogene time produced a broad, rolling surface surrounding residual highlands and east-trending fluvial channels filled with coarse, boulder gravel.
Significant global cooling during the Pliocene led to glaciation during the Quaternary. In the Rocky Mountain region, renewed uplift allowed erosion to accentuate the topographic relief across the high mountains of the map area and established the elevations necessary to trigger accumulation of persistent snow and ice. Mountain glaciers advanced and retreated during at least three glacial-interglacial cycles during the middle and late Pleistocene in this area.
Erosion continues to this day on the High Plains east of the mountain front, and progressive incision of the drainage is recorded by at least five major gravel-clad terrace and pediment surfaces along the major fluvial channels that connect to the South Platte River system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3399","usgsCitation":"Workman, J.B., Cole, J.C., Shroba, R.R., Kellogg, K.S., and Premo, W.R., 2018, Geologic map of the Fort Collins 30'×60' quadrangle, Larimer and Jackson Counties, Colorado, and Albany and Laramie Counties, Wyoming: U.S. Geological Survey Scientific Investigations Map 3399, pamphlet 83 p., scale 1:100,000, https://doi.org/10.3133/sim3399/.","productDescription":"Report: vii, 83 p.; 2 Maps: 59.0 x 38.5 inches; Data Release; Read Me","onlineOnly":"Y","ipdsId":"IP-078484","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":359260,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7G44PHV","text":"USGS data release","linkHelpText":"Data release for geologic map of the Fort Collins 30' x 60' quadrangle, Larimer and Jackson Counties, Colorado and Albany and Laramie Counties, Wyoming"},{"id":359258,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_sheet_georeferenced.pdf","text":"Georeferenced Map","size":"59.2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3399 Georeferenced Map"},{"id":359257,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_sheet.pdf","text":"Map","size":"57.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3399 Map"},{"id":359259,"rank":5,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_Readme.txt","text":"Read Me","size":"8.0 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3399 Read Me"},{"id":359255,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3399/coverthb2.jpg"},{"id":359256,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3399/sim3399_pamphlet.pdf","text":"Report","size":"19.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3399 Pamphlet"}],"country":"United States","state":"Colorado, Wyoming","county":"Albany County, Jackson County, Laramie County, Larimer County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106,\n 40.5\n ],\n [\n -105,\n 40.5\n ],\n [\n -105,\n 41\n ],\n [\n -106,\n 41\n ],\n [\n -106,\n 40.5\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS-980
Denver, CO 80225-0046
Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
GenEst (a generalized estimator of mortality) is a suite of statistical models and software tools for generalized mortality estimation. It was specifically designed for estimating the number of bird and bat fatalities at solar and wind power facilities, but both the software (Dalthorp and others, 2018) and the underlying statistical models are general enough to be useful in various situations to estimate the size of open populations when detection probabilities and search coverages are less than 1. In this report, we outline the statistical models and data structures underlying the estimator. The models are numerous, complex, and intricately interwoven. Discussion begins with broad, high-level overviews of the general models. The lower-level technical details are then gradually added. Broader and less technical discussions on the general context and applications of the models and the use of the software are available in the software user guide (Simonis and others, 2018), vignettes bundled with the software, and the help files within the software itself.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Section A: Algorithm in Book 7:Automated data processing and computations","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm7A2","collaboration":"Prepared in cooperation with Bureau of Land Management and the National Renewable Energy Laboratory","usgsCitation":"Dalthorp, D., Madsen, L., Huso, M., Rabie, P., Wolpert, R., Studyvin, J., Simonis, J., and Mintz, J., 2018, GenEst statistical models—A generalized estimator of mortality: U.S. Geological Survey Techniques and Methods, book 7, chap. A2, 13 p., https://doi.org/10.3133/tm7A2.","productDescription":"Report: iv, 13 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-101458","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":359137,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/7a2/coverthb.jpg"},{"id":359138,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/7a2/tm7a2.pdf","text":"Report","size":"371 KB","linkFileType":{"id":1,"text":"pdf"},"description":"TM 7-A2"},{"id":359326,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9O9BATL","text":"USGS data release","linkHelpText":"Generalized Mortality Estimator (GenEst) - R code & GUI"},{"id":359139,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/tm7C19","text":"Techniques and Methods 7-C19 —","description":"TM 7-C19","linkHelpText":"GenEst User Guide—Software for a Generalized Estimator of Mortality"}],"publicComments":"This report is Chapter 2 of Section A: Algorithm in Book 7:Automated data processing and computations.","contact":"Director, Forest and Rangeland Ecosystem Science Center
U.S. Geological Survey
777 NW 9th St., Suite 400
Corvallis, Oregon 97330
No abstract available.
","language":"English","publisher":"Taylor & Francis","doi":"10.1080/0005772X.2018.1535951","usgsCitation":"Corby-Harris, V., Bowsher, J.H., Carr-Markell, M., Carroll, M., Centrella, M., Cook, S.C., Couvillon, M., DeGrandi-Hoffman, G., Dolezal, A., Jones, J.C., Mogren, C., Otto, C., Lau, P., Rangel, J., Schurch, R., and St. Clair, A., 2018, Emerging themes from the ESA symposium entitled “Pollinator nutrition: Lessons from bees at individual to landscape levels”: Bee World, v. 96, no. 1, p. 3-9, https://doi.org/10.1080/0005772X.2018.1535951.","productDescription":"7 p.","startPage":"3","endPage":"9","ipdsId":"IP-099977","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":359670,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"96","issue":"1","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2018-11-07","publicationStatus":"PW","scienceBaseUri":"5bfd146ee4b0815414ca38f2","contributors":{"authors":[{"text":"Corby-Harris, Vanessa","contributorId":210710,"corporation":false,"usgs":false,"family":"Corby-Harris","given":"Vanessa","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":751522,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bowsher, Julia H.","contributorId":210712,"corporation":false,"usgs":false,"family":"Bowsher","given":"Julia","email":"","middleInitial":"H.","affiliations":[{"id":12471,"text":"North Dakota State University","active":true,"usgs":false}],"preferred":false,"id":751524,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Carr-Markell, Morgan","contributorId":210713,"corporation":false,"usgs":false,"family":"Carr-Markell","given":"Morgan","email":"","affiliations":[{"id":38132,"text":"Minnesota","active":true,"usgs":false}],"preferred":false,"id":751525,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carroll, Mark J.","contributorId":210793,"corporation":false,"usgs":false,"family":"Carroll","given":"Mark J.","affiliations":[],"preferred":false,"id":751526,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Centrella, Mary","contributorId":210796,"corporation":false,"usgs":false,"family":"Centrella","given":"Mary","email":"","affiliations":[],"preferred":false,"id":751985,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cook, Steven C.","contributorId":210795,"corporation":false,"usgs":false,"family":"Cook","given":"Steven","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":751527,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Couvillon, Margaret","contributorId":210714,"corporation":false,"usgs":false,"family":"Couvillon","given":"Margaret","email":"","affiliations":[{"id":38133,"text":"Virginia, Tech","active":true,"usgs":false}],"preferred":false,"id":751528,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"DeGrandi-Hoffman, Gloria","contributorId":210715,"corporation":false,"usgs":false,"family":"DeGrandi-Hoffman","given":"Gloria","email":"","affiliations":[{"id":38134,"text":"ARS Tucson","active":true,"usgs":false}],"preferred":false,"id":751529,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Dolezal, Adam","contributorId":210716,"corporation":false,"usgs":false,"family":"Dolezal","given":"Adam","email":"","affiliations":[{"id":38135,"text":"Illinois","active":true,"usgs":false}],"preferred":false,"id":751530,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Jones, Julia C.","contributorId":210794,"corporation":false,"usgs":false,"family":"Jones","given":"Julia","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":751531,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Mogren, Christina","contributorId":210711,"corporation":false,"usgs":false,"family":"Mogren","given":"Christina","email":"","affiliations":[{"id":6758,"text":"USDA-ARS","active":true,"usgs":false}],"preferred":false,"id":751523,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Otto, Clint 0000-0002-7582-3525 cotto@usgs.gov","orcid":"https://orcid.org/0000-0002-7582-3525","contributorId":5426,"corporation":false,"usgs":true,"family":"Otto","given":"Clint","email":"cotto@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":751521,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Lau, Pierre","contributorId":210718,"corporation":false,"usgs":false,"family":"Lau","given":"Pierre","email":"","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":751532,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Rangel, Juliana","contributorId":210719,"corporation":false,"usgs":false,"family":"Rangel","given":"Juliana","email":"","affiliations":[{"id":36313,"text":"Texas A&M","active":true,"usgs":false}],"preferred":false,"id":751533,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Schurch, Roger","contributorId":210720,"corporation":false,"usgs":false,"family":"Schurch","given":"Roger","email":"","affiliations":[{"id":12694,"text":"Virginia Tech","active":true,"usgs":false}],"preferred":false,"id":751534,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"St. Clair, Ashley","contributorId":210721,"corporation":false,"usgs":false,"family":"St. Clair","given":"Ashley","email":"","affiliations":[{"id":38137,"text":"Iowa State","active":true,"usgs":false}],"preferred":false,"id":751535,"contributorType":{"id":1,"text":"Authors"},"rank":16}]}} ]}