The U.S. Geological Survey has been collaborating with the Bureau of Land Management to develop statistically reliable methods for wild horse and burro aerial survey data collection and analysis for more than a decade. In cooperation with Colorado State University, the U.S. Geological Survey tested several methods in herds with known abundance, resulting in two scientifically defensible aerial survey and population estimation techniques. These methods are now being applied by the Bureau of Land Management across the western United States, enabling better management of wild horses and burros. The purpose of these Standard Operating Procedures (SOPs) is to provide detailed instructions to the Bureau of Land Management wild horse and burro specialists who need to fly aerial surveys for management.
This report provides multiple SOPs that are related to Equus caballus (wild horse) and Equus asinus (wild burro) double-observer aerial surveys, along with datasheets, pre-survey checklists, and a quick-guide to the methods. SOP 1 describes how to carry out wild horse and burro aerial surveys as an aviation crew member. SOP 2, SOP 3, and SOP 4 relate to data management, and are important for the wild horse and burro specialist or other lead staff who will be responsible for documenting and archiving records from the survey. SOP 5 details double-observer reporting via a data entry spreadsheet and provides reference for analyzing double observer data to obtain population estimates. SOP 6 presents general principles for preparing aerial survey flight lines. SOP 7 provides instructions for using abundance estimates from aerial surveys to project population size forward in time. The appendixes provide survey datasheets, pre-survey checklists, and a quick-guide to SOP 1.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/tm2A16","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Griffin, P.C., Ekernas, L.S., Schoenecker, K.A., and Lubow, B.C., 2020, Standard operating procedures for wild horse and burro double-observer aerial surveys: U.S. Geological Survey Techniques and Methods, book 2, chap. A16, 76 p., https://doi.org/10.3133/tm2A16","productDescription":"Report: viii, 76 p.; Data Release","numberOfPages":"88","onlineOnly":"Y","ipdsId":"IP-099245","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437070,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P946MHTZ","text":"USGS data release","linkHelpText":"Wild horse aerial double observer survey analysis R script"},{"id":372919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/tm/02/a16/coverthb.jpg"},{"id":372920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/tm/02/a16/tm2a16.pdf","text":"Report","size":"13.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"T&M 2–A16"},{"id":372921,"rank":3,"type":{"id":4,"text":"Application Site"},"url":"https://doi.org/10.5066/P946MHTZ","text":"Software","description":"USGS Data Release","linkHelpText":"– R script to analyze simultaneous double observer wild horse and burro aerial surveys"}],"contact":"Center Director, Fort Collins Science Center
U.S. Geological Survey
2150 Centre Ave., Bldg. C
Fort Collins, CO 80526–8118
An emerging approach to ecosystem monitoring involves the use of physiological biomarker analyses in combination with gene transcription assays. For the first time, we employed these tools to evaluate the Pacific razor clam (Siliqua patula), which is important both economically and ecologically, as a bioindicator species in the northeast Pacific. Our objectives were to (1) develop biomarker and gene transcription assays with which to monitor the health of the Pacific razor clam, (2) acquire baseline biomarker and gene transcription reference ranges for razor clams, (3) assess the relationship between physiological and gene transcription assays and (4) determine if site-level differences were present. Pacific razor clams were collected in July 2015 and 2016 at three sites within each of two national parks in southcentral Alaska. In addition to determining reference ranges, we found differences in biomarker assay and gene transcription results between parks and sites which indicate variation in both large-scale and local environmental conditions. Our intent is to employ these methods to evaluate Pacific razor clams as a bioindicator of nearshore ecosystem health. Links between the results of the biomarker and gene transcription assays were observed that support the applicability of both assays in ecosystem monitoring. However, we recognize the need for controlled studies to examine the range of responses in physiology and gene transcripts to different stressors.sors.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.8761","usgsCitation":"Bowen, L., Counihan, K., Ballachey, B.E., Colletti, H.A., Hollmen, T.E., Pister, B., and Wilson, T.L., 2020, Monitoring nearshore ecosystem health using Pacific razor clams (Siliqua patula) as an indicator species: PeerJ, v. 8, e8761, 30 p., https://doi.org/10.7717/peerj.8761.","productDescription":"e8761, 30 p.","ipdsId":"IP-116067","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457492,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.8761","text":"Publisher Index Page"},{"id":379759,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","otherGeospatial":"Katmai National Park and Preserve, Lake Clark National Park and Preserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -152.68798828125,\n 59.87239799228177\n ],\n [\n -152.259521484375,\n 60.973107109199404\n ],\n [\n -152.874755859375,\n 61.03169171684717\n ],\n [\n -152.9296875,\n 61.53840616716746\n ],\n [\n -153.775634765625,\n 61.52269494598361\n ],\n [\n -154.68749999999997,\n 60.94644199944748\n ],\n [\n -155.10498046875,\n 59.85585085709834\n ],\n [\n -153.028564453125,\n 59.80063426102869\n ],\n [\n -152.68798828125,\n 59.87239799228177\n ]\n ]\n ]\n }\n },\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -153.248291015625,\n 58.89897119532359\n ],\n [\n -153.79760742187497,\n 59.130863097255904\n ],\n [\n -154.2919921875,\n 59.06315402462662\n ],\n [\n -155.06103515624997,\n 59.153403092050375\n ],\n [\n -155.313720703125,\n 59.03490244176445\n ],\n [\n -155.797119140625,\n 59.01794033995248\n ],\n [\n -156.55517578125,\n 58.73970633523893\n ],\n [\n -156.59912109375,\n 58.510913712234455\n ],\n [\n -156.302490234375,\n 58.39019698411526\n ],\n [\n -155.950927734375,\n 57.99645479966997\n ],\n [\n -155.45654296875,\n 57.87981645527839\n ],\n [\n -155.203857421875,\n 57.745213216291866\n ],\n [\n -154.193115234375,\n 58.13592099138227\n ],\n [\n -153.248291015625,\n 58.89897119532359\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","noUsgsAuthors":false,"publicationDate":"2020-03-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Bowen, Lizabeth 0000-0001-9115-4336 lbowen@usgs.gov","orcid":"https://orcid.org/0000-0001-9115-4336","contributorId":4539,"corporation":false,"usgs":true,"family":"Bowen","given":"Lizabeth","email":"lbowen@usgs.gov","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":802973,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Counihan, Katrina","contributorId":140780,"corporation":false,"usgs":false,"family":"Counihan","given":"Katrina","affiliations":[{"id":13561,"text":"Alaska Sea Life Center, Seward, AK","active":true,"usgs":false}],"preferred":false,"id":802974,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ballachey, Brenda E. 0000-0003-1855-9171 bballachey@usgs.gov","orcid":"https://orcid.org/0000-0003-1855-9171","contributorId":2966,"corporation":false,"usgs":true,"family":"Ballachey","given":"Brenda","email":"bballachey@usgs.gov","middleInitial":"E.","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":802975,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Colletti, Heather A","contributorId":199047,"corporation":false,"usgs":false,"family":"Colletti","given":"Heather","email":"","middleInitial":"A","affiliations":[],"preferred":false,"id":802976,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hollmen, Tuula E.","contributorId":211728,"corporation":false,"usgs":false,"family":"Hollmen","given":"Tuula","email":"","middleInitial":"E.","affiliations":[{"id":16211,"text":"Alaska SeaLife Center","active":true,"usgs":false}],"preferred":false,"id":802977,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Pister, Benjamin","contributorId":219669,"corporation":false,"usgs":false,"family":"Pister","given":"Benjamin","email":"","affiliations":[{"id":40046,"text":"Ocean Alaska Science and Learning Center, National Park Service","active":true,"usgs":false}],"preferred":false,"id":802978,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilson, Tammy L","contributorId":219670,"corporation":false,"usgs":false,"family":"Wilson","given":"Tammy","email":"","middleInitial":"L","affiliations":[{"id":40047,"text":"7Department of Natural Resource Management, South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":802979,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70206596,"text":"pp1863 - 2020 - Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole","interactions":[],"lastModifiedDate":"2022-04-22T19:10:54.810814","indexId":"pp1863","displayToPublicDate":"2020-03-05T09:14:28","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":331,"text":"Professional Paper","code":"PP","onlineIssn":"2330-7102","printIssn":"1044-9612","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"1863","displayTitle":"Groundwater Characterization and Effects of Pumping in the Death Valley Regional Groundwater Flow System, Nevada and California, with Special Reference to Devils Hole","title":"Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole","docAbstract":"Groundwater flow and development were characterized in four groundwater basins of the Death Valley regional flow system in Nevada and California with calibrated, groundwater-flow models. Natural groundwater discharges in the Furnace Creek, Lower Amargosa, and Saratoga Spring areas were defined and distributed consistently with a revised hydrogeologic framework. This simplified hydrogeologic framework was limited to four hydraulically unique, hydrogeologic units: (1) basin fill; (2) carbonate rocks; (3) volcanic rocks; and (4) low-permeability granitic and siliciclastic rocks. Hydrogeologic units and division of carbonate and volcanic rocks between shallow and deep were supported by results from 271 aquifer tests and specific-capacity estimates. Greater than 90 percent of field-estimated transmissivity occurred within 1,600 feet (ft) of the water table. Pumping in the study area from 1960 to 2010 averaged 46,000 acre-feet per year (acre-ft/yr), which is 80 percent of the predevelopment discharge. The central Amargosa Desert and Pahrump Valley were the two primary pumping centers and measurably affected water levels across 900 square miles in 2018.
Water levels in Devils Hole were a special focus because endangered Devils Hole pupfish (Cyprinodon diabolis) are affected by water-level declines. Pumping 42,100 acre-ft by Cappaert Enterprises, formerly Spring Meadows, Inc., caused a 2.3-ft water-level decline in Devils Hole, which temporarily reduced habitat of Devils Hole pupfish by 85 percent in 1972. If no pumping occurred, water levels in Devils Hole would have risen naturally about 1 ft between 1973 and 2018 from temporal variations in recharge. The 2.6-ft range of measured water-level changes in Devils Hole was simulated with a root-mean-square error of 0.2 ft during the 70-year period of record. Simulated water-level declines from pumping totaled 1.4 ft in 2018, with 25 and 34 percent attributed to pumping by Cappaert Enterprises and the central Amargosa Desert, respectively. Water levels in Devils Hole will decline at rates of 0.1–0.2 ft per decade if pumping from Ash Meadows groundwater basin and the central Amargosa Desert continue at current rates. Effects of future natural water-level fluctuations remain unknown.
Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins are hydraulically connected near well AD-4, about 5 miles south of the town of Amargosa Valley, Nevada. About 40 percent of the discharge from the Furnace Creek area is recharged in the Ash Meadows groundwater basin. Basin fill in the central Amargosa Desert hydraulically connects carbonate rocks east of well AD-4 with saturated carbonate rocks in the Funeral Range. About 7 percent of the 960,000 acre-ft pumped from Ash Meadows and Alkali Flat–Furnace Creek Ranch groundwater basins prior to 2019 was captured discharge from springs and phreatophytes. Greater than 40 percent of the 2,080,000 acre-ft pumped from Pahrump Valley between 1910 and 2019 was capture that primarily discharged from Bennetts and Manse Springs.
Simulated advective-flow distances and velocities from underground nuclear tests are within the range of advective transport calculations from tritium data and previous radionuclide transport investigations. Boundary conditions and flow rates from the regional model in this study are plausible for local-scale flow and radionuclide transport models. Simulated 165-year groundwater-flow paths do not extend into pumping areas and effects of regional pumping on advective transport are negligible.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/pp1863","collaboration":"Prepared in cooperation with the U.S. Department of Energy Office of Environmental Management, National Nuclear Security Administration, Nevada Site Office, under Interagency Agreement DE-EM0004969","usgsCitation":"Halford, K.J., and Jackson, T.R., 2020, Groundwater characterization and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole: U.S. Geological Survey Professional Paper 1863, 178 p., https://doi.org/10.3133/pp1863.","productDescription":"Report: xvi, 178 p.; Data Release","ipdsId":"IP-105994","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":372815,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HIYVG2","text":"USGS data release","description":"USGS Data Release","linkHelpText":"MODFLOW-2005 model and supplementary data used to characterize groundwater flow and effects of pumping in the Death Valley regional groundwater flow system, Nevada and California, with special reference to Devils Hole"},{"id":399508,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109738.htm"},{"id":372814,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/pp/1863/pp1863.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"PP 1863"},{"id":372813,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/pp/1863/coverthb2.jpg"}],"country":"United States","state":"California, Nevada","otherGeospatial":"Death Valley, Devils Hole","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117,\n 35.6464\n ],\n [\n -115.0611,\n 35.6464\n ],\n [\n -115.0611,\n 37.7214\n ],\n [\n -117,\n 37.7214\n ],\n [\n -117,\n 35.6464\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 89701
We have assembled a comprehensive and publicly accessible U.S. Geological Survey (USGS) streamflow measurement data set, called HYDRoSWOT, from a USGS National Water Information System archive of acoustic Doppler current profiler river discharge measurements collected from a wide range of rivers throughout the United States. The data set provides a wealth of information on the range of hydraulic characteristics of river cross sections in the United States. Preliminary exploration of the data set, filtered for quality control, indicates that rivers tend toward consistent and predictable forms as discharge increases. The ratio of maximum-to-mean depth is highly predictable and is remarkably consistent across all river sizes and discharges. Distributions of hydraulic characteristics provide a large-scale perspective on the general hydraulic characteristics of rivers. The data set affords the opportunity to analyze hydraulic relations for individual rivers as a function of stage, geomorphic setting, and energy environments and, combined with additional information contained in this data set, might yield predictive relations that could help constrain and parameterize river hydraulic models.
Despite consensus that impervious surfaces increase flooding, the magnitude of the increase remains uncertain. This uncertainty largely stems from the challenge of isolating the effect of changes in impervious cover separate from other factors that also affect flooding. To control for these factors, prior study designs rely on either temporal or spatial variation in impervious cover. We leverage both temporal and spatial variation in a panel data regression design to isolate the effect of impervious cover on floods. With 39 years of data from 280 U.S. streamgages, we estimate that a one percentage point increase in impervious basin cover causes a 3.3% increase in annual flood magnitude (95%CI: 1.9%, 4.7%) on average. Using 2,109 streamgages, some of which have upstream regulation and/or overlapping basins, we estimate a larger effect: 4.6% (CI: 3.5%, 5.6%). The approach introduced here can be extended to estimate the causal effects of other drivers of hydrologic change.
We evaluated the status of a population of Mojave Desert Tortoises (Gopherus agassizii), a threatened species, in the El Paso Mountains of the northwestern Mojave Desert in California, USA. The study area lies north of and adjacent to a designated critical habitat unit for the species, is adjacent to a state park, and is a short distance from the Desert Tortoise Research Natural Area. We randomly sampled 373 1-ha plots from a 239.1-km2 area in the mountain range to determine demographic attributes of the population, vegetation associations, predator presence, and human uses. Live and dead G. agassizii and sign (burrows, scats, tracks) occurred on 35.7% of plots. Densities of adults were higher than in adjacent critical habitat, and threats (traumatic injuries, infectious and other diseases) were similar to those reported elsewhere in the geographic range. Signs of human use were evident on 98.4% of plots. We used a multimodel approach to determine distribution of G. agassizii in relation to vegetation, anthropogenic, and predator variables. Vegetation, predators, trash, mining activity, and vehicles were important factors affecting the distribution and intensity of tortoise sign. We concluded that this population is in a downward trend, like other populations in the western Mojave Desert. The high death rate of adults, low population density, high human visitor use, and ongoing decline in the adjacent critical habitat unit indicate that a viable population is unlikely to persist in the study area. The future for the population found in the El Paso Mountains might depend on survival in the adjacent roadless El Paso Mountains Wilderness Area.
","language":"English","publisher":"The Herpetologists' League, Inc","doi":"10.1655/Herpetologica-D-18-00033","usgsCitation":"Berry, K.H., Yee, J.L., Lyren, L.L., and Mack, J., 2020, An uncertain future for a population of desert tortoises experiencing human impacts: Herpetologica, v. 76, no. 1, p. 1-11, https://doi.org/10.1655/Herpetologica-D-18-00033.","productDescription":"11 p.","startPage":"1","endPage":"11","ipdsId":"IP-016876","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457504,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1655/herpetologica-d-18-00033","text":"Publisher Index Page"},{"id":372962,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","county":"Kern County","otherGeospatial":"El Paso Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.14971923828124,\n 35.29607300397548\n ],\n [\n -117.60177612304688,\n 35.29607300397548\n ],\n [\n -117.60177612304688,\n 35.66399091134812\n ],\n [\n -118.14971923828124,\n 35.66399091134812\n ],\n [\n -118.14971923828124,\n 35.29607300397548\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"76","issue":"1","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783498,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":783499,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lyren, Lisa L.","contributorId":166968,"corporation":false,"usgs":false,"family":"Lyren","given":"Lisa","email":"","middleInitial":"L.","affiliations":[{"id":24583,"text":"former USGS employee","active":true,"usgs":false}],"preferred":false,"id":783500,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mack, Jeremy S 0000-0002-3394-8493","orcid":"https://orcid.org/0000-0002-3394-8493","contributorId":206166,"corporation":false,"usgs":false,"family":"Mack","given":"Jeremy S","affiliations":[{"id":37269,"text":"Crater Lake National Park (formerly USGS - WERC)","active":true,"usgs":false}],"preferred":false,"id":783501,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208487,"text":"sir20205012 - 2020 - Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17","interactions":[],"lastModifiedDate":"2022-04-25T21:42:26.20684","indexId":"sir20205012","displayToPublicDate":"2020-03-04T14:44:16","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2020-5012","displayTitle":"Estimates of Water Use Associated with Continuous Oil and Gas Development in the Williston Basin, North Dakota and Montana, 2007–17","title":"Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17","docAbstract":"This study of water use associated with development of continuous oil and gas resources in the Williston Basin is intended to provide a preliminary model-based analysis of water use in major regions of production of continuous oil and gas resources in the United States. Direct, indirect, and ancillary water use associated with development of continuous oil and gas resources in the Williston Basin was estimated in North Dakota and Montana from 2007 to 2017. Water-use data were aggregated by county and year, which were the sampling units used in this analysis. Linear and quantile regression models of water use in relation to the number of oil and gas wells developed were fit for the direct, indirect, and ancillary water-use categories for each State. A 95-percent confidence interval for each parameter estimate from the linear regression models was computed as a measure of uncertainty. Additional information on uncertainty can be gained from modeling other distribution parameters, so quantile regression models of the 5th, 50th, and 95th percentiles also were fit. To assess uncertainty in the estimates from the regression models of direct, indirect, and ancillary water use, leave-one-out cross-validation was used. Model performance was evaluated with three goodness-of-fit metrics used to compare the estimates and observations of water use.
Mean annual direct and indirect water use for development of continuous oil and gas resources in North Dakota was estimated at 4,512 million gallons (Mgal) per year (Mgal/yr), with a 95-percent confidence interval of 4,021–5,152 Mgal/yr, and in Montana was estimated at 196 Mgal/yr, with a 95-percent confidence interval of 189–203 Mgal/yr. Ancillary water use (for domestic and public supply) had an estimated annual mean of 2,753 Mgal/yr in North Dakota and 396 Mgal/yr in Montana. The coefficient from the linear regression model of direct water use was 3.86 Mgal per well and hydraulic fracturing water use was 3.70 Mgal per well for North Dakota. The mean estimate of direct water use had a 95-percent confidence interval of 3.48–4.23 Mgal per well. For North Dakota, the coefficient from the linear regression model of indirect water use was 0.453 Mgal per well, with a 95-percent confidence interval of 0.415–0.492 Mgal per well. Direct and indirect water use had a mean estimate of about 4.31 Mgal per well in North Dakota. The mean estimate of ancillary water use (for domestic and public supply) in North Dakota was 2.03 Mgal per well, with a 95-percent confidence interval of 1.76–2.31 Mgal per well. For Montana, the linear regression model of hydraulic fracturing water use had a mean estimate of 2.04 Mgal per well. The 95-percent confidence interval for the mean estimate was 1.80–2.28 Mgal per well. Direct and indirect water use in Montana had a mean estimate of 2.49 Mgal per well. The mean estimate of ancillary water use (for domestic and public supply) in Montana was 2.43 Mgal per well, with a 95-percent confidence interval of 1.76–3.11 Mgal per well.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205012","collaboration":"Water Availability and Use Science Program","usgsCitation":"McShane, R.R., Barnhart, T.B., Valder, J.F., Haines, S.S., Macek-Rowland, K.M., Carter, J.M., Delzer, G.C., and Thamke, J.N., 2020, Estimates of water use associated with continuous oil and gas development in the Williston Basin, North Dakota and Montana, 2007–17: U.S. Geological Survey Scientific Investigations Report 2020–5012, 26 p., https://doi.org/10.3133/sir20205012","productDescription":"Report: vii, 26 p.; 2 Appendixes; Data Release","numberOfPages":"38","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-112448","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":399633,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109737.htm"},{"id":372867,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix2.zip","text":"Appendix 2","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 2","linkHelpText":"– Water-Use Estimates and Coefficients"},{"id":372866,"rank":3,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012_appendix1.zip","text":"Appendix 1","linkFileType":{"id":6,"text":"zip"},"description":"SIR 2020–5012 Appendix 1","linkHelpText":"– R Scripts"},{"id":372864,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5012/coverthb2.jpg"},{"id":372868,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9CPKRLW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Data to Estimate Water Use Associated with Continuous Oil and Gas Development, Williston Basin, United States, 1980-2017 (ver. 2.0, September 2019)"},{"id":372865,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5012/sir20205012.pdf","text":"Report","size":"2.14 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5012"}],"country":"United States","state":"Montana, North Dakota, South Dakota","otherGeospatial":"Williston Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.8333,\n 44.8333\n ],\n [\n -99,\n 44.8333\n ],\n [\n -99,\n 49\n ],\n [\n -106.8333,\n 49\n ],\n [\n -106.8333,\n 44.8333\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wyoming-Montana Water Science Center
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We introduced a multilevel model of value shift to describe the changing social context of wildlife conservation. Our model depicts how cultural-level processes driven by modernization (e.g., increased wealth, education, and urbanization) affect changes in individual-level cognition that prompt a shift from domination to mutualism wildlife values. Domination values promote beliefs that wildlife should be used primarily to benefit humans, whereas mutualism values adopt a view that wildlife are part of one's social network and worthy of care and compassion. Such shifts create emergent effects (e.g., new interest groups) and challenges to wildlife management organizations (e.g., increased conflict) and dramatically alter the sociopolitical context of conservation decisions. Although this model is likely applicable to many modernized countries, we tested it with data from a 2017–2018 nationwide survey (mail and email panel) of 43,949 residents in the United States. We conducted hierarchical linear modeling and correlational analysis to examine relationships. Modernization variables had strong state-level effects on domination and mutualism. Higher levels of education, income, and urbanization were associated with higher percentages of mutualists and lower percentages of traditionalists, who have strong domination values. Values affected attitudes toward wildlife management challenges; for example, states with higher proportions of mutualists were less supportive of lethal control of wolves (Canis lupus) and had lower percentages of active hunters, who represent the traditional clientele of state wildlife agencies in the United States. We contend that agencies will need to embrace new strategies to engage and represent a growing segment of the public with mutualism values. Our model merits testing for application in other countries.
The fire regime is a central framing concept in wildfire science and ecology and describes how a range of wildfire characteristics vary geographically over time. Understanding and mapping fire regimes is important for guiding appropriate management and risk reduction strategies and for informing research on drivers of global change and altered fire patterns. Most efforts to spatially delineate fire regimes have been conducted by identifying natural groupings of fire parameters based on available historical fire data. This can result in classes with similar fire characteristics but wide differences in ecosystem types. We took a different approach and defined fire regime ecoregions for California to better align with ecosystem types, without using fire as part of the definition. We used an unsupervised classification algorithm to segregate the state into spatial clusters based on distinctive biophysical and anthropogenic attributes that drive fire regimes – and then used historical fire data to evaluate the ecoregions. The fire regime ecoregion map corresponded well with the major land cover types of the state and provided clear separation of historical patterns in fire frequency and size, with lower variability in fire severity. This methodology could be used for mapping fire regimes in other regions with limited historical fire data or forecasting future fire regimes based on expected changes in biophysical characteristics.
Sediment is one of the leading pollutants in rivers and streams across the United States (US) and the world. Between 1992 and 2012, concentrations of annual mean suspended sediment decreased at over half of the 137 stream sites assessed across the contiguous US. Increases occurred at less than 25 % of the sites, and the direction of change was uncertain at the remaining 25 %. Sediment trends were characterized using the Weighted Regressions on Time, Discharge, and Season (WRTDS) model, and decreases in sediment ranged from −95 % to −8.5 % of the 1992 concentration. To explore potential drivers of these changes, the sediment trends were (1) parsed into two broad contributors of change, changes in land management versus changes in the streamflow regime, and (2) grouped by land use of the watershed and correlated to concurrent changes in land use or land cover (land use/cover), hydrology and climate variables and static/long-term watershed characteristics. At 83 % of the sites, changes in land management (captured by changes in the concentration–streamflow relationship over time; C–Q relationship) contributed more to the change in the sediment trend than changes in the streamflow regime alone (i.e., any systematic change in the magnitude, frequency or timing of flows). However, at >50 % of the sites, changes in the streamflow regime contributed at least a 5 % change in sediment, and at 11 sites changes in the streamflow regime contributed over half the change in sediment, indicating that at many sites changes in streamflow were not the main driver of changes in sediment but were often an important supporting factor. Correlations between sediment trends and concurrent changes in land use/cover, hydrology and climate were often stronger at sites draining watersheds with more homogenous, human-related land uses (i.e., agricultural and urban lands) compared to mixed-use or undeveloped lands. At many sites, decreases in sediment occurred despite small-to-moderate increases in the amount of urban or agricultural land in the watershed, suggesting conservation efforts and best-management practices (BMPs) used to reduce sediment runoff to streams may be successful, up to a point, as lands are converted to urban and agricultural uses.
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Division","active":true,"usgs":true},{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"preferred":false,"id":783837,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70208666,"text":"70208666 - 2020 - Climate change: Flowering time may be shifting in surprising ways","interactions":[],"lastModifiedDate":"2020-03-05T14:10:34","indexId":"70208666","displayToPublicDate":"2020-03-03T14:09:19","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1352,"text":"Current Biology","active":true,"publicationSubtype":{"id":10}},"title":"Climate change: Flowering time may be shifting in surprising ways","docAbstract":"Climate change is known to affect regional weather patterns and phenology; however, we lack under-standing of how climate drives phenological change across local spatial gradients. This spatial variation is critical for determining whether subpopulations and metacommunities are changing in unison or diverging in phenology. Divergent responses could reduce synchrony both within species (disrupting gene flow among subpopulations) and among species (disrupting interspecific interactions in communities). We also lack understanding of phenological change in environments where life history events are frequently aseasonal, such as the tropical, arid,and semi-arid ecosystems that cover vast areas.Using a 33-year-long dataset spanning a 1,267-m semi-arid elevational gradient in the southwestern United States, we test whether flowering phenology diverged among subpopulations within species and among five communities comprising 590 species. Applying circular statistics to test for changes in year-round flowering, we show flowering has become earlier for all communities except at the highest elevations. However, flowering times shifted at different rates across elevations likely because of elevation-specific changes in temperature and precipitation, indicating diverging phenologies of neighboring communities. Subpopulations of individual species also diverged at mid-elevation but converged in phenology at high elevation. These changes in flowering phenology among communities and subpopulations are undetectable when data are pooled across the gradient. Furthermore, we show that nonlinear changes in flowering times over the 33-year record are obscured by traditional calculations of long-term trends. These findings reveal greater spatiotemporal complexity in phenological responses than previously recognized and indicate climate is driving phenological reshuffling acrosslocal spatial gradients.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.cub.2019.12.009","usgsCitation":"Prevey, J.S., 2020, Climate change: Flowering time may be shifting in surprising ways: Current Biology, v. 30, no. 3, p. R112-R114, https://doi.org/10.1016/j.cub.2019.12.009.","productDescription":"3 p.","startPage":"R112","endPage":"R114","ipdsId":"IP-114283","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":457512,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.cub.2019.12.009","text":"Publisher Index Page"},{"id":372955,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","issue":"3","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Prevey, Janet S. 0000-0003-2879-6453","orcid":"https://orcid.org/0000-0003-2879-6453","contributorId":222702,"corporation":false,"usgs":true,"family":"Prevey","given":"Janet","email":"","middleInitial":"S.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":782946,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70209057,"text":"70209057 - 2020 - Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database","interactions":[],"lastModifiedDate":"2020-03-12T12:52:37","indexId":"70209057","displayToPublicDate":"2020-03-03T12:46:56","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1958,"text":"ISPRS Journal of Photogrammetry and Remote Sensing","active":true,"publicationSubtype":{"id":10}},"title":"Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database","docAbstract":"The 2016 National Land Cover Database (NLCD) product suite (available on www.mrlc.gov), includes Landsat-based, 30 m resolution products over the conterminous (CONUS) United States (U.S.) for land cover, urban imperviousness, and tree, shrub, herbaceous and bare ground fractional percentages. The release of NLCD 2016 provides important new information on land change patterns across CONUS from 2001-2016. For land cover, seven epochs were concurrently generated for years 2001, 2004, 2006, 2008, 2011, 2013, and 2016. Products reveal that land cover change is significant across most land cover classes and time periods. The land cover product was validated using existing reference data from the legacy NLCD 2011 accuracy assessment, applied to the 2011 epoch of the NLCD 2016 product line. The legacy and new NLCD 2011 overall accuracies were 82% and 83%, respectively, (standard error was 0.5%), demonstrating a small but significant increase in overall accuracy. Between 2001-2016, the CONUS landscape experienced significant change, with almost 8% of the landscape having experienced a land cover change at least once during this time. Nearly 50% of that change involves forest, driven by change agents of harvest, fire, disease and pests that resulted in an overall forest decline, including increasing fragmentation and loss of interior forest. Agricultural change represented 15.9% of the change, with total agricultural spatial extent showing only a slight increase of 4,778 km2, however there was a substantial decline (7.94%) in pasture/hay during this time, transitioning mostly to cultivated crop. Water and wetland change comprised 15.2% of change and represent highly dynamic land cover classes from epoch to epoch, heavily influenced by precipitation. Grass and shrub change comprise 14.5% of the total change, with most change resulting from fire. Developed change was the most persistent and permanent land change increase adding almost 29,000 km2 over 15 years (5.6% of total CONUS change), with southern states exhibiting expansion much faster than most of the northern states. Temporal rates of developed change increased in 2001-2006 at twice the rate of 2011-2016, reflecting a slowdown in CONUS economic activity. Future NLCD plans include increasing monitoring frequency, reducing latency time between satellite imaging and product delivery, improving accuracy and expanding the variety of products available in an integrated database.","language":"English","publisher":"Elsevier","doi":"10.1016/j.isprsjprs.2020.02.019","usgsCitation":"Homer, C.G., Dewitz, J., Jin, S., Xian, G.Z., Costello, C., Danielson, P., Gass, L., Funk, M., Wickham, J., Stehman, S., Auch, R.F., and Riitters, K.H., 2020, Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database: ISPRS Journal of Photogrammetry and Remote Sensing, v. 162, p. 184-199, https://doi.org/10.1016/j.isprsjprs.2020.02.019.","productDescription":"16 p.","startPage":"184","endPage":"199","ipdsId":"IP-113469","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":457514,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index 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Center","active":true,"usgs":true}],"preferred":true,"id":784664,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Riitters, Kurt H. 0000-0003-3901-4453","orcid":"https://orcid.org/0000-0003-3901-4453","contributorId":139788,"corporation":false,"usgs":false,"family":"Riitters","given":"Kurt","email":"","middleInitial":"H.","affiliations":[{"id":36400,"text":"US Forest Service","active":true,"usgs":false}],"preferred":false,"id":784665,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70210746,"text":"70210746 - 2020 - Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams","interactions":[],"lastModifiedDate":"2020-06-23T14:52:36.144242","indexId":"70210746","displayToPublicDate":"2020-03-03T09:48:29","publicationYear":"2020","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}},"title":"Legacy and current‐use contaminants in sediments alter macroinvertebrate communities in southeastern US Streams","docAbstract":"Sediment contamination of freshwater streams in urban areas is a recognized and growing concern. As a part of a comprehensive regional stream‐quality assessment, stream‐bed sediment was sampled from streams spanning a gradient of urban intensity in the Piedmont ecoregion of the southeastern United States. We evaluated relations between a broad suite of sediment contaminants (metals, current‐use pesticides, organochlorine pesticides, polychlorinated biphenyls, brominated diphenyl ethers, and polycyclic aromatic hydrocarbons), ambient sediment toxicity, and macroinvertebrate communities from 76 sites. Sediment toxicity was evaluated by conducting whole‐sediment laboratory toxicity testing with the amphipod Hyalella azteca (for 28 d) and the midge Chironomus dilutus (for 10 d). Approximately one‐third of the sediment samples were identified as toxic for at least one test species endpoint, although concentrations of contaminants infrequently exceeded toxicity benchmarks. Ratios of contaminant concentrations relative to their benchmarks, both individually and as summed benchmark quotients, were explored on a carbon‐normalized and a dry‐weight basis. Invertebrate taxa measures from ecological surveys tended to decline with increasing urbanization and with sediment contamination. Toxicity test endpoints were more strongly related to sediment contamination than invertebrate community measures were. Sediment chemistry and sediment toxicity provided moderate and weak, respectively, explanatory power for the similarity/dissimilarity of invertebrate communities. The results indicate that current single‐chemical sediment benchmarks may underestimate the effects from mixtures of sediment contaminants experienced by lotic invertebrates.
All five species of sea turtle that occur in Florida are in danger of extinction. Many of the reasons these turtles are declining are a result of people’s actions on beaches and in shallow waters. Environmental education is needed to increase awareness and appreciation for sea turtles, and to teach about the potential harmful impacts human behaviors can have on these animals. This document describes topics that are frequently misunderstood and discusses common human actions that are harmful to sea turtles, providing insight on which topics could be addressed during environmental education efforts.
","language":"English","publisher":"UFIFAS Extension","doi":"","collaboration":"","usgsCitation":"Swindall, J.E., Ober, H.K., Lamont, M., and Carthy, R.R., 2020, Sea turtle conservation: Priorities for environmental education efforts: EDIS, v. 2, no. 4, UW46500, 4 p., https://doi.org/.","productDescription":"UW46500, 4 p.","ipdsId":"IP-116612","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":373861,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":373768,"type":{"id":15,"text":"Index Page"},"url":"https://journals.flvc.org/edis/article/view/117285"}],"country":"United 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Research Center","active":true,"usgs":true}],"preferred":true,"id":786429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Carthy, Raymond R. 0000-0001-8978-5083","orcid":"https://orcid.org/0000-0001-8978-5083","contributorId":223853,"corporation":false,"usgs":true,"family":"Carthy","given":"Raymond","email":"","middleInitial":"R.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":786430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70228433,"text":"70228433 - 2020 - Estimating population persistence for at-risk species using citizen science data","interactions":[],"lastModifiedDate":"2022-02-10T13:24:33.608697","indexId":"70228433","displayToPublicDate":"2020-03-03T07:22:20","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Estimating population persistence for at-risk species using citizen science data","docAbstract":"Population persistence probability is valuable for characterizing risk to species and informing listing and conservation decisions but is challenging to estimate through traditional methods for rare, data-limited species. Modeling approaches have used citizen science data to mitigate data limitations of focal species and better estimate parameters such as occupancy and detection, but their use to estimate persistence and inform conservation decisions is limited. We developed an approach to estimate persistence using only occurrence records of the target species and citizen science occurrence data of non-target species to account for search effort and imperfect detection. We applied the approach to a highly cryptic and data-limited species, the southern hognose snake (Heterodon simus), as part of its USFWS Species Status Assessment, and estimated current (in 2018) and future persistence under plausible scenarios of varying levels of urbanization, sea level rise, and management. Of 222 known populations, 133 (60%) are likely extirpated currently (persistence probability < 50%), and 165 (74%) populations are likely to be extirpated by 2080 with no additional management. Future management scenarios that included strategies to acquire and improve habitat on currently unprotected lands with existing populations lessened the estimated rate of population declines. These results can directly inform listing decisions and conservation planning for the southern hognose snake by Federal, State, and other partners. Our approach – using occurrence records and auxiliary data from non-target species to estimate population persistence – is applicable across rare and at-risk species for evaluating extinction risk with limited data and prioritizing management actions.
Several species of ciscoes Coregonus, subgenus Leucichthys that are native to the Laurentian Great Lakes are rare or extirpated. The restoration of Coregonus fishes is being actively pursued through stocking, and success may depend on the availability of unoccupied niche space. We described the spring–summer habitat occupancy and diets of three native cisco species (Bloater Coregonus hoyi, Cisco C. artedi, and Kiyi C. Kiyi) and invasive Rainbow Smelt Osmerus mordax in Lake Superior and measured niche overlap among these species for both small and large sizes. The potential habitat area was highest for Cisco and Kiyi, followed by Bloater and Rainbow Smelt. The probability of overlap in habitat occupation, as measured by bathymetric depth, fish capture depth, distance from shore, and fish capture water temperature was highest for small Rainbow Smelt and Cisco. Trophic overlap, as measured by stomach contents and stable isotopes, was highest between small Bloater and Cisco and between large Bloater and Kiyi. All of the species showed significant ontogenetic change in both habitat occupation and diet. The overall niche overlap in spring–summer habitat and diet was greatest between small Cisco and Rainbow Smelt and between large Bloater and Kiyi; however, differences in individual niche dimensions likely limit competition for both species pairs. Synthesizing the diet and habitat niche data revealed nuanced niches that allow these seemingly similar planktivorous species to coexist. Kiyi had the least niche overlap with other cisco species and Rainbow Smelt, so from an available niche perspective Kiyi would be a strong candidate for reintroduction into lakes from which they were extirpated.
","language":"English","publisher":"American Fisheries Society","doi":"10.1002/tafs.10219","usgsCitation":"Rosinski, C.L., Vinson, M., and Yule, D.L., 2020, Niche partitioning among native ciscoes and nonnative Rainbow Smelt in Lake Superior: Transactions of the American Fisheries Society, v. 149, no. 2, p. 184-203, https://doi.org/10.1002/tafs.10219.","productDescription":"10 p.","startPage":"184","endPage":"203","ipdsId":"IP-113030","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":382868,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States, Canada","otherGeospatial":"Lake Superior","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -88.11035156249999,\n 49.009050809382046\n ],\n [\n -89.1650390625,\n 48.574789910928864\n ],\n [\n -89.4287109375,\n 48.019324184801185\n ],\n [\n -90.703125,\n 47.724544549099676\n ],\n [\n -92.1533203125,\n 46.6795944656402\n ],\n [\n -90.8349609375,\n 46.9502622421856\n ],\n [\n -90.8349609375,\n 46.558860303117164\n ],\n [\n -90,\n 46.76996843356982\n ],\n [\n -88.9892578125,\n 47.07012182383309\n ],\n [\n -87.978515625,\n 47.338822694822\n ],\n [\n -88.505859375,\n 46.76996843356982\n ],\n [\n -88.11035156249999,\n 46.9502622421856\n ],\n [\n -87.451171875,\n 46.558860303117164\n ],\n [\n -86.3525390625,\n 46.46813299215554\n ],\n [\n -85.4736328125,\n 46.70973594407157\n ],\n [\n -85.0341796875,\n 46.70973594407157\n ],\n [\n -84.8583984375,\n 46.31658418182218\n ],\n [\n -84.3310546875,\n 46.49839225859763\n ],\n [\n -84.5068359375,\n 47.07012182383309\n ],\n [\n -84.90234375,\n 47.989921667414194\n ],\n [\n -85.95703125,\n 48.10743118848039\n ],\n [\n -86.3525390625,\n 48.719961222646276\n ],\n [\n -88.11035156249999,\n 49.009050809382046\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"149","issue":"2","noUsgsAuthors":false,"publicationDate":"2020-03-03","publicationStatus":"PW","contributors":{"authors":[{"text":"Rosinski, Caroline Lynn 0000-0003-3635-2748","orcid":"https://orcid.org/0000-0003-3635-2748","contributorId":248618,"corporation":false,"usgs":true,"family":"Rosinski","given":"Caroline","email":"","middleInitial":"Lynn","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809624,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Vinson, Mark 0000-0001-5256-9539 mvinson@usgs.gov","orcid":"https://orcid.org/0000-0001-5256-9539","contributorId":3800,"corporation":false,"usgs":true,"family":"Vinson","given":"Mark","email":"mvinson@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809625,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Yule, Daniel L. 0000-0002-0117-5115","orcid":"https://orcid.org/0000-0002-0117-5115","contributorId":248693,"corporation":false,"usgs":true,"family":"Yule","given":"Daniel","middleInitial":"L.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":809626,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211932,"text":"70211932 - 2020 - Mercury export from Arctic great rivers","interactions":[],"lastModifiedDate":"2020-08-11T21:05:02.516262","indexId":"70211932","displayToPublicDate":"2020-03-02T16:04:25","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1565,"text":"Environmental Science & Technology","onlineIssn":"1520-5851","printIssn":"0013-936X","active":true,"publicationSubtype":{"id":10}},"title":"Mercury export from Arctic great rivers","docAbstract":"Land–ocean linkages are strong across the circumpolar north, where the Arctic Ocean accounts for 1% of the global ocean volume and receives more than 10% of the global river discharge. Yet estimates of Arctic riverine mercury (Hg) export constrained from direct Hg measurements remain sparse. Here, we report results from a coordinated, year-round sampling program that focused on the six major Arctic rivers to establish a contemporary (2012–2017) benchmark of riverine Hg export. We determine that the six major Arctic rivers exported an average of 20 000 kg y–1 of total Hg (THg, all forms of Hg). Upscaled to the pan-Arctic, we estimate THg flux of 37 000 kg y–1. More than 90% of THg flux occurred during peak river discharge in spring and summer. Normalizing fluxes to watershed area (yield) reveals higher THg yields in regions where greater denudation likely enhances Hg mobilization. River discharge, suspended sediment, and dissolved organic carbon predicted THg concentration with moderate fidelity, while suspended sediment and water yields predicted THg yield with high fidelity. These findings establish a benchmark in the face of rapid Arctic warming and an intensifying hydrologic cycle, which will likely accelerate Hg cycling in tandem with changing inputs from thawing permafrost and industrial activity.
","language":"English","publisher":"ACS Publications","doi":"10.1021/acs.est.9b07145","usgsCitation":"Zolkos, S., Krabbenhoft, D.P., Suslova, A., Tank, S.E., McClelland, J.W., Spencer, R.G., Shiklomanov, A., Zhulidov, A.V., Gurtovaya, T., Zimov, N., Zimov, S., Mutter, E., Kutny, L., Amos, E., and Holmes, R.M., 2020, Mercury export from Arctic great rivers: Environmental Science & Technology, v. 54, no. 7, p. 4140-4148, https://doi.org/10.1021/acs.est.9b07145.","productDescription":"9 p.","startPage":"4140","endPage":"4148","ipdsId":"IP-115773","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":377394,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, Russia, United States","volume":"54","issue":"7","noUsgsAuthors":false,"publicationDate":"2020-03-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Zolkos, Scott 0000-0001-9945-6945","orcid":"https://orcid.org/0000-0001-9945-6945","contributorId":238024,"corporation":false,"usgs":false,"family":"Zolkos","given":"Scott","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Krabbenhoft, David P. 0000-0003-1964-5020 dpkrabbe@usgs.gov","orcid":"https://orcid.org/0000-0003-1964-5020","contributorId":1658,"corporation":false,"usgs":true,"family":"Krabbenhoft","given":"David","email":"dpkrabbe@usgs.gov","middleInitial":"P.","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true},{"id":677,"text":"Wisconsin Water Science Center","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"preferred":true,"id":795853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Suslova, Anya","contributorId":238025,"corporation":false,"usgs":false,"family":"Suslova","given":"Anya","email":"","affiliations":[{"id":16705,"text":"Woods Hole Research Center","active":true,"usgs":false}],"preferred":false,"id":795854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Tank, Suzanne E. 0000-0002-5371-6577","orcid":"https://orcid.org/0000-0002-5371-6577","contributorId":238026,"corporation":false,"usgs":false,"family":"Tank","given":"Suzanne","email":"","middleInitial":"E.","affiliations":[{"id":47684,"text":"Department of Biological Sciences, University of Alberta","active":true,"usgs":false}],"preferred":false,"id":795855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"McClelland, James W. 0000-0001-9619-8194","orcid":"https://orcid.org/0000-0001-9619-8194","contributorId":238027,"corporation":false,"usgs":false,"family":"McClelland","given":"James","email":"","middleInitial":"W.","affiliations":[{"id":47685,"text":"Marine Science Institute, University of Texas at Austin","active":true,"usgs":false}],"preferred":false,"id":795856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Spencer, Robert G. M. 0000-0003-0777-0748","orcid":"https://orcid.org/0000-0003-0777-0748","contributorId":238028,"corporation":false,"usgs":false,"family":"Spencer","given":"Robert","email":"","middleInitial":"G. M.","affiliations":[{"id":47686,"text":"Department of Earth, Ocean and Atmospheric Science, Florida State University","active":true,"usgs":false}],"preferred":false,"id":795857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shiklomanov, Alexander","contributorId":238029,"corporation":false,"usgs":false,"family":"Shiklomanov","given":"Alexander","affiliations":[{"id":47687,"text":"Institute for the Study of Earth, Oceans, and Space, University of New Hampshire","active":true,"usgs":false}],"preferred":false,"id":795858,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Zhulidov, Alexander V.","contributorId":238030,"corporation":false,"usgs":false,"family":"Zhulidov","given":"Alexander","email":"","middleInitial":"V.","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795859,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Gurtovaya, Tatiana","contributorId":238031,"corporation":false,"usgs":false,"family":"Gurtovaya","given":"Tatiana","email":"","affiliations":[{"id":47688,"text":"South Russia Centre for Preparation and Implementation of International Projects, Rostov-on-Don, Russia","active":true,"usgs":false}],"preferred":false,"id":795860,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Zimov, Nikita","contributorId":238032,"corporation":false,"usgs":false,"family":"Zimov","given":"Nikita","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795861,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Zimov, Sergey","contributorId":238033,"corporation":false,"usgs":false,"family":"Zimov","given":"Sergey","email":"","affiliations":[{"id":47689,"text":"Northeast Science Station, Far Eastern Branch of Russian Academy of Science, Chersky, Russia","active":true,"usgs":false}],"preferred":false,"id":795862,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Mutter, Edda A.","contributorId":238034,"corporation":false,"usgs":false,"family":"Mutter","given":"Edda A.","affiliations":[{"id":47690,"text":"˚Yukon River Inter-Tribal Watershed Council, Anchorage, Alaska","active":true,"usgs":false}],"preferred":false,"id":795863,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Kutny, Les","contributorId":238035,"corporation":false,"usgs":false,"family":"Kutny","given":"Les","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795864,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Amos, Edwin","contributorId":238036,"corporation":false,"usgs":false,"family":"Amos","given":"Edwin","email":"","affiliations":[{"id":47691,"text":"Western Arctic Research Centre, Inuvik, Northwest Territories, Canada","active":true,"usgs":false}],"preferred":false,"id":795865,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Holmes, Robert M.","contributorId":178901,"corporation":false,"usgs":false,"family":"Holmes","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":795866,"contributorType":{"id":1,"text":"Authors"},"rank":15}]}} ,{"id":70208879,"text":"70208879 - 2020 - Gas hydrate petroleum systems: What constitutes the “seal”?","interactions":[],"lastModifiedDate":"2020-06-04T16:58:08.036025","indexId":"70208879","displayToPublicDate":"2020-03-02T15:50:00","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3906,"text":"Interpretation","active":true,"publicationSubtype":{"id":10}},"title":"Gas hydrate petroleum systems: What constitutes the “seal”?","docAbstract":"The gas hydrate petroleum system (GHPS) approach, which has been used to characterize gas hydrates in nature, utilizes three distinct components: a methane source, a methane migration pathway, and a reservoir that not only contains gas hydrate, but also acts as a seal to prevent methane loss. Unlike GHPS, a traditional petroleum system (PS) approach further distinguishes between the reservoir, a unit with generally coarser sediment grains, and a separate overlying seal unit with generally finer sediment grains. Adopting this traditional PS distinction in the GHPS approach facilitates assessments of reservoir growth and production potential. The significance of the seal for the formation of a gas hydrate reservoir as well as for the efficiency in methane extraction from the reservoir as an energy resource is evident in the findings from recent offshore field expeditions, such as India’s second National Gas Hydrate Program expedition (NGHP-02). In regards to gas hydrate-bearing reservoir formation, the NGHP-02 gas chemistry data indicate a primarily microbial methane source. Fine-grained seal sediment in contact with coarser-grained reservoir sediment can facilitate that microbial methane production. Logging-while-drilling and sediment core data also indicate that the overlying fine-grained seal sediment is less permeable than the underlying, highly gas hydrate-saturated reservoir sediment. The overlying seal’s capacity to act as a low-permeability boundary is important not only for preventing methane migration out of the reservoir over time, but for also preventing water invasion into the reservoir during methane extraction from the reservoir. Ultimately, the presence of an overlying, fine-grained, low-permeability “Seal”? influences how gas hydrate initially forms in a coarse-grained reservoir and dictates how efficiently methane can be extracted as an energy resource from the gas hydrate reservoir via depressurization.","language":"English","publisher":"Society of Exploration Geophysicists","doi":"10.1190/int-2019-0026.1","usgsCitation":"Jang, J., Waite, W., and Stern, L.A., 2020, Gas hydrate petroleum systems: What constitutes the “seal”?: Interpretation, v. 8, no. 2, p. T231-T248, https://doi.org/10.1190/int-2019-0026.1.","productDescription":"18 p.","startPage":"T231","endPage":"T248","ipdsId":"IP-104479","costCenters":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":372926,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"India","otherGeospatial":"Bay of Bengal","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 84.44091796875,\n 18.47960905583197\n ],\n [\n 82.41943359375,\n 17.11979250078707\n ],\n [\n 82.44140625,\n 16.720385051694\n ],\n [\n 82.1337890625,\n 16.172472808397515\n ],\n [\n 81.40869140625,\n 16.25686733062344\n ],\n [\n 81.10107421874999,\n 15.665354182093287\n ],\n [\n 82.90283203125,\n 14.817370620155254\n ],\n [\n 86.396484375,\n 17.434510551522894\n ],\n [\n 84.44091796875,\n 18.47960905583197\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"2","publishingServiceCenter":{"id":11,"text":"Pembroke PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Jang, Junbong 0000-0001-5500-7558 jjang@usgs.gov","orcid":"https://orcid.org/0000-0001-5500-7558","contributorId":189400,"corporation":false,"usgs":true,"family":"Jang","given":"Junbong","email":"jjang@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":783810,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waite, William F. 0000-0002-9436-4109 wwaite@usgs.gov","orcid":"https://orcid.org/0000-0002-9436-4109","contributorId":625,"corporation":false,"usgs":true,"family":"Waite","given":"William F.","email":"wwaite@usgs.gov","affiliations":[{"id":678,"text":"Woods Hole Coastal and Marine Science Center","active":true,"usgs":true},{"id":186,"text":"Coastal and Marine Geology Program","active":true,"usgs":true}],"preferred":true,"id":783811,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stern, Laura A. 0000-0003-3440-5674","orcid":"https://orcid.org/0000-0003-3440-5674","contributorId":212238,"corporation":false,"usgs":true,"family":"Stern","given":"Laura","email":"","middleInitial":"A.","affiliations":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true},{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":783812,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70211218,"text":"70211218 - 2020 - Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda","interactions":[],"lastModifiedDate":"2020-07-20T12:55:11.299767","indexId":"70211218","displayToPublicDate":"2020-03-02T15:31:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3219,"text":"Quaternary Science Reviews","active":true,"publicationSubtype":{"id":10}},"title":"Testing glacial isostatic adjustment models of last-interglacial sea level history in the Bahamas and Bermuda","docAbstract":"Part of the spatial variation in the apparent sea-level record of the last interglacial (LIG) period is due to the diverse response of coastlines to glacial isostatic adjustment (GIA) processes, particularly where coastlines were close to the Laurentide Ice Sheet during the past two glacial periods. We tested modeled LIG paleo-sea levels on New Providence Island (NPI), Bahamas and Bermuda by investigating emergent coral patch reefs and oolitic/peloidal beach deposits. Corals with closed-system histories collected from patch reefs on NPI have ages of 128-118 ka and ooids/peloids from beach ridges have closed-system ages of 128-116 ka. Elevations of patch reefs indicate a LIG paleo-sea level of at least ∼7 m to ∼9 m above present. Beach ridge sediments indicate paleo-sea levels of ∼5 m to ∼14 m (assuming subsidence, ∼7 m to ∼16 m) above present during the LIG. Some, though not all of these measurements are in good agreement with GIA models of paleo-sea level that have been simulated for the Bahamas. On Bermuda, corals with closed-system histories collected from marine deposits have ages of 126-114 ka. Although coral-bearing marine deposits on Bermuda lack the precise indication of paleo-sea level provided by patch reefs and oolitic beach ridges, these sediments nevertheless provide at least a first-order estimate of paleo-sea level. Paleo-sea level records on Bermuda are consistently lower (∼2 m to ∼7 m) than what GIA models simulate for the LIG. The reason for the reasonable agreement with models for the Bahamas and poor agreement for Bermuda is not understood, but needs further investigation in light of the probability of a higher sea level in the near future.
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