Understanding the geochemistry of waters produced during petroleum extraction is essential to informing the best treatment and reuse options, which can potentially be optimized for a given geologic basin. Here, we used the US Geological Survey’s National Produced Waters Geochemical Database (PWGD) to determine if major ion chemistry could be used to classify accurately a produced water sample to a given geologic basin based on similarities to a given training dataset. Two datasets were derived from the PWGD: one with seven features but more samples (PWGD7), and another with nine features but fewer samples (PWGD9). The seven-feature dataset, prior to randomly generating a training and testing (i.e., validation) dataset, had 58,541 samples, 20 basins, and was classified based on total dissolved solids (TDS), bicarbonate (HCO3), Ca, Na, Cl, Mg, and sulfate (SO4). The nine-feature dataset, prior to randomly splitting into a training and testing (i.e., validation) dataset, contained 33,271 samples, 19 basins, and was classified based on TDS, HCO3, Ca, Na, Cl, Mg, SO4, pH, and specific gravity. Three supervised machine learning algorithms—Random Forest, k-Nearest Neighbors, and Naïve Bayes—were used to develop multi-class classification models to predict a basin of origin for produced waters using major ion chemistry. After training, the models were tested on three different datasets: Validation7, Validation9, and one based on data absent from the PWGD. Prediction accuracies across the models ranged from 23.5 to 73.5% when tested on the two PWGD-based datasets. A model using the Random Forest algorithm predicted most accurately compared to all other models tested. The models generally predicted basin of origin more accurately on the PWGD7-based dataset than on the PWGD9-based dataset. An additional dataset, which contained data not in the PWGD, was used to test the most accurate model; results suggest that some basins may lack geochemical diversity or may not be well described, while others may be geochemically diverse or are well described. A compelling result of this work is that a produced water basin of origin can be determined using major ions alone and, therefore, deep basinal fluid compositions may not be as variable within a given basin as previously thought. Applications include predicting the geochemistry of produced fluid prior to drilling at different intervals and assigning historical produced water data to a producing basin.
","language":"English","publisher":"Springer Link","doi":"10.1007/s11053-021-09949-8","usgsCitation":"Shelton, J., Jubb, A., Saxe, S., Attanasi, E., Milkov, A., Engle, M.A., Freeman, P., Shaffer, C., and Blondes, M., 2021, Machine learning can assign geologic basin to produced water samples using major ion geochemistry: Natural Resources Research, v. 30, p. 4147-4163, https://doi.org/10.1007/s11053-021-09949-8.","productDescription":"17 p.","startPage":"4147","endPage":"4163","ipdsId":"IP-126045","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":450614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1007/s11053-021-09949-8","text":"Publisher Index Page"},{"id":390110,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"30","noUsgsAuthors":false,"publicationDate":"2021-09-30","publicationStatus":"PW","contributors":{"authors":[{"text":"Shelton, Jenna L. 0000-0002-1377-0675 jlshelton@usgs.gov","orcid":"https://orcid.org/0000-0002-1377-0675","contributorId":5025,"corporation":false,"usgs":true,"family":"Shelton","given":"Jenna L.","email":"jlshelton@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824454,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Jubb, Aaron M. 0000-0001-6875-1079","orcid":"https://orcid.org/0000-0001-6875-1079","contributorId":201978,"corporation":false,"usgs":true,"family":"Jubb","given":"Aaron M.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824455,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Saxe, Samuel 0000-0003-1151-8908","orcid":"https://orcid.org/0000-0003-1151-8908","contributorId":215753,"corporation":false,"usgs":true,"family":"Saxe","given":"Samuel","email":"","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"preferred":true,"id":824456,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Attanasi, Emil D. 0000-0001-6845-7160 attanasi@usgs.gov","orcid":"https://orcid.org/0000-0001-6845-7160","contributorId":198728,"corporation":false,"usgs":true,"family":"Attanasi","given":"Emil D.","email":"attanasi@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824457,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Milkov, Alexei","contributorId":266160,"corporation":false,"usgs":false,"family":"Milkov","given":"Alexei","email":"","affiliations":[{"id":6606,"text":"Colorado School of Mines","active":true,"usgs":false}],"preferred":false,"id":824458,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Engle, Mark A 0000-0001-5258-7374","orcid":"https://orcid.org/0000-0001-5258-7374","contributorId":228981,"corporation":false,"usgs":false,"family":"Engle","given":"Mark","email":"","middleInitial":"A","affiliations":[{"id":41535,"text":"The University of Texas at El Paso, Department of Geological Sciences, El Paso, TX 79968","active":true,"usgs":false}],"preferred":false,"id":824459,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Freeman, Philip A. 0000-0002-0863-7431","orcid":"https://orcid.org/0000-0002-0863-7431","contributorId":206294,"corporation":false,"usgs":true,"family":"Freeman","given":"Philip A.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824460,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Shaffer, Christopher","contributorId":266161,"corporation":false,"usgs":false,"family":"Shaffer","given":"Christopher","email":"","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":824461,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Blondes, Madalyn S. 0000-0003-0320-0107 mblondes@usgs.gov","orcid":"https://orcid.org/0000-0003-0320-0107","contributorId":3598,"corporation":false,"usgs":true,"family":"Blondes","given":"Madalyn S.","email":"mblondes@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":824462,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70225551,"text":"70225551 - 2021 - Clays are not created equal: How clay mineral type affects soil parameterization","interactions":[],"lastModifiedDate":"2021-10-22T12:42:02.867477","indexId":"70225551","displayToPublicDate":"2021-09-30T07:38:20","publicationYear":"2021","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":"Clays are not created equal: How clay mineral type affects soil parameterization","docAbstract":"Clay minerals dominate the soil colloidal fraction and its specific surface area. Differences among clay mineral types significantly influence their effects on soil hydrological and mechanical behavior. Presently, the soil clay content is used to parameterize soil hydraulic and mechanical properties (SHMP) for land surface models while disregarding the type of clay mineral. This undifferentiated use of clay leads to inconsistent parameterization, particularly between tropical and temperate soils, as shown herein. We capitalize on recent global maps of clay minerals that exhibit strong climatic and spatial segregation of active and inactive clays to consider spatially resolved clay mineral types in SHMP estimation. Clay mineral-informed pedotransfer functions and machine learning algorithms trained with datasets including different clay types and soil structure formation processes improve SHMP representation regionally with broad implications for hydrological and geomechanical Earth surface processes.
To determine trends in either frog distribution or abundance in the State of Louisiana, we reviewed and analyzed frog call data from the Louisiana Amphibian Monitoring Program (LAMP). The data were collected between 1997 and 2017 using North American Amphibian Monitoring Program protocols. Louisiana was divided into three survey regions for administration and analysis: the Florida Parishes, and 2 areas west of the Florida parishes called North and South. Fifty-four routes were surveyed with over 12,792 stops and 1,066 hours of observation. Observers heard 26 species of the 31 species reported to be in Louisiana. Three of the species not heard were natives with ranges that did not overlap with survey routes. The other two species were introduced species, the Rio Grande Chirping Frog (Eleutherodactylus cystignathoides) and the Cuban Treefrog (Osteopilus septentrionalis). Both seem to be limited to urban areas with little to no route coverage. The 15 most commonly occurring species were examined in detail using the percentage of stops at which they observed along a given survey and their call indices. Most species exhibited a multimodal, concave, or convex pattern of abundance over a 15-year period. Among LAMP survey regions, none of the species had synchronous population trends. Only one group of species, winter callers, regularly co-occur. Based on the species lists, the North region could be seen as a subset of the South. However, based on relative abundance, the North was more similar to Florida parishes for both the winter and summer survey runs. Our analyses demonstrate that long-term monitoring (10 years or more) may be necessary to determine population and occupancy trends, and that frog species may have different local demographic patterns across large geographic areas.
Long-term lake ice phenological records from around the Northern Hemisphere provide unique sensitive indicators of climatic variations, even prior to the existence of physical meteorological measurement stations. Here, we updated ice phenology records for 60 lakes with time-series ranging from 107–204 years to provide the first re-assessment of Northern Hemispheric ice trends since 2004 by adding 15 additional years of ice phenology records and 40 lakes to our study. We found that, on average, ice-on was 11.0 days later, ice-off was 6.8 days earlier, and ice duration was 17.0 days shorter per century over the entire record for each lake. Trends in ice-on and ice duration were six times faster in the last 25-year period (1992–2016) than previous quarter centuries. More extreme events in recent decades, including late ice-on, early ice-off, shorter periods of ice cover, or no ice cover at all, contribute to the increasing rate of lake ice loss. Reductions in greenhouse gas emissions could limit increases in air temperature and abate losses in lake ice cover that would subsequently limit ecological, cultural, and socioeconomic consequences, such as increased evaporation rates, warmer water temperatures, degraded water quality, and the formation of toxic algal blooms.
The Arctic cryosphere is collapsing, posing overlapping environmental risks. In particular, thawing permafrost threatens to release biological, chemical and radioactive materials that have been sequestered for tens to hundreds of thousands of years. As these constituents re-enter the environment, they have the potential to disrupt ecosystem function, reduce the populations of unique Arctic wildlife and endanger human health. Here, we review the current state of the science to identify potential hazards currently frozen in Arctic permafrost. We also consider the cascading natural and anthropogenic processes that may compound the impacts of these risks, as it is unclear whether the highly adapted Arctic ecosystems have the resilience to withstand new stresses. We conclude by recommending research priorities to address these underappreciated risks.
Croaking gourami Trichopsis vittata is a non-native fish species that has maintained a reproducing population in Florida, USA, since at least the 1970s. However, unlike most other non-native fishes in Florida, T. vittata has not spread beyond its very small (ca. 5 km²) range. We suspected the inability of T. vittata to colonize new habitats may be due to biotic resistance by the native eastern mosquitofish Gambusia holbrooki. In laboratory experiments, we show that G. holbrooki causes physical damage to T. vittata and that T. vittata’s growth is reduced in the presence of G. holbrooki. While the effects of G. holbrooki on T. vittata were sub-lethal, they were severe enough to hamper its growth and could affect recruitment in the wild. These results support the hypothesis that small non-native fishes may be excluded from establishment or may only establish small ranges due to pressure from G. holbrooki. Biotic resistance may reduce invasion success and thus consideration of species interactions is useful for natural resource managers trying to evaluate the potential risk of new invaders.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre (REABIC)","doi":"10.3391/ai.2021.16.3.08","usgsCitation":"Schofield, P., Tuckett, Q.M., Slone, D., Reaver, K., and Hill, J.H., 2021, Invasion frustration: Can biotic resistance explain the small geographic range of non-native croaking gourami Trichopsis vittata (Cuvier, 1831) in Florida, USA?: Aquatic Invasions, v. 16, no. 3, p. 512-526, https://doi.org/10.3391/ai.2021.16.3.08.","productDescription":"15 p.; Data Release","startPage":"512","endPage":"526","ipdsId":"IP-097602","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":450624,"rank":3,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/ai.2021.16.3.08","text":"Publisher Index 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There is a recognized need for additional taxa to accompany the three standard species for effluent testing. An appropriate additional taxon is unionid mussels because mussels are widely distributed, live burrowed in sediment and filter particles from the water column for food, and exhibit high sensitivity to a variety of contaminants. Multiple studies were conducted to develop a relevant and robust short-term test method for mussels. We first evaluated the comparative sensitivity of two mussel species (Villosa constricta and Lampsilis siliquoidea) and two standard species (P. promelas and C. dubia) using two mock effluents prepared by mixing ammonia and five metals (cadmium, copper, nickel, lead, and zinc) or a field-collected effluent in 7-day exposures. Both mussel species were equally or more sensitive (more than two-fold) to effluents compared with the standard species. Next, we refined the mussel test method by first determining the best feeding rate of a commercial algal mixture for three age groups (1, 2, and 3 weeks old) of L. siliquoidea in a 7-day feeding experiment, and then used the derived optimal feeding rates to assess the sensitivity of the three ages of juveniles in a 7-day reference toxicant (sodium chloride [NaCl]) test. Juvenile mussels grew substantially (30%–52% length increase) when the 1- or 2-week-old mussels were fed 2 ml twice daily and the 3-week-old mussels were fed 3 ml twice daily. The 25% inhibition concentrations (IC25s) for NaCl were similar (314–520 mg Cl/L) among the three age groups, indicating that an age range of 1- to 3-week-old mussels can be used for a 7-day test. Finally, using the refined test method, we conducted an interlaboratory study among 13 laboratories to evaluate the performance of a 7-day NaCl test with L. siliquoidea. Eleven laboratories successfully completed the test, with more than 80% control survival and reliable growth data. The IC25s ranged from 296 to 1076 mg Cl/L, with a low (34%) coefficient of variation, indicating that the proposed method for L. siliquoidea has acceptable precision. Environ Toxicol Chem 2021;40:3392–3409. © 2021 SETAC
No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Cooperative Science and Monitoring Initiative (CSMI) Lake Ontario 2018","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Indiana Illinois Sea Grant, New York Sea Grant","usgsCitation":"Elgin, A., Glyshaw, P., and Weidel, B., 2021, Lake Ontario 2018 coordinated science and monitoring initiative (CSMI) quagga mussel growth experiment and a lake-wide quagga mussel condition assessment, 13 p.","productDescription":"13 p.","startPage":"24","endPage":"36","ipdsId":"IP-126966","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":390948,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":390887,"type":{"id":15,"text":"Index Page"},"url":"https://seagrant.sunysb.edu//Images/Uploads/PDFs/GreatLakes-CSMI-LakeOntario-Report-2018.pdf"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.2001953125,\n 43.100982876188546\n ],\n [\n -75.60791015625,\n 43.100982876188546\n ],\n [\n -75.60791015625,\n 44.465151013519616\n ],\n [\n -80.2001953125,\n 44.465151013519616\n ],\n [\n -80.2001953125,\n 43.100982876188546\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Elgin, Ashley","contributorId":208535,"corporation":false,"usgs":false,"family":"Elgin","given":"Ashley","affiliations":[],"preferred":false,"id":825653,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Glyshaw, Paul","contributorId":267943,"corporation":false,"usgs":false,"family":"Glyshaw","given":"Paul","email":"","affiliations":[{"id":34438,"text":"NOAA-GLERL","active":true,"usgs":false}],"preferred":false,"id":825654,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":825655,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70225575,"text":"70225575 - 2021 - Quantifying Lake Ontario coregonine habitat use dynamic’s across space and time to inform assessment and restoration","interactions":[],"lastModifiedDate":"2021-10-26T11:05:37.60992","indexId":"70225575","displayToPublicDate":"2021-09-30T06:01:56","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":4,"text":"Other Government Series"},"displayTitle":"Quantifying Lake Ontario Coregonine Habitat Use Dynamic’s Across Space and Time to Inform Assessment and Restoration","title":"Quantifying Lake Ontario coregonine habitat use dynamic’s across space and time to inform assessment and restoration","docAbstract":"No abstract available.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Cooperative Science and Monitoring Initiative (CSMI) Lake Ontario 2018","largerWorkSubtype":{"id":4,"text":"Other Government Series"},"language":"English","publisher":"Indiana Illinois Sea Grant, New York Sea Grant","usgsCitation":"Weidel, B., Brown, T., Connerton, M., Holden, J., and Gorsky, D., 2021, Quantifying Lake Ontario coregonine habitat use dynamic’s across space and time to inform assessment and restoration, 16 p.","productDescription":"16 p.","startPage":"70","endPage":"85","ipdsId":"IP-126554","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":390888,"type":{"id":15,"text":"Index Page"},"url":"https://seagrant.sunysb.edu//Images/Uploads/PDFs/GreatLakes-CSMI-LakeOntario-Report-2018.pdf"},{"id":390947,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Canada, United States","otherGeospatial":"Lake Ontario","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.2001953125,\n 43.100982876188546\n ],\n [\n -75.60791015625,\n 43.100982876188546\n ],\n [\n -75.60791015625,\n 44.465151013519616\n ],\n [\n -80.2001953125,\n 44.465151013519616\n ],\n [\n -80.2001953125,\n 43.100982876188546\n ]\n ]\n ]\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Weidel, Brian 0000-0001-6095-2773 bweidel@usgs.gov","orcid":"https://orcid.org/0000-0001-6095-2773","contributorId":2485,"corporation":false,"usgs":true,"family":"Weidel","given":"Brian","email":"bweidel@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":825656,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Brown, Taylor","contributorId":267945,"corporation":false,"usgs":false,"family":"Brown","given":"Taylor","affiliations":[{"id":12722,"text":"Cornell University","active":true,"usgs":false}],"preferred":false,"id":825657,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Connerton, Michael","contributorId":251649,"corporation":false,"usgs":false,"family":"Connerton","given":"Michael","affiliations":[{"id":13678,"text":"New York State Department of Environmental Conservation","active":true,"usgs":false}],"preferred":false,"id":825658,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Holden, Jeremy","contributorId":139654,"corporation":false,"usgs":false,"family":"Holden","given":"Jeremy","affiliations":[{"id":12864,"text":"OMNRF","active":true,"usgs":false}],"preferred":false,"id":825659,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gorsky, Dimitry","contributorId":251650,"corporation":false,"usgs":false,"family":"Gorsky","given":"Dimitry","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":825660,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70224602,"text":"sir20215078 - 2021 - Characterization of water resources in the Big Lost River Basin, south-central Idaho","interactions":[{"subject":{"id":70224329,"text":"sir20215078A - 2021 - Hydrogeologic framework of the Big Lost River Basin, south-central Idaho, chap. 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This multichapter volume documents the findings of a hydrogeologic investigation of the Big Lost River Basin that was jointly conducted by the U.S. Geological Survey, Idaho Department of Water Resources, and Idaho Geological Survey from 2018 through 2021. Chapter A (Zinsser, 2021) describes the hydrogeologic framework of the Big Lost River Basin. The framework presents a conceptual definition of four hydrogeologic units, a three-dimensional hydrogeologic framework model representing the spatial occurrence of the hydrogeologic units, and a description of groundwater occurrence and movement. Chapter B (Dudunake and Zinsser, 2021) describes streamflow gains from and losses to groundwater in the Big Lost River between Mackay Reservoir and south of Arco. Streamflow losses and gains were estimated from a series of four measurement events completed during spring and fall conditions from 2019 to 2021. Chapter C (Clark, 2022) describes budgets for the Big Lost River Basin from 2000 to 2019. The groundwater budgets provide annual estimates for aquifer inflows and outflows and include representations of average, wet, and dry conditions. Collectively, these reports present a characterization of water resources in the Big Lost River Basin that will help address current challenges in water-resources management.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078, https://doi.org/10.3133/sir20215078.","onlineOnly":"Y","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":389977,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.16992187499999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 43.22919511396498\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
U.S. Geological Survey
230 Collins Road
Boise, Idaho 83702-4520
The Big Lost River of south-central Idaho interacts with the underlying aquifer by gaining and losing streamflow throughout various areas in the Big Lost River Valley. Surface-water and groundwater resources are used throughout the valley to sustain domestic, agricultural, and livestock needs. The U.S. Geological Survey, in cooperation with the Idaho Department of Water Resources, evaluated streamflow gains and losses by differential streamgaging in the lower Big Lost River, Idaho, during four measurement events: March 27–28, 2019; October 16–17, 2019; October 6–7, 2020; and March 30, 2021. This report presents and analyzes streamflow measurement and uncertainty data from each measurement event to describe surface-water/groundwater interactions. This report is the second chapter of a multi-chapter volume that characterizes water resources in the Big Lost River Basin.
During the four measurement events, 100 streamflow measurements were made at 46 unique sites on the Big Lost River, James Creek, and diversions or tributaries between Mackay Reservoir near Mackay and Arco, Idaho. Aquifer lithology and dimensions affected spatial patterns of streamflow gains and losses between the upper, middle, and lower reaches; changes in water supply, groundwater levels, and surface-water management affected seasonal differences within reaches. In the upper reach of the Big Lost River, streamflow losses and gains were greater during the wetter 2019 events and lesser during the drier 2020 and 2021 events. The middle reach includes the largest losses from the Big Lost River to groundwater; these losses occurred in the Darlington Sinks where 42 percent or more of streamflow was lost as the aquifer widens and groundwater deepens. These results suggest that changing surface-water supply, irrigation use, and recharge affect interannual groundwater levels and, in turn, affect patterns of streamflow gains and losses in the middle reach. Finally, surface-water management is the primary control on surface-water/groundwater interactions in the lower reach. Overall patterns of streamflow gains and losses in this study generally were consistent with previous reports. However, paired with the related hydrogeologic framework and water budget, this investigation provides new insights into how hydrogeologic conditions and interannual variability in water supply, groundwater levels, and surface-water management affect surface-water/groundwater interactions in the Big Lost River Valley.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215078B","collaboration":"Prepared in cooperation with the Idaho Department of Water Resources","usgsCitation":"Dudunake, T.J., and Zinsser, L.M., 2021, Surface-water and groundwater interactions in the Big Lost River, south-central Idaho, chap. B of Zinsser, L.M., ed., Characterization of water resources in the Big Lost River Basin, south-central Idaho: U.S. Geological Survey Scientific Investigations Report 2021–5078–B, 33 p., https://doi.org/10.3133/sir20215078B.","productDescription":"vii, 33 p.","onlineOnly":"Y","ipdsId":"IP-125229","costCenters":[{"id":343,"text":"Idaho Water Science Center","active":true,"usgs":true}],"links":[{"id":409272,"rank":5,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/versionHist.txt","size":"1 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2021-5078B Version History"},{"id":396946,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/images"},{"id":396945,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/sir20215078B.XML"},{"id":390009,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/sir20215078B.pdf","text":"Report","size":"3.8 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5078B"},{"id":390008,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5078/b/coverthb.jpg"}],"country":"United States","state":"Idaho","otherGeospatial":"Big Lost River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -114.16992187499999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 43.22919511396498\n ],\n [\n -112.82958984374999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 44.18220395771566\n ],\n [\n -114.16992187499999,\n 43.22919511396498\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Idaho Water Science Center
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230 Collins Road
Boise, Idaho 83702-4520
The State of Hawai'i passed legislation to be carbon neutral by 2045, a goal that will partly depend on carbon sequestration by terrestrial ecosystems. However, there is considerable uncertainty surrounding the future direction and magnitude of the land carbon sink in the Hawaiian Islands. We used the Land Use and Carbon Scenario Simulator (LUCAS), a spatially explicit stochastic simulation model that integrates landscape change and carbon gain-loss, to assess how projected future changes in climate and land use will influence ecosystem carbon balance in the Hawaiian Islands under all combinations of two radiative forcing scenarios (RCPs 4.5 and 8.5) and two land use scenarios (low and high) over a 90 year timespan from 2010 to 2100. Collectively, terrestrial ecosystems of the Hawaiian Islands acted as a net carbon sink under low radiative forcing (RCP 4.5) for the entire 90 year simulation period, with low land use change further enhancing carbon sink strength. In contrast, Hawaiian terrestrial ecosystems transitioned from a net sink to a net source of CO2 to the atmosphere under high radiative forcing (RCP 8.5), with high land use accelerating this transition and exacerbating net carbon loss. A sensitivity test of the CO2 fertilization effect on plant productivity revealed it to be a major source of uncertainty in projections of ecosystem carbon balance, highlighting the need for greater mechanistic understanding of plant productivity responses to rising atmospheric CO2. Long-term model projections such as ours that incorporate the interactive effects of land use and climate change on regional ecosystem carbon balance will be critical to evaluating the potential of ecosystem-based climate mitigation strategies.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/1748-9326/ac2347","usgsCitation":"Selmants, P., Sleeter, B.M., Liu, J., Wilson, T., Trauernicht, C., Frazier, A.G., and Asner, G.P., 2021, Ecosystem carbon balance in the Hawaiian Islands under different scenarios of future climate and land use change: Environmental Research Letters, v. 16, 104020, 14 p., https://doi.org/10.1088/1748-9326/ac2347.","productDescription":"104020, 14 p.","ipdsId":"IP-119813","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":450627,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/1748-9326/ac2347","text":"Publisher Index Page"},{"id":436181,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9AWLFKZ","text":"USGS data release","linkHelpText":"Land change and carbon balance projections for the Hawaiian 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University","active":true,"usgs":false}],"preferred":false,"id":824905,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70268411,"text":"70268411 - 2021 - Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations","interactions":[],"lastModifiedDate":"2025-06-25T14:13:55.200306","indexId":"70268411","displayToPublicDate":"2021-09-29T09:12:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":3,"text":"Organization Series"},"title":"Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations","docAbstract":"Traditionally, fisheries management has focused on the abundance, distribution, and size structure of populations. Although these factors remain key aspects of management, a large and growing body of evidence highlights the importance of genetics in conserving wild populations, especially when populations are small and isolated (Frankham et al. 2017). Local adaptations are very common among fishes and help populations cope with specific conditions in their local environment (Fraser et al. 2011). The field of conservation genetics and genomics is highly technical and has advanced rapidly in recent years, offering a wealth of information to support brook trout conservation and restoration. A major impediment to successfully incorporating these advances into conservation outcomes is that most fisheries managers have only a basic understanding of fish genetics and its relevance to their management decisions.","language":"English","publisher":"STAC","usgsCitation":"Kazyak, D.C., Hallerman, E.M., Maloney, L., Faulkner, S., Welsh, A., Coombs, J., Whiteley, A., Rash, J., White, S.L., Bartron, M.L., Kulp, M.A., and Meek, M., 2021, Understanding genetics for successful conservation and restoration of resilient Chesapeake Bay brook trout populations, HTML Document.","productDescription":"HTML Document","ipdsId":"IP-173712","costCenters":[{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":491275,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":491274,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://www.chesapeake.org/stac/events/understanding-genetics-for-successful-conservation-and-restoration-of-resilient-chesapeake-bay-brook-trout-populations/","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Chesapeake Bay region","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -75.71040420312731,\n 39.694677079680844\n ],\n [\n -76.93796273435163,\n 39.694677079680844\n ],\n [\n -76.93796273435163,\n 37.158701995783204\n ],\n [\n -75.71040420312731,\n 37.158701995783204\n ],\n [\n -75.71040420312731,\n 39.694677079680844\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","noUsgsAuthors":false,"publicationDate":"2021-09-29","publicationStatus":"PW","contributors":{"authors":[{"text":"Kazyak, David C. 0000-0001-9860-4045","orcid":"https://orcid.org/0000-0001-9860-4045","contributorId":140409,"corporation":false,"usgs":true,"family":"Kazyak","given":"David","email":"","middleInitial":"C.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941248,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hallerman, E. M.","contributorId":280251,"corporation":false,"usgs":false,"family":"Hallerman","given":"E.","email":"","middleInitial":"M.","affiliations":[{"id":25550,"text":"Virginia Polytechnic Institute and State University","active":true,"usgs":false}],"preferred":false,"id":941251,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Maloney, Lori","contributorId":357372,"corporation":false,"usgs":false,"family":"Maloney","given":"Lori","affiliations":[],"preferred":false,"id":941304,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Faulkner, Stephen 0000-0001-5295-1383 faulkners@usgs.gov","orcid":"https://orcid.org/0000-0001-5295-1383","contributorId":146152,"corporation":false,"usgs":true,"family":"Faulkner","given":"Stephen","email":"faulkners@usgs.gov","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941305,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Welsh, Amy","contributorId":287823,"corporation":false,"usgs":false,"family":"Welsh","given":"Amy","affiliations":[{"id":12432,"text":"West Virginia University","active":true,"usgs":false}],"preferred":false,"id":941252,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Coombs, Jason","contributorId":205478,"corporation":false,"usgs":false,"family":"Coombs","given":"Jason","affiliations":[{"id":7134,"text":"USFS","active":true,"usgs":false}],"preferred":false,"id":941306,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Whiteley, Andrew","contributorId":299020,"corporation":false,"usgs":false,"family":"Whiteley","given":"Andrew","affiliations":[{"id":48908,"text":"U Montana","active":true,"usgs":false}],"preferred":false,"id":941307,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rash, Jake","contributorId":357373,"corporation":false,"usgs":false,"family":"Rash","given":"Jake","affiliations":[],"preferred":false,"id":941308,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"White, Shannon L. 0000-0003-4687-6596","orcid":"https://orcid.org/0000-0003-4687-6596","contributorId":263424,"corporation":false,"usgs":true,"family":"White","given":"Shannon","email":"","middleInitial":"L.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":941249,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Bartron, Meredith L.","contributorId":149109,"corporation":false,"usgs":false,"family":"Bartron","given":"Meredith","email":"","middleInitial":"L.","affiliations":[{"id":26874,"text":"USFWS, Lamar, PA","active":true,"usgs":false},{"id":6678,"text":"U.S. Fish and Wildlife Service, Alaska Maritime National Wildlife Refuge","active":true,"usgs":false}],"preferred":false,"id":941250,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kulp, Matt A.","contributorId":196801,"corporation":false,"usgs":false,"family":"Kulp","given":"Matt","email":"","middleInitial":"A.","affiliations":[{"id":35484,"text":"National Park Service, Great Smoky Mountains National Park","active":true,"usgs":false}],"preferred":false,"id":941309,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Meek, Mariah","contributorId":260835,"corporation":false,"usgs":false,"family":"Meek","given":"Mariah","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":941310,"contributorType":{"id":1,"text":"Authors"},"rank":12}]}} ,{"id":70225742,"text":"70225742 - 2021 - Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA","interactions":[],"lastModifiedDate":"2021-11-09T14:39:08.788372","indexId":"70225742","displayToPublicDate":"2021-09-29T08:34:55","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA","docAbstract":"Biogenic methane is estimated to account for one-fifth of the natural gas worldwide and there is great interest in controlling methane from different sources. Biogenic coalbed methane (CBM) production relies on syntrophic associations between fermentative bacteria and methanogenic archaea to anaerobically degrade recalcitrant coal and produce methanogenic substrates. However, very little is known about how differences in geochemistry, hydrology, and microbial community composition influence subsurface carbon utilization and CBM production. The addition of an amendment consisting of microalgal biomass has previously been shown to increase CBM production while providing the possibility of a closed-loop fossil system where waste (production water) is used to grow algae to ultimately produce energy (methane). However, the efficiency of enhancing CBM production under different redox conditions remains unresolved. In this study, we focused on the U.S. Geological Survey's Birney test site (Montana, USA) that has nine wells vertically accessing four coal seams with varying geochemistry (low and high sulfate (SO42−)) and methane production rates. We used organic matter (OM) in the form of algal biomass to discern the effect of this amendment on OM degradation and microbially enhanced CBM production potential under different geochemical constraints. We tracked changes in community composition, OM composition, organic carbon (OC) concentration, methane production, and nutrients in batch systems over six months. Methane production was detected only in microcosms from low SO42− wells (168 to 800 μg methane per gram of coal). The OC consumption varied across time for all wells and the variation was greatest for the low SO42− wells. Different groups of syntrophic bacteria were associated with net‑carbon consuming microcosms, and specifically Syntrophorhabdus was identified with several different statistical methods as a potentially important coal degrader. Results from this study provide insight into potential coal-degraders, the compositional changes in some of the different OM fractions, and trends in carbon consumption related to methane production across coal seams along the vertical SO42− gradient.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.coal.2021.103860","usgsCitation":"Smith, H.J., Schweitzer, H.S., Barnhart, E.P., Orem, W.H., Gerlach, R., and Fields, M.W., 2021, Effect of an algal amendment on the microbial conversion of coal to methane at different sulfate concentrations from the Powder River Basin, USA: International Journal of Coal Geology, v. 248, 103860, 16 p., https://doi.org/10.1016/j.coal.2021.103860.","productDescription":"103860, 16 p.","ipdsId":"IP-106713","costCenters":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"links":[{"id":450630,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://hdl.handle.net/10037/24259","text":"External Repository"},{"id":391509,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Montana, Wyoming","otherGeospatial":"Powder River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -105.75439453125,\n 41.409775832009565\n ],\n [\n -104.765625,\n 41.83682786072714\n ],\n [\n -104.23828125,\n 44.59046718130883\n ],\n [\n -104.9853515625,\n 46.649436163350245\n ],\n [\n -106.58935546875,\n 46.7549166192819\n ],\n [\n -108.1494140625,\n 46.51351558059737\n ],\n [\n -108.12744140625,\n 45.38301927899065\n ],\n [\n -106.41357421875,\n 43.6599240747891\n ],\n [\n -105.99609375,\n 41.83682786072714\n ],\n [\n -105.75439453125,\n 41.409775832009565\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"248","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Heidi J.","contributorId":268344,"corporation":false,"usgs":false,"family":"Smith","given":"Heidi","email":"","middleInitial":"J.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schweitzer, Hannah S.","contributorId":268345,"corporation":false,"usgs":false,"family":"Schweitzer","given":"Hannah","email":"","middleInitial":"S.","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barnhart, Elliott P. 0000-0002-8788-8393","orcid":"https://orcid.org/0000-0002-8788-8393","contributorId":203225,"corporation":false,"usgs":true,"family":"Barnhart","given":"Elliott","middleInitial":"P.","affiliations":[{"id":5050,"text":"WY-MT Water Science Center","active":true,"usgs":true}],"preferred":true,"id":826467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Orem, William H. 0000-0003-4990-0539 borem@usgs.gov","orcid":"https://orcid.org/0000-0003-4990-0539","contributorId":577,"corporation":false,"usgs":true,"family":"Orem","given":"William","email":"borem@usgs.gov","middleInitial":"H.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":826468,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gerlach, Robin","contributorId":203247,"corporation":false,"usgs":false,"family":"Gerlach","given":"Robin","email":"","affiliations":[{"id":36555,"text":"Montana State University","active":true,"usgs":false}],"preferred":false,"id":826469,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Fields, Matthew W.","contributorId":172391,"corporation":false,"usgs":false,"family":"Fields","given":"Matthew","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":826470,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70224919,"text":"70224919 - 2021 - Compositional evolution of organic matter in Boquillas Shale across a thermal gradient at the single particle level","interactions":[],"lastModifiedDate":"2021-10-05T12:42:29.156695","indexId":"70224919","displayToPublicDate":"2021-09-29T07:40:03","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2033,"text":"International Journal of Coal Geology","active":true,"publicationSubtype":{"id":10}},"title":"Compositional evolution of organic matter in Boquillas Shale across a thermal gradient at the single particle level","docAbstract":"The molecular composition of petroliferous organic matter and its compositional evolution throughout thermal maturation provides insight for understanding petroleum generation. This information is critical for understanding hydrocarbon resources in unconventional reservoirs, as source rock organic matter is highly dispersed, in contact with the surrounding mineral matrix, and may occur as multiple organic matter maceral types. Here, Raman spectroscopy and optical microscopy approaches were applied to a marginally mature (vitrinite reflectance ~0.5%) sample of the Late Cretaceous Boquillas Shale before and after hydrous pyrolysis (HP) at 300 °C and 330 °C for 72 h. This analytical approach allowed for correlative examination of micro-scale changes in organic matter compositional properties (e.g., aromaticity) for a variety of organic matter macerals across a thermal gradient (from marginally mature into the late oil/wet gas window) at the single particle level. Results indicate that while the examined amorphous organic matter, solid bitumen, and vitrinite particles exhibit different aromatic signatures in the unheated shale, they effectively progress along a similar trend through composition space with thermal maturation. Examined inertinite fragments were generally insensitive to the applied thermal stress, reinforcing the idea that reservoir temperature may be secondary for dictating the molecular composition of inertinite. Additional analysis of Raman spectra for individual organic matter macerals was performed using multivariate curve resolution (MCR) and correlation of standard Raman and reflectance-derived thermal maturity proxies against MCR parameters shows consistent trends. This trend suggests that MCR may be a fast and statistically robust method for extracting compositional information from Raman spectra of sedimentary organic matter, and can be used to construct thermal maturity relationships. These findings inform our understanding of how different petroliferous organic matter maceral types evolve throughout thermal reactions and further demonstrate that Raman spectroscopy combined with petrographic analysis can provide complementary estimates of organic matter composition and thermal maturity.
Climate change threatens global biodiversity. Many species vulnerable to climate change are important to humans for nutritional, cultural, and economic reasons. Polar bears Ursus maritimus are threatened by sea-ice loss and represent a subsistence resource for Indigenous people. We applied a novel population modeling-management framework that is based on species life history and accounts for habitat loss to evaluate subsistence harvest for the Chukchi Sea (CS) polar bear subpopulation. Harvest strategies followed a state-dependent approach under which new data were used to update the harvest on a predetermined management interval. We found that a harvest strategy with a starting total harvest rate of 2.7% (˜85 bears/yr at current abundance), a 2:1 male-to-female ratio, and a 10-yr management interval would likely maintain subpopulation abundance above maximum net productivity level for the next 35 yr (approximately three polar bear generations), our primary criterion for sustainability. Plausible bounds on starting total harvest rate were 1.7–3.9%, where the range reflects uncertainty due to sampling variation, environmental variation, model selection, and differing levels of risk tolerance. The risk of undesired demographic outcomes (e.g., overharvest) was positively related to harvest rate, management interval, and projected declines in environmental carrying capacity; and negatively related to precision in population data. Results reflect several lines of evidence that the CS subpopulation has been productive in recent years, although it is uncertain how long this will last as sea-ice loss continues. Our methods provide a template for balancing trade-offs among protection, use, research investment, and other factors. Demographic risk assessment and state-dependent management will become increasingly important for harvested species, like polar bears, that exhibit spatiotemporal variation in their response to climate change.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/eap.2461","usgsCitation":"Regehr, E.V., Runge, M.C., Von Duyke, A.L., Wilson, R., Polasek, L., Rode, K.D., Hostetter, N.J., and Converse, S.J., 2021, Demographic risk assessment for a harvested species threatened by climate change: Polar bears in the Chukchi Sea: Ecological Applications, v. 31, no. 8, e02461, 13 p., https://doi.org/10.1002/eap.2461.","productDescription":"e02461, 13 p.","ipdsId":"IP-119837","costCenters":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":450636,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.2461","text":"External Repository"},{"id":429876,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia, United States","otherGeospatial":"Chukchi Sea","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -157.44241992486556,\n 73.07923094153199\n ],\n [\n -179.9,\n 73.07923094153199\n ],\n [\n -179.9,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 66.33440002284189\n ],\n [\n -157.44241992486556,\n 73.07923094153199\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"31","issue":"8","noUsgsAuthors":false,"publicationDate":"2021-10-26","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":902940,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Runge, Michael C. 0000-0002-8081-536X mrunge@usgs.gov","orcid":"https://orcid.org/0000-0002-8081-536X","contributorId":3358,"corporation":false,"usgs":true,"family":"Runge","given":"Michael","email":"mrunge@usgs.gov","middleInitial":"C.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":902941,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Von Duyke, Andrew L.","contributorId":214208,"corporation":false,"usgs":false,"family":"Von Duyke","given":"Andrew","email":"","middleInitial":"L.","affiliations":[{"id":38995,"text":"North Slope Borough Department of Wildlife Management","active":true,"usgs":false}],"preferred":false,"id":903129,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wilson, Ryan R. ","contributorId":222456,"corporation":false,"usgs":false,"family":"Wilson","given":"Ryan R. ","affiliations":[{"id":6654,"text":"USFWS","active":true,"usgs":false}],"preferred":false,"id":903130,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Polasek, Lori","contributorId":338318,"corporation":false,"usgs":false,"family":"Polasek","given":"Lori","email":"","affiliations":[],"preferred":false,"id":903131,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"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":116,"text":"Alaska Science Center Biology MFEB","active":true,"usgs":true},{"id":114,"text":"Alaska Science Center","active":true,"usgs":true}],"preferred":true,"id":902942,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"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":903132,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"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":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":902943,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70224534,"text":"ofr20211080 - 2021 - Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","interactions":[],"lastModifiedDate":"2021-09-29T11:36:22.700641","indexId":"ofr20211080","displayToPublicDate":"2021-09-28T09:20:00","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-1080","displayTitle":"Optimization of Salt Marsh Management at the Rachel Carson National Wildlife Refuge, Maine, Through Use of Structured Decision Making","title":"Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making","docAbstract":"Structured decision making is a systematic, transparent process for improving the quality of complex decisions by identifying measurable management objectives and feasible management actions; predicting the potential consequences of management actions relative to the stated objectives; and selecting a course of action that maximizes the total benefit achieved and balances tradeoffs among objectives. The U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, applied an existing, regional framework for structured decision making to develop an example of a prototype tool for optimizing tidal marsh management decisions for selected marsh management units at the Rachel Carson National Wildlife Refuge in Maine. The goal was to create a prototype that could be available for future implementation. Refuge biologists, refuge managers, and research scientists identified multiple potential management actions to improve the ecological integrity of seven marsh management units within the refuge and estimated the outcomes of each action in terms of regional performance metrics associated with each management objective. Value functions previously developed at the regional level were used to transform metric scores to a common utility scale, and utilities were summed to produce a single score representing the total management benefit that could be accrued from each potential management action. Constrained optimization was used to identify the set of management actions, one per marsh management unit, that could maximize total management benefits at different cost constraints at the refuge scale.
Management costs were estimated using limited available information, and estimated costs of individual management actions reflected relative differences among actions rather than actual expected expenditures. Results from this prototype showed how, for the objectives, actions, and estimated outcomes used for this example, total management benefits may increase consistently up to a certain estimated cost, and may continue to increase, at a lower rate, with further expenditures. Potential management actions in optimal portfolios at moderate total estimated costs included breaching or removing dikes, roads, or embankments; planting Spartina alterniflora (smooth cordgrass); and digging runnels, or shallow creeks, on the marsh platform to improve surface-water drainage. Potential management actions in optimal portfolios at high estimated costs (for example, up to $550,000) included breaching embankments to restore tidal exchange followed by planting salt marsh vegetation. The potential management benefits were derived from predicted increases in the numbers of tidal marsh obligate birds and spiders (as an indicator of trophic health), and expected improvement in the capacity of marsh elevation to keep pace with sea-level rise and reduced duration of marsh-surface inundation. The prototype presented here does not resolve current management decisions; rather, it provides a framework for decision making at the Rachel Carson National Wildlife Refuge that can be updated for implementation as new data and information become available. Insights from this process may also be useful to inform future habitat management planning at the refuges.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20211080","collaboration":"Prepared in cooperation with the U.S. Fish and Wildlife Service","usgsCitation":"Neckles, H.A., Lyons, J.E., Nagel, J.L., Adamowicz, S.C., Mikula, T., O’Brien, K.M., Benvenuti, B., and Kleinert, R., 2021, Optimization of salt marsh management at the Rachel Carson National Wildlife Refuge, Maine, through use of structured decision making: U.S. Geological Survey Open-File Report 2021–1080, 35 p., https://doi.org/10.3133/ofr20211080.","productDescription":"vi, 35 p.","numberOfPages":"35","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-126540","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true},{"id":50464,"text":"Eastern Ecological Science Center","active":true,"usgs":true}],"links":[{"id":389743,"rank":2,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2021/1080/coverthb.jpg"},{"id":389744,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.pdf","text":"Report","size":"4.44 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2021-1080"},{"id":389737,"rank":1,"type":{"id":9,"text":"Database"},"url":"https://ecos.fws.gov/ServCat/Reference/Profile/121918","text":"U.S. Fish and Wildlife Service database","linkHelpText":"- Salt marsh integrity and Hurricane Sandy vegetation, bird and nekton data"},{"id":389746,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2021/1080/images/"},{"id":389747,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2021/1080/ofr20211080.XML"}],"country":"United States","state":"Maine","otherGeospatial":"Rachel Carson National Wildlife Refuge","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.63796997070312,\n 43.20417480788432\n ],\n [\n -70.61325073242188,\n 43.153101551466385\n ],\n [\n -70.477294921875,\n 43.257205668363206\n ],\n [\n -70.43472290039062,\n 43.38508989465156\n ],\n [\n -70.53634643554688,\n 43.393073720674415\n ],\n [\n -70.63796997070312,\n 43.31418735795809\n ],\n [\n -70.63796997070312,\n 43.20417480788432\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Eastern Ecological Science Center
U.S. Geological Survey
11649 Leetown Road
Kearneysville, WV 25430
Using available data, the U.S. Geological Survey estimated that 306 billion cubic feet of recoverable helium is presently within the known geologic natural gas reservoirs of the United States.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215085","usgsCitation":"Brennan, S.T., Rivera, J.L., Varela, B.A., and Park, A.J., 2021, National assessment of helium resources within known natural gas reservoirs: U.S. Geological Survey Scientific Investigations Report 2021–5085, 5 p., https://doi.org/10.3133/sir20215085.","productDescription":"Report: vi, 5 p.; Data Release","numberOfPages":"5","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-112618","costCenters":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":389388,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20215085/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2021-5085"},{"id":389060,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P92QL79J","text":"USGS data release","linkHelpText":"Dataset of helium concentrations in United States wells"},{"id":389058,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5085/coverthb.jpg"},{"id":389059,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5085/sir20215085.pdf","text":"Report","size":"4.28 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021-5085"},{"id":389386,"rank":5,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2021/5085/images/"},{"id":389385,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2021/5085/sir20215085.XML"}],"contact":"Director, Energy Resources Program
U.S. Geological Survey
12201 Sunrise Valley Drive
Reston, VA 20192
The Los Angeles Coastal Plain (LACP) covers about 580 square miles and is the largest coastal plain of semiarid southern California. The LACP is heavily developed with mostly residential, commercial, and industrial land uses that rely heavily on groundwater for water supply. In 2010, the LACP was home to about 14 percent of California’s population, or about 5.4 million residents. The LACP is also a major commercial and industrial hub with industries including manufacturing, aerospace, entertainment, and tourism.
There has been a heavy reliance on groundwater from the LACP for many years. An average of 305,000 acre-feet per year (acre-ft/yr) of groundwater was used annually from the LACP from 1971 to 2015. The need to replenish the groundwater basins within the LACP was recognized as far back as the 1930s, when spreading grounds were first used to replenish groundwater basins and store water underground during times of water surplus to meet demands in times of shortage. Seawater intrusion resulting from freshwater pumping was first observed in the 1940s. As a result, injection of imported water through wells at what is now the West Coast Basin Barrier Project began on an experimental basis in 1951. Managed aquifer recharge from the spreading grounds and barrier wells is now a substantial component of the LACP’s groundwater supply. The average annual recharge from water spreading from 1971 to 2015 was about 120,000 acre-ft/yr, and the average annual injection into the barrier wells was about 33,000 acre-ft/yr. Other inflows include areal recharge, underflow from San Gabriel and San Fernando Valleys, and onshore flow from the ocean. The average annual recharge from these sources was 100,000 acre-feet (acre-ft) from 1971 to 2015. Additionally, cross-boundary flow from Orange County into the western Orange County subareas of the LACP was simulated as 48,000 acre-ft from 1971 to 2015.
This study, conducted in cooperation with the Water Replenishment District of Southern California (WRD), involved an assessment of the historical and present status of groundwater resources in the LACP and the development of tools to better understand the groundwater system. These efforts were built upon results from previous studies and incorporate new information and developments in modeling capabilities to provide a more detailed analysis of the aquifer systems.
This study includes a comprehensive compilation of geologic and hydrologic data (Chapter A), development of a chronostratigraphic model that provides a detailed description of the LACP aquifer systems (Chapter B), characterization of the groundwater hydrology of the LACP, including a down-hole analysis of grain size using lithologic and geophysical logs (Chapter C), and development and application of the Los Angeles Coastal Plain Groundwater-flow Model (LACPGM) to simulate past groundwater conditions, estimate groundwater-budget components and flow paths, and approximate future groundwater conditions under different scenarios (Chapter D).
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215088","collaboration":"Prepared in cooperation with the Water Replenishment District of Southern California","usgsCitation":"Paulinski, S., ed., 2021, Development of a groundwater-simulation model in the Los Angeles Coastal Plain, Los Angeles County, California (ver. 1.1, May 2023): U.S. Geological Survey Scientific Investigations Report 2021-5088, 489 p., https://doi.org/10.3133/sir20215088.","productDescription":"Report: xiii, 489 p.; Data Release","numberOfPages":"489","onlineOnly":"Y","ipdsId":"IP-023155","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":389755,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9H15ZAX","linkHelpText":"MODFLOW-USG model used to evaluate water management issues in the Los Angeles Coastal Plain, California"},{"id":389754,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5088/sir20215088_v1.1.pdf","text":"Report","size":"66 MB","linkFileType":{"id":1,"text":"pdf"}},{"id":389753,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5088/covrthb_.jpg"},{"id":416877,"rank":4,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2021/5088/versionHist.txt","size":"2 KB","linkFileType":{"id":2,"text":"txt"}},{"id":436182,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TJD4IE","text":"USGS data release","linkHelpText":"MODFLOW-6 model to update and extend the Los Angeles Coastal Plain Groundwater Model"},{"id":500446,"rank":6,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_111785.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"California","county":"Los Angeles County","otherGeospatial":"Los Angeles Coastal Plain","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.90802001953125,\n 33.59860671494885\n ],\n [\n -117.59490966796875,\n 33.876116579321206\n ],\n [\n -117.82012939453125,\n 34.14249823152873\n ],\n [\n -118.20327758789062,\n 34.23337699755914\n ],\n [\n -118.53973388671874,\n 34.03672867489511\n ],\n [\n -118.41476440429686,\n 33.80083235326659\n ],\n [\n -118.24722290039061,\n 33.72776616734189\n ],\n [\n -117.90802001953125,\n 33.59860671494885\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: September 2021; Version 1.1: May 2023","contact":"Director,
California Water Science Center
U.S. Geological Survey
6000 J Street, Placer Hall
Sacramento, California 95819
Some avian species have developed the capacity to leverage resource subsidies associated with human manipulated landscapes to increase population densities in habitats with naturally low carrying capacities. Elevated corvid densities and new territory establishment have led to an unsustainable increase in depredation pressure on sympatric native wildlife prey populations as well as in crop damage. Yet, subsidized predator removal programs aimed at reducing densities are likely most effective longer-term when conducted in tandem with subsidy control, habitat management, and robust assessment monitoring programs. We developed decision support software that leverages stage structured Lefkovitch population matrices to compare and identify treatment strategies that reduce subsidized avian predator densities most efficiently, in terms of limiting both cost and take levels. The StallPOPd (Version 4; available at https://doi.org/10.7298/sk2e-0c38.4) software enables managers to enter the area of their management stratum and the demographic properties (vital rates) of target bird population(s) of interest to evaluate strategies to decrease or curtail further population growth. Strategies explicitly include the reduction in fertility (i.e., eggs hatched) and/or the culling of hatchlings, non-breeders and/or breeders, but implicitly comprise reduction in survival or reproduction through subsidy denial. We illustrate the utilities of the software with examples using common ravens (Corvus corax; ravens) in the Mojave Desert of California, USA. Unfortunately, the survival and reproduction effects of each unit of a particular subsidy in that system have remained elusive, though this is the priority of current research. Because the software leverages a life history representation that is known to characterize hundreds of wildlife species in addition to ravens, the work expands the suite of tools available to wildlife managers and agricultural industry specialists to abate bird damage and impacts on sensitive wildlife in habitats with persistent human subsidies.
Schistosome parasites infect more than 200 million people annually, mostly in sub-Saharan Africa, where people may be co-infected with more than one species of the parasite. Infection risk for any single species is determined, in part, by the distribution of its obligate intermediate host snail. As the World Health Organization reprioritizes snail control to reduce the global burden of schistosomiasis, there is renewed importance in knowing when and where to target those efforts, which could vary by schistosome species. This study estimates factors associated with schistosomiasis risk in 16 villages located in the Senegal River Basin, a region hyperendemic for Schistosoma haematobium and S. mansoni. We first analyzed the spatial distributions of the two schistosomes’ intermediate host snails (Bulinus spp. and Biomphalaria pfeifferi, respectively) at village water access sites. Then, we separately evaluated the relationships between human S. haematobium and S. mansoni infections and (i) the area of remotely-sensed snail habitat across spatial extents ranging from 1 to 120 m from shorelines, and (ii) water access site size and shape characteristics. We compared the influence of snail habitat across spatial extents because, while snail sampling is traditionally done near shorelines, we hypothesized that snails further from shore also contribute to infection risk. We found that, controlling for demographic variables, human risk for S. haematobium infection was positively correlated with snail habitat when snail habitat was measured over a much greater radius from shore (45 m to 120 m) than usual. S. haematobium risk was also associated with large, open water access sites. However, S. mansoni infection risk was associated with small, sheltered water access sites, and was not positively correlated with snail habitat at any spatial sampling radius. Our findings highlight the need to consider different ecological and environmental factors driving the transmission of each schistosome species in co-endemic landscapes.