A numerical surface-water/groundwater model was developed for the lower San Antonio River Basin to evaluate the responses of low base flows and groundwater levels within the basin under conditions of reduced recharge and increased groundwater withdrawals. Batch data assimilation through history matching used a simulation of historical conditions (2006-2013); this process included history-matching to groundwater levels and base-flow estimates at several gages, and was completed in a high-dimensional (highly parameterized) framework. The model was developed in an uncertainty framework such that parameters, observations, and scenarios of interest are envisioned stochastically as distributions of potential values. Results indicate that groundwater contributions to surface water during periods of low flow may be reduced from 6% to 25% with a corresponding 25% reduction in recharge and a 25% increase in groundwater pumping over an 8-year planning period. Furthermore, results indicate groundwater-level reductions in some hydrostratigraphic units are more likely than in other hydrostratigraphic units over an 8-year period under drought conditions with the higher groundwater withdrawal scenario.
We use ground and space geodetic data to study surface deformation at Kīlauea Volcano from January to September 2015. This period includes an episode of heightened activity in April and May 2015 that culminated in a magmatic intrusion beneath the volcano's summit. The data set consists of Global Navigation Satellite System (GNSS), tilt, visual and seismic time series along with 25 descending and 15 ascending acquisitions of the Sentinel-1 satellite. We identify four different stages of surface deformation and volcanic activity, which we attribute to pressure changes and the movement of magma in response to an imbalance between magma supply and withdrawal in the shallow plumbing system, eventually leading to an intrusion beneath the summit area. In particular, we model the deformation as due to pressure changes in two subsurface magma bodies: the Halema‘uma‘u Reservoir (HMMR) and South Caldera Reservoir (SCR). The SCR was best described by an ellipsoidal source at 2.8 (2.65–3.07 at 95% confidence) km depth below the south caldera region. The HMMR was modeled as a point source located just east of Halema‘uma‘u crater at 1.5 (0.95–2.62) km depth. We suggest that a short-term increase in the magma supply rate to the volcano is a potential mechanisms for the intrusion, although other factors, like the filling of available void space or a reduced efficiency of magma transport through the volcano's East Rift Zone, may also play a role.
Scavenging is an important function within ecosystems where scavengers remove organic matter, reduce disease, stabilize food webs, and generally make ecosystems more resilient to environmental changes. Global change (i.e., changing climate and increasing human impact) is currently influencing scavenger communities. Thus, understanding what promotes species richness in scavenger communities can help prioritize management actions. Using a long-term dataset from camera traps deployed with animal carcasses as bait along a 1881 km latitudinal gradient in the Appalachian Mountains of eastern USA, we investigated the relative impact of climate and humans on the species richness and diversity of vertebrate scavengers. Our most supported models for both mammalian and avian scavengers included climatic, but not human, variables. The richness of mammalian and avian scavengers detected was highest during relatively warm (5–10°C) and dry (100–150 mm precipitation) winters, when food was likely limited and both reliance on and detection of carrion was high. The diversity of mammalian and avian scavengers detected was highest under drier conditions. We then used these results to project the future species richness of scavengers that would be detected within our sampling area and under the climate scenario of 2070 (emissions level RCP8.5). Our predictions suggest up to 80% and 67% reductions, respectively, in the richness of avian and mammalian scavengers that would be detected at baited sites. Climate-induced shifts in behavior (i.e., reduction in scavenging, even if present) at this scale could have cascading implications for ecosystem function, resilience, and human health. Further, our study highlights the importance of conducting studies of scavenger community dynamics within ecosystems across wide spatial gradients within temperate environments. More broadly, these findings build upon our understanding of the impacts of climate-induced adjustments in behavior that can likely have negative impacts on systems at a large scale.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.15653","usgsCitation":"Marneweck, C.J., Katzner, T., and Jachowski, D., 2021, Predicted climate-induced reductions in scavenging in eastern North America: Global Change Biology, v. 27, no. 14, p. 3383-3394, https://doi.org/10.1111/gcb.15653.","productDescription":"12 p.","startPage":"3383","endPage":"3394","ipdsId":"IP-125016","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":387282,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Appalachian Mountains","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -70.48828125,\n 45.27488643704891\n ],\n [\n -75.673828125,\n 43.26120612479979\n ],\n [\n -80.8154296875,\n 40.34654412118006\n ],\n [\n -83.75976562499999,\n 38.06539235133249\n ],\n [\n -81.8701171875,\n 36.94989178681327\n ],\n [\n -77.0361328125,\n 39.027718840211605\n ],\n [\n -72.0703125,\n 42.48830197960227\n ],\n [\n -69.60937499999999,\n 44.68427737181225\n ],\n [\n -70.48828125,\n 45.27488643704891\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"27","issue":"14","noUsgsAuthors":false,"publicationDate":"2021-05-10","publicationStatus":"PW","contributors":{"authors":[{"text":"Marneweck, Courtney J. 0000-0002-5064-1979","orcid":"https://orcid.org/0000-0002-5064-1979","contributorId":261261,"corporation":false,"usgs":false,"family":"Marneweck","given":"Courtney","email":"","middleInitial":"J.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":819615,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Katzner, Todd E. 0000-0003-4503-8435 tkatzner@usgs.gov","orcid":"https://orcid.org/0000-0003-4503-8435","contributorId":191353,"corporation":false,"usgs":true,"family":"Katzner","given":"Todd E.","email":"tkatzner@usgs.gov","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819616,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jachowski, David S.","contributorId":228814,"corporation":false,"usgs":false,"family":"Jachowski","given":"David S.","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":819617,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70222508,"text":"70222508 - 2021 - Mercury and water level management in lakes of northern Minnesota","interactions":[],"lastModifiedDate":"2021-08-02T15:30:54.462657","indexId":"70222508","displayToPublicDate":"2021-04-23T10:26:27","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Mercury and water level management in lakes of northern Minnesota","docAbstract":"Water level (WL) fluctuations substantially alter the fauna, flora, and microbial community of nearshore aquatic ecosystems. Water level management therefore has the potential to strongly influence a wide variety of ecosystem processes. Many northern temperate lake food webs experience substantial methylmercury contamination, which is partially mediated by the action of sulfate-reducing bacteria occurring in sediments that are periodically inundated. For lakes with elevated methylmercury, WL management could be designed to reduce methylmercury contamination. At the lake scale, this concept is supported by studies that identified statistical associations between fish mercury content and water level (WL) fluctuations. Here, we compiled a long-term dataset (1997–2015) of mercury content in young-of-year Yellow Perch (Perca flavescens) from six lakes on the border of the United States and Canada and examined whether mercury content was associated with WL fluctuation. Many WL metrics covary and appear to have strong associations with Yellow Perch mercury. However, these associations appear to vary by lake, and lake-specific models are needed to identify relationships between WL fluctuation and Yellow Perch mercury content. We used partial least-squares regression (PLSR) to identify the associations between Yellow Perch mercury content and WL metrics, temperature, and annual deposition data for lakes in northern Minnesota. These PLSR models not only showed some variation among lakes, but also supported strong associations between WL fluctuations and annual variation in Yellow Perch mercury content. The study lakes underwent a change in WL management in 2000, when winter WL minimums were increased by about 1 m in five of the six study lakes, which reduced annual WL fluctuation on those lakes. Using the PLSR models, we estimated how this change in WL management would have affected Yellow Perch mercury content. In four of the five study lakes in which annual WL fluctuation was reduced in 2000, the change in WL management likely reduced Yellow Perch mercury content, relative to the previous WL management regime.
","language":"English","publisher":"Ecological Society of America","doi":"10.1002/ecs2.3465","usgsCitation":"Larson, J.H., Maki, R., Christensen, V., Hlavacek, E., Sandheinrich, M.B., LeDuc, J.F., Kissane, C., and Knights, B.C., 2021, Mercury and water level management in lakes of northern Minnesota: Ecosphere, v. 12, no. 4, e03465, 17 p., https://doi.org/10.1002/ecs2.3465.","productDescription":"e03465, 17 p.","ipdsId":"IP-119750","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":489137,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3465","text":"Publisher Index Page"},{"id":436398,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P96TWNJL","text":"USGS data release","linkHelpText":"Mercury and water level fluctuations in lakes of northern Minnesota - sampling site land cover and inundated area data"},{"id":387629,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -93.240966796875,\n 48.16058943132621\n ],\n [\n -92.37991333007812,\n 48.16058943132621\n ],\n [\n -92.37991333007812,\n 48.62383195130112\n ],\n [\n -93.240966796875,\n 48.62383195130112\n ],\n [\n -93.240966796875,\n 48.16058943132621\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Larson, James H. 0000-0002-6414-9758 jhlarson@usgs.gov","orcid":"https://orcid.org/0000-0002-6414-9758","contributorId":4250,"corporation":false,"usgs":true,"family":"Larson","given":"James","email":"jhlarson@usgs.gov","middleInitial":"H.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820351,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Maki, Ryan P.","contributorId":190131,"corporation":false,"usgs":false,"family":"Maki","given":"Ryan P.","affiliations":[],"preferred":false,"id":820352,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Christensen, Victoria 0000-0003-4166-7461","orcid":"https://orcid.org/0000-0003-4166-7461","contributorId":220548,"corporation":false,"usgs":true,"family":"Christensen","given":"Victoria","affiliations":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"preferred":true,"id":820353,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hlavacek, Enrika 0000-0002-9872-2305 ehlavacek@usgs.gov","orcid":"https://orcid.org/0000-0002-9872-2305","contributorId":149114,"corporation":false,"usgs":true,"family":"Hlavacek","given":"Enrika","email":"ehlavacek@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820354,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Sandheinrich, Mark B.","contributorId":149084,"corporation":false,"usgs":false,"family":"Sandheinrich","given":"Mark","email":"","middleInitial":"B.","affiliations":[{"id":12793,"text":"University of Wisconsin-La Crosse","active":true,"usgs":false}],"preferred":false,"id":820355,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"LeDuc, Jaime F.","contributorId":190132,"corporation":false,"usgs":false,"family":"LeDuc","given":"Jaime","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":820356,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Kissane, Claire","contributorId":178240,"corporation":false,"usgs":false,"family":"Kissane","given":"Claire","email":"","affiliations":[{"id":12462,"text":"U.S. Department of the Interior, National Park Service","active":true,"usgs":false}],"preferred":false,"id":820357,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Knights, Brent C. 0000-0001-8526-8468 bknights@usgs.gov","orcid":"https://orcid.org/0000-0001-8526-8468","contributorId":2906,"corporation":false,"usgs":true,"family":"Knights","given":"Brent","email":"bknights@usgs.gov","middleInitial":"C.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":820358,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70220198,"text":"70220198 - 2021 - West-wide drought analysis","interactions":[],"lastModifiedDate":"2021-04-27T12:40:24.30458","indexId":"70220198","displayToPublicDate":"2021-04-23T07:33:53","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"chapter":"4","title":"West-wide drought analysis","docAbstract":"This chapter describes analyses of the variability and characteristics of drought for historical and future projected climate conditions across the Western United States. The analyses are performed using the Palmer Drought Severity Index (PDSI; Palmer, 1965) to define drought events. The advantage of using PDSI to define droughts is that it focuses explicitly on droughts driven by hydroclimate variability. The PDSI does not include anthropogenic effects, such as water management, including the effects of reservoirs and diversions. Thus, PDSI is well-suited to examine natural climate-driven drought characteristics (i.e., drought duration, severity, and frequency).\nThe next section (Section 4.1) describes the PDSI dataset and how it is used in the analyses. Section 4.2 describes the methodologies used to identify and analyze drought events. Section 4.3 presents results, along with considerations regarding the interpretations of the results. Summary and next steps emerging from the analyses are described in Section 4.4. Lastly, a listing of key findings is given in Section 4.5.","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"West-Wide Climate and Hydrology Assessment, Technical Memorandum No. ENV-2021-001","largerWorkSubtype":{"id":9,"text":"Other Report"},"language":"English","publisher":"U.S. Bureau of Reclamation","collaboration":"U.S. Bureau of Reclamation","usgsCitation":"Gangopadhyay, S., McCabe, G.J., Pruitt, T., and House, B., 2021, West-wide drought analysis, 54 p.","productDescription":"54 p.","startPage":"129","endPage":"182","ipdsId":"IP-125638","costCenters":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true}],"links":[{"id":385318,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":385309,"type":{"id":15,"text":"Index Page"},"url":"https://www.usbr.gov/climate/secure/2021secure.html"}],"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gangopadhyay, Subhrendu","contributorId":257611,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":814724,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McCabe, Gregory J. 0000-0002-9258-2997 gmccabe@usgs.gov","orcid":"https://orcid.org/0000-0002-9258-2997","contributorId":200854,"corporation":false,"usgs":true,"family":"McCabe","given":"Gregory","email":"gmccabe@usgs.gov","middleInitial":"J.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":814725,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom","contributorId":257612,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":814726,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"House, Brandon","contributorId":257613,"corporation":false,"usgs":false,"family":"House","given":"Brandon","email":"","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":814727,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222120,"text":"70222120 - 2021 - Sagebrush recovery patterns after fuel treatments mediated by disturbance type and plant functional group interactions","interactions":[],"lastModifiedDate":"2021-07-20T11:46:05.454447","indexId":"70222120","displayToPublicDate":"2021-04-23T06:43:23","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Sagebrush recovery patterns after fuel treatments mediated by disturbance type and plant functional group interactions","docAbstract":"Fire and fuel management is a high priority in North American sagebrush ecosystems where the expansion of piñon and juniper trees and the invasion of nonnative annual grasses are altering fire regimes and resulting in loss of sagebrush species and habitat. We evaluated 10-yr effects of woody fuel treatments on sagebrush recruitment and plant functional group interactions using Sagebrush Steppe Treatment Evaluation Project data. We used mixed-effects ANOVAs to examine treatment effects on sagebrush density and cover and perennial and annual grass cover in expansion woodlands (prescribed fire and cut-and-leave) and annual grass invasion areas (prescribed fire, mowing, tebuthiuron herbicide application). We used piecewise structural equation models to evaluate interactions among sagebrush seedling density, juvenile and adult density, and cover and perennial and annual grass cover. Fuel treatments were equated to pulse or press disturbances varying in resource release and subsequent intra- and interspecific interactions. Prescribed fire, a high magnitude pulse disturbance with more severe effects in warm and dry sites, reduced sagebrush cover and decoupled associations among sagebrush seedlings, juvenile and adult density, and cover indicating changed population structure. Cutting and leaving trees, a low magnitude pulse disturbance in cooler and moister woodlands, increased sagebrush density and cover and generally had lesser effects on sagebrush intraspecific associations. Mowing, a moderate magnitude pulse disturbance, and tebuthiuron herbicide application, a multiyear press disturbance, reduced sagebrush cover and disrupted intraspecific relationships. Competitive release increased cover of perennial grass in all treatments but tebuthiuron. Annual grass increased in all treatments, especially prescribed fire and tebuthiuron. Annual and perennial grass interactions with sagebrush were generally rare, but in woodland treatments perennial grass suppressed annual grass through year 6. Treatments in cooler and moister woodland sites had more positive effects on sagebrush recruitment and perennial grass cover, less negative effects on sagebrush intraspecific interactions, and smaller increases in annual grass cover indicating potential increases in resilience to fire. In warmer and drier invasion sites, reductions in woody fuels resulted in lack of sagebrush recruitment, disruption of sagebrush intraspecific interactions, and progressive increases in annual grass indicating reduced resilience to fire and resistance to invaders.
","language":"English","publisher":"Wiley","doi":"10.1002/ecs2.3450","usgsCitation":"Chambers, J., Urza, A.K., Board, D.I., Miller, R.F., Pyke, D.A., Roundy, B.A., Schupp, E.W., and Tausch, R.J., 2021, Sagebrush recovery patterns after fuel treatments mediated by disturbance type and plant functional group interactions: Ecosphere, v. 12, no. 4, e03450, 22 p., https://doi.org/10.1002/ecs2.3450.","productDescription":"e03450, 22 p.","ipdsId":"IP-123790","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":489091,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ecs2.3450","text":"Publisher Index Page"},{"id":387283,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","issue":"4","noUsgsAuthors":false,"publicationDate":"2021-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Chambers, Jeanne C.","contributorId":75889,"corporation":false,"usgs":false,"family":"Chambers","given":"Jeanne C.","affiliations":[],"preferred":false,"id":819607,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Urza, Alexandra K. 0000-0001-9795-6735","orcid":"https://orcid.org/0000-0001-9795-6735","contributorId":261259,"corporation":false,"usgs":false,"family":"Urza","given":"Alexandra","email":"","middleInitial":"K.","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":819608,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Board, David I.","contributorId":261260,"corporation":false,"usgs":false,"family":"Board","given":"David","email":"","middleInitial":"I.","affiliations":[{"id":16848,"text":"USDA Forest Service, Rocky Mountain Research Station","active":true,"usgs":false}],"preferred":false,"id":819609,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Miller, Richard F.","contributorId":178258,"corporation":false,"usgs":false,"family":"Miller","given":"Richard","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":819610,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pyke, David A. 0000-0002-4578-8335 david_a_pyke@usgs.gov","orcid":"https://orcid.org/0000-0002-4578-8335","contributorId":3118,"corporation":false,"usgs":true,"family":"Pyke","given":"David","email":"david_a_pyke@usgs.gov","middleInitial":"A.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":819611,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Roundy, Bruce A.","contributorId":178261,"corporation":false,"usgs":false,"family":"Roundy","given":"Bruce","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":819612,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Schupp, Eugene W.","contributorId":178262,"corporation":false,"usgs":false,"family":"Schupp","given":"Eugene","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":819613,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Tausch, Robin J.","contributorId":213637,"corporation":false,"usgs":false,"family":"Tausch","given":"Robin","email":"","middleInitial":"J.","affiliations":[{"id":36493,"text":"USDA Forest Service","active":true,"usgs":false}],"preferred":false,"id":819614,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70266036,"text":"70266036 - 2021 - Hydrologic effects on growth and hatching success of age-0 Channel Catfish in the Tallapoosa River basin: Implications for management in regulated systems","interactions":[],"lastModifiedDate":"2025-04-24T15:44:41.254501","indexId":"70266036","displayToPublicDate":"2021-04-23T00:00:00","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2886,"text":"North American Journal of Fisheries Management","active":true,"publicationSubtype":{"id":10}},"title":"Hydrologic effects on growth and hatching success of age-0 Channel Catfish in the Tallapoosa River basin: Implications for management in regulated systems","docAbstract":"We assessed the effects of hydrology on growth and hatching success of age‐0 Channel Catfish Ictalurus punctatus in regulated and unregulated reaches of the Tallapoosa River basin, Alabama. Age‐0 Channel Catfish (N = 91) were collected from sites in both the Coastal Plain and Piedmont regions in fall 2003 and fall 2005. Lapillus otoliths were used to estimate the daily ages of age‐0 Channel Catfish, for which hatch dates were back‐calculated. We performed growth analysis to determine growth histories of each fish at 20‐d increments from hatch. Across the 2 years of sampling, Channel Catfish hatches were documented from June 7 to September 15. Ages and growth rates of age‐0 Channel Catfish ranged from 20 to 126 d and 0.60 to 1.5 mm/d, respectively. In general, growth was highest among age‐0 Channel Catfish from unregulated sites in the lower Coastal Plain, lowest among fish from unregulated sites in the Piedmont, and intermediate from regulated sites in the Piedmont. Successful hatching typically occurred during periods when mean discharges were in the upper two quartiles of flows for each site but not during exceptionally high peaks in flow. Physiographic province, the frequency of high pulses, and the number of flow reversals were the most important factors influencing the growth of recently hatched Channel Catfish. Results suggest that a low to moderate frequency of high pulses (25–150 pulses per 20‐d increment) and a moderate number of flow reversals (~100–175 reversals per 20‐d increment) enhances early growth of Channel Catfish in the Tallapoosa River system. Managing flow, when possible, to minimize large releases of water that result in exceptionally high pulses and providing minimal hydropeaking may improve hatching success during the Channel Catfish spawning season.
","language":"English","publisher":"Oxford Academic","doi":"10.1002/nafm.10600","usgsCitation":"Erickson, K., Sakaris, P., Conner, H., and Irwin, E.R., 2021, Hydrologic effects on growth and hatching success of age-0 Channel Catfish in the Tallapoosa River basin: Implications for management in regulated systems: North American Journal of Fisheries Management, v. 41, no. S1, p. S118-S132, https://doi.org/10.1002/nafm.10600.","productDescription":"15 p.","startPage":"S118","endPage":"S132","ipdsId":"IP-117156","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":484988,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama","otherGeospatial":"Tallapoosa River basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -86.13103826076762,\n 34.346950443170655\n ],\n [\n -86.13103826076762,\n 33.770251015559566\n ],\n [\n -85.39701150663335,\n 33.770251015559566\n ],\n [\n -85.39701150663335,\n 34.346950443170655\n ],\n [\n -86.13103826076762,\n 34.346950443170655\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"41","issue":"S1","noUsgsAuthors":false,"publicationDate":"2021-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Erickson, Keith A.","contributorId":353730,"corporation":false,"usgs":false,"family":"Erickson","given":"Keith A.","affiliations":[{"id":84494,"text":"Georgia Gwinnett College","active":true,"usgs":false}],"preferred":false,"id":934427,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sakaris, Peter C.","contributorId":353731,"corporation":false,"usgs":false,"family":"Sakaris","given":"Peter C.","affiliations":[{"id":84494,"text":"Georgia Gwinnett College","active":true,"usgs":false}],"preferred":false,"id":934428,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Conner, Hannah","contributorId":353732,"corporation":false,"usgs":false,"family":"Conner","given":"Hannah","affiliations":[{"id":84494,"text":"Georgia Gwinnett College","active":true,"usgs":false}],"preferred":false,"id":934429,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Irwin, Elise R. 0000-0002-6866-4976 eirwin@usgs.gov","orcid":"https://orcid.org/0000-0002-6866-4976","contributorId":2588,"corporation":false,"usgs":true,"family":"Irwin","given":"Elise","email":"eirwin@usgs.gov","middleInitial":"R.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":934430,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70222089,"text":"70222089 - 2021 - Quantifying diagenesis, contributing factors, and resulting isotopic bias in benthic foraminifera using the Foraminiferal Preservation Index: Implications for geochemical proxy records","interactions":[],"lastModifiedDate":"2021-07-19T23:24:09.565365","indexId":"70222089","displayToPublicDate":"2021-04-22T18:19:21","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5790,"text":"Paleoceanography and Paleoclimatology","active":true,"publicationSubtype":{"id":10}},"title":"Quantifying diagenesis, contributing factors, and resulting isotopic bias in benthic foraminifera using the Foraminiferal Preservation Index: Implications for geochemical proxy records","docAbstract":"Geochemical records generated from the calcite tests of benthic foraminifera, especially those of the genera Cibicidoides and Uvigerina, provide the basis for proxy reconstructions of past climate. However, the extent to which benthic foraminifera are affected by postdepositional alteration is poorly constrained. Furthermore, how diagenesis may alter the geochemical composition of benthic foraminiferal tests, and thereby biasing a variety of proxy-based climate records, is also poorly constrained. We present the Foraminiferal Preservation Index (FPI) as a new metric to quantify preservation quality based on objective, well-defined criteria. The FPI is used to identify and quantify trends in diagenesis temporally, from late Pliocene to modern coretop samples (3.3–0 Ma), as well as spatially in the deep ocean. The FPI identifies the chemical composition of deep-ocean water masses to be the primary driver of diagenesis through time, while also serving as a supplementary method of identifying periods of changing water mass influence at a given site. Additionally, we present stable isotope data (δ18O, δ13C) generated from individual Cibicidoides specimens of various preservation quality that demonstrate the likelihood of significant biasing in a variety of geochemical proxy records, especially those used to reconstruct past changes in ice volume and sea level. These single-test data further demonstrate that when incorporating carefully selected tests of only the highest preservation quality, robust paleorecords can be generated.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020PA004110","usgsCitation":"Poirier, R., Gaetano, M.Q., Acevedo, K., Morgan F. Schaller, M.F., Raymo, M.E., and Kozdon, R., 2021, Quantifying diagenesis, contributing factors, and resulting isotopic bias in benthic foraminifera using the Foraminiferal Preservation Index: Implications for geochemical proxy records: Paleoceanography and Paleoclimatology, v. 36, no. 5, e2020PA004110, 32 p., https://doi.org/10.1029/2020PA004110.","productDescription":"e2020PA004110, 32 p.","ipdsId":"IP-121944","costCenters":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"links":[{"id":387257,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"36","issue":"5","noUsgsAuthors":false,"publicationDate":"2021-05-15","publicationStatus":"PW","contributors":{"authors":[{"text":"Poirier, Robert 0000-0001-5380-4545","orcid":"https://orcid.org/0000-0001-5380-4545","contributorId":261201,"corporation":false,"usgs":true,"family":"Poirier","given":"Robert","email":"","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true}],"preferred":true,"id":819465,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gaetano, Madison Q.","contributorId":261202,"corporation":false,"usgs":false,"family":"Gaetano","given":"Madison","email":"","middleInitial":"Q.","affiliations":[{"id":7159,"text":"University of Cincinnati","active":true,"usgs":false}],"preferred":false,"id":819466,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Acevedo, Kimberly","contributorId":261203,"corporation":false,"usgs":false,"family":"Acevedo","given":"Kimberly","email":"","affiliations":[{"id":34616,"text":"University of Massachusetts Amherst","active":true,"usgs":false}],"preferred":false,"id":819467,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Morgan F. Schaller, Morgan F. 0000-0003-2742-2126","orcid":"https://orcid.org/0000-0003-2742-2126","contributorId":261204,"corporation":false,"usgs":false,"family":"Morgan F. Schaller","given":"Morgan","email":"","middleInitial":"F.","affiliations":[{"id":12656,"text":"Rensselaer Polytechnic Institute","active":true,"usgs":false}],"preferred":false,"id":819468,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Raymo, Maureen E.","contributorId":261205,"corporation":false,"usgs":false,"family":"Raymo","given":"Maureen","email":"","middleInitial":"E.","affiliations":[{"id":28041,"text":"Lamont-Doherty Earth Observatory, Columbia University","active":true,"usgs":false}],"preferred":false,"id":819469,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kozdon, Reinhard 0000-0001-6347-456X","orcid":"https://orcid.org/0000-0001-6347-456X","contributorId":261206,"corporation":false,"usgs":false,"family":"Kozdon","given":"Reinhard","email":"","affiliations":[{"id":28041,"text":"Lamont-Doherty Earth Observatory, Columbia University","active":true,"usgs":false}],"preferred":false,"id":819470,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70220192,"text":"70220192 - 2021 - Geometry of the Bushveld Complex from 3D potential field modelling","interactions":[],"lastModifiedDate":"2021-04-26T12:19:20.73145","indexId":"70220192","displayToPublicDate":"2021-04-22T07:06:36","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Geometry of the Bushveld Complex from 3D potential field modelling","docAbstract":"A full three-dimensional (3D) potential field model of the central and southern Bushveld Complex reveals information about the Complex in areas obscured by younger geological cover. Previously, two-dimensional gravity models and a few magnetic models limited to certain sections of the Bushveld Complex have been used to propose geometries for the Rustenburg Layered Suite, especially in the western and eastern lobes. These models were often used to support different emplacement models. Although these models provided valuable information, two-and-a-half-dimensional (2.5D) potential field modelling is not well suited to modelling complex 3D geology. Also, in most cases, only the magnetic or gravity data were modelled, but jointly modelling both data sets better constrains the results, as was shown recently for a 3D model of the northern lobe. Joint 3D modelling of regional gravity and magnetic data combined with published crustal thickness models derived from broadband seismic tomography studies and constrained by density and susceptibility data, geologic mapping, boreholes and seismic reflection data were used to create a 3D model of the central and southeastern sections of the Bushveld Complex, as well as the southern part of the northern lobe. The model shows a complex geometry with thick continuous Rustenburg Layered Suite S in most of the western and southeastern lobes, but less continuous Rustenburg Layered Suite in the eastern lobe. Large domes or thick granites and granophyre in the latter interrupt the continuity of the Rustenburg Layered Suite and the western and eastern lobes are strictly speaking only partially connected in places. However, they are not separate intrusions, but one disconnected by pre-existing and synmagmatic updoming. Three possible feeders were modelled in the northern, western, and south-eastern lobes.
Annual wintertime water-level drawdowns are a common management strategy in recreational lakes; however, few studies have estimated their relative impact on lake littoral habitat among a set of typically co-occurring anthropogenic stressors including lakeshore development and herbicide application. Within 21 Massachusetts, USA lakes that represented a drawdown magnitude gradient (0.07–2.26 m), we assessed depth-specific littoral habitat (coarse wood, sediment, macrophytes) at two sites adjacent to forested or developed shorelines. Using generalized linear mixed models, we found coarse wood abundance and branching complexity was not correlated with drawdown magnitude but was primarily explained by the presence of lakeshore development. Drawdown magnitude was negatively correlated with silt cover and positively correlated with coarse substrate cover, with effects further varying by depth (0.5 m vs. 1 m). Macrophyte biomass and biovolume were negatively correlated with drawdown magnitude with effects also varying by depth for biomass. Macrophyte taxa with annual longevity strategies (e.g., Najas flexilis) and amphibious growth forms increased in biomass proportions with drawdown magnitude. Distance-based redundancy analyses suggested macrophyte taxa composition was driven by drawdown magnitude, coarse substrate, alkalinity, water transparency, and herbicide use. These results suggest the importance of water quality and depth on macrophyte assemblage responses to winter drawdowns and the potential to develop drawdown-tolerant macrophyte assemblages. Altogether, understanding the unique impacts of anthropogenic stressors on littoral zone habitat across heterogeneous environmental lake conditions can help minimize impacts to lake ecological integrity while maintaining recreational value.
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\"}}]}","volume":"12","noUsgsAuthors":false,"publicationDate":"2021-04-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Carmignani, Jason R.","contributorId":276066,"corporation":false,"usgs":false,"family":"Carmignani","given":"Jason","email":"","middleInitial":"R.","affiliations":[{"id":36396,"text":"University of Massachusetts","active":true,"usgs":false}],"preferred":false,"id":834525,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Roy, Allison H. 0000-0002-8080-2729 aroy@usgs.gov","orcid":"https://orcid.org/0000-0002-8080-2729","contributorId":4240,"corporation":false,"usgs":true,"family":"Roy","given":"Allison","email":"aroy@usgs.gov","middleInitial":"H.","affiliations":[{"id":199,"text":"Coop Res Unit Leetown","active":true,"usgs":true}],"preferred":true,"id":834524,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70227154,"text":"70227154 - 2021 - Groundwater residence time estimates obscured by anthropogenic carbonate","interactions":[],"lastModifiedDate":"2022-01-03T16:22:58.74868","indexId":"70227154","displayToPublicDate":"2021-04-21T10:09:04","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":5010,"text":"Science Advances","active":true,"publicationSubtype":{"id":10}},"title":"Groundwater residence time estimates obscured by anthropogenic carbonate","docAbstract":"Groundwater is an important source of drinking and irrigation water. Dating groundwater informs its vulnerability to contamination and aids in calibrating flow models. Here, we report measurements of multiple age tracers (14C, 3H, 39Ar, and 85Kr) and parameters relevant to dissolved inorganic carbon (DIC) from 17 wells in California’s San Joaquin Valley (SJV), an agricultural region that is heavily reliant on groundwater. We find evidence for a major mid-20th century shift in groundwater DIC input from mostly closed- to mostly open-system carbonate dissolution, which we suggest is driven by input of anthropogenic carbonate soil amendments. Crucially, enhanced open-system dissolution, in which DIC equilibrates with soil CO2, fundamentally affects the initial 14C activity of recently recharged groundwater. Conventional 14C dating of deeper SJV groundwater, assuming an open system, substantially overestimates residence time and thereby underestimates susceptibility to modern contamination. Because carbonate soil amendments are ubiquitous, other groundwater-reliant agricultural regions may be similarly affected.
","language":"English","publisher":"AAAS","doi":"10.1126/sciadv.abf3503","usgsCitation":"Seltzer, A., Bekaert, D., Barry, P.H., Durkin, K., Mace, E., Aaselth, C.E., Zappala, J., Mueller, P., Jurgens, B., and Kulongoski, J.T., 2021, Groundwater residence time estimates obscured by anthropogenic carbonate: Science Advances, v. 7, no. 17, eabf3503, 9 p., https://doi.org/10.1126/sciadv.abf3503.","productDescription":"eabf3503, 9 p.","ipdsId":"IP-119391","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"links":[{"id":452609,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1126/sciadv.abf3503","text":"External Repository"},{"id":393748,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"San Joaquin Valley","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -121.11328124999999,\n 38.58252615935333\n ],\n [\n -121.81640624999999,\n 38.37611542403604\n ],\n [\n -121.86035156249999,\n 37.96152331396614\n ],\n [\n -120.89355468749999,\n 36.58024660149866\n ],\n [\n -120.0146484375,\n 35.62158189955968\n ],\n [\n -119.33349609375,\n 34.994003757575776\n ],\n [\n -119.02587890624999,\n 34.813803317113155\n ],\n [\n -118.125,\n 35.40696093270201\n ],\n [\n -118.71826171875,\n 36.40359962073253\n ],\n [\n -120.10253906249999,\n 37.37015718405753\n ],\n [\n -120.76171875,\n 38.20365531807149\n ],\n [\n -120.89355468749999,\n 38.61687046392973\n ],\n [\n -121.11328124999999,\n 38.58252615935333\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"17","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Seltzer, Alan 0000-0003-2870-1215","orcid":"https://orcid.org/0000-0003-2870-1215","contributorId":270717,"corporation":false,"usgs":false,"family":"Seltzer","given":"Alan","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":829825,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bekaert, David 0000-0002-1062-6221","orcid":"https://orcid.org/0000-0002-1062-6221","contributorId":270718,"corporation":false,"usgs":false,"family":"Bekaert","given":"David","email":"","affiliations":[{"id":36711,"text":"Woods Hole Oceanographic Institution","active":true,"usgs":false}],"preferred":false,"id":829826,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Barry, Peter H. 0000-0002-6960-1555","orcid":"https://orcid.org/0000-0002-6960-1555","contributorId":218244,"corporation":false,"usgs":false,"family":"Barry","given":"Peter","email":"","middleInitial":"H.","affiliations":[{"id":25447,"text":"University of Oxford","active":true,"usgs":false}],"preferred":false,"id":829827,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Durkin, Kathryn 0000-0002-2146-8281","orcid":"https://orcid.org/0000-0002-2146-8281","contributorId":270719,"corporation":false,"usgs":false,"family":"Durkin","given":"Kathryn","email":"","affiliations":[{"id":39679,"text":"Scripps Institution of Oceanography, UCSD","active":true,"usgs":false}],"preferred":false,"id":829828,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Mace, Emily 0000-0002-7521-1123","orcid":"https://orcid.org/0000-0002-7521-1123","contributorId":270720,"corporation":false,"usgs":false,"family":"Mace","given":"Emily","email":"","affiliations":[{"id":38914,"text":"Pacific Northwest National Laboratory","active":true,"usgs":false}],"preferred":false,"id":829829,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Aaselth, Craig E.","contributorId":270722,"corporation":false,"usgs":false,"family":"Aaselth","given":"Craig","email":"","middleInitial":"E.","affiliations":[{"id":6727,"text":"Pacific Northwest National Laboratory, Richland, WA","active":true,"usgs":false}],"preferred":false,"id":829831,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Zappala, Jake 0000-0003-1597-6009","orcid":"https://orcid.org/0000-0003-1597-6009","contributorId":270721,"corporation":false,"usgs":false,"family":"Zappala","given":"Jake","email":"","affiliations":[{"id":17946,"text":"Argonne National Laboratory","active":true,"usgs":false}],"preferred":false,"id":829830,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Peter 0000-0002-8544-8191","orcid":"https://orcid.org/0000-0002-8544-8191","contributorId":270723,"corporation":false,"usgs":false,"family":"Mueller","given":"Peter","email":"","affiliations":[{"id":17946,"text":"Argonne National Laboratory","active":true,"usgs":false}],"preferred":false,"id":829832,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Jurgens, Bryant C. 0000-0002-1572-113X","orcid":"https://orcid.org/0000-0002-1572-113X","contributorId":203430,"corporation":false,"usgs":true,"family":"Jurgens","given":"Bryant","middleInitial":"C.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829833,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Kulongoski, Justin T. 0000-0002-3498-4154 kulongos@usgs.gov","orcid":"https://orcid.org/0000-0002-3498-4154","contributorId":173457,"corporation":false,"usgs":true,"family":"Kulongoski","given":"Justin","email":"kulongos@usgs.gov","middleInitial":"T.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":829834,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70228881,"text":"70228881 - 2021 - Informed breeding dispersal following stochastic changes to patch quality in a pond-breeding amphibian","interactions":[],"lastModifiedDate":"2022-02-23T15:53:19.487538","indexId":"70228881","displayToPublicDate":"2021-04-21T09:42:58","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2158,"text":"Journal of Animal Ecology","active":true,"publicationSubtype":{"id":10}},"title":"Informed breeding dispersal following stochastic changes to patch quality in a pond-breeding amphibian","docAbstract":"The geochemical and petrophysical complexity of source-rock reservoirs in liquids-rich unconventional (LRU) plays necessitates the implementation of a more expansive analytical protocol for initial play assessment. In this study, original samples from selected source-rock reservoirs in the USA and the UK were analyzed by 22 MHz nuclear magnetic resonance (HF-NMR) T1-T2 mapping, followed by hydrous pyrolysis, and a modified Rock-Eval pyrolysis method (multi-heating step method-MHS). The above methods were complemented by organic petrography under reflected white and UV light excitation of the original and pyrolyzed samples. The analytical protocol presented attempts to better qualify and quantify different petroleum fractions (mobile, heavy hydrocarbons, viscous, solid bitumen), thus provide valuable and refined information about producibility of target intervals during appraisal. Results show how the hydrocarbon fractions interpreted from peak locations and intensities on NMR T1-T2 maps are in good agreement with those from MHS pyrolysis in terms of hydrocarbon mobility/producibility. Results from HP (Hydrous Pyrolysis) experiments show that an exception to this general agreement between NMR and MHS estimates occurs for the Kimmeridge Blackstone Clay samples, where MHS shows an increase of >90% in producible hydrocarbon yields vs. minimal to no presence of mobile hydrocarbons in NMR T1-T2 maps. This study clarifies the role of pore structure and networks in these discrepancies of producible oil estimates when comparing programmed pyrolysis to NMR-based techniques. This novel, multi-step and multidisciplinary approach provides a more advanced screening protocol for identifying zones of higher oil-in-place (OIP) and predicting fluid mobility prior to drilling or completions.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.fuel.2021.120357","usgsCitation":"Gentzis, T., Carvajal-Ortiz, H., Xie, Z.H., Hackley, P.C., and Fowler, H., 2021, An integrated geochemical, spectroscopic, and petrographic approach to examining the producibility of hydrocarbons from liquids-rich unconventional formations: Fuel, v. 298, 120357, 20 p., https://doi.org/10.1016/j.fuel.2021.120357.","productDescription":"120357, 20 p.","ipdsId":"IP-123449","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":398464,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"298","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Gentzis, Thomas","contributorId":289978,"corporation":false,"usgs":false,"family":"Gentzis","given":"Thomas","affiliations":[],"preferred":false,"id":840114,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Carvajal-Ortiz, Humberto","contributorId":289979,"corporation":false,"usgs":false,"family":"Carvajal-Ortiz","given":"Humberto","email":"","affiliations":[],"preferred":false,"id":840115,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Xie, Z. Harry","contributorId":289982,"corporation":false,"usgs":false,"family":"Xie","given":"Z.","email":"","middleInitial":"Harry","affiliations":[],"preferred":false,"id":840116,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hackley, Paul C. 0000-0002-5957-2551 phackley@usgs.gov","orcid":"https://orcid.org/0000-0002-5957-2551","contributorId":592,"corporation":false,"usgs":true,"family":"Hackley","given":"Paul","email":"phackley@usgs.gov","middleInitial":"C.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":255,"text":"Energy Resources Program","active":true,"usgs":true}],"preferred":true,"id":840117,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Fowler, Hallie","contributorId":289986,"corporation":false,"usgs":false,"family":"Fowler","given":"Hallie","email":"","affiliations":[],"preferred":false,"id":840118,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70220410,"text":"70220410 - 2021 - Geophysical insights into Paleoproterozoic tectonics along the southern margin of the Superior Province, central Upper Peninsula, Michigan, USA","interactions":[],"lastModifiedDate":"2021-05-12T11:53:20.247964","indexId":"70220410","displayToPublicDate":"2021-04-21T06:42:06","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3112,"text":"Precambrian Research","active":true,"publicationSubtype":{"id":10}},"title":"Geophysical insights into Paleoproterozoic tectonics along the southern margin of the Superior Province, central Upper Peninsula, Michigan, USA","docAbstract":"The southern margin of the Archean Superior Province in the central Upper Peninsula of Michigan was a nexus for key Paleoproterozoic tectonic events involved in the ~2.1 Ga rifting of proposed Archean supercraton Superia and subsequent assembly of Laurentia. Interpretations of the region’s tectonic history have historically been hampered by extensive Pleistocene glacial and Paleozoic sedimentary cover and a lack of appropriate geophysical data. These rifting and orogenic events formed geologic effects that are readily mappable with modern geophysical methods. New aeromagnetic and gravity data provide a critical means of mapping and interpreting the complex geological framework through cover, allowing development of significantly richer geographical and process-based perspectives on all these tectonic events. Interpretations of Precambrian contacts and structure are here, for the first time, carried >30 km eastward under Paleozoic cover. Effects of ~2.1 Ga rifting are strongly expressed geophysically, including the Dickinson Group, perhaps a unique record of the progression of rift-related sedimentation and magmatism, shown here to be a geographically extensive and largely concealed tectonic feature of the southern Superior Province. The geophysical evidence for plausible ~2.1 Ga rift-related intrusive magmatism includes a previously unrecognized swarm of northeast-striking mafic dikes cutting Archean rocks and gravity lows produced by granites. Effects of the ~1.87–1.83 Ga Penokean orogeny include gravity and magnetic gradients and pattern breaks along the Niagara fault zone suture, abundant evidence for thin-skinned thrusting and folding in the Menominee iron district, and speculative emplacement of an allochthonous sedimentary sequence in the Calumet trough. Numerous east–west trending structures imaged geophysically likely originated, or were significantly reactivated by, post-Penokean deformation. Metamorphic events at ~1.76 Ga and ~1.65 Ga may correspond to orogenies involving younger, outboard Paleoproterozoic crustal provinces recognized in southern Laurentia. For example, the previously unrecognized West Branch fault, separating the Dickinson Group from Archean rocks, is shown to be a major structure in the region, and is a proposed expression of ~1.76 Ga thick-skinned deformation. Oblique disruptions of crudely east–west striking structures have robust geophysical expressions and are speculatively connected to transpressive deformation at ~1.65 Ga. These new geophysical observations and interpretations collectively help illuminate a critical period in the tectonic evolution of Laurentia, as it transitioned from a disparate array of Archean cratons to a more coherent, growing continent.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.precamres.2021.106205","usgsCitation":"Drenth, B.J., Cannon, W.F., Schulz, K.J., and Ayuso, R.A., 2021, Geophysical insights into Paleoproterozoic tectonics along the southern margin of the Superior Province, central Upper Peninsula, Michigan, USA: Precambrian Research, v. 359, 106205, 19 p., https://doi.org/10.1016/j.precamres.2021.106205.","productDescription":"106205, 19 p.","ipdsId":"IP-121384","costCenters":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true},{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":452613,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.precamres.2021.106205","text":"Publisher Index Page"},{"id":436400,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99X3X07","text":"USGS data release","linkHelpText":"Data Release - Geologic map of the central Upper Peninsula, Michigan"},{"id":385578,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Minnesota, Wisconsin, Michigan","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 -91.8017578125,\n 43.99281450048989\n ],\n [\n -86.81396484375,\n 43.99281450048989\n ],\n [\n -86.81396484375,\n 47.81315451752768\n ],\n [\n -91.8017578125,\n 47.81315451752768\n ],\n [\n -91.8017578125,\n 43.99281450048989\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"359","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Drenth, Benjamin J. 0000-0002-3954-8124 bdrenth@usgs.gov","orcid":"https://orcid.org/0000-0002-3954-8124","contributorId":1315,"corporation":false,"usgs":true,"family":"Drenth","given":"Benjamin","email":"bdrenth@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":815467,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cannon, William F. 0000-0002-2699-8118","orcid":"https://orcid.org/0000-0002-2699-8118","contributorId":201972,"corporation":false,"usgs":true,"family":"Cannon","given":"William","email":"","middleInitial":"F.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815468,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schulz, Klaus J. 0000-0003-2967-4765 kschulz@usgs.gov","orcid":"https://orcid.org/0000-0003-2967-4765","contributorId":2438,"corporation":false,"usgs":true,"family":"Schulz","given":"Klaus","email":"kschulz@usgs.gov","middleInitial":"J.","affiliations":[{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815469,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Ayuso, Robert A. 0000-0002-8496-9534 rayuso@usgs.gov","orcid":"https://orcid.org/0000-0002-8496-9534","contributorId":2654,"corporation":false,"usgs":true,"family":"Ayuso","given":"Robert","email":"rayuso@usgs.gov","middleInitial":"A.","affiliations":[{"id":387,"text":"Mineral Resources Program","active":true,"usgs":true},{"id":245,"text":"Eastern Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":815470,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70220099,"text":"fs20213005 - 2021 - EverForecast—A near-term forecasting application for ecological decision support","interactions":[],"lastModifiedDate":"2021-04-21T11:50:04.150156","indexId":"fs20213005","displayToPublicDate":"2021-04-20T14:48:29","publicationYear":"2021","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":313,"text":"Fact Sheet","code":"FS","onlineIssn":"2327-6932","printIssn":"2327-6916","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2021-3005","displayTitle":"EverForecast—A Near-Term Forecasting Application for Ecological Decision Support","title":"EverForecast—A near-term forecasting application for ecological decision support","docAbstract":"The Everglades Forecasting application (EverForecast) provides decision makers with a support tool to examine optimal allocations of water across the managed landscape while explicitly quantifying the conflicting needs of multiple species. Covering the Greater Everglades (a vast, subtropical wetland ecosystem in South Florida), EverForecast provides 6-month forecasts of daily projected water stage across the region. It then runs these forecasts through a suite of species models and illustrates potential tradeoffs. All output is summarized by subregion and hydrologic category. Decision makers can use these near-term forecasts to manage the transition from current conditions to future alternatives according to their management priorities.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20213005","collaboration":"U.S. Geological Survey Greater Everglades Priority Ecosystems Program","usgsCitation":"Haider, S.M., Romañach, S.S., McKelvy, M., Suir, K., and Pearlstine, L., EverForecast—A near-term forecasting application for ecological decision support: U.S. Geological Survey Fact Sheet 2021–3005, 2 p., https://doi.org/10.3133/fs20213005.","productDescription":"2 p.","numberOfPages":"2","onlineOnly":"Y","ipdsId":"IP-123566","costCenters":[{"id":566,"text":"Southeast Ecological Science Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":385203,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2021/3005/fs20213005.pdf","text":"Report","size":"3.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2021–3005"},{"id":385202,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2021/3005/coverthb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Everglades","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -81.80969238281249,\n 24.991036982463722\n ],\n [\n -80.19470214843749,\n 24.991036982463722\n ],\n [\n -80.19470214843749,\n 26.74070480712781\n ],\n [\n -81.80969238281249,\n 26.74070480712781\n ],\n [\n -81.80969238281249,\n 24.991036982463722\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Wetland and Aquatic Research Center
U.S. Geological Survey
7920 NW 71st St.
Gainesville, FL 32653
We analyze multimethod shear (SH)‐wave velocity (
Assessing the scope and severity of threats is necessary for evaluating impacts on populations to inform conservation planning. Quantitative threat assessment often requires monitoring programs that provide reliable data over relevant spatial and temporal scales, yet such programs can be difficult to justify until there is an apparent stressor. Leveraging efforts of wildlife management agencies to record winter counts of hibernating bats, we collated data for 5 species from over 200 sites across 27 U.S. states and 2 Canadian provinces from 1995 to 2018 to determine the impact of white-nose syndrome (WNS), a deadly disease of hibernating bats. We estimated declines of winter counts of bat colonies at sites where the invasive fungus that causes WNS (Pseudogymnoascus destructans) had been detected to assess the threat impact of WNS. Three species undergoing species status assessment by the U.S. Fish and Wildlife Service (Myotis septentrionalis, Myotis lucifugus, and Perimyotis subflavus) declined by more than 90%, which warrants classifying the severity of the WNS threat as extreme based on criteria used by NatureServe. The scope of the WNS threat as defined by NatureServe criteria was large (36% of Myotis lucifugus range) to pervasive (79% of Myotis septentrionalis range) for these species. Declines for 2 other species (Myotis sodalis and Eptesicus fuscus) were less severe but still qualified as moderate to serious based on NatureServe criteria. Data-sharing across jurisdictions provided a comprehensive evaluation of scope and severity of the threat of WNS and indicated regional differences that can inform response efforts at international, national, and state or provincial jurisdictions. We assessed the threat impact of an emerging infectious disease by uniting monitoring efforts across jurisdictional boundaries and demonstrated the importance of coordinated monitoring programs, such as the North American Bat Monitoring Program (NABat), for data-driven conservation assessments and planning.
Seagrasses, oyster reefs, and salt marshes are critical coastal habitats that support high densities of juvenile fish and invertebrates. Yet which species are enhanced through these nursery habitats, and to what degree, remains largely unquantified. Densities of young-of-year fish and invertebrates in seagrasses, oyster reefs, and salt marsh edges as well as in paired adjacent unstructured habitats of the northern Gulf of Mexico were compiled. Species consistently found at higher densities in the structured habitats were identified, and species-specific growth and mortality models were applied to derive production enhancement estimates arising from this enhanced density. Enhancement levels for fish and invertebrate production were similar for seagrass (1370 [SD 317] g m–2 y–1for 25 enhanced species) and salt marsh edge habitats (1222 [SD 190] g m–2 y–1, 25 spp.), whereas oyster reefs produced ~650 [SD 114] g m–2 y–1(20 spp). This difference was partly due to lower densities of juvenile blue crab (Callinectes sapidus) on oyster reefs, although only oyster reefs enhanced commercially valuable stone crabs (Menippe spp.). The production estimates were applied to Galveston Bay, Texas, and Pensacola Bay, Florida, for species known to recruit consistently in those embayments. These case studies illustrated variability in production enhancement by coastal habitats within the northern Gulf of Mexico. Quantitative estimates of production enhancement within specific embayments can be used to quantify the role of essential fish habitat, inform management decisions, and communicate the value of habitat protection and restoration.
In 2014, the U.S. Geological Survey, in cooperation with Llano Estacado Underground Water Conservation District, Sandy Land Underground Water Conservation District, and South Plains Underground Water Conservation District (hereinafter referred to collectively as the “UWCDs”), began a multiphase study in and near Gaines, Terry, and Yoakum Counties, Texas, to develop a regional conceptual model of the hydrogeologic framework, geochemistry, groundwater-flow system, and hydraulic properties, primarily for the High Plains and Edwards-Trinity aquifer system and to a lesser degree for the Dockum aquifer. The High Plains aquifer system (hereinafter referred to as the “Ogallala aquifer”), contained within the Ogallala Formation in Texas, is the shallowest aquifer in the study area and is the primary source of water for agriculture and municipal supply in the areas managed by the UWCDs. Groundwater withdrawals from deeper aquifers (primarily the Edwards-Trinity [High Plains] aquifer system that is hereinafter referred to as the “Edwards-Trinity [High Plains] aquifer”) augmented by lesser amounts from the Dockum aquifer provide additional water sources in the study area. The Edwards-Trinity (High Plains) aquifer is contained within the Trinity and Fredericksburg Groups. The Dockum aquifer, a relatively minor source of water in the study area, is contained in the Dockum Group, which was evaluated as a single unit. The potential for continual declines of the groundwater in the Ogallala aquifer in the study area and the potential changes in water quality resulting from dewatering and increased vertical groundwater movement between adjacent water-bearing units have raised concerns about the amount and quality of available groundwater.
The developed conceptual model helped in the understanding of the quantity and quality of the groundwater within the Ogallala, the Edwards-Trinity (High Plains), and to a lesser extent, the Dockum aquifers within the study area. The hydrogeologic framework was used to assess the vertical and lateral extents of hydrogeologic units, bed orientations, unit thicknesses, and location and orientation of paleochannels. In general, the Trinity and Fredericksburg Groups and Ogallala Formation exhibit a slight regional dip (dip angle of about 0.14 degrees) to the southeast with dip directions becoming more to the south with each successively overlying unit (105, 110, and 125 degrees for the bases of the Trinity and Fredericksburg Groups and Ogallala Formation, respectively). In general, the Trinity and Fredericksburg Groups thin to the south and are not present in the southern part of Gaines County, whereas the Ogallala Formation becomes thinner from west to east. The combined thickness of the Trinity and Fredericksburg Groups and Ogallala Formation is generally greatest in the north-central part of the study area and thinnest in the southeastern part of the study area. Paleochannel orientation varied over geologic time as formations were deposited and eroded.
Water-quality samples were collected from 51 wells throughout the study area to better understand general water quality and to provide insight into groundwater-flow paths and recharge areas. Groundwater samples were spatially grouped on the basis of similarities found in the physicochemical properties, major ions, trace elements, nutrients, organic compounds, and selected stable isotopes and age tracers. Three groundwater groups were identified in the study area. The first groundwater group (Group 1), represented mostly by groundwater from the Ogallala and Edwards-Trinity (High Plains) aquifers in the northern half of the study area, is considered to be recent recharge, affected by land-use activities, as explained by the younger age, higher concentrations of nitrate plus nitrite, and more frequent detections of organic compounds. Groundwater wells in the second groundwater group (Group 2) are typically in the southwestern and northwestern parts of the study area, and the groundwater in this group is considered to be groundwater recharged during the Pleistocene period, as explained by the relatively old age of the groundwater, high strontium stable isotope ratios, and hydrogen and oxygen stable isotope ratios. The last groundwater group (Group 3) is likely a mixture of groundwater from the first or second groups (or both) with a third, highly mineralized groundwater as explained by having the highest dissolved-solids concentrations in the study area and having some similarities to geochemical characteristics of samples from the first and second groups.
A groundwater-flow system analysis was done to understand the flow of groundwater throughout the aquifer system. Groundwater-level altitudes for the Ogallala, Edwards-Trinity (High Plains), and Dockum aquifers are generally higher in the northwestern part of the study area and lower in the southeastern part of the study area. Groundwater generally flows in a northwest to southeast direction across the study area in each of the aquifers. The groundwater-flow paths closely resemble the mapped paleochannels, indicating that within the study area, the groundwater flows preferentially along the paleochannels, especially within the Ogallala aquifer where dewatering of the aquifer results in a greater effect of the base structure on the flow of groundwater.
The Ogallala aquifer is unsaturated in localized areas in the study area; unsaturated areas are generally near the southern extent of the Edwards-Trinity (High Plains) aquifer, with the largest unsaturated area west of Seminole, Tex. The saturated thickness of the Ogallala aquifer is thickest (more than 125 feet) southeast of Seminole and west of Brownfield, Tex., near the border between Terry and Yoakum Counties. The saturated thickness of the combined Ogallala and Edwards-Trinity (High Plains) aquifers ranges from less than 10 feet along the far southern edge of the study area to more than 350 feet north and east of Brownfield, Tex., and along the border between Terry and Yoakum Counties.
The aquifer hydraulic properties, including hydraulic conductivity and specific yield, were estimated to better understand the ability of groundwater to move through the aquifer system and quantify the volume of available water in storage. The hydraulic-conductivity values varied greatly within the study area (ranging from about 0.03 to about 350 feet per day), and often large variations were found in the same area. Terry County contained the highest and lowest hydraulic conductivity values for the Ogallala aquifer, whereas Yoakum County contained the highest and lowest hydraulic conductivity values for the Edwards-Trinity (High Plains) aquifer. The highest hydraulic-conductivity values for the Dockum aquifer were in Gaines County, whereas the lowest hydraulic-conductivity values were in Terry County. The estimated specific yield values within the study area range from 0.01 to 0.36. Higher specific yield values generally occurred in the western part of the study area except in the Ogallala aquifer where higher specific yield values were in the east. The Ogallala aquifer had the lowest specific yield range and the least specific yield variability among the three aquifers, whereas the Dockum aquifer had the highest specific yield range and the greatest specific yield variability.
Using the estimated saturated thickness and estimated specific yield grids, the water volumes of the Ogallala and Edwards-Trinity (High Plains) aquifers and the combined Ogallala and Edwards-Trinity (High Plains) aquifers were estimated. The available water in the Edwards-Trinity (High Plains) aquifer (16.6 million acre-feet) is almost double the available water in the Ogallala aquifer (8.8 million acre-feet). Although the Edwards-Trinity (High Plains) aquifer contains more available groundwater, pumping is more difficult because of the relatively low hydraulic conductivity and specific yield values compared to the Ogallala aquifer. Overall, the available water within the combined Ogallala and Edwards-Trinity (High Plains) aquifers is about 6.6, 10.2, and 8.6 million acre-feet for Gaines, Terry, and Yoakum Counties, respectively.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20215009","collaboration":"Prepared in cooperation with Llano Estacado Underground Water Conservation District, Sandy Land Underground Water Conservation District, and South Plains Underground Water Conservation District","usgsCitation":"Teeple, A.P., Ging, P.B., Thomas, J.V., Wallace, D.S., and Payne, J.D., 2021, Hydrogeologic framework, geochemistry, groundwater-flow system, and aquifer hydraulic properties used in the development of a conceptual model of the Ogallala, Edwards-Trinity (High Plains), and Dockum aquifers in and near Gaines, Terry, and Yoakum Counties, Texas: U.S. Geological Survey Scientific Investigations Report 2021–5009, 68 p., https://doi.org/10.3133/sir20215009.","productDescription":"Report: xi, 68 p.; Data Release","numberOfPages":"85","onlineOnly":"N","ipdsId":"IP-118420","costCenters":[{"id":48595,"text":"Oklahoma-Texas Water Science Center","active":true,"usgs":true}],"links":[{"id":385110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2021/5009/coverthb.jpg"},{"id":385111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2021/5009/sir20215009.pdf","text":"Report","size":"16.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2021–5009"},{"id":385112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9N3WKQ5","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Compilation of time-domain electromagnetic surface geophysical soundings, historical borehole characteristics, water level, water quality and hydraulic properties data throughout Gaines, Yoakum, and Terry Counties in Texas, 1929–2019"}],"country":"United States","state":"Texas","county":"Gaines County, Terry County, Yoakum County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-102.2039,32.961],[-102.2038,32.5237],[-102.2109,32.524],[-103.0637,32.5215],[-103.0632,32.9589],[-103.0632,33.0017],[-103.0593,33.209],[-103.0559,33.3903],[-102.5954,33.3903],[-102.0774,33.3894],[-102.0782,32.9611],[-102.2039,32.961]]]},\"properties\":{\"name\":\"Gaines\",\"state\":\"TX\"}}]}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
Hoary bats (Lasiurus cinereus) are among the bat species most commonly killed by wind turbine strikes in the midwestern United States. The impact of this mortality on species census size is not understood, due in part to the difficulty of estimating population size for this highly migratory and elusive species. Genetic effective population size (Ne) could provide an index of changing census population size if other factors affecting Ne are stable.
We used the NeEstimator package to derive effective breeding population size (Nb) estimates for two temporally spaced cohorts: 93 hoary bats collected in 2009–2010 and an additional 93 collected in 2017–2018. We sequenced restriction-site associated polymorphisms and generated a de novo genome assembly to guide the removal of sex-linked and multi-copy loci, as well as identify physically linked markers.
Analysis of the reference genome with psmc suggested at least a doubling of Ne in the last 100,000 years, likely exceeding Ne = 10,000 in the Holocene. Allele and genotype frequency analyses confirmed that the two cohorts were comparable, although some samples had unusually high or low observed heterozygosities. Additionally, the older cohort had lower mean coverage and greater variability in coverage, and batch effects of sampling locality were observed that were consistent with sample degradation. We therefore excluded samples with low coverage or outlier heterozygosity, as well as loci with sequence coverage far from the mode value, from the final data set. Prior to excluding these outliers, contemporary Nb estimates were significantly higher in the more recent cohort, but this finding was driven by high values for the 2018 sample year and low values for all other years. In the reduced data set, Nb did not differ significantly between cohorts. We found base substitutions to be strongly biased toward cytosine to thymine or the complement, and further partitioning loci by substitution type had a strong effect on Nb estimates. Minor allele frequency and base quality bias thresholds also had strong effects on Nb estimates. Instability of Nb with respect to common data filtering parameters and empirically identified factors prevented robust comparison of the two cohorts. Given that confidence intervals frequently included infinity as the stringency of data filtering increased, contemporary trends in Nb of North American hoary bats may not be tractable with the linkage disequilibrium method, at least using the protocol employed here.
","language":"English","publisher":"PeerJ","doi":"10.7717/peerj.11285","usgsCitation":"Cornman, R.S., Fike, J., Oyler-McCance, S.J., and Cryan, P.M., 2021, Historical effective population size of North American hoary bat (Lasiurus cinereus) and challenges to estimating trends in contemporary effective breeding population size from archived samples: PeerJ, v. 9, e11285, 27 p., https://doi.org/10.7717/peerj.11285.","productDescription":"e11285, 27 p.","ipdsId":"IP-125432","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":452631,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.7717/peerj.11285","text":"Publisher Index Page"},{"id":436402,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VSG54Z","text":"USGS data release","linkHelpText":"Genetic variation in hoary bats (Lasiurus cinereus) assessed from archived samples"},{"id":385284,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"9","noUsgsAuthors":false,"publicationDate":"2021-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Cornman, Robert S. 0000-0001-9511-2192 rcornman@usgs.gov","orcid":"https://orcid.org/0000-0001-9511-2192","contributorId":5356,"corporation":false,"usgs":true,"family":"Cornman","given":"Robert","email":"rcornman@usgs.gov","middleInitial":"S.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true},{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814602,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fike, Jennifer A. 0000-0001-8797-7823","orcid":"https://orcid.org/0000-0001-8797-7823","contributorId":207268,"corporation":false,"usgs":true,"family":"Fike","given":"Jennifer A.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814603,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Oyler-McCance, Sara J. 0000-0003-1599-8769 sara_oyler-mccance@usgs.gov","orcid":"https://orcid.org/0000-0003-1599-8769","contributorId":1973,"corporation":false,"usgs":true,"family":"Oyler-McCance","given":"Sara","email":"sara_oyler-mccance@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814604,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Cryan, Paul M. 0000-0002-2915-8894 cryanp@usgs.gov","orcid":"https://orcid.org/0000-0002-2915-8894","contributorId":147942,"corporation":false,"usgs":true,"family":"Cryan","given":"Paul","email":"cryanp@usgs.gov","middleInitial":"M.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":814605,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70263927,"text":"70263927 - 2021 - Spatiotemporal clustering of great earthquakes on a transform fault controlled by geometry","interactions":[],"lastModifiedDate":"2025-02-28T15:50:17.631195","indexId":"70263927","displayToPublicDate":"2021-04-19T09:46:24","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2845,"text":"Nature Geoscience","active":true,"publicationSubtype":{"id":10}},"title":"Spatiotemporal clustering of great earthquakes on a transform fault controlled by geometry","docAbstract":"Minor changes in geometry along the length of mature strike-slip faults may act as conditional barriers to earthquake rupture, terminating some and allowing others to pass. This hypothesis remains largely untested because palaeoearthquake data that constrain spatial and temporal patterns of fault rupture are generally imprecise. Here we develop palaeoearthquake event data that encompass the last 20 major-to-great earthquakes along approximately 320 km of the Alpine Fault in New Zealand with sufficient temporal resolution and spatial coverage to reveal along-strike patterns of rupture extent. The palaeoearthquake record shows that earthquake terminations tend to cluster in time near minor along-strike changes in geometry. These terminations limit the length to which rupture can grow and produce two modes of earthquake behaviour characterized by phases of major (Mw 7–8) and great (Mw > 8) earthquakes. Physics-based simulations of seismic cycles closely resemble our observations when parameterized with realistic fault geometry. Switching between the rupture modes emerges due to heterogeneous stress states that evolve over multiple seismic cycles in response to along-strike differences in geometry. These geometric complexities exert a first-order control on rupture behaviour that is not currently accounted for in fault-source models for seismic hazard.
","language":"English","publisher":"Nature","doi":"10.1038/s41561-021-00721-4","usgsCitation":"Howarth, J., Barth, N.C., Fitzsimons, S., Richards-Dinger, K.B., Clark, K., Biasi, G., Cochran, U., Langridge, R.M., Berryman, K., and Sutherland, R., 2021, Spatiotemporal clustering of great earthquakes on a transform fault controlled by geometry: Nature Geoscience, v. 14, p. 314-320, https://doi.org/10.1038/s41561-021-00721-4.","productDescription":"7 p.","startPage":"314","endPage":"320","ipdsId":"IP-123083","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":482642,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"New Zealand","otherGeospatial":"South Island","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 166.14130042117392,\n -45.855254614357875\n ],\n [\n 167.55703138687022,\n -47.72709088799331\n ],\n [\n 171.04329453684727,\n -45.90628854385326\n ],\n [\n 172.01003756617126,\n -44.16671517350856\n ],\n [\n 173.28236889170694,\n -43.93388994721674\n ],\n [\n 173.01583587447817,\n -43.26711906105943\n ],\n [\n 174.6610670093284,\n -41.74200766025002\n ],\n [\n 174.2430988469256,\n -40.59327885176259\n ],\n [\n 172.24758921841533,\n -40.37688312712664\n ],\n [\n 171.32279188614189,\n -41.531725778001864\n ],\n [\n 169.84538818345476,\n -43.00565520661165\n ],\n [\n 167.88893104162912,\n -43.84009881514205\n ],\n [\n 166.14130042117392,\n -45.855254614357875\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"14","noUsgsAuthors":false,"publicationDate":"2021-04-19","publicationStatus":"PW","contributors":{"authors":[{"text":"Howarth, Jamie D.","contributorId":351620,"corporation":false,"usgs":false,"family":"Howarth","given":"Jamie D.","affiliations":[{"id":34109,"text":"Victoria University of Wellington, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929132,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Barth, Nicolas C.","contributorId":206132,"corporation":false,"usgs":false,"family":"Barth","given":"Nicolas","email":"","middleInitial":"C.","affiliations":[{"id":37254,"text":"University of California, Riverside, CA","active":true,"usgs":false}],"preferred":false,"id":929133,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Fitzsimons, Sean J.","contributorId":351621,"corporation":false,"usgs":false,"family":"Fitzsimons","given":"Sean J.","affiliations":[{"id":13378,"text":"University of Otago, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929134,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Richards-Dinger, Keith B.","contributorId":198155,"corporation":false,"usgs":false,"family":"Richards-Dinger","given":"Keith","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":929135,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Kate","contributorId":295749,"corporation":false,"usgs":false,"family":"Clark","given":"Kate","email":"","affiliations":[],"preferred":false,"id":929136,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Biasi, Glenn 0000-0003-0940-5488 gbiasi@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-5488","contributorId":195946,"corporation":false,"usgs":true,"family":"Biasi","given":"Glenn","email":"gbiasi@usgs.gov","affiliations":[],"preferred":true,"id":929137,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Cochran, Ursula A.","contributorId":351622,"corporation":false,"usgs":false,"family":"Cochran","given":"Ursula A.","affiliations":[{"id":26939,"text":"GNS Science, Lower Hutt, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929138,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Langridge, Robert M.","contributorId":175117,"corporation":false,"usgs":false,"family":"Langridge","given":"Robert","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":929139,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Berryman, Kelvin R.","contributorId":351623,"corporation":false,"usgs":false,"family":"Berryman","given":"Kelvin R.","affiliations":[{"id":26939,"text":"GNS Science, Lower Hutt, New Zealand","active":true,"usgs":false}],"preferred":false,"id":929140,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Sutherland, Rupert 0000-0001-7430-0055","orcid":"https://orcid.org/0000-0001-7430-0055","contributorId":278669,"corporation":false,"usgs":false,"family":"Sutherland","given":"Rupert","email":"","affiliations":[{"id":57245,"text":"School of Geography, Environment and Earth Sciences, Victoria University of Wellington","active":true,"usgs":false}],"preferred":false,"id":929141,"contributorType":{"id":1,"text":"Authors"},"rank":10}]}} ,{"id":70222437,"text":"70222437 - 2021 - Organo-facies and mineral effects on sorption capacity of low-maturity Permian Barakar shales from the Auranga Basin, Jharkhand, India","interactions":[],"lastModifiedDate":"2021-07-30T14:14:46.655583","indexId":"70222437","displayToPublicDate":"2021-04-19T09:12:49","publicationYear":"2021","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1506,"text":"Energy & Fuels","active":true,"publicationSubtype":{"id":10}},"title":"Organo-facies and mineral effects on sorption capacity of low-maturity Permian Barakar shales from the Auranga Basin, Jharkhand, India","docAbstract":"Shales associated with the Lower Permian (Barakar Formation) sediments of the Auranga Coalfield, India, occur in the immature–early mature stage. The sorption capacity of Barakar shale samples has been studied through high-pressure methane (CH4) adsorption and low-pressure N2 gas adsorption (LPN2GA) methods, supported with proximate analyses, programmed pyrolysis, optical petrography, and with energy-dispersive spectroscopy, X-ray diffraction, and inductively coupled plasma mass spectrometry. The sorption capacity is a function of the organic and inorganic constituents present in the shale samples. The methane sorption capacity (MSC) and Langmuir volume of the shale samples vary from 0.217 to 0.314 and 0.315 to 0.429 mmol/g rock, respectively. The BET-calculated surface area of the studied shales varies from 8.12 to 30.36 m2/g. The sorption capacities show the importance of the total organic content (TOC) through weak but positive correlations with MSC (r2 = 0.45) and S1 values (mg hydrocarbons/g rock from programmed pyrolysis; r2 = 0.40). Moreover, apparent inverse relationships were observed between MSC and clay mineral abundances, suggesting that individual clay mineral types may influence MSC, although more work is needed. The TOC-normalized MSC (MSC*) of shale samples shows a positive trend with quartz plus clay mineral content and ash yield of r2 = 0.64 for both. In addition, MSC* shows a negative logarithmic relationship with S1 + S2 (r2 = 0.63) and a positive linear relationship with TOC-normalized total organic matter (TOM*) (r2 = 0.88, when 5 low TOM* samples are excluded) indicating complex relationships possibly including bitumen retention in the sample pore spaces. The micropore study of the samples through LPN2GA, applying Dubinin–Radushkevich, Dubinin–Astakhov, and density functional theory models, shows the dominance of micro-mesopore concentrations in the shale matrix of ∼2 nm pore diameter. However, these pores might be present as blind or closed pores. The presence of thorium and zirconium is reflective of terrigenous detrital matter, i.e., moderately to strongly recycled sediments. The fluviatile facies of deposited shales in the Auranga Coalfield are noted by the significant presence of kaolinite (32.5–78.3%), which suggests the importance of its effect on the sorption capacity of proximal terrigenous shales.
Coking coal, or metallurgical coal, has been produced in the United States for nearly 200 years. Coking coal is primarily used in the production of coke for use in the steel industry, and for other uses (for example, foundries, blacksmithing, heating buildings, and brewing). Currently, U.S. coking coal is produced in Alabama, Arkansas, Pennsylvania, Virginia , and West Virginia. Historically, coking coal has also been produced in 15 other states (Alaska, Colorado, Georgia, Illinois, Indiana, Kentucky, Maryland, Montana, New Mexico, Ohio, Oklahoma, Tennessee, Utah, Washington, and Wyoming), but currently is not. Coals from the Appalachian, Arkoma, and Illinois basins are Pennsylvanian in age, while coals in Alaska, Colorado, Montana, New Mexico, Utah, Washington, and Wyoming range in age from Early Cretaceous through Eocene.
This Open-File Report presents the geographic locations of current and historical coking coal deposits of the United States, with additional information about recent and historical mining and exploration activities. Chemical, rheological, petrographic, and other criteria for evaluating the coking potential of coals are discussed, and historical data for coking coals in the United States are presented. In addition, new coking coal samples from Alabama, Arkansas, Kentucky, and Oklahoma were collected and analyzed for this report, and the data are presented in multiple tables, including proximate and ultimate analyses; calorific value; sulfur forms; major-, minor-, and trace-element abundances; Free-Swelling Index; Gieseler Plastometer analyses; American Society for Testing and Materials (ASTM) dilatation; coal petrography; and predicted values of Coal Stability Factor and Coal Strength after Reaction with CO2 (pCSF and pCSR, respectively). Data from previously analyzed coking coal samples in Kentucky, Pennsylvania, Virginia, and West Virginia were supplied by three companies, including results from all the tests listed above, plus oxidation, Hardgrove Grindability Index, and ash fusion (in a reducing environment) temperatures are also presented in tables in the report.
Geographic Information System (GIS) data compiled for this project are available for download for public and private utilization and may be used to create maps for a variety of energy resource studies. These GIS data are in shapefile format, and metadata files are included describing all GIS processing. Additional geographic information about coking coal areas of the United States are also presented in tabular format in the report, including the following: names of coal basins, fields, regions, districts, and areas; coal beds or zones; geographic locations including States, counties, towns, rivers, mountains, etc.; stratigraphic hierarchy and age of the coal-bearing interval; coking characteristics including sulfur content, ash yield, volatile matter, moisture, calorific value, and Free-Swelling Index; coal rank; names of coal mines and coal-mining companies; current and past mining activity; and references for reports about the coal.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201113","usgsCitation":"Trippi, M.H., Ruppert, L.F., Eble, C.F., and Hower, J.C., 2021, Coking coal of the United States—Modern and historical coking coal mining locations and chemical, rheological, petrographic, and other data from modern samples: U.S. Geological Survey Open-File Report 2020–1113, 112 p., https://doi.org/10.3133/ofr20201113.","productDescription":"Report: xi, 112 p.; Tables 1.1-21.1; Data Release; Metadata; Spatial Data","numberOfPages":"112","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-111543","costCenters":[{"id":49175,"text":"Geology, Energy & Minerals Science Center","active":true,"usgs":true}],"links":[{"id":381899,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2020/1113/ofr20201113_appendix_tables_csv.zip","text":"Tables","size":"88.9 KB","linkFileType":{"id":6,"text":"zip"},"linkHelpText":"- Zip file of tables 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U.S. Geological Survey
12201 Sunrise Valley Drive
954 National Center
Reston, VA 20192
Realistic environmental models used for decision making typically require a highly parameterized approach. Calibration of such models is computationally intensive because widely used parameter estimation approaches require individual forward runs for each parameter adjusted. These runs construct a parameter-to-observation sensitivity, or Jacobian, matrix used to develop candidate parameter upgrades. Parameter estimation algorithms are also commonly adversely affected by numerical noise in the calculated sensitivities within the Jacobian matrix, which can result in unnecessary parameter estimation iterations and less model-to-measurement fit. Ideally, approaches to reduce the computational burden of parameter estimation will also increase the signal-to-noise ratio related to observations influential to the parameter estimation even as the number of forward runs decrease. In this work a simultaneous increments, an iterative ensemble smoother (IES), and a randomized Jacobian approach were compared to a traditional approach that uses a full Jacobian matrix. All approaches were applied to the same model developed for decision making in the Mississippi Alluvial Plain, USA. Both the IES and randomized Jacobian approach achieved a desirable fit and similar parameter fields in many fewer forward runs than the traditional approach; in both cases the fit was obtained in fewer runs than the number of adjustable parameters. The simultaneous increments approach did not perform as well as the other methods due to inability to overcome suboptimal dropping of parameter sensitivities. This work indicates that use of highly efficient algorithms can greatly speed parameter estimation, which in turn increases calibration vetting and utility of realistic models used for decision making.
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