Aim
Human development and agriculture can have transformative and homogenizing effects on natural systems, shifting the composition of ecological communities towards non-native and native species that tolerate or thrive under human-dominated conditions. These impacts cannot be fully captured by summarizing species presence, as they include dramatic changes to patterns of species abundance. However, how human land use patterns and species invasions intersect to shape patterns of abundance and dominance within ecological communities is poorly understood even in well-known taxa.
Location
Conterminous United States.
Time period
2010–2012.
Major taxa studied
Passeriformes.
Methods
We analyse continental-scale monitoring data to study the proportional abundance of non-native and native synanthropic species within passerine bird communities. Synanthropic species are those that benefit from an association with humans. We estimate how the amount and configuration of human development and agriculture relate to the degree to which human-associated species dominate passerine communities across the continent.
Results
Human-associated species comprised the majority of detected passerine individuals across two-thirds of bird surveys. Non-native and synanthropic species responded differently to land cover and reached highest relative abundance in different portions of the continent. The proportional abundance of synanthropic birds increased rapidly with development, but was not related to the configuration of land cover. The proportion of non-native individuals was higher when intensively-used land cover was more aggregated.
Main conclusions
Even low amounts of intensively-used lands were associated with a dramatic reshaping of passerine communities, with consequences for patterns of relative abundance across the continent.
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Center","active":true,"usgs":true}],"preferred":true,"id":836414,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Davis, Kristin P. 0000-0003-1204-4687","orcid":"https://orcid.org/0000-0003-1204-4687","contributorId":286993,"corporation":false,"usgs":true,"family":"Davis","given":"Kristin","email":"","middleInitial":"P.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":836415,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pejchar, Liba","contributorId":225494,"corporation":false,"usgs":false,"family":"Pejchar","given":"Liba","email":"","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":836416,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70208489,"text":"ofr20191136 - 2020 - The surface trace tool — Modeling complex planar interactions using ArcGIS","interactions":[],"lastModifiedDate":"2022-04-21T19:38:18.369387","indexId":"ofr20191136","displayToPublicDate":"2020-02-12T15:40:53","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-1136","displayTitle":"The Surface Trace Tool — Modeling Complex Planar Interactions Using ArcGIS","title":"The surface trace tool — Modeling complex planar interactions using ArcGIS","docAbstract":"The surface trace tool comprises a Python script written for ArcGIS that will determine the line of intersection between a planar feature and a surface. Specifically, this tool was designed for geologic applications where geologic planar-feature orientations are reported as strike and dip, and the intersecting surface is the ground. The tool output will show how planar geologic layers intersect with topography.
Determining where geologic features crop out on the surface can be used to guide new geologic mapping as well as reviewing existing geologic mapping. This tool was developed to aid in more efficient mapping of an unknown area. These unknown areas may be missing data, either owing to a lack of suitable outcrops or being difficult to traverse, and data about the areas may be extrapolated using this tool and surrounding data to determine where planar features might appear on the ground.
Director,
Geology, Minerals, Energy, & Geophysics Science Center
Menlo Park, California
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
The North Branch Au Sable River, located in the northern lower peninsula of Michigan near Lovells, Michigan, has historically been known for its brook trout (Salvelinus fontinalis) and its status as a blue ribbon trout stream; however, within the past few decades, there has been a decline in fish population. The objectives of this study were to assess if concentrations of organic chemicals were present in quantities in the North Branch Au Sable River that may potentially harm aquatic species and to establish current baseline concentrations of organic chemicals against which future data can be compared. Passive sampling technology was used to collect information on the concentration, occurrence, transport, and fate of organic chemicals; these samplers absorb dissolved organic chemicals in the river over several weeks, as the timing and intensity of pesticide applications and the frequency of storm events and irrigation can cause fluctuations in organic chemical loading to surface waters. The chemical classes investigated as part of this study included pesticides (both legacy [organochlorine] and current use), polychlorinated biphenyls, polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs).
Passive samplers, including semipermeable membrane devices and polar organic chemical integrative samplers, were deployed at four locations along the North Branch Au Sable River, near Lovells, Mich., in June 2018 for a total of 28 days. Several organic chemicals were detected in the North Branch Au Sable River at low concentrations. Organic chemicals were detected at every sampling location on the North Branch Au Sable River; however, not all chemicals were detected at every location. The highest number of organic chemicals were detected at the most downstream sampling site (North Branch Au Sable River at Kellogg's Bridge), and the lowest number of organic chemicals were detected at the next site upstream (North Branch Au Sable River at Twin Bridge Road). The organic contaminants most frequently detected at all sampling locations include the legacy pesticides pentachloroanisole, trans-chlordane, p,p'-dichlorodiphenyldichloroethylene, and p,p'-dichlorodiphenyltrichloroethane; the PBDE PBDE-28; and the PAHs 2-methylphenanthrene and perylene.
Organic chemical concentrations detected on the North Branch Au Sable River were below almost all water-quality benchmarks included in this report. However, low concentrations of organic chemicals may still pose a risk to aquatic organisms and throughout the trophic hierarchy because of low-dose additive and synergistic mixture effects, transgenerational effects, and a lack of established water-quality benchmarks for many organic chemicals. This report provides data on the current (2018) state of the North Branch Au Sable River and provided a baseline of organic contaminant data against which future data on the North Branch Au Sable River can be evaluated.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20205002","collaboration":"Prepared for the Mason-Griffith Founders Chapter of Trout Unlimited in cooperation with Lovells Township, Michigan","usgsCitation":"Brennan, A.K., and Alvarez, D.A., 2020, Evaluation of legacy and emerging organic chemicals using passive sampling devices on the North Branch Au Sable River near Lovells, Michigan, June 2018: U.S. Geological Survey Scientific Investigations Report 2020–5002, 21 p., https://doi.org/10.3133/sir20205002.","productDescription":"vi, 21 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-112993","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":399625,"rank":3,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109682.htm"},{"id":372284,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2020/5002/sir20205002.pdf","text":"Report","size":"1.99 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2020–5002"},{"id":372283,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2020/5002/coverthb.jpg"}],"country":"United States","state":"Michigan","county":"Crawford County","city":"Lovells","otherGeospatial":"North Branch Au Sable River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.6833,\n 45\n ],\n [\n -84.25,\n 45\n ],\n [\n -84.25,\n 44.65\n ],\n [\n -84.6833,\n 44.65\n ],\n [\n -84.6833,\n 45\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Upper Midwest Water Science Center
U.S. Geological Survey
5840 Enterprise Drive
Lansing, MI 48911–4107
Following the 2019 Ridgecrest, California, earthquake sequence, we compiled ground‐motion records from multiple data centers and processed these records using newly developed ground‐motion processing software that performs quality assurance checks, performs standard time series processing steps, and computes a wide range of ground‐motion metrics. In addition, we compute station and waveform metrics such as the time‐averaged shear‐wave velocity to 30 m depth (
Many pathogens interact and evolve in communities where more than one host species is present, yet our understanding of host–pathogen specialization is mostly informed by laboratory studies with single species. Managing diseases in the wild, however, requires understanding how host–pathogen specialization affects hosts in diverse communities. Juvenile salmonid mortality in hatcheries caused by infectious hematopoietic necrosis virus (IHNV) has important implications for salmonid conservation programs. Here, we evaluate evidence for IHNV specialization on three salmonid hosts and assess how this influences intra‐ and interspecific transmission in hatchery‐reared salmonids. We expect that while more generalist viral lineages should pose an equal risk of infection across host types, viral specialization will increase intraspecific transmission. We used Bayesian models and data from 24 hatcheries in the Columbia River Basin to reconstruct the exposure history of hatcheries with two IHNV lineages, MD and UC, allowing us to estimate the probability of juvenile infection with these lineages in three salmonid host types. Our results show that lineage MD is specialized on steelhead trout and perhaps rainbow trout (both Oncorhynchus mykiss), whereas lineage UC displayed a generalist phenotype across steelhead trout, rainbow trout, and Chinook salmon. Furthermore, our results suggest the presence of specialist–generalist trade‐offs because, while lineage UC had moderate probabilities of infection across host types, lineage MD had a small probability of infection in its nonadapted host type, Chinook salmon. Thus, in addition to quantifying probabilities of infection of socially and economically important salmonid hosts with different IHNV lineages, our results provide insights into the trade‐offs that viral lineages incur in multihost communities. Our results suggest that knowledge of the specialist/generalist strategies of circulating viral lineages could be useful in salmonid conservation programs to control disease.
We develop a semiautomated method for estimating with second seismic moments the directivity, rupture area, duration, and centroid velocity of earthquakes. The method is applied to 41 southern California earthquakes with magnitude in the range 3.5–5.2 and provides stable results for 28 events. Apparent source time functions (ASTFs) of P and S phases are derived using deconvolution with three stacked empirical Green's functions (seGf). The use of seGf suppresses nongeneric source effects, improves the focal mechanism correspondence to the analyzed earthquakes, and typically allows inclusion of 5 to 15 more ASTFs compared with analysis using a single eGf. Most analyzed earthquakes in the Trifurcation area of the San Jacinto Fault have directivities toward the northwest, while events around Cajon Pass and San Gabriel Mountain tend to propagate toward the southeast. These results are generally consistent with predictions for dynamic rupture on bimaterial interfaces associated with the imaged velocity contrasts in the area. The second moment inversions also provide constraints on the upper and lower bounds of rupture areas in our data set. Stress drops and uncertainties are estimated for elliptical ruptures using the derived characteristic rupture length and width. The semiautomated second moment method with seGfs can be used for routine application to moderate earthquakes in locations with good station coverage.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019JB018566","usgsCitation":"Meng, H., McGuire, J., and Ben-Zion, Y., 2020, Semiautomated estimates of directivity and related source properties of small to moderate southern California earthquakes using second seismic moments: Journal of Geophysical Research, v. 125, no. 4, e2019JB018566, 21 p., https://doi.org/10.1029/2019JB018566.","productDescription":"e2019JB018566, 21 p.","ipdsId":"IP-110976","costCenters":[{"id":234,"text":"Earthquake Hazards Program","active":true,"usgs":true}],"links":[{"id":377209,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.20214843749999,\n 32.519026027827515\n ],\n [\n -115.059814453125,\n 32.713355353177555\n ],\n [\n -115.169677734375,\n 35.34425514918409\n ],\n [\n -116.65283203124999,\n 35.96911507577482\n ],\n [\n -120.25634765624999,\n 34.77771580360469\n ],\n [\n -120.03662109374999,\n 34.31621838080741\n ],\n [\n -117.20214843749999,\n 32.519026027827515\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"125","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-04-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Meng, Haoran","contributorId":237785,"corporation":false,"usgs":false,"family":"Meng","given":"Haoran","email":"","affiliations":[{"id":47614,"text":"University of Southern California; Florida State University","active":true,"usgs":false}],"preferred":false,"id":795292,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McGuire, Jeffrey J. 0000-0001-9235-2166","orcid":"https://orcid.org/0000-0001-9235-2166","contributorId":219786,"corporation":false,"usgs":true,"family":"McGuire","given":"Jeffrey J.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":795293,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ben-Zion, Yehuda","contributorId":195741,"corporation":false,"usgs":false,"family":"Ben-Zion","given":"Yehuda","email":"","affiliations":[{"id":16177,"text":"University of Southern California, Los Angeles, Ca.","active":true,"usgs":false}],"preferred":false,"id":795294,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70228490,"text":"70228490 - 2020 - Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment","interactions":[],"lastModifiedDate":"2022-02-11T16:09:26.967537","indexId":"70228490","displayToPublicDate":"2020-02-11T09:10:33","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1497,"text":"Endangered Species Research","active":true,"publicationSubtype":{"id":10}},"title":"Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment","docAbstract":"The US Fish and Wildlife Service has initiated a re-envisioned approach for providing decision makers with the best available science and synthesis of that information, called the Species Status Assessment (SSA), for endangered species decision making. The SSA report is a descriptive document that provides decision makers with an assessment of a species’ current status and predicted future status. These analyses support all manner of decisions under the US Endangered Species Act, such as listing, reclassification, recovery planning, etc. Novel scientific analysis and predictive modeling in SSAs could be an important part of rooting species conservation decisions in current data and cutting edge analytical and modeling techniques. Here we describe a novel analysis of available data to assess current condition of eastern black rail across its range in a dynamic occupancy analysis. We used the results of the analysis to develop a site occupancy projection model where the model parameters (initial occupancy, site persistence, colonization) were linked to environmental covariates, such as land management and land cover change (sea-level rise, development, etc.). We used the projection model to predict future conditions under multiple sea-level rise and habitat management scenarios. Occupancy probability and site colonization were low in all analysis units and site persistence was also low, suggesting low resiliency and redundancy currently. Extinction probability was high for all analysis units in all simulated scenarios except one with significant effort to preserve existing habitat, suggesting low future resiliency and redundancy. With results of these data analyses and predictive modeling, the US Fish and Wildlife Service concluded that protections of the Endangered Species Act were warranted for this subspecies.
","language":"English","publisher":"Inter-Research","doi":"10.3354/esr01063","usgsCitation":"McGowan, C.P., Angeli, N., Beisler, W., Snyder, C., Rankin, N., Woodrow, J., Wilson, J., Rivenbark, E., Schwarzer, A., Hand, C., Anthony, R., Griffin, R., Barrett, K., Haverland, A., Roach, N., Schneider, T., Smith, A.J., Smith, F., Tolliver, J., and Watts, B.D., 2020, Linking monitoring and data analysis to predictions and decisions for the range-wide eastern black rail status assessment: Endangered Species Research, v. 43, p. 209-222, https://doi.org/10.3354/esr01063.","productDescription":"14 p.","startPage":"209","endPage":"222","ipdsId":"IP-111624","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":457761,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3354/esr01063","text":"Publisher Index 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cmcgowan@usgs.gov","orcid":"https://orcid.org/0000-0002-7330-9581","contributorId":167162,"corporation":false,"usgs":true,"family":"McGowan","given":"Conor","email":"cmcgowan@usgs.gov","middleInitial":"P.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":false,"id":834417,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Angeli, N.","contributorId":275934,"corporation":false,"usgs":false,"family":"Angeli","given":"N.","email":"","affiliations":[{"id":13360,"text":"Auburn University","active":true,"usgs":false}],"preferred":false,"id":834418,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Beisler, W.","contributorId":275935,"corporation":false,"usgs":false,"family":"Beisler","given":"W.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834419,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Snyder, C.W.","contributorId":259201,"corporation":false,"usgs":false,"family":"Snyder","given":"C.W.","email":"","affiliations":[],"preferred":false,"id":834420,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Rankin, N.M.","contributorId":196484,"corporation":false,"usgs":false,"family":"Rankin","given":"N.M.","email":"","affiliations":[],"preferred":false,"id":834421,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Woodrow, J.","contributorId":275936,"corporation":false,"usgs":false,"family":"Woodrow","given":"J.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834422,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Wilson, J.","contributorId":216248,"corporation":false,"usgs":false,"family":"Wilson","given":"J.","affiliations":[],"preferred":false,"id":834423,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Rivenbark, E.","contributorId":275937,"corporation":false,"usgs":false,"family":"Rivenbark","given":"E.","email":"","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834424,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Schwarzer, A.","contributorId":275939,"corporation":false,"usgs":false,"family":"Schwarzer","given":"A.","affiliations":[{"id":12556,"text":"Florida Fish and Wildlife Conservation Commission","active":true,"usgs":false}],"preferred":false,"id":834425,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Hand, C.","contributorId":275941,"corporation":false,"usgs":false,"family":"Hand","given":"C.","email":"","affiliations":[{"id":35670,"text":"South Carolina Department of Natural Resources","active":true,"usgs":false}],"preferred":false,"id":834426,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Anthony, R.M.","contributorId":181902,"corporation":false,"usgs":false,"family":"Anthony","given":"R.M.","email":"","affiliations":[],"preferred":false,"id":834427,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Griffin, R.","contributorId":275943,"corporation":false,"usgs":false,"family":"Griffin","given":"R.","affiliations":[{"id":36188,"text":"U.S. Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":834428,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Barrett, K.","contributorId":275945,"corporation":false,"usgs":false,"family":"Barrett","given":"K.","email":"","affiliations":[{"id":7084,"text":"Clemson University","active":true,"usgs":false}],"preferred":false,"id":834429,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Haverland, A.","contributorId":275947,"corporation":false,"usgs":false,"family":"Haverland","given":"A.","email":"","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":834430,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Roach, N.","contributorId":275950,"corporation":false,"usgs":false,"family":"Roach","given":"N.","email":"","affiliations":[{"id":56911,"text":"Clemson, University","active":true,"usgs":false}],"preferred":false,"id":834431,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Schneider, T.","contributorId":216061,"corporation":false,"usgs":false,"family":"Schneider","given":"T.","affiliations":[],"preferred":false,"id":834432,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Smith, A. J.","contributorId":67040,"corporation":false,"usgs":false,"family":"Smith","given":"A.","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":834433,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Smith, F.","contributorId":275953,"corporation":false,"usgs":false,"family":"Smith","given":"F.","affiliations":[{"id":6686,"text":"College of William and Mary","active":true,"usgs":false}],"preferred":false,"id":834434,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Tolliver, J.","contributorId":275957,"corporation":false,"usgs":false,"family":"Tolliver","given":"J.","email":"","affiliations":[{"id":6677,"text":"Texas State University","active":true,"usgs":false}],"preferred":false,"id":834435,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Watts, Bryan D","contributorId":243507,"corporation":false,"usgs":false,"family":"Watts","given":"Bryan","email":"","middleInitial":"D","affiliations":[],"preferred":false,"id":834436,"contributorType":{"id":1,"text":"Authors"},"rank":20}]}} ,{"id":70219032,"text":"70219032 - 2020 - Oil-source rock correlation studies in the unconventional Upper Cretaceous Tuscaloosa marine shale (TMS) petroleum system, Mississippi and Louisiana, USA","interactions":[],"lastModifiedDate":"2021-03-22T12:07:49.855628","indexId":"70219032","displayToPublicDate":"2020-02-11T06:55:54","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2419,"text":"Journal of Petroleum Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Oil-source rock correlation studies in the unconventional Upper Cretaceous Tuscaloosa marine shale (TMS) petroleum system, Mississippi and Louisiana, USA","docAbstract":"The U.S. Geological Survey assessed undiscovered unconventional hydrocarbon resources reservoired in the Upper Cretaceous Tuscaloosa marine shale (TMS) of southern Mississippi and adjacent Louisiana in 2018. As part of the assessment, oil-source rock correlations were examined in the TMS play area where operators produce light (38–45° API), sweet oil from horizontal, hydraulically-fractured wells in an overpressured ‘high-resistivity’ (>5 Ω-m) zone at the base of the TMS. Geochemical data from 39 oil samples and 17 source rock solvent extracts collected from the TMS play area indicate close correspondence for Tuscaloosa Group oils [from lower Tuscaloosa, middle Tuscaloosa (the TMS) and upper Tuscaloosa reservoirs] in thermal maturity (computed from MPI), SARA proportions, n-alkane distributions, isoprenoid and DBT/P ratios, monoaromatic steroids, and δ13C isotopic compositions (from whole oils, saturate and aromatic fractions). Other parameters (normal steranes, extended homohopanes, C31R/C30 hopane, norhopane/hopane and tricyclic terpane ratios, gammacerane/hopane) show most oil samples have similar values, suggesting all Tuscaloosa Group oils are from a common mixed marine-terrigenous source rock. Tighter distributions for triaromatic steroid (TAS) and δ13C isotopic composition for conventional oils in lower and upper Tuscaloosa reservoirs may indicate charge occurred in a single or shorter pulse relative to TMS oils which show broader TAS and δ13C properties, possibly from their generation over an extended period of burial maturation. Dissimilarity in geochemical properties between lower Tuscaloosa source rock solvent extracts and Tuscaloosa Group oils indicates lower Tuscaloosa source rocks did not contribute significantly to conventional and unconventional Tuscaloosa Group hydrocarbon accumulations. Whereas, TMS solvent extracts are similar to Tuscaloosa Group oils, suggesting an oil-source rock correlation. Excluding the possibility for long-distance lateral migration from a similar source downdip (which is unnecessary given thermal maturity considerations), the observations indicate 1. the TMS is a self-sourced reservoir, 2. the TMS is the source of oils accumulated in nearby conventional Tuscaloosa Group reservoirs, and 3. thin organic-rich shales in the lower Tuscaloosa did not contribute substantially to any oil accumulations in the Tuscaloosa Group.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.petrol.2020.107015","usgsCitation":"Hackley, P.C., Dennen, K.O., Garza, D., Lohr, C., Valentine, B., Hatcherian, J.J., Enomoto, C., and Dulong, F.T., 2020, Oil-source rock correlation studies in the unconventional Upper Cretaceous Tuscaloosa marine shale (TMS) petroleum system, Mississippi and Louisiana, USA: Journal of Petroleum Science and Engineering, v. 190, 107015, 16 p., https://doi.org/10.1016/j.petrol.2020.107015.","productDescription":"107015, 16 p.","ipdsId":"IP-110731","costCenters":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":457773,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.petrol.2020.107015","text":"Publisher Index Page"},{"id":384492,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Louisiana, Mississippi","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -94.15283203125,\n 29.19053283229458\n ],\n [\n -88.08837890625,\n 29.19053283229458\n ],\n [\n -88.08837890625,\n 33.063924198120645\n ],\n [\n -94.15283203125,\n 33.063924198120645\n ],\n [\n -94.15283203125,\n 29.19053283229458\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"190","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"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":812499,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dennen, Kristin Opferkuch","contributorId":255529,"corporation":false,"usgs":true,"family":"Dennen","given":"Kristin","email":"","middleInitial":"Opferkuch","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812500,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Garza, Daniel","contributorId":255532,"corporation":false,"usgs":false,"family":"Garza","given":"Daniel","email":"","affiliations":[{"id":51576,"text":"Sanchez Oil & Gas Corporation","active":true,"usgs":false}],"preferred":false,"id":812501,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Lohr, Celeste 0000-0001-6287-9047 clohr@usgs.gov","orcid":"https://orcid.org/0000-0001-6287-9047","contributorId":209992,"corporation":false,"usgs":true,"family":"Lohr","given":"Celeste","email":"clohr@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812520,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Valentine, Brett 0000-0002-8678-2431 bvalentine@usgs.gov","orcid":"https://orcid.org/0000-0002-8678-2431","contributorId":209829,"corporation":false,"usgs":true,"family":"Valentine","given":"Brett","email":"bvalentine@usgs.gov","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Hatcherian, Javin J. 0000-0001-9151-6798 jhatcherian@usgs.gov","orcid":"https://orcid.org/0000-0001-9151-6798","contributorId":195770,"corporation":false,"usgs":true,"family":"Hatcherian","given":"Javin","email":"jhatcherian@usgs.gov","middleInitial":"J.","affiliations":[{"id":243,"text":"Eastern Geology and Paleoclimate Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812522,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Enomoto, Catherine B. 0000-0002-4119-1953","orcid":"https://orcid.org/0000-0002-4119-1953","contributorId":211802,"corporation":false,"usgs":true,"family":"Enomoto","given":"Catherine B.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812523,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Dulong, Frank T. 0000-0001-7388-647X fdulong@usgs.gov","orcid":"https://orcid.org/0000-0001-7388-647X","contributorId":650,"corporation":false,"usgs":true,"family":"Dulong","given":"Frank","email":"fdulong@usgs.gov","middleInitial":"T.","affiliations":[{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":812524,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70206441,"text":"sir20195122 - 2020 - Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah","interactions":[],"lastModifiedDate":"2022-04-25T19:05:32.137207","indexId":"sir20195122","displayToPublicDate":"2020-02-10T14:00:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2019-5122","displayTitle":"Hydrogeologic Characterization, Groundwater Chemistry, and Vulnerability Assessment, Ute Mountain Ute Reservation, Colorado and Utah","title":"Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah","docAbstract":"The U.S. Geological Survey, in cooperation with the Ute Mountain Ute Tribe (UMUT), initiated a study in 2016 to increase understanding of the hydrogeology and chemistry of groundwater within select areas of the Ute Mountain Ute Reservation (UMUR) in Colorado and Utah, identify vulnerabilities to the system and other natural resources, and outline information needs to aid in the understanding and protection of groundwater resources. The results presented for this study can be used to support the UMUT’s goal of protecting their vital groundwater resources on the UMUR.
Hydrogeologic conditions were characterized for the surficial aquifer contained in Quaternary-age unconsolidated surficial deposits and the Dakota aquifer contained in the Cretaceous-age Dakota Sandstone. In the surficial aquifer, median depth to water ranges from about 5.4 to 17.2 feet below land surface in the Farm and Ranch Enterprise area and 11 to 34 feet below land surface in the Towaoc area, and the water table slopes generally southwest or south. A map of depth to the top of the Dakota Sandstone was constructed from existing well data. Depths range from zero in outcrop areas to more than 3,000 feet below land surface on mesas in the southeastern part of the UMUR.
Groundwater-chemistry data were collected by the UMUT from 13 springs and 31 wells from 1996 through 2017. Specific conductance was much lower for samples from springs than from wells; median values were 512 and 6,024 microsiemens per centimeter at 25 degrees Celsius, respectively. Spring samples were well oxygenated. A few well samples were anoxic (dissolved oxygen concentrations less than 0.5 milligrams per liter [mg/L]), indicating reducing conditions in the aquifer. About 75 percent of spring samples had fresh water (total dissolved solids concentrations less than 1,000 mg/L), and about 85 percent of well samples had brackish or highly saline water (total dissolved solids concentrations greater than 1,000 mg/L). Water type for springs on the Ute Mountains was calcium bicarbonate. Lower-altitude springs had a calcium-sulfate water type. Most well samples had sodium as the dominant cation, and sulfate, bicarbonate, and chloride as the dominant anions. Fluoride concentrations in about 45 percent of well samples were greater than an agricultural-use standard of 2 mg/L.
Nitrate plus nitrite concentrations in most spring and well samples were less than about 1.6 mg/L per liter. Concentrations in samples from wells in the irrigated agricultural area were elevated; the maximum concentration was 78.5 mg/L. About one-half of the trace-element samples had concentrations that were less than laboratory reporting limits. Only aluminum, arsenic, and selenium in spring samples, and boron and selenium in well samples, were detected at concentrations greater than surface-water standards or water-quality standards for agricultural use of groundwater.
Only three organic compounds, the pesticides alachlor and atrazine and the volatile organic compound di(2-ethylhexyl) phthalate, were detected in well samples. The Escherichia coli bacteria was detected in 47 and 23 percent of samples from wells and springs, respectively. The E. coli detections included samples from three culturally significant springs, which did not meet the UMUT cultural-use standard of total absence of E. coli.
Tritium and carbon-14 were the primary environmental tracers used for interpreting groundwater ages for Lopez 2 Spring and five wells (AP–1, 5000 Block, Cottonwood Spring, Goodknight, and SE Toe). Water from the AP–1 well contained a mixture of pre- and post-1950s recharge. Tritium and carbon-14 recharge ages for Lopez 2 Spring (post-1950s in age), Goodknight and SE Toe wells (pre-1950s in age), and Cottonwood Spring well (primarily pre-1950s in age) are supported by helium-4 data. The helium-4 data for the 5000 Block well are inconsistent with the tritium and carbon-14 age of pre-1950s recharge because of interference caused by high methane concentrations in the water.
Springs and surficial deposits are more vulnerable to contamination from anthropogenic chemicals than deeper bedrock wells. Bedrock aquifers are vulnerable in areas where the geologic formations containing the aquifers are exposed at the land surface. Groundwater in deep bedrock aquifers is likely thousands of years old and is not currently affected by present-day land uses. Both shallow and deep groundwater are vulnerable to naturally occurring salts and minerals, such as of total dissolved solids, major ions, nitrate, and trace elements.
Effects of a changing climate on water resources and other ecological characteristics of the UMUR could include changes in evapotranspiration, a decrease in snowpack, decreased aquifer recharge and flow of springs, a decrease in soil moisture, and increased occurrence of wildfires and forest mortality. Of particular interest for the UMUT are possible effects of a changing climate on medicinal and culturally important plants and springs
Several information needs were identified during this study that would aid in the understanding and protection of groundwater resources on the UMUR. These include well-completion information for bedrock wells, the collection of environmental tracer data at additional wells, the addition of methane and hydrocarbon analysis to well sampling plans, and the resampling of springs and wells that were last sampled in 2002 or earlier.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston VA","doi":"10.3133/sir20195122","collaboration":"Prepared in cooperation with the Ute Mountain Ute Tribe","usgsCitation":"Bauch, N.J., and Arnold, L.R., 2020, Hydrogeologic characterization, groundwater chemistry, and vulnerability assessment, Ute Mountain Ute Reservation, Colorado and Utah: U.S. Geological Survey Scientific Investigations Report 2019–5122, 76 p., https://doi.org/10.3133/sir20195122.","productDescription":"Report: ix, 76 p.; Data Release","onlineOnly":"Y","ipdsId":"IP-095027","costCenters":[{"id":191,"text":"Colorado Water Science Center","active":true,"usgs":true}],"links":[{"id":399604,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109676.htm"},{"id":372110,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9S4MOB6","text":"USGS data release","description":"USGS data release","linkHelpText":"Geospatial datasets for estimating depth to the top of the Dakota Sandstone, Ute Mountain Ute Reservation, Colorado, 2017"},{"id":372108,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2019/5122/coverthb.jpg"},{"id":372109,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2019/5122/sir20195122.pdf","text":"Report","size":"8.40 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2019-5122"}],"country":"United States","state":"Colorado","otherGeospatial":"Ute Mountain Ute Reservation","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -109.0333,\n 37\n ],\n [\n -108.2667,\n 37\n ],\n [\n -108.2667,\n 37.3564\n ],\n [\n -109.0333,\n 37.3564\n ],\n [\n -109.0333,\n 37\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Colorado Water Science Center
U.S. Geological Survey
Box 25046, MS-415
Denver, CO 80225-0046
Pinnipeds are commonly monitored using aerial photographic surveys at land- or ice-based sites, where animals come ashore for resting, pupping, molting, and to avoid predators. Although these counts form the basis for monitoring population change over time, they do not provide information regarding where animals occur in the water, which is often of management and conservation interest. In this study, we developed a hierarchical model that links counts of pinnipeds at terrestrial sites to sightings-at-sea and estimates abundance, spatial distribution, and the proportion of time spent on land (attendance probability). The structure of the model also allows for the inclusion of predictors that may explain variation in ecological and observation processes. We applied the model to Steller sea lions (Eumetopias jubatus) in Glacier Bay, Alaska using counts of sea lions from aerial photographic surveys and opportunistic in-water sightings from vessel surveys. Glacier Bay provided an ideal test and application of the model because data are available on attendance probability based on long-term monitoring. We found that occurrence in the water was positively related to proximity to terrestrial sites, as would be expected for a species that engages in central-place foraging. The proportion of sea lions in attendance at terrestrial sites and overall abundance estimates were consistent with reports from the literature and monitoring programs. The model we describe has benefit and utility for park managers who wish to better understand the overlap between pinnipeds and visitors, and the framework that we present has potential for application across a variety of study systems and taxa.
Beaches around the world continuously adjust to daily and seasonal changes in wave and tide conditions, which are themselves changing over longer time-scales. Different approaches to predict multi-year shoreline evolution have been implemented; however, robust and reliable predictions of shoreline evolution are still problematic even in short-term scenarios (shorter than decadal). Here we show results of a modelling competition, where 19 numerical models (a mix of established shoreline models and machine learning techniques) were tested using data collected for Tairua beach, New Zealand with 18 years of daily averaged alongshore shoreline position and beach rotation (orientation) data obtained from a camera system. In general, traditional shoreline models and machine learning techniques were able to reproduce shoreline changes during the calibration period (1999–2014) for normal conditions but some of the model struggled to predict extreme and fast oscillations. During the forecast period (unseen data, 2014–2017), both approaches showed a decrease in models’ capability to predict the shoreline position. This was more evident for some of the machine learning algorithms. A model ensemble performed better than individual models and enables assessment of uncertainties in model architecture. Research-coordinated approaches (e.g., modelling competitions) can fuel advances in predictive capabilities and provide a forum for the discussion about the advantages/disadvantages of available models.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-020-59018-y","usgsCitation":"Jennifer Montaño, Coco, G., Antolinez, J., Beuzen, T., Bryan, K.R., Cagigal, L., Bruno Castelle, Davidson, M., Goldstein, E.B., Ibaceta, R., Déborah Idier, Ludka, B.C., Masoud-Ansari, S., Fernando Mendez, A. Brad Murray, Plant, N.G., Ratlif, K., Robinet, A., Ana Rueda, Nadia Sénéchal, Simmons, J., Splinter, K., Scott Stephens, Townend, I., Vitousek, S., and Vos, K., 2020, Blind testing of shoreline evolution models: Scientific Reports, v. 10, 2137, 10 p., https://doi.org/10.1038/s41598-020-59018-y.","productDescription":"2137, 10 p.","ipdsId":"IP-116455","costCenters":[{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":457790,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-020-59018-y","text":"Publisher Index Page"},{"id":376470,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"10","noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Jennifer Montaño","contributorId":229413,"corporation":false,"usgs":false,"family":"Jennifer 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0000-0003-2762-9151","orcid":"https://orcid.org/0000-0003-2762-9151","contributorId":229416,"corporation":false,"usgs":false,"family":"Beuzen","given":"Tomas","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":793157,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Bryan, Karin R.","contributorId":229417,"corporation":false,"usgs":false,"family":"Bryan","given":"Karin","middleInitial":"R.","affiliations":[{"id":12678,"text":"University of Waikato","active":true,"usgs":false}],"preferred":false,"id":793158,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Cagigal, Laura 0000-0001-5384-6382","orcid":"https://orcid.org/0000-0001-5384-6382","contributorId":229418,"corporation":false,"usgs":false,"family":"Cagigal","given":"Laura","email":"","affiliations":[{"id":38833,"text":"University of Auckland","active":true,"usgs":false}],"preferred":false,"id":793159,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Bruno Castelle","contributorId":229419,"corporation":false,"usgs":false,"family":"Bruno Castelle","affiliations":[{"id":41639,"text":"University of Bordeaux","active":true,"usgs":false}],"preferred":false,"id":793160,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Davidson, Mark","contributorId":229420,"corporation":false,"usgs":false,"family":"Davidson","given":"Mark","email":"","affiliations":[{"id":7119,"text":"Plymouth University","active":true,"usgs":false}],"preferred":false,"id":793161,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Goldstein, Evan B. 0000-0001-9358-1016","orcid":"https://orcid.org/0000-0001-9358-1016","contributorId":184210,"corporation":false,"usgs":false,"family":"Goldstein","given":"Evan","email":"","middleInitial":"B.","affiliations":[],"preferred":false,"id":793162,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Ibaceta, Raimundo 0000-0003-2203-7640","orcid":"https://orcid.org/0000-0003-2203-7640","contributorId":229421,"corporation":false,"usgs":false,"family":"Ibaceta","given":"Raimundo","email":"","affiliations":[{"id":27304,"text":"University of New South Wales","active":true,"usgs":false}],"preferred":false,"id":793163,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Déborah Idier","contributorId":229422,"corporation":false,"usgs":false,"family":"Déborah Idier","affiliations":[{"id":41640,"text":"BGRM","active":true,"usgs":false}],"preferred":false,"id":793164,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Ludka, Bonnie C. 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For bounded outcome variables restricted to the\nunit interval, however, classical modeling approaches based on mean squared error\nloss may severely suer as they do not account for heteroscedasticity in the data.\nTo address this issue, we propose a random forest approach for relating a beta dis-\ntributed outcome to a set of explanatory variables. Our approach explicitly makes\nuse of the likelihood function of the beta distribution for the selection of splits dur-\ning the tree-building procedure. In each iteration of the tree-building algorithm it\nchooses one explanatory variable in combination with a split point that maximizes\nthe log-likelihood function of the beta distribution with the parameter estimates de-\nrived from the nodes of the currently built tree. Results of several simulation studies\nand an application using data from the U.S.A. National Lakes Assessment Survey\ndemonstrate the properties and usefulness of the method, in particular when com-\npared to random forest approaches based on mean squared error loss and parametric\nregression models.","language":"English","publisher":"Taylor and Francis","doi":"10.1080/10618600.2019.1705310","usgsCitation":"Weinhold, L., Schmid, M., Mitchell, R., Maloney, K.O., Wright, M.N., and Berger, M., 2020, A random forest approach for bounded outcome variables: Journal of Computational and Graphical Statistics, v. 29, no. 3, p. 639-658, https://doi.org/10.1080/10618600.2019.1705310.","productDescription":"20 p.","startPage":"639","endPage":"658","ipdsId":"IP-107449","costCenters":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"links":[{"id":457792,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8193767","text":"External Repository"},{"id":376906,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"29","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Weinhold, Leonie","contributorId":236854,"corporation":false,"usgs":false,"family":"Weinhold","given":"Leonie","email":"","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794489,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Schmid, Matthias","contributorId":236855,"corporation":false,"usgs":false,"family":"Schmid","given":"Matthias","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794490,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Mitchell, Richard M.","contributorId":215406,"corporation":false,"usgs":false,"family":"Mitchell","given":"Richard M.","affiliations":[{"id":39239,"text":"USEPA, Washington D.C.","active":true,"usgs":false}],"preferred":false,"id":794491,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Maloney, Kelly O. 0000-0003-2304-0745 kmaloney@usgs.gov","orcid":"https://orcid.org/0000-0003-2304-0745","contributorId":4636,"corporation":false,"usgs":true,"family":"Maloney","given":"Kelly","email":"kmaloney@usgs.gov","middleInitial":"O.","affiliations":[{"id":365,"text":"Leetown Science Center","active":true,"usgs":true}],"preferred":true,"id":794492,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Wright, Marvin N.","contributorId":236856,"corporation":false,"usgs":false,"family":"Wright","given":"Marvin","email":"","middleInitial":"N.","affiliations":[{"id":47553,"text":"Leibniz Institute for Prevention Research and Epidemiology, Germany","active":true,"usgs":false}],"preferred":false,"id":794493,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Berger, Moritz","contributorId":236857,"corporation":false,"usgs":false,"family":"Berger","given":"Moritz","email":"","affiliations":[{"id":47552,"text":"University of Bonn, Germany","active":true,"usgs":false}],"preferred":false,"id":794494,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70214305,"text":"70214305 - 2020 - A spatially explicit, empirical estimate of tree-based biological nitrogen fixation in forests of the United States","interactions":[],"lastModifiedDate":"2020-09-25T14:20:41.430575","indexId":"70214305","displayToPublicDate":"2020-02-07T09:15:27","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1836,"text":"Global Biogeochemical Cycles","active":true,"publicationSubtype":{"id":10}},"title":"A spatially explicit, empirical estimate of tree-based biological nitrogen fixation in forests of the United States","docAbstract":"Quantifying human impacts on the nitrogen (N) cycle and investigating natural ecosystem N cycling depend on the magnitude of inputs from natural biological nitrogen fixation (BNF). Here, we present two bottom‐up approaches to quantify tree‐based symbiotic BNF based on forest inventory data across the coterminous United States and SE Alaska. For all major N‐fixing tree genera, we quantify BNF inputs using (1) ecosystem N accretion rates (kg N ha−1 yr−1) scaled with spatial data on tree abundance and (2) percent of N derived from fixation (%Ndfa) scaled with tree N demand (from tree growth rates and stoichiometry). We estimate that trees fix 0.30–0.88 Tg N yr−1 across the study area (1.4–3.4 kg N ha−1 yr−1). Tree‐based N fixation displays distinct spatial variation that is dominated by two genera, Robinia (64% of tree‐associated BNF) and Alnus (24%). The third most important genus, Prosopis, accounted for 5%. Compared to published estimates of other N fluxes, tree‐associated BNF accounted for 0.59 Tg N yr−1, similar to asymbiotic (0.37 Tg N yr−1) and understory symbiotic BNF (0.48 Tg N yr−1), while N deposition contributed 1.68 Tg N yr−1 and rock weathering 0.37 Tg N yr−1. Overall, our results reveal previously uncharacterized spatial patterns in tree BNF that can inform large‐scale N assessments and serve as a model for improving tree‐based BNF estimates worldwide. This updated, lower BNF estimate indicates a greater ratio of anthropogenic to natural N inputs, suggesting an even greater human impact on the N cycle.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2019GB006241","usgsCitation":"Staccone, A., Liao, W., Perakis, S.S., Compton, J., Clark, C., and Menge, D., 2020, A spatially explicit, empirical estimate of tree-based biological nitrogen fixation in forests of the United States: Global Biogeochemical Cycles, v. 34, no. 2, e2019GB006241, 18 p., https://doi.org/10.1029/2019GB006241.","productDescription":"e2019GB006241, 18 p.","ipdsId":"IP-104007","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":457798,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2019gb006241","text":"Publisher Index Page"},{"id":378747,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n 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Wenying","contributorId":241125,"corporation":false,"usgs":false,"family":"Liao","given":"Wenying","email":"","affiliations":[{"id":6644,"text":"Princeton University","active":true,"usgs":false}],"preferred":false,"id":799600,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","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":799601,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Compton, Jana","contributorId":145529,"corporation":false,"usgs":false,"family":"Compton","given":"Jana","affiliations":[{"id":6784,"text":"US EPA","active":true,"usgs":false}],"preferred":false,"id":799602,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Clark, Christopher L.","contributorId":168382,"corporation":false,"usgs":false,"family":"Clark","given":"Christopher L.","affiliations":[{"id":25276,"text":"US EPA, National Center for Envirenmental Assessment, DC","active":true,"usgs":false}],"preferred":false,"id":799603,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Menge, Duncan 0000-0003-4736-9844","orcid":"https://orcid.org/0000-0003-4736-9844","contributorId":241126,"corporation":false,"usgs":false,"family":"Menge","given":"Duncan","email":"","affiliations":[{"id":7171,"text":"Columbia University","active":true,"usgs":false}],"preferred":false,"id":799604,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70217086,"text":"70217086 - 2020 - Co-occurrence and occupancy dynamics of mourning doves and Eurasian collared-doves","interactions":[],"lastModifiedDate":"2021-01-05T13:14:55.225713","indexId":"70217086","displayToPublicDate":"2020-02-07T07:07:43","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2508,"text":"Journal of Wildlife Management","active":true,"publicationSubtype":{"id":10}},"title":"Co-occurrence and occupancy dynamics of mourning doves and Eurasian collared-doves","docAbstract":"Understanding how land cover and potential competition with invasive species shape patterns of occupancy, extirpation, and colonization of native species across a landscape can help target management for declining native populations. Mourning dove (Zenaida macroura) populations have declined throughout the United States from 1965–2015. The expansion of the Eurasian collared‐dove (Streptopelia decaocto), an introduced species with similar food preferences, may further threaten mourning dove populations. We analyzed data from 2009–2016 from a large‐scale monitoring program in the Western Great Plains of the United States in a 2‐species occupancy model to assess the effects of collared‐doves on mourning dove distributions, while accounting for imperfect detection and variation in land cover across the landscape. Mourning dove occupancy was stable or increasing across our study area, and despite overlap in resource use and co‐occurrence between mourning doves and Eurasian collared‐doves, we found no evidence that collared‐doves are extirpating mourning doves from preferred habitat during the breeding season.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/jwmg.21835","usgsCitation":"Green, A., Sofaer, H., Otis, D.L., and Van Lanen, N.J., 2020, Co-occurrence and occupancy dynamics of mourning doves and Eurasian collared-doves: Journal of Wildlife Management, v. 84, no. 4, p. 775-785, https://doi.org/10.1002/jwmg.21835.","productDescription":"11 p.","startPage":"775","endPage":"785","ipdsId":"IP-107369","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":437122,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9TYF93I","text":"USGS data release","linkHelpText":"Co-occurrence and Occupancy Dynamics of Mourning Doves and Eurasian Collared-Doves"},{"id":381870,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Texas, Oklahoma, Kansas, Nebraska, South Dakota, North Dakota, Montana, Wyoming, Colorado, New Mexico","otherGeospatial":"Badlands and Prairies and Shortgrass Prairie Bird Conservation Regions","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -101.1181640625,\n 32.95336814579932\n ],\n [\n -101.3818359375,\n 36.63316209558658\n ],\n [\n -101.6455078125,\n 43.068887774169625\n ],\n [\n -100.2392578125,\n 45.182036837015886\n ],\n [\n -100.72265625,\n 47.487513008956554\n ],\n [\n -104.9853515625,\n 48.28319289548349\n ],\n [\n -109.3359375,\n 47.60616304386874\n ],\n [\n -111.8408203125,\n 47.54687159892238\n ],\n [\n -106.171875,\n 42.71473218539458\n ],\n [\n -104.4580078125,\n 37.71859032558816\n ],\n [\n -104.4140625,\n 33.02708758002874\n ],\n [\n -101.1181640625,\n 32.95336814579932\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-02-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Green, Adam W.","contributorId":246045,"corporation":false,"usgs":false,"family":"Green","given":"Adam W.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":807560,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sofaer, Helen 0000-0002-9450-5223","orcid":"https://orcid.org/0000-0002-9450-5223","contributorId":216681,"corporation":false,"usgs":true,"family":"Sofaer","given":"Helen","email":"","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":807561,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Otis, David L","contributorId":246046,"corporation":false,"usgs":false,"family":"Otis","given":"David","email":"","middleInitial":"L","affiliations":[{"id":6621,"text":"Colorado State University","active":true,"usgs":false}],"preferred":false,"id":807562,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Van Lanen, Nicholas J.","contributorId":246047,"corporation":false,"usgs":false,"family":"Van Lanen","given":"Nicholas","middleInitial":"J.","affiliations":[{"id":25644,"text":"Bird Conservancy of the Rockies","active":true,"usgs":false}],"preferred":false,"id":807563,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208693,"text":"70208693 - 2020 - Global physical controls on estuarine habitat distribution during sea levelchange: Consequences for genetic diversification through time","interactions":[],"lastModifiedDate":"2020-02-24T19:00:02","indexId":"70208693","displayToPublicDate":"2020-02-06T18:58:35","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1844,"text":"Global and Planetary Change","active":true,"publicationSubtype":{"id":10}},"title":"Global physical controls on estuarine habitat distribution during sea levelchange: Consequences for genetic diversification through time","docAbstract":"Determining the extrinsic (physical) factors controlling speciation and diversification of species through time is\nof key interest in paleontology and evolutionary biology. The role of sea-level change in shaping species richness\npatterns of marginal marine species has received much attention, but with variable conclusions. Recent work\ncombining genetic data and Geographical Information Systems (GIS)-based habitat modeling yielded a framework\nfor how geomorphology of continental margins mediates genetic connectivity of populations during sealevel\nchange. This approach may ultimately yield insights on how distinct lineages, species, and biodiversity\naccumulate in coastal settings. Here, we expand this GIS work globally to different geomorphic settings to model\nestuarine habitat in a larger geographic framework and test how tectonic setting, oceanographic setting, climate,\nand margin age affect habitat distribution during sea-level change. In addition, independent of estuaries we\nexplore paleobiologic (e.g. Olsson, 1961) and neontolologic effects of sea-level change on evolution, and test the\nrelation between overall shelf area and species richness using data of 1721 fish species. We find 82% global\nreduction of estuarine habitat abundance at lowstand relative to highstand, and find large habitats change in size\nmuch more than small habitats. Consistent with prior work, narrow continental margins have significantly less\nhabitat at highstand and lowstand than wide margins, and narrow margins significantly associate with fore-arc\nsettings, effectively linking tectonic setting to habitat abundance. Surprisingly, narrow margins host greater\nspecies richness, a finding which violates the canonical species-area relation. This finding can be explained if: 1)\nthe physical isolation imposed by narrow margins facilitates the formation of new species over time; 2) the sizestability\nof small habitats, which disproportionately occur on narrow margins, accumulate and retain species\nextirpated in the more variable habitats on wide margins; or 3) the smaller habitats on narrow margins facilitate\ngreater species richness through greater habitat heterogeneity. These results are generally at odds with prior\ninterpretations, but the combination of richness data and population genetic principles offer a different perspective\non these long-studied questions. Finally, we emphasize that the nuance of Pleistocene-Holocene sea\nlevel oscillations should be more explicitly considered in genetic studies.","language":"English","publisher":"Elsevier","doi":"10.1016/j.gloplacha.2020.103128","usgsCitation":"Dolby, G.A., Bedolla, A.M., Bennett, S., and Jacobs, D.K., 2020, Global physical controls on estuarine habitat distribution during sea levelchange: Consequences for genetic diversification through time: Global and Planetary Change, v. 187, 103128, https://doi.org/10.1016/j.gloplacha.2020.103128.","productDescription":"103128","ipdsId":"IP-110919","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":457809,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://escholarship.org/uc/item/3qs0m0qg","text":"External Repository"},{"id":372590,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"187","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Dolby, Greer A. 0000-0002-5923-0690","orcid":"https://orcid.org/0000-0002-5923-0690","contributorId":222726,"corporation":false,"usgs":false,"family":"Dolby","given":"Greer","email":"","middleInitial":"A.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":783031,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bedolla, Arturo M.","contributorId":222727,"corporation":false,"usgs":false,"family":"Bedolla","given":"Arturo","email":"","middleInitial":"M.","affiliations":[{"id":6607,"text":"Arizona State University","active":true,"usgs":false}],"preferred":false,"id":783032,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bennett, S. 0000-0002-9772-4122","orcid":"https://orcid.org/0000-0002-9772-4122","contributorId":29230,"corporation":false,"usgs":true,"family":"Bennett","given":"S.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"preferred":false,"id":783030,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jacobs, David K.","contributorId":139394,"corporation":false,"usgs":false,"family":"Jacobs","given":"David","email":"","middleInitial":"K.","affiliations":[{"id":12763,"text":"University of California, Los Angeles","active":true,"usgs":false}],"preferred":false,"id":783033,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70208166,"text":"mcs2020 - 2020 - Mineral commodity summaries 2020","interactions":[],"lastModifiedDate":"2022-04-20T21:49:14.759817","indexId":"mcs2020","displayToPublicDate":"2020-02-06T14:25:00","publicationYear":"2020","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":6,"text":"USGS Unnumbered Series"},"seriesTitle":{"id":368,"text":"Mineral Commodity Summaries","active":false,"publicationSubtype":{"id":6}},"displayTitle":"Mineral Commodity Summaries 2020","title":"Mineral commodity summaries 2020","docAbstract":"Each chapter of the 2020 edition of the U.S. Geological Survey (USGS) Mineral Commodity Summaries (MCS) includes information on events, trends, and issues for each mineral commodity as well as discussions and tabular presentations on domestic industry structure, Government programs, tariffs, 5-year salient statistics, and world production and resources. The MCS is the earliest comprehensive source of 2019 mineral production data for the world. More than 90 individual minerals and materials are covered by two-page synopses.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/mcs2020","usgsCitation":"U.S. Geological Survey, 2020, Mineral commodity summaries 2020: U.S. Geological Survey, 200 p., https://doi.org/10.3133/mcs2020.","productDescription":"200 p.","numberOfPages":"204","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-113182","costCenters":[{"id":432,"text":"National Minerals Information Center","active":true,"usgs":true}],"links":[{"id":371766,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/periodicals/mcs2020/mcs2020.pdf","text":"Report","linkFileType":{"id":1,"text":"pdf"},"description":"MCS 2020"},{"id":371765,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/periodicals/mcs2020/coverthb.jpg"},{"id":399334,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109665.htm"},{"id":372005,"rank":4,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/nmic/commodity-statistics-and-information","text":"Commodity Statistics and Information"},{"id":371767,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://www.usgs.gov/centers/nmic/mineral-commodity-summaries","text":"Mineral Commodity Summaries Prior to 2020"}],"contact":"Director, National Minerals Information Center
U.S. Geological Survey
12201 Sunrise Valley Drive
988 National Center
Reston, VA 20192
Email: nmicrecordsmgt@usgs.gov
In 2014, groundwater samples from residential and public supply wells in the vicinity of two former U.S. Navy bases at Willow Grove and Warminster, and an active Air National Guard Station at Horsham, Bucks and Montgomery Counties, Pennsylvania, were found to have concentrations of perfluorooctanoic acid (PFOA) and perfluorooctane sulfonate (PFOS), which are per- and polyfluoroalkyl substances (PFAS), above U.S. Environmental Protection Agency (EPA) provisional health advisory (HA) levels for drinking water. Five supply wells near the bases were shut down because of PFAS contamination. In 2016, after EPA established a Lifetime HA for PFAS in drinking water that is lower than the provisional HA in place in 2014, at least 13 additional supply wells near the bases were shut down because of PFAS contamination. At the request of the U.S. Navy, and in consultation with other Federal and State agencies and local stakeholders, the U.S. Geological Survey used historical and recent data on well withdrawals, recharge rates, aquifer properties, groundwater levels, and stream base flow to evaluate regional groundwater-flow paths from identified areas of PFAS groundwater contamination or potential PFAS sources at the bases. Groundwater withdrawals near the bases from public supply and other large wells decreased substantially from the 1990s to 2017, increasing the proportion of groundwater recharge that discharged to local streams. A preliminary groundwater-flow model, calibrated using 1,009 groundwater levels and 17 stream base flow estimates, simulated regional flow paths from the bases and showed that recharge at the bases discharged to withdrawal wells and local streams, generally within a mile or two of the bases. Supply and remediation wells at the bases captured some of the recharge on base areas of possible PFAS contamination, whereas other base recharge was simulated to flow to nearby public supply wells and streams, depending on water use and aquifer recharge conditions between 1999 and 2017. The locations of many residential wells near the bases that were identified by the Navy and Air National Guard as having elevated PFAS concentrations were generally consistent with the simulated flow paths from possible sources at the bases. However, there are some areas of observed PFAS contamination where no flow paths from base sources were simulated. Additionally, no data were available on PFAS concentrations in groundwater in some areas of simulated flow paths from base sources. Data and models used for this study are provided in this report and in digital data releases to support further investigations and model revisions.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20191137","collaboration":"Prepared in cooperation with the U.S. Navy","usgsCitation":"Goode, D.J., and Senior, L.A., 2020, Groundwater withdrawals and regional flow paths at and near Willow Grove and Warminster, Pennsylvania—Data compilation and preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017: U.S. Geological Survey Open-File Report 2019–1137, 127 p., https://doi.org/10.3133/ofr20191137.","productDescription":"Report: x, 127 p.; 2 Data Releases","numberOfPages":"138","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-113639","costCenters":[{"id":532,"text":"Pennsylvania Water Science Center","active":true,"usgs":true}],"links":[{"id":399427,"rank":5,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_109664.htm"},{"id":371906,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9ZGEI67","text":"USGS data release","linkHelpText":"Groundwater levels, groundwater withdrawals, and point-source discharges to streams in the vicinity of Willow Grove and Warminster, Bucks and Montgomery Counties, Pennsylvania, for selected years during 1999–2017"},{"id":371905,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K36P5S","text":"USGS data release","linkHelpText":"MODFLOW 6 and MODPATH 7 model data sets used to evaluate groundwater flow in the vicinity of Horsham and Warminster, Bucks and Montgomery Counties, Pennsylvania—Preliminary simulations for conditions in 1999, 2010, 2013, 2016, and 2017"},{"id":372113,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2019/1137/ofr20191137.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2019-1137"},{"id":371903,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2019/1137/coverthb.jpg"}],"country":"United States","state":"Pennsylvania","county":"Bucks County, Montgomery County","city":"Warminster, Willow Grove","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.3536,\n 40.0678\n ],\n [\n -74.9167,\n 40.0678\n ],\n [\n -74.9167,\n 40.2967\n ],\n [\n -75.3536,\n 40.2967\n ],\n [\n -75.3536,\n 40.0678\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Pennsylvania Water Science Center
U.S. Geological Survey
215 Limekiln Road
New Cumberland, PA 17070
The 2016–2017 shallow submarine eruption of Bogoslof volcano in Alaska injected plumes of ash and seawater to maximum heights of ~ 12 km. More than 4550 volcanic lightning strokes were detected by the World Wide Lightning Location Network (WWLLN) and Vaisala’s Global Lightning Dataset (GLD360) over 9 months. Lightning assisted monitoring efforts by confirming ash-producing explosions in near-real time, but only 32 out of the 70 explosive events produced detectable lightning. What led to electrical activity within some of the volcanic plumes, but not others? And why did the lightning intensity wax and wane over the lifetime of individual explosions? We address these questions using multiparametric observations from ground-based lightning sensors, satellite imagery, photographs, acoustic signals, and 1D plume modeling. Detailed time-series of monitoring data show that the plumes did not produce detectable lightning until they rose higher than the atmospheric freezing level (approximated by − 20 °C temperatures). For example, on 28 May 2017 (event 40), the delayed onset of lightning coincides with modeled ice formation in upper levels of the plume. Model results suggest that microphysical conditions inside the plume rivaled those of severe thunderstorms, with liquid water contents > 5 g m−3 and vigorous updrafts > 40 m s−1 in the mixed-phase region where liquid water and ice coexist. Based on these findings, we infer that ‘thunderstorm-style’ collisional ice-charging catalyzed the volcanic lightning. However, charge mechanisms likely operated on a continuum, with silicate collisions dominating electrification in the near-vent region, and ice charging taking over in the upper-level plumes. A key implication of this study is that lightning during the Bogoslof eruption provided a reliable indicator of sustained, ash-rich plumes (and associated hazards) above the atmospheric freezing level.
The Devonian Period experienced significant fluctuations of atmospheric oxygen (O2) levels (∼25–13%), for which the extent and timing are debated. Also characteristic of the Devonian Period, at the Frasnian–Famennian (F–F) boundary, is one of the “big five” mass extinction events of the Phanerozoic. Fossilized charcoal (inertinite) provides a record of wildfire events, which in turn can provide insight into the evolution of terrestrial ecosystems and the atmospheric composition. Here, we report organic petrology, programmed pyrolysis analysis, major and trace element analyses, and initial osmium isotope (Osi) stratigraphy from five sections of Upper Devonian (F–F interval) from western New York, USA. These data are discussed to infer evidence of a wildfire event at the F–F boundary. Based on the evidence for a wildfire at the F–F boundary we also provide an estimate of atmospheric O2 levels of ∼23–25% at this interval, which is in agreement with the models that predict elevated pO2 levels during the Late Devonian. This, coupled with our Os isotope records, support the currently published Osi data that lacks any evidence for an extra-terrestrial impact or volcanic event at the F–F interval, and therefore to act as a trigger for the F–F mass extinction. The elevated O2 level at the F–F interval inferred from this study supports the hypothesis that pCO2 drawdown and associated climate cooling may have acted as a driving mechanism of the F–F mass extinction.
Effective conservation requires prioritizing areas that are vulnerable to large, irreversible changes. Unfortunately, rigorously documenting these changes with experiments and long-term monitoring is not only costly, but may provide evidence that is too late to facilitate proactive decisions.
We use a simple model to illustrate that commonly available short-term spatial, “snapshot”, data from a given ecosystem along an environmental gradient can be used to identify environmental conditions under which different ecosystem states (e.g. different species compositions) co-occur in space. These environmental conditions are those under which future perturbations have the potential for discontinuous large, sometimes irreversible, effects; and can be mapped in space to predict potential spatial hotspots of ecosystem fragility.
We apply these insights to ecologically important high-elevation subalpine meadows of the Sierra Nevada (California). Our analysis reveals specific areas within meadows that may be more vulnerable than others because their plant communities have the potential to shift to a different state. These shifts can be mechanistically explained by interactions between the vegetation and the local water regimes and/or the upper soil conditions.
Our study provides a simple workflow using commonly available data to help prioritize conservation areas based on their potential sensitivity to upcoming perturbations. Such an approach could be very valuable to make most efficient use of conservation and management resources in the context of ongoing global changes.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.biocon.2019.108388","usgsCitation":"Genin, A., Lee, S.R., Berlow, E.L., Ostoja, S., and Kefi, S., 2020, Mapping hotspots of potential ecosystem fragility using commonly available spatial data: Biological Conservation, v. 241, 108388, 11 p., https://doi.org/10.1016/j.biocon.2019.108388.","productDescription":"108388, 11 p.","ipdsId":"IP-084595","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":457858,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.biocon.2019.108388","text":"Publisher Index Page"},{"id":385279,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Sequoia National Park, Yosemite National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -117.90527343750001,\n 36.05798104702501\n ],\n [\n -118.3447265625,\n 37.29153547292737\n ],\n [\n -119.36645507812499,\n 38.30718056188316\n ],\n [\n -119.81689453125,\n 38.324420427006544\n ],\n [\n -120.047607421875,\n 37.83148014503288\n ],\n [\n -119.937744140625,\n 37.32648861334206\n ],\n [\n -119.278564453125,\n 36.77409249464195\n ],\n [\n -118.94897460937499,\n 36.20882309283712\n ],\n [\n -118.24584960937499,\n 35.47856499535729\n ],\n [\n -117.87231445312499,\n 35.43381992014202\n ],\n [\n -117.90527343750001,\n 36.05798104702501\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"241","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Genin, Alexandre","contributorId":192956,"corporation":false,"usgs":false,"family":"Genin","given":"Alexandre","email":"","affiliations":[],"preferred":false,"id":814635,"contributorType":{"id":1,"text":"Authors"},"rank":0},{"text":"Lee, Steven R. 0000-0002-4581-3684 srlee@usgs.gov","orcid":"https://orcid.org/0000-0002-4581-3684","contributorId":5630,"corporation":false,"usgs":true,"family":"Lee","given":"Steven","email":"srlee@usgs.gov","middleInitial":"R.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":814636,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Berlow, Eric L.","contributorId":91416,"corporation":false,"usgs":false,"family":"Berlow","given":"Eric","email":"","middleInitial":"L.","affiliations":[],"preferred":false,"id":814637,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Ostoja, Steven M.","contributorId":225183,"corporation":false,"usgs":false,"family":"Ostoja","given":"Steven M.","affiliations":[{"id":32922,"text":"USDA California Climate Hub","active":true,"usgs":false}],"preferred":false,"id":814638,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kefi, Sonia","contributorId":257566,"corporation":false,"usgs":false,"family":"Kefi","given":"Sonia","affiliations":[{"id":37581,"text":"Université de Montpellier, France","active":true,"usgs":false}],"preferred":false,"id":814639,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70205930,"text":"fs20193064 - 2020 - Continuous nitrate monitoring in groundwater and potential contribution to surface-water nitrogen loads in Mason County, Illinois","interactions":[],"lastModifiedDate":"2022-04-19T21:47:50.406624","indexId":"fs20193064","displayToPublicDate":"2020-02-04T09:34:19","publicationYear":"2020","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":"2019-3064","displayTitle":"Continuous Nitrate Monitoring in Groundwater and Potential Contribution to Surface-Water Nitrogen Loads in Mason County, Illinois","title":"Continuous nitrate monitoring in groundwater and potential contribution to surface-water nitrogen loads in Mason County, Illinois","docAbstract":"Illinois has some of the most productive farmland in the country. The use of fertilizers to improve crop production has increased, which has resulted in an increase in the concentration of nitrogen in many streams and aquifers. The U.S. Geological Survey, in cooperation with the Illinois Environmental Protection Agency, is continuously monitoring (one reading every 15 minutes) the concentration of nitrate plus nitrite, as nitrogen, in a groundwater well and assessing the potential contribution to surface-water nitrogen loads. Continuous monitoring of the nitrate concentration allows for the collection of a larger dataset in comparison to periodic or event-based sampling. This fact sheet describes the data collection methods, describes the overall experimental design, and displays data collected for the study. The analysis of continuous data improves understanding of the fate and transport of nitrate.","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20193064","collaboration":"Prepared in cooperation with Illinois Environmental Protection Agency","usgsCitation":"Gruhn, L.R., and Nalley, G.M., 2020, Continuous nitrate monitoring in groundwater and potential contribution to surface-water nitrogen loads in Mason County, Illinois: U.S. Geological Survey Fact Sheet 2019–3064, 4 p., https://doi.org/10.3133/fs20193064.","productDescription":"Report: 4 p.; Dataset","numberOfPages":"4","onlineOnly":"N","ipdsId":"IP-105761","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":371778,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2019/3064/coverthb2.jpg"},{"id":399142,"rank":4,"type":{"id":36,"text":"NGMDB Index 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Central Midwest Water Science Center
U.S. Geological Survey
405 N. Goodwin Ave.
Urbana, Illinois 61801