Aquatic organisms have adapted over evolutionary time-scales to hydrologic variability represented by the natural flow regime of rivers and streams in their unimpaired state. Rapid landscape change coupled with growing human demand for water have altered natural flow regimes of many rivers and streams on a global scale. Climate non-stationarity is expected to further intensify hydrologic variability, placing increased pressure on aquatic communities. Using a machine learning approach and georeferenced species occurrence data, we modeled and mapped spatial patterns of hydrologic disturbance for streams in Arkansas, Missouri, and eastern Oklahoma. Random forest (RF) models trained on fish community data, hydrologic, and landscape metrics for gaged streams in the National Hydrography (NHDPlusV2) database were used to predict a hydrologic disturbance index (HDI) for ungaged streams. The HDI is part of the USGS Geospatial Attributes of Gages for Evaluating Streamflow (GAGESII) database and is a composite index of watershed-scale disturbance from anthropogenic stressors. Fish presence/absence data had similar overall model prediction accuracy (77%; 95% CI: 0.74, 0.80) as flow variables (76%; CI: 0.73, 0.80). Including topographic variables increased the RF prediction accuracy of both the fish (90%; CI: 0.88, 0.92) and flow models (86%; CI: 0.84, 0.89). Spatial patterns of hydrologic disturbance suggest distinct ecohydrological regions exist where conservation actions may be focused. Streams with low HDI were predominately located in the Ozark Highlands, Boston Mountains, and Ouachita Mountains. Correlation analysis of HDI by flow regime showed groundwater stable streams had the lowest disturbance frequency, with over 50% of stream reaches with low HDI located in forested land cover. HDI was highest for big rivers, intermittent runoff streams and streams in areas of agricultural land use. Our results show long-term georeferenced biological data can provide a valuable resource for predictive modeling of hydrologic disturbance for ungaged rivers and streams.
Avian avulavirus (commonly known as avian paramyxovirus-1 or APMV-1) can cause disease of varying severity in both domestic and wild birds. Understanding how viruses move among hosts and geography would be useful for informing prevention and control efforts. A Bayesian statistical framework was employed to estimate the evolutionary history of 1602 complete fusion gene APMV-1 sequences collected from 1970 to 2016 in order to infer viral transmission between avian host orders and diffusion among geographic regions. Ancestral states were estimated with a non-reversible continuous-time Markov chain model, allowing transition rates between discrete states to be calculated. The evolutionary analyses were stratified by APMV-1 classes I (n = 198) and II (n = 1404), and only those sequences collected between 2006 and 2016 were allowed to contribute host and location information to the viral migration networks.
While the current data was unable to assess impact of host domestication status on APMV-1 diffusion, these analyses supported the sharing of APMV-1 among divergent host taxa. The highest supported transition rate for both classes existed from domestic chickens to Anseriformes (class I:6.18 transitions/year, 95% highest posterior density (HPD) 0.31–20.02, Bayes factor (BF) = 367.2; class II:2.88 transitions/year, 95%HPD 1.9–4.06, BF = 34,582.9). Further, among class II viruses, domestic chickens also acted as a source for Columbiformes (BF = 34,582.9), other Galliformes (BF = 34,582.9), and Psittaciformes (BF = 34,582.9). Columbiformes was also a highly supported source to Anseriformes (BF = 322.0) and domestic chickens (BF = 402.6). Additionally, our results provide support for the diffusion of viruses among continents and regions, but no interhemispheric viral exchange between 2006 and 2016. Among class II viruses, the highest transition rates were estimated from South Asia to the Middle East (1.21 transitions/year; 95%HPD 0.36–2.45; BF = 67,107.8), from Europe to East Asia (1.17 transitions/year; 95%HPD 0.12–2.61; BF = 436.2) and from Europe to Africa (1.06 transitions/year, 95%HPD 0.07–2.51; BF = 169.3).
While migration appears to occur infrequently, geographic movement may be important in determining viral diversification and population structure. In contrast, inter-order transmission of APMV-1 may occur readily, but most events are transient with few lineages persisting in novel hosts.
Groundwater inundation (GWI) is a particularly challenging consequence of sea-level rise (SLR), as it progressively inundates infrastructure located above and below the ground surface. Paths of flooding by GWI differ from other types of SLR flooding (i.e., wave overwash, storm-drain backflow) such that it is more difficult to mitigate, and thus requires a separate set of highly innovative adaptation strategies to manage. To spur consideration of GWI in planning, data-intensive numerical modeling methods have been developed that produce locally specific visualizations of GWI, though the accessibility of such methods is limited by extensive data requirements. Conversely, the hydrostatic (or 'bathtub') modeling approach is widely used in adaptation planning owing to easily accessed visualizations (i.e., NOAA SLR Viewer), yet its capacity to simulate GWI has never been tested. Given the separate actions necessary to mitigate GWI relative to marine overwash, this is a significant gap. Here we compare a simple hydrostatic modeling method with a more deterministic, dynamic and robust 3D numerical modeling approach to explore the effectiveness of the hydrostatic method in simulating equilibrium aquifer effects of multi-decadal sea-level rise, and in turn GWI for Honolulu, Hawai'i. We find hydrostatic modeling in the Honolulu area and likely other settings may yield similar results to numerical modeling when referencing the local mean higher-high water tide datum (generally typical of flood studies). These findings have the potential to spur preliminary understanding of GWI impacts in municipalities that lack the required data to conduct rigorous groundwater-modeling investigations. We note that the methods explored here for Honolulu do not simulate dynamic coastal processes (i.e., coastal erosion, sediment accretion or changes in land cover) and thus are most appropriately applied to regions that host heavily armored shorelines behind which GWI can develop.
","language":"English","publisher":"IOP Publishing","doi":"10.1088/2515-7620/ab21fe","usgsCitation":"Habel, S., Fletcher, C., Rotzoll, K., El-Kadi, A.I., and Oki, D., 2019, Comparison of a simple hydrostatic and a data-intensive 3D numerical modeling method of simulating sea-level rise induced groundwater inundation for Honolulu, Hawai'i, USA: Environmental Research Letters, v. 1, no. 4, 041005, 12 p., https://doi.org/10.1088/2515-7620/ab21fe.","productDescription":"041005, 12 p.","ipdsId":"IP-102017","costCenters":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"links":[{"id":467598,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1088/2515-7620/ab21fe","text":"Publisher Index Page"},{"id":385147,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Hawaii","otherGeospatial":"O'ahu","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -158.04931640625,\n 21.289374355860424\n ],\n [\n -157.9010009765625,\n 21.289374355860424\n ],\n [\n -157.9010009765625,\n 21.4121622297254\n ],\n [\n -158.04931640625,\n 21.4121622297254\n ],\n [\n -158.04931640625,\n 21.289374355860424\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"1","issue":"4","noUsgsAuthors":false,"publicationDate":"2019-05-24","publicationStatus":"PW","contributors":{"authors":[{"text":"Habel, Shellie 0000-0001-9295-0596","orcid":"https://orcid.org/0000-0001-9295-0596","contributorId":257499,"corporation":false,"usgs":false,"family":"Habel","given":"Shellie","email":"","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":814397,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fletcher, Charles H.","contributorId":257500,"corporation":false,"usgs":false,"family":"Fletcher","given":"Charles H.","affiliations":[{"id":39036,"text":"University of Hawaii at Manoa","active":true,"usgs":false}],"preferred":false,"id":814398,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Rotzoll, Kolja 0000-0002-5910-888X","orcid":"https://orcid.org/0000-0002-5910-888X","contributorId":201087,"corporation":false,"usgs":false,"family":"Rotzoll","given":"Kolja","affiliations":[],"preferred":false,"id":814399,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"El-Kadi, Aly I. 0000-0002-9623-5458","orcid":"https://orcid.org/0000-0002-9623-5458","contributorId":257501,"corporation":false,"usgs":false,"family":"El-Kadi","given":"Aly","email":"","middleInitial":"I.","affiliations":[{"id":35886,"text":"University of Hawaii at Manoa, Water Resources Research Center","active":true,"usgs":false}],"preferred":false,"id":814400,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Oki, Delwyn S. 0000-0002-6913-8804","orcid":"https://orcid.org/0000-0002-6913-8804","contributorId":207735,"corporation":false,"usgs":true,"family":"Oki","given":"Delwyn S.","affiliations":[{"id":525,"text":"Pacific Islands Water Science Center","active":true,"usgs":true}],"preferred":true,"id":814401,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70203310,"text":"sim3434 - 2019 - Drilling, construction, water chemistry, water levels, and regional potentiometric surface of the upper carbonate-rock aquifer in Clark County, Nevada, 2009–2015","interactions":[],"lastModifiedDate":"2019-05-24T09:10:16","indexId":"sim3434","displayToPublicDate":"2019-05-23T15:22:52","publicationYear":"2019","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":333,"text":"Scientific Investigations Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3434","displayTitle":"Drilling, Construction, Water Chemistry, Water Levels, and Regional Potentiometric Surface of the Upper Carbonate-Rock Aquifer in Clark County, Nevada, 2009–2015","title":"Drilling, construction, water chemistry, water levels, and regional potentiometric surface of the upper carbonate-rock aquifer in Clark County, Nevada, 2009–2015","docAbstract":"The U.S. Geological Survey (USGS) and the Bureau of Land Management (BLM) initiated a cooperative study through the Southern Nevada Public Land Management Act (Bureau of Land Management, 1998) to install six wells in the carbonate-rock and basin-fill aquifers of Clark County, Nevada, in areas of sparse groundwater data. This map uses water levels from these new wells, water levels from existing wells, and altitudes of spring discharge points to update a regional potentiometric map of the carbonate-rock aquifer and provide evidence to interpret the direction of regional groundwater flow. This potentiometric surface map is accompanied by drilling and borehole geophysical logs, well construction information, lithology, water chemistry, and water levels from the newly drilled wells.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3434","collaboration":"Prepared in cooperation with the Bureau of Land Management","usgsCitation":"Wilson, J.W., 2019, Drilling, construction, water chemistry, water levels, and regional potentiometric surface of the upper carbonate-rock aquifer in Clark County, Nevada, 2009–2015: U.S. Geological Survey Scientific Investigations Map 3434, scale 1:500,000, https://doi.org/10.3133/sim3434.","productDescription":"1 Sheet: 59.00 x 32.00 in.; Data Release","onlineOnly":"Y","ipdsId":"IP-050214","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":364132,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3434/sim3434.pdf","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"Scientific Investigations Map 3434"},{"id":364131,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3434/coverthb_.jpg"},{"id":364136,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9K73T7Q","linkHelpText":"Supplemental data for drilling, construction, water chemistry, water levels, and regional potentiometric surface of the upper carbonate-rock aquifer in Clark County, Nevada, 2009-2015"}],"contact":"Nevada Water Science Center
U.S. Geological Survey
2730 N. Deer Run Road
Carson City, Nevada 95819
Mosquito vectors play a crucial role in the distribution of avian Plasmodium parasites worldwide. At northern latitudes, where climate warming is most pronounced, there are questions about possible changes in the abundance and distribution of Plasmodium parasites, their vectors, and their impacts to avian hosts. To better understand the transmission of Plasmodium among local birds and to gather baseline data on potential vectors, we sampled a total of 3,909 mosquitoes from three locations in south‐central Alaska during the summer of 2016. We screened mosquitoes for the presence of Plasmodium parasites using molecular techniques and estimated Plasmodium infection rates per 1,000 mosquitoes using maximum likelihood methods. We found low estimated infection rates across all mosquitoes (1.28 per 1,000), with significantly higher rates in Culiseta mosquitoes (7.91 per 1,000) than in Aedes mosquitoes (0.57 per 1,000). We detected Plasmodium in a single head/thorax sample of Culiseta, indicating potential for transmission of these parasites by mosquitoes of this genus. Plasmodium parasite DNA isolated from mosquitoes showed a 100% identity match to the BT7 Plasmodium lineage that has been detected in numerous avian species worldwide. Additionally, microscopic analysis of blood smears collected from black‐capped chickadees (Poecile atricapillus) at the same locations revealed infection by parasites preliminarily identified as Plasmodium circumflexum. Results from our study provide the first information on Plasmodium infection rates in Alaskan mosquitoes and evidence that Culiseta species may play a role in the transmission and maintenance of Plasmodium parasites in this region.
Director,
Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
Cropland extent maps are useful components for assessing food security. Ideally, such products are a useful addition to countrywide agricultural statistics since they are not politically biased and can be used to calculate cropland area for any spatial unit from an individual farm to various administrative unites (e.g., state, county, district) within and across nations, which in turn can be used to estimate agricultural productivity as well as degree of disturbance on food security from natural disasters and political conflict. However, existing cropland extent maps over large areas (e.g., Country, region, continent, world) are derived from coarse resolution imagery (250 m to 1 km pixels) and have many limitations such as missing fragmented and\\or small farms with mixed signatures from different crop types and\\or farming practices that can be, confused with other land cover. As a result, the coarse resolution maps have limited useflness in areas where fields are small (<1 ha), such as in Southeast Asia. Furthermore, coarse resolution cropland maps have known uncertainties in both geo-precision of cropland location as well as accuracies of the product. To overcome these limitations, this research was conducted using multi-date, multi-year 30-m Landsat time-series data for 3 years chosen from 2013 to 2016 for all Southeast and Northeast Asian Countries (SNACs), which included 7 refined agro-ecological zones (RAEZ) and 12 countries (Indonesia, Thailand, Myanmar, Vietnam, Malaysia, Philippines, Cambodia, Japan, North Korea, Laos, South Korea, and Brunei). The 30-m (1 pixel = 0.09 ha) data from Landsat 8 Operational Land Imager (OLI) and Landsat 7 Enhanced Thematic Mapper (ETM+) were used in the study. Ten Landsat bands were used in the analysis (blue, green, red, NIR, SWIR1, SWIR2, Thermal, NDVI, NDWI, LSWI) along with additional layers of standard deviation of these 10 bands across 1 year, and global digital elevation model (GDEM)-derived slope and elevation bands. To reduce the impact of clouds, the Landsat imagery was time-composited over four time-periods (Period 1: January- April, Period 2: May-August, and Period 3: September-December) over 3-years. Period 4 was the standard deviation of all 10 bands taken over all images acquired during the 2015 calendar year. These four period composites, totaling 42 band data-cube, were generated for each of the 7 RAEZs. The reference training data (N = 7849) generated for the 7 RAEZ using sub-meter to 5-m very high spatial resolution imagery (VHRI) helped generate the knowledge-base to separate croplands from non-croplands. This knowledge-base was used to code and run a pixel-based random forest (RF) supervised machine learning algorithm on the Google Earth Engine (GEE) cloud computing environment to separate croplands from non-croplands. The resulting cropland extent products were evaluated using an independent reference validation dataset (N = 1750) in each of the 7 RAEZs as well as for the entire SNAC area. For the entire SNAC area, the overall accuracy was 88.1% with a producer’s accuracy of 81.6% (errors of omissions = 18.4%) and user’s accuracy of 76.7% (errors of commissions = 23.3%). For each of the 7 RAEZs overall accuracies varied from 83.2 to 96.4%. Cropland areas calculated for the 12 countries were compared with country areas reported by the United Nations Food and Agriculture Organization and other national cropland statistics resulting in an R2 value of 0.93. The cropland areas of provinces were compared with the province statistics that showed an R2 = 0.95 for South Korea and R2 = 0.94 for Thailand. The cropland products are made available on an interactive viewer at www.croplands.org and for download at National Aeronautics and Space Administration’s (NASA) Land Processes Distributed Active Archive Center (LP DAAC): https://lpdaac.usgs.gov/node/1281.
Director, Geology, Geophysics and Geochemistry Science Center
U.S. Geological Survey
Box 25046, MS-973
Denver, CO 80225-0046
Whereas research in volcano geodesy seeks to push the boundaries of our knowledge of the physics of volcanoes, monitoring looks at changes in volcano behavior to predict when a volcanic crisis might develop. To be effective, geodetic monitoring must be done before, during, and after eruptions and must be integrated with other monitoring techniques. It requires the type of long-term commitment of time and resources that academic and industry scientists generally cannot make. A few, well-placed geodetic monitoring stations can make a huge difference to a country's ability to alert its people to an imminent volcanic eruption.
Monitoring strategies vary greatly depending on several factors such as the activity of the individual volcano, access, and available personnel and funding. Rapid advances in technology allow for more precise geodetic monitoring today than was imaginable when many of the existing volcano observatories were established. Today, deformation measurements at active volcanoes are usually made with continuous Global Positioning System (CGPS) stations, supplemented by Interferometric Synthetic Aperture Radar (InSAR) images. Neither method requires a continuous presence of personnel in the field, except for the installation and maintenance of the GPS stations; however subsequent data analysis can be highly complex.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Reference Module in Earth Systems and Environmental Sciences","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Elsevier","doi":"10.1016/B978-0-12-409548-9.10902-9","usgsCitation":"Battaglia, M., Alpala, J., Alpala, R., Angarita, M., Arcos, D., Eullides, L., Euillades, P., Mueller, C., and Narvaez, L., 2019, Monitoring volcanic deformation, chap. of Reference Module in Earth Systems and Environmental Sciences, https://doi.org/10.1016/B978-0-12-409548-9.10902-9.","ipdsId":"IP-103581","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":364027,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Battaglia, Maurizio 0000-0003-4726-5287 mbattaglia@usgs.gov","orcid":"https://orcid.org/0000-0003-4726-5287","contributorId":204742,"corporation":false,"usgs":true,"family":"Battaglia","given":"Maurizio","email":"mbattaglia@usgs.gov","affiliations":[{"id":114,"text":"Alaska Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":762901,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Alpala, Jorge","contributorId":139634,"corporation":false,"usgs":false,"family":"Alpala","given":"Jorge","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762900,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Alpala, Rosa","contributorId":215654,"corporation":false,"usgs":false,"family":"Alpala","given":"Rosa","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762902,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Angarita, Mario","contributorId":215655,"corporation":false,"usgs":false,"family":"Angarita","given":"Mario","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":762903,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Arcos, Dario","contributorId":139636,"corporation":false,"usgs":false,"family":"Arcos","given":"Dario","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762904,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Eullides, Leonardo","contributorId":215656,"corporation":false,"usgs":false,"family":"Eullides","given":"Leonardo","email":"","affiliations":[],"preferred":false,"id":762905,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Euillades, Pablo","contributorId":215657,"corporation":false,"usgs":false,"family":"Euillades","given":"Pablo","email":"","affiliations":[],"preferred":false,"id":762906,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Mueller, Cyrill","contributorId":215658,"corporation":false,"usgs":false,"family":"Mueller","given":"Cyrill","email":"","affiliations":[{"id":37066,"text":"OVSICORI","active":true,"usgs":false}],"preferred":false,"id":762907,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Narvaez, Lourdes","contributorId":215659,"corporation":false,"usgs":false,"family":"Narvaez","given":"Lourdes","email":"","affiliations":[{"id":12810,"text":"Colombian Geological Survey","active":true,"usgs":false}],"preferred":false,"id":762908,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70215202,"text":"70215202 - 2019 - From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows","interactions":[],"lastModifiedDate":"2020-10-13T22:52:05.481431","indexId":"70215202","displayToPublicDate":"2019-05-21T08:49:07","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3912,"text":"Frontiers in Marine Science","onlineIssn":"2296-7745","active":true,"publicationSubtype":{"id":10}},"title":"From the oceans to the cloud: Opportunities and challenges for data, models, computation and workflows","docAbstract":"Advances in ocean observations and models mean increasing flows of data. Integrating observations between disciplines over spatial scales from regional to global presents challenges. Running ocean models and managing the results is computationally demanding. The rise of cloud computing presents an opportunity to rethink traditional approaches. This includes developing shared data processing workflows utilizing common, adaptable software to handle data ingest and storage, and an associated framework to manage and execute downstream modeling. Working in the cloud presents challenges: migration of legacy technologies and processes, cloud-to-cloud interoperability, and the translation of legislative and bureaucratic requirements for “on-premises” systems to the cloud. To respond to the scientific and societal needs of a fit-for-purpose ocean observing system, and to maximize the benefits of more integrated observing, research on utilizing cloud infrastructures for sharing data and models is underway. Cloud platforms and the services/APIs they provide offer new ways for scientists to observe and predict the ocean’s state. High-performance mass storage of observational data, coupled with on-demand computing to run model simulations in close proximity to the data, tools to manage workflows, and a framework to share and collaborate, enables a more flexible and adaptable observation and prediction computing architecture. Model outputs are stored in the cloud and researchers either download subsets for their interest/area or feed them into their own simulations without leaving the cloud. Expanded storage and computing capabilities make it easier to create, analyze, and distribute products derived from long-term datasets. In this paper, we provide an introduction to cloud computing, describe current uses of the cloud for management and analysis of observational data and model results, and describe workflows for running models and streaming observational data. We discuss topics that must be considered when moving to the cloud: costs, security, and organizational limitations on cloud use. Future uses of the cloud via computational sandboxes and the practicalities and considerations of using the cloud to archive data are explored. We also consider the ways in which the human elements of ocean observations are changing – the rise of a generation of researchers whose observations are likely to be made remotely rather than hands on – and how their expectations and needs drive research towards the cloud. In conclusion, visions of a future where cloud computing is ubiquitous are discussed.
Understanding threats to species persistence requires knowledge of where species currently occur. We explore methods for estimating two important facets of species distributions, namely where the range limit occurs and how species interactions structure distributions. Accurate understanding of range limits is crucial for predicting range dynamics and shifts in response to interspecific interactions and climate change. Additionally, species interactions are increasingly recognized as an important but not well‐understood predictor of range shifts. Our objective was to predict range limits and contact zones for two plethodontid salamanders, the highly range‐restricted Shenandoah salamander (Plethodon shenandoah) and the wide‐ranging red‐backed salamander (Plethodon cinereus). Using detection/non‐detection data, we assess four methodological decisions when estimating species’ distributions: (1) accounting for imperfect detection, (2) covariates to predict species occurrences, (3) accounting for species interactions, and (4) the inclusion of spatial autocorrelation. We found that Shenandoah salamander and red‐backed salamander co‐occurrence would have been underestimated and the range edge misidentified had we not accounted for incomplete detection. Covariates related to habitat were not sufficient to explain species’ range boundaries. Models that included spatial autocorrelation (i.e., a conditional autoregressive random effect) performed better than models that included just species interactions (i.e., detection and occurrence were conditional on the other species being present) and models that included both spatial autocorrelation and species interactions. Further, we found that the breadth of primary contact zones was typically 60–170 m, which is greater on average than previous estimates. In addition, we frequently observed secondary, disjunct contact zones along the range boundary. Understanding the extent to which species co‐occur and how the range boundaries are shaped is crucial to conservation efforts. Our work indicates that accounting for detection is crucial for accurately characterizing range edges and that spatial models may be especially effective in modeling distributions at the boundary.
In 2015, the U.S. Geological Survey (USGS) entered into a cooperative agreement with the North Carolina Department of Transportation (NCDOT) to develop a North Carolina-enhanced variation of the national Stochastic Empirical Loading and Dilution Model (SELDM) with available North Carolina-specific streamflow and water-quality data and to demonstrate use of the model by documenting selected simulation scenarios. The USGS developed the national SELDM in cooperation with the Federal Highway Administration (FHWA) to provide the tools and techniques necessary for performing stormwater-quality simulations. SELDM uses a stochastic mass-balance approach to estimate combinations of flows, concentrations, and loads of stormwater constituents from the site of interest (often a highway catchment; nonhighway areas, such as a large impervious area at a shopping center complex, also can be used) and the basin upstream from the stormwater outfall to assess the risk for adverse effects of runoff. SELDM also can be used to simulate the effectiveness of volume reduction, hydrograph extension, and water-quality concentration reductions by stormwater best management practices (BMPs), which are designed to help mitigate the effects of runoff on receiving water bodies.
Some of the statistical inputs needed for the North Carolina-enhanced SELDM were either calculated or augmented using local or regional data from North Carolina. Streamflow statistics used by SELDM were determined for 266 streamgages across North Carolina on the basis of data available through the 2015 water year. Recession ratio statistics used for triangular hydrographs were also developed for 30 streamgages across the State. The NCDOT identified previous research reports on highway-runoff and BMP studies in North Carolina for review of potential data addition to the national FHWA Highway-Runoff Database (HRDB). Following USGS review of these data, a total of 25,087 event mean concentration values and 1,140 storm events for 39 highway-runoff sites and 195 analytes were uploaded to the national HRDB from six North Carolina highway-runoff research reports and a recent USGS bridge deck runoff study. Using data for 27 streamgages in North Carolina, a total of 57 water-quality transport curves were developed for seven constituents for use in simulating water-quality conditions in the upstream basin. Performance data for three BMPs (bioretention, grass strip or swale, and wetland channel) from NCDOT research data were incorporated into the North Carolina-enhanced SELDM for volume-reduction statistics, including the effectiveness of treating four water-quality constituents (total suspended solids, total nitrogen, total phosphorus, nitrate plus nitrite) and turbidity.
Simulations using the North Carolina-enhanced SELDM are presented for two hypothetical upstream basins in the Piedmont ecoregion and one hypothetical highway site to demonstrate how simulations can be used to provide risk-based information about potential effects of stormwater runoff on downstream water quality and the potential for mitigating those risks by using BMPs. The first group of simulations explores the stochastic variability in dilution factors (the ratio of the highway runoff to the total downstream stormflow) for a hypothetical Piedmont rural creek having drainage areas ranging from 1 to 100 square miles. The second group of simulations examines dilution factors based on variations in precipitation, streamflow, and recession ratios for two hypothetical Piedmont upstream basins (rural and urban) where the drainage area was held constant at 25 square miles. These simulations indicate the sensitivity of results to variations in each of the three variables. The third group of simulations examines the effects of varied concentrations in the upstream basin on water-quality conditions downstream from the highway crossing. Variations in upstream water-quality conditions for three constituents (suspended sediment concentration, total nitrogen, and total phosphorus) are based on water-quality transport curves selected from among the 57 curves developed as part of this study to represent low-, medium-, and high-concentration statistics. Simulations completed for this third group also examine the potential effects of grass swale and bioretention BMP treatment on total nitrogen and total phosphorus concentrations in highway runoff. The BMP performance data from the NCDOT research reports were applied in this group of simulations.
The stochastic mass-balance approach used in SELDM analyses and simulations provides a strong tool for engineers and water-resource managers to use in exploring a wide range of possible hydrologic and water-quality inputs and their effects on downstream water quality. The results of this study can not only aid engineers and managers in planning for potential adverse effects of runoff at site-specific locations, they can also help the USGS and other Federal and State agencies with oversight responsibilities in stormwater-quality issues to continue gathering data on potential water-quality effects in receiving streams.
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U.S. Geological Survey
720 Gracern Road
Columbia, SC 29210
Inversion of airborne electromagnetic (AEM) data is an under-determined inverse problem, in that infinitely many resistivity models exist that will be able to explain the observed data, within measurement errors. Therefore, additional information or constraints must be taken into account to solve the inverse problem. In deterministic approaches, the goal is to locate one optimal model that can be obtained by using some form of smoothness constraints implied through a number of regularization choices. This model, however, will not necessarily represent realistic geological features. Probabilistic methods offer an alternative in which the solution is not one model, but a collection of models, whose variability represents the uncertainty. The probabilistic approach can also rely on implicit model assumptions, representing prior information (a type of regularization information) that may or may not be consistent with the actual available information. Here, we present an approach for AEM inversion in which the prior model is explicitly chosen by a user, preferably selected based on actual prior information available and then integrated with AEM data using a general Monte Carlo based sampling approach. This approach leads to a new workflow to AEM inversion in which geological prior information is independently and explicitly chosen before inversion is carried out. The main benefit of this approach is that each model obtained will, by construction, be consistent with prior (geological) information as well as geophysical data. Through examples based on synthetic and real AEM data, we will demonstrate the methodology, not least that the choice of prior information cannot be avoided: Either it is done explicitly, or it will be chosen implicitly by the choice of method used to invert the AEM data.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/gji/ggz230","usgsCitation":"Hansen, T.M., and Minsley, B.J., 2019, Inversion of airborne EM data with an explicit choice of prior model: Geophysical Journal International, v. 218, no. 2, p. 1348-1366, https://doi.org/10.1093/gji/ggz230.","productDescription":"17 p.","startPage":"1348","endPage":"1366","ipdsId":"IP-106050","costCenters":[{"id":35995,"text":"Geology, Geophysics, and Geochemistry Science Center","active":true,"usgs":true}],"links":[{"id":467607,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://pure.au.dk/portal/en/publications/731bd10f-dcc4-4142-a377-2f42a561b2c9","text":"External Repository"},{"id":382276,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"218","issue":"2","noUsgsAuthors":false,"publicationDate":"2019-05-20","publicationStatus":"PW","contributors":{"authors":[{"text":"Hansen, Thomas Mejer","contributorId":199735,"corporation":false,"usgs":false,"family":"Hansen","given":"Thomas","email":"","middleInitial":"Mejer","affiliations":[{"id":27198,"text":"Niels Bohr Institute, University of Copenhagen","active":true,"usgs":false}],"preferred":false,"id":808408,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Minsley, Burke J. 0000-0003-1689-1306 bminsley@usgs.gov","orcid":"https://orcid.org/0000-0003-1689-1306","contributorId":697,"corporation":false,"usgs":true,"family":"Minsley","given":"Burke","email":"bminsley@usgs.gov","middleInitial":"J.","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true}],"preferred":true,"id":808409,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70205612,"text":"70205612 - 2019 - Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests","interactions":[],"lastModifiedDate":"2019-09-27T10:33:51","indexId":"70205612","displayToPublicDate":"2019-05-20T10:27:40","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests","docAbstract":"Salinity loading in the Upper Colorado River Basin (UCRB) costs local economies upwards of $300 million US dollars annually. Salinity source models have generally included coarse spatial data to represent non‐agriculture sources. We developed new predictive soil property and cover maps at 30 m resolution to improve source representation in salinity modeling. Salinity loading erosion risk indices were also created based on soil properties, remotely sensed bare ground exposure, and topographic factors to examine potential surface soil erosion drivers. These new maps and data from previous SPARROW models were related to recently updated records of salinity at 309 stream gauges in the UCRB using random forest regressions. Resulting salinity yield predictions indicate more diffuse salinity sources, with slightly higher yields in more arid portions of the UCRB, and less overall load coming from irrigated agricultural sources. Model simulations still indicate irrigation to be the major human source of salinity (661,000 Mg, or 12%), but also suggest that 75,000 Mg (1.4%) of annual salinity in the UCRB is coming from areas with excessive exposed bare ground in high elevation mountain areas. Model inputs allow for field scale screening of locations that could be targeted for salinity control projects. Results confirm recent studies indicating limited surface erosional influence on salinity loading in UCRB surface waters, but impacts of monsoonal runoff events are still not fully understood, particularly in drylands. The study highlights the utility of new predictive soil maps and machine learning for environmental modeling.","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2018WR024054","usgsCitation":"Nauman, T., Ely, C., Miller, M., and Duniway, M.C., 2019, Salinity yield modeling of the Upper Colorado River Basin using 30-meter resolution soil maps and random forests: Water Resources Research, v. 55, no. 6, p. 4954-4973, https://doi.org/10.1029/2018WR024054.","productDescription":"20 p.","startPage":"4954","endPage":"4973","ipdsId":"IP-099154","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true},{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"links":[{"id":499841,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doaj.org/article/678588abb83c4b249680e0982160eaf7","text":"External Repository"},{"id":437460,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9QSFDJN","text":"USGS data release","linkHelpText":"Salinity yield modeling spatial data for the Upper Colorado River Basin, USA"},{"id":367766,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, New Mexico, Utah, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.89697265625,\n 41.062786068733026\n ],\n [\n -108.687744140625,\n 42.17154633452751\n ],\n [\n -110.225830078125,\n 43.42100882994726\n ],\n [\n -110.687255859375,\n 43.0287452513488\n ],\n [\n -110.863037109375,\n 41.09591205639546\n ],\n [\n -111.456298828125,\n 39.985538414809746\n ],\n [\n -112.203369140625,\n 36.76529191711624\n ],\n [\n -111.09374999999999,\n 36.146746777814364\n ],\n [\n -108.643798828125,\n 35.43381992014202\n ],\n [\n -107.64404296875,\n 36.33282808737917\n ],\n [\n -106.754150390625,\n 37.142803443716836\n ],\n [\n -106.6552734375,\n 37.84015683604136\n ],\n [\n -106.80908203125,\n 38.324420427006544\n ],\n [\n -106.424560546875,\n 39.32579941789298\n ],\n [\n -106.424560546875,\n 40.25437660372649\n ],\n [\n -106.89697265625,\n 41.062786068733026\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"6","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-06-22","publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":219281,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771865,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Ely, Christopher P. 0000-0001-5276-5046","orcid":"https://orcid.org/0000-0001-5276-5046","contributorId":219282,"corporation":false,"usgs":true,"family":"Ely","given":"Christopher P.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771866,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Miller, Matthew P. 0000-0002-2537-1823 mamiller@usgs.gov","orcid":"https://orcid.org/0000-0002-2537-1823","contributorId":219283,"corporation":false,"usgs":true,"family":"Miller","given":"Matthew","email":"mamiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":610,"text":"Utah Water Science Center","active":true,"usgs":true}],"preferred":true,"id":771867,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":219284,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":771868,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70215501,"text":"70215501 - 2019 - Development of microsatellite loci for two New World vultures (Cathartidae)","interactions":[],"lastModifiedDate":"2020-10-21T15:10:10.819332","indexId":"70215501","displayToPublicDate":"2019-05-19T10:05:58","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":958,"text":"BMC Research Notes","active":true,"publicationSubtype":{"id":10}},"title":"Development of microsatellite loci for two New World vultures (Cathartidae)","docAbstract":"Use next-generation sequencing to develop microsatellite loci that will provide the variability necessary for studies of genetic diversity and population connectivity of two New World vulture species.
We characterized 11 microsatellite loci for black vultures (Coragyps atratus) and 14 loci for turkey vultures (Cathartes aura). These microsatellite loci were grouped into 3 multiplex panels for each species. The number of alleles among black vulture samples ranged from 2 to 11, and 3 to 48 among turkey vulture samples.
","language":"English","publisher":"Springer Nature","doi":"10.1186/s13104-019-4295-z","usgsCitation":"Wostenberg, D.J., Fike, J., Oyler-McCance, S.J., Avery, M.L., and Piaggio, A.J., 2019, Development of microsatellite loci for two New World vultures (Cathartidae): BMC Research Notes, v. 12, 257, 6 p., https://doi.org/10.1186/s13104-019-4295-z.","productDescription":"257, 6 p.","ipdsId":"IP-102828","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":467609,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1186/s13104-019-4295-z","text":"Publisher Index Page"},{"id":379588,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"12","noUsgsAuthors":false,"publicationDate":"2019-05-09","publicationStatus":"PW","contributors":{"authors":[{"text":"Wostenberg, Darren J","contributorId":243551,"corporation":false,"usgs":false,"family":"Wostenberg","given":"Darren","email":"","middleInitial":"J","affiliations":[{"id":48728,"text":"NWRC","active":true,"usgs":false}],"preferred":false,"id":802521,"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":802522,"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":802523,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Avery, Michael L.","contributorId":211841,"corporation":false,"usgs":false,"family":"Avery","given":"Michael","email":"","middleInitial":"L.","affiliations":[{"id":36589,"text":"USDA","active":true,"usgs":false}],"preferred":false,"id":802524,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Piaggio, Antoinette J.","contributorId":174782,"corporation":false,"usgs":false,"family":"Piaggio","given":"Antoinette","email":"","middleInitial":"J.","affiliations":[{"id":12434,"text":"USDA, Wildlife Services, National Wildlife Research Center","active":true,"usgs":false}],"preferred":false,"id":802525,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70204004,"text":"70204004 - 2019 - Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran","interactions":[],"lastModifiedDate":"2019-06-26T15:50:26","indexId":"70204004","displayToPublicDate":"2019-05-17T15:37:46","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3871,"text":"Global Ecology and Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran","docAbstract":"Habitat loss and fragmentation are among the biggest threats to amphibian populations and anthropogenic climate change may exacerbate these. The response of Iran's amphibians to climate change is uncertain and yet making an accurate prediction of how the species will respond is critical for conservation. We assessed how expected future climate scenarios before the years 2050 and 2070 might influence the geographic distribution and habitat connectivity of the Lorestan Mountain Newt (Neurergus kaiseri). We examined presence data (2010–2018) of the species according to environmental and anthropogenic factors, and created an ensemble model of habitat suitability based on eight species distribution models (SDMs). Then, we used the concept of circuit theory to estimate potential linkages between the habitat patches. We applied the ensemble calibrated models and quantified spatial connectivity to assess the influence of climate change on the species range for the years 2050 and 2070 under four representative concentration pathways (RCPs) of three general circulation models (GCMs). Models using current climate predicted that 6.8% of the 267,609 km2 study area has suitable conditions for the species, but only about 7% of these climatically suitable landscapes are covered by conservation areas. Temperature and precipitation-related climatic variables made the largest contribution to the distribution model. Under projected climate conditions, we found a decline of 56–98% of the suitable habitat and predicted a potential for distributional shifts towards higher elevations by 2050 and 2070. Although there is relatively good connectivity between many habitat patches today, models predict that suitable areas available to the newt will become increasingly fragmented under projected climate change scenarios. Our findings support the hypothesis that projected climatic shifts will negatively influence suitable habitats of amphibians and likely cause upward shifts in elevation in range of some species. Identifying potentially suitable habitats and important linkages between habitat patches under different climate scenarios are crucial steps in conservation planning for the Lorestan Mountain Newt.","language":"English","doi":"10.1016/j.gecco.2019.e00637","collaboration":"Shahrekord University","usgsCitation":"Ashrafzadeh, M.R., Naghipour, A.A., Haidarian, M., Kusza, S., and Pilliod, D.S., 2019, Effects of climate change on habitat and connectivity for populations of a vulnerable, endemic salamander in Iran: Global Ecology and Conservation, v. 19, e00637; 13 p., https://doi.org/10.1016/j.gecco.2019.e00637.","productDescription":"e00637; 13 p.","ipdsId":"IP-103213","costCenters":[{"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}],"links":[{"id":467611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.gecco.2019.e00637","text":"Publisher Index Page"},{"id":365095,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Iran","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 47.30712890625,\n 33.32134852669881\n ],\n [\n 49.81201171875,\n 30.845647420182598\n ],\n [\n 50.83374023437499,\n 31.672083485607402\n ],\n [\n 48.328857421875,\n 34.298068350990825\n ],\n [\n 47.30712890625,\n 33.32134852669881\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"19","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Ashrafzadeh, Mohammad Reza","contributorId":216617,"corporation":false,"usgs":false,"family":"Ashrafzadeh","given":"Mohammad","email":"","middleInitial":"Reza","affiliations":[],"preferred":false,"id":765173,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Naghipour, Ali Asghar","contributorId":216618,"corporation":false,"usgs":false,"family":"Naghipour","given":"Ali","email":"","middleInitial":"Asghar","affiliations":[],"preferred":false,"id":765174,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haidarian, Maryam","contributorId":216619,"corporation":false,"usgs":false,"family":"Haidarian","given":"Maryam","email":"","affiliations":[],"preferred":false,"id":765175,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Kusza, Szilvia","contributorId":216620,"corporation":false,"usgs":false,"family":"Kusza","given":"Szilvia","email":"","affiliations":[],"preferred":false,"id":765176,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Pilliod, David S. 0000-0003-4207-3518 dpilliod@usgs.gov","orcid":"https://orcid.org/0000-0003-4207-3518","contributorId":149254,"corporation":false,"usgs":true,"family":"Pilliod","given":"David","email":"dpilliod@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":765167,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70228355,"text":"70228355 - 2019 - Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens","interactions":[],"lastModifiedDate":"2022-02-09T19:58:18.2157","indexId":"70228355","displayToPublicDate":"2019-05-17T13:52:38","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens","docAbstract":"Researchers and managers are often interested in monitoring the underlying state of a population (e.g., abundance), yet error in the observation process might mask underlying changes due to imperfect detection, availability for sampling, and heterogeneity in abundance. Additional heterogeneity can be introduced into a monitoring program when male-based surveys are used as an index for the total population. Often, male-based surveys are used for lekking species, as males are conspicuous and more easily monitored when lekking than females. To determine if lek surveys capture changes or trends in population abundance based on female survival and reproduction, we developed a virtual ecologist approach using the lesser prairie-chicken (Tympanuchus pallidicinctus) as an example. Our approach used an individual-based model to simulate lek counts based on female vital rate data from lesser prairie-chickens, included models where detection probability and lek attendance were <1, and analyzed using unadjusted counts and an N-mixture model to compare estimates of population abundance and growth rates. When lek attendance rates were <1, the estimate of abundance was biased low, even when using N-mixture models to account for detection probability. Additionally, using lek counts to estimate population growth rates without accounting for detection probability consistently overestimated population growth rates, indicating a stable population when the population was decreasing. Our results therefore suggest that lek-based surveys used without accounting for lek attendance and detection probability may miss important trends in population changes. Rather than population-level inference, lek-based surveys not accounting for lek attendance and detection probability may instead be better for inferring broad-scale range shifts of lesser prairie-chicken populations in a presence/absence framework.","language":"English","publisher":"PLoS","doi":"10.1371/journal.pone.0217172","usgsCitation":"Ross, B., Sullins, D.S., and Haukos, D.A., 2019, Using an individual-based model to assess common biases in lek-based count data to estimate population trajectories of lesser prairie-chickens: PLoS ONE, 0217172, 17 p., https://doi.org/10.1371/journal.pone.0217172.","productDescription":"0217172, 17 p.","ipdsId":"IP-098683","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":467613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0217172","text":"Publisher Index Page"},{"id":395719,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2019-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Ross, Beth 0000-0001-5634-4951 bross@usgs.gov","orcid":"https://orcid.org/0000-0001-5634-4951","contributorId":199242,"corporation":false,"usgs":true,"family":"Ross","given":"Beth","email":"bross@usgs.gov","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":833920,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sullins, Daniel S.","contributorId":275280,"corporation":false,"usgs":false,"family":"Sullins","given":"Daniel","email":"","middleInitial":"S.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":833921,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Haukos, David A. 0000-0001-5372-9960 dhaukos@usgs.gov","orcid":"https://orcid.org/0000-0001-5372-9960","contributorId":3664,"corporation":false,"usgs":true,"family":"Haukos","given":"David","email":"dhaukos@usgs.gov","middleInitial":"A.","affiliations":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true},{"id":200,"text":"Coop Res Unit Seattle","active":true,"usgs":true}],"preferred":true,"id":833922,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70204595,"text":"70204595 - 2019 - Volcano deformation: Insights into magmatic systems","interactions":[],"lastModifiedDate":"2019-08-07T09:07:01","indexId":"70204595","displayToPublicDate":"2019-05-17T12:29:25","publicationYear":"2019","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"title":"Volcano deformation: Insights into magmatic systems","docAbstract":"Volcano geodesy is the branch of geodetic science that deals with the changing shapes of volcanoes, whether large or small, deep-seated or surficial. Together with seismicity and volcanic gas flux, deformation of the ground surface can be a key indicator of subsurface conditions and processes at volcanoes—information that not only improves scientific understanding of magmatic systems but also is useful for assessing volcano hazards and mitigating their potential consequences. To take full advantage of such information requires detailed characterization of the deformation field in space and time. Currently, no single geodetic technique is capable of providing both the high spatial and temporal resolution required. However, important advances have been made recently by combining information from real-time in situ sensors such as continuous GPS, strainmeters, and tiltmeters with repeated campaign-style GPS, microgravity, interferometric synthetic aperture radar (InSAR), lidar, and photogrammetric observations. Continuous real-time data constrain the timing but not necessarily the spatial extent and pattern of deformation, whereas InSAR provides detailed spatial information but only at intervals of several days to weeks. Repeated microgravity surveys, when combined with independent measurements of surface height, are uniquely sensitive to changes in subsurface mass distribution and therefore can be used to distinguish among processes driven by magma, hydrous fluids, or gas. Simultaneous analysis of multiple geodetic datasets, especially when guided by information from global volcano databases and numerical simulations of volcanic processes and products, can provide a statistical basis for outcome prediction and serve as a guide for additional observations. In the foreseeable future, interactions among magmatic, tectonic, and hydrothermal systems will be monitored and modeled at regional scale in real time, enabling new insights into Earth’s subsurface environment.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Encyclopedia of Complexity and Systems Science","largerWorkSubtype":{"id":13,"text":"Handbook"},"language":"English","publisher":"Springer","doi":"10.1007/978-3-642-27737-5_635-1","usgsCitation":"Dzurisin, D., 2019, Volcano deformation: Insights into magmatic systems, chap. of Encyclopedia of Complexity and Systems Science, 15 p., https://doi.org/10.1007/978-3-642-27737-5_635-1.","productDescription":"15 p.","ipdsId":"IP-063922","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":366311,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2019-05-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Dzurisin, Daniel 0000-0002-0138-5067 dzurisin@usgs.gov","orcid":"https://orcid.org/0000-0002-0138-5067","contributorId":217868,"corporation":false,"usgs":true,"family":"Dzurisin","given":"Daniel","email":"dzurisin@usgs.gov","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":767707,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70203352,"text":"fs20193029 - 2019 - The 3D Elevation Program—Supporting California's Economy","interactions":[],"lastModifiedDate":"2019-06-25T13:17:47","indexId":"fs20193029","displayToPublicDate":"2019-05-16T15:30:00","publicationYear":"2019","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":"2019-3029","displayTitle":"The 3D Elevation Program—Supporting California's Economy","title":"The 3D Elevation Program—Supporting California's Economy","docAbstract":"California faces unprecedented challenges presented by shifting weather patterns that are defining a “new normal.” The result has been extreme weather events, prolonged drought, flooding, and debris flows. These conditions drive severe tree mortality, increase wildfire occurrence and intensity, reduce water availability, and hasten subsidence in groundwater basins. Collectively, these challenges threaten public safety, compromise infrastructure, and adversely impact the economic well-being of California's citizens. Critical applications that address these issues depend on light detection and ranging (lidar) data that provide a highly detailed, three-dimensional (3D) model of the Earth’s surface. The U.S. Geological Survey 3D Elevation Program works in partnership with Federal, State, Tribal, U.S. territorial, and local agencies to acquire consistent lidar coverage to meet the needs of California and the Nation.
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\"}}]}","edition":"Version 1.1; Revised June 24, 2019","contact":"Director, National Geospatial Program
U.S. Geological Survey, MS 511
12201 Sunrise Valley Drive
Reston, VA 20192
3D Elevation Program
Email: 3DEP@usgs.gov
This report presents revised results for four parameters reported for suspended-sediment samples that were collected in the lower Mississippi-Atchafalaya River Basin as part of a cooperative program between the U.S. Army Corps of Engineers, Mississippi Valley Division, New Orleans District and the U.S. Geological Survey (USGS). The cooperative program has been active since 1973 at seven sites: two sites on the main stem of the Mississippi River, three sites on the Atchafalaya River, one site on the Old River Outflow Channel, and one site on the lower Red River above the confluence with the Old River Outflow Channel. The four parameters—suspended-sediment concentration, percent by mass of the sediment that passes through a 0.063-millimeter (US 230) sieve, instantaneous stream discharge, and instantaneous suspended-sediment discharge—reported for 2,895 samples have been modified to reflect the findings of a full review of the cooperative program, which was initiated by both agencies in January 2015. The revised results are for samples collected from October 1989 through February 2015. Ninety-four percent of the revised values for suspended-sediment concentration are lower than their corresponding original reported values, indicating that less suspended sediment moves through the lower Mississippi River system than was previously reported. For example, the median revised instantaneous suspended-sediment discharge at the Mississippi River at Tarbert Landing, Miss. (USGS station 07295100), was 315,000 short tons per day, compared to 378,000 short tons per day as originally reported. At the Atchafalaya River at Simmesport, La. (USGS station 07381490), the median revised suspended-sediment discharge was 105,000 short tons per day, compared to 143,000 short tons per day as originally reported. The systematic downward revision in instantaneous suspended-sediment discharge values was due to a systematic downward revision in the suspended fine (less than 0.063 millimeter) sediment concentration. The effect of the revision on the suspended-sand concentration and instantaneous suspended-sand discharge was weaker. Any model of sediment load or transport processes in the basin that uses data from the affected samples should be reevaluated on the basis of the revised results.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185147","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, New Orleans District","usgsCitation":"Norton, K.K., Olsen, L.D., Baumann, T.E., Simmons, L.B., Clark, A.P., Demcheck, D.K., and Johnson, M., 2019, Revisions to suspended-sediment concentration, percent smaller than 0.063 millimeter, and instantaneous suspended-sediment discharge reported for a cooperative program between the U.S. Geological Survey and the U.S. Army Corps of Engineers in the lower Mississippi-Atchafalaya River Basin, October 1989 to February 2015: U.S. Geological Survey Scientific Investigations Report 2018–5147, 232 p., https://doi.org/10.3133/sir20185147.","productDescription":"Report: x, 232 p.; Data Release","numberOfPages":"246","onlineOnly":"N","ipdsId":"IP-088529","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":363738,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5147/coverthb.jpg"},{"id":363739,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5147/sir20185147.pdf","text":"Report","size":"7.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5147"},{"id":363740,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7K936GW","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Revised Data and Supporting Information for Seven Sites located on the Lower Mississippi and Atchafalaya Rivers sampled as part of a cooperative sediment program with the U.S. Army Corps of Engineers, October 1989 through February 2015"}],"country":"United States","otherGeospatial":"Lower Mississippi basin","geographicExtents":"{ \"type\": \"FeatureCollection\", \"features\": [ { \"type\": \"Feature\", \"properties\": {}, \"geometry\": { \"type\": \"Polygon\", \"coordinates\": [ [ [ -91.830936,29.164113 ], [ -91.830936,32.428085 ], [ -89.918735,32.428085 ], [ -89.918735,29.164113 ], [ -91.830936,29.164113 ] ] ] } } ] }","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park Drive
Nashville, TN 37211