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.
Modern microbialites are often located within groundwater discharge zones, yet the role of groundwater in microbialite accretion has yet to be resolved. To understand relationships between groundwater, microbialites, and associated microbial communities, we quantified and characterized groundwater flow and chemistry in active thrombolitic microbialites in Lake Clifton, Western Australia, and compared these observations to inactive thrombolites and lakebed sediments. Groundwater flows upward through an interconnected network of pores within the microstructure of active thrombolites, discharging directly from thrombolite heads into the lake. This upwelling groundwater is fresher than lake water and is hypothesized to support microbial mat growth by reducing salinity and providing limiting nutrients in an osmotically stressful and oligotrophic habitat. This is in contrast to inactive thrombolites that show no evidence of microbial mat colonization and are infiltrated by hypersaline lake water. Groundwater discharge through active thrombolites contrasts with the surrounding lakebed, where hypersaline lake water flows downward through sandy sediments at very low rates. Based on an appreciation for the role of microorganisms in thrombolite accretion, our findings suggest conditions favorable to thrombolite formation still exist in certain locations of Lake Clifton despite increasing lake water salinity. This study is the first to characterize groundwater flow rates, paths, and chemistry within a microbialite‐forming environment and provides new insight into how groundwater can support microbial mats believed to contribute to microbialite formation in modern and ancient environments.
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.
Simulation models are often used to identify hydrologic regimes suitable for different riparian or floodplain tree species. However, most existing models pay little attention to forest successional processes or other disturbances that may interact with the hydrologic regime of river systems to alter forest dynamics in space and time. In this study, we introduce a flood disturbance module to the LANDIS-II forest succession modelling framework to enable investigations into how inundation interacts with other disturbances and successional processes to alter floodplain forest cover and community dynamics. We illustrate the functionality of the model using a case study with multiple scenarios in the Upper Mississippi River floodplain, USA. We found that model predictions of total forest cover and the abundance of specific forest community types were generally related to uncertainty in the susceptibility of different species and age classes to inundation. By simulation year 100, increases or decreases in total forest cover and forest type distributions were roughly proportional to the initial differences in the susceptibility of species and age classes to inundation. The largest decrease in total forest cover was associated with a scenario that included disturbance by the emerald ash borer (Agrilus planipennis) and when using susceptibility parameters corresponding to the weakest flood tolerance. In contrast, changes in the composition of aboveground biomass were not sensitive to differences in susceptibility, and generally showed shifts toward later successional species with higher shade tolerance and longer lifespans for all scenarios. Our findings suggest that flood inundation interacts with other disturbances (e.g., insect outbreaks) and forest successional processes to alter forest abundance, distribution, and species composition in this system. Our modelling framework should allow for future studies that examine such interactions in other systems, and in the context of alternative hydrologic scenarios and other disturbance regimes.
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":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":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"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
The Northwest Canada Interior Basins Province is bounded by the Mackenzie and Richardson Mountains on the southwest and west, by the Eskimo Lakes Arch on the northwest, and by the erosional limit of Paleozoic strata on the east. It lies within the far northwest part of the Paleozoic continent of Laurentia. During early Paleozoic time, it was part of a passive margin formed when the Neoproterozoic supercontinent Rodinia broke apart. A Cambrian marine transgressive sequence gave way to an evaporitic intrashelf basin, succeeded by a westward-building carbonate bank from Late Cambrian through Middle Devonian time. In Late Devonian and early Carboniferous time, the region was buried by a thick succession of south-prograding clastic strata derived from orogenic belts to the northeast and north. A subsequent period of inactivity and erosion persisted until sedimentation resumed as the opening of the Canada Basin initiated an Early Cretaceous marine transgression over much of the region. Later in Cretaceous time, clastic strata derived from Cordilleran uplifts to the southwest began to prograde north, ending with eventual Laramide uplift and deformation in latest Cretaceous and Paleogene time.
Two petroleum systems are known within the province. A petroleum system in Cambrian to Middle Devonian strata, sourced by alginitic Cambrian shales and sealed by Cambrian evaporites, is proven by modest gas discoveries in the Colville Hills; generation is attributed to burial under the Upper Devonian clastic wedge. An Upper Devonian petroleum system, proven by the presence of the 250-million-barrel Norman Wells field at the southern edge of the province, was sourced by organic-rich shale of the Canol Formation. Generation is similarly inferred to have been driven by burial beneath the Devonian clastic wedge, perhaps augmented locally by additional Cretaceous burial. Potential reservoirs include Devonian reefs and sandstones stratigraphically below and laterally equivalent to the source rocks, as well as overlying sandstones in the clastic wedge. Subordinate source rocks could include organic-rich shales within the clastic wedge.
Principal risks to the lower Paleozoic petroleum system include (1) inadequate reservoir volume for a field of the minimum size and (2) petroleum loss by remigration caused by Laramide deformation. One lower Paleozoic assessment unit (AU) was quantitatively assessed, with estimated resources of 0 to 117 million barrels of oil (MMBO), mean 23 MMBO; and 0 to 1,364 billion cubic feet of gas (BCFG), mean 310 BCFG. The principle risks to the Devonian petroleum system were considered to be (1) lack of preservation due to extensive erosion before the Cretaceous, and (2) inadequate reservoir volume, because the best potential reservoir strata, Devonian reefs, may be absent throughout most of the province owing to either lack of deposition or erosion. These risks were sufficiently high that the single AU defined in the Devonian petroleum system was not quantitatively assessed, because the chance of a field of the minimum size, 50 million barrels of oil equivalent, was estimated to be only 0.1.
Contact Information, Geology, Minerals, Energy, & Geophysics Science Center—Menlo Park
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025-3591
FAX 650-329-4936
This chapter discusses the role of seed-vectored microbes in modulating seedling development and increasing fitness of plants in terms of increased biotic and abiotic stress tolerance.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":" Seed endophytes: Biology and biotechnology.","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer ","doi":"10.1007/978-3-030-10504-4","usgsCitation":"White, J.F., Kingsley, K.L., Butterworth, S., Brindisi, L., Gatei, J.W., Elmore, M.T., Verma, S.K., Yao, X., and Kowalski, K., 2019, Seed-vectored microbes: Their roles in improving seedling fitness and competitor plant suppression, chap. of Seed endophytes: Biology and biotechnology., p. 3-20, https://doi.org/10.1007/978-3-030-10504-4.","productDescription":"18 p.","startPage":"3","endPage":"20","ipdsId":"IP-101008","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":363810,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":15,"text":"Madison PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"White, James F.","contributorId":214321,"corporation":false,"usgs":false,"family":"White","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759630,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kingsley, Kathryn L.","contributorId":203176,"corporation":false,"usgs":false,"family":"Kingsley","given":"Kathryn","email":"","middleInitial":"L.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759631,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Butterworth, Susan","contributorId":214322,"corporation":false,"usgs":false,"family":"Butterworth","given":"Susan","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759632,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Brindisi, Lara","contributorId":214323,"corporation":false,"usgs":false,"family":"Brindisi","given":"Lara","email":"","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759633,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Gatei, Judy W","contributorId":214324,"corporation":false,"usgs":false,"family":"Gatei","given":"Judy","email":"","middleInitial":"W","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759634,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Elmore, Matthew T.","contributorId":206820,"corporation":false,"usgs":false,"family":"Elmore","given":"Matthew","email":"","middleInitial":"T.","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759635,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Verma, Satish Kumar","contributorId":203175,"corporation":false,"usgs":false,"family":"Verma","given":"Satish","email":"","middleInitial":"Kumar","affiliations":[{"id":12727,"text":"Rutgers University","active":true,"usgs":false}],"preferred":false,"id":759636,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Yao, Xiang","contributorId":214325,"corporation":false,"usgs":false,"family":"Yao","given":"Xiang","email":"","affiliations":[{"id":39011,"text":"Lanzhou University","active":true,"usgs":false}],"preferred":false,"id":759637,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kowalski, Kurt P. 0000-0002-8424-4701 kkowalski@usgs.gov","orcid":"https://orcid.org/0000-0002-8424-4701","contributorId":3768,"corporation":false,"usgs":true,"family":"Kowalski","given":"Kurt P.","email":"kkowalski@usgs.gov","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":759629,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70223346,"text":"70223346 - 2019 - Mechanisms underlying increased nest predation in natural gas fields: a test of the mesopredator release hypothesis","interactions":[],"lastModifiedDate":"2021-08-24T12:59:59.567114","indexId":"70223346","displayToPublicDate":"2019-05-15T07:58:03","publicationYear":"2019","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1475,"text":"Ecosphere","active":true,"publicationSubtype":{"id":10}},"title":"Mechanisms underlying increased nest predation in natural gas fields: a test of the mesopredator release hypothesis","docAbstract":"Anthropogenic activities are changing landscapes and the context in which predator–prey dynamics evolved, thereby altering key ecological processes and community structure. Yet, the specific mechanisms underlying such changes are rarely understood. We tested whether a mesopredator release explained increased rodent density and concomitant predation of songbird nests near natural gas development. From 2015 to 2016, we surveyed apex predators (coyotes, badgers, raptors, and corvids) and measured apparent survival and perceived predation risk of deer mice (Peromyscus maniculatus; a primary nest predator), at 12 plots spanning a gradient of surface disturbance caused by energy development in Wyoming, USA. Additionally, we measured densities of three nest predators: deer mice, least chipmunks (Tamias minimus), and thirteen-lined ground squirrels (Ictidomys tridecemlineatus). Contrary to the mesopredator release hypothesis, counts of apex predators and perceived predation risk of deer mice increased with surface disturbance from energy development, whereas apparent survival of mice decreased. Densities of mice and ground squirrels, however, increased with surface disturbance, despite increased predation pressure. We therefore rejected the mesopredator release hypothesis as a potential mechanism underlying altered trophic dynamics near energy development. Our results suggest that apex predator control measures would not benefit declining songbirds on natural gas fields. Rather, apex predator abundance may be regulated from the bottom-up by rodents in this system. Our results corroborate a pattern showing weakened effects of mesopredator release in habitats modified by humans. Understanding how predator–prey dynamics may be altered in novel environments requires an understanding of how predators and prey alike respond to habitat change under different contexts.
Atoll islands’ alongshore sediment transport gradients depend on how island and reef morphology affect incident wave energy. It is unclear, though, how potential atoll morphologic configurations influence shoreline erosion and/or accretion patterns, and how these relationships will respond to future sea-level rise (SLR). Schematic atoll models with varying morphologies were used to evaluate the relative control of individual morphological parameters on alongshore transport gradients. Incident wave transformations were simulated using a physics-based numerical model and alongshore erosion and accretion was calculated using empirical formulae. The magnitude of the transport gradients increased with SLR: initial erosion or accretion patterns intensified. Modeled morphologic parameters that significantly influenced alongshore transport were the atoll diameter, reef flat width, reef flat depth, and island width. Modeled atolls with comparably small diameters, narrow and deep reef flats with narrow islands displayed greater magnitudes of erosion and/or accretion, especially with SLR. Windward island shorelines are projected to accrete toward the island’s longitudinal ends and lagoon due to SLR, whereas leeward islands erode along lagoon shorelines and extend toward the island ends. Oblique island, oriented parallel to the incident deepwater wave direction, shorelines are forecast to build out leeward along the reef rim and toward the lagoon while eroding along regions exposed to direct wave attack. These findings make it possible to evaluate the relative risk of alongshore erosion/accretion on atolls due to SLR in a rapid, first-order analysis.
Choices in ecological research and natural resource management require balancing multiple, often competing objectives. Examples include maximizing species persistence in a wildlife conservation context, while minimizing cost, or balancing opposing stakeholder objectives when managing wildlife populations. Multiple-objective optimization (MOO) provides a unifying framework for solving multiple objective problems. Model selection is a critical component of ecological inference and prediction and requires balancing the competing objectives of model fit and model complexity. The tradeoff between model fit and model complexity provides a basis for describing the model-selection problem within the MOO framework. We discuss MOO and two strategies for solving the MOO problem; modeling preferences pre-optimization and post-optimization. Most conventional model selection methods can be formulated as solutions of MOO problems via specification of pre-optimization preferences. We reconcile model selection within the MOO framework. We also consider model selection using post-optimization specification of preferences. That is, by first identifying Pareto optimal solutions, and then selecting among them. We demonstrate concepts with an ecological application of model selection using avian species richness data in the continental United States.
Understanding changes in wave attenuation by emergent vegetation as wetlands degrade or accrete over time is crucial for incorporation of wetlands into holistic coastal risk management. Linked SLAMM and XBeach models were used to investigate potential future changes in wave attenuation over a 50-year period in a degrading, subtropical wetland and a prograding, temperate wetland. These contrasting systems also have differing management contexts and were contrasted to demonstrate how the linked models can provide management-relevant insights. Morphological development of wetlands for different scenarios of sea-level rise and accretion was simulated with SLAMM and then coupled with different vegetation characteristics to predict the influence on future wave attenuation using XBeach. The geomorphological context, subsidence, and accretion resulted in large predicted reductions in the extent of vegetated land (e.g., wetland) and changes in wave height reduction potential across the wetland. These were exacerbated by increases in sea-level from +0.217 m to +0.386 m over a 50-year period, especially at the lowest accretion rates in the degrading wetland. Mangrove vegetation increased wave attenuation within the degrading, subtropical, saline wetland, while grazing reduced wave attenuation in the temperate, prograding wetland. Coastal management decisions and actions, related to coastal vegetation type and structure, have the potential to change future wave attenuation at a spatial scale relevant to coastal protection planning. Therefore, a coastal management approach that includes disaster risk reduction, biodiversity, and climate change, can be informed by coastal modeling tools, such as those demonstrated here for two contrasting case studies.