From December 2 to 4, 2015, NatureServe and the U.S. Geological Survey organized and hosted a biodiversity and ecological informatics workshop at the U.S. Department of the Interior in Washington, D.C. The workshop objective was to identify user-driven future directions and areas of collaboration in advanced applications of environmental data applied to forecasting and decision making for the sustainability of biodiversity and ecosystem services. Substantial effort to recruit attendees from diverse Federal, State, and private sector organizations successfully attracted participants from 20 Federal agencies and 48 different institutions in the academic, nonprofit, State government, and commercial sectors; the total number of attendees ranged from 100 to 144 during the 3-day workshop. The first one-half of the workshop was divided into 7 plenary sessions and 3 sets of lightning talk sessions organized by sector, providing 48 oral and visual plenary presentations that shared diverse perspectives on biodiversity and ecological informatics, including original biospatial analyses from 6 graduate student map contest winners. The second one-half of the workshop focused on 10 breakout sessions with participant-driven themes from the environmental data sphere and concluded with an address by the Director of the U.S. Fish and Wildlife Service. The workshop was structured to encourage interactivity. About 80–90 percent of attendees provided direct feedback using clicker devices for specific questions related to biodiversity and ecological data uses and needs, and 10 breakout session leaders shared the highlights of their group discussions during the final workshop plenary sessions. Participants were encouraged to use the Twitter hashtag #ShareUrData. Over lunch on day 2 there were 20 simultaneous presentations of tools and apps during a special “Tools Café” session.
The 10 participant-defined breakout session topics are listed below:
Numerous common themes that emerged from the workshop include the following:
Director, Core Science Analytics Synthesis and Libraries Program
U.S. Geological Survey
W 6th Ave Kipling Street
Lakewood, CO 80225
Rapid population growth and declining annual recharge to aquifers in the upper Santa Cruz Basin area of southern Arizona, have increased the demand for additional groundwater resources. This demand is predicted to escalate in the future because of higher temperatures, longer droughts, less aquifer recharge, and decreased river and stream base flow. We conducted geologic studies to help evaluate and better understand groundwater resources in the basin. Results of these studies are presented in this report, which summarizes the basin geologic framework and hydrogeology, and presents a threedimensional (3D) hydrogeologic model for the Rio Rico and Nogales 7.5′ quadrangles. Three major hydrogeologic units are displayed in the 3D model; a lower basement confining unit, consisting of Jurassic, Cretaceous, and Tertiary (Paleocene and Oligocene) rocks; a middle unit composed entirely of the Miocene Nogales Formation; and an upper unit consisting of late Miocene to Holocene surficial deposits. The Nogales Formation and the late Miocene to Holocene sediments are the main aquifers in the upper Santa Cruz Basin. The 3D model integrates the hydrogeologic units and faults to define the geometry, structure, and thickness of the aquifer system that provides water to Nogales and surrounding communities of southernmost Arizona. The report includes an EarthVision 3D Viewer, consisting of software enabling the user to view data interactively in 3D space to help explain the internal complexities of the basin geometry, structure, stratigraphy, and hydrology. The 3D model is a synthesis of geologic data from geologic maps, cross sections, and lithologic descriptions and interpretations; and geophysical data including gravity, magnetic data, and airborne electromagnetic data.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20185062","usgsCitation":"Page, W.R., Bultman, M.W., VanSistine, D.P., Menges, C.M., Gray, Floyd, and Pantea, M.P., 2018, Geologic framework and hydrogeology of the Rio Rico and Nogales 7.5’ quadrangles, upper Santa Cruz Basin, Arizona, with three-dimensional hydrogeologic model: U.S. Geological Survey Scientific Investigations Report 2018–5062, 34 p., https://doi.org/10.3133/sir20185062.","productDescription":"Report: vi, 34 p.; Data release","onlineOnly":"Y","ipdsId":"IP-085666","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":356167,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2018/5062/coverthb.jpg"},{"id":356184,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7QJ7GHT","text":"USGS data release","linkHelpText":"Data Release for Geologic Framework and Hydrogeology of the Rico Rico and Nogales 7.5' quadrangles, Upper Santa Cruz basin, Arizona, with 3-Dimensional hydrogeologic model"},{"id":356168,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2018/5062/sir20185062.pdf","text":"Report","size":"23.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2018–5062"}],"country":"United States","state":"Arizona","otherGeospatial":"Rio Rico and Nogales 7.5’ Quadrangles, Upper Santa Cruz Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111,\n 31.33\n ],\n [\n -110.875,\n 31.33\n ],\n [\n -110.875,\n 31.5\n ],\n [\n -111,\n 31.5\n ],\n [\n -111,\n 31.33\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Geosciences and Environmental Change Science Center
U.S. Geological Survey
Box 25046, MS 980
Denver, CO 80225
Expanding human enterprise across remote environments impacts numerous wildlife species. Anthropogenic resources provide subsidies for generalist predators that can lead to cascading effects on prey species at lower trophic levels. A fundamental challenge for applied ecologists is to disentangle natural and anthropogenic influences on species occurrence, and subsequently develop spatially explicit models to help inform management and conservation decisions.
Using Bayesian hierarchical occupancy models, we mapped the broad‐scale occurrence of common ravens Corvus corax as a function of natural and anthropogenic landscape covariates using >15,000 point count surveys performed during 2007–2016 within the Great Basin region, USA. Raven abundance and distribution is substantially increasing across the American west due to unintended anthropogenic resource subsidies. Importantly, ravens prey on eggs and chicks of numerous species including greater sage‐grouse Centrocercus urophasianus, an indicator species whose decline is at the centre of national conservation strategies and land use policies.Anthropogenic factors that contributed to greater raven occurrence were: increased road density, presence of transmission lines, agricultural activity, and presence of roadside rest areas. Natural landscape characteristics included lower elevations with greener vegetation (NDVI), greater stream and habitat edge densities, and lower percentages of big sagebrush A. tridentata spp.
Interactions between anthropogenic sources of nesting substrate and food subsidies suggested that raven occurrence increased multiplicatively when these resource subsidies co‐occurred. Overall, the average probability of raven occurrence estimated within sagebrush ecosystems of the study area was ~0.83.
Synthesis and applications. We demonstrate how anthropogenic factors can be disentangled from natural effects when making spatially‐explicit predictions of subsidized predators occurring across expansive landscapes. This approach can guide management decisions where subsidized predators overlap sensitive prey habitats. For example, we identify areas where elevated raven occurrence coincides with breeding sage‐grouse concentration areas and appears to be largely driven by anthropogenic factors. Management applications could focus on reducing raven access to anthropogenic subsidies in these areas, while prioritizing habitat improvements for sage‐grouse elsewhere. Our approach is applicable to other species where widespread survey data are available.
","language":"English","publisher":"British Ecological Society","doi":"10.1111/1365-2664.13249","usgsCitation":"O’Neil, S.T., Coates, P.S., Brussee, B.E., Jackson, P.J., Howe, K., Moser, A.M., Foster, L.J., and Delehanty, D.J., 2018, Broad‐scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage‐grouse and other sensitive prey: Journal of Applied Ecology, v. 55, no. 6, p. 2641-2652, https://doi.org/10.1111/1365-2664.13249.","productDescription":"12 p.","startPage":"2641","endPage":"2652","ipdsId":"IP-097721","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":468542,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1111/1365-2664.13249","text":"Publisher Index Page"},{"id":437805,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P93ONIQT","text":"USGS data release","linkHelpText":"Data from: Broad-scale occurrence of a subsidized avian predator: reducing impacts of ravens on sage-grouse and other sensitive prey"},{"id":356082,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"55","issue":"6","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationDate":"2018-09-08","publicationStatus":"PW","scienceBaseUri":"5b6fc3ece4b0f5d57878e93b","contributors":{"authors":[{"text":"O’Neil, Shawn T.","contributorId":62533,"corporation":false,"usgs":true,"family":"O’Neil","given":"Shawn","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":741233,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Coates, Peter S. 0000-0003-2672-9994 pcoates@usgs.gov","orcid":"https://orcid.org/0000-0003-2672-9994","contributorId":3263,"corporation":false,"usgs":true,"family":"Coates","given":"Peter","email":"pcoates@usgs.gov","middleInitial":"S.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741232,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Brussee, Brianne E. 0000-0002-2452-7101 bbrussee@usgs.gov","orcid":"https://orcid.org/0000-0002-2452-7101","contributorId":4249,"corporation":false,"usgs":true,"family":"Brussee","given":"Brianne","email":"bbrussee@usgs.gov","middleInitial":"E.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":741234,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jackson, Pat J.","contributorId":206602,"corporation":false,"usgs":false,"family":"Jackson","given":"Pat","email":"","middleInitial":"J.","affiliations":[{"id":27489,"text":"Nevada Department of Wildlife","active":true,"usgs":false}],"preferred":false,"id":741235,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Howe, Kristy B.","contributorId":192078,"corporation":false,"usgs":false,"family":"Howe","given":"Kristy B.","affiliations":[],"preferred":false,"id":741236,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Moser, Ann M.","contributorId":206592,"corporation":false,"usgs":false,"family":"Moser","given":"Ann","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":741237,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Foster, Lee J.","contributorId":201654,"corporation":false,"usgs":false,"family":"Foster","given":"Lee","email":"","middleInitial":"J.","affiliations":[{"id":36223,"text":"Oregon Department of Fish and Wildlife","active":true,"usgs":false}],"preferred":false,"id":741238,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Delehanty, David J.","contributorId":195584,"corporation":false,"usgs":false,"family":"Delehanty","given":"David","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":741239,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70196119,"text":"sir20165074 - 2018 - UFINCH: A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages","interactions":[],"lastModifiedDate":"2018-07-26T14:54:27","indexId":"sir20165074","displayToPublicDate":"2018-07-26T11:30:00","publicationYear":"2018","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5074","title":"UFINCH: A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages","docAbstract":"The UFINCH (Unit Flows In Networks of Channels) computer application can be used to simulate daily and unit flows in networks of streams based on geospatial data in the National Hydrography Dataset NHDPlus (with value added attributes), and U.S. Geoogical Survey daily streamflow data from a downstream (or base) streamgage. Among streamflow augmentation methods, UFINCH has the unique capability to estimate time series of flows from a single base (downstream) streamgage to many upstream reaches, while conserving flows within the basin. UFINCH also provides a simple statistical model to adjust simulated flows to better match continuous flows from data at an upstream streamgage. Parameters of the statistical model are estimated using overlapping periods of record at the two streamgages, but the adjustment can be applied to all years of record available at the base streamgage. This report describes the main features of UFINCH and presents results from a sample application. Interactive graphical user interfaces and automated geographical information processing facilitate flow-data retrievals provide an intuitive environment for efficient and effective generation of flow information in a network. UFINCH is coded in the Matlab programming language and can be run in the Matlab programming environment, with supporting statistical, optimization, and mapping toolboxes, or from compiled code on a Microsoft Windows computer.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165074","collaboration":"National Water Quality Program","usgsCitation":"Holtschlag, D.J., 2018, UFINCH—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages: U.S. Geological Survey Scientific Investigations Report 2016-5074, 17 p., https://doi.org/10.3133/sir20165074.","productDescription":"Report: iv, 17 p.; Data release","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-073436","costCenters":[{"id":382,"text":"Michigan Water Science Center","active":true,"usgs":true}],"links":[{"id":355884,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2016/5074/sir20165074.pdf","text":"Report","size":"21.7 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2016-5074"},{"id":355883,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2016/5074/coverthb.jpg"},{"id":355885,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7319TC5","text":"USGS data release","description":"USGS data release","linkHelpText":"Code, data, executables, and other information used to run Unit Flows in Networks of Channels (UFINCH)—A method for simulating unit and daily flows in networks of channels described by NHDPlus using continuous flow data at U.S. Geological Survey streamgages"}],"contact":"Upper Midwest Water Science Center
U.S. Geological Survey
6520 Mercantile Way
Suite 5
Lansing, MI 48911
The Long Island South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy (CWRMS) provides an overview of the water-quality and ecological monitoring within the Reserve and presents suggestions from stakeholders for future data collection, data management, and coordination among monitoring programs. The South Shore Estuary Reserve, hereafter referred to as the Reserve, is a 173-square-mile network of bays and tributaries shaped by the south shore of Long Island (New York) and the barrier islands that was formed as a result of the last ice age (roughly 18,000 years ago). This overview and coordination document is based on information assembled from a series of meetings, a workshop, and individual correspondences with the CWRMS Project Advisory Committee, which was formed in 2015 to help guide the creation of the document, which reflects the current (2017) status of the Reserve and the need for additional data to address its water-quality issues and ecological health and to respond to a changing climate. The U.S. Geological Survey (USGS), in cooperation with the New York State Department of State Office of Planning, Development and Community Infrastructure and the South Shore Estuary Reserve Office, compiled information and recommendations to help stakeholders efficiently evaluate waters currently being monitored and address areas where necessary data are lacking. Water-quality monitoring in the Reserve is ongoing on the Federal, State, and local levels, and coordination among the various programs administered by the U.S. Environmental Protection Agency; National Oceanic and Atmospheric Administration; USGS; Shinnecock Tribal Nation; New York State; Nassau and Suffolk Counties; the Towns of Hempstead, Oyster Bay, Babylon, Islip, Brookhaven, and Southampton; and local universities and nonprofit organizations is necessary to ensure cooperation and efficient use of limited resources. Proper collection and archival of data are critical to the usability of data and methods—a sample of available repositories for monitoring data are provided in this report. Equally important are quality assurances of data and proper techniques of archival such that water and ecological data are collected and analyzed in a consistent manner, regardless of their sources, and that differences in methodologies are identified that might result in discrepancies in the compiled data. Details on monitoring programs, data gaps that are perceived by stakeholders and researchers in the area, and Project Advisory Committee recommendations are provided in this report to promote discussion and coordination. In most cases, resources to fill data gaps are needed, and the use of citizen science volunteers has been shown to help extend programs and provide insight into previously unaddressed areas of concern. This document, in conjunction with the CWRMS website and interactive mapper, is intended to inform the latest iteration of the Comprehensive Management Plan for the Reserve. Moreover, resource managers can use the CWRMS and mapper to identify areas of potential overlap and initiate conversations with stakeholders about addressing needs for additional monitoring of water quality and ecological health in the bays and tributaries of the Reserve.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171161","collaboration":"Prepared in cooperation with the New York State Department of State Office of Planning, Development and Community Infrastructure and the South Shore Estuary Reserve Office","usgsCitation":"Fisher, S.C., Welk, R.J., and Finkelstein, J.S., 2018, Long Island South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy: U.S. Geological Survey Open-File Report 2017–1161, 105 p., https://doi.org/10.3133/ofr20171161.","productDescription":"xi, 105 p.","numberOfPages":"122","onlineOnly":"Y","additionalOnlineFiles":"N","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":352790,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1161/coverthb.jpg"},{"id":352791,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1161/ofr20171161.pdf","text":"Report","size":"3.33 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1161"},{"id":355586,"rank":3,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://ny.water.usgs.gov/maps/sser/","linkHelpText":"- South Shore Estuary Reserve Coordinated Water Resources Monitoring Strategy mapper"}],"country":"United States","state":"New York","otherGeospatial":"Long Island, South Shore Estuary Reserve","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -74.3939208984375,\n 40.27533480732468\n ],\n [\n -71.47705078125,\n 40.27533480732468\n ],\n [\n -71.47705078125,\n 41.422134246213616\n ],\n [\n -74.3939208984375,\n 41.422134246213616\n ],\n [\n -74.3939208984375,\n 40.27533480732468\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
U.S. Geological Survey
2045 Route 112, Building 4
Coram, NY 11727
Questions
What drives individual species–area relationships in temperate coniferous forests?
Location
Two 25.6‐ha forest plots on the Pacific Slope of North America, one in California, and one in Washington State.
Methods
We mapped all trees ≥1 cm in diameter and examined tree species diversity of their local neighbourhoods by calculating the individual species–area relationship for each species and for each of three diameter classes (saplings, mature trees and large‐diameter trees).
Results
In the California plot, small trees in four of the five major species occurred in neighbourhoods with higher levels of diversity than would be expected at random. In the Washington plot, small trees for four of five abundant species had neighbourhoods with lower than expected diversity at distances ≤5 m for small trees. However, at distances >5 m, all five species showed higher than expected diversity in their neighbourhoods. Larger trees at both plots tended to occur in neighbourhoods with lower than expected diversity, and no large‐diameter focal species had neighbourhoods with higher than expected diversity.
Conclusion
Diversity and co‐existence in temperate conifer‐dominated forests do not appear to be the result of random processes. Competitive interactions appear to dominate for the largest trees of most species, resulting in neighbourhoods with lower diversity. For smaller trees, we suggest that a positive response to environmental heterogeneity is the likely driver of neighbourhoods with higher than expected diversity, although we cannot rule out the possibility that facilitation or conspecific negative density dependence (CNDD) also play a role.
The Geospatial Semantic Web (GSW) is an emerging technology that uses the Internet for more effective knowledge engineering and information extraction. Among the aims of the GSW are to structure the semantic specifications of data to reduce ambiguity and to link those data more efficiently. The data are stored as triples, the basic data unit in graph databases, which are similar to the vector data model of geographic information systems (GIS); that is, a node-edge-node model that forms a graph of semantically related information. The GSW is supported by emerging technologies such as linked geospatial data, described below, that enable it to store and manage geographical data that require new cartographic methods for visualization. This report describes a map that can interact with linked geospatial data using a simulation of a data query approach called the browsable graph to find information that is semantically related to a subject of interest, visualized using the Data Driven Documents (D3) library. Such a semantically enabled map functions as a map knowledge base (MKB) (Varanka and Usery, 2017).
A MKB differs from a database in an important way. The central element of a triple, alternatively called the edge or property, is composed of a logic formalization that structures the relation between the first and third parts, the nodes or objects. Node-edge-node represents the graphic form of the triple, and the subject-property-object terms represent the data structure. Object classes connect to build a federated graph, similar to a network in visual form. Because the triple property is a logical statement (a predicate), the data graph represents logical propositions or assertions accepted to be true about the subject matter. These logical formalizations can be manipulated to calculate new triples, representing inferred logical assertions, from the existing data.
To demonstrate a MKB system, a technical proof-of-concept is developed that uses geographically attributed Resource Description Framework (RDF) serializations of linked data for mapping. The proof-of-concept focuses on accessing triple data from visual elements of a geographic map as the interface to the MKB. The map interface is embedded with other essential functions such as SPARQL Protocol and RDF Query Language (SPARQL) data query endpoint services and reasoning capabilities of Apache Marmotta (Apache Software Foundation, 2017). An RDF database of the Geographic Names Information System (GNIS), which contains official names of domestic feature in the United States, was linked to a county data layer from The National Map of the U.S. Geological Survey. The county data are part of a broader Government Units theme offered to the public as Esri shapefiles. The shapefile used to draw the map itself was converted to a geographic-oriented JavaScript Object Notation (JSON) (GeoJSON) format and linked through various properties with a linked geodata version of the GNIS database called “GNIS–LD” (Butler and others, 2016; B. Regalia and others, University of California-Santa Barbara, written commun., 2017). The GNIS–LD files originated in Terse RDF Triple Language (Turtle) format but were converted to a JSON format specialized in linked data, “JSON–LD” (Beckett and Berners-Lee, 2011; Sorny and others, 2014). The GNIS–LD database is composed of roughly three predominant triple data graphs: Features, Names, and History. The graphs include a set of namespace prefixes used by each of the attributes. Predefining the prefixes made the conversion to the JSON–LD format simple to complete because Turtle and JSON–LD are variant specifications of the basic RDF concept.
To convert a shapefile into GeoJSON format to capture the geospatial coordinate geometry objects, an online converter, Mapshaper, was used (Bloch, 2013). To convert the Turtle files, a custom converter written in Java reconstructs the files by parsing each grouping of attributes belonging to one subject and pasting the data into a new file that follows the syntax of JSON–LD. Additionally, the Features file contained its own set of geometries, which was exported into a separate JSON–LD file along with its elevation value to form a fourth file, named “features-geo.json.” Extracted data from external files can be represented in HyperText Markup Language (HTML) path objects. The goal was to import multiple JSON–LD files using this approach.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171150","usgsCitation":"Powell, L.J., and Varanka, D.E., 2018, A linked GeoData map for enabling information access: U.S. Geological Survey Open–File Report 2017–1150, 6 p, https://doi.org/10.3133/ofr20171150.","productDescription":"iv, 6 p.","numberOfPages":"14","onlineOnly":"Y","ipdsId":"IP-090452","costCenters":[{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"links":[{"id":350413,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1150/coverthb.jpg"},{"id":350414,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1150/ofr20171150.pdf","text":"Report","size":"376 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1150"}],"contact":"Director, National Geospatial Technical Operations Center
U.S. Geological Survey
1400 Independence Road
Rolla, MO 65401
Including stakeholders in environmental model building and analysis is an increasingly popular approach to understanding ecological change. This is because stakeholders often hold valuable knowledge about socio-environmental dynamics and collaborative forms of modeling produce important boundary objects used to collectively reason about environmental problems. Although the number of participatory modeling (PM) case studies and the number of researchers adopting these approaches has grown in recent years, the lack of standardized reporting and limited reproducibility have prevented PM's establishment and advancement as a cohesive field of study. We suggest a four-dimensional framework (4P) that includes reporting on dimensions of (1) the Purpose for selecting a PM approach (the why); (2) the Process by which the public was involved in model building or evaluation (the how); (3) the Partnerships formed (the who); and (4) the Products that resulted from these efforts (the what). We highlight four case studies that use common PM software-based approaches (fuzzy cognitive mapping, agent-based modeling, system dynamics, and participatory geospatial modeling) to understand human–environment interactions and the consequences of ecological changes, including bushmeat hunting in Tanzania and Cameroon, agricultural production and deforestation in Zambia, and groundwater management in India. We demonstrate how standardizing communication about PM case studies can lead to innovation and new insights about model-based reasoning in support of ecological policy development. We suggest that our 4P framework and reporting approach provides a way for new hypotheses to be identified and tested in the growing field of PM.
","language":"English","publisher":"Wiley","doi":"10.1002/eap.1627","usgsCitation":"Gray, S., Voinov, A., Paolisso, M., Jordan, R., BenDor, T., Bommel, P., Glynn, P.D., Hedelin, B., Hubacek, K., Introne, J., Kolagani, N., Laursen, B., Prell, C., Schmitt-Olabisi, L., Singer, A., Sterling, E.J., and Zellner, M., 2018, Purpose, processes, partnerships, and products: four Ps to advance participatory socio-environmental modeling: Ecological Applications, v. 28, no. 1, p. 46-61, https://doi.org/10.1002/eap.1627.","productDescription":"16 p.","startPage":"46","endPage":"61","ipdsId":"IP-077094","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469127,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.1002/eap.1627","text":"External Repository"},{"id":351378,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"28","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-12-08","publicationStatus":"PW","scienceBaseUri":"5a7d6ffde4b00f54eb2441a7","contributors":{"authors":[{"text":"Gray, Steven","contributorId":201912,"corporation":false,"usgs":false,"family":"Gray","given":"Steven","email":"","affiliations":[{"id":36290,"text":"Michigan State University, Department of Community Sustainability, Natural Resource Building 480 Wilson Road Room 151, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":727186,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Voinov, Alexey","contributorId":191330,"corporation":false,"usgs":false,"family":"Voinov","given":"Alexey","affiliations":[],"preferred":false,"id":727187,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Paolisso, Michael","contributorId":201913,"corporation":false,"usgs":false,"family":"Paolisso","given":"Michael","email":"","affiliations":[{"id":36291,"text":"University of Maryland, Department of Anthropology, College Park, Maryland 20742 USA","active":true,"usgs":false}],"preferred":false,"id":727188,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Jordan, Rebecca","contributorId":201914,"corporation":false,"usgs":false,"family":"Jordan","given":"Rebecca","email":"","affiliations":[{"id":36292,"text":"Rutgers University, Human Ecology & Ecology, Evolution and Natural Resources School of Environmental and Biological Sciences, 59 Lipman Drive, New Brunswick, NJ 08901","active":true,"usgs":false}],"preferred":false,"id":727189,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"BenDor, Todd","contributorId":201915,"corporation":false,"usgs":false,"family":"BenDor","given":"Todd","email":"","affiliations":[{"id":36293,"text":"University of North Carolina at Chapel Hill, Department of City and Regional Planning, New East Building, CB #3140, Chapel Hill, NC 27599","active":true,"usgs":false}],"preferred":false,"id":727190,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Bommel, Pierre","contributorId":201916,"corporation":false,"usgs":false,"family":"Bommel","given":"Pierre","email":"","affiliations":[{"id":36294,"text":"CIRAD, Green Research Unit, Montpellier, France & University of Costa Rica, San José, Costa Rica","active":true,"usgs":false}],"preferred":false,"id":727191,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Glynn, Pierre D. 0000-0001-8804-7003 pglynn@usgs.gov","orcid":"https://orcid.org/0000-0001-8804-7003","contributorId":2141,"corporation":false,"usgs":true,"family":"Glynn","given":"Pierre","email":"pglynn@usgs.gov","middleInitial":"D.","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":727185,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hedelin, Beatrice","contributorId":201917,"corporation":false,"usgs":false,"family":"Hedelin","given":"Beatrice","email":"","affiliations":[{"id":36295,"text":"Karlstad University, Centre for Climate and Safety, Department of Environmental and Life Sciences, SE-651 88 Karlstad, Sweden","active":true,"usgs":false}],"preferred":false,"id":727192,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Hubacek, Klaus","contributorId":201918,"corporation":false,"usgs":false,"family":"Hubacek","given":"Klaus","email":"","affiliations":[{"id":36296,"text":"University of Maryland, Department of Geographical Sciences, College Park, MD, 20742 USA","active":true,"usgs":false}],"preferred":false,"id":727193,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Introne, Josh","contributorId":201919,"corporation":false,"usgs":false,"family":"Introne","given":"Josh","email":"","affiliations":[{"id":36297,"text":"Michigan State University, Department of Media and Information, 404 Wilson Road, Room 417, University East Lansing MI 48824","active":true,"usgs":false}],"preferred":false,"id":727194,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Kolagani, Nagesh","contributorId":191331,"corporation":false,"usgs":false,"family":"Kolagani","given":"Nagesh","email":"","affiliations":[],"preferred":false,"id":727195,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Laursen, Bethany","contributorId":201920,"corporation":false,"usgs":false,"family":"Laursen","given":"Bethany","affiliations":[{"id":36298,"text":"Michigan State University, Departments of Community Sustainability and Philosophy, Natural Resource Building 480 Wilson Road Room 151, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":727196,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Prell, Christina","contributorId":201921,"corporation":false,"usgs":false,"family":"Prell","given":"Christina","email":"","affiliations":[{"id":36299,"text":"University of Maryland, Department of Sociology, 2112 Parren Mitchell Art-Sociology Building, 3834 Campus Drive, College Park, MD 20742","active":true,"usgs":false}],"preferred":false,"id":727197,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Schmitt-Olabisi, Laura","contributorId":201922,"corporation":false,"usgs":false,"family":"Schmitt-Olabisi","given":"Laura","email":"","affiliations":[{"id":36290,"text":"Michigan State University, Department of Community Sustainability, Natural Resource Building 480 Wilson Road Room 151, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":727198,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Singer, Alison","contributorId":201923,"corporation":false,"usgs":false,"family":"Singer","given":"Alison","email":"","affiliations":[{"id":36290,"text":"Michigan State University, Department of Community Sustainability, Natural Resource Building 480 Wilson Road Room 151, East Lansing, MI 48824","active":true,"usgs":false}],"preferred":false,"id":727199,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Sterling, Eleanor J.","contributorId":145439,"corporation":false,"usgs":false,"family":"Sterling","given":"Eleanor","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":727200,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Zellner, Moira","contributorId":201924,"corporation":false,"usgs":false,"family":"Zellner","given":"Moira","affiliations":[{"id":36300,"text":"University of Illinois at Chicago, Department of Urban Planning & Policy and Institute for Environmental Science and Policy. 412 S. Peoria St., MC 348, Chicago, IL 60607","active":true,"usgs":false}],"preferred":false,"id":727201,"contributorType":{"id":1,"text":"Authors"},"rank":17}]}} ,{"id":70190589,"text":"70190589 - 2018 - The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware","interactions":[],"lastModifiedDate":"2018-03-29T12:51:13","indexId":"70190589","displayToPublicDate":"2017-06-30T00:00:00","publicationYear":"2018","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3751,"text":"Wetlands Ecology and Management","active":true,"publicationSubtype":{"id":10}},"title":"The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware","docAbstract":"The dependence of downstream waters on upstream ecosystems necessitates an improved understanding of watershed-scale hydrological interactions including connections between wetlands and streams. An evaluation of such connections is challenging when, (1) accurate and complete datasets of wetland and stream locations are often not available and (2) natural variability in surface-water extent influences the frequency and duration of wetland/stream connectivity. The Upper Choptank River watershed on the Delmarva Peninsula in eastern Maryland and Delaware is dominated by a high density of small, forested wetlands. In this analysis, wetland/stream surface water connections were quantified using multiple wetland and stream datasets, including headwater streams and depressions mapped from a lidar-derived digital elevation model. Surface-water extent was mapped across the watershed for spring 2015 using Landsat-8, Radarsat-2 and Worldview-3 imagery. The frequency of wetland/stream connections increased as a more complete and accurate stream dataset was used and surface-water extent was included, in particular when the spatial resolution of the imagery was finer (i.e., <10 m). Depending on the datasets used, 12–60% of wetlands by count (21–93% of wetlands by area) experienced surface-water interactions with streams during spring 2015. This translated into a range of 50–94% of the watershed contributing direct surface water runoff to streamflow. This finding suggests that our interpretation of the frequency and duration of wetland/stream connections will be influenced not only by the spatial and temporal characteristics of wetlands, streams and potential flowpaths, but also by the completeness, accuracy and resolution of input datasets.
","language":"English","publisher":"Springer","doi":"10.1007/s11273-017-9554-y","usgsCitation":"Vanderhoof, M.K., Distler, H., Lang, M.W., and Alexander, L.C., 2018, The influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware: Wetlands Ecology and Management, v. 26, no. 1, p. 63-86, https://doi.org/10.1007/s11273-017-9554-y.","productDescription":"24 p.","startPage":"63","endPage":"86","ipdsId":"IP-084257","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":469198,"rank":1,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/9534041","text":"External Repository"},{"id":438088,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F70C4T8F","text":"USGS data release","linkHelpText":"Data Release for the influence of data characteristics on detecting wetland/stream surface-water connections in the Delmarva Peninsula, Maryland and Delaware"},{"id":352120,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Delaware, Maryland","otherGeospatial":"Delmarva Peninsula","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -76.1,\n 38.5\n ],\n [\n -76.1,\n 39.1\n ],\n [\n -75.5,\n 39.1\n ],\n [\n -75.5,\n 38.5\n ],\n [\n -76.1,\n 38.5\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"26","issue":"1","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-06-08","publicationStatus":"PW","scienceBaseUri":"5afee787e4b0da30c1bfc2b6","contributors":{"authors":[{"text":"Vanderhoof, Melanie K. 0000-0002-0101-5533 mvanderhoof@usgs.gov","orcid":"https://orcid.org/0000-0002-0101-5533","contributorId":168395,"corporation":false,"usgs":true,"family":"Vanderhoof","given":"Melanie","email":"mvanderhoof@usgs.gov","middleInitial":"K.","affiliations":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true},{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709917,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Distler, Hayley 0000-0001-5006-1360 hdistler@usgs.gov","orcid":"https://orcid.org/0000-0001-5006-1360","contributorId":179359,"corporation":false,"usgs":true,"family":"Distler","given":"Hayley","email":"hdistler@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":709918,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Lang, Megan W.","contributorId":196284,"corporation":false,"usgs":false,"family":"Lang","given":"Megan","email":"","middleInitial":"W.","affiliations":[{"id":6661,"text":"US Fish and Wildlife Service","active":true,"usgs":false}],"preferred":false,"id":709919,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Alexander, Laurie C.","contributorId":196285,"corporation":false,"usgs":false,"family":"Alexander","given":"Laurie","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":709920,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189890,"text":"ds1060 - 2017 - Distribution of foraminifera in Chincoteague Bay and the marshes of Assateague Island and the adjacent vicinity, Maryland and Virginia","interactions":[],"lastModifiedDate":"2025-05-13T16:27:31.482105","indexId":"ds1060","displayToPublicDate":"2017-11-28T11:30:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":310,"text":"Data Series","code":"DS","onlineIssn":"2327-638X","printIssn":"2327-0271","active":false,"publicationSubtype":{"id":5}},"seriesNumber":"1060","title":"Distribution of foraminifera in Chincoteague Bay and the marshes of Assateague Island and the adjacent vicinity, Maryland and Virginia","docAbstract":"Scientists from the U.S. Geological Survey (USGS), St. Petersburg Coastal and Marine Science Center conducted a seasonal collection of estuarine, marsh, and sandy washover surface sediments from Chincoteague Bay, Tom’s Cove, and the surrounding Assateague Island and Delmarva Peninsula in March–April and October 2014, after Hurricane Sandy. Micropaleontology samples were collected as part of a complementary USGS Coastal and Marine Geology Program Sea-level and Storm Impacts on Estuarine Environments and Shorelines project study. For comparison with estuarine and overwash deposited foraminifera, a group of scientists from the USGS Woods Hole Coastal and Marine Science Center in Massachusetts collected samples offshore of Assateague Island on the inner continental shelf during a seafloor mapping study in the summer of 2014 and shipped select samples to the St. Petersburg Coastal and Marine Science Center. The micropaleontological subsamples analyzed for foraminifera at each site can be used to establish a foraminiferal baseline assemblage that takes into consideration the seasonal variability of the various species, regarding density and geographic extent, which are influenced by transient and stable environmental parameters. By understanding what parameters affect the various foraminiferal assemblages, researchers can delineate how alterations in salinity, temperature, or marsh-to-bay interactions, such as marsh erosion, might affect that assemblage.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ds1060","usgsCitation":"Ellis, A.M., Shaw, J.E., Osterman, L.E., and Smith, C.G., 2017, Distribution of foraminifera in Chincoteague Bay and the marshes of Assateague Island and the adjacent vicinity, Maryland and Virginia: U.S. Geological Survey Data Series 1060, available at https://doi.org/10.3133/ds1060.","productDescription":"HTML Document; Data Downloads","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-084010","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":347970,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/ds/1060","text":"Report HTML","linkFileType":{"id":5,"text":"html"},"description":"DS 1060"},{"id":347969,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/ds/1060/coverthb.jpg"},{"id":347971,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/ds1059","text":"Data Series 1059","linkHelpText":"- A seasonal and spatial comparison of metals, and stable carbon and nitrogen isotopes, in Chincoteague Bay and the marsh deposits of Assateague Island and the adjacent vicinity, Maryland and Virginia"},{"id":438143,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9YCK857","text":"USGS data release","linkHelpText":"Benthic Foraminiferal Data from Surface Samples and Sedimentary Cores in the Grand Bay Estuary, Mississippi and Alabama"}],"country":"United States","state":"Maryland, Virginia","otherGeospatial":"Assateague Island, Chincoteague Bay","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -75.42388916015625,\n 37.82931081282506\n ],\n [\n -75.0311279296875,\n 37.82931081282506\n ],\n [\n -75.0311279296875,\n 38.43422817624596\n ],\n [\n -75.42388916015625,\n 38.43422817624596\n ],\n [\n -75.42388916015625,\n 37.82931081282506\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"St. Petersburg Coastal and Marine Science Center
U.S. Geological Survey
600 4th Street South
St. Petersburg, FL 33701
Baseline conditions for the sand and gravel aquifers (groundwater) in McHenry County, Illinois, were assessed using data from a countywide network of 44 monitoring wells collecting continuous water-level data from 2009–14. In 2010, water-quality data were collected from 41 of the monitoring wells, along with five additional monitoring wells available from the U.S. Geological Survey National Water Quality Assessment Program. Periodic water-quality data were collected from 2010–14 from selected monitoring wells. The continuous water-level data were used to identify the natural and anthropogenic factors that influenced the water levels at each well. The water-level responses to natural influences such as precipitation, seasonal and annual variations, barometric pressure, and geology, and to anthropogenic influences such as pumping were used to determine (1) likely hydrogeologic setting (degree of aquifer confinement and interconnections) that, in part, are related to lithostratigraphy; and (2) areas of recharge and discharge related to vertical flow directions. Water-level trends generally were determined from the 6 years of data collection (2009–14) to infer effects of weather variability (drought) on recharge.
Precipitation adds an estimated 2.4 inches per year of recharge to the aquifer. Some of this recharge is subsequently discharged to streams and some is discharged to supply wells. A few areas in the eastern half of the county had higher average recharge rates, indicating a need for adequate protection of these recharge areas. Downward vertical flow gradients in upland areas indicate that recharge to the confined aquifer units occurs near upland areas. Upward vertical flow gradients in lowland areas indicate discharge at locations of surface water and groundwater interaction (wetlands, ponds, and streams).
Monitoring wells were sampled for major and minor ions, metals, and nutrients and a subset of wells was sampled for trace elements, dissolved gases, pesticides, and volatile organic compounds. The results were compared to health‑based and aesthetically based standards, which include the U.S. Environmental Protection Agency Maximum Contaminant Level (EPA MCL), and EPA Secondary Maximum Contaminant Levels (SMCL), as well as EPA Health-based Standards Drinking Water Advisories. Health‑based standards were exceeded for arsenic in 22 percent, sodium in 20 percent, and nitrates in 2 percent of the monitoring wells sampled. Aesthetically based standards were exceeded for total dissolved solids in 33 percent, chloride in 11 percent, iron in 85 percent, and manganese in 30 percent of the wells sampled. Many of these same constituents, such as arsenic, iron, and manganese, are naturally occurring but become elevated in areas that have anoxic, mixed, and suboxic conditions. Some areas of potential vulnerability to anthropogenic-sourced constituents in the sand and gravel aquifers were evidenced by trace amounts of volatile organic compounds and pesticides detected in water-quality samples from shallow wells (total depth less of than 46 feet below land surface) near urban settings, and by the detection of elevated major ions (chloride, sodium, magnesium, and calcium) associated, in part, with road-salt applications. Source analysis for chloride indicates mixtures of road salt, water softeners, and sewage.
Continuously measured specific conductance values were used as a surrogate for continuously measured chloride concentrations in the groundwater. The estimated chloride concentrations generally were highest in spring and lowest in summer, and occasionally peak during spring melt. Overall, the range of concentrations varied depending on the local thickness and hydraulic conductivity of the aquifer.
Water levels and water quality from the countywide groundwater monitoring network were compared to water levels and water-quality results in 1979 from a previous U.S. Geological Survey study. Potentiometric surface maps show areas with inferred decreases of water levels near the southern and southeastern areas of McHenry County. Significant increases were noted for total dissolved solids and specific conductance. Chloride concentrations increased as much as 521 percent in three of six wells resampled in 2015 from the previous study.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175112","collaboration":"Prepared in cooperation with McHenry County, Illinois","usgsCitation":"Gahala, A.M., 2017, Hydrogeology and water quality of sand and gravel aquifers in McHenry County, Illinois, 2009–14, and comparison to conditions in 1979 (ver. 1.1, August 2022): U.S. Geological Survey Scientific Investigations Report 2017–5112, 91 p., https://doi.org/10.3133/sir20175112.","productDescription":"ix, 91 p.","numberOfPages":"106","onlineOnly":"Y","ipdsId":"IP-067438","costCenters":[{"id":344,"text":"Illinois Water Science Center","active":true,"usgs":true}],"links":[{"id":404906,"rank":3,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5112/versionHist.txt","text":"Version History","size":"1.36 kB","linkFileType":{"id":2,"text":"txt"}},{"id":404904,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5112/coverthb2.jpg"},{"id":347422,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5112/sir20175112.pdf","text":"Report","size":"6.67 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5112"},{"id":501947,"rank":4,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_106395.htm","linkFileType":{"id":5,"text":"html"}}],"country":"United States","state":"Illinois","county":"McHenry County","geographicExtents":"{\"type\":\"FeatureCollection\",\"features\":[{\"type\":\"Feature\",\"geometry\":{\"type\":\"Polygon\",\"coordinates\":[[[-88.3016,42.4979],[-88.1971,42.4981],[-88.1979,42.4562],[-88.1974,42.4167],[-88.1966,42.3286],[-88.1994,42.2432],[-88.1992,42.1555],[-88.2382,42.155],[-88.3539,42.1547],[-88.4703,42.1552],[-88.5891,42.1556],[-88.7061,42.1564],[-88.7057,42.2418],[-88.7041,42.329],[-88.705,42.4167],[-88.7059,42.4972],[-88.6737,42.4977],[-88.6288,42.4985],[-88.5047,42.4981],[-88.4099,42.4977],[-88.3016,42.4979]]]},\"properties\":{\"name\":\"McHenry\",\"state\":\"IL\"}}]}","edition":"Version 1.0: October 26, 2017; Version 1.1: August 17, 2022","contact":"Director, Illinois Water Science Center
U.S. Geological Survey
405 N Goodwin
Urbana, IL 61801
Mammalian herbivory and exotic plant species interactions are an important ongoing research topic, due to their presumed impacts on native biodiversity. The extent to which these interactions affect forest understory plant community composition and persistence was the subject of our study. We conducted a 5-year, 2 × 2 factorial experiment in three mid-Atlantic US deciduous forests with high densities of white-tailed deer (Odocoileus virginianus) and exotic understory plants. We predicted: (i) only deer exclusion and exotic plant removal in tandem would increase native plant species metrics; and (ii) deer exclusion alone would decrease exotic plant abundance over time. Treatments combining exotic invasive plant removal and deer exclusion for plots with high initial cover, while not differing from fenced or exotic removal only plots, were the only ones to exhibit positive richness responses by native herbaceous plants compared to control plots. Woody seedling metrics were not affected by any treatments. Deer exclusion caused significant increases in abundance and richness of native woody species >30 cm in height. Abundance changes in two focal members of the native sapling community showed that oaks (Quercus spp.) increased only with combined exotic removal and deer exclusion, while shade-tolerant maples (Acer spp.) showed no changes. We also found significant declines in invasive Japanese stiltgrass (Microstegium vimineum) abundance in deer-excluded plots. Our study demonstrates alien invasive plants and deer impact different components and life-history stages of the forest plant community, and controlling both is needed to enhance understory richness and abundance. Alien plant removal combined with deer exclusion will most benefit native herbaceous species richness under high invasive cover conditions while neither action may impact native woody seedlings. For larger native woody species, only deer exclusion is needed for such increases. Deer exclusion directly facilitated declines in invasive species abundance. Resource managers should consider addressing both factors to achieve their forest management goals.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/aobpla/plx046","usgsCitation":"Bourg, N., McShea, W.J., Herrmann, V., and Stewart, C.M., 2017, Interactive effects of deer exclusion and exotic plant removal on deciduous forest understory communities: AoB PLANTS, v. 9, no. 5, p. 1-16, https://doi.org/10.1093/aobpla/plx046.","productDescription":"plx046; 16 p.","startPage":"1","endPage":"16","ipdsId":"IP-086985","costCenters":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"links":[{"id":469554,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/aobpla/plx046","text":"Publisher Index Page"},{"id":348268,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Maryland, Virginia","volume":"9","issue":"5","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationDate":"2017-09-07","publicationStatus":"PW","scienceBaseUri":"5a07e88be4b09af898c8cb87","contributors":{"authors":[{"text":"Bourg, Norman 0000-0002-7443-1992 nbourg@usgs.gov","orcid":"https://orcid.org/0000-0002-7443-1992","contributorId":197809,"corporation":false,"usgs":true,"family":"Bourg","given":"Norman","email":"nbourg@usgs.gov","affiliations":[{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":714434,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"McShea, William J.","contributorId":197834,"corporation":false,"usgs":false,"family":"McShea","given":"William","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":714435,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Herrmann, Valentine","contributorId":181782,"corporation":false,"usgs":false,"family":"Herrmann","given":"Valentine","email":"","affiliations":[],"preferred":false,"id":714436,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stewart, Chad M.","contributorId":197857,"corporation":false,"usgs":false,"family":"Stewart","given":"Chad","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":714437,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70189223,"text":"fs20173057 - 2017 - New Jersey StreamStats: A web application for streamflow statistics and basin characteristics","interactions":[],"lastModifiedDate":"2017-08-02T16:51:40","indexId":"fs20173057","displayToPublicDate":"2017-08-02T15:45:00","publicationYear":"2017","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":"2017-3057","title":"New Jersey StreamStats: A web application for streamflow statistics and basin characteristics","docAbstract":"StreamStats is an interactive, map-based web application from the U.S. Geological Survey (USGS) that allows users to easily obtain streamflow statistics and watershed characteristics for both gaged and ungaged sites on streams throughout New Jersey. Users can determine flood magnitude and frequency, monthly flow-duration, monthly low-flow frequency statistics, and watershed characteristics for ungaged sites by selecting a point along a stream, or they can obtain this information for streamgages by selecting a streamgage location on the map. StreamStats provides several additional tools useful for water-resources planning and management, as well as for engineering purposes. StreamStats is available for most states and some river basins through a single web portal.
Streamflow statistics for water resources professionals include the 1-percent annual chance flood flow (100-year peak flow) used to define flood plain areas and the monthly 7-day, 10-year low flow (M7D10Y) used in water supply management and studies of recreation, wildlife conservation, and wastewater dilution. Additionally, watershed or basin characteristics, including drainage area, percent area forested, and average percent of impervious areas, are commonly used in land-use planning and environmental assessments. These characteristics are easily derived through StreamStats.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173057","collaboration":"Prepared in cooperation with the New Jersey Department of Environmental Protection","usgsCitation":"Watson, K.M., and Janowicz, J.A., 2017, New Jersey StreamStats: A web application for streamflow statistics and basin characteristics: U.S. Geological Survey Fact Sheet 2017–3057, 4 p., https://doi.org/10.3133/fs20173057.","productDescription":"4 p.","numberOfPages":"4","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-081939","costCenters":[{"id":470,"text":"New Jersey Water Science Center","active":true,"usgs":true}],"links":[{"id":344537,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3057/coverthb.jpg"},{"id":344538,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3057/fs20173057.pdf","text":"Report","size":"479 KB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 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Jersey\",\"nation\":\"USA \"}}]}","contact":"Director, New Jersey Water Science Center
U.S. Geological Survey
3450 Princeton Pike, Suite 110
Lawrenceville, NJ 08648
The U.S. Geological Survey’s Flagstaff Science Campus is focused on interdisciplinary study of the Earth and solar system, and has the scientific expertise to detect early environmental changes and provide strategies to minimize possible adverse effects on humanity. The Flagstaff Science Campus (FSC) is located in Flagstaff, Arizona, which is situated in the northern part of the State, home to a wide variety of landscapes and natural resources, including (1) young volcanoes in the San Francisco Volcanic Field, (2) the seven ecological life zones of the San Francisco Peaks, (3) the extensive geologic record of the Colorado Plateau and Grand Canyon, (4) the Colorado River and its perennial, ephemeral, and intermittent tributaries, and (5) a multitude of canyons, mountains, arroyos, and plains. More than 200 scientists, technicians, and support staff provide research, monitoring, and technical advancements in planetary geology and mapping, biology and ecology, Earth-based geology, hydrology, and changing climate and landscapes. Scientists at the FSC work in collaboration with multiple State, Federal, Tribal, municipal, and academic partners to address regional, national, and global environmental issues, and provide scientific outreach to the general public.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20173051","usgsCitation":"Hart, R.J., Vaughan, R.G., McDougall, K., Wojtowicz, T., and Thenkenbail, P., 2017, The U.S. Geological Survey Flagstaff Science Campus—Providing expertise on planetary science, ecology, water resources, geologic processes, and human interactions with the Earth: U.S. Geological Survey Fact Sheet 2017–3051, 2 p., https://doi.org/10.3133/fs20173051.","productDescription":"2 p.","ipdsId":"IP-086978","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":343101,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2017/3051/coverthb.jpg"},{"id":343102,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2017/3051/fs20173051.pdf","text":"Report","size":"2 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2017-3051"}],"country":"United States","state":"Arizona","city":"Flagstaff","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.73027038574219,\n 35.139282901732635\n ],\n [\n -111.53594970703125,\n 35.139282901732635\n ],\n [\n -111.53594970703125,\n 35.27084997704059\n ],\n [\n -111.73027038574219,\n 35.27084997704059\n ],\n [\n -111.73027038574219,\n 35.139282901732635\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"U.S. Geological Survey
Flagstaff Science Campus
2255 N. Gemini Dr.
Flagstaff, AZ 86001
Tel: (928) 556-7000
The Rocky Mountain Association of Geologists (RMAG) is, once again, publishing portions of the 1972 Geologic Atlas of the Rocky Mountain Region (Mallory, ed., 1972) as a geospatial map and data package. Georeferenced tiff (Geo TIFF) images of map figures from this atlas has served as the basis for these data products. Shapefiles and file geodatabase features have been generated and cartographically represented for select pages from the following chapters:
• Phanerozoic Rocks (page 56)
• Cambrian System (page 63)
• Ordovician System (pages 78 and 79)
• Silurian System (pages 87 - 89)
• Devonian System (pages 93, 94, and 96 - 98)
• Mississippian System (pages 102 and 103)
• Pennsylvanian System (pages 114 and 115)
• Permian System (pages 146 and 149 - 154)
• Triassic System (pages 168 and 169)
• Jurassic System (pages 179 and 180)
• Cretaceous System (pages 197 - 201, 207 - 210, 215, - 218, 221, 222, 224, 225, and 227).
The primary purpose of this publication is to provide regional-scale, as well as local-scale, geospatial data of the Rocky Mountain Region for use in geoscience studies. An important aspect of this interactive map product is that it does not require extensive GIS experience or highly specialized software.
","language":"English","publisher":"The Rocky Mountain Association of Geologists","usgsCitation":"2017, Paleozoic and mesozoic GIS data from the Geologic Atlas of the Rocky Mountain Region: Volume 1, e-book.","productDescription":"e-book","ipdsId":"IP-079177","costCenters":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"links":[{"id":348354,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":347815,"type":{"id":15,"text":"Index Page"},"url":"https://www.rmag.org/paleozoic---mesozoic-gis-data"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"5a0425b7e4b0dc0b45b45359","contributors":{"editors":[{"text":"Graeber, Aimee 0000-0001-5671-3403 agraeber@usgs.gov","orcid":"https://orcid.org/0000-0001-5671-3403","contributorId":169548,"corporation":false,"usgs":true,"family":"Graeber","given":"Aimee","email":"agraeber@usgs.gov","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":720863,"contributorType":{"id":2,"text":"Editors"},"rank":1},{"text":"Gunther, Gregory L. 0000-0002-1761-1604 ggunther@usgs.gov","orcid":"https://orcid.org/0000-0002-1761-1604","contributorId":1581,"corporation":false,"usgs":true,"family":"Gunther","given":"Gregory","email":"ggunther@usgs.gov","middleInitial":"L.","affiliations":[{"id":164,"text":"Central Energy Resources Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true}],"preferred":true,"id":720864,"contributorType":{"id":2,"text":"Editors"},"rank":2}]}} ,{"id":70188293,"text":"70188293 - 2017 - Spatio-temporal mapping of plate boundary faults in California using geodetic imaging","interactions":[],"lastModifiedDate":"2017-11-13T15:05:50","indexId":"70188293","displayToPublicDate":"2017-06-05T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1816,"text":"Geosciences","active":true,"publicationSubtype":{"id":10}},"title":"Spatio-temporal mapping of plate boundary faults in California using geodetic imaging","docAbstract":"The Pacific–North American plate boundary in California is composed of a 400-km-wide network of faults and zones of distributed deformation. Earthquakes, even large ones, can occur along individual or combinations of faults within the larger plate boundary system. While research often focuses on the primary and secondary faults, holistic study of the plate boundary is required to answer several fundamental questions. How do plate boundary motions partition across California faults? How do faults within the plate boundary interact during earthquakes? What fraction of strain accumulation is relieved aseismically and does this provide limits on fault rupture propagation? Geodetic imaging, broadly defined as measurement of crustal deformation and topography of the Earth’s surface, enables assessment of topographic characteristics and the spatio-temporal behavior of the Earth’s crust. We focus here on crustal deformation observed with continuous Global Positioning System (GPS) data and Interferometric Synthetic Aperture Radar (InSAR) from NASA’s airborne UAVSAR platform, and on high-resolution topography acquired from lidar and Structure from Motion (SfM) methods. Combined, these measurements are used to identify active structures, past ruptures, transient motions, and distribution of deformation. The observations inform estimates of the mechanical and geometric properties of faults. We discuss five areas in California as examples of different fault behavior, fault maturity and times within the earthquake cycle: the M6.0 2014 South Napa earthquake rupture, the San Jacinto fault, the creeping and locked Carrizo sections of the San Andreas fault, the Landers rupture in the Eastern California Shear Zone, and the convergence of the Eastern California Shear Zone and San Andreas fault in southern California. These examples indicate that distribution of crustal deformation can be measured using interferometric synthetic aperture radar (InSAR), Global Navigation Satellite System (GNSS), and high-resolution topography and can improve our understanding of tectonic deformation and rupture characteristics within the broad plate boundary zone.
","language":"English","publisher":"Multidisciplinary Digital Publishing Institute","doi":"10.3390/geosciences7010015","usgsCitation":"Donnellan, A., Arrowsmith, R., and DeLong, S.B., 2017, Spatio-temporal mapping of plate boundary faults in California using geodetic imaging: Geosciences, v. 7, no. 1, p. 1-26, https://doi.org/10.3390/geosciences7010015.","productDescription":"Article 15; 26 p.","startPage":"1","endPage":"26","ipdsId":"IP-082746","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":469772,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/geosciences7010015","text":"Publisher Index Page"},{"id":342133,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"7","issue":"1","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-03-21","publicationStatus":"PW","scienceBaseUri":"59366da6e4b0f6c2d0d7d5f5","contributors":{"authors":[{"text":"Donnellan, Andrea","contributorId":176745,"corporation":false,"usgs":false,"family":"Donnellan","given":"Andrea","email":"","affiliations":[{"id":18954,"text":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA","active":true,"usgs":false}],"preferred":false,"id":697149,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Arrowsmith, Ramon","contributorId":181555,"corporation":false,"usgs":false,"family":"Arrowsmith","given":"Ramon","email":"","affiliations":[],"preferred":false,"id":697150,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"DeLong, Stephen B. 0000-0002-0945-2172 sdelong@usgs.gov","orcid":"https://orcid.org/0000-0002-0945-2172","contributorId":5240,"corporation":false,"usgs":true,"family":"DeLong","given":"Stephen","email":"sdelong@usgs.gov","middleInitial":"B.","affiliations":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"preferred":true,"id":697148,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70139441,"text":"70139441 - 2017 - Map projections and the Internet","interactions":[],"lastModifiedDate":"2020-08-20T19:28:58.81488","indexId":"70139441","displayToPublicDate":"2017-05-19T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"4","title":"Map projections and the Internet","docAbstract":"The field of map projections can be described as mathematical, static, and challenging. However, this description is evolving in concert with the development of the Internet. The Internet has enabled new outlets for software applications, learning, and interaction with and about map projections . This chapter examines specific ways in which the Internet has moved map projections from a relatively obscure paper-based setting to a more engaging and accessible online environment. After a brief overview of map projections, this chapter discusses four perspectives on how map projections have been integrated into the Internet. First, map projections and their role in web maps and mapping services is examined. Second, an overview of online atlases and the map projections chosen for their maps is presented. Third, new programming languages and code libraries that enable map projections to be included in mapping applications are reviewed. Fourth, the Internet has facilitated map projection education and research especially with the map reader’s comprehension and understanding of complex topics like map projection distortion is discussed.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Choosing a map projection","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","publisherLocation":"Cham, Switzerland","doi":"10.1007/978-3-319-51835-0_4","isbn":"978-3-319-51834-3","usgsCitation":"Kessler, F., Battersby, S.E., Finn, M.P., and Clarke, K., 2017, Map projections and the Internet, chap. 4 of Choosing a map projection, p. 117-148, https://doi.org/10.1007/978-3-319-51835-0_4.","productDescription":"32 p.","startPage":"117","endPage":"148","ipdsId":"IP-062186","costCenters":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"links":[{"id":341515,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-04-05","publicationStatus":"PW","scienceBaseUri":"5920044ae4b0ac16dbdeb787","contributors":{"authors":[{"text":"Kessler, Fritz","contributorId":138942,"corporation":false,"usgs":false,"family":"Kessler","given":"Fritz","email":"","affiliations":[{"id":12588,"text":"Frostburg State University/ Department of Geography","active":true,"usgs":false}],"preferred":false,"id":539399,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Battersby, Sarah E.","contributorId":138943,"corporation":false,"usgs":false,"family":"Battersby","given":"Sarah","email":"","middleInitial":"E.","affiliations":[{"id":12589,"text":"University of South Carolina/ Department of Geography","active":true,"usgs":false}],"preferred":false,"id":539400,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":539398,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Clarke, Keith","contributorId":13861,"corporation":false,"usgs":true,"family":"Clarke","given":"Keith","affiliations":[],"preferred":false,"id":539401,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70170060,"text":"sim3356 - 2017 - Geologic map of Meridiani Planum, Mars","interactions":[],"lastModifiedDate":"2023-03-20T18:09:16.256306","indexId":"sim3356","displayToPublicDate":"2017-04-28T00:00:00","publicationYear":"2017","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":"3356","title":"Geologic map of Meridiani Planum, Mars","docAbstract":"The Meridiani Planum region of Mars—originally named due to its proximity to the Martian prime meridian—contains a variety of geologic units, including those that are crater‑related, that span the Early Noachian to Late Amazonian Epochs. Mars Global Surveyor (MGS) data indicate this area contains extensive layered deposits, some of which are rich in the mineral hematite. The National Aeronautics and Space Administration’s (NASA) Mars Exploration Rover (MER) Opportunity landed in Meridiani Planum in early 2004 and, at the time of this writing, is still conducting operations. A variety of water-altered bedrock outcrops have been studied and contain indications of prolonged surface and near-surface fluid/rock interactions. The purpose of this study is to use the more recent orbiter data to place the rover’s findings in a broader context by assessing the geologic and hydrologic histories of the region.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3356","collaboration":"Prepared in cooperation with the National Aeronautics and Space Administration","usgsCitation":"Hynek, B.M., and Di Achille, G., 2017, Geologic map of Meridiani Planum, Mars (ver. 1.1, April 2017): U.S. Geological Survey Scientific Investigations Map 3356, pamphlet 9 p., scale 1:2,000,000, https://doi.org/10.3133/sim3356.","productDescription":"Pamphlet: i, 9 p.; Sheet: 55.90 x 40.00 inches; Metadata; Spatial Data","onlineOnly":"N","additionalOnlineFiles":"Y","ipdsId":"IP-070106","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":438359,"rank":8,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9DG4NAB","text":"USGS data release","linkHelpText":"Interactive Map: USGS SIM 3356 Geologic Map of Meridiani Planum"},{"id":405428,"rank":7,"type":{"id":2,"text":"Additional Report Piece"},"url":"https://doi.org/10.5066/P9DG4NAB","text":"Interactive Web Map","description":"Hynek, B.M., and Di Achille, G., 2017, Geologic map of Meridiani Planum, Mars (ver. 1.1, April 2017): U.S. Geological Survey Scientific Investigations Map 3356, pamphlet 9 p., scale 1:2,000,000, https://doi.org/10.3133/sim3356","linkHelpText":"- Geologic Map of Meridiani Planum, Mars, 1:2M. Hynek and Di Achille (2017)"},{"id":340635,"rank":6,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3356/sim3356_revHistory.txt","text":"Version history","size":"12.5 KB","linkFileType":{"id":2,"text":"txt"}},{"id":334386,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3356/sim3356_sheet1.pdf","text":"Map","size":"25.3 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3356"},{"id":334388,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3356/sim3356_metadata.txt","text":"Metadata","size":"12.4 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3356 Metadata"},{"id":334387,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3356/sim3356_pamphlet.pdf","text":"Pamphlet","size":"598 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3356 Pamphlet"},{"id":334385,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3356/coverthb.jpg"},{"id":334389,"rank":5,"type":{"id":23,"text":"Spatial Data"},"url":"https://pubs.usgs.gov/sim/3356/sim3356_gis.zip","text":"GIS Data","size":"293 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM 3356 GIS"}],"edition":"Version 1.0: Originally posted January 31, 2017; Version 1.1: April 28, 2017","contact":"There are undeniable practical consequences to consider when choosing an appropriate map projection for a specific region. The surface of a globe covered by global, continental, and regional maps are so singular that each type distinctively affects the amount of distortion incurred during a projection transformation because of the an assortment of effects caused by distance, direction, scale , and area. A Decision Support System (DSS) for Map Projections of Small Scale Data was previously developed to help select an appropriate projection. This paper reports on a tutorial to accompany that DSS. The DSS poses questions interactively, allowing the user to decide on the parameters, which in turn determines the logic path to a solution. The objective of including a tutorial to accompany the DSS is achieved by visually representing the path of logic that is taken to a recommended map projection derived from the parameters the user selects. The tutorial informs the DSS user about the pedigree of the projection and provides a basic explanation of the specific projection design. This information is provided by informational pop-ups and other aids.
","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Choosing a Map Projection","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Springer","publisherLocation":"Cham, Switzerland","doi":"10.1007/978-3-319-51835-0_10","usgsCitation":"Finn, M.P., Usery, E.L., Woodard, L.N., and Yamamoto, K.H., 2017, The logic of selecting an appropriate map projection in a Decision Support System (DSS), chap. of Choosing a Map Projection, p. 229-245, https://doi.org/10.1007/978-3-319-51835-0_10.","productDescription":"17 p.","startPage":"229","endPage":"245","ipdsId":"IP-053623","costCenters":[],"links":[{"id":342277,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"noUsgsAuthors":false,"publicationDate":"2017-04-05","publicationStatus":"PW","scienceBaseUri":"593910abe4b0764e6c5e884c","contributors":{"authors":[{"text":"Finn, Michael P. 0000-0003-0415-2194 mfinn@usgs.gov","orcid":"https://orcid.org/0000-0003-0415-2194","contributorId":2657,"corporation":false,"usgs":true,"family":"Finn","given":"Michael","email":"mfinn@usgs.gov","middleInitial":"P.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true},{"id":5074,"text":"Center for Geospatial Information Science (CEGIS)","active":true,"usgs":true}],"preferred":true,"id":519223,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Usery, E. Lynn 0000-0002-2766-2173 usery@usgs.gov","orcid":"https://orcid.org/0000-0002-2766-2173","contributorId":231,"corporation":false,"usgs":true,"family":"Usery","given":"E.","email":"usery@usgs.gov","middleInitial":"Lynn","affiliations":[{"id":423,"text":"National Geospatial Program","active":true,"usgs":true}],"preferred":true,"id":519222,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Woodard, Laura N.","contributorId":9733,"corporation":false,"usgs":true,"family":"Woodard","given":"Laura","email":"","middleInitial":"N.","affiliations":[],"preferred":false,"id":519225,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Yamamoto, Kristina H. khyamamoto@usgs.gov","contributorId":4490,"corporation":false,"usgs":true,"family":"Yamamoto","given":"Kristina","email":"khyamamoto@usgs.gov","middleInitial":"H.","affiliations":[{"id":5047,"text":"NGTOC Denver","active":true,"usgs":true}],"preferred":true,"id":519224,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70184404,"text":"ofr20171025 - 2017 - Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia","interactions":[],"lastModifiedDate":"2017-03-14T09:45:46","indexId":"ofr20171025","displayToPublicDate":"2017-03-10T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1025","title":"Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia","docAbstract":"Between 1997 and 2011, Mongolia established three specially protected areas in the north-central part of the country to protect various high-value resources. These areas are jointly referred to as the Ulaan Taiga Specially Protected Areas. In accordance with the goals of the draft general management plan, this report identifies options for initiating an inventory and monitoring program for the three protected areas. Together, the three areas comprise over 1.5 million hectares of mountainous terrain west of Lake Hovsgol and bordering the Darkhad Valley. The area supports numerous rare ungulates, endangered fish, and over 40 species of threatened plants. Illegal mining, illegal logging, and poaching pose the most immediate threats to resources. As a first step, a review of published literature would inform natural resource management at the Ulaan Taiga Specially Protected Areas because it would inform other inventories.
Vegetation classification and mapping also would inform other inventory efforts because the process incorporates geographic analysis to identify environmental gradients, fine-scale sampling that captures species composition and structure, and landscape-scale results that represent the variety and extent of habitats for various organisms. Mapping using satellite imagery reduces the cost per hectare.
Following a determination of existing knowledge, field surveys of vertebrates and vascular plants would serve to build species lists and fill in gaps in existing knowledge. For abiotic resources, a focus on monitoring air quality, evaluating and monitoring water quality, and assembling and storing weather data would provide information for correlating resource response status with changing environmental conditions.
Finally, we identify datasets that, if incorporated into a geographic information system, would inform resource management. They include political boundaries, infrastructure, topography, surficial geology, hydrology, fire history, and soils.
In terms of tracking high-value resources, vegetation monitoring at the plot scale would provide a basis for detecting change in such characteristics as plant species composition, vegetation structure, and productivity that are associated with landscape-scale factors such as climate change or biotic interactions. Continued population monitoring of rare ungulates, particularly argali or wild sheep (Ovis ammon), would provide information on how populations are responding to natural and anthropogenic stressors. Siberian taimen (Hucho taimen) also is an important monitoring target given ongoing threats of poaching and climate change.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171025","usgsCitation":"Moore, P.E., Meyer, J.B., and Chow, L.S., 2017, Natural resource inventory and monitoring for Ulaan Taiga Specially Protected Areas—An assessment of needs and opportunities in northern Mongolia: U.S. Geological Survey Open-File Report 2017–1025, 35 p., https://doi.org/10.3133/ofr20171025.","productDescription":"viii, 35 p.","numberOfPages":"48","onlineOnly":"Y","ipdsId":"IP-082861","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":337345,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1025/coverthb.jpg"},{"id":337346,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1025/ofr20171025.pdf","text":"Report","size":"3.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1025"}],"country":"Mongolia","otherGeospatial":"Ulaan Taiga Specially Protected Areas","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n 97.55859375,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 49.89463439573421\n ],\n [\n 102.48046875,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 52.24125614966341\n ],\n [\n 97.55859375,\n 49.89463439573421\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Western Ecological Research Center
U.S. Geological Survey
3020 State University Drive East
Sacramento, California 95819
http://www.werc.usgs.gov/
Regional-scale, three-dimensional continuous probability models, were constructed for aspects of redox conditions in the groundwater system of the Central Valley, California. These models yield grids depicting the probability that groundwater in a particular location will have dissolved oxygen (DO) concentrations less than selected threshold values representing anoxic groundwater conditions, or will have dissolved manganese (Mn) concentrations greater than selected threshold values representing secondary drinking water-quality contaminant levels (SMCL) and health-based screening levels (HBSL). The probability models were constrained by the alluvial boundary of the Central Valley to a depth of approximately 300 m. Probability distribution grids can be extracted from the 3-D models at any desired depth, and are of interest to water-resource managers, water-quality researchers, and groundwater modelers concerned with the occurrence of natural and anthropogenic contaminants related to anoxic conditions.
Models were constructed using a Boosted Regression Trees (BRT) machine learning technique that produces many trees as part of an additive model and has the ability to handle many variables, automatically incorporate interactions, and is resistant to collinearity. Machine learning methods for statistical prediction are becoming increasing popular in that they do not require assumptions associated with traditional hypothesis testing. Models were constructed using measured dissolved oxygen and manganese concentrations sampled from 2767 wells within the alluvial boundary of the Central Valley, and over 60 explanatory variables representing regional-scale soil properties, soil chemistry, land use, aquifer textures, and aquifer hydrologic properties. Models were trained on a USGS dataset of 932 wells, and evaluated on an independent hold-out dataset of 1835 wells from the California Division of Drinking Water. We used cross-validation to assess the predictive performance of models of varying complexity, as a basis for selecting final models. Trained models were applied to cross-validation testing data and a separate hold-out dataset to evaluate model predictive performance by emphasizing three model metrics of fit: Kappa; accuracy; and the area under the receiver operator characteristic curve (ROC). The final trained models were used for mapping predictions at discrete depths to a depth of 304.8 m. Trained DO and Mn models had accuracies of 86–100%, Kappa values of 0.69–0.99, and ROC values of 0.92–1.0. Model accuracies for cross-validation testing datasets were 82–95% and ROC values were 0.87–0.91, indicating good predictive performance. Kappas for the cross-validation testing dataset were 0.30–0.69, indicating fair to substantial agreement between testing observations and model predictions. Hold-out data were available for the manganese model only and indicated accuracies of 89–97%, ROC values of 0.73–0.75, and Kappa values of 0.06–0.30. The predictive performance of both the DO and Mn models was reasonable, considering all three of these fit metrics and the low percentages of low-DO and high-Mn events in the data.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jhydrol.2017.01.014","usgsCitation":"Rosecrans, C.Z., Nolan, B.T., and Gronberg, J.M., 2017, Prediction and visualization of redox conditions in the groundwater of Central Valley, California: Journal of Hydrology, v. 546, p. 341-356, https://doi.org/10.1016/j.jhydrol.2017.01.014.","productDescription":"16 p.","startPage":"341","endPage":"356","ipdsId":"IP-075668","costCenters":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"links":[{"id":336939,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Central Valley","volume":"546","publishingServiceCenter":{"id":1,"text":"Sacramento PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58bfd4f0e4b014cc3a3ba483","contributors":{"authors":[{"text":"Rosecrans, Celia Z. 0000-0003-1456-4360 crosecrans@usgs.gov","orcid":"https://orcid.org/0000-0003-1456-4360","contributorId":187542,"corporation":false,"usgs":true,"family":"Rosecrans","given":"Celia","email":"crosecrans@usgs.gov","middleInitial":"Z.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":false,"id":680860,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Nolan, Bernard T. 0000-0002-6945-9659 btnolan@usgs.gov","orcid":"https://orcid.org/0000-0002-6945-9659","contributorId":2190,"corporation":false,"usgs":true,"family":"Nolan","given":"Bernard","email":"btnolan@usgs.gov","middleInitial":"T.","affiliations":[{"id":27111,"text":"National Water Quality Program","active":true,"usgs":true},{"id":451,"text":"National Water Quality Assessment Program","active":true,"usgs":true}],"preferred":true,"id":680862,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Gronberg, JoAnn M. 0000-0003-4822-7434 jmgronbe@usgs.gov","orcid":"https://orcid.org/0000-0003-4822-7434","contributorId":3548,"corporation":false,"usgs":true,"family":"Gronberg","given":"JoAnn","email":"jmgronbe@usgs.gov","middleInitial":"M.","affiliations":[{"id":154,"text":"California Water Science Center","active":true,"usgs":true}],"preferred":true,"id":680861,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70185296,"text":"70185296 - 2017 - Computer modelling for ecosystem service assessment","interactions":[],"lastModifiedDate":"2020-08-20T19:40:54.887198","indexId":"70185296","displayToPublicDate":"2017-02-09T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":5,"text":"Book chapter"},"publicationSubtype":{"id":24,"text":"Book Chapter"},"chapter":"4.4","title":"Computer modelling for ecosystem service assessment","docAbstract":"Computer models are simplified representations of the environment that allow biophysical, ecological, and/or socio-economic characteristics to be quantified and explored. Modelling approaches differ from mapping approaches (Chapter 5) as (i) they are not forcibly spatial (although many models do produce spatial outputs); (ii) they focus on understanding and quantifying the interactions between different components of social and/or environmental systems and (iii)\nby changing parameters within models, they are capable of exploring both alternative scenarios and internal model dynamics. When applied to the assessment of ecosystem\nservices (ES), models are important tools which can quantify the relationships that underpin ES supply, demand and flows and, in some cases, produce maps representing\nthese factors. Furthermore, as models can explore scenarios, trade-offs that result from different scenarios can be assessed. This chapter provides a broad overview of\ndifferent types of models that have been applied to ES assessments and discusses, with examples, the ways that these models have the potential to be used in practice. In the context of ES, there are a number of ways of distinguishing between different\ntypes of models. Here, we distinguish between individual models focussing on single ES and modelling frameworks that can assess multiple ES within the framework of a\nsingle modelling tool.","largerWorkType":{"id":4,"text":"Book"},"largerWorkTitle":"Mapping ecosystem services","largerWorkSubtype":{"id":15,"text":"Monograph"},"language":"English","publisher":"Pensoft Publishers","publisherLocation":"Sofia, Bulgaria","doi":"10.3897/ab.e12837","usgsCitation":"Dunford, R., Harrison, P., and Bagstad, K.J., 2017, Computer modelling for ecosystem service assessment, chap. 4.4 of Mapping ecosystem services, p. 124-135, https://doi.org/10.3897/ab.e12837.","productDescription":"12 p.","startPage":"124","endPage":"135","ipdsId":"IP-074513","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":470079,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"https://doi.org/10.3897/ab.e12837","text":"External Repository"},{"id":339501,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"58ebadace4b0b4d95d320097","contributors":{"authors":[{"text":"Dunford, Robert","contributorId":189523,"corporation":false,"usgs":false,"family":"Dunford","given":"Robert","email":"","affiliations":[],"preferred":false,"id":685064,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Harrison, Paula A.","contributorId":189524,"corporation":false,"usgs":false,"family":"Harrison","given":"Paula","middleInitial":"A.","affiliations":[],"preferred":false,"id":685065,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bagstad, Kenneth J. 0000-0001-8857-5615 kjbagstad@usgs.gov","orcid":"https://orcid.org/0000-0001-8857-5615","contributorId":3680,"corporation":false,"usgs":true,"family":"Bagstad","given":"Kenneth","email":"kjbagstad@usgs.gov","middleInitial":"J.","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":685063,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70202107,"text":"70202107 - 2017 - Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S.","interactions":[],"lastModifiedDate":"2019-02-11T14:15:20","indexId":"70202107","displayToPublicDate":"2017-01-01T14:15:14","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":9,"text":"Other Report"},"title":"Validation of NEXRAD data and models of bird migration stopover sites in the Northeast U.S.","docAbstract":"The national network of weather surveillance radars (NEXRAD) detects birds in flight, and has proven to be a useful remote-sensing tool for ornithological study. We used data collected during Fall 2008 to 2014 by 16 NEXRAD and four terminal Doppler weather radars (TDWR) in the northeastern U.S. to map and study the spatial distribution of landbirds shortly after they leave daytime stopover sites to embark on nocturnal migratory flights. Given observed variability in the precise timing of migratory exodus, we developed a new method to sample the onset of migration at the point of maximum rate of increase in bird densities aloft to consistently sample exodus across radars and days.
The mean linear trend in aggregate stopover densities of migrants indicated a 4% decline per year from the 2008 baseline density (29% decline over the seven years). Regionally, coastal Virginia and Maine had the steepest declines. The steepest increases in migrant densities across years occurred within the Delmarva Peninsula and in coastal Connecticut.
We used NEXRAD observations to develop models to predict potentially important stopover sites throughout USFWS Region 5. Observed NEXRAD data were positively correlated to observations from TDWR and NASA’s S-Band Dual-Polarimetric Radar (NPOL), though not strongly. Predicted densities increased with increasing hardwood cover across multiple scales and with vegetation productivity. Contrastingly, predicted densities decreased with increasing agricultural, emergent marsh and coniferous land cover, but did not change with fraction of urban cover. Stopover density increased closer to bright areas and the Atlantic coast. Moreover, interactive effects indicated that migrants were more concentrated in forested areas that were both brightly lit and near the Atlantic coast. Large areas of predicted regionally important stopover sites were located along the coastlines of Maine, Long Island Sound, New Jersey, the lower Delmarva Peninsula, within the Adirondack Mountains, Catskill Mountains, and eastern Virginia.
We also created maps of classified stopover use during bimonthly periods and at multiple-scales. Migrant densities peaked along the Adirondack Mountains early in September, and along the Atlantic coast in late September with the passage of Neotropical migrants. Stopover densities peaked in the most northern extent of Maine and New England States in late October with the departure of temperate migrants.
Ground surveys conducted at 48 forested sites within the Delmarva Peninsula and Tidewater Virginia during Fall 2013 and 2014 revealed that nocturnal migrant densities pooled across species and for 14 individual species, after accounting for temporal phenology in their passage timing, were related to factors operating at multiple scales including food resources (primarily arthropod abundance in understory) and understory shrub density at a patch scale, and latitude and proximity to the Atlantic coast at a regional scale.
We integrated field survey and radar data to estimate relative stopover duration and to identify stopover functional types among 45 sites that included data from a past study near the Gulf of Mexico. We identified four functional types spanning the gradient of short rest stops to refueling stops with variable duration of stopover in relation to food abundance. The Mid-Atlantic sites were dominated by rest stops near coastal areas and lacked quick refueling stops due to low overall food abundance. The maps and ecological understanding produced can help inform conservation planning to protect and enhance stopover sites for migratory landbirds in the future.
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