Modeling of ecosystems is a part of the U.S. Environmental Protection Agency’s protocol for developing site-specific selenium guidelines for protection of aquatic life. Selenium as an environmental contaminant is known to bioaccumulate and cause reproductive effects in fish and wildlife. Here we apply a modeling methodology—ecosystem-scale selenium modeling—to understand and document the scientific basis for predicting and validating ecological protection for Lake Koocanusa, a transboundary reservoir between Montana and British Columbia. A comprehensive set of site-specific data compiled from public databases (Federal, State, and Provincial) and reports by Teck Coal Ltd., is available in a companion U.S. Geological Survey data release. The tissue guideline used within modeling here to assess protection is the U.S. Environmental Protection Agency’s national selenium guideline for whole-body fish (dry weight); however, other numeric values for a whole-body guideline or other tissue types may be assumed if applicable tissue-to-tissue conversion factors are available.
We consider the report assembled here as a working document that presents a model that can effectively address and structure the needs of (1) scientific understanding in representing the lake’s ecosystem and selenium biodynamics and (2) policy and management development during a decision-making process, but it is open to modification and updating as more ecologically detailed data become available. The approach brings together the main concerns involved in selenium toxicity: likelihood of high exposure, inherent species sensitivity, and close connectivity of ecosystem characteristics and behavioral ecology of predators. Detailed site-specific modeling equations are provided to document the linked factors that determine the responses of ecosystems to selenium. A series of scenarios quantifies the implications of choices of site-specific variables including food-web species, bioavailability of particulate material, and partitioning between the dissolved and particulate phases at the base of food webs. A gradient mapping tool applied to Lake Koocanusa provides a precedent for ecosystem-scale modeling of lakes by recognizing the importance of lake strata and hydrodynamics as components of modeling.
Data requirements for ecosystem modeling, including ecological and hydrological process information fundamental to the dietary biodynamics of selenium in site-specific food webs, were assessed as a precursor to model validation for Lake Koocanusa. Understanding these relationships is necessary to connect modeling outcomes to reproductive effects and establish boundaries, in the case of Lake Koocanusa, for the influences of dam operation, fish-community viability, and its Clean Water Act impaired 303(d)-listing status on ecosystem function.
We find that an assemblage of conditions affects the representation of Lake Koocanusa’s ecosystem within modeling scenarios but that the constructed gradient maps, mechanistic model, and associated bioaccumulation potentials portray and quantify the variables that are determinative to protection of predator species. Ecological and hydrological sorting of compiled individual data points on a site- and species-specific basis helps identify and address model uncertainties. Sources of uncertainty include (1) the scarcity of data for some environmental media compartments across time and locations, (2) the complexity of hydrodynamic conditions that can lead to seasonal ecological disconnects such as in selenium partitioning from water into particulates, and (3) the functional status of Lake Koocanusa’s ecosystem because of cumulative effects of various environmental stresses (for example, fish-community changes, flow regime changes, parasites, gonadal dysfunction, and increasing mining input-selenium concentrations since 1984). To this last point, it is important to determine where Lake Koocanusa is in an impairment-restoration cycle so as not to base protection on survivor bias, the maintenance of a currently degraded ecosystem, or normalized toxicity. In a broader context, one of the overall consequences of revised selenium regulations is that their derivation is now dependent on being able to define and understand the status of the ecosystem on which protection is based.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201098","collaboration":"Prepared in cooperation with the Montana Department of Environmental Quality","usgsCitation":"Presser, T.S., and Naftz, D.L., 2020, Understanding and documenting the scientific basis of selenium ecological protection in support of site-specific guidelines development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada: U.S. Geological Survey Open-File Report 2020–1098, 40 p., https://doi.org/10.3133/ofr20201098.","productDescription":"Report: viii, 40 p.; 3 Tables; Data Releases","numberOfPages":"52","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-120031","costCenters":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true}],"links":[{"id":436823,"rank":6,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P99LM27E","text":"USGS data release","linkHelpText":"Results of Ecosystem Scale Selenium Modeling in Support of Site-Specific Guidelines Development for Lake Koocanusa, Montana, U.S.A., and British Columbia, Canada, 2020"},{"id":377297,"rank":4,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9HB5S5F","text":"USGS data release","description":"USGS Data Release","linkHelpText":"USGS measurements of dissolved and suspended particulate material selenium in Lake Koocanusa in the vicinity of Libby Dam (MT), 2015–2017 (update)"},{"id":377296,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9VXYSNZ","text":"USGS data release","description":"USGS Data Release","linkHelpText":"Selenium concentrations in food webs of Lake Koocanusa in the vicinity of Libby Dam (MT) and the Elk River (BC) as the basis for applying ecosystem-scale modeling, 2008–2018"},{"id":377295,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098.pdf","text":"Report","size":"19.0 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1098"},{"id":377294,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1098/coverthb.jpg"},{"id":377363,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/of/2020/1098/ofr20201098_tables_1_and_3_to_10.xlsx","text":"Tables 1 and 3–10","size":"91.5 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"OFR 2020–1098 Tables"}],"country":"United States, Canada","state":"Montana, British Columbia","otherGeospatial":"Lake Koocanusa","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.72998046875,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 48.33251726168281\n ],\n [\n -114.90600585937499,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 49.457413352792216\n ],\n [\n -115.72998046875,\n 48.33251726168281\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Water Mission Area
U.S. Geological Survey
345 Middlefield Rd.
Menlo Park, CA 94025
The July 2019
Tallgrass prairie is a disturbance‐dependent ecosystem that has suffered steep declines in the midwestern United States. The necessity of disturbance, typically fire or grazing, presents challenges to managers who must apply them on increasingly small and fragmented parcels. The goal of this study was to compare effects of management using cattle grazing or fire on vegetation and soil characteristics to aid managers in making decisions regarding the kind of disturbance to apply. We selected 73 sites, of which 27 were managed solely by cattle grazing and 46 solely by fire, for at least 11 yr leading up to the study. We stratified the sites by prairie type (dry, mesic, and wet) and sampled frequency of plant species on randomly placed transects, supplemented with botanist‐directed walks, and collected and composited five soil cores on a randomly selected transect within each prairie type at each site. We calculated rarefied richness and Shannon evenness from the transect data and mean coefficient of conservatism (CofC) from the total list of species. Soil samples were analyzed for texture, bulk density, total N and C, and potential net N nitrification and mineralization. A nonmetric multidimensional scaling analysis of the plant community data revealed differences in species associated with mesic and wet prairies, but no separation by management type. Similarly, none of the vegetation variables we calculated varied by management type, as determined by mixed‐effects models, but soil bulk density was 17.5% higher and total N was 22% higher on grazed sites than burned sites. Sites burned more recently had higher species richness and mean CofC, but fire was not associated with any soil variables. Sites grazed more recently had higher bulk density, total N and C, and faster N cycling rates. Overall, 28% of plant species were found exclusively in one management type or the other, but these species did not vary in mean CofC. We conclude that, at the levels of burning and grazing intensity we studied, both management approaches produce similar C storage and vegetation responses. To maintain maximum diversity across the landscape, however, both approaches are necessary.
As much as 22 inches of rain fell in Oklahoma in May 2019, resulting in historic flooding along the Arkansas River and its tributaries in eastern and northeastern Oklahoma. The flooding along the Arkansas River and its tributaries that began in May continued into June 2019. Peaks of record were measured at nine U.S. Geological Survey (USGS) and U.S. Army Corps of Engineers (USACE) streamgages on various streams in eastern and northeastern Oklahoma. This report documents the peak streamflows and stages for 38 selected streamgages in eastern and northeastern Oklahoma and is a followup to a previous report by the USGS that documented flood peaks associated with the May 2019 flood event. Most of the flood peaks occurred from May 26 to June 4, 2019. This report includes data from streamgages on tributaries to the Arkansas River and uses modeling methods to extend the period of record for Arkansas River streamgages. The historic flooding caused homes to fall into the river as a result of bank erosion, forced some towns to be evacuated, and resulted in the highest flood depths in Tulsa, Oklahoma, since 1986. Several USGS and USACE streamgages along the Arkansas River and its tributaries recorded new peaks of record.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201090","collaboration":"Prepared in cooperation with the Federal Emergency Management Agency and the U.S. Army Corps of Engineers","usgsCitation":"Lewis, J.M., Williams, D.J., Harris, S.J., and Trevisan, A.R., 2020, Characterization of peak streamflow and stages at selected streamgages in eastern and northeastern Oklahoma from the May to June 2019 flood event—With an emphasis on flood peaks downstream from dams and on tributaries to the Arkansas River: U.S. Geological Survey Open-File Report 2020–1090, 18 p., https://doi.org/10.3133/ofr20201090.","productDescription":"Report: iv, 18 p.; Data Release","numberOfPages":"26","onlineOnly":"Y","ipdsId":"IP-118379","costCenters":[{"id":516,"text":"Oklahoma Water Science Center","active":true,"usgs":true}],"links":[{"id":377112,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9T3Q6MB","text":"USGS data release","description":"USGS Data Release","linkHelpText":"RiverWare model outputs for flood calculations along the Arkansas River for a flood event in eastern and northeastern Oklahoma during May–June 2019"},{"id":377111,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1090/ofr20201090.pdf","text":"Report","size":"4.47 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1090"},{"id":377110,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1090/coverthb.jpg"}],"country":"United States","state":"Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -98.61328125,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 34.59704151614417\n ],\n [\n -94.1748046875,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 37.125286284966805\n ],\n [\n -98.61328125,\n 34.59704151614417\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Oklahoma-Texas Water Science Center
U.S. Geological Survey
1505 Ferguson Lane
Austin, TX 78754–4501
The lateral extent and vertical stability of salt marshes experiencing rising sea levels depend on interacting drivers and feedbacks with potential for non‐linear behaviors. A two‐dimensional transect model was developed to examine changes in marsh and upland forest lateral extent and to explore controls on marsh inland transgression. Model behavior demonstrates limited and abrupt forest retreat with long‐term upland boundary migration rates controlled by slope, sea level rise (SLR), high water events and biotic‐abiotic interactions. For low to moderate upland slopes the landward marsh edge is controlled by the interaction of these inundation events and forest recovery resulting in punctuated transgressive events. As SLR rates increase, the importance of the timing and frequency of water level deviations diminishes, and migration rates revert back to a slope‐SLR dominated process.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020GL088998","usgsCitation":"Carr, J., Guntenspergen, G.R., and Kirwan, M.L., 2020, Modelling marsh-forest boundary transgression in response to storms and sea-level rise: Geophysical Research Letters, v. 47, no. 17, e2020GL088998, 10 p., https://doi.org/10.1029/2020GL088998.","productDescription":"e2020GL088998, 10 p.","ipdsId":"IP-112010","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":455681,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2020gl088998","text":"Publisher Index Page"},{"id":436830,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XQ27F5","text":"USGS data release","linkHelpText":"Water levels (November 11 2016 through November 11 2017) for four wells and Light intensity data (October 1 2015 through September 2019): from marsh to upland forest, for Moneystump Marsh, Blackwater National Wildlife Refuge, Maryland"},{"id":377420,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"47","issue":"17","noUsgsAuthors":false,"publicationDate":"2020-09-02","publicationStatus":"PW","contributors":{"authors":[{"text":"Carr, Joel A. 0000-0002-9164-4156 jcarr@usgs.gov","orcid":"https://orcid.org/0000-0002-9164-4156","contributorId":168645,"corporation":false,"usgs":true,"family":"Carr","given":"Joel A.","email":"jcarr@usgs.gov","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795925,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Guntenspergen, Glenn R. 0000-0002-8593-0244 glenn_guntenspergen@usgs.gov","orcid":"https://orcid.org/0000-0002-8593-0244","contributorId":2885,"corporation":false,"usgs":true,"family":"Guntenspergen","given":"Glenn","email":"glenn_guntenspergen@usgs.gov","middleInitial":"R.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":795927,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kirwan, Matt L.","contributorId":189205,"corporation":false,"usgs":false,"family":"Kirwan","given":"Matt","middleInitial":"L.","affiliations":[],"preferred":false,"id":795926,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70196827,"text":"sim3406 - 2020 - Geomorphic map of western Whatcom County, Washington","interactions":[],"lastModifiedDate":"2021-11-29T11:25:56.313865","indexId":"sim3406","displayToPublicDate":"2020-08-10T14:26:38","publicationYear":"2020","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":"3406","displayTitle":"Geomorphic Map of Western Whatcom County, Washington","title":"Geomorphic map of western Whatcom County, Washington","docAbstract":"Western Whatcom County has a rich history of glaciation, sea-level change, fluvial erosion and deposition, landsliding, nearby volcanic activity, and human landscape modification. This lidar-derived geomorphic map interprets this history from the form and position of the Earth’s surface.
The geomorphic record is broken into nine phases, beginning with the peak of the Vashon stade of the Fraser glaciation of Armstrong and others (1965) (phase 1), shortly after 16,000 years ago. The Cordilleran ice sheet was ≥1.6 km thick in the Bellingham area. Glacial lineations on high ground demonstrate that ice flow was from north to south. Storage of water in ice sheets at this time resulted in global sea level ~120 m lower than at present. The weight of the ice sheet depressed the land so that local relative sea level was at least 150 m higher than at present. As the ice sheet melted and thinned, it floated, broke up, and was replaced by salt water. The margin of the ice sheet—or at least its grounding line—retreated to the northeast of the map area during or before phase 2. Marine deposition, currents, and waves smoothed earlier-formed surfaces in the western part of the map area. Global sea level rose (because of melting of continental ice sheets), but the Fraser Lowland rose even faster (due to glacio-isostatic rebound following the loss of ice-sheet load), and thus local relative sea level fell.
The Cordilleran ice sheet readvanced during the Sumas stade of Armstrong and others (1965). Oldest Sumas moraines formed when relative sea level at Bellingham was ~55 m (phase 3). Younger moraines formed when relative sea level at Bellingham was ~25 m (phase 4). The amount of Sumas ice retreat and readvance between these times is unknown. Younger Sumas events are marked by local moraines, progressive isostatic rebound and lowering of relative sea level, and changes in the flow of ice-marginal water. During phase 5, the southeast margin of the ice sheet advanced, perhaps because capture of ice-marginal drainage by the Samish River (east and south of the map area) meant the ice sheet was no longer trimmed by high-discharge flow along Squalicum channel. Farther west and north, the ice margin retreated between phases 4 and 5. Phases 6 through 9 may mark stillstands during further ice retreat. There were glacial outburst floods (jökulhlaups) during phases 7 and 8, and perhaps during phase 5.
When Sumas ice left the area, perhaps about 11,500 years ago, the Nooksack River appears to have discharged northeast through Sumas Valley to the Fraser River. Details of the switch to its modern course are speculative, but archaeological and sediment-supply arguments suggest that the modern Nooksack River delta south of Ferndale formed within the past 5,000 years.
The foothills of the North Cascades are decorated with abundant post-glacial deep-seated landslides. Anomalously high late Holocene beaches are found at Birch Bay, Neptune Beach, perhaps at Maple Beach on the east side of Point Roberts, and perhaps at the northwest corner of the Lummi Peninsula. These beaches may have been uplifted by earthquakes that did not rupture the surface.
The low-relief landscape shaped by the Cordilleran ice sheet, along with fluvial infilling of low areas, resulted in abundant wetland, at least 70 percent of which has been diked and (or) drained to control flooding and facilitate farming.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3406","collaboration":"Prepared in cooperation with Whatcom County and the Washington State Department of Natural Resources","usgsCitation":"Kovanen, D.J., Haugerud, R.A., and Easterbrook, D.J., 2020, Geomorphic map of western Whatcom County, Washington (ver. 1.1, November 2021): U.S. Geological Survey Scientific Investigations Map 3406, pamphlet 42 p., scale 1:50,000, https://doi.org/10.3133/sim3406.","productDescription":"Pamphlet: vi, 42 p.; Plate: 65.10 x 39.00 inches; Metadata; Read Me; 4 Databases; Version History","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-086454","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true}],"links":[{"id":392128,"rank":11,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sim/3406/versionHist.txt"},{"id":377137,"rank":10,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/XML_metadata.zip","text":"XML_metadata","size":"109 KB","linkFileType":{"id":6,"text":"zip"},"description":"XML_metadata.zip"},{"id":377136,"rank":9,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-simple.zip","text":"SIM3406-simple","size":"13.9 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-simple.zip"},{"id":377135,"rank":8,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-open.zip","text":"SIM3406-open","size":"13.7 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-open.zip"},{"id":377134,"rank":7,"type":{"id":9,"text":"Database"},"url":"https://pubs.usgs.gov/sim/3406/database/SIM3406-gdb.zip","text":"SIM3406-gdb","size":"62.6 MB","linkFileType":{"id":6,"text":"zip"},"description":"SIM3406-gdb.zip"},{"id":377133,"rank":6,"type":{"id":20,"text":"Read Me"},"url":"https://pubs.usgs.gov/sim/3406/00Readme.txt","size":"3 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3406 Read Me"},{"id":377132,"rank":5,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_metadata.xml","size":"18 KB xml","description":"SIM 3406 Metadata xml"},{"id":377131,"rank":4,"type":{"id":16,"text":"Metadata"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_metadata.txt","size":"17 KB","linkFileType":{"id":2,"text":"txt"},"description":"SIM 3406 Metadata text"},{"id":377130,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3406/sim3406.pdf","text":"Map","size":"16.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3406"},{"id":377129,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sim/3406/sim3406_pamphlet_v1.1.pdf","text":"Pamphlet","size":"11.4 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3406 Pamphlet"},{"id":377128,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3406/coverthb.jpg"}],"country":"United States","state":"Washington","county":"Whatcom County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -123.1185,\n 48.6169\n ],\n [\n -122.2390,\n 48.6169\n ],\n [\n -122.2390,\n 49.0156\n ],\n [\n -123.1185,\n 49.0156\n ],\n [\n -123.1185,\n 48.6169\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: August 2020; Version 1.1: November 2021","contact":"Geology, Minerals, Energy, & Geophysics Science Center
U.S. Geological Survey
345 Middlefield Road
Menlo Park, CA 94025
Hydrodynamic model hindcasts of the surface water and groundwater of the Everglades and the greater Miami, Florida, area were used to simulate hydrology using estimated storm surge height, wind field, and rainfall for the Great Miami Hurricane (GMH), which struck on September 18, 1926. Ranked estimates of losses from hurricanes in inflation-adjusted dollars indicate that the GMH was one of the most damaging tropical cyclones to make landfall in the United States, but little hydrologic data were collected because many types of field stations did not exist at the time. Several techniques were used to estimate previously unknown critical storm variables for model input, demonstrating the value of reanalyzing historical storm events using modern hydrodynamic modeling. This representation of the 1926 GMH was then used to develop a hypothetical simulation of the hydrologic effects of a similar hurricane occurring in contemporary (1996) times. Results indicate that the 18-centimeter sea-level rise between 1926 and 1996 had a greater effect on salinity intrusion than climatic differences or the development of modern canal-based infrastructure. Moreover, the post-1926 canal infrastructure does not seem to substantially mitigate the deleterious effects of sea-level rise.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201010","usgsCitation":"Krohn, M.D., Swain, E.D., Langtimm, C.A., and Obeysekera, J., 2020, Repurposing a hindcast simulation of the 1926 Great Miami Hurricane, south Florida: U.S. Geological Survey Open-File Report 2020–1010, 9 p., https://doi.org/10.3133/ofr20201010.","productDescription":"Report: iv, 9 p.; Data Release","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-073595","costCenters":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true},{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":375607,"rank":3,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9C681IV","text":"USGS data release","linkHelpText":"FTLOADDS (combined SWIFT2D surface-water model and SEAWAT groundwater model) simulator used to repurpose a hindcast simulation of the 1926 Great Miami Hurricane using the south Florida peninsula for the Biscayne and Southern Everglades Coastal Transport (BISECT) model"},{"id":375605,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1010/coverthb.jpg"},{"id":375606,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1010/ofr20201010.pdf","text":"Report","size":"2.64 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1010"}],"country":"United States","state":"Florida","city":"Miami","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -80.55999755859375,\n 25.209911213827688\n ],\n [\n -80.28533935546875,\n 25.199970890386023\n ],\n [\n -80.04638671875,\n 25.403584973186703\n ],\n [\n -80.04638671875,\n 26.23430203240673\n ],\n [\n -80.52978515625,\n 26.23430203240673\n ],\n [\n -80.55999755859375,\n 25.209911213827688\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Science Center
U.S. Geological Survey
4446 Pet Lane, Suite 108
Lutz, Florida 33559
Aeolian sediment transport, deposition, and erosion have been ongoing throughout Mars's history. This record of widespread aeolian processes is preserved in landforms and geologic units that retain important clues about past environmental conditions including wind patterns. In this study we describe landforms within Melas Chasma, Valles Marineris, that occur in distinct groups with linear to crescentic shapes, arranged with a characteristic wavelength; some possess slope profiles analogous to modern sand dunes yet show evidence for lithification. Based on the features' dimensions, asymmetry, and spatial patterns relative to modern equivalents, we interpret these landforms to be two classes of aeolian bedforms: decameter‐scale megaripples and sand dunes. The presence of superposed erosional features and depositional units indicates that these landforms were cemented and likely ancient. Melas paleodunes are found atop Hesperian‐aged layered deposits, but we estimate them to be younger, likely lithified in the Amazonian period. Although a range of degradation was observed, some paleodunes are >10 m tall and maintain steep lee sides (>25°), an uncommon scenario for terrestrial examples as other geologic processes lead to dune obliteration. The preserved paleobedform geometries are largely consistent with those of modern aeolian indicators, suggesting no major shifts in wind regime or contributing boundary conditions. Finally, we propose that their appearance and context require sequential periods of dune migration, stabilization following catastrophic burial, cementation, differential erosion, exposure, and burial. The presence of wholly preserved duneforms appears to be more common on Mars compared to the Earth and may signal something important about Martian landscape evolution.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2020JE006510","usgsCitation":"Chojnacki, M., Fenton, L.K., Weintraub, A., Edgar, L.A., Jodhpurkar, M.J., and Edwards, C., 2020, Ancient Martian aeolian sand dune deposits recorded in the stratigraphy of Valles Marineris and implications for past climates: Journal of Geophysical Research: Planets, v. 125, no. 9, e2020JE006510, 24 p., https://doi.org/10.1029/2020JE006510.","productDescription":"e2020JE006510, 24 p.","ipdsId":"IP-118939","costCenters":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"links":[{"id":488358,"rank":0,"type":{"id":41,"text":"Open Access External Repository Page"},"url":"http://hdl.handle.net/10150/657782","text":"External Repository"},{"id":377577,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"otherGeospatial":"Mars, Valles Marineris","volume":"125","issue":"9","noUsgsAuthors":false,"publicationDate":"2020-08-31","publicationStatus":"PW","contributors":{"authors":[{"text":"Chojnacki, Matthew","contributorId":201621,"corporation":false,"usgs":false,"family":"Chojnacki","given":"Matthew","affiliations":[{"id":27205,"text":"U. Arizona","active":true,"usgs":false}],"preferred":false,"id":796518,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Fenton, Lori K.","contributorId":208682,"corporation":false,"usgs":false,"family":"Fenton","given":"Lori","email":"","middleInitial":"K.","affiliations":[{"id":37319,"text":"SETI Institute","active":true,"usgs":false}],"preferred":false,"id":796519,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Weintraub, Aaron R","contributorId":238778,"corporation":false,"usgs":false,"family":"Weintraub","given":"Aaron R","affiliations":[{"id":12698,"text":"Northern Arizona University","active":true,"usgs":false}],"preferred":false,"id":796520,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edgar, Lauren A. 0000-0001-7512-7813 ledgar@usgs.gov","orcid":"https://orcid.org/0000-0001-7512-7813","contributorId":167501,"corporation":false,"usgs":true,"family":"Edgar","given":"Lauren","email":"ledgar@usgs.gov","middleInitial":"A.","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":796522,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Jodhpurkar, Mohini Jeetendra 0000-0002-5125-6650","orcid":"https://orcid.org/0000-0002-5125-6650","contributorId":238779,"corporation":false,"usgs":true,"family":"Jodhpurkar","given":"Mohini","email":"","middleInitial":"Jeetendra","affiliations":[{"id":131,"text":"Astrogeology Science Center","active":true,"usgs":true}],"preferred":true,"id":796521,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Edwards, Christopher S.","contributorId":206168,"corporation":false,"usgs":false,"family":"Edwards","given":"Christopher S.","affiliations":[{"id":7202,"text":"NAU","active":true,"usgs":false}],"preferred":false,"id":796523,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70212497,"text":"70212497 - 2020 - Ecosystem services of riparian restoration: A review of rock detention structures in the Madrean Archipelago Ecoregion","interactions":[],"lastModifiedDate":"2020-08-18T14:46:14.015324","indexId":"70212497","displayToPublicDate":"2020-08-07T09:40:10","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":686,"text":"Air, Soil and Water Research","active":true,"publicationSubtype":{"id":10}},"title":"Ecosystem services of riparian restoration: A review of rock detention structures in the Madrean Archipelago Ecoregion","docAbstract":"In northwestern Mexico and the southwestern United States, limited water supplies and fragile landscapes jeopardize world-renowned biological diversity. Simple rock detention structures have been used to manage agricultural water for over a thousand years and are now being installed to restore ecohydrological functionality but with little scientific evidence of their success. The impacts, design, and construction of such structures has been debated among local restoration practitioners, management, and permitting agencies. This article presents archeological documentation, local contentions, and examples of available research assessments of rock detention structures in the Madrean Archipelago Ecoregion. A US Geological Survey study to quantify impacts of rock detention structures using remote-sensing analyses, hydrologic monitoring, vegetation surveys, and watershed modeling is discussed, and results rendered in terms of the critical restoration ecosystem services provided. This framework provides a means for comparing management actions that might directly or indirectly impact human populations and assessing tradeoffs between them.","language":"English","publisher":"Sage Journals","doi":"10.1177/1178622120946337","usgsCitation":"Norman, L., 2020, Ecosystem services of riparian restoration: A review of rock detention structures in the Madrean Archipelago Ecoregion: Air, Soil and Water Research, v. 13, 13 p., https://doi.org/10.1177/1178622120946337.","productDescription":"13 p.","ipdsId":"IP-114137","costCenters":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"links":[{"id":455722,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1177/1178622120946337","text":"Publisher Index Page"},{"id":377603,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Mexico, United States","state":"Arizona, Chihuahua, New Mexico, Sonora","otherGeospatial":"Madrean Archipelago Ecoregion","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -111.610107421875,\n 29.372601506681402\n ],\n [\n -108.00659179687499,\n 29.372601506681402\n ],\n [\n -108.00659179687499,\n 33.486435450999885\n ],\n [\n -111.610107421875,\n 33.486435450999885\n ],\n [\n -111.610107421875,\n 29.372601506681402\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"13","noUsgsAuthors":false,"publicationDate":"2020-08-07","publicationStatus":"PW","contributors":{"authors":[{"text":"Norman, Laura M. 0000-0002-3696-8406","orcid":"https://orcid.org/0000-0002-3696-8406","contributorId":203300,"corporation":false,"usgs":true,"family":"Norman","given":"Laura M.","affiliations":[{"id":657,"text":"Western Geographic Science Center","active":true,"usgs":true}],"preferred":true,"id":796583,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70211816,"text":"70211816 - 2020 - Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico","interactions":[],"lastModifiedDate":"2020-08-07T20:03:25.527836","indexId":"70211816","displayToPublicDate":"2020-08-06T14:44:50","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2380,"text":"Journal of Marine Science and Engineering","active":true,"publicationSubtype":{"id":10}},"title":"Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico","docAbstract":"Turbidity currents deliver sediment rapidly from the continental shelf to the slope and beyond; and can be triggered by processes such as shelf resuspension during oceanic storms; mass failure of slope deposits due to sediment- and wave-pressure loadings; and localized events that grow into sustained currents via self-amplifying ignition. Because these operate over multiple spatial and temporal scales, ranging from the eddy-scale to continental-scale; coupled numerical models that represent the full transport pathway have proved elusive though individual models have been developed to describe each of these processes. Toward a more holistic tool, a numerical workflow was developed to address pathways for sediment routing from terrestrial and coastal sources, across the continental shelf and ultimately down continental slope canyons of the northern Gulf of Mexico, where offshore infrastructure is susceptible to damage by turbidity currents. Workflow components included: 1) a calibrated simulator for fluvial discharge (Water Balance Model - Sediment; WBMsed); 2) domain grids for seabed sediment textures (dbSEABED); bathymetry, and channelization; 3) a simulator for ocean dynamics and resuspension (the Regional Ocean Modeling System; ROMS); 4) A simulator (HurriSlip) of seafloor failure and flow ignition; and 5) A Reynolds-averaged Navier–Stokes (RANS) turbidity current model (TURBINS). Model simulations explored physical oceanic conditions that might generate turbidity currents, and allowed the workflow to be tested for a year that included two hurricanes. Results showed that extreme storms were especially effective at delivering sediment from coastal source areas to the deep sea, at timescales that ranged from individual wave events (~hours), to the settling lag of fine sediment (~days).
","language":"English","publisher":"MDPI","doi":"10.3390/jmse8080586","usgsCitation":"Harris, C.K., Syvitski, J., Arango, H., Meiburg, E.H., Cohen, S., Jenkins, C., Birchler, J.J., Hutton, E.W., Kniskern, T.A., Radhakrishnan, S., and Auad, G., 2020, Data-driven, multi-model workflow suggests strong influence from hurricanes on the generation of turbidity currents in the Gulf of Mexico: Journal of Marine Science and Engineering, v. 8, no. 8, 586, 28 p., https://doi.org/10.3390/jmse8080586.","productDescription":"586, 28 p.","ipdsId":"IP-109071","costCenters":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"links":[{"id":455731,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3390/jmse8080586","text":"Publisher Index Page"},{"id":377178,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alabama, Florida, Louisiana, Mississippi, Texas","otherGeospatial":"Gulf of Mexico","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -97.53662109375,\n 27.410785702577023\n ],\n [\n -83.56201171875,\n 27.410785702577023\n ],\n [\n -83.56201171875,\n 30.581179257386985\n ],\n [\n -97.53662109375,\n 30.581179257386985\n ],\n [\n -97.53662109375,\n 27.410785702577023\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"8","issue":"8","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Harris, Courtney K.","contributorId":19620,"corporation":false,"usgs":false,"family":"Harris","given":"Courtney","email":"","middleInitial":"K.","affiliations":[{"id":6708,"text":"Virginia Institute of Marine Science","active":true,"usgs":false}],"preferred":false,"id":795214,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Syvitski, Jaia","contributorId":237738,"corporation":false,"usgs":false,"family":"Syvitski","given":"Jaia","email":"","affiliations":[],"preferred":false,"id":795215,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Arango, H.G.","contributorId":103772,"corporation":false,"usgs":true,"family":"Arango","given":"H.G.","email":"","affiliations":[],"preferred":false,"id":795216,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Meiburg, E. H.","contributorId":237739,"corporation":false,"usgs":false,"family":"Meiburg","given":"E.","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":795217,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Cohen, Sagy","contributorId":202461,"corporation":false,"usgs":false,"family":"Cohen","given":"Sagy","email":"","affiliations":[{"id":36450,"text":"Department of Geography, University of Alabama, Tuscaloosa, AL 35487, USA","active":true,"usgs":false}],"preferred":false,"id":795218,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Jenkins, C.J.","contributorId":61244,"corporation":false,"usgs":true,"family":"Jenkins","given":"C.J.","email":"","affiliations":[],"preferred":false,"id":795219,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Birchler, Justin J. 0000-0002-0379-2192 jbirchler@usgs.gov","orcid":"https://orcid.org/0000-0002-0379-2192","contributorId":169117,"corporation":false,"usgs":true,"family":"Birchler","given":"Justin","email":"jbirchler@usgs.gov","middleInitial":"J.","affiliations":[{"id":574,"text":"St. Petersburg Coastal and Marine Science Center","active":true,"usgs":true}],"preferred":true,"id":795220,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Hutton, E. W. H.","contributorId":20940,"corporation":false,"usgs":true,"family":"Hutton","given":"E.","email":"","middleInitial":"W. H.","affiliations":[],"preferred":false,"id":795221,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Kniskern, T. A.","contributorId":42807,"corporation":false,"usgs":false,"family":"Kniskern","given":"T.","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":795222,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Radhakrishnan, S.","contributorId":237740,"corporation":false,"usgs":false,"family":"Radhakrishnan","given":"S.","email":"","affiliations":[],"preferred":false,"id":795223,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Auad, Guillermo","contributorId":78120,"corporation":false,"usgs":true,"family":"Auad","given":"Guillermo","email":"","affiliations":[],"preferred":false,"id":795224,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70211637,"text":"70211637 - 2020 - A hybrid approach for predictive soil property mapping using conventional soil survey data","interactions":[],"lastModifiedDate":"2020-09-10T20:19:02.656543","indexId":"70211637","displayToPublicDate":"2020-08-06T10:36:57","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3420,"text":"Soil Science Society of America Journal","active":true,"publicationSubtype":{"id":10}},"title":"A hybrid approach for predictive soil property mapping using conventional soil survey data","docAbstract":"Soil property maps are important for land management and earth systems modeling. A new hybrid point-disaggregation predictive soil property mapping strategy improved mapping in the Colorado River Basin, and can be applied to other areas with similar data (e.g. conterminous United States). This new approach increased sample size ~6-fold over past efforts. Random forests related environmental raster layers representing soil forming factors to samples to predict 15 soil properties (pH, texture fractions, rock, electrical conductivity, gypsum, CaCO3, sodium adsorption ratio, available water capacity, bulk density, erodibility, organic matter) at 7 depths, depth to restrictive layer, and surface rock size and cover. Cross-validations resulted in coefficient of determinations averaging 0.52, with a range of 0.20 to 0.76; and mean absolute errors ranged from 3% to 98% of training data averages with a mean of 41%. Uncertainty estimates were also developed by creating relative prediction intervals (RPIs) for the entire study area, which allow end users to evaluate uncertainty relative to original data distributions. Average error increased with higher RPI values (higher uncertainty), and areas with the highest RPI are consistently under-sampled, suggesting that additional sampling in these areas may improve prediction accuracy. Greater uncertainty was also observed in areas with shale parent materials and physiographic settings uncommon relative to the broader study area.","language":"English","publisher":"Wiley","doi":"10.1002/saj2.20080","usgsCitation":"Nauman, T.W., and Duniway, M.C., 2020, A hybrid approach for predictive soil property mapping using conventional soil survey data: Soil Science Society of America Journal, v. 84, no. 4, p. 170-1194, https://doi.org/10.1002/saj2.20080.","productDescription":"25 p.","startPage":"170","endPage":"1194","onlineOnly":"Y","ipdsId":"IP-108106","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":436834,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9SK0DO2","text":"USGS data release","linkHelpText":"Predictive soil property maps with prediction uncertainty at 30-meter resolution for the Colorado River Basin above Lake Mead"},{"id":377090,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"New Mexico, Colorado, Wyoming, Utah, Nevada, Arizona","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -106.61132812499999,\n 35.67514743608467\n ],\n [\n -107.490234375,\n 39.50404070558415\n ],\n [\n -108.720703125,\n 42.68243539838623\n ],\n [\n -110.302734375,\n 42.5530802889558\n ],\n [\n -112.1484375,\n 41.21172151054787\n ],\n [\n -113.818359375,\n 38.06539235133249\n ],\n [\n -115.6201171875,\n 37.3002752813443\n ],\n [\n -116.3671875,\n 36.527294814546245\n ],\n [\n -112.587890625,\n 34.56085936708384\n ],\n [\n -106.61132812499999,\n 35.67514743608467\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"84","issue":"4","noUsgsAuthors":false,"publicationDate":"2020-07-21","publicationStatus":"PW","contributors":{"authors":[{"text":"Nauman, Travis W. 0000-0001-8004-0608 tnauman@usgs.gov","orcid":"https://orcid.org/0000-0001-8004-0608","contributorId":169241,"corporation":false,"usgs":true,"family":"Nauman","given":"Travis","email":"tnauman@usgs.gov","middleInitial":"W.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794892,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Duniway, Michael C. 0000-0002-9643-2785 mduniway@usgs.gov","orcid":"https://orcid.org/0000-0002-9643-2785","contributorId":4212,"corporation":false,"usgs":true,"family":"Duniway","given":"Michael","email":"mduniway@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":794893,"contributorType":{"id":1,"text":"Authors"},"rank":2}]}} ,{"id":70211646,"text":"70211646 - 2020 - Evaluation of genetic structuring within GIS‐derived Brook Trout management units","interactions":[],"lastModifiedDate":"2021-01-25T15:51:59.457998","indexId":"70211646","displayToPublicDate":"2020-08-06T10:05:06","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3624,"text":"Transactions of the American Fisheries Society","active":true,"publicationSubtype":{"id":10}},"title":"Evaluation of genetic structuring within GIS‐derived Brook Trout management units","docAbstract":"Delineation of management units across broad spatial scales can help to visualize population structuring and identify conservation opportunities. Geographical information system (GIS) approaches can be useful for developing broad‐scale management units, especially when paired with field data that can validate the GIS‐based delineations. Genetic data can be useful for evaluating whether management units accurately represent population structuring. The Eastern Brook Trout Joint Venture, a regionwide collaborative group, delineated patch‐based management units for Brook Trout Salvelinus fontinalis by using GIS approaches to inform conservation strategies across the eastern United States. The objectives of this research were to (1) evaluate how well the patches predicted Brook Trout genetic structuring in Connecticut, USA; (2) modify the patches as needed to represent contemporary genetic structuring; and (3) identify catchment‐ and patch‐scale riverscape characteristics that predict genetic diversity. Patches with dams and high levels of upstream impervious surfaces (>3%) had increased intrapatch genetic structuring, which we incorporated into our revised patch delineation algorithm. Patch area and catchment area were the best predictors of genetic diversity, suggesting the importance of maintaining connectivity and incorporating patch‐scale processes into conservation actions. The modified patch layer could be used as the basis for Brook Trout management units to help predict population structuring in the absence of watershed‐scale genetic data, allowing opportunities for Brook Trout conservation to be identified.
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,{"id":70211647,"text":"70211647 - 2020 - Dynamics of lake trout production in the main basin of Lake Huron","interactions":[],"lastModifiedDate":"2020-08-06T14:10:58.01762","indexId":"70211647","displayToPublicDate":"2020-08-06T09:05:18","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1936,"text":"ICES Journal of Marine Science","active":true,"publicationSubtype":{"id":10}},"title":"Dynamics of lake trout production in the main basin of Lake Huron","docAbstract":"To inform lake trout (Salvelinus namaycush) fishery management in Lake Huron that has undergone rapid ecosystem changes, we quantified lake trout production dynamics by coupling age-structured stock assessment and fish bioenergetics models. Our approach revealed the connection between piscivore production and prey consumption, included growth compensation to reproduction losses, and allowed comparisons between long-term dynamics of fishery harvests and fish production. We found that despite the collapse of alewives, a major non-native pelagic prey fish, lake trout production appeared to be sustainable. To a certain degree, the effect of recent recruitment declines on lake trout production was offset by release of harvest pressure from subadult lake trout, and reduction of fishing and sea lamprey induced mortality on adult lake trout. Evidence for sustainability also included the finding that no changes in average ratios of annual production to beginning-of-the-year biomass. Juvenile P:B ratio remained as high as 2.1. The effect of growth declines on adult and subadult production was offset by reduction in population mortality. Body growth and condition did not continue to decline when lake trout became more and more reliant on round goby as food, and the dynamics of total consumption of prey fish continued to be recipient controlled.","language":"English","publisher":"ICES Journal of Marine Science","doi":"10.1093/icesjms/fsaa030","collaboration":"Michigan Department of Natural Resources, Michigan State University","usgsCitation":"He, J.X., Bence, J., Madenjian, C.P., and Claramunt, R.M., 2020, Dynamics of lake trout production in the main basin of Lake Huron: ICES Journal of Marine Science, v. 77, no. 3, p. 975-987, https://doi.org/10.1093/icesjms/fsaa030.","productDescription":"13 p.","startPage":"975","endPage":"987","ipdsId":"IP-111403","costCenters":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"links":[{"id":377081,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","otherGeospatial":"Lake Huron","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -84.6990966796875,\n 46.02366774426006\n ],\n [\n -84.7320556640625,\n 45.767522962149876\n ],\n [\n -84.232177734375,\n 45.63324613981234\n ],\n [\n -84.0838623046875,\n 45.49094569262732\n ],\n [\n -83.6993408203125,\n 45.38301927899065\n ],\n [\n -83.507080078125,\n 45.32897866218559\n ],\n [\n -83.4027099609375,\n 45.236217535866025\n ],\n [\n -83.2928466796875,\n 45.01141864227728\n ],\n [\n -83.43017578125,\n 45.042478050891546\n ],\n [\n -83.29833984375,\n 44.867549659447214\n ],\n [\n -83.3148193359375,\n 44.54742015866826\n ],\n [\n -83.3807373046875,\n 44.296332880058706\n ],\n [\n -82.9522705078125,\n 44.26093725039923\n ],\n [\n -82.254638671875,\n 44.296332880058706\n ],\n [\n -81.4031982421875,\n 44.953136827528816\n ],\n [\n -81.727294921875,\n 45.24008561090264\n ],\n [\n -82.177734375,\n 45.598665689820635\n ],\n [\n -83.056640625,\n 45.832626782661535\n ],\n [\n -83.375244140625,\n 45.836454050187726\n ],\n [\n -83.9190673828125,\n 45.98932892799953\n ],\n [\n -84.6990966796875,\n 46.02366774426006\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"77","issue":"3","noUsgsAuthors":false,"publicationDate":"2020-03-10","publicationStatus":"PW","contributors":{"authors":[{"text":"He, Ji X.","contributorId":181528,"corporation":false,"usgs":false,"family":"He","given":"Ji","email":"","middleInitial":"X.","affiliations":[],"preferred":false,"id":794918,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Bence, James R.","contributorId":95026,"corporation":false,"usgs":false,"family":"Bence","given":"James R.","affiliations":[{"id":6601,"text":"Michigan State University","active":true,"usgs":false}],"preferred":false,"id":794919,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Madenjian, Charles P. 0000-0002-0326-164X cmadenjian@usgs.gov","orcid":"https://orcid.org/0000-0002-0326-164X","contributorId":2200,"corporation":false,"usgs":true,"family":"Madenjian","given":"Charles","email":"cmadenjian@usgs.gov","middleInitial":"P.","affiliations":[{"id":324,"text":"Great Lakes Science Center","active":true,"usgs":true}],"preferred":true,"id":794920,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Claramunt, Randall M.","contributorId":190497,"corporation":false,"usgs":false,"family":"Claramunt","given":"Randall","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":794921,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70211994,"text":"70211994 - 2020 - Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska","interactions":[],"lastModifiedDate":"2020-08-13T12:56:44.564102","indexId":"70211994","displayToPublicDate":"2020-08-06T07:53:36","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":6009,"text":"Earth System Science Data (ESSD)","active":true,"publicationSubtype":{"id":10}},"title":"Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska","docAbstract":"Boreal ecosystems comprise one tenth of the world’s land surface and contain over 20 % of the global soil carbon (C) stocks. Boreal soils are unique in that its mineral soil is covered by what can be quite thick layers of organic soil. These organic soil layers, or horizons, can differ in their state of decomposition, source vegetation, and disturbance history. These differences result in varying soil properties (bulk density, C concentration, and nitrogen (N) concentration) among soil horizons. Here we summarize these soil properties, as represented by over 3000 samples from Interior Alaska, and examine how soil drainage and stand age affect these attributes. The summary values presented here can be used to gap-fill large datasets when important soil properties were not measured, provide data to initialize process-based models, and validate model results. These data are available at https://doi.org/10.5066/P960N1F9 (Manies, 2019).","language":"English","publisher":"Copernicus Publications","doi":"10.5194/essd-12-1745-2020","usgsCitation":"Manies, K.L., Waldrop, M., and Harden, J.W., 2020, Generalized models to estimate carbon and nitrogen stocks of organic soil horizons in Interior Alaska: Earth System Science Data (ESSD), v. 12, p. 1745-1757, https://doi.org/10.5194/essd-12-1745-2020.","productDescription":"13 p.","startPage":"1745","endPage":"1757","ipdsId":"IP-109891","costCenters":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":455749,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/essd-12-1745-2020","text":"Publisher Index Page"},{"id":377481,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Alaska","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -160.3125,\n 63.54855223203644\n ],\n [\n -142.734375,\n 63.54855223203644\n ],\n [\n -142.734375,\n 68.13885164925573\n ],\n [\n -160.3125,\n 68.13885164925573\n ],\n [\n -160.3125,\n 63.54855223203644\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","noUsgsAuthors":false,"publicationDate":"2020-08-06","publicationStatus":"PW","contributors":{"authors":[{"text":"Manies, Kristen L. 0000-0003-4941-9657 kmanies@usgs.gov","orcid":"https://orcid.org/0000-0003-4941-9657","contributorId":2136,"corporation":false,"usgs":true,"family":"Manies","given":"Kristen","email":"kmanies@usgs.gov","middleInitial":"L.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":796140,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Waldrop, Mark 0000-0003-1829-7140","orcid":"https://orcid.org/0000-0003-1829-7140","contributorId":216758,"corporation":false,"usgs":true,"family":"Waldrop","given":"Mark","affiliations":[],"preferred":true,"id":796141,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Harden, Jennifer W. 0000-0002-6570-8259 jharden@usgs.gov","orcid":"https://orcid.org/0000-0002-6570-8259","contributorId":1971,"corporation":false,"usgs":true,"family":"Harden","given":"Jennifer","email":"jharden@usgs.gov","middleInitial":"W.","affiliations":[{"id":312,"text":"Geology, Minerals, Energy, and Geophysics Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":796142,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70230035,"text":"70230035 - 2020 - Understanding the uncertainty in global forest carbon turnover","interactions":[],"lastModifiedDate":"2022-03-25T13:54:17.290291","indexId":"70230035","displayToPublicDate":"2020-08-05T08:50:31","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1011,"text":"Biogeosciences","active":true,"publicationSubtype":{"id":10}},"title":"Understanding the uncertainty in global forest carbon turnover","docAbstract":"The length of time that carbon remains in forest biomass is one of the largest uncertainties in the global carbon cycle, with both recent historical baselines and future responses to environmental change poorly constrained by available observations. In the absence of large-scale observations, models used for global assessments tend to fall back on simplified assumptions of the turnover rates of biomass and soil carbon pools. In this study, the biomass carbon turnover times calculated by an ensemble of contemporary terrestrial biosphere models (TBMs) are analysed to assess their current capability to accurately estimate biomass carbon turnover times in forests and how these times are anticipated to change in the future. Modelled baseline 1985–2014 global average forest biomass turnover times vary from 12.2 to 23.5 years between TBMs. TBM differences in phenological processes, which control allocation to, and turnover rate of, leaves and fine roots, are as important as tree mortality with regard to explaining the variation in total turnover among TBMs. The different governing mechanisms exhibited by each TBM result in a wide range of plausible turnover time projections for the end of the century. Based on these simulations, it is not possible to draw robust conclusions regarding likely future changes in turnover time, and thus biomass change, for different regions. Both spatial and temporal uncertainty in turnover time are strongly linked to model assumptions concerning plant functional type distributions and their controls. Thirteen model-based hypotheses of controls on turnover time are identified, along with recommendations for pragmatic steps to test them using existing and novel observations. Efforts to resolve uncertainty in turnover time, and thus its impacts on the future evolution of biomass carbon stocks across the world's forests, will need to address both mortality and establishment components of forest demography, as well as allocation of carbon to woody versus non-woody biomass growth.","language":"English","publisher":"European Geosciences Union","doi":"10.5194/bg-17-3961-2020","usgsCitation":"Pugh, T., Rademacher, T.T., Shafer, S., Steinkamp, J., Barichivich, J., Beckage, B., Haverd, V., Harper, A., Heinke, J., Nishina, K., Rammig, A., Sato, H., Arneth, A., Hantson, S., Hickler, T., Kautz, M., Quesada, B., Smith, B., and Thonicke, K., 2020, Understanding the uncertainty in global forest carbon turnover: Biogeosciences, v. 17, p. 3961-3989, https://doi.org/10.5194/bg-17-3961-2020.","productDescription":"29 p.","startPage":"3961","endPage":"3989","ipdsId":"IP-108104","costCenters":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"links":[{"id":455754,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.5194/bg-17-3961-2020","text":"Publisher Index Page"},{"id":397598,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"17","noUsgsAuthors":false,"publicationDate":"2020-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Pugh, Thomas A. M.","contributorId":289252,"corporation":false,"usgs":false,"family":"Pugh","given":"Thomas A. M.","affiliations":[{"id":62077,"text":"School of Geography, Earth & Environmental Sciences and Birmingham Institute of Forest Research, University of Birmingham, Birmingham, B15 2TT, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":838803,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Rademacher, Tim Tito","contributorId":289253,"corporation":false,"usgs":false,"family":"Rademacher","given":"Tim","email":"","middleInitial":"Tito","affiliations":[{"id":62079,"text":"Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA, USA","active":true,"usgs":false}],"preferred":false,"id":838804,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shafer, Sarah 0000-0003-3739-2637 sshafer@usgs.gov","orcid":"https://orcid.org/0000-0003-3739-2637","contributorId":149866,"corporation":false,"usgs":true,"family":"Shafer","given":"Sarah","email":"sshafer@usgs.gov","affiliations":[{"id":318,"text":"Geosciences and Environmental Change Science Center","active":true,"usgs":true}],"preferred":true,"id":838805,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Steinkamp, Jorg","contributorId":289254,"corporation":false,"usgs":false,"family":"Steinkamp","given":"Jorg","email":"","affiliations":[{"id":62080,"text":"Senckenberg Biodiversity and Climate Research Centre (BiK-F), Senckenberganlage 25, 60325 Frankfurt/Main, Germany","active":true,"usgs":false}],"preferred":false,"id":838806,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barichivich, Jonathan","contributorId":289255,"corporation":false,"usgs":false,"family":"Barichivich","given":"Jonathan","email":"","affiliations":[{"id":62081,"text":"Instituto de Conservación Biodiversidad y Territorio, Universidad Austral de Chile, Valdivia, Chile, and Center for Climate and Resilience Research, Santiago, Chile; Instituto de Geografía, Pontificia Universidad Católica de Valparaíso, Valparaíso, Chile","active":true,"usgs":false}],"preferred":false,"id":838807,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Beckage, Brian","contributorId":289256,"corporation":false,"usgs":false,"family":"Beckage","given":"Brian","email":"","affiliations":[{"id":62082,"text":"Department of Plant Biology & Department of Computer Science, University of Vermont, Burlington, VT 05405, USA","active":true,"usgs":false}],"preferred":false,"id":838808,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Haverd, Vanessa 0000-0003-4359-5895","orcid":"https://orcid.org/0000-0003-4359-5895","contributorId":245057,"corporation":false,"usgs":false,"family":"Haverd","given":"Vanessa","email":"","affiliations":[{"id":49073,"text":"CSIRO Oceans and Atmosphere, GPO Box 1700, Canberra, ACT, 2601 Australia","active":true,"usgs":false}],"preferred":false,"id":838809,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Harper, Anna","contributorId":289257,"corporation":false,"usgs":false,"family":"Harper","given":"Anna","email":"","affiliations":[{"id":62083,"text":"College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, UK","active":true,"usgs":false}],"preferred":false,"id":838810,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Heinke, Jens","contributorId":289258,"corporation":false,"usgs":false,"family":"Heinke","given":"Jens","email":"","affiliations":[{"id":62084,"text":"Potsdam-Institute for Climate Impact Research (PIK), Telegraphenberg, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":838811,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Nishina, Kazuya","contributorId":289259,"corporation":false,"usgs":false,"family":"Nishina","given":"Kazuya","email":"","affiliations":[{"id":62085,"text":"Institute of Arctic Climate and Environment Research (IACE), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showamachi, Kanazawa-ku, Yokohama, 236-0001, Japan","active":true,"usgs":false}],"preferred":false,"id":838812,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Rammig, Anja","contributorId":289260,"corporation":false,"usgs":false,"family":"Rammig","given":"Anja","email":"","affiliations":[{"id":62086,"text":"Technical University of Munich (TUM), School of Life Sciences Weihenstephan, Freising, Germany","active":true,"usgs":false}],"preferred":false,"id":838813,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Sato, Hisashi","contributorId":289261,"corporation":false,"usgs":false,"family":"Sato","given":"Hisashi","email":"","affiliations":[{"id":62085,"text":"Institute of Arctic Climate and Environment Research (IACE), Japan Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showamachi, Kanazawa-ku, Yokohama, 236-0001, Japan","active":true,"usgs":false}],"preferred":false,"id":838814,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Arneth, Almut","contributorId":289262,"corporation":false,"usgs":false,"family":"Arneth","given":"Almut","email":"","affiliations":[{"id":62088,"text":"Karlsruhe Institute of Technology, Institute of Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany","active":true,"usgs":false}],"preferred":false,"id":838815,"contributorType":{"id":1,"text":"Authors"},"rank":13},{"text":"Hantson, Stijn","contributorId":207242,"corporation":false,"usgs":false,"family":"Hantson","given":"Stijn","email":"","affiliations":[{"id":37495,"text":"Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany","active":true,"usgs":false}],"preferred":false,"id":838816,"contributorType":{"id":1,"text":"Authors"},"rank":14},{"text":"Hickler, Thomas","contributorId":245550,"corporation":false,"usgs":false,"family":"Hickler","given":"Thomas","affiliations":[{"id":27439,"text":"Senckenberg Biodiversity and Climate Research Centre","active":true,"usgs":false}],"preferred":false,"id":838817,"contributorType":{"id":1,"text":"Authors"},"rank":15},{"text":"Kautz, Markus","contributorId":203627,"corporation":false,"usgs":false,"family":"Kautz","given":"Markus","email":"","affiliations":[],"preferred":false,"id":838818,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Quesada, Benjamin","contributorId":289263,"corporation":false,"usgs":false,"family":"Quesada","given":"Benjamin","email":"","affiliations":[{"id":62088,"text":"Karlsruhe Institute of Technology, Institute of Atmospheric Environmental Research (IMK-IFU), Kreuzeckbahnstrasse 19, 82467, Garmisch-Partenkirchen, Germany","active":true,"usgs":false}],"preferred":false,"id":838819,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Smith, Benjamin","contributorId":171838,"corporation":false,"usgs":false,"family":"Smith","given":"Benjamin","affiliations":[],"preferred":false,"id":838820,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Thonicke, Kirsten","contributorId":289264,"corporation":false,"usgs":false,"family":"Thonicke","given":"Kirsten","email":"","affiliations":[{"id":62084,"text":"Potsdam-Institute for Climate Impact Research (PIK), Telegraphenberg, 14473 Potsdam, Germany","active":true,"usgs":false}],"preferred":false,"id":838821,"contributorType":{"id":1,"text":"Authors"},"rank":19}]}} ,{"id":70220557,"text":"70220557 - 2020 - The catastrophic decline of tortoises at a fenced natural area","interactions":[],"lastModifiedDate":"2021-05-19T12:37:06.051732","indexId":"70220557","displayToPublicDate":"2020-08-05T07:32:48","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3773,"text":"Wildlife Monographs","active":true,"publicationSubtype":{"id":10}},"title":"The catastrophic decline of tortoises at a fenced natural area","docAbstract":"Agassiz’s desert tortoise (Gopherus agassizii), a threatened species of the southwestern United States, has severely declined to the point where 76% of populations in critical habitat (Tortoise Conservation Areas) are below viability. The potential for rapid recovery of wild populations is low because females require 12–20 years to reach reproductive maturity and produce few eggs annually. We report on a 34‐year mark‐recapture study of tortoises initiated in 1979 at the Desert Tortoise Research Natural Area in the western Mojave Desert, California, USA, and provide substantive data on challenges faced by the species. In 1980, the United States Congress designated the Research Natural Area and protected the land from recreational vehicles, livestock grazing, and mining with a wildlife‐permeable fence. The 7.77‐km2 study area, centered on interpretive facilities, included land both within the Natural Area and outside the fence. We expected greater benefits to accrue to the tortoises and habitat inside compared to outside. Our objectives were to conduct a demographic study, analyze and model changes in the tortoise population and habitat, and compare the effectiveness of fencing to protect populations and habitat inside the fence versus outside, where populations and habitat were unprotected. We conducted surveys in spring in each of 7 survey years from 1979, when the fence was under construction, through 2012. We compared populations inside to those outside the fence by survey year for changes in distribution, structure by size and relative age, sex ratios, death rates of adults, and causes of death for all sizes of tortoises. We used a Bayesian implementation of a Jolly Seber model for mark‐recapture data. We modeled detection, density, growth and transition of tortoises to larger size‐age classes, movements from inside the protective fence to outside and vice versa, and survival. After the second and subsequent survey years, we added surveys to monitor vegetation and habitat changes, conduct health assessments, and collect data on counts of predators and predator sign. At the beginning of the study, counts and densities for all sizes of tortoises were high, but densities were approximately 24% higher inside the fence than outside. By 2002, the low point in densities, densities had declined 90% inside the fence and 95% outside. Between 2002 and 2012, the population inside the fence showed signs of improving with a 54% increase in density. Outside the fence, densities remained low. At the end of the study, when we considered the initial differences in location, densities inside the fence were roughly 2.5 times higher than outside. The pattern of densities was similar for male and female adults. When evaluating survival by blocks of years, survivorship was higher in 1979–1989 than in 1989–2002 (the low point) and highest from 2002 to 2012. Recruitment and survival of adult females into the population was important for growing the population, but survival of all sizes, including juveniles, was also critical.
","language":"English","publisher":"The Wildlife Society","doi":"10.1002/wmon.1052","usgsCitation":"Berry, K.H., Yee, J.L., Shields, T.A., and Stockton, L., 2020, The catastrophic decline of tortoises at a fenced natural area: Wildlife Monographs, v. 205, no. 1, p. 1-53, https://doi.org/10.1002/wmon.1052.","productDescription":"53 p.","startPage":"1","endPage":"53","ipdsId":"IP-114548","costCenters":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"links":[{"id":455757,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/wmon.1052","text":"Publisher Index Page"},{"id":436835,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY0HVH","text":"USGS data release","linkHelpText":"Demography and Habitat of Desert Tortoises at the Desert Tortoise Research Natural Area, Western Mojave Desert, California (1978 - 2014)"},{"id":436836,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BY0HVH","text":"USGS data release","linkHelpText":"Demography and Habitat of Desert Tortoises at the Desert Tortoise Research Natural Area, Western Mojave Desert, California (1978 - 2014)"},{"id":385754,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","otherGeospatial":"Desert Tortoise Research Natural Area","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -118.1634521484375,\n 34.97150033361733\n ],\n [\n -117.3504638671875,\n 34.97150033361733\n ],\n [\n -117.3504638671875,\n 35.48527461007853\n ],\n [\n -118.1634521484375,\n 35.48527461007853\n ],\n [\n -118.1634521484375,\n 34.97150033361733\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"205","issue":"1","noUsgsAuthors":false,"publicationDate":"2020-08-05","publicationStatus":"PW","contributors":{"authors":[{"text":"Berry, Kristin H. 0000-0003-1591-8394 kristin_berry@usgs.gov","orcid":"https://orcid.org/0000-0003-1591-8394","contributorId":437,"corporation":false,"usgs":true,"family":"Berry","given":"Kristin","email":"kristin_berry@usgs.gov","middleInitial":"H.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815990,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Yee, Julie L. 0000-0003-1782-157X julie_yee@usgs.gov","orcid":"https://orcid.org/0000-0003-1782-157X","contributorId":3246,"corporation":false,"usgs":true,"family":"Yee","given":"Julie","email":"julie_yee@usgs.gov","middleInitial":"L.","affiliations":[{"id":651,"text":"Western Ecological Research Center","active":true,"usgs":true}],"preferred":true,"id":815991,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Shields, Timothy A.","contributorId":190759,"corporation":false,"usgs":false,"family":"Shields","given":"Timothy","email":"","middleInitial":"A.","affiliations":[],"preferred":false,"id":815992,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Stockton, Laura","contributorId":258217,"corporation":false,"usgs":false,"family":"Stockton","given":"Laura","email":"","affiliations":[{"id":52242,"text":"Bakersfield, CA","active":true,"usgs":false}],"preferred":false,"id":815993,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70225726,"text":"70225726 - 2020 - Channel cross-section analysis for automated stream head identification","interactions":[],"lastModifiedDate":"2021-11-05T11:52:59.985895","indexId":"70225726","displayToPublicDate":"2020-08-05T06:50:40","publicationYear":"2020","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":7164,"text":"Environmental Modelling & Software","active":true,"publicationSubtype":{"id":10}},"title":"Channel cross-section analysis for automated stream head identification","docAbstract":"Headwater streams account for more than half of the streams in the United States by length. The substantial occurrence and susceptibility to change of headwater streams makes regular updating of related maps vital to the accuracy of associated analysis and display. Here we present work testing new methods of completely automated remote headwater stream identification using metrics derived from channel Digital Elevation Model (DEM) cross-sections. A jump in standard deviation of curvature (sK) is found to correlate with the presence of stream heads. Field and remotely validated stream and channel initiation points from 4 diverse study areas in North Carolina as well as a simulated surface are used to test the sK findings. The sK value within individual catchments equal to 0.5*Tukey's upper inner fence is found to be a reliable threshold for identifying the upslope extent of channels in varied landscapes.
Adjusted geomagnetic data are magnetometer measurements with provisional correction factors applied such that vector quantities are oriented in a local Cartesian frame in which the X axis points north, the Y axis points east, and the Z axis points down. These correction factors are determined from so-called absolute measurements, which are “ground truth” observations made in the field using specialized magnetometers and survey equipment that are (nearly) colocated with the automated and continuously running magnetic measurement instrumentation. Correction factors can be substantial, up to hundreds of nanoTeslas, depending on the geologic and geomagnetic characteristics of the observatory site. They also tend to evolve over time because of instrument response instability and changing site characteristics. Historically, correction factors were determined offline, up to 1 year or more post-measurement, and applied to raw measurements to produce “Definitive” data for scientific analysis. Growing demand for corrected real-time geomagnetic data to better support space weather operations motivated development of an “Adjusted” geomagnetic data product. Modern computational tools, and some notable practical concerns, dictated a transition to affine transformations in lieu of more traditional baseline corrections, as well as a calibration parameter estimation algorithm that is more robust and statistically optimal, and therefore better suited for automated and unsupervised execution. A theoretical basis for this algorithm is presented, along with a demonstration and validation based on a comparison of results obtained with traditional techniques. Discrepancies between Definitive corrected data and near real-time Adjusted data obtained using affine transformations are minimal, generally much less than 5 nanoTeslas per vector component, and less than 1 nanoTesla for the total field magnitude, which satisfies International Real-Time Magnetic Observatory Network (INTERMAGNET) standards.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201053","usgsCitation":"Rigler, E.J., and Claycomb, A.E., 2020, Adjusted geomagnetic data—Theoretical basis and validation: U.S. Geological Survey Open-File Report 2020–1053, 19 p., https://doi.org/10.3133/ofr20201053.","productDescription":"iv, 19 p.","onlineOnly":"Y","costCenters":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"links":[{"id":376988,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1053/coverthb.jpg"},{"id":376989,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1053/ofr20201053.pdf","text":"Report","size":"2.15 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020-1053"}],"contact":"Director, Geologic Hazards Science Center
U.S. Geological Survey
Box 25046, MS-966
Denver, CO 80225-0046
Methane hydrate was synthesized from pure water ice and flash frozen seawater, with varying amounts of sand or silt added. Electrical conductivity was determined by impedance spectroscopy, using equivalent circuit modeling to separate the effects of electrodes and to gain insight into conduction mechanisms. Silt and sand increase the conductivity of pure hydrate, we infer by contaminant NaCl contributing to conduction in hydrate, to values in agreement with resistivities observed in well logs through hydrate The addition of silt and sand lowers the conductivity of hydrate synthesized from seawater by an amount consistent with Archie's Law. All samples were characterized using cryogenic scanning electron microscopy and energy dispersive spectroscopy, which show good connectivity of salt and brine phases. Electrical conductivity measurements of pure hydrate and hydrate mixed with silt during pressure‐induced dissociation supports previous conclusions that sediment increases dissociation rate.
There is a growing body of tools available for science support for determining the fate and behavior of industrial and agricultural chemicals that are rapidly injected (“spilled”) into aquatic environments. A 2-day roundtable-style workshop was held by the U.S. Geological Survey (USGS) in Middleton, Wisconsin, in December 2017 to describe and explore existing Federal science support for spill fate and behavior tools used for inland spills, ongoing and new fate and behavior studies, and science gaps in planning and response tools as part of the USGS Midcontinent Region’s efforts to include spill response as part of its strategic plans. A total of 28 attendees representing a variety of Federal, State, and regional entities presented on programs and tools used in various aspects of spill response. Most programs and tools discussed were for spills in riverine environments but tools and applications for spills in lakes, on land surfaces, in urban storm sewer networks, and groundwater also were discussed. A primary workshop focus was to facilitate communication and increase potential for future collaboration among agencies for inland spill science support. The role and need for more USGS science support within the inland spill community was discussed. Enhanced communication is needed within the USGS and the U.S. Department of the Interior science programs, as well as within and among other agencies that do emergency planning and response. A main conclusion of the workshop was that there are untapped resources of the USGS outlined in the agency’s science strategy that could strengthen science support for fate and behavior tools in inland areas, especially in the Upper Mississippi River, Ohio River, and Great Lakes Basins where large freshwater resources overlap with dense corridors of oil and hazardous substances, with transportation networks, and with large populations centers.
Fate and behavior tools are being developed quickly for inland spill response by multiple Federal agencies in partnership with local and regional entities. Applicability of these tools ranges from planning and preparedness, to the early stages of spill response for protection of human life and property, and to the application of monitoring and models to assess the long-term consequences of spills. Key findings from the workshop, with an emphasis on potential further development of USGS science support, include the following:
•The national and regional response to spills occurs within an established system that must be respected by all parties involved in spill response. The USGS’s role is to support spill responders who are physically working at a spill scene, deploying booms and using other efforts to contain and recover spilled materials.
•The USGS has tools that have been used throughout spill response operations, from early response to recovery and restoration. Developing a more formal role for the USGS to participate in science support for inland spills on a consistent basis is a desired outcome. This will require the USGS to improve internal and external communication and would be best accomplished by assigning one or more coordinator positions within the agency to plan and oversee USGS spill-response efforts. More involvement of the USGS on National and Regional Response Teams, especially in the realm of the Science and Technology Subcommittees, will gofar in increasing external communication and integration of fate and behavior tools.
•Rapid response to spills requires modeling and mapping of plumes and associated time-of-travel estimation for a range of stream sizes across the United States. Many existing models use USGS streamgage data and the USGS National Hydrography Dataset. Nearly all existing models would benefit from updated linkages to USGS StreamStats and its soon-to-be released time-of-travel estimates,real-time velocity, stream morphology, and slope data. Integrating USGS tools with those from other agencies could be done to better serve the larger spill response community.
• A problem is that existing models to rapidly predict plume extent, as well as more followup/longer-term fate and transport models, can be unknown or unavailable to spill responders. Thus, creating and strengthening linkages among USGS scientists skilled at using these tools is needed to support spill response with the on-scene responders.
• Research for inland spill fate and behavior done outside of an immediate spill response can assist with spill planning and preparedness by (1) revealing sites likely to experience spills in the future (high-risk sites) and (2) understanding how a spilled substance might behave under a range of environmental conditions. However, USGS research on this topic has been scarce and subject to funding availability. Examples include the 2010 Line 6B Spill release into the Kalamazoo River in Michigan, where the USGS provided science support for a variety of fate and behavior tools for stream and impoundment environments. A long-term research site in Bemidji, Minnesota, provides important insights into transformations and longevity of spilled oil in groundwater and groundwater-surface water interactions.
• Linking stream models to other components of this inland environment, including groundwater, overland flow, and karst, is needed. Stream network data can be linked to underground conduits such as storm sewers and karst groundwater systems. Stream models can also be linked with geospatial data such as that contained in U.S. Environmental Protection Agency’s
interactive mapping tools.
• The USGS is uniquely qualified to collect water-quality data during spills in the United States because of its many geographically dispersed water science centers, its knowledge and preparedness for flood measurement and documentation, and its cadre of skilled water-quality employees. Rapid-deployment gages, used for floods, could also be used for spills if they included spill-specific sensors. Coordinated expertise at USGS water and environmental science centers can be used for monitoring spill effects and for assessing risk to water quality and ecological communities.
• Scientists at the USGS have proven capable of providing science coordination and technical assistance within the Incident Command Structure at the request of the lead on-scene coordinator. This external coordination, as well as internal communication within USGS Water, Hazards, and Ecosystems Mission Areas, could be improved by establishing and naming a USGS spills coordinator. Scott Morlock, Jo Ellen Hinck, and Faith Fitzpatrick are currently (2017) serving in informal coordination roles in addition to their traditional duties.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20201063","usgsCitation":"Sullivan, D.J., and Fitzpatrick, F.A., 2020, Fate and behavior tools related to inland spill response—Workshop on the U.S. Geological Survey’s role in Federal science support: U.S. Geological Survey Open-File Report 2020–1063, 22 p., https://doi.org/10.3133/ofr20201063.","productDescription":"v, 22 p.","numberOfPages":"32","onlineOnly":"Y","ipdsId":"IP-111089","costCenters":[{"id":37947,"text":"Upper Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":376920,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2020/1063/ofr20201063.pdf","text":"Report","size":"8.66 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2020–1063"},{"id":376919,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2020/1063/coverthb.jpg"}],"contact":"Director, Upper Midwest Water Science Center
U.S Geological Survey
8505 Research Way
Middleton, WI 53562