Transgression and regression of water levels (stages) have impacted the evolution of aeolian landforms and sedimentary deposits throughout geologic history. We studied this phenomenon over a five-day period of reduced flow on the Colorado River in Grand Canyon National Park, AZ, USA, in March 2021. These transient low flows exposed river-channel sand deposits to the air, causing progressive desiccation (drying) and thereby making these deposits susceptible to aeolian transport. We measured aeolian threshold friction velocities (u*t) for sand saltation and PM10 dust emissions, as well as other characteristics, on a subaerially exposed sandbar and downwind aeolian dunefield during each day of the low river flow. The sandbar transitioned from supply-limited to transport-limited aeolian sediment transport conditions during the regression in river water stage. A possible tipping point between the two transport conditions occurred approximately 48 hours after the drop in river flow. The empirically measured u*t decreased as the sandbar sediment dried with increased subaerial exposure time. Theoretical estimates and empirical measurements of u*t corresponded closely on the aeolian dunefield and on the sandbar when it was drier during the third and fourth day of the experiment. Eighty-seven percent of the variability in u*t was explained by empirical models that provide practical estimates of aeolian transport potential of subaerial river sediment deposits using monitoring data that are commonly available in this and other river systems. The work provides theoretical insight into the response of aeolian processes to sediment supply changes driven by periods of anthropogenic activity, drought, and climate change.
","language":"English","publisher":"American Geophysical Union","doi":"10.1029/2022JF006816","usgsCitation":"Sankey, J., Caster, J., Kasprak, A., and Fairley, H.C., 2022, The influence of drying on the aeolian transport of river-sourced sand: Journal of Geophysical Research Earth Surface, v. 127, no. 12, e2022JF006816, 24 p., https://doi.org/10.1029/2022JF006816.","productDescription":"e2022JF006816, 24 p.","ipdsId":"IP-142498","costCenters":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"links":[{"id":445764,"rank":1,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1029/2022jf006816","text":"Publisher Index Page"},{"id":435603,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P91WBUYO","text":"USGS data release","linkHelpText":"Threshold friction velocities for aeolian transport of river-sourced sand, with related moisture content, grain size, topographic, and wind data from Lees Ferry, Arizona"},{"id":409919,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona","otherGeospatial":"Grand Canyon National Park","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -111.35659478532732,\n 36.965267960408156\n ],\n [\n -114.03110966943333,\n 36.965267960408156\n ],\n [\n -114.03110966943333,\n 35.544550609550456\n ],\n [\n -111.35659478532732,\n 35.544550609550456\n ],\n [\n -111.35659478532732,\n 36.965267960408156\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"127","issue":"12","noUsgsAuthors":false,"publicationDate":"2022-12-13","publicationStatus":"PW","contributors":{"authors":[{"text":"Sankey, Joel B. 0000-0003-3150-4992","orcid":"https://orcid.org/0000-0003-3150-4992","contributorId":261248,"corporation":false,"usgs":true,"family":"Sankey","given":"Joel B.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858099,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Caster, Joshua 0000-0002-2858-1228 jcaster@usgs.gov","orcid":"https://orcid.org/0000-0002-2858-1228","contributorId":199033,"corporation":false,"usgs":true,"family":"Caster","given":"Joshua","email":"jcaster@usgs.gov","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858100,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Kasprak, Alan 0000-0001-8184-6128","orcid":"https://orcid.org/0000-0001-8184-6128","contributorId":204162,"corporation":false,"usgs":true,"family":"Kasprak","given":"Alan","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":true,"id":858101,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Fairley, Helen C. 0000-0001-6151-4804 hfairley@usgs.gov","orcid":"https://orcid.org/0000-0001-6151-4804","contributorId":3040,"corporation":false,"usgs":true,"family":"Fairley","given":"Helen","email":"hfairley@usgs.gov","middleInitial":"C.","affiliations":[{"id":568,"text":"Southwest Biological Science Center","active":true,"usgs":true}],"preferred":false,"id":858102,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70238688,"text":"70238688 - 2022 - Defining biologically relevant and hierarchically nested population units to inform wildlife management","interactions":[],"lastModifiedDate":"2022-12-05T13:03:45.51127","indexId":"70238688","displayToPublicDate":"2022-11-30T06:52:31","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1467,"text":"Ecology and Evolution","active":true,"publicationSubtype":{"id":10}},"title":"Defining biologically relevant and hierarchically nested population units to inform wildlife management","docAbstract":"Wildlife populations are increasingly affected by natural and anthropogenic changes that negatively alter biotic and abiotic processes at multiple spatiotemporal scales and therefore require increased wildlife management and conservation efforts. However, wildlife management boundaries frequently lack biological context and mechanisms to assess demographic data across the multiple spatiotemporal scales influencing populations. To address these limitations, we developed a novel approach to define biologically relevant subpopulations of hierarchically nested population levels that could facilitate managing and conserving wildlife populations and habitats. Our approach relied on the Spatial “K”luster Analysis by Tree Edge Removal clustering algorithm, which we applied in an agglomerative manner (bottom-to-top). We modified the clustering algorithm using a workflow and population structure tiers from least-cost paths, which captured biological inferences of habitat conditions (functional connectivity), dispersal capabilities (potential connectivity), genetic information, and functional processes affecting movements. The approach uniquely included context of habitat resources (biotic and abiotic) summarized at multiple spatial scales surrounding locations with breeding site fidelity and constraint-based rules (number of sites grouped and population structure tiers). We applied our approach to greater sage-grouse (Centrocercus urophasianus), a species of conservation concern, across their range within the western United States. This case study produced 13 hierarchically nested population levels (akin to cluster levels, each representing a collection of subpopulations of an increasing number of breeding sites). These closely approximated population closure at finer ecological scales (smaller subpopulation extents with fewer breeding sites; cluster levels ≥2), where >92% of individual sage-grouse's time occurred within their home cluster. With available population monitoring data, our approaches can support the investigation of factors affecting population dynamics at multiple scales and assist managers with making informed, targeted, and cost-effective decisions within an adaptive management framework. Importantly, our approach provides the flexibility of including species-relevant context, thereby supporting other wildlife characterized by site fidelity.
Levittown Lake is a 30-hectare, brackish waterbody located in the municipality of Toa Baja, on the northern coast of Puerto Rico. The lake is a small, man-made feature formed by draining the marshland over which the Levittown community was built. Levittown Lake has an average depth of about 5 meters and a water level at/near mean sea level. Tidal oscillations within the lake were minimal during the study, about 10 centimeters regardless of ocean tides, and the daily flushing rate of the lake was about 2 percent of its entire water volume.
Hydrologic, water-quality, and biological data were collected in Levittown Lake and adjacent areas (specifically, the inlet/outlet channel and Caño El Hato drainage canal) between April 2010 and June 2011 (1) to establish baseline conditions and determine the water quality of the lake on the basis of preestablished standards and (2) for contrast with other, more healthy coastal lagoons. The study provides a baseline for an assessment of the potential of Levittown Lake to function as a coastal lagoon.
Water-quality properties measured onsite (temperature, pH, dissolved oxygen concentration, specific conductance, salinity, and water transparency) varied diurnally and seasonally. In general, water-quality properties were in compliance with current regulatory Class SB standards established by the Puerto Rico Environmental Quality Board, except for some dissolved oxygen concentration and pH measurements. Some dissolved oxygen concentration measurements at the water surface and all dissolved oxygen concentration measurements at the lake bottom were lower than the values recommended by the Puerto Rico Environmental Quality Board. The pH of the water at the lake surface ranged from 7.3 to 9.1, with the upper value exceeding the recommended pH values. Nutrient concentrations were below the current regulatory standards of less than 5 milligrams per liter (mg/L) for total nitrogen and 1 mg/L for total phosphorus. The measured concentrations of chlorophyll a varied throughout the year of sampling and indicate that eutrophic conditions predominate in Levittown Lake.
The phytoplankton yielded an average net productivity of 0.5 milligram of oxygen per liter per hour, as determined by light and dark bottle primary productivity studies conducted on a monthly basis and measured in the early morning hours. Because these measurements were restricted to the morning hours, a qualification of the representativeness of the results to the full diurnal cycle is necessary. The measured hourly respiration rate averaged 0.39 milligram of oxygen per liter. Diel studies were planned in the lake to assess dissolved oxygen concentration diurnal curves and ultimately to compute the community net primary productivity, respiration, and gross productivity. Conditions during the diel studies were later determined to be unsuitable, limiting the assessment of community metabolism. Another biological indicator evaluated during the study was the phytoplankton biomass, and results indicated that phytoplankton biomass measured at the Levittown Lake ranged from 6.0 to 112.5 mg/L.
Fecal indicator bacteria concentrations ranged from 10 to 1,540,000 colonies per 100 milliliters of water. Concentrations generally were greatest in and near the Caño El Hato drainage canal and, during the study, exceeded current regulatory standards established for Puerto Rico.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225096","issn":"2328-0328","collaboration":"Prepared in cooperation with the Puerto Rico Department of Natural and Environmental Resources","usgsCitation":"Soler-López, L.R., Gómez-Fragoso, J.M., and Val-Merníz, N.A., 2022, Hydrology, water quality, and biological characteristics of Levittown Lake, Toa Baja, Puerto Rico, April 2010–June 2011: U.S. Geological Survey Scientific Investigations Report 2022–5096, 32 p., https://doi.org/10.3133/sir20225096.","productDescription":"Report: vii, 32 p.; Data Release; Dataset","numberOfPages":"44","onlineOnly":"Y","ipdsId":"IP-064860","costCenters":[{"id":27821,"text":"Caribbean-Florida Water Science Center","active":true,"usgs":true}],"links":[{"id":409442,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9MC6JZ6","text":"USGS data release","linkHelpText":"Data for the hydrologic and water-quality characterization of Levittown Lake, Toa Baja, Puerto Rico, April 2010–June 2011"},{"id":409802,"rank":7,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225096/full","text":"Report","linkFileType":{"id":5,"text":"html"}},{"id":409439,"rank":3,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5096/sir20225096.pdf","text":"Report","size":"1.73 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5096"},{"id":409438,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5096/coverthb.jpg"},{"id":409440,"rank":2,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5096/sir20225096.XML"},{"id":409441,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5096/images"},{"id":409443,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Puerto Rico","otherGeospatial":"Levittown Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -66.20418646107952,\n 18.468480510318614\n ],\n [\n -66.20418646107952,\n 18.43267147514682\n ],\n [\n -66.16780969765956,\n 18.43267147514682\n ],\n [\n -66.16780969765956,\n 18.468480510318614\n ],\n [\n -66.20418646107952,\n 18.468480510318614\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Caribbean-Florida Water Science Center
U.S. Geological Survey
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More than 840 publications, 575 data releases, and 330 project web pages from the U.S. Geological Survey (USGS) pertain to the Colorado River Basin. Limited interconnections between Colorado River Basin publications, data, and web pages restrict the ability to synthesize and interpret scientific resources. Currently, these pieces are spread across multiple isolated locations, internal systems, data repositories, and local offices. The increasing size, complexity, and diversity of Colorado River Basin data creates additional need for integration. These different data types—including discrete, continuous, aerial, remote sensing, geophysical, geospatial, and other types in varied formats—are collected over numerous time and space scales and require data-intensive science and technology to integrate.
Information management technology (IMT) resources are enterprise capabilities that the USGS workforce can leverage at multiple scales with consistent interoperable solutions to better facilitate integrated science. The USGS 21st Century Science Strategy directs the USGS to establish enterprise IMT capabilities that support integrated work through interoperable software and database solutions at multiple scales. This Information Management Technology Plan identifies nine steps to leverage new and existing technologies, data, models, and scientific knowledge to support integrated science projects conducted across the Colorado River Basin. These steps are transferable to integrated-science studies in other locations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/fs20223051","usgsCitation":"Anderson, E.D, Erxleben, J.R., Qi, S.L., Monroe, A.P., and Dahm, K.G., 2022, U.S. Geological Survey Colorado River Basin Actionable and Strategic Integrated Science and Technology (ASIST)—Information Management Technology Plan: U.S. Geological Survey Fact Sheet 2022-3051, 4 p., https://doi.org/10.3133/fs20223051.","productDescription":"4 p.","onlineOnly":"Y","ipdsId":"IP-132808","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":64844,"text":"Rocky Mountain Region Director’s Office","active":true,"usgs":true}],"links":[{"id":409861,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/fs20223051/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"FS 2022-3051"},{"id":409757,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/fs/2022/3051/coverthb.jpg"},{"id":409758,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/fs/2022/3051/fs20223051.pdf","text":"Report","size":"1.26 MB","linkFileType":{"id":1,"text":"pdf"},"description":"FS 2022-3051"},{"id":409760,"rank":3,"type":{"id":22,"text":"Related Work"},"url":"https://doi.org/10.3133/fs20223010","text":"USGS Fact Sheet 2022-3010—","linkHelpText":"Addressing Stakeholder Science Needs for Integrated Drought Science in the Colorado River Basin Fact Sheet 2022-3010"},{"id":409803,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/fs/2022/3051/images"},{"id":409804,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/fs/2022/3051/fs20223051.xml"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Utah, Wyoming","otherGeospatial":"Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -115.00488281250001,\n 32.65787573695528\n ],\n [\n -114.78515624999999,\n 31.840232667909365\n ],\n [\n -113.99414062499999,\n 31.541089879585808\n ],\n [\n -113.2470703125,\n 31.015278981711266\n ],\n [\n -112.0166015625,\n 30.14512718337613\n ],\n [\n -110.654296875,\n 29.878755346037977\n ],\n [\n -109.86328125,\n 29.99300228455108\n ],\n [\n -108.720703125,\n 30.600093873550072\n ],\n [\n -108.28125,\n 31.653381399664\n ],\n [\n -108.28125,\n 32.54681317351514\n ],\n [\n -107.9736328125,\n 33.87041555094183\n ],\n [\n -107.40234375,\n 34.23451236236987\n ],\n [\n -106.9189453125,\n 35.88905007936091\n ],\n [\n -106.69921875,\n 36.35052700542763\n ],\n [\n -106.3916015625,\n 37.23032838760387\n ],\n [\n -106.3916015625,\n 38.272688535980976\n ],\n [\n -106.3916015625,\n 39.13006024213511\n ],\n [\n -106.12792968749999,\n 40.84706035607122\n ],\n [\n -106.3037109375,\n 41.47566020027821\n ],\n [\n -107.09472656249999,\n 41.96765920367816\n ],\n [\n -108.67675781249999,\n 42.32606244456202\n ],\n [\n -109.7314453125,\n 43.03677585761058\n ],\n [\n -110.654296875,\n 43.45291889355465\n ],\n [\n -111.09374999999999,\n 43.389081939117496\n ],\n [\n -111.1376953125,\n 42.61779143282346\n ],\n [\n -111.005859375,\n 42.16340342422401\n ],\n [\n -110.830078125,\n 41.376808565702355\n ],\n [\n -111.0498046875,\n 40.51379915504413\n ],\n [\n -111.4013671875,\n 39.740986355883564\n ],\n [\n -111.533203125,\n 37.68382032669382\n ],\n [\n -112.19238281249999,\n 37.43997405227057\n ],\n [\n -113.203125,\n 37.3002752813443\n ],\n [\n -114.2138671875,\n 37.37015718405753\n ],\n [\n -114.521484375,\n 38.20365531807149\n ],\n [\n -115.13671875,\n 38.51378825951165\n ],\n [\n -115.400390625,\n 37.16031654673677\n ],\n [\n -115.1806640625,\n 35.92464453144099\n ],\n [\n -114.82910156249999,\n 34.994003757575776\n ],\n [\n -114.697265625,\n 33.7243396617476\n ],\n [\n -115.00488281250001,\n 32.65787573695528\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, Region 7 - Upper Colorado Basin
U.S. Geological Survey
Box 25046, MS-911
Denver, CO 80225-0046
Extreme precipitation events may cause flooding, slope failure, erosion, deposition, and damage to infrastructure over a regional scale, but the impacts of these events are often difficult to fully characterize. Regional-scale landscape change occurred during an extreme rain event in June 2012 in northeastern Minnesota. Landscape change was documented by 8,000 km2 of airborne lidar data collected before and after the event. Following improved alignment of the lidar point data and reducing error using insight from analysis of extensive stable areas, elevation differences were classified into map objects representing geomorphic change in relation to process and landscape position using object-based image analysis. This remote mapping compares favorably to field and imagery-based mapping and provides the basis for volumetric sediment budgeting. Elevation differences in these objects indicate that 4.5 × 106 ± 1.0 × 106 m3 of sediment was eroded in the study area. Of this, 2.5 × 106 ± 3.3 × 105 m3 was deposited in deposits on hillslopes and valley floors, and 2.0 × 106 ± 4.6 × 105 m3 were removed from watersheds and exported to the Saint Louis River Estuary and Lake Superior. Multivariate logistic regression analysis emphasized that topographic slope and presence of glaciolacustrine clay lithology are the primary control on landslide occurrence, and landslides occur most frequently on slopes within tens of meters of stream channels. These results provide the basis to anticipate the impacts of similar future storm events. Because precipitation events are forecast to continue to increase in frequency and intensity owing to climate change, characterizing and anticipating their effects may support hazard planning.
The U.S. Geological Survey, in cooperation with the Mississippi Band of Choctaw Indians (MBCI), conducted a baseline assessment of the physical, chemical, and biological quality of selected streams and rivers within and contiguous to the Pearl River Community (PRC) in 2017 and 2018. The MBCI is a federally recognized tribe with territories in Mississippi and Tennessee. MBCI Tribal government and communities have sovereign authority over their natural resources and are responsible for protecting the quality of waters within the Tribal lands from sources of pollution and restoring impaired waters. The quality of these surface waters has a profound effect upon the health and welfare of MBCI Tribal members. Data generated from this study may be used with other relevant water-quality data for comparison and development of Tribal water-quality standards.
The PRC territory is drained by the Pearl River and associated tributaries. Water-quality and biological samples were collected and habitat surveys were conducted at sites on the mainstem of the Pearl River and major tributaries of the Pearl River—Wolf Creek, Beasha Creek, Jones Creek, and Kentawka Creek. The selected stream sites represent a range of land use/land cover and potential sources of alteration and contamination from within their respective drainage areas. In particular, Wolf Creek watershed has the highest relative percentage of developed land.
Ambient physicochemical properties, major ions, nutrients, and organic wastewater compounds (OWCs) were analyzed quarterly from surface-water samples from October 2017 through August 2018. Physicochemical properties were also measured in June 2018 over a continuous 48-hour period. Trace elements and polycyclic aromatic hydrocarbons were analyzed from streambed sediments in August 2018. Biological samples included the collection of periphyton algae (August 2018), benthic macroinvertebrate (March 2017 and March 2018), and fish communities (April 2018). Physical stream habitat characteristics were assessed using qualitative (March 2017 and March 2018) and quantitative surveys (August 2018).
While not directly applicable, the State of Mississippi Water Quality Standards were used as reference to evaluate Tribal water quality. Physicochemical water-quality constituents—water temperature, specific conductance (SC), pH, and dissolved oxygen (DO)—were generally within natural ranges among sites and samples, with a few exceptions that exceeded existing Mississippi water-quality standards. pH and DO periodically were below the minimum State standards at some sampled sites. Specific conductance was also relatively high at both Wolf Creek sites but did not exceed the existing maximum standard for recreational waters.
The surface water among stream sites was predominantly calcium bicarbonate type, with a shift toward sodium-bicarbonate water type at the downstream Wolf Creek (Wolf DS) site. Major ion concentrations were generally highest at the Wolf Creek sites. Nutrient concentrations were also often highest at Wolf DS, but total nitrogen and total phosphorus periodically exceeded recommended State and Federal nutrient criteria thresholds among most sampled sites. Twenty-nine OWCs, including 10 known or suspected endocrine disruptors, were detected among sites. Concentrations of OWCs were relatively low, and only 19 percent of all detections were above the reporting level.
Concentrations of copper and nickel in streambed sediments were detected above consensus-based threshold-effect concentrations (TECs) at one site each, and arsenic and chromium exceeded TECs at most sites. Concentrations of all polycyclic aromatic hydrocarbons in streambed sediments were low and well below TECs at all sites.
The periphyton, macroinvertebrate, and fish communities at most sampled sites appear typical of central Mississippi streams; however, the diversity, composition, and abundance of taxa sampled from Wolf DS were particularly distinctive compared to other sampled stream sites. Periphyton taxa richness was low at both Wolf Creek sites, and both sites had greater abundances of diatom taxa, which are indicative of high nutrient concentrations, than of soft-algae taxa. Similarly, Wolf DS had relatively low macroinvertebrate diversity, the fewest Ephemeroptera, Plecoptera, and Trichoptera taxa, a high abundance of Tubificid taxa, and the lowest overall Mississippi-Benthic Index of Stream Quality score. Fish species richness was also relatively low at Wolf DS compared to some other sampled sites.
Habitat characteristics also appeared to be generally typical of most central Mississippi streams. Qualitative habitat assessment scores were at or above the regional least disturbed streams for Wolf DS, the upstream Wolf Creek (Wolf US) site, and Jones Creek. Habitat scores among the remaining sites indicate fair conditions. Quantitative and qualitative habitat characteristics indicate relatively lower habitat quality at the two Beasha Creek sites.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225090","collaboration":"Prepared in cooperation with the Mississippi Band of Choctaw Indians","usgsCitation":"Driver, L.J., Hicks, M.B., and Gill, A.C., 2022, Characterization of water quality, biology, and habitat of the Pearl River and selected tributaries contiguous to and within Tribal lands of the Pearl River Community of the Mississippi Band of Choctaw Indians, 2017–18: U.S. Geological Survey Scientific Investigations Report 2022–5090, 64 p., https://doi.org/10.3133/sir20225090.","productDescription":"Report: xi, 64 p.; Data Release; Dataset","numberOfPages":"80","onlineOnly":"Y","ipdsId":"IP-128827","costCenters":[{"id":24708,"text":"Lower Mississippi-Gulf Water Science Center","active":true,"usgs":true}],"links":[{"id":409703,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9BX5Z48","text":"USGS data release","linkHelpText":"Habitat and biological assemblage data of streams within Tribal lands of the Pearl River Community of the Mississippi Band of Choctaw Indians, 2017–18"},{"id":409699,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5090/coverthb.jpg"},{"id":409700,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5090/sir20225090.pdf","text":"Report","size":"2.39 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022–5090"},{"id":409701,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5090/sir20225090.XML"},{"id":409702,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5090/images"},{"id":409704,"rank":6,"type":{"id":28,"text":"Dataset"},"url":"https://doi.org/10.5066/F7P55KJN","text":"USGS National Water Information System database","linkHelpText":"—USGS water data for the Nation"}],"country":"United States","state":"Mississippi","otherGeospatial":"Pearl River Community","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -89,\n 32.8667\n ],\n [\n -89.5,\n 32.8667\n ],\n [\n -89.5,\n 32.7333\n ],\n [\n -89,\n 32.7333\n ],\n [\n -89,\n 32.8667\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Lower Mississippi-Gulf Water Science Center
U.S. Geological Survey
640 Grassmere Park, Suite 100
Nashville, TN 37211
The Conservation Reserve Enhancement Program is a program administered by the U.S. Department of Agriculture’s Farm Service Agency. The Secretary of Agriculture is required to submit an annual report to Congress on Conservation Reserve Enhancement Program agreements that, among other things, reports on the progress made towards fulfilling commitments outlined in the agreements. The U.S. Geological Survey developed an online reporting form designed to ensure that consistent information is submitted to the Farm Service Agency from Conservation Reserve Enhancement Program State partners. Combined with the automated importation of text from partner-provided forms to word-processing documents, individual State reports and annual reports to Congress can now be produced efficiently and in a standardized format. Use of a standardized reporting format will also assist the Farm Service Agency in collecting information needed to support ecosystem service quantifications that go beyond the quantifications required from partners to document progress towards meeting the specific purposes and objectives identified in each agreement. Addition of these overarching conservation effect quantifications builds upon past ecosystem services modeling efforts based on the Integrated Valuation of Ecosystem Services and Tradeoffs suite of open-source software models; these offer a spatially explicit means to quantify additional ecosystem services across diverse partners in a consistent manner. Data sources are currently available to provide much of the information needed to run these models and complete simulations that would facilitate the quantification and reporting of the societal values of conservation actions taken under the Conservation Reserve Enhancement Program. It is the aim of this report to provide the information needed to move towards widescale monitoring of the Nation’s ecosystem services in a natural accounting framework, similar to the framework used to value financial and human capital.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221104","collaboration":"Prepared in cooperation with the U.S. Department of Agriculture’s Farm Production and Conservation Business Center and Farm Service Agency","usgsCitation":"Mushet, D.M., and McKenna, O.P., 2022, Development of an online reporting format to facilitate the inclusion of ecosystem services into Conservation Reserve Enhancement Program reports: U.S. Geological Survey Open-File Report 2022–1104, 19 p., https://doi.org/10.3133/ofr20221104.","productDescription":"Report: vi, 19 p.; 5 Appendixes","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-141507","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":409698,"rank":10,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221104/full","text":"Report"},{"id":409675,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1104/coverthb.jpg"},{"id":409676,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104.pdf","text":"Report","size":"725 kB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022–1104"},{"id":409677,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104.XML"},{"id":409678,"rank":4,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix1.pdf","text":"Appendix 1","description":"OFR 2022–1104, Appendix 1","linkHelpText":"—Farm Service Agency Notice Implementing Use of Online Reporting Form"},{"id":409679,"rank":5,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix2.pdf","text":"Appendix 2","description":"OFR 2022–1104, Appendix 2","linkHelpText":"—A Guide for Completing Conservation Reserve Enhancement Program Annual Reports Using the New Online Reporting Form"},{"id":409681,"rank":6,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix4.pdf","text":"Appendix 4","description":"OFR 2022–1104, Appendix 4","linkHelpText":"—Microsoft Word Mail Merge State Report Template"},{"id":409682,"rank":7,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix5.pdf","text":"Appendix 5","description":"OFR 2022–1104, Appendix 5","linkHelpText":"—Draft Text Produced for 2020 Report to Congress"},{"id":409683,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/of/2022/1104/ofr20221104_appendix6.pdf","text":"Appendix 6","description":"OFR 2022–1104, Appendix 6","linkHelpText":"—Draft Text Produced for 2021 Report to Congress"},{"id":409687,"rank":9,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1104/images"}],"contact":"Director, Northern Prairie Wildlife Research Center
U.S. Geological Survey
8711 37th Street Southeast
Jamestown, ND 58401
Management of a widely distributed species can be a challenge when management priorities, resource status, and assessment methods vary across jurisdictions. For example, restoration and preservation of coregonine species diversity is a goal of management agencies across the Laurentian Great Lakes. However, management goals and the amount of information available varies across management units, making the focus for management efforts challenging to determine. Genetic data provide a spatially consistent means to assess diversity. Therefore, we examined the genetic stock structure of cisco (Coregonus artedi) in the Great Lakes where the species is still extant. Using genotype data from 17 microsatellite DNA loci, we observed low levels of population structure among collections with most contributions to overall diversity occurring among lakes. Cisco from lakes Superior, Michigan, Ontario, and the St. Marys River could be considered single genetic populations while distinct genetic populations were observed among samples from northern Lake Huron. Significant within-lake diversity in Lake Huron is supported by populations found in embayments in northern Lake Huron. The Grand Traverse Bay population in Lake Michigan represents a distinct population with reduced levels of genetic variation when compared to other lakes. The different levels of within lake population structure we observed will be important to consider as future lake-specific management plans are developed.
Stable hydrogen, carbon, nitrogen, oxygen and sulfur (HCNOS) isotope compositions expressed as isotope-delta values are typically reported relative to international standards such as Vienna Standard Mean Ocean Water (VSMOW), Vienna Peedee belemnite (VPDB) or Vienna Cañon Diablo Troilite (VCDT). These international standards are chosen by convention and the calibration methods used to realise them in practice undergo occasional changes. To ensure longevity and reusability of published data, a comprehensive description of (1) analytical procedure, (2) traceability, (3) data processing, and (4) uncertainty evaluation is required. Following earlier International Union of Pure and Applied Chemistry documents on terminology and notations, this paper proposes minimum requirements for publishing HCNOS stable-isotope delta results. Each of the requirements are presented with illustrative examples.
","language":"English","publisher":"De Gruyter","doi":"10.1515/pac-2021-1108","usgsCitation":"Skrzypek, G., Allison, C., Bohlke, J., Bontempo, L., Brewer, P., Camin, F., Carter, J.F., Chartrand, M.M., Coplen, T.B., Groning, M., Helie, J., Esquivel-Hernandez, G., Kraft, R., Magdas, D.A., Mann, J.L., Meija, J., Meijer, H.A., Moossen, H., Ogrinc, N., Perini, M., Possolo, A., Rogers, K., Schimmelmann, A., Shemesh, A., Soto, D.X., Thomas, F., Wielgosz, R., Winchester, M.R., Yan, Z., and Dunn, P.J., 2022, Minimum requirements for publishing hydrogen, carbon, nitrogen, oxygen and sulfur stable-isotope delta results (IUPAC Technical Report): Pure and Applied Chemistry, v. 94, no. 11-12, p. 1249-1255, https://doi.org/10.1515/pac-2021-1108.","productDescription":"7 p.","startPage":"1249","endPage":"1255","ipdsId":"IP-135649","costCenters":[{"id":37464,"text":"WMA - Laboratory & Analytical Services Division","active":true,"usgs":true}],"links":[{"id":445800,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1515/pac-2021-1108","text":"Publisher Index Page"},{"id":418125,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"94","issue":"11-12","noUsgsAuthors":false,"publicationDate":"2022-11-25","publicationStatus":"PW","contributors":{"authors":[{"text":"Skrzypek, Grzegorz 0000-0002-5686-2393","orcid":"https://orcid.org/0000-0002-5686-2393","contributorId":310369,"corporation":false,"usgs":false,"family":"Skrzypek","given":"Grzegorz","email":"","affiliations":[{"id":67153,"text":"West Australian Biogeochemistry Centre, School of Biological Sciences, The University of Western Australia, Crawley, Western Australia, Australia","active":true,"usgs":false}],"preferred":false,"id":875480,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Allison, Colin 0000-0002-3942-827X","orcid":"https://orcid.org/0000-0002-3942-827X","contributorId":310370,"corporation":false,"usgs":false,"family":"Allison","given":"Colin","email":"","affiliations":[{"id":67154,"text":"Commonwealth Scientific and Industrial Research Organisation, Oceans and Atmosphere, Aspendale, Victoria, Australia","active":true,"usgs":false}],"preferred":false,"id":875481,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bohlke, J.K. 0000-0001-5693-6455 jkbohlke@usgs.gov","orcid":"https://orcid.org/0000-0001-5693-6455","contributorId":191103,"corporation":false,"usgs":true,"family":"Bohlke","given":"J.K.","email":"jkbohlke@usgs.gov","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true}],"preferred":true,"id":875482,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Bontempo, Luana 0000-0001-7583-1501","orcid":"https://orcid.org/0000-0001-7583-1501","contributorId":310371,"corporation":false,"usgs":false,"family":"Bontempo","given":"Luana","email":"","affiliations":[{"id":67155,"text":"Food Quality and Nutrition Department, Research and Innovation Centre, Adige, Italy","active":true,"usgs":false}],"preferred":false,"id":875483,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Brewer, Paul","contributorId":310372,"corporation":false,"usgs":false,"family":"Brewer","given":"Paul","email":"","affiliations":[{"id":67156,"text":"National Physical Laboratory, Teddington, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":875484,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Camin, Federica","contributorId":243295,"corporation":false,"usgs":false,"family":"Camin","given":"Federica","email":"","affiliations":[{"id":48677,"text":"University of Treno, Italy","active":true,"usgs":false}],"preferred":false,"id":875485,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Carter, James F.","contributorId":310373,"corporation":false,"usgs":false,"family":"Carter","given":"James","email":"","middleInitial":"F.","affiliations":[{"id":67159,"text":"Queensland Health Forensic and Scientific Services, Archerfield, Australia","active":true,"usgs":false}],"preferred":false,"id":875486,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Chartrand, Michelle M.G. 0000-0003-3398-7246","orcid":"https://orcid.org/0000-0003-3398-7246","contributorId":310374,"corporation":false,"usgs":false,"family":"Chartrand","given":"Michelle","email":"","middleInitial":"M.G.","affiliations":[{"id":67160,"text":"National Research Council Canada, Ottawa, Ontario, Canada","active":true,"usgs":false}],"preferred":false,"id":875487,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Coplen, Tyler B. 0000-0003-4884-6008 tbcoplen@usgs.gov","orcid":"https://orcid.org/0000-0003-4884-6008","contributorId":508,"corporation":false,"usgs":true,"family":"Coplen","given":"Tyler","email":"tbcoplen@usgs.gov","middleInitial":"B.","affiliations":[{"id":436,"text":"National Research Program - 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J.","affiliations":[{"id":63918,"text":"University of Groningen, Groningen, The Netherlands","active":true,"usgs":false}],"preferred":false,"id":875496,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Moossen, Heiko","contributorId":260393,"corporation":false,"usgs":false,"family":"Moossen","given":"Heiko","email":"","affiliations":[{"id":52579,"text":"Max Planck Institute for Biogeochemistry, Jena, Germany","active":true,"usgs":false}],"preferred":false,"id":875497,"contributorType":{"id":1,"text":"Authors"},"rank":18},{"text":"Ogrinc, Nives","contributorId":243298,"corporation":false,"usgs":false,"family":"Ogrinc","given":"Nives","email":"","affiliations":[{"id":48679,"text":"Department of Environmental Sciences, Slovenia","active":true,"usgs":false}],"preferred":false,"id":875498,"contributorType":{"id":1,"text":"Authors"},"rank":19},{"text":"Perini, Matteo 0000-0002-9880-9590","orcid":"https://orcid.org/0000-0002-9880-9590","contributorId":310379,"corporation":false,"usgs":false,"family":"Perini","given":"Matteo","email":"","affiliations":[{"id":67164,"text":"Fondazione Edmund Mach, San Michele all'Adige, Italy","active":true,"usgs":false}],"preferred":false,"id":875499,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Possolo, Antonio 0000-0002-8691-4190","orcid":"https://orcid.org/0000-0002-8691-4190","contributorId":295934,"corporation":false,"usgs":false,"family":"Possolo","given":"Antonio","email":"","affiliations":[{"id":63946,"text":"National Institute of Standards and Technology, Gaithersburg, MD","active":true,"usgs":false}],"preferred":false,"id":875500,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Rogers, Karyne 0000-0001-8464-4337","orcid":"https://orcid.org/0000-0001-8464-4337","contributorId":310380,"corporation":false,"usgs":false,"family":"Rogers","given":"Karyne","email":"","affiliations":[{"id":67165,"text":"National Isotope Centre, GNS Science, Lower Hutt, New Zealand","active":true,"usgs":false}],"preferred":false,"id":875501,"contributorType":{"id":1,"text":"Authors"},"rank":22},{"text":"Schimmelmann, Arndt 0000-0003-4648-5253","orcid":"https://orcid.org/0000-0003-4648-5253","contributorId":243293,"corporation":false,"usgs":false,"family":"Schimmelmann","given":"Arndt","email":"","affiliations":[{"id":37145,"text":"Indiana University","active":true,"usgs":false}],"preferred":false,"id":875502,"contributorType":{"id":1,"text":"Authors"},"rank":23},{"text":"Shemesh, Aldo 0000-0003-3035-6377","orcid":"https://orcid.org/0000-0003-3035-6377","contributorId":310381,"corporation":false,"usgs":false,"family":"Shemesh","given":"Aldo","email":"","affiliations":[{"id":67166,"text":"Department of Earth and Planetary Sciences, The Weizmann Institute of Science, Rehovot, Israel","active":true,"usgs":false}],"preferred":false,"id":875503,"contributorType":{"id":1,"text":"Authors"},"rank":24},{"text":"Soto, David X.","contributorId":207729,"corporation":false,"usgs":false,"family":"Soto","given":"David","email":"","middleInitial":"X.","affiliations":[{"id":36681,"text":"Environment and Climate Change Canada","active":true,"usgs":false}],"preferred":false,"id":875504,"contributorType":{"id":1,"text":"Authors"},"rank":25},{"text":"Thomas, Freddy 0000-0002-8900-3368","orcid":"https://orcid.org/0000-0002-8900-3368","contributorId":310382,"corporation":false,"usgs":false,"family":"Thomas","given":"Freddy","email":"","affiliations":[{"id":67167,"text":"Eurofins Analytics France, Nantes, France","active":true,"usgs":false}],"preferred":false,"id":875505,"contributorType":{"id":1,"text":"Authors"},"rank":26},{"text":"Wielgosz, Robert","contributorId":310383,"corporation":false,"usgs":false,"family":"Wielgosz","given":"Robert","email":"","affiliations":[{"id":67168,"text":"Bureau International des Poids et Mesures, Sevres Cedex, France","active":true,"usgs":false}],"preferred":false,"id":875506,"contributorType":{"id":1,"text":"Authors"},"rank":27},{"text":"Winchester, Michael R. 0000-0002-7436-7599","orcid":"https://orcid.org/0000-0002-7436-7599","contributorId":310384,"corporation":false,"usgs":false,"family":"Winchester","given":"Michael","email":"","middleInitial":"R.","affiliations":[{"id":67162,"text":"National Institute of Standards and Technology, United States Department of Commerce, Gaithersburg, Maryland, USA","active":true,"usgs":false}],"preferred":false,"id":875507,"contributorType":{"id":1,"text":"Authors"},"rank":28},{"text":"Yan, Zhao","contributorId":310412,"corporation":false,"usgs":false,"family":"Yan","given":"Zhao","email":"","affiliations":[],"preferred":false,"id":875551,"contributorType":{"id":1,"text":"Authors"},"rank":29},{"text":"Dunn, Philip J. H. 0000-0002-3848-6187","orcid":"https://orcid.org/0000-0002-3848-6187","contributorId":310385,"corporation":false,"usgs":false,"family":"Dunn","given":"Philip","email":"","middleInitial":"J. H.","affiliations":[{"id":67169,"text":"National Measurement Laboratory, LGC, Teddington, United Kingdom","active":true,"usgs":false}],"preferred":false,"id":875509,"contributorType":{"id":1,"text":"Authors"},"rank":30}]}} ,{"id":70246258,"text":"70246258 - 2022 - A reappraisal of explosive–effusive silicic eruption dynamics: Syn-eruptive assembly of lava from the products of cryptic fragmentation","interactions":[],"lastModifiedDate":"2023-06-28T11:47:35.561379","indexId":"70246258","displayToPublicDate":"2022-11-24T06:46:09","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2499,"text":"Journal of Volcanology and Geothermal Research","active":true,"publicationSubtype":{"id":10}},"title":"A reappraisal of explosive–effusive silicic eruption dynamics: Syn-eruptive assembly of lava from the products of cryptic fragmentation","docAbstract":"Silicic volcanic eruptions range in style from gently effusive to highly explosive, and may switch style unpredictably during a single eruption. Direct observations of subaerial rhyolitic eruptions (Chaiten 2008, Cordón Caulle 2011–2012, Chile) challenged long-standing paradigms of explosive and effusive eruptive styles and led to the formulation of new models of hybrid activity. However, the processes that govern such hybrid explosive–effusive activity remain poorly understood. Here, we bring together observations of the well-studied 2011–2012 Cordón Caulle eruption with new textural and petrologic data on erupted products, and video and still imagery of the eruption. We infer that all of the activity – explosive, effusive, and hybrid – was fed by explosive fragmentation at depth, and that effusive behaviour arose from sticking and sintering, in the shallow vent region, of the clastic products of deeper, cryptic fragmentation. We use a scaling approach to determine that there is sufficient time available, during emplacement, for diffusive pyroclast degassing and sintering to produce a degassed plug that occludes the shallow conduit, feeding clastogenic, apparently effusive, lava-like deposits. Based on evidence from Cordón Caulle, and from other similar eruptions, we further argue that hybrid explosive–effusive activity is driven by episodic gas-fracking of the occluding lava plug, fed by the underlying pressurized ash- and pyroclast-laden region. The presence of a pressurized pocket of ash-laden gas within the conduit provides a mechanism for generation of harmonic tremor, and for syn-eruptive laccolith intrusion, both of which were features of the Cordón Caulle eruption. We conclude that the cryptic fragmentation models is more consistent with available evidence than the prevailing model for effusion of silicic lava that assume coherent non-fragmental rise of magma from depth to the surface without wholesale explosive fragmentation.
The increased risk of coastal flooding associated with climate-change driven sea level rise threatens to displace communities and cause substantial damage to infrastructure. Site-specific adaptation planning is necessary to mitigate the negative impacts of flooding on coastal residents and the built environment. Cost-benefit analyses used to evaluate coastal adaption strategies have traditionally focused on economic considerations, often overlooking potential demographic impacts that can directly influence vulnerability in coastal communities. Here, we present a transferable framework that couples hydrodynamic modeling of flooding driven by sea level rise and storm scenarios with site-specific building stock and census block-level demographic data. We assess the efficacy of multiple coastal adaptation strategies at reducing flooding, economic damages, and impacts to the local population. We apply this framework to evaluate a range of engineered, nature-based, and hybrid adaptation strategies for a portion of Santa Monica Bay, California. Overall, we find that dual approaches that provide protection along beaches using dunes or seawalls and along inlets using sluice gates perform best at reducing or eliminating flooding, damages, and population impacts. Adaptation strategies that include a sluice gate and partial or no protection along the beach are effective at reducing flooding around inlets but can exacerbate flooding elsewhere, leading to unintended impacts on residents. Our results also indicate trade-offs between economic and social risk-reduction priorities. The proposed framework allows for a comprehensive evaluation of coastal protection strategies across multiple objectives. Understanding how coastal adaptation strategies affect hydrodynamic, economic, and social factors at a local scale can enable more effective and equitable planning approaches.
Outdoor recreation provides societal benefits that are often measured by the amount of use natural resource systems receive. Still, the amount of resource use natural resource systems receive is often unknown or unstudied. Monitoring and quantifying resource use is often logistically difficult and costly but is paramount to optimize societal benefits. Identifying a simple and readily available metric that can indicate the quantity of recreational use of natural resource systems would benefit natural resource management. Using recreational angler participation data during an 11-year study period from 73 public waterbodies in Nebraska, USA, we developed a resource size-use model that demonstrates the ability of natural resource system size to indicate the quantity of recreational use they receive. We demonstrate how resource size-use models can estimate use for unsampled systems, produce broad-scale estimations of use, guide the allocation of resources, and predict how changes in resource system size may affect use. Resource size-use models provide opportunities to manage recreational use, which has been previously elusive for social-ecological systems.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolind.2022.109711","usgsCitation":"Kane, D.S., Pope, K.L., Koupal, K.D., Pegg, M., Chizinski, C., and Kaemingk, M.A., 2022, Natural resource system size can be used for managing recreational use: Ecological Indicators, v. 145, 109711, 7 p., https://doi.org/10.1016/j.ecolind.2022.109711.","productDescription":"109711, 7 p.","ipdsId":"IP-136542","costCenters":[{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"links":[{"id":445826,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolind.2022.109711","text":"Publisher Index Page"},{"id":433163,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United 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\"}}]}","volume":"145","noUsgsAuthors":false,"publicationStatus":"PW","contributors":{"authors":[{"text":"Kane, Derek S.","contributorId":341369,"corporation":false,"usgs":false,"family":"Kane","given":"Derek","email":"","middleInitial":"S.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908313,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Pope, Kevin L. 0000-0003-1876-1687","orcid":"https://orcid.org/0000-0003-1876-1687","contributorId":270762,"corporation":false,"usgs":true,"family":"Pope","given":"Kevin","email":"","middleInitial":"L.","affiliations":[{"id":506,"text":"Office of the AD Ecosystems","active":true,"usgs":true},{"id":198,"text":"Coop Res Unit Atlanta","active":true,"usgs":true}],"preferred":true,"id":908314,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Koupal, Keith D.","contributorId":341370,"corporation":false,"usgs":false,"family":"Koupal","given":"Keith","email":"","middleInitial":"D.","affiliations":[{"id":17640,"text":"Nebraska Game and Parks Commission","active":true,"usgs":false}],"preferred":false,"id":908315,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Pegg, Mark A.","contributorId":341371,"corporation":false,"usgs":false,"family":"Pegg","given":"Mark A.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908316,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Chizinski, Christopher J.","contributorId":341372,"corporation":false,"usgs":false,"family":"Chizinski","given":"Christopher J.","affiliations":[{"id":36892,"text":"University of Nebraska","active":true,"usgs":false}],"preferred":false,"id":908317,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Kaemingk, Mark A.","contributorId":341373,"corporation":false,"usgs":false,"family":"Kaemingk","given":"Mark","email":"","middleInitial":"A.","affiliations":[{"id":17628,"text":"University of North Dakota","active":true,"usgs":false}],"preferred":false,"id":908318,"contributorType":{"id":1,"text":"Authors"},"rank":6}]}} ,{"id":70261202,"text":"70261202 - 2022 - Variation in carbon and nitrogen concentrations among peatland categories at the global scale","interactions":[],"lastModifiedDate":"2024-11-29T16:03:20.242547","indexId":"70261202","displayToPublicDate":"2022-11-23T09:18:33","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Variation in carbon and nitrogen concentrations among peatland categories at the global scale","docAbstract":"Peatlands account for 15 to 30% of the world’s soil carbon (C) stock and are important controls over global nitrogen (N) cycles. However, C and N concentrations are known to vary among peatlands contributing to the uncertainty of global C inventories, but there are few global studies that relate peatland classification to peat chemistry. We analyzed 436 peat cores sampled in 24 countries across six continents and measured C, N, and organic matter (OM) content at three depths down to 70 cm. Sites were distinguished between northern (387) and tropical (49) peatlands and assigned to one of six distinct broadly recognized peatland categories that vary primarily along a pH gradient. Peat C and N concentrations, OM content, and C:N ratios differed significantly among peatland categories, but few differences in chemistry with depth were found within each category. Across all peatlands C and N concentrations in the 10–20 cm layer, were 440 ± 85.1 g kg-1 and 13.9 ± 7.4 g kg-1, with an average C:N ratio of 30.1 ± 20.8. Among peatland categories, median C concentrations were highest in bogs, poor fens and tropical swamps (446–532 g kg-1) and lowest in intermediate and extremely rich fens (375–414 g kg-1). The C:OM ratio in peat was similar across most peatland categories, except in deeper samples from ombrotrophic tropical peat swamps that were higher than other peatlands categories. Peat N concentrations and C:N ratios varied approximately two-fold among peatland categories and N concentrations tended to be higher (and C:N lower) in intermediate fens compared with other peatland types. This study reports on a unique data set and demonstrates that differences in peat C and OM concentrations among broadly classified peatland categories are predictable, which can aid future studies that use land cover assessments to refine global peatland C and N stocks.
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Virginia, 2016–2020","title":"Stormwater quantity and quality in selected urban watersheds in Hampton Roads, Virginia, 2016–2020","docAbstract":"Urbanization can substantially alter sediment and nutrient loadings to streams. Although a growing body of literature has documented these processes, conditions may vary widely by region and physiographic province (PP). Substantial investments are made by localities to meet federal, state, and local water-quality goals and locally relevant monitoring data are needed to appropriately set standards and track progress. In 2016, a long-term stormwater monitoring program was initiated to characterize water-quality and streamflow conditions and compute average annual nutrient- and sediment-loading rates across the three dominant land-use types—commercial (COM), high-density residential, and single-family residential (SFR)—in the Hampton Roads metropolitan region within the Coastal Plain PP in southeastern Virginia. This report summarizes the first five years of data collection to (1) assess patterns in streamflow and water chemistry across the three major land-use types in the region; (2) compute annual sediment and nutrient loads; and (3) compare annual loading rates to those in other urbanized regions.
Patterns in watershed hydrology characteristics and conditions were similar to those observed in other urban monitoring studies. Base-flow indices were lower and stream flashiness indices were higher in the study watersheds compared to those in less developed reference watersheds. These patterns reflect a decrease in infiltration and consequent increase in storm runoff as a result of urbanization. Stream flashiness was strongly positively related to degree of impervious land cover and negatively to watershed area. Hydrologic metrics varied across the land-use gradient, reflecting greater and more rapid runoff in the COM watersheds than in SFR watersheds. Event-based analyses conducted exclusively on periods of runoff highlight longer duration events, longer time-to-peak streamflow, and a longer lag between peak precipitation and peak streamflow in SFR watersheds, and higher stormflow yields, runoff ratios, and peak flows in COM watersheds. Event-based metrics varied seasonally because of regional meteorological patterns.
Concentrations of total suspended solids (TSS) and total phosphorus (TP) were positively correlated to streamflow, whereas concentrations of total nitrogen (TN) varied little across the hydrologic regime. Phosphorus composition varied spatially and seasonally—the proportion of orthophosphate (PO43-) was highest in samples collected from stations draining residential land-use types and was elevated in summer and fall. Nitrogen composition varied with hydrologic condition: nitrate plus nitrite (NO3-) dominance during base flow shifted to total organic nitrogen (TON) dominance during periods of runoff. For all three major constituents (TSS, TP, and TN), concentrations were highest in SFR watersheds, whereas yields were greatest in COM watersheds. This seeming contradiction in concentration and yield across land-use types occurred because of spatial differences in streamflow yield.
The network average TSS yield in Hampton Roads was lower than that in comparable networks in Fairfax County, Virginia, and Gwinnett County, Georgia, a difference that may reflect dissimilarities in the topographic and soil characteristics of the Coastal Plain versus those in Piedmont PPs, as well as differences in engineered concrete stormwater conveyances versus earthen streams. The average annual TP yield in Hampton Roads was higher than averages reported in comparison studies and was primarily driven by elevated PO43-. Elevated PO43- yields may be related to unique soil and geological features of the Coastal Plain PP that limit phosphorus retention. Total nitrogen yields in the Hampton Roads and Fairfax County networks were similar; however, composition did vary, with greater total organic nitrogen yields in Hampton Roads and greater NO3- yields in Fairfax County.
Cross-correlation analyses and mass-volume curves were used to assess the timing of sediment and nutrient loadings. The majority of TSS and TP was typically transported during the initial phase of a storm-runoff event, a phenomenon commonly termed the “first flush.” Although TN concentrations typically peaked within an hour of peak streamflow, reflecting the particulate dominance of TN during stormflows, and loadings were greater during the early phase of most storm events, the stricter first-flush criterion was rarely met. This suggests that the most abundant sources of TN in these watersheds are not as directly connected to the stormwater-conveyance system as are TSS and TP.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20225111","isbn":"978-1-4113-4488-4","collaboration":"Prepared in cooperation with the Hampton Roads Planning District Commission","usgsCitation":"Porter, A.J., 2022, Stormwater quantity and quality in selected urban watersheds in Hampton Roads, Virginia, 2016–2020: U.S. Geological Survey Scientific Investigations Report 2022–5111, 77 p., https://doi.org/10.3133/sir20225111.","productDescription":"Report: xi, 77 p.; Data Release","numberOfPages":"77","onlineOnly":"N","additionalOnlineFiles":"N","ipdsId":"IP-140434","costCenters":[{"id":37280,"text":"Virginia and West Virginia Water Science Center ","active":true,"usgs":true}],"links":[{"id":409552,"rank":6,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/sir20225111/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"SIR 2022-5111"},{"id":409543,"rank":5,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9XMPEND","text":"USGS data release","linkHelpText":"Inputs and selected outputs used to assess stormwater quality and quantity in twelve urban watersheds in Hampton Roads, Virginia, 2016–2020"},{"id":409542,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sir/2022/5111/images/"},{"id":409539,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2022/5111/sir20225111.pdf","text":"Report","size":"10.9 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2022-5111"},{"id":409541,"rank":3,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sir/2022/5111/sir20225111.XML"},{"id":409538,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2022/5111/coverthb.jpg"}],"country":"United States","state":"Virginia","city":"Hampton Roads","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -76.14335620670732,\n 36.90313369880009\n ],\n [\n -76.31812685340054,\n 37.07905097755574\n ],\n [\n -76.51786473533596,\n 37.10821243098252\n ],\n [\n -76.55620727517176,\n 37.13025388344366\n ],\n [\n -76.59365716752067,\n 37.16650105362895\n ],\n [\n -76.62219115065432,\n 37.1295423598058\n ],\n [\n -76.47773786104024,\n 37.03208397181615\n ],\n [\n -76.4179948338545,\n 36.95587973488442\n ],\n [\n -76.23519900440459,\n 36.804670263024434\n ],\n [\n -76.11036282819533,\n 36.821803342395526\n ],\n [\n -76.14335620670732,\n 36.90313369880009\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Virginia and West Virginia Water Science Center
U.S. Geological Survey
1730 East Parham Road
Richmond, Virginia 23228
In this report, we consider evolutionary and ecological connectivity for westslope cutthroat trout (Oncorhynchus clarkii lewisi) and mountain whitefish (Prosopium williamsoni) within the Pend Oreille River in northeastern Washington State, northern Idaho, and adjacent portions of southeastern British Columbia, Canada. Specifically, we focused on the rationale for active translocation of individuals of these species upstream from Boundary Dam both in the context of natural patterns of pre-dam evolutionary connectivity as well as preserving contemporary ecological and evolutionary characteristics of local extant populations. Boundary Dam impounds the Pend Oreille River (called the Pend d’Oreille River in Canada) with the resulting reservoir inundating two historical barriers to upstream movement of fish (Metaline Falls and Z Canyon). Historically, it was thought these barriers impeded the upstream movement of westslope cutthroat trout and mountain whitefish, as well as Pacific salmon (Oncorhynchus spp.), steelhead trout (O. mykiss), and other resident species such as bull trout (Salvelinus confluentus). To address connectivity, we consider historical and contemporary processes and features. This review includes an assessment of postglacial processes within the Pend Oreille River and systems upstream that include Priest Lake, Lake Pend Oreille, the Clark Fork River, features of Boundary Reservoir and its tributaries, and areas downstream in the Pend Oreille River such as the Salmo River. Based on this information, we then give a more detailed review of existing genetic and ecological data to summarize what is known about connectivity for westslope cutthroat trout and mountain whitefish. Our assessment of the collective evidence leads us to conclude that moving fish upstream over Boundary Dam is not warranted if the management objective is to maintain natural patterns of evolutionary and ecological connectivity or to conserve unique ecological and evolutionary characteristics of extant local populations of these species in the system. These findings parallel that of a previous analysis of bull trout. Although we were able to arrive at well-supported conclusions in relation to Boundary Dam, we suggest that more work on connectivity further upstream in the Pend Oreille River would help to better understand the role of historical processes and dams further up in the system.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221084","collaboration":"Prepared in cooperation with the University of British Columbia, Biodiversity Research Centre and Beaty Biodiversity Museum, and Idaho State University, Department of Biological Sciences, Fish Ecology Laboratory","usgsCitation":"Dunham, J.B., Taylor, E.B., and Keeley, E.R., 2022, Evolutionary and ecological connectivity in westslope cutthroat\ntrout (Oncorhynchus clarkii lewisi) and mountain whitefish (Prosopium williamsoni) in relation to the potential\ninfluences of Boundary Dam, Washington, Idaho, and parts of British Columbia: U.S. Geological Survey Open-File\nReport 2022–1084, 22 p., https://doi.org/10.3133/ofr20221084.","productDescription":"vii, 22 p.","onlineOnly":"Y","ipdsId":"IP-137003","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":409558,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1084/coverthb.jpg"},{"id":409559,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1084/ofr20221084.pdf","text":"Report","size":"4.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1084"},{"id":409561,"rank":3,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1084/images"},{"id":409562,"rank":4,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1084/ofr20221084.XML"}],"country":"Canada, United States","state":"British Columbia, Idaho, Washington","otherGeospatial":"Boundary Dam","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -117.81638376433206,\n 49.098372085467105\n ],\n [\n -117.81638376433206,\n 47.528691502768\n ],\n [\n -113.48236882623914,\n 47.528691502768\n ],\n [\n -113.48236882623914,\n 49.098372085467105\n ],\n [\n -117.81638376433206,\n 49.098372085467105\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Forest and Rangeland Ecosystem Science Center
777 NW 9th Street, Suite 400
Corvallis, OR 97330
The design of successful invasive species control programs is often hindered by the absence of basic demographic data on the targeted population. Establishment of invasive Burmese pythons (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida USA has led to local precipitous declines (> 90%) of mesomammal populations and is also a major threat to native populations of reptiles and birds. Efforts to control this species are ongoing but are hampered by the lack of access to and information on the expected biological patterns of pythons in southern Florida. We present data from more than 4,000 wild Burmese pythons that were removed in southern Florida over 26 years (1995–2021), the most robust dataset representing this invasive population to date. We used these data to characterize Burmese python size distribution, size at maturity, clutch size, and seasonal demographic and reproductive trends. We broadened the previously described size ranges by sex and, based on our newly defined size-stage classes, showed that males are smaller than females at sexual maturity, confirmed a positive correlation between maternal body size and potential clutch size, and developed predictive equations to facilitate demographic predictions. We also refined the annual breeding season (approx.100 days December into March), oviposition timing (May), and hatchling emergence and dispersal period (July through October) using correlations of capture morphometrics with observations of seasonal gonadal recrudescence (resurgence) and regression. Determination of reproductive output and timing can inform population models and help managers arrest population growth by targeting key aspects of python life history. These results define characteristics of the species in Florida and provide an enhanced understanding of the ecology and reproductive biology of Burmese pythons in their invasive Everglades range.
","language":"English","publisher":"Pensoft","doi":"10.3897/neobiota.78.93788","usgsCitation":"Currylow, A.F., Falk, B., Yackel Adams, A.A., Romagosa, C., Josimovich, J., Rochford, M., Cherkiss, M., Nafus, M., Hart, K., Mazzotti, F., Snow, R.W., and Reed, R., 2022, Size distribution and reproductive phenology of the invasive Burmese python (Python molurus bivittatus) in the Greater Everglades Ecosystem, Florida, USA: NeoBiota, v. 78, p. 129-158, https://doi.org/10.3897/neobiota.78.93788.","productDescription":"30 p.","startPage":"129","endPage":"158","ipdsId":"IP-144474","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true},{"id":5049,"text":"Pacific Islands Ecosys Research Center","active":true,"usgs":true},{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"links":[{"id":445837,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3897/neobiota.78.93788","text":"Publisher Index Page"},{"id":409686,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Florida","otherGeospatial":"Greater Everglades Ecosystem","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -80.05393175290448,\n 26.86978990604409\n ],\n [\n -82.2443372550706,\n 26.86978990604409\n ],\n [\n -82.2443372550706,\n 24.285980539160136\n ],\n [\n -80.05393175290448,\n 24.285980539160136\n ],\n [\n -80.05393175290448,\n 26.86978990604409\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"78","noUsgsAuthors":false,"publicationDate":"2022-11-23","publicationStatus":"PW","contributors":{"authors":[{"text":"Currylow, Andrea Faye 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0000-0002-5257-7974","orcid":"https://orcid.org/0000-0002-5257-7974","contributorId":220333,"corporation":false,"usgs":true,"family":"Hart","given":"Kristen","affiliations":[{"id":17705,"text":"Wetland and Aquatic Research Center","active":true,"usgs":true}],"preferred":true,"id":857550,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Mazzotti, Frank J.","contributorId":12358,"corporation":false,"usgs":false,"family":"Mazzotti","given":"Frank J.","affiliations":[{"id":12604,"text":"Department of Wildlife Ecology and Conservation, Fort Lauderdale Research and Education Center, 3205 College Avenue, University of Florida, Davie, FL 33314, USA","active":true,"usgs":false}],"preferred":false,"id":857551,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Snow, Ray W.","contributorId":76449,"corporation":false,"usgs":false,"family":"Snow","given":"Ray","email":"","middleInitial":"W.","affiliations":[{"id":13415,"text":"Everglades National 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Map","code":"SIM","onlineIssn":"2329-132X","printIssn":"2329-1311","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"3499","displayTitle":"Bathymetric Map and Surface Area and Capacity Table for Table Rock Lake near Branson, Missouri, 2020","title":"Bathymetric map and surface area and capacity table for Table Rock Lake near Branson, Missouri, 2020","docAbstract":"Table Rock Lake was completed in 1958 on the White River in southwestern Missouri and northwestern Arkansas for flood control, hydroelectric power, public water supply, and recreation. The surface area of Table Rock Lake is about 42,400 acres, and about 715 miles of shoreline are at the conservation pool level (915 feet above the North American Vertical Datum of 1988). Sedimentation in reservoirs can result in reduced water storage capacity and a reduction in usable aquatic habitat; therefore, accurate and up-to-date estimates of reservoir water capacity are important for managing pool levels, power generation, recreation, and downstream aquatic habitat. Many of the lakes operated by the U.S. Army Corps of Engineers are periodically surveyed to monitor bathymetric changes that affect water capacity. In October and November 2020, the U.S. Geological Survey, in cooperation with the U.S. Army Corps of Engineers, completed one such survey of Table Rock Lake using a multibeam echosounder. The echosounder data were combined with U.S. Geological Survey 1/3 arc-second digital elevation model data and light detection and ranging (lidar) data, where present, to prepare a bathymetric map and a surface area and capacity table up to the flood pool elevation of 931 feet above the North American Vertical Datum of 1988.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sim3499","collaboration":"Prepared in cooperation with the U.S. Army Corps of Engineers, Southwestern Division, Little Rock District","usgsCitation":"Huizinga, R.J., Rivers, B.C., and Richards, J.M., 2022, Bathymetric map and surface area and capacity table for Table Rock Lake near Branson, Missouri, 2020: U.S. Geological Survey Scientific Investigations Map 3499, 3 sheets, https://doi.org/10.3133/sim3499.","productDescription":"3 Sheets: 40.00 × 48.00 inches or smaller; Data Release","onlineOnly":"Y","ipdsId":"IP-137684","costCenters":[{"id":36532,"text":"Central Midwest Water Science Center","active":true,"usgs":true}],"links":[{"id":501939,"rank":9,"type":{"id":36,"text":"NGMDB Index Page"},"url":"https://ngmdb.usgs.gov/Prodesc/proddesc_113828.htm","linkFileType":{"id":5,"text":"html"}},{"id":409452,"rank":8,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.er.usgs.gov/publication/sim3499/full","text":"Report"},{"id":409450,"rank":7,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/P9FAFJZG","text":"USGS data release","linkHelpText":"Bathymetric and supporting data for Table Rock Lake near Branson, Missouri, 2020"},{"id":409449,"rank":6,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/sim/3499/images"},{"id":409448,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/sim/3499/sim3499.XML"},{"id":409447,"rank":4,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3499/sim3499_sheet03.pdf","text":"Sheet 3","size":"3.43 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3499 sheet 3"},{"id":409446,"rank":3,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3499/sim3499_sheet02.pdf","text":"Sheet 2","size":"3.13 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3499 sheet 2"},{"id":409445,"rank":2,"type":{"id":26,"text":"Sheet"},"url":"https://pubs.usgs.gov/sim/3499/sim3499_sheet01.pdf","text":"Sheet 1","size":"4.77 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIM 3499 sheet 1"},{"id":409444,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sim/3499/coverthb.jpg"}],"country":"United States","state":"Missouri","city":"Branson","otherGeospatial":"Table Rock Lake","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -93.46162698604762,\n 36.673080825378904\n ],\n [\n -93.46162698604762,\n 36.45192970827965\n ],\n [\n -93.25043226339268,\n 36.45192970827965\n ],\n [\n -93.25043226339268,\n 36.673080825378904\n ],\n [\n -93.46162698604762,\n 36.673080825378904\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Central Midwest Water Science Center
U.S. Geological Survey
1400 Independence Road
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Soil moisture directly affects operational hydrology, food security, ecosystem services, and the climate system. However, the adoption of soil moisture data has been slow due to inconsistent data collection, poor standardization, and typically short record duration. Soil moisture, or quantitatively volumetric soil water content (SWC), is measured using buried, in situ sensors that infer SWC from an electromagnetic response. This signal can vary considerably with local site conditions such as clay content and mineralogy, soil salinity or bulk electrical conductivity, and soil temperature; each of these can have varying impacts depending on the sensor technology.,
Furthermore, poor soil contact and sensor degradation can affect the quality of these readings over time. Unlike more traditional environmental sensors, there are no accepted standards, maintenance practices, or quality controls for SWC data. As such, SWC is a challenging measurement for many environmental monitoring networks to implement. Here, we attempt to establish a community-based standard of practice for in situ SWC sensors so that future research and applications have consistent guidance on site selection, sensor installation, data interpretation, and long-term maintenance of monitoring stations.,
The videography focuses on a multi-agency consensus of best-practices and recommendations for the installation of in situ SWC sensors. This paper presents an overview of this protocol along with the various steps essential for high-quality and long-term SWC data collection. This protocol will be of use to scientists and engineers hoping to deploy a single station or an entire network.
","language":"English","publisher":"MyJoVE Corporation","doi":"10.3791/64498","usgsCitation":"Caldwell, T., Cosh, M.H., Evett, S.R., Edwards, N., Hofman, H., Illston, B., Meyers, T.P., Skumanich, M., and Sutcliffe, K., 2022, In situ soil moisture sensors in undisturbed soils: Journal of Visualized Experiments, no. 189, e64498, 35 p., https://doi.org/10.3791/64498.","productDescription":"e64498, 35 p.","ipdsId":"IP-131823","costCenters":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"links":[{"id":445855,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://repository.library.noaa.gov/view/noaa/62072","text":"Publisher Index Page"},{"id":409993,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"issue":"189","noUsgsAuthors":false,"publicationDate":"2022-11-18","publicationStatus":"PW","contributors":{"authors":[{"text":"Caldwell, Todd 0000-0003-4068-0648","orcid":"https://orcid.org/0000-0003-4068-0648","contributorId":217924,"corporation":false,"usgs":true,"family":"Caldwell","given":"Todd","email":"","affiliations":[{"id":465,"text":"Nevada Water Science Center","active":true,"usgs":true}],"preferred":true,"id":857431,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Cosh, Michael H.","contributorId":146998,"corporation":false,"usgs":false,"family":"Cosh","given":"Michael","email":"","middleInitial":"H.","affiliations":[],"preferred":false,"id":857432,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Evett, Steven R. 0000-0003-3418-5771","orcid":"https://orcid.org/0000-0003-3418-5771","contributorId":244949,"corporation":false,"usgs":false,"family":"Evett","given":"Steven","email":"","middleInitial":"R.","affiliations":[{"id":18168,"text":"USDA ARS","active":true,"usgs":false}],"preferred":false,"id":857433,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Edwards, Nathan","contributorId":260132,"corporation":false,"usgs":false,"family":"Edwards","given":"Nathan","email":"","affiliations":[{"id":5089,"text":"South Dakota State University","active":true,"usgs":false}],"preferred":false,"id":857434,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hofman, Heather","contributorId":260134,"corporation":false,"usgs":false,"family":"Hofman","given":"Heather","email":"","affiliations":[{"id":52518,"text":"USDA NRCS National Climate Center","active":true,"usgs":false}],"preferred":false,"id":857435,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Illston, Bradley","contributorId":299264,"corporation":false,"usgs":false,"family":"Illston","given":"Bradley","email":"","affiliations":[{"id":7062,"text":"University of Oklahoma","active":true,"usgs":false}],"preferred":false,"id":857436,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Meyers, Tilden P.","contributorId":146138,"corporation":false,"usgs":false,"family":"Meyers","given":"Tilden","email":"","middleInitial":"P.","affiliations":[{"id":16598,"text":"NOAA/ATDD","active":true,"usgs":false}],"preferred":false,"id":857437,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Skumanich, Marina","contributorId":260137,"corporation":false,"usgs":false,"family":"Skumanich","given":"Marina","email":"","affiliations":[{"id":52519,"text":"NOAA National Integrated Drought Information System","active":true,"usgs":false}],"preferred":false,"id":857438,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Sutcliffe, Kent","contributorId":299265,"corporation":false,"usgs":false,"family":"Sutcliffe","given":"Kent","email":"","affiliations":[{"id":36658,"text":"U.S. Department of Agriculture","active":true,"usgs":false}],"preferred":false,"id":857439,"contributorType":{"id":1,"text":"Authors"},"rank":9}]}} ,{"id":70257255,"text":"70257255 - 2022 - Rainforest carnivore ecology in a managed forest reserve: Differential seasonal correlates between habitat components and relative abundance","interactions":[],"lastModifiedDate":"2024-08-14T12:04:58.815419","indexId":"70257255","displayToPublicDate":"2022-11-18T07:04:02","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1015,"text":"Biological Conservation","active":true,"publicationSubtype":{"id":10}},"title":"Rainforest carnivore ecology in a managed forest reserve: Differential seasonal correlates between habitat components and relative abundance","docAbstract":"Studies of relationships between seasons and Neotropical carnivore distributions tend to focus on water and prey availability without considering other habitat components such as escape, foraging, and resting cover. Our goal was to evaluate habitat characteristics that may be important for predicting the seasonal (dry or rainy) relative abundance of four commonly captured Neotropical carnivores (i.e., jaguar [Panthera onca], puma [Puma concolor], ocelot [Leopardus pardalis], and grey fox [Urocyon cinereoargenteus]) in Chiquibul Forest Reserve in Belize, Central America. We used trail camera data and random-effect Poisson models to investigate how prey ratios (number of prey detections/total detections), cover (e.g., logs and stumps used for hiding cover from predators), vegetation structure, and environmental site characteristics (e.g., site harvest-history, slope, aspect) were related to carnivore relative abundance. Both prey ratios and vegetation structure appeared in supported models more frequently than other environmental site characteristics and were negatively correlated with carnivore relative abundance. Supported models differed for each season for all species except jaguars for which mammalian prey ratios and prey cover at sites was always negatively correlated with jaguar relative abundance. Carnivores appeared to avoid sites where vegetation created ideal escape and hiding cover for prey even though prey may be less abundant. Our data suggest that vegetation structure and composition can create conditions conducive to carnivore foraging and that these characteristics can differ by season in the tropics.
Imaging sonar was used to assess the behavior, abundance, and timing of fish at the entrances to the Selective Water Withdrawal (SWW) intake structure located in the forebay of Round Butte Dam, Oregon during the spring of 2021. The purposes of the SWW are (1) to direct surface currents in the forebay to attract and collect downriver migrating juvenile salmonid smolts (Chinook salmon [Oncorhynchus tshawytscha], sockeye salmon [O. nerka], and steelhead [O. mykiss]) from Lake Billy Chinook and (2) to enable operators of the SWW to withdraw water from surface and benthic elevations in the reservoir to manage downriver water temperatures. Part of the evaluation, to determine how well the structure performs at collecting juvenile salmonids, needs (A) to regularly assess how fish are approaching the entrance, and (B) to determine if operational flows could be optimized to increase the attraction of smolts present in the forebay of Lake Billy Chinook. The primary goals of this study were (1) to assess the abundance and behaviors of smolt-size fish observed near the SWW and (2) to provide data of the effect of two-night generation operation timing conditions on movements and behaviors of fish near the entrance to the SWW structure. The purpose of this assessment is to improve downstream passage solutions.
Two imaging sonar units were deployed during the spring 2021 smolt out-migration period. One unit monitored fish movements near the south entrance and one unit monitored movements near the north entrance of the SWW. Both smolt and bull trout (Salvelinus confluentus)-size fish were regularly observed near the entrances with greater abundances observed during night, corresponding with greater discharge through the SWW than during the day when discharge was reduced. Differences in fish abundance were observed between the night generation operation timing conditions, with increased fish counts observed when elevated discharge was extended to 6:00 a.m., rather than when discharges have been traditionally reduced in the early morning at 4:00 a.m. Fish of all size groups were primarily observed near the center of the SWW, and greater abundances of fish were observed at the south entrance. Increased counts of bull trout-size fish coincided with the increased abundances of smolt-size fish. Overall, the results indicate that (A) smolt-size fish were more abundant near the entrance of the SWW during periods of increased discharge, (B) bull trout-size fish were present at the SWW, and (C) fish were more numerous at the SWW when night generation operation timing was extended later into the morning hours rather than the traditional operation timing flow reduction.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20221098","collaboration":"Prepared in cooperation with Portland General Electric","usgsCitation":"Smith, C.D., and Hatton, T.W., 2022, Evaluation of fish behavior at the entrances to a Selective Water Withdrawal structure in Lake Billy Chinook, Oregon, 2021: U.S. Geological Survey Open-File Report 2022–1098, 28 p., https://doi.org/10.3133/ofr20221098.","productDescription":"viii, 28 p.","onlineOnly":"Y","ipdsId":"IP-139510","costCenters":[{"id":654,"text":"Western Fisheries Research Center","active":true,"usgs":true}],"links":[{"id":409425,"rank":4,"type":{"id":34,"text":"Image Folder"},"url":"https://pubs.usgs.gov/of/2022/1098/images"},{"id":409424,"rank":3,"type":{"id":39,"text":"HTML Document"},"url":"https://pubs.usgs.gov/publication/ofr20221098/full","text":"Report","linkFileType":{"id":5,"text":"html"},"description":"OFR 2022-1098"},{"id":409423,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2022/1098/ofr20221098.pdf","text":"Report","size":"36.1 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2022-1098"},{"id":409422,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2022/1098/coverthb.jpg"},{"id":409426,"rank":5,"type":{"id":31,"text":"Publication XML"},"url":"https://pubs.usgs.gov/of/2022/1098/ofr20221098.XML"}],"country":"United States","state":"Oregon","otherGeospatial":"Lake Billy Chinook","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n -121.5557866634069,\n 44.75388913871075\n ],\n [\n -121.5557866634069,\n 44.38885839267408\n ],\n [\n -121.07886358532556,\n 44.38885839267408\n ],\n [\n -121.07886358532556,\n 44.75388913871075\n ],\n [\n -121.5557866634069,\n 44.75388913871075\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","contact":"Director, Western Fisheries Research Center
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6505 NE 65th Street
Seattle, Washington 98115-5016
The 21–22 June 2019 eruption of Raikoke volcano, Russia, provided an opportunity to explore how spatial trends in volcanic lightning locations provide insights into pulsatory eruption dynamics. Using satellite-derived plume heights, we examine the development of lightning detected by Vaisala’s Global Lightning Dataset (GLD360) from eleven, closely spaced eruptive pulses. Results from one-dimensional plume modeling show that the eruptive pulses with maximum heights 9–16.5 km above sea level were capable of producing ice in the upper troposphere, which contributed variably to electrification and volcanic lightning. A key finding is that lightning locations not only followed the main dispersal direction of these ash plumes, but also tracked a lower-level cloud derived from pyroclastic density currents. We show a positive relationship between umbrella cloud expansion and the area over which lightning occurs (the ‘lightning footprint’). These observations suggest useful metrics to characterize ongoing eruptive activity in near real-time.
","language":"English","publisher":"Presses universitaires de Strasbourg","doi":"10.30909/vol.05.02.385395","usgsCitation":"Smith, C., Van Eaton, A.R., Schneider, D.J., Mastin, L.G., Matoza, R.S., McKee, K., and Maher, S., 2022, Spatial analysis of globally detected volcanic lightning from the June 2019 eruption of Raikoke volcano, Kuril Islands: Volcanica, v. 5, no. 2, p. 385-395, https://doi.org/10.30909/vol.05.02.385395.","productDescription":"11 p.","startPage":"385","endPage":"395","ipdsId":"IP-144250","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":467146,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.30909/vol.05.02.385395","text":"Publisher Index Page"},{"id":466647,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"Russia","otherGeospatial":"Kuril Islands, Raikoke volcano","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"coordinates\": [\n [\n [\n 153.22845752671452,\n 48.304580250458486\n ],\n [\n 153.22845752671452,\n 48.27759609075218\n ],\n [\n 153.27313900396035,\n 48.27759609075218\n ],\n [\n 153.27313900396035,\n 48.304580250458486\n ],\n [\n 153.22845752671452,\n 48.304580250458486\n ]\n ]\n ],\n \"type\": \"Polygon\"\n }\n }\n ]\n}","volume":"5","issue":"2","noUsgsAuthors":false,"publicationDate":"2022-11-17","publicationStatus":"PW","contributors":{"authors":[{"text":"Smith, Cassandra M.","contributorId":349097,"corporation":false,"usgs":false,"family":"Smith","given":"Cassandra M.","affiliations":[{"id":83431,"text":"NSF Postdoc, Alaska Volcano Observatory","active":true,"usgs":false}],"preferred":false,"id":924002,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Van Eaton, Alexa R. 0000-0001-6646-4594 avaneaton@usgs.gov","orcid":"https://orcid.org/0000-0001-6646-4594","contributorId":184079,"corporation":false,"usgs":true,"family":"Van Eaton","given":"Alexa","email":"avaneaton@usgs.gov","middleInitial":"R.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924003,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Schneider, David J. 0000-0001-9092-1054 djschneider@usgs.gov","orcid":"https://orcid.org/0000-0001-9092-1054","contributorId":198601,"corporation":false,"usgs":true,"family":"Schneider","given":"David","email":"djschneider@usgs.gov","middleInitial":"J.","affiliations":[],"preferred":true,"id":924004,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mastin, Larry G. 0000-0002-4795-1992","orcid":"https://orcid.org/0000-0002-4795-1992","contributorId":265985,"corporation":false,"usgs":true,"family":"Mastin","given":"Larry","email":"","middleInitial":"G.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":924005,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Matoza, Robin S.","contributorId":257265,"corporation":false,"usgs":false,"family":"Matoza","given":"Robin","email":"","middleInitial":"S.","affiliations":[{"id":36524,"text":"University of California, Santa Barbara","active":true,"usgs":false}],"preferred":false,"id":924006,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"McKee, Kathleen 0000-0003-3189-9189","orcid":"https://orcid.org/0000-0003-3189-9189","contributorId":265977,"corporation":false,"usgs":false,"family":"McKee","given":"Kathleen","email":"","affiliations":[{"id":54848,"text":"Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC, USA","active":true,"usgs":false}],"preferred":false,"id":924007,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Maher, Sean","contributorId":265979,"corporation":false,"usgs":false,"family":"Maher","given":"Sean","affiliations":[{"id":54850,"text":"Department of Earth Science and Earth Research Institute, University of California, Santa Barbara, Santa Barbara, CA, USA","active":true,"usgs":false}],"preferred":false,"id":924008,"contributorType":{"id":1,"text":"Authors"},"rank":7}]}} ,{"id":70243665,"text":"70243665 - 2022 - Assessing direct and indirect long-term economic impacts from earthquakes to the U.S. National Bridge Inventory","interactions":[],"lastModifiedDate":"2023-05-16T21:03:22.789749","indexId":"70243665","displayToPublicDate":"2022-11-16T14:00:05","publicationYear":"2022","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Assessing direct and indirect long-term economic impacts from earthquakes to the U.S. National Bridge Inventory","docAbstract":"Using the 2018 National Seismic Hazard Model and the 2018 National Bridge Inventory, an annualized earthquake loss (AEL) study was conducted for approximately 610,000 bridges in the conterminous United States, quantifying both direct and indirect economic losses. The typical AEL framework has been augmented with new replacement unit cost data and bridge-specific parameters for modifying default fragility curves. Earthquake hazard is defined using spectral acceleration hazard curves that account for location-specific soil conditions. Hazard is integrated with bridge-specific fragility curves to compute annual probabilities of exceeding various damage states. Further, economic loss for each bridge was estimated using the repair costs associated with specific damage states and indirect costs incurred from downtimes. Quantitative assessments of seismic risk, especially those that account for downtime-related impacts, enable us to illustrate the distribution of risk with respect to geographic region, era of construction, or type of bridge.
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Center","active":true,"usgs":true}],"preferred":true,"id":872852,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Kwong, N. Simon 0000-0003-3017-9585","orcid":"https://orcid.org/0000-0003-3017-9585","contributorId":241863,"corporation":false,"usgs":true,"family":"Kwong","given":"N.","email":"","middleInitial":"Simon","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":872853,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bausch, Doug","contributorId":195191,"corporation":false,"usgs":false,"family":"Bausch","given":"Doug","email":"","affiliations":[{"id":34169,"text":"Pacific Disaster Center","active":true,"usgs":false}],"preferred":false,"id":872854,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wald, David J. 0000-0002-1454-4514 wald@usgs.gov","orcid":"https://orcid.org/0000-0002-1454-4514","contributorId":795,"corporation":false,"usgs":true,"family":"Wald","given":"David","email":"wald@usgs.gov","middleInitial":"J.","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":872855,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Lin, Kuo-wan 0000-0002-7520-8151 klin@usgs.gov","orcid":"https://orcid.org/0000-0002-7520-8151","contributorId":1539,"corporation":false,"usgs":true,"family":"Lin","given":"Kuo-wan","email":"klin@usgs.gov","affiliations":[{"id":300,"text":"Geologic Hazards Science Center","active":true,"usgs":true}],"preferred":true,"id":872856,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Yen, Sharon","contributorId":265958,"corporation":false,"usgs":false,"family":"Yen","given":"Sharon","email":"","affiliations":[{"id":54842,"text":"Caltrans Division of Research, Innovation and System Information","active":true,"usgs":false}],"preferred":false,"id":872857,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Shen, Jerry","contributorId":265959,"corporation":false,"usgs":false,"family":"Shen","given":"Jerry","email":"","affiliations":[{"id":54843,"text":"Federal Highway Administration","active":true,"usgs":false}],"preferred":false,"id":872858,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Ger, Jeffrey","contributorId":265960,"corporation":false,"usgs":false,"family":"Ger","given":"Jeffrey","email":"","affiliations":[{"id":54843,"text":"Federal Highway Administration","active":true,"usgs":false}],"preferred":false,"id":872859,"contributorType":{"id":1,"text":"Authors"},"rank":8}]}} ,{"id":70238344,"text":"70238344 - 2022 - Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population","interactions":[],"lastModifiedDate":"2022-11-17T13:12:09.830263","indexId":"70238344","displayToPublicDate":"2022-11-15T07:10:07","publicationYear":"2022","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2980,"text":"PLoS ONE","active":true,"publicationSubtype":{"id":10}},"title":"Evaluating noninvasive methods for estimating cestode prevalence in a wild carnivore population","docAbstract":"Helminth infections are cryptic and can be difficult to study in wildlife species. Helminth research in wildlife hosts has historically required invasive animal handling and necropsy, while results from noninvasive parasite research, like scat analysis, may not be possible at the helminth species or individual host levels. To increase the utility of noninvasive sampling, individual hosts can be identified by applying molecular methods. This allows for longitudinal sampling of known hosts and can be paired with individual-level covariates. Here we evaluate a combination of methods and existing long-term monitoring data to identify patterns of cestode infections in gray wolves in Yellowstone National Park. Our goals were: (1) Identify the species and apparent prevalence of cestodes infecting Yellowstone wolves; (2) Assess the relationships between wolf biological and social characteristics and cestode infections; (3) Examine how wolf samples were affected by environmental conditions with respect to the success of individual genotyping. We collected over 200 wolf scats from 2018–2020 and conducted laboratory analyses including individual wolf genotyping, sex identification, cestode identification, and fecal glucocorticoid measurements. Wolf genotyping success rate was 45%, which was higher in the winter but decreased with higher precipitation and as more time elapsed between scat deposit and collection. One cestode species was detected in 28% of all fecal samples, and 38% of known individuals. The most common infection was Echinococcus granulosus sensu lato (primarily E. canadensis). Adult wolves had 4x greater odds of having a cestode infection than pups, as well as wolves sampled in the winter. Our methods provide an alternative approach to estimate cestode prevalence and to linking parasites to known individuals in a wild host system, but may be most useful when employed in existing study systems and when field collections are designed to minimize the time between fecal deposition and collection.