Human perceptions of the landscape can influence land-use and land-management decisions. Recognizing the diversity of landscape perceptions across space and time is essential to understanding land change processes and emergent landscape patterns. We summarize the role of landscape perceptions in the land change process, demonstrate advances in quantifying and mapping landscape perceptions, and describe how these spatially explicit techniques have and may benefit land change research.
Mapping landscape perceptions is becoming increasingly common, particularly in research focused on quantifying ecosystem services provision. Spatial representations of landscape perceptions, often measured in terms of landscape values and functions, provide an avenue for matching social and environmental data in land change studies. Integrating these data can provide new insights into land change processes, contribute to landscape planning strategies, and guide the design and implementation of land change models.
Challenges remain in creating spatial representations of human perceptions. Maps must be accompanied by descriptions of whose perceptions are being represented and the validity and uncertainty of those representations across space. With these considerations, rapid advancements in mapping landscape perceptions hold great promise for improving representation of human dimensions in landscape ecology and land change research.
Spruce beetle (Dendroctonus rufipennis) outbreaks are rapidly spreading throughout subalpine forests of the Rocky Mountains, raising concerns that altered fuel structures may increase the ecological severity of wildfires. Although many recent studies have found no conclusive link between beetle outbreaks and increased fire size or canopy mortality, few studies have addressed whether these combined disturbances produce compounded effects on short-term vegetation recovery. We tested for an effect of spruce beetle outbreak severity on vegetation recovery in the West Fork Complex fire in southwestern Colorado, USA, where much of the burn area had been affected by severe spruce beetle outbreaks in the decade prior to the fire. Vegetation recovery was assessed using the Landsat-derived Normalized Difference Vegetation Index (NDVI) two years after the fire, which occurred in 2013. Beetle outbreak severity, defined as the basal area of beetle-killed trees within Landsat pixels, was estimated using vegetation index differences (dVIs) derived from pre-outbreak and post-outbreak Landsat images. Of the seven dVIs tested, the change in Normalized Difference Moisture Index (dNDMI) was most strongly correlated with field measurements of beetle-killed basal area (R2 = 0.66). dNDMI was included as an explanatory variable in sequential autoregressive (SAR) models of NDVI2015. Models also included pre-disturbance NDVI, topography, and weather conditions at the time of burning as covariates. SAR results showed a significant correlation between NDVI2015 and dNDMI, with more severe spruce beetle outbreaks corresponding to reduced post-fire vegetation cover. The correlation was stronger for models which were limited to locations in the red stage of outbreak (outbreak ≤ 5 years old at the time of fire) than for models of gray-stage locations (outbreak > 5 years old at the time of fire). These results indicate that vegetation recovery processes may be negatively impacted by severe spruce beetle outbreaks occurring within a decade of stand-replacing wildfire.
","language":"English","publisher":"Public Library of Science","doi":"10.1371/journal.pone.0181778","usgsCitation":"Carlson, A., Sibold, J.S., Assal, T.J., and Negron, J.F., 2017, Evidence of compounded disturbance effects on vegetation recovery following high-severity wildfire and spruce beetle outbreak: PLoS ONE, v. 12, no. 8, Article e0181778: 24 p., https://doi.org/10.1371/journal.pone.0181778.","productDescription":"Article e0181778: 24 p.","ipdsId":"IP-083553","costCenters":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"links":[{"id":469607,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1371/journal.pone.0181778","text":"Publisher Index Page"},{"id":344878,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Colorado","otherGeospatial":"West Fork Complex","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -108.03955078125,\n 37.09023980307208\n ],\n [\n -106.3037109375,\n 37.09023980307208\n ],\n [\n -106.3037109375,\n 38.238180119798635\n ],\n [\n -108.03955078125,\n 38.238180119798635\n ],\n [\n -108.03955078125,\n 37.09023980307208\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"12","issue":"8","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-04","publicationStatus":"PW","scienceBaseUri":"59940845e4b0fe2b9fe8af8d","contributors":{"authors":[{"text":"Carlson, Amanda R. 0000-0002-0450-2636","orcid":"https://orcid.org/0000-0002-0450-2636","contributorId":195661,"corporation":false,"usgs":false,"family":"Carlson","given":"Amanda R.","affiliations":[],"preferred":false,"id":707799,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Sibold, Jason S.","contributorId":195662,"corporation":false,"usgs":false,"family":"Sibold","given":"Jason","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":707800,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Assal, Timothy J. 0000-0001-6342-2954 assalt@usgs.gov","orcid":"https://orcid.org/0000-0001-6342-2954","contributorId":2203,"corporation":false,"usgs":true,"family":"Assal","given":"Timothy","email":"assalt@usgs.gov","middleInitial":"J.","affiliations":[{"id":291,"text":"Fort Collins Science Center","active":true,"usgs":true}],"preferred":true,"id":707798,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Negron, Jose F.","contributorId":10734,"corporation":false,"usgs":true,"family":"Negron","given":"Jose","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":707801,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190102,"text":"ofr20171104 - 2017 - Modified mercalli intensities for nine earthquakes in central and western Washington between 1989 and 1999","interactions":[],"lastModifiedDate":"2017-08-21T14:04:16","indexId":"ofr20171104","displayToPublicDate":"2017-08-15T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":330,"text":"Open-File Report","code":"OFR","onlineIssn":"2331-1258","printIssn":"0196-1497","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2017-1104","title":"Modified mercalli intensities for nine earthquakes in central and western Washington between 1989 and 1999","docAbstract":"We determine Modified Mercalli (Seismic) Intensities (MMI) for nine onshore earthquakes of magnitude 4.5 and larger that occurred in central and western Washington between 1989 and 1999, on the basis of effects reported in postal questionnaires, the press, and professional collaborators. The earthquakes studied include four earthquakes of M5 and larger: the M5.0 Deming earthquake of April 13, 1990, the M5.0 Point Robinson earthquake of January 29, 1995, the M5.4 Duvall earthquake of May 3, 1996, and the M5.8 Satsop earthquake of July 3, 1999. The MMI are assigned using data and procedures that evolved at the U.S. Geological Survey (USGS) and its Department of Commerce predecessors and that were used to assign MMI to felt earthquakes occurring in the United States between 1931 and 1986. We refer to the MMI assigned in this report as traditional MMI, because they are based on responses to postal questionnaires and on newspaper reports, and to distinguish them from MMI calculated from data contributed by the public by way of the internet. Maximum traditional MMI documented for the M5 and larger earthquakes are VII for the 1990 Deming earthquake, V for the 1995 Point Robinson earthquake, VI for the 1996 Duvall earthquake, and VII for the 1999 Satsop earthquake; the five other earthquakes were variously assigned maximum intensities of IV, V, or VI. Starting in 1995, the Pacific Northwest Seismic Network (PNSN) published MMI maps for four of the studied earthquakes, based on macroseismic observations submitted by the public by way of the internet. With the availability now of the traditional USGS MMI interpreted for all the sites from which USGS postal questionnaires were returned, the four Washington earthquakes join a rather small group of earthquakes for which both traditional USGS MMI and some type of internet-based MMI have been assigned. The values and distributions of the traditional MMI are broadly similar to the internet-based PNSN intensities; we discuss some differences in detail that reflect differences in data-sampling procedure, differences in the procedure used to assign intensity numbers from macroseismic observations, and differences in how intensities are mapped.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171104","usgsCitation":"Brocher, T.M., Dewey, J.W., and Cassidy, J.F., 2017, Modified Mercalli Intensities for nine earthquakes in central and western Washington between 1989 and 1999: U.S. Geological Survey Open-File Report 2017–1104, 82 p., https://doi.org/10.3133/ofr20171104.","productDescription":"v, 82 p.","numberOfPages":"87","onlineOnly":"Y","ipdsId":"IP-080341","costCenters":[{"id":237,"text":"Earthquake Science Center","active":true,"usgs":true}],"links":[{"id":344861,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1104/ofr2017.1104.pdf","text":"Report","size":"4.25 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1104"},{"id":344860,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1104/coverthb.jpg"}],"country":"United States","state":"Oregon, Washington","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -126.925048828125,\n 44.12702800650004\n ],\n [\n -116.90551757812499,\n 44.12702800650004\n ],\n [\n -116.90551757812499,\n 49.78835749241399\n ],\n [\n -126.925048828125,\n 49.78835749241399\n ],\n [\n -126.925048828125,\n 44.12702800650004\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"USGS Earthquake Science Center
U.S. Geological Survey
345 Middlefield Road
Mail Stop 977
Menlo Park, CA 94025
The California Cascades field trip is a loop beginning and ending in Portland, Oregon. The route of day 1 goes eastward across the Cascades just south of Mount Hood, travels south along the east side of the Cascades for an overview of the central Oregon volcanoes (including Three Sisters and Newberry Volcano), and ends at Klamath Falls, Oregon. Day 2 and much of day 3 focus on Medicine Lake Volcano. The latter part of day 3 consists of a drive south across the Pit River into the Hat Creek Valley and then clockwise around Lassen Volcanic Center to the town of Chester, California. Day 4 goes from south to north across Lassen Volcanic Center, ending at Burney, California. Day 5 and the first part of day 6 follow a clockwise route around Mount Shasta. The trip returns to Portland on the latter part of day 6, west of the Cascades through the Klamath Mountains and the Willamette Valley.
Each of the three sections of this guidebook addresses one of the major volcanic regions: Lassen Volcanic Center (a volcanic field that spans the volcanic arc), Mount Shasta (a fore-arc stratocone), and Medicine Lake Volcano (a rear-arc, shield-shaped edifice). Each section of the guide provides (1) an overview of the extensive field and laboratory studies, (2) an introduction to the literature, and (3) directions to the most important and accessible field localities. The field-trip sections contain far more stops than can possibly be visited in the actual 6-day 2017 IAVCEI excursion from Portland. We have included extra stops in order to provide a field-trip guide that will have lasting utility for those who may have more time or may want to emphasize one particular volcanic area.
","largerWorkType":{"id":18,"text":"Report"},"largerWorkTitle":"Field-trip guides to selected volcanoes and volcanic landscapes of the western United States (Scientific Investigation Report 2017–5022)","largerWorkSubtype":{"id":5,"text":"USGS Numbered Series"},"language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175022K","usgsCitation":"Muffler, L.J.P., Donnelly-Nolan, J.M., Grove, T.L., Clynne, M.A., Christiansen, R.L., Calvert, A.T., and Ryan-Davis, J., 2017, Overview for geologic field-trip guides to volcanoes of the Cascades Arc in Northern California: U.S. Geological Survey Scientific Investigations Report 2017–5022–K, 6 p., https://doi.org/10.3133/sir20175022K.","productDescription":"viii, 6 p.","startPage":"1","endPage":"6","numberOfPages":"18","onlineOnly":"Y","ipdsId":"IP-077694","costCenters":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"links":[{"id":344874,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5022/k/sir20175022k_.pdf","text":"Report","size":"5.5 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017-5022-K"},{"id":344938,"rank":4,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/sir20175022K2","text":"Scientific Investigations Report 2017-5022-K2","description":"SIR 2017-5022-K2","linkHelpText":" - Chapter K2: Geologic Field-Trip Guide to the Lassen Segment of the Cascades Arc, Northern California"},{"id":344873,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5022/k/coverthb.jpg"},{"id":344937,"rank":3,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/sir20175022K1","text":"Scientific Investigations Report 2017-5022-K1","description":"SIR 2017-5022-K1","linkHelpText":" - Chapter K1: Geologic Field-Trip Guide to Medicine Lake Volcano, Northern California, Including Lava Beds National Monument"},{"id":344953,"rank":5,"type":{"id":6,"text":"Chapter"},"url":"https://doi.org/10.3133/sir20175022K3","text":"Scientific Investigations Report 2017-5022-K3","description":"SIR 2017-5022-K3","linkHelpText":" - Chapter K3: Geologic Field-Trip Guide to Mount Shasta Volcano, Northern California"}],"country":"United States","state":"California","otherGeospatial":"Cascades Volcanic Arc","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -122.64038085937499,\n 40.06125658140474\n ],\n [\n -119.981689453125,\n 40.06125658140474\n ],\n [\n -119.981689453125,\n 42.66628070564928\n ],\n [\n -122.64038085937499,\n 42.66628070564928\n ],\n [\n -122.64038085937499,\n 40.06125658140474\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Volcano Science Center - Menlo Park
U.S. Geological Survey
345 Middlefield Road, MS 910
Menlo Park, CA 94025
No abstract available.
Staghorn coral, Acropora cervicornis, is a threatened species and the primary focus of western Atlantic reef restoration efforts to date. We compared linear extension, calcification rate, and skeletal density of nursery-raised A. cervicornis branches reared for 6 months either on blocks attached to substratum or hanging from PVC trees in the water column. We demonstrate that branches grown on the substratum had significantly higher skeletal density, measured using computerized tomography, and lower linear extension rates compared to water-column fragments. Calcification rates determined with buoyant weighing were not statistically different between the two grow-out methods, but did vary among coral genotypes. Whereas skeletal density and extension rates were plastic traits that depended on grow-out method, calcification rate was conserved. Our results show that the two rearing methods generate the same amount of calcium carbonate skeleton but produce colonies with different skeletal characteristics and suggest that there is genetically based variability in coral calcification performance.
Forests cover 30% of the terrestrial Earth surface and are a major component of the global carbon (C) cycle. Humans have doubled the amount of global reactive nitrogen (N), increasing deposition of N onto forests worldwide. However, other global changes—especially climate change and elevated atmospheric carbon dioxide concentrations—are increasing demand for N, the element limiting primary productivity in temperate forests, which could be reducing N availability. To determine the long-term, integrated effects of global changes on forest N cycling, we measured stable N isotopes in wood, a proxy for N supply relative to demand, on large spatial and temporal scales across the continental U.S.A. Here, we show that forest N availability has generally declined across much of the U.S. since at least 1850 C.E. with cool, wet forests demonstrating the greatest declines. Across sites, recent trajectories of N availability were independent of recent atmospheric N deposition rates, implying a minor role for modern N deposition on the trajectory of N status of North American forests. Our results demonstrate that current trends of global changes are likely to be consistent with forest oligotrophication into the foreseeable future, further constraining forest C fixation and potentially storage.
","language":"English","publisher":"Nature","doi":"10.1038/s41598-017-08170-z","usgsCitation":"McLauchlan, K.K., Gerhart, L.M., Battles, J.J., Craine, J.M., Elmore, A.J., Higuera, P., Mack, M.M., McNeil, B.E., Nelson, D.M., Pederson, N., and Perakis, S.S., 2017, Centennial-scale reductions in nitrogen availability in temperate forests of the United States: Scientific Reports, v. 7, Article 7856: 7 p., https://doi.org/10.1038/s41598-017-08170-z.","productDescription":"Article 7856: 7 p.","ipdsId":"IP-088632","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469606,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1038/s41598-017-08170-z","text":"Publisher Index Page"},{"id":344879,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"7","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-08-10","publicationStatus":"PW","scienceBaseUri":"59940846e4b0fe2b9fe8af93","contributors":{"authors":[{"text":"McLauchlan, Kendra K.","contributorId":7994,"corporation":false,"usgs":true,"family":"McLauchlan","given":"Kendra","email":"","middleInitial":"K.","affiliations":[],"preferred":false,"id":707778,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gerhart, Laci M.","contributorId":150048,"corporation":false,"usgs":false,"family":"Gerhart","given":"Laci","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":707779,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Battles, John J.","contributorId":102006,"corporation":false,"usgs":false,"family":"Battles","given":"John","email":"","middleInitial":"J.","affiliations":[{"id":6609,"text":"UC Berkeley","active":true,"usgs":false}],"preferred":false,"id":707780,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Craine, Joseph M.","contributorId":139154,"corporation":false,"usgs":false,"family":"Craine","given":"Joseph","email":"","middleInitial":"M.","affiliations":[{"id":12661,"text":"Kansas State University","active":true,"usgs":false}],"preferred":false,"id":707781,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Elmore, Andrew J.","contributorId":29702,"corporation":false,"usgs":true,"family":"Elmore","given":"Andrew","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707782,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Higuera, Phil E.","contributorId":16736,"corporation":false,"usgs":true,"family":"Higuera","given":"Phil E.","affiliations":[],"preferred":false,"id":707783,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Mack, Michelle M","contributorId":195657,"corporation":false,"usgs":false,"family":"Mack","given":"Michelle","email":"","middleInitial":"M","affiliations":[],"preferred":false,"id":707784,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"McNeil, Brendan E.","contributorId":195658,"corporation":false,"usgs":false,"family":"McNeil","given":"Brendan","email":"","middleInitial":"E.","affiliations":[],"preferred":false,"id":707785,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"Nelson, David M.","contributorId":175098,"corporation":false,"usgs":false,"family":"Nelson","given":"David","email":"","middleInitial":"M.","affiliations":[{"id":13479,"text":"University of Maryland Center for Environmental Science, Appalachian Laboratory, 301 Braddock Road, Frostburg, Maryland","active":true,"usgs":false}],"preferred":false,"id":707786,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Pederson, Neil","contributorId":149422,"corporation":false,"usgs":false,"family":"Pederson","given":"Neil","email":"","affiliations":[{"id":17731,"text":"Research Scientist, Tree Ring Laboratory, Lamont-Doherty Earth Observatory","active":true,"usgs":false}],"preferred":false,"id":707787,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Perakis, Steven S. 0000-0003-0703-9314 sperakis@usgs.gov","orcid":"https://orcid.org/0000-0003-0703-9314","contributorId":145528,"corporation":false,"usgs":true,"family":"Perakis","given":"Steven","email":"sperakis@usgs.gov","middleInitial":"S.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":707777,"contributorType":{"id":1,"text":"Authors"},"rank":11}]}} ,{"id":70176466,"text":"sir20165129 - 2017 - Nutrient and pesticide contamination bias estimated from field blanks collected at surface-water sites in U.S. Geological Survey Water-Quality Networks, 2002–12","interactions":[],"lastModifiedDate":"2017-08-14T09:20:51","indexId":"sir20165129","displayToPublicDate":"2017-08-14T09:15:00","publicationYear":"2017","noYear":false,"publicationType":{"id":18,"text":"Report"},"publicationSubtype":{"id":5,"text":"USGS Numbered Series"},"seriesTitle":{"id":334,"text":"Scientific Investigations Report","code":"SIR","onlineIssn":"2328-0328","printIssn":"2328-031X","active":true,"publicationSubtype":{"id":5}},"seriesNumber":"2016-5129","title":"Nutrient and pesticide contamination bias estimated from field blanks collected at surface-water sites in U.S. Geological Survey Water-Quality Networks, 2002–12","docAbstract":"Potential contamination bias was estimated for 8 nutrient analytes and 40 pesticides in stream water collected by the U.S. Geological Survey at 147 stream sites from across the United States, and representing a variety of hydrologic conditions and site types, for water years 2002–12. This study updates previous U.S. Geological Survey evaluations of potential contamination bias for nutrients and pesticides. Contamination is potentially introduced to water samples by exposure to airborne gases and particulates, from inadequate cleaning of sampling or analytic equipment, and from inadvertent sources during sample collection, field processing, shipment, and laboratory analysis. Potential contamination bias, based on frequency and magnitude of detections in field blanks, is used to determine whether or under what conditions environmental data might need to be qualified for the interpretation of results in the context of comparisons with background levels, drinking-water standards, aquatic-life criteria or benchmarks, or human-health benchmarks. Environmental samples for which contamination bias as determined in this report applies are those from historical U.S. Geological Survey water-quality networks or programs that were collected during the same time frame and according to the same protocols and that were analyzed in the same laboratory as field blanks described in this report.
Results from field blanks for ammonia, nitrite, nitrite plus nitrate, orthophosphate, and total phosphorus were partitioned by analytical method; results from the most commonly used analytical method for total phosphorus were further partitioned by date. Depending on the analytical method, 3.8, 9.2, or 26.9 percent of environmental samples, the last of these percentages pertaining to all results from 2007 through 2012, were potentially affected by ammonia contamination. Nitrite contamination potentially affected up to 2.6 percent of environmental samples collected between 2002 and 2006 and affected about 3.3 percent of samples collected between 2007 and 2012. The percentages of environmental samples collected between 2002 and 2011 that were potentially affected by nitrite plus nitrate contamination were 7.3 for samples analyzed with the low-level method and 0.4 for samples analyzed with the standard-level method. These percentages increased to 14.8 and 2.2 for samples collected in 2012 and analyzed using replacement low- and standard-level methods, respectively. The maximum potentially affected concentrations for nitrite and for nitrite plus nitrate were much less than their respective maximum contamination levels for drinking-water standards. Although contamination from particulate nitrogen can potentially affect up to 21.2 percent and that from total Kjeldahl nitrogen can affect up to 16.5 percent of environmental samples, there are no critical or background levels for these substances.
For total nitrogen, orthophosphate, and total phosphorus, contamination in a small percentage of environmental samples might be consequential for comparisons relative to impairment risks or background levels. At the low ends of the respective ranges of impairment risk for these nutrients, contamination in up to 5 percent of stream samples could account for at least 23 percent of measured concentrations of total nitrogen, for at least 40 or 90 percent of concentrations of orthophosphate, depending on the analytical method, and for 31 to 76 percent of concentrations of total phosphorus, depending on the time period.
Twenty-six pesticides had no detections in field blanks. Atrazine with 12 and metolachlor with 11 had the highest number of detections, mostly occurring in spring or early summer. At a 99-percent level of confidence, contamination was estimated to be no greater than the detection limit in at least 98 percent of all samples for 38 of 40 pesticides. For metolachlor and atrazine, potential contamination was no greater than 0.0053 and 0.0093 micrograms per liter in 98 percent of samples. For 11 of 14 pesticides with at least one detection, the maximum potentially affected concentration of the environmental sample was less than their respective human-health or aquatic-life benchmarks. Small percentages of environmental samples had concentrations high enough that atrazine contamination potentially could account for the entire aquatic-life benchmark for acute effects on nonvascular plants, that dieldrin contamination could account for up to 100 percent of the cancer health-based screening level, or that chlorpyrifos contamination could account for 13 or 12 percent of the concentrations in the aquatic-life benchmarks for chronic effects on invertebrates or the criterion continuous concentration for chronic effects on aquatic life.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20165129","usgsCitation":"Medalie, Laura, and Martin, J.D., 2017, Nutrient and pesticide contamination bias estimated from field blanks collected at surface-water sites in U.S. Geological Survey water-quality networks, 2002–12: U.S. Geological Survey Scientific Investigations Report 2016–5129, 40 p., https://doi.org/10.3133/sir20165129.","productDescription":"Report: vi, 40 p.; Appendixes 1-2","numberOfPages":"50","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-070500","costCenters":[{"id":466,"text":"New England Water Science Center","active":true,"usgs":true}],"links":[{"id":344595,"rank":8,"type":{"id":3,"text":"Appendix"},"url":"https://pubs.usgs.gov/sir/2016/5129/attachments/sir20165129_appendix2_metadata.txt","text":"Appendix 2","size":"1.43 KB","linkFileType":{"id":2,"text":"txt"},"linkHelpText":"- Metadata, pesticide field-blank data from surface-water 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U.S. Geological Survey
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In 2005, the U.S. Geological Survey began a cooperative study with New York City Department of Environmental Protection to characterize the local groundwater-flow system and identify potential sources of seeps on the southern embankment at the Hillview Reservoir in southern Westchester County, New York. Monthly site inspections at the reservoir indicated an approximately 90-square-foot depression in the land surface directly upslope from a seep that has episodically flowed since 2007. In July 2008, the U.S. Geological Survey surveyed the topography of land surface in this depression area by collecting high-accuracy (resolution less than 1 inch) measurements. A point of origin was established for the topographic survey by using differentially corrected positional data collected by a global navigation satellite system. Eleven points were surveyed along the edge of the depression area and at arbitrary locations within the depression area by using robotic land-surveying techniques. The points were surveyed again in March 2012 to evaluate temporal changes in land-surface altitude. Survey measurements of the depression area indicated that the land-surface altitude at 8 of the 11 points decreased beyond the accepted measurement uncertainty during the 44 months from July 2008 to March 2012. Two additional control points were established at stable locations along Hillview Avenue, which runs parallel to the embankment. These points were measured during the July 2008 survey and measured again during the March 2012 survey to evaluate the relative accuracy of the altitude measurements. The relative horizontal and vertical (altitude) accuracies of the 11 topographic measurements collected in March 2012 were ±0.098 and ±0.060 feet (ft), respectively. Changes in topography at 8 of the 11 points ranged from 0.09 to 0.63 ft and topography remained constant, or within the measurement uncertainty, for 3 of the 11 points.
Two cross sections were constructed through the depression area by using land-surface altitude data that were interpolated from positional data collected during the two topographic surveys. Cross section A–A′ was approximately 8.5 ft long and consisted of three surveyed points that trended north to south across the depression. Land-surface altitude change decreased along the entire north-south trending cross section during the 44 months, and ranged from 0.2 to more than 0.6 ft. In general, greater land-surface altitude change was measured north of the midpoint as compared to south of the midpoint of the cross section. Cross section B–B′ was 18 ft long and consisted of six surveyed points that trended east to west across the depression. Land-surface altitude change generally decreased or remained constant along the east-west trending cross section during the 44 months and ranged from 0.0 to 0.3 ft. Volume change of the depression area was calculated by using a three-dimensional geographic information system utility that subtracts interpolated surfaces. The results indicated a net volume loss of approximately 38 ±5 cubic feet of material from the depression area during the 44 months.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/ofr20171028","collaboration":"Prepared in cooperation with the New York City Department of Environmental Protection","usgsCitation":"Noll, M.L., and Chu, Anthony, 2017, Detecting temporal change in land-surface altitude using robotic land-surveying techniques and geographic information system applications at an earthen dam site in southern Westchester County, New York: U.S. Geological Survey Open-File Report 2017–1028, 15 p., https://doi.org/10.3133/ofr20171028.","productDescription":"vi, 15 p.","numberOfPages":"26","onlineOnly":"Y","additionalOnlineFiles":"N","ipdsId":"IP-077425","costCenters":[{"id":474,"text":"New York Water Science Center","active":true,"usgs":true}],"links":[{"id":344641,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/of/2017/1028/ofr20171028.pdf","text":"Report","size":"1.03 MB","linkFileType":{"id":1,"text":"pdf"},"description":"OFR 2017-1028"},{"id":344640,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/of/2017/1028/coverthb.jpg"}],"country":"United States","state":"New York","county":"Westchester County","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -73.88648986816406,\n 40.894180824484465\n ],\n [\n -73.85250091552734,\n 40.894180824484465\n ],\n [\n -73.85250091552734,\n 40.92726192578736\n ],\n [\n -73.88648986816406,\n 40.92726192578736\n ],\n [\n -73.88648986816406,\n 40.894180824484465\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New York Water Science Center
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A study was done by the U.S. Geological Survey in cooperation with the Kansas Department of Transportation and the Federal Emergency Management Agency to develop regression models to estimate peak streamflows of annual exceedance probabilities of 50, 20, 10, 4, 2, 1, 0.5, and 0.2 percent at ungaged locations in Kansas. Peak streamflow frequency statistics from selected streamgages were related to contributing drainage area and average precipitation using generalized least-squares regression analysis. The peak streamflow statistics were derived from 151 streamgages with at least 25 years of streamflow data through 2015. The developed equations can be used to predict peak streamflow magnitude and frequency within two hydrologic regions that were defined based on the effects of irrigation. The equations developed in this report are applicable to streams in Kansas that are not substantially affected by regulation, surface-water diversions, or urbanization. The equations are intended for use for streams with contributing drainage areas ranging from 0.17 to 14,901 square miles in the nonirrigation effects region and, 1.02 to 3,555 square miles in the irrigation-affected region, corresponding to the range of drainage areas of the streamgages used in the development of the regional equations.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175063","collaboration":"Prepared in cooperation with the Kansas Department of Transportation and Federal Emergency Management Agency","usgsCitation":"Painter, C.C., Heimann, D.C., and Lanning-Rush, J.L., 2017, Methods for estimating annual exceedance-probability streamflows for streams in Kansas based on data through water year 2015 (ver. 1.1, September 2017): U.S. Geological Survey Scientific Investigations Report 2017–5063, 20 p., https://doi.org/10.3133/sir20175063.","productDescription":"Report: vi, 20 p.; 4 Tables","numberOfPages":"30","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-087048","costCenters":[{"id":353,"text":"Kansas Water Science Center","active":false,"usgs":true}],"links":[{"id":345864,"rank":7,"type":{"id":25,"text":"Version History"},"url":"https://pubs.usgs.gov/sir/2017/5063/versionHist.txt","size":"1 kB","linkFileType":{"id":2,"text":"txt"},"description":"SIR 2017–5063 Version History"},{"id":344871,"rank":6,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table5.xlsx","text":"Table 5","size":"47 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 5"},{"id":344870,"rank":5,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table4.xlsx","text":"Table 4","size":"23 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 4"},{"id":344868,"rank":3,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table2.xlsx","text":"Table 2","size":"42 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 2"},{"id":344869,"rank":4,"type":{"id":27,"text":"Table"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063_table3.xlsx","text":"Table 3","size":"60 kB","linkFileType":{"id":3,"text":"xlsx"},"description":"SIR 2017–5063 Table 3"},{"id":344698,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5063/coverthb2.jpg"},{"id":344699,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5063/sir20175063.pdf","text":"Report","size":"1.52 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5063"}],"country":"United States","state":"Colorado, Kansas, Missouri, Nebraska, Oklahoma","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -102.48046875,\n 36.38591277287651\n ],\n [\n -93.93310546875,\n 36.38591277287651\n ],\n [\n -93.93310546875,\n 40.713955826286046\n ],\n [\n -102.48046875,\n 40.713955826286046\n ],\n [\n -102.48046875,\n 36.38591277287651\n ]\n ]\n ]\n }\n }\n ]\n}","edition":"Version 1.0: Originally posted August 14, 2017; Version 1.1: September 18, 2017","contact":"Director, Kansas Water Science Center
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4821 Quail Crest Place
Lawrence, KS 66049
The Albuquerque Bernalillo County Water Utility Authority (ABCWUA) drinking water supply was almost exclusively sourced from groundwater from within the Albuquerque Basin before 2008. In 2008, the San Juan-Chama Drinking Water Project (SJCDWP) provided surface-water resources to augment the groundwater supply, allowing for a reduction in groundwater pumping in the Albuquerque Basin. In 2013, the U.S. Geological Survey, in cooperation with the ABCWUA, began a study to measure and compare aquifer-system and land-surface elevation change before and after the SJCDWP in 2008. Three methods of data collection with different temporal and spatial resolutions were used for this study: (1) aquifer-system compaction data collected continuously at a single extensometer from 1994 to 2013; (2) land-surface elevation change from Global Positioning System (GPS) surveys of a network of monuments collected in 1994–95, 2005, and 2014; and (3) spatially distributed Interferometric Synthetic Aperture Radar (InSAR) satellite data from 1993 to 2010. Collection of extensometer data allows for direct and continuous measurement of aquifer-system compaction at the extensometer location. The GPS surveys of a network of monuments allow for periodic measurements of land-surface elevation change at monument locations. Interferograms are limited in time by lifespan of the satellite, orbital pattern, and data quality but allow for measurement of gridded land-surface elevation change over the study area. Each of these methods was employed to provide a better understanding of aquifer-system compaction and land-surface elevation change for the Albuquerque Basin.
Results do not show large magnitudes of subsidence in the Albuquerque Basin. High temporal-resolution but low spatial-resolution data measurements of aquifer-system compaction at the Albuquerque extensometer show elastic aquifer-system response to recovering groundwater levels. Results from the GPS survey of the network of monuments show inconsistent land-surface elevation changes over the Albuquerque Basin, likely because of the lack of significant change and the complexity of subsurface stratigraphy in addition to the spatial and temporal heterogeneity of groundwater withdrawals over the study period. Results from the InSAR analysis show areas of land-surface elevation increase after 2008, which could be attributed to elastic recovery of the aquifer system. The spatial extent to which elastic recovery of the aquifer system has resulted in recovery of land-surface elevation is limited to the in-situ measurements at the extensometer. Examination of spatially distributed InSAR data relative to limited spatial extent of the complex heterogeneity subsurface stratigraphy may explain some of the heterogeneity of land-surface elevation changes over this study period.
","language":"English","publisher":"U.S. Geological Survey","publisherLocation":"Reston, VA","doi":"10.3133/sir20175057","collaboration":"Prepared in cooperation with the Albuquerque Bernalillo County Water Utility Authority","usgsCitation":"Driscoll, J.M., and Brandt, J.T., 2017, Land subsidence and recovery in the Albuquerque Basin, New Mexico, 1993–2014: U.S. Geological Survey Scientific Investigations Report 2017–5057, 31 p., https://doi.org/10.3133/sir20175057.","productDescription":"Report: v, 31 p.; Figures: 10A, 10B, 10C","numberOfPages":"42","onlineOnly":"Y","additionalOnlineFiles":"Y","ipdsId":"IP-071011","costCenters":[{"id":472,"text":"New Mexico Water Science Center","active":true,"usgs":true}],"links":[{"id":344697,"rank":5,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10C.pdf","text":"Figure 10C","size":"802 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10C","linkHelpText":"C. InSAR measured elevation change along geology profile CC-CC’"},{"id":344694,"rank":2,"type":{"id":11,"text":"Document"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057.pdf","text":"Report","size":"9.75 MB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057"},{"id":344695,"rank":3,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10A.pdf","text":"Figure 10A","size":"594 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10A","linkHelpText":"A. InSAR measured elevation change along geology profile AA-AA’"},{"id":344696,"rank":4,"type":{"id":29,"text":"Figure"},"url":"https://pubs.usgs.gov/sir/2017/5057/sir20175057_figure10B.pdf","text":"Figure 10B","size":"425 kB","linkFileType":{"id":1,"text":"pdf"},"description":"SIR 2017–5057 Figure 10B","linkHelpText":"B. InSAR measured elevation change along geology profile BB-BB’"},{"id":344693,"rank":1,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/sir/2017/5057/coverthb.jpg"}],"country":"United States","state":"New Mexico","otherGeospatial":"Albuquerque Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107,\n 34.85\n ],\n [\n -106.375,\n 34.85\n ],\n [\n -106.375,\n 35.4\n ],\n [\n -107,\n 35.4\n ],\n [\n -107,\n 34.85\n ]\n ]\n ]\n }\n }\n ]\n}","contact":"Director, New Mexico Water Science Center
U.S. Geological Survey
6700 Edith Blvd NE
Albuquerque NM 87113
Genetic differentiation among Spotted Owl (Strix occidentalis) subspecies has been established in prior studies. These investigations also provided evidence for introgression and hybridization among taxa but were limited by a lack of samples from geographic regions where subspecies came into close contact. We analyzed new sets of samples from Northern Spotted Owls (NSO: S. o. caurina) and California Spotted Owls (CSO: S. o. occidentalis) in northern California using mitochondrial DNA sequences (mtDNA) and 10 nuclear microsatellite loci to obtain a clearer depiction of genetic differentiation and hybridization in the region. Our analyses revealed that a NSO population close to the northern edge of the CSO range in northern California (the NSO Contact Zone population) is highly differentiated relative to other NSO populations throughout the remainder of their range. Phylogenetic analyses identified a unique lineage of mtDNA in the NSO Contact Zone, and Bayesian clustering analyses of the microsatellite data identified the Contact Zone as a third distinct population that is differentiated from CSO and NSO found in the remainder of the subspecies' range. Hybridization between NSO and CSO was readily detected in the NSO Contact Zone, with over 50% of individuals showing evidence of hybrid ancestry. Hybridization was also identified among 14% of CSO samples, which were dispersed across the subspecies' range in the Sierra Nevada Mountains. The asymmetry of hybridization suggested that the hybrid zone may be dynamic and moving. Although evidence of hybridization existed, we identified no F1 generation hybrid individuals. We instead found evidence for F2 or backcrossed individuals among our samples. The absence of F1 hybrids may indicate that (1) our 10 microsatellites were unable to distinguish hybrid types, (2) primary interactions between subspecies are occurring elsewhere on the landscape, or (3) dispersal between the subspecies' ranges is reduced relative to historical levels, potentially as a consequence of recent regional fires.
","language":"English","publisher":"Wiley","doi":"10.1002/ece3.3260","usgsCitation":"Miller, M.P., Mullins, T.D., Forsman, E.D., and Haig, S.M., 2017, Genetic differentiation and inferred dynamics of a hybrid zone between Northern Spotted Owls (Strix occidentalis caurina) and California Spotted Owls (S. o. occidentalis) in northern California: Ecology and Evolution, v. 7, no. 17, p. 6871-6883, https://doi.org/10.1002/ece3.3260.","productDescription":"13 p.","startPage":"6871","endPage":"6883","ipdsId":"IP-085456","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"links":[{"id":469608,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1002/ece3.3260","text":"Publisher Index Page"},{"id":344850,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"California","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -124.76074218749999,\n 37.49229399862877\n ],\n [\n -119.02587890624999,\n 37.49229399862877\n ],\n [\n -119.02587890624999,\n 42.58544425738491\n ],\n [\n -124.76074218749999,\n 42.58544425738491\n ],\n [\n -124.76074218749999,\n 37.49229399862877\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"7","issue":"17","publishingServiceCenter":{"id":12,"text":"Tacoma PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-27","publicationStatus":"PW","scienceBaseUri":"59b76ec2e4b08b1644ddfac6","contributors":{"authors":[{"text":"Miller, Mark P. 0000-0003-1045-1772 mpmiller@usgs.gov","orcid":"https://orcid.org/0000-0003-1045-1772","contributorId":1967,"corporation":false,"usgs":true,"family":"Miller","given":"Mark","email":"mpmiller@usgs.gov","middleInitial":"P.","affiliations":[{"id":38131,"text":"WMA - Office of Planning and Programming","active":true,"usgs":true}],"preferred":true,"id":707746,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Mullins, Thomas D. 0000-0001-8948-9604 tom_mullins@usgs.gov","orcid":"https://orcid.org/0000-0001-8948-9604","contributorId":149824,"corporation":false,"usgs":true,"family":"Mullins","given":"Thomas","email":"tom_mullins@usgs.gov","middleInitial":"D.","affiliations":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true}],"preferred":true,"id":707747,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Forsman, Eric D.","contributorId":96792,"corporation":false,"usgs":false,"family":"Forsman","given":"Eric","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":707748,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Haig, Susan M. 0000-0002-6616-7589 susan_haig@usgs.gov","orcid":"https://orcid.org/0000-0002-6616-7589","contributorId":719,"corporation":false,"usgs":true,"family":"Haig","given":"Susan","email":"susan_haig@usgs.gov","middleInitial":"M.","affiliations":[{"id":289,"text":"Forest and Rangeland Ecosys Science Center","active":true,"usgs":true},{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true}],"preferred":true,"id":707749,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ,{"id":70190155,"text":"70190155 - 2017 - Seasonal trends in eDNA detection and occupancy of bigheaded carps","interactions":[],"lastModifiedDate":"2017-08-14T17:39:33","indexId":"70190155","displayToPublicDate":"2017-08-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2330,"text":"Journal of Great Lakes Research","active":true,"publicationSubtype":{"id":10}},"title":"Seasonal trends in eDNA detection and occupancy of bigheaded carps","docAbstract":"Bigheaded carps, which include silver and bighead carp, are threatening to invade the Great Lakes. These species vary seasonally in distribution and abundance due to environmental conditions such as precipitation and temperature. Monitoring this seasonal movement is important for management to control the population size and spread of the species. We examined if environmental DNA (eDNA) approaches could detect seasonal changes of these species. To do this, we developed a novel genetic marker that was able to both detect and differentiate bighead and silver carp DNA. We used the marker, combined with a novel occupancy model, to study the occurrence of bigheaded carps at 3 sites on the Wabash River over the course of a year. We studied the Wabash River because of concerns that carps may be able to use the system to invade the Great Lakes via a now closed (ca. 2017) connection at Eagle Marsh between the Wabash River's watershed and the Great Lakes' watershed. We found seasonal trends in the probability of detection and occupancy that varied across sites. These findings demonstrate that eDNA methods can detect seasonal changes in bigheaded carps densities and suggest that the amount of eDNA present changes seasonally. The site that was farthest upstream and had the lowest carp densities exhibited the strongest seasonal trends for both detection probabilities and sample occupancy probabilities. Furthermore, other observations suggest that carps seasonally leave this site, and we were able to detect this with our eDNA approach.
","language":"English","publisher":"International Association for Great Lakes Research","doi":"10.1016/j.jglr.2017.06.003","usgsCitation":"Erickson, R.A., Merkes, C.M., Jackson, C., Goforth, R., and Amberg, J., 2017, Seasonal trends in eDNA detection and occupancy of bigheaded carps: Journal of Great Lakes Research, v. 43, no. 4, p. 762-770, https://doi.org/10.1016/j.jglr.2017.06.003.","productDescription":"9 p.","startPage":"762","endPage":"770","ipdsId":"IP-074701","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469610,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.jglr.2017.06.003","text":"Publisher Index Page"},{"id":344854,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"43","issue":"4","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b76ec2e4b08b1644ddfac8","contributors":{"authors":[{"text":"Erickson, Richard A. 0000-0003-4649-482X rerickson@usgs.gov","orcid":"https://orcid.org/0000-0003-4649-482X","contributorId":5455,"corporation":false,"usgs":true,"family":"Erickson","given":"Richard","email":"rerickson@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707727,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Merkes, Christopher M. 0000-0001-8191-627X cmerkes@usgs.gov","orcid":"https://orcid.org/0000-0001-8191-627X","contributorId":139516,"corporation":false,"usgs":true,"family":"Merkes","given":"Christopher","email":"cmerkes@usgs.gov","middleInitial":"M.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707728,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jackson, Craig 0000-0003-4023-0276 cjackson@usgs.gov","orcid":"https://orcid.org/0000-0003-4023-0276","contributorId":192276,"corporation":false,"usgs":true,"family":"Jackson","given":"Craig","email":"cjackson@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707729,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Goforth, Reuben","contributorId":192277,"corporation":false,"usgs":false,"family":"Goforth","given":"Reuben","affiliations":[],"preferred":false,"id":707730,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Amberg, Jon 0000-0002-8351-4861 jamberg@usgs.gov","orcid":"https://orcid.org/0000-0002-8351-4861","contributorId":149785,"corporation":false,"usgs":true,"family":"Amberg","given":"Jon","email":"jamberg@usgs.gov","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707731,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190052,"text":"70190052 - 2017 - Statistical relative gain calculation for Landsat 8","interactions":[],"lastModifiedDate":"2018-04-23T09:04:19","indexId":"70190052","displayToPublicDate":"2017-08-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":24,"text":"Conference Paper"},"publicationSubtype":{"id":19,"text":"Conference Paper"},"title":"Statistical relative gain calculation for Landsat 8","docAbstract":"The Landsat 8 Operational Land Imager (OLI) is an optical multispectral push-broom sensor with a focal plane consisting of over 7000 detectors per spectral band. Each of the individual imaging detectors contributes one column of pixels to an image. Any difference in the response between neighboring detectors may result in a visible stripe or band in the imagery. An accurate estimate of each detector’s relative gain is needed to account for any differences between detector responses. This paper describes a procedure for estimating relative gains which uses normally acquired Earth viewing statistics.","largerWorkTitle":"Proceedings SPIE: Optics and Photonics 2017: Remote Sensing","conferenceTitle":"SPIE Optics and Photonics","conferenceDate":"August 6-10, 2017","conferenceLocation":"San Diego, CA","language":"English","publisher":"SPIE","usgsCitation":"Anderson, C., Helder, D., and Jeno, D., 2017, Statistical relative gain calculation for Landsat 8, in Proceedings SPIE: Optics and Photonics 2017: Remote Sensing, v. 10402, San Diego, CA, August 6-10, 2017.","ipdsId":"IP-089100","costCenters":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"links":[{"id":344809,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"},{"id":344808,"rank":1,"type":{"id":15,"text":"Index Page"},"url":"https://spie.org/conferences-and-exhibitions/optics-and-photonics/proceedings?SSO=1","linkFileType":{"id":5,"text":"html"}}],"volume":"10402","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59b76ec3e4b08b1644ddfacc","contributors":{"authors":[{"text":"Anderson, Cody 0000-0001-5612-1889 chanderson@usgs.gov","orcid":"https://orcid.org/0000-0001-5612-1889","contributorId":195521,"corporation":false,"usgs":true,"family":"Anderson","given":"Cody","email":"chanderson@usgs.gov","affiliations":[{"id":222,"text":"Earth Resources Observation and Science (EROS) Center","active":true,"usgs":true}],"preferred":true,"id":707335,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Helder, Dennis 0000-0002-7379-4679","orcid":"https://orcid.org/0000-0002-7379-4679","contributorId":195522,"corporation":false,"usgs":false,"family":"Helder","given":"Dennis","affiliations":[],"preferred":false,"id":707336,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Jeno, Drake 0000-0001-6962-6616 drake.jeno.ctr@usgs.gov","orcid":"https://orcid.org/0000-0001-6962-6616","contributorId":195523,"corporation":false,"usgs":true,"family":"Jeno","given":"Drake","email":"drake.jeno.ctr@usgs.gov","affiliations":[],"preferred":true,"id":707337,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190163,"text":"70190163 - 2017 - Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands","interactions":[],"lastModifiedDate":"2017-08-15T12:10:56","indexId":"70190163","displayToPublicDate":"2017-08-14T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1837,"text":"Global Change Biology","active":true,"publicationSubtype":{"id":10}},"title":"Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands","docAbstract":"Inland waters are increasingly recognized as critical sites of methane emissions to the atmosphere, but the biogeochemical reactions driving such fluxes are less well understood. The Prairie Pothole Region (PPR) of North America is one of the largest wetland complexes in the world, containing millions of small, shallow wetlands. The sediment pore waters of PPR wetlands contain some of the highest concentrations of dissolved organic carbon (DOC) and sulfur species ever recorded in terrestrial aquatic environments. Using a suite of geochemical and microbiological analyses, we measured the impact of sedimentary carbon and sulfur transformations in these wetlands on methane fluxes to the atmosphere. This research represents the first study of coupled geochemistry and microbiology within the PPR and demonstrates how the conversion of abundant labile DOC pools into methane results in some of the highest fluxes of this greenhouse gas to the atmosphere ever reported. Abundant DOC and sulfate additionally supported some of the highest sulfate reduction rates ever measured in terrestrial aquatic environments, which we infer to account for a large fraction of carbon mineralization in this system. Methane accumulations in zones of active sulfate reduction may be due to either the transport of free methane gas from deeper locations or the co-occurrence of methanogenesis and sulfate reduction. If both respiratory processes are concurrent, any competitive inhibition of methanogenesis by sulfate-reducing bacteria may be lessened by the presence of large labile DOC pools that yield noncompetitive substrates such as methanol. Our results reveal some of the underlying mechanisms that make PPR wetlands biogeochemical hotspots, which ultimately leads to their critical, but poorly recognized role in regional greenhouse gas emissions.
","language":"English","publisher":"Wiley","doi":"10.1111/gcb.13633","usgsCitation":"Martins, P., Hoyt, D.W., Bansal, S., Mills, C., Tfaily, M., Tangen, B., Finocchiaro, R., Johnston, M.D., McAdams, B.C., Solensky, M.J., Smith, G.J., Chin, Y., and Wilkins, M.J., 2017, Abundant carbon substrates drive extremely high sulfate reduction rates and methane fluxes in Prairie Pothole Wetlands: Global Change Biology, v. 23, no. 8, p. 3107-3120, https://doi.org/10.1111/gcb.13633.","productDescription":"14 p.","startPage":"3107","endPage":"3120","ipdsId":"IP-078860","costCenters":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":438247,"rank":0,"type":{"id":30,"text":"Data Release"},"url":"https://doi.org/10.5066/F7TX3CJ7","text":"USGS data release","linkHelpText":"Dissolved greenhouse gas concentrations and fluxes from Wetlands P7 and P8 of the Cottonwood Lake Study area, Stutsman County, North Dakota, 2015"},{"id":344847,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"North Dakota","otherGeospatial":"Cottonwood Lake Study Area, Prairie Pothole Wetlands","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -99.17770385742188,\n 47.05702528260841\n ],\n [\n -99.03419494628906,\n 47.05702528260841\n ],\n [\n -99.03419494628906,\n 47.14489748555398\n ],\n [\n -99.17770385742188,\n 47.14489748555398\n ],\n [\n -99.17770385742188,\n 47.05702528260841\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"23","issue":"8","publishingServiceCenter":{"id":4,"text":"Rolla PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-23","publicationStatus":"PW","scienceBaseUri":"59940848e4b0fe2b9fe8af9e","contributors":{"authors":[{"text":"Martins, Paula","contributorId":195645,"corporation":false,"usgs":false,"family":"Martins","given":"Paula","email":"","affiliations":[],"preferred":false,"id":707758,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Hoyt, David W.","contributorId":195652,"corporation":false,"usgs":false,"family":"Hoyt","given":"David","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":707768,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Bansal, Sheel 0000-0003-1233-1707 sbansal@usgs.gov","orcid":"https://orcid.org/0000-0003-1233-1707","contributorId":167295,"corporation":false,"usgs":true,"family":"Bansal","given":"Sheel","email":"sbansal@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707757,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Mills, Christopher T. 0000-0001-8414-1414 cmills@usgs.gov","orcid":"https://orcid.org/0000-0001-8414-1414","contributorId":150137,"corporation":false,"usgs":true,"family":"Mills","given":"Christopher T.","email":"cmills@usgs.gov","affiliations":[{"id":211,"text":"Crustal Geophysics and Geochemistry Science Center","active":true,"usgs":true},{"id":171,"text":"Central Mineral and Environmental Resources Science Center","active":true,"usgs":true}],"preferred":false,"id":707769,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Tfaily, Malak","contributorId":195651,"corporation":false,"usgs":false,"family":"Tfaily","given":"Malak","affiliations":[],"preferred":false,"id":707767,"contributorType":{"id":1,"text":"Authors"},"rank":5},{"text":"Tangen, Brian 0000-0001-5157-9882 btangen@usgs.gov","orcid":"https://orcid.org/0000-0001-5157-9882","contributorId":167277,"corporation":false,"usgs":true,"family":"Tangen","given":"Brian","email":"btangen@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707766,"contributorType":{"id":1,"text":"Authors"},"rank":6},{"text":"Finocchiaro, Raymond 0000-0002-5514-8729 rfinocchiaro@usgs.gov","orcid":"https://orcid.org/0000-0002-5514-8729","contributorId":167278,"corporation":false,"usgs":true,"family":"Finocchiaro","given":"Raymond","email":"rfinocchiaro@usgs.gov","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707765,"contributorType":{"id":1,"text":"Authors"},"rank":7},{"text":"Johnston, Michael D.","contributorId":195650,"corporation":false,"usgs":false,"family":"Johnston","given":"Michael","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":707764,"contributorType":{"id":1,"text":"Authors"},"rank":8},{"text":"McAdams, Brandon C.","contributorId":195649,"corporation":false,"usgs":false,"family":"McAdams","given":"Brandon","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707763,"contributorType":{"id":1,"text":"Authors"},"rank":9},{"text":"Solensky, Matthew J. 0000-0003-4376-7765 msolensky@usgs.gov","orcid":"https://orcid.org/0000-0003-4376-7765","contributorId":4784,"corporation":false,"usgs":true,"family":"Solensky","given":"Matthew","email":"msolensky@usgs.gov","middleInitial":"J.","affiliations":[{"id":480,"text":"Northern Prairie Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707762,"contributorType":{"id":1,"text":"Authors"},"rank":10},{"text":"Smith, Garrett J.","contributorId":195646,"corporation":false,"usgs":false,"family":"Smith","given":"Garrett","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707759,"contributorType":{"id":1,"text":"Authors"},"rank":11},{"text":"Chin, Yu-Ping","contributorId":195648,"corporation":false,"usgs":false,"family":"Chin","given":"Yu-Ping","affiliations":[],"preferred":false,"id":707761,"contributorType":{"id":1,"text":"Authors"},"rank":12},{"text":"Wilkins, Michael J.","contributorId":195647,"corporation":false,"usgs":false,"family":"Wilkins","given":"Michael","email":"","middleInitial":"J.","affiliations":[],"preferred":false,"id":707760,"contributorType":{"id":1,"text":"Authors"},"rank":13}]}} ,{"id":70190127,"text":"70190127 - 2017 - Residence times and alluvial architecture of a sediment superslug in response to different flow regimes","interactions":[],"lastModifiedDate":"2017-09-25T13:46:24","indexId":"70190127","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1801,"text":"Geomorphology","active":true,"publicationSubtype":{"id":10}},"title":"Residence times and alluvial architecture of a sediment superslug in response to different flow regimes","docAbstract":"A superslug was deposited in a basin in the Colorado Front Range Mountains as a consequence of an extreme flood following a wildfire disturbance in 1996. The subsequent evolution of this superslug was measured by repeat topographic surveys (31 surveys from 1996 through 2014) of 18 cross sections approximately uniformly spaced over 1500 m immediately above the basin outlet. These surveys allowed the identification within the superslug of chronostratigraphic units deposited and eroded by different geomorphic processes in response to different flow regimes.
Over the time period of the study, the superslug went through aggradation, incision, and stabilization phases that were controlled by a shift in geomorphic processes from generally short-duration, episodic, large-magnitude floods that deposited new chronostratigraphic units to long-duration processes that eroded units. These phases were not contemporaneous at each channel cross section, which resulted in a complex response that preserved different chronostratigraphic units at each channel cross section having, in general, two dominant types of alluvial architecture—laminar and fragmented. Age and transit-time distributions for these two alluvial architectures evolved with time since the extreme flood. Because of the complex shape of the distributions they were best modeled by two-parameter Weibull functions. The Weibull scale parameter approximated the median age of the distributions, and the Weibull shape parameter generally had a linear relation that increased with time since the extreme flood. Additional results indicated that deposition of new chronostratigraphic units can be represented by a power-law frequency distribution, and that the erosion of units decreases with depth of burial to a limiting depth. These relations can be used to model other situations with different flow regimes where vertical aggradation and incision are dominant processes, to predict the residence time of possible contaminated sediment stored in channels or on floodplains, and to provide insight into the interpretation of recent or ancient fluvial deposits.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.geomorph.2017.04.012","usgsCitation":"Moody, J.A., 2017, Residence times and alluvial architecture of a sediment superslug in response to different flow regimes: Geomorphology, v. 294, p. 40-57, https://doi.org/10.1016/j.geomorph.2017.04.012.","productDescription":"18 p.","startPage":"40","endPage":"57","ipdsId":"IP-081978","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344776,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"294","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59901393e4b09fa1cb17891d","contributors":{"authors":[{"text":"Moody, John A. 0000-0003-2609-364X jamoody@usgs.gov","orcid":"https://orcid.org/0000-0003-2609-364X","contributorId":771,"corporation":false,"usgs":true,"family":"Moody","given":"John","email":"jamoody@usgs.gov","middleInitial":"A.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":707587,"contributorType":{"id":1,"text":"Authors"},"rank":1}]}} ,{"id":70190109,"text":"70190109 - 2017 - Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin","interactions":[],"lastModifiedDate":"2017-08-15T13:16:00","indexId":"70190109","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3825,"text":"Groundwater","active":true,"publicationSubtype":{"id":10}},"title":"Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin","docAbstract":"The Colorado River is an important source of water in the western United States, supplying the needs of more than 38 million people in the United States and Mexico. Groundwater discharge to streams has been shown to be a critical component of streamflow in the Upper Colorado River Basin (UCRB), particularly during low-flow periods. Understanding impacts on groundwater in the basin from projected climate change will assist water managers in the region in planning for potential changes in the river and groundwater system. A previous study on changes in basin-wide groundwater recharge in the UCRB under projected climate change found substantial increases in temperature, moderate increases in precipitation, and mostly periods of stable or slight increases in simulated groundwater recharge through 2099. This study quantifies projected spatial and seasonal changes in groundwater recharge within the UCRB from recent historical (1950 to 2015) through future (2016 to 2099) time periods, using a distributed-parameter groundwater recharge model with downscaled climate data from 97 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate projections. Simulation results indicate that projected increases in basin-wide recharge of up to 15% are not distributed uniformly within the basin or throughout the year. Northernmost subregions within the UCRB are projected an increase in groundwater recharge, while recharge in other mainly southern subregions will decline. Seasonal changes in recharge also are projected within the UCRB, with decreases of 50% or more in summer months and increases of 50% or more in winter months for all subregions, and increases of 10% or more in spring months for many subregions.
","language":"English","publisher":"National Groundwater Association","doi":"10.1111/gwat.12507","usgsCitation":"Tillman, F.D., Gangopadhyay, S., and Pruitt, T., 2017, Changes in projected spatial and seasonal groundwater recharge in the upper Colorado River Basin: Groundwater, v. 55, no. 4, p. 506-518, https://doi.org/10.1111/gwat.12507.","productDescription":"13 p.","startPage":"506","endPage":"518","ipdsId":"IP-078645","costCenters":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"links":[{"id":344783,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Arizona, Colorado, Nevada, New Mexico, Wyoming","otherGeospatial":"Upper Colorado River Basin","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -107.64404296874999,\n 42.147114459220994\n ],\n [\n -108.74267578124999,\n 42.342305278572816\n ],\n [\n -110.28076171874999,\n 41.983994270935625\n ],\n [\n -111.57714843749999,\n 40.74725696280421\n ],\n [\n -112.85156249999999,\n 38.324420427006515\n ],\n [\n -114.52148437499999,\n 37.84015683604134\n ],\n [\n -115.04882812499999,\n 37.54457732085582\n ],\n [\n -115.04882812499999,\n 36.61552763134925\n ],\n [\n -114.19189453124999,\n 34.50655662164561\n ],\n [\n -114.60937499999999,\n 33.797408767572485\n ],\n [\n -114.78515624999999,\n 32.861132322810946\n ],\n [\n -114.96093749999997,\n 32.15701248607008\n ],\n [\n -113.90624999999999,\n 31.74685416292141\n ],\n [\n -113.29101562499999,\n 31.034108344903483\n ],\n [\n -112.41210937499999,\n 30.164126343161097\n ],\n [\n -110.87402343749999,\n 30.543338954230222\n ],\n [\n -109.24804687499997,\n 31.259769987394286\n ],\n [\n -107.13867187499999,\n 32.97180377635759\n ],\n [\n -106.17187499999999,\n 36.43896124085945\n ],\n [\n -105.95214843749999,\n 39.740986355883564\n ],\n [\n -106.39160156249999,\n 41.52502957323801\n ],\n [\n -107.64404296874999,\n 42.147114459220994\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"55","issue":"4","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-02-16","publicationStatus":"PW","scienceBaseUri":"59901397e4b09fa1cb178921","contributors":{"authors":[{"text":"Tillman, Fred D. 0000-0002-2922-402X ftillman@usgs.gov","orcid":"https://orcid.org/0000-0002-2922-402X","contributorId":147809,"corporation":false,"usgs":true,"family":"Tillman","given":"Fred","email":"ftillman@usgs.gov","middleInitial":"D.","affiliations":[{"id":128,"text":"Arizona Water Science Center","active":true,"usgs":true}],"preferred":true,"id":707516,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Gangopadhyay, Subhrendu 0000-0003-3864-8251","orcid":"https://orcid.org/0000-0003-3864-8251","contributorId":173439,"corporation":false,"usgs":false,"family":"Gangopadhyay","given":"Subhrendu","affiliations":[{"id":7183,"text":"U.S. Bureau of Reclamation","active":true,"usgs":false}],"preferred":false,"id":707517,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Pruitt, Tom 0000-0002-3543-1324","orcid":"https://orcid.org/0000-0002-3543-1324","contributorId":173440,"corporation":false,"usgs":false,"family":"Pruitt","given":"Tom","email":"","affiliations":[{"id":27228,"text":"Reclamation","active":true,"usgs":false}],"preferred":false,"id":707518,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190123,"text":"70190123 - 2017 - Effects of lateral confinement in natural and leveed reaches of a gravel-bed river: Snake River, Wyoming, USA","interactions":[],"lastModifiedDate":"2017-10-16T14:23:51","indexId":"70190123","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1425,"text":"Earth Surface Processes and Landforms","active":true,"publicationSubtype":{"id":10}},"title":"Effects of lateral confinement in natural and leveed reaches of a gravel-bed river: Snake River, Wyoming, USA","docAbstract":"This study examined the effects of natural and anthropogenic changes in confining margin width by applying remote sensing techniques – fusing LiDAR topography with image-derived bathymetry – over a large spatial extent: 58 km of the Snake River, Wyoming, USA. Fused digital elevation models from 2007 and 2012 were differenced to quantify changes in the volume of stored sediment, develop morphological sediment budgets, and infer spatial gradients in bed material transport. Our study spanned two similar reaches that were subject to different controls on confining margin width: natural terraces versus artificial levees. Channel planform in reaches with similar slope and confining margin width differed depending on whether the margins were natural or anthropogenic. The effects of tributaries also differed between the two reaches. Generally, the natural reach featured greater confining margin widths and was depositional, whereas artificial lateral constriction in the leveed reach produced a sediment budget that was closer to balanced. Although our remote sensing methods provided topographic data over a large area, net volumetric changes were not statistically significant due to the uncertainty associated with bed elevation estimates. We therefore focused on along-channel spatial differences in bed material transport rather than absolute volumes of sediment. To complement indirect estimates of sediment transport derived by morphological sediment budgeting, we collected field data on bed mobility through a tracer study. Surface and subsurface grain size measurements were combined with bed mobility observations to calculate armoring and dimensionless sediment transport ratios, which indicated that sediment supply exceeded transport capacity in the natural reach and vice versa in the leveed reach. We hypothesize that constriction by levees induced an initial phase of incision and bed armoring. Because levees prevented bank erosion, the channel excavated sediment by migrating rapidly across the restricted braidplain and eroding bars and islands.
","language":"English","publisher":"British Society for Geomorphology","doi":"10.1002/esp.4157","usgsCitation":"Leonard, C., Legleiter, C.J., and Overstreet, B., 2017, Effects of lateral confinement in natural and leveed reaches of a gravel-bed river: Snake River, Wyoming, USA: Earth Surface Processes and Landforms, v. 42, no. 13, p. 2119-2138, https://doi.org/10.1002/esp.4157.","productDescription":"20 p.","startPage":"2119","endPage":"2138","ipdsId":"IP-075980","costCenters":[{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"links":[{"id":344779,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"country":"United States","state":"Wyoming","otherGeospatial":"Snake River","geographicExtents":"{\n \"type\": \"FeatureCollection\",\n \"features\": [\n {\n \"type\": \"Feature\",\n \"properties\": {},\n \"geometry\": {\n \"type\": \"Polygon\",\n \"coordinates\": [\n [\n [\n -110.58906555175781,\n 43.86720808597874\n ],\n [\n -110.61309814453125,\n 43.843936871965695\n ],\n [\n -110.61241149902344,\n 43.819665724206956\n ],\n [\n -110.65155029296875,\n 43.79042818348387\n ],\n [\n -110.70236206054688,\n 43.7492731811147\n ],\n [\n -110.72776794433592,\n 43.708586214366036\n ],\n [\n -110.73532104492186,\n 43.68277040294095\n ],\n [\n -110.73188781738281,\n 43.66042082657193\n ],\n [\n -110.71266174316406,\n 43.64054754952543\n ],\n [\n -110.68450927734375,\n 43.64452273099928\n ],\n [\n -110.6494903564453,\n 43.69419030566581\n ],\n [\n -110.60279846191405,\n 43.73488704685434\n ],\n [\n -110.54237365722656,\n 43.766135280960974\n ],\n [\n -110.49568176269531,\n 43.845917754377275\n ],\n [\n -110.50666809082031,\n 43.85879188670806\n ],\n [\n -110.5279541015625,\n 43.866713048323184\n ],\n [\n -110.58906555175781,\n 43.86720808597874\n ]\n ]\n ]\n }\n }\n ]\n}","volume":"42","issue":"13","publishingServiceCenter":{"id":2,"text":"Denver PSC"},"noUsgsAuthors":false,"publicationDate":"2017-05-31","publicationStatus":"PW","scienceBaseUri":"59901396e4b09fa1cb17891f","contributors":{"authors":[{"text":"Leonard, Christina","contributorId":195596,"corporation":false,"usgs":false,"family":"Leonard","given":"Christina","email":"","affiliations":[],"preferred":true,"id":707576,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Legleiter, Carl J. 0000-0003-0940-8013 cjl@usgs.gov","orcid":"https://orcid.org/0000-0003-0940-8013","contributorId":169002,"corporation":false,"usgs":true,"family":"Legleiter","given":"Carl","email":"cjl@usgs.gov","middleInitial":"J.","affiliations":[{"id":37778,"text":"WMA - Integrated Modeling and Prediction Division","active":true,"usgs":true},{"id":5044,"text":"National Research Program - Central Branch","active":true,"usgs":true}],"preferred":true,"id":707575,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Overstreet, Brandon T.","contributorId":195597,"corporation":false,"usgs":false,"family":"Overstreet","given":"Brandon T.","affiliations":[],"preferred":false,"id":707577,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190106,"text":"70190106 - 2017 - Use of alternating and pulsed direct current electrified fields for zebra mussel control","interactions":[],"lastModifiedDate":"2017-08-12T08:55:26","indexId":"70190106","displayToPublicDate":"2017-08-12T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2655,"text":"Management of Biological Invasions","active":true,"publicationSubtype":{"id":10}},"title":"Use of alternating and pulsed direct current electrified fields for zebra mussel control","docAbstract":"Alternatives to chemicals for controlling dreissenid mussels are desirable for environmental compatibility, but few alternatives exist. Previous studies have evaluated the use of electrified fields for stunning and/or killing planktonic life stages of dreissenid mussels, however, the available literature on the use of electrified fields to control adult dreissenid mussels is limited. We evaluated the effects of sinusoidal alternating current (AC) and 20% duty cycle square-wave pulsed direct current (PDC) exposure on the survival of adult zebra mussels at water temperatures of 10, 15, and 22 °C. Peak voltage gradients of ~ 17 and 30 Vp/cm in the AC and PDC exposures, respectively, were continuously applied for 24, 48, or 72 h. Peak power densities ranged from 77,999 to 107,199 µW/cm3 in the AC exposures and 245,320 to 313,945 µW/cm3 in the PDC exposures. The peak dose ranged from 6,739 to 27,298 Joules/cm3 and 21,306 to 80,941 Joules/cm3 in the AC and PDC exposures, respectively. The applied power ranged from 16.6 to 68.9 kWh in the AC exposures and from 22.2 to 86.4 kWh in the PDC exposures. Mortality ranged from 2.7 to 92.7% in the AC exposed groups and from 24.0 to 98.7% in PDC exposed groups. Mortality increased with corresponding increases in water temperature and exposure duration, and we observed more zebra mussel mortality in the PDC exposures. Exposures conducted with AC required less of a peak dose (Joules/cm3) but more applied power (kWh) to achieve the same level of adult zebra mussel mortality as corresponding PDC exposures. The results demonstrate that 20% duty cycle square-wave PDC requires less energy than sinusoidal AC to inducing the same level of adult zebra mussel mortality.
","language":"English","publisher":"Regional Euro-Asian Biological Invasions Centre","doi":"10.3391/mbi.2017.8.3.05","usgsCitation":"Luoma, J.A., Dean, J.C., Severson, T.J., Wise, J.K., and Barbour, M., 2017, Use of alternating and pulsed direct current electrified fields for zebra mussel control: Management of Biological Invasions, v. 8, no. 3, p. 311-324, https://doi.org/10.3391/mbi.2017.8.3.05.","productDescription":"14 p.","startPage":"311","endPage":"324","ipdsId":"IP-080213","costCenters":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"links":[{"id":469611,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.3391/mbi.2017.8.3.05","text":"Publisher Index Page"},{"id":344784,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"8","issue":"3","publishingServiceCenter":{"id":6,"text":"Columbus PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"59901398e4b09fa1cb178923","contributors":{"authors":[{"text":"Luoma, James A. 0000-0003-3556-0190 jluoma@usgs.gov","orcid":"https://orcid.org/0000-0003-3556-0190","contributorId":4449,"corporation":false,"usgs":true,"family":"Luoma","given":"James","email":"jluoma@usgs.gov","middleInitial":"A.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707507,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Dean, Jan C.","contributorId":195579,"corporation":false,"usgs":false,"family":"Dean","given":"Jan","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707508,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Severson, Todd J. 0000-0001-5282-3779 tseverson@usgs.gov","orcid":"https://orcid.org/0000-0001-5282-3779","contributorId":4749,"corporation":false,"usgs":true,"family":"Severson","given":"Todd","email":"tseverson@usgs.gov","middleInitial":"J.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707509,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Wise, Jeremy K. 0000-0003-0184-6959 jwise@usgs.gov","orcid":"https://orcid.org/0000-0003-0184-6959","contributorId":5009,"corporation":false,"usgs":true,"family":"Wise","given":"Jeremy","email":"jwise@usgs.gov","middleInitial":"K.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707510,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Barbour, Matthew T. 0000-0002-0095-9188 mbarbour@usgs.gov","orcid":"https://orcid.org/0000-0002-0095-9188","contributorId":195580,"corporation":false,"usgs":true,"family":"Barbour","given":"Matthew","email":"mbarbour@usgs.gov","middleInitial":"T.","affiliations":[{"id":606,"text":"Upper Midwest Environmental Sciences Center","active":true,"usgs":true}],"preferred":true,"id":707511,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190137,"text":"70190137 - 2017 - Comparison of sediment and nutrient export and runoff characteristics from watersheds with centralized versus distributed stormwater management","interactions":[],"lastModifiedDate":"2017-08-11T18:26:47","indexId":"70190137","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":2258,"text":"Journal of Environmental Management","active":true,"publicationSubtype":{"id":10}},"title":"Comparison of sediment and nutrient export and runoff characteristics from watersheds with centralized versus distributed stormwater management","docAbstract":"Stormwater control measures (SCMs) are used to retain stormwater and pollutants. SCMs have traditionally been installed in a centralized manner using detention to mitigate peak flows. Recently, distributed SCM networks that treat runoff near the source have been increasingly utilized. The aim of this study was to evaluate differences among watersheds that vary in SCM arrangement by assessing differences in baseflow nutrient (NOx-N and PO4−) concentrations and fluxes, stormflow export of suspended sediments and particulate phosphorus (PP), and runoff characteristics. A paired watershed approach was used to compare export between 2004 and 2016 from one forested watershed (For-MD), one suburban watershed with centralized SCMs (Cent-MD), and one suburban watershed with distributed SCMs (Dist-MD). Results indicated baseflow nitrate (NOx-N) concentrations typically exceeded 1 mg-N/L in all watersheds and were highest in Dist-MD. Over the last 10 years in Dist-MD, nitrate concentrations in both stream baseflow and in a groundwater well declined as land use shifted from agriculture to suburban. Baseflow nitrate export temporarily increased during the construction phase of SCM development in Dist-MD. This temporary pulse of nitrate may be attributed to the conversion of sediment control facilities to SCMs and increased subsurface flushing as infiltration SCMs came on line. During storm flow, Dist-MD tended to have less runoff and lower maximum specific discharge than Cent-MD for small events (<1.3 cm), but runoff responses became increasingly similar to Cent-MD with increasing precipitation (>1.3 cm). Mass export estimated during paired storm events indicated Dist-MD exported 30% less sediment and 31% more PP than Cent-MD. For large precipitation events, export of sediment and PP was similar among all three watersheds. Results suggest that distributed SCMs can reduce runoff and sediment loads during small rain events compared to centralized SCMs, but these differences become less evident for large events when peak discharge likely leads to substantial bank erosion.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.jenvman.2017.07.067","usgsCitation":"Hopkins, K.G., Loperfido, J., Craig, L.S., Noe, G.E., and Hogan, D.M., 2017, Comparison of sediment and nutrient export and runoff characteristics from watersheds with centralized versus distributed stormwater management: Journal of Environmental Management, v. 203, no. 1, p. 286-298, https://doi.org/10.1016/j.jenvman.2017.07.067.","productDescription":"13 p.","startPage":"286","endPage":"298","ipdsId":"IP-086456","costCenters":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true}],"links":[{"id":344772,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"203","issue":"1","publishingServiceCenter":{"id":9,"text":"Reston PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"598e903be4b09fa1cb16096c","contributors":{"authors":[{"text":"Hopkins, Kristina G. 0000-0003-1699-9384 khopkins@usgs.gov","orcid":"https://orcid.org/0000-0003-1699-9384","contributorId":195604,"corporation":false,"usgs":true,"family":"Hopkins","given":"Kristina","email":"khopkins@usgs.gov","middleInitial":"G.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":13634,"text":"South Atlantic Water Science Center","active":true,"usgs":true}],"preferred":true,"id":707625,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Loperfido, J.V.","contributorId":90970,"corporation":false,"usgs":true,"family":"Loperfido","given":"J.V.","email":"","affiliations":[],"preferred":false,"id":707626,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Craig, Laura S.","contributorId":195611,"corporation":false,"usgs":false,"family":"Craig","given":"Laura","email":"","middleInitial":"S.","affiliations":[],"preferred":false,"id":707627,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Noe, Gregory E. 0000-0002-6661-2646 gnoe@usgs.gov","orcid":"https://orcid.org/0000-0002-6661-2646","contributorId":139100,"corporation":false,"usgs":true,"family":"Noe","given":"Gregory","email":"gnoe@usgs.gov","middleInitial":"E.","affiliations":[{"id":40020,"text":"Florence Bascom Geoscience Center","active":true,"usgs":true},{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":436,"text":"National Research Program - Eastern Branch","active":true,"usgs":true},{"id":36183,"text":"Hydro-Ecological Interactions Branch","active":true,"usgs":true}],"preferred":true,"id":707628,"contributorType":{"id":1,"text":"Authors"},"rank":4},{"text":"Hogan, Dianna M. 0000-0003-1492-4514 dhogan@usgs.gov","orcid":"https://orcid.org/0000-0003-1492-4514","contributorId":131137,"corporation":false,"usgs":true,"family":"Hogan","given":"Dianna","email":"dhogan@usgs.gov","middleInitial":"M.","affiliations":[{"id":242,"text":"Eastern Geographic Science Center","active":true,"usgs":true},{"id":241,"text":"Eastern Energy Resources Science Center","active":true,"usgs":true},{"id":5064,"text":"Southeast Regional Director's Office","active":true,"usgs":true}],"preferred":true,"id":707629,"contributorType":{"id":1,"text":"Authors"},"rank":5}]}} ,{"id":70190134,"text":"70190134 - 2017 - Using optimal transport theory to estimate transition probabilities in metapopulation dynamics","interactions":[],"lastModifiedDate":"2017-08-11T18:29:59","indexId":"70190134","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1458,"text":"Ecological Modelling","active":true,"publicationSubtype":{"id":10}},"title":"Using optimal transport theory to estimate transition probabilities in metapopulation dynamics","docAbstract":"This work considers the estimation of transition probabilities associated with populations moving among multiple spatial locations based on numbers of individuals at each location at two points in time. The problem is generally underdetermined as there exists an extremely large number of ways in which individuals can move from one set of locations to another. A unique solution therefore requires a constraint. The theory of optimal transport provides such a constraint in the form of a cost function, to be minimized in expectation over the space of possible transition matrices. We demonstrate the optimal transport approach on marked bird data and compare to the probabilities obtained via maximum likelihood estimation based on marked individuals. It is shown that by choosing the squared Euclidean distance as the cost, the estimated transition probabilities compare favorably to those obtained via maximum likelihood with marked individuals. Other implications of this cost are discussed, including the ability to accurately interpolate the population's spatial distribution at unobserved points in time and the more general relationship between the cost and minimum transport energy.
","language":"English","publisher":"Elsevier","doi":"10.1016/j.ecolmodel.2017.06.003","usgsCitation":"Nichols, J.M., Spendelow, J.A., and Nichols, J.D., 2017, Using optimal transport theory to estimate transition probabilities in metapopulation dynamics: Ecological Modelling, v. 359, p. 311-319, https://doi.org/10.1016/j.ecolmodel.2017.06.003.","productDescription":"9 p.","startPage":"311","endPage":"319","ipdsId":"IP-085663","costCenters":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"links":[{"id":469613,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1016/j.ecolmodel.2017.06.003","text":"Publisher Index Page"},{"id":344773,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"359","publishingServiceCenter":{"id":10,"text":"Baltimore PSC"},"noUsgsAuthors":false,"publicationStatus":"PW","scienceBaseUri":"598e903be4b09fa1cb16096e","contributors":{"authors":[{"text":"Nichols, Jonathan M.","contributorId":195603,"corporation":false,"usgs":false,"family":"Nichols","given":"Jonathan","email":"","middleInitial":"M.","affiliations":[],"preferred":false,"id":707616,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Spendelow, Jeffrey A. 0000-0001-8167-0898 jspendelow@usgs.gov","orcid":"https://orcid.org/0000-0001-8167-0898","contributorId":4355,"corporation":false,"usgs":true,"family":"Spendelow","given":"Jeffrey","email":"jspendelow@usgs.gov","middleInitial":"A.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":true,"id":707615,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Nichols, James D. 0000-0002-7631-2890 jnichols@usgs.gov","orcid":"https://orcid.org/0000-0002-7631-2890","contributorId":140652,"corporation":false,"usgs":true,"family":"Nichols","given":"James","email":"jnichols@usgs.gov","middleInitial":"D.","affiliations":[{"id":531,"text":"Patuxent Wildlife Research Center","active":true,"usgs":true}],"preferred":false,"id":707617,"contributorType":{"id":1,"text":"Authors"},"rank":3}]}} ,{"id":70190141,"text":"70190141 - 2017 - Dam removal: Listening in","interactions":[],"lastModifiedDate":"2019-04-24T16:24:39","indexId":"70190141","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":3722,"text":"Water Resources Research","onlineIssn":"1944-7973","printIssn":"0043-1397","active":true,"publicationSubtype":{"id":10}},"title":"Dam removal: Listening in","docAbstract":"Dam removal is widely used as an approach for river restoration in the United States. The increase in dam removals—particularly large dams—and associated dam-removal studies over the last few decades motivated a working group at the USGS John Wesley Powell Center for Analysis and Synthesis to review and synthesize available studies of dam removals and their findings. Based on dam removals thus far, some general conclusions have emerged: (1) physical responses are typically fast, with the rate of sediment erosion largely dependent on sediment characteristics and dam-removal strategy; (2) ecological responses to dam removal differ among the affected upstream, downstream, and reservoir reaches; (3) dam removal tends to quickly reestablish connectivity, restoring the movement of material and organisms between upstream and downstream river reaches; (4) geographic context, river history, and land use significantly influence river restoration trajectories and recovery potential because they control broader physical and ecological processes and conditions; and (5) quantitative modeling capability is improving, particularly for physical and broad-scale ecological effects, and gives managers information needed to understand and predict long-term effects of dam removal on riverine ecosystems. Although these studies collectively enhance our understanding of how riverine ecosystems respond to dam removal, knowledge gaps remain because most studies have been short (< 5 years) and do not adequately represent the diversity of dam types, watershed conditions, and dam-removal methods in the U.S.
","language":"English","publisher":"American Geophysical Union","doi":"10.1002/2017WR020457","usgsCitation":"Foley, M.M., Bellmore, J., O'Connor, J., Duda, J.J., East, A., Grant, G.G., Anderson, C.W., Bountry, J.A., Collins, M.J., Connolly, P., Craig, L.S., Evans, J.E., Greene, S., Magilligan, F.J., Magirl, C.S., Major, J.J., Pess, G.R., Randle, T.J., Shafroth, P.B., Torgersen, C.E., Tullos, D.D., and Wilcox, A.C., 2017, Dam removal: Listening in: Water Resources Research, v. 53, no. 7, p. 5229-5246, https://doi.org/10.1002/2017WR020457.","productDescription":"18 p.","startPage":"5229","endPage":"5246","ipdsId":"IP-083383","costCenters":[{"id":290,"text":"Forest and Rangeland Ecosystem Science Center","active":false,"usgs":true},{"id":518,"text":"Oregon Water Science Center","active":true,"usgs":true},{"id":520,"text":"Pacific Coastal and Marine Science Center","active":true,"usgs":true},{"id":615,"text":"Volcano Hazards Program","active":true,"usgs":true},{"id":29789,"text":"John 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jjmajor@usgs.gov","orcid":"https://orcid.org/0000-0003-2449-4466","contributorId":439,"corporation":false,"usgs":true,"family":"Major","given":"Jon","email":"jjmajor@usgs.gov","middleInitial":"J.","affiliations":[{"id":617,"text":"Volcano Science Center","active":true,"usgs":true}],"preferred":true,"id":707657,"contributorType":{"id":1,"text":"Authors"},"rank":16},{"text":"Pess, George R.","contributorId":13501,"corporation":false,"usgs":false,"family":"Pess","given":"George","email":"","middleInitial":"R.","affiliations":[{"id":6578,"text":"National Marine Fisheries Service, Seattle, WA 98112, USA","active":true,"usgs":false}],"preferred":false,"id":707658,"contributorType":{"id":1,"text":"Authors"},"rank":17},{"text":"Randle, Timothy J.","contributorId":90994,"corporation":false,"usgs":false,"family":"Randle","given":"Timothy","email":"","middleInitial":"J.","affiliations":[{"id":7183,"text":"U.S. Bureau of 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Center","active":false,"usgs":true}],"preferred":true,"id":707661,"contributorType":{"id":1,"text":"Authors"},"rank":20},{"text":"Tullos, Desiree D.","contributorId":176667,"corporation":false,"usgs":false,"family":"Tullos","given":"Desiree","email":"","middleInitial":"D.","affiliations":[],"preferred":false,"id":707662,"contributorType":{"id":1,"text":"Authors"},"rank":21},{"text":"Wilcox, Andrew C. 0000-0002-6241-8977","orcid":"https://orcid.org/0000-0002-6241-8977","contributorId":195613,"corporation":false,"usgs":false,"family":"Wilcox","given":"Andrew","email":"","middleInitial":"C.","affiliations":[],"preferred":false,"id":707663,"contributorType":{"id":1,"text":"Authors"},"rank":22}]}} ,{"id":70190147,"text":"70190147 - 2017 - Autotrophic microbial arsenotrophy in arsenic-rich soda lakes","interactions":[],"lastModifiedDate":"2017-08-11T17:51:35","indexId":"70190147","displayToPublicDate":"2017-08-11T00:00:00","publicationYear":"2017","noYear":false,"publicationType":{"id":2,"text":"Article"},"publicationSubtype":{"id":10,"text":"Journal Article"},"seriesTitle":{"id":1620,"text":"FEMS Microbiology Letters","active":true,"publicationSubtype":{"id":10}},"title":"Autotrophic microbial arsenotrophy in arsenic-rich soda lakes","docAbstract":"A number of prokaryotes are capable of employing arsenic oxy-anions as either electron acceptors [arsenate; As(V)] or electron donors [arsenite; As(III)] to sustain arsenic-dependent growth (‘arsenotrophy’). A subset of these microorganisms function as either chemoautotrophs or photoautotrophs, whereby they gain sufficient energy from their redox metabolism of arsenic to completely satisfy their carbon needs for growth by autotrophy, that is the fixation of inorganic carbon (e.g. HCO3−) into their biomass. Here we review what has been learned of these processes by investigations we have undertaken in three soda lakes of the western USA and from the physiological characterizations of the relevant bacteria, which include the critical genes involved, such as respiratory arsenate reductase (arrA) and the discovery of its arsenite-oxidizing counterpart (arxA). When possible, we refer to instances of similar process occurring in other, less extreme ecosystems and by microbes other than haloalkaliphiles.
","language":"English","publisher":"Oxford Academic","doi":"10.1093/femsle/fnx146","usgsCitation":"Oremland, R.S., Saltikov, C.W., Stolz, J.F., and Hollibaugh, J.T., 2017, Autotrophic microbial arsenotrophy in arsenic-rich soda lakes: FEMS Microbiology Letters, v. 364, no. 15, Article fnx146, https://doi.org/10.1093/femsle/fnx146.","productDescription":"Article fnx146","ipdsId":"IP-087336","costCenters":[{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"links":[{"id":469614,"rank":0,"type":{"id":40,"text":"Open Access Publisher Index Page"},"url":"https://doi.org/10.1093/femsle/fnx146","text":"Publisher Index Page"},{"id":344768,"type":{"id":24,"text":"Thumbnail"},"url":"https://pubs.usgs.gov/thumbnails/outside_thumb.jpg"}],"volume":"364","issue":"15","publishingServiceCenter":{"id":14,"text":"Menlo Park PSC"},"noUsgsAuthors":false,"publicationDate":"2017-07-08","publicationStatus":"PW","scienceBaseUri":"598e9039e4b09fa1cb160968","contributors":{"authors":[{"text":"Oremland, Ronald S. 0000-0001-7382-0147 roremlan@usgs.gov","orcid":"https://orcid.org/0000-0001-7382-0147","contributorId":931,"corporation":false,"usgs":true,"family":"Oremland","given":"Ronald","email":"roremlan@usgs.gov","middleInitial":"S.","affiliations":[{"id":37277,"text":"WMA - Earth System Processes Division","active":true,"usgs":true},{"id":438,"text":"National Research Program - Western Branch","active":true,"usgs":true}],"preferred":true,"id":707696,"contributorType":{"id":1,"text":"Authors"},"rank":1},{"text":"Saltikov, Chad W.","contributorId":195632,"corporation":false,"usgs":false,"family":"Saltikov","given":"Chad","email":"","middleInitial":"W.","affiliations":[],"preferred":false,"id":707697,"contributorType":{"id":1,"text":"Authors"},"rank":2},{"text":"Stolz, John F.","contributorId":179305,"corporation":false,"usgs":false,"family":"Stolz","given":"John","email":"","middleInitial":"F.","affiliations":[],"preferred":false,"id":707698,"contributorType":{"id":1,"text":"Authors"},"rank":3},{"text":"Hollibaugh, James T.","contributorId":195633,"corporation":false,"usgs":false,"family":"Hollibaugh","given":"James","email":"","middleInitial":"T.","affiliations":[],"preferred":false,"id":707699,"contributorType":{"id":1,"text":"Authors"},"rank":4}]}} ]}